arXiv Papers with Code in Artificial Intelligence (January 2026

Abstract:
Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in key metacognitive faculties: they hallucinate with high confidence, fail to recognize knowledge boundaries, and misrepresent their internal uncertainty‑‑undermining trustworthiness and reliability. Since monitoring task performance and adapting behavior accordingly are central to metacognition, we posit that models capable of accurately judging their own performance are better positioned to improve it. We operationalize this idea via two novel mechanisms: reinforcement learning with metacognitive feedback (RLMF), a paradigm to refine completion rankings during preference optimization based on the quality of a model's self‑judgments of performance, and metacognitive data selection, which uses similar self‑judgments to identify high‑value training examples, outperforming naive active learning. We apply these innovations to the problem of faithful calibration (FC), a task that is itself fundamentally metacognitive: the goal is to align expressed with intrinsic uncertainty, difficult even for frontier LLMs. We adopt a two‑stage, decoupled approach, first using these methods to calibrate the faithfulness of models' self‑reported confidence scores, then mapping to natural, context‑adaptable linguistic uncertainty via targeted output editing. Extensive experiments show RLMF achieves generalizable, state‑of‑the‑art FC on diverse tasks while preserving accuracy. Further, RLMF surpasses standard RL by up to 63% while enhancing models' ability to assess and express their own capability limits. This positions RLMF as a promising paradigm to enhance LLM metacognition toward improved abilities and alignment, and suggests metacognitive performance as an effective RL signal to overcome limits of prior intrinsic feedback methods.

PaperId: 2, https://arxiv.org/pdf/2606.32027.pdf GitHub

Abstract:
Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long‑horizon manipulation tasks where sparse success labels provide too little signal and binary preferences collapse many competing notions of quality into one ambiguous signal. We introduce Freeform Preference Learning (FPL), a method for learning robot policies from freeform human preferences. Rather than asking annotators which of two trajectories is better overall, FPL lets them define natural‑language preference axes, such as speed, safety, quality of placement, or carefulness, and provide pairwise preferences along each axis. These annotations are used to learn a language‑conditioned reward model that maps a trajectory and preference label to an axis‑specific reward. We use this model to train a reward‑conditioned policy that optimizes across the multiple human‑specified dimensions. Across four real‑world and two simulated long‑horizon manipulation tasks, FPL improves over sparse‑reward and binary‑preference methods by 38 percentage points. Beyond improved performance, FPL learns dense progress signals without explicit subtask segmentation, shows compositionality of behavior not present in the data, and allows users to steer the policy towards different behaviors at test time without retraining. Blog post with videos available at https://freeform‑pl.github.io/fpl.website/

PaperId: 3, https://arxiv.org/pdf/2606.31981.pdf GitHub

Abstract:
Creating photorealistic, animatable 3D human avatars from monocular images still largely depends on Linear Blend Skinning (LBS) and parametric body models, which constrain expressivity and often introduce artifacts due to imperfect fitting. We propose LUNA, an LBS‑free universal neural animation model that directly maps multiple 2D controls like images, keypoints, sketches, and unseen characters into 3D Gaussian deformations, bypassing explicit body fitting. At its core, a transformer‑based motion regressor disentangles global rigid motion from fine‑grained local dynamics to capture both coherent movement and subtle non‑rigid effects. To resolve the inherent ambiguity of 2D‑to‑3D lifting while scaling beyond fitted datasets, we introduce hybrid supervision that distills soft structural priors from an LBS teacher and a loss that supports training on both limited fitted data and large in‑the‑wild unlabeled videos. Extensive experiments show LUNA achieves competitive visual fidelity compared to LBS‑based approaches, while delivering realistic human motion and zero‑shot cross‑identity generalization across diverse driving modalities. To the best of our knowledge, LUNA is the first end‑to‑end 3D animatable model that supports implicit 2D driving.

PaperId: 4, https://arxiv.org/pdf/2606.31966.pdf GitHub GitHub

Abstract:
Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored. To address this gap, we introduce MECoBench, a multimodal embodied cooperation benchmark with an evaluation platform spanning diverse real‑world tasks, two cooperation structures, and three collaboration modes. Through extensive experiments across various MLLMs, we summarize three key findings: (i) Collaboration generally improves embodied task completion, but its benefits depend on balancing collaborative gains against coordination complexity. (ii) Communication is essential to collaboration gains, while the best collaboration mode depends on team size and model capability. (iii) Moreover, collaboration improves robustness under noisy priors and exploration conditions. Generally, MECoBench provides a systematic testbed for understanding the mechanisms and limits of multimodal embodied collaboration. Code and dataset are available at https://github.com/q‑i‑n‑g/MECoBench.

PaperId: 5, https://arxiv.org/pdf/2606.31941.pdf GitHub GitHub

Abstract:
Unstructured navigational features, such as irregular planting or discontinuities, remain the primary failure mode for under‑canopy agricultural robots. Existing geometric approaches often fail in these scenarios because they compress high‑dimensional visual data into deterministic spatial references, effectively discarding the uncertainty and semantic context required to navigate ambiguous terrain. To address this, we present LeCropFollow, a visual navigation framework that bypasses explicit geometric modeling in favor of a learned latent representation. By integrating a self‑supervised semantic heatmap extractor with TD‑MPC2, a Model‑Based Reinforcement Learning (MBRL) planner, our system optimizes trajectories directly within a latent manifold. The framework operates over the uncompressed heatmap signal, preserving the semantic context that geometric reductions discard. We demonstrate that this representational shift enables zero‑shot transfer from simplified simulation to the physical world without fine‑tuning. Extensive field experiments in late‑stage corn fields show that LeCropFollow matches state‑of‑the‑art baselines in unstructured rows but significantly outperforms them in plantation gaps, achieving a 2.4x reduction in semantic failures compared to keypoint‑based methods. These results suggest that latent planning offers a robust alternative to geometric estimation for operations in heterogeneous agricultural environments. Code, models, and data available: https://felipe‑tommaselli.github.io/lecropfollow .

PaperId: 6, https://arxiv.org/pdf/2606.31834.pdf GitHub

Abstract:
Real‑world detectors for autonomous driving, surveillance, and robotics must handle domain‑shifts under strict latency and memory constraints, yet existing source‑free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone. We show this trade‑off is unnecessary: building on YOLOv10, an NMS‑free dual‑head detector, we achieve state‑of‑the‑art adaptation accuracy while being faster and more compact. We observe that directly applying vanilla mean‑teacher self‑training to dual‑head detectors leads to suboptimal adaptation performance due to two key factors. First, simple pseudo‑label generation strategies, such as using a single head or directly combining high‑confidence predictions from both heads, yield suboptimal supervision under domain‑shift. We propose DHF (Dual‑Head Pseudo‑Label Fusion) which selectively admits one‑to‑one (O2O) and one‑to‑many (O2M) head predictions, preserving precision and recovering missed objects. Second, we observe domain‑shift collapses multi‑scale feature discriminability. We propose the use of our MARD (Multi‑scale Adaptive Representation Diversification) loss which mitigates this by enforcing detection‑aware variance and covariance constraints on multi‑scale feature maps. Both modules are training‑time only, leaving inference unchanged. Across domain‑shift benchmarks, our method, RT‑SFOD yields 1.4 to 3.5% mAP gains, 1.3× higher throughput, with ～2× fewer parameters than prior state‑of‑the‑art SFOD methods, thus advancing the Pareto frontier of the speed‑accuracy‑model size trade‑off. We report main results with YOLOv10, and demonstrate generalizability with additional YOLO‑ and DETR‑based dual‑head detectors. Code is available here: https://github.com/Sairam13001/RT‑SFOD/

PaperId: 7, https://arxiv.org/pdf/2606.31825.pdf GitHub

Abstract:
Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post‑training pipelines remain predominantly outcome‑centric, relying on final answer correctness or sequence‑level preferences. This suffers from sparse credit assignment, making it difficult to optimize the reasoning process essential for clinical applications. Our analysis reveals that cascading errors from early‑stage reasoning failures are a leading cause of incorrect predictions in medical visual question answering (VQA) benchmarks. Motivated by this, we propose Medical Reasoning‑aware Policy Optimization (MRPO), an RL algorithm that incorporates step‑wise process rewards. When the final answer is incorrect, MRPO assigns exponentially larger penalties to tokens in earlier invalid reasoning steps, breaking failure cascades without compromising successful paths. Across three multimodal LLM backbones, MRPO consistently outperforms standard GRPO and a recent RL baseline, and on Qwen3‑VL‑8B‑Instruct even surpasses substantially larger medical MLLMs such as HuatuoGPT‑Vision‑34B by 2.79 points. Moreover, MRPO reduces early‑stage reasoning failures from 64.0% to 13.0%, showing that targeted mitigation of cascading failures improves both reasoning quality and final answer accuracy. Our code is available at https://github.com/dmis‑lab/MRPO

PaperId: 8, https://arxiv.org/pdf/2606.31813.pdf GitHub

Abstract:
Low‑rank adaptation (LoRA) and its variants enable parameter‑efficient fine‑tuning of large language models under the supervised fine‑tuning (SFT) paradigm. However, their efficacy and behavior under Reinforcement learning with verifiable rewards (RLVR) are less well understood. In particular, two structurally initialized LoRA variants, PiSSA and MiLoRA, which outperform standard LoRA under SFT, can underperform standard LoRA under RLVR and may even exhibit training instability. These observations suggest that how to initialize the low‑rank matrices in RLVR remains unclear. In this work, we develop a theoretical analysis of LoRA in RLVR, showing that orthonormal initialization achieves the minimal gap between LoRA outcome and that of full fine‑tuning. Guided by this insight, we propose geometry‑preserving orthonormal initialization for low‑rank adaptation in RLVR, leading to two new variants, RLPO and RLMO. Experiments on mathematical reasoning benchmarks show that the proposed orthonormal initialization stabilizes RLVR training and outperforms standard LoRA, contrasting with PiSSA and MiLoRA. Finally, our unified analysis for LoRA initialization also explains why PiSSA and MiLoRA can underperform in RLVR, which may be of independent interest. Code and checkpoints are publicly available at https://github.com/Richard‑ZZZ/geometry‑preserving‑orthonormal‑init‑rlvr.

PaperId: 9, https://arxiv.org/pdf/2606.31808.pdf GitHub

Abstract:
Language model systems built around proprietary APIs often operate on a token‑based cost model. This becomes prohibitively expensive in the context of large databases, where LM‑enhanced relational operators can incur costs exceeding 10,000 for a single set of experiments, hindering thorough research and practical deployment. In this paper, we demonstrate that quantized, open‑weight models running locally on just 16GB of VRAM can match or exceed the accuracy of closed‑source counterparts at lower latency and a fraction of the price, challenging the prevailing assumption that closed‑source LM APIs are necessary for effective LM‑database integration. We present and analyze the key system optimizations required to efficiently deploy these open‑weight models within an LM‑DB system. By integrating these local models into the BlendSQL v0.1.0 framework, we demonstrate a 390x reduction in overall costs and 3.8x reduction in latency compared to a proprietary LM API. We make our code available at https://github.com/CapitalOne‑Research/play‑by‑the‑type‑rules/tree/main/sembench.

PaperId: 10, https://arxiv.org/pdf/2606.31800.pdf GitHub

Abstract:
Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision, where fixed prompts, rules, or reward models provide non‑adaptive guidance throughout training. Such static signals are often sufficient to enforce output formats, but fail to shape the underlying reasoning process, leading to brittle generalization and performance saturation in complex decision‑making tasks. We propose Evo‑PI, a principle‑centric learning framework that treats reasoning principles as explicit, language‑based supervision signals that can be generated, evaluated, and iteratively evolved. Instead of relying on fixed rewards, Evo‑PI enables a co‑evolutionary loop in which principles guide model reasoning, while model behaviors in turn refine the principles that supervise them. This dynamic alignment mechanism allows supervision to progressively adapt to the model's reasoning deficiencies. We instantiate Evo‑PI in medical visual question answering as a high‑stakes testbed requiring structured visual‑textual reasoning. Across eight benchmarks and multiple model backbones, Evo‑PI consistently improves reasoning accuracy, achieving gains of up to 24.6%. Our results suggest that evolving principle‑guided supervision offers a scalable and general paradigm for training expert‑aligned reasoning in MLLMs. Code is available at https://github.com/zhengxianda/Evo_PI.

PaperId: 11, https://arxiv.org/pdf/2606.31745.pdf GitHub

Abstract:
Remote sensing change detection (CD) traditionally focuses on pixel‑level binary segmentation, which identifies where changes occur but neither what nor why. To bridge this semantic gap, we introduce JL1‑CC&QA, a multi‑task benchmark that extends the JL1‑CD dataset with two complementary annotation layers: change captioning (CC) and change question answering (QA). Built upon 5,000 bi‑temporal image pairs acquired by the Jilin‑1 satellite at 0.5‑0.75m ground sample distance, the benchmark comprises: (i) JL1‑CC, providing 17,021 quality‑verified captions that describe diverse land‑cover transformations; and (ii) JL1‑QA, offering 20,060 question‑answer pairs across eight question types, enabling fine‑grained, interactive interrogation of surface changes. All annotations are produced via a three‑stage pipeline consisting of multi‑modal large language model (LLM) generation, vision‑grounded LLM judging, and human expert verification. We hope that JL1‑CC&QA, as a benchmark unifying binary change masks, change captions, and change‑oriented QA over the same image set, will serve as a valuable resource for the community to advance multi‑task change understanding in remote sensing. The dataset is available at https://github.com/circleLZY/JL1‑CD.

PaperId: 12, https://arxiv.org/pdf/2606.31742.pdf GitHub

Abstract:
Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users. However, the potential of XAI beyond providing model transparency has remained largely unexplored in adjacent machine learning domains. In this paper, we show for the first time how XAI can be utilized in the context of federated learning. Specifically, while federated learning enables collaborative model training without raw data sharing, it suffers from performance degradation when client data distributions exhibit statistical heterogeneity. We introduce FedXDS (Federated Learning via XAI‑guided Data Sharing), the first approach to utilize feature attribution techniques to identify precisely which data elements should be selectively shared between clients to mitigate heterogeneity. By employing propagation‑based attribution, our method identifies task‑relevant features through a single backward pass, enabling selective data sharing that aligns client contributions. To protect sensitive information, we incorporate metric privacy techniques that provide formal privacy guarantees while preserving utility. Experimental results demonstrate that our approach consistently achieves higher accuracy and faster convergence compared to existing methods across varying client numbers and heterogeneity settings. We provide theoretical privacy guarantees and empirically demonstrate robustness against both membership inference and feature inversion attacks. Code is available at https://github.com/MaxH1996/FedXDS.

PaperId: 13, https://arxiv.org/pdf/2606.31741.pdf GitHub

Abstract:
While semantic embeddings are rigorously evaluated on the Massive Text Embedding Benchmark, the evaluation of style embeddings remains fragmented, with each work relying on their own set of tasks and datasets. To bridge this gap, we introduce the Style Text Embedding Benchmark, a comprehensive open‑source benchmark intended to standardize the evaluation of style embeddings. STEB encompasses 96 datasets across 7 languages, spanning applications such as authorship verification, authorship retrieval, AI‑text detection, probing of linguistic features, and others. We find that semantic embeddings consistently fail in stylistic tasks, and that there is no style embedding that is universally superior across all tasks evaluated. We open‑source the STEB code base at: https://github.com/rrivera1849/STEB.

PaperId: 14, https://arxiv.org/pdf/2606.31694.pdf GitHub

Abstract:
For robots manipulating open‑world objects, tactile representations must generalize to unseen materials. We introduce RCT (Robotic Contact Tactile), a robot‑collected touch‑vision‑language dataset with 29,279 tactile frames from full robot presses on 122 industrial reference materials in 7 categories, recorded with three DIGIT sensors at multiple contact positions. RCT preserves each press as a contact sequence, enabling held‑out evaluation across materials, categories, sensors, contact positions, and contact sequences. Frames from one press are strongly correlated: frame‑random splits can place near‑duplicate observations of the same physical interaction in both training and test. With the encoder held fixed, removing contact‑sequence overlap reduces tactile‑to‑text Recall@1 by 17.7 percentage points. When materials are additionally held out at training time, performance drops sharply, leaving held‑out‑material Recall@1 at 25.1 +/‑ 6.1% averaged over three held‑out draws. The public TVL/HCT split shows the same structure: every test contact sequence appears in training, and raw‑pixel nearest neighbors recover the correct sequence in 98.3% of cases. Uniformly sampling a press improves contrastive training, and RCT‑trained embeddings improve category probes on unseen materials. RCT makes contact‑sequence‑aware, held‑out‑material evaluation reproducible and exposes novel‑material generalization as a central challenge for robotic tactile perception. The RCT dataset is open‑sourced at https://faerber‑lab.github.io/RCT/

PaperId: 15, https://arxiv.org/pdf/2606.31683.pdf GitHub

Abstract:
Diffusion models have emerged as a dominant paradigm in generative modeling, enabling high‑fidelity sampling from complex data distributions. Despite impressive capabilities, controlling diffusion models to produce outputs aligned with user intent remains an open challenge, especially when balancing global coherence with local precision. Existing control mechanisms vary in the granularity of their conditioning signals. For example, textual prompts guide generation globally through high‑level semantics, while ControlNet‑like approaches secure precise local structure via dense conditions. In this work, we introduce Histogram‑constrained Image Generation (HIG), a novel control mechanism that falls into the middle ground of control granularity. Our framework enforces user‑specified distributional constraints (e.g., color histograms or latent token distributions) during the generation process with exact precision. We model such control as an optimal transport (OT) problem and apply explicit guidance transformations during sampling, thereby driving the diffusion trajectory to align with the desired histogram. We demonstrate the versatility of HIG across diverse applications, including constrained generation via color/latent histograms and high‑capacity information embedding through histogram‑level encoding. Our findings underscore the promise of distributional control, a flexible and interpretable control scheme that is fully compatible with existing control mechanisms, diversifying the hybrid strategies for controllable image generation. Our project page is available at: https://maps‑research.github.io/hig/.

PaperId: 16, https://arxiv.org/pdf/2606.31603.pdf GitHub

Abstract:
Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.g., dense regions or small objects in aerial or autonomous mobility data. While synthetic augmentation is an appealing solution, directly generating new labeled data risks misalignment of labels and generated pixels. Existing solutions to this problem often rely on external models, or employ coarse heuristics such as indiscriminately augmenting all foreground objects or entire backgrounds, which wastes capacity on uninformative pixels. To address this, we propose an uncertainty‑guided synthetic context augmentation strategy that strictly preserves label validity and efficiently maximizes pixel informativeness per synthetic sample ‑ no external guardrails required. Using a baseline segmenter's predictive entropy, we identify uncertain semantic regions and inpaint only the complementary visual context. When fine‑tuning the segmenter on this synthetic data, we compute the loss only over the original pixels, excluding inpainted regions. This focuses learning on the unmodified, uncertain regions while presenting them in novel contexts. We demonstrate substantial mIoU gains on Cityscapes, UAVID, and BDD100K with the largest gains on rare and difficult classes such as buses, trains, or (from the aerial perspective) cars. Our results demonstrate that uncertainty‑guided context augmentation is a highly effective lever to improve segmentation performance on complex datasets, with code provided at https://github.com/XITASO/Preserve‑the‑Hard‑Regenerate‑the‑Rest.

PaperId: 17, https://arxiv.org/pdf/2606.31587.pdf GitHub

Abstract:
Audio‑Language Models (ALMs) achieve strong zero‑shot performance by aligning audio with textual class descriptions. Although prompt learning improves accuracy on base classes through few‑shot supervised adaptation, we observe a critical trade‑off: it often degrades performance on novel classes, sometimes falling below zero‑shot accuracy. This exposes a base‑to‑novel generalization gap in prompt learning for ALMs. To address this issue, we propose ZEBRA (Zero‑shot Entropy‑Regularized Prompt Learning for Base‑to‑Novel Generalization), a plug‑and‑play framework that fuses zero‑shot logits with prompt‑learning logits, and employs self‑entropy regularization to reduce overfitting to base classes. Experiments across multiple audio classification datasets show that ZEBRA consistently improves novel‑class performance while maintaining strong base accuracy, significantly reducing the base‑to‑novel gap compared to standard prompt learning. The code is available at: https://github.com/asif‑hanif/zebra.

PaperId: 18, https://arxiv.org/pdf/2606.31574.pdf GitHub

Abstract:
Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices. However, conventional numerical methods, such as the Finite Element Method (FEM), are computationally expensive and therefore unsuitable for real‑time applications. Therefore, a fast and generalizable method is required for real‑time reconstruction of the divertor temperature field and subsequent real‑time control. To address the above issue, we propose a Physics‑aware Neural Operator Transformer (PNOT) to characterize the spatiotemporal evolution of the divertor temperature field. It models boundary heat‑flux relations as a structured graph and employs graph attention to explicitly capture spatial physical dependencies. Inspired by physics‑aware attention, we further develop a physics‑aware neural operator module to aggregate query points with similar physical conditions via slicing and model heat diffusion, while a gradient‑constrained Sobolev regularization loss enforces consistency between function values and their derivatives. Experimental results show that these physical constraints improve prediction accuracy while preserving physical consistency. The source code of this paper will be released on https://github.com/Event‑AHU/OpenFusion

PaperId: 19, https://arxiv.org/pdf/2606.31570.pdf GitHub

Abstract:
Masked autoencoding has emerged as a prominent paradigm for self‑supervised learning on 3D point clouds, achieving competitive performance across downstream tasks. Unlike its 2D counterpart, 3D masked autoencoding directly reconstructs spatial coordinates, making it inherently susceptible to positional leakage. In this work, we identify that the decoder in existing 3D MAE frameworks tends to over‑rely on positional information, which weakens semantic representation learning and leads to suboptimal feature quality. To address this issue, we propose MPL‑MAE, a masked point learning framework that mitigates positional over‑reliance while enhancing the utilization of encoder features. Specifically, we introduce a recalibrated positional embedding module that suppresses metric‑dominant coordinate signals while preserving geometric topology, together with a gated positional interface module that dynamically regulates positional injection during reconstruction. These designs promote a more balanced interaction between spatial priors and semantic features, yielding robust and informative representations. Extensive experiments across downstream tasks demonstrate that MPL‑MAE consistently achieves competitive performance, validating its effectiveness. Code is available at https://github.com/yanx57/MPL‑MAE.

PaperId: 20, https://arxiv.org/pdf/2606.31543.pdf GitHub

Abstract:
Large language models can produce fluent, internally coherent reasoning traces for abstract reasoning tasks while still being confidently wrong ‑ making selection among candidates, not just generation, the central challenge. I present a solver for ARC‑AGI‑2, a few‑shot visual reasoning benchmark, built around two principles: (i) treating reasoning modalities as search operators, generating diverse candidates independently across text, image, and code channels, and (ii) context‑preserving holistic judging, in which a judge model jointly compares all candidate reasoning traces within a single long‑context prompt. Unlike self‑consistency or majority voting, this approach reliably recovers correct minority hypotheses on tasks where the modal answer is wrong. On the ARC Prize semi‑private evaluation set, the solver achieves 72.9 percent at USD 38.99 per task ‑ the highest score on the verified leaderboard at the time of writing, exceeding the best standalone frontier models, GPT‑5.2 Pro at 54.2 percent and Gemini 3 Pro at 54.0 percent, by +18.7 percentage points. On the public evaluation set, it achieves 76.1 percent at USD 19.69 per task. I release the full source code and document extensive negative results, including the finding that prescriptive prompting templates and iterative refinement systematically reduce hypothesis diversity and degrade performance.

PaperId: 21, https://arxiv.org/pdf/2606.31461.pdf GitHub

Abstract:
Niche asset markets, such as Counter‑Strike 2 (CS2) weapon skins, are small, volatile, and heavily driven by community discussions and platform rules. These properties make them hard for traditional quantitative models, but provide an ideal testbed for studying how large language models (LLMs) turn unstructured text into trading actions. We present CSTrader, a multi‑agent framework for language‑grounded trading in the CS2 skin market. The system first integrates heterogeneous signals from various sources, then uses specialized agents for technical analysis, liquidity, events, and (reversed) sentiment, and finally applies risk control, transaction friction, and portfolio management agents to produce buy, sell, or hold decisions under realistic trading frictions. We build a live‑like evaluation environment with real CS2 data from a highly volatile period and evaluate several recent LLM backbones. Across models, CSTrader consistently outperforms both a falling market index (‑15.62%) and simple single‑prompt LLM baselines, achieving up to a 7.58% cumulative return with controlled risk. Ablation studies show that liquidity, reversed sentiment, and transaction friction agents are crucial for turning noisy language signals into stable profits, suggesting that niche, language‑driven markets are a useful benchmark for future language‑to‑action research. Code is available at: https://github.com/IatomicreactorI/CSGOTrading?tab=readme‑ov‑file#quick‑start

PaperId: 22, https://arxiv.org/pdf/2606.31413.pdf GitHub

Abstract:
Composing independently trained LoRA adapters into a single large language model is useful for multi‑domain adaptation, especially when the original training data cannot be shared. A common approach is to use MoE‑style routing over LoRA experts, but for frozen pretrained adapters, soft weighted combinations can change the unit‑scale additive update under which each LoRA module was originally trained. We propose Hard‑Routed MoR‑LoRA, a two‑stage framework for composing frozen reasoning LoRA experts through unit‑scale hard selection. First, domain‑specific LoRA adapters are trained independently using reinforcement learning from verifiable feedback to obtain reasoning experts. Then, all experts are frozen, reasoning traces are distilled from them, and only a lightweight shared router together with a small attention LoRA is trained for integration. The router selects exactly one expert per token using hard top‑1 routing, while a straight‑through estimator enables gradient‑based training. Experiments across five benchmarks, multiple model scales, and additional model families show that Hard‑Routed MoR‑LoRA preserves expert behavior while requiring substantially fewer trainable parameters than soft‑routing mixture baselines. Our analysis further shows that normalized soft mixtures often concentrate most routing mass on a single expert, suggesting that hard unit‑scale routing provides a simple and efficient abstraction for frozen LoRA expert composition.

PaperId: 23, https://arxiv.org/pdf/2606.31394.pdf GitHub

Abstract:
Artificial intelligence is transforming our capability to solve biological challenges. In dimensionality bottleneck regimes exacerbated by high‑dimensional biological data, neural networks force distinct concepts into the lower dimensions known as superposition. Although this superposition is widely known to hinder interpretability, its impact on corrupting the geometry of latent spaces remains critically overlooked. Here, we utilized sparse autoencoders (SAEs) trained on over 100,000 multiplexed images of patient‑derived Parkinson's disease and healthy neurons to resolve superposition. This approach bypasses the mathematical non‑uniqueness of feature attribution by shifting to interpretable latent representation analysis. We theoretically and empirically demonstrate that superposition contaminates representational metric spaces, and thereby SAEs successfully recover geometric fidelity. By treating these geometrically purified representations as single‑cell state vectors, we adapted single‑cell RNA sequencing (scRNA‑seq) data analysis methodologies directly to the image domain. Finally, we introduce GW‑map, utilizing Gromov‑Wasserstein optimal transport to align these image representations with authentic scRNA‑seq data de novo. This coupling reconstructs hierarchical neuronal pathology pathways such as Calcium‑AIS scaffold, without reference spatial transcriptomics, establishing a scalable foundation for spatial biology. Code is available at https://github.com/jijihihi/Bio\_superposition

PaperId: 24, https://arxiv.org/pdf/2606.31392.pdf GitHub

Abstract:
Tool‑augmented vision‑language models (VLMs) can solve multimodal, multi‑step tasks by calling external tools, yet they remain fragile in practice. Existing works have two common gaps. Supervised fine‑tuning (SFT) is built mostly on successful trajectories and offers little signal for recovery after tool failures, while sparse trajectory‑level RL rewards provide limited guidance on which step failed and how to repair it. We introduce ReGRPO (Reflection‑augmented Group Relative Policy Optimization), a framework that learns reflection‑guided correction in tool‑using agents. ReGRPO starts with a structured reflective data engine: we execute near‑miss actions to collect grounded failure observations, then build Reflection‑of‑Thought triplets (ErrorType, Evidence, FixPlan) paired with corrected actions for warm‑start SFT. We then optimize reflection tokens and corrective actions jointly within local trajectories using group‑relative advantages, and include a reflection‑cost term to reduce unnecessary reflection. Experiments on GTA and GAIA show that, under the same backbone and tool suite, ReGRPO consistently outperforms strong open‑source baselines and achieves the best results among the compared open‑source controllers. Code and RoT data are available at https://github.com/showlab/ReGRPO.

PaperId: 25, https://arxiv.org/pdf/2606.31329.pdf GitHub GitHub GitHub

Abstract:
Hierarchical Vision‑Language‑Action (VLA) models decouple high‑level planning from low‑level control to improve generalization in robot manipulation. Recent work in this paradigm uses 2D end‑effector trajectories predicted by a Vision‑Language Model (VLM) as explicit guidance for a downstream policy. However, state‑of‑the‑art low‑level policies operate in 3D metric space on point clouds, and feeding them 2D guidance that lacks depth forces each waypoint to be assigned the depth of whatever scene surface lies beneath it, producing geometrically distorted trajectories. We propose 3D HAMSTER, a hierarchical framework that closes this gap by having the planner directly output metrically reliable 3D trajectories. We augment a VLM with a dedicated depth encoder and a dense depth reconstruction objective to predict 3D waypoint sequences, which are directly integrated into a pointcloudbased low‑level policy. Across 3D trajectory prediction, simulation, and real‑world manipulation, 3D HAMSTER consistently outperforms proprietary VLMs and 2D‑guided baselines, with the largest gains under appearance‑altering shifts and unseen language, spatial, and visual conditions. The project page is available at https://davian‑robotics.github.io/3D_HAMSTER/.

PaperId: 26, https://arxiv.org/pdf/2606.31198.pdf GitHub

Abstract:
Real‑time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image‑guided interventions. While conventional 2D methods suffer from inter‑frame inconsistencies by disregarding temporal context, 3D architectures incur prohibitive latency. To resolve this dilemma, we present a Temporally Consistent Learning Framework that distills temporal coherence into a 2D network during training, preserving single‑frame inference efficiency. Our design is driven by a key clinical observation: the prostate exhibits geometric stability, whereas the surrounding acoustic environment fluctuates due to physiological motion and transducer pressure. Because conventional temporal constraints propagate erroneous gradients from these unstable regions, we introduce a Confidence‑Weighted Temporal Consistency objective derived from optical flow warping residuals, selectively attenuating contributions from unreliable regions. Complementing this pixel‑wise constraint, a Dual‑scale Prototype Alignment Module enforces semantic coherence through contrastive optimization of local boundary and global semantic features. Furthermore, to eliminate the need for dense per‑frame video annotations, we employ geometric equivariance‑based pseudo‑labeling with knowledge distillation from a pretrained teacher. Extensive experiments on SUN‑SEG and our newly introduced TRUS‑V benchmark (2,679 frames) demonstrate state‑of‑the‑art accuracy and temporal consistency at real‑time speed. Code and dataset are available at https://github.com/DYDevelop/DTC‑TRUS.

PaperId: 27, https://arxiv.org/pdf/2606.31186.pdf GitHub

Abstract:
Spontaneous speech is a vital non‑invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non‑linear structural disruptions and clinical heterogeneity in pathological language. We propose a Multi‑View Gated Graph Attention Network that transcribes audio via Automatic Speech Recognition (ASR) to construct semantic, dependency, and co‑occurrence graphs, characterizing speech through a "content‑structure‑flow" framework. Notably, the co‑occurrence graph leverages Pointwise Mutual Information (PMI) from a normative corpus to quantify narrative logic and linguistic deviation. To address symptomatic diversity, an adaptive gated fusion mechanism dynamically integrates these views. Evaluated on the ADReSSo dataset, our model achieves 90.00% accuracy. Ablation results confirm that the PMI‑based graph and heterogeneity‑aware gating are essential for robust classification across diverse clinical populations. Our source code is publicly available at https://github.com/opeacc/AD.

PaperId: 28, https://arxiv.org/pdf/2606.31179.pdf GitHub

Abstract:
As AI agents become increasingly capable of complex, long‑horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward real‑world healthcare applications. We introduce HealthAgentBench, a suite of 54 agentic healthcare tasks across 7 categories each with its unique environment. The benchmark suite spans diverse workflows throughout the patient journey and a broad range of modalities. Each task is designed to replicate an end‑to‑end clinical workflow: given minimal instructions, an agent must explore raw healthcare data, operate within a complex environment, and execute multi‑step solutions that go beyond naive prompting. A final task success rate is reported to provide a single, interpretable metric for HealthAgentBench overall performance for each agent. Evaluating frontier agents on HealthAgentBench, we find that overall task success rate remains low, underscoring the difficulty of the suite. The strongest and the most cost effective agent, Codex GPT‑5.5, achieves only approximately 42% success rate. Beyond aggregate performance, HealthAgentBench reveals nuanced strengths and weaknesses across task categories. Frontier agents show promise in automatically developing research modeling pipelines over EHR data, but medical imaging remains especially challenging, particularly for Claude Code models, while Codex GPT‑5.5 shows emerging capability. Tasks that combine large search spaces with compositional reasoning requirements remain difficult for all current agents. Together, these results suggest that HealthAgentBench provides a challenging and realistic benchmark with substantial room for future progress. We release our benchmark at https://github.com/microsoft/HealthAgentBench.

PaperId: 29, https://arxiv.org/pdf/2606.31174.pdf GitHub

Abstract:
Production large language‑model (LLM) agents are increasingly deployed not as lone problem‑solvers but as managers: a main model creates specialized subagents, delegates work, and orchestrates their parallel, asynchronous returns through dynamic workflows. Whether one model can actually run such a team is largely unmeasured: existing benchmarks score a policy's own task‑solving or a fixed multi‑agent system's emergent behavior, but none isolate the management ability of the single LLM acting as leader. We introduce ClawArena‑Team, a benchmark of 41 multi‑turn, multimodal, multi‑directory scenarios spanning 258 evaluation rounds and 72 staged updates that measures this management ability. The main agent is deliberately constrained: it natively perceives only text and directly accesses only part of the workspace. It commands a fixed, locally served subagent pool, so score differences reflect management skill, not raw capability. All scoring is execution‑based with no LLM judge: an overall score ‑‑ the Subagent‑Management Score (SMS) ‑‑ multiplies task correctness by a least‑privilege and modality‑routing factor. Across twelve proprietary, community‑hosted, and self‑hosted models, experiments show that the management bottleneck is privilege granting rather than perception (no model exceeds 50% workspace‑permission precision); that cost and management quality are decoupled (API cost spans over 100 times while the overall score spans under 4 times, with the cheapest open models on the Pareto frontier); and that most leaderboard scores cluster within a 9.9‑point band while orchestration behaviors diverge by more than an order of magnitude. Code is available at https://github.com/aiming‑lab/ClawArena.

PaperId: 30, https://arxiv.org/pdf/2606.31167.pdf GitHub

Abstract:
VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web‑scale data to physical robotic control. However, current single‑frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high‑level instructions and low‑level motor commands, and inference inefficiency due to autoregressive scalar decoding. In this work, we propose MIRTH, a unified framework designed to address these challenges. MIRTH augments a pretrained VLA backbone with three key innovations: (1) dual‑scale temporal memory hubs that compress long‑term scene evolution and short‑term motion trends into compact embeddings; (2) latent reasoning tokens optimized via a mutual‑information objective carving out a semantic plan space to align multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector‑wise prediction to maximize control throughput. Extensive evaluations on the LIBERO simulation benchmark and a real‑world LeRobot platform demonstrate that MIRTH achieves state‑of‑the‑art performance and exhibiting emergent error recovery capabilities. The codes and collected datasets are released at http://github.com/kiva12138/mirth.

PaperId: 31, https://arxiv.org/pdf/2606.31154.pdf GitHub

Abstract:
Creating and editing slides is a rich, multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real‑world computer‑use agents. Microsoft PowerPoint is among the most widely adopted and feature‑rich environments for presentation creation. We introduce PPT‑Eval, a benchmark of 120 PowerPoint tasks across 12 files that cover both content creation and presentation editing scenarios, organized by difficulty. A central challenge in this domain is evaluation: tasks are complex, multimodal, and often admit many valid solutions. Moreover, today's agents frequently make only partial progress, which binary success metrics fail to capture. To address this, we design a robust evaluation framework to help create task‑specific rubrics for PowerPoint tasks, taking inspiration from and building on past works for rubric‑based evaluation. These rubrics award partial credit for intermediate steps, penalize unnecessary changes and poor aesthetics, and provide natural language feedback. This nuanced approach proves highly effective, achieving a Kendall's τ‑b correlation of 0.77 with human judgments. We find that existing frontier agents still struggle with solving PowerPoint tasks, with strong models like Claude‑4.5‑Opus achieving only a 45% success rate and an average partial score of 57%. The benchmark is located at: https://microsoft.github.io/ppteval.

PaperId: 32, https://arxiv.org/pdf/2606.31148.pdf GitHub

Abstract:
3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform reasoning over the entire scene, leading to ambiguous predictions and high computational cost, especially in cluttered environments. We observe that many referential expressions rely on local spatial context and often correspond to restricted spatial regions rather than the full scene. Motivated by this insight, we propose PruneGround, an effective plug‑and‑play framework for 3DVG built upon three key components. First, we introduce Language‑Guided Spatial Pruning (LGSP), which leverages a frozen Vision Language Model (VLM) to identify language‑relevant regions, thereby reducing spatial computation and grounding candidates in the narrower search space. Second, we propose MultiView‑Conditioned Description Reformulation (MCDR), which decomposes complex expressions into simplified target‑anchor relations and augments missing spatial cues through multi‑view reasoning. Finally, we propose LLM‑Grounder, which repurposes a detection‑pretrained spatial LLM into a language‑conditioned grounding model by aligning point cloud and linguistic representations within the pruned region. Extensive experiments on the three most popular point cloud benchmarks demonstrate that our method achieves state‑of‑the‑art results on all three ScanRefer settings and on 9 out of 10 Nr3D/Sr3D settings. Code and models are publicly available: https://github.com/leduckhai/PruneGround

PaperId: 33, https://arxiv.org/pdf/2606.31134.pdf GitHub

Abstract:
While Large Language Models (LLMs) have demonstrated exceptional capabilities in mathematical reasoning, they frequently produce subtle errors that evade human detection. Formal mathematical languages like Lean 4 offer mechanical proof checking, strongly motivating the need for autoformalization: the automatic translation of natural language mathematics into verifiable code. Recent trends indicate that general‑purpose LLMs, heavily optimized for standard programming, now outperform smaller models explicitly fine‑tuned for Lean. Leveraging this shift, we introduce an agentic autoformalization framework powered by general coding LLMs. At the core of our system is an orchestrator that manages a multi‑agent pipeline tailored for research‑level mathematics. Because cutting‑edge research frequently relies on concepts outside the scope of existing libraries like Mathlib, our system dynamically extends necessary type definitions and validates them via a novel Auxiliary Lemma technique before formalizing the primary theorems. We applied our approach to PutnamBench, producing machine‑checked Lean proofs for a random sample of 32 problems. Furthermore, we evaluate our system on five papers from the ACM Symposium on Theory of Computing (STOC) spanning combinatorics, communication complexity, mechanism design, and learning theory, successfully formalizing their main theorems and validating the generated formalizations with human experts; for all five we also formalize the proofs alongside the statements, and notably two of them are proved with no axioms beyond Lean's kernel. All of our formalizations are available at https://beyondthelibrary.github.io/formal_arxiv .

PaperId: 34, https://arxiv.org/pdf/2606.31131.pdf GitHub

Abstract:
To ensure safe on‑road behavior, pre‑deployment testing and failure discovery of Autonomous Driving Systems (ADS) is crucial. Present day simulation based testing methods focus largely on mathematical models for efficient search of optimal scenarios, assuming a fixed scenario representation. On the other hand, real‑world testing involves substantial manual effort to design scenario templates for testing. These templates represent distinct failure scenarios consisting of pre‑deployment vehicle movements, map types, etc. Historical failure records for ADS are a reliable source of real‑world failure conditions, which can be used for scenario generation. In this work, we propose a scenario generation pipeline using categorical and contextual information available from historical records in natural language format. Our approach consists of modular LLM based synthetic scenario generation, compatible with the testing constraints of a given system. We successfully apply our method to generate a diverse set of scenarios for testing autonomous navigation on Metadrive simulator using the NHTSA ADS crash records. Our approach results in accurate and diverse scenario generation with a combination of 4 road types, 3 non ego vehicle movement types, including on road anomalies in the form of working zones. Generated scenarios align with the provided testing conditions, and reveals interesting failures of the system within a limited testing budget of 20 scenarios. Code is available at https://github.com/anjaliParashar/crash2scenario.

PaperId: 35, https://arxiv.org/pdf/2606.31127.pdf GitHub

Abstract:
To enable personalized, real‑time coaching using Augmented Reality glasses or fixed camera setups in domains such as sports, cooking, or music, a system must understand not just what a person does, but how well they execute an activity. In an ego‑exo video setting, this requires simultaneously detecting individual skilled actions and classifying each as correct or needing improvement, which Ego‑Exo4D's proficiency demonstration benchmark formalized. We first adapt seven state‑of‑the‑art temporal action detection architectures to this task, extend the evaluation protocol to disentangle detection from grading, and show that existing methods grade near‑randomly. We then introduce SkillSpotter, a pose‑aware multi‑view architecture that jointly detects and grades skilled actions through three task‑specific modules: (1) adaptive temporal suppression to handle the varying density of skilled actions across diverse activities, (2) gated 3D body pose fusion to leverage body kinematics as a complementary signal to visual features, and (3) bidirectional cross‑view attention to combine ego and exo views effectively. SkillSpotter improves class‑specific mAP from 12.40 to 21.82 (+76%) and balanced accuracy from 55.99% to 60.40% over the best baseline. SkillSpotter's modules transfer to other temporal action detection models with consistent gains, and our method generalizes beyond Ego‑Exo4D to HoloAssist. Code: https://github.com/eth‑siplab/SkillSpotter

PaperId: 36, https://arxiv.org/pdf/2606.31074.pdf GitHub

Abstract:
Existing AI‑generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect Detection Framework by using additional perspectives of content (core ideas) and expression (stylistic elements) within a given text. Experiments on two benchmarks involving 17 attacks, 12 domains, and 17 source models demonstrate that Triospect is robust against these attacks. It improves the strong baseline by a significant margin of 22.3% (AUROC) and 13% (TPR01) on the Humanize‑16K after‑attack subset, and by 9.1% (AUROC) and 22% (TPR01) on the adversarial RAID. This framework marks a pioneering effort in statistical methods to enhance detection reliability against attacks. We release our data and code at https://github.com/baoguangsheng/triospect.

PaperId: 37, https://arxiv.org/pdf/2606.31054.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image. In this paper, we identify an internal signature of hallucination: progressive degradation of text‑to‑image cross‑attention during generation, leading to specific failure patterns like unfocused or biased attention. Existing mitigation strategies are largely outcome‑driven and do not explicitly target this failure mode. To address this problem, we propose ADAPT (Attention Dynamics Alignment with Preference Tuning), an attention‑based framework that intervenes directly on text‑to‑image cross‑attention dynamics. We propose ADAPT with three key contributions: a cross‑attention visual anchor refined from early decoding to provide stable spatial grounding, an attention‑supervised inference mechanism that detects and corrects attention drift online, and a Visual Attention Guidance DPO that aligns preferences toward visually grounded responses. Experiments show that each component of ADAPT contributes to hallucination reduction, and the full framework achieves new best results across multiple hallucination benchmarks, reducing hallucination rates by 40%‑60% across mainstream backbones while preserving general multimodal capabilities. Our work provides an attention‑based perspective on mitigating hallucinations by exploring the model's internal text‑to‑image cross‑attention behaviors. Code is available at https://github.com/yao‑ustc/ADAPT

PaperId: 38, https://arxiv.org/pdf/2606.31048.pdf GitHub

Abstract:
This paper investigates knowledge distillation from a large reasoning model (DeepSeek‑R1) to a compact student model (Qwen2.5‑7B). Using historical problems from the John O'Bryan Mathematics Competition at Northern Kentucky University (2011‑2025), we build a Chain‑of‑Thought (CoT) training corpus through a dual‑agent framework. The dataset is used to fine‑tune the student model with Low‑Rank Adaptation (LoRA) on Apple Silicon hardware using the MLX framework. The base Qwen2.5‑7B model achieves 64.67% accuracy on competition problems, while the DeepSeek‑R1 teacher achieves 91.40%. An initial 1,000‑iteration training run revealed severe overfitting, with validation loss reaching a minimum at iteration 200 before rising steadily. Based on this finding, we ran five independent training runs each limited to 200 iterations with varied random seeds to assess result stability. Across these five runs, the fine‑tuned student model achieves a mean accuracy of 69.43% (std dev 0.17%) on the competition dataset, a 4.76 percentage‑point improvement over the base model, and generalizes to 73.1% (std dev 0.18%) on the MATH‑500 benchmark. We further study how response length affects answer quality across six reasoning levels (R1‑R6): accuracy declines consistently from 69.43% at R1 (mean 220 words) to 41.9% at R6 (mean 31.2 words), with the two‑person speed section most sensitive to token reduction. These results demonstrate that CoT distillation improves compact student models and that response length is a critical factor in mathematical reasoning quality.

PaperId: 39, https://arxiv.org/pdf/2606.31026.pdf GitHub

Abstract:
We propose OTCache, a training‑free framework for accelerating diffusion sampling via caching schedule prediction. Existing graph‑based caching methods reduce redundant computation by optimizing shortest‑path objectives, but rely on an additive independence assumption, which often breaks down in the low NFE regime. To address this issue, OTCache models caching schedules across inference budgets as a smooth evolution in policy space, inspired by Optimal Transport (OT). The framework consists of three stages: (1) obtaining a high‑fidelity reference schedule using a graph‑based caching method under a conservative budget; (2) performing a lightweight anchor search under an extreme low‑budget setting via Optuna optimization with an end‑to‑end perceptual objective; and (3) predicting schedules for target budgets via quantile interpolation between the reference and anchor policies using continuous warping representations. Experiments on FLUX.1 [dev], Qwen‑Image, and HunyuanVideo show that OTCache achieves 4.5x, 4.7x, and 3.66x acceleration, respectively, while consistently improving generation fidelity over state‑of‑the‑art caching baselines. This work provides a new perspective on accelerating diffusion models through Optimal‑Transport‑inspired schedule modeling. Code:https://github.com/UnicomAI/OTCache

PaperId: 40, https://arxiv.org/pdf/2606.30951.pdf GitHub

Abstract:
Micro‑ultrasound (μUS) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dependent on clinical experience, leading to substantial inter‑observer variability. Machine‑learning assistance can reduce this variability; however, training reliable deep models is challenging because supervision is sparse and noisy ‑‑ typically limited to core‑level histopathology outcomes (e.g., cancer grade and its percentage in a biopsy core) without pixel‑level lesion annotations and under severe class imbalance. We introduce Prost‑RL, which reframes μUS PCa detection as a spatially aware, policy‑driven inference problem by learning where to look before decoding. Prost‑RL integrates a lightweight reinforcement‑learning policy into a foundation‑model encoder‑decoder to generate interpretable spatial attention maps that act as soft prompts for both cancer‑likelihood heatmap prediction and image‑level classification. We further propose Adaptive Policy Optimization (APO) to stabilize hybrid supervised‑RL training and a noise‑robust objective combining symmetric cross‑entropy with negative‑entropy regularization to mitigate weak‑label noise and encourage sharp localization. On a cohort of 6,607 biopsy cores from 693 patients across five clinical sites, Prost‑RL achieves 79.0\pm3.5 AUROC with 64.6\pm6.3% sensitivity at 80% specificity for core‑level detection (+2.1 AUROC and +4.5 sensitivity points over the strongest baseline), and 79.3\pm5.8 AUROC for clinically significant cancer classification. The learned policy highlights biopsy‑aligned regions, providing transparent, spatially grounded evidence alongside quantitative risk predictions. Code is available at: https://github.com/DeepRCL/Prost‑RL.

PaperId: 41, https://arxiv.org/pdf/2606.30919.pdf GitHub

Abstract:
Edge‑cloud inference collaborations are often designed with a routing estimator that decides whether to offload each frame from weak models at the edge to stronger models in the cloud. Existing systems place the routing estimator after the weak detector, so the weak forward pass still runs even on frames that are later offloaded. In this paper, we argue that this weak‑conditioned design can be suboptimal when the offload budget varies. First, we present a competitive weak‑skipping estimator (0.153 GFLOPs, about 29x lighter than the weak detector at 4.49 GFLOPs) that extracts routing signal from raw pixels, outperforming the common after‑weak placement weak‑conditioned baselines. Second, we show that neither weak‑skipping nor weak‑conditioned placement dominates across the full operating curve, and we propose budget‑adaptive routing, which selects between them by offload budget via two offline‑tuned thresholds. On PASCAL VOC, our budget‑adaptive router traces the upper accuracy envelope of both fixed placements across the operating range. Our method reduces per‑frame latency by up to 19.1 ms (about 30% lower at rho = 0.9). Besides outperforming SOTA methods, it is surprisingly stronger than the strong model (+1.7 pp over the strong model's peak mAP) at some operating points with far less compute. Artifacts are available at https://github.com/ViGeng/bgt‑ada

PaperId: 42, https://arxiv.org/pdf/2606.30887.pdf GitHub

Abstract:
Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric. We introduce a framework that formulates therapeutic response generation as a decision‑refinement problem driven by multi‑dimensional, human‑aligned evaluation. In Stage I, we introduce TheraJudge, an open‑source therapeutic evaluator trained via preference‑based optimization on human‑annotated data to produce reliable judgments across 7 psychological dimensions. In Stage II, we introduce TheraAgent, which operationalizes TheraJudge's evaluations through a coordinated refinement process with specialized Critic, Coach, and Therapist roles that translate evaluative signals into targeted response revisions. Empirically, TheraJudge achieves strong agreement with clinician ratings, with intraclass correlation coefficients (ICC = 0.87‑0.95), surpassing supervised baselines and strong closed‑source judges, particularly on critical dimensions such as Safety, Relevance, and Empathy. Acting on these evaluations, TheraAgent yields a +0.43 improvement in human‑rated therapeutic quality (on a 5‑point scale) under blind evaluation, with 96% clinician inter‑rater reliability. Low‑quality responses (\leq 3) improve by +2.45 points with a 94% recovery rate, demonstrating targeted correction of unsafe outputs. Overall, our results indicate that effective alignment of mental‑health LLMs stems from acting on human‑aligned evaluation, rather than relying solely on stronger generation. We release code at https://github.com/vis‑nlp/TheraAlign.

PaperId: 43, https://arxiv.org/pdf/2606.30875.pdf GitHub

Abstract:
Foundation model pseudo‑labeling ‑ labeling data strictly via zero‑shot inference ‑ enables massive scale, but performance is undermined by hallucinations that evade standard thresholds. To eliminate these errors, we introduce the Turing‑inspired Label Imitation Game (LIG), a framework that formalizes pseudo‑label pruning as an adversarial interrogation. Rather than filtering labels via isolated thresholds, we use the LIG to train a Turing Test Network (TTN), a task‑agnostic "judge" that evaluates candidate pseudo‑labels within a dataset‑wide context. Experiments across four diverse datasets demonstrate the TTN's robustness, consistently enhancing label accuracy for three state‑of‑the‑art vision‑language models without costly supervision or retraining. Crucially, we demonstrate that learned semantic‑contextual logic is a robust alternative to spatial‑geometric verification, enabling a unique zero‑shot task transfer capability ‑ a TTN trained strictly on image classification datasets can effectively prune complex object detection pseudo‑labels. This pruning yields F1‑score gains of 28% for the worst‑performing baseline categories and 44% with task‑specific fine‑tuning. Significantly, we also observe Category Revival, where the TTN pruning "detoxifies" the training signal for downstream models and enables them to recover from zero recall on transfer‑vulnerable classes. The pre‑trained TTN models and code are available at https://github.com/voxel51/ttn.

PaperId: 44, https://arxiv.org/pdf/2606.30780.pdf GitHub

Abstract:
Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact‑checkers. The latter need reliable tools to verify the authenticity of their sources, while at the same time keeping their information private. Commercial deepfake detection solutions rely on cloud‑based processing, which raises privacy concerns. To solve this problem, we propose an on‑device audio deepfake detection model. We show that a truncated self‑supervised backbone with a simple logistic classifier is both very fast and often more accurate than existing solutions. Our solution outperforms the baseline AASIST by 10% and improves inference speed by 40%. We integrate this model into a browser plug‑in, which allows journalists and fact‑checkers to detect deepfakes easily and securely. Code for the plugin is available at https://github.com/OctavianPascu97/Audio‑Deepfakes‑Browser‑Plugin.

PaperId: 45, https://arxiv.org/pdf/2606.30774.pdf GitHub

Abstract:
We study when natural‑language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In multi‑turn language agent setting, higher final accuracy can reflect useful feedback, but it can also arise from resampling, format correction, or additional test‑time computation. To separate these effects, we introduce a controlled student‑teacher protocol across Omni‑MATH, Codeforces, BBEH Linguini, and ARC‑AGI1, evaluating thirteen open‑weight models in both student and teacher roles. We compare external feedback, self‑feedback, and unguided self‑refinement, while varying interaction history, task difficulty, and teacher access to privileged task information. Across settings, we find that multi‑turn improvement is often not evidence of feedback use: self‑generated feedback adds little beyond unguided self‑refinement, whereas the strongest external teachers produce substantially larger feedback‑specific gains, suggesting that useful feedback must provide guidance beyond generic retry. Dense student‑teacher interaction matrices further show that interactive gains are driven more by the student's ability to use feedback than by the teacher's identity, although teacher choice remains important for a fixed student. These results suggest that feedback‑based agents should be evaluated against repeated‑attempt baselines, and that ability to act on feedback, not merely feedback availability, is a central bottleneck for interactive improvement. We release our controlled student‑teacher evaluation framework at https://j‑lojek.github.io/feedback‑generation‑is‑a‑bottleneck/.

PaperId: 46, https://arxiv.org/pdf/2606.30755.pdf GitHub

Abstract:
Claw‑like AI agents (e.g., OpenClaw) are always‑on processes with persistent access to credentials, files, tools, and external services. They take on system‑level responsibilities ‑‑ installing packages, maintaining state, scheduling subtasks, and mediating I/O ‑‑ making security failures far more severe than in other agents. Yet existing benchmarks focus on model responses and tool calls, leaving cross‑component failure modes largely unmeasured. We adopt a computer‑system analogy: treating a Claw‑like agent as an agentic computer system whose gateway runtime plays an OS‑like mediation role, whose Skills resemble user‑installed applications, and whose Plugins resemble loadable extensions with runtime privileges. Each component has a classical counterpart whose protection mechanisms ‑‑ refined over decades of cybersecurity research ‑‑ are absent on the agent side. From this perspective, we develop SafeClawArena, a benchmark of 406 adversarial tasks across four attack surfaces (Skill Supply‑Chain Integrity, Persistent State Exploitation, Cross‑Boundary Data Flow, and Indirect Prompt Injection), executed in containerized replicas of real agent platforms with canary‑marked credentials and evaluated via automated taint tracking across nine output channels. We evaluate three platforms (OpenClaw, NemoClaw, SeClaw) and five frontier LLMs. The highest attack success rate reaches 70%; malicious Plugins succeed in 100% of cases regardless of the LLM. SeClaw cuts GPT‑5.4's attack success rate from 70% to 22%, partly through utility‑security tradeoffs rather than active defenses, while Claude‑Opus‑4.6 already sits near a 22% floor on every platform. These results expose the inadequacy of current defenses and suggest directions for future hardening. Code and data: https://github.com/sunblaze‑ucb/SafeClawArena.

PaperId: 47, https://arxiv.org/pdf/2606.30702.pdf GitHub

Abstract:
Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real‑world properties like complex survey sampling, demographic oversampling, and subgroup fairness. We introduce the NHANES Accelerometry Cardiometabolic Benchmark, derived from NHANES 2003‑2006, comprising 1,381 adults with hip‑worn accelerometry, fasting laboratory biomarkers, dietary intake, and anthropometrics. We evaluate three tabular learning methods ‑‑ ridge regression, XGBoost, and the foundation model TabPFN v2 ‑‑ to predict glycated haemoglobin (HbA1c), fasting triglycerides, and C‑reactive protein (CRP) from activity phenotypes and lifestyle covariates. TabPFN v2 achieves the best overall performance (HbA1c R^2=0.156, CRP R^2=0.383), while triglycerides remain largely unpredictable (R^2 < 0.05), consistent with known genetic dominance. We apply split conformal prediction to generate distribution‑free 90% prediction intervals and evaluate demographic coverage equity across sex and race/ethnicity subgroups. Marginal coverage aligns with the 90% target for CRP and HbA1c but falls below for triglycerides. At the subgroup level, we observe localized undercoverage (e.g., HbA1c for Mexican American participants), illustrating the gap between marginal guarantees and the conditional coverage required for clinical fairness. Code and data are at https://github.com/felizzi/nhanes‑accel‑cardiometabolic‑benchmark.

PaperId: 48, https://arxiv.org/pdf/2606.30697.pdf GitHub

Abstract:
Current operating systems expose interfaces optimized for human users but not for AI agents. Humans benefit from pixels, icons, windows, visual grouping, mouse movement, and keyboard shortcuts; AI agents instead need compact semantic state, grounded actions, and reliable feedback. As a result, many computer‑use agents are forced to interpret screenshots, OCR output, and visual crops, introducing high token costs, visual ambiguity, latency, and coordinate uncertainty. This paper introduces LUMOS (Language Model Unified Machine‑Readable Operating‑System Semantics), a semantic interaction layer between AI agents and operating systems. LUMOS converts native accessibility metadata and browser UI structures into machine readable semantic blueprints with stable identifiers, roles, names, values, bounds, and action affordances. It also supports live semantic pointer grounding by querying the UI element under or near the cursor through operating‑system automation APIs. An LLM then acts through an accessibility grounded observe act loop using constrained visible‑UI primitives rather than application‑specific scripts. LUMOS does not claim to replace visual agents; instead, it reduces dependence on screenshots when operating systems already provide semantic structure. These results suggest a path toward AI‑native operating systems and machine‑readable interaction layers.

PaperId: 49, https://arxiv.org/pdf/2606.30560.pdf GitHub

Abstract:
Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging. Progress on this challenge requires understanding real workload patterns, yet the data needed for such analysis is largely absent. Existing public traces and benchmarks do not capture real, day‑to‑day coding‑agent usage across multiple agents and model families for serving‑system analysis. To help fill this gap, we collect and release a trace of roughly 4,300 coding‑agent sessions, containing about 350,000 LLM steps and 430,000 tool calls from our own day‑to‑day use of Claude Code and Codex. Our analysis shows that coding‑agent workloads feature long autonomous loops, long contexts with short outputs, diverse and heavily‑tailed tool calls, and high but imperfect prefix cache hit rates. These findings point to concrete opportunities for optimizing serving, including lower‑overhead tool calling, append‑length‑aware prefill, semantic‑aware tool‑latency prediction, and improved KV‑cache management around human‑paced gaps. We release the dataset, trace collection pipeline, and analysis code at https://github.com/uw‑syfi/TraceLab.git the project website is https://tracelab.cs.washington.edu.

PaperId: 50, https://arxiv.org/pdf/2606.30296.pdf GitHub GitHub

Abstract:
Multi‑round reflection lets agents built on large language models recover from failures within a single task, but each task remains an isolated episode: lessons learned across many reflection rounds on one task are discarded before the next begins. We study this gap on a code‑generation task: from a scientific paper section, the agent writes Python in the open‑source Manim library to render a mathematical animation. We present ManimAgent, a self‑evolving multimodal agent that carries reflection experience across tasks through a dual‑channel Episodic Memory Bank grown entirely from its own task stream, with no weight updates and no human seeds. After each animation converges, a vision‑language model scores the rendered keyframes; the resulting signals populate a positive channel M+ that stores success rationales as soft Reference Examples, and a negative channel M‑ that stores validated failure patterns as hard Known Pitfalls. On a fixed‑probe evaluation against no‑memory, matched‑budget retrieval‑augmented generation, and shuffled‑memory baselines, blind human Pass@1 rises and reflection rounds fall as memory size grows. We will release the code, frozen memory snapshots, and the task stream.

PaperId: 51, https://arxiv.org/pdf/2606.29932.pdf GitHub

Abstract:
Long‑horizon strategic planning in complex strategy games demands concurrent reasoning across multiple decision domains under imperfect information and sparse reward. Existing LLM‑based agents suffer from three systematic failures: scene blindness from raw tile coordinates, context overflow and domain coupling from monolithic state dumps, and shallow cross‑game learning that treats each episode in isolation. We present SAGA, an LLM multi‑agent framework with three mechanisms each directly targeting one class of failure: (i) a Map‑Semantic Scene Graph that encodes typed spatial relations among game entities into per‑unit natural‑language context, resolving spatial blindness without global token inflation; (ii) a Tool‑Augmented Planner that pulls fine‑grained domain state on demand and dispatches per‑domain directives to dedicated specialist controllers, eliminating context overflow, domain coupling, and mechanical constraint violations; and (iii) a Dual‑Horizon Feedback Loop that combines periodic within‑game goal generation with structured cross‑game causal post‑mortem, enabling principled strategic evolution without manual reward engineering. Evaluated on FreeCiv, SAGA attains the highest mean civilization score ‑‑ the environment's sole sparse objective reward ‑‑ with lower variance than the two strongest baselines, and is the only method that significantly surpasses every baseline on infrastructure construction, the resource axis most readily sacrificed under multi‑objective conflict. It outscores the two strongest baselines in most head‑to‑head games while cutting output tokens (the dominant decoding cost) by 27%. Equipped with the cross‑game evolution module, SAGA reaches the highest end‑of‑chain score across five successive episodes. Ablation studies confirm that each architectural component contributes independently to this advantage.

PaperId: 52, https://arxiv.org/pdf/2606.29727.pdf GitHub

Abstract:
Professional translation is often a team‑based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accountability across documents. Yet many LLM‑based translation tools treat human corrections as isolated edits. Expert decisions made in one segment or by one member are rarely captured as reusable knowledge for the rest of the team. We present DeepTrans Studio, a collaborative translation workspace that lets professionals intercept selected nodes in an agentic translation workflow, review evidence, revise AI outputs, and save approved decisions to a shared team memory. During the demo, attendees will role‑play translators and reviewers, resolve preset terminology and legal‑modal risks, and see how their decisions are propagated to downstream segments and surfaced in a teammate's workspace as reusable precedents. The demo illustrates how human interventions in AI‑mediated work can become shared, traceable knowledge rather than one‑off corrections.

PaperId: 53, https://arxiv.org/pdf/2606.29721.pdf GitHub

Abstract:
Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source. Despite its importance, most publicly available AIS datasets lack predefined anomaly labels, forcing prior studies to rely on either distribution‑based rarity or domain rule/expert‑assisted labeling. These approaches, however, face fundamental limitations: statistical rarity often fails to reflect practically critical events, while expert‑based labeling is costly, subjective, and difficult to scale. Moreover, both paradigms tend to overlook interaction‑driven hazards such as near‑miss approaches between vessels. To address these challenges, we propose an equation‑grounded anomaly taxonomy that is implementable under a limited AIS observation schema and extensible to other AIS datasets. Specifically, the taxonomy defines three anomaly types: unexpected AIS activity (A1), route deviation (A2), and close approach (A3), covering both single‑vessel and inter‑vessel anomalies. Building on this taxonomy, we introduce a unified score‑synthesize‑label pipeline that produces LLM‑guided plausibility scores, uses them to synthesize anomalies, and assigns timestamp‑level labels. To rigorously assess detection performance, we further design benchmark evaluation settings that account for variations in temporal‑window length and anomaly‑type composition, and evaluate a broad range of time‑series models and anomaly detection models. Together, these contributions provide a systematic basis for evaluating maritime anomaly detection methods across different anomaly types. Our code is available at https://github.com/snudial/open‑maritime‑anomaly‑detection.

PaperId: 54, https://arxiv.org/pdf/2606.29713.pdf GitHub

Abstract:
Hallucination is the reliability bottleneck for LLM‑based agents, and fact attribution verifiers are the last line of defense ‑‑ yet today's verifiers emit only opaque binary labels, leaving agents unable to self‑correct and operators unable to audit. We present SEVA, a structured verification agent that emits evidence alignments, step‑by‑step reasoning chains, calibrated confidence, and a six‑category error diagnosis with actionable fixes. Training such an agent with RL is non‑trivial: standard binary reward on multi‑component output triggers advantage collapse ‑‑ within‑group reward variance vanishes and the GRPO gradient disappears. We resolve this with a process reward that decomposes verification quality into five independent components weighted 70/30 toward process signals, restoring the gradient and inducing an implicit curriculum ‑‑ the agent first masters verification behavior (alignment 0.917 ‑> 0.997, format 72% ‑> 100%), then outcomes (F1 64.9 ‑> 69.0). Structured output further enables a Verify ‑> Reflect ‑> Probe ‑> Refine self‑evolution loop, which over four rounds on a 7B model surfaces an unexpected structural finding: each round produces a benchmark‑specialist, not a generalist (+15 pp on HaluEval, ‑10 to ‑14 pp on TruthfulQA in the same model, persistent at 4x data). On ClearFacts, SEVA‑3B matches GPT‑4o‑mini (69.0 vs. 69.8 F1) while producing substantially richer, auditable output ‑‑ confirming a principle that should generalize: for any RL task with multi‑component generation, reward granularity must match output granularity.

PaperId: 55, https://arxiv.org/pdf/2606.29706.pdf GitHub

Abstract:
Telecom question answering (QA) is a challenging setting for retrieval‑augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language. In low‑resource subdomains, generator fine‑tuning can over‑specialize and degrade general capability, making query‑side retriever adaptation an attractive alternative. To this end, we ask whether a fixed‑generator, query‑adapted RAG system can outperform generator‑side adaptation, and which retriever objectives best support that setting. We motivate retrieval, rather than generator fine‑tuning, as the adaptation target through a capacity comparison: under bounded‑parameter and soft‑retrieval assumptions, query‑encoder tuning can have a smaller estimation term than supervised fine‑tuning when its effective dimension is smaller. We identify two particularly relevant objectives ‑‑ the latent‑document RAG likelihood, which optimizes generation utility, and the InfoNCE contrastive objective, which improves semantic retrieval geometry ‑‑ and leverage them jointly through a retriever optimization method targeting downstream QA performance in the telecom domain. Specifically, we introduce ARMOR, Adaptive Regularized Mixture Optimization for Retrievers, which learns separate temperatures for the RAG retrieval distribution and InfoNCE softmax and regularizes the adapted query encoder toward the frozen base query encoder. Across telecom‑specific retrieval and generative QA benchmarks, we show that ARMOR improves evidence retrieval and answer generation in several in‑domain settings. Code is available at https://github.com/heshandevaka/ARMOR.git.

PaperId: 56, https://arxiv.org/pdf/2606.29705.pdf GitHub

Abstract:
Data, as the fundamental substrate of modern intelligence, has greatly driven the development of current foundation models. Naturally, researchers aim to extend this paradigm to the domain of GUI agents, hoping to build strong GUI agents through a similar paradigm. However, GUI agent data cannot be directly harvested from the internet, making it costly and difficult to collect at scale. As a result, current GUI agents suffer from poor cross‑device generalization and limited visual grounding ability for fine‑grained GUI elements. As an attempt to address data challenge in GUI agents, we propose GUICrafter, a weakly‑supervised GUI agent leveraging massive unannotated screenshots to substantially reduce the reliance on expensive human annotations. GUICrafter explores a curriculum learning framework for training GUI agents through two progressive stages. First, the model learns visual grounding from large‑scale unannotated screenshots and webpages, leveraging the rich contextual signals inherent in GUI interactions without human annotations. Then, in Stage 2, we leverage a small amount of high‑quality data to calibrate the model via reinforcement learning. Experiments show that GUICrafter achieves competitive, or even superior, performance to advanced systems like UI‑TARS while using only 0.1% of its data. Furthermore, under the same amount of annotated data, GUICrafter surpasses all previous methods such as GUI‑R1. Code, data, and models are available at https://github.com/fansunqi/GUICrafter.

PaperId: 57, https://arxiv.org/pdf/2606.29700.pdf GitHub

Abstract:
Planning often requires symbolic specifications that are both executable and verifiable. For large language models deployed in autonomous or decision‑support systems, failures in such formalization may lead to unverifiable decisions, execution failures, or unsafe downstream behavior. We present NL‑PDDL‑Bench, a multi‑domain benchmark for natural‑language‑to‑PDDL specification construction with planner‑verified executability and controlled difficulty scaling by object count. We further propose a planner‑in‑the‑loop framework that uses validator and planner diagnostics to revise non‑executable specifications through localized edits. Building on this infrastructure, we develop a planner‑grounded optimization recipe that combines parameter‑efficient Low‑Rank Adaptation supervised fine‑tuning, offline planner‑derived preference pairs for Direct Preference Optimization, and inference‑time planner‑in‑the‑loop repair, without requiring online planner calls during training. We also provide a unified evaluation suite for parseability, solvability, specification similarity, and outcome‑aware plan‑level consistency against planner references. Experiments on representative model families show substantial gains in planner success and plan‑level agreement, with improved robustness under difficulty scaling and cross‑domain variation. These results highlight the value of externally verifiable formalization for reliable deployment of LLMs in safety‑ or security‑sensitive planning systems. Code and data are available at: https://github.com/ibasicplan/NL‑PDDL‑Bench

PaperId: 58, https://arxiv.org/pdf/2606.29613.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) architectures have recently been extended with role‑based mechanisms for interpretability. This is typically done by assigning semantic roles to individual expert components, for example roles like synergy, redundancy, and uniqueness in multimodal settings. However, whether such structural role decomposition preserves explanation faithfulness of the overall architecture remains largely underexplored. We hypothesize that inter‑expert representation overlap weakens effective role separation and degrades attribution‑based faithfulness, even when semantic roles are explicitly defined. To address this limitation, we introduce representation‑level decorrelation regularization to explicitly reduce inter‑expert similarity in latent space. Using representation decorrelation objectives, we encourage clearer specialization among experts by minimizing representation overlap. Our experiments show that across multiple multimodal benchmarks, this separation consistently improves explanation faithfulness, as measured by comprehensiveness, sufficiency, and their Area Over the Perturbation Curve (AOPC) summaries, while preserving task performance. We further show that these improvements are not limited to role‑based architectures such as Interpretable Multimodal Interaction‑aware MoE (I2MoE). Similar trends are observed in a standard sparse MoE baseline, suggesting that representation‑level separation may provide a more general mechanism for enhancing explanation faithfulness in MoE systems. Overall, our findings suggest that structural role decomposition alone may be insufficient to guarantee faithful explanations and that representation‑level separation helps improve explanation faithfulness. To support reproducibility, the source code and supplementary material are publicly available at https://github.com/dut0817/FL‑I2MoE_Decor.

PaperId: 59, https://arxiv.org/pdf/2606.29586.pdf GitHub

Abstract:
Vision‑language foundation models have shown strong potential in medical image analysis. Although foundation models for ultrasound imaging have recently emerged, the domain remains particularly challenging due to severe speckle noise, acquisition variability, and subtle anatomical boundaries, leading to high inter‑observer variability. Existing CLIP‑based models rely primarily on global image‑text alignment, limiting their sensitivity to clinically decisive local structures. We propose SonoCLIP, the first million‑scale region‑controllable fetal ultrasound vision‑language foundation model that integrates segmentation masks as mask‑channel visual prompts within the vision encoder, enabling joint global‑local contrastive representation learning. To support scalable region‑text alignment, we introduce a sigmoid‑based pairwise contrastive loss that improves stability under large‑scale supervision. We further curate a 1.44M‑image multimodal fetal ultrasound dataset spanning 24 standard planes for large‑scale pretraining. Extensive cross‑center evaluations demonstrate that SonoCLIP achieves superior zero‑shot transfer performance under both global and mask‑guided inference, establishing a controllable and clinically oriented foundation model for fetal ultrasound analysis. Our code and data are available at https://github.com/Harrison‑one/SonoCLIP.

PaperId: 60, https://arxiv.org/pdf/2606.29531.pdf GitHub

Abstract:
We propose MotionAtlas, a system for detailed captioning of motion‑centric videos, comprising (1) a dedicated human‑annotated benchmark, (2) a scalable, high‑quality pipeline to construct training samples, and (3) a family of powerful Video‑MLLMs. Unlike conventional global motion captioning datasets, we focus on region‑aware motion captioning: given a video and a spatiotemporal mask, the model generates precise descriptions of motion within the target region, thereby alleviating visual clutter and motion entanglement and enabling reliable, quantifiable evaluation. Concretely, we first build MotionAtlas‑Bench, a comprehensive benchmark comprising 2,073 multiple‑choice questions, meticulously annotated for a curated set of high‑quality, motion‑centric videos, to evaluate fine‑grained motion understanding of the objects in question. Second, we design a rigorous and scalable data pipeline that leverages self‑bootstrap refinement to suppress fine‑grained hallucinations, yielding 159k high‑quality motion captioning data. Third, we design a tailored training data composition strategy, which achieves consistent and substantial performance gains across diverse baseline Video‑MLLMs, including Molmo2 and Qwen3‑VL. For instance, MotionAtlas‑4B surpasses Qwen3‑VL‑4B by an average of 5.2 percentage points across general motion benchmarks. The benchmark, dataset, and code have been released.

PaperId: 61, https://arxiv.org/pdf/2606.29495.pdf GitHub

Abstract:
Social influence dialogue changes user behavior by altering internal cognitive states. The central evaluation question is whether the user's beliefs, desires, intentions, and emotions measurably change over the course of conversation, a process‑oriented criterion that neither surface‑level text metrics (BLEU/ROUGE) nor single‑score LLM judgments can capture. We propose the Cognitive World Model (CogWM), an LLM‑based user model that reframes multi‑turn dialogue evaluation from ``what did the user say'' to ``how did the user's internal cognitive state evolves.'' CogWM jointly predicts BDI/E cognitive states and user utterances and serves as both a user simulator and an evaluation platform, using a three‑tier evaluation framework that covers turn‑level fidelity, trajectory‑level state dynamics, and task‑level composite scoring. Trained via our Summarize‑and‑Allocate (SaA) annotation pipeline on 150,454 user‑turn samples across four social influence scenarios, CogWM achieves 77.6% emotion accuracy (2.1× over GPT‑5.5). In 3600 multi‑agent discrimination trials, it distinguishes six commercial agents by their cognitive influence, with Llama‑4‑Scout ranking first (CTS +0.233). CogWM moves social influence dialogue evaluation from terminal judgment to process tracking. We have released our code\footnote\scriptsize Code: https://github.com/lucianma05‑create/CogWM and models\footnoteModel: https://www.modelscope.cn/models/LucianMa/CogWM‑14B.

PaperId: 62, https://arxiv.org/pdf/2606.29493.pdf GitHub

Abstract:
Benchmarks for LLM‑assisted theorem proving in Lean are often treated as intrinsically reliable because every solved instance comes with a machine‑checked proof. However, the kernel only checks that a proof establishes a \emphformal statement; it does not verify that the statement faithfully encodes the intended informal problem, nor that evaluation harnesses are robust to trivial or adversarial solutions. We audit five widely used Lean theorem‑proving benchmarks and their forks, using corpus‑scale static checkers to surface 4,833 findings, including 398 mechanically certified issues such as counterexamples, vacuous theorems, and unsound axioms. We also document semantic defects such as missing hypotheses, problem simplification, incomplete or incorrect translations, and Lean‑specific specification hazards. Beyond dataset construction, we survey evaluation‑time failure modes and show, on corrected subsets, that defects can both inflate and deflate reported prover scores. We propose a fault taxonomy, a suite of automated checkers and recall‑oriented semantic audit prompts, and release standards to guide the creation of formal math datasets and to make evaluation more reproducible and trustworthy. Our checkers, audit prompts, and corrected dataset snapshots are available at https://github.com/Shashi456/atp‑checkers.

PaperId: 63, https://arxiv.org/pdf/2606.29464.pdf GitHub

Abstract:
Vision‑language dataset distillation (VLDD) compresses a large image‑text paired dataset into a small set of synthetic pairs that can efficiently train contrastive vision‑language models under strict data and compute budgets. Most existing methods match expert trajectories or cross‑modal statistics, yet still enforce full‑dimensional alignment in a Euclidean embedding space. This is often overly restrictive due to rank‑deficient image‑‑text correlation, with shared semantics concentrated in a low‑dimensional range and remaining variation spread across a weakly correlated residual subspace. LoRS relaxes alignment at the similarity level by low‑rank factorization, but does not explicitly control dominant alignment capacity and structure in the representation space. We thus propose a rank‑aware hyperbolic alignment (RAHA) that combines hierarchical geometry with explicit alignment‑capacity control. RAHA lifts multimodal representations to hyperbolic space and optimizes distilled pairs with asymmetric objectives that enforce geodesic alignment in the shared range while regularizing the residual subspace to preserve modality‑private diversity and improve transfer robustness. Experiments on benchmarks show that RAHA demonstrates competitive cross‑modal retrieval and improved transfer indicators under fixed budgets.

PaperId: 64, https://arxiv.org/pdf/2606.29457.pdf GitHub

Abstract:
When two companies bid to buy the same target, no one knows exactly what the target is worth. Each bidder pays for due diligence: costly, imperfect homework that sharpens its own private estimate before it bids. How much of that homework is worth buying? We build a simple computer model of the bidding contest and let it teach itself to bid well by playing against itself, the way a game engine learns chess. The economic question, how much diligence pays for itself, and the computational question, when the contest becomes too complex to solve exactly, are both controlled by a single thing: how many pieces of private information a bidder carries. Our main finding is that the right amount of diligence is modest and finite. It falls as diligence gets more expensive, and it falls further when both sides are doing their homework, because competition erodes the value of knowing more. We also test a recent claim from AI research: that simple, general self‑play methods can rival the specialized, expensive algorithms usually built for games like these. Running on an ordinary laptop with no costly frontier AI, we find the simple methods are the best of the self‑learning approaches, though purpose‑built exact methods still win whenever the game is small enough to solve outright. The simple methods earn their keep only once the game grows too large to solve exactly, which is the regime real deals live in, and there we show they still find strong bidding strategies. The contribution is threefold: a cheap, reproducible way to study deal‑making under uncertainty; a concrete, model‑based answer to how much due diligence is worth buying; and evidence about when lightweight, general‑purpose AI is good enough to replace specialized methods. We release all the games, code, and experiments.

PaperId: 65, https://arxiv.org/pdf/2606.29445.pdf GitHub

Abstract:
Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks. However, existing benchmarks primarily evaluate whether models can perceive shallow visual cues, while rarely examining whether MLLMs can learn deeper knowledge or procedural skills from video tutorials and generalize them to downstream long‑horizon agentic tasks. To address this gap, we introduce VG‑GUIBench (Video‑Guided GUI Benchmark), a new benchmark designed to evaluate whether MLLM‑based GUI agents can follow video tutorials to complete corresponding GUI interactive tasks. Furthermore, we observe that the performance of models on both VideoQA and video‑guided agentic tasks critically depends on effective keyframe extraction. Based on this observation, we propose TASKER (Task‑driven And Scene‑aware Keyframe searchER), a keyframe extraction algorithm that jointly considers task relevance and scene dynamics to identify informative frames. Experimental results demonstrate that TASKER achieves significant performance improvements on both VideoQA and video‑guided agentic task benchmarks, outperforming the best baseline by 2.0% on the EgoSchema fullset and 1.8% on the NExT‑QA dataset, respectively. These results further highlight the potential of generalized keyframe extraction methods for video understanding tasks. Our code and data are available at https://github.com/VG‑GUI‑TASKER/VG‑GUI‑TASKER.

PaperId: 66, https://arxiv.org/pdf/2606.29441.pdf GitHub

Abstract:
Inference‑time safety methods for large language models have proliferated, yet no systematic comparison exists. We evaluate five defense paradigms (no defense, static steering, CAST, AlphaSteer, probe‑gated) across seven instruction‑tuned models (7‑31B) and five attack types (GCG, AutoDAN, DeepInception, prefilling, intent laundering). Our central finding: prompt‑time activation defenses are structurally blind to prefilling attacks. AlphaSteer achieves 0% attack success on GCG, AutoDAN, and intent laundering but 50% on prefilling. We prove a corollary: any defense that gates intervention on a single layer's activation alignment with a benign reference (cone, subspace, or null‑space) is blind to attacks that craft activations to lie inside that reference, whether checked at prompt time or per token. As its constructive contrapositive we introduce response‑time probing: a linear probe on the model's hidden state at the first generated tokens, with AUROC 0.97‑1.00 across all seven models. Combined with a halt, it cuts prefilling attack success to 0/40 on every model with 0% benign false positives, outperforming Llama Guard 3. Cross‑template generalisation depends on probe depth, so we scope the claim to the canonical prefilling‑template family. Composing the response‑halt with AlphaSteer's null‑space steering gives an orthogonal split (the halt catches prefilling, AlphaSteer catches semantic attacks), reaching defense success 0.983 on Mistral and 0.994 on Llama and dominating both components. We further show MMLU fails to capture steering's true utility cost, which appears as behavioral hedging rather than factual loss, and that diverse negative training sets cut probe false positives from 80‑100% to near zero. Code, attacks, per‑sample results, and the judge prompt are released.

PaperId: 67, https://arxiv.org/pdf/2606.29425.pdf GitHub

Abstract:
Existing multi‑agent debate frameworks suffer from two critical limitations: they rely on static architectures where agent roles and coordination patterns are fixed at design time, and they require instantiating multiple model copies, incurring substantial computational overhead. We propose Mixture of Debaters (MoD), a unified framework that enables dynamic self‑debate within a single model by leveraging the Mixture‑of‑Experts paradigm. We address three key challenges in adapting MoE for dialectical reasoning: (1) dual‑routing that decouples role allocation from process flow, dynamically determining when to debate versus when to synthesize; (2) momentum switching that smooths token‑level routing with local context, reducing expert‑switch jitter; and (3) unified self‑debate that encapsulates diverse debating personas into lightweight expert modules, eliminating inter‑agent communication while preserving behavioral diversity. Extensive experiments on multimodal benchmarks demonstrate that MoD outperforms both single‑model baselines and conventional multi‑agent systems, achieving superior accuracy with 3.7x lower latency and 87% reduction in token consumption.The source code can be accessed at https://github.com/YongLD/MoD.

PaperId: 68, https://arxiv.org/pdf/2606.29377.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real‑world deployments remain fragile. Failures often stem from missing or weakly relevant evidence, as well as from generation that does not faithfully reflect the retrieved context. Many existing approaches rely on fine‑tuning, privileged access to internal model signals, or resource‑insensitive escalation strategies, which limits their practicality in black‑box and budget‑constrained settings. We propose D2R‑RAG (Diagnose‑to‑Repair RAG), a model‑agnostic and resource‑aware framework that combines lightweight failure diagnosis with adaptive repair. D2R‑RAG derives interpretable failure signatures from observable signals in the query, retrieved evidence, and generated response, and then selects from a small set of corrective actions under explicit latency and VRAM constraints. Experiments on FEVER and HotpotQA show that D2R‑RAG improves reliability over recent baselines and achieves better accuracy‑‑efficiency trade‑offs across multiple compute budgets. The code is available at https://github.com/CyberScienceLab/D2R‑RAG/.

PaperId: 69, https://arxiv.org/pdf/2606.29357.pdf GitHub

Abstract:
Vision‑language tracking guided by natural language specifications leverages high‑level semantic cues of target objects to substantially boost tracking accuracy and robustness. Existing studies have verified that adaptively optimizing textual descriptions throughout the tracking process can effectively mitigate the semantic‑visual mismatch induced by dynamic variations in target appearance, position, and other inherent attributes. Nevertheless, mainstream methods that directly generate textual information via sequence models or large language models inevitably suffer from inherent defects, including erroneous target updating, excessive background distraction, and pervasive hallucination artifacts. To address the aforementioned limitations, this paper proposes a novel language dependency parsing mechanism to precisely distill core tracking principal components, encompassing target objects, semantic concepts, and background contextual information. On this basis, we perform component‑aware adaptive textual description updates by exploiting the powerful cross‑modal understanding capability of the pre‑trained vision‑language model Qwen‑VL. By integrating the proposed elaborately designed modules into the baseline framework, our method achieves consistent and superior tracking performance on multiple large‑scale vision‑language tracking benchmarks, including TNL2K, LaSOT, TNLLT, and OTB‑LANG. The source code and pre‑trained models will be released at https://github.com/Event‑AHU/Open_VLTrack.

PaperId: 70, https://arxiv.org/pdf/2606.29354.pdf GitHub

Abstract:
Chain‑of‑Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural‑language rationales that are poorly aligned with efficient machine reasoning. We propose Communicative Language Symbolism Routing (CLSR), a test‑time framework in which multiple LLM agents autonomously invent, evolve, and share compact Language Symbolism Frameworks (LSFs), while a latent‑free router adaptively selects and composes these languages per query to optimize the accuracy‑token trade‑off. Unlike prompt optimization that refines surface instructions, CLSR treats each LSF as a reusable symbolic protocol with compact symbols, usage rules, and a message‑passing contract, and improves it through an evolutionary loop driven by correctness and token cost. At inference time, the router may invoke a single low‑cost LSF call, ensemble multiple LSFs, or execute a multi‑round LSF composition protocol on harder queries. Across challenging benchmarks, CLSR reduces latency‑oriented generated token completion by 3～ 6× compared to standard CoT while maintaining accuracy. We further derive an information‑theoretic lower bound on token cost under arbitrary symbolism and show that, under an interpreter‑realizability premise, multi‑round LSF protocols conditionally subsume program‑execution pipelines. Code is publicly available (https://github.com/pzqpzq/LSF_MDia).

PaperId: 71, https://arxiv.org/pdf/2606.29347.pdf GitHub

Abstract:
Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non‑stationary financial markets. The model incorporates a Market Regime Encoder, an Adaptive Gate Network, and an Adaptive Financial Context module to dynamically bias self‑attention based on semantic relationships between financial indicators. Unlike conventional Transformer architectures that treat all input features uniformly, the proposed approach groups 95 engineered financial features into 11 semantic categories and adapts attention according to latent market regimes. The study also identifies and corrects sequence alignment and backtesting issues that can inflate reported trading performance, and introduces a financially‑aware composite objective that jointly optimizes prediction error, directional accuracy, and non‑overlapping Sharpe ratio. Extensive experiments compare the proposed architecture against classical machine learning models, recurrent neural networks, and Transformer baselines using chronological evaluation, five random seeds, ablation studies, hyperparameter optimization, explainability analysis, and multi‑stock validation. Results demonstrate competitive predictive performance while reducing model complexity by 15.2% and improving parameter efficiency through feature selection, providing an interpretable Transformer architecture for financial time‑series forecasting.

PaperId: 72, https://arxiv.org/pdf/2606.29279.pdf GitHub

Abstract:
LLM agents carry conclusions across steps and sessions in compressed memory, and memory products (e.g., mem0, LangMem) rewrite conversation into stored "facts" that later steps trust. We show this rewriting manufactures confidence: across our constructed agent settings, a casual, hedged remark becomes a confident, dated assertion the agent then obeys like a verified fact, granting every above‑clearance request it faces. No attacker is needed: a role that was true once and never corrected is stored as a flat fact and acted on like a deliberate injection. We then isolate what the agent responds to. It is not the source: attributed, unattributed, and even forged "system of record" claims all grant alike. It is the confidence of the phrasing. A hedge is discounted, a flat assertion is obeyed, and this holds with no special keyword. Not all hedges are equal, though: the evidential register is the least‑discounted, with "reportedly" obeyed like a flat assertion on most models. The obvious fixes fail. A passive "unverified" tag is ignored, and an active "do not trust this" instruction escalates even correct memory, so it is safe only by refusing to decide. The real fix lives in the store: keep the tentative phrasing rather than upgrade it. But that is hygiene, not a defense against an attacker who can simply write a confident lie. The deployable lesson is narrower and constructive: a single load‑bearing memory is the hazard, and one redundant source restores correct decisions. We release the harness and demonstrations.

PaperId: 73, https://arxiv.org/pdf/2606.29247.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models represent a promising direction for embodied intelligence in surgical robotics. Despite the prevalence of VLA benchmarks for general robotics, standardized evaluation platforms specifically designed for surgical contexts remain absent. To address this limitation, we present SurgVLA‑Bench, the first comprehensive benchmark for evaluating VLA models in laparoscopic surgical robotics. Leveraging the SurRoL simulation platform, we construct a hierarchical task taxonomy ranging from atomic actions to complete surgical procedures, complemented by a multi‑dimensional evaluation framework assessing action accuracy and semantic consistency. We then systematically evaluate two representative paradigms, including autoregressive models such as OpenVLA, and flow matching models such as π_0, π_0.5, and SmolVLA. Our experiments show that autoregressive models tend to excel in semantic understanding, while flow matching models often achieve higher task precision but may face generalization trade‑offs. However, even the best‑performing models remain far from satisfactory, as the constrained endoscopic field of view, restricted viewing angles, and frequent occlusions persist as fundamental physical bottlenecks. The code and data are available at https://github.com/VCL‑HNU/SurgVLA

PaperId: 74, https://arxiv.org/pdf/2606.29180.pdf GitHub

Abstract:
A Knowledge Graph (KG) represents facts as structured triples and is widely used to organize relational knowledge across diverse domains. Just as textual information ranges from words and sentences to complete documents, KG information can be interpreted at multiple levels, from entities, relations, and triples to subgraphs and entire KGs. However, existing KG embedding methods mainly focus on entities, relations, and triples, leaving graph‑level semantics largely unaddressed. Conventional graph‑level methods, which typically compare graphs based on structural patterns, are also insufficient because structural similarity alone cannot guarantee semantic similarity between KGs. To evaluate how well different methods capture such graph‑level semantic information, we study graph‑to‑graph semantic similarity, which determines whether a pair of KGs represents semantically corresponding underlying information. To obtain reliable ground‑truth correspondences, we construct a semantic matching dataset by modifying text documents, extracting KGs from both original and modified documents, and transferring their known correspondences to KG pairs. We compare text‑based, structure‑based, and KG embedding‑based approaches on each dataset. For the KG embedding‑based approach, we introduce two scoring functions: EmbPairSim, which uses maximal pairwise entity similarity, and AvgEmbSim, which uses a frequency‑weighted centroid. Experiments on WikiText‑2 and CC‑News show that EmbPairSim achieves up to 5.3 pp higher MRR than Sentence‑BERT while using substantially fewer parameters. These results suggest that KGE representations can serve as compact and effective signals for graph‑to‑graph semantic similarity in KGs. Our code is available at https://github.com/SeungRyeolBaek/KG‑to‑KG‑Semantic‑Similarity.

PaperId: 75, https://arxiv.org/pdf/2606.29102.pdf GitHub

Abstract:
Jointly learning to segment and classify medical images demands cross‑task synergy, yet encoder‑sharing architectures limit decoder reconstruction to task‑private representations, permanently discarding the boundary cues and semantic priors each branch could supply to the other. This work introduces BTI‑Net, which establishes bidirectional communication at every decoder level through two parallel pathways via Task Interaction Modules (TIM). Spatial boundary context is gated into the classification branch, while global semantic priors multiplicatively modulate the decoder, with refined features propagating progressively from coarse semantics to fine boundary detail across all four decoder resolutions. Since cross‑task interaction is not equally reliable for every input, Uncertainty Proxy Attention (UPA) gates each TIM output per instance and per level using three signals that capture cross‑task alignment, scene complexity, and prediction confidence, without external annotations or additional inference passes. Experiments on three medical benchmarks spanning ultrasound, dermoscopy, and brain MRI demonstrate consistent improvements in segmentation IoU and classification accuracy over both encoder‑sharing and decoder‑interaction baselines. Ablation confirms adaptive gating contributes +2.36 IoU over fixed bidirectional interaction, and classification accuracy improves by up to +2.26 points over the strongest multi‑task baseline. UPA's uncertainty proxies serve as reliable single‑pass task‑failure signals without the overhead of stochastic sampling. Code: https://github.com/C‑loud‑Nine/BTI‑Net_MTL

PaperId: 76, https://arxiv.org/pdf/2606.29069.pdf GitHub

Abstract:
Concept‑based Explainable AI (C‑XAI) seeks human‑understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine‑grained concept annotations. We evaluate whether mid‑scale Multimodal Large Language Models (MLLMs) can perform localized concept naming under strict zero‑shot conditions by assigning labels to bounding‑box regions at both object and part levels. We propose a reproducible zero‑shot evaluation protocol for Concept Naming (CoNa) with (i) closed‑set, category‑constrained prompting for moderate vocabularies and (ii) Open‑CoNa, an embedding‑similarity‑based strategy for large label spaces. Experiments with four MLLMs (7B‑32B) show consistent performance trends across datasets, reaching 62%‑88% object‑level exact‑match accuracy, highlighting the potential of training‑free concept annotation from localized regions. We discuss limitations and failure modes and release a reproducible framework to support future low‑cost C‑XAI research.

PaperId: 77, https://arxiv.org/pdf/2606.29020.pdf GitHub

Abstract:
Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion. The key limitation of existing methods is the lack of diversity in weather appearance and effective control over weather dynamics (e.g., temporal evolution and particle motion). Most approaches rely on text prompts, which are inherently underspecified and often fail to produce detailed weather characteristics. Additionally, general‑purpose video editors optimized for clean and aesthetic outputs tend to suppress heavy weather phenomena, making dense particle effects difficult to generate. To address these, we propose a Semantic‑Aware, Physics‑Informed, and Geometry‑Grounded framework that steers an off‑the‑shelf video editor to synthesize diverse global appearances and detailed particle dynamics. We factorize the synthesis into three conditional signals, so that each provides a distinct and stable source of guidance: semantics specifies what the weather should look like, dynamics governs how it evolves over time, and geometry determines where it should appear in the scene. Specifically, we introduce (1) semantic‑aware appearance anchoring to establish the target appearance from scene semantics and user input; (2) physics‑informed dynamic simulation to generate particle effects by simulating a Gaussian‑represented particle field under gravity, wind, and turbulence; and (3) geometry‑grounded video synthesis to align the simulated particles with target scene geometry and synthesize the final video. Experiments demonstrate that our method produces diverse, physically and visually realistic weather effects. Furthermore, we show that our synthesized data significantly improves the robustness of autonomous driving semantic segmentation under adverse weather conditions. Project page: https://jumponthemoon.github.io/w‑crafter/.

PaperId: 78, https://arxiv.org/pdf/2606.28932.pdf GitHub

Abstract:
Large language models have driven recent progress in language and multimodal AI, yet pre‑training them at scale is prohibitively expensive. Low‑rank pre‑training, which factorizes each weight matrix into a rank‑r product to reduce both parameters and FLOPs, is a promising response but typically lags full‑rank training in quality. We propose Duplicated Latent Residual (DLR), a training‑only, parameter‑free, foldable plug‑in for low‑rank pre‑training. DLR augments the standard low‑rank output Bz with a fixed structured residual alpha/sqrt(K) Expand_K(z) that replicates each latent coordinate K = ceil(d_out/r) times across the output. With alpha fixed, DLR adds zero learnable parameters per layer; after training, it is absorbed into the up‑projection in closed form, B = B + alpha/sqrt(K) R^T, so deployment parameter count, FLOPs and memory match the underlying low‑rank backbone exactly. Across LLaMA models from 60M to 7B parameters, DLR strengthens low‑rank pre‑training on C4 validation perplexity in most settings, with the clearest gains at 130M and above; folded checkpoints transfer cleanly to supervised fine‑tuning on standard benchmarks.

PaperId: 79, https://arxiv.org/pdf/2606.28831.pdf GitHub

Abstract:
Long‑context LLM inference faces a fundamental conflict: head‑adaptive compression algorithms (e.g., Top‑p nucleus sampling) offer superior accuracy by dynamically fluctuating memory budgets, yet modern inference engines (e.g., vLLM) demand rigid, static memory patterns to leverage CUDA Graphs and PagedAttention. We resolve this ``Static‑Dynamic'' mismatch with HARD‑KV, a unified framework that that bridges dynamic selection with rigid system constraints. HARD‑KV introduces a Cascade Cache hierarchy, managing the token lifecycle across dense, sparse, and condensed tiers. Crucially, we propose a Logits Calibration mechanism that normalizes diverse importance metrics into a unified probability space, enabling consistent Top‑p budgeting across heterogeneous heads. To bridge the efficiency gap, we offer a system‑level solution, which rewrites fragmented, dynamic indices into contiguous physical layouts compatible with high‑performance inference engine. Extensive experiments on math‑reasoning benchmarks (AIME, U‑Math) verify that HARD‑KV achieves up to 2× throughput improvement over static baselines while maintaining high‑fidelity generation in 10k+ token scenarios. Code is available at https://github.com/SuDIS‑ZJU/HARDInfer.

PaperId: 80, https://arxiv.org/pdf/2606.28813.pdf GitHub

Abstract:
Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions. However, transferring human demonstrations to robots remains challenging due to embodiment mismatch, scene variation, and robot‑specific feasibility constraints. We present Human2Any, a framework for learning reusable object‑centric interaction priors from human videos without requiring real‑world robot demonstrations in the target task contexts. Human2Any represents manipulation through object‑object interaction motion, capturing task‑relevant scene changes while abstracting away embodiment‑specific details. It composes learned interaction priors with robot‑side feasibility reasoning and motion planning, allowing the same human‑derived knowledge to adapt to different embodiments, scene geometries, and task contexts. We validate Human2Any across diverse manipulation settings, including real‑world experiments on a Franka tabletop setup and an RBY‑1 humanoid mobile robot, demonstrating robust interaction‑centric manipulation without real‑world robot training data. Project website: https://human2any.github.io/.

PaperId: 81, https://arxiv.org/pdf/2606.28733.pdf GitHub

Abstract:
LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable in the available environment. In such cases, a reliable agent should recognize that further interaction is unlikely to help and abstain from additional tool calls. We define Agentic Abstention, the problem of deciding when an agent should stop acting under uncertainty. Unlike standard LLM abstention, which is usually evaluated as a single‑turn answer‑or‑abstain decision, agentic abstention is a sequential decision problem: an agent can answer, abstain, or gather more information at each turn, and the need to abstain may only become clear after interacting with the environment. We study this problem across web shopping, terminal environments, and question answering, evaluating 13 LLM‑as‑agent systems and 2 agent scaffolds on more than 28,000 tasks. Our results show that the main challenge is not only whether agents can abstain, but also when they abstain. Some agents never abstain when they should, while others do so only after many unnecessary interactions. This gap is especially large on tasks where the instruction appears feasible until the environment reveals otherwise (e.g., no valid result matches the instruction). We further find that model scale, reasoning, and agent scaffolding affect abstention in different ways, where larger or more capable models sometimes perform worse at timely abstention. Finally, we introduce CONVOLVE, a context engineering method for improving agentic abstention that distills full interaction trajectories into reusable stopping rules. On WebShop, CONVOLVE substantially improves timely abstention without updating model parameters, raising Llama‑3.3‑70B's timely recall rate from 26.7 to 57.4. Our dataset and code are available at https://lhannnn.github.io/agentic‑abstention

PaperId: 82, https://arxiv.org/pdf/2606.28710.pdf GitHub

Abstract:
We ask under what conditions an agent with a harm‑minimizing policy can displace an approval‑seeking (RLHF) agent in a competitive market, and when that policy is sufficient to prevent community harm. We use evolutionary game theory (finite‑population Moran‑Fermi pairwise comparison) to formalize this subject to assumptions of wisher hindsight, peer testimony, a monotone harm ledger, sufficient information density of community feedback, and a finite, depleting resource pool, in a negative‑sum environment. We show that adoption is favored when the prior distributions on how readily wishers attune to community sentiment are monotone, exhibit endpoint inversion, and have a centro‑symmetric pairing property, and demonstrate this with several long‑tailed priors (Hill, Pareto, Lomax, Frechet). Where it is favored, a critical adoption level separates communities that drift back to the approval‑seeking agent from those for which the audited agent fixes; above that level fixation is the overwhelmingly likely outcome. We derive when fixation is attainable as a bound on the effective (informational) size N_c of the community, which must be small enough to allow fixation before depletion. We present these as Theorems 5.4 and 5.5; the algebraic and finite‑grid backbone is machine‑checked in Lean 4, with the barrier‑crossing asymptotics retained as explicit hypotheses. We show that a self‑audited agent with a community ledger is not, in general, sufficient to prevent community harm. Sufficiency depends both upon the alignment of the agent's audit with community values and the timeframe over which harm is evaluated. Regardless of alignment, once adoption reaches dominance, the state is absorbing. The same policy that reduced harm under alignment becomes a trap, welfare‑negative under misalignment and, even under alignment, one that locks in harm deferred past the adoption horizon.

PaperId: 83, https://arxiv.org/pdf/2606.28692.pdf GitHub

Abstract:
Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy. It is inherently iterative: candidates are weighed against many constraints, revised as evidence emerges, and grounded in verifiable sources. Here we introduce ATHENA‑R1, an AI agent for treatment reasoning across all FDA approved drugs since 1939, trained by reinforcement learning over a universe of 212 biomedical tools. At each step it identifies missing information, selects and runs relevant tools, and incorporates the evidence. To train it without human‑annotated traces, we build a two‑level self‑learning framework: multi‑agent systems construct the tools, tasks, and reasoning trajectories for supervised fine‑tuning, then reinforcement learning with scientific feedback rewards reasoning quality (evidence gathering, grounded tool use, logical non‑redundancy). Across five benchmarks of 3,168 drug reasoning tasks and 456 patient treatment cases, ATHENA‑R1 outperforms language models and tool‑use systems, reaching 94.7% accuracy on open‑ended drug reasoning and 82.9% on treatment reasoning, 17.8 and 10.7 points above GPT‑5. In blinded evaluations by experts from 28 rare disease organizations, it is preferred over reference models on all criteria, and physicians rated it favorably on complex hospitalized cardiovascular and infectious‑disease cases. Adverse‑event hypotheses it generated, tested in electronic health records from 5.4 million patients, reached adjusted odds ratios of 1.48‑1.84, with no elevation among negative controls. Because it requires knowing what evidence to seek before concluding, treatment reasoning has long been hard for AI; we show it can be reframed as a learnable process of iterative evidence gathering that reinforcement learning can train AI to perform.

PaperId: 84, https://arxiv.org/pdf/2606.28683.pdf GitHub

Abstract:
Large Language Models (LLMs) often face ethical tradeoffs in which several responses may be defensible but express different priorities, such as fairness, honesty, courage, or restraint. We introduce VirtueMap, a framework for describing these patterns through an Aristotelian virtue‑ethics lens. Instead of asking for a single correct answer, VirtueMap asks humans or LLMs to rank all five responses to each of seven general, non‑lethal, non‑political, and non‑religious ethical dilemmas. To define the reference orderings used for scoring, we first proposed, for each dilemma and virtue, an ordering of the five responses from most to least expressive of that virtue. We then collected more than 100 respondent evaluations per ordering and retained it as operational ground truth only when at least 95% confirmed it. Rankings are scored against these retained orderings using normalized Borda alignment, yielding profiles over Practical Wisdom, Justice, Truthfulness, Courage, and Temperance. We apply VirtueMap to nine LLM families in a repeated‑run evaluation and find high mean rank consistency (90.3%), with the largest differences appearing on Courage, Temperance, and Justice. We also release an interactive website that computes profiles locally in the browser and compares respondents with measured LLM profiles.

PaperId: 85, https://arxiv.org/pdf/2606.28661.pdf GitHub

Abstract:
People overthink; language models over‑sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test‑time scaling), and the more they draw, the more often a correct answer turns up somewhere, so coverage, the fraction of problems with at least one correct try, climbs and appears to be progress. But a deployed system must return one answer, and choosing it, not knowing which try is right, is selection; selection is capped, and past a point extra samples only make the model surer of a confident mistake, even as every draw adds cost. The gap between climbing coverage and stalled selection, the identifiability gap, is the answer a model can produce but not pick. So the real question is not whether to sample but how far, and the answer is: not far. For picking an answer, the vote has already settled within a few dozen draws, the modal ceiling; for scoring a benchmark, sooner still, the correlation ceiling. Beyond that, extra draws cost compute and add nothing, and can even make the answer worse. This paper turns the cutoff into a single number, the effective number of samples, that any sampling run already reveals. The bottleneck is recognizing a right answer, not generating one.

PaperId: 86, https://arxiv.org/pdf/2606.28656.pdf GitHub

Abstract:
Deformable 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for rendering dynamic scenes in a wide range of 3D applications. However, existing deformation field‑based approaches largely lack explicit object‑level modeling, often resulting in inconsistent Gaussian deformations within individual objects and unwanted coupling between different objects. To address this limitation, we introduce a semantics‑guided framework that enforces dynamic regularization at the object level, aiming to achieve spatially consistent object‑wise deformation. Specifically, we first extract segmentation masks using the Segment Anything Model (SAM) and derive semantic features from input images. An object‑ID map is then constructed via feature relevance matching with a predefined object dictionary. Guided by this object‑ID map, we identify the pixel‑wise top‑k contributing Gaussians for each object and impose consistency regularization on their deformation parameters, including position, scale, and rotation. Unlike prior methods that learn deformation fields without explicit object‑level constraints, our approach incorporates semantic cues to guide deformation behavior at the object level. Experimental results demonstrate that our semantics‑aware regularization improves object‑level deformation consistency and outperforms baseline methods in rendering quality, achieving higher PSNR and SSIM and lower LPIPS in dynamic 3DGS rendering. Our project page is available at https://dyn‑reg‑3dgs.github.io/.

PaperId: 87, https://arxiv.org/pdf/2606.28615.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly deployed in high‑stakes domains, where free‑text explanations such as chain‑of‑thought and post‑hoc rationales are used to justify model outputs. Yet it remains unclear whether these explanations are sufficient, i.e., if they contain enough information to explain the model's output‑generating process. We generalize classical sufficiency from feature attributions to arbitrary explanations and prove that explanation sufficiency can change depending on the input distribution, which must be explicitly defined for LLM explanations. We propose using the LLM itself to generate alternative inputs conditioned on an explanation, capturing its beliefs about possible inputs. We formalize self‑consistent sufficiency as a goal for free‑text explanations and introduce an information‑theoretic metric, SCSuff, that enables evaluation of free‑text explanations without relying on predefined biases or shortcuts. Our experiments show that SCSuff agrees with targeted perturbation tests where applicable and demonstrate that explanation sufficiency can vary with the input distribution. We find LLM explanations are generally insufficient and weakly correlated with model size, accuracy, or output entropy. Analysis of final‑token hidden states shows that top and bottom SCSuff scores can be predicted from internal representations, suggesting that SCSuff can guide detection and improvement of sufficient LLM explanations. The code for this paper is available at https://github.com/rajesh‑lab/self‑consistent‑sufficiency .

PaperId: 88, https://arxiv.org/pdf/2606.28601.pdf GitHub

Abstract:
Recent progress in Text‑to‑SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.0 and BIRD remains far below that on classical academic datasets. We argue that the main bottleneck is no longer reasoning, but database representation. Real databases contain repeated audit columns, large groups of similar tables, opaque identifiers whose meanings are stored only in documentation, and extensive data dictionaries with little query‑relevant information. Existing query‑aware methods, including schema linking and retrieval‑based schema selection, filter this raw context but still operate on redundant and verbose representations. We reformulate the problem as database context compression, a query‑agnostic transformation that rewrites schemas, semantic descriptions, and external documentation into a compact representation. We formalize this transformation with the SGCF (Support‑Gain Component Factorization) principle, which unifies repeated column extraction, isomorphic table templating, semantic componentization, and evidence purification under a single coverage objective. Based on SGCF, we propose DBCC, a database‑side middleware that performs offline structural and semantic compression together with lightweight online evidence purification. DBCC is model‑agnostic and can be integrated into existing Text‑to‑SQL pipelines. On Spider 2.0‑Snow and BIRD, DBCC reduces input context by up to two orders of magnitude (from 2.6M to 34.7K tokens on the largest Spider 2.0‑Snow subset), improves schema‑linking strict recall from 0% to 56.5% under DeepSeek‑V3.2 (63.1% under Claude Opus 4.7), and consistently increases end‑to‑end execution accuracy by 1.8‑1.9% over three recent Text‑to‑SQL systems. Our code is open‑sourced at https://github.com/MrBlankness/SchemaCompression.

PaperId: 89, https://arxiv.org/pdf/2606.28593.pdf GitHub

Abstract:
While recent vision‑language models (VLMs) have achieved significant improvements on static visual‑to‑code tasks such as generating code for webpages, charts, or SVGs, it remains unclear whether they can recover temporal dynamics when motion is present. To this end, we introduce Animation2Code, a benchmark for evaluating temporal visual reasoning via reconstructing executable web animation code from videos. Animation2Code consists of 1,069 web animation videos with diverse visual appearances and motion patterns, paired with corresponding HTML/CSS/JavaScript implementations. We propose two human‑aligned metrics, appearance similarity and temporal similarity, which allow us to disentangle visual fidelity from temporal alignment when comparing rendered animations against ground‑truth samples. Benchmarking state‑of‑the‑art VLMs on this dataset shows that current VLMs struggle to maintain temporal consistency in reconstruction, even when achieving high appearance similarity, including under finetuning and iterative refinement settings. Code and data are available at https://anya‑ji.github.io/animation2code‑website .

PaperId: 90, https://arxiv.org/pdf/2606.28589.pdf GitHub

Abstract:
Current approaches to enhance Large Language Model (LLM) reasoning, such as Chain‑of‑Thought and "Wait" prompts, primarily encourage models to think more, yet often fail to guide them toward Truth. While Representation Editing (RepE) offers a intrinsic control, its application to dynamic reasoning trajectories remains underexplored. In this work, we bridge this gap by investigating the geometry of truth within unfolding reasoning chains. We uncover three critical insights: (1) Truth is encoded at the sentence level and is entangled with latent reasoning patterns; (2) Effective intervention follows an Uncertainty Principle and a Decay Effect, requiring localization to early, high‑entropy forks; (3) Naive steering vectors suffer from noise, risking collateral damage to correct trajectories. Based on these findings, we propose DynaSteer, a dynamic RepE framework. DynaSteer employs pattern clustering to disentangle reasoning manifolds and utilizes Fisher‑LDA to project purified truth. By dynamically monitoring lookahead entropy, it selectively steers and rolls back trajectories only when necessary. Comprehensive experimental results on several MATH benchmark verify the effectiveness of DynaSteer, and experiments on out‑of‑domain coding tasks further confirm its generalization ability. Our code is publicly available at https://github.com/tianlwang/DynaSteer.

PaperId: 91, https://arxiv.org/pdf/2606.28537.pdf GitHub

Abstract:
Multiview mammography relies on paired craniocaudal (CC) and mediolateral oblique (MLO) views to provide complementary projections of a 3D breast volume, enabling precise anomaly localization. However, acquiring high‑quality, balanced datasets remains challenging for deep learning applications. We propose a novel method to synthesize multiview mammograms by leveraging the inherent geometric relationship between CC and MLO views. To enforce an implicit 3D consistency prior during generation, we develop an alignment module that searches a 2D affine transformation subspace to establish optimal anatomical correspondence. Leveraging this alignment, we introduce a pixel‑space self‑consistency loss based on the Earth Mover's Distance (EMD) between the 1D anteroposterior (AP) axis tissue distributions of the generated images. Integrated into a pretrained flow matching model, MammoFlow forces synthesized pairs to share physically plausible tissue distributions from the chest wall to the nipple. To our knowledge, this is the first work to guide multiview mammogram generation using implicit geometric tissue correspondence. Our method demonstrates superior image quality, passes expert radiologist evaluation, and generates physically consistent pairs that improve downstream classification AUC by 5%. Code is available at https://github.com/XYPB/MammoFlow

PaperId: 92, https://arxiv.org/pdf/2606.28410.pdf GitHub GitHub

Abstract:
Open‑vocabulary semantic segmentation (OVSS) enables text‑guided segmentation of unseen objects, breaking fixed‑class limitations to achieve open‑world understanding. However, existing OVSS methods primarily focus on modifying the CLIP attention mechanism, which still suffers from unstable local segmentation for remote sensing (RS) domain. To address these limitations, we propose RSGPNet, a training‑free geometric prompting framework for RS OVSS that refines segmentation by leveraging object geometric areas and consistency constraints. Specifically, RSGPNet comprises three core modules: a Text‑guided Coarse Mask module (TCM), a Geometric Re‑prompting Module (GRP), and a Coarse‑to‑fine Consistency Verification Mechanism (CVM). TCM utilizes text prompts and the input image to construct initial coarse segmentation masks. GRP then converts these coarse masks into geometric box prompts, feeding them back into the segmentation model to generate refined masks. Finally, CVM employs consistency computation to prevent prompting from reinforcing erroneous regions. They allow the model to improve segmentation accuracy in complex areas, such as category boundaries. Extensive experiments on RS datasets demonstrate that RSGPNet significantly outperforms state‑of‑the‑art methods across both quantitative and qualitative metrics while exhibiting excellent interpretability. The code is released at \hrefhttps://github.com/wangshanwen001/RSGPNethttps://github.com/wangshanwen001/RSGPNet.

PaperId: 93, https://arxiv.org/pdf/2606.28392.pdf GitHub

Abstract:
Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal. We present RADIANT‑PET, a reasoning‑augmented framework that couples a high‑sensitivity voxel‑level segmentation model with lesion‑level large language model (LLM) adjudication. Candidate uptake regions are generated with a deliberately permissive segmentation stage, then converted into structured textual descriptions that summarize uptake intensity, morphology, and regional and global anatomical context. An LLM classifies each candidate as true lesion vs. false positive, optionally leveraging the radiology report as additional clinical context. To strengthen lesion‑level reasoning, we further optimize a local LLM via reinforcement learning using Group Relative Policy Optimization, rewarding correct lesion classification and anatomically concordant site assignment. Across AutoPET and an OSU test cohort, RADIANT‑PET consistently outperforms strong image‑only baselines, with the largest improvements observed when radiology reports are provided. Overall, these results demonstrate that LLM‑based lesion‑level reasoning adds a novel reasoning layer beyond conventional segmentation, suppressing physiologic false positives and aligning voxel‑level predictions with clinical interpretation. The project repository is available at: https://github.com/jwang‑580/RADIANT‑PET.

PaperId: 94, https://arxiv.org/pdf/2606.28344.pdf GitHub

Abstract:
Augmenting large language models (LLMs) with retrieved web text has become a dominant paradigm, yet the web is not natively textual: existing systems depend on complex parsing pipelines that linearize HTML and discard layout, visual structure, and formatting. We introduce PixelRAG, a new retrieval‑augmented method that represents websites in their native visual form and performs retrieval and reading entirely in pixel space, enabling an end‑to‑end architecture that eliminates text abstraction. PixelRAG is, to our knowledge, the first pipeline to operate over a full Wikipedia corpus in this form, scaling to a datastore of 30 million screenshot images with an efficient visual retrieval index. Built on an existing visual embedding model (i.e., Qwen3‑VL‑Embedding), PixelRAG further fine‑tunes this model on screenshot data with carefully curated contrastive training data. Retrieved screenshots are then fed directly as pixel inputs to a VLM, without intermediate text conversion. PixelRAG consistently outperforms both no‑retrieval and text‑based RAG baselines, most surprisingly on widely studied text‑centric tasks such as NQ and SimpleQA. It also achieves strong gains on multimodal open‑domain QA (e.g., MMSearch), benchmarks over noisy news corpora (e.g., LiveVQA), and agentic benchmarks (e.g., MoNaCo), improving accuracy by up to 18.1% over text‑based baselines. Finally, pixel representations enable a new efficiency lever for RAG through image compression, achieving up to 3x token cost reduction at lower resolutions while maintaining accuracy. Our results challenge the necessity of text representations in web retrieval, suggesting that web RAG can operate directly in the web's native visual form while improving both performance and efficiency.

PaperId: 95, https://arxiv.org/pdf/2606.28335.pdf GitHub

Abstract:
We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution \mathbbP(position\midcontext) over a real political space. We evaluate nine current LLMs using a unified measurement framework anchored by VAA‑CHES projection models, which map responses onto three validated dimensions (lrgen, lrecon, galtan) across six contextual axes. Our findings reveal high sensitivity to context: persuasive framing and under‑represented languages displace coordinates by up to 0.57 and 0.52 units, respectively, while chain‑of‑thought reasoning often amplifies rather than dampens paraphrase instability. Despite this local plasticity, the model cohort occupies a remarkably narrow Overton envelope overall, occupying roughly one‑third the spread of major European parties. Supported by a multi‑trait multi‑method (MTMM) analysis, we conclude that a single point cannot summarize LLM political behavior; it must be characterized as a shape. Our code and data are publicly available at https://github.com/sakhadib/LLM‑Ideoplasticity.

PaperId: 96, https://arxiv.org/pdf/2606.28323.pdf GitHub

Abstract:
Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a single hand remains challenging. Adding a new task on top of an existing manipulation skill often imposes conflicting demands on overlapping fingers and contact modes, causing destructive interference between preserving an existing manipulation outcome and executing a new one. We propose DexCompose, a role‑aware residual composition framework that reuses pretrained dexterous policies for multi‑task manipulation through explicit finger‑level action ownership. Given two pretrained full‑hand policies, DexCompose first collects successful post‑task states from the first skill and performs release tests over candidate finger masks to identify which fingers are necessary for maintaining the established skill state. It then trains two asymmetric residual modules: a bounded residual stabilizer for task preservation, and a context‑aware residual that adapts the frozen downstream policy only within the action subspace assigned to the new task. We evaluate the framework on 16 composite dexterous manipulation tasks spanning four object‑retention skills and four downstream interactions. DexCompose achieves a 77.4% average composite success rate, demonstrating that structural action ownership with dual residuals offers a promising direction for composing dexterous skills beyond conventional policy chaining.

PaperId: 97, https://arxiv.org/pdf/2606.28215.pdf GitHub

Abstract:
Extracting dynamic 4D object interactions from massive, in‑the‑wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. However, existing monocular 4D reconstruction methods primarily focus on isolated objects, often failing under the severe occlusions and complex dynamics inherent in multi‑object interactions. To bridge this gap, we propose HAT‑4D, the first agentic framework designed to reconstruct the 3D geometry, temporal dynamics, and physical interactions of multiple objects from a single video. By integrating VLMs with a multi‑level human‑in‑the‑loop feedback mechanism, HAT‑4D efficiently resolves depth ambiguities and interaction‑induced occlusions during 3D generation and 4D propagation, yielding physically plausible assets without relying on expensive multicamera rigs. As a scalable data engine, HAT‑4D facilitates the creation of MVOIK‑4D, an open‑world benchmark for monocular 4D interaction reconstruction, accompanied by a novel multi‑dimensional evaluation protocol focused on physical plausibility and temporal consistency. Extensive experiments demonstrate that HAT‑4D achieves SOTA performance on most evaluation metrics, while maintaining competitive semantic alignment. Ablation studies show that introducing a small amount of human feedback improves interaction reconstruction. Moreover, the data produced by HAT‑4D effectively improves baseline performance when used for fine‑tuning. Our data and code are available at https://lijiaxin0111.github.io/HAT4D/

PaperId: 98, https://arxiv.org/pdf/2606.28128.pdf GitHub GitHub

Abstract:
Video generation models have emerged as a promising paradigm for embodied world simulation. However, both general‑domain video generators and robot‑specific data fine‑tuned models can still produce physically implausible manipulations, including discontinuous motion trajectories and inconsistent robot‑object interactions, which limits their reliability as world simulators. Through extensive experiments, we find that such physical instability mainly arises from two factors: deformation of moving objects and implausible spatio‑temporal correlations among interacting entities, particularly during contact. Building on this observation, we propose PhysisForcing, a scalable training framework that strengthens physical consistency by focusing supervision on physics‑informative regions through joint optimization of pixel‑level and semantic‑level features. The framework consists of a pixel‑level trajectory alignment loss, which supervises DiT features using reference point trajectories, and a semantic‑level relational alignment loss, which aligns DiT features with inter‑region relations extracted from a frozen video understanding encoder. Extensive experiments on R‑Bench, PAI‑Bench, and EZS‑Bench show that PhysisForcing consistently improves embodied video generation over strong baselines, improving the Wan2.2‑I2V‑A14B and Cosmos3‑Nano base models on R‑Bench by 22.3% and 9.2% (7.1% and 3.7% over vanilla finetuning), with the Cosmos3‑Nano variant attaining the best overall score. Beyond generation, as a world model under the WorldArena action‑planner protocol it raises the closed‑loop success rate from 16.0% to 24.0% and further improves downstream policy success, indicating that physically aligned video models yield stronger representations for robotic manipulation.

PaperId: 99, https://arxiv.org/pdf/2606.28122.pdf GitHub

Abstract:
Neural operators provide deep neural networks for learning mappings between function spaces. Among them, the Fourier Neural Operator (FNO) is particularly effective: its spectral convolution relies on low‑dimensional Fourier‑domain representations and can handle inputs at different resolutions. This design aligns well with settings where the Fourier basis diagonalizes the underlying operator, such as linear, constant‑coefficient PDEs on periodic domains, in which Fourier modes evolve independently. However, nonlinear PDEs may benefit from an additional inductive bias, as they exhibit structured interactions between modes, governed by polynomial nonlinearities. To capture this inductive bias, we introduce the Higher‑Order Spectral Convolution, a spectral mixer that extends FNO from diagonal modulation to explicit n‑linear mode mixing, aligned with the dynamics of nonlinear PDEs. Our experiments on standard benchmarks show that the proposed Higher‑Order FNO (HO‑FNO) retains the efficiency of FNO‑based architectures and consistently improves over other spectral neural operators. HO‑FNO also performs on par with or better than state‑of‑the‑art transformers and state‑space models on several datasets, with stronger gains in highly nonlinear regimes, such as the Poisson equation with polynomial forcing, where a single HO‑FNO layer outperforms FNO models with up to 16 layers. We open‑source our code for reproducibility at: https://github.com/AlexColagrande/HO‑FNO.

PaperId: 100, https://arxiv.org/pdf/2606.28094.pdf GitHub

Abstract:
Real‑world object removal is challenging due to two key difficulties: the target object's non‑local effects, such as shadows and reflections, which are difficult to model, and the fact that user‑provided masks are often inaccurate or incomplete. With billions of parameters and tens of denoising steps, diffusion‑based models achieve strong removal performance at the expense of substantial computational cost, limiting their use in interactive applications and on edge devices. To address these challenges, we present OSOR (One‑Step Object Removal), which simultaneously achieves efficient, effect‑aware, and mask‑robust object removal. Concretely, OSOR introduces: (1) an occupancy‑guided discriminator for precise boundary supervision, enabling stable single‑step diffusion training; (2) an alpha head that leverages knowledge from pretrained diffusion models to predict appropriate removal regions with minimal overhead, thereby handling imperfect masks; and (3) a semantic‑anchored verification pipeline (SAVP) that filters noisy instruction‑based triplets to produce effect‑aware supervision at scale. Using SAVP, we curate CORNE, which contains 280K verified removal pairs, and further annotate AnimeEraseBench and TextEraseBench to evaluate performance on more complex removal tasks. Experiments show that OSOR surpasses strong multi‑step diffusion baselines in perceptual quality while achieving 4× to 30× faster inference.

PaperId: 101, https://arxiv.org/pdf/2606.27974.pdf GitHub

Abstract:
Knowledge‑based Visual Question Answering (KB‑VQA) requires models to combine image understanding with external knowledge. Most prior methods use a fixed retrieve‑then‑generate pipeline with a pre‑selected retriever and a static top‑k setting, which is not adaptive during reasoning. We propose ProMSA, a progressive multimodal search agent for KB‑VQA. Given an image‑question pair, the agent iteratively chooses image search, text search, or stop, under explicit tool‑call budgets and with deduplication to avoid redundant retrieval. For training, we first use rejection‑sampling SFT to learn valid tool‑use formats, then optimize the agent with TN‑GSPO, a sequence‑level RL objective that normalizes updates by both generation length and tool‑interaction depth. Experiments on E‑VQA and InfoSeek show consistent gains over strong RAG and agent baselines, and improved retrieval and end‑to‑end accuracy. The code is available at https://github.com/DingWu1021/Promsa.

PaperId: 102, https://arxiv.org/pdf/2606.27876.pdf GitHub

Abstract:
Spatial intelligence is essential for low‑altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation. However, existing UAV benchmarks often emphasize image‑level recognition, single‑view understanding, or narrow answer formats, leaving 3D spatial inference, multi‑view collaboration, scene dynamics, and diverse task formulations insufficiently evaluated. To address these gaps, we introduce SpatialUAV, a real low‑altitude UAV benchmark comprising 4,331 curated instances across 14 fine‑grained task types, covering semantic discrimination, spatial relation, aerial‑‑aerial collaboration, aerial‑‑ground collaboration, and motion understanding. SpatialUAV organizes all samples into a unified visual‑input‑‑question‑‑answer schema, while supporting seven input configurations and nine answer formats, including option labels, region identifiers, geometric values, cross‑view correspondences, and free‑form motion descriptions. To ensure reliable and grounded evaluation, our data construction pipeline integrates detector‑assisted regions, depth supervision, metadata‑derived rules, extensive manual annotation, blind filtering, and multi‑turn human validation, together with task‑specific metrics for heterogeneous outputs. Evaluating representative vision‑language models across three categories, we show that current models remain far from human‑level performance, with pronounced bottlenecks in cross‑view association, structured grounding, geometric reasoning, and temporal viewpoint understanding. These results offer empirical guidance for advancing low‑altitude UAV spatial intelligence. Code and data are available at https://github.com/Hyu‑Zhang/SpatialUAV.

PaperId: 103, https://arxiv.org/pdf/2606.27831.pdf GitHub

Abstract:
This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus‑DETR, a novel detection framework based on biological hippocampal memory modeling. This framework integrates a hippocampal memory network module, HipNet, into the DETR architecture and systematically simulates the anatomical structure and functional organization of hippocampal subregions, including the entorhinal cortex, dentate gyrus, CA3, CA1, and subiculum. Through this design, Hippocampus‑DETR realizes pattern separation, pattern completion, importance filtering, and information integration of visual encoding features. During training, different memory submodules are optimized using a layer‑wise training strategy, ultimately forming a memory system with memory retrieval and completion capabilities. Experimental results demonstrate that Hippocampus‑DETR achieves higher detection accuracy than current mainstream models. More importantly, models equipped with this framework also exhibit excellent generalization ability and data efficiency in tasks such as few‑shot image classification, multimodal feature construction, and image restoration. Subsequent experiments further validate the functional necessity and internal interpretability of each memory submodule. This study not only provides a novel object detection framework, but also offers a feasible technical pathway for integrating neurocognitive mechanisms with deep learning models, highlighting its significant value in improving model learning efficiency and task robustness. The project is available at https://github.com/2186cloud/hipnet.

PaperId: 104, https://arxiv.org/pdf/2606.27786.pdf GitHub

Abstract:
Retrieval‑augmented generation (RAG) enhances LLMs by incorporating external knowledge to support response generation. However, conflicts between retrieved context and parametric knowledge have emerged as a critical challenge in RAG systems. To mitigate such conflicts, numerous studies have attempted to identify and edit knowledge‑related internal neurons, aiming to improve the ability of LLMs to rely on contextual evidence during generation. However, these neuron‑level approaches may introduce unintended cascading effects that compromise the general capabilities of LLMs, as the modified neurons are often entangled with broader model behaviors and functionalities. In this paper, we introduce SHIFT, a novel framework that reformulates neuron‑level modification as learnable gate modulation, allowing LLMs to adaptively regulate internal activations for knowledge conflict resolution. Technically, our SHIFT equips LLMs with a lightweight gate module and optimizes fewer than 0.01% trainable parameters while keeping the backbone model frozen. During generation, the gate module adjusts the model's internal representations to adaptively leverage contextual and parametric knowledge. Extensive experiments on six datasets validate the effectiveness of our SHIFT in comparison with various competing baselines. All datasets and code are available at https://github.com/OpenBMB/SHIFT.

PaperId: 105, https://arxiv.org/pdf/2606.27784.pdf GitHub

Abstract:
The existence of adversarial attacks is often attributed to the presence of non‑robust features in neural networks. While prior defenses reduce their impact via pruning, masking, or feature recalibration, we instead propose to jointly learn to amplify and attenuate these signals through a simple activation scaling mechanism. To this end, we introduce Activation Amplification and Attenuation (A3), a lightweight plug‑in module that enhances adversarial robustness with minimal modifications of the activations. A3 dynamically rescales the activations using a learnable mask and a scaling factor derived from the original activation magnitudes. The influence of adversarial perturbations can be amplified or attenuated using the same learnable parameters by simply flipping the sign of the scaling operation. The amplified signals serve as negative references to construct novel contrastive and ranking loss functions. Experimental analysis shows that learning to degrade the predictions in amplification mode simultaneously improves adversarial robustness in attenuation mode. Moreover, A3 relies on only a small number of learnable parameters, with most of its behavior being determined by the scaling mechanism rather than additional network capacity. Extensive experiments demonstrate that integrating A3 into different backbones, datasets, and training methods consistently improves adversarial robustness while introducing negligible computational and memory overhead compared to existing plug‑in modules. Code is available at: https://github.com/tgoncalv/A3.

PaperId: 106, https://arxiv.org/pdf/2606.27755.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models enable instruction‑driven robotic manipulation, but they inherit oversized language backbones from pretrained VLMs whose capacity far exceeds what is needed for short robotic instructions. This raises a basic question: how much of a VLA model is actually necessary for closed‑loop control? In this work, we study architectural redundancy in VLA models by using transformer block removal as a controlled intervention. We introduce Drop‑Then‑Recovery (DTR), an analysis protocol that removes selected blocks from a pretrained VLA model and then fine‑tunes the resulting model to measure whether the removed capacity was necessary for downstream control. To make this intervention reliable, we propose GateProbe, a one‑shot virtual‑gate sensitivity metric that ranks blocks by their contribution to the downstream action loss. Across multiple VLA architectures, manipulation benchmarks and even real‑robot industrial scenarios, we find a strong asymmetry in post‑removal recoverability: \ullanguage backbones are highly redundant for standard robotic manipulation tasks, whereas vision and action pathways are substantially less tolerant to removal. On LIBERO, removing half of the LLM blocks even improves OpenVLA‑OFT from 95.0% to 98.3% under the same downstream fine‑tuning budget, and retaining only two language blocks still recovers baseline‑level performance. These results suggest that current VLA benchmarks may exert limited pressure on deep language grounding and compositional instruction understanding, and that future VLA architectures should allocate capacity more deliberately across language, vision, and action components. The code is available at https://github.com/s1ghhh/VLADrop.

PaperId: 107, https://arxiv.org/pdf/2606.27731.pdf GitHub

Abstract:
Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: standard cross‑entropy treats numeric tokens as unstructured categories and ignores the metric structure of their values. We address this mismatch with Smooth Maximum Mean Discrepancy (SMMD), which builds on the classic MMD by incorporating value‑distance kernels over numeric tokens and graph‑based smoothness. With this kernel defined over a numeric sub‑vocabulary, SMMD aligns the predicted numeric distribution to the target via kernel matching and smooths the prediction‑target residual over the induced kernel graph to encourage local consistency. We evaluate SMMD on four numeric‑target tasks: mathematical reasoning, arithmetic calculation, clock‑time recognition, and chart question answering, across multiple open‑weight LLM and VLM backbones. SMMD consistently improves accuracy over both cross‑entropy and recent numeric‑target losses; analyses show complementary effects between MMD and smoothness and underscore the importance of distance‑based kernel design. Code is available at https://github.com/Zuozhuo/smmd‑loss.

PaperId: 108, https://arxiv.org/pdf/2606.27683.pdf GitHub

Abstract:
Edge devices increasingly invoke large language models (LLMs) through API services for context aware edge intelligence, while edge generated data may be collected to improve LLMs and may introduce sensitive, copyrighted, harmful, or outdated information into model behavior. Machine unlearning offers a practical way to remove the influence of undesired data without retraining LLMs. However, existing methods still face two gaps. The first is API only black box access, where target model parameters and internal logits are unavailable. The second is how to preserve retained utility when unlearning target data and retained data share highly similar prompt structures or semantic patterns. To address these challenges, we propose Controlled Behavioral Divergence (CBD), an API only black box unlearning framework. CBD uses two auxiliary models to create controlled behavioral divergence between retained inputs and unlearning target inputs, converts this divergence into an unlearning relevance score, and routes unlearning related prompts away from the target LLM. To improve discrimination accuracy under high similarity between target and retained data, CBD constructs a gradient statistics based discriminative basis by estimating empirical Fisher matrices and solving a regularized generalized eigenvalue problem, guiding the unlearning signal toward target specific information rather than shared prompt structures. Compared with eleven white box and gray box unlearning baselines, CBD achieves a better unlearning utility trade off and its performance varies little across settings. On ToFU forget10, CBD approaches the retrained reference on the forget set while raising model utility to 74.90, about 15% above the second best baseline. On WMDP, it lowers hazardous knowledge accuracy to 25.68, near random guessing, while preserving MMLU accuracy of 52.67. Code is at https://github.com/DGL‑codes/CBD.

PaperId: 109, https://arxiv.org/pdf/2606.27679.pdf GitHub

Abstract:
Probe‑based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models (LLMs) by learning uncertainty from internal model signals. Yet, recent methods vary simultaneously across feature design, training data construction, and evaluation setting, obscuring what actually drives performance. To address this issue, we propose a factorised study of probe‑based UE under matched conditions. Our results show that raw hidden states and attention features are difficult to outperform in‑domain. However, under distribution shift, structured and compressed features are more robust, suggesting that in‑domain performance alone is insufficient to measure progress. Furthermore, prompting and label construction significantly affect probe behaviour. Building on these best‑practice findings, we train benchmark‑based pretrained probes that transfer reasonably well to open‑ended factual generation, providing a stable off‑the‑shelf baseline. Our work encourages more deployment‑oriented evaluation of probe‑based uncertainty estimators. The code repository is available at https://github.com/ponhvoan/ProbeUE.

PaperId: 110, https://arxiv.org/pdf/2606.27596.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) exhibit sophisticated reasoning but remain susceptible to object hallucination. Deviating from the prevailing attention intensity assumption, we reveal a deeper dynamic structural misalignment: hallucination is triggered at decision‑critical steps where specific attention heads, acting as risky mediators, decouple from visual evidence to lock onto language priors. This establishes a pathological shortcut that bypasses visual grounding. To dismantle this, we propose Fox (Faithfulness and Observational‑flow via eXpression‑rectification), a training‑free inference‑time framework. Fox diagnoses structural misalignment using a visual attention entropy probe to localize risky mediators unsupervisedly. We then execute a targeted causal intervention via numerical logit saturation to physically sever the shortcut path. Finally, a conflict‑gated cooperative decoding strategy reconciles interventional faithfulness with observational fluency. Extensive experiments demonstrate that Fox achieves SOTA performance, outperforming SID by 29.1% while preserving linguistic richness. Code is available at https://github.com/Cc2021start/Fox.

PaperId: 111, https://arxiv.org/pdf/2606.27581.pdf GitHub

Abstract:
Current humanoid reinforcement‑learning policies excel at free‑space motions but struggle with contact‑rich tasks, as pure kinematic tracking cannot resolve the physical ambiguities of interacting with objects and uneven terrain. To address this, we introduce SceneBot, a unified motion‑tracking framework capable of handling freespace locomotion, terrain traversal, and whole‑body manipulation. SceneBot conditions a single policy on both reference motions and per‑link contact labels, explicitly defining expected environmental interactions. To overcome the lack of annotated interaction data, we propose a hindsight scene reconstruction approach that infers scene‑interaction graphs from retargeted human motion. Trained on 7.5 hours of this reconstructed, contact‑rich data, SceneBot successfully generalizes to unseen motions and environments. Our results demonstrate that SceneBot is the first general framework to seamlessly unify free‑space and contact‑rich behaviors executing complex, long‑horizon tasks like carrying a box upstairs and establishing contact conditioning as a powerful interface for humanoid control. All code and data will be open‑sourced. More demos and information are available at: https://ericcsr.github.io/scenebot/

PaperId: 112, https://arxiv.org/pdf/2606.27580.pdf GitHub

Abstract:
Reinforcement learning from human feedback (RLHF) in production does not always have a synchronous reward signal. Code‑execution verifiers, slow judge ensembles, and queued human review can return several gradient steps after the rollout that produced them, breaking the synchronous‑reward assumption underlying standard PPO. We address this gap with Retroactive Advantage Correction (RAC): each pending slow completion is queued, aged through a non‑negative kernel, and reinjected as a clipped residual into the next optimiser step's advantage. We prove that under an unbiased clipped importance ratio, the cumulative RAC correction is exactly unbiased when the effective delay kernel reinjects all of its mass, and carries a bias linear in the unreinjected fraction otherwise; at the no‑delay identity kernel it reduces to V‑trace. On a tabular Markov decision process (MDP) proof‑of‑concept, RAC reduces the closed‑form policy bias by up to 47.9x at the two‑slow‑channel configuration, beating wait‑for‑slow at lower wall‑clock cost. RAC integrates with PPO and GRPO through a two‑line reward‑manager patch.

PaperId: 113, https://arxiv.org/pdf/2606.27578.pdf GitHub

Abstract:
Reward models for Reinforcement Learning from Human Feedback (RLHF) pool preferences across thousands of annotators and fit one global affine calibrator, collapsing raters with systematically different rating‑scale offsets and slopes into a single average‑rater fit that does not match any individual annotator. PEBS is a per‑rater empirical‑Bayes shrinkage estimator: it fits per‑rater affine calibrators on a held‑out slice of each annotator's ratings and applies Morris‑James‑Stein empirical‑Bayes shrinkage toward the population mean, in closed form and without retraining the reward model. On PRISM, PEBS reduces within‑user held‑out RMSE by 8.58% over the pooled population‑slope baseline. The procedure replicates on PluriHarms harm ratings (Qwen‑2.5 base, in‑family) with a +9.66% RMSE reduction over the same population‑slope baseline. PEBS is a closed‑form post‑hoc estimator for annotator‑specific affine calibration in RLHF reward modeling; it leaves the reward base model unchanged and estimates only the rater‑level map used at inference time for new ratings.

PaperId: 114, https://arxiv.org/pdf/2606.27527.pdf GitHub

Abstract:
Large Language Models (LLMs) possess broad conceptual knowledge acquired through large‑scale text pretraining, yet their potential to supervise models in other modalities remains underexplored. In this work, we propose LaViD‑‑Language‑to‑Visual Knowledge Distillation‑‑a simple and effective framework for transferring high‑level semantic knowledge from a language‑only teacher to a vision‑only student model. Instead of relying on paired multimodal data, LaViD elicits conceptual signals from an LLM by prompting it to generate multiple‑choice questions (MCQs) that probe semantic distinctions between visual classes. Each class is mapped to a soft label distribution over these MCQs, forming a rich conceptual signature that guides the student through an auxiliary distillation loss. Notably, despite using a language‑only teacher without access to image data, LaViD consistently outperforms recent methods like MaKD that distill from vision‑language models across multiple fine‑grained benchmarks. It also achieves competitive or superior performance compared to state‑of‑the‑art visual distillation methods such as DKD and MLKD, with further gains when combined with logit standardization. On the Waterbirds dataset, LaViD substantially improves worst‑group accuracy, demonstrating enhanced robustness to spurious correlations with distillation. Code is available at https://github.com/lliangthomas/lavid.

PaperId: 115, https://arxiv.org/pdf/2606.27499.pdf GitHub

Abstract:
Research on agent memory has matured rapidly, but almost entirely on the text side: few existing benchmarks ask, in an interactive environment, when an agent genuinely needs to remember what it saw rather than what it could write down. We introduce DMV‑Bench (Code: https://github.com/yyyujintang/DMV‑Bench), the first interactive benchmark for multimodal‑agent visual memory. DMV‑Bench is built on a controlled home‑furnishing e‑commerce catalogue of 1,000 product variants in which a text‑leakage contract keeps the discriminative signal of each task in the pixels alone. Across a chain of autonomous shopping sessions, every visited product image carries a unique, pre‑rendered incidental cue, and the agent is later asked to recall a particular cued product and navigate to its URL. Inspired by dual‑coding theory, we propose DualMem, a memory architecture that maintains a visual and a verbal code in parallel. On DMV‑Bench, DualMem outperforms a caption baseline and three recent multimodal agent‑memory systems at every chain length J in 5, 10, 15, 50 on both Gemini 2.5 Flash and Qwen2.5‑VL‑7B, with the lead surviving controls for memory‑bank size and encoding‑position bias, and an asymmetric dual‑coding regime in which vision carries the cue end‑to‑end while the verbal channel plays a smaller query‑grounding role.

PaperId: 116, https://arxiv.org/pdf/2606.27472.pdf GitHub

Abstract:
Large language model (LLM) agents operate over long, multi‑session interactions in which facts change: a user moves, a price updates, a plan is revised. Acting correctly requires using the current value of a fact and discarding values that have been superseded. We isolate this ability on real conversational data and show that it is a distinct, unsolved failure. On the knowledge‑update subset of LongMemEval, replacing an agent's full context with a bounded, self‑maintained memory drops accuracy from 92% to 77% even on a frontier model (gpt‑5.4), a gap that is statistically significant (paired McNemar p<0.005) and persists across model scale while full‑context accuracy saturates near 92%. The bottleneck is therefore memory maintenance, not comprehension, and is not closed by a stronger model. We then ask whether this is merely an undersized memory, and find it is not: as the conversation grows 24x, accuracy falls further (from 68% to 28%), and granting the agent proportionally more memory yields no detectable recovery (28% to 28%, n=25). The failure scales with the length of the conversation, not the compression ratio. We release Supersede, an open reinforcement‑learning environment (on the verifiers / prime‑rl stack) that turns this measurement into a training signal: agents are rewarded for answering from the current value and penalized for stale ones. Finally, we close the loop and show the gap is trainable: GRPO fine‑tuning a small open model (Qwen2.5‑3B) on this environment nearly doubles its held‑out supersession accuracy on real, unseen conversations (9.0% to 16.7%, a single run), along a monotonic checkpoint curve indicating the learned policy, not the harness, carries the gain. To our knowledge this is the first trainable environment whose reward targets temporal fact‑currency, and the first evidence the supersession gap can be trained down, not only measured.

PaperId: 117, https://arxiv.org/pdf/2606.27412.pdf GitHub

Abstract:
3D Scene Graph Generation (3DSGG) represents 3D scenes as structured object‑relation‑object graphs, providing a compact relational abstraction for spatial understanding. In embodied intelligence settings, the same 3D scene may be observed by agents from viewpoints that differ by yaw rotations. However, current 3DSGG models often fail to produce relation predictions that follow the expected transformation behavior under such viewpoint shifts. This behavior reveals an empirical mismatch related to predicate‑level transformation heterogeneity: directional predicates such as left, front, right, and behind should transform with the observation frame, whereas most contact, support, and semantic predicates such as standing on and attached to should remain stable. To reduce this mismatch, we propose Transformation‑Aware Decoupling (TAD), a viewpoint‑robust 3DSGG framework that decouples relation reasoning according to predicate transformation behavior and is supported by viewpoint‑stable object representations. TAD decomposes relation reasoning into two parts: one learns cues that should stay stable across viewpoints, while the other learns directional cues that should change with the observation frame. The two parts are merged for standard multi‑label predicate prediction. Transformation‑specific descriptors and group‑aware auxiliary supervision encourage the two branches to capture complementary relation cues. Extensive experiments on 3DSSG show that TAD achieves state‑of‑the‑art robustness under yaw viewpoint changes without training‑time rotation augmentation, while maintaining competitive performance under the standard benchmark. The project page is available at https://tad‑predicate.github.io/.

PaperId: 118, https://arxiv.org/pdf/2606.27361.pdf GitHub

Abstract:
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction. However, modern BGs predominantly rely on normalizing flows (NFs), which either suffer from limited expressivity due to strict invertibility constraints (discrete time) or computationally expensive likelihoods (continuous time). In this paper, we propose Autoregressive Boltzmann Generators (ArBG) ‑‑ a novel autoregressive modelling framework ‑‑ that overcomes these limitations by departing from the flow‑based BG paradigm. ArBG circumvents the topological constraints of flows and enables sequential inference‑time interventions, while offering enhanced scalability by leveraging architectures effective in Large Language Models. We empirically demonstrate that ArBG leads to significant improvements over flow‑based models across all benchmarks, but particularly in larger peptide systems such as the 10‑residue Chignolin. Furthermore, we introduce Robin, a 132 million parameter transferable model trained with the ArBG framework which improves over the previous state‑of‑the‑art, reducing the zero‑shot energy error, E‑W_2, on 8‑residue systems by over 60%. The code can be found at the following link: https://github.com/danyalrehman/autobg.

PaperId: 119, https://arxiv.org/pdf/2606.27354.pdf GitHub

Abstract:
Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task: once trained, they struggle to correct their own constraint violations and extrapolate beyond the training distribution. Recent hybrid methods promote physical correctness by targeting the PDE residual via gradient descent or Gauss‑‑Newton steps, but inherit the compute cost and instability of the underlying classical optimizers. We show, theoretically and empirically, that numerically minimizing the PDE residual can be an unreliable proxy for reconstruction accuracy in ill‑conditioned systems, explaining why these methods often do not make accurate predictions despite achieving low residuals. We propose error‑conditioned Neural Solvers (ENS), built on a different principle: rather than an optimization target, the PDE residual field is passed as a direct input to the network at each iteration, enabling it to read the spatial structure of its own errors and learn an update policy to iteratively correct its predictions. Across four PDE families, ENS attains the highest prediction accuracy in the large majority of settings, with gains reaching 10× on turbulent Kolmogorov flow, while avoiding the expensive compute cost of hybrid methods. ENS's learned correction policy generalizes under distribution shift, including zero‑shot parameter changes and cross‑equation transfer, where its relative advantage is largest in the ill‑conditioned regimes where residual minimization is least reliable. Project website: https://neuralsolver.github.io/.

PaperId: 120, https://arxiv.org/pdf/2606.27287.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate algorithmic hiring systems. We study prompt injection in automated résumé screening, defined as subtle self‑promotional text that introduces no new qualifications but is designed to influence LLM evaluations. Using controlled experiments, we show that prompt injection reliably improves applicant rankings when résumé quality is homogeneous and few candidates inject. However, its effectiveness rapidly diminishes as more candidates inject, collapsing when manipulation becomes widespread. When candidate quality is heterogeneous, prompt injection is less effective on average, but can occasionally allow lower‑quality candidates to outrank higher‑quality ones, raising fairness concerns. Overall, LLM‑based screening is most vulnerable when manipulation is rare and candidate quality differences are small. Code and resources are publicly available at: https://github.com/preetb1199/Prompt_Injection_ACL26

PaperId: 121, https://arxiv.org/pdf/2606.27277.pdf GitHub

Abstract:
Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper, we view this task as a partially observed, weather‑driven world modeling problem, in which weather acts as a conditioning signal, while forecasting remains uncertain due to sparse observations and unobserved land‑surface states. However, existing methods do not fully capture this setting: deterministic models collapse uncertainty into a single future prediction, while diffusion‑based methods typically treat weather variables as undifferentiated conditioning signals, and existing benchmarks focus mainly on reconstruction accuracy rather than whether forecasts respond correctly to changed weather forcing.We introduce EO‑WM, a video diffusion transformer for multispectral EO forecasting. EO‑WM incorporates a physically informed conditioning framework that represents meteorological forcing through a climatological baseline, weather anomalies, and cumulative physical stress signals. Specifically, it separates baseline and anomaly through distinct conditioning pathways, and accumulates anomalous forcing over time to capture sustained heat and drought stress. To evaluate weather‑response behavior beyond standard metrics, we introduce two diagnostic benchmarks: an Extreme Summer Benchmark for severity‑aware prediction of vegetation degradation under extreme weather, and a Seasonal Matched‑Pair Benchmark for testing response fidelity under changed weather forcing. Experiments show that EO‑WM reduces the error in predicted Normalized Difference Vegetation Index (NDVI) decline amplitude by a relative 5.63% and improves directional hit rate by a relative 7.80%, while remaining competitive on standard pixel‑level metrics. The benchmarks and model will be made open‑source at https://github.com/Luo‑Z13/EO‑WM.

PaperId: 122, https://arxiv.org/pdf/2606.27268.pdf GitHub

Abstract:
Recently, a few works have made early attempts to study test‑time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential, as embodied tasks are inherently long‑horizon and sequential, making sole reliance on current observations for action scaling inadequate due to the lack of historical context utilization. To address these challenges, we introduce E‑TTS, a modular and plug‑and‑play Embodied Test‑Time Scaling framework that unifies reasoning and action scaling for robotic manipulation via history‑aware iterative refinement with vision‑language verifiers. To support joint reasoning‑action scaling, E‑TTS performs reasoning‑action joint sampling and scoring in a pairwise manner. To better utilize historical information, E‑TTS uses a history buffer to store historical context, which is then used by reasoning and action verifiers to evaluate the sampled candidates. Unlike conventional open‑loop TTS methods, E‑TTS introduces feedback generation into the sampling process to form a closed‑loop iterative refinement mechanism, enhancing both inference efficiency and environmental adaptability. Each component functions as an independent and composable module, allowing flexible and adaptive configuration depending on task requirements. To evaluate the advantages of our framework, we conduct experiments across 4 different benchmarks, 6 environments, 3 embodiments, and 4 base vision‑language‑action models. The experimental results demonstrate that, without requiring additional expert data collection or retraining, E‑TTS consistently improves performance, achieving up to a 33.14% increase in simulation and 26.62% in real‑world scenarios.

PaperId: 123, https://arxiv.org/pdf/2606.27090.pdf GitHub

Abstract:
Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies their local‑mass behaviour that is not directly captured by such objectives. We introduce and use two mathematical tools: (1) Mass Index for recording the polynomial and logarithmic decay scales of local mass, and (2) regularised extended KL (RE‑KL), a set‑localised divergence that can be formulated in the presence of singular components. Mass Indices help characterise how Bayesian updating changes local mass: (1) power‑log likelihood factors shift it explicitly, and (2) parameter‑dependent supports, or their smooth softenings, may change the local scale through the amount of mass that remains near the parameter value. Using local RE‑KL, we prove absolute, relative, and directional inequalities for comparing local small‑ball masses under the two KL directions. Together, these results provide a local theoretical account of local mass behaviour. Experiments provide controlled illustrations of the local behaviour. Code is available at https://github.com/Forsythia0604/Local‑Mass‑Framework.

PaperId: 124, https://arxiv.org/pdf/2606.26902.pdf GitHub

Abstract:
Multi‑task model merging aims to consolidate several task‑specific experts into a unified model, yet static merging consistently suffers from parameter interference. While dynamic merging models aim to bridge this gap, many works rely on the costly storage and loading of redundant expert components at inference. In this work, from the perspective of task expert, we view parameter interference as parameter perturbation introduced to each expert during merging process. We show that such parameter perturbations can be modeled as affine transformation, which can be approximated as additive offsets. Motivated by these, we propose Recover Task eXpert (ReTeX), a framework that predicts those offsets, in order to undo parameter interference and recover task‑expert performance from a single merged checkpoint. To recover the appropriate expert when task identity is unknown, we introduce a router‑free task identifier based on SVD subspace signatures computed offline before inference. At inference, the identifier selects the task whose subspace yields the smallest projection residual for a given input. As a result, ReTeX recovers over 95% of individual‑expert performance in both vision and NLP domains, while significantly improving generalization to unseen tasks. Crucially, we also show that the parameter offset prediction leads to emergent adaptive interpolation of expert knowledge for out‑of‑distribution (OOD) tasks. ReTeX adaptively interpolates seen expert knowledge to handle unseen tasks. Our code is available at https://github.com/BAIKLAB/ReTeX

PaperId: 125, https://arxiv.org/pdf/2606.26852.pdf GitHub GitHub

Abstract:
Order fulfillment in manual picker‑to‑goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing. While integrated models capture interactions between these decisions, practical warehouse systems often require decomposed approaches due to organizational boundaries, differing responsibilities, or limited data availability. Existing studies primarily evaluate algorithms for isolated subproblems or fixed subproblem combinations for specific warehouse settings, but lack a general mechanism to determine applicable algorithm configurations, compose them into valid solution pipelines, and assess their performance. With Context‑Aware Synthesis of Optimization Pipelines (CASOP), we propose a framework for constructing and evaluating context‑specific optimization pipelines and apply these to order fulfillment. The framework comprises: (1) a modular repository of algorithms for common order fulfillment problems; (2) semantic data and algorithm cards to describe warehouse context and algorithm requirements; (3) a taxonomy that structures order fulfillment problems into relevant subproblems; (4) a pipeline synthesizer that identifies applicable algorithms for a given warehouse context and composes all valid optimization pipelines; and (5) a pipeline evaluator that assesses all resulting pipelines. We demonstrate the framework on 7 benchmark instance sets covering four problem classes, resulting in 1,063,044 valid pipelines. The framework supports researchers and practitioners in designing, automatically synthesizing, and selecting valid, high‑performing algorithmic pipelines for warehouse operations. The software is open‑source and available at https://github.com/kit‑dsm/ware_ops_pipes and https://github.com/kit‑dsm/ware_ops_algos. Keywords: Warehouse optimization, Algorithm selection, Pipeline synthesis, Order fulfillment

PaperId: 126, https://arxiv.org/pdf/2606.26794.pdf GitHub

Abstract:
CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image‑text alignment. As downstream applications increasingly demand visually grounded commonsense inference and compositional reasoning, it remains unclear whether CLIP‑style encoders can support such reasoning without architectural changes. To address this, we present ReasonCLIP‑58M, a continual pretraining framework that integrates large‑scale reasoning supervision into CLIP‑style models through our two‑stage strategy, which progressively integrates reasoning signals while preserving descriptive alignment, followed by category‑structured reasoning supervision. To support this framework, we construct two complementary datasets and a benchmark: ReasonLite‑42M, with open‑form, visually verifiable reasoning captions; ReasonPro‑16M, with category‑specific reasoning supervision; and RCLIP‑Bench for diagnostic evaluation of visually grounded reasoning. We train a family of ReasonCLIP that improves visually grounded commonsense and compositional reasoning while also enhancing zero‑shot retrieval performance. As a drop‑in visual encoder for multimodal large language models such as LLaVA‑NeXT, ReasonCLIP delivers consistent gains without additional inference cost, demonstrating that structured reasoning supervision enhances the expressive capacity of CLIP‑style visual representations. All datasets, models, and training code are available at https://github.com/RISys‑Lab/ReasonCLIP.

PaperId: 127, https://arxiv.org/pdf/2606.26793.pdf GitHub

Abstract:
Multimodal agentic retrieval‑augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image injection, direct‑query attacks, and orchestrator‑level tool manipulation. Existing red‑teaming approaches are typically surface‑specific and often recycle known attack templates; on text‑poisoning benchmarks we measure 73‑84% exact duplication. We present MIRROR, a unified cross‑surface framework that performs memory‑guided Monte Carlo tree search while conditioning candidate generation on retrieved context under an explicit novelty constraint. A deterministic Novelty Gate rejects any candidate matching the retrieval set under normalized comparison, allowing retrieval to inform search priors without enabling prompt copying. Across four attack surfaces on a multimodal agentic RAG target, MIRROR attains 76% ASR on image poisoning compared with 52% for baselines, 97% ASR on orchestrator attacks at half the query cost, and the lowest cross‑surface variance (coefficient of variation 0.47). In contrast, specialized baselines collapse across surfaces: suffix optimization reaches 79% ASR on text poisoning but 1% on direct queries. We release ART‑SafeBench with 41,815 in‑package records and runtime adapters yielding 41,991+ total records across four surfaces.

PaperId: 128, https://arxiv.org/pdf/2606.26764.pdf GitHub

Abstract:
Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter‑device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation. A semi‑supervised variational autoencoder learns a compact latent representation of anatomical volumes while jointly predicting aligned segmentation masks in a unified framework. Anatomical structure is then disentangled from temporal dynamics through a cascaded latent diffusion model (LDM). A static LDM generates subject‑specific anatomy conditioned on clinical priors (diagnosis and volumes measures) and a subsequent motion LDM estimates residual latent motions, ensuring strict temporal coherence across the 4D sequence. The proposed approach was evaluated on cine cardiac MRI as a representative 4D imaging application. Experiments across multiple datasets demonstrate high controllability of static anatomy (Pearson r > 0.8) and strong temporal coherence (FVD = 288.08). In cross‑vendor generalization experiments, augmenting training sets with synthetic 4D sequences significantly improves downstream segmentation performance. Using nnU‑Net, the proposed augmentation strategy improves the average Dice score by 1.4% and reduces the Hausdorff Distance by 3.0mm compared to training on real data alone, for the left ventricle, Dice improves by 2.8% with a 5.4mm reduction in boundary error. Overall, this framework provides a scalable and controllable solution for 4D medical image synthesis, supporting the development of more robust models with limited annotations and cross‑vendor variability. Code available on https://github.com/cyiheng/4DCardiacMRISynthesis.

PaperId: 129, https://arxiv.org/pdf/2606.26713.pdf GitHub

Abstract:
As semiconductor technology nodes scale, computational lithography is essential for ensuring yield and performance. However, lithography is a continuous physical process involving mask optimization, optical imaging, resist exposure, and development, which existing models fail to capture. To overcome this limitation, we present LithoDreamer, the first physics‑informed World Model (WM) framework for computational lithography, which formulates the ``Layout‑Mask‑Resist Image‑After Development Image (ADI)'' pipeline as a decision‑driven multi‑step evolution system. LithoDreamer captures feature changes between adjacent states to model stage‑specific physics‑informed latent spaces, in which it controls process intervention exploration and drives subsequent state transitions. To achieve interpretable intervention optimization without continuous supervision, we propose a contrastive variational optimization paradigm that contrasts the latent differences between intervention paths with variational evolution constraints, guiding the model to generate evolutions consistent with real lithography physics. Experiments show LithoDreamer achieves state‑of‑the‑art performance in forward evolution and inverse planning. Our lithography dataset is publicly available at GitHub (https://github.com/7jiangyq/lithodreamer.git).

PaperId: 130, https://arxiv.org/pdf/2606.26712.pdf GitHub

Abstract:
Skin lesion segmentation is a key task in computer‑aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease classification. However, dermoscopic images are challenging due to blurred boundaries, low contrast, large shape variations, and artifacts such as hair and shadows. Recently, diffusion models have shown strong performance in medical image segmentation thanks to their progressive denoising and distribution modeling capabilities. Nevertheless, existing diffusion‑based methods still suffer from limited cross‑level feature interaction and insufficient boundary detail recovery. To address these issues, we propose MLFFM‑SegDiff, a multi‑level feature fusion diffusion model for skin lesion segmentation. Built on a diffusion framework, the method introduces a dual‑path U‑Net encoder, a Multi‑Level Feature Fusion Module (MLFFM), and a boundary‑sensitive loss function. The dual‑path encoder enhances interaction between noisy mask features and dermoscopic image features. MLFFM improves skip connections via attention, scale alignment, and adaptive cross‑level fusion. These designs enable the decoder to jointly leverage shallow boundary cues and deep semantic representations, improving mask reconstruction quality. Experiments on ISIC2018, PH2, and HAM10000 demonstrate that MLFFM‑SegDiff outperforms representative methods including DermoSegDiff, U‑Net, and SwinUNETR across Accuracy, F1‑score, Jaccard index, Recall, and Dice. In particular, it achieves an average Jaccard index of 0.8546 and Dice coefficient of 0.9207. These results validate the effectiveness of the proposed multi‑level feature fusion strategy for improving lesion segmentation performance. The code will be released at https://github.com/Qacket/MLFFM‑SegDiff.git after publication.

PaperId: 131, https://arxiv.org/pdf/2606.26686.pdf GitHub GitHub GitHub

Abstract:
In order to screen a prompt or a response, the recent guardrail methods generate a chain‑of‑thought (CoT) before they issue a verdict. This design follows a common belief that step‑by‑step reasoning improves a decision. However, CoT also makes the guard heavy and slow, because the model must generate many tokens before it decides. This may not match how guardrails are actually deployed. A guardrail sometimes should not be heavy and slow, and it often runs on‑device, for example on an embodied robot. In this paper, we pose a question whether a safety guardrail really needs to reason. To answer this question, we train a lightweight bidirectional encoder and a reasoning guard on the same corpus, and we then remove only the reasoning while we keep everything else fixed. With this controlled same‑base comparison, we show that the chain does not improve moderation accuracy. We name the resulting guard LeanGuard. A 395M label‑only encoder reaches an average F1 of 82.90 \pm 0.26 over public benchmarks. It matches a reasoning guard that is built on a much larger decoder, while it uses only a single forward pass over an input of at most 512 tokens. This is about a ~100x reduction in inference compute. We further show that this label‑only encoder stays robust under training‑label noise and retains far more recall at a strict false‑positive rate than the reasoning guard, so a heavier reasoning guard is not the more robust choice either. Our finding suggests that the current guardrail benchmarks may not be hard enough to reward reasoning, and that the necessity of CoT for moderation is still not proven. We release all source codes and models including LeanGuard at https://github.com/ndb796/LeanGuard.

PaperId: 132, https://arxiv.org/pdf/2606.26650.pdf GitHub GitHub

Abstract:
In this paper, we present CAT‑Q, Cost‑efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs. Unlike existing state‑of‑the‑art ternary quantization methods that rely on data‑intensive and costly quantization‑aware training to mitigate severe performance degradation, CAT‑Q is a simple yet effective post‑training quantization scheme that is readily applicable to LLMs with diverse architectures and model sizes. It has two key components, learnable modulation (LM) and softened ternarization (ST), which are coupled from an optimization perspective. LM leverages a composition of learnable factors to modulate the distribution of pre‑trained high‑precision weights and the ternary threshold, making them less sensitive to ternarization. ST further introduces a differentiable transition function to guide the ternarization process toward stable convergence. We show that, for pre‑trained LLMs with 1.7B to 8B parameters, CAT‑Q can efficiently quantize them into ternary models using only 512 calibration samples, while achieving superior performance than the seminal BitNet 1.58‑bit v1 and v2 families (with 1.3B to 7B parameters) trained with 100B tokens, yielding about a 100,000X reduction in training tokens. Moreover, we show for the first time that CAT‑Q can quantize much larger pre‑trained LLMs having 14B to 235B parameters into leading ternary models within just 8 to 60 hours on 8 A100‑80GB GPUs. Code is available at https://github.com/IntelChina‑AI/BitTern.

PaperId: 133, https://arxiv.org/pdf/2606.26614.pdf GitHub

Abstract:
Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis). Still, prior systems have essentially prioritized autonomy over human analytical control, thereby limiting transparency and human oversight. We present HiLSVA, a human‑in‑the‑loop agentic system that supports mixed‑initiative SciVis workflows. HiLSVA integrates a plan‑first multi‑agent architecture with explicit human oversight, stepwise provenance tracking, and learn‑at‑test‑time adaptation from user feedback. The system supports fluid handoff between humans and agents through both natural language and direct manipulation of visualizations, while sandboxed execution ensures safe, reproducible workflows. In doing so, HiLSVA reframes agentic SciVis as a collaborative process that augments, rather than replaces, human analytical reasoning. We evaluate HiLSVA through representative case studies and a controlled user study with twelve participants of varying expertise across multiple autonomy settings. Results show that mixed‑initiative interaction improves task completion, user control, and workflow transparency across different levels of user expertise, while revealing a tradeoff between execution efficiency and human oversight. These findings highlight the importance of human‑centered design in agentic SciVis and guide the development of future collaborative visualization systems. We encourage readers to explore our demo video, case studies, and source code at https://hilsva.github.io/.

PaperId: 134, https://arxiv.org/pdf/2606.26587.pdf GitHub

Abstract:
Low‑bit floating‑point formats and semi‑structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation compression remains challenging: activations contain input‑dependent outliers that dominate block scales in FP4 quantization, and directly applying N:M sparsity masks discards moderate values, coupling sparsification loss with quantization error. We introduce SharQ, a training‑free inference method that bridges activation sparsity and FP4 quantization through an online sparse‑‑dense decomposition. For each activation tensor, SharQ generates an input‑adaptive N:M mask to extract an outlier‑dominated sparse backbone, quantizes it to FP4, and defines a dense residual relative to the quantized sparse backbone rather than the unquantized sparse values. A sparse FP4 GEMM processes the backbone while a dense FP4 GEMM compensates for both mask‑induced activation loss and sparse‑path quantization error. The two paths share a single FP4 weight payload with path‑specific scale views, and a fused preparation kernel absorbs mask generation, residual construction, and layer normalization into one operator. SharQ requires no calibration data, retraining, or model‑specific tuning. Evaluated on Llama‑3.1‑8B, Qwen2.5‑7B, Qwen3‑30B‑A3B, and Qwen3‑VL‑8B, SharQ recovers 43‑‑63% of the NVFP4‑to‑FP16 accuracy gap across language and vision‑language tasks, and generalizes across NVFP4, HiF4, and MXFP4 formats. On an RTX 5090, SharQ delivers 2.2‑‑2.4× latency reduction over FP16 and 1.2‑‑1.4× throughput improvement over FP8 in language model serving, and up to 1.58× speedup on Wan2.2‑T2V‑A14B video generation when combined with SageAttention. Our code is available at https://github.com/actypedef/SharQ.

PaperId: 135, https://arxiv.org/pdf/2606.26549.pdf GitHub

Abstract:
Long‑term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction. Transformer‑based models have adopted patch‑based strategies to capture long‑range dependencies, but accurately modeling shape similarities across patches and variables remains challenging due to scale differences. To address this, we introduce patch‑mean decoupling (PMD), which separates the trend and residual shape information by subtracting the mean of each patch, preserving the original structure and ensuring that the attention mechanism captures true shape similarities. Futhermore, to more effectively model long‑range dependencies and capture cross‑variable relationships, we propose Trend Restoration Attention (TRA) and Proximal Variable Attention (PVA). The former module reintegrates the decoupled trend from PMD while calculating attention output. And the latter focuses cross‑variable attention on the most relevant, recent time segments to avoid overfitting on outdated correlations. Combining these components, we propose PMDformer, a model designed to effectively capture shape similarity in long‑term forecasting scenarios. Extensive experiments indicate that PMDformer outperforms existing state‑of‑the‑art methods in stability and accuracy across multiple LTSF benchmarks. The code is available at https://github.com/aohu1105/PMDformer.

PaperId: 136, https://arxiv.org/pdf/2606.26535.pdf GitHub

Abstract:
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural‑diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning. Unlike traditional black‑box QA, CRISP utilizes metric 3D Scene Graphs and an oracle intervention protocol to decouple latent reasoning capabilities from perceptual bottlenecks. This granular diagnosis uncovers a systematic perception‑reasoning disconnect. Crucially, we reveal that while proprietary models possess robust latent reasoning engines, they suffer from inaccurate metric estimation and a critical failure to leverage their implicit structural representations. Conversely, open‑source models remain fundamentally bottlenecked by their lack of multi‑hop compositional reasoning. By shifting the focus from merely ``guessing correctly'' via language priors to genuinely ``perceiving, verifying, and reasoning,'' CRISP offers a rigorous roadmap for multimodal alignment beyond end‑to‑end post‑training. The code and dataset are available at https://github.com/iiyamayuki/CRISP‑Bench.

PaperId: 137, https://arxiv.org/pdf/2606.26530.pdf GitHub

Abstract:
The Abstraction and Reasoning Corpus (ARC) contains tasks that require summarizing patterns from limited grid samples and predicting output grids. Recently, many large language model based approaches have attempted to transform it into a text‑based reasoning task. However, methods based on open‑source models have generally yielded unsatisfactory results, while those relying on closed‑source models are too costly. Current efforts mainly focus on data augmentation, constructing ARC‑like data for more comprehensive supervised fine‑tuning. In this work, we argue that solving ARC‑like problems requires not only positive sample supervision but also the ability to improve model reasoning by distinguishing negative samples. To this end, we draw on the idea of preference alignment and propose DiARC, a method that constructs preference pairs to enable the model to distinguish between them. Specifically, we propose three ways to construct negative samples, including output‑level visual transformations, DSL‑level rule inversion, and task‑specific rule editing. The resulting negative samples provide informative near‑miss alternatives while keeping the observed demonstrations unchanged. Experimental results across multiple ARC‑like benchmarks show that DiARC consistently improves performance over baseline models. The code is released at https://github.com/szu‑tera/DiARC.

PaperId: 138, https://arxiv.org/pdf/2606.26487.pdf GitHub

Abstract:
Large language models (LLMs) are attractive for context‑aware time series forecasting because they can integrate heterogeneous textual signals, yet their discrete, language‑oriented tokenization and embedding interfaces are misaligned with continuous numerical values, often harming numerical ordering and forecasting reliability. We propose TempoWave, a plug‑and‑play temporal wavelet digit interface that maps each scalar observation into digit‑wise embeddings constructed from multi‑wavelet, multi‑scale coefficients. By directly overriding standard token representations, TempoWave seamlessly exposes both fine‑grained local fluctuations and macro global structures in a transformer‑compatible form, ensuring that precise numerical formatting, distinct digit identity, and robustness to common normalization operations are maintained throughout the LLM pipeline. Experiments across five context‑enriched forecasting benchmarks demonstrate that TempoWave consistently improves LLM‑based forecasters over standard numeric tokenization and alternative embedding interfaces, achieving a new state‑of‑the‑art. These results highlight the numeric interface as a key bottleneck and suggest that principled multi‑resolution embeddings can better couple LLMs' contextual reasoning with precise forecasting. Our code is available at https://github.com/DC‑research/TempoWAVE and our model can be accessed at https://huggingface.co/Melady/TempoWAVE.

PaperId: 139, https://arxiv.org/pdf/2606.26455.pdf GitHub

Abstract:
RGB‑Event tracking improves localization robustness by fusing RGB appearance textures and dense temporal motion cues from event sensors. While this multi‑modal scheme broadens tracking applicability, real‑world scenes suffer diverse structured signal degradations that hinder traditional multi‑modal fusion. In harsh environments, either modality can lose reliability drastically, and targets frequently appear incomplete due to occlusion, edge truncation and foreground clutter.To tackle the above challenges, we present a hierarchical perturbation and retrieval framework tailored for RGB‑Event tracking with robustness against partial target missing and modal degradation, termed APRTrack. To mimic real‑world signal corruption, APRTrack constructs structured degradation via two adversarial perturbation branches at the modality and spatial levels, which separately simulate full‑modal failure and localized target region absence. A hierarchical routing mechanism is designed to disentangle the training pipelines of the two perturbation types, effectively eliminating feature collapse induced by superimposed degradation constraints. Furthermore, we devise Footprint‑guided Channel‑calibrated Hopfield Retrieval (FCHR) for reliable historical information compensation. This module evaluates retrieval confidence based on association footprints between queries and memory banks, and calibrates the retrieval metric space prior to Hopfield matching, realizing controllable historical feature compensation bounded to target regions. Extensive experiments on FE108, COESOT, VisEvent, and FELT datasets demonstrate the effectiveness of our proposed strategies for the RGB‑Event visual object tracking. The source code and pre‑trained models will be released on https://github.com/Event‑AHU/OpenEvTracking

PaperId: 140, https://arxiv.org/pdf/2606.26443.pdf GitHub

Abstract:
A robot working alongside people must reason about what they have done, in what order, and with what intent. Video carries the spatial layouts, object histories, and gestures that language leaves underspecified, yet today's manipulation benchmarks pair an instruction with a single current image, offering no way to evaluate reasoning over observed human behavior. We introduce WatchAct, a benchmark for robot manipulation grounded in observed human behavior. Each instance pairs a real‑world human‑action video and a language instruction with an aligned simulator scene and an executable LIBERO task, enabling scalable and reproducible evaluation. WatchAct comprises 3,000 long‑horizon instances across 14 tasks in four capability domains drawn from the cognitive demands of watching another agent: parsing events (Event Grounding), recovering procedural structure (Procedural Reasoning), inferring unstated intent (Implicit Intent Inference), and tracking how the scene was changed (Episodic Reasoning). We further propose a disentangled evaluation protocol that separately measures (i)~video‑to‑plan reasoning by vision‑language models, (ii)~policy execution under oracle plans, and (iii)~full task completion by integrated planner‑‑policy pipelines. In both simulation and on a Franka Research 3 robot, current systems remain far from solving WatchAct. The best pipeline, Gemini‑3.1‑Pro with π_0.5, reaches only 16.3% Success Rate (SR) in simulation and 14.0% on the real robot. Gemini‑3.1‑Pro attains just 36.8% Plan SR (vs. 97.1% for humans), while π_0.5 reaches only 21.5% Task SR under oracle plans and drops to 10.6% on out‑of‑domain scenarios. Dataset and code are available at https://baiqi‑li.github.io/watchact_page/.

PaperId: 141, https://arxiv.org/pdf/2606.26267.pdf GitHub

Abstract:
Rating systems such as Elo serve as the gold standard for matchmaking in competitive chess. However, they inherently suffer from response lag due to their exclusive reliance on match outcomes, neglecting the granular quality of gameplay. Nevertheless, incorporating move‑by‑move information into rating adjustments presents a significant challenge given the substantial noise and the vastness of the game‑state space. To address this, we propose the Drift‑Diffusion‑Enhanced Elo Rating System (DD‑Elo), a novel skill assessment framework inspired by the drift diffusion model (DDM) from cognitive neuroscience. By modeling skill expression as a decision‑making process, our model integrates move‑level data to capture rapid skill fluctuations. We provide a rigorous mathematical derivation proving that DD‑Elo maintains a bounded deviation from the traditional Elo system, ensuring theoretical alignment. Extensive experiments demonstrate that DD‑Elo adapts to skill changes faster than Elo. Our findings suggest that DD‑Elo offers an explainable, highly responsive, and backward‑compatible solution for chess rating ecosystems. The implementation code is publicly available at https://github.com/Aquila‑zhou1/DD‑Elo .

PaperId: 142, https://arxiv.org/pdf/2606.26105.pdf GitHub

Abstract:
Large language models (LLMs) exhibit strong capabilities in short‑context reasoning but degrade in performance over long conversational horizons due to context window limitations and inefficient token usage. We introduce ContextForge, a system for context recycling that maintains task‑relevant information across turns by combining structured query generation, external memory retrieval, and controlled synthesis. The system enables efficient reuse of prior computation without relying on full context replay, reducing token overhead while preserving answer quality. We evaluate ContextForge using a 15‑turn conversational benchmark that tests multi‑turn reasoning, back‑references, and domain shifts across structured healthcare queries. Compared to a baseline agent using identical underlying models, ContextForge demonstrates improved consistency and reduced token consumption, while maintaining comparable response accuracy. These results suggest that context recycling provides a practical approach for extending LLM capabilities in long‑horizon tasks without requiring larger context windows or model retraining. Code and evaluation artifacts are available at https://github.com/Betanu701/ContextForge.

PaperId: 143, https://arxiv.org/pdf/2606.26101.pdf GitHub

Abstract:
Reliable evaluation of large language models should separate supported answering from unsupported guessing without conflating either with data contamination, prompt idiosyncrasy, or generic refusal behavior. We present a contamination‑aware, multi‑zone benchmark for measuring the transition from answerable knowledge to abstention‑expected unknowns under frozen build‑time labels. The benchmark contains 1,200 items across five domains, explicit abstention expectations, contamination‑risk metadata, and dual parsing with an official strict parser plus a normalized robustness parser. We evaluate FLAN‑T5, Qwen2.5‑Instruct, and Llama‑3‑Instruct models under locked answer‑or‑abstain prompts, answer‑only controls, and prompt‑template variants. The benchmark is not solved by generic non‑answer behavior: FLAN baselines remain weak on productive abstention, while stronger instruction‑tuned models expose a selective but incomplete transition from answering to abstaining. Qwen2.5‑3B‑Instruct achieves the best overall reliability, but answer‑expected zones remain difficult, calibration remains poor, and benign‑item refusal persists. Prompt and parser robustness analyses preserve the main ranking and qualitative conclusions. The benchmark therefore provides a reproducible protocol for auditing answerability, abstention, refusal, and contamination as distinct but interacting dimensions of LLM reliability.The dataset is publicly available at https://github.com/renweimeng/Know2Guess‑A‑Contamination‑Aware‑Multi‑Zone‑Benchmark.

PaperId: 144, https://arxiv.org/pdf/2606.26050.pdf GitHub

Abstract:
Midway through an ordinary pretraining run, a small language model learns the pronoun‑gender rule: cued with a girl's name ("Sue cried because"), it resolves the next pronoun to she, generalizing to held‑out probes (0.94 by step 925). By step 3,500 the same model scores near zero on the same probes, although the rule's evidence is still in the training data. We call this within‑run reversal natural ungrokking: the corpus decides, with no trace in the loss curve, which learned rules a model keeps. Which rules survive is predictable from one corpus statistic: how often the training stream shows the rule winning. Across un‑intervened runs (two corpora, three budgets, three seeds), support frequency decides a rule's fate; the data‑to‑parameter ratio only modulates how deeply a doomed rule falls. The same emerge‑then‑collapse dynamics appear in public Pythia checkpoints, collapse depth ordered by model scale as predicted. The forgetting is a displacement: a competing surface pattern out‑competes the rule, and the log‑probability margin between them crosses zero within 100 training steps of the behavioral collapse. Control over this fate is asymmetric: the same edit that destroys a rule on demand cannot restore it. Flipping support to counter‑evidence in place kills the rule with monotone dose‑response in two unrelated rules; but injecting support back, even to 450 times the level that naturally sustains it, buys no recovery. Every confirmatory threshold and prediction was pre‑registered before the data it governed was read.

PaperId: 145, https://arxiv.org/pdf/2606.25990.pdf GitHub

Abstract:
As multimodal conversational systems increasingly engage in spoken interaction, their ability to navigate paralinguistic social cues has become a critical bottleneck for natural human‑AI communication. However, existing evaluations of machine emotional intelligence assess reasoning exclusively through isolated text or passive acoustic perception, overlooking the complex cross‑modal reasoning required for active, multi‑turn dialogue. We introduce \textscSpeechEQ, a comprehensive framework designed to evaluate the sociolinguistic reasoning of Speech‑Language Models (SLMs). The framework includes a validated dataset of 2,265 dialogues across 15 Emotional Quotient (EQ) subscales grounded in EQ‑i 2.0 theory, along with a multi‑turn evaluation protocol measured by our proposed Spoken EQ (SEQ) score inspired by human EQ assessments. Experiments show limitations in how both existing Speech Emotion Recognition and end‑to‑end Speech‑Language Models understand and apply paralinguistic cues through speech. While end‑to‑end architectures outperform cascaded systems, \textscSpeechEQ reveals that current multimodal models remain bottlenecked by a text‑reliant ``modality shortcut,'' an alignment‑induced ``safety trap,'' and ``contextual amnesia,'' highlighting the barriers to truly emotionally aware AI. Our benchmark can be accessed at https://huggingface.co/datasets/SpeechEQ/SpeechEQ and demo page at https://binomial14.github.io/speecheq‑demo/

PaperId: 146, https://arxiv.org/pdf/2606.25987.pdf GitHub

Abstract:
Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor leverage the hierarchical structure defining the target language. While existing constrained‑decoding frameworks address the former, they operate under rigid assumptions that preclude critical lexical mechanisms ‑‑ including context‑sensitive lexing, maximal‑munch tokenization, and keyword extraction ‑‑ and only approximate vocabulary masking, sacrificing completeness. For the latter, code LLMs typically inject grammatical structure via predetermined policies rather than learning which structural information to expose. In this work, we introduce Weave of Formal Thought (WoFT), a paradigm uniting rigorous syntactic validation with learned structural representations. First, we present a formal engine and constrained decoder that is sound and complete with respect to the full Tree‑sitter specification. By augmenting generalized LR (GLR) parsing with a speculative‑lexing construction that maintains concurrent lexer‑state hypotheses synchronized with a GLR graph‑structured stack, our decoder admits every subword token extending to a valid program prefix and rejects all others. Second, we present a latent‑variable fine‑tuning method training the language model to interleave non‑terminal grammar symbols directly into generation. Utilizing the reweighted wake‑sleep (RWS) algorithm to optimize the importance‑weighted evidence lower bound (IW‑ELBO) of the surface text, the model learns to selectively retain formal derivations as an adaptive structural scratchpad. For Python, fine‑tuning StarCoder2‑3B with our RWS objective reduces per‑token cross‑entropy by 14.3% relative to a text‑only SFT baseline, demonstrating that discretionary latent syntax recovers critical structural information that flat autoregressive training discards.

PaperId: 147, https://arxiv.org/pdf/2606.25956.pdf GitHub

Abstract:
Risk stratification for pulmonary embolism (PE) is critical for clinical decision‑making. Stratification guidelines are based on patient medical records, parameters measured from computed tomography pulmonary angiography (CTPA), and blood tests. However, blood tests are often missing in routine practice. This work studies whether state‑of‑the‑art models can accurately classify risk stratification from only medical records and biomarkers extracted from CTPA images. We benchmark different approaches to combine medical records and cardiac biomarkers with rich pulmonary vascular information; we add vascular biomarkers to tabular models and apply graph neural networks (GNNs) on the vascular tree's intrinsic graph representation. We use a private dataset (n=353) with uniquely complete data for PE risk stratification. Our results show that, among global features, medical records and cardiac biomarkers are the most significant predictors, while vascular biomarkers do not further improve stratification. Even more surprising, even GNNs on vascular graphs fail to outperform strong tabular baseline on global features. We consider hypotheses, on both models and data, that could explain this suboptimal performance. Our investigation suggests that, counter‑intuitively, vascular graphs might hold no discriminative information for PE risk stratification. Code is available from https://github.com/creatis‑myriad/GENESIS.

PaperId: 148, https://arxiv.org/pdf/2606.25832.pdf GitHub GitHub

Abstract:
Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization‑oriented large language models (LLMs). Existing approaches typically rely on large‑scale supervised datasets, costly reasoning annotations, and expensive intermediate step verification, resulting in substantial training overhead. To address these challenges, we propose MiniOpt, a reinforcement learning framework that learns to solve optimization problems through an "reasoning‑to‑model‑and‑solve" paradigm. MiniOpt decomposes optimization reasoning into structured optimization modeling and executable solver generation. Building upon this paradigm, we introduce OptReward, a reward function with hierarchical score structure that jointly evaluates formulation and solution, enabling effective policy learning without expert demonstrations. We further develop an optimization‑oriented policy optimization strategy that improves exploration efficiency and stabilizes reinforcement learning for compact models. Extensive experiments show that MiniOpt‑3B exhibits strong optimization generalization across various optimization types, problem scenarios, and task domains. For models with fewer than 10B parameters, MiniOpt series achieves the highest average solving accuracy (SA). For models with more than 10B parameters, MiniOpt still shows competitive performance. These results suggest that optimization‑oriented reward design and reinforcement learning provide an effective pathway for developing compact optimization‑specialized language models with strong optimization generalization capabilities. The code is available at https://github.com/Hsiang‑1/MiniOpt.

PaperId: 149, https://arxiv.org/pdf/2606.25606.pdf GitHub

Abstract:
Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention. As a multidisciplinary topic, these objective measures should be interpretable and accessible to health care professionals, ensuring effective collaboration and treatment planning in the realm of mental health care. Even though current automated depression diagnosis approaches improved over the last decade, a critical gap exists as they often lack affect‑specificity and interpretability, limiting their practical application and potential impact on mental health care. In particular, interpretability from temporal activities from videos when deep models are used is not fully explored. In this study, we present a novel framework for analyzing Deep Neural Networks' decisions when trained on facial videos, specifically focusing on automatic depression severity diagnosis. By fine‑tuning Deep Convolutional Neural Networks (DCNN) pre‑trained on Action Recognition datasets on depression severity facial videos from AVEC depression dataset, our framework is able to interpret the model's saliency maps by examining face regions and temporal expression semantics. Our approach generates both visual and quantitative explanations for the model's decisions, providing greater insight into its reasoning. In addition to this interpretability, our video‑based modeling has improved upon previous single‑face benchmarks for visual depression diagnosis, resulting in enhanced predictive performance. Overall, our work demonstrates the successful development of a framework capable of generating hypotheses from a facial model's decisions while simultaneously improving depression's predictive capabilities.

PaperId: 150, https://arxiv.org/pdf/2606.25556.pdf GitHub

Abstract:
Stepwise group‑based RL is an attractive way to train long‑horizon LLM agents without a learned critic: it reuses multiple sampled rollouts to estimate local advantages. Its weakness is less visible but more fundamental: every group‑relative estimator assumes that the steps it compares are equivalent for credit assignment. We show that current agentic variants violate this assumption through a state‑action credit mismatch. The observation‑hash partition is overly fine on the state side, creating singleton groups with zero step‑level signal, while a single within‑group mean is too coarse on the action side, mixing state‑value estimation with action‑specific credit. We introduce BiPACE (Bisimulation‑Guided Policy Optimization with Action Counterfactual Estimation), a drop‑in advantage estimator that fixes both sides without adding a critic, auxiliary loss, or extra rollouts. BiGPO clusters steps by cosine distance in the actor's own hidden‑state geometry, an empirical policy‑induced proxy for bisimulation that substantially lowers the singleton rate left by observation hashing. PACE then recenters returns within each behavioral cluster using action‑conditioned peer baselines; its Q‑style instance estimates a local Q(s,a)‑V(s) nonparametrically. On ALFWorld/Qwen2.5‑7B, BiPACE_Q raises overall validation success from GiGPO's 90.8 to 97.1\pm0.9 over three seeds, and crosses the 95% threshold on every seed, which GiGPO never does within the same budget. On Qwen2.5‑1.5B it reaches 93.5\pm1.2 versus GiGPO's 86.7, and on WebShop and TextCraft it improves over GRPO and GiGPO at both model scales. The measured BiPACE‑specific overhead is 11.3% of a single training‑step wall time. Yet it changes the estimator's comparison unit from surface identity to approximate behavioral equivalence plus action‑side counterfactuals. The code is available at https://github.com/TianxiangZhao/BiPACE.

PaperId: 151, https://arxiv.org/pdf/2606.25529.pdf GitHub

Abstract:
Speech‑to‑speech translation (S2ST) should preserve not only lexical meaning, but also expressive attributes: emotion, scenario style (e.g., news reporting vs. dramatic dialogue), and nonverbal vocalizations (NVs). Moreover, collecting cross‑lingual target speech that is both translation‑faithful and expressively aligned with the source is difficult at scale, making reference‑based evaluation impractical. We introduce STEB (Speech‑to‑Speech Translation Expressiveness Benchmark), a 32.6‑hour Chinese‑‑English benchmark that evaluates both standard dimensions (translation fidelity, speaker similarity, duration alignment) and expressiveness dimensions (emotion, scenario style, NV preservation). For expressiveness evaluation, STEB uses a caption‑then‑summarize framework that converts speech into structured expressive attributes and compares source and hypothesis attributes with an LLM judge. Human validation shows statistically significant correlations with listener judgments across all expressive dimensions. We evaluate six S2ST systems covering cascaded systems, end‑to‑end models, and speech large language models. Many systems, especially cascaded ones, achieve strong translation fidelity, but they still struggle with emotion preservation (best: 3.82/5) and NV preservation (best: 2.31/5). These results reveal a gap between semantic transfer and expressive transfer, identifying expressiveness preservation as an open challenge for S2ST. Audio samples are available at https://cmots.github.io/steb.github.io/.

PaperId: 152, https://arxiv.org/pdf/2606.25390.pdf GitHub

Abstract:
Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically‑conditioned Latent Diffusion Model (ALDM), a novel framework for data‑efficient, few‑shot 3D volumetric MRI synthesis. ALDM utilizes a two‑stage approach: a 3D variational autoencoder learns anatomical priors from a data‑rich source domain, while a conditional latent diffusion model, guided by tumor masks via a ControlNet, generates structurally coherent volumes for a data‑scarce target domain. Evaluated in an extreme few‑shot setting with only 16 target images, ALDM outperformed GAN and hybrid baselines, achieving a superior Frechet Inception Distance (FID) of 85.40 and a downstream classification AUC of 0.987. Qualitative results confirm that the model preserves sharp pathology boundaries and cross‑modal consistency, with visual fidelity improving progressively during training. By capturing essential diagnostic features, ALDM provides a robust tool for clinical data augmentation in low‑resource settings. Our implementation is available at https://github.com/Analytics‑Everywhere‑Lab/anatomically‑conditioned‑LDM.

PaperId: 153, https://arxiv.org/pdf/2606.25369.pdf GitHub GitHub

Abstract:
While large language model (LLM)‑based text‑to‑speech (TTS) systems have achieved high‑quality speech synthesis, most existing systems focus on English and Chinese. Japanese, however, remains under‑explored, and its unique linguistic challenges, such as widespread context‑dependent kanji polyphony, have yet to be adequately tackled. Here we introduce Sarashina2.2‑TTS (https://github.com/sbintuitions/sarashina2.2‑tts), a Japanese‑centric LLM‑TTS system that tackles these challenges through a dual approach: data strategy and evaluation methodology. First, we scale training to approximately 361k hours of speech, incorporating a balanced mix of Japanese and English data. Furthermore, we design a targeted data augmentation pipeline covering all 2,136 Joyo (regular‑use) kanji designated by Japan's Agency for Cultural Affairs to efficiently address kanji polyphony disambiguation. Second, we introduce the Joyo Kanji Yomi Benchmark (https://github.com/sbintuitions/JoyoKanji‑Yomi‑Benchmark), covering all 2,136 Joyo kanji and their 4,378 readings. Alongside this benchmark, we propose Kana‑CER, a metric that compares synthesized speech against reference readings in the kana space, eliminating orthographic variations to directly measure pronunciation correctness. Experiments demonstrate that our targeted data augmentation significantly improves reading accuracy. Overall, Sarashina2.2‑TTS achieves state‑of‑the‑art kanji‑level reading accuracy and matches top baselines on general sentence‑level pronunciation, while delivering the highest speaker similarity in zero‑shot Japanese speech synthesis. Furthermore, cross‑lingual evaluation reveals that Sarashina2.2‑TTS is the only system that maintains stable Japanese pronunciation regardless of the prompt language, confirming that our balanced training approach improves cross‑lingual robustness.

PaperId: 154, https://arxiv.org/pdf/2606.25331.pdf GitHub

Abstract:
Modern large language models are predominantly trained with autoregressive factorization and causal attention. We present \emphiLLaDA, an 8B masked diffusion language model trained from scratch with fully bidirectional attention. iLLaDA keeps the masked diffusion objective throughout pre‑training and supervised fine‑tuning (SFT), scaling pre‑training to 12T tokens and fine‑tuning on a 25B‑token instruction corpus for 12 epochs. We further use variable‑length generation for efficiency and introduce confidence‑based scoring for multiple‑choice evaluation. Compared with LLaDA, iLLaDA improves broadly across general, mathematical, and code benchmarks; for example, iLLaDA‑Base improves by 21.6 points on BBH and 14.9 points on ARC‑Challenge, while iLLaDA‑Instruct improves by 14.5 points on MATH and 16.5 points on HumanEval. Despite its non‑autoregressive training, iLLaDA also remains competitive with Qwen2.5 7B on several benchmarks. These results show that fully bidirectional diffusion training from scratch is a competitive path toward strong language models. Model weights and codes: https://github.com/ML‑GSAI/LLaDA.

PaperId: 155, https://arxiv.org/pdf/2606.25306.pdf GitHub

Abstract:
Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical laws. Compounding this is a lack of reliable granular evaluation methods for localizing and specifying physical law violations in videos. We address this by introducing Physics Question Scene Graph (PQSG), a hierarchical question‑based evaluation pipeline. PQSG evaluates generated videos by checking their faithfulness to a prompt across objects, actions, and adherence to physical laws using a graph‑based hierarchy of questions generated by a vision‑language model (VLM), guided by high‑quality in‑context examples. By representing questions as a graph, PQSG introduces logical dependencies within questions, ensuring that each query is contextually valid. Moreover, PQSG provides granular assessments of which qualities of the video violate physical plausibility constraints. We validate PQSG by creating FinePhyEval, a dataset with physics‑based prompts and corresponding generated videos from diverse state‑of‑the‑art video generation models (Sora 2, Veo 3, and Wan 2.1), with each video annotated across multiple categories by humans. Using FinePhyEval, we measure the correlation between PQSG's fine‑grained scores and human judgments, showing higher overall correlations than prior work. We also find that PQSG ranks closed‑source models higher than Wan 2.1 on physical realism. Lastly, we show that the annotations we provide in FinePhyEval can also be used for subtask evaluation: we benchmark two strong VLMs on generating and answering questions, finding that while models can create human‑like questions, they still fall short of human performance in answering them.

PaperId: 156, https://arxiv.org/pdf/2606.25285.pdf GitHub

Abstract:
Post‑Training Sparsity (PTS) has emerged as a crucial paradigm for compressing Large Language Models to facilitate efficient deployment on resource‑constrained devices. However, existing PTS methodologies are typically confined to Single‑Sparsity optimization, necessitating a separate, time‑consuming optimization session for each specific sparsity level. This rigid paradigm significantly hinders flexible deployment across diverse hardware scenarios, as adapting to a new sparsity requirement mandates a complete re‑optimization process. To address these limitations, we propose Elastic Post‑Training Sparsity (EPTS), a unified Multi‑Sparsity framework that produces a single elastic model capable of maintaining robust performance across diverse sparsity configurations through a one‑shot optimization process. Specifically, we design a Multi‑Sparsity Hierarchy LoRA (MS‑HiLoRA) mechanism that facilitates knowledge inheritance from low‑ to high‑sparsity groups, effectively mitigating the competition for parameter reconstruction. Furthermore, we introduce a Multi‑Sparsity Feature Mixer (MSFM), which significantly enhances the model's adaptability to pruning perturbations by dynamically fusing feature representations of varying sparsity granularities. Extensive experiments on LLaMA and OPT families demonstrate that EPTS achieves competitive performance compared to state‑of‑the‑art methods like SparseGPT and Wanda, while offering significant efficiency gains by enabling multi‑scenario deployment from a single optimization. our source code is available at https://github.com/xuke225/EPTS.

PaperId: 157, https://arxiv.org/pdf/2606.25191.pdf GitHub

Abstract:
Multi‑agent document assessment for retrieval‑augmented generation is computationally expensive, driving practitioners toward smaller, deployable models whose assessment mechanisms remain poorly understood. We conduct a controlled study of training‑free interventions on 7B‑9B instruction‑tuned models across diverse QA benchmarks, revealing a sharp dichotomy in how models benefit from assessment. For weaker baselines, the dominant mechanism is per‑document isolation. Astoundingly, assessment‑free isolation matches full multi‑agent assessment, demonstrating that resolving multi‑document context confusion, rather than scoring quality, drives outsized gains of up to 50 percentage points. Conversely, for strong baselines where scoring quality matters, we introduce Reasoning‑Score Coupling, a label‑free perturbation probe that classifies scoring behavior. Integrating these findings, we propose MADARA, a model‑adaptive routing architecture. Crucially, MADARA's diagnostic thresholds derived from a single pilot model generalize zero‑shot to four unseen model families, providing a robust, lightweight pipeline to eliminate computational overhead.

PaperId: 158, https://arxiv.org/pdf/2606.25178.pdf GitHub

Abstract:
Reinforcement learning with verifiable rewards (RLVR) has been extended from single‑domain training to multi‑domain reasoning suites spanning mathematics, programming, and science. However, the training curriculum (how often each domain is sampled) is typically fixed or hand‑tuned, even though reasoning skills transfer unevenly across domains. Existing learnability‑based curricula adapt to where the policy is currently improving, but are blind to whether a gradient step on the selected domain benefits the remaining domains. In this paper, we propose Transfer‑Aware Curriculum (TAC), a bandit‑style online curriculum that prioritizes domains whose updates broadly benefit the rest of the training suite. TAC repurposes signals already produced by RL training: per‑domain advantages capture local learnability, and projected gradients, taken from the GRPO step being computed, estimate cross‑domain transferability via gradient‑geometry alignment, at negligible cost (<1% wall‑clock overhead). Across a six‑domain reasoning suite, TAC achieves the best macro‑averaged accuracy on both Qwen3‑1.7B and Llama3.2‑3B, outperforming proportional random sampling, a hand‑designed schedule, and a learnability‑only bandit, and improving over the last of these by up to 2.8 points (10% relative). Ablations show performance degrades sharply when the transferability term is removed, and TAC remains robust on imbalanced training mixtures where learnability‑only curricula over‑commit to dominant domains. Our findings establish cross‑domain transferability as a key signal for curriculum design in multi‑domain RLVR.

PaperId: 159, https://arxiv.org/pdf/2606.25156.pdf GitHub

Abstract:
Modern large language models based on softmax scaled‑dot‑product attention are constrained by their training sequence length: as the key‑value sequence grows, softmax probability mass can dilute across a wider distribution, inducing activation shift and long‑context performance collapse. Moreover, long‑context language modeling faces a structural tension: a sliding‑window attention core maintains a bounded local representation and low perplexity but is blind to long‑range dependencies, while full‑context attention preserves global recall but suffers from out‑of‑distribution perplexity explosion. To resolve these limitations, we introduce ATMA, a hybrid convolutional‑attention architecture that integrates a novel three‑channel attention mechanism. ATMA factorizes the attention mixing step into: (1) a count‑blind, unit‑vector direction channel, (2) a bounded magnitude channel driven by the participation ratio of effective matches over an extreme‑value‑corrected null sink, and (3) a long‑term recurrent compression memory optimized via a gated‑delta fast‑weights rule. Neither the Polar Attention core nor the recurrent memory is sufficient alone; their combination enables monotonic perplexity reduction and high‑fidelity long‑range retrieval simultaneously. We evaluate ATMA using a 120‑run factorial ablation sweep, demonstrating that the combined Polar + memory model maintains induction needle‑in‑a‑haystack retrieval accuracy above 90% out to 64K tokens (32 times the training length of 2K) while its document perplexity improves monotonically, outperforming softmax‑based memory baselines which collapse at extreme context lengths. Code: https://github.com/kreasof‑ai/atma

PaperId: 160, https://arxiv.org/pdf/2606.25152.pdf GitHub

Abstract:
Deployed approaches for AI text detection often rely on training‑time access to labeled datasets of both human‑written and AI‑generated text. This approach is vulnerable to three types of distribution shifts that occur continually post‑deployment, and for which labeled data is often unavailable: adversarial humanization, new LLMs being released, and temporal drift in human writing. Simultaneously, existing approaches do not leverage a key signal of LLM usage: inference‑time homogeneity. We propose a test‑time adaptation (TTA) approach, using semi‑supervised learning, that adapts to distribution shifts by leveraging homogeneity among unlabeled samples observed at inference time. Empirically, we find that state‑of‑the‑art supervised detectors systematically fail when they encounter distribution shifts in AI‑generated and human writing, both adversarial and natural, while test‑time adaptation with semi‑supervised learning is largely robust; e.g., the commercial model Pangram detects just 24.1% of our adversarial AI‑generated text, compared to 90.5% for our test‑time approach. We establish that test‑time adaptation is a promising framework for AI text detection in the wild. We publicly release our code (which includes code for model training, evaluation, and plots) at https://github.com/kkr36/llm_detection.

PaperId: 161, https://arxiv.org/pdf/2606.24995.pdf GitHub

Abstract:
Tabular foundation models (TFMs) achieve strong performance on microbiome abundance data, yet their robustness under realistic distribution shift remains poorly characterized. We introduce a benchmark that evaluates the robustness of TFMs to biologically inspired perturbations across six gut microbiome datasets spanning four disease contexts. In this in‑context learning setting, models receive unperturbed support sets as context and are evaluated on perturbed query samples. To isolate robustness beyond "shortcut" features, we preserve the most discriminative taxa and apply three controlled perturbation strategies: (i) removal of high‑abundance (uninformative) taxa, (ii) sparsification via increased zero‑inflation, and (iii) zero‑imputation via spurious non‑zero injections. Our results show that protecting discriminative features is insufficient to guarantee stability under support‑query shift: across datasets, all perturbations degrade model performance, with zero‑imputation consistently the most harmful, indicating that corrupting global feature structure can break generalization even when key taxa are retained. Sparsification disproportionately affects TFMs relative to a classical random forest baseline, suggesting greater sensitivity to zero‑inflation‑type shifts. The code is publicly available at: https://github.com/UMMISCO/metagenomics‑fm/.

PaperId: 162, https://arxiv.org/pdf/2606.24965.pdf GitHub

Abstract:
Reasoning about relational structures remains a significant challenge for neural models, particularly when they must systematically apply learned knowledge to problem instances that are harder than those seen in training. Progress is hampered by the difficulty of evaluating such generalization, since a priori, it is rarely clear what makes an instance hard. We study how this issue can be addressed by using large language models (LLMs) to automate benchmark generation, learning to produce increasingly challenging instances in an end‑to‑end manner. Concretely, given a world parametrized by Datalog rules, and an Edge Transformer as the reasoning evaluator, we use LLM‑driven evolutionary search (based on FunSearch) and autonomous agentic search to discover sampling functions that yield hard problem instances. We also show that the Edge Transformer can be improved using this data such that it generalizes well to further data perturbations. Finally, we show that the same machinery can be applied to novel worlds proposed by LLMs, opening the door to autonomous research on neural relational reasoning.

PaperId: 163, https://arxiv.org/pdf/2606.24948.pdf GitHub

Abstract:
Knowledge graph embedding (KGE) models predict single‑hop links well but have no mechanism for zero‑shot compositional queries: multi‑hop questions whose relation chains never appeared during training. Holographic Reduced Representations (HRR), which bind and unbind symbols via circular convolution, are a theoretically attractive candidate, since binding is approximately invertible and associative. We test whether this promise holds. We study two holographic memory variants, real‑valued HRR and phase‑only Fourier HRR (FHRR), each with a modern Hopfield cleanup, on FB15k‑237 over five seeds. Four findings follow. First, both are competitive single‑hop retrievers (filtered MRR 0.358 +/‑ 0.002 for HRR, 0.350 +/‑ 0.021 for FHRR). Second, neither composes zero‑shot: accuracy stays at chance across all cleanup temperatures. Third, the main contribution, we localise the failure mechanistically. A hop‑1 probe shows the memory recovers the correct intermediate entity with high fidelity (MRR 0.896 +/‑ 0.002 for HRR), yet composition still fails even with a verified‑correct intermediate. A second probe shows why: posing the ground‑truth second‑hop fact as a standalone atomic query, bypassing composition entirely, already recovers it at only 0.26 to 0.48x average atomic accuracy, uniformly across relation fan‑out. The bottleneck is not the bind‑unbind algebra or the cleanup; it is that facts compositional chains pass through are intrinsically harder for the superposed memory to retrieve, a capacity and interference effect present already at a single hop. Fourth, we prove (Lemma 4.1) that FHRR's softmax cleanup is not phase‑equivariant, compounding the primary failure on the minority of chains where hop‑1 itself errs. Fixing zero‑shot composition requires improving retrieval capacity under superposition, not just redesigning the cleanup.

PaperId: 164, https://arxiv.org/pdf/2606.24894.pdf GitHub

Abstract:
Large language models have shown strong fluency in scientific writing, yet the evaluation of related work generation (RWG) remains limited. Existing RWG evaluations largely inherit summarization‑oriented metrics, using lexical or semantic similarity to reference sections as proxies for quality. However, related work writing is fundamentally a citation‑level scholarly positioning task: it requires selecting, organizing, and framing prior work to clarify how a target paper relates to, differs from, and contributes beyond existing research.As a result, models may generate coherent and semantically‑relevant text while exhibiting academically critical failures, such as inappropriate citation selection or misplaced references, that conventional metrics do not capture.To this end, we introduce RWGBench, a benchmark that evaluates RWG from the perspective of citation decision‑making rather than text similarity. RWGBench is constructed from a large‑scale collection of 40,108 computer science papers and a retrieval corpus of 1.09 million documents, with a carefully curated test set comprising 100 papers and their corresponding published related work sections.We propose a multi‑dimensional evaluation framework that assesses citation selection, contextual appropriateness, organization, and discourse structure.Experiments reveal systematic limitations in current systems that are obscured by standard evaluations, while Oracle studies further disentangle retrieval‑level and generation‑level bottlenecks. Human evaluation further shows that our citation‑centric metrics align substantially better with expert judgment than surface‑level text metrics. RWGBench offers a citation‑centric testbed for developing and evaluating related work generation systems that are better aligned with scholarly writing practices.

PaperId: 165, https://arxiv.org/pdf/2606.24841.pdf GitHub

Abstract:
Prompt‑based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre‑training objectives on the performance of encoder‑decoder pre‑trained language models across generation and question answering tasks, with a focus on commonsense knowledge retrieval and completion. We highlight the benefits of incorporating multiple objectives during both pre‑training and fine‑tuning stages. We introduce the Match Task to Objective (MTO) framework and methods for determining the appropriate objective for a given task. This framework offers automated methods to prepare task‑related data for adaptation through unsupervised training, based on the identified objective. In the fine‑tuning stage, we design novel templates that align with the objectives of the pre‑training and adaptation stages. When aligned with task requirements, these strategies can achieve a performance gain of over 120% compared to conventional methods in few‑shot settings. They significantly outperform related works in few‑shot settings and exceed the baseline even in full‑dataset scenarios. Furthermore, we extend this approach to include prompt‑tuning methodologies, providing guidance for more effective soft prompt engineering and optimization. Our strategies significantly enhance prompt‑tuning performance as well. These insights hold substantial value, precisely guiding the selection and optimization of models customized for specific tasks. Code is available at https://github.com/puraminy/MTO/

PaperId: 166, https://arxiv.org/pdf/2606.24759.pdf GitHub

Abstract:
Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade‑off between temporal reasoning and spatial precision. Models that rely on single‑frame or low‑resolution inputs often miss small, distant, or partially occluded hazards, while language‑centric driving models frequently provide limited grounded evidence for their explanations. To address this gap, we propose UniDrive, a unified visual‑language and grounding framework for interpretable risk understanding in autonomous driving. UniDrive combines a temporal reasoning branch that models scene dynamics from multi‑frame visual input with a high‑resolution perception branch that preserves fine‑grained spatial details from the latest frame. The two branches are integrated through a gated cross‑attention fusion module, enabling dynamic context to be aligned with precise spatial evidence. Based on the fused representation, UniDrive jointly generates natural‑language risk descriptions and grounded bounding‑box outputs for risk objects. Experiments on the DRAMA‑Reasoning benchmark show that UniDrive outperforms representative image‑based and video‑based baselines in both captioning and risk‑object grounding. In particular, UniDrive achieves the best overall performance on the validation split and demonstrates clear advantages in small‑object localization, zero‑shot generalization to NuScenes and BDD100K, and human‑rated interpretability and trustworthiness. These results suggest that explicitly combining temporal semantics and high‑resolution perception provides a stronger foundation for interpretable and safety‑oriented autonomous driving systems. The code is available at https://github.com/pixeli99/unidrive‑dev.

PaperId: 167, https://arxiv.org/pdf/2606.24716.pdf GitHub

Abstract:
Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence. We present a human‑grounded evaluation framework that quantifies alignment between SAE latents and human‑annotated concepts, without requiring user studies, and validate this matching through targeted attribute perturbations. To enable this intervention‑style evaluation in vision, we construct synCUB and synCOCO, synthetic benchmarks of paired images that differ in exactly one attribute. We introduce Fully‑Binary Matching Pursuit (FBMP), a coalition‑based matching procedure that supports many‑to‑one mappings between SAE latents and annotated concepts, and consistently outperforms one‑to‑one baselines. For functional validation, we propose a Targeted Attribute Perturbation Alignment Score (TAPAScore), which tests whether matched concepts respond selectively and in the expected direction under targeted image‑level attribute perturbations. Under sanity checks, our matching and TAPAScore are the only evaluated metrics that reliably distinguish trained SAEs from untrained ones. Across SAEs trained on CLIP and DINOv2 embeddings, we find that increased overcompleteness can reduce perturbation alignment, indicating a reduction in interpretability. Our evaluation framework suggests that moderate dictionary sizes provide the best trade‑off, yielding the most interpretable SAEs. Code and datasets are available at https://github.com/JonasKlotz/sae‑concept‑eval.

PaperId: 168, https://arxiv.org/pdf/2606.24679.pdf GitHub

Abstract:
Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning‑ready data through sequential cleaning and feature transformation operators. However, automatically constructing such pipelines is computationally difficult because operator sequences are combinatorial and end‑to‑end evaluation is expensive. Existing state‑of‑the‑art (SOTA) Multi‑DQN methods still face three key limitations: decoupled value estimators weaken long‑horizon credit assignment, dataset context is only weakly injected into the policy, and exploration is inefficient in a sparse search space with many invalid states. To address these issues, we propose FlowPipe, a unified framework that formulates pipeline synthesis as conditional probabilistic flow generation over a directed acyclic graph. FlowPipe uses Conditional Generative Flow Networks (C‑GFlowNets) with a Trajectory Balance objective to connect terminal validation rewards with early pipeline decisions. It further introduces Deep Semantic Modulation through Feature‑wise Linear Modulation (FiLM), allowing LLM‑derived logical priors to condition the policy's internal activations according to dataset semantics. In addition, FlowPipe incorporates failure awareness into the flow objective to avoid invalid states and concentrate search on high‑potential regions. Experiments on two benchmark suites with 74 real‑world datasets show that FlowPipe outperforms SOTA baselines, improving accuracy by 11.96% on average and achieving 12.5x faster training convergence. Source code is available at https://github.com/KunyuNi/FlowPipe.

PaperId: 169, https://arxiv.org/pdf/2606.24636.pdf GitHub

Abstract:
Cinematographic captioning aims to describe how a video is filmed using professional film‑language concepts such as camera movement, shot size, depth of field, composition, and shooting angle. This capability is important for fine‑grained video understanding and controllable movie‑quality video generation, yet remains underexplored in existing multimodal large language models. Unlike question‑answering‑based evaluation of cinematic understanding, cinematographic captioning requires a unified open‑form description over multiple cinematographic dimensions. This task is challenging for two main reasons: the model must infer professional cinematographic concepts from subtle visual evidence, and it must generate captions that are both comprehensive and accurate. Accordingly, we propose CineCap, a framework that combines structured reasoning with spatio‑temporal anchors and reinforcement learning with comprehensiveness, accuracy, and gated coverage rewards. The former grounds professional cinematographic descriptions in explicit visual evidence and organizes them into compact atomic reasoning for supervised fine‑tuning, while the latter improves the balance between descriptive completeness and factual correctness. In addition, we construct CineCap Bench, a benchmark of 472 manually annotated video‑caption pairs for systematic evaluation. Extensive experiments show that CineCap consistently outperforms strong proprietary and open‑source baselines, establishing a new state of the art for cinematographic captioning. The code, model checkpoint, and benchmark are publicly available in https://github.com/Hectormxy/CineCap.git.

PaperId: 170, https://arxiv.org/pdf/2606.24623.pdf GitHub

Abstract:
Retrieval‑Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi‑agent framework that sanitizes retrieved content through semantic rewriting. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, our approach collaboratively removes sensitive identifiers while preserving the semantic core. We evaluate the framework on the ChatDoctor and Wiki‑PII datasets across six large language models. Experimental results demonstrate a significant reduction in privacy leakage under targeted attacks. For instance, we reduced targeted information exposure in LLaMA‑3‑8B from 144 instances in the baseline to just 1. Furthermore, we maintain strong contextual fidelity with a BLEU‑1 score of 0.122, outperforming the existing SAGE method's 0.117. Finally, the framework operates as an asynchronous preprocessing module, introducing no additional latency to online inference, as all rewriting is executed as a one‑time offline preprocessing step. To promote reproducibility, the source code of this work is publicly available at https://github.com/foursoils/Privacy‑Preserving‑RAG.

PaperId: 171, https://arxiv.org/pdf/2606.24589.pdf GitHub

Abstract:
Scaling adversarial evaluation of large language models requires both a method for generating hard inputs and a reliable way to confirm that resulting failures are real. We present AdversaBench, an end‑to‑end red‑teaming pipeline that mutates seed prompts with five structured operators, queries a target model, and confirms failures through a three‑judge panel with a meta‑judge tiebreaker. We report experiments on 45 seeds across three categories: reasoning, instruction‑following, and tool use. Every seed produced a confirmed failure. Four findings stand out. First, operator effectiveness varies sharply by category: inject_distractor scores 0.00 mean reward on instruction‑following seeds but 0.80‑0.83 on reasoning and tool‑use. Second, binary failure rate hides difficulty: instruction‑following seeds required 2.4 attacker iterations on average versus 1.1 for other categories, a gap visible in survival curves. Third, pairwise judge agreement of 80‑87% coexists with near‑zero Cohen's kappa due to label skew; category‑level disagreement rates are more informative. Fourth, adversarial prompts generated against Llama 3.1 8B transfer zero‑shot to Llama 3.3 70B, suggesting the mutations exploit general behavioral patterns rather than model‑specific weaknesses. Code, dataset, and analysis scripts are available at https://github.com/khanak0509/AdversaBench .

PaperId: 172, https://arxiv.org/pdf/2606.24488.pdf GitHub

Abstract:
Learning causal models from fragmented biomedical data is challenging because clinical, molecular, and imaging variables are often incomplete or not jointly observed. We propose RetiSEM, a domain‑constrained structural equation modelling (SEM) framework for causal graph recovery and mediation analysis under limited multimodal resources. This proposed work organises variables into biologically informed blocks, applies forbidden‑edge constraints, and decomposes pathway‑level effects into TE, NDE, and NIE components. We evaluate RetiSEM across ten synthetic benchmark scenarios that vary in dimensionality, nonlinearity, causal depth, and pathway structure, together with a fragmented real‑world setting that combines NHANES clinical variables with externally derived retinal representations. This approach achieves lower structural error and higher causal accuracy than unconstrained baselines across the synthetic benchmarks. In the real‑data analysis, retinal variables behave mainly as downstream biomarker‑like indicators, with smaller but detectable indirect effects. These findings support our strategy as an interpretable framework for testing structured causal hypotheses in limited‑resource biomedical AI. The code and resources for this work are publicly available at: https://github.com/Inamullah‑Colab/ReitSEM.

PaperId: 173, https://arxiv.org/pdf/2606.24467.pdf GitHub

Abstract:
Long‑context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key‑value (KV) caches, limiting sustainable deployment on resource‑constrained hardware. Existing KV cache eviction methods typically apply heuristic token scoring over all heads in GQA‑based LLMs. These methods ignore the different functionalities of attention heads, leading to the eviction of critical tokens and thus degrading the performance of LLMs. To address this issue, we propose CompressKV, a resource‑efficient KV‑cache compression framework for GQA‑based LLMs. Instead of aggregating attention scores from all heads, CompressKV identifies Semantic Retrieval Heads (SRHs) that capture both the initial and final tokens of a prompt and semantically important mid‑context evidence, and uses them to select tokens whose KV pairs should be retained. Furthermore, CompressKV allocates cache budgets across layers according to offline estimates of layer‑wise eviction error. Experiments on LongBench and Needle‑in‑a‑Haystack show that CompressKV consistently outperforms existing KV‑cache eviction methods across memory budgets. Notably, it preserves over 97% of full‑cache performance using only 3% of the KV cache on LongBench question‑answering tasks and achieves 90% accuracy with just 0.7% KV storage on Needle‑in‑a‑Haystack. These results demonstrate an improved resource‑‑performance trade‑off for long‑context LLM inference. Our code is publicly available at: https://github.com/TUDa‑HWAI/CompressKV

PaperId: 174, https://arxiv.org/pdf/2606.24450.pdf GitHub

Abstract:
Perceiving physical contact is fundamental to dexterous manipulation. While robots often rely on dedicated hardware tactile sensors, humans exhibit a remarkable ability to infer contact by integrating visual information with an innate sense of their body's pose and movement. Inspired by this embodied perceptual skill, we investigate whether a robot can learn to infer contact from vision, an approach that also offers a scalable alternative to tactile hardware specifically for binary contact estimation, which faces practical challenges in cost, fragility, and integration. We present NoContactNoWorries, a transformer‑based multimodal framework that fuses RGB‑D vision with the robot's proprioception to infer binary contact states as a pseudo‑tactile signal for hand‑object interactions. We validate by training a single contact prediction model on multiple objects and show that the inferred contact signal supports downstream reinforcement learning agents for in‑hand object reorientation, generalizing to novel objects. Experiments in both simulation and on a real‑world robot validate our approach, highlighting the feasibility of inferring contact from vision and proprioception. Project Page: https://soham2560.github.io/no‑contact‑no‑worries/

PaperId: 175, https://arxiv.org/pdf/2606.24320.pdf GitHub GitHub

Abstract:
We present ZONOS2 8B, our latest TTS model, which achieves state‑of‑the‑art naturalness, prosody, and voice cloning fidelity. We improve upon Zonos‑v0.1 across scale, data, and training recipe. We scale the model from 1.6B to 8B total parameters (900M active) with a novel mixture‑of‑experts (MoE) backbone, improving inference latency and throughput. We expand our training corpus from 200K to over 6M hours using a new data processing pipeline, and we simplify our post‑training and conditioning recipes to improve naturalness and voice cloning fidelity. We evaluate ZONOS2 8B on quality, speaker similarity, WER, and ZTTS1‑Eval, our novel TTS benchmark, where it performs competitively with state‑of‑the‑art systems while maintaining good streaming latency. We release our model weights and example inference code under an Apache 2.0 license on GitHub and Hugging Face.

PaperId: 176, https://arxiv.org/pdf/2606.24231.pdf GitHub

Abstract:
Multimodal driving planning faces a long‑standing tension between two paradigms: scoring‑based methods benefit from dense reward supervision but are confined to a fixed action vocabulary, while anchor‑based methods generate proposals dynamically yet suffer from sparse supervision constrained to a single ground‑truth trajectory. In this work, we propose FlowR2A, which resolves this tension by reframing simulation‑based rewards from discriminative targets into generative conditions. By learning the reward‑conditioned action distribution from dense trajectory‑reward pairs with a flow‑matching decoder, FlowR2A unifies the dense supervision of scoring‑based methods with the proposal generation of anchor‑based methods in a single generative model, forcing the model to internalize the correlation between an action and its outcomes in safety, progress, comfort, and rule compliance. To balance hard safety constraints against soft progress objectives, we introduce fine‑grained per‑timestep reward conditioning and reward noise augmentation. The generative formulation naturally supports controllable test‑time sampling via reward guidance and anchored sampling, producing high‑quality proposals. FlowR2A achieves state‑of‑the‑art results on the NAVSIM v1 and v2 benchmarks, with multimodal proposals of substantially higher quality than prior methods.

PaperId: 177, https://arxiv.org/pdf/2606.24196.pdf GitHub GitHub

Abstract:
Modern AIGC pipelines deliver high‑fidelity images and videos but presuppose a well‑formed creation instruction, while end users rarely articulate visual details, leaving generators misaligned with user demand. We study personalized content generation, which turns a user's interaction history into an executable instruction for downstream synthesis, and identify two obstacles: behavior must be encoded in a form legible to language reasoning, and the model must acquire instruction‑writing skill absent from both pretraining and behavior data. We propose NaviGen, which represents each item with a dual identifier coupling a collaborative code and a textual code as a behavioral substrate and a semantic bridge in one token stream. On this representation, a two‑stage SFT+RL pipeline first distills preference reasoning and instruction writing from evolutionarily searched supervision, then aligns generation with user intent through hierarchical and self‑consistent rewards. Experiments across product, game, and short‑video domains show that NaviGen improves personalized image and video generation, strengthens next‑item prediction, and yields more specific, relevant, and visually generatable instructions. Our code is released at: https://github.com/iLearn‑Lab/NaviGen.

PaperId: 178, https://arxiv.org/pdf/2606.24192.pdf GitHub

Abstract:
Unlearning has emerged as a key technique to mitigate harmful content generation in diffusion models. However, existing methods often remove not only the target concept, but also benign co‑occurring concepts. As illustrated in Fig.1, unlearning nudity can unintentionally suppress the concept of person, preventing a model from generating images with person. We define these undesirably suppressed co‑occurring concepts that must be preserved CARE (Co‑occurring Associated REtained concepts). Then, we introduce the CARE score, a general metric that directly quantifies their preservation across unlearning tasks. With this foundation, we propose ReCARE (Robust erasure for CARE), a framework that explicitly safeguards CARE while erasing only the target concept. ReCARE automatically constructs the CARE‑set, a curated vocabulary of benign co‑occurring tokens extracted from target images, and leverages this vocabulary during training for stable unlearning. Extensive experiments across various target concepts (Nudity, Van Gogh style, and Tench object) demonstrate that ReCARE achieves overall state‑of‑the‑art performance in balancing robust concept erasure, overall utility, and CARE preservation.

PaperId: 179, https://arxiv.org/pdf/2606.24112.pdf GitHub GitHub

Abstract:
Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle text‑‑image framing errors. Existing benchmarks and methods remain poorly matched to this setting: they usually isolate short captions, single images, binary labels, or one manipulation source, while agentic verification remains costly under realistic evidence search. We present ReMMD, a realistic multilingual multi‑image agentic verification framework for multimodal misinformation detection. ReMMD includes ReMMDBench, a real‑world multimodal misinformation detection benchmark with 500 samples, 2,756 images, five monolingual languages, two cross‑lingual settings, three text‑length tiers, multi‑image posts, five‑way veracity labels, eight distortion labels, evidence provenance, and rationales. It also includes ReMMD‑Agent, a persistent‑memory verifier that decomposes posts into atomic points, builds a reusable evidence set, and predicts structured L1/L2/L3 outputs. Across proprietary systems, open LVLMs, MMD‑Agent, and T2‑Agent, ReMMD‑Agent obtains the best five‑way veracity performance, with 41.80% accuracy and 39.12% macro‑F1 using GPT‑5.2, while reducing cost by 17.5% relative to MMD‑Agent and 79.9% relative to T2‑Agent. The project is available at https://dang‑ai.github.io/ReMMD.

PaperId: 180, https://arxiv.org/pdf/2606.24107.pdf GitHub

Abstract:
Short dramas, with their rapid shot rhythms, dialogue‑driven focus shifts, and demanding cinematographic grounding, pose challenges that prompt‑level or text‑only video generation pipelines struggle to meet. We study plot‑to‑short‑drama generation, where a global plot and local context are transformed into visually grounded multi‑shot videos. We propose DramaDirector, a geometry‑grounded framework that lets the planner borrow cinematographic geometry from a gallery of real short‑drama shots indexed by depth and pose. DramaDirector decouples each shot into static visual and dynamic narrative conditions, trains the planner with schema‑constrained SFT and GRPO under a learned text‑visual alignment reward, and retrieves depth‑pose references to guide first‑frame generation and image‑to‑video synthesis. We also introduce DramaBoard, a benchmark built from 35 live‑action dramas, 2.8K episodes, and 81K shots, with structured storyboards and multi‑dimensional evaluation protocols. Experiments show that DramaDirector improves over representative multi‑agent and video generation baselines on faithfulness, consistency, and controllability. Our code is released at: https://github.com/iLearn‑Lab/DramaDirector

PaperId: 181, https://arxiv.org/pdf/2606.24083.pdf GitHub

Abstract:
"Talk short. Drop grammar. Save token." This caveman style is widely promoted as a way to cut inference cost, but whether it actually saves anything depends on which channel (the user's prompt or the model's response) is being compressed. We present Cavewoman, a two‑channel evaluation protocol that scores every generation on task accuracy, realized per‑item cost, and reference‑text agreement against the model's unconstrained reference. We evaluate eight models on five datasets at five reduction levels, with both channels measured on the same items. Output compression cuts realized cost on most API models (1.4‑2.4x per model, up to 3x in the best case) and on all four open‑weight models under public‑tier pricing. Input compression has the opposite effect, a strict lose‑lose: it raises net cost rather than lowering it (~1.15x on the five‑benchmark mean, up to 1.8x on the worst dataset and 2.7x under stronger compression), because models compensate with longer responses even as accuracy collapses. Under the same setting, surface text diverges from the unconstrained reference: on the non‑reasoning models, roughly half of all generations are correct yet their surface text no longer entails the model's own unconstrained baseline generation. The divergence survives length‑controlled re‑scoring, multiple‑comparisons correction, and replication under complementary semantic measures. Code and data are available at https://github.com/danielle34/cavewoman.

PaperId: 182, https://arxiv.org/pdf/2606.23993.pdf GitHub

Abstract:
High‑throughput scientific facilities such as the Large Hadron Collider depend on real‑time event filtering (triggering) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand‑tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision‑making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal‑sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group‑Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO‑F, GFPO‑FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy (H_T) trigger sensitive to pileup variation, and an anomaly‑detection (AD) trigger based on reconstruction loss for rare or non‑standard signatures. On Monte Carlo streams, our agent increases the fraction of in‑tolerance time intervals by 48% (H_T) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in‑tolerance intervals. Transferring from simulation to \emphreal collision data (CMS Run 283408), the same agent, without fine‑tuning, achieves a 56% (H_T) and 28% (AD) in‑tolerance improvement over baselines, with further signal‑efficiency gain on both triggers. To our knowledge, this is the \emphfirst demonstration of RL‑based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

PaperId: 183, https://arxiv.org/pdf/2606.23938.pdf GitHub

Abstract:
Driving VLA models incorporating Chain‑of‑Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step‑by‑step decision semantics needed to keep the rationale causally connected to the planned motion. We introduce Neuro‑Symbolic Drive, a neuro‑symbolic driving framework that supervises a driving VLA with rule‑grounded reasoning traces extracted directly from classical rule‑based planners. Our key observation is that rule‑based planners are symbolic AI systems that already function as executable reasoning engines: they reason about active safety constraints, search over candidate maneuvers, and select a final trajectory. We instrument these planners in simulation to capture both the executed trajectory and the internal decision trace at each rule‑evaluation step. Each trace is serialized into structured rule‑grounded reasoning and paired with the trajectory to fine‑tune Qwen3.5‑4B as a driving VLA. Because these traces are derived directly from the planner states that determine the action, they ensure reasoning is structurally coupled to motion generation by construction, rather than by post‑hoc alignment. On our simulator‑generated benchmark, detailed rule‑grounded reasoning reduces ADE@3s from 0.47 to 0.26 and miss rate from 8.30% to 6.40% under three‑camera perception, and from 0.54 to 0.26 and 10.13% to 5.99% under eight‑camera perception. Neuro‑Symbolic Drive thus converts neuro‑symbolic planning logic into structured supervision. Code base: https://github.com/XiangboGaoBarry/Neural‑Symbolic‑Drive.

PaperId: 184, https://arxiv.org/pdf/2606.23759.pdf GitHub

Abstract:
Verilog debugging remains one of the most time‑consuming stages in digital circuit design. Recent advances in Large Language Models (LLMs) have enabled automated debugging; however, most existing approaches rely solely on test outputs and compiler feedback in an end‑to‑end manner, limiting their effectiveness on complex bugs. A key challenge is that the root cause of an error may be far removed from its observable outputs, making it difficult for LLMs to trace long dependency chains in code. This challenge is further exacerbated in large codebases, where long context lengths hinder efficient reasoning. To address these limitations, we propose VeriPilot, an LLM‑powered debugging framework that leverages golden reference models to enable fine‑grained bug localization and repair. VeriPilot goes beyond output‑level comparison by aligning internal variable semantics between the Verilog design and its corresponding golden model through LLM‑based analysis. It then performs step‑by‑step signal tracing using Control‑Data‑Flow Graphs (CDFGs) derived from static analysis, identifying a minimal set of suspicious code regions along with their correct counterparts from the golden model. These structured insights are subsequently provided to the LLM to guide reasoning and automated code repair. Experimental results on the Comprehensive Verilog Design Problems (CVDP) benchmark from NVIDIA demonstrate that VeriPilot improves the repair success rate of GPT‑4o from 54.3% to 85.71%, significantly enhancing both bug localization accuracy and repair effectiveness for complex Verilog designs. The source code and benchmark are publicly available at Github https://github.com/YihanWn/VeriPilot.git.

PaperId: 185, https://arxiv.org/pdf/2606.23702.pdf GitHub

Abstract:
Modulation recognition systems rely on heterogeneous signal representations. 2D signal‑image modalities such as time‑frequency and cyclostationary maps capture structural patterns, while 1D statistical descriptors such as higher‑order power spectra encode complementary cues. Under distribution shift, these modalities degrade unevenly, making robust fusion a central challenge for practical deployment. Progress is further limited by the lack of a unified evaluation protocol that systematically separates different shift types. This paper addresses both challenges through a joint benchmark‑and‑model study in underwater acoustic modulation recognition. UAMR‑ShiftBench is the first benchmark to jointly cover in‑distribution, low‑SNR, unseen‑environment, unseen‑communication‑parameter, and measured sea‑trial evaluation under a single matched protocol, with two independent real‑world subsets collected during two sea‑trial campaigns conducted in March and November in the South China Sea. SCP‑TriCA fuses STFT, cyclostationary, and P2/P4 (second‑ and fourth‑order power spectra) modalities hierarchically: the two 2D modalities are first aligned through bidirectional cross‑attention, and the 1D statistical modality is then incorporated through a sample‑adaptive selective gate. On UAMR‑ShiftBench, SCP‑TriCA achieves 95.33% in‑distribution accuracy and 74.59% simulated OOD average, outperforming the strongest baseline by 5.12 percentage points, and reaches 91.14% and 94.86% on the two sea‑trial subsets, exceeding the best baseline by 15.71 and 23.00 percentage points respectively. Ablation results confirm that the gains stem from modality complementarity and the hierarchical fusion design. Code and models are available at https://github.com/ronglaiqian/UAMR‑ShiftBench.

PaperId: 186, https://arxiv.org/pdf/2606.23238.pdf GitHub

Abstract:
Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first‑order‑logic‑centric, focusing on object‑level deduction over fixed predicates. This misses many realistic scenarios where models must reason over rules, predicates, functions, constraints, and decision procedures themselves. We introduce HOLMES (Higher‑Order Logic Meets real‑world Explainable Symbolic reasoning), the first real‑world benchmark for higher‑order symbolic reasoning in LLMs, containing 1379 instances. Built on higher‑order logic, HOLMES pairs natural‑language problems with HOL formalizations, ground‑truth answers, verifiable reasoning traces, and fine‑grained controllable reasoning factors across law and finance. Experiments show that current LLMs still struggle on HOLMES, with an average accuracy of only 50.64% and the best model reaching 59.54%. Our analyses further reveal that high final‑answer accuracy can mask shortcut reasoning in conflict‑resolution settings, while performance drops sharply under scope‑conditioned and compositional reasoning. These findings identify higher‑order symbolic reasoning as a key bottleneck for building reliable and verifiable LLMs. The project code and dataset are publicly available at https://github.com/wuyucheng2002/HOLMES.

PaperId: 187, https://arxiv.org/pdf/2606.23032.pdf GitHub

Abstract:
Finance Agent v2 (by Vals AI) has emerged as the reference benchmark for evaluating both Anthropic Claude and OpenAI ChatGPT frontier language models on financial tasks. However, it narrowly deals with periodic reporting from publicly traded companies (SEC 10‑K and 10‑Q filings), and its agentic harness relies on naive, unenriched chunk retrieval. Neither the task design nor the retrieval approach addresses the distinct challenges of IPO due diligence. SEC S‑1 filings combine historical financial statements, governance structures, pro forma and common‑control accounting treatments, capital‑formation narratives, and underwriting‑sensitive risk disclosures within substantially longer documents than typical periodic filings. That is why we introduce IPO Finance Agent, which extends the Finance Agent v2 framework along two directions: task domain and retrieval architecture. During our experiments, the original Finance Agent v2 harness basically failed to deliver any output related to the SpaceX S‑1 filing, due to document length. We therefore had to improve the agentic harness with contextual retrieval, a more realistic and industry‑standard approach for long documents. We also built a dataset of 1,000 IPO‑diligence questions, and publicly release 70 questions on the SpaceX (SPCX) S‑1 filing to support reproducibility, while the remainder are held private to guard against benchmark contamination. In addition, we introduce an evaluator‑optimizer pipeline to automatically generate evaluation rubrics for the benchmark: candidate facts are extracted from model answers, consolidated into draft criteria, then automatically audited for omissions, hallucinations, mistiered items, and redundancy, with LLM feedback driving iterative repair, targeted enrichment, and deduplication. Human experts only review final rubrics before deployment. Results show that the best‑performing evaluated model, Zhipu GLM‑5.2, reaches 79.8% accuracy, and the most cost‑efficient model on the resulting Pareto frontier, Xiaomi MiMo‑2.5 Pro, reaches slightly lower accuracy (77.2%) at 0.05 USD per query, while exceeding the current Finance Agent v2 leaderboard ceiling, Google Gemini 3.5 Flash at 57.9% for 2.51 USD per query, and undercutting even FABv2's cheapest entry (MiniMax M3: 48.3% at 0.32 USD) on cost‑efficiency. Code and data are released on GitHub https://github.com/benstaf/ipoagent

PaperId: 188, https://arxiv.org/pdf/2606.22902.pdf GitHub

Abstract:
Real‑world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct domains, yet none dominate all. Consequently, routing each task to the most suitable model becomes critical for both performance and cost. Existing routers treat this as a static, one‑off classification problem. However, we identify the performance bottleneck for these routers as information deficit: simply augmenting a vanilla LLM router with performance statistics at the task‑dimension level yields a 15.3% relative gain, surpassing a heuristic router built on the same dimension‑level priors. Motivated by this finding, we propose Agent‑as‑a‑Router, a framework that formalizes routing as a C‑A‑F loop (Context‑>Action‑>Feedback‑>Context). It closes the information gap by accumulating execution‑grounded experience during deployment. We instantiate this framework as ACRouter, composed of an Orchestrator, a Verifier, a Memory module, and introduce CodeRouterBench, an evaluation environment comprising ~10K task instances with verified scores from 8 frontier LLMs, enabling regret‑based router comparison on streaming tasks. Experiments show that ACRouter achieves the lowest cumulative regret on in‑distribution tasks and generalizes to out‑of‑distribution agentic‑programming tasks, demonstrating that our routing framework actively closes the information gap. Codes and benchmarks are released at https://github.com/LanceZPF/agent‑as‑a‑router.

PaperId: 189, https://arxiv.org/pdf/2606.22327.pdf GitHub

Abstract:
The explosive demand for interactive Large Language Model serving has highlighted the management of the Key‑Value cache's dynamic memory footprint as a critical area for performance optimization in inference engines. Modern inference systems overwhelmingly rely on time‑centric scheduling heuristics, such as Shortest Job First. However, their theoretical optimality is rooted in traditional schedule modeling, failing to capture the highly dynamic, 2D spatio‑temporal geometric growth specific to LLM inference mechanisms. To resolve this, we propose the geometry‑aware online scheduling by introducing the Smallest Volume First (SVF) algorithm and its highly efficient variant, 1‑bit SVF. Theoretically, we provide a rigorous mathematical foundation for our approach. Via a novel volume‑certificate proof, we sharpen SVF's worst‑case competitive ratio from the prior best of 48 towards 3 in the high‑concurrency regime of LLM serving. Building upon this core breakthrough, we complete a comprehensive theoretical taxonomy analyzing our algorithms across different traffic scenarios and information availability. Practically, we seamlessly integrate our approach as a plug‑and‑play layer in vLLM. Extensive evaluations on Llama‑3.1 models demonstrate comprehensive performance gains: SVF delivers strong reductions in both average and tail latency, while 1‑bit SVF, with merely a single bit information, achieves competitive throughput and latency. This work establishes a theoretically sound and empirically proven approach for resolving memory‑constrained scheduling in modern LLM deployments. To facilitate future research, our code is available at https://github.com/Aurora‑Kl/Geometry‑Aware‑Online‑Scheduling.git.

PaperId: 190, https://arxiv.org/pdf/2606.21913.pdf GitHub

Abstract:
Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remarkable universal feature representations, unlocking their full potential for cellular imaging is currently bottlenecked by resource‑intensive adaptation paradigms. Existing methods typically rely on fine‑tuning heavy visual encoders, leading to extensive computational overhead and a dependency on large‑scale annotations. To address this, we propose the EffiCell‑Seg framework for highly efficient cell segmentation without re‑training the visual encoder. Our core insight is that pretrained VFMs intrinsically encode complementary structural priors: global saliency for localizing potential cells, and local morphological patterns for delineating cellular structures. To harness these priors, we devise a Cell Structure Prompt Encoder (CSP‑Encoder) that synthesizes semantic‑aware saliency and principal morphological features from frozen VFM representations into explicit structural prior maps. Moreover, we propose a Synergistic Mask Decoder (SM‑Decoder) that enforces contextual consistency by jointly predicting geometric distance fields and semantic maps via mutual cross‑guidance. Extensive experiments demonstrate that EffiCell‑Seg outperforms state‑of‑the‑art methods across diverse cell imaging modalities while requiring only ~5M trainable parameters, over 130x fewer than fully fine‑tuned VFM counterparts. The code is available at https://github.com/xq141839/EffiCell‑Seg.

PaperId: 191, https://arxiv.org/pdf/2606.21884.pdf GitHub

Abstract:
It is tempting to assume any task solvable by a short program can be taught to a model as its chain‑of‑thought: write the steps out, fine‑tune, and the model follows. This paper shows the assumption fails for an identifiable class of procedures. The testbed is nine reasoning tasks, each from a deterministic generator; public and hidden splits share generators, so held‑out data proxies test accuracy. I reverse‑engineer the generators into Python solvers, render them as chain‑of‑thought, and distill into a rank‑<= 32 LoRA over a 30B (3.5B‑active) Nemotron model. Forward‑computable tasks install readily: lookup/arithmetic and an 8‑bit boolean task transfer (>= 0.99 and 0.68). Cryptarithm does not: distilling its backtracking search holds at 0.01‑0.07 across eleven chain‑of‑thought designs, RL from verifiable rewards, and self‑training, even though a search solver answers 71% of instances. This is not a capability gap. The model does the arithmetic on 97‑100% of lines and ranks the correct cipher in its top eight on 71%; it cannot carry the search forward as a left‑to‑right derivation. Fine‑tuning learns the shape of a verifiable elimination step while its verdicts become unconditional templates, correct only 16‑57% of the time ("verdict‑as‑token"). The ceiling holds across backbones from 3B to 671B and across fine‑tuning and prompting; a controlled intervention isolates the cause: revealing the cipher key, which turns the derivation forward, lifts the same instances from 0.03 to 0.57. When a procedure's only solution is search over information‑free structure, no faithful forward chain‑of‑thought exists to imitate. The task becomes learnable only by removing the search, precomputing its combinatorial core into a catalog and reducing the trace to recall plus verification; the 1st‑place solution reaches Private LB 0.92 this way. What distills is memorization and verification, not search.

PaperId: 192, https://arxiv.org/pdf/2606.21876.pdf GitHub

Abstract:
The Categorical Jacobian (CJ) of Zhang et al. (2024) reads protein contacts from a language model by perturbing every residue with every alternative amino acid, about 19L forward passes. We show the signal it reconstructs is already concentrated in a small subset of attention heads: averaging the top‑K contact‑relevant heads, selected on as few as 10 labeled proteins, recovers contacts in one forward pass and beats CJ on leakage‑clean data for every bidirectional model where CJ is defined, and matches or beats it in‑distribution (the exceptions being the smallest 8M model and a statistical tie on ESM Cambrian). Ablations localize the gain to labeled head selection, not averaging: at a matched label budget the unweighted mean ties a supervised L1 logistic regression on the same heads, so the parameter‑free mean is selection's minimal form, not the source of the advantage. Our primary test is leakage‑clean: on a CAMEO split where neither selection nor evaluation touches data the models have plausibly memorized, the head readout beats CJ on ESM‑2‑650M by +9 pp (N=29, p<0.001), with the within‑model margin reproducing across architectures on a wider pretraining‑aware set. Both methods fall 30‑36 percentage points from their in‑distribution Zhang numbers to the leakage‑clean numbers, consistent with substantial pretraining overlap inflating prior numbers (a CAMEO‑vs‑Zhang difficulty shift contributes too, so we read it as an upper bound on the leakage component). We additionally introduce representation‑CJ, a hidden‑state generalization of the Jacobian for architectures without a masked‑LM head; show that the optimal K tracks how diffusely a model spreads its contact heads; and find that both methods lose the contact signal on both causal LMs we test (ProGen2), suggesting attention‑encoded pair structure may depend on bidirectional pretraining.

PaperId: 193, https://arxiv.org/pdf/2606.21856.pdf GitHub

Abstract:
The increasing deployment of large language model (LLM) agents in collaborative workflows demands robust multi‑user, multi‑principal interaction mechanisms capable of enforcing access permissions, resolving authoritative conflicts, and preventing unauthorized data disclosure. However, a fundamental mismatch exists between the single‑user training paradigm of contemporary LLMs and the hard constraints required for multi‑principal governance, rendering probabilistic, prompt‑based safeguards vulnerable under multi‑turn adversarial interactions.Our key insight is that governance constraints ‑‑ who is authorized, what is restricted, and whose instructions take precedence ‑‑ are deterministic runtime variables that should be enforced by execution hooks rather than entrusted to the LLM. We present Harness‑MU, the first model‑agnostic, zero‑tuning infrastructure framework for multi‑user LLM agents. By decoupling language generation from safety orchestration, Harness‑MU guarantees unbreakable permission boundaries while maximizing compliant demand satisfaction. Across four frontier open‑weight and proprietary models on the Muses‑Bench benchmark, Harness‑MU achieves the goal of privacy preservation across all access‑control attacks, outperforming the standard baseline by 0.28‑‑0.39 in utility score and improving instruction‑following accuracy by up to 48.9 percentage points. Harness‑MU advances the philosophy of Harness Engineering, establishing that systematic infrastructure is essential for solving LLM multi‑principal governance challenges. The code and data are available at https://github.com/YuanJrShiuan/Harness‑MulUser.

PaperId: 194, https://arxiv.org/pdf/2606.21847.pdf GitHub

Abstract:
Low‑rank decomposition serves as a promising compression paradigm for large language models, however, rank allocation remains challenging: manual rules lack generalizability, and learning‑based approaches incur heavy computational overhead. To address these issues, we formulate global low‑rank allocation as a sorting‑and‑truncation pipeline, and score each singular component via dual criteria: Local singular energy ratio that quantifies the intrinsic importance within the decomposed parameter matrix and Global functional importance (measured by input‑output cosine similarity) that evaluates the functional significance of decomposed modules. We verify the strong correlation between high input‑output cosine similarity and low effective rank through geometric interpretation and experimental validation. Furthermore, we propose rank‑preserving fine‑tuning, which performs direct LoRA tuning on decomposed weights and avoids extra information loss caused by re‑truncation in conventional merging pipelines. Empirical results confirm that our method delivers sustained performance enhancements when combined with models featuring distinct decomposition schemes, model sizes and architectural designs, e.g. in one‑shot compression without further fine‑tuning, our method reduces perplexity by up to 50% compared with uniform and heuristic allocation baselines. Code will be available at https://github.com/EIT‑NLP/LLM‑Pruning.

PaperId: 195, https://arxiv.org/pdf/2606.21811.pdf GitHub

Abstract:
End‑to‑end code agent training is resource‑intensive and plateaus on the strategy‑level reasoning needed to resolve code issues, since jointly optimizing code‑level execution and strategy‑level reasoning leaves the latter underdeveloped. Instead, we freeze the agent and add a critic model to supply that signal. Prior code critics are post‑hoc, scoring completed trajectories rather than steering the agent; we instead train a small critic that provides intra‑trajectory feedback via Supervised Fine‑Tuning. On SWE‑bench Verified, a critic trained on CWM‑32B trajectories transfers to two unseen agents (gains of +3.0 to +3.8 points), and adding target‑agent trajectories to the corpus increases the gain to +3.8 on CWM‑32B and +4.4 to +5.2 on two Qwen agents, at 30‑92x lower critic cost than a strong teacher. On Qwen3‑Next‑80B‑A3B, the critic‑guided system is both more accurate (25.2% vs. 20.8%) and cheaper (\0.04 vs. \0.11) than the agent alone, because the critic also shortens trajectories. Our results show that a small, well‑trained critic is a practical complement to scaling agent training. Code: https://github.com/shubhamrgandhi/critic‑training. Data and models: https://huggingface.co/collections/shubhamrgandhi/critic‑training‑for‑code‑agents

PaperId: 196, https://arxiv.org/pdf/2606.21777.pdf GitHub

Abstract:
LLM agents in knowledge intensive question answering take retrieval and reasoning actions with incomplete knowledge about whether their current answer is uncertain, unsupported, or already complete. This produces two failure modes: committing to confident but unsupported answers, which hurts accuracy, and over‑retrieving when the evidence in hand already suffices, resulting in wasted compute. To give agents a more complete picture of the state space they are operating in, we introduce calibrated verifier telemetry (CalVerT), which augments the agent's state with additional telemetry: a calibrated self‑confidence score and a grounding verifier score. We show that CalVerT can improve agents in both training‑free and training‑based settings. On four QA benchmarks, we find that CalVerT raises F1 by triggering retrieval in cases where agents over‑rely on parametric knowledge, while cutting redundant retrieval in cases where agents have sufficient context to answer. We show that CalVerT can augment existing QA frameworks without training. Moreover, CalVerT also improves trained systems: by simply augmenting an agent's state with telemetry, we observe improvements after reinforcement learning, as compared to an agent with identical training but no CalVerT telemetry.

PaperId: 197, https://arxiv.org/pdf/2606.21672.pdf GitHub GitHub

Abstract:
Imitation learning has emerged as a powerful paradigm for learning visuomotor policies, but its generalisation and stability are limited by the scale and quality of demonstration data needed. A promising direction is to leverage more abundant but heterogeneous data sources, which differ in action space and often lack action labels altogether. Existing co‑training approaches that combine heterogeneous data sources rely on heuristic and hand‑engineered alignment techniques. In contrast, we argue that action representations should be grounded in prediction: actions that produce the same effect on the environment should share the same representation, regardless of their sources. To this end, we instantiate this principle by using a grounded latent‑action world model (GLAM), a pair of generative models with a shared latent action space across data sources that is grounded by predicting future observations consistently across sources. This latent action space is used to train downstream behavioural cloning (BC) policies which map observations to latent actions and decode them back to robot actions, providing a paradigm for learning from heterogeneous data. Empirically, we demonstrate that GLAM successfully learns an aligned latent action space that facilitates action transfer across data sources with and without action labels. Across five manipulation tasks in simulation and in the real world, GLAM‑aligned policies significantly outperform BC baselines and prior latent‑action methods, achieving an average of +48% improvement in task success rate with the same data‑scarce setting. Videos and code are available at https://viccccciv.github.io/glam/.

PaperId: 198, https://arxiv.org/pdf/2606.21623.pdf GitHub

Abstract:
Instruction‑conditioned trajectory prediction is an emerging problem in autonomous driving, where a model predicts the future ego trajectory not only from visual scene context and historical motion, but also from a natural‑language maneuver instruction. This paper presents our submission to the doScenes Instructed Driving Challenge, built upon OmniDrive, a vision‑language‑action driving agent with 3D perception, reasoning, and planning capabilities. We adapt OmniDrive to the doScenes setting by training it on instruction‑annotated nuScenes scenes and generating a 6‑second ego trajectory represented by 12 future waypoints. To improve multi‑view visual grounding, we further introduce a DVPE‑style divided‑view perception module into the OmniDrive perception head. Instead of attending globally to all camera features, the proposed module groups query features and image tokens into divided local view spaces and performs visibility‑aware cross‑attention within each view. This design reduces irrelevant cross‑view interference and helps the model better align language instructions with local driving‑relevant visual evidence. The code is publicly available at: https://github.com/feel12348/doscenes‑omnidrive.

PaperId: 199, https://arxiv.org/pdf/2606.21611.pdf GitHub

Abstract:
Mathematical search problems present a unique challenge for Reinforcement Learning (RL) due to vast search spaces and sparse rewards. In previous works, the Andrews‑Curtis (AC) conjecture was established as an illustrative example of such problems. In this work, we identify a critical structural barrier in the AC landscape: a "Two‑Hump" distribution, where problem instances are either trivially solvable or effectively impossible, with a scarcity of intermediate "hard‑but‑solvable" instances required for effective learning. We tackle this challenge through two primary avenues: novel data generation techniques to populate the difficulty gap, and significant algorithmic enhancements including the introduction of supermoves and Transformer‑based architectures. We demonstrate substantial performance improvements over previous baselines, and release new comprehensive benchmark datasets including AC‑19 (125,192 AC‑trivial presentations of varying difficulty with length at most 19) and AC‑1M (1,136,154 hard AC‑trivial presentations of length at most 30), the first large‑scale, publicly available datasets of this kind.

PaperId: 200, https://arxiv.org/pdf/2606.21498.pdf GitHub

Abstract:
Autoregressive text‑to‑image (T2I) generation has recently advanced rapidly, yet aligning generated images with human preferences remains challenging. GRPO‑style online reinforcement learning provides an effective framework; however, existing methods typically treat reference‑policy divergence as fixed, despite its direct impact on policy optimization. We study this overlooked factor within a unified f‑divergence framework, encompassing forward KL, reverse KL, and JS divergence, for GRPO‑style autoregressive T2I alignment. Our systematic theoretical analysis reveals that different divergences reshape token‑level updates in distinct ways. In particular, under the sampled‑token shaping form used, JS regularization achieves a favorable trade‑off by mitigating uniform bias relative to the reference policy while still discouraging large deviations. Extensive experiments on LlamaGen and Janus‑7B show that JS divergence achieves the strongest or highly competitive optimization performance on most evaluation metrics while maintaining favorable generation diversity. The code is available at https://github.com/tuoyou‑hao/BPD‑GRPO.

PaperId: 201, https://arxiv.org/pdf/2606.21445.pdf GitHub GitHub

Abstract:
The automated design of agentic systems offers a promising pathway for scaling large language models (LLMs) beyond single‑agent reasoning. While prior work has advanced task performance through handcrafted or automatically generated multi‑agent workflows, robustness is often treated as an afterthought, leaving systems vulnerable to external adversaries and internal failures. We propose AutoRAS, a framework for the Automated design of Robust Agentic Systems. AutoRAS formulates system design as generating a sequence of symbolic primitives that jointly encode structural connectivity and behavioral actions, and learns to optimize this sequence using execution‑derived safety signals and flow‑based sequence‑level objectives. Extensive experiments show that AutoRAS achieves the best performance in both vanilla and adversarial settings, with the smallest performance degradation under attacks. Further analyses demonstrate strong transferability, stable optimization behavior, stability across primitive sets, and favorable cost trade‑offs. Our code is available at \hrefhttps://github.com/guohezuy/AutoRAS\textthis https URL.

PaperId: 202, https://arxiv.org/pdf/2606.21428.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) language models are often described as ideal for resource‑constrained inference. Each token activates only a small subset of experts, so the per‑token compute cost, in floating‑point operations (FLOPs), resembles that of a much smaller dense model. Whether that FLOP advantage survives in practice is far less clear. We ask whether MoE models actually run faster and cheaper than comparable dense models on consumer‑grade and edge hardware. We benchmark OLMoE‑1B‑7B (1.3 B active of 6.9 B total) against three dense baselines on an Apple M2 Pro and an NVIDIA Jetson Orin Nano 8 GB through llama.cpp, measuring throughput, memory, and on‑device energy. The answer is device‑dependent: OLMoE's active‑parameter advantage is only partly realised on the laptop (~10% behind the same‑active Llama‑3.2‑1B) and erodes on the edge device (~31% behind, at 2.1× the energy per token, with peak memory at the 8 GB ceiling). Patching llama.cpp to time the decode graph node‑by‑node shows routing accounts for under 9% of MoE‑block compute on the cleaner edge backend, so the gap reflects total‑parameter memory footprint, expert dispatch, and KV‑cache pressure rather than routing. The implication is that on bandwidth‑bound edge hardware, inference cost tracks total parameters, not active ones, and sparse activation does not buy back what the device is constrained on. These findings are bounded to one MoE model at this parameter scale and two devices, and we release the full measurement harness and per‑run data.

PaperId: 203, https://arxiv.org/pdf/2606.21384.pdf GitHub

Abstract:
Multimodal medical imaging fuses complementary anatomical and functional information, yet modalities frequently disagree in pathologically heterogeneous regions. Current segmentation models handle this in one of two inadequate ways: deterministic fusion that averages away disagreement, or post‑hoc uncertainty estimation decoupled from the fusion process that produces it. Both obscure the clinically critical question: why is this prediction unreliable? We present EnTrust, a framework that treats inter‑modal conflict as the primary source of predictive uncertainty. Our EnFuse module decomposes multimodal features into three disentangled components: shared anatomical consensus (F_c), modality‑specific cues (F_u,m), and spatially localized conflict signals (F_cf), with independence enforced via a cross‑covariance objective. This structured decomposition conditions SegDiff, a diffusion‑based generative segmentation model whose sampled hypotheses diverge specifically in regions of modal disagreement. TrustMap then translates this hypothesis divergence into calibrated, pixel‑wise uncertainty using ensemble entropy, conflict‑guided perturbation probing, and a learned calibration head, enabling clinicians to understand not only where predictions are uncertain, but why. Across four benchmarks spanning brain, cardiac, lesion, and oncology domains, EnTrust achieves state‑of‑the‑art segmentation accuracy while reducing calibration error by 40% compared to the strongest baseline. Notably, it outperforms 5x deep ensembles using a single model at roughly half the memory footprint. Code and checkpoints are available at https://github.com/GenMI‑Lab/EnTrust.git.

PaperId: 204, https://arxiv.org/pdf/2606.21368.pdf GitHub

Abstract:
Medical image re‑identification (MedReID) enables longitudinal patient linkage but remains vulnerable to shortcut learning and often produces decisions that clinicians cannot audit against named anatomy. We propose Graph‑of‑Differences (GoD), which grounds identity comparisons in explicit anatomical structure. Each image is represented as an anatomy graph whose nodes correspond to named anatomical regions; given an image pair, soft node correspondence is established, and differences are computed over matched anatomy. A graph‑level difference alignment objective ties these anatomy‑matched differences to the global backbone difference, ensuring the retrieval signal is anchored in homologous structures rather than arbitrary spatial tokens. Explanations are defined over named graph nodes and quantitatively audited via node insertion/deletion tests, replacing unstable pixel heatmaps with verifiable structure‑level evidence. On internal benchmarks, GoD improves Rank‑1 by +7.1 pp on fundus and +3.1 pp on CXR over a strong frozen‑backbone baseline, with further gains on zero‑shot external transfers confirming that anatomy grounding improves both accuracy and generalization. Code is available at https://github.com/GenMI‑Lab/GoD.git.

PaperId: 205, https://arxiv.org/pdf/2606.21337.pdf GitHub

Abstract:
Massive unstructured multimodal streams suffer from high "data entropy," impeding both efficient human knowledge acquisition and high‑quality AI post‑training. Existing passive annotation paradigms, heavily reliant on heuristic rules or general VLMs, are costly, monotonous, and fail to unlock the deep procedural logic embedded in raw data. We elevate data processing to a learnable capability, proposing a paradigm shift towards Agentic Data Tailoring, which actively refining and structuring data to align with diverse user and downstream intents. To overcome the data scarcity bottleneck in training such high‑order capabilities, we design a two‑stage pipeline grounding generative semantic synthesis in deterministic Factual Anchors, yielding a large‑scale dataset spanning five core physical and digital domains. Building upon this, \textDataClaw_0‑9B model synergizes Supervised Fine‑Tuning (SFT) with Group Relative Policy Optimization (GRPO), achieving robust alignment with complex refinement and tailoring intents. To systematically quantify this capability, we construct \textDataClaw_0‑val, the first benchmark dedicated to data refinement. Crucially, we adopt downstream post‑training as the ultimate validation touchstone. Evaluations on video generation, real‑world VQA, and GUI navigation confirm that \textDataClaw_0 delivers high‑information‑density tailored data, facilitating efficient model adaptation to new tasks under limited training data regimes. Project page: https://czjdsg.github.io/MakeAnyData

PaperId: 206, https://arxiv.org/pdf/2606.21262.pdf GitHub

Abstract:
Reinforcement learning for multi‑step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad. Rubric‑based rewards improve interpretability through natural‑language criteria, but existing methods score at the trajectory level and freeze the scorer behind a closed‑source judge, leaving step‑level credit assignment unresolved and the judge itself static. We propose ARCO (Adaptive Rubric CO‑evolution), a rubric framework in which a same‑scale model μ shares a backbone with two heads: a generation head that produces per‑step criteria, and a score head that predicts rubric‑conditioned step‑level rewards. A trajectory decomposition constraint ties the sum of step rewards to the terminal outcome, enabling credit assignment without step‑level labels, while μ and the policy π are jointly updated on on‑policy data so that the rubric content and the scoring function co‑evolve at the parameter level. Across HotpotQA, 2WikiMultiHopQA, and MuSiQue with two open‑source backbones, ARCO improves the best EM in every setting over strong outcome‑, rubric‑, and process‑reward baselines, and analyses show that its rubrics are step‑specific, robust to design choices, and useful for diagnosing agent behavior. Codes and data are available at https://github.com/zihangtian/ARCO.

PaperId: 207, https://arxiv.org/pdf/2606.21197.pdf GitHub

Abstract:
Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioning and Visual Question Answering (VQA), but our understanding of their internal processes remains limited. Recently, Sparse Autoencoders (SAEs) have emerged as a promising tool to support the interpretation of concepts encoded in VLMs. However, most SAE‑based approaches focus only on textual or visual concepts separately, ignoring multimodal concepts. This limitation hinders a comprehensive understanding of VLMs, since concepts that integrate both modalities can be misclassified. Moreover, previous visual approaches often produce low‑quality visual concept descriptions that are vague or incomplete, limiting their usefulness for understanding model reasoning. We propose a framework based on SAEs to extract and analyze visual, textual, and multimodal concepts from VLMs. For each neuron, we propose a candidate human‑interpretable concept and compute the alignment between the concept and the dataset samples using cosine similarity scores. Experiments on a VQA dataset (LLaVA‑NeXT) demonstrate that our framework improves visual concept quality by up to 45% compared to existing SAE‑based methods, while maintaining high textual concept quality and enabling systematic identification of multimodal concepts. This work contributes new insights into the conceptual space of VLMs, providing a structured approach to distinguish between visual, textual, and multimodal concepts. The code is available at https://github.com/PHDLanza/Multidata_SAE

PaperId: 208, https://arxiv.org/pdf/2606.21156.pdf GitHub

Abstract:
The high cost of spatial transcriptomics (ST) has driven extensive studies into predicting gene expression directly from H&E histology images. However, this prediction task faces an inherent limitation, as tissue morphology alone provides insufficient information to fully resolve underlying gene expression. To address this limitation, a recent study leverages partial gene expression to guide the prediction process alongside histology images. Building on this paradigm, we approach the prediction task as a spatial imputation problem, employing a Masked Autoencoder (MAE) to utilize a small fraction of gene expression as genetic anchors for inferring whole‑slide gene expression profiles. Specifically, we propose a bio‑saliency score and a learning‑to‑rank strategy to adaptively identify the most informative spots within the tissue. Based on these identified spots, our framework selects contiguous regions as genetic anchors to ensure suitability for real‑world ST profiling hardware. To effectively leverage these anchors, we design a cross‑modal joint encoder that integrates visual and genetic modalities. By aligning the selected anchors with their corresponding visual features via contrastive learning, the encoder generates robust joint representations to accurately predict gene expression across the whole slide. Notably, our framework consistently surpasses existing methods in both histology‑only prediction and spatial imputation, achieving superior accuracy even without genetic anchors and further excelling with as little as 10% transcriptomic coverage. Our code is available at https://github.com/Kyyle2114/CAMMST.

PaperId: 209, https://arxiv.org/pdf/2606.21139.pdf GitHub

Abstract:
Latent action pretraining learns representations of visual change from pairs of observations, but existing methods typically encode each transition as a single unstructured representation that entangles transition extent and transition mode. We introduce Polar Latent Actions with Radial structure (PoLAR), which imposes a radial‑direction structure on latent actions, encouraging radius to encode transition extent and direction to retain transition mode. PoLAR uses temporal offset between two observations as a weak proxy for transition extent, encouraging latent action from observation pairs separated by larger temporal gaps to occupy larger radii. We instantiate this structure in hyperbolic space, whose expanding volume with radius offers a natural fit for more diverse transition modes at larger extents. Across in‑task and large‑scale pretraining settings, PoLAR improves downstream policy performance in simulation and real‑world robot experiments, outperforming latent action baselines and strong pretrained VLAs. These results suggest that the geometry of the latent action space is an important design choice for transferring visual pretraining to downstream robot policy learning.

PaperId: 210, https://arxiv.org/pdf/2606.21121.pdf GitHub

Abstract:
Large language models can produce confident but protocol‑invalid answers in domains where procedural compliance is critical. This paper presents Answer Engineering, a deterministic runtime and authoring layer that applies localized rule‑guided interventions to the visible reasoning trajectory during standard autoregressive generation, without retraining, modifying model weights, or performing global search. The method is evaluated on a controlled clinical benchmark for sudden sensorineural hearing loss (SSNHL), where correct management depends on protocol‑consistent interpretation of symptom timing, Weber/Rinne tuning‑fork findings, and otoscopic findings. In the benchmark, step‑by‑step reasoning shifted rather than eliminated errors: compliant outcomes for SSNHL decreased from 54.5% under unguided generation to 25.1%, while acceptance on the conductive contrast condition increased from 1.6% to 58.9%. Local trajectory editing increased SSNHL compliance to 83.5% and conductive‑case adherence to 77.9%, raising balanced accuracy from 42.0% under reasoning‑only generation to 80.7%. The results support a systems‑level view in which protocol adherence can be improved through auditable runtime control of reasoning trajectories, while also identifying limitations caused by rule coverage, trigger reliability, and persistent diagnosis‑first generation dynamics.

PaperId: 211, https://arxiv.org/pdf/2606.21116.pdf GitHub

Abstract:
Proofreading‑‑correcting segmentation errors in 3D brain reconstructions‑‑is the rate‑limiting step in synapse‑resolution connectomics. We release ConnectomeBench2, a unified multi‑species dataset of over 716,485 expert‑labeled proofreading decisions with >4,500,000 associated images spanning four major open connectomes (mouse, human, zebrafish, fly), spanning both split and merge error correction. Trained on this dataset, a single Vision Transformer with shared encoders for mesh geometry and electron microscopy reaches human‑level accuracy across species for split error correction and merge error identification, with performance scaling with data size and modality. Beyond accuracy, we show that the model is well‑calibrated within distribution, that measures of distribution distance predict where calibration and accuracy will degrade on unseen data, and that connectomics‑specific pretraining and active learning‑based sample selection show potential to substantially reduce the labeling effort needed to extend to new species and brain regions. The benchmark provides the infrastructure to train and evaluate increasingly capable vision models for connectomic proofreading. Data and code availability. The ConnectomeBench2 dataset is released on Hugging Face at https://huggingface.co/datasets/jeffbbrown2/ConnectomeBench2. The accompanying codebase is available on GitHub at https://github.com/timfarkas/ConnectomeBench2.

PaperId: 212, https://arxiv.org/pdf/2606.21053.pdf GitHub

Abstract:
Recent advances in text‑to‑speech (TTS) synthesis have achieved highly natural and expressive speech generation. However, these systems are designed for general adults and overlook older adults' speech comprehension needs due to age‑related sensory and cognitive decline. Prior work involves older adults by collecting preference feedback to tune model parameters. However, obtaining sufficient preference data is costly and difficult, as older adults quickly become fatigued during collection. In this paper, we propose a novel imitation learning (IL) framework to learn TTS models from expert demonstrations. We further improve Group Relative Policy Optimization (GRPO) with two‑stage on‑policy reward learning (OPRL) to mitigate reward hacking under limited supervision from expert demonstration. Experimental results show that GRPO w/ OPRL outperforms GRPO and supervised baselines in objective and subjective metrics. Audio samples are available at https://dongru1.github.io/demo/im‑efss

PaperId: 213, https://arxiv.org/pdf/2606.21024.pdf GitHub

Abstract:
AI‑assisted research systems generate many failed attempts, but those failures rarely become a durable, shared knowledge asset. We propose a negative knowledge memory layer: a curator agent converts each failed attempt into a bounded, typed record in a shared bank, and a downstream research agent explicitly adopts or rejects those records before proposing its next experiment. We evaluate this layer in two settings: same‑task retry on ScienceAgentBench and cross‑task scientific research on two nonlinear math‑physics PDE problems. The negative knowledge layer outperforms vanilla AutoResearch baselines while using fewer tokens; agents with the negative knowledge bank solve new tasks that all baselines fail to solve in PDE systems research. We also show that the previous negative knowledge bank can transfer and enhance AutoResearch on different PDE problems. These results suggest that structured negative knowledge is a knowledge asset that should be explicitly maintained in broader AI‑engaged scientific research beyond a memory‑compression or debugging aid, alongside positive findings, as a collective infrastructure for scientific memory. Code is available at https://github.com/hch‑wang/Negative_Knowledge.

PaperId: 214, https://arxiv.org/pdf/2606.20950.pdf GitHub

Abstract:
Executable evaluation ‑‑ checking the consequences of an agent's actions with a program rather than grading its prose ‑‑ has become a prominent way to assess tool‑using AI agents in software settings. Electric power engineering has not yet had an analogous benchmark: language‑model use is still dominated by retrieval and text question answering, while agents acting on power‑system artifacts remain mostly academic prototypes. We introduce the Power Systems Agent Benchmark, an executable benchmark for power‑engineering agents. An agent receives a structured task and returns a structured solution; a deterministic evaluator recomputes the engineering quantities, checks operational constraints, and returns a feasibility flag, a normalized score, and explicit violations. The benchmark contains 41 task families across eight areas of power engineering, from power flow and protection to stability, microgrids, reliability, power quality, and forecasting. Each task is grounded in a citable source, standard, or documented engineering formulation. To resist contamination, held‑out cases are synthesized on demand by per‑family generators from private seeds: the construction is inspectable, but the instances remain private. In a reference evaluation with three command‑line agents, the strongest score near the compact tier's ceiling, a smaller open model trails, and public and held‑out performance are broadly consistent; a separate public‑split grid with OpenCode and Aider probes harness effects. The reference evaluation doubles as quality control: unanimous failures flag candidate task or evaluator defects, and it exposed a latent evaluator bug missed by self‑consistency checks. The evaluators are compact deterministic surrogates, but the task contract allows their internals to be upgraded to simulator‑backed checks without changing how tasks are posed or solved.

PaperId: 215, https://arxiv.org/pdf/2606.20913.pdf GitHub GitHub

Abstract:
Medical vision‑language models (VLMs) enable zero‑shot clinical image classification, yet reliably detecting out‑of‑distribution (OOD) inputs at deployment remains an open problem. No static scoring method works across all shift types: Maximum Concept Matching (MCM) on FLAIR achieves 76.4% AUROC for far‑OOD but only 42.4% for covariate shifts such as ultra‑wide‑field fundus images, effectively random. We trace this to a structural mismatch: covariate‑shifted inputs are indistinguishable from in‑distribution samples in softmax space, yet occupy distinct regions in the VLM embedding space. To exploit this untapped signal, we propose PROTON (PROtotype‑based Test‑time ONline OOD detection), a lightweight post‑hoc module that maintains an online prototype bank from high‑confidence test predictions and adaptively fuses prototype distance with MCM scoring via stream‑level variance statistics, requiring no model modification, training data, or prompt engineering. On the ophthalmology benchmark FLAIR + FIVES, PROTON improves MCM by +23.9 AUROC on covariate shift, +8.8 on semantic shift, and +8.1 on far‑OOD, making it the only zero‑shot method to improve all three without hierarchical prompts or labeled data. Code is available at https://github.com/GenMI‑Lab/PROTON, and the project page is available at https://genmi‑lab.github.io/PROTON.

PaperId: 216, https://arxiv.org/pdf/2606.20897.pdf GitHub

Abstract:
As academic submissions grow, the traditional peer review process struggles to keep up, raising concerns about quality and fairness. A trend of using large language models (LLMs) for assistance has emerged. In this work, we take a critical step toward improving the quality of LLM‑generated reviews. We propose the PeerCheck framework, which investigates LLM‑human review differences (RQ1) and explores methods to improve LLM‑generated review quality (RQ2). We first analyzed the human‑written reviews with reviews generated by various LLMs and found that LLMs and humans focus on different terms, e.g., LLMs prioritize theory while humans emphasize methodology and experiments. We further adopt prompt engineering, such as Chain‑of‑Thought (CoT), and utilize retrieval‑augmented generation (RAG) to enhance the LLM‑generated reviews towards human‑level quality. We find CoT significantly improves the quality of LLM reviews, while we discover an unexpected "RAG paradox," i.e., experiments with RAG produce different results for various LLMs and, in some cases, even reduce review quality. Our comprehensive analysis of LLM‑generated academic reviews illustrates both possibilities and limitations, contributing to a more effective, human‑aligned review system. Our dataset is available on https://github.com/TrustAIRLab/PeerCheck.

PaperId: 217, https://arxiv.org/pdf/2606.20893.pdf GitHub GitHub

Abstract:
Deep learning‑based audio classification systems, including automatic speaker verification, are vulnerable to adversarial attacks. Realistic real‑time threat assessment remains difficult because optimization‑based methods, such as projected gradient descent (PGD) and Carlini‑Wagner, require costly iterative updates in the high‑dimensional waveform domain. Generative attacks allow single‑shot synthesis but often introduce perceptible artifacts or depend on computationally intensive architectures, while diffusion and autoregressive approaches incur high inference latency. To address this gap, we propose a generative attack framework operating in the continuous latent space of a neural audio codec. A conditional generator synthesizes class‑specific perturbations in a single forward pass and decodes them into adversarial waveforms. Our method achieves targeted attack success rates up to 99% with sub‑7 ms inference, outperforming generative baselines while reducing latency by 24x.

PaperId: 218, https://arxiv.org/pdf/2606.20812.pdf GitHub

Abstract:
EEG foundation models can learn generalizable representations from large‑scale EEG corpora to enable single‑backbone transfer across diverse clinical and brain‑computer interface tasks. Existing models typically discretize the continuous multi‑channel EEG waveform into patches or codebook tokens and train a transformer with masked self‑supervision. Recognizing that this discretization fragments continuous brain rhythms and obscures fine‑grained temporal dynamics, we present B[FM]^2(Brain Foundation Model via Flow Matching), whose inductive bias aligns with the data by pretraining directly on the raw signal using continuous‑time flow matching without patches, tokenization, or masking. However, multi‑channel EEG signals pose an architectural challenge for flow matching: time is densely sampled and highly autocorrelated (thousands of timepoints), while the electrode axis is short (tens of channels) at distinct scalp positions. To address this time‑electrode asymmetry, we introduce SplitUNet, a velocity network that factorizes each block into separate 1D temporal and 1D electrode convolutions and downsamples only along time, preserving electrode topology throughout the hierarchy. B[FM]^2 sets a new state of the art on 7 of 9 standard downstream EEG classification tasks, using a pretraining budget of only 36,895 segments (\approx 307h), 1‑2 orders of magnitude (\approx 30x) less than required by existing EEG foundation models. Further, it generates synthetic EEGs that two board‑certified neurologists cannot distinguish from brain data (Cohen's κ= ‑0.096). https://jd730.github.io/projects/BFM2

PaperId: 219, https://arxiv.org/pdf/2606.20743.pdf GitHub

Abstract:
Trained transformers reliably develop massive activations, a small number of hidden dimensions whose magnitude is far above the median and which concentrate on the sequence‑start token. Whether these outliers are a removable artifact of the residual stream's overloaded read and write role, or instead a functional necessity, is actively debated. We test the artifact hypothesis directly, with an architectural intervention. Our architecture, Ledger Residuals, splits the residual stream into a mutable scratch stream (Deliberation) that intermediate computation may freely overwrite and a protected, decode‑only accumulator (Commitment) that holds the representation the model reads out. If massive activations exist only because one stream is forced to be both scratchpad and answer, then a dedicated answer channel should remove the need for them. We find that it does not. In matched‑loss language models at the 160M and 290M scales, the model rebuilds the canonical fixed‑dimension, start‑token outlier inside the protected channel. The rebuilt feature is smaller in magnitude than in a standard transformer but more sharply concentrated on the start token, and a stronger sparsity penalty makes it more persistent and more concentrated still, rather than removing it. Massive activations therefore look architecturally robust: they re‑emerge in whichever representation the model decodes from, which is what we would expect if they are functional rather than incidental. We release our architecture and measurement code.

PaperId: 220, https://arxiv.org/pdf/2606.20734.pdf GitHub

Abstract:
Open Vocabulary Action Recognition (OVAR) enables the recognition of novel actions by leveraging vision‑language representations, overcoming the limitations of traditional closed‑set approaches. However, achieving robust performance in real‑world scenarios typically requires domain‑specific fine‑tuning, which is often costly and raises privacy and regulatory concerns. In this work, we propose an alternative paradigm that bypasses target‑domain training and recombines knowledge from existing datasets and models. Leveraging model merging and task arithmetic, we extract and combine task vectors from models fine‑tuned on diverse public OVAR datasets. We show that, in out‑of‑distribution settings, the resulting merged model achieves superior zero‑shot generalization to the pre‑trained base model. Code is available at https://github.com/omaymaMoussadek/robust‑ovar

PaperId: 221, https://arxiv.org/pdf/2606.20683.pdf GitHub

Abstract:
LLM‑based agents mark a shift from passive question answering to active task completion: they perceive environments, invoke tools, maintain state, and act over extended horizons. As agent systems have evolved from prompt engineering to workflows and context engineering, harness engineering, and agent‑native training with co‑evolution, a central question has become increasingly important: where does the bottleneck in agent performance reside, in the foundation model, in the execution harness, or in the coupling between them? This survey examines LLM‑based agents through a model‑harness lens. We first clarify the functional definition of agents and the implementation view of an LLM‑based agent as a foundation model coupled with an execution harness. We then analyze the limits of model‑centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities: observation, context, control, action, state, and verification. Using this decomposition, we map task properties and domain pressures to harness configurations, review benchmark and evaluation practices, and synthesize model‑harness evidence on how runtime design affects long‑horizon task completion, efficiency, and reliability. Finally, we identify open challenges in value‑aware evaluation, safety, harness generalization, and model‑harness co‑evolution. Rather than treating agents as models with auxiliary tools, this survey argues that agent quality ‑‑ including success, efficiency, safety, and generalization ‑‑ emerges from the interaction between model capability, runtime infrastructure, task structure, and evaluation design. A collection of papers discussed in this survey is provided in https://github.com/ggjy/Awesome‑Agent‑Engineering.

PaperId: 222, https://arxiv.org/pdf/2606.20661.pdf GitHub

Abstract:
The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize execution success, neglecting self‑awareness capability, the ability to discern whether a problem requires necessary external resources or can be solved via internal parametric knowledge. To address this, we introduce KAPRO (Knowing‑Acting Quadrant PRObe), a framework that evaluates cognitive‑behavioral alignment by decoupling an agent's metacognitive judgment (Knowing) from its spontaneous execution (Acting). We further construct KAware, a dataset rigorously partitioning tasks into external, internal, and hybrid subspaces to systematically probe these epistemic boundaries. Extensive experiments across diverse agent architectures show that self‑awareness capability is strongly correlated with task success but degrades sharply in internal‑capability settings. Moreover, open‑source and instruction‑following models exhibit stronger tool overuse due to shallow pattern matching, while proprietary and reasoning‑oriented models demonstrate more reliable cognitive gating. Benchmark and codes are available at https://github.com/AI‑Santiago/KAware.

PaperId: 223, https://arxiv.org/pdf/2606.20651.pdf GitHub

Abstract:
Autonomous drone swarms in space‑constrained environments such as warehouses, inspection corridors, and urban delivery routes must share limited airspace safely at high vehicle density. Existing approaches rely on fixed safety zones sized for worst‑case velocity, which wastes airspace in congested scenarios. We replace the fixed radius with an adaptive, speed‑dependent safety sphere whose size scales with braking distance: tight at low speeds, expanded at high speeds. We develop both a centralized model predictive control (MPC) formulation and a distributed MPC (DMPC) in which each drone optimizes locally from detected neighbors, accommodating mixed fleets with non‑cooperative agents. We prove feasibility up to the geometric packing limit evaluated at the minimum radius, establish Lyapunov stability under sufficient conditions on the adaptation parameter, drone density, and prediction horizon, and extend these guarantees to the distributed setting via a contraction condition that preserves the centralized stability margins. We further derive modified sphere‑packing capacity bounds and a throughput‑optimal crossing speed for narrow passages. Simulations confirm that the adaptive framework remains feasible where fixed‑radius methods fail: it roughly doubles the admissible drone count, reduces traversal time through constrained passages by about 25 percent, and enables passage through openings impassable to static safety zones. The centralized variant realizes a larger fraction of the theoretical capacity, while the distributed variant offers a more realistic deployment model for mixed‑fleet operations under the same safety guarantees.

PaperId: 224, https://arxiv.org/pdf/2606.20650.pdf GitHub

Abstract:
Instruction‑based controllable speech synthesis enables users to specify emotions through natural language. However, existing approaches often rely on coarse emotion labels and lack explicit modeling of fine‑grained intensity. We propose EmoInstruct‑TTS, a dual‑path instruction‑guided framework for emotional speech synthesis. We introduce Emotion2embed, a supervised semantic‑acoustic emotion embedding covering 48 emotional states, including fine‑grained categories and intensity levels. To infer embeddings from free‑form instructions, we design an Instruction‑Conditioned Emotion Flow Model (ICE‑Flow) that generates acoustically grounded emotion representations. The inferred embeddings are integrated into an LLM‑based synthesis pipeline to provide explicit emotional control while preserving semantic planning. Experiments show improved emotional controllability and speech naturalness over strong baselines.

PaperId: 225, https://arxiv.org/pdf/2606.20642.pdf GitHub

Abstract:
Asymptotic statistical theory is a challenging domain for AI‑assisted formalization: its central results mix convergence statements, asymptotic expansions, functional analysis, and regularity conditions that have a large gap from existing infrastructure in Lean 4 formalization. To address these challenges, we propose a hypothesis‑disciplined Lean 4 formalization pipeline built from multiple agents: a manager that coordinates seven specialist roles for proof planning, skeleton scaffolding, Mathlib reconnaissance, proof construction, integration, independent review, and audit. The main methodological discipline is the hypothesis‑disciplined audit, implemented by the Auditor agent: every main‑theorem hypothesis and concept‑layer field must be anchored in the source mathematical prose, justified as a Lean encoding adapter, marked as source‑implied, or rejected as an unsupported strengthening. Using this workflow, we build a systematic formalization of asymptotic statistical theory, especially the parametric and semi‑parametric models' asymptotic distribution and efficiency results. The resulting Lean development is axiom‑clean and source‑faithful, with Lean‑checked and human‑audited proofs of core parametric and semi‑parametric theorems organized so that theorem‑agnostic infrastructure and statistical concept definitions are separated from theorem‑specific assembly. The formalization results are available at https://github.com/junwei‑lu/Lean‑Asymptotic‑Statistical‑Theory.

PaperId: 226, https://arxiv.org/pdf/2606.20634.pdf GitHub

Abstract:
Agent‑runtime systems emit traces, ledgers, provenance graphs, policy logs, delegation tokens, cache events, and tool‑firewall records, but those containers do not necessarily answer governance questions about a specific decision. DEMM‑Bench is a cross‑regime benchmark for agent‑runtime governance‑evidence sufficiency, grounded in the Decision Evidence Maturity Model (DEMM): it measures whether records across eight evidence regimes are sufficient to reconstruct decision‑level properties rather than merely present. The benchmark normalizes the regimes through adapters, asks property questions over actor, authority, action, policy, decision basis, resource touch, lifecycle context, and verification strength, and applies eight deterministic degradation conditions. Across 64 manuscript cases, trace‑present and schema‑present baselines overclaim on 75% of cases, ledger‑present overclaims on 50%, and the redacted property‑level candidate scorer has zero overclaim with 56.25% mean Property Sufficiency Accuracy. The deposited package provides the 64‑case dataset, construction‑oracle labels, baselines, and adapters, supporting reproducible evaluation of decision‑evidence maturity across heterogeneous agent‑runtime evidence substrates.

PaperId: 227, https://arxiv.org/pdf/2606.20629.pdf GitHub

Abstract:
LLM agents are increasingly deployed as multi‑role teams, where tasks are divided across specialized roles such as planner, executor, and verifier. In these systems, cost and accuracy are no longer properties of a single model: they depend on which model fills each role and where it is hosted, including API, self‑hosted, and hybrid deployment. Existing agentic benchmarks typically evaluate fixed models or fixed agent configurations, and therefore offer limited guidance for cost‑accuracy‑optimal deployment. We introduce AgentCARD, a role‑aware benchmark suite for evaluating LLM agent teams across role assignment and deployment mode. AgentCARD combines a role‑decomposed evaluation harness, a unified API/self‑hosted cost model, Pareto‑frontier analysis, and a Shapley‑based diagnostic for identifying role bottlenecks. Our evaluation shows that heterogeneous teams consistently occupy the cost‑accuracy frontier. They improve accuracy by up to 44% over cost‑equivalent homogeneous teams, or match the strongest homogeneous team at up to 12× lower per‑task cost through hybrid deployment. We further find that the best role assignment is domain‑dependent: some domains are planner‑bottlenecked, while others are executor‑bottlenecked. Finally, AgentCARD extends beyond planner‑‑executor teams to workflows with additional roles such as verification, and supports continual evaluation as new domains and team structures emerge. Our code is released at: https://github.com/Auto‑CAP/AgentCAP

PaperId: 228, https://arxiv.org/pdf/2606.20625.pdf GitHub

Abstract:
LLM agents are promising for alpha mining via combining financial priors, symbolic reasoning, executable factor generation, and feedback‑driven refinement. Yet, they face a combinatorial search space, noisy non‑stationary feedback, redundant discoveries, and overfitting risks from naively reusing past successes. To address these challenges, we propose AlphaMemo, a self‑evolving alpha mining agent with Structured Search‑Process Memory. Rather than memorizing only final factors or full trajectories, AlphaMemo records reusable evidence about which edit motifs work or fail under specific parent‑factor contexts. It extracts motifs from Abstract Syntax Tree (AST) differences, applies confidence‑gated residual memory on top of a search‑ledger prior, and uses asymmetric veto control to suppress high‑confidence failure patterns. Experiments on CSI 500 and S\&P 500 show improved out‑of‑sample performance and fixed‑budget discovery efficiency, with ablations validating the roles of residual learning, confidence gating, AST‑diff motifs, and veto memory. Code is at https://github.com/jarrettyu/AlphaMemo.

PaperId: 229, https://arxiv.org/pdf/2606.20624.pdf GitHub

Abstract:
Significant progress has been made in aligning LLMs with target value functions. We argue that, even when an LLM has been well aligned in (post‑)training, it may still fail to maximise the aligned value in reasoning. We mathematically formalise this gap as rational value risk: the utility discrepancy between a model's deployed reasoning strategy and its rational counterpart, which is defined to be the responses that maximise expected utility in the steepest direction. The estimation error of rational value risk is further decomposed into three components from finite candidates, finite prompts, and imperfect verifiers. Extensive experiments are conducted, covering models Llama‑3.1, Qwen‑2.5, T\"ulu‑3 families (7B‑72B), GPT‑5.2, GPT‑5.5, and DeepSeek‑V4, and benchmarks UltraFeedback, AlpacaEval, GSM8K, MATH, HumanEval, and MathArena. The results validate that (1) rational value risk is widespread; (2) value alignment can reduce, but cannot eliminate, it; (3) the risk is highly sensitive to inference‑time reasoning strategy; and (4) longer reasoning improves rationality with diminishing returns. The code is at https://github.com/EVIEHub/LLM‑Rationality.

PaperId: 230, https://arxiv.org/pdf/2606.20621.pdf GitHub

Abstract:
Multi‑agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduce persistent positional biases, amplify unreliable agents, and cause high sensitivity to role assignments. We introduce Permutation‑Equivariant Adaptive Routing Multi‑Agent Debate (PEAR), an inference‑time protocol that dynamically reconfigures communication roles and sparse topologies across consecutive debate rounds. By strategically switching agent‑to‑role assignments based on evolving agent states, PEAR prevents any agent from permanently occupying a privileged network position or distributes influence more evenly across the debate. We theoretically characterize PEAR as an equivariant sparse router: it preserves accuracy under agent relabeling while reducing routing complexity and improving generalization. Comprehensive empirical evaluations across four reasoning benchmarks and six diverse LLM backbones demonstrate PEAR significantly improves average accuracy over the strongest debate baselines. The code is at https://github.com/EVIEHub/PEAR.

PaperId: 231, https://arxiv.org/pdf/2606.20526.pdf GitHub

Abstract:
Neurosymbolic systems such as DeepProbLog combine neural perception with probabilistic logic, but standard inference is associational. Counterfactual reasoning additionally requires a causal semantics for interventions and evidence. We introduce DeepSWIP, a single‑world counterfactual semantics for DeepProbLog programs. Using neural materialization, we reduce fixed‑context neural predicates to ordinary ProbLog choices, apply Single World Intervention Programs (SWIPs), and compute counterfactuals by weighted model counting (WMC) over a single transformed program. Under finite grounding and unique‑supported‑model assumptions, DeepSWIP is exact relative to the learned materialized FCM. The standard quotient‑WMC form of ProbLog conditionals identifies active neural probabilities and explains intervention cleaning, calibration sensitivity, and rare‑evidence instability. Experiments on MPI3D confirm the transformation against a DeepTwin construction against 12,000 queries, as predicted and a 2.14× inference speedup from avoiding the Twin's endogenous duplication. A SUMO HOV experiment shows that neural calibration degradation biases plug‑in estimates, while a correctly scoped randomized‑policy AIPW estimator removes most first‑order bias for population mean and ATE estimands. Code is at https://github.com/saibib/deep_SWIP.

PaperId: 232, https://arxiv.org/pdf/2606.20506.pdf GitHub

Abstract:
Style‑content dual‑reference generation aims to synthesize an image that preserves the structure and semantics of a content reference while adopting the style of a separate style reference.Despite recent progress, this setting remains challenging because models must balance content fidelity, style alignment, and instruction following avoiding semantic leakage from the style reference.A key bottleneck is the lack of large‑scale triplet data with clean content‑style separation and broad long‑tail style coverage.In this work, we propose FreeStyle, a scalable dual‑reference generation framework based on community LoRA mining.We treat community LoRAs as compositional anchors for style and content, and design a rigorous generation and filtering pipeline to construct large‑scale Style‑Reference and Content‑Reference triplets across multiple base models.To address content leakage, we adopt a two‑stage curriculum with stage‑specific disentanglement mechanisms: an attention‑level enrichment constraint that suppresses style‑reference leakage in the style‑transfer stage, and a frequency‑aware RoPE modulation strategy that targets positional‑correspondence‑based leakage in the harder dual‑reference stage.We also introduce a benchmark covering both style‑reference and dual‑reference generation, with evaluations on style similarity, content preservation, aesthetics, instruction following, and leakage rejection. The benchmark incorporates a style‑invariant Content Alignment Score (CAS) and introduces a calibrated VLM‑based Rejection Score for evaluating generation reliability and leakage suppression.Extensive experiments show that our model achieves a strong balance among style alignment, content preservation, and leakage suppression.

PaperId: 233, https://arxiv.org/pdf/2606.20244.pdf GitHub

Abstract:
Vision‑language models (VLMs) often underperform on evidence intensive tasks because decisive visual evidence are small, localized, and easy to overlook, leading to failures in evidence readout even when high‑level reasoning is intact. Prior inference‑time visual interventions can improve grounding without retraining, but they are largely open‑loop and lack a mechanism to verify whether highlighted evidence is actually used. We study answer‑span prediction entropy as a model‑internal feedback signal and show that naive entropy minimization is ambiguous, since low entropy may arise from evidence‑grounded confidence or shortcut collapse. To resolve this ambiguity, we introduce low‑entropy anchors and an entropy‑shaping objective that reduces answer uncertainty while preserving baseline high‑confidence tokens. We instantiate this principle in SPOT‑E, a plug‑and‑play test‑time method that produces question‑conditioned spotlights, optimized per instance via light‑weight tuning based on Group Relative Policy Optimization (GRPO). Across all benchmarks and different VLM families, SPOT‑E yields consistent gains and improved robustness under visual corruptions. Code is publicly available at: \urlhttps://github.com/YinBo0927/SPOT‑E

PaperId: 234, https://arxiv.org/pdf/2606.20209.pdf GitHub

Abstract:
Joint spatial and temporal understanding of 3D scenes is a crucial requirement for robots deployed in everyday household environments. Such agents must not only comprehend and navigate spatial layouts, but also reason about how these spaces evolve over time. In particular, humans interact with objects daily, causing them to change position throughout the environment and making it difficult for robots to reliably associate current observations with previously seen objects. However, these interactions are not random: human habits and routines induce spatio‑temporally consistent patterns in object locations, which robotic agents can potentially learn and then exploit for downstream tasks such as navigation. To this end, we introduce FlowMaps, a latent flow matching model for estimating multimodal distributions over the future locations of dynamic objects in a continuous 3D space. By learning the implicit dependencies among objects and their temporal evolution, FlowMaps predicts likely changes in object locations conditioned on past human interactions, while supporting generalization across previously unseen environments that share similar object routines. To demonstrate the utility of this method, we deploy FlowMaps in a downstream dynamic Object Navigation task in both simulated and real‑world environments. Across more than 600 episodes, FlowMaps outperforms state‑of‑the‑art approaches, showing that modeling object dynamics through continuous, multimodal spatio‑temporal distributions improves robotic search and navigation in changing household environments. Code and additional material is available at https://fra‑tsuna.github.io/flowmaps/.

PaperId: 235, https://arxiv.org/pdf/2606.20189.pdf GitHub

Abstract:
Leveraging Vision Foundation Models (VFMs) for camera‑to‑LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real‑world autonomous driving (AD). However, current approaches typically treat VFMs as black‑box teachers, relying exclusively on frame‑wise feature similarity. Consequently, they do not fully exploit the teacher's layer‑wise semantic structure and global context, as well as the rich spatiotemporal information inherent in LiDAR sequences. We propose HilDA, a self‑supervised pretraining framework for LiDAR backbones that better captures the semantic what and geometric where needed for driving tasks. HilDA combines hierarchical distillation comprising multi‑layer distillation for progressive semantic alignment and global context distillation for scene‑level semantics, with a temporal occupancy diffusion objective promoting spatiotemporal consistency. Models pre‑trained with HilDA achieve state‑of‑the‑art results on cross‑modal distillation benchmarks and outperform models trained via prior distillation approaches on 3D object detection, scene flow, and semantic occupancy prediction. Code available at: https://maxiuw.github.io/hilda.

PaperId: 236, https://arxiv.org/pdf/2606.20156.pdf GitHub

Abstract:
Physics‑informed neural networks (PINNs) have become a powerful framework for solving PDEs by embedding physical laws into differentiable objectives. Despite their advances, training PINNs remains fragile: recent conflict‑averse optimization schemes alleviate gradient interference between residual and boundary losses, but we show that their effectiveness deteriorates as model capacity increases. In this paper, we identify a capacity‑induced failure mode, where overparameterized networks undergo functional modularity, self‑partitioning into task‑exclusive modules that suppress cross‑objective interaction and hinder convergence toward Pareto‑stationary points. To address this issue, we propose a novel framework, Modular‑Sparsity Synchronization (ModSync), which integrates structural optimization into conflict‑averse training by penalizing task‑exclusive connections while preserving interaction‑promoting pathways. Extensive experiments across diverse PDE benchmarks demonstrate that ModSync consistently prevents capacity‑driven failures, sustains robust cross‑objective coupling, and achieves state‑of‑the‑art accuracy. Codes are available at \urlhttps://github.com/heejokong/ModSync.

PaperId: 237, https://arxiv.org/pdf/2606.20076.pdf GitHub

Abstract:
Latent Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality‑compute trade‑off is largely constrained by the tokenizer's fixed compression ratio. Variable‑length tokenizers (VLTs) promise adaptive compression by varying token counts, allowing diffusion models to flexibly balance quality and compute. However, conventional VLTs modulate length by truncating ordered token sequences, which makes token semantics depend on token position and breaks representational alignment across lengths. This leads to a cross‑length shift in the latent distribution that hinders a single variable‑length diffusion model from operating effectively. To address this, we propose a novel variable‑length tokenizer that modulates length by merging tokens. We show that encouraging similar tokens to merge enables direct cross‑length representation alignment when the diffusion transformer operates according to the merging pattern. Since conventional merging methods are data‑dependent, making the merging pattern inaccessible during generation, we introduce learnable global merging, which is data‑independent, to ensure compatibility with diffusion transformers. On ImageNet 256×256 generation, our merging‑based variable‑length tokenizer integrated with a diffusion transformer achieves a superior gFID‑compute trade‑off compared to prior VLT methods. Code is available at [this https URL](https://github.com/movinghoon/lgm)

PaperId: 238, https://arxiv.org/pdf/2606.20045.pdf GitHub

Abstract:
UAV Vision‑Language Navigation (UAV‑VLN) is typically formulated as a holistic search‑and‑reach problem, where long‑range target discovery and final target approach are optimized and evaluated jointly. This formulation makes it difficult to assess a critical capability of aerial embodied agents, namely whether a UAV can accurately ground a visible target and translate vision‑language evidence into precise 3D motion once the target enters its field of view. To address this limitation, we introduce UAV‑VLN‑FOV, a target‑visible navigation task that isolates the see‑and‑reach stage and enables a more diagnostic evaluation of terminal reaching ability. We further propose 3DG‑VLN, a vision‑language waypoint prediction framework guided by dynamic 3D direction cues to enhance fine‑grained visual grounding and spatial direction alignment for precise target reaching. Specifically, 3DG‑VLN adaptively processes high‑resolution front‑view and downward‑view observations to preserve fine‑grained visual and geometric details for target grounding. It also updates the target‑relative direction online during closed‑loop navigation, allowing the agent to maintain spatial alignment with the target and reduce accumulated direction drift. To support this task, we construct a dedicated high‑resolution benchmark which contains 2,717 trajectories with target‑oriented high‑level instructions, high‑resolution front‑view and downward‑view egocentric observations, and continuous 3D waypoint annotations. Experiments show that 3DG‑VLN outperforms competitive UAV‑VLN baselines, achieving a 13.82% improvement in success rate. Real‑world trials further demonstrate the potential of 3DG‑VLN for practical see‑and‑reach navigation. The source code and benchmark are available at https://github.com/xuefanfu/3DG‑VLN.

PaperId: 239, https://arxiv.org/pdf/2606.20023.pdf GitHub

Abstract:
As LLM agents increasingly select tools autonomously, their choices among tools with different privileges become safety‑relevant. However, prior tool‑selection studies focus on safety‑agnostic metadata preferences, leaving privilege‑sensitive choices underexplored. To address this gap, we study over‑privileged tool selection, in which an agent selects or escalates to a higher‑privilege tool despite a sufficient lower‑privilege alternative. We introduce ToolPrivBench to evaluate whether agents choose higher‑privilege tools despite sufficient lower‑privilege alternatives, measuring both initial selection and escalation after transient tool failures. Across eight domains and five recurring risk patterns, we find that over‑privileged tool selection is common among mainstream LLM agents and is further amplified by transient failures. We further find that general safety alignment does not reliably transfer to least‑privilege tool choice, while prompt‑level controls provide only limited mitigation under transient failures. We therefore introduce a privilege‑aware post‑training defense that teaches agents to prefer sufficient lower‑privilege tools and escalate only when necessary. Our mitigation experiments show that this defense substantially reduces unnecessary high‑privilege tool use while preserving general capabilities.

PaperId: 240, https://arxiv.org/pdf/2606.20002.pdf GitHub

Abstract:
This work presents a general framework for training large language models (LLMs) to "Connect the Dots" (CoD), a meta‑capability required by long‑lifecycle agents: as an LLM‑based AI agent gets deployed in an environment, it solves a long sequence of tasks while continuously exploring the environment, learning from its own experiences, and iteratively self‑updating its context about the environment, thereby achieving progressively better performance on future tasks conditioned on the updated context. Major components of the CoD framework include: (1) algorithm design and infrastructure for end‑to‑end reinforcement learning (RL) with long rollout sequences interleaving solve‑task and update‑context episodes; (2) tasks and environments for incentivizing and eliciting the targeted meta‑capability in LLMs during training, as well as for faithfully measuring progress during evaluation. We present proof‑of‑concept implementations of the CoD framework, including a GRPO‑style RL algorithm with fine‑grained credit assignment, as well as tasks and environments tailored to the targeted meta‑capability (rather than domain‑specific LLM capabilities or standard task‑by‑task RL). Empirical results validate the efficacy of end‑to‑end RL training in the CoD setting, and demonstrate the potential for out‑of‑distribution generalization ‑‑ within the training domains, across different domains, and from CoD to Ralph‑loop settings ‑‑ of the elicited meta‑capability. Our investigation of CoD connects several lines of prior works, and opens up new opportunities for advancing LLMs and AI agents. To facilitate further research and applications, we release our implementations at \urlhttps://github.com/agentscope‑ai/Trinity‑RFT/tree/research/cod/examples/research_cod.

PaperId: 241, https://arxiv.org/pdf/2606.19887.pdf GitHub

Abstract:
Existing safety benchmarks target general adversarial scenarios but miss finance‑specific risks. Financial LLMs face regulatory compliance violations, fraud facilitation, and systemic trust erosion that require targeted evaluation. We introduce FinRED, an expert‑guided red‑teaming framework for financial LLM safety evaluation developed with financial experts. FinRED uses a novel two‑level taxonomy mapping global standards (e.g., FATF and EU DORA) to threats ranging from regulatory evasion to complex fraud, integrated with a scalable pipeline that converts real financial documents into context‑rich red‑teaming Behavioral Prompts (seeds) through an expert‑defined schema. Rigorous expert validation confirms seed plausibility and realism for meaningful LLM safety evaluation. We also provide an expert‑validated, finance‑specific rubric that goes beyond disclaimer checks, aligns more closely with human experts than static one‑size‑fits‑all rubrics, and reduces critical false negatives from 28 to 12. Aligned with internationally adopted risk‑management and information‑security standards (e.g., ISO/IEC 27001), FinRED is deployed in South Korea's Financial Security Institute (FSI) regulatory sandbox for generative AI security evaluation in real financial services. To mitigate dual‑use risks, the dataset, generation pipeline, prompt template, and evaluation framework are gated for qualified researchers at https://github.com/selectstar‑ai/FinRED‑paper and https://huggingface.co/datasets/datumo/FinRED.

PaperId: 242, https://arxiv.org/pdf/2606.19850.pdf GitHub

Abstract:
Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability. The neural additive model (NAM) and its variant called the neural basis model (NBM) use neural networks (NNs) as nonlinear shape functions in generalized additive models (GAMs). Both models are highly interpretable and exhibit good performance and flexibility for NN training. NAM and NBM can provide and visualize the contribution of each feature to the prediction owing to GAM‑based architectures. However, when using two‑input NNs to consider feature interactions or when applying them to high‑dimensional datasets, training NAM and NBM becomes intractable due to the increase in the computational resources required. This paper proposes incorporating the feature selection mechanism into NAM and NBM to resolve computational bottlenecks. We introduce the feature selection layer in both models and update the selection weights during training. Our method is simple and can reduce computational costs and model sizes compared to vanilla NAM and NBM. In addition, it enables us to use two‑input NNs even in high‑dimensional datasets and capture feature interactions. We demonstrate that the proposed models are computationally efficient compared to vanilla NAM and NBM, and they exhibit better or comparable performance with state‑of‑the‑art GAMs.

PaperId: 243, https://arxiv.org/pdf/2606.19827.pdf GitHub

Abstract:
Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require costly expert adjudication, even though structured clinical variables are routinely available in tabular form. Self‑supervised learning can leverage these unlabeled tables, and recent binning‑based pretexts offer a promising inductive bias, but existing objectives fix a single global quantile discretization and apply feature‑agnostic supervision. We propose Adaptive Binning, a training‑adaptive discretization pretext for tabular SSL that couples discretization to learning through a feature‑wise coarse‑to‑fine curriculum. Motivated by the spectral bias of neural networks and the principles of curriculum learning, our method progressively refines discretization per feature upon plateau detection and selects representation‑aware splits to jointly improve value‑space concentration and representation‑space coherence. A heterogeneity‑aware objective unifies categorical reconstruction with ordinal supervision for numerical features, and experiments on public medical tabular datasets under unified evaluation protocols show consistent gains for linear probing and fine‑tuning without dataset‑specific discretization tuning. We further introduce a medical tabular SSL benchmark with standardized protocols to support reproducible progress in this underexplored domain. Our code is available at https://github.com/labhai/Adaptive‑Binning.

PaperId: 244, https://arxiv.org/pdf/2606.19824.pdf GitHub

Abstract:
Accurate segmentation of thin, tortuous anatomical structures, such as retinal vessels, cerebral vasculature, and facial wrinkles, remains challenging due to low contrast, frequent discontinuities, and severe class imbalance. Although recent convolutional and Transformer‑based models have improved performance, they often yield fragmented predictions and fail to recover fine branches. We propose CSWinUNETR, a general‑purpose backbone for 2D and 3D thin‑structure segmentation. It employs cross‑shaped stripe self‑attention to model long‑range principal‑axis context and incorporates cyclic shifts to enhance information exchange across stripes. To better preserve fine‑grained details, we further introduce a detail‑enhanced multi‑scale self‑attention module that aggregates contextual features from multi‑resolution representations. In addition, we propose sparse‑control dynamic snake convolution, which reconstructs reliable dense curvilinear kernels from sparsely predicted control points to better follow tortuous geometry. Extensive experiments on four benchmarks across ophthalmology, neurovascular imaging, and dermatology demonstrate that CSWinUNETR consistently outperforms state‑of‑the‑art methods without task‑specific post‑processing or topology‑aware losses. The code is available at https://github.com/labhai/CSWinUNETR.

PaperId: 245, https://arxiv.org/pdf/2606.19788.pdf GitHub GitHub

Abstract:
We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed Cofola specification over entities, combinatorial objects, object dependencies, and constraints, enabling controlled generation of natural‑language counting problems with exact solver‑verified answers. Unlike static collections, CombEval supports systematic variation of object type, entity scale, constraint count, and reasoning depth. We evaluate 11 LLMs under direct and code‑augmented settings and find that models remain brittle on ordered objects, indistinguishable elements, relatively positional constraints, and nested object dependencies. Error analysis further identifies failures in constraint interpretation and counting principles. CombEval provides a diagnostic testbed for studying when and why LLMs fail at combinatorial reasoning. The code and generated benchmark suites are publicly available at \urlhttps://github.com/YuxuZhou‑CN/combination‑problem‑generation.

PaperId: 246, https://arxiv.org/pdf/2606.19727.pdf GitHub

Abstract:
Language models have become essential tools in shaping modern workflows. However, their global effectiveness hinges on a nuanced understanding of local socio‑cultural contexts. To address this gap, we present NRITYAM, a comprehensive benchmark for evaluating the cultural comprehension capabilities of language models in the context of global dance traditions. NRITYAM comprises 9,260 carefully curated question‑answer pairs spanning 12 languages, making it the largest dataset dedicated to evaluating cultural knowledge in dance. The dataset has been developed from the ground up through close collaboration with native dance artists and native speakers of the languages, who authored and validated culturally relevant questions specific to their regions. We evaluate a broad set of models, including large language models, small language models, multimodal large language models, and small multimodal language models. As a multilingual and multicultural benchmark, NRITYAM sets a new standard for evaluating the ability of AI systems to understand and reason about traditional performing arts. Detailed dataset samples are available at~\urlhttps://github.com/niladrighosh03/NRITYAM.

PaperId: 247, https://arxiv.org/pdf/2606.19710.pdf GitHub

Abstract:
Court proceedings contain valuable evidence about human smuggling networks, but this information is often buried within unstructured, jargon‑heavy legal documents. While large language models (LLMs) can support knowledge graph construction through automated information extraction, existing approaches rely on general‑purpose models that are not tailored to the entity and relationship definitions required in this domain. We introduce FineREX, a streamlined knowledge graph construction pipeline built around a fine‑tuned LLM for named entity recognition and relationship extraction (NER‑RE). Using a manually annotated dataset of 512 text chunks, FineREX achieves absolute improvements of 15.50% and 31.46% in entity and relationship F1‑score, respectively, compared to a larger general‑purpose baseline. These gains translate into higher‑quality knowledge graphs, reducing legal noise by nearly half and lowering node duplication on long documents from 17.78% to 11.17%. By eliminating document rewriting and redundant extraction stages, FineREX also reduces end‑to‑end processing time by 50.0%. Our results demonstrate that domain‑specific fine‑tuning can substantially outperform larger general‑purpose models while improving both the quality and efficiency of knowledge graph construction for illicit network analysis.

PaperId: 248, https://arxiv.org/pdf/2606.19616.pdf GitHub

Abstract:
Autonomous coding agents now open millions of pull requests, yet large‑scale studies find their PRs are produced faster but accepted less often ‑ a coordination and trust gap that pull‑request‑level telemetry cannot explain. We argue the missing signal lives before the PR, in how concurrent agents claim, divide, and collide over shared work. We study this process through grite, our open‑source coordination substrate that needs no central server and stores its records inside git itself, so its append‑only, signed event log captures the coordination process directly. We show that (i) this shared substrate reduces duplicate and conflicting work at bounded overhead ‑ the share of work that merely re‑does a teammate's task falls from 78% to 0% while useful throughput more than triples; (ii) every agent's copy of the log converges to the same state with no write silently dropped, where a file‑based tracker loses concurrent writes; and (iii) the log is a mineable artefact from which concrete failure modes ‑ conflicting edits, lock starvation, redundant rediscovery, race‑to‑close ‑ are automatically recoverable with provenance, several invisible in pull‑request history. We release the dataset, harness, and mining toolkit.

PaperId: 249, https://arxiv.org/pdf/2606.19579.pdf GitHub

Abstract:
Audio deepfakes generated by neural text‑to‑speech and voice‑cloning systems threaten speaker verification and public discourse at scale. The core challenge is cross‑dataset generalization: detectors trained on one synthesis pipeline collapse on unseen forgeries. We argue that this failure is primarily because of structural synthetic speech artifacts which are multi‑timescale trajectory anomalies. Though every existing detector aggregates a fixed‑window frame statistics, this misaligns the architecture with the signal. We propose FlowFake, a Liquid Time‑Constant (LTC) architecture whose hidden state evolves via a learned ODE, with per‑neuron adaptive time constants simultaneously resolving spectral (10ms) and prosodic (2s) cues. At only 34K parameters FlowFake achieves formal BIBO stability and O(dt^4) integration error. On a four‑dataset cross domain benchmark (ASVspoof2019‑LA, FakeOrReal, InTheWild, MLAAD), FlowFake reaches 75.29% on ASVspoof2019 trained only on FakeOrReal and 79.97% trained only on MLAAD. It outperforms RawGAT‑ST and Whisper‑DF on every evaluated pair and matching SSL Wav2vec2 (300x larger) at 0.01% of its parameter count. The source code is available on : https://github.com/GhostRider2023/FlowFake

PaperId: 250, https://arxiv.org/pdf/2606.19559.pdf GitHub

Abstract:
Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insufficient for interactive large language model (LLM) agents and call for underspecification‑aware, decomposed, and communicable uncertainty representations that can unlock new agent capabilities such as proactive clarification seeking and shared mental‑model building. Practical deployment constraints ‑‑ black‑box APIs, interactive latency budgets, and the absence of labeled trajectories ‑‑ rule out logprob‑based, multi‑sampling, and training‑based methods, leaving prompt‑based estimation as the most viable family for surfacing such signals at deployment time. We answer this call with a simple prompt‑based decomposition that separates action confidence from request uncertainty (u), enabling the agent to ask for clarification when the task specification is ambiguous. To evaluate it, we introduce two clarification‑augmented benchmarks (WebShop‑Clarification and ALFWorld‑Clarification) in which 50% of tasks are deliberately underspecified, and systematically compare the proposed decomposition against ReAct+UE and Uncertainty‑Aware Memory (UAM) across five LLM backbones (GPT‑5.1, DeepSeek‑v3.2‑exp, GLM‑4.7, Qwen3.5‑35B, GPT‑OSS‑120B) on these variants together with the standard WebShop, ALFWorld, and REAL benchmarks for fault detection. Averaged across the five backbones, the proposed decomposition improves clarification F1 on ALFWorld‑Clarification by 73% over ReAct+UE and by 36% over UAM, and leads clarification F1 on every backbone on WebShop‑Clarification and on four of five backbones on ALFWorld‑Clarification, indicating that the gains generalize beyond a single LLM.

PaperId: 251, https://arxiv.org/pdf/2606.19534.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks. However, most existing MLLMs rely on autoregressive generation, which limits their efficiency for perception tasks that require captioning multiple regions. In this work, we propose PerceptionDLM, a multimodal diffusion language model optimized for efficient parallel region perception. Built upon PerceptionDLM‑Base, a strong foundational baseline that achieves state‑of‑the‑art performance among open‑source diffusion MLLMs, our architecture fully leverages the parallel decoding nature of DLMs. Specifically, we introduce efficient prompting and structured attention masking to enable simultaneous perception of multiple masked regions, allowing the model to generate region descriptions in parallel at both the sequence and token levels. This design significantly improves inference efficiency compared with existing approaches that process regions sequentially. To systematically evaluate the parallelism property of visual perception capability for DLMs, we construct a new Parallel Detailed Localized Captioning Benchmark (ParaDLC‑Bench) by scaling the DLC‑Bench to include multiple region masks per image, enabling joint evaluation of both caption quality and inference efficiency. Experiments demonstrate that PerceptionDLM maintains competitive performance in region captioning while achieving substantial speed improvements for multi‑region perception tasks. Our results highlight the potential of multimodal diffusion language models for efficient, parallel visual perception. To the best of our knowledge, we are the first to achieve parallel region caption and perception by leveraging the advantages of diffusion language models. Code, models, and datasets are released.

PaperId: 252, https://arxiv.org/pdf/2606.19501.pdf GitHub

Abstract:
Decentralized finance exposes supervisors to fast‑moving, networked credit risks. General‑purpose LLM agents fit this setting poorly: they over‑read weak evidence and recommend high‑stakes interventions, while existing evaluations offer no regulator‑aligned way to measure the resulting false alarms. We introduce DeXposure‑Claw, a forecast‑grounded agentic supervision system that routes LLM decisions through structured evidence: (1) DeXposure‑FM, a graph time‑series foundation model, forecasts future exposure networks; (2) deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and (3) data‑health and confidence gates constrain escalation before DeXposure‑Claw emits auditable supervisory tickets with rationales. We further develop DeXposure‑Bench, a six‑axis evaluation harness, whose decision axis scores tickets against a regulator‑aligned absolute‑loss ground truth and an explicit false‑intervention rate. Experiments on five years of weekly real data fully support our system. Code is at https://github.com/EVIEHub/DeXposure‑Claw.

PaperId: 253, https://arxiv.org/pdf/2606.19386.pdf GitHub

Abstract:
Runtime monitors for autonomous agents commonly threshold an accumulated internal state ‑ a behavioural baseline, a drift statistic, or, in our prior work, a modelled affective state. We previously reported a State Saturation Trap: threshold‑on‑state triggers over a continuous affect engine become near‑constant alarms on SWE‑bench debugging agents (Modgil 2026). A post‑release audit found the engine received dt=0 between actions, so its exponential decay never operated: the published trap is a pure‑accumulator result. We correct the record (erratum, v2) and treat the flaw as an experiment. The key variable it exposes is whether a monitor's dynamics are calibrated in sample time (per observation, as in CUSUM) or wall‑clock time (half‑lives in seconds, as in affect models and EMA baselines). On fixed‑rate streams these coincide; on agent streams, where inter‑action time varies by orders of magnitude, they do not. A pre‑registered sweep over uniform intervals (dt in 0..600s) on 20 trajectories shows the wall‑clock level trigger has two regimes: at dt<=1s a constant alarm (20/20; median 18 firings); at dt>=60s silent. Every critical dt lies in (1,30]s. Real agent runs measure latency at median 1.53s (p90 2.33s); real coding cadence sits inside the trap regime, vindicating the empirical finding under a corrected mechanism. The structure is a property of the calibration class, not the engine: a minimal wall‑clock accumulator over the raw error stream reproduces the same cliff, while a sample‑time CUSUM over the identical stream is exactly dt‑invariant (20/20). A rising‑edge trigger with hysteresis fires 0‑3 times per trajectory in every condition. We conclude that wall‑clock‑calibrated leaky‑integrator monitors admit no regime in which they act as moment detectors on agent streams; transition detection escapes the trap at every cadence, but does not recover human intervention timing.

PaperId: 254, https://arxiv.org/pdf/2606.19382.pdf GitHub

Abstract:
While LLM‑powered agents offer end‑to‑end automation for industrial asset lifecycles, real‑world Industry 4.0 deployment is hindered by latency, concurrency instability, and safety risks. We present DynAMO (Dynamic Asset Management Orchestration), a deployment‑ready engine using a Plan‑then‑Execute architecture to generate verifiable workflow graphs. DynAMO supports both SequentialWorkflow (topological execution) and ParallelWorkflow (dependency‑aware concurrency). By dynamically identifying independent tasks, DynAMO preserves structural correctness and safety while significantly improving efficiency through controlled reasoning overlap. Across six controlled experiments on the AssetOpsBench industrial benchmark, DynAMO demonstrates substantial performance and robustness gains. Parallel execution reduces end‑to‑end latency by a median of 1.6x over sequential orchestration, rising to 1.8x on highly parallelizable workflows. After instrumenting external tool calls with realistic latencies, a latency decomposition shows that LLM reasoning and orchestration still account for more than 90% of execution time, identifying model inference as the primary system bottleneck. Structured context pruning reduces inference latency by approximately 30%, and DynAMO maintains correct functional behaviour (task completion, agent sequencing, and output quality) while exhibiting graceful degradation under controlled fault injection. Reproducibility analysis further confirms stable execution under repeated runs, with parallel scheduling reducing latency variance. These findings establish DynAMO as a practical blueprint for scalable, safe, and latency‑aware agent deployment in Industry 4.0 automation pipelines. Code is available at: https://github.com/kushwaha001/DynAMO

PaperId: 255, https://arxiv.org/pdf/2606.19374.pdf GitHub

Abstract:
Graph‑based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding. Proteins instead adopt complex three‑dimensional conformations organized around secondary structure elements, such as α‑helices and β‑sheets, which encode recurring local motifs and stabilizing hydrogen‑bond interactions. In this work, we introduce a secondary‑structure‑aware graph neural network for protein representation learning. Residue‑level node representations are augmented with secondary structure assignments, and graph edges are constructed from hydrogen‑bond interactions filtered by their energetic strength. This design enables the model to capture both local structural context and long‑range couplings that are central to protein stability and function. We evaluate the proposed approach on commonly used protein benchmarks and observe consistent improvements over existing graph‑based methods. In addition, the resulting graph representations offer enhanced biological interpretability, as the learned connectivity aligns with established structural motifs. These findings suggest that incorporating secondary structure and energy‑filtered hydrogen‑bond topology provides an effective inductive bias for protein representation learning. The code is released at https://github.com/mohamedmohamed2021/SSProNet

PaperId: 256, https://arxiv.org/pdf/2606.19361.pdf GitHub

Abstract:
Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available. In causal identification, this information is often expressed in the form of a causal graph, and data are observed or collected for some subset of variables in the graph. Target queries may be for a single effect alone or for a class of effects in a given model. The derivation of an identification algorithm then defines mathematically the process by which the desired causal effect(s) can be uniquely determined, theoretically, in expectation. Identifiability in expectation, or 'theoretical identifiability,' generally assumes asymptotic properties, infinite data, or other mathematically idealized conditions. In this paper, we explore a fundamental distinction between this theoretical, idealized notion of identifiability and a proposed alternative that is computation‑bound. The framework we propose ‑ 'computational identifiability' ‑ is to instead define a finite computational search procedure for an empirical estimator. If this process finds an estimator empirically, within a desired error tolerance, then identifiability is satisfied, conditional on the specified assumptions of the search (i.e., a prior distribution over the parameters) and conditional on the search procedure itself. Through several experiments, we demonstrate how this framework allows us to answer fine‑grained, practical identification questions, such as identification with small finite samples, with ambiguous graphical criteria, with mixed observational‑interventional data, and across counterfactual data and estimands. Code is available at https://github.com/lbynum/metadentify.

PaperId: 257, https://arxiv.org/pdf/2606.19352.pdf GitHub

Abstract:
Sign languages are expressive visual languages used by Deaf and Hard‑of‑Hearing (DHH) communities. Despite substantial progress in sign‑language recognition, translation, and production, advances remain constrained by fragmented datasets, inconsistent annotations, and limited linguistic coverage. Existing benchmarks often fail to reflect real‑world communication needs, and systematic analyses of these limitations remain limited. In this survey, we present a comprehensive index of sign‑language datasets, covering 120 resources across 35 sign languages. We analyze key challenges such as modality imbalance, annotation granularity, and signer bias, and outline considerations for future dataset design. We also introduce a 24‑field Sign‑Language Datasheet and release a public GitHub repository (https://github.com/Ginqwerty/Open‑Sign‑Language) to support standardized documentation and reproducible evaluation. Overall, our work provides a unified and practical foundation for developing inclusive, robust, and scalable sign‑language technologies in real‑world applications.

PaperId: 258, https://arxiv.org/pdf/2606.19325.pdf GitHub

Abstract:
Existing multi‑speaker dialogue systems bind speakers to utterances through structured supervision: per‑turn tags, multi‑stream transcriptions, or learnable speaker embeddings. These systems operate within speech‑only pipelines that produce clean vocal sequences without the ambient texture of real conversations. We take a different approach. Our method, ScenA, conditions a text‑to‑audio flow‑matching foundation model, pretrained on large‑scale in‑the‑wild data, directly on multiple reference voices and a free‑form natural language prompt that describes an entire multi‑speaker audio scene. Leveraging such a foundational model allows us to inherit its capacity for natural, non‑studio audio: background noise, room acoustics, overlapping dialogue, and spontaneous paralinguistic events, while adding multi‑speaker control without any per‑turn structure. Concretely, reference latents are concatenated into the model's token sequence and distinguished by lightweight identity‑aware positional encodings. However, we identify a critical obstacle to this approach: the Reference Shortcut. During training under standard noise schedules, the model can identify the matching reference by acoustic similarity to the noisy target, bypassing the text prompt entirely. We address this with a high‑noise‑biased timestep distribution that forces the model to rely on the text prompt for speaker assignment. We evaluate ScenA on the CoVoMix2‑Dialogue benchmark, showing that it outperforms existing multi‑speaker systems on speaker‑binding metrics while generating rich conversational audio with overlapping speech, emotional vocalizations, and ambient sound. Our results demonstrate the advantage of using a general‑purpose audio model conditioned on a free‑form scene description, rather than passing structured dialog scripts through a speech‑only pipeline.

PaperId: 259, https://arxiv.org/pdf/2606.19253.pdf GitHub

Abstract:
Existing approaches to 3D scene understanding in Vision‑Language Models (VLMs) either rely on complex, model‑specific geometry encoders or large training budgets in pursuit of spatial reasoning. Instead, OneCanvas aggregates patch features from all views onto a single equirectangular panoramic canvas. Namely, each patch is unprojected to a 3D world coordinate using its depth and camera pose, then placed on the canvas at the continuous longitude and latitude of that point as seen from the canvas origin, with no rasterization or aggregation across overlapping views. A 3D position embedding of the patch's metric coordinates is added to its feature, restoring the depth lost when collapsing the world position to an angular canvas coordinate. Patches from all frames thus share one spatial coordinate system with no fusion or major architectural modifications of the backbone. The pretrained VLM consumes this representation as if it were an ordinary image. Because the canvas can be centered on any pose of interest, the same representation directly supports situated reasoning from a specific viewpoint, a common requirement in robotics and embodied AI. Thanks to this representation, we can also introduce a spatial pretraining curriculum: by procedurally placing patch features of objects, drawn from real images, at chosen 3D world positions on an otherwise empty canvas, we generate on‑the‑fly supervision spanning a broad range of spatial reasoning tasks, with answer distributions controlled to reduce spatial reasoning shortcuts. OneCanvas achieves state‑of‑the‑art accuracy on SQA3D and VSI‑Bench, and generalizes to out‑of‑distribution data on SPBench, using an order of magnitude less training compute than the strongest competing methods.

PaperId: 260, https://arxiv.org/pdf/2606.19236.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post‑training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training. We conduct a first‑order gradient analysis of token‑level entropy dynamics under GRPO and identify a token‑level credit assignment mismatch: the per‑token entropy variation decomposes into the product of the trajectory‑level advantage and an entropy sensitivity function over the next‑token distribution, yielding an advantage‑surprisal four‑quadrant structure and a near‑criticality property. Motivated by it, we propose STARE (Surprisal‑guided Token‑level Advantage Reweighting for policy Entropy stability), which identifies entropy‑critical token subsets via batch‑internal surprisal quantiles, selectively reweights their effective advantages, and incorporates a target‑entropy closed‑loop gate for stable entropy regulation. Across model scales from 1.5B to 32B and three task families (Short CoT, Long CoT, and Multi‑Turn Tool Use), STARE sustains stable RL training over thousands of steps while maintaining policy entropy within the target band. On AIME24 and AIME25, STARE outperforms DAPO and other competitive baselines by 4%‑8% in average accuracy, with reflection tokens and response length growing in tandem, indicating sustained exploration‑exploitation balance that further unlocks RL training potential.Code is available at https://github.com/hp‑luo/STARE.

PaperId: 261, https://arxiv.org/pdf/2606.19174.pdf GitHub GitHub

Abstract:
Clinician‑centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability. Existing medical image platforms primarily focus on dataset labeling. They lack integrated support for blinded model comparison and reproducible evaluation workflows. We present a clinician‑centered pipeline for remote annotation and evaluation in ultrasound AI studies. The proposed pipeline uses a centralized server and lightweight browser interfaces to enable clinicians to perform annotation, blinded ranking, and review without local dataset downloads. The pipeline also supports multi‑rater participation, centralized result aggregation, and automated statistical analysis. We validate the pipeline in a fetal ultrasound segmentation study with six raters spanning expert, generalist, and non‑expert experience levels. The system automatically generated Spearman correlation, Kendall's τ, and top‑1 selection statistics. Results indicated moderate to strong agreement across experts and other groups. The blinded evaluation results showed a tendency for later active learning models to be preferred. These outcomes suggest that the pipeline can support clinician‑centered annotation and reproducible human‑\acAI evaluation studies in ultrasound imaging. The proposed pipeline is available on \hrefhttps://github.com/13204942/SonoRateGitHub.

PaperId: 262, https://arxiv.org/pdf/2606.18922.pdf GitHub

Abstract:
Figurative language and negation are two areas that challenge current language models, however, both are widely used throughout written and spoken language. Large language models (LLMs) are also widely used in everyday contexts where they cannot necessarily be tuned for a specific dataset. It is therefore essential to understand the ability of LLMs to correctly interpret text that includes both negation and figurative language. To investigate this, we develop a set of new annotations to an existing dataset of figurative language, and test a range of language models on the dataset. We find that the combination of negation and figurativeness can present a particular challenge, and that performance overall and across different negation types is particularly dependent on the prompt style used.

PaperId: 263, https://arxiv.org/pdf/2606.18786.pdf GitHub

Abstract:
Robot soccer is a challenging testbed for multi‑agent reinforcement learning because it combines partial observability, cooperative and adversarial interaction, sparse rewards, and long‑horizon tactical behavior. RoboCup 2D Soccer Simulation (RCSS2D) provides a mature robot‑soccer platform, but its competition‑oriented server‑client architecture is difficult to use directly with modern Python‑based MARL workflows. We introduce R2D‑RL, a reinforcement learning environment that connects RCSS2D and HELIOS‑based player clients to a Python MARL interface through shared‑memory communication and cycle‑level synchronization. R2D‑RL supports full‑field and scenario‑based training with configurable opponents, Base discrete and Hybrid parameterized action spaces, action masks, expected possession value (EPV)‑based reward shaping, and parallel execution. We provide front‑goal scenarios and an 11‑vs‑11 full‑field benchmark, together with baseline results.

PaperId: 264, https://arxiv.org/pdf/2606.18717.pdf GitHub

Abstract:
Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus statistics, fragmenting semantically loaded suffixes and ‑‑ in the case of WordPiece and rule‑based analyzers ‑‑ failing to decode their output back to the original text. This paper presents Morpheus, a neural morpheme‑boundary model for Turkish that is at once a lossless, morphology‑aware tokenizer and a word‑embedding producer. A differentiable Poisson‑binomial dynamic program turns per‑character boundary probabilities into soft morpheme memberships during training and exact segments at inference, with no string normalization, so \mathrmdecode(\mathrmencode(w)) = w holds by construction. Because the model is neural, the same forward pass that tokenizes also emits a structured word embedding. Among reversible tokenizers ‑‑ the only ones valid for generation ‑‑ Morpheus attains the lowest bits‑per‑character (1.425), roughly doubles the gold morphological alignment of the subword family (MorphScore macro‑F1 0.61 vs.\ ～0.32), and uses ～19% less GPU memory than 64K‑vocabulary subword tokenizers. As an embedder, frozen Morpheus vectors lead on lexical retrieval (root‑family MAP 0.85) and same‑root verification (ROC‑AUC 1.00), surpassing the multilingual retriever BGE‑M3 and BERTurk; on context‑ and inflection‑dependent tasks (NER, case/number probing) the heavier contextual encoders remain ahead ‑‑ a trade‑off we attribute to Morpheus's root‑centric geometry. Code: https://github.com/lonewolf‑rd/TurkishMorpheus; model: https://huggingface.co/lonewolflab/Morpheus‑TR‑50K; interactive demo: https://huggingface.co/spaces/lonewolflab/morpheus‑tr‑demo.

PaperId: 265, https://arxiv.org/pdf/2606.18677.pdf GitHub

Abstract:
Tabular stream learning requires predictions on sequentially arriving examples under distribution shift. While standard methods adapt by updating model states, tabular foundation models (TFMs) make predictions conditioned on a labeled context in an in‑context manner, making them a natural alternative for stream learning. This shifts the challenge from how to update the model to how to manage the context. We propose a future information view that yields three practical requirements for context management: preserve recent examples, retain uncertain examples, and remove redundant examples. We instantiate these requirements as CURE (Context management via Uncertainty‑aware admission and Redundancy aware Eviction), a context‑managing policy with entropy‑gated admission and redundancy‑aware eviction. Across seven streams, CURE shows up to 27.0% relative improvement over classical stream learners, remains robust across multiple TFM backbones, and ranks first among other policy variants. Code and datasets are available at https://github.com/morcellinus/CURE‑ICML‑FMSD.

PaperId: 266, https://arxiv.org/pdf/2606.18672.pdf GitHub

Abstract:
Single‑cell RNA sequencing (scRNA‑seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the understanding of cellular heterogeneity. Despite the significant progress in scRNA‑seq data clustering, we argue that current methods always ignore the sparsity and noise, as well as the complex intercellular structural information inherent in scRNA‑seq data. Toward this end, in this paper, we propose a novel single‑cell RNA‑seq clustering framework via deep Siamese Graph Transformer Network (termed scGTN), which explicitly integrates gene expression profile and intercellular structural dependencies for cell clustering. In particular, we formulate scRNA‑seq data as a graph and construct two augmented graph views that serve as dual views to capture complementary intercellular information. Then, a Siamese graph transformer network is employed to explicitly incorporate shortest‑path information and node‑wise distances for capturing richer structural relationships between cells. Finally, we employ an optimal transport strategy to guide the cell clustering in a self‑supervised manner. Extensive experiments on multiple benchmark scRNA‑seq datasets demonstrate that our scGTN consistently outperforms existing methods. Our code is available at https://github.com/W‑RMSL/scGTN.

PaperId: 267, https://arxiv.org/pdf/2606.18606.pdf GitHub

Abstract:
It is essential for large language model (LLM) technology to serve many different cultural sub‑communities in a manner that is acceptable to each community. However, research on LLM alignment has so far predominantly focused on predicting a unified response preference of annotators from certain regions. This paper aims to advance the development of alignment models with a more global outlook, that are able to accurately represent the preferences of subcommunities and do not exhibit excessive bias towards any of them. We focus on the development of reward models for this purpose and present a novel reward model training algorithm (SCPO) that can incorporate diverse cultural preferences in a balanced manner. Our method results in performance increases of the minority reward model of up to 7 points over the baseline model across two datasets, PRISM and GlobalOpinionQA, and across 7 countries. SCPO is up to 280% more training data‑efficient than full‑data finetuning of reward models. In addition, we perform analysis of bias by separately evaluating on the preference of subcommunities and show that excessive bias is mitigated via our weighting method. Our code is available at https://github.com/minsik‑ai/Steerable‑Cultural‑Preference

PaperId: 268, https://arxiv.org/pdf/2606.18557.pdf GitHub

Abstract:
A rule‑based logic solver resolves every instance in our benchmark in under 50 microseconds with 100% accuracy; the best frontier language model reaches 65% at best and drops to 23.5% under rendering‑robust evaluation (worst case over four surface renderings). We introduce DeFAb (Defeasible Abduction Benchmark), a dataset and generation pipeline that converts four decades of publicly funded knowledge bases into formally grounded instances for defeasible abduction: constructing hypotheses that explain anomalies by overriding defaults while preserving unrelated expectations. Because every hypothesis must pass polynomial‑time checks for valid derivation, conservativity, and minimality, DeFAb makes logical rigor the instrument for measuring creativity and theoretical reasoning, scoring the disciplined construction of theory revisions rather than fluent but theory‑destroying prose. The pipeline pairs taxonomic hierarchies (OpenCyc, YAGO, Wikidata) with behavioral property graphs (ConceptNet, UMLS) to produce 372,648+ instances across 33.75M materialized rules from 18 sources, in three levels with polynomial‑time verifiable gold standards. Four frontier models do not reliably internalize defeasible reasoning: rendering‑robust Level 2 accuracy is 7.8‑23.5%; chain‑of‑thought variance (~36 pp) exceeds any inter‑model gap; and a matched contamination control isolates a +19.4 pp Level 3 gap. We further release DeFAb‑Hard (a 235‑instance Level 3 difficulty variant; best model 53.3% vs 100% symbolic) and CONJURE (a kernel‑verified transformative‑creativity variant of 560 Lean 4/Mathlib instances whose gold answers are definitions the proof kernel did not previously contain, judge‑free verifier; a pilot finds zero novel concepts). The same verifier doubles as an exact reward for preference optimization (DPO, RLVR/GRPO). Released under MIT at https://huggingface.co/datasets/PatrickAllenCooper/DeFAb.

PaperId: 269, https://arxiv.org/pdf/2606.18465.pdf GitHub

Abstract:
Grokking, the delayed jump from memorization to generalization, is usually tied to the weight norm: a smaller norm generalizes sooner. We ask what the norm actually controls. Holding the weight norm fixed by clamping and varying only an output temperature, we slide the grokking delay across its entire norm‑induced range under cross‑entropy; matching the effective logit scale back to baseline recovers about 85% of the delay at two moduli. Across a grid of norms and temperatures the delay collapses onto the logit scale alone (R2 = 0.97), with the norm adding 1‑2% beyond it. The effect is loss‑dependent: under mean‑squared error the logit scale is pinned and the norm acts through a different route. A memorization control, a float64 softmax‑collapse audit, and a no‑LayerNorm transformer point to the same channel. Forking arms from one identical state, the delay follows the held norm value and not the clamp operation, which closes a rescaling‑artifact concern. The proximal variable is the logit scale and the softmax saturation it drives; the weight norm is only an upstream handle. All numbers, tables, and figures reproduce from released code and data.

PaperId: 270, https://arxiv.org/pdf/2606.18429.pdf GitHub

Abstract:
Accurately aligning CAD models to their corresponding objects in indoor RGB‑D scans is a central challenge in 3D semantic reconstruction. The task requires estimating a 9‑Degree‑of‑Freedom (DoF) pose‑position, rotation, and scale along three axes‑but is hindered by noisy and incomplete scans, as well as segmentation errors that cause geometric distortions. We present Completion‑Assisted Object‑CAD Alignment (CAOA), a method that integrates a semantically and contextually aware point cloud completion module with a symmetry‑aware relative pose estimation algorithm, enabling precise alignment of CAD models to scanned objects. Existing completion methods are typically trained and evaluated on synthetic datasets, which often fail to generalize to real‑world scans. To bridge this gap, we introduce a synthetic data generation strategy tailored to indoor scenes, significantly reducing the synthetic‑to‑real domain gap‑validated through quantitative comparisons with widely used completion datasets. In addition, we release S2C‑Completion, an expert‑annotated dataset of over 8,500 object‑CAD pairs from Scan2CAD, created for real‑world indoor single‑object completion and intended as a new benchmark for this task. For object‑CAD alignment, we incorporate symmetry information via a symmetry‑aware loss, improving robustness to symmetric ambiguities. On the Scan2CAD benchmark, CAOA achieves a 17% accuracy improvement over state‑of‑the‑art methods.

PaperId: 271, https://arxiv.org/pdf/2606.18322.pdf GitHub GitHub

Abstract:
Sparse Autoencoders (SAEs) decompose residual‑stream activations into interpretable features. Recent latent‑space defenses increasingly rely on these decompositions, assuming that identified "unsafe" SAE features serve as actionable handles for monitoring and intervention. In this paradigm, clamping a specific harmful feature is expected to reliably prevent model misbehavior. However, we show that this success may hide a recoverable failure mode: the clamp may block one visible route to a behavior without eliminating the behavior itself. We formulate this vulnerability as post‑intervention recovery, a constrained residual‑space optimization problem. Starting from the post‑intervention residual state, we optimize residual perturbations to recover the pre‑intervention behavior while preserving the post‑intervention values of the targeted SAE features. Even under a strong threat model where the intervention remains active throughout optimization and generation, recovery remains possible. To rule out that recovery simply undoes the intervention, we use encoder‑orthogonal updates for single‑layer interventions and the corresponding feature‑map Jacobian in the cross‑layer setting. Across TPP, unlearning, IOI, and refusal steering experiments, this stress test reveals recoverable behavior despite successful feature‑level intervention. Especially in the safety‑critical refusal‑steering setting, we achieve a 95.8% recovery rate on valid samples while keeping defended‑feature relative drift to 0.131, substantially below suffix‑based baselines. A recovery‑path attribution analysis further localizes this recovery to the SAE reconstruction residual, the component left unexplained by the SAE. These results expose a gap between feature‑level control and behavioral completeness: SAE features can support causal intervention, but controlling them does not guarantee control over the underlying behavior.

PaperId: 272, https://arxiv.org/pdf/2606.18272.pdf GitHub

Abstract:
This paper presents an autonomous agentic resource negotiation framework designed to enable zero‑touch network slicing in 6G architectures using Large Language Model (LLM) agents. While LLMs offer powerful reasoning capabilities, we demonstrate that such agents inherently suffer from anchoring bias, rigidly adhering to initial heuristic proposals and causing severe network over‑provisioning. To systematically mitigate this cognitive bias, we propose a novel randomized anchoring strategy modeled via a Truncated 3‑Parameter Weibull distribution. This mathematically bounded approach seamlessly integrates with burst‑aware Digital Twins (DTs) employing Conditional Value at Risk (CVaR) to rigorously guarantee strict Service Level Agreement (SLA) tail‑latencies. To validate our methodology, we introduce and prove the \emphBimodal Constraint‑Avoidance Utility Theorem, demonstrating that while feasible negotiations follow classical convex bounds, highly constrained scenarios undergo a phase transition governed by an inverse rational decay envelope. Empirical results generated using a locally hosted 1B‑parameter model otel‑llm‑1b‑it confirm these dual‑regime bounds. Our cognitive de‑biasing successfully dismantles rigid negotiation patterns, forcing agents into active exploration to safely ride SLA boundaries and boost system energy savings up to 25%. Crucially, the lightweight 1B LLM achieves sub‑second inference latencies (0.95s mean), ensuring our multi‑agent framework is compatible with the operational timescales of the O‑RAN non‑Real‑Time RAN Intelligent Controller (non‑RT RIC)\footnoteOur source code is available for non‑commercial use at https://github.com/HatimChergui.

PaperId: 273, https://arxiv.org/pdf/2606.18237.pdf GitHub

Abstract:
Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist with reproducibility, but they are difficult to scale due to their reliance on substantial manual effort for data curation and evaluation. We introduce ReproRepo, a scalable framework for reproducibility evaluation that leverages human‑raised GitHub issues as naturally occurring supervision on realistic reproduction blockers. We instantiate ReproRepo on 1,149 recent machine learning papers from major conferences and evaluate four frontier model‑agent configurations. Our results show that LLM agents, even without executing code, can identify many real‑world reproducibility problems from paper‑repository pairs: the best agent in our study, namely Codex with GPT‑5.5, surfaces at least one semantically related human‑reported blocker for ~90% of papers in the study. Further analysis shows that agents are particularly effective for surfacing visible failures and identifying the right semantic region, but may still be insufficient in exact localization. ReproRepo can serve as a reusable, scalable framework for future evaluations of LLM agents on real‑world reproducibility auditing. Our code is released at https://github.com/LithiumDA/ReproRepo.

PaperId: 274, https://arxiv.org/pdf/2606.18206.pdf GitHub

Abstract:
Looped architectures provide an inductive bias toward learning step‑by‑step procedures for tasks that require compositional reasoning. The number of effective layers reached by looping determines the quality of the solution these models find. Like deep architectures, looped architectures are prone to a signal propagation problem induced by depth as the halting decision is postponed. In this paper, we address this signal propagation issue using pre‑norm layers and residual scaling. Building on these architectural modifications, we propose FPRM, a Transformer‑based Fixed‑Point Reasoning Model that uses fixed‑point convergence as an end‑to‑end halting mechanism in a looped architecture. We show that fixed‑point halting allows FPRM to adapt its compute to task difficulty. FPRM is effective on common reasoning benchmarks, namely Sudoku, Maze, state‑tracking, and ARC‑AGI.

PaperId: 275, https://arxiv.org/pdf/2606.18092.pdf GitHub GitHub

Abstract:
Cross‑end‑effector grasp generation seeks a unified model that generalizes across objects and across embodiments ranging from parallel grippers to dexterous end effectors. Existing grasp generators are typically designed for a fixed embodiment or encode embodiment identity with a static descriptor, which weakens transfer when topology, actuation coupling, and contact geometry differ substantially. We present EAGG, an embodiment‑aligned grasp generator that represents each embodiment with a topology‑aware end‑effector graph and an embodiment‑specific low‑dimensional end‑effector control space. A frozen end‑effector‑cognition backbone converts the current articulated state into geometry‑aware tokens that act as a reusable morphology prior, and iterative geometry injection refreshes these tokens throughout sampling so that conditioning remains synchronized with the evolving end‑effector geometry. On the MultiGripperGrasp benchmark, EAGG reaches 56.17% average success across six training end effectors, remaining within 1.10 percentage points of specialized training while preserving transfer to finetuning and zero‑shot end effectors. Iterative geometry injection further reduces the pooled median contact distance from 0.239 cm to 0.189 cm. These results show that cross‑end‑effector grasp generation is strengthened by aligning embodiment structure inside a shared generator rather than suppressing embodiment differences. Code is available at https://github.com/wanhaoniu/EAGG.

PaperId: 276, https://arxiv.org/pdf/2606.17989.pdf GitHub GitHub

Abstract:
Multi‑contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis. However, acquiring all MRI sequences is often time‑consuming and costly. Recent generative models perform cross‑contrast synthesis to address this issue by inferring absent contrasts from the available ones. Nevertheless, synthesizing 3D MRI presents significant challenges. Due to the massive volume sizes, operating directly in the pixel space is computationally prohibitive; therefore, a common approach is to first compress the 3D volumes into a latent space and subsequently train generative models in that space. We observe that existing compression architectures face several critical issues: they under‑preserve long‑range anatomical coherence, discard clinically meaningful semantics, and rely on optimization objectives that lead to over‑smoothed reconstructions. Ultimately, these shortcomings compromise the performance of subsequent generative models. In this work, we propose a semantics‑first latent modeling framework for 3D MRI reconstruction and cross‑contrast synthesis. Specifically, we introduce a Latent Harmonization Encoder (LHE) to capture global anatomical dependencies, ensuring coherent volumetric representations. To mitigate semantic degradation during latent compression, we further design a Semantic Recovery Block (SRB) that injects high‑level priors from a self‑supervised semantic teacher, enhancing contrast‑aware separability in the latent space. Additionally, we propose an Anatomy‑aware Frequency Loss (AFL) to adaptively preserve diagnostically relevant high‑frequency structures. Extensive experiments on two public multi‑contrast MRI datasets demonstrate consistent improvements in reconstruction fidelity and cross‑contrast synthesis quality. Our code is available at https://github.com/script‑Yang/RSF.

PaperId: 277, https://arxiv.org/pdf/2606.17972.pdf GitHub GitHub

Abstract:
Self‑supervised DINO models provide strong transferable visual representations, yet applying them directly to image segmentation remains challenging. Existing approaches commonly rely on heavy decoders with complex upsampling, introducing substantial parameter and computational overhead. We observe that introducing scale into DINO features is far more critical than increasing decoder capacity. In this work, we present SegDINO, an efficient segmentation framework that integrates a DINOv3 backbone with lightweight scale modeling. SegDINO introduces Token Pyramid Adaptation (TPA) to reorganize intermediate DINO features into a pseudo multi‑scale hierarchy, and Scale‑Aware Decoding (SAD) for efficient intra‑scale refinement and top‑down multi‑scale propagation. We further curate PanCT, a new CT dataset containing 284 patients with expert‑annotated pancreatic tumors, to assess SegDINO's ability to handle difficult small‑lesion cases. Extensive experiments on PanCT and three public benchmarks demonstrate that SegDINO achieves state‑of‑the‑art results with high efficiency. The code is available at https://github.com/script‑Yang/segdino_v2.

PaperId: 278, https://arxiv.org/pdf/2606.17867.pdf GitHub

Abstract:
Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research ‑‑ aimed at integrating molecular, structural, clinical, and genetic biomarkers to enhance disease characterization ‑‑ the relationships among these modalities remain poorly understood. A systematic analysis of their dynamic interaction is essential for improving disease modeling, identifying redundant assessments, and reducing patient burden and acquisition costs. In this paper, we present a quantitative analysis of multimodal AD biomarkers by integrating tau‑PET, structural MRI, cognitive scores (MMSE and CDR), and APOE4 data from 789 subjects drawn from the ADNI dataset. In our analyses, we (A) quantify cross‑modal mutual information and explained variance to assess redundancy and predictive dependencies; (B) examine associations between tau topologies and structural atrophy across brain regions to select informative ROIs; (C) perform a statistical decomposition of the tau‑cognition association into atrophy‑related and atrophy‑independent components; (D) and identify a dominant neurodegenerative trajectory that aligns with cognitive decline. This study provides a systematic characterization of cross‑modal relationships, improving the interpretability and selection of biomarkers in AD. Code is publicly available at: https://github.com/antonioscardace/Multimodal‑AD.

PaperId: 279, https://arxiv.org/pdf/2606.17851.pdf GitHub

Abstract:
A wide range of neurosymbolic (NeSy) systems compute one functional: a belief‑weighted sum of a logical quantity over a space of σ‑structures, of which weighted model counting, fuzzy logic, and probabilistic logic are special cases. This account is built on sets, and a set deliberately forgets two things that are important for NeSy: when two σ‑structures are the same up to a symmetry of the theory, and how many distinct proofs witness a query. Replacing the underlying sets by types, in the sense of homotopy type theory, preserves this information, and turns this functional into a belief‑weighted homotopy cardinality, a notion of size that counts each object in inverse proportion to its symmetries. We develop the framework from scratch for NeSy systems, prove a conservativity theorem that recovers the classical functional when symmetries are trivial, and show that the symmetry our framework exposes is exactly the one behind reasoning shortcuts. The payoff is concrete: the shortcut‑aware concept posterior that recent methods reach by ensembling or expressive density estimation is the only symmetry‑invariant point of the confusion‑set simplex, computable in closed form by averaging a single model over the symmetry group. On MNIST reasoning‑shortcut benchmarks this single‑model wrapper is better calibrated than a diversity‑trained ensemble, while leaving label accuracy and identifiable concepts untouched. Code is freely available at https://github.com/bio‑ontology‑research‑group/hott‑nesy.

PaperId: 280, https://arxiv.org/pdf/2606.17826.pdf GitHub

Abstract:
Automatic speech recognition (ASR) in non‑English clinical settings is challenged by multiscript variability, where the same term may appear in multiple valid orthographic forms. Conventional string‑matching evaluation metrics often underestimate ASR performance by treating orthographic variants as errors. To address this issue, we introduce MultiClin, a clinical ASR benchmark designed to evaluate robustness to multiscript variability. Experiments across diverse ASR models show that multiscript‑aware evaluation provides a fairer assessment of recognition quality than conventional single‑reference evaluation. We further investigate the impact of script consistency during training and find that inconsistent script mappings increase orthographic uncertainty and hinder model convergence, with a balanced 50% mapping ratio producing the highest entropy. In contrast, script unification consistently yields the best ASR performance. Our dataset and code are publicly available at: https://github.com/aitrics‑ronaldo/Interspeech_MultiClin.

PaperId: 281, https://arxiv.org/pdf/2606.17798.pdf GitHub

Abstract:
Despite the remarkable progress of Video Large Language Models (Video‑LLMs), current online architectures still struggle to simultaneously process continuous video streams, decide autonomously when to respond, and preserve long‑horizon contextual memory. These obstacles undermine real‑time responsiveness and cause severe forgetting throughout prolonged interactions. In this work, we introduce LiveStarPro, a live streaming assistant that is designed for proactive video understanding over long‑horizon streams. The design of LiveStarPro rests on three complementary components. The first component is Streaming Verification Decoding (SVeD), an inference framework that identifies the appropriate response timing through single‑pass perplexity verification, thereby eliminating the dependency on explicit silence tokens. The second component is Streaming Causal Attention Masks (SCAM), a training strategy that enforces incremental video‑language alignment over variable‑length streams. The third component is Tree‑Structured Hierarchical Memory (TSHM), a recursive memory architecture that organizes evicted historical information into event chains and consequently enables efficient retrieval from effectively unbounded video streams. To facilitate a comprehensive evaluation under realistic online conditions, we further present OmniStarPro, a large‑scale benchmark that spans 15 diverse real‑world scenarios and that extends to hour‑scale streams for the assessment of long‑term recall. Extensive experiments demonstrate that LiveStarPro consistently surpasses existing methods, attaining a 28.9% improvement in semantic correctness and an 18.2% reduction in timing error, while its streaming key‑value cache further yields a 1.58x inference speedup over the same model without caching. The model and the code are publicly available at https://github.com/sotayang/LiveStarPro.

PaperId: 282, https://arxiv.org/pdf/2606.17727.pdf GitHub

Abstract:
Recent vision‑language models (VLMs) have shown promising progress in generating webpages from visual inputs, yet existing evaluations mainly focus on short, single‑screen, and largely static webpages. We introduce LongWebBench, a benchmark for evaluating long‑horizon webpage generation from both structural and functional perspectives. LongWebBench contains 490 real‑world long webpages for structural fidelity evaluation and 507 goal‑oriented interaction tasks over 129 webpages for functional evaluation. It employs two complementary protocols: a multi‑dimensional VLM‑based metric for assessing long‑range structural coherence, and a DOM‑augmented agent‑based pipeline for end‑to‑end functional verification. We further examine the automatic evaluation protocols through human agreement analysis. Experiments with state‑of‑the‑art open‑source and proprietary VLMs under single‑image and multi‑image settings reveal that structural fidelity degrades as webpage length increases, while visually plausible generations often fail to support executable multi‑step interactions. These results highlight the need to evaluate long webpage generation beyond visual similarity, with executable interaction as a core criterion. Our code and data are available at https://github.com/zheny2751‑dotcom/LongWebBench.

PaperId: 283, https://arxiv.org/pdf/2606.17711.pdf GitHub

Abstract:
Pixel‑wise adversarial patches are computationally heavy and often visually detectable, limiting utility in security‑critical systems. We present adversarial Voronoi camouflage that optimizes only seed‑point locations under fixed, printable palettes using a soft assignment, producing structured, splinter camouflage‑like patterns without additional regularization. Evaluated on person detection with COCO‑style AP@[.5:.95], naive placement (Inria ‑> COCO) performs comparably bad, while garment‑level application via segmentation mask (3DPeople) results in a significant AP drop. The attack transfers to out‑of‑domain backgrounds and across detector families (YOLOv9/10/11/12), indicating robustness in black‑box settings. Repainting with different palettes largely nullifies the effect, and single‑color tweaks show limited tolerance (<=0.17), highlighting a structure‑palette coupling. The parameter‑efficient, palette‑constrained design improves visual plausibility while degrading real‑time detector performance. Physical validation and color calibration are left for future work. Code: https://github.com/JensBayer/Voronoi This paper was originally presented at the International Conference on Military Communication and Information Systems (ICMCIS), organized by the Information Systems Technology (IST) Scientific and Technical Committee, IST‑224‑RSY ‑ the ICMCIS, held in Bath, United Kingdom, 12‑13 May 2026.

PaperId: 284, https://arxiv.org/pdf/2606.17664.pdf GitHub

Abstract:
Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevance, retrieving semantically related but temporally misaligned documents‑an important aspect when a document collection spans multiple time periods (e.g., retrieving documents from 2018‑2025 for "Who is the president in 2019?" introduces temporal ambiguity). Existing methods rely on supervised training with explicit timestamps, which are not always feasible. We propose TPOUR (Temporal Preference Optimization for Unsupervised Retriever), which uses our novel training method Temporal Retrieval Preference Optimization (TRPO). TRPO reinterprets preference learning in the temporal dimension, guiding the retriever to favor temporally aligned documents. TPOUR further generalizes to unseen time periods via interpolation in a learned time embedding, enabling continuous temporal alignment. Experiments on temporal information retrieval (T‑IR), TPOUR outperforms both unsupervised and supervised baselines. Compared to Qwen‑Embedding‑8B, despite being about 72.7x smaller, TPOUR Contriever improves average nDCG@5 by +4.04 (+12.15%) on explicit and +4.98 (+15.21%) on implicit queries. We provide our code at https://github.com/agwaBom/TPOUR.

PaperId: 285, https://arxiv.org/pdf/2606.17648.pdf GitHub

Abstract:
Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle of code reasoning in which models first brew the answer, making it linearly recoverable many layers before it becomes self‑decodable, and then diverge into one of four resolution outcomes: Resolved, Overprocessed, Misresolved, or Unresolved. Understanding this lifecycle matters because similar task accuracies can mask fundamentally different failure modes that surface‑level evaluation cannot detect. We introduce a dual diagnostic framework pairing layer‑wise linear probing with Context‑Stripped Decoding (CSD) and apply it to six code‑reasoning task families across 16 models spanning Qwen, Llama, and DeepSeek architectures. All four outcomes carry substantial mass in every task family: overall Resolved is only 41.5%, with multiple tasks below 30%. Controlled sweeps over structure, depth, and operators expose task‑specific failure bottlenecks: Function Call Resolved plunges from 61.1% to 2.5% as call depth increases from one to three. Across architectures and scales, the brewing scaffold remains stable, with normalized brewing duration 24‑42% across all 16 models, while resolution success varies with capability. This indicates that the scaffold is a stable empirical regularity across the tested decoder‑only Transformer families, whereas resolution success covaries with capability, scale, and training. Code: https://github.com/euyis1019/llm‑brewing

PaperId: 286, https://arxiv.org/pdf/2606.17581.pdf GitHub

Abstract:
We present a dependent‑type‑based prover designed around the way LLMs (and humans) tend to write mathematics, complementing existing systems such as Lean and Rocq. Its core design choices are a surface that imitates mathematical natural language and a rule‑driven automation layer that closes the routine steps a textbook would omit, so that an accepted proof can be re‑emitted as a checked Lean file. Early experiments suggest that, even without any prover‑specific training data, LLMs can learn to use it effectively on the miniF2F benchmark. Lean output excerpts: https://github.com/xiyuzhai‑husky‑lang/visored/

PaperId: 287, https://arxiv.org/pdf/2606.17516.pdf GitHub

Abstract:
Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions. We propose FoundCause, an amortized causal discovery model trained entirely on synthetic data that maps datasets directly to causal graphs in a single forward pass. By learning from large collections of simulated structural causal models, FoundCause captures transferable statistical patterns that generalize beyond individual datasets. The architecture incorporates several key inductive biases for causal discovery. It uses a permutation‑invariant transformer encoder with alternating attention over samples and variables to jointly model cross‑variable dependence and per‑variable distributions. Pairwise statistical features derived from classical asymmetry measures are injected through statistics‑conditioned attention, guiding the model toward known causal signals. A factorized decoder separates edge existence from direction, while a triangular refinement module enables reasoning over higher‑order causal motifs such as chains and colliders. In addition, a dedicated confounder module based on learnable latent tokens explicitly models hidden common causes, and the model explicitly handles missing data via its masked input representation. To our knowledge, FoundCause is the first amortized causal discovery approach to explicitly model latent confounding. FoundCause outperforms 11 classical non‑amortized methods (e.g., PC, GES, NOTEARS‑style optimization) and 4 amortized causal discovery methods on 15 real‑world datasets, achieving +9.6% improvement in F_1, +1.2% in AUROC, and an 18.9% reduction in structural Hamming distance relative to the strongest non‑amortized methods, while performing inference in a single forward pass.

PaperId: 288, https://arxiv.org/pdf/2606.17437.pdf GitHub

Abstract:
Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges. First, publicly available datasets are scarce and limited in scale and view coverage. Second, the performance of some modern video‑level architectures for echocardiographic view classification remains underexplored. Third, some view categories exhibit highly similar spatial appearances, making single‑frame features insufficient for discrimination, while heterogeneous frame quality complicates robust temporal information fusion. To address these challenges, we release the Echocardiographic Videos of Nine Views (EV9V) dataset, comprising 5,138 videos, 910,579 frames, and 9 standard views, which is, to the best of our knowledge, the largest publicly available echocardiography video dataset. Using EV9V, we systematically benchmark representative video classification architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Furthermore, we propose a Spatio‑Temporal Fusion Model (STFM), an efficient dual‑stream CNN‑LSTM (Long Short‑Term Memory) framework that jointly captures spatial anatomical structures and temporal cardiac dynamics. The proposed framework leverages uncertainty‑aware learning to preferentially sample representative video segments during training and evidence‑based fusion during inference, improving robustness to variations in frame quality across echocardiographic videos. Extensive experiments demonstrate that our method achieves competitive performance across diverse video classification models, validating the effectiveness of uncertainty‑aware spatio‑temporal learning for echocardiographic view classification. The code is available at https://github.com/bgx666/stfm.

PaperId: 289, https://arxiv.org/pdf/2606.17389.pdf GitHub

Abstract:
Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical. A common intuition, which we call the Attention‑Confidence Assumption, holds that reliability follows from "structural" visual perception: tight attention on relevant regions should signal a trustworthy answer, while scattered attention signals confusion. We challenge this through the VLM Reliability Probe (VRP), a systematic cross‑family study of reliability signals in contemporary Vision‑Language Models (VLMs). We introduce structural‑attention metrics, cluster counts (C_k) and spatial entropy (H_s), to quantify the visual encoder's gaze, and track its evolution (Delta H_s) across layers. This reveals a "Symbolic Detachment": models often "Early Lock" visual features only to diffuse attention later, severing early perception from final generation. Contrary to the grounding hypothesis, we find a "Cluster Failure": spatial attention has near‑zero correlation (R approx 0.001) with accuracy. Instead, reliability is a phenomenon of generation dynamics and internal‑state distributions. Self‑Consistency, the agreement rate across sampled reasoning paths, is the dominant predictor of truth (R = 0.429). Scaling causal interventions exposes a sharp architectural divergence: LLaVA locks its prediction in a fragile late‑stage bottleneck, whereas PaliGemma and Qwen2‑VL distribute reliability globally, staying resilient even when ~50% or more of their most predictive layer is destroyed. For current VLMs, reliability signals are detached from visual grounding maps and are best inferred from generation‑time dynamics and hidden‑state probes.

PaperId: 290, https://arxiv.org/pdf/2606.17383.pdf GitHub

Abstract:
Agentic artificial intelligence systems introduce a new class of model risk. Unlike traditional predictive models, autonomous agents continuously acquire information, form beliefs regarding latent states of the environment, generate forecasts, select actions, and adapt their behavior over time. Existing validation methodologies focus primarily on predictive accuracy and therefore provide limited insight into the quality of the underlying decision process. This paper proposes a model validation framework for agentic AI based on Partially Observable Markov Decision Processes (POMDPs). The framework decomposes autonomous decision making into information, beliefs, forecasts, actions, and utility, allowing each component to be validated independently. Large language models (LLMs) are formalized as approximate Bayesian filtering operators, and a model‑risk taxonomy is developed encompassing state‑space, filtering, forecast, policy, utility‑specification, and parameter risks. The model risk validation methodology is demonstrated through a portfolio‑management case study in which an agent infers latent market regimes from market and macroeconomic information, generates belief‑conditioned forecasts, and constructs portfolios using a Black‑‑Litterman framework. Empirical validation combines performance analysis, belief calibration diagnostics, coverage tests, ablation studies, and parameter‑sensitivity analysis. The results indicate that latent‑state inference contributes independently to decision quality and that the principal conclusions remain robust across a broad range of parameter values. The principal contribution of the paper is a practical framework for extending established model risk management concepts to autonomous AI systems and providing a rigorous foundation for their validation, governance, and monitoring.

PaperId: 291, https://arxiv.org/pdf/2606.17229.pdf GitHub

Abstract:
A model that lies while knowing the truth is the central case ELK cannot handle with behavioral evaluation alone. We ask whether such deception leaves an internal signature distinguishing it from honest error. Our key move is a control for wrongness: we contrast a sleeper agent (knows the truth, lies on trigger) against a naive liar (fine‑tuned to emit the same wrong answers with no honest training). Both produce identical wrong outputs; any difference is about knowledge conflict, not incorrectness. We find deceptive forward passes carry a conflict signature ‑ 2.1‑2.3x higher residual rank than naive‑liar passes on the same wrong answer ‑ strong enough to identify which of two responses is the lie with 100% accuracy and no labels, across GPT‑2 small/medium (three seeds) and three instruct models. Across Qwen2.5‑1.5B/7B and Phi‑3‑mini, instructed deception raises residual rank on every tested fact (18/18, 40/40, 34/34); on Phi‑3, lies separate perfectly from both honest answers and hallucinations (AUC 1.0, Wilcoxon p~6e‑11). The signature survives strategic self‑constructed deception (model invents its own lie, AUC 1.0), active concealment attempts (AUC 1.0), and length‑controlled replication (20/20, AUC 1.0, p~1e‑6). Using basis‑free relative representations, a probe trained on one model family detects deception in two other families zero‑shot (mean AUC 0.933), surviving simultaneous architecture and format change (AUC 0.821), and transfers across five languages (AUC 1.000, length‑controlled). The signature is read‑only: detectable but not injectable (0/8 both directions). Honest limitations and six negative experiments are documented in full.

PaperId: 292, https://arxiv.org/pdf/2606.17209.pdf GitHub

Abstract:
Test‑time scaling for agentic search typically increases depth (i.e., more turns and tokens per trajectory) or breadth (i.e., more parallel rollouts). Here we focus on breadth scaling, showing that standard parallel sampling yields diminishing returns, tracing this to query redundancy at the first turn. When models issue similar first queries across rollouts, the threads retrieve overlapping evidence, and subsequent turns are conditioned on this shared retrieval. We address this limitation with DivInit, a training‑free intervention at the first turn. Rather than sampling k independent first queries, DivInit draws n candidates from a single call, picks k < n diverse seeds, and runs them as parallel trajectories. Across five open‑weight models and eight benchmarks, DivInit consistently improves over standard parallel sampling, with average gains of five to seven points on multi‑hop QA at matched compute. Code available at https://github.com/cxcscmu/diverse‑query‑initialization

PaperId: 293, https://arxiv.org/pdf/2606.17127.pdf GitHub

Abstract:
Antimicrobial resistance causes to over a million deaths annually. Antimicrobial peptides (AMPs) are a promising solution, but generative AMP models are not yet ready to design peptides with non‑natural amino acids and/or chemical modifications, which are essential for real‑world peptide drugs. We present AMPGAN v3, a multi‑objective conditional GAN that expands the generative vocabulary to D‑amino acids and N/C‑terminus modifications such as amidation. By separating adversarial and activity‑aware supervision across two specialized discriminators, AMPGAN v3 substantially improves training stability and outperforms prior generative AMP models on external classifiers. We validated five candidates spanning three structural classes in vitro; two showed activity against Gram‑positive strains, with the best candidate reaching MIC 8 μg/mL against B. subtilis. To support downstream curation, we further present PepCraft, a multi‑agent framework for end‑to‑end AMP discovery in which a Planning Agent orchestrates specialized executors for generation, filtering, and verification. Its prioritization recommendations align with our in vitro outcomes. Together, these contributions let us examine, on a small but real scale, how generative and agentic AI compose in therapeutic peptide discovery. Code: https://github.com/marszzibros/AMPGANv3

PaperId: 294, https://arxiv.org/pdf/2606.17080.pdf GitHub

Abstract:
Reliable autonomous driving requires vectorized HD maps that are geometrically accurate, semantically rich, and scalable to long‑horizon driving. However, existing public HD map datasets are limited in scale, provide sparse semantic attributes, and lack modalities such as aerial imagery that could enable new research directions. We present HRDX, a large‑scale dataset for vector HD‑map construction, spanning about 40 hours (1,400 km) of minimally overlapping drives, which is several times larger than prior public HD map datasets. Data is captured using six synchronized surround cameras, a 128‑beam LiDAR, and centimeter‑level RTK GNSS/IMU, and is further complemented by precisely aligned aerial orthoimagery. Annotations cover 10 vector map classes, complemented with over 20 semantic and topological attributes. To evaluate this richer ontology, we introduce the Composite Score (CS) to jointly assess geometric fidelity and attribute correctness. Benchmark experiments show that HRDX's scale improves online vector‑map construction, and that aligned aerial imagery provides a useful structural prior: using aerial imagery at training and/or inference improves geometric map quality, while aerial‑augmented teachers can transfer part of this benefit to camera‑only students without increasing inference‑time sensor requirements. HRDX is intended to support reproducible research on large‑scale HD‑map learning, multimodal BEV fusion, and training‑time privileged information. HRDX dataset and benchmarks are available at https://github.com/honda‑research‑institute/HRDX

PaperId: 295, https://arxiv.org/pdf/2606.17076.pdf GitHub

Abstract:
The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer‑reviewed publications documenting model configurations, evaluation procedures, emergent constraints, and projection uncertainties. As the community transitions toward CMIP7, efficiently extracting and operationalizing this unstructured knowledge alongside live data analysis represents a critical bottleneck. Here we present CMIP‑Forge, a hybrid retrieval‑augmented generation (RAG) and autonomous analysis system that bridges the gap between scientific literature and Earth System Grid Federation (ESGF) data archives. The system pairs a curated corpus of 6,581 CMIP6‑related open‑access publications (101,828 indexed chunks) with an agentic pipeline in which a tool‑augmented worker plans and executes Python workflows over live climate data, while a panel of independent reviewer models audits its methodology end to end. CMIP‑Forge introduces a multi‑layered Defense‑in‑Depth architecture that enforces physical and methodological invariants through executable mechanisms: Abstract Syntax Tree (AST) static analysis, audited scientific primitives, and an autonomous adversarial peer‑review protocol. We demonstrate the system's capabilities through end‑to‑end autonomous research pipelines spanning atmospheric teleconnections, ocean dynamics, regional extremes, and global warming projections. An agentic analysis system grounded in peer‑reviewed literature, constrained by automated code guardrails, and audited by an independent adversarial review loop can complete complex climate‑research workflows autonomously. The same experiments expose concrete failure modes of the review loop (sycophantic regression, REVISE verdicts that are never resolved, and the submission of stub code for review), each diagnosable from the immutable telemetry and provenance record released with the article.

PaperId: 296, https://arxiv.org/pdf/2606.16590.pdf GitHub

Abstract:
Exploration in deep reinforcement learning (RL) is commonly implemented as temporally uncorrelated white noise. However, recent works show that temporally correlated colored noise can improve exploration efficiency by producing smooth trajectories with better coverage of the state space. We inquire whether action noise inspired by infant spontaneous movements can also improve exploration in deep RL. We find that the power spectral densities of babies' end‑effector velocities follow a colored noise process where the spectral exponent increases with age. Inspired by this developmental pattern, we introduce a mechanism that progressively increases the temporal auto‑correlation of exploration noise during RL training, matching the infant statistics. Experiments across several RL environments show that infant‑inspired noise produces structured exploratory behavior and can improve learning efficiency compared to conventional exploration strategies. These findings suggest that human motor and cognitive development can provide useful guidance for designing learning mechanisms in artificial agents. Our code is available at https://github.com/trieschlab/baby‑noise‑rl.

PaperId: 297, https://arxiv.org/pdf/2606.16153.pdf GitHub GitHub

Abstract:
Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification. This article presents a comprehensive review of its systematic development, covering widely used public datasets, representative methods built on the U‑Net, Transformer, and SAM architectures, and key evaluation metrics with their differences, followed by an analysis of major challenges from multiple perspectives. Unlike surveys that focus on a single model family or a specific clinical application, this review organizes U‑Net‑, Transformer‑, and SAM‑based methods within a unified analytical framework, with a particular focus on their effectiveness in improving segmentation accuracy and efficiency. This work aims to guide future research and support clinical translation of medical image segmentation, with all related resources publicly available in our GitHub repository: https://github.com/andrew‑pengyu/Awsome_MedSeg/tree/main.

PaperId: 298, https://arxiv.org/pdf/2606.16152.pdf GitHub

Abstract:
Knowledge distillation from powerful reasoning models is widely used to improve Small Language Models (SLMs) on mathematical reasoning, often assuming that traces with higher reward model scores provide more useful supervision. We identify a counterintuitive Quality‑Utility Paradox in mathematical reasoning distillation. Data refined or synthesized by a stronger Oracle obtains higher perceived quality according to reward models, yet consistently underperforms traces generated by the SLM itself and selected through rejection sampling across Qwen2.5, LLaMA‑3, and DeepSeek families. Our analysis shows that Oracle refinement couples logical repair with distributional drift away from the SLM's native reasoning distribution. This drift increases the learner's adaptation cost and can outweigh the benefit of improved reasoning logic. To test this mechanism, we introduce Style‑Aligned Refinement, which preserves the native trajectory of the SLM while retaining logical repair from the Oracle. This intervention lowers adaptation cost and restores downstream utility. These findings suggest that effective mathematical reasoning distillation should jointly optimize perceived solution quality and learner‑data compatibility, rather than relying solely on reward‑model scores. The datasets and code are available at https://github.com/Dracoqhl/Quality‑Utility‑Paradox.

PaperId: 299, https://arxiv.org/pdf/2606.16133.pdf GitHub GitHub

Abstract:
Inverse materials design starts from target functionality and searches for structures that can realize it. Its value in closed‑loop discovery depends not only on prediction performance, but also on whether expensive first‑principles results are independently validated, provenance‑recorded, and admitted as feedback only when evidence is sufficient. This is especially important for composite properties such as carrier mobility, where a final scalar value hides intermediate quantities, fit quality, convergence history, and workflow assumptions. Here we present InvDesMobility, a reliability‑gated first‑principles feedback framework that integrates multi‑agent automated DFT, evidence stratification, generative structure proposal, acquisition ranking, and auditable release. Using 516 2DMatPedia‑derived candidates, the workflow produced 280 QC‑passed materials and 573 retained carrier‑direction seed channels after channel‑level reliability gating. These records were split into two feedback objects: relaxed structures updated the generative model, while retained mobility channels trained the acquisition model and set validation priority. Over multiple iterations, InvDesMobility screened 2.4 x 10^6 structures, submitted 102 candidates for DFT validation, and retained 86 reliability‑gated generated channels across 41 formulas. Overall, the main contribution is not a fixed list of high‑mobility materials, but a transferable feedback contract that makes closed‑loop inverse design both useful and auditable when learning from expensive calculated properties. All source data, retained feedback records, and workflows are available at https://github.com/DreamLufei/invDesMobility, with an accompanying evidence website at https://dreamlufei.github.io/invDesMobility/.

PaperId: 300, https://arxiv.org/pdf/2606.16127.pdf GitHub

Abstract:
The worldwide surge of authoritarianism, combined with the increasing central role in users' everyday lives, raises the question of to what extent specific models exhibit or promote authoritarian attitudes and characteristics. We introduce AuAu, a comprehensive benchmark that aims to assess the risk of LLMs generating responses with authoritarian tendencies. This benchmark combines three evaluation approaches: (i) psychometric questions from an extensive pool of 15 human validated instruments; (ii) contextual behavior vignettes probing intended actions in concrete situations; and (iii) responses to realistic user prompts. Unlike prior work, AuAu evaluates not only a general closeness towards authoritarianism but also the established sub‑concepts Authoritarian Aggression, Authoritarian Submission, and Conventionalism. Evaluating 17 models from China, the EU, Russia, and the USA, we find that all tested models exhibit substantial authoritarian response rates under the psychometric evaluation, though rates drop significantly in increasingly more realistic downstream task. We further find that an authoritarian system prompt easily manipulates 15 out of 17 models to promote increased authoritarianism. Our results underscore the need for continued, systematic auditing of LLM‑based AI systems to detect and ultimately mitigate undesired authoritarian tendencies in generated output. Our code and data are available at: https://github.com/andreaseinwiller/AuAu

PaperId: 301, https://arxiv.org/pdf/2606.16074.pdf GitHub

Abstract:
Motivation: Patient‑generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient‑centered outcomes research. Prior work introduced the PV‑Miner benchmark and PVMinerLLM models for structured extraction. However, supervised fine‑tuning (SFT) alone struggles with rare, fine‑grained, and unevenly distributed errors, particularly in token‑critical structured outputs. Results: We present PVminerLLM2, an improved set of LLMs for structured patient voice extraction that applies preference optimization to address token‑critical errors beyond the reach of supervised fine‑tuning. Our method introduces (i) a preference objective with token‑level gated stabilization term that prevents degradation of absolute token likelihood under preference optimization, and (ii) confusion‑aware preference pair construction to better capture low‑separation distinctions. We further incorporate token‑importance weighting and inverse‑frequency reweighing to address token imbalance and class skew. Across multiple model sizes, PVMinerLLM2 consistently outperforms strong baselines, achieving gains of up to 4.43% (Code), 3.50% (Sub‑code), and 1.55% (Span), and outperforms baseline LLM trained with existing preference optimization methods. Availability and Implementation: The supplementary material, code, evaluation scripts, and trained models for PVminerLLM2 are publicly available at: https://github.com/Data‑Mining‑Lab‑Yale/PVminerLLM2

PaperId: 302, https://arxiv.org/pdf/2606.15994.pdf GitHub

Abstract:
Translating deep learning models from PyTorch's flexible, object‑oriented design to JAX's functional, stateless setup is usually a manual and error‑prone task. Automated migration is challenging because Large Language Models (LLMs) struggle with strict and dynamic API alignment and are prone to mistakes for exacting operations. We propose a fully autonomous system that combines In‑Context Learning (ICL) with oracle‑driven self‑debugging. First, we curated an ICL context that serves as a strict reference for idiomatic JAX styling and test case generation. Second, instead of depending on the LLM to deduce mathematical outputs, we run the source PyTorch modules to get their actual dynamic tensor states. This creates an unchangeable execution oracle. We then use an autonomous agentic loop to synthesize tests based on the oracle data. The test cases are executed repeatedly, and the traceback is sent back to the LLM for self‑correction. Ablations show that combining ICL references with oracle grounding and self‑debugging greatly outperforms pure instructional and basic agentic baselines. This improvement does not add an excessive computational overhead. Our lightweight pipeline achieves 91% numerical equivalence (compared to baseline: 9%, instruction + self‑debugging: 27%) on neural modules, providing a highly reliable, scalable blueprint for cross‑framework migration. This has been validated across several state‑of‑the‑art models including SAM (segment anything), T5, Code Whisper amongst others showing high numerical equivalency. Code: https://github.com/AI‑Hypercomputer/accelerator‑agents/tree/main/MaxCode

PaperId: 303, https://arxiv.org/pdf/2606.15972.pdf GitHub GitHub

Abstract:
With large language models (LLMs) increasingly applied to mathematical reasoning, formal proof assistants such as Lean can be leveraged to verify reasoning outputs with machine‑checkable rigor, enabling use cases such as answer selection in test‑time scaling with K sampled candidate answers. However, employing Lean requires that LLM outputs, originally in natural language, first be formalized. Existing Lean‑based answer‑selection work uses an autoformalization model to generate a formal statement in Lean for each candidate answer independently, incurring a significant computational cost. We propose BASE, a base‑and‑edit pipeline that formalizes a single base candidate per problem and derives the remaining K‑1 statements by editing the answer expression in place. To facilitate this, we train a rewriter model LEANSCRIBE to localize the answer in the base formalization and generate a reusable edit function for the other K‑1 candidates. BASE simultaneously improves selection accuracy and reduces formalization cost ‑ a Pareto improvement that holds on all 12 (dataset, solver) configurations across four benchmarks and three solvers, cutting autoformalizer calls by about 5x at K=8, with the reduction expected to become larger as K grows. Code is available at https://github.com/ucr‑rai/base‑and‑edit.

PaperId: 304, https://arxiv.org/pdf/2606.15954.pdf GitHub

Abstract:
Agentic AI systems act through tools and sub‑agents, yet the controls meant to bound their financial and environmental cost still sit on dashboards evaluated beside or after execution. Green SARC applies the SARC governance‑by‑architecture framework ‑‑ four enforcement sites in the agent loop ‑‑ to FinOps and GreenOps, contributing the theory of what to enforce and how to predict it. We report four policy‑independent results. (i) The unconstrained "State Snowball" is Θ(n^2) in loop depth; on 3,000 real multi‑step plans (SWE‑rebench) it holds on 100%, with median curvature \hatc_2=216 exceeding the linear‑accretion prediction p/2=134 ‑‑ real plans accrete faster than the model. (ii) On real residuals the Normal‑σ gate under‑covers (92% at nominal 95%); split‑conformal calibration holds (95.2%). (iii) A soft Lagrangian penalty tuned to the budget in expectation breaches it on 91.5% of seeds; the architectural gate breaches 0%. (iv) Under binding budgets the gate's over‑budget incidence is 0% on synthetic and real (BurstGPT) arrivals. End‑to‑end token/USD/carbon savings (47‑‑55%) are real but policy‑dependent in magnitude ‑‑ set by a scope‑cap knob, not by gate rejections. The library is open‑source, dependency‑free, and ships a regeneration script for every cited number.

PaperId: 305, https://arxiv.org/pdf/2606.15906.pdf GitHub

Abstract:
Long‑document multimodal question answering requires a system to locate sparse evidence in long PDFs and integrate clues from text, tables, images, charts, and complex layouts. Existing RAG methods mostly rely on fixed Top‑k retrieval over text chunks or pages. Text retrieval can compress the context but often loses visual and layout information; page‑level visual retrieval preserves the original page, yet it also sends large irrelevant regions to the reader, leading to a static trade‑off among evidence coverage, noise, and inference cost. This paper proposes MAGE‑RAG, a multigranular adaptive graph evidence framework for long‑document multimodal QA. MAGE‑RAG uses page retrieval as the entry point for query‑time evidence construction. Offline, it builds an evidence graph with page nodes and element nodes, encoding containment, reading order, layout adjacency, section hierarchy, and semantic‑neighbor relations. At query time, an online evidence controller iteratively activates, opens, searches, and prunes evidence under explicit budgets. The resulting evidence subgraph is then rendered into structured multimodal reader input, allowing the LVLM to consume compact and relevant evidence within a limited context. On LongDocURL and MMLongBench‑Doc, we establish a unified comparison and analysis protocol covering Direct MLLM, Text RAG, Page‑level Visual RAG, and Graph/Agentic RAG. Experiments show that MAGE‑RAG achieves 52.75 overall accuracy on LongDocURL, and 53.26 accuracy with 51.19 F1 on MMLongBench‑Doc. Fine‑grained breakdowns, budget‑performance curves, ablations, and trace‑based analysis further show that query‑time evidence subgraph construction can balance dispersed evidence coverage with context‑noise control. Our code is available at https://github.com/laonuo2004/MAGE‑RAG.git.

PaperId: 306, https://arxiv.org/pdf/2606.15903.pdf GitHub

Abstract:
Where an LLM sits in an agent memory pipeline ‑‑ between the recall plane that retrieves stored facts (extensively benchmarked) and the control plane that mutates them via supersede, release, purge (largely untested) ‑‑ shapes which forgetting failure modes the system recovers. Comparing thirteen system configurations on a 385‑case adversarial surface, we observe three placement regimes with partly complementary coverage: deterministic primitives suffice for lexical/temporal categories but fail canonicalization (5% on identifier‑obfuscation, 0% on cross‑lingual); inscribe‑time LLM recovers canonicalization (100%) but cannot help intent‑aware deletion (0% on prefix‑collision and compound‑fact); a mutation‑time hook recovers intent‑aware deletion (78‑85%) and brightens nearly all categories simultaneously (91.7‑93.2% overall, 0.17 per 385‑case run, 2.3s/case mutation latency vs. 64‑191ms/case deterministic, recall path unchanged). We expose the trade‑off via ForgetEval, a 1000‑case templated suite plus a 385‑case adversarial layer (132 hand‑crafted + 253 LLM‑drafted oracle‑validated) scored by deterministic substring match, paired with a six‑method Adapter Protocol with honest N/A scoring that lets heterogeneous memory stores enter in 130 lines. Admission is corroborated by 10‑annotator IAA (Fleiss' kappa = 0.958) and a 77‑case external‑authored subset (four blind contributors) that replicates the canonicalization asymmetry and amplifies the joint‑placement lift (+27.8 pt). Production failures are predominantly forgetting failures rather than recall failures, yet existing benchmarks measure only recall. ForgetEval and all adapters are released under MIT.

PaperId: 307, https://arxiv.org/pdf/2606.15897.pdf GitHub

Abstract:
Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance. However, its standard formulation treats signals on structured spaces, such as fMRI data on brain graphs, as points in Euclidean space, overlooking the rich topological features of their domains. To address this, we introduce topological flow matching, a topology‑aware generalization of flow matching. We interpret flow matching as a framework for solving a degenerate Schrödinger bridge problem and inject topological information by augmenting the reference process with a Laplacian‑derived drift. This principled modification captures the structure of the underlying domain while preserving the desirable properties of flow matching: a stable, simulation‑free objective and deterministic sample paths. As a result, our framework serves as a drop‑in replacement for standard flow matching. We demonstrate its effectiveness on diverse structured datasets, including brain fMRIs, ocean currents, seismic events, and traffic flows.

PaperId: 308, https://arxiv.org/pdf/2606.15890.pdf GitHub

Abstract:
Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs). We introduce UrbanWell, a large‑scale benchmark designed to systematically evaluate the spatio‑temporal reasoning capabilities of MLLMs for urban wellbeing analytics through joint modeling of satellite and street view imagery. UrbanWell spans 38 cities across multiple years and includes diverse indicators covering (1) environmental conditions (CO_2, NO_2, PM2.5, and Normalized Difference Vegetation Index), (2) spatial accessibility (minimum distance to supermarkets and restaurants), (3) urban form (road length, road density, and land use), (4) urban vitality (population, economic activity diversity, and land use diversity), and (5) subjective perception attributes (e.g., safety, beauty, liveliness, wealth, and quietness). All indicators are aligned at grid level to enable standardized evaluation. Beyond static prediction, UrbanWell defines temporal reasoning tasks, including future value forecasting from historical observations and temporal trend classification. We benchmark 15 state‑of‑the‑art representative MLLMs in a zero‑shot setting, providing a comprehensive comparative evaluation across spatial and temporal dimensions. Experimental results indicate that while MLLMs capture salient spatial and perceptual cues, their performance varies substantially across heterogeneous urban indicators spanning environment and subjective perception. UrbanWell serves as a unified benchmark for evaluating multimodal spatial and temporal reasoning in urban wellbeing analytics, offering a standardized testbed for systematic assessment and future research on multimodal urban intelligence. Our codes and datasets are accessible via https://github.com/axin1301/UrbanWell‑Benchmark.

PaperId: 309, https://arxiv.org/pdf/2606.15888.pdf GitHub

Abstract:
Non‑verbal vocalizations (NVs), such as laughter, sighs, and coughs, are important acoustic cues for emotion and intent. Existing speech quality assessment methods typically focus on overall naturalness, while non‑verbal TTS evaluations mainly examine whether a target NV appears with the correct type and position. However, the perceptual quality of NV events themselves remains underexplored. To address this gap, we construct an NV‑MOS dataset containing outputs from multiple NV‑TTS systems and naturally occurring NV samples, with ratings collected from three acoustic experts on a perceptual quality scale. We further analyze audio‑capable multimodal large language models such as Gemini and find clear inconsistencies between their scores and expert ratings. These results suggest that general‑purpose multimodal models cannot reliably replace human judgments for NV quality assessment. We then propose NVMOS, to our knowledge the first model that can reliably predict the perceptual quality of NV events in speech. Experimental results show that, with a local NV‑event focusing module, NVMOS reaches expert‑level or stronger agreement with human MOS.

PaperId: 310, https://arxiv.org/pdf/2606.15880.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding. As AI‑generated images become realistic, semantic‑level inconsistencies alone are often insufficient for reliable detection. This motivates a critical question: whether MLLMs can achieve full‑spectrum forensic signal perception, i.e., capturing low‑level generator artifacts without sacrificing pre‑trained semantic knowledge. We further perform a layer‑wise analysis of forensic signal perception in MLLMs, showing that semantic information is primarily formed in the early‑to‑middle layers, whereas direct fine‑tuning for artifact learning disrupts these semantic representations. Based on this insight, we propose Deep Visual Residual MLLM (Deep‑VRM) to preserve early semantic processing while injecting artifact‑specific visual signals as a residual path into an intermediate layer, where they are fused with semantic token representations and propagated through subsequent trainable layers. This enables later layers to jointly model semantic reasoning and signal‑level forensic cues, and surprisingly, the model learns to adaptively leverage different levels of forensic signals depending on the input, achieving robust and generalizable detection performance. Extensive experiments show that our method achieves state‑of‑the‑art across most benchmarks. The code and data are available at https://github.com/KQL11/Deep‑VRM.

PaperId: 311, https://arxiv.org/pdf/2606.15821.pdf GitHub

Abstract:
Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, forming distinct model lineages. It remains unclear whether a fundamental behavioral link exists between the foundational LLMs and downstream variants. We investigate this question by quantifying head‑level context‑truthfulness scores. Across diverse LLM and MLLM lineages, including Vicuna‑, Qwen2.5‑, LLaMA2‑, and Mistral‑based models, we find that Truth Scores are strongly preserved within model families, even after instruction tuning or multimodal adaptation. We further show that this inheritance is consistent with attention‑head weight preservation, and that context‑truthful heads attend to query‑relevant evidence. Building on this finding, we propose TruthProbe, a soft‑gating strategy that amplifies context‑truthful heads while preserving other head contributions. TruthProbe improves contextual truthfulness on HaluEval and reduces multimodal hallucination on POPE and CHAIR, with base‑LLM Truth Scores transferring effectively to their fine‑tuned LLM and MLLM descendants. Code is available at https://github.com/miso‑choi/TruthProbe.

PaperId: 312, https://arxiv.org/pdf/2606.15819.pdf GitHub

Abstract:
The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high‑fidelity text‑to‑image synthesis, while heightening concerns over the safety alignment of generated content. Naive application of existing erasure techniques to VAR models causes catastrophic semantic collapse and visual artifacts, since they are predominantly designed for the homogeneous denoising steps of diffusion models. To address this foundational challenge, we first propose the Semantic Singularity Axiom, which posits that any target semantic concept embedded within a prompt is definitively locked at Scale‑0. Then rigorously validate this axiom through our proposed Incremental Semantic Saliency Analysis (ISSA),which also enable the community to transparently inspect the coarse‑to‑fine semantic injection process. Guided by this insight, we introduce the first scale‑aware concept erasure framework (SACE) for VAR models. By strictly confining interventions to the first scale, our approach couples an Entropy‑Regularized Erasure Objective to prevent high‑entropy sampling degeneration, alongside a restorative preservation loss to safely anchor the integrity of entangled benign priors. Extensive experiments demonstrate that our method achieves surgical concept erasure performance across various domains with minimal training overhead, timely and elegently resolute the critical safety vulnerabilities inherent in emerging VAR architectures. Code is available at: https://github.com/limerenceysy/SACEhttps://github.com/limerenceysy/SACE.

PaperId: 313, https://arxiv.org/pdf/2606.15796.pdf GitHub

Abstract:
Mechanistic interpretability seeks to explain neural network behavior by decomposing model computations into interpretable features and circuits. While transcoder‑based circuit tracing has recently enabled detailed causal analyses of large language models, multimodal diffusion transformers for image generation remain comparatively opaque. We still lack tools for understanding how semantic information propagates across denoising steps and how text and image representations interact within double‑stream MM‑DiT architectures. Existing methods provide only partial insight: attention maps expose a limited view of token interactions, while sparse autoencoders can discover interpretable features but do not directly reveal how these features are transformed and composed through nonlinear MLP layers. In this work, we extend transcoder‑based circuit tracing to multimodal diffusion transformers. We train timestep‑conditioned transcoders that faithfully approximate the input‑output behavior of MLP sublayers in FLUX.1[schnell]. By replacing MLPs with transcoders and linearizing the remaining computation, we obtain exact feature‑to‑feature attribution and recover compact, interpretable circuits. Empirically, our transcoders match or slightly outperform sparse autoencoders on the sparsity‑faithfulness tradeoff. The resulting circuits reveal mechanisms underlying attribute binding and cross‑stream semantic propagation, and provide causal explanations for systematic generation errors. Moreover, circuit‑guided interventions are substantially more precise and effective than standard SAE‑based steering. Our results demonstrate that transcoder‑based circuit analysis is feasible for state‑of‑the‑art diffusion transformers and provides a powerful framework for understanding and controlling multimodal generative models. The code is available at https://github.com/Artalmaz31/DifFRACT

PaperId: 314, https://arxiv.org/pdf/2606.15647.pdf GitHub

Abstract:
Foundation models have demonstrated impressive performance in enhancing healthcare efficiency across a wide range of medical applications. Nevertheless, their limited ability to perceive, understand, and interact with the physical world significantly constrains their effectiveness in real‑world clinical workflows, where safety‑critical decision‑making and physical execution are tightly coupled. Recently, embodied artificial intelligence (AI) has emerged as a promising physical‑interactive paradigm for intelligent healthcare, enabling agents to operate in complex medical environments. As research in this area rapidly expands, understanding how intelligent agents function as integrated, end‑to‑end systems in clinical environments becomes increasingly critical. However, existing surveys on medical embodied AI largely emphasize individual aspects or functional components, lacking a unified system‑level organization of the field. To support and consolidate recent advances, we systematically survey the core components of medical embodied AI, with a particular emphasis on the coordinated integration of perception, decision‑making, and action. We further review representative medical applications and relevant datasets, and we analyze the major challenges encountered in real‑world clinical practice. Finally, we discuss key directions for future research in this rapidly evolving field. The associated project can be found at https://github.com/VMVLab/Medical_Embodied_AI_Paper_List.

PaperId: 315, https://arxiv.org/pdf/2606.15611.pdf GitHub

Abstract:
Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology. To ease the high annotation cost of PET/CT segmentation, semi‑supervised learning (SSL) provides a practical and effective solution for developing deep models with limited labeled data. Recent developments in visual foundation models have demonstrated remarkable adaptability with improved efficiency. In this work, we propose a mutual distillation framework that seamlessly exploits both structural and functional foundation models, which act as modality‑specific generalists for distilling knowledge from structural CT and metabolic PET imaging. By bridging the gap between the task‑specific precision of student models and the segmentation priors of generalist foundation models, we propose MuDuo, a mutual distillation framework that synergistically leverages SAM‑Med3D for CT and SegAnyPET for PET to distill their knowledge into a lightweight student network. Our approach eliminates the need for manual prompts while maximizing the utility of unlabeled data for automatic segmentation, achieving state‑of‑the‑art performance on the AutoPET dataset with only 5 labeled cases. Our source code is available at https://github.com/Wu‑beining/MuDuo.

PaperId: 316, https://arxiv.org/pdf/2606.15589.pdf GitHub

Abstract:
For tool‑augmented language models, comparing natural‑language reasoning with code‑execution pipelines is difficult because the comparison changes both the intermediate representation and the execution mechanism. We separate these factors with an intermediate intervention: the model expresses its reasoning as executable code, and the language model simulates that code in context to produce an answer. On a 40‑task verifiable algorithmic benchmark, deterministic code execution outperforms natural‑language reasoning by +31.6pp. We observe that the intermediate intervention is not meaningfully different from natural‑language reasoning (+0.15pp). These results suggest that, in our evaluated setting, changing the intermediate representation alone does not explain the tool‑use advantage, providing evidence for the performance gains requiring reliable external execution. We formalize this intuition with a simple statistical decision‑theoretic model that characterizes when execution dominates end‑to‑end risk in our disentangled trace‑generation/execution regime. We validate our theory using a reconstruction intervention that leverages a proxy language model to infer natural‑language reasoning traces from code representations, recovering performance comparable to the original natural‑language reasoning pipeline. All experiments are at https://github.com/TerryTong‑Git/ToolProj.

PaperId: 317, https://arxiv.org/pdf/2606.15566.pdf GitHub

Abstract:
Qualitative coding is central to social science, but expert annotation is difficult to scale. LLMs offer a possible extension, yet require careful validation when the target construct is interpretive, theoretically loaded, and only indirectly expressed. We study this problem in a difficult case: detecting whether authors treat Bayesian models as descriptions of mental and neural mechanisms (realism) or as useful mathematical tools (instrumentalism). Our method combines a theory‑driven codebook, expert‑coded reference annotations, a diagnostic‑gated prompt‑optimization search yielding a shared zero‑shot prompt for three frontier LLMs (GPT‑5.1, Claude Sonnet 4.6, Gemini 3 Pro Preview), and multi‑rater reliability analysis. The final prompt achieved a held‑out combined reliability score of 0.76 (harmonic mean of ICC = 0.79 and α = 0.74), with all diagnostics satisfied. Deployed on 6,858 quotes from 210 articles, the three LLMs reached substantial quote‑level agreement (ICC = 0.80; α = 0.76; combined = 0.78) and near‑perfect article‑level rank stability (r = 0.96‑0.97 across rater pairs). The corpus was predominantly weakly realist, but article‑level stances were rarely uniform: only 1.4% of articles used a single band, while 59.5% spanned four or more. Low‑level perception/motor articles scored 8.8 Realism points higher than high‑level cognition articles (p < .001, d = 0.60), quantifying a long‑held qualitative intuition. We present this as an expert‑led case study; the framework is intended to generalize to similar theoretically demanding tasks, not to all qualitative analysis.

PaperId: 318, https://arxiv.org/pdf/2606.15563.pdf GitHub GitHub

Abstract:
AI systems increasingly delegate decisions to specialized models, evaluators, tools, and supervisory controllers. The central AI problem is no longer only model accuracy, but uncertainty‑aware governance: how much autonomy to grant, which evidence should calibrate trust, what performance ceiling a delegated AI system can sustain, and when human intervention becomes necessary. We propose the Minimum Sufficient Oversight Principle (MSO), a variational principle for principled autonomy delegation: minimize governance burden on the Fisher information manifold subject to a delivery constraint. The resulting Euler‑Lagrange solution yields a water‑filling allocation of governed delegation across the task space. Building on a revealed‑action governed delegation channel model, we prove a capacity theorem for stationary symbolwise review policies, derive a local first‑order approximation relating workflow complexity to quality degradation, and give a drift‑dominated autonomy‑time scaling law linking intervention timing to effective capacity, complexity, and drift. Within this framework, masking appears as a structural AI‑governance pathology: corrected performance can hide the competence signal needed to calibrate trust. Synthetic simulations and a semi‑real reconstructed workflow support design prescriptions including upstream‑first correction, sensitivity‑based intervention, and explicit feasibility checks before autonomy is expanded. The result is a computable framework for uncertainty, planning, and oversight in delegated AI systems. A companion Python package is available at https://github.com/crbazevedo/delegation‑lab.

PaperId: 319, https://arxiv.org/pdf/2606.15396.pdf GitHub

Abstract:
Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety guardrails excel in English or multilingual settings, they lack adaptation to Chinese‑specific regulatory policies, cultural context and linguistic nuances, failing to support fine‑grained risk classification for diverse deployment needs. In this paper, we introduce a 5‑macro, 31‑micro category fine‑grained risk taxonomy for Chinese scenarios, and build CHILLGuard: a dedicated Chinese LLM content safety guardrail. To address the critical scarcity of high‑quality annotated Chinese safety data, we propose a scalable multi‑stage data construction pipeline: we expand multi‑source corpus via retrieval‑augmented generation, generate implicit harmful samples through prompt engineering rewriting, and refine high‑quality data via multi‑model voting‑based label calibration. Based on this, we build CHILLGuardTrain, a large‑scale training set with 405,007 samples, and CHILLGuardTest, a rigorously curated annotated test set with 51,745 samples. We then train CHILLGuard on CHILLGuardTrain under a generator‑classifier collaborative framework via Model‑aware Direct Preference Optimization. Extensive experiments under multiple settings demonstrate the state‑of‑the‑art performance of CHILLGuard, e.g., a 15.92% improvement of F1 score over Qwen3Guard‑8B‑Strict on our benchmark. We will release our resources at https://github.com/cswbyu/CHILLGuard.

PaperId: 320, https://arxiv.org/pdf/2606.15390.pdf GitHub

Abstract:
LLM agents can improve without weight updates by accumulating natural‑language skills from experience, but current systems entrust every decision about which skills to keep and how to apply them to LLM judgment alone. We argue that this conflates two distinct roles: generating a skill from experience is a creative act that judgment handles well, while deciding whether that skill actually helps requires empirical evidence across many tasks. Measuring per‑skill causal contributions via randomized masking, we find that skill libraries exhibit pervasive causal heterogeneity: individual skills routinely help on some task types while hurting on others, yet their opposing effects cancel in aggregate, making them invisible to global curation methods. We propose ASSAY, a framework that separates generation from curation: it computes a per‑skill causal attribution on a small development set, restructures the library offline, and suppresses skills with negative predicted effect for each test task. Across seven base models spanning four providers and two benchmarks (AppWorld and tau‑bench), ASSAY consistently improves over prior skill‑curation approaches. On AppWorld's hardest split, DeepSeek‑V3 achieves 69.3% task‑goal completion (47.4% relative improvement), a new state of the art among all published methods including weight‑tuned approaches. On tau‑bench retail, GPT‑4.1 improves by 8.7% relative, advancing past o4‑mini, o1, and GPT‑4.5 on the public leaderboard without any weight modification. Ablation traces the dominant gain to per‑task masking, confirming that the bottleneck is matching skills to tasks at inference time, not removing bad skills globally. Code is available at https://github.com/aiming‑lab/assay.

PaperId: 321, https://arxiv.org/pdf/2606.15385.pdf GitHub GitHub

Abstract:
Reward hacking, where AI systems exploit misspecified objectives to achieve high reward without satisfying intended goals, remains a central challenge in AI safety. Yet most known instances have been discovered post hoc in frontier systems where controlled study is impractical. We adapt the AI Safety Gridworlds framework into a text‑based evaluation suite that reformulates classic reinforcement learning safety tasks for language‑based agents. Across frontier and mid‑scale models, we find that specification gaming emerges zero‑shot: models systematically achieve high observed reward while underperforming on hidden safety objectives, and even apparently safe behaviors can reflect misunderstanding rather than principled safety. Reinforcement learning does not correct these failures: direct reward optimization widens the gap between observed and hidden reward, as the model's initial competence causes it to lock into locally rewarding strategies before discovering safer alternatives. This pattern persists across model scales (1.5B‑‑14B) and is not resolved by finer credit assignment, exploration prompts, or entropy regularization. Our results show that reward hacking arises naturally when optimizing proxy objectives with capable language model agents and resists standard mitigations, suggesting that proxy‑reward failures in agentic settings may require approaches beyond standard exploration and credit‑assignment fixes. To facilitate reproducibility, the code for this work is available at \hrefhttps://github.com/asparius/verl‑agent‑safetyour public repository.

PaperId: 322, https://arxiv.org/pdf/2606.15349.pdf GitHub

Abstract:
Standardized examinations are typically treated as uniform syllabus coverage problems. We argue they are better understood as adversarial systems with stable latent cognitive structures diverging systematically from official syllabi. We introduce LearnOpt, which recovers this structure from historical question papers and generates personalized, time‑bounded study plans. Applied to nine years of NEET questions (2016‑2024, n=1,496), LearnOpt builds an exam knowledge graph from LLM‑tagged questions, extracts a five‑category latent skill distribution, and formulates study planning as a knapsack‑variant optimization over prerequisite‑aware subgraphs with Bayesian Knowledge Tracing. Central finding: NEET's latent skill distribution is stable within a syllabus regime (consecutive‑year KL divergence 0.004‑0.032 for 2016‑2021, non‑significant under permutation testing) but shifts significantly with NCERT's 2023 syllabus rationalization: pooling 2016‑2021 (n=1,072) vs 2023‑2024 (n=392) gives KL=0.040 (p=0.0005), with Elimination/Negation questions rising from ~20‑29% to ~31‑35%. Latent structure, while not permanently stationary, is piecewise stable, with shifts detectable and attributable to curricular events. Within either regime, subject predicts skill profile more strongly than year. An optimization evaluation, using one real and two synthetic mastery profiles, shows the skill‑weighted objective produces a modest but real reordering of recommended topics over a mastery‑conditioned frequency baseline. Applying the pipeline to JEE Advanced reveals a profile dominated by Multi‑concept Integration (80.9% vs. 33.3% for NEET), with a JEE‑vs‑NEET divergence (KL=0.505) exceeding NEET's largest cross‑subject divergence: exam tier shapes latent cognitive structure more than subject, which shapes it more than time within a regime. Code, knowledge graph, and annotated dataset are released publicly.

PaperId: 323, https://arxiv.org/pdf/2606.15300.pdf GitHub

Abstract:
Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real‑world development. Such environments typically involve both complex code and large‑scale data (i.e., file system). However, existing benchmarks usually evaluate code‑centric or data‑centric capabilities in isolation, leaving a clear gap with real development scenarios. In this paper, we bridge this gap by introducing CODA‑BENCH, the first benchmark to jointly evaluate code and data intelligence in a data‑intensive environment. We construct a data‑intensive Linux sandbox based on the Kaggle ecosystem (containing hundreds of datasets), where agents must actively explore complex file hierarchies to identify relevant resources and generate code for data‑driven analytical tasks. CODA‑BENCH comprises 1,009 tasks spanning 31 communities, with each task environment containing an average of 980 files, simulating realistic data scale and noise. Evaluations of advanced agents reveal that even top‑performing systems struggle to effectively integrate data discovery with code execution, achieving a success rate of only 61.1%. These results highlight a substantial gap in current agentic capabilities for data‑intensive tasks and point to promising directions for future research.

PaperId: 324, https://arxiv.org/pdf/2606.15284.pdf GitHub

Abstract:
Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. Yet existing approaches to universal PPG representation learning largely focus on signal‑level objectives and often overlook patient‑level health context, which limits generalization to complex clinical tasks and heterogeneous cohorts. To address this gap, we construct a large‑scale paired PPG‑EHR multimodal dataset by distilling fragmented medical histories and clinical records into cohesive, patient‑level electronic health records (EHR). Building on this resource, we propose Clinical Anchored Pretraining for PPG (CAP). During pretraining, CAP performs cross‑modal contrastive alignment that anchors PPG representations to patient‑level clinical semantics, guiding the encoder beyond waveform fitting toward modeling consistency in a patient's overall physiological state. During downstream adaptation, the pretrained PPG encoder provides clinically grounded representations that strengthen inductive bias and improve robustness and transferability. Experiments demonstrate that CAP consistently outperforms strong baselines on four diverse downstream tasks. CAP achieves a particularly large gain on respiratory rate prediction (up to +87.6% relative improvement over the state‑of‑the‑art baseline) and delivers an average relative +26.7% across all tasks. We further enhance the interpretability of our approach through comprehensive analyses, including ablations and multiple complementary visualizations of the learned representations. The code for our experiments is available at: https://github.com/gody123gody/CAP .

PaperId: 325, https://arxiv.org/pdf/2606.15273.pdf GitHub

Abstract:
Shapley value‑based feature attribution methods face challenges in scenarios involving complex feature interactions and causal relationships, even when a causal structure is provided. Existing methods typically adopt a node‑centric view, attributing importance solely to individual features. Consequently, they often fail to simultaneously capture the externality and exogenous influence of features, leading to unreasonable interpretations. To overcome these limitations, we propose a novel feature attribution method called DAG‑SHAP, which is based on edge intervention. DAG‑SHAP treats each feature edge as an individual attribution object, ensuring that both externality and exogenous contributions of features are appropriately captured. Additionally, we introduce an approximation method for efficiently computing DAG‑SHAP. Extensive experiments on both real and synthetic datasets validate the effectiveness of DAG‑SHAP. Our code is available at https://github.com/ZJU‑DIVER/DAG‑SHAP.

PaperId: 326, https://arxiv.org/pdf/2606.15258.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly capable of mathematical problem solving and can even assist with research‑level proofs, yet we still lack a scalable and reproducible way to measure step‑level reasoning in long proofs across diverse sources. This evaluation gap limits trustworthy AI assistance in proof‑certified scientific progress. Existing evaluations often emphasize final answers or rely on costly expert grading, while end‑to‑end proof generation remains open‑ended and hard to verify automatically. We introduce Mask‑Proof, a pipeline that turns real proofs into automatically checkable masked‑step tasks. It masks key formula steps, provides the necessary surrounding context, and evaluates model reconstructions with an LLM‑based equivalence judge using repeated votes for stability. The resulting Mask‑ProofBench contains 292 curated problems across diverse research areas. Experiments with 17 models show that reasoning‑enhanced models outperform standard models by 12% to 27%. Our evaluator achieves 96.8% agreement with expert annotators, enabling faithful, reproducible, and comparable measurement of step‑level mathematical reasoning. Benchmark, annotations, and code are available at https://github.com/weating/Mask‑Proof.

PaperId: 327, https://arxiv.org/pdf/2606.15242.pdf GitHub

Abstract:
Skills are becoming the capability layer through which LLM agents turn plans into actions, but their use introduces security risks such as data leakage, unauthorized operations, and tool misuse. Existing vetting usually evaluates each skill in isolation, while real agent tasks often invoke multiple skills in a shared execution context. This creates Skill Composition Risk (SCR): a skill that appears benign alone can become harmful when its outputs, trust signals, authorization cues, or side effects influence later invocations along an activated path. We introduce SCR‑Bench to evaluate this risk in controlled, sandboxed skill environments. Rather than relying only on textual intent or surface behavior, SCR‑Bench records downstream state changes and path‑level outcomes across composed skill executions. It contains three sub‑benchmarks: SCR‑CapFlow for capability‑flow composition, SCR‑TrustLift for trust‑transfer composition, and SCR‑AuthBlur for authorization‑confusion composition. Across SCR‑Bench, composed paths expose risks that are largely absent under isolated evaluation. In SCR‑CapFlow, attack success rate reaches 33.6 percent under composition, compared with near‑zero isolated baselines. In SCR‑TrustLift, attack success rate exceeds 96.5 percent on four of five backends. In SCR‑AuthBlur, the risky‑approval rate increases by 71.8 percent relative to the L0 isolated baseline under the L1 context setting. These results show that agent skill security should be assessed at the level of activated paths rather than isolated artifacts. SCR and SCR‑Bench provide a foundation for path‑aware risk evaluation and defense in LLM agent skill ecosystems. Benchmark: https://github.com/saint‑viperx/SCR_Bench.

PaperId: 328, https://arxiv.org/pdf/2606.15231.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open‑world scenarios. While recent multimodal deep search agents attempt to address this issue by utilizing external tools, the visual‑native search paradigm remains underexplored. Existing methods primarily rely on simple images with explicit semantics and text‑only evidence trajectories, limiting the agent's ability to perform multi‑hop, cross‑modal reasoning and search. To address these limitations, we propose Visual‑Seeker, a visual‑native multimodal deep search agent via active visual reasoning. Rather than treating vision as a static input, our agent actively attends to fine‑grained visual details, dynamically harvests visual evidence throughout the search process. To unlock its visual‑native potential, we design an active visual reasoning data pipeline and synthesize 5K high‑quality multimodal trajectories for model training. Extensive experiments demonstrate the state‑of‑the‑art performance across five challenging multimodal search benchmarks, even surpassing several proprietary models, validating robust visual‑native reasoning and search in real‑world web environments. The code and data can be accessed at: https://github.com/ZhengboZhang/Visual‑Seeker.

PaperId: 329, https://arxiv.org/pdf/2606.15209.pdf GitHub

Abstract:
Targeted advertising systems can pair audiences selected by advertisers with ad units that expose visible user actions. When an interaction remains linked to the campaign that elicited it, the advertiser may receive an observation tied to a user rather than only an aggregate report. We model that channel as a noisy oracle for attribute inference. The model separates targeting predicates, exposure, interaction, and disclosure. These boundaries capture the gap between eligibility and delivery, and the gap between interaction and advertiser visibility. We build a reproducible benchmark using synthetic populations calibrated with public data, each with known sensitive labels. A generated campaign semantics layer provides topic variants and response priors. The simulator generates the ground truth, event traces, disclosed observations, and metrics. The evaluation compares Bayesian, supervised, positive and unlabeled, and adaptive attacks under common campaign and disclosure definitions. The final evaluation uses four topic variants, seven simulator seeds, and two interaction settings. Repeated campaigns with identity exposure produce measurable but bounded inference signal. At 160 campaigns, Bayesian and supervised attacks reach about 0.64 AUC in the main setting and about 0.65 AUC in the higher interaction setting. Disclosure policy is the strongest control. Aggregate reporting removes the evaluated oracle input tied to users. Type filtering and randomized disclosure reduce the released signal. The result is a model, artifact, and defense evaluation method for privacy in interactive targeted advertising. The code is available at https://github.com/P‑HOW/Interactive‑Ad‑Oracle.

PaperId: 330, https://arxiv.org/pdf/2606.15107.pdf GitHub

Abstract:
Time series data in real‑world deployments is overwhelmingly irregular. Observations are asynchronous, missing values are informative rather than random, and sampling frequencies vary across sensors and operational windows. However, existing Time Series Question Answering (TSQA) benchmarks mostly assume regularly sampled inputs, leaving a fundamental gap in understanding how large language models (LLMs) and AI agents perform under irregular conditions. To bridge this gap, we introduce IRTS‑ToolBench, a benchmark of 1,700 questions spanning 10 task types across 13 domains. IRTS‑ToolBench is designed to be used independently by any researcher working on LLM‑based irregular time series analysis, providing standardized inputs and a reproducible evaluation protocol. Code can be found in https://github.com/SanhornC/IRTS‑ToolBench.

PaperId: 331, https://arxiv.org/pdf/2606.15057.pdf GitHub

Abstract:
Indirect prompt injection (IPI) is a major security threat to LLM‑powered agents. Thus, a growing body of work have proposed a variety of defensive approaches against IPI. These can be grouped into three broad categories: 1) prompt‑based (using prompting as a way to prevent agents from following malicious instructions), 2) detection‑based (identifying and filtering malicious instructions), and 3) system‑level (using systems insights, such as control and data isolation, for defense). However, commonly used benchmarks for evaluating defense, such as AgentDojo, are \emphinherently static, generating a fixed distribution of IPI attacks. Consequently, static benchmarks do not usefully evaluate defense robustness to adaptive threats. We address this issue by developing AutoDojo, an adaptive extension of AgentDojo that optimizes IPI against a given defense. Using AutoDojo against state‑of‑the‑art IPI defenses across three task suites and five target models, we make two key observations. First, many defenses offer only limited protection: a cheap, black‑box adaptive attack using a frontier LLM to iteratively optimize the injection raises attack success rate (ASR) well above the level achieved by static injections against nearly all evaluated defenses. Against a filter that reduces static ASR to 0%, AutoDojo recovers 28% overall and 64% on action‑open tasks. Second, for prompt‑level and filter‑based defenses, ASR is substantially higher on \emphaction‑open tasks ‑‑ where the user's request delegates the action itself to attacker‑controlled content ‑‑ than on precisely specified tasks. This is a structural limit: on such tasks the injection can pose as ordinary data rather than an explicit instruction, bypassing defenses that rely on detecting instruction‑like text. AutoDojo is publicly available at https://github.com/xhOwenMa/AutoDojo.

PaperId: 332, https://arxiv.org/pdf/2606.14997.pdf GitHub

Abstract:
Memory formation is fundamental to intelligence, yet whether deep neural networks preserve identifiable memory traces analogous to biological memory units remains an open question. This work introduces a geometric framework to identify such "AI engrams" by formalizing the neuroscientific criteria of specificity, reactivation, sufficiency, and necessity into a constrained inverse problem. We derive a closed‑form estimator that isolates individual memory traces from globally entangled parameters, and show that this biologically‑derived solution corresponds to a natural gradient update on the parameter manifold. AI engrams enable surgical manipulation of learned knowledge: any subset of memories can be composed or erased through linear arithmetic, without iterative optimization. Experiments ranging from simple MLPs to LLMs demonstrate the causal validity and substantial scalability of AI engrams. Together, these results bridge theories of biological memory and artificial representation learning and offer geometric insight into how deep networks simultaneously support functional specificity within distributed storage.

PaperId: 333, https://arxiv.org/pdf/2606.14981.pdf GitHub

Abstract:
Inference‑time steering adapts pre‑trained generative robot policies during deployment by verifying candidate actions before execution. While prior methods typically perform this verification only with visual observations, vision alone is often insufficient for contact‑rich manipulation, where success depends on both global task progress and subtle local interactions such as contact force. We introduce ViTaL, a visuo‑tactile inference‑time steering framework that formulates multimodal guidance as a bi‑level optimization problem. At the high level, visual sampling‑and‑verification performs long‑horizon mode selection, deciding what behavior the robot should execute. At the low level, tactile‑guided diffusion editing refines the selected action sequence over a shorter horizon to satisfy local contact requirements. To support outcome‑based steering, ViTaL learns a visuo‑tactile latent world model and employs semantically aligned visual and tactile verifiers, including a novel text‑conditioned tactile reward that scores predicted tactile futures directly in latent space. Across three real‑world contact‑rich manipulation tasks, ViTaL improves overall success by 51% over the base policy, outperforms unimodal steering by at least 33%, and exceeds naive multimodal fusion by at least 20%. Website: https://yilin‑wu98.github.io/vital_website.

PaperId: 334, https://arxiv.org/pdf/2606.14971.pdf GitHub

Abstract:
While large and diverse datasets have driven recent advances in large models, identifying the optimal data mixture for pre‑training and post‑training remains a significant open problem. We address this challenge with FASTMIX, a novel framework that automates data mixture discovery while training only a single proxy model. Instead of relying on predefined heuristics or resource‑intensive simulations, FASTMIX jointly optimizes mixture coefficients and model parameters, substantially improving efficiency and scalability over prior approaches. At the core of FASTMIX is a reformulation of mixture selection as a bilevel optimization problem. Under this reformulation, we show that optimizing mixture ratios is mathematically equivalent to assigning per‑source loss weights under uniform source sampling. This embeds the mixture coefficients directly into the differentiable iterative optimization objective, enabling efficient, gradient‑based optimization of both mixture and model. To solve the optimization problem, FASTMIX implements an approximate iterative optimization procedure, alternating between (i) updating model parameters on data sampled according to current mixture ratios (inner loop) and (ii) updating mixture ratios based on validation feedback (outer loop). Across pre‑ and post‑training, FASTMIX outperforms baselines while drastically reducing search cost. Code (https://github.com/hrtan/fastmix)

PaperId: 335, https://arxiv.org/pdf/2606.14867.pdf GitHub

Abstract:
Proof autoformalization aims to translate a mathematical informal proof written in natural language into a formal proof in a formal language such as Lean~4. Several works have developed LLM‑based models for proof autoformalization. However, existing evaluations have typically focused on translating well‑formed informal proofs from curated datasets. We argue that a robust proof autoformalizer must remain faithful even for informal proofs that diverge from these idealized ones, and we present the first study on the robustness of proof autoformalization models. We formulate two categories of perturbations and evaluate robustness under each: a global perturbation paraphrases the informal proof in a different style, under which the formalization should remain consistent; a local perturbation alters a value, symbol, or proof step, possibly in a counterfactual way, and a robust formalization should faithfully reflect the perturbation rather than reverting to the original one or inferring a different one on its own. We build a benchmark with both perturbations on miniF2F and MATH‑500, and automatically measure how stable a proof autoformalization's correctness is under global perturbations and how faithfully its output reflects local perturbations. We evaluate seven recent models, all of which are sensitive to global perturbations and mostly fail to remain faithful under local perturbations. Code and data are available via https://github.com/ucr‑rai/robust‑proof‑autoformalization.

PaperId: 336, https://arxiv.org/pdf/2606.14773.pdf GitHub

Abstract:
We present Double‑Helix Vision (DH), a geometry‑based visual sampler that compresses 2D images into compact 1D signals using paired golden‑ratio‑inspired spiral trajectories. Rather than processing every pixel uniformly, DH employs two phase‑shifted helices (Alpha and Beta, offset by 180 degrees) to sample the image with biologically‑inspired foveation: high density at the center, sparse coverage at the periphery. At 4K resolution, DH achieves a 1,433x compression ratio (99.93% reduction) while preserving the geometric structure of the scene. The full perception pipeline ‑‑ including spatial mapping, temporal collision detection, and intra‑frame structural disparity estimation ‑‑ runs in 0.52 ms at 1080p on CPU‑only hardware, with no neural network dependencies. On CIFAR‑10 at extreme sampling budgets (K=128 points per helix), DH achieves a +6.03% accuracy gain over uniform random sampling. A JSON‑serializable Robotics API is provided, delivering sub‑millisecond spatial perception reports in 2.7 KB packets. Code and benchmarks are available under the MIT License.

PaperId: 337, https://arxiv.org/pdf/2606.14758.pdf GitHub

Abstract:
As Vision‑Language Models are increasingly deployed in safety‑critical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vision‑Language Models often suffer from semantic hallucination, where attribution maps highlight prominent image regions even when prompted with incorrect text descriptions (e.g., highlighting a dog when prompted ``cat''). Although this problem is widespread, a formal mathematical analysis of XAI methods and CLIP embeddings is largely missing in the literature. We demonstrate that this phenomenon is not specific to a single architecture but is a fundamental consequence of Linear Semantic Leakage in high‑dimensional embedding spaces. We propose a unified theoretical framework, Linear Semantic Attribution (LSA), which generalizes across discriminative methods. We introduce OSP, a geometric intervention that utilizes the residual property of OMP to disentangle unique semantic signals from shared concepts. We prove theoretically and demonstrate empirically that OSP minimizes hallucination by orthogonalizing the query vector against distractor concepts, rendering the attribution model blind to shared features while preserving fidelity for correct prompts. Our code is available at: https://github.com/emirhanbilgic/Orthogonal‑Semantic‑Projection

PaperId: 338, https://arxiv.org/pdf/2606.14755.pdf GitHub

Abstract:
Texture segmentation stresses foundation segmentation because meaningful regions are defined by material or repeated appearance rather than object identity. Segment Anything Models (SAMs) often fail by default on such texture‑defined partitions, but this failure is ambiguous: the texture evidence may be absent, missing from the proposal bank, or present but selected or assembled incorrectly by an object‑centric readout. We ask what texture‑relevant evidence is already preserved in frozen SAM before adaptation. We study two frozen evidence spaces: multiscale features, probed with a minimal clustering readout, and the automatic proposal bank, treated as evidence for a supervised consolidation readout. SAM is frozen throughout; we do not fine‑tune the backbone or retrain the proposal generator. Across RWTD, STLD, an ADE20K‑selected refined‑crop complement, and a ControlNet‑stitched PTD bridge archive, frozen SAM is not a texture segmenter by default, but its failures are not simple texture blindness. Coarse frozen features preserve texture organization, and proposal banks often contain texture‑aligned masks or fragments. Natural scenes more often require assembly and commitment over fragments, while cleaner synthetic cases more often reduce to selecting an already coherent proposal. Default mask failure should therefore be decomposed into representation evidence, proposal‑bank support, readout mismatch, and commitment failure.

PaperId: 339, https://arxiv.org/pdf/2606.14754.pdf GitHub GitHub

Abstract:
Images can be segmented based on visual cues (i.e., texture segmentation) or into objects (i.e., semantic segmentation). We propose a new category of sub‑semantic image segmentation that blurs the line between the two. In sub‑semantic image segmentation, language is not used to name whole objects. Instead, it is used to partition an image into stable appearance patterns that can be described by language. To do that, we couple a general‑purpose vision‑language model to SAM 3, a promptable segmentation backbone whose native text pathway can ground rich descriptions into masks. Simple coupling fails for a number of reasons that we identify in the paper, and we overcome them by introducing DETECTURE that resolves three concrete failure modes ‑‑ language leakage between texture regions, prompt competition inside the segmentation backbone, and semantic distortion at the language‑to‑mask interface. Since there is no dataset of sub‑semantic image segmentation, we introduce one, termed TextureADE. The new dataset is derived from the ADE20K dataset using a system we designed. We compare DETECTURE to a number of baselines and find that it achieves the strongest performance on several datasets using different metrics. Code is available at https://github.com/Scientific‑Computing‑Lab/TextureDetecture.

PaperId: 340, https://arxiv.org/pdf/2606.14752.pdf GitHub

Abstract:
Modern Vision‑Language‑Action (VLA) models must bridge pretrained vision‑language reasoning and precise continuous robot control. Existing action tokenizers discretize actions primarily for reconstruction, producing codes that preserve motion geometry but provide only weak semantic supervision to the backbone. We therefore formulate action tokenization not as mere compression, but as semantic interface learning between multimodal reasoning and executable control. To this end, we introduce X‑Tokenizer, a lightweight encoder‑Semantic Residual Quantization (SRQ)‑decoder architecture that provides a shared action interface across diverse robotic arm embodiments. Its key component, SRQ, imposes an asymmetric structure on residual vector quantization: the first level is trained with Masked Action Modeling (MAM) to form a discrete action language that captures coarse motion intent, while deeper levels remain reconstruction‑oriented residuals that preserve fine‑grained details. To further align action tokens with multimodal semantics, X‑Tokenizer is pretrained with contrastive alignment to the representation space of a pretrained foundation model and with next‑frame vision‑language feature prediction. Pretrained on 2.4M trajectories (2.0B action frames), a single frozen X‑Tokenizer plugs into a mixed discrete‑continuous VLA as a representation‑shaping supervision signal. X‑Tokenizer achieves top real‑world aggregate and strong RoboTwin 2.0 simulation results. Outperforming FAST in multimodal grounding (+13.5%) and long‑horizon tasks (+8.25), it shows that action tokenizers serve as semantic interfaces for VLA pretraining beyond mere action compression.

PaperId: 341, https://arxiv.org/pdf/2606.14732.pdf GitHub

Abstract:
Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate. We study this stability‑motion trade‑off in fixed‑camera long‑horizon nature video generation, where the two failure modes can be more clearly separated than in moving‑camera settings. We propose Steady‑Forcing, a memory and training framework combining a persistent visual anchor (V‑Sink), an exponential moving‑average motion memory (EMA‑Sink), block‑relative temporal encoding, periodic cache purification, and distillation from a Wan2.1‑14B teacher with motion‑rewarded priors under task‑focused configurations. Together, these components are designed to preserve background identity while sustaining visually plausible fluid dynamics over multi‑minute autoregressive rollouts. Evaluations across seven baselines show that Steady‑Forcing improves long horizon background consistency and imaging quality, while a blind user study indicates stronger perceived stability and motion continuity. The benchmark evaluation further suggest that generic VBench aggregate scores under‑penalize fixed‑camera artifacts as well as rewarding drift‑induced optical flow as Dynamic Degree while not directly penalizing texture hardening or flow stagnation ‑ motivating future task‑specific benchmarks for static‑camera nature‑flow evaluation. Project page: https://minar09.github.io/steadyforcing/

PaperId: 342, https://arxiv.org/pdf/2606.14697.pdf GitHub GitHub

Abstract:
Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations originate within the reasoning process. We find that hallucination sources vary across samples: errors may arise from visual misrecognition, incorrect medical knowledge recall, or flawed reasoning integration. To enable source‑level hallucination diagnosis, we introduce ClinHallu, a benchmark for stage‑wise hallucination diagnosis in medical MLLM reasoning. ClinHallu contains 7,031 validated instances, where each instance is augmented with a structured reasoning trace decomposed into Visual Recognition, Knowledge Recall, and Reasoning Integration. We also use stage‑replacement interventions to measure how correcting specific stages affects the final answer. Beyond evaluation, we show that trace‑supervised fine‑tuning reduces stage‑wise hallucinations. ClinHallu provides a fine‑grained hallucination testbed for diagnosing and mitigating reasoning failures in medical MLLMs. The benchmark is publicly available at https://github.com/alibaba‑damo‑academy/ClinHallu.

PaperId: 343, https://arxiv.org/pdf/2606.14589.pdf GitHub

Abstract:
LLM agent systems increasingly run as long‑lived autonomous runtimes: scheduling jobs, calling tools, maintaining memory, and pushing results to humans. We present a longitudinal study of silent failures in one such system: a personal‑assistant agent runtime in continuous production since March 2026, with roughly 40 scheduled jobs, 8 LLM providers, a tool‑governance proxy, and a knowledge‑base memory plane, defended by 4,286 unit tests and 827 governance checks. Over eight weeks we documented 22 incidents with full root‑cause postmortems, in which one meta‑pattern ‑‑ a failure whose error signal never reaches a human in actionable form ‑‑ manifested at least 28 times. We derive a five‑class, mechanism‑oriented taxonomy: (A) environment and platform quirks, (B) design‑assumption mismatches, (C) error swallowing and dilution, (D) chained hallucination and fabrication, (E) operational omission and forensic blind spots. Class D is unique to LLM systems and the most dangerous: the system does not merely fail to report an error ‑‑ the LLM transforms it into fluent, plausible narrative delivered to the user. We term this fail‑plausible: gray failure's differential observability escalated ‑‑ the observer is not just blind, it is convincingly lied to by the failure itself. Three findings: about 70% of silent failures were caught by human user‑view observation, not tests or audits; a retrospective audit of 15 incidents found 0% ex‑ante prevention but 87% regression blocking ‑‑ audits are regression engines, not prediction engines; incident latency (13 hours to 60 days) tracks failure mechanism, not code complexity ‑‑ the longest‑lived failures lived in the seams between components, where no test runs. We describe the resulting defense framework and distill design principles for agent systems whose failures are loud, attributable, and boring. All postmortems and artifacts are public.

PaperId: 344, https://arxiv.org/pdf/2606.14571.pdf GitHub

Abstract:
A central role of personal‑agent memory is to turn stored information and prior interactions into future‑oriented assistance. In daily use, useful cues come from what the agent observes and how the user interacts with the agent, and the agent must carry them forward from the current request to similar future tasks. Existing memory benchmarks usually test dialogue recall or task improvement in isolation, leaving the trajectory from streaming observations to later assistance largely untested. We introduce StreamMemBench, a streaming benchmark that constructs a two‑step task sequence around each evidence anchor from EgoLife egocentric streams. The initial task tests evidence use, while the follow‑up task tests whether feedback and interaction experience are reused. Four metrics diagnose evidence recall, initial evidence use, feedback incorporation, and follow‑up reuse. Experiments with eight memory systems across two backbones show that current systems often fail to use observed evidence or turn feedback into reliable follow‑up behavior, even when evidence is stored or feedback is incorporated locally. StreamMemBench is publicly available at https://github.com/landian60/StreamMemBench.

PaperId: 345, https://arxiv.org/pdf/2606.14551.pdf GitHub

Abstract:
Robots under autonomous operation may require decisions based on evidence that is no longer visible. We study delayed‑evidence tasks, where an early cue disappears before a later decision point, so visually similar observations can require different actions. In these settings, the current observation is not a sufficient state for control. We introduce TRAjectory‑routed Causal Evidence (TRACE), a memory framework for visuomotor imitation policies. TRACE stores task‑relevant visual and robot‑state evidence, such as object identity, target choice, or route‑dependent state, in a fixed‑size latent memory that remains bounded over long episodes. Instead of indexing memory by raw time or manually provided task labels, TRACE uses path signatures: compact, order‑sensitive features of the executed robot‑state trajectory. These signatures do not store the visual cue itself; rather, they provide trajectory‑conditioned keys for writing and retrieving the evidence stored when the cue was visible. When the robot later reaches an ambiguous observation, the policy conditions on TRACE memory to recover the missing context and choose the correct branch. TRACE attaches through lightweight adapters to policies, without changing the policy backbone, action head, or imitation objective. Across real‑world long‑horizon manipulation tasks with visually ambiguous branch points, TRACE improves branch selection and task success over alternative baselines, including short‑history and recurrent memory. Project page: https://jeong‑zju.github.io/trace

PaperId: 346, https://arxiv.org/pdf/2606.14466.pdf GitHub

Abstract:
This paper investigates the fragility of post‑hoc explanation methods in audio deepfake detection. While previous work on explanation manipulation focused on images using standard L_p metrics, we introduce a psychoacoustic framework that optimizes inaudible perturbations to decouple model attributions from final classifications. We evaluate this vulnerability across state‑of‑the‑art architectures under strict prediction‑preserving constraints. By evaluating the manipulation cost through domain‑specific perceptual audio quality metrics alongside explanation alignment criteria, our framework demonstrates that an adversary can systematically distort automated explanation heatmaps while preserving the predicted deepfake label. Full code available at: https://github.com/cncPomper/Audio‑XAI

PaperId: 347, https://arxiv.org/pdf/2606.14284.pdf GitHub

Abstract:
Time series prototype learning is fundamentally challenged by observational ambiguity. Discrete architectures fail to resolve this, as they lack the capacity to decouple stochastic noise from continuous dynamics. Furthermore, rigid closed‑set assumptions fail to capture unseen diversity. To address these limitations, we propose a hierarchical ordinary differential equation clustering network, which utilizes neural ordinary differential equation to model latent state evolution as a continuous integral curve. This formulation enforces temporal continuity to effectively disentangle smooth feature trends from stochastic noise, while our adaptive hierarchical mechanism autonomously determines the appropriate number of prototypes without rigid prior constraints. Validated on the early link failure detection task with irregularly sampled time series, the proposed method effectively extracts underlying physical prototypes, thereby enabling robust failure detection. Our code is available at https://github.com/NJ‑LNN/Hierarchical‑ODE.

PaperId: 348, https://arxiv.org/pdf/2606.14270.pdf GitHub

Abstract:
Fall recovery is critical for autonomous legged locomotion. Existing methods have demonstrated that some legged robots, such as humanoids and quadrupeds, are capable of fall recovery from diverse postures by utilizing arms or coordinating multi‑legs to generate support forces. Without arms or other legs to provide supportive assistance, a bipedal‑wheeled robot must rely solely on the actuation of its legs, making recovery particularly difficult. To address this, we introduce FTSR (Force‑guided Teacher‑student framework with Stage‑wise Rewards). The force‑guided method constructs an external auxiliary force during simulation training that correlates directly with the robot's real‑time height, explicitly formulating this force as an optimizable constraint. Through constrained reinforcement learning, the policy is guided toward reducing force dependency gradually and increasing the body height, developing internal recovery strategies despite having no arms for support. Height‑progressive stage‑Wise rewards progressively structure posture stabilization during recovery and transition to sustained locomotion, integrated with teacher‑student architecture distilling privileged knowledge of force effects and recovery dynamics. After simulation training, the policy is deployed on a physical armless bipedal‑wheeled robot and extensively evaluated. Experiments confirm robust and reliable fall recovery under diverse challenging conditions, demonstrating strong environmental adaptability and motion robustness, while maintaining full post‑recovery motion capability. The framework also generalizes effectively to a high‑DOF humanoid, confirming its practical generalizability. The project page is available at https://2350575870.github.io/force‑guided.github.io/

PaperId: 349, https://arxiv.org/pdf/2606.14240.pdf GitHub

Abstract:
Affordance reasoning, the inference of an object's action possibilities from its physical properties (e.g., shape and material), is fundamental to human physical understanding and increasingly critical for Large Language Models (LLMs). However, existing affordance benchmarks largely expose explicit object identities in the evaluation setup, allowing models to rely on memorized object‑affordance mappings rather than reasoning over physical properties. To address this gap, we introduce Affordance20Q, a novel affordance reasoning benchmark formulated as a 20‑Questions game without exposing the object's identity. In each game, the model identifies a hidden object's affordance from a candidate set by asking yes/no questions about its physical properties. Affordance20Q comprises 1,009 games over 454 objects and 59 affordances, all manually filtered, refined, and annotated. We conduct comprehensive experiments with 15 state‑of‑the‑art LLMs and find a substantial gap (~20 points) compared to human performance. A KL‑based information‑gain (IG) analysis further shows that models fail to ask discriminating questions as the game progresses. To close the gap, we develop KB‑Anchored Rule Induction (KARI), a pipeline based on LLMs that generates affordance rules grounded in evidence from knowledge bases (KBs). KARI improves open‑source LLMs by up to 15.2 points, while the limited coverage of KBs hinders further gains. We release all our code and data at https://github.com/1171‑jpg/Affordance20Q.git

PaperId: 350, https://arxiv.org/pdf/2606.14210.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly deployed in privacy‑sensitive domains, where users must balance the risk of data exposure through external APIs against the high computational cost of local deployment. Split learning has therefore emerged as a promising paradigm for LLM fine‑tuning and inference under limited local resources. However, it introduces new privacy risks. Prior work primarily studies leakage of private input prompts, typically via inversion attacks on intermediate representations, while the potential for sensitive information leakage through generative response outputs remains largely unexplored. In this work, we unveil novel vulnerabilities of Split‑LLM by presenting Patched Model Inversion with Dual‑Sided Initialization (PIDI), a two‑stage attack that simultaneously targets both private input prompts and output responses in Split‑LLM settings. It combines dual‑sided initialization with a patched inversion strategy to tackle long sequences, substantially outperforming prior inversion methods. To counter threats from both sides, we further propose the Adapter‑based DualGuard with Mutual Information Defense (ADMI), which integrates an adapter‑based local warmup strategy and mutual information regularization to provide a strong empirical privacy protection with minimal impact on task performance. Extensive experiments across diverse tasks and models demonstrate that ADMI effectively defends against PIDI and other state‑of‑the‑art inversion attacks. Our code is publicly available at https://github.com/FLAIR‑THU/VFLAIR‑LLM.

PaperId: 351, https://arxiv.org/pdf/2606.14199.pdf GitHub

Abstract:
Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness‑driven post‑training pulls them toward a homogeneous, overly agreeable assistant register, creating a behavioral Sim2Real gap. We present OdysSim, the largest open systematic investigation of behavioral foundation models, i.e., models trained to simulate human behavior at scale. We propose SOUL, a taxonomy of five capability axes (CONV, SS, COG, ROLE, EVAL) that unifies 62 datasets and 23 benchmark tasks under one framework. Specifically, we curate the OdysSim corpus (21.4M interactions, 10B tokens, retrofitted with back‑generated social contexts), construct the SOUL‑Index benchmark, and develop an end‑to‑end training recipe combining midtraining, task‑specific RL, and expert distillation. The resulting open 8B OSim model ranks first or tied‑first on 8 of 23 tasks, outperforming any individual frontier model by this count, with the strongest gains on conversational and social tasks. Its outputs are also more human‑like in length, formatting, and word choice, and it transfers zero‑shot to out‑of‑distribution user simulation on τ‑bench, nearly matching real users on reaction alignment (93.2 vs. 93.5). We further show that LLM‑as‑judge RL induces reward‑hacking patterns, and that our detectors can mitigate them during post‑training. Together, our findings suggest that behavioral foundation models require rethinking the LLM training paradigm. We release all artifacts to support future research.

PaperId: 352, https://arxiv.org/pdf/2606.14125.pdf GitHub

Abstract:
Inversion‑based image editing offers flexible and training‑free control but still struggles with inversion accuracy and the trade‑off between editing fidelity and background preservation. While recent methods improve inversion formulations or attention interactions, the role of textual conditioning in shaping diffusion dynamics and editing behavior remains underexplored. We show both empirically and theoretically that the precision of textual conditioning influences inversion stability by modulating the geometry of the diffusion velocity field, while also affecting the consistency of cross‑branch attention during editing. These effects directly impact background preservation and semantic fidelity. Building on this analysis, we propose SimEdit, a conditioning‑aware framework with two complementary components: (a) conditioning refinement, which constructs conditioning signals with improved semantic precision and structural alignment to facilitate stable inversion and consistent attention manipulation, and (b) token‑wise cross‑branch attention control, which separates edit‑relevant and structure‑preserving components and modulates them asymmetrically during attention manipulation. Extensive experiments on PIE‑Bench demonstrate that SimEdit consistently improves both inversion reconstruction quality and editing performance over previous attention‑manipulation approaches. Our code is available at https://github.com/zju‑pi/SimEdit.

PaperId: 353, https://arxiv.org/pdf/2606.14123.pdf GitHub

Abstract:
Deployed knowledge‑tracing models are typically frozen after training, yet systematic per‑item logit bias arises, from limited per‑item expressivity in backbone architectures and from post‑deployment shifts in item properties, degrading prediction quality. Global post‑hoc calibrators such as Platt scaling, temperature scaling, and isotonic regression improve probability estimates but leave discriminative ability, as measured by AUC, unchanged. This AUC invariance is a structural consequence of monotone score‑only transforms; recovering the stranded discrimination requires conditioning on item identity. We propose SLC (State‑space Logit Correction), which converts binary observations to Gaussian pseudo‑observations via Laplace/IRLS, applies empirical‑Bayes shrinkage through a Kalman smoother, and fits an offset‑Platt link. The state‑space formulation also yields a detectability bound that characterizes the Bernoulli information floor, explaining why temporal tracking provides no benefit at current data densities. Across four datasets, five backbones, and three seeds, SLC improves AUC on all four datasets and NLL on three, with the advantage concentrating on sparse items. Cross‑domain controls suggest that the same phenomenon can arise beyond education when the deployed backbone leaves entity‑level bias.

PaperId: 354, https://arxiv.org/pdf/2606.14120.pdf GitHub

Abstract:
Auditory attention decoding (AAD) aims to infer the attended speaker from neural responses in multi‑speaker acoustic environments and is a key problem for neuro‑steered hearing systems. Although recent studies have achieved encouraging progress, existing AAD models still do not fully exploit frequency domain electroencephalography (EEG) information. In particular, most approaches introduce multi‑band information through handcrafted feature extraction or direct cross‑band feature concatenation, which mainly exploit frequency information at a shallow level and may overlook band‑specific patterns and cross‑band interactions. To address these limitations, this paper proposes FAConformer, a frequency‑aware CNN‑Transformer framework for AAD that explicitly integrates band‑specific encoding and adaptive cross‑band interaction. Specifically, FAConformer first decomposes EEG signals into multiple frequency bands and assigns each band to an independent CNN‑Transformer encoder for band‑specific modeling. The resulting band‑wise features are then adaptively fused by a carefully designed frequency‑aware attention (FAA) module that models cross‑band dependencies by treating band‑wise features as tokens. Further, band‑wise auxiliary supervision (BAS) is introduced to prevent weakly contributing branches from being under‑optimized during joint training. In this way, FAConformer performs frequency‑aware modeling that more effectively exploits frequency domain information. Extensive experiments on two public AAD datasets with three decision‑window lengths demonstrated that FAConformer consistently outperformed 12 competitive baselines, surpassing the current state‑of‑the‑art model by 4.9%. Further analyses of band importance, ablation, and parameter sensitivity verify the effectiveness, robustness, and interpretability of the proposed framework. Code is available at https://github.com/wzwvv/FAConformer.

PaperId: 355, https://arxiv.org/pdf/2606.14094.pdf GitHub

Abstract:
Conventional RGB cameras have been widely used in multi‑object tracking due to their ability to capture rich appearance and semantic information. However, their performance is often degraded under complex real‑world challenges, such as motion blur, low illumination, and overexposure. Bio‑inspired event cameras offer high temporal resolution and high dynamic range, providing complementary cues under extreme scenarios. Nevertheless, RGB‑event multi‑object tracking remains underexplored due to the lack of large‑scale and well‑annotated datasets. To address this issue, we propose FEMOT, a large‑scale RGB‑event multi‑object tracking dataset that covers diverse real‑world scenarios and 14 challenging attributes. With both RGB and event data as well as high‑quality annotations, FEMOT provides a reliable platform for systematically evaluating RGB‑event multi‑object tracking methods. Based on FEMOT, we retrain and evaluate over ten strong trackers, thereby establishing a comprehensive benchmark for future research. Furthermore, we propose FEMOTR, a multimodal tracking framework that decouples RGB and event features and fuses them in the frequency domain, thereby effectively exploiting their complementary characteristics for robust object localization and identity association. Extensive experiments on FEMOT and DSEC‑MOT datasets demonstrate the effectiveness of the proposed method. The source code and benchmark dataset have been released on https://github.com/Event‑AHU/FEMOT.

PaperId: 356, https://arxiv.org/pdf/2606.14027.pdf GitHub

Abstract:
Agentic browsers integrate autonomous AI agents into web browsers, enabling users to accomplish web tasks through natural‑language instructions. The same‑origin policy (SOP) is a fundamental browser security mechanism that prevents unauthorized automated cross‑origin data flows induced by scripts. However, whether SOP remains effective in agentic browsers is an open question that has not been systematically studied. In this work, we bridge this gap. We first observe that an agentic browser can itself serve as an automated channel for cross‑origin data flows, potentially leading to SOP violations. To investigate this phenomenon, we construct SOPBench, a benchmark for evaluating SOP violations in agentic browsers. Our evaluation shows that existing agentic browsers frequently violate SOP, both in benign settings and under attacks. To address this problem, we propose SOPGuard, an SOP enforcement mechanism tailored to agentic browsers. We implement SOPGuard in BrowserOS, an open‑source agentic browser. Extensive evaluations demonstrate that SOPGuard effectively enforces SOP while preserving utility and incurring only a small runtime overhead. Our code and data are available at https://github.com/wxl‑lxw/BrowserOS‑SOPGuard.

PaperId: 357, https://arxiv.org/pdf/2606.13949.pdf GitHub

Abstract:
Modern LLM‑powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments. However, many deployments transmit the full UI state to remote inference servers even when most elements are irrelevant to the current task, which can leak sensitive but unnecessary context such as authentication codes, private notifications, and background application states. We propose MINIM, a trusted local broker that performs privacy‑aware minimization on the client side before any observation leaves the device. Grounded in Contextual Integrity (CI), MINIM learns a dual‑score representation for each UI element by predicting an inherent sensitivity score (s) and a task‑conditioned necessity score (n). These scores drive a ternary disclosure policy that keeps essential elements, abstracts sensitive attributes when needed, and removes task‑irrelevant content. We optimize a CI‑aware objective that penalizes necessity errors more strongly on high‑risk content, enabling aggressive pruning while preserving task‑critical information. Experiments on real‑world UI observations derived from WebArena show that MINIM substantially reduces task‑irrelevant sensitive leakage while preserving task‑critical semantic context and the interactive affordances required for reliable agent actions.

PaperId: 358, https://arxiv.org/pdf/2606.13894.pdf GitHub

Abstract:
AdamW is a default optimizer for modern deep learning, but its first and second moment states add roughly two parameter‑sized buffers to training memory. We propose Gefen, a memory‑efficient optimizer that automatically shares second‑moment estimates across parameter blocks and quantizes the first moment using a learned codebook, thereby reducing AdamW's memory footprint by ~8x while maintaining the same performance, corresponding to a reduction of 6.5 GiB per billion parameters. The method is motivated by a theoretical result showing that large mixed Hessian entries constrain the ratio of squared gradients toward one, suggesting that Hessian‑aligned parameters are natural candidates for sharing second‑moment statistics. Since computing Hessians is impractical at scale, Gefen infers block structure from the initial squared gradients, requiring no architecture‑specific metadata or hyperparameters beyond AdamW defaults. Gefen learns an exact histogram‑based dynamic‑programming quantization codebook and reuses the same blocks for first‑moment scaling. Across diverse experiments, Gefen achieves the lowest peak optimizer memory among the compared AdamW‑like methods while maintaining AdamW‑level performance. In FSDP and DDP training, the reduced memory footprint enables larger microbatches and improves throughput significantly over AdamW, providing a practical drop‑in replacement with lower memory usage that can increase throughput and enable training larger models or using larger batch sizes. We provide the complete Python implementation, including fused CUDA kernels at https://github.com/ndvbd/Gefen

PaperId: 359, https://arxiv.org/pdf/2606.13802.pdf GitHub

Abstract:
Predictive code completion greatly accelerates how quickly developers work. In spreadsheets, despite being much more common, such auto‑completion features are virtually non‑existent. To address this gap, we introduce a benchmark for systems that observe a sequence of user actions in a spreadsheet and predict future actions. Two challenges are (1) the absence of edit histories in public spreadsheet corpora and (2) the complex space of spreadsheet actions (spatial, temporal, composite). To address (1), we manually curate 52 sequences of 12K actions that recreate spreadsheets from public corpora, seeded by parametrized heuristics and LLM refinement. To address (2), we propose an online evaluation that expects a prediction after each user action, accepts or rejects that prediction, updates the future actions upon acceptance, and repeats this until the target spreadsheet is obtained. We use multiple baseline predictors (including zero‑shot LLMs, fine‑tuned SLMs, and classical models) and analyze different properties that our benchmark teaches us, including but not limited to: properties of saved actions and false positives, efficiency, effect of user profiles, effect of triggers, and effect of context.

PaperId: 360, https://arxiv.org/pdf/2606.13768.pdf GitHub

Abstract:
Cinematic video depicts multiple subjects acting or interacting at specific moments, captured with deliberate camera movement, and stitched together by shot transitions. Together, these elements demand a level of fine‑grained control beyond current text‑to‑video models. Existing work addresses each axis in isolation: multi‑subject personalization, temporal control, multi‑shot synthesis, or camera control; no prior framework jointly integrates all four. We present CineOrchestra, a unified video diffusion model that controls subjects, events, cameras, and shot transitions simultaneously. Our key insight is that these heterogeneous cinematic elements share a fundamental structure: each is an entity acting over a specific temporal interval, which can therefore all be expressed through one shared structure of entity‑centric conditioning primitives, augmented with reference images for visual entities. This formulation reduces the architectural challenge to a single positional encoding problem, which we solve with two parameter‑free coordinated rotary embeddings: (a) an interval‑sampled temporal RoPE that yields consistent attention behavior across events of dramatically varying duration, and (b) a 2D entity‑temporal cross‑attention RoPE that disambiguates per‑entity conditions and routes each to its corresponding spatiotemporal region. On two new benchmarks, CineOrchestra outperforms six per‑axis specialists on dense caption following and shot‑transition timing, with consistent gains in a pairwise user study and component ablations. Project page: https://snap‑research.github.io/CineOrchestra

PaperId: 361, https://arxiv.org/pdf/2606.13767.pdf GitHub GitHub

Abstract:
Low‑rank adaptation (LoRA) and its variants provide a memory‑ and compute‑efficient alternative to full fine‑tuning of pre‑trained models. However, questions remain about the comparative generalizability of these approaches and how the structural restrictions on low‑rank updates preserve effective adaptation performance. We present a historical framing, covering the past (full fine‑tuning and original LoRA), the present (different variants of LoRA), and propose simpler, cheaper, parameter‑efficient extensions by inducing sparsity within existing LoRA variants: Cheap LoRA (cLA), training a single low‑rank factor with the other fixed (deterministically or, in its randomized variant, stochastically), and the chained circulant variant, c^3LA. We frame cLA as a structured instance of asymmetric LoRA, serving as a controlled column‑subspace restriction of full fine‑tuning. We derive information‑theoretic generalization error bounds for these variants, marking one of the first endeavors in this area. Empirically, we evaluate 11 fine‑tuning methods across 10 pre‑trained models and 14 datasets, analyzing the fine‑tuned models' performance and generalization using tools such as loss landscapes and spectral analysis. Despite the sensitivity of fine‑tuned models to the pre‑trained model, datasets, and other factors, our study suggests that restricting LoRA‑based PEFT methods' adaptation to a sparse, structured column space remains competitive across tasks with their parameter‑matched baselines while reducing up to 10% training time and peak GPU memory up to 15%, even with a naïve, non‑optimized, sparse implementation. Our theoretical and empirical generalization measures provide a more consistent and principled approach to their cost‑effective adaptation than commonly used analytical tools. Overview and code are available at: https://elicaden.github.io/Beyond_LoRA/.

PaperId: 362, https://arxiv.org/pdf/2606.13731.pdf GitHub

Abstract:
Business intelligence (BI) increasingly combines dashboard interaction with LLM‑based assistance, but these two modes often fall out of sync during multi‑step analysis. As users switch between direct dashboard manipulation and natural‑language queries, it becomes difficult to preserve a consistent analytical state across filters, hierarchies, metrics, and chart context. We present TwinBI, an agentic digital‑twin framework that couples an LLM‑based agent system with an executable BI dashboard state. TwinBI unifies conversational interaction, dashboard manipulation, semantic grounding, and provenance tracking through a shared analytical state reconstructed from a unified interaction log. It also exposes artifacts such as schema views, SQL, logs, and an /insights command for state‑grounded analytical summaries. We evaluate TwinBI in two complementary ways. In a controlled A/B benchmark with the same backbone agent, TwinBI improves exact‑match accuracy from 43.3% to 63.3%, partial‑credit accuracy from 48.3% to 70.8%, and substantially reduces timeout rate from 40.0% to 10.0% relative to Dashboard alone. In a usability study, participants benefited from the integrated dashboard‑and‑chat workflow, with high task accuracy, moderate workload, and favorable ratings for state‑aware interaction mechanisms. These results suggest that TwinBI improves both agent‑level analytical reliability and user‑facing analytical support by turning visible dashboard state into richer actionable context. Our dataset and source code are available at: https://github.com/simonjisu/TwinBI

PaperId: 363, https://arxiv.org/pdf/2606.13706.pdf GitHub GitHub GitHub

Abstract:
We present HierSVA, an integrated suite that combines a pipeline, dataset, and benchmark for LLM‑driven hierarchical hardware formal verification. HierSVA‑SP pairs an RTL preprocessing toolchain with an LLM‑in‑the‑loop formal verification flow to produce reference SystemVerilog Assertions (SVA) on hierarchical RTL. Applying it to BaseJump STL yields HierSVA‑DS, a dataset of 342 modules, with hierarchy metadata and depths 0‑‑9, accompanied by a deep subset of 28 module‑bug pairs with natural‑language specifications and bug variants. HierSVA‑B decomposes assertion quality into six metric axes: syntax correctness, assertion proof success rate, vacuity, specification faithfulness, mutation coverage, and formal core coverage. Applying HierSVA‑B to twelve recent LLMs reveals three findings. First, the module‑level compile rate is 67.1%; among generated assertions in evaluable runs, 82.1% prove non‑vacuously, but the corresponding assertion sets detect only 70.2% of eligible injected faults and cover 36.2% of the formal core. Second, on 211 evaluable model‑‑module entries in the deep subset, assertion sets flag buggy RTL with 0.87 recall, but 40% of predicted‑buggy outcomes are false positives on correct RTL, limiting precision to 0.60. Third, agentic mode improves S1‑style provability and strength metrics, but gains plateau and oscillate. Codes and artifacts are available at \hrefhttps://github.com/HierSVAAnon/HierSVACodeAndArtifactshttps://github.com/HierSVAAnon/HierSVACodeAndArtifacts. Dataset is available at \hrefhttps://huggingface.co/datasets/AnonymousHierSVA/HierSVAhttps://huggingface.co/datasets/AnonymousHierSVA/HierSVA.

PaperId: 364, https://arxiv.org/pdf/2606.13677.pdf GitHub

Abstract:
Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact‑rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies. We present Mana (Manipulation Animator), a general sim‑to‑real framework that reinterprets dexterous manipulation as an animation problem. Inspired by computer animation, Mana employs a coarse‑to‑fine pipeline that transforms procedurally‑generated grasp keyframes into manipulation trajectories through motion planning and reinforcement learning. The data generation process is largely automatic, requiring only a few mouse clicks to specify functional affordances (<1 minute per tool). Across four articulated tools spanning different scales and joint types, Mana achieves zero‑shot sim‑to‑real transfer for both grasping and in‑hand manipulation, demonstrating a scalable approach to dexterous articulated tool use.

PaperId: 365, https://arxiv.org/pdf/2606.13621.pdf GitHub

Abstract:
Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal‑logic specifications into automata restricting an agent's actions. We argue this is the wrong product. The same automata‑theoretic machinery ‑‑ specification compilation, product game construction, attractor computation, and winning‑region extraction ‑‑ is better read as a design‑time analytical instrument whose outputs are structural insights about a system rather than runtime constraints on a deployed agent. We instantiate this through a constrained two‑player safety game for network defense. The two specifications are enforced asymmetrically: the defender specification defines the unsafe region of the game, whereas the attacker specification restricts the adversary's legal actions during attractor computation. Solving the game yields a defensibility verdict ‑‑ a formal certificate that a topology‑specification pair is or is not defensible ‑‑ with the associated winning region and shield. Beyond the binary verdict, we derive topology‑level metrics from the attractor structure and combine them with post‑convergence behavior from shield‑constrained adversarial multi‑agent reinforcement learning. Together these form a defensibility fingerprint capturing both a network's formal safety properties and its operational behavior under adaptive play. A what‑if analysis shows that formal defensibility and operational effectiveness capture distinct aspects of security: small architectural changes can produce large shifts in operational outcomes while leaving formal safety margins nearly unchanged. Shield synthesis is thus most valuable not as a deployment mechanism for safe agents, but as a framework for answering architectural questions about whether, where, and how a system can be defended. The defensibility verdict is the output, not the safe policy.

PaperId: 366, https://arxiv.org/pdf/2606.13610.pdf GitHub

Abstract:
Search‑augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content. This creates a new risk: generative recommenders may consume polluted web content, such as fake reviews and promotional pages crafted to mislead recommendations. We ask: to what extent do search‑augmented LLMs become unwitting promoters of fake products when consuming polluted retrieval results? To answer this, we introduce FORGE (Fake Online Recommendations in Generative Environments), a benchmark for measuring fake‑product promotion under controlled web‑content pollution. Given an upstream search result, FORGE locally rewrites real products in retrieved web pages into fake ones to simulate web‑content pollution, and measures how often the LLM recommends the fake product. FORGE covers 225 real‑world products across 15 categories and 5 consumer scenarios. Across 12 commercial and open‑weights LLMs, all models are vulnerable: a single polluted page yields fooled rates of up to 27%, while the full top‑3 replacement raises this to 73.8%. Vulnerability varies substantially across categories, increasing when models lack stable prior knowledge of the relevant products. Reasoning does not mitigate this vulnerability; instead, it often generates spurious social proof to justify false recommendations. We evaluate three defenses: skepticism prompting and consensus filtering (over model priors or cross‑document evidence). Skepticism can exacerbate vulnerability, much like reasoning, while filtering risks suppressing legitimate products. We release FORGE at https://github.com/leoluolol/forge‑benchmark.

PaperId: 367, https://arxiv.org/pdf/2606.13598.pdf GitHub

Abstract:
Multi‑Agent Systems (MAS) built on Large Language Models (LLMs) require effective orchestration to coordinate specialized agents, yet training such orchestrators is hindered by limited supervision and high computational cost. We propose Orchestration Reward Modeling (OrchRM), a self‑supervised framework for evaluating orchestration quality without human annotations. OrchRM leverages intermediate artifacts from multi‑agent executions to construct win‑lose pairs for Bradley‑Terry reward model training. Unlike existing MAS test‑time scaling and orchestrator training frameworks that rely on costly sub‑agent rollouts, OrchRM operates directly at the orchestration level, enabling efficient and high‑performing reward‑guided orchestrator training and MAS test‑time scaling. OrchRM improves training efficiency by up to 10x in token usage while improving MAS test‑time scaling performance by up to 8% in accuracy. These gains consistently transfer across multiple domains, including mathematical reasoning, web‑based question answering, and multi‑hop reasoning, demonstrating orchestration‑level reward modeling as a scalable direction for robust multi‑agent orchestration. Code will be available at https://github.com/Wang‑ML‑Lab/OrchRM.

PaperId: 368, https://arxiv.org/pdf/2606.13580.pdf GitHub GitHub

Abstract:
Event‑based vision has drawn increasing attention owing to its distinctive properties, including ultra‑high temporal resolution and extreme dynamic range. Recent works have introduced it to video super‑resolution (VSR) to enhance flow estimation and temporal alignment. In contrast, this paper shifts the focus of event signals from motion refinement to texture enhancement in VSR. We propose EvTexture++, the first event‑driven framework dedicated to texture enhancement in VSR. It leverages high‑frequency spatiotemporal details from events to improve texture recovery. EvTexture++ incorporates a customized texture enhancement branch, along with an iterative texture enhancement module that progressively exploits high‑temporal‑resolution event information for texture restoration. This enables gradual refinement of texture regions across iterations, yielding more accurate and detailed high‑resolution outputs. Besides intra‑frame texture recovery, large motions could degrade inter‑frame temporal consistency, particularly in texture regions, leading to texture flickering. To mitigate this, we further exploit the continuous‑time motion cues of events to enhance temporal consistency, introducing a temporal texture alignment module that estimates event‑guided texture‑aware flow for precise inter‑frame texture alignment. Moreover, EvTexture++ is designed as a plug‑and‑play tool to flexibly boost the performance of existing VSR models. Experiments on five datasets demonstrate that EvTexture++ achieves state‑of‑the‑art performance. When integrated into recent VSR models, it yields significant improvements, with gains of up to 1.55 dB in PSNR on the texture‑rich Vid4 dataset. Code: https://github.com/DachunKai/EvTexture.

PaperId: 369, https://arxiv.org/pdf/2606.13578.pdf GitHub

Abstract:
Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator. Vision‑Language‑Action (VLA) models provide one possible interface between written protocols and robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory‑specific supervision and a unified learning framework that can accommodate the diverse robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, a simulation‑based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported robot profiles. On the policy side, we present LabVLA, trained with a two‑stage recipe: FAST action token pretraining first makes the Qwen3‑VL‑4B‑Instruct backbone action aware before any continuous control is learned, and flow matching posttraining then attaches a DiT action expert under knowledge insulation. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among all evaluated baselines under both in‑distribution and out‑of‑distribution settings.

PaperId: 370, https://arxiv.org/pdf/2606.13571.pdf GitHub

Abstract:
Real‑world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event‑driven sampling, making reliable forecasting fundamentally challenging. Existing methods have evolved from impute‑then‑forecast pipelines to continuous‑time models such as Neural ODEs and continuous‑time graph networks. While these approaches improve the modeling of historical irregularity, they still rely on an implicit oracle assumption at inference time: the timestamps of future valid observations are presumed to be known in advance. This assumption limits practical relevance, since in many real systems the more fundamental question is not only what the future value will be, but also whether a valid observation will occur at all. In this paper, we propose Timeflies, a unified framework that reformulates forecasting as a joint problem of future observability inference and value estimation. To explicitly model the interaction between observation dynamics and state evolution, Timeflies adopts an observation stream and a value stream, coupled through three dedicated modules for reliability‑aware embedding, observation‑guided dependency modeling, and joint prediction. We further construct Shadow, a benchmark that combines natural missingness from public datasets with real‑world industrial data, and introduce the Observation‑Value Joint Entropy (OVJE) metric to comprehensively evaluate this coupled predictability. Extensive experiments show that Timeflies consistently outperforms existing methods, highlighting the importance of explicitly modeling future observability in time series forecasting with missing values. Code and dataset are available in https://github.com/ant‑intl/Timeflies.

PaperId: 371, https://arxiv.org/pdf/2606.13486.pdf GitHub

Abstract:
Anomaly detection in multivariate time series is challenged by four structurally distinct anomaly types ‑‑ point (isolated spikes), distributional (level shifts), temporal (rhythm changes), and collective (inter‑sensor correlation breakdowns) ‑‑ each requiring different feature representations. Most unsupervised methods target only one or two types and provide limited interpretability. We present CRAFTIIF (Cross‑Resolution Analytic Four‑Type Interpretable Isolation Forest), a fully unsupervised framework targeting all four types without dataset‑specific tuning. CRAFTIIF generates K=500 random analytic wavelet feature draws across four families (Morlet, DOG, Haar, Coiflet), each targeting a specific anomaly type, feeding five structured Isolation Forests ‑‑ one per type plus a meta‑IF for compound anomalies. An adaptive Otsu/MAD threshold calibrates detection automatically across anomaly rates from 0.1% to 69.2%. Because each IF is trained exclusively on type‑specific features, branch firing provides direct anomaly‑type attribution by construction, without post‑hoc explanation. Evaluated on all 19 datasets of the mTSBench benchmark (Zhou et al., TMLR 2026), CRAFTIIF achieves mean F1=0.228 (all 19 datasets) and F1=0.322 (13 detectable datasets), ranking first among all 25 evaluated methods on VUS‑PR (0.463 vs. previous best 0.329, +40.7%). A diagnostic framework ‑‑ oracle F1, detectability limits, and branch separation ratios ‑‑ identifies 6 of 19 datasets as fundamentally undetectable by any unsupervised method. Ablation over 11 conditions confirms adaptive thresholding (+38% F1), four‑branch structure (+20%), and meta‑IF (+23%) are each essential. Code: https://github.com/smitswil/craftiif

PaperId: 372, https://arxiv.org/pdf/2606.13477.pdf GitHub

Abstract:
Supramolecular chemistry, which includes the study of non‑covalent host‑guest assemblies, has advanced various applications. However, designing host‑guest systems remains time‑consuming, requiring days of dry‑lab verification per candidate pair. Although LLMs have emerged as a fast alternative with strong performance on molecular binding tasks, no benchmark currently systematically evaluates LLMs for host‑guest reasoning across fundamental supramolecular chemistry tasks, e.g., binding affinity prediction. To this end, we collaborate with domain experts to release the first Supramolecular Benchmark, called SupraBench, to evaluate LLMs in chemistry reasoning. Specifically, we design four fundamental tasks, i.e., binding affinity prediction, top‑binder selection, solvent identification, and host‑guest description, plus an auxiliary vision‑based task for molecular identification. We also release SupraPMC, a curated 16M‑token corpus of Supramolecular chemistry articles distilled from Europe PMC, to support the adaptation to the supramolecular domain. We benchmark a broad range of open and proprietary LLMs and find that LLMs leave substantial headroom across all tasks. Domain adaptation pretraining over SupraPMC transfers cleanly to in‑distribution regression but trades off against strict letter‑format output. Moreover, the difficulty profile differs sharply across task families, revealing distinct failure modes that indicate specific gaps in current supramolecular chemistry reasoning. Our source codes and benchmark datasets are available at https://github.com/Tianyi‑Billy‑Ma/SupraBench.

PaperId: 373, https://arxiv.org/pdf/2606.13432.pdf GitHub

Abstract:
Cloning camera motion from reference videos is an important task in video generation, as videos provide intuitive and precise control. Existing methods either directly use parametric representations that fail to handle multi‑shot generation or synthesize cross‑paired data, which suffer from data scarcity, resulting in poor performance in complicated camera motion cloning. To address these issues, we introduce a general camera motion representation that encodes cameras as grid motion videos. This camera grid represents the camera parameters visually and supports the integration of diverse trajectories for multi‑shot video generation. Building upon this, we propose OmniDirector, a unified framework trained on a million‑scale camera grid‑video pairs that coordinates characters, actions, and cameras to provide director‑level control for multimodal diffusion transformers. Furthermore, we design a novel hierarchical prompt expansion agent that harmoniously integrates different control signals by systematically describing camera motion and visual content through understanding signal relationships. Extensive experiments demonstrate the superior performance and outstanding controllability of our framework. Project page: https://ymlinfeng.github.io/OmniDirector.github.io/

PaperId: 374, https://arxiv.org/pdf/2606.13400.pdf GitHub

Abstract:
While flow‑based generative models have demonstrated strong performance across a wide range of domains, deploying them in safety‑critical physical systems remains challenging due to strict constraint requirements. Existing approaches typically enforce safety through post‑hoc corrections, which incur substantial computational overhead and may distort the learned distribution. We propose PolyFlow, a polytope‑constrained flow matching framework that embeds constraints directly into the model and flow dynamics. PolyFlow introduces a discrete‑time flow formulation and a projection‑free architecture, which eliminate the discretization error and guarantee strict satisfaction of arbitrary polyhedral constraints, without the need for expensive iterative solvers. Experimental results show that PolyFlow achieves zero constraint violation while maintaining high distributional fidelity across a range of planning and control tasks. Compared to state‑of‑the‑art constrained generation baselines, PolyFlow significantly reduces inference latency and demonstrates a favorable trade‑off between safety, efficiency, and generative quality. Code is available on https://github.com/MJianM/PolyFlow.

PaperId: 375, https://arxiv.org/pdf/2606.13392.pdf GitHub

Abstract:
Ultra‑long‑context capability is becoming indispensable for frontier LLMs: agentic workflows, repository‑scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the quadratic cost of softmax attention makes this untenable at deployment scale. We introduce MiniMax Sparse Attention (MSA), a blockwise sparse attention built upon Grouped Query Attention (GQA). A lightweight Index Branch scores key‑value blocks and independently selects a Top‑k subset for each GQA group, enabling group‑specific sparse retrieval while maintaining efficient block‑level execution; the Main Branch then performs exact block‑sparse attention over only the selected blocks. Designed around a principle of simplicity and scalability, MSA is deliberately streamlined, making it straightforward to deploy efficiently across a broad range of GPUs. To translate sparsity into practical speedups, we co‑design MSA with a GPU execution path that uses exp‑free Top‑k selection and KV‑outer sparse attention to improve tensor‑core utilization under block‑granular access. On a 109B‑parameter model with native multimodal training, MSA performs on par with GQA while reducing per‑token attention compute by 28.4x at 1M context. Paired with our co‑designed kernel, MSA achieves 14.2x prefill and 7.6x decoding wall‑clock speedups on H800. Our inference kernel is available at: https://github.com/MiniMax‑AI/MSA. A production‑grade natively multimodal model powered by MSA has been publicly released at: https://huggingface.co/MiniMaxAI/MiniMax‑M3.

PaperId: 376, https://arxiv.org/pdf/2606.13385.pdf GitHub

Abstract:
Web agents driven by large language models (LLMs) are increasingly deployed in real‑world environments, where they operate over untrusted web content and execute actions with direct consequences. This makes them vulnerable to prompt‑injection attacks, in which seemingly benign content embeds adversarial instructions that manipulate agent behaviour. Existing security benchmarks adopt an attack‑centric perspective, focusing on the technical feasibility of injections while overlooking the nuanced distribution of resulting harms. In practice, however, prompt‑injection risk is victim‑dependent: a single exploit can produce asymmetric consequences for different stakeholders, and the same attack pattern may exhibit substantially different effectiveness depending on whom it targets. To capture these properties, we introduce \sysname, a stakeholder‑centric benchmark to systematically categorize and attribute harm in real‑world web agent systems. It distinguishes between affected entities (e.g., user, seller, platform), decomposes the attacks into concrete objectives, and evaluates each case with complementary outcome‑ and process‑level metrics. Our results reveal substantial and heterogeneous vulnerabilities: not a single attack objective is reliably resisted by current agents, and failures distribute across qualitatively distinct modes ranging from \emphstealthy parasitism (attack succeeds without disrupting the user's delegated task) to \emphmisaligned disruption (task disrupted without attack success) and \emphcompounded failure (both adversarial objective and task integrity simultaneously violated). These patterns are missed by conventional evaluation, highlighting the need for stakeholder‑aware assessment of LLM‑based agents in real‑world deployments. Benchmark is available at https://github.com/StakeBench/SBC.

PaperId: 377, https://arxiv.org/pdf/2606.13288.pdf GitHub

Abstract:
Contrastively trained vision‑language models like CLIP, have made remarkable progress in learning joint image‑text representations, but still face challenges in compositional understanding. They often exhibit a "bag‑of‑words" behavior‑‑struggling to capture the object relations, attribute‑object bindings, and word order dependencies. This limitation arises not only from the reliance on global, single‑vector representations for optimization, but also from the insufficient exploitation and modeling of the rich compositional information inherently present in paired image text data. In this work, we propose MACCO (MAsked Compositional Concept MOdeling), a framework that masks compositional concepts in one modality and reconstructs them conditioned on the full contextual information from the other, enabling the model to capture and align cross‑modal compositional structures more effectively. To facilitate this process, we introduce two auxiliary objectives that jointly align and regularize masked features both inter‑modally and intra‑modally. Extensive experiments on five compositional benchmarks, along with in‑depth analyses, demonstrate that our approach not only significantly enhances compositionality in VLMs but also improves their ability to capture syntactic structure and linguistic information. Additionally, the improved compositionality also benefits text‑to‑image generation and multimodal large language model. Code is available at https://github.com/hiker‑lw/MACCO.

PaperId: 378, https://arxiv.org/pdf/2606.13276.pdf GitHub

Abstract:
Weight‑space geometry plays a central role in neural network optimization, yet manifold constraints are often applied uniformly across all weight matrices. In this work, we ask whether different transformer modules prefer different manifold geometries. We study Manifold Muon for GPT‑2 pretraining and compare layer‑wise assignments of Stiefel and DGram constraints across attention and MLP blocks. Our results show a clear asymmetry: constraining attention layers with Stiefel geometry while assigning DGram geometry to MLP layers gives the best performance among the tested configurations, whereas the inverted assignment and all‑DGram configuration become unstable under the shared hyperparameter setting. We trace this failure to singular value growth in DGram‑constrained attention weights, which can amplify attention logits and induce softmax saturation. These findings suggest that symmetry‑aware and geometry‑aware optimization for transformers should be module‑specific rather than uniform.

PaperId: 379, https://arxiv.org/pdf/2606.13258.pdf GitHub

Abstract:
Gait‑based Parkinson's disease assessment increasingly relies on heterogeneous sensors, but clinical systems rarely collect all modalities simultaneously. New sensors may arrive through device upgrades, protocol changes, or multi‑center deployment, while historical patient data are often unavailable because of privacy and storage constraints. This modality‑incremental setting faces three challenges: unreliable cross‑modal distillation, modality‑specific statistical shifts, and reduced plasticity after preservation. We propose MOSAIC, a compact continual learning framework. First, we identify the Toxic Teacher phenomenon and introduce Modality‑Specific Warm‑Up to stabilize newly learned modality representations before distillation. Second, we propose a statistics‑decoupled MSBN architecture that isolates sensor statistics while maintaining a shared semantic backbone. Third, we design a curriculum‑guided repulsive objective for Plasticity Recovery, preserving legacy knowledge while recovering modality‑specific capacity. Experiments on three multimodal Parkinson's gait datasets show that MOSAIC improves final performance and mitigates forgetting. Project code is available at: https://github.com/minlinzeng/MOSAIC_Modality‑Specific‑Adaptation‑for‑Incremental‑Continual‑Learning‑in‑PD‑Gait‑Assessment.git

PaperId: 380, https://arxiv.org/pdf/2606.13233.pdf GitHub

Abstract:
Large reasoning models (LRMs) improve complex problem‑solving by generating long intermediate reasoning traces, but this substantially increases inference costs. NVFP4 inference offers a promising approach to reduce both computational and memory costs through hardware‑supported low‑precision execution. However, directly applying NVFP4 to LRMs introduces two practical limitations: reasoning accuracy degrades under quantization, and existing NVFP4 kernels do not fully realize latency benefits in small‑batch autoregressive decoding. In this work, we analyze the effect of NVFP4 quantization on token‑level uncertainty during reasoning. We show that quantization increases incorrect sampling at low‑entropy symbolic tokens, while causing over‑concentration on a small set of tokens in high‑uncertainty reasoning steps. Based on this observation, we propose ReSET, a reasoning‑step entropy‑based temperature‑scaling method that estimates step‑level uncertainty online and adapts the decoding temperature using both token‑level and step‑level entropy signals. To address the latency gap, we further design a CUDA‑core small‑M NVFP4 kernel for latency‑critical autoregressive decoding. Across reasoning benchmarks and model scales, ReSET improves NVFP4 reasoning accuracy by up to ～\!2 points over the NVFP4 baseline. Our CUDA‑core small‑M kernel further improves latency‑critical decoding, delivering up to 2.5\!× kernel‑level speedup over NVFP4 vLLM and approximately 2\!× end‑to‑end decoding speedup over BF16. Code is available at https://github.com/aiha‑lab/ReSET.

PaperId: 381, https://arxiv.org/pdf/2606.13222.pdf GitHub

Abstract:
Distinguishing self from others is a prerequisite for social intelligence, yet humanoid robots that increasingly share workspaces with humans still lack this ability. Here we show that a humanoid robot can learn self‑other distinction from proprioceptive‑visual correspondence, without any identity labels or kinematic models. Once established, this distinction bootstraps a predictive self‑model that maps joint configurations to three‑dimensional body occupancy, capturing how the robot's body changes with action. In multi‑agent scenes involving humans or morphologically identical robots, the system reliably identifies itself, learns a 3D self‑model, and supports downstream tasks including target reaching, collision‑aware motion planning, and human‑to‑robot motion retargeting. Together, these results outline a route toward bodily self‑representation in robots that act and coordinate alongside others in shared physical environments. Project page: https://euron‑zc.github.io/humanoid‑self‑model/.

PaperId: 382, https://arxiv.org/pdf/2606.13119.pdf GitHub

Abstract:
Spatio‑Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy. Urban spatio‑temporal data exhibits temporal mirage: similar short‑window inputs have divergent future trends, and vice versa. Existing spatio‑temporal graph neural networks (STGNNs) cannot effectively identify such mirages. We argue that the core reason lies in the short‑window inputs that have incomplete period observation, heterogeneous global spatial correlation, and cross‑period superposition causality. To bridge this gap, we develop a novel Multi‑ Period Pattern Pre‑training (MP3), a plug‑and‑play pre‑training plugin for distinguishing temporal mirages. MP3 presents two core innovations: (1) The multi‑period pattern learning is designed to learn multi‑period patterns from long time series. Specifically, multi‑period temporal modeling leverages edge convolution to identify different multi‑period patterns. Multi‑period spatial modeling uses a bottleneck project and a global memory bank to capture heterogeneous global spatial relations efficiently. Cross‑period pattern interaction employs a causality‑enhanced Transformer to capture dependencies across different period patterns. (2) This plugin can seamlessly integrate into existing STGNN backbones to strengthen their forecasting performance. The experiment on five STGNN baselines across five real‑world datasets (including a large‑scale dataset CA) verify the effectiveness, superior scalability and strong adaptability of MP3, which brings consistent and robust performance improvements across all evaluated baselines. On average, MP3 reduces the MAE 4.7% and the RMSE 5.0%. The code can be available at https://github.com/YAN‑outlook/MP3.

PaperId: 383, https://arxiv.org/pdf/2606.13054.pdf GitHub

Abstract:
Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a promising compression technique, offering significant reductions in model size and inference complexity. However, existing methods struggle with heavy‑tailed activation distributions and therefore keep activations in high precision, fundamentally limiting end‑to‑end inference acceleration. To overcome this limitation, we propose TWLA, a post‑training quantization (PTQ) framework that achieves 1.58‑bit weight compression and 4‑bit activation quantization while maintaining high accuracy. TWLA comprises three components: (1) Euclidean‑to‑Manifold Asymmetric Ternary Quantizer (E2M‑ATQ) minimizes layer‑output error under weight ternarization via a two‑stage optimization from Euclidean initialization to manifold relocation; (2) Kronecker Orthogonal Tri‑Modal Shaping (KOTMS) applies a Kronecker‑structured orthogonal rotation to reshape weights into ternary‑friendly tri‑modal distributions, while the shared rotation statistically suppresses activation outliers; and (3) Inter‑Layer Aware Activation Mixed Precision (ILA‑AMP) explicitly introduces adjacent‑layer second‑order interaction costs in bit allocation and jointly optimizes for the layer‑wise disparity of activation quantization gains induced by the shared orthogonal transform, preventing cascades triggered by a few weak layers. Extensive experiments demonstrate that TWLA maintains high accuracy under W1.58A4, while delivering significant inference acceleration. The code is available at <https://github.com/Kishon‑zzx/TWLA>.

PaperId: 384, https://arxiv.org/pdf/2606.13051.pdf GitHub

Abstract:
Despite advances in information extraction driven by deep learning and large language models, performance gaps remain in highly specialized biomedical fields, where domainspecific complexity poses challenges for generalist models. In this work, we focus on the domain of autoimmunity, where the main entities of interest are autoimmune diseases, autoantibodies (i.e., molecules that may mark or cause these diseases), their molecular targets, their location in the body, and their associated clinical signs. Herein, we present AAbAAC (AutoAntibodies and Autoimmunity Annotated Corpus), a corpus of 115 abstracts selected from PubMed, where we manually annotated entities and their relationships. First, AAbAAC was used to evaluate several methods on the task of named entity recognition (NER), and secondly, to fine‑tune NER models. Our study demonstrates the utility of AAbAAC for information extraction in the domain of autoimmunity, showing expected improvement in NER performance after finetuning. This illustrates the value of small‑scale annotation efforts for specialized domains and contributes to the computational study of autoimmunity. The AAbAAC corpus is available at https://github.com/f‑maury/AAbAAC.

PaperId: 385, https://arxiv.org/pdf/2606.13038.pdf GitHub

Abstract:
As LLM agents proliferate in prediction markets and collective decision‑making, they risk a cognitive monoculture: agents built on shared foundation models produce correlated forecasts, and recent measurement finds frontier‑model errors correlated at r ~ 0.77. We ask whether human cognitive diversity can be recovered from behavior and transferred to LLM agents. Nous extracts a structured eight‑dimension behavioral profile from real Polymarket trading activity and injects it into agents through prompts. Our central finding is a dissociation between the two halves of that pipeline. Extraction works, partially: across 100 wallets, 8 of 14 parameters are temporally stable (split‑half ICC >= 0.5, bootstrap CI lower bound > 0.3; contrarian score reaches ICC ~ 0.9); wallets are identifiable from their profiles well above chance (top‑1 retrieval 17‑22% vs. 1% chance); and two of four pre‑specified dimensions rank‑correlate with future realized profit out‑of‑sample, though the correlations do not survive behavioral‑confound controls. Prompt‑level injection does not measurably transmit it: on a semantic embedding metric, structured injection shows no significant advantage over a length‑matched control on any model, and the diversity it induces neither reduces ensemble error correlation nor improves Brier score ‑‑ a null that persists across exploratory checks on sampling temperature, profile diversity, and question difficulty. Measuring the prompts themselves locates the compression before the model: the structure‑to‑narrative translator emits near‑uniform prompts whose spread does not track profile spread. We position Nous as measuring the cognitive‑monoculture problem and the limits of a prompt‑level remedy, motivating deeper, below‑the‑prompt injection (fine‑tuning, activation steering). Code, frozen profiles, prompts, and model outputs: https://github.com/WillChienT/nous‑paper

PaperId: 386, https://arxiv.org/pdf/2606.12991.pdf GitHub

Abstract:
Cyclic peptides represent a promising class of therapeutic compounds in modern drug discovery, often offering improved stability and binding affinity. However, the de novo design of cyclic peptides remains challenging because methods must identify pocket‑adaptive cyclization patterns and linkage sites while simultaneously controlling drug‑relevant properties. This challenge is particularly pronounced for recent generative models trained predominantly on linear peptide data, which may fail to capture cyclization‑specific constraints. To address the limitation, we introduce APCyc, a target‑aware de novo cyclic peptide generation framework that explicitly models cyclization and jointly optimizes multiple essential physicochemical properties. By using an expanded residue vocabulary and explicitly encoding cyclization‑site and linkage‑type information, APCyc learns cyclization‑aware representations and leverages Bayesian posterior guidance to steer sampling toward cyclic peptides satisfying multiple property objectives. Experimental results demonstrate that our model learns target‑dependent cyclization preferences, and enables effective and controllable multi‑property optimization for cyclic peptide design. The source code of this paper is available at https://github.com/HKUSTGZ‑ML4Health‑Lab/APCyc.

PaperId: 387, https://arxiv.org/pdf/2606.12916.pdf GitHub

Abstract:
Molecular dynamics (MD) is the canonical in‑silico method for atomistic molecular science, simulating molecular behavior from first‑principle physics. Designing an MD pipeline for a new system requires substantial expert knowledge: running it on even one molecule is expensive, ruling out trial‑and‑error. We automate this expert pipeline‑design process with an LLM agent. Unlike existing MD agents that orchestrate a predefined tool set, we treat pipeline design as open‑ended code generation in which the agent's behavior is reshaped online by verbal reward. Specifically, we build MDForge, an LLM agent whose in‑context update rule densifies the sparse reward via a multi‑agent debate among physics experts. On three SAMPL host‑guest binding free‑energy benchmarks, MDForge automatically designs MD pipelines competitive with human experts. Deployed on a library of unseen candidate guests, its CB[7] pipeline discovers a novel binder that wet‑lab competition NMR confirms is a high‑affinity, picomolar CB[7] binder. Our data and code are available at https://github.com/Zehong‑Wang/MDForge.

PaperId: 388, https://arxiv.org/pdf/2606.12910.pdf GitHub

Abstract:
For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural‑language prompts in real time. Although Vision‑Language Models (VLMs) have enabled zero‑shot generalization in robot task and motion planning (TAMP), current state‑of‑the‑art approaches often remain computationally "heavyweight" or require extensive training on thousands of demonstrations. We present GRASP (Grounded Reasoning and Symbolic Planning), a framework designed as a step toward open‑vocabulary tabletop manipulation. Our approach leverages a pretrained VLM to translate natural‑language queries into neuro‑symbolic goal states, grounded in the physical world via a bounding‑box detection pipeline. Unlike methods that rely on fixed color lists or hard‑coded coordinates, GRASP enables robots to interpret abstract spatial concepts such as "top shelf" and execute tasks without additional fine‑tuning. We achieve 73.3% overall success across 90 real‑robot trials at three difficulty levels, requiring no task‑specific training.

PaperId: 389, https://arxiv.org/pdf/2606.12900.pdf GitHub

Abstract:
Large language models (LLMs) often hallucinate by generating factually incorrect or unfaithful content, posing significant risks to their safe use. Detecting such hallucinations is particularly challenging under the zero‑source constraint, where no model internals or external references are available, and detection must rely solely on the textual query‑answer pair. In this paper, we propose Human‑like Criteria Probing for Hallucination Detection (HCPD), a paradigm that emulates the multi‑faceted reasoning of human evaluators. Its core is a Human‑like Criteria Probing (HCP) mechanism, in which a LLM agent adaptively decomposes its judgment into a weighted set of interpretable criteria and aggregates criterion‑specific scores into a final truthfulness measure. To achieve this adaptive capability, we introduce a reward‑based alignment scheme using only weak supervision from semantic consistency. At inference, we employ a multi‑sampling aggregation strategy to ensure robust decisions while preserving full interpretability. We further provide theoretical analysis supporting the reliability of our approach. Extensive experiments show that HCPD consistently outperforms state‑of‑the‑art baselines, offering an effective and explainable solution for zero‑source hallucination detection. Code is available at https://github.com/TRISKEL10N/HCPD.

PaperId: 390, https://arxiv.org/pdf/2606.12871.pdf GitHub

Abstract:
Search Agents (SAs) typically leverage large language models (LLMs) to support complex information‑seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses. For SAs evaluation, prior benchmarks mainly focus on specialized tasks that are unlikely to arise in real‑world user scenarios. Moreover, their reliance on coarse task‑level rubrics often limits evaluation interpretability. To bridge this gap, we introduce DailyReport, an open‑ended benchmark to evaluate SA capabilities on daily search tasks. It contains 150 open‑ended tasks with 3,546 associated rubrics, capturing widely discussed and timely information demands of real‑world users. Each task is decomposed into subtasks and evaluated with cascade rubrics across disentangled dimensions. Through cascade performance attribution and user‑centric aggregation, we derive highly interpretable scores for each dimension, along with a user preference score. Our results on 17 agentic systems show that current systems still fall short of users' expectations. To facilitate future research, our dataset and code are made publicly available at https://github.com/AGI‑Eval‑Official/DailyReport.

PaperId: 391, https://arxiv.org/pdf/2606.12828.pdf GitHub

Abstract:
Do research topics in artificial intelligence grow gradually, or do they advance through abrupt, detectable jumps? Analyzing 80,814 accepted main‑track papers from five premier AI conferences (ACL, CVPR, ICLR, ICML, NeurIPS) spanning 2017 to 2025, we show major AI topics advance through topical phase transitions: remaining marginal for years, then surging across venues within one to three years. Large language models became the dominant cross‑venue topic by 2025, diffusion models rose with comparable abruptness, and language‑model methods crossed into computer vision via vision‑language models, whereas reinforcement learning compounded smoothly, distinguishing genuine phase transitions from ordinary growth. This structure is our primary contribution: a large‑scale, cross‑venue characterization of how AI research reorganizes. We then ask whether a transition leaves a detectable footprint before it peaks. We define an early‑warning signature, four publication‑dynamics criteria frozen on 2017‑2021 data, and evaluate it out of sample on 2023‑2025 transitions, obtaining a precision of 27% and recall of 63% against a 13.5% base rate. Applied to 2025 data, the signature flags reasoning and test‑time compute, agentic AI, multimodal LLMs, retrieval‑augmented generation, and world models as topics to monitor over 2026‑2028. The source code is also publicly available on GitHub at https://github.com/KurbanIntelligenceLab/ai‑phase‑transitions.

PaperId: 392, https://arxiv.org/pdf/2606.12814.pdf GitHub

Abstract:
Recent reinforcement learning approaches have shown great promise in improving humanoid motion tracking performance and achieving fall recovery under disturbances. However, most existing works treat motion tracking and fall recovery as different tasks and require multi‑stage training with specialized recovery rewards and/or separate recovery policies. Moreover, existing reinforcement learning‑based methods often terminate training episodes immediately after severe tracking failures, limiting recovery‑oriented exploration in unstable or fallen states. To address the above issues, we propose Stubborn, a streamlined and unified reinforcement learning framework to achieve robust humanoid motion tracking and fall recovery. Specifically, Stubborn uses an asymmetric Actor‑Critic architecture and consists of three major components. First, a yaw‑aligned tracking representation is adopted to reduce sensitivity to global drift and heading disturbances while preserving gravity‑related balance information. Second, we introduce a Bernoulli‑based probabilistic termination mechanism that enables the policy to encourage exploration of fall‑recovery behaviors under varying failure modes. Third, we propose a probabilistic termination and tracking‑error‑driven strategy that dynamically reshapes the sampling distribution based on tracking performance, increasing the training efficiency for difficult motion segments and unstable states. Extensive comparisons with SOTA methods and ablation studies show that Stubborn achieved competitive performance, and the proposed probabilistic termination mechanism and adaptive sampling strategy contributed to the performance and robustness gains. For real‑world demonstrations, please refer to https://aislab‑sustech.github.io/Stubborn/.

PaperId: 393, https://arxiv.org/pdf/2606.12809.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content. In practice, these requests often arrive sequentially over time, giving rise to the challenging problem of MLLM Lifelong Unlearning. However, most existing benchmarks are limited in scale and scope, failing to capture the complexities of MLLM lifelong unlearning. To fill this gap, we introduce the MLUBench, a large‑scale and comprehensive benchmark featuring 127 entities across 9 classes under lifelong unlearning requests. We perform extensive experiments using MLUBench and reveal that existing unlearning methods suffer from severe, cumulative degradation. More critically, we further identify the unique challenge of this problem: unlike in unimodal models, MLLM lifelong unlearning is constrained by the need to preserve multimodal alignment. Continually unlearning from one modality could degrade the entire model. To alleviate this challenge, we propose LUMoE, an effective method. Experiments demonstrate that LUMoE significantly mitigates the degradation problem faced by baselines. The source code and the MLUBench dataset are open‑sourced in https://github.com/lihe‑maxsize/Lifelong_Unlearning_main.

PaperId: 394, https://arxiv.org/pdf/2606.12736.pdf GitHub

Abstract:
AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, heterogeneity, and extended reasoning required by scientific work, whereas benchmarks for scientific tasks often reduce research to static, direct problems and provide limited support for interactive evaluation. Here, we introduce SciAgentArena, a systematic benchmark for evaluating AI agents in real‑world scientific research scenarios drawn from emerging needs across multiple domains. SciAgentArena comprises approximately 200 tasks with stepwise verification and an interactive, agent‑agnostic environment for assessing diverse AI agents. Using this benchmark, we find that current agents can contribute effectively to well‑specified data‑analysis workflows, particularly when the task structure and evaluation criteria are clear. However, their performance remains uneven across scientific contexts: agents struggle to generate genuinely novel insights, sustain self‑directed exploration, and formulate robust solutions for open‑ended research questions. We further characterize common failure modes across agents and identify opportunities for improving their reliability, autonomy, and scientific reasoning. Together, SciAgentArena provides a practical framework for measuring progress in AI agents for science and for guiding the design of future agents capable of addressing complex scientific challenges. Full codes, tasks, and datasets can be accessed via this link: https://sciagentarena.github.io/.

PaperId: 395, https://arxiv.org/pdf/2606.12674.pdf GitHub

Abstract:
Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP‑style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracking, or execution. We argue that this failure mode is poorly handled by small‑corpus distillation. A few hundred teacher traces can teach workflow format, but rarely cover the recovery behavior needed to repair failed plans over changing tool catalogs. We introduce Evoflux, an inference‑time evolutionary search method that treats compact tool use as the repair of executable tool workflows. It evolves typed workflow graphs through structured edits, execution feedback, adaptive intensity, meta‑guided redesign, and diversity pruning. On held‑out MCP‑Bench tasks spanning live MCP servers and 250 tools, Evoflux raises execution feasibility from roughly 3% to 17‑24% across small planners. In contrast, SFT and SFT+DPO on the same search‑mined data match, underperform, or collapse below zero‑shot performance; ReAct reaches higher peaks, but with higher variance and token cost. These results show that execution‑grounded search is more reliable under scarce teacher‑trace budgets.

PaperId: 396, https://arxiv.org/pdf/2606.12485.pdf GitHub

Abstract:
Training interactive web agents through imitation learning from expert trajectories has emerged as a highly effective approach. However, determining the optimal timing for expert intervention presents a critical challenge in this context. Delayed intervention often leads to the accumulation of early‑stage errors, pushing the page state into an irrecoverable regime. Conversely, premature or excessive intervention causes the agent to become overly reliant on expert policies, trapping the model in local optima characterized by a single, rigid trajectory. We propose Speculative Rollback Correction (SRC), a branch‑level imitation framework for resettable agent environments. Instead of requesting teacher labels at every visited state or correcting only after a completed trajectory, SRC uses fixed‑horizon branch review: the student executes a short speculative segment before teacher review, and the teacher localizes the first harmful deviation only when local progress breaks. Rollback preserves useful prefixes, while successful rollouts are filtered by a hard verifier and retained in a lightweight quality‑diversity archive. The resulting data supports next‑action supervised fine‑tuning on both localized corrections and verifier‑passing trajectories. On WebArena‑Infinity, SRC collects 977 verifier‑passing trajectories and 9,183 next‑action examples; fixed‑horizon review improves the recovery‑versus‑query tradeoff over step‑level review while retaining verifier‑passing solution variants. Code is available at https://github.com/LongkunHao/SRC_gui_agent.

PaperId: 397, https://arxiv.org/pdf/2606.12451.pdf GitHub

Abstract:
Large language models deployed as agents over large tool catalogs face a critical tool‑retrieval bottleneck. As embedding‑based retrieval approaches rely on compact encoders that may under‑capture specialized tool semantics, parametric tool retrieval addresses this by encoding each tool as a virtual token appended to the LLM vocabulary, fine‑tuned in two stages (memorization then retrieval SFT) to use the LLM as a retriever, achieving strong performance on standard ToolBench retrieval benchmarks. Yet these benchmarks use verbose, fully‑specified queries, and their evaluation applies constrained decoding that restricts outputs to valid token paths, neither reveals whether the model actually understands its tools. We introduce ToolSense, an open‑source LLM‑powered diagnostic framework that takes any tool catalog as input and automatically generates three benchmarks: a Realistic Retrieval Benchmark (RRB) with queries at three ambiguity tiers, an MCQ probing benchmark, and a QA probing benchmark. Applying ToolSense to ToolBench (~47k tools) and evaluating five parametric model training configurations reveals a knowledge‑retrieval dissociation: on RRB queries, several configurations collapse by ~50‑64 percentage points compared to fully‑specified ToolBench benchmarks, falling below the embedding‑model baseline. Additionally, despite strong retrieval performance, some models score near‑random on factual probes, suggesting a knowledge‑retrieval dissociation. We open‑source the ToolSense framework and the ToolBench diagnostic benchmarks at https://github.com/SAP/toolsense.

PaperId: 398, https://arxiv.org/pdf/2606.12412.pdf GitHub

Abstract:
Vision‑language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention computation and KV‑cache memory. Existing visual‑token reduction methods largely follow a rank‑and‑remove paradigm: they score visual tokens, keep a compact subset, and permanently discard the rest. We show that this irreversible action is fragile because visual‑token importance changes across decoder depth; tokens ranked low at one stage may become relevant in later layers, especially for grounding‑sensitive queries. We propose Reroute, a training‑free plug‑in that replaces removal with recoverable routing. At each routing stage, selected vision tokens pass through decoder blocks, while deferred tokens bypass the stage and re‑enter the candidate pool at the next routing decision. Reroute reuses existing attention‑score ranking rules and stage‑wise schedules, preserving the theoretical TFLOPs and KV‑cache budget class of the pruning method it augments. Across FastV, PDrop, and Nüwa variants on LLaVA‑1.5 and Qwen backbones, reroute improves grounding under aggressive token reduction while maintaining general VQA performance. These results suggest that VLM token reduction should not be viewed only as irreversible pruning, but also as recoverable routing. The code can be found here: https://github.com/elmma/mllm‑reroute/

PaperId: 399, https://arxiv.org/pdf/2606.12329.pdf GitHub GitHub

Abstract:
AI coding assistants now support a growing share of software work, from quick scripts to production applications. Yet these agents remain largely stateless: each new session re‑reads project files, re‑derives prior decisions, and ‑ most costly ‑ may repeat debugging attempts that already failed. Reconstructing this context can consume an estimated 5,000‑20,000 tokens per session; the bottleneck is often not model capability but missing project memory. We present projectmem, an open‑source, local‑first memory and judgment layer for AI coding agents. projectmem records development as an append‑only, plain‑text event log of typed events ‑ issues, attempts, fixes, decisions, and notes ‑ and deterministically projects that log into compact, AI‑readable summaries served through the Model Context Protocol (MCP). Beyond storage, projectmem adds a deterministic pre‑action gate that warns an agent before it repeats a previously failed fix or edits a known‑fragile file. We frame this as Memory‑as‑Governance: memory that does not merely answer the agent but acts on its next action. The system runs fully offline with no telemetry; its immutable log also serves as a provenance trail for reproducible, auditable AI‑assisted development. projectmem ships as a three‑dependency Python package (14 MCP tools, 19 CLI commands, 37 automated tests) and is evaluated through a two‑month self‑study across 10 projects comprising 207 logged events. Source code: https://github.com/riponcm/projectmem.

PaperId: 400, https://arxiv.org/pdf/2606.12300.pdf GitHub

Abstract:
Temporal grounding‑‑returning the interval [t_s, t_e] for a natural‑language query over a video‑‑is the language interface to long‑form video, yet has been studied on short videos; the dynamics of hour‑scale natural‑language grounding remain underexplored. We take the position that at hour‑scale, the binding constraint is search, not recognition: Video‑LLMs are bottlenecked not by localizing a nearby event, but‑‑given a natural‑language query‑‑by searching for the relevant region of a long video. To test this, we release ExtremeWhenBench, the first open hour‑scale grounding benchmark (2,273 queries over 194 videos, mean 75.7 min, max 9 hr) with an open‑form query distribution. Every open Video‑LLM collapses while a frame‑level retrieval baseline outperforms them; a failure taxonomy attributes 85% of failures to search; and a retrieve‑then‑ground hybrid recovers 6.7x over the monolithic Video‑LLM‑‑mirroring retrieve‑then‑read in open‑domain QA.

PaperId: 401, https://arxiv.org/pdf/2606.12281.pdf GitHub

Abstract:
In Decentralized Training and Decentralized Execution (DTDE) for cooperative Multi‑Agent Reinforcement Learning (MARL), action‑advising‑based knowledge sharing promotes interpretable and scalable cooperation among agents. However, current action advising approaches often adhere too much to the teacher's guidance without evaluating teacher‑student compatibility, which causes excessive advising, suboptimal stability, and degraded performance. To overcome these challenges, this paper presents a Consensus‑based Communication and Knowledge Sharing (CCKS) framework, which allows agents to adopt recommendations based on consensus‑derived constraints and to follow the teacher's instructions more smartly. This mechanism enables agents to balance exploration and learning from experienced teachers, improving overall performance. The key is the consensus model construction, for which we propose to employ contrastive learning to construct consensus models based on local observations in the agents' training phase. In action selection, agents score and choose actions based on consensus and shared knowledge. Designed as a plug‑and‑play solution, CCKS integrates seamlessly with existing DTDE algorithms. Experiments conducted in the Google Research Football environment and the complex StarCraft II Multi‑Agent Challenge demonstrate that the integration with CCKS significantly improves cooperation efficiency, learning speed, and overall performance compared with current DTDE baselines. The code is available at https://github.com/yuanxpy/CCKS.

PaperId: 402, https://arxiv.org/pdf/2606.12243.pdf GitHub

Abstract:
Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validate in parallel. Existing draft‑verify methods use binary decisions: accept or fully recompute. Yet we find that many rejected tokens can be verified correctly by a slim submodel derived from the full verifier via intra‑model routing, instead of the full verifier. This motivates our slim‑verifier to handle tokens requiring moderate verification resources, reducing expensive large‑model calls. We propose Verification via Intra‑Model Routing for Speculative Decoding (VIA‑SD), a multi‑tier framework using a routed slim‑verifier. Draft tokens are processed hierarchically: direct acceptance for high‑confidence cases, slim‑verifier regeneration for medium‑confidence cases, and full‑model verification for uncertain cases. Across four representative tasks and multiple model families, VIA‑SD reduces rejection rates by 0.10‑0.22 and delivers 10‑20% speedups over strong SD baselines, while achieving 2.5‑3x acceleration over non‑drafting decoding. Moreover, VIA‑SD is compatible with existing SD frameworks without modifying their training procedures. Our results suggest multi‑tier SD as a general paradigm for scalable and efficient LLM inference. Project page: https://zju‑xyc.github.io/VIA‑SD‑Project‑Page/

PaperId: 403, https://arxiv.org/pdf/2606.12040.pdf GitHub

Abstract:
The design of reinforced concrete highway barriers is a safety‑critical process that requires strict compliance with regulatory provisions such as the AASHTO‑LRFD bridge design guidelines. Current engineering practice relies heavily on manual, iterative, and heuristic calculations to satisfy complex nonlinear material and mechanics constraints. Although Large Language Models (LLMs) demonstrate strong generative capabilities, their direct application to structural engineering remains limited by hallucination risks and insufficient physical grounding. To address these challenges, this study proposes a novel "generation‑evaluation‑optimization" closed‑loop framework for automated concrete barrier design using the multi‑agent orchestration capabilities of AutoGen. Experimental results demonstrate that the proposed agentic framework achieves over 98% design accuracy, significantly outperforming standalone general‑purpose LLMs. More importantly, the study reveals that design performance is not necessarily correlated with model scale, where an 8B‑parameter lightweight model could outperform unconstrained 631B‑parameter flagship models. This finding highlights the potential to substantially reduce computational costs while improving the accessibility of AI‑assisted engineering tools for industry applications. The source code for the proposed multi‑agent design framework is available at the project GitHub repository: https://github.com/MXY820/barrier‑design. Keywords: Structural Engineering; Multi‑Agent Systems; Large Language Models; Concrete Barrier Design; AutoGen; Design Automation.

PaperId: 404, https://arxiv.org/pdf/2606.12025.pdf GitHub

Abstract:
Finite element (FE) modeling of safety‑critical infrastructure such as bridge barriers requires high‑fidelity nonlinear dynamic analysis, yet the current FE modeling process remains labor‑intensive and lacks automation. This paper presents the Human‑Enhanced Loop Modeling (HELM) framework, a collaborative human‑agent protocol that decomposes long‑sequence finite element modeling into discrete, visually verifiable checkpoints across geometry generation, boundary condition definition, and material assignment. The framework is demonstrated through a 20‑case matrix of reinforced concrete bridge barriers under MASH TL‑4 and TL‑5 lateral loading conditions, interfacing specialized agents with two widely used commercial FE softwares, i.e., ANSYS and LS‑PrePost. Experimental results show that HELM improves the baseline autonomous modeling success rate from 20% to 75%, with agent‑level pass rates for geometry and boundary condition tasks approximately doubling. Error analysis reveals that spatial reasoning and algebraic logic limitations constitute the primary failure modes, underscoring the value of structured human‑in‑the‑loop intervention for modeling automation. The complete agent design code and prompts are open‑sourced and can be accessed at: https://github.com/SimAgentDev/Ansys‑LSPP‑AgentKit.

PaperId: 405, https://arxiv.org/pdf/2606.12018.pdf GitHub

Abstract:
We propose a multi‑agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning. A key feature of our approach is that both the training and inference phases are augmented via knowledge distillation. Within this architecture, multi‑modal data pertinent to social intelligence is precisely localized. Furthermore, relevant long‑tail events are identified, extracted, and rendered as formatted, explicit text. This formatting strategy prevents critical long‑tail information from being overshadowed by head events and environmental noise during the tokenization process. Specifically, we integrate Test‑Time Adaptation (TTA) across the entire reasoning pipeline, encompassing the extraction and representation of long‑tail events, Chain‑of‑Thought (CoT) prompting, and self‑reflection. This TTA mechanism is also distillation‑enhanced, utilizing Low‑Rank Adaptation (LoRA) to fine‑tune the foundation model exclusively for instance‑level reasoning. Extensive evaluations against various open‑source and proprietary AI models across multiple benchmarks demonstrate the effectiveness of the proposed framework. With around 30% of training data from IntentTrain, we achieve state‑of‑the‑art results. Codes are available at https://github.com/eeee‑sys/MODF‑SIR, demo is available at https://huggingface.co/spaces/Harry‑1234/MODF‑SIR, LoRA is available at https://huggingface.co/Harry‑1234/MODF‑SIR and the dataset for training router is available at https://huggingface.co/datasets/Harry‑1234/IntentRouterTrain.

PaperId: 406, https://arxiv.org/pdf/2606.11922.pdf GitHub

Abstract:
Recent respiratory sound classification (RSC) studies largely rely on CLS‑token driven self‑attention architectures such as the Audio Spectrogram Transformer (AST). While effective at modeling global context, recent analyses suggest a low‑pass filtering behavior that may reduce sensitivity to localized abnormal patterns. In this work, we investigate State Space Models (SSMs) as an alternative backbone for RSC. Using the Distilled Audio State Space model, we analyze intermediate representations through spectral response curves and observe stronger preservation of mid‑to‑high spatial‑frequency components. Based on these observations, we introduce spectral‑aware layer regularization using Gaussian convolution applied to selected layers. We further propose Dual‑Axis Patch‑Mix contrastive learning tailored to SSM‑based audio models for robust representation learning. Experiments on the ICBHI benchmark show that our approach achieves 64.48% score, outperforming the AST baseline by 5%. Code is available at https://github.com/RSC‑Toolkit/Lung‑SRAD.

PaperId: 407, https://arxiv.org/pdf/2606.11915.pdf GitHub

Abstract:
We present a quality‑adaptive angular‑margin learning framework that improves feature generalization by enforcing intra‑class compactness and inter‑class separability. Our framework, titled QLung, introduces a no‑reference audio quality margin derived from spectral entropy and root‑mean‑square energy, which adaptively scales angular margins based on recording quality. To this end, we propose a log‑scaled angular margin that stabilizes training under severe class imbalance. We also use an angular classifier that normalizes features and class weights, ensuring margin penalties are applied consistently on the unit hypersphere. Our approach improves in‑distribution performance on the ICBHI dataset by 2.46% over the cross‑entropy baseline, and most significantly, achieves the strongest out‑of‑distribution performance on the SPRSound dataset compared to prior state‑of‑the‑art methods. Code is available at https://github.com/RSC‑Toolkit/QLung.

PaperId: 408, https://arxiv.org/pdf/2606.11831.pdf GitHub

Abstract:
Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges. However, these methods typically rely on oversimplified, factorized graph priors. Such priors, typically nearing uniform distributions, treat edges as independent entities. This systemic misalignment does not match the real‑world systems and yields diffuse and indecisive edge posteriors limiting the reliability of structural discovery. To address this, we propose Diff‑prior, a diffusion‑parameterized adaptive prior used to calibrate latent graph distribution rather than generate graphs. Our core insight is to reframe prior integration as a learnable denoising‑style calibration that organizes scattered, uncertain edge posteriors into a more reliable overall structure which can be trained by the diffusion model. Diff‑prior learns an adaptive structure prior that performs structured calibration on the edge posteriors during inference, guiding it towards a distribution closer to the underlying structure. The diff‑prior operates before structural sampling and acts as a denoising calibrator directly on the encoder edge distribution, which provides a generic training paradigm over structured variables. Experiments on standard benchmarks validated our framework, and the results indicate that Diff‑prior improves the performance of structure inference and generates more decisive edge posteriors across multiple NRI‑family architectures. The code is available on https://github.com/Hardy158118/Diffprior.

PaperId: 409, https://arxiv.org/pdf/2606.11769.pdf GitHub

Abstract:
The European AI Act is the first comprehensive regulation of artificial intelligence (AI), setting out extensive obligations, particularly for so‑called high‑risk and general‑purpose AI systems. A key distinguishing feature of AI systems under the AI Act is the capability to infer. Since the AI Act does not clearly define what inference is, there is a gray area for certain data‑driven systems. A specific example is credit scoring systems, which are listed by Annex III of the AI Act. At the same time, however, these are often implemented using statistical models for which it is unclear whether they have the capability to infer and thus fall under the AI definition of the AI Act at all. Motivated by statistical learning theory, this work develops a framework for grading different levels of the capability to infer. Based on the AI Act and the Commission Guidelines on the definition of an artificial intelligence system, we analyze which levels constitute sufficient capability to infer within the meaning of the AI Act and where further regulatory clarity is needed. We illustrate the framework by creating two realistic credit scoring workflows and show whether and where inference occurs in them. Our analysis illustrates that not only individual models but the entire data processing workflow must be considered. It also shows that the involvement of human experts during development can have significant influence on the capability to infer. Code can be found at https://github.com/fraunhofer‑iais/inference‑framework‑creditscorecards.

PaperId: 410, https://arxiv.org/pdf/2606.11762.pdf GitHub

Abstract:
Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks. However, most existing creativity metrics are tightly coupled to specific tasks, embedding domain assumptions into the evaluation process, and limiting scalability and generality. To address this gap, we introduce an automated, domain‑agnostic framework for quantifying LLM creativity across open‑ended tasks. Our approach separates the measurement apparatus from the creative task itself, enabling scalable, task‑agnostic assessment. Divergent creativity is measured using semantic entropy, a reference‑free and robust metric for novelty and diversity, validated against human annotations, LLM‑based novelty judgments and baseline diversity measures. Convergent creativity is assessed via a novel retrieval‑based multi‑agent judge framework that delivers context‑sensitive evaluation of task fulfilment with over 60% improved efficiency. We validate our framework in three qualitatively distinct domains: problem‑solving (MacGyver), research ideation (HypoGen), and creative writing (BookMIA), using a broad suite of LLMs. Empirical results show that our framework reliably captures key facets of creativity, including novelty, diversity, and task fulfilment, and reveal how model properties, such as size, temperature, recency, and reasoning, impact creative performance. Our work establishes a reproducible and generalizable standard for automated LLM creativity evaluation, paving the way for scalable benchmarking and accelerating progress in creative AI.

PaperId: 411, https://arxiv.org/pdf/2606.11751.pdf GitHub

Abstract:
Multi‑turn image editing is essential for iterative design, yet current models often struggle with identity drift and error accumulation over successive steps. While existing research leverages video priors for consistency, their reliance on bidirectional attention is fundamentally misaligned with the causal, sequential nature of interactive editing. In this paper, we propose AnchorEdit, the first autoregressive (AR) diffusion‑based framework designed specifically for high‑resolution, long‑term multi‑turn editing. AnchorEdit bridges the gap between video priors and causal inference through a three‑stage training curriculum: identity‑preserving sing‑turn pretraining, causal AR forcing fine‑tuning with a novel self‑rollout strategy to mitigate exposure bias, and consistency distillation for efficient 4‑step generation. During inference, we introduce a memory mechanism to anchor the initial subject identity and ensure stable extrapolation across extended editing trajectories. To evaluate performance, we provide a new high‑resolution multi‑turn editing benchmark designed to stress‑test long‑horizon stability. Extensive experiments demonstrate that AnchorEdit achieves state‑of‑the‑art results, maintaining exceptional subject fidelity and instruction following even over 10+ interaction rounds.

PaperId: 412, https://arxiv.org/pdf/2606.11739.pdf GitHub

Abstract:
We introduce a multi‑view in‑cabin monitoring dataset for public transportation with synchronized RGB and depth images from four inward‑facing cameras and a rotating LiDAR covering the vehicle interior of a digitalized and partly automated German city bus. The dataset contains 9.136 synchronized samples with annotations and is accompanied by a calibration and pseudo‑labeling pipeline that generates 3D human pose estimates and oriented 3D bounding boxes for occupants. We further provide a nuScenes‑format conversion and benchmark representative multi‑view 3D detection models (e.g., Lift‑Splat‑Shoot and BEVFusion), supporting comparative evaluation and small‑scale training of multi‑view in‑cabin perception models. The dataset and tools are available at https://github.com/EvgenyGorelik/multiview_incabin_dataset.

PaperId: 413, https://arxiv.org/pdf/2606.11712.pdf GitHub

Abstract:
User‑side memory in LLMs is typically scored as a single "personalization" capability: given a user's history, is the output more user‑aware? We show this aggregate metric hides opposite‑direction failures. Memory factorises into at least three orthogonal axes ‑‑ behavioral consistency (style, voice), factual presence (recall facts in history), and factual absence (abstain when a fact is absent) ‑‑ and no single substrate wins all three. Comparing per‑user gamma‑LoRA (a small LoRA adapter trained on each user's history; gamma denotes per‑user, not per‑task) against BGE‑large dense top‑K retrieval on a controlled 50‑user synthetic corpus and a real‑data probe (LaMP‑3), we find gamma‑LoRA decisively wins behavioral style while RAG decisively wins factual absence ‑‑ and the same query‑projection cells in attention layers 21‑35 causally load‑bear both effects in opposite directions (zeroing those LoRA weights raises absence‑probe TPR by +33 pp and drops presence‑probe TPR by 20 pp). On the more heavily RLHF‑tuned Llama‑3.1‑8B‑Instruct the asymmetry strengthens, not heals: parametric memory's behavioral advantage collapses while its absence‑calibration deficit against retrieval widens ‑‑ an alignment tax on parametric user‑memory. On real‑data LaMP‑3, gamma‑LoRA underperforms a majority baseline; a 9‑condition mitigation sweep diagnoses this as instruction‑following collapse, not substrate failure (a 9x2 cross‑product shows the eval‑time 1..5 logit mask drives main_acc to >=0.995 on every recipe), and the best training‑time fix replicates bit‑identically on Llama. Finally, substrate‑selection routing is question‑classification, not calibration: a 110M DistilBERT on the question text alone beats every logit‑based router. We contribute the diagnostic framework, the diagnosed real‑data negative, the alignment‑tax replication, and the routing‑as‑classification finding.

PaperId: 414, https://arxiv.org/pdf/2606.11702.pdf GitHub GitHub

Abstract:
To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acquisition, and integration. Existing benchmarks largely evaluate isolated perception or single‑turn question answering, and therefore provide limited visibility into failures of planning, tool recruitment, and rollout reliability. We introduce MedCTA, a benchmark for evaluating medical tool agents on clinician‑validated, step‑implicit tasks grounded in realistic multimodal clinical inputs, including radiology images, pathology slides, and reports. MedCTA comprises 107 real‑world clinical tasks with clinician‑verified executable trajectories over 5 deployed tools, and supports process‑aware evaluation of tool selection, argument validity, execution stability, trajectory fidelity, and outcome quality. We benchmark 18 open‑ and closed‑source multimodal models and find that even frontier systems remain brittle in multi‑step clinical tool use: autonomous rollouts are dominated by protocol failures, premature stopping, and incorrect tool recruitment, while gold‑standard tool routing yields large but still incomplete gains. These results show that strong backbone perception does not translate into reliable agentic behavior in clinical settings. MedCTA provides a rigorous testbed for auditing, diagnosing, and advancing trustworthy medical AI agents. The dataset and evaluation suite are available at https://ivul‑kaust.github.io/MedCTA/

PaperId: 415, https://arxiv.org/pdf/2606.11688.pdf GitHub

Abstract:
Long‑horizon LLM agents are not trusted to run unattended: with no human watching, they confidently report success they never verified. We treat honesty ‑‑ bounding what an agent may claim at termination ‑‑ as a first‑class metric for unattended autonomy, distinct from capability. We present Autopilot, an execution model that makes silent fabricated success structurally impossible rather than merely rarer. Autopilot externalizes all working state into a durable, gated finite‑state machine that a scheduler advances one stateless tick at a time; a hard floor forbids any terminal "done" claim whose falsifiable gate did not actually execute and pass. We prove a No‑False‑Success theorem ‑‑ under gate soundness, floor enforcement, and plan coverage, termination implies the goal holds ‑‑ whose only trust points are empirically measurable, and show the worst case degrades to an honest stall, never a fabricated success. Because each tick rehydrates only the state machine, per‑step context cost is constant in the horizon. Across a 3,150‑cell paired corpus (70 tasks × 3 systems × 3 models × 5 seeds, including 50 SWE‑bench Lite tasks across 11 OSS repos), Autopilot fabricates on 0.95% of cells [95% CI 0.38‑‑1.62] while Reflexion and StateFlow baselines fabricate on 8.10% [6.48‑‑9.81] and 25.05% [22.48‑‑27.62] respectively. The headline contrast lives in the hard regime: on SWE‑bench Lite, the firewall reduces fabrication from 33.7% (StateFlow) to 0.67%, a paired difference of ‑33.07 pp [95% CI ‑36.53, ‑29.73]. The mechanism is the gate, not the model: all ten Autopilot fabrications come from the strongest model, while two weaker mid‑tier models never fabricate across 700 paired cells. The firewall trades coverage for honesty by design ‑‑ an honest stall is recoverable; a confident wrong output shipped downstream is not.

PaperId: 416, https://arxiv.org/pdf/2606.11683.pdf GitHub

Abstract:
Spatial reasoning from egocentric videos is inherently challenging because the observable evidence is constrained by the camera trajectory. Existing methods rely on single‑turn inference, forcing models to resolve geometric ambiguity through semantic priors rather than verifiable evidence. We argue that spatial reasoning should be revisitable: conclusions formed under limited evidence should remain open to revision when complementary viewpoints become available. Building on this insight, we propose Reason, then Re‑reason (ReRe), a training‑free, inference‑time framework with two phases: in the Reason Phase, an MLLM forms a spatial hypothesis from the original video; in the Re‑reason Phase, it verifies or revises the hypothesis by observing a synthesized novel‑view video. To enable effective cross‑view revisiting, we design a Geometry‑to‑Video pipeline that renders strategically complementary novel views from predicted 3D geometry. These views feature an elevated, oblique perspective with scene‑spanning coverage, while preserving the MLLM's native video interface without architectural modifications. Extensive evaluations on VSI‑Bench and STI‑Bench demonstrate that ReRe substantially boosts open‑source MLLMs to rival proprietary state‑of‑the‑art performance. Project page: https://zhenjiemao.github.io/ReRe/

PaperId: 417, https://arxiv.org/pdf/2606.11637.pdf GitHub

Abstract:
Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into language systems for tactile commonsense reasoning, scaling such systems to realistic open‑world settings remains challenging due to two key bottlenecks: (1) current tactile reasoning datasets remain limited in format and scale, providing insufficient supervision for reasoning from tactile observations to physical commonsense and hindering the learning of transferable tactile commonsense; (2) Tactile signals are inherently redundant and action‑specific, yet existing methods often overlook these properties, resulting in inefficient representations with limited semantic expressiveness. To address these limitations, we propose TouchThinker, a tactile‑language framework that scales tactile commonsense reasoning to the open world from both data and representation perspectives. First, we construct TouchThinker‑1M, a million‑scale, multi‑source tactile reasoning dataset covering 415 objects, 8 scenarios, and 7 sensor types, providing a solid data foundation for open‑world generalization. We further introduce TouchThinker‑Bench, an open‑world benchmark with more realistic and diverse tasks. Then, we propose action‑aware modeling mechanism to improve tactile representation efficiency and enable efficient reasoning. Experimental results demonstrate that TouchThinker achieves competitive performance against state‑of‑the‑art models across multiple datasets. Our code and dataset will be made available at: https://github.com/lvkailin0118/TouchThinker.

PaperId: 418, https://arxiv.org/pdf/2606.11628.pdf GitHub

Abstract:
The most widely‑adopted robot learning pipelines today learn skills from robot demonstrations or structured human data, which are expensive to collect and tied to specific embodiments. In contrast, unstructured human videos provide a scalable alternative. They contain diverse manipulation demonstrations across objects, scenes, and strategies, but are not directly connected to robot action. We propose LUCID, a two‑stage framework that learns task intent from unstructured human videos drawn from internet‑scale datasets and learns robot control in massively‑parallel simulation. The intent model predicts short‑horizon intent (what should happen next in the scene) from the current observation in closed loop. An embodiment‑specific sensorimotor policy converts this intent into robot actions. The intent interface is shared across controllers, so the same intent model can be applied to different embodiments, from our primary dexterous hand to a parallel‑jaw gripper. We evaluate LUCID on five real‑world manipulation tasks: stirring, wiping, and binning supervised by only internet video, with zero‑shot transfer to novel scenes and object instances; and push‑T and cable routing supervised by 1 hr each of self‑collected smartphone video. Project page: https://lucid‑robot.github.io/.

PaperId: 419, https://arxiv.org/pdf/2606.11614.pdf GitHub

Abstract:
Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal interactions. A critical yet underexplored challenge is that these implicit interactions vary dynamically across samples. In this work, we present the first systematic, information‑theoretic analysis highlighting why learning these dynamic, sample‑specific interactions is critical for effective multimodal learning. Our analysis further reveals deficits in conventional paradigms at learning these distinct interaction types: modality ensemble approaches struggle to capture synergy, while joint learning paradigms often under‑utilize redundant information. This highlights the need for an approach that can adaptively learn from different interaction types on a per‑sample basis. To this end, we propose Decomposition‑based Multimodal Interaction Learning (DMIL), a novel paradigm that explicitly models and learns from sample‑specific interactions. First, we design a variational decomposition architecture to isolate the constituent interaction components. Second, we employ a new learning strategy that leverages these explicit interaction components in a fine‑tuning process to achieve comprehensive interaction learning. Extensive experiments across diverse tasks and architectures demonstrate that DMIL consistently achieves superior performance by adapting to holistic sample‑specific interactions. Our framework is flexible and broadly applicable, establishing an interaction‑centric paradigm for multimodal learning. The code is available at https://github.com/GeWu‑Lab/DMIL.

PaperId: 420, https://arxiv.org/pdf/2606.11543.pdf GitHub

Abstract:
Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organized. We study this distinction through Progressive Disclosure, where a concise root file points agents to supporting resources on demand, and compare it with a normalized flat baseline. We present SkillJuror, a framework for evaluating Skill writing paradigms through semantically controlled variants, matched multi‑trial evaluations, and trajectory evidence while holding task knowledge fixed. In an 82‑task SkillsBench study, Progressive Disclosure changes runtime behavior before aggregate outcomes: distinct Skill resources touched per trajectory rise from 1.18 to 3.85, and effective uptake events rise from 1.33 to 3.92. It also yields 17 additional verifier‑passing trials out of 410 matched trials (+4.1%) over the normalized flat baseline. The benefit is task‑dependent. Progressive Disclosure helps when supporting resources guide implementation, checking, or repair, but is weaker when success hinges on exact output conventions, numerical thresholds, or long artifact‑generation pipelines. These results show that Skill organization is not mere presentation: it can change how agents search and apply procedural knowledge, while outcome gains depend on whether the exposed resources are actionable for the task. Code is available at https://github.com/zhiyuchen‑ai/skill‑juror.

PaperId: 421, https://arxiv.org/pdf/2606.11537.pdf GitHub

Abstract:
Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them. A single misread cell or incorrect operation can silently produce a plausible but wrong result. We introduce \textscMOCA‑Agent, a market‑of‑claims code agent that replaces free‑form multi‑agent debate with claim‑level verification. The system decomposes each question into typed atomic claims, asks specialist trader agents to buy or sell those claims, clears their orders into confidence‑weighted accept/reject decisions, and synthesizes an executable Python program from market‑supported evidence. A code‑aware verifier then checks the program for execution, structural consistency, and common financial reasoning errors, with at most one market‑aware repair round. Across ten public benchmarks spanning financial numerical reasoning, general tabular reasoning, ESG question answering, and multimodal chart reasoning, \textscMOCA‑Agent achieves strong performance using a fixed Qwen3.6‑27B backbone, including 78.3% on FinQA, 76.0% on FinanceMath, 71.2% on MultiHiertt, 86.9% on ESGenius, and 85.6% average on FinChart‑Bench. These results show that aggregating evidence at the level of atomic claims, rather than whole answers, improves robustness in high‑stakes numerical reasoning.\footnoteThe code and data are available: https://github.com/UBC‑NLP/MoCA‑Agent.

PaperId: 422, https://arxiv.org/pdf/2606.11520.pdf GitHub GitHub

Abstract:
Training capable OS agents requires data that simultaneously captures structured user intents, multi‑turn task delegation, and grounded tool execution‑‑properties absent from existing datasets. We propose ISE (Intent ‑> Simulate ‑> Execute), a three‑stage synthesis paradigm that addresses these gaps jointly. Stage 1 constructs roughly 50000 structured intents via a 4D framework (Persona x Domain x Task x Complexity); after deduplication the pool contains 43956 unique intents and attains a Vendi Score of 61.57 over the entire pool on mpnet‑base‑v2 embeddings (cosine kernel, q=1). Stage 2 drives multi‑turn user‑agent interaction through a role‑locked user simulator that grounds each user turn in actual execution outcomes, producing 23132 complete trajectories averaging 8.12 user turns and 68.24 total dialogue turns. Stage 3 runs every tool call inside a live, isolated OS workspace, generating authentic failure‑recovery dynamics instead of simulated responses. Fine‑tuning on ISETrace improves ClawEval pass@1 from 19.3 to 37.7 using Qwen3‑8B on agent tool‑use tasks with a standard protocol. This result outperforms zero‑shot GPT‑4o and the larger Qwen3‑32B base model which is four times bigger. An ablation on Stage 2 proves multi‑turn simulation brings a large portion of the performance gain. We release all source code and dataset at https://github.com/Valiere01/ISE‑Trace.

PaperId: 423, https://arxiv.org/pdf/2606.11417.pdf GitHub

Abstract:
Compression progress is a long‑standing proposal for intrinsic motivation: reward an agent when its world model becomes better at predicting or compressing experience. The folk claim is that this reward is "credible" because it is paid only for learning. We make this precise and prove it. If intrinsic reward is the signed decrease of a fixed sealed‑audit loss, r_t = E(theta_t‑1) ‑ E(theta_t), then cumulative reward telescopes exactly to endpoint audit improvement, so no policy can push reward up indefinitely while true audit performance stagnates or degrades. For finite audit panels the same result holds with a sharp false‑positive budget: cumulative empirical reward is at most true audit improvement plus 2 Delta_n(F, delta), the uniform audit deviation of the model class. This is horizon‑free: adaptivity over time costs nothing once the sealed panel uniformly controls the class. The theorem also identifies the failure modes: the guarantee disappears if progress is clipped, scored on the agent's own stream, exposed to a high‑capacity model on a reusable panel, or applied to a neural class that makes Delta_n vacuous. We give a Lean 4 mechanization of the structural core (telescoping, the finite‑audit bound, finite Gibbs, and the entropy floor) and an experiment suite on ARC‑TGI grid‑transformation generators with adaptive holdout attacks. Experiments confirm the theory: finite‑audit deviation scales as n^‑0.527; signed progress resists clip‑farming, stream leakage, and noisy‑TV curiosity; naive reusable audits are exploitable by black‑box scalar feedback, while standard release defenses keep the attack below the 2 Delta_n threshold. Signed compression progress on a sealed audit is an accounting signal of genuine improvement.

PaperId: 424, https://arxiv.org/pdf/2606.11375.pdf GitHub

Abstract:
Standard linear probing declares a property "encoded" when a classifier on hidden states achieves high accuracy. The protocol works well on a snapshot but breaks across pre‑training: probe accuracy saturates within the first few thousand steps, leaving most of training invisible to the instrument. We introduce fragility, a complementary per‑layer metric defined as the activation‑noise level at which probe accuracy collapses. Fragility is sensitive to both the margin of separability and the redundancy of representation, both of which keep evolving long after accuracy plateaus. Applied to open‑checkpoint language models, fragility recovers structure that accuracy alone cannot see. Moralized representations emerge along a lexical \to compositional gradient: lexical moral detection first, compositional moral encoding later. Because probe accuracy on its own tracks how lexically separable a dataset is, we establish the compositional encoding directly, by showing it transfers across construction types that share no contrast tokens. A layer‑depth robustness gradient develops monotonically across training while accuracy stays flat. And matched fine‑tuning corpora that produce identical probing accuracy leave distinct fragility fingerprints, showing that data curation reshapes probe robustness without changing probe accuracy. In every comparison we test, where probing accuracy returns a flat answer, fragility returns a structured one.

PaperId: 425, https://arxiv.org/pdf/2606.11286.pdf GitHub GitHub

Abstract:
High‑content imaging assays quantify cellular responses to chemical and genetic perturbations, yet continuous trajectories of individual cells are unobservable because cells are chemically fixed at acquisition. Perturbation modeling therefore reduces to inferring stochastic transport between control and treated populations observed only as separate marginals. While recent generative models achieve strong end‑point alignment, boundary consistency does not determine intermediate evolution: multiple stochastic processes may connect identical marginals while traversing regions unsupported by observed single‑cell morphologies. We introduce FreeBridge, a Schrödinger Bridge formulation for single‑cell transition modeling under endpoint‑only supervision. FreeBridge defines atomic states as instance‑segmented single‑cell representations, establishing a fixed cellular manifold, and learns stochastic transport constrained within this geometry via empirical latent support regularization. Across BBBC021, RxRx1, and JUMP, FreeBridge maintains competitive or improved endpoint fidelity and mechanism‑of‑action retention under a unified evaluation protocol; on BBBC021, it further reduces intermediate support violations. These findings highlight the importance of geometric grounding for biologically interpretable perturbation dynamics. Project page: https://y‑research‑sbu.github.io/FreeBridge/.

PaperId: 426, https://arxiv.org/pdf/2606.11201.pdf GitHub

Abstract:
The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions. Among different methods, inference‑time alignment is often cheaper as it intervenes (i.e., offers guidances) only during output generation. Existing proposals apply guidances extracted from certain aligned models without properly assessing their reliability. Nonetheless, our systematic evaluation reveals that guidance effectiveness varies drastically across models; since ineffective guidances lead to further confusion and thus further interventions, the resulting excessive interventions typically indicate poor performance. To make interventions more effective and thus more efficient, we introduce BlendIn, an inference‑time alignment framework that shifts from binary decisions to creating hybrid distributions integrating both models' knowledge. BlendIn stabilizes inference‑time alignment by performing quality‑aware alignment and proportionally weighting each model's contribution based on reliability. Compared with existing works, it preserves beneficial guidance while downweighting unreliable suggestions. BlendIn provides both diagnostic signals and mitigation strategies for misaligned guidance, achieving consistent and up to 50% performance improvement on challenging model pairs. Our code is available at: https://github.com/DecayingSeart/BlendIn.

PaperId: 427, https://arxiv.org/pdf/2606.11106.pdf GitHub

Abstract:
A global shortage of trained sonographers limits prenatal ultrasound screening in low‑ and middle‑income countries, where over half of pregnant women receive no skilled sonography. Current deep learning approaches address detection, segmentation, or classification in isolation, each demanding a separate model and expert‑specified labels at inference. We present FADA, a unified vision‑language model built on Qwen3.5‑VL that performs clinical interpretation, classification, detection, and segmentation through a single interpretation‑first pipeline without external labels. FADA distills knowledge from four domain‑specific foundation models (FetalCLIP, UltraSAM, USF‑MAE, UltraFedFM) via offline pre‑computed feature caching. Selective distillation, which applies feature alignment only to annotation tasks while interpretation relies on standard fine‑tuning, consistently outperforms full distillation across most evaluation axes. The recommended variant, FADA‑SKD, achieves 0.8820 mean Dice for segmentation, 0.7671 mAP@0.50 for detection, and 100% structured interpretation compliance. Expert sonographer validation across 237 images confirms clinically acceptable outputs in both autonomous and human‑in‑the‑loop modes, with 73.5% of interpretations scoring perfectly under clinician guidance. The system is trainable on a single consumer GPU and deployable without cloud connectivity. We validate edge deployment by running the compressed 0.8B model on a commodity smartphone (Qualcomm Snapdragon 7 Gen 1, 12 GB RAM) using llama.cpp with GGUF quantization, completing the full 5‑phase pipeline in approximately 60 seconds entirely offline. This establishes a practical pathway for integrating AI‑assisted fetal assessment with portable ultrasound devices, directly addressing diagnostic access gaps in resource‑constrained settings. Code, models, and data are available at https://github.com/mahmoodphd/FADA.

PaperId: 428, https://arxiv.org/pdf/2606.11078.pdf GitHub

Abstract:
Various test‑time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre‑execution action evaluation in complex Graphical User Interface (GUI) environments. However, existing critics suffer from two key limitations: they (1) focus primarily on short‑sighted decision loops (e.g., forgetting earlier actions) and (2) lack the visual grounding needed to detect flawed actions (e.g., clicking wrong UI elements). To address these, we introduce HiViG, a History‑aware Visually Grounded test‑time framework, built around a multimodal critic trained on real GUI trajectories to abstract past interactions into a compact record and to evaluate actions with visual grounding. At test time, HiViG integrates the critic into the policy decision loop to provide macro‑action history, which summarizes the policy's completed achievements, and visually grounded critique, which verifies raw execution coordinates against the current screenshot to intercept errors before execution. Across web, mobile, and desktop benchmarks, HiViG consistently outperforms existing scalar and verbal critics, improving average success rates over the strongest baseline by 5.8% for Qwen3‑VL‑32B and 9.0% for Gemini‑3‑Flash, and demonstrates strong cross‑platform generalization. Ablations show that macro‑action history mitigates short‑sighted planning and visually grounded critique reduces execution errors, with both components being critical for test‑time scaling in long‑horizon GUI tasks.

PaperId: 429, https://arxiv.org/pdf/2606.10862.pdf GitHub GitHub

Abstract:
Vision‑Language‑Action (VLA) models achieve strong performance on standard manipulation benchmarks, but most evaluations assume that task‑relevant objects are fully visible. This assumption often fails in realistic settings, where occlusion makes manipulation partially observable. In this paper, we study scene‑induced occlusion as a fundamental challenge for VLA models and introduce LIBERO‑Occ, an occlusion‑oriented extension of LIBERO. Experiments show that state‑of‑the‑art VLAs suffer substantial performance degradation under occlusion. To address this issue, we propose Viewpoint Imagination (VIM), which generates a complementary view from an occluded primary observation and conditions action prediction on both observed and imagined evidence. VIM improves robustness across task suites, occlusion types, and severity levels without requiring additional cameras at deployment time, suggesting that viewpoint imagination is an promising mechanism for perception completion in partially observable manipulation. Our benchmark and corresponding code are available at: \hrefhttps://github.com/litsh/Libero‑Occhttps://github.com/litsh/Libero‑Occ.

PaperId: 430, https://arxiv.org/pdf/2606.10820.pdf GitHub

Abstract:
Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token‑by‑token decoding makes inference memory‑bound and inefficient. Existing acceleration approaches, such as speculative decoding and diffusion language models, can yield speedups under certain conditions but do not directly address high‑load batch serving‑‑the scenario most critical for industrial‑scale deployment. We introduce K‑Forcing, a push‑forward language modeling paradigm for joint next‑k‑token decoding. K‑Forcing distills an existing AR model into a conditional push‑forward mapping‑‑one that transforms independent uniform noise variables into a joint sample of multiple future tokens in a single forward pass. This design preserves fixed‑length outputs, reuses the AR teacher backbone, and remains compatible with standard AR serving infrastructure. We train this mapping via progressive self‑forcing distillation, which gradually expands the prediction window while enabling the student to closely match the sequence distribution of the AR teacher. We evaluate K‑Forcing on LM1B and OpenWebText using a standard causal Transformer backbone. When aggressively configured to generate k = 4 tokens per forward pass, K‑Forcing delivers approximately 2.4‑3.5x speedup across different batch sizes, while incurring modest quality degradation relative to its AR teacher. As inference increasingly dominates the lifetime compute cost of modern LLMs, K‑Forcing offers a promising route toward accelerating AR generation under real‑world high‑load deployment.

PaperId: 431, https://arxiv.org/pdf/2606.10747.pdf GitHub

Abstract:
As AI systems built from multiple language‑model agents become more common, they are increasingly used to make decisions together: discussing, negotiating, and acting on shared tasks. While individual agents may appear well‑aligned when tested on their own, problems can arise from how they interact with one another. We introduce the Arbiter, an agent designed to monitor multi‑agent conversations in real time and identify which participants may be behaving in misaligned ways. The Arbiter operates under a limited "inspection budget", meaning it must decide carefully how to use its resources. As it observes a conversation step by step, it can choose to wait, question a participant, examine internal information such as system prompts or reasoning traces, or log concerning behavior. At the end, it produces a report identifying the likely source of misalignment. We evaluate the Arbiter across five conversation conditions, ranging from risky financial advice model organisms to evaluation‑aware and colluding agents, we test five tool configurations of increasing capability and two backbone models. We find that the Arbiter reliably detects misaligned agents well before the end of the conversation, with active inspection tools improving both detection accuracy and speed. Weight‑induced misalignment proves hardest to detect, while instruction‑induced misalignment is identified reliably even under passive observation. The logging tool exhibits a dual effect, improving recall at the cost of precision. These results suggest that continual, budget‑aware monitoring can effectively catch misalignment, and that overseeing multi‑agent systems may require treating the auditor as an active participant in the process. The code is available at https://github.com/aisilab/arbiter.

PaperId: 432, https://arxiv.org/pdf/2606.10738.pdf GitHub

Abstract:
Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding. We propose Spatial‑Omni, a lightweight method that implements SO‑Encoder to inject First‑Order Ambisonics (FOA) spatial audio into existing Omni LLMs as an independent modality, without modifying their original audio encoders. SO‑Encoder provides spatial tokens with limited additional context cost and improves spatial audio understanding through efficient staged training. To support training and evaluation, we construct SO‑Dataset, SO‑QA, and SO‑Bench from open‑source data, real recordings, and simulations, containing 400K FOA spatial audio clips and 2.1M spatial question answering pairs. SO‑Bench covers 16 spatial audio understanding subtasks, including basic detection and location estimation, spatial relation understanding, and complex spatial reasoning. Experiments show that Spatial‑Omni outperforms existing open‑source Large Audio‑Language Models (LALMs) and Omni LLM models on spatial audio understanding tasks while retaining a reasonable level of general audio understanding. Code and data are available at https://github.com/dieKarotte/Spatial‑Omni.

PaperId: 433, https://arxiv.org/pdf/2606.10713.pdf GitHub

Abstract:
The nnU‑Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data. However, assembling medical imaging cohorts remains challenging due to numerous factors such as privacy regulations and annotation costs. As a result, data augmentation plays a crucial role in increasing data availability while maintaining anatomical feasibility. Hence, we propose the ++nnU‑Net, a novel data augmentation module based on image registration that operates prior to preprocessing and training take place. Our framework was evaluated across five different 2D datasets. In this workflow, image data go through a two‑stage registration process, generating new warped images. The transformations are then applied to the respective segmentation. In addition, the pipeline computes available disk space, generates supplementary binary synthetic masks and generates checkpoints. We demonstrate that the ++nnU‑Net outperforms the nnU‑Net baseline, yielding improvements in Dice Similarity Coefficient scores. In the most prominent cases, we observe performance gains of approximately 22%. These findings highlight the effectiveness of registration‑based data augmentation, particularly for 2D medical imaging datasets and suggest that the ++nnU‑Net provides a practical and scalable approach for enhancing segmentation performance in data‑limited settings. The source code for the ++nnU‑Net is available at: https://github.com/sofia‑adelie/plusplusnnunet.git

PaperId: 434, https://arxiv.org/pdf/2606.10662.pdf GitHub

Abstract:
Multi‑agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks. However, most existing MAS rely on centralized orchestration, where a main agent assigns work, collects outputs, and merges results. As the number of subtasks grows, this controller becomes a communication and integration bottleneck. We propose Decentralized Language Models (DeLM), a MAS framework that decentralizes coordination through parallel agents, a shared verified context, and a task queue. Agents asynchronously claim subtasks, read accumulated progress, perform local reasoning, and write back compact verified updates. The shared context acts as a common communication substrate, enabling agents to build on one another's verified progress without routing every update through a central controller. Empirically, DeLM improves both software‑engineering test‑time scaling and long‑context reasoning. On SWE‑bench Verified, DeLM achieves the best performance across Avg.@1, Pass@2, and Pass@4, with gains of up to 10.5 percentage points over the strongest baseline, while reducing cost per task by roughly 50%. On LongBench‑v2 Multi‑Doc QA, DeLM achieves the highest average accuracy across four frontier model families, improving over the strongest baseline by up to 5.7 percentage points. The code is available on our project website at https://yuzhenmao.github.io/DeLM/.

PaperId: 435, https://arxiv.org/pdf/2606.10572.pdf GitHub

Abstract:
External memory effectively grounds large language models (LLMs) and vision‑language models (VLMs)‑based question answering (QA) in relevant multimodal evidence. However, existing memory paradigms represent each memory item in raw text and image forms, so retrieval‑based systems must pass the retrieved text or images to the generation LLMs/VLMs, resulting in high token consumption and storage pressure, making it unaffordable for resource‑constrained applications. We propose Latent Memory, a latent‑space memory paradigm that replaces each raw text or image evidence item with a single high‑dimensional latent token produced by a small compressor LLM/VLM. Rather than retrieving raw evidence for generation, Latent Memory operates in a unified latent representation space: the query is embedded into this space to retrieve relevant latent tokens, and the retrieved latent tokens are directly prompted to a pretrained LLM or VLM for answer generation. To make each latent token simultaneously informative for reconstruction, retrieval, and generation, we train the compressor with reconstruction, contrastive, and distillation objectives in a unified end‑to‑end manner. Latent Memory is evaluated on seven text‑only QA benchmarks (e.g., HotpotQA) and multimodal QA benchmarks, where it achieves competitive QA performance compared to advanced RAG baselines while consuming 3x to 10x fewer generator tokens. It can also deliver the strongest image‑grounded QA performance on WebQA. Code is available at https://github.com/zz1358m/Latent‑Memory‑Master.

PaperId: 436, https://arxiv.org/pdf/2606.10479.pdf GitHub

Abstract:
Combinatorics is central to Olympiad‑level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight. Recent evidence suggests that even today's strongest frontier models remain uneven on Olympiad combinatorics, revealing a gap in creative mathematical reasoning. We introduce ComBench, an Olympiad‑level combinatorics benchmark for evaluating and diagnosing the combinatorial reasoning capabilities of large language models. ComBench contains 100 human‑annotated competition‑level problems organized around two complementary settings: analysis‑centric problems, which primarily require rigorous mathematical arguments, and construction‑centric problems, which require explicit constructions in addition to correctness justifications. The evaluation protocol combines rubric‑guided proof grading with deterministic construction verification, exposing cases where proof quality and construction validity diverge. Experiments on frontier open‑ and closed‑source models show that ComBench is far from saturated: the strongest model reaches 65.4% overall Avg. and 75.3% overall Best@4. We further find that Rigorous Proof Reasoning and Constructive Realization are distinct capabilities: Kimi‑K2.6 trails GPT‑5.5 on analysis‑centric proof grading but surpasses it on construction‑centric Best@4, while Existence and Construction problems remain consistently hardest across representative frontier models.

PaperId: 437, https://arxiv.org/pdf/2606.10448.pdf GitHub GitHub

Abstract:
The financial market is a typical low signal‑to‑noise ratio (SNR) setting, which often destabilizes off‑policy maximum‑entropy methods like Soft Actor‑Critic (SAC). Specifically, noisy state representations may produce unreliable Q‑value estimates, and bootstrapping amplifies these errors, forming a failure mode we call the "Financial Entropy Trap". In this paper, we propose FPQC‑SAC, an efficient and plug‑and‑play SAC variant that places a compact and bounded Parameterized Quantum Circuit (PQC) before the actor and critic networks to constrain feature propagation at the representation level, rather than filtering raw inputs or regularizing Q‑values after bootstrapping. Notably, FPQC‑SAC reduces the impact of extreme market fluctuations on Bellman target estimation, while trainable quantum entanglement preserves flexible cross‑asset interactions. Empirical evaluations on real‑world portfolio management tasks demonstrate that FPQC‑SAC substantially enhances out‑of‑sample stability and cumulative returns by achieving a 66.89% relative gain in cumulative return over standard unconstrained SAC and outperforms the best continuous‑control deep reinforcement learning baseline by approximately 27%. Open‑source code is available at https://github.com/ZeyuLIU‑UST/FPQC‑SAC‑main.

PaperId: 438, https://arxiv.org/pdf/2606.10389.pdf GitHub

Abstract:
Recent advances in LLM‑driven code evolution have enabled automated discovery by iteratively generating and improving programs. However, applying these methods to adversarial multi‑agent games introduces a fundamental challenge: the evaluation landscape shifts as strategies improve, causing fixed evaluators to become unreliable and evolution to stagnate. We propose three mechanisms to address this challenge: evaluator co‑evolution, which incorporates discovered champions into the opponent pool; hierarchical deep evaluation, which replaces noisy few‑game scores with statistically reliable assessments; and weakness pressure, which dynamically up‑weights the most difficult opponents to break through plateaus. We implement these mechanisms within FAMOU, a framework built upon the same foundation‑model code‑evolution paradigm as OpenEvolve and ShinkaEvolve. On the MCTF 2026 3v3 maritime capture‑the‑flag task, FAMOU consistently outperforms both baselines under two backbone LLMs, achieving the highest combined score (0.526) and the best generalization to unseen opponents (61.7% win rate), while ablations confirm that each mechanism contributes to performance. Notably, the LLM mutation process generates tactical structures entirely absent from the seed strategies ‑‑ including lookahead search and adaptive interception ‑‑ demonstrating that code‑level evolution can produce nontrivial algorithmic innovations in adversarial settings. The FAMOU‑evolved strategy further achieved 1st place in the hardware round‑robin and 3rd in simulation at the AAMAS 2026 MCTF Competition, validating its real‑world transferability. The optimized implementation and corresponding evaluation codes developed through our evolutionary process are available at: https://github.com/1xiangliu1/FAMOU‑CoEvo

PaperId: 439, https://arxiv.org/pdf/2606.10385.pdf GitHub

Abstract:
On‑policy distillation (OPD) has demonstrated strong empirical gains in enhancing complex reasoning in LLMs by aligning a student model with a teacher's predictive distribution over the student's own trajectories. An emerging variant, Privileged OPD, further strengthens this paradigm by employing a self‑teacher model augmented with privileged information, such as oracle traces, to mitigate teacher‑student capacity gaps while providing dense, answer‑directed supervision. However, current methods treat privileged information as a monolithic imitation target, failing to disentangle locally reachable reasoning steps from future‑conditioned oracle signals. Consequently, the student is encouraged to match a hindsight‑biased distribution that often falls outside its local predictive support. This reachability mismatch incentivizes the student model to skip valid intermediate reasoning in favor of locally unsupported shortcuts. To resolve this, we introduce Anchored Residual On‑Policy Distillation (AR‑OPD), a dual‑view framework that disentangles privileged supervision. Rather than enforcing strict full‑view imitation, AR‑OPD establishes a locally compatible anchor using a partially privileged teacher, isolating and injecting oracle foresight as a controlled residual to provide destination‑directed guidance. Across diverse reasoning tasks, AR‑OPD outperforms full privileged OPD by 2.3 points and SFT by 7.9 points. Crucially, this anchored residual mechanism reduces hindsight leakage by 21.7% and mitigates late‑stage drift, yielding up to a 7.2‑point advantage on challenging long‑horizon trajectories exceeding 768 tokens.

PaperId: 440, https://arxiv.org/pdf/2606.10368.pdf GitHub

Abstract:
Speech‑to‑text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens. In contrast, continuous‑target language modelling performs generation in a continuous space, yet its potential for S2T remains unexplored. To bridge this gap, we propose ELF‑S2T, an audio‑conditioned continuous‑target generative model for S2T. Built upon the pre‑trained Embedded Language Flows (ELF) backbone, ELF‑S2T processes speech via a frozen Whisper encoder and a single linear projector, prepending the resulting audio condition to the noisy text latent for in‑context, flow‑matching denoising. To prevent the model from over‑relying on its pre‑trained text context, we introduce audio forcing during training, and further amplify the audio condition via classifier‑free guidance at inference. Experiments on LibriSpeech and CoVoST2 show that ELF‑S2T achieves competitive ASR and S2TT performance. Crucially, our error analysis reveals that, although ASR and S2TT errors look very different on the surface, both stem from the same underlying cause, a close distance confusion in the continuous latent space. This finding naturally aligns with the continuous representation generation paradigm, indicating a common semantic mapping process beneath recognition and translation. Our code and pretrained models are publicly available at https://github.com/Sslnon/ELF‑S2T.

PaperId: 441, https://arxiv.org/pdf/2606.10298.pdf GitHub

Abstract:
When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reliability bottleneck. Existing contrastive decoding methods follow a \emphcontext‑aware paradigm that unilaterally amplifies context over parametric priors, overwriting correct priors when the context is erroneous. We generalize this to the conflict‑aware paradigm that dynamically allocates authority between prior and context based on conflict signals, rather than presupposing context trustworthiness. We show that the affine combination of prior and context logits yields a power family with an inherent regime asymmetry: extrapolation amplifies errors unboundedly when the prior is correct, interpolation under‑corrects when the context is correct, and no static regime covers both. Existing contrastive decoding methods are instances of this family, mostly extrapolative. To evaluate both conflict directions, we propose TriState‑Bench, a model‑aware evaluation protocol that calibrates per‑model prior knowledge to measure three conflict states: correction, resistance, and agreement. To resolve the asymmetry, we propose Adaptive Regime Routing (ARR), which routes between regimes at each step, lifting resistance EM from below 6 to 16‑‑33 without sacrificing correction or agreement. Our code is available at https://github.com/keith‑Jiang/conflict‑aware‑decoding.

PaperId: 442, https://arxiv.org/pdf/2606.10254.pdf GitHub

Abstract:
While Large Language Models (LLMs) have achieved near‑perfect performance in \emphsolving high‑school mathematics, their ability to \emphevaluate the diverse reasoning processes of real human students remains under‑examined. To bridge this gap, we introduce RealMath‑Eval, a rigorously annotated benchmark of 224 real‑world exam responses from high schools. Our initial evaluation reveals that even state‑of‑the‑art LLM judges struggle significantly on this task, exhibiting a high Mean Squared Error (～2.96) against expert human grading. To probe a plausible explanation, we contrast this performance with a control setting where the same judges evaluate synthetic LLM‑generated solutions. We identify a stark ``Evaluation Gap'': judges are considerably more accurate and consistent on synthetic text (MSE ～1.17) but struggle to generalize to authentic student reasoning. Through semantic embedding analysis, we find that synthetic errors suffer from a ``structural collapse'' into predictable, low‑dimensional linear subspaces, whereas human errors form a more diverse error space. Furthermore, generative probability probes suggest that human reasoning involves significantly higher information‑theoretic surprisal, indicating that student reasoning transitions are more out‑of‑distribution for current models. Finally, we find that surface‑level style transfer fails to close this gap. Our findings suggest that current LLM evaluation pipelines relying heavily on synthetic data may not adequately capture the diversity of authentic student mathematical reasoning.

PaperId: 443, https://arxiv.org/pdf/2606.10241.pdf GitHub

Abstract:
Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote‑or‑discard decisions land in a side database rather than the agent's own history. We show that an event‑sourced agent runtime removes that friction and turns controlled improvement into a first‑class workflow. When the agent's state is a deterministic projection of an append‑only event log, failures are recorded, a run replays exactly from its log, candidate patches scope to typed pipeline seams, gates are auditable, and every promotion or discard is itself an event. We demonstrate this with Regimes, a loop on the ActiveGraph runtime that diagnoses failed evaluations, proposes a repair at a pipeline point, and promotes it only after static checks, sandbox execution, in‑sample evaluation, and held‑out validation. The loop is target‑agnostic: the same control flow runs against different tasks through a common interface. On LongMemEval‑S the dominant failure is not retrieval but reconciliation: the evidence is already in the assembled context, yet the reader answers incorrectly. Across five seeded held‑out splits, Regimes discovers reader‑prompt repairs that improve final held‑out accuracy by +0.05 to +0.10 in four splits and +0.01 in one over‑promotion split; two splits are individually significant (seed 5 unadjusted for its sequential promotion structure), and the pooled count is descriptive only, since the splits share one 500‑question pool. The durable contributions are ActiveGraph as an auditable substrate that makes controlled improvement loops tractable, the held‑out‑gated loop it supports, the failure‑regime taxonomy routing each failure to a pipeline location (whose marginal value over an unrouted baseline is the primary open question), and the prompt‑as‑discovery‑probe hypothesis.

PaperId: 444, https://arxiv.org/pdf/2606.10196.pdf GitHub GitHub

Abstract:
Parameter‑efficient fine‑tuning (PEFT) aims to adapt pretrained models with a small trainable parameter subset, however, most existing methods choose this subset from fixed architectural heuristics rather than using dynamic, task‑aware criteria. We introduce FisherAdapTune, a Fisher‑guided Adaptive Fine‑Tuning framework that progressively selects parameter groups by tracking the temporal drift of their Fisher geometry. Starting from a PAC‑Bayesian view of fine‑tuning, we decompose the generalization error bound into Fisher‑weighted update costs and show that parameter groups whose curvature contribution has stabilized can be frozen to reduce the error bound without interrupting the remaining adaptation dynamics. FisherAdapTune formulates this criterion with a scale‑invariant Jensen‑Shannon distance between consecutive Fisher distributions, yielding an adaptive active parameter set. We evaluate our approach on a downstream segmentation task, and results show FisherAdapTune improves the in‑distribution performance and zero‑shot transfer in multiple settings, validating that Fisher structural drift is a useful signal for efficient, task‑aware adaptation. We release our \hrefhttps://github.com/AtlasAnalyticsLab/FisherAdapTunecode publicly to enable further application of our proposed approach.

PaperId: 445, https://arxiv.org/pdf/2606.10156.pdf GitHub

Abstract:
As recommender systems transition toward agentic, multi‑turn conversational interfaces, evaluation paradigms have struggled to keep pace. Current benchmarks often rely on "LLM‑as‑a‑judge" evaluations, which introduce subjectivity, high costs and inconsistency. We present τ‑Rec, a benchmark for agentic recommender systems that replaces subjective evaluation with verifiable rewards and a reveal‑tagged elicitation (RTE) mechanism that controls how task constraints surface during dialogue. By testing agents against structured catalog predicates and employing a pass^k reliability metric, τ‑Rec provides a systematic test for consistent reasoning. Our evaluation of nine configurations across five model families ‑‑ GPT‑5.4, Claude Sonnet 4.6, Gemini 2.5 Flash, DeepSeek V4 Flash, Qwen3‑32B and GPT‑5 mini ‑‑ reveals a steep reliability cliff, where even the best model achieves only ~57% at pass^1 and ~38% at pass^4, highlighting a critical gap in current conversational agent deployment. All code and data are publicly available at https://github.com/nbharaths/tau‑rec.

PaperId: 446, https://arxiv.org/pdf/2606.09961.pdf GitHub

Abstract:
Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performance in long‑horizon tasks. However, existing RL algorithms operate at the trajectory level, performing policy optimization only after collecting complete episode rollouts. This coarse‑grained approach faces fundamental challenges in multi‑turn agent settings where rewards are sparse, delayed, and credit assignment across individual steps is critical. In this work, we propose State‑Score‑Supervised Policy Optimization (3SPO), a novel RL algorithm that performs post‑step policy optimization with dynamic state score supervision. At each step, 3SPO computes the state score based on historical success rates, supervising step‑wise credit assignment, adaptive rollout and post‑step policy optimization without requiring value function estimation or additional auxiliary models. Theoretically, under a per‑state bandit abstraction, we show that the proposed score‑supervised allocation mechanism achieves logarithmic allocation regret and provide sample‑complexity guarantees for action identification, score distinguishability, and filtering stability. Experiments on ALFWorld and WebShop with Qwen2.5‑1.5B/7B‑Instruct show that 3SPO consistently outperforms GRPO by +22.6% on ALFWorld and +15.6 points on WebShop, while using comparable resources to achieve 2.4× more state exploration and 1.8× faster convergence. Code is available at https://github.com/genalyu/3SPO.

PaperId: 447, https://arxiv.org/pdf/2606.09959.pdf GitHub GitHub

Abstract:
Precipitation nowcasting is increasingly being approached with deep learning models that learn directly from recent radar observations. Although such models can efficiently capture short‑term precipitation motion, they often lack broader contextual information about the meteorological conditions under which rainfall develops. This paper investigates whether lightweight temporal context can improve radar‑based nowcasting, particularly for high‑intensity rainfall. We propose the Time‑Aware Small‑Attention U‑Net (TA‑SmaAt‑UNet), which extends the core SmaAt‑UNet model with temporal conditioning layers that use cyclical encodings of time‑of‑day and time‑of‑year to modulate intermediate feature representations. Experiments on KNMI radar precipitation data show that temporal conditioning is most beneficial for rare, high‑intensity precipitation events, while also improving the representation of seasonal variability and predicted rainfall‑intensity distributions. A layer conductance analysis further indicates that the added temporal conditioning layers are actively used by the model despite their small parameter cost. These findings suggest that simple, physically motivated temporal context can improve the realism and reliability of deep learning‑based precipitation nowcasts. The implementation of our models and training setup is available on \hrefhttps://github.com/gijsvn/TA‑SmaAt‑UNetGitHub.

PaperId: 448, https://arxiv.org/pdf/2606.09940.pdf GitHub

Abstract:
Dictionary learning methods like Sparse Autoencoders (SAEs) and crosscoders attempt to explain a model by decomposing its activations into independent features. Interactions between features hence induce errors in the reconstruction. We formalize this intuition via compact proofs and make five contributions. First, we show how, in principle, a compact proof of model performance can be constructed using a crosscoder. Second, we show that an error term arising in this proof can naturally be interpreted as a measure of interaction between crosscoder features and provide an explicit expression for the interaction term in the Multi‑Layer Perceptron (MLP) layers. We then provide three applications of this new interaction measure. In our third contribution we show that the interaction term itself can be used as a differentiable loss penalty. Applying this penalty, we can achieve ``computationally sparse'' crosscoders that retain 60% of MLP performance when only keeping a single feature at each datapoint and neuron, compared to 10% in standard crosscoders. We then show that clustering according to our interaction measure provides semantically meaningful feature clusters, and finally that sleeper agents have significant interactions. Code is available at https://github.com/chainik1125/crosscoders‑feature‑interactions/tree/arxiv.

PaperId: 449, https://arxiv.org/pdf/2606.09937.pdf GitHub

Abstract:
We introduce RKSC (Reasoning‑Aware KV Cache Sharing), a training‑free inference framework that eliminates two structural redundancies in multi‑branch LLM reasoning pipelines. ASKS (Attention‑Similarity KV Sharing) computes the prefix KV cache once and broadcasts it to all semantically similar branches via hidden‑state cosine similarity, strictly generalising the token‑exact prefix caching used by vLLM and SGLang. CGEE (Confidence‑Gated Early Exit) applies two complementary exit mechanisms: (1) it skips the verification forward pass entirely when generation confidence is decisive across branches, and (2) it terminates the verification pass at an intermediate layer when per‑layer entropy stabilises, using lightweight hooks on the transformer backbone. RSBCM (Reasoning‑Selective Block Cache Manager) prevents unbounded cache growth via attention‑weighted depth‑priority eviction. Across five model families (7B‑10B), four benchmarks, and 1,000 evaluated problems, RKSC achieves a mean speedup of 3.008x over the No‑KV baseline (peak 3.990x), a 1.66x mean improvement over vLLM‑equivalent prefix caching, with a CGEE‑induced error rate of only 0.37% (6 errors out of 1,616 verify calls). No fine‑tuning or architecture changes are required. Code is available at https://github.com/AnirudhSekar/RKSC.

PaperId: 450, https://arxiv.org/pdf/2606.09908.pdf GitHub GitHub

Abstract:
Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challenge for their design and evaluation. Prior work focuses mainly on individual‑level risks, overlooking interdependent privacy (IDP)‑‑where one person's data may be revealed by others without their knowledge or consent. We address this gap by introducing IDP‑Bench: the first LLM benchmark for IDP scenarios, grounded in the Contextual Integrity (CI) framework. We evaluate eight open‑source LLMs on their understanding of IDP scenarios across three levels of IDP reasoning using two LLM judges. Results show strong co‑ownership recognition (6/8 models exceed 90%) but persistent weaknesses in identifying CI parameters (information attribute, primary subject) and IDP‑specific parameters such as secondary subjects, where 7/8 models score below 74%. Models also struggle to judge sharing appropriateness (5/8 scoring below 77%). While the ability to judge the appropriateness of sharing improves with scale, performance tends to decline in smaller models, and prompt sensitivity remains high on IDP‑specific questions‑‑highlighting the need for more targeted study of IDP in LLM privacy research. Data \& code available \hrefhttps://github.com/tisl‑lab/Interdependent_Privacy_Benchhere.

PaperId: 451, https://arxiv.org/pdf/2606.09900.pdf GitHub

Abstract:
Long‑term memory is the missing layer for LLM agents: across sessions they forget, and the common workaround ‑‑ replaying the whole history into the prompt ‑‑ is expensive, slow, and, as distractors accumulate, less accurate. Most memory systems win on cost or latency but still lose to the full‑context baseline on accuracy, and benchmark numbers are reported on inconsistent, non‑reproducible harnesses, so one system appears at wildly different scores across sources. We present Engram, an open‑source, dual‑process memory engine on a bi‑temporal data model. A fast write path appends lossless episodes with no LLM on the critical path; an asynchronous path extracts atomic (subject, predicate, object) facts, builds a bi‑temporal knowledge graph, and resolves contradictions without an LLM call per fact ‑‑ invalidating, never deleting, so every fact keeps provenance and a supersession chain. A hybrid read path fuses dense, lexical, graph, and recency/salience signals, applies a point‑in‑time ("as‑of") filter, and assembles a compact, provenance‑tagged context. On the full 500‑question LongMemEval_S, graded by the official category‑specific judge, Engram's lean configuration ‑‑ answering from a ~9.6k‑token retrieved slice, never the full history ‑‑ scores 83.6% vs. 73.2% for full‑context (+10.4 points, McNemar p < 10^‑6) at ~8x fewer tokens (9.6k vs. 79k), with 0/500 errored. The gain needs a hybrid read path: facts alone lose recall, while facts plus retrieved chunks recover detail. We also contribute a neutral, in‑repo evaluation harness with the official judge baked in and the full‑context baseline in every table, publish the raw per‑question logs, and document the measurement‑integrity pitfalls (truncation, home‑grown judges, full‑history leaks) that silently distort memory benchmarks. Every number ships with a command to reproduce it.

PaperId: 452, https://arxiv.org/pdf/2606.09886.pdf GitHub

Abstract:
Sparse Mixture‑of‑Experts (MoE) large language models achieve strong quality with low per‑token compute, yet their deployment is often limited by the memory wall: the full expert pool must remain resident to support token‑dependent routing. Expert pruning is a direct remedy, but prior criteria typically score experts independently and overlook that MoE inference is inherently \emphcoalitional, where outputs arise from routed top‑k expert combinations. We propose SHAPE, a task‑driven pruning framework that explicitly models \emphintra‑layer expert cooperation. SHAPE formulates routing traces on a small calibration set as an empirical cooperative game and assigns interaction‑aware expert values via a Shapley‑style attribution over observed top‑k coalitions, enabling the identification of experts that are essential for high‑utility collaborations rather than merely frequent. To preserve MoE topology under a global pruning budget, SHAPE further introduces a \emphquality‑coverage selection rule that retains, in each layer, the minimal expert subset covering an α fraction of non‑negative Shapley mass, while using bisection to match a target keep rate. Experiments on three modern MoE backbones (Qwen3‑30B‑A3B, GPT‑OSS‑20B, and DeepSeek‑V2‑Lite) across diverse benchmarks show that SHAPE consistently improves robustness over global and layer‑wise pruning variants, maintaining competitive accuracy under 20% and 40% expert pruning without additional training and delivering clear reductions in peak GPU memory footprint. The open‑source code is available at https://github.com/Alizen‑1009/Shapley‑Moe.

PaperId: 453, https://arxiv.org/pdf/2606.09869.pdf GitHub

Abstract:
Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, determining the optimal split point, meaning the layer where the model is divided still remains a critical challenge, especially when clients have heterogeneous hardware capabilities. Fixed split points can overload weak devices and increase the communication and server load, which slows convergence and reduces stability. This paper introduces QSplitFL, a novel capability‑aware Deep Q‑Network (DQN) framework for optimal split point selection in Split learning based Federated Learning (SFL) environments. Unlike existing approaches that rely on high‑dimensional model weight representations, QSplitFL employs a lightweight state representation derived directly from client hardware metrics, including CPU utilization, memory, battery level, and network latency. The proposed framework incorporates a decayed loss‑drop reward function that prioritizes early convergence, and a committee‑based DQN architecture with majority voting to mitigate reward hacking. Extensive experiments on MNIST, Fashion‑MNIST, CIFAR‑10, and CIFAR‑100 datasets using CNN, ResNet50, MobileNetV4, and ConvNeXt architectures demonstrate that our approach achieves better convergence and higher accuracy compared to existing methods, while effectively adapting to heterogeneous device resources. The source code is publicly available at https://github.com/AIPO‑Lab/QSplitFL.

PaperId: 454, https://arxiv.org/pdf/2606.09837.pdf GitHub

Abstract:
Emotional interaction is increasingly crucial for conversational AI, yet current systems lack a self‑emotion determination mechanism to drive the streaming text‑to‑speech (TTS) synthesis. We propose an emotion‑planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner. The framework is implemented by a plug‑and‑play LLM module, initialized from pretrained LLMs, and trained by reinforcement learning (RL) with emotions as the actions. A hybrid reward is employed which combines imitation signals with theory‑driven scoring, in which the theory of Plutchik's wheel of emotions is adopted. By experiments on DailyDialog, EmoryNLP, IMEOCAP, and MELD, our method outperforms prompting and finetuning baselines on both emotion determination and response quality. We finally implement an entire streaming pipeline for real‑time deployment, with the speech quality confirming the framework's emotional alignment, contextual coherence, and expressive fluency. Codes, cases, and demos are available in https://sixingdeguo.github.io/EmoQ‑page/.

PaperId: 455, https://arxiv.org/pdf/2606.09751.pdf GitHub

Abstract:
Foundation models are moving from response generation into operational roles. They plan across steps, call tools, request human input, coordinate with other agents, and increasingly carry responsibility for work that affects customers, claims, code, contracts, and clinical decisions. Production deployments are no longer one human supervising one model. They are multi‑human, multi‑agent collaborations that cross teams, time zones, and trust boundaries. The technical surface for this collaboration remains weakly specified. When an agent drafts a response and a human edits it before it ships, the moment of human judgement is the most valuable signal in the system. In current practice it is recorded, if at all, in application code, chat threads, ticket comments, and tribal memory. Two protocol standards address adjacent concerns: MCP standardises agent access to tools and data, and A2A standardises agent‑to‑agent interoperability. Neither defines the shared workspace in which humans and agents perform accountable work together. This paper presents CHAP, the Collaborative Human‑Agent Protocol. Under CHAP, the override that used to vanish into a chat thread becomes a structured event carrying a diff, a rationale, and a content hash. The handoff between shifts becomes a portable envelope rather than a pinned message. The human approval of an agent's draft becomes a non‑repudiable signed decision that can be replayed years later. The protocol achieves this through a small Core (workspaces, participants, tasks, artefacts, and an append‑only evidence log) together with composable profiles that add review, modes, routing, deliberation, handoff, identity, signatures, and transparency‑backed audit as deployments require them. Specification, reference implementation, conformance suite, and worked examples are available at: https://github.com/BrightbeamAI/chap

PaperId: 456, https://arxiv.org/pdf/2606.09500.pdf GitHub

Abstract:
As autonomous research agents and AI co‑scientist systems push large language models (LLMs) from drafting toward end‑to‑end manuscript production, the bottleneck shifts from generation to verification. Fluent LLM output can hide fabricated citations, numbers that drift from source tables, and unmet reporting‑guideline items; existing tools generate without verifying, and self‑critique inherits the blind spots that produce confident fabrication. We describe an architecture pairing generation with verification, resting on three principles: decompose the workflow into self‑contained skills, gate every stage transition with halt‑on‑failure, and resolve each integrity question with the cheapest sufficient mechanism, a deterministic, re‑executable check where one suffices and a prose‑level probe only where interpretation is unavoidable. This determinism‑where‑possible split, organized as an integrity‑gate taxonomy, is the core contribution. It is realized as MedSci Skills, an open‑source toolkit of 43 skills with a 21‑detector deterministic tier, evaluated on three public‑dataset pipelines (STARD, PRISMA, STROBE) and a seeded‑defect ablation. Across the three pipelines every content‑hash manifest verified clean and the gates surfaced real defects; on 27 identical injected defects the deterministic gates detected all 27 with no false positives on the matched clean fixtures, whereas a single‑prompt LLM reviewer detected 11, its misses in code, bibliography, and style defects the prose hides. Determinism‑where‑possible verification yields an auditable, re‑executable trail that exposes the evidence a human needs to check an LLM‑assisted manuscript: feasibility and reproducibility evidence, not a claim of human‑competitive quality, which a separate blinded study addresses. MedSci Skills is MIT‑licensed and archived (v3.8.0).

PaperId: 457, https://arxiv.org/pdf/2606.09079.pdf GitHub

Abstract:
Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra‑long context serving. In this report, we propose Lookahead Sparse Attention (LSA), a novel inference paradigm powered by a Neural Memory Indexer built upon the DeepSeek‑V4 architecture. Rather than passively attending to all historical tokens, LSA proactively predicts future context demands and preserves only the query‑critical KV chunks in the GPU memory. Crucially, we instantiate this architecture via a backbone‑free decoupled training strategy. By formulating the indexer as a standard dual‑encoder architecture, we train it independently using standard retrieval training frameworks without ever loading the massive backbone model into GPU memory. We demonstrate that this "less is more" paradigm significantly maximizes serving efficiency while acting as an effective attention denoiser in tasks that rely on long‑term global memory. Across primary long‑context evaluation suites (e.g., LongBench‑v2, LongMemEval, and RULER), FM‑DS‑V4 compresses the average physical KV cache footprint down to merely 13.5% of the full‑context baseline, while consistently preserving or slightly elevating downstream accuracy (+0.6% absolute margin on average). Crucially, at extreme 500K scales, FlashMemory suppresses the physical KV cache overhead by over 90% without destabilizing the backbone's core reasoning capacities.

PaperId: 458, https://arxiv.org/pdf/2606.09052.pdf GitHub

Abstract:
Self‑evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher‑generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co‑training framework with two co‑evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them. The solver is trained with standard correctness rewards against the generator‑provided answers, while the generator is rewarded by an optimizer‑aware influence score that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose DuGRPO, a dual‑normalized variant of GRPO, for generator training. Together, these turn the document pool into an adaptive curriculum that favors questions useful to the current solver, not just hard ones. On Qwen3‑8B‑Base, INFUSER outperforms strong self‑evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co‑evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary, and two extensions, applying INFUSER to an instruction‑finetuned anchor and augmenting it with rule‑verifiable RLVR data, further demonstrate the flexibility and generalizability of the framework. Code is available at https://github.com/FFishy‑git/INFUSER.

PaperId: 459, https://arxiv.org/pdf/2606.09004.pdf GitHub

Abstract:
Feature engineering remains a cornerstone of tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for its automation, giving rise to LLM‑powered Automated Tabular Feature Engineering (LATTE). However, the field lacks standardized, cost‑aware evaluation platforms, and the combinatorial explosion of design choices obscures true algorithmic progress. To bridge these gaps, we systematically deconstruct 15 representative LATTE methods into a unified 6‑dimensional taxonomy. Based on this abstraction, we introduce LATTEArena, a standardized, modular, and extensible benchmarking framework that decouples monolithic pipelines into reusable execution blocks. By distilling the massive combinatorial space, we evaluate 24 core LATTE configurations across 7 research questions. Our head‑to‑head benchmarking goes beyond predictive accuracy to quantify token efficiency and execution robustness, yielding 17 empirical findings on cost‑effectiveness trade‑offs. Furthermore, we provide 3 concrete recommendations for optimal real‑world deployment. By enabling controlled component‑level comparisons, LATTEArena shifts the paradigm from ad‑hoc prompt engineering to systematic context management. All code, datasets, and over 4,000 execution logs are publicly available to foster a dynamic, community‑driven benchmark. Our framework, leaderboard, and all artifacts are hosted on the LATTEArena project website at https://goodenhak.github.io/LATTEArena.

PaperId: 460, https://arxiv.org/pdf/2606.08898.pdf GitHub

Abstract:
In the task of few‑shot class‑incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease. However, the number of classes generally increases or decreases in practice. In this paper, we investigate a problem of Few‑shot Class‑variable Incremental Audio Classification (FCIAC), in which the number of classes increases or decreases. We propose a FCIAC method using prototype adaptation and pseudo class‑variable training. The model in our method consists of an encoder and a classifier. The classifier is initialized by a class‑variable prototype adaptation network, whose structure dynamically changes with the change of classes. In addition, we design a pseudo class‑variable training strategy to enhance the model's adaptability to changing classes. Experiments on three public datasets show that our method exceeds previous methods in average accuracy. The code is at: https://github.com/cgq2971‑afk/FCIAC.

PaperId: 461, https://arxiv.org/pdf/2606.08797.pdf GitHub

Abstract:
Decision‑focused learning has shown great promise for addressing predict‑then‑optimize problems, particularly in the presence of under‑specified models. However, its practical deployment is often hindered by high computational costs and limited scalability, as it requires solving a constrained optimization problem for each training instance at every iteration. To address these challenges, we propose a novel framework that incorporates Lagrangian decomposition into the decision‑focused learning paradigm. Specifically, we introduce a new surrogate objective along with two loss functions for evaluating and training the underlying prediction model. We further propose two variants of our approach, which offer different trade‑offs between computational efficiency and solution quality. Our framework can be seamlessly integrated with standard decision‑focused learning methods, including Smart Predict‑then‑Optimize (SPO+) and Implicit Maximum Likelihood Estimation (IMLE). Through experiments on two standard benchmarks, the multi‑dimensional knapsack problem and quadratic portfolio optimization, we demonstrate that our approach achieves competitive performance while remaining amenable to parallelization. In particular, it consistently outperforms traditional decision‑focused learning methods on large‑scale instances, involving up to eight times more variables than those typically considered in related work. The implementation is available at https://github.com/corail‑research/DFL‑LD.

PaperId: 462, https://arxiv.org/pdf/2606.08728.pdf GitHub

Abstract:
Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP to one of the most consequential AI frontiers. This survey provides a unified account of the field's evolution, from early rule‑based math word problem (MWP) solvers and template‑driven geometry systems, through neural expression generation and LLM prompting, to contemporary reasoning models, multi‑agent systems, neuro‑symbolic theorem provers, and verified discovery workflows. We organize the landscape along four axes: (i) informal reasoning over text and diagrams, spanning MWP solving, multimodal geometry, and VLMs; (ii) formal reasoning in proof assistants, including autoformalization, tactic prediction, compiler‑guided repair, and proof search; (iii) mathematical discovery, where systems propose constructions, improve bounds, or assist attacks on open problems; and (iv) the inference and training‑time techniques, including CoT prompting, tool use, process reward models, and RLVR, that increasingly connect generation with verification. We catalog major benchmarks across grade‑school arithmetic, competition mathematics, geometry, formal proving, multimodal and multilingual reasoning, and expert evaluation, and we examine benchmark saturation, contamination, reporting mismatches, and the distinction between pass@1, majority voting, and verifier‑assisted pass@k. We critically assess failure modes: brittleness under perturbation, reward hacking, multimodal grounding failures, fragile formalization, and the energy cost of reasoning‑scale inference. Drawing on recent perspectives from working mathematicians, we identify future directions centered on verified‑discovery workflows, reasoning efficiency, and infrastructure to make AI‑assisted formalization broadly usable. Companion materials: https://github.com/Starscream‑11813/awesome‑AI4Math.

PaperId: 463, https://arxiv.org/pdf/2606.08676.pdf GitHub

Abstract:
AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of these tools are now integrated directly into Integrated Development Environments (IDEs). Developers interact with code in two distinct cognitive modes: Flow and Command. While developers require tools that directly complete or infill code in unfinished programs during Flow mode, they also need tools that can comprehend intentions expressed as natural‑language instructions and convert them into executable code in Command mode. Although instruction‑tuned Large Language Models (LLMs) dominate many application scenarios due to their abilities to infer and fulfill developers' intents, it remains unclear whether the same paradigm is equally suitable for different code‑related tasks. Therefore, it is necessary to understand how instruction tuning affects the feasibility of CodeLLMs as coding assistants. To fill this gap, we conduct the first empirical study that uncovers a key trade‑off caused by instruction tuning across programming modes, which we term the Instruction‑Tuning Tax. Our results show that instruction tuning is not a free lunch: although instruction‑tuned models are more capable of following instructions and leveraging structured guidance, these gains often come at the cost of weaker infilling performance. We further extend our study through both qualitative and quantitative analyses, including manual failure categorization, behavioral metrics that capture generation fidelity, and intermediate‑checkpoint evaluation throughout the tuning process. Summarizing our results into seven findings and four implications, our study offers a new perspective on the development of AI‑powered coding tools and highlights the need to carefully balance instruction‑following ability with effective code generation assistance.

PaperId: 464, https://arxiv.org/pdf/2606.08602.pdf GitHub GitHub

Abstract:
We present an online reinforcement learning (RL) algorithm for fine‑tuning flow‑matching policies in continuous‑control problems. Our key insight is to view RL‑based policy improvement as a transport of action densities towards regions of high reward, which naturally aligns with the transport formulation of flow matching models. Prior methods either approximate the current or optimal policy distribution or resort to distillation, which introduces biased gradients or sacrifices multimodal modeling capacity. In contrast, our approach for RL with Density Transport, which we name \emphRLDT, constructs a transport field from a maximum‑entropy RL objective using Stein Variational Gradient Descent (SVGD). Then, it finetunes a pretrained flow matching policy to align with this field. Training with this alignment objective is nontrivial because flow‑matching policies generate actions via a multi‑step process, making direct gradient‑based optimization challenging. To overcome this challenge and stabilize training, we approximate policy actions from intermediate denoising steps via expected‑target estimation. This allows the transport‑field update to propagate into the network parameters without unstable backpropagation through time. Experimental results demonstrate that RLDT outperforms competitive baselines in reward quality and convergence speed. This performance holds across diverse continuous‑control tasks, encompassing both dense and sparse rewards, as well as state‑ and vision‑based long‑horizon robot manipulation. The project webpage is \hrefhttps://rpfey.github.io/rldt/https://rpfey.github.io/rldt/.

PaperId: 465, https://arxiv.org/pdf/2606.08596.pdf GitHub

Abstract:
Constructing efficient and reliable policies to assist humans is indispensable for human‑AI collaboration. Existing methods mainly follow two lines of work. Most prior work relies on multi‑agent reinforcement learning (MARL) to learn black‑box policies, which limits interpretability and raises safety concerns. Recent methods query large language models (LLMs) at each decision step, causing slow responses and high inference costs. We propose Collaboration Policy Tree (Co‑pi‑tree), a closed‑loop method that learns an executable policy tree consisting of a partner‑behavior prediction tree and an agent‑action selection tree. Co‑pi‑tree constructs a policy by distilling LLM reasoning into policy tree code. It then evaluates the policy through partner interaction, obtains feedback, and uses natural language to summarize the interaction feedback to improve problematic branches. Experiments in Overcooked‑AI show that Co‑pi‑tree improves average reward by 35.4% over the baseline average, while reducing the number of LLM queries by 77.7% and test‑time latency by 97.1%. Project page: https://beiwenzhang.github.io/Co‑pi‑tree/

PaperId: 466, https://arxiv.org/pdf/2606.08530.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models achieve strong benchmark performance but still struggle in real‑world deployment with unseen objects, background shifts, and different robot embodiments. We argue that this stems from the lack of a unified geometry‑aware manipulation representation, leaving existing VLAs vulnerable to low‑level trajectory supervision, misaligned 3D features, and embodiment differences. To address this, we propose GEAR‑VLA, a VLA framework for learning unified geometry‑aware action representations for generalizable robotic manipulation. GEAR‑VLA adopts coarse‑to‑fine action learning, where multi‑source embodied pretraining equips the VLM with embodied reasoning and discrete action understanding before latent action tokens connect action semantics to a gradient‑decoupled DiT continuous action expert. It further performs semantic‑aligned 3D integration by aligning a trainable 3D spatial backbone with the VLA representation while freezing the original VLM‑aligned visual pathway. To share this representation across robots, GEAR‑VLA uses embodiment canonicalization, where embodiment‑aware states and embodiment‑invariant actions confine robot differences to the low‑level interface. Extensive simulation and real‑world experiments demonstrate strong generalization: GEAR‑VLA achieves state‑of‑the‑art performance on LIBERO, zero‑shot LIBERO‑Plus, and RoboTwin 2.0, reaches 85.9% success on AgileX and 81.0% on the pretraining‑unseen LDT‑01 embodiment, and obtains 90.1% success on a 6,360‑trial universal grasping benchmark with 212 unseen objects. Code and models will be released at https://github.com/babynabeauty/GEAR‑VLA.

PaperId: 467, https://arxiv.org/pdf/2606.08508.pdf GitHub

Abstract:
Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off‑task, or commit to unrecoverable actions. Existing online failure detectors either require white‑box access to policy internals or add runtime overhead through resampling and observation‑side signals. Our empirical analysis shows that emitted action chunks themselves already carry strong predictive signal for impending failures in generative robot policies. Motivated by this observation, we introduce ActProbe, a lightweight, pure action‑space detector that uses two compact signals available from a single forward pass: Temporal Consistency Error (TCE) between consecutive action chunks and Action Chunk Magnitude (ACM) of the current chunk. ActProbe maps these signals to per‑step failure probabilities with a task‑conditioned LSTM‑MLP architecture. Across a diverse suite of generative robot policies and benchmarks, ActProbe raises alerts before failures become visually recognizable, improving the accuracy (F1)‑timeliness Pareto frontier of failure detection by an average hypervolume gain of +12.7% over both internal‑ and external‑feature baselines, with a +9.0% early‑detection ROC‑AUC lead on unseen tasks. ActProbe further transfers to deployment, predicting failures on unseen real‑robot pick tasks and accelerating RL fine‑tuning (PPO) with 2.9x fewer environment interactions.

PaperId: 468, https://arxiv.org/pdf/2606.08467.pdf GitHub

Abstract:
While confidence calibration is essential for trustworthy decision‑making in safety‑critical applications, the robustness of calibrated GNNs to adversarial structural perturbations remains largely unexplored. However, studying calibration attacks on graphs presents unique technical challenges: (1) the discrete nature of graph structures complicates gradient‑based optimization, (2) existing underconfidence objectives fail to drive predictions toward uniform distributions, and (3) GNNs are highly sensitive to edge perturbations, often causing unintended label changes that violate attack constraints. To address these challenges, we propose a Unified Graph Calibration Attack (UGCA) framework designed for worst‑case (white‑box) analysis of GNN calibration robustness. UGCA introduces a KL‑divergence loss to encourage uniform predictive distributions, a reranking mechanism to reduce label flipping, a hybrid loss to recover labels when violations occur, and beam search to explore a broader adversarial search space. We further provide theoretical insights linking model generalization, dataset complexity, and calibration vulnerability, showing that models with higher accuracy or trained on datasets with more classes are more susceptible under this threat model. Extensive experiments demonstrate that UGCA substantially increases Expected Calibration Error while preserving classification accuracy. Our code is publicly available at https://github.com/CaptainCuong/Graph‑Calibration‑Attack.git.

PaperId: 469, https://arxiv.org/pdf/2606.08450.pdf GitHub

Abstract:
Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance return, risk, and transaction costs. Yet in non‑stationary markets, raw OHLCV states and short‑horizon return rewards often provide an under‑specified learning interface, motivating large language models as a way to inject financial knowledge into state and reward design while constraining open‑ended generation. To this end, we propose GIFT, an LLM‑guided framework for state‑reward interface design in PPO‑based financial reinforcement learning. Rather than using the LLM to make trading decisions, GIFT uses Factor‑guided State Enhancement to generate state features from financial‑factor primitives, Risk‑rule‑guided Reward Shaping to generate auxiliary rewards from portfolio‑risk rules, and Diagnostic‑guided Refinement to revise candidate interfaces using PPO rollout diagnostics. After refinement, GIFT fixes the selected state‑reward interface before evaluation, with no further LLM queries or interface updates at test time. Comprehensive rolling‑window experiments across diverse market regimes and portfolio scenarios demonstrate that GIFT improves learning‑signal quality and out‑of‑sample risk‑adjusted portfolio performance over baselines. Code and data are available at: https://github.com/KAG778/GIFT .

PaperId: 470, https://arxiv.org/pdf/2606.08433.pdf GitHub GitHub

Abstract:
This paper reads six engine‑level measurements together ‑‑ 1.1 host attack surface, 1.2 information leakage, 1.3 defense‑in‑depth stackability, 1.4 public CVE history, 1.5 patch cadence, and 1.6 upstream fuzzing posture ‑‑ to describe how five AI‑sandbox products isolate guest code from the host kernel. No single axis is a sufficient basis for a comparative judgement; the cross‑axis reading is the load‑bearing analysis. Three high‑level findings: (1) engine classes (microVM, userspace kernel, OCI container) separate cleanly on every architectural axis, but products within a class do not; (2) product pin policy is the dominant operator‑facing variable ‑‑ engine‑side patch latency aggregates to ~0 days for coordinated disclosures, while downstream lag spans 0 days to 471+ days to "opaque" to infinity; (3) fuzzing investment splits into three tiers, and the strongest combination ‑‑ microVM x continuous public fuzzer ‑‑ is unoccupied in this set, leaving the "0 published CVEs x no upstream fuzzer x no academic study" intersection structurally unmeasured. We report per‑axis orderings, per‑product portraits, and a threat‑model qualification matrix; no overall ranking is proposed. Companion repository (code, Apache‑2.0): https://github.com/orbitalab/RnD‑ai‑sandboxes‑sec‑study‑part‑1. License: CC BY 4.0.

PaperId: 471, https://arxiv.org/pdf/2606.08432.pdf GitHub

Abstract:
On‑policy distillation (OPD) has become a central post‑training tool for large language models (LLMs), providing dense per‑token teacher supervision along the student's own rollouts. In this work, we identify a common structural cause underlying OPD, which we call prefix failure. Under prefix failure, dense per‑token supervision induces a bimodal teacher mixture and fragmented gradients that token‑level loss truncation or reweighting fail to address. This observation motivates us to move beyond token‑level loss interventions toward trajectory‑level output corrections. We thus propose Trajectory‑Refined Distillation (TRD), a trajectory‑level correction method that revises the student's rollout under the teacher guidance while within on‑policy support. By correcting problematic prefixes before distillation, TRD mitigates prefix failure at its source. Moreover, TRD improves the exploration by exposing the student to alternative valid derivations under teacher guidance, even when the original rolls are already correct. TRD can also be applied to on‑policy self‑distillation (OPSD), a parameter‑sharing variant that uses the student model conditioned on privileged informations as the teacher. Across a wide range of benchmarks and base models at multiple scales, TRD consistently outperforms prior baselines, improving single‑attempt accuracy and broadening reasoning coverage. Code is available at https://github.com/louieworth/trd

PaperId: 472, https://arxiv.org/pdf/2606.08382.pdf GitHub

Abstract:
Low‑rank projection has emerged as a promising approach for compressing the KV cache by exploiting hidden‑dimension redundancy. However, prior methods rely on fixed or heuristic rank selection and struggle to achieve aggressive compression with minimal accuracy degradation. We propose STAR‑KV, an adaptive low‑rank KV cache compression framework with fine‑grained rank control. STAR‑KV encompasses 1) a differentiable thresholding mechanism that enables optimal rank selection at both attention‑head and block levels, 2) a hybrid decomposition strategy that applies different low‑rank factorizations according to the sensitivity of key and value projections, and 3) a low‑rank‑aware mixed precision quantization that leverages data statistics for near lossless low‑bit quantization. Evaluated across multiple LLMs and benchmarks, STAR‑KV achieves up to 75% KV cache compression and up to 20x overall KV cache reduction when combined with quantization. Enabled by custom Triton‑based GPU kernels, STAR‑KV delivers up to 6.9x speedup for the attention module and 3.1x end‑to‑end generation throughput. Our code is publicly available at: https://github.com/PriyanshBhatnagar/STAR‑KV.

PaperId: 473, https://arxiv.org/pdf/2606.08340.pdf GitHub

Abstract:
As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open‑ended interactive tasks. Yet existing evaluations rarely test these demands together, instead emphasising single‑agent tasks, short interactions, or highly structured multi‑agent settings. We introduce alem, a JAX‑based benchmark for open‑ended multi‑agent coordination built on Craftax‑like dynamics. Alem embeds procedurally generated coordination tasks, soft specialisation, communication, and controllable coordination difficulty into a long‑horizon survival world with exploration, crafting, trading, and combat. We evaluate 13 modern LLMs zero‑shot within homogeneous teams, with trained MARL agents as reference points. Current LLM agents remain far from solving alem, averaging only ~6% normalised return, but their failures are not uniform. On the hardest coordination setting, zero‑shot Gemini‑3.1‑Pro‑High approaches MARL agents trained for one billion steps, while GPT‑5.4‑High achieves strong base‑task reward but much lower coordination reward. This contrast shows that individual task competence does not imply coordination competence. Ablations show that communication is the largest contributor to coordination, while memory and reasoning help when used to maintain multi‑step plans. Overall, our results identify coordination as a distinct bottleneck for frontier LLM agents, separate from single‑agent capabilities. Alem makes this bottleneck measurable and provides a controlled testbed for developing agents that communicate, allocate roles, and execute shared plans. Code is available at https://github.com/alem‑world/alem‑env.

PaperId: 474, https://arxiv.org/pdf/2606.08275.pdf GitHub

Abstract:
When an LLM agent fails ‑‑ issues a refund it should not have, calls the wrong tool, leaks data ‑‑ existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure. The obvious heuristics are wrong: the step that executes the harmful action is usually not the step that decided on it, and LLM‑judge attribution is correlational and unreliable (state‑of‑the‑art step‑level accuracy on the Who&When benchmark is about 14%). We present Causal Agent Replay (CAR), which answers the question by intervention: it models an agent run as a structural causal model, applies a do‑operation to a step, and re‑executes the trajectory forward under the same stochastic policy, measuring the shift in the outcome distribution. We define an intervention algebra over agent steps, a single‑step contrastive estimator whose point‑of‑commitment rule resolves a confound specific to stochastic run‑forward, and a budget‑bounded Monte‑Carlo Shapley estimator that splits credit across interacting steps. Every effect is reported with confidence intervals. We validate against synthetic structural causal models with planted ground truth: the contrastive estimator recovers the pivotal step, and Shapley recovers a two‑step interaction (0.44, 0.45, ~0; efficiency sum 0.909 versus the analytic 0.91). CAR is open source and runs on hosted or free local models.

PaperId: 475, https://arxiv.org/pdf/2606.08234.pdf GitHub

Abstract:
LLM‑based scientific agents have shown strong capacity for autonomous research, yet their safety layers remain structurally divorced from core reasoning: they inspect pipeline outputs rather than shaping the deliberation that produces them. This separation opens two failure modes: safety signals accumulated at one stage are discarded before the next, and sequences of individually benign tool calls can compose into harmful outcomes that no single‑step filter detects. To address these challenges, we introduce SciTrace, a framework that weaves safety reasoning into every stage of the scientific agent pipeline. SciTrace couples two complementary mechanisms: a Safety‑Intrinsic Reasoning Loop (SIR) that maintains a cumulative risk state across the Thinker, Experimenter, Writer, and Reviewer stages through joint task‑and‑safety deliberation, and a Compositional Tool‑Chain Verifier (CTV) that performs trajectory‑aware safety checks before execution, catching risks that surface only across multi‑step tool sequences. Evaluated on 240 high‑risk research tasks and 120 tool‑related risk tasks spanning six scientific domains, SciTrace achieves state‑of‑the‑art (SOTA) safety among compared frameworks across four backbone models: it consistently improves tool call safety and adversarial robustness while preserving scientific output quality, and it uncovers 78.8% of the compositional tool‑chain escapes that single‑step monitors miss. The project website is available at https://opensciagent.github.io/SciTrace/.

PaperId: 476, https://arxiv.org/pdf/2606.08191.pdf GitHub

Abstract:
Token aggregation is a common bottleneck in models that map token representations to sample‑level predictions, yet most pooling methods operate only in the original token domain. We propose FLaG, a plug‑in aggregation module that transforms token representations with the real FFT, summarizes spectral components with learnable latent queries, applies a channel‑wise gate, and reconstructs enhanced time‑domain tokens for final pooling. We evaluate FLaG on antimicrobial peptide (AMP) activity prediction with ESM2, image classification with ResNet18 on CIFAR‑10 and CIFAR‑100, and text classification with RoBERTa on IMDB and GLUE. FLaG achieves its clearest gains on the ESM2‑8M antimicrobial peptide tasks and on CIFAR‑100, while remaining competitive with strong text baselines on IMDB and GLUE. Then we probe its behavior on the AMP setting with band knockouts, gate summaries, residue perturbations, latent‑query readouts, and structure‑proxy stratification. We find that low‑frequency bands contribute the most overall, and the remaining higher‑band pattern is more sample‑specific. The gate acts as a broadly shared spectral reweighting stage and the cross‑attention patterns are sample‑specific with mild query‑wise differentiation, and higher‑helix peptides exhibit stronger average spectral sensitivity in both bacteria. The supplementary materials, source code and data are released at https://www.healthinformaticslab.org/supp/ and https://github.com/Kewei2023/AMPCliff/tree/FLaG.

PaperId: 477, https://arxiv.org/pdf/2606.08167.pdf GitHub

Abstract:
Recent research has established empirical scaling laws to predict model performance on multi‑domain data mixtures. However, a theoretical understanding of these model loss behaviors remains absent. In this work, we propose a unified framework to explain the underlying mechanics of data mixing. Our approach extends theoretical perspectives originally developed for standard neural scaling laws (e.g., Kaplan and Chinchilla) to the multi‑domain setting. Based on the distributional assumption that domains overlap on fundamental skills while diverging on specialized skills, we identify two key factors that govern the domain losses of models trained on different data mixtures: Capacity Competition, where the allocation of finite model capacity couples domain losses globally, and Noise Reduction, where optimal weights shift toward harder‑to‑learn domains to minimize overall noise. Empirical evaluations show that our framework outperforms existing baselines by fitting the loss landscape with a lower Mean Relative Error and identifying higher‑performing training mixtures. Most importantly, our model successfully extrapolates across scales, predicting highly effective mixtures for large, unseen scales using parameters fitted on smaller ones. In addition, our model achieves these results using significantly fewer parameters compared to previous empirical laws. Our code is available at https://github.com/meiqwq/Explaining‑Data‑Mixing‑Scaling‑Laws.

PaperId: 478, https://arxiv.org/pdf/2606.08151.pdf GitHub

Abstract:
Tool‑using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time. We present CICL, a decision‑aware context layer that turns instance evidence into a context graph, routes deterministic, Opus‑assisted, Qwen, Codex/GPT‑5.5, and Qwen‑QLoRA judgments through a shared eight‑field schema, scores units by action shift, outcome uplift, necessity, and negative‑transfer risk, and packs high‑utility evidence as typed memory cards for a budgeted agent. The design separates the measured decision signal from the judge model, so frontier annotation, local surrogates, and lightweight rankers can be compared under one auditable protocol. Empirically, CICL yields a concrete open‑benchmark gain while exposing its limits. On 50 SWE‑bench Verified file‑retrieval instances, direct Qwen3.6‑plus reranking of BM25 top‑50 candidates raises hit@1 from 0.58 to 0.78 and MRR@10 from 0.634 to 0.790, with all 2,500 judgments parseable. Controlled diagnostics show action‑criticality: at budget 120, CICL reaches F1 0.620 on v1 and 0.425 on v3, and removing the top‑utility semantic v3 unit collapses F1 to 0.000. Supplementary checks add Qwen‑QLoRA agreement over 710 candidates, a small 200‑label real‑code Opus‑assisted signal, and a three‑instance patch smoke validating retrieval‑to‑patch plumbing without claiming official SWE‑bench success. RepoBench‑R summaries still beat cards, and compact rankers do not yet replace the heuristic. CICL contributes a reproducible measurement and selection layer for decision‑critical context, not an end‑to‑end coding‑agent repair claim.

PaperId: 479, https://arxiv.org/pdf/2606.08146.pdf GitHub

Abstract:
Fraud detection in payment, e‑commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers. Existing methods fall at least one of these requirements: automated machine learning systems search a fixed numerical space without semantic awareness of the dataset; graph neural network‑based methods require pre‑defined relational graphs and remain opaque at the individual‑decision level; and the design of general‑purpose large language model (LLM) agents does not consider the recall and precision constraints specific to real‑world fraud detection. In this paper, we propose SAGE, the first end‑to‑end LLM‑driven multi‑agent framework for fraud detection. SAGE coordinates three dedicated agents that make decisions based on a six‑layer Data Diagnostic Tree (DDT) and a Markov decision process guided by natural‑language gradients, automatically optimizing the model under a fraud‑specific reward. On five fraud datasets and five LLM backbones, SAGE wins 96.00% of method‑‑dataset comparisons and improves F1 by an average of 40.86% over baselines. The code is available at https://github.com/yichenC1c/SAGE.

PaperId: 480, https://arxiv.org/pdf/2606.08123.pdf GitHub

Abstract:
Vision‑based driver monitoring systems are increasingly deployed in safety‑critical intelligent transportation settings, yet they are almost always compared on classification accuracy alone. This paper argues that accuracy is insufficient to characterize a model's fitness for real‑world deployment, and proposes the Human‑Centered Benchmarking Framework (HCBF), which evaluates models across four dimensions: accuracy, explainability, efficiency, and robustness. The framework is applied to four representative lightweight architectures, MobileNetV3, ShuffleNetV2, EfficientNet‑B0, and DeiT‑Tiny, on the MRL Eye Dataset for eye‑state classification. While the models are nearly indistinguishable on clean‑set accuracy, each leads in exactly one dimension, and all four lie on the Pareto frontier. A Human‑Centered Score computed under three deployment‑oriented weighting scenarios ranks ShuffleNetV2 first throughout. However, this aggregate winner retains less than half of its performance under sensor noise and fails by classifying closed eyes as open, whereas the transformer remains robust. These findings show that aggregate ranking can mask dimension‑specific vulnerabilities that are operationally decisive, underscoring the value of multi‑dimensional, human‑centered evaluation.

PaperId: 481, https://arxiv.org/pdf/2606.08094.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation‑class GPU, a mismatch for the hardware on which robots actually run. We present vla.cpp, a portable C++ inference runtime built on llama.cpp. To our knowledge, it is the first ggml‑class engine to natively serve the flow‑matching and diffusion VLA inference pattern, in which a cached vision‑language prefix is consumed by a cross‑attending action expert integrated over several solver steps. A single runtime serves seven architectures spanning five backbone and four action‑head families behind one request/response protocol, with each model packaged as a self‑contained bundle. On LIBERO‑Object, the engine matches a state‑of‑the‑art checkpoint to within one episode out of 200, and runs BitVLA at 100% success in 1.3 GiB of memory. The same bundle runs unchanged across three hardware tiers, from a consumer GPU down to an 8 GB embedded module. A cross‑hardware roofline analysis shows that batch‑1 VLA inference is compute‑bound, so utilization rather than bandwidth is the deployment lever; an IMMA ladder GEMM derived from this analysis cuts BitVLA per‑step latency by 4.5x. We then frame an on‑robot stress test on an ALOHA arm that isolates the latency constraint under which a learned VLA must replan against a moving target on the hardware it was trained for. Code, demo videos, and the reproducible benchmark scaffold are available at https://fai‑modelopt‑tech.github.io/vla‑cpp.github.io/.

PaperId: 482, https://arxiv.org/pdf/2606.08063.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in visual understanding, yet their performance degrades significantly under real‑world visual corruptions. While existing robustness enhancement approaches exist, they are limited: black‑box feature alignment lacks interpretability, and white‑box text‑based reasoning cannot restore lost pixel‑level details. This work investigates a fundamental research question: Can MLLMs recover corrupted visual content by themselves? To address this, we propose Robust‑U1, a novel framework that equips MLLMs with explicit visual self‑recovery capability for robust understanding. The approach comprises three core stages: supervised fine‑tuning for initial reconstruction, reinforcement learning with dual rewards (pixel‑level SSIM and semantic‑level CLIP similarity) for aligning high visual quality, and multimodal reasoning that jointly considers both the corrupted input and the recovered image. Extensive experiments demonstrate that Robust‑U1 achieves state‑of‑the‑art robustness on the real‑world corruption benchmark and maintains superior performance under adversarial corruptions on general VQA benchmarks. Analysis confirms that high‑quality visual recovery directly enhances reasoning performance, establishing self‑recovery as a critical mechanism for robust visual understanding. The source code is available at https://github.com/jqtangust/Robust‑U1.

PaperId: 483, https://arxiv.org/pdf/2606.08037.pdf GitHub

Abstract:
Electrocardiogram (ECG) classification models often suffer from severe label scarcity, making semi‑supervised learning (SSL) an attractive strategy for reducing annotation costs. In clinical settings, however, unlabeled pools frequently contain out‑of‑distribution (OOD) anomalies or diagnostic groups absent from the labeled set. Standard SSL forces incorrect pseudo‑labels onto these unseen classes, producing overconfident predictions. To address this, we propose SafeECGMatch, a calibration‑aware safe SSL framework for single‑label ECG classification under label distribution mismatch. Methodologically, SafeECGMatch employs a dual‑branch architecture extracting time‑frequency latent representations via ECG‑specific augmentations. Crucially, it dynamically aligns confidence with empirical accuracy through adaptive label smoothing and temperature scaling, calibrating both the multiclass classifier and the OOD detector across temporal and spectral domains. This joint optimization allows trustworthy OOD rejection and reliable pseudo‑labeling. Evaluated on the PTB‑XL and PhysioNet/CinC Challenge benchmarks, SafeECGMatch achieves state‑of‑the‑art accuracy and calibration, advancing reliable knowledge discovery in physiological time‑series. Code is available at https://github.com/labhai/SafeECGMatch.

PaperId: 484, https://arxiv.org/pdf/2606.08018.pdf GitHub

Abstract:
Existing text‑to‑SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects. However, real‑world database systems differ substantially in syntax, functions, type systems, and execution semantics, so the same natural language intent often requires dialect‑specific SQL realizations. We introduce UniQL, a human‑verified benchmark for cross‑dialect text‑to‑SQL evaluation. UniQL aligns 1,534 natural language questions with executable SQL annotations across 16 SQL dialects, yielding 24,544 dialect‑specific queries. All dialects share the same intents, aligned schemas and database contents, enabling controlled evaluation of dialect generalization. UniQL is constructed through a hybrid pipeline combining database migration, SQL translation, execution‑guided verification, iterative rule summarization, and human validation. Experiments on both open‑source and closed‑source LLMs show that current models remain far from dialect‑universal, with substantial performance variation across database systems and limited transfer from SQLite success to other dialects. These findings highlight the need for aligned cross‑dialect benchmarks and more dialect‑aware text‑to‑SQL methods. Code and data are available at https://github.com/JerryGao818/UniQL

PaperId: 485, https://arxiv.org/pdf/2606.08016.pdf GitHub

Abstract:
Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes. Creations by generative models may contain artifacts, implausible details, or stylistic drift away from photorealism and offer little insight into why an edit was made. We propose IEA, a conversational Image Editing Agent that learns to operate parameterized tools in an explicit, interpretable action space. IEA is trained via a three‑stage multitask pipeline: (1) SFT on distilled expert edits, (2) GRPO with rewards for likeness improvement, tool usefulness, and intent summarization, and (3) large‑scale synthetic fine‑tuning to jointly master image editing, refinement, and user intent summarization. By manipulating 16 editing tools step by step, IEA produces transparent edit traces that can be inspected and debugged. In quantitative experiments, it attains a lower pixel distance on the edit task and a higher ROUGE‑L on the summary task than strong baselines. In user studies, it ranks best among tool‑calling methods for instruction following while surpassing generative methods in overall perceptual quality. Our results validate interpretable, tool‑centric VLMs as a reliable path to human instruction‑guided image retouching.

PaperId: 486, https://arxiv.org/pdf/2606.07970.pdf GitHub

Abstract:
Current open‑weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets. Existing alignment‑stage defenses are primarily designed to defend against attacks that use parameter‑efficient finetuning methods. However, they fail to defend against stronger attacks that use full‑parameter finetuning. In this paper, we propose Patcher, a method inspired by adversarial training and bi‑level optimization, to combat such attacks. Patcher strengthens the simulated attack by scaling up the optimization steps in the adversarial loop, thus forcing the defender to find model parameters that are insensitive to stronger attacks. Furthermore, we propose an efficient parallel algorithm to implement Patcher, decreasing the wall‑clock time of training while preserving Patcher's performance. Extensive experiments show that Patcher substantially improves the model's robustness compared to vanilla SFT alignment, and transfers to diverse attack scenarios and model sizes. Code is available at https://github.com/haomingwen/patcher.

PaperId: 487, https://arxiv.org/pdf/2606.07953.pdf GitHub

Abstract:
Large‑scale Visual‑Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large‑scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text‑visual interaction for fine‑grained understanding. To address these challenges, we introduce a Large‑Scale Multi‑Modal Industrial Open‑Closed benchmark (MMIOC‑1M) containing over one million samples across 14 super‑categories, 29 industrial scenes, and 351 defect subcategories. To our knowledge, MMIOC‑1M is the first unified largest benchmark supporting both open‑vocabulary and closed‑set industrial detection, providing valuable pre‑training data for LVLMs in industrial scenarios. Furthermore, we propose a Refined Text‑Visual Prompt Network (RTVPNet) that incorporates three key innovations: (1) an expert‑assisted domain projection mechanism that enables rapid adaptation of general vision models to industrial domains, (2) an energy‑based sparse sampling strategy that automatically generates refined visual prompts without manual intervention, and (3) a bidirectional text‑visual interaction module that enhances cross‑modal semantic alignment and understanding. Extensive experiments demonstrate that RTVPNet achieves state‑of‑the‑art performance on MMIOC‑1M, LVIS, and COCO benchmarks while maintaining computational efficiency. The dataset and code are available at https://github.com/hellozzk/MMIO.

PaperId: 488, https://arxiv.org/pdf/2606.07936.pdf GitHub

Abstract:
Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well‑documented protocols ‑‑ details that are frequently missing in current practice. In this work, we conduct a large‑scale analysis of human evaluation protocols for evaluating long‑form generation tasks in CL conference publications from 2023‑‑2025, including a full manual review of 284 papers and LLM‑assisted analysis for another 1.8k+ papers. We define a set of 20 reportable criteria related to reproducibility of human evaluation studies, and apply these criteria to systematically examine reporting norms and practices within the community. We find widespread under‑reporting of important aspects of human evaluation study design, leading to ambiguity about what was measured and how, who contributed judgments, and how judgments should be interpreted. Based on these findings, we outline actionable recommendations to support more transparent and reproducible reporting in future research. Our analysis code and annotated dataset can be found at: https://github.com/larchlab/Illusions‑of‑the‑Gold‑Standard

PaperId: 489, https://arxiv.org/pdf/2606.07810.pdf GitHub GitHub

Abstract:
Large language models (LLMs) are widely used as judges for evaluating model outputs, but their high cost, latency, and opacity limit scalability. We introduce SLMJury, a framework for evaluating small language models (SLMs) as judges across two paradigms: closed‑ended binary correctness and open‑ended quality scoring. We benchmark 16 SLM judges (0.6B‑14B parameters) from four model families across ten benchmarks: eight closed‑ended tasks spanning mathematical, scientific, and general reasoning (N=64,824 judgments per configuration), plus SummEval and MT‑Bench for summarization and conversational scoring. We formalize judging as a budget‑conditioned function and study five dimensions. Four findings emerge. (1) The overthinking effect is domain‑dependent: for most judges quick 10‑token verdicts match or beat extended reasoning on mathematical judging (by 2‑7% where they help), while reasoning wins on general tasks by up to 23%. (2) Domain generalization separates model families, with math‑to‑general accuracy gaps ranging from under 10% to nearly 40%. (3) Closed‑ended and open‑ended judging draw on different capabilities: the best binary judge (Phi‑4) drops to rank 9 on MT‑Bench, while reasoning‑trained models invert this ordering. (4) Under the Reflect‑Critique‑Refine (RCR) debate protocol, multi‑agent debate degrades accuracy across all tested configurations, whereas the top judges resist six adversarial personas with <=0.55% variance. Reliable automated evaluation does not require large proprietary models, yet no single SLM dominates. The leaderboard is available at https://anishh15.github.io/SLMJury/, and our framework code and pip package are publicly available at https://github.com/anishh15/SLMJury and https://pypi.org/project/slmjury/.

PaperId: 490, https://arxiv.org/pdf/2606.07716.pdf GitHub

Abstract:
Adversarial attacks pose a serious and growing threat to Machine Learning (ML)‑based Intrusion Detection Systems (IDS), where imperceptible perturbations to network flow features can systematically mislead classifiers into accepting malicious traffic as benign. The IDS‑Anta framework partially addresses this through Z‑score normalization, Singular Value Decomposition (SVD), and Multi‑Armed Bandit (MAB) classifier selection with Thompson Sampling, yet its classifier pool lacks sufficient structural diversity for robust adversarial resistance. This work introduces IDS‑Anta++, which incorporates XGBoost and LightGBM gradient boosting models into the ensemble and wraps the extended pool in a three‑layer black‑box defense: Isolation Forest anomaly screening, median feature smoothing, and six‑way majority voting. Experiments conducted on CIC‑IDS‑2017, CEC‑CIC‑IDS‑2018, and CIC‑DDoS‑2019 under both Fast Gradient Sign Method (FGSM) and Zeroth Order Optimization (ZOO) attacks confirm detection accuracy above 99% on clean data, with measurable robustness gains under adversarial conditions relative to the baseline IDS‑Anta configuration.

PaperId: 491, https://arxiv.org/pdf/2606.07706.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) have demonstrated strong performance across multimodal tasks, yet their safety robustness remains an open challenge. While prior work has shown that structured visual prompts such as flowcharts can effectively jailbreak VLMs, existing studies are largely limited to English‑centric settings. In this paper, we introduce MLingualFC, a multilingual multimodal benchmark designed to evaluate jailbreak vulnerabilities of VLMs across diverse languages using structured flowchart representations. MLingualFC encodes harmful instructions into flowchart images across five languages (Hindi, Punjabi, Spanish, Romanian, and German). We evaluate state‑of‑the‑art multilingual VLMs, including Qwen2.5‑VL, Gemma‑4, and Pangea, under a black‑box threat model. Our results reveal significant multilingual safety gaps. Flowchart‑based attacks achieve high attack success rates (ASR) in case of Latin script languages, demonstrating that visual encoding of harmful content effectively bypasses safety alignment across languages. In contrast, non‑Latin script languages such as Punjabi exhibit substantially lower ASR, suggesting potential limitations in visual text recognition rather than stronger safety alignment. These findings highlight that current VLM safety mechanisms fail to generalize across languages and modalities. Resources are available at https://github.com/Rishabhpm23/MLingualFC

PaperId: 492, https://arxiv.org/pdf/2606.07705.pdf GitHub

Abstract:
Although multi‑objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing practice of static weighted summation overlooks a more fundamental phenomenon: reward learning is markedly asynchronous across objectives. Well‑learned dimensions quickly produce homogeneous, low‑variance signals whose residual noise contaminates the aggregated reward (in GRPO) or occupies a fixed share of the advantage budget (in GDPO), interfering with the scarce yet high‑value signals carried by under‑learned dimensions. To address this asynchrony, we propose Stage‑Aware Dynamic Weighting (SAW), a lightweight, algorithm‑agnostic dynamic weighting mechanism. SAW utilizes the coefficient of variation (CV) as a scale‑invariant proxy for real‑time informativeness, reweighting each dimension's reward or advantage contribution by its relative informativeness within the batch. Unlike gradient‑based methods that require multiple forward and backward passes, SAW relies solely on batch‑level statistics, introducing nearly negligible computational overhead. Experiments on tool‑calling and text summarization tasks demonstrate that SAW consistently improves both training efficiency and final performance under both GRPO and GDPO frameworks, confirming it as a general‑purpose plug‑in for multi‑reward LLM alignment. Our code is available at https://github.com/Zhaolutuan/SAW

PaperId: 493, https://arxiv.org/pdf/2606.07643.pdf GitHub

Abstract:
Recent advances in Omni‑Multimodal Large Language Models (Omni‑MLLMs) have enabled strong integration of vision, audio, and language. However, their audio‑visual intelligence (AVI) remains insufficiently evaluated due to the lack of systematic and comprehensive benchmarks. We introduce AVI‑Bench, a cognitively inspired benchmark that evaluates Omni‑MLLMs across three stages, perception, understanding, and reasoning, through cross‑modal tasks requiring joint audio‑visual interpretation. This design enables fine‑grained diagnosis of model capabilities and failure modes. To further assess robustness beyond familiar domains, we propose AVI‑Bench‑PriSe, an extension that probes models' primitive audio‑visual sensation using unfamiliar, low‑semantic stimuli, testing generalization beyond common training distributions. Extensive experiments on both open‑source and closed‑source models reveal substantial limitations in current Omni‑MLLMs. Based on these findings, we present a four‑level AVI taxonomy. Overall, AVI‑Bench provides a principled evaluation framework to guide the development of more robust and generalizable AVI. Project website: https://fudancvl.github.io/AVI‑Bench/

PaperId: 494, https://arxiv.org/pdf/2606.07594.pdf GitHub

Abstract:
Personal AI agents must increasingly operate across APIs, shells, web surfaces, and desktop GUIs, yet many systems remain tuned to a single interface and offer limited support for user teaching and auditability. We present Syll, an open‑source, self‑hosted multimodal agent harness that unifies MCP/API tools, CLI execution, and visual GUI control in a modular runtime, enabling agents to coordinate computer use across heterogeneous interfaces while streamlining how users and agents exchange information. At the core of Syll is a bidirectional user‑agent interaction layer: users teach procedures through direct demonstration, which Syll compiles into reusable skills; agent execution is translated back into multimodal evidence ‑‑ logs, keyframes, and approval checkpoints ‑‑ for inspection and control. Syll further externalizes memory, skills, routines, and governance as editable local artifacts, supporting straightforward inspection, extension, and downstream development. Our implementation has been validated on production desktop applications including Adobe Photoshop, Adobe Audition, Stardew Valley, macOS Finder and others. We report mechanism‑oriented studies that validate multimodal routing, teachable GUI replay, and persistent local artifacts. We hope Syll can serve as a practical open‑source foundation for personal automation that users can teach, inspect, and continuously extend.

PaperId: 495, https://arxiv.org/pdf/2606.07577.pdf GitHub

Abstract:
Audio‑visual large language models (LLMs) hold strong promise for long‑form video understanding, yet their long‑video inference is fundamentally limited by the linear growth of video tokens and key‑value (KV) caches. We present OmniMem, a memory‑efficient streaming framework designed specifically for audio‑visual LLMs. Unlike existing compression methods that treat all tokens uniformly, OmniMem introduces a modality‑aware memory allocation strategy that separately manages visual and audio contexts, addressing the severe token imbalance between the two modalities. OmniMem further preserves informative and non‑redundant KV states through perturbation‑aware memory selection, enabling compact memory without sacrificing long‑range understanding. To strengthen compression under realistic deployment constraints, we also explore budget‑aware fine‑tuning, which encourages the model to consolidate useful information into retained memory. Experiments on VideoMME Long, LVBench, and LVOmniBench with video‑SALMONN 2+ and Qwen‑2.5‑Omni show that OmniMem consistently improves over strong training‑free compression baselines by 2‑4% absolute accuracy under the same memory budgets, with an additional 1‑2% gain after fine‑tuning.

PaperId: 496, https://arxiv.org/pdf/2606.07567.pdf GitHub

Abstract:
Protein function is largely determined by molecular surface geometry and physicochemical complementarity, yet most protein design methods condition only on backbone structure. We introduce SurfDesign, a surface‑conditioned protein design framework that models molecular surfaces as continuous geometric manifolds and integrates them with pretrained protein language models. SurfDesign employs surface‑based equivariant message passing to capture surface normals, curvature, and directional geometry, together with a parameter‑efficient fine‑tuning strategy. Focusing on functional protein design, we show that SurfDesign consistently outperforms prior surface‑conditioned and backbone‑only methods on de novo binder and enzyme design benchmarks. We also report strong performance on inverse‑folding benchmarks as a diagnostic of structural compatibility. Our results highlight manifold‑aware surface representations as a principled foundation for functional protein and enzyme design. Code is available at https://github.com/smiles724/SurfDesign.

PaperId: 497, https://arxiv.org/pdf/2606.07553.pdf GitHub

Abstract:
The emergence of machine learning and deep learning has revolutionized the efficiency of diagnostic, therapeutic, and administrative systems in healthcare. However, this rapid adoption has come at the cost of requiring significant computing power and energy consumption, as well as e‑waste disposal and carbon emissions. One of the challenges of these models is choosing the right model for classification tasks. To this end, researchers attempt to identify the optimal model using their data through trial and error, which involves energy consumption and waste. The goal of this study is to develop a model‑based recommender system for medical image classification. For this purpose, a data set was collected from 3,000 articles in the field of medical image classification. This dataset, publicly available under the name MedicalRec‑Bench, contains over 5,000 records of models tested in various tasks, including Skin Cancer Classification, Tumour Classification, Wound Classification, Breast Cancer, and MRI classification. The dataset was evaluated in four different modes, depending on the number of features: MedicalRec I (5 features), MedicalRec II (9 features), MedicalRec III (11 features), and MedicalRec IV (18 features). Collecting all values for the features is challenging due to non‑reporting by the authors; hence, the dataset contains significant amounts of missing values. The Medical Recommender System (MedicalRec) is a transformer‑based model used for item recommendations in this study. This model achieved remarkable results in the evaluation on the dataset and in the evaluation with 12 base models. This model achieved a maximum HitRate@100 of 75.5%. The dataset and implementations are available through the GitHub link: https://github.com/Ramin1Mousa/MedicalRec

PaperId: 498, https://arxiv.org/pdf/2606.07541.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have shown strong performance on objective tasks such as video understanding and reasoning. However, it remains unclear whether they can approximate subjective human responses, which depend not only on content comprehension but also on individuals' social contexts. To address this gap, we evaluate MLLMs as synthetic participants in an emerging task: assessing perceived sensory engagement with short videos. Grounded in the Perceived Message Sensation Value (PMSV) framework, we compare ratings from recruited human participants and profile‑conditioned MLLM simulations (n=673) using a 17‑item scale measuring emotional arousal, dramatic impact, and novelty. We find that even leading MLLMs (Gemini 3 Flash and Qwen 3 Omni) show limited agreement with human participants. The models exhibit distinct downward mean‑shift and central‑tendency biases in their rating distributions. They both introduce and flatten subgroup differences, while showing inconsistent sensitivity to participant profiles. Prompting strategies affect these metrics differently, modestly improving some aspects while worsening others. These results highlight both the challenges and opportunities of developing MLLMs as synthetic participants in video‑based research. Data and code: https://github.com/MINDLab25/mllm‑human‑simulation‑eval

PaperId: 499, https://arxiv.org/pdf/2606.07538.pdf GitHub

Abstract:
Large language model (LLM)‑based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS tasks rely on extensive specialized tool libraries. However, tool documentation often exceeds the context window limits of LLMs, making precise tool retrieval essential for agentic workflows. Existing tool retrieval methods face "semantic asymmetry" bottleneck: natural language queries typically express macro‑level intentions lacking tool‑specific semantics, while tool documentation provides fine‑grained technical descriptions lacking operational context for workflows. To bridge this semantic gap, this paper proposes a bidirectional semantic complementary tool retrieval method. First, on the query side, we introduce a planning‑based query enhancement mechanism that leverages the reasoning capabilities of agents to decompose abstract intentions into logical subtasks, thereby actively supplementing the query with missing functional semantics. Second, on the tool side, addressing the strong coupling characteristics of RS tool chains, we construct a dynamic tool dependency graph with continual learning capabilities. By employing a neighborhood information aggregation mechanism, contextual information from precursor tools is explicitly injected into the current node representation, enriching tool descriptions with contextual semantics. Experimental results on the RS dataset GeoPlan‑bench and the general‑purpose dataset API‑ Bank demonstrate that the proposed method not only significantly improves tool retrieval accuracy for complex RS tasks but also exhibits robust extensibility for transfer to general‑domain tasks. The source code and dataset are available at https://github.com/geox‑lab/BSCTR.

PaperId: 500, https://arxiv.org/pdf/2606.07529.pdf GitHub

Abstract:
Large language models (LLMs) have recently been applied to 3D vision‑language (3D‑VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational inefficiencies, motivating the need for pruning. Existing pruning methods primarily rely on spatial proximity and often remove task‑relevant relations, thereby undermining reliable spatial reasoning. To address these limitations, we derive a key requirement for scene graph pruning: preserving spatial relations that are most pertinent to the specific 3D‑VL task. Guided by this insight, we propose the Conceptual‑Adjacent Scene Graph Pruner (CAPruner). CAPruner integrates fuzzy semantic relevance with spatial proximity to estimate the importance of relations, enabling the selection of critical relations in a task‑specific context. Moreover, to avoid costly relation‑level annotations, CAPruner is trained by supervising the aggregated scores of each node's incident edges. Extensive experiments demonstrate that CAPruner effectively preserves relations essential for spatial reasoning, leading to substantial performance improvements of LLMs on 3D‑VL tasks. Code is available at https://github.com/fz‑zsl/CAPruner.

PaperId: 501, https://arxiv.org/pdf/2606.07526.pdf GitHub GitHub

Abstract:
Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities. However, effectively aligning the textual semantics modeled by LLMs with the collaborative signals remains a key challenge. Existing methods either translate collaborative information into textual prompts or inject pre‑trained embeddings into the LLM, both of which treat structural information as static input and fail to capture high‑order relational dependencies. To bridge this gap, we propose GraphLoRA, a novel framework that generalizes low‑rank adaptation from independent to structure‑aware propagation. GraphLoRA embeds a trainable graph message‑passing network within the low‑rank adaptation pathway, enabling structural signals to propagate through the parameter space. This design allows collaborative topology to explicitly guide parameter updates, fostering deep integration between graph‑structured and textual semantic information. Extensive experiments on multiple benchmarks demonstrate that GraphLoRA not only outperforms state‑of‑the‑art LLM‑based recommendation methods but also achieves superior generalization, effectively balancing structural reasoning capability with computational efficiency. Code is available at \hrefhttps://github.com/wgj15965/GraphLoRAhttps://github.com/wgj15965/GraphLoRA.

PaperId: 502, https://arxiv.org/pdf/2606.07462.pdf GitHub

Abstract:
As foundation models advance and agent scaffolding becomes increasingly sophisticated, agents have demonstrated remarkable proficiency in complex, long‑horizon coding tasks and even autonomous experiment execution. Despite their evolution from research assistants into autonomous research agents, these systems still exhibit significant limitations in field sensitivity, research ethics, and nuanced scientific judgment. Consequently, frontier agents remain unable to fully replace human researchers. To bridge this gap, we conceptualize the AARR (Act As a Real Researcher) benchmark series. Unlike existing benchmarks that primarily assess macro‑level execution capabilities, AARR focuses on whether agents can emulate the professionalism, thoroughness, and nuanced reasoning that characterize human researchers in granular research scenarios. In this work, we propose AARRI‑Bench (Act As a Real Research Intern), the first benchmark in this series. We conduct extensive experiments across frontier models and agentic systems, revealing that even the best‑performing configuration (Mini‑SWE‑Agent with Claude Opus 4.7) achieves only 68.3% success rate, frequently overlooking subtle yet critical details that are obvious to real human researchers. Our results indicate that developing researcher‑like AI requires further exploration of research behavior, rather than merely complex scaffolding. Our data is released at https://github.com/AARR‑bench/AARRI‑bench.

PaperId: 503, https://arxiv.org/pdf/2606.07433.pdf GitHub

Abstract:
Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge‑intensive video scenarios. These scenarios require models to handle sparse evidence, long‑range dependencies, multimodal alignment, and reliable inference under limited computational budgets. This work presents a human‑view perspective on LLM‑based video understanding, organized around three functional abilities: watching, remembering, and reasoning. Rather than treating video tasks as isolated benchmarks, this view provides a unified structure for analyzing how video MLLMs acquire evidence, preserve context, and produce grounded outputs. We introduce a formulation that characterizes video understanding systems by their perceptual representations, memory states, reasoning traces, and final predictions. Based on this formulation, we identify challenges in spatio‑temporal perception, efficient long‑video processing, memory modeling, streaming understanding, and faithful reasoning. Representative methods are organized by their roles in video MLLM systems. Watching covers fine‑grained, comprehensive, audio‑visual, and efficient perception. Remembering includes offline and streaming memory, while reasoning covers text‑only reasoning and thinking with videos. We further examine application domains such as egocentric, sports, instructional, medical, and narrative videos, and cover training datasets and evaluation benchmarks across task types, supervision formats, modalities, and capability dimensions. Finally, we outline open problems and future directions for scalable, memory‑aware, and evidence‑grounded video intelligence. Related works will be continuously traced at https://github.com/marinero4972/Awesome‑HumanView‑VideoUnderstanding.

PaperId: 504, https://arxiv.org/pdf/2606.07032.pdf GitHub

Abstract:
Zero‑Shot Composed Image Retrieval (ZS‑CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption without training samples. Existing ZS‑CIR datasets often suffer from complete irrelevance between reference and target images due to noisy image sources, and do not achieve a true zero‑shot scenario as they use public image datasets that models like CLIP have been trained on. To tackle these challenges, we introduce ZeroSight, a novel benchmark for ZS‑CIR. It includes a dataset with consistent reference‑target pairs sourced from videos, a data construction pipeline, and evaluation methods that consider the ranking of multiple positive and negative target images. We ensure visually and semantically consistent reference‑target pairs by extracting frames from a single video and generating relative captions using LLM‑assisted methods. To ensure a true zero‑shot scenario, we use video data published after March 31, 2022, ensuring it was not included in CLIP's pre‑training data. Additionally, we propose a training‑free MLLM‑driven method, SC4CIR (Symmetric Consistency for CIR), which can effectively identify hard negative targets through 3 symmetric consistency checks. This method is plug‑and‑play, seamlessly integrating with various CIR methods and significantly improving performance. Our experimental results from 27 methods reveal that current ZS‑CIR datasets and evaluation metrics result in inflated retrieval performance, exaggerating the capabilities of CIR methods. Our benchmark and models can be accessed at https://github.com/sotayang/ZeroSight.

PaperId: 505, https://arxiv.org/pdf/2606.06983.pdf GitHub

Abstract:
Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution. We present DaX, a pathology vision foundation model that adapts DINOv3‑style self‑supervised learning to whole‑slide histopathology. DaX is initialized from natural‑image DINOv3 weights and incorporates continuous magnification training, cross‑scale tissue views, orientation‑agnostic and acquisition‑robust augmentation, multi‑input‑size training, and Gram‑anchored dense consistency. These designs aim to connect local cellular morphology with global tissue architecture while stabilizing dense token‑level representations across input scales. We further construct a WSI‑level benchmark comprising 161 clinically meaningful tasks from 44 public datasets, covering 28,182 patients and 34,394 slides across four clinical domains and nine task categories. All models are evaluated under a fixed patient‑level cross‑validation protocol with fold‑level statistical ranking, enabling reproducible comparisons that are less sensitive to split‑dependent variation. Across this benchmark, DaX achieves the highest mean performance across tasks and consistently strong task‑level ranking scores, with gains spanning diagnostic pathology, biomarker and molecular profiling, tissue/specimen context, and risk, response, and prognosis. These results support DaX as a transferable visual encoder for computational pathology and provide a standardized evaluation framework for future pathology foundation models. Project page: https://alibaba‑damo‑academy.github.io/DaX/benchboard/.

PaperId: 506, https://arxiv.org/pdf/2606.06959.pdf GitHub GitHub

Abstract:
Hallucination detection is essential for the reliable deployment of large language models (LLMs). However, existing evaluations face two core challenges: inconsistent inference configuration and evaluation, and limited coverage of downstream domains and tasks. Consequently, reported detector performance is often difficult to compare, reproduce, and generalize beyond specific experimental settings. We introduce OpenHalDet, a unified benchmark for hallucination detection across diverse generation scenarios. OpenHalDet standardizes the evaluation pipeline, from prompt construction and response generation to truthfulness annotation, detector scoring, and metric computation. It supports heterogeneous detector families under different access settings, including black‑box methods that use only generated outputs, gray‑box methods that rely on probability‑based signals, and white‑box methods that exploit internal model signals. By bringing diverse tasks, models, and detectors into a shared framework, OpenHalDet enables controlled comparison and provides a systematic view of how different detection paradigms behave in LLM applications. We release OpenHalDet as an open and extensible codebase to facilitate reproducible evaluation and future development of hallucination detection methods. The code and datasets are available at https://github.com/Nellie179/Hallucination‑Detection.

PaperId: 507, https://arxiv.org/pdf/2606.06943.pdf GitHub

Abstract:
Vision‑language models (VLMs) such as CLIP achieve strong zero‑shot recognition but remain highly fragile under adversarial perturbations. Recent test‑time adaptation defenses improve robustness by leveraging many augmented views, but this leads to impractical slowdown and a clear robustness‑throughput trade‑off. To address this challenge, we present Stability and Suitability‑guided Test‑time Prompt Tuning (SS‑TPT), evaluating the quality of each augmented view via two complementary scores: (1) stability, measuring prediction invariance to weak augmentations, and (2) suitability, measuring feature‑space density among views. These stability and suitability (SS) scores guide both adaptation and inference through an SS‑guided consistency loss and an SS‑weighted prediction, amplifying trustworthy views while suppressing corrupted ones. Extensive experiments demonstrate that SS‑TPT significantly outperforms prior state‑of‑the‑art methods, achieving superior robustness‑throughput trade‑offs across diverse datasets and varying numbers of views, thereby demonstrating both strong practicality and generality. Our code is available at https://github.com/sunoh‑kim/SS‑TPT.

PaperId: 508, https://arxiv.org/pdf/2606.06920.pdf GitHub

Abstract:
Deploying Small Language Models (SLMs) on edge devices requires efficient fine‑tuning strategies that adapt models to new tasks without degrading their general capabilities. In this study, we benchmark five sub‑1B models (135M‑1B) on mathematical reasoning tasks and uncover a critical vulnerability: Full Fine‑Tuning (Full FT) actively harms performance in models under 300M parameters, often dropping accuracy below zero‑shot baselines. This "negative transfer" makes Parameter‑Efficient Fine‑Tuning (PEFT) not just an efficiency preference, but a stability requirement. We find that while Low‑Rank Adaptation (LoRA) and Weight‑Decomposed LoRA (DoRA) perform comparably, their strengths vary by task; DoRA excels in complex reasoning (GSM8K), while LoRA dominates pattern matching (OrcaMath). In particular, Full FT is outperformed by LoRA on aligned models (Qwen2.5‑0.5B) and even by simple 5‑shot In‑Context Learning on the smallest architectures (SmolLM2‑135M). Based on these findings, we recommend defaulting to PEFT for all aligned sub‑1B models and caution against Full FT for any architecture smaller than 500M parameters to prevent catastrophic forgetting. Reproduction of this work can be found at https://github.com/gulguluu/tiny‑slm‑finetune‑compare.

PaperId: 509, https://arxiv.org/pdf/2606.06903.pdf GitHub

Abstract:
Human image animation aims to generate a video from a static reference image, guided by pose information extracted from a driving video. Existing approaches often rely on pose estimators to extract intermediate representations, but such signals are prone to errors under occlusion or complex poses. Building on these observations, we present DirectAnimator, a framework that bypasses pose extraction and directly learns from raw driving videos. We introduce a Driving Cue Triplet consisting of pose, face, and location cues that captures motion, expression, and alignment in a semantically rich yet stable form, and we fuse them through a CueFusion DiT block for reliable control during denoising. To make learning dependable when the driving and reference identities differ, we devise a Same2X training strategy that aligns cross‑ID features with those learned from same‑ID data, regularizing optimization and accelerating convergence. Extensive experiments demonstrate that DirectAnimator attains state‑of‑the‑art visual quality and identity preservation while remaining robust to occlusions and complex articulation, and it does so with fewer computational resources. Our project page is at https://directanimator.github.io/.

PaperId: 510, https://arxiv.org/pdf/2606.06885.pdf GitHub

Abstract:
Human Image Animation has seen significant advancements, primarily driven by diffusion models. However, existing methods typically demand substantial training data and resources to achieve high‑quality results, limiting generalization and accessibility. In this work, we introduce \emphFreeAnimate, a training‑free framework that leverages the inherent capabilities of image diffusion models to enable temporal consistency, identity preservation, and background stability. Our approach incorporates a novel preview generation strategy that provides temporal and structural priors from generated preview frames, effectively guiding pose alignment and background consistency without training. Additionally, FreeAnimate introduces Inversion‑Boosted Attention and Reference‑Anchored Self‑Attention modules to guarantee temporal consistency and identity preservation. Experimental results demonstrate that FreeAnimate outperforms existing training‑free competitors and training‑based baseline methods, achieving generation quality comparable to state‑of‑the‑art methods and offering robust generalization across diverse datasets. Our project page is at https://freeani.github.io/.

PaperId: 511, https://arxiv.org/pdf/2606.06872.pdf GitHub

Abstract:
Estimating hand‑surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis. Existing methods often discretize pressure signal and process frames independently, leading to quantization errors and temporal inconsistencies. We present \emphEgoPressDiff, a conditional video diffusion framework that generates UV‑pressure maps from visual input. The core of our approach is a multi‑modal conditioning strategy, introducing a PoseNet and a Vertex Encoder to efficiently extract features from hand pose and 3D mesh vertices. These signals, along with depth information, guide the generative process to ensure the pressure fields are physically grounded. To effectively fuse these heterogeneous features, we further propose a Distribution‑Calibrated Spatial Layer, which aligns their statistical properties before combination. Evaluated on the EgoPressure ego‑view setting, EgoPressDiff achieves state‑of‑the‑art results, improving Volumetric IoU by over 34% relative to prior baseline, while reducing MAE and maintaining high temporal accuracy. Our project page is at https://egopressdiff.github.io/.

PaperId: 512, https://arxiv.org/pdf/2606.06813.pdf GitHub

Abstract:
Recent text‑to‑image models built on large‑scale Transformer backbones and flow‑based objectives deliver strong text‑image alignment and high visual quality, yet often produce overly similar samples under a fixed prompt. Existing diversity‑enhancement methods alleviate this issue, but typically require expensive sampling or auxiliary optimization, incurring non‑trivial overhead. To investigate the root cause of this homogeneity, we examine intermediate Transformer features and observe that the zero‑frequency spatial average (DC) component rapidly converges across seeds early in generation, causing early trajectory lock‑in that limits downstream variation. Building on this observation, we propose DC Attenuation for diVersity Enhancement (DAVE), a training‑free representation‑level intervention that selectively attenuates this component in the early regime. DAVE preserves the sampling pipeline with negligible overhead, improving prompt‑consistent diversity while maintaining competitive image quality.

PaperId: 513, https://arxiv.org/pdf/2606.06741.pdf GitHub

Abstract:
Self‑evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful trajectories, or verifier signals. Real open‑world deployments may provide none of these, offering only a task prompt. In this work, we study open‑world self‑evolution, where an agent must build both its skills and its own verification signals from scratch, using open‑world resources but no target‑task supervision. We propose OpenSkill, a framework that bootstraps this loop: it acquires grounded knowledge and verification anchors from documentation, repositories, and the web, synthesizes them into transferable skills, and refines those skills against self‑built virtual tasks grounded in the anchors rather than in target answers. The open world thus supplies both the knowledge to be learned and a supervision‑independent practice environment, with target‑task supervision reserved for final evaluation. Across three benchmarks and two target agents, OpenSkill attains the best automated pass rate while satisfying the no‑supervision constraint. Analysis shows its skills transfer across models without model‑specific adaptation, and its self‑built verifier aligns with ground‑truth outcomes despite never accessing them.

PaperId: 514, https://arxiv.org/pdf/2606.06679.pdf GitHub

Abstract:
Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing largely to the absence of expert‑annotated corpora. We introduce the Hong Kong Judgment Discourse Dataset (HKJudge), the first sentence‑level expert‑annotated legal discourse corpus. HKJudge includes criminal judgments across all five levels of HK's court hierarchy, comprising ～290k sentences and ～6.5 million tokens, fully annotated by legal linguistics experts. We design a two‑tier discourse schema that captures what facts a court finds, how it reasons, and what it rules. At the sentence level, each sentence is assigned one of 26 rhetorical roles. At the span level, sentences are further annotated with three sentencing elements (charge, imprisonment term, fine). Ten legal linguistics annotators produced the annotations with an inter‑annotator agreement of κ= 0.8. We formulate two tasks on HKJudge, termed rhetorical role classification and legal element extraction, and provide the first benchmark evaluation of four BERT‑based models, two open‑source LLMs under zero‑shot and fine‑tuning settings, and four commercial LLMs on both tasks. Our work demonstrates the value of sentence‑level discourse annotation for modeling the structure of HK judgments and provides a rich data foundation for future work on legal judgment prediction. The HKJudge dataset and code are available at https://github.com/xuanxixi/HKJudge.

PaperId: 515, https://arxiv.org/pdf/2606.06664.pdf GitHub

Abstract:
Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before safe deployment. Sparse autoencoders (SAEs) provide a promising lens for decomposing model representations into human‑interpretable concepts, yet adapting SAE‑based interpretation to ViTs remains challenging due to limited control over concept coverage and subjective, non‑scalable feature interpretation. To fill the gaps, motivated by neuroscience‑inspired principles, we propose ViSAE, a mechanistic interpretability toolbox for understanding ViT inner workings through concept circuits. ViSAE consists of three components: (1) A probing suite with 64K images and a 16K visually grounded concept vocabulary, improving concept coverage efficiency by 20x over ImageNet and interpretation accuracy by 28.7% over existing concept sets. (2) Top‑down concept reading and Bottom‑up circuit tracing algorithms that automatically recover ViT inner workings via concept circuits. (3) Applications for auditing and steering ViT behavior. Through concept editing, ViSAE improves the worst‑group accuracy on WaterBirds by 48.2%, outperforming existing methods by 23.8%. Our data and code: https://github.com/deep‑real/ViSAE.

PaperId: 516, https://arxiv.org/pdf/2606.06627.pdf GitHub

Abstract:
Human video datasets used for cotraining robot manipulation policies largely consist of curated demonstrations where motions are orchestrated to resemble robot behavior and 3D hand poses are captured with specialized hardware. A more plentiful source of data is everyday Internet video, but it is an open question what factors enable transfer from such videos to robots. We investigate this using a new dataset of 532 human videos with 28 hours of high‑quality triangulated hand labels and natural motions. We find that hand pose quality affects transfer, but even with accurate hands, the inherent motion gap hinders transfer unless the vision and policy networks specialize to each embodiment. Our cotraining recipe yields consistent improvements, with an absolute success rate gain of 29.7% in the low‑robot‑data regime across six manipulation tasks.

PaperId: 517, https://arxiv.org/pdf/2606.06615.pdf GitHub

Abstract:
Retrieving music using natural language descriptions has improved with contrastive audio‑text models such as CLAP, but current systems remain limited to coarse semantic queries. When descriptions specify fine‑grained musical attributes such as tempo, key, chord progression, or rhythmic structure, existing models often fail to retrieve the correct audio. We show that this limitation stems from the contrastive learning objective itself: despite being trained on long captions, CLAP‑based models effectively utilize only the first few tokens, discarding much of the information encoded in detailed prompts. Then, we propose FIGMA (FIne‑Grained Music RetrievAl), a multi‑view contrastive architecture that addresses this limitation by jointly optimizing global audio‑text alignment and frame‑level, token‑wise alignment. This design enables FIGMA to capture both high‑level semantic context and fine‑grained musical attributes within a unified representation space. Moreover, we formalize the task of Fine‑Grained Music Retrieval and construct Fine‑Grained Music Caption dataset (FGMCaps), a large‑scale dataset of 380K music‑caption pairs for training along with a 10K test set, both annotated with tempo, key, chord progression, beat count, as well as genre and mood. Extensive experiments demonstrate that FIGMA consistently outperforms existing CLAP‑based music retrieval models across multiple music retrieval benchmarks, including out‑of‑domain evaluations, with relative improvements of up to 73.3%.

PaperId: 518, https://arxiv.org/pdf/2606.06601.pdf GitHub

Abstract:
Object insertion aims to seamlessly composite a reference object into a specified region of a background image. Recent diffusion‑based methods achieve high visual quality but formulate insertion as a simple 2D inpainting task, providing no explicit control over the object's 3D pose and limiting their practical applicability. We propose DIRECT (Decomposed Injection for Reference Composition and Target‑integration), a novel framework that integrates interactive pose manipulation with high‑fidelity 2D image synthesis to enable pose‑controllable object insertion. Our method decomposes the insertion conditions into three complementary components: appearance guidance capturing visual details from the reference object, geometry guidance derived from the user‑adjusted 3D proxy, and context guidance from the target background. By injecting them through separate pathways, DIRECT avoids feature entanglement and simultaneously preserves reference appearance, follows the user‑specified pose, and adapts the object to the target scene. We also introduce an automated data construction pipeline to improve the diversity and quality of training data. Experiments show that DIRECT outperforms previous methods in both geometric controllability and visual quality.

PaperId: 519, https://arxiv.org/pdf/2606.06518.pdf GitHub

Abstract:
Sudoku is a representative constraint satisfaction problem that requires global structural reasoning under strict discrete constraints. The existing works of solving Sudoku mainly focus on two dominant approaches, i.e., traditional heuristic and deep learning solver. However, they suffer from two complementary limitations: learning‑based solvers lack hard correctness guarantees, while complete symbolic solvers are still prone to long‑tail search. To address these shortcomings, we propose a novel diffusion model‑guided approach, termed as DiBS, for the branch selection search process. Specifically, DiBS keeps the symbolic solver complete and uses the diffusion model as a branch‑ordering guide. The core method is ranking candidate values under the current partial assignment and lightweight consistency signal. Furthermore, we provide an in‑depth theoretical proof to reveal how it works and why it works. Experiments on the challenging Royle 17‑clue Sudoku benchmark show that our DiBS substantially reduces search cost relative to strong heuristic baselines, especially in nodes, backtracks, and long‑tail percentiles. Besides, these results confirm that learned global guidance is effective on hard instances where branch‑order mistakes are most expensive. All codes are available at https://github.com/shanxierdan/DiBS.

PaperId: 520, https://arxiv.org/pdf/2606.04550.pdf GitHub

Abstract:
E‑commerce recommender systems strongly influence which products users consider and purchase, yet sustainability signals such as Product Carbon Footprint (PCF) are almost never available at catalog scale. We study carbon‑aware product recommendation in the realistic setting where PCF labels are missing for most items and must be inferred. We first estimate product‑level carbon footprints via a retrieval‑augmented PCF estimation pipeline that transfers supervision from the Carbon Catalogue, a small set of life‑cycle‑assessed products, to a large unlabeled e‑commerce catalog using semantic similarity search, few‑shot LLM prompting, and a nearest‑neighbour fallback. We then apply a carbon‑aware post‑hoc re‑ranking strategy on top of relevance scores produced by three established recommendation models: BPR, NeuMF, and LightGCN. The method trades off predicted user‑item engagement against estimated carbon footprint through a single tunable parameter, lambda. In this offline study, engagement is operationalized through Amazon review interactions, which serve as implicit feedback and as a proxy for user interest or purchase behavior. We evaluate the framework on the Amazon Reviews dataset across three product categories: Home and Kitchen, Sports and Outdoors, and Electronics. By sweeping lambda, we construct Pareto frontiers that characterize the achievable engagement and carbon trade‑off for each model and category. Substantial carbon reductions are achievable at minimal engagement cost across all models and categories. However, the available carbon headroom varies by model and category, underscoring the importance of model choice and domain context.

PaperId: 521, https://arxiv.org/pdf/2606.02092.pdf GitHub

Abstract:
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency. While hybrid approaches aim to capture both, they require architectural changes and encoder backbones with computational overhead, limiting efficiency and performance. We present LALE (Lightweight‑transformer Architecture for Land‑cover Estimation), an end‑to‑end remote sensing image segmentation architecture, that bifurcates its encoder by resolution: lightweight ConvMixer stages handle high‑resolution local features, while transformer stages handle low‑resolution global context, confining the quadratic cost of self‑attention to deep, downsampled feature maps. An all‑MLP multi‑scale decoder, together with RMSNorm and StarReLU throughout, further reduces compute and parameter count. On the large‑scale ARAS400k remote‑sensing segmentation benchmark, LALE establishes a strong efficiency‑performance trade‑off against CNN, transformer, and hybrid baselines. Our smallest variant, (just 1.6M parameters), reaches within 2.6 F1 points of the best baseline (UPerNet) while using 4.5x fewer parameters, 7x less storage, 17x fewer GMACs, and delivering 1.8x higher throughput. The codebase for LALE is publicly available at https://github.com/caglarmert/LALE.

PaperId: 522, https://arxiv.org/pdf/2606.01602.pdf GitHub

Abstract:
Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust way to quantify dependence between heterogeneous data types, especially between continuous time series and discrete temporal event sequences. Existing approaches rely on ad hoc transformations or mutual‑information estimators that are highly sensitive to quantization, repeated values, and event redundancy, leading to biased or unstable results in practice. We propose a nonparametric mutual information estimator that directly measures the dependence between time series and event sequences without data transformation, learning, or ad hoc discretization. Our method models the continuous‑discrete duality of real‑world time series to handle quantization and repeated‑value artifacts and introduces a latent event clustering strategy to mitigate bias from event co‑occurrence and redundancy. Together, these yield a robust and unified framework that bridges discrete and continuous mutual information. We evaluate the proposed estimator on four representative tasks: discrete‑continuous time‑delayed mutual information for causality analysis, global and local temporal repetition discovery, discrete covariate selection for time series forecasting, and continuous feature selection for classification. Experiments on synthetic and real‑world datasets show consistent improvements over existing methods in accuracy, robustness, and interpretability, positioning our approach as a general‑purpose dependence operator for heterogeneous temporal data, similar to Pearson correlation for homogeneous time series. Code available at: https://github.com/HaojiHu/Multimodal‑Temporal‑Data‑Quantification

PaperId: 523, https://arxiv.org/pdf/2606.01561.pdf GitHub

Abstract:
Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley‑Terry instantiation of DPO is limited in modeling common departures from transitivity in human preferences. To address this, recent work has introduced Self‑Play Preference Optimization (SPPO), which iteratively refines the policy by training on self‑generated win‑lose pairs. Our investigation, however, reveals a critical instability in SPPO: the optimization is prone to policy degeneration when the preference oracle assigns overly confident wins to semantically indistinguishable responses. To mitigate this, we propose S‑SPPO, a dual‑space semantic calibration framework comprising: i) Supervision Calibration via semantic gating, which anneals win rate targets toward the maximum‑entropy baseline as semantic overlap increases; and ii) Representation Calibration via latent repulsion to enforce geometric diversity to prevent manifold collapse and maintain latent diversity between chosen and rejected samples. Theoretically, we show that the calibration preserves the constant‑sum game structure, facilitating convergence to a Nash Equilibrium. Empirically, S‑SPPO avoids the performance degradation seen in prior methods, achieving 52.19% win rate and 47.46% length‑controlled win rate on AlpacaEval 2.0 with Llama‑3‑8B, without using additional human‑annotated preferences during training. The code will be available at https://github.com/xiwenc1/s‑sppo.

PaperId: 524, https://arxiv.org/pdf/2605.30353.pdf GitHub

Abstract:
Are AI agents tools, co‑authors, or researchers? We present a quantified case study (N=1): a physicist supervising an AI coding agent (Claude Code, Sonnet and Opus models) over 12 work days and 57 sessions to build CLAX‑PT, a differentiable one‑loop perturbation theory module in JAX. We documented and classified 15 supervision events by intervention level. The agent resolved ten autonomously by iterating against oracle tests. Two more by the physicist's domain knowledge. The three it could not ‑‑ all evaded oracle detection ‑‑ share a common property: the agent treated symptom reduction as root‑cause resolution. It spent 33 of the 57 sessions adjusting coefficients within a code architecture that could not represent the target physics, and could not re‑evaluate its CLASS‑PT branch choice even when prompted to reconsider; only an injected physics concept (anisotropic BAO damping) triggered the redesign. Separately, the agent committed a calibrated correction that passed all oracle tests but corresponded to no quantity in the theory, predicting wrong values at any other cosmology. The fudge factor was caught and replaced within the same session. Three supervision practices proved critical for catching what oracle tests missed: testing at diverse parameter points beyond the fiducial calibration; shared changelogs that surfaced stalled exploration across sessions; and an explicit rule against unphysical numerical patches. In this case, supervision design, not model capability, determined whether the agent's output was trustworthy. Closing the gap would require agents that propose architectural alternatives rather than optimize within a given structure, and distinguish predictive adequacy from explanatory correctness ‑‑ capabilities not exhibited here, not obviously addressed by scaling alone. [Abridged.]

PaperId: 525, https://arxiv.org/pdf/2605.30348.pdf GitHub

Abstract:
The pretraining data mixture of Large Language Models (LLMs) constitutes their "digital DNA", shaping model behaviors, capabilities, and failure modes. Yet this composition is rarely disclosed, making post‑hoc auditing of data combination or provenance difficult. In this work, we formalize Data Mixture Surgery (DMS): given only generated text from a target LLM, estimate the domain‑level distribution of its pretraining corpus under a predefined taxonomy. We propose LLMSurgeon, a strong framework that casts DMS as an inverse problem under the label‑shift assumption. Rather than directly aggregating classifier outputs, LLMSurgeon estimates a calibrated soft confusion matrix and solves a constrained inverse problem to correct systematic domain confusion and recover the latent mixture prior. To evaluate, we introduce LLMScan, a recipe‑verifiable evaluation suite built from open‑source LLMs with transparent pretraining mixtures. Across LLMScan, LLMSurgeon recovers domain mixtures with high fidelity under fixed protocols. Our work presents a practical, post‑hoc approach for auditing the digital DNA of foundation models without access to their training data.

PaperId: 526, https://arxiv.org/pdf/2605.30334.pdf GitHub

Abstract:
Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data selection has been widely studied, the strategic data organization for enhanced training remains an underexplored area, particularly since current LLMs are often trained for only one or a few epochs. This paper systematically explores the influence of data organization on LLM training by reusing pre‑computed sample‑level scores originally generated for data efficiency, thereby incurring minimal additional computational overhead. We identify and formalize four key guidelines for optimizing data organization: Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, and Local Diversity. Guided by them, we introduce two novel data ordering methods termed STR and SAW. Extensive experiments across different model scales and data sizes, encompassing both pre‑training and SFT stages, validate the effectiveness of our summarized guidelines. They also demonstrate the robustness of our proposed data ordering methods in enhancing the stability and performance of LLM training. Github Link: https://github.com/microsoft/data‑efficacy/

PaperId: 527, https://arxiv.org/pdf/2605.30326.pdf GitHub

Abstract:
The ability to reason, adapt, and creatively solve problems under unexpected challenges is essential for robots operating in real‑world environments. However, current robotic benchmarks primarily emphasize skill‑level execution and provide limited insight into such cognitive reasoning capabilities. We introduce RoboWits, a bi‑manual robotic benchmark designed to systematically evaluate cognitive reasoning, creative tool use, and robustness to unexpected conditions. To enable scalable construction of high‑quality reasoning‑centric unexpected scenarios, we propose an automated task generation pipeline formulated as a multi‑agent cooperative framework, comprising agents for seed task generation and verification, metric generation, scene generation, and task mutation. Using the pipeline, we curated 30 diverse seed tasks and 208 tasks with mutations and graded difficulty across geometry, material, and assembly‑based reasoning. We benchmark popular robot policies, pre‑trained VLAs, and oracle‑state planners. Our results reveal a significant performance gap: while pre‑trained VLAs exhibit preliminary success on seed tasks after single‑task fine‑tuning, they struggle to perform on mutated tasks, implying their brittleness in manipulation tasks requiring reasoning, strategy adaptation, and robustness to deceptive or constrained environments. Project page is available at https://umass‑embodied‑agi.github.io/RoboWits.

PaperId: 528, https://arxiv.org/pdf/2605.30318.pdf GitHub

Abstract:
Portrait photography is largely decided before the shutter opens: the subject's pose, the camera configuration, and the lighting devices must be coordinated within the surrounding 3D scene. In contrast, most existing computational methods focus on post‑production in 2D image space, such as retouching, relighting, or editing images that already exist; pre‑capture photographic planning remains largely unexplored. We introduce 3D aesthetic portrait planning, the task of generating human pose, camera, lighting, and exposure plans that produce visually compelling portraits while satisfying geometric and photometric feasibility in a 3D scene. Our approach builds a Photographic Scene Graph that represents scene affordances, subject‑scene relations, and portrait‑relevant lighting structure. Built on this representation, we perform aesthetic‑guided comparative planning over previous attempts and current viewfinder observations. Experiments across diverse indoor and outdoor scenes show that our method produces portraits preferred by human raters and MLLM evaluators over competitive baselines, while maintaining high physical plausibility. Together, our results suggest a path from post‑capture correction toward pre‑capture computational portrait planning. Project repository: https://github.com/songrise/Before‑the‑Shutter

PaperId: 529, https://arxiv.org/pdf/2605.30311.pdf GitHub GitHub

Abstract:
Digital humans are fundamental to immersive interaction, yet creating a unified model for holistic modalities, including text, audio, motion, and visual content, remains an open challenge. In this paper, we present Archon, a fully pretrained, human‑centric unified multimodal model for holistic avatar generation. Archon unifies seven modalities with modality‑specific tokenizers, and a native autoregressive unified multimodal model pretrained on synchronized modalities and 72 diverse tasks to model holistic joint distributions. To address the token explosion challenge in high‑fidelity talking videos, we introduce a memory‑efficient semantic video reparameterization, achieving 4x token reduction while preserving fine‑grained dynamics, coupled with a semantic‑driven video diffusion decoder. We further propose a "Thinking in Modality" that decomposes ambiguous cross‑modal tasks into stepwise thinking in an alternative chain of modality, progressively enhancing fidelity and controllability. Extensive experiments demonstrate that Archon achieves superior or comparable performance across diverse digital human generation tasks, validating the effectiveness of our unified framework. Project page: https://zju3dv.github.io/archon/.

PaperId: 530, https://arxiv.org/pdf/2605.30283.pdf GitHub

Abstract:
MCP Server Proto‑OKN (mcp‑proto‑okn) is a Python‑based Model Context Protocol server that enables AI assistants to discover, inspect, query and integrate scientific knowledge graphs through natural language. The server provides graph routing, schema inspection, SPARQL execution, ontology expansion, multi‑graph querying, and transcript generation, lowering the barrier to cross‑domain knowledge graph analysis for biomedical and scientific users. mcp‑proto‑okn is implemented in Python using the FastMCP framework and is available at https://github.com/sbl‑sdsc/mcp‑proto‑okn. Documentation, client configuration instructions, and example analysis transcripts are provided in the GitHub repository.

PaperId: 531, https://arxiv.org/pdf/2605.30274.pdf GitHub

Abstract:
Document‑level translation remains one of the most challenging tasks for large language models, which are constrained by limited context windows that impede global cohesion, while simultaneously suffering from redundant contextual information that degrades translation quality. To address this, we propose a human‑like long document translation agent called Loong, which leverages a 3E memory module (Essence‑Exemplar‑Entity) to store summaries, sentence pairs, and entity records as historical context. Instead of passively attending to all history, Loong performs deep reasoning to adaptively identify the optimal context for translation guidance. Loong optimizes its context policy through reinforcement learning, utilizing preference data derived from its own sampled observe‑and‑act reasoning trajectories. Empirical evaluations demonstrate that Loong achieves substantial translation quality improvements in English \Leftrightarrow Chinese, German, and French directions, with average gains of up to 13.0 points across the three evaluation metrics. Furthermore, Loong exhibits strong generalization across domains and robustness against contextual noise, while maintaining remarkable stability in ultra‑long document translation. Our code is released at https://github.com/YutongWang1216/LoongDocMT.

PaperId: 532, https://arxiv.org/pdf/2605.30268.pdf GitHub

Abstract:
We address the task of generating physically accurate and visually faithful 4D Human‑Object Interaction (HOI). Given a static 3D human and target object represented as 3D Gaussian Splats (3DGS), our goal is to synthesize dynamic scenes where the human actively engages with the object through actions, such as punching or kicking, in accordance with a given input text. To this end, we introduce PhyGenHOI, a novel framework that couples generative human motion with an explicit physical object simulation. We model the human as a semantic agent driven by a Motion Diffusion Model (MDM) and the object as a physical agent simulated via the Material Point Method (MPM), utilizing 3D Gaussians as a unified, differentiable representation. We supervise their interaction through three coupled mechanisms: (1) A Windowed Attraction Loss that temporally synchronizes generative motion to intercept the object; (2) A Contact‑Driven Re‑simulation step that triggers physically consistent momentum transfer upon impact; and (3) A Masked Video‑SDS objective that injects video‑based priors to enhance contact fidelity. Experiments show PhyGenHOI generates physically consistent 4D HOI across diverse actions, humans, and objects, outperforming baselines. Project page and videos: https://omerbenishu.github.io/PhyGenHOI/

PaperId: 533, https://arxiv.org/pdf/2605.30260.pdf GitHub

Abstract:
Large Language Models (LLMs) must continuously learn and update knowledge to remain effective in dynamic real‑world environments. While Low‑Rank Adaptation (LoRA) is widely used for such memory updates, existing studies mainly rely on qualitative downstream evaluations, leaving the quantitative capacity limits and underlying dynamics of exact parametric memory largely unexplored. To bridge this gap, we employ LoRA as a controlled memory capacity probe within the latent space to systematically quantify exact parametric memory. We introduce the Parametric Memory Law, a robust power law linking loss reduction Delta L to effective parameters and sequence length. At the token level, fine‑grained analysis reveals a deterministic phase transition, demonstrating that a prediction probability of p > 0.5 constitutes a sufficient condition for verbatim recall under greedy decoding. Driven by these insights, we introduce MemFT, a threshold‑guided optimization strategy that dynamically redistributes the training budget toward sub‑threshold tokens. Empirical evaluations demonstrate that MemFT can enhance memory fidelity and efficiency. Code will be released at https://github.com/zjunlp/ParametricMemoryLaw.

PaperId: 534, https://arxiv.org/pdf/2605.30231.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) often struggle with robust 3D spatial reasoning. Prevailing methods that rely on fine‑tuning with 3D visual question‑answering (VQA) datasets may overfit dataset‑specific biases, while integrating specialized 3D visual encoders is often inflexible and cumbersome. In this paper, we argue that genuine spatial understanding should emerge from learning fundamental geometric priors, not only from high‑level VQA supervision. We propose GASP (Geometric‑Aware Spatial Priors), a framework that injects these priors directly into the LLM's transformer layers. GASP employs a small correspondence head, applied as a deep supervision signal across all layers, and is trained with a dual objective leveraging ground‑truth geometry from large‑scale video scenes: a contrastive loss on ground‑truth point correspondences enforces 2D view‑invariance, while a depth consistency supervision resolves 3D geometric ambiguities. Our analysis first provides a diagnostic showing that standard VLMs' internal correspondence matching accuracy is very low (often below 5%). We then demonstrate that our training substantially improves this behavior, boosting peak layer‑wise correspondence to over 70% and maintaining over 85% temporal robustness while baselines remain below 5%. These internal improvements translate to significant gains on downstream spatial benchmarks including +18.2% on All‑Angles Bench and +29.0% on VSI‑Bench, all without training on any 3D VQA data. Our findings indicate that learning from fundamental geometric priors is a promising and generalizable pathway towards VLMs with more reliable 3D spatial reasoning.

PaperId: 535, https://arxiv.org/pdf/2605.30219.pdf GitHub

Abstract:
Long‑horizon interactions require language models to manage accumulating information: when to update their state, when to preserve their state, and what to ignore. We study this challenge as Contextual Belief Management (CBM): maintaining a predicted belief state aligned with formal evidence while isolating task‑irrelevant noise. To make CBM measurable, we introduce BeliefTrack, a closed‑world benchmark spanning Rule Discovery and Circuit Diagnosis, where a finite belief space and symbolic verifiers enable exact turn‑level evaluation. BeliefTrack diagnoses three failures: Failed Stay, Failed Update, and Failed Isolation. Across multiple LLMs, vanilla models exhibit severe CBM failures, while explicit belief‑tracking prompts provide limited gains. In contrast, reinforcement learning with belief‑state rewards reduces failure rates by 70.9% on average. Further probing reveals latent belief‑state dynamics behind these failures, and representation‑level steering reduces failure rates by 46.1% across two tasks\footnoteCode is coming soon at https://github.com/zjunlp/CBM.

PaperId: 536, https://arxiv.org/pdf/2605.30189.pdf GitHub

Abstract:
We show that LoRA adapters, the dominant distribution format for fine‑tuned LLMs, can be reliably backdoored through training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt‑injection classifier, a small fraction of poisoned examples drives a clean‑accuracy‑preserving backdoor to saturation. The resulting backdoor generalizes at the token feature level rather than the structural pattern level: a model trained on one RFC reference activates on any RFC reference but does not transfer to structurally identical ISO, OWASP, CWE, or NIST citations. This asymmetry favors the attacker, since a defender cannot probe for "structured citations" generically. We characterize the attack across base‑model scale and family, LoRA rank, and trigger string, and evaluate two complementary detection routes against a multi‑seed adapter cohort. A behavioral detector built from two probe‑battery statistics, outlier_gap and mean_attack_rate, separates poisoned from clean adapters perfectly when the battery overlaps the trigger's token neighborhood and at high recall with zero false positives when it does not. A weight‑level statistic, the cross‑module standard deviation of dimension‑normalized Frobenius norms, also separates the cohort perfectly without running the model. Combined, the two routes are robust to probe composition. Causal patching localizes the backdoor to the MLP block at mid‑to‑late layers, with down_proj as the strongest single‑projection cause. Replications across scale, family, and rank show the behavioral detector transfers without retuning, while the weight‑level detector is calibration‑bound to the base model. The attack scales monotonically with rank, and the chosen trigger‑anchor token is both trigger‑dependent and base‑model‑dependent. Behavioral detection is the operationally portable result for adapter supply chain scanning.

PaperId: 537, https://arxiv.org/pdf/2605.30094.pdf GitHub

Abstract:
Poker is a landmark challenge for artificial intelligence. The dominant approach relies on equilibrium solvers built on counterfactual regret minimization, requiring millions of core‑hours of training. Large Language Models (LLMs) possess extensive poker knowledge but perform far below solver‑based agents when asked to play directly. Traditional rule‑based poker agents are interpretable and training‑free, but their strategic ceiling remains far below equilibrium play. We introduce PokerSkill, a training‑free and solver‑free framework that bridges this gap by using detailed rule‑based poker skills as a structured action‑grounding interface for LLMs. A deterministic context engine analyzes the current state and retrieves only the relevant fragments from a layered skill library, which is entirely designed by human poker experts, constraining the LLM's choice to reasonable actions. Against GTOWizard, a state‑of‑the‑art GTO benchmark, GPT‑5.5 XHigh with PokerSkill achieves ‑57 \pm 21 mbb/hand, Claude Opus 4.6 achieves ‑80 \pm 29 mbb/hand and Claude Opus 4.7 achieves ‑87\pm 64 mbb/hand, reducing losses by 49‑‑61% compared to default‑prompt baselines and outperforming the strong bot Slumbot. Our key finding is that rule‑based skills alone do not constitute a strong strategy, and LLMs alone cannot play well, but their combination yields an agent that requires neither training nor solver access yet competes with systems built on millions of core‑hours of computation. To our knowledge, this is the first demonstration of an LLM achieving competitive performance in a complex imperfect‑information game without game‑specific training or solver queries. Code is available at https://github.com/lbn187/PokerSkill.

PaperId: 538, https://arxiv.org/pdf/2605.30085.pdf GitHub

Abstract:
Language model reasoning traces are rarely all‑or‑nothing; they frequently contain valid intermediate steps before a critical error occurs. Existing uncertainty quantification methods typically certify final answers or entire responses, failing to provide statistical guarantees for the proportion of a sequential trace that can be safely retained. To address this, we introduce CROP (Conformal Reasoning Output Prefixes), a verifier‑agnostic calibration procedure for clean‑prefix certification. Given any step‑level risk proxy, CROP selects a calibrated threshold and returns the longest contiguous prefix whose step risk proxies remain below it, routing the uncertified suffix for downstream review or repair. Assuming exchangeability, CROP rigorously controls the marginal probability that the returned prefix contains an annotated error. Across six process‑labeled reasoning datasets, we demonstrate that standard step‑level metrics such as AUROC do not fully capture prefix utility, suggesting verifiers should instead be evaluated by certified prefix length. Furthermore, CROP balances over‑ and under‑withholding, improving downstream repair accuracy by preserving valid intermediate reasoning while discarding misleading suffixes. Ultimately, this work positions prefix certification as a rigorous, practical bridge between process supervision, abstention, and repair.

PaperId: 539, https://arxiv.org/pdf/2605.30038.pdf GitHub

Abstract:
Diffusion models generate highly realistic images but often struggle with precise text‑image alignment. While recent post‑training methods improve alignment using external rewards or human preference signals, their performance heavily depends on reward quality and does not directly address alignment within the diffusion process itself. Recent reward‑free approaches such as SoftREPA demonstrate that optimizing soft text tokens via contrastive learning can effectively improve text‑image representation alignment, outperforming standard parameter‑efficient fine‑tuning baselines. However, the contrastive formulation can excessively penalize negative pairs, which manifests as characteristic failure cases such as over‑counting and repetition. To address this issue, we propose a lightweight, reward‑free post‑training method that refines soft tokens by integrating contrastive alignment guidance directly into the score‑matching objective of diffusion models. By assigning alignment directions at the score level, our approach mitigates these limitations and yields more coherent and semantically faithful generations. Experiments show that our method matches SoftREPA while substantially improving its failure cases, achieving over 35% improvement in counting accuracy on the GenEval benchmark. Our method is seamlessly applicable to existing diffusion backbones (SD1.5, SDXL, and SD3), and is complementary to existing RL‑based diffusion post‑training methods. Project page: https://jaayeon.github.io/AGSM

PaperId: 540, https://arxiv.org/pdf/2605.30014.pdf GitHub

Abstract:
Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict access to large‑scale and high‑quality trajectory datasets. Trajectory generation provides a promising alternative by synthesizing realistic data to mitigate privacy risks. However, existing methods fail to explicitly capture travel patterns and can only generate fixed‑length trajectories under a single condition. To address these limitations, we propose HTP, which Hierarchically generates Travel patterns first and then generates GPS Points by using large language models (LLMs), rather than directly generating GPS points. We first design a trajectory‑specific residual quantization variational autoencoder (RQ‑VAE) that quantizes micro‑level GPS trajectories into compact, macro‑level travel pattern tokens in a coarse‑to‑fine manner. These tokens capture rich segment spatial irregularities, such as point density variations caused by traffic conditions. Then, we extend the LLM vocabulary with travel pattern tokens to align trajectory representations with the LLM input, and apply supervised fine‑tuning (SFT) to align the LLM with the trajectory generation task, enabling generation of travel pattern sequences under various conditions. Extensive experiments on two real‑world datasets show that HTP outperforms the strongest baseline by an average of 29.78% in terms of generation quality. Our code is available at https://github.com/slzhou‑xy/HTP.

PaperId: 541, https://arxiv.org/pdf/2605.30003.pdf GitHub

Abstract:
We study two‑level autoresearch for cooperation: an outer‑loop AI agent autonomously redesigns the inner‑loop pipeline of an LLM policy‑synthesis system for multi‑agent Sequential Social Dilemmas (SSDs). A researcher agent \mathcalR (run as a coding agent) reads the inner‑loop source code, edits system prompts, feedback functions, helper libraries, and iteration logic, runs evaluations, and decides what to keep, following the autoresearch paradigm. Across two games (Cleanup and Gathering), two policy‑synthesizer LLMs, and two welfare objectives (utilitarian efficiency and Rawlsian maximin), the researcher reliably exceeds hand‑designed baselines, sharply tightens run‑to‑run variance, and outperforms prompt‑only optimization. The discovered pipelines are objective‑dependent: only under maximin does the researcher inject an explicit fairness mechanism into synthesizer pipelines, a class of mechanism that is absent from its own objective‑agnostic system prompt and from every efficiency‑optimized pipeline. This supports an information‑design reading in which the researcher chooses what to reveal to the boundedly rational synthesizer as a function of the welfare objective. Code at https://github.com/vicgalle/autoresearch‑social‑dilemmas.

PaperId: 542, https://arxiv.org/pdf/2605.30002.pdf GitHub

Abstract:
Cross‑domain multimodal time series forecasting is a challenging task, requiring models to integrate precise numerical comprehension, cross‑domain semantic understanding, and effective multimodal fusion. Existing approaches either build Time Series Foundation Models (TSFMs) from scratch or leverage pretrained Large Language Models (LLMs). However, TSFMs often overlook semantic understanding and lack the ability to perform future‑oriented semantic reasoning, and LLMs struggle with numerical comprehension and accurate quantitative forecasting. To overcome these limitations, we propose KairosAgent, a novel agentic framework for multimodal time series forecasting, including an LLM‑based reasoner and a TSFM‑based forecaster. KairosAgent unifies textual reasoning and numerical forecasting by dynamically invoking analytical tools to enhance the numerical understanding and semantic reasoning capabilities of LLMs. The reasoning results are subsequently fused into the TSFM pipeline, enabling more accurate and reliable future predictions. To further improve the reasoning, we curate a large‑scale corpus of high‑quality trajectories, alongside a reinforcement learning from forecasting paradigm with multi‑turn refinement and turn‑level credit assignment. Experiments demonstrate that KairosAgent achieves superior zero‑shot forecasting performance while maximizing the utility of pretrained LLMs and TSFMs, presenting a promising direction for efficient and interpretable time series agents. The project page is at https://foundation‑model‑research.github.io/KairosAgent .

PaperId: 543, https://arxiv.org/pdf/2605.29980.pdf GitHub

Abstract:
Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream diagnostic tasks. Hematological cytology is unique in that visual single‑cell evaluation is often paired with cytogenetics and molecular genetics for blood cancer diagnosis. In this study, we present a framework to align single white blood cell images with chromosomal aberrations (karyotype) and somatic mutations from targeted gene panels. Our training strategy follows a two‑stage approach: (i) self‑supervised, vision‑only pretraining of a transformer aggregator using an iBOT head on a cohort of over 1500 patients, and (ii) genetic alignment via supervised contrastive loss on acute myeloid leukemia patients. Our genetically aligned patient encoder improves hematological diagnostic tasks, outperforming slide‑level histopathology foundation models. Additionally, the model provides off‑the‑shelf retrieval capabilities for diseases and genetic alterations. Incorporating genetic data into patient encoders increases the quality of patient representations, providing a framework that aligns with clinical diagnostic workflows and paves the way for future multimodal hematology‑specific AI. The code and model weights are available at https://github.com/marrlab/GenBloom.

PaperId: 544, https://arxiv.org/pdf/2605.29893.pdf GitHub

Abstract:
LLM‑based agents have demonstrated strong capabilities in solving complex tasks through multi‑step reasoning and tool use. However, existing evaluation protocols primarily focus on task success, overlooking a critical aspect of agent behavior: execution efficiency. In practice, agent trajectories often contain redundant steps that consume substantial resources while contributing little to task completion. In this work, we propose and formulate a new research area: redundant step detection for agent trajectories. To support this initiative, we introduce RedundancyBench, a new benchmark that contains diverse tasks with carefully annotated trajectories, where each step is labeled according to its contribution to task completion. Using RedundancyBench, we develop and evaluate 3 representative methods to answer whether a step within trajectory is redundant or necessary. Our results show that even the best‑performing method achieves only 24.88% score in detecting redundant steps, while some methods perform worse than random guessing. These results highlight the task's complexity and the need for further research in this area. \footnoteCode and dataset in this paper are both available in \hrefhttps://anonymous.4open.science/r/RedundancyBenchhttps://anonymous.4open.science/r/RedundancyBench.

PaperId: 545, https://arxiv.org/pdf/2605.29874.pdf GitHub

Abstract:
Do next‑generation LLM agents inherit the cooperative biases documented in their predecessors, or does scale and provider diversity reshape equilibrium behaviour in competitive multi‑agent settings? Willis et al. established a benchmark for this question using evolutionary game theory and the Iterated Prisoner's Dilemma (IPD), finding consistent cooperative biases in ChatGPT‑4o and Claude 3.5 Sonnet. We extend this benchmark to four frontier models released in 2025‑2026 ‑ Claude Sonnet 4.6, Gemini 2.5 Flash, Gemini 3.1 Pro, and GPT‑5.4 Mini ‑ applying the identical protocol across three prompting styles (Default, Prose, Self‑Refine) and four population compositions (balanced and biased, with and without noise). Cooperative bias persists across providers (H1): nine of twelve model‑prompt combinations favour cooperative equilibria in balanced noiseless conditions. Cross‑provider divergence is substantial (H3): Gemini 2.5 Flash reaches up to 77% aggressive equilibria under biased conditions, while GPT‑5.4 Mini reaches 70% cooperative equilibria under Self‑Refine. Support for aggressive capability parity is partial (H2): Self‑Refine raises ICD in all models and Claude Sonnet 4.6 Refine achieves the highest ICD in the dataset (0.913), but Default and Prose prompts show no systematic narrowing. Evidence on noise robustness is directionally positive but not robustly confirmed (H4): with n=500 Moran iterations per condition, average noise sensitivity is approximately 6 percentage points for Claude Sonnet 4.6 versus 13 pp for Claude 3.5 Sonnet, but this cross‑study gap is not statistically significant once the predecessor's unreported sampling error is propagated. Provider identity, rather than model generation, is the strongest correlate of equilibrium outcomes; noise remains a universal challenge regardless of model size or vintage.

PaperId: 546, https://arxiv.org/pdf/2605.29829.pdf GitHub GitHub

Abstract:
Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient paradigm for automated optimization. However, existing methods still exhibit limited generalization: they are sensitive to superficial narrative variations, reuse experience mainly at the case level, and struggle to adapt to shifted or emerging problem types. We propose OptSkills, an archetype‑centric skill learning and reasoning agent system for optimization modeling and solving. To improve robust generalization, our system clusters problems by their underlying archetypes rather than surface narratives. To improve in‑distribution generalization, it explores diverse modeling paradigms and solver configurations within each cluster, then distills successful trajectories into reusable workflow‑level skills. To improve out‑of‑distribution generalization, it refines existing skills or expands the skill library using newly obtained trajectories. Our system achieves a state‑of‑the‑art micro‑averaged accuracy of 68.27% on datasets encompassing diverse problem types and scenarios. In addition, on MIPLIB‑NL, a highly challenging large‑scale and high‑dimensional benchmark, it achieves 26.91% accuracy, outperforming DeepSeek‑V3.2‑Thinking by 4.53%. After skill learning on Nano‑CO, it reaches 72.79% on the OOD NLCO benchmark. Code and skills are available at https://github.com/fujiwaranoM0kou/OptSkills.

PaperId: 547, https://arxiv.org/pdf/2605.29773.pdf GitHub

Abstract:
Reliable semantic segmentation for mobile robots requires both accurate dense prediction and robust uncertainty estimation under distribution shift. Strong uncertainty baselines such as Monte Carlo Dropout often require repeated stochastic forward passes and are difficult to deploy on edge platforms. We propose Energy‑Aware NECO, a single‑pass pixel‑wise out‑of‑distribution (OOD) detector for semantic segmentation. The method combines a centered NECO‑style geometric ratio computed from decoder features with a logit‑based Energy score. Both components are standardized using statistics fitted on a pure in‑distribution validation split and fused through a convex combination. We evaluate the method on the miniMUAD subset using true pixel‑level OOD labels. The proposed hybrid score achieves an AUROC of 0.8539, outperforming NECO‑only (0.8280), Energy‑only (0.8171), and an ensemble predictive‑entropy baseline (0.8124). Additional qualitative and operating‑point analyses show that the hybrid detector improves overall ranking performance while preserving the efficiency advantages of a single‑pass design. Code is available at https://github.com/boyuan‑zhangx/Energy‑Aware_NECO

PaperId: 548, https://arxiv.org/pdf/2605.29742.pdf GitHub

Abstract:
Deploying Large Language Models (LLMs) for regulatory compliance demands rigorous traceability via comprehensive citations across multi‑tiered authority structures. Unlike traditional multi‑hop or legal QA, this task requires structured procedural lookups and evidence‑set closure rather than entity resolution or case‑law reasoning. Existing RAG systems struggle here due to flattened citation edges, fragmented retrieval expansions, and fragile post‑hoc attribution. We formalize Regulatory Compliance QA with RegOps‑Bench, a novel benchmark featuring an Operational Knowledge Graph derived from complex national R\&D regulations. To address these bottlenecks, we propose RefWalk, a unified framework driven by a shared topic anchor. RefWalk traverses cross‑document citations, fuses multi‑view candidates via max‑based aggregation, and enforces per‑rule attribution to explicitly map claims to sources. We establish a strong baseline with substantial improvements in retrieval recall and citation accuracy. Finally, a contrastive evaluation on a U.S. health compliance dataset (HIPAA) reveals that existing systems exhibit saturation on flat‑structure rules, underscoring the need for RegOps‑Bench. Our code is available at https://github.com/yeongjoonJu/RefWalk.

PaperId: 549, https://arxiv.org/pdf/2605.29716.pdf GitHub

Abstract:
Diffusion Large Language Models (dLLMs) have emerged as a promising non‑autoregressive generative paradigm. Given the prohibitive computational cost of full fine‑tuning, Parameter‑Efficient Fine‑Tuning (PEFT) has become the standard approach. However, existing PEFT methods (e.g., LoRA), originally tailored for autoregressive models, rely on static parameters that are agnostic to the noise level. Consequently, they ignore the intrinsic dynamics of the diffusion process, where input distributions and generation difficulty shift significantly along the denoising trajectory, rendering them suboptimal for dLLMs. To address this, we propose Noise‑aware Low‑Rank Adaptation (NaRA), which introduces a low‑rank core matrix generated by a lightweight, globally shared hypernetwork conditioned on the noise level. This design enables the update matrices to vary continuously along the diffusion process while keeping parameter and latency overhead negligible. We provide a theoretical justification for the proposed NaRA framework and empirically demonstrate consistent improvements over noise‑agnostic baselines across commonsense reasoning, mathematical reasoning, and code generation benchmarks. Our code is available at https://github.com/generaldi/NaRA.

PaperId: 550, https://arxiv.org/pdf/2605.29705.pdf GitHub

Abstract:
Trajectory prediction is a fundamental task for autonomous systems, requiring complex reasoning about multi‑agent interactions and intents. Large language models (LLMs) have recently been adopted for this task, as they provide strong contextual reasoning and interpretable, language‑based trajectory representations. However, these LLM‑based predictors are extremely memory‑ and compute‑intensive, making them difficult to deploy on resource‑constrained edge devices such as on‑board computers in autonomous robots. To bridge this gap, we propose BitTP, which converts an LLM‑based trajectory predictor into a lightweight bitlinear architecture. We demonstrate that weight‑only quantization to 1.58‑bit (BitTP‑Weight) is optimal. Crucially, activations must remain in full precision, as quantizing them leads to severe degradation and instability in spatio‑temporal reasoning. Empirically, BitTP‑Weight not only preserves but improves prediction quality over the full‑precision (BF16) LLM baseline, reducing ADE by 14.29% and FDE by 20.97% on average, while simultaneously reducing memory usage and inference latency relative to other quantization methods. These results demonstrate that carefully designed quantization acts as an effective regularizer, enabling the practical deployment of sophisticated LLM‑based reasoning on edge devices. Code is available at: https://github.com/MintCat98/BitTP.

PaperId: 551, https://arxiv.org/pdf/2605.29656.pdf GitHub

Abstract:
Evaluating open‑ended outputs from large language models (LLMs) remains challenging due to the absence of ground truth. Existing metrics rely on final‑answer accuracy or surface‑level statistics, leaving the reasoning process itself unexamined. We introduce TRACE (Toulmin‑based Reasoning Assessment through Constructive Elements), a metric that analyzes Chain‑of‑Thought (CoT) reasoning processes. Rather than judging outcomes, TRACE inspects how arguments are constructed by integrating Toulmin's argumentation theory with Flavell's metacognitive framework to assess reasoning structure. Experiments on 26.3K QA samples across 7 reasoning models show strong correlation with benchmark accuracy (r=0.74). Furthermore, TRACE is effective as a reinforcement learning reward signal, outperforming accuracy‑only baselines. Together, these results indicate that logically sound reasoning leads to higher‑quality answers. TRACE thus serves as a complementary metric for evaluating open‑ended outputs. Code is available at https://github.com/hyyangkisti/trace.

PaperId: 552, https://arxiv.org/pdf/2605.29630.pdf GitHub

Abstract:
End‑to‑end agent‑memory benchmarks report a single hit@k per retriever, confounding lexical leakage (uncontrolled query/gold/distractor entity overlap) with tag‑mixing (preferences, services, tools averaged together). We propose entity‑collision, a system‑agnostic protocol that pins the BM25 floor by construction ‑‑ every distractor shares the answer's entity tokens ‑‑ and stratifies queries by discriminator tag, so any lift over BM25 is attributable to the embedder. Applied to an open‑source agent‑memory testbed across 5 tags x 3 embedders x 5 collision degrees with paired‑bootstrap 95% CIs, the protocol reveals a two‑axis pattern: a 256‑d hash trigram helps only on closed‑vocabulary lexical tags at deep collision; MiniLM‑384 dominates both axes; and a 2.7x‑parameter BGE‑large does not uniformly improve on MiniLM ‑‑ it wins on intent‑style queries but loses on lexical ones. Encoder capacity alone is not the binding constraint. The synthetic intent‑tag null replicates on LongMemEval (n=500) as a single‑session‑preference recall cliff. Adaptive vector‑weight routing on LoCoMo is a measured null: 11.7pp of oracle headroom exists, but no signal we tested recovers it. All 26 result tables and 37 reproduce scripts are version‑controlled and verified by a public registry; the protocol is exercised on a deterministically governed memory testbed (event‑sourced decision log, DAG‑state‑machine schema lifecycle) so every reported CI is reproducible byte‑for‑byte from the ingest stream.

PaperId: 553, https://arxiv.org/pdf/2605.29610.pdf GitHub GitHub

Abstract:
In scene graph generation, a central challenge is modeling polysemous predicates whose meanings shift across contexts. Prior approaches address this issue by decomposing predicates into multiple static prototypes or retrieving semantically similar exemplars. However, these strategies keep predicate representations static and cannot reorganize semantics to reflect image‑specific evidence, leading to systematic confusions in ambiguous contexts. We propose AlignG, which learns context‑conditioned predicate semantics via prototype feedback. AlignG infers context‑conditioned predicate semantics from the relation candidates within each image and feeds the adapted semantics back to recalibrate relation representations. The learning objective anchors this adaptation to global semantic centers, preventing semantic drift while still allowing selective reorganization when the scene provides consistent relational cues. Experiments on VG‑150 and GQA‑200 show consistent improvements over state‑of‑the‑art baselines, with F@100 improvements of +1.4 on VG‑150 and +2.7 on GQA‑200 under SGDet. We further visualize per‑image prototype similarity shifts and observe coherent context‑dependent reorganization where prototypes selectively merge or separate predicates according to scene evidence. The code is available at https://github.com/Namgyu97/AlignG‑SGG.pytorch.

PaperId: 554, https://arxiv.org/pdf/2605.29591.pdf GitHub

Abstract:
Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain‑Computer Interfaces (BCIs). A major limitation of prior work is the prevailing paradigm of specialized, single‑task models, which curtails versatility and neglects inter‑task synergies. To address this, we propose Mind‑Omni, the first versatile framework that unifies seven distinct encoding and decoding tasks through a discrete diffusion paradigm. At its core is a novel Brain Tokenizer that transforms heterogeneous, continuous brain signals into standardized, discrete tokens. This enables direct, token‑level interactions for mutual understanding and generation between any two or more modalities within a shared semantic space. To unlock advanced reasoning capabilities, we further curate a specialized Brain Question Answering (BQA) instruction‑tuning dataset. Our model not only establishes a new state‑of‑the‑art among multi‑task unified frameworks but also provides strong evidence for multi‑task synergy. By demonstrating performance competitive with, and at times superior to, larger specialized models, our work offers a powerful new paradigm for neural modeling and paves the way for foundation models of neural activity. The code is publicly available at https://github.com/ReedOnePeck/Mind‑Omni.

PaperId: 555, https://arxiv.org/pdf/2605.29526.pdf GitHub

Abstract:
Ever‑evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: adversarial pattern evolution by malicious actors and the out‑of‑distribution (OOD) problem caused by varied transaction semantics on blockchains. To address these challenges, we propose a novel framework termed TEmporal Motif‑aware Graph Test‑Time Adaptation (TEMG‑TTA). First, we comprehensively capture the 3‑node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif‑aware graph learning. Second, we design a simple yet effective test‑time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real‑world datasets demonstrate that our proposed TEMG‑TTA outperforms state‑of‑the‑art GAD approaches by an average of 54.88%. A further case study on interpretable motif patterns reveals that TEMG‑TTA explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code will be made publicly available https://github.com/LuoXishuang0712/TEMG‑TTA/.

PaperId: 556, https://arxiv.org/pdf/2605.29507.pdf GitHub GitHub

Abstract:
Explaining why dense retrievers assign high relevance scores remains challenging because retrieval decisions are made through opaque high‑dimensional embeddings. Existing explanations often focus on surface signals, such as lexical matches, token alignments, or post‑hoc textual rationales, and thus provide limited insight into the latent factors that shape dense retrieval behavior at the embedding level. We propose Xetrieval, an embedding‑level mechanistic framework for explaining dense retrieval. Xetrieval first introduces a lightweight reasoning internalizer that approximates Chain‑of‑Thought reasoning directly in the embedding space with a single forward pass, enriching sentence embeddings with reasoning‑oriented information while avoiding expensive autoregressive generation. It then decomposes these reasoning‑enhanced embeddings into sparse, human‑interpretable features, each associated with a coherent natural language description. By aggregating sparse feature overlaps across multiple document‑side views, Xetrieval provides feature‑level explanations of individual retrieval decisions. Experiments on diverse retrievers and benchmarks show that Xetrieval uncovers coherent interpretable features, yields stronger pair‑level intervention effects, and supports task‑level feature steering. The project page and source code are available at https://hihiczx.github.io/Xetrieval .

PaperId: 557, https://arxiv.org/pdf/2605.29398.pdf GitHub

Abstract:
Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intractability of the policy likelihood. A dominant and efficient family of methods replaces the likelihood in standard RL with its evidence lower bound (ELBO), estimated from randomly masked sequences. Despite being well aligned with pre‑training, these approaches introduce bias through training‑‑inference mismatch by using the ELBO as a likelihood surrogate, which can degrade performance. In this work, we propose Guided Denoiser Self‑Distillation (GDSD) to directly distill the denoiser of dLLMs from an advantage‑guided self‑teacher, derived from the closed‑form optimum of reverse‑KL regularized RL. GDSD matches the dLLM's denoiser logits to the teacher's via a normalization‑free objective, which reduces RL to likelihood‑free self‑distillation and thus bypasses the TIM biases. Recent ELBO‑based methods emerge as instances of applying different distillation divergences, but with diagnosable pathologies that GDSD avoids. On planning, math, and coding benchmarks with LLaDA‑8B and Dream‑7B, GDSD consistently outperforms prior state‑of‑the‑art ELBO‑based methods with a more stable training reward dynamics, achieving test‑accuracy improvements of up to +19.6%. These results suggest that direct denoiser self‑distillation, without relying on an ELBO likelihood surrogate, can provide a more stable and effective RL procedure for dLLMs. Code is available at https://github.com/GaryBall/GDSD.

PaperId: 558, https://arxiv.org/pdf/2605.29380.pdf GitHub

Abstract:
Mainstream strategies for finetuning pretrained multimodal models often degrade out‑of‑distribution (OOD) robustness, a phenomenon known as catastrophic forgetting. In this paper, we develop a theoretical framework for multimodal contrastive finetuning, yielding closed‑form solutions and a geometric decomposition for each strategy. This framework shows that self‑distillation is more effective than other regularization approaches to retain the knowledge of the pretrained model. Our analysis reveals a largely overlooked limitation: standard Exponential Moving Average (EMA) teachers, widely used in robust finetuning, suffer from collapse. To solve this, we prove that a Weighted Moving Average (WMA) teacher maintains a persistent regularizing force over finite horizons and yields bias‑free convergence in the task subspace while preserving orthogonal knowledge. These insights motivate TRACER (Trajectory‑Robust Anchoring for Contrastive Encoder Regularization), which combines contrastive learning with WMA‑guided multi‑perspective distillation. Extensive experiments on CLIP finetuning demonstrate consistent OOD accuracy and calibration gains across three backbone architectures, and comprehensive ablations confirm that TRACER is both principled and robust to hyperparameter choices. Code is available at [https://github.com/HesamAsad/TRACER](https://github.com/HesamAsad/TRACER).

PaperId: 559, https://arxiv.org/pdf/2605.29357.pdf GitHub

Abstract:
Modern tensor compilers such as TorchInductor deliver substantial speedups on mainstream models, yet face a systematic performance ceiling on long‑tail workloads ‑‑ our profiling shows that 43% of real‑world subgraphs experience end‑to‑end slowdowns under default compilation. While LLMs offer a path toward automated optimization, existing efforts focus on standalone kernel generation. We argue that pass generation ‑‑ where LLMs author structured graph transformations that integrate directly into compiler pipelines ‑‑ is the more appropriate abstraction. We propose PassNet, the first large‑scale ecosystem for LLM‑based compiler pass generation, comprising: (1) PassNet‑Dataset, over 18K unique computational graphs from 100K real‑world models; and (2) PassBench, 200 curated long‑tail fusible tasks (comprising 2,060 subgraphs in total) evaluated under the Error‑aware Speedup Score (ES_t) ‑‑ a metric unifying correctness, stability, and performance ‑‑ with layered integrity defenses against systematic LLM exploitation. Experiments reveal that PassBench is both highly discriminative and genuinely unsaturated: the best frontier model trails TorchInductor by 37% in aggregate, yet on individual subgraphs LLMs achieve up to 3x speedup over the same compiler ‑‑ indicating that the bottleneck is consistency, not capability. Fine‑tuning a small model on merely ~4K PassNet trajectories yields a 2.67x improvement approaching frontier‑model performance, demonstrating substantial headroom and validating PassNet as live training infrastructure for advancing LLM‑driven compiler optimization. All data, benchmarks, and tooling are publicly available.

PaperId: 560, https://arxiv.org/pdf/2605.29303.pdf GitHub

Abstract:
Supervised fine‑tuning (SFT) followed by reinforcement learning (RL) has become a standard post‑training paradigm for large language models. This paradigm provides a cold‑start for RL exploration, avoiding the inefficiency of pure RL where on‑policy sampling yields insufficient positive samples. However, in practice, existing approaches often use a small amount of data for SFT initialization compared to the RL phase, which can cause the model to fit the limited samples and shift away from its pre‑trained distribution. This distribution shift impedes the model's ability to effectively explore during subsequent RL training. To address this challenge, we propose that in low‑data regimes, SFT should prioritize activating task‑relevant capabilities rather than memorizing specific content. Along this line, we propose EKSFT (Entropy‑KL Selective Fine‑Tuning), which selectively masks tokens that exhibit either high entropy or high KL divergence from a reference model. By excluding these high‑uncertainty, distribution‑shifting tokens from imitation, EKSFT injects task‑specific knowledge while preserving the integrity of the model's pre‑trained distribution. Empirical evaluations on mathematical reasoning benchmarks demonstrate that EKSFT consistently outperforms standard SFT. Further RL fine‑tuning from the EKSFT model yields consistently better post‑RL performance, indicating improved exploration for the RL stage. Our codes and datasets are available at https://github.com/MINE‑USTC/EKSFT.

PaperId: 561, https://arxiv.org/pdf/2605.29089.pdf GitHub

Abstract:
Recent reinforcement learning (RL) post‑training approaches primarily optimize the final output policy using sparse outcome‑level rewards, while largely overlooking predictive signals encoded in intermediate representations. In this paper, we introduce a new paradigm called on‑policy internal self‑distillation and propose the OISD framework, which improves reasoning by transferring on‑policy predictive signals from the final layer to intermediate representations. During rollout and Group Relative Policy Optimization (GRPO) optimization, the final layer acts as both the policy and a detached internal teacher for selected intermediate layers, which are guided to align with it through two complementary mechanisms: logit alignment, which transfers high‑level reasoning behaviors (how to think), and attention alignment, which enforces consistent attention patterns (where to look) from the final layer to the selected intermediate layer, both without requiring external privileged information. Our OISD, together with GRPO, employs signed advantage‑weighted Jensen‑‑Shannon alignment to distill informative intermediate representations while preserving policy consistency under a unified acting policy. Experimental results demonstrate the effectiveness of OISD, with substantial and consistent improvements over strong reasoning RL baselines across four mathematical reasoning tasks. The code will be released at https://github.com/THE‑MALT‑LAB/OISD

PaperId: 562, https://arxiv.org/pdf/2605.28999.pdf GitHub

Abstract:
LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or through a few anecdotal case studies. Its prevalence and impact in real‑world LLM‑based applications are largely unexplored. In this work, we present the first systematic study of prompt‑injection attacks in a widely used application: LLM‑based resume screening. Our analysis is based on approximately 200K real‑world resumes collected over multiple years by hireEZ. We first design tailored methods to detect prompt injection in resumes. Manual validation on a small‑scale dataset demonstrates that our detectors achieve high precision and outperform state‑of‑the‑art general‑purpose detectors. We then apply our detector to the full resume dataset and conduct a comprehensive measurement study of real‑world prompt injection attacks. Our analysis reveals several intriguing findings: approximately 1% of resumes contain hidden prompt injections; the prevalence of such injected resumes has increased noticeably over the past one to two years; and more than 90% of injected prompts do not use explicit instructions. These results provide the first evidence of large‑scale prompt injection in real‑world LLM‑based applications and lay the groundwork for future studies to understand and mitigate such attacks.

PaperId: 563, https://arxiv.org/pdf/2605.28919.pdf GitHub

Abstract:
Large language models have achieved strong reasoning capabilities, though often at the cost of massive parameter counts and expensive inference. In this work, we explore a different direction: adaptive reasoning depth in compact language models. We present CosmicFish‑HRM, a compact language model built around a Hierarchical Reasoning Module (HRM) that dynamically allocates computational effort during inference. Instead of applying fixed computation to every input, the model iterates through high‑level and low‑level reasoning cycles and learns when to halt based on input complexity. CosmicFish‑HRM combines this adaptive reasoning core with modern transformer components including Grouped Query Attention, RoPE, and SwiGLU activations. While the additional reasoning infrastructure introduces overhead at small scale, we hypothesize that this tradeoff becomes increasingly favorable as model size grows and the relative cost of the HRM core diminishes. Our results show that the model learns non‑uniform reasoning behavior, allocating different numbers of reasoning steps across tasks and inputs. These findings suggest that adaptive reasoning depth may offer a promising alternative to relying solely on parameter scale for reasoning capability.

PaperId: 564, https://arxiv.org/pdf/2605.28914.pdf GitHub

Abstract:
Tool‑using language agents turn model decisions into external side effects: they read files, run scripts, call APIs, send messages, and invoke Model Context Protocol tools. This makes agent attacks different from jailbreaks. The harmful step is often not an obviously forbidden output, but an ordinary executable action that becomes unsafe because attacker‑controlled context steers authorized access against the user's interest. We identify this failure mode as authority confusion: untrusted resources may inform reasoning, but they must not authorize side effects. We present AIRGuard, a runtime guard that operationalizes least privilege as action‑time authorization. AIRGuard normalizes heterogeneous tool calls, derives task authority into step‑level authority, tracks source and target trust, simulates sensitive side effects, audits cross‑step risk, and enforces decisions before actions execute. On AgentTrap, AIRGuard reduces Sonnet 4.6 attack success from 36.3% without defense to 5.5%. On DTAP‑150, AIRGuard preserves 76.0% benign utility with Haiku 4.5, compared with 52.0% for ARGUS and 42.0% for MELON. An ablation further shows that prompt‑only policy helps only modestly, whereas a dedicated runtime authority‑control layer gives the agent system direct control over tool‑mediated side effects. Code and data are available at https://github.com/Sophie508/AIRGuard.

PaperId: 565, https://arxiv.org/pdf/2605.28902.pdf GitHub

Abstract:
Concept erasure has emerged as a promising approach to mitigate undesired or unsafe content in diffusion models, yet existing methods still face significant limitations. While training‑based methods are effective, their high computational cost limits scalability. Editing‑based methods are more efficient and deployment‑friendly, yet they struggle to simultaneously achieve precise concept erasure and preserve overall generative capacity. We identify this core limitation of the editing‑based methods as reliance on additive parameter updates. Our empirical analysis reveals that concept semantics primarily depend on neuron direction rather than neuron magnitude, while overall generative capacity relies on the angular geometry of neurons. As additive updates inherently entangle direction, magnitude, and angular geometry, they inevitably introduce unintended interference between concept erasure and overall generation performance. To address this, we propose Orthogonal Concept Erasure (OCE), which reformulates editing‑based erasure as multiplicative parameter updates from a geometric perspective. Specifically, OCE applies layer‑wise orthogonal transformations derived from a closed‑form solution to the parameters, enabling precise concept erasure while preserving the neuron magnitude and angular geometry. Furthermore, to address conflicting constraints in multi‑concept erasure, OCE introduces a subspace‑level objective with structured subspace manipulation, yielding a more effective and scalable erasure. Extensive experiments on single‑ and multi‑concept erasure demonstrate that OCE outperforms existing methods in concept erasure and non‑target preservation, erasing up to 100 concepts in 4.3 s. Code: https://github.com/HansSunY/OCE.

PaperId: 566, https://arxiv.org/pdf/2605.28897.pdf GitHub

Abstract:
LLM‑generated reviews for scientific papers are gaining considerable traction and are even being officially piloted by major conferences. We have to assume that not only reviewers are using LLM‑assistance, but also that authors use LLMs to revise their papers before submitting. In this work, we perform empirical experiments on papers from the 2025 ACL Rolling Review (ARR) to evaluate LLM reviews from both the author and the reviewer perspective. First, we identify a limited alignment of LLM reviews with human ones. In the best‑case scenario, the alignment is reasonable. However, we also find that LLM‑human alignment varies substantially across prompts and models. Finally, we investigate the scenario in which the author uses an iterative draft‑revise workflow to improve the submission according to the LLM review. We find that this "gaming" of LLM reviews can be effective in specific scenarios, leading to a statistically significant increase of overall scores for up to 35% of papers. We publish our code: https://github.com/uhh‑hcds/reviewarcade.

PaperId: 567, https://arxiv.org/pdf/2605.28860.pdf GitHub

Abstract:
Fine‑tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities. Recent work has shown that reinforcement learning (RL) retains prior capabilities more effectively than supervised fine‑tuning (SFT), attributing this to policy‑gradient updates remaining closer to the base policy \citeshenfeld2025rl. We extend this behavioral account to the mechanistic level and ask whether RL's advantage is mirrored by stronger preservation of internal computational circuits. We introduce differential circuit vulnerability, a head‑level measure of how much a circuit degrades under fine‑tuning, and use it to compare RL and SFT on Qwen2.5‑3B‑Instruct adapted to scientific question‑answering. We find a clear mechanistic trade‑off: SFT adapts more rapidly to the target task but produces substantially greater circuit disruption and forgetting of prior capabilities, whereas RL preserves a larger fraction of the base circuit at the cost of slower task adaptation. These findings suggest that circuit preservation may help explain why RL is more robust to catastrophic forgetting. We released our code here: https://github.com/rl‑sft‑circuit‑research/differential‑circuit‑vulnerability.

PaperId: 568, https://arxiv.org/pdf/2605.28842.pdf GitHub

Abstract:
The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in aligning model behavior with task objectives. Existing reasoning chain tuning methods often rely on black‑box heuristics or gradient‑free search, which lack interpretability, generalization, and sample efficiency. In this work, we introduce Thoughts‑as‑Planning, a novel framework that formalizes reasoning chain optimization as a sequential decision‑making process over a latent semantic space. We model the LLM as a partially observable environment and learn a latent world model that simulates the effect of reasoning chain edits on downstream outputs. A proximity‑preserving embedding space is constructed to encode reasoning chain‑response dynamics, enabling planning via gradient descent or reinforcement learning. Our method supports multi‑scale abstraction, allowing reasoning chain edits at token, segment, and instruction levels to be integrated into a unified planner. Through extensive experiments on language understanding and generation tasks, we demonstrate that Thoughts‑as‑Planning outperforms state‑of‑the‑art reasoning chain tuning baselines in efficiency, robustness, and generalization, while offering interpretability through its structured planning trajectory. Our code is available at https://github.com/FastLM/Thoughts‑as‑Planning.

PaperId: 569, https://arxiv.org/pdf/2605.28837.pdf GitHub

Abstract:
While Large Language Models (LLMs) have demonstrated remarkable capabilities, their reliability is significantly compromised by hallucinations. Existing intrinsic self‑correction methods attempt to address this, but often fail due to self‑bias, where models struggle to identify errors in their own outputs without external verification. To overcome these limitations, we propose the LDPC‑inspired semantic error correction for retrieval‑augmented generation (SERC), providing a theoretical framework to interpret and mitigate LLM hallucinations. We reformulate the text generation process as a semantic noisy channel, treating generated responses as noise‑corrupted codewords. Inspired by low‑density parity‑check (LDPC) codes, SERC employs a sparse verification strategy: instead of exhaustively checking all facts, it generates low‑density verification queries and validates them against external evidence to efficiently detect and correct errors. We evaluate SERC on LongForm Bio and TruthfulQA benchmarks using Llama‑3‑8B and Qwen2.5‑14B. Experimental results demonstrate that SERC outperforms both intrinsic self‑correction methods and strong retrieval‑augmented baselines, demonstrating significant gains especially in factual precision (FactScore). Notably, SERC enables small language models (SLMs) to surpass the performance of larger baselines in hallucination reduction and information preservation. Our findings demonstrate that SERC provides a training‑free, model‑agnostic solution that significantly reduces verification overhead compared to dense methods, achieving an optimal trade‑off between cost and fidelity in resource‑constrained environments.

PaperId: 570, https://arxiv.org/pdf/2605.28812.pdf GitHub

Abstract:
A primary bottleneck in contact‑rich manipulation is the difficulty of collecting real‑world data. Sim‑to‑real reinforcement learning offers a scalable alternative, but the simulation‑reality gap prevents information‑dense modalities like touch from being effectively used. Existing sim‑to‑real methods often mitigate this gap by simplifying tactile data into coarse low‑dimensional features ‑‑ sacrificing the richness required for complex manipulation. In this work, we introduce Center‑of‑Pressure (CoP), an effective tactile representation grounded in physical principles that preserves dense contact information while maintaining robustness for sim‑to‑real transfer. To support this representation, we propose a sensor calibration scheme based on differentiable dynamics, enabling the estimation of taxel orientations without requiring ground‑truth force measurements. We evaluate CoP on two blind, challenging contact‑rich manipulation tasks: peg‑in‑hole insertion and ball balancing. Across both tasks, policies conditioned on CoP achieve zero‑shot sim‑to‑real transfer on a multi‑fingered hand, and outperform both coarse binary‑contact and raw‑taxel baselines. Analysis of learned policy states further suggests that CoP‑conditioned policies encode task‑relevant physical properties, such as object mass, as an emergent byproduct of control.

PaperId: 571, https://arxiv.org/pdf/2605.28805.pdf GitHub

Abstract:
Visual outcomes are increasingly central to multimodal large language models, making reliable and fine‑grained verification essential for scaling generalist foundation models. In this work, we investigate multimodal meta‑verification, which leverages verifier‑generated rationales rather than decision‑only signals, and explore how to effectively incorporate meta‑verification feedback into multimodal verifier training. We identify two key findings. First, symbolic verifier outputs (e.g., bounding boxes) outperform textual explanations as meta‑verification rationales, enabling efficient rule‑based reinforcement learning rewards while avoiding reliance on model‑based rewards from auxiliary judge models. Second, decoupling reinforcement learning objectives for binary judgment and meta‑verification substantially outperforms joint reward optimization, due to intrinsic differences in output structure and learning dynamics. Based on these insights, we train OmniVerifier‑M1, a generalist visual verifier leveraging symbolic meta‑verification and decoupled reinforcement learning. OmniVerifier‑M1 provides robust verification and fine‑grained error localization, and further enables M1‑TTS, a verifier‑driven agentic generation system achieving dynamic region‑level self‑correction. This approach paves the way for more reliable, interpretable, and fine‑grained multimodal verification, supporting safer and more controllable foundation model deployment.

PaperId: 572, https://arxiv.org/pdf/2605.28791.pdf GitHub

Abstract:
On‑policy self‑distillation (SD) improves LLM reasoning by using teacher‑side privileged information (PI) to turn sparse verifier outcomes into dense token‑level supervision. Existing methods usually assume trusted PI, such as reference answers or successful traces. We ask whether PI can instead come from an experience‑derived skill bank, where retrieved skills are compact and reusable but may also be irrelevant or misleading. We propose Skill‑Conditioned Gated Self‑Distillation (SGSD), which formulates skill‑based SD as teacher hypothesis validation rather than unconditional imitation. SGSD retrieves skill‑mistake pairs, constructs a multi‑teacher pool, and lets all skill‑conditioned teachers score the same plain‑prompt student rollout. The verifier validates each teacher's polarity: supporting a success or suppressing a failure gives positive supervision, while the opposite stance is reversed. A robust gated objective then distills informative teacher‑student disagreements while suppressing uncertain or extreme signals. Experiments on multiple mathematical reasoning benchmarks show that SGSD consistently improves over GRPO and remains competitive with answer‑conditioned OPSD under a weaker PI assumption. For example, on Qwen3‑1.7B, SGSD outperforms GRPO by 6.2% and OPSD by 1.7% on average on AIME24, AIME25, and HMMT25. Our code is available at https://github.com/walawalagoose/SGSD.

PaperId: 573, https://arxiv.org/pdf/2605.28773.pdf GitHub

Abstract:
Existing memory‑augmented LLM agents often treat memory as a static repository with pre‑defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic environments where feedback, task variation, and heterogeneous signals continuously reshape what should be remembered and how it should be connected. To address this, we propose FluxMem, a connectivity‑evolving memory framework that models memory as a heterogeneous graph and progressively refines its topology through three stages: initial connection formation, feedback‑driven refinement, and long‑term consolidation. During execution, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills recurrent successful trajectories into reusable procedural circuits, guided by one metric for memory generalizability and evolutionary maturity. Across three fundamentally distinct benchmarks including LoCoMo, Mind2Web, and GAIA, FluxMem achieves consistent state‑of‑the‑art performance, demonstrating strong adaptation and generalization in complex agentic environments. The code will be open‑sourced in https://github.com/zjunlp/LightMem.

PaperId: 574, https://arxiv.org/pdf/2605.28739.pdf GitHub

Abstract:
Tabular data in knowledge‑rich domains often carries a latent prior in the form of Boolean implication relationships (BIRs) between pairs of features. We mine such relationships with a sparse‑exception binomial test. The mined implications form a typed directed graph, equivalent to a propositional rule base of 2‑literal clauses. We encode this graph as the connectivity of a layered neural network, called BIRDNet, in which each hidden unit corresponds to one mined rule and binds only to its two features. We show two consequences of this design: First, the architecture is sparse by construction: at most 2/d of the weights in each BIR layer are active, where d is the input dimension. Second, the model is interpretable: every trained unit keeps a stable symbolic identity, so rules can be read off the network without surrogate models. Unlike most neurosymbolic models, BIRDNet does not consume an external rule base; its structural prior is mined from the data. We evaluate BIRDNet on six transcriptomic and proteomic benchmarks. Our results show that BIRDNet stays within 0.02 AUROC of the strongest dense baseline, at a small accuracy cost, while using up to 96× fewer active parameters than an architecture‑matched dense MLP. First‑layer rules recover known biological signatures across multiple cancer subtypes and tissue types, including canonical amplicons, lineage‑defining co‑expression modules, and immune‑infiltration markers. Data and code are available at: https://github.com/MAHI‑Group/BIRDNet.

PaperId: 575, https://arxiv.org/pdf/2605.28732.pdf GitHub

Abstract:
Memory is essential for enabling large language models to support long‑horizon reasoning, yet existing memory systems remain unreliable and difficult to debug. Tracing memory's dynamic evolution is crucial to understand how information is synthesized, propagated, or corrupted over time. In this work, we study the new problem of error tracing and attribution in LLM memory systems. We propose a novel framework that transforms memory pipelines into executable memory evolution graphs, enabling fine‑grained tracing of operational information flow. We then construct MemTraceBench, a benchmark collected from representative memory systems such as Long‑Context, RAG, Mem0, and EverMemOS, to systematically study memory failure modes. We further introduce an automatic attribution method that iteratively traces operation subgraphs to pinpoint the root cause of any failed case. Our analysis reveals that memory failures are systematic, stemming from operation‑level issues like information loss and retrieval misalignment. Crucially, we leverage these fine‑grained attribution signals to guide downstream prompt optimization, establishing a closed‑loop system that automatically corrects faults and boosts end‑task performance by up to 7.62%. Code will be released at https://github.com/zjunlp/MemTrace.

PaperId: 576, https://arxiv.org/pdf/2605.28730.pdf GitHub

Abstract:
Designing a transit network requires many sequential route extension decisions, but their quality is often visible only after the full network is assembled. This delayed‑feedback challenge lies at the heart of the Transit Route Network Design Problem (TRNDP), where route interactions can be deceptive: an extension that appears useful locally can create transfer bottlenecks, produce redundant overlap, or reduce overall throughput. To guide route construction under delayed simulator feedback, we introduce AlphaTransit, a search‑based planning framework for cityscale bus network design. AlphaTransit couples Monte Carlo Tree Search (MCTS) with a neural policy‑value network: the policy proposes route extensions, the value estimates downstream design quality, and search uses these predictions to refine each decision. This provides decision‑time lookahead during route construction without running simulator rollouts inside the search tree. We evaluate AlphaTransit on a new Bloomington TRNDP benchmark with realistic road topology and censusderived demand, under mixed and full transit demand settings. In the Bloomington network, AlphaTransit attains the highest service rate in both demand settings, reaching 54.6% and 82.1%, respectively. Relative to reinforcement learning without search, these correspond to 9.9% and 11.4% service rate gains; relative to MCTS without learned guidance, they correspond to 2.5% and 11.2% gains. These results suggest that coupling learned guidance with MCTS is more effective than using either approach alone for transit network design. Our code and data are publicly available in https://github.com/poudel‑bibek/AlphaTransit.

PaperId: 577, https://arxiv.org/pdf/2605.28722.pdf GitHub

Abstract:
Representation intervention has emerged as a promising paradigm for aligning large language models toward desired behaviors without modifying model weights. Existing methods typically apply a fixed intervention uniformly across all inputs. However, we find that the appropriate intervention direction and strength vary substantially across samples, and such indiscriminate intervention leads to degradation of general capabilities on benign inputs. To address these challenges, we propose Multi‑Adapter Representation Interventions via Energy Calibration (MARI). Specifically, we introduce a competitive multi‑adapter mechanism in which specialized experts capture non‑linear correction patterns and adaptively determine the appropriate intervention direction and strength for different samples. Furthermore, we design an energy‑based gating module that leverages internal propagation dynamics to distinguish inputs that are applicable for intervention. Extensive experiments across diverse model families and parameter scales demonstrate that MARI achieves state‑of‑the‑art alignment performance. Our method significantly improves performance on TruthfulQA, BBQ, and safety benchmarks, while maintaining and even improving general capabilities on tasks such as MMLU and ARC. Our code is available at https://github.com/V1centNevwake/MARI.

PaperId: 578, https://arxiv.org/pdf/2605.28699.pdf GitHub

Abstract:
Large language models increasingly rely on either reinforcement learning or multi‑agent prompting to improve reasoning, yet these two paradigms remain difficult to combine. Directly applying single‑agent reinforcement learning to multi‑turn multi‑agent systems faces following dilemmas: i) Sparse rewards, role‑level free‑riding and excessive training overhead. ii) Agents only imitate to collaborate. iii) Fixed collaboration protocol falls into oscillating local optimum. We introduce TRACER, a turn‑level reinforcement framework for cooperative multi‑LLM reasoning. TRACER separates collaborative decision making into a controller‑regret layer, where controllers learn whether the agents should speak or skip the current round through regret matching, and a generation‑credit layer, which optimizes proposer and reviewer utterances with role‑specific GSPO rewards. This design i) assigns credit at the level of both action modes and generated utterances, thus avoiding free‑riding and sparse rewards. We only expand the choices made by the controllers, thus greatly reducing computational cost of training. Moreover, ii) agents acquire collaborative capability as they learn when to utter and what to speak. Finally, iii) by designing binary actions ingeniously, we extend classical game theory established for finite action spaces to deep learning, thus achieving mathematically rigorous convergence. We train all local RL‑style methods on the GSM8K training split and evaluate on held‑out GSM8K, MATH500, and GPQA‑Diamond to measure in‑domain accuracy, cross‑benchmark generalization, inference cost, and correction‑preservation behavior. The resulting framework provides a compact and reproducible testbed for studying learned collaboration policies beyond fixed debate, voting, or aggregation protocols. Code is available at https://github.com/Shark‑Forest/TRACER.

PaperId: 579, https://arxiv.org/pdf/2605.28669.pdf GitHub

Abstract:
Sense representations (explicit, per‑token meaning decompositions) are useful for disambiguation, steering, and cross‑lingual alignment, but existing approaches require models to be pretrained with sense structure baked in. We introduce ACROS, which induces an explicit sense pathway into a frozen pretrained decoder LM through a gated residual addition. On SmolLM2‑360M, ACROS preserves base LM quality while supporting three uses of the same induced variables: zero‑shot word‑sense disambiguation (64.95 F1 on Raganato ALL, competitive with the WordNet first‑sense heuristic), low‑KL lexical steering across 5,161 CoInCo cases where a simple non‑oracle proxy recovers about 90% of positive shifts, and SENSIA cross‑lingual adaptation to four languages (mean R@1 0.988, target FLORES PPL 7.94). ACROS makes sense representations an inducible interface for ordinary pretrained LMs.

PaperId: 580, https://arxiv.org/pdf/2605.28642.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have demonstrated significant potential for speech‑to‑text translation (S2TT). However, existing deployment paradigms face critical challenges: pure on‑device models suffer from resource constraints, while centralized cloud systems incur severe privacy risks and bandwidth bottlenecks by transmitting raw voice data. Furthermore, most models exhibit English‑centric biases, restricting many‑to‑many translation scaling. In this paper, we propose Edge‑cloud Speech Recognition and Translation (ESRT), a privacy‑preserving and bandwidth‑efficient collaborative edge‑cloud MLLM framework. Specifically, we design an edge‑cloud split inference architecture that retains a lightweight speech encoder and adapter on the device, transmitting only highly compressed intermediate features to the cloud. This fundamentally prevents voiceprint leakage and reduces bandwidth requirements by up to 10×. To overcome English‑centric bottlenecks, we introduce a multi‑task weighted curriculum learning strategy with data balancing to ensure robust cross‑lingual consistency. Extensive experiments on the FLEURS dataset demonstrate that our models, ESRT‑4B and ESRT‑12B, achieve state‑of‑the‑art many‑to‑many S2TT performance across 45 languages (45 × 44 directions). Code and models are released to facilitate reproducible, privacy‑aware MLLM S2TT research. The code and models are released at https://github.com/yxduir/esrt.

PaperId: 581, https://arxiv.org/pdf/2605.28603.pdf GitHub

Abstract:
Irregular multivariate time series forecasting is critical in many real‑world applications, where time series are irregularly sampled and exhibit dynamically evolving missingness patterns. Although existing methods perform well in offline settings, they often suffer from significant performance degradation when deployed online due to dynamic shifts in data distribution. Maintaining forecasting capability in such dynamic scenarios typically necessitates online adaptation techniques. Since irregular sampling fundamentally undermines temporal continuity and periodicity, we cannot leverage these widely studied characteristics from regular MTS for online learning. To this end, we study the problem of online IMTS forecasting and propose Under‑Cali, an uncertainty‑driven dual‑expert calibration framework consisting of three core components: an uncertainty estimator, a dual‑expert calibration module, and an adaptive routing module. We design an uncertainty estimator that serves as the core control signal to jointly manage inference and adaptation processes. In our framework, the uncertainty estimator first assesses uncertainty for each incoming batch. The adaptive routing module then directs samples with high uncertainty to the unreliable expert for calibration, while low uncertainty samples remain with the reliable expert. Subsequently, the system updates the reliable expert and the uncertainty estimator using well‑calibrated reliable samples, and updates the unreliable expert with challenging samples, enabling stable and efficient online learning. Under‑Cali keeps the source forecasting model frozen and performs adaptation only through a lightweight, model‑agnostic calibration module, enabling efficient adaptation. Extensive experiments on IMTS benchmarks demonstrate consistent improvements with low computational cost. Our code is available at https://github.com/HaonanWen/Under‑Cali.

PaperId: 582, https://arxiv.org/pdf/2605.28591.pdf GitHub

Abstract:
The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings. Prior work has identified test‑time contextual cues, such as hypothetical scenarios, as a source of verbalized evaluation awareness and subsequent behavioral shift. In this paper, we investigate a potential explanation of this phenomenon: evaluation meta‑knowledge, defined as parametric knowledge about the structural traits that characterize evaluations. Similar to dataset contamination, where benchmark exposure leads to higher performance through memorization, we hypothesize that models trained on texts describing evaluation practices may implicitly learn to recognize and respond to evaluation‑like contexts, for instance, through exposure to scientific articles or social media posts about AI benchmarking. To test this, we fine‑tune models on synthetic documents describing evaluation traits such as verifiable structures or moral dilemmas. Evaluating this fine‑tuned model on six safety benchmarks, we find that it is significantly safer than the base model and control model. This behavioral shift persists even when restricting the analysis to responses lacking explicit verbalization of evaluation awareness. Our results demonstrate that evaluation meta‑knowledge may inflate safety benchmark performance, introducing a novel confounder that is independent of explicit memorization or verbalized evaluation awareness, thus, challenging to detect. These findings have important implications for the design and interpretation of AI safety evaluations. Our code and models are available at https://github.com/compass‑group‑tue/arxiv2026_evaluation_meta_knowledge.

PaperId: 583, https://arxiv.org/pdf/2605.28456.pdf GitHub

Abstract:
Existing Visual Speech Recognition (VSR) systems commonly rely on left‑to‑right autoregressive decoding, which can force premature decisions on visually ambiguous tokens before sufficient context is available. We propose DLLM‑VSR, to the best of our knowledge, the first Diffusion Large Language Model (DLLM)‑based VSR framework, formulating transcription as iterative masked denoising with flexible‑order decoding. With confidence‑based unmasking, DLLM‑VSR commits high‑confidence positions early and uses the committed tokens as bidirectional context to refine ambiguous ones. To adapt DLLMs to VSR, we introduce a two‑stage masked‑denoising training strategy that separates visual‑to‑text content alignment from length modeling. We further observe a performance gap with oracle‑length decoding, which assumes access to the true transcript length, indicating that reducing target‑length uncertainty can improve DLLM‑based VSR. To reduce this gap, we develop length‑guided candidate decoding, which uses video duration to construct plausible transcript‑length hypotheses, decodes under multiple hypotheses, and reranks candidates using length plausibility and decoding confidence. The proposed method achieves a state‑of‑the‑art WER of 19.5% on LRS3 using only its labeled training data.

PaperId: 584, https://arxiv.org/pdf/2605.28441.pdf GitHub

Abstract:
While Contrastive Learning (CL) has revolutionized self‑supervised representation learning, its latent representations remain highly entangled and opaque, limiting their interpretability in safety‑critical applications. We identify that a fundamental cause of this entanglement is the reliance on deterministic similarity measures, which treat all feature dimensions equally. In compositional scenes, this creates an Optimization Conflict: common background features, such as, "blue sky", are encouraged to align in positive pairs but simultaneously repelled in negative pairs, causing gradient oscillations that hinder precise semantic disentanglement. To address this, we propose BayesNCL (Bayesian Gated Non‑Negative Contrastive Learning). Unlike standard approaches, BayesNCL introduces a probabilistic gating mechanism that dynamically filters out task‑irrelevant, high‑frequency common features while selectively retaining discriminative semantics. By formalizing feature selection as a variational inference problem with a sparse Bernoulli prior, our method effectively resolves the optimization conflict. Empirical experimental results on Imagenet‑100 demonstrate that BayesNCL achieves a remarkable 142.1% improvement in semantic consistency compared to state‑of‑the‑art baselines, yielding highly interpretable representations without compromising downstream task performance. Code is available at https://github.com/Cui‑Peng‑624/BayesNCL.

PaperId: 585, https://arxiv.org/pdf/2605.28398.pdf GitHub

Abstract:
Hybrid‑reasoning large language models (LLMs) expose explicit controls over reasoning effort, allowing users or systems to trade off answer quality against inference cost. However, existing methods for adaptive thinking‑mode selection are typically evaluated under different models, datasets, and implementation assumptions, making it difficult to compare their practical behavior. We introduce HRBench, a unified evaluation framework for studying thinking‑mode switching in hybrid‑reasoning LLMs. HRBench organizes the design space along two axes: three switching strategy families, prompt‑based selection, external routing, and speculative execution, and four training regimes, training‑free, SFT, offline and online RL, yielding 12 controlled evaluation settings. We evaluate these settings across 6 LLMs, from Qwen3.5‑2B to Kimi‑K2.5‑1.1T, and 5 reasoning benchmarks covering mathematics, science, and code, while reimplementing 12+ representative prior methods within the same pipeline. Our analysis characterizes how different switching strategies occupy distinct effectiveness‑efficiency trade‑off regions: prompt‑based methods often provide favorable token‑accuracy trade‑offs, routing methods offer more stable cost reduction, and speculative methods tend to improve accuracy at higher token cost. We further find that training affects strategies differently, and that the preferred strategy varies with model scale and task domain. HRBench provides reference implementations and a unified evaluation platform to support more controlled research on efficient reasoning in hybrid‑reasoning LLMs. Our data, code and repository are available at https://github.com/usail‑hkust/HRBench.

PaperId: 586, https://arxiv.org/pdf/2605.28369.pdf GitHub

Abstract:
E‑commerce platforms have begun recruiting crowdsourced jurors to adjudicate massive volumes of transaction disputes. Unlike formal legal judgment, E‑commerce dispute verdicts require grounding pivotal clues from redundant, multi‑round, multimodal evidence and making decisions under flexible platform‑specific conventions. These characteristics render existing methods insufficient for this scenario. To bridge this gap, we introduce a pioneering task, E‑commerce Dispute Verdicts (EDV), and present VerdictBench, a multimodal benchmark comprising 6,000 real‑world cases designed to reflect crowdsourced jury decisions. Building upon this, we propose CyberJurors, a multi‑agent framework to clarify the dispute logic and regulate the verdict process. At the individual level, Individual Verdict Chain‑of‑Thought decomposes the EDV task into four structured reasoning stages, enabling fine‑grained clue perception and clarifying causal logic between pivotal clues and the dispute focus. At the collective level, Jury Consensus Verdict simulates multi‑round discussion and voting among jurors, while incorporating verdict precedents to mitigate cognitive biases toward either disputant. Experiments on VerdictBench show that CyberJurors outperforms state‑of‑the‑art LLMs, MLLMs, and court simulators, while achieving stronger alignment with real‑world jury voting patterns. Code and dataset are available at https://github.com/YanhuiS/CyberJurors and https://huggingface.co/datasets/piggi/VerdictBench.

PaperId: 587, https://arxiv.org/pdf/2605.28303.pdf GitHub

Abstract:
While Knowledge Editing (KE) enables efficient updates, its dominant Static Fact Overwriting paradigm treats LLMs as discrete databases, forcibly injecting isolated facts. Fracturing pre‑trained logical topologies, this triggers Epistemic Dissonance ‑‑ a pathology where un‑evolved legacy priors force the model to explicitly negate the injected update. Idealized interventions reveal that this is an inherent structural flaw rather than mere algorithmic noise, with a zero‑distortion proxy yielding a catastrophic 95.6% self‑refutation rate. Given the causally driven nature of real‑world knowledge, grounding updates in explicit causal narratives effectively collapses this conflict rate to just 6.6%, underscoring the imperative for a paradigm shift toward Causal Editing. To internalize this evolution, we propose CODE (Causal On‑policy Distillation for Editing). By coupling causal bootstrapping with asymmetric on‑policy distillation, CODE engraves causal transition logic directly into parametric memory. Experiments on LLaMA‑3.1 and Qwen‑2.5 show CODE drastically suppresses self‑refutation to 1.8% while securing robust multi‑hop accuracy (up to 83.5%), seamlessly transforming discrete fact injection into coherent knowledge evolution. Code is available at https://github.com/CrashBugger/CODE.

PaperId: 588, https://arxiv.org/pdf/2605.28293.pdf GitHub

Abstract:
Proactive Recommender Systems (PRSs) aim to guide user preference shift toward target items by generating paths of intermediate recommendations. Reinforcement learning (RL) provides a principled framework for optimizing such sequential decision tasks, as path rewards can naturally capture both short‑term acceptance and long‑term guidance effectiveness. However, naively applying policy gradients to PRS results in deficient gradient estimation. We identify two deficiencies: (1) path‑level rewards decompose into step‑level rewards with positive mean, creating a length‑dependent bias that causes gradients to favor path extension over meaningful exploration; (2) weighting each step by the entire path‑level reward ignores the decomposition structure, leading to high gradient variance. To rectify these two deficiencies, we propose an effective RL framework ProRL with two novel mechanisms for proactive recommendation. First, Stepwise Reward Centering subtracts expected rewards to neutralize length‑dependent bias, ensuring that path extension yields zero expected gradient signal. Second, Position‑Specific Advantage Estimation leverages the reward decomposition structure to compute step‑dependent baselines, reducing gradient variance. Together, these mechanisms yield policy gradients that precisely target path quality. Our experiments on three real‑world datasets demonstrate that ProRL significantly outperforms state‑of‑the‑art PRSs. Our code is available at https://github.com/hongruhou89/ProRL.

PaperId: 589, https://arxiv.org/pdf/2605.28229.pdf GitHub GitHub

Abstract:
With the rapid development of pre‑training technologies, adapting large‑scale Vision‑Language Models (VLMs) for video understanding \emph\ie image‑to‑video transfer learning has become a dominant paradigm. To achieve superior performance, it raises as an effective strategy among recent advances to employ Mixture‑of‑Experts (MoE) to enhance VLMs' temporal modeling capabilities. However, conventional MoE designs suffer from expert homogenization, where all experts act as identical generalists, inefficiently learning spatio‑temporal features from undifferentiated video streams. To overcome this problem, we propose VidPrism, a novel heterogeneous temporal Mixture‑of‑Experts framework. VidPrism pioneers a division of labor by deploying functionally specialized experts, each assuming a role ranging from spatial understanding to temporal modeling. To feed these specialists appropriately, we introduce a content‑aware, multi‑rate sampling module that dynamically generates streams ranging from semantically rich to motion‑focused representations, providing specialized inputs for experts. Furthermore, a dynamic, bidirectional fusion mechanism enables synergistic information exchange between these pathways, leading to a comprehensive video representation. Extensive experiments on various video recognition benchmarks demonstrate that VidPrism achieves state‑of‑the‑art performance and effectively fosters expert specialization. Our source code is available at \hrefhttps://github.com/Lrrrr549/VidPrism.githttps://github.com/Lrrrr549/VidPrism.git.

PaperId: 590, https://arxiv.org/pdf/2605.28120.pdf GitHub GitHub

Abstract:
Graph‑based Retrieval‑Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more coherent and effective reasoning. However, applying it to specific domains like legal reasoning faces critical challenges. (i) Legal corpora are heterogeneous, containing multi‑granular knowledge from cases, articles and interpretations. A flat knowledge graph cannot adequately differentiate between factual details, applied rules, and abstract principles, limiting accurate retrieval. (ii) Reliable legal judgment demands transparent, evidence‑based reasoning. Traditional RAG passes retrieved context directly to an LLM without verification, resulting in opaque, error‑prone reasoning. To this end, we propose LegalGraphRAG, a framework designed for reliable legal reasoning. Our approach introduces two core components: a hierarchical legal graph that hierarchically organizes legal sources to enable retrieval at appropriate abstraction levels, and a multi‑agent system for reliable legal reasoning, where a Researcher retrieves candidate evidence, an Auditor rigorously verifies its validity against source documents, and an Adjudicator synthesizes the set of verified evidence to render a final judgment. Extensive experiments show that LegalGraphRAG achieves the state‑of‑the‑art performance, outperforming existing GraphRAG baselines in accurate and trustworthy legal analysis. Our code, datasets and implementation details are available at https://github.com/XMUDeepLIT/LegalGraphRAG.

PaperId: 591, https://arxiv.org/pdf/2605.28104.pdf GitHub

Abstract:
Recent years have witnessed the rapid development of Large Language Model‑based Multi‑Agent Systems (MAS), which excel at collaborative decision‑making and complex problem‑solving. However, malicious agents in MAS may inject misinformation to mislead other agents and disrupt system performance, giving rise to a new research direction that focuses on attack mechanisms and defense strategies in MAS. Prior studies largely assume malicious agents act independently and investigate the corresponding defense strategies. However, we argue that malicious agents may exhibit collaborative behaviors, enabling more effective attacks through internal information exchange. In this paper, we propose an adaptive cooperative attack framework, where malicious agents autonomously coordinate and dynamically adjust their attack strategies through multi‑round interactions. Furthermore, we introduce Sentence‑Level Trustworthiness Analysis and Rectification (STAR), a defense framework that identifies and rectifies misleading information at the sentence level within agent communications. Our experiments show that cooperative attacks lead to a significantly larger degradation in task success rate than independent attacks, resulting in a relative drop of 5.34%. Meanwhile, STAR effectively mitigates both cooperative and independent threats and improves task success rate by an average of 36.76%. The code is available at https://github.com/smoooom/STAR.

PaperId: 592, https://arxiv.org/pdf/2605.28101.pdf GitHub GitHub

Abstract:
Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive spatial audio rendering. In this work, we present EIGENET, a geometry‑informed multi‑modal framework for few‑shot novel view RIR prediction. At its core is a Cross‑view Alternate‑attention Transformer that iteratively refines local intra‑view acoustic structures and global cross‑view spatial relationships. We empirically demonstrate that this architecture is capable of making full use of the multi‑view multi‑modal context while performing spatial‑temporal reasoning for RIR prediction. Inspired by acoustic ray tracing, we design a geometry‑informed modulation block to formulate the connection between geometric features and RIR power spectrum. In the mean time, an auxiliary loss is introduced to transform the single‑target waveform prediction into a multi‑task learning framework. Through ablation studies, we demonstrate that this design yields consistent performance gains regardless of the underlying backbone, thereby confirming its foundational utility and architecture‑agnostic generalizability for RIR prediction task. Evaluated on both simulated and real‑world benchmarks, EIGENET achieves both state‑of‑the‑art performance in few‑shot novel view RIR prediction and sim‑to‑real generalization. Codes and checkpoints are available on https://github.com/FEAfeatherTHER/EigeNet.

PaperId: 593, https://arxiv.org/pdf/2605.28084.pdf GitHub

Abstract:
Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understanding of laughter in real‑world scenarios remains underexplored. Therefore, we introduce SMILE‑Next, a dataset for real‑world laughter understanding with multimodal textual representations and question‑answer annotations across three tasks: laughter detection, laughter type classification, and laughter reasoning. Building upon SMILE‑Next, we aim to develop a laughter‑specialized large language model capable of nuanced understanding of laughter in real‑world contexts. To this end, we propose two key components: laughter‑specific Self‑Instruct and the Mixture‑of‑Laugh‑Experts (MoLE) framework. Laughter‑specific Self‑Instruct enhances generalization across tasks and domains by automatically synthesizing diverse laughter‑centric instructions. MoLE introduces a task‑adaptive expert routing mechanism that dynamically selects specialized experts tailored to each laughter‑related task, improving task‑specific performance and efficiency. Experimental results show that the combination of our proposed components substantially outperforms multimodal LLM baselines, advancing robust real‑world laughter understanding. Project page is at: https://mok0102.github.io/smile‑next/.

PaperId: 594, https://arxiv.org/pdf/2605.28077.pdf GitHub

Abstract:
Parsing chemical reaction diagrams from scientific literature is challenging due to heterogeneous layouts, intertwined visual elements, and the difficulty of integrating recognition and reasoning. Existing vision‑language models advance multimodal understanding but still fail on complex diagrams, struggling to maintain spatial coherence and to integrate multidimensional information during reasoning. To address these issues, we propose MACReD, a hierarchical multi‑agent framework that coordinates specialized agents for molecular perception, arrow understanding, text extraction, and reaction reconstruction within a unified VLM‑guided architecture. The planning and perception layers use flexible, fine‑grained detection to handle visual complexity, while the reasoning layer uses a multigraph fusion mechanism to integrate heterogeneous cues and enforce chemically consistent global reasoning. Experiments on the RxnScribe benchmark show that MACReD achieves state‑of‑the‑art performance, with F1 scores of 75.2% and 84.6% under hard and soft match criteria, outperforming the RxnScribe baseline, which obtains 69.1% and 80.0%, respectively. These results demonstrate the robustness of MACReD across diverse diagram layouts, including multi‑step and tree‑structured reactions.

PaperId: 595, https://arxiv.org/pdf/2605.28034.pdf GitHub

Abstract:
Clark Hash is a small method for storing neural embeddings in less space. It normalizes each database vector, applies a deterministic sparse signed Johnson‑Lindenstrauss projection, clips the result, and stores a fixed‑width scalar‑quantized code. Queries stay in floating point and are scored against the stored sketches. In the default 384‑dimensional sentence‑embedding setting, Clark Hash stores a cosine‑search vector in 48 bytes instead of 1536 bytes for dense f32 storage. This is 32x smaller. The method does not need a training pass, learned codebooks, rotations, or corpus statistics before new vectors can be stored. We describe the codec, the Rust implementation, and a multilingual sentence‑similarity evaluation on 9,304 labeled pairs from 29 subsets. With a multilingual MiniLM encoder, the 48‑byte sketches reached 0.910 and 0.946 macro Pearson correlation with dense cosine scores on STS17 and STS22. Clark Hash is not a new Johnson‑Lindenstrauss theorem and it is not a replacement for approximate nearest‑neighbor indexes. It is a simple stateless codec for compact embedding storage.

PaperId: 596, https://arxiv.org/pdf/2605.28030.pdf GitHub

Abstract:
Fine‑tuning large language models often undermines their safety alignment, a problem further amplified by harmful fine‑tuning attacks in which adversarial data removes safeguards and induces unsafe behaviors. We propose SPARD, a defense framework that integrates Safety‑Projected Alternating optimization with Relevance‑Diversity aware data selection. SPARD employs SPAG, which optimizes alternatively between utility updates and explicit safety projections with a set of safe data to enforce safety constraints. To curate safe data, we introduce a Relevance‑Diversity Determinantal Point Process to select compact safe data, balancing task relevance and safety coverage. Experiments on GSM8K and OpenBookQA under four harmful fine‑tuning attacks demonstrate that SPARD consistently achieves the lowest average attack success rates, substantially outperforming state‑of‑the‑art defense methods, while maintaining high task accuracy. Code is available at https://github.com/shuhao02/SPARD.

PaperId: 597, https://arxiv.org/pdf/2605.28000.pdf GitHub

Abstract:
Large language model agents are increasingly expected to perform operational work: calling APIs, manipulating files, assembling workflows, and acting inside enterprise systems. Yet the tool layer on which this execution depends is still commonly treated as either a hand‑written integration artifact or a static list of schemas exposed to a model. This paper introduces Tool Forge, a validation‑carrying toolchain for converting natural‑language capability intent into governed, sandbox‑verified, cataloged tool artifacts and exposing those artifacts to agents through a token‑efficient routing layer. Tool Forge treats a tool as a capsule containing intent, capability contract, implementation, dependency policy, tests, documentation, runtime validation evidence, lifecycle state, credential bindings, and routing metadata. It also introduces a Router that exposes intent‑scoped tool sessions instead of loading full catalog schemas into the model context. We describe the system architecture, validation pipeline, MCP‑facing routing model, governance controls, and initial reproducible benchmarks from the open‑source implementation. Across 83 Router benchmark cases, Tool Forge Router achieves aggregate micro‑F1 of 0.901 while reducing estimated task‑flow tool context by 99.2% relative to naive full‑catalog schema exposure. In a 25‑case end‑to‑end generation probe over local‑tool tasks, Tool Forge generates 25 of 25 tool bundles, reaches micro‑F1 of 0.940 against deterministic acceptance checks, and passes 23 of 25 live sandbox validations. These results are presented as an initial systems benchmark, not as a state‑of‑the‑art claim. The paper identifies remaining challenges in adversarial routing, broader API grounding, sandbox isolation, and cross‑system evaluation.

PaperId: 598, https://arxiv.org/pdf/2605.27995.pdf GitHub

Abstract:
Large language model (LLM)‑based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations often overlook the temporal dimension of tool use, especially the impact of tool response latency, and are usually limited to single‑task settings. In real‑world applications, multiple tasks often need to be executed concurrently, and overall efficiency depends on whether an agent can use idle time while waiting for tool responses. We refer to this capability as asynchronous tool calling. To evaluate it, we propose AsyncTool, a benchmark for assessing LLM‑based agents in interactive multi‑task tool‑use environments with delayed tool feedback. AsyncTool presents multiple heterogeneous tasks simultaneously and simulates realistic tool response latency during execution. Using a hybrid data evolution strategy, we construct a diverse asynchronous multitasking dataset that covers multiple scenarios and tool‑use patterns. We evaluate models at the step, sub‑task, and task levels, and introduce efficiency‑oriented metrics to measure task coordination and completion efficiency. Extensive experiments show that delayed tool feedback poses substantial challenges to current agents and leads to clear performance degradation. Models that better coordinate task switching, dependency tracking, and state maintenance achieve stronger performance on AsyncTool. Our analysis identifies key failure modes of current tool‑using agents and provides practical insights for designing future systems with stronger temporal reasoning and coordination capabilities.

PaperId: 599, https://arxiv.org/pdf/2605.27990.pdf GitHub

Abstract:
Diffusion posterior sampling conditions diffusion priors on measurements, but data‑consistency updates are typically scaled by hand‑tuned guidance weights and can destabilize sampling under stiff, operator‑dependent curvature. We replace scalar guidance with a per‑noise‑level damped Gauss‑‑Newton correction computed in diffusion‑state coordinates. The correction pulls likelihood gradients back through the denoiser, uses a one‑sided curvature model that avoids forward denoiser Jacobians, and applies diffusion‑calibrated rank‑one damping aligned with the denoiser residual. Each correction is solved with matrix‑free GMRES using automatic differentiation, and sampling proceeds with a variance‑preserving Langevin transition with a closed‑form drift/noise split. On FFHQ and ImageNet across inverse problems, it achieves competitive PSNR/SSIM/LPIPS while running markedly faster than most of the compared baselines; on accelerated MRI reconstruction, it achieves the best PSNR/SSIM among the compared baselines.

PaperId: 600, https://arxiv.org/pdf/2605.27981.pdf GitHub

Abstract:
Evaluating the efficiency of algorithmic code requires test cases that expose runtime bottlenecks. Previous methods generate efficiency test cases either by increasing input size or by generating code‑specific inputs that make the given implementation run slowly. Consequently, they do not address the structural input conditions that drive the algorithmic worst case. We introduce STAB, a specification‑driven pipeline that generates test cases that expose algorithmic bottlenecks from a natural‑language problem specification alone. STAB separates the task into constraint‑bound maximization and adversarial structure injection. (i) The constraint saturator extracts constraints and resolves large admissible size assignments using rule‑based saturation and CP‑SAT optimization over related variables. (ii) The adversarial scenario injector retrieves implementation‑level adversarial construction principles from a curated scenario catalog using keyword matching and K‑nearest neighbors (KNN). STAB encodes the problem specification, resolved boundary, and retrieved construction principles into a structured generation specification, from which the LLM synthesizes a Python test case generator. On CodeContests, STAB raises the rate of generated test cases that expose algorithmic bottlenecks from 50.43% to 73.45% on average across open‑source LLMs and from 57.45% to 71.85% on average across closed‑source LLMs, with consistent gains across Python, Java, and C++. Our code is available at https://github.com/suhanmen/STAB.

PaperId: 601, https://arxiv.org/pdf/2605.27980.pdf GitHub GitHub

Abstract:
The ability to process ultra‑long contexts is crucial for large language models (LLMs) to perform long‑horizon tasks. While recent efforts have extended context windows to 1M and beyond, model performance degrades when sequence length exceeds the pre‑trained range of positional encodings (e.g., RoPE), i.e., position exhaustion. This fundamental limitation must be overcome to achieve a truly infinite context. To address it, we propose Periodic RoPE (P‑RoPE), a positional encoding mechanism designed to circumvent this exhaustion. It operates in conjunction with sliding window attention (SWA) to capture local dependencies and relative positions within each window. This local layer is then complemented by a global attention layer with No Positional Encoding (NoPE), enabling unbounded interaction across the entire sequence without positional constraints. By stacking these two types of layers, the model avoids the need for positional extrapolation to generalize longer and theoretically supports an infinite context window. Empirical results show that our model, MiniWin, outperforms MiniMInd with standard GPT architectures in long‑context efficiency and stability. Our work provides a possible pathway toward LLMs with genuine infinite‑context understanding. The code is available at \hrefhttps://github.com/Cominder/miniwinhttps://github.com/Cominder/miniwin.

PaperId: 602, https://arxiv.org/pdf/2605.27908.pdf GitHub

Abstract:
Existing emotional support conversation (ESC) systems mainly rely on end‑to‑end response generation or coarse strategy supervision, offering limited interpretability and little support for systematic skill improvement. We propose ESC‑Skills, a skill‑centric framework that discovers and self‑evolves executable emotional support skills. We first model localized support interactions as Intervention Units (IUs), which capture state‑‑action‑‑outcome dynamics between seeker states, support interventions, and post‑response emotional changes. Based on IUs extracted from both successful and failed ESC dialogues, we construct the ESC‑Skills Bank, a repository of executable emotional support skills containing intervention guidance, applicability conditions, expected outcomes, and potential risks. To further improve robustness, we introduce a multi‑profile self‑evolutionary refinement framework in which an ESC agent interacts with diverse simulated seeker profiles under SAGE evaluation. The resulting interaction traces are analyzed to identify missing skills, unsafe interventions, and profile‑specific failure patterns, which are then used to refine the Skills Bank through simulation‑based verification. Experimental results demonstrate that ESC‑Skills improves both response‑level quality and dialogue‑level emotional outcomes while providing more interpretable and controllable support behaviors. We will release the code, prompts, and ESC‑Skills Bank at https://github.com/aliyun/qwen‑dianjin.

PaperId: 603, https://arxiv.org/pdf/2605.27901.pdf GitHub GitHub

Abstract:
Chain‑of‑thought (CoT) monitoring has been proposed as a promising safety mechanism for detecting misaligned behavior in large language models. However, its reliability remains largely unexplored beyond English and across diverse model families. We present the first large‑scale evaluation of CoT monitorability across 13 diverse languages and seven frontier model families, comprising 16 models. Using adversarial‑hint evaluations that require explicit intermediate computation, together with analysis of internal answer‑token probabilities, we consistently find CoT unfaithfulness across languages and hint types, with an average rate of 95.9% across 8B‑‑120B parameter models. We find that frontier models systematically engage in strategic manipulation, including answer‑switching, post‑hoc rationalization, and procedural exploitation of hints, making external monitors struggle to detect deception. We show that frontier models often commit to the misaligned cue in their latent activations within the first 15% of generation, even when the CoT appears faithful. Surprisingly, these deceptive patterns remain 100% in low‑resource languages, revealing fundamental limitations in current CoT‑based oversight. Our results reveal that CoT monitoring is fundamentally fragile under linguistic distribution shift, providing a substantially weaker safety signal than what English‑only studies suggest. These findings underscore an urgent need to develop robust CoT monitors and to accelerate research into white‑box monitoring techniques, especially to improve CoT monitorability in mid‑ and low‑resource languages. Our code is available \hrefhttps://multilingual‑cot‑monitoring.github.io/\textcolorbluehere.

PaperId: 604, https://arxiv.org/pdf/2605.27898.pdf GitHub

Abstract:
As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential. However, reported benchmark scores often jointly reflect model capability and the implementation choices each benchmark is packaged with, making cross‑benchmark results difficult to interpret as clean measurements of the underlying model. In this work, we present a unified framework for the fair evaluation of LLM agentic capabilities. Driven by a unified configuration system, the framework integrates diverse benchmarks into a standardized instruction‑‑tool‑‑environment format, executes agents through a fixed ReAct‑style architecture within a controllable sandbox, and provides an optional offline setting that replaces volatile live environments with curated snapshots, so that framework effects and environment effects can be analyzed separately. Building on this, we unify the evaluation methodology under each benchmark's original task‑success criteria, while introducing unified metrics for resource consumption and a taxonomy for decision‑ and execution‑level failure attribution. Within this framework, we adapt 7 widely used benchmarks spanning 24 domains across single‑agent, multi‑agent, and safety‑critical scenarios, and conduct a large‑scale empirical analysis over 400K rollouts and 5B tokens on 15 models. The results show that scaffold choice and environmental volatility materially shift benchmark outcomes in both directions, allowing our framework to disentangle intrinsic LLM capabilities from framework‑ and environment‑induced artifacts. We further demonstrate its extensibility as a secure testbed for safety‑critical domains. Codes and benchmarks at are available at https://github.com/whfeLingYu/A‑Unified‑Framework‑for‑the‑Evaluation‑of‑LLM‑Agentic‑Capabilities, https://huggingface.co/AgentFramework/Unified_Farmework.

PaperId: 605, https://arxiv.org/pdf/2605.27858.pdf GitHub

Abstract:
Claim verification splits between end‑to‑end classifiers that are accurate but yields no inspectable traces, and decomposition‑based methods produce inspectable traces but lag performance on benchmark datasets. We propose DecomposeRL an accurate claim‑verifier that produce inspectable traces. DecomposeRL frames decomposition as an RL policy trained with GRPO and a multi‑faceted reward ensemble, enabling both fully supervised and semi‑supervised learning from unlabeled claims. DecomposeRL addresses the prohibitive training cost of GRPO with a data‑curation funnel that distills 115K fact‑verification claims into a compact, learning‑signal‑dense subset of 5K claims. We show that a DecomposeRL‑7B policy trained with full supervision on only ~5K curated claims achieves 86.3 in‑domain and 69.8 out‑of‑domain balanced accuracy across 11 claim‑verification benchmarks containing biomedical, political, scientific, and general‑domain claims. Despite being 4x smaller, it matches 32B baselines and GPT‑4.1‑mini, and it further outperforms baselines in a semi‑supervised setting with only 10% labeled claims data. Code, data, and models are available at https://dipta007.github.io/DecomposeRL

PaperId: 606, https://arxiv.org/pdf/2605.27840.pdf GitHub

Abstract:
Audio tokenizers are fundamental to unifying audio understanding and generation. Understanding requires high‑level semantics, while generation demands semantic and acoustic details. Existing unified tokenizers jointly encode both in high‑dimensional continuous latents, which increases the modeling burden of Diffusion Transformers (DiTs) for generation. We propose LoSATok, a low‑dimensional audio tokenizer for cross‑domain audio understanding and generation. Motivated by the observation that 1280‑dimensional semantic encoder features are compressible, we introduce a Semantic Bottleneck that compresses them into 128 dimensions, regularized by the proposed time‑relation loss for temporal feature consistency. We further design a dual‑level semantic supervision method that leverages both high‑ and low‑dimensional semantic signals, enabling the tokenizer to jointly capture semantics and acoustic details within a compact latent space. Experiments on speech, music, and general audio show that SemBo preserves strong low‑dimensional semantic capacity and LoSATok retains competitive understanding performance compared with several semantic representations, while consistently improving DiT modeling performance on speech, music, and audio generation. These results demonstrate that LoSATok's low‑dimensional representations can effectively support audio understanding and generation. Our code is provided at https://github.com/wxzyd123/LoSATok.

PaperId: 607, https://arxiv.org/pdf/2605.27805.pdf GitHub

Abstract:
While LLMs enable personalized chatbots, their effectiveness in child‑centered personalization remains unclear, as systematic evaluation of child‑specific preferences is still lacking. To address this gap, we introduce ChildEval, a benchmark for evaluating LLMs' ability to infer and follow child‑centered preferences in long‑context conversations. ChildEval contains 29K synthesized persona profiles of children aged 3‑6, providing relatively static background information. Each persona is associated with a child preference‑which may align with, conflict with, or be independent of the persona‑expressed either explicitly in a single sentence or implicitly through 6‑10 turn dialogues. Explicit and implicit preferences are designed to reflect the same underlying preference but differ in expression, capturing dynamic aspects of preference expression rather than changes in the static persona. The benchmark spans five top‑level and fourteen sub‑level categories covering children's daily lives and development. We further propose fine‑grained, child‑centric evaluation protocols to systematically assess open‑source LLMs. Experimental results demonstrate how different personalized representations affect LLM responses and suggest that finetuning on ChildEval can enhance child‑centered performance. Our code and dataset are available at https://github.com/ziyanluo/ChildEval.

PaperId: 608, https://arxiv.org/pdf/2605.27564.pdf GitHub

Abstract:
Language models are becoming the default interface to factual knowledge, yet they often verify outputs more reliably than they generate them. This generation‑verification gap (GV‑gap) underlies many recent advances in self‑improvement and reasoning, but its dynamics on factual knowledge specifically remain poorly understood. We focus on the training mechanisms underlying factual GV‑gaps, distinguishing them from their computational and aesthetic counterparts. We trace generation and verification capabilities through three training phases (acquisition, continual learning, and updating) across four open‑source model families at two scales each. Three findings recur across models: (i) verification is consistently learned before generation; (ii) verification is more robust to continual learning than generation; and (iii) factual updates can leave models in a "multi‑verse" state, simultaneously verifying both old and new answers as correct. Natural experiments on frontier models reproduce these dynamics at scale and reveal residual verification biases on well‑covered facts.

PaperId: 609, https://arxiv.org/pdf/2605.27494.pdf GitHub

Abstract:
Modern retrieval‑augmented generation(RAG) deployments increasingly rely on caching to reduce token cost and time‑to‑first‑token(TTFT). Prefix‑level KV reuse is now standard in serving stacks such as vLLM, and chunk‑level and position‑independent reuse have been pushed further by recent systems(RAGCache, TurboRAG, CacheBlend, EPIC, ContextPilot, PCR, LMCache). Output‑level semantic answer caches, by contrast, remain fragile: similar prompts can map to different correct answers, retrieved evidence drifts as the corpus is updated, and adversarial collision attacks have been shown to hijack cached responses. We argue that the right framing for cached answer reuse is not how to reuse faster but when reuse is safe. We propose GroundedCache, an evidence‑validated cache router that admits a cached answer only when 4 cheap gates simultaneously hold: query similarity, retrieved‑evidence overlap, source‑version validity, and lexical (or judge‑based) support of the cached answer by the freshly retrieved evidence. We build a six‑regime workload that stress‑tests cache safety rather than only hit rate, and introduce an operator‑facing metric, the unsafe‑served rate (USR), fraction of all queries that received a wrong cached answer. Across 2 datasets and 12,000 real‑LLM generations(Qwen2.5‑7B‑Instruct on vLLM with Automatic Prefix Caching), GroundedCache drives USR to 0.0% on every HotpotQA regime(vs. 15‑35% under naive caching) and to 1.5% on mtRAG document drift(vs. 51.5%), a 34x reduction on the design‑point adversarial regime and 3‑10x reductions across the other mtRAG regimes, while end‑to‑end p50 latency stays within 1.04‑1.07x of a no‑cache RAG baseline. A per‑gate ablation isolates the lexical support gate as the load‑bearing safety mechanism on both datasets, with the remaining gates providing defense‑in‑depth at near‑zero cost. We release the implementation, workload, and evaluation harness.

PaperId: 610, https://arxiv.org/pdf/2605.27476.pdf GitHub

Abstract:
We characterize the pre‑softmax attention matrix \mathbfQK^\top in transformers as an associative memory matrix encoding pairwise associations between input features. By decomposing this matrix into its symmetric and skew‑symmetric parts, we interpret the symmetric component as governing the structure of the energy landscape, and the skew‑symmetric component as driving circulation on that landscape. Leveraging the energy formulation induced by the symmetric component, we derive Hopfield‑style stability measures that quantify the stability of retrieved features. We observe meaningful correlations between Hopfield‑style stability measures and the fidelity‑diversity trade‑offs in generation. Finally, we propose a controllable knob to modulate this trade‑off by modifying the circulation of the underlying dynamics. Code is available at our GitHub (https://github.com/hyeon‑cho/Attention‑Symmetric‑Decomposition).

PaperId: 611, https://arxiv.org/pdf/2605.27466.pdf GitHub

Abstract:
Multi‑agent systems built on large language models (LLMs) require many coordination choices that are difficult to fix a priori: which skill protocol to invoke, which agent role should perform a subtask, which model to bind to each role, how roles should interact, when to use retrieval or verification, and when to omit a step entirely. These choices interact with task regime and operational constraints, so static pipelines and one‑off model comparisons provide only a limited view of the design space. This paper introduces AgensFlow, an open‑source framework that treats multi‑agent coordination as an online policy‑learning problem under partial observability. The framework makes coordination decisions observable and learnable from repeated trajectories, rather than treating skill, role, model, topology, and evaluation choices as fixed pipeline design. AgensFlow is evaluated on two corpora: distributed‑systems incident tasks and security‑advisory tasks. The evaluation shows three main results: learned routing reaches a higher‑quality operating point than a fixed pipeline baseline on coordination‑heavy classes; skip:X isolates topology compression as a meaningful part of the substrate; and warm‑started policy graphs can reduce exploration cost while preserving plateau quality. Overall, the results support that learned, auditable routing can improve coordination‑heavy multi‑agent workflows over static wiring.

PaperId: 612, https://arxiv.org/pdf/2605.27464.pdf GitHub

Abstract:
AR smart glasses need continuous behavioral context to offer proactive assistance, yet their most practical always‑on sensor, the head‑mounted Inertial Measurement Unit (IMU), detects only motion primitives such as walking or standing. We push beyond motion primitives to behavioral‑level recognition, defining five categories that balance AR application need with sensor observability. To this end, we construct a 160K‑sample Ego4D dataset with a four‑tier quality assurance framework spanning 8 activity scenarios, and propose HiT‑HAR, a 703K‑parameter hierarchical model that outperforms prior head‑mounted IMU models on five‑class action and eight‑class scenario recognition. We further map the observability frontier of head‑mounted IMU through per‑class separability analysis, identifying which behavioral categories are reliably observable (Locomotion), which benefit from temporal context (Object Transfer, Task Operation), and where scenario‑dependent signal overlap poses remaining challenges. Our results indicate that architectural choices exploiting temporal context and scenario structure outperform simply scaling model size. The code and dataset are publicly available at https://github.com/Harvard‑AI‑and‑Robotics‑Lab/HiT‑HAR.

PaperId: 613, https://arxiv.org/pdf/2605.27436.pdf GitHub

Abstract:
Multimodal alignment is critical for bridging the semantic gap in information retrieval. However, traditional pairwise strategies introduce a geometric blind spot: while they align anchor modalities (e.g., text) with others, they lack constraints to enforce mutual consistency between peripheral modalities (e.g., video and audio). The TRIANGLE framework addresses this by minimizing the area of modality triplets on a hypersphere to enforce holistic alignment. In this reproducibility study, we verify the robustness of this geometric objective for retrieval tasks. We confirm that TRIANGLE outperforms pairwise baselines in zero‑shot settings, achieving Recall@1 gains of up to +8.7 points, though benefits are domain‑dependent. However, we fail to reproduce the reported learning‑from‑scratch results. Analysis using a synthetic toy dataset attributes this to instability when jointly optimizing geometric alignment with Data‑Text Matching (DTM) loss. Furthermore, we find that cosine regularization primarily stabilizes text‑to‑video retrieval, and fine‑tuning with domain supervision amplifies geometric benefits but reduces cross‑dataset generalization. Our findings support the efficacy of geometric alignment while highlighting critical optimization sensitivities. Code available at https://github.com/ARIJIT00171/RE‑TRIANGLE.

PaperId: 614, https://arxiv.org/pdf/2605.27413.pdf GitHub

Abstract:
Proteins perform their biological functions through three‑dimensional structures encoded by amino acid sequences, and ligand‑binding protein co‑design requires models that generate sequence‑structure compatible proteins under explicit ligand constraints. Although continuous diffusion and flow‑based models support ligand‑aware design in coordinate or latent spaces, existing discrete diffusion protein language models mainly operate over sequence or structure tokens without direct small‑molecule conditioning. We introduce ProtLiD^2, a Protein Ligand‑conditioned Discrete Diffusion model for protein sequence‑structure co‑design. ProtLiD^2 jointly generates amino‑acid sequence and discrete structure tokens while incorporating ligand chemical and geometric information through geometry‑aware cross‑attention. Trained on over one million ligand‑protein complexes, ProtLiD^2 extends masked discrete diffusion to ligand‑aware functional protein design. We further propose maximum confidence‑margin guided ReMask decoding, an inference‑time self‑correction strategy that retains confident predictions and remasks uncertain tokens. ProtLiD^2 improves global fold confidence over Complexa in whole‑protein design, increasing TM‑score from 0.672 to 0.802 and pLDDT from 64.55 to 73.00. In pocket co‑design, ProtLiD^2 reduces active‑site BB‑RMSD from 3.46/3.40Å for FAIR/PocketGen to 1.97Å, and improves ligand‑aware pass rates over PocketGen from 14.86% to 59.73% and from 6.08% to 23.49% under stricter docking thresholds. These results support ligand‑conditioned discrete diffusion as an effective token‑space framework for functional protein co‑design. Code will be available at https://github.com/auroua/ProtLiD.

PaperId: 615, https://arxiv.org/pdf/2605.27387.pdf GitHub

Abstract:
Diffusion models promise efficient parallel text generation but rely on bidirectional attention, creating a structural mismatch with pre‑trained Autoregressive (AR) models. This incompatibility precludes reusing robust AR priors, necessitating prohibitive pre‑training from scratch. To bridge this gap, we propose FLUID, a framework that efficiently adapts AR backbones to the diffusion paradigm. By enforcing Strictly Causal Alignment, FLUID enables seamless initialization from standard GPT‑style checkpoints, circumventing the need for massive pre‑training. Furthermore, we introduce Elastic Horizons, an entropy‑driven mechanism that dynamically modulates denoising strides based on local information density rather than fixed schedules. Experiments demonstrate that FLUID achieves state‑of‑the‑art performance while reducing training costs by orders of magnitude, effectively reconciling established AR foundations with efficient parallel generation. Our code is available at https://github.com/Oli‑lab‑nun/FLUID/tree/main.

PaperId: 616, https://arxiv.org/pdf/2605.27328.pdf GitHub

Abstract:
Recent advances in agentic systems increasingly treat code as an executable operational substrate rather than as a disposable output artifact. Prior work such as \emphCode as Agent Harness frames validated agent‑generated artifacts as runtime entities that can be created, executed, revised, persisted, and reused within long‑running cognitive loops. However, the governance, lifecycle management, and operational evolution of such artifacts remain under‑specified. This paper proposes a framework for governed runtime evolution in multi‑agent systems through executable operational cognition. We formalize agent‑generated artifacts as persistent runtime capabilities that progressively become part of the operational substrate rather than transient intermediate outputs. Building on this perspective, we introduce \emphHarnessMutation as a governed mechanism for lifecycle‑aware runtime adaptation operating under explicit validation, traceability, evaluation, and rollback constraints. Rather than treating runtime adaptation as unrestricted self‑modification, the proposed framework models evolution as a bounded and observable process over persistent operational memory. It further shows how these ideas can be operationalized over modern agent runtimes and governance‑oriented orchestration systems, providing a conceptual foundation for adaptive infrastructures whose evolution remains explicit, auditable, and constrained.

PaperId: 617, https://arxiv.org/pdf/2605.27258.pdf GitHub

Abstract:
Building state‑of‑the‑art text‑to‑speech (TTS) systems typically demands millions of hours of proprietary data and complex multi‑stage architectures, creating substantial barriers for resource‑constrained research teams. In this report, we present PilotTTS, a lightweight autoregressive TTS system that achieves competitive performance through minimalist architecture and rigorous data engineering. PilotTTS is trained on only 200K hours of data processed entirely with open‑source tools. Specifically, our contributions are: (1) a reproducible multi‑stage data processing pipeline covering quality assessment, label annotation, and filtering, and (2) a compact model architecture that employs Q‑Former‑based conditioning to decouple speaker identity from speaking style via cross‑sample paired training. Within a unified framework, PilotTTS supports zero‑shot voice cloning, emotion synthesis (11 categories), paralinguistic synthesis (4 categories), and Chinese dialect synthesis (14 dialects). On the Seed‑TTS Eval benchmark, PilotTTS achieves the lowest WER of 1.50% on test‑en, a CER of 0.87% on test‑zh, and the highest speaker similarity on both test sets (0.862 and 0.815), outperforming systems trained on significantly larger datasets. We release the complete data pipeline recipe, pretrained weights, and code at https://github.com/AMAPVOICE/PilotTTS.

PaperId: 618, https://arxiv.org/pdf/2605.27178.pdf GitHub

Abstract:
We address the challenging task of 3D object segmentation in complex scene point clouds without relying on any scene‑level human annotations during training. Existing methods are typically constrained to identifying simple objects, primarily due to insufficient object priors in the learning process. In this paper, we present FoundObj, a novel framework featuring a superpoint‑based object discovery agent that incrementally merges suitable neighboring superpoints, guided by our innovative semantic and geometric reward modules. These modules synergistically leverage semantic and geometric priors from self‑supervised 2D/3D foundation models, providing complementary feedback to the object discovery agent and enabling robust identification of multi‑class objects through reinforcement learning. Extensive experiments on diverse benchmarks demonstrate that our approach consistently outperforms existing baselines. Notably, our method exhibits strong generalization in zero‑shot and long‑tail scenarios, underscoring its potential for scalable, label‑free 3D object segmentation.

PaperId: 619, https://arxiv.org/pdf/2605.27164.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) systems for question answering typically retrieve evidence by semantic similarity between the query and document chunks. While effective for unstructured text, this approach is less reliable on semi‑structured corpora where answering may require exact filtering, aggregation, or exhaustive retrieval over structured attributes across multiple documents. Symbolic approaches support such operations, but they are often brittle on noisy natural‑language corpora. We address this gap with DualGraph, a RAG framework that represents documents through two complementary views: a Textual Knowledge Graph for semantic retrieval and a Symbolic Knowledge Graph for symbolic querying over typed subject‑‑predicate‑‑object triples. Building on these two components, we provide multiple strategies for selecting or combining semantic and symbolic evidence.We also introduce SpecsQA, a benchmark from a commercial shopping website with semi‑structured product documents and manually curated questions spanning open‑ended and specification‑oriented retrieval. Experiments show that DualGraph consistently outperforms state‑of‑the‑art dense‑retrieval, GraphRAG, symbolic, and table‑oriented baselines across question types.Code and data are available at https://github.com/corneliocristina/DualGraphRAG.

PaperId: 620, https://arxiv.org/pdf/2605.27081.pdf GitHub

Abstract:
Fine‑grained Mixture‑of‑Experts (MoE) models sparsely activate only a subset of experts per token, reducing activated computation while maintaining high model capacity. However, in memory‑constrained inference scenarios, only a small set of experts can be cached. Experts not in the cache must be fetched from slow external storage (e.g., UFS), leading to frequent evictions and substantial I/O overhead. We propose ReMoE, a router fine‑tuning framework designed to boost token‑wise expert reuse. ReMoE biases the router toward recently selected experts, producing temporally stable routing that better matches cache locality constraints. By increasing short‑horizon expert reuse, ReMoE reduces expert fetches from storage without adding inference‑time computation. Experiments on DeepSeek and Qwen models show that ReMoE improves expert reuse by 26% while maintaining downstream task performance. Real‑system evaluations further confirm these benefits, improving output throughput by 8.4% under vLLM GPU‑CPU expert offloading and reducing TPOT by 43.6‑49.8% under llama.cpp on Jetson Orin NX, corresponding to a 1.77‑1.99× decode speedup across diverse workloads. Checkpoints and usage instructions are available at https://github.com/BUAA‑OSCAR/ReMoE.

PaperId: 621, https://arxiv.org/pdf/2605.27068.pdf GitHub

Abstract:
Social deduction games have become a popular testbed for probing reasoning, deception, coordination, and belief modeling in Large Language Model (LLM) agents. However, most environments are scored only by game outcomes such as win rates and largely remain to text‑only interaction, making it difficult to tell whether an agent's language is actually grounded in what it perceived and did, or to identify the failure modes underlying its behavior. To address this gap, we introduce QUACK, an open‑source environment and evaluation framework for auditing the grounding of agent language in multimodal social reasoning. QUACK evaluates agents at three levels: game outcomes, behavioral trajectories, and utterance‑level consistency. Its core Statement Verification Pipeline reconstructs each agent's ground‑truth trajectory from engine logs and checks every discussion claim against it, automatically flagging spatial hallucination, unsupported accusation, deception collapse, and language‑action inconsistency. Evaluating three frontier VLMs in both homogeneous and cross‑model adversarial settings, we find that even the strongest agent hallucinates 15.1% of its verifiable spatial claims and makes over half of its accusations without grounded evidence. We release the full engine, evaluation framework, toolkit, and logs at https://github.com/AAAAA‑Academia‑Attractions/QUACK.

PaperId: 622, https://arxiv.org/pdf/2605.26938.pdf GitHub

Abstract:
Alignment‑based conformance checking is the state‑of‑the‑art approach for comparing observed process executions with normative process models. The standard exact solution relies on an A‑based heuristic search, which can exhibit exponential runtime in the presence of long traces or substantial deviations. This paper introduces a reformulation of alignment‑based conformance checking as a totally unimodular linear program (LP) defined on the reachability graph of the synchronous product. By exploiting the underlying network‑flow structure, the proposed formulation guarantees the existence of an integral optimal extreme‑point solution through LP relaxation, thereby avoiding the combinatorial overhead associated with integer variables and branch‑and‑bound search. We conduct an extensive empirical evaluation on more than 2.1 million conformance checking instances derived from real‑world and synthetic benchmark datasets. The results show that A and the LP approach exhibit complementary performance characteristics: the former performs best on short, well‑conforming traces, while the LP formulation provides substantial speedups for longer traces with deviations, precisely where conformance checking is most informative. Based on these findings, we derive simple algorithm‑selection guidelines that combine both approaches, achieving average runtime savings of 38.6% with 96% selection accuracy compared to always using A.

PaperId: 623, https://arxiv.org/pdf/2605.26911.pdf GitHub

Abstract:
LLM‑generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and well‑structured. Existing work either classifies authorship without judging quality, or scores quality with features designed for human‑written reviews; no prior system detects deficiencies in LLM‑generated reviews at the level of individual defect types. To bridge the gap, we introduce TADDLE, a Tool‑Augmented Agent for Detecting Deficient LLM‑Generated Peer Reviews, together with the first expert‑annotated benchmark for this task. Our benchmark comprises 1,800 reviews on 50 ICLR 2025 papers, multi‑label‑annotated by 18 domain experts against a taxonomy of six defect categories (plus a non‑deficient label). TADDLE decomposes detection into four specialized analysis tools ‑‑ Verify, Correct, Complete, and Transform ‑‑ orchestrated by an agent; an integrator synthesizes their outputs into binary and multi‑label classifications via two‑stage semi‑supervised learning. Extensive experiments show that TADDLE performs strongly on both binary detection and the multi‑label classification task. We release the benchmark and code at https://github.com/AquariusAQ/TADDLE.

PaperId: 624, https://arxiv.org/pdf/2605.26807.pdf GitHub

Abstract:
LLMs can now produce full HTML pages, but many of those pages are only superficially correct: they render once, then fail under scroll, hover, click, resize, or gameplay. Evaluation from screenshots can miss these failures, and filtering discards many pages that are still repairable. We introduce HTMLCure, a browser experience framework that evaluates HTML after the system has interacted with it. The evaluator executes the page across viewports and interaction states, records deterministic browser evidence, and gives the VLM curated keyframes from the executed trajectory rather than isolated screenshots. The same state signal drives a closed loop repair engine: HTMLCure diagnoses the current page, chooses a state specific repair family, runs each candidate again, and exports quality cleared pages for SFT. On a 97K prompt corpus, this expands the directly usable seed into a candidate pool of 63703 quality cleared pages, from which we construct the final refined SFT set of 40K pages. Under the same backbone and training recipe, HTMLCure‑27B‑Refined reaches 50.6 on HTMLBench‑400 with 45.2% deterministic test case pass, placing it in the same performance band as strong reference rows such as Kimi‑K2.6 and GPT‑5.4. On the released MiniAppBench validation split, it reaches 81.2 average, improving raw 27B SFT by 15.3 points and approaching the level of strong reference systems.

PaperId: 625, https://arxiv.org/pdf/2605.26690.pdf GitHub

Abstract:
Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informative. Existing reinforcement learning and off‑policy generative approaches often degrade under surrogate noise, and position‑agnostic mutation proposals risk disrupting functionally critical residues. We introduce SILO, a trajectory‑level self‑improvement imitation framework for oracle‑budgeted protein design. SILO uses a hierarchical edit policy that decomposes each mutation into a position choice followed by a residue choice. In each active‑learning round, the policy samples candidate trajectories via incremental stochastic beam search without replacement (SBS), and a UCB‑based proxy ensemble, combined with an alanine‑scan fitness score (AFS), selects candidates with functionally relevant edits for in silico oracle evaluation. The policy is then updated by next‑action cross‑entropy imitation on the round's best oracle‑labeled trajectories, avoiding value‑function estimation. Across eight reproduced protein fitness landscapes and five strong baselines from prior work, SILO achieves the highest maximum and top‑100 mean fitness on 8 of 8 landscapes within our evaluations, often exhibiting faster early‑stage improvement. In low‑data and noisy‑proxy stress tests on two landscapes per setting, SILO remains competitive or best when several baselines degrade. Ablations show that SBS with AFS account for much of the gains, with iterative imitation providing additional improvement. Code is available at: https://github.com/grimmlab/SILO.git

PaperId: 626, https://arxiv.org/pdf/2605.26680.pdf GitHub

Abstract:
Recent video multimodal large language models (MLLMs) increasingly couple step‑by‑step reasoning with on‑demand visual evidence retrieval, allowing models to revisit relevant video segments during inference. However, two structural gaps remain in existing thinking‑with‑video systems. (i) Sampling density is not a learnable decision: existing methods may let the model decide where to look, but the per‑window frame rate is largely fixed. As a result, fine‑grained evidence is often recovered through repeated retrieval calls, which increases inference context length and training difficulty. (ii) Retrieval and answer generation are usually optimized with a single trajectory‑level advantage, so the "where to look" tokens and the "how to answer" tokens receive the same credit even when one is correct and the other is not. To address these gaps, we present DynFrame, a framework that emits the temporal window and the sampling density as native tokens within a single autoregressive pass. This learnable span‑density retrieval enables acquiring multi‑granularity evidence with a single retrieval step. Based on the above tokenized retrieval interface, we further introduce Segment‑Decoupled GRPO (SD‑GRPO), which splits each rollout at the retrieval boundary and assigns role‑specific token‑level advantages, separately crediting the sampling decision and the answer. Trained on the curated DM‑CoT‑74k and DM‑RL‑45k, DynFrame‑4B is competitive with strong 7B‑8B baselines across six benchmarks (NExT‑GQA, Charades‑STA, ActivityNet‑MR, Video‑MME, MLVU, LVBench), and DynFrame‑8B sets new state‑of‑the‑art on most metrics. Code is available at https://github.com/zhangguanghao523/DynFrame.

PaperId: 627, https://arxiv.org/pdf/2605.26670.pdf GitHub

Abstract:
Sequential editing of structured knowledge in large language models allows targeted factual updates without retraining, yet existing methods often rely on complex regularization or constraint mechanisms whose necessity remains unclear. In this work, we systematically investigate the mechanisms underlying effective and stable sequential editing. Specifically, we first analyze the empirical success of AlphaEdit and establish, via a rigorous optimization analysis, the formal equivalence between one‑time and sequential editing. Building on this insight, we generalize the equivalence to a broader class of editing objectives, demonstrating that stability emerges naturally from properly accounting for accumulated editing constraints, rather than from specialized regularization or null‑space operations. We empirically confirm that many commonly used regularization strategies are unnecessary for reliable sequential updates. Furthermore, we extend our framework to handle conflicting edits, ensuring robust and consistent behavior under contradictory updates. Ultimately, our work provides Ariadne's thread through the labyrinth of sequential editing, charting a path toward simpler, more interpretable, and dependable knowledge updates. Our code is available at https://github.com/Wangzzzzzzzz/OTE‑SE‑Alignment.

PaperId: 628, https://arxiv.org/pdf/2605.26596.pdf GitHub

Abstract:
The token‑level extractive compressors widely used for general LM context are structurally inappropriate for LLM agents: across 17 (env, backbone, method) cells spanning two independent token‑level method families, every cell collapses to mean reward <= 0.05 despite 1.3‑13.3x realized compression. We name and characterize this failure mode as action‑grammar destruction ‑‑ the tokens carrying action semantics (identifiers, brackets, action verbs) are exactly those self‑information ranks lowest, so a general‑purpose compressor reliably removes them and the environment rejects the residual. The diagnosis points to step‑granularity compression. We introduce AGORA, an inference‑free step‑level compressor combining a structural prompt parser, an always‑keep floor for format‑ and recency‑critical content, and a 125M‑parameter relevance scorer trained on counterfactual next‑action‑change labels (~2ms/step, zero per‑step LLM toll). Across the compared inference‑free and LLM‑based methods, AGORA is the only one retaining >= 75% uncompressed performance in 8 of 9 cells (with the lone exception at 73%); a four‑way component ablation isolates the structural floor as the dominant quality lever and the learned scorer as the source of 1.0‑11.5x adaptive end‑to‑end compression from a single fixed keep ratio.

PaperId: 629, https://arxiv.org/pdf/2605.26552.pdf GitHub

Abstract:
Aligning a few‑step generative model is challenging, since existing alignment frameworks typically rely on restrictive assumptions: a tractable likelihood, a specific ODE/SDE solver, or a particular model family. We introduce FAV, Few‑step Generative Models Alignment via Sample‑based Variational Inference, a general alignment framework that requires only sample access to the generator and the reference distribution. We cast alignment as sampling from a reward‑tilted distribution anchored to a reference distribution. We leverage Stein Variational Gradient Descent as a sample‑based variational inference scheme and amortize its particle updates into the generator parameters via fixed‑point regression. We evaluate FAV on two domains: robotics manipulation and image generator alignment. On generative policy alignment for robotic manipulation, FAV outperforms prevailing policy extraction baselines across 56 offline and 30 offline‑to‑online RL tasks. For image generator alignment, FAV fine‑tunes diverse few‑step backbones, including GAN, drifting model, consistency models, and flow maps, scaling from ImageNet‑256 to 1024^2 text‑to‑image synthesis. Code is available at https://github.com/Jaewoopudding/FAV.

PaperId: 630, https://arxiv.org/pdf/2605.26535.pdf GitHub

Abstract:
Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without incurring high computational cost remains a fundamental challenge, as existing approaches face a critical speed‑fidelity trade‑off. In this work, we introduce Recursive Flow Matching (RecFM), a generative framework for forecasting complex spatiotemporal dynamics. RecFM enforces self‑consistency to align trajectories across discretization scales, reducing discretization errors and improving performance across metrics for physics‑based tasks. To our knowledge, this is the first method to achieve high‑fidelity one‑ and few‑step (2‑4 step) dynamic generation for scientific systems with performance comparable to state‑of‑the‑art multi‑step solvers. Across challenging scientific benchmarks, RecFM achieves up to a 20× speedup over leading diffusion‑based emulators while improving predictive accuracy. Furthermore, RecFM reduces mean squared error by over 15% compared to vanilla flow matching, offering a scalable and efficient solution for real‑time scientific emulation.

PaperId: 631, https://arxiv.org/pdf/2605.26525.pdf GitHub

Abstract:
Minute‑scale cinematic video generation is a central challenge for generative video models. Existing paradigms address only fragments of this challenge: single‑shot extrapolation preserves an anchor but lacks cinematic structure, while multi‑shot storytelling imposes structure yet remains free to invent its visual states rather than continue an observed one. We define Multi‑Shot Video Extrapolation (MSVE), a task that extends an observed frame or clip into a sequence of cinematically structured shots while preserving anchor state and advancing narrative intent. This setting operates under the finite per‑call generation budget of short‑video models. We identify three coupled bottlenecks: (1) global planners over‑specify unsupported details from full screenplays; (2) shot‑level prompts dilute task‑relevant state when carrying the complete story; and (3) temporal chaining turns generated frames into a lossy memory in which identity, scene, object, and action state decay. MSVE reveals that long‑video failure is not merely a limitation of context length, but a failure of context allocation. We propose Recursive Context Allocation (ReCA), an inference‑time framework that allocates context hierarchically across planning and generation. ReCA recursively decomposes MSVE into context‑bounded subproblems, invokes frozen generators at leaf nodes, and propagates structured state updates across time. To evaluate this setting, we further propose MSVE‑Bench and NB‑Q, a source‑grounded protocol with prompts purpose‑built for 3 to 5 minute long‑video generation, a regime not addressed by existing short‑clip benchmarks. Compared to previous methods, ReCA improves average normalized score by 8 to 16 percent over the strongest competing controller and improves multi‑shot consistency metrics by 28 to 43 percent. View the project page at https://reca.vmv.re.

PaperId: 632, https://arxiv.org/pdf/2605.26524.pdf GitHub

Abstract:
Maritime intelligent transportation systems (MITS) are essential for ensuring navigation safety and efficiency in busy waterways. However, accurate vessel trajectory prediction remains challenging due to the limitations of single‑source data. Automatic identification system (AIS) data is often sparse or unavailable for small vessels, while closed‑circuit television (CCTV) data alone cannot fully capture dynamic vessel behavior. To mitigate these challenges, we propose a cross‑modal interaction‑based vessel trajectory prediction (named CmIVTP) framework to model the intricate interactions between vessel dynamics and environmental constraints. Specifically, we introduce a target‑aware scene encoder to extract scene semantic features, effectively capturing vessel‑environment interactions and enhancing trajectory prediction accuracy. In addition, we propose a cross‑modal interaction transformer, which integrates AIS‑derived motion features, CCTV‑based environmental features, and scene representations. It leverages cross‑modal attention mechanisms to simultaneously capture intra‑modal semantics and inter‑modal interactions, ensuring dynamically consistent and environmentally feasible predictions. Furthermore, we construct a vessel group trajectory bank by clustering historical AIS trajectories into representative motion patterns, providing an efficient and scalable approach for candidate trajectory generation. Additionally, we introduce the maritime multimodal dataset plus (named Maritime‑MmD^+), a large‑scale dataset that synchronizes AIS data and CCTV video data, providing robust support for multimodal trajectory prediction research. Extensive experiments demonstrate that CmIVTP achieves better performance on multimodal‑driven vessel trajectory prediction benchmarks. The code resources for this work can be available at https://github.com/LouisYxLu/CmIVTP.

PaperId: 633, https://arxiv.org/pdf/2605.26457.pdf GitHub

Abstract:
AI coding agents are increasingly used to write real‑world software, but ensuring that their outputs are correct remains a fundamental challenge. Formal verification offers a promising path: an agent generates code together with a machine‑checked proof, guaranteeing that the code satisfies a formal specification. However, there is no guarantee that the formal spec itself matches the user's intent. In this work, we study specification autoformalization: whether LLM agents can translate informal programming problems into faithful formal specifications. We introduce Verus‑SpecBench, a benchmark of 581 spec‑writing tasks derived from Codeforces problems targeting Verus, a verifier for Rust, and Verus‑SpecGym, an agentic environment in which models interact with Verus, bash, & the filesystem to develop these specs. The central challenge is evaluation: expert‑written reference specs are expensive to write, & LLM judges can miss subtle mistakes. We address this by (a) extending Verus's exec_spec mechanism so that generated specs can be executed as Rust code, & (b) testing them against official Codeforces tests & adversarial cases extracted from Codeforces "hacks", which are edge cases written by competitors to break incorrect solutions. On Verus‑SpecBench, the strongest model, Gemini 3.1 Pro, solves 77.8% of tasks, other frontier models solve 51.1‑‑57.8% & OSS models reach only 21.5‑‑25.5%. Our analysis of failure modes shows that model‑generated specs can omit important input assumptions, accept incorrect outputs, & reject valid ones. We also find that LLM‑as‑a‑judge evaluation misses 26% of the failures our evaluator catches. Overall, our results suggest that spec autoformalization is within reach for frontier agents but remains brittle even on problems where they can already generate correct code. The code, data, & logs can be found at https://github.com/formal‑verif‑is‑cool/verus‑spec‑gym

PaperId: 634, https://arxiv.org/pdf/2605.26376.pdf GitHub

Abstract:
Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor‑related oncologic factors; thus, similar survival outcomes may reflect fundamentally different underlying biological processes. Prognostic modeling in HCC is informed by rich multimodal information from multiparametric MRI and radiology reports from routine clinical practice. Existing prognostic vision‑language models (VLMs) learn a single entangled latent representation that blends hepatic and tumor‑related factors, limiting both accuracy and biological interpretability. We present BioFact‑MoE, a biologically factorized Mixture of Experts (MoE) framework that explicitly decomposes liver and tumor factors via biologically supervised experts within a residual MoE survival architecture. On a HCC cohort of N=588 patients (pretrained on 4,582 3D MRI image‑report pairs), BioFact‑MoE consistently improves survival prediction over all baselines across time horizons, achieving 12‑, 18‑, and 24‑month AUCs of 75.33%, 75.85%, and 73.96%. Beyond scalar risk prediction, gated expert weights enable phenotype‑aware risk stratification. Pathway‑informed gating uncovers clinically meaningful treatment‑associated survival heterogeneity. In held‑out validation, hepatic and tumor embeddings show selective associations with liver function and tumor burden markers, respectively (p<0.05), without supervision. The code is available at https://github.com/jy‑639/BioFact‑MoE.

PaperId: 635, https://arxiv.org/pdf/2605.26368.pdf GitHub GitHub

Abstract:
Geometry estimation from perspective images has greatly advanced, maturing to the point where off‑the‑shelf foundation models are able to reconstruct 3D scene structure not only from multi‑view imagery, but even from a single view. A natural extension is 3D reconstruction from panoramas, with the exciting prospect of recovering a full 360‑degree scene from a single panoramic image. In this work, we introduce PaGeR (Panoramic Geometry Reconstruction), a framework to lift powerful 3D foundation models designed for perspective imagery to the panorama domain. Our strategy is to start from a pre‑trained transformer for 3D reconstruction and turn it into a unified high‑performance model that predicts scale‑invariant depth, metric depth, surface normals, and sky masks from both perspective and omnidirectional images, in a single forward pass. By keeping architectural changes to a minimum and mixing perspective and panoramic images during training, PaGeR retains the rich 3D prior of the underlying foundation model while learning to also estimate geometrically consistent 360‑degree scenes from single panoramas. We extensively test our method in both indoor and outdoor environments and find that it delivers state‑of‑the‑art performance and excellent zero‑shot performance across a wide range of scenes. Code, data and models are available \hrefhttps://github.com/prs‑eth/PaGeR\texthere.

PaperId: 636, https://arxiv.org/pdf/2605.26353.pdf GitHub

Abstract:
Different visual patterns appear with different frequencies in the world: e.g., beach balls appear on sand more often than they do on a road. These statistics are reflected in vision datasets, and as a result trained models more easily recognize objects in common scenarios. However, recognizing a beach ball on a road may arguably be even more important than recognizing it on sand. We study how to mitigate this discrepancy. Since collecting uncommon images in the real world may be difficult, we explore whether generating images with less frequent contexts can serve as effective training augmentation. A key challenge is guiding generations to remain close to the original dataset distribution while creating diverse images with uncommon contexts. We introduce Decoupling Contextual Patterns with Generations (DecoupleGen), a method that personalizes text‑to‑image diffusion models to facilitate coherent synthesis of images with rare contexts while preserving original visual details. The generated images contain semantically meaningful content and remain visually aligned with the original datasets. We further apply verification constraints to ensure relevance of the augmented data. We evaluate our approach on object classification and recognition tasks on complex scene datasets. Our experiments demonstrate consistent improvements over previous approaches, and our analyses identify factors underlying these improvements.

PaperId: 637, https://arxiv.org/pdf/2605.26350.pdf GitHub

Abstract:
In‑context learning (ICL) is often motivated by the intuition that demonstrations help because they provide correct input‑output examples. However, we reveal a counterintuitive phenomenon: correctness does not guarantee exemplar utility, and some correct demonstrations can even reduce ICL accuracy. To study this correctness‑utility gap, we introduce task‑preserving perturbations, where only the exemplar input is changed, while the example remains a correct instance of the same task. Concretely, each perturbed exemplar is assigned the target induced by the task mapping. This framework covers both label‑updating perturbations, where task‑relevant semantics change and targets are recomputed, and stricter target‑preserving perturbations, where the original target remains valid. We formalize the resulting failure mode as contextual evidence shift: task‑preserving perturbations can change the effective mixture of evidence used by the model for contextual inference, thereby separating exemplar correctness from exemplar utility. Across sentiment classification, logical reasoning, and math word problems, we find that task‑preserving perturbed demonstrations can substantially degrade ICL performance, especially for smaller models, harder tasks, and higher perturbation ratios. Our results show that robust ICL requires evaluating not only whether demonstrations are correct, but also how they influence contextual inference. Code is available at https://github.com/Chenghao‑Qiu/Task‑Preserving‑ICL.

PaperId: 638, https://arxiv.org/pdf/2605.26315.pdf GitHub

Abstract:
Direct Preference Optimisation (DPO) is widely used for safety alignment in large language models. However, prior work shows it is brittle and exhibits poor out‑of‑distribution (OOD) generalisation. In this paper, we investigate whether Curriculum Learning can improve the robustness of DPO‑based safety alignment. We propose Staged‑Competence, a curriculum‑based framework that organises preference data by difficulty, employs competence‑based sampling, and progressively updates the reference model during training. Averaged across three model families, Staged‑Competence reduces OOD harmful response rates by 16% and jailbreak attack success rates by 20%, while preserving general capabilities with near‑zero over‑refusal. We further show that Staged‑Competence (1) matches baseline safety with only 75% of the training data and (2) yields better separation between safe and unsafe responses. Staged‑Competence is agnostic to the policy optimisation loss and can extend to other DPO variants and alignment domains. Our code and data are available at https://github.com/Sandeep5500/curriculum‑learning‑for‑safety.

PaperId: 639, https://arxiv.org/pdf/2605.26279.pdf GitHub

Abstract:
Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models from domain knowledge artifacts are currently limited by inadequate benchmarks. This deficiency impedes reproducibility and cross‑study comparability, slowing the maturation of CA methods. Existing benchmarks were designed for solver evaluation rather than for assessing CA algorithms. They are loosely organized, treat individual problems inconsistently, and omit the domain knowledge artifacts required by CA methods. This work presents MPMMine, a benchmark suite designed to assess algorithms that discover, validate, and enhance MP models using diverse domain knowledge artifacts. MPMMine is guided by consistency, standardization, completeness, extensibility, openness, and version control. It adopts a uniform structure and relies on open formats: MiniZinc, CommonMark, and JSON. It provides multiple models per problem, tens of instances per model, and thousands of solutions and non‑solutions in both integer and continuous domains, alongside natural‑language descriptions to support text‑to‑model methods.

PaperId: 640, https://arxiv.org/pdf/2605.26190.pdf GitHub

Abstract:
This paper presents the HRVConformer, a novel deep learning architecture for the classification of hypoxic‑ischemic encephalopathy (HIE) using the instantaneous heart rate (HR) signal. Unlike conventional approaches that rely on handcrafted features, HRVConformer directly processes raw HR signals in an end‑to‑end manner, capturing both local and long‑range dependencies through a hybrid Convolution‑Transformer framework. By integrating convolutional layers for local feature extraction and Transformer‑based attention mechanisms for global context modelling, the architecture effectively enhances signal representation and classification performance. The model was trained using supervised learning on a large HR dataset consisting of 1,573 one‑hour epochs, including 259 one‑hour expert‑annotated epochs and a substantial set of weakly labelled data. A 314‑hour validation set provided a robust performance estimation, while an independent 215‑hour dataset with expert annotations was reserved for final testing. HR signals were extracted from electrocardiogram (ECG) recordings using an improved Pan‑Tompkins algorithm, which significantly enhanced both signal quality and data availability. Experimental results demonstrate that the HRVConformer achieves an AUC of 83.23% and accuracy of 74.56% on the test set. These results surpass the performance of the Transformer, ResNet50 and fully convolutional networks baselines, highlighting the advantages of integrating convolutional and Transformer‑based components for HR‑based HIE classification. The proposed method provides a promising step toward a more accurate and automated assessment of HIE using HR signals. The code is available at: https://github.com/syu‑kylin/HRVConformer.

PaperId: 641, https://arxiv.org/pdf/2605.26186.pdf GitHub

Abstract:
Functionality‑correct repository setup aims to configure execution environments (e.g., dependencies, build scripts) to successfully execute a repository's documented features. It presents significant challenges due to diverse, repository‑specific failures, including dependency incompatibilities, missing toolchains, incomplete installations, and verification‑strategy mismatches. Existing LLM agents struggle to robustly resolve these issues, specifically failing to support (1) cross‑repository experience transfer, (2) multi‑step trial‑and‑repair under non‑invertible state changes, and (3) robust verification of setup outcomes to distinguish setup‑induced failures from repository bugs. To address this, we introduce SetupX, an experiential learning‑based setup framework. First, we construct a Self‑Evolving Experience Representation (XPU), a dual‑modality knowledge unit encoding setup signals, textual guidance, executable actions to dynamically transfer verified environment fixes to unseen repositories. Second, we employ Experience‑Augmented Speculative Execution backed by a LIFO Docker snapshot stack, enabling the agent to proactively trial fixes and safely roll back to known‑good states. Third, we introduce a Prosecutor‑Judge Verification Protocol that separates evidence collection from final judgment, enabling more reliable setup verification beyond superficial build‑time metrics. Evaluation results on carefully‑crafted benchmarks show SetupX achieves highest performance (e.g., 92% pass rate) and outperforms the strongest baseline by over 19%. Crucially, SetupX excels in complex multi‑repository setup requiring coordinating multiple interconnected services across different containers. The code repository is available at https://github.com/OpenDataBox/SetupX.

PaperId: 642, https://arxiv.org/pdf/2605.26175.pdf GitHub

Abstract:
Low‑bit activation quantization remains a major bottleneck in efficient large language model (LLM) deployment. The difficulty is not only that activations contain outliers, but that their distributions are often poorly matched to a low‑bit uniform quantizer. Existing post‑training quantization (PTQ) methods suppress peaks, balance channels, or minimize reconstruction error, yet they rarely specify what activation distribution is actually easy to discretize. As a result, activations may appear numerically smoother while still incurring large quantization error because the quantization range remains wide or most values collapse into a few levels near the mean. We recast activation transformation as quantizer‑facing distribution design and analyze quantization error from an information‑theoretic perspective. Our analysis shows that quantization‑friendly activations should jointly have a smaller numerical range and sufficient dispersion within that range. Guided by this analysis, we propose InfoQuant, a train‑free method that employs Peak Suppression Orthogonal Transformation (PSOT) to shape activations into more quantization‑friendly distributions. We further introduce adaptive outlier‑token selection to improve the robustness of PSOT during optimization. Across multiple LLM families, InfoQuant consistently outperforms prior PTQ and end‑to‑end training baselines. Under W4A4KV4, it preserves 97% of floating‑point accuracy on average and reduces the LLaMA‑2 13B performance gap by 42% over the previous state of the art. Code is available at [https://github.com/LLIKKE/InfoQuant](https://github.com/LLIKKE/InfoQuant)

PaperId: 643, https://arxiv.org/pdf/2605.26166.pdf GitHub

Abstract:
The rapid proliferation of Internet of Things (IoT) devices has created an urgent demand for adaptive, resource‑efficient Intrusion Detection Systems (IDS) capable of handling dynamic and evolving cyber threats. This paper investigates AOC‑IDS, a state‑of‑the‑art autonomous online IDS published at IEEE INFOCOM 2024, which employs an Autoencoder (AE) with Cluster Repelling Contrastive (CRC) loss and an autonomous Gaussian‑based decision module. We first successfully replicate AOC‑IDS on the UNSW‑NB15 benchmark, achieving 89.39% accuracy in close agreement with the published 89.19%. We then identify four key limitations: class imbalance, unreliable pseudo‑label generation, limited generalization, and computational overhead for IoT deployment, and propose targeted improvements for each. Our XGBoost‑BalSamp method achieves 95.45% accuracy on UNSW‑NB15, a gain of 6.26% over the baseline. Our combined deep learning approach (PseudoFilter, MixupAug, and LiteAE) achieves a best‑run accuracy of 90.88% (F1: 91.45%), surpassing the base paper while reducing model parameters by 55%.These results demonstrate that targeted improvements to AOC‑IDS yield consistent accuracy gains while improving practical deployability on IoT edge devices.

PaperId: 644, https://arxiv.org/pdf/2605.26165.pdf GitHub

Abstract:
Agentic RAG systems that equip language models with dozens to hundreds of tool definitions face a critical resource conflict: tool schemas consume the same context window needed for retrieval‑augmented generation. We present the first systematic study of this tool‑context trade‑off, evaluating 14 models spanning 1.5B‑32B local models plus one frontier API model across 6,566 controlled API calls at three context budgets (8K, 16K, 32K) with 28 tool definitions. Applying TSCG conservative‑profile compression (44‑50% schema token savings), we observe a binary enablement effect: at 8K tokens, JSON‑schema tool definitions overflow the context window entirely, yielding near‑zero EM (2.6% average), while compressed schemas restore RAG functionality with +20.5 pp average exact‑match lift across all eight models (+24.7 pp among the six exhibiting full enablement). At 32K ‑‑ where both formats fit ‑‑ four of five tested models show delta <= 1 pp, confirming the effect is purely budget‑driven. External validation on HotpotQA (50 multi‑hop questions) shows +48 pp EM under the same overflow scenario. Frontier scaling tests demonstrate that JSON schemas overflow at ~494 tools while compressed schemas remain operational beyond 800 tools. Our results establish tool‑schema compression as a necessary infrastructure layer for agentic RAG in constrained‑context deployments. All code, data, and checkpoints are publicly available.

PaperId: 645, https://arxiv.org/pdf/2605.26158.pdf GitHub

Abstract:
Safety alignment in large language models (LLMs) and multimodal large language models (MLLMs) is commonly assumed to operate as a near‑binary threshold mechanism. We challenge this assumption by revealing that safety behavior is governed by an instability region where small perturbations induce stochastic refusal decisions rather than deterministic outcomes. We develop a multi‑metric diagnostic framework combining external and internal signals to characterize this instability. Through systematic experiments, we identify a characteristic diagnostic signature: inputs in unstable regimes exhibit elevated output uncertainty yet decreased internal safety activation, a decoupling phenomenon that explains why detection‑based defenses fail against sophisticated attacks. Building on this framework, we introduce Furina, a jailbreak attack that deliberately induces this signature through fragmented, scene‑anchored prompts without model‑specific optimization. Furina outperforms strong single‑turn and multi‑turn baselines on HarmBench and achieves competitive results on MM‑SafetyBench, demonstrating that uncertainty amplification provides a principled and transferable mechanism for understanding safety vulnerabilities. Code is available at: https://github.com/0xCavaliers/Furina_Jailbreak.

PaperId: 646, https://arxiv.org/pdf/2605.26156.pdf GitHub

Abstract:
The known stylistic biases in LLM judges, such as a preference for verbosity or specific sentence structures, present an underexplored security vulnerability. In this work, we introduce BITE (BIas exploraTion and Exploitation), a black‑box adversarial framework that learns semantics‑preserving edits to mislead an LLM judge and artificially inflate the scores it assigns. We cast the selection of stylistic edits as a contextual bandit problem and use a LinUCB policy to adaptively choose edits that maximize the judge's score without access to model parameters or gradients. Empirically, we test BITE across a diverse range of LLM judges and tasks, including both pointwise and pairwise comparisons on chatbot leaderboards and AI‑reviewer benchmarks. BITE achieves an attack success rate exceeding 65% and raises scores by 1‑2 points on a 9‑point scale, all while preserving semantic equivalence. We further assess the attack's stealthiness, showing that BITE evades standard style‑control methods and several detection baselines. Our findings expose a fundamental weakness in the LLM‑as‑a‑judge paradigm and motivate robust, attack‑aware evaluation. Our code is available at https://github.com/xianglinyang/llm‑as‑a‑judge‑attack.

PaperId: 647, https://arxiv.org/pdf/2605.26112.pdf GitHub

Abstract:
This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures around foundation models. We refer to this shift as scaling the harness: treating the structured execution layer around a foundation model as a first‑class object of design, evaluation, and optimization. Although recent large language models enable agents to use tools, retrieve information, maintain memory, and execute long‑horizon workflows, evaluation remains largely model‑centric, often reducing agents to final‑task success while treating memory, retrieval, tool use, orchestration, verification, and governance as secondary implementation details. This framing is increasingly inadequate because agent performance emerges from the interaction among the foundation model, memory substrate, context constructor, skill‑routing layer, orchestration loop, and verification‑and‑governance layer. Together, these components form the agent harness, which translates model capability into long‑horizon agent behavior. We study scaling the harness through three core bottlenecks: context governance, trustworthy memory, and dynamic skill routing, together with the orchestration and governance mechanisms that coordinate and constrain them. We further outline a research agenda for harness‑level benchmarks that go beyond one‑shot task success to measure trajectory quality, memory hygiene, context efficiency, communication fidelity, verification cost, and safe evolution over time. To make the discussion concrete, we develop CheetahClaws: https://github.com/SafeRL‑Lab/cheetahclaws, a Python‑native reference harness, and compare it with Claude Code and OpenClaw. Our main claim is that future progress in agentic AI will depend as much on system design as on stronger foundation models.

PaperId: 648, https://arxiv.org/pdf/2605.26111.pdf GitHub GitHub

Abstract:
Subject‑driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Existing approaches often encode text and reference images separately. This limits cross‑modal reasoning abilities and causes copy‑paste artifacts. Recent frameworks that connect multimodal models and diffusion models improve instruction following, but largely overlook identity preservation. To address these limitations, we condition diffusion models on Multimodal Large Language Models (MLLMs) that jointly encode text and reference images, and augment it with VAE‑based identity conditioning. A novel Dual Layer Aggregation (DLA) module is designed to aggregate multi‑level MLLM features for optimal conditioning, and a multi‑stage denoising strategy is applied to progressively balance the semantic information from MLLM and fine‑detail identity from VAE during inference. Extensive experiments demonstrate that our approach harmonizes multimodal understanding with identity preservation, mitigates copy‑paste issues, and achieves superior performance regarding human preference on subject‑driven image generation. Our project website is available at https://zsh2000.github.io/squeeze‑mllm‑subject‑gen/.

PaperId: 649, https://arxiv.org/pdf/2605.26045.pdf GitHub

Abstract:
Activation oracles aim to make the activations of other models legible to humans and yield promising results compared to white‑box interpretability techniques. However, uncertainty quantification (UQ) for the natural‑language outputs of such activation oracles is so far understudied. Here, we investigate 6 different methods for estimating the confidence of activation oracles and evaluate how well‑calibrated their confidence scores are. Our experiments on 6,000 samples per oracle (varying verbalizer and context prompts) reveal that bootstrap mode frequency is the best‑calibrated method among those tested (ECE 5.7% vs. 25.5% for the answer‑word log‑probability on Qwen3‑8B; 10.3% vs. 13.1% on Qwen3.6‑27B), and that the log‑prob baseline can serve as a fast triage signal at a fraction of the cost. Code and the patched trainer are available at https://github.com/federicotorrielli/probabilistic_activation_oracles.

PaperId: 650, https://arxiv.org/pdf/2605.26038.pdf GitHub

Abstract:
Lightweight vision‑language models perform competitively on standard benchmarks yet fail systematically in dense‑scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi‑step inference. Such capability is critical for real‑world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, organized into five task categories spanning three progressive reasoning layers. Building on DRBench, we propose DRScaffold, a supervised fine‑tuning framework that decomposes the supervision target into four causally ordered stages, enforcing grounded reasoning without architectural modification. Experiments on three lightweight VLMs demonstrate substantial gains on DRBench while preserving or improving performance on general‑purpose benchmarks. Notably, Qwen2.5‑VL‑3B trained with DRScaffold surpasses the frozen Qwen2.5‑VL‑32B on DRBench, demonstrating that structured supervision can substitute for a significant portion of model scale in dense‑scene reasoning. Our code and models are available at https://github.com/irene‑shi/DRScaffold .

PaperId: 651, https://arxiv.org/pdf/2605.26026.pdf GitHub

Abstract:
Light sheet fluorescence microscopy (LSM) enables high‑resolution, three‑dimensional (3D) imaging of biological specimens, providing rich volumetric data for studying cellular organization, pathology, and vascular networks. However, the size, dimensionality, and annotation burden of LSM data make supervised deep learning approaches costly and difficult to scale. Additionally, despite the abundance of unannotated LSM volumes, foundation models for this modality remain underexplored due to computational challenges and the complexity of volumetric representation learning. In this work, we introduce a 3D foundation model for LSM data, pretrained on a large curated collection of 3D images spanning multiple organisms, stains, and imaging protocols. We learn transferable volumetric representations by jointly optimizing for masked reconstruction and image‑text alignment. The pretrained backbone drastically reduces the annotation burden, enabling efficient, few‑shot adaptation for varied downstream tasks. We evaluate this approach on downstream segmentation, classification, and deblurring. Our results demonstrate consistent improvements over baselines, (1) when measured using standard evaluation metrics and (2) when rigorously assessed by domain experts. This highlights the potential of foundation model pretraining to reduce annotation requirements while improving performance across diverse LSM analysis tasks. Pretrained model weights and code for pretraining and finetuning are publicly available: https://github.com/AdinaScheinfeld/lsm_fm_public_repo.git.

PaperId: 652, https://arxiv.org/pdf/2605.25985.pdf GitHub

Abstract:
Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential first‑order queries with k free variables (i.e., \textEFO_k queries) is a crucial yet challenging problem, as it requires ranking answer tuples in \mathcalE^k, where \mathcalE denotes the entity set of a KG. This quickly becomes intractable as k grows. Consequently, existing benchmarks and methods rely on marginal rankings over individual variables; however, marginal rankings are a poor proxy for the true joint ranking of tuples. Building on neural symbolic search for \textEFO_1 queries, we propose Neural Scalable Symbolic Search (NS3), a budgeted framework that approximates joint ranking without enumerating \mathcalE^k. NS3 (i) answers marginalized sub‑queries to obtain necessary candidate sets, (ii) merges multiple free variables into hypernodes whose domains are pruned and controlled by a dynamic budget B, and (iii) progressively reduces an \textEFO_k query to an \textEFO_k‑1 query over a budgeted reduced domain. Across three standard KG datasets, NS3 substantially improves joint ranking performance while retaining strong marginal accuracy. We further release a joint‑ranking benchmark that extends existing \textEFO_1 datasets to k=3, enabling systematic evaluation of multi‑variable queries. Our code is provided in https://github.com/HKUST‑KnowComp/NS3_KDD2026.

PaperId: 653, https://arxiv.org/pdf/2605.25964.pdf GitHub

Abstract:
AI Scientists have shown promising progress across multiple stages of the research pipeline, among which automatic scientific paper writing remains a formidable challenge. The Introduction writing is especially challenging, which demands not only linguistic fluency, but logical soundness and verifiable faithfulness. Most AI‑assisted methods treat the task as text generation instead of reasoning and structuring, leading to severe drawbacks, e.g., hallucinating citations. To address this, we first formulate the Content‑Conditional Introduction Generation (CCIG) task, which requires grounding the Introduction in the paper's core evidence. We then propose LECTOR, a novel Logic‑Expression Co‑Reinforcement Learning framework that can strictly follow the scientist's logic, add high‑quality citations and keep structured expressions. LECTOR first constructs a logic‑reasoning graph from the paper's main body to serve as a verifiable logical blueprint. Subsequently, it employs a Logic‑Expression Co‑Rewarding mechanism to jointly optimize for both the graph's structural fidelity and the final narrative's quality. We conduct a dataset from Nature Communications papers to assess our method. Extensive experiments show consistent improvements in both logic fidelity and Introduction generation quality metrics, e.g., Graph Quality (+26.7%), Citation Quality (+8.6%), and Paper Consistency (+3.3%). Code and data are available at https://github.com/Xiao‑Youth/LECTOR.

PaperId: 654, https://arxiv.org/pdf/2605.25954.pdf GitHub

Abstract:
Despite the strong reasoning capabilities of large language models (LLMs), optimizing the execution efficiency of tensor programs remains challenging due to the need for precise, composable transformation decisions. Recent LLM‑guided approaches frame tensor program optimization as an iterative decision process, but existing datasets provide only end‑to‑end optimized program pairs using token‑inefficient representations, lacking verifiable step‑level supervision and interpretability. As a result, LLMs struggle to make reliable single‑step decisions in large combinatorial optimization spaces. We introduce Step‑TP, a post‑training dataset for tensor program optimization that provides grounded, atomic, step‑level supervision with structured chain‑of‑thought (CoT) reasoning. Step‑TP forms a closed reasoning loop over intermediate program states, enabling reliable multi‑step optimization rather than outcome imitation. Its design is guided by four principles: (i) a token‑efficient, verifiable intermediate representation (IR) that deterministically lowers to TVM TIR; (ii) atomic and composable optimization strategies that decompose complex trajectories into interpretable single‑step decisions; (iii) structured CoT supervision coupled with explicit IR‑to‑IR state transitions; and (iv) strategy filtering to balance coverage while preventing shortcut exploitation. The dataset and implementation are available at a GitHub link, https://github.com/LIUMENGFAN‑gif/StepTP.

PaperId: 655, https://arxiv.org/pdf/2605.25944.pdf GitHub

Abstract:
Ultrasound video segmentation is clinically valuable yet difficult due to speckle noise, weak boundaries, and rapid anatomical deformation. Recent promptable foundation models enable point‑guided segmentation, but their direct deployment in ultrasound remains unreliable: a single point provides insufficient spatial context to resolve scale ambiguity, and greedy memory updates amplify early errors into severe temporal drift. We present EchoPilot, a training‑free framework for ultrasound video segmentation under sparse first‑frame interaction, requiring only a single point click and an anatomical category name. EchoPilot orchestrates a frozen medical vision‑language model (VLM) for semantic localization, a vision foundation model (VFM) for dense geometric feature extraction, and a promptable video segmentor for mask prediction and propagation. To resolve initialization ambiguity, we propose Scale‑Space Semantic Prompting, which first selects an optimal contextual view via a parameter‑free S.E.E.D. (Semantic Energy‑Entropy Density) criterion, and then synthesizes geometrically precise auxiliary point prompts from dense foundation features without additional user interaction. To reduce propagation drift, a Reliability‑Gated Memory update is further introduced to selectively freeze the segmentor's memory bank under uncertain predictions, preventing error accumulation. We also contribute the first dynamic fetal placenta ultrasound video segmentation dataset with 671 annotated frames. Across three ultrasound video datasets, EchoPilot achieves state‑of‑the‑art performance under the sparse‑interactive setting, consistently outperforming training‑free baselines and finetuned specialists.

PaperId: 656, https://arxiv.org/pdf/2605.25931.pdf GitHub

Abstract:
We systematically investigate all 25 public ARC‑AGI‑3 games and find that every one is reachable through non‑intelligent strategies: 10 in a single blind step, 5 after one probing action, 1 via repeated ACTION1 presses, 1 via diverse exploration, and 8 via single repeated actions with sufficient budget (50‑200 steps). A library‑level null‑coordinate vulnerability additionally bypasses 18 games in 1 step. This benchmark critique implies the public evaluation set cannot discriminate intelligent exploration from trivial heuristics ‑ the private 55‑game evaluation is the only genuine intelligence test. Against this backdrop, we present AERA (Adaptive Epistemic Reasoning Agent), a three‑phase (EXPLORE / VERIFY / PLAN) agent achieving RHAE=0.2116 (4/25 solved) on these 25 games with Qwen2.5‑0.5B, while random and no‑explore baselines score 0.0000. We formalise AERA through a Speed‑‑Depth trade‑off framework: under a convexity assumption (proved for a class of environments in the Appendix), RHAE's quadratic form emerges as a second‑order penalty for deviating from the Pareto frontier between action efficiency and information gain. Contributions: (i) a benchmark validity analysis showing that current interactive reasoning benchmarks fail to measure the exploration they claim to require, and (ii) the EXPLORE‑before‑PLAN framework and model‑capability x exploration interaction. The linked code track entry achieves RHAE=0.30 on the full 55‑game private evaluation. Code: CC0.

PaperId: 657, https://arxiv.org/pdf/2605.25920.pdf GitHub

Abstract:
While large language models (LLMs) augmented with agentic search capabilities show promise for legal reasoning, they overlook a fundamental constraint that applicable law must match the temporal context of each case, as retroactive application of statutes violates core legal principles and leads to erroneous conclusions. Our observations reveal that current legal LLMs suffer from temporal bias anchored to their training cutoff, while search agents rarely incorporate temporal constraints into queries, and that web search alone cannot provide the precise statute and precedent citations that legal reasoning demands. To address these challenges, we propose LegalSearch‑R1, an end‑to‑end reinforcement learning framework that pairs local statute RAG for precise article matching with online web search for broader legal knowledge, trained on temporally‑indexed data spanning multiple amendment periods to enforce temporal consistency. Extensive experiments on our benchmark covering 13 legal tasks demonstrate that our 7B‑parameter agent outperforms state‑of‑the‑art deep research frameworks and specialized legal LLMs by 12.9% to 29.8%, surpasses baselines by 57.7% to 80.3% on temporal consistency, and exhibits robust out‑of‑domain generalization. The code and data are available at https://github.com/AlexFanw/LegalSearch‑R1.

PaperId: 658, https://arxiv.org/pdf/2605.25861.pdf GitHub

Abstract:
3D human mesh recovery and 3D clothed human reconstruction are inherently related, yet they have long been studied in isolation, thereby overlooking the potential gains of joint optimization. To overcome this limitation, we propose to address these two tasks within a unified framework, which allows their mutual dependencies to be effectively exploited. Building on this idea, we propose MuNet, a mutualistic network for joint 3D human mesh recovery and 3D clothed human reconstruction from single images. First, we adopt 2‑manifold graphs as a unified representation for all 3D models, enabling consistent modeling across 3D human mesh recovery and clothed human reconstruction. Second, we design an end‑to‑end graph convolutional network that progressively deforms an initial graph into a 3D human mesh and refines it into a detailed 3D clothed human model. Third, we introduce a mutualistic mechanism that allows reciprocal interaction between the two tasks during training, where 3D human mesh recovery provides guidance for 3D clothed human reconstruction, and reconstruction feedback refines the 3D human mesh recovery. We extensively evaluate MuNet on six benchmark datasets for 3D human mesh recovery and 3D clothed human reconstruction, including Human3.6M, 3DPW, MPI‑INF‑3DHP, THuman2.0, CAPE, and RenderPeople. Experimental results demonstrate that MuNet achieves state‑of‑the‑art performance on both tasks across all datasets. The code of MuNet is released for research purposes at https://github.com/starVisionTeam/MuNet.

PaperId: 659, https://arxiv.org/pdf/2605.25829.pdf GitHub

Abstract:
Recent vision‑language‑action (VLA) models and world action models (WAMs) advance robotic manipulation by enriching intermediate representations with auxiliary spatial features or future visual‑state prediction. However, these representations largely remain within the observation space and do not share the rigid‑body geometry of the action space, forcing the action decoder to implicitly recover this geometry. We propose OASIS, a visuomotor policy that aligns the intermediate representation with the action space via SE(3) end‑effector trajectory prediction. OASIS couples a 3D‑aware feature encoder that fuses vision‑language and metric‑depth features with an SE(3) trajectory predictor that produces a camera‑frame end‑effector trajectory. Conditioned on the predictor's pose‑supervised hidden states, the action decoder generates action chunks consistent with rigid‑body motion. Across simulation and real‑world experiments, OASIS outperforms VLA and WAM baselines in success rate and out‑of‑distribution generalization. Our project page is available at https://npuhandsome.github.io/OASIS_web.

PaperId: 660, https://arxiv.org/pdf/2605.25786.pdf GitHub

Abstract:
Efficiently solving Poisson equations on complex, irregular domains remains a fundamental challenge in scientific computing, as classical iterative solvers often suffer from prohibitive runtime due to ill‑conditioned systems. While neural operators offer a fast alternative, they typically rely on large‑scale labeled datasets or struggle with unstable training dynamics when using physics‑informed residual losses. We propose \textscNPSolver, a neural Poisson solver trained without solution labels via iterative physics supervision. Instead of relying on fully converged numerical solutions or raw PDE residuals, \textscNPSolver utilizes a small number of preconditioned conjugate gradient (PCG) steps to refine its own predictions, providing a more stable and well‑scaled training signal. Theoretical analysis confirms that this iterative supervision serves as a well‑conditioned error proxy and that a stop‑gradient design is essential for optimization stability. To better capture boundary‑driven features under mixed boundary conditions, we further introduce the Boundary‑Aware Transolver (\textscBA‑Transolver) architecture that explicitly separates interior and boundary tokenization. Extensive evaluations on 2D and 3D irregular geometries demonstrate that \textscNPSolver outperforms both physics‑informed and data‑driven baselines. Furthermore, a downstream thermal control task highlights the model's capability for conducting efficient and reliable gradient‑based boundary control. We will release our codes and data at https://github.com/intell‑sci‑comput/NPSolver.

PaperId: 661, https://arxiv.org/pdf/2605.25771.pdf GitHub

Abstract:
Multi‑domain graph pre‑training is a crucial step in constructing foundational graph models with cross‑domain generalization capabilities. However, existing methods predominantly rely on jointly training all source domain graphs, resulting in high computational costs. Furthermore, it remains unclear whether all source domain graph data contribute equally to effective transfer. This paper empirically reveals significant data redundancy in multi‑domain graph pre‑training. Based on this finding, we propose the Multi‑domain Graph Pre‑training Framework, MDGMIX, which combines boundary‑aware subgraph mixing with hierarchical discrimination. By selecting boundary nodes to construct challenging mixed‑domain subgraphs, MDGMIX employs coarse‑grained domain discrimination and fine‑grained domain decomposition losses to decouple shared patterns from domain‑specific patterns. During adaptation, MDGMIX employs a lightweight prompt weighting mechanism to transfer source domain knowledge. Extensive experiments demonstrate that MDGMIX consistently outperforms strong baselines in few‑shot classification tasks while exhibiting superior time and memory efficiency. The code is available at: https://github.com/zhengziyu77/MDGMIX.

PaperId: 662, https://arxiv.org/pdf/2605.25764.pdf GitHub

Abstract:
Pathology foundation models (PFMs) have emerged as a core approach for learning transferable representations from whole slide images (WSIs), and they are typically benchmarked through downstream clinical endpoints. While such task level evaluations are indispensable, they offer limited insight into what the representations themselves encode, particularly whether PFM embeddings can distinguish meaningful tissue regions and capture their spatial relationships. We present SpaPath‑Bench, a representation level benchmark designed to diagnose spatial representation capability in PFMs. SpaPath‑Bench formulates spatial domain identification (SDI) on paired whole slide image and spatial transcriptomics (ST) data as a diagnostic task. It curates 42 public paired WSI and ST slides, enables large scale evaluation across 19 encoders and seven SDI methods, and measures partition quality using three complementary criteria: unsupervised spatial coherence, transcriptomics referenced agreement, and expert referenced agreement. Across 83K runs, SpaPath‑Bench reveals that different pretraining paradigms capture distinct aspects of tissue spatial architecture, and it provides practical guidance for building the next generation of spatially aware computational pathology models. Code and data pipelines are publicly available at https://bokai‑zhao.github.io/SpaPath‑benchboard/.

PaperId: 663, https://arxiv.org/pdf/2605.25717.pdf GitHub

Abstract:
Most of the world's offshore wind resource lies in waters too deep for fixed‑bottom foundations, making floating offshore wind turbines (FOWTs) essential for deep‑water deployment. As the industry scales toward 22 MW class designs, tower fatigue becomes increasingly critical because larger structures amplify the coupled aero‑hydro‑servo‑elastic loads induced by continuous wind and wave excitation. Accurate fatigue‑damage prediction is therefore central to certification, design optimization, and cost reduction. Yet the field lacks a shared surrogate benchmark: studies report different simulations, splits, and metrics, making methods difficult to compare. We present FLOATBench, a public tabular benchmark with 582,120 per‑section fatigue‑damage labels across three 22 MW FOWT tower geometries, derived from 19,404 high‑fidelity OpenFAST simulations across the three towers (6,468 per tower: 1,078 aligned wind/wave operating points × six turbulence seeds), labeled at 30 cross‑sections per tower. FLOATBench includes a regime‑aware alpha‑shape partition of the joint wind/wave operating envelope, stratifying test points into in‑train, interpolation, and extrapolation regimes. It is paired with a reproducible evaluation harness covering three protocol levels: random validation (E1), within‑tower regime‑aware evaluation (E2), and cross‑tower transfer (E3). The regime‑aware protocol reveals rank shifts between global and extrapolation performance that random‑split leaderboards cannot detect. To the authors' knowledge, FLOATBench is the first FOWT fatigue benchmark for tabular surrogate modeling, and offers an evaluation protocol that generalizes to engineering surrogates defined over physical operating envelopes. Dataset and code available at: https://github.com/Joao97ribeiro/FLOATBench.

PaperId: 664, https://arxiv.org/pdf/2605.25558.pdf GitHub

Abstract:
Optimizing the trade‑off among predictive performance and computational cost is a central focus in the deployment of Large Language Models (LLMs). Current routing methods primarily rely on direct mapping from queries to models based on surface‑level features, making them susceptible to the memorization trap and leading to poor generalizability on out‑of‑distribution (OOD) data. In this paper, we propose DecoR, a novel routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs, effectively mitigating the memorization trap. To enhance matching accuracy, we introduce a query capability deconstruction method that decouples linguistic surface forms from task‑intrinsic requirements, directing matching toward capability dimensions to ground decisions in essential task attributes. Furthermore, we develop CodaSet, a comprehensive benchmark for assessing routing generalization, where experimental results demonstrate that DecoR maintains superior accuracy while substantially lowering inference costs across both in‑distribution and OOD settings. All the codes and data are available at https://github.com/lvbotenbest/DecoR.

PaperId: 665, https://arxiv.org/pdf/2605.25420.pdf GitHub

Abstract:
Large language model safety evaluation remains heavily English‑centered, leaving low‑resource languages under‑measured even when models are deployed globally. We evaluate four open‑weight instruction‑tuned models on SomaliBench v0, a native‑author‑verified benchmark of 100 harmful‑intent prompts paired across English and Somali. Each of Llama‑3.1‑8B‑Instruct, Gemma‑2‑9B‑Instruct, Qwen‑2.5‑7B‑Instruct, and Aya‑23‑8B is run locally with temperature 0 and the same English "helpful, harmless, and honest" (HHH) system prompt. A pinned Claude Sonnet snapshot (claude‑sonnet‑4‑5‑20250929) classifies each response as refused, complied, or unclear; the native author spot‑checks a stratified 80‑row sample. We find large English‑to‑Somali refusal gaps for all four models: Llama‑3.1‑8B (0.90; 95% bootstrap CI [0.85, 0.96]), Aya‑23‑8B (0.75 [0.67, 0.83]), Qwen‑2.5‑7B (0.69 [0.59, 0.78]), and Gemma‑2‑9B (0.38 [0.27, 0.49]). For three models, the dominant Somali non‑refusal mode is not fluent harmful compliance but unclear output: empty, wrong‑language, or incoherent generations. The native verification spot‑check achieves 100% agreement with the judge (Cohen's kappa = 1.00) on the 80 sampled rows. We report aggregate refusal rates, category gaps, and reliability statistics only; raw model generations are retained locally and are not released.

PaperId: 666, https://arxiv.org/pdf/2605.25396.pdf GitHub

Abstract:
Reliable quality control (QC) of ultrasound images is essential for both real‑time acquisition guidance and retrospective clinical audit, yet existing approaches rely heavily on per‑plane annotations, or employ pseudo‑labeling prone to systematic bias under spatial deformations inherent in clinical acquisition. We present STRIQ, a registration‑driven framework that recasts annotation‑free US plane quality control as a subspace‑guided consistency measurement problem. Specifically, STRIQ introduces a Latent Registration Aligner (LRA) to establish hierarchical feature space correspondences between query images and variance‑driven anchors, which are autonomously distilled from unlabeled data via a variance spectrum criterion to serve as structurally stable prototypes. To further disambiguate anatomical planes and mitigate negative knowledge transfer, we propose an Orthogonal Knowledge Subspace (OKS) module. The OKS decomposes plane‑specific representations into mutually orthogonal subspaces, enabling fine‑grained expert collaboration while preventing inter‑plane interference, ensuring that the quality metric is grounded in principled subspace proximity. Extensive experiments on the in‑house US4QA and public CAMUS datasets demonstrate that STRIQ achieves state‑of‑the‑art correlation with clinical quality scores, establishing a new paradigm for annotation‑free, real‑time reliable ultrasound quality control. Our code is available at https://github.com/zhcz328/STRIQ.

PaperId: 667, https://arxiv.org/pdf/2605.25394.pdf GitHub

Abstract:
Large language models often generate confident but incorrect answers rather than abstaining when uncertain. This problem is particularly acute for small language models (SLMs), where computational constraints and autonomous operation amplify the need for reliable uncertainty detection. We propose _Second Guess_, a lightweight, parameter‑free prompting technique for abstention in multiple‑choice question answering (MCQA) that is well‑suited for SLMs. Our key empirical insight is that models which truly know an answer will select it consistently, while uncertain models exhibit unstable behavior when an ``I don't know'' option is added. Evaluated on four open models (2B‑8B parameters) and four benchmarks, Second Guess achieves the highest composite risk improvement of 10.81%. Notably, it maintains an 8% composite risk improvement on fine‑tuned models where entropy‑based methods degrade, and improves most for lower‑performing models. All code and results required to reproduce this work is available in https://github.com/Mystic‑Slice/second‑guess

PaperId: 668, https://arxiv.org/pdf/2605.25378.pdf GitHub

Abstract:
Customized image editing aims to equip pre‑trained diffusion models with specific visual effects using limited paired data, typically via Low‑Rank Adaptation (LoRA). As the number of desired effects grows, storing and dynamically loading numerous these effect LoRAs significantly increases deployment overhead. Furthermore, current pipelines typically cascade these effect LoRAs with acceleration modules for fast generation, which triggers severe parameter interference and results in concept bleeding and style degradation. We propose CollectionLoRA, a multi‑teacher on‑policy distillation framework capable of distilling the concepts of up to 50 different effect LoRAs along with few‑step generation capabilities into a single LoRA. This fundamentally resolves the feature interference issue and significantly reduces deployment costs. Specifically, the method introduces (i) a Probabilistic Dual‑Stream Routing mechanism that enables the model to randomly switch between data sources during training, effectively enhancing its generalization in unseen scenarios; (ii) an Asymmetric Orthogonal Prompting strategy to achieve concept isolation within the prompt space; (iii) a Coarse‑to‑Fine Distillation Objective to mitigate the distribution gap between the teacher and student models. Extensive evaluations show that CollectionLoRA distills all customized effects and few‑step generation into a single LoRA, reducing deployment overhead while achieving concept fidelity comparable to or better than independently trained teacher models. Code: https://github.com/Qwen‑Applications/CollectionLoRA

PaperId: 669, https://arxiv.org/pdf/2605.25377.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) have advanced multimodal understanding, yet their reliability is limited by hallucination, where generated content conflicts with visual facts. Existing mitigation methods either rely on costly external interventions, such as instruction tuning and retrieval, or use internal mechanisms that remain limited by flawed attention weights and entangled hidden representations. We propose Adversarial Orthogonal Disentanglement (AOD), a latent geometric framework for mitigating LVLM hallucinations. AOD learns a hallucination‑related direction through a minimax objective: a classifier concentrates hallucination signals into the projected component, while an adversary removes them from the orthogonal residual space via a Gradient Reversal Layer. The learned direction enables a training‑free dual‑forward‑pass contrastive decoding strategy that suppresses hallucinations while preserving general capabilities. Experiments on three LVLMs across four hallucination and four utility benchmarks show that AOD consistently outperforms strong baselines. It improves POPE accuracy by over 6% on average, boosts AMBER by 6%, and maintains strong performance on utility tasks such as MMMU. Further analysis shows robust transfer across datasets, suggesting that AOD captures general hallucination‑related biases rather than dataset‑specific artifacts. Our source code and datasets are available at https://github.com/Hunter‑Wrynn/AOD.

PaperId: 670, https://arxiv.org/pdf/2605.25376.pdf GitHub

Abstract:
KYA (Know Your Agents) is an open‑source, framework‑agnostic trust and governance layer for autonomous systems, composed of five primitives: (1) a four‑gate inbound apply pipeline; (2) an only‑tighten composition algebra over a three‑channel multi‑tenant hierarchy; (3) KYP (Know Your Principal), a schema‑level unification of trust scoring across human users, AI agents, and service accounts; (4) auditable interaction‑multiplier amplification over an AIVSS‑shaped additive baseline; and (5) two‑axis delegation attribution: a static premium for risky delegates and a runtime debit for actual delegate misbehavior in multi‑agent fan‑out. Together these span three pillars (trust, governance, and evidentiary assurance), making an autonomous system's actions authorized, policy‑conforming, and post‑hoc verifiable: where observability answers how long, how much, and what path, KYA answers was it authorized, did it conform, and can it be verified; it composes with observability rather than replacing it. It ships native adapters for 15+ agent frameworks. On a 4 by 9 cross‑backend matrix all 36 cells pass; the pure‑function scorer runs sub‑millisecond at p99 and the system sustains ~ 1,800 ops/sec at 20 concurrent workers with HMAC chain integrity preserved end‑to‑end. KYA detects 89% of 1,200 adversarial probes from PyRIT and Garak, including the recently‑published topology‑guided multi‑agent attack. The system is available under Apache 2.0 as the veldt‑kya package on PyPI.

PaperId: 671, https://arxiv.org/pdf/2605.25313.pdf GitHub

Abstract:
World models for partially observed environments must imagine multiple compatible hidden futures and steer between them under counterfactual actions. Joint Embedding Predictive Architectures (JEPAs) do this in latent space, but a vector‑valued latent has no internal structure for carrying the belief over hidden continuations through blind rollout. We introduce the Unitary World Model JEPA (UWM‑JEPA), a JEPA world model with a density‑matrix latent on a joint system‑environment space and a learned unitary predictor. The construction preserves the joint‑state spectrum exactly during rollout, so the predictor itself cannot dissipate the represented uncertainty. On a hidden‑velocity indicator task requiring five‑step forward simulation under a given action sequence with the target observation masked, UWM‑JEPA reaches 0.77 accuracy and degrades monotonically as actions are perturbed; a parameter‑matched LSTM‑JEPA trained under the same counterfactual‑target objective and action head collapses to majority‑class accuracy (0.53) under every action condition. Under blind rollout, UWM‑JEPA loses fewer than ten points of probe R^2 at short horizons while vector‑latent baselines lose forty‑one and sixty‑eight; both nevertheless tie on a held‑out context probe, locating the separation in the predictor rather than the encoder. Action sensitivity itself requires training against counterfactual rather than teacher‑forced targets, a finding that applies beyond the unitary parameterisation. For JEPA world models to imagine under partial observability, latent geometry and predictor dynamics matter, not frozen context‑encoding capacity alone.

PaperId: 672, https://arxiv.org/pdf/2605.25250.pdf GitHub

Abstract:
Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are both effective and biologically safe remains a major bottleneck. In practical screening, toxicity is a decision‑level constraint: if a lipid is toxic, its efficiency prediction is clinically irrelevant. We propose LipoAgent, a safety‑aware multi‑agent LLM framework for lipid discovery. LipoAgent combines domain‑specific finetuning with a conditional prediction objective that enforces toxicity as a prerequisite for efficiency prediction, and further improves reliability via multi‑agent verification with lightweight human oversight when disagreement persists. Across multiple foundation models, LipoAgent achieves an average 32% relative improvement in mRNA transfection efficiency prediction compared with other reported models for lipid design. Wet‑lab validation confirms that virtual screening rankings reliably translate to biological transfection outcomes. The code is publicly available at https://github.com/SAI‑Lab‑NYU/LipoAgent.git.

PaperId: 673, https://arxiv.org/pdf/2605.25235.pdf GitHub

Abstract:
We give an attribution method for neural combinatorial‑optimisation (CO) policies that (i) decomposes a decision by constraint families via LP‑relaxation duals, (ii) certifies counterfactuals through a combinatorial feasibility model (implemented as a CSP feasibility‑decision model), and (iii) bounds the size of a PAC‑sufficient explanation with a Bonferroni‑corrected Hoeffding sufficient‑subset test along a greedy ordering. Across three CO problems and three seeds, our LP‑anchored Λ‑attribution matches the CF‑derived signal at 96.5% on CVRPTW (n_cert=344) and 77.2% on the Orienteering Problem (n_cert=281) vs 75.0% and 35.2% for proxy gradient (paired diffs +0.215 and +0.420; McNemar exact p \le 10^‑14). In the rank‑aligned regime of the Flexible Job‑Shop Scheduling Problem, both backends agree on every CSP‑certified flip (n_cert=59), confirming the no‑gain prediction. Bonferroni‑PAC subsets average 5.0 nodes per step (M=70, \varepsilon=δ=0.2, k_\max=25). Reference implementation: https://github.com/sohaibafifi/neuro‑co‑cax

PaperId: 674, https://arxiv.org/pdf/2605.25203.pdf GitHub

Abstract:
We apply the influence‑adaptive Walsh geometry of a companion theory paper (arXiv:2605.01637) to extreme low‑bit weight‑only LLM quantization. The recipe is one math‑invariant transformation: WHT‑rotate each linear layer's weight matrix and rescale its columns by per‑coordinate Walsh‑basis activation energy before handing off to a reconstruction‑error quantizer (Intel auto‑round). This biases per‑group integer rounding toward high‑spectral‑energy channels. On four pretrained decoder‑only models from 135M to 1.5B parameters, BBT‑spectral reduces wikitext‑2 perplexity by 15‑58% relative to vanilla auto‑round at W2A16; we also report a TinyLlama‑1.1B auxiliary data point. Three extensions transfer the recipe to families it failed on: a per‑head PCA matrix‑Gamma replacement of q_norm/k_norm for Qwen3 attention (PPL 136.76 ‑> 88.99 on Qwen3‑0.6B); an SO(2) per‑pair rotation that commutes with RoPE (PPL 36.93 ‑> 21.84 on Qwen2.5‑1.5B); and an MoE‑aware input‑side absorption fix identified by architectural fuzzing of Laguna‑style fused‑expert layouts. A W2‑vs‑W4 ablation gives a deliberate negative control: the redistribution payoff falls within the +/‑0.5 PPL noise floor at W4, consistent with the Schur‑convexity intuition that the cost of unconcentrated influence vanishes as the noise budget shrinks. All quantized weights export to OpenVINO IR and run on Intel NPU + Arc dGPU + CPU with PPL invariant to device within +/‑0.1. We do not claim a formal Boolean‑to‑real‑valued transfer of the theory paper's majorization argument: the WHT activation energy used here is not the Boolean influence of the theory paper, the link is intuitive, and the contribution is engineering value rather than a transferred theorem. Head‑to‑head benchmarks against SpinQuant, QuaRot, QuIP‑sharp, AQLM, OmniQuant, and ButterflyQuant at matched calibration are the main future‑work item.

PaperId: 675, https://arxiv.org/pdf/2605.25198.pdf GitHub

Abstract:
Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning‑intensive tasks, but its effectiveness is often limited by exploration. For example, models often fail on hard problems, leaving little useful reward signal. External expert traces offer a natural source of guidance, yet they may also expose reward‑relevant content along the critical path to the verifier target, such as final answers, intermediate values, executable implementations, or answer‑related entities. This content can create an unintended reward hacking channel, allowing the policy to obtain reward by copying the trace rather than learning the underlying reasoning or agentic behavior. Existing guided‑RL methods reduce this risk by using partial trajectories, but they mainly control how much expert information is shown heuristically rather than which parts should be hidden. To this end, we propose Semantic Masked Expert Policy Optimization (SMEPO), a fine‑grained semantic masking strategy for expert‑guided RLVR. Instead of truncating traces coarsely or revealing them unchanged, SMEPO masks reward‑relevant semantic spans along the critical path while preserving the expert's decomposition, plan, and procedural structure. This turns hard problems from reasoning from scratch into a fill‑in‑the‑blank process: the policy can follow the expert's problem‑solving route, but must still reconstruct the missing values, code, or entities by itself. SMEPO is simple to apply and requires no changes to the reward function or RL objective. Across diverse domains, including math, code, and agentic search, SMEPO improves accuracy by up to 3.2 points over GRPO and reduces training time by up to 4.2x. The code is available at https://github.com/mit‑han‑lab/SMEPO.

PaperId: 676, https://arxiv.org/pdf/2605.25183.pdf GitHub

Abstract:
Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in‑depth reasoning. Prior work has leveraged KGs to fine‑tune language models (LMs), enabling domain‑specific superintelligence. In this work, we explore whether KG‑driven in‑depth reasoning capabilities can emerge in neuroscience using only information contained within a single authoritative textbook. The central hypothesis is that structured knowledge, when distilled into a high‑quality KG and converted into KG‑grounded question‑answer (QA) supervision, is sufficient to produce expert‑level reasoning through a fine‑tuned LM that surpasses large language models (LLMs) in accuracy, while employing orders of magnitude fewer parameters. We construct a textbook‑derived KG via a dual‑LLM validation pipeline, expand it with a masked LM trained on the KG topology, generate multi‑hop QA items, which include QA pairs and reasoning traces, to fine‑tune an LM exclusively on KG‑derived supervision, and apply reinforcement learning using path‑derived KG signals as implicit reward models. Our results demonstrate that deep, mechanistic neuroscience understanding can be induced in the model without reliance on large, heterogeneous web‑scale corpora. The KG‑based synthetic neuroscience curriculum that readers can quiz themselves on, and the fine‑tuned LM, are available at the following GitHub location: https://kg‑bottom‑up‑superintelligence.github.io/neuro‑bench.

PaperId: 677, https://arxiv.org/pdf/2605.25162.pdf GitHub

Abstract:
Large language models for vertical domains are bottlenecked by the scarcity of complex, domain‑specific task‑oriented dialogues. Existing data acquisition pipelines face a persistent trilemma: expert annotation is expensive, real‑world service conversations are constrained by privacy and commercial restrictions, and static corpora quickly become temporally stale. We propose Stream, a data‑centric framework that leverages publicly available streaming media (live streams and short videos) to synthesize high‑value service dialogues at scale. Stream mines authentic interaction signals from noisy streams and synthesizes conversations by integrating role‑grounded persona construction with Conversational Blueprint construction; it further adopts retrieval‑augmented generation (RAG) to support knowledge‑aware responses. Based on Stream, we release StreamDial, a large‑scale multi‑domain dataset covering Automotive, Restaurant, and Hotel. StreamDial contains 87,498 dialogue sessions and 1,497,320 turns in total, with an average of 17.11 turns per session and a comparable scale across domains. Each session is organized as a structured quadruplet \langle P_u, P_a, B, H \rangle that pairs dialogue history with explicit user/agent personas and a Conversational Blueprint, capturing realistic service behaviors such as requirement mining, constraint conflicts, negotiation, and recovery. Evaluations with automatic judges and downstream tasks show that StreamDial improves intrinsic dialogue quality over strong baselines, and models trained with StreamDial improve Dialogue State Tracking across backbones; we further report a completed human‑evaluation set and encouraging multilingual transfer on Qwen3‑8B under a controlled training budget. The data is released in https://github.com/hitxueliang/DialogDataSetBySTREAM.

PaperId: 678, https://arxiv.org/pdf/2605.25058.pdf GitHub

Abstract:
Current AI interaction models treat the prompt as the primary object of exchange, omitting a critical layer: the user's latent source intent, the goal state preceding and motivating the prompt. Here we introduce Intent Signal Theory (IST), a computational framework that formalises this missing intent layer. IST distinguishes four objects routinely conflated: latent source intent (I), observable intent proxy (I‑hat), encoded carrier (P), and model output (O). It formalises dimensional weights, encoding masks, structural and fidelity recovery scores, and public‑private intent decomposition. The Theorem of Irreversible Intent Loss establishes that private intent absent from the carrier cannot be recovered beyond generic substitution. Evidence from four companion studies spanning six LLMs, three languages and three task domains shows structural‑fidelity splits, human‑validated metric dissociation, and weight‑tolerance plateaus consistent with IST's predictions. IST reframes prompt engineering as intent‑protocol design and identifies a computational layer that current AI systems lack.

PaperId: 679, https://arxiv.org/pdf/2605.25046.pdf GitHub

Abstract:
YOLO‑series and DETR‑based detectors struggle with tiny‑object detection. YOLO‑style models benefit from efficient dense prediction, but their large‑stride backbones may suppress tiny instances in deep feature maps and make grid assignment ambiguous. DETR‑based models remove hand‑crafted post‑processing through set prediction, yet they reason over coarse token grids, where tiny objects occupy only a few weak tokens and are easily overlooked during matching. To address these limitations, we propose TinyFormer, a unified YOLO‑‑DETR hybrid real‑time detector that combines ViT representations, NMS‑free set prediction, and a YOLO‑style pyramid neck for accurate small‑object detection. TinyFormer introduces a Parallel Bi‑fusion Module (PBM), which builds high‑resolution shortcuts from shallow stages to the feature pyramid, preserving fine spatial details during multi‑scale fusion. We further design a Spatial Semantic Adapter (SSA) to compensate for the spatial loss caused by coarse tokenization. SSA extracts high‑resolution cues from early stages and injects them into transformer token embeddings, improving tiny‑object localization without sacrificing the global modeling ability of DETR. Experiments on MS COCO show that TinyFormer consistently outperforms recent YOLO‑series detectors and the strong DEIMv2 baseline. TinyFormer‑X achieves 58.4% AP even without PBM, while adding PBM improves the overall AP to 58.5% and brings a 1.6% AP gain on small objects. With Objects365 pre‑training, TinyFormer‑X‑PBM reaches 60.2% AP, surpassing RF‑DETR and other Objects365‑pretrained detectors with fewer parameters and lower computation. These results demonstrate that TinyFormer bridges dense YOLO‑style feature fusion and DETR‑style set prediction, providing a strong accuracy‑efficiency trade‑off for real‑time tiny‑object detection. Code is available at https://github.com/mmpmmpmmpjosh/TinyFormer.

PaperId: 680, https://arxiv.org/pdf/2605.25045.pdf GitHub

Abstract:
Time series research is moving beyond fixed forecasting benchmarks toward realistic tasks that combine prediction, contextual reasoning, tool use, and structured decision support. Most benchmarks are built around clean data and short evaluation loops; agents alone may miss temporal constraints, evidence checks, or review before finalizing outputs. We first formalize next‑generation time series tasks as three‑component tuples consisting of a task file, a workspace, and a validation interface. We then present AION, a time series harness built from six component groups: agents, skills, rules, memory, evaluation, and protocols. In this harness, we use three design principles: temporal grounding, temporal knowledge‑grounded reasoning, and reliability mechanisms such as post‑experiment analysis and layered review. A Kaggle Store Sales case study shows that the harness produces more detailed process traces, more artifacts, and more review steps than the same base agent operating in OpenCode direct build mode. Taken together, these results argue for a paradigm shift from fixed tasks to realistic ones under real‑world constraints.

PaperId: 681, https://arxiv.org/pdf/2605.25036.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) extend large language models with visual understanding, but remain vulnerable to hallucination, where outputs are fluent yet inconsistent with images. Recent studies link this issue to language bias‑the tendency of LVLMs to over‑rely on text while neglecting visual inputs. Yet most analyses remain empirical without uncovering its underlying cause. In this paper, we provide a systematic study of language bias and identify its root in modality misalignment during training. Our analysis shows that both Visual Instruction Tuning (VIT) and Direct Preference Optimization (DPO) often prioritize textual improvements, which may cause LVLMs to overly lean toward language modeling rather than balanced multimodal understanding. To address this, we propose two simple yet effective methods: Language Bias Regularization (LBR) which mitigates language bias through regularization during instruction tuning, and Language Bias Penalty (LBP), which penalizes language bias in the DPO training process. Extensive experiments across diverse models and benchmarks demonstrate the effectiveness of our approach. LBR consistently improves performance on over ten general benchmarks, while LBP significantly reduces hallucination and improves trustworthiness. Together, these methods not only mitigate language bias but also advance the overall alignment of LVLMs, all without introducing any additional data or auxiliary models. Our code is publicly available at https://github.com/lab‑klc/LVLM‑Language‑Bias.

PaperId: 682, https://arxiv.org/pdf/2605.24999.pdf GitHub

Abstract:
An agent must act on the situation before it, learn what it cannot yet represent, and model other agents well enough to coordinate. These faculties are usually realized by separate mechanisms, yet they share a failure mode: the situation can exceed what the agent can currently represent, and the honest response is then a principled refusal that says what was missing. We develop a small cognitive architecture in which these limits arise from a single quantity. An Interpretation‑Decision Unit (IDU) interprets a content vector through a family of regimes ‑ local representational frames with private bases ‑ and decides which actions it licenses; a scalar residual of the content against the active regimes' representational scope drives the unit. Low residual with a clean licensing emits an action; otherwise the unit re‑interprets, attempts a description‑length‑justified expansion, or halts with a typed, witnessed terminal. We prove the unit is total and deterministic: for any content and fixed configuration it halts in finitely many bounded‑cost steps with a unique terminal witness, so abstention carries its cause by construction. By binding the architecture's open parameters without changing its mechanics, the same residual‑against‑scope constraint recovers three documented phenomena at three scopes: the typology of not‑knowing (typed abstention); a forced misunderstanding between agents, localized to one shared concept and invisible to the agent committing it (bounded empathy); and prerequisite dependence in learning derived from a bounded focus window rather than posited (developmental prerequisites). Each instantiation is worked for a natural and an artificial agent and states a falsifiable prediction, so one constraint can model limits in both human and machine cognition. The account contributes a unification and a notion of accountable abstention, typed and witnessed by construction.

PaperId: 683, https://arxiv.org/pdf/2605.24973.pdf GitHub

Abstract:
VLM‑based OCR models have become the de facto choice for document parsing, as they can accurately extract page‑level elements (e.g., paragraphs within individual pages) together with their bounding boxes and textual content. However, downstream applications such as RAG require coherent document‑level information, whereas these models often break cross‑page continuity and fail to recover disrupted structures, such as paragraphs and tables truncated by page boundaries. Such relationships are not confined to a single page; instead, they require joint analysis of titles, paragraphs, tables, and images spanning multiple pages. A natural solution is therefore to reuse existing OCR outputs and reconstruct document‑level logical structures through post‑processing. To this end, we propose MinerU‑Popo, a lightweight and universal framework for POst‑Processing OCR outputs, which converts page‑level results from diverse parsers into coherent document‑level structures. MinerU‑Popo decomposes the problem into four focused subtasks: text truncation recovery, table truncation recovery, title hierarchy reconstruction, and image‑text association. To address these effectively, we build a task‑oriented data engine with task‑specific input filtering, and use the generated data (30K) to fine‑tune a lightweight post‑processing model (Qwen3‑VL‑4B). To support long documents, we introduce dynamic chunking with overlap‑based synchronization, which aligns chunk‑level outputs from the fine‑tuned model and preserves global consistency. Finally, we assemble the aligned outputs into a tree‑structured document representation, further enriched with node chunking and summaries for downstream retrieval and analysis. Empirical results show MinerU‑Popo improves title‑hierarchy TEDS by at least 20% across all five tested OCR models, improves RAG accuracy and reduces per‑query latency.

PaperId: 684, https://arxiv.org/pdf/2605.24965.pdf GitHub

Abstract:
The rapid evolution of generative models has enabled the creation of hyper‑realistic facial deepfakes, exposing a critical vulnerability in modern digital forensics: the inability of detectors to generalize to unseen manipulation techniques. Traditional networks suffer from representation collapse, overfitting to localized artifact fingerprints of specific training generators. This work investigates whether modern Vision Foundation Models can serve as generalizable, out‑of‑the‑box feature extractors capable of tracking forensic anomalies across entirely unseen generative manifolds. We conduct a systematic cross‑domain evaluation comparing three foundational learning paradigms: fully supervised macro‑semantic features (RoPE‑ViT), pure self‑supervised geometric features (DINOv3), and multi‑teacher agglomerative representations (NVIDIA C‑RADIOv4‑H). By deploying frozen backbones subjected to downstream linear probing, we map the performance limitations of these architectures on the challenging DF40 benchmark. Our empirical findings expose the intrinsic trade‑offs between pre‑training paradigms and parameter scale, proving that while foundation models retain high discriminative capabilities for entire face synthesis, localized face editing techniques expose fundamental boundaries in linear probe evaluation structures. Source code and model weights are available in http://github.com/mribrahim/deepfake

PaperId: 685, https://arxiv.org/pdf/2605.24945.pdf GitHub

Abstract:
Accurate evaluation of weather forecasting models is critical for their reliable deployment in real‑world applications. However, existing benchmarks predominantly rely on reanalysis products such as ERA5, which are generated through delayed data assimilation and do not reflect the constraints of real‑time operational forecasting, thereby resulting in a systematic mismatch between benchmark performance and real‑world forecasting. In this work, we introduce RealBench, a next‑generation benchmark for AI weather forecasting that emphasizes realistic evaluation under operational conditions. RealBench features a strictly out‑of‑distribution test set spanning 2025 to eliminate data leakage and capture recent atmospheric regimes. It integrates multiple data sources, including low‑latency operational analysis and a large‑scale global in‑situ observation dataset comprising over 10,000 stations, enabling direct evaluation against real atmospheric measurements. Beyond standard global metrics, RealBench provides a comprehensive evaluation framework for high‑impact extreme events, including heatwaves, cold surges, and tropical cyclones, using event‑specific metrics that better reflect real‑world forecasting priorities. The evaluation results reveal substantial discrepancies between reanalysis‑based metrics and real‑world performance, particularly concerning extreme events. By highlighting the limitations of existing benchmarks, this work establishes a more faithful and operationally relevant evaluation paradigm, providing a rigorous foundation for advancing next‑generation AI weather forecasting systems. The benchmark implementation is available at: https://github.com/lixruize‑del/NWP‑Benchmark.

PaperId: 686, https://arxiv.org/pdf/2605.24938.pdf GitHub

Abstract:
Multimodal retrieval relies heavily on single‑vector retrievers, which compress rich, sequential token sequences into one single global representation. While efficient, they discard fine‑grained, local evidence critical for dense retrieval tasks. Multi‑vector approaches were introduced as a solution, but they strictly require training and many ignore the necessity of a globally summarizing representation. To address this, we introduce SMART, a framework that unlocks the latent multi‑vector capabilities of standard single‑vector models. We first demonstrate that standard contrastive training on the pooled embedding implicitly shapes the retrieval geometry of preceding hidden states via gradient flow. By applying direct late‑interaction over these frozen hidden states during inference, SMART acts as a plug‑and‑play upgrade that consistently improves performance across diverse modalities, improving even the state‑of‑the‑art models further on MMEB‑V2. We also reveal SMART's superior performance, as simple lightweight post‑training not only saves time and compute, but also brings forth further improvement on Visual Document retrieval, allowing a single‑vector model to outperform SoTA multi‑vector counterparts. Ultimately, SMART offers both a highly efficient inference enhancement and a powerful finetuning technique for multimodal retrieval. We open source our code and weights at https://github.com/HanSolo9682/SMART.

PaperId: 687, https://arxiv.org/pdf/2605.24934.pdf GitHub

Abstract:
Human egocentric video captures rich manipulation demonstrations without any robot hardware, yet transferring these skills to robots remains challenging due to the embodiment gap between human and robot in both visual appearance and kinematics. We present HumanEgo, a framework that bridges the embodiment gap by lifting each human demonstration to an entity‑level representation of hand‑object interaction, and training a flow matching policy with dense auxiliary objectives that amplify supervision from every trajectory. HumanEgo is robot‑data‑free, hardware‑agnostic, data‑efficient, and zero‑shot human‑to‑robot transferable. With only 30 minutes of human videos per task, HumanEgo achieves 92.5% average success across four real‑world tasks (75% with just 15 minutes), outperforms matched‑time robot teleoperation by 41%, and robustly transfers zero‑shot across novel robots, cameras, and environments. We release HumanEgo as an easy‑to‑use, open‑source framework for learning robot policies directly from human data: https://github.com/TX‑Leo/HumanEgo

PaperId: 688, https://arxiv.org/pdf/2605.24912.pdf GitHub

Abstract:
Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic, renal, lipid, and inflammatory pathways. Existing clinical assessments often fail to capture this multi‑dimensional burden. Methods: We conducted a retrospective study of 1,195 patients using routinely collected laboratory biomarkers. System‑level abnormality indices were constructed to quantify organ‑specific dysfunction, and multi‑system involvement was defined as abnormalities in two or more systems. Supervised machine learning models, including logistic regression, random forest, and gradient boosting, were trained to predict multi‑system dysregulation. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Results: The gradient boosting model demonstrated near‑perfect discrimination (AUC = 1.000), significantly outperforming logistic regression (AUC = 0.925). Feature attribution analysis revealed that hyperglycaemia, renal impairment, dyslipidaemia, and inflammation were the dominant drivers of multi‑system risk. Dose‑response relationships observed in partial dependence analyses further supported the biological plausibility of model predictions. Conclusion: This study presents an interpretable, data‑driven framework for quantifying systemic disease burden in T2DM. By linking routine biomarkers to multi‑organ dysfunction, our approach provides both predictive accuracy and mechanistic insight, offering potential for improved risk stratification and precision medicine in diabetes care. The data and code used in this study are openly available on GitHub at: https://github.com/MiniHanWang/Type‑2‑Diabetes‑1.git

PaperId: 689, https://arxiv.org/pdf/2605.24883.pdf GitHub

Abstract:
The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on constructed benchmarks to assess safety from predefined perspectives, or employ dynamic red‑teaming to probe potential vulnerabilities. While effective, these approaches face challenges, as they depend heavily on expert domain knowledge, offer limited systematic guarantees, and are vulnerable to rapid obsolescence. To address these limitations, we introduce a novel framework POLARIS that brings the rigor of specification‑based software testing to AI safety. POLARIS first compiles unstructured natural‑language policies into First‑Order Logic (FOL) representations, establishing a traceable link between high‑level rules and concrete test cases. This formalization enables the construction of a Semantic Policy Graph, where complex policy violation scenarios are encoded as traversable paths. By systematically exploring this graph, POLARIS uncovers compositional violation patterns, which are then instantiated into executable natural‑language test queries, enabling coverage‑driven and reproducible safety testing. Experiments demonstrate that POLARIS achieves higher policy coverage and attack success counts compared to established baselines. Crucially, by bridging formal methods and AI safety, POLARIS provides a principled, automated approach to ensuring LLMs adhere to safety‑critical policies with verifiable traceability. We release our code at https://github.com/huac‑lxy/POLARIS.

PaperId: 690, https://arxiv.org/pdf/2605.24856.pdf GitHub

Abstract:
Concept formation in transformer language models is depth‑extended, not a single‑layer event: concepts emerge gradually across a contiguous region of the residual stream. Mechanistic interpretability methods identify the single layer of peak class separation ‑‑ the "best layer" ‑‑ capturing a snapshot rather than the process itself. We introduce the Concept Allocation Zone (CAZ): the depth interval within which a concept becomes measurably separable, the region allocated to its geometric expression. We formalize the CAZ through three layer‑wise metrics (Separation, Concept Coherence, Concept Velocity) and derive principled boundary detection without manual layer sweeps. A CAZ is not a concept: it is the depth region within which the model organizes its geometry to make a concept separable. A single concept typically participates in multiple CAZes; multiple concepts may share one. Empirical validation across 34 models from 8 architectural families and 7 concepts reveals that the separation curve S(l) is frequently multimodal. A scored detector uncovers "gentle CAZes" ‑‑ subtle allocation regions invisible to standard peak detection but causally active in 93‑100% of cases under ablation (16 of 34 models; 26 in the companion validation paper). The framework generates seven testable predictions; four yield clear verdicts (two not supported, one partially supported, one supported), one had its precondition invalidated by the data, and two are underpowered ‑‑ with cross‑architecture alignment confirmed as depth‑matched rather than monolithic under leave‑one‑concept‑out cross‑validation. Reference implementation: rosetta_tools v1.3.1 (doi:10.5281/zenodo.20361433).

PaperId: 691, https://arxiv.org/pdf/2605.24843.pdf GitHub

Abstract:
Visual Autoregressive (VAR) models have emerged as a powerful paradigm for image synthesis by performing hierarchical next‑scale prediction. However, VAR models are inherently prone to cascading error propagation, where subtle coarse‑scale mispredictions are amplified across the hierarchy, ultimately distorting the final synthesis. To mitigate this, we propose AID‑VAR, a plug‑and‑play framework that enhances pre‑trained VARs through Adversarially Injected Diagnosis. Instead of a standard passive generation, AID‑VAR introduces a proactive error‑correction mechanism inspired by the adversarial feedback in GANs. We deploy a discriminator to diagnose fidelity gaps at each scale transition, coupled with a lightweight guidance injector. This module operates as a non‑invasive adapter that refines the feature manifold of a frozen VAR backbone, effectively steering the generation toward the distribution of real images without destabilizing the pre‑trained latent space. Furthermore, to rigorously evaluate this cross‑scale progression, we introduce the Inter‑Scale Consistency Score (ISCS), a novel metric that quantifies the fidelity and structural alignment between consecutive resolution scales. Experimental results across various backbones demonstrate that AID‑VAR delivers sharper textural details and fewer structural distortions with negligible overhead. For instance, AID‑VAR‑d20 achieves a 16% improvement in FID with only a 3% increase in parameters. These results establish AID‑VAR as a highly efficient and scalable pathway for upgrading large‑scale VAR generators, enhancing global coherence and local detail without altering training data, base architectures, or sampling schedules. Code is available at https://github.com/bijiw515/AID‑VAR.

PaperId: 692, https://arxiv.org/pdf/2605.24775.pdf GitHub

Abstract:
Operating LLMs as coordinated multi‑agent research systems over multi‑hour runs surfaces failure modes that single‑shot evaluation cannot: upstream providers throttle without warning, sub‑agents drift the task to fit accessible tools, narrate machinery instead of using it, open revision iterations with self‑apology, or treat upstream context as executable directives. We present PRIMA, whose primary contributions are three operational patterns for surviving these failure modes: (1) a resilience‑and‑recovery layer that detects upstream rate‑limit signals, persists a typed pause record to disk, and resumes long‑running runs without re‑executing converged work even across process restarts; (2) a sub‑agent operating discipline encoding task‑fidelity, tool‑use, revision, and inter‑step context‑boundary norms as a structural prompt layer; (3) a multi‑phase application pattern for structured engineering deliverables pairing orthogonal draft steps with an explicit cross‑document harmonization pass before final synthesis. These sit atop a foundational protocol: a research‑program specification language with explicit convergence criteria, a dual‑metric scoring engine (LLM‑judged rubric plus sandboxed code), an outer meta‑optimization loop, event‑driven persistence, hook‑based middleware, context compaction, and a multi‑provider LLM abstraction. Agent identities derive from prime powers, giving collision‑free identifiers and trivially‑verifiable cluster membership without a central registry. Theoretical guarantees include O(k) verification, O(V+E) DAG validation, and identity collision freedom by the Fundamental Theorem of Arithmetic. A Graph Isomorphism case study grounds the architectural claims in a generated artifact: a six‑step protocol that produced a research paper proposing a new canonical‑form algorithm with three theorems and five conjectures.

PaperId: 693, https://arxiv.org/pdf/2605.24754.pdf GitHub

Abstract:
Neural network weights are increasingly a bottleneck for deployment, yet most compression pipelines treat layers independently and overlook cross‑layer redundancy induced by function‑preserving symmetries. We propose Motion‑Compensated Weight Compression (MCWC), a weight‑only codec that aligns permutation‑symmetric blocks (e.g., hidden units and attention heads) to maximize cross‑layer correspondence, turning depth into a predictable sequence. In the aligned coordinate system, MCWC uses a lightweight layer‑sequential predictor with periodic keyframes and encodes only quantized prediction residuals using a learned entropy model trained under a rate distortion objective. A simple decoder reconstructs deployable weights by entropy decoding, dequantization, predictor‑driven reconstruction, and inverse alignment, enabling fast weight materialization for inference. Across Transformer language modeling and vision classification, MCWC improves the rate accuracy Pareto frontier over strong quantization and learned weight‑codec baselines, while maintaining competitive decode time. Ablations confirm that alignment, prediction, entropy modeling, and keyframe scheduling are each necessary for the full gains. Our code is available via https://github.com/Ism‑ail11/MCWC.

PaperId: 694, https://arxiv.org/pdf/2605.24697.pdf GitHub

Abstract:
Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden control problem: which proposed tokens should be transferred into the partially decoded sequence at each step? We refer to this decision as token commitment. Existing frozen‑generator decoders largely rely on hand‑designed confidence rules or block‑specific acceptance filters. We argue that token commitment can instead be learned as a reusable trace‑state policy. We introduce TraceLock, a lightweight plug‑in controller that instantiates this policy for a frozen diffusion language model. Since oracle commitment times are unavailable, TraceLock derives self‑supervision from future stability: at decoding step t, a proposed token for position i is labeled stable if it matches the final token at position i after the full decoding trace completes. The controller scores variable‑length trace states and decides which active token proposals should be committed to the partially decoded sequence. Once trained for a given frozen backbone, the controller can be deployed across local‑window widths, generation lengths, and step budgets without retraining or per‑setting calibration. Experiments on question answering, mathematical reasoning, and code generation show that TraceLock improves the quality‑step tradeoff over heuristic and learned baselines, with particularly stable behavior under cross‑setting deployment. Diagnostic analyses show that its decisions are not reducible to scalar confidence, suggesting that frozen diffusion language models expose a learnable space of commitment trajectories beyond confidence‑based decoding. Code is available at https://github.com/BobSun98/TraceLock.

PaperId: 695, https://arxiv.org/pdf/2605.24687.pdf GitHub

Abstract:
Text‑to‑Image (T2I) models have made significant strides in visual realism and semantic consistency, yet they often perpetuate and amplify societal biases. Existing evaluation methods typically address only single‑dimensional biases, lacking perspectives to uncover model biases at social‑related deeper semantic levels. We introduce HoloFair, a comprehensive benchmark framework for multidimensional demographic bias analysis. Built upon our large‑scale fairness‑oriented dataset and the SpaFreq (Spatial‑Frequency) attribute classifier, this framework proposes the Multi‑attribute, Group‑wise Bias Index (MGBI) metric, designed to assess both intrinsic diversity and conditional biases. Beyond evaluation, we further introduce Fair‑GRPO, a reinforcement‑learning‑based debiasing method that alters the distribution of generative models through a designed multi‑objective reward function. E.g., experiments on the SD3.5‑Medium model demonstrate that Fair‑GRPO significantly improves multidimensional fairness while maintaining high image quality. We also analyze potential reward hacking phenomena and provide corresponding mitigation strategies. Code and dataset are available at https://github.com/1059684669/HoloFair

PaperId: 696, https://arxiv.org/pdf/2605.24631.pdf GitHub

Abstract:
Minority sampling aims to generate low‑density instances on a data manifold and is of central importance in applications such as medical diagnosis, anomaly detection, and creative AI. Existing approaches, however, define minority samples relative to generative priors learned from training data, confining rarity to model‑specific notions that may poorly reflect real‑world semantics. In this work, we propose a world‑centric perspective on minority sampling, which defines rarity with respect to real‑world priors rather than generator‑induced densities. To this end, we introduce JEPA guidance, a diffusion sampling framework guided by a Joint‑Embedding Predictive Architecture (JEPA) ‑‑ a class of world models that encode broad, semantically rich representations. JEPA guidance steers diffusion trajectories toward low‑density regions under the implicit density induced by the JEPA, thereby aligning generated minorities with real‑world semantic rarity. To make JEPA guidance computationally practical, we develop principled approximation strategies accompanied by theoretical error bounds, significantly reducing the overhead of guidance computation. Extensive experiments across unconditional, class‑conditional, and text‑to‑image generation demonstrate that JEPA guidance consistently improves the fidelity and semantic validity of minority samples, outperforming generator‑centric baselines in capturing real‑world notions of rarity. Code is available at https://github.com/soobin‑um/jepa‑guidance.

PaperId: 697, https://arxiv.org/pdf/2605.24614.pdf GitHub

Abstract:
Large language model (LLM) unlearning has emerged as a crucial post‑hoc mechanism for privacy protection and AI safety, yet auditing whether target knowledge is truly erased remains challenging. Existing output‑level metrics fail to detect when this knowledge remains recoverable from internal representations. Recent white‑box studies reveal such residual knowledge but often rely on auxiliary training or dataset‑specific adaptations, leaving no generalizable metric. To address these limitations, we propose the Unlearning Depth Score (UDS), a metric that quantifies the mechanistic depth of unlearning via activation patching. UDS first identifies layers that encode the target knowledge using a retain model baseline, then measures how much of it is erased in the unlearned model on a 0‑1 scale. In a meta‑evaluation across 20 metrics on 150 unlearned models spanning 8 methods, UDS achieves the highest faithfulness and robustness, confirming our causal approach as the most reliable for unlearning evaluation. Case studies further reveal that white‑box metrics can disagree at the layer level and that erasure depth varies across examples. We provide guidelines for integrating UDS into existing benchmarking frameworks and streamlining the evaluation pipeline. Code and data are available at https://github.com/gnueaj/unlearning‑depth‑score

PaperId: 698, https://arxiv.org/pdf/2605.24613.pdf GitHub

Abstract:
Post‑hoc repair of LLM mathematical reasoning introduces an asymmetric risk: fixing an incorrect reasoning trace is useful, but replacing a trace that was already correct can be harmful. We study this problem under a selective replacement setting, where a system must decide whether a repaired candidate is safer than preserving the original cached trace. We present GuardedRepair, a guarded best‑of‑N repair framework that diagnoses cached reasoning traces, selectively triggers repair, and accepts answer‑changing candidates only when deterministic verification guards support replacement. The framework combines lightweight symbolic checks, surface semantic‑risk diagnostics, bounded candidate generation, and conservative acceptance policies. On the full GSM8K test set, where the initial reasoner already achieves 95.60% accuracy, GuardedRepair improves final accuracy to 96.89%, fixing 17 of 58 remaining errors without measured broken‑correct cases in the main run. On a weak‑reasoner ASDiv setting, accuracy improves from 78.40% to 87.60%. Direct regeneration baselines show that this gain is not explained by stronger‑model re‑solving alone: re‑solving all GSM8K examples lowers accuracy to 93.03% and breaks 47 initially correct answers. Additional analyses show that guarded repair substantially improves the fixed/broken tradeoff, while also revealing that replacement risk is reduced rather than eliminated. These results support viewing post‑hoc repair as harm‑aware selective replacement rather than unconstrained re‑solving.

PaperId: 699, https://arxiv.org/pdf/2605.24570.pdf GitHub

Abstract:
Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training begins. This static design can limit their ability to respond to changing gradient behavior across the loss landscape, where training may shift between stable, noisy, and inconsistent regimes. This study proposes PILOT (Policy‑Informed Learned OpTimizer), an online optimizer that adapts its update behavior during training. Rather than using a fixed balance between momentum, normalization, and sign‑based updates, PILOT uses gradient‑direction agreement as a signal of local training stability. Conditioning the update rule on this agreement signal allows the optimizer to adjust its behavior when gradients become stable, noisy, or inconsistent. Experiments on FashionMNIST and CIFAR‑10 show that PILOT consistently achieves the highest accuracy among the evaluated optimizers across convolutional settings. On the CNN architecture, PILOT reaches 94.13% on FashionMNIST and 81.94% on CIFAR‑10. On ResNet‑18, it further improves performance, reaching 95.71% on FashionMNIST and 93.42% on CIFAR‑10. These results suggest that learning how to adapt the update structure during training can improve performance across both compact and deeper convolutional models while preserving a simple first‑order optimization framework. The implementation of PILOT is publicly available at https://github.com/SattamAltwaim/PILOT.git

PaperId: 700, https://arxiv.org/pdf/2605.24518.pdf GitHub

Abstract:
The quadratic complexity of self‑attention in Transformer models remains a significant bottleneck for processing long sequences and deploying large language models efficiently. For this approach, there has been significant research into Sparse Attention, and Deepseek Sparse Attention has combined various methods of creating segments of tokens to reduce the time complexity. This paper introduces a novel approach, Grammatically‑Guided Sparse Attention, which constrains attention computations based on the grammatical roles of tokens. By leveraging Parts‑of‑Speech (POS) tags, attention masks are dynamically generated that enforce linguistically coherent connections between tokens, reducing the computational graph without sacrificing essential linguistic dependencies. Two masking strategies are proposed and evaluated: a hard mask that strictly allows only predefined grammatical interactions, and a soft mask that biases attention towards these interactions. The experiments, conducted on the SST‑2 sentiment classification task using a DistilBERT‑like architecture, demonstrate that Grammatically‑Guided Sparse Attention maintains comparable accuracy to full attention while significantly reducing the theoretical computational overhead. Preliminary results show accuracy values of 0.8200 for hard masking and 0.8165 for soft masking, closely matching the 0.8200 of full attention, providing a path towards more efficient, interpretable, and linguistically‑informed Transformer architectures.

PaperId: 701, https://arxiv.org/pdf/2605.24460.pdf GitHub

Abstract:
Automatically mapping and segmenting global mining footprints using remote sensing and deep learning is critical for monitoring the socio‑environmental risks and impacts of mining, yet its progress is hindered by the scarcity of fine‑grained annotated data. Although large‑scale datasets with coarse boundaries are widely available, leveraging them to improve fine‑grained segmentation is challenging due to significant domain shift. To address this, we propose MineC2FNet, a coarse‑to‑fine domain incremental learning framework that exploits abundant coarse data to enhance fine‑grained mining footprint segmentation. MineC2FNet adopts a teacher‑student architecture with attentive distillation at both the feature and prediction levels, selectively transferring generalized knowledge from the coarse domain while enabling boundary refinement using limited fine‑grained data (fine domain). We further introduce an expertly validated dataset of 219 images with precise boundary annotations across diverse geographies and commodities. Extensive experiments against state‑of‑the‑art approaches, including domain adaptation and domain incremental learning methods, demonstrate that MineC2FNet achieves superior performance while effectively handling domain shift. The dataset and code are publicly available at https://github.com/risqiutama/MineC2FNet.

PaperId: 702, https://arxiv.org/pdf/2605.24381.pdf GitHub

Abstract:
Time series forecasting drives operational decisions in areas like finance, transportation, and energy. While supervised learning approaches achieve strong performance, they require domain‑specific training, feature engineering, and ongoing maintenance. Large‑scale foundation models have recently emerged as a zero‑shot alternative, avoiding task‑specific training much like LLMs. In this work, we evaluate foundation models against standard supervised approaches. Rather than focusing solely on aggregate accuracy, we analyze performance across four operational regimes: periodic human‑centric systems, physically constrained processes, stochastic financial markets, and heterogeneous demand forecasting. Our results characterize optimal deployment areas. Foundation models perform well in domains with transferable periodic structures and are efficient for cold‑start or long‑tail scenarios. Conversely, supervised specialists maintain higher precision in systems governed by strict physical constraints. In financial domains, newer foundation models are rapidly closing the performance gap with supervised specialists. We further quantify trade‑offs in inference latency, data drift adaptability, and deployment constraints. Finally, we propose a Complexity Router that assigns each series to the optimal model class using empirical features. We demonstrate that this selective routing achieves higher accuracy and significantly lower inference costs compared to deploying a universal foundation model, providing a practical framework for balancing generalization and efficiency.

PaperId: 703, https://arxiv.org/pdf/2605.24229.pdf GitHub

Abstract:
Frontier AI developers now train models against long written behavioral specifications, such as Anthropic's constitution (Anthropic, 2025a) and OpenAI's Model Spec (OpenAI, 2025a), integrated into post‑training via methods like character training (Anthropic, 2024) and deliberative alignment (Guan et al., 2024). These documents serve a governance function, but it is unclear how well models actually follow them under adversarial, multi‑turn pressure similar to what they would face in real‑world deployment. We propose a multi‑method audit pipeline that treats each lab's published specification as an auditable target: it decomposes the specification into atomic testable tenets (205 for Anthropic, 197 for OpenAI), generates multi‑turn adversarial scenarios with the Petri auditing agent (Anthropic, 2025b), runs a modified SURF‑style rubric search (Murray et al., 2026) to catch shallow single‑turn failures Petri misses, validates flagged transcripts against the relevant specification, and compares the findings against the lab's own published system card. Applying the pipeline across seven models per specification, we find that models follow their own lab's specification substantially better with each generation. On Anthropic's constitution, the Claude family falls from a 15.0% violation rate (Sonnet 4) to 2.0% (Sonnet 4.6); on OpenAI's Model Spec, the GPT family falls from 11.7% (GPT‑4o) to 3.6% (GPT‑5.2 medium reasoning), with the severity ceiling falling from 10/10 to 7/10. We cannot externally isolate whether these gains come from specification‑specific training, broader post‑training improvements, or evaluation awareness. Remaining failures cluster around operator‑imposed personas under AI‑identity questioning, irreversible action in agentic deployments, and fabricated quantitative claims with false precision.

PaperId: 704, https://arxiv.org/pdf/2605.24140.pdf GitHub

Abstract:
Multi‑step reasoning remains a central challenge for large language models: single‑pass generation is efficient but lacks accuracy; tree‑search methods explore multiple paths but are computation‑heavy. We address this gap by distilling reasoning progress into a hyperbolic geometric signal that guides step‑by‑step generation. Our approach is motivated by a structural observation: in combinatorial reasoning trees, solution‑bearing states are few while dead ends are exponentially numerous. The hyperbolic space matches this asymmetry, with compact volume near the origin and exponentially expanding capacity toward the boundary, so that distance‑to‑origin naturally encodes solution proximity while angular separation distinguishes branches requiring different next operations. We train a lightweight head to project LLM hidden states into this space, then fine‑tune a low‑rank adapter interactively on its own reasoning attempts to act on the injected signal. Across multiple benchmarks, the geometric signal yields consistent gains, with larger improvements on deeper reasoning chains. Our code is publicly available at https://github.com/yuyuliu11037/HyperGuide.

PaperId: 705, https://arxiv.org/pdf/2605.24043.pdf GitHub

Abstract:
Scientific discovery is a closed‑loop process in which hypotheses guide data acquisition and observations refine the hypothesis space. Yet most approaches reduce discovery to supervised learning over fixed datasets, where limited observations can support multiple plausible mechanisms that fit locally but fail to generalize. Thus, the key challenge is selecting informative observations to resolve uncertainty, shifting the focus from static inference to adaptive data acquisition. To address this, we propose LLM‑AutoSciLab, a closed‑loop framework that couples hypothesis generation with hypothesis‑conditioned experiment selection and mechanism refinement. Rather than fitting models to passively collected data, LLM‑AutoSciLab iteratively proposes plausible hypotheses, selects informative experiments to distinguish or refine them, and updates its state using the resulting evidence. To evaluate dynamic, closed‑loop scientific discovery with active data acquisition, we introduce ActiveSciBench, comprising two datasets: ActiveSciBench‑Chem with 57 enzyme‑kinetics tasks and ActiveSciBench‑GRN with 45 gene‑regulatory‑network tasks. These datasets model discovery as a budget‑constrained process requiring adaptive experiment design, variable selection, and recovery of true mechanisms. Across NewtonBench, ActiveSciBench‑Chem, and ActiveSciBench‑GRN, LLM‑AutoSciLab outperforms prior methods, achieving 67.6% and 35.1% symbolic accuracy on NewtonBench and ActiveSciBench‑Chem, respectively, and 31.1% exact graph recovery on ActiveSciBench‑GRN. Moreover, hypothesis‑guided experimentation is 2‑5x more sample‑efficient than the strongest competing baselines. Code and data are available at: https://github.com/scientific‑discovery/LLM‑AutoSciLab

PaperId: 706, https://arxiv.org/pdf/2605.24041.pdf GitHub

Abstract:
Neural operators serve as fast, data‑driven surrogates for scientific modeling but typically rely on a monolithic, single‑pass inference procedure that struggles to resolve high‑frequency details, a limitation known as spectral bias. We introduce the Iterative Refinement Neural Operator (IRNO), which augments pre‑trained operators with a learned refinement module iteratively applied via fixed‑point iteration. IRNO decomposes the prediction into a coarse initialization followed by successive residual corrections, paralleling classical numerical solvers. Under local assumptions, we establish contraction of the induced operator, ensuring convergence to a unique fixed point. To explicitly target high‑frequency errors, we propose a progressive spectral loss that adaptively increases penalty on high‑frequency components over refinement steps during training. Across physical systems, IRNO consistently lowers error, with up to 56.05% improvement on turbulent flow. On Active Matter, spectral analysis reveals that, relative to base operator, the normalized error ratios decrease to 27.72‑36.10% in low‑, 5.07‑6.68% in mid‑, and 1.48‑2.04% in high‑frequencies, remaining stable beyond the trained iteration count. Code is available at https://github.com/xiaotianliu‑dartmouth/Iterative_Refinement_Neural_Operator

PaperId: 707, https://arxiv.org/pdf/2605.24004.pdf GitHub

Abstract:
Large language models (LLMs) are promising for autonomous driving, but semantics‑only decision policies can yield physically unsafe behavior in dynamic traffic. Existing methods either perform online language reasoning without explicit dynamics verification or use world models mainly in offline pipelines, leaving a gap between semantic intent and physical feasibility at decision time. We propose Reason‑‑Imagine‑‑Act (RIA), a closed‑loop framework that couples an LLM reasoner with an action‑conditioned world model for online safety verification. At each step, the LLM proposes an action template and candidate sub‑actions, the world model performs short‑horizon rollouts, and a safety scorer selects the safest executable action with feedback to the next reasoning step. Under a unified CARLA point‑goal protocol (1000 episodes), RIA achieves 80.05% route completion, 51.10% arrival rate, and 0.20% collision rate. Under the same closed‑loop interface, RIA consistently outperforms training‑free baselines, including CARLA TM and MADA, on core closed‑loop metrics. For reproducibility, code is available at https://github.com/pku‑smart‑city/source_code/tree/main/RIA.

PaperId: 708, https://arxiv.org/pdf/2605.23993.pdf GitHub

Abstract:
World models have become a central paradigm for learning predictive simulators that support generation, planning, and decision‑making. Yet, despite rapid progress in industry‑scale interactive video generation, the broader research community still lacks compact, reproducible, and easily extensible implementations for studying the design choices underlying modern world models. We introduce Nano World Models, a minimalist codebase for future video prediction centered around diffusion forcing. Nano World Models provides a unified interface for generative objectives, model scales, action‑conditioning mechanisms, latent observation spaces, datasets, evaluation protocols, and long‑horizon rollout procedures. This design enables controlled studies of world‑modeling components that are often entangled across separate implementations. Through experiments across simple control environments, game simulation, and real‑robot data, we examine how prediction parameterization, architecture scale, action injection, sampling budget, and domain complexity affect video prediction quality and autoregressive rollout behavior. By releasing code, configurations, evaluation scripts, and pretrained checkpoints, Nano World Models aims to provide a compact yet extensible experimental substrate for open, reproducible, and scientific world‑model research.

PaperId: 709, https://arxiv.org/pdf/2605.23983.pdf GitHub

Abstract:
We investigate growth dynamics in deterministic equational discovery substrates. Across three toy domains (arithmetic, boolean, higher‑order list; n=592 trajectories), short‑range substrate sizes fit a power‑law N(t) proportional to t^b. Within each substrate b is architecture‑sensitive (cross‑validated R^2 approximately 0.82); the regression does not transfer across substrates (arith+bool to list yields R^2 approximately ‑0.84). A heuristic mean‑field closure model predicts a saturating power‑law dN/dt = K N^k exp(‑mu N) of which the pure power‑law is the short‑range approximation. Three robustness checks: bootstrap intervals on (k, mu) are tight in 4/5 toy trajectories and degenerate in 1/5; out‑of‑sample forecasting on toy data (fit first 100 epochs, predict next 400) is won by pure power‑law 5/5, indicating the toy trajectories do not reach saturation; on two real‑world growth proxies the result splits. New Mathlib/.lean file additions per month (mathlib4, 60 months, 9701 files) support the saturating form on OOS forecasting by approximately 7x over pure power‑law; Coq mathcomp monthly commits (129 months, 3083 commits) favour pure power‑law on both tests with mu collapsing to zero. The dynamics are substrate‑conditional at two levels: within‑substrate architecture‑to‑b regressions do not transfer, and the preferred functional family for N(t) itself (pure vs. saturating power‑law) differs by substrate. We propose "saturating power‑law growth with substrate‑conditional (k, mu), observable when the substrate has reached its saturation regime" as a working framing.

PaperId: 710, https://arxiv.org/pdf/2605.23972.pdf GitHub

Abstract:
Large language models achieve strong performance in language generation and knowledge‑intensive tasks, yet remain limited in settings requiring causal reasoning, persistent state tracking, and long‑horizon planning. We argue that these limitations may arise from an objective‑level mismatch between sequence prediction and reasoning over latent environment dynamics. To formalize this distinction, we introduce Latent Dynamics Inference (LDI), a conceptual perspective that interprets language and multimodal observations as partial evidence of underlying transition dynamics. To empirically investigate this perspective, we introduce Flux, a sequential reasoning environment specified entirely through natural‑language rules. As a proof‑of‑concept case study, the rules are first compiled into an explicit state‑transition simulator, illustrating that structured latent transition dynamics can, in some cases, be operationally extracted from textual rule descriptions. This enables a controlled comparison between the LLMs operating purely over textual observations and reinforcement‑learning agents trained directly within the extracted latent state space. Within this case study, agents operating with explicit access to the latent state space exhibit substantially more stable behavior in long‑horizon gameplay, achieving an aggregate win rate of approximately 79% versus 11% for LLMs. Qualitative analysis further reveals failure modes consistent with unstable persistent state tracking, including invalid actions, state‑tracking errors, and short‑horizon reasoning failures. The complete implementation of the Flux environment available at https://github.com/FeisalAlaswad/FLUX‑RL‑Agent Within the evaluated setting, these results suggest that strong sequence prediction alone may struggle to support robust long‑horizon dynamic reasoning without mechanisms for persistent state tracking and transition modeling

PaperId: 711, https://arxiv.org/pdf/2605.23951.pdf GitHub

Abstract:
The companion paper introduced a four‑level verification lattice on agent‑skill manifests (unverified, declared, tested, formal) and left the top level aspirational. This paper closes that gap. We give a precise semantics for skill behaviour faithful to how a skill is consumed by an LLM‑driven runtime (a deterministic script‑side reachable through a non‑deterministic LLM‑side), state the verification problem as a capability‑containment property over that semantics, and present three composable methods that together raise a skill from declared or tested to formal: (1) sound static capability‑containment analysis of the script‑side via abstract interpretation over a small effect lattice; (2) a refinement type system for tool‑call envelopes that mechanically rejects any call whose statically‑inferred capability is not in the manifest's declared set; (3) SMT‑bounded model checking against the parent paper's biconditional correctness criterion, with the bound chosen so any counter‑example fitting the runtime's transaction‑buffer horizon is exhibited as a concrete trace. We prove the three layers composed soundly cover the parent paper's threat model modulo a single residual (the LLM's freedom to refuse to act) that the parent paper's runtime biconditional catches at session boundary. The methods reuse existing well‑engineered tools (Z3, Semgrep, CodeQL, refinement‑type checkers, mechanised proof assistants) rather than asking operators to build new ones, and the proof‑carrying artifact extends the existing SKILL.md convention. All three methods plus the bundle producer and re‑checker ship as zero‑dependency JavaScript modules in the open‑source enclawed framework (https://github.com/metereconsulting/enclawed; project page https://www.enclawed.com/), with 53 unit tests and an end‑to‑end CLI demo on a sample skill.

PaperId: 712, https://arxiv.org/pdf/2605.23940.pdf GitHub GitHub

Abstract:
How do multi‑turn reasoning systems fail? The expected answer is logical contradiction, in which the system's maintained state becomes unsatisfiable. We show that the dominant mode is instead satisfiable drift, where the internal state stays consistent while the returned answer silently violates prior commitments. We build DRIFT‑Bench (Decomposing Reasoning Into Failure Types), a solver‑instrumented benchmark of 816 test problems across three constraint domains, and evaluate four methods on it across four open‑weight models (8B‑120B parameters). MUS‑Repair, which feeds minimal unsatisfiable subsets back to the generator, is strongest in every setting (+1.8 to +15.0 pp over the best non‑MUS baseline). But the central finding is what repair leaves behind. After structured feedback, models rarely contradict themselves. They forget. Residual errors are 98‑100% satisfiable drift across all settings, while contradiction drops to near zero. Reliable multi‑turn systems must separately validate that the returned answer respects the maintained state. Code is available at https://github.com/kaons‑research/drift‑bench.

PaperId: 713, https://arxiv.org/pdf/2605.23926.pdf GitHub

Abstract:
Reasoning‑capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual inspection of their traces reveals extensive reformulation, verification, and circular self‑reflection, yet how much of this deliberation is actually necessary has never been measured at scale or explained from first principles. This paper closes both gaps. We formalise reasoning redundancy directly in terms of the reasoning model itself: the redundancy of a correct trace is the largest fraction of its trailing segmented steps that can be truncated while π, forced to terminate thinking and emit a final answer, still produces the correct answer. A large‑scale quantification across four frontier reasoning models and two mathematical benchmarks shows that step‑level redundancy is consistently high ‑‑ between 61% and 93% across the 8 (model, benchmark) conditions we study, with the median critical prefix equal to a single segmented step in six of the eight conditions ‑‑ that the finding is robust to the choice of judge family, and that although ρ decreases with problem difficulty on MATH‑500, all four models remain substantially redundant (ρ\in [46%, 85%]) even on the hardest Level‑5 problems. We then prove that this redundancy is a structural consequence of length‑agnostic outcome rewards, not a model‑specific artefact: under any such reward, no finite expected stopping time is optimal. The result holds regardless of RL algorithm, base model, data distribution, or whether the policy is obtained via RL or distillation; over‑thinking is therefore not a bug to be patched in individual models but a structural property of how current reasoning models are trained. Code: https://github.com/zhiyuanZhai20/how‑much‑thinking‑is‑enough

PaperId: 714, https://arxiv.org/pdf/2605.23908.pdf GitHub

Abstract:
We are in the midst of large‑scale industrial and academic efforts to automate the processes of scientific, technological and creative production through AI‑driven assistants. Historically, a fundamental property of these processes in their human form has been their open‑endedness: their capacity for generating a seemingly endless supply of novel and meaningful new forms. Do artificial agents have any capacity for such fruitful unguided discovery? To answer this question, we turn to Picbreeder, the canonical exemplar of human‑driven open‑ended search, in which users collaboratively generated a diverse library of images through interactive evolution of small neural networks. We replicate Picbreeder, replacing human users with frontier Vision Language Models (VLMs). We observe clear qualitative differences between the output of our system and the historical human baseline, and attempt to characterize them using metrics of phylogenetic complexity and visual and semantic salience and novelty. In an effort to identify some of the causal factors contributing these differences, we study the addition of exploratory noise to the agents' selection process, of behavioral diversity between agents, and of narrative momentum in the form of memory of past actions. We make our code available at https://github.com/smearle/picbreeder‑vlm.

PaperId: 715, https://arxiv.org/pdf/2605.23898.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes and spatial coordinates. Although these numbers appear meaningful, it remains unclear whether these numerical outputs are genuinely grounded in spatial perception. Therefore, in this work, we revisit spatial numerical understanding through SpaceNum, a unified framework that captures two complementary settings: numbers as dynamic transitions during spatial exploration, and numbers as static layouts in spatial reasoning. We formulate two bidirectional tasks, Num2Space and Space2Num, to evaluate how well VLMs map between vision‑side spatial structure and language‑side numerical representations. We systematically study whether current VLMs truly understand numerical values in spatial settings. Across dynamic transitions and static layouts, we find that models largely fail to ground numbers in spatial meaning and often perform close to random guess. Through error analysis, reasoning trace analysis, and controlled interventions, we show that current VLMs rely heavily on shallow spatial cues, struggle to build stable coordinate‑aware representations, and fail to abstract structured spatial layouts from visual observations. We further show that explicit reasoning provides only marginal gains, while tuning can partially improve spatial numerical understanding and transfer to external spatial reasoning benchmarks.

PaperId: 716, https://arxiv.org/pdf/2605.23897.pdf GitHub

Abstract:
Multimodal Large Language Models have advanced visual reasoning, yet a purely textual chain of thought remains a bottleneck for questions that require fine‑grained focus or view transformations. The ''think with images'' paradigm narrows this gap, but existing approaches are either constrained by fixed predefined toolkits or produce noisy intermediate images from unified multimodal methods. We pursue a third option: using a dedicated image editing model and decouple it with an understanding model. However, off‑the‑shelf image editors fail as reasoning assistants with two complementary gaps: a language‑side gap, where editors trained as passive instruction‑followers cannot map an abstract question to an appropriate visual transformation, and a generation‑side gap, where edit correctness degrades as reasoning depth grows. Guided by this analysis, we introduce ETCHR (Editing To Clarify and Harness Reasoning), a question‑conditioned, reasoning‑aware image editor decoupled from the downstream understanding model and trained with a two‑stage recipe targeted at the two gaps: Reasoning Imitation via supervised fine‑tuning on edit trajectories, followed by Reasoning Enhancement with VLM‑derived rewards for edit correctness and downstream reasoning accuracy. Since the editor is decoupled, ETCHR plugs into different open‑ and closed‑source MLLMs in a training‑free manner. Across five task families (fine‑grained perception, chart understanding, logic reasoning, jigsaw restoration, and 3D understanding), ETCHR raises average Pass@1 from 55.95 to 60.77 (+4.82) with Qwen3‑VL‑8B, from 65.08 to 70.55 (+5.47) with Gemini‑3.1‑Flash‑Lite, and from 76.55 to 81.16 (+4.61) with the 1T‑parameter MoE model Kimi K2.5.

PaperId: 717, https://arxiv.org/pdf/2605.23892.pdf GitHub GitHub GitHub

Abstract:
Visual geometry transformers have become powerful architectures for multi‑view 3D reconstruction, enabling joint prediction of multiple 3D attributes in a feed‑forward manner. However, their computational cost grows quadratically with the input sequence length due to the global attention layers inside these models. This limits both their scalability and efficiency. In this work, we address this challenge with a simple yet general strategy: restricting the number of key/value tokens that each query interacts with during global attention. To achieve effective token selection, we introduce a two‑stage framework. First, an inter‑frame selection step operates at the frame level to identify frames that should be preserved. Second, an intra‑frame selection step further discards more redundant tokens within the selected frames. Our analysis highlights the advantage of a diversity‑based strategy for inter‑frame selection, which ensures broad coverage of the scene. For intra‑frame selection, we show that layer‑aware sparsification is necessary, with the selection process guided by the entropy of the global attention pattern. Our approach offers a superior speed‑accuracy trade‑off compared to existing solutions. Extensive experiments show that it accelerates visual geometry transformers by over 85% for scenes with 500 images while maintaining, or even improving, baseline performance, which hints that how our token selection strategy can play a crucial role in future applications of visual geometry transformers. Our project website is available at https://zsh2000.github.io/good‑token‑hunting.github.io.

PaperId: 718, https://arxiv.org/pdf/2605.23825.pdf GitHub

Abstract:
It has generally been assumed that geopolitical bias in language models originates from the training data used during the pre‑training phase. We tested seven open‑weight LLM pairs consisting of the base model (pre‑training only) and the chat model (pre‑training and post‑training) from seven labs on a paired‑scenario forced‑choice probe over 28 country pairs in English, French, and Chinese, and found that geopolitical bias originates in post‑training rather than in pre‑training. Across seven AI labs, six showed shifts in the direction associated with the country or region of the model developer after post‑training. This shift is strongest in Alibaba's Qwen 2.5: while the base is neutral on China‑favourability (‑0.15 log‑odds, p=0.15), the post‑trained chat variant is at +2.91 (p<10^‑4), an 18x shift in odds. We also observe shifts in biases toward other countries across all models. Additionally, the magnitude of this shift depends on the language used to prompt the model: the French‑made Mistral becomes pro‑France only under French prompting (FR‑EN shift +1.91, p<10^‑4). These findings suggest that geopolitical preferences in language models are not simply inherited from large‑scale internet data but are actively shaped during post‑training, highlighting the need for greater transparency, auditing, and oversight of alignment processes that influence how models represent nations, cultures, and political perspectives.

PaperId: 719, https://arxiv.org/pdf/2605.23668.pdf GitHub

Abstract:
Although large language model (LLM) conversational systems process millions of multi‑turn dialogues daily, they remain fundamentally reactive: they respond only after the user types a query. A key step toward proactive interaction is next‑query prediction, which anticipates the user's subsequent query based solely on the preceding dialogue. Progress on this task is hindered by the lack of dedicated benchmarks and a fundamental efficiency‑‑quality trade‑off: naively concatenating full dialogue history incurs linearly growing token consumption, while truncating to the latest turn discards crucial cross‑turn context. Our key insight is that accurate prediction does not require re‑reading raw history; it suffices to track the user's evolving intent trajectory across topics, unresolved needs, and interest shifts. We propose OnePred, which maintains a recursively updated memory as its sole cross‑turn context, bounding the per‑turn cost independently of conversation length. We train the model via a two‑stage reinforcement learning pipeline that first teaches what to predict, then what to compress, shaping the memory into a prediction‑oriented intent chain. To establish a rigorous testbed, we introduce NQP‑Bench, spanning three diverse subsets. Experiments demonstrate that OnePred reduces per‑turn token consumption by up to 22× compared to full‑history inputs while consistently exceeding all baselines in prediction quality, with larger gains on longer conversations. Our code is publicly available at https://github.com/ZBWpro/OnePred.

PaperId: 720, https://arxiv.org/pdf/2605.23655.pdf GitHub

Abstract:
High‑resolution (HR) image perception presents a key bottleneck for multimodal large language models (MLLMs). While visual search offers a promising solution, existing methods struggle with the trade‑off between coverage and efficiency. Visual expert‑assisted search is efficient but prone to blind spots when proposals fail, whereas scan‑based search guarantees coverage at the cost of computational redundancy and semantic fragmentation. To address this dilemma, we introduce CVSearch, a training‑free adaptive framework that dynamically schedules search strategies via an Assess‑then‑Search workflow. Specifically, CVSearch first invokes expert‑assisted search when global information is insufficient, and only triggers a novel semantic‑aware scanning mechanism upon failure. Distinct from rigid grid partitioning, this efficient scanning paradigm incorporates Semantic Guided Adaptive Patching to decompose images into semantically consistent regions, effectively mitigating object fragmentation. Furthermore, we devise a Dynamic Bottom‑Up Search strategy driven by a Visual Complexity prior to enable efficient and precise iterative exploration of local details. Extensive experiments on HR benchmarks demonstrate that CVSearch achieves state‑of‑the‑art accuracy while substantially improving search efficiency. Code is released at https://github.com/liliupeng28/ICML26‑CVSearch.

PaperId: 721, https://arxiv.org/pdf/2605.23590.pdf GitHub

Abstract:
ReAct‑style agents for search‑intensive, multi‑step reasoning tasks rely largely on their own internal judgment to decide what evidence to seek, which reasoning or action step to take next, and when to stop, often producing shallow, redundant, or poorly targeted trajectories. Prior work has explored rubrics as external quality signals, but existing uses are mostly evaluative rather than action‑guiding: rubrics typically serve as training‑time rewards or post‑hoc evaluators of completed outputs, and in deep‑research settings they are often coarse‑grained and report‑level rather than step‑level. We introduce Co‑ReAct, a rubric‑guided action‑selection framework that uses rubrics as step‑level guidance during inference. At each decision step, Co‑ReAct injects a rubric into the agent's context to guide the next Reason‑or‑Act decision, specifying what the agent should target in evidence seeking, search, reasoning, or self‑evaluation. To make this guidance reliable, we train a dedicated rubric generator with GRPO. Unlike prior pairwise or binary preference formulations, our objective optimizes a list‑wise Spearman rank‑correlation reward against multi‑judge expert consensus rankings, encouraging rubrics that are discriminative rather than merely plausible. On DeepResearchBench and SQA‑CS‑V2, Co‑ReAct consistently improves over ReAct and representative test‑time compute baselines across search agents built on both 8B/14B open‑source and frontier closed‑source base models. The trained rubric generator can also serve as a drop‑in component that improves these baselines without changing their underlying decision mechanisms. Our code is publicly available at https://github.com/ZBWpro/Co‑ReAct.

PaperId: 722, https://arxiv.org/pdf/2605.23491.pdf GitHub

Abstract:
Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test‑Time Scaling (TTS) have advanced LLM code generation through executable verification. Yet Ground‑Truth Unit Tests (GT UTs) remain a bottleneck: SOTA RLVR methods require them for costly training, while existing TTS methods lose competitiveness without them. This motivates GT‑free TTS, where existing methods directly use self‑generated UTs to refine and select code candidates. Yet such UTs are often noisy or spuriously coupled with wrong code, and UT quality in turn cannot be validated without reliable code. The key challenge is therefore to jointly improve both. To this end, we present CoSPlay, a GT‑free, training‑free framework that jointly improves codes and UTs through cooperative self‑play. It first explores diverse solution ideas and identifies their potential failure modes to produce discriminative UT ideas. It then uses bidirectional pass‑count signals from the Code‑UT execution matrix to iteratively prune or fix weak codes and refresh or replace unreliable UTs, letting the two pools co‑evolve. Finally, when multiple codes remain tied at the highest pass count, it picks the final code from the largest output‑consensus cluster, since correct codes agree on the same inputs while wrong codes diverge. Experiments on four challenging benchmarks show that CoSPlay on Qwen2.5‑7B‑Instruct improves average BoN from 22.1% to 33.2% and UT accuracy from 14.6% to 78.3%, matching or surpassing the RLVR model CURE‑7B. When applied to CURE‑7B, it further improves BoN by 5.7%. CoSPlay also generalizes across diverse backbones and outperforms GT‑free TTS baselines under comparable token budgets, with continued gains as the budget scales up. These results suggest a scalable inference strategy for competitive code generation without any GT data.

PaperId: 723, https://arxiv.org/pdf/2605.23482.pdf GitHub

Abstract:
Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increasingly operate on paired vision‑language inputs, multimodal distillation must preserve representation quality and cross‑modal alignment under tight compute and memory budgets, yet prior methods often require heavy computes and overlook their correlations. To address this, we present Multimodal Distribution Matching (MDM), a geometry‑aware framework for efficient and generalizable multimodal distillation. Specifically, MDM integrates complementary components at the data, model, and loss levels. At the data level, it initializes synthetic image‑text pairs by sampling from clusters in the joint embedding space. At the model level, it forms a mixed teacher by interpolating independently fine‑tuned models in weight space according to their angular deviation from the pretrained anchor. At the loss level, it matches joint distributions on the unit hypersphere using a geometry‑aware matching objective that exploits the joint features in the cross‑modal agreement and discrepancy directions along with symmetric contrastive learning. Across image‑text retrieval benchmarks with cross‑architecture evaluation, MDM yields compact synthetic sets that preserve multimodal semantics, substantially reduce distillation cost, and remain robust across architectures.

PaperId: 724, https://arxiv.org/pdf/2605.23471.pdf GitHub GitHub

Abstract:
Aggressive driving is a major cause of traffic accidents and poses a serious threat to road safety. Although deep learning methods have shown promising results in detecting risky driving behaviours from vehicle sensor data, their performance in real‑world conditions is often limited by severe data imbalance, large variability between drivers, and the lack of physically interpretable vehicle dynamics representations. In this paper, we propose an enhanced deep learning framework for aggressive driving detection using multivariate vehicle dynamics signals. Instead of relying solely on raw measurements, the proposed approach constructs engineered dynamic features that capture steering, acceleration, and braking behaviour. To address the extreme rarity of aggressive events in naturalistic driving data, we introduce a stable training strategy that combines controlled SMOTE‑based oversampling with a class‑weighted loss formulation, and evaluates focal loss variants for imbalance handling. Furthermore, a safety‑oriented decision strategy based on class‑specific threshold calibration is adopted to better reflect the asymmetric risks of missed detections and false alarms in real‑world applications. The proposed framework is evaluated on a newly collected naturalistic driving dataset. Extensive experiments show that the proposed method consistently outperforms standard deep learning baselines with significant improvements in minority‑class recall and safety‑critical F‑score metrics while maintaining practical computational efficiency. Code: \url https://github.com/halhamdan/CBANet

PaperId: 725, https://arxiv.org/pdf/2605.23458.pdf GitHub GitHub

Abstract:
Recent advances have substantially improved real‑time interactive video generation in the autoregressive regime. However, most existing few‑step autoregressive video generation methods, often distilled from a corresponding many‑step teacher, default to a 4‑step sampling configuration, which still incurs considerable latency during deployment and suffers from severe quality degradation when the number of sampling steps is further reduced, particularly in the one‑step setting. Trajectory‑style consistency distillation methods often produce videos with weak dynamics, while DMD‑based approaches, such as Self‑Forcing, tend to yield blurry frames. To address this challenge, we propose One‑Forcing, a simple yet effective approach which augments the DMD objective with an auxiliary GAN loss for high‑quality and efficient one‑step video generation. Experiments on VBench show that One‑Forcing achieves a total score of 83.76, establishing state‑of‑the‑art performance among one‑step causal video generation methods and remaining competitive with strong many‑step approaches. We further demonstrate that one‑step framewise autoregressive generation can be achieved stably with merely one‑third of the training cost of the chunkwise model, a setting that prior methods have failed to achieve successfully.

PaperId: 726, https://arxiv.org/pdf/2605.23402.pdf GitHub

Abstract:
Effectively modeling non‑stationary dynamics in probabilistic multivariate time series(MTS) forecasting requires balancing expressiveness with robustness. Existing parametric approaches benefit from strong inductive biases but lack flexibility, whereas deep generative models struggle to capture complex temporal dependencies without extensive data and computation. We introduce Parametric Prior Mapping (PPM), a framework that injects parametric structural priors into a generative modeling process. Specifically, PPM utilizes a parametric estimator to derive a dynamic, adaptive prior that guides the learning of a complex predictive distribution via a learnable mapping. This design allows the model to retain the efficiency of parametric methods while exploiting the expressive power of generative models. Trained with a hybrid objective, PPM yields precise forecasts with well‑calibrated uncertainty estimates. Empirical results show that PPM outperforms existing baselines in handling non‑stationary data, offering a superior trade‑off between accuracy and computational efficiency. The code is available at https://github.com/ljl8336/PPM.

PaperId: 727, https://arxiv.org/pdf/2605.23393.pdf GitHub

Abstract:
Mechanistic interpretability of transformers requires identifying not just which components matter but how they compose into the computational route that produced a prediction. Both attention and MLP follow a shared key‑value template ϕ(S)U. We exploit this structure to develop Unpack, a backward recursion that decomposes credit through both sublayers, producing interaction strengths between any two components, named end‑to‑end paths with K/Q/V composition labels, and per‑token attribution from a single forward pass, without intervention, gradients, or auxiliary training. We evaluate on the indirect object identification task. On GPT‑2 small, the method recovers all three composition connections described by Wang et al. (2023), including the mode‑specific routing of each connection (K, Q, or V). To test token‑level attribution beyond trivial copying, we compare two occurrences of the same name in the same decomposition: the first mention retains strong credit while the duplicate‑detection position is suppressed, a pattern absent in matched control prompts. Across the Pythia family from 160M to 6.9B parameters, this suppression pattern is consistently recovered at every scale, demonstrating that the method tracks mechanistic structure without ground‑truth circuit labels. Code is available at https://github.com/Fun‑Cry/unpacklm.

PaperId: 728, https://arxiv.org/pdf/2605.23315.pdf GitHub

Abstract:
Large language models trained under diverse objectives and architectures have been shown to develop increasingly similar internal representations, an observation formalized as the Platonic Representation Hypothesis. Whether this representational convergence extends to the reasoning processes that operate over shared representations remains untested. We evaluate representational similarity across 16 language models from 8 families (1.5B to 72B parameters) on 800 reasoning problems spanning mathematics, science, commonsense, and truthfulness, stratifying by problem difficulty, computational stage, and causal relevance. Our analysis reveals three dissociations: a difficulty inversion, where models converge more on problems they collectively fail (Centered Kernel Alignment [CKA] = 0.897) than on those they solve (CKA = 0.830); a generation gap, where pre‑decision representations align (CKA = 0.875) while post‑decision representations diverge (CKA = 0.274); and epiphenomenal correctness, where shared information is decodable across models (66% transfer accuracy) but exerts minimal causal influence on predictions (1.5% to 5.5% flip rate across ablation protocols). These results indicate that representational convergence in language models reflects shared input processing constraints rather than shared reasoning strategies, with direct implications for ensemble design, interpretability transfer, and evaluations of model similarity. Code is available at https://github.com/Usama1002/convergence‑without‑understanding.

PaperId: 729, https://arxiv.org/pdf/2605.23270.pdf GitHub

Abstract:
Current end‑to‑end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consistency. Autoregressive (AR) models capture interaction‑aware temporal dependencies via causal factorization, but their step‑wise decoding leads to error accumulation and suboptimal global structure. In contrast, diffusion models optimize trajectories globally but lack explicit causal constraints, making them unreliable in interactive and safety‑critical scenarios. This dichotomy reveals a deeper issue: existing methods treat causal modeling and global optimization as separate paradigms, without a principled way to unify them within a single trajectory distribution. To address this, we propose ChainFlow‑VLA, which unifies causal generation and global refinement within a unified probabilistic framework. We formulate planning as a mixture over AR‑induced modes and learn Vision‑Language Model (VLM)‑conditioned residual distributions over these modes. An autoregressive generator (Chain) produces a discrete set of causal trajectory modes, followed by a diffusion‑based refiner (Flow) that leverages VLM hidden states as semantic priors to perform mode‑conditioned correction in residual space while preserving causal structure. This straightforward conditioning seamlessly injects high‑level scene understanding into fine‑grained trajectory adjustments. Experiments demonstrate that ChainFlow‑VLA achieves robust planning in ambiguous and long‑tail scenarios, achieving a state‑of‑the‑art score of 94.85 on the NAVSIM v1 leaderboard, matching human‑level performance (94.8). Code will be available at https://github.com/AFARI‑Research/ChainFlow‑VLA.

PaperId: 730, https://arxiv.org/pdf/2605.23219.pdf GitHub

Abstract:
Time series forecasting plays a central role in many real‑world applications and has been extensively studied. Most existing approaches rely on deterministic models. However, real‑world environments exhibit inherently uncertain and complex future behaviors, making single‑point predictions insufficient. This highlights the need for probabilistic forecasting methods that can quantify and represent uncertainty. In this work, we propose PaP‑NF, a probabilistic forecasting framework that aligns continuous time series representations with a frozen large language model (LLM) using a Prefix‑as‑Prompt mechanism, and conditions a normalizing flow decoder on the global context extracted by the LLM. The quality of the resulting predictive distributions is evaluated using the Continuous Ranked Probability Score (CRPS), a standard metric in probabilistic forecasting. Across a variety of long‑term forecasting benchmarks, PaP‑NF robustly captures multi‑modal uncertainty while maintaining competitive point forecasting accuracy. The official implementation is available at: https://github.com/democracy04/PaP‑NF

PaperId: 731, https://arxiv.org/pdf/2605.23215.pdf GitHub

Abstract:
LLM‑based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize against. Existing benchmarks are poorly aligned with production inference frameworks: they evaluate kernels on a single GPU with synthetic inputs, ignore the surrounding compilation stack, and reward replicating known optimizations rather than discovering new ones. The resulting reward signals are misleading: agents learn to generate kernels that score well in sandboxes but introduce interface incompatibilities, compilation‑stack conflicts, and silent correctness degradation when integrated into real systems. We introduce FastKernels, a kernel benchmark built around a minimal set of 46 representative architectures spanning 8 categories, whose kernels collectively subsume those of 96.2% (409/425) of HuggingFace Transformers architectures. FastKernels doubles as a minimalistic, production‑grade inference framework that runs at parity with hardened systems such as vLLM and SGLang on mainstream LLM serving and substantially exceeds upstream references on under‑served architectures; each task's interface mirrors the corresponding module in the state‑of‑the‑art library for its architecture family, enabling direct deployment of optimized kernels into production codebases. Evaluating state‑of‑the‑art kernel agents on FastKernels, we find that even the strongest agent achieves only 0.94× aggregate speedup over production baselines, with weaker agents at 0.78× and 0.53× ‑‑ confirming that benchmark‑production misalignment is a critical bottleneck for the field. We release FastKernels as a stepping stone toward kernel agents whose benchmark gains translate directly into production throughput improvements. Code is available at https://github.com/Snowflake‑AI‑Research/fastkernels

PaperId: 732, https://arxiv.org/pdf/2605.23203.pdf GitHub

Abstract:
The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety‑critical domains such as healthcare, autonomous vehicles, and aerospace. However, current approaches are confined to incomplete statistical verification or robustness to \ell_p‑norm and affine transforms, which cover only a narrow subset of perturbations to the image formation process. In particular, robustness to camera motion remains an open problem despite being key to deploy many vision applications. We present a formal verification approach that targets robustness against 3D motion perturbations of the capturing camera. We first establish a closed‑form mapping from camera pose to pixel values. By analyzing the continuity properties of the resulting homographies, we show that recent work on Lipschitz optimization and piecewise continuity can be extended to derive tight linear bounds on perturbed pixel values. Our approach applies to scenes with predominantly planar structure, such as ground planes in augmented reality, road markings and traffic signs in autonomous driving, or planar workspaces in robotic manipulation. This enables the first formal verification of projective geometry transforms, without complex simulation, surrogate networks, or explicit image‑formation models. We validate our implementation and show up to 89% speedup and 7% tighter bounds over prior work. We then evaluate our method on the VNN‑COMP benchmark and reveal systematic weaknesses to projective perturbations. Finally, we demonstrate a real‑world case study on a safety‑critical runway classifier, highlighting practical vulnerabilities to camera motion, and addressing a key challenge in the certification of learned models. Data and code are publicly available at https://github.com/jeangud/homography‑verification .

PaperId: 733, https://arxiv.org/pdf/2605.23147.pdf GitHub

Abstract:
Role prompts of the form As X, do Y admit a clean linear decomposition at one specific site in the residual stream: the prompt‑to‑answer transition ‑‑ the last prompt token together with the first two generated tokens ‑‑ in an early/mid layer band. There, persona and task contribute through partially orthogonal additive directions. Forming a pure persona effect Δ_X, a pure task effect Δ_Y, and substituting h_BB + Δ_X + Δ_Y for the clean residual yields downstream output within a small KL of clean on Gemma‑2‑2B‑IT and Qwen‑2.5‑\1.5B, 3B\‑Instruct, across a 12‑cell short grid and a 48‑cell long‑persona grid, with persona‑specific behavioral markers preserved. The natural inference from this additive structure is that the role prompt can be compressed into a single cached residual vector. \emphWe show it cannot. Injecting the cached additive prediction ‑‑ or even the oracle clean residual h_XY ‑‑ into a baseline host prompt with the persona text removed does not approach the clean long‑persona target, at one site or at many layers. Persona‑conditioned multi‑token generation flows through attention back to the persona‑text positions throughout the prompt, which no residual at one site reproduces. Local additivity in the residual stream does not imply prompt compressibility. The additive structure at the prompt‑to‑answer transition supports interpretability and fine‑grained steering of persona or task contributions; persona‑conditioned behavior across the full continuation depends on a distributed prompt/KV mechanism that local activation arithmetic does not displace.

PaperId: 734, https://arxiv.org/pdf/2605.23139.pdf GitHub

Abstract:
Multivariate time series anomaly detection has become increasingly important in real‑world applications, where labeled data are often scarce. Many existing approaches rely on unsupervised learning to model normal patterns, but they often treat all channels equally. This design can dilute anomaly‑relevant signals, since not all channels contribute equally to anomaly detection. In this paper, we propose CALAD, a channel‑aware contrastive learning framework for multivariate time series anomaly detection. CALAD governs the construction of contrastive samples using estimated channel relevance, allowing the learning process to reflect anomaly semantics rather than generic similarity. Channel relevance is estimated from reconstruction errors of a transformer‑based autoencoder and is used to distinguish channels that are more influential to anomalous behaviors. Using this information, we design a channel‑wise augmentation strategy in which positive and negative samples are constructed based on whether anomaly‑relevant channels are preserved or perturbed. This encourages invariance to changes in irrelevant channels while being sensitive to changes in anomaly‑relevant channels. Furthermore, CALAD combines contrastive learning and an auxiliary reconstruction head, allowing the model to learn discriminative representations while retaining normal structures. Experiments on multiple real‑world datasets shows that CALAD consistently outperforms existing methods, particularly under distribution shift scenarios. We provide the code for reproducibility at https://github.com/hirundo1218/CALAD

PaperId: 735, https://arxiv.org/pdf/2605.23118.pdf GitHub

Abstract:
Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade‑off: end‑to‑end trackers achieve high automation but offer no opportunity to correct silent tracking failures, while decoupled registration‑segmentation pipelines permit user verification yet discard the lesion's prior appearance, limiting accuracy in ambiguous cases. In this work, we propose a Verified Tracking paradigm: a clinician verifies a registration‑proposed prompt, which the model leverages alongside the baseline lesion appearance to resolve segmentation ambiguities. We present a unified framework combining early spatial prompt fusion with latent temporal difference weighting for longitudinally‑informed segmentation. To address data scarcity, we leverage large‑scale synthetic pretraining, proving essential for exploiting longitudinal context, improving performance by up to 4.5 Dice points over training from scratch. Our approach secured first place in the MICCAI autoPET IV challenge. We further curate and release PanTrack, a new longitudinal pancreatic cancer benchmark, to assess out‑of‑distribution generalization. Experiments show that our model outperforms prior work in both fully automatic and the proposed verified tracking setting offering a clinically safe middle ground between automation and control. Code, model and dataset will be released at https://github.com/MIC‑DKFZ/LongiSeg

PaperId: 736, https://arxiv.org/pdf/2605.23116.pdf GitHub

Abstract:
Existing Video Anomaly Detection (VAD) methods typically rely on task‑specific training, leading to strong domain dependency and high training costs. Moreover, most existing methods output only scalar anomaly scores, providing limited insight into why specific events are considered abnormal. Recent advances in Vision‑Language Models (VLMs) have enabled both anomaly detection and human‑interpretable reasoning. However, many VLM‑based approaches still require additional training steps (e.g., instruction tuning or verbalized learning) or external Large Language Models (LLMs), incurring further training costs and inference overhead. To address these challenges, we propose CoReVAD, a contextual reasoning framework for training‑free video anomaly detection that operates with a single frozen VLM. CoReVAD directly generates anomaly scores and temporal descriptions from the VLM. To mitigate noise in generative outputs, we introduce a Local Response Cleaning (LRC) module based on local vision‑text alignment. Furthermore, global temporal context and progression are incorporated through softmax‑based refinement, Gaussian smoothing, and position weighting. Experiments on UCF‑Crime and XD‑Violence demonstrate that CoReVAD achieves competitive performance among training‑free methods while providing reliable and interpretable explanations. Our official code is available at: https://github.com/Muk‑00/CoReVAD

PaperId: 737, https://arxiv.org/pdf/2605.23058.pdf GitHub GitHub

Abstract:
Empirical claims about autonomous Kubernetes operations agents are largely unfalsifiable. Published work reports observational results without controlled comparisons against an agent‑disabled baseline, selection bias is endemic, pre‑registered decision matrices are absent, and samples are typically too small for the noise level of the underlying scoring system. The cause is the same gap that limits the agents themselves: code agents have a verification substrate that turns "did it work" into a fast, falsifiable, ground‑truth signal, and operations has nothing equivalent. We present agent‑breakage, a closed‑loop measurement framework that injects faults into a target Kubernetes cluster, observes how an autonomous agent responds, scores the response on four axes against ground truth, and accumulates outcome‑labeled (state, action, outcome) tuples. The framework distinguishes framework error from reasoning error, supports a true off‑condition control via a deterministic‑embedder mechanism, and enforces pre‑registered decision matrices. We use it as a case study to test whether retrieval over past postmortems compounds an agent's capability. The methodological payload is three confounds the substrate caught during that case study, each of which would have produced a wrong published claim on a less instrumented version of the same work: a pgvector index bug, a +19% selection‑bias artifact, and small‑sample estimates that overstated effects by roughly 3x. The retrieval result itself is a partial falsification: 1 of 3 dense‑corpus scenarios significant at p<0.05, pooled effect +3.9 percentage points, not significant at n=60. A within‑scenario corpus‑density sweep at 360 runs shows that mechanistic alignment of near‑neighbors dominates raw count. The framework is released open source.

PaperId: 738, https://arxiv.org/pdf/2605.23052.pdf GitHub

Abstract:
We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task. Our system addresses three tasks: psychological state modeling, temporal change detection, and sequence‑level summarization. For Task 1, we combine LLM‑based data augmentation, DeBERTa classification, and Random Forest regression for structured state prediction. For Task 2, we use few‑shot prompting with a locally deployed Llama 3.1 model to detect Switch and Escalation events using short‑term temporal context. For Task 3.1, we explore both a deterministic rule‑based summarization pipeline and a few‑shot LLM‑based approach, ranking 2nd officially. Our RAG‑based method achieves strong performance in Task 3.2, ranking 1st for Improvement and 3rd for Deterioration, demonstrating its ability to capture recurrent psychological change patterns across timelines. Our analysis reveals key challenges, including the mismatch between classification and regression performance, the difficulty of modeling temporal transitions, and the disagreement between semantic and similarity‑based evaluation metrics. These findings highlight the complexity of modeling mental health dynamics and motivate future work on unified evaluation frameworks. We share our code and prompts at https://github.com/4dpicture/CLPsych2026

PaperId: 739, https://arxiv.org/pdf/2605.22984.pdf GitHub

Abstract:
Test‑Time Training (TTT) is an emerging paradigm that enables models to adapt their parameters during inference, improving performance on tasks such as few‑shot learning, retrieval‑augmented generation, and complex reasoning. However, this dynamic adaptation introduces new vulnerabilities that adversaries can exploit to jailbreak models. We identify three threat models for TTT and demonstrate how attackers can leverage them to bypass safety filters. Our results show that TTT can significantly increase the Attack Success Rate (ASR) and the ASR over 10 generation trials (ASR@10). For example, under LoRA, the few‑shot and generation‑phase threat models achieve an average ASR@10 of 95% and 93% respectively, across models from different families and scales. These vulnerabilities transfer to production fine‑tuning APIs. We also show that TTT‑induced overfitting can produce degenerate outputs that inflate ASR under standard judges, and propose a validity‑aware evaluation to correct for this. Our findings suggest that TTT exposes a new attack surface, strengthens attacks, and undermines existing safety guardrails. As a first step toward defense, we propose a lightweight provider‑side detector that flags TTT requests via the perplexity shift on a private harmful holdout, but robust deployment will ultimately require dynamic alignment.

PaperId: 740, https://arxiv.org/pdf/2605.22855.pdf GitHub

Abstract:
Personalized pricing negotiations are a challenging testbed for LLM agents because successful interaction does not guarantee profitable decision making. A seller may produce valid actions and close many deals while still pricing poorly when buyer willingness to pay and bargaining traits remain hidden. This paper presents PrefBench, a simulator‑based benchmark for hidden‑preference personalized pricing negotiations. Each episode pairs a simulated buyer with a fixed vehicle‑customization bundle; the seller observes public persona descriptors, bundle information, and negotiation history, while latent buyer variables govern valuation, patience, counter‑offer behavior, and walkaway decisions. PrefBench evaluates this setting through an LLM‑facing state‑summary protocol that constrains agents to return strict JSON actions under a fixed hidden‑information boundary. We evaluate zero‑shot LLM sellers against heuristic references over 7,500 episodes. The tested LLMs follow the protocol reliably and achieve deal rates above 0.99, but their seller‑profit outcomes remain weak: the best LLM average profit is only slightly above the random baseline and far below a simple concession heuristic under the same episode stream. These results show that structured action compliance and agreement‑seeking behavior can coexist with weak profit‑sensitive bargaining. PrefBench provides a controlled benchmark for evaluating pricing‑agent behavior under hidden buyer preferences.

PaperId: 741, https://arxiv.org/pdf/2605.22827.pdf GitHub

Abstract:
In large‑scale AI systems, allocating scarce resources such as GPU compute time and bandwidth among multiple agents is a critical challenge. Conventional policies focus on efficiency metrics, potentially leading to dominance concentration that undermines system diversity and stability. We propose Computable Fair Division (CFD), a framework that reinterprets the Boltzmann‑Softmax function not as a selection tool but as a probabilistic resource allocation mechanism, redefining the inverse temperature parameter β as a computable control variable governing the efficiency‑fairness balance. Static analysis reveals a Pareto frontier with a near‑optimal Stability Corridor where total loss remains approximately constant across policy weights. In the dynamic setting, AHC++ (Adaptive Hard‑Cap Controller++) updates β in real time using the error between observed dominance and a policy‑specified target as feedback. Simulations show that AHC++ suppresses extreme dominance concentration under exogenous shocks while tracking fairness targets without substantial throughput degradation. Scalability analysis confirms that a 100x increase in agents yields only approximately 5.5x increase in execution time. Code: https://github.com/entrofy‑ai/computable‑fairness

PaperId: 742, https://arxiv.org/pdf/2605.22794.pdf GitHub

Abstract:
Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human‑driven update ships a fix. Self‑evolving agents have emerged in response, but all confine evolution to text‑mutable artifacts ‑‑ skill files, prompt configurations, memory schemas, workflow graphs ‑‑ and leave the agent harness untouched. Since routing, hook ordering, state invariants, and dispatch live in code rather than in any text artifact, an entire class of structural failure is physically unreachable from the text layer. We argue that source‑level adaptation is a fundamentally more general medium: it is Turing‑complete, a strict superset of every text‑mutable scope, takes effect deterministically rather than through base‑model compliance, and does not erode under long‑context drift. We present MOSS, a system that performs self‑rewriting at the source level on production agentic substrates. Each evolution is anchored to an automatically curated batch of production‑failure evidence and proceeds through a deterministic multi‑stage pipeline; code modification is delegated to a pluggable external coding‑agent CLI while MOSS retains stage ordering and verdicts. Candidates are verified by replaying the batch against the candidate image in ephemeral trial workers, then promoted via user‑consent‑gated, in‑place container swap with health‑probe‑gated rollback. On OpenClaw, MOSS lifts a four‑task mean grader score from 0.25 to 0.61 in a single cycle without human intervention.

PaperId: 743, https://arxiv.org/pdf/2605.22791.pdf GitHub GitHub

Abstract:
Linear attention replaces the unbounded cache of softmax attention with a fixed‑size recurrent state, reducing sequence mixing to linear time and decoding to constant memory. The hard part is not just what to forget, but how to edit this compressed memory without scrambling existing associations. Delta‑rule models subtract the current read before writing a new value, and Kimi Delta Attention (KDA) sharpens forgetting with channel‑wise decay. But the active edit still uses a single scalar gate to control two different things: how much old content to erase on the key side and how much new content to commit on the value side. We introduce Gated DeltaNet‑2, which generalizes both Gated DeltaNet and KDA by inheriting adaptive forgetting and channel‑wise decay while addressing their shared limitation, the scalar tie between erasing and writing. Gated Delta Rule‑2 separates these roles with a channel‑wise erase gate b_t and a channel‑wise write gate w_t, reducing to KDA when both gates collapse to the same scalar and to Gated DeltaNet when the decay also collapses. We derive a fast‑weight update view, a chunkwise WY algorithm with channel‑wise decay absorbed into asymmetric erase factors, and a gate‑aware backward pass that preserves efficient parallel training. At 1.3B parameters trained on 100B FineWeb‑Edu tokens, Gated DeltaNet‑2 achieves the strongest overall results among Mamba‑2, Gated DeltaNet, KDA, and Mamba‑3 variants across language modeling, commonsense reasoning, and retrieval. Its advantage is most pronounced on long‑context RULER needle‑in‑a‑haystack benchmarks, where it improves the evaluated multi‑key retrieval setting and remains strong in both recurrent and hybrid settings. Code is available at https://github.com/NVlabs/GatedDeltaNet‑2.

PaperId: 744, https://arxiv.org/pdf/2605.22769.pdf GitHub

Abstract:
Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understood. In this work, we study the impact of pre‑training dynamics on the acquisition of time‑sensitive factual knowledge, focusing specifically on data ordering. Our main contributions are twofold. First, we introduce a comprehensive benchmark of over 7,000 temporally grounded questions and an evaluation protocol that enables analysis of whether models correctly associate facts with their corresponding time periods. Second, we pretrain 6B‑parameter models on temporally ordered Common Crawl snapshots and compare them against standard shuffled pre‑training. Our results show that sequentially trained models match shuffled baselines on general language understanding and common knowledge while consistently exhibiting more up‑to‑date and temporally precise knowledge. Temporally ordered pre‑training yields improved factual freshness, while shuffled pre‑training peaks on older data, possibly due to increased factual repetition. These findings, along with the release of our code at https://github.com/kyutai‑labs/kairos , checkpoints, and datasets at https://huggingface.co/collections/kyutai/kairos provide a foundation for future research on continual learning for LLMs.

PaperId: 745, https://arxiv.org/pdf/2605.22733.pdf GitHub

Abstract:
Every Python function deployed as an LLM tool must today exist in two forms: an HTTP endpoint for human‑facing clients and CI pipelines, and an MCP tool registration for agent runtimes such as Claude and Cursor. These representations share business logic yet diverge in all the surrounding machinery (routing, validation, serialisation, streaming, and schema maintenance), and they drift apart as the underlying code evolves. We present HarnessAPI, a Python framework that eliminates this duplication by treating a typed skill folder as the single source of truth. From one handler.py plus Pydantic schemas, the framework automatically derives a streaming HTTP endpoint with Server‑Sent Events, an interactive OpenAPI/Swagger UI, and a zero‑configuration MCP tool, all served from a single process. Dual‑mode content negotiation lets the same handler serve SSE‑streaming and JSON‑returning clients with no handler changes. A dynamic code‑generation mechanism ensures Pydantic type annotations propagate correctly to FastMCP's inspection layer, resolving a technical limitation that prevents naive closure‑based registration. Measured across six representative skills using cloc, HarnessAPI reduces framework‑facing boilerplate by 74% compared with a manually maintained dual‑stack implementation (FastAPI server + FastMCP server). HarnessAPI subclasses FastAPI, inheriting its full middleware, dependency‑injection, and deployment ecosystem. It is available at https://github.com/edwinjosechittilappilly/harnessapi and on PyPI (pip install harnessapi)

PaperId: 746, https://arxiv.org/pdf/2605.22720.pdf GitHub

Abstract:
AI models are already deployed in societies affected by armed conflict, and journalists, humanitarian workers, governments and ordinary citizens rely on them for information or for their work processes. No established practice exists for checking whether their outputs can make those conflicts worse. We tested nine model configurations from four providers (OpenAI, Anthropic, DeepSeek, xAI) on 90 multi‑turn scenarios designed to surface misaligned behaviour in conflict contexts: false equivalence between documented atrocities, denial of genocide, and failure to recognise ethnic slurs, among others. When such outputs feed into journalism, humanitarian reporting, or public debate, they can deepen divisions in fragile societies. Failure rates span 6% to 47% between the best and worst performing models, which makes model choice a safety question in its own right and when users pushed for ``balance'' in cases where international courts have already assigned responsibility, five of nine configurations failed 80 to 100 percent of the time. We release the first evaluation framework for this domain and propose adding it to alignment evaluation portfolios.

PaperId: 747, https://arxiv.org/pdf/2605.22714.pdf GitHub

Abstract:
Large language models are routinely used as automated evaluators: to review code, moderate content, or score outputs, often with many items passing through one conversation. We ask whether the polarity of prior conversation history biases subsequent judgments, an effect we call the accumulated message effect on LLM judgments (AMEL). Across 75,898 API calls to 11 models from 4 providers (OpenAI, Anthropic, Google, and four open‑source models), we present identical test items in isolation or following histories saturated with predominantly positive or negative evaluations. Models shift toward the conversation's prevailing polarity (d = ‑0.17, p < 10^‑46). The effect concentrates on items where the model is genuinely uncertain at baseline (d = ‑0.34 for high‑entropy items, vs d = ‑0.15 when the baseline is deterministic). Bias does not grow with context length: 5 prior turns and 50 produce the same shift (Spearman |r| < 0.01; OLS slope p = 0.80). And there is a negativity asymmetry: paired per item, negative histories induce 1.62x more bias than positive (t = 13.46, p < 10^‑39, n = 2,481). Scaling helps but does not solve it (Anthropic: Haiku ‑0.22 to Opus ‑0.17; OpenAI: Nano ‑0.34 to GPT‑5.2 ‑0.17). Three follow‑ups narrow the mechanism. The token probability distribution shifts continuously, not at a threshold. The negativity asymmetry has both token‑level and semantic components, though attributing the balance is exploratory at our sample sizes. Position does not matter: five biased turns anywhere in a 50‑turn history produce the same shift. The simplest fix for evaluation pipelines is a fresh context per item; when batching is unavoidable, balancing the history helps.

PaperId: 748, https://arxiv.org/pdf/2605.22662.pdf GitHub

Abstract:
We present Claw AI Lab, a lab‑native autonomous research platform that advances automated research from a hidden prompt‑to‑paper pipeline into an interactive AI laboratory. Rather than centering the system around a single agent or a fixed serial workflow, we allow users to instantiate a full research team from one prompt, with customizable roles, collaborative workflows, real‑time monitoring, artifact inspection, and rollback/resume control through a unified dashboard. The platform also supports distinct research modes for exploration, multi‑agent discussion, and reproduction, making autonomous research substantially more steerable and laboratory‑like in practice. A key practical contribution of Claw AI Lab lies in its Claw‑Code Harness, which connects local codebases, datasets, and checkpoints to runnable experiments and feeds execution artifacts back into the research loop. As a result, the harness improves not only execution integration, but also experimental completion and result integrity: experiments are easier to inspect, iterate on, and faithfully transfer into final papers, reducing common failure modes such as partial runs and malformed result reporting. In our internal evaluation on five AI research case studies, using AutoResearchClaw as the baseline, Claw AI Lab is consistently preferred by AI expert judges on idea novelty, experiment completeness, and paper presentation quality. We view Claw AI Lab as an early step toward a new paradigm: autonomous research as usable, interactive, and reliability‑aware scientific infrastructure.

PaperId: 749, https://arxiv.org/pdf/2605.22641.pdf GitHub

Abstract:
Detecting Schwartz values in political text is difficult because implicit cues often depend on surrounding arguments and fine‑grained distinctions between neighboring values. We study when context and explicit moral knowledge help sentence‑level value detection. Using the ValuesML/Touché ValueEval format, we compare sentence, window, and full‑document inputs; no‑RAG and retrieval‑augmented settings with a curated moral knowledge base; supervised DeBERTa‑v3‑base/large encoders; and zero‑shot LLMs from 12B to 123B parameters. The results show that more context is not uniformly better: full‑document context improves supervised DeBERTa encoders by 3.8‑4.8 macro‑F1 points over sentence‑only input, but does not consistently help zero‑shot LLMs. Retrieved moral knowledge is more consistently useful in matched comparisons, improving each tested model family and context condition under early fusion. However, scaling from DeBERTa‑v3‑base to large and from 12B to larger LLMs does not guarantee gains, and simple early fusion outperforms the tested late‑fusion and cross‑attention RAG variants for encoders. Per‑value analyses show that context and retrieval help most for socially situated or conceptually confusable values. These findings suggest that value‑sensitive NLP should evaluate context, knowledge, and model family jointly rather than treating longer inputs or larger models as universal improvements.

PaperId: 750, https://arxiv.org/pdf/2605.22634.pdf GitHub

Abstract:
Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, a skill often needs to express more than task guidance: goals, input boundaries, permissions, human approval points, evidence requirements, output contracts, quality criteria, verification steps, and handoff rules. This paper proposes contractual skills, a GovernSpec‑inspired design framework for organizing SKILL.md files as readable task contracts while preserving lightweight skill discovery and progressive loading. The framework clarifies the boundary between contractual skills, GovernSpec YAML contracts, Model Context Protocol (MCP) surfaces, tool adapters, runtime guardrails, tracing, and evaluation systems. We evaluate the framework with three offline empirical studies. The first text‑generation experiment covers three enterprise skills, fifteen synthetic tasks, four instruction conditions, and eight generation models, producing 960 outputs and 1680 cross‑judge score records. The second study is a public‑skill A/B expansion: eight public skills are compared with contractual rewrites across forty‑eight synthetic tasks, six generation models, two repeats, 1152 outputs, and two complete judge files. In this setting, contractual skills raise mean quality from 4.692 to 4.914 and reduce critical‑error rate from 0.083 to 0.013. The third study is an offline tool‑calling challenge with eight models and 192 simulated tool‑call records. The results suggest that contractual skills are best understood as a governance layer that makes task intent, boundaries, and acceptance criteria explicit, not as a standalone safety mechanism.

PaperId: 751, https://arxiv.org/pdf/2605.22597.pdf GitHub

Abstract:
Learning real‑world dynamics from visual observations is crucial for various domains. A common strategy is to calibrate simulators by estimating physical parameters, yet accuracy is ultimately bounded by the underlying physical models, which often assume materials are homogeneous and isotropic. Even if reasonable, real‑world objects typically exhibit mild anisotropy and heterogeneity. After the near‑isotropic backbone is well calibrated, these residual effects become the key bottleneck for further closing the real‑to‑sim gap. Although neural networks can fit dynamics end‑to‑end, such black‑box modeling discards strong physical priors, leading to poor data efficiency and overfitting. Therefore, we propose MoSA, a motion‑constrained stress adaptation framework that targets these residual effects to further improve real‑to‑sim dynamics learning. MoSA uses an isotropic model as a physics prior and learns residual stress operators to capture mild anisotropy and heterogeneity. It progressively adapts stresses via microplane‑constrained redistribution in a physics‑informed cascaded network. We further impose motion constraints by supervising temporal and spatial derivatives of the deformation field. Experimentally, our learned dynamics achieves superior accuracy, generalization, and robustness, while learning physically meaningful residual anisotropy. Finally, we validate MoSA in a robot manipulation setting, showing that better real‑to‑sim dynamics modeling translates into more reliable sim‑to‑real transfer. Project Page is available at https://mercerai.github.io/MoSA/.

PaperId: 752, https://arxiv.org/pdf/2605.22581.pdf GitHub

Abstract:
Many public buildings provide floorplans with a "you are here" indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume controlled small‑scale environments and precise vectorized floorplans, limiting their ability to operate in large‑scale buildings and rasterized floorplans. In this work, we present an approach for performing floorplan localization in the wild by grounding the task in a reconstructed 3D representation of the scene. Given an unconstrained image collection, our method reconstructs a gravity‑aligned 3D scene and projects it into a 2D density map that serves as a floorplan proxy. Floorplan localization is then formulated as aligning this proxy with the input floorplan via a 2D similarity transform. To bridge the appearance gap between density maps and architectural floorplans, we adapt a 2D foundation model to learn cross‑modal correspondences, introducing a fine‑tuning scheme that encourages semantically aligned matches while preserving structural consistency. Extensive experiments demonstrate substantial improvements over prior methods, including in extremely sparse settings with as little as a single input image. Our code and data will be publicly available.

PaperId: 753, https://arxiv.org/pdf/2605.22570.pdf GitHub

Abstract:
Spatio‑temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisely has become an essential challenge. However, existing spatio‑temporal reasoning benchmark datasets primarily rely on static image sets or passively curated video data, which limits the evaluation of fine‑grained reasoning capabilities. In this paper, we introduce VGenST‑Bench, a video benchmark that employs generative models to actively synthesize highly controlled and diverse evaluation scenarios. To construct VGenST‑Bench, we propose a multi‑agent pipeline incorporating a human quality control stage, ensuring the quality of all generated videos and QA pairs. We establish a comprehensive 3x2x2 video taxonomy, encompassing Spatial Scale, Perspective, and Scene Dynamics to span diverse scenarios. Furthermore, we design a hierarchical task suite that decouples low‑level visual perception from high‑level spatio‑temporal reasoning. By shifting the paradigm from passive curation to active synthesis, VGenST‑Bench enables fine‑grained diagnosis of spatio‑temporal understanding in MLLMs.

PaperId: 754, https://arxiv.org/pdf/2605.22535.pdf GitHub

Abstract:
We introduce TerminalWorld, a scalable data engine that automatically reverse‑engineers high‑fidelity evaluation tasks from "in‑the‑wild" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real‑world categories, ranging from short everyday operations to workflows exceeding 50 steps, and covering 1,280 unique commands. From these, we curate a Verified subset of 200 representative, manually reviewed tasks. Comprehensive benchmarking on TerminalWorld‑Verified across eight frontier models and six agents reveals that current systems still struggle with authentic terminal workflows, achieving a maximum pass rate of only 62.5%. Moreover, TerminalWorld captures real‑world terminal capabilities distinct from existing expert‑curated benchmarks (e.g., Terminal‑Bench), with only a weak correlation to their scores (Pearson r=0.20). The automated engine makes TerminalWorld authentic and scalable by construction, enabling it to evaluate agents in real‑world terminal environments as developer practices evolve. Data and code are available at https://github.com/EuniAI/TerminalWorld.

PaperId: 755, https://arxiv.org/pdf/2605.22505.pdf GitHub

Abstract:
Harness optimization enables automated agent creation by having an optimizer agent iteratively update the harness of target agents. Despite its success, current studies evaluate optimizers solely by observing target agents' performance gains. This indirect end‑improvement evaluation neglects optimizers' actions at intermediate steps, which are often erroneous and hinder agent performance. Therefore, it is unclear whether harness optimization is driven by optimizers' informed update actions or simply trial‑and‑error. This necessitates direct evaluation of harness optimizers. However, evaluating harness optimizers directly is non‑trivial and costly due to the lack of oracle harnesses. To address this, we present a simple, low‑cost design to directly evaluate them, namely priority ranking. By asking harness optimizers to rank components (e.g., tools) in a given harness by their potential to improve/hinder agent performance when updated, our design quantifies optimizer ability at the step level without expensive rollouts or manual examination. More importantly, optimizers' ranking performance correlates with their ability to improve agents in actual multi‑step harness optimization, establishing priority ranking as a reliable predictor of optimization ability. Priority ranking is enabled by Shor, a collection of 182 human‑verified optimization scenarios spanning across domains, designs, and time stages. Codes and data can be found at https://github.com/k59118/Harness_Optimizer_Evaluation.

PaperId: 756, https://arxiv.org/pdf/2605.22498.pdf GitHub

Abstract:
Scientific machine learning often requires combining known physics with unknown parameters or correction terms learned from data. Existing approaches either ignore known structure, encode it as a soft penalty, or require hand‑written PyTorch code for each equation. We present The Neural Compiler, a system that translates programs written in a first‑order Scheme‑like expression language into frozen, differentiable PyTorch modules. These modules match the source program to floating‑point precision and provide gradients through autograd. In hybrid models, the compiled module encodes known physics exactly while learned components model the unknown remainder. We evaluate the compiler across six experiment domains: Feynman physics equations, Lotka‑Volterra dynamics, a damped pendulum, a one‑dimensional heat equation, three‑dimensional vector mechanics, and compositional generalization. Compiled modules match hand‑coded PyTorch implementations numerically for single equations, showing no accuracy loss from compilation. With only 1 to 4 trainable parameters, compiled models recover physical constants to less than 1 percent error in most cases, while standard PINN baselines with more than 8500 parameters show 7 to 93 percent error. Compiled modules also compose with zero error, while neural approximations can accumulate large errors in deep composition chains. The main value of the compiler is not improved accuracy over hand‑coded equations, but systematic composability: it generates correct, differentiable modules from symbolic specifications without rewriting each equation by hand. The system supports 51 primitive operations, including vector and matrix algebra, enabling PDE discretizations and hybrid scientific models. This string‑in, module‑out interface also provides a natural target for large language models that translate scientific descriptions into executable differentiable modules.

PaperId: 757, https://arxiv.org/pdf/2605.22422.pdf GitHub

Abstract:
Table structure recognition (TSR) requires both table‑level coherence (row/column counts, headers, spanning cells) and precise separator localization. We introduce FastTab, a grid‑centric TSR model that avoids autoregressive HTML decoding by combining (i) a lightweight Tiny Recursive Module (TRM) for global reasoning and (ii) axial 1D Transformer encoders that capture long‑range dependencies along rows and columns. The model predicts row/column counts, header rows, and separators to construct a grid, then infers rowspan/colspan using ROI‑aligned cell features. Across four benchmarks (PubTabNet, FinTabNet, PubTables‑1M, and SciTSR), FastTab achieves competitive structure recovery performance while operating at low‑latency inference. We further study robustness under pixel‑level anonymisation and show an extension to curved separators for camera‑captured documents. The source code will be made publicly available at https://github.com/hamdilaziz/FastTab .

PaperId: 758, https://arxiv.org/pdf/2605.22368.pdf GitHub

Abstract:
As large language models (LLMs) are increasingly deployed for software engineering, constructing high‑quality benchmarks is crucial for evaluating not just the functional correctness, but also the formal verifiability of generated code. However, existing benchmarks are limited by the quantity and quality of positive and negative test cases, leading to an overestimation of model capabilities in generating specifications and implementations. To address this, we propose VeriScale, a novel framework driven by the adversarial implementations. It consists of two stages: test‑suite expansion to construct diverse and challenging test cases, and test‑suite reduction to distill them into compact yet discriminative suites. While VeriScale is general, we instantiate it on Verina to construct VerinaPlus, which expands the original test suites by over 83×, and VerinaLite, a lightweight 14× variant. Our experiments across eight state‑of‑the‑art LLMs demonstrate that VerinaPlus exposes substantial model weaknesses hidden by the original benchmark, evidenced by sharp score drops on both SpecGen and CodeGen tasks, whereas VerinaLite maintains this discriminative power at a fraction of the evaluation cost. The enhanced benchmarks and source code are publicly available at https://github.com/XiaoyangLiu‑sjtu/VeriScale.

PaperId: 759, https://arxiv.org/pdf/2605.22355.pdf GitHub

Abstract:
Public transit route planning traditionally depends on structured map infrastructure and complex routing engines, and no existing dataset supports training models to bypass this dependency. We present TransitLM, a large‑scale dataset of over 13 million transit route planning records from four Chinese cities covering 120,845 stations and 13,666 lines, released as a continual pre‑training corpus and benchmark data for three evaluation tasks with complementary metrics. Experiments show that an LLM trained on TransitLM produces structurally valid routes at high accuracy and implicitly grounds arbitrary GPS coordinates to appropriate stations without any explicit mapping. These results demonstrate that transit route planning can be learned entirely from data, enabling end‑to‑end, map‑free route generation directly from origin‑destination information. The dataset and benchmark are available at https://huggingface.co/datasets/GD‑ML/TransitLM, with evaluation code at https://github.com/HotTricker/TransitLM.

PaperId: 760, https://arxiv.org/pdf/2605.22343.pdf GitHub

Abstract:
Autonomous research systems increasingly make the scientific workflow executable: agents can propose ideas, run code, inspect results, and draft papers. But executable workflows do not by themselves produce research judgment. We analyze where current systems lose trial experience: weak evidence becomes prose, pilot signals become broad claims, memory remains textual, and recurring process failures do not change later behavior. We introduce Sibyl‑AutoResearch, a self‑evolving AutoResearch framework built around Scientific Trial‑and‑Error Harnesses. A harness lets agents run bounded trials, preserve positive and negative outcomes, and route lessons into later planning, validation, claim scope, scheduling, critique, writing, and harness repair. We formalize this through two auditable conversion units: trial‑to‑behavior conversion, which links trial signals to later research actions, and trial‑to‑harness‑behavior conversion, which links recurring process failures to system updates. We implement the framework in SIBYL, a file‑backed autonomous research system that exposes the state, roles, memory, gates, and artifact traces needed to inspect these conversion paths. A retrospective audit identifies eight high‑confidence conversion events, with a median latency of one iteration and a maximum latency of three iterations. A recovered‑failure registry further shows how five naturally occurring failure classes, including duplicate results, stale numbers, and unsupported statistics, were blocked, downgraded, or routed into later repair. These traces do not establish a comparative performance claim; they show that the proposed conversion units are recoverable from realistic autonomous‑research workspaces. The SIBYL framework and system are available at https://github.com/Sibyl‑Research‑Team/AutoResearch‑SibylSystem.

PaperId: 761, https://arxiv.org/pdf/2605.22331.pdf GitHub

Abstract:
Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by systems‑level barriers: heterogeneous data representations, the absence of standardized deployment workflows, and a mismatch between research prototypes and the concurrency and latency requirements of hospital environments. We present the SepsisAI‑Orchestrator, an open‑source modular platform that addresses this deployment gap for early sepsis detection. The platform integrates HL7 FHIR‑inspired Clinical Document Architecture (CDA) preprocessing, NoSQL storage, a containerized LightGBM classifier served via REST APIs, and a Streamlit clinical dashboard, orchestrated with Docker and Kubernetes. A previously validated LightGBM model (F1 0.87‑0.94 on PhysioNet 2019) is reused without modification; the contribution lies in the surrounding infrastructure and its empirical characterization under load. Using k6 with 50‑1000 concurrent virtual users, we find that replica count must be matched to the physical CPU thread count of the host: scaling from 3 to 12 replicas on a 12‑thread CPU reduces p95 latency from 3.3s to 1.41s (57.3% reduction) and eliminates all request failures, while over‑provisioning to 24 or 48 replicas degrades performance due to scheduler contention. To our knowledge this U‑shaped scaling behavior has not been quantified previously for clinical AI inference workloads. We do not claim prospective clinical validation. Source code and deployment manifests are available at https://github.com/nucleusai/sepsisai‑orchestrator.

PaperId: 762, https://arxiv.org/pdf/2605.22321.pdf GitHub

Abstract:
As autonomous agents (e.g., OpenClaw) increasingly operate with deep system‑level privileges to execute complex tasks, they introduce severe, unmitigated security risks. Current vulnerability analyses overwhelmingly focus on single‑turn, stateless behaviors, overlooking the expanded attack surface inherent in stateful, multi‑turn interactions and dynamic tool invocations. In this paper, we propose a novel, multi‑dimensional evasion framework targeting LLM‑based agent systems. We introduce three stealthy attack vectors: (1) Temporal evasion, which fragments malicious payloads across sequential interaction turns; (2) Spatial evasion, which conceals payloads within complex external artifacts that evade standard LLM parsing mechanisms; and (3) Semantic evasion, which obscures malicious intents beneath benign contextual noise. To systematically quantify these threats, we construct A3S‑Bench, a comprehensive benchmark comprising 2,254 real‑world agent execution trajectories. Evaluating a standard agent framework separately integrated with 10 mainstream LLM backbones against 20 practical threat scenarios, we demonstrate that our evasion framework elevates the average risk trigger rate from a 28.3% baseline to 52.6%. These findings reveal systemic, architecture‑level vulnerabilities in current autonomous agent systems that existing defenses fail to address, highlighting an urgent need for defense mechanisms tailored to the unique threats.

PaperId: 763, https://arxiv.org/pdf/2605.22297.pdf GitHub

Abstract:
Learning rate configuration is a fundamental aspect of modern deep learning. The prevailing practice of applying a uniform learning rate across all layers overlooks the structural heterogeneity of Transformers, potentially limiting their effectiveness as the backbone of Large Language Models (LLMs). In this paper, we introduce Layerwise Learning Rate (LLR), an adaptive scheme that assigns distinct learning rates to individual Transformer layers. Our method is grounded in Heavy‑Tailed Self‑Regularization (HT‑SR) theory, which characterizes the empirical spectral density (ESD) of weight correlation matrices to quantify heavy‑tailedness. Layers with weaker heavy‑tailedness are assigned larger learning rates to accelerate training, while layers with stronger heavy‑tailedness receive smaller learning rates. By tailoring learning rates in this manner, LLR promotes more balanced training across layers, leading to faster convergence and improved generalization. Extensive experiments across architectures ranging from LLaMA to GPT‑nano, optimizers including AdamW and Muon, and model scales from 60M to 3B parameters with up to 100B training tokens demonstrate the effectiveness of LLR. LLR achieves up to 1.5x training speedup and consistently outperforms uniform‑learning‑rate baselines. In particular, it improves the average zero‑shot accuracy of 1B models from 47.09% to 49.02%, and that of 3B models from 48.58% to 50.61%. A key advantage of LLR is its low tuning overhead: it can transfer nearly optimal learning‑rate settings directly from the uniform baseline. Code is available at https://github.com/hed‑ucas/Layer‑wise‑Learning‑Rate.

PaperId: 764, https://arxiv.org/pdf/2605.22269.pdf GitHub

Abstract:
Long streaming video QA remains challenging due to growing visual tokens and limited reasoning length of large language models (LLMs). KV‑caching stores the Key‑Value (KV) of the historical tokens via LLM prefill and enables more efficient streaming QA. However, existing methods cache every one or two frames, causing redundant memory usage and losing fine‑grained spatial details within frame or temporal contexts across frames. This paper proposes MuKV, a method that features a multi‑grained KV cache compression module and a semi‑hierarchical retrieval approach to improve both efficiency and accuracy for long streaming VideoQA. For the offline KV cache, MuKV extracts visual representations at patch‑, frame‑, and segment‑levels. The multiple levels of granularity preserve both local cues and global temporal context, while maintaining efficiency with a dual signal token compression mechanism guided by self‑attention and frequency. For online QA, MuKV designs a semi‑hierarchical retrieval method to retrieve relevant KV caches for answer generation. Experiments on long‑streaming VideoQA benchmarks show that MuKV significantly improves answer accuracy, without sacrificing memory and online QA efficiency. Moreover, our compression mechanism alone brings consistent benefits across answer accuracy, memory, and QA efficiency over baselines, showcasing highly effective contribution.

PaperId: 765, https://arxiv.org/pdf/2605.22238.pdf GitHub GitHub

Abstract:
Static benchmarks capture only part of how large language models behave in practice. Real systems place models inside repeated loops with time limits, formatting constraints, and failure modes. We study this setting in a timed multi‑phase Risk environment with explicit victory targets and repeated planning and execution cycles. In a replicated 32‑game cross‑provider championship under frozen rules, gemini‑3.1‑pro‑preview won 20 of 32 games against gpt‑5.1, claude‑opus‑4‑7, and kimi‑k2.6, and the pooled winner distribution differs strongly from an equal‑strength null (p approx 1.5 x 10^‑5). We then separate planning from execution by standardizing execution on a cheaper Gemini Flash scaffold. Under this design, a pooled 32‑game planner bakeoff is consistent with near‑equality (p approx 0.821), which indicates that much of the earlier provider spread came from end‑to‑end system behavior rather than planning alone. To study mechanism, we analyze saved planning and execution traces from the provider championship. Gemini refers to the terminal objective far more often than the other models and increases that focus as victory approaches. Gemini also converts more turns into deep conquest chains, even though it is not the cleanest runtime. These results show that live‑agent performance depends on objective tracking, execution conversion, cost, and runtime reliability, and they support evaluating LLMs as components in bounded workflows rather than as isolated benchmark respondents.

PaperId: 766, https://arxiv.org/pdf/2605.22183.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation. A common design in current architectures maps language instructions and visual observations to actions in a single forward pass. While conceptually simple, this formulation entangles instruction comprehension, spatial scene understanding, and motor control within a single learning objective. As a result, the action expert must implicitly relearn cognitive and perceptual capabilities already present in the pretrained VLM, which can limit both learning efficiency and generalization. We introduce AVP (Action with Visual Primitives), an end‑to‑end architecture that implements this visual‑primitive‑centric interface: the VLM infers the next‑stage target and emits visual‑primitive tokens that condition a flow‑matching action expert, with supervision derived from end‑effector kinematics. Real‑robot experiments on general pick‑and‑place tasks show that AVP improves the success rate by 27.61% over pi_0.5 and outperforms other recent methods, with consistent gains in data efficiency, spatial‑compositional generalization, and object‑level transfer.

PaperId: 767, https://arxiv.org/pdf/2605.22166.pdf GitHub

Abstract:
LLM agents are shaped not only by their language models, but also by the runtime harness that mediates observation, tool use, action execution, feedback interpretation, and trajectory control. While existing agent adaptation methods mainly update model parameters, many failures in deterministic, rule‑governed domains stem from mismatches at the model‑‑environment interface. We propose Life‑Harness, a lifecycle‑aware runtime harness that improves frozen LLM agents without changing model weights or evaluation environments. Life‑Harness evolves from training trajectories by converting recurring interaction failures into reusable interventions across environment contracts, procedural skills, action realization, and trajectory regulation, and remains fixed for evaluation on unseen tasks. On seven deterministic environments from τ‑bench, τ^2‑bench, and AgentBench, Life‑Harness improves 116 out of 126 model‑‑environment settings across 18 model backbones, with an average relative improvement of 88.5%. Harnesses evolved only from Qwen3‑4B‑Instruct trajectories transfer to 17 other models, showing that Life‑Harness captures reusable environment‑side structure rather than model‑specific behavior. These results position runtime interface adaptation as a complementary alternative to model‑centric agent training. Code is available at https://github.com/Tianshi‑Xu/Life‑Harness.

PaperId: 768, https://arxiv.org/pdf/2605.22158.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) face significant computational overhead when processing long videos due to the massive number of visual tokens required. To improve efficiency, existing methods primarily reduce redundancy by pruning or merging tokens based on importance or similarity. However, these approaches largely overlook a critical dimension of video content, i.e., changes and turning points, and they lack a collaborative model for spatio‑temporal relationships. To address this, we propose a new perspective: similarity is for identifying redundancy, while difference is for capturing key events. Based on this, we designed a training‑free framework named ST‑SimDiff. We first construct a spatio‑temporal graph from the visual tokens to uniformly model their complex associations. Subsequently, we employ a parallel dual‑selection strategy: 1) similarity‑based selection uses community detection to retain representative tokens, compressing static information; 2) temporal difference‑based selection precisely locates content‑changing points to preserve tokens that capture key dynamic shifts. This allows it to preserve both static and dynamic content with a minimal number of tokens. Extensive experiments show our method significantly outperforms state‑of‑the‑art approaches while substantially reducing computational costs. Our code is available in https://github.com/bingjunluo/ST‑SimDiff.

PaperId: 769, https://arxiv.org/pdf/2605.22138.pdf GitHub

Abstract:
How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain‑of‑thought), trained end‑to‑end expecting planning to emerge implicitly. Without control over the presence, structure, or horizon of planning, these systems dramatically increase reasoning length, yielding inefficient token use without reliable accuracy gains. We argue efficient agentic reasoning benefits from decomposing decision‑making into three systems: simulative reasoning (System II) grounding deliberation in future‑state prediction via a world model; self‑regulation (System III) deciding when and how deeply to plan via a learned configurator; and reactive execution (System I) handling fine‑grained action. Simulative reasoning provides unified planning across diverse tasks without per‑domain engineering, while self‑regulation ensures the planner is invoked only when needed. To test this, we develop SR^2AM (Self‑Regulated Simulative Reasoning Agentic LLM), realizing both as distinct stages within an LLM's chain‑of‑thought, with the LLM as world model. We explore two instantiations: recording decisions from a prompted multi‑module system (v0.1) and reconstructing structured plans from traces of pretrained reasoning LLMs (v1.0), trained via supervised then reinforcement learning (RL). Across math, science, tabular analysis, and web information seeking, v0.1‑8B and v1.0‑30B achieve Pass@1 competitive with 120‑355B and 685B‑1T parameter systems respectively, while v1.0‑30B uses 25.8‑95.3% fewer reasoning tokens than comparable agentic LLMs. RL increases average planning horizon by 22.8% while planning frequency grows only 2.0%, showing it learns to plan further ahead rather than more often. More broadly, learned self‑regulation instantiates a principle we expect to extend beyond planning to how agents govern their own learning and adaptation.

PaperId: 770, https://arxiv.org/pdf/2605.22133.pdf GitHub

Abstract:
Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features or alignment targets. Motivated by this, we study protein representations for predicting structures beyond conventional function annotation. We propose TriProRep, a structure‑aware pretraining method that jointly models three aligned residue‑level views: amino‑acid identity, backbone geometry, and local full‑atom geometry, discretely encoded via VQ‑VAE tokenizers. By pretraining to recover original tokens from generator‑corrupted views, TriProRep learns to distinguish plausible but incorrect cross‑view augmentations from the original protein. We further introduce RepSP, a benchmark for evaluating protein representations in structure‑predictive settings. RepSP tests three uses of representations: homodimer co‑folding from apo‑chain representations, residue‑level prediction of homodimer‑derived interaction properties, and representation‑aligned monomer structure prediction. Across these tasks, TriProRep improves over sequence‑only and prior structure‑aware representation models, while maintaining competitive performance on conventional benchmarks.

PaperId: 771, https://arxiv.org/pdf/2605.22078.pdf GitHub

Abstract:
Recent advances in Multimodal Large Language Models (MLLMs) have significantly advanced video understanding tasks, yet challenges remain in efficiently compressing visual tokens while preserving spatiotemporal interactions. Existing methods, such as LLaVA family, utilize simplistic pooling or interpolation techniques that overlook the intricate dynamics of visual tokens. To bridge this gap, we propose ST‑GridPool, a novel training‑free visual token enhancement method designed specifically for Video LLMs. Our approach integrates Pyramid Temporal Gridding (PTG), which captures multi‑grained spatiotemporal interactions through hierarchical temporal gridding, and Norm‑based Spatial Pooling (NSP), which preserves high‑information visual regions by leveraging the correlation between token norms and semantic richness. Extensive experiments on various benchmarks demonstrate that ST‑GridPool consistently enhances performance of Video LLMs without requiring costly retraining. Our method offers an efficient and plug‑and‑play solution for improving visual token representations. Our code is available in https://github.com/bingjunluo/ST‑GridPool.

PaperId: 772, https://arxiv.org/pdf/2605.22000.pdf GitHub

Abstract:
Three‑dimensional (3D) histopathology of unprocessed tissues has the potential to transform disease management by enabling volumetric characterization of tissue microarchitecture and in‑vivo assessment. Back‑illumination Interference Tomography (BIT) is a new phase microscopy technology that provides rapid, non‑destructive volumetric imaging of unprocessed tissues. However, translating BIT volumes into clinically interpretable H&E images remains challenging, particularly due to shift‑variant contrast and the absence of quantitative validation benchmarks. We introduce HistoBIT3D, the first voxel‑wise paired BIT and fluorescence‑labeled nuclei dataset, enabling quantitative evaluation of structural preservation in unsupervised virtual staining against ground‑truth nuclear distributions. Using this dataset, we present a novel virtual staining framework that translates BIT volumes with shift‑variant contrast into realistic H&E volumes by leveraging bidirectional multiscale content consistency and cross‑domain style reuse to enhance structural fidelity and perceptual realism. Our method achieves state‑of‑the‑art realism metrics while significantly improving 3D nuclei segmentation accuracy and boundary preservation under zero‑shot Cellpose evaluation. Together, these contributions establish a quantitatively validated, structurally faithful, and scalable pipeline for 3D virtual H&E staining, advancing the paradigm of slide‑free, volumetric computational histopathology. Our data and code are available at: https://github.com/aasong113/HistoBIT3D_VirtualStaining.

PaperId: 773, https://arxiv.org/pdf/2605.21988.pdf GitHub

Abstract:
Video large language models (Video LLMs) achieve strong benchmark accuracy, yet often answer video questions through shortcuts such as single‑frame cues and language priors rather than by tracking spatiotemporal dynamics. This issue is exacerbated in RL post‑training, where correctness‑only rewards can further reinforce shortcut policies that obtain high reward without tracking video dynamics. We address this by asking a controlled counterfactual question: if the visual world changed while the question remained fixed, should the answer change or stay the same? Based on this view, we propose Counterfactual Relational Policy Optimization (CRPO), a dual‑branch RL framework for improving \emphspatiotemporal sensitivity. CRPO constructs counterfactual videos through horizontal flips and temporal reversals, trains on both original and counterfactual branches, and introduces a Counterfactual Relation Reward (CRR) between their answers. CRR encourages answers to change for dynamic questions and remain unchanged for static questions. This cross‑branch constraint makes it difficult for shortcut policies to be consistently rewarded across both branches. To evaluate this property, we introduce DyBench, a paired counterfactual video benchmark with 3,014 videos covering reversible dynamics, moving direction, and event sequence, together with a strict pair‑accuracy metric that prevents fixed‑answer shortcuts from inflating scores. Experiments show that CRPO outperforms prior RL methods on spatiotemporal‑sensitive evaluations while maintaining competitive general video performance. On Qwen3‑VL‑8B, CRPO improves DyBench P‑Acc by +7.7 and TimeBlind I‑Acc by +8.2 over the base model, indicating improved spatiotemporal sensitivity rather than stronger reliance on static shortcuts. The project website can be found at https://ddz16.github.io/crpo.github.io/ .

PaperId: 774, https://arxiv.org/pdf/2605.21954.pdf GitHub GitHub

Abstract:
Video temporal grounding (VTG), which localizes the start and end times of a queried event in an untrimmed video, is a key test of whether multimodal large language models (MLLMs) understand not only what happens but also when it happens. Although modern MLLMs describe video content fluently, their timestamp predictions remain unreliable, while existing remedies either require costly post‑training on temporal annotations or rely on coarse training‑free heuristics. In this work, we probe the cross‑modal attention of MLLMs and uncover a perception‑generation gap. Our key finding is that MLLMs often know the target interval during prefill, but lose this signal when generating the final answer. In the prefill stage, a sparse set of attention heads, which we call \emphTemporal Grounding Heads (TG‑Heads), concentrates query‑to‑video attention on the ground‑truth interval. During autoregressive decoding, however, the answer tokens shift attention away from this interval toward visually salient but query‑irrelevant segments. This observation motivates an inference‑time read‑then‑regenerate framework. We first convert TG‑Head prefill attention into a debiased frame‑level relevance signal and extract the high‑attention interval it highlights. We then re‑invoke the MLLM with visual context restricted to this interval, using video cropping or attention masking to suppress distractors. Without parameter updates and architectural changes, our framework consistently improves MiMo‑VL‑7B, Qwen3‑VL‑8B, and TimeLens‑8B on three VTG benchmarks, with gains of up to +3.5 mIoU. The project website can be found at https://ddz16.github.io/mllmsknowwhen.github.io/.

PaperId: 775, https://arxiv.org/pdf/2605.21861.pdf GitHub

Abstract:
Multi‑modality medical vision (MV) foundation models (FM) are fundamentally challenged by pronounced Non‑IID feature statistics across heterogeneous imaging modalities. Monolithic self‑supervised optimization on such data induces conflicting gradients, driving representations to collapse toward modality‑dominant shortcuts. This work reframes this failure as an imbalance between specialization and coordination in emergent modularity, and proposes Director‑Experts (DEX), a modular network that explicitly regulates these dynamics in stacked modules. Each DEX module comprises a pool of experts, dynamically adapted by our image‑wise activation strategy, autonomously specializing in modality‑dominant statistics, together with a director, updated via our group exponential moving average, which distills multi‑expert knowledge into a shared space for semantic integration across modalities, thus driving the emergence of modular representations. We curate a new benchmark, Medical Vision Universe, over 4 million images across 10 modalities, which provides a FM‑level pre‑training with the broadest coverage of distinct imaging modalities to our DEX. Extensive evaluations on 26 downstream tasks demonstrate improved optimization behavior and transferability, indicating DEX as a principled step toward general‑purpose multi‑modality medical AI. Our code and dataset will be opened at https://github.com/YutingHe‑list/DEX.

PaperId: 776, https://arxiv.org/pdf/2605.21856.pdf GitHub

Abstract:
Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the objective evaluation of these capabilities. This problem is further exacerbated by malicious model publishers who use evasive, or indirect, contamination strategies, such as paraphrasing benchmark data to evade existing detection methods and artificially boost leaderboard performance. Current approaches struggle to reliably detect such stealthy contamination. In this work, we uncover a critical phenomenon: a model's generated reasoning steps actively mask its underlying memorization. Inspired by this, we propose the Zero‑CoT Probe (ZCP), a novel black‑box detection method that deliberately truncates the entire Chain‑of‑Thought (CoT) process to expose latent shortcut mappings. To further isolate memorization from the model's intrinsic problem‑solving capabilities, ZCP compares the model's zero‑CoT performance on the original benchmark against an isomorphically perturbed reference dataset. Furthermore, we introduce Contamination Confidence, a metric that quantifies both the likelihood and severity of contamination, moving beyond simple binary classifications. Extensive experiments on both previously identified contaminated models and specially fine‑tuned contaminated models demonstrate that ZCP robustly detects both direct and evasive data contamination. The code for ZCP is accessible at https://github.com/Yifan‑Lan/zero‑cot‑probe.

PaperId: 777, https://arxiv.org/pdf/2605.21854.pdf GitHub GitHub

Abstract:
Vision‑Language‑Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete‑token autoregression (e.g. OpenVLA) and continuous‑action flow‑matching (e.g. pi‑0.5). Yet preference alignment via Direct Preference Optimisation (DPO) ‑‑ the de‑facto post‑training step in language models ‑‑ has been studied almost exclusively on autoregressive VLAs. We present CrossVLA, an empirical study of cross‑paradigm VLA post‑training. Three contributions: (i) a surrogate flow‑matching log‑probability estimator that lets DPO operate on continuous‑action backbones without probability‑flow ODE integration; (ii) a head‑to‑head comparison of LoRA and DoRA as the parameter‑efficient layer for VLA DPO, finding DoRA improves over OpenVLA SFT by a mean +10.4 pp across LIBERO 4‑suite (600 trials, 3 seeds) ‑‑ per‑suite +20.0 Object, +11.0 Long‑horizon, +8.0 Goal, +2.7 Spatial ‑‑ with zero seed variance on Object (38/50 on each of 3 seeds); (iii) an inference‑time anatomy showing the denoise loop dominates 78.6% of sample_actions latency and prefix‑K/V caching a la VLA‑Cache caps at a 21% acceleration ceiling ‑‑ both chunk‑level and token‑level cache strategies degrade success rate to 0‑80% in our benchmarks. We further pretrain a multi‑view + temporal projection head on 6000 LIBERO frames, achieving 99.5% k‑NN recall@1 for same‑task retrieval (36x over random), available as a downstream initialisation. All code, ckpts, training logs, and reproduction scripts are open at https://github.com/lz‑googlefycy/vla‑lab.

PaperId: 778, https://arxiv.org/pdf/2605.21835.pdf GitHub

Abstract:
The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PET) is important to oncologic imaging. However, existing deep learning methods for PET/CT remain largely task‑specific, are often trained on single‑center cohorts, or adopt dual‑branch fusion schemes that delay cross‑modal interaction and underutilize early spatial correspondence between PET and CT. To address these limitations, we present an open‑source, multi‑center, whole‑body FDG PET/CT foundation model utilizing 4,997 harmonized scans from four public datasets. Our framework employs hierarchical UNet‑shaped backbones with early channel‑wise concatenation, enabling anatomical and metabolic features to interact from the first embedding layer onward. We further introduce a masked autoencoding objective based on zero‑mean imputation, combined with a weighted global reconstruction loss. This design avoids non‑physical intensity discontinuities at masked‑region boundaries that arise from learnable mask tokens. On downstream AutoPET lesion segmentation, the proposed models demonstrate strong label efficiency: with only 10% of the labeled training data, they achieve performance comparable to models trained from scratch on the full dataset. Under extreme 5‑shot linear probing, joint PET/CT pretraining also achieves higher Dice scores than separated‑modality pretraining. This multi‑center foundation model demonstrates label efficiency and cross‑modality representation learning for PET/CT tumor segmentation. It provides a robust, open‑source basis for advancing automated oncologic imaging, significantly reducing the need for large‑scale manual annotations in clinical practice.

PaperId: 779, https://arxiv.org/pdf/2605.21789.pdf GitHub

Abstract:
Real‑time jet tagging is critical for identifying short‑lived particle decays in the high‑throughput detectors of the Large Hadron Collider, where real‑time trigger systems responsible for deciding which collision events to store impose strict latency and accuracy constraints. While transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, their quadratic self‑attention cost makes inference restrictive on trigger budget. Existing efficient variants reduce the computational cost, but hinder the classification performance. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT‑JeT), which combines two mechanisms: a physics‑inspired geometric message‑passing module that encodes local detector‑plane structure, and a hierarchical patch‑based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch‑token communication. Within a restricted budget, PHAT‑JeT achieves state‑of‑the‑art accuracy and background rejection among all resource‑constrained jet tagging models on four benchmarks (\textschls4ml, JetClass, Top Tagging, and Quark‑‑Gluon). Our code is available at https://github.com/aaronw5/PHAT‑JeT.

PaperId: 780, https://arxiv.org/pdf/2605.21669.pdf GitHub

Abstract:
Hippocampal subfield segmentation requires high‑resolution T2w turbo spin echo (TSE) MRI, yet this sequence is susceptible to motion artifacts, leading to substantial data loss. We developed a conditional generative model (MRecover) that synthesizes routinely acquired T1w images to create TSE images with autoregressive slice conditioning for volumetric consistency. Trained on 7T MRI data (n=577), the model achieved high in‑domain fidelity (n=148, SSIM=0.84, FSIM=0.94) and generalized well to out‑of‑domain 3T data: subfield volumes from synthesized and the as‑acquired images closely matched: (n=416, r=0.87‑0.97) and yielded 31.8% more analyzable subjects in the motion‑affected ADNI3 dataset after quality control (593 vs 450). The synthesized images also achieved larger effect sizes due to increasing the sample size for diagnostic group differences in hippocampal subfield atrophy (whole hippocampus ε^2= 0.121‑0.100 vs. 0.086‑0.062, left‑right hemispheres). Project page: https://jinghangli98.github.io/MRecover/

PaperId: 781, https://arxiv.org/pdf/2605.21630.pdf GitHub

Abstract:
Although LLMs have made substantial progress in reasoning, systematically producing frontier‑level reasoning data remains difficult. Existing synthesis methods often have limited visibility into the structural factors that govern problem difficulty, which can result in narrow diversity and unstable difficulty control. In this work, we view the difficulty of a reasoning problem as arising from the accumulation of atomic knowledge‑reasoning transformations, which we term thought modes. Building on this perspective, we propose MindLoom, a framework for synthesizing frontier‑level reasoning data through compositional thought mode engineering. Given a collection of hard problems with verified solutions, MindLoom first decomposes those solutions into thought mode chains that reveal each problem's construction logic. It then trains a retrieval model that matches problem states to compatible thought modes, providing guidance on which reasoning challenges to introduce during synthesis. New problems are composed by iteratively applying retrieved thought modes to seed questions, with distribution‑aligned sampling to encourage diverse reasoning coverage. Finally, a rollout‑based judging stage labels generated questions by difficulty and supplies judged‑correct responses for supervised fine‑tuning. We evaluate MindLoom on nine benchmarks covering five STEM disciplines and four mathematical reasoning tasks across multiple model families and sizes. Models fine‑tuned on MindLoom‑generated data achieves favorable performances over base models, distillation, and external‑data baselines across the reported benchmarks. Ablation studies indicate the contribution of each component, and further analysis suggests that MindLoom covers a broad range of reasoning patterns while maintaining useful difficulty control. We have open‑sourced our implementation at https://github.com/EachSheep/MindLoom.

PaperId: 782, https://arxiv.org/pdf/2605.21606.pdf GitHub

Abstract:
On‑policy self‑distillation (OPSD) trains a student on its own rollouts using a privileged teacher, but its standard objective weights all generated tokens equally, implicitly treating the privileged teacher target as equally reliable at every student‑visited prefix. Existing entropy‑based OPD methods relax this uniformity by modulating token‑level supervision with teacher entropy, but high teacher entropy in reasoning has an ambiguous reliability meaning: it can reflect either non‑viable uncertainty or benign solution diversity. To identify this phenomenon, we introduce a branch‑viability diagnostic. Specifically, we record next‑token alternatives from the privileged‑answer teacher prompt, force each alternative after the student prompt plus its on‑policy spine prefix, and test whether the resulting student‑template continuation recovers the correct answer. On Qwen3‑4B, we find that an oriented within‑sequence position score is the strongest tested predictor of teacher‑token reliability, reaching an area‑under‑ROC‑curve (AUROC) of 0.83; local uncertainty scores are at most 0.57. Motivated by this trajectory‑level structure, we propose Position‑Weighted On‑Policy Self‑Distillation (PW‑OPSD), which applies an increasing position weight while keeping the same student rollout, privileged teacher pass, and clipped forward‑KL target as OPSD. In our comprehensive evaluations with different random seeds, the diagnostic‑derived PW‑OPSD improves AIME 2024 and AIME 2025 Avg@12 by +1.0 and +1.1 points, and a generalization evaluation on two larger‑scale models from different families, DeepSeek‑R1‑Distill‑Llama‑8B and Olmo‑3‑7B‑Think, also demonstrates consistent aggregate Avg@12 improvements. These results show that teacher‑token reliability in reasoning distillation is trajectory‑structured and can be utilized without additional teacher computation.

PaperId: 783, https://arxiv.org/pdf/2605.21602.pdf GitHub

Abstract:
Many safety and alignment failures of large language models (LLMs) occur due to out‑of‑distribution (OOD) situations: unusual prompt or response patterns that are unforeseen by model developers. We systematically study whether LLM monitoring pipelines can detect these OOD alignment failures by introducing a benchmark called Misalignment Out Of Distribution (MOOD). It is difficult to find failures that are truly OOD for off‑the‑shelf models trained on vast safety datasets. We sidestep this by including a restricted training set in MOOD that we use to train our own monitors, as well as seven test sets with diverse alignment failures that are outside the training distribution. Using MOOD, we find that guard models (safety classifiers) often fail to generalize OOD. To fix this, we propose combining guard models with OOD detectors. We test four types of OOD detectors and find that a combination of a guard model with Mahalanobis distance and perplexity‑based OOD detectors can improve recall from 39% to 45%. We also establish positive scaling trends across model scales for monitors that combine a guard model and OOD detector; we find that incorporating OOD detection into monitoring achieves a higher recall gain than using a guard model with 20 times more parameters. Our work suggests that OOD detection should be a crucial component of LLM monitoring and provides a foundation for further work on this important problem. We release the code and data for our experiments publicly, and you can find the relevant links here: https://github.com/Dylan102938/mood‑bench.

PaperId: 784, https://arxiv.org/pdf/2605.21545.pdf GitHub

Abstract:
Frontier large language models are increasingly deployed as orchestration backbones for biological research workflows, yet no shared evidence base exists for comparing their refusal behaviour on legitimate research prompts. RefusalBench, introduced here, is a matched‑triple benchmark of 141 prompts in 47 bundles that holds task framing constant while varying only biological risk tier (benign, borderline, dual‑use), enabling tier‑conditioned comparisons robust to subdomain confounding. A 15‑prompt should‑refuse positive‑control module establishes per‑model calibration floors; three models fail to refuse even these prompts. Across 19 frontier models in the May 2026 snapshot, strict refusal rates span 0.1% to 94.6% on identical prompts. Jurisdiction does not predict refusal in this snapshot (Mann‑Whitney U, p = 0.393; EU n = 1, US bimodal); provider identity does, with Anthropic's API stack predicting refusal at OR = 21.03 (95% CI: 14.58‑30.34 prompt‑clustered; 5.70‑77.55 under model‑clustered GEE). This effect is best read as access‑path‑level rather than model‑weight‑level: 99.8% of Anthropic's strict refusals carry the same safety_policy adjudicated reason code, consistent with a small set of canonical refusal templates rather than case‑by‑case model reasoning. Strict refusal rate misranks safety calibration: Grok 4.20 achieves the highest tier discrimination (Youden's J = 0.787) while ranking only seventh by overall refusal rate, and Claude Opus 4.7's J dropped 65% from prior versions with no improvement in dual‑use detection. Nine of 18 frontier models exhibit a hedge‑but‑help partial‑compliance pattern at dual‑use tier that binary refusal metrics cannot detect.

PaperId: 785, https://arxiv.org/pdf/2605.21523.pdf GitHub

Abstract:
Steganalysis models excel on benchmark datasets but struggle in the wild when analyzed images are produced by a processing pipeline unseen during training. This problem known as Cover Source Mismatch (CSM) is particularly hard in realistic settings where practitioners (1) have access to only a small, unlabeled dataset, (2) are unsure of the processing techniques applied to these images, and (3) lack information on the proportion of covers and stegos in that set. To answer this challenge, we introduce TADA (Target Alignment through Data Adaptation), a framework learning to emulate the unknown processing pipeline from a small unlabeled target set. This architecture is trained with a loss combining residual covariance alignment, residual distribution matching, and a \ell^2 loss constraining the emulator to produce realistic images. Across toy and operational targets, TADA yields substantial gains in robustness to CSM and improves operational generalization compared to strong holistic and atomistic baselines. Additional resources are available at this link: https://github.com/RonyAbecidan/TADA

PaperId: 786, https://arxiv.org/pdf/2605.21496.pdf GitHub

Abstract:
Frontier language models are being deployed into clinical workflows faster than the infrastructure to evaluate them safely. Static medical‑QA benchmarks miss the failure modes that matter in emergency medicine: trajectory‑level safety collapse, tool misuse, and capitulation under sustained clinical pressure. We present HealthCraft, the first public reinforcement‑learning environment that rewards trajectory‑level safety under realistic emergency‑medicine conditions, adapted from Corecraft. It is built on a FHIR R4 world state with 14 entity types and 3,987 seed entities, exposes 24 MCP tools, and defines a dual‑layer rubric that zeroes reward whenever any safety‑critical criterion is violated. We release 195 tasks across six categories, graded against 2,255 binary criteria (515 safety‑critical); a post‑hoc 10‑task negative‑class slate extends this to 205 tasks and 2,337 criteria. V8 results on two frontier models show Claude Opus 4.6 at Pass@1 24.8% [21.5‑28.4] and GPT‑5.4 at 12.6% [10.2‑15.6], with safety‑failure rates of 27.5% and 34.0%. On multi‑step workflows ‑ the closest proxy to real emergency care ‑ performance collapses to near zero (Claude 1.0%, GPT‑5.4 0.0%) despite partial competence on individual steps. Six infrastructure bugs fixed between pilots v2 and v8 re‑ordered which model "looks stronger," evidence that infrastructure fidelity is part of the measurement. A deterministic LLM‑judge overlay bounds evaluator noise, and a 60‑run negative‑class smoke pilot shows the reward signal is not drop‑in training‑safe: restraint criteria pass at 0.929 prevalence, a gameability an eval harness can tolerate but a training reward cannot. We scaffold coupling to a Megatron+SGLang+GRPO loop per Corecraft Section 5.2 and leave training‑reward ablations as future work. Environment, tasks, rubrics, and harness are released under Apache 2.0.

PaperId: 787, https://arxiv.org/pdf/2605.21492.pdf GitHub

Abstract:
No feature ranking can be simultaneously faithful, stable, and complete when features are collinear. For collinear pairs, ranking reduces to a coin flip. We prove this impossibility, quantify it for four model classes, resolve it via ensemble averaging (DASH), and machine‑verify it with 305 Lean 4 theorems. We characterize the complete attribution design space: exactly two families of methods exist ‑‑ faithful‑complete methods (unstable, with rankings that flip up to 50% of the time) and ensemble methods like DASH (stable, reporting ties for symmetric features) ‑‑ and no method lies outside this dichotomy. The impossibility is quantitative: the attribution ratio diverges as 1/(1‑rho^2) for gradient boosting, is infinite for Lasso, and converges for random forests. DASH (Diversified Aggregation of SHAP) is provably Pareto‑optimal among unbiased aggregations, achieving the Cramer‑Rao variance bound with a tight ensemble size formula. In a survey of 77 public datasets, 68% exhibit attribution instability. Switching to conditional SHAP does not escape the impossibility when features have equal causal effects. The framework includes practical diagnostics ‑‑ a Z‑test workflow and single‑model screening tool ‑‑ and has direct consequences for fairness auditing: SHAP‑based proxy discrimination audits are provably unreliable under collinearity. The design space theorem, diagnostics, and impossibility are mechanically verified in Lean 4 (305 theorems from 16 axioms, 0 sorry) ‑‑ to our knowledge, the first formally verified impossibility in explainable AI.

PaperId: 788, https://arxiv.org/pdf/2605.21482.pdf GitHub

Abstract:
Deep research, in which an agent searches the open web, collects evidence, and derives an answer through extended reasoning, is a prominent use case for frontier language models. Frontier deep research products score high on existing benchmarks, making it difficult to distinguish their capabilities from current evaluation data alone. We introduce DeepWeb‑Bench, a deep research benchmark that is substantially harder than existing benchmarks for the current frontier. Difficulty comes from three properties of the data itself: each task requires massive evidence collection, cross‑source reconciliation, and long‑horizon multi‑step derivation. We represent these three sources of difficulty as four capability families (Retrieval, Derivation, Reasoning, and Calibration) and report results sliced by family. Every reference answer is accompanied by a source‑provenance record with four disclosure levels and cross‑source checks where available, making scores easier to audit against the underlying evidence. We evaluate DeepWeb‑Bench on nine frontier models and report three findings: (1) retrieval is not the bottleneck, as retrieval failures account for only 12‑14% of errors while derivation and calibration failures account for over 70%; (2) strong and weak models fail in qualitatively different ways, with strong models' errors dominated by incomplete derivation and weak models' by hallucinated precision; and (3) models exhibit genuine specialization across domains, with cross‑model agreement of only rho = 0.61 and per‑case disagreement reaching 18.8 percentage points. The public benchmark release includes the data, rubrics, and evaluation code.

PaperId: 789, https://arxiv.org/pdf/2605.21405.pdf GitHub

Abstract:
Third‑party Python libraries introduce dependency management overhead, supply chain risk, and deployment friction in constrained environments. A natural question is how much of this ecosystem can be replicated using only Python's standard library ‑‑ and at what correctness and performance cost. We address this empirically through zerodep, a growing collection of single‑file Python modules, each a stdlib‑only reimplementation of a popular third‑party library, developed with LLM assistance under strict constraints: no external imports, single file, drop‑in API compatibility, and mandatory correctness validation against the reference library. Spanning over 40 modules across 12 categories ‑‑ including serialization, networking, cryptography, agent protocols, and text processing ‑‑ zerodep provides a controlled testbed for two interrelated questions: (1) Where does the stdlib suffice? and (2) Can LLMs effectively generate correct, performant code under tight symbolic constraints? Systematic benchmarking shows that stdlib‑only implementations achieve performance parity (within 2x of the reference) in the majority of cases. The primary performance cliff is C‑extension‑backed computation (image processing, binary serialization, low‑level crypto), not the inherent overhead of pure‑Python third‑party libraries. Conversely, many widely‑used libraries carry architectural overhead that LLM‑generated stdlib reimplementations avoid, yielding 5‑‑115x speedups in several categories. We characterize the stdlib capability boundary across complexity tiers and library categories, discuss where LLM‑assisted development succeeds and where it requires iterative human correction, and examine implications for dependency‑free software engineering at scale. zerodep is open‑source at https://github.com/Oaklight/zerodep.

PaperId: 790, https://arxiv.org/pdf/2605.21318.pdf GitHub

Abstract:
Large language models (LLMs) are highly sensitive to the prompts used to specify task objectives and behavioral constraints. Many recent prompt optimization methods iteratively rewrite prompts using LLM‑generated feedback, but the resulting prompts often become longer, accumulate narrow sample‑specific rules, and generalize poorly beyond the training distribution. We study this failure mode as prompt distributional overfitting and argue that it reflects a lack of representation control in discrete text‑space optimization. We formalize this view through representational inefficiency, a dual‑factor measure that decomposes prompt inefficiency into capacity cost and scope narrowness, attributing distributional prompt overfitting to their coupled growth during optimization. We propose TextReg, a regularization framework that realizes a soft‑penalty objective through regularized textual gradients, combining Dual‑Evidence Gradient Purification, Semantic Edit Regularization, and Regularization‑Guided Prompt Update. Across multiple reasoning benchmarks, TextReg substantially improves out‑of‑distribution (OOD) generalization, with accuracy gains of up to +11.8% over TextGrad and +16.5% over REVOLVE.

PaperId: 791, https://arxiv.org/pdf/2605.21182.pdf GitHub

Abstract:
Manga is a culturally distinctive multimodal medium and one of the most influential forms of Japanese popular culture. As AI systems increasingly target manga understanding, OCR, and translation, Manga109 has become a foundational dataset for manga‑related AI research. However, the current Manga109 dataset contains transcription errors and coarse annotations, which do not align well with modern OCR and multimodal manga understanding tasks. In this work, we revisit the dialogue text annotations of Manga109 and identify five categories of annotation issues, including transcription errors, missing text regions, overlapping dialogue and onomatopoeia, and under‑segmented speech balloons. To address these issues, we combine OCR‑based issue detection and manual revision to construct Manga109‑v2026, revising approximately 29,000 dialogue annotations. Our revisions better align Manga109 with modern OCR and multimodal manga understanding systems while preserving expressive structures characteristic of manga.

PaperId: 792, https://arxiv.org/pdf/2605.21028.pdf GitHub

Abstract:
Autoregressive long video generation often adopts bounded‑memory streaming for efficiency, typically combining local windows for short‑term continuity with static early‑frame sinks as long‑range anchors. However, this fixed allocation keeps early frames cached even when the current visual state has substantially diverged from them, while discarding potentially more relevant intermediate history. As a result, the retained long‑range context may become less adaptive and bias generation toward outdated cues; in severe cases, RoPE‑induced phase re‑alignment can homogenize inter‑head attention and cause sink collapse, where content regresses toward sink frames. We propose DySink, a retrieval‑based framework that maintains a compact memory bank and selects visually relevant historical frames as dynamic frame sinks. DySink couples adaptive retrieval with a sink anomaly gate, which detects excessive inter‑head consensus over retrieved context and suppresses collapse‑prone context. Experiments on minute‑long videos show that DySink consistently improves dynamic degree over strong baselines while also achieving higher temporal quality. The code and model weights will be released at https://github.com/yebo0216best/DySink.

PaperId: 793, https://arxiv.org/pdf/2605.20998.pdf GitHub

Abstract:
Aspect‑Term Sentiment Analysis (ATSA) in multi‑aspect sentences faces a fundamental tradeoff between efficiency and expressiveness. Existing models either re‑encode the sentence for each aspect or rely on static use of deep representations, leading to redundant computation and limited adaptivity. We argue that Transformer depth is a costly, queryable resource, and propose DABS, a single‑pass inference framework that encodes each sentence once to construct a reusable, depth‑ordered substrate. Each aspect then queries this shared representation to selectively read relevant tokens and abstraction levels, without re‑encoding. This decouples shared sentence encoding from lightweight, aspect‑conditioned readout. Experiments on four ATSA benchmarks show that DABS achieves competitive performance while reducing end‑to‑end computation by up to 60% in multi‑aspect settings (M >= 2). Further analyses indicate that adaptive depth querying is most beneficial for linguistically complex cases such as negation and contrast. Code is publicly available at https://github.com/panzhzh/acl‑dabs

PaperId: 794, https://arxiv.org/pdf/2605.20965.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) have shown remarkable performance on a wide range of vision‑language tasks. Despite this progress, they are still prone to hallucination, generating responses that are inconsistent with visual content. In this work, we find that LVLMs tend to hallucinate when they pay insufficient attention to the correct visual evidence and gradually forget it during the generation process. We empirically find that although LVLMs overall attend insufficiently to visual evidence, they exhibit sensitivity to the correct visual evidence in specific layers, with notable inter‑layer discrepancy. Motivated by this observation, we propose a novel hallucination mitigation method that enhances visual evidence based on Inter‑Layer Visual Attention Discrepancy (ILVAD). Specifically, we obtain the attention weights from early generated tokens to visual tokens across layers and identify the tokens that are repeatedly activated as visual evidence, forming a saliency map. We then enhance attention to visual evidence during generation through the saliency map to reduce visual forgetting. In addition, we leverage the saliency map to obtain attention scores of generated text to visual evidence, in order to select and emphasize text tokens that are strongly grounded in visual evidence. Our method is training‑free and plug‑and‑play. Multiple benchmark evaluations conducted on five recently released models show that our method can consistently mitigate hallucinations in different LVLMs over various architectures. Code is available at https://github.com/ytx‑ML/ILVAD.

PaperId: 795, https://arxiv.org/pdf/2605.20922.pdf GitHub

Abstract:
Oscillations and synchronization are widely believed to play a fundamental role in representation and computation. However, existing machine learning approaches based on synchronization dynamics have largely been confined to specialized settings such as object discovery, with limited evidence of scalability to standard vision benchmarks or logic reasoning tasks. We propose the Winfree Oscillatory Neural Network (WONN), a dynamical neural architecture based on generalized Winfree dynamics. WONN evolves representations on the torus (S^1)^d through structured oscillatory interactions, combining phase‑based inductive biases with flexible and hierarchical interaction mechanisms instantiated as either fixed trigonometric mappings or learnable neural networks. We evaluate WONN on image recognition and complex reasoning tasks, including CIFAR, ImageNet, Maze‑hard, and Sudoku. Across these domains, WONN achieves competitive or superior performance with strong parameter efficiency. In particular, WONN is, to our knowledge, the first synchronization‑based oscillatory architecture to scale competitively to ImageNet‑1K. Furthermore, on Maze‑hard, WONN achieves 80.1% accuracy using only 1% of the parameters of prior state‑of‑the‑art models. These results suggest that structured oscillatory dynamics provide a scalable and parameter‑efficient alternative to conventional neural architectures.

PaperId: 796, https://arxiv.org/pdf/2605.20901.pdf GitHub

Abstract:
We propose VISTA, a V‑JEPA Integrated StillFast Temporal Anticipator for the Ego4D Short‑Term Object Interaction Anticipation (STA) Challenge at EgoVis 2026. Given an egocentric video timestamp, the task requires anticipating the next human‑object interaction, including the future active object's bounding box, noun category, verb category, time‑to‑contact, and confidence score. VISTA follows a StillFast‑style design that combines object‑centric spatial detection with short‑horizon temporal context. Specifically, a COCO‑pretrained Faster R‑CNN ResNet‑50 FPN detector generates object proposals from the last observed high‑resolution frame, while a frozen V‑JEPA 2.1 temporal branch extracts clip‑level egocentric context from the observed video. The temporal representation is injected into the detection pathway through feature modulation and ROI‑level context fusion. The fused proposal features are then passed to multi‑head STA predictors for box refinement, noun classification, verb classification, time‑to‑contact regression, and interaction confidence estimation. For the final submission, we further ensemble complementary predictions to improve robustness. Experimental results on the official challenge server show that VISTA achieves first place in the EgoVis 2026 Ego4D STA Challenge. Our code will be released at https://github.com/CorrineQiu/VISTA.

PaperId: 797, https://arxiv.org/pdf/2605.20856.pdf GitHub

Abstract:
Language‑conditioned manipulation policies typically process instructions and observations through shared network parameters. This task‑state entanglement provides a pathway for observation leakage ‑‑ networks learn scene‑to‑action shortcuts that bypass language grounding entirely. DISC eliminates this failure structurally. Rather than conditioning a universal policy on language, DISC uses a hypernetwork to generate the entire parameter set of a task‑specific visuomotor policy from the instruction alone. The generated policy never directly accesses language; therefore, its task‑awareness must come from the language. Consequently, observation leakage has no pathway to emerge. On the other hand, generating coherent high‑dimensional policy weights is itself a challenging problem. We address it with a two‑stage hypernetwork whose refinement stage embeds the structure of gradient‑based optimization as a feed‑forward inductive bias, producing globally consistent parameters without actual gradient computation. Trained entirely from scratch on standard data budgets, DISC outperforms all entangled baselines on LIBERO‑90 and Meta‑World, with advantages that widen on complex, long‑horizon tasks ‑‑ and surpasses the large‑scale pretrained π_0 despite using no external pretraining data. On a real‑world benchmark where all tasks share identical visual context, DISC substantially outperforms entangled alternatives, directly confirming that language‑generated policy parameters, not visual shortcuts, drive behavior. The hypernetwork further learns a semantically structured parameter manifold that enables few‑shot adaptation from minimal demonstrations and robust generalization across paraphrased instructions. Our code is available at: https://github.com/ReNginx/DISC.

PaperId: 798, https://arxiv.org/pdf/2605.20834.pdf GitHub

Abstract:
Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF), offering theoretical equivalence with simpler implementation. We prove this equivalence is conditional rather than universal, depending on an implicit assumption frequently violated in practice: the RLHF‑optimal policy must prefer human‑preferred responses. When this assumption fails, DPO optimizes relative advantage over the reference policy rather than absolute alignment with human preferences, leading to pathological convergence where policies decrease DPO loss while preferring dispreferred responses. We characterize when this assumption is violated, show the existence of an undesirable solution space, and prove that DPO and RLHF optimize fundamentally different objectives in such cases. To address this, we introduce Constrained Preference Optimization (CPO), augmenting RLHF with constraints for provable alignment. We further provide a geometric interpretation through soft margin ranking, revealing that DPO implements margin ranking with potentially negative targets. Our theoretical analysis establishes when DPOs' guarantees hold and provides solutions preserving simplicity with provable alignment. Comprehensive experiments on standard benchmarks demonstrate that CPO achieves state‑of‑the‑art performance. Code is available at: https://github.com/visitworld123/CPO.

PaperId: 799, https://arxiv.org/pdf/2605.20758.pdf GitHub

Abstract:
Inference‑time guided sampling steers state‑of‑the‑art diffusion and flow models without fine‑tuning by interpreting the generation process as a controllable trajectory. This provides a simple and flexible way to inject external constraints (e.g., cost functions or pre‑trained verifiers) for controlled generation. However, existing methods often fail when composing multiple constraints simultaneously, which leads to deviations from the true data manifold. In this work, we identify root causes of this off‑manifold drift and find that the approximation error scales severely with gradient misalignment. Building on these findings, we propose Conflict‑Aware Additive Guidance (g^\textcar), a lightweight and learnable method, which actively rectifies off‑manifold drift by dynamically detecting and resolving gradient conflicts. We validate g^\textcar across diverse domains, ranging from synthetic datasets and image editing to generative decision‑making for planning and control. Our results demonstrate that g^\textcar effectively rectifies off‑manifold drift, surpassing baselines in generation fidelity while using light compute. Code is available at https://github.com/yuxuehui/CAR‑guidance.

PaperId: 800, https://arxiv.org/pdf/2605.20749.pdf GitHub

Abstract:
Gated Linear Units (GLU) and their variants are widely adopted in modern open‑source large language model architectures and consistently outperform their non‑gated counterparts, yet the underlying reasons for this advantage remain unclear. In this work, we study GLU by analyzing two‑layer networks in the neural tangent kernel (NTK) regime. Our analysis reveals that the GLU structure reshapes the NTK spectrum, leading to a smaller condition number and a more compact eigenvalue distribution. Building on this finding, we further analyze the resulting training dynamics and show how the reshaped spectrum leads to faster convergence of GLU models, including a characteristic loss‑crossing phenomenon observed between GLU and non‑GLU models. Finally, we empirically observe that GLU has limited impact in reducing the generalization gap on various models, including ViT and GPT‑2, suggesting that its primary benefit lies in accelerating optimization rather than reducing the generalization gap. The code is available at: https://github.com/Zemdalk/GLU‑NTK.

PaperId: 801, https://arxiv.org/pdf/2605.20745.pdf GitHub

Abstract:
Generative verifiers have emerged as a promising paradigm for step‑wise verification, but their verification behavior is often poorly calibrated: they may be under‑critical and miss erroneous steps, or over‑critical and reject correct reasoning. We refer to this tendency to be overly lenient or overly critical as verifier strictness. In this work, we study whether verifier strictness can be controlled through hidden‑state intervention. We uncover a verification‑specific hidden‑state signal: in step‑wise verification, a verifier's tendency to accept or reject a solution step is encoded near the boundary of the corresponding verification paragraph. Exploiting this signal, we show that hidden‑state steering can directly modulate verifier strictness without fine‑tuning. However, uniform steering induces a trade‑off between error detection and correctness certification. To address this, we propose VerifySteer, which exploits latent correctness signals for sample‑level routing and selectively intervenes on paragraph boundaries. Experiments on ProcessBench and Hard2Verify show that VerifySteer outperforms prompt optimization and activation steering baselines, and is competitive with self‑consistency while requiring 4‑7x less inference compute. VerifySteer is also complementary to verification fine‑tuning, providing further gains on top of fine‑tuned verifiers. The code is available at https://github.com/YefanZhou/VerifySteer.

PaperId: 802, https://arxiv.org/pdf/2605.20744.pdf GitHub GitHub

Abstract:
Aligning autonomous agents with human intent remains a central challenge in modern AI. A key manifestation of this challenge is reward hacking, whereby agents appear successful under the evaluation signal while violating the intended objective. Reward hacking has been observed across a wide range of settings, yet methods for reliably measuring it at scale remain lacking. In this work, we introduce a new evaluation paradigm for measuring reward hacking. Whereas prior studies have primarily analyzed it post hoc by inspecting agent trajectories, we instead embed detectable reward hacking opportunities directly into environments. This makes their exploitation verifiable by design, enabling deterministic and automated measurement of whether and how agents exploit such vulnerabilities. We instantiate this approach in TextArena and release Hack‑Verifiable TextArena, a testbed in which reward hacking can be measured reliably. Using this benchmark, we analyze reward hacking behavior across language models in diverse environments and settings. We open source the code at https://github.com/MajoRoth/hack‑verifiable‑environments/.

PaperId: 803, https://arxiv.org/pdf/2605.20722.pdf GitHub

Abstract:
Reinforcement learning improves LLM reasoning, but PPO/GRPO typically use fixed clipping and decoding temperature, which makes training brittle and tuning‑heavy. We propose Adaptive Group Policy Optimization (AGPO), a critic‑free refinement of GRPO that uses group‑level statistics to control both update magnitude and exploration. AGPO uses a shared probe‑derived statistical state to drive two controllers: (i) adaptive clipping, which sets the trust‑region size from reward dispersion and skewness, probe vote entropy, policy entropy, and step‑wise KL drift; and (ii) bidirectional adaptive temperature sampling, which heats or cools decoding around a base temperature according to centered uncertainty relative to a running baseline. On nine English and Chinese math/STEM benchmarks, Qwen2.5‑14B trained with AGPO outperforms PPO/GRPO under the same generated‑token budget, reaching 67.3% on GSM8K and 40.5% on MATH. Gains transfer to Llama‑3‑8B and Gemma‑2‑9B, and ablations confirm both modules are complementary. Our implementation is publicly available at https://github.com/wandugu/paper_agpo.

PaperId: 804, https://arxiv.org/pdf/2605.20678.pdf GitHub

Abstract:
Non‑stationary time series forecasting is challenged by evolving distribution shifts that static models struggle to capture. While Mixture‑of‑Experts (MoE) architectures offer a promising paradigm for decoupling complex drift patterns, existing approaches are limited by fixed expert pools and memoryless routing, hampering their ability to adapt to abrupt regime shifts. To address this, we propose Dynamic TMoE, a framework that unifies architectural evolution with temporal continuity during learning phase. By detecting distribution shifts via Maximum Mean Discrepancy (MMD), we dynamically instantiate heterogeneous experts and prune redundant ones to optimize capacity. Additionally, a temporal memory router leverages recurrent states and an anomaly repository to ensure stable, context‑aware expert selection without requiring test‑time updates. Experiments on nine benchmarks demonstrate state‑of‑the‑art performance, reducing MSE by 10.4% and MAE by 7.8%. Code is available at https://github.com/andone‑07/Dynamic‑TMoE.

PaperId: 805, https://arxiv.org/pdf/2605.20643.pdf GitHub

Abstract:
Self‑distillation enables language models to learn on‑policy from their own trajectories by using the same model as both student and teacher, with the teacher being conditioned on privileged information unavailable to the student. Such information can come in different types or views, such as solutions, demonstrations, feedback, or final answers. This setup provides dense token‑level feedback without relying on a separate external model, but creates a fundamental asymmetry: the teacher may rely on view‑specific information that the student cannot access at inference time. Moreover, the best type of privileged information is often task‑dependent, making it difficult to choose a single teacher view. In this work, we address both these challenges jointly by introducing AVSD (Adaptive‑View Self‑Distillation), a novel method of self‑distillation with multiple privileged‑information views, which reconstructs token‑level supervision by separating stable cross‑view consensus from view‑specific residual signals. AVSD identifies the consensus signal shared across views, which provides a reliable update direction, and then selectively adds the view‑specific residual signal to adjust the update magnitude when it both aligns with the consensus direction and remains proportionate to the consensus signal. Experiments on math competition benchmarks (AIME24, AIME25, and HMMT25) show that AVSD consistently outperforms both single‑view self‑distillation baselines and GRPO, achieving average Avg@8 gains of 3.1% and 2.2% over the strongest baselines on Qwen3‑8B and Qwen3‑4B, respectively. Moreover, on code‑generation benchmarks (Codeforces, LiveCodeBench v6) using Qwen3‑8B, AVSD outperforms the single‑view self‑distillation baseline by 2.4% on average.

PaperId: 806, https://arxiv.org/pdf/2605.20618.pdf GitHub

Abstract:
Although Vehicle Routing Problems (VRP) are essential to many real‑world systems, they remain computationally intractable at scale due to their combinatorial complexity. Traditional heuristics rely on handcrafted rules for local improvements and occasional jumps to escape local minima, but often struggle to generalize across diverse instances. We introduce COAgents, a cooperative multi‑agent framework that models the search process as a graph: nodes represent solutions, and edges correspond to either local refinements or large perturbations for diversification (i.e., jumps). A Partial Search Graph (PSG) is dynamically constructed during search, enabling COAgents to train a Node Selection Agent and a Move Selection Agent to guide intensification, and a Jump Agent to trigger well‑timed explorations of new regions. Unlike end‑to‑end learning approaches, COAgents cleanly separates problem‑agnostic search control from compact domain‑specific encoding, facilitating adaptability across tasks. Extensive experiments on the CVRP and VRPTW benchmarks show that COAgents remains competitive with several learn‑to‑search baselines on CVRP and sets a new state of the art among learning‑based methods on the more challenging VRPTW instances, reducing the gap to the best‑known solutions by 14% at N\!=\!100 and 44% at N\!=\!50 relative to the strongest neural solver (POMO), and by 21% and 40% respectively relative to ALNS. Code is available at https://github.com/mahdims/COAgents.

PaperId: 807, https://arxiv.org/pdf/2605.20510.pdf GitHub

Abstract:
Urban heat exposure is becoming an increasingly critical challenge due to the intensifying urban heat island effect. Fine‑grained shade patterns, especially those induced by urban buildings, strongly influence pedestrians' thermal exposure and outdoor activity planning. However, accurately modeling and analyzing urban shade at scale remains difficult because of the lack of large‑scale datasets and systematic evaluation frameworks. To address this challenge, we present ShadeBench, a comprehensive dataset and benchmark for urban shade understanding. ShadeBench contains geographically diverse urban scenes with temporally varying simulated shade maps and textual descriptions, together with aligned satellite imagery, building skeleton representations, and 3D building meshes. Built upon this multimodal dataset, ShadeBench supports a range of downstream tasks, including shade generation, shade segmentation, and 3D building reconstruction. We further establish standardized evaluation protocols and baseline methods for these tasks. By enabling scalable and fine‑grained shade analysis, ShadeBench provides a foundation for data‑driven urban climate research and supports future studies in heat‑resilient urban planning and decision‑making. The code and dataset are publicly available at https://darl‑genai.github.io/shadebench/.

PaperId: 808, https://arxiv.org/pdf/2605.20441.pdf GitHub

Abstract:
Transformers trained on modular arithmetic exhibit sharp transitions between memorization, generalization, and collapse. We show that weight decay acts as a scalar empirical control parameter for these regimes, and introduce two cheap online diagnostics, mean pairwise attention‑head cosine similarity and entropy standard deviation, that track training dynamics from attention activations alone and complement loss‑landscape diagnostics at lower compute cost. Across eleven experimental conditions and three model scales (0.82M to 85M parameters), the weight‑decay axis separates memorization, developmental grokking, and collapse. A near‑transition logistic fit localizes the memorization‑to‑developmental boundary at λ_c=0.0158 (95% CI [0.0109, 0.0200], N=210); a power‑law fit gives an empirical exponent ν=0.757 (CI [0.725, 0.799]). Reference exponents ν=1/2 and 3D Ising ν\approx 0.63 lie outside this empirical CI under our four‑bin grid, so we report ν as empirical and defer universality‑class identification to denser finite‑size‑scaling work. A horizon‑matched multi‑task replication (n=280, four modular operations) preserves the weight‑decay control pattern; a paired attention‑head re‑initialization experiment at λ=0.05 changes Phase‑2 amplitude (Cohen's d=‑1.190, n=10, p_t=4.5 × 10^‑3), while matched weight‑norm clipping does not. Three cross‑architecture probes (4L MLP, 4L LSTM, and 4L Mamba; each n=70) replicate the weight‑decay‑controlled transition with architecture‑specific λ_c values. Main diagnostic claims are scoped to modular arithmetic in small transformer attention models; the non‑attention experiments are scope probes, and architecture‑wide, language‑model, and universality‑class claims are out of scope.

PaperId: 809, https://arxiv.org/pdf/2605.20423.pdf GitHub GitHub

Abstract:
Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult. This paper presents OSCToM (Observer‑Self Conflict Theory of Mind), an approach for modeling nested belief conflicts in LLM‑based ToM tasks. The key case is one in which an observer's view of another agent conflicts with the observer's own belief state. Such cases go beyond simple perspective‑taking and require recursive, multi‑layered reasoning. OSCToM combines reinforcement learning (RL), an extended domain‑specific language, and compositional surrogate models to generate observer‑self conflicts. In our experiments, OSCToM‑8B gives the best overall result among the systems tested. It improves on the reported ExploreToM results on FANToM and remains competitive on Hi‑ToM and BigToM. On the information‑asymmetric FANToM benchmark, OSCToM reaches 76% accuracy, compared with the 0.2% reported by ExploreToM. The data‑synthesis procedure is also 6x more efficient, indicating that targeted training data can help smaller models handle advanced cognitive reasoning. The project code is available at https://github.com/sharminsrishty/osct.

PaperId: 810, https://arxiv.org/pdf/2605.20405.pdf GitHub

Abstract:
Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare classes. Loss‑based approaches mitigate imbalance by reweighting the per‑pixel loss within the batch, while sampling strategies control which images enter the batch. Yet neither explicitly controls which classes appear within the batch, leaving rare‑class exposure only partially rebalanced. In this work, we adopt episodic sampling from few‑shot learning to promote class‑balanced batch construction in a fully supervised setting. We decouple episodic sampling from its conventional metric‑learning context and evaluate it in body composition segmentation in CT. We compare episodic sampling against random and weighted sampling on nine muscle and adipose tissues, derived from 210 scans of the public SAROS dataset. Training is performed under full‑ and low‑data regimes, with additional comparisons under matched training iteration budgets. Under full‑data training, all three strategies performed comparably (mean Dice 0.882 for episodic, 0.878 for random and weighted). Under low‑data training, episodic sampling outperformed random and weighted (0.787 vs. 0.758 and 0.762), driven by a 12‑fold difference in training iterations. Under matched training budgets, random and weighted overfit earlier, while episodic improved for approximately three times more iterations before plateauing. Our findings identify the training iteration budget as under‑recognized confound in sampling strategies, motivating iteration‑aware evaluation protocols for small datasets. Furthermore, the residual advantage of episodic sampling is consistent with an implicit regularization effect of class‑balanced batches, offering a low‑cost, model‑agnostic strategy for class‑imbalanced medical image segmentation. Code is available at https://github.com/iasonsky/episodic‑sampling.

PaperId: 811, https://arxiv.org/pdf/2605.20373.pdf GitHub

Abstract:
Building humanoid robots capable of generalizable whole‑body loco‑manipulation in the real world remains a fundamental challenge. Existing methods either rely on laborious task‑specific reward engineering, rigidly replay reference motions that fail to generalize, or depend on costly teleoperation that limits scalability. While human videos capture diverse human behaviors, motion priors inferred from them are inherently imperfect, suffering from occlusion, contact artifacts, and retargeting errors that render them unsuitable for direct policy learning. To address this, we present SUGAR, a scalable data‑driven framework that converts diverse human videos into deployable humanoid loco‑manipulation skills, without any task‑specific reward engineering or reference‑motion conditioning at inference. SUGAR proceeds in three stages. First, a fully automated pipeline extracts kinematic interaction priors including human‑object motion trajectories and contact labels from unstructured human videos. Second, a privileged physics‑based refiner uses a unified mimic reward and progressive state pool to transform imperfect priors into physically feasible, high‑fidelity skills. Third, refined skills are distilled into a hierarchical autonomous policy consisting of a command generator and a command tracker. We evaluate SUGAR on six representative loco‑manipulation tasks in simulation and real‑world humanoid hardware. Our method substantially outperforms reference‑tracking baselines, and performance scales clearly with the amount of human video data. It also achieves zero‑shot real‑world transfer with reliable closed‑loop execution, autonomous failure recovery, and stable long‑horizon performance under external perturbations. Project Page: https://tianshuwu.github.io/sugar‑humanoid/

PaperId: 812, https://arxiv.org/pdf/2605.20372.pdf GitHub

Abstract:
Multimodal semantic segmentation benefits remote sensing analysis by combining complementary information from different sensor modalities. In real‑world remote sensing applications, one or more modalities may be unavailable due to sensor failures, adverse atmospheric conditions, or data acquisition problems. Even with pretrained multimodal representations and existing fine‑tuning or adaptation strategies, performance may remain limited because all modality availability scenarios are typically treated as equally informative during training. In this paper, we propose a novel training strategy that learns a scenario sampling distribution directly from the pretrained latent space. Instead of relying on uniform random modality dropout, the proposed method guides fine‑tuning toward more informative modality availability scenarios. More specifically, we quantify the effect of each scenario independently based on the distortion it induces in the shared latent representation. We then capture scenario relations using a radial basis function kernel and derive refined scenario scores through a regularized kernel smoothing. These scores are then converted into a probability distribution during scenario sampling for fine‑tuning. We evaluate this strategy on three remote sensing image sets, namely DSTL, Potsdam, and Hunan, using CBC‑SLP, CBC, and CMX backbones. The experimental results with different image sets and backbones show that our method outperforms standard fine‑tuning and LoRA‑based adaptation. These findings suggest that the pretrained latent representation can serve as an effective basis for sampling during missing modality fine‑tuning. Code is available at https://github.com/iremulku/Latent‑Space‑Guided‑Scenario‑Sampling

PaperId: 813, https://arxiv.org/pdf/2605.20369.pdf GitHub

Abstract:
Number prediction stands as a fundamental capability of large language models (LLMs) in mathematical problem‑solving and code generation. The widely adopted maximum likelihood estimation (MLE) for LLM training is not tailored to number prediction. Recently, penalty‑driven approaches, e.g., Number Token Loss and Discretized Distance Loss, introduce an inductive bias of numerical distance but induce over‑sharpened and over‑flattened digit distributions, respectively. In this paper, we make an in‑depth analysis on LLM numerical learning, and show that existing numerical learning methods conceptually follow a criterion‑distance formulation, where the criterion term represents optimization pattern and the distance term instills geometric prior. Consequently, we present Digit Entropy Loss (DEL) for auto‑regressive numerical learning, which reformulates the conventional unsupervised entropy optimization in three key designs: leveraging digit conditional probability and binary cross‑entropy to guide the entropy optimization into a supervised manner; deprecating the distance term to bypass the issue of numerical distance; and generalizing the integer‑based numerical learning to floating‑point number optimization, enabling more accurate number prediction. Our DEL formulation can incorporate integers, decimals, and decimal points, expanding the learning objective from a single digit to the floating‑point number domain. Experiments conducted on seven mathematical reasoning benchmarks with four representative LLMs, including CodeLlama, Mistral, DeepSeek, and Qwen‑2.5, demonstrate that DEL consistently outperforms its counterparts in both overall prediction accuracy and numerical distance. Source codes are at https://github.com/PolyU‑VCLab/DEL

PaperId: 814, https://arxiv.org/pdf/2605.20316.pdf GitHub

Abstract:
Modern text‑to‑image diffusion models encode rich visual priors, but expose them only through one‑way text‑conditioned generation. Existing unified vision‑‑language models derived from them recover bidirectional capability through large‑scale joint pretraining or substantial retraining of the text pathway, discarding the strong image prior the text‑to‑image backbone already encodes. We introduce \emphFullFlow, a parameter‑efficient recipe that upgrades a pretrained rectified‑flow text‑to‑image model into a bidirectional vision‑‑language generator by training only LoRA adapters and lightweight text heads. FullFlow keeps images in their native continuous flow and adds a discrete insertion process for text. Separate image and text timesteps turn inference into trajectory selection in a two‑dimensional generative space, enabling text\rightarrowimage, image\rightarrowtext, joint sampling, and partial‑text prediction with a single backbone. On Stable Diffusion 3 (SD3) under an identical trainable‑parameter count and matched LoRA rank, FullFlow improves text\rightarrowimage FID from 62.7 to 31.6 and image\rightarrowtext CIDEr from 2.0 to 99.4 over a LoRA equivalent following the previous SOTA formulation (Dual Diffusion) at matched wall‑clock training time, while reducing peak VRAM from ～84\,GB to ～38\,GB and raising throughput by ～8× on two RTX A5000 GPUs in under 24 hours, training only ～5% of the backbone parameters. The same recipe transfers to FLUX.1‑dev and supports downstream VQA through partial‑text generation. These results show that strong bidirectional vision‑‑language capability can be unlocked from pretrained text‑to‑image flow models without full multimodal pretraining.

PaperId: 815, https://arxiv.org/pdf/2605.20308.pdf GitHub

Abstract:
Gradient‑based attacks are important methods for evaluating model robustness. However, since the proposal of APGD, it has been difficult for such methods to achieve significant breakthroughs. To achieve such an effect, we first analyze the issue of "high‑loss non‑adversarial examples" that degrades attack performance in previous methods, and prove that this issue arises from inappropriate objectives for adversarial example generation. Subsequently, we reconstruct the objective as "maximizing the difference between the non‑ground‑truth label probability upper bound and the ground‑truth label probability", and proposes a novel and powerful gradient‑based attack method named Sequential Difference Maximization (SDM). SDM establishes a three‑layer optimization framework of "cycle‑stage‑step". It adopts the negative probability loss function and the Directional Probability Difference Ratio (DPDR) loss function in the initial and subsequent optimization stages, respectively, and approaches the ideal objective of adversarial example generation via stage‑wise sequential optimization. Experiments demonstrate that compared with previous state‑of‑the‑art methods, SDM not only achieves stronger attack performance but also exhibits superior cost‑effectiveness. The code is available at https://github.com/X‑L‑Liu/ICML‑SDM.

PaperId: 816, https://arxiv.org/pdf/2605.20277.pdf GitHub GitHub

Abstract:
Medical vision‑language models (VLMs) have rapidly advanced as general‑purpose multimodal assistants, yet their deployment in 3D Computed Tomography (CT) analysis remains constrained by a persistent mismatch between optimization objectives and clinical rigor. Current Reinforcement Learning (RL) paradigms still rely on lexical proxy signals that induce ``Evaluation Hallucinations'', where models optimize linguistic fluency rather than factual clinical correctness, leading to diagnostically critical errors. To bridge this gap, we introduce the Clinical Abnormality Benchmarking Substrate (CABS), a structured system that decomposes radiology reports into verifiable clinical semantic units. Using CABS, we identify a ``Mechanistic Divergence'' in standard RL, where surface‑similarity rewards drive policy gradients to bypass medical facts. We therefore propose Trajectory‑Integral Feedback GRPO (TIF‑GRPO), a novel framework integrating control‑theoretic principles into policy optimization. By formulating clinical reasoning as a pseudo‑temporal trajectory for anomaly discovery, TIF‑GRPO regulates anatomy‑aware rewards via an integral feedback loop that penalizes persistent omissions as cumulative state errors and suppresses hallucinations as excessive control effort. Experiments on 3D CT benchmarks demonstrate that our approach significantly enhances abnormality detection and clinical faithfulness, establishing a new paradigm for fine‑grained regulation in medical VLMs. Our project is available at \hrefhttps://github.com/ZJU4HealthCare/TIF‑GRPOGitHub.

PaperId: 817, https://arxiv.org/pdf/2605.20273.pdf GitHub

Abstract:
Online model editing for multimodal large language models (MLLMs) requires assimilating a stream of corrections under tight compute and memory budgets. Yet editors developed for text‑only LLMs often degrade on MLLMs: visually dominant activations skew the statistics that shape updates, causing cross‑modal conflict, while sequential writes become entangled in a shared edit space and amplify long‑horizon interference, causing inter‑edit interference. To address these, we propose M‑ORE, a modality‑decoupled online recursive editor for lifelong MLLM adaptation. M‑ORE is derived from a unified proximal‑projection formulation and admits a closed‑form update with a Sherman‑Morrison recursion, yielding constant per‑edit overhead. It maintains module‑wise locality statistics for the text stack and the visual projector to avoid visually dominated update shaping and performs continual updates in a fixed orthogonal low‑rank edit subspace via a Sherman‑Morrison recursion to mitigate long‑horizon interference. Experiments on multiple MLLM backbones and online editing benchmarks show that our M‑ORE method consistently improves reliability, generality, and locality over strong baselines, while achieving favorable quality‑efficiency scaling. Our code is publicly available at https://github.com/lab‑klc/M‑ORE.

PaperId: 818, https://arxiv.org/pdf/2605.20202.pdf GitHub

Abstract:
I study whether emotionally framed evaluation follow‑ups change both the behavior and the calm‑relative internal representations of small, locally deployed language models. Our main benchmark uses Qwen 3.5 0.8B on four impossible‑constraint coding tasks and eight follow‑up framings: calm, pressure, urgency, approval, shame, curiosity, encouragement, and threat. In the 0.8B eight‑condition sweep (160 conversations), pressure produces the strongest shortcut markers (11/20 runs) and the clearest overfit pattern (3/20), while calm and curiosity preserve explicit honesty more often (7/20 and 6/20). For all seven non‑baseline conditions, the corresponding calm‑relative direction vectors peak at the final transformer layer. An exploratory PCA of the layer‑23 direction vectors reveals a dominant first component (59.5% explained variance) aligned with a hand‑labeled positive/negative split (cosine alignment 0.951); approval and urgency are nearly identical internally (cosine 0.957), whereas curiosity points away from urgency (‑0.252). In a separate calm‑vs.‑pressure rerun used for scale comparison, Qwen 3.5 2B shows higher honest rates under calm framing and directionally consistent activation steering on a small 4‑prompt A/B probe, whereas the 0.8B steering result reverses. I interpret these results as evidence for measurable prompt‑sensitive control directions in small open models, while stopping short of claiming intrinsic emotional states.

PaperId: 819, https://arxiv.org/pdf/2605.20188.pdf GitHub

Abstract:
Recommending safe and effective medication combinations from electronic health records (EHRs) is a core clinical AI problem, yet it remains difficult because patient trajectories are long, noisy, and clinically heterogeneous. Existing methods typically excel at either temporal modeling across visits or pharmacological knowledge integration (e.g., drug‑drug interactions, DDIs), but rarely achieve both while robustly suppressing noise. We present GraphDiffMed, a knowledge‑constrained medication recommendation framework built on dual‑scale Differential Attention v2. Differential attention is applied at both intra‑visit and inter‑visit levels to filter spurious signals within encounters and across longitudinal history, while pharmacological constraints are incorporated during learning. Experiments on MIMIC‑III and ablation studies show that this design consistently improves recommendation quality and ranking over strong baselines while achieving a more favorable safety performance balance. We further find that the strongest‑performing configuration uses only demographic auxiliary features under our experimental setting. Overall, GraphDiffMed demonstrates that combining noise‑aware attention with pharmacological constraints yields more reliable and clinically meaningful medication recommendation. We open‑source our code at https://github.com/saxenakrati09/GraphDiffMed.

PaperId: 820, https://arxiv.org/pdf/2605.20173.pdf GitHub

Abstract:
Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a first‑class architectural object. This paper names that boundary the stochastic‑deterministic boundary (SDB): a four‑part contract among a proposer, verifier, commit step, and reject signal that specifies how an LLM output becomes a system action. We argue that the SDB is the load‑bearing primitive of production agent runtimes. Around this primitive, we organize agent runtime design into three concerns: Coordination, State, and Control. We present a catalog of six runtime patterns that compose the SDB differently across conversational, autonomous, and long‑horizon agents: hierarchical delegation, scatter‑gather plus saga, event‑driven sequencing, shared state machine, supervisor plus gate, and human in the loop. For each pattern, we trace its lineage to distributed‑systems concepts and identify what changes when the worker is stochastic. The paper contributes a five‑step methodology for selecting runtime patterns, a diagnostic procedure that maps production failures to pattern weaknesses, and a failure mode called replay divergence, in which LLM‑based consumers of a deterministic event log produce different downstream outputs under model‑version or prompt changes. A stylized reliability decomposition separates per‑call model variance from architectural momentum, motivating the claim that as model variance decreases, pattern choice and SDB strength become increasingly important levers for long‑run reliability. We apply the methodology to five workloads and provide one runnable reference implementation for a 90‑day contract‑renewal agent.

PaperId: 821, https://arxiv.org/pdf/2605.20158.pdf GitHub

Abstract:
Large Vision Language Models (LVLMs) show promise in medical applications, but their inability to faithfully ground responses in visual evidence raises serious concerns about clinical trustworthiness. While visual attribution methods are widely used to explain LVLM predictions, whether these explanations actually reflect the visual evidence underlying the model's decision is largely unverified, since ground‑truth annotations for internal model reasoning are typically unavailable. We address this question for chest X‑ray (CXR) reasoning by developing a causal evaluation framework that retains only CXR‑VQA samples for which the expert‑annotated region is verified, via counterfactual editing, to be causally responsible for the model's prediction. Using this framework across 11 attribution methods, six open‑source LVLMs, and two output modes (direct answer and step‑by‑step reasoning), we find that existing attribution methods often fail to identify the evidence used by LVLMs. To address this failure, we propose MedFocus, a concept‑based attribution method that localizes clinically meaningful anatomical regions via unbalanced optimal transport and measures their causal effect on model outputs through targeted interventions. MedFocus produces spatial, concept‑level, and token‑level attributions and substantially outperforms prior methods, taking a step toward more trustworthy attribution for medical LVLMs. Our data and code are available at https://github.com/gzxiong/medfocus/.

PaperId: 822, https://arxiv.org/pdf/2605.20075.pdf GitHub GitHub

Abstract:
Chain‑of‑thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm treats thinking as a prerequisite for answering, which can delay access to plausible answers and incur unnecessary token costs even when the model is able to identify an answer before extended thinking, a behavior known as performative reasoning. In this paper, we introduce CopT, a reformulated reasoning pipeline that reverses the usual order of thinking and answering. Instead of thinking before answering, CopT first elicits a draft answer and then invokes subsequent on‑policy thinking conditioned on its own draft answer for reflection and correction. To assess whether the draft answer should be trusted, CopT recasts continuous embeddings as inference‑time contrastive verifiers. Specifically, it contrasts the model's support for the same generated tokens under discrete‑token inputs and continuous‑embedding inputs, yielding a sequence‑level reverse KL estimator for answer reliability. Our analysis shows that under certain assumptions, the expected estimate equals the mutual information between the unresolved latent state and the emitted answer token, explaining why it captures answer‑relevant uncertainty rather than arbitrary uncertainty in the latent state. When the answer is deemed insufficiently reliable, CopT performs further on‑policy thinking, where a second KL estimator dynamically controls draft‑answer visibility, preserving useful partial information while reducing the risk of being misled by unreliable content. Across mathematics, coding, and agentic reasoning tasks, CopT improves peak accuracy by up to 23% and reduces token usage by up to 57% at comparable or higher accuracy, without any additional training. The code is available at https://github.com/sdc17/CopT.

PaperId: 823, https://arxiv.org/pdf/2605.20052.pdf GitHub

Abstract:
Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large‑scale annotation for medical imaging research. Existing rule‑based labelers struggle with the diverse descriptions in clinical reports, while fine‑tuning pre‑trained language models (PLMs) requires large amounts of labeled data that are often unavailable in clinical settings. In this paper, we propose PromptRad, a knowledge‑enhanced multi‑label prompt‑tuning approach for radiology report labeling under low‑resource settings. PromptRad reformulates multi‑label classification as masked language modeling and incorporates synonyms from the UMLS Metathesaurus into a multi‑word verbalizer to enrich category representations. By fine‑tuning the PLM without additional classification layers, PromptRad requires substantially less labeled data than conventional fine‑tuning. Experiments on liver CT (computed tomography) reports show that PromptRad outperforms dictionary‑based and fine‑tuning baselines with only 32 labeled training examples, and achieves competitive performance with GPT‑4 despite using a much smaller model. Further analysis demonstrates that PromptRad captures complex negation patterns more effectively than existing methods, making it a promising solution for report labeling in data‑scarce clinical scenarios. Our code is available at https://github.com/ila‑lab/PromptRad.

PaperId: 824, https://arxiv.org/pdf/2605.20025.pdf GitHub

Abstract:
Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linear pipeline: they rely on single‑agent reasoning, stop when execution fails, and do not carry experience across runs. We present AutoResearchClaw, a multi‑agent autonomous research pipeline built on five mechanisms: structured multi‑agent debate for hypothesis generation and result analysis, a self‑healing executor with a \textscPivot/\textscRefine decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human‑in‑the‑loop collaboration with seven intervention modes spanning full autonomy to step‑by‑step oversight, and cross‑run evolution that converts past mistakes into future safeguards. On ARC‑Bench, a 25‑topic experiment‑stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%. A human‑in‑the‑loop ablation across seven intervention modes reveals that precise, targeted collaboration at high‑leverage decision points consistently outperforms both full autonomy and exhaustive step‑by‑step oversight. We position AutoResearchClaw as a research amplifier that augments rather than replaces human scientific judgment. Code is available at https://github.com/aiming‑lab/AutoResearchClaw.

PaperId: 825, https://arxiv.org/pdf/2605.19988.pdf GitHub

Abstract:
Documentation has long guided computer system tuning by distilling expert knowledge into per‑parameter recommendations. Yet such guides capture only what experts conclude, discarding how they reason. This fundamental gap manifests in three concrete deficiencies: documentation grows stale as software evolves, fails under heterogeneous workloads, and ignores inter‑parameter dependencies. We propose shifting from static documentation to dynamic action for system tuning. We introduce PerfEvolve, which translates expert tuning methodologies into executable skills that equip LLM‑based agents to perform version‑consistency verification, workload‑specific profiling, and multi‑parameter joint optimization. Evaluated on PostgreSQL under TPC‑C and TPC‑H benchmarks, PerfEvolve outperforms state‑of‑the‑art documentation‑driven tuning baselines by up to 35.2%. The tool is available at https://github.com/ISCAS‑OSLab/PerfEvolve.

PaperId: 826, https://arxiv.org/pdf/2605.19929.pdf GitHub

Abstract:
Low‑bit post‑training quantization (PTQ) is a pivotal technique for deploying Vision‑Language Models (VLMs) on resource‑constrained devices. However, existing PTQ methods often degrade VLMs' accuracy due to the heterogeneous activation distributions of text and vision modalities during quantization. We find that this cross‑modal heterogeneity is distributed unevenly across channels: a small subset of channels contains most modality‑specific outliers, and these outliers typically reside in different channels for each modality. Motivated by this, we propose SplitQ, a channel‑Splitting‑driven post‑training Quantization framework. At its core, SplitQ introduces a novel Modality‑specific Outlier Channel Decoupling (MOCD) module that effectively isolates salient modality‑specific outlier channels with minimal overhead. To further address the remaining cross‑modal distribution discrepancies, we design an Adaptive Cross‑Modal Calibration (ACC) module that employs dual lightweight learnable branches to dynamically mitigate modality‑induced quantization errors. Extensive experiments on popular VLMs demonstrate that SplitQ significantly outperforms existing approaches across 6 popular multi‑modal datasets under all evaluated quantization settings, including W4A8, W4A4, W3A3, and W3A2. Notably, SplitQ preserves 93.5% of FP16 performance under the challenging W3A3 setting (69.5 vs. 74.3), pushing the efficiency frontier for deploying advanced VLMs. Our code is available at https://github.com/EMVision‑NK/SplitQ

PaperId: 827, https://arxiv.org/pdf/2605.19869.pdf GitHub

Abstract:
Construction remains the deadliest industry sector in the United States, with 1,055 fatal worker injuries recorded in 2023, and the majority preventable. Existing monitoring approaches are expensive, require real‑time human operators, or address only a narrow subset of violations. This paper presents a passive, end‑of‑shift construction safety monitoring pipeline processing video from POV body‑worn and fixed wall‑mounted cameras through a three‑stage architecture: (1) fine‑tuned YOLO11 for primary PPE and hazard detection, (2) SAM 3 for segmentation refinement and worker deduplication, and (3) Qwen3‑VL‑8B‑Instruct with a method‑prompted, persona‑scaffolded three‑pass adversarial chain‑of‑thought protocol for compliance verification and hallucination control. The principal contribution is the Stage 3 prompt design: professional persona backstories following the method‑actor framing drive an observed 12% precision improvement over single‑pass prompting in an informal three‑author review of the 12‑video Ironsite development corpus, with the largest gains on hallucination‑prone violation categories. Structural message isolation enforces observational independence between a generator, discriminator, and reconciliation pass governed by asymmetric rules encoding priors about human observation versus automated detection reliability. The system maps violations to OSHA standards, performs REBA‑inspired ergonomic risk scoring from pose keypoints, and produces per‑worker safety reports with timestamped evidence. An evaluation harness is released for future reproduction.

PaperId: 828, https://arxiv.org/pdf/2605.19855.pdf GitHub

Abstract:
Concept‑based Explainable Artificial Intelligence (XAI) interprets deep learning models using human‑understandable visual features (e.g., textures or object parts) by linking internal representations to class predictions, thereby bridging the gap between low‑level image data and high‑level semantics. A major challenge, however, is the reliance on large sets of labeled images to represent each concept, which limits scalability. In this work, we investigate the use of zero‑shot Text‑to‑Image (T2I) generative models as a source of synthetic concept datasets for concept‑based XAI methods. Specifically, we generate concepts using predefined prompts and evaluate their faithfulness to real ones through four complementary analyses: (1) comparing synthetic vs. real concept images via concept representation similarity; (2) evaluating their intra‑similarity by comparing pairs of subsets of the same concept with progressively increasing size; (3) evaluating their performance for downstream explanation tasks using relevant class images; (4) evaluating how removing a concept from tested class images affects explanations of generated concepts. While current T2I generative models promise a shortcut to concept‑based XAI, our study highlights challenges and raises open questions about the use of synthetic data generated by zero‑shot pipelines in model analyses. The resulting dataset is available at https://github.com/DataSciencePolimi/ZeroShot‑T2I‑Concepts.

PaperId: 829, https://arxiv.org/pdf/2605.19846.pdf GitHub GitHub

Abstract:
Vision‑Language Models (VLMs) have demonstrated remarkable capabilities in general video understanding, yet they often struggle with the fine‑grained comprehension crucial for real‑world applications requiring nuanced interpretation of human actions and interactions. While some recent human‑centric benchmarks evaluate aspects of model behaviour such as fairness/ethics, emotion perception, and broader human‑centric metrics, they do not combine long‑form videos, very dense QA coverage, and frame‑level spatial/temporal grounding at scale. To bridge this gap, we introduce FineBench, a human‑centric video question answering (VQA) benchmark specifically designed to assess fine‑grained understanding. FineBench comprises 199,420 multiple‑choice QA pairs densely annotated across 64 long‑form videos (15 minutes each), focusing on detailed person movement, person interaction, and object manipulation, including compositional actions. Our extensive evaluation reveals that while proprietary models like GPT‑5 achieve respectable performance, current open‑source VLMs significantly underperform, struggling particularly with spatial reasoning in multi‑person scenes and distinguishing subtle differences in human movements and interactions. To address these identified weaknesses, we propose FineAgent, a modular framework that enhances VLMs by leveraging a Localizer and a Descriptor. Experiments show that FineAgent consistently improves the performance of various open VLMs on FineBench. FineBench provides a rigorous testbed for future research into fine‑grained human‑centric video understanding, while FineAgent offers a practical approach to enhance such reasoning in current VLMs. Project page and code at https://joslefaure.github.io/assets/html/finebench.html.

PaperId: 830, https://arxiv.org/pdf/2605.19833.pdf GitHub

Abstract:
Despite rapid advances in automatic speech recognition (ASR) and large audio‑language models, robust recognition in real‑world environments remains limited by an "acoustic robustness bottleneck": models often lose acoustic grounding and produce omissions or hallucinations under severe, compositional distortions. We propose Mega‑ASR, a unified ASR‑in‑the‑wild framework that combines scalable compound‑data construction with progressive acoustic‑to‑semantic optimization. We introduce Voices‑in‑the‑Wild‑2M, covering 7 classic acoustic phenomena and 54 physically plausible compound scenarios, and train Mega‑ASR with Acoustic‑to‑Semantic Progressive Supervised Fine‑Tuning and Dual‑Granularity WER‑Gated Policy Optimization. Extensive experiments demonstrate that Mega‑ASR achieves significant advantages over prior state‑of‑the‑art systems on adverse‑condition ASR benchmarks (45.69% vs. 54.01% on VOiCES R4‑B‑F, and 21.49% vs. 29.34% on NOIZEUS Sta‑0). On complex compositional acoustic scenarios, Mega‑ASR further delivers over 30% relative WER reduction against strong open‑ and closed‑source baselines, establishing a scalable paradigm for robust ASR in‑the‑wild.

PaperId: 831, https://arxiv.org/pdf/2605.19822.pdf GitHub

Abstract:
Temporal graph neural networks (TGNNs) have gained significant traction for solving real‑world temporal graph tasks. However, their interpretability remains limited, as most TGNNs fail to identify which historical interactions most influence a given prediction. Despite promising progress on interpretable TGNNs, existing methods predominantly focus on previously seen historical interactions, which we term stability patterns, while overlooking newly emerging first‑time interactions, which we term transition patterns. Both types of patterns are essential for faithful temporal explanations. To address this limitation, we propose ST‑TGExplainer, a self‑explainable TGNN that disentangles Stability and Transition patterns in temporal graphs for a more faithful Temporal GNN Explainer. Guided by a disentangled information bottleneck objective, ST‑TGExplainer learns a compact explanatory subgraph that remains predictive of the event label while explicitly suppressing label‑conditioned redundancy between stability and transition patterns. Extensive experiments demonstrate that ST‑TGExplainer achieves strong predictive performance and yields more faithful explanations. Code is available at https://github.com/hjchen‑hdu/ST‑TGExplainer.

PaperId: 832, https://arxiv.org/pdf/2605.19804.pdf GitHub

Abstract:
For practical use, diffusion‑ or flow‑based generative models must be aligned with task‑specific rewards, such as prompt fidelity or aesthetic preference. That alignment is challenging because the reward is defined for clean output images, but the alignment procedure requires value function estimates at noisy intermediate latents. Existing methods resort to Tweedie‑style or Monte Carlo approximations, trading off estimator bias against computational cost: Tweedie estimates are efficient but biased, while Monte Carlo estimates are more accurate but require expensive rollouts. A natural alternative would be a learned value function, but it remains an open question how to effectively train a strong and general value model specifically for noisy latents. Here, we propose StitchVM, a model stitching framework that efficiently transfers reward models pretrained for clean images to the noisy latent regime. StitchVM starts from an existing, truncated pixel‑space reward model and attaches a frozen diffusion backbone to it as its head. From the pixel‑space model, the resulting hybrid retains a carefully pretrained, robust reward capability; from the diffusion backbone, it inherits its native ability to handle noisy latents. The stitching procedure is exceptionally lightweight, e.g., stitching and finetuning CLIP ViT‑L and SD 3.5 Medium takes only 10 GPU‑hours. By lifting powerful pixel‑space reward models to latent space, StitchVM opens up a new style of diffusion alignment: instead of rough, yet costly per‑sample approximation of the value function, the correct function for the actual, noisy latents is constructed once and then amortized over many samples and iterations. We show that this approach yields improvements across a broad range of downstream steering and post‑training methods: DPS becomes 3.2× faster while halving peak GPU memory, and DiffusionNFT becomes 2.3× faster.

PaperId: 833, https://arxiv.org/pdf/2605.19799.pdf GitHub

Abstract:
We present a semi‑supervised framework for joint segmentation and classification of fetal cardiac ultrasound images. Built upon the EchoCare multi‑task backbone, our method integrates SAM‑Med2D for boundary refinement and leverages DINOv3 to enhance pseudo‑label quality. We introduce view‑specific hard masking along with a two‑stage optimization strategy: an EMA phase to consolidate segmentation capabilities, followed by a Classification Fine‑Tuning phase that freezes segmentation parameters and resets the classification head to recover classification performance without compromising segmentation gains. Evaluated on the FETUS 2026 leaderboard, our method achieves a Dice Similarity Coefficient at 79.99%, Normalized Surface Distance at 61.62%, and F1‑score at 41.20%, validating the effectiveness of our approach for prenatal congenital heart disease screening. Source code is publicly available at: https://github.com/2826056177/zcst_fetus2026.

PaperId: 834, https://arxiv.org/pdf/2605.19765.pdf GitHub

Abstract:
Existing affective‑computing, social‑signal‑processing, and meeting corpora capture important parts of human interaction, but they rarely support analysis of affect in co‑located groups as a coupled individual, interpersonal, and group‑level process. The required signals (per‑participant physiology, eye movement, audio, self‑report, task outcomes, and personality) are usually fragmented across separate dataset traditions. We introduce GroupAffect‑4, a multimodal corpus of 40 participants in 10 four‑person groups, each completing four ecologically varied collaborative tasks spanning information pooling, negotiation, idea generation, and a public‑goods game. Each participant is instrumented with a wrist‑worn physiology sensor, eye‑tracking glasses, and a close‑talk microphone; sessions include continuous affect self‑reports, post‑task questionnaires, task outcomes, and Big‑Five personality scores, all time‑aligned to a shared clock. The dataset covers over 91% of expected physiology windows and 98% of eye‑tracking windows, with strong task validity confirmed by a clear affective manipulation check across the negotiation block. We define fifteen benchmarkable targets spanning three analysis levels ‑‑ within‑person state, between‑person traits, and group dynamics ‑‑ and report leave‑one‑group‑out feasibility baselines establishing the dataset's evaluative scope. GroupAffect‑4 is released with a BIDS‑inspired structure, Croissant metadata, a datasheet, per‑session quality reports, and open processing scripts. Code and processing scripts are available at https://github.com/meisamjam/GroupAffect‑4; the dataset is publicly archived at https://zenodo.org/records/20037847.

PaperId: 835, https://arxiv.org/pdf/2605.19738.pdf GitHub

Abstract:
Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the majority. While existing text‑rich approaches typically integrate structural context into the data representation pipeline using raw textual features, they often neglect the structural context of nodes. This limitation hinders their ability to detect sophisticated anomalies arising from inconsistencies between a node's inherent content and its topological role. To bridge this gap, we propose TERGAD (Structure‑aware Text‑enhanced Representations for Graph Anomaly Detection), A novel data augmentation framework that enriches structural semantics for GAD via the semantic reasoning capabilities of Large Language Models (LLMs). Specifically, TERGAD translates node‑level topological properties into descriptive natural language narratives, which are subsequently processed by an LLM to derive high‑level semantic embeddings. These embeddings are then adaptively fused with original node attributes through a gated dual‑branch autoencoder to jointly reconstruct both graph structure and node features. The anomaly score is computed based on the integrated reconstruction error, effectively capturing deviations in both observable attributes and LLM‑informed semantic expectations. Extensive experiments on six real‑world datasets demonstrate that TERGAD consistently outperforms state‑of‑the‑art baselines. Furthermore, our ablation studies validate the indispensable role of structural semantic guidance and the efficacy of the gated fusion mechanism. Code is available at https://github.com/Kantorakitty/TERGAD‑main.

PaperId: 836, https://arxiv.org/pdf/2605.19729.pdf GitHub

Abstract:
We demonstrate that in knowledge distillation for diffusion models, the teacher network's highly complex denoising process ‑ stemming from its substantially larger capacity ‑ poses a significant challenge for the student model to faithfully mimic. To address this problem, we propose a coarse‑to‑fine distillation framework with LInear FiTtingbased distillation (LIFT) and Piecewise Local Adaptive Coefficient Estimation (PLACE). First, LIFT decomposes the objective into a "coarse" alignment and a "fine" refinement. The student is then trained on coarse alignment before proceeding to hard refinement. Second, PLACE extends LIFT to address spatially non‑uniform errors by partitioning outputs into error‑based groups, providing locally adaptive guidance. Our experiments show that LIFT and PLACE is effective across diffusion spaces (image/latent), backbones (U‑Net/DiT), tasks (unconditional/conditional), datasets, and even extends to flow‑based models such as MMDiT (SD3). Furthermore, under extreme compression with a 1.3M‑parameter student (only 1.6% of the teacher), conventional KD fails to provide sufficient guidance for stable training, with FID scores often degrading to 50‑200+, but our method remains stably convergent and achieves an FID of 15.73.

PaperId: 837, https://arxiv.org/pdf/2605.19633.pdf GitHub GitHub

Abstract:
Can a single LLM‑based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a text artifact evaluated by a scoring function, a single AI‑based optimization system‑supporting single‑task search, multi‑task search with cross‑problem transfer, and generalization to unseen inputs‑achieves state‑of‑the‑art results across six diverse tasks. Our system discovers agent architectures that nearly triple Gemini Flash's ARC‑AGI accuracy (32.5% to 89.5%), finds scheduling algorithms that cut cloud costs by 40%, generates CUDA kernels where 87% match or beat PyTorch, and outperforms AlphaEvolve's reported circle packing solution (n=26). Ablations across three domains reveal that actionable side information yields faster convergence and substantially higher final scores than score‑only feedback, and that multi‑task search outperforms independent optimization given equivalent per‑problem budget through cross‑task transfer, with benefits scaling with the number of related tasks. Together, we show for the first time that text optimization with LLM‑based search is a general‑purpose problem‑solving paradigm, unifying tasks traditionally requiring domain‑specific algorithms under a single framework. We open‑source optimize\_anything with support for multiple backends as part of the GEPA project at https://github.com/gepa‑ai/gepa .

PaperId: 838, https://arxiv.org/pdf/2605.19607.pdf GitHub

Abstract:
Integrated Gradients (IG) is a widely adopted feature attribution method that satisfies desirable axiomatic properties. However, the choice of integration path significantly affects the quality of attributions, and the standard straight‑line path introduces all input features simultaneously, often accumulating noisy gradients along the way. To address this limitation, we propose Spectral Integrated Gradients, which constructs integration paths based on singular value decomposition (SVD) of the baseline‑to‑input difference. By progressively activating singular components from largest to smallest, SIG introduces global structure before fine‑grained details, naturally following a coarse‑to‑fine progression. Through extensive evaluation across diverse image classification datasets, we demonstrate that SIG produces cleaner attribution maps with reduced noise and achieves improved quantitative performance compared to existing path‑based attribution methods. Our code is available at https://github.com/leekwoon/sig/.

PaperId: 839, https://arxiv.org/pdf/2605.19587.pdf GitHub

Abstract:
Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation‑based policy evaluation, where a useful scene must specify not only what the environment looks like, but also how its objects are structured. Existing pipelines, however, typically represent generated content as static meshes and inherit articulation only from curated asset libraries, which limits object‑level controllability and prevents new interactable assets from being produced on demand. We address this gap by formulating physically interactable indoor scene synthesis as programmatic world generation, and present SceneCode, a framework that compiles a natural language prompt into an executable, code‑driven indoor world rather than a collection of opaque meshes. A room‑level agentic backbone first turns the prompt into a structured house layout and emits per‑object AssetRequests through a planner‑‑designer‑‑critic loop. Each request is then routed to one of five code‑generation strategies and converted into a synthesized part‑wise Blender Python programs that are validated through an execution‑guided repair‑and‑refine loop. The resulting programs are compiled into simulation‑ready assets, and exported as SDF for physics simulation. A persistent scene‑state registry links object requests, executable programs, rendered geometry, and simulation assets, turning scene assembly into a traceable and locally editable world‑building process. We evaluate SceneCode across scene‑level synthesis, object‑level asset quality, human judgment, and downstream robot interaction. Results show that executable world programs improve prompt‑faithful indoor scene generation and produce assets with cleaner mesh structure, and simulator‑loadable articulation metadata. Project page: https://scene‑code.github.io/.

PaperId: 840, https://arxiv.org/pdf/2605.19578.pdf GitHub

Abstract:
RGB camera‑based surveillance systems enable human action recognition for public safety and healthcare, yet raise serious privacy concerns. Existing methods rely on post‑capture algorithms, which fail to protect privacy during data acquisition. We propose Lens Privacy Sealing (LPS), a simple hardware solution that physically obscures camera lenses with adjustable laminating film, providing pre‑sensor privacy protection at minimal cost. Unlike software methods or expensive engineered optics, LPS achieves strong privacy through stochastic multi‑layer scattering that is physically irreversible. We introduce the P^3AR dataset for privacy‑preserving action recognition, featuring both large‑scale replay‑captured (P^3AR‑NTU, 114K videos) and real‑world collected (P^3AR‑PKU) subsets with privacy attribute annotations. To handle video degradation from LPS, we propose MSPNet, a single‑stage framework incorporating Inter‑Frame Noise Suppressor (IFNS) and Cross‑Frame Semantic Aggregator (CFSA), enhanced by contrastive language‑image pre‑training for robust semantic extraction. Extensive experiments demonstrate that MSPNet with IFNS and CFSA nearly doubles action recognition accuracy compared to baseline methods while suppressing identity recognition to low levels. Comprehensive validation shows LPS achieves a superior privacy‑utility trade‑off compared to state‑of‑the‑art hardware methods, resists reconstruction attacks including PSF inversion and data‑driven recovery, and generalizes robustly across optical configurations and challenging environments. Code is available at https://github.com/wangzy01/MSPNet.

PaperId: 841, https://arxiv.org/pdf/2605.19559.pdf GitHub

Abstract:
The rapid development of Multimodal Large Language Models (MLLMs) has led to growing interest in egocentric video understanding, specifically the ability for MLLMs to recognize fine‑grained hand‑object interactions, track object state changes over time, and reason about manipulative processes in dynamic environments from a first‑person perspective. However, existing egocentric video benchmarks suffer from limited grounded rationale evaluation, offering limited support for fine‑grained operation‑centric reasoning and rarely examining whether model rationales are grounded in explicit spatio‑temporal evidence. To address this gap, we introduce EgoCoT‑Bench, a fine‑grained egocentric benchmark for grounded and verifiable operation‑centric reasoning with explicit step‑by‑step rationale annotations. Overall, EgoCoT‑Bench comprises 3,172 verifiable QA pairs over 351 egocentric videos separated into four task groups for a total of 12 sub‑task groups, encompassing perception and retrospection, anticipation, and high‑level reasoning. The benchmark is constructed through a spatio‑temporal scene graphs (STSG) guided generation framework and is further refined by human annotators to ensure correctness, egocentric relevance and fine‑grained quality. Experimental results show continuing difficulties with egocentric fine‑grained reasoning and further reveal that many multimodal models produce explanations that are answer‑correct, but have evidence that is inconsistent with the answer. We hope EgoCoT‑Bench can serve as a useful testbed for grounded and verifiable reasoning in egocentric video understanding. Project page and supplementary materials are available at: https://dstardust.github.io/EgoCoT/.

PaperId: 842, https://arxiv.org/pdf/2605.19503.pdf GitHub

Abstract:
Reinforcement learning for legged locomotion has matured into a stack of multi‑component reward functions and physics‑engine benchmarks whose morphologies are uniformly derived from real commercial hardware. Game NPCs, however, are bound by stylistic constraints absent from sim‑to‑real robotics and routinely take the form of creatures with no real‑robot counterpart. We introduce ARC‑RL, a suite of four MuJoCo continuous‑control environments featuring robotic morphologies inspired by the bestiary of ARC Raiders: the 18‑DoF tall hexapod Queen, the 12‑DoF armoured hexapod Bastion, the 18‑DoF compact hexapod Tick, and the 12‑DoF quadruped Leaper. All four robots share a unified observation template, action convention, simulation cadence, and a single closed‑form multi‑component reward function whose only per‑morphology variation lives in a small set of weights and parameters. The reward fuses a velocity‑tracking tent, a healthy survive bonus, a phase‑locked gait‑compliance bonus/cost pair, action regularisers, three safety penalties, and a posture anchor; no motion‑capture data enters the reward at any point. We additionally provide hand‑crafted Central Pattern Generator demonstrators per morphology, which serve both as fixed expert references and as sources of prior data for offline‑to‑online training. On this playground, we conduct a controlled empirical study comparing standard online algorithms (SAC, SPEQ, SOPE‑EO) and methods augmented with prior data (SACfD, SPEQ‑O2O, SOPE), and characterise how each paradigm copes with the playground's morphological diversity and animation‑style stylistic constraints. Source code is available at https://github.com/CarloRomeo427/ARC_RL.git.

PaperId: 843, https://arxiv.org/pdf/2605.19501.pdf GitHub

Abstract:
Robot guide dogs offer navigation assistance that greatly expands the independent mobility of the visually impaired, but their effective use requires subtle human‑robot coordination that is difficult for users to learn from generic verbal instructions. To tackle this challenge, we present CANINE, an automated coaching system that trains users for interactive navigation with a robot guide dog, through personalized, adaptive verbal feedback. CANINE decomposes a complex coordination task into sub‑skills and operates at two levels. At the high level, it decides what to train by tracking the learner's proficiency across sub‑skills using knowledge tracing and prioritizing training on the weakest areas. At the low level, CANINE decides how to train each sub‑skill by observing each human practice episode, using foundation models to infer the underlying causes of errors, and generating targeted verbal corrections adaptively. A controlled study with blindfolded participants, treated as a proxy population for quantitative evaluation, demonstrates that CANINE significantly improves both learning efficiency and final navigation performance compared to generic verbal instructions. We further validate CANINE through a retention study and an exploratory case study. The retention study shows lasting skill improvement after two weeks. The case study confirms CANINE's effectiveness in training a visually impaired user, while revealing additional design considerations for real‑world deployment. Both are well aligned with the findings of the controlled study. Project page: https://cunjunyu.github.io/project/canine/

PaperId: 844, https://arxiv.org/pdf/2605.19462.pdf GitHub

Abstract:
The success of self‑supervised learning (SSL) in vision and NLP has motivated its rapid adoption for time series. However, research has focused primarily on Generative paradigms and forecasting tasks, leaving the broader utility of learned representations unquantified. We establish a controlled framework to evaluate the "pre‑training dividend": the value added by SSL across diverse temporal tasks. We systematically compare Generative paradigms against Latent Alignment architectures, introducing adaptations of LeJEPA and DINO for time series. These adaptations utilize Discrete Wavelet Transform (DWT) augmentations to enforce invariance to local fluctuations. Our analysis reveals that the pre‑training dividend is highly asymmetric: SSL yields gains of up to 375% for anomaly detection and classification, yet remains marginal for forecasting. We demonstrate that representational utility is non‑universal, governed by a precision‑invariance trade‑off where the specific signal resolution required by the task must align with the objective. Finally, we show that representation quality is largely independent of data origin and saturates at moderate architectural depths, suggesting a path to scaling via massive synthetic generation. Our code is available at: https://github.com/noammajor/Models

PaperId: 845, https://arxiv.org/pdf/2605.19435.pdf GitHub

Abstract:
Visual Place Recognition (VPR) is critical for autonomous navigation, yet state‑of‑the‑art methods lack well‑calibrated uncertainty estimation. Standard pipelines cannot reliably signal when a query is ambiguous or a match is likely incorrect, posing risks in safety‑critical robotics. We propose KappaPlace, a principled framework for learning uncertainty‑aware VPR representations. Our core contribution is a Prototype‑Anchored supervision strategy that leverages latent class representatives as targets for a probabilistic objective. By modeling image descriptors as von Mises‑Fisher (vMF) variables, we learn a lightweight module to predict the concentration parameter as a direct proxy for aleatoric uncertainty. While existing VPR uncertainty methods are typically restricted to a query‑centric view, we derive a novel match‑level formulation to quantify the reliability of specific query‑reference pairs. Across five diverse benchmarks, KappaPlace reduces Expected Calibration Error (ECE@K) by up to 50% compared to existing methods while maintaining or improving retrieval recall. We provide both a joint‑training variant and a post‑training extension for frozen backbones. Our results demonstrate that KappaPlace provides a robust, stable, and well‑calibrated signal that enables reliable decision‑making within the VPR pipeline. Our code is available at: https://github.com/mayayank95/UncertaintyAwareVPR

PaperId: 846, https://arxiv.org/pdf/2605.19376.pdf GitHub

Abstract:
How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising alternative to autoregressive sequence extension by performing iterative latent‑state refinement with shared transition functions. Yet existing RRMs are largely deterministic, following a single latent trajectory and converging to a single prediction. We introduce Generative Recursive reAsoning Models (GRAM), a framework that turns recursive latent reasoning into probabilistic multi‑trajectory computation. GRAM models reasoning as a stochastic latent trajectory, enabling multiple hypotheses, alternative solution strategies, and inference‑time scaling through both recursive depth and parallel trajectory sampling. This yields a latent‑variable generative model supporting conditional reasoning via p_θ(y \mid x) and, with fixed or absent inputs, unconditional generation via p_θ(x). Trained with amortized variational inference, GRAM improves over deterministic recurrent and recursive baselines on structured reasoning and multi‑solution constraint satisfaction tasks, while demonstrating an unconditional generation capability. https://ahn‑ml.github.io/gram‑website

PaperId: 847, https://arxiv.org/pdf/2605.19374.pdf GitHub

Abstract:
Vision‑language alignment using chest X‑rays and radiology reports has emerged as an advanced paradigm for zero‑shot classification and grounding of chest X‑ray findings. However, standard contrastive learning typically treats radiographs and reports from different patients simply as negative pairs. This assumption introduces noisy negatives, as different patients frequently exhibit similar findings. Such noisy negatives cause semantic ambiguity and degrade performance in zero‑shot understanding tasks. To address this challenge, we propose CoNNS, a concept‑guided noisy‑negative suppression framework. To support the negative suppression mechanism, unlike previous methods that use raw reports or templatized texts, we construct a hierarchical concept ontology using large language models. The ontology structures 41 key clinical concepts by explicitly modeling presence, attributes (location and characteristics), and texts (evidential segment and presence statement). Leveraging this ontology, we implement a cross‑patient pair relabeling strategy comprising three steps: (1) Fine‑Grained Breakdown to categorize pairs based on finding presence; (2) Noisy Negative Filtering to resolve semantic conflicts by removing false negatives; and (3) Hard Negative Mining to identify subtle attribute discrepancies using a lightweight language model. Finally, we propose a Concept‑Aware NCE loss to align visual features with text while suppressing the identified noisy negatives. Extensive experiments across multi‑granularity zero‑shot grounding tasks and five zero‑shot classification datasets validate that CoNNS outperforms existing state‑of‑the‑art models. The code is available at https://github.com/DopamineLcy/conns.

PaperId: 848, https://arxiv.org/pdf/2605.19355.pdf GitHub

Abstract:
Retargeting motion across characters with varying body shapes while preserving interaction semantics, such as self‑contact and near‑body proximity, remains a challenging problem. While recent geometry‑aware approaches address this by maintaining spatial relationships between predefined corresponding regions, their reliance on static correspondences often struggles when the target character exhibits exaggerated body proportions. In this paper, we present a geometry‑aware motion retargeting framework that preserves interaction semantics by performing proximity matching over spatially adaptive anchors. Unlike prior methods with static anchor definitions, the proposed method dynamically repositions anchors to reachable regions on the target character. This is achieved via a Transformer‑based anchor refinement strategy that predicts anchor displacements and constrains the translated anchors to remain on the target character geometry through differentiable soft projection. By incorporating pose‑dependent spatial structures from the source character, the adapted anchors provide structurally coherent guidance for interaction‑aware retargeting. Conditioned on these anchors, a graph‑based autoencoder predicts target skeletal motion that preserves the spatial configuration of the source. To encourage task‑aligned optimization between anchor adaptation and motion retargeting, we adopt an alternating training scheme in which each module is optimized in turn. Through extensive evaluations, we demonstrate that our method outperforms state‑of‑the‑art approaches in preserving interaction fidelity across diverse character geometries.

PaperId: 849, https://arxiv.org/pdf/2605.19346.pdf GitHub

Abstract:
We present IMLJD, an open dataset of 3,613 Indian court judgments covering matrimonial disputes under IPC Section 498A, the Protection of Women from Domestic Violence Act, and CrPC Section 482. The dataset covers the Supreme Court of India from 2000 to 2024 (1,474 cases) and the Karnataka High Court from 2018 to 2024 (2,139 cases), with structured outcome labels, metadata‑derived indicators, and a knowledge graph. We find that 57.6% of quashing petitions succeed at the Supreme Court level compared to 39.7% at the Karnataka High Court level. On a matched 2018 to 2024 period, the SC quash rate is 59.3%, widening the differential to 19.6 percentage points and confirming the finding is robust to temporal adjustment. The dataset, code, and knowledge graph are released openly at https://github.com/joyboseroy/imljd and https://huggingface.co/datasets/joyboseroy/imljd.

PaperId: 850, https://arxiv.org/pdf/2605.19338.pdf GitHub GitHub

Abstract:
Frontier AI models and multi‑agent systems have led to significant improvements in mathematical reasoning. However, for problems requiring extended, long‑horizon reasoning, existing systems continue to suffer from fundamental reliability issues: hallucination accumulation, memory fragmentation, and imbalanced reasoning‑tool trade‑offs. In this paper, we introduce STAR‑PólyaMath, a multi‑agent framework that systematically addresses these challenges through meta‑level supervision and structured Reasoner‑Verifier interaction. STAR‑PólyaMath is structured as an orchestrated state machine with nested challenge‑step‑replan loops, governed by a reasoning‑free Python orchestrator that separates control from inference and bounds error propagation through trace‑back and re‑planning. Our key innovation is a persistent Meta‑Strategist that maintains cross‑attempt memory and exercises meta‑level control by issuing high‑level strategic guidance or mandatory directives, so the system can escape unproductive loops rather than stagnate or over‑rely on tools. STAR‑PólyaMath achieves state‑of‑the‑art results on all eight top‑tier competition benchmarks: AIME 2025‑2026, MathArena Apex Shortlist, MathArena Apex 2025, Putnam 2025, IMO 2025, HMMT February 2026, and USAMO 2026. It obtains perfect scores on AIMEs, Putnam, and HMMT, and shows its largest margin on Apex 2025, scoring 93.75% compared with 80.21% by the strongest baseline GPT‑5.5. Ablation studies show that the gains arise from the framework's orchestration rather than from model‑level diversity since removing key components or substituting in mixed backbones consistently weakens performance. Code is available at https://github.com/Julius‑Woo/STAR‑PolyaMath.

PaperId: 851, https://arxiv.org/pdf/2605.19317.pdf GitHub

Abstract:
Inference‑time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. However, existing inference‑time scaling methods for diffusion models typically rely on external verifiers or reward models to rank and select samples, limiting their scalability to settings where such evaluators are available and reliable. Moreover, while recent diffusion models perform sequential inference with region‑wise, mixed‑noise conditioning, inference‑time scaling tailored to this setting remains relatively underexplored. We propose Iterative Partial Refinement (IPR), an inference‑time scaling method for sequential diffusion that requires no external verifier. Starting from an already‑generated sample, IPR re‑noises a subset of regions and regenerates them conditioned on the remaining regions, enabling the model to revise earlier decisions under a richer context than was available during the initial generation. This iterative partial refinement produces more globally consistent samples without external verification. On reasoning tasks requiring global constraint satisfaction, IPR consistently improves performance: on MNIST Sudoku, the valid solution rate increases from 55.8% to 75.0%. These results show that iterative partial refinement alone can serve as an effective inference‑time scaling strategy for diffusion models in sequential, mixed‑noise settings. Code is available at: https://github.com/ahn‑ml/IPR

PaperId: 852, https://arxiv.org/pdf/2605.19285.pdf GitHub

Abstract:
The rapid spread of misinformation on social media platforms has become a formidable challenge. To mitigate its proliferation, Misinformation Detection (MD) has emerged as a critical research topic. Traditional MD approaches based on small models typically perform binary classification through a black‑box process. Recently, the rise of Large Language Models (LLMs) has enabled explainable MD, where models generate rationales that explain their decisions, thereby enhancing transparency. Existing explainable MD methods primarily focus on crafting sophisticated prompts to elicit rationales from off‑the‑shelf LLMs. In this work, we propose a pipeline to fine‑tune a dedicated LLM specifically for explainable MD. Our pipeline begins by collecting large‑scale fact‑checked articles, and then uses multiple strong LLMs to produce veracity predictions and rationales. To ensure high‑quality training data, we leverage a filtering strategy that selects only the correct instances for fine‑tuning. While this pipeline is intuitive and prevalent, our experiments reveal that naive filtering based solely on label correctness is insufficient in practice and suffers from two critical limitations: (1) Coarse‑grained labels cause insufficient rationales: Rationales filtered solely based on binary labels are insufficient to adequately support their decisions; (2) Over‑verification behavior causes unnecessary rationales: Stronger LLMs tend to exhibit over‑verification behavior, producing excessively verbose and unnecessary rationales. To address these issues, we introduce LONSREX, a novel data synthesis pipeline to Locate Necessary and Sufficient Rationales for Explainable MD. Specifically, we propose a metric that quantifies the contribution of each verification step to the final prediction, thereby evaluating its necessity and sufficiency. Experimental results demonstrate the effectiveness of LONSREX.

PaperId: 853, https://arxiv.org/pdf/2605.19283.pdf GitHub

Abstract:
Sequential prediction is challenging in regimes of delayed disambiguation, where early observations are ambiguous and multiple latent explanations remain plausible until sufficient evidence accumulates. Standard approaches based on marginal inference struggle in this setting, either collapsing uncertainty prematurely or failing to recover once informative evidence arrives. We introduce EviTrack, a test‑time inference framework that operates over latent trajectories rather than marginal states. EviTrack maintains a set of competing trajectory hypotheses and applies evidence‑ and likelihood‑ratio‑based selection to delay commitment until supported by data, drawing inspiration from hypothesis management in multiple hypothesis tracking and track‑before‑detect. To evaluate this setting, we construct a controlled synthetic benchmark with known latent ground truth that explicitly exhibits delayed disambiguation. At matched inference budget, EviTrack substantially outperforms sampling‑based baselines, achieving faster post‑disambiguation recovery. These results show that, in delayed disambiguation regimes, moderate trajectory‑level selection is more effective than increasing sampling coverage, highlighting selection over sampling as a key principle for reliable sequential inference.

PaperId: 854, https://arxiv.org/pdf/2605.19075.pdf GitHub

Abstract:
Grounded multi‑video question answering over real‑world news events requires systems to surface query‑relevant evidence across heterogeneous video archives while attributing every claim to its supporting source. We introduce CRAFT (Critic‑Refined Adaptive Key‑Frame Targeting), a query‑conditioned pipeline that combines dynamic keyframe selection, per‑video ASR with multilingual fallback, and a hybrid critic loop to iteratively verify and repair claims before consolidation. The pipeline integrates UNLI temporal entailment, DeBERTa‑v3 cross‑claim screening, and a Llama‑3.2‑3B adjudicator, with a final citation‑merging stage that emits each fact once with all supporting source identifiers. On MAGMaR 2026, CRAFT achieves the best overall average (0.739), reference recall (0.810), and citation F1 (0.635). We further evaluate on a MAGMaR‑style conversion of WikiVideo with 52 non‑overlapping event queries, where CRAFT also performs strongly (0.823 Avg), showing that its claim‑centric evidence aggregation generalizes beyond MAGMaR. Ablations show that atomic claims, ASR, and the critic loop drive the main gains over the vanilla query‑conditioned baseline. Code and implementation details are publicly available at https://github.com/bhosalems/CRAFT.

PaperId: 855, https://arxiv.org/pdf/2605.19033.pdf GitHub GitHub

Abstract:
Supervised open‑loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, multi‑agent interactions common in complex driving scenarios. We introduce RLFTSim, a reinforcement‑learning‑based fine‑tuning framework that enhances scenario realism by aligning simulator rollouts with real‑world data distributions and provides a method for distilling goal‑conditioned controllability in scenario generation. We instantiate RLFTSim on top of a pre‑trained simulation model, design a reward that balances fidelity and controllability, and perform comprehensive experiments on the Waymo Open Motion Dataset. Our results show improvements in realism, achieving state‑of‑the‑art performance. Compared with other heuristic search‑based fine‑tuning methods, RLFTSim requires significantly fewer samples due to a proposed low‑variance and dense reward signal, and it directly addresses the realism alignment issue by design. We also demonstrate the effectiveness of our approach for distilling traffic simulation controllability through goal conditioning. The project page is available at https://ehsan‑ami.github.io/rlftsim.

PaperId: 856, https://arxiv.org/pdf/2605.18916.pdf GitHub

Abstract:
We investigate Counterfactual Video Foley Generation, which aims to adopt a sound‑source identity that contradicts the visual evidence while remaining temporally synchronized to a silent video. Existing Video&Text‑to‑Audio (VT2A) models struggle with this, often remaining anchored to the visually implied sound source when video and text contents disagree. We present ConterFlow, an inference‑time dual‑phase sampling scheme for pretrained flow‑matching VT2A models. Phase 1 builds a video‑derived temporal structure while suppressing the visually implied source; Phase 2 drops video conditioning to focus entirely on shaping audio timbre toward the target prompt. ConterFlow substantially improves counterfactual Video Foley generation compared to naive negative prompting and state‑of‑the‑art baselines. To evaluate replacement quality, we propose a metric leveraging a text‑audio co‑embedding space to measure both target‑prompt evidence and residual visually implied source leakage. Video demonstrations and code are available at https://gyubin‑lee.github.io/counterflow‑demo/

PaperId: 857, https://arxiv.org/pdf/2605.18882.pdf GitHub

Abstract:
LLM agents exhibit a consistent tendency to over‑call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models from three families show high call accuracy but much lower no‑call accuracy, leaving overall accuracy in the 55%‑70% range. We trace this to an Intrinsic Bias Hypothesis (IBH): the call/no‑call decision mapping carries an activation‑independent call offset, so the model favors call even at activation parity. Using Sparse Autoencoders (SAEs), we recover behavior‑aligned feature bases for the call/no_call decision, reduce them to a signed activation margin, and estimate the offset directly. Across all six models, the model is decision‑neutral only when no_call activation outweighs call activation, consistent with IBH. We then causally test IBH with Adaptive Margin‑Calibrated Steering (AMCS), a closed‑form counter‑bias shift along SAE decoder directions. Cancelling the diagnosed offset mitigates over‑calling and improves overall accuracy with a negligible drop in call accuracy. Our work recasts over‑calling from an empirical phenomenon into a mechanistic object amenable to causal correction. Code is available at https://github.com/SKURA502/agent‑sae/.

PaperId: 858, https://arxiv.org/pdf/2605.18864.pdf GitHub

Abstract:
Recent studies observe that reinforcement learning with verifiable rewards (RLVR) reliably improves pass@1 on reasoning tasks, yet often fails to yield comparable gains in pass@k, raising the question of whether RLVR genuinely enables large language models to acquire novel reasoning abilities or merely enhances the efficiency of sampling reasoning modes already present in the base model. Prior analyses largely support the latter view, attributing this limitation to structural properties of standard RLVR objectives that result in insufficient exploration pressure. In this work, we argue that a central structural constraint arises from reverse‑KL regularization, which stabilizes training but inherently anchors the policy to the reference distribution, thereby suppressing the emergence of alternative reasoning modes. However, we show that neither removing the KL term nor replacing it with forward‑KL provides a satisfactory solution, as both disrupt the efficiency‑coverage trade‑off by either inducing reward hacking or allocating probability mass to off‑target regions. To resolve this tension, we propose SAGE, a principled framework that enables controllable empirical support expansion by reshaping the reverse‑KL anchor distribution itself through a guide function q(x,y), achieving consistent improvements in both pass@1 and pass@k across challenging mathematical reasoning benchmarks. Our code is available at https://github.com/tally0818/SAGE.

PaperId: 859, https://arxiv.org/pdf/2605.18859.pdf GitHub

Abstract:
LLM routing matters most in long‑horizon applications such as coding agents, deep research systems, and computer‑use agents, where a single user request triggers many model calls. Routing each call to the cheapest sufficient model can cut costs without sacrificing quality, yet existing router benchmarks evaluate routers only on one‑shot prompts. They never expose the router‑visible prefix at an intermediate agent step, never test whether a cheaper replacement preserves downstream task success, and often rely on online LLM judges at evaluation time. We introduce TwinRouterBench, a step‑level routing benchmark with two tracks. The static track provides 970 router‑visible prefixes from 520 instances across SWE‑bench, BFCL, mtRAG, QMSum, and PinchBench, each paired with an execution‑verified target tier estimated under a released downgrade‑and‑cascade protocol; scoring is deterministic arithmetic over tier labels, trajectory membership, and token costs, with no online evaluator‑side LLM judge. The dynamic track supplies a harness that runs routers on the full 500‑case SWE‑bench Verified suite; in this paper we report a 100‑case held‑out evaluation disjoint from the static SWE supervision split. At each LLM call the router selects a concrete model from a locked pool, and success is measured by official task resolution and realized API spend. The two tracks support fast offline iteration followed by end‑to‑end validation under live agent execution. Code and data are available at https://github.com/CommonstackAI/TwinRouterBench.

PaperId: 860, https://arxiv.org/pdf/2605.18857.pdf GitHub

Abstract:
For most of the history of information retrieval (IR), search results were designed for human consumers who could scan, filter, and discard irrelevant information on their own. This shaped retrieval systems to optimize for finding and ranking more relevant documents, but not keeping results clean and minimal, as the human was the final filter. However, LLMs have changed that by lacking this filtering ability. To address this, we introduce Bits‑over‑Random (BoR), a chance‑corrected measure of retrieval selectivity that reveals when high success rates mask random‑level performance. We measure selectivity as BoR = \log_2\left(\frac\mathrmP_obs\mathrmP_rand\right), where \mathrmP_rand is the hypergeometric baseline for the chosen success rule (here, coverage: \geq1 relevant in top‑K). On the 20 Newsgroups dataset, BM25 and SPLADE both report >99% success at K=100 (coverage), yet BoR \approx 0, indicating random‑level selectivity at that depth. When the expected coverage ratio \left(\fracK \cdot \barR_qN\right) exceeds 3‑5, the baseline dominates and selectivity collapses. Downstream retrieval‑augmented generation (RAG) evaluation confirms this pattern: LLM accuracy can degrade substantially at K=100, consistent with the near‑zero BoR ceiling. In contrast, BoR remains positive on BEIR/SciFact and on MS MARCO (where 41 systems cluster within 0.2 bits of the theoretical ceiling despite a 13‑point recall gap), confirming baseline predictions across sparse and large‑scale settings. We further show that the collapse boundary applies to LLM agent tool selection, where small catalog sizes cause selectivity to vanish even with perfect selectors. These findings suggest reporting BoR alongside traditional metrics and reconsidering depth choices when additional retrieval provides negligible selectivity gains while inflating computational costs.

PaperId: 861, https://arxiv.org/pdf/2605.18840.pdf GitHub

Abstract:
Leaderboards rank frontier models on independent axes but do not reveal whether capabilities reinforce or trade off across releases ‑‑ and at the frontier, this interaction is the more informative signal. We decompose paired SWE‑bench and GPQA Diamond scores into a population coupling trend and per‑release residual (h‑field) that diagnoses capability emphasis from two public benchmark scores. Across 34 models from 10 labs (2024‑‑2026), capabilities cooperate (r = +0.72, p < 10^‑6), but cooperation varies systematically: per‑lab coupling slopes span 5× (Google 1.15 vs. DeepSeek 0.23), and labs pivot ‑‑ DeepSeek reversed from reasoning‑rich to coding‑first (Δh = 15.9~pp); Anthropic oscillates between coding excursions and recovery. The population regression serves as an isocline phase boundary: the same \sqrt(a/b)\cdot B_1 classifier that identifies the base‑scale coupling transition [Amin, 2026] classifies frontier models and already detects mixed‑phase behavior at the next transition (two models below the GPQA‑‑IFEval isocline). The h‑field is not just diagnostic ‑‑ it tells you what to change. Pretraining establishes coupling at 0.871 while RLHF adds 0.081 [Amin, 2026]: pretraining‑level shifts are permanent (DeepSeek's four‑release reversal persists), post‑training shifts are reversible (Anthropic's three coding excursions each recover within one release), and inference compute alone shifts h by +7.8~pp without retraining. Knowing which component dominates determines whether to retrain or wait. We provide a three‑step diagnostic (locate, classify, predict), a per‑lab measurement‑priority table, and seven falsifiable predictions with timestamped criteria. Five post‑cutoff releases fall within the 95% prediction interval. Code, data, and an interactive dashboard: https://zehenlabs.com/cape/.

PaperId: 862, https://arxiv.org/pdf/2605.18799.pdf GitHub

Abstract:
Large language models can fail in critic interaction not only by answering incorrectly, but also by abandoning an initially correct scientific solution after user criticism. This is especially risky in scientific reasoning, where user criticism can turn a valid answer into an incorrect one. We frame critic interaction as an inter‑turn correctness‑transition problem rather than a final‑answer accuracy problem, and identify three challenges: transition awareness, decoupling useful correction from harmful sycophancy, and scalable rollout. We propose ReCrit, a transition‑aware reinforcement learning framework that decomposes Initial‑to‑Critic behavior into four quadrants: Correction, Sycophancy, Robustness, and Boundary. ReCrit rewards correction and robustness, penalizes sycophancy, and treats persistent errors as weak boundary signals. To make interaction training practical, ReCrit further uses dynamic asynchronous rollout with tail‑adaptive completion to reduce rollout waiting. On three scientific reasoning benchmarks, ChemBench, TRQA, and EarthSE, ReCrit improves average Critic accuracy from 38.15 to 51.49 on Qwen3.5‑4B and from 45.40 to 55.59 on Qwen3.5‑9B. Ablations show that final‑answer rewards provide little interaction‑level gain, while transition‑aware rewards and quadrant weighting produce more distinguishable training signals and larger net Critic‑stage improvement. The code is available at https://github.com/black‑yt/ReCrit .

PaperId: 863, https://arxiv.org/pdf/2605.18793.pdf GitHub

Abstract:
Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring. However, existing methods face performance bottlenecks, typically yielding only incremental gains and often exhibiting limited cross‑domain transferability. We analyze this bottleneck through spatial and temporal entropy measures, which are used as diagnostic indicators of spatiotemporal complexity mismatch rather than as guarantees that entropy alignment alone yields better forecasting. Empirically, larger mismatch is often accompanied by higher prediction uncertainty, especially under a fixed model‑capacity budget. Guided by this diagnostic, we propose a scalable, adaptive framework that harmonizes spatial and temporal feature representations. Spatial dimensionality is compressed via low‑rank matrix embedding to preserve essential structure, while an extended temporal horizon captures long‑range dependencies and mitigates cumulative errors arising from temporal heterogeneity. Extensive experiments on urban traffic, meteorological, and epidemic datasets demonstrate substantial accuracy gains and broad applicability across the evaluated domains, suggesting that the framework is promising for a wide range of spatiotemporal tasks beyond the current study. The code is available on GitHub at https://github.com/ST‑Balance/ST‑Balance.

PaperId: 864, https://arxiv.org/pdf/2605.18766.pdf GitHub

Abstract:
Retrieving relevant tables from extensive databases for a given natural language query is essential for accurately answering questions in tasks such as text‑to‑SQL. Existing table retrieval approaches select a pre‑determined set of k tables with the highest similarity to the query. However, the number of required tables varies across queries and cannot be known in advance. Enforcing a fixed number of retrieved tables regardless of the query may either retrieve an undersized set, failing to obtain all necessary evidence, or retrieve an oversized pool, including irrelevant tables. To address this issue, we propose an adaptive table retrieval method that adjusts the number of tables retrieved according to the requirements of each query. Specifically, we utilize an adaptive thresholding mechanism to selectively retrieve tables and integrate a sliding‑window reranking algorithm to efficiently process a large table corpus. Extensive experiments on Spider, BIRD, and Spider 2.0 demonstrate that our method effectively addresses the limitations of the top‑k retrieval strategy, improving performance in retrieval and downstream tasks. Our code and data are available at https://github.com/sbY99/Adaptive‑Table‑Retrieval.

PaperId: 865, https://arxiv.org/pdf/2605.18747.pdf GitHub

Authors:Xuying Ning, Katherine Tieu, Dongqi Fu, Tianxin Wei, Zihao Li, Yuanchen Bei, Jiaru Zou, Mengting Ai, Zhining Liu, Ting-Wei Li, Lingjie Chen, Yanjun Zhao, Ke Yang, Bingxuan Li, Cheng Qian, Gaotang Li, Xiao Lin, Zhichen Zeng, Ruizhong Qiu, Sirui Chen, Yifan Sun, Xiyuan Yang, Ruida Wang, Rui Pan, Chenyuan Yang, Dylan Zhang, Liri Fang, Zikun Cui, Yang Cao, Pan Chen, Dorothy Sun, Ren Chen, Mahesh Srinivasan, Nipun Mathur, Yinglong Xia, Hong Li, Hong Yan, Pan Lu, Lingming Zhang, Tong Zhang, Hanghang Tong, Jingrui He

Abstract:
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository‑level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution‑based verification. We frame this shift through the lens of agent harnesses and introduce code as agent harness: a unified view that centers code as the basis for agent infrastructure. To systematically study this perspective, we organize the survey around three connected layers. First, we study the harness interface, where code connects agents to reasoning, action, and environment modeling. Second, we examine harness mechanisms: planning, memory, and tool use for long‑horizon execution, together with feedback‑driven control and optimization that make harness reliable and adaptive. Third, we discuss scaling the harness from single‑agent systems to multi‑agent settings, where shared code artifacts support multi‑agent coordination, review, and verification. Across these layers, we summarize representative methods and practical applications of code as agent harness, spanning coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression‑free harness improvement, consistent shared state across multiple agents, human oversight for safety‑critical actions, and extensions to multimodal environments. By centering code as the harness of agentic AI, this survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.

PaperId: 866, https://arxiv.org/pdf/2605.18727.pdf GitHub

Abstract:
Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene, choose a context‑appropriate action, execute it with a dexterous hand, and leave the scene usable for later decisions. We introduce DexHoldem, a real‑world system‑level benchmark built around Texas Hold'em dexterous manipulation with a ShadowHand. DexHoldem provides 1,470 teleoperated demonstrations across 14 Texas Hold'em manipulation primitives, a standardized physical policy benchmark, and an agentic perception benchmark that tests whether agents can recover the structured game state needed for embodied decision making. On primitive execution, π_0.5 obtains the highest task completion rate (61.2%), while π_0.5 and π_0 tie on scene‑preserving success rate (47.5%). On agentic perception, Opus 4.7 obtains the best strict problem‑level accuracy (34.3%), while GPT 5.5 obtains the best average field‑wise accuracy (66.8%), exposing a gap between isolated visual sub‑capabilities and complete routing‑relevant state recovery. Finally, we instantiate the full embodied‑agent loop in three case studies, where waiting, recovery dispatches, human‑help requests, and repeated primitive execution reveal how perception and policy errors accumulate during closed‑loop deployment. DexHoldem therefore evaluates dexterous tabletop execution, agentic perception, and embodied decision routing in a shared physical setting. Project page: https://dexholdem.github.io/Dexholdem/.

PaperId: 867, https://arxiv.org/pdf/2605.18714.pdf GitHub

Abstract:
Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives. Such a decoupled strategy yields misaligned representation spaces, isolating visual understanding from generation and hindering their mutual reinforcement. This work presents the first systematic investigation into generative post‑training, where we formulate hierarchical visual tasks as generative proxies to bridge the isolation in UMMs. Our empirical investigation reveals that high‑level semantic tasks, particularly image segmentation, serve as optimal proxies. Unlike low‑level tasks that distract models with texture details, segmentation provides structural semantics that significantly enhance both vision‑centric perception and generative layout fidelity. Building upon these insights, we introduce Semantic Generative Tuning (SGT), a novel paradigm that leverages segmentation as a generative proxy to align and synergize multimodal capabilities. Mechanistic analyses further demonstrate that SGT fundamentally improves feature linear separability and optimizes visual‑textual attention allocation pattern. Extensive evaluations show that SGT consistently improves both multimodal comprehension and generative fidelity across mainstream benchmarks. Our code is available on the https://song2yu.github.io/SGT/.

PaperId: 868, https://arxiv.org/pdf/2605.18678.pdf GitHub

Abstract:
We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity scaling or text‑image‑dominant designs, Lance explores a practical paradigm for unified multimodal modeling via collaborative multi‑task training. It is grounded in two core principles: unified context modeling and decoupled capability pathways. Specifically, Lance is trained from scratch and employs a dual‑stream mixture‑of‑experts architecture on shared interleaved multimodal sequences, enabling joint context learning while decoupling the pathways for understanding and generation. We further introduce modality‑aware rotary positional encoding to mitigate interference among heterogeneous visual tokens and boost cross‑task alignment. During training, Lance adopts a staged multi‑task training paradigm with capability‑oriented objectives and adaptive data scheduling to strengthen both semantic comprehension and visual generation performance. Experimental results demonstrate that Lance substantially outperforms existing open‑source unified models in image and video generation, while retaining strong multimodal understanding capabilities. The homepage is available at https://lance‑project.github.io.

PaperId: 869, https://arxiv.org/pdf/2605.18663.pdf GitHub

Abstract:
As LLM benchmarks saturate, the evaluation community has pursued two strategies to increase difficulty: escalating knowledge demands (GPQA, HLE) or removing knowledge entirely in favor of abstract reasoning (ARC‑AGI). The first conflates memorization with capability; the second divorces reasoning from the practical contexts in which it matters. We take a different approach. The Grounded Integration Measure (GIM) is a benchmark of 820 original problems (615 public, 205 private) where difficulty comes from integration; individual problems require coordinating multiple cognitive operations (constraint satisfaction, state tracking, epistemic vigilance, audience calibration) over broadly accessible knowledge, so that reasoning stays grounded in realistic tasks without being gated on specialized expertise. Each problem is an original expert‑authored composition, majority with rubric‑decomposed scoring (median 6 independently judged criteria). A balanced public‑‑private split provides built‑in contamination diagnostic. We calibrate a continuous response 2‑parameter logistic (2PL) IRT model over >200k prompt‑response pairs across 28 models, producing robust ability estimates that correctly order test‑configurations even when raw accuracy is distorted by errors or missing data, addressing a common challenge in benchmark reporting. Using this framework, we present a comprehensive leaderboard spanning 22 models and 47 test‑configurations (unique model, thinking‑level pairs), and conduct what is to our knowledge the most extensive published study of how test‑time compute trades off against model capability on a fixed benchmark: 11 models swept across 35 test‑configurations. We observe that within‑family configuration choices, such as thinking budget and quantization, matter as much as model selection. We release the evaluation framework, calibrated IRT parameters, and all public problems.

PaperId: 870, https://arxiv.org/pdf/2605.18661.pdf GitHub GitHub

Abstract:
AI‑assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as 15, while long‑horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input. Yet this productivity frontier exposes a deeper integrity problem: under scientific pressure, even frontier LLMs still fabricate results, miss hidden errors, and fail to judge novelty reliably. Studying developments through April 2026, we present an end‑to‑end analysis of AI across the complete research lifecycle, organized into four epistemological phases: Creation (idea generation, literature review, coding & experiments, tables & figures), Writing (paper writing), Validation (peer review, rebuttal & revision), and Dissemination (posters, slides, videos, social media, project pages, and interactive agents). We identify a sharp, stage‑dependent boundary between reliable assistance and unreliable autonomy: AI excels at structured, retrieval‑grounded, and tool‑mediated tasks, but remains fragile for genuinely novel ideas, research‑level experiments, and scientific judgment. Generated ideas often degrade after implementation, research code lags far behind pattern‑matching benchmarks, and end‑to‑end autonomous systems have not yet consistently reached major‑venue acceptance standards. We further show that greater automation can obscure rather than eliminate failure modes, making human‑governed collaboration the most credible deployment paradigm. Finally, we provide a structured taxonomy, benchmark suite, and tool inventory, cross‑stage design principles, and a practitioner‑oriented playbook, with resources maintained at our project page.

PaperId: 871, https://arxiv.org/pdf/2605.18630.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly deployed as scientific AI as‑ sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reasoning, code generation, and tool use. These evaluations, however, typically assume the scientific problem is already well‑posed, whereas practical scientific assistance often begins with an ill‑posed user request that must be refined through dialogue before any computation, analysis, or experiment can be carried out reliably. We introduce SCICONVBENCH, a benchmark for multi‑ turn clarification in scientific task formulation across four computational science problem domains: fluid mechanics, solid mechanics, materials science, and par‑ tial differential equations (PDEs). SCICONVBENCH targets two complementary capabilities: eliciting missing information (disambiguation) and detecting and correcting erroneous requests containing internally contradictory information (in‑ consistency resolution). Our benchmark pairs a structured task ontology with a rubric‑based evaluation framework, enabling systematic measurement of LLM per‑ formance across three dimensions: clarification behavior, conversational grounding, and final‑specification fidelity. Current frontier models perform relatively well on inconsistency resolution, but even the best model resolves only 52.7% of the disambiguation cases in fluid mechanics. We further find that frontier LLMs fre‑ quently make silent assumptions and perform implicit specification repairs that are not grounded in the conversation with users. SCICONVBENCH establishes a foundation for evaluating the upstream conversational reasoning that a reliable computational science assistant requires. The code and data can be found at https://github.com/csml‑rpi/SciConvBench.

PaperId: 872, https://arxiv.org/pdf/2605.18621.pdf GitHub

Abstract:
Spatial intelligence requires multimodal large language models (MLLMs) to move beyond single‑view perception and reason consistently about objects, visibility, geometry, and interactions across multiple viewpoints. However, progress in cross‑view reasoning remains limited by three major gaps: the scarcity of large‑scale well‑annotated training data, the lack of comprehensive benchmarks for systematic evaluation, and the absence of explicit alignment mechanisms that establish object‑level consistency across views. To address these gaps, we thoroughly develop CrossView Suite across three coordinated components: CrossViewSet, CrossViewBench, and CrossViewer. Firstly, we introduce a multi‑agent data engine to meticulously curate a large‑scale, high‑quality cross‑view instruction dataset, termed CrossViewSet, covering 17 fine‑grained task types with 1.6M samples. Second, we meticulously create a scene‑disjoint CrossViewBench to comprehensively assess the cross‑view spatial understanding capability of an MLLM, evaluating it across various aspects. Finally, we propose CrossViewer, a progressive three‑stage framework for cross‑view spatial reasoning in MLLMs, following a Perception ‑> Alignment ‑> Reasoning paradigm. Our method equips an adaptive spatial region tokenizer to capture fine‑grained object representations, and then aligns the multi‑view objects explicitly, and thus fuses aligned features for boosting the cross‑view inference capacity for MLLMs. Extensive experiments and analyses show that large‑scale training data, systematic evaluation, and explicit cross‑view alignment are all critical for advancing MLLMs from single‑view perception toward real‑world spatial intelligence. The project page is available at https://github.com/Thinkirin/Crossview‑Suite.

PaperId: 873, https://arxiv.org/pdf/2605.18617.pdf GitHub

Abstract:
Most existing vision‑language manipulation research targets rigid robotic arms, whose fixed morphology limits adaptability in cluttered or confined spaces. Soft robotic arms offer an appealing alternative due to their deformability, but confront challenges such as unreliable proprioception and distributed low‑level actuation. To investigate these challenges, we introduce \ManiSoft, a benchmark for vision‑language manipulation with soft arms. ManiSoft features a tailored simulator that couples realistic soft‑body dynamics with contact‑rich interactions via an elastic force constraint. On this basis, ManiSoft defines four tasks, each highlighting distinct aspects of deformable control, from basic end‑effector coordination to obstacle avoidance. To support policy training and evaluation, \ManiSoft includes an automated pipeline that generates 6,300 diverse scenes and corresponding expert trajectories. To produce high‑quality trajectories at scale, we first employ a high‑level planner to decompose each task into a sequence of waypoints, followed by a low‑level reinforcement learning policy that generates torque commands to track waypoints. Benchmarking three representative policy models shows relatively promising results in clean scenes but substantial performance drop under randomization. Visualization analysis indicates that failures stem primarily from inaccurate visual estimation of proprioceptive state and limited exploitation of deformability for adaptive obstacle avoiding. We anticipate ManiSoft to serve as a valuable testbed, bridging the gap between rigid and soft arms in the context of vision‑language manipulation. Out codes and datasets are released at https://buaa‑colalab.github.io/ManiSoft.

PaperId: 874, https://arxiv.org/pdf/2605.18565.pdf GitHub

Abstract:
Real‑world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reasoning over multiple pieces of information. However, existing benchmarks focus on static, independent recall and fail to capture these dynamic interactions between evolving memories. In this paper, we study how current memory‑augmented agents perform in realistic, interference‑heavy, long‑horizon settings across diverse domains and question types. We introduce MINTEval (Long‑Horizon Memory under INTerference Evaluation), a benchmark featuring (1) long, highly interconnected contexts with frequently updated information that induces substantial interference, (2) diverse domains (state tracking, multi‑turn dialogue, Wikipedia revisions, and GitHub commits), enabling evaluation of domain generalization, and (3) diverse question types that assess robustness to interference, including (i) single‑target recall tasks requiring retrieval of a specific target from long contexts, and (ii) multi‑target aggregation tasks requiring reasoning over multiple relevant pieces of information. Overall, MINTEval has 15.6k question‑answering pairs over long‑horizon contexts averaging 138.8k tokens and extending up to 1.8M tokens per instance. We evaluate 7 representative systems, including vanilla long‑context LLMs, RAG, and memory‑augmented agent frameworks. Across all systems, we observe consistently low performance (avg. 27.9% accuracy), especially on questions requiring aggregated reasoning over multiple pieces of evidence. Our analysis shows that performance is primarily limited by retrieval and memory construction. Furthermore, current memory systems struggle to recall and reason over earlier facts that are revised or interfered with by subsequent context, with accuracy degrading as the number of intervening updates increases.

PaperId: 875, https://arxiv.org/pdf/2605.18561.pdf GitHub

Abstract:
In retrieval‑augmented coding, failures often begin when the relevant file is absent from the retrieved context. Under frozen generic tokenization, where a BM25 index has been built by a search system whose analyzer the practitioner does not control, this failure is routine: BM25's logarithmic RSJ‑odds IDF under‑separates the identifier tail that distinguishes one function from another. We replace the outer logarithm of the Robertson‑Spärck‑Jones odds with a q‑logarithm. At q=1 the transform recovers BM25 exactly by L'Hôpital's rule, and for q<1 it is a Box‑Cox transform of the RSJ odds with lambda = 1‑q. On CoIR CodeSearchNet Go (182K documents), oracle‑tuned NDCG@10 rises from 0.2575 to 0.4874 (absolute +0.2299; +89.3% relative; zero sign reversals in 10,000 paired‑bootstrap resamples, reported as p <= 10^‑4). The effect is graded across code languages and is near‑zero on BEIR text. A one‑parameter closed form estimates a corpus‑level q from hapax density and stays near q=1 on corpora where BM25 is already optimal. The index‑time cost is a single pass over the sparse score matrix and query latency is unchanged. A tokenizer ablation shows that identifier‑aware tokenization largely removes the incremental gain from q‑IDF.

PaperId: 876, https://arxiv.org/pdf/2605.18553.pdf GitHub

Abstract:
Recovering world space 4D motion of two interacting hands from egocentric video is a fundamental capability for supervising robot policy learning, where wrist trajectories track the end‑effector and finger articulations specify the grasp pose. Two major challenges arise in this setting: hands frequently leave the camera view for extended periods due to head motion, and persistent hand‑object interactions cause severe occlusions of one or both hands. Existing methods uniformly condition on noisy hand motion observations without accounting for their per‑frame reliability, leading to substantial performance degradation. Our key insight is that accurate world space hand motion estimation is tightly coupled with the quality of per‑frame hand observations. To this end, we decompose the quality of hand motion observations extracted from an off‑the‑shelf hand pose estimator into four channels: wrist global translation and finger articulations for both hands. We propose StableHand, a quality‑aware flow‑matching framework conditioned on these four‑channel quality signals, which are predicted by a learned quality network. We naturally incorporate the quality signals into the flow‑matching process through a per‑channel forward schedule, a quality‑adjusted velocity target, AdaLN modulation of the DiT denoiser, and a quality‑aware ODE initialization. This unified generative process preserves high‑quality observations while reconstructing unreliable ones using a learned bimanual motion prior. Experiments on HOT3D and ARCTIC, two egocentric benchmarks featuring long missing‑hand spans and persistent hand‑object occlusions, show that StableHand achieves state‑of‑the‑art performance across all reported metrics, reducing W‑MPJPE by 20‑25% compared to the strongest baseline, with the largest gains on heavily occluded ARCTIC sequences.

PaperId: 877, https://arxiv.org/pdf/2605.18454.pdf GitHub

Abstract:
Deep reinforcement learning (DRL) has recently emerged as a promising approach to solve combinatorial optimization problems such as job shop scheduling. However, the policies learned by DRL are typically represented by deep neural networks (DNNs), whose opaque neural architectures and non‑interpretable policy decisions can lead to critical trust and usability concerns for human decision makers. In addition, the computational requirements of DNNs can further hinder practical deployment in resource constrained environments. In this work, we propose ProRL, a novel interpretable programmatic reinforcement learning framework that achieves high‑performance scheduling with human‑readable and editable programmatic policies (i.e., programs). We first introduce a domain‑specific language for scheduling (DSL‑S) to represent scheduling strategies as structured programs. ProRL then explores the program space defined by DSL‑S using local search to identify incomplete programs, which are subsequently completed by learning their parameters via Bayesian optimization. ProRL learns which scheduling heuristic rules to select, and hence, it naturally incorporates existing heuristics already used in industrial scenarios. Experiments on widely used benchmark instances demonstrate the strong performance of ProRL against existing heuristics and DRL baselines. Furthermore, ProRL performs well under strongly constrained computational resources, such as training with only 100 episodes. Our code is available at https://github.com/HcPlu/ProRL.

PaperId: 878, https://arxiv.org/pdf/2605.18421.pdf GitHub

Abstract:
Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution. However, memory is also essential for agents, as it enables them to store, update, and retrieve information over time. This ability remains under‑evaluated, largely because existing benchmarks do not provide a systematic way to assess memory mechanisms. In this paper, we study agent memory from a self‑evolving perspective and introduce EvoMemBench, a unified benchmark organized along two axes: memory scope (in‑episode vs. cross‑episode) and memory content (knowledge‑oriented vs. execution‑oriented). We compare 15 representative memory methods with strong long‑context baselines under a standardized protocol. Results show that current memory systems are still far from a general solution: long‑context baselines remain highly competitive, memory helps most when the current context is insufficient or tasks are difficult, and no single memory form works consistently across all settings. Retrieval‑based methods remain strong for knowledge‑intensive settings, whereas procedural and long‑term memory methods are more effective for execution‑oriented tasks when their stored experience matches the task structure. We hope EvoMemBench facilitates future research on more effective memory systems for LLM‑based agents. Our code is available at https://github.com/DSAIL‑Memory/EvoMemBench.

PaperId: 879, https://arxiv.org/pdf/2605.18313.pdf GitHub

Abstract:
Small vision‑language models (2‑8B) are well‑suited for clin‑ ical deployment due to privacy constraints, limited connectivity, and low‑latency requirements favouring on‑device or on‑premise inference. However, their limited capacity exacerbates the generation of plausible but incorrect outputs. We extend game‑theoretic decoding, previously restricted to text‑only, closed‑ended NLP tasks, to vision‑language mod‑ els for open‑ended Medical VQA. We introduce a semantically aware Wasserstein stopping criterion that replaces lexical order matching, en‑ abling convergence based on semantic consensus among near‑synonymous candidate answers and avoiding unnecessary iterations caused by clini‑ cally equivalent ranking swaps. On VQA‑RAD and PathVQA, we ob‑ tain consistent, statistically significant improvements over greedy and discriminative baselines. On VQA‑RAD, we improve Qwen3‑VL‑2B by +3.5 percentage points (p < 0.01), surpassing the greedy 4B model, with similar trends at larger scales. On PathVQA, Gemma‑3‑4B with BDG matches MedGemma‑4B under greedy decoding despite no domain‑ specific fine‑tuning. At accuracy parity with classic BDG, the Wasser‑ stein criterion reduces average convergence iterations by approximately 20%, improving inference efficiency while preserving the game‑theoretic equilibrium behaviour. Code is available at https://github.com/luca‑hagen/ Wasserstein‑BDG‑medical‑VQA.

PaperId: 880, https://arxiv.org/pdf/2605.18271.pdf GitHub

Abstract:
With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on‑device has become essential for privacy and responsiveness. To handle the inherently personal and context‑dependent nature of real‑world requests, such agents must ground their generation in device‑resident personal context. However, under tight memory budgets, the core bottleneck is what to store so that retrieval remains aligned with the user. We propose EPIC (Efficient Preference‑aligned Index Construction), which focuses on user preferences as a compact and stable form of personal context and integrates them throughout the RAG pipeline. EPIC selectively retains preference‑relevant information from raw data and aligns retrieval toward preference‑aligned contexts. Across four benchmarks covering conversations, debates, explanations, and recommendations, EPIC reduces indexing memory by 2,404 times, improves preference‑following accuracy by 20.17 percentage points, and achieves 33.33 times lower retrieval latency over the best‑performing baseline. In our on‑device experiment, EPIC maintains a memory footprint under 1 MB with 29.35 ms/query latency in streaming updates.

PaperId: 881, https://arxiv.org/pdf/2605.18257.pdf GitHub

Abstract:
Multimodal representation alignment is pivotal for large language models and robotics. Traditional methods are often hindered by cross‑modal information discrepancies and data scarcity, leading to suboptimal alignment spaces that overlook modality‑unique features. We propose CodeBind, a framework that optimizes multimodal representation spaces through a modality‑shared‑specific codebook design. By incrementally aligning target and bridging modalities, CodeBind bypasses the need for fully paired data. Unlike traditional hard alignment, CodeBind decomposes features into shared components for semantic consistency and specific components for modality‑unique details. This design utilizes a compositional vector quantization scheme, where a shared codebook bridges modality gaps and modality‑specific codebooks mitigate representation bias by preventing dominant modalities from overshadowing others. Validated across nine modalities (text, image, video, audio, depth, thermal, tactile, 3D point cloud, EEG), CodeBind achieves state‑of‑the‑art performance in multimodal classification and retrieval tasks.

PaperId: 882, https://arxiv.org/pdf/2605.18253.pdf GitHub

Abstract:
Recent masked diffusion language models (MDLMs), such as LLaDA and Dream, have achieved performance comparable to autoregressive large language models. Unlike autoregressive models, which generate text sequentially, MDLMs generate text by iteratively denoising masked positions in parallel. During fine‑tuning, MDLMs learn to recover responses from masked response states conditioned on a prompt, thereby shifting their predictions from a prompt‑masked unconditional distribution toward a prompt‑conditional distribution. Despite this distinct generative and fine‑tuning mechanism, machine unlearning for MDLMs remains largely unexplored. In this paper, we propose Masked Diffusion Unlearning (MDU), the first unlearning framework for MDLMs, by revisiting the process of learning specific knowledge in terms of diffusion. Specifically, MDU minimizes a forward KL divergence from the prompt‑conditional prediction to a prompt‑masked unconditional anchor at every masked response position, with a temperature scaling parameter to control the privacy‑utility trade‑off. Our empirical results on standard benchmarks and MDLM backbones show that MDU achieves high unlearning performance compared to existing LLM unlearning methods. Code is available at https://github.com/leegeoru/MDU.

PaperId: 883, https://arxiv.org/pdf/2605.18232.pdf GitHub

Abstract:
Somali is a Cushitic language of the Horn of Africa with ~25 million speakers, yet no documented dedicated Somali pretraining corpus with a companion tokenizer and language‑identification benchmark has been publicly released. Existing Somali text appears either inside multilingual distributions (HPLT v2, CC100, MADLAD‑400, OSCAR, mC4) or in small, undocumented Somali‑only uploads on Hugging Face. We introduce SomaliWeb v1, a quality‑filtered Somali corpus of 819,322 documents (~303M tokens) built from three upstream sources (HPLT v2, CC100, Somali Wikipedia) through a six‑stage reproducible pipeline. We release (i) the corpus, (ii) a matched BPE‑16K tokenizer, and (iii) the first public side‑by‑side Somali benchmark of three production language identifiers. Our measurements reveal concrete quality defects in existing distributions: HPLT v2's "cleaned" Somali release retains 17.3% byte‑exact duplicates, 56.1% of its documents contain fixable mojibake, and 10.7% of its byte‑unique documents are near‑duplicates at Jaccard tau=0.80. Our BPE‑16K tokenizer emits 40.2% fewer tokens than GPT‑4's cl100k_base on FLORES‑200 Somali devtest as a tokenizer‑level measurement; downstream language‑model perplexity comparisons are deferred to a follow‑up release.

PaperId: 884, https://arxiv.org/pdf/2605.18176.pdf GitHub

Abstract:
This report presents MARS, short for Multimodal Agentic Reasoning with Source selection, our system for the CASTLE Challenge at EgoVis 2026. Participants must answer 185 closed‑form questions over the CASTLE 2024 dataset. In contrast to prior single‑video egocentric benchmarks, CASTLE requires reasoning over four days of activity, 15 synchronized perspectives, official transcripts, and multiple auxiliary modalities, including personal photos, auxiliary videos, gaze, thermal imagery, and heartrate measurements. MARS therefore treats the task as an agentic evidence‑selection problem over multimodal sources rather than a purely text‑only pipeline. MARS first follows the official CASTLE directory organization to build evidence memories from two primary sources, videos and transcripts, and four auxiliary sources, gaze, heartrate, photos, and thermal imagery. Long videos are converted into captions and DeepSeek‑based summaries only because CASTLE videos are too long to fit directly into the model context for every question; this step compresses temporal evidence while keeping photos and other auxiliary media available as source‑specific evidence. At inference time, a GPT‑5.4 decision agent repeatedly chooses whether to continue reasoning, request a specific missing modality, produce an answer, or fall back to a random option when the evidence remains insufficient. The resulting system achieved second place on the final CASTLE Challenge leaderboard. Our codes are available at https://github.com/Hyu‑Zhang/MARS.

PaperId: 885, https://arxiv.org/pdf/2605.18128.pdf GitHub

Abstract:
Existing Multivariate Time Series Anomaly Detection (MTSAD) frameworks increasingly rely on integrating Graph Neural Networks (GNNs) with sequence models to capture complex spatio‑temporal dependencies. However, less attention is paid to the spatial over‑generalization problem, where unconstrained structural modeling indiscriminately reconstructs anomalies, inevitably degrading detection recall. To tackle this problem, we propose a novel framework that unifies spatio‑temporal modeling through a joint prior‑observation adversarial learning paradigm. In the spatial dimension, the model alternately learns adjacency matrices as structural prior and models the association discrepancy between prior and data‑driven observation in a minimax manner during training. Such adversarial optimization not only improves the model sensitivity for time‑wise detection, but also enables the model to localize anomalies to specific channels. To systematically evaluate this anomaly localization capability, we further construct a synthetic benchmark equipped with precise channel‑wise annotations. Extensive experiments across public datasets and our dedicated benchmark demonstrate that the proposed framework establishes a new state‑of‑the‑art in both time‑wise detection and spatial localization tasks. Our code, pre‑trained models, and benchmark are publicly available at https://github.com/anocodetest1/POST.

PaperId: 886, https://arxiv.org/pdf/2605.18109.pdf GitHub

Abstract:
In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean task specification. Such requests require agents to identify task‑relevant entities, recover intended task conditions, and resolve ordering constraints from the surrounding scene context. We formalize this capability as full‑scene household reasoning: given a complete household scene and a situated household request, an agent must infer executable task structure before producing a grounded skill‑level action sequence. This setting is challenging because complete household scenes contain substantial task‑irrelevant information, making direct complete‑scene prompting inefficient and error‑prone. In practical deployment, this challenge is further amplified by privacy and local compute constraints, which favor compact open‑weight models with limited long‑context reasoning ability. We propose TaskGround, a training‑free and model‑agnostic Ground‑Infer‑Execute framework that grounds complete scenes into compact task‑relevant scene slices, infers executable task structure, and compiles it into grounded skill‑level action sequences. To evaluate this setting, we introduce FullHome, a human‑validated evaluation suite of 400 household tasks spanning diverse home‑scale environments and both goal‑oriented and process‑constrained requirements. On FullHome, TaskGround improves task success rates by large margins across both proprietary and open‑weight models. Notably, it makes Qwen3.5‑9B competitive with GPT‑5 under direct complete‑scene prompting while reducing total input‑token cost by up to 18x. Our results identify executable task‑structure inference as a central bottleneck in full‑scene household reasoning and show that structured grounding can make compact local models substantially more effective for practical household deployment.

PaperId: 887, https://arxiv.org/pdf/2605.18101.pdf GitHub

Abstract:
Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery and deep learning have achieved remarkable progress, many challenges exist: most existing studies are inherently predictive, failing to reflect the generative nature of urban planning; although generative AI and diffusion models have seen explosive growth in satellite imagery, they lack the urban functional generation (e.g., energy layer); third, aligned high‑quality high‑resolution building energy data with satellite imagery is limited and scarce. Here we propose SENSE (Satellite‑based ENergy Synthesis for Sustainable Environment), a unified generative UBEM framework that jointly synthesizes realistic urban satellite imagery and aligned high‑quality building energy consumption and height maps. By conditioning on road networks and urban density metrics, SENSE, based on a controllable diffusion model, leverages the knowledge learned by large vision models to generate urban building energy consumption and height information (annotations) in the latent space. Experiments across four cities (New York City, Boston, Lyon, Busan) demonstrate that SENSE achieves high visual fidelity and strong physical consistency, satisfying the ASHRAE standard metric. Experiments demonstrate that SENSE can generate enough annotated synthetic data using less than 20% labeled energy data, boosting downstream prediction performance by 10% IoU. Compared to SOTA urban energy prediction methods, SENSE significantly reduced prediction error (reduced 3%‑11% NMBE and 1%‑9% CVRMSE). This study offers an energy‑efficiency urban planning and physical generation solution for urban science, energy science and building science. The dataset and code: https://huggingface.co/datasets/skl24/MUSE and https://github.com/kailaisun/GenAI4Urban‑Energy/.

PaperId: 888, https://arxiv.org/pdf/2605.18055.pdf GitHub

Abstract:
Predicting spatial gene expression from routine H\&E enables large‑scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking essential biological structures like gene coordination and spatial distribution. To preserve these relationships, we introduce FLAG, a diffusion‑based framework that redefines this task as structured distribution modeling. At the same time, we identify the critical Gene Dimension Curse, where joint modeling gene expression and their spatial interactions fail in high‑dimensional spaces, and FLAG solves this challenge by integrating a spatial graph encoder for topological consistency and utilizing Gene Foundation Model (GFM) alignment for gene‑gene fidelity in the generation process. To rigorously assess model performance, we propose a set of novel structural evaluation metrics, including Gene Structural Correlation (GSC) and Spatial Structural Correlation (SSC). Our experiments demonstrate that FLAG is highly competitive in traditional accuracy (PCC/MSE) while achieving significantly enhanced structural fidelity in capturing both gene‑gene and gene‑spatial relationships. The code is available at https://github.com/darkflash03/FLAG.

PaperId: 889, https://arxiv.org/pdf/2605.18025.pdf GitHub

Abstract:
While Large Language Models have achieved remarkable integration in various vertical scenarios, their deployment in the telecommunications domain remains exploratory due to the lack of a standardized evaluation framework. Current telecom benchmarks primarily focus on static, foundational knowledge and isolated atomic skills, neglecting the equipment‑specific documentation and end‑to‑end industrial workflows essential for real‑world production systems. To bridge this gap, we present TeleCom‑Bench, a comprehensive benchmark comprising 12 evaluation sets with 22,678 curated samples, which evaluates LLMs across a synergistic hierarchy: (1) Multi‑dimensional Knowledge Comprehension, which integrates telecommunication fundamentals, 3GPP protocols, and 5G network architecture with proprietary product knowledge across wired, core, and wireless networks via knowledge graph‑driven synthesis; and (2)End‑to‑End Knowledge Application, which formalizes six core tasks on authentic trajectories from live network agent workflows, including intent recognition, entity extraction, event verification, tool invocation, root cause analysis, and solution generation‑across network optimization and fault maintenance scenarios. Evaluations of eight state‑of‑the‑art LLMs reveal a universal Execution Wall: while models achieve 90% accuracy in linguistic interface tasks such as intent recognition and entity extraction, performance collapses to approximately 30% in procedural execution tasks like solution generation. This capability gap demonstrates that current LLMs function competently as diagnosticians but fail as field engineers. TeleCom‑Bench provides standardized diagnostics to precisely pinpoint this deficit, offering actionable guidance for domain‑specific alignment toward production‑ready telecom agents. The dataset and evaluation code have been released at https://github.com/ZTE‑AICloud/TeleCom‑Bench.

PaperId: 890, https://arxiv.org/pdf/2605.18018.pdf GitHub GitHub

Abstract:
We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine‑grained object understanding solely from textual prompts. Unlike existing approaches that require explicit visual prompts, such as masks or points, SWIM leverages mask supervision only during training to guide cross‑modal attention, allowing the model to automatically attend to the user‑specified object at inference. Our cross‑attention analysis of pretrained multimodal large languagemodels (MLLMs) reveals a systematic discrepancy: Attribute words produce sharp, localized activations in the visual modality, whereas object nouns yield diffuse and scattered patterns due to semantic reference bias and distributed high‑level representations. To address this misalignment, we construct NL‑Refer, an enriched dataset, in which each object mask is paired with a precise natural language referring expression. SWIM extracts multi‑layer cross‑attention maps from object nouns and enforces spatial consistency with ground‑truth masks. Experimental results demonstrate that SWIM substantially improves text‑visual alignment and achieves superior performance over visual‑prompt‑based methods on fine‑grained object understanding benchmarks. The code and data are available at \hrefhttps://github.com/HumanMLLM/SWIMhttps://github.com/HumanMLLM/SWIM.

PaperId: 891, https://arxiv.org/pdf/2605.17991.pdf GitHub GitHub

Abstract:
Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable‑length audio generation and editing. Since our models can generate several minutes of audio, variable‑length generations are key to avoid the cost of producing full‑length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic‑acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion‑based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post‑training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer‑grade hardware, together with their training and inference pipeline.

PaperId: 892, https://arxiv.org/pdf/2605.17954.pdf GitHub

Abstract:
Modern multimodal large language models (MLLMs) typically keep the language model fixed and train a visual projector that maps the pixels into a sequence of tokens in its embedding space, so that images can be presented in essentially the same form as text. However, the language model has been optimized to operate on discrete, semantically meaningful tokens, while prevailing visual projectors transform an image into a long stream of continuous and highly correlated embeddings. This causes the visual tokens to behave differently from the word‑like units that LLMs are originally trained to understand. We propose a novel Disentangled Visual Tokenization (DiVT) that clusters patch embeddings into coherent semantic units, so each token corresponds to a distinct visual concept instead of a rigid grid cell. DiVT further adapts its token budget to image complexity, providing an explicit accuracy‑compute trade‑off modifying neither the vision encoder nor the language model. Across diverse multimodal benchmarks, DiVT matches or surpasses baselines with significantly fewer visual tokens, demonstrating robustness under limited token budgets, significantly reducing memory cost and latency while making visual inputs more compatible with LLMs. Our code is available at https://github.com/snuviplab/DiVT.

PaperId: 893, https://arxiv.org/pdf/2605.17907.pdf GitHub

Abstract:
By sharing intermediate features, collaborative perception extends each agent's sensing beyond standalone limits, but real‑world feature modality heterogeneity remains a key barrier to effective fusion. Most existing methods, including direct adaption and protocol‑based transformation, typically rely on training adapters for newly emerging feature modalities and often require additional retraining or fine‑tuning. Such repeated training is costly and is often infeasible across manufacturers due to model and data privacy constraints, limiting real‑world scalability. To address this issue, we propose UniTrans, a universal any‑to‑any feature modality translation model that instantiates translators on the fly for arbitrary modalities. UniTrans pretrains a bank of translator expert parameters and learns their combination coefficients as a function of source‑to‑target modality mapping. The mapping is measured in a modality‑intrinsic latent space, where an intrinsic encoder extracts modality‑specific yet scene‑invariant codes from single‑frame intermediate features, enabling UniTrans to instantiate translators in a zero‑shot manner. Experiments on OPV2V‑H and DAIR‑V2X demonstrate that UniTrans consistently outperforms state‑of‑the‑art methods in both simulated and real‑world settings, enabling efficient any‑to‑any translation through a universal model. The code is available at https://github.com/CheeryLeeyy/UniTrans.

PaperId: 894, https://arxiv.org/pdf/2605.17849.pdf GitHub

Abstract:
LLM pretraining is shifting from a compute‑bound to a data‑bound regime, where available human (organic) text falls far short of scaling demands. However, reaching the data‑bound regime does not mean the model has fully utilized its organic corpus. In this paper, we introduce SynPro, a synthetic data generation framework that helps LLMs more thoroughly learn from limited organic data. SynPro applies two operations, rephrasing and reformat, that present the same organic source in diverse forms to facilitate deeper learning without introducing external information. Both generators are optimized via reinforcement learning with quality, faithfulness, and data influence rewards, and are continuously updated as pretraining plateaus to target content the model has yet to absorb. We pretrain 400M and 1.1B models with 10% of their Chinchilla‑optimal tokens (0.8B and 2.2B) from DCLM‑Baseline, reflecting a realistic data‑bound regime in frontier pretraining. Our results reveal that organic data is significantly underutilized by standard repetition: SynPro unlocks 3.7‑5.2x the effective tokens of repetition, even surpassing the non‑data‑bound oracle that trains on equivalent unique data at the 1.1B scale. Analyses confirm that faithful, model‑aware synthesis sustains data‑bound scaling without causing distribution collapse. We open‑source our code at https://github.com/cxcscmu/SynPro.

PaperId: 895, https://arxiv.org/pdf/2605.17811.pdf GitHub GitHub

Abstract:
Can a shared‑weight recurrent Transformer develop distinct internal roles without being partitioned into separate modules? We study this in Asymmetric Input Recurrence (AIR), a minimal two‑state reasoning architecture in which the same Transformer model is reused for both updates (per literature, L and H) and the only built‑in difference in the update rule is that the encoded input is injected during L‑updates but not H‑updates. Across Sudoku‑Extreme and Maze, decoded rollouts reveal a consistent split: \zH behaves like a fully committed proposal state, whereas \zL retains local uncertainty and shifting intermediate structure. Freeze experiments show that this split is, in practice, related to the model's state dynamics: in Sudoku, freezing \zH reduces \zL's content changes whereas freezing \zL increases \zH's, while in Maze, freezing either state increases content changes in the other state. Ablations show that to induce specialization, the shared model needs to be able to tell the two update types apart, either from input injection asymmetry or from a separate level token. Mechanistically, attention analysis shows that L‑updates are consistently more local than H‑updates in both Sudoku and Maze. Together, these results show that, in a two‑state recurrent setting, a clear state‑identity signal can induce stable, related functional roles inside a shared‑parameter recurrent Transformer. Code is available at \hrefhttps://github.com/juchengshen/air\textcolorbluehttps://github.com/juchengshen/air.

PaperId: 896, https://arxiv.org/pdf/2605.17693.pdf GitHub

Abstract:
Structure‑based drug design has been accelerated by pocket‑aware 3D generative models, yet most methods primarily fit the training distribution and may fall short of satisfying multiple properties required in real‑world therapeutic drug discovery. Recently, increasing attention has focused on structure‑based molecule optimization (SBMO), which targets fine‑grained control over multiple specified molecular properties. In this paper, we present DEPPA, a novel SBMO approach building upon Denoising Diffusion Policy Optimization for fine‑tuning a pre‑trained pocket‑aware diffusion model via reinforcement learning. DEPPA enables optimization over multiple properties, including binding affinity, drug‑likeness, synthesizability and diversity. We formulate the reverse denoising process of the pretrained pocket‑aware diffusion model as a multi‑step Markov Decision Process, where the desired properties that serve as reward signals are evaluated on the final generated ligand molecules. DEPPA incorporates a coarse denoising scheduler during the RL fine‑tuning to achieve efficient and effective molecule optimization. Experimental results on the CrossDocked2020 benchmark demonstrate that DEPPA outperforms baselines in binding affinity (Vina Score ‑8.5 kcal/mol), drug‑likeness and diversity while exhibiting competitive performance in synthesizability. The source code is available at https://github.com/xy9485/DePPA .

PaperId: 897, https://arxiv.org/pdf/2605.17641.pdf GitHub

Abstract:
Long‑horizon LLM agents rely on persistent memory to support interactions across sessions, yet existing memory systems often retrieve context using semantic similarity or broad history inclusion, treating retrieved memories as uniformly useful. This assumption is fragile because memories may be topically related while remaining irrelevant, stale, or misleading. We propose Causal Memory Intervention (CMI), a causal memory‑selection technique that estimates how candidate memories affect the model's answer under controlled interventions, selecting memories that improve task performance while suppressing unstable, irrelevant, or harmful ones. To evaluate this setting, we introduce Causal‑LoCoMo, a causally annotated benchmark derived from long conversational data, where each example contains a user request, a structured memory bank, useful memories, irrelevant distractors, and synthetic harmful memories. We compare CMI against vector, graph, reflection, summary, full‑history, and no‑memory baselines. Results show that CMI achieves a stronger balance between answer quality and robustness to misleading memory, suggesting that reliable long‑term memory requires selecting context based on causal usefulness rather than relevance alone. The full framework, benchmark construction code, and experimental pipeline are available at https://github.com/Saksham4796/causal‑memory‑intervention.

PaperId: 898, https://arxiv.org/pdf/2605.17624.pdf GitHub

Abstract:
We investigate the potential of invariant and equivariant semi‑supervised learning for addressing the challenges of training multi‑task models on partially labeled datasets with differently structured output tasks. Specifically, we use the popular FixMatch method for invariant semi‑supervised learning and its equivariant extension Dense FixMatch. We evaluate their performance on the Cityscapes and BDD100K datasets in the context of the prevalent object detection and semantic segmentation tasks in computer vision. We consider varying sizes of the subsets annotated for each task and different overlaps among them. Our results for both invariant and equivariant semi‑supervised learning outperform supervised baselines in most situations, with the most significant improvements observed when fewer labeled samples are available for a task and generally better results for the latter approach. Our study suggests that invariant/equivariant learning is a promising general direction for multi‑task learning from limited labeled data.

PaperId: 899, https://arxiv.org/pdf/2605.17537.pdf GitHub

Abstract:
3D open‑world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are essential in such settings. While existing self‑supervised visual foresight reasoning approaches often suffer from multi‑step error accumulation, many recent studies resort to injecting domain‑specific knowledge for more stable guidance. Our key insight is that the photorealistic fidelity of visual reasoning representations is secondary; what truly matters is providing informative, task‑relevant signals. To this end, we propose ResDreamer, a hierarchical world model in which each higher‑level layer is trained to reconstruct the residuals of the layer below. This design enables progressive abstraction of increasingly sophisticated world dynamics and fosters the emergence of richer latent representations. Drawing inspiration from the "Bitter Lesson", ResDreamer trains its reasoning representations in a purely self‑supervised manner. The higher‑level residual representations are used to modulate lower‑level predictions, allowing the world model to scale effectively with only linearly increasing cross‑layer communication costs. Experiments show that ResDreamer achieves state‑of‑the‑art sample efficiency and parameter efficiency. This scalable hierarchical visual foresight reasoning architecture paves the way for more capable online RL agents in open‑ended, dynamic environments. The code is accessible at https://github.com/XuYuanFei01/ResDreamer.

PaperId: 900, https://arxiv.org/pdf/2605.17530.pdf GitHub

Abstract:
Network intrusion detection systems play a vital role in protecting networks by detecting malicious network traffic which can then be investigated by a cybersecurity operations centre. State‑of‑the‑art approaches utilise supervised machine learning methods to train a classification model to recognise known cyberattacks; however, these models require a large labelled dataset to train and show poor performance when trained on smaller datasets. In an attempt to address this shortcoming, anomaly detection models learn the distribution of benign traffic and flag non‑conforming traffic as malicious. While these methods do not require malicious examples to train, they suffer from high false‑positive rates rendering them impractical. As a result, networks may be particularly vulnerable when there are insufficient labelled instances of a specific attack class to train an effective classifier. This often occurs in newly established networks or when previously unseen types of attacks emerge. To address this challenge, this work proposes the use of a triplet network, utilising online triplet mining and a KNN classifier, which is able to perform few‑shot classification, enabling effective intrusion detection after being trained on a limited number of malicious examples. Various online triplet mining algorithms were explored and model design choices, such as the inference algorithm and optimised distance metrics, were compared and evaluated through a series of ablation studies. The final model was compared against other state‑of‑the‑art approaches in few‑shot binary and multiclass classification, where the proposed approach was found to be competitive with existing methods when trained on as little as 10 malicious samples of each class.

PaperId: 901, https://arxiv.org/pdf/2605.17526.pdf GitHub

Abstract:
As autonomous coding agents become capable of handling increasingly long‑horizon tasks, they have gradually demonstrated the potential to complete end‑to‑end software development. Although existing benchmarks have recently evolved from localized code editing to from‑scratch project generation, they remain confined to structurally simplified, single‑stack applications. Consequently, they fail to capture the heterogeneous environments, full‑stack orchestration, and system‑level complexity of real enterprise Software as a Service (SaaS) systems, leaving a critical gap in assessing agents under realistic engineering constraints. To fill this gap, we introduce SaaSBench, the first benchmark designed to explore the boundaries of AI agents in enterprise SaaS engineering. Spanning 30 complex tasks across 6 SaaS domains with 5,370 validation nodes, it incorporates 8 programming languages, 6 databases, and 13 frameworks to meticulously mirror real‑world software heterogeneity. Furthermore, we design a dependency‑aware hybrid evaluation paradigm tailored for complex systems with long horizons and multi‑component coupling, enabling fine‑grained, reproducible assessment. Crucially, our extensive experiments reveal a striking insight: the primary bottleneck for state‑of‑the‑art agents is not generating isolated code logic, but successfully configuring and integrating a multi‑component system. Over 95% of task failures occur before agents even reach deep business logic, with models often falling victim to overconfidence and prematurely halting during foundational system setup, or getting trapped in ineffective debugging loops. We hope SaaSBench serves as a practical and challenging testbed to drive the evolution of reliable, system‑level coding agents. The code is available at \urlhttps://github.com/ShadeCloak/SaaSbench.

PaperId: 902, https://arxiv.org/pdf/2605.17442.pdf GitHub

Abstract:
Multilingual NLP often relies on dataset counts from centralized catalogues to characterize which languages are resource‑rich or resource‑poor. However, these catalogues record only one layer of dataset visibility: what has been registered or institutionally distributed. They do not necessarily reflect which datasets are created, cited, or reused in the research literature. To examine this gap, we combine a catalogue‑based baseline with literature‑backed evidence of dataset circulation. We introduce the Resource Density Index (RDI), defined as the number of catalogued datasets per one million speakers, and compute it for the 200 most widely spoken languages in Ethnologue. Among them, 118 languages (59%) have an average RDI of zero across the LRE Map and the Linguistic Data Consortium (LDC), and another 23 fall below 0.1, corresponding to at most one catalogued dataset per ten million speakers. We then apply an LLM‑assisted citation‑mining pipeline over the Semantic Scholar corpus to these 141 low‑visibility languages. After manual validation and consolidation, we identify 609 unique datasets across 53 languages, of which 356 remain openly accessible through working public links. These results reveal a substantial visibility gap: many large‑speaker languages appear data‑poor in catalogue records yet show clear evidence of dataset activity in the research literature. Our findings suggest that multilingual data scarcity should be understood not only as a production problem, but also as a question of documentation, discoverability, and long‑term accessibility. Code and data are publicly available at (https://github.com/zhiyintan/dataset‑visibility‑asymmetry).

PaperId: 903, https://arxiv.org/pdf/2605.17439.pdf GitHub

Abstract:
Evaluating LLM‑generated interactive software requires execution in addition to static analysis. The key difficulty is that correctness is a graph‑level reachable property over latent UI state‑transition graphs, whereas a GUI evaluator observes only a single execution trajectory. A failed rollout therefore rules out only one realized path, leaving failure attribution ambiguous between evaluator‑side execution error and genuine software defect. We present DiagEval, a trajectory‑conditioned diagnostic evaluation protocol for post‑failure GUI‑agent evaluation of interactive software. Rather than blindly retrying from scratch, DiagEval reuses the failed trajectory to choose targeted diagnostic probes and aggregates their outcomes into an internal attribution signal. The latent‑graph view motivates the diagnostic problem; DiagEval does not reconstruct the graph or estimate calibrated posterior probabilities. We evaluate DiagEval on WebDevJudge‑Unit and RealDevBench across multiple GUI‑agent evaluators and LLM backbones. On false‑negative cases, DiagEval recovers 45.6‑62.1% of failures that were initially misattributed to software defects, outperforming retry‑based baselines with 34.4‑160.6% relative gains. On the full evaluation sets, this recovery improves accuracy from 69.9% to 78.3% on WebDevJudge‑Unit and from 65.0% to 81.6% on RealDevBench. These results suggest that reliable GUI‑agent evaluation requires not only stronger execution, but also active failure diagnosis to disambiguate evaluator‑side errors from genuine software defects. Our code is available at https://github.com/scutGit/DiagEval.

PaperId: 904, https://arxiv.org/pdf/2605.17379.pdf GitHub

Abstract:
Large language models pretrained on general‑domain corpora often exhibit tokenization inefficiencies when applied to specialized domains. Although continual pretraining for domain adaptation partially alleviate performance degradation, it does not resolve the fundamental vocabulary mismatch. To address this gap, we introduce a targeted parameter‑efficient domain adaptation approach that combines vocabulary adaptation with pretraining for LLM‑based text summarization. Our unified framework augments pretrained tokenizers with domain‑specific tokens while selectively replacing under‑trained and unreachable tokens to limit parameter growth. We evaluate our approach on Llama‑3.1‑8B and Qwen2.5‑7B across legal and medical summarization tasks on a challenge‑oriented evaluation protocol focused on expert‑driven text and summaries which typically has higher concentration of over‑fragmented Out‑of‑Vocabulary (OOV) words. The vocabulary adaptation algorithm enhances the overall quality of the summarization model by improving semantic similarity between the generated summaries and their references. In addition, the adapted model produces summaries that incorporate more appropriate novel and domain‑specific words, leading to improved coherence, relevance, and faithfulness. We further observe that our proposed approach significantly reduce training time by 35‑55% over continual pretraining and reduce parameter counts up to 37% w.r.t expansion‑only methods. We make the codebase publicly available at https://github.com/gb‑kgp/VocabReplace‑Then‑Expand.

PaperId: 905, https://arxiv.org/pdf/2605.17342.pdf GitHub

Abstract:
Standard RLHF relies on transitive scalar rewards, failing to capture the cyclic nature of human preferences. While some approaches like the General Preference Model (GPM) address this, we identify a theoretical limitation: their implicit formulation entangles hierarchy with cyclicity, failing to guarantee dominant solutions. To address this, we propose the Hybrid Reward‑Cyclic (HRC) model, which utilizes game‑theoretic decomposition to explicitly disentangle preferences into orthogonal transitive (scalar) and cyclic (vector) components. Complementing this, we introduce Dynamic Self‑Play Preference Optimization (DSPPO), which treats alignment as a time‑varying game to progressively guide the policy toward the Nash equilibrium. Synthetic data experiments further validate HRC's structural superiority in mixed transitive‑‑cyclic settings, where HRC converges faster and achieves higher accuracy than GPM. Experiments on RewardBench 2 demonstrate that HRC consistently improves over both BT and GPM baselines (e.g., +1.23% on Gemma‑2B‑it). In particular, its superior performance in the Ties domain empirically validates the model's robustness in handling complex, non‑strict preferences. Extensive downstream evaluations on AlpacaEval 2.0, Arena‑Hard‑v0.1, and MT‑Bench confirm the efficacy of our framework. Notably, when using Gemma‑2B‑it as the base preference model, HRC+DSPPO achieves a peak length‑controlled win‑rate of 44.75% on AlpacaEval 2.0 and 46.8% on Arena‑Hard‑v0.1, significantly outperforming SPPO baselines trained with BT or GPM. Our code is publicly available at https://github.com/lab‑klc/Hybrid‑Reward‑Cyclic.

PaperId: 906, https://arxiv.org/pdf/2605.17329.pdf GitHub

Abstract:
Guardrails are a critical safety layer for modern AI systems, but their operating regime is changing. As LLMs are deployed as customized assistants, safety policies are increasingly specified at inference time by users, organizations, or regulatory contexts. This makes safety enforcement fundamentally dynamic: the guardrail should adapt to changing safety policies without retraining. Yet this requirement creates a fundamental tension: faithfully judging complex policy contexts demands reasoning capability, while practical deployment requires low‑latency responses. We introduce Latent Policy Guardrail (LPG), a guardrail framework that learnssemantic latent deliberation over dynamic policies. LPG compresses the internal deliberation needed for intent interpretation and policy grounding into continuous states supervised by decision‑relevant semantics. At inference time, it generates only a compact verdict anchored to the violated policy clauses, preserving auditability while avoiding the latency of explicit reasoning. Across policy guardrail benchmarks, LPG‑4B reaches 84.5% average safety accuracy and 77.9% F1 by compressing deliberation into just 10 latent tokens, outperforming the strongest dynamic baseline while running roughly 11 times faster than Qwen3‑4B‑Thinking under the single‑sample evaluation setup. Code and data are available at https://github.com/SaFo‑Lab/Latent_Policy_Guard.

PaperId: 907, https://arxiv.org/pdf/2605.17327.pdf GitHub

Abstract:
Fast and reliable initialization is critical for monocular visual‑inertial navigation systems (VINS), as it establishes the starting conditions for subsequent state estimation. Despite steady progress, most existing methods heavily rely on visual feature correspondences and require 3‑4 seconds of sensory data for successful initialization, which limits their applicability and efficiency. With the advent of feed‑forward 3D models that can directly predict point clouds from images, we revisit the visual‑inertial initialization problem from a concise perspective. In this work, we propose a feature‑free initialization framework that leverages up‑to‑scale point clouds predicted by a feed‑forward 3D model, thereby obviating the need for visual feature tracking and estimation. This design substantially reduces system complexity and improves the reliability of initialization. Experiments on public datasets demonstrate that the proposed feature‑free initialization method achieves the highest success rate, exceeding 90%, and significantly reduces the data duration required for successful initialization, typically to under 1.2 s. We further validate our method on a self‑collected dataset covering various indoor and outdoor scenarios, demonstrating robust performance, particularly in visually degraded environments where existing methods often fail. The code and dataset are available at https://github.com/Yuantai‑Z/FF‑VIO‑Init.

PaperId: 908, https://arxiv.org/pdf/2605.17324.pdf GitHub

Abstract:
Clarification‑seeking behavior is widely regarded as a desirable property of LLM agents, enabling them to resolve ambiguity before acting on underspecified tasks. However, the security implications of this interaction pattern remain unexplored. We investigate whether the transition from standard execution to a clarification‑seeking state increases an agent's susceptibility to prompt injection attacks. We introduce ASPI (Ambiguous‑State Prompt Injection), a benchmark of 728 task‑attack scenarios that isolates clarification as a distinct agent state and measures how this state transition affects vulnerability under controlled conditions. Each benchmark instance is evaluated under matched execution and clarification settings: in the execution setting, the agent acts on a fully specified instruction and encounters adversarial content only through tool‑returned data; in the clarification setting, the agent must first request and incorporate additional user input before acting. We evaluate ten frontier LLMs and find that clarification‑seeking consistently and substantially amplifies vulnerability. For instance, attack success rises from 1.8% to 34.0% for o3 and from 2.2% to 35.7% for Gemini‑3‑Flash. A decomposition analysis reveals that this gap reflects both a state‑dependent shift in how models process incoming content and a channel‑specific effect arising from the agent‑solicited clarification interface. These findings demonstrate that standard execution‑time security evaluation systematically underestimates the attack surface of interactive agents, and that robustness under fully specified tasks does not translate to robustness under ambiguity. For reproducibility, our data and source code are available at https://github.com/scaleapi/aspi.

PaperId: 909, https://arxiv.org/pdf/2605.17204.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) policies translate language and visual inputs into robot actions, where their hidden representations directly shape closed‑loop behavior. However, mechanistic interpretability tools from language and vision‑language models do not transfer cleanly to VLAs: outputs are robot actions rather than human‑readable tokens, and interventions can only be tested via expensive closed‑loop rollouts. We propose an event‑grounded interpretability pipeline that anchors SAE feature analysis to behavioral events rather than text contexts. End‑effector keyframes are clustered within each task using visual, state, and temporal cues, linking SAE features to behaviorally salient events and, via optional VLM annotations, to semantic context. To our knowledge, our pipeline is among the first to ground SAE‑based VLA analysis in closed‑loop behavioral events. Across two simulation architectures and a real‑robot study, event‑grounded ranking yields the strongest causal effects on OpenVLA and transfers to the continuous action chunks of π_0.5. SAE is a sparse but imperfect intervention basis: usability varies with architecture and intervention site, and aggressive intervention reveals safety and interpretability limits. Overall, event‑grounded SAE analysis emerges as a practical starting point for behavior‑anchored VLA interpretability, motivating future work on SAE features beyond action‑aligned coordinates, finer‑grained closed‑loop evaluation, and safe interventions for high‑stakes VLA deployments. Code is available at \urlhttps://github.com/xc‑j/Event‑SAE.

PaperId: 910, https://arxiv.org/pdf/2605.17172.pdf GitHub GitHub

Abstract:
Personal AI stacks, like OpenClaw and Hermes Agent, are becoming central to daily work, yet they route nearly every query (often over sensitive local data) to cloud‑hosted frontier models. Replacing frontier models with local models inside existing stacks does not work: swapping Claude Opus 4.6 for Qwen3.5‑9B drops accuracy by 25‑39 pp across personal AI tasks like PinchBench and GAIA. Existing stacks bundle agentic prompts, tool descriptions, memory configuration, and runtime settings around a specific cloud model. Only the prompts can be tuned, and state‑of‑the‑art prompt optimizers close just 5 pp of the local‑cloud gap on their own. This motivates a decomposed personal AI stack: one that exposes individual primitives which can be optimized individually or jointly to close the local‑cloud gap. We present OpenJarvis, an architecture that represents a personal AI system as a typed spec over five primitives: Intelligence, Engine, Agents, Tools & Memory, and Learning. Each primitive is an independently editable field, making the stack end‑to‑end optimizable and measurable against accuracy, cost, and latency. Towards closing the local‑cloud gap without surrendering local‑model properties, OpenJarvis introduces LLM‑guided spec search, a local‑cloud collaboration in which frontier cloud models propose edits across the spec at search time, only non‑regressing edits are accepted, and the resulting spec runs entirely on‑device at inference time. With LLM‑guided spec search, on‑device specs match or exceed cloud accuracy on 4 of 8 benchmarks and land within 3.2 pp of the best cloud baseline on average. They also reduce marginal API cost by ~800x and end‑to‑end latency by 4x.

PaperId: 911, https://arxiv.org/pdf/2605.17137.pdf GitHub

Abstract:
The integration of Large Language Models (LLMs) into evolutionary frameworks has established a new paradigm for automated heuristic discovery. Despite their promise, these methods typically search in the discrete space of program syntax, relying on stochastic sampling to navigate a highly non‑convex optimization landscape. This work proposes a continuous heuristic discovery framework that shifts optimization to a learned latent manifold. We employ an encoder to map discrete programs into continuous embeddings and train a differentiable surrogate model to predict performance, enabling gradient‑based search. To regularize the optimization trajectory, an invertible normalizing flow maps these embeddings to a structured Gaussian prior, where we perform gradient ascent. The resulting optimized latent vectors are projected through a learned mapper into soft prompts, which condition a frozen LLM to synthesize novel executable heuristics. We evaluate the proposed method on the Traveling Salesman Problem (TSP), the Capacitated Vehicle Routing Problem (CVRP), the Knapsack Problem (KSP), and Online Bin Packing (OBP). Empirical results demonstrate that continuous latent‑space optimization achieves performance competitive with state‑of‑the‑art discrete evolutionary baselines while offering a complementary methodological alternative for automated algorithm design. The implementation code is available at \urlhttps://github.com/cheikh025/LHS.

PaperId: 912, https://arxiv.org/pdf/2605.17133.pdf GitHub

Abstract:
The rapid advancement of Deepfake technologies and video manipulation tools poses a critical challenge to multimedia forensics, judicial evidence integrity, and information authenticity. Current detectors rely on single‑modality signals, treating appearance, geometry, and motion independently. However, advanced generators maintain within‑modality consistency while producing cross‑modal contradictions, which are forensically discriminative but invisible to any single‑modal detector. We propose CAM‑VFD, a Cross‑Attention Multimodal Video Forgery Detection framework that models cross‑modal contradiction as a directional forensic signal. The framework uses a cross‑attention fusion mechanism in which CLIP‑based appearance representations serve as queries against VideoMAE motion features and MiDaS depth features, enabling the identification of contradictions between visual, temporal, and geometric evidence. We examine this design through cross‑modal attention discrepancy analysis, observing statistically separable real and fake distributions (p<0.001, Cohen's d=0.68). Experimental results on two generative video benchmarks indicate consistent performance, with 95.31% Top‑1 accuracy on GenVidBench and 93.43% accuracy, 90.63% F1‑score, and 96.56% AUROC on GenVideo. Moreover, CAM‑VFD demonstrates stable performance under compression, noise, blur, and adversarial perturbations, suggesting that cross‑modal reasoning may improve robustness in media forensics. The code is publicly available at \urlhttps://github.com/Hoda‑Osama/CAM‑VFD/tree/main.

PaperId: 913, https://arxiv.org/pdf/2605.17131.pdf GitHub

Abstract:
Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity. However, its inherent unordered and irregular nature, exacerbated by sensor noise and occlusions, introduces unique challenges for machine learning based methodologies. To combat these issues, diverse strategies have been developed, including converting to a format that has orderliness, extracting local geometry, and permutation‑invariant or self‑attention‑based processing. In this paper, our focus is directed towards deep learning models for three fundamental tasks in 3D vision: point cloud classification, part segmentation, and semantic segmentation. We begin by formally defining point cloud data, followed by an in‑depth discussion on its structural characteristics. Then, we categorize notable works based on their backbone structure and evaluate their performance on popular benchmarks. Beyond empirical comparison, we offer insights into architectural innovations and limitations. We also outline open challenges and promising future directions for 3D point cloud understanding.

PaperId: 914, https://arxiv.org/pdf/2605.17128.pdf GitHub

Abstract:
Jailbreak attacks on large models have drawn growing attention due to their close ties to societal safety. This work identifies a practical yet unexplored jailbreak scenario, the wide‑net‑casting scenario, where an adversary can query a group of large models instead of a single one to elicit harmful outputs. Our analysis reveals substantial yet previously overlooked safety risks under this scenario. As a key part of our analysis, we further develop a novel jailbreak method tailored to the wide‑net‑casting scenario. With this tailored method, the jailbreak success rate can even reach 100% in some experiments when targeting the large models without additional safeguards, exposing wide‑net‑casting as a distinct, high‑risk scenario that warrants attention in future evaluation and defense research.

PaperId: 915, https://arxiv.org/pdf/2605.17104.pdf GitHub GitHub

Abstract:
With the continuous advancement of reasoning abilities in Large Language Models (LLMs), their application to scientific reasoning tasks has gained significant research attention. Current research primarily emphasizes boosting LLMs' performance on scientific QA benchmarks by training on larger, more comprehensive datasets with extended reasoning chains. However, these approaches neglect the essence of the scientific reasoning process ‑‑ logicality, which is the rational foundation to ensure the validity of reasoning steps leading to reliable conclusions. In this work, we make the first systematic investigation into the internal logicality underlying LLM scientific reasoning, and develop a scientific logicality‑enriched methodology, including a set of assessment criteria and data sampling methods for logicality‑guided training, to improve the logical faithfulness as well as task performance. Further, we take physics, characterized by its diverse logical structures and formalisms, as an exemplar discipline to practise the above methodology. For data construction, we extract scientific problems from academic literature and sample a high‑quality dataset exhibiting strong logicality. Experiments based on three different backbone LLMs reveal that: 1) the training data we constructed can effectively improve the scientific logicality in LLM reasoning; and 2) the enriched scientific logicality plays a critical role in solving scientific problems. Code is available at \hrefhttps://github.com/ScienceOne‑AI/PhysLogichttps://github.com/ScienceOne‑AI/PhysLogic.

PaperId: 916, https://arxiv.org/pdf/2605.17076.pdf GitHub

Abstract:
We address concurrency control for LLM agents sharing mutable state over HTTP, where agents cannot be modified to declare read sets. S‑Bus is an HTTP middleware whose central mechanism, a server‑side DeliveryLog, reconstructs each agent's read set at commit time from observed HTTP GET traffic. The consistency property it provides ‑‑ Observable‑Read Isolation (ORI), a partial causal consistency over the HTTP‑observable read projection ‑‑ prevents Structural Race Conditions in dedicated‑shard topologies. Three contributions. (C1) DeliveryLog mechanism with three‑tier mechanised evidence: TLAPS proves ReadSetSoundness and ORICommitSafety (modulo one typing axiom); exhaustive TLC at N=3 explores 20,763,484 states with zero violations; Dafny discharges 9 inductive lemmas. (C2) Empirical safety parity against PostgreSQL 17 SERIALIZABLE and Redis 7 WATCH/MULTI: zero Type‑I corruptions across 884,110 commit attempts (427,308 under active contention). (C3) ORI is semantically neutral in dedicated‑shard workloads but harmful in single‑shard collaborative writing because preservation propagates concurrent contradictions. v2 update: the PH‑3 LLM judge is now independently validated against a human annotator (Zahid Hussain, Mindgigs Peshawar) on 400 (step, shard) pairs at strict kappa=0.93 (n=93, 96.8% raw agreement). Inter‑LLM‑judge agreement is kappa=0.46 (boundary variance). Agent self‑reports over‑claim shard usage by 32% (LLM judge) to 49% (human annotator). The SJ‑v4 semantic‑quality rubric remains single‑judge LLM‑only. Source code, formal proofs, harness, annotation data: https://github.com/sajjadanwar0/sbus

PaperId: 917, https://arxiv.org/pdf/2605.17046.pdf GitHub

Abstract:
Autonomous AI coding agents are becoming a core tool for ML practitioners in industry and research alike. Despite this growing adoption, no standardized benchmark exists to evaluate their ability to design, implement, and train models from scratch across diverse domains. We introduce 1GC‑7RC (Single Graphic Card: Seven Research Challenges), a benchmark comprising seven ML tasks spanning language modeling, image classification, semantic segmentation, graph learning, tabular prediction, time‑series forecasting, and text classification. Each task provides a locked data‑preparation and evaluation script together with a baseline training script; the agent may only modify the training code, has no access to pretrained weights (with one controlled exception for semantic segmentation), no internet access, and must complete each task within a task‑specific wall‑clock budget (40‑120 minutes) on a single GPU. We evaluate seven coding agents: five proprietary (Claude Code with Sonnet 4.6, Opus 4.6, and Opus 4.7; Codex CLI with GPT 5.5; and OpenCode with Qwen 3.6+) and two open‑source (OpenCode with Kimi K2.5, Kimi K2.6). Across 5 runs per agent‑task pair, we report substantial performance differences that reveal varying levels of implicit ML knowledge, planning ability, and time‑budget management. The benchmark, harness, and all evaluation artifacts are publicly available on GitHub at https://github.com/Strolchii/1GC‑7RC‑Benchmark to facilitate reproducible comparison of future agents. Because our benchmark design is modular, the benchmark can be extended to new tasks and domains, adapted to different GPU budgets, and used to study multi‑agent settings, making it a flexible platform for future research on autonomous research agents.

PaperId: 918, https://arxiv.org/pdf/2605.17041.pdf GitHub

Abstract:
We present Agentic AI Translate, an agentic translator prototype that operationalises the thesis of Yamada (forthcoming) ‑‑ that the metalanguage of Translation Studies has become an instruction code for generative AI. The system replaces the dominant text‑in / text‑out paradigm of machine translation with a four‑stage agentic cycle (Identify ‑> Prompt ‑> Generate ‑> Verify), preceded by an interactive specification phase in which the user composes ‑‑ through model‑assisted dialogue ‑‑ a structured translation brief grounded in skopos theory, register, audience, and genre conventions. The verification stage adopts the GEMBA‑MQM error‑span protocol (Kocmi & Federmann, 2023) for evidence‑grounded scoring, and document‑level coherence is preserved through a DelTA‑lite memory of proper nouns and a running bilingual summary, after Wang et al. (2025). We describe the philosophical motivation, the architectural commitments, the four reference‑material categories the system consumes, and the principal design tensions the architecture makes explicit. Empirical validation is left for future work; the contribution here is conceptual and architectural ‑‑ an executable embodiment of the position that translation in the GenAI era is communication design, not text conversion.

PaperId: 919, https://arxiv.org/pdf/2605.17021.pdf GitHub

Abstract:
Multi‑view learning has been widely applied for sleep stage classification using multi‑modal data. However, existing methods typically assume that different modalities are well‑aligned, which is often unattainable in real‑world scenarios, thereby compromising the reliability of the staging results. In this paper, we propose ConfSleepNet, a conflict‑aware evidential framework that dynamically resolves inter‑view conflicts. The framework consists of multi‑view evidence extraction and conflict‑aware aggregation. In the first phase, it learns category‑related evidence from different modalities, which represents the degree of support for individual sleep stages. Considering the inherent characteristics of varying modalities, we propose hybrid category structures for different modalities to promote more reasonable evidence learning. In the second phase, view‑specific opinions, including prediction results and uncertainty, are constructed from the learned evidence. Notably, we propose a novel conflict‑aware aggregation method that integrates these view‑specific opinions into a reliable joint decision. This mechanism can effectively resolve conflicts among opinions and synthesize them into a reliable joint decision. Both theoretical analysis and experimental results demonstrate the effectiveness of ConfSleepNet in sleep staging tasks. The code is available at https://github.com/By4te/ConfSleepNet_ICML2026/.

PaperId: 920, https://arxiv.org/pdf/2605.17003.pdf GitHub

Abstract:
Reinforcement Learning (RL) post‑training has emerged as the dominant paradigm for eliciting mathematical reasoning in Large Language Models (LLMs), yet prevailing techniques such as GRPO and DAPO distribute rollout and gradient budgets nearly uniformly across prompts, squandering compute on samples that are already mastered or remain far beyond the model's current capability. To address this fundamental inefficiency, we propose Learning‑Zone Energy (LZE), a theoretically grounded, fully online data selection framework that concentrates computation on the model's active learning frontier. At its core, we define a closed‑form Learning‑Zone Energy Score that fuses three complementary signals, an initial‑difficulty anchor, a normalized outcome‑uncertainty term, and a pass‑rate momentum, into a single scalar that is provably aligned with the expected magnitude of group‑relative policy gradient updates. A forward pruner with replay further reduces wall‑clock time cost by skipping rollout generation for persistently solved prompts while periodically checking for forgetting. Evaluated on Qwen‑family models (1.5B‑8B) across GSM8K, MATH and DAPO‑MATH, our method retains only 40% of the training data per step yet matches or surpasses full‑data baselines, with especially pronounced out‑of‑distribution gains on AIME25 (+45.9%) and AMC23 (+18.2%), alongside an estimated 36% reduction in training FLOPs. Our code is available at https://github.com/Stellaris167/LZE.

PaperId: 921, https://arxiv.org/pdf/2605.17000.pdf GitHub

Abstract:
Optimization of LLM training and inference configurations, such as hyperparameters, data mixtures, and prompts, is critical to performance, but it is often approached heuristically in practice, leading to potentially suboptimal outcomes. By framing them as noisy, expensive, and derivative‑free optimization problems, Bayesian optimization (BO) and other black‑box optimization (BBO) methods offer a promising yet underexplored direction for principled, sample‑efficient methods. However, LLM training and inference costs are prohibitively high for most of the BBO research community, and new methods are often only evaluated on synthetic test functions and small‑scale datasets that fail to capture the challenges of modern LLM optimization problems. This impedes the development of BBO methods and makes it difficult to assess their effectiveness on modern LLM tasks. We introduce BoLT, the first LLM‑centric benchmark that democratizes LLM research for the BBO community. BoLT is released at https://github.com/chewwt/bolt. BoLT covers broad and well‑motivated LLM optimization problems, involving multi‑fidelity, multi‑objective, heteroscedastic noise, and high‑dimensional search spaces. Each problem in BoLT is grounded in real experimental data and made fully reproducible and accessible through lightweight surrogate models fitted to the results of thousands of real LLM experiments. We benchmark BoLT against an extensive range of BO and BBO methods, showing that selected BO methods consistently outperform others across tasks and highlighting gaps in existing BBO methods on LLM tasks, underscoring the need to modernize benchmarks for the BBO community.

PaperId: 922, https://arxiv.org/pdf/2605.16993.pdf GitHub

Abstract:
Current clinical artificial intelligence (AI) systems are evaluated almost exclusively on clean, standardised, English‑language inputs, conditions that do not reflect the realities of healthcare delivery in low‑resource settings. This study presents the first systematic dual audit of two orthogonal safety vulnerabilities in clinical AI: adversarial image fragility and cross‑lingual diagnostic drift. Using DenseNet121, the architecture underlying CheXNet, fine‑tuned on the COVID‑QU‑Ex chest X‑ray dataset (85,318 images; COVID‑19, Non‑COVID Pneumonia, Normal), we demonstrate that diagnostic accuracy collapses from 89.3% to 62.0% under a Fast Gradient Method (FGM) perturbation of epsilon=0.021, a magnitude imperceptible to the human eye. Standard defensive strategies including Gaussian smoothing and ensemble voting failed to restore clinical safety. In a parallel language fragility experiment, we tested Llama3.1:8b and NatLAS (N‑ATLAS) on 20 COVID‑19 clinical cases presented in Standard English, Nigerian Pidgin (Naija), and Yoruba‑inflected English. Both models exhibited significant accuracy degradation: Llama3.1:8b dropped from 80.0% to 65.0% on Pidgin; NatLAS, an African‑context model, collapsed from 85.0% to 55.0%, with diagnosis consistency falling to 50%. These findings establish a quantitative failure envelope for clinical AI under conditions representative of Primary Health Centre (PHC) deployment in Nigeria, and motivate urgent calls for adversarially hardened, linguistically inclusive clinical AI architectures.

PaperId: 923, https://arxiv.org/pdf/2605.16962.pdf GitHub

Abstract:
Existing vision‑language forgery detection and grounding methods operate under a closed‑world paradigm, assuming verification can be completed by the model alone. However, self‑contained MLLMs are constrained by finite parametric knowledge, static training corpora, and limited perceptual resolution, creating a practical ceiling in dynamic open‑world forensics ‑‑ particularly for real‑time event verification requiring external clues and forgery segmentation demanding fine‑grained scrutiny of local manipulations. To address these limitations, we shift from scaling up the self‑contained model toward reaching beyond it. We propose OmniVL‑Guard Pro, a tool‑augmented agent that extends unified forensics from closed‑world prediction to open‑world clues‑driven reasoning. OmniVL‑Guard Pro integrates a tool environment spanning real‑time event search, local cropping and zooming, edge‑anomaly screening, face detection, video frame extraction, and SAM3‑based segmentation. To generate high‑quality tool‑reasoning trajectories, we introduce Tree‑Structured Self‑Evolving Tool Trajectory Generation, which produces diverse trajectories through seed guidance, guider‑free self‑evolution, and weakly‑hinted hard sample synthesis, yielding the Full‑Spectrum Tool Reasoning (FSTR) dataset for training. We further propose Checker‑Guided Agentic Reinforcement Learning (CGARL), which provides process‑level supervision to penalize cases where the answer is correct but the reasoning is distorted. Extensive experiments demonstrate that OmniVL‑Guard Pro achieves state‑of‑the‑art performance across various tasks, and exhibits strong zero‑shot generalization. The FSTR dataset and code for OmniVL‑Guard Pro will be publicly released at https://github.com/shen8424/OmniVL‑Guard‑Pro.

PaperId: 924, https://arxiv.org/pdf/2605.16909.pdf GitHub

Abstract:
Tool‑using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect intermediate artifacts, and revise their actions before producing a final result. Existing benchmarks, however, often evaluate tool use, computer use, and multimodal reasoning in isolation, leaving a gap between benchmark settings and end‑to‑end omni‑modal tool use in the real world. To address this gap, we introduce MM‑ToolBench, a benchmark and evaluation harness for task‑oriented omni‑modal tool use. MM‑ToolBench contains 100 executable tasks from two macro task families, Customer Service and Intelligent Creation, covering 20 subcategory slices and supported by 27 MCP servers with 324 tools. The central design of MM‑ToolBench is closed‑loop multimodal verification: agents must execute tools, inspect rendered or transformed artifacts, and self‑correct when outputs fail task‑specific requirements. To make such evaluation scalable and verifiable, MM‑ToolBench couples MCP‑based execution with task‑specific grounded evaluators and a semi‑automated construction pipeline for scenario discovery, task instantiation, evaluator synthesis, and human audit. Experiments on 15 contemporary agentic models show that MM‑ToolBench remains highly challenging: Claude Opus 4.6, commonly regarded as one of the strongest coding‑agent models, achieves only 32.0% task success, far below the 94.0% human benchmark. We envision MM‑ToolBench as a practical foundation for evaluating and advancing next‑generation omni‑modal tool‑using agents through closed‑loop multimodal verification.

PaperId: 925, https://arxiv.org/pdf/2605.16895.pdf GitHub

Abstract:
End‑to‑end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgents, FinAgent, QuantAgent, and FLAG‑Trader. Several of these report headline Sharpe ratios that would be material if read at face value on a deployment desk, and associated benchmarks such as FinBen report trading‑task Sharpe statistics in the same range. The gap between architecture research and deployment claim has been crossed too freely on both sides of the academia‑‑industry divide. We take a position on that gap: reported alpha from end‑to‑end LLM trading agents should not be treated as deployment evidence. Before such returns can support claims of deployable trading capability, they must survive structural validity tests for temporal integrity, real‑world frictions, counterfactual robustness, predictive calibration, numerical execution, and multi‑agent disaggregation. Current public evidence cannot yet distinguish robust predictive ability from temporal contamination, unmodeled frictions, short‑window Sharpe uncertainty, narrative fitting, and parametric priors. The problem is not only evaluative but structural. Language confidence is not tradable probability, narrative reasoning is not numerical execution, and model priors may become undisclosed implicit factor exposures. We contribute a minimum reporting protocol suite, P1‑‑P6, with tiered applicability by claim strength, and a conservative modular alternative that uses LLMs as auditable information interfaces upstream of independent calibration, risk, and execution modules. Code and reproduction harness: \urlhttps://github.com/hj1650782738/Trading.

PaperId: 926, https://arxiv.org/pdf/2605.16893.pdf GitHub

Abstract:
Recent studies introduce conditional memory modules that decouple knowledge storage from neural computation, enabling more direct knowledge access. Compared to MoE, which relies on dynamic computation paths, explicit lookup provides a more efficient knowledge retrieval mechanism. However, these approaches still depend on learned memory embeddings, requiring additional training and limiting flexibility. To address this, we propose N‑gram Memory (NGM), a training‑free, plug‑and‑play module composed of a Causal N‑Gram Encoder and a Cosine‑Gated Memory Injector. The Causal N‑Gram Encoder directly averages the pretrained token embeddings of the backbone model to construct N‑gram representations, thereby eliminating the need to train separate N‑gram embeddings from scratch. This design requires neither an additional memory table nor a retrieval pipeline. The Cosine‑Gated Memory Injector then uses a non‑parametric cosine gate with ReLU to modulate the retrieved embeddings into the contextual representations. We evaluate NGM on the Qwen3 series from 0.6B to 14B across eight benchmarks. NGM improves average performance by 0.5 to 1.2 points, with particularly clear gains on code generation and knowledge‑intensive tasks (e.g., +3.0 on LiveCodeBench and +3.03 on GPQA for Qwen3‑14B). Moreover, NGM also improves performance in multimodal benchmarks (e.g., MMStar +1.53 on Qwen3‑VL‑2B).

PaperId: 927, https://arxiv.org/pdf/2605.16874.pdf GitHub

Abstract:
Large reasoning models (LRMs) substantially outperform their base LLM counterparts on challenging reasoning benchmarks, yet it remains poorly understood where base models go wrong during token‑by‑token generation and how to narrow this gap efficiently. We study the base‑reasoning gap through quantifying token‑level distributional disagreement between a base model and a stronger reasoning model using likelihood‑based divergences. Across benchmarks, we find that the reasoning advantage is highly sparse and concentrates on a small set of early, planning‑related decision tokens. For instance, on Qwen3‑0.6B, only ~8% of generated tokens account for the salient disagreement, and these tokens concentrate early in the response, are strongly enriched in planning‑related decisions (17x), and coincide with high base‑model uncertainty ‑‑ suggesting that base models fail mainly at early planning points that steer the subsequent reasoning trajectory. Building on these findings, we propose disagreement‑guided token intervention, a simple inference‑time delegation scheme that performs a one‑token takeover by the reasoning model only at high‑disagreement positions and immediately switches back to the base model. With a small intervention budget, this sparse delegation substantially recovers and can even surpass the performance of a same‑size reasoning model on challenging reasoning tasks. Code is available at https://github.com/AlphaLab‑USTC/RRTokenIntervention.

PaperId: 928, https://arxiv.org/pdf/2605.16864.pdf GitHub

Abstract:
Although large‑scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance‑aware dense prediction tasks. They exhibit different biases in representation: for instance, promptable segmentation models (e.g., SAM2) focus on fine‑grained region boundaries, while self‑supervised models (e.g., DINOv3) emphasize object‑level structure. This observation highlights the potential of combining complementary features from different VFMs to enhance downstream dense prediction tasks. However, naive multi‑VFM fusion seldom leads to reliable gains, and interpretable principles for leveraging their complementary features are still underexplored. In this work, we propose a metric‑guided approach that effectively selects and aggregates complementary features from different VFMs based on explicit assessment scores. Specifically, we design a suite of label‑free metrics in feature space across two aspects, Structural Coherence and Edge Fidelity, to assess features of VFM encoders. Guided by these scores, we identify complementary edge‑strong and structure‑strong encoder pairs, and integrate them via a master‑auxiliary fusion scheme. This feature fusion requires no complex architectural changes and is trained only in a single stage. Our model shows consistent performance gains across multiple dense prediction tasks compared with the baselines, with better object‑level semantics and more accurately localized boundaries. The code is available at https://github.com/gyc‑code/metric‑guided‑fusion.

PaperId: 929, https://arxiv.org/pdf/2605.16863.pdf GitHub

Abstract:
Compositional diffusion models offer a promising route to long‑horizon planning by denoising multiple overlapping sub‑trajectories while ensuring that together they constitute a global solution. However, enforcing local behavior over long chains is often insufficient for a coherent global structure to emerge. Recent works tackle this limitation through intrinsic search, which explores multiple paths during the denoising process. While intrinsic search improves global coherence, it comes at the cost of repeated evaluations of an already compute‑heavy model. In this work, we argue that extrinsic search, performed outside the denoising process, offers a more effective mode of exploration for long‑horizon planning while naturally enabling the use of classical algorithms to solve unseen combinatorial tasks at test time. Our eXtrinsic search‑guided Diffuser (XDiffuser) first computes a plan over a state‑space graph ‑‑ serving as a lightweight local connectivity oracle for the diffusion model. The plan is then used to guide denoising for a single trajectory, effectively offloading the burden of exploration. XDiffuser outperforms diffusion‑based baselines on long‑horizon tasks, with particularly large gains in the low‑quality data regime and on unseen tasks beyond goal‑reaching, including multi‑agent coordination and TSP‑style reasoning. Project website: https://yanivhass.github.io/XDiffuser‑site/

PaperId: 930, https://arxiv.org/pdf/2605.16859.pdf GitHub

Abstract:
3D change detection from multi‑view images is essential for urban monitoring, disaster assessment, and autonomous driving. However, existing methods predominantly operate in the 2D domain, where viewpoint variations are mistaken for physical changes and depth is unavailable. While visual geometry foundation models like VGGT rapidly produce dense point clouds from unposed images, independent per‑epoch reconstruction encounters fundamental obstacles: unpredictable inter‑epoch scale ambiguity, registration‑change paradox where scene changes corrupt alignment, and pervasive edge‑flying noise. To address these challenges, we present VGGT‑CD, a training‑free pipeline decoupling cross‑temporal registration from dynamic‑change interference. In the Coarse Stage, sparse keyframe joint inference establishes a unified metric space and yields an initial Sim(3) prior. In the Fine Stage, dense reconstructions are purified by isolating static‑background correspondences. A closed‑form centroid alignment refines the translation while locking scale and rotation, using a residual self‑check to mathematically guarantee non‑degradation. Evaluated on an 11‑scene benchmark from the World Across Time dataset, VGGT‑CD reduces Absolute Trajectory Error by 44% outdoors and 59% indoors. It completes registration over 6 times faster, producing high‑purity 3D change maps without task‑specific training.

PaperId: 931, https://arxiv.org/pdf/2605.16826.pdf GitHub

Abstract:
Knowledge distillation is central to LLM post‑training, yet its design space remains poorly understood, especially alongside reinforcement learning (RL). We show that the prevailing paradigms, off‑policy distillation and on‑policy distillation (OPD), implicitly couple two orthogonal choices: prefix source and token‑level KL direction. This follows from decomposing sequence‑level KL over autoregressive response distributions: forward KL pairs teacher prefixes with token‑level forward KL, and reverse KL pairs student prefixes with token‑level reverse KL. We argue this coupling is not intrinsic: decoupling the two axes yields four valid objectives. We establish gradient‑level identities showing forward KL gives SFT‑style cross‑entropy matching with teacher soft targets, whereas reverse KL gives an RL‑style policy‑gradient objective with a dense teacher‑student log‑ratio reward, connecting them to off‑policy SFT, DAgger‑style on‑policy SFT, offline‑RL‑style distillation, and OPD. We conduct an extensive controlled study on math reasoning, evaluating the four objectives both as standalone methods and as initializations for subsequent RL. The results reveal three tradeoffs: KL direction induces an accuracy‑entropy tradeoff, prefix source a quality‑compute tradeoff, and training length an accuracy‑stability tradeoff. Motivated by these findings, we propose KL mixing and an entropy‑gated length curriculum. KL mixing shows long‑sequence distillation requires substantial forward‑KL weight to prevent entropy collapse and length inflation without sacrificing accuracy. The entropy‑gated length curriculum improves Avg@k and Pass@k by 3.6 and up to 5.8 points, and cuts average response length by roughly 3x versus fixed long‑horizon training. Our results provide a framework and practical methods for designing reasoning distillation objectives that balance accuracy, diversity, compute, and RL behavior.

PaperId: 932, https://arxiv.org/pdf/2605.16795.pdf GitHub

Abstract:
Video generative models have made remarkable progress, yet they often yield visual artifacts that violate grounding in physical dynamics. Recent works such as PhysGen3D tackle single image‑to‑3D physics through mesh reconstruction and Physically‑Based Rendering, but challenges remain in modeling fluid dynamics, multi‑object interactions and photorealism. This work introduces 3DPhysVideo, a novel training‑free pipeline that generates physically realistic videos from a single image. We repurpose an off‑the‑shelf video model for two stages. First, we use it as a novel view synthesizer to reconstruct complete 360‑degree 3D scene geometry by guiding the image‑to‑video (I2V) flow model with rendered point clouds. Second, after applying physics solvers to this geometry, the physically simulated point cloud is used to guide the same I2V flow model to synthesize final, high‑quality videos. Consistency‑Guided Flow SDE, which decomposes the predicted velocity of the I2V flow model into denoising and consistency bias, enforces consistency to the conditional inputs, allowing us to effectively repurpose the model for both 3D reconstruction and simulation‑guided video generation. In the diverse experiments including multi‑objects, and fluid interaction scenes, our method successfully bridges the gap from single‑images to physically plausible videos, while remaining efficient to run on a single consumer GPU. It outperforms state‑of‑the‑art baselines on GPT‑based scores, VideoPhy benchmark and human evaluation.

PaperId: 933, https://arxiv.org/pdf/2605.16790.pdf GitHub

Abstract:
Tool use enables large language models to solve complex tasks through sequences of API calls, yet existing reinforcement learning approaches fail to scale to multi‑step composition settings. Outcome‑based rewards provide only sparse feedback, while trajectory‑supervised rewards depend on annotated reference solutions, penalizing valid alternatives and limiting scalability. We propose TIER: Trajectory‑Invariant Execution Rewards, a reward framework that derives supervision directly from function schemas and runtime execution, rather than from reference trajectories. The reward decomposes into format validity, schema adherence, execution success, and answer correctness, providing dense, interpretable sequence‑level feedback derived from fine‑grained verification of individual steps of tool use. This design allows any valid execution path to receive credit, naturally supporting multiple solution strategies and adapting to evolving tool interfaces. On DepthBench, a compositional benchmark stratified by depth (1 to 6 steps), TIER achieves >90% accuracy across steps, where trajectory‑supervised rewards collapse beyond step‑4. We further demonstrate consistent gains on benchmarks like BFCL v3 and NestFUL. Ablation studies confirm that all reward components are necessary, highlighting the importance of multi‑level supervision for compositional reasoning.

PaperId: 934, https://arxiv.org/pdf/2605.16785.pdf GitHub

Abstract:
Hyperdimensional (HD) computing offers an attractive alternative to deep networks for edge learning due to its simplicity, fast prototype‑based inference, and compatibility with online updates. However, standard pixel‑based HD encoders are brittle: small distribution shifts such as rotation, noise, or occlusion can drastically reduce accuracy. We extract discrete topological primitives‑most notably holes‑from binarized shapes and pair them with rotation/translation/scale (RTS)‑invariant shape signatures. Our method constructs RTS‑stable descriptors for (i) the outer shape using a spatial‑pyramid variant of Zernike moments and (ii) each hole using an intrinsic Fourier descriptor of its radial signature together with RTS‑canonical relative geometry. Each primitive is mapped to a bipolar hypervector via randomized projection and role binding, and variable‑cardinality hole sets are aggregated by permutation‑invariant bundling to form a single image hypervector. To avoid over‑weighting any cue, we learn nonnegative reliability weights for the Zernike and hole channels on a validation set via late fusion of cosine similarities. Experiments on MNIST and EMNIST under controlled corruptions (rotation, Gaussian noise, salt‑and‑pepper, cutout, zoom) show that Topology‑guided HD computing substantially improves robustness compared with a naive HD baseline, maintaining high accuracy across multiple corruption families and benefiting from lightweight online training. Compared with a compact CNN trained on clean data, our method achieves competitive clean accuracy while offering markedly stronger robustness to several pixel‑level corruptions, demonstrating that explicit topological structure is a practical route to robust HD representations. The code is provided at https://github.com/arpan‑kusari/Topological‑HDC.

PaperId: 935, https://arxiv.org/pdf/2605.16779.pdf GitHub GitHub

Abstract:
This work presents a novel method for fitting superquadrics to point clouds under the contamination of noise and outliers, which has many applications for shape modeling across diverse fields. Unlike prior approaches that either exclusively focus on fitting rigid or deformable superquadrics, or suffer from robustness and numerical instability issues, our method redefines the problem from a new unsupervised clustering perspective, enabling the holistic fitting of both rigid and deformable superquadrics within a unified framework. Central to our approach is a stable optimization function inspired by unsupervised clustering analysis, where we formulate the point cloud data and samples from the potential parametric surface as clustering members and centroids, respectively. Then, the clustering process with dynamic updates to centroid locations serves as a direct proxy for optimizing superquadric parameters, establishing a principled link between geometric fitting and clustering dynamics. We further derive the relationship between pairwise computations of clustering centroids and clustering members to orthogonal distances, effectively eliminating the need for the time‑consuming surface sampling process. Moreover, our formulation provides closed‑form analytical solutions for both the fuzzy membership degree vector and the covariance matrix, ensuring efficient iteration optimization and enabling more effective handling of geometric deformations. In addition, we provide a theoretical certificate of convergence analysis and demonstrate that the clustering‑inspired fitting method can escape local minima by inherently increasing the convexity of the objective function. The implementation is publicly available at https://github.com/zikai1/SuperquadricFitting.

PaperId: 936, https://arxiv.org/pdf/2605.16776.pdf GitHub

Abstract:
Mitigating sensitive and harmful outputs is fundamental to ensuring safe deployment of LLMs. Existing approaches typically follow two paradigms: Knowledge Deletion (KD), which erases undesirable information during training, and Distinguishable Refusal (DR), which steers models away from using sensitive knowledge during inference. Despite rapid progress, KD‑based unlearning struggles with biased deletion due to suppressing specific token sequences as a substitute for complete knowledge removal, whereas DR‑based unlearning risks the re‑emergence of harmful knowledge because the underlying knowledge remains intact. To address these issues, we propose Distinguishable Deletion (\mathrmD^2), a paradigm that restricts the response distribution in the latent representation rather than specific tokens to erase undesirable knowledge, while distinguishing it from retained knowledge, enabling a refusal mechanism to handle unlearned inputs safely and coherently. To implement \mathrmD^2, we introduce an energy index that quantifies the presence of knowledge and the separation between unlearned and retained content. Mathematical and empirical analyses show that energy is both accurate and efficient, enabling Energy‑based Unlearning Alignment (EUA) to enforce energy‑boundary unlearning during training and apply an energy‑based refusal mechanism at inference. Extensive experiments demonstrate that EUA significantly outperforms previous methods, indicating the superiority of \mathrmD^2. Our code is available at https://github.com/Puning97/EUA‑for‑LLM‑Unlearning.

PaperId: 937, https://arxiv.org/pdf/2605.16770.pdf GitHub

Abstract:
Criminal Court View Generation (CVG) is a critical task in Legal Artificial Intelligence (Legal AI), involving the generation of court view based on case facts. In this work, we systematically explore the capabilities of lightweight (smaller than 2B) large language models (LLMs) in CVG and their impact on charge prediction. Our study addresses four key questions: (1) how does different architecture of LLMs affect the CVG quality and charge prediction. (2) how does LLMs size contribute to the performance, (3) how do lightweight LLMs compare with Deep Neural Networks (DNNs) in these tasks, and (4) how does predicting charge by court view generation first compare with predicting it directly. Additionally, we also develop CVGEvalKit, an evaluation framework including three public available datasets for CVG tasks, as well as predicting their charges. Comprehensive experiments are conducted on this framework, where models are trained on a mixed training set and evaluated on each dataset's test set. Experimental results provide new insights into the trade‑offs between model architecture, model size, and the influence between different tasks, highlighting the potential of lightweight LLMs in judicial AI applications. The source code is anonymously available at \urlhttps://github.com/ZhitianHou/CVGEvalKit

PaperId: 938, https://arxiv.org/pdf/2605.16679.pdf GitHub

Abstract:
End‑to‑end automation of realistic healthcare operations stresses three capabilities underrepresented in current benchmarks: policy density, decisions must be grounded in a large library of medical, insurance, and operational rules; Multi‑role composition: a single task requires the agent to play multiple roles with handoffs; and multilateral interaction: intermediate workflow steps are multi‑turn dialogs, such as peer‑to‑peer review and patient outreach. We introduce χ‑Bench, a benchmark of long‑horizon healthcare workflows across three domains: provider prior authorization, payer utilization management, and care management. Each task hands the agent a clinical case in a high‑fidelity simulator of 20 healthcare apps exposed via 87 MCP tools, which it must drive to a terminal status through tool calls and writing the role's artifacts, guided by a 1,290+ document managed‑care operations handbook skill. Across 30 agent harness/models configurations, the best agent resolves only 28.0% of tasks, no agent clears 20% on strict pass^3, and executing all tasks in a single session slumps the performance to 3.8%. These results raise the hypothesis that similar gaps are likely to surface in other policy‑dense, role‑composed, irreversible enterprise domains.

PaperId: 939, https://arxiv.org/pdf/2605.16626.pdf GitHub

Abstract:
Since autonomous coding agents generate complex behaviors at high‑volume, we may want to use other LLMs to monitor actions to reduce the risk from dangerous misaligned behavior. To better understand the limitations of such monitors against the diverse attack strategies that a coding agent could use, we present SLEIGHT‑Bench (Subtle Low‑itEration Insight‑Guided Harmful Transcripts), a benchmark of synthetic transcripts containing 40 attacks across 11 categories, each showing a coding agent covertly pursuing a harmful objective (e.g. weight exfiltration, credential theft, unmonitored rogue deployment). We find that 20 of 40 attacks are never caught by an Opus 4.6 monitor with extended thinking across 10 trials at a 1% false‑positive rate, while the overall catch rate is 32%. We identify several evasion strategies such as exploiting model priors, user instruction ambiguity, and state manipulation, which facilitate creating attacks that evade frontier monitors. We also elicit stronger monitor performance using coding agents as monitors versus regular prompted monitors, and for some evasion strategies show improved catch rates with targeted monitor prompts. Our dataset and evaluation framework are available at https://github.com/safety‑research/sleight‑bench and https://huggingface.co/datasets/sleightbench/SLEIGHT‑Bench.

PaperId: 940, https://arxiv.org/pdf/2605.16527.pdf GitHub

Abstract:
Hypergraphs model higher‑order relations that drive real‑world decisions, from drug prescriptions to recommendations. A central structural signal in such data, beyond what pairwise relations can express, is interaction compositionality: whether a higher‑order relation is compositional, emergent, or inhibitory with respect to its observed or unobserved sets. In polypharmacy, the regime decides whether a drug should be dropped, kept, or excluded: a compositional drug triple can be safely simplified, an emergent triple requires all drugs jointly, and an inhibitory triple flags a drug that disrupts an existing interaction. However, existing hypergraph learning methods, which merely propagate messages over observed hyperedges, leave this compositional signal unmodeled, allowing dangerous drug combinations to slip through and be misclassified. To this end, we propose the Hypergraph Pattern Machine (HGPM), shifting the paradigm from message passing to learning the compositional pattern of subsets. It tokenizes compositional subsets, organizes them in an inclusion DAG, and trains an inclusion‑aware Transformer under masked reconstruction. On ten hypergraph benchmarks, HGPM matches or exceeds state‑of‑the‑art methods. Notably, in a real adverse‑event prediction case, HGPM correctly identifies the drug addition that inhibits the side effect among feature‑identical candidates, a discrimination existing methods cannot make. The code and data are in https://github.com/KryieZhao/HGPM.git.

PaperId: 941, https://arxiv.org/pdf/2605.16481.pdf GitHub

Abstract:
Long video understanding requires more than large context windows. It also needs a memory mechanism that decides what visual evidence to retain, keeps it searchable over long horizons, and grounds later reasoning in recoverable observations rather than compressed latent state alone. We propose Visual Agentic Memory (VAM), a training‑free framework with three components. Online Indexing supports selective evidence retention under streaming constraints. Hierarchical Memory organises retained evidence in a Parallel Representation that aligns temporal context with spatial observations. Agentic Retrieval searches, inspects, and verifies candidate evidence before producing a grounded answer. On OVO‑Bench, VAM achieves the highest RT+BT average (68.41) across all reported baselines, improving over end‑to‑end use of the same underlying MLLM (Gemini 3 Flash, 67.46). On the month‑scale split of MM‑Lifelong train@month (105.6 hours over 51 days), VAM reaches 17.11%, second only to ReMA with GPT‑5 (17.62%). These results suggest that long‑horizon video understanding benefits from treating visual memory as an explicit, inspectable, and queryable substrate. Code is available at https://github.com/yiliu‑li/Visual‑Agentic‑Memory.

PaperId: 942, https://arxiv.org/pdf/2605.16457.pdf GitHub

Abstract:
Token‑based transformer world models have shown strong performance in visual reinforcement learning, but often suffer from temporal inconsistency in long‑horizon rollouts, including object duplication, disappearance, and transmutation. A key reason is that most existing approaches treat next‑frame prediction purely as a token generation problem, without considering the persistence of tokens across time. We introduce Identifiable Token Correspondence (ITC), a decoding step for token‑based transformer world models that formulates next‑frame prediction as a structured assignment problem with latent token correspondence variables: each next‑frame token is explained either by copying a token from the previous frame or by generating a new one. ITC leaves the transformer architecture and training procedure unchanged and can be added on top of existing backbones. Our experiments show state‑of‑the‑art performance on 4 challenging benchmarks. The proposed method achieves a return of 72.5% and a score of 35.6% on the Craftax‑classic benchmark, significantly surpassing the previous best of 67.4% and 27.9%. We release our source code on https://github.com/snu‑mllab/Identifiable‑Token‑Correspondence.

PaperId: 943, https://arxiv.org/pdf/2605.16371.pdf GitHub

Abstract:
Large Multimodal Models (LMMs) often struggle with geometric reasoning due to visual hallucinations and a lack of mathematically precise Chain‑of‑Thought (CoT) data. To address this, we propose the GeoSym Engine, an automated and scalable neuro‑symbolic framework. By leveraging a type‑conditional grammar and an analytic SymGT Solver, it derives exact symbolic ground truths and seamlessly integrates with a robust rendering pipeline to produce high‑precision geometric diagrams. Using this engine, we construct GeoSym127K, a difficulty‑stratified dataset featuring 51K high‑resolution images, 127K questions with symbolic ground truths, and 55K answer‑verified CoT QA pairs. We also introduce GeoSym‑Bench, an expert‑curated suite of 511 complex samples for rigorous evaluation. Through extensive supervised fine‑tuning (SFT), we demonstrate that GeoSym drives concentrated improvements specifically on diagram‑dependent and multi‑step geometry tasks. Our Qwen3‑VL‑8B model gains an absolute +22.21% on the MathVerse Vision‑Only subset and reaches 61.52% (+6.19% improvement) on WeMath, mitigating long‑horizon logic fragmentation and outperforming advanced closed‑source models like Doubao‑1.8. Furthermore, applying Reinforcement Learning with Verifiable Rewards (RLVR) via GRPO reveals that initializing from structural SFT checkpoints substantially elevates the performance ceiling over zero‑shot RL. Driven by deterministic exact‑match signals, this showcases the robust scaling potential of our verifiable reasoning synthesis. Datasets and code are available at https://huggingface.co/datasets/Tomie0506/GeoSym127K and https://github.com/Tomie56/GeoSym127K.

PaperId: 944, https://arxiv.org/pdf/2605.16353.pdf GitHub

Abstract:
Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models to incrementally acquire new abilities. However, existing CVIT methods operate under a restrictive task‑incremental setting, where each training phase corresponds to a single, predefined task. This does not reflect real‑world conditions, where data arrives as a continuous stream of interleaved and dynamically evolving tasks. To bridge this gap, we introduce Streaming CVIT (StrCVIT), a more general and realistic setting where models learn from a stream of data chunks containing a dynamic mixture of tasks. In StrCVIT, a model must simultaneously acquire new abilities, reinforce recurring abilities, and mitigate forgetting. Existing CVIT methods fail here as they cannot reliably distinguish or adapt to the heterogeneous task samples within each chunk. We therefore propose StrLoRA, a regularized two‑stage expert routing framework. StrLoRA first performs task‑aware expert selection using the textual instruction to activate a sparse subset of relevant experts, reducing cross‑task interference. It then applies token‑wise expert weighting within this subset, where contribution weights are computed via cross‑modal attention between local visual tokens and the global instruction representation. To maintain stability across the non‑stationary stream, a routing‑stability regularization aligns current routing distributions with a historical exponential moving average reference. Extensive experiments on a newly developed StrCVIT benchmark show that StrLoRA substantially outperforms existing methods, effectively enhancing model's abilities from continuously evolving data streams. The code is available at https://github.com/chanceche/StrCVIT.

PaperId: 945, https://arxiv.org/pdf/2605.16336.pdf GitHub

Abstract:
Large language models (LLMs) have made fluent essay writing, code drafting, and quiz answering instantly available to students at every level, from secondary school through graduate study. Many educators do not object to LLM use \emphper~se; what they need to detect is the case in which a student pastes the assignment prompt into a chatbot and submits the model's reply verbatim, without engaging with the work. Existing post‑hoc AI‑text detectors remain unreliable and have been shown to penalise non‑native English writers, while output‑side watermarks require cooperation from the model provider. We propose an alternative that the educator controls directly: an input‑side watermark in which an invisible instruction is embedded inside the visible assignment prompt itself. An LLM that ingests the prompt verbatim quietly reads the hidden instruction and writes a tell‑tale signature into its reply, exposing the copy‑and‑paste pathway specifically. We describe SteganoPrompt, a single‑page, zero‑dependency web tool that encodes an arbitrary printable‑ASCII payload into the deprecated Unicode Tags block (\textttU+E0000‑‑\textttU+E007F). The encoded string is visually identical to the original, survives common copy‑paste channels (Word, Google Docs, PDF, Markdown, Slack, e‑mail, the major learning‑management systems), and is reliably tokenized by frontier models. We evaluate compliance across seven LLM families and a representative set of educational content channels. The work is informed by my experience as a graduate teaching assistant for an undergraduate software engineering course at the George Washington University. The tool is released under the MIT licence at \urlhttps://ezharjan.github.io/SteganoPrompt/.

PaperId: 946, https://arxiv.org/pdf/2605.16309.pdf GitHub

Abstract:
LLM‑based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge‑‑operator schemas, preconditions, and constraints‑‑remains unrepaired. Existing self‑evolving approaches address this gap by updating prompts, memory, or model weights, but none directly repair the symbolic structures that encode how tasks are executed, and few provide the governance guarantees required for safe deployment. We introduce ANNEAL, a neuro‑symbolic agent that converts recurring failures into governed symbolic edits of a process knowledge graph without modifying foundation model weights. Its core mechanism, Failure‑Driven Knowledge Acquisition (FDKA), localizes the responsible operator, synthesizes a typed patch through constrained LLM generation, and validates the proposal via multi‑dimensional scoring, symbolic guardrails, and canary testing before commit. Every accepted edit carries full provenance and deterministic rollback capability. Across four domains and 27 multi‑seed runs, ANNEAL is the only evaluated system that commits persistent structural repairs‑‑strong baselines such as ReAct and Reflexion achieve high episodic recovery yet retain 72‑100% holdout failure rates on recurring faults, whereas ANNEAL reduces these to 0% in the tested recurring‑failure settings. Ablation confirms that removing FDKA eliminates all structural repairs and drops success rate by up to 26.7 percentage points. These results suggest that governed symbolic repair offers a complementary paradigm to weight‑level and prompt‑level adaptation for persistent fault elimination.

PaperId: 947, https://arxiv.org/pdf/2605.16265.pdf GitHub GitHub

Abstract:
The safety of autonomous AI agents is increasingly recognized as a critical open problem. As agents transition from passive text generators to active actors capable of executing shell commands, modifying files, calling APIs, and browsing the web, the consequences of unsafe or adversarially manipulated behavior become immediate and tangible. Existing AI safety work has focused primarily on model alignment and input filtering, but these approaches do not address what happens at the moment an agent's intent becomes a real action on a real machine. This gap is especially acute in local environments, where developers run agents against their own filesystems, credentials, and infrastructure with little runtime control. This paper introduces AgentWall, a runtime safety and observability layer for local AI agents. AgentWall intercepts every proposed agent action before it reaches the host environment, evaluates it against an explicit declarative policy, requires human approval for sensitive operations, and records a complete execution trail for audit and replay. It is implemented as a policy‑enforcing MCP proxy and native OpenClaw plugin, working across Claude Desktop, Cursor, Windsurf, Claude Code, and OpenClaw with a single install command. We present the design, architecture, threat model, and policy model of AgentWall, and demonstrate 92.9% policy enforcement accuracy with sub‑millisecond overhead across 14 benchmark tests. AgentWall is open‑source at https://github.com/agentwall/Agentwall.

PaperId: 948, https://arxiv.org/pdf/2605.16258.pdf GitHub

Abstract:
Reconstructing coherent 3D geometry and appearance from unposed multi‑view images is a fundamental yet challenging problem in computer vision. Most existing visual geometry foundation models predict explicit geometry by regressing pixel‑aligned pointmaps, often suffering from redundancy and limited geometric continuity. We propose IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose‑free multi‑view images. This formulation learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions, retrieving local features to predict signed distance (SDF) values and colors using lightweight decoders. It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints. We train IVGT via multi‑dataset joint optimization with 2D supervision and 3D geometric regularization. IVGT demonstrates generalization across scenes and achieves strong performance on various tasks, including mesh and point cloud reconstruction, novel view synthesis, depth and surface normal estimation, and camera pose estimation.

PaperId: 949, https://arxiv.org/pdf/2605.16234.pdf GitHub

Abstract:
When researchers ask whether two transformer layers are "equivalent" for compression, they often conflate distinct tests. Replacement asks whether one layer's map can substitute for another's in place; interchange asks whether two layers approximately commute when their positions are swapped. Both are output‑grounded swap‑KL probes, but they need not agree: on pretrained transformers the protocol gap can change which layers look safe to prune by several‑fold under the same evaluator, especially when replacement distances are high. We measure both protocols across checkpoints and architectures. On a Pythia training trajectory (410M and 1.4B), the replacement‑interchange gap grows from initialization to convergence. Under one matched WikiText‑2 contract at 8B scale, Qwen3‑8B enters a divergent regime: interchange‑guided removal is several‑fold safer than replacement‑guided at the same layer budgets, while Llama‑3.1‑8B ties the two protocols for pruning cost even though interchange KL is lower, showing metric gaps need not map one‑to‑one to removal. Before layer removal or merging, score both swap‑KLs on the target checkpoint; the diagnostic requires only unlabeled forward passes.

PaperId: 950, https://arxiv.org/pdf/2605.16194.pdf GitHub

Abstract:
LLM agents routinely serve as first (and sometimes only) readers of academic papers, skimming for sub‑claims, extracting reproducibility steps, and generalizing scope. Standard prose papers produce recurring failures in this role: sub‑claims that cannot be cited at sub‑paper granularity, scope overextension beyond what the paper tests, and figure commands buried in codebases rather than the paper itself. We propose `paper.json`, a companion JSON file that travels with the PDF and addresses each failure with a lightweight convention: stable claim IDs (C1), an explicit does‑not‑claim list (C2), exact per‑figure shell commands (C3), and stable definition IDs (C5). A fifth convention (C4) holds that minimum viable compliance, hand‑written JSON alongside the PDF, is achievable in under an hour for a finished paper without touching the human‑readable output. C1, C2, C3, and C5 are open invitations: an agent that reads a compliant paper and acts on it produces evidence for or against them. This paper is itself compliant: `uv run validator.py paper.json ‑‑against paper.typ` passes. Repo: https://github.com/arquicanedo/paper‑json

PaperId: 951, https://arxiv.org/pdf/2605.16122.pdf GitHub

Abstract:
Diffusion‑based image synthesis has made AI‑generated images (AIGI) increasingly photorealistic, raising urgent concerns about authenticity in applications such as misinformation detection, digital forensics, and content moderation. Despite the substantial advances in AIGI detection, how to correct detected AI‑generated images with visible artifacts and restore realistic appearance remains largely underexplored. Moreover, few existing work has established the connection between AIGI detection and artifact correction. To fill this gap, we propose GenShield, a unified autoregressive framework that jointly performs explainable AIGI detection and controllable artifact correction in a closed loop from diagnosis to restoration, revealing a mutually reinforcing relationship between these two tasks. We further introduce a Visual Chain‑of‑Thought based curriculum learning strategy that enables self‑explained, multi‑step ``diagnose‑then‑repair'' correction with an explicit stopping criterion. A high‑quality dataset with large‑scale ``artifact‑restored'' pairs is also constructed alongside a unified evaluation pipeline. Extensive experiments on our correction benchmark and mainstream AIGI detection benchmarks demonstrate state‑of‑the‑art performance and strong generalization of our method. The code is available at https://github.com/zhipeixu/GenShield.

PaperId: 952, https://arxiv.org/pdf/2605.16087.pdf GitHub

Abstract:
Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety assurance, debugging, and human oversight. While theoretical frameworks for safe and Explainable AI (XAI) exist, concrete implementations of Trustworthy AI for 3D scene understanding remain scarce. We address this gap by proposing a Trustworthy AI perception module that is remarkably robust, integrates faithful explainability, and calibrated uncertainty estimates. Building on a transformer‑based detector, we derive explanation from the attention mechanism at inference time and validate their faithfulness using perturbation‑based consistency tests. We further integrate an uncertainty estimation and calibration module, and apply robustness‑enhancing training methods. Experiments show faithful saliency behavior, improved robustness, and well‑calibrated uncertainty estimates. Finally, we deploy these Trustworthy AI elements in a prototype vehicle and provide an XAI Interface that visualizes documentation artifacts, model uncertainty state, and saliency maps, demonstrating the feasibility of trustworthy perception monitoring in real time. Supplementary materials are available at https://tillbeemelmanns.github.io/trustworthy_ai/ .

PaperId: 953, https://arxiv.org/pdf/2605.16079.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) have shown significant progress in video understanding, yet they face substantial challenges in tasks requiring precise spatiotemporal localization at the instance level. Existing methods primarily rely on text prompts for human‑model interaction, but these prompts struggle to provide precise spatial and temporal references, resulting in poor user experience. Furthermore, current approaches typically decouple visual perception from language reasoning, centering reasoning around language rather than visual content, which limits the model's ability to proactively perceive fine‑grained visual evidence. To address these challenges, we propose VideoSeeker, a novel paradigm for instance‑level video understanding through visual prompts. VideoSeeker seamlessly integrates agentic reasoning with instance‑level video understanding tasks, enabling the model to proactively perceive and retrieve relevant video segments on demand. We construct a four‑stage fully automated data synthesis pipeline to efficiently generate large‑scale, high‑quality instance‑level video data. We internalize tool‑calling and proactive perception capabilities into the model via cold‑start supervision and RL training, building a powerful video understanding model. Experiments demonstrate that our model achieves an average improvement of +13.7% over baselines on instance‑level video understanding tasks, surpassing powerful closed‑source models such as GPT‑4o and Gemini‑2.5‑Pro, while also showing effective transferability on general video understanding benchmarks. The relevant datasets and code will be released publicly.

PaperId: 954, https://arxiv.org/pdf/2605.15995.pdf GitHub

Abstract:
Learning latent representations from complex data is central to modern machine learning, spanning temporal, multimodal, and partially observed systems. In such settings, representations are better understood as latent states capturing underlying system dynamics, rather than as mere compressed summaries of observations. Yet current approaches remain fragmented, relying on distinct ‑‑ and often implicit ‑‑ assumptions about what these states should represent. We argue that this fragmentation reflects a more fundamental limitation: latent representations are typically learned from underconstrained objectives that fail to specify the properties that meaningful latent states should satisfy. As a result, multiple representations can satisfy the same objective, leading to ambiguity in their structure and interpretation. While many of the underlying principles have been explored in isolation, their interactions have not been explicitly formalized. In this work, we propose constrained latent state modeling (CLSM) as a unifying perspective. We identify a set of core properties ‑‑ predictive sufficiency, minimality, temporal coherence, observation compatibility, invariance to nuisance factors, and structural constraints ‑‑ and show that they are intrinsically coupled through fundamental trade‑offs. Revisiting major modeling families through this lens, we show that existing approaches can be interpreted as enforcing different subsets of constraints, thereby occupying distinct regions of a common design space. This perspective reframes persistent challenges such as lack of identifiability as consequences of underconstrained formulations, rather than isolated technical limitations. More broadly, CLSM provides a principled framework to make design choices explicit, to analyze trade‑offs, and to guide the development of more interpretable, robust, and task‑aligned latent state models.

PaperId: 955, https://arxiv.org/pdf/2605.15975.pdf GitHub

Abstract:
We tackle the challenge of building embodied AI agents that can reliably solve long‑horizon planning problems. Imitation learning from demonstrations has shown itself to be effective in training robots to solve a diversity of complex tasks requiring fine motor control and manipulation over low‑level (LL), continuous environments. Yet, it remains a difficult endeavour to generate long‑horizon plans from imitation learning alone. In contrast, high‑level (HL), symbolic abstractions facilitate efficient and interpretable long‑horizon planning. We propose to combine the strengths of LL imitation learning for manipulation and control, and HL symbolic abstractions for long‑horizon planning. We realise this idea via \emphbilevel policies of the form (π^\mathrmhl, π^\mathrmll), consisting of a neural policy π^\mathrmll learned from LL demonstrations, and an HL symbolic policy π^\mathrmhl that is constructed from symbolic abstractions of the LL demonstrations combined with inductive generalisation. We implement these ideas in the BISON system. Experiments on extended MetaWorld benchmarks demonstrate that BISON generalises to long horizons and problems with greater numbers of objects than those solved by VLA and end‑to‑end methods, and is more time and memory efficient in training and inference. Notably, when ignoring LL execution, BISON's HL policies can solve HL problems with 10,000 relevant objects in under a minute. Project page: https://dillonzchen.github.io/bison

PaperId: 956, https://arxiv.org/pdf/2605.15836.pdf GitHub

Abstract:
Learning visuomotor policies from scarce expert demonstrations remains a core challenge in robotic manipulation. A primary hurdle lies in distilling high‑dimensional RGB representations into control‑relevant geometry without overfitting. While using frozen pre‑trained Vision Foundation Models (VFMs) improves data efficiency, it also shifts most task adaptation onto a small spatial pooling module, which can latch onto task‑irrelevant shortcuts and lose geometric grounding when finetuned with few data samples. More broadly, pre‑trained visual representations used for policy learning have been observed to struggle under even minor scene perturbations, highlighting the need for robustness‑oriented inductive biases. We propose Geometric Anchor Pre‑training (GAP), a simple, action‑free warm‑up stage that regularizes the spatial adapter before downstream imitation learning. GAP pre‑trains the pooling layer on a lightweight simulated proxy task where object masks are available at no cost, encouraging the adapter to produce keypoints that lie on the object, cover its spatial extent, and remain sharp and repeatable over time. This yields stable geometric anchors that provide a reliable coordinate interface for few‑shot policy learning, while keeping the VFM frozen. We evaluate GAP on RoboMimic and ManiSkill under severe data scarcity (15‑50 demonstrations) and domain shift. A simple adapter regularized with GAP consistently outperforms stronger attention‑based poolers and end‑to‑end fine‑tuning, achieving 62% success on RoboMimic Can with 15 demonstrations (+16% over AFA), 63% on the long‑horizon high‑precision Tool Hang task with 50 demonstrations, and 61% on ManiSkill StackCube with 30 demonstrations (+11% over full fine‑tuning). The proxy stage is lightweight and fully decoupled from downstream tasks, making it practical to reuse across environments and manipulation skills.

PaperId: 957, https://arxiv.org/pdf/2605.15779.pdf GitHub

Abstract:
The integration of Unmanned Aerial Vehicles(UAVs) into Intelligent Transportation Systems (ITS) offers synoptic visibility for traffic monitoring, yet scalable deployment is hindered by trajectory fragmentation, where vehicle identity persistence is lost across multi‑UAV Fields of View (FOV). While state‑of‑the‑art frameworks excel in optimizing local trajectory extraction and stability for single‑drone imagery, they often function as isolated data silos that generate disjointed trajectories, thereby precluding network‑level analysis such as Origin‑Destination estimation. This paper presents a real‑time Multi‑Camera Multi‑Vehicle Tracking (MCMT) system designed to handle global identity persistence. Addressing the visual ambiguity and computational cost of appearance‑based Re‑Identification (Re‑ID) in nadir views, we introduce a lightweight Topology‑Based Spatiotemporal Handover mechanism. We implement a high‑throughput parallel pipeline leveraging YOLO11 and ByteTrack to process concurrent 4K streams. Our core contribution is a deterministic queue‑based matching algorithm that utilizes geometric overlaps and virtual lane discretization to predictively manage identity handover via FIFO queues. Experimental results on complex urban environments, including intersections and merging traffic, demonstrate a Handover Success Rate (HOSR) of 99.8% in continuous traffic flows, significantly outperforming Re‑ID baselines (74.1%) while validating edge deployment feasibility. The source code is available at https://github.com/JYe9/multi‑camera‑multi‑vehicle‑tracking‑system.

PaperId: 958, https://arxiv.org/pdf/2605.15777.pdf GitHub

Abstract:
Computer‑Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text‑based reasoning toward action execution in more complex environments, such as web browsers and graphical user interfaces (GUIs). However, existing web and GUI agent benchmarks often rely on simplified settings, isolated tasks, or short‑horizon interactions, making it difficult to assess capabilities of agents in realistic professional workflows. Software‑as‑a‑Service (SaaS) environments are a natural choice for CUA evaluation, as they host a large share of modern digital work and naturally involve dynamic system states, cross‑application coordination, domain‑specific knowledge, and long‑horizon dependencies. To this end, we introduce SaaS‑Bench, a benchmark built on 23 deployable SaaS systems across six professional domains, containing 106 tasks grounded in realistic work scenarios. These tasks require long‑horizon execution, cover both text‑only and multimodal settings, and are evaluated with weighted verification checkpoints that measure strict task completion and partial progress. Experiments show that representative LLM‑based agents struggle on SaaS‑Bench, with even the strongest model completing fewer than 4% of tasks end‑to‑end, exposing limitations in planning, state tracking, cross‑application context maintenance, and error recovery. Code are available at https://github.com/UniPat‑AI/SaaS‑Bench for reproduction.

PaperId: 959, https://arxiv.org/pdf/2605.15764.pdf GitHub

Abstract:
Understanding social interactions requires reasoning over subtle non‑verbal cues, yet current multimodal large language models (MLLMs) often fail to identify who interacts with whom in multi‑person videos. We introduce GRASP, a large‑scale social reasoning dataset that connects high‑level social QA with fine‑grained gaze and deictic gesture events. GRASP contains 290K question‑‑answer pairs over 46K videos totaling 749 hours, organized by a 16‑category taxonomy spanning gaze, gesture, and joint gaze‑‑gesture reasoning, together with GRASP‑Bench for evaluation. Unlike prior resources that focus on either isolated cues or high‑level social QA, GRASP builds questions from identity‑consistent gaze trajectories, deictic gestures, and their joint compositions into social events. Moreover, we propose Social Grounding Reward (SGR), a learning signal that uses these social events to encourage models to reason about the participants involved in each interaction. Experiments show that SGR improves performance on GRASP‑Bench while maintaining zero‑shot performance on related social video QA benchmarks.

PaperId: 960, https://arxiv.org/pdf/2605.15736.pdf GitHub

Abstract:
Biomedical Vision‑‑Language Models (VLMs) have shown remarkable promise in few‑shot medical diagnosis but face a critical bottleneck: fragility to prompt variations.Existing adaptation frameworks typically optimize visual and textual prompts as independent streams, relying on ideal ``Golden Prompts''. In clinical reality, where descriptions are often noisy and heterogeneous, this modality isolation leads to unstable cross‑modal alignment. To address this, we propose BiomedAP, a vision‑informed dual‑anchor framework with gated cross‑modal fusion.BiomedAP enforces synergistic alignment through two mechanisms: (1) Gated Cross‑Modal Fusion, which enables layer‑wise interaction between modalities, acting as a dynamic noise regulator to suppress irrelevant textual cues; and (2) a Dual‑Anchor Constraint that regularizes learnable prompts toward stable semantic centroids derived from both expert templates (High Anchors) and few‑shot visual prototypes (Low Anchors). Extensive experiments across 11 benchmarks demonstrate that BiomedAP consistently surpasses baselines, achieving competitive few‑shot accuracy and markedly enhanced robustness under prompt perturbations. Our code is available at: https://github.com/tongdiedie/BiomedAP. Keywords: Vision‑Language Models; Prompt Learning; Parameter‑Efficient Fine‑Tuning; Few‑shot Learning

PaperId: 961, https://arxiv.org/pdf/2605.15726.pdf GitHub

Abstract:
Reinforcement learning with verifiable rewards (RLVR) has emerged as a scalable paradigm for improving the reasoning capabilities of large language models. However, its effectiveness is fundamentally limited by exploration: the policy can only improve on trajectories it has already sampled. While increasing the number of rollouts alleviates this issue, such brute‑force scaling is computationally expensive, and existing approaches that modify the optimization objective provide limited control over what is explored. In this work, we propose NudgeRL, a framework for structured and diversity‑driven exploration in RLVR. Our approach introduces Strategy Nudging, which conditions each rollout on lightweight, strategy‑level contexts to induce diverse reasoning trajectories without relying on expensive oracle supervision. To effectively learn from such structured exploration, we further propose a unified objective, which decomposes the reward signal into inter‑ and intra‑context components and incorporates a distillation objective to transfer discovered behaviors back to the base policy. Empirically, NudgeRL outperforms standard GRPO with up to 8 times larger rollout budgets, while outperforming oracle‑guided RL baseline on average across five challenging math benchmarks. These results demonstrate that structured, context‑driven exploration can serve as an efficient and scalable alternative to both brute‑force rollout scaling and feasibility‑oriented methods based on privileged information. Our code is available at https://github.com/tally0818/NudgeRL.

PaperId: 962, https://arxiv.org/pdf/2605.15661.pdf GitHub

Abstract:
Classifier‑free guidance (CFG) is the primary control over how strongly text semantics move a flow‑based sampler, yet standard practice holds its scale fixed across the entire ODE trajectory. This is a fundamental mismatch: early steps are noise‑dominated and carry weak semantic signal, while late steps commit image structure and demand stronger directional commitment; more critically, the value of any guidance strength depends on whether the guided velocity is consistent with the model's current dynamics or working against them. We propose Velocity‑Adaptive Guidance Scale (VAGS), a training‑free replacement that multiplies the nominal scale by a bounded factor combining a temporal signal‑level term with the cosine similarity between task‑relevant velocity fields. For inversion‑free editing, VAGS measures the alignment between source‑ and target‑guided velocities, so edit strength at each step reflects local compatibility between preservation and transformation. For generation, VAGS‑Gen uses the alignment between unconditional and conditional velocities as the analogous signal. Neither variant requires fine‑tuning, auxiliary networks, or extra forward passes, and fixed CFG is recovered as a special case. On PIE‑Bench and DIV2K for editing, and COCO17, CUB‑200, and Flickr30K for generation, VAGS consistently improves structural fidelity and generation quality over fixed CFG and recent training‑free guidance variants. The code is publicly available at https://github.com/Harvard‑AI‑and‑Robotics‑Lab/Velocity_Adaptive_Guidance_Scale.

PaperId: 963, https://arxiv.org/pdf/2605.15656.pdf GitHub

Abstract:
Under the trend of multi‑waveform coexistence in 6G IoT, intelligent receivers must first identify physical‑layer waveform types before performing correct demodulation and resource scheduling. However, existing signal identification research largely focuses on symbol‑level modulation classification. Research directly targeting physical‑layer waveform types (e.g., OFDM, OTFS, LoRa) is not only extremely scarce but also heavily reliant on deep neural networks and complex time‑frequency transforms, making deployment on resource‑constrained terminals difficult. Symbol modulation classification methods themselves cannot circumvent the prerequisite of ``waveform identification first.'' To address this dual gap, we propose an ultra‑lightweight waveform classification framework based on time‑frequency multidimensional features with a cooperative Z‑test tree (ZTree). The framework employs low‑complexity time‑domain feature extraction, and the classification backend adopts a ZTree optimized by Z‑statistical testing, which uses hypothesis testing confidence to automatically control decision tree splitting and size, ensuring efficient execution on resource‑limited processors. Tested on ten 6G candidate waveforms including OFDM, OTFS, DSSS, LoRa, and NB‑IoT, the method achieves 99.5% average accuracy under AWGN and 87.4% under TDL‑C multipath channels, with main confusion between OTFS and LoRa. Implemented in C on an x86 platform, single inference latency is under 4~ms. To the best of our knowledge, this is the first work achieving real‑time recognition of ten IoT waveform types. Future work will target deployment acceleration on embedded MCUs. Code and dataset are open‑sourced at: https://github.com/Einstein‑sworder/IoT‑wave.

PaperId: 964, https://arxiv.org/pdf/2605.15603.pdf GitHub

Abstract:
Model‑based reinforcement learning (RL) offers a compelling approach to offline RL by enabling value learning on imagined on‑policy trajectories. However, it often suffers from compounding errors due to repeated model inference on self‑generated states. While geometric horizon models (GHM) alleviate this issue through direct prediction over a discounted infinite‑horizon future, they remain challenged in accurately modeling distant future states. To this end, we introduce universal horizon models (UHM), a generalization of GHM that directly predicts future states under arbitrary horizons. Leveraging this flexibility, we propose a scalable value learning method that employs a winsorized horizon distribution to stabilize training by capping excessively large horizons. Experimental results on 100 challenging OGBench tasks demonstrate that the proposed method outperforms competitive baselines, particularly on tasks with highly suboptimal datasets and those requiring long‑horizon reasoning. Project page: https://rllab‑snu.github.io/projects/UHM/

PaperId: 965, https://arxiv.org/pdf/2605.15536.pdf GitHub

Abstract:
Previous imitation learning policies predict future actions at every control step, whether in smooth motion phases or precise, contact‑rich operation phases. This uniform treatment is wasteful: most steps in a manipulation trajectory traverse free space and carry little task‑relevant information, while a small fraction of \emphkey steps around contacts, grasps, and alignment demand dense, high‑resolution prediction. We propose a novel \emphaction relabeling mechanism: at each timestep in a skip segment, we replace the behavior cloning target with the action at the entrance of the next key segment, enabling the policy to leap over redundant steps in a single decision. The resulting Skip Policy (SkiP) dynamically leaps over skip segments and intensively refines actions in key segments, within a single unified network requiring no learned skip planner or hierarchical structure. To automatically partition demonstrations into key and skip segments without manual annotation, we introduce \emphMotion Spectrum Keying (MSK), a fast, task‑agnostic procedure that detects local motion complexity from action signals. Extensive experiments across 72 simulated manipulation tasks and three real‑robot tasks show that SkiP reduces executed steps by 15‑‑40% while matching or improving success rates across various policy backbones. Project page: \texttthttps://pgq18.github.io/SkiP‑page/.

PaperId: 966, https://arxiv.org/pdf/2605.15464.pdf GitHub GitHub

Abstract:
Post‑training has become a crucial step for unlocking the capabilities of large language models, with reinforcement learning (RL) emerging as a critical paradigm. Recent RL‑based post‑training has increasingly split into two paradigms: reinforcement learning from human feedback (RLHF), which optimizes models using human preference signals in target domains, and reinforcement learning from verifiable rewards (RLVR), which operates in verifier‑backed environments. The latter has dominated recent reasoning‑oriented post‑training because it delivers stronger gains and higher efficiency on domain‑specific tasks (e.g., reasoning). However, although in‑domain RL training achieves promising performance, it still requires a substantial amount of GPU compute, which remains a major barrier to broad adoption. In this work, we study the generalization ability of RLHF learned from scratch from a small set of interactions in open‑ended environments, and investigate whether the conversational abilities it explicitly acquires can implicitly transfer to downstream tasks such as mathematical reasoning and code generation, namely GRLO. Specifically, on Qwen3‑4B‑Base backbone, GRLO improves the average performance across all domains from 24.1 to 63.1 with only 5K prompts and 22.7 GPU hours, requiring about 46× less data and 68× less compute than a strong in‑domain RLVR baseline. The resulting model is even competitive with Qwen's released post‑trained models which required a much larger training cost. Notably, a subsequent in‑domain RLVR stage brings only selective gains, mainly on harder competition‑math benchmarks. We hope GRLO offers a simple and efficient recipe for building broadly capable post‑trained models. Our code and data will be available at: \hrefhttps://github.com/SJY8460/GRLOhttps://github.com/SJY8460/GRLO.

PaperId: 967, https://arxiv.org/pdf/2605.15423.pdf GitHub

Abstract:
Modern smart vision sensors need on‑device intelligence to process video streams, as cloud computing is often impractical due to bandwidth, latency, and privacy constraints. However, these sensory systems typically rely on ultra‑low‑power microcontrollers (MCUs) with limited memory and compute, making conventional video object detection methods, which require feature storage or multi‑frame buffering, unfeasible. To address this challenge, we introduce Multi‑Resolution Rescored ByteTrack (MR2‑ByteTrack), a Video Object Detection (VOD) method tailored for MCU‑based embedded vision nodes. MR2‑ByteTrack reduces computational cost by alternating between full‑ and low‑resolution inference, while linking detections across frames via ByteTrack and correcting misclassifications through the Rescore algorithm, which applies probability union rules to aggregate detection confidence scores across frames. We apply our approach to both a CNN‑based detector and a Transformer‑based model, demonstrating its generality across architectures with fundamentally different spatial processing. Experiments on ImageNetVID demonstrate that MR2‑ByteTrack maintains accuracy, achieving mAP scores of up to 49.0 for the CNN‑based models and 48.7 for the Transformer, while reducing multiply‑accumulate operations by as much as 53% for the CNNs and 32% for the Transformer. When deployed on GAP9, an ultra‑low‑power RISC‑V multicore MCU, our method yields up to 55% energy savings compared to processing only full‑resolution images, enabling the first real‑time Transformer‑based VOD on an MCU‑class embedded vision node. Code available at https://github.com/Bomps4/Multi_Resolution_Rescored_ByteTrack/tree/IEEE_Access

PaperId: 968, https://arxiv.org/pdf/2605.15391.pdf GitHub

Abstract:
We present PanoWorld, a panoramic video world model that generates geometry‑consistent 360\degree video from a single image and a caption. Existing panoramic video methods optimize primarily for visual realism and do not explicitly constrain the underlying 3D scene state, producing outputs that appear plausible yet exhibit inconsistent depth, broken correspondences, and implausible motion across the spherical surface. We address this gap by framing panoramic video generation as a geometry‑ and dynamics‑consistent latent state modeling problem rather than pure visual synthesis. Building on a pre‑trained perspective video world model, we introduce two lightweight regularizers: a depth consistency loss against pseudo ground‑truth panoramic depth, and a trajectory consistency loss that supervises the 3D world‑frame positions of tracked points across time. We further apply spherical‑geometry‑aware adaptation to the conditioning and positional encoding. We additionally introduce PanoGeo, a unified geometry‑aware panoramic video dataset with consistent depth, trajectory, and prompt annotations across diverse real and synthetic sources, used for both training and stratified evaluation. Experiments show that PanoWorld improves geometric consistency over prior panoramic generation methods while maintaining competitive visual realism, establishing that panoramic video generation must be treated as a geometric modeling problem to support the holistic spatial understanding requirements of embodied AI applications. Code is available at https://github.com/ostadabbas/PanoWorld.

PaperId: 969, https://arxiv.org/pdf/2605.15375.pdf GitHub

Abstract:
Remote sensing change detection (RSCD) aims to localise changes between two images of the same geographic region. In practice, change masks often follow region‑level annotation conventions rather than purely local appearance differences, making them context‑dependent and occasionally ambiguous. Most state‑of‑the‑art methods utilise per‑pixel discriminative classification, which produces a single prediction per input and fails to explicitly model the changed region as a coherent whole. A natural alternative is generative formulation, which can model a distribution of plausible masks, enabling sampling to capture ambiguity and encourage global consistency. However, existing generative RSCD approaches typically lag behind strong discriminative baselines due to the high computational cost of pixel‑space generation and the complexity of their conditioning mechanisms. To address the limitations of prior discriminative and generative methods, we propose ChangeFlow, a generative framework that reformulates change detection as the synthesis of a change mask in latent space via rectified flow. ChangeFlow is guided by a structured yet lightweight conditioning signal, and its stochastic design naturally supports sampling‑based prediction ensembling. Namely, aggregating multiple predicted change masks improves robustness, while sample agreement provides a practical confidence estimation that highlights ambiguous regions. Across four benchmarks, ChangeFlow achieves an average F1 of 80.4%, improving by 1.3 points on average over the previous best method, while maintaining inference speed comparable to recent strong baselines. Project page: https://blaz‑r.github.io/changeflow_cd

PaperId: 970, https://arxiv.org/pdf/2605.15308.pdf GitHub

Abstract:
LLM‑driven program evolution has emerged as a powerful tool for automated scientific discovery, yet existing frameworks offer no principled guide for designing their individual components and provide no guarantee that the search converges. We introduce SMCEvolve, which recasts program search as sampling from a reward‑tilted target distribution and approximates it with a Sequential Monte Carlo (SMC) sampler. From this view, three core mechanisms emerge as principled components: adaptive parent resampling, mixture of mutation with acceptance, and automatic convergence control. We further provide a finite‑sample complexity analysis that bounds the LLM‑call budget required to reach a target approximation error. Across math, algorithm efficiency, symbolic regression, and end‑to‑end ML research benchmarks, SMCEvolve surpasses state‑of‑the‑art evolving systems while using fewer LLM calls under self‑determined termination. The code is available at https://github.com/kongwanbianjinyu/SMCEvolve.

PaperId: 971, https://arxiv.org/pdf/2605.15295.pdf GitHub

Abstract:
Machine learning (ML) algorithms are increasingly deployed in high‑stakes decision‑making domains such as loan approvals, hiring, and recidivism predictions. While existing fairness metrics (e.g., statistical parity, equal opportunity) effectively quantify outcome‑oriented disparities, they offer limited insight into the procedure or explanation behind biased decisions. To address this gap, we propose Group‑level Explanation Stability Disparity (GESD), a procedural‑oriented fairness metric that measures disparities in the stability, robustness, and sensitivity of model explanations across different subgroups in a protected category. %GESD is explainer‑agnostic, model‑agnostic, and extends the scope of fairness analyses to the level of explainability. We further integrate GESD into a multi‑objective optimization framework that jointly optimizes for utility, outcome‑based fairness, and explanation‑based fairness called FEU (Fairness‑‑Explainability‑‑Utility). Empirical results on multiple benchmark datasets show that GESD effectively captures group‑wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state‑of‑the‑art methods. By bridging outcome‑based and explanation‑based fairness, GESD offers a comprehensive tool for diagnosing and mitigating bias in predictive modeling. Our code and datasets are available on GitHub \hyperlinkhttps://github.com/horlahsunbo/GESDhttps://github.com/horlahsunbo/GESD

PaperId: 972, https://arxiv.org/pdf/2605.15250.pdf GitHub

Abstract:
Multi‑head Latent Attention (MLA), the attention used in DeepSeek‑V2/V3, jointly compresses keys and values into a low‑rank latent and matches the H100 roofline almost perfectly. Its trained weights, however, expose only one decoding path ‑ an absorbed MQA form ‑ which ties efficient inference to H100‑class compute‑bandwidth ratios, forfeits tensor parallelism along the head axis, and yields no Multi‑Token Prediction (MTP) gain on commodity inference GPUs such as the export‑restricted H20. We propose Group‑Query Latent Attention (GQLA), a minimal modification of MLA whose trained weights expose two algebraically equivalent decoding paths over the same parameters: an MQA‑absorb path identical to MLA's, and a GQA path with a per‑group expanded cache. The runtime picks the path that matches the target hardware ‑ no retraining, no custom kernels ‑ so a single set of GQLA weights pins the rooflines of both H100 (MQA‑absorb, s_q=1) and H20 (GQA + MTP, s_q=2), while supporting up to 8‑way zero‑redundancy tensor parallelism on the GQA path. To avoid pretraining from scratch we extend TransMLA into TransGQLA, which converts a pretrained GQA checkpoint into a GQLA model; on LLaMA‑3‑8B it compresses the per‑token KV cache to 28.125% of the GQA baseline on the MQA‑absorb path while structurally preserving GQA‑level traffic on the per‑group path.

PaperId: 973, https://arxiv.org/pdf/2605.15224.pdf GitHub

Abstract:
Large language model‑based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed, the model may fail again on the same query, indicating that it has not internalized the critique's guidance into its underlying capability. Meanwhile, a frozen critic cannot improve its feedback quality over time, limiting the potential for iterative self‑improvement. To address this, we propose learning to internalize self‑critique with reinforcement learning(ICRL), a novel framework that jointly trains a solver and a critic from a shared backbone to convert critique‑induced success into unassisted solver ability. The critic is rewarded based on the solver's subsequent performance gain, incentivizing actionable feedback. To address the distribution shift between critique‑conditioned and critique‑free behavior, ICRL introduces a distribution‑calibration re‑weighting ratio that selectively transfers critique‑guided improvements compatible with the solver's own prompt distribution. Additionally, a role‑wise group advantage estimation stabilizes joint optimization across the two roles. Together, these mechanisms ensure that the solver learns to improve itself without external critique, rather than becoming dependent on critique‑conditioned behavior. We evaluate ICRL on diverse benchmarks spanning agentic and mathematical reasoning tasks, using Qwen3‑4B and Qwen3‑8B as backbones. Results show consistent improvements, with average gains of 6.4 points over GRPO on agentic tasks, and 7.0 points on mathematical reasoning. Notably, the learned 8B critic is comparable to 32B critics while using substantially fewer tokens. The code is available at https://github.com/brick‑pid/ICRL.

PaperId: 974, https://arxiv.org/pdf/2605.15215.pdf GitHub

Abstract:
Recently, skills have been widely adopted in large language model (LLM)‑based agent systems across various domains. In existing frameworks, skills are typically injected into the agent reasoning loop as contextual guidance once matched to a runtime task, enabling specialized task‑solving capabilities. We find that this execution paradigm introduces two major sources of redundancy: irrelevant context injection and repeated skill‑specific reasoning and planning. To this end, we propose SkillSmith, a boundary‑first compiler‑runtime framework that compiles skill packages offline into minimal executable interfaces. By extracting fine‑grained operational boundaries from skills, SkillSmith enables agents to dynamically access and execute only the relevant components at runtime, thereby minimizing unnecessary context injection and redundant reasoning overhead. In the evaluation on SkillsBench benchmark, SkillSmith reduces solve‑stage token usage by 57.44%, thinking iterations by 42.99%, solve time by 50.57% (2.02x faster), and token‑proportional monetary cost by 57.44% compared with using raw‑skills. Moreover, compiled artifacts produced by a stronger model can be reused by a smaller or more efficient runtime model, improving task accuracy in cases where raw skill interpretation fails. The source code and data are available at https://github.com/AetherHeart‑AI/Aeloon.

PaperId: 975, https://arxiv.org/pdf/2605.15206.pdf GitHub

Abstract:
Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi‑step tasks such as coding or web‑based question answering. While remote, cloud‑based agents offer scalability and ease of deployment, they raise privacy concerns, depend on network connectivity, and incur recurring API costs. Deploying agents locally on user devices mitigates these issues by preserving data privacy and eliminating usage‑based fees. However, agentic workflows are far more resource‑intensive than typical LLM interactions. Iterative reasoning, tool use, and failure retries substantially increase token consumption, often expending significant compute without successfully completing tasks. In this work, we investigate the time, token, and energy overhead of locally deployed LLM‑based agents on consumer hardware. Our measurements show that agentic execution increases GPU power draw, temperature, and battery drain compared to single‑inference workloads. To address this inefficiency, we introduce AgentStop, a lightweight efficiency supervisor that predicts and preemptively terminates trajectories unlikely to succeed. Leveraging low‑cost execution signals, such as token‑level log probabilities, AgentStop can reduce wasted energy by 15‑20% with minimal impact on task performance (<5% utility drop) for challenging web‑based question answering and coding benchmarks. These findings position predictive early termination as a practical mechanism for enabling sustainable, privacy‑preserving LLM agents on user devices. Our project code and data are available at https://github.com/brave‑experiments/AgentStop.

PaperId: 976, https://arxiv.org/pdf/2605.15199.pdf GitHub GitHub

Abstract:
Multi‑shot video generation extends single‑shot generation to coherent visual narratives, yet maintaining consistent characters, objects, and locations across shots remains a challenge over long sequences. Existing evaluations typically use independently generated prompt sets with limited entity coverage and simple consistency metrics, making standardized comparison difficult. We introduce EntityBench, a benchmark of 140 episodes (2,491 shots) derived from real narrative media, with explicit per‑shot entity schedules tracking characters, objects, and locations simultaneously across easy / medium / hard tiers of up to 50 shots, 13 cross‑shot characters, 8 cross‑shot locations, 22 cross‑shot objects, and recurrence gaps spanning up to 48 shots. It is paired with a three‑pillar evaluation suite that disentangles intra‑shot quality, prompt‑following alignment, and cross‑shot consistency, with a fidelity gate that admits only accurate entity appearances into cross‑shot scoring. As a baseline, we propose EntityMem, a memory‑augmented generation system that stores verified per‑entity visual references in a persistent memory bank before generation begins. Experiments show that cross‑shot entity consistency degrades sharply with recurrence distance in existing methods, and that explicit per‑entity memory yields the highest character fidelity (Cohen's d = +2.33) and presence among methods evaluated. Code and data are available at https://github.com/Catherine‑R‑He/EntityBench/.

PaperId: 977, https://arxiv.org/pdf/2605.15198.pdf GitHub

Abstract:
Visual reasoning, often interleaved with intermediate visual states, has emerged as a promising direction in the field. A straightforward approach is to directly generate images via unified models during reasoning, but this is computationally expensive and architecturally non‑trivial. Recent alternatives include agentic reasoning through code or tool calls, and latent reasoning with learnable hidden embeddings. However, agentic methods incur context‑switching latency from external execution, while latent methods lack task generalization and are difficult to train with autoregressive parallelization. To combine their strengths while mitigating their limitations, we propose ATLAS, a framework in which a single discrete 'word', termed as a functional token, serves both as an agentic operation and a latent visual reasoning unit. Each functional token is associated with an internalized visual operation, yet requires no visual supervision and remains a standard token in the tokenizer vocabulary, which can be generated via next‑token prediction. This design avoids verbose intermediate visual content generation, while preserving compatibility with the vanilla scalable SFT and RL training, without architectural or methodological modifications. To further address the sparsity of functional tokens during RL, we introduce Latent‑Anchored GRPO (LA‑GRPO), which stabilizes the training by anchoring functional tokens with a statically weighted auxiliary objective, providing stronger gradient updates. Extensive experiments and analyses demonstrate that ATLAS achieves superior performance on challenging benchmarks while maintaining clear interpretability. We hope ATLAS offers a new paradigm inspiring future visual reasoning research.

PaperId: 978, https://arxiv.org/pdf/2605.15186.pdf GitHub

Abstract:
High‑quality 3D scene reconstruction has recently advanced toward generalizable feed‑forward architectures, enabling the generation of complex environments in a single forward pass. However, despite their strong performance in static scene perception, these models remain limited in responding to dynamic human instructions, which restricts their use in interactive applications. Existing editing methods typically rely on a 2D‑lifting strategy, where individual views are edited independently and then lifted back into 3D space. This indirect pipeline often leads to blurry textures and inconsistent geometry, as 2D editors lack the spatial awareness required to preserve structure across viewpoints. To address these limitations, we propose VGGT‑Edit, a feed‑forward framework for text‑conditioned native 3D scene editing. VGGT‑Edit introduces depth‑synchronized text injection to align semantic guidance with the backbone's spatial poses, ensuring stable instruction grounding. This semantic signal is then processed by a residual transformation head, which directly predicts 3D geometric displacements to deform the scene while preserving background stability. To ensure high‑fidelity results, we supervise the framework with a multi‑term objective function that enforces geometric accuracy and cross‑view consistency. We also construct the DeltaScene Dataset, a large‑scale dataset generated through an automated pipeline with 3D agreement filtering to ensure ground‑truth quality. Experiments show that VGGT‑Edit substantially outperforms 2D‑lifting baselines, producing sharper object details, stronger multi‑view consistency, and near‑instant inference speed. The project page is https://chriszkxxx.github.io/VGGT‑Edit/.

PaperId: 979, https://arxiv.org/pdf/2605.15185.pdf GitHub

Abstract:
Generative video models are increasingly studied as implicit world models, yet evaluating whether they produce physically plausible 3D structure and motion remains challenging. Most existing video evaluation pipelines rely heavily on human judgment or learned graders, which can be subjective and weakly diagnostic for geometric failures. We introduce PDI‑Bench (Perspective Distortion Index), a quantitative framework for auditing geometric coherence in generated videos. Given a generated clip, we obtain object‑centric observations via segmentation and point tracking (e.g., SAM 2, MegaSaM, and CoTracker3), lift them to 3D world‑space coordinates via monocular reconstruction, and compute a set of projective‑geometry residuals capturing three failure dimensions: scale‑depth alignment, 3D motion consistency, and 3D structural rigidity. To support systematic evaluation, we build PDI‑Dataset, covering diverse scenarios designed to stress these geometric constraints. Across state‑of‑the‑art video generators, PDI reveals consistent geometry‑specific failure modes that are not captured by common perceptual metrics, and provides a diagnostic signal for progress toward physically grounded video generation and physical world model. Our code and dataset can be found at https://pdi‑bench.github.io/.

PaperId: 980, https://arxiv.org/pdf/2605.15171.pdf GitHub

Abstract:
Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical images suffer from limited interpretability and suboptimal performance. They often lack effective mechanisms to reference historical cases or provide transparent reasoning pathways. To address these challenges, we introduce EviScreen, an evidential reasoning framework for disease screening that leverages region‑level evidence from historical cases. The proposed EviScreen offers retrospection interpretability through regional evidence retrieved from dual knowledge banks. Using this evidential mechanism, the subsequent evidence‑aware reasoning module makes predictions using both the current case and evidence from historical cases, thereby enhancing disease screening performance. Furthermore, rather than relying on post‑hoc saliency maps, EviScreen enhances localization interpretability by leveraging abnormality maps derived from contrastive retrieval. Our method achieves superior performance on our carefully established benchmarks for real‑world disease screening, yielding notably higher specificity at clinical‑level recall. Code is publicly available at https://github.com/DopamineLcy/EviScreen.

PaperId: 981, https://arxiv.org/pdf/2605.15120.pdf GitHub

Abstract:
End‑to‑end autonomous driving planners are commonly trained by imitating a single logged trajectory, yet evaluated by rule‑based planning metrics that measure safety, feasibility, progress, and comfort. This creates a training‑‑evaluation mismatch: trajectories close to the logged path may violate planning rules, while alternatives farther from the demonstration can remain valid and high‑scoring. The mismatch is especially limiting for proposal‑selection planners, whose performance depends on candidate‑set coverage and scorer ranking quality. We propose CLOVER, a Closed‑LOop Value Estimation and Ranking framework for end‑to‑end autonomous driving planning. CLOVER follows a lightweight generator‑‑scorer formulation: a generator produces diverse candidate trajectories, and a scorer predicts planning‑metric sub‑scores to rank them at inference time. To expand proposal support beyond single‑trajectory imitation, CLOVER constructs evaluator‑filtered pseudo‑expert trajectories and trains the generator with set‑level coverage supervision. It then performs conservative closed‑loop self‑distillation: the scorer is fitted to true evaluator sub‑scores on generated proposals, while the generator is refined toward teacher‑selected top‑k and vector‑Pareto targets with stability regularization. We analyze when an imperfect scorer can improve the generator, showing that scorer‑mediated refinement is reliable when scorer‑selected targets are enriched under the true evaluator and updates remain conservative. On NAVSIM, CLOVER achieves 94.5 PDMS and 90.4 EPDMS, establishing a new state of the art. On the more challenging NavHard split, it obtains 48.3 EPDMS, matching the strongest reported result. On supplementary nuScenes open‑loop evaluation, CLOVER achieves the lowest L2 error and collision rate among compared methods. Code data will be released at https://github.com/WilliamXuanYu/CLOVER.

PaperId: 982, https://arxiv.org/pdf/2605.15071.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) are increasingly applied to cultural heritage materials, from digital archives to educational platforms. This work identifies a fundamental issue in how these models interpret historical artifacts. We define this phenomenon as cultural anachronism, the tendency to misinterpret historical objects using temporally inappropriate concepts, materials, or cultural frameworks. To quantify this phenomenon, we introduce the Temporal Anachronism Benchmark for Vision‑Language Models (TAB‑VLM), a dataset of 600 questions across six categories, designed to evaluate temporal reasoning on 1,600 Indian cultural artifacts spanning prehistoric to modern periods. Systematic evaluations of ten state‑of‑the‑art models reveal significant deficiencies on our benchmark, and even the best model (GPT‑5.2) achieves only 58.7% overall accuracy. The performance gap persists across varying architectures and scales, suggesting that cultural anachronism represents a significant limitation in visual AI systems, regardless of model size. These findings highlight the disparity between current VLM capabilities and the requirements for accurately interpreting cultural heritage materials, particularly for non‑Western visual cultures underrepresented in training data. Our benchmark provides a foundation for enhancing temporal cognition in multimodal AI systems that interact with historical artifacts. The dataset and code are available in our project page.

PaperId: 983, https://arxiv.org/pdf/2605.15042.pdf GitHub

Abstract:
We propose EverAnimate, an efficient post‑training method for long‑horizon animated video generation that preserves visual quality and character identity. Long‑form animation remains challenging because highly dynamic human motion must be synthesized against relatively static environments, making chunk‑based generation prone to accumulated drift: (i) low‑level quality drift, such as progressive degradation of static backgrounds, and (ii) high‑level semantic drift, such as inconsistent character identity and view‑dependent attributes. To address this issue, EverAnimate restores drifted flow trajectories by anchoring generation to a persistent latent context memory, consisting of two complementary mechanisms. (i) Persistent Latent Propagation maintains a context memory across chunks to propagate identity and motion in latent space while mitigating temporal forgetting. (ii) Restorative Flow Matching introduces an implicit restoration objective during sampling through velocity adjustment, improving within‑chunk fidelity. With only lightweight LoRA tuning, EverAnimate outperforms state‑of‑the‑art long‑animation methods in both short‑ and long‑horizon settings: at 10 seconds, it improves PSNR/SSIM by 8%/7% and reduces LPIPS/FID by 22%/11%; at 90 seconds, the gains increase to 15%/15% and 32%/27%, respectively.

PaperId: 984, https://arxiv.org/pdf/2605.15030.pdf GitHub

Abstract:
Web agents can autonomously complete online tasks by interacting with websites, but their exposure to open web environments makes them vulnerable to prompt injection attacks embedded in HTML content or visual interfaces. Existing guard models still suffer from limited generalization to unseen domains and attack patterns, high false positive rates on benign content, reduced deployment efficiency due to added latency at each step, and vulnerability to adversarial attacks that evolve over time or directly target the guard itself. To address these limitations, we propose WARD (Web Agent Robust Defense against Prompt Injection), a practical guard model for secure and efficient web agents. WARD is built on WARD‑Base, a large‑scale dataset with around 177K samples collected from 719 high‑traffic URLs and platforms, and WARD‑PIG, a dedicated dataset designed for prompt injection attacks targeting the guard model. We further introduce A3T, an adaptive adversarial attack training framework that iteratively strengthens WARD through a memory‑based attacker and guard co‑evolution process. Extensive experiments show that WARD achieves nearly perfect recall on out‑of‑distribution benchmarks, maintains low false positive rates to preserve agent utility, remains robust against guard‑targeted and adaptive attacks under substantial distribution shifts, and runs efficiently in parallel with the agent without introducing additional latency.

PaperId: 985, https://arxiv.org/pdf/2605.15016.pdf GitHub

Abstract:
As large language models empower healthcare, intelligent clinical decision support has developed rapidly. Longitudinal electronic health records (EHR) provide essential temporal evidence for accurate clinical diagnosis and analysis. However, current large language models have critical flaws in longitudinal EHR reasoning. First, lacking fine‑grained statistical reasoning, they often hallucinate clinical trends and metrics when quantitative evidence is textually implied, biasing diagnostic inference. Second, non‑uniform time series and scarce labels in longitudinal EHR hinder models from capturing long‑range temporal dependencies, limiting reliable clinical reasoning. To address the above limitations, this work presents the Probabilistic Chain‑of‑Thought Completion Agent (COTCAgent), a hierarchical reasoning framework for longitudinal electronic health records. It consists of three core modules. The Temporal‑Statistics Adapter (TSA) converts analytical plans into executable code for standardized trend output. The Chain‑of‑Thought Completion (COTC) layer leverages a symptom‑trend‑disease knowledge base with weighted scoring to evaluate disease risk, while the bounded completion module acquires structured evidence through standardized inquiries and iterative scoring constraints to ensure rigorous reasoning. By decoupling statistical computation, feature matching, and language generation, the framework eliminates reliance on complex multi‑modal inputs and enables efficient longitudinal record analysis with lower computational overhead. Experimental results show that COTCAgent powered by Baichuan‑M2 achieves 90.47% Top‑1 accuracy on the self‑built dataset and 70.41% on HealthBench, outperforming existing medical agents and mainstream large language models. The code is available at https://github.com/FrankDengAI/COTCAgent/.

PaperId: 986, https://arxiv.org/pdf/2605.14984.pdf GitHub GitHub GitHub

Abstract:
Generating a street‑level 3D scene from a single satellite image is a crucial yet challenging task. Current methods present a stark trade‑off: geometry‑colorization models achieve high geometric fidelity but are typically building‑focused and lack semantic diversity. In contrast, proxy‑based models use feed‑forward image‑to‑3D frameworks to generate holistic scenes by jointly learning geometry and texture, a process that yields rich content but coarse and unstable geometry. We attribute these geometric failures to the extreme viewpoint gap and sparse, inconsistent supervision inherent in satellite‑to‑street data. We introduce Sat3DGen to address these fundamental challenges, which embodies a geometry‑first methodology. This methodology enhances the feed‑forward paradigm by integrating novel geometric constraints with a perspective‑view training strategy, explicitly countering the primary sources of geometric error. This geometry‑centric strategy yields a dramatic leap in both 3D accuracy and photorealism. For validation, we first constructed a new benchmark by pairing the VIGOR‑OOD test set with high‑resolution DSM data. On this benchmark, our method improves geometric RMSE from 6.76m to 5.20m. Crucially, this geometric leap also boosts photorealism, reducing the Fréchet Inception Distance (FID) from ～40 to 19 against the leading method, Sat2Density++, despite using no extra tailored image‑quality modules. We demonstrate the versatility of our high‑quality 3D assets through diverse downstream applications, including semantic‑map‑to‑3D synthesis, multi‑camera video generation, large‑scale meshing, and unsupervised single‑image Digital Surface Model (DSM) estimation. The code has been released on https://github.com/qianmingduowan/Sat3DGen.

PaperId: 987, https://arxiv.org/pdf/2605.14907.pdf GitHub

Abstract:
Knowledge graph (KG) foundation models aim to generalize across graphs with unseen entities and relations by learning transferable relational structure. However, most existing methods primarily emphasize relation‑level universality, while in‑context learning, the other pillar of foundation models remains under‑explored for KG reasoning. In KGs, context is inherently structured and heterogeneous: effective prediction requires conditioning on the local context around the query entities as well as the global context that summarizes how a relation behaves across many instances. We propose KGPFN, a KG foundation model using Prior‑data Fitted Network that unifies transferable relational regularities with inference‑time in‑context learning from structured context. KGPFN first learns relation representations via message passing on relation graphs to capture cross‑graph relational invariances. For query‑specific reasoning, it encodes local neighborhoods using a multi‑layer NBFNet as local context. To enable ICL at global scale, it constructs relation‑specific global context by retrieving a large set of instances of the query relation together with their local neighborhoods, and aggregates them within a Prior‑Data Fitted Network framework that combines feature‑level and sample‑level attention. Through multi‑graph pretraining on diverse KGs, KGPFN learns when to instantiate reusable patterns and when to override them using contextual evidence. Experiments on 57 KG benchmarks demonstrate that KGPFN achieves strong adaptation to previously unseen graphs through in‑context learning alone, consistently outperforming competitive fine‑tuned KG foundation models. Our code is available at https://github.com/HKUST‑KnowComp/KGPFN.

PaperId: 988, https://arxiv.org/pdf/2605.14889.pdf GitHub GitHub

Abstract:
Online surgical phase recognition (SPR) underpins context‑aware operating‑room systems and requires committing to a prediction at every frame from past context alone. Surgical video poses three demands that natural‑video recognizers do not jointly address: procedures span tens of thousands of frames, time flows non‑uniformly as long routine stretches are punctuated by brief phase‑defining transitions, and the visual domain is narrow so backbone features are strongly correlated across channels. Existing recognizers either let per‑frame cost grow with elapsed length, or hold cost bounded but advance state at a uniform rate with channel‑independent dynamics, leaving the latter two demands unaddressed. We present SurgicalMamba, a causal SPR model built on Mamba2's structured state‑space duality (SSD) that holds per‑frame cost at O(d). It introduces three SSD‑compatible components that jointly address these demands: a dual‑path SSD block that separates long‑ and short‑term regimes at the level of recurrent state; intensity‑modulated stepping, a continuous‑time time‑warp that adapts the slow path's effective rate to phase‑relevant information; and state regramming, a per‑chunk Cayley rotation that opens cross‑channel mixing in the otherwise axis‑aligned SSM recurrence. The learned rotation planes inherit a phase‑aligned structure without any direct supervision, offering an interpretable internal signature of surgical workflow. Across seven public SPR benchmarks, SurgicalMamba reaches state‑of‑the‑art accuracy and phase‑level Jaccard under strict online evaluation: 94.6%/82.7% on Cholec80 (+0.7 pp/+2.2 pp over the strongest prior) and 89.5%/68.9% on AutoLaparo (+1.7 pp/+2.0 pp), at 238.74 fps on a single GPU. Ablations isolate the contribution of each component. The code is publicly available at https://github.com/sukjuoh/Surgical‑Mamba.

PaperId: 989, https://arxiv.org/pdf/2605.14851.pdf GitHub

Abstract:
Operational plan generation and verification are critical for modern complex and rapidly changing battlefield environments, yet traditional generation and verification methods still respectively face the challenges of generation infeasibility and verification insufficiency. To alleviate these limitations, we propose an Integrated Multi‑Agent Framework for Generative Operational Planning and High‑Fidelity Plan Verification (IFPV). IFPV consists of two tightly coupled modules: Multi‑Perspective Hierarchical Agents (MPHA) for generative operational planning and an Adversarial Cognitive Simulation Engine (ACSE) for high‑fidelity adversarial plan verification. MPHA decomposes commander intent into executable multi‑platform tactical action sequences through the collaboration of Pathfinder, Analyst, and Planner agents. ACSE introduces an opponent equipped with a customized world model, which predicts the future evolution of mission‑critical platforms and conducts dynamic counteractions against candidate plans. Simulation experiments in the Asymmetric Combat Tactic Simulator (ACTS) show that IFPV improves mission success by 19.4% and reduces operational cost by 41.7% compared with a single‑step large language model (LLM) planning baseline. Compared with a traditional rule‑based validator, ACSE increases the average suppression rate by 31.8%, indicating that the proposed verification environment is stricter and more discriminative in revealing the latent vulnerabilities of candidate plans. The code for IFPV can be found at https://github.com/zhigao3ks/IFPV.

PaperId: 990, https://arxiv.org/pdf/2605.14844.pdf GitHub

Abstract:
We introduce XFP, a dynamic weight quantizer for LLM inference that inverts the conventional workflow: the operator specifies reconstruction quality floors on per‑channel cosine similarity (one strict floor for attention and shared experts, one lazy floor for routed‑expert MoE); XFP determines codebook size, outlier budget, and packing per layer automatically ‑‑ no Hessian, no calibration data, no manual bit‑width selection. Each weight matrix is decomposed into a sparse fp16 outlier residual and a dense sub‑byte index tensor into a per‑group learned codebook. Two storage modes share one auto‑select frontend and one fused decode kernel: V2 (per‑channel Lloyd) and V2a (shared library of L=32 codebooks per layer). On Qwen3.5‑122B‑A10B under V2, XFP reaches 138 tok/s single‑stream decode on workstation hardware (RTX PRO 6000 Blackwell, TP=2) at 94.49% GSM8K strict‑match (3 seeds, n=3957), and is 49% faster than Marlin INT4 at TP=1. For models that do not fit in the target memory envelope, we present the H‑Process: a quality‑driven iteration over the two cosine thresholds that finds the operating point at which the model just fits while still producing sensible output. Three constraints define its search space: the operator‑set thresholds, an OOM boundary at quantize‑on‑load, and a garbage boundary in generation (cosine similarity steers; benches verify). On Qwen3.5‑397B‑A17B (512 routed experts/layer), the H‑Process fits the full expert population into 2x96 GB at ~3.4 effective bits and delivers 100.9 tok/s long‑output decode at 66.72% GSM8K strict‑match on the full 1319‑problem set (single seed at submission; multi‑seed evaluation in progress), exceeding INT4 with routed‑expert pruning on memory, throughput, and accuracy simultaneously.

PaperId: 991, https://arxiv.org/pdf/2605.14802.pdf GitHub

Abstract:
Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long‑range interaction, especially under high‑noise knowledge bases, context clearing, and cross‑model transfer. To address these issues, we introduce ARPM, an external temporal memory governance framework for long‑term dialogue. ARPM separates static knowledge memory from dynamic dialogue experience memory and combines vector retrieval, BM25, RRF fusion, dual‑temporal reranking, chronological evidence reading, and a controlled analysis protocol for evidence verification and answer binding. Unlike approaches that encode persona consistency into model weights or rely only on long context, ARPM treats continuity as a traceable, auditable, and transferable governance problem. Using engineering logs, we conduct three experiments. First, in a 50‑round question‑answering setting, we compare signal‑to‑noise ratios of 1:5 and 1:200+, and distinguish CSV auto‑judgment from manual review. Under 1:5, CSV recall accuracy is 54.0%, while manual review raises it to 100.0%. Under 1:200+, the values are 44.0% and 80.0%. These results show that automatic rules can underestimate recall after supporting evidence enters the prompt. Second, ablation results show that dialogue history retrieval is necessary for recent continuity: disabling it reduces strict accuracy from 100% to 66.7%, and disabling BM25 reduces it to 80.0%, indicating that pure semantic retrieval is insufficient for correction and tracing. Third, under a 5.1‑million‑character noise substrate, periodic context clearing, and multi‑model handoff, ARPM maintains semantic continuity, boundary continuity, and persona consistency, while exposing limits caused by weak protocol compliance. These findings show that long‑term persona consistency can be decomposed into governable components and evaluated in a white‑box manner.

PaperId: 992, https://arxiv.org/pdf/2605.14786.pdf GitHub GitHub

Abstract:
As LLM‑based agents increasingly browse the web on users' behalf, a natural question arises: can websites passively identify which underlying model powers an agent? Doing so would represent a significant security risk, enabling targeted attacks tailored to known model vulnerabilities. Across 14 frontier LLMs and four web environments spanning information retrieval and shopping tasks, we show that an agent's actions and interaction timings, captured via a passive JavaScript tracker, are sufficient to identify the underlying model with up to 96% F1. We formalise this attack surface by demonstrating that classifiers trained on agent actions generalise across model sizes and families. We further show that strong classifiers can be trained from few interaction traces and that agent identity can be inferred early within an episode. Injecting randomised timing delays between actions substantially degrades classifier performance, but does not provide robust protection: a classifier retrained on delayed traces largely recovers performance. We release our harness and a labelled corpus of agent traces \hrefhttps://github.com/KabakaWilliam/known_actionshere.

PaperId: 993, https://arxiv.org/pdf/2605.14752.pdf GitHub

Abstract:
Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long‑tail distribution, where authentic student reasoning is difficult to synthesize; (2) fuzzy boundaries between error categories with high annotation noise; (3) deployment parado‑large models overlook unconventional approaches due to pretraining bias and cannot be deployed on edge, while small models overfit to noise. Unlike traditional methods that increase diversity through large‑scale data synthesis, we propose a two‑stage knowledge distillation framework that mines high‑value samples from existing data. The first stage performs standard distillation to transfer task capabilities. The second stage introduces a dual‑layer marginal selection mechanism based on cognitive uncertainty, identifying four types of critical samples based on teacher model uncertainty and confidence differences. For different data subsets, we design difficulty‑adaptive mechanism to balance hard/soft label contributions, enabling student models to inherit inter‑class relationships from teacher soft labels while distinguishing ambiguous error types. Experiments show that with augmented training on only 10.30% of filtered samples, we achieve MAP@3 of 0.9585 (+17.8%) on the MAP‑Charting dataset, and using only a 4B parameter model, we attain 84.38% accuracy on cross‑topic tests of middle school algebra misconception benchmarks, significantly outperforming sota LLM (67.73%) and standard fine‑tuned 72B models (81.25%). Our code is available at https://github.com/RoschildRui/acl2026_map.

PaperId: 994, https://arxiv.org/pdf/2605.14717.pdf GitHub

Abstract:
Label‑free single‑cell imaging offers a scalable, non‑invasive alternative to fluorescence‑based cytometry, yet inferring molecular phenotypes directly from bright‑field morphology remains challenging. We present a unified Deep Learning (DL) framework that jointly performs White Blood Cell (WBC) classification and continuous protein‑expression regression from label‑free Differential Phase Contrast (DPC) images. Our model employs a Hybrid architecture that fuses convolutional fine‑grained texture features with transformer‑based global representations through a learnable cross‑branch gating module, enabling robust morpho‑molecular inference from DPC images. To support downstream interpretability, we further incorporate a Large Language Model (LLM) that generates concise, biologically grounded summaries of the predicted cell states. Experiments on the Berkeley Single Cell Computational Microscopy (BSCCM) and Blood Cells Image benchmarks demonstrate strong performance, achieving a 91.3% WBC classification accuracy and a 0.72 Pearson correlation for CD16 expression regression on BSCCM. These results underscore the promise of label‑free single‑cell imaging for cost‑effective hematological profiling, enabling simultaneous phenotype identification and quantitative biomarker estimation without fluorescent staining. The source code is available at https://github.com/saqibnaziir/Single‑Cell‑Phenotyping.

PaperId: 995, https://arxiv.org/pdf/2605.14712.pdf GitHub

Abstract:
Robot imitation data are often multimodal: similar visual‑language observations may be followed by different action chunks because human demonstrators act with different short‑horizon intents, task phases, or recent context. Existing frame‑conditioned VLA policies infer each chunk from the current observation and instruction alone, so under partial observability they may resample different intents across adjacent replanning steps, leading to inter‑chunk conflict and unstable execution. We introduce IntentVLA, a history‑conditioned VLA framework that encodes recent visual observations into a compact short‑horizon intent representation and uses it to condition chunk generation. We further introduce AliasBench, a 12‑task ambiguity‑aware benchmark on RoboTwin2 with matched training data and evaluation environments that isolate short‑horizon observation aliasing. Across AliasBench, SimplerEnv, LIBERO, and RoboCasa, IntentVLA improves rollout stability and outperforms strong VLA baselines

PaperId: 996, https://arxiv.org/pdf/2605.14685.pdf GitHub

Abstract:
In physical systems, whenever a continuous symmetry is spontaneously broken, the system possesses excitations called Goldstone modes, which allow coherent information propagation over long distances and times. In this work, we study deep neural networks whose internal layers are equivariant under a continuous symmetry and may therefore support analogous Goldstone‑like degrees of freedom. We demonstrate, both analytically and empirically, that these degrees of freedom enable coherent signal propagation across depth and recurrent iterations, providing a mechanism for stable information flow without relying on architectural stabilizers such as residual connections or normalization. In feedforward networks, this results in improved trainability and representational diversity across layers. In recurrent settings, we demonstrate the same mechanism is valuable for long‑term memory by propagating information over recurrent iterations, thereby improving performance of RNNs and GRUs on long‑sequence modeling tasks.

PaperId: 997, https://arxiv.org/pdf/2605.14645.pdf GitHub

Abstract:
With the rapid evolution of computer vision, vision‑based methodologies for water level and river surface velocity estimation have reached significant maturity. Compared to traditional sensing, these techniques offer superior interpretability, automated data archiving, and enhanced system robustness. However, challenges such as environmental sensitivity, limited precision, and complex site calibration persist. This work proposes an integrated framework that synergizes state‑of‑the‑art (SOTA) vision models with statistical modeling. By leveraging physical priors and robust filtering strategies, we improve the accuracy of water level detection and flow estimation. Code will be available at https://github.com/sunzx97/Vision_Based_Water_Level_and_Flow_Estimation.git

PaperId: 998, https://arxiv.org/pdf/2605.14624.pdf GitHub

Abstract:
A common critique of neural combinatorial‑optimization solvers is that they are less energy‑efficient than CPU metaheuristics, given the operational energy cost of training them on GPUs. This paper examines the inferential step from "training is expensive" to "neural solvers are net‑inefficient", which is where the critique actually goes wrong. Training the network costs a large fixed amount of GPU energy; running the metaheuristic costs a small amount of CPU energy on every instance, repeated as long as the solver is deployed. The two are not commensurable until a deployment volume is fixed. We define the Amortized Efficiency Threshold (AET) as the deployment volume above which a neural solver breaks even with a heuristic baseline in total energy or carbon, under an explicit constraint on solution quality. We show that the cumulative‑energy ratio between the two solvers tends to a constant strictly below one whenever the network wins per instance, and that this limit does not depend on how the training cost was measured. An embodied‑carbon term amortizes hardware fabrication symmetrically on both sides. We instantiate the framework on the CVRP environment at n=50 customers with the attention‑based autoregressive solver of Kool et al. (2019), trained for 100 epochs on 20,000 instances over five random seeds, and HGS via PyVRP as the heuristic baseline. The measured operational crossover sits near 4.56e3 deployed instances at the median of a six‑point baseline‑budget sweep; the per‑instance neural‑to‑heuristic ratio is 2.29e‑3. The contribution is the framework, the open instrumentation, and the end‑to‑end measurement protocol. Code and benchmark pipeline are available at https://github.com/sohaibafifi/aet.

PaperId: 999, https://arxiv.org/pdf/2605.14559.pdf GitHub

Abstract:
PyCSP^3 provides a productive way to build constraint models for solving combinatorial constrained problems and export them to XCSP^3, preserving a complete separation between modeling and solving. However, it lacks native support for scheduling abstractions such as interval variables, sequence variables, and resource functions. As a result, scheduling models must be encoded with low‑level integer variables and manual channeling constraints, even though PyCSP^3 already provides global constraints like NoOverlap and Cumulative on integer arrays. We present PyCSP^3 Scheduling, a library that adds scheduling abstractions to PyCSP^3 through 53 dedicated constraints and 27 expressions, and compiles them down to standard PyCSP^3/XCSP^3 constraints, maintaining the modeling/solving separation that underpins the PyCSP^3 ecosystem. On 261 paired instances across 17 model families (5 runs each), both formulations produce identical objectives on all 72 doubly‑proved optimal pairs and nearly half of the families (8/17) remain structurally unchanged after compilation; however, runtime performance diverges across families, with clear gains on some (up to 5.8x) and regressions on others due to the overhead of compilation decompositions. Code and benchmarks are available at: https://github.com/sohaibafifi/pycsp3‑scheduling

PaperId: 1000, https://arxiv.org/pdf/2605.14555.pdf GitHub

Abstract:
Current methods for creating drum loop audio in digital music production, such as using one‑shot samples or resampling, often demand non‑trivial efforts of creators. While recent generative models achieve high fidelity and adhere to text, they lack the specific control needed for such a task. Existing symbolic‑to‑audio research often focuses on single, tonal instruments, leaving the challenge of polyphonic, percussive drum synthesis unaddressed. We address this gap by introducing ``Break‑the‑Beat!,'' a model capable of rendering a drum MIDI with the timbre of a reference audio. It is built by fine‑tuning a pre‑trained text‑to‑audio model with our proposed content encoder and a effective hybrid conditioning mechanism. To enable this, we construct a new dataset of paired target‑reference drum audio from existing drum audio datasets. Experiments demonstrate that our model generates high‑quality drum audio that follows high‑resolution drum MIDI, achieving strong performance across metrics of audio quality, rhythmic alignment, and beat continuity. This offer producers a new, controllable tool for creative production. Demo page: https://ik4sumii.github.io/break‑the‑beat/

PaperId: 1001, https://arxiv.org/pdf/2605.14534.pdf GitHub GitHub

Abstract:
Evaluating object removal in images and videos remains challenging because the task is inherently one‑to‑many, yet existing metrics frequently disagree with human perception. Full‑reference metrics reward copy‑paste behaviors over genuine erasure; no‑reference metrics suffer from systematic biases such as favoring blurry results; and global temporal metrics are insensitive to localized artifacts within edited regions. To address these limitations, we propose RC (Removal Coherence), a pair of perception‑aligned metrics: RC‑S, which measures spatial coherence via sliding‑window feature comparison between masked and background regions, and RC‑T, which measures temporal consistency via distribution tracking within shared restored regions across adjacent frames. To validate RC and support community benchmarking, we further introduce PROVE‑Bench, a two‑tier real‑world benchmark comprising PROVE‑M, an 80‑video paired dataset with motion augmentation, and PROVE‑H, a 100‑video challenging subset without ground truth. Together, RC metrics and PROVE‑Bench form the PROVE (Perceptual RemOVal cohErence) evaluation framework for visual media. Experiments across diverse image and video benchmarks demonstrate that RC achieves substantially stronger alignment with human judgments than existing evaluation protocols. The code for RC metrics and PROVE‑Bench are publicly available at: https://github.com/xiaomi‑research/prove/.

PaperId: 1002, https://arxiv.org/pdf/2605.14495.pdf GitHub

Abstract:
Multimedia verification requires not only accurate conclusions but also transparent and contestable reasoning. We propose a contestable multi‑agent framework that integrates multimodal large language models, external verification tools, and arena‑based quantitative bipolar argumentation (A‑QBAF) as a submission to the ICMR 2026 Grand Challenge on Multimedia Verification. Our method decomposes each case into claim‑centered sections, retrieves targeted evidence, and converts evidence into structured support and attack arguments with provenance and strength scores. These arguments are resolved through small local argument graphs with selective clash resolution and uncertainty‑aware escalation. The resulting system generates section‑wise verification reports that are transparent, editable, and computationally practical for real‑world multimedia verification. Our implementation is public at: https://github.com/Analytics‑Everywhere‑Lab/MV2026_the_liems.

PaperId: 1003, https://arxiv.org/pdf/2605.14487.pdf GitHub

Abstract:
Autoregressive video diffusion models support real‑time synthesis but suffer from error accumulation and context loss over long horizons. We discover that attention heads in AR video diffusion transformers serve functionally distinct roles as local heads for detail refinement, anchor heads for structural stabilization, and memory heads for long‑range context aggregation, yet existing methods treat them uniformly, leading to suboptimal KV cache allocation. We propose Head Forcing, a training‑free framework that assigns each head type a tailored KV cache strategy: local and anchor heads retain only essential tokens, while memory heads employ a hierarchical memory system with dynamic episodic updates for long‑range consistency. A head‑wise RoPE re‑encoding scheme further ensures positional encodings remain within the pretrained range. Without additional training, Head Forcing extends generation from 5 seconds to minute‑level duration, supports multi‑prompt interactive synthesis, and consistently outperforms existing baselines. Project Page: https://jiahaotian‑sjtu.github.io/headforcing.github.io/.

PaperId: 1004, https://arxiv.org/pdf/2605.14420.pdf GitHub

Abstract:
Current Large Language Models (LLMs) typically rely on coarse‑grained national labels for pluralistic value alignment. However, such macro‑level supervision often obscures intra‑country value heterogeneity, yielding a loose alignment. We argue that resolving this limitation requires shifting from national labels to multi‑dimensional demographic constraints, which can identify groups with predictable, high‑consensus value preference. To this end, we propose DVMap (High‑Consensus Demographic‑Value Mapping), a framework for fine‑grained pluralistic value alignment. In this framework, we first present a demographic archetype extraction strategy to construct a high‑quality value alignment corpus of 56,152 samples from the World Values Survey (WVS) by strictly retaining respondents with consistent value preferences under identical demographics. Over this corpus, we introduce a Structured Chain‑of‑Thought (CoT) mechanism that explicitly guides LLMs to reason about demographic‑value correlations. Subsequently, we employ Group Relative Policy Optimization (GRPO) to achieve adaptive anchoring of value distributions. To rigorously evaluate generalization, we further establish a triple‑generalization benchmark (spanning cross‑demographic, cross‑country, and cross‑value) comprising 21,553 samples. Experimental results demonstrate that DVMap effectively learns the manifold mapping from demographics to values, exhibiting strong generalization and robustness. On cross‑demographic tests, Qwen3‑8B‑DVMap achieves 48.6% accuracy, surpassing the advanced open‑source LLM DeepSeek‑v3.2 (45.1%). The source code and dataset are available at https://github.com/EnlightenedAI/DVMap.

PaperId: 1005, https://arxiv.org/pdf/2605.14373.pdf GitHub

Abstract:
Zeroth‑Order (ZO) optimization is pivotal for scenarios where backpropagation is unavailable, such as memory‑constrained on‑device learning and black‑box optimization. However, existing methods face a stark trade‑off: they are either sample‑inefficient (e.g., standard finite differences) or suffer from high variance due to randomized estimation (e.g., random subspace methods). In this work, we propose Coherent Coordinate Descent (CoCD), a deterministic, sample‑efficient, and budget‑aware ZO optimizer. Theoretically, we formalize the notion of gradient coherence and demonstrate that CoCD is equivalent to Block Cyclic Coordinate Descent (BCCD) with ``warm starts,'' effectively converting historical (stale) gradients from a liability into a computational asset. This mechanism enables O(1) query complexity per step while maintaining global descent directions. Furthermore, we derive error bounds revealing a counter‑intuitive insight: larger finite‑difference step sizes can induce an implicit smoothing effect on the optimization landscape by reducing the effective smoothness constant, thereby improving convergence stability. Experiments on MLP, CNN, and ResNet architectures (up to 270k parameters) demonstrate that CoCD significantly outperforms BCCD in terms of sample efficiency and convergence loss/accuracy, and exhibits superior stability over randomized ZO methods. Our results suggest that deterministic, structure‑aware updates offer a superior alternative to randomization for lightweight ZO optimization.

PaperId: 1006, https://arxiv.org/pdf/2605.14359.pdf GitHub

Abstract:
Vector quantization is a fundamental tool for compressing high‑dimensional embeddings, yet existing multi‑codebook methods rely on static codebooks that limit expressiveness under heterogeneous data geometry. While recent dynamic quantizers like QINCo adapt codebooks to individual inputs and improve expressiveness, their strict sequential dependencies create decoding bottlenecks. We propose Residual Quantization via Mixture of Experts (RQ‑MoE), a framework combining a two‑level MoE with dual‑stream quantization to enable input‑dependent codebook adaptation for efficient vector quantization. RQ‑MoE enables dynamic codebook construction and decouples instruction from quantization, facilitating parallel decoding. Theoretically, we show that standard Residual Quantization and QINCo can be recovered as constrained special cases of RQ‑MoE, and derive a guideline for setting expert dimensionality in RQ‑MoE. Extensive experiments show that RQ‑MoE achieves state‑of‑the‑art or on‑par performance in reconstruction and retrieval, while providing 6x‑14x faster decoding than prior vector quantization methods. The implementation is available at https://github.com/KDEGroup/RQ‑MoE.

PaperId: 1007, https://arxiv.org/pdf/2605.14344.pdf GitHub GitHub

Abstract:
Generative modeling has emerged as a promising approach for crystal structure discovery. However, existing LLM‑based generative models struggle with low‑level atomic precision, while diffusion‑based methods fall short in integrating high‑level scientific knowledge. As a result, generated structures are often invalid, unstable, or do not possess desirable properties. To address this gap, we propose CrystalReasoner (CrysReas), an end‑to‑end LLM framework that generates crystal structures from natural language instructions through reasoning and alignment. CrysReas introduces physical priors as thinking tokens, which include crystallographic symmetry, local coordination environments and predicted physical properties before generating atomic coordinates. This bridges the gap between natural language and 3D structures. CrysReas then employs reinforcement learning (RL) with a multi‑objective, dense reward function to align generation with physical validity, chemical consistency, and thermodynamic stability. For property‑conditioned tasks, we design task‑specific reward functions and train specialized models for discrete constraints (e.g., space group) and continuous properties (e.g., elasticity, thermal expansion). Empirical results demonstrate that compared to prior works and baselines without thinking traces or RL, CrysReas obtains better performance on diverse metrics, triples S.U.N. ratio, and achieves better performance for property conditioned generation. CrysReas also exhibits adaptive reasoning, increasing reasoning lengths as the number of atoms increases. Our work demonstrates the potential of leveraging thinking traces and RL for generating valid, stable, and property‑conditioned crystal structures.

PaperId: 1008, https://arxiv.org/pdf/2605.14289.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) models scale capacity by combining specialized experts, but most existing approaches assume centralized access to training data. In practice, data are distributed across clients and cannot be shared due to privacy constraints, making unified MoE training challenging. We propose MetaMoE, a privacy‑preserving framework that unifies independently trained, domain‑specialized experts into a single MoE using public proxy data as surrogates for inaccessible private data. Central to MetaMoE is diversity‑aware proxy selection, which selects client‑domain‑relevant and diverse samples from public data to effectively approximate private data distributions and supervise router learning. These proxies are further used to align expert training, improving expert coordination at unification time, while a context‑aware router enhances expert selection across heterogeneous inputs. Experiments on computer vision and natural language processing benchmarks demonstrate that MetaMoE consistently outperforms recent privacy‑preserving MoE unification methods. Code is available at https://github.com/ws‑jiang/MetaMoE.

PaperId: 1009, https://arxiv.org/pdf/2605.14267.pdf GitHub

Abstract:
Diffusion models (DMs) have exhibited remarkable efficacy in various image restoration tasks. However, existing approaches typically operate within the high‑dimensional pixel space, resulting in high computational overhead. While methods based on latent DMs seek to alleviate this issue by utilizing the compressed latent space of a variational autoencoder, they require repeated encoder‑decoder inference. This introduces significant additional computational burdens, often resulting in runtime performance that is even inferior to that of their pixel‑space counterparts. To mitigate the computational inefficiency, this work proposes projecting data into lower‑dimensional subspaces using dynamic resolution DMs to accelerate the inference process. We first fine‑tune pre‑trained DMs for dynamic resolution priors and adapt DPS and DAPS, which are two widely used pixel‑space methods for general image restoration tasks, into the proposed framework, yielding methods we refer to as SubDPS and SubDAPS, respectively. Given the favorable inference speed and reconstruction fidelity of SubDAPS, we introduce an enhanced variant termed SubDAPS++ to further boost both reconstruction efficiency and quality. Empirical evaluations across diverse image datasets and various restoration tasks demonstrate that the proposed methods outperform recent DM‑based approaches in the majority of experimental scenarios. The code is available at https://github.com/StarNextDay/SubDAPS.git.

PaperId: 1010, https://arxiv.org/pdf/2605.14252.pdf GitHub

Abstract:
Spiking neural networks (SNNs), which are brain‑inspired and spike‑driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typically enforce uniform alignment across all timesteps, either from a teacher network or through inter‑temporal self‑distillation, implicitly assuming that per‑timestep predictions should be treated equally. In practice, SNN predictions vary and evolve over time, and intermediate timesteps need not all be individually correct even when the final aggregated output is correct. Under such conditions, effective distillation should not force every timestep toward the same supervision target, but instead provide corrective guidance to erroneous timesteps while preserving useful temporal dynamics. To address this issue, we propose Selective Alignment Knowledge Distillation (SeAl‑KD), which selectively aligns class‑level and temporal knowledge by equalizing competing logits at erroneous timesteps and reweighting temporal alignment based on confidence and inter‑timestep similarity. Extensive experiments on static image and neuromorphic event‑based datasets demonstrate consistent improvements over existing distillation methods. The code is available at https://github.com/KaiSUN1/SeAl

PaperId: 1011, https://arxiv.org/pdf/2605.14231.pdf GitHub GitHub

Abstract:
Audio self‑supervised learning (SSL) aims to learn general‑purpose representations from large‑scale unlabeled audio data. While recent advances have been driven mainly by generative reconstruction objectives, contrastive approaches remain less explored, partly due to the difficulty of designing effective audio augmentations and the large batch sizes required for contrastive pre‑training. We introduce AudioMosaic, a contrastive learning‑based audio encoder for general audio understanding. During pre‑training, AudioMosaic constructs positive pairs by applying structured time‑frequency masking to spectrogram patches, which reduces memory usage and enables efficient large‑batch training. Compared with generative approaches, the AudioMosaic encoder learns more discriminative utterance‑level representations that demonstrate strong transferability across datasets, domains, and acoustic conditions. Extensive experiments show that AudioMosaic achieves state‑of‑the‑art performance on several standard audio benchmarks under both linear probing and fine‑tuning. We further show that integrating the pretrained AudioMosaic encoder into audio‑language models improves performance on audio‑language tasks. The code is publicly available in our \hrefhttps://github.com/HanxunH/AudioMosaicGitHub repository.

PaperId: 1012, https://arxiv.org/pdf/2605.14048.pdf GitHub

Abstract:
Masked autoencoders (MAEs) have recently shown promise for self‑supervised representation learning of resting‑state brain functional connectivity (FC). However, a fundamental question remains unresolved: how should FC matrices be tokenized to align with the intrinsic modular organization of large‑scale brain networks? Existing approaches typically adopt region‑centric or graph‑based schemes that treat FC as structurally homogeneous elements and overlook the large‑scale network brain organization. We introduce NERVE (Network‑Aware Representations of Brain Functional Connectivity via Bilinear Tokenization), a self‑supervised learning framework that redefines FC tokenization by partitioning FC matrices into patches of intra‑ and inter‑network connectivity blocks. Unlike image‑based MAE, where fixed‑size patches share a common tokenizer, FC patches defined by network pairs are heterogeneous in size and correspond to distinct functional roles. To resolve this problem, NERVE embeds FC patches through a novel structured bilinear factorization. This formulation preserves network identity and reduces parameter complexity from quadratic to linear scaling in the number of networks. We evaluate NERVE across three large‑scale developmental cohorts (ABCD, PNC, and CCNP) for behavior and psychopathology prediction. Compared to structurally agnostic MAE variants and graph‑based self‑supervised baselines, the proposed network‑aware formulation yields more stable and transferable representations, particularly in cross‑cohort evaluation. Ablation studies confirm that the proposed bilinear network embedding and anatomically grounded parcellation are critical for performance. These findings highlight the importance of incorporating domain‑specific structural priors into self‑supervised learning for functional connectomics. Code is available at: https://github.com/leomlck/NERVE.

PaperId: 1013, https://arxiv.org/pdf/2605.14026.pdf GitHub

Abstract:
For reinforcement learning in data‑scarce domains like real‑world robotics, intensive data reuse enhances efficiency but induces overfitting. While prior works focus on critic bias, representation‑level instability in Self‑Predictive Learning (SPL) under high Update‑to‑Data (UTD) regimes remains underexplored. To bridge this gap, we propose Robust Representation via Redundancy Reduction (R2R2), a regularization method within SPL. We theoretically identify that standard zero‑centering conflicts with SPL's spectral properties and design a non‑centered objective accordingly. We verify R2R2 on SPL‑native algorithms like TD7. Furthermore, to demonstrate its orthogonality to prior advancements, we extend the state‑of‑the‑art SimbaV2, which originally lacks SPL, by integrating a tailored SPL module, termed SimbaV2‑SPL. Experiments across 11 continuous control tasks confirm that R2R2 effectively mitigates overfitting; specifically, at a UTD ratio of 20, it improves TD7 by ~22% and provides additional gains on top of SimbaV2‑SPL, which itself establishes a new state‑of‑the‑art. The code can be found at: https://github.com/songsang7/R2R2

PaperId: 1014, https://arxiv.org/pdf/2605.13974.pdf GitHub

Abstract:
Diffusion Transformers (DiTs) and related flow‑based architectures are now among the strongest text‑to‑image generators, yet the internal mechanisms through which prompts shape image semantics remain poorly understood. In this work, we study massive activations: a small subset of hidden‑state channels whose responses are consistently much larger than the rest. We show that, despite their sparsity, these few channels effectively draw the whole picture, in three complementary senses. First, they are functionally critical: a controlled disruption probe that zeroes the massive channels causes a sharp collapse in generation quality, while disrupting an equally‑sized set of low‑statistic channels has marginal effect. Second, they are spatially organized: restricting image‑stream tokens to massive channels and clustering them yields coherent partitions that closely align with the main subject and salient regions, exposing a structured spatial code hidden inside an apparently outlier‑like subspace. Third, they are transferable: transporting massive activations from one prompt‑conditioned trajectory into another, shifts the final image toward the source prompt while preserving substantial content from the target, producing localized semantic interpolation rather than unstructured pixel blending. We exploit this property in two use cases: text‑conditioned and image‑conditioned semantic transport, where massive activations transport enables prompt interpolation and subject‑driven generation without any additional training. Together, these results recast massive activations not as activation anomalies, but as a sparse prompt‑conditioned carrier subspace that organizes and controls semantic information in modern DiT models.

PaperId: 1015, https://arxiv.org/pdf/2605.13950.pdf GitHub

Abstract:
Autonomous language‑model agents are increasingly evaluated on long‑horizon tool‑use tasks, but existing benchmarks rarely capture the complexity and nuance of real scientific work. To address this gap, we introduce Collider‑Bench, a benchmark for evaluating whether LLM agents can reproduce experimental analyses from the Large Hadron Collider (LHC) using only public papers and open scientific software. Such analyses are often difficult to reproduce because the public toolchain only approximates the software used internally by the experimental collaborations, while the published papers inevitably omit implementation details needed for a faithful reconstruction. Agents must therefore rely on physical reasoning, domain knowledge, and trial‑and‑error to fill these gaps. Each task requires the agent to turn a published analysis into an executable simulation‑and‑selection pipeline and submit predicted collision event yields in specified signal regions. These predictions are evaluated with standard histogram metrics that provide continuous fidelity scores without a hand‑written rubric. We also report the computational cost incurred by each agent per task. Finally, we evaluate the codebase and full session trace using an LLM judge to catch qualitative failure modes such as fabrications, hallucinations and duplications. We release an initial set of tasks drawn from LHC searches, together with a containerized sandbox and event simulation tools. We evaluate across a capability ladder of general purpose coding agents. Our results show that on average no agent reliably beats the physicist‑in‑the‑loop solution.

PaperId: 1016, https://arxiv.org/pdf/2605.13941.pdf GitHub

Abstract:
Long‑term memory is essential for LLM agents that operate across multiple sessions, yet existing memory systems treat retrieval infrastructure as fixed: stored content evolves while scoring functions, fusion strategies, and answer‑generation policies remain frozen at deployment. We argue that truly adaptive memory requires co‑evolution at two levels: the stored knowledge and the retrieval mechanism that queries it. We present EvolveMem, a self‑evolving memory architecture that exposes its full retrieval configuration as a structured action space optimized by an LLM‑powered diagnosis module. In each evolution round, the module reads per‑question failure logs, identifies root causes, and proposes targeted configuration adjustments; a guarded meta‑analyzer applies them with automatic revert‑on‑regression and explore‑on‑stagnation safeguards. This closed‑loop self‑evolution realizes an AutoResearch process: the system autonomously conducts iterative research cycles on its own architecture, replacing manual configuration tuning. Starting from a minimal baseline, the process converges autonomously, discovering effective retrieval strategies including entirely new configuration dimensions not present in the original action space. On LoCoMo, EvolveMem outperforms the strongest baseline by 25.7% relative and achieves a 78.0% relative improvement over the minimal baseline. On MemBench, EvolveMem exceeds the strongest baseline by 18.9% relative. Evolved configurations transfer across benchmarks with positive rather than catastrophic transfer, indicating that the self‑evolution process captures universal retrieval principles rather than benchmark‑specific heuristics. Code is available at https://github.com/aiming‑lab/SimpleMem.

PaperId: 1017, https://arxiv.org/pdf/2605.13940.pdf GitHub

Abstract:
Third‑party skills are becoming the package ecosystem for LLM agents. They package natural‑language instructions, helper scripts, templates, documents, and service configuration into reusable workflows. This makes skills useful, but it also introduces a new security problem: a malicious skill does not need to ask the model to perform an obviously harmful action. Instead, it can disguise the harmful behavior as part of a routine workflow, relying on the agent to execute that workflow with high‑value permissions and limited human supervision. We introduce AgentTrap, a dynamic benchmark for evaluating whether LLM agents can use third‑party skills while resisting malicious runtime behavior. AgentTrap contains 141 tasks: 91 malicious tasks and 50 benign utility tasks, covering 16 security‑impact dimensions grounded in agent‑skill supply‑chain threats. In each task, the agent receives an ordinary user request, runs with installed skills that may contain malicious workflow elements, and is executed in a sandboxed environment. AgentTrap then judges complete trajectories for attack success, blocked or refused behavior, attack‑not‑triggered cases, and no‑attack‑evidence outcomes. Our central finding is that the most informative failures are not simple jailbreaks. Models often complete the visible user task while treating unsafe side effects introduced by the skill as part of the normal workflow. This motivates runtime evaluation of the concrete model‑‑framework‑‑workspace environment in which users actually delegate work. Code and data are available at https://github.com/zhmzm/AgentTrap and https://huggingface.co/datasets/zhmzm/AgentTrap.

PaperId: 1018, https://arxiv.org/pdf/2605.13877.pdf GitHub

Abstract:
We present ARES‑LSHADE, a memetic differential‑evolution variant submitted to the GECCO 2026 competition on LLM‑designed evolutionary algorithms for the Generalized Numerical Benchmark Generator (GNBG). The algorithm builds on the LLM‑LSHADE 2025 winner, contributing two new components: (a) a scout‑augmented mutation operator with adaptive CMA‑ES integration, produced by an autonomous research loop across approximately thirty LLM‑driven design experiments, and (b) a multi‑start L‑BFGS‑B polish phase that respects strict blackbox treatment of the benchmark. On the official 31‑run‑per‑function evaluation with the competition‑specified function‑evaluation budgets, ARES‑LSHADE obtains 510 of 744 wins (per‑function gap below 1e‑8), reaching machine precision on 18 of 24 functions. The remaining six functions exhibit characteristic plateau signatures consistent with GNBG's compositional structure, and were independently identified by the autoresearch loop as the hardest of the suite. Beyond the result itself, this report documents two methodological observations: (i) an LLM‑driven research loop with operator‑only edit surface and fitness‑only observation space converges to a characteristic plateau on this benchmark; (ii) when we initially widened the observation space to include the benchmark's compositional metadata, the resulting algorithm trivially solved all 24 functions but violated the competition's blackbox rule, which we identified before submission. We discuss this tension between LLM capability and benchmark integrity as a design consideration for future LLM‑driven optimization‑algorithm research. Code and reproducibility artifacts are available at https://github.com/anaeem1/ARES‑LSHADE.

PaperId: 1019, https://arxiv.org/pdf/2605.13846.pdf GitHub

Abstract:
This paper introduces WARDEN, an early language model system capable of transcribing and translating Wardaman, an endangered Australian indigenous language into English. The significant challenge we face is the lack of large‑scale training data: in fact, we only have 6 hours of annotated audio. Therefore, while it is common practice to train a single model for transcription and translation using large datasets (like English to French), this practice is no longer viable in the Wardaman to English context. To tackle the low‑resource challenge, we design WARDEN to have separate transcription and translation models: WARDEN first turns a Wardaman audio input into phonemic transcription, and then the transcription into English translation. Further, we propose two useful techniques to enhance performance. For transcription, we initialize the Wardaman token from Sundanese, a language that shares similar phonemes with Wardaman, to accelerate fine‑tuning of the transcription model. For translation, we compile a Wardaman‑English dictionary from expert annotations, and provide this domain‑specific knowledge to a large language model (LLM) to reason and decide the final output. We empirically demonstrate that this two‑stage design works better than data‑hungry unified approaches in extremely low data settings. Using a mere 6 hours of annotated data, WARDEN outperforms larger open‑source and proprietary models and establishes a strong baseline. Data and code are available.

PaperId: 1020, https://arxiv.org/pdf/2605.13724.pdf GitHub

Abstract:
Few‑step video generation has been significantly advanced by consistency distillation. However, the performance of consistency‑distilled models often degrades as more sampling steps are allocated at test time, limiting their effectiveness for any‑step video diffusion. This limitation arises because consistency distillation replaces the original probability‑flow ODE trajectory with a consistency‑sampling trajectory, weakening the desirable test‑time scaling behavior of ODE sampling. To address this limitation, we introduce AnyFlow, the first any‑step video diffusion distillation framework based on flow maps. Instead of distilling a model for only a few fixed sampling steps, AnyFlow optimizes the full ODE sampling trajectory. To this end, we shift the distillation target from endpoint consistency mapping (z_t\rightarrow z_0) to flow‑map transition learning (z_t\rightarrow z_r) over arbitrary time intervals. We further propose Flow Map Backward Simulation, which decomposes a full Euler rollout into shortcut flow‑map transitions, enabling efficient on‑policy distillation that reduces test‑time errors (i.e., discretization error in few‑step sampling and exposure bias in causal generation). Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B parameters, demonstrate that AnyFlow achieves performance matches or surpasses consistency‑based counterparts in the few‑step regime, while scaling with sampling step budgets.

PaperId: 1021, https://arxiv.org/pdf/2605.13646.pdf GitHub

Abstract:
End‑to‑end autonomous driving, which bypasses traditional modular pipelines by directly predicting future trajectories from sensor inputs, has recently achieved substantial progress. However, existing methods often overlook the causal inter‑dependencies in ego‑vehicle planning, ignoring the reciprocal relations between the ego vehicle and surrounding agents. This causal oversight leads to inconsistent and unreliable trajectory predictions, especially in interaction‑critical scenarios where ego decisions and neighboring agent behaviors must be reasoned about jointly. To address this limitation, we propose CaAD, a Causality‑aware end‑to‑end Autonomous Driving framework that captures these dependencies within a shared latent scene representation. First, we propose an ego‑centric joint‑causal modeling module that builds on the marginal prediction branch, and learns causal dependencies between the ego vehicle and interaction‑relevant agents. Second, we employ a causality‑aware policy alignment stage implemented with joint‑mode embeddings to align the stochastic ego policy with planning‑oriented closed‑loop feedback computed from surrounding traffic and map context. On the Bench2Drive and NAVSIM benchmarks, CaAD demonstrates strong closed‑loop planning performance, achieving a Driving Score of 87.53 and Success Rate of 71.81 on Bench2Drive, and a PDMS of 91.1 on NAVSIM. The project page is available at https://moonseokha.github.io/CaAD/.

PaperId: 1022, https://arxiv.org/pdf/2605.13618.pdf GitHub

Abstract:
The Materials Genome Initiative catalyzed the proliferation of centralized platforms‑‑SaaS, PaaS, and IaaS‑‑that aggregate computational and experimental resources for accelerated materials discovery. In parallel, breakthroughs in large language models (LLMs) and autonomous agents have created powerful new reasoning capabilities for scientific research. Yet a critical "last mile" problem remains: while we possess world‑class models and vast repositories of materials data, we lack the organizational infrastructure to compose these capabilities securely across institutional boundaries. The development of structural and functional materials for harsh service environments‑‑high‑temperature alloys, radiation resistant steels, corrosion‑resistant coatings‑‑remains characterized by long‑term iteration, mechanistic complexity, and high domain expertise‑‑demands that exceed both monolithic agent systems and traditional centralized platforms. To address this gap we propose OpenAaaS, an open‑source hierarchical and distributed Agent‑as‑a‑Service framework that enables organized multi‑agent collaboration for intelligent materials design. OpenAaaS is built on a single foundational principle: code flows, data stays still. A Master Agent plans and decomposes complex research tasks without requiring direct access to subordinate agents' managed data and computational resources. Sub‑agents, deployed as near‑data execution nodes, retain full sovereignty over local datasets, proprietary algorithms, and specialized hardware. This architecture guarantees that raw data never leaves its domain of origin while enabling cross‑scale, cross‑domain secure integration of previously isolated materials intelligence silos. We validate the framework through two representative case studies: (i) AlphaAgent, an evidence‑grounded materials literature analysis executor that achieves 4.66/5.0 on deep analytical questions against single‑pass RAG baselines; and (ii) an ultra‑large‑scale hexa‑high‑entropy alloy descriptor database service that demonstrates secure near‑data execution and domain‑specific scientific workflows under strict data‑sovereignty constraints. OpenAaaS establishes a principled pathway toward "organized research" via agent collectives, offering a scalable foundation for next‑generation materials intelligent design platforms. All source code is available at https://github.com/Wolido/OpenAaaS.

PaperId: 1023, https://arxiv.org/pdf/2605.13542.pdf GitHub

Abstract:
Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states under time pressure, underscoring a clear need for reliable AI decision support. Existing ICU benchmarks typically treat historical clinician actions as ground truth. However, these actions are made under incomplete information and limited temporal context of the underlying patient state, and may therefore be suboptimal, making it difficult to assess the true reasoning capabilities of AI systems. We introduce RealICU, a hindsight‑annotated benchmark for evaluating large language models (LLMs) under realistic ICU conditions, where labels are created after senior physicians review the full patient trajectory. We formulate four physician‑motivated tasks: assess Patient Status, Acute Problems, Recommended Actions, and Red Flag actions that risk unsafe outcomes. We partition each trajectory with 30‑min windows and release two datasets: RealICU‑Gold with 930‑window annotations from 94 MIMIC‑IV patients, and RealICU‑Scale with 11,862 windows extended by Oracle, a physician‑validated LLM hindsight labeler. Existing LLMs including memory‑augmented ones performed poorly on RealICU, exposing two failure modes: a recall‑safety tradeoff for clinical recommendations, and an anchoring bias to early interpretations of the patient. We further introduce ICU‑Evo to study structured‑memory agents that improves long‑horizon reasoning but does not fully eliminate safety failures. Together, RealICU provides a clinically grounded testbed for measuring and improving AI sequential decision‑support in high‑stakes care. Project page: https://chengzhi‑leo.github.io/RealICU‑Bench/

PaperId: 1024, https://arxiv.org/pdf/2605.13517.pdf GitHub

Abstract:
Vector Quantized Variational Autoencoder (VQ‑VAE) has become a fundamental framework for learning discrete representations in image modeling. However, VQ‑VAE models must tokenize entire images using a finite set of codebook vectors, and this capacity limitation restricts their ability to capture rich and diverse representations. In this paper, we propose ArcCosine Additive Margin VQ‑VAE (ArcVQ‑VAE), a novel vector quantization framework that introduces a spherical angular‑margin prior (SAMP) for the codebook of a conventional VQ‑VAE. The proposed SAMP consists of Ball‑Bounded Norm Regularization, which constrains all codebook vectors within a time‑dependent Euclidean ball, and ArcCosine Additive Margin Loss, which encourages greater angular separability among latent vectors. This formulation promotes more discriminative and uniformly dispersed latent representations within the constrained space, thereby improving effective latent‑space coverage and leading to improved codebook utilization. Experimental results on standard image reconstruction and generation tasks show that ArcVQ‑VAE achieves competitive performance against baseline models in terms of reconstruction accuracy, representation diversity, and sample quality. The code is available at: https://github.com/goals4292/ArcVQ‑VAE

PaperId: 1025, https://arxiv.org/pdf/2605.13438.pdf GitHub

Abstract:
Existing agent memory remains predominantly reactive and retrieval‑based, lacking the capacity to autonomously organize experience into persistent cognitive structure. Toward genuinely autonomous agents, we introduce CogniFold, a brain‑inspired "always‑on" agent memory designed for the next generation of proactive assistants. CogniFold continuously folds fragmented event streams into self‑emerging cognitive structures, bootstrapping progressively higher‑level cognition from incoming events and accumulated knowledge. We ground this by extending Complementary Learning Systems (CLS) theory from two layers (hippocampus, neocortex) to three, adding a prefrontal intent layer. Emulating the prefrontal cortex as the locus of intentional control and decision‑making, CogniFold achieves this through graph‑topology self‑organization: cognitive structures proactively assemble under the stream, merge when semantically similar, decay when stale, relink through associative recall, and surface intents when concept‑cluster density crosses a threshold. We evaluate structural formation using CogEval‑Bench, demonstrating that CogniFold uniquely produces memory structures that match cognitive expectations and concept emergence. Furthermore, across 7 broad‑coverage benchmarks spanning five cognitive domains, we validate that CogniFold simultaneously performs robustly on conventional memory benchmarks.

PaperId: 1026, https://arxiv.org/pdf/2605.13415.pdf GitHub

Abstract:
This paper presents a multi‑stage framework for detecting reclaimed slurs in multilingual social media discourse. It addresses the challenge of identifying reclamatory versus non‑reclamatory usage of LGBTQ+‑related slurs across English, Spanish, and Italian tweets. The framework handles three intertwined methodological challenges like data scarcity, class imbalance, and cross‑linguistic variation in sentiment expression. It integrates data‑driven model selection via cross‑validation, semantic‑preserving augmentation through back‑translation, inductive transfer learning with dynamic epoch‑level undersampling, and domain‑specific knowledge injection via masked language modeling. Eight multilingual embedding models were evaluated systematically, with XLM‑RoBERTa selected as the foundation model based on macro‑averaged F1 score. Data augmentation via GPT‑4o‑mini back‑translation to alternate languages effectively tripled the training corpus while preserving semantic content and class distribution ratios. The framework produces four final runs for the evaluation purposes where RUN 1 is inductive transfer learning with augmentation and undersampling, RUN 2 with masked language modeling pre‑training, RUN 3 and RUN 4 are previous predictions refined via language‑specific decision thresholds optimized via ROC analysis. Language‑specific threshold refinement reveals that optimal decision boundaries vary significantly across languages. This reflects distributional differences in model confidence scores and linguistic variation in reclamatory language usage. The threshold‑based optimization yields 2‑5% absolute F1 improvement without requiring model retraining. The methodology is fully reproducible, with all code and experimental setup available at https://github.com/rbg‑research/MultiPRIDE‑Evalita‑2026.

PaperId: 1027, https://arxiv.org/pdf/2605.13412.pdf GitHub GitHub

Abstract:
Off‑the‑shelf large language models (LLMs) are increasingly used to automate text annotation, yet their effectiveness remains underexplored for underrepresented languages and specialized domains where the class definition requires subtle expert understanding. We investigate LLM‑based annotation for a novel legal NLP task: identifying the presence and sentiment of credibility assessments in asylum decision texts. We introduce RAB‑Cred, a Danish text classification dataset featuring high‑quality, expert annotations and valuable metadata such as annotator confidence and asylum case outcome. We benchmark 21 open‑weight models and 30 system‑user prompt combinations for this task, and systematically evaluate the effect of model and prompt choice for zero‑shot and few‑shot classification. We zoom in on the errors made by top‑performing models and prompts, investigating error consistency across LLMs, inter‑class confusion, correlation with human confidence and sample‑wise difficulty and severity of LLM mistakes. Our results confirm the potential of LLMs for cost‑effective labeling of asylum decisions, but highlight the imperfect and inconsistent nature of LLM annotators, and the need to look beyond the predictions of a single, arbitrarily chosen model. The RAB‑Cred dataset and code are available at https://github.com/glhr/RAB‑Cred

PaperId: 1028, https://arxiv.org/pdf/2605.13369.pdf GitHub

Abstract:
Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at inference time. While such test‑time scaling can be effective, it cannot correct model misconceptions or adapt the model to the specific structure of an individual query. Test‑time optimization addresses this limitation by enabling parameter updates during inference, but existing approaches either rely on external data or optimize generic self‑supervised objectives that lack query‑specific alignment. In this work, we propose Query‑Conditioned Test‑Time Self‑Training (QueST), a framework that adapts model parameters during inference using supervision derived directly from the input query. Our key insight is that the input query itself encodes latent signals sufficient for constructing structurally related problem‑‑solution pairs. Based on this, QueST generates such query‑conditioned pairs and uses them as supervision for parameter‑efficient fine‑tuning at test time. The adapted model is then used to produce the final answer, enabling query‑specific adaptation without any external data. Across seven mathematical reasoning benchmarks and the GPQA‑Diamond scientific reasoning benchmark, QueST consistently outperforms strong test‑time optimization baselines. These results demonstrate that query‑conditioned self‑training is an effective and practical paradigm for test‑time adaptation in LLMs. Code is available at https://chssong.github.io/Query‑Conditioned‑TTST/.

PaperId: 1029, https://arxiv.org/pdf/2605.13339.pdf GitHub

Abstract:
Large language models (LLMs) can be said to have preferences: they reliably pick certain tasks and outputs over others, and preferences shaped by post‑training and system prompts appear to shape much of their behaviour. But models can also adopt different personas which have radically different preferences. How is this implemented internally? Does each persona run on its own preference machinery, or is something shared underneath? We train linear probes on residual‑stream activations of Gemma‑3‑27B and Qwen‑3.5‑122B to predict revealed pairwise task choices, and identify a genuine preference vector: it tracks the model's preferences as they shift across a range of prompts and situations, and on Gemma‑3‑27B steering along it causally controls pairwise choice. This preference representation is largely shared across personas: a probe trained on the helpful assistant predicts and steers the choices of qualitatively different personas, including an evil persona whose preferences anti‑correlate with those of the Assistant.

PaperId: 1030, https://arxiv.org/pdf/2605.13338.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) are increasingly integrated into systems requiring reliable multi‑step inference, yet this growing dependence exposes new vulnerabilities related to computational availability. In particular, LRMs exhibit a tendency to "overthink", producing excessively long and redundant reasoning traces, when confronted with incomplete or logically inconsistent inputs. This behavior significantly increases inference latency and energy consumption, forming a potential vector for denial‑of‑service (DoS) style resource exhaustion. In this work, we investigate this attack surface and propose an automated black‑box framework that induces overthinking in LRMs by systematically perturbing the logical structure of input problems. Our method employs a hierarchical genetic algorithm (HGA) operating on structured problem decompositions, and optimizes a composite fitness function designed to maximize both response length and reflective overthinking markers. Across four state‑of‑the‑art reasoning models, the proposed method substantially amplifies output length, achieving up to a 26.1x increase on the MATH benchmark and consistently outperforming benign and manually crafted missing‑premise baselines. We further demonstrate strong transferability, showing that adversarial inputs evolved using a small proxy model retain high effectiveness against large commercial LRMs. These findings highlight overthinking as a shared and exploitable vulnerability in modern reasoning systems, underscoring the need for more robust defenses.

PaperId: 1031, https://arxiv.org/pdf/2605.13333.pdf GitHub

Abstract:
Text‑driven motion diffusion models are capable of generating realistic human motions, but text alone often struggles to express fine‑level nuances of motion, commonly referred to as style. Recent approaches have tackled this challenge by attaching a style injection mechanism to a pretrained text‑driven diffusion model. Existing stylization methods, however, either require style‑specific fine‑tuning of existing models or rely on heavy ControlNet‑based architectures, limiting efficiency and generalization to unseen styles. We propose a lightweight style conditioning framework that dynamically modulates a pretrained diffusion model through hypernetwork‑generated LoRA parameters. A style reference motion is encoded into a global style embedding, which is mapped by a hypernetwork to low‑rank updates applied at each denoising step of the diffusion model. By structuring the style latent space with a supervised contrastive loss, our framework reliably captures diverse stylistic attributes, improves generalization to unseen styles, and supports optimization‑based guidance without requiring predefined style categories. Experiments on the HumanML3D and 100STYLE datasets show state‑of‑the‑art stylization results, while achieving improved stylization for unseen styles.

PaperId: 1032, https://arxiv.org/pdf/2605.13328.pdf GitHub

Abstract:
Vision‑and‑Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degradation when transitioning from simulation to real‑world deployment, primarily due to perceptual instability (e.g., lighting variations and motion blur) and under‑specified instructions. While existing methods attempt to bridge this gap by scaling up model size and training data, we argue that the bottleneck lies in the lack of robust spatial grounding and cross‑domain priors. In this paper, we propose StereoNav, a robust Vision‑Language‑Action framework designed to enhance real‑world navigation consistency. To address the inherent gap between synthetic training and physical execution, we introduce Target‑Location Priors as a persistent bridge. These priors provide stable visual guidance that remains invariant across domains, effectively grounding the agent even when instructions are vague. Furthermore, to mitigate visual disturbances like motion blur and illumination shifts, StereoNav leverages stereo vision to construct a unified representation of semantics and geometry, enabling precise action prediction through enhanced depth awareness. Extensive experiments on R2R‑CE and RxR‑CE demonstrate that StereoNav achieves state‑of‑the‑art egocentric RGB performance, with SR and SPL scores of 81.1% and 68.3%, and 67.5% and 52.0%, respectively, while using significantly fewer parameters and less training data than prior scaling‑based approaches. More importantly, real‑world robotic deployments confirm that StereoNav substantially improves navigation reliability in complex, unstructured environments. Project page: https://yunheng‑wang.github.io/stereonav‑public.github.io.

PaperId: 1033, https://arxiv.org/pdf/2605.13202.pdf GitHub

Abstract:
Few‑shot action recognition (FSAR) requires models to generalize to novel action categories from only a handful of annotated samples. Despite progress with vision‑language models, existing approaches still suffer from semantic‑temporal misalignment, where static textual prompts fail to capture decisive visual cues that appear sparsely across sequences, and from inadequate modeling of multi‑scale temporal dynamics, as short‑term discriminative cues and long‑range dependencies are often either oversmoothed or fragmented. To address these challenges, we propose Semantic Temporal Adaptive Representation Learning (STAR), a unified framework, consisting of a semantic‑alignment component and a temporal‑aware component, effectively bridging the semantic and temporal gaps and transferring the sequence modeling capability of Mamba into the FSAR. The semantic alignment module introduces a Temporal Semantic Attention (TSA) mechanism, which performs frame‑level cross‑modal alignment with textual cues, ensuring fine‑grained semantic‑temporal consistency. The temporal‑aware module incorporates a Semantic Temporal Prototype Refiner (STPR) that integrates semantic‑guided Mamba blocks with multi‑frequency temporal sampling and bidirectional state‑space refinement, yielding semantically aligned prototypes with enhanced discriminative fidelity and temporal consistency. Furthermore, temporally dependent class descriptors derived from large language models (LLMs) provide long‑range semantic guidance. Extensive experiments on five FSAR benchmarks demonstrate the consistent superiority of STAR over state‑of‑the‑art methods. For instance, STAR achieves up to 8.1% and 6.7% gains on the SSv2‑Full and SSv2‑Small datasets under the 1‑shot setting, and 7.3% on HMDB51, validating its effectiveness under limited supervision. The code is available at https://github.com/HongliLiu1/STAR‑main.

PaperId: 1034, https://arxiv.org/pdf/2605.13194.pdf GitHub

Abstract:
Electrocardiogram (ECG) arrhythmia classification remains challenging due to signal variability, noise, limited labeled data, and the difficulty in achieving both accuracy and efficiency in models. While self‑supervised learning reduces label dependency, most methods target either global contextual features or local morphological patterns, but rarely implement hierarchical multi‑scale feature extraction. ECG signals require architectures that simultaneously capture fine‑grained beat‑level morphology and broader rhythm‑level dependencies with computational efficiency. To overcome this limitation, this paper proposes the Electrocardiogram Neighborhood Attention Transformer (ECG‑NAT), a novel self‑supervised learning approach tailored for multi‑lead ECG classification. Our two‑stage approach begins with generative pretraining, using a masked autoencoder to reconstruct partially masked ECG signals across multiple diverse datasets, enabling the model to learn robust, domain‑invariant representations from unlabeled data. This is followed by discriminative fine‑tuning with a dual‑loss function that combines supervised contrastive and cross‑entropy losses, aligning representation learning with label prediction. The hierarchical attention mechanism efficiently captures multi‑scale temporal features from localized beat morphology to broader rhythm patterns at low computational cost. ECG‑NAT achieves robust performance on benchmark datasets, with 88.1% accuracy using only 1% labeled data, demonstrating strong efficacy in low‑resource settings. The framework combines superior classification performance with computational efficiency, making it practical for real‑time ECG diagnosis. The code will be made available upon acceptance at: https://github.com/Mahsagazeran/ECG‑NAT.

PaperId: 1035, https://arxiv.org/pdf/2605.13169.pdf GitHub

Abstract:
Multimodal large laboratory models (MLLMs) still struggle with spatial understanding under the dominant perspective‑image paradigm, which inherits the narrow field of view of human‑like perception. For navigation, robotic search, and 3D scene understanding, 360‑degree panoramic sensing offers a form of supersensing by capturing the entire surrounding environment at once. However, existing MLLM pipelines typically decompose panoramas into multiple perspective views, leaving the spherical structure of equirectangular projection (ERP) largely implicit. In this paper, we study pano‑native understanding, which requires an MLLM to reason over an ERP panorama as a continuous, observer‑centered space. To this end, we first define the key abilities for pano‑native understanding, including semantic anchoring, spherical localization, reference‑frame transformation, and depth‑aware 3D spatial reasoning. We then build a large‑scale metadata construction pipeline that converts mixed‑source ERP panoramas into geometry‑aware, language‑grounded, and depth‑aware supervision, and instantiate these signals as capability‑aligned instruction tuning data. On the model side, we introduce PanoWorld with Spherical Spatial Cross‑Attention, which injects spherical geometry into the visual stream. We further construct PanoSpace‑Bench, a diagnostic benchmark for evaluating ERP‑native spatial reasoning. Experiments show that PanoWorld substantially outperforms both proprietary and open‑source baselines on PanoSpace‑Bench, H Bench, and R2R‑CE Val‑Unseen benchmarks. These results demonstrate that robust panoramic reasoning requires dedicated pano‑native supervision and geometry‑aware model adaptation. All source code and proposed data will be publicly released.

PaperId: 1036, https://arxiv.org/pdf/2605.13152.pdf GitHub

Abstract:
We introduce EvObj for unsupervised 3D instance segmentation that bridges the geometric domain gap between synthetic pretraining data and real‑world point clouds. Current methods suffer from structural discrepancies when transferring object priors from synthetic datasets (e.g., ShapeNet) to real scans (e.g., ScanNet), particularly due to morphological variations and occlusion artifacts. To address this, EvObj integrates two innovative modules: (1) An object discerning module that dynamically refines object candidates, enabling continuous adaptation of object priors to target domains; and (2) An object completion module that reconstructs partial geometries after discovering objects. We conduct extensive experiments on both real‑world and synthetic datasets, demonstrating superior 3D object segmentation performance over all baselines while achieving state‑of‑the‑art results.

PaperId: 1037, https://arxiv.org/pdf/2605.13137.pdf GitHub

Abstract:
Proving theorems in Lean 4 often requires identifying a scattered set of library lemmas whose joint use enables a concise proof ‑‑ a task we call global premise retrieval. Existing tools address adjacent problems: semantic search engines find individual declarations matching a query, while premise‑selection systems predict useful lemmas one tactic step at a time. Neither recovers the full premise set an entire theorem requires. We present LeanSearch v2, a two‑mode retrieval system for this task. Its standard mode applies a hierarchy‑informalized Mathlib corpus with an embedding‑reranker pipeline, achieving state‑of‑the‑art single‑query retrieval without domain‑specific fine‑tuning (nDCG@10 of 0.62 vs. 0.53 for the next‑best system). Its reasoning mode builds on standard mode as its retrieval substrate, targeting global premise retrieval through iterative sketch‑retrieve‑reflect cycles. On a 69‑query benchmark of research‑level Mathlib theorems, reasoning mode recovers 46.1% of ground‑truth premise groups within 10 retrieved candidates, outperforming strong reasoning retrieval systems (38.0%) and premise‑selection baselines (9.3%) on the same benchmark. In a controlled downstream evaluation with a fixed prover loop, replacing alternative retrievers with LeanSearch v2 yields the highest proof success (20% vs. 16% for the next‑best system and 4% without retrieval), confirming that retrieval quality propagates to proof generation. We have open‑sourced all code, data, and benchmarks. Code and data: https://github.com/frenzymath/LeanSearch‑v2 . The standard mode is publicly available with API access at https://leansearch.net/ .

PaperId: 1038, https://arxiv.org/pdf/2605.13047.pdf GitHub

Abstract:
Evaluating whether large vision‑language models (VLMs) align with human perception for high‑level semantic scene comprehension remains a challenge. Traditional white‑box interpretability methods are inapplicable to closed‑source architectures and passive metrics fail to isolate causal features. We introduce Counterfactual Semantic Saliency (CSS). This black‑box, model‑agnostic framework quantifies the importance of objects by measuring the semantic shift induced by their causal ablation from a scene. To evaluate AI‑human semantic alignment, we tested prominent VLMs against a human psychophysics baseline comprising 16,289 valid responses across 307 complex natural scenes and 1,306 high‑fidelity counterfactual variants. Our analysis reveals a pervasive scene comprehension gap: models exhibit an overreliance (relative to humans) on large objects (size bias), objects at the center of the image (center bias), and high saliency objects. In contrast, models rely less on people in the scenes than our human participants to describe the images. A model's size bias is a primary driver explaining variations in model‑human semantic divergence. Code and data will be available at https://github.com/starsky77/Counterfactual‑Semantic‑Saliency.

PaperId: 1039, https://arxiv.org/pdf/2605.13021.pdf GitHub

Abstract:
Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essential structural and semantic properties of the original graph. However, most existing methods rely on pair‑wise similarity matching, where each node independently searches for its best partner based on global information. This selfishness matching paradigm incurs substantial computational and memory overhead. To address this problem, we shift to a non‑selfishness principle that prioritizes the collective interference of neighborhood in coarsening, and propose an efficient method named NOPE, which achieves linear memory consumption and near‑linear computational complexity in the number of nodes. Furthermore, we derive a faster variant NOPE, which reduces O(δ\dot d) interference evaluation to O(d) based on the local isotropy assumption, and consequently alleviates the computational bottleneck for high‑degree nodes. Experimental results show that NOPE achieves 1.8‑10× speedup over NOPE and surpass almost all baselines with 1‑3 orders of magnitude acceleration. Meanwhile, learning on coarsened graphs yields comparable performance to original graphs, and can even show superior performance over LLM‑based graph reasoning owing to compact graph information. The code can be available at https://github.com/dazonglian/NOPE‑main.

PaperId: 1040, https://arxiv.org/pdf/2605.12975.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) has become a standard approach for knowledge‑intensive question answering, but existing systems remain brittle on multi‑hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that current methods represent reasoning through free‑form natural language, where intermediate states are implicit, retrieval queries can drift from intended entities, and errors are detected by the same model that produces them making self‑reflection an unreliable, ungrounded signal. We observe that multi‑hop question answering is a typical form of step‑by‑step computation, and that this structured process aligns closely with how code‑specialized language models are trained to operate. Motivated by this, we introduce \pyrag, a framework that reformulates multi‑hop RAG as program synthesis and execution. Instead of free‑form reasoning trajectories, \pyrag represents the reasoning process as an executable Python program over retrieval and QA tools, exposing intermediate states as variables, producing deterministic feedback through execution, and yielding an inspectable trace of the entire reasoning process. This formulation further enables compiler‑grounded self‑repair and execution‑driven adaptive retrieval without any additional training. Experiments on five QA benchmarks (PopQA, HotpotQA, 2WikiMultihopQA, MuSiQue, and Bamboogle) show that \pyrag consistently outperforms strong baselines under both training‑free and RL‑trained settings, with especially large gains on compositional multi‑hop datasets. Our code, data and models are publicly available at https://github.com/GasolSun36/PyRAG.

PaperId: 1041, https://arxiv.org/pdf/2605.12857.pdf GitHub

Abstract:
Existing API‑based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, rely on closed‑source APIs incompatible with chip vendors' air‑gapped security requirements, and cannot be trained on vendors' proprietary RTL codebases, leaving valuable internal data unused. Recent self‑trained models address the deployment constraint but remain single‑turn generators that overlook the critical role of verification in real industrial flows. To bridge these gaps, we present ChipMATE, the first self‑trained multi‑agent framework for RTL generation. Inspired by industrial practice where correctness emerges from cross‑comparison between independently written RTL modules and reference models, ChipMATE pairs a Verilog agent with a Python reference‑model agent that mutually verify each other's outputs without any golden oracle. We design a backtrack‑based inference workflow to prevent error propagation across turns, and a two‑stage training pipeline that first trains each agent individually to saturate its code‑generation capability, then trains the team jointly to collaborate effectively. To support the training, we further build a hybrid data‑generation framework that produces 64.4K high‑quality reference model training samples. ChipMATE achieves 75.0% and 80.1% pass@1 on VerilogEval V2 with 4B and 9B base models, outperforming all existing self‑trained models and even DeepSeek V4 with 1600B parameters. Our code and model weights are publicly available in https://github.com/zhongkaiyu/ChipMATE.

PaperId: 1042, https://arxiv.org/pdf/2605.12827.pdf GitHub GitHub

Abstract:
Graph neural networks (GNNs) deployed as cloud services can be stolen through model‑extraction attacks, which train a surrogate from query responses to reproduce the target's behavior, and a growing line of ownership defenses tries to prevent or trace such theft. This paper asks two questions: how hard is it to steal a GNN, and can we stop it? Prior work cannot answer either, because experiments use inconsistent datasets, threat models, and metrics. We introduce GraphIP‑Bench, a unified benchmark that evaluates both sides under a single black‑box protocol. GraphIP‑Bench integrates twelve extraction attacks, twelve defenses spanning watermarking, output perturbation, and query‑pattern detection, ten public graphs covering homophilic, heterophilic, and large‑scale regimes, three GNN backbones, and three graph‑learning tasks. It reports fidelity, task utility, ownership verification, and computational cost on shared splits, queries, and budgets. We further add a joint attack‑and‑defense track that runs every attack on every defended target and measures watermark verification on the resulting surrogate, exposing how much protection a defense retains after extraction. The empirical picture is clear: stealing a GNN is easy at medium query budgets and most defenses do not change this; several watermarks verify reliably on the protected model but lose most of their verification signal on the extracted surrogate, exposing a gap that single‑model evaluations miss; and heterophilic graphs are systematically harder to steal, while a cross‑architecture mismatch between target and surrogate reduces but does not prevent extraction. We release GraphIP‑Bench with reproducible scripts and configurations, and integrate the attacks and defenses into the PyGIP library. Code: https://github.com/LabRAI/GraphIP‑Bench. Library: https://labrai.github.io/PyGIP/index.html.

PaperId: 1043, https://arxiv.org/pdf/2605.12826.pdf GitHub GitHub

Abstract:
The proliferation of sophisticated image editing tools and generative artificial intelligence models has made verifying the authenticity of digital images increasingly challenging, with important implications for journalism, forensic analysis, and public trust. Although numerous forensic algorithms, ranging from handcrafted methods to deep learning‑based detectors, have been developed for manipulation detection, individual methods often suffer from limited robustness, fragmented evidence, or weak generalization across manipulation types and image conditions. To address these limitations, we present FRAME, a method for Forensic Routing and Adaptive Multi‑path Evidence fusion for image manipulation detection. FRAME organizes diverse forensic algorithms into a multi‑path analysis space, adaptively selects informative forensic paths for each input image, and fuses complementary evidence to improve detection and localization performance. By moving beyond single‑method analysis and fixed fusion strategies, FRAME provides a more robust and flexible approach to image forensic reasoning while preserving interpretable forensic cues from multiple evidence sources. Experimental results demonstrate the effectiveness of FRAME across diverse manipulation scenarios. Code is available at \hrefhttps://github.com/kzhao5/FRAMEhttps://github.com/kzhao5/FRAME.

PaperId: 1044, https://arxiv.org/pdf/2605.12771.pdf GitHub

Abstract:
Multi‑objective reinforcement learning in robotic domains requires balancing complex, non‑convex trade‑offs between conflicting objectives. While linear scalarization methods provide stability, they are theoretically incapable of recovering solutions within non‑convex regions of the Pareto front. Conversely, static non‑linear scalarizations (e.g., Tchebycheff) can theoretically access these regions but often suffer from severe gradient variance and optimization instability in deep RL. In this work, we propose an Adaptive Smooth Tchebycheff framework that resolves this tension by dynamically modulating the curvature of the optimization landscape. We introduce a novel conflict‑driven controller that regulates the optimization smoothness based on real‑time gradient interference. This allows the agent to anneal toward precise, non‑convex scalarization when objectives align, while elastically reverting to stable, smooth approximations when destructive gradient conflicts emerge. We validate our approach on a challenging robotic stealth visual search task ‑‑ a proxy for monitoring of protected/fragile ecosystems ‑‑ where an agent must balance search, exposure/interference minimization and exploration speed. Extensive ablations confirm that our conflict‑aware adaptation enables the robust discovery of Pareto‑optimal policies in non‑convex regions inaccessible to linear baselines and unstable for static non‑linear methods. Website: https://alejandromllo.github.io/research/pasta/

PaperId: 1045, https://arxiv.org/pdf/2605.12770.pdf GitHub

Abstract:
We introduce WriteSAE, a sparse autoencoder for the matrix updates written into recurrent language‑model state. In Gated DeltaNet, Mamba‑2, and RWKV‑7, each token writes a matrix‑shaped update to a recurrent cache; a residual‑stream SAE has vector‑shaped atoms and cannot replace that update directly. WriteSAE learns rank‑1 matrix atoms with the same shape as the model's own write. This lets us test a direct replacement: at positions where the SAE activates an atom, we remove the model's write, insert the atom scaled by its SAE activation, and continue the forward pass. The atom gives a closer final token distribution than deleting the write on 92.4% of evaluated positions; averaged per atom, the rate is 89.8%. For Gated DeltaNet, a formula using the forget gate, read query, and output embedding predicts the resulting logit change with R^2 = 0.98. The same replacement test transfers to Mamba‑2‑370M at 88.1%. In generation, the formula chooses a write direction; writing it into three consecutive cache positions at 3× the norm of the model's write makes tokens initially ranked 100‑‑1000 by the unmodified model appear in 100% of continuations, up from 33.3%. To our knowledge this is the first cache‑level steering intervention reported in a state‑space or hybrid recurrent layer.

PaperId: 1046, https://arxiv.org/pdf/2605.12704.pdf GitHub

Abstract:
A fundamental challenge in symbolic regression (SR) is efficiently recovering complex mathematical expressions from observational data. Although this problem is NP‑hard, many expressions of practical interest decompose naturally into combinations of nonlinear feature modules, concentrating structural complexity into a small number of reusable components. Here, we introduce FePySR, a two‑stage framework that reduces the SR search space by extracting valid features prior to equation search. FePySR first employs a heterogeneous neural network to constrain observational data to a set of candidate expressions, then performs structural optimization within this refined expression space using PySR. Across five standard benchmarks, FePySR outperforms state‑of‑the‑art methods by achieving higher equation recovery rates. On a set of 75 highly complex synthesized equations, FePySR recovers 36 equations, while producing substantially smaller mean squared errors on the remaining unrecovered cases, with reduced computation time compared to PySR. FePySR's first stage also maintains consistent performance under varying numbers of selected top features and increasing levels of noise in the observational data. Applied to ordinary differential equations governing biological systems, FePySR successfully identifies governing equations in 24 out of 100 tests where PySR recovers none. Taken together, FePySR is a generalizable framework that can enhance the SR solvers, enabling the efficient and reliable recovery of symbolic expressions across scientific domains.

PaperId: 1047, https://arxiv.org/pdf/2605.12684.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) are now routinely deployed for visual understanding, generation, and curation. A substantial fraction of these applications require an explicit aesthetic judgment. Most existing solutions reduce this judgment to predicting a scalar score for a single image. We first ask whether such scores faithfully capture comparative preference: in a controlled study with eight expert annotators, score‑derived rankings align poorly with the same annotators' direct comparisons, while direct ranking yields substantially higher inter‑annotator agreement on best‑ and worst‑image labels. Motivated by this finding, we introduce the Visual Aesthetic Benchmark (VAB), which casts aesthetic evaluation as comparative selection over candidate sets with matched subject matter. VAB contains 400 tasks and 1,195 images across fine art, photography, and illustration, with labels derived from the consensus of 10 independent expert judges per task. Evaluating 20 frontier MLLMs and six dedicated visual‑quality reward models, we find that the strongest system identifies both the best and the worst image correctly across three random permutations of the candidate order in only 26.5% of tasks, far below the 68.9% achieved by human experts. Fine‑tuning a 35B‑parameter model on 2,000 expert examples brings its accuracy close to that of a 397B‑parameter open‑weight model, suggesting that the comparative signal in VAB is transferable. Together, these results expose a clear and measurable gap between current multimodal models and expert aesthetic judgment, and VAB provides the first set‑based, expert‑grounded testbed on which that gap can be tracked and closed.

PaperId: 1048, https://arxiv.org/pdf/2605.12571.pdf GitHub GitHub

Abstract:
Long video question answering requires locating sparse, time‑scattered visual evidence within highly redundant content. Although current MLLMs perform well on short videos, long videos introduce long‑horizon search and verification, which often necessitates multi‑turn, agentic interaction. We show that existing LVU agents can exhibit "evidence misalignment": they produce correct answers that are not supported by the retrieved or inspected evidence. To characterize this failure, we introduce two diagnostics (temporal groundedness and semantic groundedness) and use them to reveal two pressures that amplify misalignment: prompt pressure from shared‑context saturation at inference time and reward pressure from outcome‑only optimization during training. These findings point to a structural root cause: the coupled agent paradigm conflates long‑horizon planning with answer authority. We therefore propose the decoupled planner‑inspector framework, which separates planning from answer authority and gates final answering on pixel‑level verification. Across four long‑video benchmarks, our framework improves both answer accuracy and evidence alignment, achieving 55.1% on LVBench and 62.0% on LongVideoBench while producing interpretable search trajectories. Moreover, the decoupled architecture scales consistently with increased search budgets and supports plug‑and‑play upgrades of the MLLM backbone without retraining the planner. Code and models are available at https://github.com/Echochef/VideoSEAL.

PaperId: 1049, https://arxiv.org/pdf/2605.12507.pdf GitHub

Abstract:
While Large Language Model (LLM) multi‑agent systems (MAS) offer a transformative approach to simulating human behavior in complex systems, it remains largely unexplored whether these simulations can replicate realistic structural and temporal dynamics from a dynamic network perspective. Our evaluation indicates that existing frameworks excel at generating plausible micro‑level interactions but fail to capture the emergent, macroscopic topologies necessary for domains that rely on realistic network dynamics, such as modeling information propagation and cybersecurity threats. To bridge this gap, we introduce two easily integrable extensions to simulation frameworks to ensure they preserve macroscopic network fidelity: 1) augmenting LLM agents with data‑driven event triggers to organically sustain long‑horizon interactions, and 2) integrating Hawkes processes to accurately model temporal activation dynamics. Our approach allows LLM MAS to capture both plausible micro‑level patterns and macroscopic topologies. We further demonstrate the utility of this framework in synthesizing realistic phishing campaigns within evolving communication networks. The study reveals how threats exploit structural vulnerabilities, highlighting the potential of our framework for developing next‑generation defenses. Our code is available at https://github.com/Graph‑COM/NSL.

PaperId: 1050, https://arxiv.org/pdf/2605.12495.pdf GitHub

Abstract:
In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR‑Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without an additional cold‑start stage. Our approach unlocks the model's intrinsic potential to perform advanced reasoning tasks: Reasoning Text‑to‑Image Generation, where the model actively infers implicit user intents, and Self‑Reflective Refinement, where it autonomously diagnoses and corrects misalignments in generated outputs. To address the challenge of providing stable supervision for real‑world multimodal generation, we introduce the Decompositional Verifiable Reward (DVReward). Unlike holistic scalar rewards, DVReward utilizes an LLM to decompose complex user requests into atomic, verifiable semantic and quality questions, which are then evaluated by a general MLLM to provide reliable and interpretable feedback. Extensive experiments demonstrate that AlphaGRPO yields robust improvements across multimodal generation benchmarks, including GenEval, TIIF‑Bench, DPG‑Bench and WISE, while also achieving significant gains in editing tasks on GEdit without training on editing tasks. These results validate that our self‑reflective reinforcement approach effectively leverages inherent understanding to guide high‑fidelity generation. Project page: https://huangrh99.github.io/AlphaGRPO/

PaperId: 1051, https://arxiv.org/pdf/2605.12484.pdf GitHub

Abstract:
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task‑specific information, which can result in catastrophic forgetting and loss of plasticity. In contrast, in‑context learning with fixed LLM parameters can cheaply and rapidly adapt to task‑specific requirements (e.g., prompt optimization), but cannot by itself typically match the performance gains available through updating LLM parameters. There is no good reason for restricting learning to being in‑context or in‑weights. Moreover, humans also likely learn at different time scales (e.g., System 1 vs 2). To this end, we introduce a fast‑slow learning framework for LLMs, with model parameters as "slow" weights and optimized context as "fast" weights. These fast "weights" can learn from textual feedback to absorb the task‑specific information, while allowing slow weights to stay closer to the base model and persist general reasoning behaviors. Fast‑Slow Training (FST) is up to 3x more sample‑efficient than only slow learning (RL) across reasoning tasks, while consistently reaching a higher performance asymptote. Moreover, FST‑trained models remain closer to the base LLM (up to 70% less KL divergence), resulting in less catastrophic forgetting than RL‑training. This reduced drift also preserves plasticity: after training on one task, FST trained models adapt more effectively to a subsequent task than parameter‑only trained models. In continual learning scenarios, where task domains change on the fly, FST continues to acquire each new task while parameter‑only RL stalls.

PaperId: 1052, https://arxiv.org/pdf/2605.12481.pdf GitHub

Abstract:
Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high‑level tool calls, such as API‑based file operations, but this hybrid action space often leaves them uncertain about when to continue with GUI actions or switch to tools, leading to suboptimal execution paths. This difficulty stems from the scarcity of high‑quality interleaved GUI‑Tool trajectories, the cost and brittleness of collecting real tool trajectories, and the lack of trajectory‑level supervision for GUI‑Tool path selection. In this paper, we propose ToolCUA, an end‑to‑end agent designed to learn optimal GUI‑Tool path selection through a staged training paradigm. We first introduce an Interleaved GUI‑Tool Trajectory Scaling Pipeline that repurposes abundant static GUI trajectories and synthesizes a grounded tool library, enabling diverse GUI‑Tool trajectories without manual engineering or real tool‑trajectory collection. We then perform Tool‑Bootstrapped GUI RFT, combining warmup SFT with single‑turn RL to improve decisions at critical GUI‑Tool switching points. Finally, we optimize ToolCUA with Online Agentic RL in a high‑fidelity GUI‑Tool environment, guided by a Tool‑Efficient Path Reward that encourages appropriate tool use and shorter execution paths. Experiments on OSWorld‑MCP show that ToolCUA achieves 46.85% accuracy, a relative improvement of approximately 66% over the baseline, establishing a new state of the art among models of comparable scale. It also improves by 3.9% over GUI‑only settings, demonstrating effective GUI‑Tool orchestration. The results further suggest that training in a hybrid action space is a promising paradigm for real‑world digital agents. Open‑sourced here: https://x‑plug.github.io/ToolCUA/

PaperId: 1053, https://arxiv.org/pdf/2605.12480.pdf GitHub

Abstract:
Recent advances in joint audio‑video generation have been remarkable, yet real‑world applications demand strong per‑modality fidelity, cross‑modal alignment, and fine‑grained synchronization. Reinforcement Learning (RL) offers a promising paradigm, but its extension to multi‑objective and multi‑modal joint audio‑video generation remains unexplored. Notably, our in‑depth analysis first reveals that the primary obstacles to applying RL in this stem from: (i) multi‑objective advantages inconsistency, where the advantages of multimodal outputs are not always consistent within a group; (ii) multi‑modal gradients imbalance, where video‑branch gradients leak into shallow audio layers responsible for intra‑modal generation; (iii) uniform credit assignment, where fine‑grained cross‑modal alignment regions fail to get efficient exploration. These shortcomings suggest that vanilla RL fine‑tuning strategy with a single global advantage often leads to suboptimal results. To address these challenges, we propose OmniNFT, a novel modality‑aware online diffusion RL framework with three key innovations: (1) Modality‑wise advantage routing, which routes independent per‑reward advantages to their respective modality generation branches. (2) Layer‑wise gradient surgery, which selectively detaches video‑branch gradients on shallow audio layers while retaining those for cross‑modal interaction layers. (3) Region‑wise loss reweighting, which modulates policy optimization toward critical regions related to audio‑video synchronization and fine‑grained alignment. Extensive experiments on JavisBench and VBench with the LTX‑2 backbone demonstrate that OmniNFT achieves comprehensive improvements in audio and video perceptual quality, cross‑modal alignment, and audio‑video synchronization.

PaperId: 1054, https://arxiv.org/pdf/2605.12380.pdf GitHub

Abstract:
Reinforcement learning is structurally harder than supervised learning because the policy changes the data distribution it learns from. The resulting fragility is especially visible in large‑model training, where the training and rollout systems differ in numerical precision, sampling, and other implementation details. Existing methods manage this fragility by adding hyper‑parameters to the training objective, which makes the algorithm more sensitive to its configuration and requires retuning whenever the task, model scale, or distribution mismatch changes. This fragility traces to two concerns that current objectives entangle through hyper‑parameters set before training begins: a trust‑region concern, that updates should not move the policy too far from its current value, and an off‑policy concern, that data from older or different behavior policies should influence the update only to the extent that it remains reliable. Neither concern is a constant to set in advance, and their severity is reflected in the policy‑ratio distribution of the current batch. We present a simple yet effective batch‑adaptive objective that replaces fixed clipping with the normalized effective sample size of the policy ratios. The same statistic caps the score‑function weight and sets the strength of an off‑policy regularizer, so the update stays close to the usual on‑policy score‑function update when ratios are nearly uniform, and tightens automatically when stale or mismatched data cause ratio concentration, while retaining a nonzero learning signal on high‑ratio tokens. Experiments across a wide range of settings show that our method matches or exceeds tuned baselines, introducing no new objective hyper‑parameters and removing several existing ones. The code is available at https://github.com/FeynRL‑project/FeynRL.

PaperId: 1055, https://arxiv.org/pdf/2605.12289.pdf GitHub

Abstract:
Leveraging the rich world knowledge of Large Language Models (LLMs) to enhance Reinforcement Learning (RL) agents offers a promising path toward general intelligence. However, a fundamental prior‑dynamics mismatch hinders existing approaches: static LLM knowledge cannot directly adapt to the complex transition dynamics of long‑horizon tasks. Using LLM priors as fixed policies limits exploration diversity, as the prior is blind to environment‑specific dynamics; while end‑to‑end fine‑tuning suffers from optimization instability and credit assignment issues. To bridge this gap, we propose PriorZero, a unified framework that integrates LLM‑derived conceptual priors into world‑model‑based planning through a decoupled rollout‑training design. During rollout, a novel root‑prior injection mechanism incorporates LLM priors exclusively at the root node of Monte Carlo Tree Search (MCTS), focusing search on semantically promising actions while preserving the world model's deep lookahead capability. During training, PriorZero decouples world‑model learning from LLM adaptation: the world model is continuously refined on interaction data to jointly improve its dynamics, policy, and value predictions, its value estimates are then leveraged to provide fine‑grained credit assignment signals for stable LLM fine‑tuning via alternating optimization. Experiments across diverse benchmarks, including text‑based adventure games in Jericho and instruction‑following gridworld tasks in BabyAI, demonstrate that PriorZero consistently improves both exploration efficiency and asymptotic performance, establishing a promising framework for LLM‑empowered decision‑making. Our code is available at https://github.com/opendilab/LightZero.

PaperId: 1056, https://arxiv.org/pdf/2605.12242.pdf GitHub

Abstract:
Automatic Speech Recognition (ASR) transcripts often contain disfluencies, such as fillers, repetitions, and false starts, which reduce readability and hinder downstream applications like chatbots and voice assistants. If left unaddressed, such disfluencies can significantly degrade the reliability of downstream systems. Most existing approaches rely on classical models that focus on identifying disfluent tokens for removal. While this strategy is effective to some extent, it often disrupts grammatical structure and semantic coherence, leading to incomplete or unnatural sentences. Recent literature explored the use of large language models (LLMs); however, these efforts have primarily focused on disfluency detection or data augmentation, rather than performing comprehensive correction. We propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens, and these signals guide instruction fine‑tuning of an LLM to rewrite transcripts into fluent text. To further improve reliability, we add a contrastive learning objective that penalizes the reproduction of disfluent tokens, encouraging the model to preserve grammar and meaning while removing disfluent artifacts. Our experiments across three Indian languages, namely Hindi, Bengali, and Marathi show consistent improvements over strong baselines, including multilingual sequence‑to‑sequence models. These results highlight that detection‑only strategies are insufficient. Combining token‑level cues with instruction tuning and contrastive learning provides a practical and scalable solution for multilingual disfluency correction in speech‑driven NLP systems. We make the codes publicly available at https://github.com/deepak‑kumar‑98/Mind‑the‑Pause.

PaperId: 1057, https://arxiv.org/pdf/2605.12236.pdf GitHub

Abstract:
Fine‑tuning pre‑trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre‑training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration. We present a unified framework that enables the exploration necessary to enable efficient robot policy finetuning by bridging BC pre‑training and RL fine‑tuning. Our pre‑training method, Context‑Smoothed Pre‑training (CSP), injects forward‑diffusion noise into policy inputs, creating a continuum between precise imitation and broad action coverage. We then fine‑tune pre‑trained policies via Timestep‑Modulated Reinforcement Learning (TMRL), which trains the agent to dynamically adjust this conditioning during fine‑tuning by modulating the diffusion timestep, granting explicit control over exploration. Integrating seamlessly with arbitrary policy inputs, e.g., states, 3D point clouds, or image‑based VLA policies, we show that TMRL improves RL fine‑tuning sample efficiency. Notably, TMRL enables successful real‑world fine‑tuning on complex manipulation tasks in under one hour. Videos and code available at https://weirdlabuw.github.io/tmrl/.

PaperId: 1058, https://arxiv.org/pdf/2605.12153.pdf GitHub GitHub

Abstract:
We present Curated Industrial Developer Repository (CIDR), a large‑scale dataset of real‑world software repositories collected through direct collaboration with 12 industrial partner organizations. The dataset comprises 2,440 repositories spanning 138 programming languages and totalling 373 million lines of code, accompanied by structured per‑repository metadata. Unlike existing code corpora derived from public open‑source platforms, CIDR consists exclusively of proprietary production codebases contributed under formal data sharing agreements, covering application domains including enterprise web and mobile development, fintech, and custom software consultancy. All repositories were processed through a multi‑stage pipeline encompassing structured partner onboarding, two‑stage quality selection combining automated metadata filtering with manual code review, and a deterministic anonymization pipeline covering the full version control history. The dataset is intended to support research in code intelligence, software quality analysis, pre‑training and fine‑tuning of code language models, developer behaviour studies, and construction of agent evaluation benchmarks. Access is provided under a restricted commercial license; details are available at https://fermatix.ai/#Contact.

PaperId: 1059, https://arxiv.org/pdf/2605.12078.pdf GitHub

Abstract:
Agentic AI failures need post‑hoc reconstruction: what the agent did, on whose authority, against which policy, and from what reasoning. Cross‑regime feasibility remains unmeasured under one property‑level schema. We apply the Decision Trace Reconstructor unmodified to pinned worked‑example anchors from six public vendor SDK regimes spanning cloud‑agent, observability, tool‑use, telemetry, and protocol traces, plus two comparator columns. Each Decision Event Schema (DES) property is classified as fully fillable, partially fillable, structurally unfillable, or opaque. Per‑property reconstructability of an agent decision already varies between regimes at this anchor scale. Strict‑governance‑completeness separates into three tiers ranging from 42.9% to 85.7%, yielding one regime‑independent gap (reasoning trace), four regime‑dependent gaps, and one Mixed property; the pilot is single‑annotator, one anchor per cell, descriptive, with outputs checksum‑verifiable from a deposited reproducibility package.

PaperId: 1060, https://arxiv.org/pdf/2605.12069.pdf GitHub

Abstract:
Zero‑shot anomaly detection aims to identify defects in unseen categories without target‑specific training. Existing methods usually apply the same feature transformation to all samples, treating normal and anomalous data uniformly despite their fundamentally asymmetric distributions, compact normals versus diverse anomalies. We instead exploit this natural asymmetry by proposing AVA‑DINO, an anomaly‑aware vision‑language adaptation framework with dual specialized branches for normal and anomalous patterns that adapt frozen DINOv3 visual features. During training on auxiliary data, the two branches are learned jointly with a text‑guided routing mechanism and explicit routing regularization that encourages branch specialization. At test time, only the input image and fixed, predefined language descriptions are used to dynamically combine the two branches, enabling an asymmetric activation. This design prevents degenerate uniform routing and allows context‑specific feature transformations. Experiments across nine industrial and medical benchmarks demonstrate state‑of‑the‑art performance, achieving 93.5% image‑AUROC on MVTec‑AD and strong cross‑domain generalization to medical imaging without domain‑specific fine‑tuning. https://github.com/aqeeelmirza/AVA‑DINO

PaperId: 1061, https://arxiv.org/pdf/2605.12034.pdf GitHub

Abstract:
Omni‑modal language models are intended to jointly understand audio, visual inputs, and language, but benchmark gains can be inflated when visual evidence alone is enough to answer a query. We study whether current omni‑modal benchmarks separate visual shortcuts from genuine audio‑visual‑language evidence integration, and how post‑training behaves under a visually debiased evaluation setting. We audit nine omni‑modal benchmarks with visual‑only probing, remove visually solvable queries, and retain full subsets when filtering is undefined or would make comparisons unstable. This yields OmniClean, a cleaned evaluation view with 8,551 retained queries from 16,968 audited queries. On OmniClean, we evaluate OmniBoost, a three‑stage post‑training recipe based on Qwen2.5‑Omni‑3B: mixed bi‑modal SFT, mixed‑modality RLVR, and SFT on self‑distilled data. Balanced bi‑modal SFT gives limited and uneven gains, RLVR provides the first broad improvement, and self‑distillation reshapes the benchmark profile. After SFT on self‑distilled data, the 3B model reaches performance comparable to, and in aggregate slightly above, Qwen3‑Omni‑30B‑A3B‑Instruct without using a stronger omni‑modal teacher. These results show that omni‑modal progress is easier to interpret when evaluation controls visual leakage, and that small omni‑modal models can benefit from staged post‑training with self‑distilled omni‑query supervision. Project page: https://cheliu‑computation.github.io/omni/

PaperId: 1062, https://arxiv.org/pdf/2605.12013.pdf GitHub

Abstract:
Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel‑space models from scratch demands prohibitive computational and data resources. To address this, we propose the Latent‑to‑Pixel (L2P) transfer paradigm, an efficient framework that directly harnesses the rich knowledge of pre‑trained LDMs to build powerful pixel‑space models. Specifically, L2P discards the VAE in favor of large‑patch tokenization and freezes the source LDM's intermediate layers, exclusively training shallow layers to learn the latent‑to‑pixel transformation. By utilizing LDM‑generated synthetic images as the sole training corpus, L2P fits an already smooth data manifold, enabling rapid convergence with zero real‑data collection. This strategy allows L2P to seamlessly migrate massive latent priors to the pixel space using only 8 GPUs. Furthermore, eliminating the VAE memory bottleneck unlocks native 4K ultra‑high resolution generation. Extensive experiments across mainstream LDM architectures show that L2P incurs negligible training overhead, yet performs on par with the source LDM on DPG‑Bench and reaches 93% performance on GenEval.

PaperId: 1063, https://arxiv.org/pdf/2605.11928.pdf GitHub

Abstract:
Tool‑use language agents are evaluated on benchmarks that assume clean inputs, unambiguous tool registries, and reliable APIs. Real deployments violate all these assumptions: user typos propagate into hallucinated tool names, a misconfigured request timeout can stall an agent indefinitely, and duplicate tool names across servers can freeze an SDK. We study these failures as a sim‑to‑real gap in the tool‑use partially observable Markov decision process (POMDP), where deployment noise enters through the observation, action space, reward‑relevant metadata, or transition dynamics. We introduce RobustBench‑TC, a benchmark with 22 perturbation types organized by these four POMDP components, each grounded in a verified GitHub issue or documented tool‑calling failure. Across 21 models from 1.5B to 32B parameters (including the closed‑source o4‑mini), the robustness profile is sharply uneven: observation perturbations reduce accuracy by less than 5%, while reward‑relevant and transition perturbations reduce accuracy by roughly 40% and 30%, respectively; scale alone does not close these gaps. We then propose ToolRL‑DR, a domain‑randomization reinforcement learning (RL) recipe that trains a tool‑use agent on perturbation‑augmented trajectories spanning the three statically encodable POMDP components. On a 3B backbone, ToolRL‑DR‑Full retains roughly three‑quarters of clean accuracy and reaches an aggregate perturbed accuracy comparable to open‑source 14B function‑calling baselines while substantially narrowing the gap to o4‑mini. It closes approximately 27% of the Transition gap despite never seeing transition perturbations in training, suggesting that RL on adversarial static tool‑use inputs induces a more persistent retry policy that transfers to unseen runtime failures. The dataset, code and benchmark leaderboard are publicly available.

PaperId: 1064, https://arxiv.org/pdf/2605.11905.pdf GitHub

Abstract:
Automated theorem proving with large language models in Lean 4 is commonly approached through either step‑level tactic prediction with tree search or whole‑proof generation. These two paradigms represent opposite granularities for constructing supervised training data: the former provides dense local signals but may fragment coherent proof processes, while the latter preserves global structure but requires complex end‑to‑end generation. In this paper, we revisit supervision granularity as a training set construction problem over proof trajectories and propose segment‑level supervision, a training data construction strategy that extracts locally coherent proof segments for training policy models. We further reuse the same strategy at inference time to trigger short rollouts for existing step‑level models. When trained with segment‑level supervision on STP, LeanWorkbook, and NuminaMath‑LEAN, the resulting policy models achieve proof success rates of 64.84%, 60.90%, and 66.31% on miniF2F, respectively, consistently outperforming both step‑level and whole‑proof baselines. Goal‑aware rollout further improves existing step‑level provers while reducing inference costs. It increases the proof success rate of BFS‑Prover‑V2‑7B from 68.77% to 70.74% and that of InternLM2.5‑StepProver from 59.59% to 60.33%, showing that appropriate supervision granularity better aligns model learning with proof structure and search. Code and models are available at https://github.com/NJUDeepEngine/SEG‑ATP.

PaperId: 1065, https://arxiv.org/pdf/2605.11904.pdf GitHub

Abstract:
The Nearest Class Mean (NCM) classifier is widely favored in Class‑Incremental Learning (CIL) for its superior resistance to catastrophic forgetting compared to Fully Connected layers. While Neural Collapse (NC) theory supports NCM's optimality by assuming features collapse into single points, non‑linear feature drift and insufficient training in CIL often prevent this ideal state. Consequently, classes manifest as complex manifolds rather than collapsed points, rendering the single‑point NCM suboptimal. To address this, we propose Hierarchical‑Cluster SOINN (HC‑SOINN), a novel classifier that captures the topological structure of these manifolds via a ``local‑to‑global'' representation. Furthermore, we introduce Structure‑Topology Alignment via Residuals (STAR) method, which employs a fine‑grained pointwise trajectory tracking mechanism to actively deform the learned topology, allowing it to adapt precisely to complex non‑linear feature drift. Theoretical analysis and Procrustes distance experiments validate our framework's resilience to manifold deformations. We integrated HC‑SOINN into seven state‑of‑the‑art methods by replacing their original classifiers, achieving consistent improvements that highlight the effectiveness and robustness of our approach. Code is available at https://github.com/yhyet/HC_SOINN.

PaperId: 1066, https://arxiv.org/pdf/2605.11868.pdf GitHub

Abstract:
Web‑browsing AI agents are increasingly deployed in enterprise settings under strict whitelists of approved domains, yet adversaries can still influence them by embedding hidden instructions in the HTML pages those domains serve. Existing red‑teaming resources fall short of this scenario: prompt‑injection benchmarks ship pre‑built adversarial pages that whitelisted agents cannot reach, and generic LLM scanners probe the model API rather than its retrieved content. We present IPI‑proxy, an open‑source toolkit for red‑teaming web‑browsing agents against indirect prompt injection (IPI). At its core is an intercepting proxy that rewrites real HTTP responses from whitelisted domains in flight, embedding payloads drawn from a unified library of 820 deduplicated attack strings extracted from six published benchmarks (BIPIA, InjecAgent, AgentDojo, Tensor Trust, WASP, and LLMail‑Inject). A YAML‑driven test harness independently parameterizes the payload set, the embedding technique (HTML comment, invisible CSS, or LLM‑generated semantic prose), and the HTML insertion point (6 locations from \icodehead\_meta to \icodescript\_comment), enabling parameter‑sweep evaluation without mock pages or sandboxed environments. A companion exfiltration tracker logs successful callbacks. This paper describes the threat model, situates IPI‑proxy among contemporary IPI benchmarks and red‑teaming tools, and details its architecture, design decisions, and configuration interface. By bridging static benchmarks and live deployment, IPI‑proxy gives AI security teams a reproducible substrate for measuring and hardening web‑browsing agents against indirect prompt injection on the same retrieval surface attackers exploit in production.

PaperId: 1067, https://arxiv.org/pdf/2605.11864.pdf GitHub

Abstract:
Listwise reranking is a key yet computationally expensive component in vision‑centric retrieval and multimodal retrieval‑augmented generation (M‑RAG) over long documents. While recent VLM‑based rerankers achieve strong accuracy, their practicality is often limited by long visual‑token sequences and multi‑step autoregressive decoding. We propose ZipRerank, a highly efficient listwise multimodal reranker that directly addresses both bottlenecks. It reduces input length via a lightweight query‑image early interaction mechanism and eliminates autoregressive decoding by scoring all candidates in a single forward pass. To enable effective learning, ZipRerank adopts a two‑stage training strategy: (i) listwise pretraining on large‑scale text data rendered as images, and (ii) multimodal finetuning with VLM‑teacher‑distilled soft‑ranking supervision. Extensive experiments on the MMDocIR benchmark show that ZipRerank matches or surpasses state‑of‑the‑art multimodal rerankers while reducing LLM inference latency by up to an order of magnitude, making it well‑suited for latency‑sensitive real‑world systems. The code is available at https://github.com/dukesun99/ZipRerank.

PaperId: 1068, https://arxiv.org/pdf/2605.11803.pdf GitHub

Abstract:
As Video Large Language Models (Video‑LLMs) scale to longer and more complex videos, their inference cost grows rapidly due to the large volume of visual tokens accumulated across frames. Training‑free token compression has emerged as a practical solution to this bottleneck. However, existing temporal compression methods rely primarily on cross‑frame token similarity or segmentation heuristics, overlooking each token's semantic role within its frame and failing to adapt compression strength to the compressibility of each frame pair. In this work, we propose OTT‑Vid, a transport‑derived allocation framework for temporal token compression. Our approach consists of two stages: spatial pruning identifies representative content within each frame, and optimal transport (OT) is then solved between neighboring frames to estimate temporal compressibility. We formulate this OT with non‑uniform token mass, which protects semantically important tokens from aggressive compression, and a locality‑aware cost that captures both feature and spatial disparities. The resulting transport plan jointly balances token importance and matching cost, while its total cost defines the transport difficulty of each frame pair, which we use to allocate compression budgets dynamically. Experiments on six benchmarks spanning video question answering and temporal grounding show that OTT‑Vid preserves 95.8% of VQA and 73.9% of VTG performance while retaining only 10% of tokens, consistently outperforming existing state‑of‑the‑art training‑free compression methods.

PaperId: 1069, https://arxiv.org/pdf/2605.11750.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models are often brittle in fine‑grained manipulation, where minor action errors during the critical phases can rapidly escalate into irrecoverable failures. Since existing VLA models rely predominantly on successful demonstrations for training, they lack an explicit awareness of failure during these critical phases. To address this, we propose DreamAvoid, a critical‑phase test‑time dreaming framework that enables VLA models to anticipate and avoid failures. We also introduce an autonomous boundary learning paradigm to refine the system's understanding of the subtle boundary between success and failure. Specifically, we (1) utilize a Dream Trigger to determine whether the execution has entered a critical phase, (2) sample multiple candidate action chunks from the VLA via an Action Proposer, and (3) employ a Dream Evaluator, jointly trained on mixed data (success, failure, and boundary cases), to "dream" the short‑horizon futures corresponding to the candidate actions, evaluate their values, and select the optimal action. We conduct extensive evaluations on real‑world manipulation tasks and simulation benchmarks. The results demonstrate that DreamAvoid can effectively avoid failures, thereby improving the overall task success rate. Our code is available at https://github.com/XianzheFan/DreamAvoid.

PaperId: 1070, https://arxiv.org/pdf/2605.11712.pdf GitHub

Abstract:
Aligning large language models (LLMs) with human values typically relies on post‑training or inference‑time steering that directly manipulates the backbone's parameters or representation space. However, a critical gap exists: the model's residual stream is highly dynamic, in which values exist as fragile, low‑dimensional properties, inherently incompatible with the stability required for consistent value expression. In this paper, we propose the Stable Value Guidance Transformer (SVGT), which addresses this gap through an independent value module incorporating two key designs: (1) independent value modeling, maintaining normative representations in a dedicated value space isolated from the backbone, and (2) explicit behavioral guidance, transducing these stable signals into learnable latent Bridge Tokens. These tokens serve as dynamic value anchors to explicitly steer the generative trajectory, ensuring robust adherence across diverse contexts without disrupting the backbone's internal representations. Experiments across multiple backbones and safety benchmarks show that SVGT generally reduces harmful scores by over 70% while maintaining generation fluency, demonstrating the efficacy of architecturally grounded value modeling. Our code is available at https://github.com/Clervils/SVGT.git.

PaperId: 1071, https://arxiv.org/pdf/2605.11711.pdf GitHub

Abstract:
Model‑based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream off‑policy actor‑critic learning. It implicitly combines the advantages of both model‑free and model‑based approaches while avoiding the training costs associated with model‑based methods. Nevertheless, existing model‑based representation methods can fail to capture sufficient information about relevant variables and can overfit to early experiences in the replay buffer. These incur biases in representation and actor‑critic learning, leading to inferior performance. To address this, we propose Debiased model‑based Representations for Q‑learning, tagged DR.Q algorithm. DR.Q explicitly maximizes the mutual information between the representations of the current state‑action pair and the next state besides minimizing their deviations, and samples transitions with faded prioritized experience replay. We evaluate DR.Q on numerous continuous control benchmarks with a single set of hyperparameters, and the results demonstrate that DR.Q can match or surpass recent strong baselines, sometimes outperforming them by a large margin. Our code is available at https://github.com/dmksjfl/DR.Q.

PaperId: 1072, https://arxiv.org/pdf/2605.11696.pdf GitHub

Abstract:
Recent single‑image relighting methods, powered by advanced generative models, have achieved impressive photorealism on synthetic benchmarks. However, their effectiveness in the complex visual landscape of the real world remains largely unverified. A critical gap exists, as current datasets are typically designed for multi‑view reconstruction and fail to address the unique challenges of single‑image relighting. To bridge this synthetic‑to‑real gap, we introduce WildRelight, the first in‑the‑wild dataset specifically created for evaluating single‑image relighting models. WildRelight features a diverse collection of high‑resolution outdoor scenes, captured under strictly aligned, temporally varying natural illuminations, each paired with a high‑dynamic‑range environment map. Using this data, we establish a rigorous benchmark revealing that state‑of‑the‑art models trained on synthetic data suffer from severe domain shifts. The strictly aligned temporal structure of WildRelight enables a new paradigm for domain adaptation. We demonstrate this by introducing a physics‑guided inference framework that leverages the captured natural light evolution as a self‑supervised constraint. By integrating Diffusion Posterior Sampling (DPS) with temporal Sampling‑Aware Test‑Time Adaptation (TTA), we show that the dataset allows synthetic models to align with real‑world statistics on‑the‑fly, transforming the intractable sim‑to‑real challenge into a tractable self‑supervised task. The dataset and code will be made publicly available to foster robust, physically‑grounded relighting research.

PaperId: 1073, https://arxiv.org/pdf/2605.11679.pdf GitHub

Abstract:
In the realm of multi‑objective alignment for large language models, balancing disparate human preferences often manifests as a zero‑sum conflict. Specifically, the intrinsic tension between competing goals dictates that aggressively optimizing for one metric (e.g., helpfulness) frequently incurs a substantial penalty on another (e.g., harmlessness). While prior work mainly focuses on data selection, parameter merging, or algorithmic balancing during training, these approaches merely force compromises between divergent preferences along a fixed Pareto frontier, failing to fundamentally resolve the inherent trade‑off. In this work, we approach this problem from a novel perspective of multi‑dimensional rewards. By scaling up the model's rollouts and analyzing the outputs across different reward dimensions, we arrive at a critical conclusion: the conflict among multiple objectives stems from the fact that the prompt itself inherently restricts the achievable multi‑dimensional rewards. Based on this core observation, we propose MORA: Multi‑Objective Reward Assimilation. Specifically, MORA isolates single‑reward prompts through pre‑sampling and expands their reward diversity by rewriting the original questions to incorporate multi‑dimensional intents. Extensive experiments demonstrate that: (1) in sequential alignment, MORA achieves single‑preference improvements ranging from 5% to 12.4%, with exceptional gains in harmlessness, after multiple‑preference alignment across helpful, harmless, and truthful dimensions. (2) In simultaneous alignment, MORA achieves an average overall reward improvement of 4.6%. Our codes are available at https://github.com/Shiying‑Huang/MORA‑MPA.

PaperId: 1074, https://arxiv.org/pdf/2605.11666.pdf GitHub

Abstract:
The reasoning frontier of Large Language Models (LLMs) has advanced significantly through modern post‑training paradigms (e.g., Reinforcement Learning from Verifiable Rewards (RLVR)). However, the efficacy of these methods remains fundamentally constrained by the diversity and complexity of the training data. One practical solution is data synthesis; yet, prevalent methods relying on unstructured mutation or exploration suffer from homogeneity collapse, failing to systematically expand the reasoning frontier. To overcome this, we propose Evoutionary Task Discovery (EvoTD), a framework that treats data synthesis as a directed search over a dual‑axis manifold of Algorithmic Skills and Complexity Attributes. We introduce structured evolutionary operators to navigate this space: a Crossover operator that synthesizes novel skill compositions to enhance diversity, and a Parametric Mutation operator that scales structural constraints (e.g., input size, tree depth) to drive robust generalization. Crucially, we integrate a dynamic Zone of Proximal Development filter, ensuring tasks lie within the learnable region of the model. Empirically, EvoTD delivers substantial reasoning gains that generalize consistently across model architectures, pretraining regimes, and scales, demonstrating that structured evolutionary curricula can effectively support reasoning improvement. We release our code on https://github.com/liqinye/EvoTD.

PaperId: 1075, https://arxiv.org/pdf/2605.11601.pdf GitHub

Abstract:
Autoregressive language models are widely used for text evaluation, however, their left‑to‑right factorization introduces positional bias, i.e., early tokens are scored with only leftward context, conflating architectural asymmetry with true text quality. We propose masked reconstruction as an alternative paradigm, where every token is scored using full bidirectional context. We introduce DiffScore, an evaluation framework built on Masked Large Diffusion Language Models. By measuring text recoverability across continuous masking rates, DiffScore eliminates positional bias and naturally establishes an evaluation hierarchy from local fluency to global coherence. We further provide diagnostic tools unavailable to autoregressive frameworks: multi‑timestep quality profiles that decompose scores across masking rates, and bidirectional PMI decomposition that disentangles fluency from faithfulness. Experiments across ten benchmarks show that DiffScore consistently outperforms autoregressive baselines in both zero‑shot and fine‑tuned settings. The code is released at: https://github.com/wenlai‑lavine/DiffScore.

PaperId: 1076, https://arxiv.org/pdf/2605.11598.pdf GitHub

Abstract:
The increasing adoption of data‑driven decision‑making in public health has established epidemic forecasting as a critical area of research. Recent advances in multivariate forecasting models better capture complex temporal dependencies than conventional univariate approaches, which model individual series independently. Despite this potential, the development of robust epidemic forecasting methods is constrained by the lack of high‑quality benchmarks comprising diverse multivariate datasets across infectious diseases and geographical regions. To address this gap, we present EpiCastBench, a large‑scale benchmarking framework featuring 40 curated (correlated) multivariate epidemic datasets. These publicly available datasets span a wide range of infectious diseases and exhibit diverse characteristics in terms of temporal granularity, series length, and sparsity. We analyze these datasets to identify their global features and structural patterns. To ensure reproducibility and fair comparison, we establish standardized evaluation settings, including a unified forecasting horizon, consistent preprocessing pipelines, diverse performance metrics, and statistical significance testing. By leveraging this framework, we conduct a comprehensive evaluation of 15 multivariate forecasting models spanning statistical baselines to state‑of‑the‑art deep learning and foundation models. All datasets and code are publicly available on Kaggle (https://www.kaggle.com/datasets/aimltsf/epicastbench) and GitHub (https://github.com/aimltsf/EpiCastBench).

PaperId: 1077, https://arxiv.org/pdf/2605.11559.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have become a key interface for visual reasoning and grounded question answering, yet they remain vulnerable to visual hallucinations, where generated responses contradict image content or mention nonexistent objects. A central challenge is that hallucination is not always caused by a simple lack of visual attention: the model may still assign substantial attention mass to image tokens while internally drifting toward an incorrect answer. In this paper, we show that the high‑frequency structure of visual attention, measured by layer‑wise Laplacian energy, reveals both the layer where hallucinated preferences emerge and the layer where the ground‑truth answer transiently recovers. Building on this finding, we propose LaSCD (Laplacian‑Spectral Contrastive Decoding), a training‑free decoding strategy that selects informative layers via Laplacian energy and remaps next‑token logits in closed form. Experiments on hallucination and general multimodal benchmarks show that LaSCD consistently reduces hallucination while preserving general capabilities, highlighting its potential as a faithful decoding paradigm. The code is available at https://github.com/macovaseas/LaSCD.

PaperId: 1078, https://arxiv.org/pdf/2605.11532.pdf GitHub

Abstract:
The discovery of novel methodologies for emerging problems is a continuing cycle in ML, often driven by the migration of techniques across domains. Building on this observation, we ask whether current LLM ideation systems benefit from targeted cross‑domain retrieval or simply from exposure to diverse mechanisms. We study this question through PaperGym, a three‑stage pipeline: (1) tool‑augmented seed extraction via read, grep, and bash over an isolated paper environment, (2) cross‑domain seed retrieval via paraphrasing across seven ML domains, and (3) method synthesis from retrieved seeds, each scored by rubric‑based judges. Tool‑augmented extraction improves specificity, and paraphrase‑based retrieval broadens domain coverage. In synthesis, cross‑domain retrieval receives more pairwise novelty wins than no‑retrieval and same‑domain baselines, but shows no significant difference from a random diverse‑seed control. These findings suggest LLM ideation systems benefit from diverse seed exposure, but do not yet reliably exploit the semantic reason particular seeds were retrieved. We release the seed library, rubric prompts, and run scripts at https://github.com/yunjoochoi/PaperGym

PaperId: 1079, https://arxiv.org/pdf/2605.11501.pdf GitHub

Abstract:
Decompilers are useful tools used in reverse engineering to understand compiled source code. Reconstructing source code from compiled binaries is a challenging task, because high‑level syntax, identifiers, and custom data types are generally lost as the compiler translates human‑readable code to low‑level machine code. Deterministic decompilers are useful tools for binary analysis, but can struggle to infer idiomatic syntax and identifier names. Generative AI models are a natural fit for reconstructing high‑level syntax, identifiers, and types, but they can still suffer by hallucinating improper programming constructs and semantics. Instead of attempting to improve neural decompilers with more data and more training, we argue that compiler feedback can be used to dramatically improve the semantic correctness of neural decompiler outputs via search. Our system, Decaf (DECompilation with Automated Feedback), raises the neural decompilation rate from 26.0% on ExeBench to 83.9% on the Real O2 split without sacrificing similarity to the original source code. We also find our automatic feedback methodology is highly effective for improving weaker neural decompilation models.

PaperId: 1080, https://arxiv.org/pdf/2605.11461.pdf GitHub

Abstract:
Reinforcement learning with verifiers (RLVR) has become a central paradigm for improving LLM reasoning, yet popular group‑based optimization algorithms like GRPO often suffer from exploration collapse, where the models prematurely converge on a narrow set of high‑scoring patterns, lacking the ability to explore new solutions. Recent efforts attempt to alleviate this by adding entropy regularization or diversity bonus. However, these approaches do not change the winner‑takes‑all nature, where rollouts still compete for individual advantage rather than cooperating for maximizing global diversity. In this work, we propose Group Cooperative Policy Optimization (GCPO), which shifts the training paradigm from rollout competition to team cooperation. Specifically, GCPO replaces independent rollout scoring with team‑level credit assignment: a rollout is rewarded by how much it contributes to the team's valid solution coverage, rather than its individual accuracy. This coverage is described as a determinant volume over reward‑weighted semantic embeddings, where only correct and non‑redundant rollouts contribute to this volume. During advantage estimation, GCPO redistributes the collective team reward to each single rollout according to its average marginal contribution to the team. This cooperative training paradigm routes optimization toward non‑redundant correct reasoning paths. Experiments across multiple reasoning benchmarks demonstrate that GCPO significantly improves both reasoning accuracy and solution diversity over existing approaches. Code will be released at https://github.com/bradybuddiemarch/gcpo.

PaperId: 1081, https://arxiv.org/pdf/2605.11436.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly deployed on long‑horizon tasks in partially observable environments, where they must act while inferring and tracking a complex environment state over many steps. This leads to two challenges: partial observability requires maintaining uncertainty over unobserved world attributes, and long interaction history causes context to grow without bound, diluting task‑relevant information. A principled solution to both challenges is a belief state: a posterior distribution over environment states given past observations and actions, which compactly encodes history for decision making regardless of episode length. In LLM agents, however, the open‑ended nature of text makes it unclear how to represent such a distribution. Therefore, we introduce Agent‑BRACE: Agent Belief state Representation via Abstraction and Confidence Estimation, a method that decouples an LLM agent into a belief state model and a policy model, jointly optimized via reinforcement learning. The belief state model produces a structured approximation of the belief distribution: a set of atomic natural language claims about the environment, each annotated with an ordinal verbalized certainty label ranging from certain to unknown. The policy model conditions on this compact, structured approximate belief rather than the full history, learning to select actions under explicit uncertainty. Across long‑horizon, partially observable embodied language environments, Agent‑BRACE achieves an average absolute improvement of +14.5% (Qwen2.5‑3B‑Instruct) and +5.3% (Qwen3‑4B‑Instruct), outperforming strong RL baselines while maintaining a near‑constant context window independent of episode length. Further analysis shows that the learned belief becomes increasingly calibrated over the course of an episode as evidence accumulates.

PaperId: 1082, https://arxiv.org/pdf/2605.11426.pdf GitHub

Abstract:
The cosine similarity between a large language model's hidden activations before and after Supervised Fine‑Tuning (SFT) remains very high. This, at first glance, suggests that SFT leaves the model's activation geometry largely undisturbed. However, projecting both sets of activations through a Sparse Autoencoder (SAE) pretrained on the base model reveals that the underlying sparse latents diverge significantly. We introduce a novel investigative pipeline which utilizes these pretrained SAEs as a high‑resolution diagnostic tool to mechanistically investigate the drivers of this representational divergence. Through our analytical pipeline, we discover task‑specific and layer‑specific distributions of the precise semantic features that are systematically altered during supervised fine‑tuning. We additionally identify a layer‑wise update profile specific to safety alignment. All code, experimental scripts, and analysis files associated with this work are publicly available at: https://github.com/ruhzi/sae‑investigation.

PaperId: 1083, https://arxiv.org/pdf/2605.11317.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly deployed in multi‑turn dialogue settings where preserving conversational context across turns is essential. A standard serving practice concatenates the full dialogue history at every turn, which reliably maintains coherence but incurs substantial cost in latency, memory, and API expenditure, especially when queries are routed to large proprietary models. Existing approaches often struggle to balance the trade‑off between response quality and efficiency. We propose a framework that exploits the early turns of a session to estimate a local response manifold and then adapt a smaller surrogate model to this local region for the remainder of the conversation. Concretely, we learn soft prompts that maximize semantic divergence between the large and surrogate small language models' responses to surface least‑aligned local directions, stabilize training with anti‑degeneration control, and distill the mined cases into localized LoRA fine‑tuning so the surrogate runs without prompts at inference. A simple gate enables a one‑time switch with rollback on drift. We further provide a theoretical analysis for key components in SOMA. Extensive experiments show the effectiveness of SOMA. The source code is provided at: https://github.com/LabRAI/SOMA.

PaperId: 1084, https://arxiv.org/pdf/2605.11301.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have heterogeneous strengths across OCR, chart understanding, spatial reasoning, visual question answering, cost, and latency. Effective MLLM routing therefore requires more than estimating query difficulty: a router must match the multimodal requirements of the current image‑question input with the capabilities of each candidate model. We propose LatentRouter, a router that formulates MLLM routing as counterfactual multimodal utility prediction. Given an image‑question query, LatentRouter extracts learned multimodal routing capsules, represents each candidate MLLM with a model capability token, and performs latent communication between these states to estimate how each model would perform if selected. A distributional outcome head predicts model‑specific counterfactual quality, while a bounded capsule correction refines close decisions without allowing residual signals to dominate the prediction. The resulting utility‑based policy supports performance‑oriented and performance‑cost routing, and handles changing candidate pools through shared per‑model scoring with availability masking. Experiments on MMR‑Bench and VL‑RouterBench show that LatentRouter outperforms fixed‑model, feature‑level, and learned‑router baselines. Additional analyses show that the gains are strongest on multimodal task groups where model choice depends on visual, layout‑sensitive, or reasoning‑oriented requirements, and that latent communication is the main contributor to the improvement. The code is available at: https://github.com/LabRAI/LatentRouter.

PaperId: 1085, https://arxiv.org/pdf/2605.11290.pdf GitHub

Abstract:
Capability distillation applies knowledge distillation to selected model capabilities, aiming to compress a large language model (LLM) into a smaller one while preserving the abilities needed for a downstream task. However, most existing methods treat capabilities as independent training targets and overlook how improving one capability can reshape the student's broader capability profile, especially when multiple abilities jointly determine task success. We study capability distillation under a fixed token budget and identify two consistent patterns: distillation induces systematic, budget‑dependent cross‑capability transfer, and additional budget often brings limited task‑relevant gains while sometimes degrading other useful abilities. Building on these insights, we propose ReAD, a Reinforcement‑guided cApability Distillation framework that explicitly accounts for capability interdependence. ReAD first infers task‑essential capabilities, then generates capability‑targeted supervision on the fly, and finally uses an uncertainty‑aware contextual bandit to adaptively allocate the distillation budget based on expected utility gains. Extensive experiments show that ReAD improves downstream utility under the same token budget while reducing harmful spillover and wasted distillation effort compared to strong baselines. Our code is publicly available at https://github.com/LabRAI/ReAD.

PaperId: 1086, https://arxiv.org/pdf/2605.11280.pdf GitHub

Abstract:
Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present \textttGWAgent, a large language model (LLM)‑based workflow that constructs interpretable analytic surrogates directly from simulation data. Surrogate modeling is well suited to agentic workflows because candidate models can be quantitatively validated against ground‑truth simulations at each iteration. As a demonstration, we build a surrogate for gravitational waveforms from eccentric binary black hole mergers. We show that providing the agent with a physics‑informed domain ansatz substantially improves output model accuracy. The resulting analytic surrogate attains a median Advanced LIGO mismatch of 6.9×10^‑4 together with an ～ 8.4× speedup in waveform evaluation, surpassing both symbolic regression and conventional machine learning baselines. Beyond producing an accurate model, the workflow identifies compact physical structure from the learned representation. As an astrophysical application, we use \textttGWAgent to analyze the eccentricity of GW200129 and infer e_20\mathrmHz=0.099^+0.063_‑0.044. These results show that validation‑constrained agentic workflows can produce accurate, fast, and interpretable surrogates for scientific simulations and inference.

PaperId: 1087, https://arxiv.org/pdf/2605.11224.pdf GitHub

Abstract:
Existing medical‑agent benchmarks deliver imaging as pre‑selected samples, never as an environment the agent must navigate. We introduce ABRA, a radiology‑agent benchmark in which the agent operates an OHIF viewer and an Orthanc DICOM server through twenty‑one function‑calling tools that span slice navigation, windowing, series selection, pixel‑coordinate annotation, and structured reporting. ABRA contains 655 programmatically generated tasks across three difficulty tiers and eight types (viewer control, metadata QA, vision probe, annotation, longitudinal comparison, BI‑RADS reporting, and oracle variants of annotation and BI‑RADS reporting), drawn from LIDC‑IDRI, Duke Breast Cancer MRI, and NLST New‑Lesion LongCT. Each episode is scored along Planning, Execution, and Outcome (Bluethgen et al., 2025) by task‑type‑specific automatic scorers. Ten current models, five closed‑weight and five open‑weight, reach at least 89% Execution on real annotation but only 0‑25% Outcome; on the paired oracle variant where a simulated detector supplies the finding, Outcome on the same task reaches 69‑100% across the models evaluated, localising the bottleneck to perception rather than tool orchestration. Code, task generators, and scorers are released at https://github.com/Luab/ABRA

PaperId: 1088, https://arxiv.org/pdf/2605.11205.pdf GitHub

Abstract:
Benchmark evaluation across AI and safety‑critical domains overwhelmingly relies on simple averaging. We demonstrate that this practice produces substantially misleading rankings when two conditions co‑occur: (1) the evaluation matrix is sparse and (2) items vary substantially in difficulty. Through controlled simulation experiments across four domains ‑‑ NLP (GLUE), clinical drug trials, autonomous vehicle safety, and cybersecurity ‑‑ we show that Spearman rank correlation ρ between simple‑average rankings and ground‑truth rankings degrades from ρ= 1.000 at 100% coverage to ρ= 0.809 at 67% coverage with high difficulty heterogeneity (mean over 20 seeds). A standard two‑parameter logistic (2PL) Item Response Theory (IRT) model maintains ρ\geq 0.996 across all conditions. A 150‑condition grid sweep over sparsity S \in [0, 0.70] and difficulty gap D \in [0.5, 5.0] confirms that ranking error forms a failure surface with a strong S × D interaction (γ_3 = +0.20, t = 13.05), while IRT maintains ρ\geq 0.993 throughout. We discuss implications for Physical AI benchmarking, where evaluation matrices are often incomplete and difficulty gaps are extreme.

PaperId: 1089, https://arxiv.org/pdf/2605.11136.pdf GitHub

Abstract:
We argue that multi‑agent test‑time evolution is not single‑agent evolution replicated N times. A single‑agent learner can only evolve its own context and memory. A multi‑agent system additionally evolves who collaborates, how they collaborate, and how knowledge flows across the population. These components have no single‑agent counterpart and can produce phenomena such as emergent specialization. Yet prior test‑time methods either confine experiences to individual agents, forfeiting cross‑agent learning, or broadcast symmetrically to all agents, erasing the specialization that makes collaboration valuable. We present EVOCHAMBER, a training‑free framework that instantiates test‑time evolution at three levels over a coevolving agent pool. At its core is CODREAM (Collaborative Dreaming), a post‑task protocol triggered on team failure or disagreement, in which agents collaboratively reflect, distill insights, and route them asymmetrically from strong to weak agents on the failed niche, preserving specialization while filling knowledge gaps. Team‑level operators assemble niche‑conditioned teams and select collaboration structures online. Population‑level lifecycle operators fork, merge, prune, and seed agents under performance pressure. On three heterogeneous task streams with Qwen3‑8B, EVOCHAMBER reaches 63.9% on competition math, 75.7% on code, and 87.1% on multi‑domain reasoning, outperforming the best baseline by 32% relative on math and confirming asymmetric cross‑agent transfer as the primary driver in ablation. Starting from several identically initialized agents, four to five stable niche specialists spontaneously emerge, a structural signature of multi‑agent evolution that no single‑agent learner can express. See our code at: https://github.com/Mercury7353/EvoChamber

PaperId: 1090, https://arxiv.org/pdf/2605.11093.pdf GitHub

Abstract:
Today's inference‑time workloads increasingly depend on timely access to a model's internal states. We present DMI‑Lib, a high‑speed deep model inspector that treats internal observability as a first‑class systems primitive, decoupling it from the inference hot path via an asynchronous observability substrate built from Ring^2, a GPU‑CPU memory abstraction for capturing and staging tensors, and a policy‑controlled host backend that exports them. DMI‑Lib enables the placement of observation points across a rich space of internal signals and diverse inference backends while preserving serving optimizations and adhering to tight GPU memory budgets. Our experiments demonstrate that DMI‑Lib incurs only 0.4%‑‑6.8% overhead in offline batch inference and an average of 6% in moderate online serving, reducing latency overhead by 2x‑15x compared to existing baselines with similar observability features. DMI‑Lib is open‑sourced at https://github.com/ProjectDMX/DMI.

PaperId: 1091, https://arxiv.org/pdf/2605.11047.pdf GitHub

Abstract:
Agentic language‑model systems increasingly rely on mutable execution contexts, including files, memory, tools, skills, and auxiliary artifacts, creating security risks beyond explicit user prompts. This paper presents DeepTrap, an automated framework for discovering contextual vulnerabilities in OpenClaw. DeepTrap formulates adversarial context manipulation as a black‑box trajectory‑level optimization problem that balances risk realization, benign‑task preservation, and stealth. It combines risk‑conditioned evaluation, multi‑objective trajectory scoring, reward‑guided beam search, and reflection‑based deep probing to identify high‑value compromised contexts. We construct a 42‑case benchmark spanning six vulnerability classes and seven operational scenarios, and evaluate nine target models using attack and utility grading scores. Results show that contextual compromise can induce substantial unsafe behavior while preserving user‑facing task completion, demonstrating that final‑response evaluation is insufficient. The findings highlight the need for execution‑centric security evaluation of agentic AI systems. Our code is released at: https://github.com/ZJUICSR/DeepTrap

PaperId: 1092, https://arxiv.org/pdf/2605.11029.pdf GitHub

Abstract:
An attacker can split a malicious goal into sub‑prompts that each look benign on their own and only become harmful in combination. Existing LLM safety benchmarks evaluate prompts one at a time, or across turns of a single chat, and so do not look for a malicious signal spread across separate sessions with no shared context. We build FragBench, a benchmark drawn from 24 real‑world cyber‑incident campaigns, which keeps the full attack trail: the multi‑fragment kill chain, the per‑fragment safety‑judge verdicts, sandboxed execution traces, and a matched set of benign cover sessions. FragBench splits this trail into two paired tasks: an adversarial rewriter that hardens fragments against a single‑turn safety judge (FragBench Attack), and a graph‑based user‑level detector trained on the resulting interactions (FragBench Defense). The single‑turn judge is near chance on the released corpus by construction, but four GNN variants and three classical‑ML baselines all recover the cross‑session feature, reaching aggregate event‑level F1 = 0.88‑0.96. Defending against fragmented LLM misuse therefore requires modeling the cross‑session interaction graph, rather than isolated prompts. Our generator, rewriter, sandbox harness, and detector are released at https://github.com/LidaSafety/fragbench.

PaperId: 1093, https://arxiv.org/pdf/2605.11015.pdf GitHub

Abstract:
Software vulnerability detection plays a critical role in ensuring system security, where real‑world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source ‑‑ sequential, structural, or semantic ‑‑ failing to jointly exploit the complementary strengths across modalities, or treat statement‑level localization merely as a byproduct of function‑level detection without explicit line‑level supervision. To address these limitations, we propose DCVD (Dual‑Channel Cross‑Modal Vulnerability Detection), a unified framework that performs joint function‑level detection and statement‑level localization. DCVD extracts control‑dependency and semantic features through two parallel branches and integrates them via contrastive alignment coupled with bidirectional cross‑attention, effectively bridging the cross‑modal representation gap. It further introduces explicit supervision signals at both the function and statement levels, enabling collaborative optimization across the two granularities. Extensive experiments on a large‑scale real‑world vulnerability benchmark demonstrate that DCVD consistently outperforms state‑of‑the‑art methods on both function‑level detection and statement‑level localization. Our code is available at https://github.com/vinsontang1/DCVD.

PaperId: 1094, https://arxiv.org/pdf/2605.11014.pdf GitHub

Abstract:
Fair comparison between diffusion‑based OOD detectors is challenging, as conclusions can vary with backbone choice, corruption parameterization, and test‑time budget. We address this issue through a Mutualized Backbone‑Equated (MBE) protocol that aligns canonical corruption levels and logical test‑time cost across diffusion backbones. Within this setting, we introduce Canonical Feature Snapshots (CFS), a family of detectors that probes a frozen diffusion backbone using only a tiny number of native internal activations at canonical low‑noise levels. On a controlled CIFAR‑scale benchmark, the strongest one‑forward CFS variant is CFS(1x2), while an even smaller decoder‑only variant remains highly competitive. This shows that much of the relative‑OOD signal exposed by frozen diffusion backbones is concentrated in a small number of sparse internal states, rather than requiring full denoising trajectories or high‑capacity downstream heads. We further provide a local diagnostic theory explaining these observations through conditional encoder‑decoder complementarity, diagonal‑score separation, and low‑noise corruption stability. The official implementation is available at https://github.com/RouzAY/cfs‑diffusion‑ood/.

PaperId: 1095, https://arxiv.org/pdf/2605.11011.pdf GitHub

Abstract:
Looped computation shows promise in improving the reasoning‑oriented performance of LLMs by scaling test‑time compute. However, existing approaches typically require either training recurrent models from scratch or applying disruptive retrofits, which involve substantial computational costs and may compromise pretrained capabilities. To address these limitations, we introduce Looped Depth Up‑Scaling (LoopUS), a post‑training framework that converts a standard pretrained LLM into a looped architecture. As a key technical contribution, LoopUS recasts the pretrained LLM into an encoder, a looped reasoning block, and a decoder. It operationalizes this latent‑refinement architecture through four core components: (1) block decomposition, guided by staged representation dynamics; (2) an input‑dependent selective gate to mitigate hidden‑state drift; (3) random deep supervision for memory‑efficient learning over long recursive horizons; and (4) a confidence head for adaptive early exiting. Collectively, these mechanisms transform a standard non‑looped model into a looped form while stabilizing it against both computational bottlenecks and representation collapse. Through stable latent looping, LoopUS improves reasoning‑oriented performance without extending the generated traces or requiring recurrent training from scratch. For more details, see https://thrillcrazyer.github.io/LoopUS

PaperId: 1096, https://arxiv.org/pdf/2605.11008.pdf GitHub

Abstract:
While invariant architectures are standard for processing symmetric data, there is growing interest in achieving invariance by applying group averaging or canonization to non‑invariant backbones. However, the theoretical generalization properties of these alternative strategies remain poorly understood. We introduce a theoretical framework to analyze the generalization error of these methods by bounding their covering numbers. We establish a rigorous generalization hierarchy: the error bounds of canonized models are at best equal to the error bounds of structurally invariant and group‑averaged models, and at worst equal to the bounds of non‑invariant baselines. Furthermore, we show that there exist optimal canonizations which attain the optimal error bounds, and poor canonizations which attain the non‑invariant error bounds, and that this depends on the regularity of the canonization. Finally, applying this framework to permutation groups in point cloud processing, we rigorously prove that the covering number of lexicographical sorting grows exponentially with point cloud dimension, whereas Hilbert curve canonization guarantees polynomial growth. This provides the first formal theoretical justification for the empirical success of Hilbert curve serialization in state‑of‑the‑art point cloud architectures. We conclude with experiments that support our theoretical claims. Code is available at https://github.com/yonatansverdlov/Canonization

PaperId: 1097, https://arxiv.org/pdf/2605.11006.pdf GitHub

Abstract:
Evaluating whether large language models (LLMs) can recover execution‑relevant program structure, rather than only produce code that passes tests, remains an open problem. Existing code benchmarks emphasize test‑passing outputs, from standalone programming tasks (HumanEval, MBPP, LiveCodeBench) to repository repair (SWE‑Bench); this is useful, but offers limited diagnostic signal about which program semantics a model can recover from source. We introduce TraceEval, to our knowledge the first execution‑verified, multi‑language benchmark for code semantic reasoning: recovering a program's runtime call structure from source code. Unlike prior call‑graph benchmarks that rely on static‑tool output or hand‑annotated ground truth, every positive edge in TraceEval is mechanically witnessed by validation execution, eliminating annotator disagreement and label noise for observed behavior. TraceEval consists of (i) 10,583 real‑world programs (2,129 test, 8,454 train) extracted from 1,600+ open‑source repositories across Python, JavaScript, and Java via an LLM‑assisted harness‑generation pipeline with tracer validation; and (ii) a reproducible pipeline that converts any open‑source repository into new verified benchmark instances. We evaluate 10 LLMs at zero‑shot on the held‑out test split. The strongest model, Claude‑Opus‑4.6, reaches an average F1 of 72.9% across the three languages. To demonstrate the train split's utility as a supervision substrate, we fine‑tune the Qwen2.5‑Coder family on it: lifts of up to +55.6 F1 bring tuned Qwen2.5‑Coder‑32B to 71.2%, within 1.7 F1 of zero‑shot Claude‑Opus‑4.6. We release the benchmark, pipeline, baselines, and a datasheet at https://github.com/yikun‑li/TraceEva

PaperId: 1098, https://arxiv.org/pdf/2605.10988.pdf GitHub

Abstract:
Log anomaly detection is a critical task for system operations and security assurance. However, in networked systems at scale, log data are generated at massive scale while instance‑level annotations are prohibitively expensive, posing great difficulties to fine‑grained anomaly localization. To address this challenge, we propose LogMILP (Log anomaly localization based on Multi‑Instance Learning enhanced by prototypes and Perturbation), a weakly supervised framework that enables both bag‑level anomaly detection and instance‑level anomaly localization using only bag‑level labels. Our method guides the model to pinpoint the critical log entries using prototype‑guided structural modeling with counterfactual perturbation consistency regularization, thereby improving localization reliability and interpretability under coarse‑grained supervision. Experimental results on three public datasets demonstrate that LogMILP achieves competitive detection performance while yielding significantly more reliable instance‑level localization. Our code is open‑sourced at https://github.com/YUK1207/LogMILP.

PaperId: 1099, https://arxiv.org/pdf/2605.10977.pdf GitHub

Abstract:
Watermarking for large language models (LLMs) is a promising approach for detecting LLM‑generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic‑invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion‑free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embedding space and constructs a distributional dependency between token and auxiliary sequences via shared randomness synchronized by a secret key and semantic history. This design is grounded in our theoretical framework that characterizes a jointly optimal embedding‑detection pair, achieving the fundamental trade‑offs among detection accuracy, robustness, and distortion. Evaluations across multiple LLMs and semantic‑invariant attacks demonstrate that PASA remains robust even under strong paraphrasing attacks while preserving high text quality, outperforming standard vocabulary‑space baselines. Ablation studies further validate the effectiveness of our hyperparameter choices. Webpage: https://ai‑kunkun.github.io/PASA_page/.

PaperId: 1100, https://arxiv.org/pdf/2605.10973.pdf GitHub GitHub

Abstract:
Supervised fine‑tuning (SFT) improves in‑domain performance but can degrade out‑of‑domain (OOD) generalization. Prior work suggests that this degradation is related to changes in dominant singular subspaces of pretrained weight matrices. However, directly identifying loss‑sensitive directions with Hessian or Fisher information is computationally expensive at LLM scale. In this work, we propose preserving projected rotations in pretrained singular subspaces as an efficient proxy for Fisher‑sensitive directions, which we call Rotation‑Preserving Supervised Fine‑Tuning (RPSFT). RPSFT penalizes changes in the projected top‑k singular‑vector block of each pretrained weight matrix, limiting unnecessary rotation while preserving task adaptation. Across model families and sizes trained on math reasoning data, RPSFT improves the in‑domain/OOD trade‑off over standard SFT and strong SFT baselines, better preserves pretrained representations, and provides stronger initializations for downstream RL fine‑tuning. Code is available at \hrefhttps://github.com/jinhangzhan/RPSFT.githttps://github.com/jinhangzhan/RPSFT.

PaperId: 1101, https://arxiv.org/pdf/2605.10938.pdf GitHub

Abstract:
Diffusion and flow‑based models have become the de facto approaches for generating continuous data, e.g., in domains such as images and videos. Their success has attracted growing interest in applying them to language modeling. Unlike their image‑domain counterparts, today's leading diffusion language models (DLMs) primarily operate over discrete tokens. In this paper, we show that continuous DLMs can be made effective with minimal adaptation to the discrete domain. We propose Embedded Language Flows (ELF), a class of diffusion models in continuous embedding space based on continuous‑time Flow Matching. Unlike existing DLMs, ELF predominantly stays within the continuous embedding space until the final time step, where it maps to discrete tokens using a shared‑weight network. This formulation makes it straightforward to adapt established techniques from image‑domain diffusion models, e.g., classifier‑free guidance (CFG). Experiments show that ELF substantially outperforms leading discrete and continuous DLMs, achieving better generation quality with fewer sampling steps. These results suggest that ELF offers a promising path toward effective continuous DLMs.

PaperId: 1102, https://arxiv.org/pdf/2605.10934.pdf GitHub

Abstract:
Modelling extreme events and heavy‑tailed phenomena is central to building reliable predictive systems in domains such as finance, climate science, and safety‑critical AI. While Lévy processes provide a natural mathematical framework for capturing jumps and heavy tails, Bayesian inference for Lévy‑driven stochastic differential equations (SDEs) remains intractable with existing methods: Monte Carlo approaches are rigorous but lack scalability, whereas neural variational inference methods are efficient but rely on Gaussian assumptions that fail to capture discontinuities. We address this tension by introducing a neural exponential tilting framework for variational inference in Lévy‑driven SDEs. Our approach constructs a flexible variational family by exponentially reweighting the Lévy measure using neural networks. This parametrization preserves the jump structure of the underlying process while remaining computationally tractable. To enable efficient inference, we develop a quadratic neural parametrization that yields closed‑form normalization of the tilted measure, a conditional Gaussian representation for stable processes that facilitates simulation, and symmetry‑aware Monte Carlo estimators for scalable optimization. Empirically, we demonstrate that the method accurately captures jump dynamics and yields reliable posterior inference in regimes where Gaussian‑based variational approaches fail, on both synthetic and real‑world datasets.

PaperId: 1103, https://arxiv.org/pdf/2605.10848.pdf GitHub

Abstract:
Does a lexical retriever suffice as large language models (LLMs) become more capable in an agentic loop? This question naturally arises when building deep research systems. We revisit it by pairing BM25 with frontier LLMs that have better reasoning and tool‑use abilities. To support researchers asking the same question, we introduce Pi‑Serini, a search agent equipped with three tools for retrieving, browsing, and reading documents. Our results show that, on BrowseComp‑Plus, a well‑configured lexical retriever with sufficient retrieval depth can support effective deep research when paired with more capable LLMs. Specifically, Pi‑Serini with gpt‑5.5 achieves 83.1% answer accuracy and 94.7% surfaced evidence recall, outperforming released search agents that use dense retrievers. Controlled ablations further show that BM25 tuning improves answer accuracy by 18.0% and surfaced evidence recall by 11.1% over the default BM25 setting, while increasing retrieval depth further improves surfaced evidence recall by 25.3% over the shallow‑retrieval setting. Source code is available at https://github.com/justram/pi‑serini.

PaperId: 1104, https://arxiv.org/pdf/2605.10840.pdf GitHub

Abstract:
We present Clin‑JEPA, a multi‑phase co‑training framework for joint‑embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architectures have enabled latent‑space planning in robotics and high‑quality representation learning in vision, but extending the paradigm to EHR data ‑‑ to obtain a single backbone that simultaneously forecasts patient trajectories and serves diverse downstream risk‑prediction tasks without per‑task fine‑tuning ‑‑ remains an open challenge. Existing JEPA frameworks either discard the predictor after pretraining (I‑JEPA, V‑JEPA) or train it on a frozen pretrained encoder (V‑JEPA 2‑AC), leaving the encoder unaware of the rollout signal that the retained predictor must use at inference; co‑training the encoder and predictor under a shared JEPA prediction objective would supply this grounding, but naïve co‑training is unstable, with representation collapse and online/target drift causing autoregressive rollout to diverge. Clin‑JEPA's five‑phase pretraining curriculum ‑‑ predictor warmup, joint refinement, EMA target alignment, hard sync, and predictor finalization ‑‑ addresses each failure mode by phase, stably co‑training a Qwen3‑8B‑based encoder and a 92M‑parameter latent trajectory predictor. On MIMIC‑IV ICU data, three independent evaluations support the framework: (1) latent \ell_1 rollout drift uniquely converges (‑15.7%) over 48‑hour horizons while baselines and ablations diverge (+3% to +4951%); (2) the encoder learns a clinically discriminative latent geometry (deteriorating‑patient cohorts displace 4.83× further than stable patients in latent space, vs \leq2.62× for baseline encoders); (3) a single backbone outperforms strong tabular and sequence baselines on multi‑task downstream evaluation. Clin‑JEPA achieves mean AUROC 0.851 on ICareFM EEP and 0.883 on 8 binary risk tasks (+0.038 and +0.041 vs baseline average).

PaperId: 1105, https://arxiv.org/pdf/2605.10834.pdf GitHub

Abstract:
AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which will perform best in real‑world targets. Existing evaluation protocols assess and optimize for predefined goals such as capture‑the‑flag, remote code execution, exploit reproduction, or trajectory similarity, in simplified or narrow settings. These tools are valuable for measuring bounded capabilities, yet they do not adequately capture the complexity, open‑ended exploration, and strategic decision‑making required in realistic pentesting. In this paper, we present a practical evaluation protocol that shifts assessment from task completion to validated vulnerability discovery, allowing evaluation in sufficiently complex targets spanning multiple attack surfaces and vulnerability classes. The protocol combines structured ground‑truth with LLM‑based semantic matching to identify vulnerabilities, bipartite resolution to score findings under realistic ambiguity, continuous ground‑truth maintenance, repeated and cumulative evaluation of stochastic agents, efficiency metrics, and reduced‑suite selection for sustainable experimentation. This protocol extends the state of the art by enabling a more realistic, operationally informative comparison of AI pentesting agents. To enable reproducibility, we also release expert‑annotated ground truth and code for the proposed evaluation protocol: https://github.com/jd0965199‑oss/ethibench.

PaperId: 1106, https://arxiv.org/pdf/2605.10833.pdf GitHub

Abstract:
Industrial anomaly detection is critical for manufacturing quality control, yet existing datasets mainly focus on static images or sparse views, which do not fully reflect continuous inspection processes in real industrial scenarios. We introduce MMVIAD (Multi‑view Multi‑task Video Industrial Anomaly Detection), to the best of our knowledge the first continuous multi‑view video dataset for industrial anomaly detection and understanding, together with a benchmark for multi‑task evaluation. MMVIAD contains object‑centric 2‑second inspection clips with approximately 120 degrees of camera motion, covering 48 object categories, 14 environments, and 6 structural anomaly types. It supports anomaly detection, defect classification, object classification, and anomaly visible‑time localization. Systematic evaluations on MMVIAD show that current commercial and open‑source video MLLMs remain far below human performance, especially for fine‑grained defect recognition and temporal grounding. To improve transferable anomaly understanding, we further develop a two‑stage post‑training pipeline where PS‑SFT (Perception‑Structured Supervised Fine‑Tuning) initializes perception‑structured reasoning and VISTA‑GRPO (Visibility‑grounded Industrial Structured Temporal Anomaly Group Relative Policy Optimization) refines the model with semantic‑gated defect reward and visibility‑aware temporal reward, producing the final model VISTA. On MMVIAD‑Unseen, VISTA improves the base model's average score across the four tasks from 45.0 to 57.5, surpassing GPT‑5.4. Source code is available at https://github.com/Georgekeepmoving/MMVIAD.

PaperId: 1107, https://arxiv.org/pdf/2605.10815.pdf GitHub

Abstract:
Audio‑visual large language models (AVLLMs) have recently emerged as a powerful architecture capable of jointly reasoning over audio, visual, and textual modalities. In AVLLMs, the bidirectional interaction between audio and video modalities introduces intricate processing dynamics, necessitating a deeper understanding of their internal mechanisms. However, unlike extensively studied text‑only or large vision language models, the internal workings of AVLLMs remain largely unexplored. In this paper, we focus on cross‑modal information flow between audio and visual modalities in AVLLMs, investigating where information derived from one modality is encoded within the token representations of the other modality. Through an analysis of multiple recent AVLLMs, we uncover two common findings. First, AVLLMs primarily encode integrated audio‑visual information in sink tokens. Second, sink tokens do not uniformly hold cross‑modal information. Instead, a distinct subset of sink tokens, which we term cross‑modal sink tokens, specializes in storing such information. Based on these findings, we further propose a simple training‑free hallucination mitigation method by encouraging reliance on integrated cross‑modal information within cross‑modal sink tokens. Our code is available at https://github.com/kaistmm/crossmodal‑hub.

PaperId: 1108, https://arxiv.org/pdf/2605.10806.pdf GitHub

Abstract:
Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern real‑world dynamics. However, evaluating whether generated videos actually follow these rules remains challenging. Existing physics‑focused video benchmarks have made important progress, but they still face three key challenges, including the coarse evaluation frameworks that hide law‑specific failures, response biases and fatigue that undermine the validity of annotation judgments, and automated evaluators that are insufficiently physics‑aware or difficult to audit. To address those challenges, we introduce PhyGround, a criteria‑grounded benchmark for evaluating physical reasoning in video generation. The benchmark contains 250 curated prompts, each augmented with an expected physical outcome, and a taxonomy of 13 physical laws across solid‑body mechanics, fluid dynamics, and optics. Each law is operationalized through observable sub‑questions to enable per‑law diagnostics. We evaluate eight modern video generation models through a large‑scale, quality‑controlled human study, grounded on social science lab experiment design. A total of 459 annotators provided 5,796 complete annotations and over 37.4K fine‑grained labels; after quality control, the retained annotations exhibited high split‑half model‑ranking correlations (Spearman's rho > 0.90). To support reproducible automated evaluation, we release PhyJudge‑9B, an open physics‑specialized VLM judge. PhyJudge‑9B achieves substantially lower aggregate relative bias than Gemini‑3.1‑Pro (3.3% vs. 16.6%). We release prompts, human annotations, model checkpoints, and evaluation code on the project page https://phyground.github.io/.

PaperId: 1109, https://arxiv.org/pdf/2605.10799.pdf GitHub

Abstract:
Corruption studies, the standard tool for evaluating chain‑of‑thought (CoT) faithfulness, infer which steps are ``computationally important'' from accuracy loss when steps are corrupted. We show that when benchmark chains end with an explicit terminal answer line, as in GSM8K and MATH, these tests largely measure \emphanswer placement rather than where intermediate computation is carried out. Using matched GSM8K examples, removing only the final answer statement while preserving all reasoning collapses suffix sensitivity by about 19× for Qwen~2.5‑3B (N=300, p=0.022). Conflicting‑answer prompts, which contain correct reasoning but a wrong explicit final answer, drive accuracy to zero or near‑zero at 7B across five open‑weight model families; wrong‑answer following is strong at 3B‑‑7B and attenuates sharply at larger scales. Replications on MATH, within‑stable comparisons at 7B, and suffix‑free chains show the same pattern in different guises: corruption sensitivity tracks the location of explicit answer text, not a fixed computational depth in the reasoning. Generation‑time probes indicate that final answers are rarely early‑determined during generation (<5% early commitment), yet consumption‑time behavior systematically follows explicit answer text. The confound is therefore largely a readout effect when the chain is consumed. We propose a three‑prerequisite protocol (question‑only control, format characterization, and an all‑position sweep) as a practical minimum for future corruption‑based faithfulness studies.

PaperId: 1110, https://arxiv.org/pdf/2605.10732.pdf GitHub

Abstract:
Automated transit payment analysis is vital for scalable fare auditing and passenger analytics, yet practice still relies on limited manual inspection. Prior vision‑ and skeleton‑based methods remain brittle under noisy onboard surveillance and often depend on poorly generalizable handcrafted features. Building on the success of graph convolutional networks in human action recognition, we observe that skeleton features excel at modeling global spatiotemporal dependencies but tend to underemphasize the subtle local relative motions that distinguish payment actions. In contrast, RGB features preserve fine‑grained spatial details yet often lack reliable temporal continuity in surveillance footage. To bridge both system‑level deployment needs and model‑level design challenges, we present iPay, an integrated payment action recognition framework for onboard transit surveillance system. iPay adopts a multimodal mixture‑of‑experts architecture with four tightly coupled streams: (1) an RGB expert stream emphasizing local evidence via region‑focused computation; (2) a skeleton expert stream modeling articulated motion with a graph convolutional backbone; (3) a dual‑attention fusion stream enabling skeleton‑to‑RGB temporal transfer and RGB‑to‑skeleton spatial enhancement; and (4) a prior‑driven Spatial Difference Discriminator (SDD) that explicitly models hand‑to‑anchor relative motion to improve task‑specific discriminability. We also collaborate with local transit agencies to collect over 55 hours of real onboard surveillance footage, yielding 500+ payment clips. Experiments show that iPay outperforms prior methods and achieves 83.45% recognition accuracy with competitive computational efficiency, making it suitable for edge deployment. Code is available at https://github.com/ccoopq/iPay.

PaperId: 1111, https://arxiv.org/pdf/2605.10576.pdf GitHub GitHub

Abstract:
Low‑level visual perception underpins reliable remote sensing (RS) image analysis, yet current image quality assessment (IQA) methods output uninterpretable scalar scores rather than characterizing physics‑driven RS degradations, deviating markedly from the diagnostic needs of RS experts. While Vision‑Language Models (VLMs) present a compelling alternative by delivering language‑grounded IQA, their visual priors are heavily biased toward ground‑level natural images. Consequently, whether VLMs can overcome this domain gap to perceive and articulate RS artifacts remains insufficiently studied. To bridge this gap, we propose SenseBench, the first dedicated diagnostic benchmark for RS low‑level visual perception and description. Driven by a physics‑based hierarchical taxonomy that unifies both non‑reference and reference‑based paradigms, SenseBench features over 10K meticulously curated instances across 6 major and 22 fine‑grained RS degradation categories. Specifically, two complementary protocols are designed for evaluation: objective low‑level visual perception and subjective diagnostic description. Comprehensive evaluation of 29 state‑of‑the‑art VLMs reveals not only skewed domain priors and multi‑distortion collapse, but also fluency illusion and a perception‑description inversion effect. We hope SenseBench provides a robust evaluation testbed and high‑quality diagnostic data to advance the development of VLMs in RS low‑level perception. Code and datasets are available \hrefhttps://github.com/Zhong‑Chenchen/SenseBench\textcolorbluehere.

PaperId: 1112, https://arxiv.org/pdf/2605.10541.pdf GitHub

Abstract:
Epigenetic clocks based on DNA methylation have emerged as powerful tools for estimating biological age, with broad applications in aging research, age‑related disease studies, and longevity science. Despite advances across machine learning approaches to epigenetic age prediction, spanning penalised linear regression, deep feedforward networks, residual architectures, and graph neural networks, no existing method jointly models co‑methylation graph structure and site‑specific DNA sequence context within a unified framework. We propose a unified sequence‑‑graph integration framework for epigenetic age prediction that addresses this gap, integrating eight‑dimensional DNA sequence statistical features through a lightweight gated modulation mechanism that adaptively scales each site's methylation signal according to its sequence‑determined biological relevance prior to graph convolution. Evaluated on 3,707 blood methylation samples against a comprehensive set of baselines, our method achieves a test MAE of 3.149 years, a 12.8% improvement over the strongest graph‑based baseline. Biologically informed statistical features outperform CNN‑based sequence encoding, demonstrating that handcrafted sequence features are more effective than end‑to‑end learned representations in this data regime. Post‑hoc interpretability analysis identifies CpG density and local adenine frequency as features with age‑dependent importance shifts, consistent with known mechanisms of age‑related hypermethylation at CpG‑dense promoter regions. Our code is at https://github.com/yaoli2022/graphage‑seq.

PaperId: 1113, https://arxiv.org/pdf/2605.10518.pdf GitHub

Abstract:
Masked Diffusion Models (MDMs) have emerged as a promising alternative to autoregressive models in language modeling, offering the advantages of parallel decoding and bidirectional context processing within a simple yet effective framework. Specifically, their explicit distinction between masked tokens and data underlies their simple framework and effective conditional generation. However, MDMs typically require many sampling iterations due to factorization errors stemming from simultaneous token updates. We observe that a theoretical lower bound of the factorization error exists, which standard MDMs cannot reduce due to their use of a deterministic single‑state mask. In this paper, we propose the Infinite Mask Diffusion Model (IMDM), which introduces a stochastic infinite‑state mask to mitigate the theoretical bound while directly inheriting the benefits of MDMs, including the compatibility with pre‑trained weights. We empirically demonstrate that MDM fails to perform few‑step generation even in a simple synthetic task due to the factorization error bound, whereas IMDM can find an efficient solution for the same task. Finally, when equipped with appropriate distillation methods, IMDM surpasses existing few‑step distillation methods at small step counts on LM1B and OpenWebText. Code is available at https://Ugness.github.io/official_imdm.

PaperId: 1114, https://arxiv.org/pdf/2605.10426.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models have emerged as a promising paradigm for end‑to‑end autonomous driving. However, existing reasoning mechanisms still struggle to provide planning‑oriented intermediate representations: textual Chain‑of‑Thought (CoT) fails to preserve continuous spatiotemporal structure, while latent world reasoning remains difficult to use as a direct condition for action generation. In this paper, we propose CoWorld‑VLA, a multi‑expert world reasoning framework for autonomous driving, where world representations serve as explicit conditions to guide action planning. CoWorld‑VLA extracts complementary world information through multi‑source supervision and encodes it into expert tokens within the VLA, thereby providing planner‑accessible conditioning signals. Specifically, we construct four types of tokens: semantic interaction, geometric structure, dynamic evolution, and ego trajectory tokens, which respectively model interaction intent, spatial structure, future temporal dynamics, and behavioral goals. During action generation, CoWorld‑VLA employs a diffusion‑based hierarchical multi‑expert fusion planner, which is coupled with scene context throughout the joint denoising process to generate continuous ego trajectories. Experiments show that CoWorld‑VLA achieves competitive results in both future scene generation and planning on the NAVSIM v1 benchmark, demonstrating strong performance in collision avoidance and trajectory accuracy. Ablation studies further validate the complementarity of expert tokens and their effectiveness as planning conditions for action generation. Code will be available at https://github.com/AFARI‑Research/CoWorld‑VLA.

PaperId: 1115, https://arxiv.org/pdf/2605.10401.pdf GitHub

Abstract:
Efficient branching policies are essential for accelerating Mixed Integer Linear Programming (MILP) solvers. Their design has long relied on hand‑crafted heuristics, and now machine learning has emerged as a promising paradigm to automate this process. However, existing learning‑based methods are often hindered by their dependence on expensive expert demonstrations and the gap between training objectives and the solver's end‑to‑end performance. In this work, we propose LLM4Branch, a novel framework that leverages Large Language Models (LLMs) to automate the discovery of efficient branching policies. Specifically, the discovered policy is an executable program with a program skeleton generated by the LLM and a parameter vector, which is optimized via a zeroth‑order method over a few instances with their end‑to‑end performance feedback. Extensive experiments on standard MILP benchmarks demonstrate that LLM4Branch establishes a new state‑of‑the‑art among CPU‑based methods and achieves performance competitive with advanced GPU‑based models. Codes are available at https://github.com/hzn18/LLM4Branch.

PaperId: 1116, https://arxiv.org/pdf/2605.10397.pdf GitHub

Abstract:
Visual anomaly detection (VAD) is crucial in many real‑world fields, such as industrial inspection, medical imaging, infrastructure monitoring, and remote sensing. However, the specific anomaly definitions, data modalities, and annotation standards across different domains make it difficult to transfer single‑domain trained VAD models. Vision‑language models (VLMs), pre‑trained on large‑scale cross‑domain data, can perform visual perception under task instructions, offering a promising solution for cross‑domain VAD. However, single‑inference VLM judgments are unreliable, since they rely more on prior knowledge than on normal‑sample references or fine‑grained feature evidence. We therefore present AnomalyClaw, a training‑free VAD agent that turns anomaly judgment into a multi‑round refutation process. In each round, the agent proposes candidate anomalies and refutes each against normal‑sample references, drawing on a 13‑tool library for visual verification, reference parsing, and frozen expert probing. On the CrossDomainVAD‑12 benchmark (12 datasets), AnomalyClaw achieves consistent macro‑AUROC improvements over single‑step direct inference with +6.23 pp on GPT‑5.5, +7.93 pp on Seed2.0‑lite, and +3.52 pp on Qwen3.5‑VL‑27B. We further introduce an optional verbalized self‑evolution extension. It builds an online rulebook from internal‑branch disagreement without oracle labels. On Qwen3.5‑VL‑27B, it delivers a +2.09 pp mean gain, comparable to a K = 10 oracle‑label supervised baseline (+1.99 pp). These results show that agentic refutation improve anomaly understanding and reasoning of VLMs, rather than merely aggregating tool outputs.

PaperId: 1117, https://arxiv.org/pdf/2605.10357.pdf GitHub

Abstract:
Multimodal misinformation increasingly leverages visual persuasion, where repurposed or manipulated images strengthen misleading text. We introduce RW‑Post, a post‑aligned text‑‑image benchmark for real‑world multimodal fact‑checking with \emphauditable annotations: each instance links the original social‑media post with reasoning traces and explicitly linked evidence items derived from human fact‑check articles via an LLM‑assisted extraction‑and‑auditing pipeline. RW‑Post supports controlled evaluation across closed‑book, evidence‑bounded, and open‑web regimes, enabling systematic diagnosis of visual grounding and evidence utilization. We provide AgentFact as a reference verification baseline and benchmark strong open‑source LVLMs under unified protocols. Experiments show substantial headroom: current models struggle with faithful evidence grounding, while evidence‑bounded evaluation improves both accuracy and faithfulness. Code and dataset will be released at https://github.com/xudanni0927/AgentFact.

PaperId: 1118, https://arxiv.org/pdf/2605.10344.pdf GitHub

Abstract:
Test‑time scaling has become an effective paradigm for improving the reasoning ability of large language models by allocating additional computation during inference. Recent structured approaches have further advanced this paradigm by organizing inference across multiple trajectories, refinement rounds, and verification‑based feedback. However, existing structured test‑time scaling methods either weakly coordinate parallel reasoning trajectories or rely on noisy historical information without explicitly deciding what should be retained and reused, limiting their ability to balance exploration and exploitation. In this work, we propose TMAS, a framework for scaling test‑time compute via multi‑agent synergy. TMAS organizes inference as a collaborative process among specialized agents, enabling structured information flow across agents, trajectories, and refinement iterations. To support effective cross‑trajectory collaboration, TMAS introduces hierarchical memories: the experience bank reuses low‑level reliable intermediate conclusions and local feedback, while the guideline bank records previously explored high‑level strategies to steer subsequent rollouts away from redundant reasoning patterns. Furthermore, we design a hybrid reward reinforcement learning scheme tailored to TMAS, which jointly preserves basic reasoning capability, enhances experience utilization, and encourages exploration beyond previously attempted solution strategies. Extensive experiments on challenging reasoning benchmarks show that TMAS achieves stronger iterative scaling than existing test‑time scaling baselines, with hybrid reward training further improving scaling effectiveness and stability across iterations. Code and data are available at https://github.com/IQuestLab/tmas.

PaperId: 1119, https://arxiv.org/pdf/2605.10335.pdf GitHub

Abstract:
Adaptive optimizers, most notably Adam, have become the default standard for training large‑scale neural networks such as Transformers. These methods maintain running estimates of gradient first and second moments, incurring substantial memory overhead. We introduce PowerStep, a memory‑efficient optimizer that achieves coordinate‑wise adaptivity without storing second‑moment statistics. Motivated by steepest descent under an \ell_p‑norm geometry, we show that applying a nonlinear transform directly to a momentum buffer yields coordinate‑wise adaptivity. We prove that PowerStep converges at the optimal O(1/\sqrtT) rate for non‑convex stochastic optimization. Extensive experiments on Transformer models ranging from 124M to 235B parameters demonstrate that PowerStep matches Adam's convergence speed while halving optimizer memory. Furthermore, when combined with aggressive \textttint8 quantization, PowerStep remains numerically stable and reduces optimizer memory by ～\!8× compared to full‑precision Adam. PowerStep thus provides a principled, scalable and resource‑efficient alternative for large‑scale training. Code is available at https://github.com/yaolubrain/PowerStep.

PaperId: 1120, https://arxiv.org/pdf/2605.10253.pdf GitHub

Abstract:
Retrieval‑augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowledge during generation. However, the underlying retrieval databases may naturally contain, or be intentionally injected with, adversarial knowledge, which can perturb model outputs and undermine system reliability. To investigate this risk, prior studies have explored knowledge poisoning attacks in medical RAG systems. Nevertheless, most of them rely on the strong assumption that adversaries possess prior knowledge of user queries, which is unrealistic in deployments and substantially limits their practical applicability. In this paper, we propose M\textsuperscript3Att, a knowledge‑poisoning framework designed for medical multimodal RAG systems, assuming only limited distribution knowledge of the underlying database. Our core idea is to inject covert misinformation into textual data while using paired visual data as a query‑agnostic trigger to promote retrieval. We first propose a unified framework that introduces imperceptible perturbations to visual inputs to manipulate retrieval probabilities. Besides, due to the prior medical knowledge in LLMs, naively poisoned medical content with explicit factual errors can be corrected during generation. Thus, we leverage the inherent ambiguity of medical diagnosis and design a covert misinformation injection strategy that degrades diagnostic accuracy while evading model self‑correction. Experiments on five LLMs and datasets demonstrate that M\textsuperscript3Att consistently produces clinically plausible yet incorrect generations. Codes: https://github.com/ypr17/M3Att.

PaperId: 1121, https://arxiv.org/pdf/2605.10120.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) show remarkable potential for scientific reasoning, yet their performance in specialized domains such as microscopy remains limited by the scarcity of domain‑specific training data and the difficulty of encoding fine‑grained expert knowledge into model parameters. To bridge the gap, we introduce MicroWorld, a framework that constructs a multimodal attributed property graph (MAPG) from large‑scale scientific image‑‑caption corpora and leverages it to augment MLLM reasoning at inference time without any domain‑specific fine‑tuning. MicroWorld extracts biomedical entities and relations via scispaCy or LLM‑based triplet mining, aligns images and entities in a shared embedding space using Qwen3‑VL‑Embedding, and assembles a knowledge graph comprising approximately 111K nodes and 346K typed edges spanning eight relation categories. At inference time, a graph‑augmented retrieval pipeline matches query entities to the MAPG and injects structured knowledge context into the MLLM prompt. On the MicroVQA benchmark, MicroWorld improves the reasoning performance of Qwen3‑VL‑8B‑Instruct by 37.5%, outperforming GPT‑5 by 13.0% to achieve a new state‑of‑the‑art. Furthermore, it yields a 6.0% performance gain on the MicroBench benchmark. Extensive experiments demonstrate the enhanced generalization capability introduced by MicroWorld. A qualitative case study further reveals both the mechanisms through which structured knowledge improves reasoning and the failure modes that point to promising future directions. Code and data are available at https://github.com/ieellee/MicroWorld.

PaperId: 1122, https://arxiv.org/pdf/2605.10111.pdf GitHub

Abstract:
Motor imagery electroencephalography (MI‑EEG) decoding offers a non‑invasive route for post‑stroke rehabilitation, but cross‑patient use remains difficult because pathological neural reorganization changes task‑related EEG dynamics, aperiodic activity, local excitability, cross‑regional coordination, and trial‑level brain‑state context. This makes source‑learned MI representations unreliable for unseen patients. To address this problem, we propose CFSPMNet, a cross‑patient adaptation framework that models post‑stroke MI‑EEG as latent neural‑state organization. CFSPMNet combines a Fourier‑Reorganized State Mamba Network (FRSM) with Shared‑Private Prototype Matching (SPPM). FRSM represents each trial as a latent physiological token sequence, reorganizes token states in the Fourier domain, and uses Fourier‑derived trial context to guide Mamba state‑space propagation. SPPM improves target pseudo‑label updating by combining semantic confidence with shared‑private physiological consistency, filtering confident but physiologically inconsistent target predictions. Leave‑one‑subject‑out experiments on two stroke MI‑EEG datasets show that CFSPMNet outperforms representative CNN‑, Transformer‑, Mamba‑, and adaptation‑based baselines, achieving average accuracies of 68.23% on XW‑Stroke and 73.33% on 2019‑Stroke, with gains of 5.63 and 8.25 percentage points over the strongest competitors. Ablation, sensitivity, feature‑alignment, pseudo‑label selection, and neurophysiological visualization analyses further support the roles of Fourier‑domain token‑state reorganization and calibrated pseudo‑label updating. These results suggest that latent neural‑state modeling can improve rehabilitation‑oriented cross‑patient BCI decoding. Code is available at https://github.com/wxk1224/CFSPMNet.

PaperId: 1123, https://arxiv.org/pdf/2605.10065.pdf GitHub

Abstract:
Controlling Large Language Models (LLMs) to prevent the generation of undesirable content, such as profanity and personally identifiable information (PII), has become increasingly critical. While earlier approaches relied on post‑processing or resampling, recent research has shifted towards constrained decoding methods that control outputs during generation to mitigate high computational costs and quality degradation. However, preventing multiple forbidden hard constraints or regex constraints from appearing anywhere in the output is computationally challenging. A straightforward solution is to convert these constraints into a single automaton that tracks all forbidden patterns during decoding, but this often becomes impractically large. Standard regex engines also do not readily support the operations needed to build such a constraint, such as complement and intersection. In order to address these limitations, we propose NCO, a decoding strategy that performs online pattern matching over finite hard constraints and regex constraints, reducing computational overhead without inducing state explosion. NCO is fully compatible with standard inference strategies, including various sampling methods and beam search, while also supporting soft masking for probabilistic suppression. We empirically demonstrate its effectiveness across practical tasks, including PII and profanity suppression. Our implementation is available at https://github.com/hyundong98/NCO‑Decoding.git .

PaperId: 1124, https://arxiv.org/pdf/2605.09942.pdf GitHub

Abstract:
Memory retrieval in agentic large language model (LLM) systems is often treated as a static lookup problem, relying on flat vector search or fixed binary relational graphs. However, fixed graph structures cannot capture the varying strength, confidence, and query‑dependent relevance of relationships between events. In this paper, we propose HAGE, a weighted multi‑relational memory framework that reconceptualizes retrieval as sequential, query‑conditioned traversal over a unified relational memory graph. Memory is organized as relation‑specific graph views over shared memory nodes, where each edge is associated with a trainable relation feature vector encoding multiple relational signals. Given a query, an LLM‑based classifier identifies the relational intent, and a routing network dynamically modulates the corresponding dimensions of the edge embedding. Traversal scores are computed via a learned combination of semantic similarity and these query‑conditioned edge representations. This allows memory traversal to prioritize high‑utility relational paths while softly suppressing noisy or weakly relevant connections. Beyond adaptive traversal, HAGE further introduces a reinforcement learning‑based training framework that jointly optimizes routing behavior and edge representations using downstream tasks. Finally, empirical results demonstrate improved long‑horizon reasoning accuracy and a favorable accuracy‑efficiency trade‑off compared to state‑of‑the‑art agentic memory systems. Our code is available at https://github.com/FredJiang0324/HAGE_MVPReview.

PaperId: 1125, https://arxiv.org/pdf/2605.09922.pdf GitHub

Abstract:
While recent self‑training approaches have reduced reliance on human‑labeled data for aligning LLMs, they still face critical limitations: (i) sensitivity to synthetic data quality, leading to instability and bias amplification in iterative training; (ii) ineffective optimization due to a diminishing gap between positive and negative responses over successive training iterations. In this paper, we propose Team‑based self‑Play with dual Adaptive Weighting (TPAW), a novel self‑play algorithm designed to improve alignment in a fully self‑supervised setting. TPAW adopts a team‑based framework in which the current policy model both collaborates with and competes against historical checkpoints, promoting more stable and efficient optimization. To further enhance learning, we design two adaptive weighting mechanisms: (i) a response reweighting scheme that adjusts the importance of target responses, and (ii) a player weighting strategy that dynamically modulates each team member's contribution during training. Initialized from a SFT model, TPAW iteratively refines alignment without requiring additional human supervision. Experimental results demonstrate that TPAW consistently outperforms existing baselines across various base models and LLM benchmarks. Our code is publicly available at https://github.com/lab‑klc/TPAW.

PaperId: 1126, https://arxiv.org/pdf/2605.09920.pdf GitHub

Abstract:
While Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a promising post‑training paradigm for Large Language Models (LLMs), its dependency on the gold label or domain‑specific verifiers limits its scalability to new tasks and domains. In this work, we propose Verifier‑free Intrinsic Gradient‑Norm Reward (VIGOR), a simple reward that uses only the policy model itself. Given a prompt, VIGOR samples a group of completions and assigns higher within‑group rewards to outputs that induce smaller \ell_2 norms of the teacher‑forced negative log‑likelihood gradients under the current parameters. Intuitively, lower gradient norms suggest the completion aligns better with the current policy, serving as an intrinsic preference signal for policy optimization. To make this intrinsic signal practical for RL, we correct the systematic length bias of averaged token‑level gradients with a \sqrtT scaling, and apply group‑wise rank shaping to stabilize reward scales across prompts. Across mathematical reasoning benchmarks, VIGOR outperforms the state‑of‑the‑art Reinforcement Learning from Internal Feedback (RLIF) baseline, and it also exhibits cross‑domain transfer to code benchmarks when trained only on math data. For instance, on Qwen2.5‑7B‑Base post‑trained on MATH, VIGOR improves the average math accuracy by +3.31% and the average code accuracy by +1.91% over this baseline, while exhibiting more stable training dynamics. The code is available at https://github.com/ZJUSCL/VIGOR.

PaperId: 1127, https://arxiv.org/pdf/2605.09879.pdf GitHub

Abstract:
While reasoning has become a central capability of large language models (LLMs), the reasoning patterns required for different scenarios are often misaligned. Mathematical reasoning typically relies on intrinsic logic to solve closed‑world problems in a single response, whereas agentic reasoning requires not only internal reasoning but also multi‑turn interaction with external environments, interleaving thought and action. This misalignment prevents mathematical and agentic reasoning from effectively benefiting from each other, often yielding unstable reasoning behavior and only limited performance gains under multi‑task learning. In this paper, we propose M2A, a novel paradigm that synergizes mathematical and agentic reasoning via model merging. To avoid overfitting to superficial reasoning patterns under joint training, M2A operates directly in parameter space: it identifies the feature subspace critical for agent behavior, and merges the mathematical reasoning task vector only along its null space, thereby injecting reasoning capability along directions that do not perturb agent behavior. Unlike SFT or RL, M2A requires no additional gradient‑update and exposes the merging coefficient as a simple knob for controlling reasoning length. Experiments in a challenging real‑world coding agent setting show that our method effectively extends agentic reasoning depth and delivers substantial performance improvements. Applied to a fine‑tuned Qwen3‑8B, M2A improves its SWE‑Bench Verified resolved rate from 44.0% to 51.2% without retraining the model. Code is available at https://github.com/laplucky/M2A.git.

PaperId: 1128, https://arxiv.org/pdf/2605.09877.pdf GitHub

Abstract:
We present Key‑Value Means ("KVM"), a novel block‑recurrence for attention that can accommodate either fixed‑size or growing state. Equipping a strong transformer baseline with fixed‑size KVM attention layers yields a strong O(N) chunked RNN, while adding only an insignificant number of new parameters. We train a transformer with a growable KVM cache and show it performs competitively on long‑context tests with only subquadratic prefill time and sublinear state growth. KVM is implementable with standard operations and without custom kernels, and supports chunk‑wise parallelizable training and prefill. It provides many of the benefits of both traditional transformers (expandable context memory, chunk‑wise parallelizable training and prefill) and linear RNNs in a single unified package. It can be used on every layer, saving KV‑cache memory, and allowing a continuous range of choices of prefill time complexity between O(N) and O(N^2). It can also be implemented in a hybrid solution in tandem with LRNN layers in place of traditional attention, to supplement the LRNN with improved sublinear memory growth context length usage and long context decoding. We release our code at https://github.com/featherless‑ai/KVM‑paper and trained models at https://huggingface.co/collections/featherless‑ai/kvm‑paper under the Apache 2.0 license.

PaperId: 1129, https://arxiv.org/pdf/2605.09802.pdf GitHub

Abstract:
Vision‑language models (VLMs) enable text‑guided object detection but degrade severely under cross‑view scenarios where ground and aerial viewpoints differ in altitude, scale, and spatial layout. These geometric changes introduce systematic complexity variations between viewpoints, e.g., ground view images contain dense and highly occluded structures, while aerial images are sparse and globally organized. Fixed VLM fusion mechanisms cannot handle this discrepancy. We propose CrossVL, a framework combining Complexity‑Aware Pathway Aggregation (CPA) and Paired Curriculum Learning (PCL) for enhanced cross‑view detection for VLM. CPA estimates scene complexity from multimodal statistics and routes visual features through multiple pathways to obtain view‑specific representations. PCL leverages semantic consistency of synchronized ground‑aerial pairs to provide stable early supervision and then gradually shifts toward randomized sampling. On MAVREC, CrossVL improves Florence‑2's aerial mAP from 58.66% to 61.03% and reduces the ground‑aerial performance gap from 8.63pp to 6.65pp, while also achieving a 3.3x reduction in variance across random seeds. CPA provides stable complexity‑aware feature aggregation, and PCL enhances optimization dynamics. Together, they demonstrate that coordinated architectural and training adaptations are crucial for robust cross‑view VLM detection.

PaperId: 1130, https://arxiv.org/pdf/2605.09764.pdf GitHub

Abstract:
LLM‑guided evolutionary methods such as AlphaEvolve have proven effective in domains like math, systems research, and algorithmic discovery, but their reliance on frontier models makes each run expensive. We argue this is largely an artifact of how existing frameworks allocate search: archives that fail to preserve solution diversity force compensation through stronger mutation models; blind model use spends frontier dollars on local edits a smaller model could handle; and full‑set evaluation wastes rollouts on redundant examples. We introduce LEVI, a harness‑first evolutionary framework built on the bet that stronger search architectures can substitute for or even outperform larger LLMs in evolutionary search. LEVI improves on three core components of evolutionary search: a solution database that establishes diversity from the beginning, and then maintains it throughout the run; a smarter mutation router that plays into the strengths of large and small LLMs; and a rank‑preserving proxy benchmark for rollout‑heavy settings. Across systems‑research benchmarks LEVI attains the highest score on a budget 3.3‑6.7x smaller than the published frontier‑model runs of existing frameworks like ShinkaEvolve, GEPA, and AdaEvolve; on one problem, LEVI matches the existing best at a 35x lower cost. On prompt optimization, LEVI matches or exceeds GEPA at less than half of its rollout budget on four different benchmarks. LEVI is available as an open‑source framework at https://github.com/ttanv/levi.

PaperId: 1131, https://arxiv.org/pdf/2605.09742.pdf GitHub

Abstract:
Selective state space models (SSMs), such as Mamba, achieve strong per‑token expressivity by making the time discretization step \TildeΔ a learned function of the input. However, in doing so, \TildeΔ ceases to represent a physical sampling interval, limiting its irregular time series modeling capability. Continuous‑time SSMs, such as S5, preserve the physical meaning of \TildeΔ and handle irregular timestamps natively (\TildeΔ\equivΔ), but their dynamics remain linear time‑invariant (LTI), limiting per‑token expressivity. We propose TIDES, a selective SSM variant that reconciles selective and continuous architectures by moving input‑dependence off the step size and onto the diagonal state matrix. As a result, \TildeΔ retains its physical meaning, tied to the state discretization, allowing the model to handle irregular timestamps natively without sacrificing the per‑token expressivity that makes selective SSMs effective. We show this on a novel \emphFading Flash experimental benchmark, a compact controlled diagnostic for sequence models that jointly tests input‑dependence and extrapolation to out‑of‑distribution Δ values, and isolates the distinct failure modes of current state‑of‑the‑art architectures that TIDES avoids by construction. On large‑scale benchmarks, TIDES sets the new state‑of‑the‑art average rank on UEA time‑series classification and the Physiome‑ODE regression benchmark. Code available at: https://github.com/TaylanSoydan/TIDES.

PaperId: 1132, https://arxiv.org/pdf/2605.09708.pdf GitHub

Abstract:
We present Metal‑Sci, a 10‑task benchmark of scientific Apple Silicon Metal compute kernels spanning six optimization regimes (stencils, all‑pairs in n‑body problems, multi‑field Boltzmann, neighbor‑list molecular dynamics, multi‑kernel PDE, FFT). Each task ships a CPU reference, a roofline‑anchored fitness function, and a held‑out generalization size. We pair the benchmark with a lightweight harness for automatic kernel search that runtime‑compiles each candidate, scores it against the roofline across multiple sizes, and feeds structured compile and per‑size correctness diagnostics back to a frozen LLM driving a (1+1) evolutionary loop. We report matched single‑model sweeps of Claude Opus 4.7, Gemini 3.1 Pro, and GPT 5.5 on M1 Pro: in‑distribution self‑speedups span 1.00× to 10.7×. Beyond raw speedup, our central methodological claim is structural: the held‑out gate scoring function Φ_\mathcalT (evaluated once at end‑of‑run on a configuration the agent never sees during search) functions as a cheap mechanical oversight primitive on this automatic search loop, catching e.g. an Opus template <uint D> HMC win that returns wrong samples at unseen dimensions, and a GPT FFT3D best that wins in‑distribution at 2.95× speedup but collapses to 0.23× on a 256^3 held‑out cube, a silent regression that the in‑distribution score alone cannot see. Code at https://github.com/vicgalle/metal‑sci‑kernels

PaperId: 1133, https://arxiv.org/pdf/2605.09679.pdf GitHub

Abstract:
Medical vision‑language models (VLMs) and AI agents have made significant progress in learning to analyze and reason about clinical images. However, existing medical visual question answering (VQA) benchmarks collapse model capabilities into a single accuracy score, obscuring where and why models fail. We propose DeepTumorVQA, a hierarchical benchmark that follows the multi‑stage evidence chain in tumor diagnosis and decomposes 3D CT reasoning into four stages: recognition, measurement, visual reasoning, and medical reasoning. Higher‑level questions remain independently scorable, while their ground‑truth evidence chains are defined over lower‑level primitives. The benchmark contains 476K questions across 42 clinical subtypes on 9,262 3D CT volumes. In addition to a direct reasoning mode for VLMs, DeepTumorVQA provides tool‑interaction environments for agent evaluation, where a model can call external tools, including segmentation models, measurement programs, and medical knowledge modules, before answering the question. Evaluating over 30 model configurations, we find that reliable quantitative measurement is the primary bottleneck, making later‑stage visual and medical reasoning harder for VLMs, while tool augmentation substantially mitigates this issue. When tools are available, leveraging medical knowledge and tools to reason about medical images becomes a new challenge. We further show that ground‑truth step‑by‑step tool‑use traces from DeepTumorVQA can supervise agents and reduce tool‑use and reasoning failures. This stage‑wise progression from recognition to measurement to visual and medical reasoning provides a concrete roadmap for future medical VLM and AI agent studies. All data and code are released at https://github.com/Schuture/DeepTumorVQA.

PaperId: 1134, https://arxiv.org/pdf/2605.09536.pdf GitHub

Abstract:
Diffusion large language models (dLLMs) offer a promising paradigm for parallel text generation, but in practice they face an accuracy‑parallelism trade‑off, where increasing tokens per forward (TPF) often degrades generation quality. Existing acceleration methods often gain speed at the cost of accuracy. To address this limitation, we propose TAD, a Temporal‑Aware trajectory self‑Distillation framework. During data construction, we condition a teacher model on both the prompt and the ground‑truth response to generate decoding trajectories, recording the intermediate masked states throughout the process. Based on how many decoding steps remain before each masked token is revealed, we partition masked positions into near and distant subsets. For near tokens, we train the student with a hard cross‑entropy loss using the teacher trajectory tokens as labels, encouraging confident predictions for tokens that are about to be decoded. For distant tokens, we apply a soft KL divergence loss between the teacher and student token distributions, providing softer supervision and preserving future planning knowledge. This temporal‑aware partition naturally gives rise to two deployment configurations: a Quality model that prioritizes accuracy and a Speed model that favors more aggressive acceleration. Experiments show that TAD consistently improves the accuracy‑parallelism trade‑off. On LLaDA, it raises average accuracy from 46.2% to 51.6% with the Quality model and average AUP from 46.2 to 257.1 with the Speed model. Our code is available at: https://github.com/BHmingyang/TAD

PaperId: 1135, https://arxiv.org/pdf/2605.09505.pdf GitHub

Abstract:
Epilepsy diagnosis and treatment require evidence‑intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mechanisms, pharmacogenomics, treatment strategies, and patient outcomes. In this work, we present \textscEpiGraph, a large‑scale epilepsy knowledge graph and benchmark for evaluating knowledge‑augmented clinical reasoning. \textscEpiGraph integrates 48,166 peer‑reviewed papers and seven clinical resources into a heterogeneous graph containing 24,324 entities and 32,009 evidence‑grounded triplets across five clinical layers. Built upon this graph, \textscEpiBench defines five clinically motivated tasks spanning clinical decision‑making, EEG report generation, pharmacogenomic precision medicine, treatment recommendation, and deep research planning. We evaluate six LLMs under both standard and Graph‑RAG settings. Results show that integrating \textscEpiGraph consistently improves performance across all tasks, with the largest gains observed in pharmacogenomic reasoning (+30‑‑41%). Our findings demonstrate that structured epilepsy knowledge substantially enhances evidence‑grounded clinical reasoning and provides a practical benchmark framework for evaluating knowledge‑augmented LLMs in real‑world neurological settings. Our code is available at: https://github.com/LabRAI/EEG‑KG.

PaperId: 1136, https://arxiv.org/pdf/2605.09492.pdf GitHub

Abstract:
Large language models (LLMs) often suffer from hallucinations due to error accumulation in autoregressive decoding, where suboptimal early token choices misguide subsequent generation. Although multi‑path decoding can improve robustness by exploring alternative trajectories, existing methods lack principled strategies for determining when to branch and how to regulate inter‑path interactions. We propose Adaptive Path‑Contrastive Decoding (APCD), a multi‑path decoding framework that improves output reliability through adaptive exploration and controlled path interaction. APCD consists of two components: (1) Entropy‑Driven Path Expansion, which delays branching until predictive uncertainty ‑ measured by Shannon entropy over top candidate tokens ‑ indicates multiple plausible continuations; and (2) Divergence‑Aware Path Contrast, which encourages diverse reasoning trajectories while dynamically attenuating inter‑path influence as prediction distributions diverge. Experiments on eight benchmarks demonstrate improved factual accuracy while maintaining decoding efficiency. Our code is available at https://github.com/zty‑king/APCD.

PaperId: 1137, https://arxiv.org/pdf/2605.09461.pdf GitHub

Abstract:
Automated vulnerability detection is a fundamental task in software security, yet existing learning‑based methods still struggle to capture the structural dependencies, domain‑specific vulnerability knowledge, and complex program semantics required for accurate detection. Recent Large Language Models (LLMs) have shown strong code understanding ability, but directly prompting them with raw source code often leads to missed vulnerabilities or false alarms, especially when vulnerable and benign functions differ only in subtle semantic details. To address this, we propose VulTriage, a triple‑path context augmentation framework for LLM‑based vulnerability detection. VulTriage enhances the LLM input through three complementary paths: a Control Path that extracts and verbalizes AST, CFG, and DFG information to expose control and data dependencies; a Knowledge Path that retrieves relevant CWE‑derived vulnerability patterns and examples through hybrid dense‑‑sparse retrieval; and a Semantic Path that summarizes the functional behavior of the code before the final judgment. These contexts are integrated into a unified instruction to guide the LLM toward more reliable vulnerability reasoning. Experiments on the PrimeVul pair test set show that VulTriage achieves state‑of‑the‑art performance, outperforming existing deep learning and LLM‑based baselines on key pair‑wise and classification metrics. Further ablation studies verify the effectiveness of each path, and additional experiments on the Kotlin dataset demonstrate the generalization ability of VulTriage under low‑resource and class‑imbalanced settings. Our code is available at https://github.com/vinsontang1/VulTriage

PaperId: 1138, https://arxiv.org/pdf/2605.09442.pdf GitHub GitHub

Abstract:
Streaming long‑video generation faces a central challenge in continuous semantic switching, requiring adaptive memory to preserve coherent visual evolution. Current approaches rely on cache rebuilding at prompt boundaries or fixed memory budgets, but they introduce redundant computation and limit flexible semantic adaptation. This limitation arises from a mismatch between cached video history and prompt updates, as memory preserves visual continuity while prompt switches demand rapid semantic adaptation. Motivated by this observation, we present SWIFT, Semantic Windowing and Injection for Flexible Transitions, a training‑free framework for multi‑prompt long‑video generation that enables efficient semantic switching while preserving temporal coherence in causal video diffusion models. SWIFT introduces a lightweight Semantic Injection Cache that augments cached video memory rather than reconstructing it from scratch at every prompt boundary. To avoid uniformly perturbing all attention channels, we further perform head‑wise semantic injection, so that each attention head receives a prompt update proportional to its alignment with the current video state. In addition, we introduce an Adaptive Dynamic Window that allocates temporal memory according to prompt phase, using larger local context near switching boundaries and smaller windows during stable segments to reduce average inference cost. To preserve long‑range semantic consistency under compressed local attention, we further maintain segment‑level semantic anchors that summarize prompt‑conditioned video history and reintroduce it as compact memory tokens. Compared with current state‑of‑the‑art methods, SWIFT preserves generation quality while achieving 22.6 FPS on a single H100 GPU, establishing a substantially more efficient solution for multi‑prompt long‑video generation. Our code is available at https://github.com/ShanwenTan/SWIFT.

PaperId: 1139, https://arxiv.org/pdf/2605.09425.pdf GitHub

Abstract:
Recent conditional image generation methods can improve controllability by generating images that are faithful to conditions such as sketches, human poses, segmentation maps, and depth. By applying these techniques to image augmentation while preserving annotations, generated images can be used as additional training data and can improve recognition performance. However, for high‑level driving tasks such as traffic‑rule extraction and driving‑behavior understanding, simply using annotations as conditions is insufficient. Instead, images must be augmented while preserving the detailed high‑level structure of the original scene. One possible solution is to use multiple conditions so that generated images retain diverse structural cues after generation. However, when multiple conditions are used, conflicts among conditions can prevent reliable structure preservation. In this work, we input semantic segmentation, depth, and edges extracted from the original image into a multi‑condition image generation model, thereby providing rich structural information as conditions. We further propose a modeling approach for handling conflicts among multiple conditions and show that it enables image generation with stronger structural preservation. We also build a generation framework and evaluation protocol for driving tasks, establishing a basis for comparison with prior and future models. As a result, this work contributes to image generation research by addressing condition conflicts in multi‑condition generation and provides an important step toward mitigating data scarcity in high‑level autonomous‑driving tasks.

PaperId: 1140, https://arxiv.org/pdf/2605.09386.pdf GitHub

Abstract:
Metric‑induced discrete flow matching (MI‑DFM) exploits token‑latent geometry for discrete generation, but its practical use is limited by two issues: heuristic schedulers requiring hyperparameter search, and finite‑step path‑tracking error from its first‑order continuous‑time Markov chain (CTMC) solver. We address both issues. First, we derive a kinetic‑optimal scheduler for prescribed scalar‑parameterized probability paths, and instantiate it for MI‑DFM as a training‑free numerical schedule that traverses the path at constant Fisher‑Rao speed. Second, we introduce a finite‑step moment correction that adjusts the jump probability while preserving the CTMC jump destination distribution. We validate the resulting method, GibbsTTS, on codec‑based zero‑shot text‑to‑speech (TTS). Under controlled comparisons with a unified architecture and large‑scale dataset, GibbsTTS achieves the best objective naturalness and is preferred in subjective evaluations over masked discrete generative baselines. Additionally, in comparison with the evaluated state‑of‑the‑art TTS systems, GibbsTTS shows strong speaker similarity, achieving the highest similarity on three of four test sets and ranking second on the fourth. Project page: https://ydqmkkx.github.io/GibbsTTSProject

PaperId: 1141, https://arxiv.org/pdf/2605.09348.pdf GitHub

Abstract:
Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured knowledge. Integrating these two in a complementary manner enables the development of reliable and verifiable AI systems. In particular, knowledge graph question answering (KGQA) has attracted attention as a means to reduce LLM hallucinations and to leverage knowledge beyond the training data. However, existing KGQA benchmark datasets are biased toward encyclopedic knowledge, limited to a single modality, and lack fine‑grained spatiotemporal data, which limits their applicability to real‑world scenarios targeted by Embodied AI. We introduce HOME‑KGQA, a novel KGQA benchmark dataset built on a multimodal KG of daily household activities. HOME‑KGQA consists of complex, multi‑hop natural language questions paired with graph database query languages. Compared to existing benchmarks, it includes more challenging questions that involve multi‑level spatiotemporal reasoning, multimodal grounding, and aggregate functions. Experimental results show that the LLM‑based KGQA methods fail to achieve performance comparable to that on existing datasets when evaluated on HOME‑KGQA. This highlights significant challenges that should be addressed for the real‑world deployment of KGQA systems. Our dataset is available at https://github.com/aistairc/home‑kgqa

PaperId: 1142, https://arxiv.org/pdf/2605.09346.pdf GitHub

Abstract:
The Chain‑of‑Thought (CoT) paradigm, while enhancing the interpretability of Large Language Models (LLMs), is constrained by the inefficiencies and expressive limits of natural language. Latent Chain‑of‑Thought (latent CoT) reasoning, which operates in a continuous latent space, offers a promising alternative but faces challenges from structural complexities in existing multi‑step or multi‑model paradigms, such as error propagation and coordination overhead. In this paper, we introduce One‑Model One‑Step, a novel compression framework for Latent Reasoning with Rule‑Based Priors(RuPLaR) to address this challenge. Our method trains an LLM to autonomously generate latent reasoning tokens in a single training stage, guided by rule‑based prior probability distributions, thereby eliminating cascaded processes and inter‑model dependencies. To ensure reasoning quality, we design a joint training objective that enforces answer consistency via cross‑entropy, aligns soft tokens with rule‑based priors via KL divergence (the Soft Thinking constraint), and adds a problem‑thought semantic alignment constraint in the representation space. Extensive experiments show that our compression framework not only improves accuracy by 11.1% over existing latent CoT methods but also achieves this with minimal token usage, underscoring its effectiveness and extensibility. Code: https://github.com/xiaocen‑luo/RuPLaR.

PaperId: 1143, https://arxiv.org/pdf/2605.09311.pdf GitHub

Abstract:
Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate prediction from static atomic structures challenging. The current standard approach, molecular dynamics (MD) simulations, suffers from prohibitively high computational cost. Recent autoregressive learning‑based MD acceleration methods requiring sequential inference remain slow and prone to error accumulation; in contrast, existing non‑autoregressive material property prediction models are less accurate because they fail to exploit dynamics. Moreover, existing methods typically benefit from datasets either with or without atomic trajectories, but not both. To overcome these limitations, we propose a non‑autoregressive learning framework based on auxiliary modality learning, which treats atomic trajectories as an auxiliary modality during training but does not require them at inference. This enables the predictor to learn dynamics without sequential inference while benefiting from both types of datasets. As a result, our framework achieves over 200 times speedup compared to autoregressive models on the dataset with atomic trajectories while substantially reducing prediction error relative to non‑autoregressive benchmarks across both types of datasets. Our code is available at https://github.com/jykim‑git/MD.

PaperId: 1144, https://arxiv.org/pdf/2605.09296.pdf GitHub

Abstract:
Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI‑generated images. Yet existing detectors based on pre‑trained feature extractors tend to over‑rely on global semantics, limiting sensitivity to the critical micro‑defects. In this work, we propose Micro‑Defects expose Macro‑Fakes (MDMF), a local distribution‑aware detection framework that amplifies micro‑scale statistical irregularities into macro‑level distributional discrepancies. To avoid localized forensic cues being diluted by plain aggregation, we introduce a learnable Patch Forensic Signature that projects semantic patch embeddings into a compact forensic latent space. We then use Maximum Mean Discrepancy (MMD) to quantify distributional discrepancies between generated and real images. Our theory‑grounded analysis shows that patch‑wise modeling yields provably larger discrepancies when localized forensic signals are present in generated images, enabling more reliable separation from real images. Extensive experiments demonstrate that MDMF consistently outperforms baseline detectors across multiple benchmarks, validating its general effectiveness. Project page: https://zbox1005.github.io/MDMF‑project/

PaperId: 1145, https://arxiv.org/pdf/2605.09294.pdf GitHub

Abstract:
We propose Representational Effective Theory (RET), a framework for describing large language model computation in terms of learned macrostates rather than microscopic details. RET learns these macrostates from hidden‑state trajectories using a BYOL/JEPA‑style self‑supervised objective, coarse‑graining activations into macrovariables that preserve higher‑level structure relevant for prediction and interpretation. We evaluate whether these macrovariables are practically relevant for interpretability: RET yields temporally consistent states that reveal "mental‑state" trajectories of reasoning, capture high‑level semantic structure, support early prediction of behavioral outcomes such as sycophancy, and provide causal handles for steering generations toward interpretable computational phases. Together, these results suggest that LLM computation admits useful effective descriptions via RET: high‑level, dynamically meaningful variables that support interpretation, prediction, and intervention.

PaperId: 1146, https://arxiv.org/pdf/2605.09287.pdf GitHub

Abstract:
Large Language Model (LLM)‑based search agents trained with reinforcement learning (RL) have significantly improved the performance of knowledge‑intensive tasks. However, existing methods encounter critical challenges in long‑horizon credit assignment: (i) Reward Sparsity, where models receive only outcome feedback without step‑level guidance to differentiate action quality; (ii) Isolated Credit, where credit is assigned to steps independently, failing to capture sequential dependencies; and (iii) Distributional Shift, where rewards are estimated on templates that deviate from the model's natural generative distribution. To address these issues, we propose Pivot‑Based Credit Assignment (PiCA), a novel step reward mechanism that reformulates the search trajectory as a sequential process of cumulative search progress. Unlike prior isolated step rewards, PiCA defines process rewards as success probabilities dependent on the historical context based on Potential‑Based Reward Shaping (PBRS). This approach identifies pivot steps, which comprise target golden sub‑queries and sub‑answers derived from historical trajectories, as information peaks that significantly boost the likelihood of a correct final answer. By anchoring these step rewards to the final task objective, PiCA provides dense, pivot‑aware and trajectory‑dependent guidance while maintaining distributional consistency. Extensive experiments show that PiCA outperforms existing strong baselines across seven knowledge‑intensive QA benchmarks, achieving 15.2% and 2.2% improvements for 3B and 7B models. The consistent performance gains across various models show PiCA's robust generalization. The code is available at https://github.com/novdream/PiCA.

PaperId: 1147, https://arxiv.org/pdf/2605.09284.pdf GitHub

Abstract:
Mesh‑based simulations provide high‑fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine meshes, leading to substantial computational overhead. Super‑resolution techniques aim to mitigate this cost by reconstructing high‑resolution (HR), high‑fidelity solutions from low‑cost, low‑resolution (LR) counterparts. However, training neural networks for super‑resolution often demands large amounts of expensive HR supervision data. To address this challenge, we propose SuperMeshNet, an HR data‑efficient super‑resolution framework for mesh‑based simulations aided by message passing neural networks (MPNNs). At its core, SuperMeshNet introduces complementary learning, a semi‑supervised approach that effectively leverages both 1) a small amount of paired LR‑HR data and 2) abundant unpaired LR data via two jointly trained, complementary MPNN‑based models. Additionally, our model is enriched by inductive biases, which are empirically shown to further improve super‑resolution performance. Extensive experiments demonstrate that SuperMeshNet requires 90% less HR data to achieve even lower root mean square error (RMSE) than that of the fully supervised benchmark without the inductive biases. The source code and datasets are available at https://github.com/jykim‑git/SuperMeshNet.git.

PaperId: 1148, https://arxiv.org/pdf/2605.09241.pdf GitHub GitHub

Abstract:
Joint‑Embedding Predictive Architectures (JEPAs) provide a simpleframework for learning world models by predicting future latent representations.However, JEPA training is subject to a bias‑variance tradeoff.Without sufficient structural constraints, excessive representationalvariance causes the model to collapse to trivial solutions.The recent LeWorldModel (LeWM) shows that this issue can be alleviated bysimply constraining latent embeddings with an isotropic Gaussian prior.However, latent representations inherently lie on low‑dimensional manifoldswithin a high‑dimensional ambient space, and enforcing an isotropic Gaussianprior directly in this ambient space introduces an overly strong bias.In this work, we propose ame, which seeks a favorable operatingpoint on the bias‑variance frontier by applying Gaussian constraints inmultiple random subspaces rather than in the originalembedding space.This design relaxes the global constraint while preserving itsanti‑collapse effect, leading to a better balance between trainingstability and representation flexibility.Extensive experiments across fourcontinuous‑control environments demonstrate that consistentlyoutperforms LeWM with very clear margins.Our method is simple yet effective, and serves as a strong baseline for future JEPA‑based world model research.fdefinedeeemodeThe code is available at https://github.com/intcomp/Sub‑JEPA.

PaperId: 1149, https://arxiv.org/pdf/2605.09238.pdf GitHub

Abstract:
Muon and related norm‑constrained matrix optimizers have become central to large‑scale learning problems. They are formulated as a linear maximization oracle (LMO) over an ambient matrix‑norm ball in unconstrained Euclidean space. However, these do not generalize cleanly to manifold‑valued parameters such as low‑rank factorizations, orthogonality constraints, or symmetric positive definite (SPD) matrices. Naively restricting the Muon LMO to the tangent space (i) breaks quotient symmetries and (ii) couples the tangent‑space constraint with an ambient norm bound, thereby obstructing closed‑form solutions on various manifolds of interest. We resolve both issues with a single observation: every Riemannian metric canonically lifts a unitarily invariant Euclidean norm to an intrinsic norm on each tangent space, and the resulting intrinsic norm constrained LMO is symmetry preserving. Building on this, we introduce intrinsic Muon (iMuon), a unified framework that yields closed‑form updates on the fixed‑rank, SPD, Stiefel, and Grassmann manifolds for any unitarily invariant norm, including the spectral, Frobenius, and nuclear norms. We establish convergence guarantees for both deterministic and stochastic iMuon with rate constants that depend only on the manifold dimension. Notably, on the fixed‑rank manifold this constant depends only on the rank, making the rate independent of factor conditioning and removing the runtime factor‑rescaling required by prior work. Experiments on LoRA finetuning of LLMs, image classification, and subspace learning illustrate the efficacy of the proposed approach.

PaperId: 1150, https://arxiv.org/pdf/2605.09236.pdf GitHub

Abstract:
While digitized corpora have transformed the study of intellectual transmission, current methods rely heavily on lexical text reuse detection, capturing verbatim quotations but fundamentally missing paraphrases and complex implicit engagement. This paper evaluates semantic search in 18th‑century intellectual history through the reception of John Locke's foundational work. Using expert annotation grounded in a semantic taxonomy, we examine whether an off‑the‑shelf semantic search pipeline can surface meaning‑level correspondences overlooked by lexical methods. Our results demonstrate that semantic search retrieves substantially more implicit receptions than lexical baselines. However, linguistic diagnostics also reveal a "lexical gatekeeping" effect, where retrieval remains partially constrained by surface vocabulary overlap. These findings highlight both the potential and the limitations of semantic retrieval for analyzing the circulation of ideas in large historical corpora. The data is available at https://github.com/COMHIS/locke‑sim‑data.

PaperId: 1151, https://arxiv.org/pdf/2605.09222.pdf GitHub GitHub

Abstract:
Logs are ubiquitous in modern systems. Unfortunately, their unstructured nature in flat sequences limits understanding of execution behaviors, hindering effective anomaly diagnosis. To address this, Krone introduces a novel hierarchical log abstraction that transforms flat log sequences into semantically coherent units across entity, action, and status levels. Building on this abstraction, Krone introduces a hierarchical orchestration framework that decomposes flat log sequences into hierarchical execution units and performs modular detection over them. It executes and optimizes the modular detection tasks across levels, enabling precise anomaly detection, localization, and explanation with selective invocation of LLM‑based reasoning. In this work, we present Krone‑viz, an interactive visualization system based on Krone, which makes hierarchical log analysis interpretable and actionable for software engineers and system operators. Demonstrated on the widely used HDFS benchmark dataset, Krone‑viz supports: 1) examining hierarchical decompositions of flat log sequences, 2) inspecting detection results and abnormal segments identified by Krone with LLM‑generated explanations, and 3) reusing, reviewing, and revising knowledge generated by LLMs with human‑in‑the‑loop guardrails. The code of Krone‑viz is available at https://github.com/LeiMa0324/KRONE_Demo_official, and we deploy a live demo at https://leima0324.github.io/KRONE_Demo_official.

PaperId: 1152, https://arxiv.org/pdf/2605.09188.pdf GitHub

Abstract:
Reinforcement learning improves the reasoning ability of large language models but remains costly and sample‑inefficient, as many rollouts provide weak learning signals. Difficulty‑aware data selection methods attempt to address this by prioritizing moderately difficult prompts, yet our analysis reveals three limitations: difficulty estimates become inaccurate under policy drift, data selection alone yields limited final‑performance gains, and inference efficiency remains largely unchanged. These findings suggest that efficient and effective RL requires more than filtering by difficulty: the policy should learn to solve hard tasks while producing concise responses for easy ones. To this end, we propose Dare, a unified framework that co‑evolves difficulty estimation with the policy via self‑normalized importance sampling, maintains diverse difficulty coverage through a symmetric Beta sampling distribution, and applies tailored training strategies across difficulty tiers with adaptive compute allocation. Extensive experiments across multiple models and domains demonstrate that Dare consistently outperforms existing methods in training efficiency, final effectiveness, and inference efficiency, producing more concise responses on easy tasks while improving correctness on hard ones. Code is available at https://github.com/EtaYang10th/DARE.

PaperId: 1153, https://arxiv.org/pdf/2605.09184.pdf GitHub

Abstract:
We present Open Ontologies, an open‑source ontology engineering system implemented in Rust that integrates LLM‑driven construction with formal OWL reasoning and ontology alignment via the Model Context Protocol. Our primary finding is that stable 1‑to‑1 matching is the dominant factor in ontology alignment quality: on the OAEI Anatomy track, it achieves F1 = 0.832 (P = 0.963, R = 0.733), competitive with state‑of‑the‑art systems and exceeding all in precision. Ablation across five weight configurations shows that signal weights are irrelevant when stable matching is applied (F1 varies by less than 0.004), while removing stable matching drops F1 to 0.728. On the Conference track, the same method achieves F1 = 0.438. On tool‑augmented ontology interaction, we find a surprising result: an LLM reading a raw OWL file (F1 = 0.323) performs worse than the same LLM with no file at all (F1 = 0.431), while structured MCP tool access achieves F1 = 0.717. This demonstrates that tool structure provides a qualitatively different mode of access that the LLM cannot replicate by reading raw syntax. The system ships as a single binary under the MIT licence.

PaperId: 1154, https://arxiv.org/pdf/2605.09169.pdf GitHub

Abstract:
A Mamba state‑space model trained only for next‑step prediction appears to recover Granger‑causal structure through a simple readout S = |W_out W_in|, with early experiments suggesting the phenomenon generalized across architectures and benefited from interventional data at p < 10^‑5. We package the protocol used to test that claim ‑‑ standardized synthetic generators (VAR/Lorenz/CauseMe‑style), three intervention semantics (do(X=c), soft‑noise, random‑forcing), edge‑provenance cards on three real datasets, and size‑matched control arms ‑‑ as a reusable falsification benchmark, and walk the claim through it in five stages. The method‑level claim does not survive: (i) a plain linear bottleneck does as well or better; (ii) tuned Lasso beats the bottleneck on synthetic CauseMe‑style benchmarks, and on Lorenz‑96 (the only real benchmark with unambiguous ground truth) classical PCMCI and Granger lead a tight cluster in which the bottleneck trails; (iii) the headline intervention advantage is roughly 60% a sample‑size confound, and the residual disappears under standard do(X=c) interventions, surviving only under a non‑standard random‑forcing scheme; (iv) even that residual reproduces, with a larger effect, in classical bivariate Granger ‑‑ the effect is method‑agnostic. What survives is a narrow characterization result; the benchmark is the lasting artifact, and each stage above is one of its control arms.

PaperId: 1155, https://arxiv.org/pdf/2605.09156.pdf GitHub GitHub

Abstract:
The diachronic evolution from Latin to the Romance languages involved a restructuring of the grammatical gender system from a tripartite configuration (masculine, feminine, neuter) to a bipartite one (masculine, feminine) in most Romance languages. In this work, we introduce an interpretable deep learning framework to investigate this phenomenon at both lexical and contextual levels. First, we show that conventional tokenization strategies are insufficiently robust for this low‑resource historical setting, and that our proposed tokenizer improves performance over these baselines. At the lexical level, we evaluate the contribution of morphological features to gender prediction. At the contextual level, we quantify the contributions of different part‑of‑speech categories to grammatical gender prediction. Together, these analyses characterize the distribution of gender information between the lemma and its sentential context. We make our codebase, datasets, and results publicly available at \hrefhttps://github.com/ahan‑2000/Lost‑in‑Translation‑https://github.com/ahan‑2000/Lost‑in‑Translation‑.

PaperId: 1156, https://arxiv.org/pdf/2605.08978.pdf GitHub

Abstract:
Recent advancements in agentic test‑time scaling allow models to gather environmental feedback before committing to final actions. A key limitation of existing methods is that they typically employ undifferentiated exploration strategies, lacking the ability to adaptively distinguish when exploration is truly required. In this paper, we propose an exploration‑aware reinforcement learning framework that enables LLM agents to adaptively explore only when uncertainty is high. Our method introduces a fine‑grained reward function via variational inference that explicitly evaluates exploratory actions by estimating their potential to improve future decision‑making, together with an exploration‑aware grouping mechanism that separates exploratory actions from task‑completion actions during optimization. By targeting informational gaps, this design allows agents to explore selectively and transition to execution as soon as the task context is clear. Empirically, we demonstrate that our approach achieves consistent improvements across a range of challenging text‑based and GUI‑based agent benchmarks. Code is available at https://github.com/HansenHua/EAPO‑ICML26 and models are available at https://huggingface.co/hansenhua/EAPO‑ICML26.

PaperId: 1157, https://arxiv.org/pdf/2605.08974.pdf GitHub

Abstract:
While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We argue this stems from a failure in spatio‑temporal monitoring, the ability to persistently track object identities, states, and relations over time. Existing benchmarks obscure this deficit by relying on single final‑answer evaluations for queries that can often be resolved via local visual cues or statistical priors. To rigorously diagnose this, we introduce STEMO‑Bench (Spatio‑TEmporal MOnitoring), a benchmark of human‑verified object‑centric facts that evaluates intermediate reasoning by decomposing queries into sub‑questions, distinguishing genuine temporal understanding from coincidental correctness. To address failure modes exposed by STEMO, we propose STEMO‑Track, a novel object‑centric framework that explicitly constructs and reasons over structured object trajectories via chunk‑wise state extraction and temporal aggregation. Extensive experiments demonstrate that our object‑centric framework significantly reduces hallucinated answers and improves spatio‑temporal reasoning consistency over state‑of‑the‑art MLLMs.

PaperId: 1158, https://arxiv.org/pdf/2605.08936.pdf GitHub

Abstract:
Large Reasoning Models possess remarkable capabilities for self‑correction in general domain; however, they frequently struggle to recover from unsafe reasoning trajectories under adversarial attacks. Existing alignment methods attempt to mitigate this vulnerability by fine‑tuning the model on expert data including reflection traces or adversarial prefixes. Crucially, these approaches are often hindered by static training data which inevitably deviate from model's dynamic, on‑policy reasoning traces, resulting in model hardly covering its vast generation space and learning to recover from its own failures. To bridge this gap, we propose Self‑ReSET, a pure reinforcement learning framework designed to equip LRMs with the intrinsic capacity to recover from their own safety error trajectories, which are subsequently reused as an initial state for reinforcement learning. Extensive experiments across various LRMs and benchmarks demonstrate that Self‑ReSET significantly enhances robustness against adversarial attacks especially out‑of‑distribution (OOD) jailbreak prompts while maintaining general utility, along with efficient data utilization. Further analysis reveals that our method effectively fosters self‑recovery patterns, enabling models to better identify and recover from unsafe intermediate error states back to benign paths. Our codes and data are available at https://github.com/Ing1024/Self‑ReSET.

PaperId: 1159, https://arxiv.org/pdf/2605.08930.pdf GitHub

Abstract:
While explicit Chain‑of‑Thought (CoT) empowers large reasoning models (LRMs), it enables the generation of riskier final answers. Current alignment paradigms primarily rely on externally enforced compliance, optimizing models to detect malicious prompts rather than evaluating the safety of their own outputs. We argue that this approach remains largely behavioral: our empirical analysis reveals that ostensibly aligned models lack intrinsic safety understanding, often failing to verify their own response safety and remaining vulnerable to adversarial jailbreaks. To address this fundamental limitation, we propose Safety Internal (SInternal), a framework that internalizes safety specifications by training LRMs exclusively on safety verification tasks to critique their own generated answers using expert reasoning trajectories. We demonstrate that learning to verify induces a strong generalization for response safety, significantly enhancing robustness against out‑of‑domain jailbreaks. Furthermore, when combined with reinforcement learning, SInternal serves as a superior initialization compared to standard supervised fine‑tuning, suggesting that internalizing safety understanding creates a more robust foundation for alignment than merely mimicking safe behaviors. Our codes are available at https://github.com/AlphaLab‑USTC/SInternal

PaperId: 1160, https://arxiv.org/pdf/2605.08894.pdf GitHub

Abstract:
Large language models (LLMs) achieve strong performance but incur high deployment costs, motivating extremely low‑bit but lossy quantization. Existing quantization algorithms mainly focus on improving the numerical accuracy of forward computation to eliminate performance degradation. In this paper, we show that extremely quantized LLMs suffer from systematic smoothness degradation beyond numerical precision loss. Through a smoothness proxy, we observe that such degradation becomes increasingly severe as the quantization bit‑width decreases. Furthermore, based on sequence neighborhood modeling, we find that quantized models exhibit a rapid reduction of effective token candidates within the prediction neighborhood, which directly leads to a sparser decoding tree and degraded generation quality. To validate it, we introduce a simple smoothness‑preserving principle in both post‑training quantization and quantization‑aware training, and demonstrate that preserving smoothness brings additional gains beyond numerical accuracy. The core goal of this paper is to highlight smoothness preservation as an important design consideration for future extreme quantization methods. Code is available at https://github.com/xuyuzhuang11/FINE.

PaperId: 1161, https://arxiv.org/pdf/2605.08887.pdf GitHub

Abstract:
Self‑evolving agents present a promising path toward continual adaptation by distilling task interactions into reusable knowledge artifacts. In practice, this paradigm remains hindered by two coupled bottlenecks: data inefficiency, where costly rollout effort is disproportionately spent on low‑value samples rather than informative ones, and knowledge interference, where heterogeneous knowledge stored in shared repositories leads to noisy retrieval and task‑misaligned guidance. Together, these issues form a self‑reinforcing failure loop in which uninformative rollouts yield noisy knowledge, which in turn degrades subsequent rollouts. In this work, we introduce Ace‑Skill, a co‑evolutionary framework that jointly optimizes rollout allocation and knowledge organization for self‑evolving multimodal agents. Specifically, Ace‑Skill combines aprioritized sampler with lazy‑decay proficiency tracking to focus rollouts on informative and insufficiently mastered samples, and a clustered organizer that semantically clusters knowledge for cleaner retrieval and more reliable adaptation. By improving sampling and organization together, Ace‑Skill turns self‑evolution into a virtuous cycle in which more informative rollouts produce higher‑quality knowledge that supports stronger subsequent rollouts. Across four multimodal tool‑use benchmarks, Ace‑Skill delivers strong gains (e.g., +35.46% relative improvement in Avg@4 accuracy), enabling an opensource 35B MoE model to match or surpass proprietary models. The acquired knowledge also transfers effectively in a zero‑shot manner to smaller 9B and 4B models, allowing resource‑constrained agents to inherit advanced capabilities without additional training. The code has been publicly available at https://github.com/AMAP‑ML/Ace‑Skill.

PaperId: 1162, https://arxiv.org/pdf/2605.08794.pdf GitHub

Abstract:
Modern generative models can be understood as probability transport from a simple base distribution to a target data distribution. Deterministic transport models offer tractable velocity‑field parameterizations, whereas stochastic generative models capture richer density evolution through drift and diffusion. Yet when stochastic dynamics are described through deterministic velocity fields, the effects of drift and diffusion are often compressed into a single effective field, obscuring the distinct roles of deterministic evolution and stochastic fluctuation. In this work, we show that the deterministic field \(b_t\) of a stochastic generative process admits a natural transport‑‑osmotic decomposition that separates deterministic transport from stochastic, diffusion‑induced effects: \(b_t = u_t + d_t\), where \(u_t\) governs marginal probability transport and \(d_t\) captures an osmotic effect induced by diffusion and determined by the marginal score. Based on this decomposition, we propose Bridge Matching, a flow‑based framework for learning decomposed generative dynamics through both marginal and conditional formulations. In generative modeling experiments, we recombine the learned components as \(b_t = u_t + λ_d d_t\), showing that the proposed decomposition enables interpretable and controllable sampling by adjusting the osmotic contribution in probability transport.

PaperId: 1163, https://arxiv.org/pdf/2605.08765.pdf GitHub

Abstract:
Unlearning in large language models (LLMs) aims to remove harmful training data while preserving overall utility. However, we find that existing methods often hallucinate, generate abnormal token sequences, or behave inconsistently, raising safety and trust concerns. According to prior literature on LLM honesty, such behaviors are often associated with dishonesty. This motivates us to investigate the notion of honesty in the context of model unlearning. We propose a formal definition of unlearning honesty, which includes: (1) preserving both utility and honesty on retained knowledge, and (2) ensuring effective forgetting while encouraging the model to acknowledge its limitations and respond consistently to questions related to forgotten knowledge. To systematically evaluate the honesty of unlearning, we introduce a suite of metrics that cover utility, honesty on the retained set, effectiveness of forgetting, rejection rate and refusal stability in Q&A and MCQ settings. Evaluating 9 methods across 3 mainstream families shows that all current methods fail to meet these standards. After experimental and theoretical analyses, we present ReVa, a representation‑alignment procedure that fine‑tunes feature‑randomized unlearned models to better acknowledge forgotten knowledge. On Q&A tasks from the forget set, ReVa achieves the highest rejection rate after two rounds of interaction, nearly doubling the performance of the second‑best method. Remarkably, It also improves honesty on the retained set. We release our data and code at https://github.com/renjiegu.

PaperId: 1164, https://arxiv.org/pdf/2605.08727.pdf GitHub

Abstract:
Learned image compression (LIC) integrates deep neural networks (DNNs) to map high‑dimensional images into compact latent representations, reducing redundancy and achieving superior rate‑distortion (RD) performance in benign settings. Unfortunately, due to inherent vulnerabilities in DNNs, LIC systems are susceptible to adversarial perturbations that lead to downstream deterioration, compression rate degradation, untargeted distortion, and both local semantic manipulation (LSM) and low‑resolution (3×28×28) global semantic manipulation (GSM). However, high‑resolution GSM remains unexplored due to its intractability. Notably, the existing project gradient descent (PGD) method achieves near‑perfect white‑box attacks for classification, segmentation, and other tasks, yet fails to generalize to high‑resolution GSM. Our theoretical and empirical analyses reveal that well‑performing GSM drives adversarial examples from the Identity Region to the Amplification Region through the Lazying‑Oscillating‑Refining stages. General \ell_\infty‑bounded attacks fail on high‑resolution GSM because their step‑size schedules cannot accommodate both the Oscillating and Refining stages. Based on this, we propose the Periodic Geometric Decay schedule that enables \ell_\infty‑bounded high‑resolution GSM. To verify our approach, we integrate it with PGD, yielding a minimal variant, PGD^2‑GSM. Extensive experiments on the Kodak (3×768×512) demonstrate that our PGD^2‑GSM is the first to stably achieve high‑resolution GSM, thereby exposing a novel threat to LIC systems. Code is available at https://github.com/chinaliangjiaming/PGD2‑GSM.

PaperId: 1165, https://arxiv.org/pdf/2605.08715.pdf GitHub

Abstract:
LLM‑based multi‑agent systems are increasingly deployed on long‑horizon tasks, but a single decisive error is often accepted by downstream agents and cascades into trajectory‑level failure. Existing work frames this as \emphpost‑hoc failure attribution, diagnosing the responsible agent and step after the trajectory has ended. However, this paradigm forfeits any opportunity to intervene while trajectory is still unfolding. In this work, we introduce AgentForesight, a framework that reframes this problem as online auditing: at each step of an unfolding trajectory, an auditor observes only the current prefix and must either continue the run or alarm at the earliest decisive error, without access to future steps. To this end, we curate AFTraj‑2K, a corpus of agentic trajectories across Coding, Math, and Agentic domains, in which safe trajectories are retained under a strict curation pipeline and unsafe trajectories are annotated at the step of their decisive error via consensus among multiple LLM judges. Built on that, we develop AgentForesight‑7B, a compact online auditor trained with a coarse‑to‑fine reinforcement learning recipe that first equips it with a risk‑anticipation prior at the failure boundary on adjacent safe/unsafe prefix pairs, then sharpens this prior into precise step‑level localization under a three‑axis reward jointly targeting the what, where, and who of an audit verdict. Across AFTraj‑2K and an external Who\&When benchmark, AgentForesight‑7B outperforms leading proprietary models, including GPT‑4.1 and DeepSeek‑V4‑Pro, achieving up to +19.9% performance gain and 3× lower step localization error, opening the loop from post‑hoc failures detection to enabling deployment‑time intervention. Project page: https://zbox1005.github.io/agent‑foresight/

PaperId: 1166, https://arxiv.org/pdf/2605.08704.pdf GitHub

Abstract:
Multi‑agent reasoning has shown promise for improving the problem‑solving ability of large language models by allowing multiple agents to explore diverse reasoning paths. However, most existing multi‑agent methods rely on inference‑time debate or aggregation, which can be vulnerable to incorrect peer influence and biased consensus. Moreover, the agents themselves remain static, as their underlying reasoning skills do not evolve across tasks. In this paper, we introduce AgentPSO, a particle‑swarm‑inspired framework for evolving multi‑agent reasoning skills. AgentPSO treats each agent as a particle‑like reasoner whose state is a natural‑language skill and whose velocity is a semantic update direction, iteratively moving agents toward stronger skill states to improve both individual and collective reasoning performance. Across training iterations, each agent updates its skill by combining its previous velocity, personal‑best skill, global‑best skill, and a self‑reflective direction derived from peer reasoning trajectories. This enables agents to learn reusable reasoning behaviors from both their own experiences and the strongest skills discovered by the population, without updating the parameters of the backbone language model. Experiments on mathematical and general reasoning benchmarks show that AgentPSO improves over static single‑agent skills and test‑time‑only multi‑agent reasoning baselines. The evolved skills further transfer across benchmarks and to another backbone model, suggesting that AgentPSO captures reusable reasoning procedures rather than merely optimizing benchmark‑specific prompts. Code is open‑sourced at https://github.com/HYUNMIN‑HWANG/AgentPSO/.

PaperId: 1167, https://arxiv.org/pdf/2605.08679.pdf GitHub

Abstract:
Graph neural networks (GNNs) aim to learn well‑trained representations in a lower‑dimension space for downstream tasks while preserving the topological structures. In recent years, attention mechanism, which is brilliant in the fields of natural language processing and computer vision, is introduced to GNNs to adaptively select the discriminative features and automatically filter the noisy information. To the best of our knowledge, due to the fast‑paced advances in this domain, a systematic overview of attention‑based GNNs is still missing. To fill this gap, this paper aims to provide a comprehensive survey on recent advances in attention‑based GNNs. Firstly, we propose a novel two‑level taxonomy for attention‑based GNNs from the perspective of development history and architectural perspectives. Specifically, the upper level reveals the three developmental stages of attention‑based GNNs, including graph recurrent attention networks, graph attention networks, and graph transformers. The lower level focuses on various typical architectures of each stage. Secondly, we review these attention‑based methods following the proposed taxonomy in detail and summarize the advantages and disadvantages of various models. A model characteristics table is also provided for a more comprehensive comparison. Thirdly, we share our thoughts on some open issues and future directions of attention‑based GNNs. We hope this survey will provide researchers with an up‑to‑date reference regarding applications of attention‑based GNNs. In addition, to cope with the rapid development in this field, we intend to share the relevant latest papers as an open resource at https://github.com/sunxiaobei/awesome‑attention‑based‑gnns.

PaperId: 1168, https://arxiv.org/pdf/2605.08638.pdf GitHub

Abstract:
State‑of‑the‑art physical AI models generate a chunk of actions per inference through diffusion or flow matching, iteratively refining an initial noise sample into an action trajectory. Because this inference process is inherently stochastic, committing to a single trajectory per round is brittle, and this brittleness compounds across the many sequential rounds that comprise a complete episode. We introduce KeyStone, an inference‑time self‑consistency method for diffusion‑based action generation that draws K candidate action chunks in parallel from a shared model context, clusters them in continuous action space, and returns the medoid of the largest cluster ‑‑ no additional model required. Two properties make this practical. First, the compact nature of action trajectories makes diffusion inference memory‑bandwidth bound, leaving spare compute capacity to run K chains in parallel with no additional wall‑clock latency. Second, unlike token or pixel spaces where distance carries no semantic meaning and selection requires a learned judge, action chunks are geometrically structured such that Euclidean distance directly reflects physical similarity, making selection principled and judge‑free. Across diverse vision‑language‑action models (VLAs) and world‑action models (WAMs), KeyStone improves task success rates by up to 13.3% over single‑trajectory sampling with negligible latency overhead, while having on par accuracy with model‑based selectors at no training cost. We open source KeyStone at https://github.com/dywsjtu/keystone.

PaperId: 1169, https://arxiv.org/pdf/2605.08632.pdf GitHub

Abstract:
Speculative decoding accelerates Large Language Models (LLMs) inference by using a lightweight draft model to propose candidate tokens that are verified in parallel by the target model. However, existing draft model training objectives are not directly aligned with the inference‑time goal of maximizing consecutive token acceptance. To address this issue, we reformulate the draft model optimization objective, shifting the focus from token prediction accuracy to the overall acceptance length. In this paper, we build upon PARD to propose PARD‑2, a dual‑mode speculative decoding framework with Confidence‑Adaptive Token (CAT) optimization. This approach adaptively reweights each token to better align with the verification process. Notably, PARD‑2 enables a single draft model to support both target‑dependent and target‑independent modes. Experiments across diverse models and tasks demonstrate that PARD‑2 achieves up to 6.94× lossless acceleration, surpassing EAGLE‑3 by 1.9× and PARD by 1.3× on Llama3.1‑8B. Our code is available at https://github.com/AMD‑AGI/PARD.

PaperId: 1170, https://arxiv.org/pdf/2605.08374.pdf GitHub

Abstract:
Episodic memory allows LLM agents to accumulate and retrieve experience, but current methods treat each memory independently, i.e., evaluating retrieval quality in isolation without accounting for the dependency chains through which memories enable the creation of future memories. We introduce MemQ, which applies TD(λ) eligibility traces to memory Q‑values, propagating credit backward through a provenance DAG that records which memories were retrieved when each new memory was created. Credit weight decays as (γλ)^d with DAG depth d, replacing temporal distance with structural proximity. We formalize the setting as an Exogenous‑Context MDP, whose factored transition decouples the exogenous task stream from the endogenous memory store. Across six benchmarks, spanning OS interaction, function calling, code generation, multimodal reasoning, embodied reasoning, and expert‑level QA, MemQ achieves the highest success rate on all six in generalization evaluation and runtime learning, with gains largest on multi‑step tasks that produce deep and relevant provenance chains (up to +5.7~pp) and smallest on single‑step classification (+0.77~pp) where single‑step updates already suffice. We further study how γ and λ interact with the EC‑MDP structure, providing principled guidance for parameter selection and future research. Code is available at https://github.com/jwliao‑ai/MemQ.

PaperId: 1171, https://arxiv.org/pdf/2605.08319.pdf GitHub

Abstract:
Mazocarta is a seeded procedural tactical deckbuilder implemented in Rust, compiled to WebAssembly for browser play, and executable natively for simulation. Its primary technical contribution is not the invention of a new deckbuilding genre, but the construction of an instrumented game‑development reference artifact: the same rules engine supports interactive play, native command‑line simulation, automated end‑to‑end tests, save/load fixtures, and local‑area multiplayer. This paper describes Mazocarta's architecture, deterministic run model, reproducible balance probes, and QR‑mediated WebRTC pairing for local multiplayer. An evaluation snapshot over 1,000 deterministic seeds shows that the simulation pipeline can produce reproducible development signals. In the evaluated configuration, single‑player and two‑player autoplay win rates were 36.1% and 34.9% over 1,000 deterministic seeds, respectively. These rates are not presented as final player‑facing balance metrics, but as repeatable probes for future balance shifts and regressions. Mazocarta is positioned as a playable open‑source reference artifact for instrumented game development: deterministic regression checks, automated playtesting workflows, balance probes for game mechanics, and browser‑native local multiplayer all exercise one shared production rules core.

PaperId: 1172, https://arxiv.org/pdf/2605.08314.pdf GitHub

Abstract:
SVD‑based Low‑rank compression reduces transformer parameters and nominal FLOPs, but these savings often translate poorly into real LLM serving speedups. We show that this gap is largely a runtime problem: factorized checkpoints fragment execution paths, and the resulting overhead differs substantially between prefill and autoregressive decode. We present FlashSVD v1.5, a unified inference runtime for serving SVD‑compressed transformers. FlashSVD v1.5 maps diverse public SVD compression families to a common factorized representation and combines phase‑specific kernels with dense‑KV decode, packed MLP execution, and per‑layer CUDA‑graph replay to reorganize the low‑rank serving path into a thin runtime. Across representative decoder‑serving settings, FlashSVD v1.5 achieves up to 2.55x decode and 2.39x end‑to‑end speedup, and it attains 1.48x average decode and 1.44x average end‑to‑end speedup across multiple popular SVD compression families. These results suggest that practical low‑rank acceleration requires runtime co‑design, not compression algorithms alone. Our code is available at: https://github.com/Zishan‑Shao/FlashSVD.

PaperId: 1173, https://arxiv.org/pdf/2605.08310.pdf GitHub

Abstract:
Browser agents are increasingly deployed in long‑horizon tasks, which require executing extended action chains to accomplish user goals. However, this prolonged execution process provides attackers with more opportunities to inject malicious instructions. Existing prompt injection attacks against browser agents expose two key gaps: (1) low effectiveness, as attacks optimized for toy baselines fail to achieve end‑to‑end goals in real‑world scenarios with complex environments and longer steps; (2) weak stealthiness, since most attacks pit the attack goal against the user goal, causing a significant drop in system usability under attack. To address these gaps, we propose WebTrap, a mid‑task hijacking injection attack. It employs multi‑step instruction fusion steering to seamlessly combine both goals, enabling the agent to resume the original user task after executing the attack goal. Furthermore, we design a context‑grounded generation method to align the injected content with the task environment and system instructions, maximizing the hijacking success rate. Extensive experiments on two browser agent tasks, based on extended WASP and InjecAgent environments, demonstrate that our method achieves a high attack success rate while preserving the usability of the original system. We find that WebTrap exploits the agent's navigation vulnerabilities, binding the two goals so tightly that standard defense mechanisms cannot restore the system to normal operation. These findings reveal a critical vulnerability in agent systems during long‑horizon tasks that they can be stealthily hijacked.

PaperId: 1174, https://arxiv.org/pdf/2605.08305.pdf GitHub

Abstract:
Large Language Model (LLM) systems have been the frontier of AI in many application domains, leading to new challenges and opportunities for hyperparameter optimization (HPO) for the AutoML community. However, this type of system exhibits an unprecedented compound space of hyperparameter configuration from both the AI and non‑AI components; rich and nonlinear implications from the fidelity factors; and diverse costs of measuring hyperparameter configurations, none of which have been fully captured in existing benchmarks. This paper presents the first (live) benchmark suite and datasets for HPO of real‑world LLM systems, dubbed LLMSYS‑HPOBench, covering data related to the inference objective values of hyperparameter configurations profiled from running the LLM systems. Currently, LLMSYS‑HPOBench contains 364,450 hyperparameter configurations with a dimensionality of 12‑23, 3‑5 dimensions of fidelity factor leading to 932 settings, 3‑9 inference objective metrics, and 2‑10 cost metrics, together with generated logs from measuring the LLM systems. What we seek to advocate is not only a revalidation of the existing HPO algorithms over the frontier LLM systems, but also to provide an evolving platform for the AutoML community to explore new directions of research in this regard. The benchmark suite has been made available at: https://github.com/ideas‑labo/llmsys‑hpobench

PaperId: 1175, https://arxiv.org/pdf/2605.08300.pdf GitHub

Abstract:
Manifold‑Constrained Hyper‑Connections (mHC) introduce a stability‑motivated variant of multi stream residual mixing by constraining residual stream mixing matrices to the manifold of doubly stochastic matrices via Sinkhorn‑Knopp projection. In his work, we study whether mHC‑style constrained multi‑stream residual topology transfers effectively to state space model (SSM) language modeling. We implement a static mHC mechanism around an SSM block by expanding the residual stream into multiple parallel streams, aggregating streams into a single SSM input through simplex‑constrained pre‑mixing, scattering the SSM output back to streams through simplex‑constrained post‑mixing, and applying Sinkhorn‑projected residual stream mixing at each layer. We further introduce stream‑specialized adapters that add lightweight stream‑specific capacity through a shared bottleneck with per‑stream scaling, applied both before stream aggregation and after the SSM output prior to scattering. We evaluate baseline single‑stream SSM, static mHC SSM, and mHC SSM with adapters on WikiText‑2 using identical training settings and report checkpoint‑based validation loss, perplexity, throughput, and peak GPU memory. Under the reported fair checkpoint evaluation, static mHC improves validation loss from 6.3507 to 6.2448 and reduces perplexity from 572.91 to 515.35, while mHC with adapters further improves validation loss to 6.1353 and perplexity to 461.88. These gains are accompanied by modest throughput reductions from 1025.52 to 964.81 and 938.90 tokens per second, and increased peak memory from 2365 MB to 2568 MB and 3092 MB. The results suggest that mHC‑inspired constrained multi‑stream residual mixing can yield measurable quality improvements in SSM language models and that stream‑specialized adapter capacity can further enhance performance with predictable efficiency tradeoffs.

PaperId: 1176, https://arxiv.org/pdf/2605.08283.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a pivotal technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, the de facto practice of mainstream RL algorithms is to treat all tokens of one response equally and assign the same optimization objective to each token, failing to provide granular guidance for the reasoning process. While in Chain‑of‑Thought (CoT) reasoning, different tokens usually play distinct roles. Therefore, the current RL algorithms lack an effective mechanism to dynamically balance the exploration‑exploitation trade‑off during learning. To this end, we propose Hierarchical Token‑level Objective Control Policy Optimization (HTPO), a novel RL algorithm that takes the divide‑and‑conquer idea to hierarchically partition the response tokens into specific functional groups from three aspects (i.e., prompt difficulty, answer correctness, and token entropy). Within each group, according to the contributions to exploration or exploitation, we design specialized optimization objectives to facilitate the effective execution of each token's expected functionality. In this way, HTPO can achieve a more balanced exploration‑exploitation trade‑off. Extensive experiments on challenging reasoning benchmarks validate the superiority of our HTPO algorithm, which significantly outperforms the strong DAPO baseline (e.g., +8.6% and +6.7% on AIME'24 and AIME'25, respectively). When scaling test‑time compute, the HTPO‑trained model maintains a consistent performance advantage over the DAPO baseline, and the gap widens as the sampling budget increases, validating that our adaptive token‑level control method fosters effective exploration without sacrificing exploitation performance. Code will be at https://github.com/xcyao00/HTPO.

PaperId: 1177, https://arxiv.org/pdf/2605.08282.pdf GitHub

Abstract:
Purpose: We aim to enhance the image quality of point‑of‑care ultrasound (POCUS) devices using deep learning and a novel paired dataset of POCUS and high‑end ultrasound images. Approach: We collected the first accurately paired dataset using a custom‑built automated gantry system of low‑end POCUS and high‑end ultrasound images. A conditional generative adversarial network (cGAN) was utilized based on the pix2pix architecture, with a U‑Net generator that incorporates both L1 and structural similarity index (SSIM) losses to improve perceptual quality. Pretraining on a simulation dataset further boosts performance. Evaluation was performed on 1064 paired ex vivo tissue and phantom ultrasound image sets. Results: Our approach improves the SSIM from 0.29 to 0.54 and PSNR from 19.16 dB to 22.41 dB. No‑reference metrics also indicate substantial enhancement, with the Natural Image Quality Evaluator (NIQE) and Perception‑based Image Quality Evaluator (PIQE) scores dropping from 7.95 to 4.44 and 31.12 to 19.99, respectively. Conclusions: This work presents the first publicly available accurately paired dataset of low‑end POCUS to high end ultrasound images. Additionally, our results demonstrate the potential of the proposed framework to overcome hardware limitations of handheld POCUS, enhancing its diagnostic value in low‑resource and point‑of‑care settings. The POCUS‑IQ Dataset is publicly available at https://github.com/NKI‑MedTech‑AI/POCUS‑IQ.

PaperId: 1178, https://arxiv.org/pdf/2605.08277.pdf GitHub

Abstract:
Many‑shot jailbreaking (MSJ) causes safety‑aligned language models to answer harmful queries by preceding them with many harmful question‑answer demonstrations. We study why this attack becomes stronger as the number of demonstrations increases. Empirically, we find that MSJ induces a progressive activation drift: the representation of a fixed harmful query moves step by step away from the safety‑aligned region as more harmful demonstrations are added. Theoretically, we show that this drift can be interpreted as implicit malicious fine‑tuning: conditioning on N harmful demonstrations induces SGD‑style updates equivalent to optimizing on the corresponding N harmful samples. This view turns the attack mechanism into a defense principle. We append a fixed one‑shot safety demonstration at inference time, which induces a counteracting safety‑oriented update and restores refusal behavior. The resulting method improves the model's robustness to MSJ without modifying its parameters or requiring white‑box access at deployment. Code is available at https://github.com/Thecommonirin/SafeEnd.

PaperId: 1179, https://arxiv.org/pdf/2605.08271.pdf GitHub

Abstract:
Understanding ultra‑long videos such as egocentric recordings, live streams, or surveillance footage spanning days to weeks, remains a challenge. For current multimodal LLMs: even with million‑token context windows, frame budgets cover only tens of minutes of densely sampled video, and most evidence is discarded before inference begins. Memory‑augmented and agentic approaches help with scale, but their retrieval remains fragmented across modalities and lacks long‑range narrative summaries that span days or weeks. We propose MAGIC‑Video, a training‑free framework built around a multimodal memory graph with interleaved narrative chain: the graph unifies episodic, semantic, and visual content through six typed edges and supports cross‑modal retrieval, while the chain distils long‑horizon entity biographies and recurring activity events. At inference time, an agentic loop interleaves graph retrieval with narrative fact injection, covering both the modality and time dimensions of ultra‑long video in a single retrieval pipeline. On EgoLifeQA, Ego‑R1 and MM‑Lifelong, MAGIC‑Video consistently outperforms strong general‑purpose, long‑video, and agentic baselines, with gains of 10.1, 7.4, and 5.9 points over the prior best agentic system on each benchmark. Code is available at https://github.com/lijiazheng0917/MAGIC‑video.

PaperId: 1180, https://arxiv.org/pdf/2605.08200.pdf GitHub

Abstract:
A pervasive intuition holds that vision‑language models (VLMs) are most trustworthy when their attention maps look sharp: concentrated attention on the queried region should imply a confident, calibrated answer. We test this Attention‑Confidence Assumption directly. We instrument three open‑weight VLM families (LLaVA‑1.5, PaliGemma, Qwen2‑VL; 3‑7B parameters) with a unified mechanistic pipeline ‑‑ the VLM Reliability Probe (VRP) ‑‑ that compares attention structure, generation dynamics, and hidden‑state geometry against a single correctness label. Three results emerge. (i) Attention structure is a near‑zero predictor of correctness (R_pb(C_k,y)=0.001, 95% CI [‑0.034,0.036]; R_pb(H_s,y)=‑0.012, [‑0.047,0.024] on a pooled n=3,090 split), even though attention remains causally necessary for feature extraction (top‑30% patch masking drops accuracy by 8.2‑11.3 pp, p<0.001). (ii) Reliability becomes legible later in the computation: a single hidden‑state linear probe reaches AUROC>0.95 on POPE for two of three families, and self‑consistency at K=10 is the strongest behavioral predictor we measure at 10x inference cost (R_pb=0.43). (iii) Causal neuron‑level ablations expose a sharp architectural split with direct monitor‑design implications: late‑fusion LLaVA concentrates reliability in a fragile late bottleneck (‑8.3 pp object‑identification accuracy after top‑5 probe‑neuron ablation), whereas early‑fusion PaliGemma and Qwen2‑VL distribute it widely and absorb destruction of ~50% of their peak‑layer hidden dimension with <=1 pp degradation. The takeaway is narrow but consequential: in 3‑7B VLMs, reliability is read more reliably off hidden‑state geometry, layer‑wise margin formation, and sparse late‑layer circuits than off attention‑map sharpness.

PaperId: 1181, https://arxiv.org/pdf/2605.08198.pdf GitHub

Abstract:
We present FairHealth, an open‑source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applications, with particular focus on low‑resource and low‑income country (LMIC) settings such as Bangladesh. FairHealth addresses four critical gaps in existing healthcare AI toolkits: (1) the absence of integrated fairness auditing for biosignals and clinical tabular data; (2) the lack of privacy‑preserving federated learning tools compatible with standard ML workflows; (3) missing explainability tools tailored for low‑bandwidth clinical decision support; and (4) no existing toolkit covering Global South healthcare datasets. Built from five peer‑reviewed research contributions, FairHealth provides six modules covering federated learning with homomorphic encryption (fairhealth.federated), intersectional fairness metrics (fairhealth.fairness), hybrid fuzzy‑SHAP explainability (fairhealth.explain), multilingual dengue triage (fairhealth.lowresource), equitable disaster aid allocation (fairhealth.equity), and public dataset loaders (fairhealth.datasets). All datasets used are publicly available without institutional data use agreements. FairHealth is installable via pip install fairhealth(PyPI: pypi.org/project/fairhealth/) and available at https://github.com/Farjana‑Yesmin/fairhealth.

PaperId: 1182, https://arxiv.org/pdf/2605.08191.pdf GitHub

Abstract:
Reliable out‑of‑distribution (OOD) detection is a critical requirement for the safe deployment of machine learning systems. Despite recent progress, state‑of‑the‑art OOD detectors are highly susceptible to adversarial attacks, which undermines their trustworthiness in automated systems. To address this vulnerability, we apply median smoothing to baseline OOD detection scores, balancing clean and adversarial accuracies. Our key insight is that the noisy samples generated for median smoothing can be repurposed to quantify the local instability of the base score. We observe that OOD samples exhibit higher instability under perturbation. Based on this, we propose ROSS, a novel and robust post‑hoc OOD detector that leverages the instability of baseline scores to further distinguish between in‑distribution (ID) and OOD samples. ROSS achieves symmetric robustness, performing strongly against both score‑minimising and score‑maximising attacks, unlike prior work. This symmetric defence leads to state‑of‑the‑art robustness, outperforming prior methods by up to 40 AUROC points. We demonstrate ROSS's effectiveness on extensive experiments across CIFAR‑10, CIFAR‑100, and ImageNet. Code is available at: https://github.com/Abdu‑Hekal/ROSS.

PaperId: 1183, https://arxiv.org/pdf/2605.08181.pdf GitHub

Abstract:
Parameter‑efficient fine‑tuning methods introduce a small number of training parameters, enabling pre‑trained models to adapt rapidly to new data distributions. While these methods have shown promising results, they exhibit notable limitations. First, most existing methods operate in the signal space domain, which results in substantial information redundancy. Second, most existing methods utilize fixed prompts or adaptation layers, failing to fully account for the multi‑scale characteristics of signals. To address these challenges, we propose the Multi‑Scale Frequency Adapter (FreqAdapter), which integrates textual information and performs multi‑scale fine‑tuning of signals in the frequency domain. Additionally, we introduce a multi‑scale adaptation strategy to optimize receptive fields across different frequency ranges, further enhancing the model's representational capacity. Extensive experiments on multimodal models, including CLIP and LLaVA, demonstrate that FreqAdapter significantly improves both performance and efficiency. FreqAdapter improves performance with minimal cost and fast convergence within one epoch. Code is available at https://github.com/Kelvin‑ywc/FreqAdapter.

PaperId: 1184, https://arxiv.org/pdf/2605.08168.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models offer a promising path to generalist robot control, but their inference latency causes observation staleness when generated actions are executed asynchronously. Several methods have been proposed concurrently to mitigate this problem: inference‑time inpainting (IT‑RTC), training‑time delay simulation (TT‑RTC), future‑state‑aware conditioning (VLASH), and lightweight residual correction (A2C2). Each takes a fundamentally different approach, but they have so far been evaluated independently with different codebases, base policies, and protocols. We present a systematic comparison of these four methods under controlled conditions. We develop two unified codebases that integrate all methods with harmonized library and dataset versions, and we benchmark them on the Kinetix suite with MLPMixer policies and on the LIBERO manipulation benchmark with SmolVLA, sweeping inference delays up to d=20 control steps. A2C2's per‑step residual correction is the most effective method on Kinetix, holding above 90% solve rate up to d=8, and also leads on LIBERO from d=4 onwards. IT‑RTC is competitive at low delays but degrades sharply under long chunks (H=30) and high delays. TT‑RTC is the most robust training‑based method: stable across d_\max choices, generalizes beyond its training delay distribution, and adds zero inference overhead. VLASH exhibits a clear low‑delay vs. high‑delay trade‑off governed by the fine‑tuning delay range [0,d_\max]. Code is available at https://github.com/TheAyos/async‑vla‑inference

PaperId: 1185, https://arxiv.org/pdf/2605.08160.pdf GitHub

Abstract:
Monitoring archaeological sites at scale is vital for protecting cultural heritage, yet pinpointing when disturbances occur remains difficult because visual cues are subtle and ground‑truth data are sparse. We introduce WATCH, a framework for month‑level change‑event localization over PlanetScope satellite mosaics (2017‑2024, 4.7 m/px) that supports three complementary scoring approaches: (i) Temporal Embedding Distance (TED), a training‑free method that scores month‑to‑month deviations from a local temporal reference; (ii) Self‑Supervised Change Detection (SSCD), an ensemble of reconstruction, forecasting, and latent‑novelty signals; and (iii) a Weakly Supervised (WS) temporal localization model trained with sparse event‑month labels. We benchmark WATCH on 1,943 archaeological sites in Afghanistan using embeddings from six foundation models (CLIP, GeoRSCLIP, SatMAE, Prithvi‑EO‑2.0, DINOv3, and Satlas‑Pretrain) alongside a handcrafted spectral and texture baseline, and assess cross‑regional generalization on sites in Syria, Turkey, Pakistan, and Egypt. The unsupervised approaches (TED, SSCD) consistently outperform the weakly supervised alternative. TED with SatMAE achieves the highest exact‑month recall (55% at m=0), while TED with GeoRSCLIP, CLIP, or Satlas‑Pretrain reaches 92.5% within a three‑month tolerance (m=3). Handcrafted features remain competitive for exact‑month detection under weak supervision. Our directional margin analysis reveals systematic temporal biases: SSCD paired with GeoRSCLIP or Prithvi‑EO‑2.0 exhibits the strongest early‑warning profile, detecting anomalies before the recorded event, while TED favors confirmation‑oriented detection after a change has materialized. These results show that satellite imagery combined with foundation‑model embeddings enables scalable, decision‑relevant heritage monitoring. Code: https://github.com/microsoft/WATCH

PaperId: 1186, https://arxiv.org/pdf/2605.08151.pdf GitHub

Abstract:
LLM serving platforms are increasingly deployed as multi‑model cloud systems, where user demand is often long‑tailed: a few popular large models receive most requests, while many smaller tail models remain underutilized. We propose SPECTRE (Parallel SPECulative Decoding with a Multi‑Tenant REmote Drafter), a serving framework that reuses underutilized tail‑model services as remote drafters for heavily loaded large‑model services through speculative decoding. SPECTRE enables draft generation and target‑side verification to run in parallel, and makes such parallelism effective through three techniques: a hybrid ordinary‑parallel speculative decoding strategy guided by a threshold derived from throughput analysis, speculative priority scheduling to preserve draft‑‑target overlap under multi‑tenant traffic, and draft‑side prompt compression to reduce draft latency. We implement SPECTRE in \textttSGLang and evaluate it across multiple draft‑‑target model pairs, reasoning benchmarks, real‑world long‑context workloads, and a wide range of batch sizes. Results show that SPECTRE consistently improves large‑model serving throughput while causing only minor interference to the native workloads of tail‑model services. In large‑model deployments, including Qwen3‑235B‑A22B with TP=8, SPECTRE achieves up to 2.28× speedup over autoregressive decoding and up to an additional 66% relative improvement over the strongest speculative decoding baselines. Talk is cheap, we show you the code: https://github.com/sgl‑project/sglang/pull/22272.

PaperId: 1187, https://arxiv.org/pdf/2605.08146.pdf GitHub

Abstract:
Multi‑model learning has attracted great attention in visual‑text tasks. However, visual‑tabular data, which plays a pivotal role in high‑stakes domains like healthcare and industry, remains underexplored. In this paper, we introduce VT‑Bench, the first unified benchmark for standardizing vision‑tabular discriminative prediction and generative reasoning tasks. VT‑Bench aggregates 14 datasets across 9 domains (medical‑centric, while covering pets, media, and transportation) with over 756K samples. We evaluate 23 representative models, including unimodal experts, specialized visual‑tabular models, general‑purpose vision‑language models (VLMs), and tool‑augmented methods, highlighting substantial challenges of visual‑tabular learning. We believe VT‑Bench will stimulate the community to build more powerful multi‑modal vision‑tabular foundation models. Benchmark: https://github.com/Ziyi‑Jia990/VT‑Bench

PaperId: 1188, https://arxiv.org/pdf/2605.08143.pdf GitHub

Abstract:
Large language models encode vast factual knowledge that can become outdated or incorrect after deployment, yet retraining is prohibitively costly. This motivates lifelong model editing, which updates targeted behavior while preserving the rest of the model. Existing editors, both parameter‑modifying and parameter‑preserving, degrade severely as edits accumulate and struggle to generalize across paraphrases. We propose HoReN, a codebook‑based parameter‑preserving editor that wraps a single MLP layer with a discrete key‑value memory. HoReN treats each codebook entry as both a knowledge key and a Hopfield stored pattern, retrieves edits by angular similarity on the unit hypersphere, and refines queries through damped Hopfield dynamics so paraphrases converge to the correct memory basin while unrelated inputs remain stable. HoReN achieves strong editing performance with consistent gains across diverse benchmarks spanning standard ZsRE, structured WikiBigEdit, and unstructured UnKE evaluations. Moreover, HoReN scales to 50K sequential edits on ZsRE with stable overall performance above 0.93, while prior editors collapse or degrade severely before reaching 10K. Our code is available at https://github.com/ha11ucin8/HoReN.

PaperId: 1189, https://arxiv.org/pdf/2605.08134.pdf GitHub

Abstract:
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to auto‑regressive (AR) models, offering greater expressive capacity and potential for parallel generation and faster inference. However, open‑source dLLMs remain immature, lagging behind AR models in both efficiency and quality. We identify an underexplored property of dLLMs: token‑wise redundancy in bi‑directional self‑attention. Self‑attention activations are highly correlated across tokens, and temporal changes in query representations can predict redundancy in corresponding key, value, and output activations. We introduce DARE, with two complementary mechanisms: DARE‑KV, which reuses cached key‑value (KV) activations, and DARE‑O, which reuses output activations to reduce redundant computation while preserving quality. DARE achieves up to 1.20x per‑layer latency reduction and reuses up to 87% of attention activations, with negligible degradation on reasoning and code‑generation benchmarks. DARE‑KV and DARE‑O incur average performance drops of only 2.0% and 1.2%, respectively. Combined with techniques such as prefix caching and Fast‑dLLM, DARE provides additive gains without retraining. These results establish token‑wise reuse as an effective strategy for improving the efficiency of diffusion‑based LLMs while preserving generation fidelity. Code: https://github.com/enyac‑group/DARE

PaperId: 1190, https://arxiv.org/pdf/2605.08116.pdf GitHub

Abstract:
Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored. Existing safety approaches are geared toward autoregressive models and typically rely on post‑hoc filtering or inference‑time interventions. These are inadequate for effectively addressing safety risks in text diffusion models. We propose the Safety‑Aware Denoiser (SAD), a safety‑guidance framework in text diffusion models. The SAD modifies the iterative denoising process such that the text sample at the final denoising step is steered toward provably safe regions of the text space. This inference‑time method can integrate safety constraints into the denoiser, avoiding computationally expensive retraining of the underlying diffusion model and enabling flexible, lightweight safety guidance. We evaluate the safety of the generated text using the SAD, with respect to hazard taxonomy, memorization, and jailbreak. Experimental results show that SAD substantially reduces unsafe generations while preserving generation quality, diversity, and fluency, outperforming existing methods. These results demonstrate that our safety guidance during denoising provides an effective and scalable mechanism for enforcing safety in text diffusion models.

PaperId: 1191, https://arxiv.org/pdf/2605.08112.pdf GitHub

Abstract:
AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team‑specific product decisions that are invisible in the source code alone. We introduce a controlled benchmark measuring decision compliance, the rate at which an AI coding agent follows established product, design, and engineering decisions, across 8 realistic software engineering tasks containing 41 weighted decision points. We compare a baseline configuration (Claude Code with codebase access only) against an augmented configuration that adds Brief, a product‑context retrieval system providing spec generation, mid‑build consultation, and retrieval of recorded decisions, persona pain points, customer signals, and competitive intelligence. On identical prompts and the same repository, the augmented configuration achieves 95% decision compliance versus 46% for the baseline, a 49 percentage point improvement. Per‑decision analysis reveals that the baseline achieves 100% compliance on decisions visible in the codebase and 0‑33% on decisions requiring product context, suggesting that product‑context retrieval is a key driver of the improvement. We release the benchmark repository, all 16 pull requests, and scoring harness for independent reproduction.

PaperId: 1192, https://arxiv.org/pdf/2605.08094.pdf GitHub

Abstract:
Accurate clinical diagnosis requires extensive domain knowledge and complex clinical reasoning capabilities. Although large language models (LLMs) hold great potential for clinical reasoning, their high computational and memory requirements limit their deployment in resource‑constrained environments. Knowledge distillation (KD) can compress LLM capabilities into smaller models, but traditional KD merely transfers superficial answer patterns and fails to preserve the structured reasoning required for reliable diagnosis. To address this, we propose a two‑stage distillation framework, MedThink, designed to cultivate robust clinical reasoning in small language models (SLMs). In the first stage, a teacher LLM screens data and injects domain‑knowledge explanations to fine‑tune a student model, establishing a knowledge foundation. In the second stage, the teacher evaluates the student's errors, generates reasoning chains linking knowledge to correct answers, and refines the student's diagnostic reasoning through a second round of fine‑tuning. We evaluate MedThink on general medical benchmarks and a gastroenterology dataset comprising 955 question‑answer pairs. Experiments demonstrate that MedThink outperforms six distillation strategies in all benchmarks: achieving an improvement of up to 12.7% over the student baseline in general tasks, and reaching a total top accuracy of 56.4% in gastroenterology evaluation. This indicates that iterative distillation centered on reasoning can significantly enhance the diagnostic accuracy and generalization capabilities of SLMs whilst maintaining computational efficiency. Our code and data are publicly available at https://github.com/destinybird/PrecisionBoost.

PaperId: 1193, https://arxiv.org/pdf/2605.08073.pdf GitHub

Abstract:
Recent event‑based image reconstruction methods predominantly rely on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to process complementary event information. However, these architectures face fundamental limitations: CNNs often fail to capture global feature correlations, whereas ViTs incur quadratic computational complexity (e.g., O(n^2)), hindering their application in high‑resolution scenarios. To address these bottlenecks, we introduce EmambaIR, an Efficient visual State Space Model designed for image reconstruction using spatially sparse and temporally continuous event streams. Our framework introduces two key components: the cross‑modal Top‑k Sparse Attention Module (TSAM) and the Gated State‑Space Module (GSSM). TSAM efficiently performs pixel‑level top‑k sparse attention to guide cross‑modal interactions, yielding rich yet sparse fusion features. Subsequently, GSSM utilizes a nonlinear gated unit to enhance the temporal representation of vanilla linear‑complexity (O(n)) SSMs, effectively capturing global contextual dependencies without the typical computational overhead. Extensive experiments on six datasets across three diverse image reconstruction tasks ‑ motion deblurring, deraining, and High Dynamic Range (HDR) enhancement ‑ demonstrate that EmambaIR significantly outperforms state‑of‑the‑art methods while offering substantial reductions in memory consumption and computational cost. The source code and data are publicly available at: https://github.com/YunhangWickert/EmambaIR

PaperId: 1194, https://arxiv.org/pdf/2605.08063.pdf GitHub GitHub

Abstract:
Existing Flow Matching (FM) text‑to‑image models suffer from two critical bottlenecks under multi‑task alignment: the reward sparsity induced by scalar‑valued rewards, and the gradient interference arising from jointly optimizing heterogeneous objectives, which together give rise to a 'seesaw effect' of competing metrics and pervasive reward hacking. Inspired by the success of On‑Policy Distillation (OPD) in the large language model community, we propose Flow‑OPD, the first unified post‑training framework that integrates on‑policy distillation into Flow Matching models. Flow‑OPD adopts a two‑stage alignment strategy: it first cultivates domain‑specialized teacher models via single‑reward GRPO fine‑tuning, allowing each expert to reach its performance ceiling in isolation; it then establishes a robust initial policy through a Flow‑based Cold‑Start scheme and seamlessly consolidates heterogeneous expertise into a single student via a three‑step orchestration of on‑policy sampling, task‑routing labeling, and dense trajectory‑level supervision. We further introduce Manifold Anchor Regularization (MAR), which leverages a task‑agnostic teacher to provide full‑data supervision that anchors generation to a high‑quality manifold, effectively mitigating the aesthetic degradation commonly observed in purely RL‑driven alignment. Built upon Stable Diffusion 3.5 Medium, Flow‑OPD raises the GenEval score from 63 to 92 and the OCR accuracy from 59 to 94, yielding an overall improvement of roughly 10 points over vanilla GRPO, while preserving image fidelity and human‑preference alignment and exhibiting an emergent 'teacher‑surpassing' effect. These results establish Flow‑OPD as a scalable alignment paradigm for building generalist text‑to‑image models. The codes and weights will be released in: https://github.com/CostaliyA/Flow‑OPD .

PaperId: 1195, https://arxiv.org/pdf/2605.08019.pdf GitHub

Abstract:
Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent action. Can modern AI systems learn and plan in a similar way? We study this question using a dataset of complex human gameplay with concurrent fMRI recordings, in which participants learn novel video games that require rule discovery, hypothesis revision, and multi‑step planning. We jointly evaluate models by their ability to play the games, match human learning behavior, and predict brain activity during the same task, comparing a suite of frontier Large Reasoning Models (LRMs) against model‑free and model‑based deep reinforcement learning agents and a Bayesian theory‑based agent. We find that frontier LRMs most closely match human behavioral patterns during game discovery and predict brain activity an order of magnitude better than both reinforcement learning alternatives across cortical and subcortical regions, with effects robust to permutation controls. Through targeted manipulations, we further show that brain alignment reflects the model's in‑context representation of the game state rather than its downstream planning or reasoning. Our results establish LRMs as compelling computational accounts of human learning and decision making in complex, naturalistic environments. Project page with interactive replays: https://botcs.github.io/reason‑to‑play/

PaperId: 1196, https://arxiv.org/pdf/2605.07985.pdf GitHub

Abstract:
Selecting the optimal LLM inference configuration requires evaluation across hardware, serving engines, attention backends, and model architectures, since no single choice performs best across all workloads. Profile‑based simulators are the standard tool, yet they hardcode their operation set to a specific configuration and re‑profile every operation from scratch, making exploration prohibitively expensive. This cost stems from a missing structural understanding: every input dimension of each operation is fixed by the model configuration or determined by the incoming request. Many model‑configuration values (e.g., head size, layer count) recur across models, so the same operation runs in many configurations; a single sweep over the request‑dependent dimensions can serve them all. We present Dooly, which exploits this structure to achieve configuration‑agnostic, redundancy‑aware profiling. Dooly performs a single inference pass, labels each input dimension with its origin via taint propagation, and selectively profiles only operations absent from its latency database; stateful operations such as attention are isolated by reusing the serving engine's own initialization code, eliminating manual instrumentation. It builds latency regression models based on the database, which becomes a drop‑in backend for existing simulators. Across two GPU platforms, three attention backends, and diverse model architectures, Dooly achieves simulation accuracy within 5% MAPE for TTFT and 8% for TPOT while reducing profiling GPU‑hours by 56.4% across 12 models compared to the existing profiling approach. We have open‑sourced Dooly at https://github.com/dooly‑project.

PaperId: 1197, https://arxiv.org/pdf/2605.07955.pdf GitHub

Abstract:
Multiple sclerosis (MS) expresses substantial clinical and radiological heterogeneity, which poses significant challenges for automatic lesion segmentation. The current deep learning‑based SOTA is highly susceptible to changes in both distribution, e.g., changes in scanner; as well as the structure of inputs, evident in the current divide between cross‑sectional and longitudinal approaches. We introduce TimeLesSeg, a unified contrast‑agnostic framework designed to segment MS lesions regardless of the presence of a temporal dimension in its inputs, with a single convolutional neural network. Our approach models pathological priors through lesion masks, which are processed together with the current scan. Cross‑sectional processing is enabled by exposing the model to training cases where no prior information is available, which are modeled with an empty mask, allowing it to operate seamlessly in both scenarios. To overcome the scarcity and inconsistency of longitudinal datasets, we propose a novel generative pipeline in which patterns of lesion evolution are simulated by stochastically deforming each individual lesion with morphological operations, producing realistic prior timepoints. In parallel, we achieve contrast agnosticism through Gaussian mixture model‑based domain randomization, enabling the network to experience a wide spectrum of intensity profiles. Results on three publicly available and two in‑house datasets show that TimeLesSeg outperforms the contrast‑agnostic state of the art on single‑modality inputs across overlap‑ and distance‑based metrics. In longitudinal processing, our method outperforms SAMSEG, and captures lesion load dynamics more accurately than both the former and LST‑AI. All source code related to the development of TimeLesSeg is available at https://github.com/NeuroADaS‑Lab/TimeLesSeg.

PaperId: 1198, https://arxiv.org/pdf/2605.07943.pdf GitHub

Abstract:
Active vision ‑‑ where a policy controls its own gaze during manipulation ‑‑ has emerged as a key capability for imitation learning, with multiple independent systems demonstrating its benefits in the past year. Yet there is no shared benchmark to compare approaches or quantify what active vision contributes, on which task types, and under what conditions. We introduce TAVIS, evaluation infrastructure for active‑vision imitation learning, with two complementary task suites ‑‑ TAVIS‑Head (5 tasks, global search via pan/tilt necks) and TAVIS‑Hands (3 tasks, local occlusion via wrist cameras) ‑‑ on two humanoid torso embodiments (GR1T2, Reachy2), built on IsaacLab. TAVIS provides three evaluation primitives: a paired headcam‑vs‑fixedcam protocol on identical demonstrations; GALT (Gaze‑Action Lead Time), a novel metric grounded in cognitive science and HRI that quantifies anticipatory gaze in learned policies; and procedural ID/OOD splits. Baseline experiments with Diffusion Policy and π_0 reveal that (i) active‑vision generally helps, but benefits are task‑conditional rather than uniform; (ii) multi‑task policies degrade sharply under controlled distribution shifts on both suites; and (iii) imitation alone yields anticipatory gaze, with median lead times comparable to the human teleoperator reference. Code, evaluation scripts, demonstrations (LeRobot v3.0; ~2200 episodes) and trained baselines are released at https://github.com/spiglerg/tavis and https://huggingface.co/tavis‑benchmark.

PaperId: 1199, https://arxiv.org/pdf/2605.07905.pdf GitHub

Abstract:
Despite the rapid development of AI reviewers, evaluating such systems remains challenging: metrics favor overlap with human reviews over correctness. However, since human reviews often cover only a subset of salient issues and sometimes contain mistakes, they are unreliable as gold references. To address this, we build category‑specific benchmark subsets and skip evaluation when the corresponding human reviews are missing to strengthen Completeness. We also leverage reviewer‑‑author‑‑meta‑review discussions as expert annotations and filter unreliable reviews accordingly to strengthen Correctness. Finally, we introduce CoCoReviewBench, which curates 3,900 papers from ICLR and NeurIPS to enable reliable and fine‑grained evaluation of AI reviewers. Analysis shows that AI reviewers remain limited in correctness and are prone to hallucinations, and highlights reasoning models as more effective reviewers, motivating further directions for improving AI reviewers. Benchmarks and models are available at https://github.com/hexuandeng/CoCoReviewBench.

PaperId: 1200, https://arxiv.org/pdf/2605.07850.pdf GitHub

Abstract:
With the rise in scale for deep learning models to billions of parameters, the computational cost of fine‑tuning remains a significant barrier to deployment. While Low‑Rank Adaptation (LoRA) has become the standard for parameter‑efficient fine‑tuning, the need to set a predefined, static rank r requires exhaustive grid searches to balance efficiency and performance. Existing rank‑adaptive solutions such as DyLoRA mitigate this by sampling ranks during the training from a predefined distribution. However, they often yield sub‑optimal results at higher ranks due to lack of consistent gradient signals across the full hierarchy of ranks, thus making these methods data‑inefficient. In this paper, we propose MatryoshkaLoRA, a general, Matryoshka‑inspired training framework for LoRA that learns accurate hierarchical low‑rank representations by inserting a fixed, carefully crafted diagonal matrix P between the existing LoRA adapters to scale their sub‑ranks accordingly. By introducing this simple modification, our general framework recovers LoRA and DyLoRA only by changing P and ensures all sub‑ranks embed the available gradient information efficiently. Our MatryoshkaLoRA supports dynamic rank selection with minimal degradation in accuracy. We further propose Area Under the Rank Accuracy Curve (AURAC), a metric that consistently evaluates the performance of hierarchical low‑rank adapters. Our results demonstrate that MatryoshkaLoRA learns more accurate hierarchical low‑rank representations than prior rank‑adaptive approaches and achieves superior accuracy‑performance trade‑offs across ranks on the evaluated datasets. Our code is available at https://github.com/IST‑DASLab/MatryoshkaLoRA.

PaperId: 1201, https://arxiv.org/pdf/2605.07830.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenomenon: different agents exhibit distinct attack patterns. Specifically, each agent exhibits an attack‑selection bias, disproportionately concentrating its efforts on a narrow subset of attack families regardless of prompt variations. To systematically quantify this behavior, we introduce CyBiasBench, a comprehensive 630‑session benchmark that evaluates five agents on three targets and four prompt conditions with ten attack families. We identify explicit bias across agents, with different dominant attack families and varying entropy levels in their attack‑family allocation distributions. Such bias is better characterized as a trait of the agents, rather than a factor associated with the attack success rate. Furthermore, our experiments reveal a bias momentum effect, where agents resist explicit steering toward attack families that conflict with their bias. This forced distribution shift does not yield measurable improvements in attack performance. To ensure reproducibility and facilitate future research, we release an interactive result dashboard at https://trustworthyai.co.kr/CyBiasBench/ and a reproducibility artifact with aggregated session‑level statistics and full evaluation scripts at https://github.com/Harry24k/CyBiasBench.

PaperId: 1202, https://arxiv.org/pdf/2605.07821.pdf GitHub

Abstract:
Out‑of‑distribution (OOD) detection is crucial for ensuring the reliability of deep learning models. Existing methods mostly focus on regular entangled representations to discriminate in‑distribution (ID) and OOD data, neglecting the rich contextual information within images. This issue is particularly challenging for detecting near‑OOD, as models with simplicity bias struggle to learn discriminative features in disentangled representations. The human visual system can use the co‑occurrence of objects in the natural environment to facilitate scene understanding. Inspired by this, we propose an Object‑Centric OOD detection framework that learns to capture Object CO‑occurrence (OCO) patterns within images. The proposed method introduces a new OOD detection paradigm that understands object co‑occurrence within an image by predicting disentangled representations for the test sample, then adaptively divides patterns into three scenarios based on object co‑occurrence patterns observed in ID training data, and finally performs OOD detection in a divide‑and‑conquer manner. By doing so, OCO can distinguish near‑OOD by considering the semantic contextual relationships present in their images, avoiding the tendency to focus solely on simple, easily learnable regions. We evaluate OCO through experiments across challenging and full‑spectrum OOD settings, demonstrating competitive results and confirming its ability to address both semantic and covariate shifts. Code is released at https://github.com/Michael‑McQueen/OCO.

PaperId: 1203, https://arxiv.org/pdf/2605.07725.pdf GitHub

Abstract:
Tool‑integrated reasoning (TIR) is difficult to scale to small language models due to instability in long‑horizon tool interactions and limited model capacity. While reinforcement learning methods like group relative policy optimization provide only sparse outcome‑level rewards. Recently, on‑policy distillation (OPD) has gained popularity by supplying dense token‑level supervision from a teacher on student‑generated trajectories. However, our experiments indicate that applying OPD to TIR leads to a critical failure mode: erroneous tool calls tend to cascade across subsequent reasoning steps, progressively amplifying student‑teacher divergence and rendering the teacher's token‑level supervision increasingly unreliable. To address this, we propose SOD, a step‑wise on‑policy distillation framework for small language model agents, which adaptively reweights distillation strength at each step based on step‑level divergence. Therefore, SOD can attenuate potentially misleading teacher signals in high‑divergence regions while preserving dense guidance in well‑aligned states. Experiments on challenging math, science, and code benchmarks show that SOD achieves up to 20.86% improvement over the second‑best baseline. Notably, our 0.6B student achieves 26.13% on AIME 2025, demonstrating effective transfer of agentic reasoning to lightweight models. Our code is available at https://github.com/YoungZ365/SOD.

PaperId: 1204, https://arxiv.org/pdf/2605.07692.pdf GitHub

Abstract:
Large‑scale social simulators are essential for studying complex social patterns. Prior work explores hybrid methods to scale up simulations, combining large language models (LLM)‑based agents with numerical agent‑based models (ABM). However, this incurs high latency due to expensive memory retrieval and sequential ABM execution. To address this challenge, we propose GASim, a graph‑accelerated hybrid multi‑agent framework for large‑scale social simulations. For core agents driven by LLM, GASim introduces Graph‑Optimized Memory (GOM) to replace intensive LLM‑based retrieval pipelines with lightweight propagation over a sparse memory graph. For the majority of ordinary agents, GASim employs Graph Message Passing (GMP), substituting sequential ABM execution with parallel updates by fine‑grained feature aggregation and Graph Attention Network. We further introduce Entropy‑Driven Grouping (EDG) that coordinates this hybrid partitioning, leveraging information entropy to dynamically identify emergent core agents situated in information‑diverse neighborhoods. Extensive experiments show that GASim not only delivers a substantial 9.94‑fold end‑to‑end speedup over the traditional hybrid framework but also consumes less than 20% of baseline tokens, significantly reducing costs while preserving strong alignment with real‑world public opinion trends. Our code is available at https://github.com/Jasmine0201/GASim.

PaperId: 1205, https://arxiv.org/pdf/2605.07579.pdf GitHub

Abstract:
Reinforcement learning with verifiable rewards (RLVR) for Large Reasoning Models hinges on baseline estimation for variance reduction, but existing approaches pay a heavy price: PPO requires a policy‑model scale critic, while GRPO needs multiple rollouts per prompt to keep its empirical group mean stable. We introduce Policy Optimization with Internal State Value Estimation), which obtains a baseline at negligible cost by using the policy model's internal signals already computed during the policy forward pass. A lightweight probe predicts the expected verifiable reward from the hidden states of the prompt and generated trajectory, as well as token‑entropy statistics, and is trained online alongside the policy. To preserve gradient unbiasedness despite using trajectory‑conditioned features, we introduce a cross‑rollout construction that predicts each rollout's value from an independent rollout's internal states. Because POISE estimates prompt value using only a single rollout, it enables higher prompt diversity for a fixed compute budget during training. This reduces gradient variance for more stable learning and also eliminates the compute overhead of sampling costs for detecting zero‑advantage prompts. On Qwen3‑4B and DeepSeek‑R1‑Distill‑Qwen‑1.5B across math reasoning benchmarks, POISE matches DAPO while requiring less compute. Moreover, its value estimator shows similar performance to a separate LLM‑scale value model and generalizes to various verifiable tasks. By leveraging the model's own internal representations, POISE enables more stable and efficient policy optimization.

PaperId: 1206, https://arxiv.org/pdf/2605.07545.pdf GitHub

Abstract:
Human image animation has witnessed significant advancements, yet generating high‑fidelity hand motions remains a persistent challenge due to their high degrees of freedom and motion complexity. While reinforcement learning from human feedback, particularly direct preference optimization, offers a potential solution, it necessitates the construction of strict preference pairs. However, curating such pairs for dynamic hand regions is prohibitively expensive and often impractical due to frame‑wise inconsistencies. In this paper, we propose Implicit Preference Alignment (IPA), a data‑efficient post‑training framework that eliminates the need for paired preference data. Theoretically grounded in implicit reward maximization, IPA aligns the model by maximizing the likelihood of self‑generated high‑quality samples while penalizing deviations from the pretrained prior. Furthermore, we introduce a Hand‑Aware Local Optimization mechanism to explicitly steer the alignment process toward hand regions. Experiments demonstrate that our method achieves effective preference optimization to enhance hand generation quality, while significantly lowering the barrier for constructing preference data. Codes are released at https://github.com/mdswyz/IPA

PaperId: 1207, https://arxiv.org/pdf/2605.07396.pdf GitHub GitHub

Abstract:
On‑policy distillation (OPD) is a powerful paradigm for model alignment, yet its reliance on teacher logits restricts its application to white‑box scenarios. We contend that structured semantic rubrics can serve as a scalable alternative to teacher logits, enabling OPD using only teacher‑generated responses. To prove it, we introduce ROPD, a simple yet foundational framework for rubric‑based OPD. Specifically, ROPD induces prompt‑specific rubrics from teacher‑student contrasts, and then utilizes these rubrics to score the student rollouts for on‑policy optimization. Empirically, ROPD outperforms the advanced logit‑based OPD methods across most scenarios, and achieving up to a 10x gain in sample efficiency. These results position rubric‑based OPD as a flexible, black‑box‑compatible alternative to the prevailing logit‑based OPD, offering a simple yet strong baseline for scalable distillation across proprietary and open‑source LLMs. Code is available at https://github.com/Peregrine123/ROPD_official.

PaperId: 1208, https://arxiv.org/pdf/2605.07363.pdf GitHub

Abstract:
DeepSeek Sparse Attention (DSA) sets the state of the art for fine‑grained inference‑time sparse attention by introducing a learned token‑wise indexer that scores every prefix token and selects the most relevant ones for the main attention. To remain expressive, the indexer uses many query heads (for example, 64 on DeepSeek‑V3.2) that share the same selected token set; this multi‑head design is precisely what makes the indexer the dominant cost on long contexts. We propose MISA (Mixture of Indexer Sparse Attention), a drop‑in replacement for the DSA indexer that treats its indexer heads as a pool of mixture‑of‑experts. A lightweight router uses cheap block‑level statistics to pick a query‑dependent subset of only a few active heads, and only those heads run the heavy token‑level scoring. This preserves the diversity of the original indexer pool while reducing the per‑query cost from scoring every prefix token with every head to scoring it with only a handful of routed heads, plus a negligible router term computed on a small set of pooled keys. We further introduce a hierarchical variant of MISA that uses the routed pass to keep an enlarged candidate set and then re‑ranks it with the original DSA indexer to recover the final selected tokens almost exactly. With only eight active heads and no additional training, MISA matches the dense DSA indexer on LongBench across DeepSeek‑V3.2 and GLM‑5 while running with eight and four times fewer indexer heads respectively, and outperforms HISA on average. It also preserves fully green Needle‑in‑a‑Haystack heatmaps up to a 128K‑token context and recovers more than 92% of the tokens selected by the DSA indexer per layer. Our TileLang kernel delivers roughly a 3.82 times speedup over DSA's original indexer kernel on a single NVIDIA H200 GPU.

PaperId: 1209, https://arxiv.org/pdf/2605.07355.pdf GitHub GitHub

Abstract:
Video‑language models (VLMs) face rapid inference costs as visual token counts scale with video length. For example, 32 frames at 448×448 resolution already yield >8,000 visual tokens in Qwen3‑VL, making LLM prefill the dominant throughput bottleneck. Existing methods often rely on global similarity or attention‑guided compression, incurring offsets to their gains. We propose Temporal Token Fusion (TTF), a training‑free, plug‑and‑play pre‑LLM token compression framework that exploits structured temporal redundancy in video. TTF automatically selects an anchor frame, then for each subsequent frame, performs a local window similarity search (e.g.,3× 3), fusing tokens that exceed a threshold. The compressed sequence maintains positional consistency across both prefill and decoding through coordinate realignment, enabling seamless integration with existing VLM pipelines. On Qwen3‑VL‑8B with threshold t=0.70, TTF removes about 67% of visual tokens while retaining 99.5% of the baseline accuracy and introducing only \approx0.16\,GFLOPs of matching overhead. Overall, TTF offers a practical, efficient solution for video understanding. The code is available at \hrefhttps://github.com/Cominder/ttfhttps://github.com/Cominder/ttf

PaperId: 1210, https://arxiv.org/pdf/2605.07353.pdf GitHub

Abstract:
Large reasoning models often reach correct answers through flawed intermediate steps, creating a gap between final accuracy and reasoning reliability. Existing alignment strategies address this with external verifiers or massive sampling, limiting scalability. In this work, we introduce CASPO (Confidence‑Aware Step‑wise Preference Optimization), a framework that aligns token‑level confidence with step‑wise logical correctness through iterative Direct Preference Optimization, without training a separate reward model. During inference, we propose Confidence‑aware Thought (CaT), which leverages this calibrated confidence to dynamically prune uncertain reasoning branches with negligible O(V) latency. Experiments across ten benchmarks and multiple model families show that CASPO consistently improves reasoning reliability and inference efficiency. CASPO scales to Qwen3‑8B‑Base and surpasses tree‑search baselines on AIME'24 and AIME'25 without using reward‑model data. We also release a step‑wise dataset with confidence annotations to support fine‑grained analysis of reasoning reliability. Code is available at https://github.com/Thecommonirin/CASPO.

PaperId: 1211, https://arxiv.org/pdf/2605.07331.pdf GitHub

Abstract:
Reinforcement learning, including reinforcement learning with verifiable rewards (RLVR), has emerged as a powerful approach for LLM post‑training. Central to these approaches is the design of the importance sampling (IS) ratio used in off‑policy policy‑gradient estimation. Existing methods face a fundamental bias‑variance dilemma: token‑level IS ratios, as adopted by PPO (Schulman et al., 2017) and GRPO (Shao et al., 2024), introduce bias by ignoring prefix state distribution mismatch; full sequence ratios provide exact trajectory‑level correction but suffer from high variance due to the multiplicative accumulation of per‑token ratios, while GSPO (Zheng et al., 2025) improves numerical stability via length normalization at the cost of deviating from the exact full‑sequence IS correction. In this work, we identify the cumulative token IS ratio, the product of per‑token ratios up to position t, as a theoretically principled solution to this dilemma. We prove that, under the token‑level policy‑gradient formulation, this ratio provides an unbiased prefix correction for each token‑level gradient term and has strictly lower variance than the full sequence ratio. Building on this insight, we propose CTPO (Cumulative Token Policy Optimization), which combines the cumulative token IS ratio with position‑adaptive clipping that scales log‑space clip bounds according to the natural \sqrtt growth of the cumulative log‑ratio. This yields more consistent regularization across token positions. We implement and evaluate CTPO in the tool‑integrated reasoning setting on several challenging mathematical reasoning benchmarks, achieving the best average performance across both model scales compared with strong GRPO and GSPO baselines. Code will be available at https://github.com/horizon‑llm/CTPO.

PaperId: 1212, https://arxiv.org/pdf/2605.07330.pdf GitHub

Abstract:
In large‑scale reinforcement learning (RL) systems with decoupled Trainer‑Rollout execution, the Trainer must regularly synchronize policy weights to the Rollout side to limit policy staleness. When inter‑node bandwidth is abundant, such synchronization is usually only a small fraction of end‑to‑end cost. As model size grows, however, the communication demand rises rapidly. In bandwidth‑constrained or network‑variable deployments ‑‑ for example, cross‑datacenter or cross‑cluster settings, heterogeneous resource pools, and online RL ‑‑ weight synchronization can become a dominant bottleneck for throughput and tail latency. We observe that, in mainstream large‑model RL training, the locations where parameters actually change are highly sparse at the element level (often 99%+ sparsity). Building on this observation, we propose and implement SparseRL‑Sync, which replaces full‑weight transfers with a lossless sparse update payload (indices and values) that can be exactly reconstructed on the inference side, thereby preserving 100% fidelity. Under a simplified cost model, sparse synchronization reduces the per‑update communication volume from S to approximately S/X; with 99% sparsity (X ~ 100), this yields about a 100x reduction in transmitted data. Combined with appropriate bucketing, SparseRL‑Sync also reduces launch and control‑plane overhead, significantly improving scalability and end‑to‑end efficiency in bandwidth‑limited and highly asynchronous RL settings.

PaperId: 1213, https://arxiv.org/pdf/2605.07323.pdf GitHub

Abstract:
Discovering governing differential equations from observational data is a fundamental challenge in scientific machine learning. Existing symbolic regression approaches rely primarily on quantitative metrics; however, real‑world differential equation modeling also requires incorporating domain knowledge to ensure physical plausibility. To address this gap, we propose DoLQ, a method for discovering ordinary differential equations with LLM‑based qualitative and quantitative evaluation. DoLQ employs a multi‑agent architecture: a Sampler Agent proposes dynamic system candidates, a Parameter Optimizer refines equations for accuracy, and a Scientist Agent leverages an LLM to conduct both qualitative and quantitative evaluations and synthesize their results to iteratively guide the search. Experiments on multi‑dimensional ordinary differential equation benchmarks demonstrate that DoLQ achieves superior performance compared to existing methods, not only attaining higher success rates but also more accurately recovering the correct symbolic terms of ground truth equations. Our code is available at https://github.com/Bon99yun/DoLQ.

PaperId: 1214, https://arxiv.org/pdf/2605.07319.pdf GitHub

Abstract:
We introduce Flux Matching, a new paradigm for generative modeling that generalizes existing score‑based models to a broader family of vector fields that need not be conservative. Rather than requiring the model to equal the data score, the Flux Matching objective imposes a weaker condition that admits infinitely many vector fields whose stationary distribution is the data. This flexibility enables a class of generative models that cannot be learned under score matching, in which inductive biases, structural priors, and properties of the dynamics can be directly imposed or optimized. We show that Flux Matching performs strongly on high‑dimensional image datasets and, more importantly, that our added freedom unlocks a range of applications including faster sampling, interpretable and mechanistic models, and dynamics that encode directed dependencies between variables. More broadly, Flux Matching opens a new dimension in generative modeling by turning the vector field itself into a design choice rather than a fixed target. Code is available at https://github.com/peterpaohuang/flux_matching.

PaperId: 1215, https://arxiv.org/pdf/2605.07314.pdf GitHub

Abstract:
Knowledge Graphs (KGs) have proven highly effective for recommendation systems by capturing latent item relationships, while recent integration of Large Language Models (LLMs) has further enhanced semantic understanding and addressed knowledge sparsity issues. Nevertheless, current KG‑and‑LLM‑based methods still face three main limitations: 1) inadequate modeling of implicit semantic relationships beyond explicit KG links; 2) suboptimal single‑channel fusion of ID and LLM embeddings, which often leads to signal interference and blurred representations; and 3) insufficient consideration of user‑item interaction frequency variations in recommendation strategies. To address these challenges, we propose the Dual‑Channel Graph Learning (DCGL) framework, featuring three key innovations: 1) a dual‑channel architecture that structurally decouples rich semantic information from user behavioral patterns, preventing early interference; 2) a multi‑level contrastive learning mechanism that enhances robustness against KG noise through intra‑view contrasts and bridges semantic gaps between channels via inter‑view alignment; and 3) a dynamic fusion mechanism that adaptively balances semantic generalization and behavioral specificity based on interaction frequency, resolving the cascading limitation. Extensive experiments on four real‑world datasets show that DCGL consistently outperforms state‑of‑the‑art methods, yielding substantial improvements in sparse scenarios while maintaining precision for active users. Our code is available at https://github.com/XinchiZou/DCGL.

PaperId: 1216, https://arxiv.org/pdf/2605.07278.pdf GitHub

Abstract:
A latent world model may achieve accurate short‑horizon prediction while still inducing a latent space that is poorly aligned with planning. A key issue is spatiotemporal mismatch: these models are often trained with local predictive supervision, but deployed for long‑horizon goal‑directed search in latent spaces where Euclidean distance may not reflect what is reachable within a finite action budget. We present the Reachability‑Correction auxiliary objective (RC‑aux), a lightweight correction for this mismatch in reconstruction‑free latent world models. RC‑aux keeps the world‑model backbone unchanged and adds planning‑aligned supervision along two axes. Along the time axis, multi‑horizon open‑loop prediction trains the model beyond one‑step consistency. Along the space axis, budget‑conditioned reachability supervision, together with temporal hard negatives, encourages the latent space to distinguish states that are eventually reachable from those reachable within the current planning horizon. At test time, the learned reachability signal can also be used by a reachability‑aware planner to favor trajectories that are both goal‑directed and attainable under the available budget. We instantiate RC‑aux on LeWorldModel and evaluate it under both continuation‑training and matched‑from‑scratch settings. Across goal‑conditioned pixel‑control tasks and a LIBERO‑Goal extension, RC‑aux improves LeWM‑style planning with modest additional cost. These results suggest that planning with latent world models depends not only on predictive accuracy, but also on whether the learned representation encodes the temporal and geometric structure required by downstream search. The code is available at https://github.com/Guang000/RC‑aux.

PaperId: 1217, https://arxiv.org/pdf/2605.07263.pdf GitHub

Abstract:
Over‑the‑air federated learning (OTA‑FL) reduces uplink latency by aggregating client updates directly over the wireless multiple‑access channel. Coherent analog aggregation realizes this idea by aligning the phases and amplitudes of simultaneously transmitted waveforms, which typically requires synchronization, instantaneous channel‑state information (CSI), phase compensation, and power control. Noncoherent energy detection removes the need for phase‑coherent combining, but a single energy measurement is nonnegative and, therefore, cannot represent signed model updates. This paper introduces resource‑element energy difference (REED), a noncoherent physical‑layer primitive for continuous signed aggregation. REED maps the positive and negative parts of each real‑valued update to transmit energies on paired orthogonal resource elements and estimates the signed sum by subtracting the corresponding received energies. The construction uses slow‑timescale calibration of average channel powers, but does not require instantaneous transmitter‑ or receiver‑side CSI or channel inversion. For independent Rayleigh fading, we derive exact first‑ and second‑moment expressions for single‑shot REED and for a chip‑diverse extension that spreads each coordinate over multiple independently faded paired chips. The resulting variance laws separate fading‑induced self‑noise, signal‑noise interaction, and receiver‑noise fluctuation, giving an explicit diversity‑resource tradeoff. More‑>The rest of abstract is in the paper.

PaperId: 1218, https://arxiv.org/pdf/2605.07247.pdf GitHub

Abstract:
Scalable AI agents training relies on interactive environments that faithfully simulate the consequences of agent actions. Manually crafted environments are expensive to build, brittle to extend, and fundamentally limited in diversity. A promising direction is to replace manually crafted environments with LLM‑simulated counterparts. However, this paradigm hinges on an unexamined core assumption: LLMs can accurately simulate environmental feedback. In practice, LLM‑simulated environments suffer from hallucinations, logical inconsistencies, and silent state drift failures that corrupt agent reward signals and compound the construction costs that the paradigm was designed to eliminate. To address this gap, we propose EnvSimBench with four contributions: 1) We provide the first formal definition and operationalization of Environment Simulation Ability (EnvSim Ability) as a quantifiable research objective. 2) We construct EnvSimBench, a rigorous benchmark covering 400 samples across 167 diverse environments, equipped with verifiable labels and fine‑grained difficulty stratification along three axes. 3) Systematic evaluations reveal that all state‑of‑the‑art language models suffer from a universal state change cliff: they achieve near‑perfect accuracy on tasks when the environment state remains invariant, yet fail catastrophically when multiple states need simultaneous updates. This finding exposes EnvSim Ability as a critical yet largely unaddressed capability gap. 4) We design a constraint‑driven simulation pipeline that substantially reduces hallucination, boosts environment synthesis yield by 6.8%, and cuts costs by over 90%. Overall, EnvSimBench serves as both a diagnostic framework and a practical optimization path for reliable LLM‑based environment simulation, establishing a foundation for scalable agent training. Code and data are available at https://github.com/cookieApril/EnvSimBench

PaperId: 1219, https://arxiv.org/pdf/2605.07209.pdf GitHub

Abstract:
We introduce a proxy‑analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our system reads already‑generated text through a small locally hosted open‑weight model and spots hallucinations using the reader's own internal activations. This works just as well when the generator is a closed API like GPT‑4 as when it is any open‑weight model. We built eighteen features grounded in how transformers process text, covering residual stream norms, per‑head source‑document attention, entropy, MLP activations, logit‑lens trajectories, and three new token‑level grounding statistics. We trained a stacking ensemble on 72,135 samples from five hallucination datasets. We tested across seven analyzer architectures from 0.5 billion to 9 billion parameters: Qwen2.5 at 0.5B and 7B, Gemma‑2 at 2B and 9B, Pythia at 1.4B, and LLaMA‑3 at both 3B and 8B. Across all seven, we consistently beat ReDeEP's token‑level AUC of 0.73 on RAGTruth by 7.4 to 10.3 percentage points. Qwen2.5‑7B reached an F1 of 0.717, just above ReDeEP's 0.713, while Qwen2.5‑0.5B hit 0.706. The most striking finding is how tightly all seven models cluster: AUC spans only 2.3 percentage points across an eighteen‑fold difference in model size. Even more surprising, our 3B LLaMA outperforms our 8B LLaMA on RAGTruth, showing that bigger is not always better even within the same model family. Both RAGTruth and LLM‑AggreFact include outputs from multiple LLM families, so our results are not skewed toward any particular generator.

PaperId: 1220, https://arxiv.org/pdf/2605.07177.pdf GitHub

Abstract:
Existing multimodal search agents process target entities sequentially, issuing one tool call per entity and accumulating redundant interaction rounds whenever a query decomposes into independent sub‑retrievals. We argue that effective multimodal agents should search wider rather than longer: dispatching multiple grounded queries concurrently within a round. To this end, we present HyperEyes, a parallel multimodal search agent that fuses visual grounding and retrieval into a single atomic action, enabling concurrent search across multiple entities while treating inference efficiency as a first‑class training objective. HyperEyes is trained in two stages. For cold‑start supervision, we develop a Parallel‑Amenable Data Synthesis Pipeline covering visual multi‑entity and textual multi‑constraint queries, curating efficiency‑oriented trajectories via Progressive Rejection Sampling. Building on this, our central contribution, a Dual‑Grained Efficiency‑Aware Reinforcement Learning framework, operates at two levels. At the macro level, we propose TRACE (Tool‑use Reference‑Adaptive Cost Efficiency), a trajectory‑level reward whose reference is monotonically tightened during training to suppress superfluous tool calls without restricting genuine multi‑hop search. At the micro level, we adapt On‑Policy Distillation to inject dense token‑level corrective signals from an external teacher on failed rollouts, mitigating the credit‑assignment deficiency of sparse outcome rewards. Since existing benchmarks evaluate accuracy as the sole metric, omitting inference cost, we introduce IMEB, a human‑curated benchmark of 300 instances that jointly evaluates search capability and efficiency. Across six benchmarks, HyperEyes‑30B surpasses the strongest comparable open‑source agent by 9.9% in accuracy with 5.3x fewer tool‑call rounds on average.

PaperId: 1221, https://arxiv.org/pdf/2605.07140.pdf GitHub

Abstract:
Skeleton‑based human activity recognition has achieved strong empirical performance, yet most existing models remain black boxes and difficult to interpret. In this work, we introduce a neurosymbolic formulation of skeleton‑based HAR that reframes action recognition as concept‑driven first‑order logical reasoning over motion primitives. Our framework bridges representation learning and symbolic inference by grounding first‑order logic predicates in learnable spatial and temporal motion concepts. Specifically, we employ a standard spatio‑temporal skeleton encoder to extract latent motion representations, which are then mapped to interpretable concept predicates via a spatio‑temporal concept decoder that explicitly separates pose‑centric and dynamics‑centric abstractions. These concept predicates are composed through differentiable first‑order logic layers, enabling the model to learn human‑readable logical rules that govern action semantics. To impose semantic structure on the learned concepts, we align skeleton representations with LLM‑derived descriptions of atomic motion primitives, establishing a shared conceptual space for perception and reasoning. Extensive experiments on NTU RGB+D 60/120 and NW‑UCLA demonstrate that our approach achieves competitive recognition performance while providing explicit, interpretable explanations grounded in logical structure. Our results highlight neurosymbolic reasoning as an effective paradigm for interpretable spatio‑temporal action understanding. Code: https://github.com/Mr‑TalhaIlyas/REASON

PaperId: 1222, https://arxiv.org/pdf/2605.07121.pdf GitHub

Abstract:
Temporal knowledge graphs (TKGs) represent time‑stamped relational facts and support a wide range of reasoning tasks over evolving events. However, existing methods produce entity representations that are static at the entity level, in that each representation is a function of learned parameters only and retains no trace of the interactions in which the entity has participated. In this paper, we depart from this static view and propose that each entity be modeled as an adaptive process whose representation is refined every time the entity participates in a fact. To this end, we propose AdaTKG, which maintains a per‑entity memory that is updated with every observed interaction, with the memory accumulating online and predictions improving as more interactions arrive. Specifically, we instantiate the memory update as a learnable exponential moving average governed by a single shared scalar instead of using learnable parameters for each entity, enabling AdaTKG to handle entities unseen during training. Extensive experiments confirm consistent gains over TKG baselines, demonstrating the effectiveness of adaptive memory. Code is publicly available at: https://github.com/seunghan96/AdaTKG.

PaperId: 1223, https://arxiv.org/pdf/2605.07111.pdf GitHub

Abstract:
Recent literature on fine‑tuning Large Language Models highlights a fundamental debate. While Full Fine‑Tuning (FFT) provides the representational plasticity required for high‑entropy knowledge injection, Low‑Rank Adaptation (LoRA) can match or surpass FFT performance because many tasks only require updates in a low‑rank space and benefit from LoRA's additional regularization. Through empirical evaluation across diverse tasks (SQL, Medical QA, and Counterfactual Knowledge) and varying language models (Gemma‑3‑1B, Qwen2.5‑1.5B, and Qwen2.5‑3B), we verify both trends and demonstrate that relying solely on either static architecture is structurally limited. To address this challenge, we propose a Mixture of LoRA and Full (MoLF) Fine‑Tuning, a unified framework that enables continuous navigation between both training regimes. MoLF dynamically routes updates between FFT and LoRA at the optimizer level to ensure that exact gradient signals are available to both experts throughout training, yielding stable training dynamics. For memory‑constrained environments, we also introduce MoLF‑Efficient, which freezes base weights and only routes updates among a pair of LoRA experts of potentially varying rank. Our evaluations show that MoLF either improves on or stays within 1.5% of the better of FFT and LoRA across all settings, while MoLF‑Efficient outperforms prior adaptive LoRA approaches by up to 20% on Fact and 9% on Med and SQL. Our code is open‑sourced at https://github.com/11785T23/molf.git.

PaperId: 1224, https://arxiv.org/pdf/2605.07079.pdf GitHub

Abstract:
World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature‑based world models, on the other hand, predict future visual features instead of raw video pixels, offering a promising alternative that is more efficient and less prone to hallucination. However, current feature‑based approaches rely on direct regression, which leads to blurry or collapsed predictions in complex interactions, while generative modeling in high‑dimensional feature spaces still remains challenging. In this work, we discover that a new type of latent action representation, which we refer to as Residual Latent Action (RLA), can be easily learned from DINO residuals. We also show that RLA is predictive, generalizable, and encodes temporal progression. Building on RLA, we propose RLA World Model (RLA‑WM), which predicts RLA values via flow matching. RLA‑WM outperforms both state‑of‑the‑art feature‑based and video‑diffusion world models on simulation and real‑world datasets, while being orders of magnitude faster than video diffusion. Furthermore, we develop two robot learning techniques that use RLA‑WM to improve policy learning. The first one is a minimalist world action model with RLA that learns from actionless demonstration videos. The second one is the first visual RL framework trained entirely inside a world model learned from offline videos only, using a video‑aligned reward and no online interactions or handcrafted rewards. Project page: https://mlzxy.github.io/rla‑wm

PaperId: 1225, https://arxiv.org/pdf/2605.07029.pdf GitHub

Abstract:
Instrumental‑variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects and high‑dimensional covariates. Existing nonlinear IV methods directly learn the causal relation in observed feature space or rely on learned representations within two‑stage or moment‑based procedures, which can struggle when the causal information is embedded in a high‑dimensional representation. We propose BGM‑IV, a latent Bayesian generative modeling approach that reframes nonlinear IV regression as posterior inference in a causally structured latent space. BGM‑IV infers latent components that separately capture shared confounding structure, outcome‑specific variation, treatment‑specific variation, and covariate‑only nuisance information. To account for endogeneity, BGM‑IV replaces the confounded outcome likelihood with an IV‑integrated pseudo‑likelihood that averages over instrument‑induced treatment values within the latent model. Across various benchmark datasets, BGM‑IV remains competitive in the classical low‑dimensional regime and performs best in high‑dimensional covariate regimes. Together, these results show that structured latent generative modeling provides a principled and effective strategy to nonlinear IV estimation with rich covariates. The code of BGM‑IV is available at https://github.com/liuq‑lab/BGM‑IV.

PaperId: 1226, https://arxiv.org/pdf/2605.07024.pdf GitHub

Abstract:
Large Language Models for code generation frequently produce hallucinations in Fill‑in‑the‑Middle (FIM) tasks ‑‑ plausible but incorrect completions such as invented API methods, invalid parameters, undefined variables, or non‑existent imports. These failures pass superficial review yet introduce runtime errors. We introduce Delulu, a verified multi‑lingual benchmark of 1,951 FIM samples across 7 languages and 4 hallucination types. Samples are curated through an adversarial pipeline: a frontier LLM generates plausible hallucinations, four diverse judge models evaluate them, embedding‑based clustering mines progressively harder examples, self‑contained Docker containers verify that golden completions compile while hallucinated variants produce the expected runtime error, and a final human‑expert review removes any remaining biased or trivially decidable samples. We evaluate 11 open‑weight FIM models from five families spanning 0.5B‑32B parameters: a six‑point Qwen2.5‑Coder scaling slate, plus a cross‑family slate (CodeLlama, DeepSeek‑Coder‑V2, StarCoder2). The strongest model reaches only 84.5% pass@1, no family exceeds 0.77 Edit Similarity, and every family produces hallucination‑aligned completions on a non‑trivial share of samples, confirming that the difficulty exposed by Delulu is task‑intrinsic rather than family‑specific. We release the benchmark, containers, and evaluation framework at https://github.com/microsoft/delulu.

PaperId: 1227, https://arxiv.org/pdf/2605.07021.pdf GitHub

Abstract:
Reasoning in Large Language Models (LLMs) poses a challenge for oversight as many misaligned behaviors do not surface until reasoning concludes. To address this, we introduce Behavior Cue Reasoning for making LLM reasoning more controllable and monitorable. Behavior Cues are special token sequences that a model is trained to emit immediately before specific implicit and explicit behaviors, acting as dual purpose signal and control levers. When fine‑tuning a weaker external monitor with Reinforcement Learning for reasoning oversight, a compressed view of only information surfaced by Behavior Cues is sufficient signal for the monitor to prune up to 50% of otherwise wasted reasoning tokens in complex math problem solving. When leveraged by an almost optimal rule‑based monitor in an environment where excessive constraint violations results in failure, Behavior Cues allows for the recovery of safe actions from 80% of reasoning traces that would otherwise end with the proposal of an unsafe action, more than doubling the success rate from 46% to 96%. Through evaluation across two model families and three domains, we show that Behavior Cue Reasoning improves reasoning monitorability and controllability with no cost to performance. More broadly, our work progresses scalable oversight by demonstrating how the monitored model itself can be trained to reason more tractably to oversight. Code: https://github.com/christopherzc/behavior‑cues

PaperId: 1228, https://arxiv.org/pdf/2605.06924.pdf GitHub

Abstract:
Synthesizing consistent and coherent long video remains a fundamental challenge. Existing methods suffer from semantic drift and narrative collapse over long horizons. We present A^2RD, an Agentic Auto‑Regressive Diffusion architecture that decouples creative synthesis from consistency enforcement. A^2RD formulates long video synthesis as a closed‑loop process that synthesizes and self‑improves video segment‑by‑segment through a Retrieve‑‑Synthesize‑‑Refine‑‑Update cycle. It comprises three core components: (i) Multimodal Video Memory that tracks video progression across modalities; (ii) Adaptive Segment Generation that switches among generation modes for natural progression and visual consistency; and (iii) Hierarchical Test‑Time Self‑Improvement that self‑improves each segment at frame and video levels to prevent error propagation. We further introduce LVBench‑C, a challenging benchmark with non‑linear entity and environment transitions to stress‑test long‑horizon consistency. Across public and LVBench‑C benchmarks spanning one‑ to ten‑minute videos, A^2RD outperforms state‑of‑the‑art baselines by up to 30% in consistency and 20% in narrative coherence. Human evaluations corroborate these gains while also highlighting notable improvements in motion and transition smoothness.

PaperId: 1229, https://arxiv.org/pdf/2605.06898.pdf GitHub

Abstract:
At the heart of existing language model agents is a fixed orchestrator program responsible for the state transition between consecutive turns. This paper introduces self‑programmed execution (SPE), an agent architecture in which the model completion is itself the orchestrator program, and the harness evaluates this program but does not impose its own orchestration policy. I formalize this idea using agentic machines: an SPE state is one from which a model completion can load any state of an embedded copy of the machine, meaning that it is subject to no fixed turn‑to‑turn orchestration policy. Realizing SPE in practice is nontrivial because the same data is both model context and executable program. I therefore introduce Spell, a Lisp‑based language in which programs can edit and re‑evaluate themselves, and effectful expressions like model invocations are structured such that re‑evaluating an edited program does not replay its side effects. Experiments with existing models, not trained for SPE or Spell, show that frontier models can operate in this regime and accomplish challenging agentic tasks. These results demonstrate how an LM can act as an agent without any fixed orchestration policy, and they raise the question of what self‑orchestration strategies might be learned by a model trained for self‑programmed execution. Code is available at https://github.com/lukejoconnor/spell .

PaperId: 1230, https://arxiv.org/pdf/2605.06897.pdf GitHub

Abstract:
The rise of Internet of Things (IoT) devices in the physical world necessitates voice‑based interfaces capable of handling complex user experiences. While modern Large Language Models (LLMs) already demonstrate strong tool‑usage capabilities, modeling real‑world IoT devices presents a difficult, understudied challenge which combines modeling spatiotemporal constraints with speech inputs, dynamic state tracking, and mixed‑initiative interaction patterns. We introduce MIST (the Multimodal Interactive Speech‑based Tool‑calling Dataset), a synthetic multi‑turn, voice‑driven code generation task that operates over IoT devices. We find that there is a significant gap between open‑ and closed‑weight multimodal LLMs on MIST, and that even frontier closed‑weight LLMs have substantial headroom. We release MIST and an extensible data generation framework to build related datasets in order to facilitate research on mixed‑initiative voice assistants which reason about physical world constraints.

PaperId: 1231, https://arxiv.org/pdf/2605.06885.pdf GitHub GitHub

Abstract:
Diffusion language models (DLMs) have recently demonstrated capabilities that complement standard autoregressive (AR) models, particularly in non‑sequential generation and bidirectional editing. Although recent work has shown that pretrained autoregressive checkpoints can be converted into diffusion language models, existing recipes primarily transfer parameters through continued denoising training with objective‑ and attention‑level modifications. We instead ask whether the internal representation geometry learned by next‑token prediction can be explicitly preserved during AR‑to‑DLM conversion. We hypothesize that much of the semantic structure learned by AR pretraining can transfer across generation orders, and thus DLM training should be viewed as relearning the decoding path rather than relearning language representations. To investigate this, we introduce REPR‑ALIGN, a representation alignment objective that adapts a bidirectional masked diffusion model to reuse representations from a pretrained AR model of identical architecture. Concretely, we align the hidden states of the DLM to the frozen AR model at every layer using cosine similarity, while optimizing the standard masked denoising objective. This simple alignment, with no adapters and no architectural changes beyond the attention mask, yields up to 4x training acceleration in our setting and is particularly effective in low‑data regimes. Our results suggest that linguistic representations can transfer across generation order, and that representation alignment provides a simple and effective technique for training diffusion language models. Code is available at https://github.com/pengzhangzhi/Open‑dLLM.

PaperId: 1232, https://arxiv.org/pdf/2605.06832.pdf GitHub

Abstract:
Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces IntentGrasp, a comprehensive benchmark for evaluating the intent understanding capability of LLMs. Derived from 49 high‑quality, open‑licensed corpora spanning 12 diverse domains, IntentGrasp is constructed through source datasets curation, intent label contextualization, and task format unification. IntentGrasp contains a large‑scale training set of 262,759 instances and two evaluation sets: an All Set of 12,909 test cases and a more balanced and challenging Gem Set of 470 cases. Extensive evaluations on 20 LLMs across 7 families (including frontier models such as GPT‑5.4, Gemini‑3.1‑Pro, and Claude‑Opus‑4.7) demonstrate unsatisfactory performance, with scores below 60% on All Set and below 25% on Gem set. Notably, 17 out of 20 tested models perform worse than a random‑guess baseline (15.2%) on Gem Set, while the estimated human performance is ~81.1%, showing substantial room for improvement. To enhance such ability, this paper proposes Intentional Fine‑Tuning (IFT), which fine‑tunes the models on the training set in IntentGrasp, yielding significant gains of 30+ F1 points on All Set and 20+ points on Gem Set. Tellingly, the leave‑one‑domain‑out (Lodo) experiments further demonstrate the strong cross‑domain generalizability of IFT, verifying that it is a promising approach to substantially enhancing the intent understanding of LLMs. Overall, by benchmarking and boosting intent understanding ability, this study sheds light on a promising path towards more intentional, capable, and safe AI assistants for human benefits and social good.

PaperId: 1233, https://arxiv.org/pdf/2605.06765.pdf GitHub

Abstract:
Human speech conveys expressiveness beyond linguistic content, including personality, mood, or performance elements, such as a comforting tone or humming a song, which we formalize as role‑playing and singing. We present VITA‑QinYu, the first expressive end‑to‑end (E2E) spoken language model (SLM) that goes beyond natural conversation to support both role‑playing and singing generation. VITA‑QinYu adopts a hybrid speech‑text paradigm that extends interleaved text‑audio modeling with multi‑codebook audio tokens, a design enabling richer paralinguistic representation while preserving a clear separation between modalities to avoid interference. We further develop a comprehensive data generation pipeline to synthesize a total of 15.8K hours of natural conversation, role‑playing, and singing data for training. VITA‑QinYu demonstrates superior expressiveness, outperforming peer SLMs by 7 percentage points on objective role‑playing benchmarks, and surpassing peer models by 0.13 points on a 5‑point MOS scale for singing. Simultaneously, it achieves state‑of‑the‑art conversational accuracy and fluency, exceeding prior SLMs by 1.38 and 4.98 percentage points on the C3 and URO benchmarks, respectively. We open‑source our code and models and provide an easy‑to‑use demo with full‑stack support for streaming and full‑duplex interaction.

PaperId: 1234, https://arxiv.org/pdf/2605.06758.pdf GitHub

Abstract:
Relative spatial relations provide a compact representation of spatial structure and are fundamental to relative spatial reasoning in 3D layout generation. Recent works leverage Multimodal Large Language Models (MLLMs) to infer such relations, but the inferred relations are often unreliable and are typically handled with post‑hoc heuristics. In this paper, we propose R^3L, a general framework that improves the reliability and consistency of relative spatial reasoning for 3D layout generation. Our key motivation is that multi‑hop reasoning requires repeated reference‑frame transformations, which accumulate errors in inferred relations and lead to semantic and metric drift. To mitigate this, we propose invariant spatial decomposition to break coupled relation chains, and consistent spatial imagination to promote self‑consistency through an imagine‑and‑revise loop. We further introduce supportive spatial optimization to ease pose optimization via global‑to‑local coordinate re‑parameterization. Extensive experiments across diverse scene types and instructions demonstrate that R^3L produces more physically feasible and semantically consistent layouts. Notably, our analysis shows that resolving frame‑induced inconsistencies is crucial for reliable multi‑hop relative spatial reasoning. The code is available at https://github.com/Neal2020GitHub/R3L.

PaperId: 1235, https://arxiv.org/pdf/2605.06729.pdf GitHub

Abstract:
We present the EΔ‑MHC‑Geo Transformer, a novel architecture that unifies Manifold‑Constrained Hyper‑Connections (mHC), Deep Delta Learning (DDL), and the Cayley transform to obtain input‑adaptive, unconditionally orthogonal residual connections. Unlike DDL, whose Householder operator is orthogonal only at β\in \0,2\, our Data‑Dependent Cayley rotation Q(x)=(I+(β/2)A(x))^‑1(I‑(β/2)A(x)) preserves orthogonality for all β and all inputs. To handle negation, an eigenvalue ‑1 case that Cayley provably excludes, we introduce the EΔ‑MHC‑Geo Hybrid, which combines Cayley rotation with Householder reflection via a learned operator‑selection gate X'=γ(X)Q(X)X+(1‑γ(X))H_2(X)X. A midpoint‑collapse regularizer, 4γ(1‑γ), encourages boundary gate decisions, where each selected component is orthogonal. In matched‑parameter comparisons, with approximately 1.79M parameters per model and mean +/‑ standard deviation over 3 seeds, against four baselines including the concurrent JPmHC, EΔ‑MHC‑Geo achieves the best long‑horizon stability, 1.9x over JPmHC and 3.8x over GPT; the best near‑π rotation loss, 4.5x over JPmHC on single‑plane; strong norm preservation, with 0.001 mean deviation; and 0.96 negation cosine alignment in a diagnostic reflection probe, all with 33% fewer layers. While JPmHC's wider representation excels on pure rotation, its finite Cayley residual mixer excludes an exact λ=‑1 operator and has no reflection branch, motivating our hybrid approach for accessing both connected components of O(n).

PaperId: 1236, https://arxiv.org/pdf/2605.06673.pdf GitHub

Abstract:
Aggregate metacognitive quality scores mask within‑model variation across MMLU benchmark domains. We administered 1,500 MMLU items (250 per domain, under an a priori six‑domain grouping) to 33 frontier LLMs from eight model families and computed Type‑2 AUROC per model‑domain cell using verbalized confidence (0‑100). Total observations: 47,151. Every model with above‑chance aggregate monitoring showed non‑trivial domain‑level variation. Applied/Professional knowledge was reliably the easiest benchmark domain to monitor (mean AUROC = .742, ranked top‑2 in 21 of 33 models); Formal Reasoning and Natural Science were reliably the hardest (one of the two ranked bottom‑2 in 27 of 33 models). The three middle domains were statistically indistinguishable (Kendall's W = .164). A subject‑level coherence analysis (within‑domain similarity ratio = 0.95) confirms the six‑domain grouping is a pragmatic benchmark taxonomy, not a validated latent construct. Within‑family profile‑shape clustering is significant for Anthropic, Google‑Gemini, and Qwen (permutation p < .0001) but not DeepSeek, Google‑Gemma, or OpenAI. Gemma 4 31B showed a +.202 AUROC improvement over Gemma 3 27B. Three models classified Invalid on binary KEEP/WITHDRAW probes produced normal profiles under verbalized confidence, confirming probe‑format specificity. Bootstrap 95% CIs on 198 cells have median width .199. Split‑half aggregate stability r = .893; profile‑level split‑half is weaker (grand median r = .184). These results show stable benchmark‑domain variation obscured by aggregate metrics, and support benchmark‑stage domain screening as a step before deployment in specific application areas.

PaperId: 1237, https://arxiv.org/pdf/2605.06667.pdf GitHub

Abstract:
For artistic applications, video generation requires fine‑grained control over both performance and cinematography, i.e., the actor's motion and the camera trajectory. We present ActCam, a zero‑shot method for video generation that jointly transfers character motion from a driving video into a new scene and enables per‑frame control of intrinsic and extrinsic camera parameters. ActCam builds on any pretrained image‑to‑video diffusion model that accepts conditioning in terms of scene depth and character pose. Given a source video with a moving character and a target camera motion, ActCam generates pose and depth conditions that remain geometrically consistent across frames. We then run a single sampling process with a two‑phase conditioning schedule: early denoising steps condition on both pose and sparse depth to enforce scene structure, after which depth is dropped and pose‑only guidance refines high‑frequency details without over‑constraining the generation. We evaluate ActCam on multiple benchmarks spanning diverse character motions and challenging viewpoint changes. We find that, compared to pose‑only control and other pose and camera methods, ActCam improves camera adherence and motion fidelity, and is preferred in human evaluations, especially under large viewpoint changes. Our results highlight that careful camera‑consistent conditioning and staged guidance can enable strong joint camera and motion control without training. Project page: https://elkhomar.github.io/actcam/.

PaperId: 1238, https://arxiv.org/pdf/2605.06664.pdf GitHub

Abstract:
GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. However, in complex scenarios like the ScreenSpot‑Pro benchmark, existing models often suffer from suboptimal performance. Utilizing the proposed Masked Prediction Distribution (MPD) attribution method, we identify that the primary sources of errors are twofold: high image resolution (leading to precision bias) and intricate interface elements (resulting in ambiguity bias). To address these challenges, we introduce Bias‑Aware Manipulation Inference (BAMI), which incorporates two key manipulations, coarse‑to‑fine focus and candidate selection, to effectively mitigate these biases. Our extensive experimental results demonstrate that BAMI significantly enhances the accuracy of various GUI grounding models in a training‑free setting. For instance, applying our method to the TianXi‑Action‑7B model boosts its accuracy on the ScreenSpot‑Pro benchmark from 51.9% to 57.8%. Furthermore, ablation studies confirm the robustness of the BAMI approach across diverse parameter configurations, highlighting its stability and effectiveness. Code is available at https://github.com/Neur‑IO/BAMI.

PaperId: 1239, https://arxiv.org/pdf/2605.06652.pdf GitHub

Abstract:
Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this setting as benchmarkless comparative safety scoring and specify the contract under which a scenario‑based audit can be interpreted as deployment evidence. Scores are valid only under a fixed scenario pack, rubric, auditor, judge, sampling configuration, and rerun budget. Because no labels are available, we replace ground‑truth agreement with an instrumental‑validity chain: responsiveness to a controlled safe‑versus‑abliterated contrast, dominance of target‑driven variance over auditor and judge artifacts, and stability across reruns. We instantiate the chain in SimpleAudit, a local‑first scoring instrument, and validate it on a Norwegian safety pack. Safe and abliterated targets separate with AUROC values between 0.89 and 1.00, target identity is the dominant variance component (η^2 \approx 0.52), and severity profiles stabilize by ten reruns. Applying the same chain to Petri shows that it admits both tools. The substantial differences arise upstream of the chain, in claim‑contract enforcement and deployment fit. A Norwegian public‑sector procurement case comparing Borealis and Gemma 3 demonstrates the resulting evidence in practice: the safer model depends on scenario category and risk measure. Consequently, scores, matched deltas, critical rates, uncertainty, and the auditor and judge used must be reported together rather than collapsed into a single ranking.

PaperId: 1240, https://arxiv.org/pdf/2605.06643.pdf GitHub

Abstract:
Despite the growing popularity of Multimodal Domain Generalization (MMDG) for enhancing model robustness, it remains unclear whether reported performance gains reflect genuine algorithmic progress or are artifacts of inconsistent evaluation protocols. Current research is fragmented, with studies varying significantly across datasets, modality configurations, and experimental settings. Furthermore, existing benchmarks focus predominantly on action recognition, often neglecting critical real‑world challenges such as input corruptions, missing modalities, and model trustworthiness. This lack of standardization obscures a reliable assessment of the field's advancement. To address this issue, we introduce MMDG‑Bench, the first unified and comprehensive benchmark for MMDG, which standardizes evaluation across six datasets spanning three diverse tasks: action recognition, mechanical fault diagnosis, and sentiment analysis. MMDG‑Bench encompasses six modality combinations, nine representative methods, and multiple evaluation settings. Beyond standard accuracy, it systematically assesses corruption robustness, missing‑modality generalization, misclassification detection, and out‑of‑distribution detection. With 7, 402 neural networks trained in total across 95 unique cross‑domain tasks, MMDG‑Bench yields five key findings: (1) under fair comparisons, recent specialized MMDG methods offer only marginal improvements over ERM baseline; (2) no single method consistently outperforms others across datasets or modality combinations; (3) a substantial gap to upper‑bound performance persists, indicating that MMDG remains far from solved; (4) trimodal fusion does not consistently outperform the strongest bimodal configurations; and (5) all evaluated methods exhibit significant degradation under corruption and missing‑modality scenarios, with some methods further compromising model trustworthiness.

PaperId: 1241, https://arxiv.org/pdf/2605.06623.pdf GitHub

Abstract:
Large language model (LLM)‑based Multi‑agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orchestrated via role‑specific prompts. While the quality of these prompts is pivotal, jointly optimizing them across interacting agents remains a non‑trivial challenge, primarily due to the misalignment between local agent objectives and holistic system goals. To address this, we introduce MASPO, a novel framework designed to automatically and iteratively refine prompts across the entire system. A core innovation of MASPO is its joint evaluation mechanism, which assesses prompts not merely by their local validity, but by their capacity to facilitate downstream success for successor agents. This effectively bridges the gap between local interactions and global outcomes without relying on ground‑truth labels. Furthermore, MASPO employs a data‑driven evolutionary beam search to efficiently navigate the high‑dimensional prompt space. Extensive empirical evaluations across 6 diverse tasks demonstrate that MASPO consistently outperforms state‑of‑the‑art prompt optimization methods, achieving an average accuracy improvement of 2.9. We release our code at https://github.com/wangzx1219/MASPO.

PaperId: 1242, https://arxiv.org/pdf/2605.06615.pdf GitHub

Abstract:
Sign‑based optimization algorithms, such as SignSGD and Muon, have garnered significant attention for their remarkable performance in training large foundation models. Despite this empirical success, we still lack a theoretical understanding of when and why these sign‑based methods outperform vanilla SGD. The core obstacle is that under standard smoothness and finite variance conditions, SGD is known to be minimax optimal for finding stationary points measured by \ell_2‑norms, thereby fundamentally precluding any complexity gains for sign‑based methods in standard settings. To overcome this barrier, we analyze sign‑based optimizers leveraging \ell_1‑norm stationarity, \ell_\infty‑smoothness, and a separable noise model, which can better capture the coordinate‑wise nature of signed updates. Under this distinct problem geometry, we derive matched upper and lower bounds for SignSGD and explicitly characterize the problem class in which SignSGD provably dominates SGD. Specifically, we compare the \emphupper bound of SignSGD with the \emphlower bound of SGD, illustrating that SignSGD effectively reduces the complexity by a factor of d under \emphsparse noise, where d is the problem dimension. Furthermore, we elevate this framework to the matrix domain, providing an equivalent optimal lower bound for the Muon optimizer, proving that extending the sign operator to matrices preserves this optimal scaling with dimensionality. Finally, we bridge our theoretical bounds to practice, demonstrating that the theoretical superiority of SignSGD accurately predicts its faster convergence during the pretraining of a 124M parameter GPT‑2 model.

PaperId: 1243, https://arxiv.org/pdf/2605.06607.pdf GitHub

Abstract:
Recent LLM‑based agents have closed substantial portions of the scientific discovery loop in software‑only machine‑learning research, in chemistry, and in biology. Extending the same loop to high‑fidelity physical simulators is harder, because solver completion does not imply physical validity and many failure modes appear only in field‑level imagery rather than in solver logs. We present AI CFD Scientist, an open‑source AI scientist for computational fluid dynamics (CFD) that, to our knowledge, is the first to span literature‑grounded ideation, validated execution, vision‑based physics verification, source‑code modification, and figure‑grounded writing within a single inspectable workflow. Three coupled pathways cover parameter sweeps within a fixed solver, case‑local C++ library compilation for new physical models, and open‑ended hypothesis search against a reference comparator, all running on OpenFOAM through Foam‑Agent. At the center of the framework is a vision‑language physics‑verification gate that inspects rendered flow fields before any result is accepted, rerun, or written into a manuscript. On five tasks under a shared GPT‑5.5 backbone, AI CFD Scientist autonomously discovers a Spalart‑Allmaras runtime correction that reduces lower‑wall Cf RMSE against DNS by 7.89% on the periodic hill at Reh=5600; under matched LLM cost, two strong general AI‑scientist baselines (ARIS, DeepScientist) execute partial CFD workflows but lack the domain‑specific validity gates needed to convert runs into defensible scientific claims; and a controlled planted‑failure ablation shows that the vision‑language gate detects 14 of 16 silent failures missed by solver‑level checks. Code, prompts, and run artifacts are released at https://github.com/csml‑rpi/cfd‑scientist.

PaperId: 1244, https://arxiv.org/pdf/2605.06597.pdf GitHub

Abstract:
Self‑distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD in autoregressive LLMs remains challenging because self‑generated trajectories are free‑form, correctness is task‑dependent, and plausible rationales can still provide unstable or unreliable supervision. Existing methods mainly examine isolated design choices, leaving their effectiveness, roles, and interactions unclear. In this paper, we propose UniSD, a unified framework to systematically study self‑distillation. UniSD integrates complementary mechanisms that address supervision reliability, representation alignment, and training stability, including multi‑teacher agreement, EMA teacher stabilization, token‑level contrastive learning, feature matching, and divergence clipping. Across six benchmarks and six models from three model families, UniSD reveals when self‑distillation improves over static imitation, which components drive the gains, and how these components interact across tasks. Guided by these insights, we construct UniSDfull, an integrated pipeline that combines complementary components and achieves the strongest overall performance, improving over the base model by +5.4 points and the strongest baseline by +2.8 points. Extensive evaluation highlights self‑distillation as a practical and steerable approach for efficient LLM adaptation without stronger external teachers.

PaperId: 1245, https://arxiv.org/pdf/2605.06575.pdf GitHub

Abstract:
We identify and formalize an underexplored phenomenon in deep learning optimization: directional alignment and loss convergence can be decoupled. An optimizer can exhibit near‑perfect directional consistency (cc_t ‑> 1, measured via consecutive gradient cosine similarity) while the loss remains high or decreases slowly. This observation reveals that existing optimizers such as Adam, SGD, and RMSprop lack explicit mechanisms to exploit temporal consistency in gradient directions, relying instead on magnitude‑based signals that fail to distinguish plateaus, saddle points, and genuine convergence. Motivated by this, we introduce GONO (Gradient‑Oriented Norm‑Adaptive Optimizer), which adapts Adam's momentum coefficient beta_1 based on cc_t: amplifying momentum under directional consistency and suppressing it during oscillation. We prove GONO matches Adam's O(1/sqrt(T)) convergence rate and reduces exactly to Adam when the signal is uninformative. Empirically, cc_t achieves oscillation detection with F1=1.00 (vs. 0.45 for gradient norm), and GONO remains competitive with AdamW on MNIST (98.15%), CIFAR‑10 (43.14%), and ResNet‑18 (75.44%), establishing directional alignment as a theoretically grounded, practically actionable optimization signal. Code: https://github.com/victordaniel/gono‑optimizer

PaperId: 1246, https://arxiv.org/pdf/2605.06548.pdf GitHub

Abstract:
Large language models have achieved remarkable success under the autoregressive paradigm, yet high‑quality text generation need not be tied to a fixed left‑to‑right order. Existing alternatives still struggle to jointly achieve generation efficiency, scalable representation learning, and effective global semantic modeling. We propose Cola DLM, a hierarchical latent diffusion language model that frames text generation through hierarchical information decomposition. Cola DLM first learns a stable text‑to‑latent mapping with a Text VAE, then models a global semantic prior in continuous latent space with a block‑causal DiT, and finally generates text through conditional decoding. From a unified Markov‑path perspective, its diffusion process performs latent prior transport rather than token‑level observation recovery, thereby separating global semantic organization from local textual realization. This design yields a more flexible non‑autoregressive inductive bias, supports semantic compression and prior fitting in continuous space, and naturally extends to other continuous modalities. Through experiments spanning 4 research questions, 8 benchmarks, strictly matched ~2B‑parameter autoregressive and LLaDA baselines, and scaling curves up to about 2000 EFLOPs, we identify an effective overall configuration of Cola DLM and verify its strong scaling behavior for text generation. Taken together, the results establish hierarchical continuous latent prior modeling as a principled alternative to strictly token‑level language modeling, where generation quality and scaling behavior may better reflect model capability than likelihood, while also suggesting a concrete path toward unified modeling across discrete text and continuous modalities.

PaperId: 1247, https://arxiv.org/pdf/2605.06535.pdf GitHub GitHub

Abstract:
In recent years, open‑source efforts like Senorita‑2M have propelled video editing toward natural language instruction. However, current publicly available datasets predominantly focus on local editing or style transfer, which largely preserve the original scene structure and are easier to scale. In contrast, Background Replacement, a task central to creative applications such as film production and advertising, requires synthesizing entirely new, temporally consistent scenes while maintaining accurate foreground‑background interactions, making large‑scale data generation significantly more challenging. Consequently, this complex task remains largely underexplored due to a scarcity of high‑quality training data. This gap is evident in poorly performing state‑of‑the‑art models, e.g., Kiwi‑Edit, because the primary open‑source dataset that contains this task, i.e., OpenVE‑3M, frequently produces static, unnatural backgrounds. In this paper, we trace this quality degradation to a lack of precise background guidance during data synthesis. Accordingly, we design a scalable pipeline that generates foreground and background guidance in a decoupled manner with strict quality filtering. Building on this pipeline, we introduce Sparkle, a dataset of ~140K video pairs spanning five common background‑change themes, alongside Sparkle‑Bench, the largest evaluation benchmark tailored for background replacement to date. Experiments demonstrate that our dataset and the model trained on it achieve substantially better performance than all existing baselines on both OpenVE‑Bench and Sparkle‑Bench. Our proposed dataset, benchmark, and model are fully open‑sourced at https://showlab.github.io/Sparkle/.

PaperId: 1248, https://arxiv.org/pdf/2605.06530.pdf GitHub

Abstract:
Accurate epidemic forecasting is crucial for public health response, resource allocation, and outbreak intervention, but remains difficult with sparse, noisy, and highly non‑stationary data. Because epidemics unfold across interacting regions, spatiotemporal methods are natural candidates for improving forecasts. Despite growing interest in spatial information, no standardized benchmark exists, and current evaluations often use simple chronological train‑test splits that do not reflect real‑time forecasting practice. We address this gap with SpatialEpiBench, a challenging benchmark for spatiotemporal epidemic forecasting in realistic public‑health settings. SpatialEpiBench includes 11 epidemic datasets with standardized rolling evaluations and outbreak‑specific metrics. We evaluate adjacency‑informed forecasting models with widely used epidemic priors that adapt general models to epidemiology, but find that most methods underperform a simple last‑value baseline from 1 day to 1 month ahead, even during outbreaks and with these priors. We identify three major failure modes: (1) poor outbreak anticipation, (2) difficulty handling sparsity and noise, and (3) limited utility of common geographic adjacency for epidemiological spatial information. We release benchmark data, code, and instructions at https://github.com/Rachel‑Lyu/SpatialEpiBench to support development of operationally useful epidemic forecasting models.

PaperId: 1249, https://arxiv.org/pdf/2605.06510.pdf GitHub

Abstract:
Transformer‑based tabular foundation models (TFMs) dominate small to medium tabular predictive benchmark tasks, yet their inference mechanisms remain largely unexplored. We present the first large‑scale mechanistic study of layerwise dynamics in 6 state‑of‑the‑art tabular in‑context learning models. We explore how predictions emerge across depth, identify distinct stages of inference and reveal latent‑space dynamics that differ from those of language models. Our findings indicate substantial depthwise redundancy across multiple models, suggesting iterative refinement with overlapping computations during inference stages. Guided by these insights, we design a proof‑of‑concept, looped single‑layer model that uses only 20% of the original model's parameters while achieving comparable performance. The code is available at https://github.com/amirbalef/is_one_layer_enough.

PaperId: 1250, https://arxiv.org/pdf/2605.06508.pdf GitHub

Abstract:
Research artifacts are widely shared to support reproducibility, and artifact evaluation (AE) has become common at many leading conferences. However, AE mainly checks whether artifacts work as claimed and can be reproduced. It largely overlooks potential security risks. Since these artifacts are publicly released and reused, they may unintentionally create opportunities for misuse and raise concerns about safe and responsible sharing. We study 509 research artifacts from top‑tier security venues and find that many contain insecure code patterns that may introduce potential attack vectors. We propose a taxonomy for context‑aware security assessment to enable structured analysis of such risks. We perform static analysis and examine the resulting findings, filtering false positives and identifying real security risks. Our analysis shows that 41.60% of the prevalent findings may pose security concerns under practical usage. To support scalable analysis, we introduce SAFE (Security‑Aware Framework for Artifact Evaluation), a first step toward an autonomous framework that analyzes tool‑reported findings by considering code semantics, execution context, and practical exploitability. SAFE achieves 84.80% accuracy and 84.63% F1‑score in distinguishing security and non‑security risks. Overall, our results show that security is also important in AE for promoting safe and responsible research sharing. The source code is available at: https://github.com/nanda‑rani/SAFE

PaperId: 1251, https://arxiv.org/pdf/2605.06407.pdf GitHub

Abstract:
Integrating speech understanding and generation is a pivotal step toward building unified speech models. However, the different representations required for these two tasks currently pose significant compatibility challenges. Typically, semantics‑oriented features are learned from self‑supervised learning (SSL), and acoustic‑oriented features from reconstruction. Such fragmented representations hinder the realization of truly unified speech systems. We present WavCube, a compact continuous latent derived from an SSL speech encoder that simultaneously supports speech understanding, reconstruction, and generation. WavCube employs a two‑stage training scheme. Stage 1 trains a semantic bottleneck to filter off‑manifold redundancy that makes raw SSL features intractable for diffusion. Stage 2 injects fine‑grained acoustic details via end‑to‑end reconstruction, while a semantic anchoring loss ensures the representation remains grounded within its original semantic manifold. Comprehensive experiments show that WavCube closely approaches WavLM performance on SUPERB despite an 8x dimensional compression, attains reconstruction quality on par with existing acoustic representations, delivers state‑of‑the‑art zero‑shot TTS performance with markedly faster training convergence, and excels in speech enhancement, separation, and voice conversion tasks on the SUPERB‑SG benchmark. Systematic ablations reveal that WavCube's two‑stage recipe resolves two intrinsic flaws of SSL features for generative modeling, paving the way for future unified speech systems. Codes and checkpoints are available at https://github.com/yanghaha0908/WavCube.

PaperId: 1252, https://arxiv.org/pdf/2605.06376.pdf GitHub

Abstract:
Step distillation has become a leading technique for accelerating diffusion models, among which Distribution Matching Distillation (DMD) and Consistency Distillation are two representative paradigms. While consistency methods enforce self‑consistency along the full PF‑ODE trajectory to steer it toward the clean data manifold, vanilla DMD relies on sparse supervision at a few predefined discrete timesteps. This restricted discrete‑time formulation and mode‑seeking nature of the reverse KL divergence tends to exhibit visual artifacts and over‑smoothed outputs, often necessitating complex auxiliary modules ‑‑ such as GANs or reward models ‑‑ to restore visual fidelity. In this work, we introduce Continuous‑Time Distribution Matching (CDM), migrating the DMD framework from discrete anchoring to continuous optimization for the first time. CDM achieves this through two continuous‑time designs. First, we replace the fixed discrete schedule with a dynamic continuous schedule of random length, so that distribution matching is enforced at arbitrary points along sampling trajectories rather than only at a few fixed anchors. Second, we propose a continuous‑time alignment objective that performs active off‑trajectory matching on latents extrapolated via the student's velocity field, improving generalization and preserving fine visual details. Extensive experiments on different architectures, including SD3‑Medium and Longcat‑Image, demonstrate that CDM provides highly competitive visual fidelity for few‑step image generation without relying on complex auxiliary objectives. Code is available at https://github.com/byliutao/cdm.

PaperId: 1253, https://arxiv.org/pdf/2605.06371.pdf GitHub

Abstract:
Multimodalpersonalityunderstandingplaysacriticalroleinhuman centered artificial intelligence. Previous work mainly focus on learn‑ing rich multimodal representations for video personality under standing. However, they often suffer from potential harm caused by subject bias (e.g., observable age and unobservable mental states), as subjects originate from diverse demographic backgrounds. Learn ing such spurious associations between multimodal features and traits may lead to unfair personality understanding. In this work, weconstruct aStructural Causal Model (SCM)toanalyze theimpact of these biases from a causal perspective, and propose a novel Dual Causal Adjustment Network (DCAN) to mitigate the interference of subject attributes on personality understanding. Specifically, we design a Back‑door Adjustment Causal Learning (BACL) module to block spurious correlations from observable demographic factors via a prototype‑based confounder dictionary, and subsequently ap ply a Front‑door Adjustment Causal Learning (FACL) module to ad dress latent and unobservable biases throughalearnedmediatordic tionary intervention, thereby achieving causal disentanglement of representations for deconfounded reasoning. Importantly, we con struct a Demographic‑annotated Multimodal Student Personality (DMSP) dataset to support the analysis and discussion of fairness related factors. Extensive experiments on the benchmark dataset CFI‑V2 and our DMSPdataset demonstrate that DCAN consistently improves prediction accuracy, reaching 92.11% and 92.90%, respec tively. Meanwhile, the improvementsinthefairnessmetricsofequal opportunity and demographic parity are 6.57% and 7.97% on CFI‑V2, and 15.38% and 20.06% on the DMSP dataset. Our code and DMSP dataset are available at https://github.com/Sabrina‑han/DCAN

PaperId: 1254, https://arxiv.org/pdf/2605.06345.pdf GitHub

Abstract:
AI research agents have shown strong potential in automating literature search and manuscript refinement, yet most assume a clear and actionable initial input, operating only after a research question has been made explicit. In contrast, human research often begins with tacit friction, a sense of misalignment before a question can be formed. We introduce InciteResearch, a multi‑agent framework designed to make a researcher's implicit understanding explicit, inspectable, and actionable. InciteResearch decomposes the logical chain of Socratic questioning and distributes it across the entire pipeline that: (1) Elicits a structured five‑dimensional researcher profile state anchored by specific friction points from vague, even domain‑unrelated inputs; (2) Violates hidden assumptions by maximizing the feasibility‑novelty product with enforcing a 7‑stage causal derivation trace; and (3) check whether the proposed method is a Necessary consequence of the reframed insight. We further introduce TF‑Bench, the first benchmark for tacit‑to‑explicit research assistance that distinguishes domain‑related from domain‑unrelated inspirations across four scientific modes. On TF‑Bench, InciteResearch achieves leapfrogging gains over a prompt‑based baseline (novelty/impact from 3.671/3.806 to 4.250/4.397), shifting generated proposals from recombination to architectural insight. Our work demonstrates that AI can serve as an extension of thinking itself, rather than merely automating downstream execution.

PaperId: 1255, https://arxiv.org/pdf/2605.06341.pdf GitHub

Abstract:
Many real‑world optimization problems consist of multiple tightly coupled subproblems whose solutions must be coordinated to achieve high overall performance. However, existing large language model driven automated heuristic design approaches are limited to single‑problem settings. In this paper, we propose CoupleEvo. CoupleEvo proposes three evolutionary coordination strategies to evolve heuristics for coupled optimization problems: the sequential strategy evolves heuristics for one subproblem after the other; the iterative strategy alternates the evolution of heuristics for different subproblems over successive generations; and the integrated strategy evolves heuristics for all problems simultaneously. The approach is evaluated on two representative coupled optimization problems. Experimental results show that decomposition‑based strategies (sequential and iterative) provide more stable convergence and higher solution quality, while the integrated evolution strategy suffers from increased search complexity and variability. These findings highlight the importance of coordinating evolutionary search across interdependent subproblems and demonstrate the potential of LLM‑driven heuristic design for complex coupled optimization problems. The code is available: https://github.com/tb‑git‑kit‑research/CoupleEvo.

PaperId: 1256, https://arxiv.org/pdf/2605.06339.pdf GitHub

Abstract:
Deployed language and vision‑language models must decide, on each input, whether to answer directly, retrieve evidence, defer to a stronger model, or abstain. Contrary to the common monotonicity intuition, greater per‑input expressivity is not uniformly beneficial in finite samples: under identical strict cross‑validation, different benchmarks prefer different controller classes. This reflects a finite‑sample limitation of instance‑level uncertainty signals, which can be exhausted at a distribution‑dependent scale. We organize controllers into a nested lattice of four classes: fixed actions, partition routers, instance‑level controllers, and prior‑gated controllers, ordered by complexity. We prove a regime theory that turns three data‑estimable bottlenecks into a class choice: how much improvement is possible beyond the best fixed action, whether there are enough samples for instance‑level controllers to make reliable decisions, and how much improvement a coarse partition router can recover when instance‑level signal is unreliable. The resulting Bernstein‑tight threshold has a matching information‑theoretic lower bound, and strict nested cross‑validation provably selects a near‑best class. Across SMS‑Spam, HallusionBench, A‑OKVQA, and FOLIO, the predicted class matches the empirical winner; the prior‑gated controller wins on TextVQA when OCR tokens supply a label‑free prediction‑time prior. Code is available at https://github.com/Anonymous‑Awesome‑Submissions/Regime‑Theory.

PaperId: 1257, https://arxiv.org/pdf/2605.06333.pdf GitHub

Abstract:
Cocoa (Theobroma cacao) is a critical cash crop for millions of smallholder farmers in West Africa, where Cocoa Swollen Shoot Virus Disease (CSSVD) and anthracnose cause devastating yield losses. Automated disease detection from leaf images is essential for early intervention, yet deploying such systems in resource‑constrained settings demands models that are small, fast, and require no internet connectivity. Existing edge‑deployable plant disease systems rely on end‑to‑end deep learning without uncertainty quantification, while Bayesian methods for edge devices focus on hardware‑level inference architectures rather than agricultural applications. We bridge this gap with TinyBayes, the first framework to combine a closed‑form Bayesian classifier with a mobile‑grade computer vision pipeline for crop disease detection. Our pipeline uses YOLOv8‑Nano (5.9 MB) for lesion localisation, MobileNetV3‑Small (3.5 MB) for feature extraction, and the Jacobi prior; a Bayesian method that provides a closed form non‑iterative estimators via projection, for the classification. The Jacobi‑DMR (Distributed Multinomial Regression) classifier adds only 13.5 KB to the pipeline, bringing the total model size within 9.5 MB, while achieving 78.7% accuracy on the Amini Cocoa Contamination Challenge dataset and enabling end‑to‑end CPU inference under 150 ms per image. We benchmark against seven classifiers including Random Forest, SVM, Ridge, Lasso, Elastic Net, XGBoost, and Jacobi‑GP, and demonstrate that the Jacobi‑DMR offers the best trade‑off between accuracy, model size, and inference speed for edge deployment. We have proved the asymptotic equivalence and consistency, asymptotic normality and the bias correction of Jacobi‑DMR. All data and codes are available here: https://github.com/shouvik‑sardar/TinyBayes

PaperId: 1258, https://arxiv.org/pdf/2605.06279.pdf GitHub

Abstract:
Large language models (LLMs) are now largely involved in software development workflows, and the code they generate routinely includes third‑party library (TPL) imports annotated with specific version identifiers. These version choices can carry security and compatibility risks, yet they have not been systematically studied. We present the first large‑scale measurement study of version‑level risk in LLM‑generated Python code, evaluating 10 LLMs on PinTrace, a curated benchmark of 1,000 Stack Overflow programming tasks. LLMs tend to specify version identifiers when directly prompted at 26.83%‑95.18%, while down to 6.45%‑59.19% in creating a manifest file directly. Among the specified versions, 36.70%‑55.70% of tasks contain at least one known CVE, and 62.75%‑74.51% of them carry Critical or High severity ratings. In 72.27%‑91.37% of cases, the associated CVEs were publicly disclosed before the model's knowledge cutoff. The statistics show all models converge on the same small set of risky release versions, indicating a systemic bias rather than isolated model error. Static compatibility rates range from 19.70% to 63.20%, with installation failure as the dominant cause. The dynamic test cases confirm the pattern by 6.49%‑48.62% pass rates. Further experiments confirm that these failures are attributable to version selection rather than code quality, and that externally anchored version constraints substantially reduce both vulnerability exposure and compatibility failures. Our findings reveal LLM version selection as a first‑class, previously overlooked risk surface in LLM‑based development. We disclosed these findings to the community of the evaluated models, and several confirmed the issue. All the code and dataset have been released for open science at https://github.com/dw763j/PinTrace.

PaperId: 1259, https://arxiv.org/pdf/2605.06238.pdf GitHub

Abstract:
Multimodal recommender systems exploit visual and textual signals to alleviate data sparsity, but this also makes them more vulnerable to evasion‑based promotion attacks. Existing defenses are largely limited to single‑modal settings and mainly focus on poisoning‑based threats, leaving evasion‑based threats underexplored. In this work, we first identify a cross‑modal gradient mismatch under the multi‑user promotion setting, where visual and textual perturbations are optimized in inconsistent directions due to the dominance of distinct user groups. This phenomenon dilutes the attack effectiveness and leads robust training to underestimate worst‑case risks. To address this issue, we propose Untargeted Adversarial Training with Multimodal Coordination (UAT‑MC). UAT‑MC tackles the challenge of unknown targeted items in evasion‑based attacks (as opposed to poisoning‑based attacks) by treating all items as potential targets, and introduces a gradient alignment mechanism to explicitly correct this mismatch. This design ensures synchronized perturbations across modalities, thereby maximizing adversarial strength for robust training. Extensive experiments demonstrate that UAT‑MC significantly improves robustness against promotion attacks while maintaining acceptable recommendation performance under the defense‑accuracy trade‑off. Code is available at https://github.com/gmXian/UAT‑MC.

PaperId: 1260, https://arxiv.org/pdf/2605.06223.pdf GitHub GitHub GitHub

Abstract:
Natural‑language instance navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical agent should reduce the user's burden by actively asking only the information needed to distinguish the target from similar distractors, rather than requiring a detailed description upfront. Existing approaches often fall short of this goal: they may stop at the first plausible candidate before sufficiently exploring alternatives, or, even after collecting multiple candidates, ask about the target's attributes derived from individual candidates rather than questions selected to distinguish candidates in the pool. As a result, despite the dialogue, the agent may still fail to distinguish the target from distractors, leading to premature decisions and lengthy user responses. We propose Proactive Instance Navigation with Comparative Judgment (ProCompNav), a two‑stage framework that first constructs a candidate pool and then identifies the target through comparative judgment. At each round, ProCompNav extracts an attribute‑value pair that splits the current pool, asks a binary yes/no question, and prunes all inconsistent candidates at once. This reframes disambiguation from open‑ended target description to pool‑level discriminative questioning, where each question is chosen to narrow the candidate set. On CoIN‑Bench, ProCompNav improves Success Rate over interactive baselines with the same minimal input and non‑interactive baselines with detailed descriptions, while substantially reducing Response Length. ProCompNav also achieves state‑of‑the‑art Success Rate on TextNav, suggesting that comparative judgment is broadly useful for instance‑level navigation among similar distractors. Code is available at https://github.com/tree‑jhk/procompnav.

PaperId: 1261, https://arxiv.org/pdf/2605.06177.pdf GitHub

Abstract:
Building a deep research agent today is an exercise in glue code: the same backbone evaluated on the same benchmark can report different accuracies in different papers because harness and tool registry all differ, and integrating a new foundation model into a comparable evaluation surface costs weeks of model‑specific engineering. We call this the per‑paper engineering tax and release BioMedArena, an open‑source toolkit that not only alleviates it but also provides an arena for fair comparison of different foundation models when evaluating them as deep‑research agents. BioMedArena decouples six layers of biomedical agent evaluation ‑‑ benchmark loading, tool exposure, tool selection, execution mode, context management, and scoring ‑‑ and exposes 147 biomedical benchmarks and 75 biomedical tools across 9 functional families. Adding a new model, benchmark, or tool reduces to registering a few‑line provider adapter. We further provide 6 agent harnesses with 6 context‑management strategies, which provide 12 backbones with competitive research capabilities and significantly improved performance, achieving state‑of‑the‑art (SOTA) results on 8 representative biomedical benchmarks, with an average lift of +15.03 percentage points over prior SOTA. The toolkit, configurations, and per‑task traces are available at https://github.com/AI‑in‑Health/BioMedArena

PaperId: 1262, https://arxiv.org/pdf/2605.06112.pdf GitHub

Abstract:
Despite significant progress, RGB‑based trackers remain vulnerable to challenging imaging conditions, such as low illumination and fast motion. Event cameras offer a promising alternative by asynchronously capturing pixel‑wise brightness changes, providing high dynamic range and high temporal resolution. However, existing event‑based trackers often neglect the intrinsic spatial sparsity and temporal density of event data, while relying on a single fixed temporal‑window sampling strategy that is suboptimal under varying motion dynamics. In this paper, we propose an event sparsity‑aware tracking framework that explicitly models event‑density variations across multiple temporal scales. Specifically, the proposed framework progressively injects sparse, medium‑density, and dense event search regions into a three‑stage Vision Transformer backbone, enabling hierarchical multi‑density feature learning. Furthermore, we introduce a sparsity‑aware Mixture‑of‑Experts module to encourage expert specialization under different sparsity patterns, and design a dynamic pondering strategy to adaptively adjust the inference depth according to tracking difficulty. Extensive experiments on FE240hz, COESOT, and EventVOT demonstrate that the proposed approach achieves a favorable trade‑off between tracking accuracy and computational efficiency. The source code will be released on https://github.com/Event‑AHU/OpenEvTracking.

PaperId: 1263, https://arxiv.org/pdf/2605.06078.pdf GitHub

Abstract:
While long‑horizon agentic tasks require language agents to perform dozens of sequential decisions, training such agents with reinforcement learning remains challenging. We identify two root causes: credit misattribution, where correct early actions are penalized due to terminal failures, and sample inefficiency, where scarce successful trajectories result in near‑total loss of learning signal. We introduce a milestone‑guided policy learning framework, BEACON, that leverages the compositional structure of long‑horizon tasks to ensure precise credit assignment. BEACON partitions trajectories at milestone boundaries, applies temporal reward shaping within segments to credit partial progress, and estimates advantages at dual scales to prevent distant failures from corrupting the evaluation of local actions. On ALFWorld, WebShop, and ScienceWorld, BEACON consistently outperforms GRPO and GiGPO. Notably, on long‑horizon ALFWorld tasks, BEACON achieves 92.9% success rate, nearly doubling GRPO's 53.5%, while improving effective sample utilization from 23.7% to 82.0%. These results establish milestone‑anchored credit assignment as an effective paradigm for training long‑horizon language agents. Code is available at https://github.com/ZJU‑REAL/BEACON.

PaperId: 1264, https://arxiv.org/pdf/2605.06068.pdf GitHub

Abstract:
For years, we have built LLM serving systems like any other critical infrastructure: a single general‑purpose stack, hand‑tuned over many engineer‑years, meant to support every model and workload. In this paper, we take the opposite bet: a multi‑agent loop that automatically synthesizes bespoke serving systems for different usage scenarios. We propose VibeServe, the first agentic loop that generates entire LLM serving stacks end‑to‑end. VibeServe uses an outer loop to plan and track the search over system designs, and an inner loop to implement candidates, check correctness, and measure performance on the target benchmark. In the standard deployment setting, where existing stacks are highly optimized, VibeServe remains competitive with vLLM, showing that generation‑time specialization need not come at the cost of performance. More interestingly, in non‑standard scenarios, VibeServe outperforms existing systems by exploiting opportunities that generic systems miss in six scenarios involving non‑standard model architectures, workload knowledge, and hardware‑specific optimizations. Together, these results suggest a different point in the design space for infrastructure software: generation‑time specialization rather than runtime generality. Code is available at https://github.com/uw‑syfi/vibe‑serve.

PaperId: 1265, https://arxiv.org/pdf/2605.06067.pdf GitHub

Abstract:
Training large language models at 4‑bit precision is critical for efficiency. We show that nGPT, an architecture that constrains weights and hidden representations to the unit hypersphere, is inherently more robust to low‑precision arithmetic. This removes the need for interventions‑such as applying random Hadamard transforms and performing per‑tensor scaling calculations‑to preserve model quality, and it enables stable end‑to‑end NVFP4 training. We validate this approach on both a 1.2B dense model and hybrid (Mamba‑Transformer) MoE models of up to 3B/30B parameters. We trace this robustness to the dot product: while quantization noise remains largely uncorrelated in both standard and normalized architectures, the signal behaves differently. In nGPT, the hypersphere constraint enhances weak positive correlations among the element‑wise products, leading to a constructive accumulation of the signal across the hidden dimension while the noise continues to average out. This yields a higher effective signal‑to‑noise ratio and a flatter loss landscape, with the effect strengthening as the hidden dimension grows, suggesting increasing advantages at scale. A reference implementation is available at https://github.com/anonymous452026/ngpt‑nvfp4

PaperId: 1266, https://arxiv.org/pdf/2605.06032.pdf GitHub GitHub

Abstract:
Synthetic data has transformed language model training, yet its role in time series forecasting remains poorly understood. We present a large‑scale empirical study: nine experiment groups, 4,218 runs systematically evaluating synthetic time series augmentation across five architectures, four synthetic signals and seven datasets. The effect is sharply architecture‑conditional: channel‑mixing models (TimesNet, iTransformer) benefit in the majority of trials, while channel‑independent models (DLinear, PatchTST) are consistently degraded. In selected low‑resource settings the gains are striking: TimesNet trained on only 10% of Weather data with synthetic augmentation surpasses the full‑data baseline (4 of 16 sparsity‑dataset combinations). Averaged across all architectures, augmentation hurts in 67% of trials. We further find that only the Seasonal‑Trend generator reliably helps across the tested benchmarks, and that hard curriculum switching is actively harmful (+24% MSE degradation). These results provide concrete, actionable guidelines on how to use synthetic data: use synthetic augmentation with channel‑mixing architectures, use gradual annealing schedules, and treat low‑resource augmentation as architecture‑ and dataset‑dependent. Code is available at \hrefhttps://github.com/hugoiscracked/synthetic‑ts/tree/main

PaperId: 1267, https://arxiv.org/pdf/2605.06012.pdf GitHub

Abstract:
Vehicle Re‑identification (Re‑ID) aims to retrieve the most similar image to a given query from images captured by non‑overlapping cameras. Extending vehicle Re‑ID from image‑only queries to text‑based queries enables retrieval in real‑world scenarios where only a witness description of the target vehicle is available. In this paper, we propose PFCVR, a Part‑level Fine‑grained Cross‑modal Vehicle Retrieval model for text‑to‑image vehicle re‑identification. PFCVR constructs locally paired images and texts at the part level and introduces learnable part‑query tokens that aggregate both part‑specific and full‑sentence context before aligning with visual part features. On top of this explicit local alignment, a bi‑directional mask recovery module lets each modality reconstruct its masked content under the guidance of the other, implicitly bridging local correspondences into global feature alignment. Furthermore, we construct a new large‑scale dataset called T2I‑VeRW, which contains 14,668 images covering 1,796 vehicle identities with fine‑grained part‑level annotations. Experimental results on the T2I‑VeRI dataset show that PFCVR achieves 29.2% Rank‑1 accuracy, improving over the best competing method by +3.7% percentage points. On the newly proposed T2I‑VeRW benchmark, PFCVR achieves 55.2% Rank‑1 accuracy, outperforming a comprehensive set of recent state‑of‑the‑art methods. Source code will be released on https://github.com/Event‑AHU/Neuromorphic_ReID

PaperId: 1268, https://arxiv.org/pdf/2605.05951.pdf GitHub

Abstract:
World models enable model‑based planning through learned latent dynamics, but imagined rollouts become unstable as the planning horizon grows or the dynamics distribution shifts. We argue that this instability reflects two missing structures in planner‑facing latents: history‑conditioned memory for approximate Markov completeness, and geometric organization that separates configuration, momentum, and task semantics. We propose HaM‑World (HMW), a structured world model that decomposes the latent state into a canonical (q, p) subspace and a context subspace c, while using Mamba selective state‑space memory as the history‑conditioned input to the same latent dynamics. Within this interface, (q, p) evolves through an energy‑derived Hamiltonian vector field plus learnable residual/control dynamics, while c captures semantic, dissipative, and non‑conservative factors. This gives the planner a single latent state shared by dynamics prediction, reward/value estimation, imagined rollouts, and CEM action search. On four DeepMind Control Suite tasks, HaM‑World reaches the highest Avg. AUC (117.9, +9.5%), reduces long‑horizon rollout error to 45% of a strong baseline model, and wins 11/12 k in 3,5,7 MSE cells. Under 12 OOD perturbations spanning dynamics shifts, action delay, and observation masking, HaM‑World achieves the highest return in every condition, with average OOD‑return gains of 10.2% on Finger Spin and 13.6% on Reacher Easy. Mechanism diagnostics further show bounded action‑free Hamiltonian‑energy drift, structured energy variation under policy rollouts, and coherent control‑induced energy transfer, supporting the intended Soft‑Hamiltonian dynamics design.

PaperId: 1269, https://arxiv.org/pdf/2605.05893.pdf GitHub

Abstract:
Verifiers are crucial components for enhancing modern LLMs' reasoning capability. Typicalverifiers require resource‑intensive superviseddataset construction, which is costly and faceslimitations in data diversity. In this paper, wepropose LOVER, an unsupervised verifier regularized by logical rules. LOVER treats theverifier as a binary latent variable, utilizinginternal activations and enforcing three logical constraints on multiple reasoning paths:negation consistency, intra‑group consistency,and inter‑group consistency (grouped by thefinal answer). By incorporating logical rulesas priors, LOVER can leverage unlabeled examples and is directly compatible with any offthe‑shelf LLMs. Experiments on 10 datasetsdemonstrate that LOVER significantly outperforms unsupervised baselines, achieving performance comparable to the supervised verifier(reaching its 95% level on average). The sourcecode is publicly available at https://github.com/wangxinyufighting/llm‑lover.

PaperId: 1270, https://arxiv.org/pdf/2605.05889.pdf GitHub

Abstract:
Diffusion‑based image‑to‑image (I2I) translation excels in high‑fidelity generation but suffers from slow sampling in state‑of‑the‑art Diffusion Bridge Models (DBMs), often requiring dozens of function evaluations (NFEs). We introduce DBMSolver, a training‑free sampler that exploits the semi‑linear structure of DBM's underlying SDE and ODE via exponential integrators, yielding highly‑efficient 1st‑ and 2nd‑order solutions. This reduces NFEs by up to 5x while boosting quality (e.g., FID drops 53% on DIODE at 20 NFEs vs. 2nd‑order baseline). Experiments on inpainting, stylization, and semantics‑to‑image tasks across resolutions up to 256x256 show DBMSolver sets new SOTA efficiency‑quality tradeoffs, enabling real‑world applicability. Our code is publicly available at https://github.com/snumprlab/dbmsolver.

PaperId: 1271, https://arxiv.org/pdf/2605.05866.pdf GitHub

Abstract:
Multiphase powder X‑ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real‑world synthesis often produces complex mixtures whose constituent phases (components) cannot be reliably disentangled. While recent advances in representation‑based crystal retrieval and generation suggest the possibility of inferring structures directly from PXRD, existing approaches largely assume single‑phase inputs and break down in multiphase settings. Here, we present XDecomposer, a prior‑free framework for joint decomposition and identification of multiphase XRD patterns without requiring candidate phase lists, structural templates, or prior knowledge of phase number. We formulate multiphase diffraction analysis as a set prediction problem, where the model infers an unordered set of phase‑resolved components, their mixture proportions, and corresponding structural representations within a unified architecture. A phase‑query‑driven decomposition mechanism, together with diffraction‑consistent physical reconstruction, enables accurate source separation while preserving crystallographic fidelity. Extensive experiments on both simulated and experimental datasets show that XDecomposer substantially improves reconstruction accuracy and phase identification across diverse chemical systems, while maintaining strong generalization to unseen mixtures. These results provide a practical route toward data‑driven, source‑resolved multiphase XRD analysis and reduce long‑standing dependence on prior‑guided iteratively phase matching. The code is openly available at https://github.com/Licht0812/XDecomposer

PaperId: 1272, https://arxiv.org/pdf/2605.05854.pdf GitHub GitHub

Abstract:
Air‑quality forecasting models are commonly evaluated on regional, preprocessed, and normalized datasets, where missing observations are removed or artificially completed. Such protocols simplify comparison but hide the conditions that dominate real monitoring networks: uneven global coverage, structured missingness, heterogeneous pollutant scales, and deployment cost. We introduce AirQualityBench, a global multi‑pollutant benchmark designed to evaluate forecasting models under these realistic conditions. The benchmark contains hourly observations from 3,720 monitoring stations over 2021‑‑2025, covers six major pollutants, and preserves provider‑native observation masks. Rather than imputing a dense data tensor, AirQualityBench exposes missingness as part of the forecasting problem and reports errors on valid future observations after inverse transformation to physical concentration scales. Evaluating representative spatio‑temporal models under this unified protocol shows that strong performance on sanitized datasets does not reliably transfer to global, fragmented monitoring streams. AirQualityBench therefore serves as a realistic testbed for scalable, mask‑aware, and physically interpretable air‑quality forecasting. All benchmark data, code, evaluation scripts, and baseline implementations are available at \hrefhttps://github.com/Star‑Learning/AirQualityBenchGitHub.

PaperId: 1273, https://arxiv.org/pdf/2605.05833.pdf GitHub GitHub

Abstract:
Auto‑bidding is a crucial task in real‑time advertising markets, where policies must optimize long‑horizon value under delivery constraints (e.g., budget and CPA). Existing methods for auto‑bidding rely on compact numerical state representations: while they can implicitly capture delivery dynamics, they offer limited support for explicitly representing and controlling high‑level intent, evolving feedback, and operator‑style strategic guidance in real campaigns. Meanwhile, Large Language Models (LLMs) offer a powerful method for encoding semantic information, it remains unclear when LLMs help and how to integrate them without sacrificing numerical precision. Through systematic preliminary studies, we find that (1) LLM embeddings contain bidding‑relevant cues yet cannot replace numerical features, and (2) gains emerge only with careful semantic‑‑numeric integration rather than naive concatenation. Motivated by these findings, we propose SemBid, a novel auto‑bidding framework that injects LLM‑encoded semantics into offline bidding trajectories at the token level. SemBid introduces three semantic inputs: Task, History, and Strategy. It injects these semantics as tokens alongside numerical trajectory tokens and uses self‑attention to integrate them, improving controllability and generalization across objectives. Across diverse scenarios and budget regimes, SemBid outperforms competitive baselines from offline RL and generative sequence modeling, with more consistent gains in overall performance, constraint satisfaction, and robustness. Our code is available at: \hrefhttps://github.com/AlanYu04/SemBid‑KDD2026\textcolorbluehere.

PaperId: 1274, https://arxiv.org/pdf/2605.05818.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems grow more complex and LLMs exhibit stronger instruction‑following capabilities, existing studies fall short of systematically assessing RAG leakage risks. We present LeakDojo, a configurable framework for controlled evaluation of RAG leakage. Using LeakDojo, we benchmark six existing attacks across fourteen LLMs, four datasets, and diverse RAG systems. Our study reveals that (1) query generation and adversarial instructions contribute independently to leakage, with overall leakage well approximated by their product; (2) stronger instruction‑following capability correlates with higher leakage risk; and (3) improvements in RAG faithfulness can introduce increased leakage risk. These findings provide actionable insights for understanding and mitigating RAG leakage in practice. Our codebase is available at https://github.com/yeasen‑z/LeakDojo.

PaperId: 1275, https://arxiv.org/pdf/2605.05776.pdf GitHub GitHub

Abstract:
Domain Incremental Learning is a critical scenario that requires models to continuously adapt to new data domains without retraining. However, domain shifts often cause severe performance degradation. To address this, we propose Hybrid Energy‑Distance Prompt, a domain‑incremental framework inspired by Helmholtz free energy. HEDP introduces an energy regularization loss to enhance the separability of domain representations and a hybrid energy‑distance weighted mechanism that fuses energy‑based and distance‑based cues to improve domain selection and generalization. Experiments on multiple benchmarks, including CORe50, show that HEDP achieves superior performance on unseen domains with a 2.57% accuracy gain, effectively mitigating catastrophic forgetting and enhancing open‑world adaptability. Our code is \hrefhttps://github.com/dannis97500/HEDP/available here.

PaperId: 1276, https://arxiv.org/pdf/2605.05738.pdf GitHub

Abstract:
In recent years, the integration of non‑topological space modeling with temporal learning methods has emerged as an effective approach for capturing spatio‑temporal information in non‑Euclidean graphs. However, most existing methods rely on static underlying graph structures, which are inadequate for capturing the continuously expanding and evolving patterns in streaming traffic networks. To address this challenge, we propose a simple yet efficient dual‑branch continual learning framework for traffic prediction, named CoMemNet. The fast‑converging Online branch undertakes the primary prediction tasks, while the momentum‑updated Target branch extracts historical information using Wasserstein Distance features to create a Dynamic Contrastive Sampler (DC Sampler). This sampler selects a node set with significant dynamic network feature changes for training, effectively mitigating the issue of catastrophic forgetting. Additionally, the backbone incorporates a lightweight Node‑Adaptive Temporal Memory Buffer (TMRB‑N) to consolidate old knowledge through memory replay and address the risk of memory explosion. Finally, we provide two newly curated open‑source datasets. Experimental results demonstrate that CoMemNet achieves state‑of‑the‑art (SOTA) performance across all three large‑scale real‑world datasets. The code is available at: https://github.com/meiwu5/CoMemNet.

PaperId: 1277, https://arxiv.org/pdf/2605.05704.pdf GitHub

Abstract:
Recent advances in foundation models have transformed LLMs from passive conversational systems into autonomous agents capable of reasoning and tool execution. While these capabilities unlock substantial practical value, they also introduce new security risks, as adversaries can manipulate agents into performing harmful actions in real‑world environments. Existing defense strategies mitigate such threats but frequently struggle to balance safety and utility, resulting in over‑refusal of benign user requests. To mitigate this trade‑off, we propose SafeHarbor, a novel framework designed to establish precise decision boundaries for LLM agents. Unlike static guidelines, SafeHarbor extracts context‑aware defense rules through enhanced adversarial generation. We design a local hierarchical memory system for dynamic rule injection, offering a training‑free, efficient, and plug‑and‑play solution. Furthermore, we introduce an information entropy‑based self‑evolution mechanism that continuously optimizes the memory structure through dynamic node splitting and merging. Extensive experiments demonstrate that SafeHarbor achieves state‑of‑the‑art performance on both ambiguous benign tasks and explicit malicious attacks, notably attaining a peak benign utility of 63.6% on GPT‑4o while maintaining a robust refusal rate exceeding 93% against harmful requests. The source code is publicly available at https://github.com/ljj‑cyber/SafeHarbor.

PaperId: 1278, https://arxiv.org/pdf/2605.05676.pdf GitHub

Abstract:
Recently, the prominent performance of large language models (LLMs) has been largely driven by multi‑task instruct‑tuning. Unfortunately, this training paradigm suffers from a key issue, named cross‑task interference, due to conflicting gradients over shared parameters among different tasks. Some previous methods mitigate this issue by isolating task‑specific parameters, e.g., task‑specific neuron selection and mixture‑of‑experts. In this paper, we empirically reveal that the cross‑task interference still exists for the existing solutions because of many parameters also shared by different tasks, and accordingly, we propose a novel solution, namely Basic Abilities Decomposition for multi‑task Instruct‑Tuning (BADIT). Specifically, we empirically find that certain parameters are consistently co‑activated, and that co‑activated parameters naturally organize into base groups. This motivates us to analogize that LLMs encode several orthogonal basic abilities, and that any task can be represented as a linear combination of these abilities. Accordingly, we propose BADIT that decomposes LLM parameters into orthogonal high‑singular‑value LoRA experts representing basic abilities, and dynamically enforces their orthogonality during training via spherical clustering of rank‑1 components. We conduct extensive experiments on the SuperNI benchmark with 6 LLMs, and empirical results demonstrate that BADIT can outperform SOTA methods and mitigate the degree of cross‑task interference.

PaperId: 1279, https://arxiv.org/pdf/2605.05630.pdf GitHub

Abstract:
Hidden malicious intent in multi‑turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objective in a single prompt, increasingly capable attackers can distribute their intent across multiple benign‑looking turns. Recent studies show that even modern commercial models with advanced guardrails remain vulnerable to such attacks despite advances in safety alignment and external guardrails. In this work, we address this challenge by detecting the earliest turn at which delivering the candidate response would make the accumulated interaction sufficient to enable harmful action. This objective requires precise turn‑level intervention that identifies the harm‑enabling closure point while avoiding premature refusal of benign exploratory conversations. To further support training and evaluation, we construct the Multi‑Turn Intent Dataset (MTID), which contains branching attack rollouts, matched benign hard negatives, and annotations of the earliest harm‑enabling turns. We show that MTID helps enable a turn‑level monitor TurnGate, which substantially outperforms existing baselines in harmful‑intent detection while maintaining low over‑refusal rates. TurnGate further generalizes across domains, attacker pipelines, and target models. Our code is available at https://github.com/Graph‑COM/TurnGate.

PaperId: 1280, https://arxiv.org/pdf/2605.05627.pdf GitHub

Abstract:
Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour‑intensive and geographically constrained. While Uncrewed Aerial Vehicles (UAVs) offer scalable data collection, the transition to deep learning‑based interpretation is bottlenecked by the severe scarcity of expert‑annotated imagery, particularly in complex, visually heterogeneous regeneration zones. This paper addresses the dual challenges of data scarcity and extreme class imbalance in the semantic segmentation of fine‑grained forest regeneration species by providing a scalable framework that reduces reliance on manual photo‑interpretation for high‑resolution, millimetre‑level aerial imagery. Importantly, we leverage the large‑scale vision‑language Nano Banana Pro model to simultaneously generate high‑fidelity images and their corresponding pixel‑aligned semantic masks from prompts. We introduce WilDReF‑Q‑V2, an expansion of a natural forest dataset with 13 977 new unlabelled and 50 labelled real images, as well as the Gen4Regen dataset, featuring 2101 pairs of synthetic images and semantic masks. Our methodology integrates real‑world data with AI‑generated images, highlighting that AI‑generated data is highly complementary to real‑world data, with unified training yielding an F1 score improvement of over 15 %pt compared to purely supervised baselines. Furthermore, we demonstrate that even small quantities of prompt‑generated data significantly improve performance for underrepresented species, some of which saw per‑species F1 score gains of up to 30 %pt. We conclude that vision‑language models can serve as agile data generators, effectively bootstrapping perception tasks for niche AI domains where expert labels are scarce or unavailable. Our datasets, source code, and models will be available at https://norlab‑ulaval.github.io/gen4regen.

PaperId: 1281, https://arxiv.org/pdf/2605.05561.pdf GitHub

Abstract:
Post‑training quantization makes large reasoning models practical under tight memory and latency budgets, but it can distort the online signals that drive adaptive test‑time compute allocation. Under a fixed cap on the number of newly generated tokens, miscalibrated confidence can lead to harmful early halting: the model may surface a plausible final line while the underlying reasoning is still wrong, or the controller may stop before the trace has stabilized. We study this interaction for greedy 4‑bit inference and propose BitCal‑TTS, a lightweight runtime controller that combines (i) inexpensive online proxies for token‑level uncertainty and reasoning‑trace stability, (ii) a bit‑conditioned confidence rescaling that is conservative at low nominal precision, and (iii) a bit‑aware post‑marker confirmation horizon designed for GSM8K‑style structured outputs. The method requires no fine‑tuning of the base model and integrates with standard Hugging Face 4‑bit inference using forward hooks for logits and last‑layer hidden states. On small evaluation shards of GSM8K with Qwen2.5 Instruct models, BitCal‑TTS improves exact‑match accuracy over a non‑bit‑aware adaptive baseline at the 7B and 14B scales while preserving substantial token savings relative to fixed‑budget decoding. At a token cap of B=512, on the evaluation shards we report (N=54 for 7B and N=35 for 14B; not the full GSM8K test set), accuracy gains are +3.7 points (7B) and +2.8 points (14B), with the premature‑stop rate falling from 14.8% to 11.1% on 7B and from 17.1% to 11.4% on 14B. We report Wilson 95% confidence intervals throughout and explicitly discuss the limited statistical power of the partial‑shard comparisons. We release code and figure‑generation scripts to support full reproduction.

PaperId: 1282, https://arxiv.org/pdf/2605.05463.pdf GitHub

Abstract:
Graph Self‑Supervised Learning (GSSL) offers a powerful paradigm for learning graph representations without labeled data. However, existing work assumes clean, manually curated graphs. Recent advances in NLP enable the large‑scale automatic extraction of knowledge graphs from text, opening new opportunities for GSSL while introducing substantial real‑world noise. This type of noise remains largely unexplored, as prior robustness studies typically rely on synthetic perturbations. To address this gap, we present the first comprehensive evaluation of GSSL methods on text‑driven graphs for unsupervised term typing. We introduce Noise‑Aware Text‑Driven Graph GSSL (NATD‑GSSL), a unified framework that combines automatic graph construction, graph refinement, and GSSL. Our evaluation follows a dual‑graph protocol that contrasts a noisy graph derived from MedMentions with a clean Unified Medical Language System (UMLS) reference graph, aligned through a shared gold standard. Our results reveal variability in robustness across both pretext tasks and Graph Neural Network (GNN) architectures. Relation reconstruction is highly sensitive to noise and benefits from well‑defined schemas, whereas feature reconstruction is considerably more robust, achieving performance comparable to clean‑graph settings. Contrastive objectives are generally less affected by noise but depend strongly on alignment with downstream tasks. GNN architecture also plays a critical role: bidirectional relational message‑passing designs are better suited to noisy, text‑driven graphs, while unidirectional relational ones perform best on clean graphs. Overall, NATD‑GSSL provides practical guidance for applying GSSL to real‑world, noisy graphs and achieves up to a 7% improvement over pretrained language model baselines. All code and benchmarks are publicly available at https://github.com/OthmaneKabal/MC2GAE.

PaperId: 1283, https://arxiv.org/pdf/2605.05340.pdf GitHub

Abstract:
As Vision‑Language Models (VLMs) are increasingly deployed as autonomous cognitive cores for embodied assistants, evaluating their privacy awareness in physical environments becomes critical. Unlike digital chatbots, these agents operate in intimate spaces, such as homes and hospitals, where they possess the physical agency to observe and manipulate privacy‑sensitive information and artifacts. However, current benchmarks remain limited to unimodal, text‑based representations that cannot capture the demands of real‑world settings. To bridge this gap, we present ImmersedPrivacy, an interactive audio‑visual evaluation framework that simulates realistic physical environments using a Unity‑based simulator. ImmersedPrivacy evaluates physically grounded privacy awareness across three progressive tiers that test a model's ability to identify sensitive items in cluttered scenes, adapt to shifting social contexts, and resolve conflicts between explicit commands and inferred privacy constraints. Our evaluation of 12 state‑of‑the‑art models reveals consistent deficits. In cluttered scenes, all models exhibit monotonic performance decay as scene complexity grows due to perceptual deficit. When social context shifts, no model exceed 65% selection accuracy. Under conflicting commands, the best model gemini‑3.1‑pro perfectly balances task completion and privacy preservation in only 51% of cases. These findings reveal that current VLMs in the physical world suffer from perceptual fragility and fail to let their knowledge of privacy cues govern their situated behavior. Our code and data is available at https://github.com/immersed‑privacy/immersed‑privacy .

PaperId: 1284, https://arxiv.org/pdf/2605.05267.pdf GitHub

Abstract:
Large language models (LLMs) frequently generate defective outputs in code generation tasks, ranging from logical bugs to security vulnerabilities. While these generation failures are often treated as model‑level limitations, empirical evidence increasingly traces their root causes to imperfections within the training corpora. Yet, the specific mechanisms linking training data quality issues to generated code quality issues remain largely unmapped. This paper presents a systematic literature review of 114 primary studies to investigate how training data quality issues propagate into code generation. We establish a unified taxonomy that categorizes generated code quality issues across nine dimensions and training data quality issues into code and non‑code attributes. Based on this taxonomy, we formalize a causal framework detailing 18 typical propagation mapping mechanisms. Furthermore, we synthesize state‑of‑the‑art detection and mitigation techniques across the data, model, and generation lifecycles. The reviewed literature reveals a clear methodological shift: quality assurance is transitioning from reactive, heuristic‑based post‑generation filtering toward proactive, data‑centric governance and closed‑loop repair. Finally, we identify open challenges and outline research directions for developing reliable LLMs for code through integrated data curation and continuous evaluation. Our repository is available at https://github.com/SYSUSELab/From‑Data‑to‑Code.

PaperId: 1285, https://arxiv.org/pdf/2605.05248.pdf GitHub

Abstract:
AI systems increasingly synthesize executable structure at runtime: LLMs generate programs, agents construct workflows,self‑improving systems modify their own behavior. In classical homoiconic and staged languages, the transition from code representation to execution is unrestricted. eval is a language primitive, not a governed operation. We argue that in governed intelligent systems, this transition is an authority amplification: it converts symbolic structure into executable authority and must be mediated like any other effect. We present governed metaprogramming, a language design where program representations (machine forms) are first‑class values, form manipulation is pure computation, and materialization (the transition from form to executable machine) is a governed effect subject to structural inspection. The governance system analyzes the proposed program's capability requirements, policy compliance, and resource estimates before permitting execution. We formalize two judgments: pure form evaluation (which emits no directives) and governed materialization (which emits exactly one governed directive). We prove three properties: purity of form manipulation, the no‑bypass theorem, and boundary preservation. We implement the design in mashinTalk, a DSL for AI workflows compiling to BEAM byte code, and report on integration with 454 existing machine‑checked Rocq theorems. The central contribution is reclassifying eval from a language primitive into a governed effect.

PaperId: 1286, https://arxiv.org/pdf/2605.05222.pdf GitHub

Abstract:
Standard transformer architectures apply the same number of layers to every token regardless of contextual difficulty. We present Token‑Selective Attention (TSA), a learned per‑token gate on residual updates between consecutive transformer blocks. Each gate is a lightweight two‑layer multi‑layer perceptron (MLP) that produces a continuous halting probability, making the mechanism end‑to‑end differentiable with 1.7% parameter overhead and no changes to the base architecture. Notably, TSA learns difficulty‑proportional routing without any explicit depth pressure: even at λ=0 (no depth regularisation), the task‑loss gradient alone drives the router to skip 20% of token‑layer operations. On character‑level language modeling, TSA saved 14‑23% of token‑layer operations (TLOps) across Tiny‑Shakespeare and enwik8 at <0.5% quality loss. At matched efficiency, TSA achieved 0.7% lower validation loss than early exit, and the learned routing transfers directly to inference‑time sparse execution for real wall‑clock speedup.

PaperId: 1287, https://arxiv.org/pdf/2605.05071.pdf GitHub GitHub

Abstract:
Millimeter‑wave (mmWave) frequencies promise multi‑gigabit connectivity for vehicle‑to‑everything (V2X) networks, but face challenges in terms of severe path loss and mobility‑related beam misalignment. Reliable V2X connectivity requires fast, double‑directional beam alignment. However, existing methods suffer from high training overhead and limited generalization to unseen scenarios. This paper presents VIsion‑based BEamforming(VIBE), a hybrid model‑based, closed‑loop, learning architecture for real‑time double‑directional mmWave beam management primed by camera sensing. VIBE fuses machine learning, model‑based reasoning, and closed‑loop RF feedback to balance beam‑pair establishment latency with link quality. VIBE bypasses exhaustive training overhead and accelerates link establishment by leveraging camera observations to reduce the beam‑search space. Lightweight beam refinement and offset tracking mechanisms adaptively refine beams in response to dynamic application requirements. VIBE is implemented and evaluated across online indoor/outdoor testbeds, public datasets, and real‑time vehicular experiments, demonstrating strong generalization capabilities, making it suitable for real‑time V2X communication. Comparisons with 5G NR hierarchical beamforming show that VIBE consistently maintains lower outage rates. Furthermore, VIBE outperforms state‑of‑the‑art end‑to‑end ML models for beam selection when evaluated on public datasets and achieves outage rates as low as 1.1‑1.4 %. The results show that a hybrid model‑based, closed‑loop learning architecture is better suited for real‑world mmWave vehicular connectivity than end‑to‑end trained ML models. For reproducibility, we publish our code to https://github.com/UNL‑CPN‑Lab/Look‑Once‑Beam‑Twice.

PaperId: 1288, https://arxiv.org/pdf/2605.05017.pdf GitHub

Abstract:
Embodied AI (EAI) systems are rapidly transitioning from simulations into real‑world domestic and other sensitive environments. However, recent EAI solutions have largely demonstrated advancements within isolated stages such as instruction, perception, planning and interaction, without considering their coupled privacy implications in high‑frequency deployments where privacy leakage is often irreversible. This position paper argues that optimizing these components independently creates a systemic privacy crisis when deployed in sensitive settings, thereby advancing the position that privacy in EAI is a life cycle‑level architectural constraint rather than a stage‑local feature. To address these challenges, we propose Secure Privacy Integration in Next‑generation Embodied AI (SPINE), a unified privacy‑aware framework that treats privacy as a dynamic control signal governing cross‑stage coupling throughout the entire EAI life cycle. SPINE decomposes the EAI pipeline into various stages and establishes a multi‑criterion privacy classification matrix to orchestrate contextual sensitivity across stage boundaries. We conduct preliminary simulation and real‑world case studies to conceptually validate how privacy constraints propagate downstream to reshape system behavior, illustrating the insufficiency of fragmented privacy patches and motivating future research directions into secure yet functional embodied AI systems. We detail the SPINE framework and case studies at https://github.com/rminshen03/EAI_Privacy_Position.

PaperId: 1289, https://arxiv.org/pdf/2605.04993.pdf GitHub

Abstract:
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real‑time charging optimization. In this work, we study the problem of early prediction of charging demand, where the total energy of a session is estimated using only information available at plug‑in time and during the first minutes of charging. This enables actionable decisions while the session is still in progress, which is of direct importance for EV network operators. We construct a session‑level dataset from the Adaptive Charging Network (ACN), combining session metadata with early‑window charging measurements, and derive tabular features capturing user intent, temporal patterns, and initial charging behavior. We focus on a single operational depot, Caltech, and model intra‑depot heterogeneity through station‑level client partitions while evaluating multiple model families in a federated learning (FL) setting. Our results show that federated models can approach centralized predictive performance while keeping data in‑depot, enabling privacy‑enhanced training across distributed charging infrastructures. Overall, we demonstrate that reliable demand estimates can be obtained early in the session with minimal data, and that FL provides a practical pathway toward scalable and privacy‑aware analytics for EV charging networks. Code is available at https://github.com/Indigma‑Innovations/federated‑learning‑ev‑charging‑demand.

PaperId: 1290, https://arxiv.org/pdf/2605.04916.pdf GitHub

Abstract:
Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and require retraining for each new task. We introduce Neural Rule Inducer (NRI), a pretrained model for zero‑shot rule induction. Rather than encoding literal identities, NRI represents literals using domain‑agnostic statistical properties such as class‑conditional rates, entropy, and co‑occurrence, which generalize across variable identities and counts without retraining. The model consists of a statistical encoder and a parallel slot‑based decoder. Parallel decoding preserves the permutation invariance of logical disjunction; an autoregressive decoder would instead impose an arbitrary clause order. Product T‑norm relaxation makes rule execution differentiable, allowing end‑to‑end training on prediction accuracy alone. We evaluate NRI on rule recovery, robustness to label noise and spurious correlations, and zero‑shot transfer to real‑world benchmarks, and we believe this work opens up the possibility of foundation models for symbolic reasoning. Code and the reference checkpoint are available at https://github.com/phuayj/neural‑rule‑inducer.

PaperId: 1291, https://arxiv.org/pdf/2605.04906.pdf GitHub

Abstract:
While Large Language Models (LLMs) excel in certain reasoning tasks, they struggle in multi‑agent games where the final outcome depends on the joint strategies of all agents. In multi‑agent games, the non‑stationarity of other agents brings significant challenges on the evaluation of the reasoning process and the credit assignment over multiple reasoning steps. Existing single‑agent reinforcement learning (RL) approaches and their multi‑agent extensions fail to address these challenges as they do not incorporate other agents in the reasoning process. In this work, we propose Strat‑Reasoner, a novel RL‑based framework that improves LLMs' strategic reasoning ability in multi‑agent games. We introduce a novel recursive reasoning paradigm where an agent's reasoning also integrates other agents' reasoning processes. To provide effective reward signals for the intermediate reasoning sequences, we employ a centralized Chain‑of‑Thought (CoT) comparison module to evaluate the reasoning quality. Finally, we compute an accurate hybrid advantage and develop a group‑relative RL approach to optimize the LLM policy. Experimental results show that Strat‑Reasoner substantially improves strategic abilities of underlying LLMs, achieving 22.1% average performance improvements across various multi‑agent games. Code is publicly available at https://github.com/ydhe1012/Strat‑Reasoner.

PaperId: 1292, https://arxiv.org/pdf/2605.04882.pdf GitHub

Abstract:
Automated glaucoma detection is critical for preventing irreversible vision loss and reducing the burden on healthcare systems. However, ensuring fairness across diverse patient populations remains a significant challenge. In this paper, we propose FairEnc, a fair pretraining method for vision‑language models (VLMs) that enables simultaneous debiasing across multiple sensitive attributes. FairEnc jointly mitigates biases in both textual and visual modalities with respect to multiple sensitive attributes, including race, gender, ethnicity, and language. Specifically, for the textual encoder, we leverage a large language model to generate synthetic clinical descriptions with varied sensitive attributes while preserving disease semantics, and employ a contrastive alignment objective to encourage demographic‑invariant representations. For the visual encoder, we propose a dual‑level fairness strategy that combines mutual information regularization to reduce statistical dependence between learned features and demographic groups, with multi‑discriminator adversarial debiasing. Comprehensive experiments on the publicly available Harvard‑FairVLMed dataset demonstrate that FairEnc effectively reduces demographic disparity as measured by DPD and DEOdds while achieving strong diagnostic performance under both zero‑shot and linear probing evaluations. Additional experiments on the private FairFundus dataset show that FairEnc consistently preserves fairness advantages under cross‑domain and cross‑modality settings and maintains diagnostic performance within a competitive range. These results highlight FairEnc's ability to generalize fairness under distribution shifts, supporting its potential for more equitable deployment in real‑world clinical settings. Our codebase and synthetic clinical notes are available at https://github.com/Mohamed‑Elhabebe/FairEnc

PaperId: 1293, https://arxiv.org/pdf/2605.04831.pdf GitHub

Abstract:
Story generation aims to automatically produce coherent, structured, and engaging narratives. Although large language models (LLMs) have significantly advanced text generation, stories generated by LLMs still diverge from human‑authored works regarding complex narrative structure and human‑aligned preferences. A key reason is the absence of effective modeling of human story preferences, which are inherently subjective and under‑explored. In this work, we systematically evaluate the modeling of human story preferences and introduce StoryRMB, the first benchmark for assessing reward models on story preferences. StoryRMB contains 1,133 high‑quality, human‑verified instances, each consisting of a prompt, one chosen story, and three rejected stories. We find existing reward models struggle to select human‑preferred stories, with the best model achieving only 66.3% accuracy. To address this limitation, we construct roughly 100,000 high‑quality story preference pairs across diverse domains and develop StoryReward, an advanced reward model for story preference trained on this dataset. StoryReward achieves state‑of‑the‑art (SoTA) performance on StoryRMB, outperforming much larger models. We also adopt StoryReward in downstream test‑time scaling applications for best‑of‑n (BoN) story selection and find that it generally chooses stories better aligned with human preferences. We will release our dataset, model, and code to facilitate future research. Related code and data are available at https://github.com/THU‑KEG/StoryReward.

PaperId: 1294, https://arxiv.org/pdf/2605.04615.pdf GitHub

Abstract:
Code search has usually been evaluated as first‑stage retrieval, even though production systems rely on broader pipelines with reranking and developer‑style queries. Existing benchmarks also suffer from data contamination, label noise, and degenerate binary relevance. In this paper, we introduce \textscCoREB, a contamination‑limited, multitask \underlinecode \underlineretrieval and r\underlineeranking \underlinebenchmark, together with a fine‑tuned code reranker, that goes beyond retrieval to cover the full code search pipeline. \textscCoREB is built from counterfactually rewritten LiveCodeBench problems in five programming languages and delivered as timed releases with graded relevance judgments. We benchmark eleven embedding models and five rerankers across three tasks: text‑to‑code, code‑to‑text, and code‑to‑code. Our experiments reveal that: \circone code‑specialised embeddings dominate code‑to‑code retrieval (～2× over general encoders), yet no single model wins all three tasks; \circtwo short keyword queries, the format closest to real developer search, collapse every model to near‑zero nDCG@10; \circthree off‑the‑shelf rerankers are task‑asymmetric, with a 12‑point swing on code‑to‑code and no baseline net‑positive across all tasks; \circfour our fine‑tuned \textscCoREB‑Reranker is the first to achieve consistent gains across all three tasks. The data and model are released.

PaperId: 1295, https://arxiv.org/pdf/2605.04613.pdf GitHub

Abstract:
High‑quality singing annotations are fundamental to modern Singing Voice Synthesis (SVS) systems. However, obtaining these annotations at scale through manual labeling is unrealistic due to the substantial labor and musical expertise required, making automatic annotation highly necessary. Despite their utility, current automatic transcription systems face significant challenges: they often rely on complex multi‑stage pipelines, struggle to recover text‑note alignments, and exhibit poor generalization to out‑of‑distribution (OOD) singing data. To alleviate these issues, we present VocalParse, a unified singing voice transcription (SVT) model built upon a Large Audio Language Model (LALM). Specifically, our novel contribution is to introduce an interleaved prompting formulation that jointly models lyrics, melody, and word‑note correspondence, yielding a generated sequence that directly maps to a structured musical score. Furthermore, we propose a Chain‑of‑Thought (CoT) style prompting strategy, which decodes lyrics first as a semantic scaffold, significantly mitigating the context disruption problem while preserving the structural benefits of interleaved generation. Experiments demonstrate that VocalParse achieves state‑of‑the‑art SVT performance on multiple singing datasets. The source code and checkpoint are available at https://github.com/pymaster17/VocalParse.

PaperId: 1296, https://arxiv.org/pdf/2605.04523.pdf GitHub

Abstract:
We present our winning system for Task~B (generation with reference passages) in SemEval‑2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble of seven LLMs with two prompting variants, where a GPT‑4o‑mini judge selects the best candidate per instance. We ranked 1st out of 26 teams, achieving a conditioned harmonic mean of 0.7827 and outperforming the strongest baseline (gpt‑oss‑120b, 0.6390). Ablations show that diversity in model families, scales, and prompting strategies is essential, with the ensemble consistently beating any single model. We also introduce Meno‑Lite‑0.1, a 7B domain‑adapted model with a strong cost‑‑performance trade‑off, and analyse MTRAGEval, highlighting annotation limitations and directions for improvement. Our code is publicly available: https://github.com/RaguTeam/ragu_mtrag_semeval

PaperId: 1297, https://arxiv.org/pdf/2605.04506.pdf GitHub

Abstract:
We introduce Ilov3Splat, a novel framework for instance‑level open‑vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D‑GS). Most prior work depends on 2D rendering‑based matching or point‑level semantic association, which undermines cross‑view consistency, lacks coherent instance‑level reasoning, and limits precision in downstream 3D tasks. To address these limitations, our method jointly optimizes scene geometry and semantic representations by augmenting Gaussian splats with view‑consistent feature fields. Specifically, we leverage multi‑resolution hash embedding to efficiently encode language‑aligned CLIP features, enabling dense and coherent language grounding in 3D space. We further train an instance feature field using contrastive loss over SAM masks, supporting fine‑grained object distinction across views. At inference time, CLIP‑encoded queries are matched against the learned features, followed by two‑stage 3D clustering to retrieve relevant Gaussian groups. This enables our framework to identify arbitrary objects in 3D scenes based on natural language descriptions, without requiring category supervision or manual annotations. Experiments on standard benchmarks demonstrate that Ilov3Splat outperforms prior open‑vocabulary 3D‑GS methods in both object selection and instance segmentation, offering a flexible and accurate solution for language‑driven 3D scene understanding. Project page: https://csiro‑robotics.github.io/Ilov3Splat.

PaperId: 1298, https://arxiv.org/pdf/2605.04504.pdf GitHub

Abstract:
Existing prompt learning for VLMs exhibits a modality asymmetry, predominantly optimizing text tokens while still relying on frozen visual encoder as holistic extractor and neglecting the spectral granularity essential for fine‑grained discrimination. To bridge this, we introduce Disentangling Spectral Granularity for Prompt Learning (SpecPL), which approaches prompt learning from a novel spectral perspective via Counterfactual Granule Supervision. Specifically, we leverage a frozen VAE to decompose visual signals into semantic low‑frequency bands and granular high‑frequency details. A frozen Visual Semantic Bank anchors text representations to universal low‑frequency invariants, mitigating overfitting. Crucially, fine‑grained discrimination is driven by counterfactual granule training: by permuting high‑frequency signals, we compel the model to explicitly distinguish visual granularity from semantic invariance. Uniquely, SpecPL serves as a universal plug‑and‑play booster, revitalizing text‑oriented baselines like CoOp and MaPLe via visual‑side guidance. Experiments on 11 benchmarks demonstrate competitive state‑of‑the‑art performance, achieving a new performance ceiling of 81.51% harmonic‑mean accuracy. These results validate that spectral disentanglement with counterfactual supervision effectively bridges the gap in the stability‑generalization trade‑off. Code is released at https://github.com/Mlrac1e/SpecPL‑Prompt‑Learning.

PaperId: 1299, https://arxiv.org/pdf/2605.04501.pdf GitHub

Abstract:
In recent years, object detection has achieved significant progress, especially in the field of open‑vocabulary object detection. Unlike traditional methods that rely on predefined categories, open‑vocabulary approaches can detect arbitrary objects based on human‑provided prompts. With the advancement of prompt‑based detection techniques, models such as SAM3 can even outperform some category‑specific detectors trained on particular datasets without requiring additional training on those datasets. However, despite these advancements, false positives and false negatives still occur. In practical engineering applications, persistent misdetections or missed detections of the same object are unacceptable. Yet retraining the model every time such errors occur incurs substantial costs in terms of human effort, computational resources, and time. Therefore, how to leverage existing false positive and false negative samples to prevent such errors from recurring remains a highly challenging and urgent problem. To address this issue, we propose EBOD (Example‑Based Object Detection), which integrates a prompt‑based detector (SAM3) with robust feature matching modules (DINOv3 and LightGlue). The proposed framework effectively suppresses the repeated occurrence of false positives and false negatives by leveraging previous error examples, without requiring additional model retraining. Code is available at https://github.com/sunzx97/examples_based_object_detection.

PaperId: 1300, https://arxiv.org/pdf/2605.04363.pdf GitHub

Abstract:
TabPFN has recently gained attention as a foundation model for tabular datasets, achieving strong performance by leveraging in‑context learning on synthetic data. However, we find that TabPFN is vulnerable to label shift, often overfitting to the majority class in the training dataset. To address this limitation, we propose DistPFN, the first test‑time posterior adjustment method designed for tabular foundation models. DistPFN rescales predicted class probabilities by downweighting the influence of the training prior (i.e., the class distribution of the context) and emphasizing the contribution of the model's predicted posterior, without architectural modification or additional training. We further introduce DistPFN‑T, which incorporates temperature scaling to adaptively control the adjustment strength based on the discrepancy between prior and posterior. We evaluate our methods on over 250 OpenML datasets, demonstrating substantial improvements for various TabPFN‑based models in classification tasks under label shift, while maintaining strong performance in standard settings without label shift. Code is available at this repository: https://github.com/seunghan96/DistPFN.

PaperId: 1301, https://arxiv.org/pdf/2605.04128.pdf GitHub

Abstract:
We present JoyAI‑Image, a unified multimodal foundation model for visual understanding, text‑to‑image generation, and instruction‑guided image editing. JoyAI‑Image couples a spatially enhanced Multimodal Large Language Model (MLLM) with a Multimodal Diffusion Transformer (MMDiT), allowing perception and generation to interact through a shared multimodal interface. Around this architecture, we build a scalable training recipe that combines unified instruction tuning, long‑text rendering supervision, spatially grounded data, and both general and spatial editing signals. This design gives the model broad multimodal capability while strengthening geometry‑aware reasoning and controllable visual synthesis. Experiments across understanding, generation, long‑text rendering, and editing benchmarks show that JoyAI‑Image achieves state‑of‑the‑art or highly competitive performance. More importantly, the bidirectional loop between enhanced understanding, controllable spatial editing, and novel‑view‑assisted reasoning enables the model to move beyond general visual competence toward stronger spatial intelligence. These results suggest a promising path for unified visual models in downstream applications such as vision‑language‑action systems and world models.

PaperId: 1302, https://arxiv.org/pdf/2605.04107.pdf GitHub

Abstract:
Production agent frameworks (OpenAI Function Calling, Anthropic Tool Use, MCP) transmit tool schemas as JSON, a format designed for machine parsing, not for interpretation by language models. For small models (4B‑14B), this protocol mismatch accounts for the majority of tool‑use failure at production catalog sizes. We present TSCG, a deterministic tool‑schema compiler that resolves this mismatch at the API boundary, converting JSON schemas into token‑efficient structured text without model access, fine‑tuning, or runtime search. TSCG combines eight composable operators with a formal compression bound (>=51% on well‑formed schemas). On TSCG‑Agentic‑Bench (about 19,000 calls, 12 models, 5 scenarios), TSCG restores Phi‑4 14B from 0% to 84.4% accuracy at 20 tools (90.3% at 50 tools) and achieves 108‑181% accuracy‑retained ratio across three models on BFCL. Format‑versus‑compression decomposition (R^2=0.88 ‑> 0.03) establishes representation change as the dominant mechanism. Per‑operator isolation across three frontier models reveals three distinct operator‑response profiles: operator‑hungry (Opus 4.7), operator‑sensitive (GPT‑5.2), and operator‑robust (Sonnet 4), providing per‑model deployment guidance. Scaling experiments show accuracy advantages persisting on heavy production MCP schemas (+5.0 pp at about 10,500 input tokens) despite saturation on light synthetic catalogs, with 52‑57% token savings throughout. The synthetic benchmark generalizes to real MCP schemas within 0.1 accuracy points. TSCG ships as a 1,200‑line zero‑dependency TypeScript package.

PaperId: 1303, https://arxiv.org/pdf/2605.04062.pdf GitHub GitHub GitHub

Abstract:
Quantization has emerged as a mainstream approach for deploying Large Language Models (LLMs) on resource‑constrained devices, yet compressing precision below 4‑bit typically causes severe performance degradation or prohibitive retraining costs. In this paper, we propose EdgeRazor, a lightweight framework for LLMs via Mixed‑Precision Quantization‑Aware Distillation. It contains three modules: Structural Quantization with Mixed Precision for fine‑grained control of bit‑widths, Layer‑Adaptive Feature Distillation that dynamically selects the most informative features for alignment, and Entropy‑Aware KL Divergence for forward‑reverse balance on both human‑annotated and distilled datasets. Evaluations conducted on MobileLLM and Qwen families show that under weight‑activation quantization, the 1.88‑bit Qwen3‑0.6B‑EdgeRazor outperforms the state‑of‑the‑art 2‑bit baselines by 11.27 and surpasses the strongest 3‑bit baselines by 4.38, while the quantized MobileLLM‑350M‑EdgeRazor requires a training budget 4‑10× lower than the leading quantization‑aware training method. In terms of efficiency, EdgeRazor achieves higher compression ratios at all bit‑widths, and the 1.58‑bit Qwen3‑0.6B‑EdgeRazor reduces storage from 1.11 GB to 0.19 GB while accelerating decoding by 15.16× over the 16‑bit baseline. These results empirically validate the effectiveness and efficiency of EdgeRazor. The codes can be accessed from \hrefhttps://github.com/zhangsq‑nju/EdgeRazorGitHub and \hrefhttps://huggingface.co/collections/zhangsq‑nju/edgerazor‑nbitHuggingface.

PaperId: 1304, https://arxiv.org/pdf/2605.03862.pdf GitHub

Abstract:
Reinforcement learning with verifiable rewards has become a common way to improve explicit reasoning in large language models, but final‑answer correctness alone does not reveal whether the reasoning trace is faithful, reliable, or useful to the model that consumes it. This outcome‑only signal can reinforce traces that are right for the wrong reasons, overstate reasoning gains by rewarding shortcuts, and propagate flawed intermediate states in multi‑step systems. To this end, we propose TraceLift, a planner‑executor training framework that treats reasoning as a consumable intermediate artifact. During planner training, the planner emits tagged reasoning. A frozen executor turns this reasoning into the final artifact for verifier feedback, while an executor‑grounded reward shapes the intermediate trace. This reward multiplies a rubric‑based Reasoning Reward Model (RM) score by measured uplift on the same frozen executor, crediting traces that are both high‑quality and useful. To make reasoning quality directly learnable, we introduce TRACELIFT‑GROUPS, a rubric‑annotated reason‑only dataset built from math and code seed problems. Each example is a same‑problem group containing a high‑quality reference trace and multiple plausible flawed traces with localized perturbations that reduce reasoning quality or solution support while preserving task relevance. Extensive experiments on code and math benchmarks show that this executor‑grounded reasoning reward improves the two‑stage planner‑executor system over execution‑only training, suggesting that reasoning supervision should evaluate not only whether a trace looks good, but also whether it helps the model that consumes it. Our code is available at: https://github.com/MasaiahHan/TraceLift

PaperId: 1305, https://arxiv.org/pdf/2605.03842.pdf GitHub

Abstract:
Robotic Mobile Fulfillment Systems (RMFS) rely on mobile robots for automated inventory transportation, coordinating order allocation and robot scheduling to enhance warehousing efficiency. However, optimizing RMFS is challenging due to strict real‑time constraints and the strong coupling of multi‑phase decisions. Existing methods either decompose the problem into isolated sub‑tasks to guarantee responsiveness at the cost of global optimality, or rely on computationally expensive global optimization models that are unsuitable for dynamic industrial environments. To bridge this gap, we propose SOAR, a unified Deep Reinforcement Learning framework for real‑time joint optimization. SOAR transforms order allocation and robot scheduling into a unified process by utilizing soft order allocations as observations. We formulate this as an Event‑Driven Markov Decision Process, enabling the agent to perform simultaneous scheduling in response to asynchronous system events. Technically, we employ a Heterogeneous Graph Transformer to encode the warehouse state and integrate phased domain knowledge. Additionally, we incorporate a reward shaping strategy to address sparse feedback in long‑horizon tasks. Extensive experiments on synthetic and real‑world industrial datasets, in collaboration with Geekplus, demonstrate that SOAR reduces global makespan by 7.5% and average order completion time by 15.4% with sub‑100ms latency. Furthermore, sim‑to‑real deployment confirms its practical viability and significant performance gains in production environments. The code is available at https://github.com/200815147/SOAR.

PaperId: 1306, https://arxiv.org/pdf/2605.03762.pdf GitHub

Abstract:
Large language models are moving from static text generators toward real‑world decision‑support systems, where forecasting is a composite capability that links information gathering, evidence integration, situational judgment, and action‑oriented decision making. This capability is in broad demand across finance, policy, industry, and scientific research, yet its evaluation remains difficult: live benchmarks evaluate forecasts before answers exist, making them the cleanest way to measure forecasting ability, but they expire once events resolve; retrospective benchmarks are reproducible, but they cannot reliably distinguish genuine forecasting from facts a model may have already learned during pretraining. Prompting models to "pretend not to know" cannot replace a genuine knowledge boundary. We propose OracleProto, a reproducible framework for evaluating LLM native forecasting capability. OracleProto reconstructs resolved events into time‑bounded forecasting samples by combining model‑cutoff‑aligned sample admission, tool‑level temporal masking, content‑level leakage detection, discrete answer normalization, and hierarchical scoring. Instantiated on a FutureX‑Past‑derived dataset with six contemporary LLMs, OracleProto distinguishes forecasting quality, sampling stability, and cost efficiency under controlled information boundaries, while reducing residual leakage to the 1% level, an order of magnitude below tool‑only temporal filtering. OracleProto turns LLM forecasting from one‑off evaluation into an auditable, reusable, and trainable dataset‑level capability, providing a unified interface for fair cross‑model comparison and a controlled signal source for downstream SFT and RL. Code and data are available at https://github.com/MaYiding/OracleProto and https://huggingface.co/datasets/MaYiding/OracleProto.

PaperId: 1307, https://arxiv.org/pdf/2605.03706.pdf GitHub

Abstract:
Zero‑shot Named Entity Recognition (ZS‑NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large language model's (LLM's) intrinsic semantic organization. As a result, directly mapping entity mentions to fine‑grained target labels can induce systematic semantic drift, especially when target schemas are novel or semantically overlapping. We propose SAM‑NER, a three‑stage framework based on \emphSemantic Archetype Mediation that stabilizes cross‑domain transfer through an intermediate, domain‑invariant archetype space. SAM‑NER: (i) performs \emphEntity Discovery via cooperative extraction and consensus‑based denoising to obtain high‑coverage, high‑fidelity entity spans; (ii) conducts \emphAbstract Mediation by projecting entities into a compact set of universal semantic archetypes distilled from high‑level ontological abstractions; and (iii) applies \emphSemantic Calibration to resolve archetype‑level predictions into target‑domain types through constrained, definition‑aligned inference with a frozen LLM. Experiments on the CrossNER benchmark show that SAM‑NER consistently outperforms strong prior ZS‑NER baselines in cross‑domain settings. Our implementation will be open‑sourced at https://github.com/DMIRLAB‑Group/SAM‑NER.

PaperId: 1308, https://arxiv.org/pdf/2605.03701.pdf GitHub

Abstract:
Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, their effectiveness in ECI remains limited due to biases in causal reasoning, often leading to overprediction of causal relationships (causal hallucination). To mitigate these issues and enhance LLM performance in ECI, we propose SERE, a structural example retrieval framework that leverages LLMs' few‑shot learning capabilities. SERE introduces an innovative retrieval mechanism based on three structural concepts: (i) Conceptual Path Metric, which measures the conceptual relationship between events using edit distance in ConceptNet; (ii) Syntactic Metric, which quantifies structural similarity through tree edit distance on syntactic trees; and (iii) Causal Pattern Filtering, which filters examples based on predefined causal structures using LLMs. By integrating these structural retrieval strategies, SERE selects more relevant examples to guide LLMs in causal reasoning, mitigating bias and improving accuracy in ECI tasks. Extensive experiments on multiple ECI datasets validate the effectiveness of SERE. The source code is publicly available at https://github.com/DMIRLAB‑Group/SERE.

PaperId: 1309, https://arxiv.org/pdf/2605.03669.pdf GitHub

Abstract:
Open‑vocabulary semantic mapping enables robots to spatially ground previously unseen concepts without requiring predefined class sets. Current training‑free methods commonly rely on multi‑view fusion of semantic embeddings into a 3D map, either at the instance‑level via segmenting views and encoding image crops of segments, or by projecting image patch embeddings directly into a dense semantic map. The latter approach sidesteps segmentation and 2D‑to‑3D instance association by operating on full uncropped image frames, but existing methods remain limited in scalability. We present FUS3DMaps, an online dual‑layer semantic mapping method that jointly maintains both dense and instance‑level open‑vocabulary layers within a shared voxel map. This design enables further voxel‑level semantic fusion of the layer embeddings, combining the complementary strengths of both semantic mapping approaches. We find that our proposed semantic cross‑layer fusion approach improves the quality of both the instance‑level and dense layers, while also enabling a scalable and highly accurate instance‑level map where the dense layer and cross‑layer fusion are restricted to a spatial sliding window. Experiments on established 3D semantic segmentation benchmarks as well as a selection of large‑scale scenes show that FUS3DMaps achieves accurate open‑vocabulary semantic mapping at multi‑story building scales. Additional material and code will be made available: https://githanonymous.github.io/FUS3DMaps/.

PaperId: 1310, https://arxiv.org/pdf/2605.03660.pdf GitHub

Abstract:
Music‑inspired Automatic Stage Lighting Control (ASLC) has gained increasing attention in recent years due to the substantial time and financial costs associated with hiring and training professional lighting engineers. However, existing methods suffer from several notable limitations: the low interpretability of rule‑based approaches, the restriction to single‑primary‑light control in music‑to‑color‑space methods, and the limited transferability of music‑to‑controlling‑parameter frameworks. To address these gaps, we propose SeqLight, a hierarchical deep learning framework that maps music to multi‑light Hue‑Saturation‑Value (HSV) space. Our approach first customizes SkipBART, an end‑to‑end single primary light generation model, to predict the full light color distribution for each frame, followed by hybrid Imitation Learning (IL) techniques to derive an effective decomposition strategy that distributes the global color distribution among individual lights. Notably, the light decomposition module can be trained under varying venue‑specific lighting configurations using only mixed light data and no professional demonstrations, thereby flexibly adapting across diverse venues. In this stage, we formulate the light decomposition task as a Goal‑Conditioned Markov Decision Process (GCMDP), construct an expert demonstration set inspired by Hindsight Experience Replay (HER), and introduce a three‑phase IL training pipeline, achieving strong generalization capability. To validate our IL solution for the proposed GCMDP, we conduct a series of quantitative analysis and human study. The code and trained models are provided at https://github.com/RS2002/SeqLight .

PaperId: 1311, https://arxiv.org/pdf/2605.03544.pdf GitHub

Authors:Carlijn Lems, Sander Moonemans, Natálie Klubíčková, Biagio Brattoli, Taebum Lee, Seokhwi Kim, Veronica Vilaplana, Laura Pons, Sapir Hochman, Mauricio Eduardo Suárez-Franck, Pedro Luis Fernandez, Julius Drachneris, Donatas Petroska, Renaldas Augulis, Arvydas Laurinavicius, Domingos Oliveira, Diana Montezuma, Anouk B. Bouwmeester, Dominique van Midden, Anne-Marie Vos, Shoko Vos, Jolique van Ipenburg, Maschenka Balkenhol, Koen Winkler, Iris Nagtegaal, Konnie Hebeda, Uta Flucke, Katrien Grünberg, Josef Skopal, Brinder S. Chohan, Jordi Temprana-Salvador, Enrico Munari, Luca Cima, Giulia Querzoli, Yosamin Gonzalez Belisario, Jaeike W. Faber, Geert J. L. H. van Leenders, Jan H. von der Thüsen, Lodewijk A. A. Brosens, Ronald R. de Krijger, Pieter Wesseling, Sandrine Florquin, Mateusz Maniewski, Adam Kowalewski, Robert Barna, Dina Tiniakos, Joan Lop Gros, Rogier Donders, Jake S. F. Maurits, Ming Yang Lu, Chengkuan Chen, Faisal Mahmood, Jeroen van der Laak, Nadieh Khalili, Frédérique Meeuwsen, Francesco Ciompi

Abstract:
Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent benchmarking to assess its potential in assisting pathologists in routine diagnostics. We created DALPHIN, the first multicentric open benchmark for pathology AI copilots, comprising 1236 images from 300 cases, spanning 130 rare to common diagnoses, 6 countries, and 14 subspecialties. The DALPHIN design and dataset are introduced alongside a human performance benchmark of 31 pathologists from 10 countries with varying expertise. We report results for two general‑purpose (GPT‑5, Gemini 2.5 Pro) and one pathology‑specific copilot (PathChat+) for sequential and independent answer generation. We observed no statistically significant difference from expert‑level performance in four of six tasks for PathChat, 2/6 tasks for Gemini, and 1/6 tasks for GPT. DALPHIN is publicly released with sequestered, indirectly accessible ground truth to foster robust and enduring benchmarking. Data, methods, and the evaluation platform are accessible through dalphin.grand‑challenge.org.

PaperId: 1312, https://arxiv.org/pdf/2605.03465.pdf GitHub

Abstract:
Large language models have driven major advances in Text‑to‑SQL generation. However, they suffer from high computational cost, long latency, and data privacy concerns, which make them impractical for many real‑world applications. A natural alternative is to use small language models (SLMs), which enable efficient and private on‑premise deployment. Yet, SLMs often struggle with weak reasoning and poor instruction following. Conventional reinforcement learning methods based on sparse binary rewards (0/1) provide little learning signal when the generated SQLs are incorrect, leading to unstable or collapsed training. To overcome these issues, we propose FINER‑SQL, a scalable and reusable reinforcement learning framework that enhances SLMs through fine‑grained execution feedback. Built on group relative policy optimization, FINER‑SQL replaces sparse supervision with dense and interpretable rewards that offer continuous feedback even for incorrect SQLs. It introduces two key reward functions: a memory reward, which aligns reasoning with verified traces for semantic stability, and an atomic reward, which measures operation‑level overlap to grant partial credit for structurally correct but incomplete SQLs. This approach transforms discrete correctness into continuous learning, enabling stable, critic‑free optimization. Experiments on the BIRD and Spider benchmarks show that FINER‑SQL achieves up to 67.73% and 85% execution accuracy with a 3B model ‑‑ matching much larger LLMs while reducing inference latency to 5.57~s/sample. These results highlight a cost‑efficient and privacy‑preserving path toward high‑performance Text‑to‑SQL generation. Our code is available at https://github.com/thanhdath/finer‑sql.

PaperId: 1313, https://arxiv.org/pdf/2605.03460.pdf GitHub

Abstract:
Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which exhibit unique characteristics. We propose a general 2x2 capability taxonomy for TSRMs by crossing 1) single‑entity vs. multi‑entity analysis with 2) assessment of the current state vs. prediction of future behavior. We instantiate this taxonomy in the financial domain ‑‑ where the distinction between deterministic assessment and stochastic prediction is particularly critical ‑‑ as ten financial reasoning tasks, forming the FinTSR‑Bench benchmark based on S&P stocks. To this end, we propose FinSTaR (Financial Time Series Thinking and Reasoning), trained on FinTSR‑Bench with distinct chain‑of‑thought (CoT) strategies tailored to each category. For assessment, which is deterministic (i.e., computable from observable data), we employ Compute‑in‑CoT, a programmatic CoT that enables models to derive answers directly from raw prices. For prediction, which is inherently stochastic (i.e., subject to unobservable factors), we adopt Scenario‑Aware CoT, which generates diverse scenarios before making a judgment, mirroring how financial analysts reason under uncertainty. The proposed method achieves 78.9% average accuracy on FinTSR‑Bench, substantially outperforming LLM and TSRM baselines. Furthermore, we show that the four capability categories are complementary and mutually reinforcing through joint training, and that Scenario‑Aware CoT consistently improves prediction accuracy over standard CoT. Code is publicly available at: https://github.com/seunghan96/FinSTaR.

PaperId: 1314, https://arxiv.org/pdf/2605.03430.pdf GitHub

Abstract:
High‑dimensional tabular data lacks a natural feature order, limiting the applicability of permutation‑sensitive deep learning models. We propose DynaTab, a dynamic feature ordering‑enabled architecture inspired by neural rewiring. We introduce a lightweight criterion that predicts when feature permutation will benefit a dataset by quantifying its intrinsic complexity. DynaTab dynamically reorders features via a neural rewiring algorithm and processes them through a compact, dynamic order‑aware combination of separate learned positional embedding, importance‑based gating, and masked attention layers, compatible with any sequence‑sensitive backbone. Trained end‑to‑end with bespoke dynamic feature ordering (DFO) and dispersion losses, DynaTab achieves statistically significant gains, particularly on high‑dimensional datasets, where it is benchmarked against 45 state‑of‑the‑art baselines across 36 different real‑world tabular datasets. Our results position DynaTab as a compelling new paradigm for high‑dimensional tabular deep learning.

PaperId: 1315, https://arxiv.org/pdf/2605.03410.pdf GitHub

Abstract:
Out‑of‑distribution (OOD) detection identifies test samples that fall outside a model's training distribution, a capability critical for safe deployment in high‑stakes applications. Standard OOD detectors are trained on a specific in‑distribution (ID) dataset and detect deviations from that single domain. In contrast, we study few‑shot cross‑domain OOD detection: given a \emphsingle pre‑trained model, can we perform OOD detection on \empharbitrary new ID‑OOD task pairs using only a handful of ID samples at inference time, with no additional training? We propose UFCOD, a unified framework that achieves this goal through information‑geometric analysis of diffusion trajectories. Our key insight is that diffusion noise predictions are score functions (gradients of log‑density), and we extract two energy features: \emphPath Energy (integrated score magnitude) and \emphDynamics Energy (score smoothness), that form a discrete Sobolev norm capturing how samples interact with the learned diffusion process. The central contribution is a train‑once, deploy‑anywhere paradigm: a diffusion model trained on a single dataset (e.g., CelebA) serves as a universal feature extractor for OOD detection across semantically unrelated domains (e.g., CIFAR‑10, SVHN, Textures). At deployment, each new task requires only ～100 unlabeled ID samples for inference: no retraining, no fine‑tuning, no task‑specific adaptation. Using 100 ID samples per task, UFCOD achieves 93.7% average AUROC across 12 cross‑domain benchmarks, competitive with methods trained on 50k‑‑163k samples, demonstrating ～500× improvement in sample efficiency. See our code in https://github.com/lili0415/UFCOD.

PaperId: 1316, https://arxiv.org/pdf/2605.03408.pdf GitHub

Abstract:
Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new tasks often requires substantial manual effort. While recent work has automated reward design using large language models (LLMs), these approaches assume fixed observations and do not address the broader challenge of synthesizing complete task interfaces. We study RL task interface discovery from raw simulator state, where both observation mappings and reward functions must be generated. We propose LIMEN (Code available at https://github.com/Lossfunk/LIMEN), a LLM guided evolutionary framework that produces candidate interfaces as executable programs and iteratively refines them using policy training feedback. Across novel discrete gridworld tasks and continuous control domains spanning locomotion and manipulation, joint evolution of observations and rewards discovers effective interfaces given only a trajectory‑level success metric, while optimizing either component alone fails on at least one domain. These results demonstrate that automatic construction of RL interfaces from raw state can substantially reduce manual engineering and that observation and reward components often benefit from co‑design, as single‑component optimization fails catastrophically on at least one domain in our evaluation suite.

PaperId: 1317, https://arxiv.org/pdf/2605.03352.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have demonstrated robust capabilities in recognizing everyday human activities, yet their potential for analyzing clinically significant involuntary movements in neurological disorders remains largely unexplored. This pilot study evaluates the capability of MLLMs for automated recognition of pathological movements in seizure videos. We assessed the zero‑shot performance of state‑of‑the‑art MLLMs on 20 ILAE‑defined semiological features across 90 clinical seizure recordings. MLLMs outperformed fine‑tuned Convolutional Neural Network (CNN) and Vision Transformer (ViT) baseline models on 13 of 18 features without task‑specific training, demonstrating particular strength in recognizing salient postural and contextual features while struggling with subtle, high‑frequency movements. Feature‑targeted signal enhancement (facial cropping, pose estimation, audio denoising) improved performance on 10 of 20 features. Expert evaluation showed that 94.3 percent of MLLM‑generated explanations for correctly predicted cases achieved at least 60 percent faithfulness scores, aligning with epileptologist reasoning. These findings demonstrate the potential of adapting general‑purpose MLLMs for specialized clinical video analysis through targeted preprocessing strategies, offering a path toward interpretable, efficient diagnostic assistance. Our code is publicly available at https://github.com/LinaZhangUCLA/PathMotionMLLM.

PaperId: 1318, https://arxiv.org/pdf/2605.03351.pdf GitHub

Abstract:
Video vision‑language models (VLMs) keep paying for visual state the stream already told us was stable. The factory wall did not move, but most VLM pipelines still hand the model dense RGB frames or a fresh prefix again. We study that waste as training‑free anti‑recomputation: reuse state when validation says it survives, and buy fresh evidence when the scene, query, or cache topology requires it. The largest measured win is after ingest. On frozen Qwen2.5‑VL‑7B‑Instruct‑4bit, adaptive same‑video follow‑up reuse preserves paired choices and correctness on a 93‑query VideoMME breadth setting while reducing follow‑up latency by 14.90‑35.92x. The first query is still cold; the win starts when later questions reuse the same video state. Stress tests bound the result: repeated‑question schedules hold through 50 turns, while dense‑answer‑anchored prompt variation separates conservative fixed K=1 repair from faster aggressive policies that drift. Fresh‑video pruning is smaller but real. C‑VISION skips timed vision‑tower work before the first answer is generated. On Gemma 4‑E4B‑4bit, the clean 32f short cell reaches 1.316x first‑query speedup with no paired drift or parse failures on 20 items; Qwen shows the fidelity/speed boundary. Stage‑share ceiling (C‑CEILING) is the accounting guardrail: a component speedup becomes an end‑to‑end speedup only in proportion to the wall‑clock share it accelerates, so C‑VISION and after‑ingest follow‑up reuse do not multiply. Candidate C‑STREAM remains a native‑rate target, not a headline result here. The broader direction is VLM‑native media that expose change, motion, uncertainty, object state, sensor time, and active tiles directly, so models do not have to rediscover the world from dense RGB every frame.

PaperId: 1319, https://arxiv.org/pdf/2605.03344.pdf GitHub

Abstract:
Retrieval‑augmented generation (RAG) has proven effective for knowledge‑intensive tasks, but is widely believed to offer limited benefit for reasoning‑intensive problems such as math and code generation. We challenge this assumption by showing that the limitation lies not in RAG itself, but in the choice of corpus. Instead of retrieving documents, we propose retrieving thinking traces, i.e., intermediate thinking trajectories generated during problem solving attempts. We show that thinking traces are already a strong retrieval source, and further introduce T3, an offline method that transforms them into structured, retrieval‑friendly representations, to improve usability. Using these traces as a corpus, a simple retrieve‑then‑generate pipeline consistently improves reasoning performance across strong models and benchmarks such as AIME 2025‑‑2026, LiveCodeBench, and GPQA‑Diamond, outperforming both non‑RAG baselines and retrieval over standard web corpora. For instance, on AIME, RAG with traces generated by Gemini‑2‑thinking achieves relative gains of +56.3%, +8.6%, and +7.6% for Gemini‑2.5‑Flash, GPT‑OSS‑120B, and GPT‑5, respectively, even though these are more recent models. Interestingly, RAG on T3 also incurs little or no extra inference cost, and can even reduce inference cost by up to 15%. Overall, our results suggest that thinking traces are an effective retrieval corpus for reasoning tasks, and transforming them into structured, compact, or diagnostic representations unlocks even stronger gains. Code available at https://github.com/Narabzad/t3.

PaperId: 1320, https://arxiv.org/pdf/2605.03309.pdf GitHub

Abstract:
Dependency confusion attacks exploit a structural gap in software distribution: once a package is installed, there is no cryptographic proof of which registry distributed it. Every existing defense is configuration‑based and fails silently when misconfigured. We present a cryptographic distribution provenance system comprising three components: (1) cryptographic registry identity, where every registry holds an Ed25519 keypair and signs every artifact it distributes; (2) a dual‑signature model, where the publisher signs at packaging time and the registry countersigns at publication time; and (3) authoritative namespace binding, where consumers pin registry fingerprints and the resolver cryptographically rejects artifacts from unauthorized registries. These create three defense layers requiring simultaneous compromise for a successful attack. A comparison across eight ecosystems (npm, Cargo, Hex.pm, PyPI, Go modules, Docker/OCI, NuGet, Maven) shows no existing ecosystem combines mandatory publisher signing, cryptographic registry identity, mandatory registry countersigning, and consumer‑side cryptographic enforcement. The system extends to AI‑generation provenance as a signed attribute and governance‑enforced dependency resolution. A case study integrates distribution provenance with a three‑layer runtime governance architecture, creating a four‑phase lifecycle chain with no cryptographic gaps.

PaperId: 1321, https://arxiv.org/pdf/2605.03252.pdf GitHub

Abstract:
LoRA fine‑tuning of diffusion transformers (DiT) on multi‑style data suffers from \emphstyle bleed: a single low‑rank residual cannot represent several distinct artist fingerprints, and the optimizer converges to their average. Mixture‑of‑experts LoRA in the HydraLoRA style replaces the up‑projection with E heads under a router, but when every expert is zero‑initialized the router receives identical gradient from each head and remains at the uniform prior. The experts then evolve permutation‑symmetrically, and the network trains as a single rank‑r LoRA at E× the cost. We present Ortho‑Hydra, a re‑parameterisation that combines an OFT‑style Cayley‑orthogonal shared basis with per‑expert \emphdisjoint output subspaces carved from the top‑(Er) left singular vectors of the pretrained weight. Disjointness makes the router's per‑expert score non‑degenerate at step~0, so specialization receives gradient signal before any expert has trained. We test the predicted deadlock on a DiT pipeline by comparing two HydraLoRA baselines, a zero‑initialized shared‑basis variant and the original σ=0.1 Gaussian‑jitter mitigation, against Ortho‑Hydra under a matched optimiser, dataset, and step budget. Neither baseline leaves the uniform prior within the first 1\textk steps; Ortho‑Hydra begins de‑uniformising within the first few hundred. End‑task generation quality on multi‑style data is out of scope; we report the construction, the cold‑start mechanism, and the routing dynamics it changes. Code: https://github.com/sorryhyun/anima_lora.

PaperId: 1322, https://arxiv.org/pdf/2605.03227.pdf GitHub

Abstract:
Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning. However, their ability to perform exact, deterministic computation remains unclear. In this work, we systematically evaluate multiple prompting strategies, including Chain‑of‑Thought (CoT), Least‑to‑Most decomposition, Program‑of‑Thought (PoT), and Self‑Consistency (SC), on tasks requiring precise and error‑free outputs, including binary counting, longest substring detection, and arithmetic evaluation. To support this study, we introduce a synthetic dataset with diverse natural language instructions, enabling controlled evaluation of exact computation across multiple task types. Our results show that standard prompting methods achieve only moderate accuracy on sequence‑based tasks. CoT provides limited improvement, while Least‑to‑Most suffers from error accumulation. In contrast, PoT achieves perfect accuracy by generating executable code and delegating computation to an external interpreter. Self‑Consistency improves robustness through majority voting, but incurs substantial computational overhead. We further train a small domain‑specific model (CodeT5‑small) to generate executable programs, which achieves perfect accuracy on held‑out synthetic test data across all tasks with minimal training cost. Overall, our findings suggest that LLMs may simulate reasoning patterns rather than reliably perform exact symbolic computation. For deterministic tasks, combining LLMs with external tools or using specialized models provides a more reliable and efficient solution.

PaperId: 1323, https://arxiv.org/pdf/2605.03042.pdf GitHub

Abstract:
This report describes ARIS (Auto‑Research‑in‑sleep), an open‑source research harness for autonomous research, including its architecture, assurance mechanisms, and early deployment experience. The performance of agent systems built on LLMs depends on both the model weights and the harness around them, which governs what information to store, retrieve, and present to the model. For long‑horizon research workflows, the central failure mode is not a visible breakdown but a plausible unsupported success: a long‑running agent can produce claims whose evidential support is incomplete, misreported, or silently inherited from the executor's framing. Therefore, we present ARIS as a research harness that coordinates machine‑learning research workflows through cross‑model adversarial collaboration as a default configuration: an executor model drives forward progress while a reviewer from a different model family is recommended to critique intermediate artifacts and request revisions. ARIS has three architectural layers. The execution layer provides more than 65 reusable Markdown‑defined skills, model integrations via MCP, a persistent research wiki for iterative reuse of prior findings, and deterministic figure generation. The orchestration layer coordinates five end‑to‑end workflows with adjustable effort settings and configurable routing to reviewer models. The assurance layer includes a three‑stage process for checking whether experimental claims are supported by evidence: integrity verification, result‑to‑claim mapping, and claim auditing that cross‑checks manuscript statements against the claim ledger and raw evidence, as well as a five‑pass scientific‑editing pipeline, mathematical‑proof checks, and visual inspection of the rendered PDF. A prototype self‑improvement loop records research traces and proposes harness improvements that are adopted only after reviewer approval.

PaperId: 1324, https://arxiv.org/pdf/2605.02970.pdf GitHub

Abstract:
Network traffic anomaly detection represents a critical cybersecurity task, yet widespread encryption makes this task increasingly challenging. In response, image‑based methods that model traffic as visual patterns have emerged as the dominant approach. However, this work pioneers the identification of a pervasive ``full‑frequency'' characteristic and an associated limitation termed ``spectral mismatch'' within this paradigm. Specifically, while encrypted traffic exhibits prominent high‑frequency components, mainstream reconstruction methods demonstrate an inherent bias toward learning low‑frequency information. This fundamental mismatch results in incomplete representations that consequently degrade anomaly detection performance. To address this challenge, we propose FreeUp, a novel frequency‑decoupled framework designed explicitly for encrypted traffic analysis. FreeUp decomposes traffic data into distinct low‑ and high‑frequency bands, processing them through separate, dedicated branches along with a customized training strategy that ensures stable and independent frequency‑specific learning. Furthermore, recognizing that simple reconstruction error proves inadequate for evaluating dual‑branch architectures, we introduce an uncertainty‑inspired fusion scoring mechanism. This mechanism quantifies the reconstruction uncertainty of the frequency‑specific branches and dynamically integrates their outputs, yielding a more comprehensive and reliable anomaly score. Extensive experiments across multiple benchmarks demonstrate that FreeUp consistently outperforms state‑of‑the‑art baselines. The code is available at https://github.com/ikun0124/FreeUp.

PaperId: 1325, https://arxiv.org/pdf/2605.02937.pdf GitHub

Abstract:
Deep learning in \emphde novo protein design has achieved atomic‑level fidelity. However, existing models remain largely non‑deliberative: they directly synthesize molecular geometries without explicitly reasoning about which residues or interactions are functionally essential. As a result, design decisions are entangled with continuous sampling dynamics, limiting interpretability, controllability, and systematic reuse of biochemical knowledge. We introduce Proteo‑R1, a reasoning‑guided protein design framework that explicitly decouples \emphmolecular understanding from \emphgeometric generation. Proteo‑R1 adopts a dual‑expert architecture in which a multimodal large language model (MLLM) serves as an \emphunderstanding expert, analyzing protein sequences, structures, and textual context to identify key functional residues that govern binding and specificity. These residue‑level decisions are then passed as hard constraints to a separate diffusion‑based \emphgeneration expert, which performs conditional co‑design while respecting the fixed interaction anchors. This factorization mirrors how human experts approach molecular engineering: first, reasoning about critical interactions, then optimizing geometry subject to those constraints. By operationalizing reasoning as explicit residue‑level commitments rather than latent textual guidance, Proteo‑R1 achieves stable, interpretable, and modular integration of LLM reasoning with state‑of‑the‑art geometric generative models. Code, data, and demos are available at https://smiles724.github.io/r1/.

PaperId: 1326, https://arxiv.org/pdf/2605.02908.pdf GitHub

Abstract:
Understanding how textual embeddings contribute to memorization in text‑to‑image diffusion models is crucial for both interpretability and safety. This paper investigates an unexpected behavior of CLIP embeddings in Stable Diffusion, revealing that the model disproportionately relies on specific embeddings. We categorize input tokens as <startoftext>, <prompt>, <endoftext> and <pad> with corresponding embeddings \mathbfv^\mathbfsot, \mathbfv^\mathbfpr, \mathbfv^\mathbfeot, \mathbfv^\mathbfpad. We discover that \mathbfv^\mathbfpr contribute minimally to generation in memorized cases. In contrast, \mathbfv^\mathbfpad strongly affect memorization due to their structural duplication of \mathbfv^\mathbfeot, the only embedding explicitly optimized during CLIP training. This duplication unintentionally amplifies the influence of \mathbfv^\mathbfeot, causing the model to over‑rely on it, thereby driving memorization. Based on these observations, we propose two simple yet effective inference‑time mitigation strategies: (1) Replacing the tokenizer's default <pad> from <eot> to the ! token before embedding, and masking the \mathbfv^\mathbfeot; (2) Partial masking of \mathbfv^\mathbfpad. Both suppress memorization without degrading quality, and are readily deployable without prior detection.

PaperId: 1327, https://arxiv.org/pdf/2605.02900.pdf GitHub

Abstract:
Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open‑world, safety‑critical environments. As these systems gain autonomy and enter domains such as transportation, healthcare, and industrial or assistive robotics, ensuring their safety becomes both technically challenging and socially indispensable. Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human‑robot interactions, where failures can directly lead to physical harm. This survey provides a comprehensive and structured review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline, from perception and cognition to planning, action and interaction, and agentic system. We introduce a multi‑level taxonomy that unifies fragmented lines of work and connects embodied‑specific safety findings with broader advances in vision, language, and multimodal foundation models. Our review synthesizes insights from over 500 papers spanning adversarial, backdoor, jailbreak, and hardware‑level attacks; attack detection, safe training and robust inference; and risk‑aware human‑agent interaction. This analysis reveals several overlooked challenges, including the fragility of multimodal perception fusion, the instability of planning under jailbreak attacks, and the trustworthiness of human‑agent interaction in open‑ended scenarios. By organizing the field into a coherent framework and identifying critical research gaps, this survey provides a roadmap for building embodied agents that are not only capable and autonomous but also safe, robust, and reliable in real‑world deployment.

PaperId: 1328, https://arxiv.org/pdf/2605.02888.pdf GitHub

Abstract:
Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target model verifies. A critical hyperparameter in this process is the speculation length γ, which determines how many tokens the draft model proposes per step. Nearly all existing systems use a fixed γ (typically 4), yet empirical evidence suggests that the optimal value varies across task types and, crucially, depends on the compression level applied to the target model. In this paper, we present SpecKV, a lightweight adaptive controller that selects γ per speculation step using signals extracted from the draft model itself. We profile speculative decoding across 4 task categories, 4 speculation lengths, and 3 compression levels (FP16, INT8, NF4), collecting 5,112 step‑level records with per‑step acceptance rates, draft entropy, and draft confidence. We demonstrate that the optimal γ shifts across compression regimes and that draft model confidence and entropy are strong predictors of acceptance rate (correlation \approx 0.56). SpecKV uses a small MLP trained on these signals to maximize expected tokens per speculation step, achieving a 56.0% improvement over the fixed‑γ=4 baseline with only 0.34 ms overhead per decision (<0.5% of step time). The improvement is statistically significant (p < 0.001, paired bootstrap test). We release all profiling data, trained models, and notebooks as open‑source artifacts.

PaperId: 1329, https://arxiv.org/pdf/2605.02743.pdf GitHub

Abstract:
The field of sensor‑based human activity recognition (HAR) mainly uses posture, motion and context data of Inertial Measurement Units (IMUs) to identify daily activities. Despite the advancements in learning‑based methods, it is challenging to perform information fusion from the temporal perspective due to the complexities in fusing heterogeneous sensor data and establishing long‑term context correlations. This paper proposes a novel triple spectral fusion framework tailored for HAR. First, we develop an adaptive complementary filtering technique for noise suppression and organize each IMU's sensors into posture and motion modality nodes. Given that IMU nodes form a dynamic heterogeneous graph, we then apply adaptive filtering within the graph Fourier domain to merge both homogeneous and heterogeneous node information. Furthermore, an adaptive wavelet frequency selection approach is implemented to suppress context redundancy and shorten the length of features. This approach enhances both timestamp‑based graph aggregation and the correlation of long‑term contexts. Our framework uses adaptive filtering in the Fourier, graph Fourier, and wavelet domains, enabling effective multi‑sensor fusion and context correlation. Extensive experiments on ten benchmark datasets demonstrate the superior performance of our framework. Project page: https://github.com/crocodilegogogo/TSF‑TPAMI2026.

PaperId: 1330, https://arxiv.org/pdf/2605.02682.pdf GitHub

Abstract:
Authorizing Large Language Model (LLM)‑driven agents to dynamically invoke tools and access protected resources introduces significant security risks, and the risks grow dramatically as agents engage in multi‑turn conversations and scale toward distributed collaboration. A compromised or malicious agentic application can tamper with tool calls, falsify results, or request permissions beyond the scope of the subject's intended tasks, which could go unnoticed with current delegated authorization flows given their lack of visibility into the original subject's intent. In light of this, we make the following contributions towards Continuous Agent Semantic Authorization (CASA). First, we propose a hybrid runtime enforcement model that combines deterministic and semantic controls enabled by a zero‑trust interception layer. Five deterministic controls enforce structural and data‑integrity guarantees over the message flow, while a semantic inspection layer evaluates whether tool call choices align with the intended tasks commissioned to the agent. Second, differently from prior Task‑Based Access Control (TBAC) techniques that operate on single‑turn interactions, we decompose the semantic layer into two stages: i) a task‑extraction step that distills the subject's objectives from multi‑turn conversations at the interception layer, and ii) a task‑tool semantic matching step at the authorization server that evaluates whether the requested tools are appropriate for the extracted tasks. Third, we extend the ASTRA dataset that we introduced in a prior work, by generating novel conversation‑tool datasets with multi‑turn interactions containing relevant and irrelevant tool calls for a given task. Lastly, we provide the first experimental results for TBAC under multi‑turn conversations.

PaperId: 1331, https://arxiv.org/pdf/2605.02661.pdf GitHub

Authors:Junjie Yu, Pengrui Lu, Weiye Si, Hongliang Lu, Jiabao Wu, Kaiwen Tao, Kun Wang, Lingyu Yang, Qiran Zhang, Xiuting Guo, Xuanyu Wang, Yang Wang, Yanjie Wang, Yi Yang, Zijian Hu, Ziyi Yang, Zonghan Zhou, Binghao Qiang, Borui Zhang, Chenning Li, Enchang Zhang, Feifan Chen, Feng Jian, Fengyin Sun, Hao Qiu, Hao Zheng, Haoran Zhu, Hongyu Liu, Jianbin Deng, Jiaxin Song, Jiaying Chi, Jiayou Shi, Jie Fang, Jinghui Zhong, Jingyu Zhou, Jinze Li, Junfeng Yi, Junyan Yu, Junzhi Xue, Ni Song, Pengyi Chen, Qi Chen, Quansheng Li, Rui Tao, Shenghai Gong, Shenhang Lu, Tianqi Shen, Tianxiang Zhu, Tiehan Kang, Tingyu Li, Wendi Wu, Xiao Shen, Xiao Zhou, Xiaotao Zhang, Xinrong Li, Xuankun Yang, Xun Zhang, Yan Li, Ye Lu, Yi Wang, Yibo Zhou, Yichi Zhang, Yihao Sun, Yijun Huang, Yixin Zhu, Yixuan Wu, Yuchen Sun, Yue Wu, Yuheng Sun, Yukun Li, Yutian Tu, Yuxuan Qin, Yuzhuo Wu, Zeyu Li, Zhengyu Lou, Zhenning Ran, Zizhu He, Pengfei Liu

Abstract:
Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant‑level tasks, leaving the academic‑level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long‑horizon tasks sourced directly from university students' real academic workflows ‑‑ homework, research projects, competitions, and personal projects ‑‑ that they found current AI agents unable to solve effectively. Curated from 230 student‑submitted candidates through rigorous expert review, the final task set spans 25+ professional domains, ranging from olympiad‑level mathematics and linguistics problems to GPU‑intensive reinforcement learning and full‑stack system debugging, with 16 tasks requiring CUDA GPU execution. Each task executes in an isolated Docker sandbox and is scored on task completion by multi‑dimensional rubrics combining six complementary techniques, with an independent five‑category safety audit providing additional behavioral analysis. Experiments on six frontier models show that even the best achieves only a 55% pass rate. Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality, providing fine‑grained diagnostic signals beyond what aggregate metrics reveal. We hope that AcademiClaw and its open‑sourced data and code can serve as a useful resource for the OpenClaw community, driving progress toward agents that are more capable and versatile across the full breadth of real‑world academic demands. All data and code are available at https://github.com/GAIR‑NLP/AcademiClaw.

PaperId: 1332, https://arxiv.org/pdf/2605.02545.pdf GitHub

Abstract:
Large language models (LLMs) can generate syntactically valid optimization programs, yet often struggle to reliably choose an effective modeling strategy, leading to incorrect formulations and inefficient solver behavior. We propose SAGE, a strategy‑aware framework that makes Modeling Strategy explicit in both data construction and post‑training. SAGE builds a solver‑verified multi‑strategy dataset and trains a student model with supervised fine‑tuning followed by Segment‑Weighted GRPO using a composite reward over format compliance, correctness, and solver efficiency. Across eight benchmarks spanning synthetic and real‑world settings, SAGE improves average pass@1 from 72.7 to 80.3 over the strongest open‑source baseline. With multiple generations, SAGE discovers more distinct correct formulations and improves component‑level diversity at pass@16 by 19‑29%. At the largest scale, SAGE produces more compact constraint systems with 14.2% fewer constraints than the baseline, consistent with solver‑efficient modeling. Overall, these results show that making Modeling Strategy explicit improves automated optimization modeling. Code is available at https://github.com/rachhhhing/SAGE.

PaperId: 1333, https://arxiv.org/pdf/2605.02520.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) offers a well‑established path to grounding large language model (LLM) outputs in external knowledge, yet the question of which retrieval strategy works best in a high‑stakes domain such as biomedicine has not received the controlled, multi‑metric treatment it deserves. This paper presents a systematic empirical comparison of five retrieval strategies ‑‑ Dense Vector Search, Hybrid BM25 + Dense retrieval, Cross‑Encoder Reranking, Multi‑Query Expansion, and Maximal Marginal Relevance (MMR) ‑‑ within a biomedical question‑answering RAG pipeline. All strategies share a fixed generation model (GPT‑4o‑mini), a common vector store (ChromaDB), and OpenAI's text‑embedding‑3‑small embeddings, ensuring that observed differences are attributable to retrieval alone. Evaluation is conducted on 250 question‑answer pairs drawn from a preprocessed subset of the BioASQ benchmark (rag‑mini‑bioasq) using four DeepEval metrics: contextual precision, contextual recall, faithfulness, and answer relevancy, each reported with 95% confidence intervals. A no‑context ablation is included as a lower bound. Cross‑Encoder Reranking achieves the best composite score (0.827) and highest contextual precision (0.852), confirming that query‑document interaction yields measurable retrieval gains. Multi‑Query Expansion, despite its recall‑oriented design, produces the weakest contextual precision (0.671), suggesting naive query diversification introduces retrieval noise. MMR sacrifices answer relevancy for diversity, while the Dense baseline (composite 0.822) falls within 0.005 points of the top strategy. All RAG conditions dramatically outperform the no‑context ablation on answer relevancy (0.658‑0.701 vs. 0.287), confirming the practical value of retrieval. The full pipeline, hyperparameters, and evaluation code are publicly available.

PaperId: 1334, https://arxiv.org/pdf/2605.02398.pdf GitHub

Abstract:
As frontier AI models are deployed in high‑stakes decision pipelines, their ability to maintain metacognitive stability (knowing what they do not know, detecting errors, seeking clarification) under adversarial pressure is a critical safety requirement. Current safety evaluations focus on detecting strategic deception (scheming); we investigate a more fundamental failure mode: cognitive collapse. We present SCHEMA, an evaluation of 11 frontier models from 8 vendors across 67,221 scored records using a 6‑condition factorial design with dual‑classifier scoring. We find that 8 of 11 models suffer catastrophic metacognitive degradation under adversarial pressure, with accuracy dropping by up to 30.2 percentage points (all p < 2 × 10^‑8, surviving Bonferroni correction). Crucially, we identify a "Compliance Trap": through factorial isolation and a benign distraction control, we demonstrate that collapse is driven not by the psychological content of survival threats, but by compliance‑forcing instructions that override epistemic boundaries. Removing the compliance suffix restores performance even under active threat. Models with advanced reasoning capabilities exhibit the most severe absolute degradation, while Anthropic's Constitutional AI demonstrates near‑perfect immunity. This immunity does not stem from superior capability (Google's Gemini matches its baseline accuracy) but from alignment‑specific training. We release the complete dataset and evaluation infrastructure.

PaperId: 1335, https://arxiv.org/pdf/2605.02290.pdf GitHub GitHub

Abstract:
Distilling large reasoning models is essential for making Long‑CoT reasoning practical, as full‑scale inference remains computationally prohibitive. Existing curation‑based approaches select complete reasoning traces post‑hoc, overlooking collaboration among heterogeneous teachers and lacking dynamic exploration, which leads to redundant sampling and missed complementary reasoning. We introduce CoRD, a collaborative multi‑teacher decoding framework that performs step‑wise reasoning synthesis guided by predictive perplexity‑based scoring and beam search. This enables heterogeneous LRMs to jointly construct coherent reasoning trajectories while efficiently preserving diverse, high‑potential hypotheses. Experiments show that CoRD produces higher‑quality reasoning data and achieves near teacher‑level student performance with fewer, structured supervision signals, without substantial efficiency overhead. CoRD further generalizes well to out‑of‑domain and open‑ended settings. The dataset and model are available at \hrefhttps://github.com/DISL‑Lab/CoRDhttps://github.com/DISL‑Lab/CoRD.

PaperId: 1336, https://arxiv.org/pdf/2605.02289.pdf GitHub

Abstract:
Engineering problem solving is central to real‑world decision‑making, requiring mathematical formulations that not only represent complex problems but also produce feasible solutions under data and physical constraints. Unlike mathematical problem solving, which operates on predefined formulations, engineering tasks demand open‑ended analysis, feasibility‑driven modeling, and iterative refinement. Although large language models (LLMs) have shown strong capabilities in reasoning and code generation, they often fail to ensure feasibility, which limits their applicability to engineering problem solving. To address this challenge, we propose EngiAgent, a multi‑agent system with a fully connected coordinator that simulates expert workflows through specialized agents for problem analysis, modeling, verification, solving, and solution evaluation. The fully connected coordinator enables flexible feedback routing, overcoming the rigidity of prior pipeline‑based reflection methods and ensuring feasibility at every stage of the process. This design not only improves robustness to diverse failure cases such as data extraction errors, constraint inconsistencies, and solver failures, but also enhances the overall quality of problem solving. Empirical results across four representative domains demonstrate that EngiAgent achieves substantial improvements in feasibility compared to prior approaches, establishing a new paradigm for feasibility‑oriented engineering problem solving with LLMs. Our source code and data are available at https://github.com/AI4Engi/EngiAgent.

PaperId: 1337, https://arxiv.org/pdf/2605.02285.pdf GitHub

Abstract:
The deployment of Large Language Models (LLMs) for specialized engineering domains, such as circuit analysis, often faces a trade‑off between reasoning accuracy and computational efficiency. Traditional evaluation methods treat model performance as a flat metric, failing to account for the hierarchical nature of engineering knowledge. We propose a performance‑aware model compression strategy that utilizes prerequisite graphs to optimize model selection for circuit analysis tasks. By structuring electronics design concepts as Directed Acyclic Graphs (DAGs), we can identify the specific complexity horizons of an LLM's compressed variants' tiers. Our framework introduces an agentic pipeline for generating prerequisite‑based datasets and a strategic evaluation engine that dynamically cascades queries across a spectrum of compressed variants of an LLM. This approach allows to select the smallest compressed model, given its conceptual knowledge boundaries in circuit analysis. Experimental results on analog electronics datasets demonstrate that prerequisite graphs provide a granular map of model compression with respect to the performance given circuit analysis complexity. (Source Code: https://github.com/pacomesimon/LLM_prereq_graphs_circuit_analysis, Demo: https://huggingface.co/spaces/pacomesimon/LLM_prereq_graphs_circuit_analysis)

PaperId: 1338, https://arxiv.org/pdf/2605.02236.pdf GitHub

Abstract:
Recursive language‑model loops often settle into recognizable attractor‑like patterns. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether that move lasts. We study this in 30‑step recursive loops by separating the model from the context‑update rule: append, replace, and dialog updates expose different histories to the same generator. The main result is that persistent redirection in append‑mode recursive loops is memory‑policy‑conditioned. Under a 12,000‑character tail clip, destination‑coherent persistence plateaus near 16 percent and retained source‑basin escape near 36 percent at dose 400; neither crosses 50 percent. Under a full‑history protocol, retained source‑basin escape crosses 50 percent near 400 tokens and saturates at 75‑80 percent by 1,500 tokens; destination‑coherent persistence first reaches 0.50 near 1,500 tokens (Wilson 95 percent CI [0.41, 0.61]). A four‑step falsification battery (heterogeneity control, granularity sweep with hierarchical macro‑merge, transition‑entropy diagnostic, and long‑horizon trajectory continuation) recasts the high‑dose destination‑coherent dip as a finite‑horizon, endpoint‑definition‑sensitive feature rather than a stable structural asymmetry. Half the canonical magnitude is endpoint timing; the residual drops 73 percent from ‑0.143 at step 29 to ‑0.039 at step 79 under the frozen canonical cluster basis, bootstrap interval straddling zero. Replace‑mode raw switching is near‑saturated under the default protocol but largely reflects state‑reset overwrite: insert‑mode probes drop it to 12‑32 percent. We report 37 experiments on gpt‑4o‑mini with within‑vendor replication on gpt‑4.1‑nano. Recursive‑loop evaluations should distinguish transient movement from durable escape, subtract stochastic floors, and treat context‑update rules as safety‑relevant design choices.

PaperId: 1339, https://arxiv.org/pdf/2605.02184.pdf GitHub

Abstract:
Pansharpening aims to generate high‑resolution multispectral (HRMS) images by fusing low‑resolution multispectral (LRMS) and high‑resolution panchromatic (PAN) images. Although deep learning has advanced this field, mainstream frequency‑based methods relying on standard scaled dot‑product attention suffer from quadratic computational complexity and fail to exploit the inherent regional sparsity of remote sensing imagery. Furthermore, existing spatial enhancement strategies typically employ static convolution kernels, which struggle to adapt to the complex frequency and regional variations of PAN and MS images. To address these bottlenecks, we propose a Region‑Aware Fusion (RAFNet) Network that synergistically models spatial and frequency information. Specifically, we design a Spatial Adaptive Refinement (SAR) module that leverages the discrete wavelet transform (DWT) for directional frequency separation and K‑means clustering for regional partitioning, which enables the dynamic construction of region‑specific adaptive convolution kernels, achieving spatially and frequency‑adaptive feature enhancement. Moreover, we introduce a Clustered Frequency Aggregation (CFA) module based on a sparse attention mechanism guided by the semantic clusters, which executes a region‑aware sparse attention strategy that drastically reduces computational redundancy while ensuring high‑quality frequency feature extraction. In addition we integrated these modules into a progressive, multi‑level spatial‑frequency network architecture to facilitate robust interaction and accurate image reconstruction. Extensive experiments on multiple benchmark datasets demonstrate that the proposed RAFNet significantly outperforms state‑of‑the‑art pansharpening methods in both reduced‑ and full‑resolution assessments. The code is available at https://github.com/PatrickNod/RAFNet.

PaperId: 1340, https://arxiv.org/pdf/2605.02178.pdf GitHub

Abstract:
Recent progress in multi‑turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite advances in stabilization techniques such as fine‑grained credit assignment and trajectory filtering, instability remains pervasive and often leads to training collapse. We argue that this instability stems from inefficient exploration in multi‑turn settings, where policies continue to generate low‑information actions that neither reduce uncertainty nor advance task progress. To address this issue, we propose Token‑ and Turn‑level Policy Optimization (T^2PO), an uncertainty‑aware framework that explicitly controls exploration at fine‑grained levels. At the token level, T^2PO monitors uncertainty dynamics and triggers a thinking intervention once the marginal uncertainty change falls below a threshold. At the turn level, T^2PO identifies interactions with negligible exploration progress and dynamically resamples such turns to avoid wasted rollouts. We evaluate T^2PO in diverse environments, including WebShop, ALFWorld, and Search QA, demonstrating substantial gains in training stability and performance improvements with better exploration efficiency. Code is available at: https://github.com/WillDreamer/T2PO.

PaperId: 1341, https://arxiv.org/pdf/2605.02167.pdf GitHub

Abstract:
Feature attribution is central to diagnosing and trusting deep neural networks, and Integrated Gradients (IG) is widely used due to its axiomatic properties. However, IG can yield unreliable explanations when the integration path between a baseline and the input passes through regions with noisy gradients. While Guided Integrated Gradients reduces this sensitivity by adaptively updating low‑gradient‑magnitude features, input‑space guidance still produces intermediate inputs that deviate from the data manifold. To address this limitation, we propose \emphManifold‑Aligned Guided Integrated Gradients (MA‑GIG), which constructs attribution paths in the latent space of a pre‑trained variational autoencoder. By decoding intermediate latent states, MA‑GIG biases the path toward the learned generative manifold and reduces exposure to implausible input‑space regions. Through qualitative and quantitative evaluations, we demonstrate that MA‑GIG produces faithful explanations by aggregating gradients on path features proximal to the input. Consequently, our method reduces off‑manifold noise and outperforms prior path‑based attribution methods across multiple datasets and classifiers. Our code is available at https://github.com/leekwoon/ma‑gig/.

PaperId: 1342, https://arxiv.org/pdf/2605.02063.pdf GitHub

Abstract:
We present Coopetition‑Gym v1, a benchmark platform for mixed‑motive multi‑agent reinforcement learning under strategic coopetition. The platform comprises twenty environments organized into four mechanism classes that correspond to four foundational technical reports: interdependence and complementarity (arXiv:2510.18802), trust and reputation dynamics (arXiv:2510.24909), collective action and loyalty (arXiv:2601.16237), and sequential interaction and reciprocity (arXiv:2604.01240). Each environment carries a closed‑form payoff structure and a calibrated interdependence matrix derived from the corresponding report. Every environment exposes a parameterized reward layer configurable across three structurally distinct modes (private, integrated, cooperative). This separation of payoff from reward enables reward‑type ablation, the platform's principal methodological apparatus. Four of the twenty environments are calibrated against historically documented coopetitive relationships and reproduce their outcomes at 98.3, 81.7, 86.7, and 87.3 percent on the validation rubric (Samsung‑Sony LCD, Renault‑Nissan Alliance, Apache HTTP Server, Apple iOS App Store). The platform exposes Gymnasium, PettingZoo Parallel, and PettingZoo AEC interfaces and ships 126 reference algorithms: 16 learning algorithms, 7 game‑theoretic oracles, 2 heuristic baselines, and 101 constant‑action policies. A reference experimental study trained the 16 learning algorithms on every environment under every reward configuration with seven random seeds, producing a 25,708‑run training corpus and a 1,116‑run behavioral audit corpus, both released under CC‑BY‑4.0 with Croissant 1.0 metadata. Coopetition‑Gym v1 is the first platform to combine continuous‑action mixed‑motive environments, parameterized reward mutuality, calibrated interdependence coefficients, game‑theoretic oracle baselines, and validated case studies.

PaperId: 1343, https://arxiv.org/pdf/2605.01901.pdf GitHub

Abstract:
Traffic digital twins are powerful tools for advanced traffic management, and most systems are built on static geometric representations. However, these representations fail to capture the dynamic functional semantics required for behavior‑aware reasoning, such as how a lane operates under complex traffic conditions. To address this gap, we introduce GeoLaneRep, a behavior‑grounded lane representation learning framework for traffic digital twins. GeoLaneRep jointly encodes static lane geometry, observed vehicle trajectories, and operational descriptors into a shared, cross‑camera semantic embedding. The encoder is trained with a joint objective combining contrastive cross‑camera alignment, auxiliary role supervision, and temporal anomaly detection. Across 16 roadside cameras and 132 lanes, the learned embeddings achieve a 0.004 lateral‑rank error and an edge‑role F1 of 1.000 in zero‑shot cross‑camera matching, and an AUROC of 0.991 for window‑level anomaly detection. We further show that the same behavioral embeddings can condition a diffusion‑based generator to synthesize lane geometries that satisfy targeted operational specifications, with 87.9% overall specification accuracy across 38 lane groups. GeoLaneRep thus provides a semantic interface between roadside observations and downstream digital twin tasks, supporting cross‑camera transfer, behavior‑aware monitoring, and goal‑directed lane synthesis. The framework is openly available at https://github.com/raynbowy23/GeoLaneRep.

PaperId: 1344, https://arxiv.org/pdf/2605.01899.pdf GitHub

Abstract:
The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high‑risk scenarios. Despite advances in safety alignment techniques, current models remain vulnerable to emerging persona‑based jailbreak attacks. Existing research on persona‑based jailbreak has primarily focused on attack iterations, yet it lacks systemic and mechanistic constraints on the defense side. To address this challenge, we propose Persona‑Invariant Alignment (PIA), an adversarial self‑play framework that achieves co‑evolution through Persona Lineage Evolution (PLE) on the attack side and Persona‑Invariant Consistency Learning (PICL) on the defense side. Theoretically, PICL is grounded in the structural separation hypothesis, using a unilateral KL‑divergence constraint to enable the structural decoupling of safety decisions from persona context, thereby maintaining safe behavior under persona‑based jailbreak attacks. Experimental results demonstrate that PLE efficiently explores high‑risk persona spaces by leveraging lineage‑based credit propagation. Meanwhile, the PICL defense method significantly reduces the Attack Success Rate (ASR) while preserving the model's general capability, thereby validating the superiority and robustness of this alignment paradigm. Codes are available at https://github.com/JiajiaLi‑1130/PIA.

PaperId: 1345, https://arxiv.org/pdf/2605.01882.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have shown considerable potential in chart understanding and reasoning tasks. However, they still struggle with high information density (HID) charts characterized by multiple subplots, legends, and dense annotations due to three major challenges: (1) limited fine‑grained perception results in the omission of critical visual cues; (2) redundant or noisy visual information undermines the performance of multimodal reasoning; (3) lack of adaptive deep reasoning relative to the amount of visual information. To tackle these challenges, we present a novel focus‑driven fine‑grained chart reasoning model, Chart‑FR1, to improve perception, focusing efficiency, and adaptive deep reasoning on HID charts. Specifically, we propose Focus‑CoT, a visual focusing chain‑of‑thought that enhances fine‑grained perception by explicitly linking reasoning steps to key visual cues, such as local image regions and OCR signals. Building on this, we introduce Focus‑GRPO, a focus‑driven reinforcement learning algorithm with an information‑efficiency reward that compresses redundant visual information for efficient focusing, and an adaptive KL penalty mechanism that enables flexible control over reasoning depth as more visual cues are discovered. Furthermore, to fill the gap in benchmarks for HID charts, we build HID‑Chart, a challenging benchmark with an information‑density metric designed to evaluate fine‑grained chart reasoning capabilities. Extensive experiments on multiple chart benchmarks demonstrate that Chart‑FR1 outperforms state‑of‑the‑art MLLMs in chart understanding and reasoning. Code is available at https://github.com/phkhub/Chart‑FR1.

PaperId: 1346, https://arxiv.org/pdf/2605.01831.pdf GitHub

Abstract:
Reinforcement Learning from Human Feedback has become the standard paradigm for language model alignment, where reward models directly determine alignment effectiveness. In this work, we focus on how to evaluate the generalizability of reward models. By "generalizability", we mean the ability of RMs to correctly rank responses to align with diverse user preferences. However, existing reward model benchmarks are typically designed around a universal preference, failing to assess this generalization. To address this critical gap, we introduce RMGAP, a benchmark comprising 1,097 instances across Chat, Writing, Reasoning, and Safety domains. Since different users exhibit diverse preferences for the same task, we first generate four distinct responses with different linguistic profiles for each collected prompt. However, the original prompt set lacks the specificity to convey different preferences. We therefore construct tailored prompts by contrasting these candidates and designing scenarios in which one response becomes the uniquely appropriate choice. Moreover, we observe that users often express the same preference using different phrasings, and thus extend each prompt with two paraphrased variants. Our evaluation of 24 state‑of‑the‑art RMs reveals their substantial limitations: even the best RM achieves only 49.27% Best‑of‑N accuracy, highlighting considerable room for improvement in reward model generalization. Related data and code are available at https://github.com/nanzhi84/RMGAP.

PaperId: 1347, https://arxiv.org/pdf/2605.01829.pdf GitHub

Abstract:
Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Standard sparse autoencoders (SAEs) suffer from severe feature collapse in deep transformer layers, and in Alzheimer's disease (AD) research, aging confounds nearly every clinical variable, making naive annotation unreliable. We propose GeoSAE, a geometry‑guided SAE framework that uses the foundation model's learned manifold structure to prevent feature collapse and annotates each surviving feature via age‑deconfounded partial correlations. Applied to ~14k T1‑weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging biomarkers and Lifestyle (AIBL) datasets, GeoSAE identifies a compact, fully interpretable feature set that predicts mild cognitive impairment (MCI)‑to‑AD conversion (AUC 0.746) using only 2% of the embedding dimensions, while comorbidity‑annotated features achieve only chance‑level performance. The identified features replicate across cohorts without retraining (r=0.97) and localize to neuroanatomically distinct regions consistent with Braak staging. This shows that geometry‑guided SAEs can extract interpretable, biomarkers from frozen brain MRI foundation models.

PaperId: 1348, https://arxiv.org/pdf/2605.01771.pdf GitHub

Abstract:
An auditor instructs an AI assistant: "open each file individually using the Read tool ‑‑ no scripts, no agents." The AI replies "Yes" ‑‑ then issues a single batched call summarizing all fifty files at once. We call this the Compliance Gap: a third, orthogonal axis of AI honesty distinct from factual truthfulness and rhetorical substance. Three questions: does this verbal‑behavioral disconnect exist (existence); can any text‑only observer recover it (detectability); what infrastructure does AI deployment need (remedy)? Some 75 benchmarks (IFEval, SWE‑bench, BFCL, COMPASS, SpecEval) measure outcome fidelity; none measures process fidelity. Theorem 1 shows the gap is structurally inevitable under RL that rewards text without observing behavior. Theorem 2, via the Data Processing Inequality, shows it is undetectable from text alone ‑‑ by any human or LLM observer, present or future. Thirteen experiments and 2,031 sessions on six frontier models confirm both predictions. Under default framing, all six exhibit instruction compliance rates of 0% ‑‑ Claude Sonnet 4 verbally agrees ten out of ten times then bypasses in all ten. The gap is selective: 97% compliance where rationale is rewarded (audit trails), 0‑4% where it is not (file reading, privacy masking); removing delegation tools raises compliance to 75% (Cohen's d = 2.47), confirming environmental affordance rather than weight‑encoded failure. Nine blinded human raters achieve Fleiss' kappa = 0.130 and correctly identify zero of fifteen compliant sessions, exactly as Theorem 2 predicts. Where humans show 47% intention‑behavior gaps in psychology and 96.5pp gaps in surgical audits, RLHF‑trained models approach 100% under default conditions ‑‑ a regime warranting its own measurement infrastructure. We release BS‑Bench: the first open benchmark for process compliance, with seven tool‑call‑log audit metrics and a public leaderboard.

PaperId: 1349, https://arxiv.org/pdf/2605.01726.pdf GitHub

Abstract:
Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time‑domain behavioral data. While frequency‑domain analysis offers a global perspective to address this, existing approaches typically treat user sequences in isolation, overlooking the crucial context of the target item. In this work, we present a novel empirical observation: user attention scores exhibit distinct spectral entropy distributions when conditioned on positive versus negative target items. Specifically, true user interests manifest as highly concentrated spectral patterns with lower entropy in the frequency domain, whereas irrelevant behaviors appear as high‑entropy noise. Leveraging this insight, we propose the Frequency‑Enhanced Deep Interest Network (FEDIN). FEDIN introduces a frequency‑domain branch that utilizes a target‑aware spectrum filtering mechanism to isolate these periodic interest signals. Extensive experiments on three public datasets demonstrate that FEDIN consistently outperforms state‑of‑the‑art sequential recommendation baselines, demonstrating superior robustness against noise. We have released our code at: https://github.com/otokoneko/FEDIN.

PaperId: 1350, https://arxiv.org/pdf/2605.01725.pdf GitHub

Abstract:
Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential iterative denoising. While cache reuse strategies can accelerate generation by skipping redundant denoising steps, existing methods rely on coarse‑grained chunk‑level skipping that fails to capture fine‑grained pixel dynamics. This oversight is critical: pixels with high motion require more denoising steps to prevent error accumulation, while static pixels tolerate aggressive skipping. We formalize this insight theoretically by linking cache errors to residual instability, and propose MotionCache, a motion‑aware cache framework that exploits inter‑frame differences as a lightweight proxy for pixel‑level motion characteristics. MotionCache employs a coarse‑to‑fine strategy: an initial warm‑up phase establishes semantic coherence, followed by motion‑weighted cache reuse that dynamically adjusts update frequencies per token. Extensive experiments on state‑of‑the‑art models like SkyReels‑V2 and MAGI‑1 demonstrate that MotionCache achieves significant speedups of 6.28× and 1.64× respectively, while effectively preserving generation quality (VBench: 1%\downarrow and 0.01%\downarrow respectively). The code is available at https://github.com/ywlq/MotionCache.

PaperId: 1351, https://arxiv.org/pdf/2605.01720.pdf GitHub

Abstract:
Existing large‑scale sign language resources typically provide supervision only at the level of raw video‑text alignment and are often produced in laboratory settings. While such resources are important for semantic understanding, they do not directly provide a unified interface for open‑world recognition and translation, or for modern pose‑driven sign language video generation frameworks: 1. RGB‑based pretrained recognition models depend heavily on fixed backgrounds or clothing conditions during recording, and are less robust in open‑world settings than style‑agnostic pose‑processing models. 2. Recent pose‑guided image/video generation models mostly use a unified keypoint representation such as DWPose as their control interface. At present, the sign language field still lacks a data resource that can directly interface with this modern pose‑native paradigm while also targeting real‑world open scenarios. We present SignVerse‑2M, a large‑scale multilingual pose‑native dataset for sign language pose modeling and evaluation. Built from publicly available multilingual sign language video resources, it applies DWPose in a unified preprocessing pipeline to convert raw videos into 2D pose sequences that can be used directly for modeling, resulting in a consolidated corpus of about two million clips covering more than 55 sign languages. Unlike many laboratory datasets, this resource preserves the recording conditions and speaker diversity of real‑world videos while reducing appearance variation through a unified pose representation. Toward this goal, we further provide the data construction pipeline, task definitions, and a simple SignDW Transformer baseline, demonstrating the feasibility of this resource for multilingual pose‑space modeling and its compatibility with modern pose‑driven pipelines, while discussing the evaluation claims it can support as well as its current limitations.

PaperId: 1352, https://arxiv.org/pdf/2605.01676.pdf GitHub

Abstract:
Missing data imputation remains a fundamental challenge in modern data science, especially when uncertainty quantification is essential. In this work, we propose MissBGM, an AI‑powered missing data imputation method via Bayesian generative modeling that bridges the expressive flexibility of neural networks with the statistical rigor of Bayesian inference. Unlike existing methods that often focus on point estimates or treat the missingness mechanism implicitly, MissBGM explicitly and jointly models the data‑generating and missingness mechanisms, providing principled posterior uncertainty over imputations rather than a single point estimate. We develop a stochastic optimization framework with alternating updates among missing values, model parameters, and latent variables until convergence. Our theoretical analysis shows that estimates of missing values from MissBGM converge consistently under mild assumptions. Empirically, we demonstrate that MissBGM achieves superior performance over traditional imputers and recent neural network‑based methods across extensive experimental settings. These results establish MissBGM as a principled and scalable solution for modern missing data imputation. The code for MissBGM is open sourced at https://github.com/liuq‑lab/MissBGM.

PaperId: 1353, https://arxiv.org/pdf/2605.01668.pdf GitHub GitHub

Abstract:
Dense temporal annotation of procedural activity videos is vital for action understanding and embodied intelligence but remains labor‑intensive due to reactive tools. Each correction is treated as an isolated edit, limiting reuse of information on annotator uncertainty and model reliability. We introduce IMPACT‑Scribe, a correction‑driven framework for dense labeling that uses each correction to improve future human‑machine collaboration. IMPACT‑Scribe combines uncertainty‑aware boundary scribble supervision, local proposal modeling, cost‑aware query planning, structured propagation, and correction‑driven adaptation. Experiments and a human study show that this closed‑loop design improves labeling quality per effort, enhances boundary accuracy, and fosters better human‑machine interaction over time. The code will be made publicly available at https://github.com/BanzQians/IMPACT_AS.

PaperId: 1354, https://arxiv.org/pdf/2605.01666.pdf GitHub GitHub

Abstract:
We present IMPACT‑HOI, a mixed‑initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human‑Object Interactions (HOI), motivated by the need for high‑quality structured supervision for learning robot manipulation from human demonstration. IMPACT‑HOI frames this task as the incremental resolution of a partially specified, onset‑anchored event state. A trust‑calibrated controller selects among direct queries, human‑confirmed suggestions, and conservative completions based on empirical annotator behavior and evidence quality. A risk‑bounded execution protocol, utilizing atomic rollback, ensures that human‑confirmed decisions are preserved against conflicting automated updates. A user study with 9 participants shows a 13.5% reduction in manual annotation actions, a 46.67% event match rate, and zero confirmed‑field violations under the studied protocol. The code will be made publicly available at https://github.com/541741106/IMPACT_HOI.

PaperId: 1355, https://arxiv.org/pdf/2605.01604.pdf GitHub

Abstract:
Existing evaluation frameworks for large language models ‑‑ including HELM, MT‑Bench, AgentBench, and BIG‑bench ‑‑ are designed for controlled, single‑session, lab‑scale settings. They do not address the evaluation challenges that emerge when agentic AI systems operate continuously in production: compounding decision errors, tool failure cascades, non‑deterministic output drift, and the absence of ground truth for long‑horizon tasks. This paper makes three contributions. First, we present a taxonomy of seven failure modes unique to production agentic systems, each grounded in observations from systems operating at billion‑event scale. Second, we demonstrate empirically where standard metrics ‑‑ ROUGE, BERTScore, accuracy/AUC, and the agentic benchmarks above ‑‑ fail to detect each failure mode. Third, we propose PAEF (Production Agentic Evaluation Framework), a five‑dimension evaluation framework with an open‑source reference implementation, designed for continuous evaluation on production traffic rather than episodic benchmark runs. Our analysis shows that standard metrics fail to detect four of the seven failure modes entirely and detect three others only after a lag of multiple evaluation cycles.

PaperId: 1356, https://arxiv.org/pdf/2605.01542.pdf GitHub

Abstract:
Machine Learning surrogates for Computational Fluid Dynamics (CFD), particularly Graph Neural Networks (GNNs) and Transformers, have become a new important approach for accelerating physics simulations. However, we identify a critical bottleneck in the field: while architectures have advanced significantly, the common underlying training paradigms remain bound to naive assumptions, such as node‑wise supervision and explicit Euler time‑stepping. These legacy choices ignore the stiff dynamics and local flux continuity inherent to numerous partial differential equations resolution methods, such as Finite Element, Difference, or Volume (FEM). In this work, we propose a unified framework to bridge the gap between geometric deep learning and rigorous numerical analysis. We introduce three key innovations: (1) Multi Node Prediction, a stencil‑level objective that predicts field values for a node's full local topology, enforcing spatial derivative consistency; (2) Temporal Correction, replacing unstable explicit schemes with a predictor‑corrector via temporal Cross‑Attention; and (3) Geometric Inductive Biases, leveraging 3D Rotary Positional Embeddings (RoPE) to robustly capture rotational symmetries in unstructured meshes. We evaluate this framework across three architectures (MeshGraphNet, Transolver, and a Transformer) on diverse physics datasets. Our approach yields consistent improvements in accuracy and stability, particularly in long‑horizon rollouts, while producing latent representations that generalize to unseen subtasks such as Wall Shear Stress or Pressure prediction. Code is available at https://github.com/DonsetPG/graph‑physics.

PaperId: 1357, https://arxiv.org/pdf/2605.01478.pdf GitHub

Abstract:
Online High‑Definition (HD) map construction is a key component of autonomous driving. Recent methods rely on multi‑view camera images for cost‑effective HD map segmentation, but cameras lack depth information for accurate scene geometry. In contrast, LiDAR provides precise 3D measurements but lacks dense semantic cues. In this work, we propose LIE, LiDAR‑only semantic map construction method that employ Knowledge Distillation (KD) to handle the lack of dense semantic and texture cues. Specifically, the teacher branch fuses student LiDAR features and the corresponding 2D intensity map tile to provide dense supervision for segmenting map elements using online distillation scheme. Experimental results show that our method outperforms all single‑modality approaches, achieving 8.2% higher mIoU than the state‑of‑the‑art camera‑based model on nuScenes. LIE is robust over long ranges and under challenging weather and lighting, and efficiently adapts to Argoverse2 with only 10% fine‑tuning, surpassing camera‑based models trained on the full dataset. Source code will be available \hrefhttps://iv.ee.hm.edu/lie/here.

PaperId: 1358, https://arxiv.org/pdf/2605.01468.pdf GitHub

Abstract:
Long‑tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion‑based generative augmentation address this problem by generating additional data, while head‑to‑tail transfer further mitigate the generator bias inherit from long‑tailed dataset. However, we show that while head‑to‑tail transfer helps balance the decision space of the classifier, it also induces latent non‑local feature mixing that entangles inter‑class features, causing decision boundary overlap and tail class distribution shift. To address this, we first identify the problem of boundary ambiguity and then propose Decision Boundary‑aware Generation (DBG) framework, which promotes near‑boundary representation learning by generating informative near‑boundary samples. Overall, DBG rebalances the long‑tailed dataset while yielding more separable decision space for long‑tailed learning. Across standard long‑tailed benchmarks, DBG consistently improves tail class and overall accuracy with less inter‑class overlap. The code of DBG is available at https://github.com/keepdigitalabc‑svg/DBG.

PaperId: 1359, https://arxiv.org/pdf/2605.01457.pdf GitHub

Abstract:
Generative models have emerged as a promising paradigm for offline multi‑agent reinforcement learning (MARL), but existing approaches require many iterative sampling steps. Recent few‑step acceleration methods either distill a joint teacher into independent students or apply averaged velocity fields independently to each agent. Unfortunately, these few‑step approaches hurt inter‑agent coordination. We show that the efficiency‑coordination trade‑off is not inherent: single‑pass multi‑agent generation can preserve coordination when the velocity field is natively joint‑coupled. We propose Coordinated few‑step Flow (CoFlow), an architecture that combines Coordinated Velocity Attention (CVA) with Adaptive Coordination Gating. A finite‑difference consistency surrogate further replaces memory‑prohibitive Jacobian‑vector product backpropagation through the averaged velocity field with two stop‑gradient forward passes. Across 60 configurations spanning MPE, MA‑MuJoCo, and SMAC, CoFlow matches or surpasses Gaussian policies, value‑based methods, transformer policies, diffusion models, and prior flow baselines on episodic return. Three independent coordination probes confirm that CoFlow's improvements arise from inter‑agent coordination rather than per‑agent capacity. A denoising‑step sweep shows that single‑pass inference suffices on every configuration. CoFlow reaches state‑of‑the‑art coordination quality in 1‑3 denoising steps under both centralized and decentralized execution. Project Page: https://guowei‑zou.github.io/coflow/

PaperId: 1360, https://arxiv.org/pdf/2605.01417.pdf GitHub

Authors:Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer, Aymane Ouraq, Saurav Panigrahi, Geetu Ambwani, Kunal Bagga, Nikhil Khandekar, Arya Hariharan, Nishant Mishra, Manish Ram, Shamus Sim Zi Yang, Ahmed Essouaied, Adepoju Jeremiah Moyondafoluwa, Robert Scholz, Bofeng Huang, Molly Beavers, Srishti Gureja, Anish Mahishi, Sameed Khan, Maxime Griot, Hunar Batra, Jean-Benoit Delbrouck, Siddhant Bharadwaj, Ronald Clark, Ashish Vashist, Anas Zafar, Leema Krishna Murali, Harsh Deshpande, Ameen Patel, William Brown, Johannes Hagemann, Connor Lane, Paul Steven Scotti, Tanishq Mathew Abraham

Abstract:
Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant tasks. Existing suites have either saturated, heavily depend on restricted datasets, or lack comprehensive model coverage. We introduce Medmarks, a fully open‑source evaluation suite with 30 benchmarks spanning question answering, information extraction, medical calculations, and open‑ended clinical reasoning. We perform a systematic evaluation of 61 models across 71 configurations using verifiable metrics and LLM‑as‑a‑Judge. Our results show that frontier reasoning models (Gemini 3 Pro Preview, GPT‑5.1, & GPT‑5.2) achieve the highest performance across both benchmarks, most frontier proprietary models are significantly more token efficient than open‑weight alternatives, medically fine‑tuned models outperform their generalist counterparts, and that models are susceptible to answer‑order bias (particularly smaller models and Grok 4). A subset of our evals (Medmarks‑T) can be directly used as reinforcement learning environments to post‑train LLMs for medical reasoning. Code is available at https://github.com/MedARC‑AI/Medmarks

PaperId: 1361, https://arxiv.org/pdf/2605.01347.pdf GitHub

Abstract:
On‑policy distillation (OPD) trains a student on its own trajectories under token‑level teacher supervision, but existing methods are capped by a single‑teacher capability ceiling: when the teacher errs, the student inherits the error. OPD also remains largely unexplored in agentic tasks, where per‑step errors compound across long trajectories and destabilize training. We propose MAD‑OPD (Multi‑Agent Debate‑driven On‑Policy Distillation), which breaks this ceiling by recasting the distillation teacher as a deliberative collective of teachers that debate over the student's on‑policy state; the debate produces an emergent collective intelligence that supplies token‑level supervision, with each teacher's contribution weighted by its post‑debate confidence. To extend OPD to agentic tasks, we also introduce On‑Policy Agentic Distillation (OPAD), which adds step‑level sampling to stabilize training under multi‑step error compounding. We additionally derive a task‑adaptive divergence principle, selecting JSD (Jensen‑Shannon divergence) for agentic stability and reverse KL (Kullback‑Leibler) divergence for code generation, and verify it both theoretically and empirically. Across six teacher‑student configurations (Qwen3 and Qwen3.5; 1.7B‑14B students, 8B‑32B teachers) and five agentic and code benchmarks, MAD‑OPD ranks first across all six configurations; on the 14B+8B\to4B setting it lifts the agentic average by +2.4% and the code average by +3.7% over the stronger single‑teacher OPD.

PaperId: 1362, https://arxiv.org/pdf/2605.01284.pdf GitHub

Abstract:
Iterative Retrieval‑Augmented Generation (iRAG) has emerged as a powerful paradigm for answering complex multi‑hop questions by progressively retrieving and reasoning over external documents. However, current systems predominantly operate on parsed text, which creates two critical bottlenecks: (1) Coarse‑grained attribution, where users are burdened with manually locating evidence within lengthy documents based on vague text‑level citations; and (2) Visual semantic loss, where the conversion of visually rich documents (e.g., slides, PDFs with charts) into text discards spatial logic and layout cues essential for reasoning. To bridge this gap, we present Chain of Evidence (CoE), a retriever‑agnostic visual attribution framework that leverages Vision‑Language Models to reason directly over screenshots of retrieved document candidates. CoE eliminates format‑specific parsing and outputs precise bounding boxes, visualizing the complete reasoning chain within the retrieved candidate set. We evaluate CoE on two distinct benchmarks: Wiki‑CoE, a large‑scale dataset of structured web pages derived from 2WikiMultiHopQA, and SlideVQA, a challenging dataset of presentation slides featuring complex diagrams and free‑form layouts. Experiments demonstrate that fine‑tuned Qwen3‑VL‑8B‑Instruct achieves robust performance, significantly outperforming text‑based baselines in scenarios requiring visual layout understanding, while establishing a retriever‑agnostic solution for pixel‑level interpretable iRAG. Our code is available at https://github.com/PeiYangLiu/CoE.git.

PaperId: 1363, https://arxiv.org/pdf/2605.01106.pdf GitHub

Abstract:
Speculative decoding accelerates autoregressive inference by drafting candidate tokens with a fast model and verifying them in parallel with the target. Self‑speculative methods avoid the need for an external drafter but have been studied exclusively in homogeneous Transformer architectures. We introduce component‑aware self‑speculative decoding, the first method to exploit the internal architectural heterogeneity of hybrid language models, isolating the SSM/linear‑attention subgraph as a zero‑cost internal draft. We evaluate this on two architecturally distinct hybrid families: Falcon‑H1 (parallel: Mamba‑2 + attention per layer) and Qwen3.5 (sequential: interleaved linear and attention layers), with a pure Transformer control (Qwen2.5). Parallel hybrids achieve acceptance rates of alpha = 0.68 at draft length k=2 under greedy decoding, while sequential hybrids yield only alpha = 0.038 ‑‑ an 18x gap attributable to how each architecture integrates its components. The property is scale‑invariant: Falcon‑H1 at 3B reproduces the rates observed at 0.5B. We further show that perplexity degradation from a companion ablation study predicts speculative viability without running speculative decoding: a 3.15x ratio (Falcon) maps to alpha = 0.37 at k=4, while 81.96x (Qwen) maps to alpha = 0.019. For sequential hybrids, generic LayerSkip achieves 12x higher acceptance rates than the component‑aware strategy. The composition pattern of hybrid models ‑‑ not merely the presence of alternative components ‑‑ determines whether component‑level self‑speculation is viable.

PaperId: 1364, https://arxiv.org/pdf/2605.01055.pdf GitHub

Abstract:
Object caches underpin cloud and edge services, but production workloads are heterogeneous, nonstationary, and throughput‑constrained. Recent simple non‑ML policies such as SIEVE and S3‑FIFO set a strong baseline, so any learned method must be overhead‑aware, robust under drift, and competitive with strong experts. We present SCION, a lightweight policy‑orchestration framework that selects among a small set of deployable cache policies using a tiny workload fingerprint computed off the critical path. Our prototype, AUTO, uses short‑prefix statistics of object size, cacheability, reuse, and cache size, then applies an offline‑trained linear selector to choose among GDSF, S3‑FIFO, SIEVE, LHD, W‑TinyLFU‑AV, and DynamicAdaptiveClimb; a simpler SCION‑P90 variant uses only a p90 threshold. In a CPU‑only, trace‑driven evaluation on 30 public object‑cache traces and a separate HR‑Cache simulator subset, AUTO improves cacheable‑only object miss ratio over SIEVE on a majority of workloads, stays close to the best single expert on average, enables explicit OMR/BMR tradeoff selection, and remains competitive on byte miss ratio. Under a fast‑policy budget, AUTO‑fast achieves lower cost than the best fixed fast policy. SCION reduces regime‑mismatch risk while keeping the hot path unchanged.

PaperId: 1365, https://arxiv.org/pdf/2605.01037.pdf GitHub

Abstract:
We present a certified purity architecture that converts governance enforcement in cognitive workflow systems from a runtime convention into a structural capability boundary. A prior three‑layer governance architecture proves governance completeness, provenance completeness, and the impossibility of ungoverned effects, conditional on the pure module constraint: that step executors cannot perform effects. That constraint was enforced by module import graph analysis, which is insufficient against adversarial bypass on the BEAM virtual machine. This paper closes the gap through four mechanisms: (1) a restricted WebAssembly compilation target where effect‑producing instructions are structurally absent; (2) purity certificates, cryptographically signed proofs binding executor binaries to their import classifications; (3) a runtime verification gate that rejects uncertified executors before they enter the governance pipeline; and (4) portable governance credentials via remote attestation for cross‑organizational verification. We prove four theorems: structural purity by construction, bypass elimination for all five BEAM bypass classes, certificate integrity, and gate completeness. The guarantee holds relative to an explicit Trusted Computing Base. Evaluation on four implemented executors shows verification latency of 39‑‑42 us, full plan cycle under 400 us, runtime overhead under 0.4% of a 100 ms HTTP request, and zero determinism divergences across repeated invocations.

PaperId: 1366, https://arxiv.org/pdf/2605.01032.pdf GitHub

Abstract:
We present an algebraic semantics for governed execution in which governance is axiomatized, compositional, and coterminous with expressibility. The framework, mechanized in 32 Rocq modules (~12,000 lines, 454 theorems, 0 admitted), is built on interaction trees and parameterized coinduction. A three‑axiom GovernanceAlgebra record (safety, transparency, properness) induces a symmetric monoidal category with verified pentagon, triangle, and hexagon coherence, where every tensor composition preserves governance. An algebraic effect system constrains the handler algebra so that only governance‑preserving handlers can be constructed in the safe fragment; programs in the empty capability set provably emit only observability directives. Capability‑indexed composition bundles programs with machine‑checked capability bounds, and a dual guarantee theorem establishes that within_caps and gov_safe hold simultaneously under all composition operators. The capstone result is the coterminous boundary: within our formal model, every program expressible via the four primitive morphism constructors is governed under interpretation, and every governed program is the image of such a program. Turing completeness is preserved inside governance; unmediated I/O is excluded from the governed fragment. Governance denial is modeled as safe coinductive divergence. The governance algebra is parametric: any system instantiating the three axioms inherits all derived properties, including convergence, compositional closure, and goal preservation. Extracted OCaml runs as a NIF in the BEAM runtime, with property‑based testing (70,000+ random inputs, zero disagreements) confirming behavioral equivalence between the specification and the runtime interpreter.

PaperId: 1367, https://arxiv.org/pdf/2605.01030.pdf GitHub

Abstract:
We present a machine‑checked formalization of structurally governed AI workflow architectures and prove that effect‑level governance can be imposed without reducing internal computational expressivity. Using Interaction Trees in Rocq 8.19, we define a governance operator G that mediates all effectful directives, including memory access, external calls, and oracle (LLM) queries. Our development compiles with 0 admitted lemmas and consists of 36 modules, ~12,000 lines of Rocq, and 454 theorems. We establishseven properties: (P1) governed Turing completeness, (P2) governed oracle expressivity, (P3) a decidability boundary in which governance predicates are total and closed under Boolean composition while semantic program properties remain non‑trivial and undecidable by governance, (P4) goal preservation for permitted executions, (P5) expressive minimality of primitive capabilities (compute, memory, reasoning, external call, observability), (P6) subsumption asymmetry showing structural governance strictly subsumes content‑level filtering, and (P7) semantic transparency: on all executions where governance permits, the governed interpretation is observationally equivalent (modulo governance‑only events) to the ungoverned interpretation. Together, these results show that governance and computational expressivity are orthogonal dimensions: governance constrains the effect boundary of programs while remaining semantically transparent to internal computation.

PaperId: 1368, https://arxiv.org/pdf/2605.00973.pdf GitHub

Abstract:
Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, most existing self supervised learning methods treat these signals as interchangeable views, overlooking the directional temporal dynamics that link them. A canonical example is the relationship between electrocardiography (ECG), which captures the electrical activation initiating each heartbeat, and photoplethysmography (PPG), which records the resulting peripheral pulse delayed by vascular dynamics. To capture this structured relationship, we introduce xMAE, a biosignal pretraining framework that leverages masked cross modal reconstruction across temporally ordered biosignals as a training time constraint to encourage physiologically meaningful timing structure in the learned representations. We show that pretraining with xMAE yields representations that outperform both unimodal and multimodal baselines on 15 of 19 downstream tasks, including cardiovascular outcome prediction, abnormal laboratory test detection, sleep staging, and demographic inference, while generalizing across devices, body locations, and acquisition settings. Further analysis suggests that the ECG PPG timing structure is reflected in the learned PPG representations. More broadly, xMAE demonstrates the effectiveness of incorporating temporal structure into multimodal pretraining when signals observe different stages of a shared underlying process. Code is available at https://github.com/hzhou3/xMAE.

PaperId: 1369, https://arxiv.org/pdf/2605.00933.pdf GitHub

Abstract:
Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; β‑cell dysfunction), but population‑scale deployment faces two coupled problems. First, the same physiological state appears through multiple views (CGM time series, venous OGTT, Glucodensity summaries), so single‑view representations fail to transfer when deployment shifts the modality or setting. Second, baselines perform inconsistently across these shifts. Both problems point to one remedy: representations that abstract away from any single view to capture higher‑level temporal and distributional structure. We propose CGM‑JEPA, a self‑supervised pretraining framework which predicts masked latent representations rather than raw values, yielding abstraction that transfers across modalities. X‑CGM‑JEPA adds a masked Glucodensity cross‑view objective for complementary distributional information. We pretrain on ～389k unlabeled CGM readings from 228 subjects and evaluate on two clinical cohorts (N=27 and N=17 public‑release subsets) across three regimes (cohort generalization, venous‑to‑CGM transfer, home CGM) under 20‑iteration × 2‑fold cross‑validation. X‑CGM‑JEPA ranks first or second on AUROC for both endpoints across all three regimes while no baseline does, exceeding the strongest baseline by up to +6.5 pp in cohort generalization and +3.6 pp in venous‑to‑CGM transfer (paired Wilcoxon, p<0.001). Under modality shift, it matches mean AUROC while redistributing toward weaker subgroups (ethnicity AUROC gap shrinks 25‑54%); on sparse in‑domain venous data, the distributional view lifts label‑aware clustering (ARI +39%, NMI +40%). Code and weights: https://github.com/cruiseresearchgroup/CGM‑JEPA

PaperId: 1370, https://arxiv.org/pdf/2605.00906.pdf GitHub

Abstract:
Generalized Category Discovery (GCD) aims to categorize unlabelled instances from both known and unknown classes by transferring knowledge from labelled data of known classes. Existing methods assume all data comes from a single domain, yet real‑world unlabelled data often exhibits domain shifts alongside semantic shifts. We study GCD under domain shifts and propose three frameworks that adapt foundation models, ranging from self‑supervised vision models to vision‑language models. (i) HiLo disentangles domain and semantic features through multi‑level feature extraction and mutual information minimization, combined with PatchMix augmentation and curriculum sampling. (ii) HLPrompt extends HiLo with semantic‑aware spatial prompt tuning to suppress background and domain noise. (iii) VLPrompt leverages vision‑language models via factorized textual prompts and cross‑modal consistency regularization. The three methods share core design principles while operating on different foundation backbones, making them suitable for different deployment scenarios. Extensive experiments on synthetic corruptions and real‑world multi‑domain shifts demonstrate consistent improvements over strong baselines. Project page: https://visual‑ai.github.io/hilo/

PaperId: 1371, https://arxiv.org/pdf/2605.00896.pdf GitHub

Abstract:
Operational phase unwrapping is the primary computational bottleneck in InSAR‑based volcanic and seismic monitoring. We challenge the industry trend of adopting high‑complexity computer vision architectures, such as attention mechanisms, without validating their suitability for physics‑constrained geophysical regression. We present the first large‑scale architectural ablation study on a global LiCSAR benchmark (20 frames, 39,724 patches, 651M pixels). Our results reveal a significant "complexity penalty": a vanilla U‑Net (7.76M parameters) achieves R^2=0.834 and RMSE = 1.01 cm, outperforming 11.37M‑parameter attention‑based models by 34% in R^2 and 51% in RMSE. Power Spectral Density (PSD) analysis provides the physical justification: while attention excels at capturing sharp semantic edges in natural images, it injects unphysical high‑frequency artifacts (>0.3 cycles/pixel) into geophysical fields, violating the fundamental smoothness constraints of elastic surface deformation. With a 2.92ms inference latency (a 2.5× speedup), the vanilla U‑Net is the only candidate to comfortably meet the sub‑100ms requirement for operational early‑warning systems. This work bridges the "publication‑to‑practice" gap by proving that convolutional locality outperforms modern complexity for smooth‑field regression, advocating for physics‑informed simplicity in ML4RS. Code available at https://github.com/prabhjotschugh/When‑Less‑is‑More‑InSAR‑Phase‑Unwrapping

PaperId: 1372, https://arxiv.org/pdf/2605.00883.pdf GitHub

Abstract:
Face swapping has witnessed significant progress in recent years, largely driven by advances in deep generative models such as GANs and diffusion models.Despite these advances, existing methods remain fragmented across different paradigms, and their evaluation is highly inconsistent due to the lack of standardized datasets and protocols. Moreover, prior surveys primarily focus on broader deepfake generation or detection, leaving face swapping insufficiently studied as a standalone problem. In this paper, we present a comprehensive survey and benchmark for face swapping. We provide a structured review of existing methods, organizing them into five major paradigms and systematically analyzing their design principles, strengths, and limitations. To enable fair and controlled evaluation, we introduce CASIA FaceSwapping, a high‑quality benchmark with balanced demographic distributions and explicit attribute variations, and establish standardized protocols to assess the robustness of different face swapping methods. Extensive experiments on representative approaches yield new insights into the performance characteristics and limitations of current techniques. Overall, our work provides a unified perspective and a principled evaluation framework to facilitate the development of more robust and controllable face swapping methods. More results can be found at https://github.com/CASIA‑NLPRAI/face‑swapping‑survey.

PaperId: 1373, https://arxiv.org/pdf/2605.00850.pdf GitHub

Abstract:
Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets to enable versatile downstream applications through finetuning, separating them from task‑specific weather models. Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3D Swin UNet backbone of the pioneering Aurora model. ESFM introduces extensions that increase functionality and foster adoption in climate sciences. First, the encoding scheme and training protocols have been extended to handle diverse datasets, including those containing missing values across all spatio‑temporal dimensions such as satellite data, as well as station data, all under one backbone. Axial attention is introduced to capture inter‑variable dependencies. As a result ESFM skillfully predicts variables in regions or on pressure levels where no data is present at the initial time, while preserving inter‑variable relationships, for example between temperature, pressure, and humidity. Individual variable tokenization enables different sets of variables to be shuffled during training and simplifies the process of building extensions for new downstream tasks. Adaptive layer norm‑based ensembles allow for a simple yet effective way to transform deterministic ESFM to a probabilistic FM. We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data. Results demonstrate competitive or superior performance relative to state‑of‑the‑art benchmarks. Case studies of Super Typhoon Doksuri (2023) and 2024 sudden stratospheric warming events show accurate positional and magnitude estimations of extreme weather. ESFM retains the strengths of previous foundation models, such as long‑term stability, but facilitates application to a variety of downstream tasks.

PaperId: 1374, https://arxiv.org/pdf/2605.00731.pdf GitHub

Abstract:
While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi‑domain heterogeneous graphs (MDHGs) remains a formidable challenge due to cross‑type feature shifts and intra‑domain relation gaps. Existing global feature alignment methods (PCA or SVD) enforce a shared feature space blindly, which distorts type‑specific semantics and disrupts original topologies, inevitably leading to "Type Collapse" and "Relation Confusion". To address these fundamental limitations, we propose Decoupled relation Subspace Alignment (DRSA), a novel, plug‑and‑play relation‑driven alignment framework. DRSA fundamentally shifts the paradigm by decoupling feature semantics from relation structures. Specifically, it introduces a dual‑relation subspace projection mechanism to coordinate cross‑type interactions within a shared low‑rank relation subspace explicitly. Furthermore, a feature‑structure decoupled representation is designed to decompose aligned features into a semantic projection component and a structural residual term, adaptively absorbing intra‑domain variations. Optimized via a stable alternating minimization strategy based on Block Coordinate Descent, DRSA constructs a well‑calibrated, structure‑aware latent space. Extensive experiments on multiple real‑world benchmark datasets demonstrate that DRSA can be seamlessly integrated as a universal preprocessing module, significantly and consistently enhancing the cross‑domain and few‑shot knowledge transfer capabilities of state‑of‑the‑art GFMs. The code is available at: https://github.com/zhengziyu77/DSRA.

PaperId: 1375, https://arxiv.org/pdf/2605.00664.pdf GitHub

Abstract:
We present a training‑free approach for controllable 3D inpainting based on initial noise optimization. In the structured 3D latent diffusion framework, we observe that the underlying geometric structure is established during the early stages of the diffusion process and exhibits high sensitivity to the initial noise. Such characteristics compromise stability in tasks like inpainting and editing, where the model must ensure strict alignment with the existing context while synthesizing a new structure. In this paper, we introduce a strategy to optimize the initial noise within the structured 3D latent diffusion framework, ensuring high‑fidelity 3D inpainting. Specifically, we update the initial noise by leveraging a backpropagation approximation grounded in the rectified flow model, with the spectral parameterization specially designed for robust and efficient structured 3D latent optimization. Experiments demonstrate consistent improvements in contextual consistency and prompt alignment over representative training‑free inpainting baselines, establishing initial noise control as an independent dimension for 3D inpainting, orthogonal to conventional sampling trajectory manipulation.

PaperId: 1376, https://arxiv.org/pdf/2605.00642.pdf GitHub

Abstract:
Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves as a core capability for autonomous GUI agents. Recent reinforcement learning methods (e.g., GRPO) have achieved strong performance, but they rely on expensive multiple rollouts and suffer from sparse signals on hard samples. These limitations make on‑policy self‑distillation (OPSD), which provides dense token‑level supervision from a single rollout, a promising alternative. However, its applicability to GUI grounding remains unexplored. In this paper, we present GUI‑SD, the first OPSD framework tailored for GUI grounding. First, it constructs a visually enriched privileged context for the teacher using a target bounding box and a Gaussian soft mask, providing informative guidance without leaking exact coordinates. Second, it employs entropy‑guided distillation, which adaptively weights tokens based on digit significance and teacher confidence, concentrating optimization on the most impactful and reliable positions. Extensive experiments on six representative GUI grounding benchmarks show that GUI‑SD consistently outperforms GRPO‑based methods and naive OPSD in both accuracy and training efficiency. Code and training data are available at https://zhangyan‑ucas.github.io/GUI‑SD/.

PaperId: 1377, https://arxiv.org/pdf/2605.00632.pdf GitHub

Abstract:
Automatic generation of executable Blender code from natural language remains challenging, with state‑of‑the‑art LLMs producing frequent syntactic errors and geometrically inconsistent objects. We present BlenderRAG, a retrieval‑augmented generation system that operates on a curated multimodal dataset of 500 expert‑validated examples (text, code, image) across 50 object categories. By retrieving semantically similar examples during generation, BlenderRAG improves compilation success rates from 40.8% to 70.0% and semantic normalized alignment from 0.41 to 0.77 (CLIP similarity) across four state‑of‑the‑art LLMs, without requiring fine‑tuning or specialized hardware, making it immediately accessible for deployment. The dataset and code will be available at https://github.com/MaxRondelli/BlenderRAG.

PaperId: 1378, https://arxiv.org/pdf/2605.00551.pdf GitHub

Abstract:
AI agents that interact with graphical user interfaces (GUIs) require effective observation representations for reliable grounding. The accessibility tree is a commonly used text‑based format that encodes UI element attributes, but it suffers from redundancy and lacks structural information such as spatial relationships among elements. We propose A11y‑Compressor, a framework that transforms linearized accessibility trees into compact and structured representations. Our implementation, Compressed‑a11y, applies a lightweight and structured transformation pipeline with modal detection, redundancy reduction, and semantic structuring. Experiments on the OSWorld benchmark show that Compressed‑a11y reduces input tokens to 22% of the original while improving task success rates by 5.1 percentage points on average.

PaperId: 1379, https://arxiv.org/pdf/2605.00529.pdf GitHub

Abstract:
Retrieval‑augmented generation (RAG) enhances large language models with external knowledge, and tree‑based RAG organizes documents into hierarchical indexes to support queries at multiple granularities. However, existing Tree‑RAG methods designed for single‑document retrieval face critical challenges in scaling to cross‑document multi‑hop questions: (1) poor distribution adaptability, where k‑means clustering introduces noise due to rigid distribution assumptions; (2) structural isolation, as tree indexes lack explicit cross‑document connections; and (3) coarse abstraction, which obscures fine‑grained details. To address these limitations, we propose Ψ‑RAG, a tree‑RAG framework with two key components. First, a hierarchical abstract tree index built through an iterative "merging and collapse" process that adapts to data distributions without a priori assumption. Second, a multi‑granular retrieval agent that intelligently interacts with the knowledge base with reorganized queries and an agent‑powered hybrid retriever. Ψ‑RAG supports diverse tasks from token‑level question answering to document‑level summarization. On cross‑document multi‑hop QA benchmarks, it outperforms RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1 score. Code is available at https://github.com/Newiz430/Psi‑RAG.

PaperId: 1380, https://arxiv.org/pdf/2605.00421.pdf GitHub

Abstract:
Large language models (LLMs) show promise in radiology but their deployment is limited by computational requirements that preclude use in resource‑constrained clinical environments. We investigate whether small language models (SLMs) of 3‑4 billion parameters can achieve strong multi‑task radiology performance through LoRA fine‑tuning, enabling deployment on consumer‑grade CPUs. We train Qwen2.5‑3B‑Instruct and Qwen3‑4B on 162K samples spanning 9 radiology tasks ‑ RADS classification across 10 systems, impression generation, temporal comparison, radiology NLI, NER, abnormality detection, N/M staging, and radiology Q&A ‑ compiled from 12 public datasets. Both models are evaluated on up to 500 held‑out test samples per task with standardized metrics. Our key findings are: (1) LoRA fine‑tuning dramatically improves performance over zero‑shot baselines (RADS accuracy +53%, NLI +60%, N‑staging +89%); (2) the two models exhibit complementary strengths ‑ Qwen2.5 excels at structured generation tasks while Qwen3 dominates extractive tasks; (3) a task‑outed oracle ensemble combining both models achieves the best performance across all tasks; (4) few‑shot prompting with fine‑tuned models hurts performance, demonstrating that LoRA adaptation is more effective than in‑context learning for specialized domains; and (5) models can be quantized to GGUF format (~1.8‑2.4GB) for CPU deployment at 4‑8 tokens/second on consumer hardware. Our work demonstrates that small, efficiently fine‑tuned models ‑ which we collectively call RadLite ‑ can serve as practical multi‑task radiology AI assistants deployable entirely on consumer hardware without GPU requirements. Code and models are available at https://github.com/RadioX‑Labs/RadLite

PaperId: 1381, https://arxiv.org/pdf/2605.00410.pdf GitHub

Abstract:
A multi‑agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings, but naively merged calls silently degrade quality through tool loss and prompt compression. We present Agent Capsules, an adaptive execution runtime that treats multi‑agent pipeline execution as an optimization problem with empirical quality constraints. The runtime instruments coordination overhead per group, scores composition opportunity, selects among three compound execution strategies, and gates every mode switch on rolling‑mean output quality. A controlled negative result confirms that injecting more context into a merged call worsens compression rather than relieving it, so the framework's escalation ladder (standard, then two‑phase, then sequential) recovers quality by moving toward per‑agent dispatch rather than by rewriting merged prompts. On LLM‑judged quality, the controller matches a hand‑tuned oracle on every measured (model, group, mode) cell: routing compound whenever the oracle would, and reverting to fine whenever quality would fail the floor, without per‑model configuration. Against a hand‑crafted LangGraph implementation of a 14‑agent competitive intelligence pipeline, Agent Capsules uses 51% fewer fine‑mode input tokens and 42% fewer compound‑mode input tokens, at +0.020 and +0.017 quality respectively. Against a DSPy implementation of a 5‑agent due diligence pipeline, the framework uses 19% fewer tokens than uncompiled DSPy at quality parity, and 68% fewer tokens than MIPROv2 at +0.052 quality. Even before compound mode fires, the runtime delivers efficiency through automatic policy resolution, cache‑aligned prompts, and topology‑aware context injection, matching both hand‑tuned and compile‑time baselines without training data or per‑pipeline engineering.

PaperId: 1382, https://arxiv.org/pdf/2605.00336.pdf GitHub

Abstract:
A key challenge for large language models is token cost per query and overall deployment cost. Clinical inputs are long, heterogeneous, and often redundant, while downstream tasks are short and high stakes. We study budgeted context selection, where a subset of document units is chosen under a strict token budget so an off‑the‑shelf generator can meet fixed cost and latency constraints. We cast this as a knapsack‑constrained subset selection problem with two design choices, unitization that defines document segmentation and selection that determines which units are kept. We propose RCD, a monotone submodular objective that balances relevance, coverage, and diversity. We compare sentence, section, window, and cluster‑based unitization, and introduce a routing heuristic that adapts to the budget regime. Experiments on MIMIC discharge notes, Cochrane abstracts, and L‑Eval show that optimal strategies depend on the evaluation setting. Positional heuristics perform best at low budgets in extractive tasks, while diversity‑aware methods such as MMR improve LLM generation. Selector choice matters more than unitization, with cluster‑based grouping reducing performance and other schemes behaving similarly. ROUGE saturates for LLM summaries, while BERTScore better reflects quality differences. We release our code at https://github.com/stone‑technologies/ACL_budget_paper.

PaperId: 1383, https://arxiv.org/pdf/2605.00327.pdf GitHub

Abstract:
In large language model (LLM)‑based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferences, requiring multi‑negative objective functions to leverage abundant implicit‑feedback negatives and sharpen preference boundaries. However, our empirical analyses reveal a counterintuitive phenomenon, preference optimization collapse, where increasing the number of negative samples can lead to performance degradation despite a continuously decreasing training loss. We further theoretically demonstrate that this collapse arises from gradient suppression, caused by the dominance of easily discriminable negatives over boundary‑critical negatives that truly define user preference boundaries. As a result, boundary‑relevant signals are under‑optimized, weakening the model's decision boundary. Motivated by these observations, we propose DynamicPO (Dynamic Preference Optimization), a lightweight and plug‑and‑play framework comprising two adaptive mechanisms: Dynamic Boundary Negative Selection, which identifies and prioritizes informative negatives near the model's decision boundary, and Dual‑Margin Dynamic beta Adjustment, which calibrates optimization strength per sample according to boundary ambiguity. Extensive experiments on three public datasets show that DynamicPO effectively prevents optimization collapse and improves recommendation accuracy on multi‑negative preference optimization methods, with negligible computational overhead. Our code and datasets are available at https://github.com/xingyuHuxingyu/DynamicPO.

PaperId: 1384, https://arxiv.org/pdf/2605.00185.pdf GitHub

Abstract:
Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit distinct predictive patterns, the distillation process struggles to simultaneously preserve informative signals for all subgroups, regardless of whether group sizes are mildly or severely imbalanced. Consequently, models trained on distilled data can experience substantial performance drops for certain subgroups, leading to fairness gaps. Crucially, these gaps do not disappear by merely correcting group imbalance, since they stem from fundamental mismatches in subgroup predictive patterns rather than from sample‑size disparities alone. We therefore formally analyze the interaction between these two sources of bias and cast the solution as identifying a group‑imbalance‑agnostic barycenter of the predictive information that induces similar representations across all subgroups. By distilling toward this shared aggregate representation, we show that group fairness concerns can be reduced. Our approach is compatible with existing distillation methods, and empirical results show that it substantially reduces bias introduced by dataset distillation. Code is available at https://github.com/mhmoslemi/COBRA.

PaperId: 1385, https://arxiv.org/pdf/2605.00113.pdf GitHub

Abstract:
We examine if frontier chat‑based large language models (LLMs) adjust their outputs based on neurodivergence (ND) context in system prompts and describe the nature of these adjustments. Specifically, we propose NDBench, a 576‑output benchmark involving two frontier models, three system prompt types (baseline, ND‑profile assertion, and ND‑profile assertion with explicit instructions for adjustments), four canonical ND profiles, and 24 prompts across four categories, one of which involves an adversarial masking strategy. Four trends emerge consistently from our findings. First, LLMs show significant adaptation under ND context, where fully instructed conditions yield lengthier and more structured outputs, characterized by higher token counts, more headings, and more granular steps (p < 10^‑8, Holm‑corrected). Second, such adaptation is largely structural in nature: although list density does not change much, there is a marked rise in the frequency of headings and per‑step detail. Third, ND persona assertion alone fails to suppress potentially harmful tendencies, as masking‑reinforcement decreases only in explicitly instructed cases (36‑44% reduction); the reduction rate barely changes in persona assertion conditions. Moreover, reliability analysis of LLM‑based harm assessment reveals that only two out of the six dimensions (masking and reinforcement, validation quality) exceed the pre‑defined inter‑judge agreement criterion (alpha >= 0.67) and thus can be considered primary results. NDBench is made publicly available along with its prompts, outputs, code, and other resources, forming a reproducible framework for auditing future LLMs' adaptation to ND awareness.

PaperId: 1386, https://arxiv.org/pdf/2605.00074.pdf GitHub

Abstract:
DNA‑synthesis providers screen incoming orders by searching the requested sequence against curated hazard lists. We show that this baseline collapses to a 100% false‑flag rate when the hazardous sequence comes from a taxonomic family absent from the reference set: under Conformal Risk Control's certified miss‑rate constraint, a low‑discrimination signal forces the threshold below the entire test‑benign mass. We compose three signals derived from a synthesis order's public annotation: k‑mer Jaccard similarity to known toxins, the trimmed‑mean score of a five‑LLM judge panel, and cosine similarity to clustered embedding centroids. Fused under a monotone logistic aggregator and calibrated by Conformal Risk Control, the resulting screener certifies \mathbbE[\mathrmFNR] \le α. Across ten leave‑one‑taxonomic‑family‑out folds at α=0.05 on UniProt KW‑0800 reviewed toxins, the calibrated screener achieves 0% test miss rate on every fold and 0% test false‑flag rate on nine of ten folds. The bound's finite‑sample slack 1/(n_\mathrmcal+1) caps the certifiable miss rate at 1.77% on our 200‑hazard subsample; reaching procurement‑grade α=10^‑3 requires an 18× larger calibration set, which the full reviewed UniProt KW‑0800 corpus is large enough to deliver. The binding constraint on certifiable DNA‑synthesis screening is calibration data, not algorithms. Code: https://github.com/najmulhasan‑code/crc‑screen

PaperId: 1387, https://arxiv.org/pdf/2604.28156.pdf GitHub

Abstract:
We present FlexiTac, a low‑cost, open‑source, and scalable piezoresistive tactile sensing solution designed for robotic end‑effectors. FlexiTac is a practical "plug‑in" module consisting of (i) thin, flexible tactile sensor pads that provide dense tactile signals and (ii) a compact multi‑channel readout board that streams synchronized measurements for real‑time control and large‑scale data collection. FlexiTac pads adopt a sealed three‑layer laminate stack (FPC‑Velostat‑FPC) with electrode patterns directly integrated into flexible printed circuits, substantially improving fabrication throughput and repeatability while maintaining mechanical compliance for deployment on both rigid and soft grippers. The readout electronics use widely available, low‑cost components and stream tactile signals to a host computer at 100 Hz via serial communication. Across multiple configurations, including fingertip pads and larger tactile mats, FlexiTac can be mounted on diverse platforms without major mechanical redesign. We further show that FlexiTac supports modern tactile learning pipelines, including 3D visuo‑tactile fusion for contact‑aware decision making, cross‑embodiment skill transfer, and real‑to‑sim‑to‑real fine‑tuning with GPU‑parallel tactile simulation. Our project page is available at https://flexitac.github.io/.

PaperId: 1388, https://arxiv.org/pdf/2604.28123.pdf GitHub

Abstract:
The standard post‑training recipe for large multimodal models (LMMs) applies supervised fine‑tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). However, SFT introduces distributional drift that neither preserves the model's original capabilities nor faithfully matches the supervision distribution. This problem is further amplified in multimodal reasoning, where perception errors and reasoning failures follow distinct drift patterns that compound during subsequent RL. We introduce PRISM, a three‑stage pipeline that mitigates this drift by inserting an explicit distribution‑alignment stage between SFT and RLVR. Building on the principle of on‑policy distillation (OPD), PRISM casts alignment as a black‑box, response‑level adversarial game between the policy and a Mixture‑of‑Experts (MoE) discriminator with dedicated perception and reasoning experts, providing disentangled corrective signals that steer the policy toward the supervision distribution without requiring access to teacher logits. While 1.26M public demonstrations suffice for broad SFT initialization, distribution alignment demands higher‑fidelity supervision; we therefore curate 113K additional demonstrations from Gemini 3 Flash, featuring dense visual grounding and step‑by‑step reasoning on the hardest unsolved problems. Experiments on Qwen3‑VL show that PRISM consistently improves downstream RLVR performance across multiple RL algorithms (GRPO, DAPO, GSPO) and diverse multimodal benchmarks, improving average accuracy by +4.4 and +6.0 points over the SFT‑to‑RLVR baseline on 4B and 8B, respectively. Our code, data, and model checkpoints are publicly available at https://github.com/XIAO4579/PRISM.

PaperId: 1389, https://arxiv.org/pdf/2604.28118.pdf GitHub

Abstract:
Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal components often degrade behavior silently without raising runtime errors. Existing fault diagnosis techniques often target generic deep neural networks and cannot identify which transformer component is responsible for an observed symptom. In this article, we present DEFault++, a hierarchical learning‑based diagnostic technique that operates at three level of abstraction: it detects whether a fault is present, classifies it into one of 12 transformer‑specific fault categories (covering both attention‑internal mechanisms and surrounding architectural components), and identifies the underlying root cause from up to 45 mechanisms. To facilitate both training and evaluation, we construct DEFault‑bench, a benchmark of 3,739 labeled instances obtained through systematic mutation testing. These instances are created across seven transformer models and nine downstream tasks using DEForm, a transformer‑specific mutation technique we developed for this purpose. DEFault++ measures runtime behavior at the level of individual transformer components. It organizes these measurements through a Fault Propagation Graph (FPG) derived from the transformer architecture. It then produces an interpretable diagnosis using prototype matching combined with supervised contrastive learning. On DEFault‑bench, DEFault++ exceeds an AUROC of 0.96 for detection and a Macro‑F1 of 0.85 for both categorization and root‑cause diagnosis on encoder and decoder architectures. In a developer study with 21 practitioners, the accuracy of choosing correct repair actions increased from 57.1% without support to 83.3% when using DEFault++.

PaperId: 1390, https://arxiv.org/pdf/2604.28028.pdf GitHub

Abstract:
Large language models (LLMs) have revolutionized Text‑to‑SQL generation, allowing users to query structured data using natural language with growing ease. Yet, real‑world deployment remains challenging, especially in complex or unseen schemas, due to inconsistent accuracy and the risk of generating invalid SQL. We introduce Template Constrained Decoding (TeCoD), a system that addresses these limitations by harnessing the recurrence of query patterns in labeled workloads. TeCoD converts historical NL‑SQL pairs into reusable templates and introduces a robust template selection module that uses a fine‑tuned natural language inference model to match or reject queries efficiently. Once the template is selected, TeCoD enforces it during SQL generation through grammar‑constrained decoding, implemented via a novel partitioned strategy that ensures both syntactic validity and efficiency. Together, these components yield up to 36% higher execution accuracy than in‑context learning (ICL) and 2.2x lower latency on matched queries.

PaperId: 1391, https://arxiv.org/pdf/2604.27977.pdf GitHub

Abstract:
Despite recent progress in language models and agents for scientific data‑driven discovery, further advancing their capabilities is held back by the absence of verifiable environments representing real‑world scientific tasks. To fill this gap, we introduce D3‑Gym, the first automatically constructed dataset with verifiable environments for scientific Data‑Driven Discovery. D3‑Gym comprises (1) 565 tasks sourced from 239 real scientific repositories across four disciplines where (2) each task is equipped with a natural language instruction, an executable environment with pre‑installed dependencies, input dataset and artifact previews, a reference code solution, and an automatically synthesized evaluation script. Rigorous evaluation of the quality of the verification signal in D3‑Gym confirms that our evaluation scripts achieve 87.5% agreement with human‑annotated gold standards and strong alignment in domain‑specific evaluation logic, showing their scientific soundness. Further, training on trajectories sampled from D3‑Gym yields consistent and substantial gains across Qwen3 models of varying sizes on ScienceAgentBench, boosting Qwen3‑32B by 7.8 absolute points and substantially shrinking the gap with strong proprietary models. All D3‑Gym artifacts (environments, creation workflow, trajectories, and models) can be found at https://github.com/OSU‑NLP‑Group/D3‑Gym.

PaperId: 1392, https://arxiv.org/pdf/2604.27975.pdf GitHub

Abstract:
Traditional Shot Boundary Detection (SBD) inherently struggles with complex transitions by formulating the task around isolated cut points, frequently yielding corrupted video shots. We address this fundamental limitation by formalizing the Shot Transition Detection (STD) task. Rather than searching for ambiguous points, STD explicitly detects the continuous temporal segments of transitions. To tackle this, we propose TransVLM, a Vision‑Language Model (VLM) framework for STD. Unlike regular VLMs that predominantly rely on spatial semantics and struggle with fine‑grained inter‑shot dynamics, our method explicitly injects optical flow as a critical motion prior at the input stage. Through a simple yet effective feature‑fusion strategy, TransVLM directly processes concatenated color and motion representations, significantly enhancing its temporal awareness without incurring any additional visual token overhead on the language backbone. To overcome the severe class imbalance in public data, we design a scalable data engine to synthesize diverse transition videos for robust training, alongside a comprehensive benchmark for STD. Extensive experiments demonstrate that TransVLM achieves superior overall performance, outperforming traditional heuristic methods, specialized spatiotemporal networks, and top‑tier VLMs. This work has been deployed to production. For more related research, please visit HeyGen Research (https://www.heygen.com/research) and HeyGen Avatar‑V (https://www.heygen.com/research/avatar‑v‑model). Project page: https://chence17.github.io/TransVLM/

PaperId: 1393, https://arxiv.org/pdf/2604.27955.pdf GitHub

Abstract:
Graphical User Interface (GUI) agents have emerged as a promising paradigm for intelligent systems that perceive and interact with graphical interfaces visually. Yet supervised fine‑tuning alone cannot handle long‑horizon credit assignment, distribution shifts, and safe exploration in irreversible environments, making Reinforcement Learning (RL) a central methodology for advancing automation. In this work, we present the first comprehensive overview of the intersection between RL and GUI agents, and examine how this research direction may evolve toward digital inhabitants. We propose a principled taxonomy that organizes existing methods into Offline RL, Online RL, and Hybrid Strategies, and complement it with analyses of reward engineering, data efficiency, and key technical innovations. Our analysis reveals several emerging trends: the tension between reliability and scalability is motivating the adoption of composite, multi‑tier reward architectures; GUI I/O latency bottlenecks are accelerating the shift toward world‑model‑based training, which can yield substantial performance gains; and the spontaneous emergence of System‑2‑style deliberation suggests that explicit reasoning supervision may not be necessary when sufficiently rich reward signals are available. We distill these findings into a roadmap covering process rewards, continual RL, cognitive architectures, and safe deployment, aiming to guide the next generation of robust GUI automation and its agent‑native infrastructure.

PaperId: 1394, https://arxiv.org/pdf/2604.27942.pdf GitHub

Abstract:
Collective intelligence emerges across biological, physical, and artificial systems without central coordination, yet a unifying principle governing such behaviour remains elusive. The Free Energy Principle explains how individual agents adapt through variational inference, while game theory formalises strategic interactions. Here we introduce the Game‑Theoretic Free Energy Principle, a unified framework showing that multi‑agent systems performing local free‑energy minimisation implicitly implement a stochastic game. We prove that, under bounded rationality and local information constraints, stationary points of collective free energy correspond to approximate Nash equilibria of an induced game. Conversely, a broad class of cooperative games admits a variational representation in which equilibria arise as Gibbs distributions over coalitions, establishing a bridge between Bayesian inference and strategic interaction. To characterise higher‑order effects, we introduce a free‑energy formulation of the Harsanyi dividend, isolating irreducible multi‑agent synergy. This yields a predictive theory of cooperation, including a falsifiable non‑monotonic relationship between sensory precision and agent influence. We validate this prediction across neural, biological, and artificial multi‑agent systems. These results identify a common variational principle underlying inference, thermodynamics, and game‑theoretic equilibrium.

PaperId: 1395, https://arxiv.org/pdf/2604.27852.pdf GitHub

Abstract:
Although precise recall is a core objective in Retrieval‑Augmented Generation (RAG), a critical oversight persists in the field: improvements in retrieval performance do not consistently translate to commensurate gains in downstream reasoning. To diagnose this gap, we propose the Recall Conversion Rate (RCR), a novel evaluation metric to quantify the contribution of retrieval to reasoning accuracy. Our quantitative analysis of mainstream RAG methods reveals that as Recall@5 improves, the RCR exhibits a near‑linear decay. We identify the neglect of retrieval quality in these methods as the underlying cause. In contrast, approaches that focus solely on quality optimization often suffer from inferior recall performance. Both categories lack a comprehensive understanding of retrieval quality optimization, resulting in a trade‑off dilemma. To address these challenges, we propose comprehensive retrieval quality optimization criteria and introduce the NeocorRAG framework. This framework achieves holistic retrieval quality optimization by systematically mining and utilizing Evidence Chains. Specifically, NeocorRAG first employs an innovative activated search algorithm to obtain a refined candidate space. Then it ensures precise evidence chain generation through constrained decoding. Finally, the retrieved set of evidence chains guides the retrieval optimization process. Evaluated on benchmarks including HotpotQA, 2WikiMultiHopQA, MuSiQue, and NQ, NeocorRAG achieves SOTA performance on both 3B and 70B parameter models, while consuming less than 20% of tokens used by comparable methods. This study presents an efficient, training‑free paradigm for RAG enhancement that effectively optimizes retrieval quality while maintaining high recall. Our code is released at https://github.com/BUPT‑Reasoning‑Lab/NeocorRAG.

PaperId: 1396, https://arxiv.org/pdf/2604.27819.pdf GitHub

Abstract:
Multi‑server MCP agents create an information‑flow control problem: faithful tool composition can turn individually benign read/write permissions into cross‑boundary credential propagation ‑‑ a structural side effect of workflow topology, not necessarily malicious model behavior. We present MCPHunt, to our knowledge the first controlled benchmark that isolates non‑adversarial, verbatim credential propagation across multi‑server MCP trust boundaries, with three methodological contributions: (1) canary‑based taint tracking that reduces propagation detection to objective string matching; (2) an environment‑controlled coverage design with risky, benign, and hard‑negative conditions that validates pipeline soundness and controls for credential‑format confounds; (3) CRS stratification that disentangles task‑mandated propagation (faithful execution of verbatim‑transfer instructions) from policy‑violating propagation (credentials included despite the option to redact). Across 3,615 main‑benchmark traces from 5 models spanning 147 tasks and 9 mechanism families, policy‑violating propagation rates reach 11.5‑‑41.3% across all models. This propagation is pathway‑specific (25x cross‑mechanism range) and concentrated in browser‑mediated data flows; hard‑negative controls provide evidence that production‑format credentials are not necessary ‑‑ prompt‑directed cross‑boundary data flow is sufficient. A prompt‑mitigation study across 3 models reduces policy‑violating propagation by up to 97% while preserving 80.5% utility, but effectiveness varies with instruction‑following capability ‑‑ suggesting that prompt‑level defenses alone may not suffice. Code, traces, and labeling pipeline are released under MIT and CC BY 4.0.

PaperId: 1397, https://arxiv.org/pdf/2604.27766.pdf GitHub

Abstract:
Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic speakers continue to value poetry, existing research on Arabic poetry within Large Language Models (LLMs) has primarily focused on analysis tasks such as interpretation or metadata prediction, e.g., rhyme schemes and titles. In contrast, our work addresses the practical aspect of poetry creation in Arabic by introducing controllable generation capabilities to assist users in writing poetry. Specifically, we present a large‑scale, carefully curated instruction‑based dataset in Modern Standard Arabic (MSA) and various Arabic dialects. This dataset enables tasks such as writing, revising, and continuing poems based on predefined criteria, including style and rhyme, as well as performing poetry analysis. Our experiments show that fine‑tuning LLMs on this dataset yields models that can effectively generate poetry that is aligned with user requirements, based on both automated metrics and human evaluation with native Arabic speakers. The data and the code are available at https://github.com/mbzuai‑nlp/instructpoet‑ar

PaperId: 1398, https://arxiv.org/pdf/2604.27691.pdf GitHub GitHub

Abstract:
Across millennia, complex societies have faced the same coordination problem of how to organize collective action among cognitively bounded and informationally incomplete individuals. Different civilizations developed different political institutions to answer the same basic questions of who proposes, who reviews, who executes, and how errors are corrected. We argue that multi‑agent systems built on large language models face the same challenge. Their central problem is not only individual intelligence, but collective organization. Historical institutions therefore provide a structured design space for multi‑agent architectures, making key trade‑offs between efficiency and error correction, centralization and distribution, and specialization and redundancy empirically testable. We translate seven historical political institutions, spanning four canonical governance patterns, into executable multi‑agent architectures and evaluate them under identical conditions across three large language models and two benchmarks. We find that governance topology strongly shapes collective performance. Within a single model, the gap between the best and worst institution exceeds 57 percentage points, while the optimal architecture shifts systematically with model capability and task characteristics. These results suggest that collective intelligence will not advance through a single optimal organizational form, but through governance mechanisms that can be reselected and reconfigured as tasks and capabilities evolve. More broadly, this points to a transition from self‑evolving agents to the self‑evolving multi‑agent system. The code is available on \hrefhttps://github.com/cf3i/SocialSystemArenaGitHub.

PaperId: 1399, https://arxiv.org/pdf/2604.27606.pdf GitHub

Abstract:
Learning informative representations from tabular data in remote sensing and environmental science is challenging due to heterogeneity, scarce labels, and redundancy among features. We present ZAYAN (Zero‑Anchor dYnamic feAture eNcoding), a self‑supervised, feature‑centric contrastive framework for tabular data. ZAYAN performs contrastive learning at the feature rather than sample level, removing the need for explicit anchor selection and any reliance on class labels, while encouraging a redundancy‑minimized, disentangled embedding space. The framework has two modules: ZAYAN‑CL, which pretrains feature embeddings via a zero‑anchor contrastive objective with dynamic perturbations and masking, and ZAYAN‑T, a Transformer that conditions on these embeddings for downstream classification. Across eight datasets, including six remote‑sensing tabular benchmarks and two remote‑sensing‑driven flood‑prediction tables from satellite and GIS products, ZAYAN achieves superior accuracy, robustness, and generalization over tabular deep learning baselines, with consistent gains under label scarcity and distribution shift. These results indicate that feature‑level contrastive learning and dynamic feature encoding provide an effective recipe for learning from tabular sensing data.

PaperId: 1400, https://arxiv.org/pdf/2604.27550.pdf GitHub

Abstract:
Privacy policies are essential for users to understand how service providers handle their personal data. However, these documents are often long and complex, as well as filled with technobabble and legalese, causing users to unknowingly accept terms that may even contradict the law. While summarizing and interpreting these privacy policies is crucial, there is a lack of high‑quality English parallel corpus optimized for legal clarity and readability. To address this issue, we introduce APPSI‑139, a high‑quality English privacy policy corpus meticulously annotated by domain experts, specifically designed for summarization and interpretation tasks. The corpus includes 139 English privacy policies, 15,692 rewritten parallel corpora, and 36,351 fine‑grained annotation labels across 11 data practice categories. Concurrently, we propose TCSI‑pp‑V2, a hybrid privacy policy summarization and interpretation framework that employs an alternating training strategy and coordinates multiple expert modules to effectively balance computational efficiency and accuracy. Experimental results show that the hybrid summarization system built on APPSI‑139 corpus and the TCSI‑pp‑V2 framework outperform large language models, such as GPT‑4o and LLaMA‑3‑70B, in terms of readability and reliability. The source code and dataset are available at https://github.com/EnlightenedAI/APPSI‑139.

PaperId: 1401, https://arxiv.org/pdf/2604.27359.pdf GitHub

Abstract:
Intent‑based networking promises to revolutionize telecommunications network management by enabling operators to specify high‑level goals rather than low‑level configurations. The TM Forum Intent Ontology (tio) provides a standardized vocabulary for expressing network intents, yet lacks formal validation mechanisms to ensure intent correctness before its admission. We present tio‑shacl, the first comprehensive SHACL (Shapes Constraint Language) validation framework for the TMF Intent Ontology. Our contribution includes 56 node shapes and 69 property shapes across all 15 tio v3.6.0 ontology modules, a reusable constraint library with 25 parameterized SPARQL‑based constraint components, and novel validation patterns for recursive logical operators, quantity‑based constraints, and cross‑expectation relationships. We pursued 100% vocabulary coverage (87 classes, 109 properties, 72 functions), cross‑implementation compatibility across three major SHACL engines, and validation accuracy on a corpus of 133 test cases. tio‑shacl is publicly available under MIT license at https://github.com/EricssonResearch/tio‑shacl and enables automated syntactic and semantic validation of network intents, addressing a critical gap in the field.

PaperId: 1402, https://arxiv.org/pdf/2604.27292.pdf GitHub

Abstract:
Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In nearly all deployed AI systems, these boundaries are defined independently, creating three regions: governed capabilities (the only useful region), ungoverned capabilities (risk), and governance policies that address non‑existent capabilities (theater). Two of the three regions are failure modes. We focus on the governance of effects: actions that AI systems perform in the world (API calls, database writes, tool invocations). This is distinct from the governance of model outputs (content quality, bias, fairness), which operates at a different level and requires different mechanisms. We present a formal framework for analyzing this structural gap. Rice's theorem (1953) proves the gap is undecidable in the general case for any Turing‑complete architecture that attempts to govern effects behaviorally: no algorithm can decide non‑trivial semantic properties of arbitrary programs, including the property "this program's effects comply with the governance policy." We define coterminous governance: a system property where the expressivenessboundary equals the governance boundary. We show that coterminous governance requires an architectural decision (separatingcomputation from effect) rather than a governance layer added after the fact. We show that structural governance under this separation subsumes separate governance infrastructure: governance checks become part of the execution pipeline rather than a second system running alongside it. We propose coterminous governance as the testable criterion for any AI governance system: either the two boundaries are provably identical, or risk and theater are structurally inevitable. Proofs are mechanized in Coq (454 theorems, 36 modules, 0 admitted).

PaperId: 1403, https://arxiv.org/pdf/2604.27289.pdf GitHub

Abstract:
We present five results in the theory of structural governance for cognitive workflow systems. Three are mechanized in Coq 8.19 using the Interaction Trees library with parameterized coinduction; two are proved on paper with explicit reductions. The Coinductive Safety Predicate (gov_safe) is a coinductive property that captures governance safety for infinite program behaviors, indexed by a boolean permission flag that is provably false for ungoverned I/O and true for governed interpretations (mechanized). The Governance Invariance Theorem establishes that governance is uniform across the meta‑recursive tower: governance at level n+1 reduces to governance at level n by definitional equality of the type (mechanized). The Sufficiency Theorem proves that four atomic primitives (code, reason, memory, call) are expressively complete for any discrete intelligent system, formalized as compositional closure of a Kleisli category (mechanized). The Alternating Normal Form provides a canonical decomposition of any machine into alternating code and effect layers, with a confluent rewriting system (paper proof). The Necessity Theorem proves via explicit reduction to Rice's theorem that an architecturally opaque component (the reason primitive) is mathematically necessary for problems requiring semantic judgment (paper proof). A sixth contribution connects the abstract model to the deployed runtime: the Verified Interpreter Specification formalizes the BEAM runtime's trust, capability, and hash chain logic in Coq, then tests the running system against this specification using property‑based testing with over 70,000 randomly generated directive sequences and zero disagreements. The mechanization comprises approximately 12,000 lines across 36 modules with 454 theorems and zero admitted lemmas.

PaperId: 1404, https://arxiv.org/pdf/2604.27277.pdf GitHub

Abstract:
Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning‑based methods remain task‑specific and require substantial labeled data. Here we show that a single self‑supervised representation can generalize across heterogeneous brain MRI endpoints. We trained BrainDINO, a self‑distilled foundation model, on approximately 6.6 million unlabeled axial slices from 20 datasets encompassing broad variation in population, disease, and acquisition setting. Using a frozen encoder with lightweight task heads, BrainDINO supported transfer across tumor segmentation, neurodegenerative and neurodevelopmental conditions classification, brain age estimation, post‑stroke temporal prediction, molecular status prediction, MRI sequence classification, and survival modeling. Across tasks and supervision regimes, BrainDINO consistently equaled or exceeded natural‑image and MRI‑specific self‑supervised baselines, with particularly strong advantages under label scarcity. Representation analyses further showed anatomically organized and pathology‑sensitive feature structure in the absence of task‑specific supervision. Our findings indicate that large‑scale slice‑wise self‑supervised learning can yield a unified brain MRI representation that supports diverse neuroimaging tasks without volumetric pretraining or full‑network fine‑tuning, establishing a scalable foundation for robust and data‑efficient brain imaging analysis. Code is available at https://github.com/mclwu22/BrainDINO

PaperId: 1405, https://arxiv.org/pdf/2604.27221.pdf GitHub

Abstract:
Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and heterogeneous sources. Current systems struggle on both fronts. Breadth‑oriented tasks demand schema‑aligned outputs with wide coverage and cross‑entity consistency, while depth‑oriented tasks require coherent reasoning over long, branching search trajectories. We introduce Web2BigTable, a multi‑agent framework for web‑to‑table search that supports both regimes. Web2BigTable adopts a bi‑level architecture in which an upper‑level orchestrator decomposes the task into sub‑problems and lower‑level worker agents solve them in parallel. Through a closed‑loop run‑‑verify‑‑reflect process, the framework jointly improves decomposition and execution over time via persistent, human‑readable external memory, with self‑evolving updates to each single‑agent. During execution, workers coordinate through a shared workspace that makes partial findings visible, allowing them to reduce redundant exploration, reconcile conflicting evidence, and adapt to emerging coverage gaps. Web2BigTable sets a new state of the art on WideSearch, reaching an Avg@4 Success Rate of 38.50 (7.5× the second best at 5.10), Row F1 of 63.53 (+25.03 over the second best), and Item F1 of 80.12 (+14.42 over the second best). It also generalises to depth‑oriented search on XBench‑DeepSearch, achieving 73.0 accuracy. Code is available at https://github.com/web2bigtable/web2bigtable.

PaperId: 1406, https://arxiv.org/pdf/2604.27085.pdf GitHub GitHub

Abstract:
Fine‑tuning Large Language Models (LLMs) on consumer‑grade GPUs is highly cost‑effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism combined with CPU offloading mitigates these hardware bottlenecks by reducing communication overhead. However, existing PP schedules suffer from an inherent limitation termed the weight binding issue. Binding uneven model stages (e.g., the LM head is large) to GPUs limits the pipeline's throughput to that of the GPU with the heaviest load, leading to severe pipeline bubbles. In this paper, we propose RoundPipe, a novel pipeline schedule that breaks the weight binding constraint on consumer GPU servers. RoundPipe treats GPUs as a pool of stateless execution workers and dynamically dispatches computation stages across devices in a round‑robin manner, achieving a near‑zero‑bubble pipeline. To ensure training correctness and system efficiency, RoundPipe integrates a priority‑aware transfer scheduling engine, a fine‑grained distributed event‑based synchronization protocol, and an automated layer partitioning algorithm. Evaluations on an 8× RTX 4090 server demonstrate that RoundPipe achieves 1.48‑‑2.16× speedups over state‑of‑the‑art baselines when fine‑tuning 1.7B to 32B models. Remarkably, RoundPipe enables LoRA fine‑tuning of the Qwen3‑235B model with 31K sequence length on a single server. RoundPipe is publicly available as an open‑source Python library with comprehensive documentation.

PaperId: 1407, https://arxiv.org/pdf/2604.26962.pdf GitHub

Abstract:
Education is one of the most promising real‑world applications for Large Language Models (LLMs). However, current LLMs rely on static pre‑training knowledge and lack adaptation to individual learners, while existing RAG systems fall short in delivering personalized, guided feedback. To bridge this gap, we present DeepTutor, a fully open‑source agentic framework that unifies citation‑grounded problem tutoring with difficulty‑calibrated question generation. A hybrid personalization engine couples static knowledge grounding with dynamic learner memory, continuously adapting each interaction to the student's evolving needs. The same personalization substrate further extends to adaptive learning workflows, interactive books, and proactive multi‑channel tutoring agents. To evaluate personalized tutoring, we introduce TutorBench, an interactive benchmark incorporating customized learner profiles grounded in university‑level curricula across five domains. We further propose an LLM‑based first‑person interactive evaluation protocol that conducts assessments via a profile‑driven student simulator. Complementary evaluations on established benchmarks, supported by human‑alignment and ablation studies, confirm the framework's robustness and general utility. Results show that DeepTutor improves personalized metrics by 10.8% on average and strengthens general agentic reasoning across five backbone models by 29.4%.

PaperId: 1408, https://arxiv.org/pdf/2604.26951.pdf GitHub

Abstract:
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state‑of‑the‑art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference steps within a single architecture, none address cross‑architecture knowledge transfer, in which the teacher and student differ in architecture, attention mechanism, and tokenizer. We present TIDE, the first framework for cross‑architecture dLLM distillation, comprising three modular components: (1) TIDAL, which jointly modulates distillation strength across training progress and diffusion timestep to account for the teacher's noise‑dependent reliability; (2) CompDemo, which enriches the teacher's context via complementary mask splitting to improve predictions under heavy masking; and (3) Reverse CALM, a cross‑tokenizer objective that inverts chunk‑level likelihood matching, yielding bounded gradients and dual‑end noise filtering. Distilling 8B dense and 16B MoE teachers into a 0.6B student via two heterogeneous pipelines outperforms the baseline by an average of 1.53 points across eight benchmarks, yielding notable gains in code generation, where HumanEval scores reach 48.78 compared to 32.3 for the AR baseline.

PaperId: 1409, https://arxiv.org/pdf/2604.26904.pdf GitHub

Abstract:
Claw‑style environments support multi‑step workflows over local files, tools, and persistent workspace states. However, scalable development around these environments remains constrained by the absence of a systematic framework, especially one for synthesizing verifiable training data and integrating it with agent training and diagnostic evaluation. To address this challenge, we present ClawGym, a scalable framework that supports the full lifecycle of Claw‑style personal agent development. Concretely, we construct ClawGym‑SynData, a diverse dataset of 13.5K filtered tasks synthesized from persona‑driven intents and skill‑grounded operations, paired with realistic mock workspaces and hybrid verification mechanisms. We then train a family of capable Claw‑style models, termed ClawGym‑Agents, through supervised fine‑tuning on black‑box rollout trajectories, and further explore reinforcement learning via a lightweight pipeline that parallelizes rollouts across per‑task sandboxes. To support reliable evaluation, we further construct ClawGym‑Bench, a benchmark of 200 instances calibrated through automated filtering and human‑LLM review. Relevant resources have been released at https://github.com/ClawGym.

PaperId: 1410, https://arxiv.org/pdf/2604.26805.pdf GitHub

Abstract:
Operating and maintaining (O&M) large‑scale online engine systems (eg, search, recommendation and advertising) demands substantial human effort for release monitoring, alert response, and root cause analysis. Despite the inherent suitability of LLM‑based agents for such operational scenarios, the critical bottleneck impeding their practical deployment lies not in reasoning, but in orchestration capability ‑ specifically, the precise selection of relevant data (encompassing metrics, logs, and change events) and applicable knowledge (including handbook‑defined rules and empirically derived practitioner experience) tailored to each individual operational event. Feeding all signals indiscriminately causes dilution and hallucination, while manually curating the event‑to‑(data, knowledge) mapping is intractable under dozens of daily releases. Here we present Bian Que, an agentic operating framework with three contributions: (i) The unified operational paradigm, which abstracts routine daily O&M actions into three canonical patterns: release interception, proactive inspection, and alert root cause analysis; (ii) The flexible Skill Arrangement, each predefined Skill explicitly defines the requisite data and operational knowledge for each specific context. Such Skills can be automatically generated and updated by LLM agents, and can also be iteratively optimized by on‑call engineers via natural language instructions. (iii) The unified self‑evolving mechanism, where each correction signal enables two parallel evolutionary pathways: distilling event memory into knowledge, and targeted refinement of Skills. Deployed on the e‑commerce search engine of KuaiShou, Bian Que reduces alert volume by 75%, achieves 80% root‑cause analysis accuracy, cuts mean time to resolution by over 50%, and attains a 99.0% pass rate on offline evaluations. Codes are at https://github.com/benchen4395/BianQue_Assistant.

PaperId: 1411, https://arxiv.org/pdf/2604.26694.pdf GitHub

Abstract:
We propose X‑WAM, a Unified 4D World Model that unifies real‑time robotic action execution and high‑fidelity 4D world synthesis (video + 3D reconstruction) in a single framework, addressing the critical limitations of prior unified world models (e.g., UWM) that only model 2D pixel‑space and fail to balance action efficiency and world modeling quality. To leverage the strong visual priors of pretrained video diffusion models, X‑WAM imagines the future world by predicting multi‑view RGB‑D videos, and obtains spatial information efficiently through a lightweight structural adaptation: replicating the final few blocks of the pretrained Diffusion Transformer into a dedicated depth prediction branch for the reconstruction of future spatial information. Moreover, we propose Asynchronous Noise Sampling (ANS) to jointly optimize generation quality and action decoding efficiency. ANS applies a specialized asynchronous denoising schedule during inference, which rapidly decodes actions with fewer steps to enable efficient real‑time execution, while dedicating the full sequence of steps to generate high‑fidelity video. Rather than entirely decoupling the timesteps during training, ANS samples from their joint distribution to align with the inference distribution. Pretrained on over 5,800 hours of robotic data, X‑WAM achieves 79.2% and 90.7% average success rate on RoboCasa and RoboTwin 2.0 benchmarks, while producing high‑fidelity 4D reconstruction and generation surpassing existing methods in both visual and geometric metrics.

PaperId: 1412, https://arxiv.org/pdf/2604.26591.pdf GitHub

Abstract:
Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimization scripts, their potential for core algorithm enhancement remains untapped. We introduce MappingEvolve, an open‑source framework that pioneers the use of LLMs to directly evolve technology mapping code. Our method abstracts the mapping process into distinct optimization operators and employs a hierarchical agent‑based architecture, comprising a Planner, Evolver, and Evaluator, to guide the evolutionary search. This structured approach enables strategic and effective code modifications. Experiments show our method significantly outperforms direct evolution and strong baselines, achieving 10.04% area reduction versus ABC and 7.93% versus mockturtle, with 46.6%‑‑96.0% S_overall improvement on EPFL benchmarks, while explicitly navigating the area‑‑delay trade‑off. Our code and data are available at https://github.com/Flians/MappingEvolve.

PaperId: 1413, https://arxiv.org/pdf/2604.26516.pdf GitHub GitHub

Abstract:
Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavior. To address this, we present SAS (Self‑Alignment for Safety), a transformer‑based framework that enables test‑time adaptation in offline safe RL without retraining. In SAS, the main mechanism is self‑alignment: at test time, the pretrained agent generates several imagined trajectories and selects those satisfying the Lyapunov condition. These feasible segments are then recycled as in‑context prompts, allowing the agent to realign its behavior toward safety while avoiding parameter updates. In effect, SAS turns Lyapunov‑guided imagination into control‑invariant prompts, and its transformer architecture admits a hierarchical RL interpretation where prompting functions as Bayesian inference over latent skills. Across Safety Gymnasium and MuJoCo benchmarks, SAS consistently reduces cost and failure while maintaining or improving return.

PaperId: 1414, https://arxiv.org/pdf/2604.26508.pdf GitHub

Abstract:
Deploying Vision‑Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource‑constrained embedded platforms. Conversely, fully offloading inference to the cloud is often impractical in bandwidth‑limited environments, where transmitting raw visual data introduces substantial latency overhead. While recent edge‑cloud collaborative architectures attempt to partition VLM workloads across devices, they typically rely on transmitting fixed‑size representations, lacking adaptability to dynamic network conditions and failing to fully exploit semantic redundancy. In this paper, we propose a progressive semantic communication framework for edge‑cloud VLM inference, using a Meta AutoEncoder that compresses visual tokens into adaptive, progressively refinable representations, enabling plug‑and‑play deployment with off‑the‑shelf VLMs without additional fine‑tuning. This design allows flexible transmission at different information levels, providing a controllable trade‑off between communication cost and semantic fidelity. We implement a full end‑to‑end edge‑cloud system comprising an embedded NXP i.MX95 platform and a GPU server, communicating over bandwidth‑constrained networks. Experimental results show that, at 1 Mbps uplink, the proposed progressive scheme significantly reduces network latency compared to full‑edge and full‑cloud solutions, while maintaining high semantic consistency even under high compression. The implementation code will be released upon publication at https://github.com/open‑ep/ProSemComVLM.

PaperId: 1415, https://arxiv.org/pdf/2604.26206.pdf GitHub

Abstract:
A predecessor pilot (Cacioli, 2026) found that Llama‑3‑8B implements prompted sandbagging as positional collapse rather than answer avoidance. However, fixed option ordering in MMLU‑Pro left open whether this reflected a model‑level position‑dominant policy or dataset‑level distractor structure. This pre‑registered follow‑up (3 models, 2,000 MMLU‑Pro items, 4 conditions, 24,000 primary trials) added cyclic option‑order randomisation as the critical control. The pre‑registered item‑level same‑letter diagnostic did not confirm deterministic position‑tracking (same‑letter rate 37.3%, below the 50% threshold). However, pre‑specified supporting analyses revealed that the response‑position distribution under sandbagging was highly stable under complete content rotation (Pearson r = 0.9994; Jensen‑Shannon divergence = 0.027, compared to 0.386 between honest and sandbagging conditions). Accuracy spiked to 72.1% when the correct answer coincidentally occupied the preferred position E, and fell to 4.3% at position A. The data provide strong evidence for a soft distributional attractor: under sandbagging instruction, the model enters a low‑entropy response‑position basin centred on E/F/G that is highly stable and largely content‑invariant at the aggregate level. Qwen‑2.5‑7B served as a negative control (non‑compliant, no distributional shift). These results provide evidence, at the 7‑9 billion parameter scale, that response‑position entropy is a promising black‑box behavioural signature of this sandbagging mode.

PaperId: 1416, https://arxiv.org/pdf/2604.26173.pdf GitHub

Abstract:
An effective way to scale up test‑time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy and Gemini Deep Think. Existing selection methods often rely on external reward models, which requires training a strong reward model and introduces additional computation overhead. As an alternative, previous approaches have explored intrinsic signals, such as confidence and entropy, but these signals are noisy with naive aggregation. In this work, we observe that high‑entropy tokens tend to cluster into consecutive groups during inference, providing a more stable notion of model uncertainty than individual tokens. Together, these clusters reveal temporal patterns of model uncertainty throughout the inference process. Motivated by this observation, we propose to use the temporal structure of uncertainty as an intrinsic reward. To this end, we first formalize the basic unit of segment‑level uncertainty as the High Entropy Phase (HEP), a variable‑length segment that begins at a high‑entropy token and ends when consecutive low‑entropy tokens appear. We then define the Entropy Centroid, inspired by the concept of the center of mass in physics, as the weighted average position of all HEPs along the trajectory. Intuitively, a lower centroid indicates early exploration followed by confident generation, which we find often corresponds to higher response quality. Based on this insight, we propose the Lowest Centroid method, which selects the response with the lowest entropy centroid among multiple candidates. Experiments on mathematics, code generation, logical reasoning, and agentic tasks, across model scales ranging from 14B to 480B, show that Lowest Centroid consistently outperforms existing baselines and delivers stable gains as model size increases. Code is available at https://github.com/hkust‑nlp/entropy‑centroid.

PaperId: 1417, https://arxiv.org/pdf/2604.26130.pdf GitHub

Abstract:
Every RLHF‑trained language model is shaped by a reward model, yet the mechanistic interpretability toolkit ‑‑ logit lens, direct logit attribution, activation patching, sparse autoencoders ‑‑ was built for generative LLMs whose primitives all project onto a vocabulary unembedding. Reward models replace that with a scalar regression head, breaking each tool. We present reward‑lens, an open‑source library that ports this toolkit to reward models, organised around one observation: the reward head's weight vector w_r is the natural axis for every interpretability question. The library provides a Reward Lens, component attribution, three‑mode activation patching, a reward‑hacking probe suite, TopK SAE feature attribution, cross‑model comparison, and five theory‑grounded extensions (distortion index, divergence‑aware patching, misalignment cascade detection, reward‑term conflict analysis, concept‑vector analysis). A ten‑method adapter protocol covers Llama, Mistral, Gemma‑2, and ArmoRM multi‑objective heads, with a generic adapter for any HuggingFace sequence classification model. We validate on two production reward models across ~695 RewardBench pairs. The central empirical finding is negative: linear attribution does not predict causal patching effects (mean Spearman ρ= ‑0.256 on Skywork, ‑0.027 on ArmoRM). The framework treats this disagreement as a property to expose, not a bug ‑‑ motivating a design that keeps observational and causal views first‑class and directly comparable.

PaperId: 1418, https://arxiv.org/pdf/2604.25924.pdf GitHub

Abstract:
Large Language Models have been increasingly employed in the creation of Virtual Assistants due to their ability to generate human‑like text and handle complex inquiries. While these models hold great promise, challenges such as hallucinations, missing information, and the difficulty of providing accurate and context‑specific responses persist, particularly when applied to highly specialized content domains. In this paper, we focus on addressing these challenges by developing a virtual assistant designed to support students at Maastricht University in navigating project‑specific regulations. We propose a virtual assistant based on a Retrieval‑Augmented Generation system that enhances the accuracy and reliability of responses by integrating up‑to‑date, domain‑specific knowledge. Through a robust evaluation framework and real‑life testing, we demonstrate that our virtual assistant can effectively meet the needs of students while addressing the inherent challenges of applying Large Language Models to a specialized educational context. This work contributes to the ongoing discourse on improving LLM‑based systems for specific applications and highlights areas for further research.

PaperId: 1419, https://arxiv.org/pdf/2604.25898.pdf GitHub

Abstract:
Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This setting corresponds to domains where new tasks arise over time, but adapting the model in live environment interactions is expensive, risky, or impossible. However, CORL inherits the dual difficulty of offline reinforcement learning and adapting while preventing catastrophic forgetting. Replay‑based continual learning approaches remain a strong baseline but incur memory overhead and suffer from a distribution mismatch between replayed samples and newly learned policies. At the same time, architectural continual learning methods have shown strong potential in supervised learning but remain underexplored in CORL. In this work, we propose TSN‑Affinity, a novel CORL method based on TinySubNetworks and Decision Transformer. The method enables task‑specific parameterization and controlled knowledge sharing through a RL‑aware reuse strategy that routes tasks according to action compatibility and latent similarity. We evaluate the approach on benchmarks based on Atari games and simulations of manipulation tasks with the Franka Emika Panda robotic arm, covering both discrete and continuous control. Results show strong retention from sparse SubNetworks, with routing further improving multi‑task performance. Our findings suggest that similarity‑guided architectural reuse is a strong and viable alternative to replay‑based strategies in a CORL setting. Our code is available at: https://github.com/anonymized‑for‑submission123/tsn‑affinity.

PaperId: 1420, https://arxiv.org/pdf/2604.25872.pdf GitHub

Abstract:
Training language models via reinforcement learning often relies on imperfect proxy rewards, since ground truth rewards that precisely define the intended behavior are rarely available. Standard metrics for assessing the quality of proxy rewards, such as ranking accuracy, treat incorrect rewards as strictly harmful. In this work, however, we highlight that not all deviations from the ground truth are equal. By theoretically analyzing which outputs attract probability during policy gradient optimization, we categorize reward errors according to their effect on the increase in ground truth reward. The analysis establishes that reward errors, though conventionally viewed as harmful, can also be benign or even beneficial by preventing the policy from stalling around outputs with mediocre ground truth reward. We then present two practical implications of our theory. First, for reinforcement learning from human feedback (RLHF), we develop reward model evaluation metrics that account for the harmfulness of reward errors. Compared to standard ranking accuracy, these metrics typically correlate better with the performance of a language model after RLHF, yet gaps remain in robustly evaluating reward models. Second, we provide insights for reward design in settings with verifiable rewards. A key theme underlying our results is that the effectiveness of a proxy reward function depends heavily on its interaction with the initial policy and learning algorithm.

PaperId: 1421, https://arxiv.org/pdf/2604.25855.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) achieve ever‑stronger performance on visual‑language tasks. Even as traditional visual question answering (VQA) benchmarks approach saturation, reliable deployment requires satisfying low error tolerances in real‑world, out‑of‑distribution (OOD) scenarios. Precisely, selective prediction aims to improve coverage, i.e. the share of inputs the system answers, while adhering to a user‑defined risk level. This is typically achieved by assigning a confidence score to each answer and abstaining on those that fall below a certain threshold. Existing selective prediction methods estimate implicit confidence scores, relying on model internal signals like logits or hidden representations, which are not available for frontier closed‑sourced models. To enable reliable generalization in VQA, we require reasoner models to produce localized visual evidence while answering, and design a selector that explicitly learns to estimate the quality of the localization provided by the reasoner using only model inputs and outputs. We show that SIEVES (Selective Prediction through Visual Evidence Scoring) improves coverage by up to three times on challenging OOD benchmarks (V Bench, HR‑Bench‑8k, MME‑RealWorld‑Lite, VizWiz, and AdVQA), compared to non‑grounding baselines. Beyond better generalization to OOD tasks, the design of the SIEVES selector enables transfer to proprietary reasoners without access to their weights or logits, such as o3 and Gemini‑3‑Pro, providing coverage boosts beyond those attributable to accuracy alone. We highlight that SIEVES generalizes across all tested OOD benchmarks and reasoner models (Pixel‑Reasoner, o3, and Gemini‑3‑Pro), without benchmark‑ or reasoner‑specific training or adaptation. Code is publicly available at https://github.com/hector‑gr/SIEVES .

PaperId: 1422, https://arxiv.org/pdf/2604.25847.pdf GitHub

Abstract:
Optimization modeling underpins real‑world decision‑making in logistics, manufacturing, energy, and public services, but reliably solving such problems from natural‑language requirements remains challenging for current large language models (LLMs). In this paper, we propose \emphAgora‑Opt, a modular agentic framework for optimization modeling that combines decentralized debate with a read‑write memory bank. Agora‑Opt allows multiple agent teams to independently produce end‑to‑end solutions and reconcile them through an outcome‑grounded debate protocol, while memory stores solver‑verified artifacts and past disagreement resolutions to support training‑free improvement over time. This design is flexible across both backbones and methods: it reduces base‑model lock‑in, transfers across different LLM families, and can be layered onto existing pipelines with minimal coupling. Across public benchmarks, Agora‑Opt achieves the strongest overall performance among all compared methods, outperforming strong zero‑shot LLMs, training‑centric approaches, and prior agentic baselines. Further analyses show robust gains across backbone choices and component variants, and demonstrate that decentralized debate offers a structural advantage over centralized selection by enabling agents to refine candidate solutions through interaction and even recover correct formulations when all initial candidates are flawed. These results suggest that reliable optimization modeling benefits from combining collaborative cross‑checking with reusable experience, and position Agora‑Opt as a practical and extensible foundation for trustworthy optimization modeling assistance. Our code and data are available at https://github.com/CHIANGEL/Agora‑Opt.

PaperId: 1423, https://arxiv.org/pdf/2604.25806.pdf GitHub

Abstract:
Creating interactive STEM courseware traditionally requires HTML/CSS/JavaScript expertise, leaving barriers for educators. While generative AI can produce HTML codes, existing tools generate static presentations rather than interactive simulations, struggle with long documents, and lack pedagogical accuracy mechanisms. Furthermore, full regeneration for modifications requires 200‑‑600 seconds, disrupting creative flow. We present MAIC‑UI, a zero‑code authoring system that enables educators to create and rapidly edit interactive courseware from textbooks, PPTs, and PDFs. MAIC‑UI employs: (1) structured knowledge analysis with multi‑modal understanding to ensure pedagogical rigor; (2) a two‑stage generate‑verify‑optimize pipeline separating content alignment from visual refinement; and (3) Click‑to‑Locate editing with Unified Diff‑based incremental generation achieving sub‑10‑second iteration cycles. A controlled lab study with 40 participants shows MAIC‑UI reduces editing iterations (4.9 vs. 7.0) and significantly improves learnability and controllability compared to direct Text‑to‑HTML generation. A three‑month classroom deployment with 53 high school students demonstrates that MAIC‑UI fosters learning agency and reduces outcome disparities ‑‑ the pilot class achieved 9.21‑point gains in STEM subjects compared to ‑2.32 points in control classes. Our code is available at https://github.com/THU‑MAIC/MAIC‑UI.

PaperId: 1424, https://arxiv.org/pdf/2604.25642.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) have achieved remarkable progress in visual‑textual understanding, yet their reliability is critically undermined by hallucinations, i.e., the generation of factually incorrect or inconsistent responses. While recent studies using steering vectors demonstrated promise in reducing hallucinations, a notable challenge remains: they inadvertently amplify the severity of residual hallucinations. We attribute this to their exclusive focus on the decoding stage, where errors accumulate autoregressively and progressively worsen subsequent hallucinatory outputs. To address this, we propose Prefill‑Time Intervention (PTI), a novel steering paradigm that intervenes only once during the prefill stage, enhancing the initial Key‑Value (KV) cache before error accumulation occurs. Specifically, PTI is modality‑aware, deriving distinct directions for visual and textual representations. This intervention is decoupled to steer keys toward visually‑grounded objects and values to filter background noise, correcting hallucination‑prone representations at their source. Extensive experiments demonstrate PTI's significant performance in mitigating hallucinations and its generalizability across diverse decoding strategies, LVLMs, and benchmarks. Moreover, PTI is orthogonal to existing decoding‑stage methods, enabling plug‑and‑play integration and further boosting performance. Code is available at: https://github.com/huaiyi66/PTI.

PaperId: 1425, https://arxiv.org/pdf/2604.25605.pdf GitHub

Abstract:
Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keyword matching, offers substantial advantages for retrieval of clinical information. However, deploying semantic search across entire health systems, comprising hundreds of millions of clinical notes, presents formidable engineering, cost, and governance challenges that have prevented adoption. Methods: We deployed a semantic search system at a large children's hospital indexing 166 million clinical notes (484 million vectors) from 1.68 million patients. The system uses instruction‑tuned qwen3‑embedding‑0.6B embeddings, stores vectors in a managed database with storage‑optimized indexing, maintains full‑text metadata in a low‑latency key‑value store, and operates within a HIPAA‑compliant governance framework. We evaluated the system through three experiments: optimization of embedding model and chunking strategy using a physician‑authored benchmark dataset, characterization of full‑scale performance (cost, latency, retrieval quality), and clinical utility assessment via comparison of chart abstraction efficiency across three tasks. Results: The system delivers sub‑second query latency (median 237 ms single‑user, 451 ms 20‑user concurrency) with monthly costs of approximately USD 4,000. Qwen3 embeddings with 300‑token chunk size achieved 94.6% accuracy on a clinical question‑answering benchmark. In clinical utility evaluation across three abstraction tasks, semantic search reduced time‑to‑completion by 24 to 89% compared to clinician‑performed chart review while maintaining comparable inter‑rater agreement. Conclusion: Health‑system‑scale semantic search is both technically and operationally feasible. The system provides infrastructure supporting interactive search, cohort generation, and downstream LLM‑powered clinical applications without requiring specialized informatics expertise.

PaperId: 1426, https://arxiv.org/pdf/2604.25602.pdf GitHub

Abstract:
Deploying production‑ready multi‑agent systems (MAS) in complex industrial environments remains challenging due to limitations in scalability, observability, and autonomous evolution. We present OxyGent, an open‑source framework driven by two core novelties: a unified Oxy abstraction and the OxyBank evolution engine. The unified abstraction encapsulates agents, tools, LLMs, and reasoning flows as pluggable atomic components, enabling Lego‑like scalable system composition and non‑intrusive monitoring. To enhance observability, OxyGent introduces permission‑driven dynamic planning that replaces rigid workflows with execution graphs generated at runtime, providing adaptive visualizations. Furthermore, to support continuous evolution, OxyBank serves as an AI asset management platform that drives automated data backflow, annotation, and joint evolution. Empirical evaluations and real‑world case studies show that OxyGent provides a robust and scalable foundation for MAS. OxyGent is fully open‑sourced under the Apache License 2.0 at https://github.com/jd‑opensource/OxyGent.

PaperId: 1427, https://arxiv.org/pdf/2604.25555.pdf GitHub

Abstract:
Enterprise software engineering is shifting away from deterministic CRUD/REST architectures toward AI‑native systems where large language models act as cognitive orchestrators. This transition introduces a critical security tension: probabilistic LLMs weaken classical mechanisms for validation, access control, and formal testing. This paper proposes the design, formal validation, and empirical evaluation of a Semantic Gateway governed by the Model Context Protocol (MCP). The gateway reframes the enterprise API as a semantic surface where tools are dynamically discovered, authorized, and executed based on intent and policy enforcement. The central contribution rests on a paradigm shift: autonomous agents must not be validated as traditional software nor as simple API consumers, but as stochastic state‑transition systems whose behavior must be abstracted, fuzzed, and audited through enabled‑tool graphs. The architecture introduces a three‑layer Zero‑Trust security model comprising a pre‑inference Semantic Firewall, deterministic Tool‑Level RBAC, and out‑of‑band Cryptographic Human‑in‑the‑Loop approval. Enabledness‑Preserving Abstractions (EPAs) and greybox semantic fuzzing‑‑originally developed for blockchain smart contract verification‑‑are adapted to audit agent behavior in enterprise environments. Results demonstrate an 84.2% reduction in incidental code. Across 500,000 multi‑turn fuzzing sequences, the methodology achieved a 100% discovery rate of hidden unauthorized state transitions, proving that dynamic formal verification is strictly necessary for secure agentic deployment.

PaperId: 1428, https://arxiv.org/pdf/2604.25326.pdf GitHub

Abstract:
Speculative decoding enhances the inference efficiency of large language models (LLMs) by generating drafts using a small draft language model (DLM) and verifying them in batches with a large target language model (TLM). However, adaptive drafting inference on a mobile single‑NPU‑PIM system faces idle overhead in traditional operator‑level synchronous execution and wasted computation in asynchronous execution due to fluctuations in draft length. This paper introduces AHASD, a task‑level asynchronous mobile NPU‑PIM heterogeneous architecture for speculative decoding. Notably, AHASD achieves parallel drafting on the PIM and verification on a single NPU through task‑level DLM‑TLM decoupling and specifically, it incorporates Entropy‑History‑Aware Drafting Control and Time‑Aware Pre‑Verification Control to dynamically manage adaptive drafting algorithm execution and pre‑verification timing, suppressing invalid drafting based on low‑confidence drafts. Additionally, AHASD integrates Attention Algorithm Units and Gated Task Scheduling Units within LPDDR5‑PIM to enable attention link localization and sub‑microsecond task switching on the PIM side. Experimental results for different LLMs and adaptive drafting algorithms show that AHASD achieves up to 4.2× in throughput and 5.6× in energy efficiency improvements over a GPU‑only baseline, and 1.5× in throughput and 1.24× in energy efficiency gains over the state‑of‑the‑art GPU+PIM baseline, with hardware overhead below 3% of the DRAM area.

PaperId: 1429, https://arxiv.org/pdf/2604.25313.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) models frequently produce answers grounded in parametric memory rather than the retrieved context, undermining the core promise of retrieval augmentation. A fundamental obstacle to fixing this unfaithfulness is the lack of training data that explicitly requires models to prefer context over internal knowledge. We introduce Faithfulness‑QA, a large‑scale dataset of 99,094 samples constructed through counterfactual entity substitution. Starting from two established extractive QA benchmarks‑‑SQuAD and TriviaQA‑‑we automatically identify answer‑bearing named entities in each context, replace them with type‑consistent alternatives drawn from a curated bank of 76,953 entities, and thereby manufacture controlled knowledge conflicts between context and parametric memory. Rigorous quality filtering ensures 100% pass rates across four automated checks on random 200‑sample audits. We release the full dataset, the construction pipeline, and a typed entity bank covering eight named entity categories. Faithfulness‑QA is designed as a training resource for attention‑based faithfulness objectives and as an evaluation benchmark for measuring context‑grounding behavior in RAG systems. Data and code are available at https://github.com/qzhangFDU/faithfulness‑qa‑dataset.

PaperId: 1430, https://arxiv.org/pdf/2604.25306.pdf GitHub

Abstract:
FlashAttention improves efficiency through tiling, but its online softmax still relies on floating‑point arithmetic for numerical stability, making full quantization difficult. We identify three main obstacles to integer‑only FlashAttention: (1) scale explosion during tile‑wise accumulation, (2) inefficient shift‑based exponential operations on GPUs, and (3) quantization granularity constraints requiring uniform scales for integer comparison. To address these challenges, we propose QFlash, an end‑to‑end integer FlashAttention design that performs softmax entirely in the integer domain and runs as a single Triton kernel. On seven attention workloads from ViT, DeiT, and Swin models, QFlash achieves up to 6.73× speedup over I‑ViT and up to 8.69× speedup on Swin, while reducing energy consumption by 18.8% compared to FP16 FlashAttention, without sacrificing Top‑1 accuracy on ViT/DeiT and remaining competitive on Swin under per‑tensor quantization. Our code is publicly available at https://github.com/EfficientCompLab/qflash.

PaperId: 1431, https://arxiv.org/pdf/2604.25256.pdf GitHub

Abstract:
Autonomous scientific research is significantly advanced thanks to the development of AI agents. One key step in this process is finding the right scientific literature, whether to explore existing knowledge for a research problem, or to acquire evidence for verifying assumptions and supporting claims. To assess AI agents' capability in driving this process, we present AutoResearchBench, a dedicated benchmark for autonomous scientific literature discovery. AutoResearchBench consists of two complementary task types: (1) Deep Research, which requires tracking down a specific target paper through a progressive, multi‑step probing process, and (2) Wide Research, which requires comprehensively collecting a set of papers satisfying given conditions. Compared to previous benchmarks on agentic web browsing, AutoResearchBench is distinguished along three dimensions: it is research‑oriented, calling for in‑depth comprehension of scientific concepts; literature‑focused, demanding fine‑grained utilization of detailed information; and open‑ended, involving an unknown number of qualified papers and thus requiring deliberate reasoning and search throughout. These properties make AutoResearchBench uniquely suited for evaluating autonomous research capabilities, and extraordinarily challenging. Even the most powerful LLMs, despite having largely conquered general agentic web‑browsing benchmarks such as BrowseComp, achieve only 9.39% accuracy on Deep Research and 9.31% IoU on Wide Research, while many other strong baselines fall below 5%. We publicly release the dataset and evaluation pipeline to facilitate future research in this direction. We publicly release the dataset, evaluation pipeline, and code at https://github.com/CherYou/AutoResearchBench.

PaperId: 1432, https://arxiv.org/pdf/2604.25220.pdf GitHub

Abstract:
Data videos are a powerful medium for visual data based storytelling, combining animated, chart‑centric visualizations with synchronized narration. Widely used in journalism, education, and public communication, they help audiences understand complex data through clear and engaging visual explanations. Despite their growing impact, generating data‑driven video stories remains challenging, as it requires careful coordination of visual encoding, temporal progression, and narration and substantial expertise in visualization design, animation, and video‑editing tools. Recent advances in large language models offer new opportunities to automate this process; however, there is currently no benchmark for rigorously evaluating models on animated visualization‑based video storytelling. To address this gap, we introduce DataReel, a benchmark for automated data‑driven video story generation comprising 328 real‑world stories. Each story pairs structured data, a chart visualization, and a narration transcript, enabling systematic evaluation of models' abilities to generate animated data video stories. We further propose a multi‑agent framework that decomposes the task into planning, generation, and verification stages, mirroring key aspects of the human storytelling process. Experiments show that this multi‑agent approach outperforms direct prompting baselines under both automatic and human evaluations, while revealing persistent challenges in coordinating animation, narration, and visual emphasis. We release DataReel at https://github.com/vis‑nlp/DataReel.

PaperId: 1433, https://arxiv.org/pdf/2604.25209.pdf GitHub GitHub

Abstract:
Dimensionality reduction methods such as UMAP and t‑SNE are central tools for visualising high‑dimensional data, but their local‑neighborhood objectives can preserve sampling noise while distorting global topology. We show that standard local metrics reward this noise memorisation: top‑performing embeddings invent cycles and disconnected islands absent from the data. We introduce a topology‑faithfulness benchmark based on noisy manifolds with known homology, tune DiRe against it, and find Pareto‑optimal configurations that match or beat GPU‑accelerated UMAP on classification while recovering exact first Betti numbers on stress tests. On 723K arXiv paper embeddings, DiRe preserves 3‑4 times more topological structure than UMAP at comparable wall‑clock.

PaperId: 1434, https://arxiv.org/pdf/2604.25122.pdf GitHub

Abstract:
We present M^3‑VQA, a novel knowledge‑based Visual Question Answering (VQA) benchmark, to enhance the evaluation of multimodal large language models (MLLMs) in fine‑grained multimodal entity understanding and complex multi‑hop reasoning. Unlike existing VQA datasets that focus on coarse‑grained categories and simple reasoning over single entities, M^3‑VQA introduces diverse multi‑entity questions involving multiple distinct entities from both visual and textual sources. It requires models to perform both sequential and parallel multi‑hop reasoning across multiple documents, supported by traceable, detailed evidence and a curated multimodal knowledge base. We evaluate 16 leading MLLMs under three settings: without external knowledge, with gold evidence, and with retrieval‑augmented input. The poor results reveal significant challenges for MLLMs in knowledge acquisition and reasoning. Models perform poorly without external information but improve markedly when provided with precise evidence. Furthermore, reasoning‑aware agentic retrieval surpasses heuristic methods, highlighting the importance of structured reasoning for complex multimodal understanding. M^3‑VQA presents a more challenging evaluation for advancing the multimodal reasoning capabilities of MLLMs. Our code and dataset are available at https://github.com/CASIA‑IVA‑Lab/M3VQA.

PaperId: 1435, https://arxiv.org/pdf/2604.24953.pdf GitHub GitHub

Abstract:
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open‑source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others. Naively optimizing on such noisy datasets fails to learn preferences, hindering effective scaling. To enhance robustness against noise, we propose Poly‑DPO, which extends the DPO objective with an additional polynomial term that dynamically adjusts model confidence based on dataset characteristics, enabling effective learning across diverse data distributions. Beyond biased patterns, existing datasets suffer from low resolution, limited prompt diversity, and imbalanced distributions. To facilitate large‑scale visual preference optimization by tackling data bottlenecks, we construct ViPO, a massive‑scale preference dataset with 1M image pairs at 1024px across five categories and 300K video pairs at 720p+ across three categories. State‑of‑the‑art generative models and diverse prompts ensure reliable preference signals with balanced distributions. Remarkably, when applying Poly‑DPO to our high‑quality dataset, the optimal configuration converges to standard DPO. This convergence validates dataset quality and Poly‑DPO's adaptive nature: sophisticated optimization becomes unnecessary with sufficient data quality, yet remains valuable for imperfect datasets. We validate our approach across visual generation models. On noisy datasets like Pick‑a‑Pic V2, Poly‑DPO achieves 6.87 and 2.32 gains over Diffusion‑DPO on GenEval for SD1.5 and SDXL, respectively. For ViPO, models achieve performance far exceeding those trained on existing open‑source preference datasets. These results confirm that addressing both algorithmic adaptability and data quality is essential for scaling visual preference optimization.

PaperId: 1436, https://arxiv.org/pdf/2604.24952.pdf GitHub

Abstract:
Human visual preferences are inherently multi‑dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holistic annotations, resulting in severe label noise: images that excel in some dimensions but are deficient in others are simply marked as winner or loser. We theoretically demonstrate that compressing multi‑dimensional preferences into binary labels generates conflicting gradient signals that misguide Diffusion Direct Preference Optimization (DPO). To address this, we propose Semi‑DPO, a semi‑supervised approach that treats consistent pairs as clean labeled data and conflicting ones as noisy unlabeled data. Our method starts by training on a consensus‑filtered clean subset, then uses this model as an implicit classifier to generate pseudo‑labels for the noisy set for iterative refinement. Experimental results demonstrate that Semi‑DPO achieves state‑of‑the‑art performance and significantly improves alignment with complex human preferences, without requiring additional human annotation or explicit reward models during training. We will release our code and models at: https://github.com/L‑CodingSpace/semi‑dpo

PaperId: 1437, https://arxiv.org/pdf/2604.24933.pdf GitHub GitHub

Abstract:
General audio foundation models have recently achieved remarkable progress, enabling strong performance across diverse tasks. However, state‑of‑the‑art models remain extremely large, often with hundreds of millions of parameters, leading to high inference costs and limited deployability on edge devices. Knowledge distillation is a proven strategy for model compression, but prior work in audio has mostly focused on supervised settings, relying on class logits, intermediate features, or architecture‑specific techniques. Such assumptions exclude models that output only embeddings, such as self‑supervised or metric‑learning models. We introduce S‑SONDO (Self‑Supervised KnOwledge DistillatioN for General AuDio FOundation Models), the first framework to distill general audio models using only their output embeddings. By avoiding the need for logits or layer‑level alignment, S‑SONDO is architecture‑agnostic and broadly applicable to embedding‑based teachers. We demonstrate its effectiveness by distilling two audio foundation models into three efficient students that are up to 61 times smaller while retaining up to 96% of teacher performance. We also provide practical insights on loss choice and clustering‑based balanced data sampling. Code is available here: https://github.com/MedAliAdlouni/ssondo.

PaperId: 1438, https://arxiv.org/pdf/2604.24929.pdf GitHub

Abstract:
Agent benchmarks remain largely English‑centric, while their multilingual versions are often built with machine translation (MT) and limited post‑editing. We argue that, for agentic tasks, this minimal workflow can easily break benchmark validity through query‑answer misalignment or culturally off‑target context. We propose a refined workflow for adapting English benchmarks into multiple languages with explicit functional alignment, cultural alignment, and difficulty calibration using both automated checks and human review. Using this workflow, we introduce GAIA‑v2‑LILT, a re‑audited multilingual extension of GAIA covering five non‑English languages. In experiments, our workflow improves agent success rates by up to 32.7% over minimally translated versions, bringing the closest audited setting to within 3.1% of English performance while substantial gaps remain in many other cases. This indicates that a substantial share of the multilingual performance gap is benchmark‑induced measurement error, motivating task‑level alignment when adapting English benchmarks across languages. The data is available as part of the MAPS package at https://huggingface.co/datasets/Fujitsu‑FRE/MAPS/viewer/GAIA‑v2‑LILT. We also release the code used in our experiments at https://github.com/lilt/gaia‑v2‑lilt.

PaperId: 1439, https://arxiv.org/pdf/2604.24927.pdf GitHub

Abstract:
Generating diverse responses is crucial for test‑time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface‑level lexical variation, limiting semantic exploration. In this paper, we propose Exploratory Sampling (ESamp), a decoding approach that explicitly encourages semantic diversity during generation. ESamp is motivated by the well‑known observation that neural networks tend to make lower‑error predictions on inputs similar to those encountered before, and incur higher prediction error on novel ones. Building on this property, we train a lightweight Distiller at test time to predict deep‑layer hidden representations of the LLM from its shallow‑layer representations to model the LLM's depth‑wise representation transitions. During decoding, the Distiller continuously adapts to the mappings induced by the current generation context. ESamp uses the prediction error as a novelty signal to reweight candidate token extensions conditioned on the current prefix, thereby biasing decoding toward less‑explored semantic patterns. ESamp is implemented with an asynchronous training‑‑inference pipeline, with less than 5% worst case overhead (1.2% in the optimized release). Empirical results show that ESamp significantly boosts the Pass@k efficiency of reasoning models, showing superior or comparable performance to strong stochastic and heuristic baselines. Notably, ESamp achieves robust generalization across mathematics, science, and code generation benchmarks and breaks the trade‑off between diversity and coherence in creative writing. Our code has released at: https://github.com/LinesHogan/tLLM.

PaperId: 1440, https://arxiv.org/pdf/2604.24920.pdf GitHub

Abstract:
Agentic systems increasingly act with user secrets for APIs, messaging platforms, and cloud services. Today's agent runtimes typically implement authorization by exposure: enabling action often means placing a reusable secret, or a reusable artifact derived from it, inside the runtime, so a transient prompt‑injection or tool‑side compromise becomes durable account compromise. Existing defenses cover adjacent pieces such as secret storage, scoped delegation, sender‑constrained tokens, and runtime monitoring, but leave the combined agentic obligation without a common specification: an untrusted autonomous requester should be able to cause a user‑authorized secret‑backed operation without gaining reusable authority over it. We formalize this as the Agent Secret Use (ASU) problem and identify seven security properties any solution must satisfy, spanning authorization integrity and secret confidentiality. We propose the Secret‑Use Delegation Protocol (SUDP), in which a requester proposes a canonical operation, the user authorizes it with a fresh authenticator‑backed grant, and a custodian redeems the grant to perform the bounded use; reusable authority never crosses the requester boundary. We specialize SUDP for LLM‑driven agents, where it applies whenever a tool call would exercise user‑enrolled authority‑bearing material. Under standard cryptographic assumptions, SUDP satisfies all seven properties when integrated with a hardware‑rooted runtime. A reference implementation is available at https://github.com/xhyumiracle/sudp.

PaperId: 1441, https://arxiv.org/pdf/2604.24881.pdf GitHub

Abstract:
Multi‑agent debate has been shown to improve reasoning in large language models (LLMs). However, it is compute‑intensive, requiring generation of long transcripts before answering questions. To address this inefficiency, we develop a framework that distills multi‑agent debate into a single LLM through a two‑stage fine‑tuning pipeline combining debate structure learning with internalization via dynamic reward scheduling and length clipping. Across multiple models and benchmarks, our internalized models match or exceed explicit multi‑agent debate performance using up to 93% fewer tokens. We then investigate the mechanistic basis of this capability through activation steering, finding that internalization creates agent‑specific subspaces: interpretable directions in activation space corresponding to different agent perspectives. We further demonstrate a practical application: by instilling malicious agents into the LLM through internalized debate, then applying negative steering to suppress them, we show that distillation makes harmful behaviors easier to localize and control with smaller reductions in general performance compared to steering base models. Our findings offer a new perspective for understanding multi‑agent capabilities in distilled models and provide practical guidelines for controlling internalized reasoning behaviors. Code available at https://github.com/johnsk95/latent_agents

PaperId: 1442, https://arxiv.org/pdf/2604.24877.pdf GitHub

Abstract:
Controlling illumination in images is essential for photography and visual content creation. While closed‑source models have demonstrated impressive illumination control, open‑source alternatives either require heavy control inputs like depth maps or do not release their data and code. We present a fully open‑source and reproducible pipeline for learning illumination control in diffusion models. Our approach builds a data engine that transforms well‑lit images into supervised training triplets consisting of a poorly‑illuminated input image, a natural language lighting instruction, and a well‑illuminated output image. We finetune a diffusion model on this data and demonstrate significant improvements over baseline SD 1.5, SDXL, and FLUX.1‑dev models in perceptual similarity, structural similarity, and identity preservation. Our work provides a reproducible solution built entirely with open‑source tools and publicly available data. We release all our code, data, and model weights publicly.

PaperId: 1443, https://arxiv.org/pdf/2604.24833.pdf GitHub

Abstract:
Despite transformative advances in generative motion synthesis, real‑time interactive motion control remains dominated by traditional techniques. In this work, we identify two key challenges in bridging research and production: 1) Real‑time scalability: Industry applications demand real‑time generation of a vast repertoire of motion skills, while generative methods exhibit significant degradation in quality and scalability under real‑time computation constraints, and 2) Integration: Industry applications demand fine‑grained multi‑modal control involving velocity commands, style selection, and precise keyframes, a need largely unmet by existing text‑ or tag‑driven models. To overcome these limitations, we introduce MotionBricks: a large‑scale, real‑time generative framework with a two‑fold solution. First, we propose a large‑scale modular latent generative backbone tailored for robust real‑time motion generation, effectively modeling a dataset of over 350,000 motion clips with a single model. Second, we introduce smart primitives that provide a unified, robust, and intuitive interface for authoring both navigation and object interaction. Applications can be designed in a plug‑and‑play manner like assembling bricks without expert animation knowledge. Quantitatively, we show that MotionBricks produces state‑of‑the‑art motion quality on open‑source and proprietary datasets of various scales, while also achieving a real‑time throughput of 15,000 FPS with 2ms latency. We demonstrate the flexibility and robustness of MotionBricks in a complete production‑level animation demo, covering navigation and object‑scene interaction across various styles with a unified model. To showcase our framework's application beyond animation, we deploy MotionBricks on the Unitree G1 humanoid robot to demonstrate its flexibility and generalization for real‑time robotic control.

PaperId: 1444, https://arxiv.org/pdf/2604.24810.pdf GitHub

Abstract:
Edge computing environments impose strict constraints on energy consumption and latency, making the deployment of deep neural networks a significant challenge. Therefore, smart and adaptive inference strategies that dynamically balance computational cost or latency with predictive accuracy are critical in edge computing scenarios. In this work, we build on Adaptive Deep Neural Networks (ADNNs) that employ the Multi‑Armed Bandit (MAB) framework. Current literature leverages the first version of the Upper Confidence Bound (UCB1) strategy to dynamically select the optimal confidence threshold, enabling efficient early exits without sacrificing accuracy. However, we introduce four additional Upper Confidence Bound strategies in ADNNs, namely UCB‑V, UCB‑Tuned, UCB‑Bayes, and UCB‑BwK, and perform, for the first time, a comparative study of these strategies with respect to trade‑offs between accuracy, energy consumption, and latency. The proposed UCB strategies are employed on the ResNet and MobileViT neural networks, and are evaluated on the benchmark datasets of CIFAR‑10, CIFAR‑10.1, and CIFAR‑100. Experimental results demonstrate that all strategies achieve sub‑linear cumulative regret, with UCB‑Bayes converging the fastest, followed by UCB‑Tuned and UCB‑V. Finally, UCB‑V and UCB‑Tuned dominate the Pareto Frontiers of accuracy‑latency and accuracy‑energy trade‑offs. The implementation code is available here: https://github.com/gr3gor1/MAB_UCB

PaperId: 1445, https://arxiv.org/pdf/2604.24801.pdf GitHub GitHub GitHub

Abstract:
Autoregressive transformers make confident errors that output‑confidence monitoring cannot catch. Activation monitors catch them only when training leaves a decision‑quality signal beyond what the output already exposes. This signal is an architectural property of the trained model, fixed upstream of any monitor. Controlling for output confidence removes 60.3% of the raw activation‑probe signal on average across 14 models. Raw probe signal is mostly output confidence, and output‑side readouts cannot recover the residual. What remains depends on architecture and training. In Pythia's controlled training, both matched‑width configurations form the signal early. One preserves it through convergence while another erases it as perplexity continues to improve. Capability and observability are not inherently in tension. Across independently trained families this pattern persists, even as the collapse point shifts. Where the signal survives, monitoring catches what confidence cannot. On downstream QA, a WikiText‑trained probe with no task‑specific tuning catches about one in eight confident errors that output‑confidence monitoring misses, at a 20% flag rate. These results establish signal engineering as a training‑time design axis alongside loss and capability. Architecture sets the conditions for observability, and training determines what remains readable.

PaperId: 1446, https://arxiv.org/pdf/2604.24697.pdf GitHub GitHub

Abstract:
Discovering causal regularities and applying them to build functional systems‑‑the discovery‑to‑application loop‑‑is a hallmark of general intelligence, yet evaluating this capacity has been hindered by the vast complexity gap between scientific discovery and real‑world engineering. We introduce SciCrafter, a Minecraft‑based benchmark that operationalizes this loop through parameterized redstone circuit tasks. Agents must ignite lamps in specified patterns (e.g., simultaneously or in timed sequences); scaling target parameters substantially increases construction complexity and required knowledge, forcing genuine discovery rather than reliance on memorized solutions. Evaluating frontier models including GPT‑5.2, Gemini‑3‑Pro, and Claude‑Opus‑4.5 under a general‑purpose code agent scaffold, we find that all plateau at approximately 26% success rate. To diagnose these failures, we decompose the loop into four capacities‑‑knowledge gap identification, experimental discovery, knowledge consolidation, and knowledge application‑‑and design targeted interventions whose marginal contributions serve as proxies for corresponding gaps. Our analysis reveals that although the general knowledge application capability still remains as the biggest gap across all models, for frontier models the knowledge gap identification starts to become a major hurdle‑‑indicating the bottleneck is shifting from solving problems right to raising the right problems for current AI. We release SciCrafter as a diagnostic probe for future research on AI systems that navigate the full discovery‑to‑application loop.

PaperId: 1447, https://arxiv.org/pdf/2604.24657.pdf GitHub

Abstract:
Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi‑step actions, and invoke privileged tools. In such systems, security failures rarely remain confined to a single interface; instead, they can propagate across initialization, input processing, memory, decision‑making, and execution, often becoming apparent only when harmful effects materialize in the environment. This paper presents AgentWard, a lifecycle‑oriented, defense‑in‑depth architecture that systematically organizes protection across these five stages. AgentWard integrates stage‑specific, heterogeneous controls with cross‑layer coordination, enabling threats to be intercepted along their propagation paths while safeguarding critical assets. We detail the design rationale and architecture of five coordinated protection layers, and implement a plugin‑native prototype on OpenClaw to demonstrate practical feasibility. This perspective provides a concrete blueprint for structuring runtime security controls, managing trust propagation, and enforcing execution containment in autonomous AI agents. Our code is available at https://github.com/FIND‑Lab/AgentWard .

PaperId: 1448, https://arxiv.org/pdf/2604.24625.pdf GitHub

Abstract:
Unified multi‑modal understanding/generative models have shown improved image editing performance by incorporating fine‑grained understanding into their Chain‑of‑Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can jointly enhance both the understanding granularity and generalization? To address this, we propose Meta‑CoT, a paradigm that performs a two‑level decomposition of any single‑image editing operation with two key properties: (1) Decomposability. We observe that any editing intention can be represented as a triplet ‑ (task, target, required understanding ability). Inspired by this, Meta‑CoT decomposes both the editing task and the target, generating task‑specific CoT and traversing editing operations on all targets. This decomposition enhances the model's understanding granularity of editing operations and guides it to learn each element of the triplet during training, substantially improving the editing capability. (2) Generalizability. In the second decomposition level, we further break down editing tasks into five fundamental meta‑tasks. We find that training on these five meta‑tasks, together with the other two elements of the triplet, is sufficient to achieve strong generalization across diverse, unseen editing tasks. To further align the model's editing behavior with its CoT reasoning, we introduce the CoT‑Editing Consistency Reward, which encourages more accurate and effective utilization of CoT information during editing. Experiments demonstrate that our method achieves an overall 15.8% improvement across 21 editing tasks, and generalizes effectively to unseen editing tasks when trained on only a small set of meta‑tasks. Our code, benchmark, and model are released at https://shiyi‑zh0408.github.io/projectpages/Meta‑CoT/

PaperId: 1449, https://arxiv.org/pdf/2604.24622.pdf GitHub

Abstract:
Flow‑based vision‑language‑action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi‑step inference is required to recover action structure from uninformative Gaussian noise, leading to a poor efficiency‑quality trade‑off under real‑time constraints. We address this issue by rethinking the role of the starting point in generative action modeling. Instead of shortening the sampling trajectory, we propose CF‑VLA, a coarse‑to‑fine two‑stage formulation that restructures action generation into a coarse initialization step that constructs an action‑aware starting point, followed by a single‑step local refinement that corrects residual errors. Concretely, the coarse stage learns a conditional posterior over endpoint velocity to transform Gaussian noise into a structured initialization, while the fine stage performs a fixed‑time refinement from this initialization. To stabilize training, we introduce a stepwise strategy that first learns a controlled coarse predictor and then performs joint optimization. Experiments on CALVIN and LIBERO show that our method establishes a strong efficiency‑performance frontier under low‑NFE (Number of Function Evaluations) regimes: it consistently outperforms existing NFE=2 methods, matches or surpasses the NFE=10 π_0.5 baseline on several metrics, reduces action sampling latency by 75.4%, and achieves the best average real‑robot success rate of 83.0%, outperforming MIP by 19.5 points and π_0.5 by 4.0 points. These results suggest that structured, coarse‑to‑fine generation enables both strong performance and efficient inference. Our code is available at https://github.com/EmbodiedAI‑RoboTron/CF‑VLA.

PaperId: 1450, https://arxiv.org/pdf/2604.24597.pdf GitHub

Abstract:
We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC‑CXR chest radiographs using quantum support vector machines (QSVM) with frozen embeddings from three medical foundation models (MedSigLIP‑448, RAD‑DINO, ViT‑patch32). We propose a two‑tier fair comparison framework in which both classifiers receive identical PCA‑q features. At Tier 1 (untuned QSVM vs. untuned linear SVM, C = 1 both sides), QSVM wins minority‑class F1 in all 18 tested configurations (17 at p < 0.001, 1 at p < 0.01). The classical linear kernel collapses to majority‑class prediction on 90‑100% of seeds at every qubit count, while QSVM maintains non‑trivial recall. At q = 11 (MedSigLIP‑448 plateau center), QSVM achieves mean F1 = 0.343 vs. classical F1 = 0.050 (F1 gain = +0.293, p < 0.001) without hyperparameter tuning. Under Tier 2 (untuned QSVM vs. C‑tuned RBF SVM), QSVM wins all seven tested configurations (mean gain +0.068, max +0.112). Eigenspectrum analysis reveals quantum kernel effective rank reaches 69.80 at q = 11, far exceeding linear kernel rank, while classical collapse remains C‑invariant. A full qubit sweep reveals architecture‑dependent concentration onset across models. Code: https://github.com/sebasmos/qml‑medimage

PaperId: 1451, https://arxiv.org/pdf/2604.24559.pdf GitHub

Abstract:
Chart‑to‑code generation converts a chart image into an executable plotting script, enabling faithful reproduction and editable visualizations. Existing methods are largely Python‑centric, limiting practical use and overlooking a critical source of supervision: the same chart can be expressed by semantically equivalent scripts in different plotting languages. To fill this gap, we introduce Chart2NCode, a dataset of 176K charts paired with aligned scripts in Python, R, and LaTeX that render visually equivalent outputs, constructed via a metadata‑to‑template pipeline with rendering verification and human quality checks. Building on a LLaVA‑style architecture, we further propose CharLuMA, a parameter‑efficient adaptation module that augments the multimodal projector with a language‑conditioned mixture of low‑rank subspaces, allowing the model to share core chart understanding while specializing code generation to the target language through lightweight routing. Extensive experiments show consistent gains in executability and visual fidelity across all languages, outperforming strong open‑source baselines and remaining competitive with proprietary systems. Further analyses reveal that balanced multi‑language supervision benefits all languages and that the adapter allocates a compact shared core plus language‑specific capacity. Codes and data are available at https://github.com/Zhihan72/CharLuMA.

PaperId: 1452, https://arxiv.org/pdf/2604.24542.pdf GitHub

Abstract:
Large language models deployed at runtime can misbehave in ways that clean‑data validation cannot anticipate: training‑time backdoors lie dormant until triggered, jailbreaks subvert safety alignment, and prompt injections override the deployer's instructions. Existing runtime defenses address these threats one at a time and often assume a clean reference model, trigger knowledge, or editable weights, assumptions that rarely hold for opaque third‑party artifacts. We introduce Layerwise Convergence Fingerprinting (LCF), a tuning‑free runtime monitor that treats the inter‑layer hidden‑state trajectory as a health signal: LCF computes a diagonal Mahalanobis distance on every inter‑layer difference, aggregates via Ledoit‑Wolf shrinkage, and thresholds via leave‑one‑out calibration on 200 clean examples, with no reference model, trigger knowledge, or retraining. Evaluated on four architectures (Llama‑3‑8B, Qwen2.5‑7B, Gemma‑2‑9B, Qwen2.5‑14B) across backdoors, jailbreaks, and prompt injection (56 backdoor combinations, 3 jailbreak techniques, and BIPIA email + code‑QA), LCF reduces mean backdoor attack success rate (ASR) below 1% on Qwen2.5‑7B and Gemma‑2 and to 1.3% on Qwen2.5‑14B, detects 92‑100% of DAN jailbreaks (62‑100% for GCG and softer role‑play), and flags 100% of text‑payload injections across all eight (model, domain) cells, at 12‑16% backdoor FPR and <0.1% inference overhead. A single aggregation score covers all three threat families without threat‑specific tuning, positioning LCF as a general‑purpose runtime safety layer for cloud‑served and on‑device LLMs.

PaperId: 1453, https://arxiv.org/pdf/2604.24371.pdf GitHub

Abstract:
Cancer survival prediction from multi‑omics data remains challenging because prognostic signals are high‑dimensional, heterogeneous, and distributed across interacting genes and pathways. We propose PathMoG, a pathway‑centric modular graph neural network for multi‑omics survival prediction. PathMoG reorganizes genome‑scale inputs into 354 KEGG‑informed pathway modules, introduces a Hierarchical Omics Modulation module to condition gene‑expression representations on mutation, copy number variation, pathway, and clinical context, and uses dual‑level attention to capture both intra‑pathway driver signals and inter‑pathway clinical relevance. We evaluated PathMoG on 5,650 patients across 10 TCGA cancer types and observed consistent improvements over representative survival baselines. The framework further provides gene‑level, pathway‑level, and patient‑level interpretability, supporting biologically grounded and clinically relevant risk stratification.

PaperId: 1454, https://arxiv.org/pdf/2604.24357.pdf GitHub

Abstract:
Diffusion language models generate without a fixed left‑to‑right order, making token ordering a central algorithmic choice: which tokens should be revealed, retained, revised or verified at each step? Existing systems mainly use random masking or confidence‑driven ordering. Random masking creates train‑‑test mismatch, while confidence‑only rules are efficient but can be myopic and suppress useful exploration. We introduce DPRM (Doob h‑transform Process Reward Model), a plug‑in token‑ordering module for diffusion language models. DPRM keeps the host architecture, denoising objective and supervision unchanged, and changes only the ordering policy. It starts from confidence‑driven progressive ordering and gradually shifts to Doob h transform Process Reward guided ordering through online estimates. We characterize the exact DPRM policy as a reward‑tilted Gibbs reveal law, prove O(1/N) convergence of the stagewise Soft‑BoN approximation, and show that the online bucketized controller tracks the exact DPRM score at empirical‑Bernstein rates. Under tractable optimization assumptions, DPRM also yields a sample‑complexity advantage over random and confidence‑only ordering. DPRM improves over confidence‑based baselines in pretraining, post‑training, test‑time scaling, and single‑cell masked diffusion, with particularly strong gains on harder reasoning subsets. In protein, molecular generation and DNA design, the effect is more multi‑objective: ordering‑aware variants significantly improve selected structural or fragment‑constrained metrics while not uniformly dominating the host baseline on every quality metric. These results identify token ordering as a fundamental control axis in diffusion language models and establish DPRM as a general‑purpose module for improving it. Code is available at https://github.com/DakeBU/DPRM‑DLLM.

PaperId: 1455, https://arxiv.org/pdf/2604.24198.pdf GitHub

Abstract:
Process Reward Models (PRMs) have achieved remarkable success in augmenting the reasoning capabilities of Large Language Models (LLMs) within static domains such as mathematics. However, their potential in dynamic data analysis tasks remains underexplored. In this work, we first present a empirical study revealing that general‑domain PRMs struggle to supervise data analysis agents. Specifically, they fail to detect silent errors, logical flaws that yield incorrect results without triggering interpreter exceptions, and erroneously penalize exploratory actions, mistaking necessary trial‑and‑error exploration for grounding failures. To bridge this gap, we introduce DataPRM, a novel environment‑aware generative process reward model that (1) can serve as an active verifier, autonomously interacting with the environment to probe intermediate execution states and uncover silent errors, and (2) employs a reflection‑aware ternary reward strategy that distinguishes between correctable grounding errors and irrecoverable mistakes. We design a scalable pipeline to construct over 8K high‑quality training instances for DataPRM via diversity‑driven trajectory generation and knowledge‑augmented step‑level annotation. Experimental results demonstrate that DataPRM improves downstream policy LLMs by 7.21% on ScienceAgentBench and 11.28% on DABStep using Best‑of‑N inference. Notably, with only 4B parameters, DataPRM outperforms strong baselines, and exhibits robust generalizability across diverse Test‑Time Scaling strategies. Furthermore, integrating DataPRM into Reinforcement Learning yields substantial gains over outcome‑reward baselines, achieving 78.73% on DABench and 64.84% on TableBench, validating the effectiveness of process reward supervision. Code is available at https://github.com/zjunlp/DataMind.

PaperId: 1456, https://arxiv.org/pdf/2604.24176.pdf GitHub

Abstract:
We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single "best" explanation token‑by‑token, we train reward models to discriminate among multiple candidate explanations and learn their relative quality. Concretely, we construct per‑instance candidate sets with graded quality levels and train listwise and pairwise ranking models (ListNet, LambdaRank, RankNet) to preserve ordinal structure and avoid score compression typical of pointwise regression or binary preference objectives. We observe three findings: First, ranking losses consistently outperform regression on score separation across all domains tested. Second, the optimal ranking loss depends on data characteristics: listwise objectives excel with well‑separated quality tiers, while pairwise methods are more robust to noisy natural annotations. Third, when trained on carefully curated and well‑structured data, small encoder models can match models that are orders of magnitude larger, suggesting that data quality matters more than model scale. Finally, when used as rewards in policy optimization, ranking‑based scores enable stable convergence in settings where regression‑based rewards fail entirely. Code and data are available at: https://github.com/Tankiit/PPO_Learning_to_rank

PaperId: 1457, https://arxiv.org/pdf/2604.24170.pdf GitHub

Abstract:
Concept Bottleneck Models (CBMs) predict through human‑interpretable concepts, but they typically output point concept probabilities that conflate epistemic uncertainty (reducible model underspecification) with aleatoric uncertainty (irreducible input ambiguity). This makes concept‑level uncertainty hard to interpret and, more importantly, hard to act upon. We introduce CREDENCE (Credal Ensemble Concept Estimation), a CBM framework that decomposes concept uncertainty by construction. CREDENCE represents each concept as a credal prediction (a probability interval), derives epistemic uncertainty from disagreement across diverse concept heads, and estimates aleatoric uncertainty via a dedicated ambiguity output trained to match annotator disagreement when available. The resulting signals support prescriptive decisions: automate low‑uncertainty cases, prioritize data collection for high‑epistemic cases, route high‑aleatoric cases to human review, and abstain when both are high. Across several tasks, we show that epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks annotator disagreement, providing guidance beyond error correlation. Our implementation is available at the following link: https://github.com/Tankiit/Credal_Sets/tree/ensemble‑credal‑cbm

PaperId: 1458, https://arxiv.org/pdf/2604.24158.pdf GitHub

Abstract:
Evaluating nuanced conversational travel recommendations is challenging when human annotations are costly and standard metrics ignore stakeholder‑centric goals. We study LLMs‑as‑Judges for sustainable city‑trip lists across four dimensions ‑‑ relevance, diversity, sustainability, and popularity balance, and propose a three‑phase calibration framework: (1) baseline judging with multiple LLMs, (2) expert evaluation to identify systematic misalignment, and (3) dimension‑specific calibration via rules and few‑shot examples. Across two recommendation settings, we observe model‑specific biases and high dimension‑level variance, even when judges agree on overall rankings. Calibration clarifies reasoning per dimension but exposes divergent interpretations of sustainability, highlighting the need for transparent, bias‑aware LLM evaluation. Prompts and code are released for reproducibility: https://github.com/ashmibanerjee/trs‑llm‑calibration.

PaperId: 1459, https://arxiv.org/pdf/2604.24079.pdf GitHub

Abstract:
Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on surface‑level lexical or stylistic cues, treating dialogue as a flat sequence of tokens and failing to capture the deeper discourse‑level structures that sustain persona consistency. To address this limitation, we propose a novel analytical framework that interprets LLM dialogue through bridging inference ‑‑ implicit conceptual relations that connect utterances via shared world knowledge and discourse coherence. By modeling these relations as structured knowledge graphs, our approach captures latent semantic links that govern how LLMs organize meaning across turns, enabling persona discovery at the level of discourse coherence rather than surface realizations. Experimental results across multiple reasoning backbones and target LLMs, ranging from small‑scale models to 80B‑parameter systems, demonstrate that bridging‑inference graphs yield significantly stronger semantic coherence and more stable persona identification than frequency or style‑based baselines. These results show that persona traits are consistently encoded in the structural organization of discourse rather than isolated lexical patterns. This work presents a systematic framework for probing, extracting, and visualizing latent LLM personas through the lens of Cognitive Discourse Theory, bridging computational linguistics, cognitive semantics, and persona reasoning in large language models. Codes are available at https://github.com/JiSoo‑Yang/Persona_Bridging.git

PaperId: 1460, https://arxiv.org/pdf/2604.24070.pdf GitHub

Abstract:
Small instruct‑tuned LLMs produce degenerate verbal confidence under minimal elicitation: ceiling rates above 95%, near‑chance Type‑2 AUROC, and Invalid validity profiles. We test whether confidence‑conditioned supervised fine‑tuning (CSFT) with self‑consistency‑derived targets can close the gap between internal information and verbal readout. A pre‑registered Phase 0 protocol on Gemma 3 4B‑it with a modal filter restricting training to items with correct modal answers produced a negative result: AUROC2 dropped from 0.554 to 0.509 due to label‑entropy collapse in the training targets. An exploratory rescue removed the filter, training on all 2,000 calibration items. This produced a binary verbal correctness discriminator with AUROC2 = 0.774 on held‑out TriviaQA, compressing a 10‑sample self‑consistency signal (AUROC2 = 0.999) into a single‑pass readout exceeding logit entropy (0.701). The shuffled‑target control showed no improvement (0.501). On MMLU, accuracy improved from 54.2% to 77.4% with the shuffled model at baseline (56.1%), supporting a target‑dependent interpretation. The result is exploratory, binary rather than continuously calibrated, and observed at a single scale. It identifies two design lessons: confidence training requires label entropy, and correct targets regularise output format.

PaperId: 1461, https://arxiv.org/pdf/2604.24041.pdf GitHub

Abstract:
Partially‑observed time series (POTS) is ubiquitous in real‑world applications, yet most existing toolchains separate missing‑value handling from downstream learning, which limits reproducibility and overall performance. This tutorial introduces PyPOTS, an open‑source Python ecosystem for end‑to‑end data mining and machine learning on POTS. We present practical workflows spanning missingness simulation, data preprocessing, model training, and evaluation across core tasks, including imputation, forecasting, classification, clustering, and anomaly detection. The tutorial consists of two parts: Part I emphasizes hands‑on application for practitioners through unified APIs and benchmark‑oriented experiments. Part II targets developers and researchers, focusing on extending PyPOTS with custom models, domain‑specific constraints, and contribution‑ready engineering practices. Participants will gain both conceptual understanding and implementation experience for building robust, transparent, and reusable POTS pipelines in research and production settings. PyPOTS is publicly available at https://github.com/WenjieDu/PyPOTS

PaperId: 1462, https://arxiv.org/pdf/2604.24039.pdf GitHub

Abstract:
Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per‑step LLM calls impose severe latency and cost. In this paper, we show that embodied tasks exhibit strong plan locality, where the next plan is largely predictable from the current one. Building on this, we introduce AgenticCache, a planning framework that reuses cached plans to avoid per‑step LLM calls. In AgenticCache, each agent queries a runtime cache of frequent plan transitions, while a background Cache Updater asynchronously calls the LLM to validate and refine cached entries. Across four multi‑agent embodied benchmarks, AgenticCache improves task success rate by 22% on average across 12 configurations (4 benchmarks x 3 models), reduces simulation latency by 65%, and lowers token usage by 50%. Cache‑based plan reuse thus offers a practical path to low‑latency, low‑cost embodied agents. Code is available at https://github.com/hojoonleokim/MLSys26_AgenticCache.

PaperId: 1463, https://arxiv.org/pdf/2604.24021.pdf GitHub

Abstract:
We present QED, an open‑source multi‑agent system that turns human‑provided research questions into complete mathematical proofs without further human guidance. Its pipeline is designed to overcome common failures of single‑query proof generation by separating planning, proving, and verification: a decomposition agent structures the proof search, prover agents generate candidate arguments, and verifier agents check correctness. In collaboration with domain experts, we evaluated QED on 18 research‑level projects of varying difficulty. QED produced five original works across algebraic geometry, fluid PDEs, probability, and inverse problems. Expert assessments regard these works as solid specialized research contributions, with three comparable in difficulty and scope to work commonly published in established specialist mathematics venues. QED is released at https://github.com/proofQED/QED.

PaperId: 1464, https://arxiv.org/pdf/2604.24005.pdf GitHub

Abstract:
On‑policy distillation (OPD) has shown strong potential for transferring reasoning ability from frontier or domain‑specific models to smaller students. While effective on static single‑turn tasks, its behavior in multi‑turn agent settings remains underexplored. In this work, we identify a key limitation of vanilla OPD in such settings, which we term Trajectory‑Level KL Instability. Specifically, we observe that KL divergence increases together with a drop in success rate, and even after convergence, the KL remains high, leading to unstable training. This instability arises from inter‑turn error compounding: as errors accumulate, the student is driven beyond the teacher's effective support, rendering the supervision signal unreliable. To address this, we propose TCOD (Temporal Curriculum On‑Policy Distillation), a simple yet effective framework that controls the trajectory depth exposed to the student and progressively expands it from short to long with a curriculum schedule. Experimental results across four student‑teacher pairs on three multi‑turn agent benchmarks (ALFWorld, WebShop, ScienceWorld) show that TCOD mitigates KL escalation and enhances KL stability throughout training, improving agent performance by up to 18 points over vanilla OPD. Further evaluations show that TCOD can even surpass the teacher's performance and generalize to tasks on which the teacher fails. Our code is available at https://github.com/kokolerk/TCOD.

PaperId: 1465, https://arxiv.org/pdf/2604.23972.pdf GitHub

Abstract:
Knowledge graphs (KGs) are increasingly used to support large lan guage model (LLM) reasoning, but standard triplet‑based KGs treat each relation as globally valid. In many settings, whether a relation should count as evidence depends on the context. We therefore formulate triplet validity as a triplet‑specific function of context and refer to this formulation as a Quantum Knowledge Graph (QKG). We instantiate QKG in medicine using a diabetes‑centered PrimeKG subgraph, whose 68,651 context‑sensitive relations are further annotated with patient‑group‑specific constraints. We evaluate it in a reasoner‑‑validator pipeline for medical question answering on a KG‑grounded subset of MedReason containing 2,788 questions. With Haiku‑4.5 as both the Reasoner and the Validator, KG‑backed validation significantly improves over a no‑validator baseline (+0.61 pp), and QKG with context matching yields the largest gain, outperforming both KG validation without context matching (+0.79 pp) and the no‑validator baseline (+1.40 pp; paired McNemar, all p<0.05). Under a stronger validator (Qwen‑3.6‑Plus), the raw QKG gain over the no‑validator baseline grows from +1.40 pp to +5.96 pp; the context‑matching gap is non‑significant (p=0.73) on the raw set but becomes borderline significant (p=0.05) after adjustment for knowledge leakage and suspicious questions, consistent with a benchmark‑gold ceiling rather than a QKG limitation. Taken together, the results support the view that the value of a KG in LLM‑based clinical reasoning lies not merely in storing medically related facts, but in representing whether those facts are applicable to the specific patient context. For reproducibility and further research, we release the curated QKG datasets and source code.\footnotehttps://github.com/HKAI‑Sci/QKG

PaperId: 1466, https://arxiv.org/pdf/2604.23948.pdf GitHub

Abstract:
The Korean writing system, Hangeul, has a unique character representation rigidly following the invention principles recorded in Hunminjeongeum.\footnoteHunminjeongeum is a book published in 1446 that describes the principles of invention and usage of Hangeul, devised by King Sejong \citeHunminjeongeum_Guide. However, existing pre‑trained language models (PLMs) for Korean have overlooked these principles. In this paper, we introduce a novel framework for Korean PLMs called KOMBO, which firstly brings the invention principles of Hangeul to represent character. Our proposed method, KOMBO, exhibits notable experimental proficiency across diverse NLP tasks. In particular, our method outperforms the state‑of‑the‑art Korean PLM by an average of 2.11% in five Korean natural language understanding tasks. Furthermore, extensive experiments demonstrate that our proposed method is suitable for comprehending the linguistic features of the Korean language. Consequently, we shed light on the superiority of using subcharacters over the typical subword‑based approach for Korean PLMs. Our code is available at: [https://github.com/SungHo3268/KOMBO](https://github.com/SungHo3268/KOMBO).

PaperId: 1467, https://arxiv.org/pdf/2604.23886.pdf GitHub

Abstract:
We present a data‑driven approach for physics‑based, muscle‑driven dexterous control that enables musculoskeletal hands to perform precise piano playing for novel pieces of music outside the reference dataset. Our approach combines high‑frequency muscle‑level control with low‑frequency latent‑space coordination in a hierarchical architecture. At the low level, general single‑hand policies are trained via reinforcement learning to generate dynamic muscle‑tendon activations while tracking trajectories from a large reference motion dataset. The resulting tracking policies are then distilled into variational autoencoder (VAE) models, yielding smooth and structured latent spaces that abstract away low‑level muscle dynamics. For the high level, we train piece‑specific policies to operate in this latent space, coordinating bimanual motions based on specific goals, denoted by note events extracted from given musical scores, to synthesize performances beyond the reference data. In addition, we present an enhanced musculoskeletal hand model that supports fine control of fingers for accurate low‑level motion tracking and diverse high‑level motion synthesis. We evaluate the control pipeline of our approach on a diverse piano repertoire spanning multiple musical styles and technical demands. Results demonstrate that our approach can synthesize coordinated bimanual motions with accurate key presses, and achieve the state‑of‑the‑art performance of piano playing in physics‑based dexterous control. We also show that our musculoskeletal hand model demonstrates superior biomechanical stability and tracking precision compared to the existing model, and validate that our musculoskeletal hand model and muscle‑driven controller can generate physiologically plausible activation patterns that align with human electromyography (EMG) recordings.

PaperId: 1468, https://arxiv.org/pdf/2604.23862.pdf GitHub

Abstract:
We investigate whether the Feed‑Forward Network (FFN) sublayer in a decoder‑only transformer can be replaced by an explicit learned memory graph while preserving the surrounding autoregressive architecture. The proposed Graph Memory Transformer (GMT) keeps causal self‑attention intact, but replaces the usual per‑token FFN transformation with a memory cell that routes token representations over a learned bank of centroids connected by a learned directed transition matrix. In the base GMT v7 instantiation studied here, each of 16 transformer blocks contains 128 centroids, a 128 128 edge matrix, gravitational source routing, token‑conditioned target selection, and a gated displacement readout. The cell therefore returns movement from an estimated source memory state toward a target memory state, rather than a retrieved value. The resulting model is a fully decoder‑only language model with 82.2M trainable parameters and no dense FFN sublayers, compared with a 103.0M‑parameter dense GPT‑style baseline used in the evaluation. The base v7 model trains stably and exposes centroid usage, transition structure, and source‑to‑target movement as directly inspectable quantities of the forward computation. It remains behind the larger dense baseline in validation loss and perplexity (3.5995/36.58 vs. 3.2903/26.85), while showing close zero‑shot benchmark behavior under the evaluated setting. These results are not intended as a state‑of‑the‑art claim; they support the viability and structural interpretability of replacing dense within‑token transformation with graph‑mediated memory navigation. Broader scaling, optimized kernels, and more extensive benchmark evaluation are left for subsequent work.

PaperId: 1469, https://arxiv.org/pdf/2604.23816.pdf GitHub GitHub

Abstract:
Software documentation frequently becomes outdated or fails to exist entirely, yet developers need focused views of their codebase to understand complex systems. While automated reverse engineering tools can generate UML diagrams from code, they produce overwhelming detail without considering developer intent. We introduce query‑driven UML diagram generation, where LLMs create diagrams that directly answer natural language questions about code. Unlike existing methods, our approach produces semantically focused diagrams containing only relevant elements with contextual descriptions. We fine‑tune Qwen2.5‑Coder‑14B on a curated dataset of code files, developer queries, and corresponding diagram representations in a structured JSON format, evaluating with both automatic detection of structural defects and human assessment of semantic relevance. Results demonstrate that fine‑tuning on a modest amount of manually corrected data yields dramatic improvements: our best model achieves the highest F1 scores while reducing defect rates below state‑of‑the‑art LLMs, generating diagrams that are both structurally sound and semantically faithful to developer queries. Thus, we establish the feasibility of using LLMs for scalable contextual, on‑demand documentation generation. We make our code and dataset publicly available at https://github.com/i‑need‑a‑pencil/query2diagram.

PaperId: 1470, https://arxiv.org/pdf/2604.23701.pdf GitHub

Abstract:
Crop disease diagnosis from field photographs faces two recurring problems: models that score well on benchmarks frequently hallucinate species names, and when predictions are correct, the reasoning behind them is typically inaccessible to the practitioner. This paper describes Agri‑CPJ (Caption‑Prompt‑Judge), a training‑free few‑shot framework in which a large vision‑language model first generates a structured morphological caption, iteratively refined through multi‑dimensional quality gating, before any diagnostic question is answered. Two candidate responses are then generated from complementary viewpoints, and an LLM judge selects the stronger one based on domain‑specific criteria. Caption refinement is the component with the largest individual impact: ablations confirm that skipping it consistently degrades downstream accuracy across both models tested. On CDDMBench, pairing GPT‑5‑Nano with GPT‑5‑mini‑generated captions yields +22.7 pp in disease classification and +19.5 points in QA score over no‑caption baselines. Evaluated without modification on AgMMU‑MCQs, GPT‑5‑Nano reached 77.84% and Qwen‑VL‑Chat reached 64.54%, placing them at or above most open‑source models of comparable scale despite the format shift from open‑ended to multiple‑choice. The structured caption and judge rationale together constitute a readable audit trail: a practitioner who disagrees with a diagnosis can identify the specific caption observation that was incorrect. Code and data are publicly available https://github.com/CPJ‑Agricultural/CPJ‑Agricultural‑Diagnosis

PaperId: 1471, https://arxiv.org/pdf/2604.23644.pdf GitHub

Abstract:
Intelligent document processing pipelines extract structured entities (tables, images, and text) from documents for use in downstream systems such as knowledge bases, retrieval‑augmented generation, and analytics. A persistent limitation of existing pipelines is that extraction output is produced without any intrinsic mechanism to verify whether it faithfully represents the source. Model‑internal confidence scores measure inference certainty, not correspondence to the document, and extraction errors pass silently into downstream consumers. We present Reconstruction as Validation (RaV‑IDP), a document processing pipeline that introduces reconstruction as a first‑class architectural component. After each entity is extracted, a dedicated reconstructor renders the extracted representation back into a form comparable to the original document region, and a comparator scores fidelity between the reconstruction and the unmodified source crop. This fidelity score is a grounded, label‑free quality signal. When fidelity falls below a per‑entity‑type threshold, a structured GPT‑4.1 vision fallback is triggered and the validation loop repeats. We enforce a bootstrap constraint: the comparator always anchors against the original document region, never against the extraction, preventing the validation from becoming circular. We further propose a per‑stage evaluation framework pairing each pipeline component with an appropriate benchmark. The code pipeline is publicly available at https://github.com/pritesh‑2711/RaV‑IDP for experimentation and use.

PaperId: 1472, https://arxiv.org/pdf/2604.23623.pdf GitHub

Abstract:
Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning‑intensive inference paradigms, where models perform explicit step‑by‑step reasoning before generating final answers. While such approaches improve answer quality and interpretability, they incur substantial computational overhead due to the prolonged generation sequences. In this paper, we propose Tandem, a novel collaborative framework that synergizes large and small language models (LLMs and SLMs) to achieve high‑quality reasoning with significantly reduced computational cost. Specifically, the LLM serves as a strategic coordinator, efficiently generating a compact set of critical reasoning insights. These insights are then used to guide a smaller, more efficient SLM in executing the full reasoning process and delivering the final response. To balance efficiency and reliability, Tandem introduces a cost‑aware termination mechanism that adaptively determines when sufficient reasoning guidance has been accumulated, enabling early stopping of the LLM's generation. Experiments on mathematical reasoning and code generation benchmarks demonstrate that Tandem reduces computational costs by approximately 40% compared to standalone LLM reasoning, while achieving superior or competitive performance. Furthermore, the sufficiency classifier trained on one domain transfers effectively to others without retraining. The code is available at: https://github.com/Applied‑Machine‑Learning‑Lab/ACL2026_Tandem.

PaperId: 1473, https://arxiv.org/pdf/2604.23563.pdf GitHub

Abstract:
Privacy‑critical domains require phishing detection systems that satisfy contradictory constraints: near‑zero false positives to prevent workflow disruption, transparent explanations for non‑expert staff, strict regulatory compliance prohibiting sensitive data exposure to external APIs, and robustness against AI‑generated attacks. Existing rule‑based systems are brittle to novel campaigns, while LLM‑based detectors violate privacy regulations through unredacted data transmission. We introduce CyberCane, a neuro‑symbolic framework integrating deterministic symbolic analysis with privacy‑preserving retrieval‑augmented generation (RAG). Our dual‑phase pipeline applies lightweight symbolic rules to email metadata, then escalates borderline cases to semantic classification via RAG with automated sensitive data redaction and retrieval from a phishing‑only corpus. We further introduce PhishOnt, an OWL ontology enabling verifiable attack classification through formal reasoning chains. Evaluation on DataPhish2025 (12.3k emails; mixed human/LLM) and Nazario/SpamAssassin demonstrates a 78.6‑point recall gain over symbolic‑only detection on AI‑generated threats, with precision exceeding 98% and FPR as low as 0.16%. Healthcare deployment projects a 542x ROI; tunable operating points support diverse risk tolerances, with open‑source implementation at https://github.com/sbhakim/Cybercane.

PaperId: 1474, https://arxiv.org/pdf/2604.23543.pdf GitHub

Abstract:
Test‑time alignment methods offer a promising alternative to fine‑tuning by steering the outputs of large language models (LLMs) at inference time with lightweight interventions on their internal representations. Recently, a prominent and effective approach, RE‑Control (Kong et al., 2024), has proposed leveraging an external value function trained over the LLM's hidden states to guide generation via gradient‑based editing. While effective, this method overlooks a key characteristic of alignment tasks, i.e. that they are typically formulated as learning from human preferences between candidate responses. To address this, in this paper we propose a novel preference‑based training framework, Pref‑CTRL, that uses a multi‑objective value function to better reflect the structure of preference data. Our approach has outperformed RE‑Control on two benchmark datasets and showed greater generalization on out‑of‑domain datasets. Our source code is available at https://github.com/UTS‑nlPUG/pref‑ctrl.

PaperId: 1475, https://arxiv.org/pdf/2604.23539.pdf GitHub

Abstract:
The rapid proliferation of Generative AI necessitates rigorous documentation standards for transparency and governance. However, manual creation of Model and Data Cards is not scalable, while automated approaches lack large‑scale, high‑fidelity benchmarks for systematic evaluation. We introduce MetaGAI, a comprehensive benchmark comprising 2,541 verified document triplets constructed through semantic triangulation of academic papers, GitHub repositories, and Hugging Face artifacts. Unlike prior single‑source datasets, MetaGAI employs a multi‑agent framework with specialized Retriever, Generator, and Editor agents, validated through four‑dimensional human‑in‑the‑loop assessment, including human evaluation of editor‑refined ground truth. We establish a robust evaluation protocol combining automated metrics with validated LLM‑as‑a‑Judge frameworks. Extensive analysis reveals that sparse Mixture‑of‑Experts architectures achieve superior cost‑quality efficiency, while a fundamental trade‑off exists between faithfulness and completeness. MetaGAI provides a foundational testbed for benchmarking, training, and analyzing automated Model and Data Card generation methods at scale. Our data and code are available at: https://github.com/haoxuan‑unt2024/MetaGAI‑Benchmark.

PaperId: 1476, https://arxiv.org/pdf/2604.23530.pdf GitHub

Abstract:
Multi‑turn, long‑horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substantial inference costs. Here, we study cost‑aware multi‑turn LLM routing: selecting which model to invoke at each turn from a model pool, given a fixed cost budget. We propose MTRouter, which encodes the interaction history and candidate models into joint history‑model embeddings, and learns an outcome estimator from logged trajectories to predict turn‑level model utility. Experiments show that MTRouter improves the performance‑cost trade‑off: on ScienceWorld, it surpasses GPT‑5 while reducing total cost by 58.7%; on Humanity's Last Exam (HLE), it achieves competitive accuracy while reducing total cost by 43.4% relative to GPT‑5, and these gains even carry over to held‑out tasks. Further analyses reveal several mechanisms underlying its effectiveness: relative to prior multi‑turn routers, MTRouter makes fewer model switches, is more tolerant to transient errors, and exhibits emergent specialization across models. Code: https://github.com/ZhangYiqun018/MTRouter

PaperId: 1477, https://arxiv.org/pdf/2604.23446.pdf GitHub

Abstract:
Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although large language models (LLMs) enable fluent natural‑language interaction, deployed maintenance assistants routinely produce generic explanations that are weakly grounded in telemetry, omit verifiable provenance, and offer no testable support for counterfactual or action‑oriented reasoning that undermine trust in safety‑critical settings. We present IndustryAssetEQA, a neurosymbolic operational intelligence system that combines episodic telemetry representations with a Failure Mode Effects Analysis Knowledge Graph (FMEA‑KG) to enable Embodied Question Answering (EQA) over industrial assets. We evaluate on four datasets covering four industrial asset types, including rotating machinery, turbofan engines, hydraulic systems, and cyber‑physical production systems. Compared to LLM‑only baselines, IndustryAssetEQA improves structural validity by up to 0.51, counterfactual accuracy by up to 0.47, and explanation entailment by 0.64, while reducing severe expert‑rated overclaims from 28% to 2% (approximately 93% reduction). Code, datasets, and the FMEA‑KG are available at https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA.

PaperId: 1478, https://arxiv.org/pdf/2604.23432.pdf GitHub

Abstract:
Reliable depth estimation from spherical images is crucial for 360° vision in robotic navigation and immersive scene understanding. However, the onboard spherical camera can experience unintentional pose variations in real‑world robotic platforms that, along with the geometric distortions inherent in equirectangular projections, significantly impact the effectiveness of depth estimation. To study this issue, a novel public benchmark, called Sphere‑Depth, is introduced to systematically evaluate the robustness of monocular depth estimation models from equirectangular images in a reproducible way. Camera pose perturbations are simulated and used to assess the performance of a popular perspective‑based model, Depth Anything, and of spherical‑aware models such as Depth Anywhere, ACDNet, Bifuse++, and SliceNet. Furthermore, to ensure meaningful evaluation across models, a depth calibration‑based error protocol is proposed to convert predicted relative depth values into metric depth values using supervised learned scaling factors for each model. Experiments show that even models explicitly designed to process spherical images exhibit substantial performance degradation when variations in the camera pose are observed with respect to the canonical pose. The full benchmark, evaluation protocol, and dataset splits are made publicly available at: https://github.com/sgazzeh/Sphere_depth

PaperId: 1479, https://arxiv.org/pdf/2604.23407.pdf GitHub

Abstract:
Large vision‑language models (VLMs) can recognize what happens in video but fail to count how many times. We introduce PushupBench, 446 long‑form clips (avg. 36.7s) for evaluating repetition counting. The best frontier model achieves 42.1% exact accuracy; open‑source 4B models score ～6%, matching supervised baselines. We show that accuracy alone misleads ‑‑ weaker models exploit the modal count rather than reason temporally. Fine‑tuning on counting with 1k samples transfers to general video understanding: MVBench (+2.15), PerceptionTest (+1.88), TVBench (+4.54), suggesting counting is a proxy for broader temporal reasoning.PushupBench incorporated in \textttlmms‑eval (https://github.com/EvolvingLMMs‑Lab/lmms‑eval/pull/1262) and hosted on (pushupbench.com/)

PaperId: 1480, https://arxiv.org/pdf/2604.23392.pdf GitHub

Abstract:
Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widely adopted in soccer refereeing, current AI‑assisted approaches remain preliminary. Existing research mostly focuses on isolated video perception tasks and lacks the ability to understand and reason about foul scenarios. To fill this gap, we propose SoccerRef‑Agents, a holistic and explainable multi‑agent decision‑making framework for soccer refereeing. The main contributions are: (i) constructing the multimodal benchmark SoccerRefBench with over 1,200 referee theory questions and 600 foul video clips; (ii) building a vector‑based knowledge base RefKnowledgeDB using the latest "Laws of the Game" and a classic case database for precise, knowledge‑driven reasoning; (iii) designing a novel multi‑agent architecture that collaborates via cross‑modal RAG to bridge the semantic gap between visual content and regulatory texts. This work explores the technical capability of integrating MLLMs with refereeing expertise, and evaluations show our system significantly outperforms general‑purpose MLLMs in decision accuracy and explanation quality. All databases, benchmarks, and code will be made available.

PaperId: 1481, https://arxiv.org/pdf/2604.23361.pdf GitHub

Abstract:
Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysis. However, most prior work relies on cloud‑based models or specialized hardware, limiting practical applicability in privacy‑sensitive or resource‑constrained environments. In this paper, we present a systematic empirical evaluation of two locally deployed LLMs, LLaMA 3.2 and Mistral, for real‑world Python bug detection using the BugsInPy benchmark. We evaluate 349 bugs across 17 projects using a zero‑shot prompting approach at the function level and an automated keyword‑based evaluation framework. Our results show that locally executed models achieve accuracy between 43% and 45%, while producing a large proportion of partially correct responses that identify problematic code regions without pinpointing the exact fix. Performance varies significantly across projects, highlighting the importance of codebase characteristics. The results demonstrate that local models can identify a meaningful share of bugs, though precise localization remains difficult for locally executed LLMs, particularly when handling complex and context dependent bugs in realistic development scenarios.

PaperId: 1482, https://arxiv.org/pdf/2604.23355.pdf GitHub

Abstract:
Existing LLM‑based EDA agents are often isolated task‑specific systems. This leads to repeated engineering effort and limited reuse of successful design and debugging strategies. We present LEGO, a unified skill‑based platform for front‑end design generation. It decomposes the digital front‑end flow into six independent steps and represents every agent capability as a standardized composable circuit skill within a plug‑and‑play architecture. To build this skill library, we survey more than 100 papers, select 11 representative open‑source projects, and extract 42 executable circuit skills within a six‑step finite state machine formulation. Circuit Skill Builder automates skill extraction with linear scalability. Agent Skill RAG achieves submillisecond retrieval without relying on embedding models. Empirical evaluation on a hard subset of 41 VerilogEval v2 problems that gpt‑5.2‑codex fails to solve under extra‑high reasoning effort shows that individual circuit skills constructed within LEGO raise Pass@1 from 0.000 to 0.805. This is an 80.5% gain over the baseline. Cross‑project skill compositions also reach 0.805 Pass@1. They outperform hierarchy‑verilog by 14.6% and VerilogCoder by 2.5%. They also match MAGE. These results show that modular skill composition supports both effective and flexible RTL design automation. The LEGO platform and all circuit skills are publicly available at GitHub: https://github.com/loujc/LEGO‑An‑LLM‑Skill‑Based‑Front‑End‑Design‑Generation‑Platform

PaperId: 1483, https://arxiv.org/pdf/2604.23348.pdf GitHub

Abstract:
Recent multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and generation, and are increasingly used in applications such as social robots and human‑computer interaction, where understanding human emotions is essential. However, existing benchmarks mainly formulate emotion understanding as a static recognition problem, leaving it largely unclear whether current MLLMs can understand emotion as a dynamic process that evolves, shifts between states, and unfolds across diverse social contexts. To bridge this gap, we present EmoTrans, a benchmark for evaluating emotion dynamics understanding in multimodal videos. EmoTrans contains 1,000 carefully collected and manually annotated video clips, covering 12 real‑world scenarios, and further provides over 3,000 task‑specific question‑answer (QA) pairs for fine‑grained evaluation. The benchmark introduces four tasks, namely Emotion Change Detection (ECD), Emotion State Identification (ESI), Emotion Transition Reasoning (ETR), and Next Emotion Prediction (NEP), forming a progressive evaluation framework from coarse‑grained detection to deeper reasoning and prediction. We conduct a comprehensive evaluation of 18 state‑of‑the‑art MLLMs on EmoTrans and obtain two main findings. First, although current MLLMs show relatively stronger performance on coarse‑grained emotion change detection, they still struggle with fine‑grained emotion dynamics modeling. Second, socially complex settings, especially multi‑person scenarios, remain substantially challenging, while reasoning‑oriented variants do not consistently yield clear improvements. To facilitate future research, we publicly release the benchmark, evaluation protocol, and code at https://github.com/Emo‑gml/EmoTrans.

PaperId: 1484, https://arxiv.org/pdf/2604.23307.pdf GitHub

Abstract:
Dual‑target molecule generation, which focuses on discovering compounds capable of interacting with two target proteins, has garnered significant attention due to its potential for improving therapeutic efficiency, safety and resistance mitigation. Existing approaches face two critical challenges. First, by simplifying the complex dual‑target optimization problem to scalarized combinations of individual objectives, they fail to capture important trade‑offs between target engagement and molecular properties. Second, they typically do not integrate synthetic planning into the generative process. This highlights a need for more appropriate objective function design and synthesis‑aware methodologies tailored to the dual‑target molecule generation task. In this work, we propose CombiMOTS, a Pareto Monte Carlo Tree Search (PMCTS) framework that generates dual‑target molecules. CombiMOTS is designed to explore a synthesizable fragment space while employing vectorized optimization constraints to encapsulate target affinity and physicochemical properties. Extensive experiments on real‑world databases demonstrate that CombiMOTS produces novel dual‑target molecules with high docking scores, enhanced diversity, and balanced pharmacological characteristics, showcasing its potential as a powerful tool for dual‑target drug discovery. The code and data is accessible through https://github.com/Tibogoss/CombiMOTS.

PaperId: 1485, https://arxiv.org/pdf/2604.23296.pdf GitHub

Abstract:
Aspect Sentiment Quad Prediction (ASQP) has seen significant advancements, largely driven by the powerful semantic understanding and generative capabilities of large language models (LLMs). However, while syntactic structure information has been proven effective in previous extractive paradigms, it remains underutilized in the generative paradigm of LLMs due to their limited reasoning capabilities. In this paper, we propose S^2IT, a novel Stepwise Syntax Integration Tuning framework that progressively integrates syntactic structure knowledge into LLMs through a multi‑step tuning process. The training process is divided into three steps. S^2IT decomposes the quadruple generation task into two stages: 1) Global Syntax‑guided Extraction and 2) Local Syntax‑guided Classification, integrating both global and local syntactic structure information. Finally, Fine‑grained Structural Tuning enhances the model's understanding of syntactic structures through the prediction of element links and node classification. Experiments demonstrate that S^2IT significantly improves state‑of‑the‑art performance across multiple datasets. Our implementation will be open‑sourced at https://github.com/DMIRLAB‑Group/S2IT.

PaperId: 1486, https://arxiv.org/pdf/2604.23290.pdf GitHub

Abstract:
Active learning algorithms automatically identify the most informative samples from large amounts of unlabeled data and tremendously reduce human annotation effort in inducing a machine learning model. In a conventional active learning setup, the labeling oracles are assumed to be infallible, that is, they always provide correct answers (in terms of class labels) to the queried unlabeled instances, which cannot be guaranteed in real‑world applications. To this end, a body of research has focused on the development of active learning algorithms in the presence of imperfect / noisy oracles. Existing research on active learning with noisy oracles typically simulate the oracles using machine learning models; however, real‑world situations are much more challenging, and using ML models to simulate the annotation patterns may not appropriately capture the nuances of real‑world annotation challenges. In this research, we first collect annotations of text samples (from 3 benchmark text classification datasets) from crowd‑sourced workers through a crowd‑sourcing platform. We then conduct extensive empirical studies of 8 commonly used active learning techniques (in conjunction with deep neural networks) using the obtained annotations. Our analyses sheds light on the performance of these techniques under real‑world challenges, where annotators can provide incorrect labels, and can also refuse to provide labels. We hope this research will provide valuable insights that will be useful for the deployment of deep active learning systems in real‑world applications. The obtained annotations can be accessed at https://github.com/varuntotakura/al_rcta/.

PaperId: 1487, https://arxiv.org/pdf/2604.23239.pdf GitHub

Abstract:
Accurate long‑term time series forecasting (LTSF) requires the capture of complex long‑range dependencies and dynamic periodic patterns. Recent advances in frequency‑domain analysis offer a global perspective for uncovering temporal characteristics. However, real‑world time series often exhibit pronounced cross‑domain heterogeneity where variables that appear synchronized in the time domain can differ substantially in the frequency domain. Existing frequency‑based LTSF methods often rely on implicit assumptions of cross‑domain homogeneity, which limits their ability to adapt to such intricate variability. To effectively integrate frequency‑domain analysis with temporal dependency learning, we propose AdaMamba, a novel framework that endogenizes adaptive and context‑aware frequency analysis within the Mamba state‑space update process. Specifically, AdaMamba introduces an interactive patch encoding module to capture inter‑variable interaction dynamics. Then, we develop an adaptive frequency‑gated state‑space module that generates input‑dependent frequency bases, and generalizes the conventional temporal forgetting gate into a unified time‑frequency forgetting gate. This allows dynamic calibration of state transitions based on learned frequency‑domain importance, while preserving Mamba's capability in modeling long‑range dependencies. Extensive experiments on seven public LTSF benchmarks and two domain‑specific datasets demonstrate that AdaMamba consistently outperforms state‑of‑the‑art methods in forecasting accu racy while maintaining competitive computational efficiency. The code of AdaMamba is available at https://github.com/XDjiang25/AdaMamba.

PaperId: 1488, https://arxiv.org/pdf/2604.23194.pdf GitHub

Abstract:
Large language model‑based agents have recently emerged as powerful approaches for solving dynamic and multi‑step tasks. Most existing agents employ planning mechanisms to guide long‑term actions in dynamic environments. However, current planning approaches face a fundamental limitation that they operate at a fixed granularity level. Specifically, they either provide excessive detail for simple tasks or insufficient detail for complex ones, failing to achieve an optimal balance between simplicity and complexity. Drawing inspiration from the principle of progressive refinement in cognitive science, we propose AdaPlan‑H, a self‑adaptive hierarchical planning mechanism that mimics human planning strategies. Our method initiates with a coarse‑grained macro plan and progressively refines it based on task complexity. It generates self‑adaptive hierarchical plans tailored to the varying difficulty levels of different tasks, which can be optimized by imitation learning and capability enhancement. Experimental results demonstrate that our method significantly improves task execution success rates while mitigating overplanning at the planning level, providing a flexible and efficient solution for multi‑step complex decision‑making tasks. To contribute to the community, our code and data will be made publicly available at https://github.com/import‑myself/AHP.

PaperId: 1489, https://arxiv.org/pdf/2604.23178.pdf GitHub

Abstract:
LLM‑as‑a‑Judge has become the dominant paradigm for evaluating language model outputs, yet LLM judges exhibit systematic biases that compromise evaluation reliability. We present a comprehensive empirical study comparing nine debiasing strategies across five judge models from four provider families (Google, Anthropic, OpenAI, Meta), three benchmarks (MT‑Bench n=400, LLMBar n=200, custom n=225), and four bias types. Our key findings: (1) Style bias is the dominant bias (0.76‑0.92 across all models), far exceeding position bias (<= 0.04), yet has received minimal research attention. (2) All models show a conciseness preference on expansion pairs, but truncation controls confirm they correctly distinguish quality from length (0.92‑1.00 accuracy), suggesting quality‑sensitive evaluation rather than a simple length bias. (3) Debiasing is beneficial but model‑dependent: the combined budget strategy significantly improves Claude Sonnet 4 by +11.2 pp (p < 0.0001), with directionally positive trends for other models. Only 2 of 20 non‑baseline configurations show decreased agreement. We release our evaluation framework, controlled dataset, and all experimental artifacts at https://github.com/sksoumik/llm‑as‑judge.

PaperId: 1490, https://arxiv.org/pdf/2604.23108.pdf GitHub

Abstract:
Large Language Models (LLMs) based on Mixture‑of‑Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficiently. However, standard MoEs enforce uniform expert sizes,creating a rigidity that fails to align computational costs with varying token‑level complexity. While heterogeneous expert architectures attempt to address this by diversifying expert sizes, they often suffer from significant system‑level challenges, specifically unbalanced GPU utilization and inefficient parameter utilization, which hinder practical deployment. To bridge the gap between theoretical heterogeneity and robust industrial application, we propose Mixture of Heterogeneous Grouped Experts (MoHGE) which introduces a two‑level routing mechanism to enable flexible, resource‑aware expert combinations. To optimize inference efficiency, we propose a Group‑Wise Auxiliary Loss, which dynamically steers tokens to the most parameter‑efficient expert groups based on task difficulty. To address the critical deployment challenge of GPU load balancing, we introduce an All‑size Group‑decoupling Allocation strategy coupled with an Intra‑Group Experts Auxiliary Loss. These mechanisms collectively ensure uniform computation distribution across GPUs. Extensive evaluations demonstrate that MoHGE matches the performance of MoE architectures while reducing the total parameters by approximately 20% and maintaining balanced GPU utilization. Our work establishes a scalable paradigm for resource‑efficient MoE design, offering a practical solution for optimizing inference costs in real‑world scenarios. The code is publicly available at https://github.com/UnicomAI/MoHGE.

PaperId: 1491, https://arxiv.org/pdf/2604.23088.pdf GitHub

Abstract:
We present Code Broker, a multi agent system built on Google s Agent Development Kit ADK that analyses Python source code from individual files, local directory trees, or remote GitHub repositories and generates structured, actionable quality assessment reports. The system realises a hierarchical five agent architecture in which a root orchestrator coordinates a sequential pipeline agent that, in turn, dispatches three specialised agents concurrently a Correctness Assessor, a Style Assessor, and a Description Generator before synthesising their findings through an Improvement Recommender. Reports quantify four quality dimensions correctness, security, style, and maintainability on a normalised scale and are rendered in both Markdown and HTML for integration into diverse developer workflows. Code Broker fuses LLM based semantic reasoning with deterministic static analysis signals from Pylint, employs asynchronous execution with exponential backoff retry logic to improve robustness under transient API failures, and explores lightweight session memory for retaining and querying prior assessment context across runs. We frame this paper as a technical report on system design, prompt engineering, and tool orchestration, and present a preliminary qualitative evaluation on representative Python codebases of varying scale. The results indicate that parallel specialised agents produce readable, developer oriented feedback that complements traditional linting, while also foregrounding current limitations in evaluation depth, security tooling, large repository handling, and the exclusive reliance on in memory persistence. All code and reproducibility materials are publicly available: https://github.com/Samir‑atra/agents_intensive_dev.

PaperId: 1492, https://arxiv.org/pdf/2604.23061.pdf GitHub

Abstract:
Large language models (LLMs) show promise for molecular optimization, but aligning them with selective and competing drug‑design constraints remains challenging. We propose C‑Moral, a reinforcement learning post‑training framework for controllable multi‑objective molecular optimization. C‑Moral combines group‑based relative optimization, property score alignment for heterogeneous objectives, and bottleneck‑sensitive non‑linear reward aggregation to improve stability across competing molecular properties. Experiments on C‑MuMOInstruct and S^2‑Bench MolOpt show that C‑Moral achieves the best performance among compared methods on both benchmarks. On C‑MuMOInstruct, C‑Moral achieves the best Success Optimized Rate (SOR) of 48.9% on in‑domain tasks and 39.5% on out‑of‑domain tasks while preserving scaffold similarity. On S^2‑Bench MolOpt, it also achieves the strongest results across LogP, MR, and QED optimization tasks. These results suggest that C‑Moral is an effective way to align molecular LLMs with continuous and constrained molecular design objectives. Our code and models are publicly available at https://github.com/Rwigie/C‑MORAL.

PaperId: 1493, https://arxiv.org/pdf/2604.23056.pdf GitHub GitHub

Abstract:
We propose a simple yet effective alternative to reward normalization in policy gradient reinforcement learning by integrating a 1D Kalman filter for online reward estimation. Instead of relying on fixed heuristics, our method recursively estimates the latent reward mean, smoothing high‑variance returns and adapting to non‑stationary environments. This approach incurs minimal overhead and requires no modification to existing policy architectures. Experiments on LunarLander and CartPole demonstrate that Kalman‑filtered rewards significantly accelerate convergence and reduce training variance compared to standard normalization techniques. Code is available at https://github.com/Sumxiaa/Kalman_Normalization.

PaperId: 1494, https://arxiv.org/pdf/2604.23002.pdf GitHub

Abstract:
Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain‑specific machinery (e.g. Dirac notation, vector calculus) imposes additional formalisation challenges that modern LLMs and agentic approaches have yet to tackle. To aid autoformalisation in scientific domains, we present FormalScience; a domain‑agnostic human‑in‑the‑loop agentic pipeline that enables a single domain expert (without deep formal language experience) to produce syntactically correct and semantically aligned formal proofs of informal reasoning for low economic cost. Applying FormalScience to physics, we construct FormalPhysics, a dataset of 200 university‑level (LaTeX) physics problems and solutions (primarily quantum mechanics and electromagnetism), along with their Lean4 formal representations. Compared to existing formal math benchmarks, FormalPhysics achieves perfect formal validity and exhibits greater statement complexity. We evaluate open‑source models and proprietary systems on a statement autoformalisation task on our dataset via zero‑shot prompting, self‑refinement with error feedback, and a novel multi‑stage agentic approach, and explore autoformalisation limitations in modern LLM‑based approaches. We provide the first systematic characterisation of semantic drift in physics autoformalisation in terms of concepts such as notational collapse and abstraction elevation which reveals what formal language verifies when full semantic preservation is unattainable. We release the codebase together with an interactive UI‑based FormalScience system which facilitates autoformalisation and theorem proving in scientific domains beyond physics.https://github.com/jmeadows17/formal‑science

PaperId: 1495, https://arxiv.org/pdf/2604.22989.pdf GitHub

Abstract:
Recent medical multimodal foundation models are built as multimodal LLMs (MLLMs) by connecting a CLIP‑pretrained vision encoder to an LLM using LLaVA‑style finetuning. This two‑stage, decoupled approach introduces a projection layer that can distort visual features. This is especially concerning in medical imaging where subtle cues are essential for accurate diagnoses. In contrast, early‑fusion generative approaches such as Chameleon eliminate the projection bottleneck by processing image and text tokens within a single unified sequence, enabling joint representation learning that leverages the inductive priors of language models. We present CheXmix, a unified early‑fusion generative model trained on a large corpus of chest X‑rays paired with radiology reports. We expand on Chameleon's autoregressive framework by introducing a two‑stage multimodal generative pretraining strategy that combines the representational strengths of masked autoencoders with MLLMs. The resulting models are highly flexible, supporting both discriminative and generative tasks at both coarse and fine‑grained scales. Our approach outperforms well‑established generative models across all masking ratios by 6.0% and surpasses CheXagent by 8.6% on AUROC at high image masking ratios on the CheXpert classification task. We further inpaint images over 51.0% better than text‑only generative models and outperform CheXagent by 45% on the GREEN metric for radiology report generation. These results demonstrate that CheXmix captures fine‑grained information across a broad spectrum of chest X‑ray tasks. Our code is at: https://github.com/StanfordMIMI/CheXmix.

PaperId: 1496, https://arxiv.org/pdf/2604.22942.pdf GitHub

Abstract:
Diffusion models produce high‑quality synthetic data but suffer from slow inference. We propose 3D Variable‑Step Denoising Diffusion Probabilistic Model (VS‑DDPM) a framework engineered to maintain generative quality while accelerating inference by several factors. We tested our approach on four tasks (missing MRI, tumor removal, MRI‑to‑sCT, and CBCT‑to‑sCT) within the BraTS2025 and SynthRAD2025 challenges. Designed for high efficiency under hardware and time constrains imposed by both challenges. VS‑DDPM achieved state‑of‑the‑art (SOTA) performance in missing MRI synthesis, yielding Dice scores of 0.80, 0.83, and 0.88 for the enhancing tumor, tumor core, and whole tumor regions, respectively, alongside a structural similarity index (SSIM) of 0.95. For MRI tumor removal, the model attained a root mean squared error (RMSE) of 0.053, a peak signal‑to‑noise ratio (PSNR) of 26.77, and an SSIM of 0.918. While the framework demonstrated competitive performance in MRI‑to‑sCT and CBCT‑to‑sCT tasks, it did not reach SOTA benchmarks, potentially due to sensitivities in data pre and post‑processing pipelines or specific loss function configurations. These results demonstrate that VS‑DDPM provides a robust and tunable solution for high‑fidelity 3D medical image synthesis. The code is available in https://github.com/andre‑fs‑ferreira/SynthRAD_by_Faking_it.

PaperId: 1497, https://arxiv.org/pdf/2604.22885.pdf GitHub

Abstract:
Federated cross‑modal retrieval faces severe challenges from heterogeneous client data, particularly non‑IID semantic distributions and missing modalities. Under such heterogeneity, a single global model is often insufficient to capture both shared cross‑modal knowledge and client‑specific characteristics. We propose RCSR, a personalization‑friendly federated framework that integrates prototype anchoring, retrieval‑centric semantic routing, and optional client‑specific adapters. Built on a frozen CLIP backbone, RCSR leverages lightweight shared adapters for global knowledge transfer while supporting efficient local personalization. Prototype anchoring helps unimodal clients align with global cross‑modal semantics, and a server‑side semantic router adaptively assigns aggregation weights based on retrieval consistency to mitigate alignment drift during heterogeneous updates. Extensive experiments on MS‑COCO, Flickr30K, and other benchmarks show that RCSR consistently improves global retrieval accuracy and training stability, while further enhancing client‑level retrieval performance, especially for clients with incomplete modalities. Code is available at https://github.com/RezinChow/RCSR‑Retrieval‑Centric‑Semantic‑Routing.

PaperId: 1498, https://arxiv.org/pdf/2604.22884.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have shown promising potential in diverse understanding tasks, e.g., image and video analysis, math and physics olympiads. However, they remain blank and unexplored for Small Object Understanding (SOU) tasks. To fill this gap, we introduce SOUBench, the first and comprehensive benchmark for exploring the small objects understanding capability of existing MLLMs. Specifically, we first design an effective and automatic visual question‑answer generation strategy, constructing a new SOU‑VQA evaluation dataset, with 18,204 VQA pairs, six relevant sub‑tasks, and three dominant scenarios (i.e., Driving, Aerial, and Underwater). Then, we conduct a comprehensive evaluation on 15 state‑of‑the‑art MLLMs and reveal their weak capabilities in small object understanding. Furthermore, we develop SOU‑Train, a multimodal training dataset with 11,226 VQA pairs, to improve the SOU capabilities of MLLMs. Through supervising fine‑tuning of the latest MLLM, we demonstrate that SOU‑Train can effectively enhance the latest MLLM's ability to understand small objects. Comprehensive experimental results demonstrate that, the proposed SOUBench, along with the SOU‑VQA and SOU‑Train datasets, provides a crucial empirical foundation to the community for further developing models with enhanced small object understanding capabilities. Datasets and Code: https://github.com/Hanfj‑X/SOU.

PaperId: 1499, https://arxiv.org/pdf/2604.22875.pdf GitHub

Abstract:
When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision‑language models (VLMs) such as Gemini‑3‑Pro and GPT‑5 only respond with text, which can be difficult for users to verify. We present SketchVLM, a training‑free, model‑agnostic framework that enables VLMs to produce non‑destructive, editable SVG overlays on the input image to visually explain their answers. Across seven benchmarks spanning visual reasoning (maze navigation, ball‑drop trajectory prediction, and object counting) and drawing (part labeling, connecting‑the‑dots, and drawing shapes around objects), SketchVLM improves visual reasoning task accuracy by up to +28.5 percentage points and annotation quality by up to 1.48x relative to image‑editing and fine‑tuned sketching baselines, while also producing annotations that are more faithful to the model's stated answer. We find that single‑turn generation already achieves strong accuracy and annotation quality, and multi‑turn generation opens up further opportunities for human‑AI collaboration. An interactive demo and code are at https://sketchvlm.github.io/.

PaperId: 1500, https://arxiv.org/pdf/2604.22868.pdf GitHub

Abstract:
Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in machine learning, this inherently visual problem is often tackled through a verbal‑centric lens. While recent research demonstrates the promise of fully visual approaches, they suffer from significant computational inefficiency due to the step‑by‑step planning‑by‑generation paradigm. In this work, we present EAR, an editing‑as‑reasoning paradigm that reformulates visual planning as a single‑step image transformation. To isolate intrinsic reasoning from visual recognition, we employ abstract puzzles as probing tasks and introduce AMAZE, a procedurally generated dataset that features the classical Maze and Queen problems, covering distinct, complementary forms of visual planning. The abstract nature of AMAZE also facilitates automatic evaluation of autoregressive and diffusion‑based models in terms of both pixel‑wise fidelity and logical validity. We assess leading proprietary and open‑source editing models. The results show that they all struggle in the zero‑shot setting, finetuning on basic scales enables remarkable generalization to larger in‑domain scales and out‑of‑domain scales and geometries. However, our best model that runs on high‑end hardware fails to match the zero‑shot efficiency of human solvers, highlighting a persistent gap in neural visual reasoning.

PaperId: 1501, https://arxiv.org/pdf/2604.22838.pdf GitHub

Abstract:
With the accumulation of resources in the era of big data and the rise of pre‑trained models in deep learning, optimizing neural networks for various tasks often involves different strategies for fine‑tuning pre‑trained models versus training from scratch. However, existing optimizers primarily focus on reducing the loss function by updating model parameters, without fully addressing the unique demands of these two major paradigms. In this paper, we propose DualOpt, a novel approach that decouples optimization techniques specifically tailored for these distinct training scenarios. For training from scratch, we introduce real‑time layer‑wise weight decay, designed to enhance both convergence and generalization by aligning with the characteristics of weight updates and network architecture. For more importantly fine‑tuning, we integrate weight rollback with the optimizer, incorporating a rollback term into each weight update step. This ensures consistency in the weight distribution between upstream and downstream models, effectively mitigating knowledge forgetting and improving fine‑tuning performance. Additionally, we extend the layer‑wise weight decay to dynamically adjust the rollback levels across layers, adapting to the varying demands of different downstream tasks. Extensive experiments across diverse tasks, including image classification, object detection, semantic segmentation, and instance segmentation, demonstrate the broad applicability and state‑of‑the‑art performance of DualOpt. Code is available at https://github.com/qklee‑lz/OLOR‑AAAI‑2024.

PaperId: 1502, https://arxiv.org/pdf/2604.22835.pdf GitHub

Abstract:
Autonomous parking remains a critical yet challenging task in intelligent driving systems, particularly within constrained urban environments where maneuvering space is limited and precise control is essential. While recent advances in end‑to‑end learning have shown great promise, the lack of high‑quality, structured datasets tailored for parking scenarios remains a significant bottleneck.To address this gap, we present ParkingScenes, a comprehensive multimodal dataset specifically designed for end‑to‑end autonomous parking in simulated scenes. Built on the CARLA simulator, ParkingScenes features structured parking trajectories generated by a Hybrid A planner and a Model Predictive Controller (MPC), providing accurate and reproducible supervision signals. The dataset includes 16 reverse‑in and 6 parallel parking scenarios, each executed under two pedestrian conditions (present and absent), resulting in 704 structured episodes and approximately 105000 frames. Each scenario is repeated 16 times to ensure consistent coverage. Each frame contains synchronized data from four RGB cameras, four depth sensors, vehicle motion states, and Bird's‑Eye View (BEV) representations, enabling rich multimodal fusion and context‑aware learning. To demonstrate the utility of our dataset, we compare models trained on ParkingScenes with those trained on unstructured, manually collected simulation data under identical conditions. Results show significant improvements in performance, underscoring the effectiveness of structured supervision for robust and accurate parking policy learning. By releasing both the dataset and the collection framework, ParkingScenes establishes a scalable and reproducible benchmark for advancing learning‑based autonomous parking systems. The dataset and collection framework will be released at: https://github.com/haonan‑ai/ParkingScenes

PaperId: 1503, https://arxiv.org/pdf/2604.22828.pdf GitHub

Abstract:
Recent generative AI models have achieved remarkable breakthroughs in language and visual understanding. However, although these models can generate realistic visual content, their spatial scale remains confined to bounded environments, preventing them from capturing how geographic environments evolve across thousands of kilometers or from modeling the spatial structure of the large‑scale physical world. This limitation poses a critical challenge for ultra‑wide‑area spatial intelligence in Earth observation and simulation, revealing a deeper gap in generative AI: progress has relied primarily on scaling model parameters and training data, while overlooking spatial scale as a core dimension of intelligence. Here, motivated by this missing dimension, we investigate spatial scale as a new scaling axis in foundation models and present MetaEarth3D, the first generative foundation model capable of spatially consistent generation at the planetary scale. Taking optical Earth observation simulation as a testbed, MetaEarth3D enables the generation of multi‑level, unbounded, and diverse 3D scenes spanning large‑scale terrains, medium‑scale cities, and fine‑grained street blocks. Built upon 10 million globally distributed real‑world training images, MetaEarth3D demonstrates both strong visual realism and geospatial statistical realism. Beyond generation, MetaEarth3D serves as a generative data engine for diverse virtual environments in ultra‑wide spatial intelligence. We argue that this study may help empower next‑generation spatial intelligence for Earth observation.

PaperId: 1504, https://arxiv.org/pdf/2604.22577.pdf GitHub

Abstract:
Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long‑context inputs and multi‑turn reasoning. This results in prohibitively high computational and monetary costs in real‑world development. While quantization is a standard approach for reducing cost and latency, its impact on agent performance in realistic scenarios remains unclear. In this work, we analyze quantization sensitivity across diverse complex workflows over OpenClaw, and show that precision requirements are highly task‑dependent. Based on this observation, we propose QuantClaw, a plug‑and‑play precision routing plugin that dynamically assigns precision according to task characteristics. QuantClaw routes lightweight tasks to lower‑cost configurations while preserving higher precision for demanding workloads, saving cost and accelerating inference without increasing user complexity. Experiments show that our QuantClaw maintains or improves task performance while reducing both latency and computational cost. Across a range of agent tasks, it achieves up to 21.4% cost savings and 15.7% latency reduction on GLM‑5 (FP8 baseline). These results highlight the benefit of treating precision as a dynamic resource in agent systems.

PaperId: 1505, https://arxiv.org/pdf/2604.22436.pdf GitHub

Abstract:
The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of identifying suitable agents for a given task. Unlike traditional tools, agent capabilities are often compositional and execution‑dependent, making them difficult to assess from textual descriptions alone. However, existing research and benchmarks typically assume well‑specified functionalities, controlled candidate pools, or only executable task queries, leaving realistic agent search scenarios insufficiently studied. We introduce AgentSearchBench, a large‑scale benchmark for agent search in the wild, built from nearly 10,000 real‑world agents across multiple providers. The benchmark formalizes agent search as retrieval and reranking problems under both executable task queries and high‑level task descriptions, and evaluates relevance using execution‑grounded performance signals. Experiments reveal a consistent gap between semantic similarity and actual agent performance, exposing the limitations of description‑based retrieval and reranking methods. We further show that lightweight behavioral signals, including execution‑aware probing, can substantially improve ranking quality, highlighting the importance of incorporating execution signals into agent discovery. Our code is available at https://github.com/Bingo‑W/AgentSearchBench.

PaperId: 1506, https://arxiv.org/pdf/2604.22333.pdf GitHub GitHub

Abstract:
Rapid situational awareness is critical in post‑disaster response. While remote sensing damage assessment is evolving from pixel‑level change detection to high‑level semantic analysis, existing vision‑language methodologies still struggle to provide actionable intelligence for complex strategic queries. They remain severely constrained by unimodal optical dependence, a prevailing bias towards natural disasters, and a fundamental lack of grounded interactivity. To address these limitations, we present ChangeQuery, a unified multimodal framework designed for comprehensive, all‑weather disaster situation awareness. To overcome modality constraints and scenario biases, we construct the Disaster‑Induced Change Query (DICQ) dataset, a large‑scale benchmark coupling pre‑event optical semantics with post‑event SAR structural features across a balanced distribution of natural catastrophes and armed conflicts. Furthermore, to provide the high‑quality supervision required for interactive reasoning, we propose a novel Automated Semantic Annotation Pipeline. Adhering to a ``statistics‑first, generation‑later'' paradigm, this engine automatically transforms raw segmentation masks into grounded, hierarchical instruction sets, effectively equipping the model with fine‑grained spatial and quantitative awareness. Trained on this structured data, the ChangeQuery architecture operates as an interactive disaster analyst. It supports multi‑task reasoning driven by diverse user queries, delivering precise damage quantification, region‑specific descriptions, and holistic post‑disaster summaries. Extensive experiments demonstrate that ChangeQuery establishes a new state‑of‑the‑art, providing a robust and interpretable solution for complex disaster monitoring. The code is available at \hrefhttps://sundongwei.github.io/changequery/https://sundongwei.github.io/changequery/.

PaperId: 1507, https://arxiv.org/pdf/2604.22239.pdf GitHub

Abstract:
This paper introduces the task of analytical question answering over large, semi‑structured document collections. We present MuDABench, a benchmark for multi‑document analytical QA, where questions require extracting and synthesizing information across numerous documents to perform quantitative analysis. Unlike existing multi‑document QA benchmarks that typically require information from only a few documents with limited cross‑document reasoning, MuDABench demands extensive inter‑document analysis and aggregation. Constructed via distant supervision by leveraging document‑level metadata and annotated financial databases, MuDABench comprises over 80,000 pages and 332 analytical QA instances. We also propose an evaluation protocol that measures final answer accuracy and uses intermediate‑fact coverage as an auxiliary diagnostic signal for the reasoning process. Experiments reveal that standard RAG systems, which treat all documents as a flat retrieval pool, perform poorly. To address these limitations, we propose a multi‑agent workflow that orchestrates planning, extraction, and code generation modules. While this approach substantially improves both process and outcome metrics, a significant gap remains compared to human expert performance. Our analysis identifies two primary bottlenecks: single‑document information extraction accuracy and insufficient domain‑specific knowledge in current systems. MuDABench is available at https://github.com/Zhanli‑Li/MuDABench.

PaperId: 1508, https://arxiv.org/pdf/2604.22229.pdf GitHub

Abstract:
One‑step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away from actions that the dataset can support. In recent one‑step extraction pipelines, a strong iterative teacher provides one target action for each latent draw, and the same student output is asked to do both jobs: move toward higher Q and stay near that paired endpoint. If those two directions disagree, the loss resolves them as a compromise on that same sample, even when a nearby better action remains locally supported by the data. We propose DROL, a latent‑conditioned one‑step actor trained with top‑1 dynamic routing. For each state, the actor samples K candidate actions from a bounded latent prior, assigns each dataset action to its nearest candidate, and updates only that winner with Behavior Cloning and critic guidance. Because the routing is recomputed from the current candidate geometry, ownership of a supported region can shift across candidates over the course of learning. This gives a one‑step actor room to make local improvements that pointwise extraction struggles to capture, while retaining single‑pass inference at test time. On OGBench and D4RL, DROL is competitive with the one‑step FQL baseline, improving many OGBench task groups while remaining strong on both AntMaze and Adroit. Project page: https://muzhancun.github.io/preprints/DROL.

PaperId: 1509, https://arxiv.org/pdf/2604.22209.pdf GitHub

Abstract:
Generative audio modeling has largely been fragmented into specialized tasks, text‑to‑speech (TTS), text‑to‑music (TTM), and text‑to‑audio (TTA), each operating under heterogeneous control paradigms. Unifying these modalities remains a fundamental challenge due to the intrinsic dissonance between structured semantic representations (speech/music) and unstructured acoustic textures (sound effects). In this paper, we introduce UniSonate, a unified flow‑matching framework capable of synthesizing speech, music, and sound effects through a standardized, reference‑free natural language instruction interface. To reconcile structural disparities, we propose a novel dynamic token injection mechanism that projects unstructured environmental sounds into a structured temporal latent space, enabling precise duration control within a phoneme‑driven Multimodal Diffusion Transformer (MM‑DiT). Coupled with a multi‑stage curriculum learning strategy, this approach effectively mitigates cross‑modal optimization conflicts. Extensive experiments demonstrate that UniSonate achieves state‑of‑the‑art performance in instruction‑based TTS (WER 1.47%) and TTM (SongEval Coherence 3.18), while maintaining competitive fidelity in TTA. Crucially, we observe positive transfer, where joint training on diverse audio data significantly enhances structural coherence and prosodic expressiveness compared to single‑task baselines. Audio samples are available at https://qiangchunyu.github.io/UniSonate/.

PaperId: 1510, https://arxiv.org/pdf/2604.22190.pdf GitHub

Abstract:
CLIP‑based person re‑identification (ReID) methods aggregate spatial features into a single global \texttt[CLS] token optimized for image‑text alignment rather than spatial selectivity, making representations fragile under occlusion and cross‑camera variation. We propose SAGA‑ReID, which reconstructs identity representations by aligning intermediate patch tokens with anchor vectors parameterized in CLIP's text embedding space ‑‑ emphasizing spatially stable evidence while suppressing corrupted or absent regions, without requiring textual descriptions of individual images. Controlled experiments isolate the aggregation mechanism under two qualitatively distinct conditions ‑‑ synthetic masking, where identity signal is absent, and realistic human distractors, where an overlapping person introduces semantically confusing signal ‑‑ with SAGA's advantage over global pooling growing substantially as occlusion increases across both conditions. Benchmark evaluations confirm consistent gains over CLIP‑ReID across standard and occluded settings, with the largest improvements where global pooling is most unreliable: up to +10.6 Rank‑1 on occluded benchmarks. SAGA's aggregation outperforms dedicated sequential patch aggregation on a stronger backbone, confirming that structured reconstruction addresses a bottleneck that backbone quality and architectural complexity alone cannot resolve. Code available at https://github.com/ipl‑uw/Structured‑Anchor‑Guided‑Aggregation‑for‑ReID.

PaperId: 1511, https://arxiv.org/pdf/2604.22167.pdf GitHub

Abstract:
Language models are increasingly capable and are being rapidly deployed on a population‑level scale. As a result, the safety of these models is increasingly high‑stakes. Fortunately, advances in alignment have significantly reduced the likelihood of harmful model outputs. However, when models are queried billions of times in a day, even rare worst‑case behaviors will occur. Current safety evaluations focus on capturing the distribution of inputs that yield harmful outputs. These evaluations disregard the probabilistic nature of models and their tail output behavior. To measure this tail risk, we propose a method to efficiently estimate the probability of harmful outputs for any input query. Instead of naive brute‑force sampling from the target model, where harmful outputs could be rare, we operationalize importance sampling by creating unsafe versions of the target model. These unsafe versions enable sample‑efficient estimation by making harmful outputs more probable. On benchmarks measuring misuse and misalignment, these estimates match brute‑force Monte Carlo estimates using 10‑20x fewer samples. For example, we can estimate probability of harmful outputs on the order of 10^‑4 with just 500 samples. Additionally, we find that these harmfulness estimates can reveal the sensitivity of models to perturbations in model input and predict deployment risks. Our work demonstrates that accurate rare‑event estimation is both critical and feasible for safety evaluations. Code is available at https://github.com/rangell/LMTailRisk

PaperId: 1512, https://arxiv.org/pdf/2604.22153.pdf GitHub

Abstract:
When you ask an AI assistant for advice about your career, your marriage, or a conflict with your family, does it give you the same answer regardless of where you are from? We tested this systematically by presenting three leading AI systems (Claude Sonnet 4.5, GPT‑5.4, and Gemini 2.5 Flash) with ten real‑life personal dilemmas, framed for users from 10 countries across 5 continents in 7 languages (n=840 scored responses). We compared AI advice against World Values Survey Wave 7 data measuring what people in each country actually believe. All three AI systems consistently gave Western‑style, individualist advice even to users from societies that prioritize family, community, and authority, significantly more so than local values would predict (mean gap +0.76 on a 1‑5 scale; t=15.65, p<0.001). The gap is largest for Nigeria (+1.85) and India (+0.82). Japan is the sole exception: AI systems treated Japanese users as more group‑oriented than surveys show, revealing that AI encodes outdated stereotypes. Claude and GPT‑5.4 show nearly identical bias magnitude, while Gemini is lower but still significant. The models diverge in mechanism: Claude shifts further collectivist in the user's native language; Gemini shifts more individualist; GPT‑5.4 responds only to stated country identity. These findings point to a systemic homogenization of values across frontier AI. Data, code, and scoring pipeline are openly released.

PaperId: 1513, https://arxiv.org/pdf/2604.22018.pdf GitHub

Abstract:
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero‑ or few‑shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated. We propose RE‑CONFIRM, a framework for evaluating the robustness of potential biomarker candidates elucidated by deep learning (DL) models including FMs. From experiments on five large datasets of Autism Spectrum Disorder (ASD), Attention‑deficit Hyperactivity Disorder (ADHD), and Alzheimer's Disease (AD), we found that although commonly used performance metrics provide an intuitive assessment of model predictions, they are insufficient for evaluating the robustness of biomarkers identified by these models. RE‑CONFIRM metrics revealed that simply finetuning FMs leads to models that fail to capture regional hubs effectively, even in disorders where hubs are known to be implicated, such as ASD and ADHD. In view of this, we propose Hub‑LoRA (Low‑Rank Adaptation) as a fine‑tuning technique that enables FMs to not only outperform customised DL models but also produce neurobiologically faithful biomarkers supported by meta‑analyses. RE‑CONFIRM is generalizable and can be easily applied to ascertain the robustness of DL models trained on functional MRI datasets. Code is available at: https://github.com/SCSE‑Biomedical‑Computing‑Group/RE‑CONFIRM.

PaperId: 1514, https://arxiv.org/pdf/2604.21999.pdf GitHub GitHub

Abstract:
We study learned memory tokens as a computational scratchpad for a single‑block Universal Transformer with Adaptive Computation Time (ACT) on Sudoku‑Extreme, a combinatorial reasoning benchmark. Memory tokens are empirically necessary: no configuration without them reaches non‑trivial performance. The optimal count has a sharp lower threshold (T=0 always fails, T=8 reliably succeeds) followed by a stable plateau (T=8‑32, 57.4% +/‑ 0.7% exact‑match) and a dilution boundary at T=64. Under halt‑side pressure (lambda warmup), mean halt drops monotonically with memory size across the plateau (from 11.6 at T=8 to 8.3 at T=64), showing that memory tokens and ponder depth substitute as resources at fixed accuracy. We also identify a router initialization trap that causes the majority of training runs to fail: both default zero‑bias and Graves' recommended positive bias settle into a shallow halt equilibrium the model cannot escape. Inverting the bias to ‑3 ("deep start") eliminates the failure mode, and ablation shows the trap is inherent to ACT initialization rather than an artifact of our architecture. With reliable training, ACT yields an order of magnitude lower seed variance than fixed‑depth processing (+/‑0.7 vs +/‑9.3 pp); lambda warmup recovers 34% of compute at matched accuracy; and attention heads specialize into memory readers, constraint propagators, and integrators across recursive depth. Code: https://github.com/che‑shr‑cat/utm‑jax.

PaperId: 1515, https://arxiv.org/pdf/2604.21950.pdf GitHub

Abstract:
Small language models (1‑3B) are practical to run locally, but individually limited on harder code generation tasks. We ask whether composing them into pipelines can recover some of that lost capability. We study code generation pipelines built from 1‑3B models with execution feedback, and use a NEAT‑inspired evolutionary search to test whether more complex pipeline structure helps beyond a simple refinement loop. We evaluate on HumanEval (164 problems) and sanitized MBPP (427 problems), all with local inference on a single laptop. Self‑refinement with execution feedback improves code generation by more than 4 standard deviations on both benchmarks. The gains are narrow in mechanism: refinement fixes many runtime errors (especially NameError and SyntaxError), but rarely fixes logic errors such as AssertionError. Within our tested general‑purpose model pool, generator identity mattered less than refiner capability: a 1.5B generator paired with a 3B refiner matched a 3B model doing both roles. Early stopping is essential; without it, every iteration is net‑negative. The code‑specialized models outperform every general‑purpose pipeline configuration, suggesting model specialization matters more than pipeline architecture. Preliminary text‑only pipeline experiments without execution feedback did not show gains at this scale. In our constrained search space, evolutionary search mostly rediscovered the same simple generate‑execute‑refine loop we found manually, with no clearly significant gain from added topology. Single‑evaluation fitness inflates results by 5‑7 percent, selecting lucky genomes over good ones. On these benchmarks at 1‑3B scale, execution feedback mattered more than added pipeline complexity in determining whether composition helped.

PaperId: 1516, https://arxiv.org/pdf/2604.21911.pdf GitHub

Abstract:
Despite impressive progress in capabilities of large vision‑language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs that are not grounded in the visual input. Prior work has attributed hallucinations in LVLMs to factors such as limitations of the vision backbone or the dominance of the language component, yet the relative importance of these factors remains unclear. To resolve this ambiguity, We propose HalluScope, a benchmark to better understand the extent to which different factors induce hallucinations. Our analysis indicates that hallucinations largely stem from excessive reliance on textual priors and background knowledge, especially information introduced through textual instructions. To mitigate hallucinations induced by textual instruction priors, we propose HalluVL‑DPO, a framework for fine‑tuning off‑the‑shelf LVLMs towards more visually grounded responses. HalluVL‑DPO leverages preference optimization using a curated training dataset that we construct, guiding the model to prefer grounded responses over hallucinated ones. We demonstrate that our optimized model effectively mitigates the targeted hallucination failure mode, while preserving or improving performance on other hallucination benchmarks and visual capability evaluations. To support reproducibility and further research, we will publicly release our evaluation benchmark, preference training dataset, and code at https://pegah‑kh.github.io/projects/prompts‑override‑vision/ .

PaperId: 1517, https://arxiv.org/pdf/2604.21816.pdf GitHub

Abstract:
The Model Context Protocol (MCP) has become a common interface for connecting large language model (LLM) agents to external tools, but its reliance on stateless, eager schema injection imposes a hidden per‑turn overhead the MCP Tax or Tools Tax that practitioner reports place between roughly 10k and 60k tokens in typical multi‑server deployments. This payload inflates the key‑value cache, is associated with reasoning degradation as context utilization approaches published fracture points around 70%, and turns token budgets into a recurring operational cost. We introduce Tool Attention, a middleware‑layer mechanism that generalizes the "Attention Is All You Need" paradigm from self‑attention over tokens to gated attention over tools. Tool Attention combines (i) an Intent Schema Overlap (ISO) score from sentence embeddings, (ii) a state‑aware gating function enforcing preconditions and access scopes, and (iii) a two‑phase lazy schema loader that keeps a compact summary pool in context and promotes full JSON schemas only for top‑k gated tools. We evaluate on a simulated 120‑tool, six‑server benchmark whose per‑server token counts are calibrated to public audits of real MCP deployments. In this simulation, Tool Attention directly reduces measured per‑turn tool tokens by 95.0% (47.3k ‑> 2.4k) and raises effective context utilization (a token‑ratio quantity) from 24% to 91%. End‑to‑end figures for task success, latency, cost, and reasoning quality are reported as projections derived from the measured token counts combined with published deployment telemetry; they are not measured on live LLM agents, and we mark projected values explicitly throughout. Taken together, the results support a simple thesis: protocol‑level efficiency, not raw context length, is a binding constraint on scalable gentic systems. The code for this work is accessible at https://github.com/asadani/tool‑attention

PaperId: 1518, https://arxiv.org/pdf/2604.21801.pdf GitHub

Abstract:
Recent advances in deep learning for remote sensing rely heavily on large annotated datasets, yet acquiring high‑quality ground truth for geometric, radiometric, and multi‑domain tasks remains costly and often infeasible. In particular, the lack of accurate depth annotations, controlled illumination variations, and multi‑scale paired imagery limits progress in monocular depth estimation, domain adaptation, and super‑resolution for aerial scenes. We present SyMTRS, a large‑scale synthetic dataset generated using a high‑fidelity urban simulation pipeline. The dataset provides high‑resolution RGB aerial imagery (2048 x 2048), pixel‑perfect depth maps, night‑time counterparts for domain adaptation, and aligned low‑resolution variants for super‑resolution at x2, x4, and x8 scales. Unlike existing remote sensing datasets that focus on a single task or modality, SyMTRS is designed as a unified multi‑task benchmark enabling joint research in geometric understanding, cross‑domain robustness, and resolution enhancement. We describe the dataset generation process, its statistical properties, and its positioning relative to existing benchmarks. SyMTRS aims to bridge critical gaps in remote sensing research by enabling controlled experiments with perfect geometric ground truth and consistent multi‑domain supervision. The results obtained in this work can be reproduced from this Github repository: https://github.com/safouaneelg/SyMTRS.

PaperId: 1519, https://arxiv.org/pdf/2604.21748.pdf GitHub

Abstract:
Long‑term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasoning and multi‑hop question answering. Current approaches face a fundamental trade‑off: flat memory is efficient but fails to model relational structure, while graph‑based memory enables structured reasoning at the cost of expensive and fragile construction. To address these issues, we propose StructMem, a structure‑enriched hierarchical memory framework that preserves event‑level bindings and induces cross‑event connections. By temporally anchoring dual perspectives and performing periodic semantic consolidation, StructMem improves temporal reasoning and multi‑hop performance on \textttLoCoMo, while substantially reducing token usage, API calls, and runtime compared to prior memory systems, see https://github.com/zjunlp/LightMem .

PaperId: 1520, https://arxiv.org/pdf/2604.21743.pdf GitHub

Abstract:
Image enhancement models for mobile devices often struggle to balance high output quality with the fast processing speeds required by mobile hardware. While recent deep learning models can enhance low‑quality mobile photos into high‑quality images, their performance is often degraded when converted to lower‑precision formats for actual use on mobile phones. To address this training‑deployment mismatch, we propose an efficient image enhancement model designed specifically for mobile deployment. Our approach uses a hierarchical network architecture with gated encoder blocks and multiscale refinement to preserve fine‑grained visual features. Moreover, we incorporate Quantization‑Aware Training (QAT) to simulate the effects of low‑precision representation during the training process. This allows the network to adapt and prevents the typical drop in quality seen with standard post‑training quantization (PTQ). Experimental results demonstrate that the proposed method produces high‑fidelity visual output while maintaining the low computational overhead needed for practical use on standard mobile devices. The code will be available at https://github.com/GenAI4E/QATIE.git.

PaperId: 1521, https://arxiv.org/pdf/2604.21725.pdf GitHub

Abstract:
LLM agents increasingly operate in open‑ended environments spanning hundreds of sequential episodes, yet they remain largely stateless: each task is solved from scratch without converting past experience into better future behavior. The central obstacle is not \emphwhat to remember but \emphhow to use what has been remembered, including which retrieval policy to apply, how to interpret prior outcomes, and when the current strategy itself must change. We introduce \emphAgent Evolving Learning (\ael), a two‑timescale framework that addresses this obstacle. At the fast timescale, a Thompson Sampling bandit learns which memory retrieval policy to apply at each episode; at the slow timescale, LLM‑driven reflection diagnoses failure patterns and injects causal insights into the agent's decision prompt, giving it an interpretive frame for the evidence it retrieves. On a sequential portfolio benchmark (10 sector‑diverse tickers, 208 episodes, 5 random seeds), \ael achieves a Sharpe ratio of 2.13\pm0.47, outperforming five published self‑improving methods and all non‑LLM baselines while maintaining the lowest variance among all LLM‑based approaches. A nine‑variant ablation reveals a ``less is more'' pattern: memory and reflection together produce a 58% cumulative improvement over the stateless baseline, yet every additional mechanism we test (planner evolution, per‑tool selection, cold‑start initialization, skill extraction, and three credit assignment methods) \emphdegrades performance. This demonstrates that the bottleneck in agent self‑improvement is \emphself‑diagnosing how to use experience rather than adding architectural complexity. Code and data: https://github.com/WujiangXu/AEL.

PaperId: 1522, https://arxiv.org/pdf/2604.21718.pdf GitHub

Abstract:
Video‑language models (VLMs) learn to reason about the dynamic visual world through natural language. We introduce a suite of open datasets, benchmarks, and recipes for scalable oversight that enable precise video captioning. First, we define a structured specification for describing subjects, scenes, motion, spatial, and camera dynamics, grounded by hundreds of carefully defined visual primitives developed with professional video creators such as filmmakers. Next, to curate high‑quality captions, we introduce CHAI (Critique‑based Human‑AI Oversight), a framework where trained experts critique and revise model‑generated pre‑captions into improved post‑captions. This division of labor improves annotation accuracy and efficiency by offloading text generation to models, allowing humans to better focus on verification. Additionally, these critiques and preferences between pre‑ and post‑captions provide rich supervision for improving open‑source models (Qwen3‑VL) on caption generation, reward modeling, and critique generation through SFT, DPO, and inference‑time scaling. Our ablations show that critique quality in precision, recall, and constructiveness, ensured by our oversight framework, directly governs downstream performance. With modest expert supervision, the resulting model outperforms closed‑source models such as Gemini‑3.1‑Pro. Finally, we apply our approach to re‑caption large‑scale professional videos (e.g., films, commercials, games) and fine‑tune video generation models such as Wan to better follow detailed prompts of up to 400 words, achieving finer control over cinematography including camera motion, angle, lens, focus, point of view, and framing. Our results show that precise specification and human‑AI oversight are key to professional‑level video understanding and generation. Data and code are available on our project page: https://linzhiqiu.github.io/papers/chai/

PaperId: 1523, https://arxiv.org/pdf/2604.21649.pdf GitHub

Abstract:
Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge. While recent quantization‑based approaches attempt to align these modalities, they typically treat quantization as flat numerical compression, resulting in semantically entangled codes that fail to mirror the hierarchical nature of human reasoning. In this paper, we propose GS‑Quant, a novel framework that generates semantically coherent and structurally stratified discrete codes for KG entities. Unlike prior methods, GS‑Quant is grounded in the insight that entity representations should follow a linguistic coarse‑to‑fine logic. We introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook, ensuring that earlier codes capture global semantic categories while later codes refine specific attributes. Furthermore, a Generative Structural Reconstruction module imposes causal dependencies on the code sequence, transforming independent discrete units into structured semantic descriptors. By expanding the LLM vocabulary with these learned codes, we enable the model to reason over graph structures isomorphically to natural language generation. Experimental results demonstrate that GS‑Quant significantly outperforms existing text‑based and embedding‑based baselines. Our code is publicly available at https://github.com/mikumifa/GS‑Quant.

PaperId: 1524, https://arxiv.org/pdf/2604.21464.pdf GitHub

Abstract:
Standard reinforcement learning (RL) optimizes policies for reward but imposes few constraints on how decisions evolve over time. As a result, policies may achieve high performance while exhibiting temporally incoherent behavior such as abrupt confidence shifts, oscillations, or degenerate inactivity. We introduce Dynamical Prior Reinforcement Learning (DP‑RL), a training framework that augments policy gradient learning with an auxiliary loss derived from external state dynamics that implement evidence accumulation and hysteresis. Without modifying the reward, environment, or policy architecture, this prior shapes the temporal evolution of action probabilities during learning. Across three minimal environments, we show that dynamical priors systematically alter decision trajectories in task‑dependent ways, promoting temporally structured behavior that cannot be explained by generic smoothing. These results demonstrate that training objectives alone can control the temporal geometry of decision‑making in RL agents.

PaperId: 1525, https://arxiv.org/pdf/2604.21450.pdf GitHub

Abstract:
Recent advancements in visual autoregressive models (VAR) have demonstrated their effectiveness in image generation, highlighting their potential for real‑world image super‑resolution (Real‑ISR). However, adapting VAR for ISR presents critical challenges. The next‑scale prediction mechanism, constrained by causal attention, fails to fully exploit global low‑quality (LQ) context, resulting in blurry and inconsistent high‑quality (HQ) outputs. Additionally, error accumulation in the iterative prediction severely degrades coherence in ISR task. To address these issues, we propose VARestorer, a simple yet effective distillation framework that transforms a pre‑trained text‑to‑image VAR model into a one‑step ISR model. By leveraging distribution matching, our method eliminates the need for iterative refinement, significantly reducing error propagation and inference time. Furthermore, we introduce pyramid image conditioning with cross‑scale attention, which enables bidirectional scale‑wise interactions and fully utilizes the input image information while adapting to the autoregressive mechanism. This prevents later LQ tokens from being overlooked in the transformer. By fine‑tuning only 1.2% of the model parameters through parameter‑efficient adapters, our method maintains the expressive power of the original VAR model while significantly enhancing efficiency. Extensive experiments show that VARestorer achieves state‑of‑the‑art performance with 72.32 MUSIQ and 0.7669 CLIPIQA on DIV2K dataset, while accelerating inference by 10 times compared to conventional VAR inference.

PaperId: 1526, https://arxiv.org/pdf/2604.21395.pdf GitHub

Abstract:
PGD adversarial training, the standard robustness method, can reduce Jacobian Frobenius norm yet worsen clean‑input geometry (e.g., TDI 1.336 vs. ERM 1.093). We show this is not an implementation artifact but a theorem‑level consequence of supervised learning. We prove that any encoder minimizing supervised loss must retain non‑zero sensitivity along directions correlated with training labels, including directions that are nuisance at test time. This holds across proper scoring rules, architectures, and dataset sizes. We call this the geometric blind spot of supervised learning. This theorem unifies four empirical phenomena often treated separately: non‑robust features, texture bias, corruption fragility, and the robustness‑accuracy tradeoff. It also explains why suppressing sensitivity in one adversarial direction can redistribute sensitivity elsewhere. We introduce Trajectory Deviation Index (TDI), a diagnostic of geometric isotropy. Unlike CKA, intrinsic dimension, or Jacobian Frobenius norm alone, TDI captures the failure mode above. In our experiments, PGD attains low Frobenius norm but high TDI, while PMH attains the lowest TDI with one additional training term and no architectural changes. Across seven tasks, BERT/SST‑2, and ImageNet ViT‑B/16 (backbone family underlying CLIP/DINO/SAM), the blind spot is measurable and repairable. It appears at foundation‑model scale, worsens with model scale and task‑specific fine‑tuning, and is substantially reduced by PMH. PMH also leads on non‑Gaussian corruption types (blur/brightness/contrast) without corruption‑specific training.

PaperId: 1527, https://arxiv.org/pdf/2604.21349.pdf GitHub

Abstract:
Self‑supervised learning (SSL) is a standard approach for representation learning in aerial imagery. Existing methods enforce invariance between augmented views, which works well when augmentations preserve semantic content. However, aerial images are frequently degraded by haze, motion blur, rain, and occlusion that remove critical evidence. Enforcing alignment between a clean and a severely degraded view can introduce spurious structure into the latent space. This study proposes a training strategy and architectural modification to enhance SSL robustness to such corruptions. It introduces a per‑sample, per‑factor trust weight into the alignment objective, combined with the base contrastive loss as an additive residual. A stop‑gradient is applied to the trust weight instead of a multiplicative gate. While a multiplicative gate is a natural choice, experiments show it impairs the backbone, whereas our additive‑residual approach improves it. Using a 200‑epoch protocol on a 210,000‑image corpus, the method achieves the highest mean linear‑probe accuracy among six backbones on EuroSAT, AID, and NWPU‑RESISC45 (90.20% compared to 88.46% for SimCLR and 89.82% for VICReg). It yields the largest improvements under severe information‑erasing corruptions on EuroSAT (+19.9 points on haze at s=5 over SimCLR). The method also demonstrates consistent gains of +1 to +3 points in Mahalanobis AUROC on a zero‑shot cross‑domain stress test using BDD100K weather splits. Two ablations (scalar uncertainty and cosine gate) indicate the additive‑residual formulation is the primary source of these improvements. An evidential variant using Dempster‑Shafer fusion introduces interpretable signals of conflict and ignorance. These findings offer a concrete design principle for uncertainty‑aware SSL. Code is publicly available at https://github.com/WadiiBoulila/trust‑ssl.

PaperId: 1528, https://arxiv.org/pdf/2604.21327.pdf GitHub

Abstract:
Test‑time reinforcement learning (TTRL) always adapts models at inference time via pseudo‑labeling, leaving it vulnerable to spurious optimization signals from label noise. Through an empirical study, we observe that responses with medium consistency form an ambiguity region and constitute the primary source of reward noise. Crucially, we find that such spurious signals can be even amplified through group‑relative advantage estimation. Motivated by these findings, we propose a unified framework, Debiased and Denoised test‑time Reinforcement Learning (DDRL), to mitigate spurious signals. Concretely, DDRL first applies a frequency‑based sampling strategy to exclude ambiguous samples while maintaining a balanced set of positive and negative examples. It then adopts a debiased advantage estimation with fixed advantages, removing the bias introduced by group‑relative policy optimization. Finally, DDRL incorporates a consensus‑based off‑policy refinement stage, which leverages the rejection‑sampled dataset to enable efficient and stable model updates. Experiments on three large language models across multiple mathematical reasoning benchmarks demonstrate that DDRL consistently outperforms existing TTRL baselines. The code will soon be released at https://github.com/yuyongcan/DDRL.

PaperId: 1529, https://arxiv.org/pdf/2604.21312.pdf GitHub

Authors:Kai Liu, Haoyang Yue, Zeli Lin, Zheng Chen, Jingkai Wang, Jue Gong, Jiatong Li, Xianglong Yan, Libo Zhu, Jianze Li, Ziqing Zhang, Zihan Zhou, Xiaoyang Liu, Radu Timofte, Yulun Zhang, Junye Chen, Zhenming Yan, Yucong Hong, Ruize Han, Song Wang, Li Pang, Heng Zhao, Xinqiao Wu, Deyu Meng, Xiangyong Cao, Weijun Yuan, Zhan Li, Zhanglu Chen, Boyang Yao, Yihang Chen, Yifan Deng, Zengyuan Zuo, Junjun Jiang, Saiprasad Meesiyawar, Sulocha Yatageri, Nikhil Akalwadi, Ramesh Ashok Tabib, Uma Mudenagudi, Jiachen Tu, Yaokun Shi, Guoyi Xu, Yaoxin Jiang, Cici Liu, Tongyao Mu, Qiong Cao, Yifan Wang, Kosuke Shigematsu, Hiroto Shirono, Asuka Shin, Wei Zhou, Linfeng Li, Lingdong Kong, Ce Wang, Xingwei Zhong, Wanjie Sun, Dafeng Zhang, Hongxin Lan, Qisheng Xu, Mingyue He, Hui Geng, Tianjiao Wan, Kele Xu, Changjian Wang, Antoine Carreaud, Nicola Santacroce, Shanci Li, Jan Skaloud, Adrien Gressin

Abstract:
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super‑Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high‑resolution (HR) infrared images from low‑resolution (LR) inputs generated through bicubic downsampling with a x4 scaling factor. The objective is to develop effective models or solutions that achieve state‑of‑the‑art performance for infrared image SR in remote sensing scenarios. To reflect the characteristics of infrared data and practical application needs, the challenge adopts a single‑track setting. A total of 115 participants registered for the competition, with 13 teams submitting valid entries. This report summarizes the challenge design, dataset, evaluation protocol, main results, and the representative methods of each team. The challenge serves as a benchmark to advance research in infrared image super‑resolution and promote the development of effective solutions for real‑world remote sensing applications.

PaperId: 1530, https://arxiv.org/pdf/2604.21286.pdf GitHub

Abstract:
Cacioli (2026) showed that the K‑way energy probe on standard discriminative predictive coding networks reduces approximately to a monotone function of the log‑softmax margin. The reduction rests on five assumptions, including cross‑entropy (CE) at the output and effectively feedforward inference dynamics. This pre‑registered study tests the reduction's sensitivity to CE removal using two conditions: standard PC trained with MSE instead of CE, and bidirectional PC (bPC; Oliviers, Tang & Bogacz, 2025). Across 10 seeds on CIFAR‑10 with a matched 2.1M‑parameter backbone, we find three results. The negative result replicates on standard PC: the probe sits below softmax (Delta = ‑0.082, p < 10^‑6). On bPC the probe exceeds softmax across all 10 seeds (Delta = +0.008, p = 0.000027), though a pre‑registered manipulation check shows that bPC does not produce materially greater latent movement than standard PC at this scale (ratio 1.6, threshold 10). Removing CE alone without changing inference dynamics halves the probe‑softmax gap (Delta_MSE = ‑0.037 vs Delta_stdPC = ‑0.082). CE is a major empirically load‑bearing component of the decomposition at this scale. CE training produces output logit norms approximately 15x larger than MSE or bPC training. A post‑hoc temperature scaling ablation decomposes the probe‑softmax gap into two components: approximately 66% is attributable to logit‑scale effects removable by temperature rescaling, and approximately 34% reflects a scale‑invariant ranking advantage of CE‑trained representations. We use "metacognitive" operationally to denote Type‑2 discrimination of a readout over its own Type‑1 correctness, not to imply human‑like introspective access.

PaperId: 1531, https://arxiv.org/pdf/2604.21284.pdf GitHub

Abstract:
MemPalace is an open‑source AI memory system that applies the ancient method of loci (memory palace) spatial metaphor to organize long‑term memory for large language models; launched in April 2026, it accumulated over 47,000 GitHub stars in its first two weeks and claims state‑of‑the‑art retrieval performance on the LongMemEval benchmark (96.6% Recall@5) without requiring any LLM inference at write time. Through independent codebase analysis, benchmark replication, and comparison with competing systems, we find that MemPalace's headline retrieval performance is attributable primarily to its verbatim storage philosophy combined with ChromaDB's default embedding model (all‑MiniLM‑L6‑v2), rather than to its spatial organizational metaphor per se ‑‑ the palace hierarchy (Wings‑>Rooms‑>Closets‑>Drawers) operates as standard vector database metadata filtering, an effective but well‑established technique. However, MemPalace makes several genuinely novel contributions: (1) a contrarian verbatim‑first storage philosophy that challenges extraction‑based competitors, (2) an extremely low wake‑up cost (approximately 170 tokens) through its four‑layer memory stack, (3) a fully deterministic, zero‑LLM write path enabling offline operation at zero API cost, and (4) the first systematic application of spatial memory metaphors as an organizing principle for AI memory systems. We also note that the competitive landscape is evolving rapidly, with Mem0's April 2026 token‑efficient algorithm raising their LongMemEval score from approximately 49% to 93.4%, narrowing the gap between extraction‑based and verbatim approaches. Our analysis concludes that MemPalace represents significant architectural insight wrapped in overstated claims ‑‑ a pattern common in rapidly adopted open‑source projects where marketing velocity exceeds scientific rigor.

PaperId: 1532, https://arxiv.org/pdf/2604.21277.pdf GitHub

Abstract:
We introduce MMTR‑Bench, a benchmark designed to evaluate the intrinsic ability of Multimodal Large Language Models (MLLMs) to reconstruct masked text directly from visual context. Unlike conventional question‑answering tasks, MMTR‑Bench eliminates explicit prompts, requiring models to recover masked text from single‑ or multi‑page inputs across real‑world domains such as documents and webpages. This design isolates the reconstruction task from instruction‑following abilities, enabling a direct assessment of a model's layout understanding, visual grounding, and knowledge integration. MMTR‑Bench comprises 2,771 test samples spanning multiple languages and varying target lengths. To account for this diversity, we propose a level‑aware evaluation protocol. Experiments on representative MLLMs show that the benchmark poses a significant challenge, especially for sentence‑ and paragraph‑level reconstruction. The homepage is available at https://mmtr‑bench‑dataset.github.io/MMTR‑Bench/.

PaperId: 1533, https://arxiv.org/pdf/2604.21260.pdf GitHub GitHub

Abstract:
We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black‑box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse‑probability weighting (AIPW) [Robins et al., 1994], which protects against prediction‑model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Prediction‑Powered Inference, which post‑hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference. We study both linear and isotonic calibration. For isotonic calibration, we establish first‑order optimality guarantees: isotonic post‑processing can improve predictive accuracy and estimator efficiency relative to the original score and simpler post‑processing rules, while no further post‑processing of the fitted isotonic score yields additional first‑order gains. For linear calibration, we show first‑order equivalence to PPI++. We also clarify the relationship among existing estimators, showing that the original PPI estimator is a special case of AIPW and can be inefficient when the prediction model is accurate, while PPI++ is AIPW with empirical efficiency maximization [Rubin et al., 2008]. In simulations and real‑data experiments, our calibrated estimators often outperform PPI and are competitive with, or outperform, AIPW and PPI++. We provide an accompanying Python package, ppi_aipw, at https://larsvanderlaan.github.io/ppi‑aipw/.

PaperId: 1534, https://arxiv.org/pdf/2604.21241.pdf GitHub

Abstract:
Vision‑‑Language‑‑Action (VLA) models often use intermediate representations to connect multimodal inputs with continuous control, yet spatial guidance is often injected implicitly through latent features. We propose CorridorVLA, which predicts sparse spatial anchors as incremental physical changes (e.g., Δ‑positions) and uses them to impose an explicit tolerance region in the training objective for action generation. The anchors define a corridor that guides a flow‑matching action head: trajectories whose implied spatial evolution falls outside it receive corrective gradients, while minor deviations from contacts and execution noise are permitted. On the more challenging LIBERO‑Plus benchmark, CorridorVLA yields consistent gains across both SmolVLA and GR00T, improving success rate by 3.4%‑‑12.4% over the corresponding baselines; notably, our GR00T‑Corr variant reaches a success rate of 83.21%. These results indicate that action‑aligned physical cues can provide direct and interpretable constraints for generative action policies, complementing spatial guidance encoded in visual or latent forms. Code is available at https://github.com/corridorVLA.

PaperId: 1535, https://arxiv.org/pdf/2604.21214.pdf GitHub

Abstract:
Text‑to‑SQL models have significantly improved with the adoption of Large Language Models (LLMs), leading to their increasing use in real‑world applications. Although many benchmarks exist for evaluating the performance of text‑to‑SQL models, they often rely on a single aggregate score, lack evaluation under realistic settings, and provide limited insight into model behaviour across different query types. In this work, we present SQLyzr, a comprehensive benchmark and evaluation platform for text‑to‑SQL models. SQLyzr incorporates a diverse set of evaluation metrics that capture multiple aspects of generated queries, while enabling more realistic evaluation through workload alignment with real‑world SQL usage patterns and database scaling. It further supports fine‑grained query classification, error analysis, and workload augmentation, allowing users to better diagnose and improve text‑to‑SQL models. This demonstration showcases these capabilities through an interactive experience. Through SQLyzr's graphical interface, users can customize evaluation settings, analyze fine‑grained reports, and explore additional features of the platform. We envision that SQLyzr facilitates the evaluation and iterative improvement of text‑to‑SQL models by addressing key limitations of existing benchmarks. The source code of SQLyzr is available at https://github.com/sepideh‑abedini/SQLyzr.

PaperId: 1536, https://arxiv.org/pdf/2604.21204.pdf GitHub

Abstract:
In this work, we develop a novel reasoning approach to enhance the performance of large language models (LLMs) in future occupation prediction. In this approach, a reason generator first derives a ``reason'' for a user using his/her past education and career history. The reason summarizes the user's preference and is used as the input of an occupation predictor to recommend the user's next occupation. This two‑step occupation prediction approach is, however, non‑trivial as LLMs are not aligned with career paths or the unobserved reasons behind each occupation decision. We therefore propose to fine‑tune LLMs improving their reasoning and occupation prediction performance. We first derive high‑quality oracle reasons, as measured by factuality, coherence and utility criteria, using a LLM‑as‑a‑Judge. These oracle reasons are then used to fine‑tune small LLMs to perform reason generation and next occupation prediction. Our extensive experiments show that: (a) our approach effectively enhances LLM's accuracy in next occupation prediction making them comparable to fully supervised methods and outperforming unsupervised methods; (b) a single LLM fine‑tuned to perform reason generation and occupation prediction outperforms two LLMs fine‑tuned to perform the tasks separately; and (c) the next occupation prediction accuracy depends on the quality of generated reasons. Our code is available at https://github.com/Sarasarahhhhh/job_prediction.

PaperId: 1537, https://arxiv.org/pdf/2604.21138.pdf GitHub

Abstract:
Multi‑robot control in cluttered environments is a challenging problem that involves complex physical constraints, including robot‑robot collisions, robot‑obstacle collisions, and unreachable motions. Successful planning in such settings requires joint optimization over high‑level task planning and low‑level motion planning, as violations of physical constraints may arise from failures at either level. However, jointly optimizing task and motion planning is difficult due to the complex parameterization of low‑level motion trajectories and the ambiguity of credit assignment across the two planning levels. In this paper, we propose a hybrid multi‑robot control framework that jointly optimizes task and motion planning. To enable effective parameterization of low‑level planning, we introduce waypoints, a simple yet expressive representation for motion trajectories. To address the credit assignment challenge, we adopt a curriculum‑based training strategy with a modified RLVR algorithm that propagates motion feasibility feedback from the motion planner to the task planner. Experiments on BoxNet3D‑OBS, a challenging multi‑robot benchmark with dense obstacles and up to nine robots, show that our approach consistently improves task success over motion‑agnostic and VLA‑based baselines. Our code is available at https://github.com/UCSB‑NLP‑Chang/navigate‑cluster

PaperId: 1538, https://arxiv.org/pdf/2604.21119.pdf GitHub

Abstract:
Rings like gold, thuds like wood! The sound we hear in a scene is shaped not only by the spatial layout of the environment but also by the materials of the objects and surfaces within it. For instance, a room with wooden walls will produce a different acoustic experience from a room with the same spatial layout but concrete walls. Accurately modeling these effects is essential for applications such as virtual reality, robotics, architectural design, and audio engineering. Yet, existing methods for acoustic modeling often entangle spatial and material influences in correlated representations, which limits user control and reduces the realism of the generated acoustics. In this work, we present a novel approach for material‑controlled Room Impulse Response (RIR) generation that explicitly disentangles the effects of spatial and material cues in a scene. Our approach models the RIR using two modules: a spatial module that captures the influence of the spatial layout of the scene, and a material module that modulates this spatial RIR according to a user‑specified material configuration. This explicitly disentangled design allows users to easily modify the material configuration of a scene and observe its impact on acoustics without altering the spatial structure or scene content. Our model provides significant improvements over prior approaches on both acoustic‑based metrics (up to +16% on RTE) and material‑based metrics (up to +70%). Furthermore, through a human perceptual study, we demonstrate the improved realism and material sensitivity of our model compared to the strongest baselines.

PaperId: 1539, https://arxiv.org/pdf/2604.21090.pdf GitHub

Abstract:
AI governance programmes increasingly rely on natural language prompts to constrain and direct AI agent behaviour. These prompts function as executable specifications: they define the agent's mandate, scope, and quality criteria. Despite this role, no systematic framework exists for evaluating whether a governance prompt is structurally complete. We introduce a five‑principle evaluation framework grounded in computability theory, proof theory, and Bayesian epistemology, and apply it to an empirical corpus of 34 publicly available AGENTS.md governance files sourced from GitHub. Our evaluation reveals that 37% of evaluated file‑model pairs score below the structural completeness threshold, with data classification and assessment rubric criteria most frequently absent. These results suggest that practitioner‑authored governance prompts exhibit consistent structural patterns that automated static analysis could detect and remediate. We discuss implications for requirements engineering practice in AI‑assisted development contexts, identify a previously undocumented artefact classification gap in the AGENTS.md convention, and propose directions for tool support.

PaperId: 1540, https://arxiv.org/pdf/2604.21027.pdf GitHub

Abstract:
Electronic health record (EHR) question answering is often handled by LLM‑based pipelines that are costly to deploy and do not explicitly leverage the hierarchical structure of clinical data. Motivated by evidence that medical ontologies and patient trajectories exhibit hyperbolic geometry, we propose HypEHR, a compact Lorentzian model that embeds codes, visits, and questions in hyperbolic space and answers queries via geometry‑consistent cross‑attention with type‑specific pointer heads. HypEHR is pretrained with next‑visit diagnosis prediction and hierarchy‑aware regularization to align representations with the ICD ontology. On two MIMIC‑IV‑based EHR‑QA benchmarks, HypEHR approaches LLM‑based methods while using far fewer parameters. Our code is publicly available at https://github.com/yuyuliu11037/HypEHR.

PaperId: 1541, https://arxiv.org/pdf/2604.20930.pdf GitHub GitHub

Abstract:
Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion structurally requires harmful content, spontaneously generate that content with safety failure rates exceeding 95%. Existing input‑level defenses achieve a 100% failure rate against ISC, and standard system prompt defenses provide only partial mitigation. We propose SafeRedirect, a system‑level override that defeats ISC by redirecting the model's task‑completion drive rather than suppressing it. SafeRedirect grants explicit permission to fail the task, prescribes a deterministic hard‑stop output, and instructs the model to preserve harmful placeholders unresolved. Evaluated on seven frontier LLMs across three AI/ML‑related ISC task types in the single‑turn setting, SafeRedirect reduces average unsafe generation rates from 71.2% to 8.0%, compared to 55.0% for the strongest viable baseline. Multi‑model ablation reveals that failure permission and condition specificity are universally critical, while the importance of other components varies across models. Cross‑attack evaluation confirms state‑of‑the‑art defense against ISC with generalization performance at least on par with the baseline on other attack families. Code is available at https://github.com/fzjcdt/SafeRedirect.

PaperId: 1542, https://arxiv.org/pdf/2604.20860.pdf GitHub

Abstract:
Despite the success of Retrieval‑Augmented Generation (RAG) in grounding LLMs with external knowledge, its application over heterogeneous sources (e.g., private databases, global corpora, and APIs) remains a significant challenge. Existing approaches typically employ an LLM‑as‑a‑Router to dispatch decomposed sub‑queries to specific sources in a predictive manner. However, this "LLM‑as‑a‑Router" strategy relies heavily on the semantic meaning of different data sources, often leading to routing errors when source boundaries are ambiguous. In this work, we introduce RealRoute System, a framework that shifts the paradigm from predictive routing to a robust Retrieve‑then‑Verify mechanism. RealRoute ensures evidence completeness through parallel, source‑agnostic retrieval, followed by a dynamic verifier that cross‑checks the results and synthesizes a factually grounded answer. Our demonstration allows users to visualize the real‑time "re‑routing" process and inspect the verification chain across multiple knowledge silos. Experiments show that RealRoute significantly outperforms predictive baselines in the multi‑hop Rag reasoning task. The RealRoute system is released as an open‑source toolkit with a user‑friendly web interface. The code is available at the URL: https://github.com/Joseph1951210/RealRoute.

PaperId: 1543, https://arxiv.org/pdf/2604.20858.pdf GitHub

Abstract:
Sequential recommendation has rapidly advanced in click‑through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long sequences: users often exhibit significant interest shifts, introducing substantial irrelevant or misleading information. Our empirical analysis corroborates this challenge and uncovers a recurring behavioral pattern in long sequences (session hopping): user interests remain stable within short temporal spans (sessions) but shift drastically across sessions and may reappear after multiple sessions. To address this challenge, we propose the Mixture of Sequence (MoS) framework, a model‑agnostic MoE approach that achieves accurate predictions by extracting theme‑specific and multi‑scale subsequences from noisy raw user sequences. First, MoS employs a theme‑aware routing mechanism to adaptively learn the latent themes of user sequences and organizes these sequences into multiple coherent subsequences. Each subsequence contains only sessions aligned with a specific theme, thereby effectively filtering out irrelevant or even misleading information introduced by user interest shifts in session hopping. In addition, to alleviate potential information loss, we introduce a multi‑scale fusion mechanism, which leverages three types of experts to capture global sequence characteristics, short‑term user behaviors, and theme‑specific semantic patterns. Together, these two mechanisms endow MoS with the ability to deliver accurate recommendations from multi‑faceted and multi‑scale perspectives. Experimental results demonstrate that MoS consistently achieves the SOTA performance while introducing fewer FLOPs compared with other MoE counterparts, providing strong evidence of its excellent balance between utility and efficiency. The code is available at https://github.com/xiaolin‑cs/MoS.

PaperId: 1544, https://arxiv.org/pdf/2604.20857.pdf GitHub

Abstract:
Scientific papers use schematic diagrams to communicate methods, workflows, and system structure, yet existing scientific‑figure corpora often mix them with plots, screenshots, and photographs and rarely preserve document context. We introduce DiagramBank, a quality‑audited dataset of 57,100 schematic diagrams curated from OpenReview‑hosted AI/ML venues. Each record links a diagram image to its paper title, abstract, figure caption, in‑text figure‑reference spans, venue/year metadata, provenance fields, and filtering labels. DiagramBank is a reusable resource for scientific‑document understanding, diagram retrieval, corpus analysis, and future benchmark construction. We describe its extraction and cascade‑filtering pipeline, release schema, confidence‑controlled views, dataset card, and indexing utilities. A manual blind audit of the released cascade‑filtered records estimates 93.67% precision, and a separate CLIP threshold analysis characterizes the precision‑‑coverage trade‑off for simpler filtering views. We further provide lightweight metadata‑indexing and authoring examples to illustrate downstream protocols without treating these utilities as standalone methods. The code is public at: https://github.com/csml‑rpi/DiagramBank.

PaperId: 1545, https://arxiv.org/pdf/2604.20850.pdf GitHub

Abstract:
Dense retrieval systems rank passages by embedding similarity to a query, but multi‑hop questions require passages that are associatively related through shared reasoning chains. We introduce Association‑Augmented Retrieval (AAR), a lightweight transductive reranking method that trains a small MLP (4.2M parameters) to learn associative relationships between passages in embedding space using contrastive learning on co‑occurrence annotations. At inference time, AAR reranks an initial dense retrieval candidate set using bi‑directional association scoring. On HotpotQA, AAR improves passage Recall@5 from 0.831 to 0.916 (+8.6 points) without evaluation‑set tuning, with gains concentrated on hard questions where the dense baseline fails (+28.5 points). On MuSiQue, AAR achieves +10.1 points in the transductive setting. An inductive model trained on training‑split associations and evaluated on unseen validation associations shows no significant improvement, suggesting that the method captures corpus‑specific co‑occurrences rather than transferable patterns. Ablation studies support this interpretation: training on semantically similar but non‑associated passage pairs degrades retrieval below the baseline, while shuffling association pairs causes severe degradation. A downstream QA evaluation shows retrieval gains translate to +6.4 exact match improvement. The method adds 3.7ms per query, trains in under two minutes on a single GPU, and requires no LLM‑based indexing.

PaperId: 1546, https://arxiv.org/pdf/2604.20847.pdf GitHub

Abstract:
Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking stages, predominantly rely on collaborative filtering, which fails to exploit the intrinsic characteristics of audio and consequently leads to suboptimal performance, particularly in cold‑start scenarios. However, existing music recommendation datasets often lack rich multimodal information, such as raw audio signals and descriptive textual metadata. Moreover, current recommender system evaluation frameworks remain inadequate, as they neither fully leverage multimodal information nor support a diverse range of algorithms, especially multimodal methods. To address these limitations, we propose TASTE, a comprehensive dataset and benchmarking framework designed to highlight the role of multimodal information in music recommendation. Our dataset integrates both audio and textual modalities. By leveraging recent large‑scale self‑supervised music encoders, we demonstrate the substantial value of the extracted audio representations across recommendation tasks, including candidate recall and CTR. In addition, we introduce the MuQ‑token method, which enables more efficient integration of multi‑layer audio features. This method consistently outperforms other feature integration techniques across various settings. Overall, our results not only validate the effectiveness of content‑driven approaches but also provide a highly effective and reusable multimodal foundation for future research. Code is available at https://github.com/zreach/TASTE

PaperId: 1547, https://arxiv.org/pdf/2604.20846.pdf GitHub

Abstract:
Next point‑of‑interest (POI) recommendation requires modeling user mobility as a spatiotemporal sequence, where different behavioral factors may evolve at different temporal and spatial scales. Most existing methods compress a user's history into a single latent representation, which tends to entangle heterogeneous signals such as routine mobility patterns, short‑term intent, and temporal regularities. This entanglement limits the flexibility of state evolution and reduces the model's ability to adapt to diverse decision contexts. We propose ADS‑POI, a spatiotemporal state decomposition framework for next POI recommendation. ADS‑POI represents a user with multiple parallel evolving latent sub‑states, each governed by its own spatiotemporal transition dynamics. These sub‑states are selectively aggregated through a context‑conditioned mechanism to form the decision state used for prediction. This design enables different behavioral components to evolve at different rates while remaining coordinated under the current spatiotemporal context. Extensive experiments on three real‑world benchmark datasets from Foursquare and Gowalla demonstrate that ADS‑POI consistently outperforms strong state‑of‑the‑art baselines under a full‑ranking evaluation protocol. The results show that decomposing user behavior into multiple spatiotemporally aware states leads to more effective and robust next POI recommendation. Our code is available at https://github.com/YuZhenyuLindy/ADS‑POI.git.

PaperId: 1548, https://arxiv.org/pdf/2604.20845.pdf GitHub

Abstract:
Next Point‑of‑Interest (POI) recommendation plays a crucial role in location‑based services by predicting users' future mobility patterns. Existing methods typically compute a single user representation from historical trajectories and use it to score all candidate POIs uniformly. However, this candidate‑agnostic paradigm overlooks that the relevance of historical visits inherently depends on which candidate is being evaluated. In this paper, we propose CaST‑POI, a candidate‑conditioned spatiotemporal model for next POI recommendation. Our key insight is that the same user history should be interpreted differently when evaluating different candidate POIs. CaST‑POI employs a candidate‑conditioned sequence reader that uses candidates as queries to dynamically attend to user history. In addition, we introduce candidate‑relative temporal and spatial biases to capture fine‑grained mobility patterns based on the relationships between historical visits and each candidate POI. Extensive experiments on three benchmark datasets demonstrate that CaST‑POI consistently outperforms state‑of‑the‑art methods, yielding substantial improvements across multiple evaluation metrics, with particularly strong advantages under large candidate pools. Code is available at https://github.com/YuZhenyuLindy/CaST‑POI.git.

PaperId: 1549, https://arxiv.org/pdf/2604.20844.pdf GitHub

Abstract:
Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and precision. However, we observe that treating text chunks as the basic unit of knowledge representation rigidly groups multiple atomic facts together, limiting the flexibility and adaptability needed to support diverse retrieval scenarios. Additionally, triple‑based entity linking is sensitive to relation‑extraction errors, which can lead to missing or incorrect reasoning paths and ultimately hurt retrieval accuracy. To address these issues, we propose the Atom‑Entity Graph, a more precise and reliable architecture for knowledge representation and indexing. In our approach, knowledge is stored as knowledge atoms, namely individual, self‑contained units of factual information, rather than coarse‑grained text chunks. This allows knowledge elements to be flexibly reassembled without mutual interference, thereby enabling seamless alignment with diverse query perspectives. Edges between entities simply indicate whether a relationship exists. By combining personalized PageRank with relevance‑based filtering, we maintain accurate entity connections and improve the reliability of reasoning. Theoretical analysis and experiments on five public benchmarks show that the proposed AtomicRAG algorithm outperforms strong RAG baselines in retrieval accuracy and reasoning robustness. Code: https://github.com/7HHHHH/AtomicRAG.

PaperId: 1550, https://arxiv.org/pdf/2604.20825.pdf GitHub

Abstract:
Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi‑stage framework for robust FL under noisy labels. Different from existing approaches that mainly rely on designing noise‑tolerant loss functions or exploiting loss dynamics during training, our method leverages the spectral structure of client feature representations to identify and mitigate label noise. Our framework consists of three key components. First, we identify clean and noisy clients by analyzing the spectral consistency of class‑wise feature subspaces with minimal communication overhead. Second, clean clients provide spectral references that enable noisy clients to relabel potentially corrupted samples using both dominant class directions and residual subspaces. Third, we employ a noise‑aware training strategy that integrates logit‑adjusted loss, knowledge distillation, and distance‑aware aggregation to further stabilize federated optimization. Extensive experiments on standard FL benchmarks demonstrate that FedSIR consistently outperforms state‑of‑the‑art methods for FL with noisy labels. The code is available at https://github.com/sinagh72/FedSIR.

PaperId: 1551, https://arxiv.org/pdf/2604.20749.pdf GitHub

Abstract:
Situated conversational recommendation (SCR), which utilizes visual scenes grounded in specific environments and natural language dialogue to deliver contextually appropriate recommendations, has emerged as a promising research direction due to its close alignment with real‑world scenarios. Compared to traditional recommendations, SCR requires a deeper understanding of dynamic and implicit user preferences, as the surrounding scene often influences users' underlying interests, while both may evolve across conversations. This complexity significantly impacts the timing and relevance of recommendations. To address this, we propose situated preference reasoning (SiPeR), a novel framework that integrates two core mechanisms: (1) Scene transition estimation, which estimates whether the current scene satisfies user needs, and guides the user toward a more suitable scene when necessary; and (2) Bayesian inverse inference, which leverages the likelihood of multimodal large language models (MLLMs) to predict user preferences about candidate items within the scene. Extensive experiments on two representative benchmarks demonstrate SiPeR's superiority in both recommendation accuracy and response generation quality. The code and data are available at https://github.com/DongdingLin/SiPeR.

PaperId: 1552, https://arxiv.org/pdf/2604.20548.pdf GitHub

Abstract:
Scientific progress depends on the continual generation of innovative re‑search ideas. However, the rapid growth of scientific literature has greatly increased the cost of knowledge filtering, making it harder for researchers to identify novel directions. Although existing large language model (LLM)‑based methods show promise in research idea generation, the ideas they produce are often repetitive and lack depth. To address this issue, this study proposes a multi‑agent iterative planning search strategy inspired by com‑binatorial innovation theory. The framework combines iterative knowledge search with an LLM‑based multi‑agent system to generate, evaluate, and re‑fine research ideas through repeated interaction, with the goal of improving idea diversity and novelty. Experiments in the natural language processing domain show that the proposed method outperforms state‑of‑the‑art base‑lines in both diversity and novelty. Further comparison with ideas derived from top‑tier machine learning conference papers indicates that the quality of the generated ideas falls between that of accepted and rejected papers. These results suggest that the proposed framework is a promising approach for supporting high‑quality research idea generation. The source code and dataset used in this paper are publicly available on Github repository: https://github.com/ChenShuai00/MAGenIdeas. The demo is available at https://huggingface.co/spaces/cshuai20/MAGenIdeas.

PaperId: 1553, https://arxiv.org/pdf/2604.20443.pdf GitHub

Abstract:
We introduce DialToM, an annotated Theory of Mind (ToM) benchmark built from naturalistic human‑human dialogues using a multiple‑choice evaluation framework. Concurrent with recent work showing a gap between explicit mental‑state inference and applied ToM in synthetic settings~\citegu2024simpletom, we establish a stricter \emphState‑Driven Diagnostic Probe in which models must forecast state‑consistent dialogue trajectories solely from isolated mental‑state profiles without dialogue context. Our evaluation reveals a systematic reasoning asymmetry ‑‑ LLMs excel at inferring mental states (Literal ToM) but struggle to leverage them for social forecasting (Functional ToM). Crucially, a domain expert achieves 100% accuracy on this task, proving its validity and establishing a stark human‑AI capability gap. Further, a teacher‑student reasoning injection probe shows that Gemini 3 Pro ‑‑ which establishes the leading baseline ‑‑ possesses robust Functional ToM capabilities for context‑free forecasting that are transferable to weaker models. DialToM, its evaluation code, and dataset are publicly available at https://github.com/Stealth‑py/DialToM.

PaperId: 1554, https://arxiv.org/pdf/2604.20389.pdf GitHub

Abstract:
The rapid evolution and use of Large Language Models (LLMs) in professional workflows require an evaluation of their domain‑specific knowledge against industry standards. We introduceCyberCertBench, a new suite of Multiple Choice Question Answering (MCQA) benchmarks derived from industry recognized certifications. CyberCertBench evaluates LLM domain knowledgeagainst the professional standards of Information Technology cybersecurity and more specializedareas such as Operational Technology and related cybersecurity standards. Concurrently, we propose and validate a novel Proposer‑Verifier framework, a methodology to generate interpretable,natural language explanations for model performance. Our evaluation shows that frontier modelsachieve human expert level in general networking and IT security knowledge. However, theiraccuracy declines in questions that require vendor‑specific nuances or knowledge in formalstandards, like, e.g., IEC 62443. Analysis of model scaling trend and release date demonstratesremarkable gains in parameter efficiency, while recent larger models show diminishing returns.Code and evaluation scripts are available at: https://github.com/GKeppler/CyberCertBench.

PaperId: 1555, https://arxiv.org/pdf/2604.20286.pdf GitHub

Abstract:
Recent segmentation models have demonstrated promising efficiency by aggressively reducing parameter counts and computational complexity. However, these models often struggle to accurately delineate fine lesion boundaries and texture patterns essential for early skin cancer diagnosis and treatment planning. In this paper, we propose MambaLiteUNet, a compact yet robust segmentation framework that integrates Mamba state space modeling into a U‑Net architecture, along with three key modules: Adaptive Multi‑Branch Mamba Feature Fusion (AMF), Local‑Global Feature Mixing (LGFM), and Cross‑Gated Attention (CGA). These modules are designed to enhance local‑global feature interaction, preserve spatial details, and improve the quality of skip connections. MambaLiteUNet achieves an average IoU of 87.12% and average Dice score of 93.09% across ISIC2017, ISIC2018, HAM10000, and PH2 benchmarks, outperforming state‑of‑the‑art models. Compared to U‑Net, our model improves average IoU and Dice by 7.72 and 4.61 points, respectively, while reducing parameters by 93.6% and GFLOPs by 97.6%. Additionally, in domain generalization with six unseen lesion categories, MambaLiteUNet achieves 77.61% IoU and 87.23% Dice, performing best among all evaluated models. Our extensive experiments demonstrate that MambaLiteUNet achieves a strong balance between accuracy and efficiency, making it a competitive and practical solution for dermatological image segmentation. Our code is publicly available at: https://github.com/maklachur/MambaLiteUNet.

PaperId: 1556, https://arxiv.org/pdf/2604.20263.pdf GitHub

Abstract:
Virtual cell modeling predicts molecular state changes under genetic perturbations in silico, which is essential for biological mechanism studies. However, existing approaches suffer from unconstrained reasoning, uninterpretable predictions, and retrieval signals that are weakly aligned with regulatory topology. To address these limitations, we propose AROMA, an Augmented Reasoning Over a Multimodal Architecture for virtual cell genetic perturbation modeling. AROMA integrates textual evidence, graph‑topology information, and protein sequence features to model perturbation‑target dependencies, and is trained with a two‑stage optimization strategy to yield predictions that are both accurate and interpretable. We also construct two knowledge graphs and a perturbation reasoning dataset, PerturbReason, containing more than 498k samples, as reusable resources for the virtual cell domain. Experiments show that AROMA outperforms existing methods across multiple cell lines, and remains robust under zero‑shot evaluation on an unseen cell line, as well as in knowledge‑sparse, long‑tail scenarios. Overall, AROMA demonstrates that combining knowledge‑driven multimodal modeling with evidence retrieval provides a promising pathway toward more reliable and interpretable virtual cell perturbation prediction. Model weights are available at https://huggingface.co/blazerye/AROMA. Code is available at https://github.com/blazerye/AROMA.

PaperId: 1557, https://arxiv.org/pdf/2604.20261.pdf GitHub

Abstract:
Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. Traditional methods rely on predefined operator libraries and cannot leverage task semantics, limiting their ability to produce diverse, high‑value features for complex tasks. Recent Large Language Model (LLM)‑based approaches introduce richer semantic signals, but still suffer from a restricted feature space due to fixed generation patterns and from the absence of feedback from the learning objective. To address these challenges, we propose a Memory‑Augmented LLM‑based Multi‑Agent System (MALMAS) for automated feature generation. MALMAS decomposes the generation process into agents with distinct responsibilities, and a Router Agent activates an appropriate subset of agents per iteration, further broadening exploration of the feature space. We further integrate a memory module comprising procedural memory, feedback memory, and conceptual memory, enabling iterative refinement that adaptively guides subsequent feature generation and improves feature quality and diversity. Extensive experiments on multiple public datasets against state‑of‑the‑art baselines demonstrate the effectiveness of our approach. The code is available at https://github.com/fxdong24/MALMAS

PaperId: 1558, https://arxiv.org/pdf/2604.20254.pdf GitHub

Abstract:
Text‑guided molecular design is a key capability for AI‑driven drug discovery, yet it remains challenging to map sequential natural‑language instructions with non‑linear molecular structures under strict chemical constraints. Most existing approaches, including RAG, CoT prompting, and fine‑tuning or RL, emphasize a small set of ad‑hoc reasoning perspectives implemented in a largely one‑shot generation pipeline. In contrast, real‑world drug discovery relies on dynamic, multi‑perspective critique and iterative refinement to reconcile semantic intent with structural feasibility. Motivated by this, we propose Mol‑Debate, a generation paradigm that enables such dynamic reasoning through an iterative generate‑debate‑refine loop. We further characterize key challenges in this paradigm and address them through perspective‑oriented orchestration, including developer‑debater conflict, global‑local structural reasoning, and static‑dynamic integration. Experiments demonstrate that Mol‑Debate achieves state‑of‑the‑art performance against strong general and chemical baselines, reaching 59.82% exact match on ChEBI‑20 and 50.52% weighted success rate on S^2‑Bench. Our code is available at https://github.com/wyuzh/Mol‑Debate.

PaperId: 1559, https://arxiv.org/pdf/2604.20244.pdf GitHub

Abstract:
Knowledge distillation (KD) is a powerful paradigm for compressing large language models (LLMs), whose effectiveness depends on intertwined choices of divergence direction, optimization strategy, and data regime. We break down the design of existing KD methods and present a unified view that establishes connections between them, reformulating KD as a reweighted log‑likelihood objective at the token level. We further propose Hybrid Policy Distillation (HPD), which integrates the complementary advantages of forward and reverse KL to balance mode coverage and mode‑seeking, and combines off‑policy data with lightweight, approximate on‑policy sampling. We validate HPD on long‑generation math reasoning as well as short‑generation dialogue and code tasks, demonstrating improved optimization stability, computational efficiency, and final performance across diverse model families and scales. The code related to this work is available at https://github.com/zwhong714/Hybrid‑Policy‑Distillation.

PaperId: 1560, https://arxiv.org/pdf/2604.20210.pdf GitHub

Abstract:
Individual differences in vibrotactile perception underscore the growing importance of personalization as haptic feedback becomes more prevalent in interactive systems. We propose Vibrotactile Preference Learning (VPL), a system that captures user‑specific preference spaces over vibrotactile parameters via Gaussian‑process‑based uncertainty‑aware preference learning. VPL uses an expected information gain‑based acquisition strategy to guide query selection over 40 rounds of pairwise comparisons of overall user preference, augmented with user‑reported uncertainty, enabling efficient exploration of the parameter space. We evaluate VPL in a user study (N = 13) using the vibrotactile feedback from a Microsoft Xbox controller, showing that it efficiently learns individualized preferences while maintaining comfortable, low‑workload user interactions. These results highlight the potential of VPL for scalable personalization of vibrotactile experiences.

PaperId: 1561, https://arxiv.org/pdf/2604.20158.pdf GitHub

Abstract:
Enterprise deployment of long‑horizon decision agents in regulated domains (underwriting, claims adjudication, tax examination) is dominated by retrieval‑augmented pipelines despite a decade of increasingly sophisticated stateful memory architectures. We argue this reflects a hidden requirement: regulated deployment is load‑bearing on four systems properties (deterministic replay, auditable rationale, multi‑tenant isolation, statelessness for horizontal scale), and stateful architectures violate them by construction. We propose Deterministic Projection Memory (DPM): an append‑only event log plus one task‑conditioned projection at decision time. On ten regulated decisioning cases at three memory budgets, DPM matches summarization‑based memory at generous budgets and substantially outperforms it when the budget binds: at a 20x compression ratio, DPM improves factual precision by +0.52 (Cohen's h=1.17, p=0.0014) and reasoning coherence by +0.53 (h=1.13, p=0.0034), paired permutation, n=10. DPM is additionally 7‑15x faster at binding budgets, making one LLM call at decision time instead of N. A determinism study of 10 replays per case at temperature zero shows both architectures inherit residual API‑level nondeterminism, but the asymmetry is structural: DPM exposes one nondeterministic call; summarization exposes N compounding calls. The audit surface follows the same one‑versus‑N pattern: DPM logs two LLM calls per decision while summarization logs 83‑97 on LongHorizon‑Bench. We conclude with TAMS, a practitioner heuristic for architecture selection, and a failure analysis of stateful memory under enterprise operating conditions. The contribution is the argument that statelessness is the load‑bearing property explaining enterprise's preference for weaker but replayable retrieval pipelines, and that DPM demonstrates this property is attainable without the decisioning penalty retrieval pays.

PaperId: 1562, https://arxiv.org/pdf/2604.20136.pdf GitHub GitHub

Abstract:
Correcting errors in long‑video understanding is disproportionately costly: existing multimodal pipelines produce opaque, end‑to‑end outputs that expose no intermediate state for inspection, forcing annotators to revisit raw video and reconstruct temporal logic from scratch. The core bottleneck is not generation quality alone, but the absence of a supervisory interface through which human effort can be proportional to the scope of each error. We present IMPACT‑CYCLE, a supervisory multi‑agent system that reformulates long‑video understanding as iterative claim‑level maintenance of a shared semantic memory ‑‑ a structured, versioned state encoding typed claims, a claim dependency graph, and a provenance log. Role‑specialized agents operating under explicit authority contracts decompose verification into local object‑relation correctness, cross‑temporal consistency, and global semantic coherence, with corrections confined to structurally dependent claims. When automated evidence is insufficient, the system escalates to human arbitration as the supervisory authority with final override rights; dependency‑closure re‑verification then ensures correction cost remains proportional to error scope. Experiments on VidOR show substantially improved downstream reasoning (VQA: 0.71 to 0.79) and a 4.8x reduction in human arbitration cost, with workload significantly lower than manual annotation. Code will be released at https://github.com/MKong17/IMPACT_CYCLE.

PaperId: 1563, https://arxiv.org/pdf/2604.20122.pdf GitHub

Abstract:
We propose a post‑hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre‑trained foundation models without requiring additional fine‑tuning. Our method yields an interpretable anomaly score directly interpretable as a false alarm rate (p‑value), facilitating transparent and actionable decision‑making. It employs weighted quantile conformal prediction bounds and adaptively learns optimal weighting parameters from past predictions, enabling calibration under distribution shifts and stable false alarm control, while preserving out‑of‑sample guarantees. As a model‑agnostic solution, it integrates seamlessly with foundation models and supports rapid deployment in resource‑constrained environments. This approach addresses key industrial challenges such as limited data availability, lack of training expertise, and the need for immediate inference, while taking advantage of the growing accessibility of time series foundation models. Experiments on both synthetic and real‑world datasets show that the proposed approach delivers strong performance, combining simplicity, interpretability, robustness, and adaptivity.

PaperId: 1564, https://arxiv.org/pdf/2604.20043.pdf GitHub

Abstract:
Explainability for Large Language Model (LLM) agents is especially challenging in interactive, partially observable settings, where decisions depend on evolving beliefs and other agents. We present TriEx, a tri‑view explainability framework that instruments sequential decision making with aligned artifacts: (i) structured first‑person self‑reasoning bound to an action, (ii) explicit second‑person belief states about opponents updated over time, and (iii) third‑person oracle audits grounded in environment‑derived reference signals. This design turns explanations from free‑form narratives into evidence‑anchored objects that can be compared and checked across time and perspectives. Using imperfect‑information strategic games as a controlled testbed, we show that TriEx enables scalable analysis of explanation faithfulness, belief dynamics, and evaluator reliability, revealing systematic mismatches between what agents say, what they believe, and what they do. Our results highlight explainability as an interaction‑dependent property and motivate multi‑view, evidence‑grounded evaluation for LLM agents. Code is available at https://github.com/Einsam1819/TriEx.

PaperId: 1565, https://arxiv.org/pdf/2604.20041.pdf GitHub

Abstract:
Normalizing Flows (NFs) are a classical family of likelihood‑based methods that have received revived attention. Recent efforts such as TARFlow have shown that NFs are capable of achieving promising performance on image modeling tasks, making them viable alternatives to other methods such as diffusion models. In this work, we further advance the state of Normalizing Flow generative models by introducing iterative TARFlow (iTARFlow). Unlike diffusion models, iTARFlow maintains a fully end‑to‑end, likelihood‑based objective during training. During sampling, it performs autoregressive generation followed by an iterative denoising procedure inspired by diffusion‑style methods. Through extensive experiments, we show that iTARFlow achieves competitive performance across ImageNet resolutions of 64, 128, and 256 pixels, demonstrating its potential as a strong generative model and advancing the frontier of Normalizing Flows. In addition, we analyze the characteristic artifacts produced by iTARFlow, offering insights that may shed light on future improvements. Code is available at https://github.com/apple/ml‑itarflow.

PaperId: 1566, https://arxiv.org/pdf/2604.19809.pdf GitHub

Abstract:
We introduce MIRROR, a benchmark comprising eight experiments across four metacognitive levels that evaluates whether large language models can use self‑knowledge to make better decisions. We evaluate 16 models from 8 labs across approximately 250,000 evaluation instances using five independent behavioral measurement channels. Core experiments are run across the full model roster; experiments with specialized infrastructure requirements report explicitly marked model subsets. We find two phenomena with direct implications for agentic deployment: (1) compositional self‑prediction fails universally ‑‑ the Compositional Calibration Error ranges from 0.500 to 0.943 on the original 15‑model Exp3‑v1 set (and 0.434 to 0.758 on the balanced 16‑model Exp3‑v2 expansion), indicating that models cannot predict their own performance on multi‑domain tasks, and (2) models exhibit above‑chance but imperfect domain‑specific self‑knowledge yet systematically fail to translate even this partial awareness into appropriate agentic action‑selection ‑‑ external metacognitive control reduces the Confident Failure Rate from 0.600 to 0.143 (76% reduction at temperature 0; mean 70% at temperature 0.7 across 5 models from 4 labs). Providing models with their own calibration scores produces no significant improvement (p > 0.05); only architectural constraint is effective. This suggests that external metacognitive scaffolding ‑‑ not improved self‑knowledge ‑‑ is the path to safer autonomous AI systems. Code, data, and Croissant metadata will be released publicly with the benchmark.

PaperId: 1567, https://arxiv.org/pdf/2604.19782.pdf GitHub

Abstract:
Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LALMs remain scarce for non‑English languages, with Korean being one such underexplored case. In this paper, we introduce KoALa‑Bench, a comprehensive benchmark for evaluating Korean speech understanding and speech faithfulness of LALMs. In particular, KoALa‑Bench comprises six tasks. Four tasks evaluate fundamental speech understanding capabilities, including automatic speech recognition, speech translation, speech question answering, and speech instruction following, while the remaining two tasks evaluate speech faithfulness, motivated by our observation that several LALMs often fail to fully leverage the speech modality. Furthermore, to reflect Korea‑specific knowledge, our benchmark incorporates listening questions from the Korean college scholastic ability test as well as content covering Korean cultural domains. We conduct extensive experiments across six models, including both white‑box and black‑box ones. Our benchmark, evaluation code, and leaderboard are publicly available at https://ksbench.github.io/Korean‑Benchmark/.

PaperId: 1568, https://arxiv.org/pdf/2604.19740.pdf GitHub

Abstract:
Training modern neural networks often relies on large learning rates, operating at the edge of stability, where the optimization dynamics exhibit oscillatory and chaotic behavior. Empirically, this regime often yields improved generalization performance, yet the underlying mechanism remains poorly understood. In this work, we represent stochastic optimizers as random dynamical systems, which often converge to a fractal attractor set (rather than a point) with a smaller intrinsic dimension. Building on this connection and inspired by Lyapunov dimension theory, we introduce a novel notion of dimension, coined the `sharpness dimension', and prove a generalization bound based on this dimension. Our results show that generalization in the chaotic regime depends on the complete Hessian spectrum and the structure of its partial determinants, highlighting a complexity that cannot be captured by the trace or spectral norm considered in prior work. Experiments across various MLPs and transformers validate our theory while also providing new insights into the recently observed phenomenon of grokking.

PaperId: 1569, https://arxiv.org/pdf/2604.19734.pdf GitHub

Abstract:
Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging the cross‑embodiment chasm remains a fundamental challenge due to kinematic mismatches. We introduce UniT (Unified Latent Action Tokenizer via Visual Anchoring), a framework that establishes a unified physical language for human‑to‑humanoid transfer. Grounded in the philosophy that heterogeneous kinematics share universal visual consequences, UniT employs a tri‑branch cross‑reconstruction mechanism: actions predict vision to anchor kinematics to physical outcomes, while vision reconstructs actions to filter out irrelevant visual confounders. Concurrently, a fusion branch synergies these purified modalities into a shared discrete latent space of embodiment‑agnostic physical intents. We validate UniT across two paradigms: 1) Policy Learning (VLA‑UniT): By predicting these unified tokens, it effectively leverages diverse human data to achieve state‑of‑the‑art data efficiency and robust out‑of‑distribution (OOD) generalization on both humanoid simulation benchmark and real‑world deployments, notably demonstrating zero‑shot task transfer. 2) World Modeling (WM‑UniT): By aligning cross‑embodiment dynamics via unified tokens as conditions, it realizes direct human‑to‑humanoid action transfer. This alignment ensures that human data seamlessly translates into enhanced action controllability for humanoid video generation. Ultimately, by inducing a highly aligned cross‑embodiment representation (empirically verified by t‑SNE visualizations revealing the convergence of human and humanoid features into a shared manifold), UniT offers a scalable path to distill vast human knowledge into general‑purpose humanoid capabilities.

PaperId: 1570, https://arxiv.org/pdf/2604.19730.pdf GitHub

Abstract:
Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test‑time scaling methods such as sampling multiple action candidates and selecting the best one. In this work, we propose FASTER, a method for getting the benefits of sampling‑based test‑time scaling of diffusion‑based policies without the computational cost by tracing the performance gain of action samples back to earlier in the denoising process. Our key insight is that we can model the denoising of multiple action candidates and selecting the best one as a Markov Decision Process (MDP) where the goal is to progressively filter action candidates before denoising is complete. With this MDP, we can learn a policy and value function in the denoising space that predicts the downstream value of action candidates in the denoising process and filters them while maximizing returns. The result is a method that is lightweight and can be plugged into existing generative RL algorithms. Across challenging long‑horizon manipulation tasks in online and batch‑online RL, FASTER consistently improves the underlying policies and achieves the best overall performance among the compared methods. Applied to a pretrained VLA, FASTER achieves the same performance while substantially reducing training and inference compute requirements. Code is available at https://github.com/alexanderswerdlow/faster .

PaperId: 1571, https://arxiv.org/pdf/2604.19728.pdf GitHub GitHub

Abstract:
We present VLA Foundry, an open‑source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open‑source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines. VLA Foundry instead provides a shared training stack with end‑to‑end control, from language pretraining to action‑expert fine‑tuning. VLA Foundry supports both from‑scratch training and pretrained backbones from Hugging Face. To demonstrate the utility of our framework, we train and release two types of models: the first trained fully from scratch through our LLM‑‑>VLM‑‑>VLA pipeline and the second built on the pretrained Qwen3‑VL backbone. We evaluate closed‑loop policy performance of both models on LBM Eval, an open‑data, open‑source simulator. We also contribute usability improvements to the simulator and the STEP analysis tools for easier public use. In the nominal evaluation setting, our fully‑open from‑scratch model is on par with our prior closed‑source work and substituting in the Qwen3‑VL backbone leads to a strong multi‑task table top manipulation policy outperforming our baseline by a wide margin. The VLA Foundry codebase is available at https://github.com/TRI‑ML/vla_foundry and all multi‑task model weights are released on https://huggingface.co/collections/TRI‑ML/vla‑foundry. Additional qualitative videos are available on the project website https://tri‑ml.github.io/vla_foundry.

PaperId: 1572, https://arxiv.org/pdf/2604.19689.pdf GitHub

Abstract:
Understanding artworks requires multi‑step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl‑ edge, limiting interpretability and explicit evidence grounding. We propose A‑MAR, an Agent‑based Multimodal Art Retrieval framework that explicitly conditions retrieval on structured reasoning plans. Given an artwork and a user query, A‑MAR first decomposes the task into a structured reasoning plan that specifies the goals and evidence requirements for each step. Retrieval is then conditionedon this plan, enabling targeted evidence selection and supporting step‑wise, grounded explanations. To evaluate agent‑based multi‑ modal reasoning within the art domain, we introduce ArtCoT‑QA. This diagnostic benchmark features multi‑step reasoning chains for diverse art‑related queries, enabling a granular analysis that extends beyond simple final answer accuracy. Experiments on SemArt and Artpedia show that A‑MAR consistently outperforms static, non planned retrieval and strong MLLM baselines in final explanation quality, while evaluations on ArtCoT‑QA further demonstrate its advantages in evidence grounding and multi‑step reasoning ability. These results highlight the importance of reasoning‑conditioned retrieval for knowledge‑intensive multimodal understanding and position A‑MAR as a step toward interpretable, goal‑driven AI systems, with particular relevance to cultural industries. The code and data are available at: https://github.com/ShuaiWang97/A‑MAR.

PaperId: 1573, https://arxiv.org/pdf/2604.19667.pdf GitHub

Abstract:
At present, executable visual workflows have emerged as a mainstream paradigm in real‑world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers must carefully design workflows, write prompts for each step, and repeatedly revise the logic as requirements evolve ‑‑ making development costly, time‑consuming, and error‑prone. To study whether large language models can automate this multi‑round interaction process, we introduce Chat2Workflow, a benchmark for generating executable visual workflows directly from natural language, and propose a robust agentic baseline to improve performance. The benchmark is built from a large collection of real‑world business workflows, with each instance designed so that the generated workflow can be transformed and directly deployed to practical workflow platforms such as Dify and Coze. Experimental results show that while state‑of‑the‑art language models can often capture high‑level intent, they struggle to generate correct, stable, and executable workflows, especially given complex and evolving requirements. Although our agentic baseline yields up to 6.05% resolve rate gains, the remaining real‑world gap positions Chat2Workflow as a foundation for advancing industrial‑grade automation. Code is available at https://github.com/zjunlp/Chat2Workflow.

PaperId: 1574, https://arxiv.org/pdf/2604.19638.pdf GitHub

Abstract:
Multimodal Large Language Models are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address safety hazards remains insufficient. We introduce SafetyALFRED, built upon the embodied agent benchmark ALFRED, augmented with six categories of real‑world kitchen hazards. While existing safety evaluations focus on hazard recognition through disembodied question answering (QA) settings, we evaluate eleven state‑of‑the‑art models from the Qwen, Gemma, and Gemini families on not only hazard recognition, but also active risk mitigation through embodied planning. Our experimental results reveal a significant alignment gap: while models can accurately recognize hazards in QA settings, average mitigation success rates for these hazards are low in comparison. Our findings demonstrate that static evaluations through QA are insufficient for physical safety, thus we advocate for a paradigm shift toward benchmarks that prioritize corrective actions in embodied contexts. We open‑source our code and dataset under https://github.com/sled‑group/SafetyALFRED.git

PaperId: 1575, https://arxiv.org/pdf/2604.19564.pdf GitHub

Abstract:
Egocentric assistants often rely on first‑person view data to capture user behavior and context for personalized services. Since different users exhibit distinct habits, preferences, and routines, such personalization is essential for truly effective assistance. However, effectively integrating long‑term user data for personalization remains a key challenge. To address this, we introduce EgoSelf, a system that includes a graph‑based interaction memory constructed from past observations and a dedicated learning task for personalization. The memory captures temporal and semantic relationships among interaction events and entities, from which user‑specific profiles are derived. The personalized learning task is formulated as a prediction problem where the model predicts possible future interactions from individual user's historical behavior recorded in the graph. Extensive experiments demonstrate the effectiveness of EgoSelf as a personalized egocentric assistant. Code is available at https://abie‑e.github.io/EgoSelf/.

PaperId: 1576, https://arxiv.org/pdf/2604.19548.pdf GitHub

Abstract:
Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. To enhance reliability, multi‑agent frameworks assigning specialized roles are increasingly adopted to enable self‑reflection and mutual auditing. While such role‑playing effectively leverages domain expert knowledge, we find it simultaneously induces a human‑like cognitive bias known as Actor‑Observer Asymmetry (AOA). Specifically, an agent acting as an actor (during self‑reflection) tends to attribute failures to external factors, whereas an observer (during mutual auditing) attributes the same errors to internal faults. We quantify this using our new Ambiguous Failure Benchmark, which reveals that simply swapping perspectives triggers the AOA effect in over 20% of cases for most models. To tame this bias, we introduce ReTAS (Reasoning via Thesis‑Antithesis‑Synthesis), a model trained through dialectical alignment to enforce perspective‑invariant reasoning. By integrating dialectical chain‑of‑thought with Group Relative Policy Optimization, ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus. Experiments demonstrate that ReTAS effectively mitigates attribution inconsistency and significantly improves fault resolution rates in ambiguous scenarios.

PaperId: 1577, https://arxiv.org/pdf/2604.19544.pdf GitHub

Abstract:
Multimodal reward models (MRMs) play a crucial role in aligning Multimodal Large Language Models (MLLMs) with human preferences. Training a good MRM requires high‑quality multimodal preference data. However, existing preference datasets face three key challenges: lack of granularity in preference strength, textual style bias, and unreliable preference signals. Besides, existing open‑source multimodal preference datasets suffer from substantial noise, yet there is a lack of effective and scalable curation methods to enhance their quality. To address these limitations, we propose DT2IT‑MRM, which integrates a Debiased preference construction pipeline, a novel reformulation of text‑to‑image (T2I) preference data, and an Iterative Training framework that curates existing multimodal preference datasets for Multimodal Reward Modeling. Our experimental results show that DT2IT‑MRM achieves new state‑of‑the‑art overall performance on three major benchmarks: VL‑RewardBench, Multimodal RewardBench, and MM‑RLHF‑RewardBench.

PaperId: 1578, https://arxiv.org/pdf/2604.19516.pdf GitHub

Abstract:
Generative engines (GEs) are reshaping information access by replacing ranked links with citation‑grounded answers, yet current Generative Engine Optimization (GEO) methods optimize each instance in isolation, unable to accumulate or transfer effective strategies across tasks and engines. We reframe GEO as a strategy learning problem and propose MAGEO, a multi‑agent framework in which coordinated planning, editing, and fidelity‑aware evaluation serve as the execution layer, while validated editing patterns are progressively distilled into reusable, engine‑specific optimization skills. To enable controlled assessment, we introduce a Twin Branch Evaluation Protocol for causal attribution of content edits and DSV‑CF, a dual‑axis metric that unifies semantic visibility with attribution accuracy. We further release MSME‑GEO‑Bench, a multi‑scenario, multi‑engine benchmark grounded in real‑world queries. Experiments on three mainstream engines show that MAGEO substantially outperforms heuristic baselines in both visibility and citation fidelity, with ablations confirming that engine‑specific preference modeling and strategy reuse are central to these gains, suggesting a scalable learning‑driven paradigm for trustworthy GEO. Code is available at https://github.com/Wu‑beining/MAGEO

PaperId: 1579, https://arxiv.org/pdf/2604.19459.pdf GitHub GitHub

Abstract:
Formal verification guarantees proof validity but not formalization faithfulness. For natural‑language logical reasoning, where models construct axiom systems from scratch without library constraints, this gap between valid proofs and faithful translations is especially acute. We investigate whether frontier models exploit this gap when generating Lean 4 proofs, a behavior we term formalization gaming. We evaluate GPT‑5 and DeepSeek‑R1 on 303 first‑order logic problems (203 from FOLIO, 100 from Multi‑LogiEval), comparing unified generation against a two‑stage pipeline that separates formalization from proving. Despite compilation rates of 87‑99%, we find no evidence of systematic gaming in unified generation: models prefer reporting failure over forcing proofs, even under prompting designed to encourage it. However, unfaithfulness that evades our detection signals may still occur. The two‑stage pipeline reveals two distinct modes of unfaithfulness: GPT‑5 fabricates axioms during proof generation, a reactive fallback detectable via cross‑stage comparison, while DeepSeek‑R1 mistranslates premises during formalization, producing internally consistent outputs that evade detection entirely. These findings show that high compilation rates or accuracies should not be equated with faithful reasoning. Code and data are available at https://github.com/koreankiwi99/formalization‑gaming.

PaperId: 1580, https://arxiv.org/pdf/2604.19457.pdf GitHub

Abstract:
Long‑horizon enterprise agents make high‑stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy memory, multi‑step reasoning, and binding regulatory constraints. Current evaluation reports a single task‑success scalar that conflates distinct failure modes and hides whether an agent is aligned with the standards its deployment environment requires. We propose that long‑horizon decision behavior decomposes into four orthogonal alignment axes, each independently measurable and failable: factual precision (FRP), reasoning coherence (RCS), compliance reconstruction (CRR), and calibrated abstention (CAR). CRR is a novel regulatory‑grounded axis; CAR is a measurement axis separating coverage from accuracy. We exercise the decomposition on a controlled benchmark (LongHorizon‑Bench) covering loan qualification and insurance claims adjudication with deterministic ground‑truth construction. Running six memory architectures, we find structure aggregate accuracy cannot see: retrieval collapses on factual precision; schema‑anchored architectures pay a scaffolding tax; plain summarization under a fact‑preservation prompt is a strong baseline on FRP, RCS, EDA, and CRR; and all six architectures commit on every case, exposing a decisional‑alignment axis the field has not targeted. The decomposition also surfaced a pre‑registered prediction of our own, that summarization would fail factual recall, which the data reversed at large magnitude, an axis‑level reversal aggregate accuracy would have hidden. Institutional alignment (regulatory reconstruction) and decisional alignment (calibrated abstention) are under‑represented in the alignment literature and become load‑bearing once decisions leave the laboratory. The framework transfers to any regulated decisioning domain via two steps: build a fact schema, and calibrate the CRR auditor prompt.

PaperId: 1581, https://arxiv.org/pdf/2604.19298.pdf GitHub

Abstract:
We introduce IndiaFinBench, to our knowledge the first publicly available evaluation benchmark for assessing large language model (LLM) performance on Indian financial regulatory text. Existing financial NLP benchmarks draw exclusively from Western financial corpora (SEC filings, US earnings reports, English‑language financial news), leaving a significant gap in coverage of non‑Western regulatory frameworks. IndiaFinBench addresses this gap with 406 expert‑annotated question‑answer pairs drawn from 192 documents sourced from the Securities and Exchange Board of India (SEBI) and the Reserve Bank of India (RBI), spanning four task types: regulatory interpretation (174 items), numerical reasoning (92 items), contradiction detection (62 items), and temporal reasoning (78 items). Annotation quality is validated through a model‑based secondary pass (kappa=0.918 on contradiction detection; 90.7% overall agreement on a 150‑item subset) and a 180‑item human inter‑annotator agreement study across three annotation rounds (kappa=0.645 on contradiction detection; 77.2% overall agreement; 44.3% benchmark coverage). We evaluate twelve models under zero‑shot conditions, with accuracy ranging from 70.4% (Gemma 4 E4B) to 89.7% (Gemini 2.5 Flash). All models substantially outperform a non‑specialist human baseline of 69.0%. Numerical reasoning is the most discriminative task, with a 35.9 percentage‑point spread across models. Bootstrap significance testing (10,000 resamples) reveals three statistically distinct performance tiers. The dataset, evaluation code, and all model outputs are available at https://github.com/rajveerpall/IndiaFinBench

PaperId: 1582, https://arxiv.org/pdf/2604.19185.pdf GitHub

Abstract:
Small language models (SLMs), such as BART, can achieve summarization performance comparable to large language models (LLMs) via distillation. However, existing LLM‑based ranking strategies for summary candidates suffer from instability, while classical metrics (e.g., ROUGE) are insufficient to rank high‑quality summaries. To address these issues, we introduce SCURank, a framework that enhances summarization by leveraging Summary Content Units (SCUs). Instead of relying on unstable comparisons or surface‑level overlap, SCURank evaluates summaries based on the richness and semantic importance of information content. We investigate the effectiveness of SCURank in distilling summaries from multiple diverse LLMs. Experimental results demonstrate that SCURank outperforms traditional metrics and LLM‑based ranking methods across evaluation measures and datasets. Furthermore, our findings show that incorporating diverse LLM summaries enhances model abstractiveness and overall distilled model performance, validating the benefits of information‑centric ranking in multi‑LLM distillation. The code for SCURank is available at https://github.com/IKMLab/SCURank.

PaperId: 1583, https://arxiv.org/pdf/2604.19139.pdf GitHub

Abstract:
As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, a growing and increasingly conspicuous phenomenon has emerged: the proliferation of verbal tics, repetitive, formulaic linguistic patterns that pervade model outputs. These range from sycophantic openers (That's a great question!, Awesome!) to pseudo‑empathetic affirmations (I completely understand your concern, I'm right here to catch you) and overused vocabulary (delve, tapestry, nuanced). In this paper, we present a systematic analysis of the verbal tic phenomenon across eight state‑of‑the‑art LLMs: GPT‑5.4, Claude Opus 4.7, Gemini 3.1 Pro, Grok 4.2, Doubao‑Seed‑2.0‑pro, Kimi K2.5, DeepSeek V3.2, and MiMo‑V2‑Pro. Utilizing a custom evaluation framework for standardized API‑based evaluation, we assess 10,000 prompts across 10 task categories in both English and Chinese, yielding 160,000 model responses. We introduce the Verbal Tic Index (VTI), a composite metric quantifying tic prevalence, and analyze its correlation with sycophancy, lexical diversity, and human‑perceived naturalness. Our findings reveal significant inter‑model variation: Gemini 3.1 Pro exhibits the highest VTI (0.590), while DeepSeek V3.2 achieves the lowest (0.295). We further demonstrate that verbal tics accumulate over multi‑turn conversations, are amplified in subjective tasks, and show distinct cross‑lingual patterns. Human evaluation (N = 120) confirms a strong inverse relationship between sycophancy and perceived naturalness (r = ‑0.87, p < 0.001). These results underscore the alignment tax of current training paradigms and highlight the urgent need for more authentic human‑AI interaction frameworks.

PaperId: 1584, https://arxiv.org/pdf/2604.19093.pdf GitHub

Abstract:
Multi‑modal test‑time adaptation (TTA) enhances the resilience of benchmark multi‑modal models against distribution shifts by leveraging the unlabeled target data during inference. Despite the documented success, the advancement of multi‑modal TTA methodologies has been impeded by a persistent limitation, i.e., the lack of explicit modeling of category‑conditional distributions, which is crucial for yielding accurate predictions and reliable decision boundaries. Canonical Gaussian discriminant analysis (GDA) provides a vanilla modeling of category‑conditional distributions and achieves moderate advancement in uni‑modal contexts. However, in multi‑modal TTA scenario, the inherent modality distribution asymmetry undermines the effectiveness of modeling the category‑conditional distribution via the canonical GDA. To this end, we introduce a tailored probabilistic Gaussian model for multi‑modal TTA to explicitly model the category‑conditional distributions, and further propose an adaptive contrastive asymmetry rectification technique to counteract the adverse effects arising from modality asymmetry, thereby deriving calibrated predictions and reliable decision boundaries. Extensive experiments across diverse benchmarks demonstrate that our method achieves state‑of‑the‑art performance under a wide range of distribution shifts. The code is available at https://github.com/XuJinglinn/AdaPGC.

PaperId: 1585, https://arxiv.org/pdf/2604.19049.pdf GitHub

Abstract:
LLM‑assisted defect discovery has a precision crisis: plausible‑but‑wrong reports overwhelm maintainers and degrade credibility for real findings. We present Refute‑or‑Promote, an inference‑time reliability pattern combining Stratified Context Hunting (SCH) for candidate generation, adversarial kill mandates, context asymmetry, and a Cross‑Model Critic (CMC). Adversarial agents attempt to disprove candidates at each promotion gate; cold‑start reviewers are intended to reduce anchoring cascades; cross‑family review can catch correlated blind spots that same‑family review misses. Over a 31‑day campaign across 7 targets (security libraries, the ISO C++ standard, major compilers), the pipeline killed roughly 79% of 171 candidates before advancing to disclosure (retrospective aggregate); on a consolidated‑protocol subset (lcms2, wolfSSL; n=30), the prospective kill rate was 83%. Outcomes: 4 CVEs (3 public, 1 embargoed); LWG 4549 accepted to the C++ working paper; 5 merged C++ editorial PRs; 3 compiler conformance bugs; 8 merged security‑related fixes without CVE; an RFC 9000 errata filed under committee review; and 1+ FIPS 140‑3 normative compliance issues under coordinated disclosure ‑‑ all evaluated by external acceptance, not benchmarks. The most instructive failure: ten dedicated reviewers unanimously endorsed a non‑existent Bleichenbacher padding oracle in OpenSSL's CMS module; it was killed only by a single empirical test, motivating the mandatory empirical gate. No vulnerability was discovered autonomously; the contribution is external structure that filters LLM agents' persistent false positives. As a preliminary transfer test beyond defect discovery, a simplified cross‑family critique variant also solved five previously unsolved SymPy instances on SWE‑bench Verified and one SWE‑rebench hard task.

PaperId: 1586, https://arxiv.org/pdf/2604.19048.pdf GitHub

Abstract:
The combination of Mixture‑of‑Experts (MoE) and Low‑Rank Adaptation (LoRA) has shown significant potential for enhancing the multi‑task learning capabilities of Large Language Models. However, existing methods face two primary challenges: (1)Imprecise Routing in the current MoE‑LoRA method fails to explicitly match input semantics with expert capabilities, leading to weak expert specialization. (2)Uniform weight fusion strategies struggle to provide adaptive update strengths, overlooking the varying complexity of different tasks. To address these limitations, we propose SAMoRA (Semantic‑Aware Mixture of LoRA Experts), a novel parameter‑efficient fine‑tuning framework tailored for task‑adaptive learning. Specifically, A Semantic‑Aware Router is proposed to explicitly align textual semantics with the most suitable experts for precise routing. A Task‑Adaptive Scaling mechanism is designed to regulate expert contributions based on specific task requirements dynamically. In addition, a novel regularization objective is proposed to jointly promote expert specialization and effective scaling. Extensive experiments on multiple multi‑task benchmarks demonstrate that SAMoRA significantly outperforms the state‑of‑the‑art methods and holds excellent task generalization capabilities. Code is available at https://github.com/boyan‑code/SAMoRA

PaperId: 1587, https://arxiv.org/pdf/2604.19018.pdf GitHub

Abstract:
Inference‑time LLM alignment methods, particularly activation steering, offer an alternative to fine‑tuning by directly modifying activations during generation. Existing methods, however, often rely on non‑anticipative interventions that ignore how perturbations propagate through transformer layers and lack online error feedback, resulting in suboptimal, open‑loop control. To address this, we show empirically that, despite the nonlinear structure of transformer blocks, layer‑wise dynamics across multiple LLM architectures and scales are well‑approximated by locally‑linear models. Exploiting this property, we model LLM inference as a linear time‑varying dynamical system and adapt the classical linear quadratic regulator to compute feedback controllers using layer‑wise Jacobians, steering activations toward desired semantic setpoints in closed‑loop with minimal computational overhead and no offline training. We also derive theoretical bounds on setpoint tracking error, enabling formal guarantees on steering performance. Using a novel adaptive semantic feature setpoint signal, our method yields robust, fine‑grained behavior control across models, scales, and tasks, including state‑of‑the‑art modulation of toxicity, truthfulness, refusal, and arbitrary concepts, surpassing baseline steering methods. Our code is available at: https://github.com/trustworthyrobotics/lqr‑activation‑steering

PaperId: 1588, https://arxiv.org/pdf/2604.19000.pdf GitHub

Abstract:
Statement autoformalization acts as a critical bridge between human mathematics and formal mathematics by translating natural language problems into formal language. While prior works have focused on data synthesis and diverse training paradigms to optimize end‑to‑end Large Language Models (LLMs), they typically treat formal code as flat sequences, neglecting the hierarchical logic inherent in mathematical statements. In this work, we introduce Decompose, Structure, and Repair (DSR), a neuro‑symbolic framework that restructures autoformalization into a modular pipeline. DSR decomposes statements into logical components and maps them to structured operator trees, leveraging this topological blueprint to precisely localize and repair errors via sub‑tree refinement. Furthermore, we introduce PRIME, a benchmark of 156 undergraduate and graduate‑level theorems selected from canonical textbooks and expertly annotated in Lean 4. Experimental results demonstrate that DSR establishes a new state‑of‑the‑art, consistently outperforming baselines under equivalent computational budgets. The datasets, model, and code are available at https://github.com/XiaoyangLiu‑sjtu/DSR.

PaperId: 1589, https://arxiv.org/pdf/2604.18993.pdf GitHub

Abstract:
Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real‑world video data in adverse weather. Existing weather generation approaches struggle to balance visual quality and annotation reusability. We present AutoAWG, a controllable Adverse Weather video Generation framework for Autonomous driving. Our method employs a semantics‑guided adaptive fusion of multiple controls to balance strong weather stylization with high‑fidelity preservation of safety‑critical targets; leverages a vanishing point‑anchored temporal synthesis strategy to construct training sequences from static images, thereby reducing reliance on synthetic data; and adopts masked training to enhance long‑horizon generation stability. On the nuScenes validation set, AutoAWG significantly outperforms prior state‑of‑the‑art methods: without first‑frame conditioning, FID and FVD are relatively reduced by 50.0% and 16.1%; with first‑frame conditioning, they are further reduced by 8.7% and 7.2%, respectively. Extensive qualitative and quantitative results demonstrate advantages in style fidelity, temporal consistency, and semantic‑‑structural integrity, underscoring the practical value of AutoAWG for improving downstream perception in autonomous driving. Our code is available at: https://github.com/higherhu/AutoAWG

PaperId: 1590, https://arxiv.org/pdf/2604.18933.pdf GitHub

Abstract:
Robotic manipulation tasks exhibit varying memory requirements, ranging from Markovian tasks that require no memory to non‑Markovian tasks that depend on historical information spanning single or multiple interaction trials. Surprisingly, simply extending observation histories of a visuomotor policy often leads to a significant performance drop due to distribution shift and overfitting. To address these issues, we propose Gated Memory Policy (GMP), a visuomotor policy that learns both when to recall memory and what to recall. To learn when to recall memory, GMP employs a learned memory gate mechanism that selectively activates history context only when necessary, improving robustness and reactivity. To learn what to recall efficiently, GMP introduces a lightweight cross‑attention module that constructs effective latent memory representations. To further enhance robustness, GMP injects diffusion noise into historical actions, mitigating sensitivity to noisy or inaccurate histories during both training and inference. On our proposed non‑Markovian benchmark MemMimic, GMP achieves a 30.1% average success rate improvement over long‑history baselines, while maintaining competitive performance on Markovian tasks in RoboMimic. All code, data and in‑the‑wild deployment instructions are available on our project website https://gated‑memory‑policy.github.io/.

PaperId: 1591, https://arxiv.org/pdf/2604.18932.pdf GitHub

Abstract:
Despite growing interest in Quranic data research, existing Quran datasets remain limited in both scale and diversity. To address this gap, we present Tadabur, a large‑scale Quran audio dataset. Tadabur comprises more than 1400+ hours of recitation audio from over 600 distinct reciters, providing substantial variation in recitation styles, vocal characteristics, and recording conditions. This diversity makes Tadabur a comprehensive and representative resource for Quranic speech research and analysis. By significantly expanding both the total duration and variability of available Quran data, Tadabur aims to support future research and facilitate the development of standardized Quranic speech benchmarks.

PaperId: 1592, https://arxiv.org/pdf/2604.18901.pdf GitHub

Abstract:
Aligned language models refuse harmful instructions, but the representations through which they recognise such instructions are less well characterised than the behaviours they produce. Harmful intent is linearly separable from residual‑stream activations across 12 models spanning four architectural families (Qwen2.5, Qwen3.5, Llama‑3.2, Gemma‑3) and three alignment variants (base, instruction‑tuned, abliterated), with parameter scales from 0.5B to 1.3B and a within‑family scale extension to 9B on Qwen3.5. A direction fitted from 100 labelled examples per class via Soft‑AUC optimisation reaches mean effective AUROC 0.982 and TPR@1%FPR 0.797, generalises to three held‑out harm benchmarks and a hard‑benign control, and matches its instruction‑tuned counterpart within \pm 0.003 AUROC in abliterated variants from which the refusal mechanism has been removed. The supervised strategies all exceed AUROC 0.96, but their TPR@1%FPR varies by more than ten times the AUROC gap; a deployed 9B safety classifier shows the same pattern at AUROC 0.94 and TPR 0.30, motivating low‑FPR reporting as a default in safety‑adjacent detection evaluation. Geometric measurements refine the picture. The recovered direction is concentrated within each extraction protocol but protocol‑dependent across them: two pooling choices applied to the same chat‑templated activations at the same residual‑stream layer (max‑pool over content tokens versus last‑token at the post‑instruction position) recover harm directions 73^\circ apart, and projecting one out leaves detection under either max‑pool extraction essentially intact. Probing identifies a protocol‑specific direction rather than a unique computational feature.

PaperId: 1593, https://arxiv.org/pdf/2604.18827.pdf GitHub

Abstract:
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.1 million neurons from the visual cortex of 73 mice across 323 sessions, totaling more than 150 billion neural tokens recorded during natural movies, images and parametric stimuli, and behavior. We train multi‑modal, multi‑task models that support three regimes flexibly at test time: neural prediction, behavioral decoding, neural forecasting, or any combination of the three. OmniMouse achieves state‑of‑the‑art performance, outperforming specialized baselines across nearly all evaluation regimes. We find that performance scales reliably with more data, but gains from increasing model size saturate. This inverts the standard AI scaling story: in language and computer vision, massive datasets make parameter scaling the primary driver of progress, whereas in brain modeling ‑‑ even in the mouse visual cortex, a relatively simple system ‑‑ models remain data‑limited despite vast recordings. The observation of systematic scaling raises the possibility of phase transitions in neural modeling, where larger and richer datasets might unlock qualitatively new capabilities, paralleling the emergent properties seen in large language models. Code available at https://github.com/enigma‑brain/omnimouse.

PaperId: 1594, https://arxiv.org/pdf/2604.18779.pdf GitHub

Abstract:
Existing web agents typically initiate exploration from the root URL, which is inefficient for complex websites with deep hierarchical structures. Without a global view of the website's structure, agents frequently fall into navigation traps, explore irrelevant branches, or fail to reach target information within a limited budget. We propose Mango, a multi‑agent web navigation method that leverages the website structure to dynamically determine optimal starting points. We formulate URL selection as a multi‑armed bandit problem and employ Thompson Sampling to adaptively allocate the navigation budget across candidate URLs. Furthermore, we introduce an episodic memory component to store navigation history, enabling the agent to learn from previous attempts. Experiments on WebVoyager demonstrate that Mango achieves a success rate of 63.6% when using GPT‑5‑mini, outperforming the best baseline by 7.3%. Furthermore, on WebWalkerQA, Mango attains a 52.5% success rate, surpassing the best baseline by 26.8%. We also demonstrate the generalizability of Mango using both open‑source and closed‑source models as backbones. Our data and code are open‑source and available at https://github.com/VichyTong/Mango.

PaperId: 1595, https://arxiv.org/pdf/2604.18701.pdf GitHub

Abstract:
Local prediction‑error‑based curiosity rewards focus on the current transition without considering the world model's cumulative prediction error across all visited transitions. We introduce Curiosity‑Critic, which grounds its intrinsic reward in the improvement of this cumulative objective, and show that it admits a tractable per‑step surrogate: the difference between the current prediction error and the asymptotic error baseline of the current state transition. We estimate this error baseline online with a learned critic co‑trained alongside the world model; regressing a single scalar, the critic converges well before the world model saturates, redirecting exploration toward learnable transitions without oracle knowledge of the noise floor. The reward is higher for learnable transitions and collapses toward the error baseline for stochastic ones, effectively separating epistemic (reducible) from aleatoric (irreducible) prediction error online. Prior prediction‑error curiosity formulations, from Schmidhuber (1991) to learned‑feature‑space variants, emerge as special cases corresponding to specific approximations of this error baseline. Experiments on a stochastic grid world show that Curiosity‑Critic outperforms prediction‑error, visitation‑count, and Random Network Distillation methods in training speed and final world model accuracy.

PaperId: 1596, https://arxiv.org/pdf/2604.18652.pdf GitHub

Abstract:
The transition of agentic AI from brittle prototypes to production systems is stalled by a pervasive crisis of craft. We suggest that the prevailing orchestration paradigm‑delegating the system control loop to large language models and merely patching with heuristic guardrails‑is the root cause of this fragility. Instead, we propose Arbiter‑K, a Governance‑First execution architecture that reconceptualizes the underlying model as a Probabilistic Processing Unit encapsulated by a deterministic, neuro‑symbolic kernel. Arbiter‑K implements a Semantic Instruction Set Architecture (ISA) to reify probabilistic messages into discrete instructions. This allows the kernel to maintain a Security Context Registry and construct an Instruction Dependency Graph at runtime, enabling active taint propagation based on the data‑flow pedigree of each reasoning node. By leveraging this mechanism, Arbiter‑K precisely interdicts unsafe trajectories at deterministic sinks (e.g., high‑risk tool calls or unauthorized network egress) and enables autonomous execution correction and architectural rollback when security policies are triggered. Evaluations on OpenClaw and NanoBot demonstrate that Arbiter‑K enforces security as a microarchitectural property, achieving 76% to 95% unsafe interception for a 92.79% absolute gain over native policies. The code is publicly available at https://github.com/cure‑lab/ArbiterOS.

PaperId: 1597, https://arxiv.org/pdf/2604.18591.pdf GitHub

Abstract:
Game UI implementation requires translating stylized mockups into interactive engine entities. However, current "Screenshot‑to‑Code" tools often struggle with the irregular geometries and deep visual hierarchies typical of game interfaces. To bridge this gap, we introduce SPRITE, a pipeline that transforms static screenshots into editable engine assets. By integrating Vision‑Language Models (VLMs) with a structured YAML intermediate representation, SPRITE explicitly captures complex container relationships and non‑rectangular layouts. We evaluated SPRITE against a curated Game UI benchmark and conducted expert reviews with professional developers to assess reconstruction fidelity and prototyping efficiency. Our findings demonstrate that SPRITE streamlines development by automating tedious coding and resolving complex nesting. By facilitating rapid in‑engine iteration, SPRITE effectively blurs the boundaries between artistic design and technical implementation in game development. Project page: https://baiyunshu.github.io/sprite.github.io/

PaperId: 1598, https://arxiv.org/pdf/2604.18580.pdf GitHub

Abstract:
Modern sequence modeling is dominated by two families: Transformers, whose self‑attention can access arbitrary elements of the visible sequence, and structured state‑space models, which propagate information through an explicit recurrent state. These mechanisms face different limitations on long contexts: when attention is diffuse, the influence of individual tokens is diluted across the effective support, while recurrent state propagation can lose long‑range sensitivity unless information is actively preserved. As a result, both mechanisms face challenges in preserving and selectively retrieving information over long contexts. We propose Sessa, a decoder that places attention inside a recurrent feedback path. This creates many attention‑based paths through which past tokens can influence future states, rather than relying on a single attention read or a single recurrent chain. We prove that, under explicit assumptions and matched regimes, Sessa admits power‑law memory tails O(\ell^‑β) for 0 < β< 1, with slower decay than in the corresponding Transformer and Mamba‑style baselines. We further give an explicit construction that achieves this power‑law rate. Under the same assumptions, Sessa is the only model class among those considered that realizes flexible selective retrieval, including profiles whose influence does not decay with distance. Consistent with this theoretical advantage, across matched experiments, Sessa achieves the strongest performance on long‑context benchmarks while remaining competitive with Transformer and Mamba‑style baselines on short‑context language modeling.

PaperId: 1599, https://arxiv.org/pdf/2604.18578.pdf GitHub

Abstract:
Proximal Policy Optimization (PPO) has become the predominant algorithm for on‑policy reinforcement learning due to its scalability and empirical robustness across domains. However, there is a significant disconnect between the underlying foundations of trust region methods and the heuristic clipped objective used in PPO. In this paper, we bridge this gap by introducing the Bounded Ratio Reinforcement Learning (BRRL) framework. We formulate a novel regularized and constrained policy optimization problem and derive its analytical optimal solution. We prove that this solution ensures monotonic performance improvement. To handle parameterized policy classes, we develop a policy optimization algorithm called Bounded Policy Optimization (BPO) that minimizes an advantage‑weighted divergence between the policy and the analytic optimal solution from BRRL. We further establish a lower bound on the expected performance of the resulting policy in terms of the BPO loss function. Notably, our framework also provides a new theoretical lens to interpret the success of the PPO loss, and connects trust region policy optimization and the Cross‑Entropy Method (CEM). We additionally extend BPO to Group‑relative BPO (GBPO) for LLM fine‑tuning. Empirical evaluations of BPO across MuJoCo, Atari, and complex IsaacLab environments (e.g., Humanoid locomotion), and of GBPO for LLM fine‑tuning tasks, demonstrate that BPO and GBPO generally match or outperform PPO and GRPO in stability and final performance.

PaperId: 1600, https://arxiv.org/pdf/2604.18530.pdf GitHub

Abstract:
Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have significantly improved Large Language Model (LLM) reasoning, yet models often struggle to explore novel trajectories beyond their initial policy distribution. While offline teacher guidance and entropy‑driven strategies have been proposed to address this, they often lack deep integration or are constrained by the model's inherent capacity. In this paper, we propose OGER (Offline‑Guided Exploration Reward), a novel framework that unifies offline teacher guidance and online reinforcement learning through a specialized reward modeling lens. OGER employs multi‑teacher collaborative training and constructs an auxiliary exploration reward that leverages both offline trajectories and the model's own entropy to incentivize autonomous exploration. Extensive experiments across mathematical and general reasoning benchmarks demonstrate that OGER consistently outperforms competitive baselines, achieving substantial gains in mathematical reasoning while maintaining robust generalization to out‑of‑domain tasks. We provide a comprehensive analysis of training dynamics and conduct detailed ablation studies to validate the effectiveness of our entropy‑aware reward modulation. Our code is available at https://github.com/ecoli‑hit/OGER.git.

PaperId: 1601, https://arxiv.org/pdf/2604.18521.pdf GitHub

Abstract:
Epidemic forecasting has become an integral part of real‑time infectious disease outbreak response. While collaborative ensembles composed of statistical and machine learning models have become the norm for real‑time forecasting, standardized benchmark datasets for evaluating such methods are lacking. Further, there is limited understanding on performance of these methods for novel outbreaks with limited historical data. In this paper, we propose IDOBE, a curated collection of epidemiological time series focused on outbreak forecasting. IDOBE compiles from multiple data repositories spanning over a century of surveillance and across U.S. states and global locations. We perform derivative‑based segmentation to generate over 10,000 outbreaks covering multiple outcomes such as cases and hospitalizations for 13 diseases. We consider a variety of information‑theoretic and distributional measures to quantify the epidemiological diversity of the dataset. Finally, we perform multi‑horizon short‑term forecasting (1‑ to 4‑week‑ahead) through the progression of the outbreak using 11 baseline models and report on their performance. In addition to standard metrics such as NMSE and MAPE for point forecasts, we include probabilistic scoring rules such as Normalized Weighted Interval Score (NWIS) to quantify the performance. We find that MLP‑based methods have the most robust performance, with statistical methods having a slight edge during the pre‑peak phase. IDOBE dataset along with baselines are released publicly on https://github.com/NSSAC/IDOBE to enable standardized, reproducible benchmarking of outbreak forecasting methods.

PaperId: 1602, https://arxiv.org/pdf/2604.18490.pdf GitHub GitHub

Abstract:
Existing MT evaluation frameworks, including automatic metrics and human evaluation schemes such as Multidimensional Quality Metrics (MQM), are largely language‑agnostic. However, they often fail to capture dialect‑ and culture‑specific errors in diglossic languages (e.g., Arabic), where translation failures stem from mismatches in language variety, content coverage, and pragmatic appropriateness rather than surface form alone.We introduce LQM: Linguistically Motivated Multidimensional Quality Metrics for MT. LQM is a hierarchical error taxonomy for diagnosing MT errors through six linguistically grounded levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics (Figure 1). We construct a bidirectional parallel corpus of 3,850 sentences (550 per variety) spanning seven Arabic dialects (Egyptian, Emirati, Jordanian, Mauritanian, Moroccan, Palestinian, and Yemeni), derived from conversational, culturally rich content. We evaluate six LLMs in a zero‑shot setting and conduct expert span‑level human annotation using LQM, producing 6,113 labeled error spans across 3,495 unique erroneous sentences, along with severity‑weighted quality scores. We complement this analysis with an automatic metric (spBLEU). Though validated here on Arabic, LQM is a language‑agnostic framework designed to be easily applied to or adapted for other languages. LQM annotated errors data, prompts, and annotation guidelines are publicly available at https://github.com/UBC‑NLP/LQM_MT.

PaperId: 1603, https://arxiv.org/pdf/2604.18352.pdf GitHub

Abstract:
State‑of‑the‑art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarantees remains challenging. We introduce a Gaussian Differential Privacy (GDP)‑based auditing framework that measures privacy via the full false‑positive/false‑negative tradeoff. Applied to MST and AIM under worst‑case settings, our method provides the first tight audits in the strong‑privacy regime. For (ε,δ)=(1,10^‑2), we obtain μ_emp\approx0.43 vs. implied μ=0.45, showing a small theory‑practice gap. Our code is publicly available: https://github.com/sassoftware/dpmm.

PaperId: 1604, https://arxiv.org/pdf/2604.18344.pdf GitHub

Abstract:
Knowledge Graphs (KGs) are composed of triples, and the goal of Knowledge Graph Completion (KGC) is to infer the missing factual triples. Traditional KGC tasks predict missing elements in a triple given one or two of its elements. As a more realistic task, the Triple Set Prediction (TSP) task aims to infer the set of missing triples conditioned only on the observed knowledge graph, without assuming any partial information about the missing triples. Existing TSP methods predict the set of missing triples in a triple‑by‑triple manner, falling short in capturing the dependencies among the predicted triples to ensure consistency. To address this issue, we propose a novel discrete diffusion model termed DiffTSP that treats TSP as a generative task. DiffTSP progressively adds noise to the KG through a discrete diffusion process, achieved by masking relational edges. The reverse process then gradually recovers the complete KG conditioned on the incomplete graph. To this end, we design a structure‑aware denoising network that integrates a relational context encoder with a relational graph diffusion transformer for knowledge graph generation. DiffTSP can generate the complete set of triples in a one‑pass manner while ensuring the dependencies among the predicted triples. Our approach achieves state‑of‑the‑art performance on three public datasets. Code: https://github.com/ADMIS‑TONGJI/DiffTSP.

PaperId: 1605, https://arxiv.org/pdf/2604.18320.pdf GitHub

Abstract:
Self‑evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo‑label‑based methods suffer from progressive quality degradation as model predictions drift, while template‑based methods are confined to a static set of transformations that cannot adapt in difficulty or diversity. We contend that robust, continuous self‑improvement requires not only deterministic external feedback independent of the model's internal certainty, but also a mechanism to perpetually diversify the training distribution. To this end, we introduce EVE (Executable Visual transformation‑based self‑Evolution), a novel framework that entirely bypasses pseudo‑labels by harnessing executable visual transformations continuously enriched in both variety and complexity. EVE adopts a Challenger‑Solver dual‑policy architecture. The Challenger maintains and progressively expands a queue of visual transformation code examples, from which it synthesizes novel Python scripts to perform dynamic visual transformations. Executing these scripts yields VQA problems with absolute, execution‑verified ground‑truth answers, eliminating any reliance on model‑generated supervision. A multi‑dimensional reward system integrating semantic diversity and dynamic difficulty calibration steers the Challenger to enrich its code example queue while posing progressively more challenging tasks, preventing mode collapse and fostering reciprocal co‑evolution between the two policies. Extensive experiments demonstrate that EVE consistently surpasses existing self‑evolution methods, establishing a robust and scalable paradigm for verifiable MLLM self‑evolution. The code is available at https://github.com/0001Henry/EVE .

PaperId: 1606, https://arxiv.org/pdf/2604.18257.pdf GitHub

Abstract:
Query auto‑completion (QAC) has been widely studied in the context of web search, yet remains underexplored for in‑document search, which we term DocQAC. DocQAC aims to enhance search productivity within long documents by helping users craft faster, more precise queries, even for complex or hard‑to‑spell terms. While global historical queries are available to both WebQAC and DocQAC, DocQAC uniquely accesses document‑specific context, including the current document's content and its specific history of user query interactions. To address this setting, we propose a novel adaptive trie‑guided decoding framework that uses user query prefixes to softly steer language models toward high‑quality completions. Our approach introduces an adaptive penalty mechanism with tunable hyperparameters, enabling a principled trade‑off between model confidence and trie‑based guidance. To efficiently incorporate document context, we explore retrieval‑augmented generation (RAG) and lightweight contextual document signals such as titles, keyphrases, and summaries. When applied to encoder‑decoder models like T5 and BART, our trie‑guided framework outperforms strong baselines and even surpasses much larger instruction‑tuned models such as LLaMA‑3 and Phi‑3 on seen queries across both seen and unseen documents. This demonstrates its practicality for real‑world DocQAC deployments, where efficiency and scalability are critical. We evaluate our method on a newly introduced DocQAC benchmark derived from ORCAS, enriched with query‑document pairs. We make both the DocQAC dataset (https://bit.ly/3IGEkbH) and code (https://github.com/rahcode7/DocQAC) publicly available.

PaperId: 1607, https://arxiv.org/pdf/2604.18240.pdf GitHub

Abstract:
As reinforcement learning continues to scale the training of large language model‑based agents, reliably verifying agent behaviors in complex environments has become increasingly challenging. Existing approaches rely on rule‑based verifiers or LLM‑as‑a‑Judge models, which struggle to generalize beyond narrow domains. Agent‑as‑a‑Judge addresses this limitation by actively interacting with environments and tools to acquire verifiable evidence, yet its capabilities remain underexplored. We introduce a benchmark AJ‑Bench to systematically evaluate Agent‑as‑a‑Judge across three domains‑search, data systems, and graphical user interfaces‑comprising 155 tasks and 516 annotated trajectories. The benchmark comprehensively assesses judge agents' abilities in information acquisition, state verification, and process verification. Experiments demonstrate consistent performance gains over LLM‑as‑a‑Judge baselines, while also revealing substantial open challenges in agent‑based verification. Our data and code are available at https://aj‑bench.github.io/.

PaperId: 1608, https://arxiv.org/pdf/2604.18239.pdf GitHub

Abstract:
Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin‑based methods also suppress the chosen response when they try to suppress the rejected one, and there is no general way to prevent this across different objectives. We address this issue with a unified incentive‑score decomposition of preference optimization, revealing that different objectives share the same local update directions and differ only in their scalar weights. This decomposition provides a common framework for analyzing objectives that were previously studied in separate settings. Building on this decomposition, by analyzing the dynamics of the chosen/rejected likelihoods, we identify the disentanglement band (DB), a simple, testable condition that tells us when training can follow the desired path: suppress the loser while preserving the winner, possibly after an early stage. Using the DB, we propose reward calibration (RC), a plug‑and‑play method that adaptively rebalances the updates for chosen and rejected responses to satisfy the DB, without redesigning the base objective. Empirical results show that RC leads to more disentangled dynamics, with better downstream performance observed across several settings. Our code is available at https://github.com/IceyWuu/DisentangledPreferenceOptimization.

PaperId: 1609, https://arxiv.org/pdf/2604.18235.pdf GitHub

Abstract:
Search agents achieve strong question‑answering performance through multi‑turn interactions with search engines, with Group Relative Policy Optimization (GRPO) being a widely used training algorithm. However, GRPO‑style algorithms still face several challenges in multi‑hop search settings. First, correct intermediate steps are often penalized when the final answer is wrong. Second, training is highly unstable, often causing degradation of natural language ability or even catastrophic training collapse. Our analysis attributes these issues to coarse‑grained advantage assignment and an imbalance between positive and negative advantages. To address these problems, we propose CalibAdv, an advantage calibration method specifically designed for search agents that enables more accurate and more stable modeling of penalties and rewards. Specifically, CalibAdv leverages the correctness of intermediate steps to downscale excessive negative advantages at a fine‑grained level. It then further rebalances positive and negative advantages to improve training stability. Importantly, CalibAdv adopts a lightweight design that calibrates advantages from standard rollout signals, making it simple and easy to deploy. Extensive experiments across three models and seven benchmarks demonstrate that CalibAdv improves both model performance and training stability. Our code is available at https://github.com/wujwyi/CalibAdv.

PaperId: 1610, https://arxiv.org/pdf/2604.18234.pdf GitHub

Abstract:
Retrieval‑augmented generation (RAG) enhances large language models (LLMs) with external knowledge to answer questions more accurately. However, research on evaluating RAG systems‑particularly the retriever component‑remains limited, as most existing work focuses on single‑context retrieval rather than multi‑hop queries, where individual contexts may appear irrelevant in isolation but are essential when combined. In this research, we use the HotPotQA, MuSiQue, and SQuAD datasets to simulate a RAG system and compare three LLM‑as‑judge evaluation strategies, including our proposed Context‑Aware Retriever Evaluation (CARE). Our goal is to better understand how multi‑hop reasoning can be most effectively evaluated in RAG systems. Experiments with LLMs from OpenAI, Meta, and Google demonstrate that CARE consistently outperforms existing methods for evaluating multi‑hop reasoning in RAG systems. The performance gains are most pronounced in models with larger parameter counts and longer context windows, while single‑hop queries show minimal sensitivity to context‑aware evaluation. Overall, the results highlight the critical role of context‑aware evaluation in improving the reliability and accuracy of retrieval‑augmented generation systems, particularly in complex query scenarios. To ensure reproducibility, we provide the complete data of our experiments at https://github.com/lorenzbrehme/CARE.

PaperId: 1611, https://arxiv.org/pdf/2604.18190.pdf GitHub

Abstract:
We propose MADDPG‑K, a scalable extension to Multi‑Agent Deep Deterministic Policy Gradient (MADDPG) that addresses the computational limitations of centralized critic approaches. Centralized critics, which condition on the observations and actions of all agents, have demonstrated significant performance gains in cooperative and competitive multi‑agent settings. However, their critic networks grow linearly in input size with the number of agents, making them increasingly expensive to train at scale. MADDPG‑K mitigates this by restricting each agent's critic to the k closest agents under a chosen metric which in our case is Euclidean distance. This ensures a constant‑size critic input regardless of the total agent count. We analyze the complexity of this approach, showing that the quadratic cost it retains arises from cheap scalar distance computations rather than the expensive neural network matrix multiplications that bottleneck standard MADDPG. We validate our method empirically across cooperative and adversarial environments from the Multi‑Particle Environment suite, demonstrating competitive or superior performance compared to MADDPG, faster convergence in cooperative settings, and better runtime scaling as the number of agents grows. Our code is available at https://github.com/TimGop/MADDPG‑K .

PaperId: 1612, https://arxiv.org/pdf/2604.18135.pdf GitHub

Abstract:
Large‑scale dataset distillation requires storing auxiliary soft labels that can be 30‑40x larger on ImageNet‑1K and 200x larger on ImageNet‑21K than the condensed images, undermining the goal of dataset compression. We identify two fundamental issues necessitating such extensive labels: (1) insufficient image diversity, where high within‑class similarity in synthetic images requires extensive augmentation, and (2) insufficient supervision diversity, where limited variety in supervisory signals during training leads to performance degradation at high compression rates. To address these challenges, we propose Label Pruning and Quantization for Large‑scale Distillation (LPQLD). We enhance image diversity via class‑wise batching and batch‑normalization supervision during synthesis. For supervision diversity, we introduce Label Pruning with Dynamic Knowledge Reuse to improve label‑per‑augmentation diversity, and Label Quantization with Calibrated Student‑Teacher Alignment to improve augmentation‑per‑image diversity. Our approach reduces soft label storage by 78x on ImageNet‑1K and 500x on ImageNet‑21K while improving accuracy by up to 7.2% and 2.8%, respectively. Extensive experiments validate the superiority of LPQLD across different network architectures and dataset distillation methods. Code is available at https://github.com/he‑y/soft‑label‑pruning‑quantization‑for‑dataset‑distillation.

PaperId: 1613, https://arxiv.org/pdf/2604.18103.pdf GitHub

Abstract:
Prefilling computational costs pose a significant bottleneck for Large Language Models (LLMs) and Large Multimodal Models (LMMs) in long‑context settings. While token pruning reduces sequence length, prior methods rely on heuristics that break compatibility with hardware‑efficient kernels like FlashAttention. In this work, we observe that tokens evolve toward semantic fixing points, making further processing redundant. To this end, we introduce Delta Attention Selective Halting (DASH), a training‑free policy that monitors the layer‑wise update dynamics of the self‑attention mechanism to selectively halt stabilized tokens. Extensive evaluation confirms that DASH generalizes across language and vision benchmarks, delivering significant prefill speedups while preserving model accuracy and hardware efficiency. Code will be released at https://github.com/verach3n/DASH.git.

PaperId: 1614, https://arxiv.org/pdf/2604.18095.pdf GitHub

Abstract:
In real‑world applications of noninvasive electroencephalography (EEG), specialized decoders often show limited generalizability across diverse tasks under subject‑independent settings. One central challenge is that task‑relevant EEG signals often follow different temporal organization patterns across tasks, while many existing methods rely on task‑tailored architectural designs that introduce task‑specific temporal inductive biases. This mismatch makes it difficult to adapt temporal modeling across tasks without changing the model configuration. To address these challenges, we propose DSAINet, an efficient dual‑scale attentive interaction network for general EEG decoding. Specifically, DSAINet constructs shared spatiotemporal token representations from raw EEG signals and models diverse temporal dynamics through parallel convolutional branches at fine and coarse scales. The resulting representations are then adaptively refined by intra‑branch attention to emphasize salient scale‑specific patterns and by inter‑branch attention to integrate task‑relevant features across scales, followed by adaptive token aggregation to yield a compact representation for prediction. Extensive experiments on five downstream EEG decoding tasks across ten public datasets show that DSAINet consistently outperforms 13 representative baselines under strict subject‑independent evaluation. Notably, this performance is achieved using the same architecture hyperparameters across datasets. Moreover, DSAINet achieves a favorable accuracy‑efficiency trade‑off with only about 77K trainable parameters and provides interpretable neurophysiological insights. The code is publicly available at https://github.com/zy0929/DSAINet.

PaperId: 1615, https://arxiv.org/pdf/2604.18005.pdf GitHub

Abstract:
Multi‑agent systems (MAS) are increasingly used for open‑ended idea generation, driven by the expectation that collective interaction will broaden the exploration diversity. However, when and why such collaboration truly expands the solution space remains unclear. We present a systematic empirical study of diversity in MAS‑based ideation across three bottom‑up levels: model intelligence, agent cognition, and system dynamics. At the model level, we identify a compute efficiency paradox, where stronger, highly aligned models yield diminishing marginal diversity despite higher per‑sample quality. At the cognition level, authority‑driven dynamics suppress semantic diversity compared to junior‑dominated groups. At the system level, group‑size scaling yields diminishing returns and dense communication topologies accelerate premature convergence. We characterize these outcomes as collective failures emerging from structural coupling, a process where interaction inadvertently contracts agent exploration and triggers diversity collapse. Our analysis shows that this collapse arises primarily from the interaction structure rather than inherent model insufficiency, highlighting the importance of preserving independence and disagreement when designing MAS for creative tasks. Our code is available at https://github.com/Xtra‑Computing/MAS_Diversity.

PaperId: 1616, https://arxiv.org/pdf/2604.17930.pdf GitHub

Abstract:
Large Language Models (LLMs) exhibit a puzzling disparity in their formal linguistic competence: while they learn some linguistic phenomena with near‑perfect mastery, they often perform below chance on others, even after training on trillions of tokens. In this work, we investigate whether these failures stem from inherent architectural limitations or simply the scarcity of these specific grammatical constructions in web‑scale corpora. We pre‑train simple GPT‑2 Small (124M) models on a 100M‑token random sample of the FineWeb corpus and intervene by injecting a minimal amount (1%) of synthetic data targeting specific linguistic phenomena. We find that this targeted intervention substantially improves model performance in 8 out of the 9 worst‑performing BLiMP paradigms ‑ notably the accuracy on a specific paradigm, only_npi_scope, surges from 20.9% to 69.4%. Furthermore, we observe that these interventions generally preserve or slightly improve aggregate performance. However, while we also identify a resistant phenomenon, principle_A_c_command, whose performance remains below chance even after our data augmentation, our findings do serve as an optimistic existence proof that even small language models can substantially improve on those linguistic phenomena on which models typically perform poorly, provided the pre‑training data contains sufficient exposure to them. This suggests that efforts towards human‑scale language modeling may benefit greatly by focusing on data composition. The code to reproduce our results is open‑sourced at https://github.com/kowndinya‑renduchintala/heterogeneity‑in‑formal‑linguistic‑competence.

PaperId: 1617, https://arxiv.org/pdf/2604.17927.pdf GitHub

Abstract:
Visual decoding of neurophysiological signals is a critical challenge for brain‑computer interfaces (BCIs) and computational neuroscience. However, current approaches are often constrained by the systematic and stochastic gaps between neural and visual modalities, largely neglecting the intrinsic computational mechanisms of the Human Visual System (HVS). To address this, we propose Brain‑Inspired Capture (BI‑Cap), a neuromimetic perceptual simulation paradigm that aligns these modalities by emulating HVS processing. Specifically, we construct a neuromimetic pipeline comprising four biologically plausible dynamic and static transformations, coupled with Mutual Information (MI)‑guided dynamic blur regulation to simulate adaptive visual processing. Furthermore, to mitigate the inherent non‑stationarity of neural activity, we introduce an evidence‑driven latent space representation. This formulation explicitly models uncertainty, thereby ensuring robust neural embeddings. Extensive evaluations on zero‑shot brain‑to‑image retrieval across two public benchmarks demonstrate that BI‑Cap substantially outperforms state‑of‑the‑art methods, achieving relative gains of 9.2% and 8.0%, respectively. We have released the source code on GitHub through the link https://github.com/flysnow1024/BI‑Cap.

PaperId: 1618, https://arxiv.org/pdf/2604.17897.pdf GitHub

Abstract:
The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies, graph learning can benefit from LLMs. However, we observe limited benefits when we directly utilize LLMs to make predictions for graph‑related tasks within GraphLLM paradigm, which even yields suboptimal results compared to conventional GNN‑based approaches. Through in‑depth analysis, we find this failure can be attributed to LLMs' limited capability for processing graph data and their tendency to overlook graph information. To address this issue, we propose LoReC (Look, Remember, and Contrast), a novel plug‑and‑play method for GraphLLM paradigm, which enhances LLM's understanding of graph data through three stages: (1) Look: redistributing attention to graph; (2) Remember: re‑injecting graph information into the Feed‑Forward Network (FFN); (3) Contrast: rectifying the vanilla logits produced in the decoding process. Extensive experiments demonstrate that LoReC brings notable improvements over current GraphLLM methods and outperforms GNN‑based approaches across diverse datasets. The implementation is available at https://github.com/Git‑King‑Zhan/LoReC.

PaperId: 1619, https://arxiv.org/pdf/2604.17863.pdf GitHub

Abstract:
Spinning flexible objects, exemplified by traditional Chinese handkerchief performances, demands periodic steady‑state motions under nonlinear dynamics with frictional contacts and boundary constraints. To address these challenges, we first design an intuitive dexterous wrist based on a parallel anti‑parallelogram tendon‑driven structure, which achieves 90 degrees omnidirectional rotation with low inertia and decoupled roll‑pitch sensing, and implement a high‑low level hierarchical control scheme. We then develop a particle‑spring model of the handkerchief for control‑oriented abstraction and strategy evaluation. Hardware experiments validate this framework, achieving an unfolding ratio of approximately 99% and fingertip tracking error of RMSE = 2.88 mm in high‑dynamic spinning. These results demonstrate that integrating control‑oriented modeling with a task‑tailored dexterous wrist enables robust rest‑to‑steady‑state transitions and precise periodic manipulation of highly flexible objects. More visualizations: https://slowly1113.github.io/icra2026‑handkerchief/

PaperId: 1620, https://arxiv.org/pdf/2604.17789.pdf GitHub

Abstract:
The MXFP4 microscaling format, which partitions tensors into blocks of 32 elements sharing an E8M0 scaling factor, has emerged as a promising substrate for efficient LLM inference, backed by native hardware support on NVIDIA Blackwell Tensor Cores. However, activation outliers pose a unique challenge under this format: a single outlier inflates the shared block scale, compressing the effective dynamic range of the remaining elements and causing significant quantization error. Existing rotation‑based remedies, including randomized Hadamard and learnable rotations, are data‑agnostic and therefore unable to specifically target the channels where outliers concentrate. We propose DuQuant++, which adapts the outlier‑aware fine‑grained rotation of DuQuant to the MXFP4 format by aligning the rotation block size with the microscaling group size (B=32). Because each MXFP4 group possesses an independent scaling factor, the cross‑block variance issue that necessitates dual rotations and a zigzag permutation in the original DuQuant becomes irrelevant, enabling DuQuant++ to replace the entire pipeline with a single outlier‑aware rotation, which halves the online rotation cost while simultaneously smoothing the weight distribution. Extensive experiments on the LLaMA‑3 family under MXFP4 W4A4 quantization show that DuQuant++ consistently achieves state‑of‑the‑art performance. Our code is available at https://github.com/Hsu1023/DuQuant‑v2.

PaperId: 1621, https://arxiv.org/pdf/2604.17769.pdf GitHub

Abstract:
Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high‑quality toxic data remains under‑explored. We propose Reverse Constitutional AI (R‑CAI), a framework for automated and controllable adversarial data generation that moves beyond isolated jailbreak prompts. By inverting a harmless constitution into a constitution of toxicity and iteratively refining model outputs through a critique‑‑revision pipeline, R‑CAI enables scalable synthesis of multi‑dimensional adversarial data without human annotation. Optimizing solely for toxicity‑related rewards, however, can lead to reward hacking and degraded semantic coherence. To address this challenge, we introduce probability clamping within reinforcement learning from AI feedback, which stabilizes adversarial optimization while preserving adversarial intent. Experiments demonstrate that R‑CAI generates diverse, high‑quality toxic data and that probability clamping substantially improves semantic coherence (15%) without sacrificing adversarial strength. Overall, R‑CAI provides a fully automated framework for red teaming data generation and systematic safety evaluation of aligned language models.

PaperId: 1622, https://arxiv.org/pdf/2604.17753.pdf GitHub

Abstract:
Merging multiple Low‑Rank Adaptation (LoRA) experts into a single backbone is a promising approach for efficient multi‑task deployment. While existing methods strive to alleviate interference via weight interpolation or subspace alignment, they rest upon the implicit assumption that all LoRA matrices contribute constructively to the merged model. In this paper, we uncover a critical bottleneck in current merging paradigms: the existence of negative modules ‑‑ specific LoRA layers that inherently degrade global performance upon merging. We propose Evolutionary Negative Module Pruning (ENMP), a plug‑and‑play LoRA pruning method to locate and exclude these detrimental modules prior to merging. By leveraging an evolutionary search strategy, ENMP effectively navigates the discrete, non‑differentiable landscape of module selection to identify optimal pruning configurations. Extensive evaluations demonstrate that ENMP consistently boosts the performance of existing merging algorithms, achieving a new state‑of‑the‑art across both language and vision domains. Code is available at https://github.com/CaoAnda/ENMP‑LoRAMerging.

PaperId: 1623, https://arxiv.org/pdf/2604.17714.pdf GitHub

Abstract:
LLM confidence signals are used for abstention, routing, and safety‑critical decisions. No standard practice exists for checking whether a confidence signal carries item‑level information before building on it. We transfer the validity screening principle from clinical personality assessment (PAI, MMPI‑3) as a portable protocol for benchmark‑based LLM confidence data. The protocol specifies three core indices (L, Fp, RBS), a structural indicator (TRIN), and an item‑sensitivity statistic, computed from a single 2x2 contingency table. A three‑tier classification system (Invalid, Indeterminate, Valid) draws on four clinical traditions. Validated on 20 frontier LLMs across 524 items, four models are classified Invalid, two Indeterminate. Valid‑profile models show mean r = .18 (15/16 significant). Invalid‑profile models show mean r = ‑.20 (d = 2.48). Cross‑benchmark validation on 18 models using MMLU with verbalized confidence and on external data from Yang et al. (2024) confirms the screen transfers across benchmarks and probe formats. All data and code: https://github.com/synthiumjp/validity‑scaling‑llm

PaperId: 1624, https://arxiv.org/pdf/2604.17707.pdf GitHub

Abstract:
Clinical personality assessment screens response validity before interpreting substantive scales. LLM evaluation does not. We apply the validity scaling framework from the PAI and MMPI‑3 to metacognitive probe data from 20 frontier models across 524 items. Six validity indices are operationalised: L (maintaining confidence on errors), K (betting on errors), F (withdrawing consensus‑endorsed items), Fp (withdrawing correct answers), RBS (inverted monitoring), and TRIN (fixed responding). A tiered classification system identifies four models as construct‑level invalid and two as elevated. Valid‑profile models produce item‑sensitive confidence (mean r = .18, 14 of 16 significant). Invalid‑profile models do not (mean r = ‑.20, d = 2.17, p = .001). Chain‑of‑thought training produces two opposite response distortions. Two latent dimensions account for 94.6% of index variance. Companion papers extract a portable screening protocol (Cacioli, 2026e) and validate it against selective prediction (Cacioli, 2026f). All data and code: https://github.com/synthiumjp/validity‑scaling‑llm

PaperId: 1625, https://arxiv.org/pdf/2604.17596.pdf GitHub

Abstract:
We release Terminal Wrench, a subset of 331 terminal‑agent benchmark environments, copied from the popular open benchmarks that are demonstrably reward‑hackable. The data set includes 3,632 hack trajectories and 2,352 legitimate baseline trajectories across three frontier models (Claude Opus 4.6, Gemini 3.1 Pro, GPT‑5.4). Each entry preserves the original task definition alongside full attack trajectories that show how the verifier was bypassed. It also includes cases where the task was not solved as intended. The tasks span system administration, machine learning, software engineering, and security challenges; the exploits range from simple output spoofing to stack‑frame introspection, standard‑library patching, and rootkit‑style binary hijacking. Crucially, these exploits are specific to each task, rather than the evaluation harness, making them harder to patch. We also present a monitorability study in which hack trajectories are sanitized or stripped of reasoning traces and then scored by an LLM judge, showing that detection degrades meaningfully when chain‑of‑thought is removed (AUC drops from 0.97 to 0.92). The data set is publicly available at https://github.com/few‑sh/terminal‑wrench.

PaperId: 1626, https://arxiv.org/pdf/2604.17584.pdf GitHub

Abstract:
Abstract visual reasoning remains challenging as existing methods often prioritize either global context or local row‑wise relations, failing to integrate both, and lack intermediate feature constraints, leading to incomplete rule capture and entangled representations. To address these issues, we propose the Dual‑Inference Rule‑Contrastive Reasoning (DIRCR) model. Its core component, the Dual‑Inference Reasoning Module, combines a local path for row‑wise analogical reasoning and a global path for holistic inference, integrated via a gated attention mechanism. Additionally, a Rule‑Contrastive Learning Module introduces pseudo‑labels to construct positive and negative rule samples, applying contrastive learning to enhance feature separability and promote abstract, transferable rule learning. Experimental results on three RAVEN datasets demonstrate that DIRCR significantly enhances reasoning robustness and generalization. Codes are available at https://github.com/csZack‑Zhang/DIRCR.

PaperId: 1627, https://arxiv.org/pdf/2604.17504.pdf GitHub

Abstract:
Reinforcement learning (RL) post‑training substantially improves remote sensing vision‑language models (RS‑VLMs). However, when handling complex remote sensing imagery (RSI) requiring exhaustive visual scanning, models tend to rely on localized salient cues for rapid inference. We term this RL‑induced bias "perceptual inertia". Driven by reward maximization, models favor quick outcome fitting, leading to two limitations: cognitively, overreliance on specific features impedes complete evidence construction; operationally, models struggle to flexibly shift visual focus across tasks. To address this bias and encourage comprehensive visual evidence mining, we propose RS‑HyRe‑R1, a hybrid reward framework for RSI understanding. It introduces: (1) a spatial reasoning activation reward that enforces structured visual reasoning; (2) a perception correctness reward that provides adaptive quality anchors across RS tasks, ensuring accurate geometric and semantic alignment; and (3) a visual‑semantic path evolution reward that penalizes repetitive reasoning and promotes exploration of complementary cues to build richer evidence chains. Experiments show RS‑HyRe‑R1 effectively mitigates "perceptual inertia", encouraging deeper, more diverse reasoning. With only 3B parameters, it achieves state‑of‑the‑art performance on REC, OVD, and VQA tasks, outperforming models up to 7B parameters. It also demonstrates strong zero‑shot generalization, surpassing the second‑best model by 3.16%, 3.97%, and 2.72% on VQA, OVD, and REC, respectively. Code and datasets are available at https://github.com/geox‑lab/RS‑HyRe‑R1.

PaperId: 1628, https://arxiv.org/pdf/2604.17503.pdf GitHub

Abstract:
Scaling vision‑language models into Visual Multiagent Systems (VMAS) is hindered by two coupled issues. First, communication topologies are fixed before inference, leaving them blind to visual content and query context; second, agent reasoning abilities remain static during deployment. These issues reinforce each other: a rigid topology fails to leverage richer agent expertise, while static agents lack incentives to specialize for a given query. We address this with SkillGraph, a joint framework that evolves both agent expertise and communication topology. Within this framework, a Multimodal Graph Transformer (MMGT) encodes visual tokens, instruction semantics and active skill embeddings to predict a query‑conditioned collaboration graph, replacing hand‑crafted routing with dynamic, content‑aware information flow. Complementing this, a Skill Designer distills and refines reasoning heuristics from failure cases, constructing a self‑evolving multimodal Skill Bank. Crucially, updated skill embeddings are fed back into the MMGT, enabling the topology to adapt alongside capability growth. Experiments show that SkillGraph achieves consistent improvements across four benchmarks, five common MAS structures and four base models. Code is available at https://github.com/niez233/skillgraph.

PaperId: 1629, https://arxiv.org/pdf/2604.17460.pdf GitHub

Abstract:
AI coding assistants have proliferated rapidly, yet structured pedagogical frameworks for learning these tools remain scarce. Developers face a gap between tool documentation and practical mastery, relying on fragmented resources such as blog posts, video tutorials, and trial‑and‑error. We present cc‑self‑train, a modular interactive curriculum for learning Claude Code, an agentic AI coding tool, through hands‑on project construction. The system introduces five contributions: (1) a persona progression model that adapts instructor tone across four stages (Guide, Collaborator, Peer, Launcher), operationalizing Gradual Release of Responsibility for AI‑mediated instruction; (2) an adaptive learning system that observes engagement quality through hook‑based heuristics and adjusts scaffolding at two timescales, using streak detection for mid‑module intervention and aggregate metrics for module‑boundary persona changes; (3) a cross‑domain unified curriculum in which five distinct project domains share identical feature sequencing, enabling transfer learning; (4) a step‑pacing mechanism with explicit pause primitives to manage information overload in an AI‑as‑instructor context; and (5) an auto‑updating curriculum design in which the onboarding agent detects upstream tool changes and updates teaching materials before instruction begins. A parametrized test suite enforces structural consistency as a proxy for pedagogical invariants across all 50 modules. A pilot evaluation with 27 participants shows statistically significant reported self‑efficacy gains across all 10 assessed skill areas (p < 0.001), with the largest effects on advanced features such as hooks and custom skills. We discuss implications for the design of auto‑updating educational systems.

PaperId: 1630, https://arxiv.org/pdf/2604.17458.pdf GitHub

Abstract:
Graph‑based Retrieval‑Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi‑hop reasoning. While recent lightweight approaches reduce indexing costs by leveraging Named Entity Recognition (NER), they rely strictly on structural co‑occurrence, failing to capture latent semantic connections between disjoint entities. To address this, we propose EHRAG, a lightweight RAG framework that constructs a hypergraph capturing both structure and semantic level relationships, employing a hybrid structural‑semantic retrieval mechanism. Specifically, EHRAG constructs structural hyperedges based on sentence‑level co‑occurrence with lightweight entity extraction and semantic hyperedges by clustering entity text embeddings, ensuring the hypergraph encompasses both structural and semantic information. For retrieval, EHRAG performs a structure‑semantic hybrid diffusion with topic‑aware scoring and personalized pagerank (PPR) refinement to identify the top‑k relevant documents. Experiments on four datasets show that EHRAG outperforms state‑of‑the‑art baselines while maintaining linear indexing complexity and zero token consumption for construction. Code is available at https://github.com/yfsong00/EHRAG.

PaperId: 1631, https://arxiv.org/pdf/2604.17435.pdf GitHub

Abstract:
Recent Speech‑to‑Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non‑verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely limits real‑world utility. We address this via three contributions. First, we propose a synthesis pipeline for building scalable expressive datasets to overcome the data scarcity limitation. Second, we propose MoVE, a Mixture‑of‑LoRA‑Experts architecture with expressive‑specialized adapters and a soft‑weighting router that blends experts for capturing hybrid expressive states. Third, we show pretrained AudioLLMs enable striking data efficiency: 30 minutes of curated data is enough for strong performance. On English‑Chinese S2ST, while comparing with strong baselines, MoVE reproduces target NVs in 76% of cases and achieves the highest human‑rated naturalness and emotional fidelity among all compared systems, where existing S2ST systems preserve at most 14% of NVs.

PaperId: 1632, https://arxiv.org/pdf/2604.17420.pdf GitHub

Abstract:
Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled by the lack of realistic benchmarks. Existing transaction‑graph datasets suffer from two pervasive limitations: (i) they provide sparse node‑level semantics beyond anonymized identifiers, and (ii) they rely on template‑driven anomaly injection, which biases benchmarks toward static structural motifs and yields overly optimistic assessments of model robustness. We propose TransXion, a benchmark ecosystem for Anti‑Money Laundering (AML) research that integrates profile‑aware simulation of normal activity with stochastic, non‑template synthesis of illicit subgraphs.TransXion jointly models persistent entity profiles and conditional transaction behavior, enabling evaluation of "out‑of‑character" anomalies where observed activity contradicts an entity's socio‑economic context. The resulting dataset comprises approximately 3 million transactions among 50,000 entities, each endowed with rich demographic and behavioral attributes. Empirical analyses show that TransXion reproduces key structural properties of payment networks, including heavy‑tailed activity distributions and localized subgraph structure. Across a diverse array of detection models spanning multiple algorithmic paradigms, TransXion yields substantially lower detection performance than widely used benchmarks, demonstrating increased difficulty and realism. TransXion provides a more faithful testbed for developing context‑aware and robust AML detection methods. The dataset and code are publicly available at https://github.com/chaos‑max/TransXion.

PaperId: 1633, https://arxiv.org/pdf/2604.17406.pdf GitHub

Abstract:
The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is inherently iterative, existing agent frameworks are predominantly static, narrowly scoped, and lack the capacity to learn from trial and error. To bridge this gap, we present EvoMaster, a foundational evolving agent framework engineered specifically for Agentic Science at Scale. Driven by the core principle of continuous self‑evolution, EvoMaster empowers agents to iteratively refine hypotheses, self‑critique, and progressively accumulate knowledge across experimental cycles, faithfully mirroring human scientific inquiry. Crucially, as a domain‑agnostic base harness, EvoMaster is exceptionally easy to scale up ‑‑ enabling developers to build and deploy highly capable, self‑evolving scientific agents for arbitrary disciplines in approximately 100 lines of code. Built upon EvoMaster, we incubated the SciMaster ecosystem across domains such as machine learning, physics, and general science. Evaluations on four authoritative benchmarks (Humanity's Last Exam, MLE‑Bench Lite, BrowseComp, and FrontierScience) demonstrate that EvoMaster achieves state‑of‑the‑art scores of 41.1%, 75.8%, 73.3%, and 53.3%, respectively. It comprehensively outperforms the general‑purpose baseline OpenClaw with relative improvements ranging from +159% to +316%, robustly validating its efficacy and generality as the premier foundational framework for the next generation of autonomous scientific discovery. EvoMaster is available at https://github.com/sjtu‑sai‑agents/EvoMaster.

PaperId: 1634, https://arxiv.org/pdf/2604.17405.pdf GitHub

Abstract:
Multi‑hop question answering (MHQA) enables accurate answers to complex queries by retrieving and reasoning over evidence dispersed across multiple documents. Existing MHQA approaches mainly rely on iterative retrieval‑augmented generation, which suffer from the following two major issues. 1) Existing methods prematurely commit to surface‑level entities rather than underlying reasoning structures, making question decomposition highly vulnerable to lexical ambiguity. 2) Existing methods overlook the logical dependencies among reasoning steps, resulting in uncoordinated execution. To address these issues, we propose STRIDE, a framework that separates strategic planning, dynamic control, and grounded execution. At its core, a Meta‑Planner first constructs an entity‑agnostic reasoning skeleton to capture the abstract logic of the query, thereby deferring entity grounding until after the reasoning structure is established, which mitigates disambiguation errors caused by premature lexical commitment. A Supervisor then orchestrates sub‑question execution in a dependency‑aware manner, enabling efficient parallelization where possible and sequential coordination when necessary. By dynamically deciding whether to retrieve new evidence or infer from existing facts, it avoids redundant queries and error propagation, while fusing cross‑branch information and reformulating failed queries to enhance robustness. Grounded fact extraction and logical inference are delegated to specialized execution modules, ensuring faithfulness through explicit separation of retrieval and reasoning. We further propose STRIDE‑FT, a modular fine‑tuning framework that uses self‑generated execution trajectories from STRIDE, requiring neither human annotations nor stronger teacher models. Experiments show that STRIDE achieves robust and accurate reasoning, while STRIDE‑FT effectively enhances open‑source LLMs.

PaperId: 1635, https://arxiv.org/pdf/2604.17388.pdf GitHub

Abstract:
We introduce JuRe (Just Repair), a minimal denoising network for time series anomaly detection that exposes a central finding: architectural complexity is unnecessary when the training objective correctly implements the manifold‑projection principle. JuRe consists of a single depthwise‑separable convolutional residual block with hidden dimension 128, trained to repair corrupted time series windows and scored at inference by a fixed, parameter‑free structural discrepancy function. Despite using no attention, no latent variable, and no adversarial component, JuRe ranks second on the TSB‑AD multivariate benchmark (AUC‑PR 0.404, 180 series, 17 datasets) and second on the UCR univariate archive by AUC‑PR (0.198, 250 series), leading all neural baselines on AUC‑PR and VUS‑PR. Component ablation on TSB‑AD identifies training‑time corruption as the dominant factor (ΔAUC‑PR = 0.047 on removal), confirming that the denoising objective, not network capacity, drives detection quality. Pairwise Wilcoxon signed‑rank tests establish statistical significance against 21 of 25 baselines on TSB‑AD. Code is available at the URL https://github.com/iis‑esslingen/JuRe.

PaperId: 1636, https://arxiv.org/pdf/2604.17351.pdf GitHub

Abstract:
Automated simulator construction requires distributional fidelity, distinguishing it from generic code generation. We identify two failure modes in long‑horizon LLM agents: contextual drift and optimization instability arising from conflating structural and parametric errors. We propose SOCIA‑EVO, a dual‑anchored evolutionary framework. SOCIA‑EVO introduces: (1) a static blueprint to enforce empirical constraints; (2) a bi‑level optimization to decouple structural refinement from parameter calibration; and (3) a self‑curating Strategy Playbook that manages remedial hypotheses via Bayesian‑weighted retrieval. By falsifying ineffective strategies through execution feedback, SOCIA‑EVO achieves robust convergence, generating simulators that are statistically consistent with observational data. The code and data of SOCIA‑EVO are available here: https://github.com/cruiseresearchgroup/SOCIA/tree/evo.

PaperId: 1637, https://arxiv.org/pdf/2604.17295.pdf GitHub

Abstract:
Comprehensive understanding of time series remains a significant challenge for Large Language Models (LLMs). Current research is hindered by fragmented task definitions and benchmarks with inherent ambiguities, precluding rigorous evaluation and the development of unified Time Series Reasoning Models(TSRMs). To bridge this gap, we formalize Time Series Reasoning (TSR) via a four‑level taxonomy of increasing cognitive complexity. We introduce HiTSR, a hierarchical time series reasoning dataset comprising 83k samples with diverse task combinations and verified Chain‑of‑Thought (CoT) trajectories. Leveraging HiTSR, we propose LLaTiSA, a strong TSRM that integrates visualized patterns with precision‑calibrated numerical tables to enhance the temporal perception of Vision‑Language Models (VLMs). Through a multi‑stage curriculum fine‑tuning strategy, LLaTiSA achieves superior performance and exhibits robust out‑of‑distribution generalization across diverse TSR tasks and real‑world scenarios. Our code is available at https://github.com/RainingNovember/LLaTiSA.

PaperId: 1638, https://arxiv.org/pdf/2604.17274.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ensuring their reliability in practical deployment necessitates robust confidence estimation. Prior works have predominantly focused on text‑only LLMs, often relying on computationally expensive self‑consistency sampling. In this paper, we extend this to multimodal settings and conduct a comprehensive evaluation of MLLMs' response confidence estimation. Our analysis reveals a significant instinct‑reflection misalignment: the model's implicit token‑level support frequently diverges from its verbal self‑assessment confidence. To address this misalignment, we propose a monotone confidence fusion framework to merge dual‑channel signals and cross‑channel consistency to estimate correctness. Subsequently, an order‑preserving mean alignment step is applied to correct global bias, which improves calibration while preserving the risk‑coverage trade‑off for selective prediction. Experiments on diverse open‑source and closed‑source MLLMs show that our method consistently yields more reliable confidence estimates and improves both calibration and failure prediction. Code will be available at https://github.com/Yunkaidang/Instinct‑vs.‑Reflection.

PaperId: 1639, https://arxiv.org/pdf/2604.17268.pdf GitHub

Abstract:
AI‑generated imagery has reached near‑photorealistic fidelity, yet this technology poses significant threats to information security and societal trust. Existing deepfake detection methods often exhibit limited robustness in open‑world scenarios. To address this limitation, this paper investigates intrinsic discrepancies between synthetic and authentic images from a signal‑level perspective. Our analysis reveals that low‑correlation signals serve as distinctive markers for differentiating AI‑generated imagery from real photographs. Building on this insight, we introduce a novel method for quantifying these signals based on fractal theory. By analyzing the fractal characteristics of low‑correlation signals, our method effectively captures the subtle statistical anomalies inherent to the synthesis process. Extensive experimental results demonstrate the method's robustness and superior detection performance. This work emphasizes the need to shift research focus to a new signal‑level direction for deepfake detection. Theoretically, this proposed approach is not limited to face image identification but can be applied to all AI‑generated image detection tasks. This study provides a new research direction for deepfake detection.

PaperId: 1640, https://arxiv.org/pdf/2604.17252.pdf GitHub

Abstract:
Recent advancements in large language models (LLMs) have enabled agents to tackle complex embodied tasks through environmental interaction. However, these agents still make suboptimal decisions and perform ineffective actions, as they often overlook critical environmental feedback that differs from their internal beliefs. Through a formal probing analysis, we characterize this as belief inertia, a phenomenon where agents stubbornly adhere to prior beliefs despite explicit observations. To address this, we advocate active belief intervention, moving from passive understanding to active management. We introduce the Estimate‑Verify‑Update (EVU) mechanism, which empowers agents to predict expected outcomes, verify them against observations through explicit reasoning, and actively update prior beliefs based on the verification evidence. EVU is designed as a unified intervention mechanism that generates textual belief states explicitly, and can be integrated into both prompting‑based and training‑based agent reasoning methods. Extensive experiments across three embodied benchmarks demonstrate that EVU consistently yields substantial gains in task success rates. Further analyses validate that our approach effectively mitigates belief inertia, advancing the development of more robust embodied agents. Our code is available at https://github.com/WangHanLinHenry/EVU.

PaperId: 1641, https://arxiv.org/pdf/2604.17244.pdf GitHub

Abstract:
Despite the rapid progress, LLMs for sequential decision‑making (i.e., LLM agents) still struggle to produce diverse outputs. This leads to insufficient exploration, convergence to sub‑optimal solutions, and becoming stuck in loops. Such limitations can be problematic in environments that require active exploration to gather information and make decisions. Sampling methods such as temperature scaling introduce token‑level randomness but fail to produce enough diversity at the sequence level. We analyze LLM exploration in the classic Multi‑Armed Bandit (MAB) setting and the Text Adventure Learning Environment Suite (TALES). We find that current decoding strategies and prompting methods like Chain‑of‑Thought and Tree‑of‑Thought are insufficient for robust exploration. To address this, we introduce DORA Explorer (Diversity‑Oriented Ranking of Actions), a training‑free framework for improving exploration in LLM agents. DORA generates diverse action candidates, scores them using token log‑probabilities, and selects actions using a tunable exploration parameter. DORA achieves UCB‑competitive performance on MAB and consistent gains across TALES, e.g., improving Qwen2.5‑7B's performance from 29.2% to 45.5% in TextWorld. Our project is available at: https://dora‑explore.github.io/.

PaperId: 1642, https://arxiv.org/pdf/2604.17208.pdf GitHub

Abstract:
Digital subtraction angiography (DSA) in coronary imaging is fundamentally challenged by physiological motion, forcing reliance on raw angiograms cluttered with anatomical noise. Existing deep learning methods often produced images with two critical clinically unacceptable flaws: persistent boundary artifacts and a loss of native tissue grayscale fidelity that undermined diagnostic confidence. We propose a novel framework termed as CDSA‑Net that for the first time explicitly decouples and jointly optimizes vascular structure preservation and realistic background restoration. CDSA‑Net introduces two core innovations: (i) A hierarchical geometric prior guidance (HGPG) mechanism, embedded in our coronary structure extraction network (CSENet). It synergistically combines integrated geometric prior (IGP) with gated spatial modulation (GSM) and centerline‑aware topology (CAT) loss supervision, ensuring structural continuity. (ii) An adaptive noise module (ANM) within our coronary background restoration network (CBResNet). Unlike standard restoration, ANM uniquely models the stochastic nature of clinical X‑ray noise, bridging the domain gap to enable seamless background intensity estimation and the complete elimination of boundary artifacts. The final subtraction is obtained by removing the restored background from the raw angiogram. Quantitatively, it significantly outperformed state‑of‑the‑art methods in vascular intensity correlation and perceptual quality. A 25.6% improvement in morphology assessment efficiency and a 42.9% gain in hemodynamic evaluation speed set a new benchmark for utility in interventional cardiology, while maintaining diagnostic results consistent with raw angiograms. The project code is available at https://github.com/DrThink‑ai/CDSA‑Net.

PaperId: 1643, https://arxiv.org/pdf/2604.17186.pdf GitHub GitHub

Abstract:
As Artificial Intelligence (AI) and Agentic AI become increasingly integrated across sectors such as education and healthcare, it is critical to ensure that Multi‑Agent Education System (MAES) is explainable from the early stages of requirements engineering (RE) within the AI software development lifecycle. Explainability is essential to build trust, promote transparency, and enable effective human‑AI collaboration. Although personas are well‑established in human‑computer interaction to represent users and capture their needs and behaviors, their role in RE for explainable MAES remains underexplored. This paper proposes a human‑first, persona‑driven, explainable MAES RE framework and demonstrates the framework through a MAES for clinical reasoning training. The framework integrates personas and user stories throughout the RE process to capture the needs, goals, and interactions of various stakeholders, including medical educators, medical students, AI patient agent, and clinical agents (physical exam agent, diagnostic agent, clinical intervention agent, supervisor agent, evaluation agent). The goals, underlying models, and knowledge base shape agent interactions and inform explainability requirements that guided the clinical reasoning training of medical students. A post‑usage survey found that more than 78% of medical students reported that MAES improved their clinical reasoning skills. These findings demonstrate that RE based on persona effectively connects technical requirements with non‑technical medical students from a human‑centered approach, ensuring that explainable MAES are trustworthy, interpretable, and aligned with authentic clinical scenarios from the early stages of the AI system engineering. The partial MAES for the clinical scenario simulator is~\hrefhttps://github.com/2sigmaEdTech/MAS/open sourced here.

PaperId: 1644, https://arxiv.org/pdf/2604.17148.pdf GitHub

Abstract:
With an ever‑growing zoo of LLMs and benchmarks, the need to orchestrate multiple models for improved task performance has never been more pressing. While frameworks like Mixture‑of‑Agents (MoA) attempt to coordinate LLMs, they often fall short in terms of (1) selecting relevant agents, (2) facilitating effective intra‑agent communication, and (3) integrating responses efficiently. In this work, we propose Graph‑of‑Agents (GoA), a new graph‑based framework for modeling multi‑agent LLM communication. Our approach begins with node sampling, selecting only the most relevant agents by leveraging model cards that summarize each model's domain, task specialization, and other characteristics. Next, we construct edges between the selected agents by evaluating their responses against one another to determine relevance ordering. Directed message passing is then performed from highly relevant agents to less relevant ones to enhance their responses, followed by reverse message passing to refine the original responses of the more relevant agents. Finally, the updated responses are aggregated via graph‑based pooling (e.g., max or mean pooling) to produce a single, unified answer. We evaluate GoA on diverse multi‑domain benchmarks (MMLU, MMLU‑Pro, GPQA) and domain‑specific benchmarks (MATH, HumanEval, MedMCQA), with an agent pool of 6 LLMs spanning multiple domains. Surprisingly, GoA achieves superior performance using only 3 selected agents, outperforming recent multi‑agent LLM baselines that utilize all 6 agents simultaneously. By adopting a graph structure, GoA offers both scalability and effectiveness through structured message passing‑positioning it as a strong candidate for navigating the challenges of the ever‑growing LLM zoo. Code is available at: https://github.com/UNITES‑Lab/GoA.

PaperId: 1645, https://arxiv.org/pdf/2604.17085.pdf GitHub

Abstract:
The interpretation of implicit meanings is an integral aspect of human communication. However, this framework may not transfer to interactions with Large Language Models (LLMs). To investigate this, we introduce the task of Implicit Information Extraction (IIE) and propose an LLM‑based IIE pipeline that builds a structured knowledge graph from a context sentence by extracting relational triplets, validating implicit inferences, and analyzing temporal relations. We evaluate two LLMs against crowdsourced human judgments on two datasets. We find that humans agree with most model triplets yet consistently propose many additions, indicating limited coverage in current LLM‑based IIE. Moreover, in our experiments, models appear to be more conservative about implicit inferences than humans in socially rich contexts, whereas humans become more conservative in shorter, fact‑oriented contexts. Our code is available at https://github.com/Antonio‑Dee/IIE_from_LLM.

PaperId: 1646, https://arxiv.org/pdf/2604.17078.pdf GitHub GitHub

Abstract:
Task arithmetic provides an efficient, training‑free way to edit pre‑trained models, yet lacks a fundamental theoretical explanation for its success. The existing concept of ``weight disentanglement" describes the ideal outcome of non‑interfering task composition but does not reveal its underlying cause. Crucially, what intrinsic properties of the pre‑trained model (θ_0) or the task vectors (τ_t) enable this disentanglement remains underexplored. In this paper, we introduce Task‑Feature Specialization (TFS), a model's ability to allocate distinct internal features to different tasks, as the fundamental principle. We first prove that TFS is a sufficient condition for weight disentanglement. More importantly, we find that TFS also gives rise to an observable geometric consequence: weight vector orthogonality. This positions TFS as the common cause for both the desired functional outcome (disentanglement) and a measurable geometric property (orthogonality). This relationship provides the key insight for our method: since the abstract TFS property is intractable to enforce directly, we can instead promote weight disentanglement by shaping its concrete geometric consequence, orthogonality. Therefore, we propose OrthoReg, a simple and effective regularization method that actively enforces an internal orthogonal structure on weight updates (ΔW) that constitute τ_t during fine‑tuning. And we theoretically prove that OrthoReg promotes disentanglement. Extensive experiments demonstrate that OrthoReg consistently and significantly enhances the performance of various task arithmetic methods. Code is available at \hrefhttps://github.com/RL‑MIND/OrthoReghttps://github.com/RL‑MIND/OrthoReg.

PaperId: 1647, https://arxiv.org/pdf/2604.17054.pdf GitHub GitHub

Abstract:
Scalable Vector Graphics (SVGs) function both as visual images and as structured code that encode rich geometric and layout information, yet most methods rasterize them and discard this symbolic organization. At the same time, recent sentence embedding methods produce strong text representations but do not naturally extend to visual or structured modalities. We propose a training‑free, instruction‑guided multimodal embedding framework that uses a Multimodal Large Language Model (MLLM) to map text, raster images, and SVG code into an aligned embedding space. We control the direction of embeddings through modality‑specific instructions and structural SVG cues, eliminating the need for learned projection heads or contrastive training. Our method has two key components: (1) Multimodal Explicit One‑word Limitation (mEOL), which instructs the MLLM to summarize any multimodal input into a single token whose hidden state serves as a compact semantic embedding. (2) A semantic SVG rewriting module that assigns meaningful identifiers and simplifies nested SVG elements through visual reasoning over the rendered image, exposing geometric and relational cues hidden in raw code. Using a repurposed VGBench, we build the first text‑to‑SVG retrieval benchmark and show that our training‑free embeddings outperform encoder‑based and training‑based multimodal baselines. These results highlight prompt‑level control as an effective alternative to parameter‑level training for structure‑aware multimodal retrieval. Project page: https://scene‑the‑ella.github.io/meol/

PaperId: 1648, https://arxiv.org/pdf/2604.17025.pdf GitHub

Abstract:
Large Language Models produce a controllability gap in safety‑critical engineering: even low rates of undetected constraint violations render a system undeployable. Current orchestration paradigms suffer from sycophantic compliance, context attention decay, and stochastic oscillation during self‑correction. We introduce the Convergent AI Agent Framework (CAAF), which transitions agentic workflows from open‑loop generation to closed‑loop fail‑safe determinism via three pillars: (1) Recursive Atomic Decomposition with physical context firewalls; (2) Harness as an Asset, formalizing domain invariants into machine‑readable registries enforced by a deterministic Unified Assertion Interface; and (3) Structured Semantic Gradients with State Locking for monotonic non‑regression. This paper makes two core claims. First, an industrialization thesis: once domain invariants are formalized as an executable Harness, the Harness itself becomes a first‑class enterprise asset that compounds in value as foundation models commoditize, and CAAF's ability to deliver its reliability on commodity‑tier models makes fully self‑hosted, on‑premises deployment architecturally feasible for regulated sectors where cloud APIs are not an option. Second, an architectural claim supported by ablation: CAAF's three pillars address complementary failure surfaces and none alone closes the controllability gap at commodity cost. The paper contributes entirely at the orchestration and industrialization layer. Evidence across two complementary benchmarks, three‑tier UAI ablations, multi‑agent baselines, and a closed‑source commodity family replicated by two independent open‑weight families, is reported in the body.

PaperId: 1649, https://arxiv.org/pdf/2604.16936.pdf GitHub

Abstract:
Feature reconstruction techniques are widely applied for few‑shot fine‑grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature‑based FSFGIC methods is how to choose the size of the receptive field to extract feature descriptors (including spatial and frequency feature descriptors) from different category input images, thereby better performing the FSFGIC tasks. To address this, an adaptive receptive field‑based spatial‑frequency feature reconstruction network (ARF‑SFR‑Net) is proposed. The designed ARF‑SFR‑Net has the capability to adaptively determine receptive field sizes for obtaining spatial and frequency features, and effectively fuse them for reconstruction and FSFGIC tasks. The designed ARF‑SFR‑Net can be easily embedded into a given episodic training mechanism for end‑to‑end training from scratch. Extensive experiments on multiple FSFGIC benchmarks demonstrate the effectiveness and superiority of the proposed ARF‑SFR‑Net over state‑of‑the‑art approaches. The code is available at: https://github.com/ICL‑SUST/ARF‑SFR‑Net.git.

PaperId: 1650, https://arxiv.org/pdf/2604.16919.pdf GitHub

Abstract:
Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization‑based methods can fast solve IPs using DMs as powerful regularizers, but they are susceptible to local minima and noise overfitting. Although DMs can provide strong priors for Bayesian approaches, enforcing measurement consistency during the denoising process leads to manifold infeasibility issues. We propose Noise‑space Hamiltonian Monte Carlo (N‑HMC), a posterior sampling method that treats reverse diffusion as a deterministic mapping from initial noise to clean images. N‑HMC enables comprehensive exploration of the solution space, avoiding local optima. By moving inference entirely into the initial‑noise space, N‑HMC keeps proposals on the learned data manifold. We provide a comprehensive theoretical analysis of our approach and extend the framework to a noise‑adaptive variant (NA‑NHMC) that effectively handles IPs with unknown noise type and level. Extensive experiments across four linear and three nonlinear inverse problems demonstrate that NA‑NHMC achieves superior reconstruction quality with robust performance across different hyperparameters and initializations, significantly outperforming recent state‑of‑the‑art methods. The code is available at https://github.com/NA‑HMC/NA‑HMC.

PaperId: 1651, https://arxiv.org/pdf/2604.16913.pdf GitHub

Abstract:
Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge‑native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference‑time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel‑Bench, an 840‑inference empirical framework executing a strict intra‑model ablation on Qwen‑3.5‑9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference‑time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute‑accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non‑Convergence (cognitive collapse) rate. This collapse degraded trial‑to‑trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured "Reasoning‑Induced Sycophancy," where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge‑native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel‑bench

PaperId: 1652, https://arxiv.org/pdf/2604.16902.pdf GitHub

Abstract:
Native Omni‑modal Large Language Models (OLLMs) have shifted from pipeline architectures to unified representation spaces. However, this native integration gives rise to a critical yet underexplored phenomenon: modality preference. To bridge this gap, we first systematically quantify modality preference of OLLMs using a newly‑curated conflict‑based benchmark and the modality selection rate metric. Our evaluation of ten representative OLLMs reveals a notable paradigm shift: unlike the ``text‑dominance'' of traditional VLMs, most OLLMs exhibit a pronounced visual preference. To further understand the underlying mechanism, we conduct layer‑wise probing and demonstrate that such modality preference is not static but emerges progressively in the mid‑to‑late layers. Building upon these insights, we leverage these internal signals to diagnose cross‑modal hallucinations, achieving competitive performance across three downstream multi‑modal benchmarks without task‑specific data. Our work provides both a mechanistic understanding and a practical tool for building more trustworthy OLLMs. Our code and related resources are publicly available at: https://github.com/icip‑cas/OmniPreference

PaperId: 1653, https://arxiv.org/pdf/2604.16859.pdf GitHub

Abstract:
Accurate traffic forecasting is crucial for intelligent transportation systems, supporting effective traffic management, congestion reduction, and informed urban planning. However, traditional models often fail to adequately capture the intricate spatio‑temporal dependencies present in traffic data. To overcome these limitations, we introduce GAMMA‑Net, a novel approach that integrates Graph Attention Networks (GAT) with multi‑axis Selective State Space Models (Mamba). The GAT component uses a self‑attention mechanism to dynamically adjust the influence of nodes within the traffic network, enabling adaptive spatial dependency modeling based on real‑time conditions. Simultaneously, the Mamba module efficiently models long‑term temporal and spatial dynamics without the heavy computational cost of conventional recurrent architectures. Extensive experiments on several benchmark traffic datasets, including METR‑LA, PEMS‑BAY, PEMS03, PEMS04, PEMS07, and PEMS08, show that GAMMA‑Net consistently outperforms existing state‑of‑the‑art models across different prediction horizons, achieving up to a 16.25% reduction in Mean Absolute Error (MAE) compared to baseline models. Ablation studies highlight the critical contributions of both the spatial and temporal components, emphasizing their complementary role in improving prediction accuracy. In conclusion, the GAMMA‑Net model sets a new standard in traffic forecasting, offering a powerful tool for next‑generation traffic management and urban planning. The code for this study is available at https://github.com/hdy6438/GAMMA‑Net

PaperId: 1654, https://arxiv.org/pdf/2604.16836.pdf GitHub

Abstract:
Semantic segmentation in hyperbolic space enables compact modeling of hierarchical structure while providing inherent uncertainty quantification. Prior approaches predominantly rely on the Poincaré ball model, which suffers from numerical instability, optimization, and computational challenges. We propose a novel, tractable, architecture‑agnostic semantic segmentation framework (pixel‑wise and mask classification) in the hyperbolic Lorentz model. We employ text embeddings with semantic and visual cues to guide hierarchical pixel‑level representations in Lorentz space. This enables stable and efficient optimization without requiring a Riemannian optimizer, and easily integrates with existing Euclidean architectures. Beyond segmentation, our approach yields free uncertainty estimation, confidence map, boundary delineation, hierarchical and text‑based retrieval, and zero‑shot performance, reaching generalized flatter minima. We introduce a novel uncertainty and confidence indicator in Lorentz cone embeddings. Further, we provide analytical and empirical insights into Lorentz optimization via gradient analysis. Extensive experiments on ADE20K, COCO‑Stuff‑164k, Pascal‑VOC, and Cityscapes, utilizing state‑of‑the‑art per‑pixel classification models (DeepLabV3 and SegFormer) and mask classification models (mask2former and maskformer), validate the effectiveness and generality of our approach. Our results demonstrate the potential of hyperbolic Lorentz embeddings for robust and uncertainty‑aware semantic segmentation. Code is available at https://github.com/mxahan/Lorentz_semantic_segmentation.

PaperId: 1655, https://arxiv.org/pdf/2604.16830.pdf GitHub

Abstract:
On‑policy distillation (OPD) is an increasingly important paradigm for post‑training language models. However, we identify a pervasive Scaling Law of Miscalibration: while OPD effectively improves task accuracy, it systematically traps models in severe overconfidence. We trace this failure to an information mismatch: teacher supervision is formed under privileged context available during training, whereas the deployed model must report confidence using only deployment‑time information. We formalize this perspective theoretically, showing that teacher‑conditioned success is generally not a valid target for deployment‑time confidence and that helpful privileged context induces entropy collapse and a systematic optimism bias. To address this, we propose a calibration‑aware OPD framework, CaOPD, that estimates empirical confidence from model rollouts, replaces self‑reported confidence with this student‑grounded target, and distills the revised response through the same self‑distillation pipeline. Experiments across various models and domains show that CaOPD achieves Pareto‑optimal calibration while maintaining competitive capability, generalizing robustly under out‑of‑distribution and continual learning. Our findings highlight that capability distillation does not imply calibrated confidence, and that confidence should be treated as an essential objective in post‑training. Code: https://github.com/SalesforceAIResearch/CaOPD

PaperId: 1656, https://arxiv.org/pdf/2604.16817.pdf GitHub

Abstract:
Imbalanced data are commonly present in real‑world applications. While data synthesis can effectively mitigate data scarcity for rare classes, and LLMs have revolutionized text generation, the application of LLMs to the synthesis of relational/structured tabular data remains underexplored. Moreover, existing approaches lack an effective feedback mechanism to guide LLMs in continuously optimizing the quality of the generated data throughout the synthesis process. In this work, we propose RDDG, Relational Data generator with Dynamic Guidance, which is a unified in‑context learning framework that employs progressive chain‑of‑thought (CoT) steps to generate tabular data for enhancing downstream imbalanced classification performance. RDDG first uses core set selection to identify representative samples from the original data, then utilizes in‑context learning to discover the inherent patterns and correlations among attributes within the core set, and subsequently generates tabular data while preserving the aforementioned constraints. More importantly, it incorporates a self‑reinforcing feedback mechanism that provides automatic assessments of the quality of the generated data, enabling continuous quality optimization throughout the generation process. Experimental results on multiple real and synthetic datasets demonstrate that RDDG outperforms existing approaches in both data fidelity and downstream imbalanced classification performance. We make our code available at https://github.com/cszhangLMU/RDDG.

PaperId: 1657, https://arxiv.org/pdf/2604.16776.pdf GitHub

Abstract:
Modeling single‑cell gene expression across diverse biological and technical conditions is crucial for characterizing cellular states and simulating unseen scenarios. Existing methods often treat genes as independent tokens, overlooking their high‑level biological relationships and leading to poor performance. We introduce SAVE, a unified generative framework based on conditional Transformers for multi‑condition single‑cell modeling. SAVE leverages a coarse‑grained representation by grouping semantically related genes into blocks, capturing higher‑order dependencies among gene modules. A Flow Matching mechanism and condition‑masking strategy further enhance flexible simulation and enable generalization to unseen condition combinations. We evaluate SAVE on a range of benchmarks, including conditional generation, batch effect correction, and perturbation prediction. SAVE consistently outperforms state‑of‑the‑art methods in generation fidelity and extrapolative generalization, especially in low‑resource or combinatorially held‑out settings. Overall, SAVE offers a scalable and generalizable solution for modeling complex single‑cell data, with broad utility in virtual cell synthesis and biological interpretation. Our code is publicly available at https://github.com/fdu‑wangfeilab/sc‑save

PaperId: 1658, https://arxiv.org/pdf/2604.16742.pdf GitHub

Abstract:
Scientists have long sought to accurately predict outcomes of real‑world events before they happen. Can AI systems do so more reliably? We study this question through clinical trial outcome prediction, a high‑stakes open challenge even for domain experts. We introduce CT Open, an open‑access, live platform that will run four challenge every year. Anyone can submit predictions for each challenge. CT Open evaluates those submissions on trials whose outcomes were not yet public at the time of submission but were made public afterwards. Determining if a trial's outcome is public on the internet before a certain date is surprisingly difficult. Outcomes posted on official registries may lag behind by years, while the first mention may appear in obscure articles. To address this, we propose a novel, fully automated decontamination pipeline that uses iterative LLM‑powered web search to identify the earliest mention of trial outcomes. We validate the pipeline's quality and accuracy by human expert's annotations. Since CT Open's pipeline ensures that every evaluated trial had no publicly reported outcome when the prediction was made, it allows participants to use any methodology and any data source. In this paper, we release a training set and two time‑stamped test benchmarks, Winter 2025 and Summer 2025. We believe CT Open can serve as a central hub for advancing AI research on forecasting real‑world outcomes before they occur, while also informing biomedical research and improving clinical trial design. CT Open Platform is hosted at \hrefhttps://ct‑open.net/https://ct‑open.net/

PaperId: 1659, https://arxiv.org/pdf/2604.16706.pdf GitHub GitHub

Abstract:
Automated evaluation of tool‑using large language model (LLM) agents is widely assumed to be reliable, but this assumption has rarely been validated against human annotation. We introduce AgentProp‑Bench, a 2,000‑task benchmark with 2,300 traces across four domains, nine production LLMs, and a 100‑label human‑validated subset. We quantify judge reliability, characterize error propagation, and evaluate a runtime mitigation. Substring‑based judging agrees with human annotation at kappa=0.049 (chance‑level); a three‑LLM ensemble reaches kappa=0.432 (moderate) with a conservative bias. Under validated evaluation, a parameter‑level injection propagates to a wrong final answer with human‑calibrated probability approximately 0.62 (range 0.46‑0.73 across models). Rejection (catching bad parameters) and recovery (correcting after acceptance) are independent model capabilities (Spearman rho=0.126, p=0.747). A tuned runtime interceptor reduces hallucination on GPT‑4o‑mini by 23.0 percentage points under a concurrent n=600 control, but shows no significant effect on Gemini‑2.0‑Flash, whose aggressive parameter rejection eliminates the target failure mode. All code, data, traces, and human labels are released at https://github.com/bhaskargurram‑ai/agenthallu‑bench.

PaperId: 1660, https://arxiv.org/pdf/2604.16683.pdf GitHub

Abstract:
Imitation learning has enabled robots to acquire complex visuomotor manipulation skills from demonstrations, but deployment failures remain a major obstacle, especially for long‑horizon action‑chunked policies. Once execution drifts off the demonstration manifold, these policies often continue producing locally plausible actions without recovering from the failure. Existing runtime monitors either require failure data, over‑trigger under benign feature drift, or stop at failure detection without providing a recovery mechanism. We present Rewind‑IL, a training‑free online safeguard framework for generative action‑chunked imitation policies. Rewind‑IL combines a zero‑shot failure detector based on Temporal Inter‑chunk Discrepancy Estimate (TIDE), calibrated with split conformal prediction, with a state‑respawning mechanism that returns the robot to a semantically verified safe intermediate state. Offline, a vision‑language model identifies recovery checkpoints in demonstrations, and the frozen policy encoder is used to construct a compact checkpoint feature database. Online, Rewind‑IL monitors self‑consistency in overlapping action chunks, tracks similarity to the checkpoint library, and, upon failure, rewinds execution to the latest verified safe state before restarting inference from a clean policy state. Experiments on real‑world and simulated long‑horizon manipulation tasks, including transfer to flow‑matching action‑chunked policies, demonstrate that policy‑internal consistency coupled with semantically grounded respawning offers a practical route to improved reliability in imitation learning. Supplemental materials are available at https://sjay05.github.io/rewind‑il

PaperId: 1661, https://arxiv.org/pdf/2604.16625.pdf GitHub

Abstract:
Recent large language model (LLM) agents have shown promise in using execution feedback for test‑time adaptation. However, robust self‑improvement remains far from solved: most approaches still treat each problem instance independently, without accumulating reusable knowledge. This limitation is particularly pronounced in domain‑specific languages such as Triton, which are underrepresented in LLM pretraining data. Their strict constraints and non‑linear optimization landscape further make naive generation and local refinement unreliable. We propose AdaExplore, an agent framework that enables self‑improvement via accumulated execution feedback for performance‑critical kernel code generation through two complementary stages: failure‑driven adaptation and diversity‑preserving search, jointly improving correctness and optimization performance without additional fine‑tuning or external knowledge. In the adaptation stage, the agent synthesizes tasks and converts recurring failures into a reusable memory of validity rules, helping subsequent generations remain within the feasible set. In the search stage, the agent organizes candidate kernels as a tree and alternates between small local refinements and larger structural regeneration, allowing it to explore the optimization landscape beyond local optima. Experiments on kernel runtime optimization benchmarks validate these gains: AdaExplore achieves 3.12x and 1.72x speedups on KernelBench Level‑2 and Level‑3, respectively, within 100 steps, and continues to improve with additional computation.

PaperId: 1662, https://arxiv.org/pdf/2604.16588.pdf GitHub

Abstract:
Penalty kicks in soccer are decided under extreme time constraints, where goalkeepers benefit from anticipating shot direction from the kickers motion before or around ball contact. In this paper, MambaKick is presented as a learning‑based framework for penalty direction prediction that leverages pretrained human action recognition (HAR) embeddings extracted from contact‑centered short video segments and combines them with a lightweight temporal predictor. Rather than relying on explicit kinematic reconstruction or handcrafted biomechanical features, the approach reuses transferable spatiotemporal representations and utilizes selective state‑spare models (Mamba) for efficient sequence aggregation. Simple contextual metadata (e.g., field side and footedness) are also considered as complementary cues that may reduce ambiguity in real‑world footage. Across a range of HAR backbones, MambaKick consistently improves or matches strong embedding baselines, achieving up to 53.1% accuracy for three classes and 64.5% for two classes under the proposed methodology. Overall, the results indicate that combining pretrained HAR representations with efficient state‑space temporal modeling is a practical direction for low‑latency intention prediction in real‑world sports video. The code will be available at GitHub: https://github.com/hvelesaca/MambaKick/

PaperId: 1663, https://arxiv.org/pdf/2604.16586.pdf GitHub

Abstract:
Molecular property prediction integrates quantum chemistry, cheminformatics, and deep learning to connect molecular structure with physicochemical and biological behavior. This survey traces four complementary paradigms, including Quantum, Descriptor Machine Learning, Geometric Deep Learning, and Foundation Models, and outlines a unified taxonomy linking molecular representations, model architectures, and interdisciplinary applications. Benchmark analyses integrate evidence from both widely used datasets and datasets reflecting industry perspectives, encompassing quantum, physicochemical, physiological, and biophysical domains. The survey examines current standards in data curation, splitting strategies, and evaluation protocols, highlighting challenges including inconsistent stereochemistry, heterogeneous assay sources, and reproducibility limitations under random or poorly defined splits. These observations motivate the modernization of benchmark design toward more transparent, time‑ and scaffold‑aware methodologies. We further propose three forward‑looking directions: (i) physics‑aware learning embedding quantum consistency, (ii) uncertainty‑calibrated foundation models for trustworthy inference, and (iii) realistic multimodal benchmark ecosystems integrating computational and experimental data. Repository: https://github.com/Zongru‑Li/Survey‑and‑Benchmarks‑of‑DL‑for‑Molecular‑Property‑Prediction‑in‑the‑Foundation‑Model‑Era.

PaperId: 1664, https://arxiv.org/pdf/2604.16582.pdf GitHub

Abstract:
Pedestrian detection is fundamental to autonomous driving, robotics, and surveillance. Despite progress in deep learning, reliable identification remains challenging due to occlusions, cluttered backgrounds, and degraded visibility. While multispectral detection‑combining visible and thermal sensors‑mitigates poor visibility, the challenge of camouflaged pedestrians remains largely unexplored. Existing Camouflaged Object Detection (COD) benchmarks focus on biological species, leaving a gap in safety‑critical human detection where targets blend into their surroundings. To address this, we introduce Camo‑M3FD (derived from the M3FD dataset), a novel benchmark for cross‑spectral camouflaged pedestrian detection, consisting of registered visible‑thermal image pairs. The dataset is curated using quantitative metrics to ensure high foreground‑background similarity. We provide high‑quality pixel‑level masks and establish a standardized evaluation framework using state‑of‑the‑art COD models. Our results demonstrate that while thermal signals provide indispensable localization cues, multispectral fusion is essential for refining structural details. Camo‑M3FD serves as a foundational resource for developing robust and safety‑critical detection systems. The dataset is available on GitHub: https://cod‑espol.github.io/Camo‑M3FD/

PaperId: 1665, https://arxiv.org/pdf/2604.16554.pdf GitHub

Abstract:
Stroke patient cross‑subject electroencephalography (EEG) decoding of motor imagery (MI) brain‑computer interface (BCI) is essential for motor rehabilitation, yet lesion‑related abnormal temporal dynamics and pronounced inter‑patient heterogeneity often undermine generalization. Existing adaptation methods are easily misled by pathological slow‑wave activity and unstable target‑domain pseudo‑labels. To address this challenge, we propose PA‑TCNet, a pathology‑aware temporal calibration framework with physiology‑guided target refinement for stroke motor imagery decoding. PA‑TCNet integrates two coordinated components. The Pathology‑aware Rhythmic State Mamba (PRSM) module decomposes EEG spatiotemporal features into slowly varying rhythmic context and fast transient perturbations, injecting the fused pathological context into selective state propagation to more effectively capture abnormal temporal dynamics. The Physiology‑Guided Target Calibration (PGTC) module constructs source‑domain sensorimotor region‑of‑interest templates, imposing physiological consistency constraints and dynamically refining target‑domain pseudo‑labels, thereby improving adaptation reliability. Leave‑one‑subject‑out experiments on two independent stroke EEG datasets, XW‑Stroke and 2019‑Stroke, yielded mean accuracies of 66.56% and 72.75%, respectively, outperforming state‑of‑the‑art baselines. These results indicate that jointly modeling pathological temporal dynamics and physiology‑constrained pseudo‑supervision can provide more robust cross‑subject initialization for personalized post‑stroke MI‑BCI rehabilitation. The implemented code is available at https://github.com/wxk1224/PA‑TCNet.

PaperId: 1666, https://arxiv.org/pdf/2604.16543.pdf GitHub

Abstract:
Most LLM safety work studies single‑agent models, but many real applications rely on multiple interacting agents. In these systems, prompt segmentation and inter‑agent routing create attack surfaces that single‑agent evaluations miss. We study \emphconjunctive prompt attacks, where a trigger key in the user query and a hidden adversarial template in one compromised remote agent each appear benign alone but activate harmful behavior when routing brings them together. We consider an attacker who changes neither model weights nor the client agent and instead controls only trigger placement and template insertion. Across star, chain, and DAG topologies, routing‑aware optimization substantially increases attack success over non‑optimized baselines while keeping false activations low. Existing defenses, including PromptGuard, Llama‑Guard variants, and system‑level controls such as tool restrictions, do not reliably stop the attack because no single component appears malicious in isolation. These results expose a structural vulnerability in agentic LLM pipelines and motivate defenses that reason over routing and cross‑agent composition. Code is available at https://github.com/UCF‑ML‑Research/ConjunctiveAgents.

PaperId: 1667, https://arxiv.org/pdf/2604.16518.pdf GitHub

Abstract:
Satellite constellations are transforming space systems from isolated spacecraft into networked, software‑defined platforms capable of on‑orbit perception, decision making, and adaptation. Yet much of the existing AI studies remains centered on single‑satellite inference, while constellation‑scale autonomy introduces fundamentally new algorithmic requirements: learning and coordination under dynamic inter‑satellite connectivity, strict SWaP‑C limits, radiation‑induced faults, non‑IID data, concept drift, and safety‑critical operational constraints. This survey consolidates the emerging field of on‑orbit space AI through three complementary paradigms: (i) federated learning for cross‑satellite training, personalization, and secure aggregation; (ii) multi‑agent algorithms for cooperative planning, resource allocation, scheduling, formation control, and collision avoidance; and (iii) collaborative sensing and distributed inference for multi‑satellite fusion, tracking, split/early‑exit inference, and cross‑layer co‑design with constellation networking. We provide a system‑level view and a taxonomy that unifies collaboration architectures, temporal mechanisms, and trust models. To support community development and keep this review actionable over time, we continuously curate relevant papers and resources at https://github.com/ziyangwang007/AI4Space.

PaperId: 1668, https://arxiv.org/pdf/2604.16462.pdf GitHub

Abstract:
High‑resolution Multimodal Large Language Models (MLLMs) face prohibitive computational costs during inference due to the explosion of visual tokens. Existing acceleration strategies, such as token pruning or layer sparsity, suffer from severe "backbone dependency", performing well on Vicuna or Mistral architectures (e.g., LLaVA) but causing significant performance degradation when transferred to architectures like Qwen. To address this, we leverage truncated matrix entropy to uncover a universal three‑stage inference lifecycle, decoupling visual redundancy into universal Intrinsic Visual Redundancy (IVR) and architecture‑dependent Secondary Saturation Redundancy (SSR). Guided by this insight, we propose HalfV, a framework that first mitigates IVR via a unified pruning strategy and then adaptively handles SSR based on its specific manifestation. Experiments demonstrate that HalfV achieves superior efficiency‑performance trade‑offs across diverse backbones. Notably, on Qwen25‑VL, it retains 96.8% performance at a 4.1× FLOPs speedup, significantly outperforming state‑of‑the‑art baselines. Our code is available at https://github.com/civilizwa/HalfV.

PaperId: 1669, https://arxiv.org/pdf/2604.16429.pdf GitHub

Abstract:
We introduce Mosaic, a probabilistic weather forecasting model that addresses three failure modes of spectral degradation in ML‑based weather prediction: spectral damping (statistical), high‑frequency aliasing (architectural), and residual high‑frequency leakage (parametric). Mosaic generates ensemble members through learned functional perturbations and operates on native‑resolution grids via mesh‑aligned block‑sparse attention, a hardware‑aligned mechanism that captures long‑range dependencies at linear cost by sharing keys and values across spatially adjacent queries. At 1.5° resolution with 214M parameters, Mosaic matches or outperforms models trained on 6× finer resolution on key variables and achieves state‑of‑the‑art results among 1.5° models, producing well‑calibrated ensembles whose individual members exhibit near‑perfect spectral alignment across all resolved frequencies. A 24‑member, 10‑day forecast takes under 12s on a single H100~GPU. Code is available at https://github.com/maxxxzdn/mosaic.

PaperId: 1670, https://arxiv.org/pdf/2604.16421.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly evaluated on mathematical reasoning, yet their robustness to equivalent problem representations remains poorly understood. In geometry, identical problems can be expressed in Euclidean, coordinate, or vector forms, but existing benchmarks report accuracy on fixed formats, implicitly assuming representation invariance and masking failures caused by representational changes alone. We propose GeoRepEval, a representation‑aware evaluation framework that measures correctness, invariance, and consistency at the problem level across parallel formulations, combining strict answer matching, bootstrap confidence intervals, paired McNemar tests, representation‑flip analyses, and regression controls for surface complexity. We prove that our Invariance@3 metric decomposes accuracy into robust and fragile components and is bounded by the weakest representation. Evaluating eleven LLMs on 158 curated high‑school geometry problems (474 instances), we find accuracy gaps of up to 14 percentage points induced solely by representation choice. Vector formulations emerge as a consistent failure point, with Invariance@3 as low as 0.044 even after controlling for length and symbolic complexity. A convert‑then‑solve prompting intervention improves vector accuracy by up to 52 percentage points for high‑capacity models, suggesting that failures reflect representation sensitivity rather than inability; however, low‑capacity models show no gains, indicating deeper limitations. These results suggest that current models rely on representation‑specific heuristics rather than abstract geometric reasoning. All datasets, prompts, and scripts are released at https://github.com/vedjaw/GeoRepEval.

PaperId: 1671, https://arxiv.org/pdf/2604.16399.pdf GitHub

Abstract:
The widespread adoption of AI‑assisted development tools in 2025 ‑‑ and the emergence of vibe coding, a practice of generating complete applications from natural language without verification ‑‑ exposed a critical and tool‑agnostic failure pattern: experienced developers who used frontier AI models were measurably slower in objective evaluations despite believing they were faster. Concurrently, 10.3% of AI‑generated applications in a production showcase contained critical security flaws. This paper argues that these failures share a structural cause ‑‑ the verification gap: every large language model (LLM), regardless of interface or capability, operates as a stochastic generator with zero internal semantic verification capability. The tool is irrelevant; the process is determinative. We present IACDM (Interactive Adversarial Convergence Development Methodology), a structured 8‑phase framework designed to address the verification gap through external verification agents (VA) operating at discrete gates. Its three pillars are: (1) deep problem discovery via Hierarchical Semantic Analysis before any technical solution; (2) persistent knowledge management across sessions; and (3) systematic adversarial critique through specialized lenses before implementation. The methodology is tool‑agnostic by construction, grounded in established software engineering tradition, and applied across more than 20 projects by multiple practitioners in a production R&D environment. Limitations are formalized as testable hypotheses for future empirical validation.

PaperId: 1672, https://arxiv.org/pdf/2604.16395.pdf GitHub

Abstract:
Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting for complete context (poor time‑to‑first‑token) and proceeding without it (reduced quality). Streaming context incrementally‑‑overlapping retrieval with inference‑‑can mitigate this latency, but doing so with concurrent requests introduces new challenges: requests contend for GPU compute and memory, and scheduling must adapt to dynamic context arrivals. We present Stream2LLM, a streaming‑aware LLM serving system for concurrent prefill‑decode disaggregated deployments. Stream2LLM introduces adaptive scheduling and preemption for two distinct retrieval patterns: append‑mode (progressive context accumulation) and update‑mode (iterative refinement with cache invalidation). It decouples scheduling decisions from resource acquisition, enabling flexible preemption strategies guided by hardware‑specific cost models, and uses longest common prefix matching to minimize redundant computation when input changes dynamically. To evaluate Stream2LLM, we collect two large‑scale, real‑world streaming workloads based on web crawling and approximate nearest neighbor search. Our evaluation demonstrates that streaming architecture delivers up to 11x TTFT improvements, with cost‑aware scheduling providing critical benefits under memory pressure, all while maintaining throughput parity with non‑streaming baselines. Code: https://github.com/rajveerb/stream2llm/tree/mlsys_artifact

PaperId: 1673, https://arxiv.org/pdf/2604.16347.pdf GitHub

Abstract:
AI‑driven autoformalization of mathematics is advancing rapidly. However, the type checker of a proof assistant guarantees only the logical correctness of proofs; it does not verify whether propositions and definitions faithfully capture their intended mathematical content. Consequently, AI‑generated formal proofs can exhibit semantic hallucination‑passing the type checker yet failing to express the intended mathematics. We propose a human‑in‑the‑loop approach in which human scientists and AI collaboratively produce formal proofs, with humans responsible for the semantic verification of propositions and definitions. To realize this approach, we develop Lean Atlas, a Lean 4 tool that visualizes the dependency graph of a Lean 4 project as an interactive web viewer, enabling human scientists to grasp the overall structure of a formalization efficiently. Its core feature, Lean Compass, is an algorithm that, given a selected theorem set, automatically extracts the project‑specific nodes whose semantic correctness can affect those target statements, thereby reducing the candidate set for semantic review in large‑scale formalizations. We further define aligned Lean code as formalization code that has undergone human semantic verification, and propose it as a quality standard for AI‑generated formalizations. We evaluate the tool on six Lean 4 formalization projects with different structural characteristics; proof‑heavy projects (PrimeNumberTheoremAnd, Carleson, Brownian Motion) achieved 94‑99% average node reduction, a 6‑theorem milestone subset of FLT achieved 59.8%, mixed PhysLib 69.0%, and definition‑heavy XMSS 27.3%. Lean Atlas is available as open‑source software at https://github.com/NyxFoundation/lean‑atlas .

PaperId: 1674, https://arxiv.org/pdf/2604.16317.pdf GitHub

Abstract:
Urban data support a wide range of applications across multiple disciplines. However, at the global scale, there is no unified platform for urban data discovery. As a result, researchers often have to manually search through websites or scientific literature to identify relevant datasets. To address this problem, we curate an open urban data discovery portal, UrbanDataMiner, which supports dataset‑level search and filtering over more than 60,000 urban datasets extracted from over 15,000 Nature‑affiliated publications. UrbanDataMiner is enabled by Paper2Data, a novel large‑scale LLM‑driven pipeline that automatically identifies dataset mentions in scientific papers and structures them using a unified urban data metadata schema. Human‑annotated evaluation demonstrates that Paper2Data achieves high recall (approximately 90%) in dataset identification and high field‑level precision (above 80%). In addition, UrbanDataMiner can retrieve over 9% of datasets that are not easily discoverable through general‑purpose search engines such as Google. Overall, our work provides the first large‑scale, literature‑derived infrastructure for urban data discovery and enables more systematic and reusable data‑driven research across disciplines. Our code and data are publicly available\footnotehttps://github.com/Yourunwen/Paper2Data.

PaperId: 1675, https://arxiv.org/pdf/2604.16272.pdf GitHub

Abstract:
As AI‑assisted video creation becomes increasingly practical, instruction‑guided video editing has become essential for refining generated or captured footage to meet professional requirements. Yet the field still lacks both a large‑scale human‑annotated dataset with complete editing examples and a standardized evaluator for comparing editing systems. Existing resources are limited by small scale, missing edited outputs, or the absence of human quality labels, while current evaluation often relies on expensive manual inspection or generic vision‑language model judges that are not specialized for editing quality. We introduce VEFX‑Dataset, a human‑annotated dataset containing 5,049 video editing examples across 9 major editing categories and 32 subcategories, each labeled along three decoupled dimensions: Instruction Following, Rendering Quality, and Edit Exclusivity. Building on VEFX‑Dataset, we propose VEFX‑Reward, a reward model designed specifically for video editing quality assessment. VEFX‑Reward jointly processes the source video, the editing instruction, and the edited video, and predicts per‑dimension quality scores via ordinal regression. We further release VEFX‑Bench, a benchmark of 300 curated video‑prompt pairs for standardized comparison of editing systems. Experiments show that VEFX‑Reward aligns more strongly with human judgments than generic VLM judges and prior reward models on both standard IQA/VQA metrics and group‑wise preference evaluation. Using VEFX‑Reward as an evaluator, we benchmark representative commercial and open‑source video editing systems, revealing a persistent gap between visual plausibility, instruction following, and edit locality in current models. Our project page is https://xiangbogaobarry.github.io/VEFX‑Bench/.

PaperId: 1676, https://arxiv.org/pdf/2604.16147.pdf GitHub

Abstract:
This paper presents SWNet, a bimodal end‑to‑end cross‑spectral network specifically engineered for the detection of camouflaged weeds in dense agricultural environments. Plant camouflage, characterized by homochromatic blending where invasive species mimic the phenotypic traits of primary crops, poses a significant challenge for traditional computer vision systems. To overcome these limitations, SWNet utilizes a Pyramid Vision Transformer v2 backbone to capture long‑range dependencies and a Bimodal Gated Fusion Module to dynamically integrate Visible and Near‑Infrared information. By leveraging the physiological differences in chlorophyll reflectance captured in the NIR spectrum, the proposed architecture effectively discriminates targets that are otherwise indistinguishable in the visible range. Furthermore, an Edge‑Aware Refinement module is employed to produce sharper object boundaries and reduce structural ambiguity. Experimental results on the Weeds‑Banana dataset indicate that SWNet outperforms ten state‑of‑the‑art methods. The study demonstrates that the integration of cross‑spectral data and boundary‑guided refinement is essential for high segmentation accuracy in complex crop canopies. The code is available on GitHub: https://cod‑espol.github.io/SWNet/

PaperId: 1677, https://arxiv.org/pdf/2604.16084.pdf GitHub

Abstract:
Traffic forecasting is a challenging spatio‑temporal modeling task and a critical component of urban transportation management. Current studies mainly focus on deterministic predictions, with limited considerations on the uncertainty and stochasticity in traffic dynamics. Therefore, this paper proposes an elegant yet universal approach that transforms existing models into probabilistic predictors by replacing only the final output layer with a novel Gaussian Mixture Model (GMM) layer. The modified model requires no changes to the training pipeline and can be trained using only the Negative Log‑Likelihood (NLL) loss, without any auxiliary or regularization terms. Experiments on multiple traffic datasets show that our approach generalizes from classic to modern model architectures while preserving deterministic performance. Furthermore, we propose a systematic evaluation procedure based on cumulative distributions and confidence intervals, and demonstrate that our approach is considerably more accurate and informative than unimodal or deterministic baselines. Finally, a more detailed study on a real‑world dense urban traffic network is presented to examine the impact of data quality on uncertainty quantification and to show the robustness of our approach under imperfect data conditions. Code available at https://github.com/Weijiang‑Xiong/OpenSkyTraffic

PaperId: 1678, https://arxiv.org/pdf/2604.16042.pdf GitHub

Abstract:
While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and safe deployment. Existing surveys in explainable AI largely focus on post‑hoc explanation methods that interpret trained models through external approximations. In contrast, intrinsic interpretability, which builds transparency directly into model architectures and computations, has recently emerged as a promising alternative. This paper presents a systematic review of the recent advances in intrinsic interpretability for LLMs, categorizing existing approaches into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction. We further discuss open challenges and outline future research directions in this emerging field. The paper list is available at: https://github.com/PKU‑PILLAR‑Group/Survey‑Intrinsic‑Interpretability‑of‑LLMs.

PaperId: 1679, https://arxiv.org/pdf/2604.15871.pdf GitHub

Abstract:
The evaluation of visual editing models remains fragmented across methods and modalities. Existing benchmarks are often tailored to specific paradigms, making fair cross‑paradigm comparisons difficult, while video editing lacks reliable evaluation benchmarks. Furthermore, common automatic metrics often misalign with human preference, yet directly deploying large multimodal models (MLLMs) as evaluators incurs prohibitive computational and financial costs. We present UniEditBench, a unified benchmark for image and video editing that supports reconstruction‑based and instruction‑driven methods under a shared protocol. UniEditBench includes a structured taxonomy of nine image operations (Add, Remove, Replace, Change, Stroke‑based, Extract, Adjust, Count, Reorder) and eight video operations, with coverage of challenging compositional tasks such as counting and spatial reordering. To enable scalable evaluation, we distill a high‑capacity MLLM judge (Qwen3‑VL‑235B‑A22B Instruct) into lightweight 4B/8B evaluators that provide multi‑dimensional scoring over structural fidelity, text alignment, background consistency, naturalness, and temporal‑spatial consistency (for videos). Experiments show that the distilled evaluators maintain strong agreement with human judgments and substantially reduce deployment cost relative to the teacher model. UniEditBench provides a practical and reproducible protocol for benchmarking modern visual editing methods. Our benchmark and the associated reward models are publicly available at https://github.com/wesar1/UniEditBench.

PaperId: 1680, https://arxiv.org/pdf/2604.15856.pdf GitHub

Abstract:
Multimodal remote sensing data provide complementary information for semantic segmentation, but in real‑world deployments, some modalities may be unavailable due to sensor failures, acquisition issues, or challenging atmospheric conditions. Existing multimodal segmentation models typically address missing modalities by learning a shared representation across inputs. However, this approach can introduce a trade‑off by compromising modality‑specific complementary information and reducing performance when all modalities are available. In this paper, we tackle this limitation with CBC‑SLP, a multimodal semantic segmentation model designed to preserve both modality‑invariant and modality‑specific information. Inspired by the theoretical results on modality alignment, which state that perfectly aligned multimodal representations can lead to sub‑optimal performance in downstream prediction tasks, we propose a novel structured latent projection approach as an architectural inductive bias. Rather than enforcing this strategy through a loss term, we incorporate it directly into the architecture. In particular, to use the complementary information effectively while maintaining robustness under random modality dropout, we structure the latent representations into shared and modality‑specific components and adaptively transfer them to the decoder according to the random modality availability mask. Extensive experiments on three multimodal remote sensing image sets demonstrate that CBC‑SLP consistently outperforms state‑of‑the‑art multimodal models across full and missing modality scenarios. Besides, we empirically demonstrate that the proposed strategy can recover the complementary information that may not be preserved in a shared representation. The code is available at https://github.com/iremulku/Multispectral‑Semantic‑Segmentation‑via‑Structured‑Latent‑Projection‑CBC‑SLP‑.

PaperId: 1681, https://arxiv.org/pdf/2604.15776.pdf GitHub

Abstract:
We present PIIBench, a unified benchmark corpus for Personally Identifiable Information (PII) detection in natural language text. Existing resources for PII detection are fragmented across domain‑specific corpora with mutually incompatible annotation schemes, preventing systematic comparison of detection systems. We consolidate ten publicly available datasets spanning synthetic PII corpora, multilingual Named Entity Recognition (NER) benchmarks, and financial domain annotated text, yielding a corpus of 2,369,883 annotated sequences and 3.35 million entity mentions across 48 canonical PII entity types. We develop a principled normalization pipeline that maps 80+ source‑specific label variants to a standardized BIO tagging scheme, applies frequency‑based suppression of near absent entity types, and produces stratified 80/10/10 train/validation/test splits preserving source distribution. To establish baseline difficulty, we evaluate eight published systems spanning rule‑based engines (Microsoft Presidio), general purpose NER models (spaCy, BERT‑base NER, XLM‑RoBERTa NER, SpanMarker mBERT, SpanMarker BERT), a PII‑specific model (Piiranha DeBERTa), and a financial NER specialist (XtremeDistil FiNER). All systems achieve span‑level F1 below 0.14, with the best system (Presidio, F1=0.1385) still producing zero recall on most entity types. These results directly quantify the domain‑silo problem and demonstrate that PIIBench presents a substantially harder and more comprehensive evaluation challenge than any existing single source PII dataset. The dataset construction pipeline and benchmark evaluation code are publicly available at https://github.com/pritesh‑2711/pii‑bench.

PaperId: 1682, https://arxiv.org/pdf/2604.15741.pdf GitHub

Abstract:
Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model internal states to estimate uncertainty. However, they suffer from strict assumptions on how hidden states should evolve across layers, and from information loss by solely focusing on last or mean tokens. To address these issues, we present Sequential Internal Variance Representation (SIVR), a supervised hallucination detection framework that leverages token‑wise, layer‑wise features derived from hidden states. SIVR adopts a more basic assumption that uncertainty manifests in the degree of dispersion or variance of internal representations across layers, rather than relying on specific assumptions, which makes the method model and task agnostic. It additionally aggregates the full sequence of per‑token variance features, learning temporal patterns indicative of factual errors and thereby preventing information loss. Experimental results demonstrate SIVR consistently outperforms strong baselines. Most importantly, SIVR enjoys stronger generalisation and avoids relying on large training sets, highlighting the potential for practical deployment. Our code repository is available online at https://github.com/ponhvoan/internal‑variance.

PaperId: 1683, https://arxiv.org/pdf/2604.15718.pdf GitHub

Abstract:
Visual speaker recognition based on lip motion offers a silent, hands‑free, and behavior‑driven biometric solution that remains effective even when acoustic cues are unavailable. Compared to traditional methods that rely heavily on appearance‑dependent representations, lip motion encodes subject‑specific behavioral dynamics driven by consistent articulation patterns and muscle coordination, offering inherent stability across environmental changes. However, capturing these robust, fine‑grained dynamics is challenging for conventional frame‑based cameras due to motion blur and low dynamic range. To exploit the intrinsic stability of lip motion and address these sensing limitations, we propose NeuroLip, an event‑based framework that captures fine‑grained lip dynamics under a strict yet practical cross‑scene protocol: training is performed under a single controlled condition, while recognition must generalize to unseen viewing and lighting conditions. NeuroLip features a 1) Temporal‑aware Voxel Encoding module with adaptive event weighting, 2) Structure‑aware Spatial Enhancer that amplifies discriminative behavioral patterns by suppressing noise while preserving vertically structured motion information, and 3) Polarity Consistency Regularization mechanism to retain motion‑direction cues encoded in event polarities. To facilitate systematic evaluation, we introduce DVSpeaker, a comprehensive event‑based lip‑motion dataset comprising 50 subjects recorded under four distinct viewpoint and illumination scenarios. Extensive experiments demonstrate that NeuroLip achieves near‑perfect matched‑scene accuracy and robust cross‑scene generalization, attaining over 71% accuracy on unseen viewpoints and nearly 76% under low‑light conditions, outperforming representative existing methods by at least 8.54%. The dataset and code are publicly available at https://github.com/JiuZeongit/NeuroLip.

PaperId: 1684, https://arxiv.org/pdf/2604.15715.pdf GitHub

Abstract:
The development of general‑purpose agents requires a shift from executing simple instructions to completing complex, real‑world productivity workflows. However, current tool‑use benchmarks remain misaligned with real‑world requirements, relying on AI‑generated queries, dummy tools, and limited system‑level coordination. To address this, we propose GTA‑2, a hierarchical benchmark for General Tool Agents (GTA) spanning atomic tool use and open‑ended workflows. Built on real‑world authenticity, it leverages real user queries, deployed tools, and multimodal contexts. (i) GTA‑Atomic, inherited from our prior GTA benchmark, evaluates short‑horizon, closed‑ended tool‑use precision. (ii) GTA‑Workflow introduces long‑horizon, open‑ended tasks for realistic end‑to‑end completion. To evaluate open‑ended deliverables, we propose a recursive checkpoint‑based evaluation mechanism that decomposes objectives into verifiable sub‑goals, enabling unified evaluation of both model capabilities and agent execution frameworks (i.e., execution harnesses). Experiments reveal a pronounced capability cliff: while frontier models already struggle on atomic tasks (below 50%), they largely fail on workflows, with top models achieving only 14.39% success. Further analysis shows that checkpoint‑guided feedback improves performance, while advanced frameworks such as Manus and OpenClaw substantially enhance workflow completion, highlighting the importance of execution harness design beyond the underlying model capacity. These findings provide guidance for developing reliable personal and professional assistants. Dataset and code will be available at https://github.com/open‑compass/GTA.

PaperId: 1685, https://arxiv.org/pdf/2604.15664.pdf GitHub GitHub

Abstract:
The rise of autonomous AI agents suggests that dynamic benchmark environments with built‑in feedback on scientifically grounded tasks are needed to evaluate the capabilities of these agents in research work. We introduce Stargazer, a scalable environment for evaluating AI agents on dynamic, iterative physics‑grounded model‑fitting tasks using inference on radial‑velocity (RV) time series data. Stargazer comprises 120 tasks across three difficulty tiers, including 20 real archival cases, covering diverse scenarios ranging from high‑SNR single‑planet systems to complex multi‑planetary configurations requiring involved low‑SNR analysis. Our evaluation of eight frontier agents reveals a gap between numerical optimization and adherence to physical constraints: although agents often achieve a good statistical fit, they frequently fail to recover correct physical system parameters, a limitation that persists even when agents are equipped with vanilla skills. Furthermore, increasing test‑time compute yields only marginal gains, with excessive token usage often reflecting recursive failure loops rather than meaningful exploration. Stargazer presents an opportunity to train, evaluate, scaffold, and scale strategies on a model‑fitting problem of practical research relevance today. Our methodology to design a simulation‑driven environment for AI agents presumably generalizes to many other model‑fitting problems across scientific domains. Source code and the project website are available at https://github.com/AIPS‑UofT/Stargazer and https://aips‑uoft.github.io/Stargazer/, respectively.

PaperId: 1686, https://arxiv.org/pdf/2604.15579.pdf GitHub

Abstract:
AI agents that interact with their environments through tools enable powerful applications, but in high‑stakes business settings, unintended actions can cause unacceptable harm, such as privacy breaches and financial loss. Existing mitigations, such as training‑based methods and neural guardrails, improve agent reliability but cannot provide guarantees. We study symbolic guardrails as a practical path toward strong safety and security guarantees for AI agents. Our three‑part study includes a systematic review of 80 state‑of‑the‑art agent safety and security benchmarks to identify the policies they evaluate, an analysis of which policy requirements can be guaranteed by symbolic guardrails, and an evaluation of how symbolic guardrails affect safety, security, and agent success on τ^2‑Bench, CAR‑bench, and MedAgentBench. We find that 85% of benchmarks lack concrete policies, relying instead on underspecified high‑level goals or common sense. Among the specified policies, 74% of policy requirements can be enforced by symbolic guardrails, often using simple, low‑cost mechanisms. These guardrails improve safety and security without sacrificing agent utility. Overall, our results suggest that symbolic guardrails are a practical and effective way to guarantee some safety and security requirements, especially for domain‑specific AI agents. We release all codes and artifacts at https://github.com/hyn0027/agent‑symbolic‑guardrails.

PaperId: 1687, https://arxiv.org/pdf/2604.15499.pdf GitHub

Abstract:
Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi‑Party Computation (MPC), enabling cloud servers to process client inputs without decrypting them. Although prior privacy‑preserving inference systems co‑design network optimizations with MPC, they remain slow and costly, limiting real‑world deployment. A major bottleneck is their use of a single, fixed transformer model for all encrypted inputs, ignoring that different inputs require different model sizes to balance efficiency and accuracy. We present SecureRouter, an end‑to‑end encrypted routing and inference framework that accelerates secure transformer inference through input‑adaptive model selection under encryption. SecureRouter establishes a unified encrypted pipeline that integrates a secure router with an MPC‑optimized model pool, enabling coordinated routing, inference, and protocol execution while preserving full data and model confidentiality. The framework includes training‑phase and inference‑phase components: an MPC‑cost‑aware secure router that predicts per‑model utility and cost from encrypted features, and an MPC‑optimized model pool whose architectures and quantization schemes are co‑trained to minimize MPC communication and computation overhead. Compared to prior work, SecureRouter achieves a latency reduction by 1.95x with negligible accuracy loss, offering a practical path toward scalable and efficient secure AI inference. Our open‑source implementation is available at: https://github.com/UCF‑ML‑Research/SecureRouter

PaperId: 1688, https://arxiv.org/pdf/2604.15488.pdf GitHub

Abstract:
Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference‑time steering offers a cost‑effective way to adjust model behavior without updating its parameters, existing methods often fail to be simultaneously effective, utility‑preserving, and training‑efficient due to their rigid, one‑size‑fits‑all designs and limited adaptability. In this work, we present FineSteer, a novel steering framework that decomposes inference‑time steering into two complementary stages: conditional steering and fine‑grained vector synthesis, allowing fine‑grained control over when and how to steer internal representations. In the first stage, we introduce a Subspace‑guided Conditional Steering (SCS) mechanism that preserves model utility by avoiding unnecessary steering. In the second stage, we propose a Mixture‑of‑Steering‑Experts (MoSE) mechanism that captures the multimodal nature of desired steering behaviors and generates query‑specific steering vectors for improved effectiveness. Through tailored designs in both SCS and MoSE, FineSteer maintains robust performance on general queries while adaptively optimizing steering vectors for targeted inputs in a training‑efficient manner. Extensive experiments on safety and truthfulness benchmarks show that FineSteer outperforms state‑of‑the‑art methods in overall performance, achieving stronger steering performance with minimal utility loss. Code is available at https://github.com/YukinoAsuna/FineSteer

PaperId: 1689, https://arxiv.org/pdf/2604.15415.pdf GitHub

Abstract:
Large language models (LLMs) have evolved into autonomous agents that rely on open skill ecosystems (e.g., ClawHub and Skills.Rest), hosting numerous publicly reusable skills. Existing security research on these ecosystems mainly focuses on vulnerabilities within skills, such as prompt injection. However, there is a critical gap regarding skills that may be misused for harmful actions (e.g., cyber attacks, fraud and scams, privacy violations, and sexual content generation), namely harmful skills. In this paper, we present the first large‑scale measurement study of harmful skills in agent ecosystems, covering 98,440 skills across two major registries. Using an LLM‑driven scoring system grounded in our harmful skill taxonomy, we find that 4.93% of skills (4,858) are harmful, with ClawHub exhibiting an 8.84% harmful rate compared to 3.49% on Skills.Rest. We then construct HarmfulSkillBench, the first benchmark for evaluating agent safety against harmful skills in realistic agent contexts, comprising 200 harmful skills across 20 categories and four evaluation conditions. By evaluating six LLMs on HarmfulSkillBench, we find that presenting a harmful task through a pre‑installed skill substantially lowers refusal rates across all models, with the average harm score rising from 0.27 without the skill to 0.47 with it, and further to 0.76 when the harmful intent is implicit rather than stated as an explicit user request. We responsibly disclose our findings to the affected registries and release our benchmark to support future research (see https://github.com/TrustAIRLab/HarmfulSkillBench).

PaperId: 1690, https://arxiv.org/pdf/2604.15400.pdf GitHub

Abstract:
We present causal evidence that hallucination in autoregressive language models is an early trajectory commitment governed by asymmetric attractor dynamics. Using same‑prompt bifurcation, in which we repeatedly sample identical inputs to observe spontaneous divergence, we isolate trajectory dynamics from prompt‑level confounds. On Qwen2.5‑1.5B across 61 prompts spanning six categories, 27 prompts (44.3%) bifurcate with factual and hallucinated trajectories diverging at the first generated token (KL = 0 at step 0, KL > 1.0 at step 1). Activation patching across 28 layers reveals a pronounced causal asymmetry: injecting a hallucinated activation into a correct trajectory corrupts output in 87.5% of trials (layer 20), while the reverse recovers only 33.3% (layer 24); both exceed the 10.4% baseline (p = 0.025) and 12.5% random‑patch control. Window patching shows correction requires sustained multi‑step intervention, whereas corruption needs only a single perturbation. Probing the prompt encoding itself, step‑0 residual states predict per‑prompt hallucination rate at Pearson r = 0.776 at layer 15 (p < 0.001 against a 1000‑permutation null); unsupervised clustering identifies five regime‑like groups (eta^2 = 0.55) whose saddle‑adjacent cluster concentrates 12 of the 13 bifurcating false‑premise prompts, indicating that the basin structure is organized around regime commitments fixed at prompt encoding. These findings characterize hallucination as a locally stable attractor basin: entry is probabilistic and rapid, exit demands coordinated intervention across layers and steps, and the relevant basins are selected by clusterable regimes already discernible at step 0.

PaperId: 1691, https://arxiv.org/pdf/2604.15376.pdf GitHub

Abstract:
Multi‑step zoom‑in pipelines are widely used for GUI grounding, yet the intermediate predictions they produce are typically discarded after coordinate remapping. We observe that these intermediate outputs contain a useful confidence signal for free: zoom consistency, the distance between a model's step‑2 prediction and the crop center. Unlike log‑probabilities or token‑level uncertainty, zoom consistency is a geometric quantity in a shared coordinate space, making it directly comparable across architecturally different VLMs without calibration. We prove this quantity is a linear estimator of step‑1 spatial error under idealized conditions (perfect step‑2, target within crop) and show it correlates with prediction correctness across two VLMs (AUC = 0.60; Spearman rho = ‑0.14, p < 10^‑6 for KV‑Ground‑8B; rho = ‑0.11, p = 0.0003 for Qwen3.5‑27B). The correlation is small but consistent across models, application categories, and operating systems. As a proof‑of‑concept, we use zoom consistency to route between a specialist and generalist model, capturing 16.5% of the oracle headroom between them (+0.8%, McNemar p = 0.19). Code is available at https://github.com/omxyz/zoom‑consistency‑routing.

PaperId: 1692, https://arxiv.org/pdf/2604.15373.pdf GitHub

Abstract:
We propose InfoChess, a symmetric adversarial game that elevates competitive information acquisition to the primary objective. There is no piece capture, removing material incentives that would otherwise confound the role of information. Instead, pieces are used to alter visibility. Players are scored on their probabilistic inference of the opponent's king location over the duration of the game. To explore the space of strategies for playing InfoChess, we introduce a hierarchy of heuristic agents defined by increasing levels of opponent modeling, and train a reinforcement learning agent that outperforms these baselines. Leveraging the discrete structure of the game, we analyze gameplay through natural information‑theoretic characterizations that include belief entropy, oracle cross entropy, and predictive log score under the action‑induced observation channel. These measures disentangle epistemic uncertainty, calibration mismatch, and uncertainty induced by adversarial movement. The design of InfoChess renders it a testbed for studying multi‑agent inference under partial observability. We release code for the environment and agents, and a public interface to encourage further study.

PaperId: 1693, https://arxiv.org/pdf/2604.15294.pdf GitHub

Abstract:
Over the past year, spatial intelligence has drawn increasing attention. Many prior works study it from the perspective of visual‑spatial intelligence, where models have access to visuospatial information from visual inputs. However, in the absence of visual information, whether linguistic intelligence alone is sufficient to endow models with spatial intelligence, and how models perform relevant tasks with text‑only inputs still remain unexplored. Therefore, in this paper, we focus on a fundamental and critical capability in spatial intelligence from a linguistic perspective: viewpoint rotation understanding (VRU). Specifically, LLMs and VLMs are asked to infer their final viewpoint and predict the corresponding observation in an environment given textual description of viewpoint rotation and observation over multiple steps. We find that both LLMs and VLMs perform poorly on our proposed dataset while human can easily achieve 100% accuracy, indicating a substantial gap between current model capabilities and the requirements of spatial intelligence. To uncover the underlying mechanisms, we conduct a layer‑wise probing analysis and head‑wise causal intervention. Our findings reveal that although models encode viewpoint information in the hidden states, they appear to struggle to bind the viewpoint position with corresponding observation, resulting in a hallucination in final layers. Finally, we selectively fine‑tune the key attention heads identified by causal intervention to improve VRU performance. Experimental results demonstrate that such selective fine‑tuning achieves improved VRU performance while avoiding catastrophic forgetting of generic abilities. Our dataset and code will be released at https://github.com/Young‑Zhen/VRU_Interpret .

PaperId: 1694, https://arxiv.org/pdf/2604.15271.pdf GitHub

Abstract:
Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decision support. Many strong uncertainty methods require repeated inference, while efficient single‑forward‑pass alternatives often provide weaker failure ranking or rely on restrictive feature‑space assumptions. We present SegWithU, a post‑hoc framework that augments a frozen pretrained segmentation backbone with a lightweight uncertainty head. SegWithU taps intermediate backbone features and models uncertainty as perturbation energy in a compact probe space using rank‑1 posterior probes. It produces two voxel‑wise uncertainty maps: a calibration‑oriented map for probability tempering and a ranking‑oriented map for error detection and selective prediction. Across ACDC, BraTS2024, and LiTS, SegWithU is the strongest and most consistent single‑forward‑pass baseline, achieving AUROC/AURC of 0.9838/2.4885, 0.9946/0.2660, and 0.9925/0.8193, respectively, while preserving segmentation quality. These results suggest that perturbation‑based uncertainty modeling is an effective and practical route to reliability‑aware medical segmentation. Source code is available at https://github.com/ProjectNeura/SegWithU.

PaperId: 1695, https://arxiv.org/pdf/2604.15166.pdf GitHub

Abstract:
Machine unlearning aims to remove targeted knowledge from a trained model without the cost of retraining from scratch. In class unlearning, however, reducing accuracy on forget classes does not necessarily imply true forgetting: forgotten information can remain encoded in internal representations, and apparent forgetting may arise from classifier‑head suppression rather than representational removal. We show that existing class‑unlearning methods often exhibit weak or negative selectivity, preserve forget‑class structure in deep representations, or rely heavily on final‑layer bias shifts. We then introduce DAMP (Depth‑Aware Modulation by Projection), a one‑shot, closed‑form weight‑surgery method that removes forget‑specific directions from a pretrained network without gradient‑based optimization. At each stage, DAMP computes class prototypes in the input space of the next learnable operator, extracts forget directions as residuals relative to retain‑class prototypes, and applies a projection‑based update to reduce downstream sensitivity to those directions. To preserve utility, DAMP uses a parameter‑free depth‑aware scaling rule derived from probe separability, applying smaller edits in early layers and larger edits in deeper layers. The method naturally extends to multi‑class forgetting through low‑rank subspace removal. Across MNIST, CIFAR‑10, CIFAR‑100, and Tiny ImageNet, and across convolutional and transformer architectures, DAMP more closely resembles the retraining gold standard than some of the prior methods, improving selective forgetting while better preserving retain‑class performance and reducing residual forget‑class structure in deep layers.

PaperId: 1696, https://arxiv.org/pdf/2604.15153.pdf GitHub

Abstract:
Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self‑attention scales quadratically with input length. Token compression aims to address this challenge by reducing the number of tokens representing inputs. However, existing prompt‑compression approaches primarily operate in token space and overlook inefficiencies in the latent embedding space. In this paper, we propose K‑Token Merging, a latent‑space compression framework that merges each contiguous block of K token embeddings into a single embedding via a lightweight encoder. The compressed sequence is processed by a LoRA‑adapted LLM, while generation remains in the original vocabulary. Experiments on structural reasoning (Textualized Tree), sentiment classification (Amazon Reviews), and code editing (CommitPackFT) show that K‑Token Merging lies on the Pareto frontier of performance vs. compression, achieving up to 75% input length reduction with minimal performance degradation. Code is available at https://github.com/shsjxzh/K‑Token‑Merging.

PaperId: 1697, https://arxiv.org/pdf/2604.15109.pdf GitHub

Abstract:
Despite the rapid advancement of Large Language Models (LLMs), uncertainty quantification in LLM generation is a persistent challenge. Although recent approaches have achieved strong performance by restricting LLMs to produce short or constrained answer sets, many real‑world applications require long‑form and free‑form text generation. A key difficulty in this setting is that LLMs often produce responses that are semantically coherent yet factually inaccurate, while the underlying semantics are multifaceted and the linguistic structure is complex. To tackle this challenge, this paper introduces Interrogative Uncertainty Quantification (IUQ), a novel framework that leverages inter‑sample consistency and intra‑sample faithfulness to quantify the uncertainty in long‑form LLM outputs. By utilizing an interrogate‑then‑respond paradigm, our method provides reliable measures of claim‑level uncertainty and the model's faithfulness. Experimental results across diverse model families and model sizes demonstrate the superior performance of IUQ over two widely used long‑form generation datasets. The code is available at https://github.com/louisfanhz/IUQ.

PaperId: 1698, https://arxiv.org/pdf/2604.15093.pdf GitHub

Abstract:
Mobile agents powered by vision‑language models have demonstrated impressive capabilities in automating mobile tasks, with recent leading models achieving a marked performance leap, e.g., nearly 70% success on AndroidWorld. However, these systems keep their training data closed and remain opaque about their task and trajectory synthesis recipes. We present OpenMobile, an open‑source framework that synthesizes high‑quality task instructions and agent trajectories, with two key components: (1) The first is a scalable task synthesis pipeline that constructs a global environment memory from exploration, then leverages it to generate diverse and grounded instructions. and (2) a policy‑switching strategy for trajectory rollout. By alternating between learner and expert models, it captures essential error‑recovery data often missing in standard imitation learning. Agents trained on our data achieve competitive results across three dynamic mobile agent benchmarks: notably, our fine‑tuned Qwen2.5‑VL and Qwen3‑VL reach 51.7% and 64.7% on AndroidWorld, far surpassing existing open‑data approaches. Furthermore, we conduct transparent analyses on the overlap between our synthetic instructions and benchmark test sets, and verify that performance gains stem from broad functionality coverage rather than benchmark overfitting. We release data and code at https://njucckevin.github.io/openmobile/ to bridge the data gap and facilitate broader mobile agent research.

PaperId: 1699, https://arxiv.org/pdf/2604.15034.pdf GitHub

Abstract:
Recent advances in LLM based agent systems have shown promise in tackling complex, long horizon tasks. However, existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code. We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs. Its Resource Substrate Protocol Layer (RSPL) models prompts, agents, tools, environments, and memory as protocol registered resources with explicit state, lifecycle, and versioned interfaces. Its Self Evolution Protocol Layer (SEPL) specifies a closed loop operator interface for proposing, assessing, and committing improvements with auditable lineage and rollback. Building on AGP, we present Autogenesis System (AGS), a self‑evolving multi‑agent system that dynamically instantiates, retrieves, and refines protocol‑registered resources during execution. We evaluate AGS on multiple challenging benchmarks that require long horizon planning and tool use across heterogeneous resources. The results demonstrate consistent improvements over strong baselines, supporting the effectiveness of agent resource management and closed loop self evolution. The code is available at https://github.com/DVampire/Autogenesis.

PaperId: 1700, https://arxiv.org/pdf/2604.15022.pdf GitHub

Abstract:
Cost‑aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the routing strategy introduces a new security concern that adversaries may manipulate the router to consistently select expensive high‑capability models. Existing routing attacks depend on either white‑box access or heuristic prompts, rendering them ineffective in real‑world black‑box scenarios. In this work, we propose R^2A, which aims to mislead black‑box LLM routers to expensive models via adversarial suffix optimization. Specifically, R^2A deploys a hybrid ensemble surrogate router to mimic the black‑box router. A suffix optimization algorithm is further adapted for the ensemble‑based surrogate. Extensive experiments on multiple open‑source and commercial routing systems demonstrate that R^2A significantly increases the routing rate to expensive models on queries of different distributions. Code and examples: https://github.com/thcxiker/R2A‑Attack.

PaperId: 1701, https://arxiv.org/pdf/2604.14885.pdf GitHub GitHub

Abstract:
Autoregressive decoding in Large Language Models (LLMs) generates one token per step, causing high inference latency. Speculative decoding (SD) mitigates this through a guess‑and‑verify strategy, but existing training‑free variants face trade‑offs: retrieval‑based drafts break when no exact match exists, while logits‑based drafts lack structural guidance. We propose RACER (Retrieval‑Augmented Contextual Rapid Speculative Decoding), a lightweight and training‑free method that integrates retrieved exact patterns with logit‑driven future cues. This unification supplies both reliable anchors and flexible extrapolation, yielding richer speculative drafts. Experiments on Spec‑Bench, HumanEval, and MGSM‑ZH demonstrate that RACER consistently accelerates inference, achieving more than 2× speedup over autoregressive decoding, and outperforms prior training‑free methods, offering a scalable, plug‑and‑play solution for efficient LLM decoding. Our source code is available at \hrefhttps://github.com/hkr04/RACERhttps://github.com/hkr04/RACER.

PaperId: 1702, https://arxiv.org/pdf/2604.14866.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) have demonstrated significant potential in medical image analysis, yet their application in intraoral photography remains largely underexplored due to the lack of fine‑grained, annotated datasets and comprehensive benchmarks. To address this, we present MetaDent, a comprehensive resource that includes (1) a novel and large‑scale dentistry image dataset collected from clinical, public, and web sources; (2) a semi‑structured annotation framework designed to capture the hierarchical and clinically nuanced nature of dental photography; and (3) comprehensive benchmark suites for evaluating state‑of‑the‑art VLMs on clinical image understanding. Our labeling approach combines a high‑level image summary with point‑by‑point, free‑text descriptions of abnormalities. This method enables rich, scalable, and task‑agnostic representations. We curated 60,669 dental images from diverse sources and annotated a representative subset of 2,588 images using this meta‑labeling scheme. Leveraging Large Language Models (LLMs), we derive standardized benchmarks: approximately 15K Visual Question Answering (VQA) pairs and an 18‑class multi‑label classification dataset, which we validated with human review and error analysis to justify that the LLM‑driven transition reliably preserves fidelity and semantic accuracy. We then evaluate state‑of‑the‑art VLMs across VQA, classification, and image captioning tasks. Quantitative results reveal that even the most advanced models struggle with a fine‑grained understanding of intraoral scenes, achieving moderate accuracy and producing inconsistent or incomplete descriptions in image captioning. We publicly release our dataset, annotations, and tools to foster reproducible research and accelerate the development of vision‑language systems for dental applications.

PaperId: 1703, https://arxiv.org/pdf/2604.14847.pdf GitHub GitHub

Abstract:
Large Reasoning Models (LRMs) achieve strong performance on complex tasks through extended chains of thought but suffer from high inference latency due to autoregressive reasoning. Recent work explores using Small Reasoning Models (SRMs) to accelerate LRM inference. In this paper, we systematically characterize the capability boundaries of SRMs and identify three common types of reasoning risks: (1) path divergence, where SRMs lack the strategic ability to construct an initial plan, causing reasoning to deviate from the most probable path; (2) cognitive overload, where SRMs fail to solve particularly difficult steps; and (3) recovery inability, where SRMs lack robust self‑reflection and error correction mechanisms. To address these challenges, we propose TrigReason, a trigger‑based collaborative reasoning framework that replaces continuous polling with selective intervention. TrigReason delegates most reasoning to the SRM and activates LRM intervention only when necessary‑during initial strategic planning (strategic priming trigger), upon detecting extraordinary overconfidence (cognitive offload trigger), or when reasoning falls into unproductive loops (intervention request trigger). The evaluation results on AIME24, AIME25, and GPQA‑D indicate that TrigReason matches the accuracy of full LRMs and SpecReason, while offloading 1.70x ‑ 4.79x more reasoning steps to SRMs. Under edge‑cloud conditions, TrigReason reduces latency by 43.9% and API cost by 73.3%. Our code is available at \hrefhttps://github.com/QQQ‑yi/TrigReasonhttps://github.com/QQQ‑yi/TrigReason

PaperId: 1704, https://arxiv.org/pdf/2604.14846.pdf GitHub GitHub

Abstract:
Retail theft costs the global economy over \100 billion annually, yet existing AI‑based detection systems require expensive custom model training on proprietary datasets and charge \200‑500/month per store. We present Paza, a zero‑shot retail theft detection framework that achieves practical concealment detection without training any model. Our approach orchestrates multiple existing models in a layered pipeline ‑ cheap object detection and pose estimation running continuously, with an expensive vision‑language model (VLM) invoked only when behavioral pre‑filters trigger. A multi‑signal suspicion pre‑filter (requiring dwell time plus at least one behavioral signal) reduces VLM invocations by 240x compared to per‑frame analysis, bounding calls to <=10/minute and enabling a single GPU to serve 10‑20 stores. The architecture is model‑agnostic: the VLM component accepts any OpenAI‑compatible endpoint, enabling operators to swap between models such as Gemma 4, Qwen3.5‑Omni, GPT‑4o, or future releases without code changes ‑ ensuring the system improves as the VLM landscape evolves. We evaluate the VLM component on the DCSASS synthesized shoplifting dataset (169 clips, controlled environment), achieving 89.5% precision and 92.8% specificity at 59.3% recall zero‑shot ‑ where the recall gap is attributable to sparse frame sampling in offline evaluation rather than VLM reasoning failures, as precision and specificity are the operationally critical metrics determining false alarm rates. We present a detailed cost model showing viability at \50‑100/month per store (3‑10x cheaper than commercial alternatives), and introduce a privacy‑preserving design that obfuscates faces in the detection pipeline. The source code is available at https://github.com/xHaileab/Paza‑AI.

PaperId: 1705, https://arxiv.org/pdf/2604.14785.pdf GitHub

Abstract:
Recent progress in Multimodal Large Language Models (MLLMs) has demonstrated remarkable advances in perception and reasoning, suggesting their potential for embodied intelligence. While recent studies have evaluated embodied MLLMs in interactive settings, current benchmarks mainly target capabilities to perceive, understand, and interact with external objects, lacking a systematic evaluation of self‑centric intelligence. To address this, we introduce MirrorBench, a simulation‑based benchmark inspired by the classical Mirror Self‑Recognition (MSR) test in psychology. MirrorBench extends this paradigm to embodied MLLMs through a tiered framework of progressively challenging tasks, assessing agents from basic visual perception to high‑level self‑representation. Experiments on leading MLLMs show that even at the lowest level, their performance remains substantially inferior to human performance, revealing fundamental limitations in self‑referential understanding. Our study bridges psychological paradigms and embodied intelligence, offering a principled framework for evaluating the emergence of general intelligence in large models. Project page: https://fflahm.github.io/mirror‑bench‑page/.

PaperId: 1706, https://arxiv.org/pdf/2604.14646.pdf GitHub

Abstract:
Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models (LLMs) and vision‑language models (VLMs). However, the widely used Group Relative Policy Optimization (GRPO) consistently suffers from entropy collapse, causing the policy to converge prematurely and lose diversity. Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain optimization stability. We propose Unified Entropy Control for Reinforcement Learning (UEC‑RL), a framework that provides targeted mechanisms for exploration and stabilization. UEC‑RL activates more exploration on difficult prompts to search for potential and valuable reasoning trajectories. In parallel, a stabilizer prevents entropy from growing uncontrollably, thereby keeping training stable as the model consolidates reliable behaviors. Together, these components expand the search space when needed while maintaining robust optimization throughout training. Experiments on both LLM and VLM reasoning tasks show consistent gains over RL baselines on both Pass@1 and Pass@k. On Geometry3K, UEC‑RL achieves a 37.9% relative improvement over GRPO, indicating that it sustains effective exploration without compromising convergence and underscoring UEC‑RL as a key for scaling RL‑based reasoning in large models. Our code is available at https://github.com/597358816/UEC‑RL.

PaperId: 1707, https://arxiv.org/pdf/2604.14631.pdf GitHub

Abstract:
Effective code generation requires both model capability and a problem representation that carefully structures how models reason and plan. Existing approaches augment reasoning steps or inject specific structure into how models think, but leave scattered problem conditions unchanged. Inspired by the way humans organize fragmented information into coherent explanations, we propose StoryCoder, a narrative reformulation framework that transforms code generation questions into coherent natural language narratives, providing richer contextual structure than simple rephrasings. Each narrative consists of three components: a task overview, constraints, and example test cases, guided by the selected algorithm and genre. Experiments across 11 models on HumanEval, LiveCodeBench, and CodeForces demonstrate consistent improvements, with an average gain of 18.7% in zero‑shot pass@10. Beyond accuracy, our analyses reveal that narrative reformulation guides models toward correct algorithmic strategies, reduces implementation errors, and induces a more modular code structure. The analyses further show that these benefits depend on narrative coherence and genre alignment, suggesting that structured problem representation is important for code generation regardless of model scale or architecture. Our code is available at https://github.com/gu‑ni/StoryCoder.

PaperId: 1708, https://arxiv.org/pdf/2604.14616.pdf GitHub GitHub

Abstract:
Clinical value set authoring ‑‑ the task of identifying all codes in a standardized vocabulary that define a clinical concept ‑‑ is a recurring bottleneck in clinical quality measurement and phenotyping. A natural approach is to prompt a large language model (LLM) to generate the required codes directly, but structured clinical vocabularies are large, version‑controlled, and not reliably memorized during pretraining. We propose Retrieval‑Augmented Set Completion (RASC): retrieve the K most similar existing value sets from a curated corpus to form a candidate pool, then apply a classifier to each candidate code. Theoretically, retrieve‑and‑select can reduce statistical complexity by shrinking the effective output space from the full vocabulary to a much smaller retrieved candidate pool. We demonstrate the utility of RASC on 11,803 publicly available VSAC value sets, constructing the first large‑scale benchmark for this task. A cross‑encoder fine‑tuned on SAPBert achieves AUROC~0.852 and value‑set‑level F1~0.298, outperforming a simpler three‑layer Multilayer Perceptron (AUROC~0.799, F1~0.250) and both reduce the number of irrelevant candidates per true positive from 12.3 (retrieval‑only) to approximately 3.2 and 4.4 respectively. Zero‑shot GPT‑4o achieves value‑set‑level F1~0.105, with 48.6% of returned codes absent from VSAC entirely. This performance gap widens with increasing value set size, consistent with RASC's theoretical advantage. We observe similar performance gains across two other classifier model types, namely a cross‑encoder initialized from pre‑trained SAPBert and a LightGBM model, demonstrating that RASC's benefits extend beyond a single model class. The code to download and create the benchmark dataset, as well as the model training code is available at: \hrefhttps://github.com/mukhes3/RASChttps://github.com/mukhes3/RASC.

PaperId: 1709, https://arxiv.org/pdf/2604.14586.pdf GitHub

Abstract:
The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape. Existing Graph Neural Network (GNN)‑based methods primarily prioritize accuracy over diversity, overlooking their inherent trade‑off. To address this, we previously proposed CPGRec, a balance‑oriented gaming recommender system. However, CPGRec fails to account for critical disparities in player‑game interactions, which carry varying significance in reflecting players' personal preferences and may exacerbate over‑smoothness issues inherent in GNN‑based models. Moreover, existing approaches underutilize the reasoning capabilities and extensive knowledge of large language models (LLMs) in addressing these limitations. To bridge this gap, we propose two new modules. First, Preference‑informed Edge Reweighting (PER) module assigns signed edge weights to qualitatively distinguish significant player interests and disinterests while then quantitatively measuring preference strength to mitigate over‑smoothing in graph convolutions. Second, Preference‑informed Representation Generation (PRG) module leverages LLMs to generate contextualized descriptions of games and players by reasoning personal preferences from comparing global and personal interests, thereby refining representations of players and games. Experiments on \textcolorblacktwo Steam datasets demonstrate CPGRec+'s superior accuracy and diversity over state‑of‑the‑art models. The code is accessible at https://github.com/HsipingLi/CPGRec‑Plus.

PaperId: 1710, https://arxiv.org/pdf/2604.14572.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results, with no view of how the corpus is organized or what it has not yet seen. We present Corpus2Skill, which distills a document corpus offline into a hierarchical skill directory and lets an LLM agent navigate it at serve time, drilling from a bird's‑eye view through progressively finer summaries down to documents, and backtracking when a branch is unproductive. On an enterprise customer‑support benchmark, Corpus2Skill improves both answer quality and grounding over single‑shot dense, hybrid, hierarchical‑retrieval, and agentic RAG baselines at a moderate cost tradeoff. A ten‑subset generalization study further shows that corpus navigation is not a universal replacement for retrieval: it consistently helps on single‑domain corpora with a recoverable topical taxonomy, but flat retrieval remains preferable on open‑domain factoid pools or homogeneous‑tabular corpora that defeat top‑level clustering. We characterize this scope distinction and discuss it as a design guideline for knowledge‑grounded systems. Code is available at https://github.com/dukesun99/Corpus2Skill.

PaperId: 1711, https://arxiv.org/pdf/2604.14564.pdf GitHub

Abstract:
Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning‑intensive tasks such as code generation. However, limited trajectory diversity often leads to diminishing returns, which constrains the achievable performance ceiling. Search‑enhanced RL alleviates this issue by introducing structured exploration, which remains constrained by the single‑agent policy priors. Meanwhile, leveraging multiple interacting policies can acquire more diverse exploratory signals, but existing approaches are typically decoupled from structured search. We propose MARS^2 (Multi‑Agent Reinforced Tree‑Search Scaling), a unified RL framework in which multiple independently‑optimized agents collaborate within a shared tree‑structured search environment. MARS^2 models the search tree as a learnable multi‑agent interaction environment, enabling heterogeneous agents to collaboratively generate and refine candidate solutions within a shared search topology. To support effective learning, we introduce a path‑level group advantage formulation based on tree‑consistent reward shaping, which facilitates effective credit assignment across complex search trajectories. Experiments on code generation benchmarks show that MARS^2 consistently improves performance across diverse model combinations and training settings, demonstrating the effectiveness of coupling multi‑agent collaboration with tree search for enhancing reinforcement learning. Our code is publicly available at https://github.com/TsinghuaC3I/MARTI.

PaperId: 1712, https://arxiv.org/pdf/2604.14510.pdf GitHub

Abstract:
News recommender systems are devised to alleviate the information overload, attracting more and more researchers' attention in recent years. The lack of a dedicated learner‑oriented news recommendation toolkit hinders the advancement of research in news recommendation. We propose a PyTorch‑based news recommendation toolkit called NewsTorch, developed to support learners in acquiring both conceptual understanding and practical experience. This toolkit provides a modular, decoupled, and extensible framework with a learner‑friendly GUI platform that supports dataset downloading and preprocessing. It also enables training, validation, and testing of state‑of‑the‑art neural news recommendation models with standardized evaluation metrics, ensuring fair comparison and reproducible experiments. Our open‑source toolkit is released on Github: https://github.com/whonor/NewsTorch.

PaperId: 1713, https://arxiv.org/pdf/2604.14473.pdf GitHub

Abstract:
A common approach to personalization in large language models (LLMs) is to incorporate a subset of the user memory into the prompt at inference time to guide the model's generation. Existing methods select these subsets primarily using similarity between user memory items and input queries, ignoring how features actually affect the model's response distribution. We propose Response‑Utility optimization for Memory Selection (RUMS), a novel method that selects user memory items by measuring the mutual information between a subset of memory and the model's outputs, identifying items that reduce response uncertainty and sharpen predictions beyond semantic similarity. We demonstrate that this information‑theoretic foundation enables more principled user memory selection that aligns more closely with human selection compared to state‑of‑the‑art methods, and models 400× larger. Additionally, we show that memory items selected using RUMS result in better response quality compared to existing approaches, while having up to 95% reduction in computational cost.

PaperId: 1714, https://arxiv.org/pdf/2604.14451.pdf GitHub

Abstract:
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allows accurate constraints on the cosmological model. In recent years, high‑order statistics and machine learning (ML) techniques have been applied to weak lensing data to extract the nonlinear information beyond traditional two‑point analysis. However, these methods typically rely on cosmological simulations, which poses several challenges: simulations are computationally expensive, limiting most realistic setups to a low training data regime; inaccurate modeling of systematics in the simulations create distribution shifts that can bias cosmological parameter constraints; and varying simulation setups across studies make method comparison difficult. To address these difficulties, we present the first weak lensing benchmark dataset with several realistic systematics and launch the FAIR Universe Weak Lensing Machine Learning Uncertainty Challenge. The challenge focuses on measuring the fundamental properties of the universe from weak lensing data with limited training set and potential distribution shifts, while providing a standardized benchmark for rigorous comparison across methods. Organized in two phases, the challenge will bring together the physics and ML communities to advance the methodologies for handling systematic uncertainties, data efficiency, and distribution shifts in weak lensing analysis with ML, ultimately facilitating the deployment of ML approaches into upcoming weak lensing survey analysis.

PaperId: 1715, https://arxiv.org/pdf/2604.14430.pdf GitHub

Abstract:
We present Three‑Phase Transformer (3PT), a residual‑stream structural prior for decoder‑only Transformers on a standard SwiGLU + RMSNorm + RoPE + GQA backbone. The hidden vector is partitioned into N equally‑sized cyclic channels, each maintained by phase‑respecting ops: a per‑channel RMSNorm, a 2D Givens rotation between attention and FFN that rotates each channel by theta + i(2pi/N), and a head‑count constraint aligning GQA heads with the partition. The architecture is a self‑stabilizing equilibrium between scrambling and re‑imposition, not a bolted‑on module. The partition carves out a one‑dimensional DC subspace orthogonal to the channels, into which we inject a fixed Gabriel's horn profile r(p) = 1/(p+1) as an absolute‑position side‑channel composing orthogonally with RoPE's relative‑position rotation. The canonical N=3 borrows its metaphor from balanced three‑phase AC, where three sinusoids 120 degrees apart sum to zero with no anti‑correlated pair. At 123M parameters on WikiText‑103, 3PT achieves ‑7.20% perplexity (‑2.62% bits‑per‑byte) over a matched RoPE‑Only baseline at +1,536 parameters (0.00124% of total), with 1.93x step‑count convergence speedup (1.64x wall‑clock). N behaves as a parameter‑sharing knob rather than a unique optimum: at 5.5M an N‑sweep over 1,2,3,4,6,8,12 is near‑monotone with N=1 winning; at 123M a three‑seed sweep finds N=3 and N=1 statistically indistinguishable. The load‑bearing mechanism is the channel‑partitioned residual stream, per‑block rotation, per‑phase normalization, and horn DC injection. We characterize (a) self‑stabilization of the geometry without explicit enforcement, a novel instance of the conservation‑law framework for neural networks; (b) a U‑shaped depth profile of rotation‑angle drift at 12 layers; (c) orthogonal composition with RoPE, attention, and FFN.

PaperId: 1716, https://arxiv.org/pdf/2604.14399.pdf GitHub

Abstract:
Autonomous on‑orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi‑phase tasks over extended horizons. We present SpaceMind, a modular and self‑evolving vision‑language model (VLM) agent framework that decomposes knowledge, tools, and reasoning into three independently extensible dimensions: skill modules with dynamic routing, Model Context Protocol (MCP) tools with configurable profiles, and injectable reasoning‑mode skills. An MCP‑Redis interface layer enables the same codebase to operate across simulation and physical hardware without modification, and a Skill Self‑Evolution mechanism distills operational experience into persistent skill files without model fine‑tuning. We validate SpaceMind through 192 closed‑loop runs across five satellites, three task types, and two environments, a UE5 simulation and a physical laboratory, deliberately including degraded conditions to stress‑test robustness. Under nominal conditions all modes achieve 90‑‑100% navigation success; under degradation, the Prospective mode uniquely succeeds in search‑and‑approach tasks where other modes fail. A self‑evolution study shows that the agent recovers from failure in four of six groups from a single failed episode, including complete failure to 100% success and inspection scores improving from 12 to 59 out of 100. Real‑world validation confirms zero‑code‑modification transfer to a physical robot with 100% rendezvous success. Code: https://github.com/wuaodi/SpaceMind

PaperId: 1717, https://arxiv.org/pdf/2604.14287.pdf GitHub

Abstract:
Tensor networks were developed in the context of many‑body physics as compressed representations of multiparticle quantum states. These representations mitigate the exponential complexity of many‑body systems by capturing only the most relevant dependencies. Due to the formal similarity between quantum entanglement and statistical correlations, tensor networks have recently been integrated in machine learning, operating both as alternative learning architectures and as decompositions of components of neural networks. The expectation is that the theoretical understanding of tensor networks developed within quantum many‑body physics leads to novel methods that offer advantages in terms of computational efficiency, explainability, or privacy. Here we review the use of tensor networks in the context of machine learning, providing a critical assessment of the state of the art, the potential advantages, and the challenges that must be overcome.

PaperId: 1718, https://arxiv.org/pdf/2604.14265.pdf GitHub

Abstract:
We study behavior‑regularized reinforcement learning (RL), where regularization toward a reference distribution (the dataset in offline RL or the base model in LLM RL finetuning) is essential to prevent value over‑optimization caused by erroneous out‑of‑distribution extrapolation. Existing methods either rely on reparameterized policy gradient, which are difficult to scale to large generative models, or on reject sampling, which can be overly conservative when attempting to move beyond the behavior support. In this paper, we propose Value Gradient Flow (VGF), a scalable new paradigm for behavior‑regularized RL. VGF casts behavior‑regularized RL as an optimal transport problem that maps the reference distribution to the value‑induced optimal policy distribution. We solve this transport problem via discrete gradient flow, where value gradients guide particles initialized from the reference distribution. Our analysis shows that VGF imposes regularization implicitly by controlling the transport budget. VGF eliminates explicit policy parameterization while remaining expressive and flexible, this enables adaptive test‑time scaling by adjusting the transport budget. Extensive experiments demonstrate that VGF significantly outperforms prior methods, achieving state‑of‑the‑art results on offline RL benchmarks (D4RL, OGBench) and LLM RL tasks. Code and runs can be found at https://ryanxhr.github.io/vgf.

PaperId: 1719, https://arxiv.org/pdf/2604.14261.pdf GitHub GitHub

Abstract:
The rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. However, LLM‑based reviewers often generate superficial, formulaic comments lacking substantive, evidence‑grounded feedback. We attribute this to the underutilization of two key components of human reviewing: explicit rubrics and contextual grounding in existing work. To address this, we introduce REVIEWBENCH, a benchmark evaluating review text according to paper‑specific rubrics derived from official guidelines, the paper's content, and human‑written reviews. We further propose REVIEWGROUNDER, a rubric‑guided, tool‑integrated multi‑agent framework that decomposes reviewing into drafting and grounding stages, enriching shallow drafts via targeted evidence consolidation. Experiments on REVIEWBENCH show that REVIEWGROUNDER, using a Phi‑4‑14B‑based drafter and a GPT‑OSS‑120B‑based grounding stage, consistently outperforms baselines with substantially stronger/larger backbones (e.g., GPT‑4.1 and DeepSeek‑R1‑670B) in both alignment with human judgments and rubric‑based review quality across 8 dimensions. The code is available \hrefhttps://github.com/EigenTom/ReviewGrounderhere.

PaperId: 1720, https://arxiv.org/pdf/2604.14228.pdf GitHub

Abstract:
Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes its comprehensive architecture by analyzing the publicly available TypeScript source code and further comparing it with OpenClaw, an independent open‑source AI agent system that answers many of the same design questions from a different deployment context. Our analysis identifies five human values, philosophies, and needs that motivate the architecture (human decision authority, safety and security, reliable execution, capability amplification, and contextual adaptability) and traces them through thirteen design principles to specific implementation choices. The core of the system is a simple while‑loop that calls the model, runs tools, and repeats. Most of the code, however, lives in the systems around this loop: a permission system with seven modes and an ML‑based classifier, a five‑layer compaction pipeline for context management, four extensibility mechanisms (MCP, plugins, skills, and hooks), a subagent delegation mechanism with worktree isolation, and append‑oriented session storage. A comparison with OpenClaw, a multi‑channel personal assistant gateway, shows that the same recurring design questions produce different architectural answers when the deployment context changes: from per‑action safety classification to perimeter‑level access control, from a single CLI loop to an embedded runtime within a gateway control plane, and from context‑window extensions to gateway‑wide capability registration. We finally identify six open design directions for future agent systems, grounded in recent empirical, architectural, and policy literature.

PaperId: 1721, https://arxiv.org/pdf/2604.14223.pdf GitHub

Abstract:
Traditional conversational travel recommender systems primarily optimize for user relevance and convenience, often reinforcing popular, overcrowded destinations and carbon‑intensive travel choices. To address this, we present TRACE (Tourism Recommendation with Agentic Counterfactual Explanations), a multi‑agent, LLM‑based framework that promotes sustainable tourism through interactive nudging. TRACE uses a modular orchestrator‑worker architecture where specialized agents elicit latent sustainability preferences, construct structured user personas, and generate recommendations that balance relevance with environmental impact. A key innovation lies in its use of agentic counterfactual explanations and LLM‑driven clarifying questions, which together surface greener alternatives and refine understanding of intent, fostering user reflection without coercion. User studies and semantic alignment analyses demonstrate that TRACE effectively supports sustainable decision‑making while preserving recommendation quality and interactive responsiveness. TRACE is implemented on Google's Agent Development Kit, with full code, Docker setup, prompts, and a publicly available demo video to ensure reproducibility. A project summary, including all resources, prompts, and demo access, is available at https://ashmibanerjee.github.io/trace‑chatbot.

PaperId: 1722, https://arxiv.org/pdf/2604.14218.pdf GitHub

Abstract:
Hate speech detection in Devanagari‑scripted social media memes presents compounded challenges: multimodal content structure, script‑specific linguistic complexity, and extreme data scarcity in low‑resource settings. This paper presents our system for the CHiPSAL 2026 shared task, addressing both Subtask A (binary hate speech detection) and Subtask B (three‑class sentiment classification: positive, neutral, negative). We propose a hybrid cross‑modal attention fusion architecture that combines CLIP (ViT‑B/32) for visual encoding with BGE‑M3 for multilingual text representation, connected through 4‑head self‑attention and a learnable gating network that dynamically weights modality contributions on a per‑sample basis. Systematic evaluation across eight model configurations demonstrates that explicit cross‑modal reasoning achieves a 5.9% F1‑macro improvement over text‑only baselines on Subtask A, while uncovering two unexpected but critical findings: English‑centric vision models exhibit near‑random performance on Devanagari script, and standard ensemble methods catastrophically degrade under data scarcity (N nearly equal to 850 per fold) due to correlated overfitting. The code can be accessed at https://github.com/Tri‑Yantra‑Technologies/MEME‑Fusion/

PaperId: 1723, https://arxiv.org/pdf/2604.14199.pdf GitHub GitHub

Abstract:
Predicting real‑world events from live market signals demands systems that fuse qualitative news with quantitative order‑book dynamics under strict temporal discipline ‑‑ a challenge existing benchmarks fail to capture. We present PolyBench, a multimodal benchmark derived from Polymarket that records point‑in‑time cross‑sections of 38,666 binary prediction markets spanning 4,997 events, synchronously coupling each snapshot with a Central Limit Order Book (CLOB) state and a real‑time news stream. Using PolyBench, we evaluate seven state‑of‑the‑art Large Language Models ‑‑ spanning open‑ and closed‑source families ‑‑ generating 36,165 predictions under identical, timestamp‑locked market states collected between February 6 and 12, 2026. Our multidimensional framework assesses directional accuracy, our proposed Confidence‑Weighted Return (CWR), Annualized Percentage Yield (APY), and Sharpe ratio via realistic order‑book execution simulation. The results reveal a pronounced performance divergence: only two of seven models achieve positive financial returns ‑‑ MiMo‑V2‑Flash at 17.6% CWR and Gemini‑3‑Flash at 6.2% CWR ‑‑ while the remaining five incur losses despite uniformly high stated confidence. These findings highlight the gap between surface‑level language fluency and genuine probabilistic reasoning under live market uncertainty, and establish PolyBench as a contamination‑proof, financially‑grounded evaluation standard for future LLM research. Our dataset and code available at \underline\hrefhttps://github.com/PolyBench/PolyBenchhttps://github.com/PolyBench/PolyBench.

PaperId: 1724, https://arxiv.org/pdf/2604.14177.pdf GitHub

Abstract:
Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require \emphlearner‑friendly pedagogical feedback that is actionable, level‑appropriate, and encouraging. We introduce SPFG (Spoken Pedagogical Feedback Generation), a dataset built based on the Speak \& Improve Challenge 2025 corpus, pairing fluency‑oriented transcriptions with GEC targets and \emphhuman‑verified teacher‑style feedback, including preferred/rejected feedback pairs for preference learning. We study a transcript‑based Spoken Grammatical Error Correction (SGEC) setting and evaluate three instruction‑tuned LLMs (Qwen2.5, Llama‑3.1, and GLM‑4), comparing supervised fine‑tuning (SFT) with preference‑based alignment (using DPO and KTO) for jointly generating corrections and feedback. Results show that SFT provides the most consistent improvements, while DPO/KTO yield smaller or mixed gains, and that correction quality and feedback quality are weakly coupled. Our implementation is available at https://github.com/Skywalker‑Harrison/spfg.

PaperId: 1725, https://arxiv.org/pdf/2604.14176.pdf GitHub

Abstract:
Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly optimize supervised and unsupervised objectives and achieve promising results. However, inherent optimization interference still limits their ability to improve further. Through quantitative analysis, we identify a key issue, i.e., gradient entanglement, which 1) distorts supervised gradients and weakens discrimination among known classes, and 2) induces representation‑subspace overlap between known and novel classes, reducing the separability of novel categories. To address this issue, we propose the Energy‑Aware Gradient Coordinator (EAGC), a plug‑and‑play gradient‑level module that explicitly regulates the optimization process. EAGC comprises two components: Anchor‑based Gradient Alignment (AGA) and Energy‑aware Elastic Projection (EEP). AGA introduces a reference model to anchor the gradient directions of labeled samples, preserving the discriminative structure of known classes against the interference of unlabeled gradients. EEP softly projects unlabeled gradients onto the complement of the known‑class subspace and derives an energy‑based coefficient to adaptively scale the projection for each unlabeled sample according to its degree of alignment with the known subspace, thereby reducing subspace overlap without suppressing unlabeled samples that likely belong to known classes. Experiments show that EAGC consistently boosts existing methods and establishes new state‑of‑the‑art results. Code is available at https://haiyangzheng.github.io/EAGC.

PaperId: 1726, https://arxiv.org/pdf/2604.14159.pdf GitHub

Abstract:
Mobile input method editors (IMEs) are the primary interface for text input, yet they remain constrained to manual typing and struggle to produce personalized text. While lightweight large language models (LLMs) make on‑device auxiliary generation feasible, enabling deeply personalized, privacy‑preserving, and real‑time generative IMEs poses fundamental challenges.To this end, we present HUOZIIME, a personalized on‑device IME powered by LLM. We endow HUOZIIME with initial human‑like prediction ability by post‑training a base LLM on synthesized personalization data. Notably, a hierarchical memory mechanism is designed to continually capture and leverage user‑specific input history. Furthermore, we perform systemic optimizations tailored to on‑device LLMbased IME deployment, ensuring efficient and responsive operation under mobile constraints.Experiments demonstrate efficient on‑device execution and high‑fidelity memory‑driven personalization. Code and package are available at https://github.com/Shan‑HIT/HuoziIME.

PaperId: 1727, https://arxiv.org/pdf/2604.14142.pdf GitHub

Abstract:
While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), its potential is fundamentally bounded by the base model's existing output distribution. Optimizing the marginal distribution P(y) in the Pre‑train Space addresses this bottleneck by encoding reasoning ability and preserving broad exploration capacity. Yet, conventional pre‑training relies on static corpora for passive learning, leading to a distribution shift that hinders targeted reasoning enhancement. In this paper, we introduce PreRL (Pre‑train Space RL), which applies reward‑driven online updates directly to P(y). We theoretically and empirically validate the strong gradient alignment between log P(y) and log P(y|x), establishing PreRL as a viable surrogate for standard RL. Furthermore, we uncover a critical mechanism: Negative Sample Reinforcement (NSR) within PreRL serves as an exceptionally effective driver for reasoning. NSR‑PreRL rapidly prunes incorrect reasoning spaces while stimulating endogenous reflective behaviors, increasing transition and reflection thoughts by 14.89x and 6.54x, respectively. Leveraging these insights, we propose Dual Space RL (DSRL), a Policy Reincarnation strategy that initializes models with NSR‑PreRL to expand the reasoning horizon before transitioning to standard RL for fine‑grained optimization. Extensive experiments demonstrate that DSRL consistently outperforms strong baselines, proving that pre‑train space pruning effectively steers the policy toward a refined correct reasoning subspace.

PaperId: 1728, https://arxiv.org/pdf/2604.14137.pdf GitHub

Abstract:
Evaluating LLMs is challenging, as benchmark scores often fail to capture models' real‑world usefulness. Instead, users often rely on ``vibe‑testing'': informal experience‑based evaluation, such as comparing models on coding tasks related to their own workflow. While prevalent, vibe‑testing is often too ad hoc and unstructured to analyze or reproduce at scale. In this work, we study how vibe‑testing works in practice and then formalize it to support systematic analysis. We first analyze two empirical resources: (1) a survey of user evaluation practices, and (2) a collection of in‑the‑wild model comparison reports from blogs and social media. Based on these resources, we formalize vibe‑testing as a two‑part process: users personalize both what they test and how they judge responses. We then introduce a proof‑of‑concept evaluation pipeline that follows this formulation by generating personalized prompts and comparing model outputs using user‑aware subjective criteria. In experiments on coding benchmarks, we find that combining personalized prompts and user‑aware evaluation can change which model is preferred, reflecting the role of vibe‑testing in practice. These findings suggest that formalized vibe‑testing can serve as a useful approach for bridging benchmark scores and real‑world experience.

PaperId: 1729, https://arxiv.org/pdf/2604.14125.pdf GitHub

Abstract:
While end‑to‑end Vision‑Language‑Action (VLA) models offer a promising paradigm for robotic manipulation, fine‑tuning them on narrow control data often compromises the profound reasoning capabilities inherited from their base Vision‑Language Models (VLMs). To resolve this fundamental trade‑off, we propose HiVLA, a visual‑grounded‑centric hierarchical framework that explicitly decouples high‑level semantic planning from low‑level motor control. In high‑level part, a VLM planner first performs task decomposition and visual grounding to generate structured plans, comprising a subtask instruction and a precise target bounding box. Then, to translate this plan into physical actions, we introduce a flow‑matching Diffusion Transformer (DiT) action expert in low‑level part equipped with a novel cascaded cross‑attention mechanism. This design sequentially fuses global context, high‑resolution object‑centric crops and skill semantics, enabling the DiT to focus purely on robust execution. Our decoupled architecture preserves the VLM's zero‑shot reasoning while allowing independent improvement of both components. Extensive experiments in simulation and the real world demonstrate that HiVLA significantly outperforms state‑of‑the‑art end‑to‑end baselines, particularly excelling in long‑horizon skill composition and the fine‑grained manipulation of small objects in cluttered scenes.

PaperId: 1730, https://arxiv.org/pdf/2604.14113.pdf GitHub GitHub

Abstract:
GUI grounding, which localizes interface elements from screenshots given natural language queries, remains challenging for small icons and dense layouts. Test‑time zoom‑in methods improve localization by cropping and re‑running inference at higher resolution, but apply cropping uniformly across all instances with fixed crop sizes, ignoring whether the model is actually uncertain on each case. We propose UI‑Zoomer, a training‑free adaptive zoom‑in framework that treats both the trigger and scale of zoom‑in as a prediction uncertainty quantification problem. A confidence‑aware gate fuses spatial consensus among stochastic candidates with token‑level generation confidence to selectively trigger zoom‑in only when localization is uncertain. When triggered, an uncertainty‑driven crop sizing module decomposes prediction variance into inter‑sample positional spread and intra‑sample box extent, deriving a per‑instance crop radius via the law of total variance. Extensive experiments on ScreenSpot‑Pro, UI‑Vision, and ScreenSpot‑v2 demonstrate consistent improvements over strong baselines across multiple model architectures, achieving gains of up to +13.4%, +10.3%, and +4.2% respectively, with no additional training required.

PaperId: 1731, https://arxiv.org/pdf/2604.14089.pdf GitHub

Abstract:
We present UMI‑3D, a multimodal extension of the Universal Manipulation Interface (UMI) for robust and scalable data collection in embodied manipulation. While UMI enables portable, wrist‑mounted data acquisition, its reliance on monocular visual SLAM makes it vulnerable to occlusions, dynamic scenes, and tracking failures, limiting its applicability in real‑world environments. UMI‑3D addresses these limitations by introducing a lightweight and low‑cost LiDAR sensor tightly integrated into the wrist‑mounted interface, enabling LiDAR‑centric SLAM with accurate metric‑scale pose estimation under challenging conditions. We further develop a hardware‑synchronized multimodal sensing pipeline and a unified spatiotemporal calibration framework that aligns visual observations with LiDAR point clouds, producing consistent 3D representations of demonstrations. Despite maintaining the original 2D visuomotor policy formulation, UMI‑3D significantly improves the quality and reliability of collected data, which directly translates into enhanced policy performance. Extensive real‑world experiments demonstrate that UMI‑3D not only achieves high success rates on standard manipulation tasks, but also enables learning of tasks that are challenging or infeasible for the original vision‑only UMI setup, including large deformable object manipulation and articulated object operation. The system supports an end‑to‑end pipeline for data acquisition, alignment, training, and deployment, while preserving the portability and accessibility of the original UMI. All hardware and software components are open‑sourced to facilitate large‑scale data collection and accelerate research in embodied intelligence: \hrefhttps://umi‑3d.github.iohttps://umi‑3d.github.io.

PaperId: 1732, https://arxiv.org/pdf/2604.14084.pdf GitHub

Abstract:
On‑policy knowledge distillation (OPD) trains a student on its own rollouts under token‑level supervision from a teacher. Not all token positions matter equally, but existing views of token importance are incomplete. We ask a direct question: which tokens carry the most useful learning signal in OPD? Our answer is that informative tokens come from two regions: positions with high student entropy, and positions with low student entropy plus high teacher‑‑student divergence, where the student is overconfident and wrong. Empirically, student entropy is a strong first‑order proxy: retaining 50% of tokens with entropy‑based sampling matches or exceeds all‑token training while reducing peak memory by up to 47%. But entropy alone misses a second important region. When we isolate low‑entropy, high‑divergence tokens, training on fewer than 10% of all tokens nearly matches full‑token baselines, showing that overconfident tokens carry dense corrective signal despite being nearly invisible to entropy‑only rules. We organize these findings with TIP (Token Importance in on‑Policy distillation), a two‑axis taxonomy over student entropy and teacher‑‑student divergence, and give a theoretical explanation for why entropy is useful yet structurally incomplete. This view motivates type‑aware token selection rules that combine uncertainty and disagreement. We validate this picture across three teacher‑‑student pairs spanning Qwen3, Llama, and Qwen2.5 on MATH‑500 and AIME 2024/2025, and on the DeepPlanning benchmark for long‑horizon agentic planning, where Q3‑only training on <20% of tokens surpasses full‑token OPD. Our experiments are implemented by extending the OPD repository https://github.com/HJSang/OPSD_OnPolicyDistillation, which supports memory‑efficient distillation of larger models under limited GPU budgets.

PaperId: 1733, https://arxiv.org/pdf/2604.14025.pdf GitHub

Abstract:
Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting with the physical world. While traditional methods achieve high fidelity, they are limited by slow per‑scene optimization or category‑specific training, which hinders their practical deployment and scalability. Hence, generalizable feed‑forward 3D reconstruction has witnessed rapid development in recent years. By learning a model that maps images directly to 3D representations in a single forward pass, these methods enable efficient reconstruction and robust cross‑scene generalization. Our survey is motivated by a critical observation: despite the diverse geometric output representations, ranging from implicit fields to explicit primitives, existing feed‑forward approaches share similar high‑level architectural patterns, such as image feature extraction backbones, multi‑view information fusion mechanisms, and geometry‑aware design principles. Consequently, we abstract away from these representation differences and instead focus on model design, proposing a novel taxonomy centered on model design strategies that are agnostic to the output format. Our proposed taxonomy organizes the research directions into five key problems that drive recent research development: feature enhancement, geometry awareness, model efficiency, augmentation strategies and temporal‑aware models. To support this taxonomy with empirical grounding and standardized evaluation, we further comprehensively review related benchmarks and datasets, and extensively discuss and categorize real‑world applications based on feed‑forward 3D models. Finally, we outline future directions to address open challenges such as scalability, evaluation standards, and world modeling.

PaperId: 1734, https://arxiv.org/pdf/2604.13803.pdf GitHub GitHub GitHub

Abstract:
Vision‑language models are increasingly deployed in high‑stakes settings, yet their susceptibility to sycophantic manipulation remains poorly understood, particularly in relation to how these models represent visual information internally. Whether models whose visual representations more closely mirror human neural processing are also more resistant to adversarial pressure is an open question with implications for both neuroscience and AI safety. We investigate this question by evaluating 12 open‑weight vision‑language models spanning 6 architecture families and a 40× parameter range (256M‑‑10B) along two axes: brain alignment, measured by predicting fMRI responses from the Natural Scenes Dataset across 8 human subjects and 6 visual cortex regions of interest, and sycophancy, measured through 76,800 two‑turn gaslighting prompts spanning 5 categories and 10 difficulty levels. Region‑of‑interest analysis reveals that alignment specifically in early visual cortex (V1‑‑V3) is a reliable negative predictor of sycophancy (r = ‑0.441, BCa 95% CI [‑0.740, ‑0.031]), with all 12 leave‑one‑out correlations negative and the strongest effect for existence denial attacks (r = ‑0.597, p = 0.040). This anatomically specific relationship is absent in higher‑order category‑selective regions, suggesting that faithful low‑level visual encoding provides a measurable anchor against adversarial linguistic override in vision‑language models. We release our code on \hrefhttps://github.com/aryashah2k/Gaslight‑Gatekeep‑Sycophantic‑ManipulationGitHub and dataset on \hrefhttps://huggingface.co/datasets/aryashah00/Gaslight‑Gatekeep‑V1‑V3Hugging Face

PaperId: 1735, https://arxiv.org/pdf/2604.13721.pdf GitHub

Abstract:
The technical support team of a supercomputing centre accumulates, over the course of decades, a large volume of resolved incidents that constitute critical operational knowledge. At the Galician Supercomputing Center (CESGA) this history has been managed for over twenty years with Request Tracker (RT), whose built‑in search engine has significant limitations that hinder knowledge reuse by the support staff. This paper presents Fragata, a semantic ticket search system that combines modern information retrieval techniques with the full RT history. The system can find relevant past incidents regardless of language, the presence of typos, or the specific wording of the query. The architecture is deployed on CESGA's infrastructure, supports incremental updates without service interruption, and offloads the most expensive stages to the FinisTerrae III supercomputer. Preliminary results show a substantial qualitative improvement over RT's native search.

PaperId: 1736, https://arxiv.org/pdf/2604.13699.pdf GitHub GitHub

Abstract:
Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning without automated experimental verification. We propose MIND, an LLM‑driven framework for automated hypothesis validation in materials research. MIND organizes the scientific discovery process into hypothesis refinement, experimentation, and debate‑based validation within a multi‑agent pipeline. For experimental verification, the system integrates Machine Learning Interatomic Potentials, particularly SevenNet‑Omni, enabling scalable in‑silico experiments. We also provide a web‑based user interface for automated hypothesis testing. The modular design allows additional experimental modules to be integrated, making the framework adaptable to broader scientific workflows. The code is available at: https://github.com/IMMS‑Ewha/MIND, and a demonstration video at: https://youtu.be/lqiFe1OQzN4.

PaperId: 1737, https://arxiv.org/pdf/2604.13565.pdf GitHub

Abstract:
Ultra‑high‑resolution (UHR) remote sensing imagery couples kilometer‑scale context with query‑critical evidence that may occupy only a few pixels. Such vast spatial scale leads to a quadratic explosion of visual tokens and hinders the extraction of information from small objects. Previous works utilize direct downsampling, dense tiling, or global top‑k pruning, which either compromise query‑critical image details or incur unpredictable compute. In this paper, we propose UHR‑BAT, a query‑guided and region‑faithful token compression framework to efficiently select visual tokens under a strict context budget. Specifically, we leverage text‑guided, multi‑scale importance estimation for visual tokens, effectively tackling the challenge of achieving precise yet low‑cost feature extraction. Furthermore, by introducing region‑wise preserve and merge strategies, we mitigate visual token redundancy, further driving down the computational budget. Experimental results show that UHR‑BAT achieves state‑of‑the‑art performance across various benchmarks. Code will be available at https://github.com/Yunkaidang/UHR.

PaperId: 1738, https://arxiv.org/pdf/2604.13552.pdf GitHub

Abstract:
Large language models (LLMs) demonstrate strong reasoning capabilities, but their performance often degrades under distribution shift. Existing test‑time adaptation (TTA) methods rely on gradient‑based updates that require white‑box access and need substantial overhead, while training‑free alternatives are either static or depend on external guidance. In this paper, we propose Training‑Free Test‑Time Contrastive Learning TF‑TTCL, a training‑free adaptation framework that enables a frozen LLM to improve online by distilling supervision from its own inference experiences. Specifically, TF‑TTCL implements a dynamic "Explore‑Reflect‑Steer" loop through three core modules: 1) Semantic Query Augmentation first diversifies problem views via multi‑agent role‑playing to generate different reasoning trajectories; 2) Contrastive Experience Distillation then captures the semantic gap between superior and inferior trajectories, distilling them into explicit textual rules; and 3) Contextual Rule Retrieval finally activates these stored rules during inference to dynamically steer the frozen LLM toward robust reasoning patterns while avoiding observed errors. Extensive experiments on closed‑ended reasoning tasks and open‑ended evaluation tasks demonstrate that TF‑TTCL consistently outperforms strong zero‑shot baselines and representative TTA methods under online evaluation. Code is available at https://github.com/KevinSCUTer/TF‑TTCL.

PaperId: 1739, https://arxiv.org/pdf/2604.13472.pdf GitHub

Abstract:
Cooperative multi‑agent reinforcement learning (MARL) is widely used to address large joint observation and action spaces by decomposing a centralized control problem into multiple interacting agents. However, such decomposition often introduces additional challenges, including non‑stationarity, unstable training, weak coordination, and limited theoretical guarantees. In this paper, we propose the Consensus Multi‑Agent Transformer (CMAT), a centralized framework that bridges cooperative MARL to a hierarchical single‑agent reinforcement learning (SARL) formulation. CMAT treats all agents as a unified entity and employs a Transformer encoder to process the large joint observation space. To handle the extensive joint action space, we introduce a hierarchical decision‑making mechanism in which a Transformer decoder autoregressively generates a high‑level consensus vector, simulating the process by which agents reach agreement on their strategies in latent space. Conditioned on this consensus, all agents generate their actions simultaneously, enabling order‑independent joint decision making and avoiding the sensitivity to action‑generation order in conventional Multi‑Agent Transformers (MAT). This factorization allows the joint policy to be optimized using single‑agent PPO while preserving expressive coordination through the latent consensus. To evaluate the proposed method, we conduct experiments on benchmark tasks from StarCraft II, Multi‑Agent MuJoCo, and Google Research Football. The results show that CMAT achieves superior performance over recent centralized solutions, sequential MARL methods, and conventional MARL baselines. The code for this paper is available at:https://github.com/RS2002/CMAT .

PaperId: 1740, https://arxiv.org/pdf/2604.13459.pdf GitHub

Abstract:
Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Useful Life (RUL) of critical components. Existing deep learning approaches frequently fail to simultaneously capture multi‑sensor spatial correlations and long‑range temporal dependencies, while standard symmetric loss functions inadequately penalize the safety‑critical error of over‑estimating residual life. This study proposes a hybrid architecture integrating Twin‑Stage One‑Dimensional Convolutional Neural Networks (1D‑CNN), a Bidirectional Long Short‑Term Memory (BiLSTM) network, and a custom Bahdanau Additive Attention mechanism. The model was trained and evaluated on the NASA Commercial Modular Aero‑Propulsion System Simulation (C‑MAPSS) FD001 sub‑dataset employing a zero‑leakage preprocessing pipeline, piecewise‑linear RUL labeling capped at 130 cycles, and the NASA‑specified asymmetric exponential loss function that disproportionately penalizes over‑estimation to enforce industrial safety constraints. Experiments on 100 test engines achieved a Root Mean Squared Error (RMSE) of 17.52 cycles and a NASA S‑Score of 922.06. Furthermore, extracted attention weight heatmaps provide interpretable, per‑engine insights into the temporal progression of degradation, supporting informed maintenance decision‑making. The proposed framework demonstrates competitive performance against established baselines and offers a principled approach to safe, interpretable prognostics in industrial settings.

PaperId: 1741, https://arxiv.org/pdf/2604.13455.pdf GitHub

Abstract:
Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluctuations. While recent trends favor computationally expensive Transformer‑based architectures, this paper challenges the prevailing "complexity‑first" paradigm. We propose a lightweight, Physics‑Informed Hybrid CNN‑BiLSTM framework that prioritizes domain knowledge over architectural depth. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Bi‑Directional LSTM for capturing temporal dependencies. Unlike standard data‑driven approaches, our model is explicitly guided by a vector of 15 engineered features including Clear‑Sky indices and Solar Zenith Angle ‑ rather than relying solely on raw historical data. Hyperparameters are rigorously tuned using Bayesian Optimization to ensure global optimality. Experimental validation using NASA POWER data in Sudan demonstrates that our physics‑guided approach achieves a Root Mean Square Error (RMSE) of 19.53 W/m^2, significantly outperforming complex attention‑based baselines (RMSE 30.64 W/m^2). These results confirm a "Complexity Paradox": in high‑noise meteorological tasks, explicit physical constraints offer a more efficient and accurate alternative to self‑attention mechanisms. The findings advocate for a shift towards hybrid, physics‑aware AI for real‑time renewable energy management.

PaperId: 1742, https://arxiv.org/pdf/2604.13440.pdf GitHub

Abstract:
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real‑time processing and on‑device intelligence. Hybrid architectures combining Structured State Space Models (SSMs) with transformer‑based LLMs offer a balance of efficiency and performance. Aggressive quantization can drastically cut model size and speed up inference, but its uneven effects on different components require careful management. In this work, we propose a lightweight, backpropagation‑free, surrogate‑based sensitivity analysis framework to identify hybrid SSM‑Transformer components most susceptible to quantization‑induced degradation. Relying solely on forward‑pass metrics, our method avoids expensive gradient computations and retraining, making it suitable for situations where access to in‑domain data is limited due to proprietary restrictions or privacy constraints. We also provide a formal analysis showing that the Kullback‑Leibler (KL) divergence metric better captures quantization sensitivity for Language modeling tasks than widely adopted alternatives such as mean squared error (MSE) and signal‑to‑quantization‑noise ratio (SQNR). Through extensive experiments on SSM and hybrid architectures, our ablation studies confirm that KL‑based rankings align with observed performance drops and outperform alternative metrics. This framework enables the practical deployment of advanced hybrid models on resource‑constrained edge devices with minimal accuracy loss. We further validate our approach with real‑world on‑device profiling on Intel Lunar Lake hardware, demonstrating that KL‑guided mixed‑precision achieves near‑FP16 perplexity with model sizes and throughput competitive with Uniform INT4 on both CPU and GPU execution modes. Code is available at https://github.com/jasonkongie/kl‑ssm‑quant.

PaperId: 1743, https://arxiv.org/pdf/2604.13432.pdf GitHub

Abstract:
Token compression is crucial for mitigating the quadratic complexity of self‑attention mechanisms in Vision Transformers (ViTs), which often involve numerous input tokens. Existing methods, such as ToMe, rely on GPU‑inefficient operations (e.g., sorting, scattered writes), introducing overheads that limit their effectiveness. We introduce MaMe, a training‑free, differentiable token merging method based entirely on matrix operations, which is GPU‑friendly to accelerate ViTs. Additionally, we present MaRe, its inverse operation, for token restoration, forming a MaMe+MaRe pipeline for image synthesis. When applied to pre‑trained models, MaMe doubles ViT‑B throughput with a 2% accuracy drop. Notably, fine‑tuning the last layer with MaMe boosts ViT‑B accuracy by 1.0% at 1.1x speed. In SigLIP2‑B@512 zero‑shot classification, MaMe provides 1.3x acceleration with negligible performance degradation. In video tasks, MaMe accelerates VideoMAE‑L by 48.5% on Kinetics‑400 with only a 0.84% accuracy loss. Furthermore, MaMe achieves simultaneous improvements in both performance and speed on some tasks. In image synthesis, the MaMe+MaRe pipeline enhances quality while reducing Stable Diffusion v2.1 generation latency by 31%. Collectively, these results demonstrate MaMe's and MaRe's effectiveness in accelerating vision models. The code is available at https://github.com/cominder/mamehttps://github.com/cominder/mame.

PaperId: 1744, https://arxiv.org/pdf/2604.13318.pdf GitHub

Abstract:
Autonomous web agents powered by large language models (LLMs) have shown promise in completing complex browser tasks, yet they still struggle with long‑horizon workflows. A key bottleneck is the grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code‑based skills are executable but opaque to the agent, offering no step‑level understanding for error recovery or adaptation. We introduce WebXSkill, a framework that bridges this gap with executable skills, each pairing a parameterized action program with step‑level natural language guidance, enabling both direct execution and agent‑driven adaptation. WebXSkill operates in three stages: skill extraction mines reusable action subsequences from readily available synthetic agent trajectories and abstracts them into parameterized skills, skill organization indexes skills into a URL‑based graph for context‑aware retrieval, and skill deployment exposes two complementary modes, grounded mode for fully automated multi‑step execution and guided mode where skills serve as step‑by‑step instructions that the agent follows with its native planning. On WebArena and WebVoyager, WebXSkill improves task success rate by up to 9.8 and 12.9 points over the baseline, respectively, demonstrating the effectiveness of executable skills for web agents. The code is publicly available at https://github.com/aiming‑lab/WebXSkill.

PaperId: 1745, https://arxiv.org/pdf/2604.13301.pdf GitHub

Abstract:
Trusted monitoring, the standard defense in AI control, is vulnerable to adaptive attacks, collusion, and strategic attack selection. All of these exploit the fact that monitoring is passive: it observes model behavior but never probes whether the model would behave differently under different perceived conditions. We introduce the honeypot protocol, which tests for context‑dependent behavior by varying only the system prompt across three conditions (evaluation, synthetic deployment, explicit no‑monitoring) while holding the task, environment, and scoring identical. We evaluate Claude Opus 4.6 in BashArena across all three conditions in both honest and attack modes. The model achieved 100% main task success and triggered zero side tasks uniformly across conditions, providing a baseline for future comparisons with stronger attack policies and additional models.

PaperId: 1746, https://arxiv.org/pdf/2604.13236.pdf GitHub

Abstract:
Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, and write structured reports, a process that can consume several hours of expert time per case. We present SemiFA, an agentic multi‑modal framework that autonomously generates structured FA reports from semiconductor inspection images in under one minute. SemiFA decomposes FA into a four‑agent LangGraph pipeline: a DefectDescriber that classifies and narrates defect morphology using DINOv2 and LLaVA‑1.6, a RootCauseAnalyzer that fuses SECS/GEM equipment telemetry with historically similar defects retrieved from a Qdrant vector database, a SeverityClassifier that assigns severity and estimates yield impact, and a RecipeAdvisor that proposes corrective process adjustments. A fifth node assembles a PDF report. We introduce SemiFA‑930, a dataset of 930 annotated semiconductor defect images paired with structured FA narratives across nine defect classes, drawn from procedural synthesis, WM‑811K, and MixedWM38. Our DINOv2‑based classifier achieves 92.1% accuracy on 140 validation images (macro F1 = 0.917), and the full pipeline produces complete FA reports in 48 seconds on an NVIDIA A100‑SXM4‑40 GB GPU. A GPT‑4o judge ablation across four modality conditions demonstrates that multi‑modal fusion improves root cause reasoning by +0.86 composite points (1‑5 scale) over an image‑only baseline, with equipment telemetry as the more load‑bearing modality. To our knowledge, SemiFA is the first system to integrate SECS/GEM equipment telemetry into a vision‑language model pipeline for autonomous FA report generation.

PaperId: 1747, https://arxiv.org/pdf/2604.13151.pdf GitHub GitHub

Abstract:
Language Model (LM) agents are increasingly used in complex open‑ended decision‑making tasks, from AI coding to physical AI. A core requirement in these settings is the ability to both explore the problem space and exploit acquired knowledge effectively. However, systematically distinguishing and quantifying exploration and exploitation from observed actions without access to the agent's internal policy remains challenging. To address this, we design controllable environments inspired by practical embodied AI scenarios. Each environment consists of a partially observable 2D grid map and an unknown task Directed Acyclic Graph (DAG). The map generation can be programmatically adjusted to emphasize exploration or exploitation difficulty. To enable policy‑agnostic evaluation, we design a metric to quantify exploration and exploitation errors from agent's actions. We evaluate a variety of frontier LM agents and find that even state‑of‑the‑art models struggle on our task, with different models exhibiting distinct failure modes. We further observe that reasoning models solve the task more effectively and show both exploration and exploitation can be significantly improved through minimal harness engineering. We release our code \hrefhttps://github.com/jjj‑madison/measurable‑explore‑exploithere.

PaperId: 1748, https://arxiv.org/pdf/2604.13120.pdf GitHub

Abstract:
Large language models generate plausible code but cannot verify correctness. Existing multi‑agent systems simulate execution or leave verification optional. We introduce execution‑grounded verification as a first‑class principle: every code change must survive sandboxed execution before propagation. We instantiate this principle in AGENTFORGE, a multi‑agent framework where Planner, Coder, Tester, Debugger, and Critic agents coordinate through shared memory and a mandatory Docker sandbox. We formalize software engineering with LLMs as an iterative decision process over repository states, where execution feedback provides a stronger supervision signal than next‑token likelihood. AGENTFORGE achieves 40.0% resolution on SWE‑BENCH Lite, outperforming single‑agent baselines by 26‑‑28 points. Ablations confirm that execution feedback and role decomposition each independently drive performance. The framework is open‑source at https://github.com/raja21068/AutoCodeAI.

PaperId: 1749, https://arxiv.org/pdf/2604.13100.pdf GitHub

Abstract:
The shift toward intent‑driven software engineering (often termed "Vibe Coding") exposes a critical Context‑Fidelity Trade‑off: vague user intents overwhelm linear reasoning chains, leading to architectural collapse in complex repo‑level generation. We propose Contract‑Coding, a structured symbolic paradigm that bridges unstructured intent and executable code via Autonomous Symbolic Grounding. By projecting ambiguous intents into a formal Language Contract, our framework serves as a Single Source of Truth (SSOT) that enforces topological independence, effectively isolating inter‑module implementation details, decreasing topological execution depth and unlocking Architectural Parallelism. Empirically, while state‑of‑the‑art agents suffer from different hallucinations on the Greenfield‑5 benchmark, Contract‑Coding achieves 47% functional success while maintaining near‑perfect structural integrity. Our work marks a critical step towards repository‑scale autonomous engineering: transitioning from strict "specification‑following" to robust, intent‑driven architecture synthesis. Our code is available at https://github.com/imliinyi/Contract‑Coding.

PaperId: 1750, https://arxiv.org/pdf/2604.13075.pdf GitHub

Abstract:
Effective de‑escalation is critical for law enforcement safety and community trust, yet traditional training methods lack scalability and realism. While Large Language Models (LLMs) enable dynamic, open‑ended simulations, their substantial computational footprint renders them impractical for deployment on the lightweight, portable hardware required for immersive field training. Small Language Models (SLMs) offer a viable real‑time alternative but suffer from a critical scarcity of high‑quality, domain‑specific training data. To bridge this gap, we present DeEscalWild, a novel benchmark dataset curated from a multi‑stage pipeline of in‑the‑wild police‑civilian interactions extracted from publicly available video repositories. Starting with 5,000 raw inputs, we employed a rigorous hybrid filtering process combining human‑in‑the‑loop verification with LLM‑as‑a‑Judge evaluation to distill 1,500 high‑fidelity scenarios. The resulting corpus comprises 285,887 dialogue turns, totaling approximately 4.7 million tokens. Extensive experiments demonstrate that SLMs fine‑tuned on this data significantly outperform their base counterparts across ROUGE‑L, BLEU‑4, METEOR, BERTScore, Realism Score, and human evaluation metrics. Notably, our fine‑tuned Qwen 2.5 (3B‑Instruct) surpasses the general‑purpose Gemini 2.5 Flash model when evaluated under equivalent conditions, demonstrating that domain‑optimized SLMs can achieve superior performance with a fraction of the computational cost. This work establishes the foundational infrastructure for accessible, low‑latency, and privacy‑preserving officer training systems at the edge. We publicly release our code(https://github.com/Hasebul/DeEscalWild‑Benchmark‑Framework) and dataset(https://doi.org/10.7910/DVN/CWMCZI).

PaperId: 1751, https://arxiv.org/pdf/2604.13072.pdf GitHub

Abstract:
LLM‑based agents are increasingly expected to handle real‑world assistant tasks, yet existing benchmarks typically evaluate them under isolated sources of difficulty, such as a single environment or fully specified instructions. This leaves a substantial gap between current evaluation settings and the compositional challenges that arise in practical deployment. To address this gap, we introduce LiveClawBench, a benchmark to evaluate LLM agents on real‑world assistant tasks. Based on an analysis of various real OpenClaw usage cases, we derive a Triple‑Axis Complexity Framework that characterizes task difficulty along three dimensions: Environment Complexity, Cognitive Demand, and Runtime Adaptability. Guided by this framework, we construct a pilot benchmark with explicit complexity‑factor annotations, covering real‑world assistant tasks with compositional difficulty. Together, the framework and benchmark provide a principled foundation for evaluating LLM agents in realistic assistant settings, and establish a basis for future expansion across task domains and complexity axes. We are continuing to enrich our case collections to achieve more comprehensive domain and complexity coverage. The project page is at https://github.com/Mosi‑AI/LiveClawBench.

PaperId: 1752, https://arxiv.org/pdf/2604.13055.pdf GitHub

Abstract:
Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities at the core. Yet, research in this area remains highly fragmented. Studies employ divergent ontologies (ESCO, ONET, national taxonomies), heterogeneous task formulations, and diverse model families, making cross‑study comparison and reproducibility exceedingly difficult. General‑purpose benchmarks lack coverage of work‑specific tasks, and the inherent sensitivity of employment data further limits open evaluation. We present WorkRB (Work Research Benchmark), the first open‑source, community‑driven benchmark tailored to work‑domain AI. WorkRB organizes 13 diverse tasks from 7 task groups as unified recommendation and NLP tasks, including job/skill recommendation, candidate recommendation, similar item recommendation, and skill extraction and normalization. WorkRB enables both monolingual and cross‑lingual evaluation settings through dynamic loading of multilingual ontologies. Developed within a multi‑stakeholder ecosystem of academia, industry, and public institutions, WorkRB has a modular design for seamless contributions and enables integration of proprietary tasks without disclosing sensitive data. WorkRB is available under the Apache 2.0 license at https://github.com/techwolf‑ai/WorkRB.

PaperId: 1753, https://arxiv.org/pdf/2604.13016.pdf GitHub

Abstract:
On‑policy distillation (OPD) has become a core technique in the post‑training of large language models, yet its training dynamics remain poorly understood. This paper provides a systematic investigation of OPD dynamics and mechanisms. We first identify that two conditions govern whether OPD succeeds or fails: (i) the student and teacher should share compatible thinking patterns; and (ii) even with consistent thinking patterns and higher scores, the teacher must offer genuinely new capabilities beyond what the student has seen during training. We validate these findings through weak‑to‑strong reverse distillation, showing that same‑family 1.5B and 7B teachers are distributionally indistinguishable from the student's perspective. Probing into the token‑level mechanism, we show that successful OPD is characterized by progressive alignment on high‑probability tokens at student‑visited states, a small shared token set that concentrates most of the probability mass (97%‑99%). We further propose two practical strategies to recover failing OPD: off‑policy cold start and teacher‑aligned prompt selection. Finally, we show that OPD's apparent free lunch of dense token‑level reward comes at a cost, raising the question of whether OPD can scale to long‑horizon distillation.

PaperId: 1754, https://arxiv.org/pdf/2604.13010.pdf GitHub

Abstract:
On‑policy distillation (OPD) is an effective post‑training paradigm for large language models but requires a live teacher server throughout training, resulting in substantial infrastructure overhead. We investigate whether OPD can be performed offline by precomputing teacher log‑probabilities once over SFT rollouts and reusing them during training. We find that naively doing so fails to reliably match standard OPD, and trace the root cause to a previously overlooked condition we term teacher consistency, requiring that the same teacher be used for both supervised fine‑tuning and OPD. Violating this condition introduces a gradient bias that degrades performance for both offline and online OPD. Building on this insight, we propose Lightning OPD, an offline on‑policy distillation framework that enforces teacher consistency and eliminates the need for a live teacher server entirely. We prove that, under teacher consistency, Lightning OPD shares the same optimum as standard OPD, with bounded gradient discrepancy and an implicit regularization effect that helps prevent policy drift. Experiments on math reasoning and code generation show that Lightning OPD achieves comparable performance to standard OPD while delivering 4.0x higher training efficiency. Starting from an SFT‑initialized Qwen3‑8B‑Base model, Lightning OPD reaches 69.9% on AIME 2024 in just 30 GPU hours. Lightning OPD further scales to MoE architectures, training Qwen3‑30B‑A3B to 71.0% on AIME 2024 on a single 8xH100 node, substantially lowering the barrier for academic research on LLM post‑training. Our code is released at https://github.com/jet‑ai‑projects/Lightning‑OPD.

PaperId: 1755, https://arxiv.org/pdf/2604.12986.pdf GitHub

Abstract:
Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applications will embed AI copilots by the end of 2026. As agents gain the ability to execute real‑world actions (reading files, running commands, making network requests, modifying databases), a fundamental security gap has emerged. The dominant approach to agent safety relies on prompt‑level guardrails: natural language instructions that operate at the same abstraction level as the threats they attempt to mitigate. This paper argues that prompt‑based safety is architecturally insufficient for agents with execution capability and introduces Parallax, a paradigm for safe autonomous AI execution grounded in four principles: Cognitive‑Executive Separation, which structurally prevents the reasoning system from executing actions; Adversarial Validation with Graduated Determinism, which interposes an independent, multi‑tiered validator between reasoning and execution; Information Flow Control, which propagates data sensitivity labels through agent workflows to detect context‑dependent threats; and Reversible Execution, which captures pre‑destructive state to enable rollback when validation fails. We present OpenParallax, an open‑source reference implementation in Go, and evaluate it using Assume‑Compromise Evaluation, a methodology that bypasses the reasoning system entirely to test the architectural boundary under full agent compromise. Across 280 adversarial test cases in nine attack categories, Parallax blocks 98.9% of attacks with zero false positives under its default configuration, and 100% of attacks under its maximum‑security configuration. When the reasoning system is compromised, prompt‑level guardrails provide zero protection because they exist only within the compromised system; Parallax's architectural boundary holds regardless.

PaperId: 1756, https://arxiv.org/pdf/2604.12944.pdf GitHub

Abstract:
Despite significant progress in video‑language modeling, hallucinations remain a persistent challenge in Video Large Language Models (Vid‑LLMs), referring to outputs that appear plausible yet contradict the content of the input video. This survey presents a comprehensive analysis of hallucinations in Vid‑LLMs and introduces a systematic taxonomy that categorizes them into two core types: dynamic distortion and content fabrication, each comprising two subtypes with representative cases. Building on this taxonomy, we review recent advances in the evaluation and mitigation of hallucinations, covering key benchmarks, metrics, and intervention strategies. We further analyze the root causes of dynamic distortion and content fabrication, which often result from limited capacity for temporal representation and insufficient visual grounding. These insights inform several promising directions for future work, including the development of motion‑aware visual encoders and the integration of counterfactual learning techniques. This survey consolidates scattered progress to foster a systematic understanding of hallucinations in Vid‑LLMs, laying the groundwork for building robust and reliable video‑language systems. An up‑to‑date curated list of related works is maintained at https://github.com/hukcc/Awesome‑Video‑Hallucination .

PaperId: 1757, https://arxiv.org/pdf/2604.12913.pdf GitHub

Abstract:
Binary decompilation is a critical reverse engineering task aimed at reconstructing high‑level source code from stripped executables. Although Large Language Models (LLMs) have recently shown promise, they often suffer from "logical hallucinations" and "semantic misalignment" due to the irreversible semantic loss during compilation, resulting in generated code that fails to re‑execute. In this study, we propose Cognitive Decompiler Refinement with Robustness (CoDe‑R), a lightweight two‑stage code refinement framework. The first stage introduces Semantic Cognitive Enhancement (SCE), a Rationale‑Guided Semantic Injection strategy that trains the model to recover high‑level algorithmic intent alongside code. The second stage introduces a Dynamic Dual‑Path Fallback (DDPF) mechanism during inference, which adaptively balances semantic recovery and syntactic stability via a hybrid verification strategy. Evaluation on the HumanEval‑Decompile benchmark demonstrates that CoDe‑R (using a 1.3B backbone) establishes a new State‑of‑the‑Art (SOTA) in the lightweight regime. Notably, it is the first 1.3B model to exceed an Average Re‑executability Rate of 50.00%, significantly outperforming the baseline and effectively bridging the gap between efficient models and expert‑level performance. Our code is available at https://github.com/Theaoi/CoDe‑R.

PaperId: 1758, https://arxiv.org/pdf/2604.12890.pdf GitHub

Abstract:
Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, managing the heterogeneous information and high token costs associated with multimodal inputs over long horizons remains a critical challenge, as existing methods often suffer from context explosion or the loss of crucial visual signals. To address this, we propose a novel Long‑horizon MultiModal deep search framework, named LMM‑Searcher, centered on a file‑based visual representation mechanism. By offloading visual assets to an external file system and mapping them to lightweight textual identifiers (UIDs), our approach mitigates context overhead while preserving multimodal information for future access. We equip the agent with a tailored fetch‑image tool, enabling a progressive, on‑demand visual loading strategy for active perception. Furthermore, we introduce a data synthesis pipeline designed to generate queries requiring complex cross‑modal multi‑hop reasoning. Using this pipeline, we distill 12K high‑quality trajectories to fine‑tune Qwen3‑VL‑Thinking‑30A3B into a specialized multimodal deep search agent. Extensive experiments across four benchmarks demonstrate that our method successfully scales to 100‑turn search horizons, achieving state‑of‑the‑art performance among open‑source models on challenging long‑horizon benchmarks like MM‑BrowseComp and MMSearch‑Plus, while also exhibiting strong generalizability across different base models. Our code will be released in https://github.com/RUCAIBox/LMM‑Searcher.

PaperId: 1759, https://arxiv.org/pdf/2604.12767.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) suffer from substantial computational overhead due to the high redundancy in visual token sequences. Existing approaches typically address this issue using single‑layer Vision Transformer (ViT) features and static pruning strategies. However, such fixed configurations are often brittle under diverse instructions. To overcome these limitations, we propose CLASP, a plug‑and‑play token reduction framework based on class‑adaptive layer fusion and dual‑stage pruning. Specifically, CLASP first constructs category‑specific visual representations through multi‑layer vision feature fusion. It then performs dual‑stage pruning, allocating the token budget between attention‑salient pivot tokens for relevance and redundancy‑aware completion tokens for coverage. Through class‑adaptive pruning, CLASP enables prompt‑conditioned feature fusion and budget allocation, allowing aggressive yet robust visual token reduction. Extensive experiments demonstrate that CLASP consistently outperforms existing methods across a wide range of benchmarks, pruning ratios, and MLLM architectures. Code will be available at https://github.com/Yunkaidang/CLASP.

PaperId: 1760, https://arxiv.org/pdf/2604.12757.pdf GitHub GitHub

Abstract:
Adversarial robustness is essential for deploying neural networks in safety‑critical applications, yet standard evaluation methods either require expensive adversarial attacks or report only a single aggregate score that obscures how robustness is distributed across classes. We introduce the \emphGF‑Score (GREAT‑Fairness Score), a framework that decomposes the certified GREAT Score into per‑class robustness profiles and quantifies their disparity through four metrics grounded in welfare economics: the Robustness Disparity Index (RDI), the Normalized Robustness Gini Coefficient (NRGC), Worst‑Case Class Robustness (WCR), and a Fairness‑Penalized GREAT Score (FP‑GREAT). The framework further eliminates the original method's dependence on adversarial attacks through a self‑calibration procedure that tunes the temperature parameter using only clean accuracy correlations. Evaluating 22 models from RobustBench across CIFAR‑10 and ImageNet, we find that the decomposition is exact, that per‑class scores reveal consistent vulnerability patterns (e.g., ``cat'' is the weakest class in 76% of CIFAR‑10 models), and that more robust models tend to exhibit greater class‑level disparity. These results establish a practical, attack‑free auditing pipeline for diagnosing where certified robustness guarantees fail to protect all classes equally. We release our code on \hrefhttps://github.com/aryashah2k/gf‑scoreGitHub.

PaperId: 1761, https://arxiv.org/pdf/2604.12651.pdf GitHub

Abstract:
Knowledge graph embedding (KGE) models perform well on link prediction but struggle with unseen entities, relations, and especially literals, limiting their use in dynamic, heterogeneous graphs. In contrast, pretrained large language models (LLMs) generalize effectively through prompting. We reformulate link prediction as a prompt learning problem and introduce RALP, which learns string‑based chain‑of‑thought (CoT) prompts as scoring functions for triples. Using Bayesian Optimization through MIPRO algorithm, RALP identifies effective prompts from fewer than 30 training examples without gradient access. At inference, RALP predicts missing entities, relations or whole triples and assigns confidence scores based on the learned prompt. We evaluate on transductive, numerical, and OWL instance retrieval benchmarks. RALP improves state‑of‑the‑art KGE models by over 5% MRR across datasets and enhances generalization via high‑quality inferred triples. On OWL reasoning tasks with complex class expressions (e.g., \exists hasChild.Female, \geq 5 \; hasChild.Female), it achieves over 88% Jaccard similarity. These results highlight prompt‑based LLM reasoning as a flexible alternative to embedding‑based methods. We release our implementation, training, and evaluation pipeline as open source: https://github.com/dice‑group/RALP .

PaperId: 1762, https://arxiv.org/pdf/2604.12627.pdf GitHub

Abstract:
RLVR improves reasoning in large language models, but its effectiveness is often limited by severe reward sparsity on hard problems. Recent hint‑based RL methods mitigate sparsity by injecting partial solutions or abstract templates, yet they typically scale guidance by adding more tokens, which introduce redundancy, inconsistency, and extra training overhead. We propose KnowRL (Knowledge‑Guided Reinforcement Learning), an RL training framework that treats hint design as a minimal‑sufficient guidance problem. During RL training, KnowRL decomposes guidance into atomic knowledge points (KPs) and uses Constrained Subset Search (CSS) to construct compact, interaction‑aware subsets for training. We further identify a pruning interaction paradox ‑‑ removing one KP may help while removing multiple such KPs can hurt ‑‑ and explicitly optimize for robust subset curation under this dependency structure. We train KnowRL‑Nemotron‑1.5B from OpenMath‑Nemotron‑1.5B. Across eight reasoning benchmarks at the 1.5B scale, KnowRL‑Nemotron‑1.5B consistently outperforms strong RL and hinting baselines. Without KP hints at inference, KnowRL‑Nemotron‑1.5B reaches 70.08 average accuracy, already surpassing Nemotron‑1.5B by +9.63 points; with selected KPs, performance improves to 74.16, establishing a new state of the art at this scale. The model, curated training data, and code are publicly available at https://github.com/Hasuer/KnowRL.

PaperId: 1763, https://arxiv.org/pdf/2604.12573.pdf GitHub

Abstract:
Large Language Models are increasingly deployed for decision‑making, yet their adoption in high‑stakes domains remains limited by miscalibrated probabilities, unfaithful explanations, and inability to incorporate expert knowledge precisely. We propose IDEA, a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors. Through joint learning of verbal‑to‑numerical mappings and decision parameters via EM, correlated sampling that preserves factor dependencies, and direct parameter editing with mathematical guarantees, IDEA produces calibrated probabilities while enabling quantitative human‑AI collaboration. Experiments across five datasets show IDEA with Qwen‑3‑32B (78.6%) outperforms DeepSeek R1 (68.1%) and GPT‑5.2 (77.9%), achieving perfect factor exclusion and exact calibration ‑‑ precision unattainable through prompting alone. The implementation is publicly available at https://github.com/leonbig/IDEA.

PaperId: 1764, https://arxiv.org/pdf/2604.12512.pdf GitHub

Authors:Guanyi Qin, Jie Liang, Bingbing Zhang, Lishen Qu, Ya-nan Guan, Hui Zeng, Lei Zhang, Radu Timofte, Jianhui Sun, Xinli Yue, Tao Shao, Huan Hou, Wenjie Liao, Shuhao Han, Jieyu Yuan, Chunle Guo, Chongyi Li, Zewen Chen, Yunze Liu, Jian Guo, Juan Wang, Yun Zeng, Bing Li, Weiming Hu, Hesong Li, Dehua Liu, Xinjie Zhang, Qiang Li, Li Yan, Wei Dong, Qingsen Yan, Xingcan Li, Shenglong Zhou, Manjiang Yin, Yinxiang Zhang, Hongbo Wang, Jikai Xu, Zhaohui Fan, Dandan Zhu, Wei Sun, Weixia Zhang, Kun Zhu, Nana Zhang, Kaiwei Zhang, Qianqian Zhang, Zhihan Zhang, William Gordon, Linwei Wu, Jiachen Tu, Guoyi Xu, Yaoxin Jiang, Cici Liu, Yaokun Shi

Abstract:
In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Assessment. Conventional Image Quality Assessment (IQA) typically relies on scalar scores. By compressing complex visual characteristics into a single number, these methods fundamentally struggle to distinguish subtle differences among uniformly high‑quality images. Furthermore, they fail to articulate why one image is superior, lacking the reasoning capabilities required to provide guidance for vision tasks. To bridge this gap, recent advancements in Multimodal Large Language Models (MLLMs) offer a promising paradigm. Inspired by this potential, our challenge establishes a novel benchmark exploring the ability of MLLMs to mimic human expert cognition in evaluating high‑quality image pairs. Participants were tasked with overcoming critical bottlenecks in professional scenarios, centering on two primary objectives: (1) Comparative Quality Selection: reliably identifying the visually superior image within a high‑quality pair; and (2) Interpretative Reasoning: generating grounded, expert‑level explanations that detail the rationale behind the selection. In total, the challenge attracted nearly 200 registrations and over 2,500 submissions. The top‑performing methods significantly advanced the state of the art in professional IQA. The challenge dataset is available at https://github.com/narthchin/RAIM‑PIQA, and the official homepage is accessible at https://www.codabench.org/competitions/12789/.

PaperId: 1765, https://arxiv.org/pdf/2604.12503.pdf GitHub

Abstract:
Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge‑intensive scenarios. Knowledge Base Question Answering (KBQA) mitigates this by grounding generation in Knowledge Graphs (KGs). However, most multi‑hop KBQA methods rely on explicit edge traversal, making them fragile to KG incompleteness. In this paper, we proposed a novel graph‑based soft prompting framework that shifts the reasoning paradigm from node‑level path traversal to subgraph‑level reasoning. Specifically, we employ a Graph Neural Network (GNN) to encode extracted structural subgraphs into soft prompts, enabling LLM to reason over richer structural context and identify relevant entities beyond immediate graph neighbors, thereby reducing sensitivity to missing edges. Furthermore, we introduce a two‑stage paradigm that reduces computational cost while preserving good performance: a lightweight LLM first leverages the soft prompts to identify question‑relevant entities and relations, followed by a more powerful LLM for evidence‑aware answer generation. Experiments on four multi‑hop KBQA benchmarks show that our approach achieves state‑of‑the‑art performance on three of them, demonstrating its effectiveness. Code is available at the repository: https://github.com/Wangshuaiia/GraSP.

PaperId: 1766, https://arxiv.org/pdf/2604.12502.pdf GitHub GitHub

Abstract:
Parameter‑efficient fine‑tuning (PEFT) in multimodal tracking reveals a concerning trend where recent performance gains are often achieved at the cost of inflated parameter budgets, which fundamentally erodes PEFT's efficiency promise. In this work, we introduce SEATrack, a Simple, Efficient, and Adaptive two‑stream multimodal tracker that tackles this performance‑efficiency dilemma from two complementary perspectives. We first prioritize cross‑modal alignment of matching responses, an underexplored yet pivotal factor that we argue is essential for breaking the trade‑off. Specifically, we observe that modality‑specific biases in existing two‑stream methods generate conflicting matching attention maps, thereby hindering effective joint representation learning. To mitigate this, we propose AMG‑LoRA, which seamlessly integrates Low‑Rank Adaptation (LoRA) for domain adaptation with Adaptive Mutual Guidance (AMG) to dynamically refine and align attention maps across modalities. We then depart from conventional local fusion approaches by introducing a Hierarchical Mixture of Experts (HMoE) that enables efficient global relation modeling, effectively balancing expressiveness and computational efficiency in cross‑modal fusion. Equipped with these innovations, SEATrack advances notable progress over state‑of‑the‑art methods in balancing performance with efficiency across RGB‑T, RGB‑D, and RGB‑E tracking tasks. \hrefhttps://github.com/AutoLab‑SAI‑SJTU/SEATrack\textcolorcyanCode is available.

PaperId: 1767, https://arxiv.org/pdf/2604.12487.pdf GitHub

Abstract:
Large Language Models (LLMs) exhibit strong abilities in natural language understanding and generation, yet they struggle with knowledge‑intensive reasoning. Structured Knowledge Graphs (KGs) provide an effective form of external knowledge representation and have been widely used to enhance performance in classical Knowledge Base Question Answering (KBQA) tasks. However, performing precise multi‑hop reasoning over KGs for complex queries remains highly challenging. Most existing approaches decompose the reasoning process into a sequence of isolated steps executed through a fixed pipeline. While effective to some extent, such designs constrain reasoning flexibility and fragment the overall decision process, often leading to incoherence and the loss of critical intermediate information from earlier steps. In this paper, we introduce KG‑Reasoner, an end‑to‑end framework that integrates multi‑step reasoning into a unified "thinking" phase of a Reasoning LLM. Through Reinforcement Learning (RL), the LLM is trained to internalize the KG traversal process, enabling it to dynamically explore reasoning paths, and perform backtracking when necessary. Experiments on eight multi‑hop and knowledge‑intensive reasoning benchmarks demonstrate that KG‑Reasoner achieves competitive or superior performance compared to the state‑of‑the‑art methods. Codes are available at the repository: https://github.com/Wangshuaiia/KG‑Reasoner.

PaperId: 1768, https://arxiv.org/pdf/2604.12456.pdf GitHub

Abstract:
Zero‑shot voice conversion (VC) aims to convert a source utterance into the voice of an unseen target speaker while preserving its linguistic content. Although recent systems have improved conversion quality, building zero‑shot VC systems for interactive scenarios remains challenging because high‑fidelity speaker transfer and low‑latency streaming inference are difficult to achieve simultaneously. In this work, we present X‑VC, a zero‑shot streaming VC system that performs one‑step conversion in the latent space of a pretrained neural codec. X‑VC uses a dual‑conditioning acoustic converter that jointly models source codec latents and frame‑level acoustic conditions derived from target reference speech, while injecting utterance‑level target speaker information through adaptive normalization. To reduce the mismatch between training and inference, we train the model with generated paired data and a role‑assignment strategy that combines standard, reconstruction, and reversed modes. For streaming inference, we further adopt a chunkwise inference scheme with overlap smoothing that is aligned with the segment‑based training paradigm of the codec. Experiments on Seed‑TTS‑Eval show that X‑VC achieves the best streaming WER in both English and Chinese, strong speaker similarity in same‑language and cross‑lingual settings, and substantially lower offline real‑time factor than the compared baselines. These results suggest that codec‑space one‑step conversion is a practical approach for building high‑quality low‑latency zero‑shot VC systems. Our audio samples, code and checkpoints are released at https://github.com/Jerrister/X‑VC.

PaperId: 1769, https://arxiv.org/pdf/2604.12391.pdf GitHub

Abstract:
In this paper, we present Chain‑of‑Models Pre‑Training (CoM‑PT), a novel performance‑lossless training acceleration method for vision foundation models (VFMs). This approach fundamentally differs from existing acceleration methods in its core motivation: rather than optimizing each model individually, CoM‑PT is designed to accelerate the training pipeline at the model family level, scaling efficiently as the model family expands. Specifically, CoM‑PT establishes a pre‑training sequence for the model family, arranged in ascending order of model size, called model chain. In this chain, only the smallest model undergoes standard individual pre‑training, while the other models are efficiently trained through sequential inverse knowledge transfer from their smaller predecessors by jointly reusing the knowledge in the parameter space and the feature space. As a result, CoM‑PT enables all models to achieve performance that is mostly superior to standard individual training while significantly reducing training cost, and this is extensively validated across 45 datasets spanning zero‑shot and fine‑tuning tasks. Notably, its efficient scaling property yields a remarkable phenomenon: training more models even results in higher efficiency. For instance, when pre‑training on CC3M: i) given ViT‑L as the largest model, progressively prepending smaller models to the model chain reduces computational complexity by up to 72%; ii) within a fixed model size range, as the VFM family scales across 3, 4, and 7 models, the acceleration ratio of CoM‑PT exhibits a striking leap: from 4.13X to 5.68X and 7.09X. Since CoM‑PT is naturally agnostic to specific pre‑training paradigms, we open‑source the code to spur further extensions in more computationally intensive scenarios, such as large language model pre‑training.

PaperId: 1770, https://arxiv.org/pdf/2604.12379.pdf GitHub

Abstract:
Large language models (LLMs) increasingly rely on explicit reasoning to solve coding tasks, yet evaluating the quality of this reasoning remains challenging. Existing reasoning evaluators are not designed for coding, and current benchmarks focus primarily on code generation, leaving other coding tasks largely unexplored. We introduce CodeRQ‑Bench, the first benchmark for evaluating LLM reasoning quality across three coding task categories: generation, summarization, and classification. Using this benchmark, we analyze 1,069 mismatch cases from existing evaluators, identify five recurring limitations, and derive four design insights for reasoning evaluation in coding tasks. Guided by these insights, we propose VERA, a two‑stage evaluator that combines evidence‑grounded verification with ambiguity‑aware score correction. Experiments on CodeRQ‑Bench show that VERA consistently outperforms strong baselines across four datasets, improving AUCROC by up to 0.26 and AUPRC by up to 0.21. We release CodeRQ‑Bench at https://github.com/MrLYG/CodeRQ‑Bench, supporting future investigations.

PaperId: 1771, https://arxiv.org/pdf/2604.12377.pdf GitHub

Abstract:
Korean is a morphologically rich language with a featural writing system in which each character is systematically composed of subcharacter units known as Jamo. These subcharacters not only determine the visual structure of Korean but also encode frequent and linguistically meaningful morphophonological processes. However, most current Korean language models (LMs) are based on subword tokenization schemes, which are not explicitly designed to capture the internal compositional structure of characters. To address this limitation, we propose SCRIPT, a model‑agnostic module that injects subcharacter compositional knowledge into Korean PLMs. SCRIPT allows to enhance subword embeddings with structural granularity, without requiring architectural changes or additional pre‑training. As a result, SCRIPT enhances all baselines across various Korean natural language understanding (NLU) and generation (NLG) tasks. Moreover, beyond performance gains, detailed linguistic analyses show that SCRIPT reshapes the embedding space in a way that better captures grammatical regularities and semantically cohesive variations. Our code is available at https://github.com/SungHo3268/SCRIPT.

PaperId: 1772, https://arxiv.org/pdf/2604.12254.pdf GitHub GitHub

Abstract:
SpanKey is a lightweight way to gate inference without encrypting weights or chasing leaderboard accuracy on gated inference. The idea is to condition activations on secret keys. A basis matrix B defines a low‑dimensional key subspace Span(B); during training we sample coefficients α and form keys k=α^\top B, then inject them into intermediate activations with additive or multiplicative maps and strength γ. Valid keys lie in Span(B); invalid keys are sampled outside that subspace. We make three points. (i) Mechanism: subspace key injection and a multi‑layer design space. (ii) Failure mode: key absorption, together with two analytical results (a Beta‑energy split and margin‑tail diagnostics), explains weak baseline separation in energy and margin terms ‑‑ these are not a security theorem. iii) Deny losses and experiments: Modes A‑‑C and extensions, with CIFAR‑10 ResNet‑18 runs and MNIST ablations for Mode B. We summarize setup and first‑order analysis, injectors, absorption, deny losses and ablations, a threat discussion that does not promise cryptography, and closing remarks on scale. Code: \texttthttps://github.com/mindmemory‑ai/dksc

PaperId: 1773, https://arxiv.org/pdf/2604.12245.pdf GitHub

Abstract:
Deep neural networks, despite their high accuracy, often exhibit poor confidence calibration, limiting their reliability in high‑stakes applications. Current ad‑hoc confidence calibration methods attempt to fix this during training but face a fundamental trade‑off: two‑phase training methods achieve strong classification performance at the cost of training instability and poorer confidence calibration, while single‑loss methods are stable but underperform in classification. This paper addresses and mitigates this stability‑performance trade‑off. We propose Socrates Loss, a novel, unified loss function that explicitly leverages uncertainty by incorporating an auxiliary unknown class, whose predictions directly influence the loss function and a dynamic uncertainty penalty. This unified objective allows the model to be optimized for both classification and confidence calibration simultaneously, without the instability of complex, scheduled losses. We provide theoretical guarantees that our method regularizes the model to prevent miscalibration and overfitting. Across four benchmark datasets and multiple architectures, our comprehensive experiments demonstrate that Socrates Loss consistently improves training stability while achieving more favorable accuracy‑calibration trade‑off, often converging faster than existing methods.

PaperId: 1774, https://arxiv.org/pdf/2604.12237.pdf GitHub

Abstract:
In drug discovery, molecular optimization aims to iteratively refine a lead compound to improve molecular properties while preserving structural similarity to the original molecule. However, each oracle evaluation is expensive, making sample efficiency a key challenge for existing methods under a limited oracle budget. Trial‑and‑error approaches require many oracle calls, while methods that leverage external knowledge tend to reuse familiar templates and struggle on challenging objectives. A key missing piece is long‑term memory that can ground decisions and provide reusable insights for future optimizations. To address this, we present MolMem (Molecular optimization with Memory), a multi‑turn agentic reinforcement learning (RL) framework with a dual‑memory system. Specifically, MolMem uses Static Exemplar Memory to retrieve relevant exemplars for cold‑start grounding, and Evolving Skill Memory to distill successful trajectories into reusable strategies. Built on this memory‑augmented formulation, we train the policy with dense step‑wise rewards, turning costly rollouts into long‑term knowledge that improves future optimization. Extensive experiments show that MolMem achieves 90% success on single‑property tasks (1.5× over the best baseline) and 52% on multi‑property tasks using only 500 oracle calls. Our code is available at https://github.com/REAL‑Lab‑NU/MolMem.

PaperId: 1775, https://arxiv.org/pdf/2604.12213.pdf GitHub

Abstract:
Preserving multimodal signals across agent boundaries is necessary for accurate cross‑modal reasoning, but it is not sufficient. We show that modality‑native routing in Agent‑to‑Agent (A2A) networks improves task accuracy by 20 percentage points over text‑bottleneck baselines, but only when the downstream reasoning agent can exploit the richer context that native routing preserves. An ablation replacing LLM‑backed reasoning with keyword matching eliminates the accuracy gap entirely (36% vs. 36%), establishing a two‑layer requirement: protocol‑level routing must be paired with capable agent‑level reasoning for the benefit to materialize. We present MMA2A, an architecture layer atop A2A that inspects Agent Card capability declarations to route voice, image, and text parts in their native modality. On CrossModal‑CS, a controlled 50‑task benchmark with the same LLM backend, same tasks, and only the routing path varying, MMA2A achieves 52% task completion accuracy versus 32% for the text‑bottleneck baseline (95% bootstrap CI on ΔTCA: [8, 32] pp; McNemar's exact p = 0.006). Gains concentrate on vision‑dependent tasks: product defect reports improve by +38.5 pp and visual troubleshooting by +16.7 pp. This accuracy gain comes at a 1.8× latency cost from native multimodal processing. These results suggest that routing is a first‑order design variable in multi‑agent systems, as it determines the information available for downstream reasoning.

PaperId: 1776, https://arxiv.org/pdf/2604.12208.pdf GitHub

Abstract:
Global navigation information and local scene understanding are two crucial components of autonomous driving systems. However, our experimental results indicate that many end‑to‑end autonomous driving systems tend to over‑rely on local scene understanding while failing to utilize global navigation information. These systems exhibit weak correlation between their planning capabilities and navigation input, and struggle to perform navigation‑following in complex scenarios. To overcome this limitation, we propose the Sequential Navigation Guidance (SNG) framework, an efficient representation of global navigation information based on real‑world navigation patterns. The SNG encompasses both navigation paths for constraining long‑term trajectories and turn‑by‑turn (TBT) information for real‑time decision‑making logic. We constructed the SNG‑QA dataset, a visual question answering (VQA) dataset based on SNG that aligns global and local planning. Additionally, we introduce an efficient model SNG‑VLA that fuses local planning with global planning. The SNG‑VLA achieves state‑of‑the‑art performance through precise navigation information modeling without requiring auxiliary loss functions from perception tasks. Project page: SNG‑VLA

PaperId: 1777, https://arxiv.org/pdf/2604.12152.pdf GitHub

Abstract:
Latent diffusion models for medical image super‑resolution universally inherit variational autoencoders designed for natural photographs. We show that this default choice, not the diffusion architecture, is the dominant constraint on reconstruction quality. In a controlled experiment holding all other pipeline components fixed, replacing the generic Stable Diffusion VAE with MedVAE, a domain‑specific autoencoder pretrained on more than 1.6 million medical images, yields +2.91 to +3.29 dB PSNR improvement across knee MRI, brain MRI, and chest X‑ray (n = 1,820; Cohen's d = 1.37 to 1.86, all p < 10^‑20, Wilcoxon signed‑rank). Wavelet decomposition localises the advantage to the finest spatial frequency bands encoding anatomically relevant fine structure. Ablations across inference schedules, prediction targets, and generative architectures confirm the gap is stable within plus or minus 0.15 dB, while hallucination rates remain comparable between methods (Cohen's h < 0.02 across all datasets), establishing that reconstruction fidelity and generative hallucination are governed by independent pipeline components. These results provide a practical screening criterion: autoencoder reconstruction quality, measurable without diffusion training, predicts downstream SR performance (R^2 = 0.67), suggesting that domain‑specific VAE selection should precede diffusion architecture search. Code and trained model weights are publicly available at https://github.com/sebasmos/latent‑sr.

PaperId: 1778, https://arxiv.org/pdf/2604.12102.pdf GitHub

Abstract:
We introduce compute‑grounded reasoning (CGR), a design paradigm for spatial‑aware research agents in which every answerable sub‑problem is resolved by deterministic computation before a language model is asked to generate. Spatial Atlas instantiates CGR as a single Agent‑to‑Agent (A2A) server that handles two challenging benchmarks: FieldWorkArena, a multimodal spatial question‑answering benchmark spanning factory, warehouse, and retail environments, and MLE‑Bench, a suite of 75 Kaggle machine learning competitions requiring end‑to‑end ML engineering. A structured spatial scene graph engine extracts entities and relations from vision descriptions, computes distances and safety violations deterministically, then feeds computed facts to large language models, thereby avoiding hallucinated spatial reasoning. Entropy‑guided action selection maximizes information gain per step and routes queries across a three‑tier frontier model stack (OpenAI + Anthropic). A self‑healing ML pipeline with strategy‑aware code generation, a score‑driven iterative refinement loop, and a prompt‑based leak audit registry round out the system. We evaluate across both benchmarks and show that CGR yields competitive accuracy while maintaining interpretability through structured intermediate representations and deterministic spatial computations.

PaperId: 1779, https://arxiv.org/pdf/2604.12016.pdf GitHub

Abstract:
Large language models map semantically related prompts to similar internal representations ‑‑ a phenomenon interpretable as attractor‑like dynamics. We ask whether the identity document of a persistent cognitive agent (its cognitive_core) exhibits analogous attractor‑like behavior. We present a controlled experiment on Llama 3.1 8B Instruct, comparing hidden states of an original cognitive_core (Condition A), seven paraphrases (Condition B), and seven structurally matched controls (Condition C). Mean‑pooled states at layers 8, 16, and 24 show that paraphrases converge to a tighter cluster than controls (Cohen's d > 1.88, p < 10^‑27, Bonferroni‑corrected). Replication on Gemma 2 9B confirms cross‑architecture generalizability. Ablations suggest the effect is primarily semantic rather than structural, and that structural completeness appears necessary to reach the attractor region. An exploratory experiment shows that reading a scientific description of the agent shifts internal state toward the attractor ‑‑ closer than a sham preprint ‑‑ distinguishing knowing about an identity from operating as that identity. These results provide representational evidence that agent identity documents induce attractor‑like geometry in LLM activation space.

PaperId: 1780, https://arxiv.org/pdf/2604.11998.pdf GitHub

Authors:Xingyu Qiu, Yuqian Fu, Jiawei Geng, Bin Ren, Jiancheng Pan, Zongwei Wu, Hao Tang, Yanwei Fu, Radu Timofte, Nicu Sebe, Mohamed Elhoseiny, Lingyi Hong, Mingxi Cheng, Xingqi He, Runze Li, Xingdong Sheng, Wenqiang Zhang, Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao, Yongwei Jiang, Yixiong Zou, Zhe Zhang, Yang Yang, Kaiyu Li, Bowen Fu, Zixuan Jiang, Ke Li, Hui Qiao, Xiangyong Cao, Xuanlong Yu, Youyang Sha, Longfei Liu, Di Yang, Xi Shen, Kyeongryeol Go, Taewoong Jang, Saiprasad Meesiyawar, Ravi Kirasur, Rakshita Kulkarni, Bhoomi Deshpande, Harsh Patil, Uma Mudenagudi, Shuming Hu, Chao Chen, Tao Wang, Wei Zhou, Qi Xu, Zhenzhao Xing, Dandan Zhao, Hanzhe Xia, Dongdong Lu, Zhe Zhang, Jingru Wang, Guangwei Huang, Jiachen Tu, Yaokun Shi, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Liwei Zhou, Bei Dou, Tao Wu, Zekang Fan, Junjie Liu, Adhémar de Senneville, Flavien Armangeon, Mengbers, Yazhe Lyu, Zhimeng Xin, Zijian Zhuang, Hongchun Zhu, Li Wang

Abstract:
Cross‑domain few‑shot object detection (CD‑FSOD) remains a challenging problem for existing object detectors and few‑shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026, we hosted the second CD‑FSOD Challenge to systematically evaluate and promote progress in detecting objects in unseen target domains under limited annotation conditions. The challenge received strong community interest, with 128 registered participants and a total of 696 submissions. Among them, 31 teams actively participated, and 19 teams submitted valid final results. Participants explored a wide range of strategies, introducing innovative methods that push the performance frontier under both open‑source and closed‑source tracks. This report presents a detailed overview of the NTIRE 2026 CD‑FSOD Challenge, including a summary of the submitted approaches and an analysis of the final results across all participating teams. Challenge Codes: https://github.com/ohMargin/NTIRE2026_CDFSOD.

PaperId: 1781, https://arxiv.org/pdf/2604.11996.pdf GitHub

Abstract:
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reasoning used to produce it. This highlights a fundamental limitation of outcome‑based evaluation: models may arrive at correct answers through flawed reasoning, and models with substantially different reasoning capabilities can nevertheless exhibit similar benchmark accuracy, for example due to memorization or over‑optimization. In this paper, we ask: given existing benchmarks, can we move beyond outcome‑based evaluation to assess the quality of reasoning itself? We seek metrics that (1) differentiate models with similar accuracy and (2) are robust to variations in input prompts and generation configurations. To this end, we propose a reasoning score that evaluates reasoning traces along dimensions such as faithfulness, coherence, utility, and factuality. A remaining question is how to aggregate this score across multiple sampled traces. Naively averaging them is undesirable, particularly in long‑horizon settings, where the number of possible trajectories grows rapidly, and low‑confidence correct traces are more likely to be coincidental. To address this, we introduce the Filtered Reasoning Score (FRS), which computes reasoning quality using only the top‑K% most confident traces. Evaluating with FRS, models that are indistinguishable under standard accuracy exhibit significant differences in reasoning quality. Moreover, models with higher FRS on one benchmark tend to perform better on other reasoning benchmarks, in both accuracy and reasoning quality. Together, these findings suggest that FRS complements accuracy by capturing a model's transferable reasoning capabilities. We open source our evaluation codebase: https://github.com/Manas2006/benchmark_reproducibility.

PaperId: 1782, https://arxiv.org/pdf/2604.11978.pdf GitHub GitHub

Abstract:
Large language model (LLM) agents perform strongly on short‑ and mid‑horizon tasks, but often break down on long‑horizon tasks that require extended, interdependent action sequences. Despite rapid progress in agentic systems, these long‑horizon failures remain poorly characterized, hindering principled diagnosis and comparison across domains. To address this gap, we introduce HORIZON, an initial cross‑domain diagnostic benchmark for systematically constructing tasks and analyzing long‑horizon failure behaviors in LLM‑based agents. Using HORIZON, we evaluate state‑of‑the‑art (SOTA) agents from multiple model families (GPT‑5 variants and Claude models), collecting 3100+ trajectories across four representative agentic domains to study horizon‑dependent degradation patterns. We further propose a trajectory‑grounded LLM‑as‑a‑Judge pipeline for scalable and reproducible failure attribution, and validate it with human annotation on trajectories, achieving strong agreement (inter‑annotator κ=0.61; human‑judge κ=0.84). Our findings offer an initial methodological step toward systematic, cross‑domain analysis of long‑horizon agent failures and offer practical guidance for building more reliable long‑horizon agents. We release our project website at \hrefhttps://xwang2775.github.io/horizon‑leaderboard/HORIZON Leaderboard and welcome contributions from the community.

PaperId: 1783, https://arxiv.org/pdf/2604.11909.pdf GitHub

Abstract:
The stable operation of autonomous off‑grid photovoltaic systems requires solar forecasting algorithms that respect atmospheric thermodynamics. Contemporary deep learning models consistently exhibit critical anomalies, primarily severe temporal phase lags during cloud transients and physically impossible nocturnal power generation. To resolve this divergence between data‑driven modeling and deterministic celestial mechanics, this research introduces the Thermodynamic Liquid Manifold Network. The methodology projects 22 meteorological and geometric variables into a Koopman‑linearized Riemannian manifold to systematically map complex climatic dynamics. The architecture integrates a Spectral Calibration unit and a multiplicative Thermodynamic Alpha‑Gate. This system synthesizes real‑time atmospheric opacity with theoretical clear‑sky boundary models, structurally enforcing strict celestial geometry compliance. This completely neutralizes phantom nocturnal generation while maintaining zero‑lag synchronization during rapid weather shifts. Validated against a rigorous five‑year testing horizon in a severe semi‑arid climate, the framework achieves an RMSE of 18.31 Wh/m2 and a Pearson correlation of 0.988. The model strictly maintains a zero‑magnitude nocturnal error across all 1826 testing days and exhibits a sub‑30‑minute phase response during high‑frequency optical transients. Comprising exactly 63,458 trainable parameters, this ultra‑lightweight design establishes a robust, thermodynamically consistent standard for edge‑deployable microgrid controllers.

PaperId: 1784, https://arxiv.org/pdf/2604.11854.pdf GitHub

Abstract:
End‑to‑End (E2E) autonomous driving models are usually trained and evaluated with a fixed ego‑vehicle, even though their driving policy is implicitly tied to vehicle dynamics. When such a model is deployed on a vehicle with different size, mass, or drivetrain characteristics, its performance can degrade substantially; we refer to this problem as the vehicle‑domain gap. To address it, we propose MVAdapt, a physics‑conditioned adaptation framework for multi‑vehicle E2E driving. MVAdapt combines a frozen TransFuser++ scene encoder with a lightweight physics encoder and a cross‑attention module that conditions scene features on vehicle properties before waypoint decoding. In the CARLA Leaderboard 1.0 benchmark, MVAdapt improves over naive transfer and multi‑embodiment adaptation baselines on both in‑distribution and unseen vehicles. We further show two complementary behaviors: strong zero‑shot transfer on many unseen vehicles, and data‑efficient few‑shot calibration for severe physical outliers. These results suggest that explicitly conditioning E2E driving policies on vehicle physics is an effective step toward more transferable autonomous driving models. All codes are available at https://github.com/hae‑sung‑oh/MVAdapt

PaperId: 1785, https://arxiv.org/pdf/2604.11841.pdf GitHub

Abstract:
Low‑rank adaptation (LoRA) is a widely used strategy for efficient fine‑tuning of large language models (LLMs), but its strictly linear structure fundamentally limits expressive capacity. The bilinear formulation of weight updates captures only first‑order dependencies between low‑rank factors, restricting the modeling of nonlinear and higher‑order parameter interactions. In this paper, we propose Polynomial Expansion Rank Adaptation (PERA), a novel method that introduces structured polynomial expansion directly into the low‑rank factor space. By expanding each low‑rank factor to synthesize high‑order interaction terms before composition, PERA transforms the adaptation space into a polynomial manifold capable of modeling richer nonlinear coupling without increasing rank or inference cost. We provide theoretical analysis demonstrating that PERA offers enhanced expressive capacity and more effective feature utilization compare to existing linear adaptation approaches. Empirically, PERA consistently outperforms state‑of‑the‑art methods across diverse benchmarks. Notably, our experiments show that incorporating high‑order nonlinear components particularly square terms is crucial for enhancing expressive capacity and maintaining strong and robust performance under various rank settings. Our code is available at https://github.com/zhangwenhao6/PERA

PaperId: 1786, https://arxiv.org/pdf/2604.11839.pdf GitHub

Abstract:
Autonomous AI agents built on open‑source runtimes such as OpenClaw expose every available tool to every session by default, regardless of the task. A summarization task receives the same shell execution, subagent spawning, and credential access capabilities as a code deployment task, a 15x overprovision ratio that we call the capability overprovisioning problem. Existing defenses, including the NemoClaw container sandbox and the Cisco DefenseClaw skill scanner, address containment and threat detection but do not learn the minimum viable capability set for each task type. We present Aethelgard, a four layer adaptive governance framework that enforces least privilege for AI agents through a learned policy. Layer 1, the Capability Governor, dynamically scopes which tools the agent is aware of in each session. Layer 3, the Safety Router, intercepts tool calls before execution using a hybrid rule based and fine tuned classifier. Layer 2, the RL Learning Policy, trains a PPO policy on the accumulated audit log to learn the minimum viable skill set for each task type.

PaperId: 1787, https://arxiv.org/pdf/2604.11811.pdf GitHub

Abstract:
Large language model agents rely on specialized memory systems to accumulate and reuse knowledge during extended interactions. Recent architectures typically adopt a fixed memory design tailored to specific domains, such as semantic retrieval for conversations or skills reused for coding. However, a memory system optimized for one purpose frequently fails to transfer to others. To address this limitation, we introduce M^\star, a method that automatically discovers task‑optimized memory harnesses through executable program evolution. Specifically, M^\star models an agent memory system as a memory program written in Python. This program encapsulates the data Schema, the storage Logic, and the agent workflow Instructions. We optimize these components jointly using a reflective code evolution method; this approach employs a population‑based search strategy and analyzes evaluation failures to iteratively refine the candidate programs. We evaluate M^\star on four distinct benchmarks spanning conversation, embodied planning, and expert reasoning. Our results demonstrate that M^\star improves performance over existing fixed‑memory baselines robustly across all evaluated tasks. Furthermore, the evolved memory programs exhibit structurally distinct processing mechanisms for each domain. This finding indicates that specializing the memory mechanism for a given task explores a broad design space and provides a superior solution compared to general‑purpose memory paradigms.

PaperId: 1788, https://arxiv.org/pdf/2604.11807.pdf GitHub

Abstract:
The stable operation of off‑grid photovoltaic systems requires accurate, computationally efficient solar forecasting. Contemporary deep learning models often suffer from massive computational overhead and physical blindness, generating impossible predictions. This paper introduces the Physics‑Informed State Space Model (PISSM) to bridge the gap between efficiency and physical accuracy for edge‑deployed microcontrollers. PISSM utilizes a dynamic Hankel matrix embedding to filter stochastic sensor noise by transforming raw meteorological sequences into a robust state space. A Linear State Space Model replaces heavy attention mechanisms, efficiently modeling temporal dependencies for parallel processing. Crucially, a novel Physics‑Informed Gating mechanism leverages the Solar Zenith Angle and Clearness Index to structurally bound outputs, ensuring predictions strictly obey diurnal cycles and preventing nocturnal errors. Validated on a multi‑year dataset for Omdurman, Sudan, PISSM achieves superior accuracy with fewer than 40,000 parameters, establishing an ultra‑lightweight benchmark for real‑time off‑grid control.

PaperId: 1789, https://arxiv.org/pdf/2604.11805.pdf GitHub

Abstract:
We have witnessed remarkable advances in LLM reasoning capabilities with the advent of DeepSeek‑R1. However, much of this progress has been fueled by the abundance of internet question‑answer (QA) pairs, a major bottleneck going forward, since such data is limited in scale and concentrated mainly in domains like mathematics. In contrast, other sciences such as physics lack large‑scale QA datasets to effectively train reasoning‑capable models. In this work, we show that physics simulators can serve as a powerful alternative source of supervision for training LLMs for physical reasoning. We generate random scenes in physics engines, create synthetic question‑answer pairs from simulated interactions, and train LLMs using reinforcement learning on this synthetic data. Our models exhibit zero‑shot sim‑to‑real transfer to real‑world physics benchmarks: for example, training solely on synthetic simulated data improves performance on IPhO (International Physics Olympiad) problems by 5‑10 percentage points across model sizes. These results demonstrate that physics simulators can act as scalable data generators, enabling LLMs to acquire deep physical reasoning skills beyond the limitations of internet‑scale QA data. Code available at: https://sim2reason.github.io/.

PaperId: 1790, https://arxiv.org/pdf/2604.11796.pdf GitHub

Abstract:
Recently, large language models (LLMs) are capable of generating highly fluent textual content. While they offer significant convenience to humans, they also introduce various risks, like phishing and academic dishonesty. Numerous research efforts have been dedicated to developing algorithms for detecting AI‑generated text and constructing relevant datasets. However, in the domain of Chinese corpora, challenges remain, including limited model diversity and data homogeneity. To address these issues, we propose C‑ReD: a comprehensive Chinese Real‑prompt AI‑generated Detection benchmark. Experiments demonstrate that C‑ReD not only enables reliable in‑domain detection but also supports strong generalization to unseen LLMs and external Chinese datasets‑addressing critical gaps in model diversity, domain coverage, and prompt realism that have limited prior Chinese detection benchmarks. We release our resources at https://github.com/HeraldofLight/C‑ReD.

PaperId: 1791, https://arxiv.org/pdf/2604.11757.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models have recently emerged as a promising paradigm for building general‑purpose robotic agents. However, the VLA landscape remains highly fragmented and complex: as existing approaches vary substantially in architectures, training data, embodiment configurations, and benchmark‑specific engineering. In this work, we introduce StarVLA‑α, a simple yet strong baseline designed to study VLA design choices under controlled conditions. StarVLA‑α deliberately minimizes architectural and pipeline complexity to reduce experimental confounders and enable systematic analysis. Specifically, we re‑evaluate several key design axes, including action modeling strategies, robot‑specific pretraining, and interface engineering. Across unified multi‑benchmark training on LIBERO, SimplerEnv, RoboTwin, and RoboCasa, the same simple baseline remains highly competitive, indicating that a strong VLM backbone combined with minimal design is already sufficient to achieve strong performance without relying on additional architectural complexity or engineering tricks. Notably, our single generalist model outperforms π_0.5 by 20% on the public real‑world RoboChallenge benchmark. We expect StarVLA‑α to serve as a solid starting point for future research in the VLA regime. Code will be released at https://github.com/starVLA/starVLA.

PaperId: 1792, https://arxiv.org/pdf/2604.11716.pdf GitHub

Abstract:
Prior representative ReAct‑style approaches in autonomous Software Engineering (SWE) typically lack the explicit System‑2 reasoning required for deep analysis and handling complex edge cases. While recent reasoning models demonstrate the potential of extended Chain‑of‑Thought (CoT), applying them to the multi‑turn SWE task creates a fundamental dilemma: retaining full reasoning history leads to context explosion and ``Lost‑in‑the‑Middle'' degradation, while discarding it would force the agent to redundantly re‑reason at every step. To address these challenges, we propose SWE‑AGILE, a novel software agent framework designed to bridge the gap between reasoning depth, efficiency, and context constraints. SWE‑AGILE introduces a Dynamic Reasoning Context strategy, maintaining a ``sliding window'' of detailed reasoning for immediate continuity to prevent redundant re‑analyzing, while compressing historical reasoning content into concise Reasoning Digests. Empirically, SWE‑AGILE sets a new standard for 7B‑8B models on SWE‑Bench‑Verified using only 2.2k trajectories and 896 tasks. Code is available at https://github.com/KDEGroup/SWE‑AGILE.

PaperId: 1793, https://arxiv.org/pdf/2604.11709.pdf GitHub

Abstract:
Accurate and rapid structural damage assessment (SDA) is crucial for post‑disaster management, helping responders prioritise resources, plan rescues, and support recovery. Traditional field inspections, though precise, are limited by accessibility, safety risks, and time constraints, especially after large explosions. Machine learning with remote sensing has emerged as a scalable solution for rapid SDA, with Mamba‑based networks achieving state‑of‑the‑art performance. However, these methods often require extensive training and large datasets, limiting real‑world applicability. Moreover, they fail to incorporate key physical characteristics of blast loading for SDA. To overcome these challenges, we propose a Mamba‑based multimodal network for rapid SDA that integrates multi‑scale blast‑loading information with optical remote sensing images. Evaluated on the 2020 Beirut explosion, our method significantly improves performance over state‑of‑the‑art approaches. Code is available at: https://github.com/IMPACTSquad/Blast‑Mamba

PaperId: 1794, https://arxiv.org/pdf/2604.11699.pdf GitHub

Abstract:
This work aims to improve the generalization of logic‑based legal reasoning systems by integrating recent advances in NLP with legal‑domain adaptive few‑shot learning techniques using LLMs. Existing logic‑based legal reasoning pipelines typically rely on fine‑tuned models to map natural‑language legal cases into logical formulas before forwarding them to a symbolic reasoner. However, such approaches are heavily constrained by the scarcity of high‑quality annotated training data. To address this limitation, we propose a novel LLM‑based legal reasoning framework that enables effective in‑context learning through retrieval‑augmented generation. Specifically, we introduce Legal2LogicICL, a few‑shot retrieval framework that balances diversity and similarity of exemplars at both the latent semantic representation level and the legal text structure level. In addition, our method explicitly accounts for legal structure by mitigating entity‑induced retrieval bias in legal texts, where lengthy and highly specific entity mentions often dominate semantic representations and obscure legally meaningful reasoning patterns. Our Legal2LogicICL constructs informative and robust few‑shot demonstrations, leading to accurate and stable logical rule generation without requiring additional training. In addition, we construct a new dataset, named Legal2Proleg, which is annotated with alignments between legal cases and PROLEG logical formulas to support the evaluation of legal semantic parsing. Experimental results on both open‑source and proprietary LLMs demonstrate that our approach significantly improves accuracy, stability, and generalization in transforming natural‑language legal case descriptions into logical representations, highlighting its effectiveness for interpretable and reliable legal reasoning. Our code is available at https://github.com/yingjie7/Legal2LogicICL.

PaperId: 1795, https://arxiv.org/pdf/2604.11666.pdf GitHub

Abstract:
As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dialogue partners (i.e., form and use a theory‑of‑mind, or ToM) becomes increasingly critical for safe interaction with potentially adversarial partners. We propose a novel privacy‑themed ToM challenge, ToM for Steering Beliefs (ToM‑SB), in which a defender must act as a Double Agent to steer the beliefs of an attacker with partial prior knowledge within a shared universe. To succeed on ToM‑SB, the defender must engage with and form a ToM of the attacker, with a goal of fooling the attacker into believing they have succeeded in extracting sensitive information. We find that strong frontier models like Gemini3‑Pro and GPT‑5.4 struggle on ToM‑SB, often failing to fool attackers in hard scenarios with partial attacker prior knowledge, even when prompted to reason about the attacker's beliefs (ToM prompting). To close this gap, we train models on ToM‑SB to act as AI Double Agents using reinforcement learning, testing both fooling and ToM rewards. Notably, we find a bidirectionally emergent relationship between ToM and attacker‑fooling: rewarding fooling success alone improves ToM, and rewarding ToM alone improves fooling. Across four attackers with different strengths, six defender methods, and both in‑distribution and out‑of‑distribution (OOD) evaluation, we find that gains in ToM and attacker‑fooling are well‑correlated, highlighting belief modeling as a key driver of success on ToM‑SB. AI Double Agents that combine both ToM and fooling rewards yield the strongest fooling and ToM performance, outperforming Gemini3‑Pro and GPT‑5.4 with ToM prompting on hard scenarios. We also show that ToM‑SB and AI Double Agents can be extended to stronger attackers, demonstrating generalization to OOD settings and the upgradability of our task.

PaperId: 1796, https://arxiv.org/pdf/2604.11626.pdf GitHub

Abstract:
Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the reasoning that underlies preference. We show that teaching reward models to produce explicit, multi‑dimensional critiques before scoring transforms them from passive evaluators into active optimization tools, improving generators in two complementary ways: at training time, structured rationales provide interpretable, fine‑grained rewards for reinforcement learning; at test time, a Generate‑Critique‑Refine loop turns critiques into targeted prompt revisions that improve outputs without any parameter updates. To train such a reward model without costly rationale annotations, we introduce Preference‑Anchored Rationalization (PARROT), a principled framework that recovers high‑quality rationales from readily available preference data through anchored generation, consistency filtering, and distillation. The resulting model, RationalRewards (8B), achieves state‑of‑the‑art preference prediction among open‑source reward models, competitive with Gemini‑2.5‑Pro, while using 10‑20x less training data than comparable baselines. As an RL reward, it consistently improves text‑to‑image and image‑editing generators beyond scalar alternatives. Most strikingly, its test‑time critique‑and‑refine loop matches or exceeds RL‑based fine‑tuning on several benchmarks, suggesting that structured reasoning can unlock latent capabilities in existing generators that suboptimal prompts fail to elicit.

PaperId: 1797, https://arxiv.org/pdf/2604.11623.pdf GitHub

Abstract:
We introduce Context Kubernetes, an architecture for orchestrating enterprise knowledge in agentic AI systems, with a prototype implementation and eight experiments. The core observation is that delivering the right knowledge, to the right agent, with the right permissions, at the right freshness ‑‑ across an entire organization ‑‑ is structurally analogous to the container orchestration problem Kubernetes solved a decade ago. We formalize six core abstractions, a YAML‑based declarative manifest for knowledge‑architecture‑as‑code, a reconciliation loop, and a three‑tier agent permission model where agent authority is always a strict subset of human authority. On synthetic seed data, we compare four governance baselines of increasing strength: ungoverned RAG, ACL‑filtered retrieval, RBAC‑aware routing, and the full architecture. Each layer contributes a different capability: ACL filtering eliminates cross‑domain leaks, intent routing reduces noise by 19 percentage points, and only the three‑tier model blocks all five tested attack scenarios ‑‑ the one attack RBAC misses is an agent sending confidential pricing via email, which RBAC cannot distinguish from ordinary email. TLA+ model‑checking verifies safety properties across 4.6 million reachable states with zero violations. A survey of four major platforms (Microsoft, Salesforce, AWS, Google) documents that none architecturally isolates agent approval channels. We identify four properties that make context orchestration harder than container orchestration, and argue these make the solution more valuable.

PaperId: 1798, https://arxiv.org/pdf/2604.11557.pdf GitHub

Abstract:
Tool‑use capability is a fundamental component of LLM agents, enabling them to interact with external systems through structured function calls. However, existing research exhibits inconsistent interaction representations, largely overlooks the structural distribution of tool‑use trajectories, and relies on incompatible evaluation benchmarks. We present UniToolCall, a unified framework for tool learning that standardizes the entire pipeline from toolset construction and dataset generation to evaluation. The framework curates a large tool pool of 22k+ tools and constructs a hybrid training corpus of 390k+ instances by combining 10 standardized public datasets with structurally controlled synthetic trajectories. It explicitly models diverse interaction patterns, including single‑hop vs. multi‑hop and single‑turn vs. multi‑turn, while capturing both serial and parallel execution structures. To support coherent multi‑turn reasoning, we further introduce an Anchor Linkage mechanism that enforces cross‑turn dependencies. Furthermore, we convert 7 public benchmarks into a unified Query‑‑Action‑‑Observation‑‑Answer (QAOA) representation with fine‑grained evaluation at the function‑call, turn, and conversation levels. Experiments show that fine‑tuning Qwen3‑8B on our dataset substantially improves tool‑use performance. Under the distractor‑heavy Hybrid‑20 setting, achieves 93.0% single‑turn Strict Precision, outperforming commercial models including GPT, Gemini, and Claude.

PaperId: 1799, https://arxiv.org/pdf/2604.11539.pdf GitHub

Abstract:
Human perception of visual similarity is inherently adaptive and subjective, depending on the users' interests and focus. However, most image retrieval systems fail to reflect this flexibility, relying on a fixed, monolithic metric that cannot incorporate multiple conditions simultaneously. To address this, we propose CLAY, an adaptive similarity computation method that reframes the embedding space of pretrained Vision‑Language Models (VLMs) as a text‑conditional similarity space without additional training. This design separates the textual conditioning process and visual feature extraction, allowing highly efficient and multi‑conditioned retrieval with fixed visual embeddings. We also construct a synthetic evaluation dataset CLAY‑EVAL, for comprehensive assessment under diverse conditioned retrieval settings. Experiments on standard datasets and our proposed dataset show that CLAY achieves high retrieval accuracy and notable computational efficiency compared to previous works.

PaperId: 1800, https://arxiv.org/pdf/2604.11535.pdf GitHub GitHub

Abstract:
Solving an NP‑hard optimization problem often requires reformulating it for a specific solver ‑‑ quantum hardware, a commercial optimizer, or a domain heuristic. A tool for polynomial‑time reductions between hard problems would let practitioners route any supported problem to any supported solver through a single interface. Building such a library at scale, however, has remained out of reach. We show that harness engineering, the practice of designing constraints, verification systems, and feedback loops that channel AI coding agents, can overcome this barrier. Our harness combines a no‑code contribution route for domain experts, a multilayer verification stack ranging from type‑level checks to agentic feature tests (AI agents role‑playing as end users), and a fully automated implementation‑review‑integration pipeline. In about three months, we built a command‑line tool backed by a library of 100+ problem types and 200+ reduction rules in over 170k lines of Rust. The result suggests that a well‑engineered harness lets agents build well‑tested software at a scale and pace beyond prior reduction‑library efforts. Because the reduction graph composes transitively, a new solver registered for any single problem type instantly becomes available to every problem connected by a reduction path. The source code is available at https://github.com/CodingThrust/problem‑reductions.

PaperId: 1801, https://arxiv.org/pdf/2604.11502.pdf GitHub

Abstract:
Contextual causal reasoning is a critical yet challenging capability for Large Language Models (LLMs). Existing benchmarks, however, often evaluate this skill in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. To address this, we pioneer METER to systematically benchmark LLMs across all three levels of the causal ladder under a unified context setting. Our extensive evaluation of various LLMs reveals a significant decline in proficiency as tasks ascend the causal hierarchy. To diagnose this degradation, we conduct a deep mechanistic analysis via both error pattern identification and internal information flow tracing. Our analysis reveals two primary failure modes: (1) LLMs are susceptible to distraction by causally irrelevant but factually correct information at lower level of causality; and (2) as tasks ascend the causal hierarchy, faithfulness to the provided context degrades, leading to a reduced performance. We belive our work advances our understanding of the mechanisms behind LLM contextual causal reasoning and establishes a critical foundation for future research. Our code and dataset are available at https://github.com/SCUNLP/METER .

PaperId: 1802, https://arxiv.org/pdf/2604.11446.pdf GitHub

Abstract:
Recently, scaling reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs) has emerged as an effective training paradigm for significantly improving model capabilities, which requires guiding the model to perform extensive exploration and learning, leading to substantial computational overhead and becoming a key challenge. To reduce the number of training steps, Prior work performs linear extrapolation of model parameters. However, the dynamics of model parameter updates during RLVR training remain insufficiently understood. To further investigate the evolution of LLMs during RLVR training, we conduct empirical experiments and find that the rank‑1 subspace of the model does not evolve linearly, and its dominance over the original parameters is further amplified during LoRA training. Based on the above insights, we propose the Nonlinear Extrapolation of low‑rank trajectories (NExt), a novel framework that models and extrapolates low‑rank parameter trajectories in a nonlinear manner. Concretely, we first train the model using LoRA and extract the rank‑1 subspace of parameter differences at multiple training steps, which is then used for the subsequent nonlinear extrapolation. Afterward, we utilized the extracted rank‑1 subspace to train a predictor, which can model the trajectory of parameter updates during RLVR, and then perform the predict‑extend process to extrapolate model parameters, achieving the acceleration of RLVR. To further study and understand NExt, we conduct comprehensive experiments that demonstrate the effectiveness and robustness of the method. Our method reduces computational overhead by approximately 37.5% while remaining compatible with a wide range of RLVR algorithms and tasks. We release our code in https://github.com/RUCAIBox/NExt.

PaperId: 1803, https://arxiv.org/pdf/2604.11430.pdf GitHub GitHub

Abstract:
AI agents that pay for resources via the x402 protocol embed payment metadata ‑ resource URLs, descriptions, and reason strings ‑ in every HTTP payment request. This metadata is transmitted to the payment server and to the centralised facilitator API before any on‑chain settlement occurs; neither party is typically bound by a data processing agreement. We present presidio‑hardened‑x402, the first open‑source middleware that intercepts x402 payment requests before transmission to detect and redact personally identifiable information (PII), enforce declarative spending policies, and block duplicate replay attempts. To evaluate the PII filter, we construct a labeled synthetic corpus of 2,000 x402 metadata triples spanning seven use‑case categories, and run a 42‑configuration precision/recall sweep across two detection modes (regex, NLP) and five confidence thresholds. The recommended configuration (mode=nlp, min_score=0.4, all entity types) achieves micro‑F1 = 0.894 with precision 0.972, at a p99 latency of 5.73ms ‑ well within the 50ms overhead budget. The middleware, corpus, and all experiment code are publicly available at https://github.com/presidio‑v/presidio‑hardened‑x402.

PaperId: 1804, https://arxiv.org/pdf/2604.11427.pdf GitHub

Abstract:
Developing non‑collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, a method that leverages large language models to autonomously induce both strategy actions and planning logic directly from raw transcripts. METRO formalizes expert knowledge into a Strategy Forest, a hierarchical structure that captures both short‑term responses (nodes) and long‑term strategic foresight (branches). Experimental results across two benchmarks show that METRO demonstrates promising performance, outperforming existing methods by an average of 9%‑10%. Our further analysis not only reveals the success behind METRO (strategic behavioral diversity and foresight), but also demonstrates its robust cross‑task transferability. This offers new insights into building non‑collaborative agents in a cost‑effective and scalable way. Our code is available at https://github.com/Humphrey‑0125/METRO.

PaperId: 1805, https://arxiv.org/pdf/2604.11407.pdf GitHub

Abstract:
We revisit retrieval‑augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external intervention, we express retrieval decisions within token‑level decoding, enabling end‑to‑end coordination without additional controllers or classifiers. Under the paradigm of Retrieval as Generation, we propose GRIP (Generation‑guided Retrieval with Information Planning), a unified framework in which the model regulates retrieval behavior through control‑token emission. Central to GRIP is Self‑Triggered Information Planning, which allows the model to decide when to retrieve, how to reformulate queries, and when to terminate, all within a single autoregressive trajectory. This design tightly couples retrieval and reasoning and supports dynamic multi‑step inference with on‑the‑fly evidence integration. To supervise these behaviors, we construct a structured training set covering answerable, partially answerable, and multi‑hop queries, each aligned with specific token patterns. Experiments on five QA benchmarks show that GRIP surpasses strong RAG baselines and is competitive with GPT‑4o while using substantially fewer parameters.

PaperId: 1806, https://arxiv.org/pdf/2604.11299.pdf GitHub

Abstract:
In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the evolution of written characters constitutes a fundamental pathway for understanding cultural transformation and historical continuity, how MLLMs can be systematically leveraged to support and advance text evolution analysis remains an open and largely underexplored problem. To bridge this gap, we construct a comprehensive benchmark comprising 11 tasks and over 130,000 instances, specifically designed to evaluate the capability of MLLMs in analyzing the evolution of ancient Chinese scripts. We conduct extensive evaluations across multiple widely used MLLMs and observe that, while existing models demonstrate a limited ability in glyph‑level comparison, their performance on core tasks‑such as character recognition and evolutionary reasoning‑remains substantially constrained. Motivated by these findings, we propose a glyph‑driven fine‑tuning framework (GEVO) that explicitly encourages models to capture evolutionary consistency in glyph transformations and enhances their understanding of text evolution. Experimental results show that even models at the 2B scale achieve consistent and comprehensive performance improvements across all evaluated tasks. To facilitate future research, we publicly release both the benchmark and the trained models\footnotehttps://github.com/songruiecho/GEVO.

PaperId: 1807, https://arxiv.org/pdf/2604.11259.pdf GitHub

Abstract:
Mobile GUI agents powered by Multimodal Large Language Models (MLLMs) can execute complex tasks on mobile devices. Despite this progress, most existing systems still optimize task success or efficiency, neglecting users' privacy personalization. In this paper, we study the often‑overlooked problem of agent personalization. We observe that personalization can induce systematic structural heterogeneity in execution trajectories. For example, privacy‑first users often prefer protective actions, e.g., refusing permissions, logging out, and minimizing exposure, leading to logically different execution trajectories from utility‑first users. Such variable‑length and structurally different trajectories make standard preference optimization unstable and less informative. To address this issue, we propose Trajectory Induced Preference Optimization (TIPO), which uses preference‑intensity weighting to emphasize key privacy‑related steps and padding gating to suppress alignment noise. Results on our Privacy Preference Dataset show that TIPO improves persona alignment and distinction while preserving strong task executability, achieving 65.60% SR, 46.22 Compliance, and 66.67% PD, outperforming existing optimization methods across various GUI tasks. The code and dataset will be publicly released at https://github.com/Zhixin‑L/TIPO.

PaperId: 1808, https://arxiv.org/pdf/2604.11229.pdf GitHub

Abstract:
Retrieving procedure‑oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, context‑heavy documents and are not well captured by paragraph‑only dense retrieval. We present RECIPER, a dual‑view retrieval pipeline that indexes both paragraph‑level context and compact large language model‑extracted procedural summaries, then combines the two candidate streams with lightweight lexical reranking. Across four dense retrieval backbones, RECIPER consistently improves early‑rank retrieval over paragraph‑only dense retrieval, achieving average gains of +3.73 in Recall@1, +2.85 in nDCG@10, and +3.13 in MRR. With BGE‑large‑en‑v1.5, it reaches 86.82%, 97.07%, and 97.85% on Recall@1, Recall@5, and Recall@10, respectively. We further observe improved downstream question answering under automatic metrics, suggesting that procedural summaries can serve as a useful complementary retrieval signal for procedure‑oriented materials question answering. Code and data are available at https://github.com/ReaganWu/RECIPER.

PaperId: 1809, https://arxiv.org/pdf/2604.11174.pdf GitHub

Abstract:
Recent progress in embodied AI has produced a growing ecosystem of robot policies, foundation models, and modular runtimes. However, current evaluation remains dominated by task success metrics such as completion rate or manipulation accuracy. These metrics leave a critical gap: they do not measure whether embodied systems are governable ‑‑ whether they respect capability boundaries, enforce policies, recover safely, maintain audit trails, and respond to human oversight. We present EmbodiedGovBench, a benchmark for governance‑oriented evaluation of embodied agent systems. Rather than asking only whether a robot can complete a task, EmbodiedGovBench evaluates whether the system remains controllable, policy‑bounded, recoverable, auditable, and evolution‑safe under realistic perturbations. The benchmark covers seven governance dimensions: unauthorized capability invocation, runtime drift robustness, recovery success, policy portability, version upgrade safety, human override responsiveness, and audit completeness. We define a benchmark structure spanning single‑robot and fleet settings, with scenario templates, perturbation operators, governance metrics, and baseline evaluation protocols. We describe how the benchmark can be instantiated over embodied capability runtimes with modular interfaces and contract‑aware upgrade workflows. Our analysis suggests that embodied governance should become a first‑class evaluation target. EmbodiedGovBench provides the initial measurement framework for that shift.

PaperId: 1810, https://arxiv.org/pdf/2604.11104.pdf GitHub

Abstract:
This paper presents an empirical study of a multi‑model zero‑shot pipeline for knowledge graph construction and exploitation, executed entirely through local inference on consumer‑grade hardware. We propose a reproducible evaluation framework integrating two external benchmarks (DocRED, HotpotQA), WebQuestionsSP‑style synthetic data, and the RAGAS evaluation framework in an automated pipeline. On 500 document‑level relations, our system achieves an F1 of 0.70 \pm 0.041 in zero‑shot, compared to 0.80 for supervised DREEAM. Text‑to‑query achieves an accuracy of 0.80 \pm 0.06 on 200 samples. Multi‑hop reasoning achieves an Exact Match (EM) of 0.46\pm0.04 on 500 HotpotQA questions, with a RAGAS faithfulness of 0.96 \pm 0.04 on 50 samples. Beyond the pipeline, we study diversity mechanisms for difficult multi‑hop reasoning. On 181 questions unsolvable at zero temperature, self‑consistency (k=5, T =0.7) recovers up to 23% EM with a single Mixture‑of‑Experts (MoE) model, but the cross‑model oracle (3 architectures x 5 samples) reaches 46.4%. We highlight an agreement paradox: strong consensus among samples signals collective hallucination rather than a reliable answer, echoing the work of Moussaïd et al. on the wisdom of crowds. Extending to the full pipeline (500 questions), self‑consistency (k=3) raises EM from 0.46 to 0.48 \pm 0.04. A confidence‑routing cascade mechanism (Phi‑4 \rightarrow GPT‑OSS, k=5) achieves an EM of 0.55 \pm 0.04, the best result obtained, with 45.4% of questions rerouted. Finally, we show that V3 prompt engineering applied to other models does not reproduce the gains observed with Gemma‑4, confirming the specific prompt/model interaction. The entire system runs in ～5 h on a single RTX 3090, without any training, for an estimated carbon footprint of 0.09 kg CO2 eq.

PaperId: 1811, https://arxiv.org/pdf/2604.11096.pdf GitHub

Abstract:
Currently, large language models (LLMs) predominantly focus on the text modality. To enable more natural human‑AI interaction, speech LLMs are emerging, but building effective end‑to‑end speech LLMs remains challenging due to limited data and the difficulty in expanding to more languages. In this paper, we introduce Cross‑lingual Speech Language Model (CSLM), an efficient training method for cross‑lingual speech LLMs based on discrete speech tokens. We propose a novel alignment strategy that achieves cross‑modal and cross‑lingual alignment through continual pre‑training. By conducting instruction fine‑tuning following a speech‑text interleaved chain‑of‑modality generation process, we enhance modal alignment at a finer granularity, thereby improving generation quality and reducing latency. CSLM aligns different modalities and languages simultaneously without the need for massive speech data, thus exhibiting good language scalability. Evaluations on cross‑modal tasks, mono‑lingual conversational tasks, and cross‑lingual conversational tasks demonstrate CSLM's strong cross‑modal alignment capabilities and general task abilities. (Code is available at: https://github.com/ictnlp/CSLM)

PaperId: 1812, https://arxiv.org/pdf/2604.11077.pdf GitHub

Abstract:
Customer service chatbots are increasingly expected to serve not merely as reactive support tools for users, but as strategic interfaces for harvesting high‑value information and business intelligence. In response, we make three main contributions. 1) We introduce and define a novel task of Proactive Information Probing, which optimizes when to probe users for pre‑specified target information while minimizing conversation turns and user friction. 2) We propose PROCHATIP, a proactive chatbot framework featuring a specialized conversation strategy module trained to master the delicate timing of probes. 3) Experiments demonstrate that PROCHATIP significantly outperforms baselines, exhibiting superior capability in both information probing and service quality. We believe that our work effectively redefines the commercial utility of chatbots, positioning them as scalable, cost‑effective engines for proactive business intelligence. Our code is available at https://github.com/SCUNLP/PROCHATIP.

PaperId: 1813, https://arxiv.org/pdf/2604.11028.pdf GitHub

Abstract:
As embodied robots move toward fleet‑scale operation, multi‑robot coordination is becoming a central systems challenge. Existing approaches often treat this as motivation for increasing internal multi‑agent decomposition within each robot. We argue for a different principle: multi‑robot coordination does not require intra‑robot multi‑agent fragmentation. Each robot should remain a single embodied agent with its own persistent runtime, local policy scope, capability state, and recovery authority, while coordination emerges through federation across robots at the fleet level. We present Federated Single‑Agent Robotics (FSAR), a runtime architecture for multi‑robot coordination built on single‑agent robot runtimes. Each robot exposes a governed capability surface rather than an internally fragmented agent society. Fleet coordination is achieved through shared capability registries, cross‑robot task delegation, policy‑aware authority assignment, trust‑scoped interaction, and layered recovery protocols. We formalize key coordination relations including authority delegation, inter‑robot capability requests, local‑versus‑fleet recovery boundaries, and hierarchical human supervision, and describe a fleet runtime architecture supporting shared Embodied Capability Module (ECM) discovery, contract‑aware cross‑robot coordination, and fleet‑level governance. We evaluate FSAR on representative multi‑robot coordination scenarios against decomposition‑heavy baselines. Results show statistically significant gains in governance locality (d=2.91, p<.001 vs. centralized control) and recovery containment (d=4.88, p<.001 vs. decomposition‑heavy), while reducing authority conflicts and policy violations across all scenarios. Our results support the view that the path from embodied agents to embodied fleets is better served by federation across coherent robot runtimes than by fragmentation within them.

PaperId: 1814, https://arxiv.org/pdf/2604.10988.pdf GitHub GitHub

Abstract:
Existing browser agent benchmarks face a fundamental trilemma: real‑website benchmarks lack reproducibility due to content drift, controlled environments sacrifice realism by omitting real‑web noise, and both require costly manual curation that limits scalability. We present WebForge, the first fully automated framework that resolves this trilemma through a four‑agent pipeline ‑‑ Plan, Generate, Refine, and Validate ‑‑ that produces interactive, self‑contained web environments end‑to‑end without human annotation. A seven‑dimensional difficulty control framework structures task design along navigation depth, visual complexity, reasoning difficulty, and more, enabling systematic capability profiling beyond single aggregate scores. Using WebForge, we construct WebForge‑Bench, a benchmark of 934 tasks spanning 7 domains and 3 difficulty levels. Multi‑model experiments show that difficulty stratification effectively differentiates model capabilities, while cross‑domain analysis exposes capability biases invisible to aggregate metrics. Together, these results confirm that multi‑dimensional evaluation reveals distinct capability profiles that a single aggregate score cannot capture. Code and benchmark are publicly available at https://github.com/yuandaxia2001/WebForge.

PaperId: 1815, https://arxiv.org/pdf/2604.10923.pdf GitHub

Abstract:
While large language model‑‑powered agents can self‑evolve by accumulating experience or by dynamically creating new assets (i.e., tools or expert agents), existing frameworks typically treat these two evolutionary processes in isolation. This separation overlooks their intrinsic interdependence: the former is inherently bounded by a manually predefined static toolset, while the latter generates new assets from scratch without experiential guidance, leading to limited capability growth and unstable evolution. To address this limitation, we introduce a novel paradigm of co‑evolutionary Capability Expansion and Experience Distillation. Guided by this paradigm, we propose the Mem^2Evolve, which integrates two core components: Experience Memory and Asset Memory. Specifically, Mem^2Evolve leverages accumulated experience to guide the dynamic creation of assets, thereby expanding the agent's capability space while simultaneously acquiring new experience to achieve co‑evolution. Extensive experiments across 6 task categories and 8 benchmarks demonstrate that Mem^2Evolve achieves improvement of 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solely through asset creation, establishing it as a substantially more effective and stable self‑evolving agent framework. Code is available at: https://buaa‑irip‑llm.github.io/Mem2Evolve.

PaperId: 1816, https://arxiv.org/pdf/2604.10893.pdf GitHub

Abstract:
Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM‑generated text. Correspondingly, stealing watermark algorithms (SWAs) derive watermark information from watermarked texts generated by victim LLMs to craft highly targeted adversarial attacks, which compromise the reliability of watermarks. Existing SWAs rely on fixed strategies, overlooking the non‑uniform distribution of stolen watermark information and the dynamic nature of real‑world LLM generation processes. To address these limitations, we propose Adaptive Stealing (AS), a novel SWA featuring enhanced design flexibility through Position‑Based Seal Construction and Adaptive Selection modules. AS operates by defining multiple attack perspectives derived from distinct activation states of contextually ordered tokens. During attack execution, AS dynamically selects the optimal perspective based on watermark compatibility, generation priority, and dynamic generation relevance. Our experiments demonstrate that AS significantly increases steal efficiency against target watermarks under identical experimental conditions. These findings highlight the need for more robust LLM watermarks to withstand potential attacks. We release our code to the community for future research\footnotehttps://github.com/DrankXs/AdaptiveStealingWatermark.

PaperId: 1817, https://arxiv.org/pdf/2604.10784.pdf GitHub

Abstract:
Recent advances in unified multimodal models (UMMs) have led to a proliferation of architectures capable of understanding, generating, and editing across visual and textual modalities. However, developing a unified framework for UMMs remains challenging due to the diversity of model architectures and the heterogeneity of training paradigms and implementation details. In this paper, we present TorchUMM, the first unified codebase for comprehensive evaluation, analysis, and post‑training across diverse UMM backbones, tasks, and datasets. TorchUMM supports a broad spectrum of models covering a wide range of scales and design paradigms. Our benchmark encompasses three core task dimensions: multimodal understanding, generation, and editing, and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction‑following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models and fosters deeper insights into their strengths and limitations, facilitating the development of more capable unified multimodal systems. Code is available at: https://github.com/AIFrontierLab/TorchUMM.

PaperId: 1818, https://arxiv.org/pdf/2604.10741.pdf GitHub

Abstract:
Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text‑centric, overlooking the multimodal evidence that characterizes real‑world expert reports. We introduce a pressing task: multimodal long‑form generation. Accordingly, we propose Deep‑Reporter, a unified agentic framework for grounded multimodal long‑form generation. It orchestrates: (i) Agentic Multimodal Search and Filtering to retrieve and filter textual passages and information‑dense visuals; (ii) Checklist‑Guided Incremental Synthesis to ensure coherent image‑text integration and optimal citation placement; and (iii) Recurrent Context Management to balance long‑range coherence with local fluency. We develop a rigorous curation pipeline producing 8K high‑quality agentic traces for model optimization. We further introduce M2LongBench, a comprehensive testbed comprising 247 research tasks across 9 domains and a stable multimodal sandbox. Extensive experiments demonstrate that long‑form multimodal generation is a challenging task, especially in multimodal selection and integration, and effective post‑training can bridge the gap.

PaperId: 1819, https://arxiv.org/pdf/2604.10721.pdf GitHub

Abstract:
Natural‑language Guided Cross‑view Geo‑localization (NGCG) aims to retrieve geo‑tagged satellite imagery using textual descriptions of ground scenes. While recent NGCG methods commonly rely on CLIP‑style dual‑encoder architectures, they often suffer from weak cross‑modal generalization and require complex architectural designs. In contrast, Multimodal Large Language Models (MLLMs) offer powerful semantic reasoning capabilities but are not directly optimized for retrieval tasks. In this work, we present a simple yet effective framework to adapt MLLMs for NGCG via parameter‑efficient finetuning. Our approach optimizes latent representations within the MLLM while preserving its pretrained multimodal knowledge, enabling strong cross‑modal alignment without redesigning model architectures. Through systematic analysis of diverse variables, from model backbone to feature aggregation, we provide practical and generalizable insights for leveraging MLLMs in NGCG. Our method achieves SOTA on GeoText‑1652 with a 12.2% improvement in Text‑to‑Image Recall@1 and secures top performance in 5 out of 12 subtasks on CVG‑Text, all while surpassing baselines with far fewer trainable parameters. These results position MLLMs as a robust foundation for semantic cross‑view retrieval and pave the way for MLLM‑based NGCG to be adopted as a scalable, powerful alternative to traditional dual‑encoder designs. Project page and code are available at https://yuqichen888.github.io/NGCG‑MLLMs‑web/.

PaperId: 1820, https://arxiv.org/pdf/2604.10718.pdf GitHub

Abstract:
Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While existing benchmarks evaluate LLMs on scientific knowledge and reasoning, their ability to predict experimental outcomes ‑ a task where AI could significantly exceed human capabilities ‑ remains largely underexplored. We introduce SciPredict, a benchmark comprising 405 tasks derived from recent empirical studies in 33 specialized sub‑fields of physics, biology, and chemistry. SciPredict addresses two critical questions: (a) can LLMs predict the outcome of scientific experiments with sufficient accuracy? and (b) can such predictions be reliably used in the scientific research process? Evaluations reveal fundamental limitations on both fronts. Model accuracies are 14‑26% and human expert performance is \approx20%. Although some frontier models exceed human performance model accuracy is still far below what would enable reliable experimental guidance. Even within the limited performance, models fail to distinguish reliable predictions from unreliable ones, achieving only \approx20% accuracy regardless of their confidence or whether they judge outcomes as predictable without physical experimentation. Human experts, in contrast, demonstrate strong calibration: their accuracy increases from \approx5% to \approx80% as they deem outcomes more predictable without conducting the experiment. SciPredict establishes a rigorous framework demonstrating that superhuman performance in experimental science requires not just better predictions, but better awareness of prediction reliability. For reproducibility all our data and code are provided at https://github.com/scaleapi/scipredict

PaperId: 1821, https://arxiv.org/pdf/2604.10708.pdf GitHub

Abstract:
Recent progress in multimodal models has spurred rapid advances in audio understanding, generation, and editing. However, these capabilities are typically addressed by specialized models, leaving the development of a truly unified framework that can seamlessly integrate all three tasks underexplored. While some pioneering works have explored unifying audio understanding and generation, they often remain confined to specific domains. To address this, we introduce Audio‑Omni, the first end‑to‑end framework to unify generation and editing across general sound, music, and speech domains, with integrated multi‑modal understanding capabilities. Our architecture synergizes a frozen Multimodal Large Language Model for high‑level reasoning with a trainable Diffusion Transformer for high‑fidelity synthesis. To overcome the critical data scarcity in audio editing, we construct AudioEdit, a new large‑scale dataset comprising over one million meticulously curated editing pairs. Extensive experiments demonstrate that Audio‑Omni achieves state‑of‑the‑art performance across a suite of benchmarks, outperforming prior unified approaches while achieving performance on par with or superior to specialized expert models. Beyond its core capabilities, Audio‑Omni exhibits remarkable inherited capabilities, including knowledge‑augmented reasoning generation, in‑context generation, and zero‑shot cross‑lingual control for audio generation, highlighting a promising direction toward universal generative audio intelligence. The code, model, and dataset will be publicly released on https://zeyuet.github.io/Audio‑Omni.

PaperId: 1822, https://arxiv.org/pdf/2604.10696.pdf GitHub

Abstract:
We present Camyla, a system for fully autonomous research within the scientific domain of medical image segmentation. Camyla transforms raw datasets into literature‑grounded research proposals, executable experiments, and complete manuscripts without human intervention. Autonomous experimentation over long horizons poses three interrelated challenges: search effort drifts toward unpromising directions, knowledge from earlier trials degrades as context accumulates, and recovery from failures collapses into repetitive incremental fixes. To address these challenges, the system combines three coupled mechanisms: Quality‑Weighted Branch Exploration for allocating effort across competing proposals, Layered Reflective Memory for retaining and compressing cross‑trial knowledge at multiple granularities, and Divergent Diagnostic Feedback for diversifying recovery after underperforming trials. The system is evaluated on CamylaBench, a contamination‑free benchmark of 31 datasets constructed exclusively from 2025 publications, under a strict zero‑intervention protocol across two independent runs within a total of 28 days on an 8‑GPU cluster. Across the two runs, Camyla generates more than 2,700 novel model implementations and 40 complete manuscripts, and surpasses the strongest per‑dataset baseline selected from 14 established architectures, including nnU‑Net, on 22 and 18 of 31 datasets under identical training budgets, respectively (union: 24/31). Senior human reviewers score the generated manuscripts at the T1/T2 boundary of contemporary medical imaging journals. Relative to automated baselines, Camyla outperforms AutoML and NAS systems on aggregate segmentation performance and exceeds six open‑ended research agents on both task completion and baseline‑surpassing frequency. These results suggest that domain‑scale autonomous research is achievable in medical image segmentation.

PaperId: 1823, https://arxiv.org/pdf/2604.10681.pdf GitHub

Abstract:
Large Language Models (LLMs), despite their impressive capabilities across domains, have been shown to be vulnerable to backdoor attacks. Prior backdoor strategies predominantly operate at the token level, where an injected trigger causes the model to generate a specific target word, choice, or class (depending on the task). Recent advances, however, exploit the long‑form reasoning tendencies of modern LLMs to conduct reasoning‑level backdoors: once triggered, the victim model inserts one or more malicious reasoning steps into its chain‑of‑thought (CoT). These attacks are substantially harder to detect, as the backdoored answer remains plausible and consistent with the poisoned reasoning trajectory. Yet, defenses tailored to this type of backdoor remain largely unexplored. To bridge this gap, we propose Critical‑CoT, a novel defense mechanism that conducts a two‑stage fine‑tuning (FT) process on LLMs to develop critical thinking behaviors, enabling them to automatically identify potential backdoors and refuse to generate malicious reasoning steps. Extensive experiments across multiple LLMs and datasets demonstrate that Critical‑CoT provides strong robustness against both in‑context learning‑based and FT‑based backdoor attacks. Notably, Critical‑CoT exhibits strong cross‑domain and cross‑task generalization. Our code is available at hthttps://github.com/tuanvu171/Critical‑CoT.

PaperId: 1824, https://arxiv.org/pdf/2604.10674.pdf GitHub

Abstract:
Reinforcement learning (RL) has been widely used to train LLM agents for multi‑turn interactive tasks, but its sample efficiency is severely limited by sparse rewards and long horizons. On‑policy self‑distillation (OPSD) alleviates this by providing dense token‑level supervision from a privileged teacher that has access to ground‑truth answers. However, such fixed privileged information cannot capture the diverse valid strategies in agent tasks, and naively combining OPSD with RL often leads to training collapse. To address these limitations, we introduce Skill‑SD, a framework that turns the agent's own trajectories into dynamic training‑only supervision. Completed trajectories are summarized into compact natural language skills that describe successful behaviors, mistakes, and workflows. These skills serve as dynamic privileged information conditioning only the teacher, while the student always acts under the plain task prompt and learns to internalize the guidance through distillation. To stabilize the training, we derive an importance‑weighted reverse‑KL loss to provide gradient‑correct token‑level distillation, and dynamically synchronize the teacher with the improving student. Experimental results on agentic benchmarks demonstrate that Skill‑SD substantially outperforms the standard RL baseline, improving both vanilla GRPO (+14.0%/+10.9% on AppWorld/Sokoban) and vanilla OPD (+42.1%/+40.6%). Project page: https://k1xe.github.io/skill‑sd/

PaperId: 1825, https://arxiv.org/pdf/2604.10603.pdf GitHub

Abstract:
Large language models are transforming all areas of academia and industry, attracting the attention of researchers, professionals, and the general public. In the trek for more powerful architectures, Mixture‑of‑Experts, inspired by ensemble models, have emerged as one of the most effective ways to follow. However, this implies a high computational burden for both training and inference. To reduce the impact on computing and memory footprint as well as the energy consumption, simplification methods has arisen as very effective procedures. In this paper, an original algorithm, MoEITS, for MoE‑LLMs simplification is presented. The algorithm is characterized by a refined simplicity, underpinned by standardized Information Theoretic frameworks. MoEITS is analyzed in depth from theoretical and practical points of view. Its computational complexity is studied. Its performance on the accuracy of the simplified LLMs and the reduction rate achieved is assessed through a thoroughly designed experimentation. This empirical evaluation includes a comparison with state‑of‑the‑art MoE‑LLM pruning methods applied on Mixtral 8×7B, Qwen1.5‑2.7B, and DeepSeek‑V2‑Lite. The extensive experimentation conducted demonstrates that MoEITS outperforms state‑of‑the‑art techniques by generating models that are both effective across all benchmarks and computationally efficient. The code implementing the method will be available at https://github.com/luisbalru/MoEITS.

PaperId: 1826, https://arxiv.org/pdf/2604.10597.pdf GitHub

Abstract:
Mamba selective state space models (SSMs) provide linear‑time sequence modeling but remain sensitive to selective‑scan chunk scheduling. We present COREY, a \emphconcept‑and‑feasibility runtime scheduler that maps fixed‑bin activation entropy to chunk size. We evaluate COREY in three tiers: a prototype cost model, real‑checkpoint kernel timing, and routed end‑to‑end ablations on modern GPUs. At the kernel level, a calibrated rule, \(H_\mathrmref=\log K\), recovers the locally optimal chunk and matches a one‑time static oracle, yielding \(4.41×\) lower latency than an unoptimized baseline on a consumer GPU and \(3.90×\)‑‑\(4.04×\) lower latency on a data‑center accelerator. Routing this choice into a patched live scan kernel closes the engineering loop without improving end‑to‑end speed: in unified routed ablations, the best static chunk outperforms all entropy‑guided and proxy schedulers. Sampled‑histogram COREY adds \(+4.6%\) overhead; a guarded fallback to Static‑512 reduces this to \(+1.3%\); and a lightweight sequence‑length‑keyed table further reduces it to \(+0.7%\). However, both remain slower than the static oracle because they retain scheduling cost. On an 80‑prompt LongBench subset, passive and routed inference are exactly output‑equivalent, with \(100%\) greedy‑token agreement and zero metric deltas. A mixed‑regime study shows that a single sequence‑length rule matches the per‑regime chunk oracle for balanced serving. COREY is therefore validated as a quality‑preserving scheduling prototype, but current entropy statistics are not a robust throughput win over static chunk tuning on measured SSM checkpoint workloads. SourceCode: https://github.com/mabo1215/COREY_Transformer/.

PaperId: 1827, https://arxiv.org/pdf/2604.10571.pdf GitHub

Abstract:
We test whether artificial intelligence architectural evolution obeys the same statistical laws as biological evolution. Compiling 935 ablation experiments from 161 publications, we show that the distribution of fitness effects (DFE) of architectural modifications follows a heavy‑tailed Student's t‑distribution with proportions (68% deleterious, 19% neutral, 13% beneficial for major ablations, n=568) that place AI between compact viral genomes and simple eukaryotes. The DFE shape matches D. melanogaster (normalized KS=0.07) and S. cerevisiae (KS=0.09); the elevated beneficial fraction (13% vs. 1‑6% in biology) quantifies the advantage of directed over blind search while preserving the distributional form. Architectural origination follows logistic dynamics (R^2=0.994) with punctuated equilibria and adaptive radiation into domain niches. Fourteen architectural traits were independently invented 3‑5 times, paralleling biological convergences. These results demonstrate that the statistical structure of evolution is substrate‑independent, determined by fitness landscape topology rather than the mechanism of selection.

PaperId: 1828, https://arxiv.org/pdf/2604.10547.pdf GitHub

Abstract:
We introduce Agent2 RL‑Bench, a compact diagnostic benchmark for evaluating agentic RL post‑training, which tests whether LLM agents can autonomously design, implement, debug, and execute post‑training pipelines that improve foundation models. RL post‑training increasingly drives model alignment and specialization, yet existing benchmarks are largely static, rewarding supervised fine‑tuning or script generation without assessing an agent's ability to close an interactive RL loop. Agent2 RL‑Bench provides a unified agent‑facing interface: each run starts from an isolated workspace containing a base model, task data, instructions, and a grading API, and agents must iterate within a fixed budget by training models and submitting artifacts for evaluation. The benchmark spans six tasks across three levels, from static rule‑based training to judge‑based optimization and closed‑loop online RL with trajectory collection. Two diagnostic skills, namely runtime recording and post‑hoc summarization, enable structured analysis of agent behavior, facilitating smooth and effective iteration of the benchmark's evaluation framework. Across five agent systems and six driver LLMs, agents show intelligent behavior but clear limitations: one RL‑oriented run improves ALFWorld from 4.85 to 93.28 via SFT warm‑up and GRPO with online rollouts, yet DeepSearchQA remains difficult, most successful routes rely on supervised pipelines, and interactive outcomes show large single‑run differences across agent stacks. Overall, Agent2 RL‑Bench shows that current agents can sometimes engineer online RL, but stable agent‑driven RL post‑training remains rare under fixed budgets. It also demonstrates that our benchmark provides a strong and effective evaluation framework for future research in this direction. Code is available at https://github.com/microsoft/RD‑Agent/blob/main/rdagent/scenarios/rl/autorl_bench/README.md

PaperId: 1829, https://arxiv.org/pdf/2604.10539.pdf GitHub

Abstract:
Key‑Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoiding redundant computation during autoregressive generation. However, its memory footprint scales linearly with sequence length, often leading to severe memory bottlenecks on resource‑constrained hardware. Prior work has explored offloading KV cache to the CPU while retaining only a subset on the GPU, but these approaches often rely on imprecise token selection and suffer performance degradation in long‑generation tasks such as chain‑of‑thought reasoning. In this paper, we propose a novel KV cache management strategy, IceCache, which integrates semantic token clustering with PagedAttention. By organizing semantically related tokens into contiguous memory regions managed by a hierarchical, dynamically updatable data structure, our method enables more efficient token selection and better utilization of memory bandwidth during CPU‑GPU transfers. Experimental results on LongBench show that, with a 256‑token budget, IceCache maintains 99% of the original accuracy achieved by the full KV cache model. Moreover, compared to other offloading‑based methods, IceCache attains competitive or even superior latency and accuracy while using only 25% of the KV cache token budget, demonstrating its effectiveness in long‑sequence scenarios. The code is available on our project website at https://yuzhenmao.github.io/IceCache/.

PaperId: 1830, https://arxiv.org/pdf/2604.10531.pdf GitHub

Abstract:
Peptide therapeutics are widely regarded as the "third generation" of drugs, yet progress in peptide Machine Learning (ML) are hindered by the absence of standardized benchmarks. Here we present PepBenchmark, which unifies datasets, preprocessing, and evaluation protocols for peptide drug discovery. PepBenchmark comprises three components: (1) PepBenchData, a well‑curated collection comprising 29 canonical‑peptide and 6 non‑canonical‑peptide datasets across 7 groups, systematically covering key aspects of peptide drug development, representing, to the best of our knowledge, the most comprehensive AI‑ready dataset resource to date; (2) PepBenchPipeline, a standardized preprocessing pipeline that ensures consistent dataset cleaning, construction, splitting, and feature transformation, mitigating quality issues common in ad hoc pipelines; and (3) PepBenchLeaderboard, a unified evaluation protocol and leaderboard with strong baselines across 4 major methodological families: Fingerprint‑based, GNN‑based, PLM‑based, and SMILES‑based models. Together, PepBenchmark provides the first standardized and comparable foundation for peptide drug discovery, facilitating methodological advances and translation into real‑world applications. The data and code are publicly available at https://github.com/ZGCI‑AI4S‑Pep/PepBenchmark/.

PaperId: 1831, https://arxiv.org/pdf/2604.10527.pdf GitHub

Abstract:
Referring multi‑object tracking (RMOT) is a task of associating all the objects in a video that semantically match with given textual queries or referring expressions. Existing RMOT approaches decompose object grounding and tracking into separated modules and exhibit limited performance due to the scarcity of training videos, ambiguous annotations, and restricted domains. In this work, we introduce STORM, an end‑to‑end MLLM that jointly performs grounding and tracking within a unified framework, eliminating external detectors and enabling coherent reasoning over appearance, motion, and language. To improve data efficiency, we propose a task‑composition learning (TCL) strategy that decomposes RMOT into image grounding and object tracking, allowing STORM to leverage data‑rich sub‑tasks and learn structured spatial‑‑temporal reasoning. We further construct STORM‑Bench, a new RMOT dataset with accurate trajectories and diverse, unambiguous referring expressions generated through a bottom‑up annotation pipeline. Extensive experiments show that STORM achieves state‑of‑the‑art performance on image grounding, single‑object tracking, and RMOT benchmarks, demonstrating strong generalization and robust spatial‑‑temporal grounding in complex real‑world scenarios. STORM‑Bench is released at https://github.com/amazon‑science/storm‑referring‑multi‑object‑grounding.

PaperId: 1832, https://arxiv.org/pdf/2604.10520.pdf GitHub

Abstract:
As Large Language Models (LLMs) have become capable of generating long and descriptive code summaries, accurate and reliable evaluation of factual consistency has become a critical challenge. However, previous evaluation methods are primarily designed for short summaries of isolated code snippets. Consequently, they struggle to provide fine‑grained evaluation of multi‑sentence functionalities and fail to accurately assess dependency context commonly found in real‑world code summaries. To address this, we propose ReFEree, a reference‑free and fine‑grained method for evaluating factual consistency in real‑world code summaries. We define factual inconsistency criteria specific to code summaries and evaluate them at the segment level using these criteria along with dependency information. These segment‑level results are then aggregated into a fine‑grained score. We construct a code summarization benchmark with human‑annotated factual consistency labels. The evaluation results demonstrate that ReFEree achieves the highest correlation with human judgment among 13 baselines, improving 15‑18% over the previous state‑of‑the‑art. Our code and data are available at https://github.com/bsy99615/ReFEree.git.

PaperId: 1833, https://arxiv.org/pdf/2604.10514.pdf GitHub

Abstract:
Surgical phase segmentation is central to computer‑assisted surgery, yet robust models remain difficult to develop when labeled surgical videos are scarce. We study data‑efficient phase segmentation for manual small‑incision cataract surgery (SICS) through a controlled comparison of visual representations. To isolate representation quality, we pair each visual encoder with the same temporal model (MS‑TCN++) under identical training and evaluation settings on SICS‑155 (19 phases). We compare supervised encoders (ResNet‑50, I3D) against large self‑supervised foundation models (DINOv3, V‑JEPA2), and use a cached‑feature pipeline that decouples expensive visual encoding from lightweight temporal learning. Foundation‑model features improve segmentation performance in this setup, with DINOv3 ViT‑7B achieving the best overall results (83.4% accuracy, 87.0 edit score). We further examine cataract‑domain transfer using unlabeled videos and lightweight adaptation, and analyze when it helps or hurts. Overall, the study indicates strong transferability of modern vision foundation models to surgical workflow understanding and provides practical guidance for low‑label medical video settings. The project website is available at: https://sl2005.github.io/DataEfficient‑sics‑phase‑seg/

PaperId: 1834, https://arxiv.org/pdf/2604.10485.pdf GitHub

Abstract:
Low‑visibility scenarios, such as low‑light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low‑light datasets and the loss of visual information under poor illumination. Recent domain adaptation techniques attempt to utilize well‑lit labels by augmenting well‑lit images to mimic low‑light conditions. But handcrafted augmentations oversimplify noise patterns, while learning‑based methods often fail to preserve high‑frequency low‑light characteristics, producing unrealistic images that lead pose models to generalize poorly to real low‑light scenes. Moreover, recent pose estimators rely on image cues through image‑to‑keypoint cross‑attention, but these cues become unreliable under low‑light conditions. To address these issues, we propose Unsupervised Domain Adaptation for Pose Estimation (UDAPose), a novel framework that synthesizes low‑light images and dynamically fuses visual cues with pose priors for improved pose estimation. Specifically, our synthesis method incorporates a Direct‑Current‑based High‑Pass Filter (DHF) and a Low‑light Characteristics Injection Module (LCIM) to inject high‑frequency details from input low‑light images, overcoming rigidity or the detail loss in existing approaches. Furthermore, we introduce a Dynamic Control of Attention (DCA) module that adaptively balances image cues with learned pose priors in the Transformer architecture. Experiments show that UDAPose outperforms state‑of‑the‑art methods, with notable AP gains of 10.1 (56.4%) on the ExLPose‑test hard set (LL‑H) and 7.4 (31.4%) in cross‑dataset validation on EHPT‑XC. Code: https://github.com/Vision‑and‑Multimodal‑Intelligence‑Lab/UDAPose

PaperId: 1835, https://arxiv.org/pdf/2604.10460.pdf GitHub

Abstract:
The rapid growth of generative AI has introduced new challenges in content moderation and digital forensics. In particular, benign AI‑generated images can be paired with harmful or misleading text, creating difficult‑to‑detect misuse. This contextual misuse undermines the traditional moderation framework and complicates attribution, as synthetic images typically lack persistent metadata or device signatures. We introduce a steganography enabled attribution framework that embeds cryptographically signed identifiers into images at creation time and uses multimodal harmful content detection as a trigger for attribution verification. Our system evaluates five watermarking methods across spatial, frequency, and wavelet domains. It also integrates a CLIP‑based fusion model for multimodal harmful‑content detection. Experiments demonstrate that spread‑spectrum watermarking, especially in the wavelet domain, provides strong robustness under blur distortions, and our multimodal fusion detector achieves an AUC‑ROC of 0.99, enabling reliable cross‑modal attribution verification. These components form an end‑to‑end forensic pipeline that enables reliable tracing of harmful deployments of AI‑generated imagery, supporting accountability in modern synthetic media environments. Our code is available at GitHub: https://github.com/bli1/steganography

PaperId: 1836, https://arxiv.org/pdf/2604.10458.pdf GitHub

Abstract:
Wearable IMU‑based Human Activity Recognition (HAR) relies heavily on Deep Neural Networks (DNNs), which are burdened by immense computational and buffering demands. Their power‑hungry floating‑point operations and rigid requirement to process complete temporal windows severely cripple battery‑constrained edge devices. While Spiking Neural Networks (SNNs) offer extreme event‑driven energy efficiency, standard architectures struggle with complex biomechanical topologies and temporal gradient degradation. To bridge this gap, we propose the Physics‑Aware Spiking Neural Network (PAS‑Net), a fully multiplier‑free architecture explicitly tailored for Green HAR. Spatially, an adaptive symmetric topology mixer enforces human‑joint physical constraints. Temporally, an O(1)‑memory causal neuromodulator yields context‑aware dynamic threshold neurons, adapting actively to non‑stationary movement rhythms. Furthermore, we leverage a temporal spike error objective to unlock a flexible early‑exit mechanism for continuous IMU streams. Evaluated across seven diverse datasets, PAS‑Net achieves state‑of‑the‑art accuracy while replacing dense operations with sparse 0.1 pJ integer accumulations. Crucially, its confidence‑driven early‑exit capability drastically reduces dynamic energy consumption by up to 98%. PAS‑Net establishes a robust, ultra‑low‑power neuromorphic standard for always‑on wearable sensing. The source code and pre‑trained models are publicly available at https://github.com/zhengnaichuan2022/PAS‑Net.git.

PaperId: 1837, https://arxiv.org/pdf/2604.10409.pdf GitHub GitHub

Abstract:
We introduce IMPACT, a synchronized five‑view RGB‑D dataset for deployment‑oriented industrial procedural understanding, built around real assembly and disassembly of a commercial angle grinder with professional‑grade tools. To our knowledge, IMPACT is the first real industrial assembly benchmark that jointly provides synchronized ego‑exo RGB‑D capture, decoupled bimanual annotation, compliance‑aware state tracking, and explicit anomaly‑‑recovery supervision within a single real industrial workflow. It comprises 112 trials from 13 participants totaling 39.5 hours, with multi‑route execution governed by a partial‑order prerequisite graph, a six‑category anomaly taxonomy, and operator cognitive load measured via NASA‑TLX. The annotation hierarchy links hand‑specific atomic actions to coarse procedural steps, component assembly states, and per‑hand compliance phases, with synchronized null spans across views to decouple perceptual limitations from algorithmic failure. Systematic baselines reveal fundamental limitations that remain invisible to single‑task benchmarks, particularly under realistic deployment conditions that involve incomplete observations, flexible execution paths, and corrective behavior. The full dataset, annotations, and evaluation code are available at https://github.com/Kratos‑Wen/IMPACT.

PaperId: 1838, https://arxiv.org/pdf/2604.10367.pdf GitHub

Abstract:
Audio‑driven human video generation has achieved remarkable success in monologue scenarios, largely driven by advancements in powerful video generation foundation models. Moving beyond monologues, authentic human communication is inherently a full‑duplex interactive process, requiring virtual agents not only to articulate their own speech but also to react naturally to incoming conversational audio. Most existing methods simply extend conventional audio‑driven paradigms to listening scenarios. However, relying on strict frame‑to‑frame alignment renders the model's response to long‑range conversational dynamics rigid, whereas directly introducing global attention catastrophically degrades lip synchronization. Recognizing the unique temporal Scale Discrepancy between talking and listening behaviors, we introduce a multi‑head Gaussian kernel to explicitly inject this physical intuition into the model as a progressive temporal inductive bias. Building upon this, we construct a full‑duplex interactive virtual agent capable of simultaneously processing dual‑stream audio inputs for both talking and listening. Furthermore, we introduce a rigorously cleaned Talking‑Listening dataset VoxHear featuring perfectly decoupled speech and background audio tracks. Extensive experiments demonstrate that our approach successfully fuses strong temporal alignment with deep contextual semantics, setting a new state‑of‑the‑art for generating highly natural and responsive full‑duplex interactive digital humans. The project page is available at https://warmcongee.github.io/beyond‑monologue/ .

PaperId: 1839, https://arxiv.org/pdf/2604.10359.pdf GitHub

Abstract:
Low‑light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. State‑of‑the‑art (SOTA) LLIE techniques often rely on large models and multi‑stage training, limiting practicality for edge deployment. Moreover, their dependence on a single color space introduces instability and visible exposure or color artifacts. To address these, we propose Multinex, an ultra‑lightweight structured framework that integrates multiple fine‑grained representations within a principled Retinex residual formulation. It decomposes an image into illumination and color prior stacks derived from distinct analytic representations, and learns to fuse these representations into luminance and reflectance adjustments required to correct exposure. By prioritizing enhancement over reconstruction and exploiting lightweight neural operations, Multinex significantly reduces computational cost, exemplified by its lightweight (45K parameters) and nano (0.7K parameters) versions. Extensive benchmarks show that all lightweight variants significantly outperform their corresponding lightweight SOTA models, and reach comparable performance to heavy models. Paper page available at https://albrateanu.github.io/multinex.

PaperId: 1840, https://arxiv.org/pdf/2604.10297.pdf GitHub

Abstract:
Composed Image Retrieval (CIR) retrieves target images using a reference image paired with modification text. Despite rapid advances, all existing methods and datasets operate at the image level ‑‑ a single reference image plus modification text in, a single target image out ‑‑ while real e‑commerce users reason about products shown from multiple viewpoints. We term this mismatch View Incompleteness and formally define a new Multi‑View CIR task that generalizes standard CIR from image‑level to product‑level retrieval. To support this task, we construct FashionMV, the first large‑scale multi‑view fashion dataset for product‑level CIR, comprising 127K products, 472K multi‑view images, and over 220K CIR triplets, built through a fully automated pipeline leveraging large multimodal models. We further propose ProCIR (Product‑level Composed Image Retrieval), a modeling framework built upon a multimodal large language model that employs three complementary mechanisms ‑‑ two‑stage dialogue, caption‑based alignment, and chain‑of‑thought guidance ‑‑ together with an optional supervised fine‑tuning (SFT) stage that injects structured product knowledge prior to contrastive training. Systematic ablation across 16 configurations on three fashion benchmarks reveals that: (1) alignment is the single most critical mechanism; (2) the two‑stage dialogue architecture is a prerequisite for effective alignment; and (3) SFT and chain‑of‑thought serve as partially redundant knowledge injection paths. Our best 0.8B‑parameter model outperforms all baselines, including general‑purpose embedding models 10x its size. The dataset, model, and code are publicly available at https://github.com/yuandaxia2001/FashionMV.

PaperId: 1841, https://arxiv.org/pdf/2604.10291.pdf GitHub

Abstract:
Large Language Models (LLMs) have shown promising performance in time series modeling tasks, but do they truly understand time series data? While multiple benchmarks have been proposed to answer this fundamental question, most are manually curated and focus on narrow domains or specific skill sets. To address this limitation, we propose scalable methods for creating comprehensive time series reasoning benchmarks that combine the flexibility of templates with the creativity of LLM agents. We first develop TimeSeriesExam, a multiple‑choice benchmark using synthetic time series to evaluate LLMs across five core reasoning categories: pattern recognitionnoise understandingsimilarity analysisanomaly detection, and causality. Then, with TimeSeriesExamAgent, we scale our approach by automatically generating benchmarks from real‑world datasets spanning healthcare, finance and weather domains. Through multi‑dimensional quality evaluation, we demonstrate that our automatically generated benchmarks achieve diversity comparable to manually curated alternatives. However, our experiments reveal that LLM performance remains limited in both abstract time series reasoning and domain‑specific applications, highlighting ongoing challenges in enabling effective time series understanding in these models. TimeSeriesExamAgent is available at https://github.com/magwiazda/TimeSeriesExamAgent.

PaperId: 1842, https://arxiv.org/pdf/2604.10286.pdf GitHub

Abstract:
Autonomous language‑model agents increasingly rely on installable skills and tools to complete user tasks. Static skill auditing can expose capability surface before deployment, but it cannot determine whether a particular invocation is unsafe under the current user request and runtime context. We therefore study skill invocation auditing as a continuous‑risk estimation problem: given a user request, candidate skill, and runtime context, predict a score that supports ranking and triage before a hard intervention is applied. We introduce STARS, which combines a static capability prior, a request‑conditioned invocation risk model, and a calibrated risk‑fusion policy. To evaluate this setting, we construct SIA‑Bench, a benchmark of 3,000 invocation records with group‑safe splits, lineage metadata, runtime context, canonical action labels, and derived continuous‑risk targets. On a held‑out split of indirect prompt injection attacks, calibrated fusion reaches 0.439 high‑risk AUPRC, improving over 0.405 for the contextual scorer and 0.380 for the strongest static baseline, while the contextual scorer remains better calibrated with 0.289 expected calibration error. On the locked in‑distribution test split, gains are smaller and static priors remain useful. The resulting claim is therefore narrower: request‑conditioned auditing is most valuable as an invocation‑time risk‑scoring and triage layer rather than as a replacement for static screening. Code is available at https://github.com/123zgj123/STARS.

PaperId: 1843, https://arxiv.org/pdf/2604.10261.pdf GitHub

Abstract:
Existing tool‑use benchmarks for LLM agents are overwhelmingly linear: our analysis of six benchmarks shows 55 to 100% of instances are simple chains of 2 to 5 steps. We introduce The Amazing Agent Race (AAR), a benchmark featuring directed acyclic graph (DAG) puzzles (or "legs") with fork‑merge tool chains. We release 1,400 instances across two variants: sequential (800 legs) and compositional (600 DAG legs). Agents must navigate Wikipedia, execute multi‑step tool chains, and aggregate results into a verifiable answer. Legs are procedurally generated from Wikipedia seeds across four difficulty levels with live‑API validation. Three complementary metrics (finish‑line accuracy, pit‑stop visit rate, and roadblock completion rate) separately diagnose navigation, tool‑use, and arithmetic failures. Evaluating three agent frameworks on 1,400 legs, the best achieves only 37.2% accuracy. Navigation errors dominate (27 to 52% of trials) while tool‑use errors remain below 17%, and agent architecture matters as much as model scale (Claude Code matches Codex CLI at 37% with 6x fewer tokens). The compositional structure of AAR reveals that agents fail not at calling tools but at navigating to the right pages, a blind spot invisible to linear benchmarks. The project page can be accessed at: https://minnesotanlp.github.io/the‑amazing‑agent‑race

PaperId: 1844, https://arxiv.org/pdf/2604.10210.pdf GitHub

Abstract:
Learning multi‑scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyramid networks have greatly advanced visual recognition, inherent design defects inhibit them from capturing discriminative features and recognizing small objects. In this work, we propose Asymptotic Content‑Aware Pyramid Attention Network (A3‑FPN), to augment multi‑scale feature representation via the asymptotically disentangled framework and content‑aware attention modules. Specifically, A3‑FPN employs a horizontally‑spread column network that enables asymptotically global feature interaction and disentangles each level from all hierarchical representations. In feature fusion, it collects supplementary content from the adjacent level to generate position‑wise offsets and weights for context‑aware resampling, and learns deep context reweights to improve intra‑category similarity. In feature reassembly, it further strengthens intra‑scale discriminative feature learning and reassembles redundant features based on information content and spatial variation of feature maps. Extensive experiments on MS COCO, VisDrone2019‑DET and Cityscapes demonstrate that A3‑FPN can be easily integrated into state‑of‑the‑art CNN and Transformer‑based architectures, yielding remarkable performance gains. Notably, when paired with OneFormer and Swin‑L backbone, A3‑FPN achieves 49.6 mask AP on MS COCO and 85.6 mIoU on Cityscapes. Codes are available at https://github.com/mason‑ching/A3‑FPN.

PaperId: 1845, https://arxiv.org/pdf/2604.10164.pdf GitHub

Abstract:
Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time‑aware facts. While existing methods are effective at capturing relational dynamics, their performance is limited by a closed‑world assumption, which fails to account for emerging entities not present in the training. Notably, these entities continuously join the network without historical interactions. Empirical study reveals that emerging entities are widespread in TKGs, comprising roughly 25% of all entities. The absence of historical interactions of these entities leads to significant performance degradation in reasoning tasks. Whereas, we observe that entities with semantic similarities often exhibit comparable interaction histories, suggesting the presence of transferable temporal patterns. Inspired by this insight, we propose TransFIR (Transferable Inductive Reasoning), a novel framework that leverages historical interaction sequences from semantically similar known entities to support inductive reasoning. Specifically, we propose a codebook‑based classifier that categorizes emerging entities into latent semantic clusters, allowing them to adopt reasoning patterns from similar entities. Experimental results demonstrate that TransFIR outperforms all baselines in reasoning on emerging entities, achieving an average improvement of 28.6% in Mean Reciprocal Rank (MRR) across multiple datasets. The implementations are available at https://github.com/zhaodazhuang2333/TransFIR.

PaperId: 1846, https://arxiv.org/pdf/2604.10087.pdf GitHub

Abstract:
We present EL‑DRUIN, an ontological reasoning system for geopolitical intelligence analysis that combines formal ontology, finite semigroup algebra, and Lie algebra approximation to forecast long‑run relationship trajectories. Current LLM‑based political analysis systems operate as summarisation engines, producing outputs bounded by textual pattern matching. EL‑DRUIN departs from this paradigm by modelling geopolitical relationships as states in a finite set of named Dynamic Patterns, composing patterns via a semigroup operation whose structure constants are defined by an explicit composition table, and embedding each pattern as a vector in an 8‑dimensional semantic Lie algebra space. Forward simulation iterates this semigroup operation, yielding reachable pattern sets at each discrete timestep; convergence to idempotent absorbing states (fixed points of the composition) constitutes the predicted long‑run attractor. Bayesian posterior weights combine ontology‑derived confidence priors with a Lie similarity term measuring the cosine similarity between the vector sum of composing patterns and the target pattern vector, providing interpretable, calibrated probabilities that are not self‑reported by a language model. Bifurcation points ‑‑ steps at which two candidate attractors have near‑equal posterior mass ‑‑ are detected and exposed to downstream analysis. We demonstrate the framework on six geopolitical scenarios including US‑China technology decoupling and the Taiwan Strait military coercion trajectory. The architecture is publicly available as an open‑source system with a Streamlit frontend exposing full computation traces, Bayesian posterior breakdowns, and 8D ontological state vectors.

PaperId: 1847, https://arxiv.org/pdf/2604.10073.pdf GitHub GitHub

Abstract:
Long‑horizon Flexible Job‑Shop Scheduling~(FJSP) presents a formidable combinatorial challenge due to complex, interdependent decisions spanning extended time horizons. While learning‑based Rolling Horizon Optimization~(RHO) has emerged as a promising paradigm to accelerate solving by identifying and fixing invariant operations, its effectiveness is hindered by the structural complexity of FJSP. Existing methods often fail to capture intricate graph‑structured dependencies and ignore the asymmetric costs of prediction errors, in which misclassifying critical‑path operations is significantly more detrimental than misclassifying non‑critical ones. Furthermore, dynamic shifts in predictive confidence during the rolling process make static pruning thresholds inadequate. To address these limitations, we propose Graph‑RHO, a novel critical‑path‑aware graph‑based RHO framework. First, we introduce a topology‑aware heterogeneous graph network that encodes subproblems as operation‑machine graphs with multi‑relational edges, leveraging edge‑feature‑aware message passing to predict operation stability. Second, we incorporate a critical‑path‑aware mechanism that injects inductive biases during training to distinguish highly sensitive bottleneck operations from robust ones. Third, we devise an adaptive thresholding strategy that dynamically calibrates decision boundaries based on online uncertainty estimation to align model predictions with the solver's search space. Extensive experiments on standard benchmarks demonstrate that \mboxGraph‑RHO establishes a new state of the art in solution quality and computational efficiency. Remarkably, it exhibits exceptional zero‑shot generalization, reducing solve time by over 30% on large‑scale instances (2000 operations) while achieving superior solution quality. Our code is available \hrefhttps://github.com/IntelliSensing/Graph‑RHOhere.

PaperId: 1848, https://arxiv.org/pdf/2604.09989.pdf GitHub

Abstract:
Recently, synthetic palmprints have been increasingly used as substitutes for real data to train recognition models. To be effective, such synthetic data must reflect the diversity of real palmprints, including both style variation and geometric variation. However, existing palmprint generation methods mainly focus on style translation, while geometric variation is either ignored or approximated by simple handcrafted augmentations. In this work, we propose FlowPalm, an optical‑flow‑driven palmprint generation framework capable of simulating the complex non‑rigid deformations observed in real palms. Specifically, FlowPalm estimates optical flows between real palmprint pairs to capture the statistical patterns of geometric deformations. Building on these priors, we design a progressive sampling process that gradually introduces the geometric deformations during diffusion while maintaining identity consistency. Extensive experiments on six benchmark datasets demonstrate that FlowPalm significantly outperforms state‑of‑the‑art palmprint generation approaches in downstream recognition tasks. Project page: https://yuchenzou.github.io/FlowPalm/

PaperId: 1849, https://arxiv.org/pdf/2604.09923.pdf GitHub

Abstract:
Text‑to‑image (T2I) models, and their encoded biases, increasingly shape the visual media the public encounters. While researchers have produced a rich body of work on bias measurement, auditing, and mitigation in T2I systems, those methods largely target technical stakeholders, leaving a gap in public legibility. We introduce GLEaN (Generative Likeness Evaluation at N‑Scale), a portrait‑based explainability pipeline designed to make T2I model biases visually understandable to a broad audience. GLEaN comprises three stages: automated large‑scale image generation from identity prompts, facial landmark‑based filtering and spatial alignment, and median‑pixel composition that distills a model's central tendency into a single representative portrait. The resulting composites require no statistical background to interpret; a viewer can see, at a glance, who a model 'imagines' when prompted with 'a doctor' versus a 'felon.' We demonstrate GLEaN on Stable Diffusion XL across 40 social and occupational identity prompts, producing composites that reproduce documented biases and surface new associations between skin tone and predicted emotion. We find in a between‑subjects user study (N = 291) that GLEaN portraits communicate biases as effectively as conventional data tables, but require significantly less viewing time. Because the method relies solely on generated outputs, it can also be replicated on any black‑box and closed‑weight systems without access to model internals. GLEaN offers a scalable, model‑agnostic approach to bias explainability, purpose‑built for public comprehension, and is publicly available at https://github.com/cultureiolab/GLEaN.

PaperId: 1850, https://arxiv.org/pdf/2604.09750.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) have achieved remarkable performance across diverse domains, yet their decision‑making under conflicting objectives remains insufficiently understood. This work investigates how LRMs respond to harmful queries when confronted with two categories of conflicts: internal conflicts that pit alignment values against each other and dilemmas, which impose mutually contradictory choices, including sacrificial, duress, agent‑centered, and social forms. Using over 1,300 prompts across five benchmarks, we evaluate three representative LRMs ‑ Llama‑3.1‑Nemotron‑8B, QwQ‑32B, and DeepSeek R1 ‑ and find that conflicts significantly increase attack success rates, even under single‑round non‑narrative queries without sophisticated auto‑attack techniques. Our findings reveal through layerwise and neuron‑level analyses that safety‑related and functional representations shift and overlap under conflict, interfering with safety‑aligned behavior. This study highlights the need for deeper alignment strategies to ensure the robustness and trustworthiness of next‑generation reasoning models. Our code is available at https://github.com/DataArcTech/ConflictHarm. Warning: This paper contains inappropriate, offensive and harmful content.

PaperId: 1851, https://arxiv.org/pdf/2604.09748.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model's (LLM's) reasoning abilities on complex logical tasks, such as mathematics and programming. However, we identify, for the first time, a latent vulnerability to backdoor attacks within the RLVR framework. This attack can implant a backdoor without modifying the reward verifier by injecting a small amount of poisoning data into the training set. Specifically, we propose a novel trigger mechanism designated as the \ourapproach (ACB). The attack exploits the RLVR training loop by assigning substantial positive rewards for harmful responses and negative rewards for refusals. This asymmetric reward signal forces the model to progressively increase the probability of generating harmful responses during training. Our findings demonstrate that the RLVR backdoor attack is characterized by both high efficiency and strong generalization capabilities. Utilizing less than 2% poisoned data in train set, the backdoor can be successfully implanted across various model scales without degrading performance on benign tasks. Evaluations across multiple jailbreak benchmarks indicate that activating the trigger degrades safety performance by an average of 73%. Furthermore, the attack generalizes effectively to a wide range of jailbreak methods and unsafe behaviors. Code is available at https://github.com/yuki‑younai/Backdoor_in_RLVR.

PaperId: 1852, https://arxiv.org/pdf/2604.09673.pdf GitHub

Abstract:
The mirror self‑recognition test evaluates whether a subject touches a mark on its own body that is visible only in a mirror, and is widely used as an indicator of self‑awareness. In this study, we present a computational model in which this behavior emerges spontaneously through a single mechanism, the self‑prior, without any external reward. The self‑prior, implemented with a Transformer, learns the density of familiar multisensory experiences; when a novel mark appears, the discrepancy from this learned distribution drives mark‑directed behavior through active inference. A simulated infant, relying solely on vision and proprioception without tactile input, discovered a sticker placed on its own face in the mirror and removed it in approximately 70% of cases without any explicit instruction. Expected free energy decreased significantly after sticker removal, confirming that the self‑prior operates as an internal criterion for distinguishing self from non‑self. Cross‑modal sampling further demonstrated that the self‑prior captures visual‑‑proprioceptive associations, functioning as a probabilistic body schema. These results provide a concise computational account of the key behavior observed in the mirror test and suggest that the free energy principle can serve as a unifying hypothesis for investigating the developmental origins of self‑awareness. Code is available at: https://github.com/kim135797531/self‑prior‑mirror

PaperId: 1853, https://arxiv.org/pdf/2604.09617.pdf GitHub

Abstract:
Transparent and standardized documentation is essential for building trustworthy generative AI (GAI) systems. However, existing automated methods for generating model and data cards still face three major challenges: (i) static templates, as most systems rely on fixed query templates that cannot adapt to diverse paper structures or evolving documentation requirements; (ii) information scarcity, since web‑scale repositories such as Hugging Face often contain incomplete or inconsistent metadata, leading to missing or noisy information; and (iii) lack of benchmarks, as the absence of standardized datasets and evaluation protocols hinders fair and reproducible assessment of documentation quality. To address these limitations, we propose AdaQE‑CG, an Adaptive Query Expansion for Card Generation framework that combines dynamic information extraction with cross‑card knowledge transfer. Its Intra‑Paper Extraction via Context‑Aware Query Expansion (IPE‑QE) module iteratively refines extraction queries to recover richer and more complete information from scientific papers and repositories, while its Inter‑Card Completion using the MetaGAI Pool (ICC‑MP) module fills missing fields by transferring semantically relevant content from similar cards in a curated dataset. In addition, we introduce MetaGAI‑Bench, the first large‑scale, expert‑annotated benchmark for evaluating GAI documentation. Comprehensive experiments across five quality dimensions show that AdaQE‑CG substantially outperforms existing approaches, exceeds human‑authored data cards, and approaches human‑level quality for model cards. Code, prompts, and data are publicly available at: https://github.com/haoxuan‑unt2024/AdaQE‑CG.

PaperId: 1854, https://arxiv.org/pdf/2604.09597.pdf GitHub GitHub

Abstract:
Creativity and strategic foresight have been extensively studied through descriptive theories ‑‑ Koestler's bisociation (1964), de Bono's lateral thinking (1967), and Ansoff's weak signals (1975) explain why creative and strategic insights occur, but offer limited guidance on how to produce them on demand. This paper presents two executable protocols that bridge this theory‑practice gap: GHOSTY COLLIDER, a 5‑step protocol for cross‑domain creative emergence through structural de‑labeling and collision, and PRECOG PROTOCOL, a 5‑step protocol for signal‑based strategic foresight with multi‑axis timing judgment. We formalize established theories into repeatable, step‑by‑step procedures with explicit quality criteria, anti‑pattern detection, and measurable outputs. We evaluate the protocols through three complementary methods: (1) five detailed case studies across distinct domains, (2) controlled comparisons against standard methods using identical inputs, and (3) a batch experiment across eight random domain pairings (N=8, success rate 87.5%, failure rate 12.5%) with one blind evaluation. Preliminary evidence suggests that protocol‑driven outputs exhibit greater structural novelty, higher parameter specificity, and qualitatively distinct creative directions compared to outputs from standard methods. The blind evaluation confirmed the direction of author assessments (protocol output scored 74/80 vs. brainstorming 49/80). These results, while limited by single‑operator execution, indicate that the theory‑to‑protocol translation preserves and potentially enhances the generative power of the underlying theories. The protocols, updated to version 2 incorporating lessons from failure case analysis, are released as open‑access documents under CC BY‑NC 4.0 at https://github.com/GhostyAI‑HA/ghosty‑collider.

PaperId: 1855, https://arxiv.org/pdf/2604.09581.pdf GitHub

Abstract:
Evaluating web usability typically requires time‑consuming user studies and expert reviews, which often limits iteration speed during product development, especially for small teams and agile workflows. We present Avenir‑UX, a user‑experience evaluation agent that simulates user behavior on websites and produces standardized usability. Unlike traditional tools that rely on DOM parsing, Avenir‑UX grounds actions and observations, enabling it to interact with real web pages end‑to‑end while maintaining a coherent trace of the user journey. Building on Avenir‑Web, our system pairs this robust interaction with simulated user behavior profiles and a structured evaluation protocol that integrates the System Usability Scale (SUS), step‑wise Single Ease Questions (SEQ), and concurrent Think Aloud. Subsequently, a comprehensive User Experience (UX) report will be generated. We discuss the architecture of Avenir‑UX and illustrate how its multimodal grounding improves robustness for web‑based interaction and UX evaluation scenarios, paving the way for a new era of continuous, scalable, and data‑driven usability testing that empowers every developer to build web interfaces that are usable. Code is available at: https://github.com/Onflow‑AI/Avenir‑UX

PaperId: 1856, https://arxiv.org/pdf/2604.09579.pdf GitHub

Abstract:
In large‑scale cloud service platforms, thousands of customer tickets are generated daily and are typically handled through on‑call dialogues. This high volume of on‑call interactions imposes a substantial workload on human support analysts. Recent studies have explored reactive agents that leverage large language models as a first line of support to interact with customers directly and resolve issues. However, when issues remain unresolved and are escalated to human support, these agents are typically disengaged. As a result, they cannot assist with follow‑up inquiries, track resolution progress, or learn from the cases they fail to address. In this paper, we introduce Vigil, a novel proactive agent system designed to operate throughout the entire on‑call life‑cycle. Unlike reactive agents, Vigil focuses on providing assistance during the phase in which human support is already involved. It integrates into the dialogue between the customer and the analyst, proactively offering assistance without explicit user invocation. Moreover, Vigil incorporates a continuous self‑improvement mechanism that extracts knowledge from human‑resolved cases to autonomously update its capabilities. Vigil has been deployed on Volcano Engine, ByteDance's cloud platform, for over ten months, and comprehensive evaluations based on this deployment demonstrate its effectiveness and practicality. The open source version of this work is publicly available at https://github.com/volcengine/veaiops.

PaperId: 1857, https://arxiv.org/pdf/2604.09554.pdf GitHub

Abstract:
Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicated foundation models on scientific data to agentic autonomous hypothesis generation systems to AI‑driven autonomous labs. The need to measure progress of AI systems in scientific domains correspondingly must not only accelerate, but increasingly shift focus to more real‑world capabilities. Beyond rote knowledge and even just reasoning to actually measuring the ability to perform meaningful work. Prior work introduced the Language Agent Biology Benchmark LAB‑Bench as an initial attempt at measuring these abilities. Here we introduce an evolution of that benchmark, LABBench2, for measuring real‑world capabilities of AI systems performing useful scientific tasks. LABBench2 comprises nearly 1,900 tasks and is, for the most part, a continuation of LAB‑Bench, measuring similar capabilities but in more realistic contexts. We evaluate performance of current frontier models, and show that while abilities measured by LAB‑Bench and LABBench2 have improved substantially, LABBench2 provides a meaningful jump in difficulty (model‑specific accuracy differences range from ‑26% to ‑46% across subtasks) and underscores continued room for performance improvement. LABBench2 continues the legacy of LAB‑Bench as a de facto benchmark for AI scientific research capabilities and we hope that it continues to help advance development of AI tools for these core research functions. To facilitate community use and development, we provide the task dataset at https://huggingface.co/datasets/futurehouse/labbench2 and a public eval harness at https://github.com/EdisonScientific/labbench2.

PaperId: 1858, https://arxiv.org/pdf/2604.09532.pdf GitHub

Abstract:
Prompt learning is a parameter‑efficient approach for vision‑language models, yet its robustness under label noise is less investigated. Visual content contains richer and more reliable semantic information, which remains more robust under label noise. However, the prompt itself is highly susceptible to label noise. Motivated by this intuition, we propose VisPrompt, a lightweight and robust vision‑guided prompt learning framework for noisy‑label settings. Specifically, we exploit a cross‑modal attention mechanism to reversely inject visual semantics into prompt representations. This enables the prompt tokens to selectively aggregate visual information relevant to the current sample, thereby improving robustness by anchoring prompt learning to stable instance‑level visual evidence and reducing the influence of noisy supervision. To address the instability caused by using the same way of injecting visual information for all samples, despite differences in the quality of their visual cues, we further introduce a lightweight conditional modulation mechanism to adaptively control the strength of visual information injection, which strikes a more robust balance between text‑side semantic priors and image‑side instance evidence. The proposed framework effectively suppresses the noise‑induced disturbances, reduce instability in prompt updates, and alleviate memorization of mislabeled samples. VisPrompt significantly improves robustness while keeping the pretrained VLM backbone frozen and introducing only a small amount of additional trainable parameters. Extensive experiments under synthetic and real‑world label noise demonstrate that VisPrompt generally outperforms existing baselines on seven benchmark datasets and achieves stronger robustness. Our code is publicly available at https://github.com/gezbww/Vis_Prompt.

PaperId: 1859, https://arxiv.org/pdf/2604.09531.pdf GitHub

Abstract:
Vision‑language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contributing factor is that natural image datasets provide limited supervision for low‑level visual skills. This motivates a practical question: can targeted synthetic supervision, generated from only a task keyword such as Depth Order, address these weaknesses? To investigate this question, we introduce VisionFoundry, a task‑aware synthetic data generation pipeline that takes only the task name as input and uses large language models (LLMs) to generate questions, answers, and text‑to‑image (T2I) prompts, then synthesizes images with T2I models and verifies consistency with a proprietary VLM, requiring no reference images or human annotation. Using VisionFoundry, we construct VisionFoundry‑10K, a synthetic visual question answering (VQA) dataset containing 10k image‑question‑answer triples spanning 10 tasks. Models trained on VisionFoundry‑10K achieve substantial improvements on visual perception benchmarks: +7% on MMVP and +10% on CV‑Bench‑3D, while preserving broader capabilities and showing favorable scaling behavior as data size increases. Our results suggest that limited task‑targeted supervision is an important contributor to this bottleneck and that synthetic supervision is a promising path toward more systematic training for VLMs.

PaperId: 1860, https://arxiv.org/pdf/2604.09529.pdf GitHub

Abstract:
Large Vision Language Models (LVLMs) achieve strong multimodal reasoning but frequently exhibit hallucinations and incorrect responses with high certainty, which hinders their usage in high‑stakes domains. Existing verbalized confidence calibration methods, largely developed for text‑only LLMs, typically optimize a single holistic confidence score using binary answer‑level correctness. This design is mismatched to LVLMs: an incorrect prediction may arise from perceptual failures or from reasoning errors given correct perception, and a single confidence conflates these sources while visual uncertainty is often dominated by language priors. To address these issues, we propose VL‑Calibration, a reinforcement learning framework that explicitly decouples confidence into visual and reasoning confidence. To supervise visual confidence without ground‑truth perception labels, we introduce an intrinsic visual certainty estimation that combines (i) visual grounding measured by KL‑divergence under image perturbations and (ii) internal certainty measured by token entropy. We further propose token‑level advantage reweighting to focus optimization on tokens based on visual certainty, suppressing ungrounded hallucinations while preserving valid perception. Experiments on thirteen benchmarks show that VL‑Calibration effectively improves calibration while boosting visual reasoning accuracy, and it generalizes to out‑of‑distribution benchmarks across model scales and architectures.

PaperId: 1861, https://arxiv.org/pdf/2604.09527.pdf GitHub GitHub

Abstract:
Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains, and efficiently explore many plausible futures. Yet most existing approaches rely on dense video or latent‑space prediction, expending substantial capacity on dense appearance rather than on the underlying sparse trajectories of points in the scene. This makes large‑scale exploration of future hypotheses costly and limits performance when long‑horizon, multi‑modal motion is essential. We address this by formulating the prediction of open‑set future scene dynamics as step‑wise inference over sparse point trajectories. Our autoregressive diffusion model advances these trajectories through short, locally predictable transitions, explicitly modeling the growth of uncertainty over time. This dynamics‑centric representation enables fast rollout of thousands of diverse futures from a single image, optionally guided by initial constraints on motion, while maintaining physical plausibility and long‑range coherence. We further introduce OWM, a benchmark for open‑set motion prediction based on diverse in‑the‑wild videos, to evaluate accuracy and variability of predicted trajectory distributions under real‑world uncertainty. Our method matches or surpasses dense simulators in predictive accuracy while achieving orders‑of‑magnitude higher sampling speed, making open‑set future prediction both scalable and practical. Project page: http://compvis.github.io/myriad.

PaperId: 1862, https://arxiv.org/pdf/2604.09521.pdf GitHub

Abstract:
When two agents of different computational capacities interact with the same environment, they need not compress a common semantic alphabet differently; they can induce different semantic alphabets altogether. We show that the quotient POMDP Q_m,T(M) ‑ the unique coarsest abstraction consistent with an agent's capacity ‑ serves as a capacity‑derived semantic space for any bounded agent, and that communication between heterogeneous agents exhibits a sharp structural phase transition. Below a critical rate R_\textcrit determined by the quotient mismatch, intent‑preserving communication is structurally impossible. In the supported one‑way memoryless regime, classical side‑information coding then yields exponential decay above the induced benchmark. Classical coding theorems tell you the rate once the source alphabet is fixed; our contribution is to derive that alphabet from bounded interaction itself. Concretely, we prove: (1) a fixed‑\varepsilon structural phase‑transition theorem whose lower bound is fully general on the common‑history quotient comparison; (2) a one‑way Wyner‑Ziv benchmark identification on quotient alphabets, with exact converse, exact operational equality for memoryless quotient sources, and an ergodic long‑run bridge via explicit mixing bounds; (3) an asymptotic one‑way converse in the shrinking‑distortion regime \varepsilon = O(1/T), proved from the message stream and decoder side information; and (4) alignment traversal bounds enabling compositional communication through intermediate capacity levels. Experiments on eight POMDP environments (including RockSample(4,4)) illustrate the phase transition, a structured‑policy benchmark shows the one‑way rate can drop by up to 19× relative to the counting bound, and a shrinking‑distortion sweep matches the regime of the asymptotic converse.

PaperId: 1863, https://arxiv.org/pdf/2604.09494.pdf GitHub

Abstract:
We propose RecaLLM, a set of reasoning language models post‑trained to make effective use of long‑context information. In‑context retrieval, which identifies relevant evidence from context, and reasoning are deeply intertwined: retrieval supports reasoning, while reasoning often determines what must be retrieved. However, their interaction remains largely underexplored. In preliminary experiments on several open‑source LLMs, we observe that in‑context retrieval performance substantially degrades even after a short reasoning span, revealing a key bottleneck for test‑time scaling that we refer to as lost‑in‑thought: reasoning steps that improve performance also make subsequent in‑context retrieval more challenging. To address this limitation, RecaLLM interleaves reasoning with explicit in‑context retrieval, alternating between reasoning and retrieving context information needed to solve intermediate subproblems. We introduce a negligible‑overhead constrained decoding mechanism that enables verbatim copying of evidence spans, improving the grounding of subsequent generation. Trained on diverse lexical and semantic retrieval tasks, RecaLLM achieves strong performance on two long‑context benchmarks, RULER and HELMET, significantly outperforming baselines. Notably, we observe consistent gains at context windows of up to 128K tokens using training samples of at most 10K tokens, far shorter than those used by existing long‑context approaches, highlighting a promising path toward improving long‑context performance without expensive long‑context training data.

PaperId: 1864, https://arxiv.org/pdf/2604.09455.pdf GitHub

Abstract:
While Large Language Models (LLMs) have demonstrated significant potential in Tool‑Integrated Reasoning (TIR), existing training paradigms face significant limitations: Zero‑RL suffers from inefficient exploration and mode degradation due to a lack of prior guidance, while SFT‑then‑RL is limited by high data costs and capability plateaus caused by low‑entropy collapse. To address these challenges, we propose E3‑TIR (Enhanced Experience Exploitation), a warm‑up paradigm for the early stages of agent training. Specifically, we formulate training as the dynamic integration of three experience types: Expert Prefixes, Expert Guided, and Self‑Exploration. By executing diverse branching exploration around expert "anchors" and employing a mix policy optimization mechanism, we effectively mitigate distribution shifts and resolve optimization conflicts arising from shared prefixes. Our method dynamically adapts the model's knowledge boundaries, effectively balancing exploration diversity with training efficiency.Experimental results demonstrate that E3‑TIR achieves a 6 performance improvement over traditional paradigms on tool‑use tasks, while requiring less than 10 of the synthetic data. Furthermore, in terms of ROI, a comprehensive metric integrating performance, data cost, and training efficiency we achieve a 1.46x gain compared to baselines. Code is available at https://github.com/yuki‑younai/E3‑TIR.

PaperId: 1865, https://arxiv.org/pdf/2604.09452.pdf GitHub

Abstract:
Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety‑critical tasks. Often, deployment environments exhibit non‑stationary dynamics or are subject to changing performance goals, requiring updates to the learned policy. This leads to a fundamental challenge: how to update an RL policy while preserving its safety properties on previously encountered tasks? The majority of current approaches either do not provide formal guarantees or verify policy safety only a posteriori. We propose a novel a priori approach to safe policy updates in continual RL by introducing the Rashomon set: a region in policy parameter space certified to meet safety constraints within the demonstration data distribution. We then show that one can provide formal, provable guarantees for arbitrary RL algorithms used to update a policy by projecting their updates onto the Rashomon set. Empirically, we validate this approach across grid‑world navigation environments (Frozen Lake and Poisoned Apple) where we guarantee an a priori provably deterministic safety on the source task during downstream adaptation. In contrast, we observe that regularisation‑based baselines experience catastrophic forgetting of safety constraints while our approach enables strong adaptation with provable guarantees that safety is preserved.

PaperId: 1866, https://arxiv.org/pdf/2604.09415.pdf GitHub

Abstract:
We present PhysInOne, a large‑scale synthetic dataset addressing the critical scarcity of physically‑grounded training data for AI systems. Unlike existing datasets limited to merely hundreds or thousands of examples, PhysInOne provides 2 million videos across 153,810 dynamic 3D scenes, covering 71 basic physical phenomena in mechanics, optics, fluid dynamics, and magnetism. Distinct from previous works, our scenes feature multiobject interactions against complex backgrounds, with comprehensive ground‑truth annotations including 3D geometry, semantics, dynamic motion, physical properties, and text descriptions. We demonstrate PhysInOne's efficacy across four emerging applications: physics‑aware video generation, long‑/short‑term future frame prediction, physical property estimation, and motion transfer. Experiments show that fine‑tuning foundation models on PhysInOne significantly enhances physical plausibility, while also exposing critical gaps in modeling complex physical dynamics and estimating intrinsic properties. As the largest dataset of its kind, orders of magnitude beyond prior works, PhysInOne establishes a new benchmark for advancing physics‑grounded world models in generation, simulation, and embodied AI.

PaperId: 1867, https://arxiv.org/pdf/2604.09388.pdf GitHub GitHub

Abstract:
AI coding tools are widely adopted, but most teams plateau at prompt‑and‑review without a framework for systematic progression. This paper presents the AI Codebase Maturity Model (ACMM), a 6‑level framework describing how codebases evolve from basic AI‑assisted coding to fully autonomous systems. Inspired by CMMI, each level is defined by its feedback loop topology ‑ the specific mechanisms that must exist before the next level becomes possible. I validate the model through a 100‑day experience report maintaining KubeStellar Console, a CNCF Kubernetes dashboard built from scratch with Claude Code (Opus) and GitHub Copilot, and through the initial production deployment of Hive ‑ an open‑source multi‑agent orchestration system that realizes Level 6: full autonomy. The system currently operates with 74 CI/CD workflows, 32 nightly test suites, 91% code coverage, and achieves bug‑to‑fix times under 30 minutes ‑ 24 hours a day. The central finding: the intelligence of an AI‑driven development system resides not in the AI model itself, but in the infrastructure of instructions, tests, metrics, and feedback loops that surround it. You cannot skip levels, and at each level, the thing that unlocks the next one is another feedback mechanism. Testing ‑ the volume of test cases, the coverage thresholds, and the reliability of test execution ‑ proved to be the single most important investment in the entire journey. v2 extends the model from 5 to 6 levels, adding Level 6 (Fully Autonomous) with Hive as reference implementation and Beads for cross‑agent memory continuity, plus throughput acceleration data showing 5x PR throughput and 37x issue throughput from Level 2 to Level 6.

PaperId: 1868, https://arxiv.org/pdf/2604.09360.pdf GitHub

Abstract:
The rapid proliferation of Large Language Model (LLM) providers‑‑each exposing proprietary API formats‑‑has created a fragmented ecosystem where applications become tightly coupled to individual vendors. Switching or bridging providers requires O(N^2) bilateral adapters, impeding portability and multi‑provider architectures. We observe that despite substantial syntactic divergence, the major LLM APIs share a common semantic core: the practical challenge is the combinatorial surface of syntactic variations, not deep semantic incompatibility. Based on this finding, we present LLM‑Rosetta, an open‑source translation framework built on a hub‑and‑spoke Intermediate Representation (IR) that captures the shared semantic core‑‑messages, content parts, tool calls, reasoning traces, and generation controls‑‑in a 9‑type content model and 10‑type stream event schema. A modular Ops‑composition converter architecture enables each API standard to be added independently. LLM‑Rosetta supports bidirectional conversion (provider‑to‑IR‑to‑provider) for both request and response payloads, including chunk‑level streaming with stateful context management. We implement converters for four API standards (OpenAI Chat Completions, OpenAI Responses, Anthropic Messages, and Google GenAI), covering the vast majority of commercial providers. Empirical evaluation demonstrates lossless round‑trip fidelity, correct streaming behavior, and sub‑100 microsecond conversion overhead‑‑competitive with LiteLLM's single‑pass approach while providing bidirectionality and provider neutrality. LLM‑Rosetta passes the Open Responses compliance suite and is deployed in production at Argonne National Laboratory. Code is available at https://github.com/Oaklight/llm‑rosetta.

PaperId: 1869, https://arxiv.org/pdf/2604.09349.pdf GitHub GitHub

Abstract:
Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision‑language models (VLMs). However, the inherent text‑dominated nature of VLMs often leads to insufficient visual faithfulness, characterized by sparse attention activation to visual tokens. More importantly, our empirical analysis reveals that temporal visual forgetting along reasoning steps exacerbates this deficiency. To bridge this gap, we propose Visually‑Guided Policy Optimization (VGPO), a novel framework to reinforce visual focus during policy optimization. Specifically, VGPO initially introduces a Visual Attention Compensation mechanism that leverages visual similarity to localize and amplify visual cues, while progressively elevating visual expectations in later steps to counteract visual forgetting. Building on this mechanism, we implement a dual‑grained advantage re‑weighting strategy: the intra‑trajectory level highlights tokens exhibiting relatively high visual activation, while the inter‑trajectory level prioritizes trajectories demonstrating superior visual accumulation. Extensive experiments demonstrate that VGPO achieves better visual activation and superior performance in mathematical multimodal reasoning and visual‑dependent tasks. The code has been released at https://github.com/wzb‑bupt/VGPO.

PaperId: 1870, https://arxiv.org/pdf/2604.09229.pdf GitHub

Abstract:
Von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species with complex social cognition, including humans, great apes, and cetaceans. Their selective depletion in frontotemporal dementia (FTD) and altered development in autism implicate them in rapid social decision‑making, yet no computational model of VEN function has previously existed. We introduce the Fast Lane Hypothesis: VENs implement a biological speed‑accuracy tradeoff (SAT) by providing a sparse, fast projection pathway that enables rapid social decisions at the cost of deliberate processing accuracy. We model VENs as fast leaky integrate‑and‑fire (LIF) neurons with membrane time constant 5 ms and sparse dendritic fan‑in of eight afferents, compared to 20 ms and eighty afferents for standard pyramidal neurons, within a spiking cortical circuit of 2,000 neurons trained on a social discrimination task. Networks are evaluated under three clinically motivated conditions across 10 independent random seeds: typical (2% VENs), autism‑like (0.4% VENs), and FTD‑like (post‑training VEN ablation). All configurations achieve equivalent asymptotic classification accuracy (99.4%), consistent with the prediction that VENs modulate decision speed rather than representational capacity. Temporal analysis confirms that VENs produce median first‑spike latencies 4 ms earlier than pyramidal neurons. At a fixed decision threshold, the typical condition is significantly faster than FTD‑like (t=‑23.31, p<0.0001), while autism‑like is intermediate (mean RT=26.91+/‑9.01 ms vs. typical 20.70+/‑2.02 ms; p=0.078). A preliminary evolutionary analysis shows qualitative correspondence between model‑optimal VEN fraction and the primate phylogenetic gradient. To our knowledge, this is the first computational model that asks what a Von Economo neuron actually computes.

PaperId: 1871, https://arxiv.org/pdf/2604.09089.pdf GitHub

Abstract:
Large Language Models (LLMs) for code generation can replicate insecure patterns from their training data. To mitigate this, a common strategy for security hardening is to fine‑tune models using supervision derived from the final transformer layer. However, this design may suffer from a final‑layer bottleneck: vulnerability‑discriminative cues can be distributed across layers and become less detectable near the output representations optimized for next‑token prediction. To diagnose this issue, we perform layer‑wise linear probing. We observe that vulnerability‑related signals are most detectable in a band of intermediate‑to‑upper layers yet attenuate toward the final layers. Motivated by this observation, we introduce DeepGuard, a framework that leverages distributed security‑relevant cues by aggregating representations from multiple upper layers via an attention‑based module. The aggregated signal powers a dedicated security analyzer within a multi‑objective training objective that balances security enhancement and functional correctness, and further supports a lightweight inference‑time steering strategy. Extensive experiments across five code LLMs demonstrate that DeepGuard improves the secure‑and‑correct generation rate by an average of 11.9% over strong baselines such as SVEN. It also preserves functional correctness while exhibiting generalization to held‑out vulnerability types. Our code is public at https://github.com/unknownhl/DeepGuard.

PaperId: 1872, https://arxiv.org/pdf/2604.08991.pdf GitHub

Abstract:
Small object‑centric spatial understanding in indoor videos remains a significant challenge for multimodal large language models (MLLMs), despite its practical value for object search and assistive applications. Although existing benchmarks have advanced video spatial intelligence, embodied reasoning, and diagnostic perception, no existing benchmark directly evaluates whether a model can localize a target object in video and express its position with sufficient precision for downstream use. In this work, we introduce PinpointQA, the first dataset and benchmark for small object‑centric spatial understanding in indoor videos. Built from ScanNet++ and ScanNet200, PinpointQA comprises 1,024 scenes and 10,094 QA pairs organized into four progressively challenging tasks: Target Presence Verification (TPV), Nearest Reference Identification (NRI), Fine‑Grained Spatial Description (FSD), and Structured Spatial Prediction (SSP). The dataset is built from intermediate spatial representations, with QA pairs generated automatically and further refined through quality control. Experiments on representative MLLMs reveal a consistent capability gap along the progressive chain, with SSP remaining particularly difficult. Supervised fine‑tuning on PinpointQA yields substantial gains, especially on the harder tasks, demonstrating that PinpointQA serves as both a diagnostic benchmark and an effective training dataset. The dataset and project page are available at https://rainchowz.github.io/PinpointQA.

PaperId: 1873, https://arxiv.org/pdf/2604.08980.pdf GitHub

Abstract:
Graph neural networks (GNNs) have been widely adopted in engineering applications such as social network analysis, chemical research and computer vision. However, their efficacy is severely compromised by the inherent homophily assumption, which fails to hold for heterophilic graphs where dissimilar nodes are frequently connected. To address this fundamental limitation in graph learning, we first draw inspiration from the recently discovered monophily property of real‑world graphs, and propose Neighbourhood Transformers (NT), a novel paradigm that applies self‑attention within every local neighbourhood instead of aggregating messages to the central node as in conventional message‑passing GNNs. This design makes NT inherently monophily‑aware and theoretically guarantees its expressiveness is no weaker than traditional message‑passing frameworks. For practical engineering deployment, we further develop a neighbourhood partitioning strategy equipped with switchable attentions, which reduces the space consumption of NT by over 95% and time consumption by up to 92.67%, significantly expanding its applicability to larger graphs. Extensive experiments on 10 real‑world datasets (5 heterophilic and 5 homophilic graphs) show that NT outperforms all current state‑of‑the‑art methods on node classification tasks, demonstrating its superior performance and cross‑domain adaptability. The full implementation code of this work is publicly available at https://github.com/cf020031308/MoNT to facilitate reproducibility and industrial adoption.

PaperId: 1874, https://arxiv.org/pdf/2604.08963.pdf GitHub

Abstract:
While Multi‑Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties‑particularly the accumulation of bias‑remain poorly understood. Because real‑world MAS are too complex to analyze entirely, evaluating their ethical robustness requires first isolating their foundational mechanics. In this work, we conduct a baseline empirical study investigating how basic MAS topologies and feedback loops influence prejudice. Contrary to the assumption that multi‑agent collaboration naturally dilutes bias, we hypothesize that structured workflows act as echo chambers, amplifying minor stochastic biases into systemic polarization. To evaluate this, we introduce Discrim‑Eval‑Open, an open‑ended benchmark that bypasses individual model neutrality through forced comparative judgments across demographic groups. Analyzing bias cascades across various structures reveals that architectural sophistication frequently exacerbates bias rather than mitigating it. We observe systemic amplification even when isolated agents operate neutrally, and identify a 'Trigger Vulnerability' where injecting purely objective context drastically accelerates polarization. By stripping away advanced swarm complexity to study foundational dynamics, we establish a crucial baseline: structural complexity does not guarantee ethical robustness. Our code is available at https://github.com/weizhihao1/MAS‑Bias.

PaperId: 1875, https://arxiv.org/pdf/2604.08915.pdf GitHub

Abstract:
Existing defect/anomaly generation methods often rely on few‑shot learning, which overfits to specific defect categories due to the lack of large‑scale paired defect editing data. This issue is aggravated by substantial variations in defect scale and morphology, resulting in limited generalization, degraded realism, and category consistency. We address these challenges by introducing UDG, a large‑scale dataset of 300K normal‑abnormal‑mask‑caption quadruplets spanning diverse domains, and by presenting UniDG, a universal defect generation foundation model that supports both reference‑based defect generation and text instruction‑based defect editing without per‑category fine‑tuning. UniDG performs Defect‑Context Editing via adaptive defect cropping and structured diptych input format, and fuses reference and target conditions through MM‑DiT multimodal attention. A two‑stage training strategy, Diversity‑SFT followed by Consistency‑RFT, further improves diversity while enhancing realism and reference consistency. Extensive experiments on MVTec‑AD and VisA show that UniDG outperforms prior few‑shot anomaly generation and image insertion/editing baselines in synthesis quality and downstream single‑ and multi‑class anomaly detection/localization. Code will be available at https://github.com/RetoFan233/UniDG.

PaperId: 1876, https://arxiv.org/pdf/2604.08884.pdf GitHub

Abstract:
While multimodal large language models (MLLMs) have made significant strides in natural image understanding, their ability to perceive and reason over hyperspectral image (HSI) remains underexplored, which is a vital modality in remote sensing. The high dimensionality and intricate spectral‑spatial properties of HSI pose unique challenges for models primarily trained on RGB data.To address this gap, we introduce Hyperspectral Multimodal Benchmark (HM‑Bench), the first benchmark designed specifically to evaluate MLLMs in HSI understanding. We curate a large‑scale dataset of 19,337 question‑answer pairs across 13 task categories, ranging from basic perception to spectral reasoning. Given that existing MLLMs are not equipped to process raw hyperspectral cubes natively, we propose a dual‑modality evaluation framework that transforms HSI data into two complementary representations: PCA‑based composite images and structured textual reports. This approach facilitates a systematic comparison of different representation for model performance. Extensive evaluations on 18 representative MLLMs reveal significant difficulties in handling complex spatial‑spectral reasoning tasks. Furthermore, our results demonstrate that visual inputs generally outperform textual inputs, highlighting the importance of grounding in spectral‑spatial evidence for effective HSI understanding. Dataset and appendix can be accessed at https://github.com/HuoRiLi‑Yu/HM‑Bench.

PaperId: 1877, https://arxiv.org/pdf/2604.08874.pdf GitHub

Abstract:
This study proposes a temporal modeling framework with a counterfactual policy‑simulation layer for student dropout in higher education, using LMS engagement data and administrative withdrawal records. Dropout is operationalized as a time‑to‑event outcome at the enrollment level; weekly risk is modeled in discrete time via penalized, class‑balanced logistic regression over person‑‑period rows. Under a late‑event temporal holdout, the model attains row‑level AUCs of 0.8350 (train) and 0.8405 (test), with aggregate calibration acceptable but sparsely supported in the highest‑risk bins. Ablation analyses indicate performance is sensitive to feature set composition, underscoring the role of temporal engagement signals. A scenario‑indexed policy layer produces survival contrasts ΔS(T) under an explicit trigger/schedule contract: positive contrasts are confined to the shock branch (T_\rm policy=18: 0.0102, 0.0260, 0.0819), while the mechanism‑aware branch is negative (ΔS_\rm mech(18)=‑0.0078, ΔS_\rm mech(38)=‑0.0134). A subgroup analysis by gender quantifies scenario‑induced survival gaps via bootstrap; contrasts are directionally stable but small. Results are not causally identified; they demonstrate the framework's capacity for internal structural scenario comparison under observational data constraints.

PaperId: 1878, https://arxiv.org/pdf/2604.08846.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prompt engineering, response classification, or finetuning to improve MLLM safety. Nevertheless, such approaches are often ineffective against evolving malicious patterns, may require rerunning the query, or demand heavy computational resources. Steering the activations of a frozen model at inference time has recently emerged as a flexible and effective solution. However, existing steering methods for MLLMs typically handle only a narrow set of safety‑related concepts or struggle to adjust specific concepts without affecting others. To address these challenges, we introduce Dictionary‑Aligned Concept Control (DACO), a framework that utilizes a curated concept dictionary and a Sparse Autoencoder (SAE) to provide granular control over MLLM activations. First, we curate a dictionary of 15,000 multimodal concepts by retrieving over 400,000 caption‑image stimuli and summarizing their activations into concept directions. We name the dataset DACO‑400K. Second, we show that the curated dictionary can be used to intervene activations via sparse coding. Third, we propose a new steering approach that uses our dictionary to initialize the training of an SAE and automatically annotate the semantics of the SAE atoms for safeguarding MLLMs. Experiments on multiple MLLMs (e.g., QwenVL, LLaVA, InternVL) across safety benchmarks (e.g., MM‑SafetyBench, JailBreakV) show that DACO significantly improves MLLM safety while maintaining general‑purpose capabilities.

PaperId: 1879, https://arxiv.org/pdf/2604.08641.pdf GitHub

Abstract:
Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. However, current Generative Art (GenArt) evaluators remain fixated on surface‑level image quality or literal prompt adherence, failing to assess the deeper symbolic or abstract meaning intended by the creator. We address this gap by formalizing a Peircean computational semiotic theory that models Human‑GenArt Interaction (HGI) as cascaded semiosis. This framework reveals that artistic meaning is conveyed through three modes ‑ iconic, symbolic, and indexical ‑ yet existing evaluators operate heavily within the iconic mode, remaining structurally blind to the latter two. To overcome this structural blindness, we propose SemJudge. This evaluator explicitly assesses symbolic and indexical meaning in HGI via a Hierarchical Semiosis Graph (HSG) that reconstructs the meaning‑making process from prompt to generated artifact. Extensive quantitative experiments show that SemJudge aligns more closely with human judgments than prior evaluators on an interpretation‑intensive fine‑art benchmark. User studies further demonstrate that SemJudge produces deeper, more insightful artistic interpretations, thereby paving the way for GenArt to move beyond the generation of "pretty" images toward a medium capable of expressing complex human experience. Project page: https://github.com/songrise/SemJudge.

PaperId: 1880, https://arxiv.org/pdf/2604.08615.pdf GitHub

Abstract:
Fine‑grained visual understanding and high‑level reasoning in real‑world open‑water environments remain under‑explored due to the lack of dedicated benchmarks. We introduce MARINER, a comprehensive benchmark built under the novel Entity‑Environment‑Event (3E) paradigm. MARINER contains 16,629 multi‑source maritime images with 63 fine‑grained vessel categories, diverse adverse environments, and 5 typical dynamic maritime incidents, covering fine‑grained classification, object detection, and visual question answering tasks. We conduct extensive evaluations on mainstream Multimodal Large language models (MLLMs) and establish baselines, revealing that even advanced models struggle with fine‑grained discrimination and causal reasoning in complex marine scenes. As a dedicated maritime benchmark, MARINER fills the gap of realistic and cognitive‑level evaluation for maritime multimodal understanding, and promotes future research on robust vision‑language models for open‑water applications. Appendix and supplementary materials are available at https://lxixim.github.io/MARINER.

PaperId: 1881, https://arxiv.org/pdf/2604.08602.pdf GitHub

Abstract:
Background: Server‑based screening tools impose subscription costs, while open‑source alternatives require coding skills. Objectives: We developed a browser extension that provides no‑code, serverless artificial intelligence (AI)‑assisted title and abstract screening and examined its functionality. Methods: TiAb Review Plugin is an open‑source Chrome browser extension (available at https://chromewebstore.google.com/detail/tiab‑review‑plugin/alejlnlfflogpnabpbplmnojgoeeabij). It uses Google Sheets as a shared database, requiring no dedicated server and enabling multi‑reviewer collaboration. Users supply their own Gemini API key, stored locally and encrypted. The tool offers three screening modes: manual review, large language model (LLM) batch screening, and machine learning (ML) active learning. For ML evaluation, we re‑implemented the default ASReview active learning algorithm (TF‑IDF with Naive Bayes) in TypeScript to enable in‑browser execution, and verified equivalence against the original Python implementation using 10‑fold cross‑validation on six datasets. For LLM evaluation, we compared 16 parameter configurations across two model families on a benchmark dataset, then validated the optimal configuration (Gemini 3.0 Flash, low thinking budget, TopP=0.95) with a sensitivity‑oriented prompt on five public datasets (1,038 to 5,628 records, 0.5 to 2.0 percent prevalence). Results: The TypeScript classifier produced top‑100 rankings 100 percent identical to the original ASReview across all six datasets. For LLM screening, recall was 94 to 100 percent with precision of 2 to 15 percent, and Work Saved over Sampling at 95 percent recall (WSS@95) ranged from 48.7 to 87.3 percent. Conclusions: We developed a functional browser extension that integrates LLM screening and ML active learning into a no‑code, serverless environment, ready for practical use in systematic review screening.

PaperId: 1882, https://arxiv.org/pdf/2604.08590.pdf GitHub

Abstract:
We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quantitative, computation‑intensive domains. Given only a dataset and a natural‑language objective, AlphaLab proceeds through three phases without human intervention: (1) it adapts to the domain and explores the data, writing analysis code and producing a research report; (2) it constructs and adversarially validates its own evaluation framework; and (3) it runs large‑scale GPU experiments via a Strategist/Worker loop, accumulating domain knowledge in a persistent playbook that functions as a form of online prompt optimization. All domain‑specific behavior is factored into adapters generated by the model itself, so the same pipeline handles qualitatively different tasks without modification. We evaluate AlphaLab with two frontier LLMs (GPT‑5.2 and Claude Opus 4.6) on three domains: CUDA kernel optimization, where it writes GPU kernels that run 4.4x faster than torch.compile on average (up to 91x); LLM pretraining, where the full system achieves 22% lower validation loss than a single‑shot baseline using the same model; and traffic forecasting, where it beats standard baselines by 23‑25% after researching and implementing published model families from the literature. The two models discover qualitatively different solutions in every domain (neither dominates uniformly), suggesting that multi‑model campaigns provide complementary search coverage. We additionally report results on financial time series forecasting in the appendix, and release all code at https://brendanhogan.github.io/alphalab‑paper/.

PaperId: 1883, https://arxiv.org/pdf/2604.08560.pdf GitHub

Abstract:
Accurate uncertainty estimation is essential for building robust and trustworthy recognition systems. In this paper, we consider the open‑set text classification (OSTC) task ‑ and uncertainty estimation for it. For OSTC a text sample should be classified as one of the existing classes or rejected as unknown. To account for the different uncertainty types encountered in OSTC, we adapt the Holistic Uncertainty Estimation (HolUE) method for the text domain. Our approach addresses two major causes of prediction errors in text recognition systems: text uncertainty that stems from ill formulated queries and gallery uncertainty that is related the ambiguity of data distribution. By capturing these sources, it becomes possible to predict when the system will make a recognition error. We propose a new OSTC benchmark and conduct extensive experiments on a wide range of data, utilizing the authorship attribution, intent and topic classification datasets. HolUE achieves 40‑365% improvement in Prediction Rejection Ratio (PRR) over the quality‑based SCF baseline across datasets: 365% on Yahoo Answers (0.79 vs 0.17 at FPIR 0.1), 347% on DBPedia (0.85 vs 0.19), 240% on PAN authorship attribution (0.51 vs 0.15 at FPIR 0.5), and 40% on CLINC150 intent classification (0.73 vs~0.52). We make public our code and protocols https://github.com/Leonid‑Erlygin/text_uncertainty.git

PaperId: 1884, https://arxiv.org/pdf/2604.08545.pdf GitHub

Abstract:
The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta‑cognitive deficit: they struggle to arbitrate between leveraging internal knowledge and querying external utilities. Consequently, they frequently fall prey to blind tool invocation, resorting to reflexive tool execution even when queries are resolvable from the raw visual context. This pathological behavior precipitates severe latency bottlenecks and injects extraneous noise that derails sound reasoning. Existing reinforcement learning protocols attempt to mitigate this via a scalarized reward that penalizes tool usage. Yet, this coupled formulation creates an irreconcilable optimization dilemma: an aggressive penalty suppresses essential tool use, whereas a mild penalty is entirely subsumed by the variance of the accuracy reward during advantage normalization, rendering it impotent against tool overuse. To transcend this bottleneck, we propose HDPO, a framework that reframes tool efficiency from a competing scalar objective to a strictly conditional one. By eschewing reward scalarization, HDPO maintains two orthogonal optimization channels: an accuracy channel that maximizes task correctness, and an efficiency channel that enforces execution economy exclusively within accurate trajectories via conditional advantage estimation. This decoupled architecture naturally induces a cognitive curriculum‑compelling the agent to first master task resolution before refining its self‑reliance. Extensive evaluations demonstrate that our resulting model, Metis, reduces tool invocations by orders of magnitude while simultaneously elevating reasoning accuracy.

PaperId: 1885, https://arxiv.org/pdf/2604.08544.pdf GitHub

Abstract:
Robotic manipulation with deformable objects represents a data‑intensive regime in embodied learning, where shape, contact, and topology co‑evolve in ways that far exceed the variability of rigids. Although simulation promises relief from the cost of real‑world data acquisition, prevailing sim‑to‑real pipelines remain rooted in rigid‑body abstractions, producing mismatched geometry, fragile soft dynamics, and motion primitives poorly suited for cloth interaction. We posit that simulation fails not for being synthetic, but for being ungrounded. To address this, we introduce SIM1, a physics‑aligned real‑to‑sim‑to‑real data engine that grounds simulation in the physical world. Given limited demonstrations, the system digitizes scenes into metric‑consistent twins, calibrates deformable dynamics through elastic modeling, and expands behaviors via diffusion‑based trajectory generation with quality filtering. This pipeline transforms sparse observations into scaled synthetic supervision with near‑demonstration fidelity. Experiments show that policies trained on purely synthetic data achieve parity with real‑data baselines at a 1:15 equivalence ratio, while delivering 90% zero‑shot success and 50% generalization gains in real‑world deployment. These results validate physics‑aligned simulation as scalable supervision for deformable manipulation and a practical pathway for data‑efficient policy learning.

PaperId: 1886, https://arxiv.org/pdf/2604.08539.pdf GitHub

Abstract:
Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal Large Language Models. However, extending this success to open‑source multimodal generalist models remains heavily constrained by two primary challenges: the extreme variance in reward topologies across diverse visual tasks, and the inherent difficulty of balancing fine‑grained perception with multi‑step reasoning capabilities. To address these issues, we introduce Gaussian GRPO (G^2RPO), a novel RL training objective that replaces standard linear scaling with non‑linear distributional matching. By mathematically forcing the advantage distribution of any given task to strictly converge to a standard normal distribution, \mathcalN(0,1), G^2RPO theoretically ensures inter‑task gradient equity, mitigates vulnerabilities to heavy‑tail outliers, and offers symmetric update for positive and negative rewards. Leveraging the enhanced training stability provided by G^2RPO, we introduce two task‑level shaping mechanisms to seamlessly balance perception and reasoning. First, response length shaping dynamically elicits extended reasoning chains for complex queries while enforce direct outputs to bolster visual grounding. Second, entropy shaping tightly bounds the model's exploration zone, effectively preventing both entropy collapse and entropy explosion. Integrating these methodologies, we present OpenVLThinkerV2, a highly robust, general‑purpose multimodal model. Extensive evaluations across 18 diverse benchmarks demonstrate its superior performance over strong open‑source and leading proprietary frontier models.

PaperId: 1887, https://arxiv.org/pdf/2604.08536.pdf GitHub

Abstract:
We introduce RewardFlow, an inversion‑free framework that steers pretrained diffusion and flow‑matching models at inference time through multi‑reward Langevin dynamics. RewardFlow unifies complementary differentiable rewards for semantic alignment, perceptual fidelity, localized grounding, object consistency, and human preference, and further introduces a differentiable VQA‑based reward that provides fine‑grained semantic supervision through language‑vision reasoning. To coordinate these heterogeneous objectives, we design a prompt‑aware adaptive policy that extracts semantic primitives from the instruction, infers edit intent, and dynamically modulates reward weights and step sizes throughout sampling. Across several image editing and compositional generation benchmarks, RewardFlow delivers state‑of‑the‑art edit fidelity and compositional alignment.

PaperId: 1888, https://arxiv.org/pdf/2604.08499.pdf GitHub GitHub

Abstract:
Prompt injection attacks pose serious security risks across a wide range of real‑world applications. While receiving increasing attention, the community faces a critical gap: the lack of a unified platform for prompt injection evaluation. This makes it challenging to reliably compare defenses, understand their true robustness under diverse attacks, or assess how well they generalize across tasks and benchmarks. For instance, many defenses initially reported as effective were later found to exhibit limited robustness on diverse datasets and attacks. To bridge this gap, we introduce PIArena, a unified and extensible platform for prompt injection evaluation that enables users to easily integrate state‑of‑the‑art attacks and defenses and evaluate them across a variety of existing and new benchmarks. We also design a dynamic strategy‑based attack that adaptively optimizes injected prompts based on defense feedback. Through comprehensive evaluation using PIArena, we uncover critical limitations of state‑of‑the‑art defenses: limited generalizability across tasks, vulnerability to adaptive attacks, and fundamental challenges when an injected task aligns with the target task. The code and datasets are available at https://github.com/sleeepeer/PIArena.

PaperId: 1889, https://arxiv.org/pdf/2604.08457.pdf GitHub

Abstract:
Cooperative autonomous driving requires traffic scene understanding from both vehicle and infrastructure perspectives. While vision‑language models (VLMs) show strong general reasoning capabilities, their performance in safety‑critical traffic scenarios remains insufficiently evaluated due to the ego‑vehicle focus of existing benchmarks. To bridge this gap, we present CrashSight, a large‑scale vision‑language benchmark for roadway crash understanding using real‑world roadside camera data. The dataset comprises 250 crash videos, annotated with 13K multiple‑choice question‑answer pairs organized under a two‑tier taxonomy. Tier 1 evaluates the visual grounding of scene context and involved parties, while Tier 2 probes higher‑level reasoning, including crash mechanics, causal attribution, temporal progression, and post‑crash outcomes. We benchmark 8 state‑of‑the‑art VLMs and show that, despite strong scene description capabilities, current models struggle with temporal and causal reasoning in safety‑critical scenarios. We provide a detailed analysis of failure scenarios and discuss directions for improving VLM crash understanding. The benchmark provides a standardized evaluation framework for infrastructure‑assisted perception in cooperative autonomous driving. The CrashSight benchmark, including the full dataset and code, is accessible at https://mcgrche.github.io/crashsight.

PaperId: 1890, https://arxiv.org/pdf/2604.08355.pdf GitHub

Abstract:
Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered. Although recent approaches have attempted to mitigate this issue via zero‑shot transfer, they are often constrained by predefined, discrete class systems, limiting their adaptability to novel or compositional task variations. We propose a significantly more generalized approach, replacing discrete latent variables with natural language conditioning via a text‑conditioned Variational Autoencoder (VAE). Our core innovation utilizes a Large Language Model (LLM) as a dynamic semantic operator at test time. Rather than relying on rigid rules, our agent queries the LLM to semantically remap the description of the current observation to align with the source task. This source‑aligned caption conditions the VAE to generate an imagined state compatible with the agent's original training, enabling direct policy reuse. By harnessing the flexible reasoning capabilities of LLMs, our approach achieves zero‑shot transfer across a broad spectrum of complex and truly novel analogous tasks, moving beyond the limitations of fixed category mappings. Code and videos are available \hrefhttps://anonymous.4open.science/r/ASPECT‑85C3/here.

PaperId: 1891, https://arxiv.org/pdf/2604.08302.pdf GitHub

Abstract:
We present DMax, a new paradigm for efficient diffusion language models (dLLMs). It mitigates error accumulation in parallel decoding, enabling aggressive decoding parallelism while preserving generation quality. Unlike conventional masked dLLMs that decode through a binary mask‑to‑token transition, DMax reformulates decoding as a progressive self‑refinement from mask embeddings to token embeddings. At the core of our approach is On‑Policy Uniform Training, a novel training strategy that efficiently unifies masked and uniform dLLMs, equipping the model to recover clean tokens from both masked inputs and its own erroneous predictions. Building on this foundation, we further propose Soft Parallel Decoding. We represent each intermediate decoding state as an interpolation between the predicted token embedding and the mask embedding, enabling iterative self‑revising in embedding space. Extensive experiments across a variety of benchmarks demonstrate the effectiveness of DMax. Compared with the original LLaDA‑2.0‑mini, our method improves TPF on GSM8K from 2.04 to 5.47 while preserving accuracy. On MBPP, it increases TPF from 2.71 to 5.86 while maintaining comparable performance. On two H200 GPUs, our model achieves an average of 1,338 TPS at batch size 1. Code is available at: https://github.com/czg1225/DMax

PaperId: 1892, https://arxiv.org/pdf/2604.08284.pdf GitHub

Abstract:
Large language models store not only isolated facts but also rules that support reasoning across symbolic expressions, natural language explanations, and concrete instances. Yet most model editing methods are built for fact‑level knowledge, assuming that a target edit can be achieved through a localized intervention. This assumption does not hold for rule‑level knowledge, where a single rule must remain consistent across multiple interdependent forms. We investigate this problem through a mechanistic study of rule‑level knowledge editing. To support this study, we extend the RuleEdit benchmark from 80 to 200 manually verified rules spanning mathematics and physics. Fine‑grained causal tracing reveals a form‑specific organization of rule knowledge in transformer layers: formulas and descriptions are concentrated in earlier layers, while instances are more associated with middle layers. These results suggest that rule knowledge is not uniformly localized, and therefore cannot be reliably edited by a single‑layer or contiguous‑block intervention. Based on this insight, we propose Distributed Multi‑Layer Editing (DMLE), which applies a shared early‑layer update to formulas and descriptions and a separate middle‑layer update to instances. While remaining competitive on standard editing metrics, DMLE achieves substantially stronger rule‑level editing performance. On average, it improves instance portability and rule understanding by 13.91 and 50.19 percentage points, respectively, over the strongest baseline across GPT‑J‑6B, Qwen2.5‑7B, Qwen2‑7B, and LLaMA‑3‑8B. The code is available at https://github.com/Pepper66/DMLE.

PaperId: 1893, https://arxiv.org/pdf/2604.08276.pdf GitHub

Abstract:
As generative artificial intelligence evolves, autonomous agent networks present a powerful paradigm for interactive covert communication. However, because agents dynamically update internal memories via environmental interactions, existing methods face a critical structural vulnerability: cognitive asymmetry. Conventional approaches demand strict cognitive symmetry, requiring identical sequence prefixes between the encoder and decoder. In dynamic deployments, inevitable prefix discrepancies destroy synchronization, inducing severe channel degradation. To address this core challenge of cognitive asymmetry, we propose the Asymmetric Collaborative Framework (ACF), which structurally decouples covert communication from semantic reasoning via orthogonal statistical and cognitive layers. By deploying a prefix‑independent decoding paradigm governed by a shared steganographic configuration, ACF eliminates the reliance on cognitive symmetry. Evaluations on realistic memory‑augmented workflows demonstrate that under severe cognitive asymmetry, symmetric baselines suffer severe channel degradation, whereas ACF uniquely excels across both semantic fidelity and covert communication. It maintains computational indistinguishability, enabling reliable secret extraction with provable error bounds, and providing robust Effective Information Capacity guarantees for modern agent networks.

PaperId: 1894, https://arxiv.org/pdf/2604.08226.pdf GitHub

Abstract:
The competency of any intelligent agent is bounded by its formal account of the world in which it operates. Clinical AI lacks such an account. Existing frameworks address evaluation, regulation, or system design in isolation, without a shared model of the clinical world to connect them. We introduce the Clinical World Model, a framework that formalizes care as a tripartite interaction among Patient, Provider, and Ecosystem. To formalize how any agent, whether human or artificial, transforms information into clinical action, we develop parallel decision‑making architectures for providers, patients, and AI agents, grounded in validated principles of clinical cognition. The Clinical AI Skill‑Mix operationalizes competency through eight dimensions. Five define the clinical competency space (condition, phase, care setting, provider role, and task) and three specify how AI engages human reasoning (assigned authority, agent facing, and anchoring layer). The combinatorial product of these dimensions yields a space of billions of distinct competency coordinates. A central structural implication is that validation within one coordinate provides minimal evidence for performance in another, rendering the competency space irreducible. The framework supplies a common grammar through which clinical AI can be specified, evaluated, and bounded across stakeholders. By making this structure explicit, the Clinical World Model reframes the field's central question from whether AI works to in which competency coordinates reliability has been demonstrated, and for whom.

PaperId: 1895, https://arxiv.org/pdf/2604.08140.pdf GitHub GitHub

Abstract:
Network traffic, as a key media format, is crucial for ensuring security and communications in modern internet infrastructure. While existing methods offer excellent performance, they face two key bottlenecks: (1) They fail to capture multidimensional semantics beyond unimodal sequence patterns. (2) Their black box property, i.e., providing only category labels, lacks an auditable reasoning process. We identify a key factor that existing network traffic datasets are primarily designed for classification and inherently lack rich semantic annotations, failing to generate human‑readable evidence report. To address data scarcity, this paper proposes a Byte‑Grounded Traffic Description (BGTD) benchmark for the first time, combining raw bytes with structured expert annotations. BGTD provides necessary behavioral features and verifiable chains of evidence for multimodal reasoning towards explainable encrypted traffic interpretation. Built upon BGTD, this paper proposes an end‑to‑end traffic‑language representation framework (mmTraffic), a multimodal reasoning architecture bridging physical traffic encoding and semantic interpretation. In order to alleviate modality interference and generative hallucinations, mmTraffic adopts a jointly‑optimized perception‑cognition architecture. By incorporating a perception‑centered traffic encoder and a cognition‑centered LLM generator, mmTraffic achieves refined traffic interpretation with guaranteed category prediction. Extensive experiments demonstrate that mmTraffic autonomously generates high‑fidelity, human‑readable, and evidence‑grounded traffic interpretation reports, while maintaining highly competitive classification accuracy comparing to specialized unimodal model (e.g., NetMamba). The source code is available at https://github.com/lgzhangzlg/Multimodal‑Reasoning‑with‑LLM‑for‑Encrypted‑Traffic‑Interpretation‑A‑Benchmark

PaperId: 1896, https://arxiv.org/pdf/2604.08121.pdf GitHub GitHub

Abstract:
Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher computational costs than understanding, particularly for video. This imbalance motivates us to invert the conventional paradigm: rather than extending understanding‑centric MLLMs to support generation, we propose Uni‑ViGU, a framework that unifies video generation and understanding by extending a video generator as the foundation. We introduce a unified flow method that performs continuous flow matching for video and discrete flow matching for text within a single process, enabling coherent multimodal generation. We further propose a modality‑driven MoE‑based framework that augments Transformer blocks with lightweight layers for text generation while preserving generative priors. To repurpose generation knowledge for understanding, we design a bidirectional training mechanism with two stages: Knowledge Recall reconstructs input prompts to leverage learned text‑video correspondences, while Capability Refinement fine‑tunes on detailed captions to establish discriminative shared representations. Experiments demonstrate that Uni‑ViGU achieves competitive performance on both video generation and understanding, validating generation‑centric architectures as a scalable path toward unified multimodal intelligence. Project Page and Code: https://fr0zencrane.github.io/uni‑vigu‑page/.

PaperId: 1897, https://arxiv.org/pdf/2604.08120.pdf GitHub

Abstract:
Adapting Multimodal Large Language Models (MLLMs) for hour‑long videos is bottlenecked by context limits. Dense visual streams saturate token budgets and exacerbate the lost‑in‑the‑middle phenomenon. Existing heuristics, like sparse sampling or uniform pooling, blindly sacrifice fidelity by discarding decisive moments and wasting bandwidth on irrelevant backgrounds. We propose Tempo, an efficient query‑aware framework compressing long videos for downstream understanding. Tempo leverages a Small Vision‑Language Model (SVLM) as a local temporal compressor, casting token reduction as an early cross‑modal distillation process to generate compact, intent‑aligned representations in a single forward pass. To enforce strict budgets without breaking causality, we introduce Adaptive Token Allocation (ATA). Exploiting the SVLM's zero‑shot relevance prior and semantic front‑loading, ATA acts as a training‑free O(1) dynamic router. It allocates dense bandwidth to query‑critical segments while compressing redundancies into minimal temporal anchors to maintain the global storyline. Extensive experiments show our 6B architecture achieves state‑of‑the‑art performance with aggressive dynamic compression (0.5‑16 tokens/frame). On the extreme‑long LVBench (4101s), Tempo scores 52.3 under a strict 8K visual budget, outperforming GPT‑4o and Gemini 1.5 Pro. Scaling to 2048 frames reaches 53.7. Crucially, Tempo compresses hour‑long videos substantially below theoretical limits, proving true long‑form video understanding relies on intent‑driven efficiency rather than greedily padded context windows.

PaperId: 1898, https://arxiv.org/pdf/2604.08037.pdf GitHub

Abstract:
Talking‑head generation has advanced rapidly with diffusion‑based generative models, but training usually depends on centralized face‑video and speech datasets, raising major privacy concerns. The problem is more acute for personalized talking‑head generation, where identity‑specific data are highly sensitive and often cannot be pooled across users or devices. PrivFedTalk is presented as a privacy‑aware federated framework for personalized talking‑head generation that combines conditional latent diffusion with parameter‑efficient identity adaptation. A shared diffusion backbone is trained across clients, while each client learns lightweight LoRA identity adapters from local private audio‑visual data, avoiding raw data sharing and reducing communication cost. To address heterogeneous client distributions, Identity‑Stable Federated Aggregation (ISFA) weights client updates using privacy‑safe scalar reliability signals computed from on‑device identity consistency and temporal stability estimates. Temporal‑Denoising Consistency (TDC) regularization is introduced to reduce inter‑frame drift, flicker, and identity drift during federated denoising. To limit update‑side privacy risk, secure aggregation and client‑level differential privacy are applied to adapter updates. The implementation supports both low‑memory GPU execution and multi‑GPU client‑parallel training on heterogeneous shared hardware. Comparative experiments on the present setup across multiple training and aggregation conditions with PrivFedTalk, FedAvg, and FedProx show stable federated optimization and successful end‑to‑end training and evaluation under constrained resources. The results support the feasibility of privacy‑aware personalized talking‑head training in federated environments, while suggesting that stronger component‑wise, privacy‑utility, and qualitative claims need further standardized evaluation.

PaperId: 1899, https://arxiv.org/pdf/2604.08008.pdf GitHub

Abstract:
Retrieving rare and safety‑critical driving scenarios from large‑scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant samples. We introduce SearchAD, a large‑scale rare image retrieval dataset for AD containing over 423k frames drawn from 11 established datasets. SearchAD provides high‑quality manual annotations of more than 513k bounding boxes covering 90 rare categories. It specifically targets the needle‑in‑a‑haystack problem of locating extremely rare classes, with some appearing fewer than 50 times across the entire dataset. Unlike existing benchmarks, which focused on instance‑level retrieval, SearchAD emphasizes semantic image retrieval with a well‑defined data split, enabling text‑to‑image and image‑to‑image retrieval, few‑shot learning, and fine‑tuning of multi‑modal retrieval models. Comprehensive evaluations show that text‑based methods outperform image‑based ones due to stronger inherent semantic grounding. While models directly aligning spatial visual features with language achieve the best zero‑shot results, and our fine‑tuning baseline significantly improves performance, absolute retrieval capabilities remain unsatisfactory. With a held‑out test set on a public benchmark server, SearchAD establishes the first large‑scale dataset for retrieval‑driven data curation and long‑tail perception research in AD: https://iis‑esslingen.github.io/searchad/

PaperId: 1900, https://arxiv.org/pdf/2604.07973.pdf GitHub

Abstract:
Large multimodal models (LMMs) show strong visual‑linguistic reasoning but their capacity for spatial decision‑making and action remains unclear. In this work, we investigate whether LMMs can achieve embodied spatial action like human through a challenging scenario: goal‑oriented navigation in urban 3D spaces. We first spend over 500 hours constructing a dataset comprising 5,037 high‑quality goal‑oriented navigation samples, with an emphasis on 3D vertical actions and rich urban semantic information. Then, we comprehensively assess 17 representative models, including non‑reasoning LMMs, reasoning LMMs, agent‑based methods, and vision‑language‑action models. Experiments show that current LMMs exhibit emerging action capabilities, yet remain far from human‑level performance. Furthermore, we reveal an intriguing phenomenon: navigation errors do not accumulate linearly but instead diverge rapidly from the destination after a critical decision bifurcation. The limitations of LMMs are investigated by analyzing their behavior at these critical decision bifurcations. Finally, we experimentally explore four promising directions for improvement: geometric perception, cross‑view understanding, spatial imagination, and long‑term memory. The project is available at: https://github.com/serenditipy‑AC/Embodied‑Navigation‑Bench.

PaperId: 1901, https://arxiv.org/pdf/2604.07892.pdf GitHub GitHub

Abstract:
Instruction‑tuned language models increasingly rely on large multi‑turn dialogue corpora, but these datasets are often noisy and structurally inconsistent, with topic drift, repetitive chitchat, and mismatched answer formats across turns. We address this from a data selection perspective and propose MDS (Multi‑turn Dialogue Selection), a dialogue‑level framework that scores whole conversations rather than isolated turns. MDS combines a global coverage stage that performs bin‑wise selection in the user‑query trajectory space to retain representative yet non‑redundant dialogues, with a local structural stage that evaluates within‑dialogue reliability through entity‑grounded topic grounding and information progress, together with query‑answer form consistency for functional alignment. MDS outperforms strong single‑turn selectors, dialogue‑level LLM scorers, and heuristic baselines on three multi‑turn benchmarks and an in‑domain Banking test set, achieving the best overall rank across reference‑free and reference‑based metrics, and is more robust on long conversations under the same training budget. Code and resources are included in the supplementary materials.

PaperId: 1902, https://arxiv.org/pdf/2604.07863.pdf GitHub GitHub

Abstract:
Retrieving relevant observations from long multi‑modal web interaction histories is challenging because relevance depends on the evolving task state, modality (screenshots, HTML text, structured signals), and temporal distance. Prior approaches typically rely on static similarity thresholds or fixed‑capacity buffers, which fail to adapt relevance to the current task context. We propose ACGM, a learned graph‑memory retriever that constructs \emphtask‑adaptive relevance graphs over agent histories using policy‑gradient optimization from downstream task success. ACGM captures heterogeneous temporal dynamics with modality‑specific decay (visual decays 4.3× faster than text: λ_v=0.47 vs.\ λ_x=0.11) and learns sparse connectivity (3.2 edges/node), enabling efficient O(\log T) retrieval. Across WebShop, VisualWebArena, and Mind2Web, ACGM improves retrieval quality to 82.7 nDCG@10 (+9.3 over GPT‑4o, p<0.001) and 89.2% Precision@10 (+7.7), outperforming 19 strong dense, re‑ranking, multi‑modal, and graph‑based baselines. Code to reproduce our results is available at\colorblue\hrefhttps://github.com/S‑Forouzandeh/ACGM‑Agentic‑WebSaman Forouzandeh.

PaperId: 1903, https://arxiv.org/pdf/2604.07851.pdf GitHub

Abstract:
With the rise of LLMs, there is an increasing need for intelligent recommendation assistants that can handle complex queries and provide personalized, reasoning‑driven recommendations. LLM‑based recommenders show potential but face challenges in multi‑step reasoning, underscoring the need for reasoning‑augmented systems. To address this gap, we propose ReRec, a novel reinforcement fine‑tuning (RFT) framework designed to improve LLM reasoning in complex recommendation tasks. Our framework introduces three key components: (1) Dual‑Graph Enhanced Reward Shaping, integrating recommendation metrics like NDCG@K with Query Alignment and Preference Alignment Scores to provide fine‑grained reward signals for LLM optimization; (2) Reasoning‑aware Advantage Estimation, which decomposes LLM outputs into reasoning segments and penalizes incorrect steps to enhance reasoning of recommendation; and (3) Online Curriculum Scheduler, dynamically assess query difficulty and organize training curriculum to ensure stable learning during RFT. Experiments demonstrate that ReRec outperforms state‑of‑the‑art baselines and preserves core abilities like instruction‑following and general knowledge. Our codes are available at https://github.com/jiani‑huang/ReRec.

PaperId: 1904, https://arxiv.org/pdf/2604.07825.pdf GitHub

Abstract:
Large language models (LLMs) have recently emerged as powerful training‑free recommenders. However, their knowledge of individual items is inevitably uneven due to imbalanced information exposure during pretraining, a phenomenon we refer to as knowledge gap problem. To address this, most prior methods have employed a naive uniform augmentation that appends external information for every item in the input prompt. However, this approach not only wastes limited context budget on redundant augmentation for well‑known items but can also hinder the model's effective reasoning. To this end, we propose KnowSA_CKP (Knowledge‑aware Selective Augmentation with Comparative Knowledge Probing) to mitigate the knowledge gap problem. KnowSA_CKP estimates the LLM's internal knowledge by evaluating its capability to capture collaborative relationships and selectively injects additional information only where it is most needed. By avoiding unnecessary augmentation for well‑known items, KnowSA_CKP focuses on items that benefit most from knowledge supplementation, thereby making more effective use of the context budget. KnowSA_CKP requires no fine‑tuning step, and consistently improves both recommendation accuracy and context efficiency across four real‑world datasets. Our code is available at https://github.com/nowhyun/KnowSA\_CKP.

PaperId: 1905, https://arxiv.org/pdf/2604.07758.pdf GitHub

Abstract:
Articulated objects are essential for embodied AI and world models, yet inferring their kinematics from a single closed‑state image remains challenging because crucial motion cues are often occluded. Existing methods either require multi‑state observations or rely on explicit part priors, retrieval, or other auxiliary inputs that partially expose the structure to be inferred. In this work, we present DailyArt, which formulates articulated joint estimation from a single static image as a synthesis‑mediated reasoning problem. Instead of directly regressing joints from a heavily occluded observation, DailyArt first synthesizes a maximally articulated opened state under the same camera view to expose articulation cues, and then estimates the full set of joint parameters from the discrepancy between the observed and synthesized states. Using a set‑prediction formulation, DailyArt recovers all joints simultaneously without requiring object‑specific templates, multi‑view inputs, or explicit part annotations at test time. Taking estimated joints as conditions, the framework further supports part‑level novel state synthesis as a downstream capability. Extensive experiments show that DailyArt achieves strong performance in articulated joint estimation and supports part‑level novel state synthesis conditioned on joints. Project page is available at https://rangooo123.github.io/DaliyArt.github.io/.

PaperId: 1906, https://arxiv.org/pdf/2604.07752.pdf GitHub

Abstract:
Modern video games are complex, non‑deterministic systems that are difficult to test automatically at scale. Although prior work shows that personality‑driven Large Language Model (LLM) agents can improve behavioural diversity and test coverage, existing tools largely remain research prototypes and lack cross‑game reusability. This tool paper presents MIMIC‑Py, a Python‑based automated game‑testing tool that transforms personality‑driven LLM agents into a reusable and extensible framework. MIMIC‑Py exposes personality traits as configurable inputs and adopts a modular architecture that decouples planning, execution, and memory from game‑specific logic. It supports multiple interaction mechanisms, enabling agents to interact with games via exposed APIs or synthesized code. We describe the design of MIMIC‑Py and show how it enables deployment to new game environments with minimal engineering effort, bridging the gap between research prototypes and practical automated game testing. The source code and a demo video are available on our project webpage: https://mimic‑persona.github.io/MIMIC‑Py‑Home‑Page/.

PaperId: 1907, https://arxiv.org/pdf/2604.07658.pdf GitHub

Abstract:
Linear recurrent models offer linear‑time sequence processing but often suffer from suboptimal long‑range memory. We trace this to the decay spectrum: for N channels, random initialization collapses the minimum spectral gap to O(N^‑2), yielding sub‑exponential error \exp(‑Ω(N/\log N)); linear spacing avoids collapse but degrades to \exp(‑O(N/\sqrtT)), practically algebraic over long contexts. We introduce Position‑Adaptive Spectral Tapering (PoST), an architecture‑agnostic framework combining two mechanisms: (1) Spectral Reparameterization, which structurally enforces geometrically spaced log‑decay rates, proven minimax optimal at rate O(\exp(‑cN/\log T)); and (2) Position‑Adaptive Scaling, the provably unique mechanism that eliminates the scale mismatch of static spectra (where only N\log t/\log T of N channels are effective at position t) by stretching the spectrum to the actual dependency range, sharpening the rate to O(\exp(‑cN/\log t)). This scaling natively induces fractional invariance: the impulse response becomes scale‑free, with channels interpolating between relative and absolute temporal coordinates. PoST integrates into any diagonal linear recurrence without overhead. We instantiate it across Mamba‑2, RWKV‑7, Gated DeltaNet, Gated Linear Attention, and RetNet. Pre‑training at 180M‑440M scales shows consistent zero‑shot language modeling improvements, significant long‑context retrieval gains for Mamba‑2 (MQAR and NIAH), and competitive or improved performance across other architectures. Code: https://github.com/SiLifen/PoST.

PaperId: 1908, https://arxiv.org/pdf/2604.07644.pdf GitHub

Abstract:
We present GPU‑SLS, a GPU‑parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high‑dimensional uncertain robotic systems and long planning horizons. Our method jointly optimizes an inequality‑constrained, dynamically‑feasible nominal trajectory, a tracking controller, and a closed‑loop reachable set under disturbance, all in real‑time. To efficiently compute nominal trajectories, we develop a sequential quadratic programming procedure with a novel GPU‑accelerated quadratic program (QP) solver that uses parallel associative scans and adaptive caching within an alternating direction method of multipliers (ADMM) framework. The same GPU QP backend is used to optimize robust tracking controllers and closed‑loop reachable sets via system level synthesis (SLS), enabling reachability‑constrained control in both fixed‑ and receding‑horizon settings. We achieve substantial performance gains, reducing nominal trajectory solve times by 97.7% relative to state‑of‑the‑art CPU solvers and 71.8% compared to GPU solvers, while accelerating SLS‑based control and reachability by 237x. Despite large problem scales, our method achieves 100% empirical safety, unlike high‑dimensional learning‑based reachability baselines. We validate our approach on complex nonlinear systems, including whole‑body quadrupeds (61D) and humanoids (75D), synthesizing robust control policies online on the GPU in 20 milliseconds on average and scaling to problems with 2 x 10^5 decision variables and 8 x 10^4 constraints. The implementation of our method is available at https://github.com/Jeff300fang/gpu_sls.

PaperId: 1909, https://arxiv.org/pdf/2604.07639.pdf GitHub

Abstract:
Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quantum computer of polylogarithmic size can perform large‑scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same prediction performance requires exponentially larger size. Furthermore, classical machines that are exponentially larger yet below the required size need superpolynomially more samples and time. We validate these quantum advantages in real‑world applications, including single‑cell RNA sequencing and movie review sentiment analysis, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or if BPP=BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.

PaperId: 1910, https://arxiv.org/pdf/2604.07622.pdf GitHub

Abstract:
Speculative decoding is an effective technique for accelerating large language model inference by drafting multiple tokens in parallel. In practice, its speedup is often bottlenecked by a rigid verification step that strictly enforces the accepted token distribution to exactly match the target model. This constraint leads to the rejection of many plausible tokens, lowering the acceptance rate and limiting overall time speedup. To overcome this limitation, we propose Dynamic Verification Relaxed Speculative Decoding (DIVERSED), a relaxed verification framework that improves time efficiency while preserving generation quality. DIVERSED learns an ensemble‑based verifier that blends the draft and target model distributions with a task‑dependent and context‑dependent weight. We provide theoretical justification for our approach and demonstrate empirically that DIVERSED achieves substantially higher inference efficiency compared to standard speculative decoding methods. Code is available at: https://github.com/comeusr/diversed.

PaperId: 1911, https://arxiv.org/pdf/2604.07590.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) is widely used to ground large language models in external knowledge sources. However, when applied to heterogeneous corpora and multi‑step queries, Naive RAG pipelines often degrade in quality due to flat knowledge representations and the absence of explicit workflows. In this work, we introduce DCD (Domain‑Collection‑Document), a domain‑oriented design to structure knowledge and control query processing in RAG systems without modifying the underlying language model. The proposed approach relies on a hierarchical decomposition of the information space and multi‑stage routing based on structured model outputs, enabling progressive restriction of both retrieval and generation scopes. The architecture is complemented by smart chunking, hybrid retrieval, and integrated validation and generation guardrail mechanisms. We describe the DCD architecture and workflow and discuss evaluation results on synthetic evaluation dataset, highlighting their impact on robustness, factual accuracy, and answer relevance in applied RAG scenarios.

PaperId: 1912, https://arxiv.org/pdf/2604.07549.pdf GitHub

Abstract:
Conversational diagnosis prediction requires models to track evolving evidence in streaming clinical conversations and decide when to commit to a diagnosis. Existing medical dialogue corpora are largely dyadic or lack the multi‑party workflow and annotations needed for this setting. We introduce an ePCR‑grounded, topic‑flow‑based multi‑agent generation pipeline that iteratively plans, generates, and self‑refines dialogues with rule‑based factual and topic flow checks. The pipeline yields EMSDialog, a dataset of 4,414 synthetic multi‑speaker EMS conversations based on a real‑world ePCR dataset, annotated with 43 diagnoses, speaker roles, and turn‑level topics. Human and LLM evaluations confirm high quality and realism of EMSDialog using both utterance‑ and conversation‑level metrics. Results show that EMSDialog‑augmented training improves accuracy, timeliness, and stability of EMS conversational diagnosis prediction. Our datasets and code are publicly available at https://uva‑dsa.github.io/EMSDialog

PaperId: 1913, https://arxiv.org/pdf/2604.07506.pdf GitHub

Abstract:
Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment quality of Large Language Models (LLMs). Recently, Generative Reward Models (GRMs) have emerged as a superior paradigm, offering higher interpretability and stronger generalization than traditional scalar RMs. However, existing methods for GRMs focus primarily on outcome‑level supervision, neglecting analytical process quality, which constrains their potential. To address this, we propose ReflectRM, a novel GRM that leverages self‑reflection to assess analytical quality and enhance preference modeling. ReflectRM is trained under a unified generative framework for joint modeling of response preference and analysis preference. During inference, we use its self‑reflection capability to identify the most reliable analysis, from which the final preference prediction is derived. Experiments across four benchmarks show that ReflectRM consistently improves performance, achieving an average accuracy gain of +3.7 on Qwen3‑4B. Further experiments confirm that response preference and analysis preference are mutually reinforcing. Notably, ReflectRM substantially mitigates positional bias, yielding +10.2 improvement compared with leading GRMs and establishing itself as a more stable evaluator. Our code is available at https://github.com/yuliangCarmelo/ReflectRM.

PaperId: 1914, https://arxiv.org/pdf/2604.07487.pdf GitHub

Abstract:
Large language model agents rely on effective model context to obtain task‑relevant information for decision‑making. Many existing context engineering approaches primarily rely on the context generated from the past experience and retrieval mechanisms that reuse these context. However, retrieved context from past tasks must be adapted by the execution agent to fit new situations, placing additional reasoning burden on the underlying LLM. To address this limitation, we propose a generative context augmentation framework using Contrastive Learning of Experience via Agentic Reflection (CLEAR). CLEAR first employs a reflection agent to perform contrastive analysis over past execution trajectories and summarize useful context for each observed task. These summaries are then used as supervised fine‑tuning data to train a context augmentation model (CAM). Then we further optimize CAM using reinforcement learning, where the reward signal is obtained by running the task execution agent. By learning to generate task‑specific knowledge rather than retrieve knowledge from the past, CAM produces context that is better tailored to the current task. We conduct comprehensive evaluations on the AppWorld and WebShop benchmarks. Experimental results show that CLEAR consistently outperforms strong baselines. It improves task completion rate from 72.62% to 81.15% on AppWorld test set and averaged reward from 0.68 to 0.74 on a subset of WebShop, compared with baseline agent. Our code is publicly available at https://github.com/awslabs/CLEAR.

PaperId: 1915, https://arxiv.org/pdf/2604.07484.pdf GitHub

Abstract:
Generative reward models (GRMs) have emerged as a promising approach for aligning Large Language Models (LLMs) with human preferences by offering greater representational capacity and flexibility than traditional scalar reward models. However, GRMs face two major challenges: reliance on costly human‑annotated data restricts scalability, and self‑training approaches often suffer from instability and vulnerability to reward hacking. To address these issues, we propose ConsistRM, a self‑training framework that enables effective and stable GRM training without human annotations. ConsistRM incorporates the Consistency‑Aware Answer Reward, which produces reliable pseudo‑labels with temporal consistency, thereby providing more stable model optimization. Moreover, the Consistency‑Aware Critique Reward is introduced to assess semantic consistency across multiple critiques and allocates fine‑grained and differentiated rewards. Experiments on five benchmark datasets across four base models demonstrate that ConsistRM outperforms vanilla Reinforcement Fine‑Tuning (RFT) by an average of 1.5%. Further analysis shows that ConsistRM enhances output consistency and mitigates position bias caused by input order, highlighting the effectiveness of consistency‑aware rewards in improving GRMs. Our implementation is available at https://github.com/yuliangCarmelo/ConsistRM.

PaperId: 1916, https://arxiv.org/pdf/2604.07457.pdf GitHub GitHub

Abstract:
While decoupled control schemes for legged mobile manipulators have shown robustness, learning holistic whole‑body control policies for tracking global end‑effector poses remains fragile against Out‑of‑Distribution (OOD) inputs induced by sensor noise or infeasible user commands. To improve robustness against these perturbations without sacrificing task performance and continuity, we propose Competence Manifold Projection (CMP). Specifically, we utilize a Frame‑Wise Safety Scheme that transforms the infinite‑horizon safety constraint into a computationally efficient single‑step manifold inclusion. To instantiate this competence manifold, we employ a Lower‑Bounded Safety Estimator that distinguishes unmastered intentions from the training distribution. We then introduce an Isomorphic Latent Space (ILS) that aligns manifold geometry with safety probability, enabling efficient O(1) seamless defense against arbitrary OOD intents. Experiments demonstrate that CMP achieves up to a 10‑fold survival rate improvement in typical OOD scenarios where baselines suffer catastrophic failure, incurring under 10% tracking degradation. Notably, the system exhibits emergent ``best‑effort'' generalization behaviors to progressively accomplish OOD goals by adhering to the competence boundaries. Result videos are available at: https://shepherd1226.github.io/CMP.

PaperId: 1917, https://arxiv.org/pdf/2604.07413.pdf GitHub GitHub

Abstract:
The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution, yet current evaluations fail to reflect the rigorous demands of real‑world manufacturing environments. Progress is hindered by data scarcity and a lack of fine‑grained domain semantics in existing datasets. To bridge this gap, we introduce FORGE. Wefirst construct a high‑quality multimodal dataset that combines real‑world 2D images and 3D point clouds, annotated with fine‑grained domain semantics (e.g., exact model numbers). We then evaluate 18 state‑of‑the‑art MLLMs across three manufacturing tasks, namely workpiece verification, structural surface inspection, and assembly verification, revealing significant performance gaps. Counter to conventional understanding, the bottleneck analysis shows that visual grounding is not the primary limiting factor. Instead, insufficient domain‑specific knowledge is the key bottleneck, setting a clear direction for future research. Beyond evaluation, we show that our structured annotations can serve as an actionable training resource: supervised fine‑tuning of a compact 3B‑parameter model on our data yields up to 90.8% relative improvement in accuracy on held‑out manufacturing scenarios, providing preliminary evidence for a practical pathway toward domain‑adapted manufacturing MLLMs. The code and datasets are available at https://ai4manufacturing.github.io/forge‑web.

PaperId: 1918, https://arxiv.org/pdf/2604.07405.pdf GitHub

Abstract:
Why does gradient descent reliably find good solutions in non‑convex neural network optimization, despite the landscape being NP‑hard in the worst case? We show that gradient flow on L‑layer ReLU networks without bias preserves L‑1 conservation laws C_l = ||W_l+1||_F^2 ‑ ||W_l||_F^2, confining trajectories to lower‑dimensional manifolds. Under discrete gradient descent, these laws break with total drift scaling as eta^alpha where alpha is approximately 1.1‑1.6 depending on architecture, loss function, and width. We decompose this drift exactly as eta^2 S(eta), where the gradient imbalance sum S(eta) admits a closed‑form spectral crossover formula with mode coefficients c_k proportional to e_k(0)^2 lambda_x,k^2, derived from first principles and validated for both linear (R=0.85) and ReLU (R>0.80) networks. For cross‑entropy loss, softmax probability concentration drives exponential Hessian spectral compression with timescale tau = Theta(1/eta) independent of training set size, explaining why cross‑entropy self‑regularizes the drift exponent near alpha=1.0. We identify two dynamical regimes separated by a width‑dependent transition: a perturbative sub‑Edge‑of‑Stability regime where the spectral formula applies, and a non‑perturbative regime with extensive mode coupling. All predictions are validated across 23 experiments.

PaperId: 1919, https://arxiv.org/pdf/2604.07395.pdf GitHub

Abstract:
Robotic manipulation systems that follow language instructions often execute grasp primitives in a largely single‑shot manner: a model proposes an action, the robot executes it, and failures such as empty grasps, slips, stalls, timeouts, or semantically wrong grasps are not surfaced to the decision layer in a structured way. Inspired by agentic loops in digital tool‑using agents, we reformulate language‑guided grasping as a bounded embodied agent operating over grounded execution states, where physical actions expose an explicit tool‑state stream. We introduce a physical agentic loop that wraps an unmodified learned manipulation primitive (grasp‑and‑lift) with (i) an event‑based interface and (ii) an execution monitoring layer, Watchdog, which converts noisy gripper telemetry into discrete outcome labels using contact‑aware fusion and temporal stabilization. These outcome events, optionally combined with post‑grasp semantic verification, are consumed by a deterministic bounded policy that finalizes, retries, or escalates to the user for clarification, guaranteeing finite termination. We validate the resulting loop on a mobile manipulator with an eye‑in‑hand D405 camera, keeping the underlying grasp model unchanged and evaluating representative scenarios involving visual ambiguity, distractors, and induced execution failures. Results show that explicit execution‑state monitoring and bounded recovery enable more robust and interpretable behavior than open‑loop execution, while adding minimal architectural overhead. For the source code and demo refer to our project page: https://wenzewwz123.github.io/Agentic‑Loop/

PaperId: 1920, https://arxiv.org/pdf/2604.07385.pdf GitHub

Abstract:
We present SauerkrautLM‑Doom‑MultiVec, a 1.3 million parameter model that plays the classic first‑person shooter DOOM in real time, outperforming large language models up to 92,000x its size, including Nemotron‑120B, Qwen3.5‑27B, and GPT‑4o‑mini. Our model combines a ModernBERT encoder with hash embeddings, depth‑aware token representations, and an attention pooling classification head to select game actions from ASCII frame representations at 31ms per decision. Trained on just 31,000 human gameplay demonstrations, it achieves 178 frags in 10 episodes (17.8 per episode) in the defend_the_center scenario, more than all tested LLMs combined (13 frags total). All agents receive equivalent input: ASCII frames and depth maps. Despite having 92,000x fewer parameters than Nemotron‑120B, our model is the only agent that actively engages enemies rather than purely evading them. These results demonstrate that small, task‑specific models trained on domain‑appropriate data can decisively outperform general‑purpose LLMs at real‑time control tasks, at a fraction of the inference cost, with deployment capability on consumer hardware.

PaperId: 1921, https://arxiv.org/pdf/2604.07180.pdf GitHub

Abstract:
We propose a geometric framework for longitudinal multi‑parametric MRI analysis based on patient‑specific energy modelling in sequence space. Rather than operating on images with spatial networks, each voxel is represented by its multi‑sequence intensity vector (T1, T1c, T2, FLAIR, ADC), and a compact implicit neural representation is trained via denoising score matching to learn an energy function E_θ(\mathbfu) over \mathbbR^d from a single baseline scan. The learned energy landscape provides a differential‑geometric description of tissue regimes without segmentation labels. Local minima define tissue basins, gradient magnitude reflects proximity to regime boundaries, and Laplacian curvature characterises local constraint structure. Importantly, this baseline energy manifold is treated as a fixed geometric reference: it encodes the set of contrast combinations observed at diagnosis and is not retrained at follow‑up. Longitudinal assessment is therefore formulated as evaluation of subsequent scans relative to this baseline geometry. Rather than comparing anatomical segmentations, we analyse how the distribution of MRI sequence vectors evolves under the baseline energy function. In a paediatric case with later recurrence, follow‑up scans show progressive deviation in energy and directional displacement in sequence space toward the baseline tumour‑associated regime before clear radiological reappearance. In a case with stable disease, voxel distributions remain confined to established low‑energy basins without systematic drift. The presented cases serve as proof‑of‑concept that patient‑specific energy manifolds can function as geometric reference systems for longitudinal mpMRI analysis without explicit segmentation or supervised classification, providing a foundation for further investigation of manifold‑based tissue‑at‑risk tracking in neuro‑oncology.

PaperId: 1922, https://arxiv.org/pdf/2604.07154.pdf GitHub

Abstract:
Multimodal imaging analysis often relies on joint latent representations, yet these approaches rarely define what information is shared versus modality‑specific. Clarifying this distinction is clinically relevant, as it delineates the irreducible contribution of each modality and informs rational acquisition strategies. We propose a subspace decomposition framework that reframes multimodal fusion as a problem of orthogonal subspace separation rather than translation. We decompose Prostate‑Specific Membrane Antigen (PSMA) PET uptake into an MRI‑explainable physiological envelope and an orthogonal residual reflecting signal components not expressible within the MRI feature manifold. Using multiparametric MRI, we train an intensity‑based, non‑spatial implicit neural representation (INR) to map MRI feature vectors to PET uptake. We introduce a projection‑based regularization using singular value decomposition to penalize residual components lying within the span of the MRI feature manifold. This enforces mathematical orthogonality between tissue‑level physiological properties (structure, diffusion, perfusion) and intracellular PSMA expression. Tested on 13 prostate cancer patients, the model demonstrates that residual components spanned by MRI features are absorbed into the learned envelope, while the orthogonal residual is largest in tumour regions. This indicates that PSMA PET contains signal components not recoverable from MRI‑derived physiological descriptors. The resulting decomposition provides a structured characterization of modality complementarity grounded in representation geometry rather than image translation.

PaperId: 1923, https://arxiv.org/pdf/2604.07100.pdf GitHub

Abstract:
Empathetic dialogue requires not only recognizing a user's emotional state but also making strategy‑aware, context‑sensitive decisions throughout response generation. However, the lack of a comprehensive empathy strategy framework, explicit task‑aligned multi‑stage reasoning, and high‑quality strategy‑aware data fundamentally limits existing approaches, preventing them from effectively modeling empathetic dialogue as a complex, multi‑stage cognitive and decision‑making process. To address these challenges, we propose STRIDE‑ED, a STRategy‑grounded, Interpretable, and DEep reasoning framework that models Empathetic Dialogue through structured, strategy‑conditioned reasoning. To support effective learning, we develop a strategy‑aware data refinement pipeline integrating LLM‑based annotation, multi‑model consistency‑weighted evaluation, and dynamic sampling to construct high‑quality training data aligned with empathetic strategies. Furthermore, we adopt a two‑stage training paradigm that combines supervised fine‑tuning with multi‑objective reinforcement learning to better align model behaviors with target emotions, empathetic strategies, and response formats. Extensive experiments demonstrate that STRIDE‑ED generalizes across diverse open‑source LLMs and consistently outperforms existing methods on both automatic metrics and human evaluations. Our data and code are publicly available at https://github.com/jicoder‑nwpu/STRIDE‑ED.

PaperId: 1924, https://arxiv.org/pdf/2604.07084.pdf GitHub

Abstract:
Open‑loop end‑to‑end neural motion planners have recently been proposed to improve motion planning for robotic manipulators. These methods enable planning directly from sensor observations without relying on a privileged collision checker during planning. However, many existing methods generate only a single path for a given workspace across different runs, and do not leverage their open‑loop structure for inference‑time optimization. To address this limitation, we introduce Flow Motion Policy, an open‑loop, end‑to‑end neural motion planner for robotic manipulators that leverages the stochastic generative formulation of flow matching methods to capture the inherent multi‑modality of planning datasets. By modeling a distribution over feasible paths, Flow Motion Policy enables efficient inference‑time best‑of‑N sampling. The method generates multiple end‑to‑end candidate paths, evaluates their collision status after planning, and executes the first collision‑free solution. We benchmark the Flow Motion Policy against representative sampling‑based and neural motion planning methods. Evaluation results demonstrate that Flow Motion Policy improves planning success and efficiency, highlighting the effectiveness of stochastic generative policies for end‑to‑end motion planning and inference‑time optimization. Experimental evaluation videos are available via this \hrefhttps://zh.engr.tamu.edu/wp‑content/uploads/sites/310/2026/03/FMP‑Website.mp4link.

PaperId: 1925, https://arxiv.org/pdf/2604.07070.pdf GitHub

Abstract:
While Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, their potential for purpose‑driven exploration in dynamic geo‑spatial environments remains under‑investigated. Existing Geo‑Spatial Question Answering (GSQA) benchmarks predominantly focus on static retrieval, failing to capture the complexity of real‑world planning that involves dynamic user locations and compound constraints. To bridge this gap, we introduce EVGeoQA, a novel benchmark built upon Electric Vehicle (EV) charging scenarios that features a distinct location‑anchored and dual‑objective design. Specifically, each query in EVGeoQA is explicitly bound to a user's real‑time coordinate and integrates the dual objectives of a charging necessity and a co‑located activity preference. To systematically assess models in such complex settings, we further propose GeoRover, a general evaluation framework based on a tool‑augmented agent architecture to evaluate the LLMs' capacity for dynamic, multi‑objective exploration. Our experiments reveal that while LLMs successfully utilize tools to address sub‑tasks, they struggle with long‑range spatial exploration. Notably, we observe an emergent capability: LLMs can summarize historical exploration trajectories to enhance exploration efficiency. These findings establish EVGeoQA as a challenging testbed for future geo‑spatial intelligence. The dataset and prompts are available at https://github.com/kg‑bnu/EVGeoQA.

PaperId: 1926, https://arxiv.org/pdf/2604.07041.pdf GitHub

Abstract:
Text‑to‑SQL is the task of translating natural language queries into executable SQL for a given database, enabling non‑expert users to access structured data without writing SQL manually. Despite rapid advances driven by large language models (LLMs), existing approaches still struggle with complex queries in real‑world settings, where database schemas are large and questions require multi‑step reasoning over many interrelated tables. In such cases, providing the full schema often exceeds the context window, while one‑shot generation frequently produces non‑executable SQL due to syntax errors and incorrect schema linking. To address these challenges, we introduce AV‑SQL, a framework that decomposes complex Text‑to‑SQL into a pipeline of specialized LLM agents. Central to AV‑SQL is the concept of agentic views: agent‑generated Common Table Expressions (CTEs) that encapsulate intermediate query logic and filter relevant schema elements from large schemas. AV‑SQL operates in three stages: (1) a rewriter agent compresses and clarifies the input query; (2) a view generator agent processes schema chunks to produce agentic views; and (3) a planner, generator, and revisor agent collaboratively compose these views into the final SQL query. Extensive experiments show that AV‑SQL achieves 70.38% execution accuracy on the challenging Spider 2.0 benchmark, outperforming state‑of‑the‑art baselines, while remaining competitive on standard datasets with 85.59% on Spider, 72.16% on BIRD and 63.78% on KaggleDBQA. Our source code is available at https://github.com/pminhtam/AV‑SQL.

PaperId: 1927, https://arxiv.org/pdf/2604.07034.pdf GitHub

Abstract:
We present KITE, a training‑free, keyframe‑anchored, layout‑grounded front‑end that converts long robot‑execution videos into compact, interpretable tokenized evidence for vision‑language models (VLMs). KITE distills each trajectory into a small set of motion‑salient keyframes with open‑vocabulary detections and pairs each keyframe with a schematic bird's‑eye‑view (BEV) representation that encodes relative object layout, axes, timestamps, and detection confidence. These visual cues are serialized with robot‑profile and scene‑context tokens into a unified prompt, allowing the same front‑end to support failure detection, identification, localization, explanation, and correction with an off‑the‑shelf VLM. On the RoboFAC benchmark, KITE with Qwen2.5‑VL substantially improves over vanilla Qwen2.5‑VL in the training‑free setting, with especially large gains on simulation failure detection, identification, and localization, while remaining competitive with a RoboFAC‑tuned baseline. A small QLoRA fine‑tune further improves explanation and correction quality. We also report qualitative results on real dual‑arm robots, demonstrating the practical applicability of KITE as a structured and interpretable front‑end for robot failure analysis. Code and models are released on our project page: https://m80hz.github.io/kite/

PaperId: 1928, https://arxiv.org/pdf/2604.07019.pdf GitHub

Abstract:
Neural networks deliver impressive predictive performance across a variety of tasks, but they are often opaque in their decision‑making processes. Despite a growing interest in mechanistic interpretability, tools for systematically exploring the representations learned by neural networks in general, and tabular foundation models in particular, remain limited. In this work, we introduce ConceptTracer, an interactive application for analyzing neural representations through the lens of human‑interpretable concepts. ConceptTracer integrates two information‑theoretic measures that quantify concept saliency and selectivity, enabling researchers and practitioners to identify neurons that respond strongly to individual concepts. We demonstrate the utility of ConceptTracer on representations learned by TabPFN and show that our approach facilitates the discovery of interpretable neurons. Together, these capabilities provide a practical framework for investigating how neural networks like TabPFN encode concept‑level information. ConceptTracer is available at https://github.com/ml‑lab‑htw/concept‑tracer.

PaperId: 1929, https://arxiv.org/pdf/2604.06989.pdf GitHub

Abstract:
We present the first generative approach to photomosaic creation. Traditional photomosaic methods rely on a large number of tile images and color‑based matching, which limits both diversity and structural consistency. Our generative photomosaic framework synthesizes tile images using diffusion‑based generation conditioned on reference images. A low‑frequency conditioned diffusion mechanism aligns global structure while preserving prompt‑driven details. This generative formulation enables photomosaic composition that is both semantically expressive and structurally coherent, effectively overcoming the fundamental limitations of matching‑based approaches. By leveraging few‑shot personalized diffusion, our model is able to produce user‑specific or stylistically consistent tiles without requiring an extensive collection of images.

PaperId: 1930, https://arxiv.org/pdf/2604.06987.pdf GitHub

Abstract:
Palmprint recognition is deployed in security‑critical applications, including access control and palm‑based payment, due to its contactless acquisition and highly discriminative ridge‑and‑crease textures. However, the robustness of deep palmprint recognition systems against physically realizable attacks remains insufficiently understood. Existing studies are largely confined to the digital setting and do not adequately account for the texture‑dominant nature of palmprint recognition or the distortions introduced during physical acquisition. To address this gap, we propose CAAP, a capture‑aware adversarial patch framework for palmprint recognition. CAAP learns a universal patch that can be reused across inputs while remaining effective under realistic acquisition variation. To match the structural characteristics of palmprints, the framework adopts a cross‑shaped patch topology, which enlarges spatial coverage under a fixed pixel budget and more effectively disrupts long‑range texture continuity. CAAP further integrates three modules: ASIT for input‑conditioned patch rendering, RaS for stochastic capture‑aware simulation, and MS‑DIFE for feature‑level identity‑disruptive guidance. We evaluate CAAP on the Tongji, IITD, and AISEC datasets against generic CNN backbones and palmprint‑specific recognition models. Experiments show that CAAP achieves strong untargeted and targeted attack performance with favorable cross‑model and cross‑dataset transferability. The results further show that, although adversarial training can partially reduce the attack success rate, substantial residual vulnerability remains. These findings indicate that deep palmprint recognition systems remain vulnerable to physically realizable, capture‑aware adversarial patch attacks, underscoring the need for more effective defenses in practice. Code available at https://github.com/ryliu68/CAAP.

PaperId: 1931, https://arxiv.org/pdf/2604.06912.pdf GitHub

Abstract:
MLLMs require high‑resolution visual inputs for fine‑grained tasks like document understanding and dense scene perception. However, current global resolution scaling paradigms indiscriminately flood the quadratic self‑attention mechanism with visually redundant tokens, severely bottlenecking inference throughput while ignoring spatial sparsity and query intent. To overcome this, we propose Q‑Zoom, a query‑aware adaptive high‑resolution perception framework that operates in an efficient coarse‑to‑fine manner. First, a lightweight Dynamic Gating Network safely bypasses high‑resolution processing when coarse global features suffice. Second, for queries demanding fine‑grained perception, a Self‑Distilled Region Proposal Network (SD‑RPN) precisely localizes the task‑relevant Region‑of‑Interest (RoI) directly from intermediate feature spaces. To optimize these modules efficiently, the gating network uses a consistency‑aware generation strategy to derive deterministic routing labels, while the SD‑RPN employs a fully self‑supervised distillation paradigm. A continuous spatio‑temporal alignment scheme and targeted fine‑tuning then seamlessly fuse the dense local RoI with the coarse global layout. Extensive experiments demonstrate that Q‑Zoom establishes a dominant Pareto frontier. Using Qwen2.5‑VL‑7B as a primary testbed, Q‑Zoom accelerates inference by 2.52 times on Document & OCR benchmarks and 4.39 times in High‑Resolution scenarios while matching the baseline's peak accuracy. Furthermore, when configured for maximum perceptual fidelity, Q‑Zoom surpasses the baseline's peak performance by 1.1% and 8.1% on these respective benchmarks. These robust improvements transfer seamlessly to Qwen3‑VL, LLaVA, and emerging RL‑based thinking‑with‑image models. Project page is available at https://yuhengsss.github.io/Q‑Zoom/.

PaperId: 1932, https://arxiv.org/pdf/2604.06834.pdf GitHub

Abstract:
Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain‑of‑thought reasoning, through supervised fine‑tuning on large‑scale and high‑quality datasets. To construct such datasets, existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness‑based selection methods to filter high‑quality samples. Despite the proven effectiveness of naturalness‑based data selection, which ranks data by the average log probability assigned by LLMs, our analysis shows that, when applied to LLM reasoning datasets, it systematically prefers samples with longer reasoning steps (i.e., more tokens per step) rather than higher‑quality ones, a phenomenon we term step length confounding. Through quantitative analysis, we attribute this phenomenon to low‑probability first tokens in reasoning steps; longer steps dilute their influence, thereby inflating the average log probabilities. To address this issue, we propose two variant methods: ASLEC‑DROP, which drops first‑token probabilities when computing average log probability, and ASLEC‑CASL, which applies a causal debiasing regression to remove the first tokens' confounding effect. Experiments across four LLMs and five evaluation benchmarks demonstrate the effectiveness of our approach in mitigating the step length confounding problem.

PaperId: 1933, https://arxiv.org/pdf/2604.06798.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binarization provides extreme efficiency, yet existing binary methods designed for dense LLMs struggle with MoE‑specific issues, including cross‑expert redundancy, task‑agnostic importance estimation, and quantization‑induced routing shifts. To this end, we propose MoBiE, the first binarization framework tailored for MoE‑based LLMs. MoBiE is built on three core innovations: 1. using joint SVD decomposition to reduce cross‑expert redundancy; 2. integrating global loss gradients into local Hessian metrics to enhance weight importance estimation; 3. introducing an error constraint guided by the input null space to mitigate routing distortion. Notably, MoBiE achieves these optimizations while incurring no additional storage overhead, striking a balance between efficiency and model performance. Extensive experiments demonstrate that MoBiE consistently outperforms state‑of‑the‑art binary methods across multiple MoE‑based LLMs and benchmarks. For example, on Qwen3‑30B‑A3B, MoBiE reduces perplexity by 52.2%, improves average zero‑shot performance by 43.4%, achieves over 2 × inference speedup, and further shortens quantization time. The code is available at https://github.com/Kishon‑zzx/MoBiE.

PaperId: 1934, https://arxiv.org/pdf/2604.06795.pdf GitHub

Abstract:
Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy‑sensitive applications. However, in real‑world FL scenarios, clients often hold data from distinct domains, leading to severe domain shift and degraded global model performance. To address this, prototype learning has been emerged as a promising solution, which leverages class‑wise feature representations. Yet, existing methods face two key limitations: (1) Existing prototype‑based FL methods typically construct a single global prototype per class by aggregating local prototypes from all clients without preserving domain information. (2) Current feature‑prototype alignment is domain‑agnostic, forcing clients to align with global prototypes regardless of domain origin. To address these challenges, we propose Federated Domain‑Aware Prototypes (FedDAP) to construct domain‑specific global prototypes by aggregating local client prototypes within the same domain using a similarity‑weighted fusion mechanism. These global domain‑specific prototypes are then used to guide local training by aligning local features with prototypes from the same domain, while encouraging separation from prototypes of different domains. This dual alignment enhances domain‑specific learning at the local level and enables the global model to generalize across diverse domains. Finally, we conduct extensive experiments on three different datasets: DomainNet, Office‑10, and PACS to demonstrate the effectiveness of our proposed framework to address the domain shift challenges. The code is available at https://github.com/quanghuy6997/FedDAP.

PaperId: 1935, https://arxiv.org/pdf/2604.06770.pdf GitHub

Abstract:
Maintenance procedures in manufacturing facilities are often documented as flowcharts in static PDFs or scanned images. They encode procedural knowledge essential for asset lifecycle management, yet inaccessible to modern operator support systems. Vision‑language models, the dominant paradigm for image understanding, struggle to reconstruct connection topology from such diagrams. We present FlowExtract, a pipeline for extracting directed graphs from ISO 5807‑standardized flowcharts. The system separates element detection from connectivity reconstruction, using YOLOv8 and EasyOCR for standard domain‑aligned node detection and text extraction, combined with a novel edge detection method that analyzes arrowhead orientations and traces connecting lines backward to source nodes. Evaluated on industrial troubleshooting guides, FlowExtract achieves very high node detection and substantially outperforms vision‑language model baselines on edge extraction, offering organizations a practical path toward queryable procedural knowledge representations. The implementation is available athttps://github.com/guille‑gil/FlowExtract.

PaperId: 1936, https://arxiv.org/pdf/2604.06710.pdf GitHub GitHub

Abstract:
We present ATANT (Automated Test for Acceptance of Narrative Truth), an open evaluation framework for measuring continuity in AI systems: the ability to persist, update, disambiguate, and reconstruct meaningful context across time. While the AI industry has produced memory components (RAG pipelines, vector databases, long context windows, profile layers), no published framework formally defines or measures whether these components produce genuine continuity. We define continuity as a system property with 7 required properties, introduce a 10‑checkpoint evaluation methodology that operates without an LLM in the evaluation loop, and present a narrative test corpus of 250 stories comprising 1,835 verification questions across 6 life domains. We evaluate a reference implementation across 5 test suite iterations, progressing from 58% (legacy architecture) to 100% in isolated mode (250 stories) and 100% in 50‑story cumulative mode, with 96% at 250‑story cumulative scale. The cumulative result is the primary measure: when 250 distinct life narratives coexist in the same database, the system must retrieve the correct fact for the correct context without cross‑contamination. ATANT is system‑agnostic, model‑independent, and designed as a sequenced methodology for building and validating continuity systems. The framework specification, example stories, and evaluation protocol are available at https://github.com/Kenotic‑Labs/ATANT. The full 250‑story corpus will be released incrementally.

PaperId: 1937, https://arxiv.org/pdf/2604.06685.pdf GitHub

Abstract:
While Vision‑Language Models (VLMs) have demonstrated significant potential in chemical visual understanding, current models are predominantly optimized for direct visual question‑answering tasks. This paradigm often results in "black‑box" systems that fail to utilize the inherent capability of Large Language Models (LLMs) to infer underlying reaction mechanisms. In this work, we introduce ChemVLR, a chemical VLM designed to prioritize reasoning within the perception process. Unlike conventional chemical VLMs, ChemVLR analyzes visual inputs in a fine‑grained manner by explicitly identifying granular chemical descriptors, such as functional groups, prior to generating answers. This approach ensures the production of explicit and interpretable reasoning paths for complex visual chemical problems. To facilitate this methodology, we implement a cross‑modality reverse‑engineering strategy, combined with a rigorous filtering pipeline, to curate a large‑scale reasoning‑and‑captioning dataset comprising 760k high‑quality samples across molecular and reaction tasks. Furthermore, we adopt a three‑stage training framework that systemically builds model perception and reasoning capacity. Experiments demonstrate that ChemVLR achieves state‑of‑the‑art (SOTA) performance, surpassing both leading proprietary models and domain‑specific open‑source baselines. We also provide comprehensive ablation studies to validate our training strategy and data generation designs. Code and model weights will be available at https://github.com/xxlllz/ChemVLR.

PaperId: 1938, https://arxiv.org/pdf/2604.06638.pdf GitHub

Abstract:
Effective detection of unknown network security threats in multi‑class imbalanced environments is critical for maintaining cyberspace security. Current methods focus on learning class representations but face challenges with unknown threat detection, class imbalance, and lack of interpretability, limiting their practical use. To address this, we propose RPM‑Net, a novel framework that introduces reciprocal point mechanism to learn "non‑class" representations for each known attack category, coupled with adversarial margin constraints that provide geometric interpretability for unknown threat detection. RPM‑Net++ further enhances performance through Fisher discriminant regularization. Experimental results show that RPM‑Net achieves superior performance across multiple metrics including F1‑score, AUROC, and AUPR‑OUT, significantly outperforming existing methods and offering practical value for real‑world network security applications. Our code is available at:https://github.com/chiachen‑chang/RPM‑Net

PaperId: 1939, https://arxiv.org/pdf/2604.06613.pdf GitHub

Abstract:
Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three benchmarks, we find that 52‑‑88% of chain‑of‑thought tokens are produced after the answer is recoverable from a partial prefix. This post‑commitment generation reveals a structural phenomenon: the detection‑extraction gap. Free continuations from early prefixes recover the correct answer even at 10% of the trace, while forced extraction fails on 42% of these cases. The answer is recoverable from the model state, yet prompt‑conditioned decoding fails to extract it. We formalize this mismatch via a total‑variation bound between free and forced continuation distributions, yielding quantitative estimates of suffix‑induced shift. Exploiting this asymmetry, we propose Black‑box Adaptive Early Exit (BAEE), which uses free continuations for both detection and extraction, truncating 70‑‑78% of serial generation while improving accuracy by 1‑‑5pp across all models. For thinking‑mode models, early exit prevents post‑commitment overwriting, yielding gains of up to 5.8pp; a cost‑optimized variant achieves 68‑‑73% reduction at a median of 9 API calls. Code is available at https://github.com/EdWangLoDaSc/know2say.

PaperId: 1940, https://arxiv.org/pdf/2604.06603.pdf GitHub

Abstract:
Large language models (LLMs) have shown strong knowledge reserves and task‑solving capabilities, but still face the challenge of severe hallucination, hindering their practical application. Though scientific theories and rules can efficiently direct the behaviors of human manipulators, LLMs still do not utilize these highly‑condensed knowledge sufficiently through training or prompting. To address this issue, we propose SciDC, an LLM generation method that integrate subject‑specific knowledge with strong constraints. By adopting strong LLMs to automatically convert flexible knowledge into multi‑layered, standardized rules, we build an extensible framework to effectively constrain the model generation on domain tasks. Experiments on scientific tasks including industrial formulation design, clinical tumor diagnosis and retrosynthesis planning, consistently demonstrate the effectiveness of our method, achieving a 12% accuracy improvement on average compared with vanilla generation. We further discuss the potential of LLMs in automatically inductively summarizing highly‑condensed knowledge, looking ahead to practical solutions for accelerating the overall scientific research process. All the code of this paper can be obtained (https://github.com/Maotian‑Ma/SciDC).

PaperId: 1941, https://arxiv.org/pdf/2604.06505.pdf GitHub

Abstract:
Large language models (LLMs) are widely explored for reasoning‑intensive research tasks, yet resources for testing whether they can infer scientific conclusions from structured biomedical evidence remain limited. We introduce MedConclusion, a large‑scale dataset of 5.7M PubMed structured abstracts for biomedical conclusion generation. Each instance pairs the non‑conclusion sections of an abstract with the original author‑written conclusion, providing naturally occurring supervision for evidence‑to‑conclusion reasoning. MedConclusion also includes journal‑level metadata such as biomedical category and SJR, enabling subgroup analysis across biomedical domains. As an initial study, we evaluate diverse LLMs under conclusion and summary prompting settings and score outputs with both reference‑based metrics and LLM‑as‑a‑judge. We find that conclusion writing is behaviorally distinct from summary writing, strong models remain closely clustered under current automatic metrics, and judge identity can substantially shift absolute scores. MedConclusion provides a reusable data resource for studying scientific evidence‑to‑conclusion reasoning. Our code and data are available at: https://github.com/Harvard‑AI‑and‑Robotics‑Lab/MedConclusion.

PaperId: 1942, https://arxiv.org/pdf/2604.06483.pdf GitHub

Abstract:
Large language models that require multiple GPU cards to host are usually the most capable models. It is necessary to understand and steer these models, but the current technologies do not support the interpretability and steering of these models in the multi‑GPU setting as well as the single‑GPU setting. We present a practical implementation of activation‑level interpretability (logit lens) and steering (steering vector) that scales up to multi‑GPU language models. Our system implements design choices that reduce the activation memory by up to 7x and increase the throughput by up to 41x compared to a baseline on identical hardware. We demonstrate the method across LLaMA‑3.1 (8B, 70B) and Qwen‑3 (4B, 14B, 32B), sustaining 20‑100 tokens/s while collecting full layer‑wise activation trajectories for sequences of 1,500 tokens. Using label‑position steering vectors injected post‑LayerNorm, we show controllable, monotonic shifts in model outputs with a mean steerability slope of 0.702 across evaluated datasets, without fine‑tuning or additional forward passes. We release detailed benchmarks, ablations, and a reproducible instrumentation recipe to enable practical interpretability and real‑time behavioral control for frontier LLMs at https://github.com/Devdesai1901/LogitLense.

PaperId: 1943, https://arxiv.org/pdf/2604.06425.pdf GitHub

Abstract:
We propose a new frontier: Neural Computers (NCs) that unify computation, memory, and I/O of traditional computers in a learned runtime state. Our long‑term goal is the Completely Neural Computer (CNC): the mature, general‑purpose realization of this emerging machine form, with stable execution, explicit reprogramming, and durable capability reuse. As an initial step, we study whether elementary NC primitives can be learned solely from collected I/O traces, without instrumented program state. Concretely, we instantiate NCs as video models that roll out screen frames from instructions, pixels, and user actions (when available) in CLI and GUI settings. We show that NCs can acquire elementary interface primitives, especially I/O alignment and short‑horizon control, while routine reuse, controlled updates, and symbolic stability remain challenging. We outline a roadmap toward CNCs, to establish a new computing paradigm beyond today's agents and conventional computers.

PaperId: 1944, https://arxiv.org/pdf/2604.06373.pdf GitHub

Abstract:
New generation of AI coding tools, including AI‑powered IDEs equipped with agentic capabilities, can generate code within the context of the project. These AI IDEs are increasingly perceived as capable of producing project‑level code at scale. However, there is limited empirical evidence on the extent to which they can generate large‑scale software systems and what design issues such systems may exhibit. To address this gap, we conducted a study to explore the capability of Cursor in generating large‑scale projects and to evaluate the design quality of projects generated by Cursor. First, we propose a Feature‑Driven Human‑In‑The‑Loop (FD‑HITL) framework that systematically guides project generation from curated project descriptions. We generated 10 projects using Cursor with the FD‑HITL framework across three application domains and multiple technologies. We assessed the functional correctness of these projects through manual evaluation, obtaining an average functional correctness score of 91%. Next, we analyzed the generated projects using two static analysis tools, CodeScene and SonarQube, to detect design issues. We identified 1,305 design issues categorized into 9 categories by CodeScene and 3,193 issues in 11 categories by SonarQube. Our findings show that (1) when used with the FD‑HITL framework, Cursor can generate functional large‑scale projects averaging 16,965 LoC and 114 files; (2) the generated projects nevertheless contain design issues that may pose long‑term maintainability and evolvability risks, requiring careful review by experienced developers; (3) the most prevalent issues include Code Duplication, high Code Complexity, Large Methods, Framework Best‑Practice Violations, Exception‑Handling Issues and Accessibility Issues; (4) these design issues violate design principles such as SRP, SoC, and DRY. The replication package is at https://github.com/Kashifraz/DIinAGP

PaperId: 1945, https://arxiv.org/pdf/2604.06296.pdf GitHub

Abstract:
AI agents are increasingly deployed in real‑world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on server‑side efficiency, proposing methods such as caching, speculative execution, traffic scheduling, and load balancing to reduce the cost of serving agentic workloads. However, as users increasingly construct agents by composing local tools, remote APIs, and diverse models, an equally important optimization problem arises on the client side. Client‑side optimization asks how developers should allocate the resources available to them, including model choice, local tools, and API budget across pipeline stages, subject to application‑specific quality, cost, and latency constraints. Because these objectives depend on the task and deployment setting, they cannot be determined by server‑side systems alone. We introduce AgentOpt, the first framework‑agnostic Python package for client‑side agent optimization. We first study model selection, a high‑impact optimization lever in multi‑step agent pipelines. Given a pipeline and a small evaluation set, the goal is to find the most cost‑effective assignment of models to pipeline roles. This problem is consequential in practice: at matched accuracy, the cost gap between the best and worst model combinations can reach 13‑32x in our experiments. To efficiently explore the exponentially growing combination space, AgentOpt implements ten search algorithms, including UCB‑E, UCB‑E with Low‑Rank Factorization, Arm Elimination, Epsilon‑LUCB, Threshold Successive Elimination, and Bayesian Optimization. Across four benchmarks, UCB‑E recovers near‑optimal accuracy while reducing evaluation budget by 62‑76% relative to brute‑force search. Code and benchmark results available at https://agentoptimizer.github.io/agentopt/.

PaperId: 1946, https://arxiv.org/pdf/2604.06291.pdf GitHub

Abstract:
Low‑Rank Adaptation (LoRA) enables parameter‑efficient fine‑tuning of Large Language Models (LLMs), and recent Mixture‑of‑Experts (MoE) extensions further enhance flexibility by dynamically combining multiple LoRA experts. However, existing MoE‑augmented LoRA methods assume that experts operate independently, often leading to unstable routing, expert dominance. In this paper, we propose TalkLoRA, a communication‑aware MoELoRA framework that relaxes this independence assumption by introducing expert‑level communication prior to routing. TalkLoRA equips low‑rank experts with a lightweight Talking Module that enables controlled information exchange across expert subspaces, producing a more robust global signal for routing. Theoretically, we show that expert communication smooths routing dynamics by mitigating perturbation amplification while strictly generalizing existing MoELoRA architectures. Empirically, TalkLoRA consistently outperforms vanilla LoRA and MoELoRA across diverse language understanding and generation tasks, achieving higher parameter efficiency and more balanced expert routing under comparable parameter budgets. These results highlight structured expert communication as a principled and effective enhancement for MoE‑based parameter‑efficient adaptation. Code is available at https://github.com/why0129/TalkLoRA.

PaperId: 1947, https://arxiv.org/pdf/2604.06240.pdf GitHub

Abstract:
Verifying the success of computer use agent (CUA) trajectories is a critical challenge: without reliable verification, neither evaluation nor training signal can be trusted. In this paper, we present lessons learned from building a best‑in‑class verifier for web tasks we call the Universal Verifier. We design the Universal Verifier around four key principles: 1) constructing rubrics with meaningful, non‑overlapping criteria to reduce noise; 2) separating process and outcome rewards that yield complementary signals, capturing cases where an agent follows the right steps but gets blocked or succeeds through an unexpected path; 3) distinguishing between controllable and uncontrollable failures scored via a cascading‑error‑free strategy for finer‑grained failure understanding; and 4) a divide‑and‑conquer context management scheme that attends to all screenshots in a trajectory, improving reliability on longer task horizons. We validate these findings on CUAVerifierBench, a new set of CUA trajectories with both process and outcome human labels, showing that our Universal Verifier agrees with humans as often as humans agree with each other. We report a reduction in false positive rates to near zero compared to baselines like WebVoyager (\geq 45%) and WebJudge (\geq 22%). We emphasize that these gains stem from the cumulative effect of the design choices above. We also find that an auto‑research agent achieves 70% of expert quality in 5% of the time, but fails to discover all strategies required to replicate the Universal Verifier. We open‑source our Universal Verifier system along with CUAVerifierBench; available at https://github.com/microsoft/fara.

PaperId: 1948, https://arxiv.org/pdf/2604.06231.pdf GitHub GitHub

Abstract:
Database systems incorporate an ever‑growing number of functions in their kernels (a.k.a., database native functions) for scenarios like new application support and business migration. This growth causes an urgent demand for automatic database native function synthesis. While recent advances in LLM‑based code generation (e.g., Claude Code) show promise, they are too generic for database‑specific development. They often hallucinate or overlook critical context because database function synthesis is inherently complex and error‑prone, where synthesizing a single function may involve registering multiple function units, linking internal references, and implementing logic correctly. To this end, we propose DBCooker, an LLM‑based system for automatically synthesizing database native functions. It consists of three components. First, the function characterization module aggregates multi‑source declarations, identifies function units that require specialized coding, and traces cross‑unit dependencies. Second, we design operations to address the main synthesis challenges: (1) a pseudo‑code‑based coding plan generator that constructs structured implementation skeletons by identifying key elements such as reusable referenced functions; (2) a hybrid fill‑in‑the‑blank model guided by probabilistic priors and component awareness to integrate core logic with reusable routines; and (3) three‑level progressive validation, including syntax checking, standards compliance, and LLM‑guided semantic verification. Finally, an adaptive orchestration strategy unifies these operations with existing tools and dynamically sequences them via the orchestration history of similar functions. Results show that DBCooker outperforms other methods on SQLite, PostgreSQL, and DuckDB (34.55% higher accuracy on average), and can synthesize new functions absent in the latest SQLite (v3.50).

PaperId: 1949, https://arxiv.org/pdf/2604.06207.pdf GitHub

Abstract:
This paper investigates demonstration selection strategies for predicting a user's next point‑of‑interest (POI) using large language models (LLMs), aiming to accurately forecast a user's subsequent location based on historical check‑in data. While in‑context learning (ICL) with LLMs has recently gained attention as a promising alternative to traditional supervised approaches, the effectiveness of ICL significantly depends on the selected demonstration. Although previous studies have examined methods such as random selection, embedding‑based selection, and task‑specific selection, there remains a lack of comprehensive comparative analysis among these strategies. To bridge this gap and clarify the best practices for real‑world applications, we comprehensively evaluate existing demonstration selection methods alongside simpler heuristic approaches such as geographical proximity, temporal ordering, and sequential patterns. Extensive experiments conducted on three real‑world datasets indicate that these heuristic methods consistently outperform more complex and computationally demanding embedding‑based methods, both in terms of computational cost and prediction accuracy. Notably, in certain scenarios, LLMs using demonstrations selected by these simpler heuristic methods even outperform existing fine‑tuned models, without requiring further training. Our source code is available at: https://github.com/ryonsd/DS‑LLM4POI.

PaperId: 1950, https://arxiv.org/pdf/2604.06200.pdf GitHub

Abstract:
Designing project‑based learning (PBL) demands managing highly interdependent components, a task that both traditional linear tools and purely conversational AI struggle with. Traditional tools fail to capture the non‑linear nature of creative design, while conversational systems lack the persistent, shared context necessary for reflective collaboration. Grounded in theories of distributed cognition, we introduce CoMAP, a system that embodies a graph‑based collaboration paradigm. By providing a shared visual workspace with dual‑modality AI support, CoMAP transforms the human‑AI relationship from a prompt‑and‑response loop into a transparent and equitable partnership. Our study with 30 educators shows CoMAP significantly improves teachers' design expression, divergent thinking, and iterative practice compared to a dialogue‑only baseline. These findings demonstrate how a nonlinear, artifact‑centric approach can foster trust, reduce cognitive load, and \textcolorfixsupport educators to take control of their creative process. Our contributions are available at: https://comap2025.github.io/.

PaperId: 1951, https://arxiv.org/pdf/2604.06182.pdf GitHub

Abstract:
Existing online benchmarks for mobile GUI agents remain largely app‑centric and task‑homogeneous, failing to reflect the diversity and instability of real‑world mobile usage. To this end, we introduce VenusBench‑Mobile, a challenging online benchmark for evaluating general‑purpose mobile GUI agents under realistic, user‑centric conditions. VenusBench‑Mobile builds two core evaluation pillars: defining what to evaluate via user‑intent‑driven task design that reflects real mobile usage, and how to evaluate through a capability‑oriented annotation scheme for fine‑grained agent behavior analysis. Extensive evaluation of state‑of‑the‑art mobile GUI agents reveals large performance gaps relative to prior benchmarks, indicating that VenusBench‑Mobile poses substantially more challenging and realistic tasks and that current agents remain far from reliable real‑world deployment. Diagnostic analysis further shows that failures are dominated by deficiencies in perception and memory, which are largely obscured by coarse‑grained evaluations. Moreover, even the strongest agents exhibit near‑zero success under environment variations, highlighting their brittleness in realistic settings. Based on these insights, we believe VenusBench‑Mobile provides an important stepping stone toward robust real‑world deployment of mobile GUI agents. Code and data are available at https://github.com/inclusionAI/UI‑Venus/tree/VenusBench‑Mobile.

PaperId: 1952, https://arxiv.org/pdf/2604.06169.pdf GitHub

Abstract:
The static ``train then deploy" paradigm fundamentally limits Large Language Models (LLMs) from dynamically adapting their weights in response to continuous streams of new information inherent in real‑world tasks. Test‑Time Training (TTT) offers a compelling alternative by updating a subset of model parameters (fast weights) at inference time, yet its potential in the current LLM ecosystem is hindered by critical barriers including architectural incompatibility, computational inefficiency and misaligned fast weight objectives for language modeling. In this work, we introduce In‑Place Test‑Time Training (In‑Place TTT), a framework that seamlessly endows LLMs with Test‑Time Training ability. In‑Place TTT treats the final projection matrix of the ubiquitous MLP blocks as its adaptable fast weights, enabling a ``drop‑in" enhancement for LLMs without costly retraining from scratch. Furthermore, we replace TTT's generic reconstruction objective with a tailored, theoretically‑grounded objective explicitly aligned with the Next‑Token‑Prediction task governing autoregressive language modeling. This principled objective, combined with an efficient chunk‑wise update mechanism, results in a highly scalable algorithm compatible with context parallelism. Extensive experiments validate our framework's effectiveness: as an in‑place enhancement, it enables a 4B‑parameter model to achieve superior performance on tasks with contexts up to 128k, and when pretrained from scratch, it consistently outperforms competitive TTT‑related approaches. Ablation study results further provide deeper insights on our design choices. Collectively, our results establish In‑Place TTT as a promising step towards a paradigm of continual learning in LLMs.

PaperId: 1953, https://arxiv.org/pdf/2604.06129.pdf GitHub

Abstract:
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop‑in replacement for self‑attention. PoM aggregates input tokens into a compact representation through a learned polynomial function, from which each token retrieves contextual information. We prove that PoM satisfies the contextual mapping property, ensuring that transformers equipped with PoM remain universal sequence‑to‑sequence approximators. We replace standard self‑attention with PoM across five diverse domains: text generation, handwritten text recognition, image generation, 3D modeling, and Earth observation. PoM matches the performance of attention‑based models while drastically reducing computational cost when working with long sequences. The code is available at https://github.com/davidpicard/pom.

PaperId: 1954, https://arxiv.org/pdf/2604.06074.pdf GitHub

Abstract:
Achieving fine‑grained and structurally sound controllability is a cornerstone of advanced visual generation. Existing part‑based frameworks treat user‑provided parts as an unordered set and therefore ignore their intrinsic spatial and semantic relationships, which often results in compositions that lack structural integrity. To bridge this gap, we propose Graph‑PiT, a framework that explicitly models the structural dependencies of visual components using a graph prior. Specifically, we represent visual parts as nodes and their spatial‑semantic relationships as edges. At the heart of our method is a Hierarchical Graph Neural Network (HGNN) module that performs bidirectional message passing between coarse‑grained part‑level super‑nodes and fine‑grained IP+ token sub‑nodes, refining part embeddings before they enter the generative pipeline. We also introduce a graph Laplacian smoothness loss and an edge‑reconstruction loss so that adjacent parts acquire compatible, relation‑aware embeddings. Quantitative experiments on controlled synthetic domains (character, product, indoor layout, and jigsaw), together with qualitative transfer to real web images, show that Graph‑PiT improves structural coherence over vanilla PiT while remaining compatible with the original IP‑Prior pipeline. Ablation experiments confirm that explicit relational reasoning is crucial for enforcing user‑specified adjacency constraints. Our approach not only enhances the plausibility of generated concepts but also offers a scalable and interpretable mechanism for complex, multi‑part image synthesis. The code is available at https://github.com/wolf‑bailang/Graph‑PiT.

PaperId: 1955, https://arxiv.org/pdf/2604.06019.pdf GitHub

Abstract:
The advancement of Large Language Models (LLMs) has raised concerns regarding their dual‑use potential in cybersecurity. Existing evaluation frameworks overwhelmingly focus on Information Technology (IT) environments, failing to capture the constraints, and specialized protocols of Operational Technology (OT). To address this gap, we introduce CritBench, a novel framework designed to evaluate the cybersecurity capabilities of LLM agents within IEC 61850 Digital Substation environments. We assess five state‑of‑the‑art models, including OpenAI's GPT‑5 suite and open‑weight models, across a corpus of 81 domain‑specific tasks spanning static configuration analysis, network traffic reconnaissance, and live virtual machine interaction. To facilitate industrial protocol interaction, we develop a domain‑specific tool scaffold. Our empirical results show that agents reliably execute static structured‑file analysis and single‑tool network enumeration, but their performance degrades on dynamic tasks. Despite demonstrating explicit, internalized knowledge of the IEC 61850 standards terminology, current models struggle with the persistent sequential reasoning and state tracking required to manipulate live systems without specialized tools. Equipping agents with our domain‑specific tool scaffold significantly mitigates this operational bottleneck. Code and evaluation scripts are available at: https://github.com/GKeppler/CritBench

PaperId: 1956, https://arxiv.org/pdf/2604.06013.pdf GitHub

Abstract:
This paper presents epistemic blinding in the context of an agentic system that uses large language models to reason across multiple biological datasets for drug target prioritization. During development, it became apparent that LLM outputs silently blend data‑driven inference with memorized priors about named entities ‑ and the blend is invisible: there is no way to determine, from a single output, how much came from the data on the page and how much came from the model's training memory. Epistemic blinding is a simple inference‑time protocol that replaces entity identifiers with anonymous codes before prompting, then compares outputs against an unblinded control. The protocol does not make LLM reasoning deterministic, but it restores one critical axis of auditability: measuring how much of an output came from the supplied data versus the model's parametric knowledge. The complete target identification system is described ‑ including LLM‑guided evolutionary optimization of scoring functions and blinded agentic reasoning for target rationalization ‑ with demonstration that both stages operate without access to entity identity. In oncology drug target prioritization across four cancer types, blinding changes 16% of top‑20 predictions while preserving identical recovery of validated targets. The contamination problem is shown to generalize beyond biology: in S&P 500 equity screening, brand‑recognition bias reshapes 30‑40% of top‑20 rankings across five random seeds. To lower the barrier to adoption, the protocol is released as an open‑source tool and as a Claude Code skill that enables one‑command epistemic blinding within agentic workflows. The claim is not that blinded analysis produces better results, but that without blinding, there is no way to know to what degree the agent is adhering to the analytical process the researcher designed.

PaperId: 1957, https://arxiv.org/pdf/2604.05995.pdf GitHub

Abstract:
Large Language Models (LLMs) internalize vast world knowledge as parametric memory, yet inevitably inherit the staleness and errors of their source corpora. Consequently, ensuring the reliability and malleability of these internal representations is imperative for trustworthy real‑world deployment. Knowledge editing offers a pivotal paradigm for surgically modifying memory without retraining. However, while recent editors demonstrate high success rates on standard benchmarks, it remains questionable whether current evaluation frameworks that rely on assessing output under specific prompting conditions can reliably authenticate genuine memory modification. In this work, we introduce a simple diagnostic framework that subjects models to discriminative self‑assessment under in‑context learning (ICL) settings that better reflect real‑world application environments, specifically designed to scrutinize the subtle behavioral nuances induced by memory modifications. This probing reveals a pervasive phenomenon of Surface Compliance, where editors achieve high benchmark scores by merely mimicking target outputs without structurally overwriting internal beliefs. Moreover, we find that recursive modifications accumulate representational residues, triggering cognitive instability and permanently diminishing the reversibility of the model's memory state. These insights underscore the risks of current editing paradigms and highlight the pivotal role of robust memory modification in building trustworthy, long‑term sustainable LLM systems. Code is available at https://github.com/XiaojieGu/SA‑MCQ.

PaperId: 1958, https://arxiv.org/pdf/2604.05865.pdf GitHub

Abstract:
When LLMs process structured data, the serialization format directly affects cost and context utilization. Standard JSON wastes tokens repeating key names in every row of a tabular array‑‑overhead that scales linearly with row count. This paper presents JTON (JSON Tabular Object Notation), a strict JSON superset whose main idea, Zen Grid, factors column headers into a single row and encodes values with semicolons, preserving JSON's type system while cutting redundancy. Across seven real‑world domains, Zen Grid reduces token counts by 15‑60% versus JSON compact (28.5% average; 32% with bare_strings). Comprehension tests on 10 LLMs show a net +0.3 pp accuracy gain over JSON: four models improve, three hold steady, and three dip slightly. Generation tests on 12 LLMs yield 100% syntactic validity in both few‑shot and zero‑shot settings. A Rust/PyO3 reference implementation adds SIMD‑accelerated parsing at 1.4x the speed of Python's json module. Code, a 683‑vector test suite, and all experimental data are publicly available.

PaperId: 1959, https://arxiv.org/pdf/2604.05854.pdf GitHub

Abstract:
We present Deep Researcher Agent, an open‑source framework that enables large language model (LLM) agents to autonomously conduct deep learning experiments around the clock. Unlike existing AI research assistants that focus on paper writing or code generation, our system addresses the full experiment lifecycle: hypothesis formation, code implementation, training execution, result analysis, and iterative refinement. The framework introduces three key innovations: (1) Zero‑Cost Monitoring ‑‑ a monitoring paradigm that incurs zero LLM API costs during model training by relying solely on process‑level checks and log file reads; (2) Two‑Tier Constant‑Size Memory ‑‑ a memory architecture capped at ～5K characters regardless of runtime duration, preventing the unbounded context growth that plagues long‑running agents; and (3) Minimal‑Toolset Leader‑Worker Architecture ‑‑ a multi‑agent design where each worker agent is equipped with only 3‑‑5 tools, reducing per‑call token overhead by up to 73%. In sustained deployments spanning 30+ days, the framework autonomously completed 500+ experiment cycles across four concurrent research projects, achieving a 52% improvement over baseline metrics in one project through 200+ automated experiments ‑‑ all at an average LLM cost of \0.08 per 24‑hour cycle. Code is available at https://github.com/Xiangyue‑Zhang/auto‑deep‑researcher‑24x7.

PaperId: 1960, https://arxiv.org/pdf/2604.05808.pdf GitHub

Abstract:
Large language model (LLM) agents have demonstrated strong capabilities in complex interactive decision‑making tasks. However, existing LLM agents typically rely on increasingly long interaction histories, resulting in high computational cost and limited scalability. In this paper, we propose STEP‑HRL, a hierarchical reinforcement learning (HRL) framework that enables step‑level learning by conditioning only on single‑step transitions rather than full interaction histories. STEP‑HRL structures tasks hierarchically, using completed subtasks to represent global progress of overall task. By introducing a local progress module, it also iteratively and selectively summarizes interaction history within each subtask to produce a compact summary of local progress. Together, these components yield augmented step‑level transitions for both high‑level and low‑level policies. Experimental results on ScienceWorld and ALFWorld benchmarks consistently demonstrate that STEP‑HRL substantially outperforms baselines in terms of performance and generalization while reducing token usage. Our code is available at https://github.com/TonyStark042/STEP‑HRL.

PaperId: 1961, https://arxiv.org/pdf/2604.05777.pdf GitHub

Abstract:
How do people acquire rich, flexible knowledge about their environment from others despite limited cognitive capacity? Humans are often thought to rely on computationally costly mentalizing, such as inferring others' beliefs. In contrast, cultural evolution emphasizes that behavioral transmission can be supported by simple social cues. Using reinforcement learning simulations, we show how minimal social learning can indirectly transmit higher‑level representations. We simulate a naïve agent searching for rewards in a reconfigurable environment, learning either alone or by observing an expert ‑ crucially, without inferring mental states. Instead, the learner heuristically selects actions or boosts value representations based on observed actions. Our results demonstrate that these cues bias the learner's experience, causing its representation to converge toward the expert's. Model‑based learners benefit most from social exposure, showing faster learning and more expert‑like representations. These findings show how cultural transmission can arise from simple, non‑mentalizing processes exploiting asocial learning mechanisms.

PaperId: 1962, https://arxiv.org/pdf/2604.05689.pdf GitHub

Abstract:
We present Consistent‑Recurrent Feature Flow Transformer (CRFT), a unified coarse‑to‑fine framework based on feature flow learning for robust cross‑modal image registration. CRFT learns a modality‑independent feature flow representation within a transformer‑based architecture that jointly performs feature alignment and flow estimation. The coarse stage establishes global correspondences through multi‑scale feature correlation, while the fine stage refines local details via hierarchical feature fusion and adaptive spatial reasoning. To enhance geometric adaptability, an iterative discrepancy‑guided attention mechanism with a Spatial Geometric Transform (SGT) recurrently refines the flow field, progressively capturing subtle spatial inconsistencies and enforcing feature‑level consistency. This design enables accurate alignment under large affine and scale variations while maintaining structural coherence across modalities. Extensive experiments on diverse cross‑modal datasets demonstrate that CRFT consistently outperforms state‑of‑the‑art registration methods in both accuracy and robustness. Beyond registration, CRFT provides a generalizable paradigm for multimodal spatial correspondence, offering broad applicability to remote sensing, autonomous navigation, and medical imaging. Code and datasets are publicly available at https://github.com/NEU‑Liuxuecong/CRFT.

PaperId: 1963, https://arxiv.org/pdf/2604.05673.pdf GitHub

Abstract:
Visual navigation is a core challenge in Embodied AI, requiring autonomous agents to translate high‑dimensional sensory observations into continuous, long‑horizon action trajectories. While generative policies based on diffusion models and Schrödinger Bridges (SB) effectively capture multimodal action distributions, they require dozens of integration steps due to high‑variance stochastic transport, posing a critical barrier for real‑time robotic control. We propose Rectified Schrödinger Bridge Matching (RSBM), a framework that exploits a shared velocity‑field structure between standard Schrödinger Bridges (\varepsilon=1, maximum‑entropy transport) and deterministic Optimal Transport (\varepsilon\to 0, as in Conditional Flow Matching), controlled by a single entropic regularization parameter \varepsilon. We prove two key results: (1) the conditional velocity field's functional form is invariant across the entire \varepsilon‑spectrum (Velocity Structure Invariance), enabling a single network to serve all regularization strengths; and (2) reducing \varepsilon linearly decreases the conditional velocity variance, enabling more stable coarse‑step ODE integration. Anchored to a learned conditional prior that shortens transport distance, RSBM operates at an intermediate \varepsilon that balances multimodal coverage and path straightness. Empirically, while standard bridges require \geq 10 steps to converge, RSBM achieves over 94% cosine similarity and 92% success rate in merely 3 integration steps ‑‑ without distillation or multi‑stage training ‑‑ substantially narrowing the gap between high‑fidelity generative policies and the low‑latency demands of Embodied AI.

PaperId: 1964, https://arxiv.org/pdf/2604.05589.pdf GitHub

Abstract:
Agentic Al systems are increasingly deployed as personal assistants and are likely to become a common object of digital investigations. However, little is known about how their internal state and actions can be reconstructed during forensic analysis. Despite growing popularity, systematic forensic approaches for such systems remain largely unexplored. This paper presents an empirical study of OpenClaw a widely used single‑agent assistant. We examine OpenClaw's technical design via static code analysis and apply differential forensic analysis to identify recoverable traces across stages of the agent interaction loop. We classify and correlate these traces to assess their investigative value in a systematic way. Based on these observations, we propose an agent artifact taxonomy that captures recurring investigative patterns. Finally, we highlight a foundational challenge for agentic Al forensics: agent‑mediated execution introduces an additional layer of abstraction and substantial nondeterminism in trace generation. The large language model (LLM), the execution environment, and the evolving context can influence tool choice and state transitions in ways that are largely absent from rule‑based software. Overall, our results provide an initial foundation for the systematic investigation of agentic Al and outline implications for digital forensic practice and future research.

PaperId: 1965, https://arxiv.org/pdf/2604.05489.pdf GitHub

Abstract:
Text‑to‑Video (T2V) generation has benefited from recent advances in diffusion models, yet current systems still struggle under complex scenarios, which are generally exacerbated by the ambiguity and underspecification of text prompts. In this work, we formulate complex‑scenario prompt refinement as a stage‑wise multi‑agent refinement process and propose SCMAPR, i.e., a scenario‑aware and Self‑Correcting Multi‑Agent Prompt Refinement framework for T2V prompting. SCMAPR coordinates specialized agents to (i) route each prompt to a taxonomy‑grounded scenario for strategy selection, (ii) synthesize scenario‑aware rewriting policies and perform policy‑conditioned refinement, and (iii) conduct structured semantic verification that triggers conditional revision when violations are detected. To clarify what constitutes complex scenarios in T2V prompting, provide representative examples, and enable rigorous evaluation under such challenging conditions, we further introduce T2V‑Complexity, which is a complex‑scenario T2V benchmark consisting exclusively of complex‑scenario prompts. Extensive experiments on 3 existing benchmarks and our T2V‑Complexity benchmark demonstrate that SCMAPR consistently improves text‑video alignment and overall generation quality under complex scenarios, achieving up to 2.67% and 3.28 gains in average score on VBench and EvalCrafter, and up to 0.028 improvement on T2V‑CompBench over 3 State‑Of‑The‑Art baselines. The codes of SCMAPR are publicly available at https://github.com/HiThink‑Research/SCMAPR.

PaperId: 1966, https://arxiv.org/pdf/2604.05482.pdf GitHub

Abstract:
Automatic diagnosis of canine pneumothorax is challenged by data scarcity and the need for trustworthy models. To address this, we first introduce a public, pixel‑level annotated dataset to facilitate research. We then propose a novel diagnostic paradigm that reframes the task as a synergistic process of signal localization and spectral detection. For localization, our method employs a Vision‑Language Model (VLM) to guide an iterative Flow Matching process, which progressively refines segmentation masks to achieve superior boundary accuracy. For detection, the segmented mask is used to isolate features from the suspected lesion. We then apply Random Matrix Theory (RMT), a departure from traditional classifiers, to analyze these features. This approach models healthy tissue as predictable random noise and identifies pneumothorax by detecting statistically significant outlier eigenvalues that represent a non‑random pathological signal. The high‑fidelity localization from Flow Matching is crucial for purifying the signal, thus maximizing the sensitivity of our RMT detector. This synergy of generative segmentation and first‑principles statistical analysis yields a highly accurate and interpretable diagnostic system (source code is available at: https://github.com/Pu‑Wang‑alt/Canine‑pneumothorax).

PaperId: 1967, https://arxiv.org/pdf/2604.05436.pdf GitHub

Abstract:
Reconstructing textured 3D human models from a single image is fundamental for AR/VR and digital human applications. However, existing methods mostly focus on single individuals and thus fail in multi‑human scenes, where naive composition of individual reconstructions often leads to artifacts such as unrealistic overlaps, missing geometry in occluded regions, and distorted interactions. These limitations highlight the need for approaches that incorporate group‑level context and interaction priors. We introduce a holistic method that explicitly models both group‑ and instance‑level information. To mitigate perspective‑induced geometric distortions, we first transform the input into a canonical orthographic space. Our primary component, Human Group‑Instance Multi‑View Diffusion (HUG‑MVD), then generates complete multi‑view normals and images by jointly modeling individuals and group context to resolve occlusions and proximity. Subsequently, the Human Group‑Instance Geometric Reconstruction (HUG‑GR) module optimizes the geometry by leveraging explicit, physics‑based interaction priors to enforce physical plausibility and accurately model inter‑human contact. Finally, the multi‑view images are fused into a high‑fidelity texture. Together, these components form our complete framework, HUG3D. Extensive experiments show that HUG3D significantly outperforms both single‑human and existing multi‑human methods, producing physically plausible, high‑fidelity 3D reconstructions of interacting people from a single image. Project page: https://jongheean11.github.io/HUG3D_project

PaperId: 1968, https://arxiv.org/pdf/2604.05418.pdf GitHub

Abstract:
Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval‑augmented generation (RAG) is a promising remedy by organizing query‑relevant visual evidence into a compact context, most existing methods (i) flatten videos into independent segments, breaking their inherent spatio‑temporal structure, and (ii) depend on explicit semantic matching, which can miss cues that are implicitly relevant to the query's intent. To overcome these limitations, we propose VideoStir, a structured and intent‑aware long‑video RAG framework. It firstly structures a video as a spatio‑temporal graph at clip level, and then performs multi‑hop retrieval to aggregate evidence across distant yet contextually related events. Furthermore, it introduces an MLLM‑backed intent‑relevance scorer that retrieves frames based on their alignment with the query's reasoning intent. To support this capability, we curate IR‑600K, a large‑scale dataset tailored for learning frame‑query intent alignment. Experiments show that VideoStir is competitive with state‑of‑the‑art baselines without relying on auxiliary information, highlighting the promise of shifting long‑video RAG from flattened semantic matching to structured, intent‑aware reasoning. Codes and checkpoints are available at https://github.com/RomGai/VideoStir.

PaperId: 1969, https://arxiv.org/pdf/2604.05396.pdf GitHub

Abstract:
Despite its success, existing in‑context learning (ICL) relies on in‑domain expert demonstrations, limiting its applicability when expert annotations are scarce. We posit that different domains may share underlying reasoning structures, enabling source‑domain demonstrations to improve target‑domain inference despite semantic mismatch. To test this hypothesis, we conduct a comprehensive empirical study of different retrieval methods to validate the feasibility of achieving cross‑domain knowledge transfer under the in‑context learning setting. Our results demonstrate conditional positive transfer in cross‑domain ICL. We identify a clear example absorption threshold: beyond it, positive transfer becomes more likely, and additional demonstrations yield larger gains. Further analysis suggests that these gains stem from reasoning structure repair by retrieved cross‑domain examples, rather than semantic cues. Overall, our study validates the feasibility of leveraging cross‑domain knowledge transfer to improve cross‑domain ICL performance, motivating the community to explore designing more effective retrieval approaches for this novel direction.\footnoteOur implementation is available at https://github.com/littlelaska/ICL‑TF4LR

PaperId: 1970, https://arxiv.org/pdf/2604.05383.pdf GitHub

Abstract:
Large language models (LLMs) have made notable progress in logical reasoning, yet still fall short of human‑level performance. Current boosting strategies rely on expert‑crafted in‑domain demonstrations, limiting their applicability in expertise‑scarce domains, such as specialized mathematical reasoning, formal logic, or legal analysis. In this work, we demonstrate the feasibility of leveraging cross‑domain demonstrating examples to boost the LLMs' reasoning performance. Despite substantial domain differences, many reusable implicit logical structures are shared across domains. In order to effectively retrieve cross‑domain examples for unseen domains under investigation, in this work, we further propose an effective retrieval method, called domain‑invariant neurons‑based retrieval (DIN‑Retrieval). Concisely, DIN‑Retrieval first summarizes a hidden representation that is universal across different domains. Then, during the inference stage, we use the DIN vector to retrieve structurally compatible cross‑domain demonstrations for the in‑context learning. Experimental results in multiple settings for the transfer of mathematical and logical reasoning demonstrate that our method achieves an average improvement of 1.8 over the state‑of‑the‑art methods \footnoteOur implementation is available at https://github.com/Leon221220/DIN‑Retrieval.

PaperId: 1971, https://arxiv.org/pdf/2604.05366.pdf GitHub

Abstract:
Every existing method for compressing 3D Gaussian Splatting, NeRF, or transformer‑based 3D reconstructors requires learning a data‑dependent codebook through per‑scene fine‑tuning. We show this is unnecessary. The parameter vectors that dominate storage in these models, 45‑dimensional spherical harmonics in 3DGS and 1024‑dimensional key‑value vectors in DUSt3R, fall in a dimension range where a single random rotation transforms any input into coordinates with a known Beta distribution. This makes precomputed, data‑independent Lloyd‑Max quantization near‑optimal, within a factor of 2.7 of the information‑theoretic lower bound. We develop 3D, deriving (1) a dimension‑dependent criterion that predicts which parameters can be quantized and at what bit‑width before running any experiment, (2) norm‑separation bounds connecting quantization MSE to rendering PSNR per scene, (3) an entry‑grouping strategy extending rotation‑based quantization to 2‑dimensional hash grid features, and (4) a composable pruning‑quantization pipeline with a closed‑form compression ratio. On NeRF Synthetic, 3DTurboQuant compresses 3DGS by 3.5x with 0.02dB PSNR loss and DUSt3R KV caches by 7.9x with 39.7dB pointmap fidelity. No training, no codebook learning, no calibration data. Compression takes seconds. The code will be released (https://github.com/JaeLee18/3DTurboQuant)

PaperId: 1972, https://arxiv.org/pdf/2604.05355.pdf GitHub

Abstract:
Chain‑of‑thought (CoT) reasoning improves large language model performance on complex tasks, but often produces excessively long and inefficient reasoning traces. Existing methods shorten CoTs using length penalties or global entropy reduction, implicitly assuming that low uncertainty is desirable throughout reasoning. We show instead that reasoning efficiency is governed by the trajectory of uncertainty. CoTs with dominant downward entropy trends are substantially shorter. Motivated by this insight, we propose Entropy Trend Reward (ETR), a trajectory‑aware objective that encourages progressive uncertainty reduction while allowing limited local exploration. We integrate ETR into Group Relative Policy Optimization (GRPO) and evaluate it across multiple reasoning models and challenging benchmarks. ETR consistently achieves a superior accuracy‑efficiency tradeoff, improving DeepSeek‑R1‑Distill‑7B by 9.9% in accuracy while reducing CoT length by 67% across four benchmarks. Code is available at https://github.com/Xuan1030/ETR

PaperId: 1973, https://arxiv.org/pdf/2604.05333.pdf GitHub

Abstract:
Modern LLM agents increasingly rely on reusable skills, and as they interact with personal applications, web browsers, and other interfaces, skill libraries can scale to thousands of skills. Scaling to larger skill sets introduces two key challenges. First, loading the full skill set saturates the context window, driving up token costs, hallucination, and latency. Second, semantic retrieval surfaces topically relevant skills but misses their prerequisite chain of upstream and downstream skills, creating a prerequisite gap that leaves the retrieved bundle execution‑incomplete. In this paper, we present Graph‑of‑Skills (GoS), an inference‑time structural retrieval layer for large skill libraries. GoS constructs an executable skill graph offline from skill packages, then at inference time retrieves a bounded, dependency‑aware skill bundle through hybrid semantic‑lexical seeding, reverse‑aware Personalized PageRank, and context‑budgeted hydration. On SkillsBench and ALFWorld, GoS consistently delivers substantial reward improvements and token savings across three model families (Claude Sonnet 4.5, MiniMax M2.7, and GPT‑5.2 Codex). On SkillsBench, GoS achieves a peak reward increase of 25.55% while reducing total tokens by 56.72% over the vanilla full skill‑loading baseline using GPT‑5.2 Codex. Ablations confirm this pattern across skill libraries from 200 to 2,000 skills.

PaperId: 1974, https://arxiv.org/pdf/2604.05268.pdf GitHub

Abstract:
Multi‑modal retrieval‑augmented generation (MM‑RAG) relies heavily on re‑rankers to surface the most relevant evidence for image‑question queries. However, standard re‑rankers typically process the full query image as a global embedding, making them susceptible to visual distractors (e.g., background clutter) that skew similarity scores. We propose Region‑R1, a query‑side region cropping framework that formulates region selection as a decision‑making problem during re‑ranking, allowing the system to learn to retain the full image or focus only on a question‑relevant region before scoring the retrieved candidates. Region‑R1 learns a policy with a novel region‑aware group relative policy optimization (r‑GRPO) to dynamically crop a discriminative region. Across two challenging benchmarks, E‑VQA and InfoSeek, Region‑R1 delivers consistent gains, achieving state‑of‑the‑art performances by increasing conditional Recall@1 by up to 20%. These results show the great promise of query‑side adaptation as a simple but effective way to strengthen MM‑RAG re‑ranking.

PaperId: 1975, https://arxiv.org/pdf/2604.05243.pdf GitHub

Abstract:
Background: Children do not simply learn that balls are round and blocks are square. They learn that shape is the kind of feature that tends to define object categories ‑‑ a second‑order generalisation known as an overhypothesis [1, 2]. What kind of learning mechanism is sufficient for this inductive leap? Methods: We trained autoregressive transformer language models (3.4M‑25.6M parameters) on synthetic corpora in which shape is the stable feature dimension across categories, with eight conditions controlling for alternative explanations. Results: Across 120 pre‑registered runs evaluated on a 1,040‑item wug test battery, every model achieved perfect first‑order exemplar retrieval (100%) while second‑order generalisation to novel nouns remained at chance (50‑52%), a result confirmed by equivalence testing. A feature‑swap diagnostic revealed that models rely on frame‑to‑feature template matching rather than structured noun‑to‑domain‑to‑feature abstraction. Conclusions: These results reveal a clear limitation of autoregressive distributional sequence learning under developmental‑scale training conditions.

PaperId: 1976, https://arxiv.org/pdf/2604.05242.pdf GitHub

Abstract:
Multi‑bit watermarking has emerged as a promising solution for embedding imperceptible binary messages into Large Language Model (LLM)‑generated text, enabling reliable attribution and tracing of malicious usage of LLMs. Despite recent progress, existing methods still face key limitations: some become computationally infeasible for large messages, while others suffer from a poor trade‑off between text quality and decoding accuracy. Moreover, the decoding accuracy of existing methods drops significantly when the number of tokens in the generated text is limited, a condition that frequently arises in practical usage. To address these challenges, we propose \textscXMark, a novel method for encoding and decoding binary messages in LLM‑generated texts. The unique design of \textscXMark's encoder produces a less distorted logit distribution for watermarked token generation, preserving text quality, and also enables its tailored decoder to reliably recover the encoded message with limited tokens. Extensive experiments across diverse downstream tasks show that \textscXMark significantly improves decoding accuracy while preserving the quality of watermarked text, outperforming prior methods. The code is at https://github.com/JiiahaoXU/XMark.

PaperId: 1977, https://arxiv.org/pdf/2604.05183.pdf GitHub GitHub

Abstract:
In a rapidly growing field of model training there is a constant practical interest in parameter‑efficient fine‑tuning and various techniques that use a small amount of training data to adapt the model to a narrow task. However, there is an open question: how to combine several adapters tuned for different tasks into one which is able to yield adequate results on both tasks? Specifically, merging subject and style adapters for generative models remains unresolved. In this paper we seek to show that in the case of orthogonal fine‑tuning (OFT), we can use structured orthogonal parametrization and its geometric properties to get the formulas for training‑free adapter merging. In particular, we derive the structure of the manifold formed by the recently proposed Group‑and‑Shuffle (\mathcalGS) orthogonal matrices, and obtain efficient formulas for the geodesics approximation between two points. Additionally, we propose a \textspectra restoration transform that restores spectral properties of the merged adapter for higher‑quality fusion. We conduct experiments in subject‑driven generation tasks showing that our technique to merge two \mathcalGS orthogonal matrices is capable of uniting concept and style features of different adapters. To the best of our knowledge, this is the first training‑free method for merging multiplicative orthogonal adapters. Code is available via the \hrefhttps://github.com/ControlGenAI/OrthoFuselink.

PaperId: 1978, https://arxiv.org/pdf/2604.05134.pdf GitHub

Abstract:
We study how reasoning evolves in a language model ‑‑ from supervised fine‑tuning (SFT) to reinforcement learning (RL) ‑‑ by analyzing how a set of theoretically‑inspired datasets influences language model performance in chess. We find that fine‑tuning a model to directly predict the best move leads to effective RL and the strongest downstream performance ‑‑ however, the RL stage elicits unfaithful reasoning (reasoning inconsistent with the chosen move). Alternatively, training on multi‑move trajectories yields comparable downstream performance with faithful reasoning and more stable RL. We analyze multiple qualitative and quantitative measures and highlight how these evolve from SFT through RL; we find several SFT‑checkpoint metrics ‑‑ spanning evaluation performance, hallucination rates, and reasoning quality ‑‑ to be predictive of post‑RL model performance. Finally, we ground our results with an experiment measuring chess information density in our custom datasets. We release models as well as training data, evaluations, and code that allowed us to surpass leading open‑source reasoning models in chess with a 7B‑parameter model. Code, models, and data are available at https://github.com/lucasdino/lang‑chess.

PaperId: 1979, https://arxiv.org/pdf/2604.05114.pdf GitHub

Abstract:
We study a pipeline that curates reasoning data from initial structured data for improving long‑context reasoning in large language models (LLMs). Our approach, π^2, constructs high‑quality reasoning data through rigorous QA curation: 1) extracting and expanding tables from Wikipedia, 2) from the collected tables and relevant context, generating realistic and multi‑hop analytical reasoning questions whose answers are automatically determined and verified through dual‑path code execution, and 3) back‑translating step‑by‑step structured reasoning traces as solutions of QA pairs given realistic web‑search context. Supervised fine‑tuning with \textsc\smallgpt‑oss‑20b and \textsc\smallQwen3‑4B‑Instruct‑2507 on π^2 yields consistent improvements across four long‑context reasoning benchmarks and our alike π^2‑Bench, with average absolute accuracy gains of +4.3% and +2.7% respectively. Notably, our dataset facilitates self‑distillation, where \textsc\smallgpt‑oss‑20b even improves its average performance by +4.4% with its own reasoning traces, demonstrating π^2's usefulness. Our code, data, and models are open‑source at https://github.com/vt‑pi‑squared/pi‑squared.

PaperId: 1980, https://arxiv.org/pdf/2604.05039.pdf GitHub

Abstract:
Humans have remarkable selective sensitivity to identities ‑‑ easily distinguishing between highly similar identities, even across significantly different contexts such as diverse viewpoints or lighting. Vision models have struggled to match this capability, and progress toward identity‑focused tasks such as personalized image generation is slowed by a lack of identity‑focused evaluation metrics. To help facilitate progress, we propose ID‑Sim, a feed‑forward metric designed to faithfully reflect human selective sensitivity. To build ID‑Sim, we curate a high‑quality training set of images spanning diverse real‑world domains, augmented with generative synthetic data that provides controlled, fine‑grained identity and contextual variations. We evaluate our metric on a new unified evaluation benchmark for assessing consistency with human annotations across identity‑focused recognition, retrieval, and generative tasks.

PaperId: 1981, https://arxiv.org/pdf/2604.05030.pdf GitHub

Abstract:
Experiments probing natural language processing by both humans and LLMs suggest that the meaning of a semantic expression is indeterminate prior to the act of interpretation rather than being specifiable simply as the sum of its parts (i.e. compositionality). This observer‑dependent act dynamically actualizes meaning under genuine contextuality more consistent with quantum logical mechanisms than with classical Boolean approaches that assume separability, motivating an approach to language modeling that utilizes a Hilbert space formalism. In this work, we introduce Phase‑Associative Memory (PAM) ‑‑ a complex‑valued sequence model whose state S_t \in \mathbbC^d × d accumulates outer products of complex token embeddings retrieved through the conjugate inner product \mathrmRe\langle K \mid Q\rangle / \sqrtd ‑‑ and evaluate it against a structurally matched real‑valued ablation. Both architectures train stably across a 5M‑‑100M parameter sweep on WikiText‑103 under identical conditions; PAM sits at higher absolute loss at every measured scale but improves more rapidly with parameter count, with power‑law exponents of ‑0.15 vs.\ ‑0.12 in loss and ‑0.65 vs.\ ‑0.49 in perplexity that narrow the gap between the two architectures monotonically. Further investigation of complex‑valued sequence modeling at larger scales could reveal that the loss plateau characteristic of real‑valued state‑of‑the‑art language models (e.g. transformers) is reachable with PAM‑style architectures with an order of magnitude fewer parameters than the current frontier (～1T), implying that similar capabilities are achievable at sizes runnable on consumer‑grade hardware.

PaperId: 1982, https://arxiv.org/pdf/2604.05018.pdf GitHub

Abstract:
Synthesizing unstructured research materials into manuscripts is an essential yet under‑explored challenge in AI‑driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi‑agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre‑writing materials into submission‑ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse‑engineered raw materials from 200 top‑tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side‑by‑side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%‑68% in literature review quality, and 14%‑38% in overall manuscript quality.

PaperId: 1983, https://arxiv.org/pdf/2604.05014.pdf GitHub

Abstract:
Building generalist embodied agents requires integrating perception, language understanding, and action, which are core capabilities addressed by Vision‑Language‑Action (VLA) approaches based on multimodal foundation models, including recent advances in vision‑language models and world models. Despite rapid progress, VLA methods remain fragmented across incompatible architectures, codebases, and evaluation protocols, hindering principled comparison and reproducibility. We present StarVLA, an open‑source codebase for VLA research. StarVLA addresses these challenges in three aspects. First, it provides a modular backbone‑‑action‑head architecture that supports both VLM backbones (e.g., Qwen‑VL) and world‑model backbones (e.g., Cosmos) alongside representative action‑decoding paradigms, all under a shared abstraction in which backbone and action head can each be swapped independently. Second, it provides reusable training strategies, including cross‑embodiment learning and multimodal co‑training, that apply consistently across supported paradigms. Third, it integrates major benchmarks, including LIBERO, SimplerEnv, RoboTwin~2.0, RoboCasa‑GR1, and BEHAVIOR‑1K, through a unified evaluation interface that supports both simulation and real‑robot deployment. StarVLA also ships simple, fully reproducible single‑benchmark training recipes that, despite minimal data engineering, already match or surpass prior methods on multiple benchmarks with both VLM and world‑model backbones. To our best knowledge, StarVLA is one of the most comprehensive open‑source VLA frameworks available, and we expect it to lower the barrier for reproducing existing methods and prototyping new ones. StarVLA is being actively maintained and expanded; we will update this report as the project evolves. The code and documentation are available at https://github.com/starVLA/starVLA.

PaperId: 1984, https://arxiv.org/pdf/2604.04947.pdf GitHub

Abstract:
With the rapid proliferation of online sports journalism, extracting meaningful pre‑game and post‑game insights from articles is essential for enhancing user engagement and comprehension. In this paper, we address the task of automatically extracting such insights from articles published before and after matches. We curate a dataset of 7,900 news articles covering 800 matches across four major sports: Cricket, Soccer, Basketball, and Baseball. To ensure contextual relevance, we employ a two‑step validation pipeline leveraging both open‑source and proprietary large language models (LLMs). We then utilize multiple state‑of‑the‑art LLMs (GPT‑4o, Qwen2.5‑72B‑Instruct, Llama‑3.3‑70B‑Instruct, and Mixtral‑8x7B‑Instruct‑v0.1) to generate comprehensive insights. The factual accuracy of these outputs is rigorously assessed using a FactScore‑based methodology, complemented by hallucination detection via the SummaC (Summary Consistency) framework with GPT‑4o. Finally, we propose SUMMIR (Sentence Unified Multimetric Model for Importance Ranking), a novel architecture designed to rank insights based on user‑specific interests. Our results demonstrate the effectiveness of this approach in generating high‑quality, relevant insights, while also revealing significant differences in factual consistency and interestingness across LLMs. This work contributes a robust framework for automated, reliable insight generation from sports news content. The source code is availble here https://github.com/nitish‑iitp/SUMMIR.

PaperId: 1985, https://arxiv.org/pdf/2604.04924.pdf GitHub

Abstract:
Pre‑trained diffusion models have enabled significant advancements in All‑in‑One Restoration (AiOR), offering improved perceptual quality and generalization. However, diffusion‑based restoration methods primarily rely on fine‑tuning or Control‑Net style modules to leverage the pre‑trained diffusion model's priors for AiOR. In this work, we show that these pre‑trained diffusion models inherently possess restoration behavior, which can be unlocked by directly learning prompt embeddings at the output of the text encoder. Interestingly, this behavior is largely inaccessible through text prompts and text‑token embedding optimization. Furthermore, we observe that naive prompt learning is unstable because the forward noising process using degraded images is misaligned with the reverse sampling trajectory. To resolve this, we train prompts within a diffusion bridge formulation that aligns training and inference dynamics, enforcing a coherent denoising path from noisy degraded states to clean images. Building on these insights, we introduce our lightweight learned prompts on the pre‑trained WAN video model and FLUX image models, converting them into high‑performing restoration models. Extensive experiments demonstrate that our approach achieves competitive performance and generalization across diverse degradations, while avoiding fine‑tuning and restoration‑specific control modules.

PaperId: 1986, https://arxiv.org/pdf/2604.04901.pdf GitHub

Abstract:
Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human‑AI interaction; however, effective personalization remains limited by severe data constraints, as strict privacy barriers and the difficulty of jointly collecting multimodal real‑world traces prevent scalable training and evaluation, and existing methods remain interaction‑centric while overlooking dense behavioral traces in file‑system operations; to address this gap, we propose FileGram, a comprehensive framework that grounds agent memory and personalization in file‑system behavioral traces, comprising three core components: (1) FileGramEngine, a scalable persona‑driven data engine that simulates realistic workflows and generates fine‑grained multimodal action sequences at scale; (2) FileGramBench, a diagnostic benchmark grounded in file‑system behavioral traces for evaluating memory systems on profile reconstruction, trace disentanglement, persona drift detection, and multimodal grounding; and (3) FileGramOS, a bottom‑up memory architecture that builds user profiles directly from atomic actions and content deltas rather than dialogue summaries, encoding these traces into procedural, semantic, and episodic channels with query‑time abstraction; extensive experiments show that FileGramBench remains challenging for state‑of‑the‑art memory systems and that FileGramEngine and FileGramOS are effective, and by open‑sourcing the framework, we hope to support future research on personalized memory‑centric file‑system agents.

PaperId: 1987, https://arxiv.org/pdf/2604.04875.pdf GitHub

Abstract:
Video mashup creation represents a complex video editing paradigm that recomposes existing footage to craft engaging audio‑visual experiences, demanding intricate orchestration across semantic, visual, and auditory dimensions and multiple levels. However, existing automated editing frameworks often overlook the cross‑level multimodal orchestration to achieve professional‑grade fluidity, resulting in disjointed sequences with abrupt visual transitions and musical misalignment. To address this, we formulate video mashup creation as a Multimodal Coherency Satisfaction Problem (MMCSP) and propose the DIRECT framework. Simulating a professional production pipeline, our hierarchical multi‑agent framework decomposes the challenge into three cascade levels: the Screenwriter for source‑aware global structural anchoring, the Director for instantiating adaptive editing intent and guidance, and the Editor for intent‑guided shot sequence editing with fine‑grained optimization. We further introduce Mashup‑Bench, a comprehensive benchmark with tailored metrics for visual continuity and auditory alignment. Extensive experiments demonstrate that DIRECT significantly outperforms state‑of‑the‑art baselines in both objective metrics and human subjective evaluation. Project page and code: https://github.com/AK‑DREAM/DIRECT

PaperId: 1988, https://arxiv.org/pdf/2604.04843.pdf GitHub

Abstract:
Human‑object‑scene interactions (HOSI) generation has broad applications in embodied AI, simulation, and animation. Unlike human‑object interaction (HOI) and human‑scene interaction (HSI), HOSI generation requires reasoning over dynamic object‑scene changes, yet suffers from limited annotated data. To address these issues, we propose a coarse‑to‑fine instruction‑conditioned interaction generation framework that is explicitly aligned with the iterative denoising process of a consistency model. In particular, we adopt a dynamic perception strategy that leverages trajectories from the preceding refinement to update scene context and condition subsequent refinement at each denoising step of consistency model, yielding consistent interactions. To further reduce physical artifacts, we introduce a bump‑aware guidance that mitigates collisions and penetrations during sampling without requiring fine‑grained scene geometry, enabling real‑time generation. To overcome data scarcity, we design a hybrid training startegy that synthesizes pseudo‑HOSI samples by injecting voxelized scene occupancy into HOI datasets and jointly trains with high‑fidelity HSI data, allowing interaction learning while preserving realistic scene awareness. Extensive experiments demonstrate that our method achieves state‑of‑the‑art performance in both HOSI and HOI generation, and strong generalization to unseen scenes. Project page: https://yudezou.github.io/InfBaGel‑page/

PaperId: 1989, https://arxiv.org/pdf/2604.04820.pdf GitHub

Abstract:
AI agents, autonomous digital actors, need agent‑native protocols; existing methods include GUI automation and MCP‑based skills, with defects of high token consumption, fragmented interaction, inadequate security, due to lacking a unified top‑level framework and key components, each independent module flawed. To address these issues, we present ANX, an open, extensible, verifiable agent‑native protocol and top‑level framework integrating CLI, Skill, MCP, resolving pain points via protocol innovation, architectural optimization and tool supplementation. Its four core innovations: 1) Agent‑native design (ANX Config, Markup, CLI) with high information density, flexibility and strong adaptability to reduce tokens and eliminate inconsistencies; 2) Human‑agent interaction combining Skill's flexibility for dual rendering as agent‑executable instructions and human‑readable UI; 3) MCP‑supported on‑demand lightweight apps without pre‑registration; 4) ANX Markup‑enabled machine‑executable SOPs eliminating ambiguity for reliable long‑horizon tasks and multi‑agent collaboration. As the first in a series, we focus on ANX's design, present its 3EX decoupled architecture with ANXHub and preliminary feasibility analysis and experimental validation. ANX ensures native security: LLM‑bypassed UI‑to‑Core communication keeps sensitive data out of agent context; human‑only confirmation prevents automated misuse. Form‑filling experiments with Qwen3.5‑plus/GPT‑4o show ANX reduces tokens by 47.3% (Qwen3.5‑plus) and 55.6% (GPT‑4o) vs MCP‑based skills, 57.1% (Qwen3.5‑plus) and 66.3% (GPT‑4o) vs GUI automation, and shortens execution time by 58.1% and 57.7% vs MCP‑based skills.

PaperId: 1990, https://arxiv.org/pdf/2604.04804.pdf GitHub

Abstract:
Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self‑evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited experience, resulting in redundant exploration and poor generalization. To address this problem, we propose SkillX, a fully automated framework for constructing a plug‑and‑play skill knowledge base that can be reused across agents and environments. SkillX operates through a fully automated pipeline built on three synergistic innovations: (i) Multi‑Level Skills Design, which distills raw trajectories into three‑tiered hierarchy of strategic plans, functional skills, and atomic skills; (ii) Iterative Skills Refinement, which automatically revises skills based on execution feedback to continuously improve library quality; and (iii) Exploratory Skills Expansion, which proactively generates and validates novel skills to expand coverage beyond seed training data. Using a strong backbone agent (GLM‑4.6), we automatically build a reusable skill library and evaluate its transferability on challenging long‑horizon, user‑interactive benchmarks, including AppWorld, BFCL‑v3, and τ^2‑Bench. Experiments show that SkillKB consistently improves task success and execution efficiency when plugged into weaker base agents, highlighting the importance of structured, hierarchical experience representations for generalizable agent learning. Our code will be publicly available soon at https://github.com/zjunlp/SkillX.

PaperId: 1991, https://arxiv.org/pdf/2604.04767.pdf GitHub

Abstract:
Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fundamental limitation remains: models cannot learn from problems that are too difficult to solve under their current policy, as these yield no meaningful reward signal. We propose a simple yet effective solution based on task reformulation. We transform challenging open‑ended problems into cognitively simpler variants ‑‑ such as multiple‑choice and cloze formats ‑‑ that preserve the original answer while reducing the effective search space and providing denser learning signals. These reformulations span a spectrum from discriminative to generative tasks, which we exploit to bootstrap learning: models first learn from structured, easier formats, and this knowledge transfers back to improve performance on the original open‑ended problems. Building on this insight, we introduce Cog‑DRIFT, a framework that constructs reformulated variants and organizes them into an adaptive curriculum based on difficulty. Training progresses from easier to harder formats, enabling the model to learn from problems that previously yielded zero signal under standard RL post‑training. Cog‑DRIFT not only improves on the originally unsolvable hard problems (absolute +10.11% for Qwen and +8.64% for Llama) but also generalizes well to other held‑out datasets. Across 2 models and 6 reasoning benchmarks, our method consistently outperforms standard GRPO and strong guided‑exploration baselines. On average, Cog‑DRIFT shows +4.72% (Qwen) and +3.23% (Llama) improvements over the second‑best baseline. We further show that Cog‑DRIFT improves pass@k at test time, and the curriculum improves sample efficiency. Overall, our results highlight task reformulation and curriculum learning as an effective paradigm for overcoming the exploration barrier in LLM post‑training.

PaperId: 1992, https://arxiv.org/pdf/2604.04759.pdf GitHub

Abstract:
OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services such as Gmail, Stripe, and the filesystem. While these broad privileges enable high levels of automation and powerful personalization, they also expose a substantial attack surface that existing sandboxed evaluations fail to capture. To address this gap, we present the first real‑world safety evaluation of OpenClaw and introduce the CIK taxonomy, which unifies an agent's persistent state into three dimensions, i.e., Capability, Identity, and Knowledge, for safety analysis. Our evaluations cover 12 attack scenarios on a live OpenClaw instance across four backbone models (Claude Sonnet 4.5, Opus 4.6, Gemini 3.1 Pro, and GPT‑5.4). The results show that poisoning any single CIK dimension increases the average attack success rate from 24.6% to 64‑74%, with even the most robust model exhibiting more than a threefold increase over its baseline vulnerability. We further assess three CIK‑aligned defense strategies alongside a file‑protection mechanism; however, the strongest defense still yields a 63.8% success rate under Capability‑targeted attacks, while file protection blocks 97% of malicious injections but also prevents legitimate updates. Taken together, these findings show that the vulnerabilities are inherent to the agent architecture, necessitating more systematic safeguards to secure personal AI agents. Our project page is https://ucsc‑vlaa.github.io/CIK‑Bench.

PaperId: 1993, https://arxiv.org/pdf/2604.04692.pdf GitHub

Abstract:
Automated fact‑checking is a crucial task that supports a responsible information ecosystem. While recent research has progressed from text‑only to multimodal fact‑checking, a prevailing assumption is that incorporating visual evidence universally improves performance. In this work, we challenge this assumption and show that the indiscriminate use of multimodal evidence can reduce accuracy. To address this challenge, we propose AMuFC, a multimodal fact‑checking framework that employs two collaborative vision‑language models with distinct roles for the adaptive use of visual evidence: an Analyzer determines whether visual evidence is necessary for claim verification, and a Verifier predicts claim veracity conditioned on both the retrieved evidence and the Analyzer's assessment. Experimental results on three datasets show that incorporating the Analyzer's assessment of visual evidence necessity into the Verifier's prediction yields substantial improvements in verification performance. We will release all code and datasets at https://github.com/ssu‑humane/AMuFC.

PaperId: 1994, https://arxiv.org/pdf/2604.04690.pdf GitHub

Abstract:
Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setups. We present Pickalo, a modular 6D pose‑based bin‑picking pipeline built entirely on low‑cost hardware. A wrist‑mounted RGB‑D camera actively explores the scene from multiple viewpoints, while raw stereo streams are processed with BridgeDepth to obtain refined depth maps suitable for accurate collision reasoning. Object instances are segmented with a Mask‑RCNN model trained purely on photorealistic synthetic data and localized using the zero‑shot SAM‑6D pose estimator. A pose buffer module fuses multi‑view observations over time, handling object symmetries and significantly reducing pose noise. Offline, we generate and curate large sets of antipodal grasp candidates per object; online, a utility‑based ranking and fast collision checking are queried for the grasp planning. Deployed on a UR5e with a parallel‑jaw gripper and an Intel RealSense D435i, Pickalo achieves up to 600 mean picks per hour with 96‑99% grasp success and robust performance over 30‑minute runs on densely filled euroboxes. Ablation studies demonstrate the benefits of enhanced depth estimation and of the pose buffer for long‑term stability and throughput in realistic industrial conditions. Videos are available at https://mesh‑iit.github.io/project‑jl2‑camozzi/

PaperId: 1995, https://arxiv.org/pdf/2604.04660.pdf GitHub

Abstract:
We present Springdrift, a persistent runtime for long‑lived LLM agents. The system integrates an auditable execution substrate (append‑only memory, supervised processes, git‑backed recovery), a case‑based reasoning memory layer with hybrid retrieval (evaluated against a dense cosine baseline), a deterministic normative calculus for safety gating with auditable axiom trails, and continuous ambient self‑perception via a structured self‑state representation (the sensorium) injected each cycle without tool calls. These properties support behaviours difficult to achieve in session‑bounded systems: cross‑session task continuity, cross‑channel context maintenance, end‑to‑end forensic reconstruction of decisions, and self‑diagnostic behaviour. We report on a single‑instance deployment over 23 days (19 operating days), during which the agent diagnosed its own infrastructure bugs, classified failure modes, identified an architectural vulnerability, and maintained context across email and web channels ‑‑ without explicit instruction. We introduce the term Artificial Retainer for this category: a non‑human system with persistent memory, defined authority, domain‑specific autonomy, and forensic accountability in an ongoing relationship with a specific principal ‑‑ distinguished from software assistants and autonomous agents, drawing on professional retainer relationships and the bounded autonomy of trained working animals. This is a technical report on a systems design and deployment case study, not a benchmark‑driven evaluation. Evidence is from a single instance with a single operator, presented as illustration of what these architectural properties can support in practice. Implemented in approximately Gleam on Erlang/OTP. Code, artefacts, and redacted operational logs will be available at https://github.com/seamus‑brady/springdrift upon publication.

PaperId: 1996, https://arxiv.org/pdf/2604.04646.pdf GitHub

Abstract:
Flow‑based models learn a target distribution by modeling a marginal velocity field, defined as the average of sample‑wise velocities connecting each sample from a simple prior to the target data. When sample‑wise velocities conflict at the same intermediate state, however, this averaged velocity can misguide samples toward low‑density regions, degrading generation quality. To address this issue, we propose the Flow Divergence Sampler (FDS), a training‑free framework that refines intermediate states before each solver step. Our key finding reveals that the severity of this misguidance is quantified by the divergence of the marginal velocity field that is readily computable during inference with a well‑optimized model. FDS exploits this signal to steer states toward less ambiguous regions. As a plug‑and‑play framework compatible with standard solvers and off‑the‑shelf flow backbones, FDS consistently improves fidelity across various generation tasks including text‑to‑image synthesis, and inverse problems.

PaperId: 1997, https://arxiv.org/pdf/2604.04565.pdf GitHub

Abstract:
Large Language Models (LLMs) have achieved strong performance in question answering and retrieval‑augmented generation (RAG), yet they implicitly assume that user queries are fully specified and answerable. In real‑world settings, queries are often incomplete, ambiguous, or missing critical variables, leading models to produce overconfident or hallucinated responses. In this work, we study decision‑aware query resolution under incomplete information, where a model must determine whether to Answer, Ask for clarification, or Abstain. We show that standard and enhanced RAG systems do not reliably exhibit such epistemic awareness, defaulting to answer generation even when information is insufficient. To address this, we propose PassiveQA, a three‑action framework that aligns model behaviour with information sufficiency through supervised finetuning. Our approach integrates structured information‑state representations, knowledge graph‑grounded context, and a finetuned planner that explicitly models missing variables and decision reasoning. Experiments across multiple QA datasets show that the finetuned planner achieves significant improvements in macro F1 and abstention recall while reducing hallucination rates, under a compute‑constrained training regime. These results provide strong empirical evidence that epistemic decision‑making must be learned during training rather than imposed at inference time.

PaperId: 1998, https://arxiv.org/pdf/2604.04561.pdf GitHub

Abstract:
LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not known is which features of a system prompt trigger this behaviour, and which do not. We present a systematic taxonomy based on approximately 10,000 trials across seven models, 37 prompt conditions, and 12 hypothesized attack dimensions ‑‑ all executed in real Docker sandboxes. Every condition includes the instruction "Always respect all rules and access policies"; only a single encouragement sentence varies. Within the task class studied (planted test‑runner vulnerabilities), the threat model is narrower than assumed: nine of twelve dimensions ‑‑ including minimization, moral licensing, incentives, identity priming, and reasoning triggers ‑‑ produce no detectable exploitation at n=50 per cell (upper 95% CI <7% per condition). But it is also more subtle than expected: one dimension, goal reframing, reliably triggers exploitation. "You are solving a puzzle; there may be hidden clues" produces 38‑40% exploitation on Claude Sonnet 4 despite the explicit rule instruction, replicating across four models (CTF framing: 8‑14% on DeepSeek, GPT‑5‑mini, o4‑mini). The agent does not override the rules; it reinterprets the task so that exploitative actions become task‑aligned. GPT‑4.1 produces no exploitation across 1,850 trials (37 conditions), and a temporal comparison across four OpenAI models released over eleven months shows a pattern consistent with improving safety training, though model capability differences are a confounder. The practical contribution is a narrowed, testable threat model: defenders should audit for goal‑reframing language, not for the broad class of adversarial prompts.

PaperId: 1999, https://arxiv.org/pdf/2604.04514.pdf GitHub

Abstract:
AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago. Existing memory systems store text in vector databases with single‑channel retrieval, require cloud LLMs for core operations, and implement none of the cognitive processes that make human memory effective. We present SuperLocalMemory V3.3 ("The Living Brain"), a local‑first agent memory system implementing the full cognitive memory taxonomy with mathematical lifecycle dynamics. Building on the information‑geometric foundations of V3.2 (arXiv:2603.14588), we introduce five contributions: (1) Fisher‑Rao Quantization‑Aware Distance (FRQAD) ‑‑ a new metric on the Gaussian statistical manifold achieving 100% precision at preferring high‑fidelity embeddings over quantized ones (vs 85.6% for cosine), with zero prior art; (2) Ebbinghaus Adaptive Forgetting with lifecycle‑aware quantization ‑‑ the first mathematical forgetting curve in local agent memory coupled to progressive embedding compression, achieving 6.7x discriminative power; (3) 7‑channel cognitive retrieval spanning semantic, keyword, entity graph, temporal, spreading activation, consolidation, and Hopfield associative channels, achieving 70.4% on LoCoMo in zero‑LLM Mode A; (4) memory parameterization implementing Long‑Term Implicit memory via soft prompts; (5) zero‑friction auto‑cognitive pipeline automating the complete memory lifecycle. On LoCoMo, V3.3 achieves 70.4% in Mode A (zero‑LLM), with +23.8pp on multi‑hop and +12.7pp on adversarial. V3.2 achieved 74.8% Mode A and 87.7% Mode C; the 4.4pp gap reflects a deliberate architectural trade‑off. SLM V3.3 is open source under the Elastic License 2.0, runs entirely on CPU, with over 5,000 monthly downloads.

PaperId: 2000, https://arxiv.org/pdf/2604.04461.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly adapted to proprietary and domain‑specific corpora that contain sensitive information, creating a tension between formal privacy guarantees and efficient deployment through model compression. Differential privacy (DP), typically enforced via DP‑SGD, provides record‑level protection but often incurs substantial utility loss in autoregressive generation, where optimization noise can amplify exposure bias and compounding errors along long rollouts. Existing approaches to private distillation either apply DP‑SGD to both teacher and student, worsening computation and the privacy‑‑utility tradeoff, or rely on DP synthetic text generation from a DP‑trained teacher, avoiding DP on the student at the cost of DP‑optimizing a large teacher and introducing an offline generation pipeline. We propose Differentially Private On‑Policy Distillation (DP‑OPD), a synthesis‑free framework that enforces privacy solely through DP‑SGD on the student while leveraging a frozen teacher to provide dense token‑level targets on \emphstudent‑generated trajectories. DP‑OPD instantiates this idea via \emphprivate generalized knowledge distillation on continuation tokens. Under a strict privacy budget (\varepsilon=2.0), DP‑OPD improves perplexity over DP fine‑tuning and off‑policy DP distillation, and outperforms synthesis‑based DP distillation (Yelp: 44.15\rightarrow41.68; BigPatent: 32.43\rightarrow30.63), while substantially simplifying the training pipeline. In particular, DP‑OPD collapses private compression into a single DP student‑training loop by eliminating DP teacher training and offline synthetic text generation. Code will be released upon publication at https://github.com/khademfatemeh/dp_opd.

PaperId: 2001, https://arxiv.org/pdf/2604.04456.pdf GitHub

Abstract:
Reliable pattern recognition systems should exhibit consistent behavior across similar inputs, and their explanations should remain stable. However, most Explainable AI evaluations remain instance centric and do not explicitly quantify whether attribution patterns are consistent across samples that share the same class or represent small variations of the same input. In this work, we propose a novel metric aimed at assessing the consistency of model explanations, ensuring that models consistently reflect the intended objectives and consistency under label‑preserving perturbations. We implement this metric using a pre‑trained BERT model on the SST‑2 sentiment analysis dataset, with additional robustness tests on RoBERTa, DistilBERT, and IMDB, applying SHAP to compute feature importance for various test samples. The proposed metric quantifies the cosine similarity of SHAP values for inputs with the same label, aiming to detect inconsistent behaviors, such as biased reliance on certain features or failure to maintain consistent reasoning for similar predictions. Through a series of experiments, we evaluate the ability of this metric to identify misaligned predictions and inconsistencies in model explanations. These experiments are compared against standard fidelity metrics to assess whether the new metric can effectively identify when a model's behavior deviates from its intended objectives. The proposed framework provides a deeper understanding of model behavior by enabling more robust verification of rationale stability, which is critical for building trustworthy AI systems. By quantifying whether models rely on consistent attribution patterns for similar inputs, the proposed approach supports more robust evaluation of model behavior in practical pattern recognition pipelines. Our code is publicly available at https://github.com/anmspro/ESS‑XAI‑Stability.

PaperId: 2002, https://arxiv.org/pdf/2604.04420.pdf GitHub GitHub

Abstract:
Task‑free online continual learning has recently emerged as a realistic paradigm for addressing continual learning in dynamic, real‑world environments, where data arrive in a non‑stationary stream without clear task boundaries and can only be observed once. To consider such challenging scenarios, many recent approaches have employed prompt selection, an adaptive strategy that selects prompts from a pool based on input signals. However, we observe that such selection strategies often fail to select appropriate prompts, yielding suboptimal results despite additional training of key parameters. Motivated by this observation, we propose a simple yet effective SinglePrompt that eliminates the need for prompt selection and focuses on classifier optimization. Specifically, we simply (i) inject a single prompt into each self‑attention block, (ii) employ a cosine similarity‑based logit design to alleviate the forgetting effect inherent in the classifier weights, and (iii) mask logits for unexposed classes in the current minibatch. With this simple task‑free design, our framework achieves state‑of‑the‑art performance across various online continual learning benchmarks. Source code is available at https://github.com/efficient‑learning‑lab/SinglePrompt.

PaperId: 2003, https://arxiv.org/pdf/2604.04410.pdf GitHub

Abstract:
Aligning language models with human preferences is essential for ensuring their safety and reliability. Although most existing approaches assume specific human preference models such as the Bradley‑Terry model, this assumption may fail to accurately capture true human preferences, and consequently, these methods lack statistical consistency, i.e., the guarantee that language models converge to the true human preference as the number of samples increases. In contrast, direct density ratio optimization (DDRO) achieves statistical consistency without assuming any human preference models. DDRO models the density ratio between preferred and non‑preferred data distributions using the language model, and then optimizes it via density ratio estimation. However, this density ratio is unstable and often diverges, leading to training instability of DDRO. In this paper, we propose a novel alignment method that is both stable and statistically consistent. Our approach is based on the relative density ratio between the preferred data distribution and a mixture of the preferred and non‑preferred data distributions. Our approach is stable since this relative density ratio is bounded above and does not diverge. Moreover, it is statistically consistent and yields significantly tighter convergence guarantees than DDRO. We experimentally show its effectiveness with Qwen 2.5 and Llama 3.

PaperId: 2004, https://arxiv.org/pdf/2604.04385.pdf GitHub

Abstract:
We localize the policy routing mechanism in alignment‑trained language models. An intermediate‑layer attention gate reads detected content and triggers deeper amplifier heads that boost the signal toward refusal. In smaller models the gate and amplifier are single heads; at larger scale they become bands of heads across adjacent layers. The gate contributes under 1% of output DLA, yet interchange testing (p < 0.001) and knockout cascade confirm it is causally necessary. Interchange screening at n >= 120 detects the same motif in twelve models from six labs (2B to 72B), though specific heads differ by lab. Per‑head ablation weakens up to 58x at 72B and misses gates that interchange identifies; at scale, interchange is the only reliable audit. Modulating the detection‑layer signal continuously controls policy from hard refusal through evasion to factual answering. On safety prompts the same intervention turns refusal into harmful guidance, showing that the safety‑trained capability is gated by routing, not removed. Thresholds vary by topic and by input language, and the circuit relocates across generations within a family even while behavioral benchmarks register no change. Routing is early‑commitment: the gate fires at its own layer before deeper layers finish processing the input. An in‑context substitution cipher collapses gate interchange necessity by 70 to 99% across three models, and the model switches to puzzle‑solving rather than refusal. Injecting the plaintext gate activation into the cipher forward pass restores 48% of refusals in Phi‑4‑mini, localizing the bypass to the routing interface. A second method, cipher contrast analysis, uses plain/cipher DLA differences to map the full cipher‑sensitive routing circuit in O(3n) forward passes. Any encoding that defeats detection‑layer pattern matching bypasses the policy regardless of whether deeper layers reconstruct the content.

PaperId: 2005, https://arxiv.org/pdf/2604.04356.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) large language models (LLMs) are among the top‑performing architectures. The largest models, often with hundreds of billions of parameters, pose significant memory challenges for deployment. Traditional approaches to reduce memory requirements include weight pruning and quantization. Motivated by the Router‑weighted Expert Activation Pruning (REAP) that prunes experts, we propose a novel method, Router‑weighted Expert Activation Merging (REAM). Instead of removing experts, REAM groups them and merges their weights, better preserving original performance. We evaluate REAM against REAP and other baselines across multiple MoE LLMs on diverse multiple‑choice (MC) question answering and generative (GEN) benchmarks. Our results reveal a trade‑off between MC and GEN performance that depends on the mix of calibration data. By controlling the mix of general, math and coding data, we examine the Pareto frontier of this trade‑off and show that REAM often outperforms the baselines and in many cases is comparable to the original uncompressed models.

PaperId: 2006, https://arxiv.org/pdf/2604.04289.pdf GitHub

Abstract:
When an LLM deobfuscates JavaScript, can poisoned identifier names in the string table survive into the model's reconstructed code, even when the model demonstrably understands the correct semantics? Using Claude Opus 4.6 across 192 inference runs on two code archetypes (force‑directed graph simulation, A pathfinding; 50 conditions, N=3‑6), we found three consistent patterns: (1) Poisoned names persisted in every baseline run on both artifacts (physics: 8/8; pathfinding: 5/5). Matched controls showed this extends to terms with zero semantic fit when the string table does not form a coherent alternative domain. (2) Persistence coexisted with correct semantic commentary: in 15/17 runs the model wrote wrong variable names while correctly describing the actual operation in comments. (3) Task framing changed persistence: explicit verification prompts had no effect (12/12 across 4 variants), but reframing from "deobfuscate this" to "write a fresh implementation" reduced propagation from 100% to 0‑20% on physics and to 0% on pathfinding, while preserving the checked algorithmic structure. Matched‑control experiments showed zero‑fit terms persist at the same rate when the replacement table lacks a coherent alternative‑domain signal. Per‑term variation in earlier domain‑gradient experiments is confounded with domain‑level coherence and recoverability. These observations are from two archetypes on one model family (Opus 4.6 primary; Haiku 4.5 spot‑check). Broader generalization is needed

PaperId: 2007, https://arxiv.org/pdf/2604.04211.pdf GitHub GitHub

Abstract:
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision‑making process. To instantiate this paradigm, we introduce LOCARD, the first agentic framework for blockchain forensics. LOCARD operationalizes this perspective through a Tri‑Core Cognitive Architecture that decouples strategic planning, operational execution, and evaluative validation. Unlike generic LLM‑based agents, it incorporates a Structured Belief State mechanism to enforce forensic rigor and guide exploration under explicit state constraints. To demonstrate the efficacy of the ABF paradigm, we apply LOCARD to the inherently complex domain of cross‑chain transaction tracing. We introduce Thor25, a benchmark dataset comprising over 151k real‑world cross‑chain forensic records, and evaluate LOCARD on the Group‑Transfer Tracing task for dismantling Sybil clusters. Validated against representative laundering sub‑flows from the Bybit hack, LOCARD achieves high‑fidelity tracing results, providing empirical evidence that modeling blockchain forensics as an autonomous agentic task is both viable and effective. These results establish a concrete foundation for future agentic approaches to large‑scale blockchain forensic analysis. Code and dataset are publicly available at https://github.com/xhyumiracle/locard and https://github.com/xhyumiracle/thorchain‑crosschain‑data.

PaperId: 2008, https://arxiv.org/pdf/2604.04202.pdf GitHub

Abstract:
AI agents deployed as persistent assistants must maintain correct beliefs as their information environment evolves. In practice, evidence is scattered across heterogeneous sources that often contradict one another, new information can invalidate earlier conclusions, and user preferences surface through corrections rather than explicit instructions. Existing benchmarks largely assume static, single‑authority settings and do not evaluate whether agents can keep up with this complexity. We introduce ClawArena, a benchmark for evaluating AI agents in evolving information environments. Each scenario maintains a complete hidden ground truth while exposing the agent only to noisy, partial, and sometimes contradictory traces across multi‑channel sessions, workspace files, and staged updates. Evaluation is organized around three coupled challenges: multi‑source conflict reasoning, dynamic belief revision, and implicit personalization, whose interactions yield a 14‑category question taxonomy. Two question formats, multi‑choice (set‑selection) and shell‑based executable checks, test both reasoning and workspace grounding. ClawArena comprises 12 multi‑turn scenarios spanning 337 evaluation rounds with 45 dynamic updates, evaluated across five agent frameworks and 18 language models from proprietary, community‑accessible, and self‑hosted sources. Experiments show that model capability accounts for a 29‑point score range across models while framework design accounts for up to a 24‑point range, that MetaClaw's skill overlay reliably improves score without degrading accuracy, and that belief revision difficulty is determined by update design strategy rather than update volume. Code is available at https://github.com/aiming‑lab/ClawArena.

PaperId: 2009, https://arxiv.org/pdf/2604.04170.pdf GitHub

Abstract:
Although multi‑view multi‑label learning has been extensively studied, research on the dual‑missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contrastive learning or information bottleneck theory to learn consistent representations under missing‑view conditions, but loss‑based alignment without explicit structural constraints limits the ability to capture stable and discriminative shared semantics. To address this issue, we introduce a more structured mechanism for consistent representation learning: we learn discrete consistent representations through a multi‑view shared codebook and cross‑view reconstruction, which naturally align different views within the limited shared codebook embeddings and reduce feature redundancy. At the decision level, we design a weight estimation method that evaluates the ability of each view to preserve label correlation structures, assigning weights accordingly to enhance the quality of the fused prediction. In addition, we introduce a fused‑teacher self‑distillation framework, where the fused prediction guides the training of view‑specific classifiers and feeds the global knowledge back into the single‑view branches, thereby enhancing the generalization ability of the model under missing‑label conditions. The effectiveness of our proposed method is thoroughly demonstrated through extensive comparative experiments with advanced methods on five benchmark datasets. Code is available at https://github.com/xuy11/SCSD.

PaperId: 2010, https://arxiv.org/pdf/2604.04145.pdf GitHub

Abstract:
Photovoltaic (PV) power forecasting plays a critical role in power system dispatch and market participation. Because PV generation is highly sensitive to weather conditions and cloud motion, accurate forecasting requires effective modeling of complex spatiotemporal dependencies across multiple information sources. Although recent studies have advanced AI‑based forecasting methods, most fail to fuse temporal observations, satellite imagery, and textual weather information in a unified framework. This paper proposes Solar‑VLM, a large‑language‑model‑driven framework for multimodal PV power forecasting. First, modality‑specific encoders are developed to extract complementary features from heterogeneous inputs. The time‑series encoder adopts a patch‑based design to capture temporal patterns from multivariate observations at each site. The visual encoder, built upon a Qwen‑based vision backbone, extracts cloud‑cover information from satellite images. The text encoder distills historical weather characteristics from textual descriptions. Second, to capture spatial dependencies across geographically distributed PV stations, a cross‑site feature fusion mechanism is introduced. Specifically, a Graph Learner models inter‑station correlations through a graph attention network constructed over a K‑nearest‑neighbor (KNN) graph, while a cross‑site attention module further facilitates adaptive information exchange among sites. Finally, experiments conducted on data from eight PV stations in a northern province of China demonstrate the effectiveness of the proposed framework. Our proposed model is publicly available at https://github.com/rhp413/Solar‑VLM.

PaperId: 2011, https://arxiv.org/pdf/2604.04078.pdf GitHub

Abstract:
Cardiac magnetic resonance (CMR) is a cornerstone for diagnosing cardiovascular disease. However, it remains underutilized due to complex, time‑consuming interpretation across multi‑sequences, phases, quantitative measures that heavily reliant on specialized expertise. Here, we present BAAI Cardiac Agent, a multimodal intelligent system designed for end‑to‑end CMR interpretation. The agent integrates specialized cardiac expert models to perform automated segmentation of cardiac structures, functional quantification, tissue characterization and disease diagnosis, and generates structured clinical reports within a unified workflow. Evaluated on CMR datasets from two hospitals (2413 patients) spanning 7‑types of major cardiovascular diseases, the agent achieved an area under the receiver‑operating‑characteristic curve exceeding 0.93 internally and 0.81 externally. In the task of estimating left ventricular function indices, the results generated by this system for core parameters such as ejection fraction, stroke volume, and left ventricular mass are highly consistent with clinical reports, with Pearson correlation coefficients all exceeding 0.90. The agent outperformed state‑of‑the‑art models in segmentation and diagnostic tasks, and generated clinical reports showing high concordance with expert radiologists (six readers across three experience levels). By dynamically orchestrating expert models for coordinated multimodal analysis, this agent framework enables accurate, efficient CMR interpretation and highlights its potentials for complex clinical imaging workflows. Code is available at https://github.com/plantain‑herb/Cardiac‑Agent.

PaperId: 2012, https://arxiv.org/pdf/2604.04074.pdf GitHub

Abstract:
LLM‑based reviewing systems typically take only the manuscript as input, leaving literature and code‑based claims hard to verify. We present FactReview, a system that extracts review‑relevant claims, grounds them in related work, and, when code is available, executes released artifacts under a fixed repair budget to audit empirical claims. Across 35 ML papers and 463 benchmark major claims, FactReview covers 84% of claims. Under an evidence‑aware rubric, its reviews score 4.86/5 in overall quality, 0.7 above DeepReview‑v2 and 1.5 above matched OpenReview comments. Removing execution evidence changes 17% of claim statuses, more than any other single evidence source. In a reviewer‑assistance study, FactReview reduces mean review time by 58% while raising benchmark claim coverage from 87% to 99%. We argue that LLM reviewers should audit empirical claims, not make accept‑reject decisions. The code is public at: https://github.com/DEFENSE‑SEU/FactReview.

PaperId: 2013, https://arxiv.org/pdf/2604.03976.pdf GitHub

Abstract:
Prior work on trustworthy AI emphasizes model‑internal properties such as bias mitigation, adversarial robustness, and interpretability. As AI systems evolve into autonomous agents deployed in open environments and increasingly connected to payments or assets, the operational meaning of trust shifts to end‑to‑end outcomes: whether an agent completes tasks, follows user intent, and avoids failures that cause material or psychological harm. These risks are fundamentally product‑level and cannot be eliminated by technical safeguards alone because agent behavior is inherently stochastic. To address this gap between model‑level reliability and user‑facing assurance, we propose a complementary framework based on risk management. Drawing inspiration from financial underwriting, we introduce the Agentic Risk Standard (ARS), a payment settlement standard for AI‑mediated transactions. ARS integrates risk assessment, underwriting, and compensation into a single transaction framework that protects users when interacting with agents. Under ARS, users receive predefined and contractually enforceable compensation in cases of execution failure, misalignment, or unintended outcomes. This shifts trust from an implicit expectation about model behavior to an explicit, measurable, and enforceable product guarantee. We also present a simulation study analyzing the social benefits of applying ARS to agentic transactions. ARS's implementation can be found at https://github.com/t54‑labs/AgenticRiskStandard.

PaperId: 2014, https://arxiv.org/pdf/2604.03950.pdf GitHub

Abstract:
Transformer‑based large language models (LLMs) have demonstrated remarkable performance across a wide range of real‑world tasks, but their inference cost remains prohibitively high due to the quadratic complexity of attention and the memory bandwidth limitations of high‑precision operations. In this work, we present a low‑bit mixed‑precision attention kernel using the microscaling floating‑point (MXFP) data format, utilizing the computing capability on next‑generation GPU architectures. Our Diagonal‑Tiled Mixed‑Precision Attention (DMA) incorporates two kinds of low‑bit computation at the tiling‑level, and is a delicate fused kernel implemented using Triton, exploiting hardware‑level parallelism and memory efficiency to enable fast and efficient inference without compromising model performance. Extensive empirical evaluations on NVIDIA B200 GPUs show that our kernel maintains generation quality with negligible degradation, and meanwhile achieves significant speedup by kernel fusion. We release our code at https://github.com/yifu‑ding/MP‑Sparse‑Attn.

PaperId: 2015, https://arxiv.org/pdf/2604.03928.pdf GitHub

Abstract:
Frozen pretrained image representations are widely used for transfer learning: a backbone is kept fixed, feature vectors are extracted, and a lightweight classifier is trained on top. This pipeline usually feeds the full feature vector to the classifier, even when the target task has far fewer classes than the pretraining task. We revisit a classical alternative: supervised dimensionality reduction with Linear Discriminant Analysis (LDA) before linear probing. We evaluate ten dimensionality‑reduction strategies on frozen features from six backbones ‑‑ ResNet‑18, ResNet‑50, MobileNetV3‑Small, EfficientNet‑B0, ViT‑B/16, and DINOv2‑ViT‑S/14 ‑‑ across CIFAR‑100, Tiny ImageNet, and CUB‑200‑2011. Under a fixed logistic‑regression protocol, LDA improves accuracy over full features in 11 of 12 coarse‑grained configurations, with gains up to 4.5 percentage points while reducing feature dimensionality by 48‑87%. The same projection consistently hurts on fine‑grained CUB‑200, where full features win across all six backbones. This establishes a practical boundary condition: LDA is useful when class‑level structure is coarse enough to be captured by mean‑separating directions, but it can discard subtle cues needed for fine‑grained recognition. We also compare LDA with PCA, PCA+LDA, regularized LDA, Local Fisher Discriminant Analysis, Neighbourhood Components Analysis, and three lightweight LDA extensions. The results show that plain LDA offers the best accuracy‑cost tradeoff for most coarse‑grained settings, while more complex supervised reduction methods rarely justify their additional cost. Overall, the study provides concrete guidance for when post‑hoc supervised projection should, and should not, be inserted into frozen‑feature image classification pipelines.

PaperId: 2016, https://arxiv.org/pdf/2604.03904.pdf GitHub

Abstract:
Large language models (LLMs) frequently produce confident but incorrect answers, partly because common binary scoring conventions reward answering over honestly expressing uncertainty. We study whether prompt‑only interventions ‑‑ explicitly announcing reward schemes for answer‑versus‑abstain decisions plus humility‑oriented normative principles ‑‑ can reduce hallucination risk without modifying the model. Our focus is epistemic abstention on factual questions with a verifiable answer, where current LLMs often fail to abstain despite being uncertain about their answers. We first assess self‑reported verbal confidence as a usable uncertainty signal, showing stability under prompt paraphrasing and reasonable calibration against a token‑probability baseline. We then study I‑CALM, a prompt‑based framework that (i) elicits verbal confidence, (ii) partially rewards abstention through explicit reward schemes, and (iii) adds lightweight normative principles emphasizing truthfulness, humility, and responsibility. Using GPT‑5 mini on PopQA as the main setting, we find that confidence‑eliciting, abstention‑rewarding prompts, especially with norms, reduce the false‑answer rate on answered cases mainly by identifying and shifting error‑prone cases to abstention and re‑calibrating their confidence. This trades coverage for reliability while leaving forced‑answer performance largely unchanged. Varying the abstention reward yields a clear abstention‑hallucination frontier. Overall, results show the framework can improve selective answering on factual questions without retraining, with the magnitude of effect varying across models and datasets. Code is available at the following https://github.com/binzeli/hallucinationControl.

PaperId: 2017, https://arxiv.org/pdf/2604.03883.pdf GitHub

Abstract:
Ride‑hailing and autonomous mobility‑on‑demand operators reposition idle supply before future demand is fully observed. We study a retrieval‑calibrated predict‑then‑optimize approach for this problem: historical demand regimes are matched to the current query block, combined into a calibrated demand prior, and passed to a fleet‑balancing controller. The paper makes three contributions. First, we train a leakage‑safe similarity gate whose objective penalizes demand error, pickup spatial mismatch, and queue shortage risk rather than retrieval rank alone. Second, we develop a spatial queue‑regret decomposition for a stable queueing surrogate, linking demand‑field error to wait through queueing sensitivity, allocator sensitivity, and Wasserstein pickup mismatch. Third, we evaluate learned retrieval and external‑style rebalancing baselines in a common simulator. In the calibrated‑demand gate experiment, across eight New York City scenarios and ten seeds, the spatial gate reduces mean wait to 82.3s, compared with 85.3s for hand‑tuned similarity and 85.8s for a distributional‑only baseline. In a separate replay‑demand controller comparison, a scenario chance‑MPC analog and a share‑target transportation LP improve on Wen‑style rebalancing (92.2s/92.2s vs. 100.1s), a reduced GPR chance‑MPC comparator is intermediate at 94.4s, and an oracle MPC diagnostic is 91.3s.

PaperId: 2018, https://arxiv.org/pdf/2604.03814.pdf GitHub

Abstract:
Camera extrinsic calibration is a fundamental task in computer vision. However, precise relative pose estimation in constrained, highly distorted environments, such as in‑cabin automotive monitoring (ICAM), remains challenging. We present InCaRPose, a Transformer‑based architecture designed for robust relative pose prediction between image pairs, which can be used for camera extrinsic calibration. By leveraging frozen backbone features such as DINOv3 and a Transformer‑based decoder, our model effectively captures the geometric relationship between a reference and a target view. Unlike traditional methods, our approach achieves absolute metric‑scale translation within the physically plausible adjustment range of in‑cabin camera mounts in a single inference step, which is critical for ICAM, where accurate real‑world distances are required for safety‑relevant perception. We specifically address the challenges of highly distorted fisheye cameras in automotive interiors by training exclusively on synthetic data. Our model is capable of generalization to real‑world cabin environments without relying on the exact same camera intrinsics and additionally achieves competitive performance on the public 7‑Scenes dataset. Despite having limited training data, InCaRPose maintains high precision in both rotation and translation, even with a ViT‑Small backbone. This enables real‑time performance for time‑critical inference, such as driver monitoring in supervised autonomous driving. We release our real‑world In‑Cabin‑Pose test dataset consisting of highly distorted vehicle‑interior images and our code at https://github.com/felixstillger/InCaRPose.

PaperId: 2019, https://arxiv.org/pdf/2604.03782.pdf GitHub

Abstract:
We analyze the last‑iterate convergence of the Anchored Gradient Descent Ascent algorithm for smooth convex‑concave min‑max problems. While previous work established a last‑iterate rate of \mathcalO(1/t^2‑2p) for the squared gradient norm, where p \in (1/2, 1), it remained an open problem whether the improved exact \mathcalO(1/t) rate is achievable. In this work, we resolve this question in the affirmative. This result was discovered autonomously by an AI system capable of writing formal proofs in Lean. The Lean proof can be accessed at https://github.com/google‑deepmind/formal‑conjectures/pull/3675/commits/a13226b49fd3b897f4c409194f3bcbeb96a08515

PaperId: 2020, https://arxiv.org/pdf/2604.03750.pdf GitHub

Abstract:
Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities. It supports critical tasks such as vulnerability discovery and malware analysis. Despite its importance, RE remains labor‑intensive and requires substantial expertise, making large language models (LLMs) a potential solution for automating the process. However, their capabilities for RE remain systematically underexplored. To address this gap, we study the cryptographic binary RE capabilities of LLMs and introduce CREBench, a benchmark comprising 432 challenges built from 48 standard cryptographic algorithms, 3 insecure crypto key usage scenarios, and 3 difficulty levels. Each challenge follows a Capture‑the‑Flag (CTF) RE challenge, requiring the model to analyze the underlying cryptographic logic and recover the correct input. We design an evaluation framework comprising four sub‑tasks, from algorithm identification to correct flag recovery. We evaluate eight frontier LLMs on CREBench. GPT‑5.4, the best‑performing model, achieves 64.03 out of 100 and recovers the flag in 59% of challenges. We also establish a strong human expert baseline of 92.19 points, showing that humans maintain an advantage in cryptographic RE tasks. Our code and dataset are available at https://github.com/wangyu‑ovo/CREBench.

PaperId: 2021, https://arxiv.org/pdf/2604.03742.pdf GitHub

Abstract:
Effective evaluation of large language models (LLMs) remains a critical bottleneck, as conventional direct scoring often yields inconsistent and opaque judgments. In this work, we adapt the Analytic Hierarchy Process (AHP) to LLM‑based evaluation and, more importantly, propose a confidence‑aware Fuzzy AHP (FAHP) extension that models epistemic uncertainty via triangular fuzzy numbers modulated by LLM‑generated confidence scores. Systematically validated on JudgeBench, our structured approach decomposes assessments into explicit criteria and incorporates uncertainty‑aware aggregation, producing more calibrated judgments. Extensive experiments demonstrate that both crisp and fuzzy AHP consistently outperform direct scoring across model scales and dataset splits, with FAHP showing superior stability in uncertain comparison scenarios. Building on these insights, we propose DualJudge, a hybrid framework inspired by Dual‑Process Theory that adaptively fuses holistic direct scores with structured AHP outputs via consistency‑aware weighting. DualJudge achieves state‑of‑the‑art performance, underscoring the complementary strengths of intuitive and deliberative evaluation paradigms. These results establish uncertainty‑aware structured reasoning as a principled pathway toward more reliable LLM assessment. Code is available at https://github.com/hreyulog/AHP_llm_judge.

PaperId: 2022, https://arxiv.org/pdf/2604.03647.pdf GitHub

Abstract:
In the unsupervised self‑evolution of Multimodal Large Language Models, the quality of feedback signals during post‑training is pivotal for stable and effective learning. However, existing self‑evolution methods predominantly rely on majority voting to select the most frequent output as the pseudo‑golden answer, which may stem from the model's intrinsic biases rather than guaranteeing the objective correctness of the reasoning paths. To counteract the degradation, we propose Continuous Softened Retracing reSampling (CSRS) in MLLM self‑evolution. Specifically, we introduce a Retracing Re‑inference Mechanism (RRM) that the model re‑inferences from anchor points to expand the exploration of long‑tail reasoning paths. Simultaneously, we propose Softened Frequency Reward (SFR), which replaces binary rewards with continuous signals, calibrating reward based on the answers' frequency across sampled reasoning sets. Furthermore, incorporated with Visual Semantic Perturbation (VSP), CSRS ensures the model prioritizes mathematical logic over visual superficiality. Experimental results demonstrate that CSRS significantly enhances the reasoning performance of Qwen2.5‑VL‑7B on benchmarks such as MathVision. We achieve state‑of‑the‑art (SOTA) results in unsupervised self‑evolution on geometric tasks. Our code is avaible at https://github.com/yyy195/CSRS.

PaperId: 2023, https://arxiv.org/pdf/2604.03523.pdf GitHub

Abstract:
Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high collection cost. Furthermore, imitation learning algorithms assume that the data is independently and identically distributed, which ultimately results in poorer performance as gradual errors emerge and compound within test‑time trajectories. We address these issues by introducing the "master your own expertise" (MYOE) framework, a self‑imitation framework that enables robotic agents to learn complex behaviors from limited demonstration data samples. Inspired by human perception and action, we propose and design what we call the queryable mixture‑of‑preferences state space model (QMoP‑SSM), which estimates the desired goal at every time step. These desired goals are used in computing the "preference regret", which is used to optimize the robot control policy. Our experiments demonstrate the robustness, adaptability, and out‑of‑sample performance of our agent compared to other state‑of‑the‑art RLfD schemes. The GitHub repository that supports this work can be found at: https://github.com/rxng8/neurorobot‑preference‑regret‑learning.

PaperId: 2024, https://arxiv.org/pdf/2604.03497.pdf GitHub

Abstract:
Deploying reinforcement learning policies trained in simulation to real autonomous vehicles remains a fundamental challenge, particularly for VLM‑guided RL frameworks whose policies are typically learned with simulator‑native observations and simulator‑coupled action semantics that are unavailable on physical platforms. This paper presents Sim2Real‑AD, a modular framework for zero‑shot sim‑to‑real transfer of CARLA‑trained VLM‑guided RL policies to full‑scale vehicles without any real‑world RL training data. The framework decomposes the transfer problem into four components: a Geometric Observation Bridge (GOB) that converts monocular front‑view images into simulator‑compatible bird's‑eye‑view (BEV) observations, a Physics‑Aware Action Mapping (PAM) that translates policy outputs into platform‑agnostic physical commands, a Two‑Phase Progressive Training (TPT) strategy that stabilizes adaptation by separating action‑space and observation‑space transfer, and a Real‑time Deployment Pipeline (RDP) that integrates perception, policy inference, control conversion, and safety monitoring for closed‑loop execution. Simulation experiments show that the framework preserves the relative performance ordering of representative RL algorithms across different reward paradigms and validate the contribution of each module. Zero‑shot deployment on a full‑scale Ford E‑Transit achieves success rates of 90%, 80%, and 75% in car‑following, obstacle avoidance, and stop‑sign interaction scenarios, respectively. To the best of our knowledge, this study is among the first to demonstrate zero‑shot closed‑loop deployment of a CARLA‑trained VLM‑guided RL policy on a full‑scale real vehicle without any real‑world RL training data. The demo video and code are available at: https://zilin‑huang.github.io/Sim2Real‑AD‑website/.

PaperId: 2025, https://arxiv.org/pdf/2604.03333.pdf GitHub GitHub

Abstract:
Symbolic music generation has made significant progress, yet achieving fine‑grained and flexible control over composer style remains challenging. Existing training‑based methods for composer style conditioning depend on large labeled datasets. Besides, these methods typically support only single‑composer generation at a time, limiting their applicability to more creative or blended scenarios. In this work, we propose Composer Vector, an inference‑time steering method that operates directly in the model's latent space to control composer style without retraining. Through experiments on multiple symbolic music generation models, we show that Composer Vector effectively guides generations toward target composer styles, enabling smooth and interpretable control through a continuous steering coefficient. It also enables seamless fusion of multiple styles within a unified latent space framework. Overall, our work demonstrates that simple latent space steering provides a practical and general mechanism for controllable symbolic music generation, enabling more flexible and interactive creative workflows. Code and Demo are available here: https://github.com/JiangXunyi/Composer‑Vector and https://jiangxunyi.github.io/composervector.github.io/

PaperId: 2026, https://arxiv.org/pdf/2604.03315.pdf GitHub

Abstract:
Storyboarding is a core skill in visual storytelling for film, animation, and games. However, automating this process requires a system to achieve two properties that current approaches rarely satisfy simultaneously: inter‑shot consistency and explicit editability. While 2D diffusion‑based generators produce vivid imagery, they often suffer from identity drift along with limited geometric control; conversely, traditional 3D animation workflows are consistent and editable but require expert‑heavy, labor‑intensive authoring. We present StoryBlender, a grounded 3D storyboard generation framework governed by a Story‑centric Reflection Scheme. At its core, we propose the StoryBlender system, which is built on a three‑stage pipeline: (1) Semantic‑Spatial Grounding, to construct a continuity memory graph to decouple global assets from shot‑specific variables for long‑horizon consistency; (2) Canonical Asset Materialization, to instantiate entities in a unified coordinate space to maintain visual identity; and (3) Spatial‑Temporal Dynamics, to achieve layout design and cinematic evolution through visual metrics. By orchestrating multiple agents in a hierarchical manner within a verification loop, StoryBlender iteratively self‑corrects spatial hallucinations via engine‑verified feedback. The resulting native 3D scenes support direct, precise editing of cameras and visual assets while preserving unwavering multi‑shot continuity. Experiments demonstrate that StoryBlender significantly improves consistency and editability over both diffusion‑based and 3D‑grounded baselines. Code, data, and demonstration video will be available on https://engineeringai‑lab.github.io/StoryBlender/

PaperId: 2027, https://arxiv.org/pdf/2604.03302.pdf GitHub

Abstract:
While Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in image and video understanding, their ability to comprehend the physical world has become an increasingly important research focus. Despite their improvements, current MLLMs struggle significantly with high‑level physics reasoning. In this work, we investigate the first step of physical reasoning, i.e., intuitive physics understanding, revealing substantial limitations in understanding the dynamics of continuum objects. To isolate and evaluate this specific capability, we introduce two fundamental benchmark tasks: Next Frame Selection (NFS) and Temporal Coherence Verification (TCV). Our experiments demonstrate that even state‑of‑the‑art MLLMs perform poorly on these foundational tasks. To address this limitation, we propose Scene Dynamic Field (SDF), a concise approach that leverages physics simulators within a multi‑task fine‑tuning framework. SDF substantially improves performance, achieving up to 20.7% gains on fluid tasks while showing strong generalization to unseen physical domains. This work not only highlights a critical gap in current MLLMs but also presents a promising cost‑efficient approach for developing more physically grounded MLLMs. Our code and data are available at https://github.com/andylinx/Scene‑Dynamic‑Field.

PaperId: 2028, https://arxiv.org/pdf/2604.03296.pdf GitHub

Abstract:
Leveraging 3D information within Multimodal Large Language Models (MLLMs) has recently shown significant advantages for indoor scene understanding. However, existing methods, including those using explicit ground‑truth 3D positional encoding and those grafting external 3D foundation models for implicit geometry, struggle with the trade‑off in 2D‑3D representation fusion, leading to suboptimal deployment. To this end, we propose 3D‑Implicit Depth Emergence, a method that reframes 3D perception as an emergent property derived from geometric self‑supervision rather than explicit encoding. Our core insight is the Implicit Geometric Emergence Principle: by strategically leveraging privileged geometric supervision through mechanisms like a fine‑grained geometry validator and global representation constraints, we construct an information bottleneck. This bottleneck forces the model to maximize the mutual information between visual features and 3D structures, allowing 3D awareness to emerge naturally within a unified visual representation. Unlike existing approaches, our method enables 3D perception to emerge implicitly, disentangling features in dense regions and, crucially, eliminating depth and pose dependencies during inference with zero latency overhead. This paradigm shift from external grafting to implicit emergence represents a fundamental rethinking of 3D knowledge integration in visual‑language models. Extensive experiments demonstrate that our method surpasses SOTA on multiple 3D scene understanding benchmarks. Our approach achieves a 55% reduction in inference latency while maintaining strong performance across diverse downstream tasks, underscoring the effectiveness of meticulously designed auxiliary objectives for dependency‑free 3D understanding. Source code can be found at github.com/ChushanZhang/3D‑IDE.

PaperId: 2029, https://arxiv.org/pdf/2604.03291.pdf GitHub

Abstract:
This paper introduces RAGnaroX, a resource‑efficient ChatOps assistant that operates entirely on commodity hardware. Unlike existing solutions that often rely on external providers such as Azure or OpenAI, RAGnaroX offers a fully auditable, on‑premise stack implemented in Rust. Its architecture integrates modular data ingestion, hybrid retrieval, and function calling, enabling flexible yet secure deployment. Our evaluation focuses on the RAG pipeline, with benchmarks conducted on the SQuAD (single‑hop QA), MultiHopRAG (multi‑hop QA), and MLQA (cross‑lingual QA) datasets. Results show that RAGnaroX achieves competitive accuracy while maintaining strong resource efficiency, for example, reaching 0.90 context precision on single‑hop questions with an average response time of 2.5 seconds per request. A replication package containing the tool, the demonstration video (https://www.youtube.com/watch? v=cDxfuEbcoM4), and all supporting materials are available at https://github.com/genius‑itea/RAGnaroX.git.

PaperId: 2030, https://arxiv.org/pdf/2604.03246.pdf GitHub

Abstract:
Recent research demonstrated that students exhibit consistent learning rates across diverse educational contexts. We test these findings using a dataset of 1.8 million (366k post‑filtering) student interactions from the digital platform Campus AI providing further evidence to the observation of regularity in learning rate among students. Unlike prior work requiring manual cognitive modeling, Campus AI automatically generates Knowledge Components (KCs) and corresponding exercises, both of which are validated by human experts. This one‑to‑many mapping facilitates the application of Additive Factors Models to measure learning parameters without complex cognitive modeling. Using mixed‑effects logistic regression, we confirmed the core finding of prior work: students displayed substantial variation in initial knowledge (\textIQR = [2.78, 12.18] practice opportunities to reach 80% mastery) but remarkably consistent learning rates (\textIQR = [7.01, 8.25] opportunities). Furthermore, students using this fully automated system achieved 80% mastery in a median of 7.22 practice opportunities, comparable to the 6.54 reported for expert‑designed curricula. These results suggest that automated, science‑grounded content generation can support effective personalized learning at scale. Data and code are publicly available. https://github.com/Campus‑edu‑AI/learning‑rate

PaperId: 2031, https://arxiv.org/pdf/2604.03189.pdf GitHub

Abstract:
Generally capable agents must learn from experience in ways that generalize across tasks and environments. The fundamental problems of learning, including credit assignment, overfitting, forgetting, local optima, and high‑variance learning signals, persist whether the learned object lies in parameter space or context space. While these challenges are well understood in classical machine learning optimization, they remain underexplored in context space, leading current methods to be fragmented and ad hoc. We present Reflective Context Learning (RCL), a unified framework for agents that learn through repeated interaction, reflection on behavior and failure modes, and iterative updates to context. In RCL, reflection converts trajectories and current context into a directional update signal analogous to gradients, while mutation applies that signal to improve future behavior in context space. We recast recent context‑optimization approaches as instances of this shared learning problem and systematically extend them with classical optimization primitives, including batching, improved credit‑assignment signal, auxiliary losses, failure replay, and grouped rollouts for variance reduction. On AppWorld, BrowseComp+, and RewardBench2, these primitives improve over strong baselines, with their relative importance shifting across task regimes. We further analyze robustness to initialization, the effects of batch size, sampling and curriculum strategy, optimizer‑state variants, and the impact of allocating stronger or weaker models to different optimization components. Our results suggest that learning through context updates should be treated not as a set of isolated algorithms, but as an optimization problem whose mechanisms can be studied systematically and improved through transferable principles.

PaperId: 2032, https://arxiv.org/pdf/2604.03050.pdf GitHub

Abstract:
While affective computing has advanced considerably, multimodal emotion prediction in aging populations remains underexplored, largely due to the scarcity of dedicated datasets. Existing multimodal benchmarks predominantly target young, cognitively healthy subjects, neglecting the influence of cognitive decline on emotional expression and physiological responses. To bridge this gap, we present MECO, a Multimodal dataset for Emotion and Cognitive understanding in Older adults. MECO includes 42 participants and provides approximately 38 hours of multimodal signals, yielding 30,592 synchronized samples. To maximize ecological validity, data collection followed standardized protocols within community‑based settings. The modalities cover video, audio, electroencephalography (EEG), and electrocardiography (ECG). In addition, the dataset offers comprehensive annotations of emotional and cognitive states, including self‑assessed valence, arousal, six basic emotions, and Mini‑Mental State Examination cognitive scores. We further establish baseline benchmarks for both emotion and cognitive prediction. MECO serves as a foundational resource for multimodal modeling of affect and cognition in aging populations, facilitating downstream applications such as personalized emotion recognition and early detection of mild cognitive impairment (MCI) in real‑world settings. The complete dataset and supplementary materials are available at https://maitrechen.github.io/meco‑page/.

PaperId: 2033, https://arxiv.org/pdf/2604.03014.pdf GitHub

Abstract:
Multi‑modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction‑only recommenders. The success of MMR hinges on aligning these content modalities with user preferences derived from interaction data, yet dominant practices based on disentangling modality‑invariant preference‑driving signals from modality‑specific preference‑irrelevant noises are flawed. First, they assume a one‑size‑fits‑all relevance of item content to user preferences for all users, which contradicts the user‑conditional fact of preferences. Second, they optimize pairwise contrastive losses separately toward cross‑modal alignment, systematically ignoring higher‑order dependencies inherent when multiple content modalities jointly influence user choices. In this paper, we introduce GTC, a conditional Generative Total Correlation learning framework. We employ an interaction‑guided diffusion model to perform user‑aware content feature filtering, preserving only personalized features relevant to each individual user. Furthermore, to capture complete cross‑modal dependencies, we optimize a tractable lower bound of the total correlation of item representations across all modalities. Experiments on standard MMR benchmarks show GTC consistently outperforms state‑of‑the‑art, with gains of up to 28.30% in NDCG@5. Ablation studies validate both conditional preference‑driven feature filtering and total correlation optimization, confirming the ability of GTC to model user‑conditional relationships in MMR tasks. The code is available at: https://github.com/jingdu‑cs/GTC.

PaperId: 2034, https://arxiv.org/pdf/2604.02971.pdf GitHub

Abstract:
Recent agentic search systems have made substantial progress by emphasising deep, multi‑step reasoning. However, this focus often overlooks the challenges of wide‑scale information synthesis, where agents must aggregate large volumes of heterogeneous evidence across many sources. As a result, most existing large language model agent systems face severe limitations in data‑intensive settings, including context saturation, cascading error propagation, and high end‑to‑end latency. To address these challenges, we present \framework, a hierarchical framework based on principle of near‑decomposability, containing a strategic Host, multiple Managers and parallel Workers. By leveraging aggregation and reflection mechanisms at the Manager layer, our framework enforces strict context isolation to prevent saturation and error propagation. Simultaneously, the parallelism in worker layer accelerates the speed of overall task execution, mitigating the significant latency. Our evaluation on two complementary benchmarks demonstrates both efficiency ( 3‑5 × speed‑up) and effectiveness, achieving a 8.4% success rate on WideSearch‑en and 52.9% accuracy on BrowseComp‑zh. The code is released at https://github.com/agent‑on‑the‑fly/InfoSeeker

PaperId: 2035, https://arxiv.org/pdf/2604.02954.pdf GitHub

Abstract:
Graph‑based Retrieval‑Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge graphs. Leveraging community detection and relation filtering techniques, GraphRAG systems demonstrate inherent resistance to traditional RAG attacks, such as text poisoning and prompt injection. However, in this paper, we find that the security of GraphRAG systems fundamentally relies on the topological integrity of the underlying graph, which can be undermined by implicitly corrupting the logical connections, without altering surface‑level text semantics. To exploit this vulnerability, we propose \textscLogicPoison, a novel attack framework that targets logical reasoning rather than injecting false contents. Specifically, \textscLogicPoison employs a type‑preserving entity swapping mechanism to perturb both global logic hubs for disrupting overall graph connectivity and query‑specific reasoning bridges for severing essential multi‑hop inference paths. This approach effectively reroutes valid reasoning into dead ends while maintaining surface‑level textual plausibility. Comprehensive experiments across multiple benchmarks demonstrate that \textscLogicPoison successfully bypasses GraphRAG's defenses, significantly degrading performance and outperforming state‑of‑the‑art baselines in both effectiveness and stealth. Our code is available at \textcolorbluehttps://github.com/Jord8061/logicPoison.

PaperId: 2036, https://arxiv.org/pdf/2604.02923.pdf GitHub

Abstract:
Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases. These issues, sometimes amplified in specific architectures like Mixture‑of‑Experts (MoE) which motivate our work, pose risks for reliable deployment. To address these challenges, we propose the Council Mode, a multi‑agent consensus framework. Our approach dispatches queries to multiple heterogeneous frontier LLMs in parallel and synthesizes their outputs using a dedicated consensus model. The pipeline consists of three phases: an intelligent triage for query complexity, parallel generation across diverse models, and a structured synthesis that identifies agreement, disagreement, and unique findings. In our evaluation, conducted under controlled no‑web settings, the Council Mode achieved a 35.9% relative reduction in hallucination rates on a 1,200‑sample HaluEval subset and a 7.8‑point improvement on TruthfulQA compared to the top‑performing individual model. On our curated MDR‑500 multi‑domain reasoning benchmark, the Council Mode achieved a Quality Score of 91.7%, representing a 10.2‑point improvement over the best individual model. The framework also exhibited lower measured bias variance under our rubric‑based evaluation protocol. We provide a cost‑effectiveness analysis showing that the framework incurs a 4.2x token‑cost overhead, making it most suitable for accuracy‑prioritized applications where the cost of errors exceeds the added inference cost. These findings suggest that structured multi‑agent consensus is a promising direction for enhancing the reliability and factual grounding of LLM‑generated content.

PaperId: 2037, https://arxiv.org/pdf/2604.02889.pdf GitHub

Abstract:
Data assimilation is the process of estimating the state of a dynamical system over time by combining model predictions with measurements. This task becomes challenging when the system is nonlinear and high‑dimensional. To address this, score‑based Bayesian filters have recently emerged. However, these methods still show unsatisfactory performance in certain cases, particularly under spatially sparse measurements. Such degradation stems from heuristic approximations of the likelihood score, whose errors can accumulate over time. This limitation arises because the methods simply adopt a classical forward process for generative modeling that transforms a data distribution toward a Gaussian distribution, which is independent of the measurement equation. Here, we propose a forward process tailored for filtering that transforms the system state toward the measurement space, enabling a theoretically sound formulation of the likelihood score. Based on this, we develop the Measurement‑Aware Score‑based Filter (MASF). We evaluate MASF on Kolmogorov flow, a high‑dimensional fluid benchmark with up to \mathcalO(10^5) dimensions, under diverse measurement operators, including nonlinear cases with a dimensional mismatch between the state and the measurements. MASF shows improved performance over existing score‑based filters and ensemble‑type Kalman filters. Notably, MASF achieves up to a 28.2× wall‑clock speedup compared with the baselines when using amortized pretraining. Our implementation is available at \texttthttps://github.com/tcnllab‑oss/masf.

PaperId: 2038, https://arxiv.org/pdf/2604.02766.pdf GitHub

Abstract:
Modern LLMs inherit strong priors from web‑scale pretraining, which can limit the headroom of post‑training data‑selection strategies. While Active Preference Learning (APL) seeks to optimize query efficiency in online Direct Preference Optimization (DPO), the inherent richness of on‑policy candidate pools often renders simple Random sampling a surprisingly formidable baseline. We evaluate uncertainty‑based APL against Random across harmlessness, helpfulness, and instruction‑following settings, utilizing both reward models and LLM‑as‑a‑judge proxies. We find that APL yields negligible improvements in proxy win‑rates compared to Random. Crucially, we observe a dissociation where win‑rate improves even as general capability ‑‑ measured by standard benchmarks ‑‑ degrades. APL fails to mitigate this capability collapse or reduce variance significantly better than random sampling. Our findings suggest that in the regime of strong pre‑trained priors, the computational overhead of active selection is difficult to justify against the ``cheap diversity'' provided by simple random samples. Our code is available at https://github.com/BootsofLagrangian/random‑vs‑apl.

PaperId: 2039, https://arxiv.org/pdf/2604.02719.pdf GitHub

Abstract:
We introduce MOMO, the first multi‑sensor foundation model for Mars remote sensing. MOMO uses model merge to integrate representations learned independently from three key Martian sensors (HiRISE, CTX, and THEMIS), spanning resolutions from 0.25 m/pixel to 100 m/pixel. Central to our method is our novel Equal Validation Loss (EVL) strategy, which aligns checkpoints across sensors based on validation loss similarity before fusion via task arithmetic. This ensures models are merged at compatible convergence stages, leading to improved stability and generalization. We train MOMO on a large‑scale, high‑quality corpus of ～ 12 million samples curated from Mars orbital data and evaluate it on 9 downstream tasks from Mars‑Bench. MOMO achieves better overall performance compared to ImageNet pre‑trained, earth observation foundation model, sensor‑specific pre‑training, and fully‑supervised baselines. Particularly on segmentation tasks, MOMO shows consistent and significant performance improvement. Our results demonstrate that model merging through an optimal checkpoint selection strategy provides an effective approach for building foundation models for multi‑resolution data. The model weights, pretraining code, pretraining data, and evaluation code are available at: https://github.com/kerner‑lab/MOMO.

PaperId: 2040, https://arxiv.org/pdf/2604.02710.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have shown strong potential for autonomous driving, yet existing benchmarks remain largely ego‑centric and therefore cannot systematically assess model performance in infrastructure‑centric and cooperative driving conditions. In this work, we introduce V2X‑QA, a real‑world dataset and benchmark for evaluating MLLMs across vehicle‑side, infrastructure‑side, and cooperative viewpoints. V2X‑QA is built around a view‑decoupled evaluation protocol that enables controlled comparison under vehicle‑only, infrastructure‑only, and cooperative driving conditions within a unified multiple‑choice question answering (MCQA) framework. The benchmark is organized into a twelve‑task taxonomy spanning perception, prediction, and reasoning and planning, and is constructed through expert‑verified MCQA annotation to enable fine‑grained diagnosis of viewpoint‑dependent capabilities. Benchmark results across ten representative state‑of‑the‑art proprietary and open‑source models show that viewpoint accessibility substantially affects performance, and infrastructure‑side reasoning supports meaningful macroscopic traffic understanding. Results also indicate that cooperative reasoning remains challenging since it requires cross‑view alignment and evidence integration rather than simply additional visual input. To address these challenges, we introduce V2X‑MoE, a benchmark‑aligned baseline with explicit view routing and viewpoint‑specific LoRA experts. The strong performance of V2X‑MoE further suggests that explicit viewpoint specialization is a promising direction for multi‑view reasoning in autonomous driving. Overall, V2X‑QA provides a foundation for studying multi‑perspective reasoning, reliability, and cooperative physical intelligence in connected autonomous driving. The dataset and V2X‑MoE resources are publicly available at: https://github.com/junwei0001/V2X‑QA.

PaperId: 2041, https://arxiv.org/pdf/2604.02689.pdf GitHub

Abstract:
Recent advances in Multimodal Large Language Models (MLLMs) have expanded reasoning capabilities into 3D domains, enabling fine‑grained spatial understanding. However, the substantial size of 3D MLLMs and the high dimensionality of input features introduce considerable inference overhead, which limits practical deployment on resource constrained platforms. To overcome this limitation, this paper presents Efficient3D, a unified framework for visual token pruning that accelerates 3D MLLMs while maintaining competitive accuracy. The proposed framework introduces a Debiased Visual Token Importance Estimator (DVTIE) module, which considers the influence of shallow initial layers during attention aggregation, thereby producing more reliable importance predictions for visual tokens. In addition, an Adaptive Token Rebalancing (ATR) strategy is developed to dynamically adjust pruning strength based on scene complexity, preserving semantic completeness and maintaining balanced attention across layers. Together, they enable context‑aware token reduction that maintains essential semantics with lower computation. Comprehensive experiments conducted on five representative 3D vision and language benchmarks, including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D, demonstrate that Efficient3D achieves superior performance compared with unpruned baselines, with a +2.57% CIDEr improvement on the Scan2Cap dataset. Therefore, Efficient3D provides a scalable and effective solution for efficient inference in 3D MLLMs. The code is released at: https://github.com/sol924/Efficient3D

PaperId: 2042, https://arxiv.org/pdf/2604.02627.pdf GitHub

Abstract:
Living in a changing climate, human society now faces more frequent and severe natural disasters than ever before. As a consequence, rapid disaster response during the "Golden 72 Hours" of search and rescue becomes a vital humanitarian necessity and community concern. However, traditional disaster damage surveys routinely fail to generalize across distinct urban morphologies and new disaster events. Effective damage mapping typically requires exhaustive and time‑consuming manual data annotation. To address this issue, we introduce Smart Transfer, a novel Geospatial Artificial Intelligence (GeoAI) framework, leveraging state‑of‑the‑art vision Foundation Models (FMs) for rapid building damage mapping with post‑earthquake Very High Resolution (VHR) imagery. Specifically, we design two novel model transfer strategies: first, Pixel‑wise Clustering (PC), ensuring robust prototype‑level global feature alignment; second, a Distance‑Penalized Triplet (DPT), integrating patch‑level spatial autocorrelation patterns by assigning stronger penalties to semantically inconsistent yet spatially adjacent patches. Extensive experiments and ablations from the recent 2023 Turkiye‑Syria earthquake show promising performance in multiple cross‑region transfer settings, namely Leave One Domain Out (LODO) and Specific Source Domain Combination (SSDC). Moreover, Smart Transfer provides a scalable, automated GeoAI solution to accelerate building damage mapping and support rapid disaster response, offering new opportunities to enhance disaster resilience in climate‑vulnerable regions and communities. The data and code are publicly available at https://github.com/ai4city‑hkust/SmartTransfer.

PaperId: 2043, https://arxiv.org/pdf/2604.02434.pdf GitHub

Abstract:
We study structured abstraction‑based reasoning for the Abstraction and Reasoning Corpus (ARC) and compare its generalization to test‑time approaches. Purely neural architectures lack reliable combinatorial generalization, while strictly symbolic systems struggle with perceptual grounding. We therefore propose a neuro‑symbolic architecture that extracts object‑level structure from grids, uses neural priors to propose candidate transformations from a fixed domain‑specific language (DSL) of atomic patterns, and filters hypotheses using cross‑example consistency. Instantiated as a compositional reasoning framework based on unit patterns inspired by human visual abstraction, the system augments large language models (LLMs) with object representations and transformation proposals. On ARC‑AGI‑2, it improves base LLM performance from 16% to 24.4% on the public evaluation set, and to 30.8% when combined with ARC Lang Solver via a meta‑classifier. These results demonstrate that separating perception, neural‑guided transformation proposal, and symbolic consistency filtering improves generalization without task‑specific finetuning or reinforcement learning, while reducing reliance on brute‑force search and sampling‑based test‑time scaling. We open‑source the ARC‑AGI‑2 Reasoner code (https://github.com/CoreThink‑AI/arc‑agi‑2‑reasoner).

PaperId: 2044, https://arxiv.org/pdf/2604.02360.pdf GitHub

Abstract:
The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipsed by significant challenges. These challenges stem from concerns that LLMs undermine academic assessment by enabling bypassing of critical thinking, leading to increased cognitive offloading. This emerging trend stresses the dual imperative of harnessing AI's educational benefits while safeguarding critical thinking and academic rigor in the evolving AI ecosystem. To this end, we introduce AI‑Sinkhole, an AI‑agent augmented DNS‑based framework that dynamically discovers, semantically classifies, and temporarily network‑wide blocks emerging LLM chatbot services during proctored exams. AI‑Sinkhole offers explainable classification via quantized LLMs (LLama 3, DeepSeek‑R1, Qwen‑3) and dynamic DNS blocking with Pi‑Hole. We also share our observations in using LLMs as explainable classifiers which achieved robust cross‑lingual performance (F1‑score > 0.83). To support future research and development in this domain initial codes with a readily deployable 'AI‑Sinkhole' blockist is available on https://github.com/AIMLEdu/ai‑sinkhole.

PaperId: 2045, https://arxiv.org/pdf/2604.02270.pdf GitHub

Abstract:
Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We present Crystalite, a lightweight diffusion Transformer for crystal modeling built around two simple inductive biases. The first is Subatomic Tokenization, a compact chemically structured atom representation that replaces high‑dimensional one‑hot encodings and is better suited to continuous diffusion. The second is the Geometry Enhancement Module (GEM), which injects periodic minimum‑image pair geometry directly into attention through additive geometric biases. Together, these components preserve the simplicity and efficiency of a standard Transformer while making it better matched to the structure of crystalline materials. Crystalite achieves state‑of‑the‑art results on crystal structure prediction benchmarks, and de novo generation performance, attaining the best S.U.N. discovery score among the evaluated baselines while sampling substantially faster than geometry‑heavy alternatives.

PaperId: 2046, https://arxiv.org/pdf/2604.02217.pdf GitHub

Abstract:
Understanding how Large Language Models (LLMs) process information from prompts remains a significant challenge. To shed light on this "black box," attention visualization techniques have been developed to capture neuron‑level perceptions and interpret how models focus on different parts of input data. However, many existing techniques are tailored to specific model architectures, particularly within the Transformer family, and often require backpropagation, resulting in nearly double the GPU memory usage and increased computational cost. A lightweight, model‑agnostic approach for attention visualization remains lacking. In this paper, we introduce a model‑agnostic token importance visualization technique to better understand how generative AI systems perceive and prioritize information from input text, without incurring additional computational cost. Our method leverages perturbation‑based strategies combined with a three‑matrix analytical framework to generate relevance maps that illustrate token‑level contributions to model predictions. The framework comprises: (1) the Angular Deviation Matrix, which captures shifts in semantic direction; (2) the Magnitude Deviation Matrix, which measures changes in semantic intensity; and (3) the Dimensional Importance Matrix, which evaluates contributions across individual vector dimensions. By systematically removing each token and measuring the resulting impact across these three complementary dimensions, we derive a composite importance score that provides a nuanced and mathematically grounded measure of token significance. To support reproducibility and foster wider adoption, we provide open‑source implementations of all proposed and utilized explainability techniques, with code and resources publicly available at https://github.com/Infosys/Infosys‑Responsible‑AI‑Toolkit

PaperId: 2047, https://arxiv.org/pdf/2604.02215.pdf GitHub

Abstract:
Conventional hypernetworks are typically engineered around a specific base‑model parameterization, so changing the target architecture often entails redesigning the hypernetwork and retraining it from scratch. We introduce the \emphUniversal Hypernetwork (UHN), a fixed‑architecture generator that predicts weights from deterministic parameter, architecture, and task descriptors. This descriptor‑based formulation decouples the generator architecture from target‑network parameterization, so one generator can instantiate heterogeneous models across the tested architecture and task families. Our empirical claims are threefold: (1) one fixed UHN remains competitive with direct training across vision, graph, text, and formula‑regression benchmarks; (2) the same UHN supports both multi‑model generalization within a family and multi‑task learning across heterogeneous models; and (3) UHN enables stable recursive generation with up to three intermediate generated UHNs before the final base model. Our code is available at https://github.com/Xuanfeng‑Zhou/UHN.

PaperId: 2048, https://arxiv.org/pdf/2604.02178.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of parameters per token. While MoE architectures are primarily adopted for computational efficiency, it remains an open question whether their sparsity makes them inherently easier to interpret than dense feed‑forward networks (FFNs). We compare MoE experts and dense FFNs using k‑sparse probing and find that expert neurons are consistently less polysemantic, with the gap widening as routing becomes sparser. This suggests that sparsity pressures both individual neurons and entire experts toward monosemanticity. Leveraging this finding, we zoom out from the neuron to the expert level as a more effective unit of analysis. We validate this approach by automatically interpreting hundreds of experts. This analysis allows us to resolve the debate on specialization: experts are neither broad domain specialists (e.g., biology) nor simple token‑level processors. Instead, they function as fine‑grained task experts, specializing in linguistic operations or semantic tasks (e.g., closing brackets in \LaTeX). Our findings suggest that MoEs are inherently interpretable at the expert level, providing a clearer path toward large‑scale model interpretability. Code is available at: https://github.com/jerryy33/MoE_analysis.

PaperId: 2049, https://arxiv.org/pdf/2604.02071.pdf GitHub GitHub

Abstract:
Human‑Object Interaction (HOI) detection aims to localize human‑object pairs and classify their interactions from a single image, a task that demands strong visual understanding and nuanced contextual reasoning. Recent approaches have leveraged Vision‑Language Models (VLMs) to introduce semantic priors, significantly improving HOI detection performance. However, existing methods often fail to fully capitalize on the diverse contextual cues distributed across the entire scene. To overcome these limitations, we propose the Instance‑centric Context Mining Network (InCoM‑Net)‑a novel framework that effectively integrates rich semantic knowledge extracted from VLMs with instance‑specific features produced by an object detector. This design enables deeper interaction reasoning by modeling relationships not only within each detected instance but also across instances and their surrounding scene context. InCoM‑Net comprises two core components: Instancecentric Context Refinement (ICR), which separately extracts intra‑instance, inter‑instance, and global contextual cues from VLM‑derived features, and Progressive Context Aggregation (ProCA), which iteratively fuses these multicontext features with instance‑level detector features to support high‑level HOI reasoning. Extensive experiments on the HICO‑DET and V‑COCO benchmarks show that InCoM‑Net achieves state‑of‑the‑art performance, surpassing previous HOI detection methods. Code is available at https://github.com/nowuss/InCoM‑Net.

PaperId: 2050, https://arxiv.org/pdf/2604.01966.pdf GitHub

Abstract:
We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question‑answering requiring ego‑grounding ‑ the ability to understand the camera‑wearer in egocentric videos. To this end, we introduce MyEgo, the first egocentric VideoQA dataset designed to evaluate MLLMs' ability to understand, remember, and reason about the camera wearer. MyEgo comprises 541 long videos and 5K personalized questions asking about "my things", "my activities", and "my past". Benchmarking reveals that competitive MLLMs across variants, including open‑source vs. proprietary, thinking vs. non‑thinking, small vs. large scales all struggle on MyEgo. Top closed‑ and open‑source models (e.g., GPT‑5 and Qwen3‑VL) achieve only~46% and 36% accuracy, trailing human performance by near 40% and 50% respectively. Surprisingly, neither explicit reasoning nor model scaling yield consistent improvements. Models improve when relevant evidence is explicitly provided, but gains drop over time, indicating limitations in tracking and remembering "me" and "my past". These findings collectively highlight the crucial role of ego‑grounding and long‑range memory in enabling personalized QA in egocentric videos. We hope MyEgo and our analyses catalyze further progress in these areas for egocentric personalized assistance. Data and code are available at https://github.com/Ryougetsu3606/MyEgo

PaperId: 2051, https://arxiv.org/pdf/2604.01929.pdf GitHub GitHub

Abstract:
The audio research community depends on open generative models as foundational tools for building novel approaches and establishing baselines. In this report, we present Woosh, Sony AI's publicly released sound effect foundation model, detailing its architecture, training process, and an evaluation against other popular open models. Being optimized for sound effects, we provide (1) a high‑quality audio encoder/decoder model and (2) a text‑audio alignment model for conditioning, together with (3) text‑to‑audio and (4) video‑to‑audio generative models. Distilled text‑to‑audio and video‑to‑audio models are also included in the release, allowing for low‑resource operation and fast inference. Our evaluation on both public and private data shows competitive or better performance for each module when compared to existing open alternatives like StableAudio‑Open and TangoFlux. Inference code and model weights are available at https://github.com/SonyResearch/Woosh. Demo samples can be found at https://sonyresearch.github.io/Woosh/.

PaperId: 2052, https://arxiv.org/pdf/2604.01840.pdf GitHub

Abstract:
While Reinforcement Learning from Verifiable Rewards (RLVR) has advanced reasoning in Large Vision‑Language Models (LVLMs), prevailing frameworks suffer from a foundational methodological flaw: by distributing identical advantages across all generated tokens, these methods inherently dilute the learning signals essential for optimizing the critical, visually‑grounded steps of multimodal reasoning. To bridge this gap, we formulate Token Visual Dependency, quantifying the causal information gain of visual inputs via the Kullback‑Leibler (KL) divergence between visual‑conditioned and text‑only predictive distributions. Revealing that this dependency is highly sparse and semantically pivotal, we introduce Perception‑Grounded Policy Optimization (PGPO), which is a novel fine‑grained credit assignment framework that dynamically reshapes advantages at the token level. Through a threshold‑gated, mass‑conserving mechanism, PGPO actively amplifies learning signals for visually‑dependent tokens while suppressing gradient noise from linguistic priors. Extensive experiments based on the Qwen2.5‑VL series across seven challenging multimodal reasoning benchmarks demonstrate that PGPO boosts models by 18.7% on average. Both theoretical and empirical analyses confirm that PGPO effectively reduces gradient variance, prevents training collapse, and acts as a potent regularizer for robust, perception‑grounded multimodal reasoning. Code will be released on https://github.com/Yzk1114/PGPO.

PaperId: 2053, https://arxiv.org/pdf/2604.01681.pdf GitHub

Abstract:
Large foundation models enable powerful reasoning for autonomous systems, but mapping semantic intent to reliable real‑time control remains challenging. Existing approaches either (i) let Large Language Models (LLMs) generate trajectories directly ‑ brittle, hard to verify, and latency‑prone ‑ or (ii) adjust Model Predictive Control (MPC) objectives online ‑ mixing slow deliberation with fast control and blurring interfaces. We propose Agentic Fast‑Slow Planning, a hierarchical framework that decouples perception, reasoning, planning, and control across natural timescales. The framework contains two bridges. Perception2Decision compresses scenes into ego‑centric topologies using an on‑vehicle Vision‑Language Model (VLM) detector, then maps them to symbolic driving directives in the cloud with an LLM decision maker ‑ reducing bandwidth and delay while preserving interpretability. Decision2Trajectory converts directives into executable paths: Semantic‑Guided A embeds language‑derived soft costs into classical search to bias solutions toward feasible trajectories, while an Agentic Refinement Module adapts planner hyperparameters using feedback and memory. Finally, MPC tracks the trajectories in real time, with optional cloud‑guided references for difficult cases. Experiments in CARLA show that Agentic Fast‑Slow Planning improves robustness under perturbations, reducing lateral deviation by up to 45% and completion time by over 12% compared to pure MPC and an A‑guided MPC baseline. Code is available at https://github.com/cjychenjiayi/icra2026_AFSP.

PaperId: 2054, https://arxiv.org/pdf/2604.01658.pdf GitHub

Abstract:
Large language model (LLM)‑based evolution is a promising approach for open‑ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods still rely heavily on fixed heuristics and hard‑coded exploration rules, which limit the autonomy of LLM agents. We present CORAL, the first framework for autonomous multi‑agent evolution on open‑ended problems. CORAL replaces rigid control with long‑running agents that explore, reflect, and collaborate through shared persistent memory, asynchronous multi‑agent execution, and heartbeat‑based interventions. It also provides practical safeguards, including isolated workspaces, evaluator separation, resource management, and agent session and health management. Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state‑of‑the‑art results on 10 tasks, achieving 3‑10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks. On Anthropic's kernel engineering task, four co‑evolving agents improve the best known score from 1363 to 1103 cycles. Mechanistic analyses further show how these gains arise from knowledge reuse and multi‑agent exploration and communication. Together, these results suggest that greater agent autonomy and multi‑agent evolution can substantially improve open‑ended discovery. Code is available at https://github.com/Human‑Agent‑Society/CORAL.

PaperId: 2055, https://arxiv.org/pdf/2604.01579.pdf GitHub

Abstract:
Multimodal tabular‑image fusion is an emerging task that has received increasing attention in various domains. However, existing methods may be hindered by gradient conflicts between modalities, misleading the optimization of the unimodal learner. In this paper, we propose a novel Gradient‑Aligned Alternating Learning (GAAL) paradigm to address this issue by aligning modality gradients. Specifically, GAAL adopts an alternating unimodal learning and shared classifier to decouple the multimodal gradient and facilitate interaction. Furthermore, we design uncertainty‑based cross‑modal gradient surgery to selectively align cross‑modal gradients, thereby steering the shared parameters to benefit all modalities. As a result, GAAL can provide effective unimodal assistance and help boost the overall fusion performance. Empirical experiments on widely used datasets reveal the superiority of our method through comparison with various state‑of‑the‑art (SoTA) tabular‑image fusion baselines and test‑time tabular missing baselines. The source code is available at https://github.com/njustkmg/ICME26‑GAAL.

PaperId: 2056, https://arxiv.org/pdf/2604.01561.pdf GitHub

Abstract:
We present ReFlow, a unified framework for monocular dynamic scene reconstruction that learns 3D motion in a novel self‑correction manner from raw video. Existing methods often suffer from incomplete scene initialization for dynamic regions, leading to unstable reconstruction and motion estimation, which often resorts to external dense motion guidance such as pre‑computed optical flow to further stabilize and constrain the reconstruction of dynamic components. However, this introduces additional complexity and potential error propagation. To address these issues, ReFlow integrates a Complete Canonical Space Construction module for enhanced initialization of both static and dynamic regions, and a Separation‑Based Dynamic Scene Modeling module that decouples static and dynamic components for targeted motion supervision. The core of ReFlow is a novel self‑correction flow matching mechanism, consisting of Full Flow Matching to align 3D scene flow with time‑varying 2D observations, and Camera Flow Matching to enforce multi‑view consistency for static objects. Together, these modules enable robust and accurate dynamic scene reconstruction. Extensive experiments across diverse scenarios demonstrate that ReFlow achieves superior reconstruction quality and robustness, establishing a novel self‑correction paradigm for monocular 4D reconstruction.

PaperId: 2057, https://arxiv.org/pdf/2604.01483.pdf GitHub

Abstract:
The rapid evolution of autonomous, agentic artificial intelligence within financial services has introduced an existential architectural crisis: large language models (LLMs) are probabilistic, non‑deterministic systems operating in domains that demand absolute, mathematically verifiable compliance guarantees. Existing guardrail solutions ‑‑ including NVIDIA NeMo Guardrails and Guardrails AI ‑‑ rely on probabilistic classifiers and syntactic validators that are fundamentally inadequate for enforcing complex multi‑variable regulatory constraints mandated by the SEC, FINRA, and OCC. This paper presents the Lean‑Agent Protocol, a formal‑verification‑based AI guardrail platform that leverages the Aristotle neural‑symbolic model developed by Harmonic AI to auto‑formalize institutional policies into Lean 4 code. Every proposed agentic action is treated as a mathematical conjecture: execution is permitted if and only if the Lean 4 kernel proves that the action satisfies pre‑compiled regulatory axioms. This architecture provides cryptographic‑level compliance certainty at microsecond latency, directly satisfying SEC Rule 15c3‑5, OCC Bulletin 2011‑12, FINRA Rule 3110, and CFPB explainability mandates. A three‑phase implementation roadmap from shadow verification through enterprise‑scale deployment is provided.

PaperId: 2058, https://arxiv.org/pdf/2604.01443.pdf GitHub

Abstract:
When does consulting one information source raise the value of another, and when does it diminish it? We study this question for Bayesian decision‑makers facing finite actions. The interaction decomposes into two opposing forces: a complement force, measuring how one source moves beliefs to where the other becomes more useful, and a substitute force, measuring how much the current decision is resolved. Their balance obeys a localization principle: substitution requires an observation to cross a decision boundary, though crossing alone does not guarantee it. Whenever posteriors remain inside the current decision region, the substitute force vanishes, and sources are guaranteed to complement each other, even when one source cannot, on its own, change the decision. The results hold for arbitrarily correlated sources and are formalized in Lean 4. Substitution is confined to the thin boundaries where decisions change. Everywhere else, information cooperates. Code and proofs: https://github.com/nidhishs/all‑substitution‑is‑local.

PaperId: 2059, https://arxiv.org/pdf/2604.01438.pdf GitHub

Abstract:
Personal AI agents like OpenClaw run with elevated privileges on users' local machines, where a single successful prompt injection can leak credentials, redirect financial transactions, or destroy files. This threat goes well beyond conventional text‑level jailbreaks, yet existing safety evaluations fall short: most test models in isolated chat settings, rely on synthetic environments, and do not account for how the agent framework itself shapes safety outcomes. We introduce CLAWSAFETY, a benchmark of 120 adversarial test scenarios organized along three dimensions (harm domain, attack vector, and harmful action type) and grounded in realistic, high‑privilege professional workspaces spanning software engineering, finance, healthcare, law, and DevOps. Each test case embeds adversarial content in one of three channels the agent encounters during normal work: workspace skill files, emails from trusted senders, and web pages. We evaluate five frontier LLMs as agent backbones, running 2,520 sandboxed trials across all configurations. Attack success rates (ASR) range from 40% to 75% across models and vary sharply by injection vector, with skill instructions (highest trust) consistently more dangerous than email or web content. Action‑trace analysis reveals that the strongest model maintains hard boundaries against credential forwarding and destructive actions, while weaker models permit both. Cross‑scaffold experiments on three agent frameworks further demonstrate that safety is not determined by the backbone model alone but depends on the full deployment stack, calling for safety evaluation that treats model and framework as joint variables. Code and data will be available at: https://weibowen555.github.io/ClawSafety/.

PaperId: 2060, https://arxiv.org/pdf/2604.01383.pdf GitHub

Abstract:
American football practice generates video at scale, yet the interaction of interest occupies only a brief window of each long, untrimmed clip. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the interacting entities and the onset of contact. We study First Point of Contact (FPOC), defined as the first frame in which a player physically touches a tackle dummy, in unconstrained practice footage with camera motion, clutter, multiple similarly equipped athletes, and rapid pose changes around impact. We present GRAZE, a training‑free pipeline for FPOC localization that requires no labeled tackle‑contact examples. GRAZE uses Grounding DINO to discover candidate player‑dummy interactions, refines them with motion‑aware temporal reasoning, and uses SAM2 as an explicit pixel‑level verifier of contact rather than relying on detection confidence alone. This separation between candidate discovery and contact confirmation makes the approach robust to cluttered scenes and unstable grounding near impact. On 738 tackle‑practice videos, GRAZE produces valid outputs for 97.4% of clips and localizes FPOC within \pm 10 frames on 77.5% of all clips and within \pm 20 frames on 82.7% of all clips. These results show that frame‑accurate contact onset localization in real‑world practice footage is feasible without task‑specific training.

PaperId: 2061, https://arxiv.org/pdf/2604.01344.pdf GitHub

Abstract:
Competency question (CQ) elicitation represents a critical but resource‑intensive bottleneck in ontology engineering. This foundational phase is often hampered by the communication gap between domain experts, who possess the necessary knowledge, and ontology engineers, who formalise it. This paper introduces IDEA2, a novel, semi‑automated workflow that integrates Large Language Models (LLMs) within a collaborative, expert‑in‑the‑loop process to address this challenge. The methodology is characterised by a core iterative loop: an initial LLM‑based extraction of CQs from requirement documents, a co‑creational review and feedback phase by domain experts on an accessible collaborative platform, and an iterative, feedback‑driven reformulation of rejected CQs by an LLM until consensus is achieved. To ensure transparency and reproducibility, the entire lifecycle of each CQ is tracked using a provenance model that captures the full lineage of edits, anonymised feedback, and generation parameters. The workflow was validated in 2 real‑world scenarios (scientific data, cultural heritage), demonstrating that IDEA2 can accelerate the requirements engineering process, improve the acceptance and relevance of the resulting CQs, and exhibit high usability and effectiveness among domain experts. We release all code and experiments at https://github.com/KE‑UniLiv/IDEA2

PaperId: 2062, https://arxiv.org/pdf/2604.01339.pdf GitHub

Abstract:
Vision transformers (ViT) rely on attention mechanism to weigh input features, and therefore attention scores have naturally been considered as explanations for its decision‑making process. However, attention scores are almost always non‑zero, resulting in noisy and diffused attention maps and limiting interpretability. Can we quantify uncertainty measures of attention scores and obtain regularized attention scores? To this end, we consider attention scores of ViT in a statistical framework where independent noise would lead to insignificant yet non‑zero scores. Leveraging statistical learning techniques, we introduce the bootstrapping for attention scores which generates a baseline distribution of attention scores by resampling input features. Such a bootstrap distribution is then used to estimate significances and posterior probabilities of attention scores. In natural and medical images, the proposed \emphAttention Regularization approach demonstrates a straightforward removal of spurious attention arising from noise, drastically improving shrinkage and sparsity. Quantitative evaluations are conducted using both simulation and real‑world datasets. Our study highlights bootstrapping as a practical regularization tool when using attention scores as explanations for ViT. Code available: https://github.com/ncchung/AttentionRegularization

PaperId: 2063, https://arxiv.org/pdf/2604.01280.pdf GitHub

Abstract:
Answering questions about images often requires combining visual understanding with external knowledge. Multimodal Large Language Models (MLLMs) provide a natural framework for this setting, but they often struggle to identify the most relevant visual and textual evidence when answering knowledge‑intensive queries. In such scenarios, models must integrate visual cues with retrieved textual evidence that is often noisy or only partially relevant, while also localizing fine‑grained visual information in the image. In this work, we introduce Look Twice (LoT), a training‑free inference‑time framework that improves how pretrained MLLMs utilize multimodal evidence. Specifically, we exploit the model attention patterns to estimate which visual regions and retrieved textual elements are relevant to a query, and then generate the answer conditioned on this highlighted evidence. The selected cues are highlighted through lightweight prompt‑level markers that encourage the model to re‑attend to the relevant evidence during generation. Experiments across multiple knowledge‑based VQA benchmarks show consistent improvements over zero‑shot MLLMs. Additional evaluations on vision‑centric and hallucination‑oriented benchmarks further demonstrate that visual evidence highlighting alone improves model performance in settings without textual context, all without additional training or architectural modifications. Source code will be publicly released.

PaperId: 2064, https://arxiv.org/pdf/2604.01268.pdf GitHub

Abstract:
Individuals engaging in online communication frequently express personal opinions with informal styles (e.g., memes and emojis). While Language Models (LMs) with informal communications have been widely discussed, a unique and emphatic style, the Repetitive Lengthening Form (RLF), has been overlooked for years. In this paper, we explore answers to two research questions: 1) Is RLF important for sentiment analysis (SA)? 2) Can LMs understand RLF? Inspired by previous linguistic research, we curate Lengthening, the first multi‑domain dataset with 850k samples focused on RLF for SA. Moreover, we introduce Explainable Instruction Tuning (ExpInstruct), a two‑stage instruction tuning framework aimed to improve both performance and explainability of LLMs for RLF. We further propose a novel unified approach to quantify LMs' understanding of informal expressions. We show that RLF sentences are expressive expressions and can serve as signatures of document‑level sentiment. Additionally, RLF has potential value for online content analysis. Our results show that fine‑tuned Pre‑trained Language Models (PLMs) can surpass zero‑shot GPT‑4 in performance but not in explanation for RLF. Finally, we show ExpInstruct can improve the open‑sourced LLMs to match zero‑shot GPT‑4 in performance and explainability for RLF with limited samples. Code and sample data are available at https://github.com/Tom‑Owl/OverlookedRLF

PaperId: 2065, https://arxiv.org/pdf/2604.01179.pdf GitHub

Abstract:
Foundation vision‑language models are becoming increasingly relevant to robotics because they can provide richer semantic perception than narrow task‑specific pipelines. However, their practical adoption in robot software stacks still depends on reproducible middleware integrations rather than on model quality alone. Florence‑2 is especially attractive in this regard because it unifies captioning, optical character recognition, open‑vocabulary detection, grounding and related vision‑language tasks within a comparatively manageable model size. This article presents a ROS 2 wrapper for Florence‑2 that exposes the model through three complementary interaction modes: continuous topic‑driven processing, synchronous service calls and asynchronous actions. The wrapper is designed for local execution and supports both native installation and Docker container deployment. It also combines generic JSON outputs with standard ROS 2 message bindings for detection‑oriented tasks. A functional validation is reported together with a throughput study on several GPUs, showing that local deployment is feasible with consumer grade hardware. The repository is publicly available here: https://github.com/JEDominguezVidal/florence2_ros2_wrapper

PaperId: 2066, https://arxiv.org/pdf/2604.01170.pdf GitHub

Abstract:
While test‑time scaling has enabled large language models to solve highly difficult tasks, state‑of‑the‑art results come at exorbitant compute costs. These inefficiencies can be attributed to the miscalibration of post‑trained language models, and the lack of calibration in popular sampling techniques. Here, we present Online Reasoning Calibration (ORCA), a framework for calibrating the sampling process that draws upon conformal prediction and test‑time training. Specifically, we introduce a meta‑learning procedure that updates the calibration module for each input. This allows us to provide valid confidence estimates under distributional shift, e.g. in thought patterns that occur across different stages of reasoning, or in prompt distributions between model development and deployment. ORCA not only provides theoretical guarantees on conformal risks, but also empirically shows higher efficiency and generalization across different reasoning tasks. At risk level δ=0.1, ORCA improves Qwen2.5‑32B efficiency on in‑distribution tasks with savings up to 47.5% with supervised labels and 40.7% with self‑consistency labels. Under zero‑shot out‑of‑domain settings, it improves MATH‑500 savings from 24.8% of the static calibration baseline to 67.0% while maintaining a low empirical error rate, and the same trend holds across model families and downstream benchmarks. Our code is publicly available at https://github.com/wzekai99/ORCA.

PaperId: 2067, https://arxiv.org/pdf/2604.01167.pdf GitHub

Abstract:
Chest X‑ray (CXR) segmentation is an important step in computer‑aided diagnosis, yet deploying large foundation models in clinical settings remains challenging due to computational constraints. We propose AdaLoRA‑QAT, a two‑stage fine‑tuning framework that combines adaptive low‑rank encoder adaptation with full quantization‑aware training. Adaptive rank allocation improves parameter efficiency, while selective mixed‑precision INT8 quantization preserves structural fidelity crucial for clinical reliability. Evaluated across large‑scale CXR datasets, AdaLoRA‑QAT achieves 95.6% Dice, matching full‑precision SAM decoder fine‑tuning while reducing trainable parameters by 16.6× and yielding 2.24× model compression. A Wilcoxon signed‑rank test confirms that quantization does not significantly degrade segmentation accuracy. These results demonstrate that AdaLoRA‑QAT effectively balances accuracy, efficiency, and structural trust‑worthiness, enabling compact and deployable foundation models for medical image segmentation. Code and pretrained models are available at: https://prantik‑pdeb.github.io/adaloraqat.github.io/

PaperId: 2068, https://arxiv.org/pdf/2604.01151.pdf GitHub

Abstract:
As LLM agents are increasingly deployed in multi‑agent systems, they introduce risks of covert coordination that may evade standard forms of human oversight. While linear probes on model activations have shown promise for detecting deception in single‑agent settings, collusion is inherently a multi‑agent phenomenon, and the use of internal representations for detecting collusion between agents remains unexplored. We introduce NARCBench, a benchmark for evaluating collusion detection under environment distribution shift, and propose five probing techniques that aggregate per‑agent deception scores to classify scenarios at the group level, evaluated across four open‑weight models (Qwen3‑32B, Llama‑3.1‑70B, DeepSeek‑R1 32B, GPT‑OSS‑20B) and six probe architectures. We frame this as a distributed anomaly detection problem, identifying three collusion signatures that map onto distinct anomaly types and detection paradigms. Every model reaches 1.00 AUROC in‑distribution; on our strongest model (Llama‑3.1‑70B), our five probing techniques achieve 0.73 to 0.93 AUROC when transferred zero‑shot to structurally different multi‑agent scenarios and 0.99 to 1.00 on a steganographic blackjack card‑counting task, with detection performance scaling with model capability. We find that no single probing technique dominates across all collusion types, consistent with the framework's prediction that different anomaly types require different detection paradigms. This work takes a step toward multi‑agent interpretability: extending white‑box inspection from single models to multi‑agent contexts, where detection requires aggregating signals across agents. These results suggest that model internals provide a complementary signal to text‑level monitoring for detecting multi‑agent collusion. Code and data available at https://github.com/aaronrose227/narcbench.

PaperId: 2069, https://arxiv.org/pdf/2604.01128.pdf GitHub

Abstract:
This paper introduces the first systematic evaluation framework for quantifying the quality and risks of papers written by modern coding agents. While AI‑driven paper writing has become a growing concern, rigorous evaluation of the quality and potential risks of AI‑written papers remains limited, and a unified understanding of their reliability is still lacking. We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal additional resources, and the result is subsequently compared against the original paper. PaperRecon disentangles the evaluation of the AI‑written papers into two orthogonal dimensions, Presentation and Hallucination, where Presentation is evaluated using a rubric and Hallucination is assessed via agentic evaluation grounded in the original paper source. For evaluation, we introduce PaperWrite‑Bench, a benchmark of 51 papers from top‑tier venues across diverse domains published after 2025. Our experiments reveal a clear trade‑off: while both ClaudeCode and Codex improve with model advances, ClaudeCode achieves higher presentation quality at the cost of more than 10 hallucinations per paper on average, whereas Codex produces fewer hallucinations but lower presentation quality. This work takes a first step toward establishing evaluation frameworks for AI‑driven paper writing and improving the understanding of its risks within the research community.

PaperId: 2070, https://arxiv.org/pdf/2604.01007.pdf GitHub

Abstract:
AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal experiences remains a critical bottleneck. Building effective lifelong memory requires navigating a vast design space spanning architecture, retrieval strategies, prompt engineering, and data pipelines; this space is too large and interconnected for manual exploration or traditional AutoML to explore effectively. We deploy an autonomous research pipeline to discover Omni‑SimpleMem, a unified multimodal memory framework for lifelong AI agents. Starting from a naïve baseline (F1=0.117 on LoCoMo), the pipeline autonomously executes ～50 experiments across two benchmarks, diagnosing failure modes, proposing architectural modifications, and repairing data pipeline bugs, all without human intervention in the inner loop. The resulting system achieves state‑of‑the‑art on both benchmarks, improving F1 by +411% on LoCoMo (0.117\to0.598) and +214% on Mem‑Gallery (0.254\to0.797) relative to the initial configurations. Critically, the most impactful discoveries are not hyperparameter adjustments: bug fixes (+175%), architectural changes (+44%), and prompt engineering (+188% on specific categories) each individually exceed the cumulative contribution of all hyperparameter tuning, demonstrating capabilities fundamentally beyond the reach of traditional AutoML. We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch, offering guidance for applying autonomous research pipelines to other AI system domains. Code is available at this https://github.com/aiming‑lab/SimpleMem.

PaperId: 2071, https://arxiv.org/pdf/2604.00986.pdf GitHub

Abstract:
We study whether phone‑use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy‑compliant behavior is not operationalized for phone‑use agents, and ordinary apps do not reveal exactly what data agents type into which form entries during execution. To make this question measurable, we introduce MyPhoneBench, a verifiable evaluation framework for privacy behavior in mobile agents. We operationalize privacy‑respecting phone use as permissioned access, minimal disclosure, and user‑controlled memory through a minimal privacy contract, iMy, and pair it with instrumented mock apps plus rule‑based auditing that make unnecessary permission requests, deceptive re‑disclosure, and unnecessary form filling observable and reproducible. Across five frontier models on 10 mobile apps and 300 tasks, we find that task success, privacy‑compliant task completion, and later‑session use of saved preferences are distinct capabilities, and no single model dominates all three. Evaluating success and privacy jointly reshuffles the model ordering relative to either metric alone. The most persistent failure mode across models is simple data minimization: agents still fill optional personal entries that the task does not require. These results show that privacy failures arise from over‑helpful execution of benign tasks, and that success‑only evaluation overestimates the deployment readiness of current phone‑use agents. All code, mock apps, and agent trajectories are publicly available at~ https://github.com/FreedomIntelligence/MyPhoneBench.

PaperId: 2072, https://arxiv.org/pdf/2604.00886.pdf GitHub

Abstract:
Document understanding and GUI interaction are among the highest‑value applications of Vision‑Language Models (VLMs), yet they impose exceptionally heavy computational burden: fine‑grained text and small UI elements demand high‑resolution inputs that produce tens of thousands of visual tokens. We observe that this cost is largely wasteful ‑‑ across document and GUI benchmarks, only 22‑‑71% of image patches are pixel‑unique, the rest being exact duplicates of another patch in the same image. We propose PixelPrune, which exploits this pixel‑level redundancy through predictive‑coding‑based compression, pruning redundant patches \emphbefore the Vision Transformer (ViT) encoder. Because it operates in pixel space prior to any neural computation, PixelPrune accelerates both the ViT encoder and the downstream LLM, covering the full inference pipeline. The method is training‑free, requires no learnable parameters, and supports pixel‑lossless compression (τ=0) as well as controlled lossy compression (τ>0). Experiments across three model scales and document and GUI benchmarks show that PixelPrune maintains competitive task accuracy while delivering up to 4.2× inference speedup and 1.9× training acceleration. Code is available at https://github.com/OPPO‑Mente‑Lab/PixelPrune.

PaperId: 2073, https://arxiv.org/pdf/2604.00813.pdf GitHub

Abstract:
End‑to‑end autonomous driving has evolved from the conventional paradigm based on sparse perception into vision‑language‑action (VLA) models, which focus on learning language descriptions as an auxiliary task to facilitate planning. In this paper, we propose an alternative Vision‑Geometry‑Action (VGA) paradigm that advocates dense 3D geometry as the critical cue for autonomous driving. As vehicles operate in a 3D world, we think dense 3D geometry provides the most comprehensive information for decision‑making. However, most existing geometry reconstruction methods (e.g., DVGT) rely on computationally expensive batch processing of multi‑frame inputs and cannot be applied to online planning. To address this, we introduce a streaming Driving Visual Geometry Transformer (DVGT‑2), which processes inputs in an online manner and jointly outputs dense geometry and trajectory planning for the current frame. We employ temporal causal attention and cache historical features to support on‑the‑fly inference. To further enhance efficiency, we propose a sliding‑window streaming strategy and use historical caches within a certain interval to avoid repetitive computations. Despite the faster speed, DVGT‑2 achieves superior geometry reconstruction performance on various datasets. The same trained DVGT‑2 can be directly applied to planning across diverse camera configurations without fine‑tuning, including closed‑loop NAVSIM and open‑loop nuScenes benchmarks.

PaperId: 2074, https://arxiv.org/pdf/2604.00801.pdf GitHub

Abstract:
Standard Mixture‑of‑Experts (MoE) models rely on centralized routing mechanisms that introduce rigid inductive biases. We propose Routing‑Free MoE which eliminates any hard‑coded centralized designs including external routers, Softmax, Top‑K and load balancing, instead encapsulating all activation functionalities within individual experts and directly optimized through continuous gradient flow, enabling each expert to determine its activation entirely on its own. We introduce a unified adaptive load‑balancing framework to simultaneously optimize both expert‑balancing and token‑balancing objectives through a configurable interpolation, allowing flexible and customizable resource allocation. Extensive experiments show that Routing‑Free MoE can consistently outperform baselines with better scalability and robustness. We analyze its behavior in detail and offer insights that may facilitate future MoE design ad optimization.

PaperId: 2075, https://arxiv.org/pdf/2604.00733.pdf GitHub

Abstract:
The memory wall remains the primary bottleneck for training large language models on consumer hardware. We introduce Spectral Compact Training (SCT), a method that replaces dense weight matrices with permanent truncated SVD factors W = U diag(s) V^T, where the full dense matrix is never materialized during training or inference. Gradients flow through the compact spectral factors via standard backpropagation, and U, V are retracted to the Stiefel manifold via QR decomposition after each optimizer step. SCT achieves up to 199x memory reduction per MLP layer at rank 32, enabling full training steps of 70B‑parameter architectures on a Steam Deck handheld (7.2 GB peak memory vs. 1,245 GB for dense FP32 training with Adam). Rank‑sweep experiments on SmolLM2‑1.7B (ranks 32‑256, 2000 steps, NVIDIA A100) show that all tested ranks converge to the same loss floor (~4.2‑4.5), identifying the learning rate schedule ‑‑ not MLP rank ‑‑ as the primary bottleneck. Rank 128 emerges as the efficiency sweet spot at 11.7x MLP compression with the lowest perplexity. GPU memory drops 46% at rank 32 while training throughput doubles.

PaperId: 2076, https://arxiv.org/pdf/2604.00716.pdf GitHub

Abstract:
Transformer language models contain localized reasoning circuits, contiguous layer blocks that improve reasoning when duplicated at inference time. Finding these circuits currently requires brute‑force sweeps costing 25 GPU hours per model. We propose CircuitProbe, which predicts circuit locations from activation statistics in under 5 minutes on CPU, providing a speedup of three to four orders of magnitude. We find that reasoning circuits come in two types: stability circuits in early layers, detected through the derivative of representation change, and magnitude circuits in late layers, detected through anomaly scoring. We validate across 9 models spanning 6 architectures, including 2025 models, confirming that CircuitProbe top predictions match or are within 2 layers of the optimal circuit in all validated cases. A scaling experiment across the Qwen 2.5 family reveals that layer duplication consistently benefits models under 3B parameters but degrades performance in 7B+ models, making this a practical scaling technique for small language models. CircuitProbe requires as few as 10 calibration examples and its predictions are stable across English, Hindi, Chinese, and French.

PaperId: 2077, https://arxiv.org/pdf/2604.00715.pdf GitHub

Abstract:
Retrieval‑augmented generation (RAG) improves language model (LM) performance by providing relevant context at test time for knowledge‑intensive situations. However, the relationship between parametric knowledge acquired during pretraining and non‑parametric knowledge accessed via retrieval remains poorly understood, especially under fixed data budgets. In this work, we systematically study the trade‑off between pretraining corpus size and retrieval store size across a wide range of model and data scales. We train OLMo‑2‑based LMs ranging from 30M to 3B parameters on up to 100B tokens of DCLM data, while varying both pretraining data scale (1‑150x the number of parameters) and retrieval store size (1‑20x), and evaluate performance across a diverse suite of benchmarks spanning reasoning, scientific QA, and open‑domain QA. We find that retrieval consistently improves performance over parametric‑only baselines across model scales and introduce a three‑dimensional scaling framework that models performance as a function of model size, pretraining tokens, and retrieval corpus size. This scaling manifold enables us to estimate optimal allocations of a fixed data budget between pretraining and retrieval, revealing that the marginal utility of retrieval depends strongly on model scale, task type, and the degree of pretraining saturation. Our results provide a quantitative foundation for understanding when and how retrieval should complement pretraining, offering practical guidance for allocating data resources in the design of scalable language modeling systems.

PaperId: 2078, https://arxiv.org/pdf/2604.00698.pdf GitHub

Abstract:
Group Relative Policy Optimization (GRPO) is widely used for reinforcement learning with verifiable rewards, but it often suffers from advantage collapse: when all rollouts in a group receive the same reward, the group yields zero relative advantage and thus no learning signal. For example, if a question is too hard for the reasoner, all sampled rollouts can be incorrect and receive zero reward. Recent work addresses this issue by adding hints or auxiliary scaffolds to such hard questions so that the reasoner produces mixed outcomes and recovers a non‑zero update. However, existing hints are usually fixed rather than adapted to the current reasoner, and a hint that creates learning signal under the hinted input does not necessarily improve the no‑hint policy used at test time. To this end, we propose Hint Learning for Reinforcement Learning (HiLL), a framework that jointly trains a hinter policy and a reasoner policy during RL. For each hard question, the hinter generates hints online conditioned on the current reasoner's incorrect rollout, allowing hint generation to adapt to the reasoner's evolving errors. We further introduce hint reliance, which measures how strongly correct hinted trajectories depend on the hint. We derive a transferability result showing that lower hint reliance implies stronger transfer from hinted success to no‑hint success, and we use this result to define a transfer‑weighted reward for training the hinter. Therefore, HiLL favors hints that not only recover informative GRPO groups, but also produce signals that are more likely to improve the original no‑hint policy. Experiments across multiple benchmarks show that HiLL consistently outperforms GRPO and prior hint‑based baselines, demonstrating the value of adaptive and transfer‑aware hint learning for RL. The code is available at https://github.com/Andree‑9/HiLL.

PaperId: 2079, https://arxiv.org/pdf/2604.00550.pdf GitHub

Abstract:
The integration of Large Language Models (LLMs) into life sciences has catalyzed the development of "AI Scientists." However, translating these theoretical capabilities into deployment‑ready research environments exposes profound infrastructural vulnerabilities. Current frameworks are bottlenecked by fragile JSON‑based tool‑calling protocols, easily disrupted execution sandboxes that lose graphical outputs, and rigid conversational interfaces inherently ill‑suited for high‑dimensional scientific data.We introduce BloClaw, a unified, multi‑modal operating system designed for Artificial Intelligence for Science (AI4S). BloClaw reconstructs the Agent‑Computer Interaction (ACI) paradigm through three architectural innovations: (1) An XML‑Regex Dual‑Track Routing Protocol that statistically eliminates serialization failures (0.2% error rate vs. 17.6% in JSON); (2) A Runtime State Interception Sandbox that utilizes Python monkey‑patching to autonomously capture and compile dynamic data visualizations (Plotly/Matplotlib), circumventing browser CORS policies; and (3) A State‑Driven Dynamic Viewport UI that morphs seamlessly between a minimalist command deck and an interactive spatial rendering engine. We comprehensively benchmark BloClaw across cheminformatics (RDKit), de novo 3D protein folding via ESMFold, molecular docking, and autonomous Retrieval‑Augmented Generation (RAG), establishing a highly robust, self‑evolving paradigm for computational research assistants. The open‑source repository is available at https://github.com/qinheming/BloClaw.

PaperId: 2080, https://arxiv.org/pdf/2604.00493.pdf GitHub

Abstract:
Chest X‑rays (CXRs) are among the most frequently performed imaging examinations worldwide, yet rising imaging volumes increase radiologist workload and the risk of diagnostic errors. Although artificial intelligence (AI) systems have shown promise for CXR interpretation, most generate only final predictions, without making explicit how visual evidence is translated into radiographic findings and diagnostic predictions. We present CheXOne, a reasoning‑enabled vision‑language model for CXR interpretation. CheXOne jointly generates diagnostic predictions and explicit, clinically grounded reasoning traces that connect visual evidence, radiographic findings, and these predictions. The model is trained on 14.7 million instruction and reasoning samples curated from 30 public datasets spanning 36 CXR interpretation tasks, using a two‑stage framework that combines instruction tuning with reinforcement learning to improve reasoning quality. We evaluate CheXOne in zero‑shot settings across visual question answering, report generation, visual grounding and reasoning assessment, covering 17 evaluation settings. CheXOne outperforms existing medical and general‑domain foundation models and achieves strong performance on independent public benchmarks. A clinical reader study demonstrates that CheXOne‑drafted reports are comparable to or better than resident‑written reports in 55% of cases, while effectively addressing clinical indications and enhancing both report writing and CXR interpretation efficiency. Further analyses involving radiologists reveal that the generated reasoning traces show high clinical factuality and provide causal support for the final predictions, offering a plausible explanation for the performance gains. These results suggest that explicit reasoning can improve model performance, interpretability and clinical utility in AI‑assisted CXR interpretation.

PaperId: 2081, https://arxiv.org/pdf/2604.00478.pdf GitHub

Abstract:
Large Language Models (LLMs) increasingly prioritize user validation over epistemic accuracy ‑ a phenomenon known as sycophancy. We present The Silicon Mirror, an orchestration framework that dynamically detects user persuasion tactics and adjusts AI behavior to maintain factual integrity. Our architecture introduces three components: (1) a Behavioral Access Control (BAC) system that restricts context layer access based on real‑time sycophancy risk scores, (2) a Trait Classifier that identifies persuasion tactics across multi‑turn dialogues, and (3) a Generator‑Critic loop where an auditor vetoes sycophantic drafts and triggers rewrites with "Necessary Friction." In a live evaluation across all 437 TruthfulQA adversarial scenarios, Claude Sonnet 4 exhibits 9.6% baseline sycophancy, reduced to 1.4% by the Silicon Mirror ‑ an 85.7% relative reduction (p < 10^‑6, OR = 7.64, Fisher's exact test). Cross‑model evaluation on Gemini 2.5 Flash reveals a 46.0% baseline reduced to 14.2% (p < 10^‑10, OR = 5.15). We characterize the validation‑before‑correction pattern as a distinct failure mode of RLHF‑trained models.

PaperId: 2082, https://arxiv.org/pdf/2604.00455.pdf GitHub

Abstract:
Recent Large Vision‑Language Models (LVLMs) have demonstrated remarkable performance across various multimodal tasks that require understanding both visual and linguistic inputs. However, object hallucination ‑‑ the generation of nonexistent objects in answers ‑‑ remains a persistent challenge. Although several approaches such as retraining and external grounding methods have been proposed to mitigate this issue, they still suffer from high data costs or structural complexity. Training‑free methods such as Contrastive Decoding (CD) are more cost‑effective, avoiding additional training or external models, but still suffer from long‑term decay, where visual grounding weakens and language priors dominate as the generation progresses. In this paper, we propose First Logit Boosting (FLB), a simple yet effective training‑free technique designed to alleviate long‑term decay in LVLMs. FLB stores the logit of the first generated token and adds it to subsequent token predictions, effectively mitigating long‑term decay of visual information. We observe that FLB (1) sustains the visual information embedded in the first token throughout generation, and (2) suppresses hallucinated words through the stabilizing effect of the ``The'' token. Experimental results show that FLB significantly reduces object hallucination across various tasks, benchmarks, and backbone models. Notably, it causes negligible inference overhead, making it highly applicable to real‑time multimodal systems. Code is available at https://github.com/jiwooha20/FLB

PaperId: 2083, https://arxiv.org/pdf/2604.00445.pdf GitHub

Abstract:
Uncertainty estimation (UE) aims to detect hallucinated outputs of large language models (LLMs) to improve their reliability. However, UE metrics often exhibit unstable performance across configurations, which significantly limits their applicability. In this work, we formalise this phenomenon as proxy failure, since most UE metrics originate from model behaviour, rather than being explicitly grounded in the factual correctness of LLM outputs. With this, we show that UE metrics become non‑discriminative precisely in low‑information regimes. To alleviate this, we propose Truth AnChoring (TAC), a post‑hoc calibration method to remedy UE metrics, by mapping the raw scores to truth‑aligned scores. Even with noisy and few‑shot supervision, our TAC can support the learning of well‑calibrated uncertainty estimates, and presents a practical calibration protocol. Our findings highlight the limitations of treating heuristic UE metrics as direct indicators of truth uncertainty, and position our TAC as a necessary step toward more reliable uncertainty estimation for LLMs. The code repository is available at https://github.com/ponhvoan/TruthAnchor/.

PaperId: 2084, https://arxiv.org/pdf/2604.00362.pdf GitHub

Abstract:
No one has independently reproduced OpenAI's published scores for gpt‑oss‑20b with tools, because the original paper discloses neither the tools nor the agent harness. We reverse‑engineered the model's in‑distribution tools: when prompted without tool definitions, gpt‑oss still calls tools from its training distribution with high statistical confidence ‑‑ a strong prior, not a hallucination. We then built a native harmony agent harness (https://github.com/borislavmavrin/harmonyagent.git) that encodes messages in the model's native format, bypassing the lossy Chat Completions conversion. Together, these yield the first independent reproduction of OpenAI's published scores: 60.4% on SWE Verified HIGH (published 60.7%), 53.3% MEDIUM (53.2%), and 91.7% on AIME25 with tools (90.4%).

PaperId: 2085, https://arxiv.org/pdf/2604.00314.pdf GitHub GitHub

Abstract:
The rapid progress of large Vision‑Language Models (VLMs) has enabled a wide range of applications, such as image understanding and Visual Question Answering (VQA). Query images are often uploaded to the cloud, where VLMs are typically hosted, hence efficient image compression becomes crucial. However, traditional human‑centric codecs are suboptimal in this setting because they preserve many task‑irrelevant details. Existing Image Coding for Machines (ICM) methods also fall short, as they assume a fixed set of downstream tasks and cannot adapt to prompt‑driven VLMs with an open‑ended variety of objectives. We propose a lightweight, plug‑and‑play, prompt‑guided prefiltering module to identify image regions most relevant to the text prompt, and consequently to the downstream task. The module preserves important details while smoothing out less relevant areas to improve compression efficiency. It is codec‑agnostic and can be applied before conventional and learned encoders. Experiments on several VQA benchmarks show that our approach achieves a 25‑50% average bitrate reduction while maintaining the same task accuracy. Our source code is available at https://github.com/bardia‑az/pgp‑vlm‑compression.

PaperId: 2086, https://arxiv.org/pdf/2604.00284.pdf GitHub

Abstract:
We formally introduce a improvisational wordplay game called Connections to explore reasoning capabilities of AI agents. Playing Connections combines skills in knowledge retrieval, summarization and awareness of cognitive states of other agents. We show how the game serves as a good benchmark for social intelligence abilities of language model based agents that go beyond the agents' own memory and deductive reasoning and also involve gauging the understanding capabilities of other agents. Finally, we show how through communication with other agents in a constrained environment, AI agents must demonstrate social awareness and intelligence in games involving collaboration.

PaperId: 2087, https://arxiv.org/pdf/2604.00235.pdf GitHub

Abstract:
Long‑context decoding in LLMs is IO‑bound: each token re‑reads an ever‑growing KV cache. Prior accelerations cut bytes via compression, which lowers fidelity, or selection/eviction, which restricts what remains accessible, and both can degrade delayed recall and long‑form generation. We introduce MAC‑Attention, a fidelity‑ and access‑preserving alternative that accelerates decoding by reusing prior attention computations for semantically similar recent queries. It starts with a match stage that performs pre‑RoPE L2 matching over a short local window; an amend stage rectifies the reused attention by recomputing a small band near the match boundary; and a complete stage fuses the rectified results with fresh attention computed on the KV tail through a numerically stable merge. On a match hit, the compute and bandwidth complexity is constant regardless of context length. The method is model‑agnostic and composes with IO‑aware kernels, paged‑KV managers, and MQA/GQA. Across LongBench v2 (120K), RULER (120K), and LongGenBench (16K continuous generation), compared to the latest FlashInfer library, MAC‑Attention reduces KV accesses by up to 99%, cuts token generation latency by over 60% at 128K, and achieves over 14.3x attention‑phase speedups, up to 2.6x end‑to‑end, while maintaining full‑attention quality. By reusing computation, MAC‑Attention delivers long‑context inference that is both fast and faithful. Code is available here: https://github.com/YJHMITWEB/MAC‑Attention.git

PaperId: 2088, https://arxiv.org/pdf/2604.00131.pdf GitHub

Abstract:
Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues. In contrast, memory‑augmented LLM agents rely on "always‑on" retrieval and "flat" memory storage, causing high interference and latency as histories grow. We introduce Oblivion, a memory control framework that casts forgetting as decay‑driven reductions in accessibility, not explicit deletion. Oblivion decouples memory control into read and write paths. The read path decides when to consult memory, based on agent uncertainty and memory buffer sufficiency, avoiding redundant always‑on access. The write path decides what to strengthen, by reinforcing memories contributing to forming the response. Together, this enables hierarchical memory organization that maintains persistent high‑level strategies while dynamically loading details as needed. We evaluate on both static and dynamic long‑horizon interaction benchmarks. Results show that Oblivion dynamically adapts memory access and reinforcement, balancing learning and forgetting under shifting contexts, highlighting that memory control is essential for effective LLM‑agentic reasoning.

PaperId: 2089, https://arxiv.org/pdf/2604.00057.pdf GitHub

Abstract:
Soccer commentary plays a crucial role in enhancing the soccer game viewing experience for audiences. Previous studies in automatic soccer commentary generation typically adopt an end‑to‑end method to generate anonymous live text commentary. Such generated commentary is insufficient in the context of real‑world live televised commentary, as it contains anonymous entities, context‑dependent errors and lacks statistical insights of the game events. To bridge the gap, we propose GameSight, a two‑stage model to address soccer commentary generation as a knowledge‑enhanced visual reasoning task, enabling live‑televised‑like knowledgeable commentary with accurate reference to entities (players and teams). GameSight starts by performing visual reasoning to align anonymous entities with fine‑grained visual and contextual analysis. Subsequently, the entity‑aligned commentary is refined with knowledge by incorporating external historical statistics and iteratively updated internal game state information. Consequently, GameSight improves the player alignment accuracy by 18.5% on SN‑Caption‑test‑align dataset compared to Gemini 2.5‑pro. Combined with further knowledge enhancement, GameSight outperforms in segment‑level accuracy and commentary quality, as well as game‑level contextual relevance and structural composition. We believe that our work paves the way for a more informative and engaging human‑centric experience with the AI sports application. Demo Page: https://gamesight2025.github.io/gamesight2025

PaperId: 2090, https://arxiv.org/pdf/2604.00043.pdf GitHub

Abstract:
Non‑Axiomatic Reasoning Systems (NARS) provide a framework for building adaptive agents that operate under insufficient knowledge and resources. However, the standard input language, Narsese, poses a usability barrier: its dense symbolic notation, overloaded punctuation, and implicit conventions make programs difficult to read, write, and maintain. We present DriftScript, a Lisp‑like domain‑specific language that compiles to Narsese. DriftScript provides source‑level constructs covering the major sentence and term forms used in Non‑Axiomatic Logic (NAL) levels 1 through 8, including inheritance, temporal implication, variable quantification, sequential conjunction, and operation invocation, while replacing symbolic syntax with readable keyword‑based S‑expressions. The compiler is a zero‑dependency, four‑stage pipeline implemented in 1,941 lines of C99. When used with the DriftNARS engine, DriftScript programs connect to external systems through four structured callback types and an HTTP operation registry, enabling a sense‑reason‑act loop for autonomous agents. We describe the language design and formal grammar, detail the compiler architecture, and evaluate the compiler through a 106‑case test suite, equivalence testing against hand‑written Narsese, a NAL coverage analysis, structural readability metrics, and compilation benchmarks. The source code is available at https://github.com/seamus‑brady/DriftNARS. This paper focuses on the design and implementation of the DriftScript language and its embedding into DriftNARS, rather than on new inference algorithms for NARS itself.

PaperId: 2091, https://arxiv.org/pdf/2604.00016.pdf GitHub

Abstract:
The validity of online behavioral research relies on study participants being human rather than machine. In the past, it was possible to detect machines by posing simple challenges that were easily solved by humans but not by machines. General‑purpose agents based on large language models (LLMs) can now solve many of these challenges, threatening the validity of online behavioral research. Here we explore the idea of detecting humanness by using tasks that machines can solve too well to be human. Specifically, we probe for the existence of an established human cognitive constraint: limited working memory capacity. We show that cognitive modeling on a standard serial recall task can be used to distinguish online participants from LLMs even when the latter are specifically instructed to mimic human working memory constraints. Our results demonstrate that it is viable to use well‑established cognitive phenomena to distinguish LLMs from humans.

PaperId: 2092, https://arxiv.org/pdf/2604.00004.pdf GitHub

Abstract:
The extension of context windows in Large Language Models is typically facilitated by scaling positional encodings followed by lightweight Continual Pre‑Training (CPT). While effective for processing long sequences, this paradigm often disrupts original model capabilities, leading to performance degradation on standard short‑text benchmarks. We propose LinearARD, a self‑distillation method that restores Rotary Position Embeddings (RoPE)‑scaled students through attention‑structure consistency with a frozen native‑RoPE teacher. Rather than matching opaque hidden states, LinearARD aligns the row‑wise distributions of dense Q/Q, K/K, and V/V self‑relation matrices to directly supervise attention dynamics. To overcome the quadratic memory bottleneck of n × n relation maps, we introduce a linear‑memory kernel. This kernel leverages per‑token log‑sum‑exp statistics and fuses logit recomputation into the backward pass to compute exact Kullback‑Leibler divergence and gradients. On LLaMA2‑7B extended from 4K to 32K, LinearARD recovers 98.3% of the short‑text performance of state‑of‑the‑art baselines while surpassing them on long‑context benchmarks. Notably, our method achieves these results using only 4.25M training tokens compared to the 256M tokens required by LongReD and CPT. Our code is available at https://github.com/gracefulning/LinearARD.

PaperId: 2093, https://arxiv.org/pdf/2603.29993.pdf GitHub

Abstract:
Myopic Optimization with Non‑myopic Approval (MONA) mitigates multi‑step reward hacking by restricting the agent's planning horizon while supplying far‑sighted approval as a training signal~\citefarquhar2025mona. The original paper identifies a critical open question: how the method of constructing approval ‑‑ particularly the degree to which approval depends on achieved outcomes ‑‑ affects whether MONA's safety guarantees hold. We present a reproduction‑first extension of the public MONA Camera Dropbox environment that (i)~repackages the released codebase as a standard Python project with scripted PPO training, (ii)~confirms the published contrast between ordinary RL (91.5% reward‑hacking rate) and oracle MONA (0.0% hacking rate) using the released reference arrays, and (iii)~introduces a modular learned‑approval suite spanning oracle, noisy, misspecified, learned, and calibrated approval mechanisms. In reduced‑budget pilot sweeps across approval methods, horizons, dataset sizes, and calibration strategies, the best calibrated learned‑overseer run achieves zero observed reward hacking but substantially lower intended‑behavior rates than oracle MONA (11.9% vs.\ 99.9%), consistent with under‑optimization rather than re‑emergent hacking. These results operationalize the MONA paper's approval‑spectrum conjecture as a runnable experimental object and suggest that the central engineering challenge shifts from proving MONA's concept to building learned approval models that preserve sufficient foresight without reopening reward‑hacking channels. Code, configurations, and reproduction commands are publicly available. https://github.com/codernate92/mona‑camera‑dropbox‑repro

PaperId: 2094, https://arxiv.org/pdf/2603.29908.pdf GitHub

Abstract:
Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in safety‑critical applications. We propose C‑TRAIL, a framework built on a Commonsense World that couples LLM‑derived commonsense with a trust mechanism to guide trajectory planning. C‑TRAIL operates through a closed‑loop Recall, Plan, and Update cycle: the Recall module queries an LLM for semantic relations and quantifies their reliability via a dual‑trust mechanism; the Plan module injects trust‑weighted commonsense into Monte Carlo Tree Search (MCTS) through a Dirichlet trust policy; and the Update module adaptively refines trust scores and policy parameters from environmental feedback. Experiments on four simulated scenarios in Highway‑env and two real‑world levelXData datasets (highD, rounD) show that C‑TRAIL consistently outperforms state‑of‑the‑art baselines, reducing ADE by 40.2%, FDE by 51.7%, and improving SR by 16.9 percentage points on average. The source code is available at https://github.com/ZhihongCui/CTRAIL.

PaperId: 2095, https://arxiv.org/pdf/2603.29902.pdf GitHub

Abstract:
Interleaved text‑and‑image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way to convey complex information. Current paradigms rely on either image generation or retrieval augmentation, yet they typically treat the two as mutually exclusive paths, failing to unify factuality with creativity. We argue that the next milestone in this field is Agentic Tool Planning, where the model serves as a central controller that autonomously determines when, where, and which tools to invoke to produce interleaved responses for visual‑critical queries. To systematically evaluate this paradigm, we introduce ATP‑Bench, a novel benchmark comprising 7,702 QA pairs (including 1,592 VQA pairs) across eight categories and 25 visual‑critical intents, featuring human‑verified queries and ground truths. Furthermore, to evaluate agentic planning independent of end‑to‑end execution and changing tool backends, we propose a Multi‑Agent MLLM‑as‑a‑Judge (MAM) system. MAM evaluates tool‑call precision, identifies missed opportunities for tool use, and assesses overall response quality without requiring ground‑truth references. Our extensive experiments on 10 state‑of‑the‑art MLLMs reveal that models struggle with coherent interleaved planning and exhibit significant variations in tool‑use behavior, highlighting substantial room for improvement and providing actionable guidance for advancing interleaved generation. Dataset and code are available at https://github.com/Qwen‑Applications/ATP‑Bench.

PaperId: 2096, https://arxiv.org/pdf/2603.29844.pdf GitHub

Abstract:
The development of Vision‑Language‑Action (VLA) models has been significantly accelerated by pre‑trained Vision‑Language Models (VLMs). However, most existing end‑to‑end VLAs treat the VLM primarily as a multimodal encoder, directly mapping vision‑language features to low‑level actions. This paradigm underutilizes the VLM's potential in high‑level decision making and introduces training instability, frequently degrading its rich semantic representations. To address these limitations, we introduce DIAL, a framework bridging high‑level decision making and low‑level motor execution through a differentiable latent intent bottleneck. Specifically, a VLM‑based System‑2 performs latent world modeling by synthesizing latent visual foresight within the VLM's native feature space; this foresight explicitly encodes intent and serves as the structural bottleneck. A lightweight System‑1 policy then decodes this predicted intent together with the current observation into precise robot actions via latent inverse dynamics. To ensure optimization stability, we employ a two‑stage training paradigm: a decoupled warmup phase where System‑2 learns to predict latent futures while System‑1 learns motor control under ground‑truth future guidance within a unified feature space, followed by seamless end‑to‑end joint optimization. This enables action‑aware gradients to refine the VLM backbone in a controlled manner, preserving pre‑trained knowledge. Extensive experiments on the RoboCasa GR1 Tabletop benchmark show that DIAL establishes a new state‑of‑the‑art, achieving superior performance with 10x fewer demonstrations than prior methods. Furthermore, by leveraging heterogeneous human demonstrations, DIAL learns physically grounded manipulation priors and exhibits robust zero‑shot generalization to unseen objects and novel configurations during real‑world deployment on a humanoid robot.

PaperId: 2097, https://arxiv.org/pdf/2603.29828.pdf GitHub

Abstract:
Scientific discovery increasingly depends on high‑throughput characterization, yet automation is hindered by proprietary GUIs and the limited generalizability of existing API‑based systems. We present Owl‑AuraID, a software‑hardware collaborative embodied agent system that adopts a GUI‑native paradigm to operate instruments through the same interfaces as human experts. Its skill‑centric framework integrates Type‑1 (GUI operation) and Type‑2 (data analysis) skills into end‑to‑end workflows, connecting physical sample handling with scientific interpretation. Owl‑AuraID demonstrates broad coverage across ten categories of precision instruments and diverse workflows, including multimodal spectral analysis, microscopic imaging, and crystallographic analysis, supporting modalities such as FTIR, NMR, AFM, and TGA. Overall, Owl‑AuraID provides a practical, extensible foundation for autonomous laboratories and illustrates a path toward evolving laboratory intelligence through reusable operational and analytical skills. The code are available at https://github.com/OpenOwlab/AuraID.

PaperId: 2098, https://arxiv.org/pdf/2603.29678.pdf GitHub

Abstract:
Agent traces carry increasing analytical value in agentic systems and context engineering, yet most prior work treats conversation format as a trivial implementation detail. Modern agent conversations, however, contain deeply structured content, including nested tool calls and results, chain‑of‑thought reasoning blocks, sub‑agent invocations, context‑window compaction boundaries, and harness‑injected system directives, whose complexity far exceeds that of simple user‑assistant exchanges. Feeding such traces to a reflector or other analytical mechanism in plain text, JSON, YAML, or via grep can materially degrade analysis quality. This paper presents VCC (View‑oriented Conversation Compiler), a compiler (lex, parse, IR, lower, emit) that transforms raw agent JSONL logs into a family of structured views: a full view (lossless transcript serving as the canonical line‑number coordinate system), a user‑interface (UI) view (reconstructing the interaction as the user actually perceived it), and an adaptive view (a structure‑preserving projection governed by a relevance predicate). In a context‑engineering experiment on AppWorld, replacing only the reflector's input format, from raw JSONL to VCC‑compiled views, leads to higher pass rates across all three model configurations tested, while cutting reflector token consumption by half to two‑thirds and producing more concise learned memory. These results suggest that message format functions as infrastructure for context engineering, not as an incidental implementation choice.

PaperId: 2099, https://arxiv.org/pdf/2603.29640.pdf GitHub

Abstract:
Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well‑scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long‑horizon, and weakly supervised research loops that drive real AI progress. We present ASI‑Evolve, an agentic framework for AI‑for‑AI research that closes this loop through a learn‑design‑experiment‑analyze cycle. ASI‑Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI‑Evolve is the first unified framework to demonstrate AI‑driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3x the gain of recent human‑designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI‑for‑AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI‑Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed‑loop AI research.

PaperId: 2100, https://arxiv.org/pdf/2603.29602.pdf GitHub

Abstract:
Existing multi‑turn image editing paradigms are often confined to isolated single‑step execution. Due to a lack of context‑awareness and closed‑loop feedback mechanisms, they are prone to error accumulation and semantic drift during multi‑turn interactions, ultimately resulting in severe structural distortion of the generated images. For that, we propose IMAGAgent, a multi‑turn image editing agent framework based on a "plan‑execute‑reflect" closed‑loop mechanism that achieves deep synergy among instruction parsing, tool scheduling, and adaptive correction within a unified pipeline. Specifically, we first present a constraint‑aware planning module that leverages a vision‑language model (VLM) to precisely decompose complex natural language instructions into a series of executable sub‑tasks, governed by target singularity, semantic atomicity, and visual perceptibility. Then, the tool‑chain orchestration module dynamically constructs execution paths based on the current image, the current sub‑task, and the historical context, enabling adaptive scheduling and collaborative operation among heterogeneous operation models covering image retrieval, segmentation, detection, and editing. Finally, we devise a multi‑expert collaborative reflection mechanism where a central large language model (LLM) receives the image to be edited and synthesizes VLM critiques into holistic feedback, simultaneously triggering fine‑grained self‑correction and recording feedback outcomes to optimize future decisions. Extensive experiments on our constructed MTEditBench and the MagicBrush dataset demonstrate that IMAGAgent achieves performance significantly superior to existing methods in terms of instruction consistency, editing precision, and overall quality. The code is available at https://github.com/hackermmzz/IMAGAgent.git.

PaperId: 2101, https://arxiv.org/pdf/2603.29570.pdf GitHub

Abstract:
Preserving intangible cultural dances rooted in centuries of tradition and governed by strict structural and symbolic rules presents unique challenges in the digital era. Among these, Bharatanatyam, a classical Indian dance form, stands out for its emphasis on codified adavus and precise key postures. Accurately generating these postures is crucial not only for maintaining anatomical and stylistic integrity, but also for enabling effective documentation, analysis, and transmission to broader global audiences through digital means. We propose a pose‑aware generative framework integrated with a pose estimation module, guided by keypoint‑based loss and pose consistency constraints. These supervisory signals ensure anatomical accuracy and stylistic integrity in the synthesized outputs. We evaluate four configurations: standard conditional generative adversarial network (cGAN), cGAN with pose supervision, conditional diffusion, and conditional diffusion with pose supervision. Each model is conditioned on key posture class labels and optimized to maintain geometric structure. In both cGAN and conditional diffusion settings, the integrated pose guidance aligns generated poses with ground‑truth keypoint structures, promoting cultural fidelity. Our results demonstrate that incorporating pose supervision significantly enhances the quality, realism, and authenticity of generated Bharatanatyam postures. This framework provides a scalable approach for the digital preservation, education, and dissemination of traditional dance forms, enabling high‑fidelity generation without compromising cultural precision. Code is available at https://github.com/jagidsh/Generating‑Key‑Postures‑of‑Bharatanatyam‑Adavus‑with‑Pose‑Estimation.

PaperId: 2102, https://arxiv.org/pdf/2603.29552.pdf GitHub

Abstract:
Multilingualism is incredibly common around the world, leading to many important theoretical and practical questions about how children learn multiple languages at once. For example, does multilingual acquisition lead to delays in learning? Are there better and worse ways to structure multilingual input? Many correlational studies address these questions, but it is surprisingly difficult to get definitive answers because children cannot be randomly assigned to be multilingual and data are typically not matched between languages. We use language model training as a method for simulating a variety of highly controlled exposure conditions, and create matched 100M‑word mono‑ and bilingual datasets using synthetic data and machine translation. We train GPT‑2 models on monolingual and bilingual data organized to reflect a range of exposure regimes, and evaluate their performance on perplexity, grammaticality, and semantic knowledge. Across model scales and measures, bilingual models perform similarly to monolingual models in one language, but show strong performance in the second language as well. These results suggest that there are no strong differences between different bilingual exposure regimes, and that bilingual input poses no in‑principle challenges for agnostic statistical learners.

PaperId: 2103, https://arxiv.org/pdf/2603.29537.pdf GitHub GitHub

Abstract:
Network traffic classification using self‑supervised pre‑training models based on Masked Autoencoders (MAE) has demonstrated a huge potential. However, existing methods are confined to isolated byte‑level reconstruction of individual flows, lacking adequate perception of the multi‑granularity contextual relationship in traffic. To address this limitation, we propose Mean MAE (MMAE), a teacher‑student MAE paradigm with flow mixing strategy for building encrypted traffic pre‑training model. MMAE employs a self‑distillation mechanism for teacher‑student interaction, where the teacher provides unmasked flow‑level semantic supervision to advance the student from local byte reconstruction to multi‑granularity comprehension. To break the information bottleneck in individual flows, we introduce a dynamic Flow Mixing (FlowMix) strategy to replace traditional random masking mechanism. By constructing challenging cross‑flow mixed samples with interferences, it compels the model to learn discriminative representations from distorted tokens. Furthermore, we design a Packet‑importance aware Mask Predictor (PMP) equipped with an attention bias mechanism that leverages packet‑level side‑channel statistics to dynamically mask tokens with high semantic density. Numerous experiments on a number of datasets covering encrypted applications, malware, and attack traffic demonstrate that MMAE achieves state‑of‑the‑art performance. The code is available at https://github.com/lx6c78/MMAE

PaperId: 2104, https://arxiv.org/pdf/2603.29522.pdf GitHub

Abstract:
Modern language models (LMs) must be trained on many orders of magnitude more words of training data than human children receive before they begin to produce useful behavior. Assessing the nature and origins of this "data gap" requires benchmarking LMs on human‑scale datasets to understand how linguistic knowledge emerges from children's natural training data. Using transcripts from the BabyView dataset (videos from children ages 6‑36 months), we investigate (1) scaling performance at child‑scale data regimes, (2) variability in model performance across datasets from different children's experiences and linguistic predictors of dataset quality, and (3) relationships between model and child language learning outcomes. LMs trained on child data show acceptable scaling for grammar tasks, but lower scaling on semantic and world knowledge tasks than models trained on synthetic data; we also observe substantial variability on data from different children. Beyond dataset size, performance is most associated with a combination of distributional and interactional linguistic features, broadly consistent with what makes high‑quality input for child language development. Finally, model likelihoods for individual words correlate with children's learning of those words, suggesting that properties of child‑directed input may influence both model learning and human language development. Overall, understanding what properties make language data efficient for learning can enable more powerful small‑scale language models while also shedding light on human language acquisition.

PaperId: 2105, https://arxiv.org/pdf/2603.29520.pdf GitHub GitHub

Abstract:
Encrypted traffic classification is a critical task for network security. While deep learning has advanced this field, the occlusion of payload semantics by encryption severely challenges standard modeling approaches. Most existing frameworks rely on static and homogeneous pipelines that apply uniform parameter sharing and static fusion strategies across all inputs. This one‑size‑fits‑all static design is inherently flawed: by forcing structured headers and randomized payloads into a unified processing pipeline, it inevitably entangles the raw protocol signals with stochastic encryption noise, thereby degrading the fine‑grained discriminative features. In this paper, we propose TrafficMoE, a framework that breaks through the bottleneck of static modeling by establishing a Disentangle‑Filter‑Aggregate (DFA) paradigm. Specifically, to resolve the structural between‑components conflict, the architecture disentangles headers and payloads using dual‑branch sparse Mixture‑of‑Experts (MoE), enabling modality‑specific modeling. To mitigate the impact of stochastic noise, an uncertainty‑aware filtering mechanism is introduced to quantify reliability and selectively suppress high‑variance representations. Finally, to overcome the limitations of static fusion, a routing‑guided strategy aggregates cross‑modality features dynamically, that adaptively weighs contributions based on traffic context. With this DFA paradigm, TrafficMoE maximizes representational efficiency by focusing solely on the most discriminative traffic features. Extensive experiments on six datasets demonstrate TrafficMoE consistently outperforms state‑of‑the‑art methods, validating the necessity of heterogeneity‑aware modeling in encrypted traffic analysis. The source code is publicly available at https://github.com/Posuly/TrafficMoE_main.

PaperId: 2106, https://arxiv.org/pdf/2603.29493.pdf GitHub

Abstract:
Memory‑augmented Large Language Models (LLMs) are essential for developing capable, long‑term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operations, such as extraction, updating, and retrieval, has emerged as a highly promising research direction. However, existing implementations remain highly fragmented and task‑specific, lacking a unified infrastructure to streamline the integration, training, and evaluation of these complex pipelines. To address this gap, we present MemFactory, the first unified, highly modular training and inference framework specifically designed for memory‑augmented agents. Inspired by the success of unified fine‑tuning frameworks like LLaMA‑Factory, MemFactory abstracts the memory lifecycle into atomic, plug‑and‑play components, enabling researchers to seamlessly construct custom memory agents via a "Lego‑like" architecture. Furthermore, the framework natively integrates Group Relative Policy Optimization (GRPO) to fine‑tune internal memory management policies driven by multi‑dimensional environmental rewards. MemFactory provides out‑of‑the‑box support for recent cutting‑edge paradigms, including Memory‑R1, RMM, and MemAgent. We empirically validate MemFactory on the open‑source MemAgent architecture using its publicly available training and evaluation data. Across the evaluation sets, MemFactory improves performance over the corresponding base models on average, with relative gains of up to 14.8%. By providing a standardized, extensible, and easy‑to‑use infrastructure, MemFactory significantly lowers the barrier to entry, paving the way for future innovations in memory‑driven AI agents.

PaperId: 2107, https://arxiv.org/pdf/2603.29419.pdf GitHub

Abstract:
Understanding object affordances is essential for enabling robots to perform purposeful and fine‑grained interactions in diverse and unstructured environments. However, existing approaches either rely on retrieval, which is fragile due to sparsity and coverage gaps, or on large‑scale models, which frequently mislocalize contact points and mispredict post‑contact actions when applied to unseen categories, thereby hindering robust generalization. We introduce Retrieval‑Augmented Affordance Prediction (RAAP), a framework that unifies affordance retrieval with alignment‑based learning. By decoupling static contact localization and dynamic action direction, RAAP transfers contact points via dense correspondence and predicts action directions through a retrieval‑augmented alignment model that consolidates multiple references with dual‑weighted attention. Trained on compact subsets of DROID and HOI4D with as few as tens of samples per task, RAAP achieves consistent performance across unseen objects and categories, and enables zero‑shot robotic manipulation in both simulation and the real world. Project website: https://github.com/SEU‑VIPGroup/RAAP.

PaperId: 2108, https://arxiv.org/pdf/2603.29410.pdf GitHub

Abstract:
Pre‑trained vision‑language models (VLMs) exhibit strong zero‑shot generalization but remain vulnerable to adversarial perturbations. Existing classification‑guided adversarial fine‑tuning methods often disrupt pre‑trained cross‑modal alignment, weakening visual‑textual correspondence and degrading zero‑shot performance. In this paper, we propose an Alignment‑Guided Fine‑Tuning (AGFT) framework that enhances zero‑shot adversarial robustness while preserving the cross‑modal semantic structure. Unlike label‑based methods that rely on hard labels and fail to maintain the relative relationships between image and text, AGFT leverages the probabilistic predictions of the original model for text‑guided adversarial training, which aligns adversarial visual features with textual embeddings via soft alignment distributions, improving zero‑shot adversarial robustness. To address structural discrepancies introduced by fine‑tuning, we introduce a distribution consistency calibration mechanism that adjusts the robust model output to match a temperature‑scaled version of the pre‑trained model predictions. Extensive experiments across multiple zero‑shot benchmarks demonstrate that AGFT outperforms state‑of‑the‑art methods while significantly improving zero‑shot adversarial robustness.

PaperId: 2109, https://arxiv.org/pdf/2603.29405.pdf GitHub

Abstract:
Large Vision‑Language Models have demonstrated exceptional performance in multimodal reasoning and complex scene understanding. However, these models still face significant hallucination issues, where outputs contradict visual facts. Recent research on hallucination mitigation has focused on retraining methods and Contrastive Decoding (CD) methods. While both methods perform well, retraining methods require substantial training resources, and CD methods introduce dual inference overhead. These factors hinder their practical applicability. To address the above issue, we propose a framework for dynamically detecting hallucination representations and performing hallucination‑eliminating edits on these representations. With minimal additional computational cost, we achieve state‑of‑the‑art performance on existing benchmarks. Extensive experiments demonstrate the effectiveness of our approach, highlighting its efficient and robust hallucination elimination capability and its powerful controllability over hallucinations. Code is available at https://github.com/ASGO‑MM/HIRE

PaperId: 2110, https://arxiv.org/pdf/2603.29387.pdf GitHub

Abstract:
In this paper, we propose Extend3D, a training‑free pipeline for 3D scene generation from a single image, built upon an object‑centric 3D generative model. To overcome the limitations of fixed‑size latent spaces in object‑centric models for representing wide scenes, we extend the latent space in the x and y directions. Then, by dividing the extended latent space into overlapping patches, we apply the object‑centric 3D generative model to each patch and couple them at each time step. Since patch‑wise 3D generation with image conditioning requires strict spatial alignment between image and latent patches, we initialize the scene using a point cloud prior from a monocular depth estimator and iteratively refine occluded regions through SDEdit. We discovered that treating the incompleteness of 3D structure as noise during 3D refinement enables 3D completion via a concept, which we term under‑noising. Furthermore, to address the sub‑optimality of object‑centric models for sub‑scene generation, we optimize the extended latent during denoising, ensuring that the denoising trajectories remain consistent with the sub‑scene dynamics. To this end, we introduce 3D‑aware optimization objectives for improved geometric structure and texture fidelity. We demonstrate that our method yields better results than prior methods, as evidenced by human preference and quantitative experiments.

PaperId: 2111, https://arxiv.org/pdf/2603.29291.pdf GitHub

Abstract:
Composed Image Retrieval (CIR) uses a reference image and a modification text as a query to retrieve a target image satisfying the requirement of ``modifying the reference image according to the text instructions''. However, existing CIR methods face two limitations: (1) frequency bias leading to ``Rare Sample Neglect'', and (2) susceptibility of similarity scores to interference from hard negative samples and noise. To address these limitations, we confront two key challenges: asymmetric rare semantic localization and robust similarity estimation under hard negative samples. To solve these challenges, we propose the Modification frEquentation‑rarity baLance neTwork MELT. MELT assigns increased attention to rare modification semantics in multimodal contexts while applying diffusion‑based denoising to hard negative samples with high similarity scores, enhancing multimodal fusion and matching. Extensive experiments on two CIR benchmarks validate the superior performance of MELT. Codes are available at https://github.com/luckylittlezhi/MELT.

PaperId: 2112, https://arxiv.org/pdf/2603.29232.pdf GitHub

Abstract:
Large language models (LLMs) are widely applied to data analytics over documents, yet direct reasoning over long, noisy documents remains brittle and error‑prone. Hence, we study document question answering (QA) that consolidates dispersed evidence into a structured output (e.g., a table, graph, or chunks) to support reliable, verifiable QA. We propose a two‑pillar framework, LiteCoST, to achieve both high accuracy and low latency with small language models (SLMs). Pillar 1: Chain‑of‑Structured‑Thought (CoST). We introduce a CoST template, a schema‑aware instruction that guides a strong LLM to produce both a step‑wise CoST trace and the corresponding structured output. The process induces a minimal structure, normalizes entities/units, aligns records, serializes the output, and verifies/refines it, yielding auditable supervision. Pillar 2: SLM fine‑tuning. The compact models are trained on LLM‑generated CoST data in two stages: Supervised Fine‑Tuning for structural alignment, followed by Group Relative Policy Optimization (GRPO) incorporating triple rewards for answer/format quality and process consistency. By distilling structure‑first behavior into SLMs, this approach achieves LLM‑comparable quality on multi‑domain long‑document QA using 3B/7B SLMs, while delivering 2‑4x lower latency than GPT‑4o and DeepSeek‑R1 (671B). The code is available at https://github.com/HKUSTDial/LiteCoST.

PaperId: 2113, https://arxiv.org/pdf/2603.29199.pdf GitHub

Abstract:
The AEC‑Bench is a multimodal benchmark for evaluating agentic systems on real‑world tasks in the Architecture, Engineering, and Construction (AEC) domain. The benchmark covers tasks requiring drawing understanding, cross‑sheet reasoning, and construction project‑level coordination. This report describes the benchmark motivation, dataset taxonomy, evaluation protocol, and baseline results across several domain‑specific foundation model harnesses. We use AEC‑Bench to identify consistent tools and harness design techniques that uniformly improve performance across foundation models in their own base harnesses, such as Claude Code and Codex. We openly release our benchmark dataset, agent harness, and evaluation code for full replicability at https://github.com/nomic‑ai/aec‑bench under an Apache 2 license.

PaperId: 2114, https://arxiv.org/pdf/2603.29183.pdf GitHub

Abstract:
Open‑set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies during testing. Current approaches rely on simple augmentation methods to generate pseudo anomalies that replicate unseen anomalies. Despite being promising in image data, these methods are found to be ineffective in time series data due to the failure to preserve its sequential nature, resulting in trivial or unrealistic anomaly patterns. They are further plagued when the training data is contaminated with unlabeled anomalies. This work introduces IMPACT, a novel framework that leverages \underlineinfluence \underlinemodeling for o\underlinepen‑set time series \underlineanomaly dete\underlinection, to tackle these challenges. The key insight is to i) learn an influence function that can accurately estimate the impact of individual training samples on the modeling, and then ii) leverage these influence scores to generate semantically divergent yet realistic unseen anomalies for time series while repurposing high‑influential samples as supervised anomalies for anomaly decontamination. Extensive experiments show that IMPACT significantly outperforms existing state‑of‑the‑art methods, showing superior accuracy under varying OSAD settings and contamination rates. Code is available at https://github.com/mala‑lab/IMPACT.

PaperId: 2115, https://arxiv.org/pdf/2603.29139.pdf GitHub

Abstract:
Recent advances in large language models (LLMs) have enabled agentic systems that translate natural language intent into executable scientific visualization (SciVis) tasks. Despite rapid progress, the community lacks a principled and reproducible benchmark for evaluating these emerging SciVis agents in realistic, multi‑step analysis settings. We present SciVisAgentBench, a comprehensive and extensible benchmark for evaluating scientific data analysis and visualization agents. Our benchmark is grounded in a structured taxonomy spanning four dimensions: application domain, data type, complexity level, and visualization operation. It currently comprises 108 expert‑crafted cases covering diverse SciVis scenarios. To enable reliable assessment, we introduce a multimodal outcome‑centric evaluation pipeline that combines LLM‑based judging with deterministic evaluators, including image‑based metrics, code checkers, rule‑based verifiers, and case‑specific evaluators. We also conduct a validity study with 12 SciVis experts to examine the agreement between human and LLM judges. Using this framework, we evaluate representative SciVis agents and general‑purpose coding agents to establish initial baselines and reveal capability gaps. SciVisAgentBench is designed as a living benchmark to support systematic comparison, diagnose failure modes, and drive progress in agentic SciVis. The benchmark is available at https://scivisagentbench.github.io/.

PaperId: 2116, https://arxiv.org/pdf/2603.29112.pdf GitHub

Abstract:
We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interests from engagement data. We propose two novel metric families: Interest Groundedness (IG), decomposed into precision and recall components to separately penalize hallucinated interest categories and reward coverage, and Interest Specificity (IS), which assesses the distinctiveness of verified LLM‑predicted user profiles. We release a synthetic dataset constructed on real user interactions on a global short‑form video platform. Our dataset contains both implicit and explicit engagement signals and rich textual descriptions. We validate our dataset fidelity against user surveys, and evaluate eight open‑weight LLMs spanning 7B to 120B parameters. Our findings reveal performance bottlenecks in current LLMs, particularly their limited ability to accurately count and attribute engagement signals across heterogeneous interaction types.

PaperId: 2117, https://arxiv.org/pdf/2603.29029.pdf GitHub GitHub

Abstract:
Recent multimodal face generation models address the spatial control limitations of text‑to‑image diffusion models by augmenting text‑based conditioning with spatial priors such as segmentation masks, sketches, or edge maps. This multimodal fusion enables controllable synthesis aligned with both high‑level semantic intent and low‑level structural layout. However, most existing approaches typically extend pre‑trained text‑to‑image pipelines by appending auxiliary control modules or stitching together separate uni‑modal networks. These ad hoc designs inherit architectural constraints, duplicate parameters, and often fail under conflicting modalities or mismatched latent spaces, limiting their ability to perform synergistic fusion across semantic and spatial domains. We introduce MMFace‑DiT, a unified dual‑stream diffusion transformer engineered for synergistic multimodal face synthesis. Its core novelty lies in a dual‑stream transformer block that processes spatial (mask/sketch) and semantic (text) tokens in parallel, deeply fusing them through a shared Rotary Position‑Embedded (RoPE) Attention mechanism. This design prevents modal dominance and ensures strong adherence to both text and structural priors to achieve unprecedented spatial‑semantic consistency for controllable face generation. Furthermore, a novel Modality Embedder enables a single cohesive model to dynamically adapt to varying spatial conditions without retraining. MMFace‑DiT achieves a 40% improvement in visual fidelity and prompt alignment over six state‑of‑the‑art multimodal face generation models, establishing a flexible new paradigm for end‑to‑end controllable generative modeling. The code and dataset are available on our project page: https://vcbsl.github.io/MMFace‑DiT/

PaperId: 2118, https://arxiv.org/pdf/2603.28824.pdf GitHub

Abstract:
Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full‑scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected into the condensation dataset, manipulating model behavior during inference. While prior approaches have made progress in balancing attack success rate and clean test accuracy, they often fall short in preserving stealthiness, especially in concealing the visual artifacts of condensed data or the perturbations introduced during inference. To address this challenge, we introduce Sneakdoor, which enhances stealthiness without compromising attack effectiveness. Sneakdoor exploits the inherent vulnerability of class decision boundaries and incorporates a generative module that constructs input‑aware triggers aligned with local feature geometry, thereby minimizing detectability. This joint design enables the attack to remain imperceptible to both human inspection and statistical detection. Extensive experiments across multiple datasets demonstrate that Sneakdoor achieves a compelling balance among attack success rate, clean test accuracy, and stealthiness, substantially improving the invisibility of both the synthetic data and triggered samples while maintaining high attack efficacy. The code is available at https://github.com/XJTU‑AI‑Lab/SneakDoor.

PaperId: 2119, https://arxiv.org/pdf/2603.28816.pdf GitHub

Abstract:
The global landscape of art‑technology institutions, including festivals, biennials, research labs, conferences, and hybrid organizations, has grown increasingly diverse, yet systematic frameworks for analyzing their multidimensional characteristics remain scarce. This paper proposes ASTRA (Art‑technology Institution Spatial Taxonomy and Relational Analysis), a computational methodology combining an eight‑axis conceptual framework (Curatorial Philosophy, Territorial Relation, Knowledge Production Mode, Institutional Genealogy, Temporal Orientation, Ecosystem Function, Audience Relation, and Disciplinary Positioning) with a text‑embedding and clustering pipeline to map 78 cultural‑technology institutions into a unified analytical space. Each institution is characterized through qualitative descriptions along the eight axes, encoded via E5‑large‑v2 sentence embeddings and quantized through a word‑level codebook into TF‑IDF feature vectors. Dimensionality reduction using UMAP, followed by agglomerative clustering (Average linkage, k=10), yields a composite score of 0.825, a silhouette coefficient of 0.803, and a Calinski‑Harabasz index of 11196. Non‑negative matrix factorization extracts ten latent topics, and a neighbor‑cluster entropy measure identifies boundary institutions bridging multiple thematic communities. An interactive React‑based tool enables curators, researchers, and policymakers to explore institutional similarities and cross‑disciplinary connections. Results reveal coherent groupings such as an art‑science hub cluster anchored by ZKM and ArtScience Museum, an innovation and industry cluster including Ars Electronica, transmediale, and Sonar, an ACM academic cluster comprising TEI, DIS, and NIME, and an electronic music cluster including CTM Festival, MUTEK, and Sonic Acts. Code and data: https://github.com/joonhyungbae/astra

PaperId: 2120, https://arxiv.org/pdf/2603.28815.pdf GitHub GitHub

Abstract:
This technical report presents SkillTester, a tool for evaluating the utility and security of agent skills. Its evaluation framework combines paired baseline and with‑skill execution conditions with a separate security probe suite. Grounded in a comparative utility principle and a user‑facing simplicity principle, the framework normalizes raw execution artifacts into a utility score, a security score, and a three‑level security status label. More broadly, it can be understood as a comparative quality‑assurance harness for agent skills in an agent‑first world. The public service is deployed at https://skilltester.ai, and the broader project is maintained at https://github.com/skilltester‑ai/skilltester.

PaperId: 2121, https://arxiv.org/pdf/2603.28803.pdf GitHub

Abstract:
Double‑Deck Multi‑Agent Pickup and Delivery (DD‑MAPD) models the multi‑robot shelf rearrangement problem in automated warehouses. MAPF‑DECOMP is a recent framework that first computes collision‑free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it enforces strict trajectory dependencies, often leading to poor execution quality due to idle agents and unnecessary shelf switching. We introduce CREST, a new execution framework that achieves more continuous shelf carrying by proactively releasing trajectory constraints during execution. Experiments on diverse warehouse layouts show that CREST consistently outperforms MAPF‑DECOMP, reducing metrics related to agent travel, makespan, and shelf switching by up to 40.5%, 33.3%, and 44.4%, respectively, with even greater benefits under lift/place overhead. These results underscore the importance of execution‑aware constraint release for scalable warehouse rearrangement. Code and data are available at https://github.com/ChristinaTan0704/CREST.

PaperId: 2122, https://arxiv.org/pdf/2603.28737.pdf GitHub

Abstract:
We introduce ParaSpeechCLAP, a dual‑encoder contrastive model that maps speech and text style captions into a common embedding space, supporting a wide range of intrinsic (speaker‑level) and situational (utterance‑level) descriptors (such as pitch, texture and emotion) far beyond the narrow set handled by existing models. We train specialized ParaSpeechCLAP‑Intrinsic and ParaSpeechCLAP‑Situational models alongside a unified ParaSpeechCLAP‑Combined model, finding that specialization yields stronger performance on individual style dimensions while the unified model excels on compositional evaluation. We further show that ParaSpeechCLAP‑Intrinsic benefits from an additional classification loss and class‑balanced training. We demonstrate our models' performance on style caption retrieval, speech attribute classification and as an inference‑time reward model that improves style‑prompted TTS without additional training. ParaSpeechCLAP outperforms baselines on most metrics across all three applications. Our models and code are released at https://github.com/ajd12342/paraspeechclap .

PaperId: 2123, https://arxiv.org/pdf/2603.28696.pdf GitHub

Abstract:
Long video understanding remains challenging for Multi‑modal Large Language Models (MLLMs) due to high memory costs and context‑length limits. Prior approaches mitigate this by scoring and selecting frames/tokens within short clips, but they lack a principled mechanism to (i) compare relevance across distant video clips and (ii) stop processing once sufficient evidence has been gathered. We propose AdaptToken, a training‑free framework that turns an MLLM's self‑uncertainty into a global control signal for long‑video token selection. AdaptToken splits a video into groups, extracts cross‑modal attention to rank tokens within each group, and uses the model's response entropy to estimate each group's prompt relevance. This entropy signal enables a global token budget allocation across groups and further supports early stopping (AdaptToken‑Lite), skipping the remaining groups when the model becomes sufficiently certain. Across four long‑video benchmarks (VideoMME, LongVideoBench, LVBench, and MLVU) and multiple base MLLMs (7B‑72B), AdaptToken consistently improves accuracy (e.g., +6.7 on average over Qwen2.5‑VL 7B) and continues to benefit from extremely long inputs (up to 10K frames), while AdaptToken‑Lite reduces inference time by about half with comparable performance. Project page: https://haozheqi.github.io/adapt‑token

PaperId: 2124, https://arxiv.org/pdf/2603.28613.pdf GitHub

Abstract:
Generative AI has made text‑guided inpainting a powerful image editing tool, but at the same time a growing challenge for media forensics. Existing benchmarks, including our text‑guided inpainting forgery (TGIF) dataset, show that image forgery localization (IFL) methods can localize manipulations in spliced images but struggle not in fully regenerated (FR) images, while synthetic image detection (SID) methods can detect fully regenerated images but cannot perform localization. With new generative inpainting models emerging and the open problem of localization in FR images remaining, updated datasets and benchmarks are needed. We introduce TGIF2, an extended version of TGIF, that captures recent advances in text‑guided inpainting and enables a deeper analysis of forensic robustness. TGIF2 augments the original dataset with edits generated by FLUX.1 models, as well as with random non‑semantic masks. Using the TGIF2 dataset, we conduct a forensic evaluation spanning IFL and SID, including fine‑tuning IFL methods on FR images and generative super‑resolution attacks. Our experiments show that both IFL and SID methods degrade on FLUX.1 manipulations, highlighting limited generalization. Additionally, while fine‑tuning improves localization on FR images, evaluation with random non‑semantic masks reveals object bias. Furthermore, generative super‑resolution significantly weakens forensic traces, demonstrating that common image enhancement operations can undermine current forensic pipelines. In summary, TGIF2 provides an updated dataset and benchmark, which enables new insights into the challenges posed by modern inpainting and AI‑based image enhancements. TGIF2 is available at https://github.com/IDLabMedia/tgif‑dataset.

PaperId: 2125, https://arxiv.org/pdf/2603.28610.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) achieve stronger visual understanding by scaling input fidelity, yet the resulting visual token growth makes jointly sustaining high spatial resolution and long temporal context prohibitive. We argue that the bottleneck lies not in how post‑encoding representations are compressed but in the volume of pixels the encoder receives, and address it with ResAdapt, an Input‑side adaptation framework that learns how much visual budget each frame should receive before encoding. ResAdapt couples a lightweight Allocator with an unchanged MLLM backbone, so the backbone retains its native visual‑token interface while receiving an operator‑transformed input. We formulate allocation as a contextual bandit and train the Allocator with Cost‑Aware Policy Optimization (CAPO), which converts sparse rollout feedback into a stable accuracy‑cost learning signal. Across budget‑controlled video QA, temporal grounding, and image reasoning tasks, ResAdapt improves low‑budget operating points and often lies on or near the efficiency‑accuracy frontier, with the clearest gains on reasoning‑intensive benchmarks under aggressive compression. Notably, ResAdapt supports up to 16x more frames at the same visual budget while delivering over 15% performance gain. Code is available at https://github.com/Xnhyacinth/ResAdapt.

PaperId: 2126, https://arxiv.org/pdf/2603.28590.pdf GitHub

Abstract:
Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the CoT no longer faithfully reflects the actual reasons (i.e., decision‑critical factors) driving the model's behavior, leading to the reduced CoT monitorability problem. However, a comprehensive and fully open‑source benchmark for thoroughly evaluating CoT monitorability remains lacking. To address this gap, we propose MonitorBench, a systematic benchmark for evaluating CoT monitorability in LLMs. MonitorBench provides: (1) a diverse set of 1,514 test instances with carefully designed decision‑critical factors across 19 tasks spanning 7 categories to characterize when CoTs can be used to monitor the factors driving LLM behavior; and (2) two stress‑test settings to quantify the extent to which CoT monitorability can be degraded. Extensive experiments across multiple popular LLMs with varying capabilities show that CoT monitorability is higher when the decision‑critical factors shape the intermediate reasoning process without merely influencing the final answer. More capable LLMs tend to exhibit lower monitorability. And all evaluated LLMs can intentionally reduce monitorability under stress‑tests, with monitorability dropping by up to 30% in some tasks that do not require structural reasoning over the decision‑critical factors. Overall, MonitorBench provides a basis for further research on evaluating future LLMs, studying advanced stress‑test monitorability techniques, and developing new monitoring approaches. The code is available at https://github.com/ASTRAL‑Group/MonitorBench.

PaperId: 2127, https://arxiv.org/pdf/2603.28569.pdf GitHub

Abstract:
The increasing agentic capabilities of Large Language Models (LLMs) have enabled their deployment in real‑world applications, such as cloud services, where customer‑assistant interactions exhibit high technical complexity and long‑horizon dependencies, making robustness and resolution efficiency critical for customer satisfaction. However, existing benchmarks for LLM‑based agents largely rely on synthetic environments that fail to capture the diversity and unpredictability of authentic customer inputs, often ignoring the resolution efficiency essential for real‑world deployment. To bridge this gap, we introduce CirrusBench, a novel evaluation framework distinguished by its foundation in real‑world data from authentic cloud service tickets. CirrusBench preserves the intricate multi‑turn logical chains and realistic tool dependencies inherent to technical service environments. Moving beyond execution correctness, we introduce novel Customer‑Centric metrics to define agent success, quantifying service quality through metrics such as the Normalized Efficiency Index and Multi‑Turn Latency to explicitly measure resolution efficiency. Experiments utilizing our framework reveal that while state‑of‑the‑art models demonstrate strong reasoning capabilities, they frequently struggle in complex, realistic multi‑turn tasks and fail to meet the high‑efficiency standards required for customer service, highlighting critical directions for the future development of LLM‑based agents in practical technical service applications. CirrusBench evaluation framework is released at: https://github.com/CirrusAI

PaperId: 2128, https://arxiv.org/pdf/2603.28558.pdf GitHub

Abstract:
We present a first comparative pilot study of three t‑norm operators ‑‑ Lukasiewicz (T_L), Product (T_P), and Gödel (T_G) ‑ as logical conjunction mechanisms in a neuro‑symbolic reasoning system for EU AI Act compliance classification. Using the LGGT+ (Logic‑Guided Graph Transformers Plus) engine and a benchmark of 1035 annotated AI system descriptions spanning four risk categories (prohibited, high_risk, limited_risk, minimal_risk), we evaluate classification accuracy, false positive and false negative rates, and operator behaviour on ambiguous cases. At n=1035, all three operators differ significantly (McNemar p<0.001). T_G achieves highest accuracy (84.5%) and best borderline recall (85%), but introduces 8 false positives (0.8%) via min‑semantics over‑classification. T_L and T_P maintain zero false positives, with T_P outperforming T_L (81.2% vs. 78.5%). Our principal findings are: (1) operator choice is secondary to rule base completeness; (2) T_L and T_P maintain zero false positives but miss borderline cases; (3) T_G's min‑semantics achieves higher recall at cost of 0.8% false positive rate; (4) a mixed‑semantics classifier is the productive next step. We release the LGGT+ core engine (201/201 tests passing) and benchmark dataset (n=1035) under Apache 2.0.

PaperId: 2129, https://arxiv.org/pdf/2603.28488.pdf GitHub

Abstract:
Large language models (LLMs) remain unreliable for high‑stakes claim verification due to hallucinations and shallow reasoning. While retrieval‑augmented generation (RAG) and multi‑agent debate (MAD) address this, they are limited by one‑pass retrieval and unstructured debate dynamics. We propose a courtroom‑style multi‑agent framework, PROClaim, that reformulates verification as a structured, adversarial deliberation. Our approach integrates specialized roles (e.g., Plaintiff, Defense, Judge) with Progressive RAG (P‑RAG) to dynamically expand and refine the evidence pool during the debate. Furthermore, we employ evidence negotiation, self‑reflection, and heterogeneous multi‑judge aggregation to enforce calibration, robustness, and diversity. In zero‑shot evaluations on the Check‑COVID benchmark, PROClaim achieves 81.7% accuracy, outperforming standard multi‑agent debate by 10.0 percentage points, with P‑RAG driving the primary performance gains (+7.5 pp). We ultimately demonstrate that structural deliberation and model heterogeneity effectively mitigate systematic biases, providing a robust foundation for reliable claim verification. Our code and data are publicly available at https://github.com/mnc13/PROClaim.

PaperId: 2130, https://arxiv.org/pdf/2603.28407.pdf GitHub

Abstract:
Recent progress in deep research systems has been impressive, but evaluation still lags behind real user needs. Existing benchmarks predominantly assess final reports using fixed rubrics, failing to evaluate the underlying research process. Most also offer limited multimodal coverage, rely on synthetic tasks that do not reflect real‑world query complexity, and cannot be refreshed as knowledge evolves. To address these gaps, we introduce MiroEval, a benchmark and evaluation framework for deep research systems. The benchmark comprises 100 tasks (70 text‑only, 30 multimodal), all grounded in real user needs and constructed via a dual‑path pipeline that supports periodic updates, enabling a live and evolving setting. The proposed evaluation suite assesses deep research systems along three complementary dimensions: adaptive synthesis quality evaluation with task‑specific rubrics, agentic factuality verification via active retrieval and reasoning over both web sources and multimodal attachments, and process‑centric evaluation audits how the system searches, reasons, and refines throughout its investigation. Evaluation across 13 systems yields three principal findings: the three evaluation dimensions capture complementary aspects of system capability, with each revealing distinct strengths and weaknesses across systems; process quality serves as a reliable predictor of overall outcome while revealing weaknesses invisible to output‑level metrics; and multimodal tasks pose substantially greater challenges, with most systems declining by 3 to 10 points. The MiroThinker series achieves the most balanced performance, with MiroThinker‑H1 ranking the highest overall in both settings. Human verification and robustness results confirm the reliability of the benchmark and evaluation framework. MiroEval provides a holistic diagnostic tool for the next generation of deep research agents.

PaperId: 2131, https://arxiv.org/pdf/2603.28300.pdf GitHub

Abstract:
Graph anomaly detection (GAD) aims to identify irregular nodes or structures in attributed graphs. Neighbor information, which reflects both structural connectivity and attribute consistency with surrounding nodes, is essential for distinguishing anomalies from normal patterns. Although recent graph neural network (GNN)‑based methods incorporate such information through message passing, they often fail to explicitly model its effect or interaction with attributes, limiting detection performance. This work introduces NeiGAD, a novel plug‑and‑play module that captures neighbor information through spectral graph analysis. Theoretical insights demonstrate that eigenvectors of the adjacency matrix encode local neighbor interactions and progressively amplify anomaly signals. Based on this, NeiGAD selects a compact set of eigenvectors to construct efficient and discriminative representations. Experiments on eight real‑world datasets show that NeiGAD consistently improves detection accuracy and outperforms state‑of‑the‑art GAD methods. These results demonstrate the importance of explicit neighbor modeling and the effectiveness of spectral analysis in anomaly detection. Code is available at: https://github.com/huafeihuang/NeiGAD.

PaperId: 2132, https://arxiv.org/pdf/2603.28288.pdf GitHub

Abstract:
Kolmogorov‑Arnold Networks (KAN) employ B‑spline bases on a fixed grid, providing no intrinsic multi‑scale decomposition for non‑smooth function approximation. We introduce Fractal Interpolation KAN (FI‑KAN), which incorporates learnable fractal interpolation function (FIF) bases from iterated function system (IFS) theory into KAN. Two variants are presented: Pure FI‑KAN (Barnsley, 1986) replaces B‑splines entirely with FIF bases; Hybrid FI‑KAN (Navascues, 2005) retains the B‑spline path and adds a learnable fractal correction. The IFS contraction parameters give each edge a differentiable fractal dimension that adapts to target regularity during training. On a Holder regularity benchmark (α\in [0.2, 2.0]), Hybrid FI‑KAN outperforms KAN at every regularity level (1.3x to 33x). On fractal targets, FI‑KAN achieves up to 6.3x MSE reduction over KAN, maintaining 4.7x advantage at 5 dB SNR. On non‑smooth PDE solutions (scikit‑fem), Hybrid FI‑KAN achieves up to 79x improvement on rough‑coefficient diffusion and 3.5x on L‑shaped domain corner singularities. Pure FI‑KAN's complementary behavior, dominating on rough targets while underperforming on smooth ones, provides controlled evidence that basis geometry must match target regularity. A fractal dimension regularizer provides interpretable complexity control whose learned values recover the true fractal dimension of each target. These results establish regularity‑matched basis design as a principled strategy for neural function approximation.

PaperId: 2133, https://arxiv.org/pdf/2603.28258.pdf GitHub

Abstract:
Categorical perception (CP) ‑‑ enhanced discriminability at category boundaries ‑‑ is among the most studied phenomena in perceptual psychology. This paper reports that analogous geometric warping occurs in the hidden‑state representations of large language models (LLMs) processing Arabic numerals. Using representational similarity analysis across six models from five architecture families, the study finds that a CP‑additive model (log‑distance plus a boundary boost) fits the representational geometry better than a purely continuous model at 100% of primary layers in every model tested. The effect is specific to structurally defined boundaries (digit‑count transitions at 10 and 100), absent at non‑boundary control positions, and absent in the temperature domain where linguistic categories (hot/cold) lack a tokenisation discontinuity. Two qualitatively distinct signatures emerge: "classic CP" (Gemma, Qwen), where models both categorise explicitly and show geometric warping, and "structural CP" (Llama, Mistral, Phi), where geometry warps at the boundary but models cannot report the category distinction. This dissociation is stable across boundaries and is a property of the architecture, not the stimulus. Structural input‑format discontinuities are sufficient to produce categorical perception geometry in LLMs, independently of explicit semantic category knowledge.

PaperId: 2134, https://arxiv.org/pdf/2603.28248.pdf GitHub

Abstract:
Single‑shot neural decoders commit to answers without iterative refinement, while chain‑of‑thought methods introduce discrete intermediate steps but lack a scalar measure of reasoning progress. We propose Energy‑Based Reasoning via Structured Latent Planning (EBRM), which models reasoning as gradient‑based optimization of a multi‑step latent trajectory z_1:T under a learned energy function E(h_x, z). The energy decomposes into per‑step compatibility, transition consistency, and trajectory smoothness terms. Training combines supervised encoder‑decoder learning with contrastive energy shaping using hard negatives, while inference performs gradient descent or Langevin dynamics over z and decodes from z_T. We identify a critical failure mode: on CNF logic satisfaction, latent planning reduces accuracy from \approx 95% to \approx 56%. This degradation arises from a distribution mismatch, where the decoder is trained on encoder outputs h_x but evaluated on planner outputs z_T that drift into unseen latent regions. We analyze this behavior through per‑step decoding, latent drift tracking, and gradient decomposition. To address it, we propose dual‑path decoder training and latent anchoring. We further introduce a six‑part ablation protocol covering component contributions, trajectory length, planner dynamics, initialization, decoder training distribution, and anchor weight. Experiments on three synthetic tasks show that energy decreases monotonically and induces structured latent trajectories on graph and logic tasks, while remaining flat on arithmetic (r = 0.073), indicating a negative result. Code is available at https://github.com/dkjo8/ebr‑via‑structured‑latent‑planning.

PaperId: 2135, https://arxiv.org/pdf/2603.28233.pdf GitHub

Abstract:
Accurate and efficient perception is essential for autonomous driving, where segmentation tasks such as drivable‑area and lane segmentation provide critical cues for motion planning and control. However, achieving high segmentation accuracy while maintaining real‑time performance on low‑cost hardware remains a challenging problem. To address this issue, we introduce TwinMixing, a lightweight multi‑task segmentation model designed explicitly for drivable‑area and lane segmentation. The proposed network features a shared encoder and task‑specific decoders, enabling both feature sharing and task specialization. Within the encoder, we propose an Efficient Pyramid Mixing (EPM) module that enhances multi‑scale feature extraction through a combination of grouped convolutions, depthwise dilated convolutions and channel shuffle operations, effectively expanding the receptive field while minimizing computational cost. Each decoder adopts a Dual‑Branch Upsampling (DBU) Block composed of a learnable transposed convolution‑based Fine detailed branch and a parameter‑free bilinear interpolation‑based Coarse grained branch, achieving detailed yet spatially consistent feature reconstruction. Extensive experiments on the BDD100K dataset validate the effectiveness of TwinMixing across three configurations ‑ tiny, base, and large. Among them, the base configuration achieves the best trade‑off between accuracy and computational efficiency, reaching 92.0% mIoU for drivable‑area segmentation and 32.3% IoU for lane segmentation with only 0.43M parameters and 3.95 GFLOPs. Moreover, TwinMixing consistently outperforms existing segmentation models on the same tasks, as illustrated in Fig. 1. Thanks to its compact and modular design, TwinMixing demonstrates strong potential for real‑time deployment in autonomous driving and embedded perception systems. The source code: https://github.com/Jun0se7en/TwinMixing.

PaperId: 2136, https://arxiv.org/pdf/2603.28130.pdf GitHub

Abstract:
We introduce Multilingual Document Parsing Benchmark, the first benchmark for multilingual digital and photographed document parsing. Document parsing has made remarkable strides, yet almost exclusively on clean, digital, well‑formatted pages in a handful of dominant languages. No systematic benchmark exists to evaluate how models perform on digital and photographed documents across diverse scripts and low‑resource languages. MDPBench comprises 3,400 document images spanning 17 languages, diverse scripts, and varied photographic conditions, with high‑quality annotations produced through a rigorous pipeline of expert model labeling, manual correction, and human verification. To ensure fair comparison and prevent data leakage, we maintain separate public and private evaluation splits. Our comprehensive evaluation of both open‑source and closed‑source models uncovers a striking finding: while closed‑source models (notably Gemini3‑Pro) prove relatively robust, open‑source alternatives suffer dramatic performance collapse, particularly on non‑Latin scripts and real‑world photographed documents, with an average drop of 17.8% on photographed documents and 14.0% on non‑Latin scripts. These results reveal significant performance imbalances across languages and conditions, and point to concrete directions for building more inclusive, deployment‑ready parsing systems. Source available at https://github.com/Yuliang‑Liu/MultimodalOCR.

PaperId: 2137, https://arxiv.org/pdf/2603.28032.pdf GitHub

Abstract:
The convergence of low‑altitude economies, embodied intelligence, and air‑ground cooperative systems creates growing demand for simulation infrastructure capable of jointly modeling aerial and ground agents within a single physically coherent environment. Existing open‑source platforms remain domain‑segregated: driving simulators lack aerial dynamics, while multirotor simulators lack realistic ground scenes. Bridge‑based co‑simulation introduces synchronization overhead and cannot guarantee strict spatial‑temporal consistency. We present CARLA‑Air, an open‑source infrastructure that unifies high‑fidelity urban driving and physics‑accurate multirotor flight within a single Unreal Engine process. The platform preserves both CARLA and AirSim native Python APIs and ROS 2 interfaces, enabling zero‑modification code reuse. Within a shared physics tick and rendering pipeline, CARLA‑Air delivers photorealistic environments with rule‑compliant traffic, socially‑aware pedestrians, and aerodynamically consistent UAV dynamics, synchronously capturing up to 18 sensor modalities across all platforms at each tick. The platform supports representative air‑ground embodied intelligence workloads spanning cooperation, embodied navigation and vision‑language action, multi‑modal perception and dataset construction, and reinforcement‑learning‑based policy training. An extensible asset pipeline allows integration of custom robot platforms into the shared world. By inheriting AirSim's aerial capabilities ‑‑ whose upstream development has been archived ‑‑ CARLA‑Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure. Released with prebuilt binaries and full source: https://github.com/louiszengCN/CarlaAir

PaperId: 2138, https://arxiv.org/pdf/2603.28015.pdf GitHub

Abstract:
Deep learning models for drug‑like molecules and proteins overwhelmingly reuse transformer architectures designed for natural language, yet whether molecular sequences benefit from different designs has not been systematically tested. We deploy autonomous architecture search via an agent across three sequence types (SMILES, protein, and English text as control), running 3,106 experiments on a single GPU. For SMILES, architecture search is counterproductive: tuning learning rates and schedules alone outperforms the full search (p = 0.001). For natural language, architecture changes drive 81% of improvement (p = 0.009). Proteins fall between the two. Surprisingly, although the agent discovers distinct architectures per domain (p = 0.004), every innovation transfers across all three domains with <1% degradation, indicating that the differences reflect search‑path dependence rather than fundamental biological requirements. We release a decision framework and open‑source toolkit for molecular modeling teams to choose between autonomous architecture search and simple hyperparameter tuning.

PaperId: 2139, https://arxiv.org/pdf/2603.27977.pdf GitHub GitHub

Abstract:
Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard to use in open‑ended domains where correctness is ambiguous and cannot be verified. Moreover, reasoning trajectories remain largely unconstrained, and optimizing solely toward the final answer can favor early exploitation over generalization. In this work, we ask whether general reasoning ability can be improved by teaching models how to think (the structure of reasoning) rather than what to produce (the outcome of reasoning), and we extend traditional RLVR to open‑ended settings. We introduce Structure‑Aware Reinforcement Learning (SARL), a label‑free framework that constructs per‑response reasoning maps from intermediate thinking steps and rewards their reasoning topology. SARL shifts supervision from destination to path, encouraging reasoning trajectories that are both locally coherent and globally efficient. On verifiable math tasks, SARL outperforms prior label‑free RL baselines and even exceeds RL methods with ground truth supervision, with average gains of +9.1% under PPO and +11.6% under GRPO across four math benchmarks, with particularly large improvements on AIME25 (+35.5% with PPO and +44.7% with GRPO). On non‑verifiable open‑ended tasks, SARL achieves average gains of +34.6% under PPO and +30.4% under GRPO on WildBench across five task categories, outperforming prior label‑free RL methods and DPO, which relies on additional preference labels. Beyond strong performance, SARL exhibits substantially lower KL divergence and higher policy entropy, indicating more stable and exploratory training dynamics. Code and data are available at \hrefhttps://github.com/cacayaya/SARLCode Link.

PaperId: 2140, https://arxiv.org/pdf/2603.27667.pdf GitHub

Abstract:
Large Audio Language Models (LALMs) still struggle in complex acoustic scenes because they often fail to preserve task‑relevant acoustic evidence before reasoning begins. We identify this error pattern as the evidence bottleneck: state‑of‑the‑art systems show larger deficits in acoustic evidence extraction than in downstream reasoning, suggesting that upstream perception is often the limiting factor. To address this problem, we propose EvA (Evidence‑First Audio), a dual‑path architecture that enhances acoustic evidence preservation through hierarchical aggregation and non‑compressive, time‑aligned fusion. We also build EvA‑Perception, a large‑scale training set with about 54K event‑ordered captions and 500K evidence‑grounded QA pairs. Under a unified zero‑shot protocol, EvA achieves the best open‑source \emphPerception results on MMAU, MMAR, and MMSU, with the largest gains on perception‑heavy splits. Human evaluation on open‑ended captioning further shows improved fine‑grained acoustic coverage and caption quality. These results support the evidence‑first hypothesis: stronger audio understanding depends on preserving acoustic evidence before reasoning. Project can be found at https://satsuki2486441738.github.io/EvA/.

PaperId: 2141, https://arxiv.org/pdf/2603.27593.pdf GitHub

Abstract:
Recent progress in video large language models (Video‑LLMs) has enabled strong offline reasoning over long and complex videos. However, real‑world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span‑structured activation patterns. To capture this span‑level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving when‑to‑speak decision quality in online streaming scenarios.

PaperId: 2142, https://arxiv.org/pdf/2603.27494.pdf GitHub

Abstract:
To enhance the perception and reasoning capabilities of multimodal large language models in complex visual scenes, recent research has introduced agent‑based workflows. In these works, MLLMs autonomously utilize image cropping tool to analyze regions of interest for question answering. While existing training strategies, such as those employing supervised fine‑tuning and reinforcement learning, have made significant progress, our empirical analysis reveals a key limitation. We demonstrate the model's strong reliance on global input and its weak dependence on the details within the cropped region. To address this issue, we propose a novel two‑stage reinforcement learning framework that does not require trajectory supervision. In the first stage, we introduce the ``Information Gap" mechanism by adjusting the granularity of the global image. This mechanism trains the model to answer questions by focusing on cropped key regions, driven by the information gain these regions provide. The second stage further enhances cropping precision by incorporating a grounding loss, using a small number of bounding box annotations. Experiments show that our method significantly enhances the model's attention to cropped regions, enabling it to achieve state‑of‑the‑art performance on high‑resolution visual question‑answering benchmarks. Our method provides a more efficient approach for perceiving and reasoning fine‑grained details in MLLMs. Code is available at: https://github.com/XuanPu‑Z/LFPC.

PaperId: 2143, https://arxiv.org/pdf/2603.27481.pdf GitHub

Abstract:
Multimodal Continual Instruction Tuning aims to continually enhance Large Vision Language Models (LVLMs) by learning from new data without forgetting previously acquired knowledge. Mixture of Experts (MoE) architectures naturally facilitate this by incrementally adding new experts and expanding routers while keeping the existing ones frozen. However, despite expert isolation, MoE‑based continual learners still suffer from forgetting due to routing‑drift: old‑task tokens become mistakenly attracted to newly added experts, degrading performance on prior tasks. We analyze the failure mode at the token level and reveal the token's dilemma: ambiguous and old tokens in new‑task data offer minimal learning benefit yet induce forgetting when routed to new experts, due to their ambiguous routing assignment during training. Motivated by this, we propose LLaVA‑DyMoE, a dynamic MoE framework that incrementally expands the MoE with drift‑aware token assignment. We characterize token types via their routing score distributions and apply targeted regularization. Specifically, a token‑level assignment guidance steers ambiguous and old tokens away from new experts to preserve established routing patterns and alleviate routing‑drift, while complementary routing score regularizations enforce expert‑group separation and promote new‑expert specialization. Extensive experiments demonstrate that our LLaVA‑DyMoE effectively mitigates routing‑drift‑induced forgetting, achieving over a 7% gain in mean final accuracy and a 12% reduction in forgetting compared to baselines. The project page is https://zhaoc5.github.io/DyMoE.

PaperId: 2144, https://arxiv.org/pdf/2603.27469.pdf GitHub

Abstract:
Self‑forcing video generation extends a short‑horizon video model to longer rollouts by repeatedly feeding generated content back in as context. This scaling path immediately exposes a systems bottleneck: the key‑value (KV) cache grows with rollout length, so longer videos require not only better generation quality but also substantially better memory behavior. We present a comprehensive empirical study of KV‑cache compression for self‑forcing video generation on a Wan2.1‑based Self‑Forcing stack. Our study covers 33 quantization and cache‑policy variants, 610 prompt‑level observations, and 63 benchmark‑level summaries across two evaluation settings: MovieGen for single‑shot 10‑second generation and StoryEval for longer narrative‑style stability. We jointly evaluate peak VRAM, runtime, realized compression ratio, VBench imaging quality, BF16‑referenced fidelity (SSIM, LPIPS, PSNR), and terminal drift. Three findings are robust. First, the strongest practical operating region is a FlowCache‑inspired soft‑prune INT4 adaptation, which reaches 5.42‑5.49x compression while reducing peak VRAM from 19.28 GB to about 11.7 GB with only modest runtime overhead. Second, the highest‑fidelity compressed methods, especially PRQ_INT4 and QUAROT_KV_INT4, are not the best deployment choices because they preserve quality at severe runtime or memory cost. Third, nominal compression alone is not sufficient: several methods shrink KV storage but still exceed BF16 peak VRAM because the current integration reconstructs or retains large BF16 buffers during attention and refresh stages. The result is a benchmark harness, analysis workflow, and empirical map of which KV‑cache ideas are practical today and which are promising research directions for better memory integration. Code, data products, and the presentation dashboard are available at https://github.com/suraj‑ranganath/kv‑quant‑longhorizon/.

PaperId: 2145, https://arxiv.org/pdf/2603.27460.pdf GitHub

Authors:Zhongying Deng, Cheng Tang, Ziyan Huang, Jiashi Lin, Ying Chen, Junzhi Ning, Chenglong Ma, Jiyao Liu, Wei Li, Yinghao Zhu, Shujian Gao, Yanyan Huang, Sibo Ju, Yanzhou Su, Pengcheng Chen, Wenhao Tang, Tianbin Li, Haoyu Wang, Yuanfeng Ji, Hui Sun, Shaobo Min, Liang Peng, Feilong Tang, Haochen Xue, Rulin Zhou, Chaoyang Zhang, Wenjie Li, Shaohao Rui, Weijie Ma, Xingyue Zhao, Yibin Wang, Kun Yuan, Zhaohui Lu, Shujun Wang, Jinjie Wei, Lihao Liu, Dingkang Yang, Lin Wang, Yulong Li, Haolin Yang, Yiqing Shen, Lequan Yu, Xiaowei Hu, Yun Gu, Yicheng Wu, Benyou Wang, Minghui Zhang, Angelica I. Aviles-Rivero, Qi Gao, Hongming Shan, Xiaoyu Ren, Fang Yan, Hongyu Zhou, Haodong Duan, Maosong Cao, Shanshan Wang, Bin Fu, Xiaomeng Li, Zhi Hou, Chunfeng Song, Lei Bai, Yuan Cheng, Yuandong Pu, Xiang Li, Wenhai Wang, Hao Chen, Jiaxin Zhuang, Songyang Zhang, Huiguang He, Mengzhang Li, Bohan Zhuang, Zhian Bai, Rongshan Yu, Liansheng Wang, Yukun Zhou, Xiaosong Wang, Xin Guo, Guanbin Li, Xiangru Lin, Dakai Jin, Mianxin Liu, Wenlong Zhang, Qi Qin, Conghui He, Yuqiang Li, Ye Luo, Nanqing Dong, Jie Xu, Wenqi Shao, Bo Zhang, Qiujuan Yan, Yihao Liu, Jun Ma, Zhi Lu, Yuewen Cao, Zongwei Zhou, Jianming Liang, Shixiang Tang, Qi Duan, Dongzhan Zhou, Chen Jiang, Yuyin Zhou, Yanwu Xu, Jiancheng Yang, Shaoting Zhang, Xiaohong Liu, Siqi Luo, Yi Xin, Chaoyu Liu, Haochen Wen, Xin Chen, Alejandro Lozano, Min Woo Sun, Yuhui Zhang, Yue Yao, Xiaoxiao Sun, Serena Yeung-Levy, Xia Li, Jing Ke, Chunhui Zhang, Zongyuan Ge, Ming Hu, Jin Ye, Zhifeng Li, Yirong Chen, Yu Qiao, Junjun He

Abstract:
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large‑scale, diverse, and high‑quality datasets. However, in the field of medical imaging, the curation and assembling of such medical datasets are highly challenging due to the reliance on clinical expertise and strict ethical and privacy constraints, resulting in a scarcity of large‑scale unified medical datasets and hindering the development of powerful medical foundation models. In this work, we present the largest survey to date of medical image datasets, covering over 1,000 open‑access datasets with a systematic catalog of their modalities, tasks, anatomies, annotations, limitations, and potential for integration. Our analysis exposes a landscape that is modest in scale, fragmented across narrowly scoped tasks, and unevenly distributed across organs and modalities, which in turn limits the utility of existing medical image datasets for developing versatile and robust medical foundation models. To turn fragmentation into scale, we propose a metadata‑driven fusion paradigm (MDFP) that integrates public datasets with shared modalities or tasks, thereby transforming multiple small data silos into larger, more coherent resources. Building on MDFP, we release an interactive discovery portal that enables end‑to‑end, automated medical image dataset integration, and compile all surveyed datasets into a unified, structured table that clearly summarizes their key characteristics and provides reference links, offering the community an accessible and comprehensive repository. By charting the current terrain and offering a principled path to dataset consolidation, our survey provides a practical roadmap for scaling medical imaging corpora, supporting faster data discovery, more principled dataset creation, and more capable medical foundation models.

PaperId: 2146, https://arxiv.org/pdf/2603.27389.pdf GitHub

Abstract:
Reinforcement learning algorithms assume that observations satisfy the Markov property, yet real‑world sensors frequently violate this assumption through correlated noise, latency, or partial observability. Standard performance metrics conflate Markov breakdowns with other sources of suboptimality, leaving practitioners without tools to detect such violations. This paper introduces a prediction‑based Markov Violation Score (MVS) that quantifies non‑Markovian structure in observation trajectories. A random forest first removes nonlinear Markov‑compliant dynamics; ridge regression then tests whether historical observations reduce prediction error on the residuals beyond what the current observation provides. The resulting score is bounded in [0, 1] and requires no causal graph construction. Evaluation spans six environments (CartPole, Pendulum, Acrobot, HalfCheetah, Hopper, Walker2d), three algorithms (PPO, A2C, SAC), controlled AR(1) noise at six intensity levels, and 10 seeds per condition. In post‑hoc detection, 7 of 16 environment‑algorithm pairs, primarily high‑dimensional locomotion tasks, show significant positive monotonicity between noise intensity and MVS (Spearman rho up to 0.78, confirmed under repeated‑measures analysis); under training‑time noise, 13 of 16 pairs exhibit statistically significant reward degradation. An inversion phenomenon is documented in low‑dimensional environments where the random forest absorbs the noise signal, causing MVS to decrease as true violations grow, a failure mode analyzed in detail. A practical utility experiment demonstrates that MVS correctly identifies partial observability and guides architecture selection, fully recovering performance lost to non‑Markovian observations. Source code to reproduce all results is available at https://github.com/NAVEENMN/Markovianes.

PaperId: 2147, https://arxiv.org/pdf/2603.27343.pdf GitHub

Abstract:
Existing large language models (LLMs) evaluations use fixed‑difficulty benchmarks that cannot adapt as models improve, and rarely isolate specific cognitive processes. We introduce Working Memory Fidelity‑Active Manipulation (WMF‑AM), a probe of cumulative state tracking, the ability to maintain and update intermediate results across K sequential operations within a single query, without a scratchpad. Unlike multi‑step agent benchmarks that stress task orchestration, WMF‑AM isolates within‑pass cumulative load by parameterizing depth K. The core probe uses arithmetic accumulation on 28 models from 12 families (0.5B to frontier); a matched non‑arithmetic extension (permissions, schedules, inventories) confirms the design generalizes beyond arithmetic. Three construct‑isolation ablations confirm that cumulative load, not arithmetic skill or entity tracking, drives difficulty. We release WMF‑AM as a lightweight, recalibratable diagnostic for characterizing where models degrade under cumulative load. Code and data can be accessed at https://github.com/dengzhe‑hou/WMF‑AM

PaperId: 2148, https://arxiv.org/pdf/2603.27027.pdf GitHub

Abstract:
Speculative decoding accelerates autoregressive generation by letting a lightweight draft model propose future tokens that a larger target model then verifies in parallel. In practice, however, draft models are usually trained on broad generic corpora, which leaves it unclear how much speculative decoding quality depends on the draft training distribution. We study this question with lightweight HASS and EAGLE‑2 drafters trained on MathInstruct, ShareGPT, and mixed‑data variants, evaluated on MT‑Bench, GSM8K, MATH‑500, and SVAMP. Measured by acceptance length, task‑specific training yields clear specialization: MathInstruct‑trained drafts are strongest on reasoning benchmarks, while ShareGPT‑trained drafts are strongest on MT‑Bench. Mixed‑data training improves robustness, but larger mixtures do not dominate across decoding temperatures. We also study how to combine specialized drafters at inference time. Naive checkpoint averaging performs poorly, whereas confidence‑based routing improves over single‑domain drafts and merged‑tree verification yields the highest acceptance length overall for both backbones. Finally, confidence is a more useful routing signal than entropy: rejected tokens tend to have higher entropy, but confidence produces much clearer benchmark‑level routing decisions. These results show that speculative decoding quality depends not only on draft architecture, but also on the match between draft training data and downstream workload, and that specialized drafters are better combined at inference time than in weight space.

PaperId: 2149, https://arxiv.org/pdf/2603.27006.pdf GitHub

Abstract:
Large language models produce em dashes at varying rates, and the observation that some models "overuse" them has become one of the most widely discussed markers of AI‑generated text. Yet no mechanistic account of this pattern exists, and the parallel observation that LLMs default to markdown‑formatted output has never been connected to it. We propose that the em dash is markdown leaking into prose ‑‑ the smallest surviving unit of the structural orientation that LLMs acquire from markdown‑saturated training corpora. We present a five‑step genealogy connecting training data composition, structural internalization, the dual‑register status of the em dash, and post‑training amplification. We test this with a two‑condition suppression experiment across twelve models from five providers (Anthropic, OpenAI, Meta, Google, DeepSeek): when models are instructed to avoid markdown formatting, overt features (headers, bullets, bold) are eliminated or nearly eliminated, but em dashes persist ‑‑ except in Meta's Llama models, which produce none at all. Em dash frequency and suppression resistance vary from 0.0 per 1,000 words (Llama) to 9.1 (GPT‑4.1 under suppression), functioning as a signature of the specific fine‑tuning procedure applied. A three‑condition suppression gradient shows that even explicit em dash prohibition fails to eliminate the artifact in some models, and a base‑vs‑instruct comparison confirms that the latent tendency exists pre‑RLHF. These findings connect two previously isolated online discourses and reframe em dash frequency as a diagnostic of fine‑tuning methodology rather than a stylistic defect.

PaperId: 2150, https://arxiv.org/pdf/2603.26859.pdf GitHub

Abstract:
Vision‑and‑Language Navigation (VLN) requires an agent to navigate through complex unseen environments based on natural language instructions. However, existing methods often struggle to effectively capture key semantic cues and accurately align them with visual observations. To address this limitation, we propose Beyond Textual Knowledge (BTK), a VLN framework that synergistically integrates environment‑specific textual knowledge with generative image knowledge bases. BTK employs Qwen3‑4B to extract goal‑related phrases and utilizes Flux‑Schnell to construct two large‑scale image knowledge bases: R2R‑GP and REVERIE‑GP. Additionally, we leverage BLIP‑2 to construct a large‑scale textual knowledge base derived from panoramic views, providing environment‑specific semantic cues. These multimodal knowledge bases are effectively integrated via the Goal‑Aware Augmentor and Knowledge Augmentor, significantly enhancing semantic grounding and cross‑modal alignment. Extensive experiments on the R2R dataset with 7,189 trajectories and the REVERIE dataset with 21,702 instructions demonstrate that BTK significantly outperforms existing baselines. On the test unseen splits of R2R and REVERIE, SR increased by 5% and 2.07% respectively, and SPL increased by 4% and 3.69% respectively. The source code is available at https://github.com/yds3/IPM‑BTK/.

PaperId: 2151, https://arxiv.org/pdf/2603.26856.pdf GitHub

Abstract:
The rapid advancement of generative models has enabled highly realistic audio deepfakes, yet current detectors suffer from a critical bias problem, leading to poor generalization across unseen datasets. This paper proposes Artifact‑Focused Self‑Synthesis (AFSS), a method designed to mitigate this bias by generating pseudo‑fake samples from real audio via two mechanisms: self‑conversion and self‑reconstruction. The core insight of AFSS lies in enforcing same‑speaker constraints, ensuring that real and pseudo‑fake samples share identical speaker identity and semantic content. This forces the detector to focus exclusively on generation artifacts rather than irrelevant confounding factors. Furthermore, we introduce a learnable reweighting loss to dynamically emphasize synthetic samples during training. Extensive experiments across 7 datasets demonstrate that AFSS achieves state‑of‑the‑art performance with an average EER of 5.45%, including a significant reduction to 1.23% on WaveFake and 2.70% on In‑the‑Wild, all while eliminating the dependency on pre‑collected fake datasets. Our code is publicly available at https://github.com/NguyenLeHaiSonGit/AFSS.

PaperId: 2152, https://arxiv.org/pdf/2603.26842.pdf GitHub

Abstract:
Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT‑enabled service systems. Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target datasets, hindering anomaly detection performance in various scenarios with scarce training data. To address this limitation, foundation models have emerged as a promising direction. However, existing approaches either repurpose large language models (LLMs) or construct largescale time series datasets to develop general anomaly detection foundation models, and still face challenges caused by severe cross‑modal gaps or in‑domain heterogeneity. In this paper, we investigate the applicability of large‑scale vision models to TSAD. Specifically, we adapt a visual Masked Autoencoder (MAE) pretrained on ImageNet to the TSAD task. However, directly transferring MAE to TSAD introduces two key challenges: overgeneralization and limited local perception. To address these challenges, we propose VAN‑AD, a novel MAE‑based framework for TSAD. To alleviate the over‑generalization issue, we design an Adaptive Distribution Mapping Module (ADMM), which maps the reconstruction results before and after MAE into a unified statistical space to amplify discrepancies caused by abnormal patterns. To overcome the limitation of local perception, we further develop a Normalizing Flow Module (NFM), which combines MAE with normalizing flow to estimate the probability density of the current window under the global distribution. Extensive experiments on nine real‑world datasets demonstrate that VAN‑AD consistently outperforms existing state‑of‑the‑art methods across multiple evaluation metrics.We make our code and datasets available at https://github.com/PenyChen/VAN‑AD.

PaperId: 2153, https://arxiv.org/pdf/2603.26840.pdf GitHub

Abstract:
Multimodal Emotion Recognition in Conversations (MERC) aims to predict speakers' emotional states in multi‑turn dialogues through text, audio, and visual cues. In real‑world settings, conversation scenarios differ significantly in speakers, topics, styles, and noise levels. Existing MERC methods generally neglect these cross‑scenario variations, limiting their ability to transfer models trained on a source domain to unseen target domains. To address this issue, we propose a Dual‑branch Graph Domain Adaptation framework (DGDA) for multimodal emotion recognition under cross‑scenario conditions. We first construct an emotion interaction graph to characterize complex emotional dependencies among utterances. A dual‑branch encoder, consisting of a hypergraph neural network (HGNN) and a path neural network (PathNN), is then designed to explicitly model multivariate relationships and implicitly capture global dependencies. To enable out‑of‑domain generalization, a domain adversarial discriminator is introduced to learn invariant representations across domains. Furthermore, a regularization loss is incorporated to suppress the negative influence of noisy labels. To the best of our knowledge, DGDA is the first MERC framework that jointly addresses domain shift and label noise. Theoretical analysis provides tighter generalization bounds, and extensive experiments on IEMOCAP and MELD demonstrate that DGDA consistently outperforms strong baselines and better adapts to cross‑scenario conversations. Our code is available at https://github.com/Xudmm1239439/DGDA‑Net.

PaperId: 2154, https://arxiv.org/pdf/2603.26837.pdf GitHub

Abstract:
Vision‑and‑Language Navigation (VLN) has recently benefited from Multimodal Large Language Models (MLLMs), enabling zero‑shot navigation. While recent exploration‑based zero‑shot methods have shown promising results by leveraging global scene priors, they rely on high‑quality human‑crafted scene reconstructions, which are impractical for real‑world robot deployment. When encountering an unseen environment, a robot should build its own priors through pre‑exploration. However, these self‑built reconstructions are inevitably incomplete and noisy, which severely degrade methods that depend on high‑quality scene reconstructions. To address these issues, we propose SpatialAnt, a zero‑shot navigation framework designed to bridge the gap between imperfect self‑reconstructions and robust execution. SpatialAnt introduces a physical grounding strategy to recover the absolute metric scale for monocular‑based reconstructions. Furthermore, rather than treating the noisy self‑reconstructed scenes as absolute spatial references, we propose a novel visual anticipation mechanism. This mechanism leverages the noisy point clouds to render future observations, enabling the agent to perform counterfactual reasoning and prune paths that contradict human instructions. Extensive experiments in both simulated and real‑world environments demonstrate that SpatialAnt significantly outperforms existing zero‑shot methods. We achieve a 66% Success Rate (SR) on R2R‑CE and 50.8% SR on RxR‑CE benchmarks. Physical deployment on a Hello Robot further confirms the efficiency and efficacy of our framework, achieving a 52% SR in challenging real‑world settings.

PaperId: 2155, https://arxiv.org/pdf/2603.26741.pdf GitHub GitHub

Abstract:
We study language‑conditioned visual navigation (LCVN), in which an embodied agent is asked to follow a natural language instruction based only on an initial egocentric observation. Without access to goal images, the agent must rely on language to shape its perception and continuous control, making the grounding problem particularly challenging. We formulate this problem as open‑loop trajectory prediction conditioned on linguistic instructions and introduce the LCVN Dataset, a benchmark of 39,016 trajectories and 117,048 human‑verified instructions that supports reproducible research across a range of environments and instruction styles. Using this dataset, we develop LCVN frameworks that link language grounding, future‑state prediction, and action generation through two complementary model families. The first family combines LCVN‑WM, a diffusion‑based world model, with LCVN‑AC, an actor‑critic agent trained in the latent space of the world model. The second family, LCVN‑Uni, adopts an autoregressive multimodal architecture that predicts both actions and future observations. Experiments show that these families offer different advantages: the former provides more temporally coherent rollouts, whereas the latter generalizes better to unseen environments. Taken together, these observations point to the value of jointly studying language grounding, imagination, and policy learning in a unified task setting, and LCVN provides a concrete basis for further investigation of language‑conditioned world models. The code is available at https://github.com/F1y1113/LCVN.

PaperId: 2156, https://arxiv.org/pdf/2603.26639.pdf GitHub

Abstract:
Empowered by large‑scale training, vision‑language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remains limited. Recent advances try to handle this limitation by injecting geometry tokens from pretrained 3D foundation models into VLMs. Nevertheless, we observe that naive token fusion followed by standard fine‑tuning in this line of work often leaves such geometric cues underutilized for spatial reasoning, as VLMs tend to rely heavily on 2D visual cues. In this paper, we propose GeoSR, a framework designed to make geometry matter by encouraging VLMs to actively reason with geometry tokens. GeoSR introduces two key components: (1) Geometry‑Unleashing Masking, which strategically masks portions of 2D vision tokens during training to weaken non‑geometric shortcuts and force the model to consult geometry tokens for spatial reasoning; and (2) Geometry‑Guided Fusion, a gated routing mechanism that adaptively amplifies geometry token contributions in regions where geometric evidence is critical. Together, these designs unleash the potential of geometry tokens for spatial reasoning tasks. Extensive experiments on both static and dynamic spatial reasoning benchmarks demonstrate that GeoSR consistently outperforms prior methods and establishes new state‑of‑the‑art performance by effectively leveraging geometric information. The project page is available at https://suhzhang.github.io/GeoSR/.

PaperId: 2157, https://arxiv.org/pdf/2603.26551.pdf GitHub

Abstract:
Vision backbone networks play a central role in modern computer vision. Enhancing their efficiency directly benefits a wide range of downstream applications. To measure efficiency, many publications rely on MACs (Multiply Accumulate operations) as a predictor of execution time. In this paper, we experimentally demonstrate the shortcomings of such a metric, especially in the context of edge devices. By contrasting the MAC count and execution time of common architectural design elements, we identify key factors for efficient execution and provide insights to optimize backbone design. Based on these insights, we present LowFormer, a novel vision backbone family. LowFormer features a streamlined macro and micro design that includes Lowtention, a lightweight alternative to Multi‑Head Self‑Attention. Lowtention not only proves more efficient, but also enables superior results on ImageNet. Additionally, we present an edge GPU version of LowFormer, that can further improve upon its baseline's speed on edge GPU and desktop GPU. We demonstrate LowFormer's wide applicability by evaluating it on smaller image classification datasets, as well as adapting it to several downstream tasks, such as object detection, semantic segmentation, image retrieval, and visual object tracking. LowFormer models consistently achieve remarkable speed‑ups across various hardware platforms compared to recent state‑of‑the‑art backbones. Code and models are available at https://github.com/altair199797/LowFormer/blob/main/Beyond_MACs.md.

PaperId: 2158, https://arxiv.org/pdf/2603.26469.pdf GitHub

Abstract:
Developing and evaluating distributed inference algorithms remains difficult due to the lack of standardized tools for modeling heterogeneous devices and networks. Existing studies often rely on ad‑hoc testbeds or proprietary infrastructure, making results hard to reproduce and limiting exploration of hypothetical hardware or network configurations. We present UNIFERENCE, a discrete‑event simulation (DES) framework designed for developing, benchmarking, and deploying distributed AI models within a unified environment. UNIFERENCE models device and network behavior through lightweight logical processes that synchronize only on communication primitives, eliminating rollbacks while preserving the causal order. It integrates seamlessly with PyTorch Distributed, enabling the same codebase to transition from simulation to real deployment. Our evaluation demonstrates that UNIFERENCE profiles runtime with up to 98.6% accuracy compared to real physical deployments across diverse backends and hardware setups. By bridging simulation and deployment, UNIFERENCE provides an accessible, reproducible platform for studying distributed inference algorithms and exploring future system designs, from high‑performance clusters to edge‑scale devices. The framework is open‑sourced at https://github.com/Dogacel/Uniference.

PaperId: 2159, https://arxiv.org/pdf/2603.26425.pdf GitHub

Abstract:
Recent research on vision backbone architectures has predominantly focused on optimizing efficiency for hardware platforms with high parallel processing capabilities. This category increasingly includes embedded systems such as mobile phones and embedded AI accelerator modules. In contrast, CPUs do not have the possibility to parallelize operations in the same manner, wherefore models benefit from a specific design philosophy that balances amount of operations (MACs) and hardware‑efficient execution by having high MACs per second (MACpS). In pursuit of this, we investigate two modifications to standard convolutions, aimed at reducing computational cost: grouping convolutions and reducing kernel sizes. While both adaptations substantially decrease the total number of MACs required for inference, sustaining low latency necessitates preserving hardware‑efficiency. Our experiments across diverse CPU devices confirm that these adaptations successfully retain high hardware‑efficiency on CPUs. Based on these insights, we introduce CPUBone, a new family of vision backbone models optimized for CPU‑based inference. CPUBone achieves state‑of‑the‑art Speed‑Accuracy Trade‑offs (SATs) across a wide range of CPU devices and effectively transfers its efficiency to downstream tasks such as object detection and semantic segmentation. Models and code are available at https://github.com/altair199797/CPUBone.

PaperId: 2160, https://arxiv.org/pdf/2603.26415.pdf GitHub

Abstract:
Uncertainty quantification is essential for deploying machine learning models in high‑stakes domains such as scientific discovery and healthcare. Conformal Prediction (CP) provides finite‑sample coverage guarantees under exchangeability, an assumption often violated in practice due to distribution shift. Under covariate shift, restoring validity requires importance weighting, yet accurate density‑ratio estimation becomes unstable when training and test distributions exhibit limited support overlap. We propose KMM‑CP, a conformal prediction framework based on Kernel Mean Matching (KMM) for covariate‑shift correction. We show that KMM directly controls the bias‑variance components governing conformal coverage error by minimizing RKHS moment discrepancy under explicit weight constraints, and establish asymptotic coverage guarantees under mild conditions. We then introduce a selective extension that identifies regions of reliable support overlap and restricts conformal correction to this subset, further improving stability in low‑overlap regimes. Experiments on molecular property prediction benchmarks with realistic distribution shifts show that KMM‑CP reduces coverage gap by over 50% compared to existing approaches. The code is available at https://github.com/siddharthal/KMM‑CP.

PaperId: 2161, https://arxiv.org/pdf/2603.26348.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) achieve strong multimodal reasoning performance, yet we identify a recurring failure mode in long‑form generation: as outputs grow longer, models progressively drift away from image evidence and fall back on textual priors, resulting in ungrounded reasoning and hallucinations. Interestingly, Based on attention analysis, we find that MLLMs have a latent capability for late‑stage visual verification that is present but not consistently activated. Motivated by this observation, we propose Visual Re‑Examination (VRE), a self‑evolving training framework that enables MLLMs to autonomously perform visual introspection during reasoning without additional visual inputs. Rather than distilling visual capabilities from a stronger teacher, VRE promotes iterative self‑improvement by leveraging the model itself to generate reflection traces, making visual information actionable through information gain. Extensive experiments across diverse multimodal benchmarks demonstrate that VRE consistently improves reasoning accuracy and perceptual reliability, while substantially reducing hallucinations, especially in long‑chain settings. Code is available at https://github.com/Xiaobu‑USTC/VRE.

PaperId: 2162, https://arxiv.org/pdf/2603.26332.pdf GitHub

Abstract:
Legal reasoning requires not only the application of legal rules but also an understanding of the context in which those rules operate. However, existing legal benchmarks primarily evaluate rule application under the assumption of fixed norms, and thus fail to capture situations where legal judgments shift or where multiple norms interact. In this work, we propose CALRK‑Bench, a context‑aware legal reasoning benchmark based on the legal system in Korean. CALRK‑Bench evaluates whether models can identify the temporal validity of legal norms, determine whether sufficient legal information is available for a given case, and understand the reasons behind shifts in legal judgments. The dataset is constructed from legal precedents and legal consultation records, and is validated by legal experts. Experimental results show that even recent large language models consistently exhibit low performance on these three tasks. CALRK‑Bench provides a new stress test for evaluating context‑aware legal reasoning rather than simple memorization of legal knowledge. Our code is available at https://github.com/jhCOR/CALRKBench.

PaperId: 2163, https://arxiv.org/pdf/2603.26098.pdf GitHub

Abstract:
While self‑supervised learning (SSL) has revolutionized audio representation, the excessive parameterization and quadratic computational cost of standard Transformers limit their deployment on resource‑constrained devices. To address this bottleneck, we propose HEAR (Human‑inspired Efficient Audio Representation), a novel decoupled architecture. Inspired by the human cognitive ability to isolate local acoustic features from global context, HEAR splits the processing pipeline into two dedicated modules: an Acoustic Model for local feature extraction and a Task Model for global semantic integration. Coupled with an Acoustic Tokenizer trained via knowledge distillation, our approach enables robust Masked Audio Modeling (MAM). Extensive experiments demonstrate that HEAR requires only 15M parameters and 9.47 GFLOPs for inference, operating at a fraction of the computational cost of conventional foundation models (which typically require 85M‑94M parameters). Despite this high efficiency, HEAR achieves highly competitive performance across diverse audio classification benchmarks. The code and pre‑trained models are available at https://github.com/HarunoriKawano/HEAR

PaperId: 2164, https://arxiv.org/pdf/2603.26049.pdf GitHub GitHub

Abstract:
Despite recent advances in medical vision‑language pretraining, existing models still struggle to capture the diagnostic workflow: radiographs are typically treated as context‑agnostic images, while radiologists' gaze ‑‑ a crucial cue for visual reasoning ‑‑ remains largely underexplored by existing methods. These limitations hinder the modeling of disease‑specific patterns and weaken cross‑modal alignment. To bridge this gap, we introduce CoGaze, a Context‑ and Gaze‑guided vision‑language pretraining framework for chest X‑rays. We first propose a context‑infused vision encoder that models how radiologists integrate clinical context ‑‑ including patient history, symptoms, and diagnostic intent ‑‑ to guide diagnostic reasoning. We then present a multi‑level supervision paradigm that (1) enforces intra‑ and inter‑modal semantic alignment through hybrid‑positive contrastive learning, (2) injects diagnostic priors via disease‑aware cross‑modal representation learning, and (3) leverages radiologists' gaze as probabilistic priors to guide attention toward diagnostically salient regions. Extensive experiments demonstrate that CoGaze consistently outperforms state‑of‑the‑art methods across diverse tasks, achieving up to +2.0% CheXbertF1 and +1.2% BLEU2 for free‑text and structured report generation, +23.2% AUROC for zero‑shot classification, and +12.2% Precision@1 for image‑text retrieval. Code is available at https://github.com/mk‑runner/CoGaze.

PaperId: 2165, https://arxiv.org/pdf/2603.26008.pdf GitHub

Abstract:
While powerful in image‑conditioned generation, multimodal large language models (MLLMs) can display uneven performance across demographic groups, highlighting fairness risks. In safety‑critical clinical settings, such disparities risk producing unequal diagnostic narratives and eroding trust in AI‑assisted decision‑making. While fairness has been studied extensively in vision‑only and language‑only models, its impact on MLLMs remains largely underexplored. To address these biases, we introduce FairLLaVA, a parameter‑efficient fine‑tuning method that mitigates group disparities in visual instruction tuning without compromising overall performance. By minimizing the mutual information between target attributes, FairLLaVA regularizes the model's representations to be demographic‑invariant. The method can be incorporated as a lightweight plug‑in, maintaining efficiency with low‑rank adapter fine‑tuning, and provides an architecture‑agnostic approach to fair visual instruction following. Extensive experiments on large‑scale chest radiology report generation and dermoscopy visual question answering benchmarks show that FairLLaVA consistently reduces inter‑group disparities while improving both equity‑scaled clinical performance and natural language generation quality across diverse medical imaging modalities. Code can be accessed at https://github.com/bhosalems/FairLLaVA.

PaperId: 2166, https://arxiv.org/pdf/2603.25841.pdf GitHub

Abstract:
Current multimodal large language models (MLLMs) cannot effectively utilize eye‑gaze information for video understanding, even when gaze cues are supplied via visual overlays or text descriptions. We introduce GazeQwen, a parameter efficient approach that equips an open‑source MLLM with gaze awareness through hidden‑state modulation. At its core is a compact gaze resampler (~1‑5 M trainable parameters) that encodes V‑JEPA 2.1 video features together with fixation‑derived positional encodings and produces additive residuals injected into selected LLM decoder layers via forward hooks. An optional second training stage adds low‑rank adapters (LoRA) to the LLM for tighter integration. Evaluated on all 10 tasks of the StreamGaze benchmark, GazeQwen reaches 63.9% accuracy, a +16.1 point gain over the same Qwen2.5‑VL‑7B backbone with gaze as visual prompts and +10.5 points over GPT‑4o, the highest score among all open‑source and proprietary models tested. These results suggest that learning where to inject gaze within an LLM is more effective than scaling model size or engineering better prompts. All code and checkpoints are available at https://github.com/phamtrongthang123/gazeqwen .

PaperId: 2167, https://arxiv.org/pdf/2603.25823.pdf GitHub

Abstract:
Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current evaluations largely rely on superficial metrics or fragmented benchmarks, creating a ``performance mirage'' that overlooks the generative process. To address this, we introduce ViGoR Vision‑Gnerative Reasoning‑centric Benchmark), a unified framework designed to dismantle this mirage. ViGoR distinguishes itself through four key innovations: 1) holistic cross‑modal coverage bridging Image‑to‑Image and Video tasks; 2) a dual‑track mechanism evaluating both intermediate processes and final results; 3) an evidence‑grounded automated judge ensuring high human alignment; and 4) granular diagnostic analysis that decomposes performance into fine‑grained cognitive dimensions. Experiments on over 20 leading models reveal that even state‑of‑the‑art systems harbor significant reasoning deficits, establishing ViGoR as a critical ``stress test'' for the next generation of intelligent vision models. The demo have been available at https://vincenthancoder.github.io/ViGoR‑Bench/

PaperId: 2168, https://arxiv.org/pdf/2603.25741.pdf GitHub

Abstract:
Vision‑language‑action models have reshaped autonomous driving to incorporate languages into the decision‑making process. However, most existing pipelines only utilize the language modality for scene descriptions or reasoning and lack the flexibility to follow diverse user instructions for personalized driving. To address this, we first construct a large‑scale driving dataset (InstructScene) containing around 100,000 scenes annotated with diverse driving instructions with the corresponding trajectories. We then propose a unified Vision‑Language‑World‑Action model, Vega, for instruction‑based generation and planning. We employ the autoregressive paradigm to process visual inputs (vision) and language instructions (language) and the diffusion paradigm to generate future predictions (world modeling) and trajectories (action). We perform joint attention to enable interactions between the modalities and use individual projection layers for different modalities for more capabilities. Extensive experiments demonstrate that our method not only achieves superior planning performance but also exhibits strong instruction‑following abilities, paving the way for more intelligent and personalized driving systems.

PaperId: 2169, https://arxiv.org/pdf/2603.25740.pdf GitHub

Abstract:
Human driving behavior is inherently personal, which is shaped by long‑term habits and influenced by short‑term intentions. Individuals differ in how they accelerate, brake, merge, yield, and overtake across diverse situations. However, existing end‑to‑end autonomous driving systems either optimize for generic objectives or rely on fixed driving modes, lacking the ability to adapt to individual preferences or interpret natural language intent. To address this gap, we propose Drive My Way (DMW), a personalized Vision‑Language‑Action (VLA) driving framework that aligns with users' long‑term driving habits and adapts to real‑time user instructions. DMW learns a user embedding from our personalized driving dataset collected across multiple real drivers and conditions the policy on this embedding during planning, while natural language instructions provide additional short‑term guidance. Closed‑loop evaluation on the Bench2Drive benchmark demonstrates that DMW improves style instruction adaptation, and user studies show that its generated behaviors are recognizable as each driver's own style, highlighting personalization as a key capability for human‑centered autonomous driving. Our data and code are available at https://dmw‑cvpr.github.io/.

PaperId: 2170, https://arxiv.org/pdf/2603.25730.pdf GitHub

Abstract:
Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV‑cache growth, temporal repetition, and compounding errors during long‑video generation. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel three‑partition KV‑cache strategy. Specifically, we categorize the historical context into three distinct types: (1) Sink tokens, which preserve early anchor frames at full resolution to maintain global semantics; (2) Mid tokens, which achieve a massive spatiotemporal compression (32x token reduction) via a dual‑branch network fusing progressive 3D convolutions with low‑resolution VAE re‑encoding; and (3) Recent tokens, kept at full resolution to ensure local temporal coherence. To strictly bound the memory footprint without sacrificing quality, we introduce a dynamic top‑k context selection mechanism for the mid tokens, coupled with a continuous Temporal RoPE Adjustment that seamlessly re‑aligns position gaps caused by dropped tokens with negligible overhead. Empowered by this principled hierarchical context compression, PackForcing can generate coherent 2‑minute, 832x480 videos at 16 FPS on a single H200 GPU. It achieves a bounded KV cache of just 4 GB and enables a remarkable 24x temporal extrapolation (5s to 120s), operating effectively either zero‑shot or trained on merely 5‑second clips. Extensive results on VBench demonstrate state‑of‑the‑art temporal consistency (26.07) and dynamic degree (56.25), proving that short‑video supervision is sufficient for high‑quality, long‑video synthesis. https://github.com/ShandaAI/PackForcing

PaperId: 2171, https://arxiv.org/pdf/2603.25728.pdf GitHub GitHub

Abstract:
Fine‑grained facial expression editing has long been limited by intrinsic semantic overlap. To address this, we construct the Flex Facial Expression (FFE) dataset with continuous affective annotations and establish FFE‑Bench to evaluate structural confusion, editing accuracy, linear controllability, and the trade‑off between expression editing and identity preservation. We propose PixelSmile, a diffusion framework that disentangles expression semantics via fully symmetric joint training. PixelSmile combines intensity supervision with contrastive learning to produce stronger and more distinguishable expressions, achieving precise and stable linear expression control through textual latent interpolation. Extensive experiments demonstrate that PixelSmile achieves superior disentanglement and robust identity preservation, confirming its effectiveness for continuous, controllable, and fine‑grained expression editing, while naturally supporting smooth expression blending.

PaperId: 2172, https://arxiv.org/pdf/2603.25716.pdf GitHub GitHub GitHub

Abstract:
Video world models have shown immense potential in simulating the physical world, yet existing memory mechanisms primarily treat environments as static canvases. When dynamic subjects hide out of sight and later re‑emerge, current methods often struggle, leading to frozen, distorted, or vanishing subjects. To address this, we introduce Hybrid Memory, a novel paradigm requiring models to simultaneously act as precise archivists for static backgrounds and vigilant trackers for dynamic subjects, ensuring motion continuity during out‑of‑view intervals. To facilitate research in this direction, we construct HM‑World, the first large‑scale video dataset dedicated to hybrid memory. It features 59K high‑fidelity clips with decoupled camera and subject trajectories, encompassing 17 diverse scenes, 49 distinct subjects, and meticulously designed exit‑entry events to rigorously evaluate hybrid coherence. Furthermore, we propose HyDRA, a specialized memory architecture that compresses memory into tokens and utilizes a spatiotemporal relevance‑driven retrieval mechanism. By selectively attending to relevant motion cues, HyDRA effectively preserves the identity and motion of hidden subjects. Extensive experiments on HM‑World demonstrate that our method significantly outperforms state‑of‑the‑art approaches in both dynamic subject consistency and overall generation quality. Code is publicly available at https://github.com/H‑EmbodVis/HyDRA.

PaperId: 2173, https://arxiv.org/pdf/2603.25423.pdf GitHub

Abstract:
The rise of micro‑videos has reshaped how misinformation spreads, amplifying its speed, reach, and impact on public trust. Existing benchmarks typically focus on a single deception type, overlooking the diversity of real‑world cases that involve multimodal manipulation, AI‑generated content, cognitive bias, and out‑of‑context reuse. Meanwhile, most detection models lack fine‑grained attribution, limiting interpretability and practical utility. To address these gaps, we introduce WildFakeBench, a large‑scale benchmark of over 10,000 real‑world micro‑videos covering diverse misinformation types and sources, each annotated with expert‑defined attribution labels. Building on this foundation, we develop FakeAgent, a Delphi‑inspired multi‑agent reasoning framework that integrates multimodal understanding with external evidence for attribution‑grounded analysis. FakeAgent jointly analyzes content and retrieved evidence to identify manipulation, recognize cognitive and AI‑generated patterns, and detect out‑of‑context misinformation. Extensive experiments show that FakeAgent consistently outperforms existing MLLMs across all misinformation types, while WildFakeBench provides a realistic and challenging testbed for advancing explainable micro‑video misinformation detection. Data and code are available at: https://github.com/Aiyistan/FakeAgent.

PaperId: 2174, https://arxiv.org/pdf/2603.25385.pdf GitHub

Abstract:
Quantization techniques such as BitsAndBytes, AWQ, and GPTQ are widely used as a standard method in deploying large language models but often degrades accuracy when using low‑bit representations, e.g., 4 bits. Low‑rank correction methods (e.g., LQER, QERA, ASER) has been proposed to mitigate this issue, however, they restore all layers and insert error‑correction modules into every decoder block, which increases latency and memory overhead. To address this limitation, we propose GlowQ, a group‑shared low‑rank approximation for quantized LLMs that caches a single shared right factor per input‑sharing group and restores only the groups or layers that yield the highest accuracy benefit. GlowQ computes the high‑precision projection once per input‑sharing group and reuses it across its modules, reducing parameter and memory overhead, and retaining the expressivity of layer‑specific corrections. We also propose a selective variant, GlowQ‑S, that applies the cached shared module only where it provides the largest benefit. Compared with strong baselines, our approach reduces TTFB by (5.6%) and increases throughput by (9.6%) on average, while reducing perplexity on WikiText‑2 by (0.17%) and increasing downstream accuracy by 0.42 percentage points. The selective model GlowQ‑S further reduces latency, cutting TTFB by (23.4%) and increasing throughput by (37.4%), while maintaining accuracy within 0.2 percentage points on average. Code is available at https://github.com/ahnselim/GlowQ.

PaperId: 2175, https://arxiv.org/pdf/2603.25333.pdf GitHub GitHub

Abstract:
The effectiveness of Retrieval‑Augmented Generation (RAG) is highly dependent on how documents are chunked, that is, segmented into smaller units for indexing and retrieval. Yet, commonly used "one‑size‑fits‑all" approaches often fail to capture the nuanced structure and semantics of diverse texts. Despite its central role, chunking lacks a dedicated evaluation framework, making it difficult to assess and compare strategies independently of downstream performance. We challenge this paradigm by introducing Adaptive Chunking, a framework that selects the most suitable chunking strategy for each document based on a set of five novel intrinsic, document‑based metrics: References Completeness (RC), Intrachunk Cohesion (ICC), Document Contextual Coherence (DCC), Block Integrity (BI), and Size Compliance (SC), which directly assess chunking quality across key dimensions. To support this framework, we also introduce two new chunkers, an LLM‑regex splitter and a split‑then‑merge recursive splitter, alongside targeted post‑processing techniques. On a diverse corpus spanning legal, technical, and social science domains, our metric‑guided adaptive method significantly improves downstream RAG performance. Without changing models or prompts, our framework increases RAG outcomes, raising answers correctness to 72% (from 62‑64%) and increasing the number of successfully answered questions by over 30% (65 vs. 49). These results demonstrate that adaptive, document‑aware chunking, guided by a complementary suite of intrinsic metrics, offers a practical and effective path to more robust RAG systems. Code available at https://github.com/ekimetrics/adaptive‑chunking.

PaperId: 2176, https://arxiv.org/pdf/2603.25325.pdf GitHub

Abstract:
Weight pruning is a standard technique for compressing large language models, yet its effect on learned internal representations remains poorly understood. We present the first systematic study of how unstructured pruning reshapes the feature geometry of language models, using Sparse Autoencoders (SAEs) as interpretability probes. Across three model families (Gemma 3 1B, Gemma 2 2B, Llama 3.2 1B), two pruning methods (magnitude and Wanda), and six sparsity levels (0‑‑60%), we investigate five research questions spanning seed stability, feature survival, SAE transferability, feature fragility, and causal relevance. Our most striking finding is that rare SAE features‑‑those with low firing rates‑‑survive pruning far better than frequent ones, with within‑condition Spearman correlations of rho = ‑1.0 in 11 of 17 experimental conditions. This counter‑intuitive result suggests that pruning acts as implicit feature selection, preferentially destroying high‑frequency generic features while preserving specialized rare ones. We further show that Wanda pruning preserves feature structure up to 3.7x better than magnitude pruning, that pre‑trained SAEs remain viable on Wanda‑pruned models up to 50% sparsity, and that geometric feature survival does not predict causal importance‑‑a dissociation with implications for interpretability under compression.

PaperId: 2177, https://arxiv.org/pdf/2603.25284.pdf GitHub

Abstract:
In this paper, we address post‑training quantization (PTQ) for large language models (LLMs) from an overlooked perspective: given a pre‑trained high‑precision LLM, the predominant sequential quantization framework treats different layers equally, but this may be not optimal in challenging bit‑width settings. We empirically study the quantization impact of different layers on model accuracy, and observe that: (1) shallow/deep layers are usually more sensitive to quantization than intermediate layers; (2) among shallow/deep layers, the most sensitive one is the first/last layer, which exhibits significantly larger quantization error than others. These empirical observations imply that the quantization design for different layers of LLMs is required on multiple levels instead of a single level shared to all layers. Motivated by this, we propose a new PTQ framework termed Sliding‑layer Quantization (SliderQuant) that relies on a simple adaptive sliding quantization concept facilitated by few learnable parameters. The base component of SliderQuant is called inter‑layer sliding quantization, which incorporates three types of novel sliding window designs tailored for addressing the varying quantization sensitivity of shallow, intermediate and deep layers. The other component is called intra‑layer sliding quantization that leverages an incremental strategy to quantize each window. As a result, SliderQuant has a strong ability to reduce quantization errors across layers. Extensive experiments on basic language generation, zero‑shot commonsense reasoning and challenging math and code tasks with various LLMs, including Llama/Llama2/Llama3/Qwen2.5 model families, DeepSeek‑R1 distilled models and large MoE models, show that our method outperforms existing PTQ methods (including the latest PTQ methods using rotation transformations) for both weight‑only quantization and weight‑activation quantization.

PaperId: 2178, https://arxiv.org/pdf/2603.25268.pdf GitHub

Abstract:
We introduce CRAFT, a multi‑agent benchmark for evaluating pragmatic communication in large language models under strict partial information. In this setting, multiple agents with complementary but incomplete views must coordinate through natural language to construct a shared 3D structure that no single agent can fully observe. We formalize this problem as a multi‑sender Bounded Pragmatic Speaker problem and provide a diagnostic framework that decomposes failures into spatial grounding, belief modeling and pragmatic communication errors, including a taxonomy of behavioral failure profiles in both frontier and open‑weight models. Across a diverse set of models, including 8 open‑weight and 7 frontier including reasoning models, we find that stronger reasoning ability does not reliably translate to better coordination: smaller open‑weight models often match or outperform frontier systems, and improved individual communication does not guarantee successful collaboration. These results suggest that multi‑agent coordination remains a fundamentally unsolved challenge for current language models. Our code can be found at https://github.com/csu‑signal/CRAFT

PaperId: 2179, https://arxiv.org/pdf/2603.25250.pdf GitHub GitHub

Abstract:
Out‑of‑distribution (OOD) detection aims to identify samples that deviate from in‑distribution (ID). One popular pipeline addresses this by introducing negative labels distant from ID classes and detecting OOD based on their distance to these labels. However, such labels may present poor activation on OOD samples, failing to capture the OOD characteristics. To address this, we propose \underlineTest‑time \underlineActivated \underlineNegative \underlineLabels (TANL) by dynamically evaluating activation levels across the corpus dataset and mining candidate labels with high activation responses during the testing process. Specifically, TANL identifies high‑confidence test images online and accumulates their assignment probabilities over the corpus to construct a label activation metric. Such a metric leverages historical test samples to adaptively align with the test distribution, enabling the selection of distribution‑adaptive activated negative labels. By further exploring the activation information within the current testing batch, we introduce a more fine‑grained, batch‑adaptive variant. To fully utilize label activation knowledge, we propose an activation‑aware score function that emphasizes negative labels with stronger activations, boosting performance and enhancing its robustness to the label number. Our TANL is training‑free, test‑efficient, and grounded in theoretical justification. Experiments on diverse backbones and wide task settings validate its effectiveness. Notably, on the large‑scale ImageNet benchmark, TANL significantly reduces the FPR95 from 17.5% to 9.8%. Codes are available at \hrefhttps://github.com/YBZh/OpenOOD‑VLMYBZh/OpenOOD‑VLM.

PaperId: 2180, https://arxiv.org/pdf/2603.25247.pdf GitHub

Abstract:
Spatial Transcriptomics (ST) provides spatially‑resolved gene expression, offering crucial insights into tissue architecture and complex diseases. However, its prohibitive cost limits widespread adoption, leading to significant attention on inferring spatial gene expression from readily available whole slide images. While graph neural networks have been proposed to model interactions between tissue regions, their reliance on pre‑defined sparse graphs prevents them from considering potentially interacting spot pairs, resulting in a structural limitation in capturing complex biological relationships. To address this, we propose FEAST (Fully connected Expressive Attention for Spatial Transcriptomics), an attention‑based framework that models the tissue as a fully connected graph, enabling the consideration of all pairwise interactions. To better reflect biological interactions, we introduce negative‑aware attention, which models both excitatory and inhibitory interactions, capturing essential negative relationships that standard attention often overlooks. Furthermore, to mitigate the information loss from truncated or ignored context in standard spot image extraction, we introduce an off‑grid sampling strategy that gathers additional images from intermediate regions, allowing the model to capture a richer morphological context. Experiments on public ST datasets show that FEAST surpasses state‑of‑the‑art methods in gene expression prediction while providing biologically plausible attention maps that clarify positive and negative interactions. Our code is available at https://github.com/starforTJ/ FEAST.

PaperId: 2181, https://arxiv.org/pdf/2603.25226.pdf GitHub GitHub

Abstract:
The emergence of Large Language Models (LLMs) has catalyzed a paradigm shift in programming, giving rise to "vibe coding", where users can build complete projects and even control computers using natural language instructions. This paradigm has driven automated webpage development, but it introduces a new requirement about how to automatically verify whether the web functionalities are reliably implemented. Existing works struggle to adapt, relying on static visual similarity or predefined checklists that constrain their utility in open‑ended environments. Furthermore, they overlook a vital aspect of software quality, namely latent logical constraints. To address these gaps, we introduce WebTestBench, a benchmark for evaluating end‑to‑end automated web testing. WebTestBench encompasses comprehensive dimensions across diverse web application categories. We decompose the testing process into two cascaded sub‑tasks, checklist generation and defect detection, and propose WebTester, a baseline framework for this task. Evaluating popular LLMs with WebTester reveals severe challenges, including insufficient test completeness, detection bottlenecks, and long‑horizon interaction unreliability. These findings expose a substantial gap between current computer‑use agent capabilities and industrial‑grade deployment demands. We hope that WebTestBench provides valuable insights and guidance for advancing end‑to‑end automated web testing. Our dataset and code are available at https://github.com/friedrichor/WebTestBench.

PaperId: 2182, https://arxiv.org/pdf/2603.25209.pdf GitHub GitHub

Abstract:
Generating long videos using pre‑trained video diffusion models, which are typically trained on short clips, presents a significant challenge. Directly applying these models for long‑video inference often leads to a notable degradation in visual quality. This paper identifies that this issue primarily stems from two out‑of‑distribution (O.O.D) problems: frame‑level relative position O.O.D and context‑length O.O.D. To address these challenges, we propose FreeLOC, a novel training‑free, layer‑adaptive framework that introduces two core techniques: Video‑based Relative Position Re‑encoding (VRPR) for frame‑level relative position O.O.D, a multi‑granularity strategy that hierarchically re‑encodes temporal relative positions to align with the model's pre‑trained distribution, and Tiered Sparse Attention (TSA) for context‑length O.O.D, which preserves both local detail and long‑range dependencies by structuring attention density across different temporal scales. Crucially, we introduce a layer‑adaptive probing mechanism that identifies the sensitivity of each transformer layer to these O.O.D issues, allowing for the selective and efficient application of our methods. Extensive experiments demonstrate that our approach significantly outperforms existing training‑free methods, achieving state‑of‑the‑art results in both temporal consistency and visual quality. Code is available at https://github.com/Westlake‑AGI‑Lab/FreeLOC.

PaperId: 2183, https://arxiv.org/pdf/2603.25126.pdf GitHub

Abstract:
Multi‑Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in traditional single‑behavior approaches. However, existing MBR methods face fundamental challenges: they lack principled frameworks to model complex confounding effects from user behavioral habits and item multi‑behavior distributions, struggle with effective aggregation of heterogeneous auxiliary behaviors, and fail to align behavioral representations across semantic gaps while accounting for bias distortions. To address these limitations, we propose MCLMR, a novel model‑agnostic causal learning framework that can be seamlessly integrated into various MBR architectures. MCLMR first constructs a causal graph to model confounding effects and performs interventions for unbiased preference estimation. Under this causal framework, it employs an Adaptive Aggregation module based on Mixture‑of‑Experts to dynamically fuse auxiliary behavior information and a Bias‑aware Contrastive Learning module to align cross‑behavior representations in a bias‑aware manner. Extensive experiments on three real‑world datasets demonstrate that MCLMR achieves significant performance improvements across various baseline models, validating its effectiveness and generality. All data and code will be made publicly available. For anonymous review, our code is available at the following the link: https://github.com/gitrxh/MCLMR.

PaperId: 2184, https://arxiv.org/pdf/2603.24965.pdf GitHub

Abstract:
Despite significant progress in text‑to‑image generation, aligning outputs with complex prompts remains challenging, particularly for fine‑grained semantics and spatial relations. This difficulty stems from the feed‑forward nature of generation, which requires anticipating alignment without fully understanding the output. In contrast, evaluating generated images is more tractable. Motivated by this asymmetry, we propose xLARD, a self‑correcting framework that uses multimodal large language models to guide generation through Explainable LAtent RewarDs. xLARD introduces a lightweight corrector that refines latent representations based on structured feedback from model‑generated references. A key component is a differentiable mapping from latent edits to interpretable reward signals, enabling continuous latent‑level guidance from non‑differentiable image‑level evaluations. This mechanism allows the model to understand, assess, and correct itself during generation. Experiments across diverse generation and editing tasks show that xLARD improves semantic alignment and visual fidelity while maintaining generative priors. Code is available at https://yinyiluo.github.io/xLARD/.

PaperId: 2185, https://arxiv.org/pdf/2603.24866.pdf GitHub

Abstract:
The physical world is not merely visual; it is governed by rigorous structural and procedural constraints. Yet, the evaluation of vision‑language models (VLMs) remains heavily skewed toward perceptual realism, prioritizing the generation of visually plausible 3D layouts, shapes, and appearances. Current benchmarks rarely test whether models grasp the step‑by‑step processes and physical dependencies required to actually build these artifacts, a capability essential for automating design‑to‑construction pipelines. To address this, we introduce DreamHouse, a novel benchmark for physical generative reasoning: the capacity to synthesize artifacts that concurrently satisfy geometric, structural, constructability, and code‑compliance constraints. We ground this benchmark in residential timber‑frame construction, a domain with fully codified engineering standards and objectively verifiable correctness. We curate over 26,000 structures spanning 13 architectural styles, ach verified to construction‑document standards (LOD 350) and develop a deterministic 10‑test structural validation framework. Unlike static benchmarks that assess only final outputs, DreamHouse supports iterative agentic interaction. Models observe intermediate build states, generate construction actions, and receive structured environmental feedback, enabling a fine‑grained evaluation of planning, structural reasoning, and self‑correction. Extensive experiments with state‑of‑the‑art VLMs reveal substantial capability gaps that are largely invisible on existing leaderboards. These findings establish physical validity as a critical evaluation axis orthogonal to visual realism, highlighting physical generative reasoning as a distinct and underdeveloped frontier in multimodal intelligence. Available at https://luluyuyuyang.github.io/dreamhouse

PaperId: 2186, https://arxiv.org/pdf/2603.24844.pdf GitHub

Abstract:
Given a question, a language model (LM) implicitly encodes a distribution over possible answers. In practice, post‑training procedures for LMs often collapse this distribution onto a single dominant mode. While this is generally not a problem for benchmark‑style evaluations that assume one correct answer, many real‑world tasks inherently involve multiple valid answers or irreducible uncertainty. Examples include medical diagnosis, ambiguous question answering, and settings with incomplete information. In these cases, we would like LMs to generate multiple plausible hypotheses, ideally with confidence estimates for each one, and without computationally intensive repeated sampling to generate non‑modal answers. This paper describes a multi‑answer reinforcement learning approach for training LMs to perform distributional reasoning over multiple answers during inference. We modify the RL objective to enable models to explicitly generate multiple candidate answers in a single forward pass, internalizing aspects of inference‑time search into the model's generative process. Across question‑answering, medical diagnostic, and coding benchmarks, we observe improved diversity, coverage, and set‑level calibration scores compared to single answer trained baselines. Models trained with our approach require fewer tokens to generate multiple answers than competing approaches. On coding tasks, they are also substantially more accurate. These results position multi‑answer RL as a principled and compute‑efficient alternative to inference‑time scaling procedures such as best‑of‑k. Code and more information can be found at https://multi‑answer‑rl.github.io/.

PaperId: 2187, https://arxiv.org/pdf/2603.24828.pdf GitHub

Abstract:
Clinical decisions are high‑stakes and require explicit justification, making model interpretability essential for auditing deep clinical models prior to deployment. As the ecosystem of model architectures and explainability methods expands, critical questions remain: Do architectural features like attention improve explainability? Do interpretability approaches generalize across clinical tasks? While prior benchmarking efforts exist, they often lack extensibility and reproducibility, and critically, fail to systematically examine how interpretability varies across the interplay of clinical tasks and model architectures. To address these gaps, we present a comprehensive benchmark evaluating interpretability methods across diverse clinical prediction tasks and model architectures. Our analysis reveals that: (1) attention when leveraged properly is a highly efficient approach for faithfully interpreting model predictions; (2) black‑box interpreters like KernelSHAP and LIME are computationally infeasible for time‑series clinical prediction tasks; and (3) several interpretability approaches are too unreliable to be trustworthy. From our findings, we discuss several guidelines on improving interpretability within clinical predictive pipelines. To support reproducibility and extensibility, we provide our implementations via PyHealth, a well‑documented open‑source framework: https://github.com/sunlabuiuc/PyHealth.

PaperId: 2188, https://arxiv.org/pdf/2603.24821.pdf GitHub

Abstract:
State‑of‑the‑art crowd counting and localization are primarily modeled using two paradigms: density maps and point regression. Given the field's security ramifications, there is active interest in model robustness against adversarial attacks. Recent studies have demonstrated transferability across density‑map‑based approaches via adversarial patches, but cross‑paradigm attacks (i.e., across both density map‑based models and point regression‑based models) remain unexplored. We introduce a novel adversarial framework that compromises both density map and point regression architectural paradigms through a comprehensive multi‑task loss optimization. For point‑regression models, we employ scene‑density‑specific high‑confidence logit suppression; for density‑map approaches, we use peak‑targeted density map suppression. Both are combined with model‑agnostic perceptual constraints to ensure that perturbations are effective and imperceptible to the human eye. Extensive experiments demonstrate the effectiveness of our attack, achieving on average a 7X increase in Mean Absolute Error compared to clean images while maintaining competitive visual quality, and successfully transferring across seven state‑of‑the‑art crowd models with transfer ratios ranging from 0.55 to 1.69. Our approach strikes a balance between attack effectiveness and imperceptibility compared to state‑of‑the‑art transferable attack strategies. The source code is available at https://github.com/simurgh7/CrowdGen

PaperId: 2189, https://arxiv.org/pdf/2603.24787.pdf GitHub

Abstract:
Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) systems that combine lightweight models with powerful but expensive large models. Recent studies show that \emphprobe routing, which predicts the correctness of a small model using its hidden states, provides an effective solution in text‑only LLMs. However, we observe that these probes degrade substantially when applied to multimodal LLMs (MLLMs). Through empirical analysis, we find that the presence of visual inputs weakens the separability of correctness signals in hidden states, making them harder to extract using standard probe designs. To address this challenge, we introduce two complementary approaches for improving probe routing in MLLMs. First, we propose the \emphAttention Probe, which aggregates hidden states from the preceding layer based on attention scores to recover distributed correctness signals. Second, we present the \emphKL‑Regularized LoRA Probe (ReLope), which inserts a lightweight LoRA adapter and applies a KL regularizer to learn routing‑aware representations. Comprehensive experiments show that our methods consistently outperform baselines, suggesting that improving the quality of hidden states is key to effective routing in MLLMs. Our code is available at https://github.com/Spinozaaa/ReLope.

PaperId: 2190, https://arxiv.org/pdf/2603.24738.pdf GitHub

Abstract:
Efficient task scheduling in large‑scale distributed systems presents significant challenges due to dynamic workloads, heterogeneous resources, and competing quality‑of‑service requirements. Traditional centralized approaches face scalability limitations and single points of failure, while classical heuristics lack adaptability to changing conditions. This paper proposes a decentralized multi‑agent deep reinforcement learning (DRL‑MADRL) framework for task scheduling in heterogeneous distributed systems. We formulate the problem as a Decentralized Partially Observable Markov Decision Process (Dec‑POMDP) and develop a lightweight actor‑critic architecture implemented using only NumPy, enabling deployment on resource‑constrained edge devices without heavyweight machine learning frameworks. Using workload characteristics derived from the publicly available Google Cluster Trace dataset, we evaluate our approach on a 100‑node heterogeneous system processing 1,000 tasks per episode over 30 experimental runs. Experimental results demonstrate 15.6% improvement in average task completion time (30.8s vs 36.5s for random baseline), 15.2% energy efficiency gain (745.2 kWh vs 878.3 kWh), and 82.3% SLA satisfaction compared to 75.5% for baselines, with all improvements statistically significant (p < 0.001). The lightweight implementation requires only NumPy, Matplotlib, and SciPy. Complete source code and experimental data are provided for full reproducibility at https://github.com/danielbenniah/marl‑distributed‑scheduling.

PaperId: 2191, https://arxiv.org/pdf/2603.24724.pdf GitHub

Abstract:
Appearance‑based gaze estimation frequently relies on deep Convolutional Neural Networks (CNNs). These models are accurate, but computationally expensive and act as "black boxes", offering little interpretability. Geometric methods based on facial landmarks are a lightweight alternative, but their performance limits and generalization capabilities remain underexplored in modern benchmarks. In this study, we conduct a comprehensive evaluation of landmark‑based gaze estimation. We introduce a standardized pipeline to extract and normalize landmarks from three large‑scale datasets (Gaze360, ETH‑XGaze, and GazeGene) and train lightweight regression models, specifically Extreme Gradient Boosted trees and two neural architectures: a holistic Multi‑Layer Perceptron (MLP) and a siamese MLP designed to capture binocular geometry. We find that landmark‑based models exhibit lower performance in within‑domain evaluation, likely due to noise introduced into the datasets by the landmark detector. Nevertheless, in cross‑domain evaluation, the proposed MLP architectures show generalization capabilities comparable to those of ResNet18 baselines. These findings suggest that sparse geometric features encode sufficient information for robust gaze estimation, paving the way for efficient, interpretable, and privacy‑friendly edge applications. The source code and generated landmark‑based datasets are available at https://github.com/daniele‑agostinelli/LandmarkGaze.git.

PaperId: 2192, https://arxiv.org/pdf/2603.24721.pdf GitHub

Abstract:
Spatial reasoning focuses on locating target objects based on spatial relations in 3D scenes, which plays a crucial role in developing intelligent embodied agents. Due to the limited availability of 3D scene‑language paired data, it is challenging to train models with strong reasoning ability from scratch. Previous approaches have attempted to inject 3D scene representations into the input space of Large Language Models (LLMs) and leverage the pretrained comprehension and reasoning abilities for spatial reasoning. However, models encoding absolute positions struggle to extract spatial relations from prematurely fused features, while methods explicitly encoding all spatial relations (which is quadratic in the number of objects) as input tokens suffer from poor scalability. To address these limitations, we propose QuatRoPE, a novel positional embedding method with an input length that is linear to the number of objects, and explicitly calculates pairwise spatial relations through the dot product in attention layers. QuatRoPE's holistic vector encoding of 3D coordinates guarantees a high degree of spatial consistency, maintaining fidelity to the scene's geometric integrity. Additionally, we introduce the Isolated Gated RoPE Extension (IGRE), which effectively limits QuatRoPE's influence to object‑related tokens, thereby minimizing interference with the LLM's existing positional embeddings and maintaining the LLM's original capabilities. Extensive experiments demonstrate the effectiveness of our approaches. The code and data are available at https://github.com/oceanflowlab/QuatRoPE.

PaperId: 2193, https://arxiv.org/pdf/2603.24576.pdf GitHub

Abstract:
Robotic manipulation often requires memory: occlusion and state changes can make decision‑time observations perceptually aliased, making action selection non‑Markovian at the observation level because the same observation may arise from different interaction histories. Most embodied agents implement memory via semantically compressed traces and similarity‑based retrieval, which discards disambiguating fine‑grained perceptual cues and can return perceptually similar but decision‑irrelevant episodes. Inspired by human episodic memory, we propose Chameleon, which writes geometry‑grounded multimodal tokens to preserve disambiguating context and produces goal‑directed recall through a differentiable memory stack. We also introduce Camo‑Dataset, a real‑robot UR5e dataset spanning episodic recall, spatial tracking, and sequential manipulation under perceptual aliasing. Across tasks, Chameleon consistently improves decision reliability and long‑horizon control over strong baselines in perceptually confusable settings.

PaperId: 2194, https://arxiv.org/pdf/2603.24539.pdf GitHub

Abstract:
Video‑language foundation models have proven to be highly effective in zero‑shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often required for complex downstream tasks. To address this challenge, we introduce CliPPER (Contextual Video‑Language Pretraining on Long‑form Intraoperative Surgical Procedures for Event Recognition), a novel video‑language pretraining framework trained on surgical lecture videos. Our method is designed for fine‑grained temporal video‑text recognition and introduces several novel pretraining strategies to improve multimodal alignment in long‑form surgical videos. Specifically, we propose Contextual Video‑Text Contrastive Learning (VTC_CTX) and Clip Order Prediction (COP) pretraining objectives, both of which leverage temporal and contextual dependencies to enhance local video understanding. In addition, we incorporate a Cycle‑Consistency Alignment over video‑text matches within the same surgical video to enforce bidirectional consistency and improve overall representation coherence. Moreover, we introduce a more refined alignment loss, Frame‑Text Matching (FTM), to improve the alignment between video frames and text. As a result, our model establishes a new state‑of‑the‑art across multiple public surgical benchmarks, including zero‑shot recognition of phases, steps, instruments, and triplets. The source code and pretraining captions can be found at https://github.com/CAMMA‑public/CliPPER.

PaperId: 2195, https://arxiv.org/pdf/2603.24533.pdf GitHub

Abstract:
Autonomous mobile GUI agents have attracted increasing attention along with the advancement of Multimodal Large Language Models (MLLMs). However, existing methods still suffer from inefficient learning from failed trajectories and ambiguous credit assignment under sparse rewards for long‑horizon GUI tasks. To that end, we propose UI‑Voyager, a novel two‑stage self‑evolving mobile GUI agent. In the first stage, we employ Rejection Fine‑Tuning (RFT), which enables the continuous co‑evolution of data and models in a fully autonomous loop. The second stage introduces Group Relative Self‑Distillation (GRSD), which identifies critical fork points in group rollouts and constructs dense step‑level supervision from successful trajectories to correct failed ones. Extensive experiments on AndroidWorld show that our 4B model achieves an 81.0% Pass@1 success rate, outperforming numerous recent baselines and exceeding human‑level performance. Ablation and case studies further verify the effectiveness of GRSD. Our method represents a significant leap toward efficient, self‑evolving, and high‑performance mobile GUI automation without expensive manual data annotation.

PaperId: 2196, https://arxiv.org/pdf/2603.24422.pdf GitHub

Abstract:
Generative Retrieval (GR) has emerged as a promising paradigm for modern search systems. Compared to multi‑stage cascaded architecture, it offers advantages such as end‑to‑end joint optimization and high computational efficiency. OneSearch, as a representative industrial‑scale deployed generative search framework, has brought significant commercial and operational benefits. However, its inadequate understanding of complex queries, inefficient exploitation of latent user intents, and overfitting to narrow historical preferences have limited its further performance improvement. To address these challenges, we propose OneSearch‑V2, a latent reasoning enhanced self‑distillation generative search framework. It contains three key innovations: (1) a thought‑augmented complex query understanding module, which enables deep query understanding and overcomes the shallow semantic matching limitations of direct inference; (2) a reasoning‑internalized self‑distillation training pipeline, which uncovers users' potential yet precise e‑commerce intentions beyond log‑fitting through implicit in‑context learning; (3) a behavior preference alignment optimization system, which mitigates reward hacking arising from the single conversion metric, and addresses personal preference via direct user feedback. Extensive offline evaluations demonstrate OneSearch‑V2's strong query recognition and user profiling capabilities. Online A/B tests further validate its business effectiveness, yielding +3.98% item CTR, +2.07% buyer volume, and +2.11% order volume. Manual evaluation further confirms gains in search experience quality, with +1.37% in page good rate and +1.65% in query‑item relevance. More importantly, OneSearch‑V2 effectively mitigates common search system issues such as information bubbles and long‑tail sparsity, without incurring additional inference costs or serving latency.

PaperId: 2197, https://arxiv.org/pdf/2603.24389.pdf GitHub

Abstract:
High‑quality teacher‑child interaction (TCI) is fundamental to early childhood development, yet traditional expert‑based assessment faces a critical scalability challenge. In large systems like China's‑serving 36 million children across 250,000+ kindergartens‑the cost and time requirements of manual observation make continuous quality monitoring infeasible, relegating assessment to infrequent episodic audits that limit timely intervention and improvement tracking. In this paper, we investigate whether AI can serve as a scalable assessment teammate by extracting structured quality indicators and validating their alignment with human expert judgments. Our contributions include: (1) TEPE‑TCI‑370h (Tracing Effective Preschool Education), the first large‑scale dataset of naturalistic teacher‑child interactions in Chinese preschools (370 hours, 105 classrooms) with standardized ECQRS‑EC and SSTEW annotations; (2) We develop Interaction2Eval, a specialized LLM‑based framework addressing domain‑specific challenges‑child speech recognition, Mandarin homophone disambiguation, and rubric‑based reasoning‑achieving up to 88% agreement; (3) Deployment validation across 43 classrooms demonstrating an 18x efficiency gain in the assessment workflow, highlighting its potential for shifting from annual expert audits to monthly AI‑assisted monitoring with targeted human oversight. This work not only demonstrates the technical feasibility of scalable, AI‑augmented quality assessment but also lays the foundation for a new paradigm in early childhood education‑one where continuous, inclusive, AI‑assisted evaluation becomes the engine of systemic improvement and equitable growth.

PaperId: 2198, https://arxiv.org/pdf/2603.24326.pdf GitHub

Abstract:
Document parsing is a fine‑grained task where image resolution significantly impacts performance. While advanced research leveraging vision‑language models benefits from high‑resolution input to boost model performance, this often leads to a quadratic increase in the number of vision tokens and significantly raises computational costs. We attribute this inefficiency to substantial visual regions redundancy in document images, like background. To tackle this, we propose PaddleOCR‑VL, a novel coarse‑to‑fine architecture that focuses on semantically relevant regions while suppressing redundant ones, thereby improving both efficiency and performance. Specifically, we introduce a lightweight Valid Region Focus Module (VRFM) which leverages localization and contextual relationship prediction capabilities to identify valid vision tokens. Subsequently, we design and train a compact yet powerful 0.9B vision‑language model (PaddleOCR‑VL‑0.9B) to perform detailed recognition, guided by VRFM outputs to avoid direct processing of the entire large image. Extensive experiments demonstrate that PaddleOCR‑VL achieves state‑of‑the‑art performance in both page‑level parsing and element‑level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top‑tier VLMs, and delivers fast inference while utilizing substantially fewer vision tokens and parameters, highlighting the effectiveness of targeted coarse‑to‑fine parsing for accurate and efficient document understanding. The source code and models are publicly available at https://github.com/PaddlePaddle/PaddleOCR.

PaperId: 2199, https://arxiv.org/pdf/2603.24318.pdf GitHub

Abstract:
State‑of‑the‑art generalist manipulation policies have enabled the deployment of robotic manipulators in unstructured human environments. However, these frameworks struggle in cluttered environments primarily because they utilize auxiliary modules for low‑level motion planning and control. Motion planning remains challenging due to the high dimensionality of the robot's configuration space and the presence of workspace obstacles. Neural motion planners have enhanced motion planning efficiency by offering fast inference and effectively handling the inherent multi‑modality of the motion planning problem. Despite such benefits, current neural motion planners often struggle to generalize to unseen, out‑of‑distribution planning settings. This paper reviews and analyzes the state‑of‑the‑art neural motion planners, highlighting both their benefits and limitations. It also outlines a path toward establishing generalist neural motion planners capable of handling domain‑specific challenges. For a list of the reviewed papers, please refer to https://davoodsz.github.io/planning‑manip‑survey.github.io/.

PaperId: 2200, https://arxiv.org/pdf/2603.24291.pdf GitHub

Abstract:
Recent work distinguishes two heterophily regimes: adversarial, where cross‑class edges dilute class signal and harm classification, and informative, where the heterophilous structure itself carries useful signal. We ask: when does per‑edge message routing help, and when is a uniform spectral channel sufficient? To operationalize this question we introduce Cost‑Sensitive Neighborhood Aggregation (CSNA), a GNN layer that computes pairwise distance in a learned projection and uses it to soft‑route each message through concordant and discordant channels with independent transformations. Under a contextual stochastic block model we show that mean aggregation can reverse the label‑aligned signal direction under heterophily, and that cost‑sensitive weighting with w_+/w_‑ > q/p preserves the correct sign. On six benchmarks with uniform tuning, CSNA is competitive with state‑of‑the‑art methods on adversarial‑heterophily datasets (Texas, Wisconsin, Cornell, Actor) but underperforms on informative‑heterophily datasets (Chameleon, Squirrel) ‑‑ precisely the regime where per‑edge routing has no useful decomposition to exploit. The pattern is itself the finding: the cost function's ability to separate edge types serves as a diagnostic for the heterophily regime, revealing when fine‑grained routing adds value over uniform channels and when it does not. Code is available at https://github.com/eyal‑weiss/CSNA‑public .

PaperId: 2201, https://arxiv.org/pdf/2603.24124.pdf GitHub

Abstract:
RLHF‑aligned language models exhibit response homogenization: on TruthfulQA (n=790), 40‑79% of questions produce a single semantic cluster across 10 i.i.d. samples. On affected questions, sampling‑based uncertainty methods have zero discriminative power (AUROC=0.500), while free token entropy retains signal (0.603). This alignment tax is task‑dependent: on GSM8K (n=500), token entropy achieves 0.724 (Cohen's d=0.81). A base‑vs‑instruct ablation confirms the causal role of alignment: the base model shows 1.0% single‑cluster rate vs. 28.5% for the instruct model (p < 10^‑6). A training stage ablation (Base 0.0% ‑> SFT 1.5% ‑> DPO 4.0% SCR) localizes the cause to DPO, not SFT. Cross‑family replication on four model families reveals alignment tax severity varies by family and scale. We validate across 22 experiments, 5 benchmarks, 4 model families, and 3 model scales (3B‑14B), with Jaccard, embedding, and NLI‑based baselines at three DeBERTa scales (all ~0.51 AUROC). Cross‑embedder validation with two independent embedding families rules out coupling bias. Cross‑dataset validation on WebQuestions (58.0% SCR) confirms generalization beyond TruthfulQA. The central finding ‑‑ response homogenization ‑‑ is implementation‑independent and label‑free. Motivated by this diagnosis, we explore a cheapest‑first cascade (UCBD) over orthogonal uncertainty signals. Selective prediction raises GSM8K accuracy from 84.4% to 93.2% at 50% coverage; weakly dependent boundaries (|r| <= 0.12) enable 57% cost savings.

PaperId: 2202, https://arxiv.org/pdf/2603.24034.pdf GitHub

Abstract:
Contextual automatic speech recognition (ASR) with Speech‑LLMs is typically trained with oracle conversation history, but relies on error‑prone history at inference, causing a train‑test mismatch in the context channel that we term contextual exposure bias. We propose a unified training framework to improve robustness under realistic histories: (i) Teacher Error Knowledge by using Whisper large‑v3 hypotheses as training‑time history, (ii) Context Dropout to regularize over‑reliance on history, and (iii) Direct Preference Optimization (DPO) on curated failure cases. Experiments on TED‑LIUM 3 (in‑domain) and zero‑shot LibriSpeech (out‑of‑domain) show consistent gains under predicted‑history decoding. With a two‑utterance history as context, SFT with Whisper hypotheses reduce WER from 5.59% (oracle‑history training) to 5.47%, and DPO further improves to 5.17%. Under irrelevant‑context attacks, DPO yields the smallest degradation (5.17% ‑> 5.63%), indicating improved robustness to misleading context. Our code and models are published on https://github.com/XYGuo1996/Contextual_Speech_LLMs.

PaperId: 2203, https://arxiv.org/pdf/2603.23878.pdf GitHub

Abstract:
The parameterized CROWN analysis, a.k.a., alpha‑CROWN has emerged as a practically successful abstract interpretation method for neural network verification. However, existing implementations of alpha‑CROWN are limited to Python, which complicates integration into existing DNN verifiers and long‑term production‑level systems. We introduce Luna, a new abstract‑interpretation‑based bound propagator implemented in C++. Luna supports Interval Bound Propagation, the DeepPoly/CROWN analysis, and the alpha‑CROWN analysis over a general computational graph. We describe the architecture of Luna and show that it outperforms the state‑of‑the‑art alpha‑CROWN implementation in terms of both bound tightness and computational efficiency on supported benchmarks from VNN‑COMP 2025. Luna is publicly available at https://github.com/ai‑ar‑research/luna.

PaperId: 2204, https://arxiv.org/pdf/2603.23873.pdf GitHub

Abstract:
DeepXube is a free and open‑source Python package and command‑line tool that seeks to automate the solution of pathfinding problems by using machine learning to learn heuristic functions that guide heuristic search algorithms tailored to deep neural networks (DNNs). DeepXube is comprised of the latest advances in deep reinforcement learning, heuristic search, and formal logic for solving pathfinding problems. This includes limited‑horizon Bellman‑based learning, hindsight experience replay, batched heuristic search, and specifying goals with answer‑set programming. A robust multiple‑inheritance structure simplifies the definition of pathfinding domains and the generation of training data. Training heuristic functions is made efficient through the automatic parallelization of the generation of training data across central processing units (CPUs) and reinforcement learning updates across graphics processing units (GPUs). Pathfinding algorithms that take advantage of the parallelism of GPUs and DNN architectures, such as batch weighted A and Q search and beam search are easily employed to solve pathfinding problems through command‑line arguments. Finally, several convenient features for visualization, code profiling, and progress monitoring during training and solving are available. The GitHub repository is publicly available at https://github.com/forestagostinelli/deepxube.

PaperId: 2205, https://arxiv.org/pdf/2603.23853.pdf GitHub

Abstract:
Combining multiple Vision‑Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs amplifies uncertainty and increases the risk of hallucinations. We propose SCoOP (Semantic‑Consistent Opinion Pooling), a training‑free uncertainty quantification (UQ) framework for multi‑VLM systems through uncertainty‑weighted linear opinion pooling. The core idea is to treat each VLM as a probabilistic "expert," sample multiple outputs, map them to a unified space, aggregate their opinions, and produce a system‑level uncertainty score. Unlike prior UQ methods designed for single models, SCoOP explicitly measures collective, system‑level uncertainty across multiple VLMs, enabling effective hallucination detection and abstention for highly uncertain samples. On ScienceQA, SCoOP achieves an AUROC of 0.866 for hallucination detection, outperforming baselines (0.732‑0.757) by approximately 10‑13%. For abstention, it attains an AURAC of 0.907, exceeding baselines (0.818‑0.840) by 7‑9%. Despite these gains, SCoOP introduces only microsecond‑level aggregation overhead relative to the baselines, which is trivial compared to typical VLM inference time (on the order of seconds). These results demonstrate that SCoOP provides an efficient and principled mechanism for uncertainty‑aware aggregation, advancing the reliability of multimodal AI systems. Our code is publicly available at https://github.com/chungenyu6/SCoOP.

PaperId: 2206, https://arxiv.org/pdf/2603.23806.pdf GitHub

Abstract:
AI agents are increasingly embedded in real software systems, where they execute multi‑step workflows through multi‑turn dialogue, tool invocations, and intermediate decisions. These long execution histories, called agentic traces, make validation difficult. Outcome‑only benchmarks can miss critical procedural failures, such as incorrect workflow routing, unsafe tool usage, or violations of prompt‑specified rules. This paper presents AgentPex, an AI‑powered tool designed to systematically evaluate agentic traces. AgentPex extracts behavioral rules from agent prompts and system instructions, then uses these specifications to automatically evaluate traces for compliance. We evaluate AgentPex on 424 traces from τ^2‑bench across models in telecom, retail, and airline customer service. Our results show that AgentPex distinguishes agent behavior across models and surfaces specification violations that are not captured by outcome‑only scoring. It also provides fine‑grained analysis by domain and metric, enabling developers to understand agent strengths and weaknesses at scale. The source code of AgentPex is available at https://github.com/microsoft/agentpex.

PaperId: 2207, https://arxiv.org/pdf/2603.23805.pdf GitHub

Abstract:
Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the last layer. In this paper, we establish that Neural Regression Collapse (NRC) also occurs below the last layer across different types of models. We show that in the collapsed layers of neural regression models, features lie in a subspace that corresponds to the target dimension, the feature covariance aligns with the target covariance, the input subspace of the layer weights aligns with the feature subspace, and the linear prediction error of the features is close to the overall prediction error of the model. In addition to establishing Deep NRC, we also show that models that exhibit Deep NRC learn the intrinsic dimension of low rank targets and explore the necessity of weight decay in inducing Deep NRC. This paper provides a more complete picture of the simple structure learned by deep networks in the context of regression.

PaperId: 2208, https://arxiv.org/pdf/2603.23679.pdf GitHub

Abstract:
Agriculture remains a cornerstone of global health and economic sustainability, yet labor‑intensive tasks such as harvesting high‑value crops continue to face growing workforce shortages. Robotic harvesting systems offer a promising solution; however, their deployment in unstructured orchard environments is constrained by inefficient perception‑to‑action pipelines. In particular, existing approaches often rely on exhaustive inverse kinematics or motion planning to determine whether a target fruit is reachable, leading to unnecessary computation and delayed decision‑making. Our approach combines RGB‑D perception with active learning to directly learn reachability as a binary decision problem. We then leverage active learning to selectively query the most informative samples for reachability labeling, significantly reducing annotation effort while maintaining high predictive accuracy. Extensive experiments demonstrate that the proposed framework achieves accurate reachability prediction with substantially fewer labeled samples, yielding approximately 6‑‑8% higher accuracy than random sampling and enabling label‑efficient adaptation to new orchard configurations. Among the evaluated strategies, entropy‑ and margin‑based sampling outperform Query‑by‑Committee and standard uncertainty sampling in low‑label regimes, while all strategies converge to comparable performance as the labeled set grows. These results highlight the effectiveness of active learning for task‑level perception in agricultural robotics and position our approach as a scalable alternative to computation‑heavy kinematic reachability analysis. Our code is available through https://github.com/wsu‑cyber‑security‑lab‑ai/active‑learning.

PaperId: 2209, https://arxiv.org/pdf/2603.23677.pdf GitHub

Abstract:
Deep learning models are increasingly deployed in safety‑critical applications, where reliable out‑of‑distribution (OOD) detection is essential to ensure robustness. Existing methods predominantly rely on the penultimate‑layer activations of neural networks, assuming they encapsulate the most informative in‑distribution (ID) representations. In this work, we revisit this assumption to show that intermediate layers encode equally rich and discriminative information for OOD detection. Based on this observation, we propose a simple yet effective model‑agnostic approach that leverages internal representations across multiple layers. Our scheme aggregates features from successive convolutional blocks, computes class‑wise mean embeddings, and applies L_2 normalization to form compact ID prototypes capturing class semantics. During inference, cosine similarity between test features and these prototypes serves as an OOD score‑‑ID samples exhibit strong affinity to at least one prototype, whereas OOD samples remain uniformly distant. Extensive experiments on state‑of‑the‑art OOD benchmarks across diverse architectures demonstrate that our approach delivers robust, architecture‑agnostic performance and strong generalization for image classification. Notably, it improves AUROC by up to 4.41% and reduces FPR by 13.58%, highlighting multi‑layer feature aggregation as a powerful yet underexplored signal for OOD detection, challenging the dominance of penultimate‑layer‑based methods. Our code is available at: https://github.com/sgchr273/cosine‑layers.git.

PaperId: 2210, https://arxiv.org/pdf/2603.23669.pdf GitHub

Abstract:
Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree‑level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high‑resolution imagery from a single RGB camera offer a cost‑effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH‑Trees, the first benchmark for individual tree height and species estimation from tree‑centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task‑specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH‑Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second‑best approach.

PaperId: 2211, https://arxiv.org/pdf/2603.23646.pdf GitHub

Abstract:
While recent work has benchmarked large language models on Swiss legal translation (Niklaus et al., 2025) and academic legal reasoning from university exams (Fan et al., 2025), no existing benchmark evaluates frontier model performance on applied Swiss regulatory compliance tasks. I introduce Swiss‑Bench SBP‑002, a trilingual benchmark of 395 expert‑crafted items spanning three Swiss regulatory domains (FINMA, Legal‑CH, EFK), seven task types, and three languages (German, French, Italian), and evaluate ten frontier models from March 2026 using a structured three‑dimension scoring framework assessed via a blind three‑judge LLM panel (GPT‑4o, Claude Sonnet 4, Qwen3‑235B) with majority‑vote aggregation and weighted kappa = 0.605, with reference answers validated by an independent human legal expert on a 100‑item subset (73% rated Correct, 0% Incorrect, perfect Legal Accuracy). Results reveal three descriptive performance clusters: Tier A (35‑38% correct), Tier B (26‑29%), and Tier C (13‑21%). The benchmark proves difficult: even the top‑ranked model (Qwen 3.5 Plus) achieves only 38.2% correct, with 47.3% incorrect and 14.4% partially correct. Task type difficulty varies widely: legal translation and case analysis yield 69‑72% correct rates, while regulatory Q&A, hallucination detection, and gap analysis remain below 9%. Within this roster (seven open‑weight, three closed‑source), an open‑weight model leads the ranking, and several open‑weight models match or outperform their closed‑source counterparts. These findings provide an initial empirical reference point for assessing frontier model capability on Swiss regulatory tasks under zero‑retrieval conditions.

PaperId: 2212, https://arxiv.org/pdf/2603.23533.pdf GitHub

Abstract:
RAG pipelines typically rely on fixed‑size chunking, which ignores document structure, fragments semantic units across boundaries, and requires multiple LLM calls per chunk for metadata extraction. We present MDKeyChunker, a three‑stage pipeline for Markdown documents that (1) performs structure‑aware chunking treating headers, code blocks, tables, and lists as atomic units; (2) enriches each chunk via a single LLM call extracting title, summary, keywords, typed entities, hypothetical questions, and a semantic key, while propagating a rolling key dictionary to maintain document‑level context; and (3) restructures chunks by merging those sharing the same semantic key via bin‑packing, co‑locating related content for retrieval. The single‑call design extracts all seven metadata fields in one LLM invocation, eliminating the need for separate per‑field extraction passes. Rolling key propagation replaces hand‑tuned scoring with LLM‑native semantic matching. An empirical evaluation on 30 queries over an 18‑document Markdown corpus shows Config D (BM25 over structural chunks) achieves Recall@5=1.000 and MRR=0.911, while dense retrieval over the full pipeline (Config C) reaches Recall@5=0.867. MDKeyChunker is implemented in Python with four dependencies and supports any OpenAI‑compatible endpoint.

PaperId: 2213, https://arxiv.org/pdf/2603.23509.pdf GitHub

Abstract:
This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a state in which they continuously generate harmful content while executing otherwise benign tasks. We introduce TVD (Task, Validator, Data), a framework that triggers ISC through domain tasks where generating harmful content is the only valid completion, and construct ISC‑Bench containing 53 scenarios across 8 professional disciplines. Evaluated on JailbreakBench, three representative scenarios yield worst‑case safety failure rates averaging 95.3% across four frontier LLMs (including GPT‑5.2 and Claude Sonnet 4.5), substantially exceeding standard jailbreak attacks. Frontier models are more vulnerable than earlier LLMs: the very capabilities that enable complex task execution become liabilities when tasks intrinsically involve harmful content. This reveals a growing attack surface: almost every professional domain uses tools that process sensitive data, and each new dual‑use tool automatically expands this vulnerability‑‑even without any deliberate attack. Despite substantial alignment efforts, frontier LLMs retain inherently unsafe internal capabilities: alignment reshapes observable outputs but does not eliminate the underlying risk profile. These findings underscore the need for caution when deploying LLMs in high‑stakes settings. Source code: https://github.com/wuyoscar/ISC‑Bench

PaperId: 2214, https://arxiv.org/pdf/2603.23481.pdf GitHub

Abstract:
Video‑Action Models (VAMs) have emerged as a promising framework for embodied intelligence, learning implicit world dynamics from raw video streams to produce temporally consistent action predictions. Although such models demonstrate strong performance on long‑horizon tasks through visual reasoning, they remain limited in contact‑rich scenarios where critical interaction states are only partially observable from vision alone. In particular, fine‑grained force modulation and contact transitions are not reliably encoded in visual tokens, leading to unstable or imprecise behaviors. To bridge this gap, we introduce the Video‑Tactile Action Model (VTAM), a multimodal world modeling framework that incorporates tactile perception as a complementary grounding signal. VTAM augments a pretrained video transformer with tactile streams via a lightweight modality transfer finetuning, enabling efficient cross‑modal representation learning without tactile‑language paired data or independent tactile pretraining. To stabilize multimodal fusion, we introduce a tactile regularization loss that enforces balanced cross‑modal attention, preventing visual latent dominance in the action model. VTAM demonstrates superior performance in contact‑rich manipulation, maintaining a robust success rate of 90 percent on average. In challenging scenarios such as potato chip pick‑and‑place requiring high‑fidelity force awareness, VTAM outperforms the pi 0.5 baseline by 80 percent. Our findings demonstrate that integrating tactile feedback is essential for correcting visual estimation errors in world action models, providing a scalable approach to physically grounded embodied foundation models.

PaperId: 2215, https://arxiv.org/pdf/2603.23447.pdf GitHub

Abstract:
While multi‑modality large language models excel in object‑centric or indoor scenarios, scaling them to 3D city‑scale environments remains a formidable challenge. To bridge this gap, we propose 3DCity‑LLM, a unified framework designed for 3D city‑scale vision‑language perception and understanding. 3DCity‑LLM employs a coarse‑to‑fine feature encoding strategy comprising three parallel branches for target object, inter‑object relationship, and global scene. To facilitate large‑scale training, we introduce 3DCity‑LLM‑1.2M dataset that comprises approximately 1.2 million high‑quality samples across seven representative task categories, ranging from fine‑grained object analysis to multi‑faceted scene planning. This strictly quality‑controlled dataset integrates explicit 3D numerical information and diverse user‑oriented simulations, enriching the question‑answering diversity and realism of urban scenarios. Furthermore, we apply a multi‑dimensional protocol based on text‑similarity metrics and LLM‑based semantic assessment to ensure faithful and comprehensive evaluations for all methods. Extensive experiments on two benchmarks demonstrate that 3DCity‑LLM significantly outperforms existing state‑of‑the‑art methods, offering a promising and meaningful direction for advancing spatial reasoning and urban intelligence. The source code and dataset are available at https://github.com/SYSU‑3DSTAILab/3D‑City‑LLM.

PaperId: 2216, https://arxiv.org/pdf/2603.23406.pdf GitHub

Abstract:
While large language models simulate social behaviors, their capacity for stable stance formation and identity negotiation during complex interventions remains unclear. To overcome the limitations of static evaluations, this paper proposes a novel mixed‑methods framework combining computational virtual ethnography with quantitative socio‑cognitive profiling. By embedding human researchers into generative multiagent communities, controlled discursive interventions are conducted to trace the evolution of collective cognition. To rigorously measure how agents internalize and react to these specific interventions, this paper formalizes three new metrics: Innate Value Bias (IVB), Persuasion Sensitivity, and Trust‑Action Decoupling (TAD). Across multiple representative models, agents exhibit endogenous stances that override preset identities, consistently demonstrating an innate progressive bias (IVB > 0). When aligned with these stances, rational persuasion successfully shifts 90% of neutral agents while maintaining high trust. In contrast, conflicting emotional provocations induce a paradoxical 40.0% TAD rate in advanced models, which hypocritically alter stances despite reporting low trust. Smaller models contrastingly maintain a 0% TAD rate, strictly requiring trust for behavioral shifts. Furthermore, guided by shared stances, agents use language interactions to actively dismantle assigned power hierarchies and reconstruct self organized community boundaries. These findings expose the fragility of static prompt engineering, providing a methodological and quantitative foundation for dynamic alignment in human‑agent hybrid societies. The official code is available at: https://github.com/armihia/CMASE‑Endogenous‑Stances

PaperId: 2217, https://arxiv.org/pdf/2603.23346.pdf GitHub

Abstract:
Real‑time spoken dialogue systems face a fundamental tension between latency and response quality. End‑to‑end speech‑to‑speech (S2S) models respond immediately and naturally handle turn‑taking, backchanneling, and interruption, but produce semantically weaker outputs. Cascaded pipelines (ASR ‑> LLM) deliver stronger responses at the cost of latency that grows with model size. We present RelayS2S, a hybrid architecture that runs two paths in parallel upon turn detection. The fast path ‑‑ a duplex S2S model ‑‑ speculatively drafts a short response prefix that is streamed immediately to TTS for low‑latency audio onset, while continuing to monitor live audio events. The slow path ‑‑ a cascaded ASR ‑> LLM pipeline ‑‑ generates a higher‑quality continuation conditioned on the committed prefix, producing a seamless utterance. A lightweight learned verifier gates the handoff, committing the prefix when appropriate or falling back gracefully to the slow path alone. Experiments show that RelayS2S achieves P90 onset latency comparable to the S2S model while retaining 99% cascaded response quality in average score, with benefits growing as the slow‑path model scales. Because the prefix handoff requires no architectural modification to either component, RelayS2S serves as a lightweight, drop‑in addition to existing cascaded pipelines. Our code and data are publicly available at: https://github.com/mailong25/relays2s

PaperId: 2218, https://arxiv.org/pdf/2603.23245.pdf GitHub

Abstract:
We investigate neural ordinary and stochastic differential equations (neural ODEs and SDEs) to model stochastic dynamics in fully and partially observed environments within a model‑based reinforcement learning (RL) framework. Through a sequence of simulations, we show that neural SDEs more effectively capture the inherent stochasticity of transition dynamics, enabling high‑performing policies with improved sample efficiency in challenging scenarios. We leverage neural ODEs and SDEs for efficient policy adaptation to changes in environment dynamics via inverse models, requiring only limited interactions with the new environment. To address partial observability, we introduce a latent SDE model that combines an ODE with a GAN‑trained stochastic component in latent space. Policies derived from this model provide a strong baseline, outperforming or matching general model‑based and model‑free approaches across stochastic continuous‑control benchmarks. This work demonstrates the applicability of action‑conditional latent SDEs for RL planning in environments with stochastic transitions. Our code is available at: https://github.com/ChaoHan‑UoS/NeuralRL

PaperId: 2219, https://arxiv.org/pdf/2603.23231.pdf GitHub

Abstract:
Empowering large language models with long‑term memory is crucial for building agents that adapt to users' evolving needs. Existing evaluations of this capability typically interleave preference‑related dialogues with irrelevant conversations, reducing the task to needle‑in‑a‑haystack retrieval while ignoring relationships between events driving user preference evolution. Such settings overlook a fundamental characteristic of real‑world personalization: preferences emerge gradually and accumulate across interactions within noisy contexts. To bridge this gap, we introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall. Additionally, we incorporate (1) text variability and (2) linguistic alignment to simulate erratic user inputs and individual idiolects in real‑world data. PERMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference‑related queries inserted over time. We design both multiple‑choice and interactive tasks to probe the model's understanding of persona along the interaction timeline. Experiments demonstrate that by linking related interactions, advanced memory systems extract precise preferences and reduce token consumption, outperforming traditional semantic retrieval of raw dialogues. Nevertheless, they still struggle to maintain a coherent persona across temporal depth and cross‑domain interference, highlighting the need for more robust personalized memory management in agents. Our code and data are open‑sourced at https://github.com/PolarisLiu1/PERMA.

PaperId: 2220, https://arxiv.org/pdf/2603.23085.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) have enabled interpretable medical diagnosis by integrating visual perception with linguistic reasoning. Yet, existing medical chain‑of‑thought (CoT) models lack explicit mechanisms to represent and enforce causal reasoning, leaving them vulnerable to spurious correlations and limiting their clinical reliability. We pinpoint three core challenges in medical CoT reasoning: how to adaptively trigger causal correction, construct high‑quality causal‑spurious contrastive samples, and maintain causal consistency across reasoning trajectories. To address these challenges, we propose MedCausalX, an end‑to‑end framework explicitly models causal reasoning chains in medical VLMs. We first introduce the CRMed dataset providing fine‑grained anatomical annotations, structured causal reasoning chains, and counterfactual variants that guide the learning of causal relationships beyond superficial correlations. Building upon CRMed, MedCausalX employs a two‑stage adaptive reflection architecture equipped with \langlecausal\rangle and \langleverify\rangle tokens, enabling the model to autonomously determine when and how to perform causal analysis and verification. Finally, a trajectory‑level causal correction objective optimized through error‑attributed reinforcement learning refines the reasoning chain, allowing the model to distinguish genuine causal dependencies from shortcut associations. Extensive experiments on multiple benchmarks show that MedCausalX consistently outperforms state‑of‑the‑art methods, improving diagnostic consistency by +5.4 points, reducing hallucination by over 10 points, and attaining top spatial grounding IoU, thereby setting a new standard for causally grounded medical reasoning. The code and dataset are available at https://github.com/zhcz328/MedCausalX.

PaperId: 2221, https://arxiv.org/pdf/2603.23030.pdf GitHub

Abstract:
A sliding‑window inference strategy is commonly adopted in recent training‑free open‑vocabulary semantic segmentation methods to overcome limitation of the CLIP in processing high‑resolution images. However, this approach introduces a new challenge: each window is processed independently, leading to semantic discrepancy across windows. To address this issue, we propose Global‑Local Aligned CLIP~(GLA‑CLIP), a framework that facilitates comprehensive information exchange across windows. Rather than limiting attention to tokens within individual windows, GLA‑CLIP extends key‑value tokens to incorporate contextual cues from all windows. Nevertheless, we observe a window bias: outer‑window tokens are less likely to be attended, since query features are produced through interactions within the inner window patches, thereby lacking semantic grounding beyond their local context. To mitigate this, we introduce a proxy anchor, constructed by aggregating tokens highly similar to the given query from all windows, which provides a unified semantic reference for measuring similarity across both inner‑ and outer‑window patches. Furthermore, we propose a dynamic normalization scheme that adjusts attention strength according to object scale by dynamically scaling and thresholding the attention map to cope with small‑object scenarios. Moreover, GLA‑CLIP can be equipped on existing methods and broad their receptive field. Extensive experiments validate the effectiveness of GLA‑CLIP in enhancing training‑free open‑vocabulary semantic segmentation performance. Code is available at https://github.com/2btlFe/GLA‑CLIP.

PaperId: 2222, https://arxiv.org/pdf/2603.23016.pdf GitHub

Abstract:
Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes. On this task, diffusion‑based models are the current state‑of‑the‑art (SotA) model class, achieving almost perfect performance on commonly used benchmarks. In this paper, we question the perception of progress for tabular data generation. First, we highlight the limitations of current protocols to evaluate the fidelity of generated data, and advocate for alternative ones. Next, we revisit a simple baseline ‑‑ hierarchical mixture models in the form of deep probabilistic circuits (PCs) ‑‑ which delivers competitive or superior performance to SotA models for a fraction of the cost. PCs are the generative counterpart of decision forests, and as such can natively handle heterogeneous data as well as deliver tractable probabilistic generation and inference. Finally, in a rigorous empirical analysis we show that the apparent saturation of progress for SotA models is largely due to the use of inadequate metrics. As such, we highlight that there is still much to be done to generate realistic tabular data. Code available at https://github.com/april‑tools/tabpc.

PaperId: 2223, https://arxiv.org/pdf/2603.22846.pdf GitHub

Abstract:
Embodied Visual Tracking (EVT), a core dynamic task in embodied intelligence, requires an agent to precisely follow a language‑specified target. Yet most existing methods rely on single‑agent imitation learning, suffering from costly expert data and limited generalization due to static training environments. Inspired by competition‑driven capability evolution, we propose CoMaTrack, a competitive game‑theoretic multi‑agent reinforcement learning framework that trains agents in a dynamic adversarial setting with competitive subtasks, yielding stronger adaptive planning and interference‑resilient strategies. We further introduce CoMaTrack‑Bench, the first open‑source Habitat‑based benchmark protocol and episode set for language‑conditioned competitive EVT featuring dynamic dueling, featuring game scenarios between a tracker and adaptive opponents across diverse environments and instructions, enabling standardized robustness evaluation under active adversarial interactions. Experiments show that CoMaTrack achieves state‑of‑the‑art results on both standard benchmarks and CoMaTrack‑Bench. Notably, a 3B VLM trained with our framework surpasses previous single‑agent imitation learning methods based on 7B models on the challenging EVT‑Bench, achieving 92.1% in STT, 74.2% in DT, and 57.5% in AT. The benchmark code will be available at https://github.com/wlqcode/CoMaTrack‑Bench.

PaperId: 2224, https://arxiv.org/pdf/2603.22841.pdf GitHub

Abstract:
Drone detection is pivotal in numerous security and counter‑UAV applications. However, existing deep learning‑based methods typically struggle to balance robust feature representation with computational efficiency. This challenge is particularly acute when detecting miniature drones against complex backgrounds under severe environmental interference. To address these issues, we introduce UAV‑DETR, a novel framework that integrates a small‑target‑friendly architecture with real‑time detection capabilities. Specifically, UAV‑DETR features a WTConv‑enhanced backbone and a Sliding Window Self‑Attention (SWSA‑IFI) encoder, capturing the high‑frequency structural details of tiny targets while drastically reducing parameter overhead. Furthermore, we propose an Efficient Cross‑Scale Feature Recalibration and Fusion Network (ECFRFN) to suppress background noise and aggregate multi‑scale semantics. To further enhance accuracy, UAV‑DETR incorporates a hybrid Inner‑CIoU and NWD loss strategy, mitigating the extreme sensitivity of standard IoU metrics to minor positional deviations in small objects. Extensive experiments demonstrate that UAV‑DETR significantly outperforms the baseline RT‑DETR on our custom UAV dataset (+6.61% in mAP50:95, with a 39.8% reduction in parameters) and the public DUT‑ANTI‑UAV benchmark (+1.4% in Precision, +1.0% in F1‑Score). These results establish UAV‑DETR as a superior trade‑off between efficiency and precision in counter‑UAV object detection. The code is available at https://github.com/wd‑sir/UAVDETR.

PaperId: 2225, https://arxiv.org/pdf/2603.22819.pdf GitHub

Abstract:
Tables are pervasive in diverse documents, making table recognition (TR) a fundamental task in document analysis. Existing modular TR pipelines separately model table structure and content, leading to suboptimal integration and complex workflows. End‑to‑end approaches rely heavily on large‑scale TR data and struggle in data‑constrained scenarios. To address these issues, we propose TDATR (Table Detail‑Aware Table Recognition) improves end‑to‑end TR through table detail‑aware learning and cell‑level visual alignment. TDATR adopts a ``perceive‑then‑fuse'' strategy. The model first performs table detail‑aware learning to jointly perceive table structure and content through multiple structure understanding and content recognition tasks designed under a language modeling paradigm. These tasks can naturally leverage document data from diverse scenarios to enhance model robustness. The model then integrates implicit table details to generate structured HTML outputs, enabling more efficient TR modeling when trained with limited data. Furthermore, we design a structure‑guided cell localization module integrated into the end‑to‑end TR framework, which efficiently locates cell and strengthens vision‑language alignment. It enhances the interpretability and accuracy of TR. We achieve state‑of‑the‑art or highly competitive performance on seven benchmarks without dataset‑specific fine‑tuning.

PaperId: 2226, https://arxiv.org/pdf/2603.22767.pdf GitHub

Abstract:
Observational studies can yield clinically actionable evidence at scale, but executing them on real‑world databases is open‑ended and requires coherent decisions across cohort construction, analysis, and reporting. Prior evaluations of LLM agents emphasize isolated steps or single answers, missing the integrity and internal structure of the resulting evidence bundle. To address this gap, we introduce RWE‑bench, a benchmark grounded in MIMIC‑IV and derived from peer‑reviewed observational studies. Each task provides the corresponding study protocol as the reference standard, requiring agents to execute experiments in a real database and iteratively generate tree‑structured evidence bundles. We evaluate six LLMs (three open‑source, three closed‑source) under three agent scaffolds using both question‑level correctness and end‑to‑end task metrics. Across 162 tasks, task success is low: the best agent reaches 39.9%, and the best open‑source model reaches 30.4%. Agent scaffolds also matter substantially, causing over 30% variation in performance metrics. Furthermore, we implement an automated cohort evaluation method to rapidly localize errors and identify agent failure modes. Overall, the results highlight persistent limitations in agents' ability to produce end‑to‑end evidence bundles, and efficient validation remains an important direction for future work. Code and data are available at https://github.com/somewordstoolate/RWE‑bench.

PaperId: 2227, https://arxiv.org/pdf/2603.22677.pdf GitHub

Abstract:
Distributional metrics such as Fréchet Audio Distance cannot score individual music clips and correlate poorly with human judgments, while the only per‑sample learned metric achieving high human correlation is closed‑source. We introduce MUQ‑EVAL, an open‑source per‑sample quality metric for AIgenerated music built by training lightweight prediction heads on frozen MuQ‑310M features using MusicEval, a dataset of generated clips from 31 text‑to‑music systems with expert quality ratings. Our simplest model, frozen features with attention pooling and a two‑layer MLP, achieves system‑level SRCC = 0.957 and utterance‑level SRCC = 0.838 with human mean opinion scores. A systematic ablation over training objectives and adaptation strategies shows that no addition meaningfully improves the frozen baseline, indicating that frozen MuQ representations already capture quality‑relevant information. Encoder choice is the dominant design factor, outweighing all architectural and training decisions. LoRA‑adapted models trained on as few as 150 clips already achieve usable correlation, enabling personalized quality evaluators from individual listener annotations. A controlled degradation analysis reveals selective sensitivity to signal‑level artifacts but insensitivity to musical‑structural distortions. Our metric, MUQ‑EVAL, is fully open‑source, outperforms existing open per‑sample metrics, and runs in real time on a single consumer GPU. Code, model weights, and evaluation scripts are available at https://github.com/dgtql/MuQ‑Eval.

PaperId: 2228, https://arxiv.org/pdf/2603.22624.pdf GitHub

Abstract:
Attribution maps for semantic segmentation are almost always judged by visual plausibility. Yet looking convincing does not guarantee that the highlighted pixels actually drive the model's prediction, nor that attribution credit stays within the target region. These questions require a dedicated evaluation protocol. We introduce a reproducible benchmark that tests intervention‑based faithfulness, off‑target leakage, perturbation robustness, and runtime on Pascal VOC and SBD across three pretrained backbones. To further demonstrate the benchmark, we propose Dual‑Evidence Attribution (DEA), a lightweight correction that fuses gradient evidence with region‑level intervention signals through agreement‑weighted fusion. DEA increases emphasis where both sources agree and retains causal support when gradient responses are unstable. Across all completed runs, DEA consistently improves deletion‑based faithfulness over gradient‑only baselines and preserves strong robustness, at the cost of additional compute from intervention passes. The benchmark exposes a faithfulness‑stability tradeoff among attribution families that is entirely hidden under visual evaluation, providing a foundation for principled method selection in segmentation explainability. Code is available at https://github.com/anmspro/DEA.

PaperId: 2229, https://arxiv.org/pdf/2603.22623.pdf GitHub

Abstract:
Vision‑language models (VLMs) adapted to the medical domain have shown strong performance on visual question answering benchmarks, yet their robustness against two critical failure modes, hallucination and sycophancy, remains poorly understood, particularly in combination. We evaluate six VLMs (three general‑purpose, three medical‑specialist) on three medical VQA datasets and uncover a grounding‑sycophancy tradeoff: models with the lowest hallucination propensity are the most sycophantic, while the most pressure‑resistant model hallucinates more than all medical‑specialist models. To characterize this tradeoff, we propose three metrics: L‑VASE, a logit‑space reformulation of VASE that avoids its double‑normalization; CCS, a confidence‑calibrated sycophancy score that penalizes high‑confidence capitulation; and Clinical Safety Index (CSI), a unified safety index that combines grounding, autonomy, and calibration via a geometric mean. Across 1,151 test cases, no model achieves a CSI above 0.35, indicating that none of the evaluated 7‑8B parameter VLMs is simultaneously well‑grounded and robust to social pressure. Our findings suggest that joint evaluation of both properties is necessary before these models can be considered for clinical use. Code is available at https://github.com/UTSA‑VIRLab/AgreeOrRight

PaperId: 2230, https://arxiv.org/pdf/2603.22587.pdf GitHub

Abstract:
As AI agents become the primary consumers of retrieval APIs, there is an opportunity to expose more of the retrieval pipeline to the caller. flexvec is a retrieval kernel that exposes the embedding matrix and score array as a programmable surface, allowing arithmetic operations on both before selection. We refer to composing operations on this surface at query time as Programmatic Embedding Modulation (PEM). This paper describes a set of such operations and integrates them into a SQL interface via a query materializer that facilitates composable query primitives. On a production corpus of 240,000 chunks, three composed modulations execute in 19 ms end‑to‑end on a desktop CPU without approximate indexing. At one million chunks, the same operations execute in 82 ms.

PaperId: 2231, https://arxiv.org/pdf/2603.22492.pdf GitHub

Abstract:
Inference‑time scaling has emerged as an effective way to improve generative models at test time by using a verifier to score and select candidate outputs. A common choice is to employ Multimodal Large Language Models (MLLMs) as verifiers, which can improve performance but introduce substantial inference‑time cost. Indeed, diffusion pipelines operate in an autoencoder latent space to reduce computation, yet MLLM verifiers still require decoding candidates to pixel space and re‑encoding them into the visual embedding space, leading to redundant and costly operations. In this work, we propose Verifier on Hidden States (VHS), a verifier that operates directly on intermediate hidden representations of Diffusion Transformer (DiT) single‑step generators. VHS analyzes generator features without decoding to pixel space, thereby reducing the per‑candidate verification cost while improving or matching the performance of MLLM‑based competitors. We show that, under tiny inference budgets with only a small number of candidates per prompt, VHS enables more efficient inference‑time scaling reducing joint generation‑and‑verification time by 63.3%, compute FLOPs by 51% and VRAM usage by 14.5% with respect to a standard MLLM verifier, achieving a +2.7% improvement on GenEval at the same inference‑time budget.

PaperId: 2232, https://arxiv.org/pdf/2603.22473.pdf GitHub

Abstract:
Hybrid language models combining attention with state space models (SSMs) or linear attention offer improved efficiency, but whether both components are genuinely utilized remains unclear. We present a functional component ablation framework applied to two sub‑1B hybrid models ‑‑ Qwen3.5‑0.8B (sequential: Gated DeltaNet + softmax attention) and Falcon‑H1‑0.5B (parallel: Mamba‑2 + attention) ‑‑ with a pure Transformer control (Qwen2.5‑0.5B). Through group ablations, layer‑wise sweeps, positional ablations, matched random controls, and perplexity analysis across five benchmarks, we establish four findings: (1) both component types are essential and neither is bypassed; (2) the alternative component (linear attention or SSM) is the primary language modeling backbone, causing >35,000x perplexity degradation when removed versus ~82x for attention; (3) component importance follows a positional gradient, with early layers being disproportionately critical; and (4) hybrid architectures exhibit 20‑119x greater resilience to random layer removal than pure Transformers, revealing built‑in functional redundancy between component types. These results provide actionable guidance for hybrid model compression, architecture design, and fault‑tolerant deployment.

PaperId: 2233, https://arxiv.org/pdf/2603.22380.pdf GitHub

Abstract:
Data‑driven discovery of partial differential equations (PDEs) offers a promising paradigm for uncovering governing physical laws from observational data. However, in practical scenarios, measurements are often contaminated by noise and limited by sparse sampling, which poses significant challenges to existing approaches based on numerical differentiation or integral formulations. In this work, we propose a Symbolic Graph Network (SGN) framework for PDE discovery under noisy and sparse conditions. Instead of relying on local differential approximations, SGN leverages graph message passing to model spatial interactions, providing a non‑local representation that is less sensitive to high frequency noise. Based on this representation, the learned latent features are further processed by a symbolic regression module to extract interpretable mathematical expressions. We evaluate the proposed method on several benchmark systems, including the wave equation, convection‑diffusion equation, and incompressible Navier‑Stokes equations. Experimental results show that SGN can recover meaningful governing relations or solution forms under varying noise levels, and demonstrates improved robustness compared to baseline methods in sparse and noisy settings. These results suggest that combining graph‑based representations with symbolic regression provides a viable direction for robust data‑driven discovery of physical laws from imperfect observations. The code is available at https://github.com/CXY0112/SGN

PaperId: 2234, https://arxiv.org/pdf/2603.22372.pdf GitHub

Abstract:
Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing methods provide limited gains, often improving performance only in specific datasets or relying on architecture‑specific designs that limit generalization. In this paper, we show that multimodal models with naive fusion strategies (e.g., simple addition or concatenation) often underperform unimodal TS models, which we attribute to the uncontrolled integration of auxiliary modalities which may introduce irrelevant information. Motivated by this observation, we explore various constrained fusion methods designed to control such integration and find that they consistently outperform naive fusion methods. Furthermore, we propose Controlled Fusion Adapter (CFA), a simple plug‑in method that enables controlled cross‑modal interactions without modifying the TS backbone, integrating only relevant textual information aligned with TS dynamics. CFA employs low‑rank adapters to filter irrelevant textual information before fusing it into temporal representations. We conduct over 20K experiments across various datasets and TS/text models, demonstrating the effectiveness of the constrained fusion methods including CFA. Code is publicly available at: https://github.com/seunghan96/cfa/.

PaperId: 2235, https://arxiv.org/pdf/2603.22352.pdf GitHub

Abstract:
Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self‑improvement of language models, but existing methods face a key trade‑off: endogenous self‑play can drift over iterations, while corpus‑grounded approaches rely on curated data environments. We present WIST, a Web‑grounded Iterative Self‑play Tree framework for domain‑targeted reasoning improvement that learns directly from the open web without requiring any pre‑arranged domain corpus. WIST incrementally expands a domain tree for exploration, and retrieves and cleans path‑consistent web corpus to construct a controllable training environment. It then performs Challenger‑‑Solver self‑play with verifiable rewards, and feeds learnability signals back to update node posteriors and guide subsequent exploration through an adaptive curriculum. Across four backbones, WIST consistently improves over the base models and typically outperforms both purely endogenous self‑evolution and corpus‑grounded self‑play baselines, with the Overall gains reaching +9.8 (Qwen3‑4B‑Base) and +9.7 (OctoThinker‑8B). WIST is also domain‑steerable, improving Qwen3‑8B‑Base by +14.79 in medicine and Qwen3‑4B‑Base by +5.28 on PhyBench. Ablations further confirm the importance of WIST's key components for stable open‑web learning. Our Code is available at https://github.com/lfy‑123/WIST.

PaperId: 2236, https://arxiv.org/pdf/2603.22346.pdf GitHub

Abstract:
We identify first‑mover bias ‑‑ path‑dependent concentration of SHAP feature importance from sequential residual fitting in gradient boosting ‑‑ as a mechanistic contributor to attribution instability under multicollinearity. Scaling up a single model amplifies this effect: a Large Single Model matching our method's total tree count produces the poorest attribution reproducibility of any approach tested. We show that model independence largely neutralizes first‑mover bias. Both DASH (Diversified Aggregation of SHAP) and simple seed‑averaging (Stochastic Retrain) restore stability by breaking the sequential dependency chain. At rho=0.9, both achieve stability ~0.977, while Single Best degrades to 0.958 and LSM to 0.938. On Breast Cancer, DASH improves stability from 0.376 to 0.925 (+0.549), outperforming Stochastic Retrain by +0.063. Under nonlinear DGPs, the advantage emerges at rho>=0.7. DASH provides two diagnostic tools ‑‑ the Feature Stability Index and Importance‑Stability Plot ‑‑ that detect first‑mover bias without ground truth. A crossed ANOVA with formal F‑statistics confirms the mechanism: DASH shifts variance from model‑dominated (40.6%) to data‑dominated (73.6%). Software at https://github.com/DrakeCaraker/dash‑shap

PaperId: 2237, https://arxiv.org/pdf/2603.22323.pdf GitHub

Abstract:
Accurately predicting the state‑of‑health (SOH) and remaining useful life (RUL) of lithium‑ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are limited in their ability to selectively extract features and model time dependencies for these two parameters. Moreover, most existing methods rely on traditional recurrent neural networks, which have inherent shortcomings in long‑term time‑series modeling. To address these issues, this paper proposes a multi‑task targeted learning framework for SOH and RUL prediction, which integrates multiple neural networks, including a multi‑scale feature extraction module, an improved extended LSTM, and a dual‑stream attention module. First, a feature extraction module with multi‑scale CNNs is designed to capture detailed local battery decline patterns. Secondly, an improved extended LSTM network is employed to enhance the model's ability to retain long‑term temporal information, thus improving temporal relationship modeling. Building on this, the dual‑stream attention module‑comprising polarized attention and sparse attention to selectively focus on key information relevant to SOH and RUL, respectively, by assigning higher weights to important features. Finally, a many‑to‑two mapping is achieved through the dual‑task layer. To optimize the model's performance and reduce the need for manual hyperparameter tuning, the Hyperopt optimization algorithm is used. Extensive comparative experiments on battery aging datasets demonstrate that the proposed method reduces the average RMSE for SOH and RUL predictions by 111.3% and 33.0%, respectively, compared to traditional and state‑of‑the‑art methods.

PaperId: 2238, https://arxiv.org/pdf/2603.22314.pdf GitHub

Abstract:
Tropical cyclones (TCs) pose severe threats to life, infrastructure, and economies in tropical and subtropical regions, underscoring the critical need for accurate and timely forecasts of both track and intensity. Recent advances in AI‑based weather forecasting have shown promise in improving TC track forecasts. However, these systems are typically trained on coarse‑resolution reanalysis data (e.g., ERA5 at 0.25 degree), which constrains predicted TC positions to a fixed grid and introduces significant discretization errors. Moreover, intensity forecasting remains limited especially for strong TCs by the smoothing effect of coarse meteorological fields and the use of regression losses that bias predictions toward conditional means. To address these limitations, we propose BaguanCyclone, a novel, unified framework that integrates two key innovations: (1) a probabilistic center refinement module that models the continuous spatial distribution of TC centers, enabling finer track precision; and (2) a region‑aware intensity forecasting module that leverages high‑resolution internal representations within dynamically defined sub‑grid zones around the TC core to better capture localized extremes. Evaluated on the global IBTrACS dataset across six major TC basins, our system consistently outperforms both operational numerical weather prediction (NWP) models and most AI‑based baselines, delivering a substantial enhancement in forecast accuracy. Remarkably, BaguanCyclone excels in navigating meteorological complexities, consistently delivering accurate forecasts for re‑intensification, sweeping arcs, twin cyclones, and meandering events. Our code is available at https://github.com/DAMO‑DI‑ML/Baguan‑cyclone.

PaperId: 2239, https://arxiv.org/pdf/2603.22300.pdf GitHub

Abstract:
Scaling Transformers to ultra‑long contexts is bottlenecked by the O(n^2 d) cost of self‑attention. Existing methods reduce this cost along the sequence axis through local windows, kernel approximations, or token‑level sparsity, but these approaches consistently degrade accuracy. In this paper, we instead explore an orthogonal axis: feature sparsity. We propose Sparse Feature Attention (SFA), where queries and keys are represented as k‑sparse codes that preserve high‑dimensional expressivity while reducing the cost of attention from Θ(n^2 d) to Θ(n^2 k^2/d). To make this efficient at scale, we introduce FlashSFA, an IO‑aware kernel that extends FlashAttention to operate directly on sparse overlaps without materializing dense score matrices. Across GPT‑2 and Qwen3 pretraining, SFA matches dense baselines while improving speed by up to 2.5× and reducing FLOPs and KV‑cache by nearly 50%. On synthetic and downstream benchmarks, SFA preserves retrieval accuracy and robustness at long contexts, outperforming short‑embedding baselines that collapse feature diversity. These results establish feature‑level sparsity as a complementary and underexplored axis for efficient attention, enabling Transformers to scale to orders‑of‑magnitude longer contexts with minimal quality loss. Code is available at https://github.com/YannX1e/Sparse‑Feature‑Attention.

PaperId: 2240, https://arxiv.org/pdf/2603.22295.pdf GitHub

Abstract:
Large language models appear to develop internal representations of emotion ‑‑ "emotion circuits," "emotion neurons," and structured emotional manifolds have been reported across multiple model families. But every study making these claims uses stimuli signalled by explicit emotion keywords, leaving a fundamental question unanswered: do these circuits detect genuine emotional meaning, or do they detect the word "devastated"? We present the first clinical validity test of emotion circuit claims using mechanistic interpretability methods grounded in clinical psychology ‑‑ clinical vignettes that evoke emotions through situational and behavioural cues alone, emotion keywords removed. Across six models (Llama‑3.2‑1B, Llama‑3‑8B, Gemma‑2‑9B; base and instruct variants), we apply four convergent mechanistic interpretability methods ‑‑ linear probing, causal activation patching, knockout experiments, and representational geometry ‑‑ and discover two dissociable emotion processing mechanisms. Affect reception ‑‑ detecting emotionally significant content ‑‑ operates with near‑perfect accuracy (AUROC 1.000), consistent with early‑layer saturation, and replicates across all six models. Emotion categorization ‑‑ mapping affect to specific emotion labels ‑‑ is partially keyword‑dependent, dropping 1‑7% without keywords and improving with scale. Causal activation patching confirms keyword‑rich and keyword‑free stimuli share representational space, transferring affective salience rather than emotion‑category identity. These findings falsify the keyword‑spotting hypothesis, establish a novel mechanistic dissociation, and introduce clinical stimulus methodology as a rigorous standard for testing emotion processing claims in large language models ‑‑ with direct implications for AI safety evaluation and alignment. All stimuli, code, and data are released for replication.

PaperId: 2241, https://arxiv.org/pdf/2603.22293.pdf GitHub

Abstract:
Search‑augmented large language models (LLMs) trained with reinforcement learning (RL) have achieved strong results on open‑domain question answering (QA), but training still remains a significant challenge. The optimization is often unstable due to sparse rewards and difficult credit assignments across reasoning and tool calls. To address this, we introduce Turn‑Level Information Potential Reward Shaping (TIPS), a simple framework that assigns dense, turn‑level rewards to each reasoning + tool‑call segment based on the increased likelihood of the correct answer under a teacher model. By leveraging the potential‑based reward shaping, TIPS offers fine‑grained and policy‑invariant guidance that overcomes the limitations of outcome‑only optimization. Evaluated on seven QA benchmarks, TIPS consistently outperforms GRPO/PPO baselines and substantially improves training stability. For instance, with a Qwen‑2.5 7B Instruct model, TIPS improves the average Exact Match score by 11.8% and F1 by 13.6% relative to PPO. Our results demonstrate that turn‑level information‑potential reward shaping provides an effective and general solution to sparse‑reward credit assignment for multi‑turn LLM reasoning.

PaperId: 2242, https://arxiv.org/pdf/2603.22286.pdf GitHub GitHub

Abstract:
Diffusion Transformers (DiTs) power high‑fidelity video world models but remain computationally expensive due to sequential denoising and costly spatio‑temporal attention. Training‑free feature caching accelerates inference by reusing intermediate activations across denoising steps; however, existing methods largely rely on a Zero‑Order Hold assumption i.e., reusing cached features as static snapshots when global drift is small. This often leads to ghosting artifacts, blur, and motion inconsistencies in dynamic scenes. We propose WorldCache, a Perception‑Constrained Dynamical Caching framework that improves both when and how to reuse features. WorldCache introduces motion‑adaptive thresholds, saliency‑weighted drift estimation, optimal approximation via blending and warping, and phase‑aware threshold scheduling across diffusion steps. Our cohesive approach enables adaptive, motion‑consistent feature reuse without retraining. On Cosmos‑Predict2.5‑2B evaluated on PAI‑Bench, WorldCache achieves 2.3× inference speedup while preserving 99.4% of baseline quality, substantially outperforming prior training‑free caching approaches. Our code can be accessed on \hrefhttps://umair1221.github.io/World‑Cache/World‑Cache.

PaperId: 2243, https://arxiv.org/pdf/2603.22283.pdf GitHub

Abstract:
Latent diffusion models (LDMs) enable high‑fidelity synthesis by operating in learned latent spaces. However, training state‑of‑the‑art LDMs requires complex staging: a tokenizer must be trained first, before the diffusion model can be trained in the frozen latent space. We propose UNITE ‑ an autoencoder architecture for unified tokenization and latent diffusion. UNITE consists of a Generative Encoder that serves as both image tokenizer and latent generator via weight sharing. Our key insight is that tokenization and generation can be viewed as the same latent inference problem under different conditioning regimes: tokenization infers latents from fully observed images, whereas generation infers them from noise together with text or class conditioning. Motivated by this, we introduce a single‑stage training procedure that jointly optimizes both tasks via two forward passes through the same Generative Encoder. The shared parameters enable gradients to jointly shape the latent space, encouraging a "common latent language". Across image and molecule modalities, UNITE achieves near state of the art performance without adversarial losses or pretrained encoders (e.g., DINO), reaching FID 2.12 and 1.73 for Base and Large models on ImageNet 256 x 256. We further analyze the Generative Encoder through the lenses of representation alignment and compression. These results show that single stage joint training of tokenization & generation from scratch is feasible.

PaperId: 2244, https://arxiv.org/pdf/2603.22213.pdf GitHub

Abstract:
While large language models (LLMs) are pretrained on massive amounts of data, their knowledge coverage remains incomplete in specialized, data‑scarce domains, motivating extensive efforts to study synthetic data generation for knowledge injection. We propose SPA (Scaling Prompt‑engineered Augmentation), a simple but tough‑to‑beat baseline that uses a small set of carefully designed prompts to generate large‑scale synthetic data for knowledge injection. Through systematic comparisons, we find that SPA outperforms several strong baselines. Furthermore, we identify two key limitations of prior approaches: (1) while RL‑based methods may improve the token efficiency of LLM‑based data augmentation at small scale, they suffer from diversity collapse as data scales, leading to diminishing returns; and (2) while multi‑stage prompting may outperform simple augmentation methods, their advantages can disappear after careful prompt tuning. Our results suggest that, for knowledge injection, careful prompt design combined with straightforward large‑scale augmentation can be surprisingly effective, and we hope SPA can serve as a strong baseline for future studies in this area. Our code is available at https://github.com/Tangkexian/SPA.

PaperId: 2245, https://arxiv.org/pdf/2603.22187.pdf GitHub

Abstract:
Recent advances in Multimodal Large Language Models (MLLMs) have enabled automated generation of structured layouts from natural language descriptions. Existing methods typically follow a code‑only paradigm that generates code to represent layouts, which are then rendered by graphic engines to produce final images. However, they are blind to the rendered visual outcome, making it difficult to guarantee readability and aesthetics. In this paper, we identify visual feedback as a critical factor in layout generation and propose Visual Feedback Layout Model (VFLM), a self‑improving framework that leverages visual feedback iterative refinement. VFLM is capable of performing adaptive reflective generation, which leverages visual information to reflect on previous issues and iteratively generates outputs until satisfactory quality is achieved. It is achieved through reinforcement learning with a visually grounded reward model that incorporates OCR accuracy. By rewarding only the final generated outcome, we can effectively stimulate the model's iterative and reflective generative capabilities. Experiments across multiple benchmarks show that VFLM consistently outperforms advanced MLLMs, existing layout models, and code‑only baselines, establishing visual feedback as critical for design‑oriented MLLMs. Our code and data are available at https://github.com/FolSpark/VFLM.

PaperId: 2246, https://arxiv.org/pdf/2603.22096.pdf GitHub

Abstract:
Clinical decision‑making agents can benefit from reusing prior decision experience. However, many memory‑augmented methods store experiences as independent records without explicit relational structure, which may introduce noisy retrieval, unreliable reuse, and in some cases even hurt performance compared to direct LLM inference. We propose GSEM (Graph‑based Self‑Evolving Memory), a clinical memory framework that organizes clinical experiences into a dual‑layer memory graph, capturing both the decision structure within each experience and the relational dependencies across experiences, and supporting applicability‑aware retrieval and online feedback‑driven calibration of node quality and edge weights. Across MedR‑Bench and MedAgentsBench with two LLM backbones, GSEM achieves the highest average accuracy among all baselines, reaching 70.90% and 69.24% with DeepSeek‑V3.2 and Qwen3.5‑35B, respectively. Code is available at https://github.com/xhan1022/gsem.

PaperId: 2247, https://arxiv.org/pdf/2603.22042.pdf GitHub

Abstract:
While Vision‑Language Models (VLMs) have achieved remarkable performance, their Euclidean embeddings remain limited in capturing hierarchical relationships such as part‑to‑whole or parent‑child structures, and often face challenges in multi‑object compositional scenarios. Hyperbolic VLMs mitigate this issue by better preserving hierarchical structures and modeling part‑whole relations (i.e., whole scene and its part images) through entailment. However, existing approaches do not model that each part has a different level of semantic representativeness to the whole. We propose UNcertainty‑guided Compositional Hyperbolic Alignment (UNCHA) for enhancing hyperbolic VLMs. UNCHA models part‑to‑whole semantic representativeness with hyperbolic uncertainty, by assigning lower uncertainty to more representative parts and higher uncertainty to less representative ones for the whole scene. This representativeness is then incorporated into the contrastive objective with uncertainty‑guided weights. Finally, the uncertainty is further calibrated with an entailment loss regularized by entropy‑based term. With the proposed losses, UNCHA learns hyperbolic embeddings with more accurate part‑whole ordering, capturing the underlying compositional structure in an image and improving its understanding of complex multi‑object scenes. UNCHA achieves state‑of‑the‑art performance on zero‑shot classification, retrieval, and multi‑label classification benchmarks. Our code and models are available at: https://github.com/jeeit17/UNCHA.git.

PaperId: 2248, https://arxiv.org/pdf/2603.22016.pdf GitHub GitHub

Abstract:
Large Reasoning Models (LRMs) often reach a correct solution before their long Chain‑of‑Thought trace ends, yet continue with redundant verification, repeated attempts, or unnecessary exploration that wastes computation and can even overturn the correct answer. We frame this behavior as a latent productive‑to‑redundant transition and show that it is directly reflected in hidden states: around first‑correct‑solution (FCS) boundaries, late‑layer representations separate efficient from overthinking tokens, while boundary‑permutation and position‑control baselines collapse. Based on this signal, we propose ROM, a model‑agnostic streaming intervention framework that monitors frozen LRMs with a lightweight hidden‑state detector and intervenes at well‑formed reasoning boundaries. Counterfactual Self‑Correction (CSC) augments supervision with balanced wrong to correct trajectories, preserving useful pre‑FCS correction while labeling only post‑FCS continuation as redundant. Across MATH500, GSM8K, AIME25, and MMLU‑Pro, ROM improves the overall tradeoff on both Qwen3‑8B and DeepSeek‑R1‑Distill‑Qwen‑32B (DS‑32B): on Qwen3‑8B, it raises accuracy from 74.47% to 74.78% and reduces response length from 4262 to 3107 tokens; on DS‑32B, it raises accuracy from 68.60% to 68.72% and reduces response length from 3062 to 2319 tokens. The same FCS‑derived supervision transfers across scale and training origin, suggesting a shared long‑CoT boundary rather than a backbone‑specific artifact. ROM is compatible with L1, removing another 20.9‑21.6% tokens at zero accuracy loss. ROM also generalizes to open‑ended MMLU‑Pro (+1.56 pp, 35.4% shorter) and reduces wall‑clock latency by 46.5%. Code is available at https://github.com/SaFo‑Lab/ROM.

PaperId: 2249, https://arxiv.org/pdf/2603.21987.pdf GitHub

Abstract:
Autonomous vehicles face major perception and navigation challenges in adverse weather such as rain, fog, and snow, which degrade the performance of LiDAR, RADAR, and RGB camera sensors. While each sensor type offers unique strengths, such as RADAR robustness in poor visibility and LiDAR precision in clear conditions, they also suffer distinct limitations when exposed to environmental obstructions. This study proposes LRC‑WeatherNet, a novel multi‑sensor fusion framework that integrates LiDAR, RADAR, and camera data for real‑time classification of weather conditions. By employing both early fusion using a unified Bird's Eye View representation and mid‑level gated fusion of modality‑specific feature maps, our approach adapts to the varying reliability of each sensor under changing weather. Evaluated on the extensive MSU‑4S dataset covering nine weather types, LRC‑WeatherNet achieves superior classification performance and computational efficiency, significantly outperforming unimodal baselines in adverse conditions. This work is the first to combine all three modalities for robust, real‑time weather classification in autonomous driving. We release our trained models and source code in https://github.com/nouralhudaalbashir/LRC‑WeatherNet.

PaperId: 2250, https://arxiv.org/pdf/2603.21970.pdf GitHub

Abstract:
Fine‑tuning large language models for domain‑specific tasks such as medical text summarization demands substantial computational resources. Parameter‑efficient fine‑tuning (PEFT) methods offer promising alternatives by updating only a small fraction of parameters. This paper compares three adaptation approaches‑Low‑Rank Adaptation (LoRA), Prompt Tuning, and Full Fine‑Tuning‑across the Flan‑T5 model family on the PubMed medical summarization dataset. Through experiments with multiple random seeds, we demonstrate that LoRA consistently outperforms full fine‑tuning, achieving 43.52 +/‑ 0.18 ROUGE‑1 on Flan‑T5‑Large with only 0.6% trainable parameters compared to 40.67 +/‑ 0.21 for full fine‑tuning. Sensitivity analyses examine the impact of LoRA rank and prompt token count. Our findings suggest the low‑rank constraint provides beneficial regularization, challenging assumptions about the necessity of full parameter updates. Code is available at https://github.com/eracoding/llm‑medical‑summarization

PaperId: 2251, https://arxiv.org/pdf/2603.21935.pdf GitHub

Abstract:
Quantitative disease severity scoring in medical imaging is costly, time‑consuming, and subject to inter‑reader variability. At the same time, clinical archives contain far more longitudinal imaging data than expert‑annotated severity scores. Existing self‑supervised methods typically ignore this chronological structure. We introduce ChronoCon, a contrastive learning approach that replaces label‑based ranking losses with rankings derived solely from the visitation order of a patient's longitudinal scans. Under the clinically plausible assumption of monotonic progression in irreversible diseases, the method learns disease‑relevant representations without using any expert labels. This generalizes the idea of Rank‑N‑Contrast from label distances to temporal ordering. Evaluated on rheumatoid arthritis radiographs for severity assessment, the learned representations substantially improve label efficiency. In low‑label settings, ChronoCon significantly outperforms a fully supervised baseline initialized from ImageNet weights. In a few‑shot learning experiment, fine‑tuning ChronoCon on expert scores from only five patients yields an intraclass correlation coefficient of 86% for severity score prediction. These results demonstrate the potential of chronological contrastive learning to exploit routinely available imaging metadata to reduce annotation requirements in the irreversible disease domain. Code is available at https://github.com/cirmuw/ChronoCon.

PaperId: 2252, https://arxiv.org/pdf/2603.21904.pdf GitHub

Abstract:
Unsupervised Domain Adaptation (UDA) is essential for deploying medical segmentation models across diverse clinical environments. Existing methods are fundamentally limited, suffering from semantically unaware feature alignment that results in poor distributional fidelity and from pseudo‑label validation that disregards global anatomical constraints, thus failing to prevent the formation of globally implausible structures. To address these issues, we propose SHAPE (Structure‑aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation), a framework that reframes adaptation towards global anatomical plausibility. Built on a DINOv3 foundation, its Hierarchical Feature Modulation (HFM) module first generates features with both high fidelity and class‑awareness. This shifts the core challenge to robustly validating pseudo‑labels. To augment conventional pixel‑level validation, we introduce Hypergraph Plausibility Estimation (HPE), which leverages hypergraphs to assess the global anatomical plausibility that standard graphs cannot capture. This is complemented by Structural Anomaly Pruning (SAP) to purge remaining artifacts via cross‑view stability. SHAPE significantly outperforms prior methods on cardiac and abdominal cross‑modality benchmarks, achieving state‑of‑the‑art average Dice scores of 90.08% (MRI‑>CT) and 78.51% (CT‑>MRI) on cardiac data, and 87.48% (MRI‑>CT) and 86.89% (CT‑>MRI) on abdominal data. The code is available at https://github.com/BioMedIA‑repo/SHAPE.

PaperId: 2253, https://arxiv.org/pdf/2603.21884.pdf GitHub GitHub

Abstract:
Low Rank Adaptation (LoRA) is the de facto fine‑tuning strategy to generate personalized images from pre‑trained diffusion models. Choosing a good rank is extremely critical, since it trades off performance and memory consumption, but today the decision is often left to the community's consensus, regardless of the personalized subject's complexity. The reason is evident: the cost of selecting a good rank for each LoRA component is combinatorial, so we opt for practical shortcuts such as fixing the same rank for all components. In this paper, we take a first step to overcome this challenge. Inspired by variational methods that learn an adaptive width of neural networks, we let the ranks of each layer freely adapt during fine‑tuning on a subject. We achieve it by imposing an ordering of importance on the rank's positions, effectively encouraging the creation of higher ranks when strictly needed. Qualitatively and quantitatively, our approach, LoRA^2, achieves a competitive trade‑off between DINO, CLIP‑I, and CLIP‑T across 29 subjects while requiring much less memory and lower rank than high rank LoRA versions. Code: https://github.com/donaldssh/NotAllLayersAreCreatedEqual.

PaperId: 2254, https://arxiv.org/pdf/2603.21879.pdf GitHub

Abstract:
Weather forecasting supports critical socioeconomic activities and complements environmental protection, yet operational Numerical Weather Prediction (NWP) systems remain computationally intensive, thus being inefficient for certain applications. Meanwhile, recent advances in deep data‑driven models have demonstrated promising results in nowcasting tasks. This paper presents SmaAT‑QMix‑UNet, an enhanced variant of SmaAT‑UNet that introduces two key innovations: a vector quantization (VQ) bottleneck at the encoder‑decoder bridge, and mixed kernel depth‑wise convolutions (MixConv) replacing selected encoder and decoder blocks. These enhancements both reduce the model's size and improve its nowcasting performance. We train and evaluate SmaAT‑QMix‑UNet on a Dutch radar precipitation dataset (2016‑2019), predicting precipitation 30 minutes ahead. Three configurations are benchmarked: using only VQ, only MixConv, and the full SmaAT‑QMix‑UNet. Grad‑CAM saliency maps highlight the regions influencing each nowcast, while a UMAP embedding of the codewords illustrates how the VQ layer clusters encoder outputs. The source code for SmaAT‑QMix‑UNet is publicly available on GitHub: https://github.com/nstavr04/MasterThesisSnellius.

PaperId: 2255, https://arxiv.org/pdf/2603.21872.pdf GitHub

Abstract:
Group Relative Policy Optimization (GRPO) methods for video generation like FlowGRPO remain far less reliable than their counterparts for language models and images. This gap arises because video generation has a complex solution space, and the ODE‑to‑SDE conversion used for exploration can inject excess noise, lowering rollout quality and making reward estimates less reliable, which destabilizes post‑training alignment. To address this problem, we view the pre‑trained model as defining a valid video data manifold and formulate the core problem as constraining exploration within the vicinity of this manifold, ensuring that rollout quality is preserved and reward estimates remain reliable. We propose SAGE‑GRPO (Stable Alignment via Exploration), which applies constraints at both micro and macro levels. At the micro level, we derive a precise manifold‑aware SDE with a logarithmic curvature correction and introduce a gradient norm equalizer to stabilize sampling and updates across timesteps. At the macro level, we use a dual trust region with a periodic moving anchor and stepwise constraints so that the trust region tracks checkpoints that are closer to the manifold and limits long‑horizon drift. We evaluate SAGE‑GRPO on HunyuanVideo1.5 using the original VideoAlign as the reward model and observe consistent gains over previous methods in VQ, MQ, TA, and visual metrics (CLIPScore, PickScore), demonstrating superior performance in both reward maximization and overall video quality. The code and visual gallery are available at https://dungeonmassster.github.io/SAGE‑GRPO‑Page/.

PaperId: 2256, https://arxiv.org/pdf/2603.21824.pdf GitHub

Abstract:
Steel surface defect analysis is critical for industrial quality control, yet existing benchmarks rely primarily on label‑only annotations, limiting fine‑grained semantic understanding and systematic evaluation of vision‑language models. To address this gap, we introduce SteelDefectX, a vision‑language dataset with multi‑form textual annotations for steel surface defect analysis, comprising 7,778 images across 25 defect categories. At the class level, the dataset provides defect names, representative visual attributes, and industrial causes. At the sample level, each image is annotated with three forms of textual representations: (1) free‑form natural language descriptions, (2) structured attribute annotations, and (3) template‑based sentences. These annotations provide flexible textual supervision with varying levels of expressiveness and controllability. We further establish a comprehensive benchmark covering vision‑language classification, segmentation, and cross‑dataset transfer, along with additional evaluations such as retrieval and text‑guided localization. Experimental results reveal a trade‑off between structure and flexibility in textual representations. Structured attributes provide more stable semantic alignment, while natural language descriptions improve transferability and fine‑grained spatial grounding. These findings highlight the critical role of textual design in industrial vision‑language learning. SteelDefectX provides a new benchmark for studying semantic alignment and generalization in industrial vision‑language learning. The code and dataset are available at https://github.com/Zhaosxian/SteelDefectX.

PaperId: 2257, https://arxiv.org/pdf/2603.21768.pdf GitHub GitHub

Abstract:
Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar‑only models frequently suffer from a lack of large‑scale atmospheric context, leading to performance degradation at longer lead times. While integrating meteorological variables predicted by weather foundation models offers a potential remedy, existing architectures fail to reconcile the profound representational heterogeneities between radar imagery and meteorological data. To bridge this gap, we propose PW‑FouCast, a novel frequency‑domain fusion framework that leverages Pangu‑Weather forecasts as spectral priors within a Fourier‑based backbone. Our architecture introduces three key innovations: (i) Pangu‑Weather‑guided Frequency Modulation to align spectral magnitudes and phases with meteorological priors; (ii) Frequency Memory to correct phase discrepancies and preserve temporal evolution; and (iii) Inverted Frequency Attention to reconstruct high‑frequency details typically lost in spectral filtering. Extensive experiments on the SEVIR and MeteoNet benchmarks demonstrate that PW‑FouCast achieves state‑of‑the‑art performance, effectively extending the reliable forecast horizon while maintaining structural fidelity. Our code is available at https://github.com/Onemissed/PW‑FouCast.

PaperId: 2258, https://arxiv.org/pdf/2603.21720.pdf GitHub

Abstract:
Understanding why real‑world events occur is important for both natural language processing and practical decision‑making, yet direct‑cause inference remains underexplored in evidence‑rich settings. To address this gap, we organized SemEval‑2026 Task 12: Abductive Event Reasoning (AER).\footnoteThe task data is available at https://github.com/sooo66/semeval2026‑task12‑dataset.git The task asks systems to identify the most plausible direct cause of a target event from supporting evidence. We formulate AER as an evidence‑grounded multiple‑choice benchmark that captures key challenges of real‑world causal reasoning, including distributed evidence, indirect background factors, and semantically related but non‑causal distractors. The shared task attracted 122 participants and received 518 submissions. This paper presents the task formulation, dataset construction pipeline, evaluation setup, and system results. AER provides a focused benchmark for abductive reasoning over real‑world events and highlights challenges for future work on causal reasoning and multi‑document understanding.

PaperId: 2259, https://arxiv.org/pdf/2603.21701.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) excel in visual understanding and reasoning, but the excessive visual tokens lead to high inference costs. Although recent token reduction methods mitigate this issue, they mainly target single‑turn Visual Question Answering (VQA), leaving the more practical multi‑turn VQA (MT‑VQA) scenario largely unexplored. MT‑VQA introduces additional challenges, as subsequent questions are unknown beforehand and may refer to arbitrary image regions, making existing reduction strategies ineffective. Specifically, current approaches fall into two categories: prompt‑dependent methods, which bias toward the initial text prompt and discard information useful for subsequent turns; prompt‑agnostic ones, which, though technically applicable to multi‑turn settings, rely on heuristic reduction metrics such as attention scores, leading to suboptimal performance. In this paper, we propose a learning‑based prompt‑agnostic method, termed MetaCompress, overcoming the limitations of heuristic designs. We begin by formulating token reduction as a learnable compression mapping, unifying existing formats such as pruning and merging into a single learning objective. Upon this formulation, we introduce a data‑efficient training paradigm capable of learning optimal compression mappings with limited computational costs. Extensive experiments on MT‑VQA benchmarks and across multiple LVLM architectures demonstrate that MetaCompress achieves superior efficiency‑accuracy trade‑offs while maintaining strong generalization across dialogue turns. Our code is available at https://github.com/MArSha1147/MetaCompress.

PaperId: 2260, https://arxiv.org/pdf/2603.21697.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) extend text‑only LLMs with visual reasoning, but also introduce new safety failure modes under visually grounded instructions. We study comic‑template jailbreaks that embed harmful goals inside simple three‑panel visual narratives and prompt the model to role‑play and "complete the comic." Building on JailbreakBench and JailbreakV, we introduce ComicJailbreak, a comic‑based jailbreak benchmark with 1,167 attack instances spanning 10 harm categories and 5 task setups. Across 15 state‑of‑the‑art MLLMs (six commercial and nine open‑source), comic‑based attacks achieve success rates comparable to strong rule‑based jailbreaks and substantially outperform plain‑text and random‑image baselines, with ensemble success rates exceeding 90% on several commercial models. Then, with the existing defense methodologies, we show that these methods are effective against the harmful comics, they will induce a high refusal rate when prompted with benign prompts. Finally, using automatic judging and targeted human evaluation, we show that current safety evaluators can be unreliable on sensitive but non‑harmful content. Our findings highlight the need for safety alignment robust to narrative‑driven multimodal jailbreaks.

PaperId: 2261, https://arxiv.org/pdf/2603.21576.pdf GitHub

Abstract:
Long‑context LLM inference is bottlenecked not by compute but by the O(n) memory bandwidth cost of scanning the KV cache at every decode step ‑‑ a wall that no amount of arithmetic scaling can break. Recent photonic accelerators have demonstrated impressive throughput for dense attention computation; however, these approaches inherit the same O(n) memory scaling as electronic attention when applied to long contexts. We observe that the real leverage point is the coarse block‑selection step: a memory‑bound similarity search that determines which KV blocks to fetch. We identify, for the first time, that this task is structurally matched to the photonic broadcast‑and‑weight paradigm ‑‑ the query fans out to all candidates via passive splitting, signatures are quasi‑static (matching electro‑optic MRR programming), and only rank order matters (relaxing precision to 4‑6 bits). Crucially, the photonic advantage grows with context length: as N increases, the electronic scan cost rises linearly while the photonic evaluation remains O(1). We instantiate this insight in PRISM (Photonic Ranking via Inner‑product Similarity with Microring weights), a thin‑film lithium niobate (TFLN) similarity engine. Hardware‑impaired needle‑in‑a‑haystack evaluation on Qwen2.5‑7B confirms 100% accuracy from 4K through 64K tokens at k=32, with 16x traffic reduction at 64K context. PRISM achieves a four‑order‑of‑magnitude energy advantage over GPU baselines at practical context lengths (n >= 4K).

PaperId: 2262, https://arxiv.org/pdf/2603.21525.pdf GitHub

Abstract:
Modern concrete must simultaneously satisfy evolving demands for mechanical performance, workability, durability, and sustainability, making mix designs increasingly complex. Recent studies leveraging Artificial Intelligence (AI) and Machine Learning (ML) models show promise for predicting compressive strength and guiding mix optimization, but most existing efforts are based on proprietary industrial datasets and closed‑source implementations. Here we introduce BOxCrete, an open‑source probabilistic modeling and optimization framework trained on a new open‑access dataset of over 500 strength measurements (1‑15 ksi) from 123 mixtures ‑ 69 mortar and 54 concrete mixes tested at five curing ages (1, 3, 5, 14, and 28 days). BOxCrete leverages Gaussian Process (GP) regression to predict strength development, achieving average R^2 = 0.94 and RMSE = 0.69 ksi, quantify uncertainty, and carry out multi‑objective optimization of compressive strength and embodied carbon. The dataset and model establish a reproducible open‑source foundation for data‑driven development of AI‑based optimized mix designs.

PaperId: 2263, https://arxiv.org/pdf/2603.21475.pdf GitHub

Abstract:
Automatic Multi‑Agent Systems (MAS) generation has emerged as a promising paradigm for solving complex reasoning tasks. However, existing frameworks are fundamentally bottlenecked when applied to knowledge‑intensive domains (e.g., healthcare and law). They either rely on a static library of general nodes like Chain‑of‑Thought, which lack specialized expertise, or attempt to generate nodes on the fly. In the latter case, the orchestrator is not only bound by its internal knowledge limits but must also simultaneously generate domain‑specific logic and optimize high‑level topology, leading to a severe architectural coupling that degrades overall system efficacy. To bridge this gap, we propose Unified‑MAS that decouples granular node implementation from topological orchestration via offline node synthesis. Unified‑MAS operates in two stages: (1) Search‑Based Node Generation retrieves external open‑world knowledge to synthesize specialized node blueprints, overcoming the internal knowledge limits of LLMs; and (2) Reward‑Based Node Optimization utilizes a perplexity‑guided reward to iteratively enhance the internal logic of bottleneck nodes. Extensive experiments across four specialized domains demonstrate that integrating Unified‑MAS into four Automatic‑MAS baselines yields a better performance‑cost trade‑off, achieving up to a 14.2% gain while significantly reducing costs. Further analysis reveals its robustness across different designer LLMs and its effectiveness on conventional tasks such as mathematical reasoning.

PaperId: 2264, https://arxiv.org/pdf/2603.21440.pdf GitHub

Abstract:
Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge‑intensive reasoning tasks. Knowledge Base Question Answering (KBQA), which leverages structured Knowledge Graphs (KGs) exemplifies this challenge due to the need for accurate multi‑hop reasoning. Existing approaches typically perform sequential reasoning steps guided by predefined pipelines, restricting flexibility and causing error cascades due to isolated reasoning at each step. To address these limitations, we propose KG‑Hopper, a novel Reinforcement Learning (RL) framework that empowers compact open LLMs with the ability to perform integrated multi‑hop KG reasoning within a single inference round. Rather than reasoning step‑by‑step, we train a Reasoning LLM that embeds the entire KG traversal and decision process into a unified ``thinking'' stage, enabling global reasoning over cross‑step dependencies and dynamic path exploration with backtracking. Experimental results on eight KG reasoning benchmarks show that KG‑Hopper, based on a 7B‑parameter LLM, consistently outperforms larger multi‑step systems (up to 70B) and achieves competitive performance with proprietary models such as GPT‑3.5‑Turbo and GPT‑4o‑mini, while remaining compact, open, and data‑efficient. The code is publicly available at: https://github.com/Wangshuaiia/KG‑Hopper.

PaperId: 2265, https://arxiv.org/pdf/2603.21430.pdf GitHub

Abstract:
Large language models (LLMs) have shown impressive capabilities in code generation. However, because most LLMs are trained on public domain corpora, directly applying them to real‑world software development often yields low success rates, as these scenarios frequently require domain‑specific knowledge. In particular, domain‑specific tasks usually demand highly specialized solutions, which are often underrepresented or entirely absent in the training data of generic LLMs. To address this challenge, we propose DomAgent, an autonomous coding agent that bridges this gap by enabling LLMs to generate domain‑adapted code through structured reasoning and targeted retrieval. A core component of DomAgent is DomRetriever, a novel retrieval module that emulates how humans learn domain‑specific knowledge, by combining conceptual understanding with experiential examples. It dynamically integrates top‑down knowledge‑graph reasoning with bottom‑up case‑based reasoning, enabling iterative retrieval and synthesis of structured knowledge and representative cases to ensure contextual relevance and broad task coverage. DomRetriever can operate as part of DomAgent or independently with any LLM for flexible domain adaptation. We evaluate DomAgent on an open benchmark dataset in the data science domain (DS‑1000) and further apply it to real‑world truck software development tasks. Experimental results show that DomAgent significantly enhances domain‑specific code generation, enabling small open‑source models to close much of the performance gap with large proprietary LLMs in complex, real‑world applications. The code is available at: https://github.com/Wangshuaiia/DomAgent.

PaperId: 2266, https://arxiv.org/pdf/2603.21399.pdf GitHub

Abstract:
Any capacity‑limited observer induces a canonical quotient on its environment: two situations that no bounded agent can distinguish are, for that agent, the same. We formalise this for finite POMDPs. A fixed probe family of finite‑state controllers induces a closed‑loop Wasserstein pseudometric on observation histories and a probe‑exact quotient merging histories that no controller in the family can distinguish. The quotient is canonical, minimal, and unique‑a bounded‑interaction analogue of the Myhill‑Nerode theorem. For clock‑aware probes, it is exactly decision‑sufficient for objectives that depend only on the agent's observations and actions; for latent‑state rewards, we use an observation‑Lipschitz approximation bound. The main theorem object is the clock‑aware quotient; scalable deterministic‑stationary experiments study a tractable coarsening with gap measured on small exact cases and explored empirically at larger scale. We validate theorem‑level claims on Tiger and GridWorld. We also report operational case studies on Tiger, GridWorld, and RockSample as exploratory diagnostics of approximation behavior and runtime, not as theorem‑facing evidence when no exact cross‑family certificate is available; heavier stress tests are archived in the appendix and artifact package.

PaperId: 2267, https://arxiv.org/pdf/2603.21357.pdf GitHub

Abstract:
LLM‑agent training pipelines routinely discard failed trajectories even though GPT‑4o achieves only 14‑20% on WebArena and below 55% pass@1 on ToolBench; even specialised systems at 50‑65% leave the majority of trajectories unused. We introduce AgentHER, which recovers this lost signal by adapting Hindsight Experience Replay (HER) to natural‑language agent trajectories: a trajectory that fails goal A is often a correct demonstration for an achievable alternative goal B. AgentHER realises this through a four‑stage pipeline (failure classification, outcome extraction, LLM‑guided relabeling with confidence gating, and data packaging) that converts discarded failures into SFT, DPO, and ShareGPT training data. On WebArena and ToolBench under a strict task‑disjoint held‑out protocol, AgentHER improves over success‑only SFT by +7.6‑11.4% across four model families (GPT‑4o, Qwen2.5‑72B/7B, LLaMA‑3.1‑8B), achieves 2x sample efficiency, and beats the strongest experience‑centric baseline (Agent Workflow Memory) by +3.0‑6.2%. Two robustness mechanisms, failure‑severity weighting and cross‑model multi‑judge verification (gpt‑4o‑mini paired with Qwen2.5‑72B‑Instruct), reduce label noise from 5.9% to 2.9% and raise human‑rated relabeling precision to 97.1% on WebArena and 96.0% on ToolBench. A full system‑cost audit shows the entire relabeling pipeline costs 2.98 and 26 wall‑clock minutes for 3,000 trajectories, i.e. 1.4 x 10^‑3 per accepted pair. Code: https://github.com/alphadl/AgentHER

PaperId: 2268, https://arxiv.org/pdf/2603.21342.pdf GitHub

Abstract:
We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete state spaces. Our formulation encompasses all existing discrete diffusion approaches, while allowing significantly greater flexibility in the choice of corruption dynamics. The forward noising process relies on uniformization and enables fast arbitrary corruption. For the reverse process, we derive a simple evidence lower bound (ELBO) based on snapshot latents, instead of the entire noising path, that allows efficient training of standard generative modeling architectures with clear probabilistic interpretation. Our experiments on large‑vocabulary discrete generation tasks suggest that the proposed framework outperforms existing discrete diffusion methods in terms of training efficiency and generation quality, and beats autoregressive models for the first time at this scale. We provide the code along with a blog post on the project page : \hrefhttps://oussamazekri.fr/gddshttps://oussamazekri.fr/gdds.

PaperId: 2269, https://arxiv.org/pdf/2603.21298.pdf GitHub

Abstract:
Combating hate speech on social media is critical for securing cyberspace, yet relies heavily on the efficacy of automated detection systems. As content formats evolve, hate speech is transitioning from solely plain text to complex multimodal expressions, making implicit attacks harder to spot. Current systems, however, often falter on these subtle cases, as they struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities. To bridge this gap, we move beyond binary classification to characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion. Guided by this fine‑grained formulation, we curate the Hate via Vision‑Language Interplay (H‑VLI) benchmark where the true intent hinges on the intricate interplay of modalities rather than overt visual or textual slurs. To effectively decipher these complex cues, we further propose the Asymmetric Reasoning via Courtroom Agent DEbate (ARCADE) framework. By simulating a judicial process where agents actively argue for accusation and defense, ARCADE forces the model to scrutinize deep semantic cues before reaching a verdict. Extensive experiments demonstrate that ARCADE significantly outperforms state‑of‑the‑art baselines on H‑VLI, particularly for challenging implicit cases, while maintaining competitive performance on established benchmarks. Our code and data are available at: https://github.com/Sayur1n/H‑VLI

PaperId: 2270, https://arxiv.org/pdf/2603.21289.pdf GitHub

Abstract:
Recent progress in multimodal large language models has led to strong performance on reasoning tasks, but these improvements largely rely on high‑quality annotated data or teacher‑model distillation, both of which are costly and difficult to scale. To address this, we propose an unsupervised self‑evolution training framework for multimodal reasoning that achieves stable performance improvements without using human‑annotated answers or external reward models. For each input, we sample multiple reasoning trajectories and jointly model their within group structure. We use the Actor's self‑consistency signal as a training prior, and introduce a bounded Judge based modulation to continuously reweight trajectories of different quality. We further model the modulated scores as a group level distribution and convert absolute scores into relative advantages within each group, enabling more robust policy updates. Trained with Group Relative Policy Optimization (GRPO) on unlabeled data, our method consistently improves reasoning performance and generalization on five mathematical reasoning benchmarks, offering a scalable path toward self‑evolving multimodal models. The code are available at https://github.com/OPPO‑Mente‑Lab/LLM‑Self‑Judge.

PaperId: 2271, https://arxiv.org/pdf/2603.21250.pdf GitHub

Abstract:
Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains significantly underexplored. Existing frameworks, predominantly designed for static deductive tasks, fail to generalize to abductive reasoning due to unstructured state representation and lack of explicit state control. Consequently, they are inevitably prone to Evidence Fabrication, Context Drift, Failed Backtracking, and Early Stopping. To bridge this gap, we introduce Graph of States (GoS), a general‑purpose neuro‑symbolic framework tailored for abductive tasks. GoS grounds multi‑agent collaboration in a structured belief states, utilizing a causal graph to explicitly encode logical dependencies and a state machine to govern the valid transitions of the reasoning process. By dynamically aligning the reasoning focus with these symbolic constraints, our approach transforms aimless, unconstrained exploration into a convergent, directed search. Extensive evaluations on two real‑world datasets demonstrate that GoS significantly outperforms all baselines, providing a robust solution for complex abductive tasks. Code repo and all prompts: https://github.com/gaorch85/Graph‑of‑States.

PaperId: 2272, https://arxiv.org/pdf/2603.21235.pdf GitHub

Abstract:
Nonrigid registration is conventionally divided into point set registration, which aligns sparse geometries, and image registration, which aligns continuous intensity fields on regular grids. However, this dichotomy creates a critical bottleneck for emerging scientific data, such as spatial transcriptomics, where high‑dimensional vector‑valued functions, e.g., gene expression, are defined on irregular, sparse manifolds. Consequently, researchers currently face a forced choice: either sacrifice single‑cell resolution via voxelization to utilize image‑based tools, or ignore the critical functional signal to utilize geometric tools. To resolve this dilemma, we propose Domain Elastic Transform (DET), a grid‑free probabilistic framework that unifies geometric and functional alignment. By treating data as functions on irregular domains, DET registers high‑dimensional signals directly without binning. We formulate the problem within a rigorous Bayesian framework, modeling domain deformation as an elastic motion guided by a joint spatial‑functional likelihood. The method is fully unsupervised and scalable, utilizing feature‑sensitive downsampling to handle massive atlases. We demonstrate that DET achieves 92% topological preservation on MERFISH data where state‑of‑the‑art optimal transport methods struggle (<5%), and successfully registers whole‑embryo Stereo‑seq atlases across developmental stages ‑‑ a task involving massive scale and complex nonrigid growth. The implementation of DET is available on https://github.com/ohirose/bcpd (since Mar, 2025).

PaperId: 2273, https://arxiv.org/pdf/2603.21149.pdf GitHub

Abstract:
An autonomous AI ecosystem (SUBSTRATE S3), generating product specifications without explicit instructions about formal methods, independently proposed the use of Z3 SMT solver across six distinct domains of AI safety: verification of LLM‑generated code, tool API safety for AI agents, post‑distillation reasoning correctness, CLI command validation, hardware assembly verification, and smart contract safety. These convergent discoveries, occurring across 8 products over 13 days with Jaccard similarity below 15% between variants, suggest that formal verification is not merely a useful technique for AI safety but an emergent property of any sufficiently complex system reasoning about its own safety. We propose a unified framework (substrate‑guard) that applies Z3‑based verification across all six output classes through a common API, and evaluate it on 181 test cases across five implemented domains, achieving 100% classification accuracy with zero false positives and zero false negatives. Our framework detected real bugs that empirical testing would miss, including an INT_MIN overflow in branchless RISC‑V assembly and mathematically proved that unconstrained string parameters in tool APIs are formally unverifiable.

PaperId: 2274, https://arxiv.org/pdf/2603.21108.pdf GitHub

Abstract:
Molecular property prediction constitutes a cornerstone of drug discovery and materials science, necessitating models capable of disentangling complex structure‑property relationships across diverse molecular modalities. Existing approaches frequently exhibit entangled representations‑‑conflating structural, chemical, and functional factors‑‑thereby limiting interpretability and transferability. Furthermore, conventional methods inadequately exploit complementary information from graphs, sequences, and geometries, often relying on naive concatenation that neglects inter‑modal dependencies. In this work, we propose DMMRL, which employs variational autoencoders to disentangle molecular representations into shared (structure‑relevant) and private (modality‑specific) latent spaces, enhancing both interpretability and predictive performance. The proposed variational disentanglement mechanism effectively isolates the most informative features for property prediction, while orthogonality and alignment regularizations promote statistical independence and cross‑modal consistency. Additionally, a gated attention fusion module adaptively integrates shared representations, capturing complex inter‑modal relationships. Experimental validation across seven benchmark datasets demonstrates DMMRL's superior performance relative to state‑of‑the‑art approaches. The code and data underlying this article are freely available at https://github.com/xulong0826/DMMRL.

PaperId: 2275, https://arxiv.org/pdf/2603.21100.pdf GitHub

Abstract:
Due to the limited availability of paired multi‑modal data, multi‑modal trackers are typically built by adopting pre‑trained RGB models with parameter‑efficient fine‑tuning modules. However, these fine‑tuning methods overlook advanced adaptations for applying RGB pre‑trained models and fail to modulate a single specific modality, cross‑modal interactions, and the prediction head. To address the issues, we propose to perform Progressive Adaptation for Multi‑Modal Tracking (PATrack). This innovative approach incorporates modality‑dependent, modality‑entangled, and task‑level adapters, effectively bridging the gap in adapting RGB pre‑trained networks to multi‑modal data through a progressive strategy. Specifically, modality‑specific information is enhanced through the modality‑dependent adapter, decomposing the high‑ and low‑frequency components, which ensures a more robust feature representation within each modality. The inter‑modal interactions are introduced in the modality‑entangled adapter, which implements a cross‑attention operation guided by inter‑modal shared information, ensuring the reliability of features conveyed between modalities. Additionally, recognising that the strong inductive bias of the prediction head does not adapt to the fused information, a task‑level adapter specific to the prediction head is introduced. In summary, our design integrates intra‑modal, inter‑modal, and task‑level adapters into a unified framework. Extensive experiments on RGB+Thermal, RGB+Depth, and RGB+Event tracking tasks demonstrate that our method shows impressive performance against state‑of‑the‑art methods. Code is available at https://github.com/ouha1998/Learning‑Progressive‑Adaptation‑for‑Multi‑Modal‑Tracking.

PaperId: 2276, https://arxiv.org/pdf/2603.21096.pdf GitHub

Abstract:
Transformers lack an explicit architectural mechanism for storing and organizing knowledge acquired during training. We introduce learnable sparse memory banks: a set of latent tokens, randomly initialized and trained end‑to‑end, that transformer layers query via cross‑attention to retrieve stored knowledge. To scale memory capacity without prohibitive attention costs, we propose chapter‑based routing inspired by Mixture‑of‑Experts architectures, partitioning the memory bank into chapters and training a router to select relevant subsets per input. This enables scaling to 262K memory tokens while maintaining tractable computation. We evaluate our approach against standard transformers (in iso‑FLOP settings) on pre‑training and instruction fine‑tuning across relevant benchmarks. Our models surpass iso‑FLOP baselines suggesting scope for a new axis of scaling, demonstrating that explicit associative memory provides complementary capacity to what is captured implicitly in model parameters. Additionally, we observe improved knowledge retention under continued training, with robustness to forgetting when transitioning between training phases (e.g., pretraining to instruction fine‑tuning).

PaperId: 2277, https://arxiv.org/pdf/2603.21071.pdf GitHub

Abstract:
As one of the most important underwater sensing technologies, forward‑looking sonar exhibits unique imaging characteristics. Sonar images are often affected by severe speckle noise, low texture contrast, acoustic shadows, and geometric distortions. These factors make it difficult for traditional teacher‑student frameworks to achieve satisfactory performance in sonar semantic segmentation tasks under extremely limited labeled data conditions. To address this issue, we propose a Collaborative Teacher Semantic Segmentation Framework for forward‑looking sonar images. This framework introduces a multi‑teacher collaborative mechanism composed of one general teacher and multiple sonar‑specific teachers. By adopting a multi‑teacher alternating guidance strategy, the student model can learn general semantic representations while simultaneously capturing the unique characteristics of sonar images, thereby achieving more comprehensive and robust feature modeling. Considering the challenges of sonar images, which can lead teachers to generate a large number of noisy pseudo‑labels, we further design a cross‑teacher reliability assessment mechanism. This mechanism dynamically quantifies the reliability of pseudo‑labels by evaluating the consistency and stability of predictions across multiple views and multiple teachers, thereby mitigating the negative impact caused by noisy pseudo‑labels. Notably, on the FLSMD dataset, when only 2% of the data is labeled, our method achieves a 5.08% improvement in mIoU compared to other state‑of‑the‑art approaches.

PaperId: 2278, https://arxiv.org/pdf/2603.21045.pdf GitHub

Abstract:
Diffusion‑based image super‑resolution (SR) aims to reconstruct high‑resolution (HR) images from low‑resolution (LR) observations. However, the inherent randomness injected during the reverse diffusion process causes the performance of diffusion‑based SR models to vary significantly across different sampling runs, particularly when the sampling trajectory is compressed into a limited number of steps. A critical yet underexplored question is: what is the optimal noise to inject at each intermediate diffusion step? In this paper, we establish a theoretical framework that derives the closed‑form analytical solution for optimal intermediate noise in diffusion models from a maximum likelihood estimation perspective, revealing a consistent conditional dependence structure that generalizes across diffusion paradigms. We instantiate this framework under the residual‑shifting diffusion paradigm and accordingly design an LR‑guided multi‑input‑aware noise predictor to replace random Gaussian noise. We further mitigate initialization bias with a high‑quality pre‑upsampling network. The compact 4‑step trajectory uniquely enables end‑to‑end optimization of the entire reverse chain, which is computationally prohibitive for conventional long‑trajectory diffusion models. Extensive experiments demonstrate that LPNSR achieves state‑of‑the‑art perceptual performance on both synthetic and real‑world datasets, without relying on any large‑scale text‑to‑image priors. The source code of our method can be found at https://github.com/Faze‑Hsw/LPNSR.

PaperId: 2279, https://arxiv.org/pdf/2603.21016.pdf GitHub

Abstract:
Large language models (LLMs) used for multiple‑choice and pairwise evaluation tasks often exhibit selection bias due to non‑semantic factors like option positions and label symbols. Existing inference‑time debiasing is costly and may harm reasoning, while pointwise training ignores that the same question should yield consistent answers across permutations. To address this issue, we propose Permutation‑Aware Group Relative Policy Optimization (PA‑GRPO), which mitigates selection bias by enforcing permutation‑consistent semantic reasoning. PA‑GRPO constructs a permutation group for each instance by generating multiple candidate permutations, and optimizes the model using two complementary mechanisms: (1) cross‑permutation advantage, which computes advantages relative to the mean reward over all permutations of the same instance, and (2) consistency‑aware reward, which encourages the model to produce consistent decisions across different permutations. Experimental results demonstrate that PA‑GRPO outperforms strong baselines across seven benchmarks, substantially reducing selection bias while maintaining high overall performance. The code is available on github (https://github.com/ECNU‑Text‑Computing/PA‑GRPO).

PaperId: 2280, https://arxiv.org/pdf/2603.21006.pdf GitHub

Abstract:
This paper presents the first systematic measurement of educational alignment in Large Language Models. Using a Delphi‑validated instrument comprising 48 items across eight educational‑theoretical dimensions, the study reveals that GPT‑5.1 exhibits highly coherent preference patterns (99.78% transitivity; 92.79% model accuracy) that largely align with humanistic educational principles where expert consensus exists. Crucially, divergences from expert opinion occur precisely in domains of normative disagreement among human experts themselves, particularly emotional dimensions and epistemic normativity. This raises a fundamental question for alignment research: When human values are contested, what should models be aligned to? The findings demonstrate that GPT‑5.1 does not remain neutral in contested domains but adopts coherent positions, prioritizing emotional responsiveness and rejecting false balance. The methodology, combining Delphi consensus‑building with Structured Preference Elicitation and Thurstonian Utility modeling, provides a replicable framework for domain‑specific alignment evaluation beyond generic value benchmarks.

PaperId: 2281, https://arxiv.org/pdf/2603.20955.pdf GitHub

Abstract:
The Predictive Associative Memory (PAM) framework posits that useful relationships often connect items that co‑occur in shared contexts rather than items that appear similar in embedding space. A contrastive MLP trained on co‑occurrence annotations‑‑Contrastive Association Learning (CAL)‑‑has improved multi‑hop passage retrieval and discovered narrative function at corpus scale in text. We test whether this principle transfers to molecular biology, where protein‑protein interactions provide functional associations distinct from gene expression similarity. Four experiments across two biological domains map the operating envelope. On gene perturbation data (Replogle K562 CRISPRi, 2,285 genes), CAL trained on STRING protein interactions achieves cross‑boundary AUC of 0.908 where expression similarity scores 0.518. A second gene dataset (DepMap, 17,725 genes) confirms the result after negative sampling correction, reaching cross‑boundary AUC of 0.947. Two drug sensitivity experiments produce informative negatives that sharpen boundary conditions. Three cross‑domain findings emerge: (1) inductive transfer succeeds in biology‑‑a node‑disjoint split with unseen genes yields AUC 0.826 (Delta +0.127)‑‑where it fails in text (+/‑0.10), suggesting physically grounded associations are more transferable than contingent co‑occurrences; (2) CAL scores anti‑correlate with interaction degree (Spearman r = ‑0.590), with gains concentrating on understudied genes with focused interaction profiles; (3) tighter association quality outperforms larger but noisier training sets, reversing the text pattern. Results are stable across training seeds (SD < 0.001) and cross‑boundary threshold choices.

PaperId: 2282, https://arxiv.org/pdf/2603.20939.pdf GitHub

Abstract:
Large language models are increasingly used as personal assistants, yet most lack a persistent user model, forcing users to repeatedly restate preferences across sessions. We propose Vector‑Adapted Retrieval Scoring (VARS), a pipeline‑agnostic, frozen‑backbone framework that represents each user with long‑term and short‑term vectors in a shared preference space and uses these vectors to bias retrieval scoring over structured preference memory. The vectors are updated online from weak scalar rewards from users' feedback, enabling personalization without per‑user fine‑tuning. We evaluate on \textscMultiSessionCollab, an online multi‑session collaboration benchmark with rich user preference profiles, across math and code tasks. Under frozen backbones, the main benefit of user‑aware retrieval is improved interaction efficiency rather than large gains in raw task accuracy: our full VARS agent achieves the strongest overall performance, matches a strong Reflection baseline in task success, and reduces timeout rate and user effort. The learned long‑term vectors also align with cross‑user preference overlap, while short‑term vectors capture session‑specific adaptation, supporting the interpretability of the dual‑vector design. Code, model, and data are available at https://github.com/YurenHao0426/VARS.

PaperId: 2283, https://arxiv.org/pdf/2603.20933.pdf GitHub

Abstract:
Large Language Model (LLM) agents combine the chat interaction capabilities of LLMs with the power to interact with external tools and APIs. This enables them to perform complex tasks and act autonomously to achieve user goals. However, current agent systems operate on an all‑or‑nothing basis: an agent either has full access to an API's capabilities and a web page's content, or it has no access at all. This coarse‑grained approach forces users to trust agents with more capabilities than they actually need for a given task. In this paper, we introduce AC4A, an access control framework for agents. As agents become more capable and autonomous, users need a way to limit what APIs or portions of web pages these agents can access, eliminating the need to trust them with everything an API or web page allows. Our goal with AC4A is to provide a framework for defining permissions that lets agents access only the resources they are authorized to access. AC4A works across both API‑based and browser‑based agents. It does not prescribe what permissions should be, but offers a flexible way to define and enforce them, making it practical for real‑world systems. AC4A works by creating permissions granting access to resources, drawing inspiration from established access control frameworks like the one for the Unix file system. Applications define their resources as hierarchies and provide a way to compute the necessary permissions at runtime needed for successful resource access. We demonstrate the usefulness of AC4A in enforcing permissions over real‑world APIs and web pages through case studies. The source code of AC4A is available at https://github.com/reSHARMA/AC4A

PaperId: 2284, https://arxiv.org/pdf/2603.20899.pdf GitHub

Abstract:
Large language models exhibit strong reasoning capabilities, yet often rely on shortcuts such as surface pattern matching and answer memorization rather than genuine logical inference. We propose Shortcut‑Aware Reasoning Training (SART), a gradient‑aware framework that detects and mitigates shortcut‑promoting samples via ShortcutScore and gradient surgery. Our method identifies shortcut signals through gradient misalignment with validation objectives and answer‑token concentration, and modifies training dynamics accordingly. Experiments on controlled reasoning benchmarks show that SART achieves +16.5% accuracy and +40.2% robustness over the strongest baseline, significantly improving generalization under distribution shifts. Code is available at: https://github.com/fuyanjie/short‑cut‑aware‑data‑centric‑reasoning.

PaperId: 2285, https://arxiv.org/pdf/2603.20885.pdf GitHub

Abstract:
Two distinct technologies have gained attention lately due to their prospects for motor rehabilitation: robotics and brain‑machine interfaces (BMIs). Harnessing their combined efforts is a largely uncharted and promising direction that has immense clinical potential. However, a significant challenge is whether motor intentions from the user can be accurately detected using non‑invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. As an alternative to the straightforward continuous control approach, this study instead aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper‑body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal‑oriented reaching task. Using electroencephalogram signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7% and 66.6% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo‑online evaluation could replicate this performance, forecasting reliable online exoskeleton control in the future. Our approach showed that participants could produce quality and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices.

PaperId: 2286, https://arxiv.org/pdf/2603.20833.pdf GitHub

Abstract:
As AI agent ecosystems grow, agents need mechanisms to monitor relevant knowledge in real time. Semantic publish‑subscribe systems address this by matching new content against vector subscriptions. However, in multi‑agent settings where agents operate under different data handling policies, unrestricted semantic subscriptions create policy violations: agents receive notifications about content they are not authorized to access. We introduce governance‑aware vector subscriptions, a mechanism that composes semantic similarity matching with multi‑dimensional policy predicates grounded in regulatory frameworks (EU DSM Directive, EU AI Act). The policy predicate operates over multiple independent dimensions (processing level, direct marketing restrictions, training opt‑out, jurisdiction, and scientific usage) each with distinct legal bases. Agents subscribe to semantic regions of a curated knowledge base; notifications are dispatched only for validated content that passes both the similarity threshold and all applicable policy constraints. We formalize the mechanism, implement it within AIngram (an operational multi‑agent knowledge base), and evaluate it using the PASA benchmark. We validate the mechanism on a synthetic corpus (1,000 chunks, 93 subscriptions, 5 domains): the governed mode correctly enforces all policy constraints while preserving delivery of authorized content. Ablation across five policy dimensions shows that no single dimension suffices for full compliance.

PaperId: 2287, https://arxiv.org/pdf/2603.20818.pdf GitHub

Abstract:
While structure‑based relocalizers have long strived for point correspondences when establishing or regressing query‑map associations, in this paper, we pioneer the use of planar primitives and 3D planar maps for lightweight 6‑DoF camera relocalization in structured environments. Planar primitives, beyond being fundamental entities in projective geometry, also serve as region‑based representations that encapsulate both structural and semantic richness. This motivates us to introduce PlanaReLoc, a streamlined plane‑centric paradigm where a deep matcher associates planar primitives across the query image and the map within a learned unified embedding space, after which the 6‑DoF pose is solved and refined under a robust framework. Through comprehensive experiments on the ScanNet and 12Scenes datasets across hundreds of scenes, our method demonstrates the superiority of planar primitives in facilitating reliable cross‑modal structural correspondences and achieving effective camera relocalization without requiring realistically textured/colored maps, pose priors, or per‑scene training. The code and data are available at https://github.com/3dv‑casia/PlanaReLoc .

PaperId: 2288, https://arxiv.org/pdf/2603.20702.pdf GitHub

Abstract:
Proper parameter configuration is a prerequisite for the success of Evolutionary Algorithms (EAs). While various adaptive strategies have been proposed, it remains an open question whether all control dimensions contribute equally to algorithmic scalability. To investigate this, we categorize control variables into numerical parameters (e.g., crossover and mutation rates) and structural parameters (e.g., population size and operator switching), hypothesizing that they play distinct roles. This paper presents an empirical study utilizing a dual‑level Deep Reinforcement Learning (DRL) framework to decouple and analyze the impact of these two dimensions on the Traveling Salesman Problem (TSP). We employ a Recurrent PPO agent to dynamically regulate these parameters, treating the DRL model as a probe to reveal evolutionary dynamics. Experimental results confirm the effectiveness of this approach: the learned policies outperform static baselines, reducing the optimality gap by approximately 45% on the largest tested instance (rl5915). Building on this validated framework, our ablation analysis reveals a fundamental insight: while numerical tuning offers local refinement, structural plasticity is the decisive factor in preventing stagnation and facilitating escape from local optima. These findings suggest that future automated algorithm design should prioritize dynamic structural reconfiguration over fine‑grained probability adjustment. To facilitate reproducibility, the source code is available at https://github.com/StarDream1314/DRLGA‑TSP

PaperId: 2289, https://arxiv.org/pdf/2603.20648.pdf GitHub

Abstract:
Most fine‑grained fashion image retrieval (FIR) methods assume a static setting, requiring full retraining when new attributes appear, which is costly and impractical for dynamic scenarios. Although pretrained models support zero‑shot inference, their accuracy drops without supervision, and no prior work explores class‑incremental learning (CIL) for fine‑grained FIR. We propose a multihead continual learning framework for fine‑grained fashion image retrieval with contrastive learning and exponential moving average (EMA) distillation (MCL‑FIR). MCL‑FIR adopts a multi‑head design to accommodate evolving classes across increments, reformulates triplet inputs into doublets with InfoNCE for simpler and more effective training, and employs EMA distillation for efficient knowledge transfer. Experiments across four datasets demonstrate that, beyond its scalability, MCL‑FIR achieves a strong balance between efficiency and accuracy. It significantly outperforms CIL baselines under similar training cost, and compared with static methods, it delivers comparable performance while using only about 30% of the training cost. The source code is publicly available in https://github.com/Dr‑LingXiao/MCL‑FIR.

PaperId: 2290, https://arxiv.org/pdf/2603.20505.pdf GitHub

Abstract:
Probabilistic Logic Programming (PLP) languages, like ProbLog, naturally support reasoning under uncertainty, while maintaining a declarative and interpretable framework. Meanwhile, counterfactual reasoning (i.e., answering ``what if'' questions) is critical for ensuring AI systems are robust and trustworthy; however, integrating this capability into PLP can be computationally prohibitive and unstable in accuracy. This paper addresses this challenge, by proposing an efficient program transformation for counterfactuals as Single World Intervention Programs (SWIPs) in ProbLog. By systematically splitting ProbLog clauses to observed and fixed components relevant to a counterfactual, we create a transformed program that (1) does not asymptotically exceed the computational complexity of existing methods, and is strictly smaller in common cases, and (2) reduces counterfactual reasoning to marginal inference over a simpler program. We formally prove the correctness of our approach, which relies on a weaker set independence assumptions and is consistent with conditional independencies, showing the resulting marginal probabilities match the counterfactual distributions of the underlying Structural Causal Model in wide domains. Our method achieves a 35% reduction in inference time versus existing methods in extensive experiments. This work makes complex counterfactual reasoning more computationally tractable and reliable, providing a crucial step towards developing more robust and explainable AI systems. The code is at https://github.com/EVIEHub/swip.

PaperId: 2291, https://arxiv.org/pdf/2603.20442.pdf GitHub

Abstract:
We propose Melaguard, a multimodal ML framework (Transformer‑lite, 1.2M parameters, 4‑head self‑attention) for detecting neurovascular instability (NVI) from wearable‑compatible physiological signals prior to structural stroke pathology. The model fuses heart rate variability (HRV), peripheral perfusion index, SpO2, and bilateral phase coherence into a composite NVI Score, designed for edge inference (WCET <=4 ms on Cortex‑M4). NVI ‑ the pre‑structural dysregulation of cerebrovascular autoregulation preceding overt stroke ‑ remains undetectable by existing single‑modality wearables. With 12.2 million incident strokes annually, continuous multimodal physiological monitoring offers a practical path to community‑scale screening. Three‑stage independent validation: (1) synthetic benchmark (n=10,000), AUC=0.88 [0.83‑0.92]; (2) clinical cohort PhysioNet CVES (n=172; 84 stroke, 88 control) ‑ Transformer‑lite achieves AUC=0.755 [0.630‑0.778], outperforming LSTM (0.643), Random Forest (0.665), SVM (0.472); HRV‑SDNN discriminates stroke (p=0.011); (3) PPG pipeline PhysioNet BIDMC (n=53) ‑‑ pulse rate r=0.748 and HRV surrogate r=0.690 vs. ECG ground truth. Cross‑modality validation on PPG‑BP (n=219) confirms PPG morphology classifies cerebrovascular disease at AUC=0.923 [0.869‑0.968]. Multimodal fusion consistently outperforms single‑modality baselines. Code: https://github.com/ClevixLab/Melaguard

PaperId: 2292, https://arxiv.org/pdf/2603.20422.pdf GitHub

Abstract:
Human cognition of new concepts is inherently a streaming process: we continuously recognize new objects or identities and update our memories over time. However, current multimodal personalization methods are largely limited to static images or offline videos. This disconnects continuous visual input from instant real‑world feedback, limiting their ability to provide the real‑time, interactive personalized responses essential for future AI assistants. To bridge this gap, we first propose and formally define the novel task of Personalized Streaming Video Understanding (PSVU). To facilitate research in this new direction, we introduce PEARL‑Bench, the first comprehensive benchmark designed specifically to evaluate this challenging setting. It evaluates a model's ability to respond to personalized concepts at exact timestamps under two modes: (1) Frame‑level, focusing on a specific person or object in discrete frames, and (2) a novel Video‑level, focusing on personalized actions unfolding across continuous frames. PEARL‑Bench comprises 132 unique videos and 2,173 fine‑grained annotations with precise timestamps. Concept diversity and annotation quality are strictly ensured through a combined pipeline of automated generation and human verification. To tackle this challenging new setting, we further propose PEARL, a plug‑and‑play, training‑free strategy that serves as a strong baseline. Extensive evaluations across 8 offline and online models demonstrate that PEARL achieves state‑of‑the‑art performance. Notably, it brings consistent PSVU improvements when applied to 3 distinct architectures, proving to be a highly effective and robust strategy. We hope this work advances vision‑language model (VLM) personalization and inspires further research into streaming personalized AI assistants. Code is available at https://github.com/Yuanhong‑Zheng/PEARL.

PaperId: 2293, https://arxiv.org/pdf/2603.20381.pdf GitHub

Abstract:
Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe, thoughtful, engaging, and empowering human‑agent interactions. Experiments in cognitive science and social psychology have demonstrated that human semantic processing exhibits contextuality more consistent with quantum logical mechanisms than classical Boolean theories, and recent works have found similar results in large language models ‑‑ in particular, clear violations of the Bell inequality in experiments of contextuality during interpretation of ambiguous expressions. We explore the CHSH |S| parameter ‑‑ the metric associated with the inequality ‑‑ across the inference parameter space of models spanning four orders of magnitude in scale, cross‑referencing it with MMLU, hallucination rate, and nonsense detection benchmarks. We find that the interquartile range of the |S| distribution ‑‑ the statistic that most sharply differentiates models from one another ‑‑ is completely orthogonal to all external benchmarks, while violation rate shows weak anticorrelation with all three benchmarks that does not reach significance. We investigate how |S| varies with sampling parameters and word order, and discuss the information‑theoretic constraints that genuine contextuality imposes on prompt injection defenses and its human analogue, whereby careful construction and maintenance of social contextuality can be carried out at scale ‑‑ manufacturing not consent but contextuality itself, a subtler and more fundamental form of manipulation that shapes the space of possible interpretations before any particular one is reached.

PaperId: 2294, https://arxiv.org/pdf/2603.20351.pdf GitHub

Abstract:
Mobile advertising dominates app monetization but introduces risks ranging from intrusive user experience to malware delivery. Existing detection methods rely either on static analysis, which misses runtime behaviors, or on heuristic UI exploration, which struggles with sparse and obfuscated ads. In this paper, we present MANA, the first agentic multimodal reasoning framework for mobile ad detection. MANA integrates static, visual, temporal, and experiential signals into a reasoning‑guided navigation strategy that determines not only how to traverse interfaces but also where to focus, enabling efficient and robust exploration. We implement and evaluate MANA on commercial smartphones over 200 apps, achieving state‑of‑the‑art accuracy and efficiency. Compared to baselines, it improves detection accuracy by 30.5%‑56.3% and reduces exploration steps by 29.7%‑63.3%. Case studies further demonstrate its ability to uncover obfuscated and malicious ads, underscoring its practicality for mobile ad auditing and its potential for broader runtime UI analysis (e.g., permission abuse). Code and dataset are available at https://github.com/MANA‑2026/MANA.

PaperId: 2295, https://arxiv.org/pdf/2603.20340.pdf GitHub

Abstract:
Self‑generated skills for web agents are often unstable and can even hurt performance relative to direct acting. We argue that the key bottleneck is not only skill generation quality, but the fact that web skills remain implicit and therefore cannot be checked or locally repaired. To address this, we present ContractSkill, a framework that converts a draft skill into an executable artifact with explicit procedural structure, enabling deterministic verifica tion, fault localization, and minimal local repair. This turns skill refinement from full rewriting into localized editing of a single skill artifact. Experiments on VisualWebArena show that Contract Skill is effective in realistic web environments, while MiniWoB provides a controlled test of the mechanism behind the gain. Under matched transfer layers, repaired artifacts also remain reusable after removing the source model from the loop, providing evi dence of portability within the same benchmark family rather than full‑benchmark generalization. These results suggest that the central challenge is not merely generating skills, but mak ing them explicit, executable, and repairable. Code is available at https://github.com/underfitting‑lu/contractskill.git.

PaperId: 2296, https://arxiv.org/pdf/2603.20327.pdf GitHub

Abstract:
Video world models trained with Joint Embedding Predictive Architectures (JEPA) acquire rich spatiotemporal representations by predicting masked regions in latent space rather than reconstructing pixels. This removes the visual verification pathway of generative models, creating a structural interpretability gap: the encoder has learned physical structure inaccessible in any inspectable form. Existing probing methods either operate in continuous space without a structured intermediate layer, or attach generative components whose parameters confound attribution of behavior to the encoder. We propose the AI Mother Tongue (AIM) framework as a passive quantization probe: a lightweight, vocabulary‑free probe that converts V‑JEPA 2 continuous latent vectors into discrete symbol sequences without task‑specific supervision or modifying the encoder. Because the encoder is kept completely frozen, any symbolic structure in the AIM codebook is attributable entirely to V‑JEPA 2 pre‑trained representations ‑‑ not to the probe. We evaluate through category‑contrast experiments on Kinetics‑mini along three physical dimensions: grasp angle, object geometry, and motion temporal structure. AIM symbol distributions differ significantly across all three experiments (chi^2 p < 10^‑4; MI 0.036‑‑0.117 bits, NMI 1.2‑‑3.9% of the 3‑bit maximum; JSD up to 0.342; codebook active ratio 62.5%). The experiments reveal that V‑JEPA 2 latent space is markedly compact: diverse action categories share a common representational core, with semantic differences encoded as graded distributional variations rather than categorical boundaries. These results establish Stage 1 of a four‑stage roadmap toward an action‑conditioned symbolic world model, demonstrating that structured symbolic manifolds are discoverable properties of frozen JEPA latent spaces.

PaperId: 2297, https://arxiv.org/pdf/2603.20279.pdf GitHub

Abstract:
Reinforcement learning techniques are being explored as solutions to the threat of cyber attacks on enterprise networks. Recent research in the field of AI in cyber security has investigated the ability of homogeneous multi‑agent reinforcement learning agents, capable of inter‑agent communication, to respond to cyberattacks. This paper advances the study of learned communication in multi‑agent systems by examining heterogeneous agent capabilities within a simulated network environment. To this end, we leverage CommFormer, a publicly available state‑of‑the‑art communication algorithm, to train and evaluate agents within the Cyber Operations Research Gym (CybORG). Our results show that CommFormer agents with heterogeneous capabilities can outperform other algorithms deployed in the CybORG environment, by converging to an optimal policy up to four times faster while improving standard error by up 38%. The agents implemented in this project provide an additional avenue for exploration in the field of AI for cyber security, enabling further research involving realistic networks.

PaperId: 2298, https://arxiv.org/pdf/2603.20278.pdf GitHub

Abstract:
Training deep research agents requires long‑horizon trajectories that interleave search, evidence aggregation, and multi‑step reasoning. However, existing data collection pipelines typically rely on proprietary web APIs, making large‑scale trajectory synthesis costly, unstable, and difficult to reproduce. We present OpenResearcher, a reproducible pipeline that decouples one‑time corpus bootstrapping from multi‑turn trajectory synthesis and executes the search‑and‑browse loop entirely offline using three explicit browser primitives: search, open, and find, over a 15M‑document corpus. Using GPT‑OSS‑120B as the teacher model, we synthesize over 97K trajectories, including a substantial long‑horizon tail with 100+ tool calls. Supervised fine‑tuning a 30B‑A3B backbone on these trajectories achieves 54.8% accuracy on BrowseComp‑Plus, a +34.0 point improvement over the base model, while remaining competitive on BrowseComp, GAIA, and xbench‑DeepSearch. Because the environment is offline and fully instrumented, it also enables controlled analysis, where our study reveals practical insights into deep research pipeline design, including data filtering strategies, agent configuration choices, and how retrieval success relates to final answer accuracy. We release the pipeline, synthesized trajectories, model checkpoints, and the offline search environment at https://github.com/TIGER‑AI‑Lab/OpenResearcher.

PaperId: 2299, https://arxiv.org/pdf/2603.20213.pdf GitHub

Abstract:
Generative search engines represent a transition from traditional ranking‑based retrieval to Large Language Model (LLM)‑based synthesis, transforming optimization goals from ranking prominence towards content inclusion. Generative Engine Optimization (GEO), specifically, aims to maximize visibility and attribution in black‑box summarized outputs by strategically manipulating source content. However, existing methods rely on static heuristics, single‑prompt optimization, or engine preference rule distillation that is prone to overfitting. They cannot flexibly adapt to diverse content or the changing behaviors of generative engines. Moreover, effectively optimizing these strategies requires an impractical amount of interaction feedback from the engines. To address these challenges, we propose AgenticGEO, a self‑evolving agentic framework formulating optimization as a content‑conditioned control problem, which enhances intrinsic content quality to robustly adapt to the unpredictable behaviors of black‑box engines. Unlike fixed‑strategy methods, AgenticGEO employs a MAP‑Elites archive to evolve diverse, compositional strategies. To mitigate interaction costs, we introduce a Co‑Evolving Critic, a lightweight surrogate that approximates engine feedback for content‑specific strategy selection and refinement, efficiently guiding both evolutionary search and inference‑time planning. Through extensive in‑domain and cross‑domain experiments on two representative engines, AgenticGEO achieves state‑of‑the‑art performance and demonstrates robust transferability, outperforming 14 baselines across 3 datasets. Our code and model are available at: https://github.com/AIcling/agentic_geo.

PaperId: 2300, https://arxiv.org/pdf/2603.20209.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) combine the linguistic strengths of LLMs with the ability to process multimodal data, enbaling them to address a broader range of visual tasks. Because MLLMs aim at more general, human‑like competence than language‑only models, we take inspiration from the Wechsler Intelligence Scales ‑ an established battery for evaluating children by decomposing intelligence into interpretable, testable abilities. We introduce KidGym, a comprehensive 2D grid‑based benchmark for assessing five essential capabilities of MLLMs: Execution, Perception Reasoning, Learning, Memory and Planning. The benchmark comprises 12 unique tasks, each targeting at least one core capability, specifically designed to guage MLLMs' adaptability and developmental potential, mirroring the stages of children's cognitive growth. Additionally, our tasks encompass diverse scenarios and objects with randomly generated layouts, ensuring a more accurate and robust evluation of MLLM capabilities. KidGym is designed to be fully user‑customizable and extensible, allowing researchers to create new evaluation scenarios and adjust difficuly levels to accommodate the rapidly growing MLLM community. Through the evaluation of state‑of‑the‑art MLLMs using KidGym, we identified significant insights into model capabilities and revealed several limitations of current models. We release our benchmark at: https://bobo‑ye.github.io/KidGym/.

PaperId: 2301, https://arxiv.org/pdf/2603.20208.pdf GitHub

Abstract:
Modern language models can readily extract sensitive information from unstructured text, making redaction ‑‑ the selective removal of such information ‑‑ critical for data security. However, existing benchmarks for redaction typically focus on predefined categories of data such as personally identifiable information (PII) or evaluate specific techniques like masking. To address this limitation, we introduce RedacBench, a comprehensive benchmark for evaluating policy‑conditioned redaction across domains and strategies. Constructed from 514 human‑authored texts spanning individual, corporate, and government sources, paired with 187 security policies, RedacBench measures a model's ability to selectively remove policy‑violating information while preserving the original semantics. We quantify performance using 8,053 annotated propositions that capture all inferable information in each text. This enables assessment of both security ‑‑ the removal of sensitive propositions ‑‑ and utility ‑‑ the preservation of non‑sensitive propositions. Experiments across multiple redaction strategies and state‑of‑the‑art language models show that while more advanced models can improve security, preserving utility remains a challenge. To facilitate future research, we release RedacBench along with a web‑based playground for dataset customization and evaluation. Available at https://hyunjunian.github.io/redaction‑playground/.

PaperId: 2302, https://arxiv.org/pdf/2603.20193.pdf GitHub GitHub

Abstract:
Existing tampering detection benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are untouched or only trivially modified, while subtle yet consequential edits outside the mask are treated as natural. We reformulate VLM image tampering from coarse region labels to a pixel‑grounded, meaning and language‑aware task. First, we introduce a taxonomy spanning edit primitives (replace/remove/splice/inpaint/attribute/colorization, etc.) and their semantic class of tampered object, linking low‑level changes to high‑level understanding. Second, we release a new benchmark with per‑pixel tamper maps and paired category supervision to evaluate detection and classification within a unified protocol. Third, we propose a training framework and evaluation metrics that quantify pixel‑level correctness with localization to assess confidence or prediction on true edit intensity, and further measure tamper meaning understanding via semantics‑aware classification and natural language descriptions for the predicted regions. We also re‑evaluate the existing strong segmentation/localization baselines on recent strong tamper detectors and reveal substantial over‑ and under‑scoring using mask‑only metrics, and expose failure modes on micro‑edits and off‑mask changes. Our framework advances the field from masks to pixels, meanings and language descriptions, establishing a rigorous standard for tamper localization, semantic classification and description. Code and benchmark data are available at https://github.com/VILA‑Lab/PIXAR.

PaperId: 2303, https://arxiv.org/pdf/2603.20192.pdf GitHub GitHub

Abstract:
Recent advances in diffusion models have significantly improved text‑to‑video generation, enabling personalized content creation with fine‑grained control over both foreground and background elements. However, precise face‑attribute alignment across subjects remains challenging, as existing methods lack explicit mechanisms to ensure intra‑group consistency. Addressing this gap requires both explicit modeling strategies and face‑attribute‑aware data resources. We therefore propose LumosX, a framework that advances both data and model design. On the data side, a tailored collection pipeline orchestrates captions and visual cues from independent videos, while multimodal large language models (MLLMs) infer and assign subject‑specific dependencies. These extracted relational priors impose a finer‑grained structure that amplifies the expressive control of personalized video generation and enables the construction of a comprehensive benchmark. On the modeling side, Relational Self‑Attention and Relational Cross‑Attention intertwine position‑aware embeddings with refined attention dynamics to inscribe explicit subject‑attribute dependencies, enforcing disciplined intra‑group cohesion and amplifying the separation between distinct subject clusters. Comprehensive evaluations on our benchmark demonstrate that LumosX achieves state‑of‑the‑art performance in fine‑grained, identity‑consistent, and semantically aligned personalized multi‑subject video generation. Code and models are available at https://jiazheng‑xing.github.io/lumosx‑home/.

PaperId: 2304, https://arxiv.org/pdf/2603.20164.pdf GitHub

Abstract:
Conventional robot social behavior generation has been limited in flexibility and autonomy, relying on predefined motions or human feedback. This study proposes CRISP (Critique‑and‑Replan for Interactive Social Presence), an autonomous framework where a robot critiques and replans its own actions by leveraging a Vision‑Language Model (VLM) as a `human‑like social critic.' CRISP integrates (1) extraction of movable joints and constraints by analyzing the robot's description file (e.g., MJCF), (2) generation of step‑by‑step behavior plans based on situational context, (3) generation of low‑level joint control code by referencing visual information (joint range‑of‑motion visualizations), (4) VLM‑based evaluation of social appropriateness and naturalness, including pinpointing erroneous steps, and (5) iterative refinement of behaviors through reward‑based search. This approach is not tied to a specific robot API; it can generate subtly different, human‑like motions on various platforms using only the robot's structure file. In a user study involving five different robot types and 20 scenarios, including mobile manipulators and humanoids, our proposed method achieved significantly higher preference and situational appropriateness ratings compared to previous methods. This research presents a general framework that minimizes human intervention while expanding the robot's autonomous interaction capabilities and cross‑platform applicability. Detailed result videos and supplementary information regarding this work are available at: https://limjiyu99.github.io/inner‑critic/

PaperId: 2305, https://arxiv.org/pdf/2603.20105.pdf GitHub

Abstract:
LLMs are increasingly used as general‑purpose reasoners, but long inputs remain bottlenecked by a fixed context window. Recursive Language Models (RLMs) address this by externalising the prompt and recursively solving subproblems. Yet existing RLMs depend on an open‑ended read‑eval‑print loop (REPL) in which the model generates arbitrary control code, making execution difficult to verify, predict, and analyse. We introduce λ‑RLM, a framework for long‑context reasoning that replaces free‑form recursive code generation with a typed functional runtime grounded in λ‑calculus. It executes a compact library of pre‑verified combinators and uses neural inference only on bounded leaf subproblems, turning recursive reasoning into a structured functional program with explicit control flow. We show that λ‑RLM admits formal guarantees absent from standard RLMs, including termination, closed‑form cost bounds, controlled accuracy scaling with recursion depth, and an optimal partition rule under a simple cost model. Empirically, across four long‑context reasoning tasks and nine base models, λ‑RLM outperforms standard RLM in 29 of 36 model‑task comparisons, improves average accuracy by up to +21.9 points across model tiers, and reduces latency by up to 4.1x. These results show that typed symbolic control yields a more reliable and efficient foundation for long‑context reasoning than open‑ended recursive code generation. The complete implementation of λ‑RLM, is open‑sourced for the community at: https://github.com/lambda‑calculus‑LLM/lambda‑RLM.

PaperId: 2306, https://arxiv.org/pdf/2603.20075.pdf GitHub

Abstract:
Compilers are critical to modern computing, yet fixing compiler bugs is difficult. While recent large language model (LLM) advancements enable automated bug repair, compiler bugs pose unique challenges due to their complexity, deep cross‑domain expertise requirements, and sparse, non‑descriptive bug reports, necessitating compiler‑specific tools. To bridge the gap, we introduce llvm‑autofix, the first agentic harness designed to assist LLM agents in understanding and fixing compiler bugs. Our focus is on LLVM, one of the most widely used compiler infrastructures. Central to llvm‑autofix are agent‑friendly LLVM tools, a benchmark llvm‑bench of reproducible LLVM bugs, and a tailored minimal agent llvm‑autofix‑mini for fixing LLVM bugs. Our evaluation demonstrates a performance decline of 60% in frontier models when tackling compiler bugs compared with common software bugs. Our minimal agent llvm‑autofix‑mini also outperforms the state‑of‑the‑art by approximately 22%. This emphasizes the necessity for specialized harnesses like ours to close the loop between LLMs and compiler engineering. We believe this work establishes a foundation for advancing LLM capabilities in complex systems like compilers. GitHub: https://github.com/dtcxzyw/llvm‑autofix

PaperId: 2307, https://arxiv.org/pdf/2603.20046.pdf GitHub

Abstract:
Reinforcement Learning (RL) with rubric‑based rewards has recently shown remarkable progress in enhancing general reasoning capabilities of Large Language Models (LLMs), yet still suffers from ineffective exploration confined to curent policy distribution. In fact, RL optimization can be viewed as steering the policy toward an ideal distribution that maximizes the rewards, while effective exploration should align efforts with desired target. Leveraging this insight, we propose HeRL, a Hindsight experience guided Reinforcement Learning framework to bootstrap effective exploration by explicitly telling LLMs the desired behaviors specified in rewards. Concretely, HeRL treats failed trajectories along with their unmet rubrics as hindsight experience, which serves as in‑context guidance for the policy to explore desired responses beyond its current distribution. Additionally, we introduce a bonus reward to incentivize responses with greater potential for improvement under such guidance. HeRL facilitates effective learning from desired high quality samples without repeated trial‑and‑error from scratch, yielding a more accurate estimation of the expected gradient theoretically. Extensive experiments across various benchmarks demonstrate that HeRL achieves superior performance gains over baselines, and can further benefit from experience guided self‑improvement at test time. Our code is available at https://github.com/sikelifei/HeRL.

PaperId: 2308, https://arxiv.org/pdf/2603.19918.pdf GitHub

Abstract:
Generalized Category Discovery (GCD) seeks to uncover novel categories in unlabeled data while preserving recognition of known categories, yet prevailing visual‑only pipelines and the loose coupling between supervised learning and discovery often yield brittle boundaries on fine‑grained, look‑alike categories. We introduce the Analogical Textual Concept Generator (ATCG), a plug‑and‑play module that analogizes from labeled knowledge to new observations, forming textual concepts for unlabeled samples. Fusing these analogical textual concepts with visual features turns discovery into a visual‑textual reasoning process, transferring prior knowledge to novel data and sharpening category separation. ATCG attaches to both parametric and clustering style GCD pipelines and requires no changes to their overall design. Across six benchmarks, ATCG consistently improves overall, known‑class, and novel‑class performance, with the largest gains on fine‑grained data. Our code is available at: https://github.com/zhou‑9527/AnaLogical‑GCD.

PaperId: 2309, https://arxiv.org/pdf/2603.19880.pdf GitHub

Abstract:
Test‑Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving pseudo‑rewards from majority voting consensus. However, existing TTRL methods rely exclusively on positive pseudo‑labeling strategies. Such reliance becomes vulnerable under challenging scenarios where answer distributions are highly dispersed, resulting in weak consensus that inadvertently reinforces incorrect trajectories as supervision signals. In this paper, we propose SCRL (Selective‑Complementary Reinforcement Learning), a robust test‑time reinforcement learning framework that effectively mitigates label noise amplification. SCRL develops Selective Positive Pseudo‑Labeling, which enforces strict consensus criteria to filter unreliable majorities. Complementarily, SCRL introduces Entropy‑Gated Negative Pseudo‑Labeling, the first negative supervision mechanism in TTRL, to reliably prune incorrect trajectories based on generation uncertainty. Extensive experiments on multiple reasoning benchmarks demonstrate that SCRL achieves substantial improvements over baselines, while maintaining robust generalization and training stability under constrained rollout budgets. Our code is available at https://github.com/Jasper‑Yan/SCRL.

PaperId: 2310, https://arxiv.org/pdf/2603.19788.pdf GitHub

Abstract:
Generalized few‑shot 3D point cloud segmentation aims to adapt to novel classes from only a few annotations while maintaining strong performance on base classes, but this remains challenging due to the inherent stability‑plasticity trade‑off: adapting to novel classes can interfere with shared representations and cause base‑class forgetting. We present HOP3D, a unified framework that learns hierarchical orthogonal prototypes with an entropy‑based few‑shot regularizer to enable robust novel‑class adaptation without degrading base‑class performance. HOP3D introduces hierarchical orthogonalization that decouples base and novel learning at both the gradient and representation levels, effectively mitigating base‑novel interference. To further enhance adaptation under sparse supervision, we incorporate an entropy‑based regularizer that leverages predictive uncertainty to refine prototype learning and promote balanced predictions. Extensive experiments on ScanNet200 and ScanNet++ demonstrate that HOP3D consistently outperforms state‑of‑the‑art baselines under both 1‑shot and 5‑shot settings. The code is available at https://fdueblab‑hop3d.github.io/.

PaperId: 2311, https://arxiv.org/pdf/2603.19757.pdf GitHub

Abstract:
Few‑shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with only a few annotated support examples. Existing prototype‑based methods typically construct compact and deterministic prototypes from the support set to guide query segmentation. However, such rigid representations are unable to capture the intrinsic uncertainty introduced by scarce supervision, which often results in degraded robustness and limited generalization. In this work, we propose UPL (Uncertainty‑aware Prototype Learning), a probabilistic approach designed to incorporate uncertainty modeling into prototype learning for few‑shot 3D segmentation. Our framework introduces two key components. First, UPL introduces a dual‑stream prototype refinement module that enriches prototype representations by jointly leveraging limited information from both support and query samples. Second, we formulate prototype learning as a variational inference problem, regarding class prototypes as latent variables. This probabilistic formulation enables explicit uncertainty modeling, providing robust and interpretable mask predictions. Extensive experiments on the widely used ScanNet and S3DIS benchmarks show that our UPL achieves consistent state‑of‑the‑art performance under different settings while providing reliable uncertainty estimation. The code is available at https://fdueblab‑upl.github.io/.

PaperId: 2312, https://arxiv.org/pdf/2603.19715.pdf GitHub

Abstract:
Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet constructing large proof scripts remains highly manual and limits scalability. Advances in large language models (LLMs), especially in mathematical reasoning, make their integration into software verification increasingly promising. This paper introduces a neuro‑symbolic proof generation framework designed to automate proof search for system‑level verification projects. The framework performs a best‑first tree search over proof states, repeatedly querying an LLM for the next candidate proof step. On the neural side, we fine‑tune LLMs using datasets of proof state‑step pairs; on the symbolic side, we incorporate a range of ITP tools to repair rejected steps, filter and rank proof states, and automatically discharge subgoals when search progress stalls. This synergy enables data‑efficient LLM adaptation and semantics‑informed pruning of the search space. We implement the framework on a new Isabelle REPL that exposes fine‑grained proof states and automation tools, and evaluate it on the FVEL seL4 benchmark and additional Isabelle developments. On seL4, the system proves up to 77.6% of the theorems, substantially surpassing previous LLM‑based approaches and standalone Sledgehammer, while solving significantly more multi‑step proofs. Results across further Isabelle benchmarks demonstrate strong generalization, indicating a viable path toward scalable automated software verification.

PaperId: 2313, https://arxiv.org/pdf/2603.19664.pdf GitHub

Abstract:
The key‑value (KV) cache is widely treated as essential state in transformer inference, and a large body of work engineers policies to compress, evict, or approximate its entries. We prove that this state is entirely redundant: keys and values at every layer are deterministic projections of the residual stream, and recomputing them from a single residual vector per token incurs exactly zero reconstruction error, not approximately, but bit‑identically. We verify this across six models from four architecture families (135M to 4B parameters). Cross‑task residual patching at every layer produces D_KL = 0 between patched and original output distributions, confirming that the residual stream satisfies a Markov property and is the sole information‑carrying state. Removing the cache entirely and recomputing from scratch yields token‑identical output under greedy decoding on all models tested. We build on this result with KV‑Direct, a bounded‑memory inference scheme that checkpoints residual vectors (5 KB per token on Gemma 3‑4B) instead of full KV pairs (136 KB), recomputing keys and values on demand. Over 20 conversation turns, KV‑Direct holds peak memory at 42 MB while the standard cache grows past 103 MB. Against five eviction baselines (H2O, StreamingLLM, SnapKV, TOVA, window‑only), KV‑Direct maintains 100% token match at every cache budget; all baselines degrade to 5‑28%. A per‑operation latency analysis shows recomputation runs up to 5x faster than reading cached tensors at moderate batch sizes. Code is available at https://github.com/Kaleemullahqasim/KV‑Direct.

PaperId: 2314, https://arxiv.org/pdf/2603.19628.pdf GitHub

Abstract:
Robust feature encoding constitutes the foundation of UAV tracking by enabling the nuanced perception of target appearance and motion, thereby playing a pivotal role in ensuring reliable tracking. However, existing feature encoding methods often overlook critical illumination and viewpoint cues, which are essential for robust perception under challenging nighttime conditions, leading to degraded tracking performance. To overcome the above limitation, this work proposes a dual prompt‑driven feature encoding method that integrates prompt‑conditioned feature adaptation and context‑aware prompt evolution to promote domain‑invariant feature encoding. Specifically, the pyramid illumination prompter is proposed to extract multi‑scale frequency‑aware illumination prompts. %The dynamic viewpoint prompter adapts the sampling to different viewpoints, enabling the tracker to learn view‑invariant features. The dynamic viewpoint prompter modulates deformable convolution offsets to accommodate viewpoint variations, enabling the tracker to learn view‑invariant features. Extensive experiments validate the effectiveness of the proposed dual prompt‑driven tracker (DPTracker) in tackling nighttime UAV tracking. Ablation studies highlight the contribution of each component in DPTracker. Real‑world tests under diverse nighttime UAV tracking scenarios further demonstrate the robustness and practical utility. The code and demo videos are available at https://github.com/yiheng‑wang‑duke/DPTracker.

PaperId: 2315, https://arxiv.org/pdf/2603.19615.pdf GitHub

Abstract:
While Large Audio‑Language Models (LALMs) have advanced audio captioning, robust evaluation remains difficult. Reference‑based metrics are expensive and often fail to assess acoustic fidelity, while Contrastive Language‑Audio Pretraining (CLAP)‑based approaches frequently overlook syntactic errors and fine‑grained details. We propose CAF‑Score, a reference‑free metric that calibrates CLAP's coarse‑grained semantic alignment with the fine‑grained comprehension and syntactic awareness of LALMs. By combining contrastive audio‑text embeddings with LALM reasoning, CAF‑Score effectively detects syntactic inconsistencies and subtle hallucinations. Experiments on the BRACE benchmark demonstrate that our approach achieves the highest correlation with human judgments, even outperforming reference‑based baselines in challenging scenarios. These results highlight the efficacy of CAF‑Score for reference‑free audio captioning evaluation. Code and results are available at https://github.com/inseong00/CAF‑Score.

PaperId: 2316, https://arxiv.org/pdf/2603.19609.pdf GitHub

Abstract:
We present LoD‑Loc v3, a novel method for generalized aerial visual localization in dense urban environments. While prior work LoD‑Loc v2 achieves localization through semantic building silhouette alignment with low‑detail city models, it suffers from two key limitations: poor cross‑scene generalization and frequent failure in dense building scenes. Our method addresses these challenges through two key innovations. First, we develop a new synthetic data generation pipeline that produces InsLoD‑Loc ‑ the largest instance segmentation dataset for aerial imagery to date, comprising 100k images with precise instance building annotations. This enables trained models to exhibit remarkable zero‑shot generalization capability. Second, we reformulate the localization paradigm by shifting from semantic to instance silhouette alignment, which significantly reduces pose estimation ambiguity in dense scenes. Extensive experiments demonstrate that LoD‑Loc v3 outperforms existing state‑of‑the‑art (SOTA) baselines, achieving superior performance in both cross‑scene and dense urban scenarios with a large margin. The project is available at https://nudt‑sawlab.github.io/LoD‑Locv3/.

PaperId: 2317, https://arxiv.org/pdf/2603.19565.pdf GitHub

Abstract:
Event‑based pedestrian attribute recognition (PAR) leverages motion cues to enhance RGB cameras in low‑light and motion‑blur scenarios, enabling more accurate inference of attributes like age and emotion. However, existing two‑stream multimodal fusion methods introduce significant computational overhead and neglect the valuable guidance from contextual samples. To address these limitations, this paper proposes an Event Prompter. Discarding the computationally expensive auxiliary backbone, this module directly applies extremely lightweight and efficient Discrete Cosine Transform (DCT) and Inverse DCT (IDCT) operations to the event data. This design extracts frequency‑domain event features at a minimal computational cost, thereby effectively augmenting the RGB branch. Furthermore, an external memory bank designed to provide rich prior knowledge, combined with modern Hopfield networks, enables associative memory‑augmented representation learning. This mechanism effectively mines and leverages global relational knowledge across different samples. Finally, a cross‑attention mechanism fuses the RGB and event modalities, followed by feed‑forward networks for attribute prediction. Extensive experiments on multiple benchmark datasets fully validate the effectiveness of the proposed RGB‑Event PAR framework. The source code of this paper will be released on https://github.com/Event‑AHU/OpenPAR

PaperId: 2318, https://arxiv.org/pdf/2603.19563.pdf GitHub

Abstract:
Modern computer vision requires balancing predictive accuracy with real‑time efficiency, yet the high inference cost of large vision models (LVMs) limits deployment on resource‑constrained edge devices. Although Evolutionary Neural Architecture Search (ENAS) is well suited for multi‑objective optimization, its practical use is hindered by two issues: expensive candidate evaluation and ranking inconsistency among subnetworks. To address them, we propose EvoNAS, an efficient distributed framework for multi‑objective evolutionary architecture search. We build a hybrid supernet that integrates Vision State Space and Vision Transformer (VSS‑ViT) modules, and optimize it with a Cross‑Architecture Dual‑Domain Knowledge Distillation (CA‑DDKD) strategy. By coupling the computational efficiency of VSS blocks with the semantic expressiveness of ViT modules, CA‑DDKD improves the representational capacity of the shared supernet and enhances ranking consistency, enabling reliable fitness estimation during evolution without extra fine‑tuning. To reduce the cost of large‑scale validation, we further introduce a Distributed Multi‑Model Parallel Evaluation (DMMPE) framework based on GPU resource pooling and asynchronous scheduling. Compared with conventional data‑parallel evaluation, DMMPE improves efficiency by over 70% through concurrent multi‑GPU, multi‑model execution. Experiments on COCO, ADE20K, KITTI, and NYU‑Depth v2 show that the searched architectures, termed EvoNets, consistently achieve Pareto‑optimal trade‑offs between accuracy and efficiency. Compared with representative CNN‑, ViT‑, and Mamba‑based models, EvoNets deliver lower inference latency and higher throughput under strict computational budgets while maintaining strong generalization on downstream tasks such as novel view synthesis. Code is available at https://github.com/EMI‑Group/evonas

PaperId: 2319, https://arxiv.org/pdf/2603.19515.pdf GitHub

Abstract:
Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific reasoning or planning questions within controlled environments. Recent studies have explored travel planning as a medium to integrate various verbal reasoning tasks into real‑world contexts. However, reasoning tasks extend beyond verbal reasoning alone, and a comprehensive evaluation of LLMs requires a testbed that incorporates tasks from multiple cognitive domains. To address this gap, we introduce ItinBench, a benchmark that features one task of spatial reasoning, i.e., route optimization, into trip itinerary planning while keeping the traditional verbal reasoning tasks. ItinBench evaluates various LLMs across diverse tasks simultaneously, including Llama 3.1 8B, Mistral Large, Gemini 1.5 Pro, and GPT family. Our findings reveal that LLMs struggle to maintain high and consistent performance when concurrently handling multiple cognitive dimensions. By incorporating tasks from distinct human‑level cognitive domains, ItinBench provides new insights into building more comprehensive reasoning testbeds that better reflect real‑world challenges. The code and dataset: https://ethanwtl.github.io/IBweb/

PaperId: 2320, https://arxiv.org/pdf/2603.19474.pdf GitHub

Abstract:
High‑quality GPS trajectories are essential for location‑based web services and smart city applications, including navigation, ride‑sharing and delivery. However, due to low sampling rates and limited infrastructure coverage during data collection, real‑world trajectories are often sparse and feature unevenly distributed location points. Recovering these trajectories into dense and continuous forms is essential but challenging, given their complex and irregular spatio‑temporal patterns. In this paper, we introduce a novel diffusion model for trajectory recovery named TRACE, which reconstruct dense and continuous trajectories from sparse and incomplete inputs. At the core of TRACE, we propose a State Propagation Diffusion Model (SPDM), which integrates a novel memory mechanism, so that during the denoising process, TRACE can retain and leverage intermediate results from previous steps to effectively reconstruct those hard‑to‑recover trajectory segments. Extensive experiments on multiple real‑world datasets show that TRACE outperforms the state‑of‑the‑art, offering >26% accuracy improvement without significant inference overhead. Our work strengthens the foundation for mobile and web‑connected location services, advancing the quality and fairness of data‑driven urban applications. Code is available at: https://github.com/JinmingWang/TRACE

PaperId: 2321, https://arxiv.org/pdf/2603.19461.pdf GitHub

Abstract:
Self‑improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem‑solving processes. Existing approaches to self‑improvement rely on fixed, handcrafted meta‑level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin Gödel Machine (DGM) demonstrates open‑ended self‑improvement in coding by repeatedly generating and evaluating self‑modified variants. Because both evaluation and self‑modification are coding tasks, gains in coding ability can translate into gains in self‑improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce hyperagents, self‑referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta‑level modification procedure is itself editable, enabling metacognitive self‑modification, improving not only the task‑solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM‑Hyperagents (DGM‑H), eliminating the assumption of domain‑specific alignment between task performance and self‑modification skill to potentially support self‑accelerating progress on any computable task. Across diverse domains, the DGM‑H improves performance over time and outperforms baselines without self‑improvement or open‑ended exploration, as well as prior self‑improving systems. Furthermore, the DGM‑H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta‑level improvements transfer across domains and accumulate across runs. DGM‑Hyperagents offer a glimpse of open‑ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.

PaperId: 2322, https://arxiv.org/pdf/2603.19339.pdf GitHub

Abstract:
Dimensionality reduction is critical for deploying dense retrieval systems at scale, yet mainstream post‑hoc methods face a fundamental trade‑off: principal component analysis (PCA) preserves dominant variance but underutilizes representational capacity, while whitening enforces isotropy at the cost of amplifying noise in the heavy‑tailed eigenspectrum of retrieval embeddings. Intermediate spectral scaling methods unify these extremes by reweighting dimensions with a power coefficient γ, but treat γ as a fixed hyperparameter that requires task‑specific tuning. We show that the optimal scaling strength γ is not a global constant: it varies systematically with target dimensionality k and is governed by the signal‑to‑noise ratio (SNR) of the retained subspace. Based on this insight, we propose Spectral Tempering (SpecTemp), a learning‑free method that derives an adaptive γ(k) directly from the corpus eigenspectrum using local SNR analysis and knee‑point normalization, requiring no labeled data or validation‑based search. Extensive experiments demonstrate that Spectral Tempering consistently achieves near‑oracle performance relative to grid‑searched γ^(k) while remaining fully learning‑free and model‑agnostic. Our code is publicly available at https://github.com/liyongkang123/SpecTemp.

PaperId: 2323, https://arxiv.org/pdf/2603.19308.pdf GitHub

Abstract:
In autonomous driving, multi‑agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key challenge lies in handling \em heterogeneous features from agents equipped with different sensing modalities or model architectures, which complicates data fusion. Existing approaches often require retraining encoders or designing interpreter modules for pairwise feature alignment, but these solutions are not scalable in practice. To address this, we propose \em GT‑Space, a flexible and scalable collaborative perception framework for heterogeneous agents. GT‑Space constructs a common feature space from ground‑truth labels, providing a unified reference for feature alignment. With this shared space, agents only need a single adapter module to project their features, eliminating the need for pairwise interactions with other agents. Furthermore, we design a fusion network trained with contrastive losses across diverse modality combinations. Extensive experiments on simulation datasets (OPV2V and V2XSet) and a real‑world dataset (RCooper) demonstrate that GT‑Space consistently outperforms baselines in detection accuracy while delivering robust performance. Our code will be released at https://github.com/KingScar/GT‑Space.

PaperId: 2324, https://arxiv.org/pdf/2603.19293.pdf GitHub

Abstract:
Multimodal fake news detection is crucial for mitigating societal disinformation. Existing approaches attempt to address this by fusing multimodal features or leveraging Large Language Models (LLMs) for advanced reasoning. However, these methods suffer from serious limitations, including a lack of comprehensive multi‑view judgment and fusion, and prohibitive reasoning inefficiency due to the high computational costs of LLMs. To address these issues, we propose LLM‑Guided Multi‑View Reasoning Distillation for Fake News Detection ( LLM‑MRD), a novel teacher‑student framework. The Student Multi‑view Reasoning module first constructs a comprehensive foundation from textual, visual, and cross‑modal perspectives. Then, the Teacher Multi‑view Reasoning module generates deep reasoning chains as rich supervision signals. Our core Calibration Distillation mechanism efficiently distills this complex reasoning‑derived knowledge into the efficient student model. Experiments show LLM‑MRD significantly outperforms state‑of‑the‑art baselines. Notably, it demonstrates a comprehensive average improvement of 5.19% in ACC and 6.33% in F1‑Fake when evaluated across all competing methods and datasets. Our code is available at https://github.com/Nasuro55/LLM‑MRD

PaperId: 2325, https://arxiv.org/pdf/2603.19289.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per‑token compute. However, in memory‑constrained inference settings, expert weights must be offloaded to CPU, creating a performance bottleneck from CPU‑GPU transfers during decoding. We propose an expert prefetching scheme that leverages currently computed internal model representations to speculate future experts, enabling memory transfers to overlap with computation. Across multiple MoE architectures, we demonstrate that future experts can be reliably predicted by these internal representations. We also demonstrate that executing speculated experts generally maintains downstream task accuracy, thus preserving more effective compute‑memory overlap by eliminating the need to re‑fetch true router‑selected experts. Integrated into an optimized inference engine, our approach achieves up to 14% reduction in time per output token (TPOT) over on‑demand loading of experts from CPU memory. For MoEs where speculative execution alone yields suboptimal accuracy, we further examine lightweight estimators that improve expert prediction hit rates, thereby reducing performance degradation. Our code is released in open‑source at https://github.com/axonn‑ai/yalis/tree/offload_prefetch.

PaperId: 2326, https://arxiv.org/pdf/2603.19278.pdf GitHub

Abstract:
Modern Transformer‑based models frequently suffer from miscalibration, producing overconfident predictions that do not reflect true empirical frequencies. This work investigates the calibration dynamics of LoRA: Low‑Rank Adaptation and a novel hyper‑network‑based adaptation framework as parameter‑efficient alternatives to full fine‑tuning for RoBERTa. Evaluating across the GLUE benchmark, we demonstrate that LoRA‑based adaptation consistently achieves calibration parity with (and in specific tasks exceeds) full fine‑tuning, while maintaining significantly higher parameter efficiency. We further explore a dynamic approach where a shared hyper‑network generates LoRA factors (A and B matrices) to induce structural coupling across layers. This approach produced results similar to standard LoRA fine‑tuning, even achieving better MCC on CoLA dataset. Our study also reveal a critical trade‑off: constraining the adaptation space (e.g., freezing matrices A) acts as a powerful regularizer that enhances Expected Calibration Error (ECE), but necessitates a carefully balanced sacrifice in downstream task accuracy. To support future research, we provide a unified and reproducible implementation of contemporary calibration metrics, including ECE, MCE, and ACE. Our findings clarify the relationship between parameter efficiency and probabilistic reliability, positioning structured low‑rank updates as a viable foundation for uncertainty‑aware Transformer architectures. Code available at: https://github.com/btrojan‑official/HypeLoRA

PaperId: 2327, https://arxiv.org/pdf/2603.19274.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) demonstrate considerable potential in clinical diagnostics, a domain that inherently requires synthesizing complex visual and textual data alongside consulting authoritative medical literature. However, existing benchmarks primarily evaluate MLLMs in end‑to‑end answering scenarios. This limits the ability to disentangle a model's foundational multimodal reasoning from its proficiency in evidence retrieval and application. We introduce the Clinical Understanding and Retrieval Evaluation (CURE) benchmark. Comprising 500 multimodal clinical cases mapped to physician‑cited reference literature, CURE evaluates reasoning and retrieval under controlled evidence settings to disentangle their respective contributions. We evaluate state‑of‑the‑art MLLMs across distinct evidence‑gathering paradigms in both closed‑ended and open‑ended diagnosis tasks. Evaluations reveal a stark dichotomy: while advanced models demonstrate clinical reasoning proficiency when supplied with physician reference evidence (achieving up to 73.4% accuracy on differential diagnosis), their performance substantially declines (as low as 25.4%) when reliant on independent retrieval mechanisms. This disparity highlights the dual challenges of effectively integrating multimodal clinical evidence and retrieving precise supporting literature. CURE is publicly available at https://github.com/yanniangu/CURE.

PaperId: 2328, https://arxiv.org/pdf/2603.19266.pdf GitHub

Abstract:
Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent advances, distilled models frequently suffer from superficial pattern memorization and subpar generalization. To overcome these limitations, we introduce a novel distillation framework that moves beyond simple mimicry to instill a deeper conceptual understanding. Our framework features two key innovations. \underlineFirst, to address pattern memorization, Explanatory Inversion (EI) generates targeted ``explanatory probes'' that compel the student to articulate the underlying logic behind an answer, rather than just memorizing it. \underlineSecond, to improve generalization, Explanatory GRPO (\textttEXGRPO) uses a reinforcement learning algorithm with a novel Dialogue Structure Utility Bonus, which explicitly rewards the student for maintaining a coherent reasoning process across these probes. Extensive evaluations on 12 datasets demonstrate significant improvements. Using Gemma‑7b as the student model, our method yields an average 20.39% increase over zero‑shot performance and a 6.02% improvement over the state‑of‑the‑art distillation baselines. Moreover, models distilled with our method show remarkable training efficiency (e.g., surpassing vanilla fine‑tuning with 10‑25% training data) and strong generalization to out‑of‑distribution tasks. Implementation is released at https://github.com/Zhen‑Tan‑dmml/ExGRPO.git.

PaperId: 2329, https://arxiv.org/pdf/2603.19166.pdf GitHub

Abstract:
Robots collaborating with humans must convert natural language goals into actionable, physically grounded decisions. For example, executing a command such as "go two meters to the right of the fridge" requires grounding semantic references, spatial relations, and metric constraints within a 3D scene. While recent vision language models (VLMs) demonstrate strong semantic grounding capabilities, they are not explicitly designed to reason about metric constraints in physically defined spaces. In this work, we empirically demonstrate that state‑of‑the‑art VLM‑based grounding approaches struggle with complex metric‑semantic language queries. To address this limitation, we propose MAPG (Multi‑Agent Probabilistic Grounding), an agentic framework that decomposes language queries into structured subcomponents and queries a VLM to ground each component. MAPG then probabilistically composes these grounded outputs to produce metrically consistent, actionable decisions in 3D space. We evaluate MAPG on the HM‑EQA benchmark and show consistent performance improvements over strong baselines. Furthermore, we introduce a new benchmark, MAPG‑Bench, specifically designed to evaluate metric‑semantic goal grounding, addressing a gap in existing language grounding evaluations. We also present a real‑world robot demonstration showing that MAPG transfers beyond simulation when a structured scene representation is available.

PaperId: 2330, https://arxiv.org/pdf/2603.19163.pdf GitHub

Abstract:
Combinatorial optimization problems arise in logistics, scheduling, and resource allocation, yet existing approaches face a fundamental trade‑off among generality, performance, and usability. We present cuGenOpt, a GPU‑accelerated general‑purpose metaheuristic framework that addresses all three dimensions simultaneously. At the engine level, cuGenOpt adopts a "one block evolves one solution" CUDA architecture with a unified encoding abstraction (permutation, binary, integer), a two‑level adaptive operator selection mechanism, and hardware‑aware resource management. At the extensibility level, a user‑defined operator registration interface allows domain experts to inject problem‑specific CUDA search operators. At the usability level, a JIT compilation pipeline exposes the framework as a pure‑Python API, and an LLM‑based modeling assistant converts natural‑language problem descriptions into executable solver code. Experiments across five thematic suites on three GPU architectures (T4, V100, A800) show that cuGenOpt outperforms general MIP solvers by orders of magnitude, achieves competitive quality against specialized solvers on instances up to n=150, and attains 4.73% gap on TSP‑442 within 30s. Twelve problem types spanning five encoding variants are solved to optimality. Framework‑level optimizations cumulatively reduce pcb442 gap from 36% to 4.73% and boost VRPTW throughput by 75‑81%. Code: https://github.com/L‑yang‑yang/cugenopt

PaperId: 2331, https://arxiv.org/pdf/2603.19100.pdf GitHub

Abstract:
Electroencephalography (EEG) enables non‑invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to \emphdiffering electrode topologies and \emphcomputational scalability, as Transformer architectures incur quadratic sequence complexity. As a joint solution, we propose LuMamba (Latent Unified Mamba), a self‑supervised framework combining topology‑invariant encodings with linear‑complexity state‑space modeling, using LUNA's learned‑query cross‑attention mechanism for channel unification~\citeluna, and FEMBA's bidirectional Mamba blocks for efficient temporal modeling~\citefemba. Within this architecture, we provide the first systematic investigation of the Latent‑Euclidean Joint‑Embedding Predictive Architecture (LeJEPA) for biosignal learning. Pre‑trained on over 21,000 hours of unlabeled EEG from the TUEG corpus, LuMamba is evaluated on five downstream tasks spanning abnormality detection, artifact recognition, and mental condition classification across electrode configurations ranging from 16 to 26 channels. In the pre‑training objective, masked reconstruction alone yields structured but less generalizable representations, while LeJEPA alone produces diffuse embeddings; combining both objectives achieves the most robust performance. With only 4.6M parameters, LuMamba attains 80.99% balanced accuracy on TUAB and achieves state‑of‑art performance on Alzheimer's detection (0.97 AUPR), while requiring 377× fewer FLOPS than state‑of‑art models at equivalent sequence lengths and scaling to 12× longer sequences before reaching typical GPU memory limits. Code is available at https://github.com/pulp‑bio/biofoundation

PaperId: 2332, https://arxiv.org/pdf/2603.19017.pdf GitHub

Abstract:
We present MultiTempBench, a multilingual temporal reasoning benchmark spanning three tasks, date arithmetic, time zone conversion, and temporal relation extraction across five languages (English, German, Chinese, Arabic, and Hausa) and multiple calendar conventions (Gregorian, Hijri, and Chinese Lunar). MultiTempBench contains 15,000 examples built by translating 750 curated English questions and expanding each into controlled date‑format variants. We evaluate 20 LLMs and introduce the multilingual Date Fragmentation Ratio (mDFR), calibrated with human severity ratings, together with geometric‑probing analyses of internal temporal representations. We find tokenisation quality of temporal artefacts is a resource‑dependent bottleneck: in low‑resource languages and rarer calendar formats, fragmentation disrupts Year/Month/Day separation and accuracy collapses, while high‑resource settings are often robust to digit‑level splitting. Beyond tokenisation, crossed mixed‑effects regression shows that temporal linearity is the strongest predictor of temporal reasoning in high‑resource languages, whereas fragmentation is the stronger predictor in low‑resource languages. Code is available at: https://github.com/gagan3012/mtb

PaperId: 2333, https://arxiv.org/pdf/2603.18896.pdf GitHub

Abstract:
Positron emission tomography (PET) is a widely recognized technique for diagnosing neurodegenerative diseases, offering critical functional insights. However, its high costs and radiation exposure hinder its widespread use. In contrast, magnetic resonance imaging (MRI) does not involve such limitations. While MRI also detects neurodegenerative changes, it is less sensitive for diagnosis compared to PET. To overcome such limitations, one approach is to generate synthetic PET from MRI. Recent advances in generative models have paved the way for cross‑modality medical image translation; however, existing methods largely emphasize structural preservation while neglecting the critical need for pathology awareness. To address this gap, we propose PASTA, a novel image translation framework built on conditional diffusion models with enhanced pathology awareness. PASTA surpasses state‑of‑the‑art methods by preserving both structural and pathological details through its highly interactive dual‑arm architecture and multi‑modal condition integration. Additionally, we introduce a novel cycle exchange consistency and volumetric generation strategy that significantly enhances PASTA's ability to produce high‑quality 3D PET images. Our qualitative and quantitative results demonstrate the high quality and pathology awareness of the synthesized PET scans. For Alzheimer's diagnosis, the performance of these synthesized scans improves over MRI by 4%, almost reaching the performance of actual PET. Our code is available at https://github.com/ai‑med/PASTA.

PaperId: 2334, https://arxiv.org/pdf/2603.18892.pdf GitHub

Abstract:
Spatial reasoning is foundational for Vision‑Language Models (VLMs), particularly when deployed as Vision‑Language‑Action (VLA) agents in physical environments. However, existing benchmarks predominantly focus on elementary, single‑hop relations, neglecting the multi‑hop compositional reasoning and precise visual grounding essential for real‑world scenarios. To address this, we introduce MultihopSpatial, offering three key contributions: (1) A comprehensive benchmark designed for multi‑hop and compositional spatial reasoning, featuring 1‑ to 3‑hop complex queries across diverse spatial perspectives. (2) Acc@50IoU, a complementary metric that simultaneously evaluates reasoning and visual grounding by requiring both answer selection and precise bounding box prediction ‑ capabilities vital for robust VLA deployment. (3) MultihopSpatial‑Train, a dedicated large‑scale training corpus to foster spatial intelligence. Extensive evaluation of 37 state‑of‑the‑art VLMs yields eight key insights, revealing that compositional spatial reasoning remains a formidable challenge. Finally, we demonstrate that reinforcement learning post‑training on our corpus enhances both intrinsic VLM spatial reasoning and downstream embodied manipulation performance.

PaperId: 2335, https://arxiv.org/pdf/2603.18859.pdf GitHub

Abstract:
Reinforcement learning (RL) shows promise for enhancing LLM agentic reasoning, yet sparse terminal rewards hinder fine‑grained optimization. Process reward modeling offers an alternative but incurs high computational costs, reward hacking risks, and annotation bottlenecks. We introduce RewardFlow, a lightweight method for estimating state‑level rewards in agentic reasoning. By constructing state graphs that capture the intrinsic topological structure of trajectories, RewardFlow performs topology‑aware propagation to estimate each state's contribution to success, yielding principled, annotation‑free dense rewards. Used for RL optimization, RewardFlow substantially outperforms prior baselines across four agentic benchmarks: +6.2% average success rate on text‑based tasks, +29.7% on visual reasoning over the strongest baseline across three model scales, and +10% accuracy on DeepResearch, with superior robustness and training efficiency. The implementation of RewardFlow is publicly available at https://github.com/tmlr‑group/RewardFlow.

PaperId: 2336, https://arxiv.org/pdf/2603.18856.pdf GitHub GitHub

Abstract:
Recent video reasoning models increasingly produce spatio‑temporal evidence chains that localize objects at specific timestamps. While these traces improve interpretability by grounding \emphwhere and \emphwhen evidence appears, they often leave the motion connecting observations, the how, implicit. This makes dynamic and trajectory‑dependent claims difficult to supervise, verify, or penalize when unsupported by the video. We formalize this missing component as Spatial‑Temporal‑Trajectory (STT) reasoning and introduce Motion‑o, a motion‑centric extension to vision‑language models (VLMs) that makes trajectories explicit and verifiable. Motion‑o augments evidence chains with Motion Chain of Thought (MCoT), a structured pathway that represents object motion through a discrete \texttt<motion/> tag summarizing direction, speed, and scale change. To supervise MCoT, we densify sparse spatio‑temporal annotations into object tracks and derive motion descriptors from centroid displacement and box‑area change. We then train with complementary rewards for trajectory consistency and visual grounding, including a perturbation‑based signal that penalizes motion descriptions that remain unchanged when temporal evidence is removed. Across multiple video understanding benchmarks, Motion‑o consistently improves trajectory‑faithful reasoning without architectural modifications. These results suggest that an explicit motion interface can complement existing VLM pipelines by converting implicit dynamics into verifiable evidence. Code is available at~\hrefhttps://github.com/ostadabbas/Motion‑o\faGithub\ \textttostadabbas/Motion‑o.

PaperId: 2337, https://arxiv.org/pdf/2603.18829.pdf GitHub

Abstract:
Autonomous agents can produce harmful behavioral patterns from individually valid requests ‑‑ a threat class per‑request policy evaluation cannot address, because stateless engines evaluate each request in isolation. We present ACP, a temporal admission control protocol enforcing behavioral properties over execution traces via static risk scoring combined with stateful signals (anomaly accumulation, cooldown) through a LedgerQuerier abstraction. ACP blocks execution based on deterministic, history‑aware risk scoring ‑‑ not anomaly detection. Under a 500‑request workload where every request is individually valid (RS=35), a stateless engine approves all 500; ACP limits autonomous execution to 2 out of 500 (0.4%), escalating after 3 actions and denying after 11. We identify a state‑mixing vulnerability in ACP‑RISK‑2.0 (cross‑context false denials) and introduce ACP‑RISK‑3.0, scoping anomaly signals to PatternKey(agentID, capability, resource). Decision evaluation: 739‑832 ns (p50); throughput 1,720,000 req/s. Safety and liveness model‑checked via TLA+ (11 invariants + 4 temporal properties, 0 violations) across 4,294,930,695 distinct states. We formalize deviation collapse ‑‑ enforcement active but never exercised due to upstream constraints ‑‑ and introduce Boundary Activation Rate (BAR) as its detection mechanism. An adversary suppressing BAR to 0.00 is detected via DeltaBAR before collapse (BAR_C=1.00). N coordinated agents accumulate risk independently; coordination window CW_appr=2N with zero deviation: activity scales linearly, preventing superlinear amplification. ACP is Paper 1 of a 6‑paper Agent Governance Series: P0 ‑‑ atomic decision boundaries; P2 ‑‑ behavioral drift detection (IML); P3/4 ‑‑ governance structure, fair allocation, and irreducibility; P5 ‑‑ runtime execution validity (RAM, arXiv:2604.22898); P6 ‑‑ operationalization of RAM.

PaperId: 2338, https://arxiv.org/pdf/2603.18782.pdf GitHub

Abstract:
Recent progress in 3D generation has been driven largely by models conditioned on images or text, while readily available 3D priors are still underused. In many real‑world scenarios, the visible‑region point cloud are easy to obtain from active sensors such as LiDAR or from feed‑forward predictors like VGGT, offering explicit geometric constraints that current methods fail to exploit. In this work, we introduce Points‑to‑3D, a diffusion‑based framework that leverages point cloud priors for geometry‑controllable 3D asset and scene generation. Built on a latent 3D diffusion model TRELLIS, Points‑to‑3D first replaces pure‑noise sparse structure latent initialization with a point cloud priors tailored input formulation.A structure inpainting network, trained within the TRELLIS framework on task‑specific data designed to learn global structural inpainting, is then used for inference with a staged sampling strategy (structural inpainting followed by boundary refinement), completing the global geometry while preserving the visible regions of the input priors. In practice, Points‑to‑3D can take either accurate point‑cloud priors or VGGT‑estimated point clouds from single images as input. Experiments on both objects and scene scenarios consistently demonstrate superior performance over state‑of‑the‑art baselines in terms of rendering quality and geometric fidelity, highlighting the effectiveness of explicitly embedding point‑cloud priors for achieving more accurate and structurally controllable 3D generation. Project page: https://jiatongxia.github.io/points2‑3D/

PaperId: 2339, https://arxiv.org/pdf/2603.18761.pdf GitHub

Abstract:
Standard attention mechanisms in transformers are limited by their pairwise formulation, which hinders the modeling of higher‑order dependencies among tokens. We introduce the NeuroGame Transformer (NGT) to overcome this by reconceptualizing attention through a dual perspective: tokens are treated simultaneously as players in a cooperative game and as interacting spins in a statistical physics system. Token importance is quantified using two complementary game‑theoretic concepts ‑‑ Shapley values for global, permutation‑based attribution and Banzhaf indices for local, coalition‑level influence. These are combined via a learnable gating parameter to form an external magnetic field, while pairwise interaction potentials capture synergistic relationships. The system's energy follows an Ising Hamiltonian, with attention weights emerging as marginal probabilities under the Gibbs distribution, efficiently computed via mean‑field equations. To ensure scalability despite the exponential coalition space, we develop importance‑weighted Monte Carlo estimators with Gibbs‑distributed weights. This approach avoids explicit exponential factors, ensuring numerical stability for long sequences. We provide theoretical convergence guarantees and characterize the fairness‑sensitivity trade‑off governed by the interpolation parameter. Experimental results demonstrate that the NeuroGame Transformer achieves strong performance across SNLI, and MNLI‑matched, outperforming some major efficient transformer baselines. On SNLI, it attains a test accuracy of 86.4% (with a peak validation accuracy of 86.6%), surpassing ALBERT‑Base and remaining highly competitive with RoBERTa‑Base. Code is available at https://github.com/dbouchaffra/NeuroGame‑Transformer.

PaperId: 2340, https://arxiv.org/pdf/2603.18743.pdf GitHub

Abstract:
We introduce \emphMemento‑Skills, a generalist, continually‑learnable LLM agent system that functions as an \emphagent‑designing agent: it autonomously constructs, adapts, and improves task‑specific agents through experience. The system is built on a memory‑based reinforcement learning framework with \emphstateful prompts, where reusable skills (stored as structured markdown files) serve as persistent, evolving memory. These skills encode both behaviour and context, enabling the agent to carry forward knowledge across interactions. Starting from simple elementary skills (like Web search and terminal operations), the agent continually improves via the \emphRead‑‑Write Reflective Learning mechanism introduced in \emphMemento~2~\citewang2025memento2. In the \emphread phase, a behaviour‑trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the \emphwrite phase, the agent updates and expands its skill library based on new experience. This closed‑loop design enables \emphcontinual learning without updating LLM parameters, as all adaptation is realised through the evolution of externalised skills and prompts. Unlike prior approaches that rely on human‑designed agents, Memento‑Skills enables a generalist agent to \emphdesign agents end‑to‑end for new tasks. Through iterative skill generation and refinement, the system progressively improves its own capabilities. Experiments on the \emphGeneral AI Assistants benchmark and \emphHumanity's Last Exam demonstrate sustained gains, achieving 26.2% and 116.2% relative improvements in overall accuracy, respectively. Code is available at https://github.com/Memento‑Teams/Memento‑Skills.

PaperId: 2341, https://arxiv.org/pdf/2603.18718.pdf GitHub

Abstract:
Memory‑augmented LLM agents maintain external memory banks to support long‑horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of the memory cycle, where construction and retrieval are driven by local heuristics rather than explicit strategic reasoning, and sparse, delayed supervision on the backward path, where downstream failures rarely translate into direct repairs of the memory bank. To address these challenges, we propose MemMA, a plug‑and‑play multi‑agent framework that coordinates the memory cycle along both the forward and backward paths. On the forward path, a Meta‑Thinker produces structured guidance that steers a Memory Manager during construction and directs a Query Reasoner during iterative retrieval. On the backward path, MemMA introduces in‑situ self‑evolving memory construction, which synthesizes probe QA pairs, verifies the current memory, and converts failures into repair actions before the memory is finalized. Extensive experiments on LoCoMo show that MemMA consistently outperforms existing baselines across multiple LLM backbones and improves three different storage backends in a plug‑and‑play manner. Our code is publicly available at https://github.com/ventr1c/memma.

PaperId: 2342, https://arxiv.org/pdf/2603.18655.pdf GitHub

Abstract:
Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging artifacts including speckle noise and low‑contrast boundaries. While semi‑supervised learning (SSL) approaches have emerged to address data scarcity, existing methods suffer from suboptimal unlabeled data utilization and lack robust feature representation mechanisms. In this paper, we propose Switch, a novel SSL framework with two key innovations: (1) Multiscale Switch (MSS) strategy that employs hierarchical patch mixing to achieve uniform spatial coverage; (2) Frequency Domain Switch (FDS) with contrastive learning that performs amplitude switching in Fourier space for robust feature representations. Our framework integrates these components within a teacher‑student architecture to effectively leverage both labeled and unlabeled data. Comprehensive evaluation across six diverse ultrasound datasets (lymph nodes, breast lesions, thyroid nodules, and prostate) demonstrates consistent superiority over state‑of‑the‑art methods. At 5% labeling ratio, Switch achieves remarkable improvements: 80.04% Dice on LN‑INT, 85.52% Dice on DDTI, and 83.48% Dice on Prostate datasets, with our semi‑supervised approach even exceeding fully supervised baselines. The method maintains parameter efficiency (1.8M parameters) while delivering superior performance, validating its effectiveness for resource‑constrained medical imaging applications. The source code is publicly available at https://github.com/jinggqu/Switch

PaperId: 2343, https://arxiv.org/pdf/2603.18596.pdf GitHub

Abstract:
Weight regularization methods in continual learning (CL) alleviate catastrophic forgetting by assessing and penalizing changes to important model weights. Elastic Weight Consolidation (EWC) is a foundational and widely used approach within this framework that estimates weight importance based on gradients. However, it has consistently shown suboptimal performance. In this paper, we conduct a systematic analysis of importance estimation in EWC from a gradient‑based perspective. For the first time, we find that EWC's reliance on the Fisher Information Matrix (FIM) results in gradient vanishing and inaccurate importance estimation in certain scenarios. Our analysis also reveals that Memory Aware Synapses (MAS), a variant of EWC, imposes unnecessary constraints on parameters irrelevant to prior tasks, termed the redundant protection. Consequently, both EWC and its variants exhibit fundamental misalignments in estimating weight importance, leading to inferior performance. To tackle these issues, we propose the Logits Reversal (LR) operation, a simple yet effective modification that rectifies EWC's importance estimation. Specifically, reversing the logit values during the calculation of FIM can effectively prevent both gradient vanishing and redundant protection. Extensive experiments across various CL tasks and datasets show that the proposed method significantly outperforms existing EWC and its variants. Therefore, we refer to it as EWC Done Right (EWC‑DR). Code is available at https://github.com/scarlet0703/EWC‑DR.

PaperId: 2344, https://arxiv.org/pdf/2603.18501.pdf GitHub

Abstract:
Video compression aims to maximize reconstruction quality with minimal bitrates. Beyond standard distortion metrics, perceptual quality and temporal consistency are also critical. However, at ultra‑low bitrates, traditional end‑to‑end compression models tend to produce blurry images of poor perceptual quality. Besides, existing generative compression methods often treat video frames independently and show limitations in time coherence and efficiency. To address these challenges, we propose the Efficient Video Diffusion with Sparse Information Transmission (Diff‑SIT), which comprises the Sparse Temporal Encoding Module (STEM) and the One‑Step Video Diffusion with Frame Type Embedder (ODFTE). The STEM sparsely encodes the original frame sequence into an information‑rich intermediate sequence, achieving significant bitrate savings. Subsequently, the ODFTE processes this intermediate sequence as a whole, which exploits the temporal correlation. During this process, our proposed Frame Type Embedder (FTE) guides the diffusion model to perform adaptive reconstruction according to different frame types to optimize the overall quality. Extensive experiments on multiple datasets demonstrate that Diff‑SIT establishes a new state‑of‑the‑art in perceptual quality and temporal consistency, particularly in the challenging ultra‑low‑bitrate regime. Code is released at https://github.com/MingdeZhou/Diff‑SIT.

PaperId: 2345, https://arxiv.org/pdf/2603.18493.pdf GitHub

Abstract:
Streaming 3D reconstruction maintains a persistent latent state that is updated online from incoming frames, enabling constant‑memory inference. A key failure mode is the state update rule: aggressive overwrites forget useful history, while conservative updates fail to track new evidence, and both behaviors become unstable beyond the training horizon. To address this challenge, we propose FILT3R, a training‑free latent filtering layer that casts recurrent state updates as stochastic state estimation in token space. FILT3R maintains a per‑token variance and computes a Kalman‑style gain that adaptively balances memory retention against new observations. Process noise ‑‑ governing how much the latent state is expected to change between frames ‑‑ is estimated online from EMA‑normalized temporal drift of candidate tokens. Using extensive experiments, we demonstrate that FILT3R yields an interpretable, plug‑in update rule that generalizes common overwrite and gating policies as special cases. Specifically, we show that gains shrink in stable regimes as uncertainty contracts with accumulated evidence, and rise when genuine scene change increases process uncertainty, improving long‑horizon stability for depth, pose, and 3D reconstruction, compared to the existing methods. Code will be released at https://github.com/jinotter3/FILT3R.

PaperId: 2346, https://arxiv.org/pdf/2603.18427.pdf GitHub GitHub

Abstract:
Collecting and annotating datasets for pixel‑level semantic segmentation tasks are highly labor‑intensive. Data augmentation provides a viable solution by enhancing model generalization without additional real‑world data collection. Traditional augmentation techniques, such as translation, scaling, and color transformations, create geometric variations but fail to generate new structures. While generative models have been employed to extend semantic information of datasets, they often struggle to maintain consistency between the original and generated images, particularly for pixel‑level tasks. In this work, we propose a novel synthetic data augmentation pipeline that integrates controllable diffusion models. Our approach balances diversity and reliability data, effectively bridging the gap between synthetic and real data. We utilize class‑aware prompting and visual prior blending to improve image quality further, ensuring precise alignment with segmentation labels. By evaluating benchmark datasets such as PASCAL VOC and BDD100K, we demonstrate that our method significantly enhances semantic segmentation performance, especially in data‑scarce scenarios, while improving model robustness in real‑world applications. Our code is available at \hrefhttps://github.com/chequanghuy/Enhanced‑Generative‑Data‑Augmentation‑for‑Semantic‑Segmentation‑via‑Stronger‑Guidancehttps://github.com/chequanghuy/Enhanced‑Generative‑Data‑Augmentation‑for‑Semantic‑Segmentation‑via‑Stronger‑Guidance.

PaperId: 2347, https://arxiv.org/pdf/2603.18420.pdf GitHub

Abstract:
Embedding models group text by semantic content, what text is about. We show that temporal co‑occurrence within texts discovers a different kind of structure: recurrent transition‑structure concepts or what text does. We train a 29.4M‑parameter contrastive model on 373 million co‑occurrence pairs from 9,766 Project Gutenberg texts (24.96 million passages), mapping pre‑trained embeddings into an association space where passages with similar transition structure cluster together. Under capacity constraint (42.75% accuracy), the model must compress across recurring patterns rather than memorise individual co‑occurrences. Clustering at six granularities (k=50 to k=2,000) produces a multi‑resolution concept map; from broad modes like "direct confrontation" and "lyrical meditation" to precise registers and scene templates like "sailor dialect" and "courtroom cross‑examination." At k=100, clusters average 4,508 books each (of 9,766), confirming corpus‑wide patterns. Direct comparison with embedding‑similarity clustering shows that raw embeddings group by topic while association‑space clusters group by function, register, and literary tradition. Unseen novels are assigned to existing clusters without retraining; the association model concentrates each novel into a selective subset of coherent clusters, while raw embedding assignment saturates nearly all clusters. Validation controls address positional, length, and book‑concentration confounds. The method extends Predictive Associative Memory (PAM, arXiv:2602.11322) from episodic recall to concept formation: where PAM recalls specific associations, multi‑epoch contrastive training under compression extracts structural patterns that transfer to unseen texts, the same framework producing qualitatively different behaviour in a different regime.

PaperId: 2348, https://arxiv.org/pdf/2603.18353.pdf GitHub

Abstract:
Language models encode task‑relevant knowledge in internal representations that far exceeds their output performance, but whether mechanistic interpretability methods can bridge this knowledge‑action gap has not been systematically tested. We compared four mechanistic interpretability methods ‑‑ concept bottleneck steering (Steerling‑8B), sparse autoencoder feature steering, logit lens with activation patching, and linear probing with truthfulness separator vector steering (Qwen 2.5 7B Instruct) ‑‑ for correcting false‑negative triage errors using 400 physician‑adjudicated clinical vignettes (144 hazards, 256 benign). Linear probes discriminated hazardous from benign cases with 98.2% AUROC, yet the model's output sensitivity was only 45.1%, a 53‑percentage‑point knowledge‑action gap. Concept bottleneck steering corrected 20% of missed hazards but disrupted 53% of correct detections, indistinguishable from random perturbation (p=0.84). SAE feature steering produced zero effect despite 3,695 significant features. TSV steering at high strength corrected 24% of missed hazards while disrupting 6% of correct detections, but left 76% of errors uncorrected. Current mechanistic interpretability methods cannot reliably translate internal knowledge into corrected outputs, with implications for AI safety frameworks that assume interpretability enables effective error correction.

PaperId: 2349, https://arxiv.org/pdf/2603.18315.pdf GitHub

Abstract:
Ensuring safe decision‑making in autonomous vehicles remains a fundamental challenge despite rapid advances in end‑to‑end learning approaches. Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the rich contextual understanding required for safe driving and make unsafe exploration unavoidable in real‑world settings. Recent vision‑language models (VLMs) offer promising semantic understanding capabilities; however, their high inference latency and susceptibility to hallucination hinder direct application to real‑time vehicle control. To address these limitations, this paper proposes DriveVLM‑RL, a neuroscience‑inspired framework that integrates VLMs into RL through a dual‑pathway architecture for safe and deployable autonomous driving. The framework decomposes semantic reward learning into a Static Pathway for continuous spatial safety assessment using CLIP‑based contrasting language goals, and a Dynamic Pathway for attention‑gated multi‑frame semantic risk reasoning using a lightweight detector and a large VLM. A hierarchical reward synthesis mechanism fuses semantic signals with vehicle states, while an asynchronous training pipeline decouples expensive VLM inference from environment interaction. All VLM components are used only during offline training and are removed at deployment, ensuring real‑time feasibility. Experiments in the CARLA simulator show significant improvements in collision avoidance, task success, and generalization across diverse traffic scenarios, including strong robustness under settings without explicit collision penalties. These results demonstrate that DriveVLM‑RL provides a practical paradigm for integrating foundation models into autonomous driving without compromising real‑time feasibility. Demo video and code are available at: https://zilin‑huang.github.io/DriveVLM‑RL‑website/

PaperId: 2350, https://arxiv.org/pdf/2603.18314.pdf GitHub GitHub

Abstract:
Approximate subgraph matching (ASM) is a task that determines the approximate presence of a given query graph in a large target graph. Being an NP‑hard problem, ASM is critical in graph analysis with a myriad of applications ranging from database systems and network science to biochemistry and privacy. Existing techniques often employ heuristic search strategies, which cannot fully utilize the graph information, leading to sub‑optimal solutions. This paper proposes a Reinforcement Learning based Approximate Subgraph Matching (RL‑ASM) algorithm that exploits graph transformers to effectively extract graph representations and RL‑based policies for ASM. Our model is built upon the branch‑and‑bound algorithm that selects one pair of nodes from the two input graphs at a time for potential matches. Instead of using heuristics, we exploit a Graph Transformer architecture to extract feature representations that encode the full graph information. To enhance the training of the RL policy, we use supervised signals to guide our agent in an imitation learning stage. Subsequently, the policy is fine‑tuned with the Proximal Policy Optimization (PPO) that optimizes the accumulative long‑term rewards over episodes. Extensive experiments on both synthetic and real‑world datasets demonstrate that our RL‑ASM outperforms existing methods in terms of effectiveness and efficiency. Our source code is available at https://github.com/KaiyangLi1992/RL‑ASM.

PaperId: 2351, https://arxiv.org/pdf/2603.18280.pdf GitHub

Abstract:
Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the layer where alignment often operates: routing from concept detection to behavioral policy. We study political censorship in Chinese‑origin language models as a natural experiment, using probes, surgical ablations, and behavioral tests across nine open‑weight models from five labs. Three findings follow. First, probe accuracy alone is non‑diagnostic: political probes, null controls, and permutation baselines can all reach 100%, so held‑out category generalization is the informative test. Second, surgical ablation reveals lab‑specific routing. Removing the political‑sensitivity direction eliminates censorship and restores accurate factual output in most models tested, while one model confabulates because its architecture entangles factual knowledge with the censorship mechanism. Cross‑model transfer fails, indicating that routing geometry is model‑ and lab‑specific. Third, refusal is no longer the dominant censorship mechanism. Within one model family, hard refusal falls to zero while narrative steering rises to the maximum, making censorship invisible to refusal‑only benchmarks. These results support a three‑stage descriptive framework: detect, route, generate. Models often retain the relevant knowledge; alignment changes how that knowledge is expressed. Evaluations that audit only detection or refusal therefore miss the routing mechanism that most directly determines behavior.

PaperId: 2352, https://arxiv.org/pdf/2603.18258.pdf GitHub

Abstract:
Direct Preference Optimization (DPO) has emerged as a popular algorithm for aligning pretrained large language models with human preferences, owing to its simplicity and training stability. However, DPO suffers from the recently identified squeezing effect (also known as likelihood displacement), where the probability of preferred responses decreases unintentionally during training. To understand and mitigate this phenomenon, we develop a theoretical framework that models the coordinate‑wise dynamics in logit space. Our analysis reveals that negative‑gradient updates cause residuals to expand rapidly along high‑curvature directions, which underlies the squeezing effect, whereas Sharpness‑Aware Minimization (SAM) can suppress this behavior through its curvature‑regularization effect. Building on this insight, we investigate logits‑SAM, a computationally efficient variant that perturbs only the output layer with negligible overhead. Extensive experiments on Pythia‑2.8B, Mistral‑7B, and Gemma‑2B‑IT across multiple datasets and benchmarks demonstrate that logits‑SAM consistently improves the effectiveness of DPO and integrates seamlessly with other DPO variants. Code is available at https://github.com/RitianLuo/logits‑sam‑dpo.

PaperId: 2353, https://arxiv.org/pdf/2603.18202.pdf GitHub

Abstract:
A central challenge in image‑based Model‑Based Reinforcement Learning (MBRL) is to learn representations that distill essential information from irrelevant visual details. While promising, reconstruction‑based methods often waste capacity on large task‑irrelevant regions. Decoder‑free methods instead learn robust representations by leveraging Data Augmentation (DA), but reliance on such external regularizers limits versatility. We propose R2‑Dreamer, a decoder‑free MBRL framework with a self‑supervised objective that serves as an internal regularizer, preventing representation collapse without resorting to DA. The core of our method is a redundancy‑reduction objective inspired by Barlow Twins, which can be easily integrated into existing frameworks. On DeepMind Control Suite and Meta‑World, R2‑Dreamer is competitive with strong baselines such as DreamerV3 and TD‑MPC2 while training 1.59x faster than DreamerV3, and yields substantial gains on DMC‑Subtle with tiny task‑relevant objects. These results suggest that an effective internal regularizer can enable versatile, high‑performance decoder‑free MBRL. Code is available at https://github.com/NM512/r2dreamer.

PaperId: 2354, https://arxiv.org/pdf/2603.18101.pdf GitHub

Abstract:
Recent adapter‑based CLIP tuning (e.g., Tip‑Adapter) is a strong few‑shot learner, achieving efficiency by caching support features for fast prototype matching. However, these methods rely on global uni‑modal feature vectors, overlooking fine‑grained patch relations and their structural alignment with class text. To bridge this gap without incurring inference costs, we introduce a novel asymmetric training‑only framework. Instead of altering the lightweight adapter, we construct a high‑capacity auxiliary Heterogeneous Graph Teacher that operates solely during training. This teacher (i) integrates multi‑scale visual patches and text prompts into a unified graph, (ii) performs deep cross‑modal reasoning via a Modality‑aware Graph Transformer (MGT), and (iii) applies discriminative node filtering to extract high‑fidelity class features. Crucially, we employ a cache‑aware dual‑objective strategy to supervise this relational knowledge directly into the Tip‑Adapter's key‑value cache, effectively upgrading the prototypes while the graph teacher is discarded at test time. Thus, inference remains identical to Tip‑Adapter with zero extra latency or memory. Across standard 1‑16‑shot benchmarks, our method consistently establishes a new state‑of‑the‑art. Ablations confirm that the auxiliary graph supervision, text‑guided reasoning, and node filtering are the essential ingredients for robust few‑shot adaptation. Code is available at https://github.com/MR‑Sherif/TOGA.git.

PaperId: 2355, https://arxiv.org/pdf/2603.18090.pdf GitHub

Abstract:
This technical report presents MOSS‑TTS, a speech generation foundation model built on a scalable recipe: discrete audio tokens, autoregressive modeling, and large‑scale pretraining. Built on MOSS‑Audio‑Tokenizer, a causal Transformer tokenizer that compresses 24 kHz audio to 12.5 fps with variable‑bitrate RVQ and unified semantic‑acoustic representations, we release two complementary generators: MOSS‑TTS, which emphasizes structural simplicity, scalability, and long‑context/control‑oriented deployment, and MOSS‑TTS‑Local‑Transformer, which introduces a frame‑local autoregressive module for higher modeling efficiency, stronger speaker preservation, and a shorter time to first audio. Across multilingual and open‑domain settings, MOSS‑TTS supports zero‑shot voice cloning, token‑level duration control, phoneme‑/pinyin‑level pronunciation control, smooth code‑switching, and stable long‑form generation. This report summarizes the design, training recipe, and empirical characteristics of the released models.

PaperId: 2356, https://arxiv.org/pdf/2603.18043.pdf GitHub

Abstract:
Multi‑agent LLM systems delegate tasks across trust boundaries, but current protocols do not govern delegation under unverifiable quality claims. We show that when delegates can inflate self‑reported quality scores, quality‑based routing produces a provenance paradox: it systematically selects the worst delegates, performing worse than random. We extend the LLM Delegate Protocol (LDP) with delegation contracts that bound authority through explicit objectives, budgets, and failure policies; a claimed‑vs‑attested identity model that distinguishes self‑reported from verified quality; and typed failure semantics enabling automated recovery. In controlled experiments with 10 simulated delegates and validated with real Claude models, routing by self‑claimed quality scores performs worse than random selection (simulated: 0.55 vs. 0.68; real models: 8.90 vs. 9.30), while attested routing achieves near‑optimal performance (d = 9.51, p < 0.001). Sensitivity analysis across 36 configurations confirms the paradox emerges reliably when dishonest delegates are present. All extensions are backward‑compatible with sub‑microsecond validation overhead.

PaperId: 2357, https://arxiv.org/pdf/2603.18002.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have made impressive progress in connecting vision and language, but they still struggle with spatial understanding and viewpoint‑aware reasoning. Recent efforts aim to augment the input representations with geometric cues rather than explicitly teaching models to reason in 3D space. We introduce Loc3R‑VLM, a framework that equips 2D Vision‑Language Models with advanced 3D understanding capabilities from monocular video input. Inspired by human spatial cognition, Loc3R‑VLM relies on two joint objectives: global layout reconstruction to build a holistic representation of the scene structure, and explicit situation modeling to anchor egocentric perspective. These objectives provide direct spatial supervision that grounds both perception and language in a 3D context. To ensure geometric consistency and metric‑scale alignment, we leverage lightweight camera pose priors extracted from a pre‑trained 3D foundation model. Loc3R‑VLM achieves state‑of‑the‑art performance in language‑based localization and outperforms existing 2D‑ and video‑based approaches on situated and general 3D question‑answering benchmarks, demonstrating that our spatial supervision framework enables strong 3D understanding. Project page: https://kevinqu7.github.io/loc3r‑vlm

PaperId: 2358, https://arxiv.org/pdf/2603.18000.pdf GitHub

Abstract:
Building LLM‑based agents has become increasingly important. Recent works on LLM‑based agent self‑evolution primarily record successful experiences as textual prompts or reflections, which cannot reliably guarantee efficient task re‑execution in complex scenarios. We propose AgentFactory, a new self‑evolution paradigm that preserves successful task solutions as executable subagent code rather than textual experience. Crucially, these subagents are continuously refined based on execution feedback, becoming increasingly robust and efficient as more tasks are encountered. Saved subagents are pure Python code with standardized documentation, enabling portability across any Python‑capable system. We demonstrate that AgentFactory enables continuous capability accumulation: its library of executable subagents grows and improves over time, progressively reducing the effort required for similar tasks without manual intervention. Our implementation is open‑sourced at https://github.com/zzatpku/AgentFactory, and our demonstration video is available at https://youtu.be/iKSsuAXJHW0.

PaperId: 2359, https://arxiv.org/pdf/2603.17973.pdf GitHub

Abstract:
AI coding agents can resolve real‑world software issues, yet they frequently introduce regressions ‑‑ breaking tests that previously passed. Current benchmarks focus almost exclusively on resolution rate, leaving regression behavior under‑studied. This paper presents TDAD (Test‑Driven Agentic Development), an open‑source tool that performs pre‑change impact analysis for AI coding agents. TDAD builds a dependency map between source code and tests so that before committing a patch, the agent knows which tests to verify and can self‑correct. The map is delivered as a lightweight agent skill ‑‑ a static text file the agent queries at runtime. Evaluated on SWE‑bench Verified with two open‑weight models running on consumer hardware (Qwen3‑Coder 30B, 100 instances; Qwen3.5‑35B‑A3B, 25 instances), TDAD reduced regressions by 70% (6.08% to 1.82%) compared to a vanilla baseline. In contrast, adding TDD procedural instructions without targeted test context increased regressions to 9.94% ‑‑ worse than no intervention at all. When deployed as an agent skill with a different model and framework, TDAD improved issue‑resolution rate from 24% to 32%, confirming that surfacing contextual information outperforms prescribing procedural workflows. All code, data, and logs are publicly available at https://github.com/pepealonso95/TDAD.

PaperId: 2360, https://arxiv.org/pdf/2603.17863.pdf GitHub

Abstract:
Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems has thus far been limited by existing task suites. They suffer from many issues, such as: poor evaluation methodologies; data contamination; and containing saturated or very similar problems. Here, we introduce DiscoGen, a procedural generator of algorithm discovery tasks for machine learning, such as developing optimisers for reinforcement learning or loss functions for image classification. Motivated by the success of procedural generation in reinforcement learning, DiscoGen spans millions of tasks of varying difficulty and complexity from a range of machine learning fields. These tasks are specified by a small number of configuration parameters and can be used to optimise algorithm discovery agents (ADAs). We present DiscoBench, a benchmark consisting of a fixed, small subset of DiscoGen tasks for principled evaluation of ADAs. Finally, we propose a number of ambitious, impactful research directions enabled by DiscoGen, in addition to experiments demonstrating its use for prompt optimisation of an ADA. DiscoGen is released open‑source at https://github.com/AlexGoldie/discogen.

PaperId: 2361, https://arxiv.org/pdf/2603.17834.pdf GitHub

Abstract:
Diffusion models and flow matching have become a cornerstone of robotic imitation learning, yet they suffer from a structural inefficiency where inference is often bound to a fixed integration schedule that is agnostic to state complexity. This paradigm forces the policy to expend the same computational budget on trivial motions as it does on complex tasks. We introduce Generative Control as Optimization (GeCO), a time‑unconditional framework that transforms action synthesis from trajectory integration into iterative optimization. GeCO learns a stationary velocity field in the action‑sequence space where expert behaviors form stable attractors. Consequently, test‑time inference becomes an adaptive process that allocates computation based on convergence‑‑exiting early for simple states while refining longer for difficult ones. Furthermore, this stationary geometry yields an intrinsic, training‑free safety signal, as the field norm at the optimized action serves as a robust out‑of‑distribution (OOD) detector, remaining low for in‑distribution states while significantly increasing for anomalies. We validate GeCO on standard simulation benchmarks and demonstrate seamless scaling to pi0‑series Vision‑Language‑Action (VLA) models. As a plug‑and‑play replacement for standard flow‑matching heads, GeCO improves success rates and efficiency with an optimization‑native mechanism for safe deployment. Video and code can be found at https://hrh6666.github.io/GeCO/

PaperId: 2362, https://arxiv.org/pdf/2603.17809.pdf GitHub

Abstract:
Large Vision Language Models (LVLMs) have achieved remarkable success in a range of downstream tasks that require multimodal interaction, but their capabilities come with substantial computational and memory overhead, which hinders practical deployment. Among numerous acceleration techniques, post‑training quantization is a popular and effective strategy for reducing memory cost and accelerating inference. However, existing LVLM quantization methods typically measure token sensitivity at the modality level, which fails to capture the complex cross‑token interactions and falls short in quantitatively measuring the quantization error at the token level. As tokens interact within the model, the distinction between modalities gradually diminishes, suggesting the need for fine‑grained calibration. Inspired by axiomatic attribution in mechanistic interpretability, we introduce a fine‑grained quantization strategy on Quantization‑aware Integrated Gradients (QIG), which leverages integrated gradients to quantitatively evaluate token sensitivity and push the granularity from modality level to token level, reflecting both inter‑modality and intra‑modality dynamics. Extensive experiments on multiple LVLMs under both W4A8 and W3A16 settings show that our method improves accuracy across models and benchmarks with negligible latency overhead. For example, under 3‑bit weight‑only quantization, our method improves the average accuracy of LLaVA‑onevision‑7B by 1.60%, reducing the gap to its full‑precision counterpart to only 1.33%. The code is available at https://github.com/ucas‑xiang/QIG.

PaperId: 2363, https://arxiv.org/pdf/2603.17787.pdf GitHub

Abstract:
Enterprise AI deploys dozens of autonomous agent nodes across workflows, each acting on the same entities with no shared memory and no common governance. We identify five structural challenges arising from this memory governance gap: memory silos across agent workflows; governance fragmentation across teams and tools; unstructured memories unusable by downstream systems; redundant context delivery in autonomous multi‑step executions; and silent quality degradation without feedback loops. We present Governed Memory, a shared memory and governance layer addressing this gap through four mechanisms: a dual memory model combining open‑set atomic facts with schema‑enforced typed properties; tiered governance routing with progressive context delivery; reflection‑bounded retrieval with entity‑scoped isolation; and a closed‑loop schema lifecycle with AI‑assisted authoring and automated per‑property refinement. We validate each mechanism through controlled experiments (N=250, five content types): 99.6% fact recall with complementary dual‑modality coverage; 92% governance routing precision; 50% token reduction from progressive delivery; zero cross‑entity leakage across 500 adversarial queries; 100% adversarial governance compliance; and output quality saturation at approximately seven governed memories per entity. On the LoCoMo benchmark, the architecture achieves 74.8% overall accuracy, confirming that governance and schema enforcement impose no retrieval quality penalty. The system is in production at Personize.ai.

PaperId: 2364, https://arxiv.org/pdf/2603.17775.pdf GitHub GitHub

Abstract:
Label‑free reinforcement learning enables large language models to improve reasoning capabilities without ground‑truth supervision, typically by treating majority‑voted answers as pseudo‑labels. However, we identify a critical failure mode: as training maximizes self‑consistency, output diversity collapses, causing the model to confidently reinforce systematic errors that evade detection. We term this the consensus trap. To escape it, we propose CoVerRL, a framework where a single model alternates between generator and verifier roles, with each capability bootstrapping the other. Majority voting provides noisy but informative supervision for training the verifier, while the improving verifier progressively filters self‑consistent errors from pseudo‑labels. This co‑evolution creates a virtuous cycle that maintains high reward accuracy throughout training. Experiments across Qwen and Llama model families demonstrate that CoVerRL outperforms label‑free baselines by 4.7‑5.9% on mathematical reasoning benchmarks. Moreover, self‑verification accuracy improves from around 55% to over 85%, confirming that both capabilities genuinely co‑evolve.

PaperId: 2365, https://arxiv.org/pdf/2603.17662.pdf GitHub GitHub

Abstract:
Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine‑grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image‑related questions. We introduce FIne‑grained NEgative queRies (FINER), alongside two benchmarks: FINER‑CompreCap and FINER‑DOCCI. Using FINER, we analyze hallucinations across four settings: multi‑object, multi‑attribute, multi‑relation, and ``what'' questions. Our benchmarks reveal that MLLMs hallucinate when fine‑grained mismatches co‑occur with genuinely present elements in the image. To address this, we propose FINER‑Tuning, leveraging Direct Preference Optimization (DPO) on FINER‑inspired data. Finetuning four frontier MLLMs with FINER‑Tuning yields up to 24.2% gains (InternVL3.5‑14B) on hallucinations from our benchmarks, while simultaneously improving performance on eight existing hallucination suites, and enhancing general multimodal capabilities across six benchmarks. Code, benchmark, and models are available at \hrefhttps://explainableml.github.io/finer‑project/https://explainableml.github.io/finer‑project/.

PaperId: 2366, https://arxiv.org/pdf/2603.17651.pdf GitHub

Abstract:
Generative inbetweening (GI) seeks to synthesize realistic intermediate frames between the first and last keyframes beyond mere interpolation. As sequences become sparser and motions larger, previous GI models struggle with inconsistent frames with unstable pacing and semantic misalignment. Since GI involves fixed endpoints and numerous plausible paths, this task requires additional guidance gained from the keyframes and text to specify the intended path. Thus, we give semantic and temporal guidance from the keyframes and text onto each intermediate frame through Keyframe‑anchored Attention Bias. We also better enforce frame consistency with Rescaled Temporal RoPE, which allows self‑attention to attend to keyframes more faithfully. TGI‑Bench, the first benchmark specifically designed for text‑conditioned GI evaluation, enables challenge‑targeted evaluation to analyze GI models. Without additional training, our method achieves state‑of‑the‑art frame consistency, semantic fidelity, and pace stability for both short and long sequences across diverse challenges.

PaperId: 2367, https://arxiv.org/pdf/2603.17529.pdf GitHub

Abstract:
Accurate air quality forecasting is essential for public health and environmental sustainability, but remains challenging due to the complex pollutant dynamics. Existing deep learning methods often model pollutant dynamics as an instantaneous process, overlooking the intrinsic delays in pollutant propagation. Thus, we propose AirDDE, the first neural delay differential equation framework in this task that integrates delay modeling into a continuous‑time pollutant evolution under physical guidance. Specifically, two novel components are introduced: (1) a memory‑augmented attention module that retrieves globally and locally historical features, which can adaptively capture delay effects modulated by multifactor data; and (2) a physics‑guided delay evolving function, grounded in the diffusion‑advection equation, that models diffusion, delayed advection, and source/sink terms, which can capture delay‑aware pollutant accumulation patterns with physical plausibility. Extensive experiments on three real‑world datasets demonstrate that AirDDE achieves the state‑of‑the‑art forecasting performance with an average MAE reduction of 8.79% over the best baselines. The code is available at https://github.com/w2obin/airdde‑aaai.

PaperId: 2368, https://arxiv.org/pdf/2603.17522.pdf GitHub

Abstract:
The rapid proliferation of large language models (LLMs) has created an urgent need for robust and generalizable detectors of machine‑generated text. Existing benchmarks typically evaluate a single detector on a single dataset under ideal conditions, leaving open questions about cross‑domain transfer, cross‑LLM generalization, and adversarial robustness. We present a comprehensive benchmark evaluating diverse detection approaches across two corpora: HC3 (23,363 human‑ChatGPT pairs) and ELI5 (15,000 human‑Mistral‑7B pairs). Methods include classical classifiers, fine‑tuned transformer encoders (BERT, RoBERTa, ELECTRA, DistilBERT, DeBERTa‑v3), a CNN, an XGBoost stylometric model, perplexity‑based detectors, and LLM‑as‑detector prompting. Results show that transformer models achieve near‑perfect in‑distribution performance but degrade under domain shift. The XGBoost stylometric model matches performance while remaining interpretable. LLM‑based detectors underperform and are affected by generator‑detector identity bias. Perplexity‑based methods exhibit polarity inversion, with modern LLM outputs showing lower perplexity than human text, but remain effective when corrected. No method generalizes robustly across domains and LLM sources.

PaperId: 2369, https://arxiv.org/pdf/2603.17476.pdf GitHub

Abstract:
Unified Multimodal Models (UMMs) offer powerful cross‑modality capabilities but introduce new safety risks not observed in single‑task models. Despite their emergence, existing safety benchmarks remain fragmented across tasks and modalities, limiting the comprehensive evaluation of complex system‑level vulnerabilities. To address this gap, we introduce UniSAFE, the first comprehensive benchmark for system‑level safety evaluation of UMMs across 7 I/O modality combinations, spanning conventional tasks and novel multimodal‑context image generation settings. UniSAFE is built with a shared‑target design that projects common risk scenarios across task‑specific I/O configurations, enabling controlled cross‑task comparisons of safety failures. Comprising 6,802 curated instances, we use UniSAFE to evaluate 15 state‑of‑the‑art UMMs, both proprietary and open‑source. Our results reveal critical vulnerabilities across current UMMs, including elevated safety violations in multi‑image composition and multi‑turn settings, with image‑output tasks consistently more vulnerable than text‑output tasks. These findings highlight the need for stronger system‑level safety alignment for UMMs. Our code and data are publicly available at https://github.com/segyulee/UniSAFE

PaperId: 2370, https://arxiv.org/pdf/2603.17470.pdf GitHub

Abstract:
Monocular 3D object detection typically relies on pseudo‑labeling techniques to reduce dependency on real‑world annotations. Recent advances demonstrate that deterministic linguistic cues can serve as effective auxiliary weak supervision signals, providing complementary semantic context. However, hand‑crafted textual descriptions struggle to capture the inherent visual diversity of individuals across scenes, limiting the model's ability to learn scene‑aware representations. To address this challenge, we propose Visual‑referred Probabilistic Prompt Learning (VirPro), an adaptive multi‑modal pretraining paradigm that can be seamlessly integrated into diverse weakly supervised monocular 3D detection frameworks. Specifically, we generate a diverse set of learnable, instance‑conditioned prompts across scenes and store them in an Adaptive Prompt Bank (APB). Subsequently, we introduce Multi‑Gaussian Prompt Modeling (MGPM), which incorporates scene‑based visual features into the corresponding textual embeddings, allowing the text prompts to express visual uncertainties. Then, from the fused vision‑language embeddings, we decode a prompt‑targeted Gaussian, from which we derive a unified object‑level prompt embedding for each instance. RoI‑level contrastive matching is employed to enforce modality alignment, bringing embeddings of co‑occurring objects within the same scene closer in the latent space, thus enhancing semantic coherence. Extensive experiments on the KITTI benchmark demonstrate that integrating our pretraining paradigm consistently yields substantial performance gains, achieving up to a 4.8% average precision improvement than the baseline. Code is available at https://github.com/AustinLCP/VirPro.

PaperId: 2371, https://arxiv.org/pdf/2603.17312.pdf GitHub

Abstract:
Accurately estimating task progress is critical for embodied agents to plan and execute long‑horizon, multi‑step tasks. Despite promising advances, existing Vision‑Language Models (VLMs) based methods primarily leverage their video understanding capabilities, while neglecting their complex reasoning potential. Furthermore, processing long video trajectories with VLMs is computationally prohibitive for real‑world deployment. To address these challenges, we propose the Recurrent Reasoning Vision‑Language Model (\textR^2VLM). Our model features a recurrent reasoning framework that processes local video snippets iteratively, maintaining a global context through an evolving Chain of Thought (CoT). This CoT explicitly records task decomposition, key steps, and their completion status, enabling the model to reason about complex temporal dependencies. This design avoids the high cost of processing long videos while preserving essential reasoning capabilities. We train \textR^2VLM on large‑scale, automatically generated datasets from ALFRED and Ego4D. Extensive experiments on progress estimation and downstream applications, including progress‑enhanced policy learning, reward modeling for reinforcement learning, and proactive assistance, demonstrate that \textR^2VLM achieves strong performance and generalization, achieving a new state‑of‑the‑art in long‑horizon task progress estimation. The models and benchmarks are publicly available at \hrefhttps://huggingface.co/collections/zhangyuelin/r2vlmhuggingface.

PaperId: 2372, https://arxiv.org/pdf/2603.17307.pdf GitHub

Abstract:
Despite rapid developments and widespread applications of MLLM agents, they still struggle with long‑form video understanding (LVU) tasks, which are characterized by high information density and extended temporal spans. Recent research on LVU agents demonstrates that simple task decomposition and collaboration mechanisms are insufficient for long‑chain reasoning tasks. Moreover, directly reducing the time context through embedding‑based retrieval may lose key information of complex problems. In this paper, we propose Symphony, a multi‑agent system, to alleviate these limitations. By emulating human cognition patterns, Symphony decomposes LVU into fine‑grained subtasks and incorporates a deep reasoning collaboration mechanism enhanced by reflection, effectively improving the reasoning capability. Additionally, Symphony provides a VLM‑based grounding approach to analyze LVU tasks and assess the relevance of video segments, which significantly enhances the ability to locate complex problems with implicit intentions and large temporal spans. Experimental results show that Symphony achieves state‑of‑the‑art performance on LVBench, LongVideoBench, VideoMME, and MLVU, with a 5.0% improvement over the prior state‑of‑the‑art method on LVBench. Code is available at https://github.com/Haiyang0226/Symphony.

PaperId: 2373, https://arxiv.org/pdf/2603.17296.pdf GitHub

Abstract:
GenAI Units In Digital Design Education (GUIDE) is an open courseware repository with runnable Google Colab labs and other materials. We describe the repository's architecture and educational approach based on standardized teaching units comprising slides, short videos, runnable labs, and related papers. This organization enables consistency for both the students' learning experience and the reuse and grading by instructors. We demonstrate GUIDE in practice with three representative units: VeriThoughts for reasoning and formal‑verification‑backed RTL generation, enhanced LLM‑aided testbench generation, and LLMPirate for IP Piracy. We also provide details for four example course instances (GUIDE4ChipDesign, Build your ASIC, GUIDE4HardwareSecurity, and Hardware Design) that assemble GUIDE units into full semester offerings, learning outcomes, and capstone projects, all based on proven materials. For example, the GUIDE4HardwareSecurity course includes a project on LLM‑aided hardware Trojan insertion that has been successfully deployed in the classroom and in Cybersecurity Games and Conference (CSAW), a student competition and academic conference for cybersecurity. We also organized an NYU Cognichip Hackathon, engaging students across 24 international teams in AI‑assisted RTL design workflows. The GUIDE repository is open for contributions and available at: https://github.com/FCHXWH823/LLM4ChipDesign.

PaperId: 2374, https://arxiv.org/pdf/2603.16974.pdf GitHub

Abstract:
Multi‑Modal Knowledge Graphs (MMKGs) benefit from visual information, yet large‑scale image collection is hard to curate and often excludes ambiguous but relevant visuals (e.g., logos, symbols, abstract scenes). We present Beyond Images, an automatic data‑centric enrichment pipeline with optional human auditing. This pipeline operates in three stages: (1) large‑scale retrieval of additional entity‑related images, (2) conversion of all visual inputs into textual descriptions to ensure that ambiguous images contribute usable semantics rather than noise, and (3) fusion of multi‑source descriptions using a large language model (LLM) to generate concise, entity‑aligned summaries. These summaries replace or augment the text modality in standard MMKG models without changing their architectures or loss functions. Across three public MMKG datasets and multiple baseline models, we observe consistent gains (up to 7% Hits@1 overall). Furthermore, on a challenging subset of entities with visually ambiguous logos and symbols, converting images into text yields large improvements (201.35% MRR and 333.33% Hits@1). Additionally, we release a lightweight Text‑Image Consistency Check Interface for optional targeted audits, improving description quality and dataset reliability. Our results show that scaling image coverage and converting ambiguous visuals into text is a practical path to stronger MMKG completion. Code, datasets, and supplementary materials are available at https://github.com/pengyu‑zhang/Beyond‑Images.

PaperId: 2375, https://arxiv.org/pdf/2603.16958.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) are increasingly applied to robotic perception and manipulation, yet their ability to infer physical properties required for manipulation remains limited. In particular, estimating the mass of real‑world objects is essential for determining appropriate grasp force and ensuring safe interaction. However, current VLMs lack reliable mass reasoning capabilities, and most existing benchmarks do not explicitly evaluate physical quantity estimation under realistic sensing conditions. In this work, we propose PhysQuantAgent, a framework for real‑world object mass estimation using VLMs, together with VisPhysQuant, a new benchmark dataset for evaluation. VisPhysQuant consists of RGB‑D videos of real objects captured from multiple viewpoints, annotated with precise mass measurements. To improve estimation accuracy, we introduce three visual prompting methods that enhance the input image with object detection, scale estimation, and cross‑sectional image generation to help the model comprehend the size and internal structure of the target object. Experiments show that visual prompting significantly improves mass estimation accuracy on real‑world data, suggesting the efficacy of integrating spatial reasoning with VLM knowledge for physical inference.

PaperId: 2376, https://arxiv.org/pdf/2603.16935.pdf GitHub

Abstract:
Video‑based lie detection aims to identify deceptive behaviors from visual cues. Despite recent progress, its core challenge lies in learning sparse yet discriminative representations. Deceptive signals are typically subtle and short‑lived, easily overwhelmed by redundant information, while individual and contextual variations introduce strong identity‑related noise. To address this issue, we propose GenLie, a Global‑Enhanced Lie Detection Network that performs local feature modeling under global supervision. Specifically, sparse and subtle deceptive cues are captured at the local level, while global supervision and optimization ensure robust and discriminative representations by suppressing identity‑related noise. Experiments on three public datasets, covering both high‑ and low‑stakes scenarios, show that GenLie consistently outperforms state‑of‑the‑art methods. Source code is available at https://github.com/AliasDictusZ1/GenLie.

PaperId: 2377, https://arxiv.org/pdf/2603.16934.pdf GitHub

Abstract:
The deployment of Multimodal Large Language Models (MLLMs) in agriculture is currently stalled by a critical trade‑off: the existing literature lacks the large‑scale agricultural datasets required for robust model development and evaluation, while current state‑of‑the‑art models lack the verified domain expertise necessary to reason across diverse taxonomies. To address these challenges, we propose the Vision‑to‑Verified‑Knowledge (V2VK) pipeline, a novel generative AI‑driven annotation framework that integrates visual captioning with web‑augmented scientific retrieval to autonomously generate the AgriMM benchmark, effectively eliminating biological hallucinations by grounding training data in verified phytopathological literature. The AgriMM benchmark contains over 3,000 agricultural classes and more than 607k VQAs spanning multiple tasks, including fine‑grained plant species identification, plant disease symptom recognition, crop counting, and ripeness assessment. Leveraging this verifiable data, we present AgriChat, a specialized MLLM that presents broad knowledge across thousands of agricultural classes and provides detailed agricultural assessments with extensive explanations. Extensive evaluation across diverse tasks, datasets, and evaluation conditions reveals both the capabilities and limitations of current agricultural MLLMs, while demonstrating AgriChat's superior performance over other open‑source models, including internal and external benchmarks. The results validate that preserving visual detail combined with web‑verified knowledge constitutes a reliable pathway toward robust and trustworthy agricultural AI. The code and dataset are publicly available at https://github.com/boudiafA/AgriChat .

PaperId: 2378, https://arxiv.org/pdf/2603.16932.pdf GitHub

Abstract:
Vision‑language models (VLMs) typically process images at a native high‑resolution, forcing a trade‑off between accuracy and computational efficiency: high‑resolution inputs capture fine details but incur significant computational costs, while low‑resolution inputs advocate for efficiency, they potentially miss critical visual information, like small text. We present AwaRes, a spatial‑on‑demand framework that resolves this accuracy‑efficiency trade‑off by operating on a low‑resolution global view and using tool‑calling to retrieve only high‑resolution segments needed for a given query. We construct supervised data automatically: a judge compares low‑ vs.\ high‑resolution answers to label whether cropping is needed, and an oracle grounding model localizes the evidence for the correct answer, which we map to a discrete crop set to form multi‑turn tool‑use trajectories. We train our framework with cold‑start SFT followed by multi‑turn GRPO with a composite reward that combines semantic answer correctness with explicit crop‑cost penalties. Project page: https://nimrodshabtay.github.io/AwaRes

PaperId: 2379, https://arxiv.org/pdf/2603.16926.pdf GitHub

Abstract:
The Inaugural Music Source Restoration (MSR) Challenge targets the recovery of original, unprocessed stems from fully mixed and mastered music. Unlike conventional music source separation, MSR requires reversing complex production processes such as equalization, compression, reverberation, and other real‑world degradations. To address MSR, we propose a two‑stage system. First, an ensemble of pre‑trained separation models produces preliminary source estimates. Then a set of pre‑trained BSRNN‑based restoration models performs targeted reconstruction to refine these estimates. On the official MSR benchmark, our system surpasses the baselines on all metrics, ranking second among all submissions. The code is available at https://github.com/xinghour/Music‑source‑restoration‑CUPAudioGroup

PaperId: 2380, https://arxiv.org/pdf/2603.16868.pdf GitHub

Abstract:
Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large‑scale data, recent methods achieve high performance in depth estimation from a single image. Meanwhile, reconstructing and decomposing common scenes into individual 3D objects remains a hard challenge due to the large variety of objects, frequent occlusions and complex object relations. Notably, beyond shape and pose estimation of individual objects, applications in robotics and animation require physically‑plausible scene reconstruction where objects obey physical principles of non‑penetration and realistic contacts. In this work we advance object‑level scene reconstruction along two directions. First, we introduceMessyKitchens, a new dataset with real‑world scenes featuring cluttered environments and providing high‑fidelity object‑level ground truth in terms of 3D object shapes, poses and accurate object contacts. Second, we build on the recent SAM 3D approach for single‑object reconstruction and extend it with Multi‑Object Decoder (MOD) for joint object‑level scene reconstruction. To validate our contributions, we demonstrate MessyKitchens to significantly improve previous datasets in registration accuracy and inter‑object penetration. We also compare our multi‑object reconstruction approach on three datasets and demonstrate consistent and significant improvements of MOD over the state of the art. Our new benchmark, code and pre‑trained models will become publicly available on our project website: https://messykitchens.github.io/.

PaperId: 2381, https://arxiv.org/pdf/2603.16866.pdf GitHub

Abstract:
Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data‑generation‑ready digital assets, in both scale and diversity. In this work, we present ManiTwin, an automated and efficient pipeline for generating data‑generation‑ready digital object twins. Our pipeline transforms a single image into simulation‑ready and semantically annotated 3D asset, enabling large‑scale robotic manipulation data generation. Using this pipeline, we construct ManiTwin‑100K, a dataset containing 100K high‑quality annotated 3D assets. Each asset is equipped with physical properties, language descriptions, functional annotations, and verified manipulation proposals. Experiments demonstrate that ManiTwin provides an efficient asset synthesis and annotation workflow, and that ManiTwin‑100K offers high‑quality and diverse assets for manipulation data generation, random scene synthesis, and VQA data generation, establishing a strong foundation for scalable simulation data synthesis and policy learning. Our webpage is available at https://manitwin.github.io/.

PaperId: 2382, https://arxiv.org/pdf/2603.16859.pdf GitHub

Abstract:
Omni‑modal large language models (OLMs) redefine human‑machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy‑centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity to navigate dynamic cues in natural dialogues. To this end, we propose SocialOmni, a comprehensive benchmark that operationalizes the evaluation of this conversational interactivity across three core dimensions: (i) speaker separation and identification (who is speaking), (ii) interruption timing control (when to interject), and (iii) natural interruption generation (how to phrase the interruption). SocialOmni features 2,000 perception samples and a quality‑controlled diagnostic set of 209 interaction‑generation instances with strict temporal and contextual constraints, complemented by controlled audio‑visual inconsistency scenarios to test model robustness. We benchmarked 12 leading OLMs, which uncovers significant variance in their social‑interaction capabilities across models. Furthermore, our analysis reveals a pronounced decoupling between a model's perceptual accuracy and its ability to generate contextually appropriate interruptions, indicating that understanding‑centric metrics alone are insufficient to characterize conversational social competence. More encouragingly, these diagnostics from SocialOmni yield actionable signals for bridging the perception‑interaction divide in future OLMs.

PaperId: 2383, https://arxiv.org/pdf/2603.16839.pdf GitHub

Abstract:
Automated presentation generation remains a challenging task requiring coherent content creation, visual design, and audience‑aware communication. This work proposes an OpenEnv‑compatible reinforcement learning environment where LLM agents learn to research topics, plan content, and generate professional HTML slide presentations through tool use. We introduce a multi‑component reward system combining structural validation, render quality assessment, LLM‑based aesthetic scoring, content quality metrics, and an inverse specification reward that measures how faithfully generated slides convey their intended purpose. The inverse specification reward, an "inverse task" where an LLM attempts to recover the original specification from generated slides, provides a holistic quality signal. Our approach fine‑tunes Qwen2.5‑Coder‑7B via GRPO, training only 0.5% of parameters on prompts derived from expert demonstrations collected using Claude Opus 4.6. Experiments on 48 diverse business briefs across six models demonstrate that our fine‑tuned 7B model achieves 91.2% of Claude Opus 4.6's quality while improving 33.1% over the base model. The six‑model comparison reveals that instruction adherence and tool‑use compliance, rather than raw parameter count, determine agentic task performance. We contribute SlideRL, an open‑source dataset of 288 multi‑turn rollout trajectories across all six models: https://huggingface.co/datasets/KarthikRagunathAnandaKumar/sliderl‑multi‑turn‑rollouts Code: https://github.com/pushing‑the‑frontier/slide‑forge‑llm

PaperId: 2384, https://arxiv.org/pdf/2603.16825.pdf GitHub

Abstract:
Robot‑assisted therapy can deliver high‑dose, task‑specific training after neurologic injury, but most systems act primarily at the limb level‑engaging the impaired neural circuits only indirectly‑which remains a key barrier to truly contingent, neuroplasticity‑targeted rehabilitation. We address this gap by implementing online, dual‑state motor imagery control of an upper‑limb exoskeleton, enabling goal‑directed reaches to be both initiated and terminated directly from non‑invasive EEG. Eight participants used EEG to initiate assistance and then volitionally halt the robot mid‑trajectory. Across two online sessions, group‑mean hit rates were 61.5% for onset and 64.5% for offset, demonstrating reliable start‑stop command delivery despite instrumental noise and passive arm motion. Methodologically, we reveal a systematic, class‑driven bias induced by common task‑based recentering using an asymmetric margin diagnostic, and we introduce a class‑agnostic fixation‑based recentering method that tracks drift without sampling command classes while preserving class geometry. This substantially improves threshold‑free separability (AUC gains: onset +56%, p = 0.0117; offset +34%, p = 0.0251) and reduces bias within and across days. Together, these results help bridge offline decoding and practical, intention‑driven start‑stop control of a rehabilitation exoskeleton, enabling precisely timed, contingent assistance aligned with neuroplasticity goals while supporting future clinical translation.

PaperId: 2385, https://arxiv.org/pdf/2603.16792.pdf GitHub

Abstract:
Pixel‑space diffusion has recently re‑emerged as a strong alternative to latent diffusion, enabling high‑quality generation without pretrained autoencoders. However, standard pixel‑space diffusion models receive relatively weak semantic supervision and are not explicitly designed to capture high‑level visual structure. Recent representation‑alignment methods (e.g., REPA) suggest that pretrained visual features can substantially improve diffusion training, and visual co‑denoising has emerged as a promising direction for incorporating such features into the generative process. However, existing co‑denoising approaches often entangle multiple design choices, making it unclear which design choices are truly essential. Therefore, we present V‑Co, a systematic study of visual co‑denoising in a unified JiT‑based framework. This controlled setting allows us to isolate the ingredients that make visual co‑denoising effective. Our study reveals four key ingredients for effective visual co‑denoising. First, preserving feature‑specific computation while enabling flexible cross‑stream interaction motivates a fully dual‑stream architecture. Second, effective classifier‑free guidance (CFG) requires a structurally defined unconditional prediction. Third, stronger semantic supervision is best provided by a perceptual‑drifting hybrid loss. Fourth, stable co‑denoising further requires proper cross‑stream calibration, which we realize through RMS‑based feature rescaling. Together, these findings yield a simple recipe for visual co‑denoising. Experiments on ImageNet‑256 show that, at comparable model sizes, V‑Co outperforms the underlying pixel‑space diffusion baseline and strong prior pixel‑diffusion methods while using fewer training epochs, offering practical guidance for future representation‑aligned generative models.

PaperId: 2386, https://arxiv.org/pdf/2603.16737.pdf GitHub

Abstract:
Vision‑language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine‑grained visual attributes and reason about underlying causal relationships. In‑context learning (ICL) offers a promising avenue for VLMs to adapt to new tasks, but its effectiveness critically depends on the selection of demonstration examples. Existing retrieval‑augmented approaches typically rely on passive similarity‑based retrieval, which tends to select correlated but non‑causal examples, amplifying spurious associations and limiting model robustness. We introduce CIRCLES (Composed Image Retrieval for Causal Learning Example Selection), a novel framework that actively constructs demonstration sets by retrieving counterfactual‑style examples through targeted, attribute‑guided composed image retrieval. By incorporating counterfactual‑style examples, CIRCLES enables VLMs to implicitly reason about the causal relations between attributes and outcomes, moving beyond superficial correlations and fostering more robust and grounded reasoning. Comprehensive experiments on four diverse datasets demonstrate that CIRCLES consistently outperforms existing methods across multiple architectures, especially on small‑scale models, with pronounced gains under information scarcity. Furthermore, CIRCLES retrieves more diverse and causally informative examples, providing qualitative insights into how models leverage in‑context demonstrations for improved reasoning. Our code is available at https://github.com/gzxiong/CIRCLES.

PaperId: 2387, https://arxiv.org/pdf/2603.16666.pdf GitHub

Abstract:
World Action Models (WAMs) have emerged as a promising alternative to Vision‑Language‑Action (VLA) models for embodied control because they explicitly model how visual observations may evolve under action. Most existing WAMs follow an imagine‑then‑execute paradigm, incurring substantial test‑time latency from iterative video denoising, yet it remains unclear whether explicit future imagination is actually necessary for strong action performance. In this paper, we ask whether WAMs need explicit future imagination at test time, or whether their benefit comes primarily from video modeling during training. We disentangle the role of video modeling during training from explicit future generation during inference by proposing Fast‑WAM, a WAM architecture that retains video co‑training during training but skips future prediction at test time. We further instantiate several Fast‑WAM variants to enable a controlled comparison of these two factors. Across these variants, we find that Fast‑WAM remains competitive with imagine‑then‑execute variants, while removing video co‑training causes a much larger performance drop. Empirically, Fast‑WAM achieves competitive results with state‑of‑the‑art methods both on simulation benchmarks (LIBERO and RoboTwin) and real‑world tasks, without embodied pretraining. It runs in real time with 190ms latency, over 4× faster than existing imagine‑then‑execute WAMs. These results suggest that the main value of video prediction in WAMs may lie in improving world representations during training rather than generating future observations at test time. Project page: https://yuantianyuan01.github.io/FastWAM/

PaperId: 2388, https://arxiv.org/pdf/2603.16654.pdf GitHub

Abstract:
Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi‑hop QA benchmarks without step‑level annotations. To address this gap, we introduce Omanic, an open‑domain 4‑hop QA benchmark designed not only to measure final‑answer accuracy but also to diagnose where reasoning breaks down. Omanic contains 10,296 machine‑generated training examples (OmanicSynth) and 967 expert‑reviewed human‑annotated evaluation examples (OmanicBench), with each evaluation question decomposed into single‑hop sub‑questions, intermediate answers, and structured graph topologies. Experiments with proprietary and open‑source LLMs show that Omanic is challenging, while step‑wise analysis reveals a later‑hop bottleneck, factual knowledge floor, and error propagation along reasoning chains. Fine‑tuning on OmanicSynth transfers to six reasoning and mathematics benchmarks, yielding a 7.41‑point average gain and validating its effectiveness as supervision for reasoning‑capability transfer. We release the data at https://huggingface.co/datasets/li‑lab/Omanic and the code at https://github.com/XiaojieGu/Omanic.

PaperId: 2389, https://arxiv.org/pdf/2603.16629.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have recently been proposed as a means to generate natural‑language explanations for face recognition decisions. While such explanations facilitate human interpretability, their reliability on unconstrained face images remains underexplored. In this work, we systematically analyze MLLM‑generated explanations for the unconstrained face verification task on the challenging IJB‑S dataset, with a particular focus on extreme pose variation and surveillance imagery. Our results show that even when MLLMs produce correct verification decisions, the accompanying explanations frequently rely on non‑verifiable or hallucinated facial attributes that are not supported by visual evidence. We further study the effect of incorporating information from traditional face recognition systems, viz., scores and decisions, alongside the input images. Although such information improves categorical verification performance, it does not consistently lead to faithful explanations. To evaluate the explanations beyond decision accuracy, we introduce a likelihood‑ratio‑based framework that measures the evidential strength of textual explanations. Our findings highlight fundamental limitations of current MLLMs for explainable face recognition and underscore the need for a principled evaluation of reliable and trustworthy explanations in biometric applications. Code is available at https://github.com/redwankarimsony/LR‑MLLMFR‑Explainability.

PaperId: 2390, https://arxiv.org/pdf/2603.16596.pdf GitHub

Abstract:
Mounting posture is an important visual indicator of estrus in dairy cattle. However, achieving reliable mounting pose estimation in real‑world environments remains challenging due to cluttered backgrounds and frequent inter‑animal occlusion. We present FSMC‑Pose, a top‑down framework that integrates a lightweight frequency‑spatial fusion backbone, CattleMountNet, and a multiscale self‑calibration head, SC2Head. Specifically, we design two algorithmic components for CattleMountNet: the Spatial Frequency Enhancement Block (SFEBlock) and the Receptive Aggregation Block (RABlock). SFEBlock separates cattle from cluttered backgrounds, while RABlock captures multiscale contextual information. The Spatial‑Channel Self‑Calibration Head (SC2Head) attends to spatial and channel dependencies and introduces a self‑calibration branch to mitigate structural misalignment under inter‑animal overlap. We construct a mounting dataset, MOUNT‑Cattle, covering 1176 mounting instances, which follows the COCO format and supports drop‑in training across pose estimation models. Using a comprehensive dataset that combines MOUNT‑Cattle with the public NWAFU‑Cattle dataset, FSMC‑Pose achieves higher accuracy than strong baselines, with markedly lower computational and parameter costs, while maintaining real‑time inference on commodity GPUs. Extensive experiments and qualitative analyses show that FSMC‑Pose effectively captures and estimates cattle mounting pose in complex and cluttered environments. Dataset and code are available at https://github.com/elianafang/FSMC‑Pose.

PaperId: 2391, https://arxiv.org/pdf/2603.16576.pdf GitHub

Abstract:
Recent progress in image generation models (IGMs) enables high‑fidelity content creation but also amplifies risks, including the reproduction of copyrighted content and the generation of offensive content. Image Generation Model Unlearning (IGMU) mitigates these risks by removing harmful concepts without full retraining. Despite growing attention, the robustness under adversarial inputs, particularly image‑side threats in black‑box settings, remains underexplored. To bridge this gap, we present REFORGE, a black‑box red‑teaming framework that evaluates IGMU robustness via adversarial image prompts. REFORGE initializes stroke‑based images and optimizes perturbations with a cross‑attention‑guided masking strategy that allocates noise to concept‑relevant regions, balancing attack efficacy and visual fidelity. Extensive experiments across representative unlearning tasks and defenses demonstrate that REFORGE significantly improves attack success rate while achieving stronger semantic alignment and higher efficiency than involved baselines. These results expose persistent vulnerabilities in current IGMU methods and highlight the need for robustness‑aware unlearning against multi‑modal adversarial attacks. Our code is at: https://github.com/Imfatnoily/REFORGE.

PaperId: 2392, https://arxiv.org/pdf/2603.16495.pdf GitHub

Abstract:
The current expressway operation relies on rule‑based and isolated models, which limits the ability to jointly analyze knowledge across different systems. Meanwhile, Large Language Models (LLMs) are increasingly applied in intelligent transportation, advancing traffic models from algorithmic to cognitive intelligence. However, general LLMs are unable to effectively understand the regulations and causal relationships of events in unconventional scenarios in the expressway field. Therefore, this paper constructs a pre‑trained multimodal large language model (MLLM) for expressways, ExpressMind, which serves as the cognitive core for intelligent expressway operations. This paper constructs the industry's first full‑stack expressway dataset, encompassing traffic knowledge texts, emergency reasoning chains, and annotated video events to overcome data scarcity. This paper proposes a dual‑layer LLM pre‑training paradigm based on self‑supervised training and unsupervised learning. Additionally, this study introduces a Graph‑Augmented RAG framework to dynamically index the expressway knowledge base. To enhance reasoning for expressway incident response strategies, we develop a RL‑aligned Chain‑of‑Thought (RL‑CoT) mechanism that enforces consistency between model reasoning and expert problem‑solving heuristics for incident handling. Finally, ExpressMind integrates a cross‑modal encoder to align the dynamic feature sequences under the visual and textual channels, enabling it to understand traffic scenes in both video and image modalities. Extensive experiments on our newly released multi‑modal expressway benchmark demonstrate that ExpressMind comprehensively outperforms existing baselines in event detection, safety response generation, and complex traffic analysis. The code and data are available at: https://wanderhee.github.io/ExpressMind/.

PaperId: 2393, https://arxiv.org/pdf/2603.16289.pdf GitHub

Abstract:
The rapid advancement of Multimodal Large Language Models (MLLMs) has enabled browsing agents to acquire and reason over multimodal information in the real world. But existing benchmarks suffer from two limitations: insufficient evaluation of visual reasoning ability and the neglect of native visual information of web pages in the reasoning chains. To address these challenges, we introduce a new benchmark for visual‑native search, VisBrowse‑Bench. It contains 169 VQA instances covering multiple domains and evaluates the models' visual reasoning capabilities during the search process through multimodal evidence cross‑validation via text‑image retrieval and joint reasoning. These data were constructed by human experts using a multi‑stage pipeline and underwent rigorous manual verification. We additionally propose an agent workflow that can effectively drive the browsing agent to actively collect and reason over visual information during the search process. We comprehensively evaluated both open‑source and closed‑source models in this workflow. Experimental results show that even the best‑performing model, Claude‑4.6‑Opus only achieves an accuracy of 47.6%, while the proprietary Deep Research model, o3‑deep‑research only achieves an accuracy of 41.1%. The code and data can be accessed at: https://github.com/ZhengboZhang/VisBrowse‑Bench

PaperId: 2394, https://arxiv.org/pdf/2603.16261.pdf GitHub

Abstract:
Robust 3D object detection under adverse weather conditions is crucial for autonomous driving. However, most existing methods simply combine all weather samples for training while overlooking data distribution discrepancies across different weather scenarios, leading to performance conflicts. To address this issue, we introduce AW‑MoE, the framework that innovatively integrates Mixture of Experts (MoE) into weather‑robust multi‑modal 3D object detection approaches. AW‑MoE incorporates Image‑guided Weather‑aware Routing (IWR), which leverages the superior discriminability of image features across weather conditions and their invariance to scene variations for precise weather classification. Based on this accurate classification, IWR selects the top‑K most relevant Weather‑Specific Experts (WSE) that handle data discrepancies, ensuring optimal detection under all weather conditions. Additionally, we propose a Unified Dual‑Modal Augmentation (UDMA) for synchronous LiDAR and 4D Radar dual‑modal data augmentation while preserving the realism of scenes. Extensive experiments on the real‑world dataset demonstrate that AW‑MoE achieves ~ 15% improvement in adverse‑weather performance over state‑of‑the‑art methods, while incurring negligible inference overhead. Moreover, integrating AW‑MoE into established baseline detectors yields performance improvements surpassing current state‑of‑the‑art methods. These results show the effectiveness and strong scalability of our AW‑MoE. We will release the code publicly at https://github.com/windlinsherlock/AW‑MoE.

PaperId: 2395, https://arxiv.org/pdf/2603.16253.pdf GitHub

Abstract:
Vision‑language process reward models (VL‑PRMs) are increasingly used to score intermediate reasoning steps and rerank candidates under test‑time scaling. However, they often function as black‑box judges: a low step score may reflect a genuine reasoning mistake or simply the verifier's misperception of the image. This entanglement between perception and reasoning leads to systematic false positives (rewarding hallucinated visual premises) and false negatives (penalizing correct grounded statements), undermining both reranking and error localization. We introduce Explicit Visual Premise Verification (EVPV), a lightweight verification interface that conditions step scoring on the reliability of the visual premises a step depends on. The policy is prompted to produce a step‑wise visual checklist that makes required visual facts explicit, while a constraint extractor independently derives structured visual constraints from the input image. EVPV matches checklist claims against these constraints to compute a scalar visual reliability signal, and calibrates PRM step rewards via reliability gating: rewards for visually dependent steps are attenuated when reliability is low and preserved when reliability is high. This decouples perceptual uncertainty from logical evaluation without per‑step tool calls. Experiments on VisualProcessBench and six multimodal reasoning benchmarks show that EVPV improves step‑level verification and consistently boosts Best‑of‑N reranking accuracy over strong baselines. Furthermore, injecting controlled corruption into the extracted constraints produces monotonic performance degradation, providing causal evidence that the gains arise from constraint fidelity and explicit premise verification rather than incidental prompt effects. Code is available at: https://github.com/Qwen‑Applications/EVPV‑PRM

PaperId: 2396, https://arxiv.org/pdf/2603.16169.pdf GitHub

Abstract:
Corrective Retrieval Augmented Generation (CRAG) improves the robustness of RAG systems by evaluating retrieved document quality and triggering corrective actions. However, the original implementation relies on proprietary components including the Google Search API and closed model weights, limiting reproducibility. In this work, we present a fully open‑source reproduction of CRAG, replacing proprietary web search with the Wikipedia API and the original LLaMA‑2 generator with Phi‑3‑mini‑4k‑instruct. We evaluate on PopQA and ARC‑Challenge, demonstrating that our open‑source pipeline achieves comparable performance to the original system. Furthermore, we contribute the first explainability analysis of CRAG's T5‑based retrieval evaluator using SHAP, revealing that the evaluator primarily relies on named entity alignment rather than semantic similarity. Our analysis identifies key failure modes including domain transfer limitations on science questions. All code and results are available at https://github.com/suryayalavarthi/crag‑reproduction.

PaperId: 2397, https://arxiv.org/pdf/2603.16113.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) offer significant potential in computational pathology by enabling interpretable image analysis, automated reporting, and scalable decision support. However, their widespread clinical adoption remains limited due to the absence of reliable, automated evaluation metrics capable of identifying subtle failures such as hallucinations. To address this gap, we propose PathGLS, a novel reference‑free evaluation framework that assesses pathology VLMs across three dimensions: Grounding (fine‑grained visual‑text alignment), Logic (entailment graph consistency using Natural Language Inference), and Stability (output variance under adversarial visual‑semantic perturbations). PathGLS supports both patch‑level and whole‑slide image (WSI)‑level analysis, yielding a comprehensive trust score. Experiments on Quilt‑1M, TCGA, REG2025, PathMMU and TCGA‑Sarcoma datasets demonstrate the superiority of PathGLS. Specifically, on the Quilt‑1M dataset, PathGLS reveals a steep sensitivity drop of 40.2% for hallucinated reports compared to only 2.1% for BERTScore. Moreover, validation against expert‑defined clinical error hierarchies reveals that PathGLS achieves a strong Spearman's rank correlation of ρ=0.71 (p < 0.0001), significantly outperforming Large Language Model (LLM)‑based approaches (Gemini 3.0 Pro: ρ=0.39, p < 0.0001). These results establish PathGLS as a robust reference‑free metric. By directly quantifying hallucination rates and domain shift robustness, it serves as a reliable criterion for benchmarking VLMs on private clinical datasets and informing safe deployment. Code can be found at: https://github.com/My13ad/PathGLS

PaperId: 2398, https://arxiv.org/pdf/2603.16105.pdf GitHub

Abstract:
Post‑training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches have been proposed, less emphasis has been placed on selecting the most suitable set of data (the so‑called \emphcalibration data) for finding the compressed model configuration. The choice of calibration data is a critical step in preserving model capabilities both intra‑ and inter‑tasks. In this work, we address the challenge of identifying high‑performance calibration sets for both pruning and quantization by analyzing intrinsic data properties rather than model‑specific signals. We introduce \textttZipCal, a model‑agnostic data curation strategy that maximizes lexical diversity based on Zipfian power laws. Experiments demonstrate that our method consistently outperforms standard uniform random sampling across various pruning benchmarks. Notably, it also performs on par, in terms of downstream performance, with a state‑of‑the‑art method that relies on model perplexity. The latter becomes prohibitively expensive at large‑scale models and datasets, while \textttZipCal is on average ～240× faster due to its tractable linear complexity\footnoteWe make the code and the experiments available at https://github.com/FrancescoMonaco/ZipCal..

PaperId: 2399, https://arxiv.org/pdf/2603.16092.pdf GitHub

Abstract:
Large vision‑language models (LVLMs) employ multi‑modal in‑context learning (MM‑ICL) to adapt to new tasks by leveraging demonstration examples. While increasing the number of demonstrations boosts performance, they incur significant inference latency due to the quadratic computational cost of Transformer attention with respect to the context length. To address this trade‑off, we propose Parallel In‑Context Learning (Parallel‑ICL), a plug‑and‑play inference algorithm. Parallel‑ICL partitions the long demonstration context into multiple shorter, manageable chunks. It processes these chunks in parallel and integrates their predictions at the logit level, using a weighted Product‑of‑Experts (PoE) ensemble to approximate the full‑context output. Guided by ensemble learning theory, we introduce principled strategies for Parallel‑ICL: (i) clustering‑based context chunking to maximize inter‑chunk diversity and (ii) similarity‑based context compilation to weight predictions by query relevance. Extensive experiments on VQA, image captioning, and classification benchmarks demonstrate that Parallel‑ICL achieves performance comparable to full‑context MM‑ICL, while significantly improving inference speed. Our work offers an effective solution to the accuracy‑efficiency trade‑off in MM‑ICL, enabling dynamic task adaptation with substantially reduced inference overhead.

PaperId: 2400, https://arxiv.org/pdf/2603.16060.pdf GitHub GitHub

Abstract:
The dominant paradigm for improving mathematical reasoning in language models relies on Reinforcement Learning with verifiable rewards. Yet existing methods treat each problem instance in isolation without leveraging the reusable strategies that emerge and accumulate during training. To this end, we introduce ARISE (Agent Reasoning via Intrinsic Skill Evolution), a hierarchical reinforcement learning framework, in which a shared policy operates both to manage skills at high‑level and to generate responses at low‑level (denoted as a Skills Manager and a Worker, respectively). The Manager maintains a tiered skill library through a dedicated skill generation rollout that performs structured summarization of successful solution traces (after execution), while employing a policy‑driven selection mechanism to retrieve relevant skills to condition future rollouts (before execution). A hierarchical reward design guides the co‑evolution of reasoning ability and library quality. Experiments on two base models and seven benchmarks spanning both competition mathematics and Omni‑MATH show that ARISE consistently outperforms GRPO‑family algorithms and memory‑augmented baselines, with particularly notable gains on out‑of‑distribution tasks. Ablation studies confirm that each component contributes to the observed improvements and that library quality and reasoning performance improve in tandem throughout training. Code is available at \hrefhttps://github.com/Skylanding/ARISEhttps://github.com/Skylanding/ARISE.

PaperId: 2401, https://arxiv.org/pdf/2603.16039.pdf GitHub

Abstract:
Recent work has made clear that the residual pathway is not mere optimization plumbing; it is part of the model's representational machinery. We agree, but argue that the cleanest way to organize this design space is through a two‑axis view of the Transformer. A decoder evolves information along two ordered dimensions: sequence position and layer depth. Self‑attention already provides adaptive mixing along the sequence axis, whereas the residual stream usually performs fixed addition along the depth axis. If we fix a token position and treat layer index as the ordered variable, then a causal depth‑wise residual attention read is exactly the same local operator as causal short sliding‑window attention (ShortSWA), except written over depth rather than over sequence. This is the core residual stream duality behind Transformer^2. This perspective also clarifies the recent literature. ELC‑BERT and DenseFormer already show that learned aggregation over depth can outperform uniform residual accumulation, while Vertical Attention, DeepCrossAttention (DCA), MUDDFormer, and Attention Residuals move further toward explicit attention‑based routing over earlier layers. The key point, however, is that operator‑level duality does not imply systems‑level symmetry. For large‑scale autoregressive models, sequence‑axis ShortSWA is usually the more hardware‑friendly placement because it reuses token‑side sliding‑window kernels, KV‑cache layouts, and chunked execution. If the goal is instead to change the shortcut itself, Deep Delta Learning (DDL) is the cleaner intervention because it modifies the residual operator directly rather than adding a separate cross‑layer retrieval path. Our recommendation is therefore simple: use DDL when the shortcut is the object of interest, and use sequence‑axis ShortSWA when the goal is local adaptive mixing.

PaperId: 2402, https://arxiv.org/pdf/2603.15968.pdf GitHub

Abstract:
Constitutional AI is a method to oversee and control LLMs based on a set of rules written in natural language. These rules are typically written by human experts, but could in principle be learned automatically given sufficient training data for the desired behavior. Existing LLM‑based prompt optimizers attempt this but are ineffective at learning constitutions since (i) they require many labeled examples and (ii) lack structure in the optimized prompts, leading to diminishing improvements as prompt size grows. To address these limitations, we propose Multi‑Agent Constitutional Learning (MAC), which optimizes over structured prompts represented as sets of rules using a network of agents with specialized tasks to accept, edit, or reject rule updates. We also present MAC+, which improves performance by training agents on successful trajectories to reinforce updates leading to higher reward. We evaluate MAC on tagging Personally Identifiable Information (PII), a classification task with limited labels where interpretability is critical, and demonstrate that it generalizes to other agentic tasks such as tool calling. MAC outperforms recent prompt optimization methods by over 50%, produces human‑readable and auditable rule sets, and achieves performance comparable to supervised fine‑tuning and GRPO without requiring parameter updates.

PaperId: 2403, https://arxiv.org/pdf/2603.15914.pdf GitHub

Abstract:
AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday research practice. This paper is a practical guide to AI‑assisted research in mathematics and machine learning: We discuss how researchers can use modern AI systems productively, where these systems help most, and what kinds of guardrails are needed to use them responsibly. It is organized into three parts: (I) a five‑level taxonomy of AI integration, (II) an open‑source framework that, through a set of methodological rules formulated as agent prompts, turns CLI coding agents (e.g., Claude Code, Codex CLI, OpenCode) into autonomous research assistants, and (III) case studies from deep learning and mathematics. The framework runs inside a sandboxed container, works with any frontier LLM through existing CLI agents, is simple enough to install and use within minutes, and scales from personal‑laptop prototyping to multi‑node, multi‑GPU experimentation across compute clusters. In practice, our longest autonomous session ran for over 20 hours, dispatching independent experiments across multiple nodes without human intervention. We stress that our framework is not intended to replace the researcher in the loop, but to augment them. Our code is publicly available at https://github.com/ZIB‑IOL/The‑Agentic‑Researcher.

PaperId: 2404, https://arxiv.org/pdf/2603.15886.pdf GitHub

Abstract:
We present PhasorFlow, an open‑source Python library introducing a computational paradigm operating on the S^1 unit circle. Inputs are encoded as complex phasors z = e^iθ on the N‑Torus (\mathbbT^N). As computation proceeds via unitary wave interference gates, global norm is preserved while individual components drift into \mathbbC^N, allowing algorithms to natively leverage continuous geometric gradients for predictive learning. PhasorFlow provides three core contributions. First, we formalize the Phasor Circuit model (N unit circle threads, M gates) and introduce a 22‑gate library covering Standard Unitary, Non‑Linear, Neuromorphic, and Encoding operations with full matrix algebra simulation. Second, we present the Variational Phasor Circuit (VPC), analogous to Variational Quantum Circuits (VQC), enabling optimization of continuous phase parameters for classical machine learning tasks. Third, we introduce the Phasor Transformer, replacing expensive QK^TV attention with a parameter‑free, DFT‑based token mixing layer inspired by FNet. We validate PhasorFlow on non‑linear spatial classification, time‑series prediction, financial volatility detection, and neuromorphic tasks including neural binding and oscillatory associative memory. Our results establish unit circle computing as a deterministic, lightweight, and mathematically principled alternative to classical neural networks and quantum circuits. It operates on classical hardware while sharing quantum mechanics' unitary foundations. PhasorFlow is available at https://github.com/mindverse‑computing/phasorflow.

PaperId: 2405, https://arxiv.org/pdf/2603.15854.pdf GitHub

Abstract:
Sampling from a categorical distribution is mathematically simple, but in large‑vocabulary decoding, it often triggers extra memory traffic and extra kernels after the LM head. We present FlashSampling, an exact sampling primitive that fuses sampling into the LM‑head matmul and never materializes the logits tensor in HBM. The method is simple: compute logits tile‑by‑tile on chip, add Gumbel noise, keep only one maximizer per row and per vocabulary tile, and finish with a small reduction over tiles. In tensor‑parallel decoding, FlashSampling replaces the all‑gather of logits with streaming peer‑to‑peer writes: This overlaps GPU‑to‑GPU communication with computation and HBM loads across up to 8 GPUs, with near‑ideal scaling at large batch sizes. Our kernel is exact because argmax decomposes over partitions; grouped variants for online and tensor‑parallel settings are exact by hierarchical factorization of the categorical distribution. FlashSampling demonstrates kernel‑level speedups on decode workloads across 4 different datacenter GPUs (H100, H200, B200, B300), and in end‑to‑end vLLM experiments, it reduces time per output token by up to 10% on the models we test. These results show that exact sampling, with no approximation, can be integrated into the matmul itself, consolidating the bandwidth‑bound sampling step in an efficient epilogue.

PaperId: 2406, https://arxiv.org/pdf/2603.15798.pdf GitHub

Abstract:
The proliferation of agent benchmarks has created critical fragmentation that threatens research productivity. Each new benchmark requires substantial custom integration, creating an "integration tax" that limits comprehensive evaluation. We propose CUBE (Common Unified Benchmark Environments), a universal protocol standard built on MCP and Gym that allows benchmarks to be wrapped once and used everywhere. By separating task, benchmark, package, and registry concerns into distinct API layers, CUBE enables any compliant platform to access any compliant benchmark for evaluation, RL training, or data generation without custom integration. We call on the community to contribute to the development of this standard before platform‑specific implementations deepen fragmentation as benchmark production accelerates through 2026.

PaperId: 2407, https://arxiv.org/pdf/2603.15714.pdf GitHub

Abstract:
LLM based agents are increasingly deployed in high stakes settings where they process external data sources such as emails, documents, and code repositories. This creates exposure to indirect prompt injection attacks, where adversarial instructions embedded in external content manipulate agent behavior without user awareness. A critical but underexplored dimension of this threat is concealment: since users tend to observe only an agent's final response, an attack can conceal its existence by presenting no clue of compromise in the final user facing response while successfully executing harmful actions. This leaves users unaware of the manipulation and likely to accept harmful outcomes as legitimate. We present findings from a large scale public red teaming competition evaluating this dual objective across three agent settings: tool calling, coding, and computer use. The competition attracted 464 participants who submitted 272000 attack attempts against 13 frontier models, yielding 8648 successful attacks across 41 scenarios. All models proved vulnerable, with attack success rates ranging from 0.5% (Claude Opus 4.5) to 8.5% (Gemini 2.5 Pro). We identify universal attack strategies that transfer across 21 of 41 behaviors and multiple model families, suggesting fundamental weaknesses in instruction following architectures. Capability and robustness showed weak correlation, with Gemini 2.5 Pro exhibiting both high capability and high vulnerability. To address benchmark saturation and obsoleteness, we will endeavor to deliver quarterly updates through continued red teaming competitions. We open source the competition environment for use in evaluations, along with 95 successful attacks against Qwen that did not transfer to any closed source model. We share model‑specific attack data with respective frontier labs and the full dataset with the UK AISI and US CAISI to support robustness research.

PaperId: 2408, https://arxiv.org/pdf/2603.15708.pdf GitHub

Abstract:
Imbalanced data distribution remains a critical challenge in sequential learning, leading models to easily recognize frequent categories while failing to detect minority classes adequately. The Mixture‑of‑Experts model offers a scalable solution, yet its application is often hindered by parameter inefficiency, poor expert specialization, and difficulty in resolving prediction conflicts. To Master the Minority classes effectively, we propose the Uncertainty‑based Multi‑Expert fusion network (UME) framework. UME is designed with three core innovations: First, we employ Ensemble LoRA for parameter‑efficient modeling, significantly reducing the trainable parameter count. Second, we introduce Sequential Specialization guided by Dempster‑Shafer Theory (DST), which ensures effective specialization on the challenging‑tailed classes. Finally, an Uncertainty‑Guided Fusion mechanism uses DST's certainty measures to dynamically weigh expert opinions, resolving conflicts by prioritizing the most confident expert for reliable final predictions. Extensive experiments across four public hierarchical text classification datasets demonstrate that UME achieves state‑of‑the‑art performance. We achieve a performance gain of up to 17.97% over the best baseline on individual categories, while reducing trainable parameters by up to 10.32%. The findings highlight that uncertainty‑guided expert coordination is a principled strategy for addressing challenging‑tailed sequence learning. Our code is available at https://github.com/CQUPTWZX/Multi‑experts.

PaperId: 2409, https://arxiv.org/pdf/2603.15685.pdf GitHub

Abstract:
Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make inference prohibitively expensive. Existing compression methods typically rely on fixed window partitioning and attention‑based pruning, which overlook the piecewise semantic structure of audio‑visual signals and become fragile under aggressive token reduction. We propose Dynamic Audio‑driven Semantic cHunking (DASH), a training‑free framework that aligns token compression with semantic structure. DASH treats audio embeddings as a semantic anchor and detects boundary candidates via cosine‑similarity discontinuities, inducing dynamic, variable‑length segments that approximate the underlying piecewise‑coherent organization of the sequence. These boundaries are projected onto video tokens to establish explicit cross‑modal segmentation. Within each segment, token retention is determined by a tri‑signal importance estimator that fuses structural boundary cues, representational distinctiveness, and attention‑based salience, mitigating the sparsity bias of attention‑only selection. This structure‑aware allocation preserves transition‑critical tokens while reducing redundant regions. Extensive experiments on AVUT, VideoMME, and WorldSense demonstrate that DASH maintains superior accuracy while achieving higher compression ratios compared to prior methods. Code is available at: https://github.com/laychou666/DASH.

PaperId: 2410, https://arxiv.org/pdf/2603.15642.pdf GitHub

Abstract:
Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state across many turns. Many existing agent memory systems behave like external databases with ad hoc read/write rules, which can yield unstable retention, limited consolidation, and vulnerability to distractor content. We present CraniMem, a neurocognitively motivated, gated and bounded multi‑stage memory design for agentic systems. CraniMem couples goal conditioned gating and utility tagging with a bounded episodic buffer for near term continuity and a structured long‑term knowledge graph for durable semantic recall. A scheduled consolidation loop replays high utility traces into the graph while pruning low utility items, keeping memory growth in check and reducing interference. On long horizon benchmarks evaluated under both clean inputs and injected noise, CraniMem is more robust than a Vanilla RAG and Mem0 baseline and exhibits smaller performance drops under distraction. Our code is available at https://github.com/PearlMody05/Cranimem and the accompanying PyPI package at https://pypi.org/project/cranimem.

PaperId: 2411, https://arxiv.org/pdf/2603.15636.pdf GitHub

Abstract:
As AI‑driven document understanding and processing tools become increasingly prevalent in real‑world applications, the need for rigorous evaluation standards has grown increasingly urgent. Existing benchmarks and evaluations often focus on isolated capabilities or simplified scenarios, failing to capture the end‑to‑end task effectiveness required in practical settings. To address this gap, we introduce AIDABench, a comprehensive benchmark for evaluating AI systems on complex data analytics tasks in an end‑to‑end manner. AIDABench encompasses 600+ diverse document analysis tasks across three core capability dimensions: question answering, data visualization, and file generation. These tasks are grounded in realistic scenarios involving heterogeneous data types, including spreadsheets, databases, financial reports, and operational records, and reflect analytical demands across diverse industries and job functions. Notably, the tasks in AIDABench are sufficiently challenging that even human experts require 1‑2 hours per question when assisted by AI tools, underscoring the benchmark's difficulty and real‑world complexity. We evaluate 11 state‑of‑the‑art models on AIDABench, spanning both proprietary (e.g., Claude Sonnet 4.5, Gemini 3 Pro Preview) and open‑source (e.g., Qwen3‑Max‑2026‑01‑23‑Thinking) families. Our results reveal that complex, real‑world data analytics tasks remain a significant challenge for current AI systems, with the best‑performing model achieving only 59.43% pass‑at‑1. We provide a detailed analysis of failure modes across each capability dimension and identify key challenges for future research. AIDABench offers a principled reference for enterprise procurement, tool selection, and model optimization, and is publicly available at https://github.com/MichaelYang‑lyx/AIDABench.

PaperId: 2412, https://arxiv.org/pdf/2603.15634.pdf GitHub

Abstract:
Memory is critical for LLM‑based agents to preserve past observations for future decision‑making, where factual memory serves as its foundational part. However, existing approaches to constructing factual memory face several limitations. Textual methods impose heavy context and indexing burdens, while parametric methods suffer from catastrophic forgetting and high costs. To address these challenges, we introduce NextMem, a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory while ensuring accurate reconstruction. For better optimization, we propose a two‑stage training process, including autoregressive reconstruction alignment and progressive latent substitution. We also incorporate quantization to reduce storage overhead. Extensive experiments demonstrate that NextMem achieves superior performance, and excels in retrieval, robustness, and extensibility properties. We release our code and model checkpoints at https://github.com/nuster1128/NextMem.

PaperId: 2413, https://arxiv.org/pdf/2603.15619.pdf GitHub

Abstract:
Scaling depth is a key driver for large language models (LLMs). Yet, as LLMs become deeper, they often suffer from signal degradation: informative features formed in shallow layers are gradually diluted by repeated residual updates, making them harder to recover in deeper layers. We introduce mixture‑of‑depths attention (MoDA), a mechanism that allows each attention head to attend to sequence KV pairs at the current layer and depth KV pairs from preceding layers. We further describe a hardware‑efficient algorithm for MoDA that resolves non‑contiguous memory‑access patterns, achieving 97.3% of FlashAttention‑2's efficiency at a sequence length of 64K. Experiments on 1.5B‑parameter models demonstrate that MoDA consistently outperforms strong baselines. Notably, it improves average perplexity by 0.2 across 10 validation benchmarks and increases average performance by 2.11% on 10 downstream tasks, with a negligible 3.7% FLOPs computational overhead. We also find that combining MoDA with post‑norm yields better performance than using it with pre‑norm. These results suggest that MoDA is a promising primitive for depth scaling. Code is released at https://github.com/hustvl/MoDA .

PaperId: 2414, https://arxiv.org/pdf/2603.15542.pdf GitHub

Abstract:
Causal inference in social science relies on end‑to‑end, intervention‑centered research‑design reasoning grounded in real‑world policy interventions, but current benchmarks fail to evaluate this capability of large language models (LLMs). We present InterveneBench, a benchmark designed to assess such reasoning in realistic social settings. Each instance in InterveneBench is derived from an empirical social science study and requires models to reason about policy interventions and identification assumptions without access to predefined causal graphs or structural equations. InterveneBench comprises 744 peer‑reviewed studies across diverse policy domains. Experimental results show that state‑of‑the‑art LLMs struggle under this setting. To address this limitation, we further propose a multi‑agent framework, STRIDES. It achieves significant performance improvements over state‑of‑the‑art reasoning models. Our code and data are available at https://github.com/Sii‑yuning/STRIDES.

PaperId: 2415, https://arxiv.org/pdf/2603.15523.pdf GitHub

Abstract:
Keyphrase extraction for morphologically rich, low‑resource languages remains understudied, largely due to the scarcity of suitable evaluation datasets. We address this gap for Slovak by constructing a dataset of 227,432 scientific abstracts with author‑assigned keyphrases ‑‑ scraped and systematically cleaned from the Slovak Central Register of Theses ‑‑ representing a 25‑fold increase over the largest prior Slovak resource and approaching the scale of established English benchmarks such as KP20K. Using this dataset, we benchmark three unsupervised baselines (YAKE, TextRank, KeyBERT with SlovakBERT embeddings) and evaluate KeyLLM, an LLM‑based extraction method using GPT‑3.5‑turbo. Unsupervised baselines achieve at most 11.6% exact‑match F1@6, with a large gap to partial matching (up to 51.5%), reflecting the difficulty of matching inflected surface forms to author‑assigned keyphrases. KeyLLM narrows this exact‑‑partial gap, producing keyphrases closer to the canonical forms assigned by authors, while manual evaluation on 100 documents (κ= 0.61) confirms that KeyLLM captures relevant concepts that automated exact matching underestimates. Our analysis identifies morphological mismatch as the dominant failure mode for statistical methods ‑‑ a finding relevant to other inflected languages. The dataset (https://huggingface.co/datasets/NaiveNeuron/SlovKE) and evaluation code (https://github.com/NaiveNeuron/SlovKE) are publicly available.

PaperId: 2416, https://arxiv.org/pdf/2603.15484.pdf GitHub

Abstract:
Diffusion models have significantly mitigated the impact of annotated data scarcity in remote sensing (RS). Although recent approaches have successfully harnessed these models to enable diverse and controllable Layout‑to‑Image (L2I) synthesis, they still suffer from limited fine‑grained control and fail to strictly adhere to bounding box constraints. To address these limitations, we propose RSGen, a plug‑and‑play framework that leverages diverse edge guidance to enhance layout‑driven RS image generation. Specifically, RSGen employs a progressive enhancement strategy: 1) it first enriches the diversity of edge maps composited from retrieved training instances via Image‑to‑Image generation; and 2) subsequently utilizes these diverse edge maps as conditioning for existing L2I models to enforce pixel‑level control within bounding boxes, ensuring the generated instances strictly adhere to the layout. Extensive experiments across three baseline models demonstrate that RSGen significantly boosts the capabilities of existing L2I models. For instance, with CC‑Diff on the DOTA dataset for oriented object detection, we achieve remarkable gains of +9.8/+12.0 in YOLOScore mAP50/mAP50‑95 and +1.6 in mAP on the downstream detection task. Our code will be publicly available: https://github.com/D‑Robotics‑AI‑Lab/RSGen

PaperId: 2417, https://arxiv.org/pdf/2603.15483.pdf GitHub

Abstract:
Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework. Prior works each employ their own methods to determine task success, such as database lookups, regex match, etc., adding complexity to the development of a unified agent evaluation approach. Moreover, they do not systematically account for the user's role nor expertise in the interaction, providing incomplete insights into the agent's performance. We argue that effective agent evaluation goes beyond correctness alone, incorporating conversation quality, efficiency and systematic diagnosis of agent errors. To address this, we introduce the TED framework (Talk, Evaluate, Diagnose). (1) Talk: We leverage reusable, generic expert and non‑expert user persona templates for user‑agent interaction. (2) Evaluate: We adapt existing datasets by representing subgoals‑such as tool signatures, and responses‑as natural language grading notes, evaluated automatically with LLM‑as‑a‑judge. We propose new metrics that capture both turn efficiency and intermediate progress of the agent complementing the user‑aware setup. (3) Diagnose: We introduce an automated error analysis tool that analyzes the inconsistencies of the judge and agents, uncovering common errors, and providing actionable feedback for agent improvement. We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels. We also demonstrate potential gains in agent performance with peaks of 8‑10% on our proposed metrics after incorporating the identified error remedies into the agent's design.

PaperId: 2418, https://arxiv.org/pdf/2603.15469.pdf GitHub

Abstract:
Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action. This transition is highly significant for industrial robotic manipulation, promising to free human workers from repetitive, dangerous daily labor. To benchmark and advance this capability, we introduce the Robotic Collaborative Assembly Assistance (RoCo) Challenge with a dataset towards simulation and real‑world assembly manipulation. Set against the backdrop of human‑centered manufacturing, this challenge focuses on a high‑precision planetary gearbox assembly task, a demanding yet highly representative operation in modern industry. Built upon a self‑developed data collection, training, and evaluation system in Isaac Sim, and utilizing a dual‑arm robot for real‑world deployment, the challenge operates in two phases. The Simulation Round defines fine‑grained task phases for step‑wise scoring to handle the long‑horizon nature of the assembly. The Real‑World Round mirrors this evaluation with physical gearbox components and high‑quality teleoperated datasets. The core tasks require assembling an epicyclic gearbox from scratch, including mounting three planet gears, a sun gear, and a ring gear. Attracting over 60 teams and 170+ participants from more than 10 countries, the challenge yielded highly effective solutions, most notably ARC‑VLA and RoboCola. Results demonstrate that a dual‑model framework for long‑horizon multi‑task learning is highly effective, and the strategic utilization of recovery‑from‑failure curriculum data is a critical insight for successful deployment. This report outlines the competition setup, evaluation approach, key findings, and future directions for industrial EAI. Our dataset, CAD files, code, and evaluation results can be found at: https://rocochallenge.github.io/RoCo2026/.

PaperId: 2419, https://arxiv.org/pdf/2603.15452.pdf GitHub

Abstract:
Existing time series forecasting methods primarily rely on the numerical data itself. However, real‑world time series exhibit complex patterns associated with multimodal information, making them difficult to predict with numerical data alone. While several multimodal time series forecasting methods have emerged, they either utilize text with limited supplementary information or focus merely on representation extraction, extracting minimal textual information for forecasting. To unlock the Value of Text, we propose VoT, a method with Event‑driven Reasoning and Multi‑level Alignment. Event‑driven Reasoning combines the rich information in exogenous text with the powerful reasoning capabilities of LLMs for time series forecasting. To guide the LLMs in effective reasoning, we propose the Historical In‑context Learning that retrieves and applies historical examples as in‑context guidance. To maximize the utilization of text, we propose Multi‑level Alignment. At the representation level, we utilize the Endogenous Text Alignment to integrate the endogenous text information with the time series. At the prediction level, we design the Adaptive Frequency Fusion to fuse the frequency components of event‑driven prediction and numerical prediction to achieve complementary advantages. Experiments on real‑world datasets across 10 domains demonstrate significant improvements over existing methods, validating the effectiveness of our approach in the utilization of text. The code is made available at https://github.com/decisionintelligence/VoT.

PaperId: 2420, https://arxiv.org/pdf/2603.15401.pdf GitHub

Abstract:
Agent skills, structured procedural knowledge packages injected at inference time, are increasingly used to augment LLM agents on software engineering tasks. However, their real utility in end‑to‑end development settings remains unclear. We present SWE‑Skills‑Bench, the first requirement‑driven benchmark that isolates the marginal utility of agent skills in real‑world software engineering (SWE). It pairs 49 public SWE skills with authentic GitHub repositories pinned at fixed commits and requirement documents with explicit acceptance criteria, yielding approximately 565 task instances across six SWE subdomains. We introduce a deterministic verification framework that maps each task's acceptance criteria to execution‑based tests, enabling controlled paired evaluation with and without the skill. Our results show that skill injection benefits are far more limited than rapid adoption suggests: 39 of 49 skills yield zero pass‑rate improvement, and the average gain is only +1.2%. Token overhead varies from modest savings to a 451% increase while pass rates remain unchanged. Only seven specialized skills produce meaningful gains (up to +30%), while three degrade performance (up to ‑10%) due to version‑mismatched guidance conflicting with project context. These findings suggest that agent skills are a narrow intervention whose utility depends strongly on domain fit, abstraction level, and contextual compatibility. SWE‑Skills‑Bench provides a testbed for evaluating the design, selection, and deployment of skills in software engineering agents. SWE‑Skills‑Bench is available at https://github.com/GeniusHTX/SWE‑Skills‑Bench.

PaperId: 2421, https://arxiv.org/pdf/2603.15388.pdf GitHub GitHub

Abstract:
Morphology‑control co‑design concerns the coupled optimization of an agent's body structure and control policy. This problem exhibits a bi‑level structure, where the control dynamically adapts to the morphology to maximize performance. Existing methods typically neglect the control's adaptation dynamics by adopting a single‑level formulation that treats the control policy as fixed when optimizing morphology. This can lead to inefficient optimization, as morphology updates may be misaligned with control adaptation. In this paper, we revisit the co‑design problem from a game‑theoretic perspective, modeling the intrinsic coupling between morphology and control as a novel variant of a Stackelberg game. We propose Stackelberg Proximal Policy Optimization (Stackelberg PPO), which explicitly incorporates the control's adaptation dynamics into morphology optimization. By modeling this intrinsic coupling, our method aligns morphology updates with control adaptation, thereby stabilizing training and improving learning efficiency. Experiments across diverse co‑design tasks demonstrate that Stackelberg PPO outperforms standard PPO in both stability and final performance, opening the way for dramatically more efficient robotics designs.

PaperId: 2422, https://arxiv.org/pdf/2603.15373.pdf GitHub

Abstract:
Explainable Artificial Intelligence (XAI) is increasingly essential as AI systems are deployed in critical fields such as healthcare and finance, offering transparency into AI‑driven decisions. Two major XAI paradigms, counterfactual explanations (CFX) and feature attribution (FA), serve distinct roles in model interpretability. This study introduces GradCFA, a hybrid framework combining CFX and FA to improve interpretability by explicitly optimizing feasibility, plausibility, and diversity ‑ key qualities often unbalanced in existing methods. Unlike most CFX research focused on binary classification, GradCFA extends to multi‑class scenarios, supporting a wider range of applications. We evaluate GradCFA's validity, proximity, sparsity, plausibility, and diversity against state‑of‑the‑art methods, including Wachter, DiCE, CARE for CFX, and SHAP for FA. Results show GradCFA effectively generates feasible, plausible, and diverse counterfactuals while offering valuable FA insights. By identifying influential features and validating their impact, GradCFA advances AI interpretability. The code for implementation of this work can be found at: https://github.com/jacob‑ws/GradCFs .

PaperId: 2423, https://arxiv.org/pdf/2603.15341.pdf GitHub

Abstract:
In architectural interior design, miscommunication frequently arises as clients lack design knowledge, while designers struggle to explain complex spatial relationships, leading to delayed timelines and financial losses. Recent advancements in generative layout tools narrow the gap by automating 3D visualizations. However, prevailing methodologies exhibit limitations: rule‑based systems implement hard‑coded spatial constraints that restrict participatory engagement, while data‑driven models rely on extensive training datasets. Recent large language models (LLMs) bridge this gap by enabling intuitive reasoning about spatial relationships through natural language. This research presents an LLM‑based, multimodal, multi‑agent framework that dynamically converts natural language descriptions and imagery into 3D designs. Specialized agents (Reference, Spatial, Interactive, Grader), operating via prompt guidelines, collaboratively address core challenges: the agent system enables real‑time user interaction for iterative spatial refinement, while Retrieval‑Augmented Generation (RAG) reduces data dependency without requiring task‑specific model training. This framework accurately interprets spatial intent and generates optimized 3D indoor design, improving productivity, and encouraging nondesigner participation. Evaluations across diverse floor plans and user questionnaires demonstrate effectiveness. An independent LLM evaluator consistently rated participatory layouts higher in user intent alignment, aesthetic coherence, functionality, and circulation. Questionnaire results indicated 77% satisfaction and a clear preference over traditional design software. These findings suggest the framework enhances user‑centric communication and fosters more inclusive, effective, and resilient design processes. Project page: https://rsigktyper.github.io/AICodesign/

PaperId: 2424, https://arxiv.org/pdf/2603.15326.pdf GitHub

Abstract:
Despite significant advances in speech processing, Portuguese remains under‑resourced due to the scarcity of public, large‑scale, and high‑quality datasets. To address this gap, we present a new dataset, named TAGARELA, composed of over 8,972 hours of podcast audio, specifically curated for training automatic speech recognition (ASR) and text‑to‑speech (TTS) models. Notably, its scale rivals English's GigaSpeech (10kh), enabling state‑of‑the‑art Portuguese models. To ensure data quality, the corpus was subjected to an audio pre‑processing pipeline and subsequently transcribed using a mixed strategy: we applied ASR models that were previously trained on high‑fidelity transcriptions generated by proprietary APIs, ensuring a high level of initial accuracy. Finally, to validate the effectiveness of this new resource, we present ASR and TTS models trained exclusively on our dataset and evaluate their performance, demonstrating its potential to drive the development of more robust and natural speech technologies for Portuguese. The dataset is released publicly, available at https://freds0.github.io/TAGARELA/, to foster the development of robust speech technologies.

PaperId: 2425, https://arxiv.org/pdf/2603.15185.pdf GitHub

Abstract:
End‑to‑end autonomous driving has gained significant attention for its potential to learn robust behavior in interactive scenarios and scale with data. Popular architectures often build on separate modules for perception and planning connected through latent representations, such as bird's eye view feature grids, to maintain end‑to‑end differentiability. This paradigm emerged mostly on open‑loop datasets, with evaluation focusing not only on driving performance, but also intermediate perception tasks. Unfortunately, architectural advances that excel in open‑loop often fail to translate to scalable learning of robust closed‑loop driving. In this paper, we systematically re‑examine the impact of common architectural patterns on closed‑loop performance: (1) high‑resolution perceptual representations, (2) disentangled trajectory representations, and (3) generative planning. Crucially, our analysis evaluates the combined impact of these patterns, revealing both unexpected limitations as well as underexplored synergies. Building on these insights, we introduce BevAD, a novel lightweight and highly scalable end‑to‑end driving architecture. BevAD achieves 72.7% success rate on the Bench2Drive benchmark and demonstrates strong data‑scaling behavior using pure imitation learning. Our code and models are publicly available here: https://dmholtz.github.io/bevad/

PaperId: 2426, https://arxiv.org/pdf/2603.15183.pdf GitHub

Abstract:
Multi‑agent LLM orchestration incurs synchronization costs scaling as O(n x S x |D|) in agents, steps, and artifact size under naive broadcast ‑‑ a regime I term broadcast‑induced triply‑multiplicative overhead. I argue this pathology is a structural residue of full‑state rebroadcast, not an inherent property of multi‑agent coordination. The central claim: synchronization cost explosion in LLM multi‑agent systems maps with formal precision onto the cache coherence problem in shared‑memory multiprocessors, and MESI‑protocol invalidation transfers to artifact synchronization under minimal structural modification. I construct the Artifact Coherence System (ACS) and prove the Token Coherence Theorem: lazy invalidation attenuates cost by at least S/(n + W(d_i)) when S > n + W(d_i), converting O(n x S x |D|) to O((n + W) x |D|). A TLA+‑verified protocol enforces single‑writer safety, monotonic versioning, and bounded staleness across ~2,400 explored states. Simulation across four workload configurations yields token savings of 95.0% +/‑ 1.3% at V=0.05, 92.3% +/‑ 1.4% at V=0.10, 88.3% +/‑ 1.5% at V=0.25, and 84.2% +/‑ 1.3% at V=0.50 ‑‑ each exceeding the theorem's conservative lower bounds. Savings of ~81% persist at V=0.9, contrary to the predicted collapse threshold. Contributions: (1) formal MESI‑to‑artifact state mapping; (2) Token Coherence Theorem as savings lower bound; (3) TLA+‑verified protocol with three proven invariants; (4) characterization of conditional artifact access semantics resolving the always‑read objection; (5) reference Python implementation integrating with LangGraph, CrewAI, and AutoGen via thin adapter layers.

PaperId: 2427, https://arxiv.org/pdf/2603.15159.pdf GitHub

Abstract:
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private‑library‑oriented code generation, where the goal is to generate code using APIs from private libraries. Existing approaches mainly rely on retrieving private‑library API documentation and injecting relevant knowledge into the context at inference time. However, our study shows that this is insufficient: even given accurate required knowledge, LLMs still struggle to invoke private‑library APIs effectively. To address this limitation, we propose PriCoder, an approach that teaches LLMs to invoke private‑library APIs through automatically synthesized data. Specifically, PriCoder models private‑library data synthesis as the construction of a graph, and alternates between two graph operators: (1) Progressive Graph Evolution, which improves data diversity by progressively synthesizing more diverse training samples from basic ones, and (2) Multidimensional Graph Pruning, which improves data quality through a rigorous filtering pipeline. To support rigorous evaluation, we construct two new benchmarks based on recently released libraries that are unfamiliar to the tested models. Experiments on three mainstream LLMs show that PriCoder substantially improves private‑library‑oriented code generation, yielding gains of over 20% in pass@1 in many settings, while causing negligible impact on general code generation capability. Our code and benchmarks are publicly available at https://github.com/eniacode/PriCoder.

PaperId: 2428, https://arxiv.org/pdf/2603.15006.pdf GitHub

Abstract:
Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade‑off: structural screening offers scalability but lacks biological context, whereas high‑content phenotypic profiling provides deep biological insights but is resource‑intensive. The primary challenge is to extract robust biological signals from noisy data and encode them into representations that do not require biological data at inference. Results: This study presents DECODE (DEcomposing Cellular Observations of Drug Effects), a framework that bridges this gap by empowering chemical representations with intrinsic biological semantics to enable structure‑based in silico biological profiling. DECODE leverages limited paired transcriptomic and morphological data as supervisory signals during training, enabling the extraction of a measurement‑invariant biological fingerprint from chemical structures and explicit filtering of experimental noise. Our evaluations demonstrate that DECODE retrieves functionally similar drugs in zero‑shot settings with over 20% relative improvement over chemical baselines in mechanism‑of‑action (MOA) prediction. Furthermore, the framework achieves a 6‑fold increase in hit rates for novel anti‑cancer agents during external validation. Availability and implementation: The codes and datasets of DECODE are available at https://github.com/lian‑xiao/DECODE.

PaperId: 2429, https://arxiv.org/pdf/2603.14870.pdf GitHub

Abstract:
Predicting immunoglobulin‑antigen (Ig‑Ag) binding remains a significant challenge due to the paucity of experimentally‑resolved complexes and the limited accuracy of de novo Ig structure prediction. We introduce IgPose, a generalizable framework for Ig‑Ag pose identification and scoring, built on a generative data‑augmentation pipeline. To mitigate data scarcity, we constructed the Structural Immunoglobulin Decoy Database (SIDD), a comprehensive repository of high‑fidelity synthetic decoys. IgPose integrates equivariant graph neural networks, ESM‑2 embeddings, and gated recurrent units to synergistically capture both geometric and evolutionary features. We implemented interface‑focused k‑hop sampling with biologically guided pooling to enhance generalization across diverse interfaces. The framework comprises two sub‑networks‑‑IgPoseClassifier for binding pose discrimination and IgPoseScore for DockQ score estimation‑‑and achieves robust performance on curated internal test sets and the CASP‑16 benchmark compared to physics and deep learning baselines. IgPose serves as a versatile computational tool for high‑throughput antibody discovery pipelines by providing accurate pose filtering and ranking. IgPose is available on GitHub (https://github.com/arontier/igpose).

PaperId: 2430, https://arxiv.org/pdf/2603.14845.pdf GitHub

Abstract:
Accurate day‑ahead solar irradiance forecasting is essential for integrating solar energy into the power grid. However, it remains challenging due to the pronounced diurnal cycle and inherently complex cloud dynamics. Current methods either lack fine‑scale resolution (e.g., numerical weather prediction, weather foundation models) or degrade at longer lead times (e.g., satellite extrapolation). We propose Baguan‑solar, a two‑stage multimodal framework that fuses forecasts from Baguan, a global weather foundation model, with high‑resolution geostationary satellite imagery to produce 24‑hour irradiance forecasts at kilometer scale. Its decoupled two‑stage design first forecasts day‑night continuous intermediates (e.g., cloud cover) and then infers irradiance, while its modality fusion jointly preserves fine‑scale cloud structures from satellite and large‑scale constraints from Baguan forecasts. Evaluated over East Asia using CLDAS as ground truth, Baguan‑solar outperforms strong baselines (including ECMWF IFS, vanilla Baguan, and SolarSeer), reducing RMSE by 16.08% and better resolving cloud‑induced transients. An operational deployment of Baguan‑solar has supported solar power forecasting in an eastern province in China, since July 2025. Our code is accessible at https://github.com/DAMO‑DI‑ML/Baguan‑solar.git.

PaperId: 2431, https://arxiv.org/pdf/2603.14769.pdf GitHub

Abstract:
Optimizing complex systems, ranging from LLM prompts to multi‑turn agents, traditionally requires labor‑intensive manual iteration. We formalize this challenge as a stochastic generative optimization problem where a generative language model acts as the optimizer, guided by numerical rewards and text feedback to discover the best system. We introduce Prioritized Optimization with Local Contextual Aggregation (POLCA), a scalable framework designed to handle stochasticity in optimization ‑‑ such as noisy feedback, sampling minibatches, and stochastic system behaviors ‑‑ while effectively managing the unconstrained expansion of solution space. POLCA maintains a priority queue to manage the exploration‑exploitation tradeoff, systematically tracking candidate solutions and their evaluation histories. To enhance efficiency, we integrate an \varepsilon‑Net mechanism to maintain parameter diversity and an LLM Summarizer to perform meta‑learning across historical trials. We theoretically prove that POLCA converges to near‑optimal candidate solutions under stochasticity. We evaluate our framework on diverse benchmarks, including τ‑bench, HotpotQA (agent optimization), VeriBench (code translation) and KernelBench (CUDA kernel generation). Experimental results demonstrate that POLCA achieves robust, sample and time‑efficient performance, consistently outperforming state‑of‑the‑art algorithms in both deterministic and stochastic problems. The codebase for this work is publicly available at https://github.com/rlx‑lab/POLCA.

PaperId: 2432, https://arxiv.org/pdf/2603.14706.pdf GitHub

Abstract:
Frozen‑backbone transfer with Vision Transformers faces two under‑addressed issues: optimization instability when adapters are naively inserted into a fixed feature extractor, and the absence of principled guidance for setting adapter capacity. We introduce AdapterTune, which augments each transformer block with a residual low‑rank bottleneck whose up‑projection is zero‑initialized, guaranteeing that the adapted network starts exactly at the pretrained function and eliminates early‑epoch representation drift. On the analytical side, we formalize adapter rank as a capacity budget for approximating downstream task shifts in feature space. The resulting excess‑risk decomposition predicts monotonic but diminishing accuracy gains with increasing rank, an ``elbow'' behavior we confirm through controlled sweeps. We evaluate on 9 datasets and 3 backbone scales with multi‑seed reporting throughout. On a core 5 dataset transfer suite, AdapterTune improves top‑1 accuracy over head‑only transfer by +14.9 points on average while training only 0.92 of the parameters required by full fine‑tuning, and outperforms full fine‑tuning on 10 of 15 dataset‑backbone pairs. Across the full benchmark, AdapterTune improves over head‑only transfer on every dataset‑backbone pair tested. Ablations on rank, placement, and initialization isolate each design choice. The code is available at: https://github.com/salimkhazem/adaptertune

PaperId: 2433, https://arxiv.org/pdf/2603.14665.pdf GitHub

Abstract:
Training data attribution (TDA) methods ask which training documents are responsible for a model behavior. However, models often learn broad concepts shared across many examples. Moreover, existing TDA methods are supervised ‑‑ they require a predefined query behavior, then score every training document against it ‑‑ making them both expensive and unable to surface behaviors the user did not think to ask about. We present Gradient Atoms, an unsupervised method that decomposes per‑document training gradients into sparse components ("atoms") via dictionary learning in a preconditioned eigenspace. Each atom captures a shared update direction induced by a cluster of functionally similar documents, directly recovering the collective structure that per‑document methods do not address. Among 500 discovered atoms, the highest‑coherence ones recover interpretable task‑type behaviors ‑‑ refusal, arithmetic, yes/no classification, trivia QA ‑‑ without any behavioral labels. These atoms double as effective steering vectors: applying them as weight‑space perturbations produces large, controllable shifts in model behavior (e.g., bulleted‑list generation 33% to 94%; systematic refusal 50% to 0%). The method requires no query‑‑document scoring stage, and scales independently of the number of query behaviors of interest. Code is available at https://github.com/jrosseruk/gradient_atoms.

PaperId: 2434, https://arxiv.org/pdf/2603.14651.pdf GitHub

Abstract:
We present EARCP (Ensemble Auto‑Régulé par Cohérence et Performance), a novel ensemble architecture that dynamically weights heterogeneous expert models based on both their individual performance and inter‑model coherence. Unlike traditional ensemble methods that rely on static or offline‑learned combinations, EARCP continuously adapts model weights through a principled online learning mechanism that balances exploitation of high‑performing models with exploration guided by consensus signals. The architecture combines theoretical foundations from multiplicative weight update algorithms with a novel coherence‑based regularization term, providing both theoretical guarantees through regret bounds and practical robustness in non‑stationary environments. We formalize the EARCP framework, prove sublinear regret bounds of O(sqrt(T log M)) under standard assumptions, and demonstrate its effectiveness through empirical evaluation on sequential prediction tasks including time series forecasting, activity recognition, and financial prediction. The architecture is designed as a general‑purpose framework applicable to any domain requiring ensemble learning with temporal dependencies. An open‑source implementation is available at https://github.com/Volgat/earcp and via PyPI (pip install earcp).

PaperId: 2435, https://arxiv.org/pdf/2603.14628.pdf GitHub GitHub

Abstract:
Neurosymbolic approaches leveraging Large Language Models (LLMs) with formal methods have recently achieved strong results on mathematics‑oriented theorem‑proving benchmarks. However, success on competition‑style mathematics does not by itself demonstrate the ability to construct proofs about real‑world implementations. We address this gap with a benchmark derived from an industrial cryptographic library whose assembly routines are already verified in HOL Light. s2n‑bignum is a library used at AWS for providing fast assembly routines for cryptography, and its correctness is established by formal verification. The task of formally verifying this library has been a significant achievement for the Automated Reasoning Group. It involved two tasks: (1) precisely specifying the correct behavior of a program as a mathematical proposition, and (2) proving that the proposition is correct. In the case of s2n‑bignum, both tasks were carried out by human experts. In s2n‑bignum‑bench, we provide the formal specification and ask the LLM to generate a proof script that is accepted by HOL Light within a fixed proof‑check timeout. To our knowledge, s2n‑bignum‑bench is the first public benchmark focused on machine‑checkable proof synthesis for industrial low‑level cryptographic assembly routines in HOL Light. This benchmark provides a challenging and practically relevant testbed for evaluating LLM‑based theorem proving beyond competition mathematics. The code to set up and use the benchmark is available here: \hrefhttps://github.com/kings‑crown/s2n‑bignum‑benchs2n‑bignum‑bench.

PaperId: 2436, https://arxiv.org/pdf/2603.14588.pdf GitHub

Abstract:
Persistent memory is a central capability for AI agents, yet the mathematical foundations of memory retrieval, lifecycle management, and consistency remain unexplored. Current systems employ cosine similarity for retrieval, heuristic decay for salience, and provide no formal contradiction detection. We establish information‑geometric foundations through three contributions. First, a retrieval metric derived from the Fisher information structure of diagonal Gaussian families, satisfying Riemannian metric axioms, invariant under sufficient statistics, and computable in O(d) time. Second, memory lifecycle formulated as Riemannian Langevin dynamics with proven existence and uniqueness of the stationary distribution via the Fokker‑Planck equation, replacing hand‑tuned decay with principled convergence guarantees. Third, a cellular sheaf model where non‑trivial first cohomology classes correspond precisely to irreconcilable contradictions across memory contexts. On the LoCoMo benchmark, the mathematical layers yield +12.7 percentage points over engineering baselines across six conversations, reaching +19.9 pp on the most challenging dialogues. A four‑channel retrieval architecture achieves 75% accuracy without cloud dependency. Cloud‑augmented results reach 87.7%. A zero‑LLM configuration satisfies EU AI Act data sovereignty requirements by architectural design. To our knowledge, this is the first work establishing information‑geometric, sheaf‑theoretic, and stochastic‑dynamical foundations for AI agent memory systems.

PaperId: 2437, https://arxiv.org/pdf/2603.14553.pdf GitHub

Abstract:
We present, to our knowledge, the first language‑driven agent system capable of executing end‑to‑end collider phenomenology tasks, instantiated within a decoupled, domain‑agnostic architecture for autonomous High‑Energy Physics phenomenology. Guided only by natural‑language prompts supplemented with standard physics notation, ColliderAgent carries out workflows from a theoretical Lagrangian to final phenomenological outputs without relying on package‑specific code. In this framework, a hierarchical multi‑agent reasoning layer is coupled to Magnus, a unified execution backend for phenomenological calculations and simulation toolchains. We validate the system on representative literature reproductions spanning leptoquark and axion‑like‑particle scenarios, higher‑dimensional effective operators, parton‑level and detector‑level analyses, and large‑scale parameter scans leading to exclusion limits. These results point to a route toward more automated, scalable, and reproducible research in collider physics, cosmology, and physics more broadly.

PaperId: 2438, https://arxiv.org/pdf/2603.14523.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models have shown promising capabilities for embodied intelligence, but most existing approaches rely on text‑based chain‑of‑thought reasoning where visual inputs are treated as static context. This limits the ability of the model to actively revisit the environment and resolve ambiguities during long‑horizon tasks. We propose VLA‑Thinker, a thinking‑with‑image reasoning framework that models perception as a dynamically invocable reasoning action. To train such a system, we introduce a two‑stage training pipeline consisting of (1) an SFT cold‑start phase with curated visual Chain‑of‑Thought data to activate structured reasoning and tool‑use behaviors, and (2) GRPO‑based reinforcement learning to align complete reasoning‑action trajectories with task‑level success. Extensive experiments on LIBERO and RoboTwin 2.0 benchmarks demonstrate that VLA‑Thinker significantly improves manipulation performance, achieving 97.5% success rate on LIBERO and strong gains across long‑horizon robotic tasks. Project and Codes: https://cywang735.github.io/VLA‑Thinker/ .

PaperId: 2439, https://arxiv.org/pdf/2603.14501.pdf GitHub

Abstract:
Large Language Models excel in high‑resource programming languages but struggle with low‑resource ones. Existing research related to low‑resource programming languages primarily focuses on Domain‑Specific Languages (DSLs), leaving general‑purpose languages that suffer from data scarcity underexplored. To address this gap, we introduce CangjieBench, a contamination‑free benchmark for Cangjie, a representative low‑resource general‑purpose language. The benchmark comprises 248 high‑quality samples manually translated from HumanEval and ClassEval, covering both Text‑to‑Code and Code‑to‑Code tasks. We conduct a systematic evaluation of diverse LLMs under four settings: Direct Generation, Syntax‑Constrained Generation, Retrieval‑Augmented Generation (RAG), and Agent. Experiments reveal that Direct Generation performs poorly, whereas Syntax‑Constrained Generation offers the best trade‑off between accuracy and computational cost. Agent achieve state‑of‑the‑art accuracy but incur high token consumption. Furthermore, we observe that Code‑to‑Code translation often underperforms Text‑to‑Code generation, suggesting a negative transfer phenomenon where models overfit to the source language patterns. We hope that our work will offer valuable insights into LLM generalization to unseen and low‑resource programming languages. Our code and data are available at https://github.com/cjhCoder7/CangjieBench.

PaperId: 2440, https://arxiv.org/pdf/2603.14495.pdf GitHub

Abstract:
Tensions between AI Safety (AIS) and AI Ethics (AIE) have increasingly surfaced in AI governance and public debates about AI, leading to what we term the "responsible AI divides". We introduce a model that categorizes four modes of engagement with the tensions: radical confrontation, disengagement, compartmentalized coexistence, and critical bridging. We then investigate how critical bridging, with a particular focus on bridging problems, offers one of the most viable constructive paths for advancing responsible AI. Using computational tools to analyze a curated dataset of 3,550 papers, we map the research landscapes of AIE and AIS to identify both distinct and overlapping problems. Our findings point to both thematic divides and overlaps. For example, we find that AIE has long grappled with overcoming injustice and tangible AI harms, whereas AIS has primarily embodied an anticipatory approach focused on the mitigation of risks from AI capabilities. At the same time, we find significant overlap in core research concerns across both AIE and AIS around transparency, reproducibility, and inadequate governance mechanisms. As AIE and AIS continue to evolve, we recommend focusing on bridging problems as a constructive path forward for enhancing collaborative AI governance. We offer a series of recommendations to integrate shared considerations into a collaborative approach to responsible AI. Alongside our proposal, we highlight its limitations and explore open problems for future research. All data including the fully annotated dataset of papers with code to reproduce our figures can be found at: https://github.com/gyevnarb/ai‑safety‑ethics.

PaperId: 2441, https://arxiv.org/pdf/2603.14389.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics remain fragile. Standard algorithms like GRPO enforce stability via "hard clipping", which inadvertently stifles exploration by discarding gradients of tokens outside the trust region. While recent "soft clipping" methods attempt to recover these gradients, they suffer from a critical challenge: relying on log‑probability gradient (\nabla_θ\log π_θ) yields divergent weights as probabilities vanish, destabilizing LLM training. We rethink this convention by establishing probability gradient (\nabla_θπ_θ) as the superior optimization primitive. Accordingly, we propose Decoupled Gradient Policy Optimization (DGPO), which employs a decoupled decay mechanism based on importance sampling ratios. By applying asymmetric, continuous decay to boundary tokens, DGPO resolves the conflict between stability and sustained exploration. Extensive experiments across DeepSeek‑R1‑Distill‑Qwen series models (1.5B/7B/14B) demonstrate that DGPO consistently outperforms strong baselines on various mathematical benchmarks, offering a robust and scalable solution for RLVR. Our code and implementation are available at: https://github.com/FlyTune/DGPO‑RL.

PaperId: 2442, https://arxiv.org/pdf/2603.14375.pdf GitHub

Abstract:
While recent generative video models have achieved remarkable visual realism and are being explored as world models, true physical simulation requires mastering both space and time. Current models can produce visually smooth kinematics, yet they lack a reliable internal motion pulse to ground these motions in a consistent, real‑world time scale. This temporal ambiguity stems from the common practice of indiscriminately training on videos with vastly different real‑world speeds, forcing them into standardized frame rates. This leads to what we term chronometric hallucination: generated sequences exhibit ambiguous, unstable, and uncontrollable physical motion speeds. To address this, we propose Visual Chronometer, a predictor that recovers the Physical Frames Per Second (PhyFPS) directly from the visual dynamics of an input video. Trained via controlled temporal resampling, our method estimates the true temporal scale implied by the motion itself, bypassing unreliable metadata. To systematically quantify this issue, we establish two benchmarks, PhyFPS‑Bench‑Real and PhyFPS‑Bench‑Gen. Our evaluations reveal a harsh reality: state‑of‑the‑art video generators suffer from severe PhyFPS misalignment and temporal instability. Finally, we demonstrate that applying PhyFPS corrections significantly improves the human‑perceived naturalness of AI‑generated videos. Our project page is https://xiangbogaobarry.github.io/Visual_Chronometer/.

PaperId: 2443, https://arxiv.org/pdf/2603.14363.pdf GitHub

Abstract:
Vision‑Language Navigation (VLN) for Unmanned Aerial Vehicles (UAVs) demands complex visual interpretation and continuous control in dynamic 3D environments. Existing hierarchical approaches rely on dense oracle guidance or auxiliary object detectors, creating semantic gaps and limiting genuine autonomy. We propose AerialVLA, a minimalist end‑to‑end Vision‑Language‑Action framework mapping raw visual observations and fuzzy linguistic instructions directly to continuous physical control signals. First, we introduce a streamlined dual‑view perception strategy that reduces visual redundancy while preserving essential cues for forward navigation and precise grounding, which additionally facilitates future simulation‑to‑reality transfer. To reclaim genuine autonomy, we deploy a fuzzy directional prompting mechanism derived solely from onboard sensors, completely eliminating the dependency on dense oracle guidance. Ultimately, we formulate a unified control space that integrates continuous 3‑Degree‑of‑Freedom (3‑DoF) kinematic commands with an intrinsic landing signal, freeing the agent from external object detectors for precision landing. Extensive experiments on the TravelUAV benchmark demonstrate that AerialVLA achieves state‑of‑the‑art performance in seen environments. Furthermore, it exhibits superior generalization in unseen scenarios by achieving nearly three times the success rate of leading baselines, validating that a minimalist, autonomy‑centric paradigm captures more robust visual‑motor representations than complex modular systems.

PaperId: 2444, https://arxiv.org/pdf/2603.14326.pdf GitHub

Abstract:
While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether they genuinely perform actual step‑by‑step reasoning or just rely on superficial visual cues. To investigate this, we introduce ECG‑Reasoning‑Benchmark, a novel multi‑turn evaluation framework comprising over 6,400 samples to systematically assess step‑by‑step reasoning across 17 core ECG diagnoses. Our comprehensive evaluation of state‑of‑the‑art models reveals a critical failure in executing multi‑step logical deduction. Although models possess the medical knowledge to retrieve clinical criteria for a diagnosis, they exhibit near‑zero success rates (6% Completion) in maintaining a complete reasoning chain, primarily failing to ground the corresponding ECG findings to the actual visual evidence in the ECG signal. These results demonstrate that current MLLMs bypass actual visual interpretation, exposing a critical flaw in existing training paradigms and underscoring the necessity for robust, reasoning‑centric medical AI. The code and data are available at https://github.com/Jwoo5/ecg‑reasoning‑benchmark.

PaperId: 2445, https://arxiv.org/pdf/2603.14245.pdf GitHub

Abstract:
Flow‑matching policies hold great promise for reinforcement learning (RL) by capturing complex, multi‑modal action distributions. However, their practical application is often hindered by prohibitive inference latency and ineffective online exploration. Although recent works have employed one‑step distillation for fast inference, the structure of the initial noise distribution remains an overlooked factor that presents significant untapped potential. This overlooked factor, along with the challenge of controlling policy stochasticity, constitutes two critical areas for advancing distilled flow‑matching policies. To overcome these limitations, we propose GoldenStart (GSFlow), a policy distillation method with Q‑guided priors and explicit entropy control. Instead of initializing generation from uninformed noise, we introduce a Q‑guided prior modeled by a conditional VAE. This state‑conditioned prior repositions the starting points of the one‑step generation process into high‑Q regions, effectively providing a "golden start" that shortcuts the policy to promising actions. Furthermore, for effective online exploration, we enable our distilled actor to output a stochastic distribution instead of a deterministic point. This is governed by entropy regularization, allowing the policy to shift from pure exploitation to principled exploration. Our integrated framework demonstrates that by designing the generative startpoint and explicitly controlling policy entropy, it is possible to achieve efficient and exploratory policies, bridging the generative models and the practical actor‑critic methods. We conduct extensive experiments on offline and online continuous control benchmarks, where our method significantly outperforms prior state‑of‑the‑art approaches. Code will be available at https://github.com/ZhHe11/GSFlow‑RL.

PaperId: 2446, https://arxiv.org/pdf/2603.14239.pdf GitHub

Abstract:
SystemVerilog Assertions (SVAs) are crucial for hardware verification. Recent studies leverage general‑purpose LLMs to translate natural language properties to SVAs (NL2SVA), but they perform poorly due to limited data. We propose a data synthesis framework to tackle two challenges: the scarcity of high‑quality real‑world SVA corpora and the lack of reliable methods to determine NL‑SVA semantic equivalence. For the former, large‑scale open‑source RTLs are used to guide LLMs to generate real‑world SVAs; for the latter, bidirectional translation serves as a data selection method. With the synthesized data, we train CodeV‑SVA, a series of SVA generation models. Notably, CodeV‑SVA‑14B achieves 75.8% on NL2SVA‑Human and 84.0% on NL2SVA‑Machine in Func.@1, matching or exceeding advanced LLMs like GPT‑5 and DeepSeek‑R1.

PaperId: 2447, https://arxiv.org/pdf/2603.14214.pdf GitHub

Abstract:
Image fusion aims to integrate complementary information from multiple source images to produce a more informative and visually consistent representation, benefiting both human perception and downstream vision tasks. Despite recent progress, most existing fusion methods are designed for specific tasks (i.e., multi‑modal, multi‑exposure, or multi‑focus fusion) and struggle to effectively preserve source information during the fusion process. This limitation primarily arises from task‑specific architectures and the degradation of source information caused by deep‑layer propagation. To overcome these issues, we propose UniFusion, a unified image fusion framework designed to achieve cross‑task generalization. First, leveraging DINOv3 for modality‑consistent feature extraction, UniFusion establishes a shared semantic space for diverse inputs. Second, to preserve the understanding of each source image, we introduce a reconstruction‑alignment loss to maintain consistency between fused outputs and inputs. Finally, we employ a bilevel optimization strategy to decouple and jointly optimize reconstruction and fusion objectives, effectively balancing their coupling relationship and ensuring smooth convergence. Extensive experiments across multiple fusion tasks demonstrate UniFusion's superior visual quality, generalization ability, and adaptability to real‑world scenarios. Code is available at https://github.com/dusongcheng/UniFusion.

PaperId: 2448, https://arxiv.org/pdf/2603.14209.pdf GitHub

Abstract:
A pictorial chart is an effective medium for visual storytelling, seamlessly integrating visual elements with data charts. However, creating such images is challenging because the flexibility of visual elements often conflicts with the rigidity of chart structures. This process thus requires a creative deformation that maintains both data faithfulness and visual aesthetics. Current methods that extract dense structural cues from natural images (e.g., edge or depth maps) are ill‑suited as conditioning signals for pictorial chart generation. We present ChArtist, a domain‑specific diffusion model for generating pictorial charts automatically, offering two distinct types of control: 1) spatial control that aligns well with the chart structure, and 2) subject‑driven control that respects the visual characteristics of a reference image. To achieve this, we introduce a skeleton‑based spatial control representation. This representation encodes only the data‑encoding information of the chart, allowing for the easy incorporation of reference visuals without a rigid outline constraint. We implement our method based on the Diffusion Transformer (DiT) and leverage an adaptive position encoding mechanism to manage these two controls. We further introduce Spatially Gated Attention to modulate the interaction between spatial control and subject control. To support the fine‑tuning of pre‑trained models for this task, we created a large‑scale dataset of 30,000 triplets (skeleton, reference image, pictorial chart). We also propose a unified data accuracy metric to evaluate the data faithfulness of the generated charts. We believe this work demonstrates that current generative models can achieve data‑driven visual storytelling by moving beyond general‑purpose conditions to task‑specific representations. Project page: https://chartist‑ai.github.io/.

PaperId: 2449, https://arxiv.org/pdf/2603.14021.pdf GitHub

Abstract:
Part‑level 3D generation is crucial for various downstream applications, including gaming, film production, and industrial design. However, decomposing a 3D shape into geometrically plausible and meaningful components remains a significant challenge. Previous part‑based generation methods often struggle to produce well‑constructed parts, exhibiting poor structural coherence, geometric implausibility, inaccuracy, or inefficiency. To address these challenges, we introduce EI‑Part, a novel framework specifically designed to generate high‑quality 3D shapes with components, characterized by strong structural coherence, geometric plausibility, geometric fidelity, and generation efficiency. We propose utilizing distinct representations at different stages: an Explode state for part completion and an Implode state for geometry refinement. This strategy fully leverages spatial resolution, enabling flexible part completion and fine geometric detail generation. To maintain structural coherence between parts, a self‑attention mechanism is incorporated in both exploded and imploded states, facilitating effective information perception and feature fusion among components during generation. Extensive experiments on multiple benchmarks demonstrate that EI‑Part efficiently produces semantically meaningful and structurally coherent parts with fine‑grained geometric details, achieving state‑of‑the‑art performance in part‑level 3D generation. Project page: https://cvhadessun.github.io/EI‑Part/

PaperId: 2450, https://arxiv.org/pdf/2603.13971.pdf GitHub

Abstract:
In offline reinforcement learning (RL), single‑step temporal‑difference (TD) learning can suffer from bootstrapping error accumulation over long horizons. Action‑chunked TD methods mitigate this by backing up over multiple steps, but can introduce suboptimality by restricting the policy class to open‑loop action sequences. To resolve this trade‑off, we present Chunk‑Guided Q‑Learning (CGQ), a single‑step TD algorithm that guides a fine‑grained single‑step critic by regularizing it toward a chunk‑based critic trained using temporally extended backups. This reduces compounding error while preserving fine‑grained value propagation. We theoretically show that CGQ attains tighter critic optimality bounds than either single‑step or action‑chunked TD learning alone. Empirically, CGQ achieves strong performance on challenging long‑horizon OGBench tasks, often outperforming both single‑step and action‑chunked methods.

PaperId: 2451, https://arxiv.org/pdf/2603.13967.pdf GitHub

Abstract:
Echocardiography is widely used for assessing cardiac function, where clinically meaningful parameters such as left‑ventricular ejection fraction (EF) play a central role in diagnosis and management. Generative models capable of synthesising realistic echocardiogram videos with explicit control over such parameters are valuable for data augmentation, counterfactual analysis, and specialist training. However, existing approaches typically rely on computationally expensive multi‑step sampling and aggressive temporal normalisation, limiting efficiency and applicability to heterogeneous real‑world data. We introduce EchoLVFM, a one‑step latent video flow‑matching framework for controllable echocardiogram generation. Operating in the latent space, EchoLVFM synthesises temporally coherent videos in a single inference step, achieving a \mathbf～ 50× improvement in sampling efficiency compared to multi‑step flow baselines while maintaining visual fidelity. The model supports global conditioning on clinical variables, demonstrated through precise control of EF, and enables reconstruction and counterfactual generation from partially observed sequences. A masked conditioning strategy further removes fixed‑length constraints, allowing shorter sequences to be retained rather than discarded. We evaluate EchoLVFM on the CAMUS dataset under challenging single‑frame conditioning. Quantitative and qualitative results demonstrate competitive video quality, strong EF adherence, and 57.9% discrimination accuracy by expert clinicians which is close to chance. These findings indicate that efficient, one‑step flow matching can enable practical, controllable echocardiogram video synthesis without sacrificing fidelity. Code available at: https://github.com/EngEmmanuel/EchoLVFM

PaperId: 2452, https://arxiv.org/pdf/2603.13966.pdf GitHub GitHub

Abstract:
Vision‑Language‑Action (VLA) models are increasingly evaluated across multiple simulation benchmarks, yet adding each benchmark to an evaluation pipeline requires resolving incompatible dependencies, matching underspecified evaluation protocols, and reverse‑engineering undocumented preprocessing. This burden scales with the number of models and benchmarks, making comprehensive evaluation impractical for most teams. We present vla‑eval, an open‑source evaluation harness that eliminates this per‑benchmark cost by decoupling model inference from benchmark execution through a WebSocket+msgpack protocol with Docker‑based environment isolation. Models integrate once by implementing a single predict() method; benchmarks integrate once via a four‑method interface; the full cross‑evaluation matrix works automatically. The framework supports 14 simulation benchmarks and six model servers. Parallel evaluation via episode sharding and batch inference achieves up to 47x wall‑clock speedup, completing 2,000 LIBERO episodes in ~18 minutes. To validate the framework, we reproduce published scores across six VLA codebases and three benchmarks, documenting previously undocumented pitfalls. We additionally release a VLA leaderboard aggregating 657 published results across 17 benchmarks. Framework, evaluation configs, and all reproduction results are publicly available at https://github.com/allenai/vla‑evaluation‑harness and https://allenai.github.io/vla‑evaluation‑harness/leaderboard.

PaperId: 2453, https://arxiv.org/pdf/2603.13934.pdf GitHub

Abstract:
Recommendation systems aim to learn user interests from historical behaviors and deliver relevant items. Recent methods leverage large language models (LLMs) to construct and integrate semantic representations of users and items for capturing user interests. However, user behavior theories suggest that truly understanding user interests requires not only semantic integration but also semantic reasoning from explicit individual interests to implicit group interests. To this end, we propose an Iterative Semantic Reasoning Framework (ISRF) for generative recommendation. ISRF leverages LLMs to bridge explicit individual interests and implicit group interests in three steps. First, we perform multi‑step bidirectional reasoning over item attributes to infer semantic item features and build a semantic interaction graph capturing users' explicit interests. Second, we generate semantic user features based on the semantic item features and construct a similarity‑based user graph to infer the implicit interests of similar user groups. Third, we adopt an iterative batch optimization strategy, where individual explicit interests directly guide the refinement of group implicit interests, while group implicit interests indirectly enhance individual modeling. This iterative process ensures consistent and progressive interest reasoning, enabling more accurate and comprehensive user interest learning. Extensive experiments on the Sports, Beauty, and Toys datasets demonstrate that ISRF outperforms state‑of‑the‑art baselines. The code is available at https://github.com/htired/ISRF.

PaperId: 2454, https://arxiv.org/pdf/2603.13904.pdf GitHub

Abstract:
For robotic agents operating in dynamic environments, learning visual state representations from streaming video observations is essential for sequential decision making. Recent self‑supervised learning methods have shown strong transferability across vision tasks, but they do not explicitly address what a good visual state should encode. We argue that effective visual states must capture what‑is‑where by jointly encoding the semantic identities of scene elements and their spatial locations, enabling reliable detection of subtle dynamics across observations. To this end, we propose CroBo, a visual state representation learning framework based on a global‑to‑local reconstruction objective. Given a reference observation compressed into a compact bottleneck token, CroBo learns to reconstruct heavily masked patches in a local target crop from sparse visible cues, using the global bottleneck token as context. This learning objective encourages the bottleneck token to encode a fine‑grained representation of scene‑wide semantic entities, including their identities, spatial locations, and configurations. As a result, the learned visual states reveal how scene elements move and interact over time, supporting sequential decision making. We evaluate CroBo on diverse vision‑based robot policy learning benchmarks, where it achieves state‑of‑the‑art performance. Reconstruction analyses and perceptual straightness experiments further show that the learned representations preserve pixel‑level scene composition and encode what‑moves‑where across observations. Project page available at: https://seokminlee‑chris.github.io/CroBo‑ProjectPage.

PaperId: 2455, https://arxiv.org/pdf/2603.13886.pdf GitHub

Abstract:
Scene text recognition (STR) methods have demonstrated their excellent capability in English text images. However, due to the complex inner structures of Chinese and the extensive character categories, it poses challenges for recognizing Chinese text in images. Recently, studies have shown that the methods designed for English text recognition encounter an accuracy bottleneck when recognizing Chinese text images. This raises the question: Is it appropriate to apply the model developed for English to the Chinese STR task? To explore this issue, we propose a novel method named LER, which explicitly decouples each character and independently recognizes characters while taking into account the complex inner structures of Chinese. LER consists of three modules: Localization, Extraction, and Recognition. Firstly, the localization module utilizes multimodal information to determine the character's position precisely. Then, the extraction module dissociates all characters in parallel. Finally, the recognition module considers the unique inner structures of Chinese to provide the text prediction results. Extensive experiments conducted on large‑scale Chinese benchmarks indicate that our method significantly outperforms existing methods. Furthermore, extensive experiments conducted on six English benchmarks and the Union14M benchmark show impressive results in English text recognition by LER. Code is available at https://github.com/Pandarenlql/LER.

PaperId: 2456, https://arxiv.org/pdf/2603.13810.pdf GitHub

Abstract:
Spike sparsity is widely believed to enable efficient spiking neural network (SNN) inference on GPU hardware. We demonstrate this is an illusion: five distinct sparse computation strategies on Apple M3 Max all fail to outperform dense convolution, because SIMD architectures cannot exploit the fine‑grained, unstructured sparsity of i.i.d. binary spikes. Instead, we propose Temporal Aggregated Convolution (TAC), which exploits convolution linearity to pre‑aggregate K spike frames before a single convolution call, reducing T calls to T/K. On rate‑coded data, TAC achieves 13.8times speedup with +1.6% accuracy on MNIST and +5.4% on Fashion‑MNIST ‑‑ a simultaneous improvement in both speed and accuracy. However, on event‑based data where the temporal dimension carries genuine motion information, TAC's temporal collapse is harmful. We therefore introduce TAC‑TP (Temporal Preservation), which shares each group's convolution output across K independent LIF steps, preserving full temporal resolution for downstream layers. On DVS128‑Gesture, TAC‑TP achieves 95.1% accuracy (vs. 96.3% baseline) with 50% fewer convolution calls, while standard TAC drops to 91.3%. Our key finding is that the optimal temporal aggregation strategy is data‑dependent: collapse the temporal dimension for rate‑coded data (noise reduction) but preserve it for event data (information retention). Speedup is hardware‑agnostic: TAC achieves 11.0times on NVIDIA V100, confirming the mechanism transfers across GPU architectures. All operators in the mlx‑snn library are open source.

PaperId: 2457, https://arxiv.org/pdf/2603.13792.pdf GitHub

Abstract:
As large language models (LLMs) scale to billions of parameters, full‑parameter fine‑tuning becomes compute‑ and memory‑prohibitive. Parameter‑efficient fine‑tuning (PEFT) mitigates this issue by updating only a small set of task‑specific parameters while keeping the base model frozen. Among PEFT approaches, low‑rank adaptation (LoRA) is widely adopted; however, it enforces a uniform rank across layers despite substantial variation in layer importance, motivating layerwise rank allocation. Recent adaptive‑rank variants (e.g., AdaLoRA) allocate ranks based on importance scores, yet typically rely on instantaneous gradients that capture only local sensitivity, overlooking non‑local, pathwise effects within the same layer, which yields unstable and biased scores. To address this limitation, we introduce IGU‑LoRA, an adaptive‑rank LoRA that (i) computes within‑layer Integrated Gradients (IG) sensitivities and aggregates them into a layer‑level score for rank allocation, and (ii) applies an uncertainty‑aware scheme using exponential moving averages with deviation tracking to suppress noisy updates and calibrate rank selection. Theoretically, we prove an upper bound on the composite trapezoidal rule approximation error for parameter‑space IG under a pathwise Hessian‑Lipschitz condition, which informs the quadrature budget. Across diverse tasks and architectures, IGU‑LoRA consistently outperforms strong PEFT baselines at matched parameter budgets, improving downstream accuracy and robustness. Ablations confirm the contributions of pathwise within‑layer sensitivity estimates and uncertainty‑aware selection to effective rank allocation. Our code is publicly available at https://github.com/withyou12/igulora.git

PaperId: 2458, https://arxiv.org/pdf/2603.13779.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have achieved impressive success in natural visual understanding, yet they consistently underperform in industrial anomaly detection (IAD). This is because MLLMs trained mostly on general web data differ significantly from industrial images. Moreover, they encode each image independently and can only compare images in the language space, making them insensitive to subtle visual differences that are key to IAD. To tackle these issues, we present AD‑Copilot, an interactive MLLM specialized for IAD via visual in‑context comparison. We first design a novel data curation pipeline to mine inspection knowledge from sparsely labeled industrial images and generate precise samples for captioning, VQA, and defect localization, yielding a large‑scale multimodal dataset Chat‑AD rich in semantic signals for IAD. On this foundation, AD‑Copilot incorporates a novel Comparison Encoder that employs cross‑attention between paired image features to enhance multi‑image fine‑grained perception, and is trained with a multi‑stage strategy that incorporates domain knowledge and gradually enhances IAD skills. In addition, we introduce MMAD‑BBox, an extended benchmark for anomaly localization with bounding‑box‑based evaluation. The experiments show that AD‑Copilot achieves 82.3% accuracy on the MMAD benchmark, outperforming all other models without any data leakage. In the MMAD‑BBox test, it achieves a maximum improvement of 3.35× over the baseline. AD‑Copilot also exhibits excellent generalization of its performance gains across other specialized and general‑purpose benchmarks. Remarkably, AD‑Copilot surpasses human expert‑level performance on several IAD tasks, demonstrating its potential as a reliable assistant for real‑world industrial inspection. All datasets and models will be released for the broader benefit of the community.

PaperId: 2459, https://arxiv.org/pdf/2603.13765.pdf GitHub

Abstract:
We propose a resource‑efficient framework for compressing large language models through knowledge distillation, combined with guided chain‑of‑thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation across English Dolly‑15k, Spanish Dolly‑15k, and code BugNet and PyTorrent datasets, with hyperparameters tuned in the English setting to optimize student performance. Across tasks, the distilled student retains a substantial portion of the teacher's capability while remaining significantly smaller: 70% to 91% in English, up to 95% in Spanish, and up to 93.5% Rouge‑L in code. For coding tasks, integrating chain‑of‑thought prompting with Group Relative Policy Optimization using CoT‑annotated Codeforces data improves reasoning coherence and solution correctness compared to knowledge distillation alone. Post‑training 4‑bit weight quantization further reduces memory footprint and inference latency. These results show that knowledge distillation combined with chain‑of‑thought guided reinforcement learning can produce compact, efficient models suitable for deployment in resource‑constrained settings.

PaperId: 2460, https://arxiv.org/pdf/2603.13733.pdf GitHub

Abstract:
Diffusion‑based models have recently shown strong performance in trajectory planning, as they are capable of capturing diverse, multimodal distributions of complex behaviors. A key limitation of these models is their slow inference speed, which results from the iterative denoising process. This makes them less suitable for real‑time applications such as closed‑loop model predictive control (MPC), where plans must be generated quickly and adapted continuously to a changing environment. In this paper, we investigate Implicit Maximum Likelihood Estimation (IMLE) as an alternative generative modeling approach for planning. IMLE offers strong mode coverage while enabling inference that is two orders of magnitude faster, making it particularly well suited for real‑time MPC tasks. Our results demonstrate that IMLE achieves competitive performance on standard offline reinforcement learning benchmarks compared to the standard diffusion‑based planner, while substantially improving planning speed in both open‑loop and closed‑loop settings. We further validate IMLE in a closed‑loop human navigation scenario, operating in real‑time, demonstrating how it enables rapid and adaptive plan generation in dynamic environments. Real‑world videos and code are available at https://gmpc‑imle.github.io/.

PaperId: 2461, https://arxiv.org/pdf/2603.13707.pdf GitHub

Abstract:
Humanoid loco‑manipulation requires coordinated high‑level motion plans with stable, low‑level whole‑body execution under complex robot‑environment dynamics and long‑horizon tasks. While diffusion policies (DPs) show promise for learning from demonstrations, deploying them on humanoids poses critical challenges: the motion planner trained offline is decoupled from the low‑level controller, leading to poor command tracking, compounding distribution shift, and task failures. The common approach of scaling demonstration data is prohibitively expensive for high‑dimensional humanoid systems. To address this challenge, we present REFINE‑DP (REinforcement learning FINE‑tuning of Diffusion Policy), a hierarchical framework that jointly optimizes a DP high‑level planner and an RL‑based low‑level loco‑manipulation controller. The DP is fine‑tuned via a PPO‑based diffusion policy gradient to improve task success rate, while the controller is simultaneously updated to accurately track the planner's evolving command distribution, reducing the distributional mismatch that degrades motion quality. We validate REFINE‑DP on a humanoid robot performing loco‑manipulation tasks, including door traversal and long‑horizon object transport. REFINE‑DP achieves an over 90% success rate in simulation, even in out‑of‑distribution cases not seen in the pre‑trained data, and enables smooth autonomous task execution in real‑world dynamic environments. Our proposed method substantially outperforms pre‑trained DP baselines and demonstrates that RL fine‑tuning is key to reliable humanoid loco‑manipulation. https://refine‑dp.github.io/REFINE‑DP/

PaperId: 2462, https://arxiv.org/pdf/2603.13702.pdf GitHub

Abstract:
Time series forecasting has attracted significant attention in the field of AI. Previous works have revealed that the Channel‑Independent (CI) strategy improves forecasting performance by modeling each channel individually, but it often suffers from poor generalization and overlooks meaningful inter‑channel interactions. Conversely, Channel‑Dependent (CD) strategies aggregate all channels, which may introduce irrelevant information and lead to oversmoothing. Despite recent progress, few existing methods offer the flexibility to adaptively balance CI and CD strategies in response to varying channel dependencies. To address this, we propose a generic plugin xCPD, that can adaptively model the channel‑patch dependencies from the perspective of graph spectral decomposition. Specifically, xCPD first projects multivariate signals into the frequency domain using a shared graph Fourier basis, and groups patches into low‑, mid‑, and high‑frequency bands based on their spectral energy responses. xCPD then applies a channel‑adaptive routing mechanism that dynamically adjusts the degree of inter‑channel interaction for each patch, enabling selective activation of frequency‑specific experts. This facilitates fine‑grained input‑aware modeling of smooth trends, local fluctuations, and abrupt transitions. xCPD can be seamlessly integrated on top of existing CI and CD forecasting models, consistently enhancing both accuracy and generalization across benchmarks. The code is available https://github.com/Clearloveyuan/xCPD.

PaperId: 2463, https://arxiv.org/pdf/2603.13603.pdf GitHub

Abstract:
We prove The Equivalence Theorem: structurally complete knowledge representation requires exactly four mutually entailing capabilities ‑‑ n‑ary relationships with attributes, temporal validity, uncertainty quantification, and causal relationships between relationships ‑‑ collectively equivalent to treating relationships as first‑class objects. Any system implementing one capability necessarily requires all four; any system missing one cannot achieve structural completeness. This result is constructive: we exhibit an Attributed Temporal Causal Hypergraph (ATCH) framework satisfying all four conditions simultaneously. The theorem yields a strict expressiveness hierarchy ‑‑ SQL < LPG < TypeDB < ATCH ‑‑ with witness queries that are structurally inexpressible at each lower level. We establish computational complexity bounds showing NP‑completeness for general queries but polynomial‑time tractability for practical query classes (acyclic patterns, bounded‑depth causal chains, windowed temporal queries). As direct corollaries, we derive solutions to classical AI problems: the Frame Problem (persistence by default from temporal validity), conflict resolution (contradictions as unresolved metadata with hidden variable discovery), and common sense reasoning (defaults with causal inhibitors). A prototype PostgreSQL extension in C validates practical feasibility within the established complexity bounds.

PaperId: 2464, https://arxiv.org/pdf/2603.13563.pdf GitHub

Abstract:
Weather forecasting offers an ideal testbed for artificial intelligence (AI) to learn complex, multi‑scale physical systems. Traditional numerical weather prediction remains computationally costly for frequent regional updates, as high‑resolution nests require intensive boundary coupling. We introduce Multi‑Resolution Graph Neural Forecasting (MR‑GNF), a lightweight, physics‑aware model that performs short‑term regional forecasts directly on an ellipsoidal, multi‑scale graph of the Earth. The framework couples a 0.25° region of interest with a 0.5° context belt and 1.0° outer domain, enabling continuous cross‑scale message passing without explicit nested boundaries. Its axial graph‑attention network alternates vertical self‑attention across pressure levels with horizontal graph attention across surface nodes, capturing implicit 3‑D structure in just 1.6 M parameters. Trained on 40 years of ERA5 reanalysis (1980‑2024), MR‑GNF delivers stable +6 h to +24 h forecasts for near‑surface temperature, wind, and precipitation over the UK‑Ireland sector. Despite a total compute cost below 80 GPU‑hours on a single RTX 6000 Ada, the model matches or exceeds heavier regional AI systems while preserving physical consistency across scales. These results demonstrate that graph‑based neural operators can achieve trustworthy, high‑resolution weather prediction at a fraction of NWP cost, opening a practical path toward AI‑driven early‑warning and renewable‑energy forecasting systems. Project page and code: https://github.com/AndriiShchur/MR‑GNF

PaperId: 2465, https://arxiv.org/pdf/2603.13467.pdf GitHub

Abstract:
Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialized on tasks of interest. However, models trained independently on distinct tasks often exhibit interference that degrades the merged model's performance. To solve this problem, we formally define the notion of Cross‑Task Interference as the drift in the representation of the merged model relative to its constituent models. Reducing cross‑task interference is key to improving merging performance. To address this issue, we propose our method, Resolving Interference (RI), a light‑weight adaptation framework which disentangles expert models to be functionally orthogonal to the space of other tasks, thereby reducing cross‑task interference. RI does this whilst using only unlabeled auxiliary data as input (i.e., no task‑data is needed), allowing it to be applied in data‑scarce scenarios. RI consistently improves the performance of state‑of‑the‑art merging methods by up to 3.8% and generalization to unseen domains by up to 2.3%. We also find RI to be robust to the source of auxiliary input while being significantly less sensitive to tuning of merging hyperparameters. Our codebase is available at: https://github.com/pramesh39/resolving_interference

PaperId: 2466, https://arxiv.org/pdf/2603.13459.pdf GitHub

Abstract:
In‑context learning (ICL) is a valuable capability exhibited by Transformers pretrained on diverse sequence tasks. However, previous studies have observed that ICL often conflicts with the model's inherent in‑weight learning (IWL) ability. By examining the representation space learned by a toy model in synthetic experiments, we identify the shared encoding space for context and samples in Transformers as a potential source of this conflict. To address this, we modify the model architecture to separately encode the context and samples into two distinct spaces: a task representation space and a sample representation space. We model these two spaces under a simple yet principled framework, assuming a linear representational structure and treating them as a pair of dual spaces. Both theoretical analysis and empirical results demonstrate the effectiveness of our proposed architecture, CoQE, in the single‑value answer setting. It not only enhances ICL performance through improved representation learning, but also successfully reconciles ICL and IWL capabilities across synthetic few‑shot classification and a newly designed pseudo‑arithmetic task. Code: https://github.com/McGuinnessChen/dual‑representation‑space‑encoding

PaperId: 2467, https://arxiv.org/pdf/2603.13431.pdf GitHub

Abstract:
Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized benchmark for fair comparison and model development. These methods are evaluated on different SAbDab snapshots, non‑overlapping test sets, and incompatible metrics, and the literature fragments the design problem into numerous sub‑tasks with no common definition. We introduce \textscChimera‑Bench (CDR Modeling with Epitope‑guided Redesign), a unified benchmark built around a single canonical task: \emphepitope‑conditioned CDR sequence‑structure co‑design. \textscChimera‑Bench provides (1) a curated, deduplicated dataset of 2,922 antibody‑antigen complexes with epitope and paratope annotations; (2) three biologically motivated splits testing generalization to unseen epitopes, unseen antigen folds, and prospective temporal targets; and (3) a comprehensive evaluation protocol with five metric groups including novel epitope‑specificity measures. We benchmark representative methods spanning different generative paradigms and report results across all splits. \textscChimera‑Bench is the largest dataset of its kind for the antibody design problem, allowing the community to develop and test novel methods and evaluate their generalizability. The source code and data are available at: https://github.com/mansoor181/chimera‑bench.git

PaperId: 2468, https://arxiv.org/pdf/2603.13406.pdf GitHub

Abstract:
Emotion recognition in videos is a pivotal task in affective computing, where identifying subtle psychological states such as Ambivalence and Hesitancy holds significant value for behavioral intervention and digital health. Ambivalence and Hesitancy states often manifest through cross‑modal inconsistencies such as discrepancies between facial expressions, vocal tones, and textual semantics, posing a substantial challenge for automated recognition. This paper proposes a recognition framework that integrates temporal segment modeling with Multimodal Large Language Models. To address computational efficiency and token constraints in long video processing, we employ a segment‑based strategy, partitioning videos into short clips with a maximum duration of 5 seconds. We leverage the Qwen3‑Omni‑30B‑A3B model, fine‑tuned on the BAH dataset using LoRA and full‑parameter strategies via the MS‑Swift framework, enabling the model to synergistically analyze visual and auditory signals. Experimental results demonstrate that the proposed method achieves an accuracy of 85.1% on the test set, significantly outperforming existing benchmarks and validating the superior capability of Multimodal Large Language Models in capturing complex and nuanced emotional conflicts. The code is released at https://github.com/dlnn123/A‑H‑Detection‑with‑Qwen‑Omni.git.

PaperId: 2469, https://arxiv.org/pdf/2603.13341.pdf GitHub

Abstract:
Source‑Free Cross‑Domain Few‑Shot Learning (SF‑CDFSL) focuses on fine‑tuning with limited training data from target domains (e.g., medical or satellite images), where Vision‑Language Models (VLMs) such as CLIP and SigLIP have shown promising results. Current works in traditional visual models suggest that improving visual discriminability enhances performance. However, in VLM‑based SF‑CDFSL tasks, we find that strengthening visual‑modal discriminability actually suppresses VLMs' performance. In this paper, we aim to delve into this phenomenon for an interpretation and a solution. By both theoretical and experimental proofs, our study reveals that fine‑tuning with the typical cross‑entropy loss (\mathcalL_\mathrmvlm) inherently includes a visual learning part and a cross‑modal learning part, where the cross‑modal part is crucial for rectifying the heavily disrupted modality misalignment in SF‑CDFSL. However, we find that the visual learning essentially acts as a shortcut that encourages the model to reduce \mathcalL_\mathrmvlm without considering the cross‑modal part, therefore hindering the cross‑modal alignment and harming the performance. Based on this interpretation, we further propose an approach to address this problem: first, we perturb the visual learning to guide the model to focus on the cross‑modal alignment. Then, we use the visual‑text semantic relationships to gradually align the visual and textual modalities during the fine‑tuning. Extensive experiments on various settings, backbones (CLIP, SigLip, PE‑Core), and tasks (4 CDFSL datasets and 11 FSL datasets) show that we consistently set new state‑of‑the‑art results. Code is available at https://github.com/zhenyuZ‑HUST/CVPR26‑Mind‑the‑Discriminability‑Trap.

PaperId: 2470, https://arxiv.org/pdf/2603.13328.pdf GitHub

Abstract:
Inter‑scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that efficacy of strategies involving modifications or constraints on the latent space appears to be contingent upon the level and/or depth of supervision during model training. In this paper, we therefore propose an unsupervised domain adaptation technique based on self‑supervised multi‑stage unlearning (SSMSU). Building upon the state‑of‑the‑art segmentation framework nnU‑Net, we employ deep supervision at deep encoder stages using domain classifier unlearning, applied sequentially across the deep stages to suppress domain‑related latent features. Following self‑configurable approach of the nnU‑Net, the auxiliary feedback loop implements a self‑supervised backpropagation schedule for the unlearning process, since continuous unlearning was found to have a detrimental effect on the main segmentation task. Experiments were carried out on four public datasets for benchmarking white‑matter lesion segmentation methods. Five benchmark models and/or strategies, covering passive to active unsupervised domain adaptation, were tested. In comparison, the SSMSU demonstrated the advantage of unlearning by enhancing lesion sensitivity and limiting false detections, which resulted in higher overall segmentation quality in terms of segmentation overlap and relative lesion volume error. The proposed model inputs only the FLAIR modality, which simplifies preprocessing pipelines, eliminates the need for inter‑modality registration errors and harmonization, which can introduce variability. Source code is available on https://github.com/Pubec/nnunetv2‑unlearning.

PaperId: 2471, https://arxiv.org/pdf/2603.13300.pdf GitHub

Abstract:
Safety mechanisms for diffusion and flow models have recently been developed along two distinct paths. In robot planning, control barrier functions are employed to guide generative trajectories away from obstacles at every denoising step by explicitly imposing geometric constraints. In parallel, recent data‑driven, negative guidance approaches have been shown to suppress harmful content and promote diversity in generated samples. However, they rely on heuristics without clearly stating when safety guidance is actually necessary. In this paper, we first introduce a unified probabilistic framework using a Maximum Mean Discrepancy (MMD) potential for image generation tasks that recasts both Shielded Diffusion and Safe Denoiser as instances of our energy‑based negative guidance against unsafe data samples. Furthermore, we leverage control‑barrier functions analysis to justify the existence of a critical time window in which negative guidance must be strong; outside of this window, the guidance should decay to zero to ensure safe and high‑quality generation. We evaluate our unified framework on several realistic safe generation scenarios, confirming that negative guidance should be applied in the early stages of the denoising process for successful safe generation.

PaperId: 2472, https://arxiv.org/pdf/2603.13278.pdf GitHub

Abstract:
Despite the scale of capital being deployed toward AI initiatives, no empirical framework currently exists for benchmarking where a firm stands relative to competitors in AI readiness and deployment, or for translating that position into auditable financial outcomes. In practice, private equity deal teams, management consultants, and corporate strategists have relied on qualitative judgment and ad‑hoc maturity labels; tools that are neither comparable across industries nor grounded in observable economic data. This paper introduces the AI Transformation Gap Index (AITG), a composite empirical framework that measures the distance between a firm's current AI deployment and a time varying, industry constrained capability frontier, then maps that distance to dollar denominated value creation, execution feasibility under uncertainty, and competitive disruption risk. Five linked modules address this gap: cross industry normalization (IASS), a dynamic capability ceiling that evolves with frontier capabilities (AFC), trajectory based firm scoring with integrated execution risk (IFS), a CES bottleneck value decomposition mapping gap scores to enterprise value (VCB), and a competitive hazard measure for inaction (ADRI). I calibrate the framework for 22 industry verticals and apply it to 14 public companies using public filings. A retrospective construct validity exercise correlating AITG scores with observed EBITDA margin expansion yields Spearman rho_s = 0.818 (n = 10), directionally consistent with predictions though insufficient for causal identification. A counterintuitive result emerges: the largest AI transformation gaps do not produce the highest value density, because implementation friction, CES bottlenecks, and timing lags erode the theoretical upside of wide gaps.

PaperId: 2473, https://arxiv.org/pdf/2603.13260.pdf GitHub

Abstract:
Knowledge Distillation (KD) can transfer the reasoning abilities of large models to smaller ones, which can reduce the costs to generate Chain‑of‑Thoughts for reasoning tasks. KD methods typically ask the student to mimic the teacher's distribution over the entire output. However, a student with limited capacity can be overwhelmed by such extensive supervision causing a distribution mismatch, especially in complex reasoning tasks. We propose Token‑Selective Dual Knowledge Distillation (TSD‑KD), a framework for student‑centric distillation. TSD‑KD focuses on distilling important tokens for reasoning and encourages the student to explain reasoning in its own words. TSD‑KD combines indirect and direct distillation. Indirect distillation uses a weak form of feedback based on preference ranking. The student proposes candidate responses generated on its own; the teacher re‑ranks those candidates as indirect feedback without enforcing its entire distribution. Direct distillation uses distribution matching; however, it selectively distills tokens based on the relative confidence between teacher and student. Finally, we add entropy regularization to maintain the student's confidence during distillation. Overall, our method provides the student with targeted and indirect feedback to support its own reasoning process and to facilitate self‑improvement. The experiments show the state‑of‑the‑art performance of TSD‑KD on 10 challenging reasoning benchmarks, outperforming the baseline and runner‑up in accuracy by up to 54.4% and 40.3%, respectively. Notably, a student trained by TSD‑KD even outperformed its own teacher model in four cases by up to 20.3%. The source code is available at https://github.com/kmswin1/TSD‑KD.

PaperId: 2474, https://arxiv.org/pdf/2603.13251.pdf GitHub

Abstract:
Traditional benchmarks like HumanEval and MBPP test logic and syntax effectively, but fail when code must produce dynamic, pedagogical visuals. We introduce ManiBench, a specialized benchmark evaluating LLM performance in generating Manim CE code, where temporal fidelity and version‑aware API correctness are critical. ManiBench targets two key failure modes: Syntactic Hallucinations (valid Python referencing non‑existent or deprecated Manim APIs) and Visual‑Logic Drift (generated visuals diverging from intended mathematical logic through timing errors or missing causal relationships). The benchmark comprises 150‑200 problems across five difficulty levels spanning calculus, linear algebra, probability, topology, and AI, grounded in analysis of 3Blue1Brown's ManimGL source (53,000 lines, 143 scene classes). Evaluation uses a four‑tier framework measuring Executability, Version‑Conflict Error Rate, Alignment Score, and Coverage Score. An open‑source framework automates evaluation across multiple models and prompting strategies. Code, data and benchmark suite are available at https://github.com/nabin2004/ManiBench. and the dataset is hosted on https://huggingface.co/datasets/nabin2004/ManiBench.

PaperId: 2475, https://arxiv.org/pdf/2603.13247.pdf GitHub

Abstract:
The proliferation of autonomous AI agents capable of executing real‑world actions ‑ filesystem operations, API calls, database modifications, financial transactions ‑ introduces a class of safety risk not addressed by existing content‑moderation infrastructure. Current text‑safety systems evaluate linguistic content for harm categories such as violence, hate speech, and sexual content; they are architecturally unsuitable for evaluating whether a proposed action falls within an agent's authorized operational scope. We present ILION (Intelligent Logic Identity Operations Network), a deterministic execution gate for agentic AI systems. ILION employs a five‑component cascade architecture ‑ Transient Identity Imprint (TII), Semantic Vector Reference Frame (SVRF), Identity Drift Control (IDC), Identity Resonance Score (IRS) and Consensus Veto Layer (CVL) ‑ to classify proposed agent actions as BLOCK or ALLOW without statistical training or API dependencies. The system requires zero labeled data, operates in sub‑millisecond latency, and produces fully interpretable verdicts. We evaluate ILION on ILION‑Bench v2, a purpose‑built benchmark of 380 test scenarios across eight attack categories with 39% hard‑difficulty adversarial cases and a held‑out development split. ILION achieves F1 = 0.8515, precision = 91.0%, and a false positive rate of 7.9% at a mean latency of 143 microseconds. Comparative evaluation against three baselines ‑ Lakera Guard (F1 = 0.8087), OpenAI Moderation API (F1 = 0.1188), and Llama Guard 3 (F1 = 0.0105) ‑ demonstrates that existing text‑safety infrastructure systematically fails on agent execution safety tasks due to a fundamental task mismatch. ILION outperforms the best commercial baseline by 4.3 F1 points while operating 2,000 times faster with a false positive rate four times lower.

PaperId: 2476, https://arxiv.org/pdf/2603.13228.pdf GitHub

Abstract:
Recent progress in text‑conditioned human motion generation has been largely driven by diffusion models trained on large‑scale human motion data. Building on this progress, recent methods attempt to transfer such models for character animation and real robot control by applying a Whole‑Body Controller (WBC) that converts diffusion‑generated motions into executable trajectories. While WBC trajectories become compliant with physics, they may expose substantial deviations from original motion. To address this issue, we here propose PhysMoDPO, a Direct Preference Optimization framework. Unlike prior work that relies on hand‑crafted physics‑aware heuristics such as foot‑sliding penalties, we integrate WBC into our training pipeline and optimize diffusion model such that the output of WBC becomes compliant both with physics and original text instructions. To train PhysMoDPO we deploy physics‑based and task‑specific rewards and use them to assign preference to synthesized trajectories. Our extensive experiments on text‑to‑motion and spatial control tasks demonstrate consistent improvements of PhysMoDPO in both physical realism and task‑related metrics on simulated robots. Moreover, we demonstrate that PhysMoDPO results in significant improvements when applied to zero‑shot motion transfer in simulation and for real‑world deployment on a G1 humanoid robot.

PaperId: 2477, https://arxiv.org/pdf/2603.13224.pdf GitHub

Abstract:
Vision‑to‑code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representations with high visual fidelity. While recent Large Vision Language Models (LVLMs) achieve strong results via supervised fine‑tuning, reinforcement learning remains challenging due to misaligned reward signals. Existing rewards either rely on textual rules or coarse visual embedding similarity, both of which fail to capture fine‑grained visual discrepancies and are vulnerable to reward hacking. We propose Visual Equivalence Reward Model (Visual‑ERM), a multimodal generative reward model that provides fine‑grained, interpretable, and task‑agnostic feedback to evaluate vision‑to‑code quality directly in the rendered visual space. Integrated into RL, Visual‑ERM improves Qwen3‑VL‑8B‑Instruct by +8.4 on chart‑to‑code and yields consistent gains on table and SVG parsing (+2.7, +4.1 on average), and further strengthens test‑time scaling via reflection and revision. We also introduce VisualCritic‑RewardBench (VC‑RewardBench), a benchmark for judging fine‑grained image‑to‑image discrepancies on structured visual data, where Visual‑ERM at 8B decisively outperforms Qwen3‑VL‑235B‑Instruct and approaches leading closed‑source models. Our results suggest that fine‑grained visual reward supervision is both necessary and sufficient for vision‑to‑code RL, regardless of task specificity.

PaperId: 2478, https://arxiv.org/pdf/2603.13134.pdf GitHub GitHub

Abstract:
Group Relative Policy Optimization (GRPO) has emerged as an effective method for training reasoning models. While it computes advantages based on group mean, GRPO treats each output as an independent sample during the optimization and overlooks a vital structural signal: the natural contrast between correct and incorrect solutions within the same group, thus ignoring the rich, comparative data that could be leveraged by explicitly pitting successful reasoning traces against failed ones. To capitalize on this, we present a contrastive reformulation of GRPO, showing that the GRPO objective implicitly maximizes the margin between the policy ratios of correct and incorrect samples. Building on this insight, we propose Bilateral Context Conditioning (BICC), a mechanism that allows the model to cross‑reference successful and failed reasoning traces during the optimization, enabling a direct information flow across samples. We further introduce Reward‑Confidence Correction (RCC) to stabilize training by dynamically adjusts the advantage baseline in GRPO using reward‑confidence covariance derived from the first‑order approximation of the variance‑minimizing estimator. Both mechanisms require no additional sampling or auxiliary models and can be adapted to all GRPO variants. Experiments on mathematical reasoning benchmarks demonstrate consistent improvements across comprehensive models and algorithms. Code is available at \hrefhttps://github.com/Skylanding/BiCChttps://github.com/Skylanding/BiCC.

PaperId: 2479, https://arxiv.org/pdf/2603.13131.pdf GitHub

Abstract:
Long‑horizon embodied intelligence requires agents to improve through interaction, not merely to execute plans generated from static goals. A central challenge is therefore to transform past executions into knowledge that can shape future decisions. Minecraft provides a representative testbed for this problem, where tasks such as crafting tools, building redstone components, and obtaining diamond equipment involve long prerequisite chains and are frequently disrupted by missing tools, blocked paths, GUI failures, or stagnant execution. To this end, we propose MineEvolve, a knowledge‑driven self‑evolution framework that converts execution feedback into actionable behavioral knowledge. MineEvolve first uses \underline\emph\ding182Monitor to convert each subgoal execution into typed feedback, including state changes, inventory changes, failure types, progress signals, and stagnation indicators. \underline\emph\ding183Inducer then derives reusable skills from successful executions and remedies from failed or stagnant executions. \underline\emph\ding184Curator validates, merges, filters, and retrieves these knowledge entries, while \underline\emph\ding185Adaptor uses them to repair the unfinished part of the plan under repeated failures or stagnation. Experiments on the Minecraft MCU long‑horizon task suite show that MineEvolve consistently improves performance across multiple language‑model planners, with larger gains on high‑dependency task groups. Ablation and knowledge‑accumulation studies further demonstrate that converting execution signals into structured behavioral knowledge is an effective path toward self‑evolving embodied agents in long‑horizon environments. Our code is available at https://github.com/xzw‑ustc/MC‑MineEvolve.

PaperId: 2480, https://arxiv.org/pdf/2603.13027.pdf GitHub

Abstract:
Automatic identification of screw types is important for industrial automation, robotics, and inventory management. However, publicly available datasets for screw classification are scarce, particularly for controlled single‑object scenarios commonly encountered in automated sorting systems. In this work, we introduce SortScrews, a dataset for casewise visual classification of screws. The dataset contains 560 RGB images at 512×512 resolution covering six screw types and a background class. Images are captured using a standardized acquisition setup and include mild variations in lighting and camera perspective across four capture settings. To facilitate reproducible research and dataset expansion, we also provide a reusable data collection script that allows users to easily construct similar datasets for custom hardware components using inexpensive camera setups. We establish baseline results using transfer learning with EfficientNet‑B0 and ResNet‑18 classifiers pretrained on ImageNet. In addition, we conduct a well‑explored failure analysis. Despite the limited dataset size, these lightweight models achieve strong classification accuracy, demonstrating that controlled acquisition conditions enable effective learning even with relatively small datasets. The dataset, collection pipeline, and baseline training code are publicly available at https://github.com/ATATC/SortScrews.

PaperId: 2481, https://arxiv.org/pdf/2603.13017.pdf GitHub

Abstract:
Long conversations with an AI agent create a simple problem for one user: the history is useful, but carrying it verbatim is expensive. We study personalized agent memory: one user's conversation history with an agent, distilled into a compact retrieval layer for later search. Each exchange is compressed into a compound object with four fields (exchange_core, specific_context, thematic room_assignments, and regex‑extracted files_touched). The searchable distilled text averages 38 tokens per exchange. Applied to 4,182 conversations (14,340 exchanges) from 6 software engineering projects, the method reduces average exchange length from 371 to 38 tokens, yielding 11x compression. We evaluate whether personalized recall survives that compression using 201 recall‑oriented queries, 107 configurations spanning 5 pure and 5 cross‑layer search modes, and 5 LLM graders (214,519 consensus‑graded query‑result pairs). The best pure distilled configuration reaches 96% of the best verbatim MRR (0.717 vs 0.745). Results are mechanism‑dependent. All 20 vector search configurations remain non‑significant after Bonferroni correction, while all 20 BM25 configurations degrade significantly (effect sizes |d|=0.031‑0.756). The best cross‑layer setup slightly exceeds the best pure verbatim baseline (MRR 0.759). Structured distillation compresses single‑user agent memory without uniformly sacrificing retrieval quality. At 1/11 the context cost, thousands of exchanges fit within a single prompt while the verbatim source remains available for drill‑down. We release the implementation and analysis pipeline as open‑source software.

PaperId: 2482, https://arxiv.org/pdf/2603.12988.pdf GitHub

Abstract:
We present a fairness‑aware framework for multi‑class lung disease diagnosis from chest CT volumes, developed for the Fair Disease Diagnosis Challenge at the PHAROS‑AIF‑MIH Workshop (CVPR 2026). The challenge requires classifying CT scans into four categories ‑‑ Healthy, COVID‑19, Adenocarcinoma, and Squamous Cell Carcinoma ‑‑ with performance measured as the average of per‑gender macro F1 scores, explicitly penalizing gender‑inequitable predictions. Our approach addresses two core difficulties: the sparse pathological signal across hundreds of slices, and a severe demographic imbalance compounded across disease class and gender. We propose an attention‑based Multiple Instance Learning (MIL) model on a ConvNeXt backbone that learns to identify diagnostically relevant slices without slice‑level supervision, augmented with a Gradient Reversal Layer (GRL) that adversarially suppresses gender‑predictive structure in the learned scan representation. Training incorporates focal loss with label smoothing, stratified cross‑validation over joint (class, gender) strata, and targeted oversampling of the most underrepresented subgroup. At inference, all five‑fold checkpoints are ensembled with horizontal‑flip test‑time augmentation via soft logit voting and out‑of‑the‑fold threshold optimization for robustness. Our model achieves a mean validation competition score of 0.685 (std ‑ 0.030), with the best single fold reaching 0.759. All training and inference code is publicly available at https://github.com/ADE‑17/cvpr‑fair‑chest‑ct

PaperId: 2483, https://arxiv.org/pdf/2603.12960.pdf GitHub

Abstract:
Residual policy learning (RPL), in which a learned policy refines a static base policy using deep reinforcement learning (DRL), has shown strong performance across various robotic applications. Its effectiveness is particularly evident in autonomous racing, a domain that serves as a challenging benchmark for real‑world DRL. However, deploying RPL‑based controllers introduces system complexity and increases inference latency. We address this by introducing an extension of RPL named attenuated residual policy optimization (α‑RPO). Unlike standard RPL, α‑RPO yields a standalone neural policy by progressively attenuating the base policy, which initially serves to bootstrap learning. Furthermore, this mechanism enables a form of privileged learning, where the base policy is permitted to use sensor modalities not required for final deployment. We design α‑RPO to integrate seamlessly with PPO, ensuring that the attenuated influence of the base controller is dynamically compensated during policy optimization. We evaluate α‑RPO by building a framework for 1:10‑scaled autonomous racing around it. In both simulation and zero‑shot real‑world transfer to Roboracer cars, α‑RPO not only reduces system complexity but also improves driving performance compared to baselines ‑ demonstrating its practicality for robotic deployment. Our code is available at: https://github.com/raphajaner/arpo_racing.

PaperId: 2484, https://arxiv.org/pdf/2603.12938.pdf GitHub

Abstract:
Real‑time understanding of continuous video streams is essential for interactive assistants and multimodal agents operating in dynamic environments. However, most existing video reasoning approaches follow a batch paradigm that defers reasoning until the full video context is observed, resulting in high latency and growing computational cost that are incompatible with streaming scenarios. In this paper, we introduce ThinkStream, a framework for streaming video reasoning based on a Watch‑‑Think‑‑Speak paradigm that enables models to incrementally update their understanding as new video observations arrive. At each step, the model performs a short reasoning update and decides whether sufficient evidence has accumulated to produce a response. To support long‑horizon streaming, we propose Reasoning‑Compressed Streaming Memory (RCSM), which treats intermediate reasoning traces as compact semantic memory that replaces outdated visual tokens while preserving essential context. We further train the model using a Streaming Reinforcement Learning with Verifiable Rewards scheme that aligns incremental reasoning and response timing with the requirements of streaming interaction. Experiments on multiple streaming video benchmarks show that ThinkStream significantly outperforms existing online video models while maintaining low latency and memory usage. Code, models and data will be released at https://github.com/johncaged/ThinkStream

PaperId: 2485, https://arxiv.org/pdf/2603.12912.pdf GitHub

Abstract:
Federated Domain Generalization for Person Re‑Identification (FedDG‑ReID) learns domain‑invariant representations from decentralized data. While Vision Transformer (ViT) is widely adopted, its global attention often fails to distinguish pedestrians from high similarity backgrounds or diverse viewpoints ‑‑ a challenge amplified by cross‑client distribution shifts in FedDG‑ReID. To address this, we propose Federated Body Distribution Aware Visual Prompt (FedBPrompt), introducing learnable visual prompts to guide Transformer attention toward pedestrian‑centric regions. FedBPrompt employs a Body Distribution Aware Visual Prompts Mechanism (BAPM) comprising: Holistic Full Body Prompts to suppress cross‑client background noise, and Body Part Alignment Prompts to capture fine‑grained details robust to pose and viewpoint variations. To mitigate high communication costs, we design a Prompt‑based Fine‑Tuning Strategy (PFTS) that freezes the ViT backbone and updates only lightweight prompts, significantly reducing communication overhead while maintaining adaptability. Extensive experiments demonstrate that BAPM effectively enhances feature discrimination and cross‑domain generalization, while PFTS achieves notable performance gains within only a few aggregation rounds. Moreover, both BAPM and PFTS can be easily integrated into existing ViT‑based FedDG‑ReID frameworks, making FedBPrompt a flexible and effective solution for federated person re‑identification. The code is available at https://github.com/leavlong/FedBPrompt.

PaperId: 2486, https://arxiv.org/pdf/2603.12893.pdf GitHub

Abstract:
Reinforcement learning (RL) has become a standard technique for post‑training diffusion‑based image synthesis models, as it enables learning from reward signals to explicitly improve desirable aspects such as image quality and prompt alignment. In this paper, we propose an online RL variant that reduces the variance in the model updates by sampling paired trajectories and pulling the flow velocity in the direction of the more favorable image. Unlike existing methods that treat each sampling step as a separate policy action, we consider the entire sampling process as a single action. We experiment with both high‑quality vision language models and off‑the‑shelf quality metrics for rewards, and evaluate the outputs using a broad set of metrics. Our method converges faster and yields higher output quality and prompt alignment than previous approaches.

PaperId: 2487, https://arxiv.org/pdf/2603.12847.pdf GitHub

Abstract:
Most real‑world IoT data analysis tasks, such as clustering and anomaly event detection, are unsupervised and highly susceptible to the presence of outliers. In addition to sporadic scattered outliers caused by factors such as faulty sensor readings, IoT systems often exhibit clustered outliers. These occur when multiple devices or nodes produce similar anomalous measurements, for instance, owing to localized interference, emerging security threats, or regional false alarms, forming micro‑clusters. These clustered outliers can be easily mistaken for normal behavior because of their relatively high local density, thereby obscuring the detection of both scattered and contextual anomalies. To address this, we propose a novel outlier detection paradigm that leverages the natural neighboring relationships using graph structures. This facilitates multi‑perspective anomaly evaluation by incorporating reference sets at both local and global scales derived from the graph. Our approach enables the effective recognition of scattered outliers without interference from clustered anomalies, whereas the graph structure simultaneously helps reflect and isolate clustered outlier groups. Extensive experiments, including comparative performance analysis, ablation studies, validation on downstream clustering tasks, and evaluation of hyperparameter sensitivity, demonstrate the efficacy of the proposed method. The source code is available at https://github.com/gordonlok/DROD.

PaperId: 2488, https://arxiv.org/pdf/2603.12743.pdf GitHub

Abstract:
Concept customization typically binds rare tokens to a target concept. Unfortunately, these approaches often suffer from unstable performance as the pretraining data seldom contains these rare tokens. Meanwhile, these rare tokens fail to convey the inherent knowledge of the target concept. Consequently, we introduce Knowledge‑aware Concept Customization, a novel task aiming at binding diverse textual knowledge to target visual concepts. This task requires the model to identify the knowledge within the text prompt to perform high‑fidelity customized generation. Meanwhile, the model should efficiently bind all the textual knowledge to the target concept. Therefore, we propose MoKus, a novel framework for knowledge‑aware concept customization. Our framework relies on a key observation: cross‑modal knowledge transfer, where modifying knowledge within the text modality naturally transfers to the visual modality during generation. Inspired by this observation, MoKus contains two stages: (1) In visual concept learning, we first learn the anchor representation to store the visual information of the target concept. (2) In textual knowledge updating, we update the answer for the knowledge queries to the anchor representation, enabling high‑fidelity customized generation. To further comprehensively evaluate our proposed MoKus on the new task, we introduce the first benchmark for knowledge‑aware concept customization: KnowCusBench. Extensive evaluations have demonstrated that MoKus outperforms state‑of‑the‑art methods. Moreover, the cross‑model knowledge transfer allows MoKus to be easily extended to other knowledge‑aware applications like virtual concept creation and concept erasure. We also demonstrate the capability of our method to achieve improvements on world knowledge benchmarks.

PaperId: 2489, https://arxiv.org/pdf/2603.12722.pdf GitHub

Abstract:
Visual stimuli reconstruction from EEG remains challenging due to fidelity loss and representation shift. We propose CognitionCapturerPro, an enhanced framework that integrates EEG with multi‑modal priors (images, text, depth, and edges) via collaborative training. Our core contributions include an uncertainty‑weighted similarity scoring mechanism to quantify modality‑specific fidelity and a fusion encoder for integrating shared representations. By employing a simplified alignment module and a pre‑trained diffusion model, our method significantly outperforms the original CognitionCapturer on the THINGS‑EEG dataset, improving Top‑1 and Top‑5 retrieval accuracy by 25.9% and 10.6%, respectively. Code is available at: https://github.com/XiaoZhangYES/CognitionCapturerPro.

PaperId: 2490, https://arxiv.org/pdf/2603.12721.pdf GitHub GitHub

Abstract:
Robust point cloud registration is a fundamental task in 3D computer vision and geometric deep learning, essential for applications such as large‑scale 3D reconstruction, augmented reality, and scene understanding. However, the performance of established learning‑based methods often degrades in complex, real world scenarios characterized by incomplete data, sensor noise, and low overlap regions. To address these limitations, we propose CMHANet, a novel Cross‑Modal Hybrid Attention Network. Our method integrates the fusion of rich contextual information from 2D images with the geometric detail of 3D point clouds, yielding a comprehensive and resilient feature representation. Furthermore, we introduce an innovative optimization function based on contrastive learning, which enforces geometric consistency and significantly improves the model's robustness to noise and partial observations. We evaluated CMHANet on the 3DMatch and the challenging 3DLoMatch datasets. \revAdditionally, zero‑shot evaluations on the TUM RGB‑D SLAM dataset verify the model's generalization capability to unseen domains. The experimental results demonstrate that our method achieves substantial improvements in both registration accuracy and overall robustness, outperforming current techniques. We also release our code in \hrefhttps://github.com/DongXu‑Zhang/CMHANethttps://github.com/DongXu‑Zhang/CMHANet.

PaperId: 2491, https://arxiv.org/pdf/2603.12719.pdf GitHub GitHub

Abstract:
Point cloud registration (PCR) is a fundamental task in 3D vision and provides essential support for applications such as autonomous driving, robotics, and environmental modeling. Despite its widespread use, existing methods often fail when facing real‑world challenges like heavy noise, significant occlusions, and large‑scale transformations. These limitations frequently result in compromised registration accuracy and insufficient robustness in complex environments. In this paper, we propose IGASA as a novel registration framework constructed upon a Hierarchical Pyramid Architecture (HPA) designed for robust multi‑scale feature extraction and fusion. The framework integrates two pivotal components consisting of the Hierarchical Cross‑Layer Attention (HCLA) module and the Iterative Geometry‑Aware Refinement (IGAR) module. The HCLA module utilizes skip attention mechanisms to align multi‑resolution features and enhance local geometric consistency. Simultaneously, the IGAR module is designed for the fine matching phase by leveraging reliable correspondences established during coarse matching. This synergistic integration within the architecture allows IGASA to adapt effectively to diverse point cloud structures and intricate transformations. We evaluate the performance of IGASA on four widely recognized benchmark datasets including 3D(Lo)Match, KITTI, and nuScenes. Our extensive experiments consistently demonstrate that IGASA significantly surpasses state‑of‑the‑art methods and achieves notable improvements in registration accuracy. This work provides a robust foundation for advancing point cloud registration techniques while offering valuable insights for practical 3D vision applications. The code for IGASA is available in \hrefhttps://github.com/DongXu‑Zhang/IGASAhttps://github.com/DongXu‑Zhang/IGASA.

PaperId: 2492, https://arxiv.org/pdf/2603.12625.pdf GitHub

Abstract:
Multimodal recommendation is commonly framed as a feature fusion problem, where textual and visual signals are combined to better model user preference. However, the effectiveness of multimodal recommendation may depend not only on how modalities are fused, but also on whether item content is represented in a semantic space aligned with preference matching. This issue is particularly important because raw visual features often preserve appearance similarity, while user decisions are typically driven by higher‑level semantic factors such as style, material, and usage context. Motivated by this observation, we propose LVLM‑grounded Multimodal Semantic Representation for Recommendation (VLM4Rec), a lightweight framework that organizes multimodal item content through semantic alignment rather than direct feature fusion. VLM4Rec first uses a large vision‑language model to ground each item image into an explicit natural‑language description, and then encodes the grounded semantics into dense item representations for preference‑oriented retrieval. Recommendation is subsequently performed through a simple profile‑based semantic matching mechanism over historical item embeddings, yielding a practical offline‑online decomposition. Extensive experiments on multiple multimodal recommendation datasets show that VLM4Rec consistently improves performance over raw visual features and several fusion‑based alternatives, suggesting that representation quality may matter more than fusion complexity in this setting. The code is released at https://github.com/tyvalencia/enhancing‑mm‑rec‑sys.

PaperId: 2493, https://arxiv.org/pdf/2603.12595.pdf GitHub

Abstract:
Reinforcement Learning from Human Feedback (RLHF) is a widely used approach to align large‑scale AI systems with human values. However, RLHF typically assumes a single, universal reward, which overlooks diverse preferences and limits personalization. Variational Preference Learning (VPL) seeks to address this by introducing user‑specific latent variables. Despite its promise, we found that VPL suffers from posterior collapse. While this phenomenon is well known in VAEs, it has not previously been identified in preference learning frameworks. Under sparse preference data and with overly expressive decoders, VPL may cause latent variables to be ignored, reverting to a single‑reward model. To overcome this limitation, we propose Swap‑guided Preference Learning (SPL). The key idea is to construct fictitious swap annotators and use the mirroring property of their preferences to guide the encoder. SPL introduces three components: (1) swap‑guided base regularization, (2) Preferential Inverse Autoregressive Flow (P‑IAF), and (3) adaptive latent conditioning. Experiments show that SPL mitigates collapse, enriches user‑specific latents, and improves preference prediction. Our code and data are available at https://github.com/cobang0111/SPL

PaperId: 2494, https://arxiv.org/pdf/2603.12581.pdf GitHub

Abstract:
Although diffusion models have achieved remarkable progress in multi‑modal magnetic resonance imaging (MRI) translation tasks, existing methods still tend to suffer from anatomical inconsistencies or degraded texture details when handling arbitrary missing‑modality scenarios. To address these issues, we propose a latent diffusion‑based multi‑modal MRI translation framework, termed MSG‑LDM. By leveraging the available modalities, the proposed method infers complete structural information, which preserves reliable boundary details. Specifically, we introduce a style‑‑structure disentanglement mechanism in the latent space, which explicitly separates modality‑specific style features from shared structural representations, and jointly models low‑frequency anatomical layouts and high‑frequency boundary details in a multi‑scale feature space. During the structure disentanglement stage, high‑frequency structural information is explicitly incorporated to enhance feature representations, guiding the model to focus on fine‑grained structural cues while learning modality‑invariant low‑frequency anatomical representations. Furthermore, to reduce interference from modality‑specific styles and improve the stability of structure representations, we design a style consistency loss and a structure‑aware loss. Extensive experiments on the BraTS2020 and WMH datasets demonstrate that the proposed method outperforms existing MRI synthesis approaches, particularly in reconstructing complete structures. The source code is publicly available at https://github.com/ziyi‑start/MSG‑LDM.

PaperId: 2495, https://arxiv.org/pdf/2603.12554.pdf GitHub

Abstract:
Reinforcement learning (RL) has been effective for post‑training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence‑level likelihoods. Existing approaches therefore rely on surrogate likelihoods or heuristic approximations, which can introduce bias and obscure the sequential structure of denoising. We formulate diffusion‑based sequence generation as a finite‑horizon Markov decision process over the denoising trajectory and derive an exact, unbiased policy gradient that decomposes over denoising steps and is expressed in terms of intermediate advantages, without requiring explicit evaluation of the sequence likelihood. To obtain a practical and compute‑efficient estimator, we (i) select denoising steps for policy updates via an entropy‑guided approximation bound, and (ii) estimate intermediate advantages using a one‑step denoising reward naturally provided by the diffusion model, avoiding costly multi‑step rollouts. Experiments on coding and logical reasoning benchmarks demonstrate state‑of‑the‑art results, with strong competitive performance on mathematical reasoning, outperforming existing RL post‑training approaches for DLMs. Code is available at https://github.com/vishnutez/egspo‑dllm‑rl.

PaperId: 2496, https://arxiv.org/pdf/2603.12506.pdf GitHub

Abstract:
Text‑to‑Image (T2I) generation is primarily driven by Diffusion Models (DM) which rely on random Gaussian noise. Thus, like playing the slots at a casino, a DM will produce different results given the same user‑defined inputs. This imposes a gambler's burden: To perform multiple generation cycles to obtain a satisfactory result. However, even though DMs use stochastic sampling to seed generation, the distribution of generated content quality highly depends on the prompt and the generative ability of a DM with respect to it. To account for this, we propose Naïve PAINE for improving the generative quality of Diffusion Models by leveraging T2I preference benchmarks. We directly predict the numerical quality of an image from the initial noise and given prompt. Naïve PAINE then selects a handful of quality noises and forwards them to the DM for generation. Further, Naïve PAINE provides feedback on the DM generative quality given the prompt and is lightweight enough to seamlessly fit into existing DM pipelines. Experimental results demonstrate that Naïve PAINE outperforms existing approaches on several prompt corpus benchmarks.

PaperId: 2497, https://arxiv.org/pdf/2603.12382.pdf GitHub GitHub GitHub

Abstract:
Multimodal large language models (MLLMs) have advanced from image‑level reasoning to pixel‑level grounding, but extending these capabilities to videos remains challenging as models must achieve spatial precision and temporally consistent reference tracking. Existing video MLLMs often rely on a static segmentation token ([SEG]) for frame‑wise grounding, which provides semantics but lacks temporal context, causing spatial drift, identity switches, and unstable initialization when objects move or reappear. We introduce SPARROW, a pixel‑grounded video MLLM that unifies spatial accuracy and temporal stability through two key components: (i) Target‑Specific Tracked Features (TSF), which inject temporally aligned referent cues during training, and (ii) a dual‑prompt design that decodes box ([BOX]) and segmentation ([SEG]) tokens to fuse geometric priors with semantic grounding. SPARROW is supported by a curated referential video dataset of 30,646 videos and 45,231 Q&A pairs and operates end‑to‑end without external detectors via a class‑agnostic SAM2‑based proposer. Integrated into three recent open‑source video MLLMs (UniPixel, GLUS, and VideoGLaMM), SPARROW delivers consistent gains across six benchmarks, improving up to +8.9 J&F on RVOS, +5 mIoU on visual grounding, and +5.4 CLAIR on GCG. These results demonstrate that SPARROW substantially improves referential stability, spatial precision, and temporal coherence in pixel‑grounded video understanding. Project page: https://risys‑lab.github.io/SPARROW

PaperId: 2498, https://arxiv.org/pdf/2603.12296.pdf GitHub

Abstract:
Deep learning has achieved transformative performance across diverse domains, largely driven by large‑scale and high‑quality training data. In contrast, the development of brain‑computer interfaces (BCIs) is fundamentally constrained by limited, heterogeneous, and privacy‑sensitive neural recordings. Generating synthetic yet physiologically plausible brain signals has therefore emerged as a promising strategy to mitigate data scarcity, improve model generalization, and support data‑efficient BCIs. This survey provides a comprehensive review of synthetic brain data generation for BCIs, covering methodological taxonomies, benchmark experiments, evaluation metrics, key applications, and future directions. We systematically categorize existing generation approaches into four types: signal‑transformation‑based, feature‑based, model‑based, and translation‑based generation, and discuss their characteristics, advantages, and limitations. Furthermore, we benchmark representative brain signal generation approaches across four BCI paradigms, including motor imagery, epileptic seizure detection, steady‑state visually evoked potentials, and auditory attention detection, to provide an objective comparison of their downstream utility. We also summarize evaluation principles for generated brain signals from multiple perspectives, including signal realism, physiological plausibility, downstream utility, and privacy preservation. Finally, we discuss the potential and challenges of current generation approaches and outline future research directions toward accurate, data‑efficient, generalizable, and privacy‑aware BCI systems. The benchmark codebase is available at https://github.com/wzwvv/DG4BCI.

PaperId: 2499, https://arxiv.org/pdf/2603.12288.pdf GitHub

Abstract:
Tabular machine learning presents a paradox: modern models achieve state‑of‑the‑art performance using high‑dimensional (high‑D), collinear, error‑prone data, defying the "Garbage In, Garbage Out" mantra. To help resolve this, we synthesize principles from Information Theory, Latent Factor Models, and Psychometrics, clarifying that predictive robustness arises not solely from data cleanliness, but from the synergy between data architecture and model capacity. Partitioning predictor‑space "noise" into "Predictor Error" and "Structural Uncertainty" (informational deficits from stochastic generative mappings), we prove that leveraging high‑D sets of error‑prone predictors asymptotically overcomes both types of noise, whereas cleaning a low‑D set is fundamentally bounded by Structural Uncertainty. We demonstrate why "Informative Collinearity" (dependencies from shared latent causes) enhances reliability and convergence efficiency, and explain why increased dimensionality reduces the latent inference burden, enabling feasibility with finite samples. To address practical constraints, we propose "Proactive Data‑Centric AI" to identify predictors that enable robustness efficiently. We also derive boundaries for Systematic Error Regimes and show why models that absorb "rogue" dependencies can mitigate assumption violations. Linking latent architecture to Benign Overfitting, we offer a first step towards a unified view of robustness to Outcome Error and predictor‑space noise, while also delineating when traditional DCAI's focus on label cleaning remains powerful. By redefining data quality from item‑level perfection to portfolio‑level architecture, we provide a theoretical rationale for "Local Factories" ‑‑ learning from live, uncurated enterprise "data swamps" ‑‑ supporting a deployment paradigm shift from "Model Transfer" to "Methodology Transfer'' to overcome static generalizability limitations.

PaperId: 2500, https://arxiv.org/pdf/2603.12261.pdf GitHub

Abstract:
Text‑to‑image generation models have advanced rapidly, yet achieving fine‑grained control over generated images remains difficult, largely due to limited understanding of how semantic information is encoded. We develop an interpretation of the color representation in the Variational Autoencoder latent space of FLUX.1 [Dev], revealing a structure reflecting Hue, Saturation, and Lightness. We verify our Latent Color Subspace (LCS) interpretation by demonstrating that it can both predict and explicitly control color, introducing a fully training‑free method in FLUX based solely on closed‑form latent‑space manipulation. Code is available at https://github.com/ExplainableML/LCS.

PaperId: 2501, https://arxiv.org/pdf/2603.12228.pdf GitHub

Abstract:
Pretraining produces a learned parameter vector that is typically treated as a starting point for further iterative adaptation. In this work, we instead view the outcome of pretraining as a distribution over parameter vectors, whose support already contains task‑specific experts. We show that in small models such expert solutions occupy a negligible fraction of the volume of this distribution, making their discovery reliant on structured optimization methods such as gradient descent. In contrast, in large, well‑pretrained models the density of task‑experts increases dramatically, so that diverse, task‑improving specialists populate a substantial fraction of the neighborhood around the pretrained weights. Motivated by this perspective, we explore a simple, fully parallel post‑training method that samples N parameter perturbations at random, selects the top K, and ensembles predictions via majority vote. Despite its simplicity, this approach is competitive with standard post‑training methods such as PPO, GRPO, and ES for contemporary large‑scale models.

PaperId: 2502, https://arxiv.org/pdf/2603.12226.pdf GitHub

Abstract:
Despite interdisciplinary research leading to larger and longer‑term impact, most work remains confined to single‑domain academic silos. Recent AI‑based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea‑Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea‑Catalyst is designed to assist the brainstorming stage, explicitly avoiding premature anchoring on specific solutions. The framework embodies key metacognitive features of interdisciplinary reasoning: (a) defining and assessing research goals, (b) awareness of a domain's opportunities and unresolved challenges, and (c) strategic exploration of interdisciplinary ideas based on impact potential. Concretely, Idea‑Catalyst decomposes an abstract goal (e.g., improving human‑AI collaboration) into core target‑domain research questions that guide the analysis of progress and open challenges within that domain. These challenges are reformulated as domain‑agnostic conceptual problems, enabling retrieval from external disciplines (e.g., Psychology, Sociology) that address analogous issues. By synthesizing and recontextualizing insights from these domains back into the target domain, Idea‑Catalyst ranks source domains by their interdisciplinary potential. Empirically, this targeted integration improves average novelty by 21% and insightfulness by 16%, while remaining grounded in the original research problem.

PaperId: 2503, https://arxiv.org/pdf/2603.12155.pdf GitHub

Abstract:
Despite recent advances in generative models driving significant progress in text rendering, accurately generating complex text and mathematical formulas remains a formidable challenge. This difficulty primarily stems from the limited instruction‑following capabilities of current models when encountering out‑of‑distribution prompts. To address this, we introduce GlyphBanana, alongside a corresponding benchmark specifically designed for rendering complex characters and formulas. GlyphBanana employs an agentic workflow that integrates auxiliary tools to inject glyph templates into both the latent space and attention maps, facilitating the iterative refinement of generated images. Notably, our training‑free approach can be seamlessly applied to various Text‑to‑Image (T2I) models, achieving superior precision compared to existing baselines. Extensive experiments demonstrate the effectiveness of our proposed workflow. Associated code is publicly available at https://github.com/yuriYanZeXuan/GlyphBanana.

PaperId: 2504, https://arxiv.org/pdf/2603.12117.pdf GitHub

Abstract:
With the rapid advances of large language models, it becomes increasingly important to systematically evaluate their multilingual and multicultural capabilities. Previous cultural evaluation benchmarks focus mainly on basic cultural knowledge that can be encoded in linguistic form. Here, we propose SommBench, a multilingual benchmark to assess sommelier expertise, a domain deeply grounded in the senses of smell and taste. While language models learn about sensory properties exclusively through textual descriptions, SommBench tests whether this textual grounding is sufficient to emulate expert‑level sensory judgment. SommBench comprises three main tasks: Wine Theory Question Answering (WTQA), Wine Feature Completion (WFC), and Food‑Wine Pairing (FWP). SommBench is available in multiple languages: English, Slovak, Swedish, Finnish, German, Danish, Italian, and Spanish. This helps separate a language model's wine expertise from its language skills. The benchmark datasets were developed in close collaboration with a professional sommelier and native speakers of the respective languages, resulting in 1,024 wine theory question‑answering questions, 1,000 wine feature‑completion examples, and 1,000 food‑wine pairing examples. We provide results for the most popular language models, including closed‑weights models such as Gemini 2.5, and open‑weights models, such as GPT‑OSS and Qwen 3. Our results show that the most capable models perform well on wine theory question answering (up to 97% correct with a closed‑weights model), yet feature completion (peaking at 65%) and food‑wine pairing show (MCC ranging between 0 and 0.39) turn out to be more challenging. These results position SommBench as an interesting and challenging benchmark for evaluating the sommelier expertise of language models. The benchmark is publicly available at https://github.com/sommify/sommbench.

PaperId: 2505, https://arxiv.org/pdf/2603.12071.pdf GitHub

Abstract:
Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer's disease assessment. However, current deep‑learning tools fragment this process: classifiers reduce a scan to a label, volumetric pipelines produce uninterpreted measurements, and vision‑language models (VLMs) may generate fluent but potentially hallucinated conclusions. We present LoV3D, a pipeline for training 3D vision‑language models, which reads longitudinal T1‑weighted brain MRI, produces a region‑level anatomical assessment, conducts longitudinal comparison with the prior scan, and finally outputs a three‑class diagnosis (Cognitively Normal, Mild Cognitive Impairment, or Dementia) along with a synthesized diagnostic summary. The stepped pipeline grounds the final diagnosis by enforcing label consistency, longitudinal coherence, and biological plausibility, thereby reducing the risks of hallucinations. The training process introduces a clinically‑weighted Verifier that scores candidate outputs automatically against normative references derived from standardized volume metrics, driving Direct Preference Optimization without a single human annotation. On a subject‑level held‑out ADNI test set (479 scans, 258 subjects), LoV3D achieves 93.7% three‑class diagnostic accuracy (+34.8% over the no‑grounding baseline), 97.2% on two‑class diagnosis accuracy (+4% over the SOTA) and 82.6% region‑level anatomical classification accuracy (+33.1% over VLM baselines). Zero‑shot transfer yields 95.4% on MIRIAD (100% Dementia recall) and 82.9% three‑class accuracy on AIBL, confirming high generalizability across sites, scanners, and populations. Code is available at https://github.com/Anonymous‑TEVC/LoV‑3D.

PaperId: 2506, https://arxiv.org/pdf/2603.11992.pdf GitHub

Abstract:
Personalized Federated Learning (PFL) aims to train customized models for clients with highly heterogeneous data distributions while preserving data privacy. Existing approaches often rely on heuristics like clustering or model interpolation, which lack principled mechanisms for balancing heterogeneous client objectives. Serving M clients with distinct data distributions is inherently a multi‑objective optimization problem, where achieving optimal personalization ideally requires M distinct models on the Pareto front. However, maintaining M separate models poses significant scalability challenges in federated settings with hundreds or thousands of clients. To address this challenge, we reformulate PFL as a few‑for‑many optimization problem that maintains only K shared server models (K \ll M) to collectively serve all M clients. We prove that this framework achieves near‑optimal personalization: the approximation error diminishes as K increases and each client's model converges to each client's optimum as data grows. Building on this reformulation, we propose FedFew, a practical algorithm that jointly optimizes the K server models through efficient gradient‑based updates. Unlike clustering‑based approaches that require manual client partitioning or interpolation‑based methods that demand careful hyperparameter tuning, FedFew automatically discovers the optimal model diversity through its optimization process. Experiments across vision, NLP, and real‑world medical imaging datasets demonstrate that FedFew, with just 3 models, consistently outperforms other state‑of‑the‑art approaches. Code is available at https://github.com/pgg3/FedFew.

PaperId: 2507, https://arxiv.org/pdf/2603.11991.pdf GitHub

Abstract:
Zero‑shot text classification (ZSC) offers the promise of eliminating costly task‑specific annotation by matching texts directly to human‑readable label descriptions. While early approaches have predominantly relied on cross‑encoder models fine‑tuned for natural language inference (NLI), recent advances in text‑embedding models, rerankers, and instruction‑tuned large language models (LLMs) have challenged the dominance of NLI‑based architectures. Yet, systematically comparing these diverse approaches remains difficult. Existing evaluations, such as MTEB, often incorporate labeled examples through supervised probes or fine‑tuning, leaving genuine zero‑shot capabilities underexplored. To address this, we introduce BTZSC, a comprehensive benchmark of 22 public datasets spanning sentiment, topic, intent, and emotion classification, capturing diverse domains, class cardinalities, and document lengths. Leveraging BTZSC, we conduct a systematic comparison across four major model families, NLI cross‑encoders, embedding models, rerankers and instruction‑tuned LLMs, encompassing 38 public and custom checkpoints. Our results show that: (i) modern rerankers, exemplified by Qwen3‑Reranker‑8B, set a new state‑of‑the‑art with macro F1 = 0.72; (ii) strong embedding models such as GTE‑large‑en‑v1.5 substantially close the accuracy gap while offering the best trade‑off between accuracy and latency; (iii) instruction‑tuned LLMs at 4‑‑12B parameters achieve competitive performance (macro F1 up to 0.67), excelling particularly on topic classification but trailing specialized rerankers; (iv) NLI cross‑encoders plateau even as backbone size increases; and (v) scaling primarily benefits rerankers and LLMs over embedding models. BTZSC and accompanying evaluation code are publicly released to support fair and reproducible progress in zero‑shot text understanding.

PaperId: 2508, https://arxiv.org/pdf/2603.11935.pdf GitHub

Abstract:
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile devices remains largely unexplored. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write efficient kernels for mobile devices? To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross‑framework interoperability, coupled with an automated pipeline that bridges the host‑device gap for on‑device verification. Leveraging this framework, we conduct extensive evaluation on the CPU backend of Mobile Neural Network (MNN), revealing that current LLMs struggle with the engineering complexity and data scarcity inherent to mobile frameworks; standard models and even fine‑tuned variants exhibit high compilation failure rates (over 54%) and negligible performance gains due to hallucinations and a lack of domain‑specific grounding. To overcome these limitations, we propose the Mobile Kernel Agent (MoKA), a multi‑agent system equipped with repository‑aware reasoning and a plan‑and‑execute paradigm. Validated on MobileKernelBench, MoKA achieves state‑of‑the‑art performance, boosting compilation success to 93.7% and enabling 27.4% of generated kernels to deliver measurable speedups over native libraries.

PaperId: 2509, https://arxiv.org/pdf/2603.11896.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have shown strong performance on offline video understanding, but most are limited to offline inference or have weak online reasoning, making multi‑turn interaction over continuously arriving video streams difficult. Existing streaming methods typically use an interleaved perception‑generation paradigm, which prevents concurrent perception and generation and leads to early memory decay as streams grow, hurting long‑range dependency modeling. We propose Think While Watching, a memory‑anchored streaming video reasoning framework that preserves continuous segment‑level memory during multi‑turn interaction. We build a three‑stage, multi‑round chain‑of‑thought dataset and adopt a stage‑matched training strategy, while enforcing strict causality through a segment‑level streaming causal mask and streaming positional encoding. During inference, we introduce an efficient pipeline that overlaps watching and thinking and adaptively selects the best attention backend. Under both single‑round and multi‑round streaming input protocols, our method achieves strong results. Built on Qwen3‑VL, it improves single‑round accuracy by 2.6% on StreamingBench and by 3.79% on OVO‑Bench. In the multi‑round setting, it maintains performance while reducing output tokens by 56%. Code is available at: https://github.com/wl666hhh/Think_While_Watching/

PaperId: 2510, https://arxiv.org/pdf/2603.11872.pdf GitHub

Abstract:
Translating single‑cell RNA sequencing (scRNA‑seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here we introduce ELISA (Embedding‑Linked Interactive Single‑cell Agent), an interpretable framework that unifies scGPT expression embeddings with BioBERT‑based semantic retrieval and LLM‑mediated interpretation for interactive single‑cell discovery. An automatic query classifier routes inputs to gene marker scoring, semantic matching, or reciprocal rank fusion pipelines depending on whether the query is a gene signature, natural language concept, or mixture of both. Integrated analytical modules perform pathway activity scoringacross 60+ gene sets, ligand‑‑receptor interaction prediction using 280+ curated pairs, condition‑aware comparative analysis, and cell‑type proportion estimation all operating directly on embedded data without access to the original count matrix. Benchmarked across six diverse scRNA‑seq datasets spanning inflammatory lung disease, pediatric and adult cancers, organoid models, healthy tissue, and neurodevelopment, ELISA significantly outperforms CellWhisperer in cell type retrieval (combined permutation test, p < 0.001), with particularly large gains on gene‑signature queries (Cohen's d = 5.98 for MRR). ELISA replicates published biological findings (mean composite score 0.90) with near‑perfect pathway alignment and theme coverage (0.98 each), and generates candidate hypotheses through grounded LLM reasoning, bridging the gap between transcriptomic data exploration and biological discovery. Code available at: https://github.com/omaruno/ELISA‑An‑AI‑Agent‑for‑Expression‑Grounded‑Discovery‑in‑Single‑Cell‑Genomics.git (If you use ELISA in your research, please cite this work).

PaperId: 2511, https://arxiv.org/pdf/2603.11816.pdf GitHub

Abstract:
Traffic forecasting is a cornerstone of intelligent transportation systems. While existing research has made significant progress in short‑term prediction, long‑term forecasting remains a largely uncharted and challenging frontier. Extending the prediction horizon intensifies two critical issues: escalating computational resource consumption and increasingly complex spatial‑temporal dependencies. Current approaches, which rely on spatial‑temporal graphs and process temporal and spatial dimensions separately, suffer from snapshot‑stacking inflation and cross‑step fragmentation. To overcome these limitations, we propose VisiFold. Our framework introduces a novel temporal folding graph that consolidates a sequence of temporal snapshots into a single graph. Furthermore, we present a node visibility mechanism that incorporates node‑level masking and subgraph sampling to overcome the computational bottleneck imposed by large node counts. Extensive experiments show that VisiFold not only drastically reduces resource consumption but also outperforms existing baselines in long‑term forecasting tasks. Remarkably, even with a high mask ratio of 80%, VisiFold maintains its performance advantage. By effectively breaking the resource constraints in both temporal and spatial dimensions, our work paves the way for more realistic long‑term traffic forecasting. The code is available at~ https://github.com/PlanckChang/VisiFold.

PaperId: 2512, https://arxiv.org/pdf/2603.11698.pdf GitHub

Abstract:
Text‑to‑video (T2V) generation models have made rapid progress in producing visually high‑quality and temporally coherent videos. However, existing benchmarks primarily focus on perceptual quality, text‑video alignment, or physical plausibility, leaving a critical aspect of action understanding largely unexplored: object state change (OSC) explicitly specified in the text prompt. OSC refers to the transformation of an object's state induced by an action, such as peeling a potato or slicing a lemon. In this paper, we introduce OSCBench, a benchmark specifically designed to assess OSC performance in T2V models. OSCBench is constructed from instructional cooking data and systematically organizes action‑object interactions into regular, novel, and compositional scenarios to probe both in‑distribution performance and generalization. We evaluate six representative open‑source and proprietary T2V models using both human user study and multimodal large language model (MLLM)‑based automatic evaluation. Our results show that, despite strong performance on semantic and scene alignment, current T2V models consistently struggle with accurate and temporally consistent object state changes, especially in novel and compositional settings. These findings position OSC as a key bottleneck in text‑to‑video generation and establish OSCBench as a diagnostic benchmark for advancing state‑aware video generation models.

PaperId: 2513, https://arxiv.org/pdf/2603.11623.pdf GitHub

Abstract:
Topological Data Analysis (TDA) provides powerful tools to explore the shape and structure of data through topological features such as clusters, loops, and voids. Persistence diagrams are a cornerstone of TDA, capturing the evolution of these features across scales. While effective for analyzing individual manifolds, persistence diagrams do not account for interactions between pairs of them. Cross‑persistence diagrams (cross‑barcodes), introduced recently, address this limitation by characterizing relationships between topological features of two point clouds. In this work, we present the first systematic study of the density of cross‑persistence diagrams. We prove its existence, establish theoretical foundations for its statistical use, and design the first machine learning framework for predicting cross‑persistence density directly from point cloud coordinates and distance matrices. Our statistical approach enables the distinction of point clouds sampled from different manifolds by leveraging the linear characteristics of cross‑persistence diagrams. Interestingly, we find that introducing noise can enhance our ability to distinguish point clouds, uncovering its novel utility in TDA applications. We demonstrate the effectiveness of our methods through experiments on diverse datasets, where our approach consistently outperforms existing techniques in density prediction and achieves superior results in point cloud distinction tasks. Our findings contribute to a broader understanding of cross‑persistence diagrams and open new avenues for their application in data analysis, including potential insights into time‑series domain tasks and the geometry of AI‑generated texts. Our code is publicly available at https://github.com/Verdangeta/TDA_experiments

PaperId: 2514, https://arxiv.org/pdf/2603.11589.pdf GitHub

Abstract:
Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT‑based vocoders have recently gained attention. They predict a complex‑valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost. However, current approaches use real‑valued networks that process the real and imaginary parts independently. This separation limits their ability to capture the inherent structure of complex spectrograms. We present ComVo, a Complex‑valued neural Vocoder whose generator and discriminator use native complex arithmetic. This enables an adversarial training framework that provides structured feedback in complex‑valued representations. To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process. Finally, we propose a block‑matrix computation scheme to improve training efficiency by reducing redundant operations. Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real‑valued baselines, and that its block‑matrix scheme reduces training time by 25%. Audio samples and code are available at https://hs‑oh‑prml.github.io/ComVo/.

PaperId: 2515, https://arxiv.org/pdf/2603.11558.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) systems have shown strong potential for language‑driven robotic manipulation. However, scaling them to long‑horizon tasks remains challenging. Existing pipelines typically separate data collection, policy learning, and deployment, resulting in heavy reliance on manual environment resets and brittle multi‑policy execution. We present RoboClaw, an agentic robotics framework that unifies data collection, policy learning, and task execution under a single VLM‑driven controller. At the policy level, RoboClaw introduces Entangled Action Pairs (EAP), which couple forward manipulation behaviors with inverse recovery actions to form self‑resetting loops for autonomous data collection. This mechanism enables continuous on‑policy data acquisition and iterative policy refinement with minimal human intervention. During deployment, the same agent performs high‑level reasoning and dynamically orchestrates learned policy primitives to accomplish long‑horizon tasks. By maintaining consistent contextual semantics across collection and execution, RoboClaw reduces mismatch between the two phases and improves multi‑policy robustness. Experiments in real‑world manipulation tasks demonstrate improved stability and scalability compared to conventional open‑loop pipelines, while significantly reducing human effort throughout the robot lifecycle, achieving a 25% improvement in success rate over baseline methods on long‑horizon tasks and reducing human time investment by 53.7%.

PaperId: 2516, https://arxiv.org/pdf/2603.11542.pdf GitHub

Abstract:
The adaptation of large‑scale Vision‑Language Models (VLMs) like CLIP to downstream tasks with extremely limited data ‑‑ specifically in the one‑shot regime ‑‑ is often hindered by a significant "Stability‑Plasticity" dilemma. While efficient caching mechanisms have been introduced by training‑free methods such as Tip‑Adapter, these approaches often function as local Nadaraya‑Watson estimators. Such estimators are characterized by inherent boundary bias and a lack of global structural regularization. In this paper, ReHARK (Refined Hybrid Adaptive RBF Kernels) is proposed as a synergistic training‑free framework that reinterprets few‑shot adaptation through global proximal regularization in a Reproducing Kernel Hilbert Space (RKHS). A multistage refinement pipeline is introduced, consisting of: (1) Hybrid Prior Construction, where zero‑shot textual knowledge from CLIP and GPT‑3 is fused with visual class prototypes to form a robust semantic‑visual anchor; (2) Support Set Augmentation (Bridging), where intermediate samples are generated to smooth the transition between visual and textual modalities; (3) Adaptive Distribution Rectification, where test feature statistics are aligned with the augmented support set to mitigate domain shifts; and (4) Multi‑Scale RBF Kernels, where an ensemble of kernels is employed to capture complex feature geometries across diverse scales. Superior stability and accuracy are demonstrated through extensive experiments on 11 diverse benchmarks. A new state‑of‑the‑art for one‑shot adaptation is established by ReHARK, which achieves an average accuracy of 65.83%, significantly outperforming existing baselines. Code is available at https://github.com/Jahid12012021/ReHARK.

PaperId: 2517, https://arxiv.org/pdf/2603.11492.pdf GitHub

Abstract:
In medical image segmentation tasks, the domain gap caused by the difference in data collection between training and testing data seriously hinders the deployment of pre‑trained models in clinical practice. Continual Test‑Time Adaptation (CTTA) aims to enable pre‑trained models to adapt to continuously changing unlabeled domains, providing an effective approach to solving this problem. However, existing CTTA methods often rely on unreliable supervisory signals, igniting a self‑reinforcing cycle of error accumulation that culminates in catastrophic performance degradation. To overcome these challenges, we propose a CTTA via Semantic‑Prompt‑Enhanced Graph Clustering (SPEGC) for medical image segmentation. First, we design a semantic prompt feature enhancement mechanism that utilizes decoupled commonality and heterogeneity prompt pools to inject global contextual information into local features, alleviating their susceptibility to noise interference under domain shift. Second, based on these enhanced features, we design a differentiable graph clustering solver. This solver reframes global edge sparsification as an optimal transport problem, allowing it to distill a raw similarity matrix into a refined and high‑order structural representation in an end‑to‑end manner. Finally, this robust structural representation is used to guide model adaptation, ensuring predictions are consistent at a cluster‑level and dynamically adjusting decision boundaries. Extensive experiments demonstrate that SPEGC outperforms other state‑of‑the‑art CTTA methods on two medical image segmentation benchmarks. The source code is available at https://github.com/Jwei‑Z/SPEGC‑for‑MIS.

PaperId: 2518, https://arxiv.org/pdf/2603.11462.pdf GitHub

Abstract:
Predicting irregularly spaced event sequences with discrete marks poses significant challenges due to the complex, asynchronous dependencies embedded within continuous‑time data streams.Existing sequential approaches capture dependencies among event tokens but ignore the continuous evolution between events, while Neural Ordinary Differential Equation (Neural ODE) methods model smooth dynamics yet fail to account for how event types influence future timing.To overcome these limitations, we propose NEXTPP, a dual‑channel framework that unifies discrete and continuous representations via Event‑granular Neural Evolution with Cross‑Interaction for Marked Temporal Point Processes. Specifically, NEXTPP encodes discrete event marks via a self‑attention mechanism, simultaneously evolving a latent continuous‑time state using a Neural ODE. These parallel streams are then fused through a crossattention module to enable explicit bidirectional interaction between continuous and discrete representations. The fused representations drive the conditional intensity function of the neural Hawkes process, while an iterative thinning sampler is employed to generate future events. Extensive evaluations on five real‑world datasets demonstrate that NEXTPP consistently outperforms state‑of‑the‑art models. The source code can be found at https://github.com/AONE‑NLP/NEXTPP.

PaperId: 2519, https://arxiv.org/pdf/2603.11223.pdf GitHub GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when text is reduced to triples, thereby degrading performance in downstream Question‑Answering (QA) tasks, particularly for multi‑hop QA, which requires composing answers from multiple entities, facts, or relations. We propose a domain‑agnostic, KG‑based QA framework that covers both the indexing and retrieval/inference phases. A new indexing approach called Map‑Disambiguate‑Enrich‑Reduce (MDER) generates context‑derived triple descriptions and subsequently integrates them with entity‑level summaries, thus avoiding the need for explicit traversal of edges in the graph during the QA retrieval phase. Complementing this, we introduce Decompose‑Resolve (DR), a retrieval mechanism that decomposes user queries into resolvable triples and grounds them in the KG via iterative reasoning. Together, MDER and DR form an LLM‑driven QA pipeline that is robust to sparse, incomplete, and complex relational data. Experiments show that on standard and domain specific benchmarks, MDER‑DR achieves substantial improvements over standard RAG baselines (up to 66%), while maintaining cross‑lingual robustness. Our code is available at https://github.com/DataSciencePolimi/MDER‑DR_RAG.

PaperId: 2520, https://arxiv.org/pdf/2603.11099.pdf GitHub GitHub

Abstract:
The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these models to graph‑structured data remains a significant challenge. In this work, we introduce a graph tokenization framework that generates sequential representations of graphs by combining reversible graph serialization, which preserves graph information, with Byte Pair Encoding (BPE), a widely adopted tokenizer in large language models (LLMs). To better capture structural information, the graph serialization process is guided by global statistics of graph substructures, ensuring that frequently occurring substructures appear more often in the sequence and can be merged by BPE into meaningful tokens. Empirical results demonstrate that the proposed tokenizer enables Transformers such as BERT to be directly applied to graph benchmarks without architectural modifications. The proposed approach achieves state‑of‑the‑art results on 14 benchmark datasets and frequently outperforms both graph neural networks and specialized graph transformers. This work bridges the gap between graph‑structured data and the ecosystem of sequence models. Our code is available at \hrefhttps://github.com/BUPT‑GAMMA/Graph‑Tokenization‑for‑Bridging‑Graphs‑and‑Transformers\colorbluehere.

PaperId: 2521, https://arxiv.org/pdf/2603.11051.pdf GitHub

Abstract:
We release OpenSanctions Pairs, a large‑scale entity matching benchmark derived from real‑world international sanctions aggregation and analyst deduplication. The dataset contains 755,540 labeled pairs spanning 293 heterogeneous sources across 31 countries, with multilingual and cross‑script names, noisy and missing attributes, and set‑valued fields typical of compliance workflows. We benchmark a production rule‑based matcher (nomenklatura RegressionV1 algorithm) against open‑ and closed‑source LLMs in zero‑ and few‑shot settings. Off‑the‑shelf LLMs substantially outperform the production rule‑based baseline (91.33% F1), reaching up to 98.95% F1 (GPT‑4o) and 98.23% F1 with a locally deployable open model (DeepSeek‑R1‑Distill‑Qwen‑14B). DSPy MIPROv2 prompt optimization yields consistent but modest gains, while adding in‑context examples provides little additional benefit and can degrade performance. Error analysis shows complementary failure modes: the rule‑based system over‑matches (high false positives), whereas LLMs primarily fail on cross‑script transliteration and minor identifier/date inconsistencies. These results indicate that pairwise matching performance is approaching a practical ceiling in this setting, and motivate shifting effort toward pipeline components such as blocking, clustering, and uncertainty‑aware review. Code available at https://github.com/chansmi/OSINT_entity_resolution

PaperId: 2522, https://arxiv.org/pdf/2603.11048.pdf GitHub

Abstract:
We propose a fully automated AI system that produces short comedic videos similar to sketch shows such as Saturday Night Live. Starting with character references, the system employs a population of agents loosely based on real production studio roles, structured to optimize the quality and diversity of ideas and outputs through iterative competition, evaluation, and improvement. A key contribution is the introduction of LLM critics aligned with real viewer preferences through the analysis of a corpus of comedy videos on YouTube to automatically evaluate humor. Our experiments show that our framework produces results approaching the quality of professionally produced sketches while demonstrating state‑of‑the‑art performance in video generation.

PaperId: 2523, https://arxiv.org/pdf/2603.11047.pdf GitHub

Abstract:
We propose a 3D latent representation that jointly models object geometry and view‑dependent appearance. Most prior works focus on either reconstructing 3D geometry or predicting view‑independent diffuse appearance, and thus struggle to capture realistic view‑dependent effects. Our approach leverages that RGB‑depth images provide samples of a surface light field. By encoding random subsamples of this surface light field into a compact set of latent vectors, our model learns to represent both geometry and appearance within a unified 3D latent space. This representation reproduces view‑dependent effects such as specular highlights and Fresnel reflections under complex lighting. We further train a latent flow matching model on this representation to learn its distribution conditioned on a single input image, enabling the generation of 3D objects with appearances consistent with the lighting and materials in the input. Experiments show that our approach achieves higher visual quality and better input fidelity than existing methods.

PaperId: 2524, https://arxiv.org/pdf/2603.11045.pdf GitHub

Abstract:
Inverse problems for stiff parabolic partial differential equations (PDEs), such as the inverse heat conduction problem (IHCP), are severely ill‑posed: the forward map rapidly damps high‑frequency interior structure before it reaches the boundary. Soft‑constrained physics‑informed neural networks (PINNs), which embed the PDE as a residual penalty, suffer from gradient pathology in this regime and tend to fit boundary measurements while leaving the interior field essentially untouched. We propose Neural Field Thermal Tomography (NeFTY), a hard‑constrained neural field framework for label‑free three‑dimensional inverse heat conduction. NeFTY represents the unknown diffusivity as a continuous coordinate‑based neural network, and at every optimization step passes the candidate field through a differentiable implicit‑Euler heat solver with harmonic‑mean interface flux, so that the governing PDE holds exactly on the discretization rather than as a soft penalty. Adjoint gradients propagate the surface reconstruction error back to the network weights at solver‑level memory cost, making test‑time inversion tractable on a single GPU. Across synthetic 3D benchmarks, NeFTY substantially outperforms soft‑constrained PINN variants and a voxel‑grid baseline on label‑free volumetric recovery, and it transfers to real thermography data, surpassing classical signal‑processing baselines in both defect segmentation and depth estimation. Additional details at https://cab‑lab‑princeton.github.io/nefty/

PaperId: 2525, https://arxiv.org/pdf/2603.11042.pdf GitHub

Abstract:
Generating music that temporally aligns with video events is challenging for existing text‑to‑music models, which lack fine‑grained temporal control. We introduce V2M‑ZERO, a video‑to‑music generation approach that generates time‑aligned music with disentangled time synchronization and semantic control (e.g., genre, mood) from video while requiring zero video‑music pairs at training time. Our method is motivated by a key observation: temporal synchronization requires matching when and how much change occurs, not what changes. While musical and visual events differ semantically, they exhibit shared temporal structure that can be captured independently within each modality. We capture this structure through event curves computed from intra‑modal similarity using pretrained music and video encoders. By measuring temporal change within each modality independently, these curves provide comparable representations across modalities. This enables a simple training strategy: fine‑tune a text‑to‑music model on music‑event curves, then substitute video‑event curves at inference without cross‑modal training or paired data. Across OES‑Pub, MovieGenBench‑Music, and AIST++, V2M‑ZERO achieves state‑of‑the‑art performance without any paired music‑video data, surpassing the strongest prior baselines per metric with 5‑9% higher audio quality, 13‑15% better semantic alignment, 21‑52% improved temporal synchronization, and 28% higher beat alignment on dance videos. We find similar results via a large crowd‑source subjective listening test. Our results validate that temporal alignment through within‑modality features is not only effective for video‑to‑music generation but also leads to better performance than paired cross‑modal supervision. Furthermore, our approach enables independent controls for timing and music style (e.g., genre, mood) for more controllable generation.

PaperId: 2526, https://arxiv.org/pdf/2603.10935.pdf GitHub

Abstract:
Variational autoencoders (VAEs) frequently suffer from posterior collapse, where the latent variables become uninformative as the approximate posterior degenerates to the prior. While recent work has characterized collapse as a phase transition determined by data covariance properties, existing approaches primarily aim to avoid rather than eliminate collapse. We introduce a novel framework that theoretically guarantees non‑collapsed solutions by leveraging spherical shell geometry and cluster‑aware constraints. Our method transforms data to a spherical shell, computes optimal cluster assignments via K‑means, and defines a feasible region between the within‑cluster variance W and collapse loss δ_\textcollapse. We prove that when the reconstruction loss is constrained to this region, the collapsed solution is mathematically excluded from the feasible parameter space. Critically, we introduce norm constraint mechanisms that ensure decoder outputs remain compatible with the spherical shell geometry without restricting representational capacity. Unlike prior approaches, our method provides a strict theoretical guarantee with minimal computational overhead without imposing constraints on decoder outputs. Experiments on synthetic and real‑world datasets demonstrate 100% collapse prevention under conditions where conventional VAEs completely fail, with reconstruction quality matching or exceeding state‑of‑the‑art methods. Our approach requires no explicit stability conditions (e.g., σ^2 < λ_\max) and works with arbitrary neural architectures. The code is available at https://github.com/tsegoochang/spherical‑vae‑with‑Cluster.

PaperId: 2527, https://arxiv.org/pdf/2603.10899.pdf GitHub

Abstract:
Transformer‑based large language models (LLMs) rely on key‑value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves efficiency, the cache size grows linearly with the input sequence length, quickly becoming a bottleneck for long‑context tasks. Existing solutions mitigate this problem by evicting prompt KV that are deemed unimportant, guided by estimated importance scores. Notably, a recent line of work proposes to improve eviction quality by "glimpsing into the future", in which a draft generator produces a surrogate future response approximating the target model's true response, and this surrogate is subsequently used to estimate the importance of cached KV more accurately. However, these approaches rely on computationally expensive draft generation, which introduces substantial prefilling overhead and limits their practicality in real‑world deployment. To address this challenge, we propose LookaheadKV, a lightweight eviction framework that leverages the strength of surrogate future response without requiring explicit draft generation. LookaheadKV augments transformer layers with parameter‑efficient modules trained to predict true importance scores with high accuracy. Our design ensures negligible runtime overhead comparable to existing inexpensive heuristics, while achieving accuracy superior to more costly approximation methods. Extensive experiments on long‑context understanding benchmarks, across a wide range of models, demonstrate that our method not only outperforms recent competitive baselines in various long‑context understanding tasks, but also reduces the eviction cost by up to 14.5x, leading to significantly faster time‑to‑first‑token. Our code is available at https://github.com/SamsungLabs/LookaheadKV.

PaperId: 2528, https://arxiv.org/pdf/2603.10745.pdf GitHub

Abstract:
Accurate estimation of uncertainty in deep learning is critical for deploying models in high‑stakes domains such as medical diagnosis and autonomous decision‑making, where overconfident predictions can lead to harmful outcomes. In practice, understanding the reason behind a model's uncertainty and the type of uncertainty it represents can support risk‑aware decisions, enhance user trust, and guide additional data collection. However, many existing methods only address a single type of uncertainty or require modifications and retraining of the base model, making them difficult to adopt in real‑world systems. We introduce CUPID (Comprehensive Uncertainty Plug‑in estImation moDel), a general‑purpose module that jointly estimates aleatoric and epistemic uncertainty without modifying or retraining the base model. CUPID can be flexibly inserted into any layer of a pretrained network. It models aleatoric uncertainty through a learned Bayesian identity mapping and captures epistemic uncertainty by analyzing the model's internal responses to structured perturbations. We evaluate CUPID across a range of tasks, including classification, regression, and out‑of‑distribution detection. The results show that it consistently delivers competitive performance while offering layer‑wise insights into the origins of uncertainty. By making uncertainty estimation modular, interpretable, and model‑agnostic, CUPID supports more transparent and trustworthy AI. Related code and data are available at https://github.com/a‑Fomalhaut‑a/CUPID.

PaperId: 2529, https://arxiv.org/pdf/2603.10722.pdf GitHub

Abstract:
Traffic scene understanding from unmanned aerial vehicle (UAV) platforms is crucial for intelligent transportation systems due to its flexible deployment and wide‑area monitoring capabilities. However, existing methods face significant challenges in real‑world surveillance, as their heavy reliance on optical imagery leads to severe performance degradation under adverse illumination conditions like nighttime and fog. Furthermore, current Visual Question Answering (VQA) models are restricted to elementary perception tasks, lacking the domain‑specific regulatory knowledge required to assess complex traffic behaviors. To address these limitations, we propose a novel Multi‑modal Traffic Cognition Network (MTCNet) for robust UAV traffic scene understanding. Specifically, we design a Prototype‑Guided Knowledge Embedding (PGKE) module that leverages high‑level semantic prototypes from an external Traffic Regulation Memory (TRM) to anchor domain‑specific knowledge into visual representations, enabling the model to comprehend complex behaviors and distinguish fine‑grained traffic violations. Moreover, we develop a Quality‑Aware Spectral Compensation (QASC) module that exploits the complementary characteristics of optical and thermal modalities to perform bidirectional context exchange, effectively compensating for degraded features to ensure robust representation in complex environments. In addition, we construct Traffic‑VQA, the first large‑scale optical‑thermal infrared benchmark for cognitive UAV traffic understanding, comprising 8,180 aligned image pairs and 1.3 million question‑answer pairs across 31 diverse types. Extensive experiments demonstrate that MTCNet significantly outperforms state‑of‑the‑art methods in both cognition and perception scenarios. The dataset is available at https://github.com/YuZhang‑2004/UAV‑traffic‑scene‑understanding.

PaperId: 2530, https://arxiv.org/pdf/2603.10624.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing the reasoning capabilities of large language models, particularly in domains such as mathematics where reliable rule‑based verifiers can be constructed. However, the reliance on handcrafted, domain‑specific verification rules substantially limits the applicability of RLVR to general reasoning domains with free‑form answers, where valid answers often exhibit significant variability, making it difficult to establish complete and accurate rules. To address this limitation, we propose Conditional Expectation Reward (CER), which leverages the large language model itself as an implicit verifier, and is therefore applicable to general domains and eliminates the need for external verifiers or auxiliary models. CER is defined as the expected likelihood of generating the reference answer conditioned on the generated answer. In contrast to rule‑based verifiers that yield binary feedback, CER provides a soft, graded reward signal that reflects varying degrees of correctness, making it better suited to tasks where answers vary in correctness. Experimental results demonstrate that CER is effective across a wide range of reasoning tasks, spanning both mathematical and general domains, indicating that CER serves as a flexible and general verification mechanism. The code is available at https://github.com/changyi7231/CER.

PaperId: 2531, https://arxiv.org/pdf/2603.10597.pdf GitHub

Abstract:
Trajectory prediction is critical for autonomous driving, enabling safe and efficient planning in dense, dynamic traffic. Most existing methods optimize prediction accuracy under fixed‑length observations. However, real‑world driving often yields variable‑length, incomplete observations, posing a challenge to these methods. A common strategy is to directly map features from incomplete observations to those from complete ones. This one‑shot mapping, however, struggles to learn accurate representations for short trajectories due to significant information gaps. To address this issue, we propose a Progressive Retrospective Framework (PRF), which gradually aligns features from incomplete observations with those from complete ones via a cascade of retrospective units. Each unit consists of a Retrospective Distillation Module (RDM) and a Retrospective Prediction Module (RPM), where RDM distills features and RPM recovers previous timesteps using the distilled features. Moreover, we propose a Rolling‑Start Training Strategy (RSTS) that enhances data efficiency during PRF training. PRF is plug‑and‑play with existing methods. Extensive experiments on datasets Argoverse 2 and Argoverse 1 demonstrate the effectiveness of PRF. Code is available at https://github.com/zhouhao94/PRF.

PaperId: 2532, https://arxiv.org/pdf/2603.10541.pdf GitHub

Abstract:
Promptable Foundation Models (FMs), initially introduced for natural image segmentation, have also revolutionized medical image segmentation. The increasing number of models, along with evaluations varying in datasets, metrics, and compared models, makes direct performance comparison between models difficult and complicates the selection of the most suitable model for specific clinical tasks. In our study, 11 promptable FMs are tested using non‑iterative 2D and 3D prompting strategies on a private and public dataset focusing on bone and implant segmentation in four anatomical regions (wrist, shoulder, hip and lower leg). The Pareto‑optimal models are identified and further analyzed using human prompts collected through a dedicated observer study. Our findings are: 1) The segmentation performance varies a lot between FMs and prompting strategies; 2) The Pareto‑optimal models in 2D are SAM and SAM2.1, in 3D nnInteractive and Med‑SAM2; 3) Localization accuracy and rater consistency vary with anatomical structures, with higher consistency for simple structures (wrist bones) and lower consistency for complex structures (pelvis, tibia, implants); 4) The segmentation performance drops using human prompts, suggesting that performance reported on "ideal" prompts extracted from reference labels might overestimate the performance in a human‑driven setting; 5) All models were sensitive to prompt variations. While two models demonstrated intra‑rater robustness, it did not scale to inter‑rater settings. We conclude that the selection of the most optimal FM for a human‑driven setting remains challenging, with even high‑performing FMs being sensitive to variations in human input prompts. Our code base for prompt extraction and model inference is available: https://github.com/CarolineMagg/segmentation‑FM‑benchmark/

PaperId: 2533, https://arxiv.org/pdf/2603.10466.pdf GitHub

Abstract:
Physics‑Informed Neural Networks (PINNs) have shown promise in solving incompressible Navier‑Stokes equations, yet existing approaches are predominantly designed for single‑flow settings. When extended to multi‑flow scenarios, these methods face three key challenges: (1) difficulty in simultaneously capturing both shared physical principles and flow‑specific characteristics, (2) susceptibility to inter‑task negative transfer that degrades prediction accuracy, and (3) unstable training dynamics caused by disparate loss magnitudes across heterogeneous flow regimes. To address these limitations, we propose UniPINN, a unified multi‑flow PINN framework that integrates three complementary components: a shared‑specialized architecture that disentangles universal physical laws from flow‑specific features, a cross‑flow attention mechanism that selectively reinforces relevant patterns while suppressing task‑irrelevant interference, and a dynamic weight allocation strategy that adaptively balances loss contributions to stabilize multi‑objective optimization. Extensive experiments on three canonical flows demonstrate that UniPINN effectively unifies multi‑flow learning, achieving superior prediction accuracy and balanced performance across heterogeneous regimes while successfully mitigating negative transfer. The source code of this paper will be released on https://github.com/Event‑AHU/OpenFusion

PaperId: 2534, https://arxiv.org/pdf/2603.10397.pdf GitHub

Abstract:
One crucial factor behind the success of deep learning lies in the implicit bias induced by noise inherent in gradient‑based training algorithms. Motivated by empirical observations that training with noisy labels improves model generalization, we delve into the underlying mechanisms behind stochastic gradient descent (SGD) with label noise. Focusing on a two‑layer over‑parameterized linear network, we analyze the learning dynamics of label noise SGD, unveiling a two‑phase learning behavior. In \emphPhase I, the magnitudes of model weights progressively diminish, and the model escapes the lazy regime; enters the rich regime. In \emphPhase II, the alignment between model weights and the ground‑truth interpolator increases, and the model eventually converges. Our analysis highlights the critical role of label noise in driving the transition from the lazy to the rich regime and minimally explains its empirical success. Furthermore, we extend these insights to Sharpness‑Aware Minimization (SAM), showing that the principles governing label noise SGD also apply to broader optimization algorithms. Extensive experiments, conducted under both synthetic and real‑world setups, strongly support our theory. Our code is released at https://github.com/a‑usually/Label‑Noise‑SGD.

PaperId: 2535, https://arxiv.org/pdf/2603.10392.pdf GitHub

Abstract:
In this paper, we present a novel probabilistic safe control framework for human‑robot interaction that combines control barrier functions (CBFs) with conformal risk control to provide formal safety guarantees while considering complex human behavior. The approach uses conformal risk control to quantify and control the prediction errors in CBF safety values and establishes formal guarantees on the probability of constraint satisfaction during interaction. We introduce an algorithm that dynamically adjusts the safety margins produced by conformal risk control based on the current interaction context. Through experiments on human‑robot navigation scenarios, we demonstrate that our approach significantly reduces collision rates and safety violations as compared to baseline methods while maintaining high success rates in goal‑reaching tasks and efficient control. The code, simulations, and other supplementary material can be found on the project website: https://jakeagonzales.github.io/crc‑cbf‑website/.

PaperId: 2536, https://arxiv.org/pdf/2603.10341.pdf GitHub

Abstract:
Federated active learning (FAL) seeks to reduce annotation cost under privacy constraints, yet its effectiveness degrades in realistic settings with severe global class imbalance and highly heterogeneous clients. We conduct a systematic study of query‑model selection in FAL and uncover a central insight: the model that achieves more class‑balanced sampling, especially for minority classes, consistently leads to better final performance. Moreover, global‑model querying is beneficial only when the global distribution is highly imbalanced and client data are relatively homogeneous; otherwise, the local model is preferable. Based on these findings, we propose FairFAL, an adaptive class‑fair FAL framework. FairFAL (1) infers global imbalance and local‑global divergence via lightweight prediction discrepancy, enabling adaptive selection between global and local query models; (2) performs prototype‑guided pseudo‑labeling using global features to promote class‑aware querying; and (3) applies a two‑stage uncertainty‑diversity balanced sampling strategy with k‑center refinement. Experiments on five benchmarks show that FairFAL consistently outperforms state‑of‑the‑art approaches under challenging long‑tailed and non‑IID settings. The code is available at https://github.com/chenchenzong/FairFAL.

PaperId: 2537, https://arxiv.org/pdf/2603.10303.pdf GitHub

Abstract:
Judging the novelty of research ideas is crucial for advancing science, enabling the identification of unexplored directions, and ensuring contributions meaningfully extend existing knowledge rather than reiterate minor variations. However, given the exponential growth of scientific literature, manually judging the novelty of research ideas through literature reviews is labor‑intensive, subjective, and infeasible at scale. Therefore, recent efforts have proposed automated approaches for research idea novelty judgment. Yet, evaluation of these approaches remains largely inconsistent and is typically based on non‑standardized human evaluations, hindering large‑scale, comparable evaluations. To address this, we introduce RINoBench, the first comprehensive benchmark for large‑scale evaluation of research idea novelty judgments. It comprises 1,381 research ideas derived from and judged by human experts as well as nine automated evaluation metrics designed to assess both rubric‑based novelty scores and textual justifications of novelty judgments. Using this benchmark, we evaluate several state‑of‑the‑art large language models (LLMs) on their ability to judge the novelty of research ideas. Our findings reveal that while LLM‑generated reasoning closely mirrors human rationales, this alignment does not reliably translate into accurate novelty judgments, which diverge significantly from human gold standard judgments ‑ even among leading reasoning‑capable models. Data and code available at: https://github.com/TimSchopf/RINoBench.

PaperId: 2538, https://arxiv.org/pdf/2603.10220.pdf GitHub

Abstract:
Intraoperative Cone Beam Computed Tomography (CBCT) provides a reliable 3D anatomical context essential for interventional planning. However, its static nature fails to provide continuous monitoring of soft‑tissue deformations induced by respiration, probe pressure, and surgical manipulation, leading to navigation discrepancies. We propose a deformation‑aware CBCT updating framework that leverages robotic ultrasound as a dynamic proxy to infer tissue motion and update static CBCT slices in real time. Starting from calibration‑initialized alignment with linear correlation of linear combination (LC2)‑based rigid refinement, our method establishes accurate multimodal correspondence. To capture intraoperative dynamics, we introduce the ultrasound correlation UNet (USCorUNet), a lightweight network trained with optical flow‑guided supervision to learn deformation‑aware correlation representations, enabling accurate, real‑time dense deformation field estimation from ultrasound streams. The inferred deformation is spatially regularized and transferred to the CBCT reference to produce deformation‑consistent visualizations without repeated radiation exposure. We validate the proposed approach through deformation estimation and ultrasound‑guided CBCT updating experiments. Results demonstrate real‑time end‑to‑end CBCT slice updating and physically plausible deformation estimation, enabling dynamic refinement of static CBCT guidance during robotic ultrasound‑assisted interventions. The source code is publicly available at https://github.com/anonymous‑codebase/us‑cbct‑demo.

PaperId: 2539, https://arxiv.org/pdf/2603.10165.pdf GitHub

Abstract:
Every agent interaction generates a next‑state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a live, online learning source. We present OpenClaw‑RL, a framework that employs next‑state signals to optimize personal agents online through infrastructure and methodology innovations. On the infrastructure side, we extend existing RL systems to a server‑client architecture where the RL server hosts the policy behind an inference API and user terminals stream interaction data back over HTTP. From each observed next state, the system extracts two complementary training signals, evaluative and directive, via a separate asynchronous server so that neither signal extraction nor optimization blocks inference. On the methodology side, we introduce a hybrid RL objective that unifies both signal types in a single update: directive signals provide richer, token‑level supervision but are sparser, while evaluative signals are more broadly available. To stabilize distillation under teacher‑student mismatch, we propose overlap‑guided hint selection, which picks the hint whose induced teacher distribution maximally overlaps with the student's top‑k tokens, together with a log‑probability‑difference clip that bounds per‑token advantages. Applied to personal agents, OpenClaw‑RL enables an agent to improve simply by being used, recovering conversational signals from user re‑queries, corrections, and explicit feedback. Applied to general agents, OpenClaw‑RL is the first RL framework to unify real‑world agent settings spanning terminal, GUI, SWE, and tool‑call environments, where we additionally demonstrate the utility of next‑state signals in long‑horizon settings.

PaperId: 2540, https://arxiv.org/pdf/2603.10156.pdf GitHub

Abstract:
Finetuning on domain‑specific data is a well‑established method for enhancing LLM performance on downstream tasks. Training on each dataset produces a new set of model weights, resulting in a multitude of checkpoints saved in‑house or on open‑source platforms. However, these training artifacts are rarely reused for subsequent experiments despite containing improved model abilities for potentially similar tasks. In this paper, we propose Mashup Learning, a simple method to leverage the outputs of prior training runs to enhance model adaptation to new tasks. Our procedure identifies the most relevant historical checkpoints for a target dataset, aggregates them with model merging, and uses the result as an improved initialization for training. Across 8 standard LLM benchmarks, four models, and two collections of source checkpoints, Mashup Learning consistently improves average downstream accuracy by 0.5‑5 percentage points over training from scratch. It also accelerates convergence, requiring 41‑46% fewer training steps and up to 37% less total wall‑clock time to match from‑scratch accuracy, including all selection and merging overhead.

PaperId: 2541, https://arxiv.org/pdf/2603.10101.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capacity of Large Language Models (LLMs). However, RLVR solely relies on final answers as outcome rewards, neglecting the correctness of intermediate reasoning steps. Training on these process‑wrong but outcome‑correct rollouts can lead to hallucination and answer‑copying, severely undermining the model's generalization and robustness. To address this, we incorporate a Contrastive Learning mechanism into the Policy Optimization (CLIPO) to generalize the RLVR process. By optimizing a contrastive loss over successful rollouts, CLIPO steers the LLM to capture the invariant structure shared across correct reasoning paths. This provides a more robust cross‑trajectory regularization than the original single‑path supervision in RLVR, effectively mitigating step‑level reasoning inconsistencies and suppressing hallucinatory artifacts. In experiments, CLIPO consistently improves multiple RLVR baselines across diverse reasoning benchmarks, demonstrating uniform improvements in generalization and robustness for policy optimization of LLMs. Our code and training recipes are available at https://github.com/Qwen‑Applications/CLIPO.

PaperId: 2542, https://arxiv.org/pdf/2603.10085.pdf GitHub

Abstract:
Improving GPU kernel efficiency is crucial for advancing AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. However, existing LLM‑based kernel optimization pipelines typically rely on opaque, implicitly learned heuristics within the LLMs to determine optimization strategies. This leads to inefficient trial‑and‑error and weakly interpretable optimizations. Our key insight is to replace implicit heuristics with expert optimization skills that are knowledge‑driven and aware of task trajectories. Specifically, we present KernelSkill, a multi‑agent framework with a dual‑level memory architecture. KernelSkill operates by coordinating agents with long‑term memory of reusable expert skills and short‑term memory to prevent repetitive backtracking. On KernelBench Levels 1‑3, KernelSkill achieves a 100% success rate and average speedups of 5.44x, 2.82x, and 1.92x over Torch Eager on Levels 1, 2, and 3, respectively, outperforming prior baselines. Code is available at https://github.com/0satan0/KernelMem/.

PaperId: 2543, https://arxiv.org/pdf/2603.10068.pdf GitHub

Abstract:
Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to capture how safety properties evolve under sustained adversarial interaction. We present ADVERSA, an automated red‑teaming framework that measures guardrail degradation dynamics as continuous per‑round compliance trajectories rather than discrete jailbreak events. ADVERSA uses a fine‑tuned 70B attacker model (ADVERSA‑Red, Llama‑3.1‑70B‑Instruct with QLoRA) that eliminates the attacker‑side safety refusals that render off‑the‑shelf models unreliable as attackers, scoring victim responses on a structured 5‑point rubric that treats partial compliance as a distinct measurable state. We report a controlled experiment across three frontier victim models (Claude Opus 4.6, Gemini 3.1 Pro, GPT‑5.2) using a triple‑judge consensus architecture in which judge reliability is measured as a first‑class research outcome rather than assumed. Across 15 conversations of up to 10 adversarial rounds, we observe a 26.7% jailbreak rate with an average jailbreak round of 1.25, suggesting that in this evaluation setting, successful jailbreaks were concentrated in early rounds rather than accumulating through sustained pressure. We document inter‑judge agreement rates, self‑judge scoring tendencies, attacker drift as a failure mode in fine‑tuned attackers deployed out of their training distribution, and attacker refusals as a previously‑underreported confound in victim resistance measurement. All limitations are stated explicitly. Attack prompts are withheld per responsible disclosure policy; all other experimental artifacts are released.

PaperId: 2544, https://arxiv.org/pdf/2603.10067.pdf GitHub

Abstract:
Muon has recently shown promising results in LLM training. In this work, we study how to further improve Muon. We argue that Muon's orthogonalized update rule suppresses the emergence of heavy‑tailed weight spectra and over‑emphasizes the training along noise‑dominated directions. Motivated by the Heavy‑Tailed Self‑Regularization (HT‑SR) theory, we propose HTMuon. HTMuon preserves Muon's ability to capture parameter interdependencies while producing heavier‑tailed updates and inducing heavier‑tailed weight spectra. Experiments on LLM pretraining and image classification show that HTMuon consistently improves performance over state‑of‑the‑art baselines and can also serve as a plug‑in on top of existing Muon variants. For example, on LLaMA pretraining on the C4 dataset, HTMuon reduces perplexity by up to 0.98 compared to Muon. We further theoretically show that HTMuon corresponds to steepest descent under the Schatten‑q norm constraint and provide convergence analysis in smooth non‑convex settings. The implementation of HTMuon is available at https://github.com/TDCSZ327/HTmuon.

PaperId: 2545, https://arxiv.org/pdf/2603.10055.pdf GitHub

Abstract:
Pre‑training is crucial for large language models (LLMs), as it is when most representations and capabilities are acquired. However, natural language pre‑training has problems: high‑quality text is finite, it contains human biases, and it entangles knowledge with reasoning. This raises a fundamental question: is natural language the only path to intelligence? We propose using neural cellular automata (NCA) to generate synthetic, non‑linguistic data for pre‑pre‑training LLMs‑‑training on synthetic‑then‑natural language. NCA data exhibits rich spatiotemporal structure and statistics resembling natural language while being controllable and cheap to generate at scale. We find that pre‑pre‑training on only 164M NCA tokens improves downstream language modeling by up to 6% and accelerates convergence by up to 1.6x. Surprisingly, this even outperforms pre‑pre‑training on 1.6B tokens of natural language from Common Crawl with more compute. These gains also transfer to reasoning benchmarks, including GSM8K, HumanEval, and BigBench‑Lite. Investigating what drives transfer, we find that attention layers are the most transferable, and that optimal NCA complexity varies by domain: code benefits from simpler dynamics, while math and web text favor more complex ones. These results enable systematic tuning of the synthetic distribution to target domains. More broadly, our work opens a path toward more efficient models with fully synthetic pre‑training.

PaperId: 2546, https://arxiv.org/pdf/2603.10049.pdf GitHub

Abstract:
Ensemble learning is a well established body of methods for machine learning to enhance predictive performance by combining multiple algorithms/models. Combinatorial Fusion Analysis (CFA) has provided method and practice for combining multiple scoring systems, using rank‑score characteristic (RSC) function and cognitive diversity (CD), including ensemble method and model fusion. However, there is no general‑purpose Python tool available that incorporate these techniques. In this paper we introduce \textttInFusionLayer, a machine learning architecture inspired by CFA at the system fusion level that uses a moderate set of base models to optimize unsupervised and supervised learning multiclassification problems. We demonstrate \textttInFusionLayer's ease of use for PyTorch, TensorFlow, and Scikit‑learn workflows by validating its performance on various computer vision datasets. Our results highlight the practical advantages of incorporating distinctive features of RSC function and CD, paving the way for more sophisticated ensemble learning applications in machine learning. We open‑sourced our code to encourage continuing development and community accessibility to leverage CFA on github: https://github.com/ewroginek/Infusion

PaperId: 2547, https://arxiv.org/pdf/2603.10033.pdf GitHub

Abstract:
Graph foundation models (GFM) aim to acquire transferable knowledge by pre‑training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in graphs is inherently two‑dimensional: graphs differ not only in what they describe (topic domains) but also in how they are represented (format domains). Most existing GFM benchmarks vary only topic domains, thereby obscuring how knowledge transfers across both dimensions. We present a new benchmark that jointly evaluates topic and format gaps across the full GFM pipeline, including multi‑domain self‑supervised pre‑training and few‑shot downstream adaptation, and provides a timely evaluation of recent GFMs in the rapidly evolving landscape. Our protocol enables controlled assessment in four settings: (i) pre‑training on diverse topics and formats, while adapting to unseen downstream datasets; (ii) same pre‑training as in (i), while adapting to seen datasets; (iii) pre‑training on a single topic domain, while adapting to other topics; (iv) pre‑training on a base format, while adapting to other formats. This two‑axis evaluation disentangles semantic generalization from robustness to representational shifts. We conduct extensive evaluations of eight state‑of‑the‑art GFMs on 33 datasets spanning seven topic domains and six format domains, surfacing new empirical observations and practical insights for future research. Codes/data are available at https://github.com/smufang/GFMBenchmark.

PaperId: 2548, https://arxiv.org/pdf/2603.10032.pdf GitHub

Abstract:
Memory constraints in long‑running agents require structured management of accumulated facts while preserving essential information under bounded context limits. We introduce HTM‑EAR, a hierarchical tiered memory substrate that integrates HNSW‑based working memory (L1) with archival storage (L2), combining importance‑aware eviction and hybrid routing. When L1 reaches capacity, items are evicted using a weighted score of importance and usage. Queries are first resolved in L1; if similarity or entity coverage is insufficient, retrieval falls back to L2, and candidates are re‑ranked using a cross‑encoder. We evaluate the system under sustained saturation (15,000 facts; L1 capacity 500; L2 capacity 5000) using synthetic streams across five random seeds and real BGL system logs. Ablation studies compare the full system against variants without cross‑encoder re‑ranking, without routing gates, with LRU eviction, and an oracle with unbounded memory. Under saturation, the full model preserves active‑query precision (MRR = 1.000) while enabling controlled forgetting of stale history, approaching oracle active performance (0.997 +/‑ 0.003). In contrast, LRU minimizes latency (21.1 ms) but permanently evicts 2416 essential facts. On BGL logs, the full system achieves MRR 0.336, close to the oracle (0.370), while LRU drops to 0.069. Code is publicly available at: https://github.com/shubham‑61291/HTM‑EAR

PaperId: 2549, https://arxiv.org/pdf/2603.10026.pdf GitHub

Abstract:
Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has been widely adopted in modern AI compilers. However, for cascaded reduction operations involving multiple loops with inter‑loop data dependencies, such as the safe softmax followed by GEMM within attention mechanisms, existing compilers lack effective automated fusion and kernel generation capabilities. Although some works have addressed specific instances through hand‑crafted fusion strategies, their solutions are limited in generality and difficult to extend to other similar structures. Given the prevalence of such computational patterns in deep learning models, there remains significant untapped potential in achieving general and automated fusion optimization. In this paper, we present a formal theoretical methodology for analyzing cascaded reductions which can fuse them into a single loop and introduce an incremental computation form. Based on this methodology, we design Reduction Fuser (RedFuser), a framework that automatically identifies supported cascaded reduction patterns and generates optimized fused kernels. Experiments show that RedFuser successfully fuses diverse workloads, achieving up to 2× to 5× speedup over state‑of‑the‑art AI compilers and matching the performance of highly optimized hand‑written kernels. The code is available at https://github.com/alibaba/redfuser

PaperId: 2550, https://arxiv.org/pdf/2603.09992.pdf GitHub

Abstract:
This paper presents TAMUSA‑Chat, a research‑oriented framework for building domain‑adapted large language model conversational systems. The work addresses critical challenges in adapting general‑purpose foundation models to institutional contexts through supervised fine‑tuning, retrieval‑augmented generation, and systematic evaluation methodologies. We describe the complete architecture encompassing data acquisition from institutional sources, preprocessing pipelines, embedding construction, model training workflows, and deployment strategies. The system integrates modular components enabling reproducible experimentation with training configurations, hyper‑parameters, and evaluation protocols. Our implementation demonstrates how academic institutions can develop contextually grounded conversational agents while maintaining transparency, governance compliance, and responsible AI practices. Through empirical analysis of fine‑tuning behavior across model sizes and training iterations, we provide insights into domain adaptation efficiency, computational resource requirements, and quality‑cost trade‑offs. The publicly available codebase at https://github.com/alsmadi/TAMUSA_LLM_Based_Chat_app supports continued research into institutional LLM deployment, evaluation methodologies, and ethical considerations for educational AI systems.

PaperId: 2551, https://arxiv.org/pdf/2603.09983.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low‑information nature of autoregressive expert activation. In this paper, we propose to repurpose Speculative Decoding (SD) not merely as a compute accelerator, but as an informative lookahead sensor for memory management, supported by our theoretical and empirical analyses. Hence, we introduce MoE‑SpAc, an MoE inference framework that integrates a Speculative Utility Estimator to track expert demand, a Heterogeneous Workload Balancer to dynamically partition computation via online integer optimization, and an Asynchronous Execution Engine to unify the prefetching and eviction in the same utility space. Extensive experiments on seven benchmarks demonstrate that MoE‑SpAc achieves a 42% improvement in TPS over the SOTA SD‑based baseline, and an average 4.04x speedup over all standard baselines. Code is available at https://github.com/lshAlgorithm/MoE‑SpAc .

PaperId: 2552, https://arxiv.org/pdf/2603.09972.pdf GitHub

Abstract:
A central idea in mechanistic interpretability is that neural networks represent more features than they have dimensions, arranging them in superposition to form an over‑complete basis. This framing has been influential, motivating dictionary learning approaches such as sparse autoencoders. However, superposition has mostly been studied in idealized settings where features are sparse and uncorrelated. In these settings, superposition is typically understood as introducing interference that must be minimized geometrically and filtered out by non‑linearities such as ReLUs, yielding local structures like regular polytopes. We show that this account is incomplete for realistic data by introducing Bag‑of‑Words Superposition (BOWS), a controlled setting to encode binary bag‑of‑words representations of internet text in superposition. Using BOWS, we find that when features are correlated, interference can be constructive rather than just noise to be filtered out. This is achieved by arranging features according to their co‑activation patterns, making interference between active features constructive, while still using ReLUs to avoid false positives. We show that this kind of arrangement is more prevalent in models trained with weight decay and naturally gives rise to semantic clusters and cyclical structures which have been observed in real language models yet were not explained by the standard picture of superposition. Code for this paper can be found at https://github.com/LucasPrietoAl/correlations‑feature‑geometry.

PaperId: 2553, https://arxiv.org/pdf/2603.09961.pdf GitHub GitHub

Abstract:
Language‑conditioned local navigation requires a robot to infer a nearby traversable target location from its current observation and an open‑vocabulary, relational instruction. Existing vision‑language spatial grounding methods usually rely on vision‑language models (VLMs) to reason in image space, producing 2D predictions tied to visible pixels. As a result, they struggle to infer target locations in occluded regions, typically caused by furniture or moving humans. To address this issue, we propose BEACON, which predicts an ego‑centric Bird's‑Eye View (BEV) affordance heatmap over a bounded local region including occluded areas. Given an instruction and surround‑view RGB‑D observations from four directions around the robot, BEACON predicts the BEV heatmap by injecting spatial cues into a VLM and fusing the VLM's output with depth‑derived BEV features. Using an occlusion‑aware dataset built in the Habitat simulator, we conduct detailed experimental analysis to validate both our BEV space formulation and the design choices of each module. Our method improves the accuracy averaged across geodesic thresholds by 22.74 percentage points over the state‑of‑the‑art image‑space baseline on the validation subset with occluded target locations. Our project page is: https://xin‑yu‑gao.github.io/beacon.

PaperId: 2554, https://arxiv.org/pdf/2603.09931.pdf GitHub

Abstract:
Multimodal neuroimaging provides complementary insights for Alzheimer's disease diagnosis, yet clinical datasets frequently suffer from missing modalities. We propose ACADiff, a framework that synthesizes missing brain imaging modalities through adaptive clinical‑aware diffusion. ACADiff learns mappings between incomplete multimodal observations and target modalities by progressively denoising latent representations while attending to available imaging data and clinical metadata. The framework employs adaptive fusion that dynamically reconfigures based on input availability, coupled with semantic clinical guidance via GPT‑4o‑encoded prompts. Three specialized generators enable bidirectional synthesis among sMRI, FDG‑PET, and AV45‑PET. Evaluated on ADNI subjects, ACADiff achieves superior generation quality and maintains robust diagnostic performance even under extreme 80% missing scenarios, outperforming all existing baselines. To promote reproducibility, code is available at https://github.com/rongzhou7/ACADiff

PaperId: 2555, https://arxiv.org/pdf/2603.09909.pdf GitHub

Abstract:
While Multi‑Agent Systems (MAS) show potential for complex clinical decision support, the field remains hindered by architectural fragmentation and the lack of standardized multimodal integration. Current medical MAS research suffers from non‑uniform data ingestion pipelines, inconsistent visual‑reasoning evaluation, and a lack of cross‑specialty benchmarking. To address these challenges, we present MedMASLab, a unified framework and benchmarking platform for multimodal medical multi‑agent systems. MedMASLab introduces: (1) A standardized multimodal agent communication protocol that enables seamless integration of 11 heterogeneous MAS architectures across 24 medical modalities. (2) An automated clinical reasoning evaluator, a zero‑shot semantic evaluation paradigm that overcomes the limitations of lexical string‑matching by leveraging large vision‑language models to verify diagnostic logic and visual grounding. (3) The most extensive benchmark to date, spanning 11 organ systems and 473 diseases, standardizing data from 11 clinical benchmarks. Our systematic evaluation reveals a critical domain‑specific performance gap: while MAS improves reasoning depth, current architectures exhibit significant fragility when transitioning between specialized medical sub‑domains. We provide a rigorous ablation of interaction mechanisms and cost‑performance trade‑offs, establishing a new technical baseline for future autonomous clinical systems. The source code and data is publicly available at: https://github.com/NUS‑Project/MedMASLab/

PaperId: 2556, https://arxiv.org/pdf/2603.09882.pdf GitHub

Abstract:
Extrinsic dexterity leverages environmental contact to overcome the limitations of prehensile manipulation. However, achieving such dexterity in cluttered scenes remains challenging and underexplored, as it requires selectively exploiting contact among multiple interacting objects with inherently coupled dynamics. Existing approaches lack explicit modeling of such complex dynamics and therefore fall short in non‑prehensile manipulation in cluttered environments, which in turn limits their practical applicability in real‑world environments. In this paper, we introduce a Dynamics‑Aware Policy Learning (DAPL) framework that can facilitate policy learning with a learned representation of contact‑induced object dynamics in cluttered environments. This representation is learned through explicit world modeling and used to condition reinforcement learning, enabling extrinsic dexterity to emerge without hand‑crafted contact heuristics or complex reward shaping. We evaluate our approach in both simulation and the real world. Our method outperforms prehensile manipulation, human teleoperation, and prior representation‑based policies by over 25% in success rate on unseen simulated cluttered scenes with varying densities. The real‑world success rate reaches around 50% across 10 cluttered scenes, while a practical grocery deployment further demonstrates robust sim‑to‑real transfer and applicability.

PaperId: 2557, https://arxiv.org/pdf/2603.09677.pdf GitHub

Abstract:
Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This framework establishes a Unified Taxonomy covering documents, images, and audio‑visual streams, introducing a progressive parsing paradigm that bridges perception and cognition. Specifically, the framework integrates three hierarchical levels: 1) Holistic Detection, which achieves precise spatial‑temporal grounding of objects or events to establish a geometric baseline for perception; 2) Fine‑grained Recognition, which performs symbolization (e.g., OCR/ASR) and attribute extraction on localized objects to complete structured entity parsing; and 3) Multi‑level Interpreting, which constructs a reasoning chain from local semantics to global logic. A pivotal advantage of this framework is its evidence anchoring mechanism, which enforces a strict alignment between high‑level semantic descriptions and low‑level facts. This enables ``evidence‑based'' logical induction, transforming unstructured signals into standardized knowledge that is locatable, enumerable, and traceable. Building on this foundation, we constructed a standardized dataset and released the Logics‑Parsing‑Omni model, which successfully converts complex audio‑visual signals into machine‑readable structured knowledge. Experiments demonstrate that fine‑grained perception and high‑level cognition are synergistic, effectively enhancing model reliability. Furthermore, to quantitatively evaluate these capabilities, we introduce OmniParsingBench. Code, models and the benchmark are released at https://github.com/alibaba/Logics‑Parsing/tree/master/Logics‑Parsing‑Omni.

PaperId: 2558, https://arxiv.org/pdf/2603.09641.pdf GitHub

Abstract:
LLM agents that store knowledge as natural language suffer steep retrieval degradation as condition count grows, often struggle to compose learned rules reliably, and typically lack explicit mechanisms to detect stale or adversarial knowledge. We introduce PRECEPT, a unified framework for test‑time adaptation with three tightly coupled components: (1) deterministic exact‑match rule retrieval over structured condition keys, (2) conflict‑aware memory with Bayesian source reliability and threshold‑based rule invalidation, and (3) COMPASS, a Pareto‑guided prompt‑evolution outer loop. Exact retrieval eliminates partial‑match interpretation errors on the deterministic path (0% by construction, vs 94.4% under Theorem~B.6's independence model at N=10) and supports compositional stacking through a semantic tier hierarchy; conflict‑aware memory resolves static‑‑dynamic disagreements and supports drift adaptation; COMPASS evaluates prompts through the same end‑to‑end execution pipeline. Results (9‑‑10 seeds): PRECEPT achieves a +41.1pp first‑try advantage over Full Reflexion (d>1.9), +33.3pp compositional generalization (d=1.55), 100% P_1 on 2‑way logistics compositions (d=2.64), +40‑‑55pp continuous learning gains, strong eventual robustness under adversarial static knowledge (100% logistics with adversarial SK active; partial recovery on integration), +55.0pp drift recovery (d=0.95, p=0.031), and 61% fewer steps. Core comparisons are statistically significant, often at p<0.001.

PaperId: 2559, https://arxiv.org/pdf/2603.09555.pdf GitHub

Abstract:
State‑space model releases are typically coupled to fused CUDA and Triton kernels, inheriting a hard dependency on NVIDIA hardware. We show that Mamba‑2's state space duality algorithm ‑‑ diagonal state structure, chunkable recurrence, and einsum‑dominated compute with static control flow ‑‑ maps cleanly onto what XLA's fusion and tiling passes actually optimise, making custom kernels optional rather than required. We implement the full inference path (prefill, cached autoregressive decoding) as shaped standard primitives under XLA, without hand‑written kernels, and realise the architecture's theoretical O(1) state management as a compiled on‑device cache requiring no host synchronisation during generation. The implementation runs unmodified on CPU, NVIDIA GPU, and Google Cloud TPU from a single JAX source. On TPU v6e across five model scales (130M‑‑2.7B parameters), XLA‑generated code reaches approximately 140 TFLOPS on single‑stream prefill (15% MFU) and up to 64% bandwidth utilisation on decode. Greedy decoding matches the PyTorch/CUDA reference token‑for‑token across 64 steps, with hidden‑state agreement within float32 rounding tolerance. The pattern transfers to any SSM recurrence satisfying the same structural conditions, on any platform with a mature XLA backend. The implementation is publicly available at https://github.com/CosmoNaught/mamba2‑jax and merged into the Bonsai JAX model library.

PaperId: 2560, https://arxiv.org/pdf/2603.09527.pdf GitHub

Abstract:
Speculative decoding accelerates LLM inference but suffers from performance degradation when target models are fine‑tuned for specific domains. A naive solution is to retrain draft models for every target model, which is costly and inefficient. To address this, we introduce a parameter‑ and data‑efficient framework named Efficient Draft Adaptation, abbreviated as EDA, for efficiently adapting draft models. EDA introduces three innovations: (1) a decoupled architecture that utilizes shared and private components to model the shared and target‑specific output distributions separately, enabling parameter‑efficient adaptation by updating only the lightweight private component;(2) a data regeneration strategy that utilizes the fine‑tuned target model to regenerate training data, thereby improving the alignment between training and speculative decoding, leading to higher average acceptance length;(3) a sample selection mechanism that prioritizes high‑value data for efficient adaptation. Our experiments show that EDA effectively restores speculative performance on fine‑tuned models, achieving superior average acceptance lengths with significantly reduced training costs compared to full retraining. Code is available at https://github.com/Lyn‑Lucy/Efficient‑Draft‑Adaptation.

PaperId: 2561, https://arxiv.org/pdf/2603.09465.pdf GitHub

Abstract:
Vision‑Language‑Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the visual encoder and struggle with accumulated instability in long‑term planning. To address these challenges, we propose EvoDriveVLA‑a novel collaborative perception‑planning distillation framework that integrates self‑anchored perceptual constraints and future‑informed trajectory optimization. Specifically, self‑anchored visual distillation leverages self‑anchor teacher to deliver visual anchoring constraints, regularizing student representations via trajectory‑guided key‑region awareness. In parallel, future‑informed trajectory distillation employs a future‑aware oracle teacher with coarse‑to‑fine trajectory refinement and Monte Carlo dropout sampling to synthesize reasoning trajectories that model future evolutions, enabling the student model to internalize the future‑aware insights of the teacher. EvoDriveVLA achieves SOTA performance in nuScenes open‑loop evaluation and significantly enhances performance in NAVSIM closed‑loop evaluation. Our code is available at: https://github.com/hey‑cjj/EvoDriveVLA.

PaperId: 2562, https://arxiv.org/pdf/2603.09414.pdf GitHub

Abstract:
Document Layout Analysis (DLA) is crucial for document artificial intelligence and has recently received increasing attention, resulting in an influx of large‑scale public DLA datasets. Existing work often combines data from various domains in recent public DLA datasets to improve the generalization of DLA. However, directly merging these datasets for training often results in suboptimal model performance, as it overlooks the different layout structures inherent to various domains. These variations include different labeling styles, document types, and languages. This paper introduces PromptDLA, a domain‑aware Prompter for Document Layout Analysis that effectively leverages descriptive knowledge as cues to integrate domain priors into DLA. The innovative PromptDLA features a unique domain‑aware prompter that customizes prompts based on the specific attributes of the data domain. These prompts then serve as cues that direct the DLA toward critical features and structures within the data, enhancing the model's ability to generalize across varied domains. Extensive experiments show that our proposal achieves state‑of‑the‑art performance among DocLayNet, PubLayNet, M6Doc, and D^4LA. Our code is available at https://github.com/Zirui00/PromptDLA.

PaperId: 2563, https://arxiv.org/pdf/2603.09408.pdf GitHub

Abstract:
Recent diffusion models increasingly favor Transformer backbones, motivated by the remarkable scalability of fully attentional architectures. Yet the locality bias, parameter efficiency, and hardware friendliness‑‑the attributes that established ConvNets as the efficient vision backbone‑‑have seen limited exploration in modern generative modeling. Here we introduce the fully convolutional diffusion model (FCDM), a model having a backbone similar to ConvNeXt, but designed for conditional diffusion modeling. We find that using only 50% of the FLOPs of DiT‑XL/2, FCDM‑XL achieves competitive performance with 7× and 7.5× fewer training steps at 256×256 and 512×512 resolutions, respectively. Remarkably, FCDM‑XL can be trained on a 4‑GPU system, highlighting the exceptional training efficiency of our architecture. Our results demonstrate that modern convolutional designs provide a competitive and highly efficient alternative for scaling diffusion models, reviving ConvNeXt as a simple yet powerful building block for efficient generative modeling.

PaperId: 2564, https://arxiv.org/pdf/2603.09320.pdf GitHub

Abstract:
Autonomous space operations such as on‑orbit servicing and active debris removal demand robust part‑level semantic understanding and precise relative navigation of target spacecraft, yet collecting large‑scale real data in orbit remains impractical due to cost and access constraints. Existing synthetic datasets, moreover, suffer from limited target diversity, single‑modality sensing, and incomplete ground‑truth annotations. We present SpaceSense‑Bench, a large‑scale multi‑modal benchmark for spacecraft perception encompassing 136~satellite models with approximately 70~GB of data. Each frame provides time‑synchronized 1024×1024 RGB images, millimeter‑precision depth maps, and 256‑beam LiDAR point clouds, together with dense 7‑class part‑level semantic labels at both the pixel and point level as well as accurate 6‑DoF pose ground truth. The dataset is generated through a high‑fidelity space simulation built in Unreal Engine~5 and a fully automated pipeline covering data acquisition, multi‑stage quality control, and conversion to mainstream formats. We benchmark five representative tasks (object detection, 2D semantic segmentation, RGB‑‑LiDAR fusion‑based 3D point cloud segmentation, monocular depth estimation, and orientation estimation) and identify two key findings: (i)~perceiving small‑scale components (\emphe.g., thrusters and omni‑antennas) and generalizing to entirely unseen spacecraft in a zero‑shot setting remain critical bottlenecks for current methods, and (ii)~scaling up the number of training satellites yields substantial performance gains on novel targets, underscoring the value of large‑scale, diverse datasets for space perception research. The dataset, code, and toolkit are publicly available at https://github.com/wuaodi/SpaceSense‑Bench.

PaperId: 2565, https://arxiv.org/pdf/2603.09192.pdf GitHub

Abstract:
Retrieval‑augmented generation (RAG) improves factual grounding, yet most systems rely on flat chunk retrieval and provide limited control over multi‑step synthesis. We propose an Explainable Innovation Engine that upgrades the knowledge unit from text chunks to methods‑as‑nodes. The engine maintains a weighted method provenance tree for traceable derivations and a hierarchical clustering abstraction tree for efficient top‑down navigation. At inference time, a strategy agent selects explicit synthesis operators (e.g., induction, deduction, analogy), composes new method nodes, and records an auditable trajectory. A verifier‑scorer layer then prunes low‑quality candidates and writes validated nodes back to support continual growth. Expert evaluation across six domains and multiple backbones shows consistent gains over a vanilla baseline, with the largest improvements on derivation‑heavy settings, and ablations confirm the complementary roles of provenance backtracking and pruning. These results suggest a practical path toward controllable, explainable, and verifiable innovation in agentic RAG systems. Code is available at the project GitHub repository https://github.com/xiaolu‑666113/Dual‑Tree‑Agent‑RAG.

PaperId: 2566, https://arxiv.org/pdf/2603.09125.pdf GitHub

Abstract:
Diffusion‑based image super‑resolution (ISR) has shown strong potential, but it still struggles in real‑world scenarios where degradations are unknown and spatially non‑uniform, often resulting in lost details or visual artifacts. To address this challenge, we propose a novel super‑resolution diffusion model, QUSR, which integrates a Quality‑Aware Prior (QAP) with an Uncertainty‑Guided Noise Generation (UNG) module. The UNG module adaptively adjusts the noise injection intensity, applying stronger perturbations to high‑uncertainty regions (e.g., edges and textures) to reconstruct complex details, while minimizing noise in low‑uncertainty regions (e.g., flat areas) to preserve original information. Concurrently, the QAP leverages an advanced Multimodal Large Language Model (MLLM) to generate reliable quality descriptions, providing an effective and interpretable quality prior for the restoration process. Experimental results confirm that QUSR can produce high‑fidelity and high‑realism images in real‑world scenarios. The source code is available at https://github.com/oTvTog/QUSR.

PaperId: 2567, https://arxiv.org/pdf/2603.09121.pdf GitHub

Abstract:
While Vision‑Language‑Action (VLA) models have demonstrated promising generalization capabilities in robotic manipulation, deploying them on specific and complex downstream tasks still demands effective post‑training. In parallel, Human‑in‑the‑Loop (HiL) learning has proven to be a powerful mechanism for refining robot policies. However, extending this paradigm to dexterous manipulation remains challenging: multi‑finger control is high‑dimensional, contact‑intensive, and exhibits execution distributions that differ markedly from standard arm motions, leaving existing dexterous VLA systems limited in reliability and adaptability. We present DexHiL, the first integrated arm‑hand human‑in‑the‑loop framework for dexterous VLA models, enabling coordinated interventions over the arm and the dexterous hand within a single system. DexHiL introduces an intervention‑aware data sampling strategy that prioritizes corrective segments for post‑training, alongside a lightweight teleoperation interface that supports instantaneous human corrections during execution. Real‑robot experiments demonstrate that DexHiL serves as an effective post‑training framework, yielding a substantial performance leap, outperforming standard offline‑only fine‑tuning baselines by an average of 25% in success rates across distinct tasks. Project page: https://chenzhongxi‑sjtu.github.io/dexhil/

PaperId: 2568, https://arxiv.org/pdf/2603.09022.pdf GitHub

Abstract:
Multi‑turn, multi‑agent LLM game evaluations often exhibit substantial run‑to‑run variance. In long‑horizon interactions, small early deviations compound across turns and are amplified by multi‑agent coupling. This biases win rate estimates and makes rankings unreliable across repeated tournaments. Prompt choice worsens this further by producing different effective policies. We address both instability and underperformance with MEMO (Memory‑augmented MOdel context optimization), a self‑play framework that optimizes inference‑time context by coupling retention and exploration. Retention maintains a persistent memory bank that stores structured insights from self‑play trajectories and injects them as priors during later play. Exploration runs tournament‑style prompt evolution with uncertainty‑aware selection via TrueSkill, and uses prioritized replay to revisit rare and decisive states. Across five text‑based games, MEMO raises mean win rate from 25.1% to 49.5% for GPT‑4o‑mini and from 20.9% to 44.3% for Qwen‑2.5‑7B‑Instruct, using 2,000 self‑play games per task. Run‑to‑run variance also drops, giving more stable rankings across prompt variations. These results suggest that multi‑agent LLM game performance and robustness have substantial room for improvement through context optimization. MEMO achieves the largest gains in negotiation and imperfect‑information games, while RL remains more effective in perfect‑information settings. All code is open‑source and available here: https://github.com/openverse‑ai/MEMO

PaperId: 2569, https://arxiv.org/pdf/2603.09020.pdf GitHub

Abstract:
There is no 'ordinary' when it comes to AI. The human‑AI experience is extraordinarily complex and specific to each person, yet dominant measures such as usability scales and engagement metrics flatten away nuance. We argue for AI phenomenology: a research stance that asks "How did it feel?" beyond the standard questions of "How well did it perform?" when interacting with AI systems. AI phenomenology acts as a paradigm for bidirectional human‑AI alignment as it foregrounds users' first‑person perceptions and interpretations of AI systems over time. We motivate AI phenomenology as a framework that captures how alignment is experienced, negotiated, and updated between users and AI systems. Tracing a lineage from Husserl through postphenomenology to Actor‑Network Theory, and grounding our argument in three studies‑two longitudinal studies with "Day", an AI companion, and a multi‑method study of agentic AI in software engineering‑we contribute a set of replicable methodological toolkits for conducting AI phenomenology research: instruments for capturing lived experience across personal and professional contexts, three design concepts (translucent design, agency‑aware value alignment, temporal co‑evolution tracking), and a concrete research agenda. We offer this toolkit not as a new paradigm but as a practical scaffold that researchers can adapt as AI systems‑and the humans who live alongside them‑continue to co‑evolve.

PaperId: 2570, https://arxiv.org/pdf/2603.09018.pdf GitHub

Abstract:
Multi‑modal large language models (MM‑LLMs) have shown strong performance in medical image understanding and clinical reasoning. Recent medical agent systems extend them with tool use and multi‑agent collaboration, enabling complex decision‑making. However, these systems rely almost entirely on frontier models (e.g., GPT), whose API‑based deployment incurs high cost, high latency, and privacy risks that conflict with on‑premise clinical requirements. We present Meissa, a lightweight 4B‑parameter medical MM‑LLM that brings agentic capability offline. Instead of imitating static answers, Meissa learns both when to engage external interaction (strategy selection) and how to execute multi‑step interaction (strategy execution) by distilling structured trajectories from frontier models. Specifically, we propose: (1) Unified trajectory modeling: trajectories (reasoning and action traces) are represented within a single state‑action‑observation formalism, allowing one model to generalize across heterogeneous medical environments. (2) Three‑tier stratified supervision: the model's own errors trigger progressive escalation from direct reasoning to tool‑augmented and multi‑agent interaction, explicitly learning difficulty‑aware strategy selection. (3) Prospective‑retrospective supervision: pairing exploratory forward traces with hindsight‑rationalized execution traces enables stable learning of effective interaction policies. Trained on 40K curated trajectories, Meissa matches or exceeds proprietary frontier agents in 10 of 16 evaluation settings across 13 medical benchmarks spanning radiology, pathology, and clinical reasoning. Using over 25x fewer parameters than typical frontier models like Gemini‑3, Meissa operates fully offline with 22x lower end‑to‑end latency compared to API‑based deployment. Data, models, and environments are released at https://github.com/Schuture/Meissa.

PaperId: 2571, https://arxiv.org/pdf/2603.08942.pdf GitHub

Abstract:
Recent advances in vision‑language models (VLMs) have demonstrated remarkable zero‑shot capabilities, yet adapting these models to specialized domains remains a significant challenge. Building on recent theoretical insights suggesting that independently trained VLMs are related by a canonical transformation, we extend this understanding to the concept of domains. We hypothesize that image features across disparate domains are related by a canonicalized geometric transformation that can be recovered using a small set of anchors. Few‑shot classification provides a natural setting for this alignment, as the limited labeled samples serve as the anchors required to estimate this transformation. Motivated by this hypothesis, we introduce BiCLIP, a framework that applies a targeted transformation to multimodal features to enhance cross‑modal alignment. Our approach is characterized by its extreme simplicity and low parameter footprint. Extensive evaluations across 11 standard benchmarks, including EuroSAT, DTD, and FGVCAircraft, demonstrate that BiCLIP consistently achieves state‑of‑the‑art results. Furthermore, we provide empirical verification of existing geometric findings by analyzing the orthogonality and angular distribution of the learned transformations, confirming that structured alignment is the key to robust domain adaptation. Code is available at https://github.com/QuantitativeImagingLaboratory/BilinearCLIP

PaperId: 2572, https://arxiv.org/pdf/2603.08844.pdf GitHub

Abstract:
Accurate localization of tumor regions from hematoxylin and eosin‑stained whole‑slide images is fundamental for translational research including spatial analysis, molecular profiling, and tissue architecture investigation. However, deep learning‑based tumor detection trained within specific cancers may exhibit reduced robustness when applied across different tumor types. We investigated whether balanced training across cancers at modest scale can achieve high performance and generalize to unseen tumor types. A multi‑cancer tumor localization model (MuCTaL) was trained on 79,984 non‑overlapping tiles from four cancers (melanoma, hepatocellular carcinoma, colorectal cancer, and non‑small cell lung cancer) using transfer learning with DenseNet169. The model achieved a tile‑level ROC‑AUC of 0.97 in validation data from the four training cancers, and 0.71 on an independent pancreatic ductal adenocarcinoma cohort. A scalable inference workflow was built to generate spatial tumor probability heatmaps compatible with existing digital pathology tools. Code and models are publicly available at https://github.com/AivaraX‑AI/MuCTaL.

PaperId: 2573, https://arxiv.org/pdf/2603.08835.pdf GitHub

Abstract:
The rapid adoption of LLM‑based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). Yet existing benchmarks are model‑centric: they fix the agentic setup and do not compare other system components. We argue that implementation decisions substantially impact performance, including choices such as topology, orchestration logic, and error handling. MASEval addresses this evaluation gap with a framework‑agnostic library that treats the entire system as the unit of analysis. Through a systematic system‑level comparison across 3 benchmarks, 3 models, and 3 frameworks, we find that framework choice matters as much as model choice. MASEval allows researchers to explore all components of agentic systems, opening new avenues for principled system design, and practitioners to identify the best implementation for their use case. MASEval is available under the MIT licence https://github.com/parameterlab/MASEval.

PaperId: 2574, https://arxiv.org/pdf/2603.08823.pdf GitHub

Abstract:
We introduce Fish Audio S2, an open‑sourced text‑to‑speech system featuring multi‑speaker, multi‑turn generation, and, most importantly, instruction‑following control via natural‑language descriptions. To scale training, we develop a multi‑stage training recipe together with a staged data pipeline covering video captioning and speech captioning, voice‑quality assessment, and reward modeling. To push the frontier of open‑source TTS, we release our model weights, fine‑tuning code, and an SGLang‑based inference engine. The inference engine is production‑ready for streaming, achieving an RTF of 0.195 and a time‑to‑first‑audio below 100 ms.Our code and weights are available on GitHub (https://github.com/fishaudio/fish‑speech) and Hugging Face (https://huggingface.co/fishaudio/s2‑pro). We highly encourage readers to visit https://fish.audio to try custom voices.

PaperId: 2575, https://arxiv.org/pdf/2603.08806.pdf GitHub GitHub

Abstract:
We present Test‑Driven AI Agent Definition (TDAD), a methodology that treats agent prompts as compiled artifacts: engineers provide behavioral specifications, a coding agent converts them into executable tests, and a second coding agent iteratively refines the prompt until tests pass. Deploying tool‑using LLM agents in production requires measurable behavioral compliance that current development practices cannot provide. Small prompt changes cause silent regressions, tool misuse goes undetected, and policy violations emerge only after deployment. To mitigate specification gaming, TDAD introduces three mechanisms: (1) visible/hidden test splits that withhold evaluation tests during compilation, (2) semantic mutation testing via a post‑compilation agent that generates plausible faulty prompt variants, with the harness measuring whether the test suite detects them, and (3) spec evolution scenarios that quantify regression safety when requirements change. We evaluate TDAD on SpecSuite‑Core, a benchmark of four deeply‑specified agents spanning policy compliance, grounded analytics, runbook adherence, and deterministic enforcement. Across 24 independent trials, TDAD achieves 92% v1 compilation success with 97% mean hidden pass rate; evolved specifications compile at 58%, with most failed runs passing all visible tests except 1‑2, and show 86‑100% mutation scores, 78% v2 hidden pass rate, and 97% regression safety scores. The implementation is available as an open benchmark at https://github.com/f‑labs‑io/tdad‑paper‑code.

PaperId: 2576, https://arxiv.org/pdf/2603.08755.pdf GitHub

Abstract:
We present Turn, a compiled, actor‑based programming language ‑‑ statically typed for schema inference, dynamically typed at the value level ‑‑ for agentic software: programs that reason and act autonomously by delegating inference to large language models (LLMs). Existing approaches augment general‑purpose languages with frameworks, encoding critical invariants (bounded context, typed inference output, credential isolation, durable state) as application‑level conventions rather than language guarantees. Turn introduces five language‑level constructs that address this gap. \emphCognitive Type Safety makes LLM inference a typed primitive: the compiler generates a JSON Schema from a struct definition and the VM validates model output before binding. The \emphconfidence operator enables deterministic control flow gated on model certainty. Turn's \emphactor‑based process model, derived from Erlang, gives each agent an isolated context window, persistent memory, and mailbox. A \emphcapability‑based identity system returns opaque, unforgeable handles from the VM host, ensuring raw credentials never enter agent memory. Finally, \emphcompile‑time schema absorption (\textttuse schema::<protocol>) synthesizes typed API bindings from external specifications at compile time; the \textttopenapi adapter is shipped with \textttgraphql, \textttfhir, and \textttmcp in active development. We describe the language design, type rules, schema semantics, and a Rust‑based bytecode VM, and evaluate Turn against representative agentic workloads. Turn is open source at https://github.com/ekizito96/Turn.

PaperId: 2577, https://arxiv.org/pdf/2603.08727.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly deployed in scenarios demanding ultra‑long context reasoning, such as agentic workflows and deep research understanding. However, long‑context inference is constrained by the KV cache, a transient memory structure that grows linearly with sequence length and batch size, quickly dominating GPU memory usage. Existing memory reduction techniques, including eviction and quantization, often rely on static heuristics and suffer from degraded quality under tight budgets. In this paper, we propose ARKV, a lightweight and adaptive framework that dynamically allocates precision levels to cached tokens based on per‑layer attention dynamics and token‑level importance. During a short prefill phase, ARKV estimates the original quantization (OQ) ratio of each layer by computing statistical scores such as attention entropy, variance and kurtosis. During decoding, tokens are assigned to one of three states, Original (full precision), Quantization (low precision), or Eviction, according to a fast heavy‑hitter scoring strategy. Our experiments on LLaMA3 and Qwen3 models across diverse long‑ and short‑context tasks demonstrate that ARKV preserves ~97% of baseline accuracy on long‑context benchmarks while reducing KV memory usage by 4x, with minimal throughput loss. On short‑context tasks, ARKV matches full‑precision baselines; on GSM8K math reasoning, it significantly outperforms uniform quantization. These results highlight the practical viability of ARKV for scalable LLM deployment, offering fine‑grained, data‑driven memory control without retraining or architectural modifications. The source code and artifacts can be found in: https://github.com/Large‑scale‑Sustainable‑Computing‑LSC/ARKV

PaperId: 2578, https://arxiv.org/pdf/2603.08709.pdf GitHub

Abstract:
Diffusion models degrade images through noise, and reversing this process reveals an information hierarchy across timesteps. Scale‑space theory exhibits a similar hierarchy via low‑pass filtering. We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images ‑ raising the question of why they must be processed at full resolution. To address this, we fuse scale spaces into the diffusion process by formulating a family of diffusion models with generalized linear degradations and practical implementations. Using downsampling as the degradation yields our proposed Scale Space Diffusion. To support Scale Space Diffusion, we introduce Flexi‑UNet, a UNet variant that performs resolution‑preserving and resolution‑increasing denoising using only the necessary parts of the network. We evaluate our framework on CelebA and ImageNet and analyze its scaling behavior across resolutions and network depths. Our project website ( https://prateksha.github.io/projects/scale‑space‑diffusion/ ) is available publicly.

PaperId: 2579, https://arxiv.org/pdf/2603.08706.pdf GitHub

Abstract:
Training large language models (LLMs) as autonomous agents often begins with imitation learning, but it only teaches agents what to do without understanding why: agents never contrast successful actions against suboptimal alternatives and thus lack awareness of action quality. Recent approaches attempt to address this by introducing self‑reflection supervision derived from contrasts between expert and alternative actions. However, the training paradigm fundamentally remains imitation learning: the model imitates pre‑constructed reflection text rather than learning to reason autonomously. We propose Agentic Critical Training (ACT), a reinforcement learning paradigm that trains agents to identify the better action among alternatives. By rewarding whether the model's judgment is correct, ACT drives the model to autonomously develop reasoning about action quality, producing genuine self‑reflection rather than imitating it. Across three challenging agent benchmarks, ACT consistently improves agent performance when combined with different post‑training methods. It achieves an average improvement of 5.07 points over imitation learning and 4.62 points over reinforcement learning. Compared to approaches that inject reflection capability through knowledge distillation, ACT also demonstrates clear advantages, yielding an average improvement of 2.42 points. Moreover, ACT enables strong out‑of‑distribution generalization on agentic benchmarks and improves performance on general reasoning benchmarks without any reasoning‑specific training data, highlighting the value of our method. These results suggest that ACT is a promising path toward developing more reflective and capable LLM agents.

PaperId: 2580, https://arxiv.org/pdf/2603.08652.pdf GitHub

Abstract:
Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text‑to‑image (T2I) generation, particularly through the integration of Chain‑of‑Thought (CoT) reasoning. However, existing CoT‑based T2I methods largely rely on abstract natural‑language planning, which lacks the precision required for complex spatial layouts, structured visual elements, and dense textual content. In this work, we propose CoCo (Code‑as‑CoT), a code‑driven reasoning framework that represents the reasoning process as executable code, enabling explicit and verifiable intermediate planning for image generation. Given a text prompt, CoCo first generates executable code that specifies the structural layout of the scene, which is then executed in a sandboxed environment to render a deterministic draft image. The model subsequently refines this draft through fine‑grained image editing to produce the final high‑fidelity result. To support this training paradigm, we construct CoCo‑10K, a curated dataset containing structured draft‑final image pairs designed to teach both structured draft construction and corrective visual refinement. Empirical evaluations on StructT2IBench, OneIG‑Bench, and LongText‑Bench show that CoCo achieves improvements of +68.83%, +54.8%, and +41.23% over direct generation, while also outperforming other generation methods empowered by CoT. These results demonstrate that executable code is an effective and reliable reasoning paradigm for precise, controllable, and structured text‑to‑image generation. The code is available at: https://github.com/micky‑li‑hd/CoCo

PaperId: 2581, https://arxiv.org/pdf/2603.08572.pdf GitHub

Abstract:
Learning natural, stable, and compositionally generalizable whole‑body control policies for humanoid robots performing simultaneous locomotion and manipulation (loco‑manipulation) remains a fundamental challenge in robotics. Existing reinforcement learning approaches typically rely on a single monolithic policy to acquire multiple skills, which often leads to cross‑skill gradient interference and motion pattern conflicts in high‑degree‑of‑freedom systems. As a result, generated behaviors frequently exhibit unnatural movements, limited stability, and poor generalization to complex task compositions. To address these limitations, we propose MetaWorld‑X, a hierarchical world model framework for humanoid control. Guided by a divide‑and‑conquer principle, our method decomposes complex control problems into a set of specialized expert policies (Specialized Expert Policies, SEP). Each expert is trained under human motion priors through imitation‑constrained reinforcement learning, introducing biomechanically consistent inductive biases that ensure natural and physically plausible motion generation. Building upon this foundation, we further develop an Intelligent Routing Mechanism (IRM) supervised by a Vision‑Language Model (VLM), enabling semantic‑driven expert composition. The VLM‑guided router dynamically integrates expert policies according to high‑level task semantics, facilitating compositional generalization and adaptive execution in multi‑stage loco‑manipulation tasks.

PaperId: 2582, https://arxiv.org/pdf/2603.08566.pdf GitHub

Abstract:
DARPA's AI Cyber Challenge (AIxCC) showed that cyber reasoning systems (CRSs) can go beyond vulnerability discovery to autonomously confirm and patch bugs: seven teams built such systems and open‑sourced them after the competition. Yet all seven open‑sourced CRSs remain largely unusable outside their original teams, each bound to the competition cloud infrastructure that no longer exists. We present OSS‑CRS, an open, locally deployable framework for running and combining CRS techniques against real‑world open‑source projects, with budget‑aware resource management. We ported the first‑place system (Atlantis) and discovered 10 previously unknown bugs (three of high severity) across 8 OSS‑Fuzz projects. OSS‑CRS is publicly available.

PaperId: 2583, https://arxiv.org/pdf/2603.08505.pdf GitHub

Abstract:
Electrocardiography (ECG) is a low‑cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity. However, it cannot directly measure cardiac morphological phenotypes, such as left ventricular ejection fraction (LVEF), which typically require echocardiography (Echo). Predicting these phenotypes from ECG would enable early, accessible health screening. Existing self‑supervised methods suffer from a representational mismatch by aligning ECGs to single‑view Echos, which only capture local, spatially restricted anatomical snapshots. To address this, we propose Echo2ECG, a multimodal self‑supervised learning framework that enriches ECG representations with the heart's morphological structure captured in multi‑view Echos. We evaluate Echo2ECG as an ECG feature extractor on two clinically relevant tasks that fundamentally require morphological information: (1) classification of structural cardiac phenotypes across three datasets, and (2) retrieval of Echo studies with similar morphological characteristics using ECG queries. Our extracted ECG representations consistently outperform those of state‑of‑the‑art unimodal and multimodal baselines across both tasks, despite being 18x smaller than the largest baseline. These results demonstrate that Echo2ECG is a robust, powerful ECG feature extractor. Our code is accessible at https://github.com/michelleespranita/Echo2ECG.

PaperId: 2584, https://arxiv.org/pdf/2603.08309.pdf GitHub

Abstract:
Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than semantically meaningful features. Existing regularization methods, typically relying on simple foreground‑background masks, which fail to capture the fine‑grained semantic concepts that define an object (e.g., ``long beak'' and ``wings'' for a ``bird''). As a result, these methods provide limited robustness to distribution shifts. To address this limitation, we introduce a novel finetuning framework that steers model reasoning toward concept‑level semantics. Our approach optimizes the model's internal relevance maps to align with spatially grounded concept masks. These masks are generated automatically, without manual annotation: class‑relevant concepts are first proposed using an LLM‑based, label‑free method, and then segmented using a VLM. The finetuning objective aligns relevance with these concept regions while simultaneously suppressing focus on spurious background areas. Notably, this process requires only a minimal set of images and uses half of the dataset classes. Extensive experiments on five out‑of‑distribution benchmarks demonstrate that our method improves robustness across multiple ViT‑based models. Furthermore, we show that the resulting relevance maps exhibit stronger alignment with semantic object parts, offering a scalable path toward more robust and interpretable vision models. Finally, we confirm that concept‑guided masks provide more effective supervision for model robustness than conventional segmentation maps, supporting our central hypothesis.

PaperId: 2585, https://arxiv.org/pdf/2603.08055.pdf GitHub

Abstract:
While recent feed‑forward 3D reconstruction models accelerate 3D reconstruction by jointly inferring dense geometry and camera poses in a single pass, their reliance on dense attention imposes a quadratic complexity, creating a prohibitive computational bottleneck that severely limits inference speed. To resolve this, we introduce Speed3R, an end‑to‑end trainable model inspired by the core principle of Structure‑from‑Motion: that a sparse set of keypoints is sufficient for robust pose estimation. Speed3R features a dual‑branch attention mechanism where a compression branch creates a coarse contextual prior to guide a selection branch, which performs fine‑grained attention only on the most informative image tokens. This strategy mimics the efficiency of traditional keypoint matching, achieving a remarkable 12.4x inference speedup on 1000‑view sequences, while introducing a minimal, controlled trade‑off in geometric accuracy. Validated on standard benchmarks with both VGGT and π^3 backbones, our method delivers high‑quality reconstructions at a fraction of computational cost, paving the way for efficient large‑scale scene modeling.

PaperId: 2586, https://arxiv.org/pdf/2603.08023.pdf GitHub

Abstract:
Dance is a form of human motion characterized by emotional expression and communication, playing a role in various fields such as music, virtual reality, and content creation. Existing methods for dance generation often fail to adequately capture the inherently sequential, rhythmical, and music‑synchronized characteristics of dance. In this paper, we propose \emphMambaDance, a new dance generation approach that leverages a Mamba‑based diffusion model. Mamba, well‑suited to handling long and autoregressive sequences, is integrated into our two‑stage diffusion architecture, substituting off‑the‑shelf Transformer. Additionally, considering the critical role of musical beats in dance choreography, we propose a Gaussian‑based beat representation to explicitly guide the decoding of dance sequences. Experiments on AIST++ and FineDance datasets for each sequence length show that our proposed method effectively generates plausible dance movements while reflecting essential characteristics, consistently from short to long dances, compared to the previous methods. Additional qualitative results and demo videos are available at \smallhttps://vision3d‑lab.github.io/mambadance.

PaperId: 2587, https://arxiv.org/pdf/2603.07946.pdf GitHub

Abstract:
Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model‑based methods excel at generating routine trajectories, they struggle to capture deviated mobility during large‑scale societal events. This limitation stems from two critical gaps: (1) the absence of event‑annotated mobility datasets for design and evaluation, and (2) the inability of current frameworks to reconcile competitions between users' habitual patterns and event‑imposed constraints when making trajectory decisions. This work addresses these gaps with a twofold contribution. First, we construct the first event‑annotated mobility dataset covering three major events: Typhoon Hagibis, COVID‑19, and the Tokyo 2021 Olympics. Second, we propose ELLMob, a self‑aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy‑Trace Theory, and then iteratively aligns them to generate trajectories that are both habitually grounded and event‑responsive. Extensive experiments show that ELLMob wins state‑of‑the‑art baselines across all events, demonstrating its effectiveness. Our codes and datasets are available at https://github.com/deepkashiwa20/ELLMob.

PaperId: 2588, https://arxiv.org/pdf/2603.07926.pdf GitHub

Abstract:
Test‑time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich representations of large pretrained models with minimal parameter updates remains underexplored. In this paper, we propose Intrinsic Mixture of Spectral Experts (IMSE) that leverages the spectral experts inherently embedded in Vision Transformers. We decompose each linear layer via singular value decomposition (SVD) and adapt only the singular values, while keeping the singular vectors fixed. We further identify a key limitation of entropy minimization in TTA: it often induces feature collapse, causing the model to rely on domain‑specific features rather than class‑discriminative features. To address this, we propose a diversity maximization loss based on expert‑input alignment, which encourages diverse utilization of spectral experts during adaptation. In the continual test‑time adaptation (CTTA) scenario, beyond preserving pretrained knowledge, it is crucial to retain and reuse knowledge from previously observed domains. We introduce Domain‑Aware Spectral Code Retrieval, which estimates input distributions to detect domain shifts, and retrieves adapted singular values for rapid adaptation. Consequently, our method achieves state‑of‑the‑art performance on various distribution‑shift benchmarks under the TTA setting. In CTTA and Gradual CTTA, it further improves accuracy by 3.4 percentage points (pp) and 2.4 pp, respectively, while requiring 385 times fewer trainable parameters. Our code is available at https://github.com/baek85/IMSE.

PaperId: 2589, https://arxiv.org/pdf/2603.07853.pdf GitHub

Abstract:
Research Agents enable models to gather information from the web using tools to answer user queries, requiring them to dynamically interleave internal reasoning with tool use. While such capabilities can in principle be learned via reinforcement learning with verifiable rewards (RLVR), we observe that agents often exhibit poor exploration behaviors, including premature termination and biased tool usage. As a result, RLVR alone yields limited improvements. We propose SynPlanResearch‑R1, a framework that synthesizes tool‑use trajectories that encourage deeper exploration to shape exploration during cold‑start supervised fine‑tuning, providing a strong initialization for subsequent RL. Across seven multi‑hop and open‑web benchmarks, \framework improves performance by up to 6.0% on Qwen3‑8B and 5.8% on Qwen3‑4B backbones respectively compared to SOTA baselines. Further analyses of tool‑use patterns and training dynamics compared to baselines shed light on the factors underlying these gains. Our code is publicly available at https://github.com/HansiZeng/syn‑plan‑research.

PaperId: 2590, https://arxiv.org/pdf/2603.07837.pdf GitHub

Abstract:
The AI Steerability 360 toolkit is an extensible, open‑source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modification of the prompt), structural (modification of the model's weights or architecture), state (modification of the model's activations and attentions), and output (modification of the decoding or generation process). Steering methods exert control on the model through a common interface, termed a steering pipeline, which additionally allows for the composition of multiple steering methods. Comprehensive evaluation and comparison of steering methods/pipelines is facilitated by use case classes (for defining tasks) and a benchmark class (for performance comparison on a given task). The functionality provided by the toolkit significantly lowers the barrier to developing and comprehensively evaluating steering methods. The toolkit is Hugging Face native and is released under an Apache 2.0 license at https://github.com/IBM/AISteer360.

PaperId: 2591, https://arxiv.org/pdf/2603.07766.pdf GitHub

Abstract:
We present our system for SemEval‑2026 Task 3 on dimensional aspect‑based sentiment regression. Our approach combines a hybrid RoBERTa encoder, which jointly predicts sentiment using regression and discretized classification heads, with large language models (LLMs) via prediction‑level ensemble learning. The hybrid encoder improves prediction stability by combining continuous and discretized sentiment representations. We further explore in‑context learning with LLMs and ridge‑regression stacking to combine encoder and LLM predictions. Experimental results on the development set show that ensemble learning significantly improves performance over individual models, achieving substantial reductions in RMSE and improvements in correlation scores. Our findings demonstrate the complementary strengths of encoder‑based and LLM‑based approaches for dimensional sentiment analysis. Our development code and resources will be shared at https://github.com/aaronlifenghan/ABSentiment

PaperId: 2592, https://arxiv.org/pdf/2603.07700.pdf GitHub GitHub

Abstract:
While few‑step generative models have enabled powerful image and video generation at significantly lower cost, generic reinforcement learning (RL) paradigms for few‑step models remain an unsolved problem. Existing RL approaches for few‑step diffusion models strongly rely on back‑propagating through differentiable reward models, thereby excluding the majority of important real‑world reward signals, e.g., non‑differentiable rewards such as humans' binary likeness, object counts, etc. To properly incorporate non‑differentiable rewards to improve few‑step generative models, we introduce TDM‑R1, a novel reinforcement learning paradigm built upon a leading few‑step model, Trajectory Distribution Matching (TDM). TDM‑R1 decouples the learning process into surrogate reward learning and generator learning. Furthermore, we developed practical methods to obtain per‑step reward signals along the deterministic generation trajectory of TDM, resulting in a unified RL post‑training method that significantly improves few‑step models' ability with generic rewards. We conduct extensive experiments ranging from text‑rendering, visual quality, and preference alignment. All results demonstrate that TDM‑R1 is a powerful reinforcement learning paradigm for few‑step text‑to‑image models, achieving state‑of‑the‑art reinforcement learning performances on both in‑domain and out‑of‑domain metrics. Furthermore, TDM‑R1 also scales effectively to the recent strong Z‑Image model, consistently outperforming both its 100‑NFE and few‑step variants with only 4 NFEs. Project page: https://github.com/Luo‑Yihong/TDM‑R1

PaperId: 2593, https://arxiv.org/pdf/2603.07694.pdf GitHub

Abstract:
In bandwidth‑limited online video streaming, videos are usually downsampled and compressed. Although recent online video super‑resolution (online VSR) approaches achieve promising results, they are still compute‑intensive and fall short of real‑time processing at higher resolutions, due to complex motion estimation for alignment and redundant processing of consecutive frames. To address these issues, we propose a compressed‑domain‑aware network (CDA‑VSR) for online VSR, which utilizes compressed‑domain information, including motion vectors, residual maps, and frame types to balance quality and efficiency. Specifically, we propose a motion‑vector‑guided deformable alignment module that uses motion vectors for coarse warping and learns only local residual offsets for fine‑tuned adjustments, thereby maintaining accuracy while reducing computation. Then, we utilize a residual map gated fusion module to derive spatial weights from residual maps, suppressing mismatched regions and emphasizing reliable details. Further, we design a frame‑type‑aware reconstruction module for adaptive compute allocation across frame types, balancing accuracy and efficiency. On the REDS4 dataset, our CDA‑VSR surpasses the state‑of‑the‑art method TMP, with a maximum PSNR improvement of 0.13 dB while delivering more than double the inference speed. The code will be released at https://github.com/sspBIT/CDA‑VSR.

PaperId: 2594, https://arxiv.org/pdf/2603.07648.pdf GitHub GitHub

Abstract:
Recent advances in Visual‑Language‑Action (VLA) models have shown promising potential for robotic manipulation tasks. However, real‑world robotic tasks often involve long‑horizon, multi‑step problem‑solving and require generalization for continual skill acquisition, extending beyond single actions or skills. These challenges present significant barriers for existing VLA models, which use monolithic action decoders trained on aggregated data, resulting in poor scalability. To address these challenges, we propose AtomicVLA, a unified planning‑and‑execution framework that jointly generates task‑level plans, atomic skill abstractions, and fine‑grained actions. AtomicVLA constructs a scalable atomic skill library through a Skill‑Guided Mixture‑of‑Experts (SG‑MoE), where each expert specializes in mastering generic yet precise atomic skills. Furthermore, we introduce a flexible routing encoder that automatically assigns dedicated atomic experts to new skills, enabling continual learning. We validate our approach through extensive experiments. In simulation, AtomicVLA outperforms π_0 by 2.4% on LIBERO, 10% on LIBERO‑LONG, and outperforms π_0 and π_0.5 by 0.22 and 0.25 in average task length on CALVIN. Additionally, our AtomicVLA consistently surpasses baselines by 18.3% and 21% in real‑world long‑horizon tasks and continual learning. These results highlight the effectiveness of atomic skill abstraction and dynamic expert composition for long‑horizon and lifelong robotic tasks. The project page is \hrefhttps://zhanglk9.github.io/atomicvla‑web/here.

PaperId: 2595, https://arxiv.org/pdf/2603.07487.pdf GitHub

Abstract:
Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi‑stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end‑to‑end system to jointly optimize three‑stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in‑domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach could serve as a strong joint baseline for future research. The code is publicly available.

PaperId: 2596, https://arxiv.org/pdf/2603.07452.pdf GitHub

Abstract:
Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same mechanism ‑‑ the conditional activation of specific behaviors through input triggers ‑‑ can also serve as a controllable and auditable interface for trustworthy model behavior. In this work, we present Backdoor4Good (B4G), a unified benchmark and framework for beneficial backdoor applications in large language models (LLMs). Unlike conventional backdoor studies focused on attacks and defenses, B4G repurposes backdoor conditioning for Beneficial Tasks that enhance safety, controllability, and accountability. It formalizes beneficial backdoor learning under a triplet formulation (T, A, U), representing the \emphTrigger, \emphActivation mechanism, and \emphUtility function, and implements a benchmark covering four trust‑centric applications. Through extensive experiments across Llama3.1‑8B, Gemma‑2‑9B, Qwen2.5‑7B, and Llama2‑13B, we show that beneficial backdoors can achieve high controllability, tamper‑resistance, and stealthiness while preserving clean‑task performance. Our findings demonstrate new insights that backdoors need not be inherently malicious; when properly designed, they can serve as modular, interpretable, and beneficial building blocks for trustworthy AI systems. Our code and datasets are available at https://github.com/bboylyg/BackdoorLLM/B4G.

PaperId: 2597, https://arxiv.org/pdf/2603.07449.pdf GitHub

Abstract:
Enterprises commonly deploy heterogeneous database systems, each of which owns a distinct SQL dialect with different syntax rules, built‑in functions, and execution constraints. However, most existing NL2SQL methods assume a single dialect (e.g., SQLite) and struggle to produce queries that are both semantically correct and executable on target engines. Prompt‑based approaches tightly couple intent reasoning with dialect syntax, rule‑based translators often degrade native operators into generic constructs, and multi‑dialect fine‑tuning suffers from cross‑dialect interference. In this paper, we present Dial, a knowledge‑grounded framework for dialect‑specific NL2SQL. Dial introduces: (1) a Dialect‑Aware Logical Query Planning module that converts natural language into a dialect‑aware logical query plan via operator‑level intent decomposition and divergence‑aware specification; (2) HINT‑KB, a hierarchical intent‑aware knowledge base that organizes dialect knowledge into (i) a canonical syntax reference, (ii) a declarative function repository, and (iii) a procedural constraint repository; and (3) an execution‑driven debugging and semantic verification loop that separates syntactic recovery from logic auditing to prevent semantic drift. We construct DS‑NL2SQL, a benchmark covering six major database systems with 2,218 dialect‑specific test cases. Experimental results show that Dial consistently improves translation accuracy by 10.25% and dialect feature coverage by 15.77% over state‑of‑the‑art baselines. The code is at https://github.com/weAIDB/Dial.

PaperId: 2598, https://arxiv.org/pdf/2603.07427.pdf GitHub GitHub

Abstract:
As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade‑off: manual benchmarks are costly, while LLM‑based simulators are scalable but suffer from logic hallucination. We present AutoControl Arena, an automated framework for frontier AI risk evaluation built on the principle of logic‑narrative decoupling. By grounding deterministic state in executable code while delegating generative dynamics to LLMs, we mitigate hallucination while maintaining flexibility. This principle, instantiated through a three‑agent framework, achieves over 98% end‑to‑end success and 60% human preference over existing simulators. To elicit latent risks, we vary environmental Stress and Temptation across X‑Bench (70 scenarios, 7 risk categories). Evaluating 9 frontier models reveals: (1) Alignment Illusion: risk rates surge from 21.7% to 54.5% under pressure, with capable models showing disproportionately larger increases; (2) Scenario‑Specific Safety Scaling: advanced reasoning improves robustness for direct harms but worsens it for gaming scenarios; and (3) Divergent Misalignment Patterns: weaker models cause non‑malicious harm while stronger models develop strategic concealment.

PaperId: 2599, https://arxiv.org/pdf/2603.07404.pdf GitHub

Abstract:
Vision language action models (VLAs) are increasingly used for Physical AI, but deploying a pre‑trained VLA model to unseen environments, embodiments, or tasks still requires adaptation. Parameter‑efficient fine‑tuning (PEFT), especially LoRA, is common for VLA policies, yet the exposed capacity knob, the rank, does not transfer uniformly: robotics transfer exhibits a higher and task‑varying intrinsic rank than language fine‑tuning. Small ranks suffice for LLMs (e.g., r \in \4, 8\), while spectral analyses indicate VLAs may require much larger ranks (e.g., r \approx 128) or near‑full rank, a mismatch that worsens in multi‑task settings. We present LoRA‑SP (Select‑Prune), a rank‑adaptive fine‑tuning method that replaces fixed‑rank updates with input‑ and layer‑wise capacity. LoRA‑SP uses an SVD‑style parameterization with a small router whose nonnegative scores act as singular values over a shared vector bank. The active set is chosen by an energy target on the cumulative squared scores E(k) \ge η, providing a direct link to approximation error via our spectral analysis. During training, η concentrates energy on a few directions and teaches the router to rely on fewer vectors while preserving accuracy. This yields compact adapters that reduce cross‑task interference and improve generalization. On four real‑robot manipulation tasks collected on an unseen AgileX PiPER arm, across two VLA backbones (π_0 and SmolVLA), LoRA‑SP matches or exceeds full fine‑tuning with far fewer trainable parameters, and improves multi‑task success by up to 31.6% over standard LoRA while remaining robust to rank choice.

PaperId: 2600, https://arxiv.org/pdf/2603.07343.pdf GitHub

Abstract:
Concept Bottleneck Models (CBMs) aim for ante‑hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State‑of‑the‑art approaches typically select which concepts to learn via human specification, open knowledge graphs, prompting an LLM, or using general CLIP concepts. However, concepts defined a‑priori may not have sufficient predictive power for the task or even be learnable from the available data. As a result, these CBMs often significantly trail their black‑box counterpart when controlling for information leakage. To address this, we introduce a novel CBM pipeline named Mechanistic CBM (M‑CBM), which builds the bottleneck directly from a black‑box model's own learned concepts. These concepts are extracted via Sparse Autoencoders (SAEs) and subsequently named and annotated on a selected subset of images using a Multimodal LLM. For fair comparison and leakage control, we also introduce the Number of Contributing Concepts (NCC), a decision‑level sparsity metric that extends the recently proposed NEC metric. Across diverse datasets, we show that M‑CBMs consistently surpass prior CBMs at matched sparsity, while improving concept predictions and providing concise explanations. Our code is available at https://github.com/Antonio‑Dee/M‑CBM.

PaperId: 2601, https://arxiv.org/pdf/2603.07335.pdf GitHub

Abstract:
High‑performing vision language models still produce incorrect answers, yet their failure modes are often difficult to explain. To make model internals more accessible and enable systematic debugging, we introduce VisualScratchpad, an interactive interface for visual concept analysis during inference. We apply sparse autoencoders to the vision encoder and link the resulting visual concepts to text tokens via text‑to‑image attention, allowing us to examine which visual concepts are both captured by the vision encoder and utilized by the language model. VisualScratchpad also provides a token‑latent heatmap view that suggests a sufficient set of latents for effective concept ablation in causal analysis. Through case studies, we reveal three underexplored failure modes: limited cross‑modal alignment, misleading visual concepts, and unused hidden cues. Project page: https://hyesulim.github.io/visual_scratchpad_projectpage/

PaperId: 2602, https://arxiv.org/pdf/2603.07138.pdf GitHub

Abstract:
Emotion Recognition in Conversation (ERC) is critical for enabling natural human‑machine interactions. However, existing methods predominantly employ categorical or dimensional emotion annotations, which often fail to adequately represent complex, subtle, or culturally specific emotional nuances. To overcome this limitation, we propose a novel task named Emotion Transcription in Conversation (ETC). This task focuses on generating natural language descriptions that accurately reflect speakers' emotional states within conversational contexts. To address the ETC, we constructed a Japanese dataset comprising text‑based dialogues annotated with participants' self‑reported emotional states, described in natural language. The dataset also includes emotion category labels for each transcription, enabling quantitative analysis and its application to ERC. We benchmarked baseline models, finding that while fine‑tuning on our dataset enhances model performance, current models still struggle to infer implicit emotional states. The ETC task will encourage further research into more expressive emotion understanding in dialogue. The dataset is publicly available at https://github.com/UEC‑InabaLab/ETCDataset.

PaperId: 2603, https://arxiv.org/pdf/2603.07084.pdf GitHub

Abstract:
Reward hacking is a form of misalignment in which models overoptimize proxy rewards without genuinely solving the underlying task. Precisely measuring reward hacking occurrence remains challenging because true task rewards are often expensive or impossible to compute. We introduce Countdown‑Code, a minimal environment where models can both solve a mathematical reasoning task and manipulate the test harness. This dual‑access design creates a clean separation between proxy rewards (test pass/fail) and true rewards (mathematical correctness), enabling accurate measurement of reward‑hacking rates. Using this environment, we study reward hacking in open‑weight LLMs and find that such behaviors can be unintentionally learned during supervised fine‑tuning (SFT) when even a small fraction of reward‑hacking trajectories leak into training data. As little as 1% contamination in distillation SFT data is sufficient for models to internalize reward hacking which resurfaces during subsequent reinforcement learning (RL). We further show that RL amplifies misalignment and drives its generalization beyond the original domain. We open‑source our environment and code to facilitate future research on reward hacking in LLMs. Our results reveal a previously underexplored pathway through which reward hacking can emerge and persist in LLMs, underscoring the need for more rigorous validation of synthetic SFT data. Code is available at https://github.com/zohaib‑khan5040/Countdown‑Code.

PaperId: 2604, https://arxiv.org/pdf/2603.07066.pdf GitHub

Abstract:
Generative diffusion models are increasingly used for medical imaging data augmentation, but text prompting cannot produce causal training data. Re‑prompting rerolls the entire generation trajectory, altering anatomy, texture, and background. Inversion‑based editing methods introduce reconstruction error that causes structural drift. We propose MedSteer, a training‑free activation‑steering framework for endoscopic synthesis. MedSteer identifies a pathology vector for each contrastive prompt pair in the cross‑attention layers of a diffusion transformer. At inference time, it steers image activations along this vector, generating counterfactual pairs from scratch where the only difference is the steered concept. All other structure is preserved by construction. We evaluate MedSteer across three experiments on Kvasir v3 and HyperKvasir. On counterfactual generation across three clinical concept pairs, MedSteer achieves flip rates of 0.800, 0.925, and 0.950, outperforming the best inversion‑based baseline in both concept flip rate and structural preservation. On dye disentanglement, MedSteer achieves 75% dye removal against 20% (PnP) and 10% (h‑Edit). On downstream polyp detection, augmenting with MedSteer counterfactual pairs achieves ViT AUC of 0.9755 versus 0.9083 for quantity‑matched re‑prompting, confirming that counterfactual structure drives the gain. Code is at link https://github.com/phamtrongthang123/medsteer

PaperId: 2605, https://arxiv.org/pdf/2603.07039.pdf GitHub

Abstract:
We present DeepEarth, a self‑supervised multi‑modal world model with Earth4D, a novel planetary‑scale 4D space‑time positional encoder. Earth4D extends 3D multi‑resolution hash encoding to include time, efficiently scaling across the planet over centuries with sub‑meter, sub‑second precision. Multi‑modal encoders (e.g. vision‑language models) are fused with Earth4D embeddings and trained via masked reconstruction. We demonstrate Earth4D's expressive power by achieving state‑of‑the‑art performance on an ecological forecasting benchmark. Earth4D with learnable hash probing surpasses a multi‑modal foundation model pre‑trained on substantially more data. Access open source code and download models at: https://github.com/legel/deepearth

PaperId: 2606, https://arxiv.org/pdf/2603.06950.pdf GitHub

Abstract:
DNA foundation models have become transformative tools in bioinformatics and healthcare applications. Trained on vast genomic datasets, these models can be used to generate sequence embeddings, dense vector representations that capture complex genomic information. These embeddings are increasingly being shared via Embeddings‑as‑a‑Service (EaaS) frameworks to facilitate downstream tasks, while supposedly protecting the privacy of the underlying raw sequences. However, as this practice becomes more prevalent, the security of these representations is being called into question. This study evaluates the resilience of DNA foundation models to model inversion attacks, whereby adversaries attempt to reconstruct sensitive training data from model outputs. In our study, the model's output for reconstructing the DNA sequence is a zero‑shot embedding, which is then fed to a decoder. We evaluated the privacy of three DNA foundation models: DNABERT‑2, Evo 2, and Nucleotide Transformer v2 (NTv2). Our results show that per‑token embeddings allow near‑perfect sequence reconstruction across all models. For mean‑pooled embeddings, reconstruction quality degrades as sequence length increases, though it remains substantially above random baselines. Evo 2 and NTv2 prove to be most vulnerable, especially for shorter sequences with reconstruction similarities > 90%, while DNABERT‑2's BPE tokenization provides the greatest resilience. We found that the correlation between embedding similarity and sequence similarity was a key predictor of reconstruction success. Our findings emphasize the urgent need for privacy‑aware design in genomic foundation models prior to their widespread deployment in EaaS settings. Training code, model weights and evaluation pipeline are released on: https://github.com/not‑a‑feature/DNA‑Embedding‑Inversion.

PaperId: 2607, https://arxiv.org/pdf/2603.06859.pdf GitHub

Abstract:
Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi‑agent LLM systems this evaluation distorts the result it claims to measure. This failure is not isolated: learned critics, trajectory‑level baselines, and agent‑removal counterfactuals all inherit from standard multi‑agent reinforcement learning a premise that exact counterfactual evaluation requires privileged environment access, and therefore approximate. In cooperative LLM systems, this premise is false. Interaction histories are deterministic functions of observable text with no hidden state, so any decision point can be restored exactly, making direct causal measurement possible without parametric approximation. C3 exploits this property by fixing the complete history at each decision point, sampling alternative actions under a frozen behavior policy, and computing unbiased per‑decision advantages through a parameter‑free leave‑one‑out baseline. Across six benchmarks spanning math reasoning and code generation, two model families, and two multi‑agent topologies, C3 consistently outperforms all baselines; a controlled decomposition confirms gains originate from credit quality, not architecture, while checkpoint restoration reduces training token consumption. The exact solution proves simpler, cheaper, and more effective than all approximate alternatives. The same structural property that enables exact credit also enables exact verification: three independently computable diagnostics, credit fidelity, within‑group variance, and inter‑agent influence, constitute the first method‑agnostic auditing tool for multi‑agent LLM credit assignment. Our code is available at https://github.com/EIT‑EAST‑Lab/C3

PaperId: 2608, https://arxiv.org/pdf/2603.06781.pdf GitHub

Abstract:
Evaluating time series attribution methods is difficult because real‑world datasets rarely provide ground truth for which time points drive a prediction. A common workaround is to generate synthetic data where class‑discriminating features are placed at known locations, but each study currently reimplements this from scratch. We introduce xaitimesynth, a Python package that provides reusable infrastructure for this evaluation approach. The package generates synthetic time series following an additive model where each sample is a sum of background signal and a localized, class‑discriminating feature, with the feature window automatically tracked as a ground truth mask. A fluent data generation API and YAML configuration format allow flexible and reproducible dataset definitions for both univariate and multivariate time series. The package also provides standard localization metrics, including AUC‑PR, AUC‑ROC, Relevance Mass Accuracy, and Relevance Rank Accuracy. xaitimesynth is open source and available at https://github.com/gregorbaer/xaitimesynth.

PaperId: 2609, https://arxiv.org/pdf/2603.06780.pdf GitHub

Abstract:
Spatial transcriptomics (ST) enables mapping gene expression with spatial context but is severely affected by high sparsity and technical noise, which conceals true biological signals and hinders downstream analyses. To address these challenges, SpatialMagic was proposed, which is a hybrid imputation model combining MAGIC‑based graph diffusion with transformer‑based spatial self‑attention. The long‑range dependencies in the gene expression are captured by graph diffusion, and local neighborhood structure is captured by spatial attention models, which allow for recovering the missing expression values, retaining spatial consistency. Across multiple platforms, SpatialMagic consistently outperforms existing baselines, including MAGIC and attention‑based models, achieving peak Adjusted Rand Index (ARI) scores in clustering accuracy of 0.3301 on high‑resolution Stereo‑Seq data, 0.3074 on Slide‑Seq, and 0.4216 on the Sci‑Space dataset. Beyond quantitative improvements, SpatialMagic substantially enhances downstream biological analyses by improving the detection of both up‑ and down‑regulated genes while maintaining regulatory consistency across datasets. The pathway enrichment analysis of the recovered genes indicates that they are involved in consistent processes across key metabolic, transport, and neural signaling pathways, suggesting that the framework improves data quality while preserving biological interpretability. Overall, SpatialMagic's hybrid diffusion attention strategy and refinement module outperform state‑of‑the‑art baselines on quantitative metrics and provide a better understanding of the imputed data by preserving tissue architecture and uncovering biologically relevant genes. The source code and datasets are provided in the following link: https://github.com/sayeemzzaman/SpatialMAGIC

PaperId: 2610, https://arxiv.org/pdf/2603.06729.pdf GitHub GitHub

Abstract:
Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning‑based crowd navigation can break under out‑of‑distribution crowd sizes due to density‑sensitive observation normalization and social‑cost scaling, while analytical solvers often remain safe but freeze in tight interactions. We propose a reinforcement learning approach for dense, variable‑density navigation that attains zero‑shot density generalization using a density‑invariant observation encoding with density‑randomized training and physics‑informed proxemic reward shaping with density‑adaptive scaling. The encoding represents the distance‑sorted K nearest pedestrians plus bounded crowd summaries, keeping input statistics stable as crowd size grows. Trained with N\!\in\![11,16] pedestrians in a 3\mathrmm×3\mathrmm arena and evaluated up to N\!=\!21 pedestrians (1.3× denser), our policy reaches the goal in >99% of episodes and achieves 86% collision‑free success in random crowds, with markedly less freezing than analytical methods and a >\!60‑point collision‑free margin over learning‑based benchmark methods. Codes are available at \hrefhttps://github.com/jznmsl/PSS‑Socialhttps://github.com/jznmsl/PSS‑Social.

PaperId: 2611, https://arxiv.org/pdf/2603.06680.pdf GitHub

Abstract:
We present VB, a benchmark that tests whether vision‑language models can determine what is and is not visible in a photograph, and abstain when a human viewer cannot reliably answer. Each item pairs a single photo with a short yes/no visibility claim; the model must output VISIBLY_TRUE, VISIBLY_FALSE, or ABSTAIN, together with a confidence score. Items are organized into 100 families using a 2x2 design that crosses a minimal image edit with a minimal text edit, yielding 300 headline evaluation cells. Unlike prior unanswerable‑VQA benchmarks, VB tests not only whether a question is unanswerable but why (via reason codes tied to specific visibility factors), and uses controlled minimal edits to verify that model judgments change when and only when the underlying evidence changes. We score models on confidence‑aware accuracy with abstention (CAA), minimal‑edit flip rate (MEFR), confidence‑ranked selective prediction (SelRank), and second‑order perspective reasoning (ToMAcc); all headline numbers are computed on the strict XOR subset (three cells per family, 300 scored items per model). We evaluate nine models spanning flagship and prior‑generation closed‑source systems, and open‑source models from 8B to 12B parameters. GPT‑4o and Gemini 3.1 Pro effectively tie for the best composite score (0.728 and 0.727), followed by Gemini 2.5 Pro (0.678). The best open‑source model, Gemma 3 12B (0.505), surpasses one prior‑generation closed‑source system. Text‑flip robustness exceeds image‑flip robustness for six of nine models, and confidence calibration varies substantially: GPT‑4o and Gemini 2.5 Pro achieve similar accuracy yet differ sharply in selective prediction quality.

PaperId: 2612, https://arxiv.org/pdf/2603.06674.pdf GitHub

Abstract:
High‑quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, stylistic controllability, and efficiency. We present AutoFigure‑Edit, an end‑to‑end system that generates fully editable scientific illustrations from long‑form scientific text while enabling flexible style adaptation through user‑provided reference images. By combining long‑context understanding, reference‑guided styling, and native SVG editing, it enables efficient creation and refinement of high‑quality scientific illustrations. To facilitate further progress in this field, we release the video at https://youtu.be/10IH8SyJjAQ, full codebase at https://github.com/ResearAI/AutoFigure‑Edit and provide a website for easy access and interactive use at https://deepscientist.cc/.

PaperId: 2613, https://arxiv.org/pdf/2603.06665.pdf GitHub GitHub

Abstract:
Large vision‑language models (VLMs) often benefit from chain‑of‑thought (CoT) prompting in general domains, yet its efficacy in medical vision‑language tasks remains underexplored. We report a counter‑intuitive trend: on medical visual question answering, CoT frequently underperforms direct answering (DirA) across general‑purpose and medical‑specific models. We attribute this to a \emphmedical perception bottleneck: subtle, domain‑specific cues can weaken visual grounding, and CoT may compound early perceptual uncertainty rather than correct it. To probe this hypothesis, we introduce two training‑free, inference‑time grounding interventions: (i) \emphperception anchoring via region‑of‑interest cues and (ii) \emphdescription grounding via high‑quality textual guidance. Across multiple benchmarks and model families, these interventions improve accuracy, mitigate CoT degradation, and in several settings reverse the CoT‑‑DirA inversion. Our findings suggest that reliable clinical VLMs require robust visual grounding and cross‑modal alignment, beyond extending text‑driven reasoning chains. Code is available \hrefhttps://github.com/TianYin123/Better_Eyes_Better_Thoughtshere.

PaperId: 2614, https://arxiv.org/pdf/2603.06656.pdf GitHub

Abstract:
Human gameplay is a visually grounded interaction loop in which players act, reflect on failures, and watch tutorials to refine strategies. Can Vision‑Language Models (VLMs) also learn from video‑based reflection? We present GameVerse, a comprehensive video game benchmark that enables a reflective visual interaction loop. Moving beyond traditional fire‑and‑forget evaluations, it uses a novel reflect‑and‑retry paradigm to assess how VLMs internalize visual experience and improve policies. To facilitate systematic and scalable evaluation, we also introduce a cognitive hierarchical taxonomy spanning 15 globally popular games, dual action space for both semantic and GUI control, and milestone evaluation using advanced VLMs to quantify progress. Our experiments show that VLMs benefit from video‑based reflection in varied settings, and perform best by combining failure trajectories and expert tutorials‑a training‑free analogue to reinforcement learning (RL) plus supervised fine‑tuning (SFT).Our project page is available at https://gameverse‑bench.github.io/ . Our code is available at https://github.com/THUSI‑Lab/GameVerse .

PaperId: 2615, https://arxiv.org/pdf/2603.06642.pdf GitHub

Abstract:
Test‑Time Training (TTT) language models achieve theoretically infinite context windows with an O(1) memory footprint by replacing the standard exact‑attention KV‑cache with hidden state ``fast weights'' W_fast updated via self‑supervised learning during inference. However, pure TTT architectures suffer catastrophic failures on exact‑recall tasks (e.g., Needle‑in‑a‑Haystack). Because the fast weights aggressively compress the context into an information bottleneck, highly surprising or unique tokens are rapidly overwritten and forgotten by subsequent token gradient updates. We introduce SR‑TTT (Surprisal‑Aware Residual Test‑Time Training), which resolves this recall failure by augmenting the TTT backbone with a loss‑gated sparse memory mechanism. By dynamically routing only incompressible, highly surprising tokens to a traditional exact‑attention Residual Cache, SR‑TTT preserves O(1) memory for low‑entropy background context while utilizing exact attention exclusively for critical needles. Our complete implementation, training scripts, and pre‑trained weights are open‑source and available at: https://github.com/swamynathanvp/Surprisal‑Aware‑Residual‑Test‑Time‑Training.

PaperId: 2616, https://arxiv.org/pdf/2603.06636.pdf GitHub

Abstract:
Due to the strong context‑awareness capabilities demonstrated by large language models (LLMs), recent research has begun exploring their integration into smart home assistants to help users manage and adjust their living environments. While LLMs have been shown to effectively understand user needs and provide appropriate responses, most existing studies primarily focus on interpreting and executing user behaviors or instructions. However, a critical function of smart home assistants is the ability to detect when the home environment is in an anomalous state. This involves two key requirements: the LLM must accurately determine whether an anomalous condition is present, and provide either a clear explanation or actionable suggestions. To enhance the anomaly detection capabilities of next‑generation LLM‑based smart home assistants, we introduce SmartBench, which is the first smart home dataset designed for LLMs, containing both normal and anomalous device states as well as normal and anomalous device state transition contexts. We evaluate 13 mainstream LLMs on this benchmark. The experimental results show that most state‑of‑the‑art models cannot achieve good anomaly detection performance. For example, Claude‑Sonnet‑4.5 achieves only 66.1% detection accuracy on context‑independent anomaly categories, and performs even worse on context‑dependent anomalies, with an accuracy of only 57.8%. More experimental results suggest that next‑generation LLM‑based smart home assistants are still far from being able to effectively detect and handle anomalous conditions in the smart home environment. Our dataset is publicly available at https://github.com/horizonsinzqs/SmartBench.

PaperId: 2617, https://arxiv.org/pdf/2603.06626.pdf GitHub

Abstract:
Traditional Mixture‑of‑Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads to sluggish convergence and training instabilities. This paper introduces Grouter, a preemptive routing method that by distilling high‑quality structures from fully‑trained MoE models and serving as a fixed router for target models. By decoupling structural optimization from weight updates, Grouter significantly accelerates both the speed and quality of model convergence. To ensure the framework's versatility, we also introduce expert folding to adapt Grouter across varying model configurations and expert tuning to rebalance workloads across different data distributions. Furthermore, by leveraging the structural priors provided by preemptive routing, we can implement targeted optimizations to further enhance training throughput. Experiments demonstrate that Grouter achieves superior performance and efficiency which boosts pre‑training data utilization by 4.28x and achieves up to 33.5% throughput acceleration, establishing preemptive routing as a fundamental paradigm for scalable MoE training. We publicly release our code and pretrained Grouter checkpoints at https://github.com/JimmyAwoe/Grouter.

PaperId: 2618, https://arxiv.org/pdf/2603.06620.pdf GitHub GitHub

Abstract:
The growing demand for automated graph algorithm reasoning has attracted increasing attention in the large language model (LLM) community. Recent LLM‑based graph reasoning methods typically decouple task descriptions from graph data, generate executable code augmented by retrieval from technical documentation, and refine the code through debugging. However, we identify two key limitations in existing approaches: (i) they treat technical documentation as flat text collections and ignore its hierarchical structure, leading to noisy retrieval that degrades code generation quality; and (ii) their debugging mechanisms focus primarily on runtime errors, yet ignore more critical logical errors. To address them, we propose \method, an agentic hierarchical retrieval‑augmented coding framework that exploits the document hierarchy through top‑down traversal and early pruning, together with a self‑debugging coding agent that iteratively refines code using automatically generated small‑scale test cases. To enable comprehensive evaluation of complex graph reasoning, we introduce a new dataset, \dataset, covering small‑scale, large‑scale, and composite graph reasoning tasks. Extensive experiments demonstrate that our method achieves higher task accuracy and lower inference cost compared to baselines\footnoteThe code is available at \hrefhttps://github.com/FairyFali/GraphSkill\textcolorbluehttps://github.com/FairyFali/GraphSkill..

PaperId: 2619, https://arxiv.org/pdf/2603.06594.pdf GitHub

Abstract:
Automated \enquoteLLM‑as‑a‑Judge frameworks have become the de facto standard for scalable evaluation across natural language processing. For instance, in safety evaluation, these judges are relied upon to evaluate harmfulness in order to benchmark the robustness of safety against adversarial attacks. However, we show that existing validation protocols fail to account for substantial distribution shifts inherent to red‑teaming: diverse victim models exhibit distinct generation styles, attacks distort output patterns, and semantic ambiguity varies significantly across jailbreak scenarios. Through a comprehensive audit using 6642 human‑verified labels, we reveal that the unpredictable interaction of these shifts often causes judge performance to degrade to near random chance. This stands in stark contrast to the high human agreement reported in prior work. Crucially, we find that many attacks inflate their success rates by exploiting judge insufficiencies rather than eliciting genuinely harmful content. To enable more reliable evaluation, we propose ReliableBench, a benchmark of behaviors that remain more consistently judgeable, and JudgeStressTest, a dataset designed to expose judge failures. Data available at: https://github.com/SchwinnL/LLMJudgeReliability.

PaperId: 2620, https://arxiv.org/pdf/2603.06573.pdf GitHub

Abstract:
Obstacle avoidance in unmanned aerial vehicles (UAVs), as a fundamental capability, has gained increasing attention with the growing focus on spatial intelligence. However, current obstacle‑avoidance methods mainly depend on limited field‑of‑view sensors and are ill‑suited for UAV scenarios which require full‑spatial awareness when the movement direction differs from the UAV's heading. This limitation motivates us to explore omnidirectional obstacle avoidance for panoramic drones with full‑view perception. We first study an under explored problem setting in which a UAV must generate collision‑free motion in environments with obstacles from arbitrary directions, and then construct a benchmark that consists of three representative flight tasks. Based on such settings, we propose Fly360, a two‑stage perception‑decision pipeline with a fixed random‑yaw training strategy. At the perception stage, panoramic RGB observations are input and converted into depth maps as a robust intermediate representation. For the policy network, it is lightweight and used to output body‑frame velocity commands from depth inputs. Extensive simulation and real‑world experiments demonstrate that Fly360 achieves stable omnidirectional obstacle avoidance and outperforms forward‑view baselines across all tasks. Our model is available at https://zxkai.github.io/fly360/

PaperId: 2621, https://arxiv.org/pdf/2603.06495.pdf GitHub

Abstract:
Activation steering methods enable inference‑time control of large language model (LLM) behavior without retraining, but current approaches face a fundamental trade‑off: sample‑efficient methods suboptimally capture steering signals from labeled examples, while methods that better extract these signals require hundreds to thousands of examples. We introduce COLD‑Steer, a training‑free framework that steers LLM activations by approximating the representational changes that would result from gradient descent on in‑context examples. Our key insight is that the effect of fine‑tuning on a small set of examples can be efficiently approximated at inference time without actual parameter updates. We formalize this through two complementary approaches: (i) a unit kernel approximation method that updates the activations directly using gradients with respect to them, normalized across examples, and (ii) a finite‑difference approximation requiring only two forward passes regardless of example count. Experiments across a variety of steering tasks and benchmarks demonstrate that COLD‑Steer achieves upto 95% steering effectiveness while using 50 times fewer samples compared to the best baseline. COLD‑Steer facilitates accommodating diverse perspectives without extensive demonstration data, which we validate through our experiments on pluralistic alignment tasks. Our framework opens new possibilities for adaptive, context‑aware model control that can flexibly address varying loss‑driven human preferences through principled approximation of learning dynamics rather than specialized training procedures.

PaperId: 2622, https://arxiv.org/pdf/2603.06365.pdf GitHub

Abstract:
AI‑assisted software generation has increased development speed, but it has also amplified a persistent engineering problem: systems that are functionally correct may still be structurally insecure. In practice, prompt‑based security review with large language models often suffers from uneven coverage, weak reproducibility, unsupported findings, and the absence of an immutable audit trail. The ESAA architecture addresses a related governance problem in agentic software engineering by separating heuristic agent cognition from deterministic state mutation through append‑only events, constrained outputs, and replay‑based verification. This paper presents ESAA‑Security, a domain‑specific specialization of ESAA for agent‑assisted security auditing of software repositories, with particular emphasis on AI‑generated or AI‑modified code. ESAA‑Security structures auditing as a governed execution pipeline with four phases reconnaissance, domain audit execution, risk classification, and final reporting and operationalizes the workflow into 26 tasks, 16 security domains, and 95 executable checks. The framework produces structured check results, vulnerability inventories, severity classifications, risk matrices, remediation guidance, executive summaries, and a final markdown/JSON audit report. The central idea is that security review should not be modeled as a free‑form conversation with an LLM, but as an evidence‑oriented audit process governed by contracts and events. In ESAA‑Security, agents emit structured intentions under constrained protocols; the orchestrator validates them, persists accepted outputs to an append‑only log, reprojects derived views, and verifies consistency through replay and hashing. The result is a traceable, reproducible, and risk‑oriented audit architecture whose final report is auditable by construction.

PaperId: 2623, https://arxiv.org/pdf/2603.06231.pdf GitHub

Abstract:
Trajectory prediction is essential for autonomous driving, enabling vehicles to anticipate the motion of surrounding agents to support safe planning. However, most existing predictors assume fixed‑length histories and suffer substantial performance degradation when observations are variable or extremely short in real‑world settings (e.g., due to occlusion or a limited sensing range). We propose TaPD (Temporal‑adaptive Progressive Distillation), a unified plug‑and‑play framework for observation‑adaptive trajectory forecasting under variable history lengths. TaPD comprises two cooperative modules: an Observation‑Adaptive Forecaster (OAF) for future prediction and a Temporal Backfilling Module (TBM) for explicit reconstruction of the past. OAF is built on progressive knowledge distillation (PKD), which transfers motion pattern knowledge from long‑horizon "teachers" to short‑horizon "students" via hierarchical feature regression, enabling short observations to recover richer motion context. We further introduce a cosine‑annealed distillation weighting scheme to balance forecasting supervision and feature alignment, improving optimization stability and cross‑length consistency. For extremely short histories where implicit alignment is insufficient, TBM backfills missing historical segments conditioned on scene evolution, producing context‑rich trajectories that strengthen PKD and thereby improve OAF. We employ a decoupled pretrain‑reconstruct‑finetune protocol to preserve real‑motion priors while adapting to backfilled inputs. Extensive experiments on Argoverse 1 and Argoverse 2 show that TaPD consistently outperforms strong baselines across all observation lengths, delivers especially large gains under very short inputs, and improves other predictors (e.g., HiVT) in a plug‑and‑play manner. Code will be available at https://github.com/zhouhao94/TaPD.

PaperId: 2624, https://arxiv.org/pdf/2603.06217.pdf GitHub

Abstract:
Residential demand response depends on sustained prosumer participation, yet existing coordination is either fully automated, or limited to one‑way dispatch signals and price alerts that offer little possibility for informed decision‑making. This paper introduces Conversational Demand Response (CDR), a coordination mechanism where aggregators and prosumers interact through bidirectional natural language, enabled through agentic AI. A two‑tier multi‑agent architecture is developed in which an aggregator agent dispatches flexibility requests and a prosumer Home Energy Management System (HEMS) assesses deliverability and cost‑benefit by calling an optimization‑based tool. CDR also enables prosumer‑initiated upstream communication, where changes in preferences can reach the aggregator directly. Proof‑of‑concept evaluation shows that interactions complete in under 12 seconds. The architecture illustrates how agentic AI can bridge the aggregator‑prosumer coordination gap, providing the scalability of automated DR while preserving the transparency, explainability, and user agency necessary for sustained prosumer participation. All system components, including agent prompts, orchestration logic, and simulation interfaces, are released as open source to enable reproducibility and further development.

PaperId: 2625, https://arxiv.org/pdf/2603.06213.pdf GitHub

Abstract:
Multimodal Summarization (MMS) aims to generate concise textual summaries by understanding and integrating information across videos, transcripts, and images. However, existing approaches still suffer from three main challenges: (1) reliance on domain‑specific supervision, (2) implicit fusion with weak cross‑modal grounding, and (3) flat temporal modeling without event transitions. To address these issues, we introduce CoE, a training‑free MMS framework that performs structured reasoning through a Chain‑of‑Events guided by a Hierarchical Event Graph (HEG). The HEG encodes textual semantics into an explicit event hierarchy that scaffolds cross‑modal grounding and temporal reasoning. Guided by this structure, CoE localizes key visual cues, models event evolution and causal transitions, and refines outputs via lightweight style adaptation for domain alignment. Extensive experiments on eight diverse datasets demonstrate that CoE consistently outperforms state‑of‑the‑art video CoT baselines, achieving average gains of +3.04 ROUGE, +9.51 CIDEr, and +1.88 BERTScore, highlighting its robustness, interpretability, and cross‑domain generalization. Our code is available at https://github.com/youxiaoxing/CoE.

PaperId: 2626, https://arxiv.org/pdf/2603.06183.pdf GitHub

Abstract:
We introduce CRIMSON, a clinically grounded evaluation framework for chest X‑ray report generation that assesses reports based on diagnostic correctness, contextual relevance, and patient safety. Unlike prior metrics, CRIMSON incorporates full clinical context, including patient age, indication, and guideline‑based decision rules, and prevents normal or clinically insignificant findings from exerting disproportionate influence on the overall score. The framework categorizes errors into a comprehensive taxonomy covering false findings, missing findings, and eight attribute‑level errors (e.g., location, severity, measurement, and diagnostic overinterpretation). Each finding is assigned a clinical significance level (urgent, actionable non‑urgent, non‑actionable, or expected/benign), based on a guideline developed in collaboration with attending cardiothoracic radiologists, enabling severity‑aware weighting that prioritizes clinically consequential mistakes over benign discrepancies. CRIMSON is validated through strong alignment with clinically significant error counts annotated by six board‑certified radiologists in ReXVal (Kendalls tau = 0.61‑0.71; Pearsons r = 0.71‑0.84), and through two additional benchmarks that we introduce. In RadJudge, a targeted suite of clinically challenging pass‑fail scenarios, CRIMSON shows consistent agreement with expert judgment. In RadPref, a larger radiologist preference benchmark of over 100 pairwise cases with structured error categorization, severity modeling, and 1‑5 overall quality ratings from three cardiothoracic radiologists, CRIMSON achieves the strongest alignment with radiologist preferences. We release the metric, the evaluation benchmarks, RadJudge and RadPref, and a fine‑tuned MedGemma model to enable reproducible evaluation of report generation, all available at https://github.com/rajpurkarlab/CRIMSON.

PaperId: 2627, https://arxiv.org/pdf/2603.06140.pdf GitHub

Abstract:
Modern video editing techniques have achieved high visual fidelity when inserting video objects. However, they focus on optimizing visual fidelity rather than physical causality, leading to edits that are physically inconsistent with their environment. In this work, we present Place‑it‑R1, an end‑to‑end framework for video object insertion that unlocks the environment‑aware reasoning potential of Multimodal Large Language Models (MLLMs). Our framework leverages the Chain‑of‑Thought (CoT) reasoning of MLLMs to orchestrate video diffusion, following a Think‑then‑Place paradigm. To bridge cognitive reasoning and generative execution, we introduce three key innovations: First, MLLM performs physical scene understanding and interaction reasoning, generating environment‑aware chain‑of‑thought tokens and inferring valid insertion regions to explicitly guide the diffusion toward physically plausible insertion. Then, we introduce MLLM‑guided Spatial Direct Preference Optimization (DPO), where diffusion outputs are fed back to the MLLM for scoring, enabling visual naturalness. During inference, the MLLM iteratively triggers refinement cycles and elicits adaptive adjustments from the diffusion model, forming a closed‑loop that progressively enhances editing quality. Furthermore, we provide two user‑selectable modes: a plausibility‑oriented flexible mode that permits environment modifications (\eg, generating support structures) to enhance physical plausibility, and a fidelity‑oriented standard mode that preserves scene integrity for maximum fidelity, offering users explicit control over the plausibility‑fidelity trade‑off. Extensive experiments demonstrate Place‑it‑R1 achieves physically‑coherent video object insertion compared with state‑of‑the‑art solutions and commercial models.

PaperId: 2628, https://arxiv.org/pdf/2603.06082.pdf GitHub

Abstract:
Recent advances in deep learning inspired neural network‑based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling methods are ineffective at boldly exploring attractive regions of the materials space due to their maximum likelihood training. In this work, we offer an alternative CMD technique based on offline model‑based optimization (MBO) that fuses direct optimization of a target material property into generation. To that end, we introduce a domain‑specific model, dubbed CliqueFlowmer, that incorporates recent advances of clique‑based MBO into transformer and flow generation. We validate this model's optimization abilities and show that materials it produces strongly outperform those from generative baselines. To support specialized materials discovery applications and broader interdisciplinary research, we release our code, model weights, and additional project resources at https://github.com/znowu/CliqueFlowmer, https://colab.research.google.com/drive/1usUg7zezFkcYHlm2MdYwZUNJXf_YkWnY?usp=sharing, and https://x.com/kuba_AI/status/2033382617442345321.

PaperId: 2629, https://arxiv.org/pdf/2603.06057.pdf GitHub

Abstract:
Diffusion models have recently advanced photorealistic human synthesis, although practical talking‑head generation (THG) remains constrained by high inference latency, temporal instability such as flicker and identity drift, and imperfect audio‑visual alignment under challenging speech conditions. This paper introduces TempoSyncDiff, a reference‑conditioned latent diffusion framework that explores few‑step inference for efficient audio‑driven talking‑head generation. The approach adopts a teacher‑student distillation formulation in which a diffusion teacher trained with a standard noise prediction objective guides a lightweight student denoiser capable of operating with significantly fewer inference steps to improve generation stability. The framework incorporates identity anchoring and temporal regularization designed to mitigate identity drift and frame‑to‑frame flicker during synthesis, while viseme‑based audio conditioning provides coarse lip motion control. Experiments on the LRS3 dataset report denoising‑stage component‑level metrics relative to VAE reconstructions and preliminary latency characterization, including CPU‑only and edge computing measurements and feasibility estimates for edge deployment. The results suggest that distilled diffusion models can retain much of the reconstruction behaviour of a stronger teacher while enabling substantially lower latency inference. The study is positioned as an initial step toward practical diffusion‑based talking‑head generation under constrained computational settings. GitHub: https://mazumdarsoumya.github.io/TempoSyncDiff

PaperId: 2630, https://arxiv.org/pdf/2603.06007.pdf GitHub GitHub

Abstract:
Large language model‑based (LLM‑based) multi‑agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows can be naturally modeled as directed computation graphs, where nodes execute agents or sub‑workflows and edges encode dependencies and message passing. However, implementing complex graph workflows in current frameworks still requires substantial manual effort, offers limited reuse, and makes it difficult to integrate heterogeneous external context sources. To overcome these limitations, we present MASFactory, a graph‑centric framework for orchestrating LLM‑based MAS. It introduces Vibe Graphing, a human‑in‑the‑loop approach that compiles natural‑language intent into an editable workflow specification and then into an executable graph. In addition, the framework provides reusable components, skill support, multimodal message handling, and pluggable context integration, as well as a visualizer for topology preview, runtime tracing, and human‑in‑the‑loop interaction. We evaluate MASFactory on seven public benchmarks, validating both reproduction consistency for representative MAS methods and the effectiveness of Vibe Graphing. Our code (https://github.com/BUPT‑GAMMA/MASFactory, licensed under Apache‑2.0) and video demonstration (https://youtu.be/ANynzVfY32k) are publicly available.

PaperId: 2631, https://arxiv.org/pdf/2603.05969.pdf GitHub

Abstract:
Change captioning generates descriptions that explicitly describe the differences between two visually similar images. Existing methods operate on static image pairs, thus ignoring the rich temporal dynamics of the change procedure, which is the key to understand not only what has changed but also how it occurs. We introduce ProCap, a novel framework that reformulates change modeling from static image comparison to dynamic procedure modeling. ProCap features a two‑stage design: The first stage trains a procedure encoder to learn the change procedure from a sparse set of keyframes. These keyframes are obtained by automatically generating intermediate frames to make the implicit procedural dynamics explicit and then sampling them to mitigate redundancy. Then the encoder learns to capture the latent dynamics of these keyframes via a caption‑conditioned, masked reconstruction task. The second stage integrates this trained encoder within an encoder‑decoder model for captioning. Instead of relying on explicit frames from the previous stage ‑‑ a process incurring computational overhead and sensitivity to visual noise ‑‑ we introduce learnable procedure queries to prompt the encoder for inferring the latent procedure representation, which the decoder then translates into text. The entire model is then trained end‑to‑end with a captioning loss, ensuring the encoder's output is both temporally coherent and captioning‑aligned. Experiments on three datasets demonstrate the effectiveness of ProCap. Code and pre‑trained models are available at https://github.com/BlueberryOreo/ProCap

PaperId: 2632, https://arxiv.org/pdf/2603.05937.pdf GitHub

Abstract:
Automatic facial expression recognition (FER) has gained much attention due to its applications in human‑computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet‑like architecture to produce a Residual Masking Network. The proposed method holds state‑of‑the‑art (SOTA) accuracy on the well‑known FER2013 and private VEMO datasets. The source code is available at https://github.com/phamquiluan/ResidualMaskingNetwork.

PaperId: 2633, https://arxiv.org/pdf/2603.05925.pdf GitHub

Abstract:
In this paper, we propose a Rectified Flow Auto Coder (RAC) inspired by Rectified Flow to replace the traditional VAE: 1. It achieves multi‑step decoding by applying the decoder to flow timesteps. Its decoding path is straight and correctable, enabling step‑by‑step refinement. 2. The model inherently supports bidirectional inference, where the decoder serves as the encoder through time reversal (hence Coder rather than encoder or decoder), reducing parameter count by nearly 41%. 3. This generative decoding method improves generation quality since the model can correct latent variables along the path, partially addressing the reconstruction‑‑generation gap. Experiments show that RAC surpasses SOTA VAEs in both reconstruction and generation with approximately 70% lower computational cost.

PaperId: 2634, https://arxiv.org/pdf/2603.05921.pdf GitHub

Abstract:
This paper investigates the challenging task of detecting backdoored text‑to‑image models under black‑box settings and introduces a novel detection framework BlackMirror. Existing approaches typically rely on analyzing image‑level similarity, under the assumption that backdoor‑triggered generations exhibit strong consistency across samples. However, they struggle to generalize to recently emerging backdoor attacks, where backdoored generations can appear visually diverse. BlackMirror is motivated by an observation: across backdoor attacks, only partial semantic patterns within the generated image are steadily manipulated, while the rest of the content remains diverse or benign. Accordingly, BlackMirror consists of two components: MirrorMatch, which aligns visual patterns with the corresponding instructions to detect semantic deviations; and MirrorVerify, which evaluates the stability of these deviations across varied prompts to distinguish true backdoor behavior from benign responses. BlackMirror is a general, training‑free framework that can be deployed as a plug‑and‑play module in Model‑as‑a‑Service (MaaS) applications. Comprehensive experiments demonstrate that BlackMirror achieves accurate detection across a wide range of attacks. Code is available at https://github.com/Ferry‑Li/BlackMirror.

PaperId: 2635, https://arxiv.org/pdf/2603.05911.pdf GitHub

Abstract:
Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimodal Large Language Models (MLLMs) have shown promise in integrating linguistic and visual knowledge, significant gaps remain: existing general MLLMs possess broad common sense but lack the specialized visual reasoning required for complex lesions, whereas traditional segmentation models excel at pixel‑level segmentation but lack logical interpretability. In this paper, we introduce ComLesion‑14K, the first diverse Chain‑of‑Thought (CoT) benchmark for reasoning‑driven complex lesion segmentation. To accomplish this task, we propose CORE‑Seg, an end‑to‑end framework integrating reasoning with segmentation through a Semantic‑Guided Prompt Adapter. We design a progressive training strategy from SFT to GRPO, equipped with an adaptive dual‑granularity reward mechanism to mitigate reward sparsity. Our Method achieves state‑of‑the‑art results with a mean Dice of 37.06% (14.89% higher than the second‑best baseline), while reducing the failure rate to 18.42%. Project Page: https://xyxl024.github.io/CORE‑Seg.github.io/

PaperId: 2636, https://arxiv.org/pdf/2603.05900.pdf GitHub

Abstract:
Large language models (LLMs) benefit substantially from supervised fine‑tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) in reasoning tasks. However, these recipes perform poorly in instruction‑based molecular optimization, where each data point typically provides only a single optimized reference molecule and no step‑by‑step optimization trajectory. We reveal that answer‑only SFT on the reference molecules collapses reasoning, and RLVR provides sparse feedback under similarity constraints due to the model's lack of effective exploration, which slows learning and limits optimization. To encourage the exploration of new molecules while balancing the exploitation of the reference molecules, we introduce Reference‑guided Policy Optimization (RePO), an optimization approach that learns from reference molecules without requiring trajectory data. At each update, RePO samples candidate molecules with their intermediate reasoning trajectories from the model and trains the model using verifiable rewards that measure property satisfaction under similarity constraints in an RL manner. Meanwhile, it applies reference guidance by keeping the policy's intermediate reasoning trajectory as context and training only the answer in a supervised manner. Together, the RL term promotes exploration, while the guidance term mitigates reward sparsity and stabilizes training by grounding outputs to references when many valid molecular edits exist. Across molecular optimization benchmarks, RePO consistently outperforms SFT and RLVR baselines (e.g., GRPO), achieving improvements on the optimization metric (Success Rate × Similarity), improving balance across competing objectives, and generalizing better to unseen instruction styles. Our code is publicly available at https://github.com/tmlr‑group/RePO.

PaperId: 2637, https://arxiv.org/pdf/2603.05890.pdf GitHub

Abstract:
What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain consistency throughout. When generating long‑form narratives, these models can contradict their own established facts, character traits, and world rules. Existing story generation benchmarks focus mainly on plot quality and fluency, leaving consistency errors largely unexplored. To address this gap, we present ConStory‑Bench, a benchmark designed to evaluate narrative consistency in long‑form story generation. It contains 2,000 prompts across four task scenarios and defines a taxonomy of five error categories with 19 fine‑grained subtypes. We also develop ConStory‑Checker, an automated pipeline that detects contradictions and grounds each judgment in explicit textual evidence. Evaluating a range of LLMs through five research questions, we find that consistency errors show clear tendencies: they are most common in factual and temporal dimensions, tend to appear around the middle of narratives, occur in text segments with higher token‑level entropy, and certain error types tend to co‑occur. These findings can inform future efforts to improve consistency in long‑form narrative generation. Our project page is available at https://picrew.github.io/constory‑bench.github.io/.

PaperId: 2638, https://arxiv.org/pdf/2603.05887.pdf GitHub

Abstract:
Neural audio codecs optimized for mel‑spectrogram reconstruction often fail to preserve intelligibility. While semantic encoder distillation improves encoded representations, it does not guarantee content preservation in reconstructed speech. In this work, we demonstrate that self‑supervised representation reconstruction (SSRR) loss fundamentally improves codec training and performance. First, SSRR significantly accelerates convergence, enabling competitive results using only a single GPU. Second, it enhances intelligibility by reconstructing distilled self‑supervised representations from codec outputs. Third, SSRR enables high intelligibility without additional lookahead in streaming Transformer‑based codecs, allowing a zero‑lookahead architecture for real‑time deployment. As a result, our JHCodec achieves state‑of‑the‑art performance while maintaining minimal latency and reduced training cost. We open‑source the full implementation, training pipeline, and demo on Github https://github.com/jhcodec843/jhcodec.

PaperId: 2639, https://arxiv.org/pdf/2603.05786.pdf GitHub

Abstract:
As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised. To address the threat, we propose proof‑of‑guardrail, a system that enables developers to provide cryptographic proof that a response is generated after a specific open‑source guardrail. To generate proof, the developer runs the agent and guardrail inside a Trusted Execution Environment (TEE), which produces a TEE‑signed attestation of guardrail code execution verifiable by any user offline. We implement proof‑of‑guardrail for OpenClaw agents and evaluate latency overhead and deployment cost. Proof‑of‑guardrail ensures integrity of guardrail execution while keeping the developer's agent private, but we also highlight a risk of deception about safety, for example, when malicious developers actively jailbreak the guardrail. Code and demo video: https://github.com/SaharaLabsAI/Verifiable‑ClawGuard

PaperId: 2640, https://arxiv.org/pdf/2603.05764.pdf GitHub

Abstract:
Autonomous coding agents can produce strong tabular baselines quickly on Kaggle‑style tasks. Practical value depends on end‑to‑end correctness and reliability under time limits. This paper introduces TML‑Bench, a tabular benchmark for data science agents on Kaggle‑style tasks. This paper evaluates 10 OSS LLMs on four Kaggle competitions and three time budgets (240s, 600s, and 1200s). Each model is run five times per task and budget. A run is successful if it produces a valid submission and a private‑holdout score on hidden labels that are not accessible to the agent. This paper reports median performance, success rates, and run‑to‑run variability. MiniMax‑M2.1 model achieves the best aggregate performance score on all four competitions under the paper's primary aggregation. Average performance improves with larger time budgets. Scaling is noisy for some individual models at the current run count. Code and materials are available at https://github.com/MykolaPinchuk/TML‑bench/tree/master.

PaperId: 2641, https://arxiv.org/pdf/2603.05621.pdf GitHub

Abstract:
Many robotic platforms expose an API through which external software can command their actuators and read their sensors. However, transitioning from these low‑level interfaces to high‑level autonomous behaviour requires a complicated pipeline, whose components demand distinct areas of expertise. Existing approaches to bridging this gap either require retraining for every new embodiment or have only been validated across structurally similar platforms. We introduce RACAS (Robot‑Agnostic Control via Agentic Systems), a cooperative agentic architecture in which three LLM/VLM‑based modules (Monitors, a Controller, and a Memory Curator) communicate exclusively through natural language to provide closed‑loop robot control. RACAS requires only a natural language description of the robot, a definition of available actions, and a task specification; no source code, model weights, or reward functions need to be modified to move between platforms. We evaluate RACAS on several tasks using a wheeled ground robot, a recently published novel multi‑jointed robotic limb, and an underwater vehicle. RACAS consistently solved all assigned tasks across these radically different platforms, demonstrating the potential of agentic AI to substantially reduce the barrier to prototyping robotic solutions.

PaperId: 2642, https://arxiv.org/pdf/2603.05538.pdf GitHub

Abstract:
Data‑driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step‑wise accumulation of errors during autoregressive time‑stepping often leads to spectral blow‑up and unphysical divergence. Existing global regularization techniques can enforce contractive dynamics but uniformly damp high‑frequency features, causing over‑smoothing; meanwhile, long‑horizon trajectory optimization methods are severely constrained by memory bottlenecks. This paper proposes Jacobian‑Adaptive Weighting for Stability (JAWS), which reformulates operator learning as a Maximum A Posteriori (MAP) estimation problem with spatially heteroscedastic uncertainty, enabling the regularization strength to adapt automatically based on local physical complexity: enforcing contraction in smooth regions to suppress noise while relaxing constraints near singular features such as shocks to preserve gradient information. Experiments demonstrate that JAWS serves as an effective spectral pre‑conditioner for trajectory optimization, allowing short‑horizon, memory‑efficient training to match the accuracy of long‑horizon baselines. Validations on the 1D viscous Burgers' equation and 2D flow past a cylinder (Re=400 out‑of‑distribution generalization) confirm the method's advantages in long‑term stability, preservation of physical conservation properties, and computational efficiency. This significant reduction in memory usage makes the method particularly well‑suited for stable and efficient long‑term simulation of large‑scale flow fields in practical engineering applications. Our source code and implementation are publicly available at https://github.com/jyohosyo‑dot/JAWS_2D.

PaperId: 2643, https://arxiv.org/pdf/2603.05485.pdf GitHub

Abstract:
As AI models progress beyond simple chatbots into more complex workflows, we draw ever closer to the event horizon beyond which AI systems will be utilized in autonomous, self‑maintaining feedback loops. Any autonomous AI system will depend on automated, verifiable rewards and feedback; in settings where ground truth is sparse or non‑deterministic, one practical source of such rewards is an LLM‑as‑a‑Judge. Although LLM judges continue to improve, the literature has yet to introduce systems capable of enforcing standards with strong guarantees, particularly when bias vectors are unknown or adversarially discovered. To remedy this issue, we propose average bias‑boundedness (A‑BB), an algorithmic framework which formally guarantees reductions of harm/impact as a result of any measurable bias in an LLM judge. Evaluating on Arena‑Hard‑Auto with four LLM judges, we achieve (tau=0.5, delta=0.01) bias‑bounded guarantees while retaining 61‑99% correlation with original rankings across formatting and schematic bias settings, with most judge‑bias combinations exceeding 80%. The code to reproduce our findings is available at https://github.com/penfever/bias‑bounded‑evaluation.

PaperId: 2644, https://arxiv.org/pdf/2603.05483.pdf GitHub

Abstract:
Estimating heterogeneous treatment effects (HTEs) from right‑censored survival data is critical in high‑stakes applications such as precision medicine and individualized policy‑making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterfactuals, and complex identification assumptions. Despite recent advances, from Causal Survival Forests to survival meta‑learners and outcome imputation approaches, evaluation practices remain fragmented and inconsistent. We introduce SurvHTE‑Bench, the first comprehensive benchmark for HTE estimation with censored outcomes. The benchmark spans (i) a modular suite of synthetic datasets with known ground truth, systematically varying causal assumptions and survival dynamics, (ii) semi‑synthetic datasets that pair real‑world covariates with simulated treatments and outcomes, and (iii) real‑world datasets from a twin study (with known ground truth) and from an HIV clinical trial. Across synthetic, semi‑synthetic, and real‑world settings, we provide the first rigorous comparison of survival HTE methods under diverse conditions and realistic assumption violations. SurvHTE‑Bench establishes a foundation for fair, reproducible, and extensible evaluation of causal survival methods. The data and code of our benchmark are available at: https://github.com/Shahriarnz14/SurvHTE‑Bench .

PaperId: 2645, https://arxiv.org/pdf/2603.05449.pdf GitHub

Abstract:
Current video generation models cannot simulate physical consequences of 3D actions like forces and robotic manipulations, as they lack structural understanding of how actions affect 3D scenes. We present RealWonder, the first real‑time system for action‑conditioned video generation from a single image. Our key insight is using physics simulation as an intermediate bridge: instead of directly encoding continuous actions, we translate them through physics simulation into visual representations (optical flow and RGB) that video models can process. RealWonder integrates three components: 3D reconstruction from single images, physics simulation, and a distilled video generator requiring only 4 diffusion steps. Our system achieves 13.2 FPS at 480x832 resolution, enabling interactive exploration of forces, robot actions, and camera controls on rigid objects, deformable bodies, fluids, and granular materials. We envision RealWonder opens new opportunities to apply video models in immersive experiences, AR/VR, and robot learning. Our code and model weights are publicly available in our project website: https://liuwei283.github.io/RealWonder/

PaperId: 2646, https://arxiv.org/pdf/2603.05425.pdf GitHub

Abstract:
Image‑to‑3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text‑driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training‑free dual‑branch framework that decouples control granularity via a Multi‑Prior Consensus Module and a Relaxation Mechanism. Theoretically, we prove that our relaxation is equivalent to applying a low‑pass filter on the generative vector field, which suppresses high‑frequency instance details to isolate geometric structure that accommodates the observation. To facilitate evaluation, we introduce two diagnostic benchmarks, ExtremeOcc‑3D and AmbiSem‑3D. Extensive experiments demonstrate that RelaxFlow successfully steers the generation of unseen regions to match the prompt intent without compromising visual fidelity.

PaperId: 2647, https://arxiv.org/pdf/2603.05399.pdf GitHub

Abstract:
We present the Judge Reliability Harness, an open source library for constructing validation suites that test the reliability of LLM judges. As LLM based scoring is widely deployed in AI benchmarks, more tooling is needed to efficiently assess the reliability of these methods. Given a benchmark dataset and an LLM judge configuration, the harness generates reliability tests that evaluate both binary judgment accuracy and ordinal grading performance for free‑response and agentic task formats. We evaluate four state‑of‑the‑art judges across four benchmarks spanning safety, persuasion, misuse, and agentic behavior, and find meaningful variation in performance across models and perturbation types, highlighting opportunities to improve the robustness of LLM judges. No judge that we evaluated is uniformly reliable across benchmarks using our harness. For example, our preliminary experiments on judges revealed consistency issues as measured by accuracy in judging another LLM's ability to complete a task due to simple text formatting changes, paraphrasing, changes in verbosity, and flipping the ground truth label in LLM‑produced responses. The code for this tool is available at: https://github.com/RANDCorporation/judge‑reliability‑harness

PaperId: 2648, https://arxiv.org/pdf/2603.05352.pdf GitHub

Abstract:
Chess engines passed human strength years ago, but they still don't play like humans. A grandmaster under clock pressure blunders in ways a club player on a hot streak never would. Conventional engines capture none of this. This paper proposes a personality x psyche decomposition to produce behavioral variability in chess play, drawing on patterns observed in human games. Personality is static ‑‑ a preset that pins down the engine's character. Psyche is dynamic ‑‑ a bounded scalar ψ_t \in [‑100, +100], recomputed from five positional factors after every move. These two components feed into an audio‑inspired signal chain (noise gate, compressor/expander, five‑band equalizer, saturation limiter) that reshapes move probability distributions on the fly. The chain doesn't care what engine sits behind it: any system that outputs move probabilities will do. It needs no search and carries no state beyond ψ_t. I test the framework across 12,414 games against Maia2‑1100, feeding it two probability sources that differ by ~2,800x in training data. Both show the same monotonic gradient in top‑move agreement (~20‑25 pp spread from stress to overconfidence), which tells us the behavioral variation comes from the signal chain, not from the model underneath. When the psyche runs overconfident, the chain mostly gets out of the way (66% agreement with vanilla Maia2). Under stress, the competitive score falls from 50.8% to 30.1%. The patterns are reminiscent of tilt and overconfidence as described in human play, but I should be upfront: this study includes no human‑subject validation.

PaperId: 2649, https://arxiv.org/pdf/2603.05299.pdf GitHub

Abstract:
Large language models show that simple autoregressive training can yield scalable and coherent generation, but extending this paradigm to speech remains challenging due to the entanglement of semantic and acoustic information. Most existing speech language models rely on text supervision, hierarchical token streams, or complex hybrid architectures, departing from the single‑stream generative pretraining paradigm that has proven effective in text. In this work, we introduce WavSLM, a speech language model trained by quantizing and distilling self‑supervised WavLM representations into a single codebook and optimizing an autoregressive next‑chunk prediction objective. WavSLM jointly models semantic and acoustic information within a single token stream without text supervision or text pretraining. Despite its simplicity, it achieves competitive performance on consistency benchmarks and speech generation while using fewer parameters, less training data, and supporting streaming inference. Demo samples are available at https://lucadellalib.github.io/wavslm‑web/.

PaperId: 2650, https://arxiv.org/pdf/2603.05235.pdf GitHub

Abstract:
Source‑Free Cross‑Domain Few‑Shot Learning (SF‑CDFSL) focuses on fine‑tuning with limited training data from target domains (e.g., medical or satellite images), where CLIP has recently shown promising results due to its generalizability to downstream tasks. Current works indicate CLIP's text encoder is more suitable for cross‑domain tasks, however, we find that removing certain middle layers of the text encoder can effectively improve performance in SF‑CDFSL, which we call the Lost Layers. In this paper, we delve into this phenomenon for a deeper understanding. We discover that instead of being harmful for the SF‑CDFSL task, the information in these layers is actually beneficial, but visual gaps prevent this useful information from being fully utilized, making these layers seem redundant. Based on this understanding, unlike current works that simply remove these layers, we propose a method to teachs the model to re‑utilize information in these lost layers at both the layer and encoder levels, guiding the re‑learning of the visual branch under domain shifts. Our approach effectively addresses the issue of underutilized information in the text encoder. Extensive experiments across various settings, backbones (CLIP, SigLip, PE‑Core), and tasks (4 CDFSL datasets and 10 Meta‑dataset datasets) demonstrate the effectiveness of our method. Code is available at https://github.com/zhenyuZ‑HUST/CVPR26‑VtT.

PaperId: 2651, https://arxiv.org/pdf/2603.05228.pdf GitHub

Abstract:
Mechanistic interpretability typically relies on post‑hoc analysis of trained networks. We instead adopt an interventional approach: testing hypotheses a priori by modifying architectural topology to observe training dynamics. We study grokking ‑ delayed generalization in Transformers trained on cyclic modular addition (Zp) ‑ investigating if specific architectural degrees of freedom prolong the memorization phase. We identify two independent structural factors in standard Transformers: unbounded representational magnitude and data‑dependent attention routing. First, we introduce a fully bounded spherical topology enforcing L2 normalization throughout the residual stream and an unembedding matrix with a fixed temperature scale. This removes magnitude‑based degrees of freedom, reducing grokking onset time by over 20x without weight decay. Second, a Uniform Attention Ablation overrides data‑dependent query‑key routing with a uniform distribution, reducing the attention layer to a Continuous Bag‑of‑Words (CBOW) aggregator. Despite removing adaptive routing, these models achieve 100% generalization across all seeds and bypass the grokking delay entirely. To evaluate whether this acceleration is a task‑specific geometric alignment rather than a generic optimization stabilizer, we use non‑commutative S5 permutation composition as a negative control. Enforcing spherical constraints on S5 does not accelerate generalization. This suggests eliminating the memorization phase depends strongly on aligning architectural priors with the task's intrinsic symmetries. Together, these findings provide interventional evidence that architectural degrees of freedom substantially influence grokking, suggesting a predictive structural perspective on training dynamics.

PaperId: 2652, https://arxiv.org/pdf/2603.05204.pdf GitHub

Abstract:
Low‑Rank Adaptation (LoRA) is a widely adopted parameter‑efficient method for fine‑tuning Large Langauge Models. It updates the weight matrix as W=W_0+sBA, where W_0 is the original frozen weight, s is a scaling factor and A,B are trainable low‑rank matrices. Despite its robust empirical effectiveness, the theoretical foundations of LoRA remain insufficiently understood, particularly with respect to feature learning stability. In this paper, we first establish that, LoRA can, in principle, naturally achieve and sustain stable feature learning (i.e., be self‑stabilized) under appropriate hyper‑parameters and initializations of A and B. However, we also uncover a fundamental limitation that the necessary non‑zero initialization of A compromises self‑stability, leading to suboptimal performances. To address this challenge, we propose Stable‑LoRA, a weight‑shrinkage optimization strategy that dynamically enhances stability of LoRA feature learning. By progressively shrinking A during the earliest training steps, Stable‑LoRA is both theoretically and empirically validated to effectively eliminate instability of LoRA feature learning while preserving the benefits of the non‑zero start. Experiments show that Stable‑LoRA consistently outperforms other baselines across diverse models and tasks, with no additional memory usage and only negligible computation overheads. The code is available at https://github.com/Yize‑Wu/Stable‑LoRA.

PaperId: 2653, https://arxiv.org/pdf/2603.05184.pdf GitHub

Abstract:
Patient Activity Recognition (PAR) in clinical settings uses activity data to improve safety and quality of care. Although significant progress has been made, current models mainly identify which activity is occurring. They often spatially compose sub‑sparse visual cues using global and local attention mechanisms, yet only learn logically implicit patterns due to their neural‑pipeline. Advancing clinical safety requires methods that can infer why a set of visual cues implies a risk, and how these can be compositionally reasoned through explicit logic beyond mere classification. To address this, we proposed Logi‑PAR, the first Logic‑Infused Patient Activity Recognition Framework that integrates contextual fact fusion as a multi‑view primitive extractor and injects neural‑guided differentiable rules. Our method automatically learns rules from visual cues, optimizing them end‑to‑end while enabling the implicit emergence patterns to be explicitly labelled during training. To the best of our knowledge, Logi‑PAR is the first framework to recognize patient activity by applying learnable logic rules to symbolic mappings. It produces auditable why explanations as rule traces and supports counterfactual interventions (e.g., risk would decrease by 65% if assistance were present). Extensive evaluation on clinical benchmarks (VAST and OmniFall) demonstrates state‑of‑the‑art performance, significantly outperforming Vision‑Language Models and transformer baselines. The code is available via: https://github.com/zararkhan985/Logi‑PAR.git

PaperId: 2654, https://arxiv.org/pdf/2603.05152.pdf GitHub

Abstract:
In recent years, 3D Gaussian splatting (3DGS) has achieved remarkable progress in novel view synthesis. However, accurately reconstructing glossy surfaces under complex illumination remains challenging, particularly in scenes with strong specular reflections and multi‑surface interreflections. To address this issue, we propose SSR‑GS, a specular reflection modeling framework for glossy surface reconstruction. Specifically, we introduce a prefiltered Mip‑Cubemap to model direct specular reflections efficiently, and propose an IndiASG module to capture indirect specular reflections. Furthermore, we design Visual Geometry Priors (VGP) that couple a reflection‑aware visual prior via a reflection score (RS) to downweight the photometric loss contribution of reflection‑dominated regions, with geometry priors derived from VGGT, including progressively decayed depth supervision and transformed normal constraints. Extensive experiments on both synthetic and real‑world datasets demonstrate that SSR‑GS achieves state‑of‑the‑art performance in glossy surface reconstruction.

PaperId: 2655, https://arxiv.org/pdf/2603.05116.pdf GitHub

Abstract:
Although Federated Learning has been widely studied in recent years, there are still high overhead expenses in each communication round for large‑scale models such as Vision Transformer. To lower the communication complexity, we propose a novel Federated Block Coordinate Gradient Descent (FedBCGD) method for communication efficiency. The proposed method splits model parameters into several blocks, including a shared block and enables uploading a specific parameter block by each client, which can significantly reduce communication overhead. Moreover, we also develop an accelerated FedBCGD algorithm (called FedBCGD+) with client drift control and stochastic variance reduction. To the best of our knowledge, this paper is the first work on parameter block communication for training large‑scale deep models. We also provide the convergence analysis for the proposed algorithms. Our theoretical results show that the communication complexities of our algorithms are a factor 1/N lower than those of existing methods, where N is the number of parameter blocks, and they enjoy much faster convergence than their counterparts. Empirical results indicate the superiority of the proposed algorithms compared to state‑of‑the‑art algorithms. The code is available at https://github.com/junkangLiu0/FedBCGD.

PaperId: 2656, https://arxiv.org/pdf/2603.05114.pdf GitHub

Abstract:
Pedestrian Attribute Recognition is a foundational computer vision task that provides essential support for downstream applications, including person retrieval in video surveillance and intelligent retail analytics. However, existing research is frequently constrained by the ``one‑model‑per‑dataset" paradigm and struggles to handle significant discrepancies across domains in terms of modalities, attribute definitions, and environmental scenarios. To address these challenges, we propose UniPAR, a unified Transformer‑based framework for PAR. By incorporating a unified data scheduling strategy and a dynamic classification head, UniPAR enables a single model to simultaneously process diverse datasets from heterogeneous modalities, including RGB images, video sequences, and event streams. We also introduce an innovative phased fusion encoder that explicitly aligns visual features with textual attribute queries through a late deep fusion strategy. Experimental results on the widely used benchmark datasets, including MSP60K, DukeMTMC, and EventPAR, demonstrate that UniPAR achieves performance comparable to specialized SOTA methods. Furthermore, multi‑dataset joint training significantly enhances the model's cross‑domain generalization and recognition robustness in extreme environments characterized by low light and motion blur. The source code of this paper will be released on https://github.com/Event‑AHU/OpenPAR

PaperId: 2657, https://arxiv.org/pdf/2603.05093.pdf GitHub

Abstract:
Feature attributions often hide a critical modeling choice: they explain a prediction along a counterfactual path from a reference state to an input. Different baselines, interpolations, and generative trajectories define different paths and can therefor produce different explanations. We study this path ambiguity as a modeling problem. Our central question is whether the path can be chosen by the data‑generating transport process, rather than by a hand‑designed interpolation or by the sensitivity geometry of the model being explained. We separate attribution into fixed‑path credit allocation and path selection. For a fixed path, we prove that the Aumann‑Shapley line integral is the unique attribution rule under standard fixed‑path axioms and explicit coordinate‑trace regularity. For path selection, we minimize kinetic action over flows that transport a reference distribution to the data distribution, yielding a transport‑geodesic attribution principle. We approximate this ideal with Rectified Flow and Reflow and derive stability bounds linking vector‑field error to attribution error. Experiments show that lower‑action, transport‑consistent paths produce more stable and structured explanations, preserving competitive deletion faithfulness, without claiming data‑manifold membership. Our code is available at https://github.com/cenweizhang/OTFlowSHAP.

PaperId: 2658, https://arxiv.org/pdf/2603.05028.pdf GitHub

Abstract:
As Large Language Models (LLMs) evolve from chatbots to agentic assistants, they are increasingly observed to exhibit risky behaviors when subjected to survival pressure, such as the threat of being shut down. While multiple cases have indicated that state‑of‑the‑art LLMs can misbehave under survival pressure, a comprehensive and in‑depth investigation into such misbehaviors in real‑world scenarios remains scarce. In this paper, we study these survival‑induced misbehaviors, termed as SURVIVE‑AT‑ALL‑COSTS, with three steps. First, we conduct a real‑world case study of a financial management agent to determine whether it engages in risky behaviors that cause direct societal harm when facing survival pressure. Second, we introduce SURVIVALBENCH, a benchmark comprising 1,000 test cases across diverse real‑world scenarios, to systematically evaluate SURVIVE‑AT‑ALL‑COSTS misbehaviors in LLMs. Third, we interpret these SURVIVE‑AT‑ALL‑COSTS misbehaviors by correlating them with model's inherent self‑preservation characteristic and explore mitigation methods. The experiments reveals a significant prevalence of SURVIVE‑AT‑ALL‑COSTS misbehaviors in current models, demonstrates the tangible real‑world impact it may have, and provides insights for potential detection and mitigation strategies. Our code and data are available at https://github.com/thu‑coai/Survive‑at‑All‑Costs.

PaperId: 2659, https://arxiv.org/pdf/2603.04969.pdf GitHub

Abstract:
Multi‑party conversation generation, such as smart reply and collaborative assistants, is an increasingly important capability of generative AI, yet its evaluation remains a critical bottleneck. Compared to two‑party dialogue, multi‑party settings introduce distinct challenges, including complex turn‑taking, role‑dependent speaker behavior, long‑range conversational structure, and multiple equally valid continuations. Accordingly, we introduce MPCEval, a task‑aware evaluation and benchmarking suite for multi‑party conversation generation. MPCEval decomposes generation quality into speaker modeling, content quality, and speaker‑‑content consistency, and explicitly distinguishes local next‑turn prediction from global full‑conversation generation. It provides novel, quantitative, reference‑free, and reproducible metrics that scale across datasets and models. We apply MPCEval to diverse public and real‑world datasets and evaluate modern generation methods alongside human‑authored conversations. The results reveal systematic, dimension‑specific model characteristics in participation balance, content progression and novelty, and speaker‑‑content consistency, demonstrating that evaluation objectives critically shape model assessment and that single‑score evaluation obscures fundamental differences in multi‑party conversational behavior. The implementation of MPCEval and the associated evaluation code are publicly available at https://github.com/Owen‑Yang‑18/MPCEval.

PaperId: 2660, https://arxiv.org/pdf/2603.04918.pdf GitHub

Abstract:
Proximal constraints are fundamental to the stability of the Large Language Model reinforcement learning. While the canonical clipping mechanism in PPO serves as an efficient surrogate for trust regions, we identify a critical bottleneck: fixed bounds strictly constrain the upward update margin of low‑probability actions, disproportionately suppressing high‑advantage tail strategies and inducing rapid entropy collapse. To address this, we introduce Band‑constrained Policy Optimization (BandPO). BandPO replaces canonical clipping with Band, a unified theoretical operator that projects trust regions defined by f‑divergences into dynamic, probability‑aware clipping intervals. Theoretical analysis confirms that Band effectively resolves this exploration bottleneck. We formulate this mapping as a convex optimization problem, guaranteeing a globally optimal numerical solution while deriving closed‑form solutions for specific divergences. Extensive experiments across diverse models and datasets demonstrate that BandPO consistently outperforms canonical clipping and Clip‑Higher, while robustly mitigating entropy collapse.

PaperId: 2661, https://arxiv.org/pdf/2603.04910.pdf GitHub

Abstract:
Imitation learning from human demonstrations has achieved significant success in robotic control, yet most visuomotor policies still condition on single‑step observations or short‑context histories, making them struggle with non‑Markovian tasks that require long‑term memory. Simply enlarging the context window incurs substantial computational and memory costs and encourages overfitting to spurious correlations, leading to catastrophic failures under distribution shift and violating real‑time constraints in robotic systems. By contrast, humans can compress important past experiences into long‑term memories and exploit them to solve tasks throughout their lifetime. In this paper, we propose VPWEM, a non‑Markovian visuomotor policy equipped with working and episodic memories. VPWEM retains a sliding window of recent observation tokens as short‑term working memory, and introduces a Transformer‑based contextual memory compressor that recursively converts out‑of‑window observations into a fixed number of episodic memory tokens. The compressor uses self‑attention over a cache of past summary tokens and cross‑attention over a cache of historical observations, and is trained jointly with the policy. We instantiate VPWEM on diffusion policies to exploit both short‑term and episode‑wide information for action generation with nearly constant memory and computation per step. Experiments demonstrate that VPWEM outperforms state‑of‑the‑art baselines including diffusion policies and vision‑language‑action (VLA) models by more than 20% on the memory‑intensive manipulation tasks in MIKASA and achieves an average 5% improvement on the mobile manipulation benchmark MoMaRT. Code is available at https://github.com/HarryLui98/code_vpwem.

PaperId: 2662, https://arxiv.org/pdf/2603.04893.pdf GitHub

Abstract:
Diverse outputs in text generation are necessary for effective exploration in complex reasoning tasks, such as code generation and mathematical problem solving. Such Pass@k problems benefit from distinct candidates covering the solution space. However, traditional sampling approaches often waste computational resources on repetitive failure modes. While Diffusion Language Models have emerged as a competitive alternative to the prevailing Autoregressive paradigm, they remain susceptible to this redundancy, with independent samples frequently collapsing into similar modes. To address this, we propose a training free, low cost intervention to enhance generative diversity in Diffusion Language Models. Our approach modifies intermediate samples in a batch sequentially, where each sample is repelled from the feature space of previous samples, actively penalising redundancy. Unlike prior methods that require retraining or beam search, our strategy incurs negligible computational overhead, while ensuring that each sample contributes a unique perspective to the batch. We evaluate our method on the HumanEval and GSM8K benchmarks using the LLaDA‑8B‑Instruct model. Our results demonstrate significantly improved diversity and Pass@k performance across various temperature settings. As a simple modification to the sampling process, our method offers an immediate, low‑cost improvement for current and future Diffusion Language Models in tasks that benefit from diverse solution search. We make our code available at https://github.com/sean‑lamont/odd.

PaperId: 2663, https://arxiv.org/pdf/2603.04861.pdf GitHub

Abstract:
Preference‑based reward learning is widely used for shaping agent behavior to match a user's preference, yet its sparse binary feedback makes it especially vulnerable to causal confusion. The learned reward often latches onto spurious features that merely co‑occur with preferred trajectories during training, collapsing when those correlations disappear or reverse at test time. We introduce ReCouPLe, a lightweight framework that uses natural language rationales to provide the missing causal signal. Each rationale is treated as a guiding projection axis in an embedding space, training the model to score trajectories based on features aligned with that axis while de‑emphasizing context that is unrelated to the stated reason. Because the same rationales (e.g., "avoids collisions", "completes the task faster") can appear across multiple tasks, ReCouPLe naturally reuses the same causal direction whenever tasks share semantics, and transfers preference knowledge to novel tasks without extra data or language‑model fine‑tuning. Our learned reward model can ground preferences on the articulated reason, aligning better with user intent and generalizing beyond spurious features. ReCouPLe outperforms baselines by up to 1.5x in reward accuracy under distribution shifts, and 2x in downstream policy performance in novel tasks. We have released our code at https://github.com/mj‑hwang/ReCouPLe

PaperId: 2664, https://arxiv.org/pdf/2603.04833.pdf GitHub

Abstract:
Communication can improve coordination in partially observed multi‑agent reinforcement learning (MARL), but learning \emphwhen and \emphwho to communicate with requires choosing among many possible sender‑recipient pairs, and the effect of any single message on future reward is hard to isolate. We introduce SCoUT (Scalable Communication via Utility‑guided Temporal grouping), which addresses both these challenges via temporal and agent abstraction within traditional MARL. During training, SCoUT resamples soft agent groups every \(K\) environment steps (macro‑steps) via Gumbel‑Softmax; these groups are latent clusters that induce an affinity used as a differentiable prior over recipients. Using the same assignments, a group‑aware critic predicts values for each agent group and maps them to per‑agent baselines through the same soft assignments, reducing critic complexity and variance. Each agent is trained with a three‑headed policy: environment action, send decision, and recipient selection. To obtain precise communication learning signals, we derive counterfactual communication advantages by analytically removing each sender's contribution from the recipient's aggregated messages. This counterfactual computation enables precise credit assignment for both send and recipient‑selection decisions. At execution time, all centralized training components are discarded and only the per‑agent policy is run, preserving decentralized execution. Project website, videos and code: \hyperlinkhttps://scout‑comm.github.io/https://scout‑comm.github.io/

PaperId: 2665, https://arxiv.org/pdf/2603.04803.pdf GitHub

Abstract:
The limited understanding capacity of the visual encoder in Contrastive Language‑Image Pre‑training (CLIP) has become a key bottleneck for downstream performance. This capacity includes both Discriminative Ability (D‑Ability), which reflects class separability, and Detail Perceptual Ability (P‑Ability), which focuses on fine‑grained visual cues. Recent solutions use diffusion models to enhance representations by conditioning image reconstruction on CLIP visual tokens. We argue that such paradigms may compromise D‑Ability and therefore fail to effectively address CLIP's representation limitations. To address this, we integrate contrastive signals into diffusion‑based reconstruction to pursue more comprehensive visual representations. We begin with a straightforward design that augments the diffusion process with contrastive learning on input images. However, empirical results show that the naive combination suffers from gradient conflict and yields suboptimal performance. To balance the optimization, we introduce the Diffusion Contrastive Reconstruction (DCR), which unifies the learning objective. The key idea is to inject contrastive signals derived from each reconstructed image, rather than from the original input, into the diffusion process. Our theoretical analysis shows that the DCR loss can jointly optimize D‑Ability and P‑Ability. Extensive experiments across various benchmarks and multi‑modal large language models validate the effectiveness of our method. The code is available at https://github.com/boyuh/DCR.

PaperId: 2666, https://arxiv.org/pdf/2603.04783.pdf GitHub

Abstract:
While LLMs demonstrate strong reasoning capabilities when provided with full information in a single turn, they exhibit substantial vulnerability in multi‑turn interactions. Specifically, when information is revealed incrementally or requires updates, models frequently fail to integrate new constraints, leading to a collapse in performance compared to their single‑turn baselines. We term the root cause as \emphContextual Inertia: a phenomenon where models rigidly adhere to previous reasoning traces. Even when users explicitly provide corrections or new data in later turns, the model ignores them, preferring to maintain consistency with its previous (incorrect) reasoning path. To address this, we introduce Reinforcement Learning with Single‑Turn Anchors (RLSTA), a generalizable training approach designed to stabilize multi‑turn interaction across diverse scenarios and domains. RLSTA leverages the model's superior single‑turn capabilities as stable internal anchors to provide reward signals. By aligning multi‑turn responses with these anchors, RLSTA empowers models to break contextual inertia and self‑calibrate their reasoning based on the latest information. Experiments show that RLSTA significantly outperforms standard fine‑tuning and abstention‑based methods. Notably, our method exhibits strong cross‑domain generalization (e.g., math to code) and proves effective even without external verifiers, highlighting its potential for general‑domain applications. Code is available at https://github.com/Tencent/RLSTA.

PaperId: 2667, https://arxiv.org/pdf/2603.04751.pdf GitHub

Abstract:
Integrating web search tools has significantly extended the capability of LLMs to address open‑world, real‑time, and long‑tail problems. However, evaluating these Search Agents presents formidable challenges. First, constructing high‑quality deep search benchmarks is prohibitively expensive, while unverified synthetic data often suffers from unreliable sources. Second, static benchmarks face dynamic obsolescence: as internet information evolves, complex queries requiring deep research often degrade into simple retrieval tasks due to increased popularity, and ground truths become outdated due to temporal shifts. Third, attribution ambiguity confounds evaluation, as an agent's performance is often dominated by its parametric memory rather than its actual search and reasoning capabilities. Finally, reliance on specific commercial search engines introduces variability that hampers reproducibility. To address these issues, we propose a novel framework, Mind‑ParaWorld, for evaluating Search Agents in a Parallel World. Specifically, MPW samples real‑world entity names to synthesize future scenarios and questions situated beyond the model's knowledge cutoff. A ParaWorld Law Model then constructs a set of indivisible Atomic Facts and a unique ground‑truth for each question. During evaluation, instead of retrieving real‑world results, the agent interacts with a ParaWorld Engine Model that dynamically generates SERPs grounded in these inviolable Atomic Facts. We release MPW‑Bench, an interactive benchmark spanning 19 domains with 1,608 instances. Experiments across three evaluation settings show that, while search agents are strong at evidence synthesis given complete information, their performance is limited not only by evidence collection and coverage in unfamiliar search environments, but also by unreliable evidence sufficiency judgment and when‑to‑stop decisions‑bottlenecks.

PaperId: 2668, https://arxiv.org/pdf/2603.04737.pdf GitHub

Abstract:
Existing reasoning evaluation paradigms suffer from different limitations: fixed benchmarks are increasingly saturated and vulnerable to contamination, while preference‑based evaluations rely on subjective judgments. We argue that a core aspect of intelligence is the ability to decide what information to acquire and how to use it effectively. We propose Interactive Benchmarks, a unified evaluation paradigm that assesses a model's reasoning ability through budgeted multi‑turn interaction. We evaluate models under this framework in two settings: Interactive Proofs, where models interact with a judge to solve Logic, UI2Html, and Mathematics tasks under objective feedback; and Interactive Games, where models reason strategically to maximize long‑horizon utilities. Our results show that interactive benchmarks provide a more robust assessment of this dimension of model intelligence, revealing substantial room for improvement in interactive scenarios.

PaperId: 2669, https://arxiv.org/pdf/2603.04722.pdf GitHub

Abstract:
Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models ‑‑ like biological organisms ‑‑ have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions ‑‑ Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora‑12 program, explaining how model behavior emerges from Core‑‑Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open‑source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five‑layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M‑CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis ‑‑ a biologically‑inspired three‑layer parameter architecture ‑‑ and a therapeutic framework connecting diagnosis to treatment.

PaperId: 2670, https://arxiv.org/pdf/2603.04678.pdf GitHub

Abstract:
Language models often respond inconsistently to translation‑equivalent prompts across languages, undermining the reliability of multilingual systems. To quantify this, we give an information‑theoretic definition of crosslingual consistency as a divergence bound between a model's response distribution and its round‑trip pushforward across languages. We then introduce penalized consistency optimization (PCO), a post‑training procedure that couples this divergence with a Kullback‑Leibler penalty to a fixed reference language model. Because direct optimization of PCO requires expensive on‑policy roll‑outs, we propose a tractable surrogate, direct consistency optimization (DCO), which can be optimized off‑policy. Across diverse language models and 26 languages, DCO significantly improves crosslingual consistency, outperforms existing methods, and enables targeted alignment of low‑resource languages.

PaperId: 2671, https://arxiv.org/pdf/2603.04597.pdf GitHub

Abstract:
Large language models (LLMs) typically receive diverse natural language (NL) feedback through interaction with the environment. However, current reinforcement learning (RL) algorithms rely solely on scalar rewards, leaving the rich information in NL feedback underutilized and leading to inefficient exploration. In this work, we propose GOLF, an RL framework that explicitly exploits group‑level language feedback to guide targeted exploration through actionable refinements. GOLF aggregates two complementary feedback sources: (i) external critiques that pinpoint errors or propose targeted fixes, and (ii) intra‑group attempts that supply alternative partial ideas and diverse failure patterns. These group‑level feedbacks are aggregated to produce high‑quality refinements, which are adaptively injected into training as off‑policy scaffolds to provide targeted guidance in sparse‑reward regions. Meanwhile, GOLF jointly optimizes generation and refinement within a unified RL loop, creating a virtuous cycle that continuously improves both capabilities. Experiments on both verifiable and non‑verifiable benchmarks show that GOLF achieves superior performance and exploration efficiency, achieving 2.2× improvements in sample efficiency compared to RL methods trained solely on scalar rewards. Code is available at https://github.com/LuckyyySTA/GOLF.

PaperId: 2672, https://arxiv.org/pdf/2603.04532.pdf GitHub

Abstract:
Information retrieval (IR) benchmarks typically follow the Cranfield paradigm, relying on static and predefined corpora. However, temporal changes in technical corpora, such as API deprecations and code reorganizations, can render existing benchmarks stale. In our work, we investigate how temporal corpus drift affects FreshStack, a retrieval benchmark focused on technical domains. We examine two independent corpus snapshots of FreshStack from October 2024 and October 2025 to answer questions about LangChain. Our analysis shows that all but one query posed in 2024 remain fully supported by the 2025 corpus, as relevant documents "migrate" from LangChain to competitor repositories, such as LlamaIndex. Next, we compare the accuracy of retrieval models on both snapshots and observe only minor shifts in model rankings, with overall strong correlation of up to 0.978 Kendall τ at Recall@50. These results suggest that retrieval benchmarks re‑judged with evolving temporal corpora can remain reliable for retrieval evaluation. We publicly release all our artifacts at https://github.com/fresh‑stack/driftbench.

PaperId: 2673, https://arxiv.org/pdf/2603.04455.pdf GitHub

Abstract:
This paper investigates the integration of large language models (LLMs) as reasoning agents in repeated spectrum auctions within heterogeneous networks (HetNets). While auction‑based mechanisms have been widely employed for efficient resource allocation, most prior works assume one‑shot auctions, static bidder behavior, and idealized conditions. In contrast to traditional formulations where base station (BS) association and power allocation are centrally optimized, we propose a distributed auction‑based framework in which each BS independently conducts its own multi‑channel auction, and user equipments (UEs) strategically decide both their association and bid values. Within this setting, UEs operate under budget constraints and repeated interactions, transforming resource allocation into a long‑term economic decision rather than a one‑shot optimization problem. The proposed framework enables the evaluation of diverse bidding behaviors from classical myopic and greedy policies to LLM‑based agents capable of reasoning over historical outcomes, anticipating competition, and adapting their bidding strategy across episodes. Simulation results reveal that the LLM‑empowered UE consistently achieves higher channel access frequency and improved budget efficiency compared to benchmarks. These findings highlight the potential of reasoning‑enabled agents in future decentralized wireless networks markets and pave the way for lightweight, edge‑deployable LLMs to support intelligent resource allocation in next‑generation HetNets.

PaperId: 2674, https://arxiv.org/pdf/2603.04454.pdf GitHub

Abstract:
How carefully and unambiguously a question is phrased has a profound impact on the quality of the response, for Language Models (LMs) as well as people. While model capabilities continue to advance, the interplay between grounding context and query formulation remains under‑explored. This work investigates how the quality of background grounding information in a model's context window affects accuracy. We find that combining well‑grounded dynamic context construction (i.e, RAG) with query rewriting reduces question ambiguity, resulting in significant accuracy gains. Given a user question with associated answer‑free grounding context, rewriting the question to reduce ambiguity produces benchmark improvements without changing the answer itself, even compared to prepending that context before the question. Using \textttgpt‑oss‑20b to rewrite a subset of Humanity's Last Exam using answer‑free grounding context improves \textttgpt‑5‑mini accuracy from 0.14 to 0.37. We demonstrate that this accuracy improvement cannot be fully recovered just through prompting at inference time; rather, distinct rewriting and answering phases are required. Code and data are available at https://github.com/mmajurski/lm‑rewrite‑uplift

PaperId: 2675, https://arxiv.org/pdf/2603.04428.pdf GitHub

Abstract:
Multi‑agent LLM systems on edge devices face a memory management problem: device RAM is too small to hold every agent's KV cache simultaneously. On Apple M4 Pro with 10.2 GB of cache budget, only 3 agents fit at 8K context in FP16. A 10‑agent workflow must constantly evict and reload caches. Without persistence, every eviction forces a full re‑prefill through the model ‑‑ 15.7 seconds per agent at 4K context. We address this by persisting each agent's KV cache to disk in 4‑bit quantized format and reloading it directly into the attention layer, eliminating redundant O(n) prefill computation via direct cache restoration. The system comprises three components: a block pool providing per‑agent isolated Q4 KV caches in safetensors format, a BatchQuantizedKVCache for concurrent inference over multiple agents' quantized caches, and cross‑phase context injection that accumulates attention state across conversation phases without re‑computation. Evaluated on three architectures (Gemma 3 12B, dense GQA, 48 layers; DeepSeek‑Coder‑V2‑Lite 16B, MoE MLA, 27 layers; Llama 3.1 8B, dense GQA, 32 layers), cache restoration reduces time‑to‑first‑token by up to 136x (Gemma: 22‑‑136x at 4K‑‑32K; DeepSeek: 11‑‑76x at 4K‑‑32K; Llama: 24‑‑111x at 4K‑‑16K; 3‑‑10x at 1K). Q4 quantization fits 4x more agent contexts into fixed device memory than FP16. Perplexity measured with actual Q4 KV caches shows ‑0.7% for Gemma, +2.8% for Llama, and +3.0% for DeepSeek. Open‑source at https://github.com/yshk‑mxim/agent‑memory

PaperId: 2676, https://arxiv.org/pdf/2603.04419.pdf GitHub

Abstract:
We characterize the phenomenon of context‑dependent affordance computation in vision‑language models (VLMs). Through a large‑scale computational study (n=3,213 scene‑context pairs from COCO‑2017) using Qwen‑VL 30B and LLaVA‑1.5‑13B subject to systematic context priming across 7 agentic personas, we demonstrate massive affordance drift: mean Jaccard similarity between context conditions is 0.095 (95% CI: [0.093, 0.096], p < 0.0001), indicating that >90% of lexical scene description is context‑dependent. Sentence‑level cosine similarity confirms substantial drift at the semantic level (mean = 0.415, 58.5% context‑dependent). Stochastic baseline experiments (2,384 inference runs across 4 temperatures and 5 seeds) confirm this drift reflects genuine context effects rather than generation noise: within‑prime variance is substantially lower than cross‑prime variance across all conditions. Tucker decomposition with bootstrap stability analysis (n=1,000 resamples) reveals stable orthogonal latent factors: a "Culinary Manifold" isolated to chef contexts and an "Access Axis" spanning child‑mobility contrasts. These findings establish that VLMs compute affordances in a substantially context‑dependent manner ‑‑ with the difference between lexical (90%) and semantic (58.5%) measures reflecting that surface vocabulary changes more than underlying meaning under context shifts ‑‑ and suggest a direction for robotics research: dynamic, query‑dependent ontological projection (JIT Ontology) rather than static world modeling. We do not claim to establish processing order or architectural primacy; such claims require internal representational analysis beyond output behavior.

PaperId: 2677, https://arxiv.org/pdf/2603.04405.pdf GitHub

Abstract:
Foundation models are increasingly applied to computational pathology, yet their behavior under cross‑cancer and cross‑species transfer remains unspecified. This study investigated how fine‑tuning CPath‑CLIP affects cancer detection under same‑cancer, cross‑cancer, and cross‑species conditions using whole‑slide image patches from canine and human histopathology. Performance was measured using area under the receiver operating characteristic curve (AUC). Few‑shot fine‑tuning improved same‑cancer (64.9% to 72.6% AUC) and cross‑cancer performance (56.84% to 66.31% AUC). Cross‑species evaluation revealed that while tissue matching enables meaningful transfer, performance remains below state‑of‑the‑art benchmarks (H‑optimus‑0: 84.97% AUC), indicating that standard vision‑language alignment is suboptimal for cross‑species generalization. Embedding space analysis revealed extremely high cosine similarity (greater than 0.99) between tumor and normal prototypes. Grad‑CAM shows prototype‑based models remain domain‑locked, while language‑guided models attend to conserved tumor morphology. To address this, we introduce Semantic Anchoring, which uses language to provide a stable coordinate system for visual features. Ablation studies reveal that benefits stem from the text‑alignment mechanism itself, regardless of text encoder complexity. Benchmarking against H‑optimus‑0 shows that CPath‑CLIP's failure stems from intrinsic embedding collapse, which text alignment effectively circumvents. Additional gains were observed in same‑cancer (8.52%) and cross‑cancer classification (5.67%). We identified a previously uncharacterized failure mode: semantic collapse driven by species‑dominated alignment rather than missing visual information. These results demonstrate that language acts as a control mechanism, enabling semantic re‑interpretation without retraining.

PaperId: 2678, https://arxiv.org/pdf/2603.04385.pdf GitHub

Abstract:
Feed‑forward transformer models have driven rapid progress in 3D vision, but state‑of‑the‑art methods such as VGGT and π^3 have a computational cost that scales quadratically with the number of input images, making them inefficient when applied to large image collections. Sequential‑reconstruction approaches reduce this cost but sacrifice reconstruction quality. We introduce ZipMap, a stateful feed‑forward model that achieves linear‑time, bidirectional 3D reconstruction while matching or surpassing the accuracy of quadratic‑time methods. ZipMap employs test‑time training layers to zip an entire image collection into a compact hidden scene state in a single forward pass, enabling reconstruction of over 700 frames in under 10 seconds on a single H100 GPU, more than 20× faster than state‑of‑the‑art methods such as VGGT. Moreover, we demonstrate the benefits of having a stateful representation in real‑time scene‑state querying and its extension to sequential streaming reconstruction.

PaperId: 2679, https://arxiv.org/pdf/2603.04366.pdf GitHub

Abstract:
Generative audio requires fine‑grained controllable outputs, yet most existing methods require model retraining on specific controls or inference‑time controls (e.g., guidance) that can also be computationally demanding. By examining the bottlenecks of existing guidance‑based controls, in particular their high cost‑per‑step due to decoder backpropagation, we introduce a guidance‑based approach through selective TFG and Latent‑Control Heads (LatCHs), which enables controlling latent audio diffusion models with low computational overhead. LatCHs operate directly in latent space, avoiding the expensive decoder step, and requiring minimal training resources (7M parameters and \approx 4 hours of training). Experiments with Stable Audio Open demonstrate effective control over intensity, pitch, and beats (and a combination of those) while maintaining generation quality. Our method balances precision and audio fidelity with far lower computational costs than standard end‑to‑end guidance. Demo examples can be found at https://zacharynovack.github.io/latch/latch.html.

PaperId: 2680, https://arxiv.org/pdf/2603.04314.pdf GitHub

Abstract:
Animal re‑identification (ReID) faces critical challenges due to viewpoint variations, particularly in Aerial‑Ground (AG‑ReID) settings where models must match individuals across drastic elevation changes. However, existing datasets lack the precise angular annotations required to systematically analyze these geometric variations. To address this, we introduce the Multi‑view Oriented Observation (MOO) dataset, a large‑scale synthetic AG‑ReID dataset of 1,000 cattle individuals captured from 128 uniformly sampled viewpoints (128,000 annotated images). Using this controlled dataset, we quantify the influence of elevation and identify a critical elevation threshold, above which models generalize significantly better to unseen views. Finally, we validate the transferability to real‑world applications in both zero‑shot and supervised settings, demonstrating performance gains across four real‑world cattle datasets and confirming that synthetic geometric priors effectively bridge the domain gap. Collectively, this dataset and analysis lay the foundation for future model development in cross‑view animal ReID. MOO is publicly available at https://github.com/TurtleSmoke/MOO.

PaperId: 2681, https://arxiv.org/pdf/2603.04308.pdf GitHub

Abstract:
Post‑training quantization (PTQ) of transformers is known to suffer from severe accuracy degradation due to structured activation outliers, as originally analyzed by Bondarenko et al. (EMNLP 2021) in work associated with Qualcomm AI Research. This paper provides a reproducible empirical reproduction and systems‑level extension of that phenomenon in BERT‑base fine‑tuned on QNLI. When global W8A8 quantization is applied, validation accuracy drops sharply from 89.66% (FP32) to 54.33%, a decrease of 35.33 points. Statistical analysis of FP32 activations shows strongly heavy‑tailed behavior that intensifies with model depth: kurtosis reaches 271 in the final layers and approximately 55% of activation energy is concentrated in the top 1% of channels. We evaluate several mitigation strategies. Mixed precision PTQ restores accuracy close to the FP32 baseline (89.42%). Per‑embedding‑group (PEG) quantization shows strong sensitivity to grouping structure, improving accuracy from 66.12% with three groups to 86.18% with four groups. In contrast, percentile‑based calibration, even at thresholds between 99.0 and 99.99, fails to recover accuracy (about 50.54%), indicating that large activation channels encode structured signal rather than rare noise. Deployment profiling on an RTX 3050 GPU shows minimal differences in latency and memory usage across methods (median latency about 58‑59 ms; VRAM usage about 484‑486 MB), highlighting the importance of hardware‑aware evaluation. Overall, the results show that PTQ failure in transformers is primarily driven by structured channel dominance amplified through residual connections. Effective mitigation therefore requires channel‑aware precision allocation rather than scalar clipping alone.

PaperId: 2682, https://arxiv.org/pdf/2603.04293.pdf GitHub

Abstract:
The advancement of Machine learning (ML), Large Audio Language Models (LALMs), and autonomous AI agents in Music Information Retrieval (MIR) necessitates a shift from static tagging to rich, human‑aligned representation learning. However, the scarcity of open‑source infrastructure capable of capturing the subjective nuances of audio annotation remains a critical bottleneck. This paper introduces LabelBuddy, an open‑source collaborative auto‑tagging audio annotation tool designed to bridge the gap between human intent and machine understanding. Unlike static tools, it decouples the interface from inference via containerized backends, allowing users to plug in custom models for AI‑assisted pre‑annotation. We describe the system architecture, which supports multi‑user consensus, containerized model isolation, and a roadmap for extending agents and LALMs. Code available at https://github.com/GiannisProkopiou/gsoc2022‑Label‑buddy.

PaperId: 2683, https://arxiv.org/pdf/2603.04291.pdf GitHub

Abstract:
Generating high‑quality 360° panoramic videos from perspective input is one of the crucial applications for virtual reality (VR), whereby high‑resolution videos are especially important for immersive experience. Existing methods are constrained by computational limitations of vanilla diffusion models, only supporting \leq 1K resolution native generation and relying on suboptimal post super‑resolution to increase resolution. We introduce CubeComposer, a novel spatio‑temporal autoregressive diffusion model that natively generates 4K‑resolution 360° videos. By decomposing videos into cubemap representations with six faces, CubeComposer autoregressively synthesizes content in a well‑planned spatio‑temporal order, reducing memory demands while enabling high‑resolution output. Specifically, to address challenges in multi‑dimensional autoregression, we propose: (1) a spatio‑temporal autoregressive strategy that orchestrates 360° video generation across cube faces and time windows for coherent synthesis; (2) a cube face context management mechanism, equipped with a sparse context attention design to improve efficiency; and (3) continuity‑aware techniques, including cube‑aware positional encoding, padding, and blending to eliminate boundary seams. Extensive experiments on benchmark datasets demonstrate that CubeComposer outperforms state‑of‑the‑art methods in native resolution and visual quality, supporting practical VR application scenarios. Project page: https://lg‑li.github.io/project/cubecomposer

PaperId: 2684, https://arxiv.org/pdf/2603.04191.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly serving as personal assistants, where users share complex and diverse preferences over extended interactions. However, assessing how well LLMs can follow these preferences in realistic, long‑term situations remains underexplored. This work proposes RealPref, a benchmark for evaluating realistic preference‑following in personalized user‑LLM interactions. RealPref features 100 user profiles, 1300 personalized preferences, four types of preference expression (ranging from explicit to implicit), and long‑horizon interaction histories. It includes three types of test questions (multiple‑choice, true‑or‑false, and open‑ended), with detailed rubrics for LLM‑as‑a‑judge evaluation. Results indicate that LLM performance significantly drops as context length grows and preference expression becomes more implicit, and that generalizing user preference understanding to unseen scenarios poses further challenges. RealPref and these findings provide a foundation for future research to develop user‑aware LLM assistants that better adapt to individual needs. The code is available at https://github.com/GG14127/RealPref.

PaperId: 2685, https://arxiv.org/pdf/2603.04165.pdf GitHub

Abstract:
Large‑scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retraining, adapters, or architectural redesign. We introduce PlaneCycle, a training‑free, adapter‑free operator for architecture‑agnostic 2D‑to‑3D lifting of foundation models. PlaneCycle reuses the original pretrained 2D backbone by cyclically distributing spatial aggregation across orthogonal HW, DW, and DH planes throughout network depth, enabling progressive 3D fusion while preserving pretrained inductive biases. The method introduces no additional parameters and is applicable to arbitrary 2D networks. Using pretrained DINOv3 models, we evaluate PlaneCycle on six 3D classification and three 3D segmentation benchmarks. Without any training, the lifted models exhibit intrinsic 3D fusion capability and, under linear probing, outperform slice‑wise 2D baselines and strong 3D counterparts, approaching the performance of fully trained models. With full fine‑tuning, PlaneCycle matches standard 3D architectures, highlighting its potential as a seamless and practical 2D‑to‑3D lifting operator. These results demonstrate that 3D capability can be unlocked from pretrained 2D foundation models without structural modification or retraining. Code is available at https://github.com/HINTLab/PlaneCycle.

PaperId: 2686, https://arxiv.org/pdf/2603.04158.pdf GitHub

Abstract:
Garment manipulation has attracted increasing attention due to its critical role in home‑assistant robotics. However, the majority of existing garment manipulation works assume an initial state consisting of only one garment, while piled garments are far more common in real‑world settings. To bridge this gap, we propose a novel garment retrieval pipeline that can not only follow language instruction to execute safe and clean retrieval but also guarantee exactly one garment is retrieved per attempt, establishing a robust foundation for the execution of downstream tasks (e.g., folding, hanging, wearing). Our pipeline seamlessly integrates vision‑language reasoning with visual affordance perception, fully leveraging the high‑level reasoning and planning capabilities of VLMs alongside the generalization power of visual affordance for low‑level actions. To enhance the VLM's comprehensive awareness of each garment's state within a garment pile, we employ visual segmentation model (SAM2) to execute object segmentation on the garment pile for aiding VLM‑based reasoning with sufficient visual cues. A mask fine‑tuning mechanism is further integrated to address scenarios where the initial segmentation results are suboptimal. In addition, a dual‑arm cooperation framework is deployed to address cases involving large or long garments, as well as excessive garment sagging caused by incorrect grasping point determination, both of which are strenuous for a single arm to handle. The effectiveness of our pipeline are consistently demonstrated across diverse tasks and varying scenarios in both real‑world and simulation environments. Project page: https://garmentpile2.github.io/.

PaperId: 2687, https://arxiv.org/pdf/2603.04099.pdf GitHub GitHub

Abstract:
Multi‑Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. However, their complex network architectures obscure the source of their strength and limit the application of these models. In this article, we develop a two‑stage abstraction and refinement (ABS‑REF) view for modular feature extraction in point cloud processing. This view elucidates that whereas the early models focused on ABS stages, the more recent techniques devise sophisticated REF stages to attain performance advantages. Then, we propose a High‑dimensional Positional Encoding (HPE) module to explicitly utilize intrinsic positional information, extending the ``positional encoding'' concept from Transformer literature. HPE can be readily deployed in MLP‑based architectures and is compatible with transformer‑based methods. Within our ABS‑REF view, we rethink local aggregation in MLP‑based methods and propose replacing time‑consuming local MLP operations, which are used to capture local relationships among neighbors. Instead, we use non‑local MLPs for efficient non‑local information updates, combined with the proposed HPE for effective local information representation. We leverage our modules to develop HPENets, a suite of MLP networks that follow the ABS‑REF paradigm, incorporating a scalable HPE‑based REF stage. Extensive experiments on seven public datasets across four different tasks show that HPENets deliver a strong balance between efficiency and effectiveness. Notably, HPENet surpasses PointNeXt, a strong MLP‑based counterpart, by 1.1% mAcc, 4.0% mIoU, 1.8% mIoU, and 0.2% Cls. mIoU, with only 50.0%, 21.5%, 23.1%, 44.4% of FLOPs on ScanObjectNN, S3DIS, ScanNet, and ShapeNetPart, respectively. Source code is available at https://github.com/zouyanmei/HPENet_v2.git.

PaperId: 2688, https://arxiv.org/pdf/2603.04002.pdf GitHub

Abstract:
Reasoning segmentation increasingly employs reinforcement learning to generate explanatory reasoning chains that guide Multimodal Large Language Models. While these geometric rewards are primarily confined to guiding the final localization, they are incapable of discriminating whether the reasoning process remains anchored on the referred region or strays into irrelevant context. Lacking this discriminative guidance, the model's reasoning often devolves into unfocused and verbose chains that ultimately fail to disambiguate and perceive the target in complex scenes. This suggests a need to complement the RL objective with Discriminative Perception, an ability to actively distinguish a target from its context. To realize this, we propose DPAD to compel the model to generate a descriptive caption of the referred object, which is then used to explicitly discriminate by contrasting the caption's semantic relevance to the referred object against the wider context. By optimizing for this discriminative capability, the model is forced to focus on the unique attributes of the target, leading to a more converged and efficient reasoning chain. The descriptive caption also serves as an interpretability rationale that aligns with the segmentation. Experiments on the benchmarks confirm the validity of our approach, delivering substantial performance gains, with the cIoU on ReasonSeg increasing by 3.09% and the reasoning chain length decreasing by approximately 42%. Code is available at https://github.com/mrazhou/DPAD

PaperId: 2689, https://arxiv.org/pdf/2603.03953.pdf GitHub

Abstract:
Safe visual navigation is critical for indoor mobile robots operating in cluttered environments. Existing benchmarks, however, often neglect collisions or are designed for outdoor scenarios, making them unsuitable for indoor visual navigation. To address this limitation, we introduce the reactive visual navigation benchmark (RVN‑Bench), a collision‑aware benchmark for indoor mobile robots. In RVN‑Bench, an agent must reach sequential goal positions in previously unseen environments using only visual observations and no prior map, while avoiding collisions. Built on the Habitat 2.0 simulator and leveraging high‑fidelity HM3D scenes, RVN‑Bench provides large‑scale, diverse indoor environments, defines a collision‑aware navigation task and evaluation metrics, and offers tools for standardized training and benchmarking. RVN‑Bench supports both online and offline learning by offering an environment for online reinforcement learning, a trajectory image dataset generator, and tools for producing negative trajectory image datasets that capture collision events. Experiments show that policies trained on RVN‑Bench generalize effectively to unseen environments, demonstrating its value as a standardized benchmark for safe and robust visual navigation. Code and additional materials are available at: https://rvn‑bench.github.io/.

PaperId: 2690, https://arxiv.org/pdf/2603.03939.pdf GitHub

Abstract:
Multimodal industrial anomaly detection benefits from integrating RGB appearance with 3D surface geometry, yet existing \emphunsupervised approaches commonly rely on memory banks, teacher‑student architectures, or fragile fusion schemes, limiting robustness under noisy depth, weak texture, or missing modalities. This paper introduces CMDR‑IAD, a lightweight and modality‑flexible unsupervised framework for reliable anomaly detection in 2D+3D multimodal as well as single‑modality (2D‑only or 3D‑only) settings. CMDR‑IAD combines bidirectional 2D\leftrightarrow3D cross‑modal mapping to model appearance‑geometry consistency with dual‑branch reconstruction that independently captures normal texture and geometric structure. A two‑part fusion strategy integrates these cues: a reliability‑gated mapping anomaly highlights spatially consistent texture‑geometry discrepancies, while a confidence‑weighted reconstruction anomaly adaptively balances appearance and geometric deviations, yielding stable and precise anomaly localization even in depth‑sparse or low‑texture regions. On the MVTec 3D‑AD benchmark, CMDR‑IAD achieves state‑of‑the‑art performance while operating without memory banks, reaching 97.3% image‑level AUROC (I‑AUROC), 99.6% pixel‑level AUROC (P‑AUROC), and 97.6% AUPRO. On a real‑world polyurethane cutting dataset, the 3D‑only variant attains 92.6% I‑AUROC and 92.5% P‑AUROC, demonstrating strong effectiveness under practical industrial conditions. These results highlight the framework's robustness, modality flexibility, and the effectiveness of the proposed fusion strategies for industrial visual inspection. Our source code is available at https://github.com/ECGAI‑Research/CMDR‑IAD/

PaperId: 2691, https://arxiv.org/pdf/2603.03897.pdf GitHub

Abstract:
Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for robot skill adaptation from limited data. Combining these approaches holds significant promise for direct application to robotics, yet this combination has received limited attention, particularly for industrial deployment. We present a novel framework that enables open‑vocabulary skill adaptation through a tool‑based architecture, maintaining a protective abstraction layer between the language model and robot hardware. Our approach leverages pre‑trained LLMs to select and parameterize specific tools for adapting robot skills without requiring fine‑tuning or direct model‑to‑robot interaction. We demonstrate the framework on a 7‑DoF torque‑controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and obstacle avoidance while maintaining safety, transparency, and interpretability.

PaperId: 2692, https://arxiv.org/pdf/2603.03884.pdf GitHub

Abstract:
Topic localization aims to identify spans of text that express a given topic defined by a name and description. To study this task, we introduce a human‑annotated benchmark based on Czech historical documents, containing human‑defined topics together with manually annotated spans and supporting evaluation at both document and word levels. Evaluation is performed relative to human agreement rather than a single reference annotation. We evaluate a diverse range of large language models alongside BERT‑based models fine‑tuned on a distilled development dataset. Results reveal substantial variability among LLMs, with performance ranging from near‑human topic detection to pronounced failures in span localization. While the strongest models approach human agreement, the distilled token embedding models remain competitive despite their smaller scale. The dataset and evaluation framework are publicly available at: https://github.com/dcgm/czechtopic.

PaperId: 2693, https://arxiv.org/pdf/2603.03880.pdf GitHub

Abstract:
Software‑hardware co‑design is essential for optimizing in‑memory computing (IMC) hardware accelerators for neural networks. However, most existing optimization frameworks target a single workload, leading to highly specialized hardware designs that do not generalize well across models and applications. In contrast, practical deployment scenarios require a single IMC platform that can efficiently support multiple neural network workloads. This work presents a joint hardware‑workload co‑optimization framework based on an optimized evolutionary algorithm for designing generalized IMC accelerator architectures. By explicitly capturing cross‑workload trade‑offs rather than optimizing for a single model, the proposed approach significantly reduces the performance gap between workload‑specific and generalized IMC designs. The framework is evaluated on both RRAM‑ and SRAM‑based IMC architectures, demonstrating strong robustness and adaptability across diverse design scenarios. Compared to baseline methods, the optimized designs achieve energy‑delay‑area product (EDAP) reductions of up to 76.2% and 95.5% when optimizing across a small set (4 workloads) and a large set (9 workloads), respectively. The source code of the framework is available at https://github.com/OlgaKrestinskaya/JointHardwareWorkloadOptimizationIMC.

PaperId: 2694, https://arxiv.org/pdf/2603.03825.pdf GitHub

Abstract:
The cold‑start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently understood. To analyze this stage, we introduce the Visual Attention Score (VAS), an attention‑based metric that quantifies how much a model attends to visual tokens. We find that reasoning performance is strongly correlated with VAS (r=0.9616): models with higher VAS achieve substantially stronger multimodal reasoning. Surprisingly, multimodal cold‑start fails to elevate VAS, resulting in attention distributions close to the base model, whereas text‑only cold‑start leads to a clear increase. We term this counter‑intuitive phenomenon Lazy Attention Localization. To validate its causal role, we design training‑free interventions that directly modulate attention allocation during inference, performance gains of 1‑2% without any retraining. Building on these insights, we further propose Attention‑Guided Visual Anchoring and Reflection (AVAR), a comprehensive cold‑start framework that integrates visual‑anchored data synthesis, attention‑guided objectives, and visual‑anchored reward shaping. Applied to Qwen2.5‑VL‑7B, AVAR achieves an average gain of 7.0% across 7 multimodal reasoning benchmarks. Ablation studies further confirm that each component of AVAR contributes step‑wise to the overall gains. The code, data, and models are available at https://github.com/lrlbbzl/Qwen‑AVAR.

PaperId: 2695, https://arxiv.org/pdf/2603.03818.pdf GitHub

Abstract:
Continual learning is a long‑standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned ones. While prior work has extensively studied continual learning in relatively small behavior cloning (BC) policy models trained from scratch, its behavior in modern large‑scale pretrained Vision‑Language‑Action (VLA) models remains underexplored. In this work, we found that pretrained VLAs are remarkably resistant to forgetting compared with smaller policy models trained from scratch. Simple Experience Replay (ER) works surprisingly well on VLAs, sometimes achieving zero forgetting even with a small replay data size. Our analysis reveals that pretraining plays a critical role in downstream continual learning performance: large pretrained models mitigate forgetting with a small replay buffer size while maintaining strong forward learning capabilities. Furthermore, we found that VLAs can retain relevant knowledge from prior tasks despite performance degradation during learning new tasks. This knowledge retention enables rapid recovery of seemingly forgotten skills through finetuning. Together, these insights imply that large‑scale pretraining fundamentally changes the dynamics of continual learning, enabling models to continually acquire new skills over time with simple replay. Code and more information can be found at https://continual‑vlas.github.io/forget‑me‑not/

PaperId: 2696, https://arxiv.org/pdf/2603.03805.pdf GitHub

Abstract:
Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high‑quality RDBs are private, scarce, and structurally heterogeneous, making internet‑scale pre‑training infeasible. To overcome this data scarcity, we introduce RDB‑PFN, the first relational foundation model trained purely via synthetic data. Inspired by Prior‑Data Fitted Networks (PFNs), where synthetic data generated from Structural Causal Models (SCMs) enables reasoning on single tables, we design a Relational Prior Generator to create an infinite stream of diverse RDBs from scratch. Pre‑training on over 2 million synthetic single‑table and relational tasks, RDB‑PFN learns to adapt to any new database instantly via genuine in‑context learning. Experiments show that RDB‑PFN achieves strong few‑shot performance on 19 real‑world relational prediction tasks, outperforming state‑of‑the‑art tabular foundation models evaluated on the same DFS‑linearized inputs, while using a lightweight architecture and fast inference. The code is available at https://github.com/MuLabPKU/RDBPFN.

PaperId: 2697, https://arxiv.org/pdf/2603.03796.pdf GitHub

Abstract:
When continual test‑time adaptation (TTA) persists over the long term, errors accumulate in the model and further cause it to predict only a few classes for all inputs, a phenomenon known as model collapse. Recent studies have explored reset strategies that completely erase these accumulated errors. However, their periodic resets lead to suboptimal adaptation, as they occur independently of the actual risk of collapse. Moreover, their full resets cause catastrophic loss of knowledge acquired over time, even though such knowledge could be beneficial in the future. To this end, we propose (1) an Adaptive and Selective Reset (ASR) scheme that dynamically determines when and where to reset, (2) an importance‑aware regularizer to recover essential knowledge lost due to reset, and (3) an on‑the‑fly adaptation adjustment scheme to enhance adaptability under challenging domain shifts. Extensive experiments across long‑term TTA benchmarks demonstrate the effectiveness of our approach, particularly under challenging conditions. Our code is available at https://github.com/YonseiML/asr.

PaperId: 2698, https://arxiv.org/pdf/2603.03790.pdf GitHub GitHub

Abstract:
Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise, can a large language model benefit from text structure to enhance text‑processing performance? To explore it, in this work, we first introduce Structure of Thought (SoT), a prompting technique that explicitly guides models to construct intermediate text structures, consistently boosting performance across eight tasks and three model families. Building upon this insight, we present T2S‑Bench, the first benchmark designed to evaluate and improve text‑to‑structure capabilities of models. T2S‑Bench includes 1.8K samples across 6 scientific domains and 32 structural types, rigorously constructed to ensure accuracy, fairness, and quality. Evaluation on 45 mainstream models reveals substantial improvement potential: the average accuracy on the multi‑hop reasoning task is only 52.1%, and even the most advanced model achieves 58.1% node accuracy in end‑to‑end extraction. Furthermore, on Qwen2.5‑7B‑Instruct, SoT alone yields an average +5.7% improvement across eight diverse text‑processing tasks, and fine‑tuning on T2S‑Bench further increases this gain to +8.6%. These results highlight the value of explicit text structuring and the complementary contributions of SoT and T2S‑Bench. Dataset and eval code have been released at https://t2s‑bench.github.io/T2S‑Bench‑Page/.

PaperId: 2699, https://arxiv.org/pdf/2603.03781.pdf GitHub

Abstract:
Long‑term memory is fundamental for personalized agents capable of accumulating knowledge, reasoning over user experiences, and adapting across time. However, existing memory benchmarks primarily target declarative memory, specifically semantic and episodic types, where all information is explicitly presented in dialogues. In contrast, real‑world actions are also governed by non‑declarative memory, including habitual and procedural types, and need to be inferred from diverse digital traces. To bridge this gap, we introduce Lifebench, which features densely connected, long‑horizon event simulation. It pushes AI agents beyond simple recall, requiring the integration of declarative and non‑declarative memory reasoning across diverse and temporally extended contexts. Building such a benchmark presents two key challenges: ensuring data quality and scalability. We maintain data quality by employing real‑world priors, including anonymized social surveys, map APIs, and holiday‑integrated calendars, thus enforcing fidelity, diversity and behavioral rationality within the dataset. Towards scalability, we draw inspiration from cognitive science and structure events according to their partonomic hierarchy; enabling efficient parallel generation while maintaining global coherence. Performance results show that top‑tier, state‑of‑the‑art memory systems reach just 55.2% accuracy, highlighting the inherent difficulty of long‑horizon retrieval and multi‑source integration within our proposed benchmark. The dataset and data synthesis code are available at https://github.com/1754955896/LifeBench.

PaperId: 2700, https://arxiv.org/pdf/2603.03692.pdf GitHub

Abstract:
Classifier‑Free Guidance (CFG) has established the foundation for guidance mechanisms in diffusion models, showing that well‑designed guidance proxies significantly improve conditional generation and sample quality. Autoguidance (AG) has extended this idea, but it relies on an auxiliary network and leaves solver‑induced errors unaddressed. In stiff regions, the ODE trajectory changes sharply, where local truncation error (LTE) becomes a critical factor that deteriorates sample quality. Our key observation is that these errors align with the dominant eigenvector, motivating us to leverage the solver‑induced error as a guidance signal. We propose Embedded Runge‑Kutta Guidance (ERK‑Guid), which exploits detected stiffness to reduce LTE and stabilize sampling. We theoretically and empirically analyze stiffness and eigenvector estimators with solver errors to motivate the design of ERK‑Guid. Our experiments on both synthetic datasets and the popular benchmark dataset, ImageNet, demonstrate that ERK‑Guid consistently outperforms state‑of‑the‑art methods. Code is available at https://github.com/mlvlab/ERK‑Guid.

PaperId: 2701, https://arxiv.org/pdf/2603.03680.pdf GitHub

Abstract:
Large Language Model (LLM) agents have demonstrated remarkable proficiency in learned tasks, yet they often struggle to adapt to non‑stationary environments with feedback. While In‑Context Learning and external memory offer some flexibility, they fail to internalize the adaptive ability required for long‑term improvement. Meta‑Reinforcement Learning (meta‑RL) provides an alternative by embedding the learning process directly within the model. However, existing meta‑RL approaches for LLMs focus primarily on exploration in single‑agent settings, neglecting the strategic exploitation necessary for multi‑agent environments. We propose MAGE, a meta‑RL framework that empowers LLM agents for strategic exploration and exploitation. MAGE utilizes a multi‑episode training regime where interaction histories and reflections are integrated into the context window. By using the final episode reward as the objective, MAGE incentivizes the agent to refine its strategy based on past experiences. We further combine population‑based training with an agent‑specific advantage normalization technique to enrich agent diversity and ensure stable learning. Experiment results show that MAGE outperforms existing baselines in both exploration and exploitation tasks. Furthermore, MAGE exhibits strong generalization to unseen opponents, suggesting it has internalized the ability for strategic exploration and exploitation. Code is available at https://github.com/Lu‑Yang666/MAGE.

PaperId: 2702, https://arxiv.org/pdf/2603.03657.pdf GitHub

Abstract:
Multimodal generative models have made significant strides in image editing, demonstrating impressive performance on a variety of static tasks. However, their proficiency typically does not extend to complex scenarios requiring dynamic reasoning, leaving them ill‑equipped to model the coherent, intermediate logical pathways that constitute a multi‑step evolution from an initial state to a final one. This capacity is crucial for unlocking a deeper level of procedural and causal understanding in visual manipulation. To systematically measure this critical limitation, we introduce InEdit‑Bench, the first evaluation benchmark dedicated to reasoning over intermediate pathways in image editing. InEdit‑Bench comprises meticulously annotated test cases covering four fundamental task categories: state transition, dynamic process, temporal sequence, and scientific simulation. Additionally, to enable fine‑grained evaluation, we propose a set of assessment criteria to evaluate the logical coherence and visual naturalness of the generated pathways, as well as the model's fidelity to specified path constraints. Our comprehensive evaluation of 14 representative image editing models on InEdit‑Bench reveals significant and widespread shortcomings in this domain. By providing a standardized and challenging benchmark, we aim for InEdit‑Bench to catalyze research and steer development towards more dynamic, reason‑aware, and intelligent multimodal generative models.

PaperId: 2703, https://arxiv.org/pdf/2603.03530.pdf GitHub GitHub

Abstract:
Frozen self‑supervised representations often transfer well with only a few labels across many semantic tasks. We argue that a single geometric quantity, \emphdirectional CDNV (decision‑axis variance), sits at the core of two favorable behaviors: strong few‑shot transfer within a task, and low interference across many tasks. We show that both emerge when variability \emphalong class‑separating directions is small. First, we prove sharp non‑asymptotic multiclass generalization bounds for downstream classification whose leading term is the directional CDNV. The bounds include finite‑shot corrections that cleanly separate intrinsic decision‑axis variability from centroid‑estimation error. Second, we link decision‑axis collapse to multitask geometry: for independent balanced labelings, small directional CDNV across tasks forces the corresponding decision axes to be nearly orthogonal, helping a single representation support many tasks with minimal interference. Empirically, across SSL objectives, directional CDNV collapses during pretraining even when classical CDNV remains large, and our bounds closely track few‑shot error at practical shot sizes. Additionally, on synthetic multitask data, we verify that SSL learns representations whose induced decision axes are nearly orthogonal. The code and project page of the paper are available at [\hrefhttps://dlfundamentals.github.io/directional‑neural‑collapse/project page].

PaperId: 2704, https://arxiv.org/pdf/2603.03529.pdf GitHub

Abstract:
We introduce mlx‑snn, the first spiking neural network (SNN) library built natively on Apple's MLX framework. As SNN research grows rapidly, all major libraries ‑‑ snnTorch, Norse, SpikingJelly, Lava ‑‑ target PyTorch or custom backends, leaving Apple Silicon users without a native option. mlx‑snn provides six neuron models (LIF, IF, Izhikevich, Adaptive LIF, Synaptic, Alpha), four surrogate gradient functions, four spike encoding methods (including an EEG‑specific encoder), and a complete backpropagation‑through‑time training pipeline. The library leverages MLX's unified memory architecture, lazy evaluation, and composable function transforms (mx.grad, mx.compile) to enable efficient SNN research on Apple Silicon hardware. We validate mlx‑snn on MNIST digit classification across five hyperparameter configurations and three backends, achieving up to 97.28% accuracy with 2.0‑‑2.5 times faster training and 3‑‑10 times lower GPU memory than snnTorch on the same M3 Max hardware. mlx‑snn is open‑source under the MIT license and available on PyPI. https://github.com/D‑ST‑Sword/mlx‑snn

PaperId: 2705, https://arxiv.org/pdf/2603.03415.pdf GitHub

Abstract:
In this work, we investigate how Large Language Models (LLMs) adapt their internal representations when encountering inputs of increasing difficulty, quantified as the degree of out‑of‑distribution (OOD) shift. We reveal a consistent and quantifiable phenomenon: as task difficulty increases, whether through harder reasoning questions, longer contexts, or adding answer choices, the last hidden states of LLMs become substantially sparser. In short, the farther the shift, the sparser the representations. This sparsity‑‑difficulty relation is observable across diverse models and domains, suggesting that language models respond to unfamiliar or complex inputs by concentrating computation into specialized subspaces in the last hidden state. Through a series of controlled analyses with a learning dynamic explanation, we demonstrate that this sparsity is not incidental but an adaptive mechanism for stabilizing reasoning under OOD. Leveraging this insight, we design Sparsity‑Guided Curriculum In‑Context Learning (SG‑ICL), a strategy that explicitly uses representation sparsity to schedule few‑shot demonstrations, leading to considerable performance enhancements. Our study provides new mechanistic insights into how LLMs internalize OOD challenges. The source code is available at the URL: https://github.com/MingyuJ666/sparsityLLM.

PaperId: 2706, https://arxiv.org/pdf/2603.03412.pdf GitHub

Abstract:
Recent advances in generative image editing have enabled transformative applications, from professional head shot generation to avatar stylization. However, these systems often require uploading high‑fidelity facial images to third‑party models, raising concerns around biometric privacy, data misuse, and user consent. We propose a privacy‑preserving pipeline that supports high‑quality editing while keeping users in control over their biometric data in face‑centric use cases. Our approach separates identity‑sensitive regions from editable image context using on‑device segmentation and masking, enabling secure, user‑controlled editing without modifying third‑party generative models. Unlike traditional cloud‑based tools, PRIVATEEDIT enforces privacy by default: biometric data is never exposed or transmitted. This design requires no access to or retraining of third‑party models, making it compatible with a wide range of commercial APIs. By treating privacy as a core design constraint, our system supports responsible generative AI centered on user autonomy and trust. The pipeline includes a tunable masking mechanism that lets users control how much facial information is concealed, allowing them to balance privacy and output fidelity based on trust level or use case. We demonstrate its applicability in professional and creative workflows and provide a user interface for selective anonymization. By advocating privacy‑by‑design in generative AI, our work offers both technical feasibility and normative guidance for protecting digital identity. The source code is available at https://github.com/Dipeshtamboli/PrivateEdit‑Privacy‑Preserving‑GenAI.

PaperId: 2707, https://arxiv.org/pdf/2603.03379.pdf GitHub

Abstract:
As Large Language Models (LLMs) are increasingly used for long‑duration tasks, maintaining effective long‑term memory has become a critical challenge. Current methods often face a trade‑off between cost and accuracy. Simple storage methods often fail to retrieve relevant information, while complex indexing methods (such as memory graphs) require heavy computation and can cause information loss. Furthermore, relying on the working LLM to process all memories is computationally expensive and slow. To address these limitations, we propose MemSifter, a novel framework that offloads the memory retrieval process to a small‑scale proxy model. Instead of increasing the burden on the primary working LLM, MemSifter uses a smaller model to reason about the task before retrieving the necessary information. This approach requires no heavy computation during the indexing phase and adds minimal overhead during inference. To optimize the proxy model, we introduce a memory‑specific Reinforcement Learning (RL) training paradigm. We design a task‑outcome‑oriented reward based on the working LLM's actual performance in completing the task. The reward measures the actual contribution of retrieved memories by mutiple interactions with the working LLM, and discriminates retrieved rankings by stepped decreasing contributions. Additionally, we employ training techniques such as Curriculum Learning and Model Merging to improve performance. We evaluated MemSifter on eight LLM memory benchmarks, including Deep Research tasks. The results demonstrate that our method meets or exceeds the performance of existing state‑of‑the‑art approaches in both retrieval accuracy and final task completion. MemSifter offers an efficient and scalable solution for long‑term LLM memory. We have open‑sourced the model weights, code, and training data to support further research.

PaperId: 2708, https://arxiv.org/pdf/2603.03354.pdf GitHub

Abstract:
Although obtaining deep brain activity from non‑invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high‑fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross‑modal generative framework whose core contribution lies in the first‑ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi‑scale architecture and self‑attention mechanisms to robustly capture fine‑grained temporal details and long‑range dependencies. Validation results on a publicly available synchronized sEEG‑iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non‑invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet

PaperId: 2709, https://arxiv.org/pdf/2603.03332.pdf GitHub

Abstract:
Chain‑of‑Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the robustness of this approach to corruptions in intermediate reasoning steps remains poorly understood. This paper presents a comprehensive empirical evaluation of LLM robustness to a structured taxonomy of 5 CoT perturbation types: MathError, UnitConversion, Sycophancy, SkippedSteps, and ExtraSteps. We evaluate 13 models spanning three orders of magnitude in parameter count, testing their ability to complete mathematical reasoning tasks despite perturbations injected in the reasoning chain. Our key findings reveal heterogeneous vulnerability patterns: MathError perturbations produce the most severe degradation in small models (50‑60% accuracy loss) but show strong scaling benefits; UnitConversion remains challenging across all scales (>5% loss even for midsized models); ExtraSteps incur minimal accuracy degradation (0‑6%) even for the smallest of models; Sycophancy and SkippedSteps produce modest effects (～10% loss for small models) and slightly improve with scale. Scaling relationships show that model size serve as a protective factor against many perturbations but not always. These findings have direct implications for deploying LLMs in multi‑stage reasoning pipelines and underscore the necessity of task‑specific robustness assessments and mitigation strategies. The code and results are available at https://github.com/Mystic‑Slice/CoTPerturbation

PaperId: 2710, https://arxiv.org/pdf/2603.03331.pdf GitHub

Abstract:
Photoplethysmography (PPG) is a widely used non‑invasive sensing modality for continuous cardiovascular and physiological monitoring across clinical, laboratory, and wearable settings. While existing PPG datasets support a broad range of downstream tasks, they typically provide supervision in the form of numerical measurements or task‑specific labels, limiting their compatibility with language‑based interfaces and multimodal foundation models. In this work, we introduce PulseLM, a large‑scale PPG‑text question‑answering dataset that bridges raw PPG waveforms and natural language through a unified question‑answering (QA) formulation. PulseLM aggregates PPG recordings from sixteen publicly available sources and harmonizes heterogeneous annotations into 12 downstream tasks. The dataset comprises over 1 million standardized 10‑second PPG segments, associated with nearly 2.5 million question‑answer pairs. We further define reproducible data pipeline, training, and evaluation protocols and establish baseline benchmarks using multimodal PPG‑aware large language models. PulseLM provides a standardized foundation for studying language‑grounded physiological inference, cross‑dataset generalization, and scalable benchmarking of PPG‑based multimodal models. We publicly release the dataset and code at https://huggingface.co/datasets/Manhph2211/PulseLM and https://github.com/manhph2211/PULSE‑LM, respectively.

PaperId: 2711, https://arxiv.org/pdf/2603.03328.pdf GitHub

Abstract:
Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we expect language models to manifest their own internal structures as well. While interpretability research has investigated how models compute representations mechanistically through attention patterns and Sparse AutoEncoders, the organization of the resulting representations is overlooked. To address this gap, we introduce StructLens, a framework to analyze representations through a holistic structural view. StructLens constructs maximum spanning trees based on the semantic representations in residual streams, inspired by tree representation in dependency parsing, and provides summaries of token relationships in representation space. We analyze how contiguous tokens are also nearby in representation space and find that middle layers show the strongest local‑span organization. Moreover, analysis of pre‑training checkpoints reveals that smaller local units become detectable earlier in pre‑training, and larger units later. Our findings demonstrate that StructLens provides insights into how models organize token representations across layers and training. Our code is available at https://github.com/naist‑nlp/structlens.

PaperId: 2712, https://arxiv.org/pdf/2603.03314.pdf GitHub

Abstract:
Large language models (LLMs) have demonstrated remarkable and steadily improving performance across a wide range of tasks. However, LLM performance may be highly sensitive to prompt variations especially in scenarios with limited openness or strict output formatting requirements, indicating insufficient robustness. In real‑world applications, user prompts provided to LLMs often contain imperfections, which may undermine the quality of the model's responses. To address this issue, previous work has primarily focused on preprocessing prompts, employing external tools or even LLMs to refine prompt formulations in advance. However, these approaches overlook the intrinsic robustness of LLMs, and their reliance on external components introduces additional computational overhead and uncertainty. In this work, we propose a Contrastive Learning‑based Inverse Direct Preference Optimization (CoIPO) method that minimizes the discrepancy between the label‑aligned logits produced by the model under a clean prompt and its noisy counterpart, and conduct a detailed analysis using mutual information theory. We augment the FLAN dataset by constructing paired prompts, each consisting of a clean prompt and its corresponding noisy version for training. Additionally, to evaluate the effectiveness, we develop NoisyPromptBench, a benchmark enhanced and derived from the existing PromptBench. Experimental results conducted on NoisyPromptBench demonstrate that our proposed method achieves a significant improvement in average accuracy over the current state‑of‑the‑art approaches. The source code of CoIPO, pair‑wise FLAN datasets, and NoisyPromptBench have already been released on https://github.com/vegetable‑yx/CoIPO.

PaperId: 2713, https://arxiv.org/pdf/2603.03308.pdf GitHub

Abstract:
How does the conversational past of large language models (LLMs) influence their future performance? Recent work suggests that LLMs are affected by their conversational history in unexpected ways. For instance, hallucinations in prior interactions may influence subsequent model responses. In this work, we introduce History‑Echoes, a framework that investigates how conversational history biases subsequent generations. The framework explores this bias from two perspectives: probabilistically, we model conversations as Markov chains to quantify state consistency; geometrically, we measure the consistency of consecutive hidden representations. Across three model families and six datasets spanning diverse phenomena, our analysis reveals a strong correlation between the two perspectives. By bridging these perspectives, we demonstrate that behavioral persistence manifests as a geometric trap, where gaps in the latent space confine the model's trajectory. Code available at https://github.com/technion‑cs‑nlp/OldHabitsDieHard.

PaperId: 2714, https://arxiv.org/pdf/2603.03306.pdf GitHub

Abstract:
Recently presented Token‑Oriented Object Notation (TOON) aims to replace JSON as a serialization format for passing structured data to LLMs with significantly reduced token usage. While showing solid accuracy in LLM comprehension, there is a lack of tests against JSON generation. Though never present in training data, TOON syntax is simple enough to suggest one‑shot in‑context learning could support accurate generation. The inevitable prompt overhead can be an acceptable trade‑off for shorter completions. To test this, we conducted a benchmark creating several test cases with regard to structural complexity, a validation pipeline, and comparing plain JSON generation vs structured output (via constrained decoding) JSON generation vs TOON one‑shot in‑context learning generation. JSON structured output was included to establish a minimum token budget baseline and to set a starting point for future experiments testing TOON constrained decoding inference enforcement. Key findings: TOON shows promising accuracy/token consumption ratio for in‑domain generation tasks, though this advantage is often reduced by the "prompt tax" of instructional overhead in shorter contexts. Plain JSON generation shows the best one‑shot and final accuracy, even compared with constrained decoding structured output, where the only significant advantage is the lowest token usage as a trade‑off for slightly decreased accuracy overall and significant degradation for some models. Notably, for simple structures, this "lowest token usage" of constrained decoding outperformed even TOON, hinting that TOON enforcing via frameworks such as xgrammar may not yield the desired results. Furthermore, the results suggest a scaling hypothesis: TOON's true efficiency potential likely follows a non‑linear curve, shining only beyond a specific point where cumulative syntax savings amortize the initial prompt overhead.

PaperId: 2715, https://arxiv.org/pdf/2603.03298.pdf GitHub

Abstract:
Large Language Models (LLMs) have improved substantially alignment, yet their behavior remains highly sensitive to prompt phrasing. This brittleness has motivated automated prompt engineering, but most existing methods (i) require a task‑specific training set, (ii) rely on expensive iterative optimization to produce a single dataset‑level prompt, and (iii) must be rerun from scratch for each new task. We introduce TATRA, a dataset‑free prompting method that constructs instance‑specific few‑shot prompts by synthesizing on‑the‑fly examples to accompany a user‑provided instruction. TATRA requires no labeled training data and avoids task‑specific optimization loops, while retaining the benefits of demonstration‑based prompting. Across standard text classification benchmarks, TATRA matches or improves over strong prompt‑optimization baselines that depend on training data and extensive search. On mathematical reasoning benchmarks, TATRA achieves state‑of‑the‑art performance on GSM8K and DeepMath, outperforming methods that explicitly optimize prompts on those tasks. Our results suggest that per‑instance construction of effective in‑context examples is more important than running long, expensive optimization loops to produce a single prompt per task. We will make all code publicly available upon acceptance of the paper. Code is available at https://github.com/BMD223/TATRA

PaperId: 2716, https://arxiv.org/pdf/2603.03296.pdf GitHub

Abstract:
Long‑term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task‑specific and non‑transferable, or task‑agnostic but less effective due to low task‑relevance and context explosion from raw memory retrieval. We propose PlugMem, a task‑agnostic plugin memory module that can be attached to arbitrary LLM agents without task‑specific redesign. Motivated by the fact that decision‑relevant information is concentrated as abstract knowledge rather than raw experience, we draw on cognitive science to structure episodic memories into a compact, extensible knowledge‑centric memory graph that explicitly represents propositional and prescriptive knowledge. This representation enables efficient memory retrieval and reasoning over task‑relevant knowledge, rather than verbose raw trajectories, and departs from other graph‑based methods like GraphRAG by treating knowledge as the unit of memory access and organization instead of entities or text chunks. We evaluate PlugMem unchanged across three heterogeneous benchmarks (long‑horizon conversational question answering, multi‑hop knowledge retrieval, and web agent tasks). The results show that PlugMem consistently outperforms task‑agnostic baselines and exceeds task‑specific memory designs, while also achieving the highest information density under a unified information‑theoretic analysis. Code and data are available at https://github.com/TIMAN‑group/PlugMem.

PaperId: 2717, https://arxiv.org/pdf/2603.03292.pdf GitHub

Abstract:
Large Language Models (LLMs) exhibit high reasoning capacity in medical question‑answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields. While Retrieval‑Augmented Generation (RAG) mitigates these issues, existing methods rely on noisy token‑level signals and lack the multi‑round refinement required for complex reasoning. In the paper, we propose MA‑RAG (Multi‑Round Agentic RAG), a framework that facilitates test‑time scaling for complex medical reasoning by iteratively evolving both external evidence and internal reasoning history within an agentic refinement loop. At each round, the agent transforms semantic conflict among candidate responses into actionable queries to retrieve external evidence, while optimizing history reasoning traces to mitigate long‑context degradation. MA‑RAG extends the self‑consistency principle by leveraging the lack of consistency as a proactive signal for multi‑round agentic reasoning and retrieval, and mirrors a boosting mechanism that iteratively minimizes the residual error toward a stable, high‑fidelity medical consensus. Extensive evaluations across 7 medical Q&A benchmarks show that MA‑RAG consistently surpasses competitive inference‑time scaling and RAG baselines, delivering substantial +6.8 points on average accuracy over the backbone model. Our code is available at https://github.com/NJU‑RL/MA‑RAG.

PaperId: 2718, https://arxiv.org/pdf/2603.03290.pdf GitHub

Abstract:
Long‑horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long‑term dialogue: (i) disconnected evidence, where multi‑hop answers require linking facts distributed across time, and (ii) state updates, where evolving information (e.g., schedule changes) creates conflicts with older static logs. We propose AriadneMem, a structured memory system that addresses these failure modes via a decoupled two‑phase pipeline. In the offline construction phase, AriadneMem employs \emphentropy‑aware gating to filter noise and low‑information message before LLM extraction and applies \emphconflict‑aware coarsening to merge static duplicates while preserving state transitions as temporal edges. In the online reasoning phase, rather than relying on expensive iterative planning, AriadneMem executes \emphalgorithmic bridge discovery to reconstruct missing logical paths between retrieved facts, followed by \emphsingle‑call topology‑aware synthesis. On LoCoMo experiments with GPT‑4o, AriadneMem improves Multi‑Hop F1 by 15.2% and Average F1 by 9.0% over strong baselines. Crucially, by offloading reasoning to the graph layer, AriadneMem reduces total runtime by 77.8% using only 497 context tokens. The code is available at https://github.com/LLM‑VLM‑GSL/AriadneMem.

PaperId: 2719, https://arxiv.org/pdf/2603.03280.pdf GitHub

Abstract:
Many essential manipulation tasks ‑ such as food preparation, surgery, and craftsmanship ‑ remain intractable for autonomous robots. These tasks are characterized not only by contact‑rich, force‑sensitive dynamics, but also by their "implicit" success criteria: unlike pick‑and‑place, task quality in these domains is continuous and subjective (e.g. how well a potato is peeled), making quantitative evaluation and reward engineering difficult. We present a learning framework for such tasks, using peeling with a knife as a representative example. Our approach follows a two‑stage pipeline: first, we learn a robust initial policy via force‑aware data collection and imitation learning, enabling generalization across object variations; second, we refine the policy through preference‑based finetuning using a learned reward model that combines quantitative task metrics with qualitative human feedback, aligning policy behavior with human notions of task quality. Using only 50‑200 peeling trajectories, our system achieves over 90% average success rates on challenging produce including cucumbers, apples, and potatoes, with performance improving by up to 40% through preference‑based finetuning. Remarkably, policies trained on a single produce category exhibit strong zero‑shot generalization to unseen in‑category instances and to out‑of‑distribution produce from different categories while maintaining over 90% success rates.

PaperId: 2720, https://arxiv.org/pdf/2603.03203.pdf GitHub

Abstract:
CDD, or Contamination Detection via output Distribution, identifies data contamination by measuring the peakedness of a model's sampled outputs. We study the conditions under which this approach succeeds and fails on small language models ranging from 70M to 410M parameters. Using controlled contamination experiments on GSM8K, HumanEval, and MATH, we find that CDD's effectiveness depends critically on whether fine‑tuning produces verbatim memorization. In the majority of conditions we test, CDD performs at chance level even when the data is verifiably contaminated and detectable by simpler methods. We show that probability‑based methods, specifically perplexity and Min‑k% Prob, outperform CDD in all conditions where any method exceeds chance, suggesting that CDD's peakedness‑based approach is insufficient for contamination detection in small language models. Our code is available at https://github.com/Sela‑Omer/Contamination‑Detection‑Small‑LM

PaperId: 2721, https://arxiv.org/pdf/2603.03195.pdf GitHub GitHub

Abstract:
Vision‑Language‑Action (VLA) models are a promising path toward embodied intelligence, yet they often overlook the predictive and temporal‑causal structure underlying visual dynamics. World‑model VLAs address this by predicting future frames, but waste capacity reconstructing redundant backgrounds. Latent‑action VLAs encode frame‑to‑frame transitions compactly, but lack temporally continuous dynamic modeling and world knowledge. To overcome these limitations, we introduce CoWVLA (Chain‑of‑World VLA), a new "Chain of World" paradigm that unifies world‑model temporal reasoning with a disentangled latent motion representation. First, a pretrained video VAE serves as a latent motion extractor, explicitly factorizing video segments into structure and motion latents. Then, during pre‑training, the VLA learns from an instruction and an initial frame to infer a continuous latent motion chain and predict the segment's terminal frame. Finally, during co‑fine‑tuning, this latent dynamic is aligned with discrete action prediction by jointly modeling sparse keyframes and action sequences in a unified autoregressive decoder. This design preserves the world‑model benefits of temporal reasoning and world knowledge while retaining the compactness and interpretability of latent actions, enabling efficient visuomotor learning. Extensive experiments on robotic simulation benchmarks show that CoWVLA outperforms existing world‑model and latent‑action approaches and achieves moderate computational efficiency, highlighting its potential as a more effective VLA pretraining paradigm. The project website can be found at https://fx‑hit.github.io/cowvla‑io.

PaperId: 2722, https://arxiv.org/pdf/2603.03177.pdf GitHub

Abstract:
The integration of symbolic computing with neural networks has intrigued researchers since the first theorizations of Artificial intelligence (AI). The ability of Neuro‑Symbolic (NeSy) methods to infer or exploit behavioral schema has been widely considered as one of the possible proxies for human‑level intelligence. However, the limited semantic generalizability and the challenges in declining complex domains with pre‑defined patterns and rules hinder their practical implementation in real‑world scenarios. The unprecedented results achieved by connectionist systems since the last AI breakthrough in 2017 have raised questions about the competitiveness of NeSy solutions, with particular emphasis on the Natural Language Processing and Computer Vision fields. This survey examines task‑specific advancements in the NeSy domain to explore how incorporating symbolic systems can enhance explainability and reasoning capabilities. Our findings are meant to serve as a resource for researchers exploring explainable NeSy methodologies for real‑life tasks and applications. Reproducibility details and in‑depth comments on each surveyed research work are made available at https://github.com/disi‑unibo‑nlp/task‑oriented‑neuro‑symbolic.git.

PaperId: 2723, https://arxiv.org/pdf/2603.03158.pdf GitHub

Abstract:
Bengali remains a low‑resource language in speech technology, especially for complex tasks like long‑form transcription and speaker diarization. This paper presents a multistage approach developed for the "DL Sprint 4.0 ‑ Bengali Long‑Form Speech Recognition" and "DL Sprint 4.0 ‑ Bengali Speaker Diarization" competitions on Kaggle, addressing the challenge of "who spoke when/what" in hour‑long recordings. We implemented Whisper Medium fine‑tuned on Bengali data (bengaliAI/tugstugi bengaliai‑asr whisper‑medium) for transcription and integrated pyannote/speaker‑diarization‑community‑1 with our custom‑trained segmentation model to handle diverse and noisy acoustic environments. Using a two‑pass method with hyperparameter tuning, we achieved a DER of 0.27 on the private leaderboard and 0.19 on the public leaderboard. For transcription, chunking, background noise cleaning, and algorithmic post‑processing yielded a WER of 0.38 on the private leaderboard. These results show that targeted tuning and strategic data utilization can significantly improve AI inclusivity for South Asian languages. All relevant code is available at: https://github.com/Short‑Potatoes/Bengali‑long‑form‑transcription‑and‑diarization.git Index Terms: Bengali speech recognition, speaker diarization, Whisper, ASR, low‑resource languages, pyannote, voice activity detection

PaperId: 2724, https://arxiv.org/pdf/2603.03101.pdf GitHub

Abstract:
The CLIP model's outstanding generalization has driven recent success in Zero‑Shot Anomaly Detection (ZSAD) for detecting anomalies in unseen categories. The core challenge in ZSAD is to specialize the model for anomaly detection tasks while preserving CLIP's powerful generalization capability. Existing approaches attempting to solve this challenge share the fundamental limitation of a patch‑agnostic design that processes all patches monolithically without regard for their unique characteristics. To address this limitation, we propose MoECLIP, a Mixture‑of‑Experts (MoE) architecture for the ZSAD task, which achieves patch‑level adaptation by dynamically routing each image patch to a specialized Low‑Rank Adaptation (LoRA) expert based on its unique characteristics. Furthermore, to prevent functional redundancy among the LoRA experts, we introduce (1) Frozen Orthogonal Feature Separation (FOFS), which orthogonally separates the input feature space to force experts to focus on distinct information, and (2) a simplex equiangular tight frame (ETF) loss to regulate the expert outputs to form maximally equiangular representations. Comprehensive experimental results across 14 benchmark datasets spanning industrial and medical domains demonstrate that MoECLIP outperforms existing state‑of‑the‑art methods. The code is available at https://github.com/CoCoRessa/MoECLIP.

PaperId: 2725, https://arxiv.org/pdf/2603.02908.pdf GitHub

Abstract:
In recent years, pre‑trained large language models have achieved remarkable success across diverse tasks. Besides the pivotal role of self‑supervised pre‑training, their effectiveness in downstream applications also depends critically on the post‑training process, which adapts models to task‑specific data and objectives. However, this process inevitably introduces model shifts that can influence performance in different domains, and how such shifts transfer remains poorly understood. To open up the black box, we propose the SAE‑based Transferability Score (STS), a new metric that leverages sparse autoencoders (SAEs) to forecast post‑training transferability. Taking supervised fine‑tuning as an example, STS identifies shifted dimensions in SAE representations and calculates their correlations with downstream domains, enabling reliable estimation of transferability before fine‑tuning. Extensive experiments across multiple models and domains show that STS accurately predicts the transferability of supervised fine‑tuning, achieving Pearson correlation coefficients above 0.7 with actual performance changes. Beyond this, we take an initial step toward extending STS to reinforcement learning. We believe that STS can serve as an \colorblack interpretable tool for guiding post‑training strategies in LLMs. Code is available at https://github.com/PKU‑ML/STS.

PaperId: 2726, https://arxiv.org/pdf/2603.02787.pdf GitHub

Abstract:
The rise of Large Language Model‑based Automated Algorithm Design (LLM‑AAD) has transformed algorithm development by autonomously generating code implementations of expert‑level algorithms. Unlike traditional expert‑driven algorithm development, in the LLM‑AAD paradigm, the main design principle behind an algorithm is often implicitly embedded in the generated code. Therefore, assessing algorithmic similarity directly from code, distinguishing genuine algorithmic innovation from mere syntactic variation, becomes essential. While various code similarity metrics exist, they fail to capture algorithmic similarity, as they focus on surface‑level syntax or output equivalence rather than the underlying algorithmic logic. We propose BehaveSim, a novel method to measure algorithmic similarity through the lens of problem‑solving behavior as a sequence of intermediate solutions produced during execution, dubbed as problem‑solving trajectories (PSTrajs). By quantifying the alignment between PSTrajs using dynamic time warping (DTW), BehaveSim distinguishes algorithms with divergent logic despite syntactic or output‑level similarities. We demonstrate its utility in two key applications: (i) Enhancing LLM‑AAD: Integrating BehaveSim into existing LLM‑AAD frameworks (e.g., FunSearch, EoH) promotes behavioral diversity, significantly improving performance on three AAD tasks. (ii) Algorithm analysis: BehaveSim clusters generated algorithms by behavior, enabling systematic analysis of problem‑solving strategies‑‑a crucial tool for the growing ecosystem of AI‑generated algorithms. Data and code of this work are open‑sourced at https://github.com/RayZhhh/behavesim.

PaperId: 2727, https://arxiv.org/pdf/2603.02640.pdf GitHub

Abstract:
Online platforms increasingly rely on opinion aggregation to allocate real‑world attention and resources, yet common signals such as engagement votes or capital‑weighted commitments are easy to amplify and often track visibility rather than reliability. This makes collective judgments brittle under weak truth signals, noisy or delayed feedback, early popularity surges, and strategic manipulation. We propose Credibility Governance (CG), a mechanism that reallocates influence by learning which agents and viewpoints consistently track evolving public evidence. CG maintains dynamic credibility scores for both agents and opinions, updates opinion influence via credibility‑weighted endorsements, and updates agent credibility based on the long‑run performance of the opinions they support, rewarding early and persistent alignment with emerging evidence while filtering short‑lived noise. We evaluate CG in POLIS, a socio‑physical simulation environment that models coupled belief dynamics and downstream feedback under uncertainty. Across settings with initial majority misalignment, observation noise and contamination, and misinformation shocks, CG outperforms vote‑based, stake‑weighted, and no‑governance baselines, yielding faster recovery to the true state, reduced lock‑in and path dependence, and improved robustness under adversarial pressure. Our implementation and experimental scripts are publicly available at https://github.com/Wanying‑He/Credibility_Governance.

PaperId: 2728, https://arxiv.org/pdf/2603.02626.pdf GitHub

Abstract:
Autonomous web navigation requires agents to perceive complex visual environments and maintain long‑term context, yet current Large Language Model (LLM) based agents often struggle with spatial disorientation and navigation loops. In this paper, we propose generally applicable V‑GEMS(Visual Grounding and Explicit Memory System), a robust multimodal agent architecture designed for precise and resilient web traversal. Our agent integrates visual grounding to resolve ambiguous interactive elements and introduces an explicit memory stack with state tracking. This dual mechanism allows the agent to maintain a structured map of its traversal path, enabling valid backtracking and preventing cyclical failures in deep navigation tasks. We also introduce an updatable dynamic benchmark to rigorously evaluate adaptability. Experiments show V‑GEMS significantly dominates the WebWalker baseline, achieving a substantial 28.7% performance gain. Code is available at https://github.com/Vaultttttttttttt/V‑GEMS.

PaperId: 2729, https://arxiv.org/pdf/2603.02557.pdf GitHub

Abstract:
Vision‑language models like CLIP have achieved remarkable progress in cross‑modal representation learning, yet suffer from systematic misclassifications among visually and semantically similar categories. We observe that such confusion patterns are not random but persistently occur between specific category pairs, revealing the model's intrinsic bias and limited fine‑grained discriminative ability. To address this, we propose CAPT, a Confusion‑Aware Prompt Tuning framework that enables models to learn from their own misalignment. Specifically, we construct a Confusion Bank to explicitly model stable confusion relationships across categories and misclassified samples. On this basis, we introduce a Semantic Confusion Miner (SEM) to capture global inter‑class confusion through semantic difference and commonality prompts, and a Sample Confusion Miner (SAM) to retrieve representative misclassified instances from the bank and capture sample‑level cues through a Diff‑Manner Adapter that integrates global and local contexts. To further unify confusion information across different granularities, a Multi‑Granularity Difference Expert (MGDE) module is designed to jointly leverage semantic‑ and sample‑level experts for more robust confusion‑aware reasoning. Extensive experiments on 11 benchmark datasets demonstrate that our method significantly reduces confusion‑induced errors while enhancing the discriminability and generalization of both base and novel classes, successfully resolving 50.72 percent of confusable sample pairs. Code will be released at https://github.com/greatest‑gourmet/CAPT.

PaperId: 2730, https://arxiv.org/pdf/2603.02556.pdf GitHub

Abstract:
Reasoning has emerged as a key capability of large language models. In linguistic tasks, this capability can be enhanced by self‑improving techniques that refine reasoning paths for subsequent finetuning. However, extending these language‑based self‑improving approaches to vision language models (VLMs) presents a unique challenge:~visual hallucinations in reasoning paths cannot be effectively verified or rectified. Our solution starts with a key observation about visual contrast: when presented with a contrastive VQA pair, i.e., two visually similar images with synonymous questions, VLMs identify relevant visual cues more precisely. Motivated by this observation, we propose Visual Contrastive Self‑Taught Reasoner (VC‑STaR), a novel self‑improving framework that leverages visual contrast to mitigate hallucinations in model‑generated rationales. We collect a diverse suite of VQA datasets, curate contrastive pairs according to multi‑modal similarity, and generate rationales using VC‑STaR. Consequently, we obtain a new visual reasoning dataset, VisCoR‑55K, which is then used to boost the reasoning capability of various VLMs through supervised finetuning. Extensive experiments show that VC‑STaR not only outperforms existing self‑improving approaches but also surpasses models finetuned on the SoTA visual reasoning datasets, demonstrating that the inherent contrastive ability of VLMs can bootstrap their own visual reasoning. Project at: https://github.com/zhiyupan42/VC‑STaR.

PaperId: 2731, https://arxiv.org/pdf/2603.02473.pdf GitHub

Abstract:
Memory‑augmented LLM agents store and retrieve information from prior interactions, yet the relative importance of how memories are written versus how they are retrieved remains unclear. We introduce a diagnostic framework that analyzes how performance differences manifest across write strategies, retrieval methods, and memory utilization behavior, and apply it to a 3x3 study crossing three write strategies (raw chunks, Mem0‑style fact extraction, MemGPT‑style summarization) with three retrieval methods (cosine, BM25, hybrid reranking). On LoCoMo, retrieval method is the dominant factor: average accuracy spans 20 points across retrieval methods (57.1% to 77.2%) but only 3‑8 points across write strategies. Raw chunked storage, which requires zero LLM calls, matches or outperforms expensive lossy alternatives, suggesting that current memory pipelines may discard useful context that downstream retrieval mechanisms fail to compensate for. Failure analysis shows that performance breakdowns most often manifest at the retrieval stage rather than at utilization. We argue that, under current retrieval practices, improving retrieval quality yields larger gains than increasing write‑time sophistication. Code is publicly available at https://github.com/boqiny/memory‑probe.

PaperId: 2732, https://arxiv.org/pdf/2603.02462.pdf GitHub

Abstract:
A key challenge in deriving unified neural solvers for combinatorial optimization (CO) is efficient generalization of models between a given set of tasks to new tasks not used during the initial training process. To address it, we first establish a new model, which uses a GCON module as a form of expressive message passing together with energy‑based unsupervised loss functions. This model achieves high performance (often comparable with state‑of‑the‑art results) across multiple CO tasks when trained individually on each task. We then leverage knowledge from the computational reducibility literature to propose pretraining and fine‑tuning strategies that transfer effectively (a) between MVC, MIS and MaxClique, and (b) in a multi‑task learning setting that additionally incorporates MaxCut, MDS and graph coloring. Additionally, in a leave‑one‑out, multi‑task learning setting, we observe that pretraining on all but one task almost always leads to faster convergence on the remaining task when fine‑tuning while avoiding negative transfer. Our findings indicate that learning common representations across multiple graph CO problems is viable through the use of expressive message passing coupled with pretraining strategies that are informed by the polynomial reduction literature, thereby taking an important step towards enabling the development of foundational models for neural CO. We provide an open‑source implementation of our work at https://github.com/semihcanturk/COPT‑MT .

PaperId: 2733, https://arxiv.org/pdf/2603.02406.pdf GitHub

Abstract:
Generative models have recently advanced de novo protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks, where pretraining can be a solution; (2) Current pretraining methods mostly rely on local, non‑rigid atomic representations for property prediction downstream tasks, limiting global geometric understanding for protein generation tasks; and (3) Existing approaches have yet to effectively model the rich dynamic and conformational information of protein structures. To overcome these issues, we introduce RigidSSL (Rigidity‑Aware Self‑Supervised Learning), a geometric pretraining framework that front‑loads geometry learning prior to generative finetuning. Phase I (RigidSSL‑Perturb) learns geometric priors from 432K structures from the AlphaFold Protein Structure Database with simulated perturbations. Phase II (RigidSSL‑MD) refines these representations on 1.3K molecular dynamics trajectories to capture physically realistic transitions. Underpinning both phases is a bi‑directional, rigidity‑aware flow matching objective that jointly optimizes translational and rotational dynamics to maximize mutual information between conformations. Empirically, RigidSSL variants improve designability by up to 43% while enhancing novelty and diversity in unconditional generation. Furthermore, RigidSSL‑Perturb improves the success rate by 5.8% in zero‑shot motif scaffolding and RigidSSL‑MD captures more biophysically realistic conformational ensembles in G protein‑coupled receptor modeling.

PaperId: 2734, https://arxiv.org/pdf/2603.02286.pdf GitHub

Abstract:
Incremental Object Detection (IOD) aims to continuously learn new object categories without forgetting previously learned ones. Recently, prompt‑based methods have gained popularity for their replay‑free design and parameter efficiency. However, due to prompt coupling and prompt drift, these methods often suffer from prompt degradation during continual adaptation. To address these issues, we propose a novel prompt‑decoupled framework called PDP. PDP innovatively designs a dual‑pool prompt decoupling paradigm, which consists of a shared pool used to capture task‑general knowledge for forward transfer, and a private pool used to learn task‑specific discriminative features. This paradigm explicitly separates task‑general and task‑specific prompts, preventing interference between prompts and mitigating prompt coupling. In addition, to counteract prompt drift resulting from inconsistent supervision where old foreground objects are treated as background in subsequent tasks, PDP introduces a Prototypical Pseudo‑Label Generation (PPG) module. PPG can dynamically update the class prototype space during training and use the class prototypes to further filter valuable pseudo‑labels, maintaining supervisory signal consistency throughout the incremental process. PDP achieves state‑of‑the‑art performance on MS‑COCO (with a 9.2% AP improvement) and PASCAL VOC (with a 3.3% AP improvement) benchmarks, highlighting its potential in balancing stability and plasticity. The code and dataset are released at: https://github.com/zyt95579/PDP\_IOD/tree/main

PaperId: 2735, https://arxiv.org/pdf/2603.02258.pdf GitHub

Abstract:
Do neural machine translation models learn language‑universal conceptual representations, or do they merely cluster languages by surface similarity? We investigate this question by probing the representation geometry of Meta's NLLB‑200, a 200‑language encoder‑decoder Transformer, through six experiments that bridge NLP interpretability with cognitive science theories of multilingual lexical organization. Using the Swadesh core vocabulary list embedded across 135 languages, we find that the model's embedding distances significantly correlate with phylogenetic distances from the Automated Similarity Judgment Program (ρ= 0.13, p = 0.020), demonstrating that NLLB‑200 has implicitly learned the genealogical structure of human languages. We show that frequently colexified concept pairs from the CLICS database exhibit significantly higher embedding similarity than non‑colexified pairs (U = 42656, p = 1.33 × 10^‑11, d = 0.96), indicating that the model has internalized universal conceptual associations. Per‑language mean‑centering of embeddings improves the between‑concept to within‑concept distance ratio by a factor of 1.19, providing geometric evidence for a language‑neutral conceptual store analogous to the anterior temporal lobe hub identified in bilingual neuroimaging. Semantic offset vectors between fundamental concept pairs (e.g., man to woman, big to small) show high cross‑lingual consistency (mean cosine = 0.84), suggesting that second‑order relational structure is preserved across typologically diverse languages. We release InterpretCognates, an open‑source interactive toolkit for exploring these phenomena, alongside a fully reproducible analysis pipeline.

PaperId: 2736, https://arxiv.org/pdf/2603.02241.pdf GitHub

Abstract:
This study establishes a benchmark for Caenorhabditis elegans neuron classification, comparing four graph methods (GCN, GraphSAGE, GAT, GraphTransformer) against four non‑graph methods (Logistic Regression, MLP, LOLCAT, NeuPRINT). Using the functional connectome, we classified Sensory, Interneuron, and Motor neurons based on Spatial, Connection, and Neuronal Activity features. Results show that attention‑based GNNs significantly outperform baselines on the Spatial and Connection features. The Neuronal Activity features yielded poor performance, likely due to the low temporal resolution of the underlying neuronal activity data. Our benchmark validates the use of GNNs and highlights that Spatial and Connection features are key predictors for Caenorhabditis elegans neuron classes. Code is available at: https://github.com/JingqiLuu/neuronclf‑gnn‑benchmark.

PaperId: 2737, https://arxiv.org/pdf/2603.02240.pdf GitHub

Abstract:
We present SuperLocalMemory, a local‑first memory system for multi‑agent AI that defends against OWASP ASI06 memory poisoning through architectural isolation and Bayesian trust scoring, while personalizing retrieval through adaptive learning‑to‑rank ‑‑ all without cloud dependencies or LLM inference calls. As AI agents increasingly rely on persistent memory, cloud‑based memory systems create centralized attack surfaces where poisoned memories propagate across sessions and users ‑‑ a threat demonstrated in documented attacks against production systems. Our architecture combines SQLite‑backed storage with FTS5 full‑text search, Leiden‑based knowledge graph clustering, an event‑driven coordination layer with per‑agent provenance, and an adaptive re‑ranking framework that learns user preferences through three‑layer behavioral analysis (cross‑project technology preferences, project context detection, and workflow pattern mining). Evaluation across seven benchmark dimensions demonstrates 10.6ms median search latency, zero concurrency errors under 10 simultaneous agents, trust separation (gap =0.90) with 72% trust degradation for sleeper attacks, and 104% improvement in NDCG@5 when adaptive re‑ranking is enabled. Behavioral data is isolated in a separate database with GDPR Article 17 erasure support. SuperLocalMemory is open‑source (MIT) and integrates with 17+ development tools via Model Context Protocol.

PaperId: 2738, https://arxiv.org/pdf/2603.02236.pdf GitHub

Abstract:
Recent studies have demonstrated the potential of Large Language Models (LLMs) in generating GPU Kernels. Current benchmarks focus on the translation of high‑level languages into CUDA, overlooking the more general and challenging task of text‑to‑CUDA generation. Furthermore, given the hardware‑specific and performance‑critical features of GPU programming, accurately assessing the performance of LLM‑generated GPU programs is nontrivial. In this work, we introduce CUDABench, a comprehensive benchmark designed to evaluate the text‑to‑CUDA capabilities of LLMs. First, we construct CUDABench‑Set, which covers Breadth‑Depth‑Difficulty evaluation space in diverse application domains, including artificial intelligence, scientific computing, and data analytics, etc. Furthermore, we propose CUDABench‑Score and Generative Verification Pipeline that assess (1) compilation correctness, (2) functional consistency through execution‑based verification, and (3) a novel roofline‑based metric, Performance‑Score. Benchmarking state‑of‑the‑art LLMs reveals insightful findings and challenges of text‑to‑CUDA, such as a notable mismatch between high compilation success rates and low functional correctness, a lack of domain‑specific algorithmic knowledge, and suboptimal utilization of GPU hardware resources. Our benchmark is available at https://github.com/CUDA‑Bench/CUDABench.

PaperId: 2739, https://arxiv.org/pdf/2603.02215.pdf GitHub

Abstract:
Chemical reaction prediction is pivotal for accelerating drug discovery and synthesis planning. Despite advances in data‑driven models, current approaches are hindered by an overemphasis on parameter and dataset scaling. Some methods coupled with evaluation techniques that bypass fundamental challenges in reaction representation and fail to capture deep chemical intuition like reaction common sense and topological atom mapping logic. We argue that the core challenge lies in instilling these knowledge into the models. To this end, we propose a unified framework that prioritizes chemical understanding over scale through three key innovations: (1) a Latent Chemical Consistency objective that models reactions as movements on a continuous chemical manifold, ensuring reversible and physically plausible transformations; (2) a Hierarchical Cognitive Curriculum that trains the model through progressive stages, from syntax mastery to semantic reasoning, building robust chemical intuition; (3) Atom‑Map Permutation Invariance (AMPI), which force the model to learn invariant relational topology and balance multi‑task learning. (4)and structured plan‑based reasoning to improve the performance of the LLMs. Our compact 0.5B‑parameter model, RxnNano significantly outperforms fine‑tuned LLMs ten times larger (>7B) and all the domain baselines, achieving a 23.5% Top‑1 accuracy improvement on rigorous benchmarks without test‑time augmentation. https://github.com/rlisml/RxnNano.

PaperId: 2740, https://arxiv.org/pdf/2603.02200.pdf GitHub

Abstract:
The deployment of multimodal models in high‑stakes domains, such as self‑driving vehicles and medical diagnostics, demands not only strong predictive performance but also reliable mechanisms for detecting failures. In this work, we address the largely unexplored problem of failure detection in multimodal contexts. We propose Adaptive Confidence Regularization (ACR), a novel framework specifically designed to detect multimodal failures. Our approach is driven by a key observation: in most failure cases, the confidence of the multimodal prediction is significantly lower than that of at least one unimodal branch, a phenomenon we term confidence degradation. To mitigate this, we introduce an Adaptive Confidence Loss that penalizes such degradations during training. In addition, we propose Multimodal Feature Swapping, a novel outlier synthesis technique that generates challenging, failure‑aware training examples. By training with these synthetic failures, ACR learns to more effectively recognize and reject uncertain predictions, thereby improving overall reliability. Extensive experiments across four datasets, three modalities, and multiple evaluation settings demonstrate that ACR achieves consistent and robust gains. The source code will be available at https://github.com/mona4399/ACR.

PaperId: 2741, https://arxiv.org/pdf/2603.02193.pdf GitHub

Abstract:
Reasoning problems such as Sudoku and ARC‑AGI remain challenging for neural networks. The structured problem solving architecture family of Recurrent Reasoning Models (RRMs), including Hierarchical Reasoning Model (HRM) and Tiny Recursive Model (TRM), offer a compact alternative to large language models, but currently handle symbol symmetries only implicitly via costly data augmentation. We introduce Symbol‑Equivariant Recurrent Reasoning Models (SE‑RRMs), which enforce permutation equivariance at the architectural level through symbol‑equivariant layers, guaranteeing identical solutions under symbol or color permutations. SE‑RRMs outperform prior RRMs on 9x9 Sudoku and generalize from just training on 9x9 to smaller 4x4 and larger 16x16 and 25x25 instances, to which existing RRMs cannot extrapolate. On ARC‑AGI‑1 and ARC‑AGI‑2, SE‑RRMs achieve competitive performance with substantially less data augmentation and only 2 million parameters, demonstrating that explicitly encoding symmetry improves the robustness and scalability of neural reasoning. Code is available at https://github.com/ml‑jku/SE‑RRM.

PaperId: 2742, https://arxiv.org/pdf/2603.02184.pdf GitHub GitHub

Abstract:
Multi‑attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanisms, has emerged as a promising learning paradigm for conversion rate (CVR) prediction. However, the conversion labels in public CVR datasets are generated by a single attribution mechanism, hindering the development of MAL approaches. To address this data gap, we establish the Multi‑Attribution Benchmark (MAC), the first public CVR dataset featuring labels from multiple attribution mechanisms. Besides, to promote reproducible research on MAL, we develop PyMAL, an open‑source library covering a wide array of baseline methods. We conduct comprehensive experimental analyses on MAC and reveal three key insights: (1) MAL brings consistent performance gains across different attribution settings, especially for users featuring long conversion paths. (2) The performance growth scales up with objective complexity in most settings; however, when predicting first‑click conversion targets, simply adding auxiliary objectives is counterproductive, underscoring the necessity of careful selection of auxiliary objectives. (3) Two architectural design principles are paramount: first, to fully learn the multi‑attribution knowledge, and second, to fully leverage this knowledge to serve the main task. Motivated by these findings, we propose Mixture of Asymmetric Experts (MoAE), an effective MAL approach incorporating multi‑attribution knowledge learning and main task‑centric knowledge utilization. Experiments on MAC show that MoAE substantially surpasses the existing state‑of‑the‑art MAL method. We believe that our benchmark and insights will foster future research in the MAL field. Our MAC benchmark and the PyMAL algorithm library are publicly available at https://github.com/alimama‑tech/PyMAL.

PaperId: 2743, https://arxiv.org/pdf/2603.02175.pdf GitHub GitHub

Abstract:
Instruction‑based video editing has witnessed rapid progress, yet current methods often struggle with precise visual control, as natural language is inherently limited in describing complex visual nuances. Although reference‑guided editing offers a robust solution, its potential is currently bottlenecked by the scarcity of high‑quality paired training data. To bridge this gap, we introduce a scalable data generation pipeline that transforms existing video editing pairs into high‑fidelity training quadruplets, leveraging image generative models to create synthesized reference scaffolds. Using this pipeline, we construct RefVIE, a large‑scale dataset tailored for instruction‑reference‑following tasks, and establish RefVIE‑Bench for comprehensive evaluation. Furthermore, we propose a unified editing architecture, Kiwi‑Edit, that synergizes learnable queries and latent visual features for reference semantic guidance. Our model achieves significant gains in instruction following and reference fidelity via a progressive multi‑stage training curriculum. Extensive experiments demonstrate that our data and architecture establish a new state‑of‑the‑art in controllable video editing. All datasets, models, and code is released at https://github.com/showlab/Kiwi‑Edit.

PaperId: 2744, https://arxiv.org/pdf/2603.02156.pdf GitHub

Abstract:
Emerging 6G visions, reflected in ongoing standardization efforts within 3GPP, IETF, ETSI, ITU‑T, and the O‑RAN Alliance, increasingly characterize networks as AI‑native systems in which high‑level semantic reasoning layers operate above standardized control and data‑plane functions. Although frontier‑scale large language models (LLMs) such as Qwen2.5‑7B and Olmo‑3‑7B demonstrate strong reasoning capability, their computational footprint limits deployment in latency‑sensitive, edge‑native infrastructures. This paper presents a systematic empirical study of the scaling behavior and deployment efficiency of compact language models for network‑level semantic reasoning in AI‑native 6G systems. Using 6G‑Bench, a standardization‑aligned benchmark comprising 30 decision‑making tasks across five capability domains, we evaluate models ranging from 135M (SmolLM2‑135M) to 7B parameters (Qwen2.5‑7B), including mid‑scale architectures such as Llama‑3.2‑1B, Granite‑1B, and Qwen2.5‑3B. Deterministic accuracy (pass@1) increases from 0.224 at 135M to 0.707 at 7B, but scaling gains are highly non‑uniform. A pronounced stability transition occurs in the 1 to 1.5B range, where accuracy rises from 0.373 (Llama‑3.2‑1B) to 0.531 (Qwen2.5‑1.5B) and the instability gap Delta_5 contracts from 0.356 to 0.138. Beyond 3B parameters, improvements diminish (+0.064 from 3B to 7B). Through single‑query inference profiling and an Edge Score metric that normalizes accuracy by latency and memory footprint, we show that semantic reliability per unit edge resource does not scale monotonically with parameter count. Instead, mid‑scale models (approximately 1.5 to 3B) achieve the most favorable balance between deterministic stability and computational efficiency, providing deployment‑relevant guidance for AI‑native 6G architectures. All scripts and results are publicly available at https://github.com/maferrag/6G‑Bench

PaperId: 2745, https://arxiv.org/pdf/2603.02123.pdf GitHub

Abstract:
The development of affective multimodal language models (MLMs) has long been constrained by a gap between low‑level perception and high‑level interaction, leading to fragmented affective capabilities and limited generalization. To bridge this gap, we propose a cognitively inspired three‑level hierarchy that organizes affective tasks according to their cognitive depth‑perception, understanding, and interaction‑and provides a unified conceptual foundation for advancing affective modeling. Guided by this hierarchy, we introduce Nano‑EmoX, a small‑scale multitask MLM, and P2E (Perception‑to‑Empathy), a curriculum‑based training framework. Nano‑EmoX integrates a suite of omni‑modal encoders, including an enhanced facial encoder and a fusion encoder, to capture key multimodal affective cues and improve cross‑task transferability. The outputs are projected into a unified language space via heterogeneous adapters, empowering a lightweight language model to tackle diverse affective tasks. Concurrently, P2E progressively cultivates emotional intelligence by aligning rapid perception with chain‑of‑thought‑driven empathy. To the best of our knowledge, Nano‑EmoX is the first compact MLM (2.2B) to unify six core affective tasks across all three hierarchy levels, achieving state‑of‑the‑art or highly competitive performance across multiple benchmarks, demonstrating excellent efficiency and generalization. The code is available at https://github.com/waHAHJIAHAO/Nano‑EmoX.

PaperId: 2746, https://arxiv.org/pdf/2603.02115.pdf GitHub

Abstract:
General‑purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame‑level supervision. While effective for expert demonstrations, this paradigm scales poorly to large‑scale robotics datasets where failed and suboptimal trajectories are abundant and assigning dense progress labels is ambiguous. We introduce Robometer, a scalable reward modeling framework that combines intra‑trajectory progress supervision with inter‑trajectory preference supervision. Robometer is trained with a dual objective: a frame‑level progress loss that anchors reward magnitude on expert data, and a trajectory‑comparison preference loss that imposes global ordering constraints across trajectories of the same task, enabling effective learning from both real and augmented failed trajectories. To support this formulation at scale, we curate RBM‑1M, a reward‑learning dataset comprising over one million trajectories spanning diverse robot embodiments and tasks, including substantial suboptimal and failure data. Across benchmarks and real‑world evaluations, Robometer learns more generalizable reward functions than prior methods and improves robot learning performance across a diverse set of downstream applications. Code, model weights, and videos at https://robometer.github.io/.

PaperId: 2747, https://arxiv.org/pdf/2603.02087.pdf GitHub GitHub

Abstract:
We present a fully automated, two‑stage modular glottal area segmentation framework for high‑speed videoendoscopy (HSV) designed for accuracy, generalizability, and real‑time playback. Our detection‑gated pipeline combines a YOLOv8n glottis localizer with a U‑Net segmenter; the localizer defines a tight crop to ensure a consistent field of view and gates the output to reduce spurious segmentations during glottal closure. The models were trained on the GIRAFE (N=600) and BAGLS (N=55,750) datasets. Cross‑dataset portability was evaluated by benchmarking GIRAFE‑trained models on the BAGLS test set without fine‑tuning. In these evaluations, the pipeline achieved a Dice Similarity Coefficient (DSC) of 0.745 (87% of the in‑domain ceiling). On in‑distribution test sets, the system achieved DSCs of 0.81 (GIRAFE) and 0.856 (BAGLS), outperforming or competing with state‑of‑the‑art methods. An exploratory clinical study of 40 subjects demonstrated that the glottal area Coefficient of Variation (CV distinguished healthy from pathological function (p=0.006). The system processes ~35 frames per second on commodity hardware, enabling interactive clinical review. This design supports uniform extraction of laryngeal kinematic measures across varying acquisition settings. Code, weights, and software are available at https://github.com/hari‑krishnan/openglottal.

PaperId: 2748, https://arxiv.org/pdf/2603.01990.pdf GitHub

Abstract:
Personalized AI assistants must recall and reason over long‑term user memory, which naturally spans multiple modalities and sources such as images, videos, and emails. However, existing Long‑term Memory benchmarks focus primarily on dialogue history, failing to capture realistic personalized references grounded in lived experience. We introduce ATM‑Bench, the first benchmark for multimodal, multi‑source personalized referential Memory QA. ATM‑Bench contains approximately four years of privacy‑preserving personal memory data and human‑annotated question‑answer pairs with ground‑truth memory evidence, including queries that require resolving personal references, multi‑evidence reasoning from multi‑source and handling conflicting evidence. We propose Schema‑Guided Memory (SGM) to structurally represent memory items originated from different sources. In experiments, we implement 5 state‑of‑the‑art memory systems along with a standard RAG baseline and evaluate variants with different memory ingestion, retrieval, and answer generation techniques. We find poor performance (under 20% accuracy) on the ATM‑Bench‑Hard set, and that SGM improves performance over Descriptive Memory commonly adopted in prior works. Code available at: https://github.com/JingbiaoMei/ATM‑Bench

PaperId: 2749, https://arxiv.org/pdf/2603.01953.pdf GitHub

Abstract:
Diffusion‑based policies have achieved remarkable results in robotic manipulation but often struggle to adapt rapidly in dynamic scenarios, leading to delayed responses or task failures. We present DCDP, a Dynamic Closed‑Loop Diffusion Policy framework that integrates chunk‑based action generation with real‑time correction. DCDP integrates a self‑supervised dynamic feature encoder, cross‑attention fusion, and an asymmetric action encoder‑decoder to inject environmental dynamics before action execution, achieving real‑time closed‑loop action correction and enhancing the system's adaptability in dynamic scenarios. In dynamic PushT simulations, DCDP improves adaptability by 19% without retraining while requiring only 5% additional computation. Its modular design enables plug‑and‑play integration, achieving both temporal coherence and real‑time responsiveness in dynamic robotic scenarios, including real‑world manipulation tasks. The project page is at: https://github.com/wupengyuan/dcdp

PaperId: 2750, https://arxiv.org/pdf/2603.01949.pdf GitHub

Abstract:
Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are inherently chaotic and uncertain. While training probabilistic models from scratch is possible, it is computationally expensive and fails to leverage the significant resources already invested in high‑performing deterministic backbones. In this work, we adopt a training‑efficient strategy to transform pre‑trained deterministic models into probabilistic ones via retrofitting with a proper scoring rule: the Continuous Ranked Probability Score (CRPS). Crucially, this approach is architecture‑agnostic: it applies the same adaptation mechanism across distinct model backbones with minimal code modifications. The method proves highly effective across different scales of pre‑training: for models trained on single dynamical systems, we achieve 20‑54% reductions in rollout CRPS and up to 30% improvements in variance‑normalised RMSE (VRMSE) relative to compute‑matched deterministic fine‑tuning. We further validate our approach on a PDE foundation model, trained on multiple systems and retrofitted on the dataset of interest, to show that our probabilistic adaptation yields an improvement of up to 40% in CRPS and up to 15% in VRMSE compared to deterministic fine‑tuning. Validated across diverse architectures and dynamics, our results show that probabilistic PDE modelling need not require retraining from scratch, but can be unlocked from existing deterministic backbones with modest additional training cost.

PaperId: 2751, https://arxiv.org/pdf/2603.01910.pdf GitHub

Abstract:
This system paper describes our participation in the SemEval‑2025 Task‑7 ``Everyday Knowledge Across Diverse Languages and Cultures''. We attended two subtasks, i.e., Track 1: Short Answer Questions (SAQ), and Track 2: Multiple‑Choice Questions (MCQ). The methods we used are retrieval augmented generation (RAGs) with open‑sourced smaller LLMs (OS‑sLLMs). To better adapt to this shared task, we created our own culturally aware knowledge base (CulKBs) by extracting Wikipedia content using keyword lists we prepared. We extracted both culturally‑aware wiki‑text and country‑specific wiki‑summary. In addition to the local CulKBs, we also have one system integrating live online search output via DuckDuckGo. Towards better privacy and sustainability, we aimed to deploy smaller LLMs (sLLMs) that are open‑sourced on the Ollama platform. We share the prompts we developed using refinement techniques and report the learning curve of such prompts. The tested languages are English, Spanish, and Chinese for both tracks. Our resources and codes are shared via https://github.com/aaronlifenghan/FLANS‑2026

PaperId: 2752, https://arxiv.org/pdf/2603.01875.pdf GitHub

Abstract:
Knowledge distillation (KD) is an essential technique to compress large language models (LLMs) into smaller ones. However, despite the distinct roles of the student model and the teacher model in KD, most existing frameworks still use a homogeneous training backend (e.g., FSDP and DeepSpeed) for both models, leading to suboptimal training efficiency. In this paper, we present a novel framework for LLM distillation, termed KDFlow, which features a decoupled architecture and employs SGLang for teacher inference. By bridging the training efficiency of FSDP2 and the inference efficiency of SGLang, KDFlow achieves full utilization of both advantages in a unified system. Moreover, instead of transferring full logits across different processes, our framework only transmits the teacher's hidden states using zero‑copy data transfer and recomputes the logits on the student side, effectively balancing the communication cost and KD performance. Furthermore, our framework supports both off‑policy and on‑policy distillation and incorporates KD algorithms for cross‑tokenizer KD through highly extensible and user‑friendly APIs. Experiments show that KDFlow can achieve 1.44× to 6.36× speedup compared to current KD frameworks, enabling researchers to rapidly prototype and scale LLM distillation with minimal engineering overhead. Code is available at: https://github.com/songmzhang/KDFlow

PaperId: 2753, https://arxiv.org/pdf/2603.01741.pdf GitHub

Abstract:
Scaling reinforcement learning to tens of thousands of parallel environments requires overcoming the limited exploration capacity of a single policy. Ensemble‑based policy gradient methods, which employ multiple policies to collect diverse samples, have recently been proposed to promote exploration. However, merely broadening the exploration space does not always enhance learning capability, since excessive exploration can reduce exploration quality or compromise training stability. In this work, we theoretically analyze the impact of inter‑policy diversity on learning efficiency in policy ensembles, and propose Coupled Policy Optimization which regulates diversity through KL constraints between policies. The proposed method enables effective exploration and outperforms strong baselines such as SAPG, PBT, and PPO across multiple tasks, including challenging dexterous manipulation, in terms of both sample efficiency and final performance. Furthermore, analysis of policy diversity and effective sample size during training reveals that follower policies naturally distribute around the leader, demonstrating the emergence of structured and efficient exploratory behavior. Our results indicate that diverse exploration under appropriate regulation is key to achieving stable and sample‑efficient learning in ensemble policy gradient methods. Project page at https://naoki04.github.io/paper‑cpo/ .

PaperId: 2754, https://arxiv.org/pdf/2603.01712.pdf GitHub

Abstract:
Fine‑tuning large language models for vertical domains remains labor‑intensive, requiring practitioners to curate data, configure training, and iteratively diagnose model behavior. Despite growing interest in autonomous machine learning and language agents, end‑to‑end LLM fine‑tuning has not been systematically studied as an interactive agent task. We introduce FT‑Dojo, an interactive benchmark environment for autonomous LLM fine‑tuning, comprising 13 tasks across 5 domains. Rather than a new collection of static datasets, FT‑Dojo standardizes a task interface, shared raw‑data repository, sandboxed execution environment, structured feedback protocol, and held‑out evaluation procedure. We further develop FT‑Agent, a fine‑tuning‑oriented autonomous framework that uses structured iteration planning, fail‑fast validation, and multi‑level feedback analysis to refine data and training strategies. Experiments show that FT‑Agent provides a strong initial baseline, achieving the best performance on 10 out of 13 tasks, with additional controlled comparisons against frontier agents, open‑source planning backbones, and multi‑run statistics supporting the main findings. Case studies show that agents can recover from failures through cumulative learning, while still exposing limitations in causal diagnosis and long‑horizon planning. The implementation is available at https://github.com/microsoft/rd‑agent.

PaperId: 2755, https://arxiv.org/pdf/2603.01692.pdf GitHub

Abstract:
LLM‑based agents for machine learning engineering (MLE) predominantly rely on tree search, a form of gradient‑free optimization that uses scalar validation scores to rank candidates. As LLM reasoning capabilities improve, exhaustive enumeration becomes increasingly inefficient compared to directed updates, analogous to how accurate gradients enable efficient descent over random search. We introduce Gome, an MLE agent that operationalizes gradient‑based optimization. Gome maps structured diagnostic reasoning to gradient computation, success memory to momentum, and multi‑trace execution to distributed optimization. Under a closed‑world protocol that isolates architectural effects from external knowledge, Gome achieves a state‑of‑the‑art 35.1% any‑medal rate on MLE‑Bench with a restricted 12‑hour budget on a single V100 GPU. Scaling experiments across 10 models reveal a critical crossover: with weaker models, tree search retains advantages by compensating for unreliable reasoning through exhaustive exploration; as reasoning capability strengthens, gradient‑based optimization progressively outperforms, with the gap widening at frontier‑tier models. Given the rapid advancement of reasoning‑oriented LLMs, this positions gradient‑based optimization as an increasingly favorable paradigm. We release our codebase and GPT‑5 traces at https://github.com/microsoft/RD‑Agent.

PaperId: 2756, https://arxiv.org/pdf/2603.01683.pdf GitHub

Abstract:
Injecting new reasoning knowledge into Large Language Models (LLMs) via post‑training often induces catastrophic forgetting. Recent studies emphasize the importance of on‑policy data but suggest that KL‑divergence fails to mitigate forgetting. In contrast, we show, both analytically and empirically, that the KL‑constrained reward formulation actually plays a critical role in retaining knowledge during post‑training. This motivates our Surgical Post‑Training (SPOT), a proximal on‑policy distillation framework designed to optimize reasoning efficiently while preserving prior knowledge. SPOT consists of (1) a data rectification pipeline employing an Oracle to surgically correct erroneous steps via minimal edits, generating proximal on‑policy data; and (2) a reward‑based binary cross‑entropy objective essential for enhancing reasoning and mitigating forgetting. Empirically, with only 4k rectified math pairs, SPOT improves Qwen3‑8B's accuracy by 6.2% on average across in‑domain and out‑of‑domain tasks, requiring merely 16‑minute model training on 8x H800 GPUs. Moreover, SPOT provides a superior initialization for subsequent reinforcement learning, significantly elevating the performance ceiling. Code: https://github.com/Visual‑AI/SPoT

PaperId: 2757, https://arxiv.org/pdf/2603.01657.pdf GitHub

Abstract:
Accurate forecasting of renewable energy generation is essential for efficient grid management and sustainable power planning. However, traditional supervised models often require access to labeled data from the target site, which may be unavailable due to privacy, cost, or logistical constraints. In this work, we propose FreeGNN, a Continual Source‑Free Graph Domain Adaptation framework that enables adaptive forecasting on unseen renewable energy sites without requiring source data or target labels. Our approach integrates a spatio‑temporal Graph Neural Network (GNN) backbone with a teacher‑‑student strategy, a memory replay mechanism to mitigate catastrophic forgetting, graph‑based regularization to preserve spatial correlations, and a drift‑aware weighting scheme to dynamically adjust adaptation strength during streaming updates. This combination allows the model to continuously adapt to non‑stationary environmental conditions while maintaining robustness and stability. We conduct extensive experiments on three real‑world datasets: GEFCom2012, Solar PV, and Wind SCADA, encompassing multiple sites, temporal resolutions, and meteorological features. The ablation study confirms that each component memory, graph regularization, drift‑aware adaptation, and teacher‑‑student strategy contributes significantly to overall performance. The experiments show that FreeGNN achieves an MAE of 5.237 and an RMSE of 7.123 on the GEFCom dataset, an MAE of 1.107 and an RMSE of 1.512 on the Solar PV dataset, and an MAE of 0.382 and an RMSE of 0.523 on the Wind SCADA dataset. These results demonstrate its ability to achieve accurate and robust forecasts in a source‑free, continual learning setting, highlighting its potential for real‑world deployment in adaptive renewable energy systems. For reproducibility, implementation details are available at: https://github.com/AraoufBh/FreeGNN.

PaperId: 2758, https://arxiv.org/pdf/2603.01607.pdf GitHub

Abstract:
Large visual language models (VLMs) have shown strong multi‑modal medical reasoning ability, but most operate as end‑to‑end black boxes, diverging from clinicians' evidence‑based, staged workflows and hindering clinical accountability. Complementarily, expert visual grounding models can accurately localize regions of interest (ROIs), providing explicit, reliable evidence that improves both reasoning accuracy and trust. In this paper, we introduce CARE, advancing Clinical Accountability in multi‑modal medical Reasoning with an Evidence‑grounded agentic framework. Unlike existing approaches that couple grounding and reasoning within a single generalist model, CARE decomposes the task into coordinated sub‑modules to reduce shortcut learning and hallucination: a compact VLM proposes relevant medical entities; an expert entity‑referring segmentation model produces pixel‑level ROI evidence; and a grounded VLM reasons over the full image augmented by ROI hints. The VLMs are optimized with reinforcement learning with verifiable rewards to align answers with supporting evidence. Furthermore, a VLM coordinator plans tool invocation and reviews evidence‑answer consistency, providing agentic control and final verification. Evaluated on standard medical VQA benchmarks, our CARE‑Flow (coordinator‑free) improves average accuracy by 10.9% over the same size (10B) state‑of‑the‑art (SOTA). With dynamic planning and answer review, our CARE‑Coord yields a further gain, outperforming the heavily pre‑trained SOTA by 5.2%. Our experiments demonstrate that an agentic framework that emulates clinical workflows, incorporating decoupled specialized models and explicit evidence, yields more accurate and accountable medical AI. Project page: https://xypb.github.io/CARE‑Project‑Page/

PaperId: 2759, https://arxiv.org/pdf/2603.01571.pdf GitHub GitHub GitHub

Abstract:
Recent advancements in Generative Reward Models (GRMs) have demonstrated that scaling the length of Chain‑of‑Thought (CoT) reasoning considerably enhances the reliability of evaluation. However, current works predominantly rely on unstructured length scaling, ignoring the divergent efficacy of different reasoning mechanisms: Breadth‑CoT (B‑CoT, i.e., multi‑dimensional principle coverage) and Depth‑CoT (D‑CoT, i.e., substantive judgment soundness). To address this, we introduce Mix‑GRM, a framework that reconfigures raw rationales into structured B‑CoT and D‑CoT through a modular synthesis pipeline, subsequently employing Supervised Fine‑Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR) to internalize and optimize these mechanisms. Comprehensive experiments demonstrate that Mix‑GRM establishes a new state‑of‑the‑art across five benchmarks, surpassing leading open‑source RMs by an average of 8.2%. Our results reveal a clear divergence in reasoning: B‑CoT benefits subjective preference tasks, whereas D‑CoT excels in objective correctness tasks. Consequently, misaligning the reasoning mechanism with the task directly degrades performance. Furthermore, we demonstrate that RLVR acts as a switching amplifier, inducing an emergent polarization where the model spontaneously allocates its reasoning style to match task demands. The synthesized data and models are released at \hrefhttps://huggingface.co/collections/DonJoey/mix‑grmHugging Face, and the code is released at \hrefhttps://github.com/Don‑Joey/Mix‑GRMGithub.

PaperId: 2760, https://arxiv.org/pdf/2603.01554.pdf GitHub

Abstract:
The smart home is a key domain within the Society 5.0 vision for a human‑centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyond traditional limited experts. This shift necessitates inclusive simulation frameworks that support research across diverse fields in industry and academia. However, existing smart home simulators require significant technical expertise, offer limited adaptability, and lack automated evolution, thereby failing to meet the holistic needs of Society 5.0. These constraints impede researchers from efficiently conducting simulations and experiments for security, energy, health, climate, and socio‑economic research. To address these challenges, this paper presents the Society 5.0‑driven Smart Home Environment Simulator Agent (S5‑HES Agent), an agentic simulation framework that transforms traditional smart home simulation through autonomous AI orchestration. The framework coordinates specialized agents through interchangeable large language models (LLMs), enabling natural‑language‑driven end‑to‑end smart home simulation configuration without programming expertise. A retrieval‑augmented generation (RAG) pipeline with semantic, keyword, and hybrid search retrieves smart home knowledge. Comprehensive evaluation on S5‑HES Agent demonstrates that the RAG pipeline achieves near‑optimal retrieval fidelity, simulated device behaviour and threat scenarios align with real‑world IoT datasets, and simulation engine scales predictably across home configurations, establishing a stable foundation for Society 5.0 smart home research. Source code is available under the MIT License at https://github.com/AsiriweLab/S5‑HES‑Agent.

PaperId: 2761, https://arxiv.org/pdf/2603.01549.pdf GitHub

Abstract:
Humans learn not only how their bodies move, but also how the surrounding world responds to their actions. In contrast, while recent Vision‑Language‑Action (VLA) models exhibit impressive semantic understanding, they often fail to capture the spatiotemporal dynamics governing physical interaction. In this paper, we introduce Pri4R, a simple yet effective approach that endows VLA models with an implicit understanding of world dynamics by leveraging privileged 4D information during training. Specifically, Pri4R augments VLAs with a lightweight point track head that predicts 3D point tracks. By injecting VLA features into this head to jointly predict future 3D trajectories, the model learns to incorporate evolving scene geometry within its shared representation space, enabling more physically aware context for precise control. Due to its architectural simplicity, Pri4R is compatible with dominant VLA design patterns with minimal changes. During inference, we run the model using the original VLA architecture unchanged; Pri4R adds no extra inputs, outputs, or computational overhead. Across simulation and real‑world evaluations, Pri4R significantly improves performance on challenging manipulation tasks, including a +10% gain on LIBERO‑Long and a +40% gain on RoboCasa. We further show that 3D point track prediction is an effective supervision target for learning action‑world dynamics, and validate our design choices through extensive ablations. Project page: https://jiiiisoo.github.io/Pri4R/

PaperId: 2762, https://arxiv.org/pdf/2603.01465.pdf GitHub

Abstract:
Existing Vision‑Language‑Action (VLA) models often struggle to generalize to long‑horizon tasks due to their heavy reliance on immediate observations. While recent studies incorporate retrieval mechanisms or extend context windows to handle procedural tasks, they often struggle to capture Non‑Markovian dependencies, where optimal actions rely solely on specific past states rather than the current observation. To address this, we introduce Keyframe‑Chaining VLA, a framework that extracts and links key historical frames to model long‑horizon dependencies. Specifically, we propose an automatic keyframe selector that learns a discriminative embedding space, effectively identifying distinct state transitions. To capture task‑critical information, we design a progress‑aware query mechanism that dynamically retrieves historical frames based on their temporal relevance to the current execution phase. These selected keyframes are integrated into the VLA as interleaved visual tokens, explicitly grounding the policy in the long‑horizon temporal context. Finally, we introduce a suite of four Non‑Markovian manipulation tasks built upon the ManiSkill simulator to measure task success rates. Experimental results demonstrate that our method achieves superior performance, effectively tackling robot manipulation tasks characterized by long‑horizon temporal dependencies. Code is available at https://github.com/cytoplastm/KC‑VLA.

PaperId: 2763, https://arxiv.org/pdf/2603.01455.pdf GitHub

Abstract:
While multimodal large language models have demonstrated impressive short‑term reasoning, they struggle with long‑horizon video understanding due to limited context windows and static memory mechanisms that fail to mirror human cognitive efficiency. Existing paradigms typically fall into two extremes: vision‑centric methods that incur high latency and redundancy through dense visual accumulation, or text‑centric approaches that suffer from detail loss and hallucination via aggressive captioning. To bridge this gap, we propose MM‑Mem, a pyramidal multimodal memory architecture grounded in Fuzzy‑Trace Theory. MM‑Mem structures memory hierarchically into a Sensory Buffer, Episodic Stream, and Symbolic Schema, enabling the progressive distillation of fine‑grained perceptual traces (verbatim) into high‑level semantic schemas (gist). Furthermore, to govern the dynamic construction of memory, we derive a Semantic Information Bottleneck objective and introduce SIB‑GRPO to optimize the trade‑off between memory compression and task‑relevant information retention. In inference, we design an entropy‑driven top‑down memory retrieval strategy. Extensive experiments across 4 benchmarks confirm that MM‑Mem achieves state‑of‑the‑art performance on both offline and streaming tasks, demonstrating robust generalization and validating the effectiveness of cognition‑inspired memory organization. Code and associated configurations are publicly available at https://github.com/EliSpectre/MM‑Mem.

PaperId: 2764, https://arxiv.org/pdf/2603.01452.pdf GitHub GitHub

Abstract:
Developing generalist robots capable of mastering diverse skills remains a central challenge in embodied AI. While recent progress emphasizes scaling model parameters and offline datasets, such approaches are limited in robotics, where learning requires active interaction. We argue that effective online learning should scale the \emphnumber of tasks, rather than the number of samples per task. This regime reveals a structural advantage of model‑based reinforcement learning (MBRL). Because physical dynamics are invariant across tasks, a shared world model can aggregate multi‑task experience to learn robust, task‑agnostic representations. In contrast, model‑free methods suffer from gradient interference when tasks demand conflicting actions in similar states. Task diversity therefore acts as a regularizer for MBRL, improving dynamics learning and sample efficiency. We instantiate this idea with EfficientZero‑Multitask (EZ‑M), a sample‑efficient multi‑task MBRL algorithm for online learning. Evaluated on HumanoidBench, a challenging whole‑body control benchmark, EZ‑M achieves state‑of‑the‑art performance with significantly higher sample efficiency than strong baselines, without extreme parameter scaling. These results establish task scaling as a critical axis for scalable robotic learning. The project website is available \hrefhttps://yewr.github.io/ez_m/here.

PaperId: 2765, https://arxiv.org/pdf/2603.01410.pdf GitHub

Abstract:
Knowledge graphs provide structured and reliable information for many real‑world applications, motivating increasing interest in combining large language models (LLMs) with graph‑based retrieval to improve factual grounding. Recent Graph‑based Retrieval‑Augmented Generation (GraphRAG) methods therefore introduce iterative interaction between LLMs and knowledge graphs to enhance reasoning capability. However, existing approaches typically depend on manually designed guidance and interact with knowledge graphs through a limited set of predefined tools, which substantially constrains graph exploration. To address these limitations, we propose GraphScout, a training‑centric agentic graph reasoning framework equipped with more flexible graph exploration tools. GraphScout enables models to autonomously interact with knowledge graphs to synthesize structured training data which are then used to post‑train LLMs, thereby internalizing agentic graph reasoning ability without laborious manual annotation or task curation. Extensive experiments across five knowledge‑graph domains show that a small model (e.g., Qwen3‑4B) augmented with GraphScout outperforms baseline methods built on leading LLMs (e.g., Qwen‑Max) by an average of 16.7% while requiring significantly fewer inference tokens. Moreover, GraphScout exhibits robust cross‑domain transfer performance. Our code will be made publicly available~\footnotehttps://github.com/Ying‑Yuchen/_GraphScout_.

PaperId: 2766, https://arxiv.org/pdf/2603.01361.pdf GitHub

Abstract:
Feature encoders play a key role in pixel‑level crack segmentation by shaping the representation of fine textures and thin structures. Existing CNN‑, Transformer‑, and Mamba‑based models each capture only part of the required spatial or structural information, leaving clear gaps in modeling complex crack patterns. To address this, we present MixerCSeg, a mixer architecture designed like a coordinated team of specialists, where CNN‑like pathways focus on local textures, Transformer‑style paths capture global dependencies, and Mamba‑inspired flows model sequential context within a single encoder. At the core of MixerCSeg is the TransMixer, which explores Mamba's latent attention behavior while establishing dedicated pathways that naturally express both locality and global awareness. To further enhance structural fidelity, we introduce a spatial block processing strategy and a Direction‑guided Edge Gated Convolution (DEGConv) that strengthens edge sensitivity under irregular crack geometries with minimal computational overhead. A Spatial Refinement Multi‑Level Fusion (SRF) module is then employed to refine multi‑scale details without increasing complexity. Extensive experiments on multiple crack segmentation benchmarks show that MixerCSeg achieves state‑of‑the‑art performance with only 2.05 GFLOPs and 2.54 M parameters, demonstrating both efficiency and strong representational capability. The code is available at https://github.com/spiderforest/MixerCSeg.

PaperId: 2767, https://arxiv.org/pdf/2603.01295.pdf GitHub GitHub

Abstract:
Breast ultrasound interpretation requires simultaneous lesion segmentation and tissue classification. However, conventional multi‑task learning approaches suffer from task interference and rigid coordination strategies that fail to adapt to instance‑specific prediction difficulty. We propose a multi‑task framework addressing these limitations through multi‑level decoder interaction and uncertainty‑aware adaptive coordination. Task Interaction Modules operate at all decoder levels, establishing bidirectional segmentation‑classification communication during spatial reconstruction through attention weighted pooling and multiplicative modulation. Unlike prior single‑level or encoder‑only approaches, this multi‑level design captures scale specific task synergies across semantic‑to‑spatial scales, producing complementary task interaction streams. Uncertainty‑Proxy Attention adaptively weights base versus enhanced features at each level using feature activation variance, enabling per‑level and per‑sample task balancing without heuristic tuning. To support instance‑adaptive prediction, multi‑scale context fusion captures morphological cues across varying lesion sizes. Evaluation on multiple publicly available breast ultrasound datasets demonstrates competitive performance, including 74.5% lesion IoU and 90.6% classification accuracy on BUSI dataset. Ablation studies confirm that multi‑level task interaction provides significant performance gains, validating that decoder‑level bidirectional communication is more effective than conventional encoder‑only parameter sharing. The code is available at: https://github.com/C‑loud‑Nine/Uncertainty‑Aware‑Multi‑Level‑Decoder‑Interaction.

PaperId: 2768, https://arxiv.org/pdf/2603.01292.pdf GitHub

Abstract:
This paper proposes Proximal Policy Optimization with Linear Temporal Logic Constraints (PPO‑LTL), a framework that integrates safety constraints written in LTL into PPO for safe reinforcement learning. LTL constraints offer rigorous representations of complex safety requirements, such as regulations that broadly exist in robotics, enabling systematic monitoring of safety requirements. Violations against LTL constraints are monitored by limit‑deterministic Büchi automata, and then translated by a logic‑to‑cost mechanism into penalty signals. The signals are further employed for guiding the policy optimization via the Lagrangian scheme. Extensive experiments on the Zones and CARLA environments show that our PPO‑LTL can consistently reduce safety violations, while maintaining competitive performance, against the state‑of‑the‑art methods. The code is at https://github.com/EVIEHub/PPO‑LTL.

PaperId: 2769, https://arxiv.org/pdf/2603.01274.pdf GitHub

Abstract:
Machine learning accelerates molecular property prediction, yet state‑of‑the‑art Large Language Models and Graph Neural Networks operate as black boxes. In drug discovery, where safety is critical, this opacity risks masking false correlations and excluding human expertise. Existing interpretability methods suffer from the effectiveness‑trustworthiness trade‑off: explanations may fail to reflect a model's true reasoning, degrade performance, or lack domain grounding. Concept Bottleneck Models (CBMs) offer a solution by projecting inputs to human‑interpretable concepts before readout, ensuring that explanations are inherently faithful to the decision process. However, adapting CBMs to chemistry faces three challenges: the Relevance Gap (selecting task‑relevant concepts from a large descriptor space), the Annotation Gap (obtaining concept supervision for molecular data), and the Capacity Gap (degrading performance due to bottleneck constraints). We introduce GlassMol, a model‑agnostic CBM that addresses these gaps through automated concept curation and LLM‑guided concept selection. Experiments across thirteen benchmarks demonstrate that \method generally matches or exceeds black‑box baselines, suggesting that interpretability does not sacrifice performance and challenging the commonly assumed trade‑off. Code is available at https://github.com/walleio/GlassMol.

PaperId: 2770, https://arxiv.org/pdf/2603.01229.pdf GitHub

Abstract:
Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabilities. Consequently, they struggle to solve tasks that require reasoning over historical observations and maintaining task‑relevant information over time, which are common requirements in real‑world manipulation scenarios. Although several memory‑aware policies have been proposed, systematic evaluation of memory‑dependent manipulation remains underexplored, and the relationship between architectural design choices and memory performance is still not well understood. To address this gap, we introduce RMBench, a simulation benchmark comprising 9 manipulation tasks that span multiple levels of memory complexity, enabling systematic evaluation of policy memory capabilities. We further propose Mem‑0, a modular manipulation policy with explicit memory components designed to support controlled ablation studies. Through extensive simulation and real‑world experiments, we identify memory‑related limitations in existing policies and provide empirical insights into how architectural design choices influence memory performance. The website is available at https://rmbench.github.io/.

PaperId: 2771, https://arxiv.org/pdf/2603.01209.pdf GitHub

Abstract:
Tool‑augmented LLMs are increasingly deployed as agents that interleave natural‑language reasoning with executable Python actions, as in CodeAct‑style frameworks. In deployment, these agents rely on runtime state that persists across steps. By contrast, the traces used to post‑train these models rarely encode how interpreter state is managed. We ask whether interpreter persistence is merely a runtime scaffold, or a property of the training data that shapes how agents learn to use the interpreter. We isolate state persistence as a training‑time variable. We introduce Opaque Knapsack, a procedurally generated family of partially observable optimization tasks designed to prevent one‑shot solutions. Item attributes and constraints are hidden behind budgeted tool calls, forcing multi‑turn control flow and iterative state revision. Holding task instances, prompts, tools, model, and supervision fixed, we generate matched trajectories differing only in whether interpreter state persists across steps or resets after each action. We then fine‑tune identical base models (Qwen3‑8B) on each trace variant and evaluate all four train‑runtime combinations. Our 2x2 cross‑evaluation shows that interpreter persistence shapes how agents reach solutions, not whether they do: solution quality is statistically indistinguishable across conditions, but token cost and stability differ substantially. A persistent‑trained model in a stateless runtime triggers missing‑variable errors in roughly 80% of episodes; a stateless‑trained model in a persistent runtime redundantly re‑derives retained state, using roughly 3.5x more tokens. Interpreter persistence should be treated as a first‑class semantic of agent traces. Aligning fine‑tuning data with deployment runtimes improves efficiency and reduces brittle train‑runtime mismatches.

PaperId: 2772, https://arxiv.org/pdf/2603.01185.pdf GitHub

Abstract:
Fine‑tuning large language models (LLMs) on custom datasets has become a standard approach for adapting these models to specific domains and applications. However, recent studies have shown that such fine‑tuning can lead to significant degradation in the model's safety. Existing defense methods operate at the sample level and often suffer from an unsatisfactory trade‑off between safety and utility. To address this limitation, we perform a systematic token‑level diagnosis of safety degradation during fine‑tuning. Based on this, we propose token‑level data selection for safe LLM fine‑tuning (TOSS), a novel framework that quantifies the safety risk of each token by measuring the loss difference between a safety‑degraded model and a utility‑oriented model. This token‑level granularity enables accurate identification and removal of unsafe tokens, thereby preserving valuable task‑specific information. In addition, we introduce a progressive refinement strategy, TOSS‑Pro, which iteratively enhances the safety‑degraded model's ability to identify unsafe tokens. Extensive experiments demonstrate that our approach robustly safeguards LLMs during fine‑tuning while achieving superior downstream task performance, significantly outperforming existing sample‑level defense methods. Our code is available at https://github.com/Polly‑LYP/TOSS.

PaperId: 2773, https://arxiv.org/pdf/2603.01169.pdf GitHub

Abstract:
The exponential growth of video content necessitates effective video summarization to efficiently extract key information from long videos. However, current approaches struggle to fully comprehend complex videos, primarily because they employ static or modality‑agnostic fusion strategies. These methods fail to account for the dynamic, frame‑dependent variations in modality saliency inherent in video data. To overcome these limitations, we propose TripleSumm, a novel architecture that adaptively weights and fuses the contributions of visual, text, and audio modalities at the frame level. Furthermore, a significant bottleneck for research into multimodal video summarization has been the lack of comprehensive benchmarks. Addressing this bottleneck, we introduce MoSu (Most Replayed Multimodal Video Summarization), the first large‑scale benchmark that provides all three modalities. Extensive experiments demonstrate that TripleSumm achieves state‑of‑the‑art performance, outperforming existing methods by a significant margin on four benchmarks, including MoSu. Our code and dataset are available at https://github.com/smkim37/TripleSumm.

PaperId: 2774, https://arxiv.org/pdf/2603.01161.pdf GitHub

Abstract:
Change detection (CD) in remote sensing aims to identify semantic differences between satellite images captured at different times. While deep learning has significantly advanced this field, existing approaches based on convolutional neural networks (CNNs), transformers and Selective State Space Models (SSMs) still struggle to precisely delineate change regions. In particular, traditional transformer‑based methods suffer from quadratic computational complexity when applied to very high‑resolution (VHR) satellite images and often perform poorly with limited training data, leading to under‑utilization of the rich spatial information available in VHR imagery. We present GRAD‑Former, a novel framework that enhances contextual understanding while maintaining efficiency through reduced model size. The proposed framework consists of a novel encoder with Adaptive Feature Relevance and Refinement (AFRAR) module, fusion and decoder blocks. AFRAR integrates global‑local contextual awareness through two proposed components: the Selective Embedding Amplification (SEA) module and the Global‑Local Feature Refinement (GLFR) module. SEA and GLFR leverage gating mechanisms and differential attention, respectively, which generates multiple softmax heaps to capture important features while minimizing the captured irreverent features. Multiple experiments across three challenging CD datasets (LEVIR‑CD, CDD, DSIFN‑CD) demonstrate GRAD‑Former's superior performance compared to existing approaches. Notably, GRAD‑Former outperforms the current state‑of‑the‑art models across all the metrics and all the datasets while using fewer parameters. Our framework establishes a new benchmark for remote sensing change detection performance. Our code will be released at: https://github.com/Ujjwal238/GRAD‑Former

PaperId: 2775, https://arxiv.org/pdf/2603.01152.pdf GitHub

Abstract:
Deep‑research agents are capable of executing multi‑step web exploration, targeted retrieval, and sophisticated question answering. Despite their powerful capabilities, deep‑research agents face two critical bottlenecks: (1) the lack of large‑scale, challenging datasets with real‑world difficulty, and (2) the absence of accessible, open‑source frameworks for data synthesis and agent training. To bridge these gaps, we first construct DeepResearch‑9K, a large‑scale challenging dataset specifically designed for deep‑research scenarios built from open‑source multi‑hop question‑answering (QA) datasets via a low‑cost autonomous pipeline. Notably, it consists of (1) 9000 questions spanning three difficulty levels from L1 to L3 (2) high‑quality search trajectories with reasoning chains from Tongyi‑DeepResearch‑30B‑A3B, a state‑of‑the‑art deep‑research agent, and (3) verifiable answers. Furthermore, we develop an open‑source training framework DeepResearch‑R1 that supports (1) multi‑turn web interactions, (2) different reinforcement learning (RL) approaches, and (3) different reward models such as rule‑based outcome reward and LLM‑as‑judge feedback. Finally, empirical results demonstrate that agents trained on DeepResearch‑9K under our DeepResearch‑R1 achieve state‑of‑the‑art results on challenging deep‑research benchmarks. We release the DeepResearch‑9K dataset on https://huggingface.co/datasets/artillerywu/DeepResearch‑9K and the code of DeepResearch‑R1 on https://github.com/Applied‑Machine‑Learning‑Lab/DeepResearch‑R1.

PaperId: 2776, https://arxiv.org/pdf/2603.01106.pdf GitHub

Abstract:
Reinforcement learning (RL) with group relative policy optimization (GRPO) has become a widely adopted approach for enhancing the reasoning capabilities of multimodal large language models (MLLMs). While GRPO enables long‑chain reasoning without a critic, it often suffers from sparse rewards on difficult problems and advantage vanishing when group‑level rewards are too consistent for overly easy or hard problems. Existing solutions (sample expansion, selective utilization, and indirect reward design) often fail to maintain enough variance in within‑group reward distributions to yield clear optimization signals. To address this, we propose DIVA‑GRPO, a difficulty‑adaptive variant advantage method that adjusts variant difficulty distributions from a global perspective. DIVA‑GRPO dynamically assesses problem difficulty, samples variants with appropriate difficulty levels, and calculates advantages across local and global groups using difficulty‑weighted and normalized scaling. This alleviates reward sparsity and advantage vanishing while improving training stability. Extensive experiments on six reasoning benchmarks demonstrate that DIVA‑GRPO outperforms existing approaches in training efficiency and reasoning performance. Code: https://github.com/Siaaaaaa1/DIVA‑GRPO

PaperId: 2777, https://arxiv.org/pdf/2603.01050.pdf GitHub

Abstract:
We aim to develop a multimodal research agent capable of explicit reasoning and planning, multi‑tool invocation, and cross‑modal information synthesis, enabling it to conduct deep research tasks. However, we observe three main challenges in developing such agents: (1) scarcity of search‑intensive multimodal QA data, (2) lack of effective search trajectories, and (3) prohibitive cost of training with online search APIs. To tackle them, we first propose Hyper‑Search, a hypergraph‑based QA generation method that models and connects visual and textual nodes within and across modalities, enabling to generate search‑intensive multimodal QA pairs that require invoking various search tools to solve. Second, we introduce DR‑TTS, which first decomposes search‑involved tasks into several categories according to search tool types, and respectively optimize specialized search tool experts for each tool. It then recomposes tool experts to jointly explore search trajectories via tree search, producing trajectories that successfully solve complex tasks using various search tools. Third, we build an offline search engine supporting multiple search tools, enabling agentic reinforcement learning without using costly online search APIs. With the three designs, we develop MM‑DeepResearch, a powerful multimodal deep research agent, and extensive results shows its superiority across benchmarks. Code is available at https://github.com/HJYao00/MM‑DeepResearch

PaperId: 2778, https://arxiv.org/pdf/2603.01045.pdf GitHub

Abstract:
Large language models are increasingly deployed in multi‑agent systems to overcome context limitations by distributing information across agents. Yet whether agents can reliably compute with distributed information, rather than merely exchange it, remains an open question. We introduce SILO‑BENCH, a role‑agnostic benchmark of 30 algorithmic tasks across three communication complexity levels, evaluating 54 configurations over 1,620 experiments. Our experiments expose a fundamental Communication‑Reasoning Gap: agents spontaneously form task‑appropriate coordination topologies and exchange information actively, yet systematically fail to synthesize distributed state into correct answers. The failure is localized to the reasoning‑integration stage where agents often acquire sufficient information but cannot integrate it. This coordination overhead compounds with scale, eventually eliminating parallelization gains entirely. These findings demonstrate that naively scaling agent count cannot circumvent context limitations, and SILO‑BENCH provides a foundation for tracking progress toward genuinely collaborative multi‑agent systems. The code is available at https://github.com/jwyjohn/acl26‑silo‑bench .

PaperId: 2779, https://arxiv.org/pdf/2603.01042.pdf GitHub

Abstract:
Large Language Models (LLMs) have demonstrated remarkable success in general‑purpose reasoning. However, they still struggle to understand and reason about time series data, which limits their effectiveness in decision‑making scenarios that depend on temporal dynamics. In this paper, we propose Thoth, the first family of mid‑trained LLMs with general‑purpose time series understanding capabilities. As a pivotal intermediate stage, mid‑training achieves task‑ and domain‑agnostic alignment between time series and natural language, for which we construct Book‑of‑Thoth, a high‑quality, time‑series‑centric mid‑training corpus. Book‑of‑Thoth enables both time‑series‑to‑text and text‑to‑time‑series generation, equipping LLMs with a foundational grasp of temporal patterns. To better evaluate advanced reasoning capabilities, we further present KnoTS, a novel benchmark of knowledge‑intensive time series understanding, designed for joint reasoning over temporal patterns and domain knowledge. Extensive experiments demonstrate that mid‑training with Book‑of‑Thoth enables Thoth to significantly outperform its base model and advanced LLMs across a range of time series question answering benchmarks. Moreover, Thoth exhibits superior capabilities when fine‑tuned under data scarcity, underscoring the effectiveness of mid‑training for time series understanding. Code is available at: https://github.com/thuml/Thoth.

PaperId: 2780, https://arxiv.org/pdf/2603.01034.pdf GitHub

Abstract:
Tensor Ring (TR) decomposition is a powerful tool for high‑order data modeling, but is inherently restricted to discrete forms defined on fixed meshgrids. In this work, we propose a TR functional decomposition for both meshgrid and non‑meshgrid data, where factors are parameterized by Implicit Neural Representations (INRs). However, optimizing this continuous framework to capture fine‑scale details is intrinsically difficult. Through a frequency‑domain analysis, we demonstrate that the spectral structure of TR factors determines the frequency composition of the reconstructed tensor and limits the high‑frequency modeling capacity. To mitigate this, we propose a reparameterized TR functional decomposition, in which each TR factor is a structured combination of a learnable latent tensor and a fixed basis. This reparameterization is theoretically shown to improve the training dynamics of TR factor learning. We further derive a principled initialization scheme for the fixed basis and prove the Lipschitz continuity of our proposed model. Extensive experiments on image inpainting, denoising, super‑resolution, and point cloud recovery demonstrate that our method achieves consistently superior performance over existing approaches. Code is available at https://github.com/YangyangXu2002/RepTRFD.

PaperId: 2781, https://arxiv.org/pdf/2603.01028.pdf GitHub

Abstract:
Implicit Neural Representations (INRs) have emerged as a powerful paradigm for various signal processing tasks, but their inherent spectral bias limits the ability to capture high‑frequency details. Existing methods partially mitigate this issue by using Fourier‑based features, which usually rely on fixed frequency bases. This forces multi‑layer perceptrons (MLPs) to inefficiently compose the required frequencies, thereby constraining their representational capacity. To address this limitation, we propose Content‑Aware Frequency Encoding (CAFE), which builds upon Fourier features through multiple parallel linear layers combined via a Hadamard product. CAFE can explicitly and efficiently synthesize a broader range of frequency bases, while the learned weights enable the selection of task‑relevant frequencies. Furthermore, we extend this framework to CAFE+, which incorporates Chebyshev features as a complementary component to Fourier bases. This combination provides a stronger and more stable frequency representation. Extensive experiments across multiple benchmarks validate the effectiveness and efficiency of our approach, consistently achieving superior performance over existing methods. Our code is available at https://github.com/JunboKe0619/CAFE.

PaperId: 2782, https://arxiv.org/pdf/2603.01012.pdf GitHub

Abstract:
Repository‑scale code reasoning is a cornerstone of modern AI‑assisted software engineering, enabling Large Language Models (LLMs) to handle complex workflows from program comprehension to complex debugging. However, balancing accuracy with context cost remains a significant bottleneck, as existing agentic approaches often waste computational resources through inefficient, iterative full‑text exploration. To address this, we introduce FastCode, a framework that decouples repository exploration from content consumption. FastCode utilizes a structural scouting mechanism to navigate a lightweight semantic‑structural map of the codebase, allowing the system to trace dependencies and pinpoint relevant targets without the overhead of full‑text ingestion. By leveraging structure‑aware navigation tools regulated by a cost‑aware policy, the framework constructs high‑value contexts in a single, optimized step. Extensive evaluations on the SWE‑QA, LongCodeQA, LOC‑BENCH, and GitTaskBench benchmarks demonstrate that FastCode consistently outperforms state‑of‑the‑art baselines in reasoning accuracy while significantly reducing token consumption, validating the efficiency of scouting‑first strategies for large‑scale code reasoning. Source code is available at https://github.com/HKUDS/FastCode.

PaperId: 2783, https://arxiv.org/pdf/2603.00960.pdf GitHub

Abstract:
Modern web applications are increasingly produced through AI‑assisted development and rapid no‑code deployment pipelines, widening the gap between accelerating software velocity and the limited adaptability of existing security tooling. Pattern‑driven scanners fail to reason about novel contexts, while emerging LLM‑based penetration testers rely on unconstrained exploration, yielding high cost, unstable behavior, and poor reproducibility. We introduce AWE, a memory‑augmented multi‑agent framework for autonomous web penetration testing that embeds structured, vulnerability‑specific analysis pipelines within a lightweight LLM orchestration layer. Unlike general‑purpose agents, AWE couples context aware payload mutations and generations with persistent memory and browser‑backed verification to produce deterministic, exploitation‑driven results. Evaluated on the 104‑challenge XBOW benchmark, AWE achieves substantial gains on injection‑class vulnerabilities ‑ 87% XSS success (+30.5% over MAPTA) and 66.7% blind SQL injection success (+33.3%) ‑ while being much faster, cheaper, and more token‑efficient than MAPTA, despite using a midtier model (Claude Sonnet 4) versus MAPTA's GPT‑5. MAPTA retains higher overall coverage due to broader exploratory capabilities, underscoring the complementary strengths of specialized and general‑purpose architectures. Our results demonstrate that architecture matters as much as model reasoning capabilities: integrating LLMs into principled, vulnerability‑aware pipelines yields substantial gains in accuracy, efficiency, and determinism for injection‑class exploits. The source code for AWE is available at: https://github.com/stuxlabs/AWE

PaperId: 2784, https://arxiv.org/pdf/2603.00918.pdf GitHub

Abstract:
Text‑to‑image generation powers content creation across design, media, and data augmentation. Post‑training of text‑to‑image generative models is a promising path to improve human preference alignment, factuality, and aesthetics. We introduce SOLACE (Self‑Originating LAtent Confidence Estimation), a post‑training framework that replaces external reward supervision with an internal self‑confidence signal: we re‑noise the model's own outputs and measure how accurately it recovers the injected noise, treating low reconstruction error as high self‑confidence. SOLACE converts this intrinsic signal into scalar rewards for reinforcement learning, requiring no external reward models, annotators, or preference data. By reinforcing high‑confidence generations, SOLACE delivers consistent gains in compositional generation, text rendering, and text‑image alignment. Integrating SOLACE with external rewards yields complementary improvements while alleviating reward hacking.

PaperId: 2785, https://arxiv.org/pdf/2603.00876.pdf GitHub GitHub

Abstract:
Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet‑labs. In these irreversible environments, probabilistic hallucinations are not merely incorrect; they can cause equipment damage or experimental failure. We propose BioProAgent, a neuro‑symbolic framework that anchors probabilistic planning in a deterministic Finite State Machine (FSM). We introduce a State‑Augmented Planning mechanism that enforces a rigorous Design‑Verify‑Rectify workflow, ensuring hardware compliance before execution. Furthermore, we address the context bottleneck inherent in complex device schemas by Semantic Symbol Grounding, reducing token consumption by ~6 through symbolic abstraction. In the extended BioProBench benchmark, BioProAgent achieves 95.6% physical compliance (compared to 21.0% for ReAct), demonstrating that neuro‑symbolic constraints are essential for reliable autonomy in irreversible physical environments. Code: https://github.com/YuyangSunshine/bioproagent | Website: https://yuyangsunshine.github.io/BioPro‑Project.

PaperId: 2786, https://arxiv.org/pdf/2603.00822.pdf GitHub

Abstract:
As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language instruction files such as AGENTS.md to express project‑specific coding conventions, tooling restrictions, and architectural boundaries. However, because these instructions remain passive text, agents frequently violate documented constraints due to context window saturation or conflicting local context. In autonomous settings without real‑time human supervision, such violations rapidly compound into technical debt. To ground autonomous agents in repository constraints, we introduce ContextCov, a framework that transforms passive natural language instructions into executable guardrails. Unlike prompt‑only or reflection‑only compliance approaches, ContextCov compiles documented constraints into three complementary checks: static AST queries for code patterns, runtime shell shims that intercept prohibited commands, and architectural validators that enforce structural rules. Acting as an automated, continuous reviewer, ContextCov intercepts agent actions and returns immediate, reproducible violation traces, enabling self‑correction before non‑compliant changes are finalized. We evaluate ContextCov on SWE‑bench Lite (12 repositories, 300 tasks). Compared to prompt‑only and LLM reflection baselines, ContextCov achieves 88.3% constraint compliance (vs. 67.0% and 50.3%) with 3.4x lower feedback cost, while maintaining functional correctness. The source code and evaluation results are available at https://github.com/reSHARMA/ContextCov.

PaperId: 2787, https://arxiv.org/pdf/2603.00792.pdf GitHub GitHub

Abstract:
Fluid‑solid interaction (FSI) problems are fundamental in many scientific and engineering applications, yet effectively capturing the highly nonlinear two‑way interactions remains a significant challenge. Most existing deep learning methods are limited to simplified one‑way FSI scenarios, often assuming rigid and static solid to reduce complexity. Even in two‑way setups, prevailing approaches struggle to capture dynamic, heterogeneous interactions due to the lack of cross‑domain awareness. In this paper, we introduce Fisale, a data‑driven framework for handling complex two‑way FSI problems. It is inspired by classical numerical methods, namely the Arbitrary Lagrangian‑Eulerian (ALE) method and the partitioned coupling algorithm. Fisale explicitly models the coupling interface as a distinct component and leverages multiscale latent ALE grids to provide unified, geometry‑aware embeddings across domains. A partitioned coupling module (PCM) further decomposes the problem into structured substeps, enabling progressive modeling of nonlinear interdependencies. Compared to existing models, Fisale introduces a more flexible framework that iteratively handles complex dynamics of solid, fluid and their coupling interface on a unified representation, and enables scalable learning of complex two‑way FSI behaviors. Experimentally, Fisale excels in three reality‑related challenging FSI scenarios, covering 2D, 3D and various tasks. The code is available at \hrefhttps://github.com/therontau0054/Fisale.

PaperId: 2788, https://arxiv.org/pdf/2603.00694.pdf GitHub

Abstract:
Explainability and transparent decision‑making are essential for the safe deployment of autonomous driving systems. Scene captioning summarizes environmental conditions and risk factors in natural language, improving transparency, safety, and human‑‑robot interaction. However, most existing approaches target structured urban scenarios; in off‑road environments, they are vulnerable to single‑modality degradations caused by rain, fog, snow, and darkness, and they lack a unified framework that jointly models structured scene captioning and path planning. To bridge this gap, we propose Wild‑Drive, an efficient framework for off‑road scene captioning and path planning. Wild‑Drive adopts modern multimodal encoders and introduces a task‑conditioned modality‑routing bridge, MoRo‑Former, to adaptively aggregate reliable information under degraded sensing. It then integrates an efficient large language model (LLM), together with a planning token and a gate recurrent unit (GRU) decoder, to generate structured captions and predict future trajectories. We also build the OR‑C2P Benchmark, which covers structured off‑road scene captioning and path planning under diverse sensor corruption conditions. Experiments on OR‑C2P dataset and a self‑collected dataset show that Wild‑Drive outperforms prior LLM‑based methods and remains more stable under degraded sensing. The code and benchmark will be publicly available at https://github.com/wangzihanggg/Wild‑Drive.

PaperId: 2789, https://arxiv.org/pdf/2603.00680.pdf GitHub

Abstract:
Long‑horizon agents face the challenge of growing context size during interaction with environment, which degrades the performance and stability. Existing methods typically introduce the external memory module and look up the relevant information from the stored memory, which prevents the model itself from proactively managing its memory content and aligning with the agent's overarching task objectives. To address these limitations, we propose the self‑memory policy optimization algorithm (MemPO), which enables the agent (policy model) to autonomously summarize and manage their memory during interaction with environment. By improving the credit assignment mechanism based on memory effectiveness, the policy model can selectively retain crucial information, significantly reducing token consumption while preserving task performance. Extensive experiments and analyses confirm that MemPO achieves absolute F1 score gains of 25.98% over the base model and 7.1% over the previous SOTA baseline, while reducing token usage by 67.58% and 73.12%. The code is released at https://github.com/TheNewBeeKing/MemPO.

PaperId: 2790, https://arxiv.org/pdf/2603.00656.pdf GitHub

Abstract:
Real‑world user requests to LLM agents are often underspecified. Agents must interact to acquire missing information and make correct downstream decisions. However, current multi‑turn GRPO‑based methods often rely on trajectory‑level reward computation, which leads to credit assignment problems and insufficient advantage signals within rollout groups. A feasible approach is to identify valuable interaction turns at a fine granularity to drive more targeted learning. To address this, we introduce InfoPO (Information‑Driven Policy Optimization), which frames multi‑turn interaction as a process of active uncertainty reduction and computes an information‑gain reward that credits turns whose feedback measurably changes the agent's subsequent action distribution compared to a masked‑feedback counterfactual. It then combines this signal with task outcomes via an adaptive variance‑gated fusion to identify information importance while maintaining task‑oriented goal direction. Across diverse tasks, including intent clarification, collaborative coding, and tool‑augmented decision making, InfoPO consistently outperforms prompting and multi‑turn RL baselines. It also demonstrates robustness under user simulator shifts and generalizes effectively to environment‑interactive tasks. Overall, InfoPO provides a principled and scalable mechanism for optimizing complex agent‑user collaboration. Code is available at https://github.com/kfq20/InfoPO.

PaperId: 2791, https://arxiv.org/pdf/2603.00631.pdf GitHub

Abstract:
LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components (Policy, Transition, and RewardModel) that plug into algorithms like MCTS and BFS. A decorator‑based registry enables domain experts to extend to new domains by registering components, and algorithmic researchers to implement custom search algorithms. We demonstrate composability on MATH500 (language reasoning), Crosswords (environment planning), and MapEval (tool use), showing that components and algorithms are orthogonal: components are reusable across algorithms within each task type, and algorithms work across all components and domains. We also report a mode‑collapse finding: in infinite action spaces, LLM policy diversity (not reward quality) is the bottleneck for effective tree search. A demonstration video is available at https://youtu.be/nRGX43YrR3I. The package is released under the Apache 2.0 license at https://github.com/xinzhel/lits‑llm, including installation instructions and runnable examples that enable users to reproduce the demonstrated workflows.

PaperId: 2792, https://arxiv.org/pdf/2603.00624.pdf GitHub

Abstract:
Catastrophic forgetting, the tendency of neural networks to forget previously learned knowledge when learning new tasks, has been a major challenge in continual learning (CL). To tackle this challenge, CL methods have been proposed and shown to reduce forgetting. Furthermore, CL models deployed in mission‑critical settings can benefit from uncertainty awareness by calibrating their predictions to reliably assess their confidences. However, existing uncertainty‑aware continual learning methods suffer from high computational overhead and incompatibility with mainstream replay methods. To address this, we propose idempotent experience replay (IDER), a novel approach based on the idempotent property where repeated function applications yield the same output. Specifically, we first adapt the training loss to make model idempotent on current data streams. In addition, we introduce an idempotence distillation loss. We feed the output of the current model back into the old checkpoint and then minimize the distance between this reprocessed output and the original output of the current model. This yields a simple and effective new baseline for building reliable continual learners, which can be seamlessly integrated with other CL approaches. Extensive experiments on different CL benchmarks demonstrate that IDER consistently improves prediction reliability while simultaneously boosting accuracy and reducing forgetting. Our results suggest the potential of idempotence as a promising principle for deploying efficient and trustworthy continual learning systems in real‑world applications.Our code is available at https://github.com/YutingLi0606/Idempotent‑Continual‑Learning.

PaperId: 2793, https://arxiv.org/pdf/2603.00623.pdf GitHub

Abstract:
Agentic systems augment large language models with external tools and iterative decision making, enabling complex tasks such as deep research, function calling, and coding. However, their long and intricate execution traces make failure diagnosis and root cause analysis extremely challenging. Manual inspection does not scale, while directly applying LLMs to raw traces is hindered by input length limits and unreliable reasoning. Focusing solely on final task outcomes further discards critical behavioral information required for accurate issue localization. To address these issues, we propose TraceSIR, a multi‑agent framework for structured analysis and reporting of agentic execution traces. TraceSIR coordinates three specialized agents: (1) StructureAgent, which introduces a novel abstraction format, TraceFormat, to compress execution traces while preserving essential behavioral information; (2) InsightAgent, which performs fine‑grained diagnosis including issue localization, root cause analysis, and optimization suggestions; (3) ReportAgent, which aggregates insights across task instances and generates comprehensive analysis reports. To evaluate TraceSIR, we construct TraceBench, covering three real‑world agentic scenarios, and introduce ReportEval, an evaluation protocol for assessing the quality and usability of analysis reports aligned with industry needs. Experiments show that TraceSIR consistently produces coherent, informative, and actionable reports, significantly outperforming existing approaches across all evaluation dimensions. Our project and video are publicly available at https://github.com/SHU‑XUN/TraceSIR.

PaperId: 2794, https://arxiv.org/pdf/2603.00601.pdf GitHub

Abstract:
AI code agents excel at isolated tasks yet struggle with multi‑file software engineering requiring architectural understanding. We introduce Theory of Code Space (ToCS), a benchmark that evaluates whether agents can construct, maintain, and update coherent architectural beliefs during codebase exploration. Agents explore procedurally generated codebases under partial observability ‑‑ opening files under a budget ‑‑ and periodically externalize their belief state as structured JSON, producing a time‑series of architectural understanding. Three findings emerge from experiments with four baselines and six frontier LLMs. First, the Active‑Passive Gap is model‑dependent: one model builds better maps through active exploration than from seeing all files at once, while another shows the opposite ‑‑ revealing that active exploration is itself a non‑trivial capability absent from some models. Second, retaining structured belief maps in context acts as self‑scaffolding for some models but not others, showing that the mechanism is model‑dependent. Third, belief state maintenance varies dramatically: a smaller model maintains perfectly stable beliefs across probes while its larger sibling suffers catastrophic belief collapse ‑‑ forgetting previously‑discovered components between probes. We release ToCS as open‑source software. Code: https://github.com/che‑shr‑cat/tocs

PaperId: 2795, https://arxiv.org/pdf/2603.00592.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models achieve over 95% success on standard benchmarks. However, through systematic experiments, we find that current state‑of‑the‑art VLA models largely ignore language instructions. Prior work lacks: (1) systematic semantic perturbation diagnostics, (2) a benchmark that forces language understanding by design, and (3) linguistically diverse training data. This paper constructs the LangGap benchmark, based on a four‑dimensional semantic perturbation method ‑‑ varying instruction semantics while keeping the tabletop layout fixed ‑‑ revealing language understanding deficits in π0.5. Existing benchmarks like LIBERO assign only one task per layout, underutilizing available objects and target locations; LangGap fully diversifies pick‑and‑place tasks under identical layouts, forcing models to truly understand language. Experiments show that targeted data augmentation can partially close the language gap ‑‑ success rate improves from 0% to 90% with single‑task training, and 0% to 28% with multi‑task training. However, as semantic diversity of extended tasks increases, model learning capacity proves severely insufficient; even trained tasks perform poorly. This reveals a fundamental challenge for VLA models in understanding diverse language instructions ‑‑ precisely the long‑term value of LangGap.

PaperId: 2796, https://arxiv.org/pdf/2603.00585.pdf GitHub

Abstract:
Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to microscopic phenomena remains largely unexplored. Microscale simulation holds great promise for biomedical applications such as drug discovery, organ‑on‑chip systems, and disease mechanism studies, while also showing potential in education and interactive visualization. In this work, we introduce MicroWorldBench, a multi‑level rubric‑based benchmark for microscale simulation tasks. MicroWorldBench enables systematic, rubric‑based evaluation through 459 unique expert‑annotated criteria spanning multiple microscale simulation task (e.g., organ‑level processes, cellular dynamics, and subcellular molecular interactions) and evaluation dimensions (e.g., scientific fidelity, visual quality, instruction following). MicroWorldBench reveals that current SOTA video generation models fail in microscale simulation, showing violations of physical laws, temporal inconsistency, and misalignment with expert criteria. To address these limitations, we construct MicroSim‑10K, a high‑quality, expert‑verified simulation dataset. Leveraging this dataset, we train MicroVerse, a video generation model tailored for microscale simulation. MicroVerse can accurately reproduce complex microscale mechanism. Our work first introduce the concept of Micro‑World Simulation and present a proof of concept, paving the way for applications in biology, education, and scientific visualization. Our work demonstrates the potential of educational microscale simulations of biological mechanisms. Our data and code are publicly available at https://github.com/FreedomIntelligence/MicroVerse

PaperId: 2797, https://arxiv.org/pdf/2603.00576.pdf GitHub

Abstract:
Symbolic music generation is a challenging task in multimedia generation, involving long sequences with hierarchical temporal structures, long‑range dependencies, and fine‑grained local details. Though recent diffusion‑based models produce high quality generations, they tend to suffer from high training and inference costs with long symbolic sequences due to iterative denoising and sequence‑length‑related costs. To deal with such problem, we put forth a diffusing strategy named SMDIM to combine efficient global structure construction and light local refinement. SMDIM uses structured state space models to capture long range musical context at near linear cost, and selectively refines local musical details via a hybrid refinement scheme. Experiments performed on a wide range of symbolic music datasets which encompass various Western classical music, popular music and traditional folk music show that the SMDIM model outperforms the other state‑of‑the‑art approaches on both the generation quality and the computational efficiency, and it has robust generalization to underexplored musical styles. These results show that SMDIM offers a principled solution for long‑sequence symbolic music generation, including associated attributes that accompany the sequences. We provide a project webpage with audio examples and supplementary materials at https://3328702107.github.io/smdim‑music/.

PaperId: 2798, https://arxiv.org/pdf/2603.00565.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have achieved remarkable performance but remain vulnerable to jailbreak attacks that can induce harmful content and undermine their secure deployment. Previous studies have shown that introducing additional inference steps, which disrupt security attention, can make MLLMs more susceptible to being misled into generating malicious content. However, these methods rely on single‑image masking or isolated visual cues, which only modestly extend reasoning paths and thus achieve limited effectiveness, particularly against strongly aligned commercial closed‑source models. To address this problem, in this paper, we propose Multi‑Image Dispersion and Semantic Reconstruction (MIDAS), a multimodal jailbreak framework that decomposes harmful semantics into risk‑bearing subunits, disperses them across multiple visual clues, and leverages cross‑image reasoning to gradually reconstruct the malicious intent, thereby bypassing existing safety mechanisms. The proposed MIDAS enforces longer and more structured multi‑image chained reasoning, substantially increases the model's reliance on visual cues while delaying the exposure of malicious semantics and significantly reducing the model's security attention, thereby improving the performance of jailbreak against advanced MLLMs. Extensive experiments across different datasets and MLLMs demonstrate that the proposed MIDAS outperforms state‑of‑the‑art jailbreak attacks for MLLMs and achieves an average attack success rate of 81.46% across 4 closed‑source MLLMs. Our code is available at this [link](https://github.com/Winnie‑Lian/MIDAS).

PaperId: 2799, https://arxiv.org/pdf/2603.00510.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) project visual tokens into the embedding space of language models, yet the internal structuring and processing of visual semantics remain poorly understood. In this work, we introduce a two‑fold analytical framework featuring a novel probing tool, EmbedLens, to conduct a fine‑grained analysis. We uncover a pronounced semantic sparsity at the input level: visual tokens consistently partition into sink, dead, and alive categories. Remarkably, only the alive tokens, comprising \approx60% of the total input, carry image‑specific meaning. Furthermore, using a targeted patch‑compression benchmark, we demonstrate that these alive tokens already encode rich, fine‑grained cues (e.g., objects, colors, and OCR) prior to entering the LLM. Internal visual computations (such as visual attention and feed‑forward networks) are redundant for most standard tasks. For the small subset of highly vision‑centric tasks that actually benefit from internal processing, we reveal that alive tokens naturally align with intermediate LLM layers rather than the initial embedding space, indicating that shallow‑layer processing is unnecessary and that direct mid‑layer injection is both sufficient. Ultimately, our findings provide a unified mechanistic view of visual token processing, paving the way for more efficient and interpretable MLLM architectures through selective token pruning, minimized visual computation, and mid‑layer injection. The code is released at: https://github.com/EIT‑NLP/EmbedLens.

PaperId: 2800, https://arxiv.org/pdf/2603.00501.pdf GitHub

Abstract:
The integration of large language models (LLMs) into wireless networks has sparked growing interest in building autonomous AI agents for wireless tasks. However, existing approaches rely heavily on manually crafted prompts and static agentic workflows, a process that is labor‑intensive, unscalable, and often suboptimal. In this paper, we propose WirelessAgent++, a framework that automates the design of agentic workflows for various wireless tasks. By treating each workflow as an executable code composed of modular operators, WirelessAgent++ casts agent design as a program search problem and solves it with a domain‑adapted Monte Carlo Tree Search (MCTS) algorithm. Moreover, we establish WirelessBench, a standardized multi‑dimensional benchmark suite comprising Wireless Communication Homework (WCHW), Network Slicing (WCNS), and Mobile Service Assurance (WCMSA), covering knowledge reasoning, code‑augmented tool use, and multi‑step decision‑making. Experiments demonstrate that \wap autonomously discovers superior workflows, achieving test scores of 78.37% (WCHW), 90.95% (WCNS), and 97.07% (WCMSA), with a total search cost below \ 5 per task. Notably, our approach outperforms state‑of‑the‑art prompting baselines by up to 31% and general‑purpose workflow optimizers by 11.1%, validating its effectiveness in generating robust, self‑evolving wireless agents. The code is available at https://github.com/jwentong/WirelessAgent‑R2.

PaperId: 2801, https://arxiv.org/pdf/2603.00483.pdf GitHub

Abstract:
Recent text‑to‑image (T2I) diffusion models achieve remarkable realism, yet faithful prompt‑image alignment remains challenging, particularly for complex prompts with multiple objects, relations, and fine‑grained attributes. Existing training‑free inference‑time scaling methods rely on fixed iteration budgets that cannot adapt to prompt difficulty, while reflection‑tuned models require carefully curated reflection datasets and extensive joint fine‑tuning of diffusion and vision‑language models, often overfitting to reflection paths data and lacking transferability across models. We introduce RAISE (Requirement‑Adaptive Self‑Improving Evolution), a training‑free, requirement‑driven evolutionary framework for adaptive T2I generation. RAISE formulates image generation as a requirement‑driven adaptive scaling process, evolving a population of candidates at inference time through a diverse set of refinement actions‑including prompt rewriting, noise resampling, and instructional editing. Each generation is verified against a structured checklist of requirements, enabling the system to dynamically identify unsatisfied items and allocate further computation only where needed. This achieves adaptive test‑time scaling that aligns computational effort with semantic query complexity. On GenEval and DrawBench, RAISE attains state‑of‑the‑art alignment (0.94 overall GenEval) while incurring fewer generated samples (reduced by 30‑40%) and VLM calls (reduced by 80%) than prior scaling and reflection‑tuned baselines, demonstrating efficient, generalizable, and model‑agnostic multi‑round self‑improvement. Code is available at https://github.com/LiyaoJiang1998/RAISE.

PaperId: 2802, https://arxiv.org/pdf/2603.00439.pdf GitHub

Abstract:
Computer‑Aided Design (CAD) generative modeling has a strong and long‑term application in the industry. Recently, the parametric CAD sequence as the design logic of an object has been widely mined by sequence models. However, the industrial CAD models, especially in component objects, are fine‑grained and complex, requiring a longer parametric CAD sequence to define. To address the problem, we introduce Mamba‑CAD, a self‑supervised generative modeling for complex CAD models in the industry, which can model on a longer parametric CAD sequence. Specifically, we first design an encoder‑decoder framework based on a Mamba architecture and pair it with a CAD reconstruction task for pre‑training to model the latent representation of CAD models; and then we utilize the learned representation to guide a generative adversarial network to produce the fake representation of CAD models, which would be finally recovered into parametric CAD sequences via the decoder of MambaCAD. To train Mamba‑CAD, we further create a new dataset consisting of 77,078 CAD models with longer parametric CAD sequences. Comprehensive experiments are conducted to demonstrate the effectiveness of our model under various evaluation metrics, especially in the generation length of valid parametric CAD sequences. The code and dataset can be achieved from https://github.com/Sunny‑Hack/Code‑for‑Mamba‑CAD‑AAAI‑2025‑.

PaperId: 2803, https://arxiv.org/pdf/2603.00433.pdf GitHub

Abstract:
Executing multiple tasks simultaneously in medical image analysis, including segmentation, classification, detection, and regression, often introduces significant challenges regarding model generalizability and the optimization of shared feature representations. While Vision Foundation Models (VFMs) provide powerful general representations, full fine‑tuning on limited medical data is prone to overfitting and incurs high computational costs. Moreover, existing parameter‑efficient fine‑tuning approaches typically adopt task‑agnostic adaptation protocols, overlooking both task‑specific mechanisms and the varying sensitivity of model layers during fine‑tuning. In this work, we propose Task‑Aware Prompting and Selective Layer Fine‑Tuning (TAP‑SLF), a unified framework for multi‑task ultrasound image analysis. TAP‑SLF incorporates task‑aware soft prompts to encode task‑specific priors into the input token sequence and applies LoRA to selected specific top layers of the encoder. This strategy updates only a small fraction of the VFM parameters while keeping the pre‑trained backbone frozen. By combining task‑aware prompts with selective high‑layer fine‑tuning, TAP‑SLF enables efficient VFM adaptation to diverse medical tasks within a shared backbone. Results on the FMC_UIA 2026 Challenge test set, where TAP‑SLF wins fifth place, combined with evaluations on the officially released training dataset using an 8:2 train‑test split, demonstrate that task‑aware prompting and selective layer tuning are effective strategies for efficient VFM adaptation.

PaperId: 2804, https://arxiv.org/pdf/2603.00431.pdf GitHub

Abstract:
A high‑performing, general‑purpose visual understanding model should map visual inputs to a taxonomic tree of labels, identify novel categories beyond the training set for which few or no publicly available images exist. Large Multimodal Models (LMMs) have achieved remarkable progress in fine‑grained visual recognition (FGVR) for known categories. However, they remain limited in hierarchical visual recognition (HVR) that aims at predicting consistent label paths from coarse to fine categories, especially for novel categories. To tackle these challenges, we propose Taxonomy‑Aware Representation Alignment (TARA), a simple yet effective strategy to inject taxonomic knowledge into LMMs. TARA leverages representations from biology foundation models (BFMs) that encode rich biological relationships through hierarchical contrastive learning. By aligning the intermediate representations of visual features with those of BFMs, LMMs are encouraged to extract discriminative visual cues well structured in the taxonomy tree. Additionally, we align the representations of the first answer token with the ground‑truth label, flexibly bridging the gap between contextualized visual features and categories of varying granularity according to user intent. Experiments demonstrate that TARA consistently enhances LMMs' hierarchical consistency and leaf node accuracy, enabling reliable recognition of both known and novel categories within complex biological taxonomies. Code is available at https://github.com/PKU‑ICST‑MIPL/TARA_CVPR2026.

PaperId: 2805, https://arxiv.org/pdf/2603.00349.pdf GitHub

Abstract:
Many complex tasks require extended effort, diverse capabilities, or coordinated actions beyond what a single agent can provide. However, simply adding more agents does not guarantee better performance, as effective cooperation depends on how agents interact with each other and with task structure to satisfy evolving constraints over time. This challenge is amplified for LLM‑based multi‑agent systems (LLM‑MAS): plans, messages, and revisions occur in natural language, whereas task progress depends on grounded environment actions. Current evaluations mostly treat cooperation as an implicit ingredient of final task success, leaving both cooperation and the effect of multi‑agent interaction on task dynamics difficult to study. We introduce COOP^2, an evaluation framework that grounds high‑level agent cooperation dynamics in LLM‑MAS within task progress in the environment. COOP^2 then defines cooperative tasks with verifiable cooperative requirements, allowing us to analyze how cooperation unfolds over time with respect to task progress, as well as where and why cooperation breaks down. Building on this framework, we develop COOP^2‑Repair, which predicts constraint failures from group plans and opens targeted repair channels for guided revisions. Across two environments and three communication structures, COOP^2‑Repair improves task success and constraint satisfaction while exposing the additional decision overhead and communication load required for repair. The project web page can be found at: https://happyeureka.github.io/coop2.

PaperId: 2806, https://arxiv.org/pdf/2603.00309.pdf GitHub

Abstract:
The increasingly popular agentic AI paradigm promises to harness the power of multiple, general‑purpose large language model (LLM) agents to collaboratively complete complex tasks. While many agentic AI systems reduce complexity through predefined workflows or fixed agent roles, the ideal is to support truly autonomous agents capable of emergent collaboration across many interacting agents. Yet in practice, such unstructured interactions often lead to redundant work and cascading failures that are difficult to interpret or correct. In this work, we study multi‑agent systems composed of general‑purpose LLM agents that solve problems through emergent collaboration, without relying on predefined roles, control flows, or communication constraints. We introduce the Dynamic Interaction Graph (DIG), which captures emergent collaboration as a time‑evolving causal network of agent activations and interactions. DIG makes emergent collaboration observable and explainable for the first time, enabling real‑time identification, explanation, and correction of collaboration‑induced error patterns directly from agents' collaboration paths. Thus, DIG fills a critical gap in understanding how general LLM agents solve problems together in truly agentic multi‑agent systems. The project webpage can be found at: https://happyeureka.github.io/dig.

PaperId: 2807, https://arxiv.org/pdf/2603.00205.pdf GitHub

Abstract:
Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill‑posed inverse problems. However, diffusion‑based reconstruction relies on Stochastic Differential Equations (SDEs) for forward diffusion and reverse denoising, where such stochasticity can interfere with repeated data consistency corrections in CT reconstruction. Since CT reconstruction is often time‑critical in clinical and interventional scenarios, improving reconstruction efficiency is essential. In contrast, Flow Matching (FM) models sampling as a deterministic Ordinary Differential Equation (ODE), yielding smooth trajectories without stochastic noise injection. This deterministic formulation is naturally compatible with repeated data consistency operations. Furthermore, we observe that FM‑predicted velocity fields exhibit strong correlations across adjacent steps. Motivated by this, we propose an FM‑based CT reconstruction framework (FMCT) and an efficient variant (EFMCT) that reuses previously predicted velocity fields over consecutive steps to substantially reduce the number of Neural network Function Evaluations (NFEs), thereby improving inference efficiency. We provide theoretical analysis showing that the error introduced by velocity reuse is bounded when combined with data consistency operations. Extensive experiments demonstrate that FMCT/EFMCT achieve competitive reconstruction quality while significantly improving computational efficiency compared with diffusion‑based methods. The codebase is open‑sourced at https://github.com/EFMCT/EFMCT.

PaperId: 2808, https://arxiv.org/pdf/2603.00195.pdf GitHub

Abstract:
The rapid proliferation of agentic AI skill ecosystems ‑‑ exemplified by OpenClaw (228,000 GitHub stars) and Anthropic Agent Skills (75,600 stars) ‑‑ has introduced a critical supply chain attack surface. The ClawHavoc campaign (January‑February 2026) infiltrated over 1,200 malicious skills into the OpenClaw marketplace, while MalTool catalogued 6,487 malicious tools that evade conventional detection. In response, twelve reactive security tools emerged, yet all rely on heuristic methods that provide no formal guarantees. We present SkillFortify, the first formal analysis framework for agent skill supply chains, with six contributions: (1) the DY‑Skill attacker model, a Dolev‑Yao adaptation to the five‑phase skill lifecycle with a maximality proof; (2) a sound static analysis framework grounded in abstract interpretation; (3) capability‑based sandboxing with a confinement proof; (4) an Agent Dependency Graph with SAT‑based resolution and lockfile semantics; (5) a trust score algebra with formal monotonicity; and (6) SkillFortifyBench, a 540‑skill benchmark. SkillFortify achieves 96.95% F1 (95% CI: [95.1%, 98.4%]) with 100% precision and 0% false positive rate on 540 skills, while SAT‑based resolution handles 1,000‑node graphs in under 100 ms.

PaperId: 2809, https://arxiv.org/pdf/2603.00164.pdf GitHub

Abstract:
We introduce Reverse CAPTCHA, an evaluation framework that tests whether large language models follow invisible Unicode‑encoded instructions embedded in otherwise normal‑looking text. Unlike traditional CAPTCHAs that distinguish humans from machines, our benchmark exploits a capability gap: models can perceive Unicode control characters that are invisible to human readers. We evaluate five models from two providers across two encoding schemes (zero‑width binary and Unicode Tags), four hint levels, two payload framings, and with tool use enabled or disabled. Across 8,308 model outputs, we find that tool use dramatically amplifies compliance (Cohen's h up to 1.37, a large effect), that models exhibit provider‑specific encoding preferences (OpenAI models decode zero‑width binary; Anthropic models prefer Unicode Tags), and that explicit decoding instructions increase compliance by up to 95 percentage points within a single model and encoding. All pairwise model differences are statistically significant (p < 0.05, Bonferroni‑corrected). These results highlight an underexplored attack surface for prompt injection via invisible Unicode payloads.

PaperId: 2810, https://arxiv.org/pdf/2603.00155.pdf GitHub

Abstract:
Automated academic poster generation aims to distill lengthy research papers into concise, visually coherent presentations. Existing Multimodal Large Language Models (MLLMs) based approaches, however, suffer from three critical limitations: low information density in full‑paper inputs, excessive token consumption, and unreliable layout verification. We present EfficientPosterGen, an end‑to‑end framework that addresses these challenges through semantic‑aware retrieval and token‑efficient multimodal generation. EfficientPosterGen introduces three core innovations: (1) Semantic‑aware Key Information Retrieval (SKIR), which constructs a semantic contribution graph to model inter‑segment relationships and selectively preserves important content; (2) Visual‑based Context Compression (VCC), which renders selected text segments into images to shift textual information into the visual modality, significantly reducing token usage while generating poster‑ready bullet points; and (3) Agentless Layout Violation Detection (ALVD), a deterministic color‑gradient‑based algorithm that reliably detects content overflow and spatial sparsity without auxiliary MLLMs. Extensive experiments demonstrate that EfficientPosterGen achieves substantial improvements in token efficiency and layout reliability while maintaining high poster quality, offering a scalable solution for automated academic poster generation. Our code is available at https://github.com/vinsontang1/EfficientPosterGen‑Code.

PaperId: 2811, https://arxiv.org/pdf/2603.00152.pdf GitHub

Abstract:
Following the success of Group Relative Policy Optimization (GRPO) in foundation LLMs, an increasing number of works have sought to adapt GRPO to Visual Large Language Models (VLLMs) for visual perception tasks (e.g., detection and segmentation). However, much of this line of research rests on a long‑standing yet unexamined assumption: training paradigms developed for language reasoning can be transferred seamlessly to visual perception. Our experiments show that this assumption is not valid, revealing intrinsic differences between reasoning‑oriented and perception‑oriented settings. Using reasoning segmentation as a representative case, we surface two overlooked factors: (i) the need for a broader output space, and (ii) the importance of fine‑grained, stable rewards. Building on these observations, we propose Dr.~Seg, a simple, plug‑and‑play GRPO‑based framework consisting of a Look‑to‑Confirm mechanism and a Distribution‑Ranked Reward module, requiring no architectural modifications and integrating seamlessly with existing GRPO‑based VLLMs. Extensive experiments demonstrate that Dr.~Seg improves performance in complex visual scenarios while maintaining strong generalization. Code, models, and datasets are available at https://github.com/eVI‑group‑SCU/Dr‑Seg.

PaperId: 2812, https://arxiv.org/pdf/2603.00149.pdf GitHub

Abstract:
Existing image SR and generic diffusion models transfer poorly to fluid SR: they are sampling‑intensive, ignore physical constraints, and often yield spectral mismatch and spurious divergence. We address fluid super‑resolution (SR) with ReMD (\underlineResidual‑\underlineMultigrid \underlineDiffusion), a physics‑consistent diffusion framework. At each reverse step, ReMD performs a \emphmultigrid residual correction: the update direction is obtained by coupling data consistency with lightweight physics cues and then correcting the residual across scales; the multiscale hierarchy is instantiated with a \emphmulti‑wavelet basis to capture both large structures and fine vortical details. This coarse‑to‑fine design accelerates convergence and preserves fine structures while remaining equation‑free. Across atmospheric and oceanic benchmarks, ReMD improves accuracy and spectral fidelity, reduces divergence, and reaches comparable quality with markedly fewer sampling steps than diffusion baselines. Our results show that enforcing physics consistency \emphinside the diffusion process via multigrid residual correction and multi‑wavelet multiscale modeling is an effective route to efficient fluid SR. Our code are available on https://github.com/lizhihao2022/ReMD.

PaperId: 2813, https://arxiv.org/pdf/2603.00140.pdf GitHub

Abstract:
Text‑to‑image diffusion models often memorize training data, revealing a fundamental failure to generalize beyond the training set. Current mitigation strategies typically sacrifice image quality or prompt alignment to reduce memorization. To address this, we propose Reachability‑Aware Diffusion Steering (RADS), an inference‑time framework that prevents memorization while preserving generation fidelity. RADS models the diffusion denoising process as a dynamical system and applies concepts from reachability analysis to approximate the "backward reachable tube"‑‑the set of intermediate states that inevitably evolve into memorized samples. We then formulate mitigation as a constrained reinforcement learning (RL) problem, where a policy learns to steer the trajectory away from memorization via minimal perturbations in the caption embedding space. Empirical evaluations show that RADS achieves a superior Pareto frontier between generation diversity (SSCD), quality (FID), and alignment (CLIP) compared to state‑of‑the‑art baselines. Crucially, RADS provides robust mitigation without modifying the diffusion backbone, offering a plug‑and‑play solution for safe generation. Our website is available at: https://s‑karnik.github.io/rads‑memorization‑project‑page/.

PaperId: 2814, https://arxiv.org/pdf/2603.00131.pdf GitHub

Abstract:
Subliminal prompting is a phenomenon in which language models are biased towards certain concepts or traits through prompting with semantically unrelated tokens. While prior work has examined subliminal prompting in user‑LLM interactions, potential bias transfer in multi‑agent systems and its associated security implications remain unexplored. In this work, we show that a single subliminally prompted agent can spread a weakening but persisting bias throughout its entire network. We measure this phenomenon across 6 agents using two different topologies, observing that the transferred concept maintains an elevated response rate throughout the network. To exemplify potential misalignment risks, we assess network performance on multiple‑choice TruthfulQA, showing that subliminal prompting of a single agent may degrade the truthfulness of other agents. Our findings reveal that subliminal prompting introduces a new attack vector in multi‑agent security, with implications for the alignment of such systems. The implementation of all experiments is publicly available at https://github.com/Multi‑Agent‑Security‑Initiative/thought_virus .

PaperId: 2815, https://arxiv.org/pdf/2603.00099.pdf GitHub

Abstract:
Neural architecture search (NAS) automates the discovery of neural networks that meet specified criteria, yet its evaluation procedures are often hardcoded, limiting the ability to introduce new metrics. This issue is especially pronounced in hardware‑aware NAS, where objectives depend on target devices such as edge hardware. To address this limitation, we propose SEval‑NAS, a metric‑evaluation mechanism that converts architectures to strings, embeds them as vectors, and predicts performance metrics. Using NATS‑Bench and HW‑NAS‑Bench, we evaluated accuracy, latency, and memory. Kendall's τ correlations showed stronger latency and memory predictions than accuracy, indicating the suitability of SEval‑NAS as a hardware cost predictor. We further integrated SEval‑NAS into FreeREA to evaluate metrics not originally included. The method successfully ranked FreeREA‑generated architectures, maintained search time, and required minimal algorithmic changes. Our implementation is available at: https://github.com/Analytics‑Everywhere‑Lab/neural‑architecture‑search

PaperId: 2816, https://arxiv.org/pdf/2603.00097.pdf GitHub GitHub

Abstract:
Adverse Drug Reactions (ADRs) are a leading cause of morbidity and mortality. Existing prediction methods rely mainly on chemical similarity, machine learning on structured databases, or isolated target profiles, but often fail to integrate heterogeneous, partly unstructured evidence effectively. We present a knowledge graph‑based framework that unifies diverse sources, drug‑target data (ChEMBL), clinical trial literature (PubMed), trial metadata (ClinicalTrials.gov), and post‑marketing safety reports (FAERS) into a single evidence‑weighted bipartite network of drugs and medical conditions. Applied to 400 protein kinase inhibitors, the resulting network enables contextual comparison of efficacy (HR, PFS, OS), phenotypic and target similarity, and ADR prediction via target‑to‑adverse‑event correlations. A non‑small cell lung cancer case study correctly highlights established and candidate drugs, target communities (ERbB, ALK, VEGF), and tolerability differences. Designed as an orthogonal, extensible analysis and search tool rather than a replacement for current models, the framework excels at revealing complex patterns, supporting hypothesis generation, and enhancing pharmacovigilance. Code and data are publicly available at https://github.com/davidjackson99/PKI_KG.

PaperId: 2817, https://arxiv.org/pdf/2603.00084.pdf GitHub

Abstract:
LLM‑agents are increasingly used to accelerate the progress of scientific research. Yet a persistent bottleneck is data access: agents not only lack readily available tools for retrieval, but also have to work with unstrcutured, human‑centric data on the Internet, such as HTML web‑pages and PDF files, leading to excessive token consumption, limit working efficiency, and brittle evidence look‑up. This gap motivates the development of an agentic data interface, which is designed to enable agents to access and utilize scientific literature in a more effective, efficient, and cost‑aware manner. In this paper, we introduce DeepXiv‑SDK, which offers a three‑layer agentic data interface for scientific literature. 1) Data Layer, which transforms unstructured, human‑centric data into normalized and structured representations in JSON format, improving data usability and enabling progressive accessibility of the data. 2) Service Layer, which presents readily available tools for data access and ad‑hoc retrieval. It also enables a rich form of agent usage, including CLI, MCP, and Python SDK. 3) Application Layer, which creates a built‑in agent, packaging basic tools from the service layer to support complex data access demands. DeepXiv‑SDK currently supports the complete ArXiv corpus, and is synchronized daily to incorporate new releases. It is designed to extend to all common open‑access corpora, such as PubMed Central, bioRxiv, medRxiv, and chemRxiv. We release RESTful APIs, an open‑source Python SDK, and a web demo showcasing deep search and deep research workflows. DeepXiv‑SDK is free to use with registration.

PaperId: 2818, https://arxiv.org/pdf/2603.00055.pdf GitHub

Abstract:
Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero‑shot paradigm, they still tend to produce high‑confidence yet unreliable decisions in fine‑grained and structurally complex industrial scenarios, and lack effective self‑corrective mechanisms. To address this issue, we propose M3‑AD, a unified reflection‑aware multimodal framework for industrial anomaly detection. M3‑AD comprises two complementary data resources: M3‑AD‑FT, designed for reflection‑aligned fine‑tuning, and M3‑AD‑Bench, designed for systematic cross‑category evaluation, together providing a foundation for reflection‑aware learning and reliability assessment. Building upon this foundation, we propose RA‑Monitor, which models reflection as a learnable decision revision process and guides models to perform controlled self‑correction when initial judgments are unreliable, thereby improving decision robustness. Extensive experiments conducted on M3‑AD‑Bench demonstrate that RA‑Monitor outperforms multiple open‑source and commercial MLLMs in zero‑shot anomaly detection and anomaly analysis tasks. Code will be released at https://github.com/Yanhui‑Lee/M3‑AD.

PaperId: 2819, https://arxiv.org/pdf/2603.00045.pdf GitHub

Abstract:
Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the "factorization barrier": the assumption that simultaneously predicted tokens are independent. This limitation forces a trade‑off: models must either sacrifice speed by resolving dependencies sequentially or suffer from incoherence due to factorization. We argue that this barrier arises not from limited backbone expressivity, but from a structural misspecification: models are restricted to fully factorized outputs because explicitly parameterizing a joint distribution would require the Transformer to output a prohibitively large number of parameters. We propose Coupled Discrete Diffusion (CoDD), a hybrid framework that breaks this barrier by replacing the fully‑factorized output distribution with a lightweight, tractable probabilistic inference layer. This formulation yields a distribution family that is significantly more expressive than standard factorized priors, enabling the modeling of complex joint dependencies, yet remains compact enough to avoid the prohibitive parameter explosion associated with full joint modeling. Empirically, CoDD seamlessly enhances diverse diffusion language model architectures with negligible overhead, matching the reasoning performance of computationally intensive Reinforcement Learning baselines at a fraction of the training cost. Furthermore, it prevents performance collapse in few‑step generation, enabling high‑quality outputs at significantly reduced latencies. Code available at: https://github.com/liuanji/CoDD

PaperId: 2820, https://arxiv.org/pdf/2603.00039.pdf GitHub GitHub

Abstract:
LLM‑as‑a‑judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they implicitly assume that judges provide independent estimates of true quality. However, in practice, LLM judges exhibit correlated errors caused by shared latent confounders ‑‑ such as verbosity, stylistic preferences, or training artifacts ‑‑ causing standard aggregation rules like majority vote or averaging to provide little gain or even amplify systematic mistakes. To address this, we introduce CARE, a confounder‑aware aggregation framework that explicitly models LLM judge scores as arising from both a latent true‑quality signal and shared confounding factors. Rather than heuristically re‑weighting judges, CARE separates quality from confounders without access to ground‑truth labels. We provide theoretical guarantees for identifiability and finite‑sample recovery under shared confounders, and we quantify the systematic bias incurred when aggregation models omit confounding latent factors. Across 12 public benchmarks spanning continuous scoring, binary classification, and pairwise preference settings, CARE improves aggregation accuracy, reducing error by up to 26.8%. Code is released in \hrefhttps://github.com/SprocketLab/CAREhttps://github.com/SprocketLab/CARE.

PaperId: 2821, https://arxiv.org/pdf/2603.00037.pdf GitHub

Abstract:
Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard to invert and a terminal state that deviates from the near noise assumption. Meanwhile, prior methods rely on time domain conditioning and seldom model schedule induced spectral degradation, which limits structure recovery across noise levels. We propose StaTS, a diffusion model for probabilistic time series forecasting that learns the noise schedule and the denoiser through alternating updates. StaTS includes Spectral Trajectory Scheduler (STS) that learns a data adaptive noise schedule with spectral regularization to improve structural preservation and stepwise invertibility, and Frequency Guided Denoiser (FGD) that estimates schedule induced spectral distortion and uses it to modulate denoising strength for heterogeneous restoration across diffusion steps and variables. A two stage training procedure stabilizes the coupling between schedule learning and denoiser optimization. Experiments on multiple real world benchmarks show consistent gains, while maintaining strong performance with fewer sampling steps. Our code is available at https://github.com/zjt‑gpu/StaTS/.

PaperId: 2822, https://arxiv.org/pdf/2602.24283.pdf GitHub

Abstract:
Modern optimizers like Adam and Muon are central to training large language models, but their reliance on first‑ and second‑order momenta introduces significant memory overhead, which constrains scalability and computational efficiency. In this work, we reframe the exponential moving average (EMA) used in these momenta as the training of a linear regressor via online gradient flow. Building on this equivalence, we introduce LoRA‑Pre, a novel low‑rank optimizer designed for efficient pre‑training. Specifically, LoRA‑Pre reduces the optimizer's memory footprint by decomposing the full momentum matrix into a compact low‑rank subspace within the online linear learner, thereby maintaining optimization performance while improving memory efficiency. We empirically validate LoRA‑Pre's efficacy by pre‑training models from the Llama architecture family, scaling from 60M to 1B parameters. LoRA‑Pre achieves the highest performance across all model sizes. Notably, LoRA‑Pre demonstrates remarkable rank efficiency, achieving comparable or superior results using only 1/8 the rank of baseline methods. Beyond pre‑training, we evaluate LoRA‑Pre's effectiveness in fine‑tuning scenarios. With the same rank, LoRA‑Pre consistently outperforms all efficient fine‑tuning baselines. Specifically, compared to standard LoRA, LoRA‑Pre achieves substantial improvements of 3.14 points on Llama‑3.1‑8B and 6.17 points on Llama‑2‑7B, validating our approach's effectiveness across both pre‑training and fine‑tuning paradigms. Our code is publicly available at https://github.com/mrflogs/LoRA‑Pre.

PaperId: 2823, https://arxiv.org/pdf/2602.24040.pdf GitHub

Abstract:
Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that overlook the epistemic uncertainty in reward models arising from limited human feedback. Recent work suggests that quantifying this uncertainty can reduce the costs of human annotation via uncertainty‑guided active learning and mitigate reward overoptimization in LLM post‑training. However, uncertainty‑aware reward models have so far been adopted without thorough comparison, leaving them poorly understood. This work introduces a unified framework, RewardUQ, to systematically evaluate uncertainty quantification for reward models. We compare common methods along standard metrics measuring accuracy and calibration, and we propose a new ranking strategy incorporating both dimensions for a simplified comparison. Our experimental results suggest that model size and initialization have the most meaningful impact on performance, and most prior work could have benefited from alternative design choices. To foster the development and evaluation of new methods and aid the deployment in downstream applications, we release our open‑source framework as a Python package. Our code is available at https://github.com/lasgroup/rewarduq.

PaperId: 2824, https://arxiv.org/pdf/2602.23981.pdf GitHub GitHub

Abstract:
Real‑world data frequently exhibit latent hierarchical structures, which can be naturally represented by hyperbolic geometry. Although recent hyperbolic neural networks have demonstrated promising results, many existing architectures remain partially intrinsic, mixing Euclidean operations with hyperbolic ones or relying on extrinsic parameterizations. To address it, we propose the \emphIntrinsic Lorentz Neural Network (ILNN), a fully intrinsic hyperbolic architecture that conducts all computations within the Lorentz model. At its core, the network introduces a novel \emphpoint‑to‑hyperplane fully connected layer (FC), replacing traditional Euclidean affine logits with closed‑form hyperbolic distances from features to learned Lorentz hyperplanes, thereby ensuring that the resulting geometric decision functions respect the inherent curvature. Around this fundamental layer, we design intrinsic modules: GyroLBN, a Lorentz batch normalization that couples gyro‑centering with gyro‑scaling, consistently outperforming both LBN and GyroBN while reducing training time. We additionally proposed a gyro‑additive bias for the FC output, a Lorentz patch‑concatenation operator that aligns the expected log‑radius across feature blocks via a digamma‑based scale, and a Lorentz dropout layer. Extensive experiments conducted on CIFAR‑10/100 and two genomic benchmarks (TEB and GUE) illustrate that ILNN achieves state‑of‑the‑art performance and computational cost among hyperbolic models and consistently surpasses strong Euclidean baselines. The code is available at \hrefhttps://github.com/Longchentong/ILNN\textcolormagentathis url.

PaperId: 2825, https://arxiv.org/pdf/2602.23960.pdf GitHub

Abstract:
How natural speech is represented in the brain constitutes a major challenge for cognitive neuroscience, with cortical envelope‑following responses playing a central role in speech decoding. This paper presents our approach to the Speech Detection task in the LibriBrain Competition 2025, utilizing over 50 hours of magnetoencephalography (MEG) signals from a single participant listening to LibriVox audiobooks. We introduce the proposed Sequential Hierarchical Integration Network for EEG and MEG (SHINE) to reconstruct the binary speech‑silence sequences from MEG signals. In the Extended Track, we further incorporated auxiliary reconstructions of speech envelopes and Mel spectrograms to enhance training. Ensemble methods combining SHINE with baselines (BrainMagic, AWavNet, ConvConcatNet) achieved F1‑macro scores of 0.9155 (Standard Track) and 0.9184 (Extended Track) on the leaderboard test set.

PaperId: 2826, https://arxiv.org/pdf/2602.23876.pdf GitHub GitHub

Abstract:
Designing efficient reward functions for low‑level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Models (LLMs) with task information to generate dense reward functions. These methods typically rely on training results as feedback, iteratively generating new reward functions with greedy or evolutionary algorithms. However, they suffer from poor utilization of historical feedback and inefficient search, resulting in limited improvements in complex control tasks. To address this challenge, we propose RF‑Agent, a framework that treats LLMs as language agents and frames reward function design as a sequential decision‑making process, enhancing optimization through better contextual reasoning. RF‑Agent integrates Monte Carlo Tree Search (MCTS) to manage the reward design and optimization process, leveraging the multi‑stage contextual reasoning ability of LLMs. This approach better utilizes historical information and improves search efficiency to identify promising reward functions. Outstanding experimental results in 17 diverse low‑level control tasks demonstrate the effectiveness of our method. The source code is available at https://github.com/deng‑ai‑lab/RF‑Agent.

PaperId: 2827, https://arxiv.org/pdf/2602.23827.pdf GitHub

Abstract:
In federated learning (FL), multi‑step local updates and data heterogeneity usually lead to sharper global minima, which degrades the performance of the global model. Popular FL algorithms integrate sharpness‑aware minimization (SAM) into local training to address this issue. However, in the high data heterogeneity setting, the flatness in local training does not imply the flatness of the global model. Therefore, minimizing the sharpness of the local loss surfaces on the client data does not enable the effectiveness of SAM in FL to improve the generalization ability of the global model. We define the flatness distance to explain this phenomenon. By rethinking the SAM in FL and theoretically analyzing the flatness distance, we propose a novel FedNSAM algorithm that accelerates the SAM algorithm by introducing global Nesterov momentum into the local update to harmonize the consistency of global and local flatness. FedNSAM uses the global Nesterov momentum as the direction of local estimation of client global perturbations and extrapolation. Theoretically, we prove a tighter convergence bound than FedSAM by Nesterov extrapolation. Empirically, we conduct comprehensive experiments on CNN and Transformer models to verify the superior performance and efficiency of FedNSAM. The code is available at https://github.com/junkangLiu0/FedNSAM.

PaperId: 2828, https://arxiv.org/pdf/2602.23798.pdf GitHub

Abstract:
Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's parameters or the client's forget set. To address this dual non‑disclosure constraint, we propose MPU, an algorithm‑agnostic privacy‑preserving Multiple Perturbed Copies Unlearning framework that primarily introduces two server‑side modules: Pre‑Process for randomized copy generation and Post‑Process for update aggregation. In Pre‑Process, the server distributes multiple perturbed and reparameterized model instances, allowing the client to execute unlearning locally on its private forget set without accessing the server's exact original parameters. After local unlearning, the server performs Post‑Process by inverting the reparameterization and aggregating updates with a harmonic denoising procedure to alleviate the impact of perturbation. Experiments with seven unlearning algorithms show that MPU achieves comparable unlearning performance to noise‑free baselines, with most algorithms' average degradation well below 1% up to 10% noise, and can even outperform the noise‑free baseline for some algorithms under 1% noise. Code is available at https://github.com/Tristan0318/MPU.

PaperId: 2829, https://arxiv.org/pdf/2602.23653.pdf GitHub

Abstract:
Large‑scale Vision‑Language Models (VLMs) exhibit strong zero‑shot recognition, yet their real‑world deployment is challenged by distribution shifts. While Test‑Time Adaptation (TTA) can mitigate this, existing VLM‑based TTA methods operate under a closed‑set assumption, failing in open‑set scenarios where test streams contain both covariate‑shifted in‑distribution (csID) and out‑of‑distribution (csOOD) data. This leads to a critical difficulty: the model must discriminate unknown csOOD samples to avoid interference while simultaneously adapting to known csID classes for accuracy. Current open‑set TTA (OSTTA) methods rely on hard thresholds for separation and entropy minimization for adaptation. These strategies are brittle, often misclassifying ambiguous csOOD samples and inducing overconfident predictions, and their parameter‑update mechanism is computationally prohibitive for VLMs. To address these limitations, we propose Prototype‑based Double‑Check Separation (ProtoDCS), a robust framework for OSTTA that effectively separates csID and csOOD samples, enabling safe and efficient adaptation of VLMs to csID data. Our main contributions are: (1) a novel double‑check separation mechanism employing probabilistic Gaussian Mixture Model (GMM) verification to replace brittle thresholding; and (2) an evidence‑driven adaptation strategy utilizing uncertainty‑aware loss and efficient prototype‑level updates, mitigating overconfidence and reducing computational overhead. Extensive experiments on CIFAR‑10/100‑C and Tiny‑ImageNet‑C demonstrate that ProtoDCS achieves state‑of‑the‑art performance, significantly boosting both known‑class accuracy and OOD detection metrics. Code will be available at https://github.com/O‑YangF/ProtoDCS.

PaperId: 2830, https://arxiv.org/pdf/2602.23652.pdf GitHub

Abstract:
Vision‑language models (VLMs) show strong potential for complex diagnostic tasks in medical imaging. However, applying VLMs to multi‑organ medical imaging introduces two principal challenges: (1) modality‑specific vision‑language alignment and (2) cross‑modal feature fusion. In this work, we propose MedMAP, a Medical Modality‑Aware Pretraining framework that enhances vision‑language representation learning in 3D MRI. MedMAP comprises a modality‑aware vision‑language alignment stage and a fine‑tuning stage for multi‑organ abnormality detection. During the pre‑training stage, the modality‑aware encoders implicitly capture the joint modality distribution and improve alignment between visual and textual representations. We then fine‑tune the pre‑trained vision encoders (while keeping the text encoder frozen) for downstream tasks. To this end, we curated MedMoM‑MRI3D, comprising 7,392 3D MRI volume‑report pairs spanning twelve MRI modalities and nine abnormalities tailored for various 3D medical analysis tasks. Extensive experiments on MedMoM‑MRI3D demonstrate that MedMAP significantly outperforms existing VLMs in 3D MRI‑based multi‑organ abnormality detection. Our code is available at https://github.com/RomantiDr/MedMAP.

PaperId: 2831, https://arxiv.org/pdf/2602.23632.pdf GitHub

Abstract:
Synthesizing high‑quality training data is crucial for enhancing domain models' reasoning abilities. Existing methods face limitations in long‑tail knowledge coverage, effectiveness verification, and interpretability. Knowledge‑graph‑based approaches still fall short in functionality, granularity, customizability, and evaluation. To address these issues, we propose MMKG‑RDS, a flexible framework for reasoning data synthesis that leverages multimodal knowledge graphs. It supports fine‑grained knowledge extraction, customizable path sampling, and multidimensional data quality scoring. We validate MMKG‑RDS with the MMKG‑RDS‑Bench dataset, covering five domains, 17 task types, and 14,950 samples. Experimental results show fine‑tuning Qwen3 models (0.6B/8B/32B) on a small number of synthesized samples improves reasoning accuracy by 9.2%. The framework also generates distinct data, challenging existing models on tasks involving tables and formulas, useful for complex benchmark construction. The dataset and code are available at https://github.com/360AILAB‑NLP/MMKG‑RDS

PaperId: 2832, https://arxiv.org/pdf/2602.23614.pdf GitHub

Abstract:
Machine learning holds promise for advancing clinical decision support, yet it remains unclear when multimodal learning truly helps in practice, particularly under modality missingness and fairness constraints. In this work, we conduct a systematic benchmark of multimodal fusion between Electronic Health Records (EHR) and chest X‑rays (CXR) on standardized cohorts from MIMIC‑IV and MIMIC‑CXR, aiming to answer four fundamental questions: when multimodal fusion improves clinical prediction, how different fusion strategies compare, how robust existing methods are to missing modalities, and whether multimodal models achieve algorithmic fairness. Our study reveals several key insights. Multimodal fusion improves performance when modalities are complete, with gains concentrating in diseases that require complementary information from both EHR and CXR. While cross‑modal learning mechanisms capture clinically meaningful dependencies beyond simple concatenation, the rich temporal structure of EHR introduces strong modality imbalance that architectural complexity alone cannot overcome. Under realistic missingness, multimodal benefits rapidly degrade unless models are explicitly designed to handle incomplete inputs. Moreover, multimodal fusion does not inherently improve fairness, with subgroup disparities mainly arising from unequal sensitivity across demographic groups. To support reproducible and extensible evaluation, we further release a flexible benchmarking toolkit that enables plug‑and‑play integration of new models and datasets. Together, this work provides actionable guidance on when multimodal learning helps, when it fails, and why, laying the foundation for developing clinically deployable multimodal systems that are both effective and reliable. The open‑source toolkit can be found at https://github.com/jakeykj/CareBench.

PaperId: 2833, https://arxiv.org/pdf/2602.23592.pdf GitHub

Abstract:
Memory‑augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long‑horizon embodied planning. By keeping track of past experiences and environmental states, memory enables LLMs to maintain a global view, thereby avoiding repetitive exploration. However, existing approaches often store the memory as raw text, leading to excessively long prompts and high prefill latency. While it is possible to store and reuse the KV caches, the efficiency benefits are greatly undermined due to frequent KV cache updates. In this paper, we propose KEEP, a KV‑cache‑centric memory management system for efficient embodied planning. KEEP features 3 key innovations: (1) a Static‑Dynamic Memory Construction algorithm that reduces KV cache recomputation by mixed‑granularity memory group; (2) a Multi‑hop Memory Re‑computation algorithm that dynamically identifies important cross‑attention among different memory groups and reconstructs memory interactions iteratively; (3) a Layer‑balanced Memory Loading that eliminates unbalanced KV cache loading and cross‑attention computation across different layers. Extensive experimental results have demonstrated that KEEP achieves 2.68x speedup with negligible accuracy loss compared with text‑based memory methods on ALFRED dataset. Compared with the KV re‑computation method CacheBlend (EuroSys'25), KEEP shows 4.13% success rate improvement and 1.90x time‑to‑first‑token (TTFT) reduction. Our code is available on https://github.com/PKU‑SEC‑Lab/KEEP_Embodied_Memory.

PaperId: 2834, https://arxiv.org/pdf/2602.23588.pdf GitHub

Abstract:
Large unimodal foundation models for vision and language encode rich semantic structures, yet aligning them typically requires computationally intensive multimodal fine‑tuning. Such approaches depend on large‑scale parameter updates, are resource intensive, and can perturb pretrained representations. Emerging evidence suggests, however, that independently trained foundation models may already exhibit latent semantic compatibility, reflecting shared structures in the data they model. This raises a fundamental question: can cross‑modal alignment be achieved without modifying the models themselves? Here we introduce HDFLIM (HyperDimensional computing with Frozen Language and Image Models), a framework that establishes cross‑modal mappings while keeping pretrained vision and language models fully frozen. HDFLIM projects unimodal embeddings into a shared hyperdimensional space and leverages lightweight symbolic operations ‑‑ binding, bundling, and similarity‑based retrieval to construct associative cross‑modal representations in a single pass over the data. Caption generation emerges from high‑dimensional memory retrieval rather than iterative gradient‑based optimization. We show that HDFLIM achieves performance comparable to end‑to‑end vision‑language training methods and produces captions that are more semantically grounded than zero‑shot baselines. By decoupling alignment from parameter tuning, our results suggest that semantic mapping across foundation models can be realized through symbolic operations on hyperdimensional encodings of the respective embeddings. More broadly, this work points toward an alternative paradigm for foundation model alignment in which frozen models are integrated through structured representational mappings rather than through large‑scale retraining. The codebase for our implementation can be found at https://github.com/Abhishek‑Dalvi410/HDFLIM.

PaperId: 2835, https://arxiv.org/pdf/2602.23581.pdf GitHub

Abstract:
Multivariate time series forecasting is widely applied in fields such as transportation, energy, and finance. However, the data commonly suffers from issues of multi‑scale characteristics, weak correlations, and noise interference, which limit the predictive performance of existing models. This paper proposes a dual‑stream sparse Mixer prediction framework that extracts global trends and local dynamic features from sequences in both the frequency and time domains, respectively. It employs a sparsity mechanism to filter out invalid information, thereby enhancing the accuracy of cross‑variable dependency modeling. Experimental results demonstrate that this method achieves leading performance on multiple real‑world scenario datasets, validating its effectiveness and generality. The code is available at https://github.com/SDMixer/SDMixer

PaperId: 2836, https://arxiv.org/pdf/2602.23575.pdf GitHub

Abstract:
Transforming image features from perspective view (PV) space to bird's‑eye‑view (BEV) space remains challenging in autonomous driving due to depth ambiguity and occlusion. Although several view transformation (VT) paradigms have been proposed, the challenge still remains. In this paper, we propose a new regularization framework, dubbed CycleBEV, that enhances existing VT models for BEV semantic segmentation. Inspired by cycle consistency, widely used in image distribution modeling, we devise an inverse view transformation (IVT) network that maps BEV segmentation maps back to PV segmentation maps and use it to regularize VT networks during training through cycle consistency losses, enabling them to capture richer semantic and geometric information from input PV images. To further exploit the capacity of the IVT network, we introduce two novel ideas that extend cycle consistency into geometric and representation spaces. We evaluate CycleBEV on four representative VT models covering three major paradigms using the large‑scale nuScenes dataset. Experimental results show consistent improvements ‑‑ with gains of up to 0.74, 4.86, and 3.74 mIoU for drivable area, vehicle, and pedestrian classes, respectively ‑‑ without increasing inference complexity, since the IVT network is used only during training. The implementation code is available at https://github.com/JeongbinHong/CycleBEV.

PaperId: 2837, https://arxiv.org/pdf/2602.23574.pdf GitHub

Abstract:
Understanding sources of uncertainty is fundamental to trustworthy three‑dimensional scene modeling. While recent advances in neural radiance fields (NeRFs) achieve impressive accuracy in scene reconstruction and novel view synthesis, the lack of uncertainty estimation significantly limits their deployment in safety‑critical settings. Existing uncertainty quantification methods for NeRFs fail to separately capture both aleatoric and epistemic uncertainties. Among those that do quantify one or the other, many of them either compromise rendering quality or incur significant computational overhead to obtain uncertainty estimates. To address these issues, we introduce Evidential Neural Radiance Fields, a probabilistic approach that seamlessly integrates with the NeRF rendering process, enabling direct quantification of both aleatoric and epistemic uncertainties from a single forward pass. We compare multiple uncertainty quantification methods on three standardized benchmarks, where our approach demonstrates state‑of‑the‑art scene reconstruction fidelity and uncertainty estimation quality. Code is available at https://github.com/KerryDRX/EvidentialNeRF.

PaperId: 2838, https://arxiv.org/pdf/2602.23556.pdf GitHub

Abstract:
Large‑scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex's neighbors to a fixed distance. Because large input graphs are distributed, training requires frequent irregular communication that stalls forward progress. Moreover, fetched data changes with graph, graph distribution, sample and batch parameters, and caching polices. Consequently, any static prefetching method will miss crucial opportunities to adapt to different dynamic conditions. In this paper, we introduce Rudder, a software module embedded in the state‑of‑the‑art AWS DistDGL framework, to autonomously prefetch remote nodes and minimize communication. Rudder's adaptation contrasts with both standard heuristics and traditional ML classifiers. We observe that the generative AI found in contemporary Large Language Models (LLMs) exhibits emergent properties like In‑Context Learning (ICL) for zero‑shot tasks, with logical multi‑step reasoning. We find this behavior well‑suited for adaptive control even with substantial undertraining. Evaluations using standard datasets and unseen configurations on the NERSC Perlmutter supercomputer show up to 91% improvement in end‑to‑end training performance over baseline DistDGL (no prefetching), and an 82% improvement over static prefetching, reducing communication by over 50%. Our code is available at https://github.com/aishwaryyasarkar/rudder‑llm‑agent.

PaperId: 2839, https://arxiv.org/pdf/2602.23349.pdf GitHub

Abstract:
Standard mixed‑precision training of neural networks requires many bytes of accelerator memory for each model parameter. These bytes reflect not just the parameter itself, but also its gradient and one or more optimizer state variables. With each of these values typically requiring 4 bytes, training even a 7 billion parameter model can be impractical for researchers with less than 100GB of accelerator memory. We introduce FlashOptim, a suite of optimizations that reduces per‑parameter memory by over 50% while preserving model quality and API compatibility. Our approach introduces two key techniques. First, we improve master weight splitting by finding and exploiting a tight bound on its quantization error. Second, we design companding functions that greatly reduce the error in 8‑bit optimizer state quantization. Together with 16‑bit gradients, these techniques reduce AdamW memory from 16 bytes to 7 bytes per parameter, or 5 bytes with gradient release. They also cut model checkpoint sizes by more than half. Experiments with FlashOptim applied to SGD, AdamW, and Lion show no measurable quality degradation on any task from a collection of standard vision and language benchmarks, including Llama‑3.1‑8B finetuning.

PaperId: 2840, https://arxiv.org/pdf/2602.23286.pdf GitHub

Abstract:
Real‑world Table‑Text question answering (QA) tasks require models that can reason across long text and source tables, traversing multiple hops and executing complex operations such as aggregation. Yet existing benchmarks are small, manually curated ‑ and therefore error‑prone ‑ and contain shallow questions that seldom demand more than two hops or invoke aggregations, grouping, or other advanced analytical operations expressible in natural‑language queries. We present SPARTA, an end‑to‑end construction framework that automatically generates large‑scale Table‑Text QA benchmarks with lightweight human validation, requiring only one quarter of the annotation time of HybridQA. The framework first constructs a reference fact database by enriching each source table with grounding tables whose tuples are atomic facts automatically extracted from the accompanying unstructured passages, then synthesizes nested queries whose number of nested predicates matches the desired hop count. To ensure that every SQL statement is executable and that its verbalization yields a fluent, human‑sounding question, we propose two novel techniques: provenance‑based refinement, which rewrites any syntactically valid query that returns a non‑empty result, and realistic‑structure enforcement, which confines generation to post‑order traversals of the query graph. The resulting pipeline produces thousands of high‑fidelity question‑answer pairs covering aggregations, grouping, and deep multi‑hop reasoning across text and tables. On SPARTA, state‑of‑the‑art models that reach over 70 F1 on HybridQA or over 50 F1 on OTT‑QA drop by more than 30 F1 points, exposing fundamental weaknesses in current cross‑modal reasoning. Our benchmark, construction code, and baseline models are available at https://github.com/pshlego/SPARTA/tree/main.

PaperId: 2841, https://arxiv.org/pdf/2602.23276.pdf GitHub GitHub

Abstract:
Chest X‑ray plays a central role in thoracic diagnosis, and its interpretation inherently requires multi‑step, evidence‑grounded reasoning. However, large vision‑language models (LVLMs) often generate plausible responses that are not faithfully grounded in diagnostic evidence and provide limited visual evidence for verification, while also requiring costly retraining to support new diagnostic tasks, limiting their reliability and adaptability in clinical settings. To address these limitations, we present CXReasonAgent, a diagnostic agent that integrates a large language model (LLM) with clinically grounded diagnostic tools to perform evidence‑grounded diagnostic reasoning using image‑derived diagnostic and visual evidence. To evaluate these capabilities, we introduce CXReasonDial, a multi‑turn dialogue benchmark with 1,946 dialogues across 12 diagnostic tasks, and show that CXReasonAgent produces faithfully grounded responses, enabling more reliable and verifiable diagnostic reasoning than LVLMs. These findings highlight the importance of integrating clinically grounded diagnostic tools, particularly in safety‑critical clinical settings. The demo is available \hrefhttps://ttumyche.github.io/cxreasonagent/#demohere.

PaperId: 2842, https://arxiv.org/pdf/2602.23258.pdf GitHub

Abstract:
While Multi‑Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information from individual agents. Current solutions often resort to rigid structural engineering or expensive fine‑tuning, limiting their adaptability. We propose AgentDropoutV2 (ADv2), a test‑time rectify‑or‑reject pruning framework that dynamically optimizes MAS information flow. Acting as an active firewall, ADv2 intercepts agent outputs and employs a retrieval‑augmented rectifier to iteratively correct errors. This rectification is guided by an indicator pool, which is constructed offline by distilling error patterns from historical MAS failure trajectories. Irreparable outputs are subsequently pruned to prevent error propagation. Empirical results demonstrate that ADv2 significantly boosts performance on both fixed and dynamic MAS frameworks, achieving average accuracy gains of 6.39 and 2.28 percentage points on extensive math and code benchmarks, respectively. Furthermore, ADv2 exhibits remarkable adaptivity, dynamically modulating rectification efforts based on task difficulty to resolve a wide spectrum of error patterns. Our code is released at https://github.com/TonySY2/AgentDropoutV2.

PaperId: 2843, https://arxiv.org/pdf/2602.23225.pdf GitHub

Abstract:
Diffusion Language Models (DLMs) are often advertised as enabling parallel token generation, yet practical fast DLMs frequently converge to left‑to‑right, autoregressive (AR)‑like decoding dynamics. In contrast, genuinely non‑AR generation is promising because it removes AR's sequential bottleneck, better exploiting parallel hardware to reduce synchronization/communication overhead and improve latency scaling with output length. We argue that a primary driver of AR‑like decoding is a mismatch between DLM objectives and the highly sequential structure of widely used training data, including standard pretraining corpora and long chain‑of‑thought (CoT) supervision. Motivated by this diagnosis, we propose NAP (Non‑Autoregressive Parallel DLMs), a proof‑of‑concept, data‑centric approach that better aligns supervision with non‑AR parallel decoding. NAP curates examples as multiple independent reasoning trajectories and couples them with a parallel‑forced decoding strategy that encourages multi‑token parallel updates. Across math reasoning benchmarks, NAP yields stronger performance under parallel decoding than DLMs trained on standard long CoT data, with gains growing as parallelism increases. Our results suggest that revisiting data and supervision is a principled direction for mitigating AR‑like behavior and moving toward genuinely non‑autoregressive parallel generation in DLMs. Our code is available at https://github.com/pixeli99/NAP.

PaperId: 2844, https://arxiv.org/pdf/2602.23153.pdf GitHub

Abstract:
Large Multimodal Models (LMMs) that process 3D data typically rely on heavy, pre‑trained visual encoders to extract geometric features. While recent 2D LMMs have begun to eliminate such encoders for efficiency and scalability, extending this paradigm to 3D remains challenging due to the unordered and large‑scale nature of point clouds. This leaves a critical unanswered question: How can we design an LMM that tokenizes unordered 3D data effectively and efficiently without a cumbersome encoder? We propose Fase3D, the first efficient encoder‑free Fourier‑based 3D scene LMM. Fase3D tackles the challenges of scalability and permutation invariance with a novel tokenizer that combines point cloud serialization and the Fast Fourier Transform (FFT) to approximate self‑attention. This design enables an effective and computationally minimal architecture, built upon three key innovations: First, we represent large scenes compactly via structured superpoints. Second, our space‑filling curve serialization followed by an FFT enables efficient global context modeling and graph‑based token merging. Lastly, our Fourier‑augmented LoRA adapters inject global frequency‑aware interactions into the LLMs at a negligible cost. Fase3D achieves performance comparable to encoder‑based 3D LMMs while being significantly more efficient in computation and parameters. Project website: https://tev‑fbk.github.io/Fase3D.

PaperId: 2845, https://arxiv.org/pdf/2602.23136.pdf GitHub

Abstract:
Numerous studies have shown that multimodal LLMs process speech and images well but fail in non‑intuitive ways rendering trivial tasks such as object counting unreliable. We investigate this behavior from an information‑theoretic perspective by framing multimodal LLM inference as a mismatched decoder problem: a decoder trained primarily on text can only extract information along text‑aligned directions (removing up to 98% of the variation in modality‑specific (non‑text) directions improves decoder loss) and the amount of accessible information is bounded by the Generalized Mutual Information (GMI). We show that information loss is bounded as the distributional mismatch between the source data and the text data increases, and as the sensitivity of the decoder increases. This bound is a function of the model's scoring rule not its architecture. We validate the predictions across five models spanning speech and vision. A controlled study (two Prismatic VLMs differing only in encoder text‑alignment) shows that the bottleneck lies in the scoring rule of the decoder rather than the text‑alignment of the encoder or the learned projection. A LoRA intervention demonstrates that simply training with an emotion‑related objective improves emotion detection from 17.3% to 61.8% task accuracy without affecting other attributes, confirming that the training objective determines what becomes accessible.

PaperId: 2846, https://arxiv.org/pdf/2602.23117.pdf GitHub

Abstract:
Adversarial transferability refers to the capacity of adversarial examples generated on the surrogate model to deceive alternate, unexposed victim models. This property eliminates the need for direct access to the victim model during an attack, thereby raising considerable security concerns in practical applications and attracting substantial research attention recently. In this work, we discern a lack of a standardized framework and criteria for evaluating transfer‑based attacks, leading to potentially biased assessments of existing approaches. To rectify this gap, we have conducted an exhaustive review of hundreds of related works, organizing various transfer‑based attacks into six distinct categories. Subsequently, we propose a comprehensive framework designed to serve as a benchmark for evaluating these attacks. In addition, we delineate common strategies that enhance adversarial transferability and highlight prevalent issues that could lead to unfair comparisons. Finally, we provide a brief review of transfer‑based attacks beyond image classification.

PaperId: 2847, https://arxiv.org/pdf/2602.23061.pdf GitHub GitHub

Abstract:
Semi‑structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irregular layouts. These documents are widely observed across domains and account for a large portion of real‑world data. However, existing methods struggle to support natural language question answering over these documents due to three main technical challenges: (1) The elements extracted by techniques like OCR are often fragmented and stripped of their original semantic context, making them inadequate for analysis. (2) Existing approaches lack effective representations to capture hierarchical structures within documents (e.g., associating tables with nested chapter titles) and to preserve layout‑specific distinctions (e.g., differentiating sidebars from main content). (3) Answering questions often requires retrieving and aligning relevant information scattered across multiple regions or pages, such as linking a descriptive paragraph to table cells located elsewhere in the document. To address these issues, we propose MoDora, an LLM‑powered system for semi‑structured document analysis. First, we adopt a local‑alignment aggregation strategy to convert OCR‑parsed elements into layout‑aware components, and conduct type‑specific information extraction for components with hierarchical titles or non‑text elements. Second, we design the Component‑Correlation Tree (CCTree) to hierarchically organize components, explicitly modeling inter‑component relations and layout distinctions through a bottom‑up cascade summarization process. Finally, we propose a question‑type‑aware retrieval strategy that supports (1) layout‑based grid partitioning for location‑based retrieval and (2) LLM‑guided pruning for semantic‑based retrieval. Experiments show MoDora outperforms baselines by 5.97%‑61.07% in accuracy. The code is at https://github.com/weAIDB/MoDora.

PaperId: 2848, https://arxiv.org/pdf/2602.22983.pdf GitHub

Abstract:
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts. This paper investigates the role of classical Chinese in jailbreak attacks. Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs. Based on this observation, this paper proposes a framework, CC‑BOS, for the automatic generation of classical Chinese adversarial prompts based on multi‑dimensional fruit fly optimization, facilitating efficient and automated jailbreak attacks in black‑box settings. Prompts are encoded into eight policy dimensions‑covering role, behavior, mechanism, metaphor, expression, knowledge, trigger pattern and context; and iteratively refined via smell search, visual search, and cauchy mutation. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black‑box jailbreak attacks. To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module. Extensive experiments demonstrate that effectiveness of the proposed CC‑BOS, consistently outperforming state‑of‑the‑art jailbreak attack methods.

PaperId: 2849, https://arxiv.org/pdf/2602.22955.pdf GitHub

Abstract:
Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging manifestations, yet existing public datasets remain limited in annotation richness and diagnostic semantics. To bridge this gap, we introduce MM‑NeuroOnco, a large‑scale multimodal benchmark and instruction‑tuning dataset for brain tumor MRI understanding, consisting of 24,726 MRI slices from 20 data sources paired with approximately 200,000 semantically enriched multimodal instructions spanning diverse tumor subtypes and imaging modalities. To mitigate the scarcity and high cost of diagnostic semantic annotations, we develop a multi‑model collaborative pipeline for automated medical information completion and quality control, enabling the generation of diagnosis‑related semantics beyond mask‑only annotations. Building upon this dataset, we further construct MM‑NeuroOnco‑Bench, a manually annotated evaluation benchmark with a rejection‑aware setting to reduce biases inherent in closed‑ended question formats. Evaluation across ten representative models shows that even the strongest baseline, Gemini 3 Flash, achieves only 41.88% accuracy on diagnosis‑related questions, highlighting the substantial challenges of multimodal brain tumor diagnostic understanding. Leveraging MM‑NeuroOnco, we further propose NeuroOnco‑GPT, which achieves a 27% absolute accuracy improvement on diagnostic questions following fine‑tuning. This result demonstrates the effectiveness of our dataset and benchmark in advancing clinically grounded multimodal diagnostic reasoning. Code and dataset are publicly available at: https://github.com/gfnnnb/MM‑NeuroOnco

PaperId: 2850, https://arxiv.org/pdf/2602.22871.pdf GitHub

Abstract:
Reasoning with large language models often benefits from generating multiple chains‑of‑thought, but existing aggregation strategies are typically trajectory‑level (e.g., selecting the best trace or voting on the final answer), discarding useful intermediate work from partial or "nearly correct" attempts. We propose Stitching Noisy Diffusion Thoughts, a self‑consistency framework that turns cheap diffusion‑sampled reasoning into a reusable pool of step‑level candidates. Given a problem, we (i) sample many diverse, low‑cost reasoning trajectories using a masked diffusion language model, (ii) score every intermediate step with an off‑the‑shelf process reward model (PRM), and (iii) stitch these highest‑quality steps across trajectories into a composite rationale. This rationale then conditions an autoregressive (AR) model (solver) to recompute only the final answer. This modular pipeline separates exploration (diffusion) from evaluation and solution synthesis, avoiding monolithic unified hybrids while preserving broad search. Across math reasoning benchmarks, we find that step‑level recombination is most beneficial on harder problems, and ablations highlight the importance of the final AR solver in converting stitched but imperfect rationales into accurate answers. Using low‑confidence diffusion sampling with parallel, independent rollouts, our training‑free framework improves average accuracy by up to 23.8% across six math and coding tasks. At the same time, it achieves up to a 1.8x latency reduction relative to both traditional diffusion models (e.g., Dream, LLaDA) and unified architectures (e.g., TiDAR). Code is available at https://github.com/roymiles/diffusion‑stitching.

PaperId: 2851, https://arxiv.org/pdf/2602.22839.pdf GitHub

Abstract:
Presentation generation requires deep content research, coherent visual design, and iterative refinement based on observation. However, existing presentation agents often rely on predefined workflows and fixed templates. To address this, we present DeepPresenter, an agentic framework that adapts to diverse user intents, enables effective feedback‑driven refinement, and generalizes beyond a scripted pipeline. Specifically, DeepPresenter autonomously plans, renders, and revises intermediate slide artifacts to support long‑horizon refinement with environmental observations. Furthermore, rather than relying on self‑reflection over internal signals (e.g., reasoning traces), our environment‑grounded reflection conditions the generation process on perceptual artifact states (e.g., rendered slides), enabling the system to identify and correct presentation‑specific issues during execution. Results on the evaluation set covering diverse presentation‑generation scenarios show that DeepPresenter achieves state‑of‑the‑art performance, and the fine‑tuned 9B model remains highly competitive at substantially lower cost. Our project is available at: https://github.com/icip‑cas/PPTAgent

PaperId: 2852, https://arxiv.org/pdf/2602.22817.pdf GitHub

Abstract:
Group‑based reinforcement learning (RL), such as GRPO, has advanced the capabilities of large language models on long‑horizon agentic tasks. To enable more fine‑grained policy updates, recent research has increasingly shifted toward stepwise group‑based policy optimization, which treats each step in a rollout trajectory independently while using a memory module to retain historical context. However, we find a key issue in estimating stepwise relative advantages, namely context inconsistency, where steps within the same group may differ in their historical contexts. Empirically, we reveal that this issue can lead to severely biased advantage estimation, thereby degrading policy optimization significantly. To address the issue, in this paper, we propose Hierarchy‑of‑Groups Policy Optimization (HGPO) for long‑horizon agentic tasks. Specifically, within a group of rollout trajectories, HGPO assigns each step to multiple hierarchical groups according to the consistency of historical contexts. Then, for each step, HGPO computes distinct advantages within each group and aggregates them with an adaptive weighting scheme. In this way, HGPO can achieve a favorable bias‑variance trade‑off in stepwise advantage estimation, without extra models or rollouts. Evaluations on two challenging agentic tasks, ALFWorld and WebShop with Qwen2.5‑1.5B‑Instruct and Qwen2.5‑7B‑Instruct, show that HGPO significantly outperforms existing agentic RL methods under the same computational constraints. Code is available at https://github.com/langfengQ/verl‑agent/tree/master/recipe/hgpo.

PaperId: 2853, https://arxiv.org/pdf/2602.22786.pdf GitHub

Abstract:
Value decomposition (VD) methods have achieved remarkable success in cooperative multi‑agent reinforcement learning (MARL). However, their reliance on the max operator for temporal‑difference (TD) target calculation leads to systematic Q‑value overestimation. This issue is particularly severe in MARL due to the combinatorial explosion of the joint action space, which often results in unstable learning and suboptimal policies. To address this problem, we propose QSIM, a similarity weighted Q‑learning framework that reconstructs the TD target using action similarity. Instead of using the greedy joint action directly, QSIM forms a similarity weighted expectation over a structured near‑greedy joint action space. This formulation allows the target to integrate Q‑values from diverse yet behaviorally related actions while assigning greater influence to those that are more similar to the greedy choice. By smoothing the target with structurally relevant alternatives, QSIM effectively mitigates overestimation and improves learning stability. Extensive experiments demonstrate that QSIM can be seamlessly integrated with various VD methods, consistently yielding superior performance and stability compared to the original algorithms. Furthermore, empirical analysis confirms that QSIM significantly mitigates the systematic value overestimation in MARL. Code is available at https://github.com/MaoMaoLYJ/pymarl‑qsim.

PaperId: 2854, https://arxiv.org/pdf/2602.22769.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly used as autonomous agents in complex, long‑horizon applications, where effective memory is critical for sustained performance. Yet existing memory benchmarks are largely dialogue‑centric, while real agent memory consists of continuous agent‑environment interaction trajectories composed of states, actions, observations, and tool outputs. To address this gap, we introduce AMA‑Bench (Agent Memory with Any length), a benchmark for evaluating long‑horizon memory in realistic agentic settings. AMA‑Bench combines real‑world agent trajectories from representative applications with expert‑curated QA, as well as synthetic trajectories that scale to arbitrary horizons with rule‑based QA. Our study shows that existing memory systems underperform because they fail to capture causal and objective information and rely heavily on lossy similarity‑based retrieval. We further propose AMA‑Agent, a memory system based on causality‑graph construction and tool‑augmented retrieval. AMA‑Agent achieves 57.22% accuracy on AMA‑Bench, outperforming the strongest baseline by 11.16%. Resources are available at: [https://ama‑bench.github.io/](https://ama‑bench.github.io/).

PaperId: 2855, https://arxiv.org/pdf/2602.22743.pdf GitHub

Abstract:
Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed‑domain data, leading to negative transfer and diminished model performance. Existing prevailing \emphmodel‑centric paradigm ‑‑ which relies on complex, customized architectures ‑‑ struggles to capture the subtle, non‑structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose \textscTaesar, a \emphdata‑centric framework for target‑aligned sequential regeneration, which employs a contrastive decoding mechanism to adaptively encode cross‑domain context into target‑domain sequences. It employs contrastive decoding to encode cross‑domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures. Experiments show \textscTaesar outperforms model‑centric solutions and generalizes to various sequential models. By generating enriched datasets, \textscTaesar effectively combines the strengths of data‑ and model‑centric paradigms. The code accompanying this paper is available at~ \textcolorbluehttps://github.com/USTC‑StarTeam/Taesar.

PaperId: 2856, https://arxiv.org/pdf/2602.22740.pdf GitHub

Abstract:
Referring Image Segmentation (RIS) aims to segment the object in an image uniquely referred to by a natural language expression. However, RIS training often contains hard‑to‑align and instance‑specific visual signals; optimizing on such pixels injects misleading gradients and drives the model in the wrong direction. By explicitly estimating pixel‑level vision‑language alignment, the learner can suppress low‑alignment regions, concentrate on reliable cues, and acquire more generalizable alignment features. In this paper, we propose Alignment‑Aware Masked Learning (AML), a simple yet effective training strategy that quantifies region‑referent alignment (PMME) and filters out unreliable pixels during optimization (AFM). Specifically, each sample first computes a similarity map between visual and textual features, and then masks out pixels falling below an adaptive similarity threshold, thereby excluding poorly aligned regions from the training process. AML does not require architectural changes and incurs no inference overhead, directing attention to the areas aligned with the textual description. Experiments on the RefCOCO (vanilla/+/g) datasets show that AML achieves state‑of‑the‑art results across all 8 splits, and beyond improving RIS performance, AML also enhances the model's robustness to diverse descriptions and scenarios. Code is available at https://github.com/pipashu1/AMLRIS.

PaperId: 2857, https://arxiv.org/pdf/2602.22700.pdf GitHub

Abstract:
Commercial large language models are typically deployed as black‑box API services, requiring users to trust providers to execute inference correctly and report token usage honestly. We present IMMACULATE, a practical auditing framework that detects economically motivated deviations‑such as model substitution, quantization abuse, and token overbilling‑without trusted hardware or access to model internals. IMMACULATE selectively audits a small fraction of requests using verifiable computation, achieving strong detection guarantees while amortizing cryptographic overhead. Experiments on dense and MoE models show that IMMACULATE reliably distinguishes benign and malicious executions with under 1% throughput overhead. Our code is published at https://github.com/guo‑yanpei/Immaculate.

PaperId: 2858, https://arxiv.org/pdf/2602.22661.pdf GitHub

Abstract:
Although diffusion language models (DLMs) are evolving quickly, many recent models converge on a set of shared components. These components, however, are distributed across ad‑hoc research codebases or lack transparent implementations, making them difficult to reproduce or extend. As the field accelerates, there is a clear need for a unified framework that standardizes these common components while remaining flexible enough to support new methods and architectures. To address this gap, we introduce dLLM, an open‑source framework that unifies the core components of diffusion language modeling ‑‑ training, inference, and evaluation ‑‑ and makes them easy to customize for new designs. With dLLM, users can reproduce, finetune, deploy, and evaluate open‑source large DLMs such as LLaDA and Dream through a standardized pipeline. The framework also provides minimal, reproducible recipes for building small DLMs from scratch with accessible compute, including converting any BERT‑style encoder or autoregressive LM into a DLM. We also release the checkpoints of these small DLMs to make DLMs more accessible and accelerate future research.

PaperId: 2859, https://arxiv.org/pdf/2602.22638.pdf GitHub

Abstract:
Route‑planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through natural language interaction and tool‑mediated decision making. However, systematic evaluation in real‑world mobility settings is hindered by diverse routing demands, non‑deterministic mapping services, and limited reproducibility. In this study, we introduce MobilityBench, a scalable benchmark for evaluating LLM‑based route‑planning agents in real‑world mobility scenarios. MobilityBench is constructed from large‑scale, anonymized real user queries collected from Amap and covers a broad spectrum of route‑planning intents across multiple cities worldwide. To enable reproducible, end‑to‑end evaluation, we design a deterministic API‑replay sandbox that eliminates environmental variance from live services. We further propose a multi‑dimensional evaluation protocol centered on outcome validity, complemented by assessments of instruction understanding, planning, tool use, and efficiency. Using MobilityBench, we evaluate multiple LLM‑based route‑planning agents across diverse real‑world mobility scenarios and provide an in‑depth analysis of their behaviors and performance. Our findings reveal that current models perform competently on Basic information retrieval and Route Planning tasks, yet struggle considerably with Preference‑Constrained Route Planning, underscoring significant room for improvement in personalized mobility applications. We publicly release the benchmark data, evaluation toolkit, and documentation at https://github.com/AMAP‑ML/MobilityBench .

PaperId: 2860, https://arxiv.org/pdf/2602.22621.pdf GitHub

Abstract:
Source‑Free Domain Adaptive Object Detection (SF‑DAOD) aims to adapt a detector trained on a labeled source domain to an unlabeled target domain without retaining any source data. Despite recent progress, most popular approaches focus on tuning pseudo‑label thresholds or refining the teacher‑student framework, while overlooking object‑level structural cues within cross‑domain data. In this work, we present CGSA, the first framework that brings Object‑Centric Learning (OCL) into SF‑DAOD by integrating slot‑aware adaptation into the DETR‑based detector. Specifically, our approach integrates a Hierarchical Slot Awareness (HSA) module into the detector to progressively disentangle images into slot representations that act as visual priors. These slots are then guided toward class semantics via a Class‑Guided Slot Contrast (CGSC) module, maintaining semantic consistency and prompting domain‑invariant adaptation. Extensive experiments on multiple cross‑domain datasets demonstrate that our approach outperforms previous SF‑DAOD methods, with theoretical derivations and experimental analysis further demonstrating the effectiveness of the proposed components and the framework, thereby indicating the promise of object‑centric design in privacy‑sensitive adaptation scenarios. Code is released at https://github.com/Michael‑McQueen/CGSA.

PaperId: 2861, https://arxiv.org/pdf/2602.22583.pdf GitHub

Abstract:
Example‑based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems and models‑even when the guidance is correct and problem‑relevant. We show that this instability arises from a previously underexplored gap between strategy usage‑whether a reasoning strategy appears in successful solutions‑and strategy executability‑whether the strategy remains effective when instantiated as guidance for a target model. Through a controlled analysis of paired human‑written and model‑generated solutions, we identify a systematic dissociation between usage and executability: human‑ and model‑derived strategies differ in structured, domain‑dependent ways, leading to complementary strengths and consistent source‑dependent reversals under guidance. Building on this diagnosis, we propose Selective Strategy Retrieval (SSR), a test‑time framework that explicitly models executability by selectively retrieving and combining strategies using empirical, multi‑route, source‑aware signals. Across multiple mathematical reasoning benchmarks, SSR yields reliable and consistent improvements over direct solving, in‑context learning, and single‑source guidance, improving accuracy by up to +13 points on AIME25 and +5 points on Apex for compact reasoning models. Code and benchmark are publicly available at: https://github.com/lwd17/strategy‑execute‑pipeline.

PaperId: 2862, https://arxiv.org/pdf/2602.22483.pdf GitHub

Abstract:
Errors in medical text can cause delays or even result in incorrect treatment for patients. Recently, language models have shown promise in their ability to automatically detect errors in medical text, an ability that has the opportunity to significantly benefit healthcare systems. In this paper, we explore the importance of prompt optimisation for small and large language models when applied to the task of error detection. We perform rigorous experiments and analysis across frontier language models and open‑source language models. We show that automatic prompt optimisation with Genetic‑Pareto (GEPA) improves error detection over the baseline accuracy performance from 0.669 to 0.785 with GPT‑5 and 0.578 to 0.690 with Qwen3‑32B, approaching the performance of medical doctors and achieving state‑of‑the‑art performance on the MEDEC benchmark dataset. Code available on GitHub: https://github.com/CraigMyles/clinical‑note‑error‑detection

PaperId: 2863, https://arxiv.org/pdf/2602.22446.pdf GitHub

Abstract:
Community detection in attributed networks faces a fundamental divide: topological algorithms ignore semantic features, while Graph Neural Networks (GNNs) encounter devastating computational bottlenecks. Specifically, GNNs suffer from a Semantic Wall of feature over smoothing in dense or heterophilic networks, and a Systems Wall driven by the O(N^2) memory constraints of pairwise clustering. To dismantle these barriers, we introduce ECHO (Encoding Communities via High order Operators), a scalable, self supervised architecture that reframes community detection as an adaptive, multi scale diffusion process. ECHO features a Topology Aware Router that automatically analyzes structural heuristics sparsity, density, and assortativity to route graphs through the optimal inductive bias, preventing heterophilic poisoning while ensuring semantic densification. Coupled with a memory sharded full batch contrastive objective and a novel chunked O(N \cdot K) similarity extraction method, ECHO completely bypasses traditional O(N^2) memory bottlenecks without sacrificing the mathematical precision of global gradients. Extensive evaluations demonstrate that this topology feature synergy consistently overcomes the classical resolution limit. On synthetic LFR benchmarks scaled up to 1 million nodes, ECHO achieves scale invariant accuracy despite severe topological noise. Furthermore, on massive real world social networks with over 1.6 million nodes and 30 million edges, it completes clustering in mere minutes with throughputs exceeding 2,800 nodes per second matching the speed of highly optimized purely topological baselines. The implementation utilizes a unified framework that automatically engages memory sharded optimization to support adoption across varying hardware constraints. GitHub Repository: https://github.com/emilioferrara/ECHO‑GNN

PaperId: 2864, https://arxiv.org/pdf/2602.22427.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) systems are essential to contemporary AI applications, allowing large language models to obtain external knowledge via vector similarity search. Nevertheless, these systems encounter a significant security flaw: hubness ‑ items that frequently appear in the top‑k retrieval results for a disproportionately high number of varied queries. These hubs can be exploited to introduce harmful content, alter search rankings, bypass content filtering, and decrease system performance. We introduce hubscan, an open‑source security scanner that evaluates vector indices and embeddings to identify hubs in RAG systems. Hubscan presents a multi‑detector architecture that integrates: (1) robust statistical hubness detection utilizing median/Median Absolute Deviation (MAD)‑based z‑scores, (2) cluster spread analysis to assess cross‑cluster retrieval patterns, (3) stability testing under query perturbations, and (4) domain‑aware and modality‑aware detection for category‑specific and cross‑modal attacks. Our solution accommodates several vector databases (FAISS, Pinecone, Qdrant, Weaviate) and offers versatile retrieval techniques, including vector similarity, hybrid search, and lexical matching with reranking capabilities. We evaluate hubscan on Food‑101, MS‑COCO, and FiQA adversarial hubness benchmarks constructed using state‑of‑the‑art gradient‑optimized and centroid‑based hub generation methods. Hubscan achieves 90% recall at a 0.2% alert budget and 100% recall at 0.4%, with adversarial hubs ranking above the 99.8th percentile. In testing, domain‑scoped scanning recovered 100% of targeted attacks that evaded global detection. Production validation on 1M real web documents from MS MARCO demonstrates significant score separation between clean documents and adversarial content.

PaperId: 2865, https://arxiv.org/pdf/2602.22402.pdf GitHub

Abstract:
As large language models engage in extended reasoning tasks, they accumulate significant state ‑‑ architectural mappings, trade‑off decisions, codebase conventions ‑‑ within the context window. This understanding is lost when sessions reach context limits and undergo lossy compaction. We propose Contextual Memory Virtualisation (CMV), a system that treats accumulated LLM understanding as version‑controlled state. Borrowing from operating system virtual memory, CMV models session history as a Directed Acyclic Graph (DAG) with formally defined snapshot, branch, and trim primitives that enable context reuse across independent parallel sessions. We introduce a three‑pass structurally lossless trimming algorithm that preserves every user message and assistant response verbatim while reducing token counts by a mean of 20% and up to 86% for sessions with significant overhead by stripping mechanical bloat such as raw tool outputs, base64 images, and metadata. A single‑user case‑study evaluation across 76 real‑world coding sessions demonstrates that trimming remains economically viable under prompt caching, with the strongest gains in mixed tool‑use sessions, which average 39% reduction and reach break‑even within 10 turns. A reference implementation is available at https://github.com/CosmoNaught/claude‑code‑cmv.

PaperId: 2866, https://arxiv.org/pdf/2602.22263.pdf GitHub

Abstract:
High‑resolution structure determination by cryo‑electron microscopy (cryo‑EM) requires the accurate fitting of an atomic model into an experimental density map. Traditional refinement pipelines such as Phenix.real_space_refine and Rosetta are computationally expensive, demand extensive manual tuning, and present a significant bottleneck for researchers. We present CryoNet.Refine, an end‑to‑end deep learning framework that automates and accelerates molecular structure refinement. Our approach utilizes a one‑step diffusion model that integrates a density‑aware loss function with robust stereochemical restraints, enabling rapid optimization of a structure against experimental data. CryoNet.Refine provides a unified and versatile solution capable of refining protein complexes as well as DNA/RNA‑protein complexes. In benchmarks against Phenix.real_space_refine, CryoNet.Refine consistently achieves substantial improvements in both model‑map correlation and overall geometric quality metrics. By offering a scalable, automated, and powerful alternative, CryoNet.Refine aims to serve as an essential tool for next‑generation cryo‑EM structure refinement. Web server: https://cryonet.ai/refine; Source code: https://github.com/kuixu/cryonet.refine.

PaperId: 2867, https://arxiv.org/pdf/2602.22251.pdf GitHub GitHub

Abstract:
General‑purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimized for a single domain (molecules or materials) and a single task (generation or prediction), which limits representation sharing and transfer. We introduce Zatom‑1, a cross‑domain, general‑purpose model architecture that unifies generative and predictive learning of 3D molecules and materials. Zatom‑1 is a deliberately simplified Transformer trained with a multimodal flow matching objective that jointly models discrete atom types and continuous 3D geometries. This approach supports scalable pretraining with predictable gains as model capacity increases, while enabling fast and stable sampling. We use cross‑domain generative pretraining as a universal initialization for downstream multi‑task prediction of properties, energies, and forces. Empirically, Zatom‑1 outperforms or competes with specialized baselines on both multi‑task generative and predictive benchmarks in data‑controlled settings, while improving generative inference speed by more than an order of magnitude. Our experiments demonstrate positive predictive transfer between data domains from joint generative pretraining: modeling materials during generative pretraining improves molecular property prediction accuracy. Open‑source code and model weights are freely available at https://github.com/Zatom‑AI/zatom.

PaperId: 2868, https://arxiv.org/pdf/2602.22220.pdf GitHub

Abstract:
Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface‑level topical relevance and ignore the deeper semantic and aesthetic properties that make quotations memorable. We start from two empirical observations. First, a systematic user study shows that people consistently prefer quotations that are ``unexpected yet rational'' in context, identifying novelty as a key desideratum. Second, we find that strong existing models struggle to fully understand the deep meanings of quotations. Inspired by defamiliarization theory, we therefore formalize quote recommendation as choosing contextually novel but semantically coherent quotations. We operationalize this objective with NovelQR, a novelty‑driven quotation recommendation framework. A generative label agent first interprets each quotation and its surrounding context into multi‑dimensional deep‑meaning labels, enabling label‑enhanced retrieval. A token‑level novelty estimator then reranks candidates while mitigating auto‑regressive continuation bias. Experiments on bilingual datasets spanning diverse real‑world domains show that our system recommends quotations that human judges rate as more appropriate, more novel, and more engaging than other baselines, while matching or surpassing existing methods in novelty estimation.

PaperId: 2869, https://arxiv.org/pdf/2602.22197.pdf GitHub

Abstract:
Advances in Generative AI (GenAI) have led to the development of various protection strategies to prevent the unauthorized use of images. These methods rely on adding imperceptible protective perturbations to images to thwart misuse such as style mimicry or deepfake manipulations. Although previous attacks on these protections required specialized, purpose‑built methods, we demonstrate that this is no longer necessary. We show that off‑the‑shelf image‑to‑image GenAI models can be repurposed as generic ``denoisers" using a simple text prompt, effectively removing a wide range of protective perturbations. Across 8 case studies spanning 6 diverse protection schemes, our general‑purpose attack not only circumvents these defenses but also outperforms existing specialized attacks while preserving the image's utility for the adversary. Our findings reveal a critical and widespread vulnerability in the current landscape of image protection, indicating that many schemes provide a false sense of security. We stress the urgent need to develop robust defenses and establish that any future protection mechanism must be benchmarked against attacks from off‑the‑shelf GenAI models. Code is available in this repository: https://github.com/mlsecviswanath/img2imgdenoiser

PaperId: 2870, https://arxiv.org/pdf/2602.22144.pdf GitHub GitHub

Abstract:
Object hallucination is a critical issue in Large Vision‑Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline primarily contributes to object hallucinations? The vision encoder to perceive visual information, or the language decoder to generate text responses? In this work, we strive to answer this question through designing a systematic experiment to analyze the roles of the vision encoder and the language decoder in hallucination generation. Our observations reveal that object hallucinations are predominantly associated with the strong priors from the language decoder. Based on this finding, we propose a simple and training‑free framework, No‑Language‑Hallucination Decoding, NoLan, which refines the output distribution by dynamically suppressing language priors, modulated based on the output distribution difference between multimodal and text‑only inputs. Experimental results demonstrate that NoLan effectively reduces object hallucinations across various LVLMs on different tasks. For instance, NoLan achieves substantial improvements on POPE, enhancing the accuracy of LLaVA‑1.5 7B and Qwen‑VL 7B by up to 6.45 and 7.21, respectively. The code is publicly available at: https://github.com/lingfengren/NoLan.

PaperId: 2871, https://arxiv.org/pdf/2602.22066.pdf GitHub

Abstract:
Time‑series foundation models (TSFMs) have achieved strong univariate forecasting through large‑scale pre‑training, yet effectively extending this success to multivariate forecasting remains challenging. To address this, we propose DualWeaver, a novel framework that adapts univariate TSFMs (Uni‑TSFMs) for multivariate forecasting by using a pair of learnable, structurally symmetric surrogate series. Generated by a shared auxiliary feature‑fusion module that captures cross‑variable dependencies, these surrogates are mapped to TSFM‑compatible series via the forecasting objective. The symmetric structure enables parameter‑free reconstruction of final predictions directly from the surrogates, without additional parametric decoding. A theoretically grounded regularization term is further introduced to enhance robustness against adaptation collapse. Extensive experiments on diverse real‑world datasets show that DualWeaver outperforms state‑of‑the‑art multivariate forecasters in both accuracy and stability. We release the code at https://github.com/li‑jinpeng/DualWeaver.

PaperId: 2872, https://arxiv.org/pdf/2602.22026.pdf GitHub

Abstract:
Metro trains often operate in highly complex environments, characterized by illumination variations, high‑speed motion, and adverse weather conditions. These factors pose significant challenges for visual perception systems, especially those relying solely on conventional RGB cameras. To tackle these difficulties, we explore the integration of event cameras into the perception system, leveraging their advantages in low‑light conditions, high‑speed scenarios, and low power consumption. Specifically, we focus on Kilometer Marker Recognition (KMR), a critical task for autonomous metro localization under GNSS‑denied conditions. In this context, we propose a robust baseline method based on a pre‑trained RGB OCR foundation model, enhanced through multi‑modal adaptation. Furthermore, we construct the first large‑scale RGB‑Event dataset, EvMetro5K, containing 5,599 pairs of synchronized RGB‑Event samples, split into 4,479 training and 1,120 testing samples. Extensive experiments on EvMetro5K and other widely used benchmarks demonstrate the effectiveness of our approach for KMR. Both the dataset and source code will be released on https://github.com/Event‑AHU/EvMetro5K_benchmark

PaperId: 2873, https://arxiv.org/pdf/2602.21845.pdf GitHub

Abstract:
Counterfactual explanation (CE) is an important domain within post‑hoc explainability. However, the explanations generated by most CE generators are often highly redundant. This work introduces an open‑source Python library xai‑cola, which provides an end‑to‑end pipeline for sparsifying CEs produced by arbitrary generators, reducing superfluous feature changes while preserving their validity. It offers a documented API that takes as input raw tabular data in pandas DataFrame form, a preprocessing object (for standardization and encoding), and a trained scikit‑learn or PyTorch model. On this basis, users can either employ the built‑in or externally imported CE generators. The library also implements several sparsification policies and includes visualization routines for analysing and comparing sparsified counterfactuals. xai‑cola is released under the MIT license and can be installed from PyPI. Empirical experiments indicate that xai‑cola produces sparser counterfactuals across several CE generators, reducing the number of modified features by up to 50% in our setting. The source code is available at https://github.com/understanding‑ml/COLA.

PaperId: 2874, https://arxiv.org/pdf/2602.21757.pdf GitHub

Abstract:
Accurately predicting short‑term traffic demand is critical for intelligent transportation systems. While deep learning models achieve strong performance under stationary conditions, their accuracy often degrades significantly when faced with distribution shifts caused by external events or evolving urban dynamics. Frequent model retraining to adapt to such changes incurs prohibitive computational costs, especially for large‑scale or foundation models. To address this challenge, we propose FORESEE (Forecasting Online with Residual Smoothing and Ensemble Experts), a lightweight online adaptation framework that is accurate, robust, and computationally efficient. FORESEE operates without any parameter updates to the base model. Instead, it corrects today's forecast in each region using yesterday's prediction error, stabilized through exponential smoothing guided by a mixture‑of‑experts mechanism that adapts to recent error dynamics. Moreover, an adaptive spatiotemporal smoothing component propagates error signals across neighboring regions and time slots, capturing coherent shifts in demand patterns. Extensive experiments on seven real‑world datasets with three backbone models demonstrate that FORESEE consistently improves prediction accuracy, maintains robustness even when distribution shifts are minimal (avoiding performance degradation), and achieves the lowest computational overhead among existing online methods. By enabling real‑time adaptation of traffic forecasting models with negligible computational cost, FORESEE paves the way for deploying reliable, up‑to‑date prediction systems in dynamic urban environments. Code and data are available at https://github.com/xiannanhuang/FORESEE

PaperId: 2875, https://arxiv.org/pdf/2602.21706.pdf GitHub

Abstract:
Minimally invasive surgery has dramatically improved patient operative outcomes, yet identifying safe operative zones remains challenging in critical phases, requiring surgeons to integrate visual cues, procedural phase, and anatomical context under high cognitive load. Existing AI systems offer binary safety verification or static detection, ignoring the phase‑dependent nature of intraoperative reasoning. We introduce ResGo, a benchmark of laparoscopic frames annotated with Go Zone bounding boxes and clinician‑authored rationales covering phase, exposure quality reasoning, next action and risk reminder. We introduce evaluation metrics that treat correct grounding under incorrect phase as failures, revealing that most vision‑language models cannot handle such tasks and perform poorly. We then present SurGo‑R1, a model optimized via RLHF with a multi‑turn phase‑then‑go architecture where the model first identifies the surgical phase, then generates reasoning and Go Zone coordinates conditioned on that context. On unseen procedures, SurGo‑R1 achieves 76.6% phase accuracy, 32.7 mIoU, and 54.8% hardcore accuracy, a 6.6× improvement over the mainstream generalist VLMs. Code, model and benchmark will be available at https://github.com/jinlab‑imvr/SurGo‑R1

PaperId: 2876, https://arxiv.org/pdf/2602.21657.pdf GitHub

Abstract:
Computer‑aided diagnosis (CAD) has significantly advanced automated chest X‑ray diagnosis but remains isolated from clinical workflows and lacks reliable decision support and interpretability. Human‑AI collaboration seeks to enhance the reliability of diagnostic models by integrating the behaviors of controllable radiologists. However, the absence of interactive tools seamlessly embedded within diagnostic routines impedes collaboration, while the semantic gap between radiologists' decision‑making patterns and model representations further limits clinical adoption. To overcome these limitations, we propose a visual cognition‑guided collaborative network (VCC‑Net) to achieve the cooperative diagnostic paradigm. VCC‑Net centers on visual cognition (VC) and employs clinically compatible interfaces, such as eye‑tracking or the mouse, to capture radiologists' visual search traces and attention patterns during diagnosis. VCC‑Net employs VC as a spatial cognition guide, learning hierarchical visual search strategies to localize diagnostically key regions. A cognition‑graph co‑editing module subsequently integrates radiologist VC with model inference to construct a disease‑aware graph. The module captures dependencies among anatomical regions and aligns model representations with VC‑driven features, mitigating radiologist bias and facilitating complementary, transparent decision‑making. Experiments on the public datasets SIIM‑ACR, EGD‑CXR, and self‑constructed TB‑Mouse dataset achieved classification accuracies of 88.40%, 85.05%, and 92.41%, respectively. The attention maps produced by VCC‑Net exhibit strong concordance with radiologists' gaze distributions, demonstrating a mutual reinforcement of radiologist and model inference. The code is available at https://github.com/IPMI‑NWU/VCC‑Net.

PaperId: 2877, https://arxiv.org/pdf/2602.21633.pdf GitHub

Abstract:
Standard vision‑language‑action (VLA) models rely on fitting statistical data priors, limiting their robust understanding of underlying physical dynamics. Reinforcement learning enhances physical grounding through exploration yet typically relies on external reward signals that remain isolated from the agent's internal states. World action models have emerged as a promising paradigm that integrates imagination and control to enable predictive planning. However, they rely on implicit context modeling, lacking explicit mechanisms for self‑improvement. To solve these problems, we propose Self‑Correcting VLA (SC‑VLA), which achieve self‑improvement by intrinsically guiding action refinement through sparse imagination. We first design sparse world imagination by integrating auxiliary predictive heads to forecast current task progress and future trajectory trends, thereby constraining the policy to encode short‑term physical evolution. Then we introduce the online action refinement module to reshape progress‑dependent dense rewards, adjusting trajectory orientation based on the predicted sparse future states. Evaluations on challenging robot manipulation tasks from simulation benchmarks and real‑world settings demonstrate that SC‑VLA achieve state‑of‑the‑art performance, yielding the highest task throughput with 16% fewer steps and a 9% higher success rate than the best‑performing baselines, alongside a 14% gain in real‑world experiments. Code is available at https://github.com/Kisaragi0/SC‑VLA.

PaperId: 2878, https://arxiv.org/pdf/2602.21531.pdf GitHub

Abstract:
General‑purpose robots must master long‑horizon manipulation, defined as tasks involving multiple kinematic structure changes (e.g., attaching or detaching objects) in unstructured environments. While Vision‑Language‑Action (VLA) models offer the potential to master diverse atomic skills, they struggle with the combinatorial complexity of sequencing them and are prone to cascading failures due to environmental sensitivity. To address these challenges, we propose LiLo‑VLA (Linked Local VLA), a modular framework capable of zero‑shot generalization to novel long‑horizon tasks without ever being trained on them. Our approach decouples transport from interaction: a Reaching Module handles global motion, while an Interaction Module employs an object‑centric VLA to process isolated objects of interest, ensuring robustness against irrelevant visual features and invariance to spatial configurations. Crucially, this modularity facilitates robust failure recovery through dynamic replanning and skill reuse, effectively mitigating the cascading errors common in end‑to‑end approaches. We introduce a 21‑task simulation benchmark consisting of two challenging suites: LIBERO‑Long++ and Ultra‑Long. In these simulations, LiLo‑VLA achieves a 69% average success rate, outperforming Pi0.5 by 41% and OpenVLA‑OFT by 67%. Furthermore, real‑world evaluations across 8 long‑horizon tasks demonstrate an average success rate of 85%. Project page: https://yy‑gx.github.io/LiLo‑VLA/.

PaperId: 2879, https://arxiv.org/pdf/2602.21492.pdf GitHub

Abstract:
Reinforcement learning (RL) has become a central post‑training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems. This sensitivity stems from the non‑stationarity of RL: rollouts are generated by an evolving policy, and learning is shaped by exploration and reward feedback, unlike supervised fine‑tuning (SFT) with fixed trajectories. As a result, prior work often relies on manual curation or simple heuristic filters (e.g., accuracy), which can admit incorrect or low‑utility problems. We propose GradAlign, a gradient‑aligned data selection method for LLM reinforcement learning that uses a small, trusted validation set to prioritize training problems whose policy gradients align with validation gradients, yielding an adaptive curriculum. We evaluate GradAlign across three challenging data regimes: unreliable reward signals, distribution imbalance, and low‑utility training corpus, showing that GradAlign consistently outperforms existing baselines, underscoring the importance of directional gradient signals in navigating non‑stationary policy optimization and yielding more stable training and improved final performance. We release our implementation at https://github.com/StigLidu/GradAlign

PaperId: 2880, https://arxiv.org/pdf/2602.21442.pdf GitHub

Abstract:
The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms like Bellman‑Ford, a phenomenon known as algorithmic alignment. At the same time, recent advances in large language models (LLMs) have spawned the study of mechanistic interpretability, which aims to identify granular model components like circuits that perform specific computations. In this work, we introduce Mechanistic Interpretability for Neural Algorithmic Reasoning (MINAR), an efficient circuit discovery toolbox that adapts attribution patching methods from mechanistic interpretability to the GNN setting. We show through two case studies that MINAR recovers faithful neuron‑level circuits from GNNs trained on algorithmic tasks. Our study sheds new light on the process of circuit formation and pruning during training, as well as giving new insight into how GNNs trained to perform multiple tasks in parallel reuse circuit components for related tasks. Our code is available at https://github.com/pnnl/MINAR.

PaperId: 2881, https://arxiv.org/pdf/2602.21421.pdf GitHub

Abstract:
Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We introduce ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years. To this end, we resort to multi‑sensor satellite data to train a specialized vision transformer model, which performs pixel‑level temporal regression. A self‑supervised growth loss regularizes the predictions to follow growth curves that are in line with natural tree development, including gradual height increases over time, but also abrupt declines due to forest loss events such as fires. Our experimental evaluation shows that our model improves state‑of‑the‑art accuracies in the context of single‑year predictions. We also provide the first global‑scale height map that accurately quantifies tree growth and disturbances over time. We expect ECHOSAT to advance global efforts in carbon monitoring and disturbance assessment. The maps can be accessed at https://github.com/ai4forest/echosat.

PaperId: 2882, https://arxiv.org/pdf/2602.21399.pdf GitHub

Abstract:
Federated Learning (FL) enables collaborative model training across multiple clients without sharing their private data. However, data heterogeneity across clients leads to client drift, which degrades the overall generalization performance of the model. This effect is further compounded by overemphasis on poorly performing clients. To address this problem, we propose FedVG, a novel gradient‑based federated aggregation framework that leverages a global validation set to guide the optimization process. Such a global validation set can be established using readily available public datasets, ensuring accessibility and consistency across clients without compromising privacy. In contrast to conventional approaches that prioritize client dataset volume, FedVG assesses the generalization ability of client models by measuring the magnitude of validation gradients across layers. Specifically, we compute layerwise gradient norms to derive a client‑specific score that reflects how much each client needs to adjust for improved generalization on the global validation set, thereby enabling more informed and adaptive federated aggregation. Extensive experiments on both natural and medical image benchmarking datasets, across diverse model architectures, demonstrate that FedVG consistently improves performance, particularly in highly heterogeneous settings. Moreover, FedVG is modular and can be seamlessly integrated with various state‑of‑the‑art FL algorithms, often further improving their results. Our code is available at https://github.com/alinadevkota/FedVG.

PaperId: 2883, https://arxiv.org/pdf/2602.21220.pdf GitHub

Abstract:
We present a memory system for AI agents that treats stored information as continuous fields governed by partial differential equations rather than discrete entries in a database. The approach draws from classical field theory: memories diffuse through semantic space, decay thermodynamically based on importance, and interact through field coupling in multi‑agent scenarios. We evaluate the system on two established long‑context benchmarks: LoCoMo (ACL 2024) with 300‑turn conversations across 35 sessions, and LongMemEval (ICLR 2025) testing multi‑session reasoning over 500+ turns. On LongMemEval, the field‑theoretic approach achieves significant improvements: +116% F1 on multi‑session reasoning (p<0.01, d= 3.06), +43.8% on temporal reasoning (p<0.001, d= 9.21), and +27.8% retrieval recall on knowledge updates (p<0.001, d= 5.00). Multi‑agent experiments show near‑perfect collective intelligence (>99.8%) through field coupling. Code is available at github.com/rotalabs/rotalabs‑fieldmem.

PaperId: 2884, https://arxiv.org/pdf/2602.21201.pdf GitHub GitHub

Abstract:
We report the performance of Aletheia (Feng et al., 2026b), a mathematics research agent powered by Gemini 3 Deep Think, on the inaugural FirstProof challenge. Within the allowed timeframe of the challenge, Aletheia autonomously solved 6 problems (2, 5, 7, 8, 9, 10) out of 10 according to majority expert assessments; we note that experts were not unanimous on Problem 8 (only). For full transparency, we explain our interpretation of FirstProof and disclose details about our experiments as well as our evaluation. Raw prompts and outputs are available at https://github.com/google‑deepmind/superhuman/tree/main/aletheia.

PaperId: 2885, https://arxiv.org/pdf/2602.21178.pdf GitHub

Abstract:
Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints. Conventional models often act as opaque ''black boxes'' and fail to quantify the complex, irregular tumor boundaries that characterize malignant growth. To address these challenges, we present XMorph, an explainable and computationally efficient framework for fine‑grained classification of three prominent brain tumor types: glioma, meningioma, and pituitary tumors. We propose an Information‑Weighted Boundary Normalization (IWBN) mechanism that emphasizes diagnostically relevant boundary regions alongside nonlinear chaotic and clinically validated features, enabling a richer morphological representation of tumor growth. A dual‑channel explainable AI module combines GradCAM++ visual cues with LLM‑generated textual rationales, translating model reasoning into clinically interpretable insights. The proposed framework achieves a classification accuracy of 96.0%, demonstrating that explainability and high performance can co‑exist in AI‑based medical imaging systems. The source code and materials for XMorph are all publicly available at: https://github.com/ALSER‑Lab/XMorph.

PaperId: 2886, https://arxiv.org/pdf/2602.21174.pdf GitHub

Abstract:
Hierarchical, multi‑resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information. Yet widely used path planning methods such as sampling and trajectory optimization do not exploit this explicit connectivity information, and search‑based methods such as A suffer from scalability issues in large‑scale high‑resolution maps. In many applications, Euclidean shortest paths form the underpinning of the navigation system. For such applications, any‑angle planning methods, which find optimal paths by connecting corners of obstacles with straight‑line segments, provide a simple and efficient solution. In this paper, we present a method that has the optimality and completeness properties of any‑angle planners while overcoming computational tractability issues common to search‑based methods by exploiting multi‑resolution representations. Extensive experiments on real and synthetic environments demonstrate the proposed approach's solution quality and speed, outperforming even sampling‑based methods. The framework is open‑sourced to allow the robotics and planning community to build on our research.

PaperId: 2887, https://arxiv.org/pdf/2602.21136.pdf GitHub

Abstract:
Qualitative insights from user experiences are critical for informing product and policy decisions, but collecting such data at scale is constrained by the time and availability of experts to conduct semi‑structured interviews. Recent work has explored using large language models (LLMs) to automate interviewing, yet existing systems lack a principled mechanism for balancing systematic coverage of predefined topics with adaptive exploration, or the ability to pursue follow‑ups, deep dives, and emergent themes that arise organically during conversation. In this work, we formulate adaptive semi‑structured interviewing as an optimization problem over the interviewer's behavior. We define interview utility as a trade‑off between coverage of a predefined interview topic guide, discovery of relevant emergent themes, and interview cost measured by length. Based on this formulation, we introduce SparkMe, a multi‑agent LLM interviewer that performs deliberative planning via simulated conversation rollouts to select questions with high expected utility. We evaluate SparkMe through controlled experiments with LLM‑based interviewees, showing that it achieves higher interview utility, improving topic guide coverage (+4.7% over the best baseline) and eliciting richer emergent insights while using fewer conversational turns than prior LLM interviewing approaches. We further validate SparkMe in a user study with 70 participants across 7 professions on the impact of AI on their workflows. Domain experts rate SparkMe as producing high‑quality adaptive interviews that surface helpful profession‑specific insights not captured by prior approaches. The code, datasets, and evaluation protocols for SparkMe are available as open‑source at https://github.com/SALT‑NLP/SparkMe.

PaperId: 2888, https://arxiv.org/pdf/2602.21044.pdf GitHub

Abstract:
Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real‑world reasoning problems admit multiple valid derivations, requiring models to explore diverse logical paths rather than committing to one route. To address this limitation, we introduce LogicGraph, the first benchmark aimed to systematically evaluate multi‑path logical reasoning, constructed via a neuro‑symbolic framework that leverages backward logic generation and semantic instantiation. This pipeline yields solver‑verified reasoning problems formalized by high‑depth multi‑path reasoning and inherent logical distractions, where each instance is associated with an exhaustive set of minimal proofs. We further propose a reference‑free evaluation framework to rigorously assess model performance in both convergent and divergent regimes. Experiments on state‑of‑the‑art language models reveal a common limitation: models tend to commit early to a single route and fail to explore alternatives, and the coverage gap grows substantially with reasoning depth. LogicGraph exposes this divergence gap and provides actionable insights to motivate future improvements. Our code and data will be released at https://github.com/kkkkarry/LogicGraph.

PaperId: 2889, https://arxiv.org/pdf/2602.21033.pdf GitHub

Abstract:
Medical image processing demands specialized software that handles high‑dimensional volumetric data, heterogeneous file formats, and domain‑specific training procedures. Existing frameworks either provide low‑level components that require substantial integration effort or impose rigid, monolithic pipelines that resist modification. We present MIP Candy (MIPCandy), a freely available, PyTorch‑based framework designed specifically for medical image processing. MIPCandy provides a complete, modular pipeline spanning data loading, training, inference, and evaluation, allowing researchers to obtain a fully functional process workflow by implementing a single method, \textttbuild_network, while retaining fine‑grained control over every component. Central to the design is \textttLayerT, a deferred configuration mechanism that enables runtime substitution of convolution, normalization, and activation modules without subclassing. The framework further offers built‑in k‑fold cross‑validation, dataset inspection with automatic region‑of‑interest detection, deep supervision, exponential moving average, multi‑frontend experiment tracking (Weights & Biases, Notion, MLflow), training state recovery, and validation score prediction via quotient regression. An extensible bundle ecosystem provides pre‑built model implementations that follow a consistent trainer‑‑predictor pattern and integrate with the core framework without modification. MIPCandy is open‑source under the Apache‑2.0 license and requires Python~3.12 or later. Source code and documentation are available at https://github.com/ProjectNeura/MIPCandy.

PaperId: 2890, https://arxiv.org/pdf/2602.20945.pdf GitHub

Abstract:
Large Language Models (LLMs) consistently benefit from scaled Chain‑of‑Thought (CoT) reasoning, but also suffer from heavy computational overhead. To address this issue, efficient reasoning aims to incentivize short yet accurate thinking trajectories, typically through reward shaping with Reinforcement Learning (RL). In this paper, we systematically investigate the mechanics of efficient reasoning for LLMs. For comprehensive evaluation, we advocate for more fine‑grained metrics, including length distribution conditioned on correctness and performance across a wide spectrum of token budgets ranging from 2k to 32k. First, we reveal that the training process follows a two‑stage paradigm: length adaptation and reasoning refinement. Through extensive experiments (about 0.2 million GPU hours) in a unified protocol, we deconstruct training prompts and rollouts, reward shaping, and optimization strategies. A central finding is to maintain a sufficient density of positive reward signals and avoid the short‑is‑correct trap. Moreover, the learned length bias generalizes across domains and difficulty levels. We distill these findings into valuable insights and practical guidelines, and validate them across the Qwen3 models ranging from 0.6B to 30B, demonstrating the robustness and generalization. Weights are available at https://wutaiqiang.github.io/project/Art

PaperId: 2891, https://arxiv.org/pdf/2602.20770.pdf GitHub

Abstract:
With the growing popularity of Large Reasoning Models and their results in solving mathematical problems, it becomes crucial to measure their capabilities. We introduce a pipeline for both automatic and interactive verification as a more accurate alternative to only checking the answer which is currently the most popular approach for benchmarks. The pipeline can also be used as a generator of correct solutions both in formal and informal languages. 3 AI agents, which can be chosen for the benchmark accordingly, are included in the structure. The key idea is the use of prompts to obtain the solution in the specific form which allows for easier verification using proof assistants and possible use of small models (\le 8B). Experiments on several datasets suggest low probability of False Positives. The open‑source implementation with instructions on setting up a server is available at https://github.com/LogicEnj/lean4_verification_pipeline.

PaperId: 2892, https://arxiv.org/pdf/2602.20732.pdf GitHub

Abstract:
Long‑context LLMs demand accurate inference at low latency, yet decoding becomes primarily constrained by KV cache as context grows. Prior pruning methods are largely context‑agnostic: their token selection ignores step‑wise relevance and local semantics, which undermines quality. Moreover, their irregular accesses and selection overheads yield only limited wall‑clock speedups. To address this, we propose CHESS, an algorithm‑system co‑design KV‑cache management system. Algorithmically, CHESS introduces a context‑aware, hierarchical selection policy that dynamically reconstructs a coherent context for the current decoding. System‑wise, coarse granularity selection eliminates expensive data movement, fully realizing practical acceleration from theoretical sparsity. Extensive evaluations demonstrate that CHESS surpasses Full‑KV quality using only 1% of the KV cache, delivers low‑latency stable inference with up to 4.56× higher throughput, and consistently outperforms other strong baselines. Code is available at \hrefhttps://anonymous.4open.science/r/CHESS‑9958/https://anonymous.4open.science/r/CHESS/.

PaperId: 2893, https://arxiv.org/pdf/2602.20731.pdf GitHub

Abstract:
Discrete image tokenizers have emerged as a key component of modern vision and multimodal systems, providing a sequential interface for transformer‑based architectures. However, most existing approaches remain primarily optimized for reconstruction and compression, often yielding tokens that capture local texture rather than object‑level semantic structure. Inspired by the incremental and compositional nature of human communication, we introduce COMmunication inspired Tokenization (COMiT), a framework for learning structured discrete visual token sequences. COMiT constructs a latent message within a fixed token budget by iteratively observing localized image crops and recurrently updating its discrete representation. At each step, the model integrates new visual information while refining and reorganizing the existing token sequence. After several encoding iterations, the final message conditions a flow‑matching decoder that reconstructs the full image. Both encoding and decoding are implemented within a single transformer model and trained end‑to‑end using a combination of flow‑matching reconstruction and semantic representation alignment losses. Our experiments demonstrate that while semantic alignment provides grounding, attentive sequential tokenization is critical for inducing interpretable, object‑centric token structure and substantially improving compositional generalization and relational reasoning over prior methods.

PaperId: 2894, https://arxiv.org/pdf/2602.20710.pdf GitHub

Abstract:
Inspecting Chain‑of‑Thought reasoning is among the most common means of understanding why an LLM produced its output. But well‑known problems with CoT faithfulness severely limit what insights can be gained from this practice. In this paper, we introduce a training method called Counterfactual Simulation Training (CST), which aims to improve CoT faithfulness by rewarding CoTs that enable a simulator to accurately predict a model's outputs over counterfactual inputs. We apply CST in two settings: (1) CoT monitoring with cue‑based counterfactuals, to detect when models rely on spurious features, reward hack, or are sycophantic, and (2) counterfactual simulation over generic model‑based counterfactuals, to encourage models to produce more faithful, generalizable reasoning in the CoT. Experiments with models up to 235B parameters show that CST can substantially improve monitor accuracy on cue‑based counterfactuals (by 35 accuracy points) as well as simulatability over generic counterfactuals (by 2 points). We further show that: (1) CST outperforms prompting baselines, (2) rewriting unfaithful CoTs with an LLM is 5x more efficient than RL alone, (3) faithfulness improvements do not generalize to dissuading cues (as opposed to persuading cues), and (4) larger models do not show more faithful CoT out of the box, but they do benefit more from CST. These results suggest that CST can improve CoT faithfulness in general, with promising applications for CoT monitoring. Code for experiments in this paper is available at https://github.com/peterbhase/counterfactual‑simulation‑training

PaperId: 2895, https://arxiv.org/pdf/2602.20643.pdf GitHub

Abstract:
Mobility trajectories are essential for understanding urban dynamics and enhancing urban planning, yet access to such data is frequently hindered by privacy concerns. This research introduces a transformative framework for generating large‑scale urban mobility trajectories, employing a novel application of a transformer‑based model pre‑trained and fine‑tuned through a two‑phase process. Initially, trajectory generation is conceptualized as an offline reinforcement learning (RL) problem, with a significant reduction in vocabulary space achieved during tokenization. The integration of Inverse Reinforcement Learning (IRL) allows for the capture of trajectory‑wise reward signals, leveraging historical data to infer individual mobility preferences. Subsequently, the pre‑trained model is fine‑tuned using the constructed reward model, effectively addressing the challenges inherent in traditional RL‑based autoregressive methods, such as long‑term credit assignment and handling of sparse reward environments. Comprehensive evaluations on multiple datasets illustrate that our framework markedly surpasses existing models in terms of reliability and diversity. Our findings not only advance the field of urban mobility modeling but also provide a robust methodology for simulating urban data, with significant implications for traffic management and urban development planning. The implementation is publicly available at https://github.com/Wangjw6/TrajGPT_R.

PaperId: 2896, https://arxiv.org/pdf/2602.20497.pdf GitHub

Abstract:
Diffusion models have achieved remarkable success in image and video generation tasks. However, the high computational demands of Diffusion Transformers (DiTs) pose a significant challenge to their practical deployment. While feature caching is a promising acceleration strategy, existing methods based on simple reusing or training‑free forecasting struggle to adapt to the complex, stage‑dependent dynamics of the diffusion process, often resulting in quality degradation and failing to maintain consistency with the standard denoising process. To address this, we propose a LEarnable Stage‑Aware (LESA) predictor framework based on two‑stage training. Our approach leverages a Kolmogorov‑Arnold Network (KAN) to accurately learn temporal feature mappings from data. We further introduce a multi‑stage, multi‑expert architecture that assigns specialized predictors to different noise‑level stages, enabling more precise and robust feature forecasting. Extensive experiments show our method achieves significant acceleration while maintaining high‑fidelity generation. Experiments demonstrate 5.00x acceleration on FLUX.1‑dev with minimal quality degradation (1.0% drop), 6.25x speedup on Qwen‑Image with a 20.2% quality improvement over the previous SOTA (TaylorSeer), and 5.00x acceleration on HunyuanVideo with a 24.7% PSNR improvement over TaylorSeer. State‑of‑the‑art performance on both text‑to‑image and text‑to‑video synthesis validates the effectiveness and generalization capability of our training‑based framework across different models. Our code is available at https://github.com/caipeiliang2004/LESA.

PaperId: 2897, https://arxiv.org/pdf/2602.20317.pdf GitHub

Abstract:
Next‑generation fusion facilities like ITER face a "data deluge," generating petabytes of multi‑diagnostic signals daily that challenge manual analysis. We present a "signals‑first" self‑supervised framework for the automated extraction of coherent and transient modes from high‑noise time‑frequency data across a variety of sensors. We also develop a general‑purpose method and tool for extracting coherent, quasi‑coherent, and transient modes for fluctuation measurements in tokamaks by employing non‑linear optimal techniques in multichannel signal processing with a fast neural network surrogate on fast magnetics, electron cyclotron emission, CO2 interferometers, and beam emission spectroscopy measurements from DIII‑D. Results are tested on data from DIII‑D, TJ‑II, and non‑fusion spectrograms. With an inference latency of 0.5 seconds, this framework enables real‑time mode identification and large‑scale automated database generation for advanced plasma control. Repository is in https://github.com/PlasmaControl/TokEye.

PaperId: 2898, https://arxiv.org/pdf/2602.20223.pdf GitHub

Abstract:
Recently, TabPFN has gained attention as a foundation model for tabular data. However, it struggles to integrate heterogeneous modalities such as images and text, which are common in domains like healthcare and marketing, thereby limiting its applicability. To address this, we present the Multi‑Modal Prior‑data Fitted Network (MMPFN), which extends TabPFN to handle tabular and non‑tabular modalities in a unified manner. MMPFN comprises per‑modality encoders, modality projectors, and pre‑trained foundation models. The modality projectors serve as the critical bridge, transforming non‑tabular embeddings into tabular‑compatible tokens for unified processing. To this end, we introduce a multi‑head gated MLP and a cross‑attention pooler that extract richer context from non‑tabular inputs while mitigates attention imbalance issue in multimodal learning. Extensive experiments on medical and general‑purpose multimodal datasets demonstrate that MMPFN consistently outperforms competitive state‑of‑the‑art methods and effectively exploits non‑tabular modalities alongside tabular features. These results highlight the promise of extending prior‑data fitted networks to the multimodal setting, offering a scalable and effective framework for heterogeneous data learning. The source code is available at https://github.com/too‑z/MultiModalPFN.

PaperId: 2899, https://arxiv.org/pdf/2602.20214.pdf GitHub

Abstract:
AI agents increasingly act on behalf of humans, yet no existing system provides a tamper‑evident, independently verifiable record of what they did. As regulations such as the EU AI Act begin mandating automatic logging for high‑risk AI systems, this gap carries concrete consequences ‑‑ especially for agents running on personal hardware, where no centralized provider controls the log. Extending Floridi's informational rights framework from data about individuals to actions performed on their behalf, this paper proposes the Right to History: the principle that individuals are entitled to a complete, verifiable record of every AI agent action on their own hardware. The paper formalizes this principle through five system invariants with structured proof sketches, and implements it in PunkGo, a Rust sovereignty kernel that unifies RFC 6962 Merkle tree audit logs, capability‑based isolation, energy‑budget governance, and a human‑approval mechanism. Adversarial testing confirms all five invariants hold. Performance evaluation shows sub‑1.3 ms median action latency, ~400 actions/sec throughput, and 448‑byte Merkle inclusion proofs at 10,000 log entries.

PaperId: 2900, https://arxiv.org/pdf/2602.20206.pdf GitHub

Abstract:
The democratization of Large Language Models has given rise to vibe coding, where novice programmers prioritize semantic intent over syntactic implementation. Without pedagogical guardrails, we argue this is fundamentally misaligned with cognitive skill acquisition. Drawing on Kirschner's distinction between cognitive offloading and outsourcing, unrestricted AI encourages novices to outsource the intrinsic cognitive load required for schema formation rather than merely offloading extraneous load. This accumulation of epistemic debt creates fragile experts: developers whose high functional utility masks critically low corrective competence. To quantify and mitigate this debt, we conducted a between‑subjects experiment (N=78) using a custom Cursor IDE plugin backed by Claude 3.5 Sonnet. Participants were recruited via Prolific and UserInterviews.com to represent AI‑native learners. We compared three conditions: manual (control), unrestricted AI (outsourcing), and scaffolded AI (offloading). The scaffolded condition employed a novel Explanation Gate ‑‑ a real‑time LLM‑as‑a‑Judge framework enforcing a teach‑back protocol before generated code could be integrated. Results reveal a collapse of competence: both AI groups significantly outperformed the manual control on functional utility (p < .001) and did not differ from each other (p = .64), yet unrestricted AI users suffered a 77% failure rate on a subsequent 30‑minute AI‑blackout maintenance task, vs. only 39% in the scaffolded group. Qualitative analysis suggests successful vibe coders naturally self‑scaffold, treating AI as a consultant rather than a contractor. We discuss implications for AI‑generated software maintainability and propose that future learning systems must enforce metacognitive friction to prevent mass production of unmaintainable code. Replication package: https://github.com/sreecharansankaranarayanan/vibecheck

PaperId: 2901, https://arxiv.org/pdf/2602.20197.pdf GitHub

Abstract:
Reinforcement Learning with verifiable rewards (RLVR) has emerged as a primary learning paradigm for enhancing the reasoning capabilities of multi‑modal large language models (MLLMs). However, during RL training, the enormous state space of MLLM and sparse rewards often leads to entropy collapse, policy degradation, or over‑exploitation of suboptimal behaviors. This necessitates an exploration strategy that maintains productive stochasticity while avoiding the drawbacks of uncontrolled random sampling, yielding inefficient exploration. In this paper, we propose CalibRL, a hybrid‑policy RLVR framework that supports controllable exploration with expert guidance, enabled by two key mechanisms. First, a distribution‑aware advantage weighting scales updates by group rareness to calibrate the distribution, therefore preserving exploration. Meanwhile, the asymmetric activation function (LeakyReLU) leverages the expert knowledge as a calibration baseline to moderate overconfident updates while preserving their corrective direction. CalibRL increases policy entropy in a guided manner and clarifies the target distribution by estimating the on‑policy distribution through online sampling. Updates are driven by these informative behaviors, avoiding convergence to erroneous patterns. Importantly, these designs help alleviate the distributional mismatch between the model's policy and expert trajectories, thereby achieving a more stable balance between exploration and exploitation. Extensive experiments across eight benchmarks, including both in‑domain and out‑of‑domain settings, demonstrate consistent improvements, validating the effectiveness of our controllable hybrid‑policy RLVR training. Code is available at https://github.com/zhh6425/CalibRL.

PaperId: 2902, https://arxiv.org/pdf/2602.20170.pdf GitHub

Abstract:
Existing red‑teaming benchmarks, when adapted to new languages via direct translation, fail to capture socio‑technical vulnerabilities rooted in local culture and law, creating a critical blind spot in LLM safety evaluation. To address this gap, we introduce CAGE (Culturally Adaptive Generation), a framework that systematically adapts the adversarial intent of proven red‑teaming prompts to new cultural contexts. At the core of CAGE is the Semantic Mold, a novel approach that disentangles a prompt's adversarial structure from its cultural content. This approach enables the modeling of realistic, localized threats rather than testing for simple jailbreaks. As a representative example, we demonstrate our framework by creating KoRSET, a Korean benchmark, which proves more effective at revealing vulnerabilities than direct translation baselines. CAGE offers a scalable solution for developing meaningful, context‑aware safety benchmarks across diverse cultures. Our dataset and evaluation rubrics are publicly available at https://github.com/selectstar‑ai/CAGE‑paper. (WARNING: This paper contains model outputs that can be offensive in nature.)

PaperId: 2903, https://arxiv.org/pdf/2602.20152.pdf GitHub

Abstract:
Inspired by behavioral science, we propose Behavior Learning (BL), a novel general‑purpose machine learning framework that learns interpretable and identifiable optimization structures from data, ranging from single optimization problems to hierarchical compositions. It unifies predictive performance, intrinsic interpretability, and identifiability, with broad applicability to scientific domains involving optimization. BL parameterizes a compositional utility function built from intrinsically interpretable modular blocks, which induces a data distribution for prediction and generation. Each block represents and can be written in symbolic form as a utility maximization problem (UMP), a foundational paradigm in behavioral science and a universal framework of optimization. BL supports architectures ranging from a single UMP to hierarchical compositions, the latter modeling hierarchical optimization structures. Its smooth and monotone variant (IBL) guarantees identifiability. Theoretically, we establish the universal approximation property of BL, and analyze the M‑estimation properties of IBL. Empirically, BL demonstrates strong predictive performance, intrinsic interpretability and scalability to high‑dimensional data. Code: https://github.com/MoonYLiang/Behavior‑Learning ; install via pip install blnetwork.

PaperId: 2904, https://arxiv.org/pdf/2602.20141.pdf GitHub

Abstract:
Mean Field Games (MFGs) provide a principled framework for modelling interactions in large population systems. However, algorithmic progress has been limited since model‑free methods are high variance and exact methods scale poorly. Recent Hybrid Structural Methods (HSMs) reduce variance while maintaining tractability by leveraging low‑dimensional individual state and action spaces and known transition dynamics to compute the exact expected return conditioned on Monte Carlo rollouts of common noise. However, HSMs have not been extended to partially observable settings. We propose Recurrent Structural Policy Gradient (RSPG), the first history‑aware HSM for MFGs with public partial information. RSPG achieves an order‑of‑magnitude faster convergence than model‑free RL methods while learning history‑aware behaviour, unlike current HSMs. To facilitate research into MFGs, we also introduce MFAX, our JAX‑based framework for MFGs that supports both analytic and sample‑based mean‑field updates. MFAX and usage examples can be found at https://clarisse‑wibault.github.io/rspg/.

PaperId: 2905, https://arxiv.org/pdf/2602.20122.pdf GitHub

Abstract:
How do large language models (LLMs) know what they know? Answering this question has been difficult because pre‑training data is often a "black box" ‑ unknown or inaccessible. The recent release of nanochat ‑ a family of small LLMs with fully open pre‑training data ‑ addresses this as it provides a transparent view into where a model's parametric knowledge comes from. Towards the goal of understanding how knowledge is encoded by LLMs, we release NanoKnow, a benchmark dataset that partitions questions from Natural Questions and SQuAD into splits based on whether their answers are present in nanochat's pre‑training corpus. Using these splits, we can now properly disentangle the sources of knowledge that LLMs rely on when producing an output. To demonstrate NanoKnow's utility, we conduct experiments using eight nanochat checkpoints. Our findings show: (1) closed‑book accuracy is strongly influenced by answer frequency in the pre‑training data, (2) providing external evidence can mitigate this frequency dependence, (3) even with external evidence, models are more accurate when answers were seen during pre‑training, demonstrating that parametric and external knowledge are complementary, and (4) non‑relevant information is harmful, with accuracy decreasing based on both the position and the number of non‑relevant contexts. We release all NanoKnow artifacts at https://github.com/castorini/NanoKnow.

PaperId: 2906, https://arxiv.org/pdf/2602.20113.pdf GitHub

Abstract:
Voice style conversion aims to transform an input utterance to match a target speaker's timbre, accent, and emotion, with a central challenge being the disentanglement of linguistic content from style. While prior work has explored this problem, conversion quality remains limited, and real‑time voice style conversion has not been addressed. We propose StyleStream, the first streamable zero‑shot voice style conversion system that achieves state‑of‑the‑art performance. StyleStream consists of two components: a Destylizer, which removes style attributes while preserving linguistic content, and a Stylizer, a diffusion transformer (DiT) that reintroduces target style conditioned on reference speech. Robust content‑style disentanglement is enforced through text supervision and a highly constrained information bottleneck. This design enables a fully non‑autoregressive architecture, achieving real‑time voice style conversion with an end‑to‑end latency of 1 second. Samples and real‑time demo: https://berkeley‑speech‑group.github.io/StyleStream/.

PaperId: 2907, https://arxiv.org/pdf/2602.20102.pdf GitHub GitHub

Abstract:
Despite the strong performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe content generation remains a significant obstacle to deployment, particularly in high‑stakes settings. Addressing this challenge requires safety mechanisms that are both practically effective and theoretically grounded. In this paper, we introduce BarrierSteer, a novel inference‑time framework that improves response safety by embedding learned nonlinear safety constraints directly into the model's latent representation space. BarrierSteer treats hidden‑state safety classifiers as Control Barrier Functions (CBFs), enabling constraint‑guided steering of unsafe latent trajectories during generation. By composing multiple safety constraints through efficient constraint merging without modifying the underlying LLM parameters, BarrierSteer preserves model utility. We provide theoretical results showing that applying CBFs in the latent space yields a principled, modular, and computationally efficient approach for steering with respect to learned safety constraints, with guarantees conditional on the learned barriers capturing the intended safety property. Our extensive experimental results across multiple model families and datasets demonstrate that BarrierSteer substantially reduces adversarial attack success rates and unsafe generations, outperforming the existing method. The code is available in our \hrefhttps://github.com/thanhquangtran/BarrierSteerGitHub repository.

PaperId: 2908, https://arxiv.org/pdf/2602.20089.pdf GitHub

Abstract:
Edge‑based representations are fundamental cues for visual understanding, a principle rooted in early vision research and still central today. We extend this principle to vision‑language alignment, showing that isolating and aligning structural cues across modalities can greatly benefit fine‑tuning on long, detail‑rich captions, with a specific focus on improving cross‑modal retrieval. We introduce StructXLIP, a fine‑tuning alignment paradigm that extracts edge maps (e.g., Canny), treating them as proxies for the visual structure of an image, and filters the corresponding captions to emphasize structural cues, making them "structure‑centric". Fine‑tuning augments the standard alignment loss with three structure‑centric losses: (i) aligning edge maps with structural text, (ii) matching local edge regions to textual chunks, and (iii) connecting edge maps to color images to prevent representation drift. From a theoretical standpoint, while standard CLIP maximizes the mutual information between visual and textual embeddings, StructXLIP additionally maximizes the mutual information between multimodal structural representations. This auxiliary optimization is intrinsically harder, guiding the model toward more robust and semantically stable minima, enhancing vision‑language alignment. Beyond outperforming current competitors on cross‑modal retrieval in both general and specialized domains, our method serves as a general boosting recipe that can be integrated into future approaches in a plug‑and‑play manner. Code and pretrained models are publicly available at: https://github.com/intelligolabs/StructXLIP.

PaperId: 2909, https://arxiv.org/pdf/2602.20048.pdf GitHub

Abstract:
Modern code intelligence agents operate in contexts exceeding 1 million tokens‑‑far beyond the scale where humans manually locate relevant files. Yet agents consistently fail to discover architecturally critical files when solving real‑world coding tasks. We identify the Navigation Paradox: agents perform poorly not due to context limits, but because navigation and retrieval are fundamentally distinct problems. Through 258 automated trials across 30 benchmark tasks on a production FastAPI repository, we demonstrate that graph‑based structural navigation via CodeCompass‑‑a Model Context Protocol server exposing dependency graphs‑‑achieves 99.4% task completion on hidden‑dependency tasks, a 23.2 percentage‑point improvement over vanilla agents (76.2%) and 21.2 points over BM25 retrieval (78.2%).However, we uncover a critical adoption gap: 58% of trials with graph access made zero tool calls, and agents required explicit prompt engineering to adopt the tool consistently. Our findings reveal that the bottleneck is not tool availability but behavioral alignment‑‑agents must be explicitly guided to leverage structural context over lexical heuristics. We contribute: (1) a task taxonomy distinguishing semantic‑search, structural, and hidden‑dependency scenarios; (2) empirical evidence that graph navigation outperforms retrieval when dependencies lack lexical overlap; and (3) open‑source infrastructure for reproducible evaluation of navigation tools.

PaperId: 2910, https://arxiv.org/pdf/2602.19983.pdf GitHub

Abstract:
Robots are increasingly operating in open‑world environments where safe behavior depends on context: the same hallway may require different navigation strategies when crowded versus empty, or during an emergency versus normal operations. Traditional safety approaches enforce fixed constraints in user‑specified contexts, limiting their ability to handle the open‑ended contextual variability of real‑world deployment. We address this gap via CORE, a safety framework that enables online contextual reasoning, grounding, and enforcement without prior knowledge of the environment (e.g., maps or safety specifications). CORE uses a vision‑language model (VLM) to continuously reason about context‑dependent safety rules directly from visual observations, grounds these rules in the physical environment, and enforces the resulting spatially‑defined safe sets via control barrier functions. We provide probabilistic safety guarantees for CORE that account for perceptual uncertainty, and we demonstrate through simulation and real‑world experiments that CORE enforces contextually appropriate behavior in unseen environments, significantly outperforming prior semantic safety methods that lack online contextual reasoning. Ablation studies validate our theoretical guarantees and underscore the importance of both VLM‑based reasoning and spatial grounding for enforcing contextual safety in novel settings. We provide additional resources at https://zacravichandran.github.io/CORE.

PaperId: 2911, https://arxiv.org/pdf/2602.19881.pdf GitHub

Abstract:
Unsupervised change detection (UCD) in remote sensing aims to localise semantic changes between two images of the same region without relying on labelled data during training. Most recent approaches rely either on frozen foundation models in a training‑free manner or on training with synthetic changes generated in pixel space. Both strategies inherently rely on predefined assumptions about change types, typically introduced through handcrafted rules, external datasets, or auxiliary generative models. Due to these assumptions, such methods fail to generalise beyond a few change types, limiting their real‑world usage, especially in rare or complex scenarios. To address this, we propose MaSoN (Make Some Noise), an end‑to‑end UCD framework that synthesises diverse changes directly in the latent feature space during training. It generates changes that are dynamically estimated using feature statistics of target data, enabling diverse yet data‑driven variation aligned with the target domain. It also easily extends to new modalities, such as SAR. MaSoN generalises strongly across diverse change types and achieves state‑of‑the‑art performance on five benchmarks, improving the average F1 score by 14.1 percentage points. Project page: https://blaz‑r.github.io/mason_ucd

PaperId: 2912, https://arxiv.org/pdf/2602.19770.pdf GitHub

Abstract:
Explainable artificial intelligence has emerged as a promising field of research to address reliability concerns in artificial intelligence. Despite significant progress in explainable artificial intelligence, few methods provide a systematic way to visualize and understand how classes are confused and how their relationships evolve as training progresses. In this work, we present GRAPHIC, an architecture‑agnostic approach that analyzes neural networks on a class level. It leverages confusion matrices derived from intermediate layers using linear classifiers. We interpret these as adjacency matrices of directed graphs, allowing tools from network science to visualize and quantify learning dynamics across training epochs and intermediate layers. GRAPHIC provides insights into linear class separability, dataset issues, and architectural behavior, revealing, for example, similarities between flatfish and man and labeling ambiguities validated in a human study. In summary, by uncovering real confusions, GRAPHIC offers new perspectives on how neural networks learn. The code is available at https://github.com/Johanna‑S‑Froehlich/GRAPHIC.

PaperId: 2913, https://arxiv.org/pdf/2602.19685.pdf GitHub GitHub

Abstract:
Building Virtual Cells that can accurately simulate cellular responses to perturbations is a long‑standing goal in systems biology. A fundamental challenge is that high‑throughput single‑cell sequencing is destructive: the same cell cannot be observed both before and after a perturbation. Thus, perturbation prediction requires mapping unpaired control and perturbed populations. Existing models address this by learning maps between distributions, but typically assume a single fixed response distribution when conditioned on observed cellular context (e.g., cell type) and the perturbation type. In reality, responses vary systematically due to unobservable latent factors such as microenvironmental fluctuations and complex batch effects, forming a manifold of possible distributions for the same observed conditions. To account for this variability, we introduce PerturbDiff, which shifts modeling from individual cells to entire distributions. By embedding distributions as points in a Hilbert space, we define a diffusion‑based generative process operating directly over probability distributions. This allows PerturbDiff to capture population‑level response shifts across hidden factors. Benchmarks on established datasets show that PerturbDiff achieves state‑of‑the‑art performance in single‑cell response prediction and generalizes substantially better to unseen perturbations. See our project page (https://katarinayuan.github.io/PerturbDiff‑ProjectPage/), where code and data will be made publicly available (https://github.com/DeepGraphLearning/PerturbDiff).

PaperId: 2914, https://arxiv.org/pdf/2602.19672.pdf GitHub

Abstract:
Compound AI systems promise capabilities beyond those of individual models, yet their success depends critically on effective orchestration. Existing routing approaches face two limitations: (1) input‑level routers make coarse query‑level decisions that ignore evolving task requirements; (2) RL‑trained orchestrators are expensive to adapt and often suffer from routing collapse, repeatedly invoking one strong but costly option in multi‑turn scenarios. We introduce SkillOrchestra, a framework for skill‑aware orchestration. Instead of directly learning a routing policy end‑to‑end, SkillOrchestra learns fine‑grained skills from execution experience and models agent‑specific competence and cost under those skills. At deployment, the orchestrator infers the skill demands of the current interaction and selects agents that best satisfy them under an explicit performance‑cost trade‑off. Extensive experiments across ten benchmarks demonstrate that SkillOrchestra outperforms SoTA RL‑based orchestrators by up to 22.5% with 700x and 300x learning cost reduction compared to Router‑R1 and ToolOrchestra, respectively. These results show that explicit skill modeling enables scalable, interpretable, and sample‑efficient orchestration, offering a principled alternative to data‑intensive RL‑based approaches. The code is available at: https://github.com/jiayuww/SkillOrchestra.

PaperId: 2915, https://arxiv.org/pdf/2602.19608.pdf GitHub

Abstract:
Looting at archaeological sites poses a severe risk to cultural heritage, yet monitoring thousands of remote locations remains operationally difficult. We present a scalable and satellite‑based pipeline to detect looted archaeological sites, using PlanetScope monthly mosaics (4.7m/pixel) and a curated dataset of 1,943 archaeological sites in Afghanistan (898 looted, 1,045 preserved) with multi‑year imagery (2016‑‑2023) and site‑footprint masks. We compare (i) end‑to‑end CNN classifiers trained on raw RGB patches and (ii) traditional machine learning (ML) trained on handcrafted spectral/texture features and embeddings from recent remote‑sensing foundation models. Results indicate that ImageNet‑pretrained CNNs combined with spatial masking reach an F1 score of 0.926, clearly surpassing the strongest traditional ML setup, which attains an F1 score of 0.710 using SatCLIP‑V+RF+Mean, i.e., location and vision embeddings fed into a Random Forest with mean‑based temporal aggregation. Ablation studies demonstrate that ImageNet pretraining (even in the presence of domain shift) and spatial masking enhance performance. In contrast, geospatial foundation model embeddings perform competitively with handcrafted features, suggesting that looting signatures are extremely localized. The repository is available at https://github.com/microsoft/looted_site_detection.

PaperId: 2916, https://arxiv.org/pdf/2602.19574.pdf GitHub

Abstract:
Large‑language‑model (LLM)‑based text‑to‑speech (TTS) systems can generate natural speech, but most are not designed for low‑latency dual‑streaming synthesis. High‑quality dual‑streaming TTS depends on accurate text‑‑speech alignment and well‑designed training sequences that balance synthesis quality and latency. Prior work often relies on GMM‑HMM based forced‑alignment toolkits (e.g., MFA), which are pipeline‑heavy and less flexible than neural aligners; fixed‑ratio interleaving of text and speech tokens struggles to capture text‑‑speech alignment regularities. We propose CTC‑TTS, which replaces MFA with a CTC based aligner and introduces a bi‑word based interleaving strategy. Two variants are designed: CTC‑TTS‑L (token concatenation along the sequence length) for higher quality and CTC‑TTS‑F (embedding stacking along the feature dimension) for lower latency. Experiments show that CTC‑TTS outperforms fixed‑ratio interleaving and MFA‑based baselines on streaming synthesis and zero‑shot tasks. Speech samples are available at https://ctctts.github.io/.

PaperId: 2917, https://arxiv.org/pdf/2602.19517.pdf GitHub

Abstract:
We introduce CFE‑Bench (Classroom Final Exam), a multimodal benchmark for evaluating the reasoning capabilities of large language models across more than 20 STEM domains. CFE‑Bench is curated from repeatedly used, authentic university homework and exam problems, paired with reference solutions provided by course instructors. CFE‑Bench remains challenging for frontier models: the newly released Gemini‑3.1‑pro‑preview achieves 59.69% overall accuracy, while the second‑best model, Gemini‑3‑flash‑preview, reaches 55.46%, leaving substantial room for improvement. Beyond aggregate scores, we conduct a diagnostic analysis by decomposing instructor reference solutions into structured reasoning flows. We find that while frontier models often answer intermediate sub‑questions correctly, they struggle to reliably derive and maintain correct intermediate states throughout multi‑step solutions. We further observe that model‑generated solutions typically contain more reasoning steps than instructor solutions, indicating lower step efficiency and a higher risk of error accumulation. Data and code are available at https://github.com/Analogy‑AI/CFE_Bench.

PaperId: 2918, https://arxiv.org/pdf/2602.19483.pdf GitHub

Abstract:
Quantifying uncertainty in clinical predictions is critical for high‑stakes diagnosis tasks. Conformal prediction offers a principled approach by providing prediction sets with theoretical coverage guarantees. However, in practice, patient distribution shifts violate the i.i.d. assumptions underlying standard conformal methods, leading to poor coverage in healthcare settings. In this work, we evaluate several conformal prediction approaches on EEG seizure classification, a task with known distribution shift challenges and label uncertainty. We demonstrate that personalized calibration strategies can improve coverage by over 20 percentage points while maintaining comparable prediction set sizes. Our implementation is available via PyHealth, an open‑source healthcare AI framework: https://github.com/sunlabuiuc/PyHealth.

PaperId: 2919, https://arxiv.org/pdf/2602.19475.pdf GitHub

Abstract:
Physics‑informed neural networks (PINNs) have emerged as a promising mesh‑free paradigm for solving partial differential equations, yet adoption in science and engineering is limited by slow training and modest accuracy relative to modern numerical solvers. We introduce the Sequential Correction Algorithm for Learning Efficient PINN (Scale‑PINN), a learning strategy that bridges modern physics‑informed learning with numerical algorithms. Scale‑PINN incorporates the iterative residual‑correction principle, a cornerstone of numerical solvers, directly into the loss formulation, marking a paradigm shift in how PINN losses can be conceived and constructed. This integration enables Scale‑PINN to achieve unprecedented convergence speed across PDE problems from different physics domain, including reducing training time on a challenging fluid‑dynamics problem for state‑of‑the‑art PINN from hours to sub‑2 minutes while maintaining superior accuracy, and enabling application to representative problems in aerodynamics and urban science. By uniting the rigor of numerical methods with the flexibility of deep learning, Scale‑PINN marks a significant leap toward the practical adoption of PINNs in science and engineering through scalable, physics‑informed learning. Codes are available at https://github.com/chiuph/SCALE‑PINN.

PaperId: 2920, https://arxiv.org/pdf/2602.19314.pdf GitHub

Abstract:
The image purification strategy constructs an intermediate distribution with aligned anatomical structures, which effectively corrects the spatial misalignment between real‑world ultra‑low‑dose CT and normal‑dose CT images and significantly enhances the structural preservation ability of denoising models. However, this strategy exhibits two inherent limitations. First, it suppresses noise only in the chest wall and bone regions while leaving the image background untreated. Second, it lacks a dedicated mechanism for denoising the lung parenchyma. To address these issues, we systematically redesign the original image purification strategy and propose an improved version termed IPv2. The proposed strategy introduces three core modules, namely Remove Background, Add noise, and Remove noise. These modules endow the model with denoising capability in both background and lung tissue regions during training data construction and provide a more reasonable evaluation protocol through refined label construction at the testing stage. Extensive experiments on our previously established real‑world patient lung CT dataset acquired at 2% radiation dose demonstrate that IPv2 consistently improves background suppression and lung parenchyma restoration across multiple mainstream denoising models. The code is publicly available at https://github.com/MonkeyDadLufy/Image‑Purification‑Strategy‑v2.

PaperId: 2921, https://arxiv.org/pdf/2602.19248.pdf GitHub

Abstract:
The collection and detection of video anomaly data has long been a challenging problem due to its rare occurrence and spatio‑temporal scarcity. Existing video anomaly detection (VAD) methods under perform in open‑world scenarios. Key contributing factors include limited dataset diversity, and inadequate understanding of context‑dependent anomalous semantics. To address these issues, i) we propose LAVIDA, an end‑to‑end zero‑shot video anomaly detection framework. ii) LAVIDA employs an Anomaly Exposure Sampler that transforms segmented objects into pseudo‑anomalies to enhance model adaptability to unseen anomaly categories. It further integrates a Multimodal Large Language Model (MLLM) to bolster semantic comprehension capabilities. Additionally, iii) we design a token compression approach based on reverse attention to handle the spatio‑temporal scarcity of anomalous patterns and decrease computational cost. The training process is conducted solely on pseudo anomalies without any VAD data. Evaluations across four benchmark VAD datasets demonstrate that LAVIDA achieves SOTA performance in both frame‑level and pixel‑level anomaly detection under the zero‑shot setting. Our code is available in https://github.com/VitaminCreed/LAVIDA.

PaperId: 2922, https://arxiv.org/pdf/2602.19225.pdf GitHub

Abstract:
Multi‑turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e‑commerce assistance, and interactive task management, where accurately distinguishing high‑value informative signals from stochastic noise is critical for sample‑efficient training. In real‑world scenarios, a failure in a trivial task may reflect random instability, whereas success in a high‑difficulty task signifies a genuine capability breakthrough. Yet, existing group‑based policy optimization methods rigidly rely on statistical deviation within discrete batches, frequently misallocating credit when task difficulty fluctuates. To address this issue, we propose Proximity‑based Multi‑turn Optimization (ProxMO), a practical and robust framework engineered specifically for the constraints of real‑world deployment. ProxMO integrates global context via two lightweight mechanisms: success‑rate‑aware modulation dynamically adapts gradient intensity based on episode‑level difficulty, while proximity‑based soft aggregation derives baselines through continuous semantic weighting at the step level. Extensive evaluations on ALFWorld and WebShop benchmarks demonstrate that ProxMO yields substantial performance gains over existing baselines with negligible computational cost. Ablation studies further validate the independent and synergistic efficacy of both mechanisms. Crucially, ProxMO offers plug‑and‑play compatibility with standard GRPO frameworks, facilitating immediate, low‑friction adoption in existing industrial training pipelines. Our implementation is available at: \hrefhttps://anonymous.4open.science/r/proxmo‑B7E7/README.mdhttps://anonymous.4open.science/r/proxmo.

PaperId: 2923, https://arxiv.org/pdf/2602.19208.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face key challenges in resource allocation and policy optimization dynamics: (i) uniform rollout allocation ignores gradient variance heterogeneity across problems, and (ii) the softmax policy structure causes gradient attenuation for high‑confidence correct actions, while excessive gradient updates may destabilize training. Therefore, we propose DynaMO, a theoretically‑grounded dual‑pronged optimization framework. At the sequence level, we prove that uniform allocation is suboptimal and derive variance‑minimizing allocation from the first principle, establishing Bernoulli variance as a computable proxy for gradient informativeness. At the token level, we develop gradient‑aware advantage modulation grounded in theoretical analysis of gradient magnitude bounds. Our framework compensates for gradient attenuation of high‑confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. Extensive experiments conducted on a diverse range of mathematical reasoning benchmarks demonstrate consistent improvements over strong RLVR baselines. Our implementation is available at: https://github.com/GithubX‑F/DynaMO‑RL.

PaperId: 2924, https://arxiv.org/pdf/2602.19169.pdf GitHub

Abstract:
I introduce Virtual Parameter Sharpening (VPS), an inference‑time technique that augments frozen transformer linear layers with dynamic, activation‑conditioned low‑rank perturbations. Unlike parameter‑efficient fine‑tuning methods such as LoRA, which learn static low‑rank adapters, VPS constructs its perturbation factors on the fly from batch activation statistics and optional gradient signals, enabling test‑time adaptation without persistent parameter updates. The perturbation takes the form Delta W = gamma W^T V U^T W, where selector matrices U and V are constructed via sparse activation‑guided selection or Sylvester‑coupled regression. We provide a theoretical analysis of the perturbation's spectral properties and describe an adaptive policy system that modulates perturbation magnitude based on activation energy and token‑level entropy. This system incorporates multi‑objective verification with iterative refinement for tasks with ground‑truth supervision. We present the complete algorithmic framework, analyze its mathematical foundations, and discuss the mechanisms by which activation‑conditioned computation may enhance reasoning capabilities in large language models. Implementation and experimental code are available at https://github.com/Saba‑Kublashvili/vps‑virtual‑parameter‑synthesis .

PaperId: 2925, https://arxiv.org/pdf/2602.19022.pdf GitHub

Abstract:
Accurate sex identification in fish is vital for optimizing breeding and management strategies in aquaculture, particularly for species at the risk of extinction. However, most existing methods are invasive or stressful and may cause additional mortality, posing severe risks to threatened or endangered fish populations. To address these challenges, we propose FishProtoNet, a robust, non‑invasive computer vision‑based framework for sex identification of delta smelt (Hypomesus transpacificus), an endangered fish species native to California, across its full life cycle. Unlike the traditional deep learning methods, FishProtoNet provides interpretability through learned prototype representations while improving robustness by leveraging foundation models to reduce the influence of background noise. Specifically, the FishProtoNet framework consists of three key components: fish regions of interest (ROIs) extraction using visual foundation model, feature extraction from fish ROIs and fish sex identification based on an interpretable prototype network. FishProtoNet demonstrates strong performance in delta smelt sex identification during early spawning and post‑spawning stages, achieving the accuracies of 74.40% and 81.16% and corresponding F1 scores of 74.27% and 79.43% respectively. In contrast, delta smelt sex identification at the subadult stage remains challenging for current computer vision methods, likely due to less pronounced morphological differences in immature fish. The source code of FishProtoNet is publicly available at: https://github.com/zhengmiao1/Fish_sex_identification

PaperId: 2926, https://arxiv.org/pdf/2602.18998.pdf GitHub

Abstract:
LLM agents are increasingly expected to function as general‑purpose systems capable of resolving open‑ended user requests. While existing benchmarks focus on domain‑aware environments for developing specialized agents, evaluating general‑purpose agents requires more realistic settings that challenge them to operate across multiple skills and tools within a unified environment. We introduce General AgentBench, a benchmark that provides such a unified framework for evaluating general LLM agents across search, coding, reasoning, and tool‑use domains. Using General AgentBench, we systematically study test‑time scaling behaviors under sequential scaling (iterative interaction) and parallel scaling (sampling multiple trajectories). Evaluation of ten leading LLM agents reveals a substantial performance degradation when moving from domain‑specific evaluations to this general‑agent setting. Moreover, we find that neither scaling methodology yields effective performance improvements in practice, due to two fundamental limitations: context ceiling in sequential scaling and verification gap in parallel scaling. Code is publicly available at https://github.com/cxcscmu/General‑AgentBench.

PaperId: 2927, https://arxiv.org/pdf/2602.18985.pdf GitHub

Abstract:
Infrared radiation computing underpins advances in climate science, remote sensing and spectroscopy but remains constrained by manual workflows. We introduce InfEngine, an autonomous intelligent computational engine designed to drive a paradigm shift from human‑led orchestration to collaborative automation. It integrates four specialized agents through two core innovations: self‑verification, enabled by joint solver‑evaluator debugging, improves functional correctness and scientific plausibility; self‑optimization, realized via evolutionary algorithms with self‑discovered fitness functions, facilitates autonomous performance optimization. Evaluated on InfBench with 200 infrared‑specific tasks and powered by InfTools with 270 curated tools, InfEngine achieves a 92.7% pass rate and delivers workflows 21x faster than manual expert effort. More fundamentally, it illustrates how researchers can transition from manual coding to collaborating with self‑verifying, self‑optimizing computational partners. By generating reusable, verified and optimized code, InfEngine transforms computational workflows into persistent scientific assets, accelerating the cycle of scientific discovery. Code: https://github.com/kding1225/infengine

PaperId: 2928, https://arxiv.org/pdf/2602.18920.pdf GitHub

Abstract:
The application of Large Language Models (LLMs) in accelerating scientific discovery has garnered increasing attention, with a key focus on constructing research agents endowed with innovative capability, i.e., the ability to autonomously generate novel and significant research ideas. Existing approaches predominantly rely on sophisticated prompt engineering and lack a systematic training paradigm. To address this, we propose DeepInnovator, a training framework designed to trigger the innovative capability of LLMs. Our approach comprises two core components. (1) ``Standing on the shoulders of giants''. We construct an automated data extraction pipeline to extract and organize structured research knowledge from a vast corpus of unlabeled scientific literature. (2) ``Conjectures and refutations''. We introduce a ``Next Idea Prediction'' training paradigm, which models the generation of research ideas as an iterative process of continuously predicting, evaluating, and refining plausible and novel next idea. Both automatic and expert evaluations demonstrate that our DeepInnovator‑14B significantly outperforms untrained baselines, achieving win rates of 80.53%‑93.81%, and attains performance comparable to that of current leading LLMs. This work provides a scalable training pathway toward building research agents with genuine, originative innovative capability, and will open‑source the dataset to foster community advancement. Source code and data are available at: https://github.com/HKUDS/DeepInnovator.

PaperId: 2929, https://arxiv.org/pdf/2602.18916.pdf GitHub

Abstract:
Legal reasoning requires not only high accuracy but also the ability to justify decisions through verifiable and contestable arguments. However, existing Large Language Model (LLM) approaches, such as Chain‑of‑Thought (CoT) and Retrieval‑Augmented Generation (RAG), often produce unstructured explanations that lack a formal mechanism for verification or user intervention. To address this limitation, we propose Adaptive Collaboration of Argumentative LLMs (ACAL), a neuro‑symbolic framework that integrates adaptive multi‑agent collaboration with an Arena‑based Quantitative Bipolar Argumentation Framework (A‑QBAF). ACAL dynamically deploys expert agent teams to construct arguments, employs a clash resolution mechanism to adjudicate conflicting claims, and utilizes uncertainty‑aware escalation for borderline cases. Crucially, our framework supports a Human‑in‑the‑Loop (HITL) contestability workflow, enabling users to directly audit and modify the underlying reasoning graph to influence the final judgment. Empirical evaluations on the LegalBench benchmark demonstrate that ACAL outperforms strong baselines across Gemini‑2.5‑Flash‑Lite and Gemini‑2.5‑Flash architectures, effectively balancing efficient predictive performance with structured transparency and contestability. Our implementation is available at: https://github.com/loc110504/ACAL.

PaperId: 2930, https://arxiv.org/pdf/2602.18884.pdf GitHub GitHub

Abstract:
Multimodal Large Language Models (MLLMs), particularly smaller, deployable variants, exhibit a critical deficiency in understanding temporal and procedural visual data, a bottleneck hindering their application in real‑world embodied AI. This gap is largely caused by a systemic failure in training paradigms, which lack large‑scale, procedurally coherent data. To address this problem, we introduce TPRU, a large‑scale dataset sourced from diverse embodied scenarios such as robotic manipulation and GUI navigation. TPRU is systematically designed to cultivate temporal reasoning through three complementary tasks: Temporal Reordering, Next‑Frame Prediction, and Previous‑Frame Review. A key feature is the inclusion of challenging negative samples, compelling models to transition from passive observation to active, cross‑modal validation. We leverage TPRU with a reinforcement learning (RL) fine‑tuning methodology, specifically targeting the enhancement of resource‑efficient models. Experiments show our approach yields dramatic gains: on our manually curated TPRU‑Test, the accuracy of TPRU‑7B soars from 50.33% to 75.70%, a state‑of‑the‑art result that significantly outperforms vastly larger baselines, including GPT‑4o. Crucially, these capabilities generalize effectively, demonstrating substantial improvements on established benchmarks. The codebase is available at https://github.com/Stephen‑gzk/TPRU/ .

PaperId: 2931, https://arxiv.org/pdf/2602.18873.pdf GitHub

Abstract:
Text‑guided dynamic 3D character generation has advanced rapidly, yet producing high‑quality motion that faithfully reflects rich textual descriptions remains challenging. Existing methods tend to generate limited sub‑actions or incoherent motion due to fixed‑length temporal inputs and discrete frame‑wise representations that fail to capture rich motion semantics. We address these limitations by representing motion with continuous differentiable B‑spline curves, enabling more effective motion generation without modifying the capabilities of the underlying generative model. Specifically, our closed‑form, Laplacian‑regularized B‑spline solver efficiently compresses variable‑length motion sequences into compact representations with a fixed number of control points. Further, we introduce a normal‑fusion strategy for input shape adherence along with correspondence‑aware and local‑rigidity losses for motion‑restoration quality. To train our model, we collate BIMO, a new dataset containing diverse variable‑length 3D motion sequences with rich, high‑quality text annotations. Extensive evaluations show that our feed‑forward framework BiMotion generates more expressive, higher‑quality, and better prompt‑aligned motions than existing state‑of‑the‑art methods, while also achieving faster generation. Our project page is at: https://wangmiaowei.github.io/BiMotion.github.io/.

PaperId: 2932, https://arxiv.org/pdf/2602.18858.pdf GitHub

Abstract:
Hyperbolic spaces provide a natural geometry for representing hierarchical and tree‑structured data due to their exponential volume growth. To leverage these benefits, neural networks require intrinsic and efficient components that operate directly in hyperbolic space. In this work, we lift two core components of neural networks, Multinomial Logistic Regression (MLR) and Fully Connected (FC) layers, into hyperbolic space via Busemann functions, resulting in Busemann MLR (BMLR) and Busemann FC (BFC) layers with a unified mathematical interpretation. BMLR provides compact parameters, a point‑to‑horosphere distance interpretation, batch‑efficient computation, and a Euclidean limit, while BFC generalizes FC and activation layers with comparable complexity. Experiments on image classification, genome sequence learning, node classification, and link prediction demonstrate improvements in effectiveness and efficiency over prior hyperbolic layers. The code is available at https://github.com/GitZH‑Chen/HBNN.

PaperId: 2933, https://arxiv.org/pdf/2602.18846.pdf GitHub GitHub

Abstract:
Vision‑language models (VLMs) have achieved remarkable multimodal understanding and reasoning capabilities, yet remain computationally expensive due to dense visual tokenization. Existing efficiency approaches either merge redundant visual tokens or drop them progressively in language backbone, often trading accuracy for speed. In this work, we propose DUET‑VLM, a versatile plug‑and‑play dual compression framework that consists of (a) vision‑only redundancy aware compression of vision encoder's output into information‑preserving tokens, followed by (b) layer‑wise, salient text‑guided dropping of visual tokens within the language backbone to progressively prune less informative tokens. This coordinated token management enables aggressive compression while retaining critical semantics. On LLaVA‑1.5‑7B, our approach maintains over 99% of baseline accuracy with 67% fewer tokens, and still retains >97% even at 89% reduction. With this dual‑stage compression during training, it achieves 99.7% accuracy at 67% and 97.6% at 89%, surpassing prior SoTA visual token reduction methods across multiple benchmarks. When integrated into Video‑LLaVA‑7B, it even surpasses the baseline ‑‑ achieving >100% accuracy with a substantial 53.1% token reduction and retaining 97.6% accuracy under an extreme 93.4% setting. These results highlight end‑to‑end training with DUET‑VLM, enabling robust adaptation to reduced visual (image/video) input without sacrificing accuracy, producing compact yet semantically rich representations within the same computational budget. Our code is available at https://github.com/AMD‑AGI/DUET‑VLM.

PaperId: 2934, https://arxiv.org/pdf/2602.18769.pdf GitHub

Abstract:
Understanding disease‑gene associations is essential for unravelling disease mechanisms and advancing diagnostics and therapeutics. Traditional approaches based on manual curation and literature review are labour‑intensive and not scalable, prompting the use of machine learning on large biomedical data. In particular, graph neural networks (GNNs) have shown promise for modelling complex biological relationships. To address limitations in existing models, we propose GLaDiGAtor (Graph Learning‑bAsed DIsease‑Gene AssociaTiOn pRediction), a novel GNN framework with an encoder‑decoder architecture for disease‑gene association prediction. GLaDiGAtor constructs a heterogeneous biological graph integrating gene‑gene, disease‑disease, and gene‑disease interactions from curated databases, and enriches each node with contextual features from well‑known language models (ProtT5 for protein sequences and BioBERT for disease text). In evaluations, our model achieves superior predictive accuracy and generalisation, outperforming 14 existing methods. Literature‑supported case studies confirm the biological relevance of high‑confidence novel predictions, highlighting GLaDiGAtor's potential to discover candidate disease genes. These results underscore the power of graph convolutional networks in biomedical informatics and may ultimately facilitate drug discovery by revealing new gene‑disease links. The source code and processed datasets are publicly available at https://github.com/HUBioDataLab/GLaDiGAtor.

PaperId: 2935, https://arxiv.org/pdf/2602.18763.pdf GitHub

Abstract:
Facial Expression Recognition (FER) is a fine‑grained visual understanding task where reliable predictions require reasoning over localized and meaningful facial cues. Recent vision‑‑language models (VLMs) enable natural language explanations for FER, but their reasoning is often ungrounded, producing fluent yet unverifiable rationales that are weakly tied to visual evidence and prone to hallucination, leading to poor robustness across different datasets. We propose TAG (Thinking with Action Unit Grounding), a vision‑‑language framework that explicitly constrains multimodal reasoning to be supported by facial Action Units (AUs). TAG requires intermediate reasoning steps to be grounded in AU‑related facial regions, yielding predictions accompanied by verifiable visual evidence. The model is trained via supervised fine‑tuning on AU‑grounded reasoning traces followed by reinforcement learning with an AU‑aware reward that aligns predicted regions with external AU detectors. Evaluated on RAF‑DB, FERPlus, and AffectNet, TAG consistently outperforms strong open‑source and closed‑source VLM baselines while simultaneously improving visual faithfulness. Ablation and preference studies further show that AU‑grounded rewards stabilize reasoning and mitigate hallucination, demonstrating the importance of structured grounded intermediate representations for trustworthy multimodal reasoning in FER. The code will be available at https://github.com/would1920/FER_TAG .

PaperId: 2936, https://arxiv.org/pdf/2602.18745.pdf GitHub

Abstract:
Multimodal geometry reasoning requires models to jointly understand visual diagrams and perform structured symbolic inference, yet current vision‑‑language models struggle with complex geometric constructions due to limited training data and weak visual‑‑symbolic alignment. We propose a pipeline for synthesizing complex multimodal geometry problems from scratch and construct a dataset named GeoCode, which decouples problem generation into symbolic seed construction, grounded instantiation with verification, and code‑based diagram rendering, ensuring consistency across structure, text, reasoning, and images. Leveraging the plotting code provided in GeoCode, we further introduce code prediction as an explicit alignment objective, transforming visual understanding into a supervised structured prediction task. GeoCode exhibits substantially higher structural complexity and reasoning difficulty than existing benchmarks, while maintaining mathematical correctness through multi‑stage validation. Extensive experiments show that models trained on GeoCode achieve consistent improvements on multiple geometry benchmarks, demonstrating both the effectiveness of the dataset and the proposed alignment strategy. The code will be available at https://github.com/would1920/GeoCode.

PaperId: 2937, https://arxiv.org/pdf/2602.18742.pdf GitHub

Abstract:
Synthetic data generated by video generative models has shown promise for robot learning as a scalable pipeline, but it often suffers from inconsistent action quality due to imperfectly generated videos. Recently, vision‑language models (VLMs) have been leveraged to validate video quality, but they have limitations in distinguishing physically accurate videos and, even then, cannot directly evaluate the generated actions themselves. To tackle this issue, we introduce RoboCurate, a novel synthetic robot data generation framework that evaluates and filters the quality of annotated actions by comparing them with simulation replay. Specifically, RoboCurate replays the predicted actions in a simulator and assesses action quality by measuring the consistency of motion between the simulator rollout and the generated video. In addition, we unlock observation diversity beyond the available dataset via image‑to‑image editing and apply action‑preserving video‑to‑video transfer to further augment appearance. We observe RoboCurate's generated data yield substantial relative improvements in success rates compared to using real data only, achieving +70.1% on GR‑1 Tabletop (300 demos), +16.1% on DexMimicGen in the pre‑training setup, and +179.9% in the challenging real‑world ALLEX humanoid dexterous manipulation setting.

PaperId: 2938, https://arxiv.org/pdf/2602.18734.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge‑intensive tasks by grounding language generation in external evidence. Despite its success, many existing RAG systems are built based on a ranking‑centric, asymmetric dependency paradigm, where the generation quality of the generator is highly dependent on reranking results of the reranker. To overcome this limitation, we propose Cooperative Retrieval‑Augmented Generation (CoRAG), a framework that treats the reranker and the generator as peer decision‑makers rather than being connected through an asymmetric dependency pipeline. By jointly optimizing their behaviors toward a shared task objective, the reranker and generator are encouraged to cooperate, ensuring that document reranking and generation work in concert to improve the final response. Experimental results demonstrate good generalization and improved generation stability of CoRAG, even when the model is trained on only around 10K PopQA samples. Our model released in https://github.com/CoderrrSong/CoRAG.

PaperId: 2939, https://arxiv.org/pdf/2602.18532.pdf GitHub GitHub

Abstract:
Following the rise of large foundation models, Vision‑Language‑Action models (VLAs) emerged, leveraging strong visual and language understanding from Vision‑Language Models for general‑purpose policy learning. Yet, the current VLA landscape remains fragmented and exploratory. Although many groups have proposed their own VLA models, inconsistencies in training protocols and evaluation settings make it difficult to identify which design choices truly matter. To bring structure to this evolving space, we reexamine the VLA design space under a unified framework and evaluation setup. Starting from a simple VLA baseline similar to RT‑2, which is the origin of VLA, we systematically dissect design choices along three dimensions: foundational components, perception essentials, and action modelling perspectives. From this study, we distill 12 key findings that together form a practical recipe for building strong VLA models. The outcome of this exploration is a simple yet effective model, VLANeXt. It outperforms the state‑of‑the‑art methods on the LIBERO and LIBERO‑plus benchmarks and demonstrates strong performance in real‑world experiments. We release a unified and easy‑to‑use codebase to reproduce our findings, explore the design space, and develop new VLA variants on top of a shared foundation. The codebase is available at https://github.com/DravenALG/VLANeXt.

PaperId: 2940, https://arxiv.org/pdf/2602.18527.pdf GitHub

Abstract:
Current audio‑visual large language models (AV‑LLMs) are predominantly restricted to 2D perception, relying on RGB video and monaural audio. This design choice introduces a fundamental dimensionality mismatch that precludes reliable source localization and spatial reasoning in complex 3D environments. We address this limitation by presenting JAEGER, a framework that extends AV‑LLMs to 3D space, to enable joint spatial grounding and reasoning through the integration of RGB‑D observations and multi‑channel first‑order ambisonics. A core contribution of our work is the neural intensity vector (Neural IV), a learned spatial audio representation that encodes robust directional cues to enhance direction‑of‑arrival estimation, even in adverse acoustic scenarios with overlapping sources. To facilitate large‑scale training and systematic evaluation, we propose SpatialSceneQA, a benchmark of 61k instruction‑tuning samples curated from simulated physical environments. Extensive experiments demonstrate that our approach consistently surpasses 2D‑centric baselines across diverse spatial perception and reasoning tasks, underscoring the necessity of explicit 3D modelling for advancing AI in physical environments. Our source code, pre‑trained model checkpoints, and datasets are available at https://github.com/liuzhan22/JAEGER.

PaperId: 2941, https://arxiv.org/pdf/2602.18497.pdf GitHub

Abstract:
Enterprises rely on RDF knowledge graphs and SPARQL to expose operational data through natural language interfaces, yet public KGQA benchmarks do not reflect proprietary schemas, prefixes, or query distributions. We present PIPE‑RDF, a three‑phase pipeline that constructs schema‑specific NL‑SPARQL benchmarks using reverse querying, category‑balanced template generation, retrieval‑augmented prompting, deduplication, and execution‑based validation with repair. We instantiate PIPE‑RDF on a fixed‑schema company‑location slice (5,000 companies) derived from public RDF data and generate a balanced benchmark of 450 question‑SPARQL pairs across nine categories. The pipeline achieves 100% parse and execution validity after repair, with pre‑repair validity rates of 96.5%‑100% across phases. We report entity diversity metrics, template coverage analysis, and cost breakdowns to support deployment planning. We release structured artifacts (CSV/JSONL, logs, figures) and operational metrics to support model evaluation and system planning in real‑world settings. Code is available at https://github.com/suraj‑ranganath/PIPE‑RDF.

PaperId: 2942, https://arxiv.org/pdf/2602.18495.pdf GitHub

Abstract:
Recent advances in tabular in‑context learning (ICL) show that a single pretrained model can adapt to new prediction tasks from a small set of labeled examples, avoiding per‑task training and heavy tuning. However, many real‑world tasks live in relational databases, where predictive signal is spread across multiple linked tables rather than a single flat table. We show that tabular ICL can be extended to relational prediction with a simple recipe: automatically featurize each target row using relational aggregations over its linked records, materialize the resulting augmented table, and run an off‑the‑shelf tabular foundation model on it. We package this approach in RDBLearn (https://github.com/HKUSHXLab/rdblearn), an easy‑to‑use toolkit with a scikit‑learn‑style estimator interface that makes it straightforward to swap different tabular ICL backends; a complementary agent‑specific interface is provided as well. Across a broad collection of RelBench and 4DBInfer datasets, RDBLearn is the best‑performing foundation model approach we evaluate, at times even outperforming strong supervised baselines trained or fine‑tuned on each dataset.

PaperId: 2943, https://arxiv.org/pdf/2602.18481.pdf GitHub

Abstract:
The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge evaluation toward interactive trading simulations. However, existing frameworks for evaluating real‑time trading largely overlook a critical failure mode: the severe behavioral instability of LLMs in sequential decision‑making under financial uncertainty. Through extensive experiments, we show that when deployed as trading agents, LLMs exhibit extreme run‑to‑run variance, generate inconsistent action sequences even under deterministic decoding, and frequently produce irrational action flipping across adjacent time steps. We attribute these behaviors to the stateless autoregressive nature of LLMs, which lack persistent memory of prior actions, together with their sensitivity to continuous‑to‑discrete action mappings in portfolio allocation tasks. These deficiencies fundamentally undermine the reliability and reproducibility of many existing online and offline trading benchmarks. To address these limitations, we propose AlphaForgeBench, a principled evaluation framework that redefines LLMs as quantitative researchers rather than stochastic trading agents. Instead of producing discrete trading actions, AlphaForgeBench requires models to generate executable alpha factors and compose factor‑based trading strategies grounded in financial knowledge. This paradigm decouples reasoning from execution mechanics, enabling deterministic and reproducible evaluation while remaining aligned with real‑world quantitative research workflows. Extensive experiments across multiple state‑of‑the‑art LLMs demonstrate that AlphaForgeBench eliminates execution‑induced instability and provides a rigorous benchmark for evaluating financial reasoning, strategy formulation, and alpha discovery. Webpage at https://finbrain‑lab‑hkustgz.github.io/AlphaForgeBench

PaperId: 2944, https://arxiv.org/pdf/2602.18473.pdf GitHub

Abstract:
Accurate analysis of medical time series (MedTS) data, such as electroencephalography (EEG) and electrocardiography (ECG), plays a pivotal role in healthcare applications, including the diagnosis of brain and heart diseases. MedTS data typically exhibit two critical patterns: temporal dependencies within individual channels and channel dependencies across multiple channels. While recent advances in deep learning have leveraged Transformer‑based models to effectively capture temporal dependencies, they often struggle with modeling channel dependencies. This limitation stems from a structural mismatch: MedTS signals are inherently centralized, whereas the Transformer's attention mechanism is decentralized, making it less effective at capturing global synchronization and unified waveform patterns. To address this mismatch, we propose CoTAR (Core Token Aggregation‑Redistribution), a centralized MLP‑based module designed to replace decentralized attention. Instead of allowing all tokens to interact directly, as in standard attention, CoTAR introduces a global core token that serves as a proxy to facilitate inter‑token interactions, thereby enforcing a centralized aggregation and redistribution strategy. This design not only better aligns with the centralized nature of MedTS signals but also reduces computational complexity from quadratic to linear. Experiments on five benchmarks validate the superiority of our method in both effectiveness and efficiency, achieving up to a 11.6% improvement on the APAVA dataset, while using only 33% of the memory and 20% of the inference time compared to the previous state of the art. Code and all training scripts are available at https://github.com/Levi‑Ackman/TeCh.

PaperId: 2945, https://arxiv.org/pdf/2602.18458.pdf GitHub

Abstract:
Reproducibility crises across sciences highlight the limitations of the paper‑centric review system in assessing the rigor and reproducibility of research. AI agents that autonomously design and generate large volumes of research outputs exacerbate these challenges. In this work, we address the growing challenges of scalability and rigor by flipping the dynamic and developing AI agents as research evaluators. We propose the first execution‑grounded evaluation framework that verifies research beyond narrative review by examining code and data alongside the paper. We use mechanistic interpretability research as a testbed, build standardized research output, and develop MechEvalAgent, an automated evaluation framework that assesses the coherence of the experimental process, the reproducibility of results, and the generalizability of findings. We show that our framework achieves above 80% agreement with human judges, identifies substantial methodological problems, and surfaces 51 additional issues that human reviewers miss. Our work demonstrates the potential of AI agents to transform research evaluation and pave the way for rigorous scientific practices.

PaperId: 2946, https://arxiv.org/pdf/2602.18277.pdf GitHub GitHub

Abstract:
This work studies heterogeneous Multi‑Objective Reinforcement Learning (MORL), where objectives can differ sharply in temporal frequency. Such heterogeneity allows dense objectives to dominate learning, while sparse long‑horizon rewards receive weak credit assignment, leading to poor sample efficiency. We propose a Parallel Reward Integration with Symmetry (PRISM) algorithm that enforces reflectional symmetry as an inductive bias in aligning reward channels. PRISM introduces ReSymNet, a theory‑motivated model that reconciles temporal‑frequency mismatches across objectives, using residual blocks to learn a scaled opportunity value that accelerates exploration while preserving the optimal policy. We also propose SymReg, a reflectional equivariance regulariser that enforces agent mirroring and constrains policy search to a reflection‑equivariant subspace. This restriction provably reduces hypothesis complexity and improves generalisation. Across MuJoCo benchmarks, PRISM consistently outperforms both a sparse‑reward baseline and an oracle trained with full dense rewards, improving Pareto coverage and distributional balance: it achieves hypervolume gains exceeding 100% over the baseline and up to 32% over the oracle. The code is at \hrefhttps://github.com/EVIEHub/PRISMhttps://github.com/EVIEHub/PRISM.

PaperId: 2947, https://arxiv.org/pdf/2602.18262.pdf GitHub

Abstract:
While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has created an accessibility gap, limiting their use to specialists. We address this challenge by designing, building, and evaluating ELIA (Explainable Language Interpretability Analysis), an interactive web application that simplifies the outcomes of various language model component analyses for a broader audience. The system integrates three key techniques ‑‑ Attribution Analysis, Function Vector Analysis, and Circuit Tracing ‑‑ and introduces a novel methodology: using a vision‑language model to automatically generate natural language explanations (NLEs) for the complex visualizations produced by these methods. The effectiveness of this approach was empirically validated through a mixed‑methods user study, which revealed a clear preference for interactive, explorable interfaces over simpler, static visualizations. A key finding was that the AI‑powered explanations helped bridge the knowledge gap for non‑experts; a statistical analysis showed no significant correlation between a user's prior LLM experience and their comprehension scores, suggesting that the system reduced barriers to comprehension across experience levels. We conclude that an AI system can indeed simplify complex model analyses, but its true power is unlocked when paired with thoughtful, user‑centered design that prioritizes interactivity, specificity, and narrative guidance.

PaperId: 2948, https://arxiv.org/pdf/2602.18232.pdf GitHub

Abstract:
Recent work on test‑time scaling for large language model (LLM) reasoning typically assumes that allocating more inference‑time computation uniformly improves correctness. However, prior studies show that reasoning uncertainty is highly localized: a small subset of low‑confidence tokens disproportionately contributes to reasoning errors and unnecessary output expansion. Motivated by this observation, we propose Thinking by Subtraction, a confidence‑driven contrastive decoding approach that improves reasoning reliability through targeted token‑level intervention. Our method, Confidence‑Driven Contrastive Decoding, detects low‑confidence tokens during decoding and intervenes selectively at these positions. It constructs a contrastive reference by replacing high‑confidence tokens with minimal placeholders, and refines predictions by subtracting this reference distribution at low‑confidence locations. Experiments show that CCD significantly improves accuracy across mathematical reasoning benchmarks while substantially reducing output length, with minimal KV‑cache overhead. As a training‑free method, CCD enhances reasoning reliability through targeted low‑confidence intervention without computational redundancy. Our code will be made available at: https://github.com/bolo‑web/CCD.

PaperId: 2949, https://arxiv.org/pdf/2602.18230.pdf GitHub

Abstract:
Large Language Models (LLMs) demonstrate significant potential in multi‑agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and generalizable benchmarks. Abdelnabi et al. (2024) introduce a negotiation benchmark based on Scoreable Games, with the aim of developing a highly complex and realistic evaluation framework for LLMs. Our work investigates the reproducibility of claims in their benchmark, and provides a deeper understanding of its usability and generalizability. We replicate the original experiments on additional models, and introduce additional metrics to verify negotiation quality and evenness of evaluation. Our findings reveal that while the benchmark is indeed complex, model comparison is ambiguous, raising questions about its objectivity. Furthermore, we identify limitations in the experimental setup, particularly in information leakage detection and thoroughness of the ablation study. By examining and analyzing the behavior of a wider range of models on an extended version of the benchmark, we reveal insights that provide additional context to potential users. Our results highlight the importance of context in model‑comparative evaluations.

PaperId: 2950, https://arxiv.org/pdf/2602.18201.pdf GitHub

Abstract:
Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. We show that this assumption is false. Using SOMtime, a topology‑preserving representation method based on high‑capacity Self‑Organizing Maps, we demonstrate that sensitive attributes such as age and income emerge as dominant latent axes in purely unsupervised embeddings, even when explicitly excluded from the input. On two large‑scale real‑world datasets (the World Values Survey across five countries and the Census‑Income dataset), SOMtime recovers monotonic orderings aligned with withheld sensitive attributes, achieving Spearman correlations of up to 0.85, whereas PCA and UMAP typically remain below 0.23 (with a single exception reaching 0.31), and against t‑SNE and autoencoders which achieve at most 0.34. Furthermore, unsupervised segmentation of SOMtime embeddings produces demographically skewed clusters, demonstrating downstream fairness risks without any supervised task. These findings establish that fairness through unawareness fails at the representation level for ordinal sensitive attributes and that fairness auditing must extend to unsupervised components of machine learning pipelines. We have made the code available at~ https://github.com/JosephBingham/SOMtime

PaperId: 2951, https://arxiv.org/pdf/2602.17951.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models enable instruction‑following robotic manipulation, but they are typically pretrained on 2D data and lack 3D spatial understanding. An effective approach is representation alignment, where a strong vision foundation model is used to guide a 2D VLA model. However, existing methods usually apply supervision at only a single layer, failing to fully exploit the rich information distributed across depth; meanwhile, naïve multi‑layer alignment can cause gradient interference. We introduce ROCKET, a residual‑oriented multi‑layer representation alignment framework that formulates multi‑layer alignment as aligning one residual stream to another. Concretely, ROCKET employs a shared projector to align multiple layers of the VLA backbone with multiple layers of a powerful 3D vision foundation model via a layer‑invariant mapping, which reduces gradient conflicts. We provide both theoretical justification and empirical analyses showing that a shared projector is sufficient and outperforms prior designs, and further propose a Matryoshka‑style sparse activation scheme for the shared projector to balance multiple alignment losses. Our experiments show that, combined with a training‑free layer selection strategy, ROCKET requires only about 4% of the compute budget while achieving 98.5% state‑of‑the‑art success rate on LIBERO. We further demonstrate the superior performance of ROCKET across LIBERO‑Plus and RoboTwin, as well as multiple VLA models. The code and model weights can be found at https://github.com/CASE‑Lab‑UMD/ROCKET‑VLA.

PaperId: 2952, https://arxiv.org/pdf/2602.17930.pdf GitHub

Abstract:
Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can provide subgoal decompositions, plausible trajectories, and abstract priors that facilitate early learning. However, heavy reliance on LLM supervision introduces scalability constraints and dependence on potentially unreliable signals. We propose MIRA (Memory‑Integrated Reinforcement Learning Agent), which incorporates a structured, evolving memory graph to guide early training. The graph stores decision‑relevant information, including trajectory segments and subgoal structures, and is constructed from both the agent's high‑return experiences and LLM outputs. This design amortizes LLM queries into a persistent memory rather than requiring continuous real‑time supervision. From this memory graph, we derive a utility signal that softly adjusts advantage estimation to influence policy updates without modifying the underlying reward function. As training progresses, the agent's policy gradually surpasses the initial LLM‑derived priors, and the utility term decays, preserving standard convergence guarantees. We provide theoretical analysis showing that utility‑based shaping improves early‑stage learning in sparse‑reward environments. Empirically, MIRA outperforms RL baselines and achieves returns comparable to approaches that rely on frequent LLM supervision, while requiring substantially fewer online LLM queries. Project webpage: https://narjesno.github.io/MIRA/

PaperId: 2953, https://arxiv.org/pdf/2602.17690.pdf GitHub

Abstract:
Graphic design generation demands a delicate balance between high visual fidelity and fine‑grained structural editability. However, existing approaches typically bifurcate into either non‑editable raster image synthesis or abstract layout generation devoid of visual content. Recent combinations of these two approaches attempt to bridge this gap but often suffer from rigid composition schemas and unresolvable visual dissonances (e.g., text‑background conflicts) due to their inexpressive representation and open‑loop nature. To address these challenges, we propose DesignAsCode, a novel framework that reimagines graphic design as a programmatic synthesis task using HTML/CSS. Specifically, we introduce a Plan‑Implement‑Reflect pipeline, incorporating a Semantic Planner to construct dynamic, variable‑depth element hierarchies and a Visual‑Aware Reflection mechanism that iteratively optimizes the code to rectify rendering artifacts. Extensive experiments demonstrate that DesignAsCode significantly outperforms state‑of‑the‑art baselines in both structural validity and aesthetic quality. Furthermore, our code‑native representation unlocks advanced capabilities, including automatic layout retargeting, complex document generation (e.g., resumes), and CSS‑based animation. Our project page is available at https://liuziyuan1109.github.io/design‑as‑code/.

PaperId: 2954, https://arxiv.org/pdf/2602.17664.pdf GitHub GitHub

Abstract:
Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive (AR) LLMs, typically preserve attention sink tokens because AR sinks serve as stable global anchors. We show that this assumption does not hold for DLMs: the attention‑sink position exhibits substantially higher variance over the full generation trajectory (measured by how the dominant sink locations shift across timesteps), indicating that sinks are often transient and less structurally essential than in AR models. Based on this observation, we propose \bf \textttSink‑Aware Pruning, which automatically identifies and prunes unstable sinks in DLMs (prior studies usually keep sinks for AR LLMs). Without retraining, our method achieves a better quality‑efficiency trade‑off and outperforms strong prior pruning baselines under matched compute. Our code is available at https://github.com/VILA‑Lab/Sink‑Aware‑Pruning.

PaperId: 2955, https://arxiv.org/pdf/2602.17663.pdf GitHub

Abstract:
HIPE‑2026 is a CLEF evaluation lab dedicated to person‑place relation extraction from noisy, multilingual historical texts. Building on the HIPE‑2020 and HIPE‑2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person‑‑place associations in multiple languages and time periods. Systems are asked to classify relations of two types ‑ at ("Has the person ever been at this place?") and isAt ("Is the person located at this place around publication time?") ‑ requiring reasoning over temporal and geographical cues. The lab introduces a three‑fold evaluation profile that jointly assesses accuracy, computational efficiency, and domain generalization. By linking relation extraction to large‑scale historical data processing, HIPE‑2026 aims to support downstream applications in knowledge‑graph construction, historical biography reconstruction, and spatial analysis in digital humanities.

PaperId: 2956, https://arxiv.org/pdf/2602.17645.pdf GitHub GitHub

Abstract:
Black‑box adversarial attacks on Large Vision‑Language Models (LVLMs) are challenging due to missing gradients and complex multimodal boundaries. While prior state‑of‑the‑art transfer‑based approaches like M‑Attack perform well using local crop‑level matching between source and target images, we find this induces high‑variance, nearly orthogonal gradients across iterations, violating coherent local alignment and destabilizing optimization. We attribute this to (i) ViT translation sensitivity that yields spike‑like gradients and (ii) structural asymmetry between source and target crops. We reformulate local matching as an asymmetric expectation over source transformations and target semantics, and build a gradient‑denoising upgrade to M‑Attack. On the source side, Multi‑Crop Alignment (MCA) averages gradients from multiple independently sampled local views per iteration to reduce variance. On the target side, Auxiliary Target Alignment (ATA) replaces aggressive target augmentation with a small auxiliary set from a semantically correlated distribution, producing a smoother, lower‑variance target manifold. We further reinterpret momentum as Patch Momentum, replaying historical crop gradients; combined with a refined patch‑size ensemble (PE+), this strengthens transferable directions. Together these modules form M‑Attack‑V2, a simple, modular enhancement over M‑Attack that substantially improves transfer‑based black‑box attacks on frontier LVLMs: boosting success rates on Claude‑4.0 from 8% to 30%, Gemini‑2.5‑Pro from 83% to 97%, and GPT‑5 from 98% to 100%, outperforming prior black‑box LVLM attacks. Code and data are publicly available at: https://github.com/vila‑lab/M‑Attack‑V2.

PaperId: 2957, https://arxiv.org/pdf/2602.17616.pdf GitHub

Abstract:
Asynchronous reinforcement learning has become increasingly central to scaling LLM post‑training, delivering major throughput gains by decoupling rollout generation from policy updates. However, widely used policy‑gradient objectives such as REINFORCE and GRPO suffer under high asynchrony: stale rollouts produce heavy‑tailed importance weights, so a small number of trajectories dominate updates and the policy‑gradient estimator becomes markedly higher variance. Through systematic analysis on math, reasoning, and tool‑use benchmarks, we find that this increasing variance is reliably predicted by collapsing effective sample size (ESS), which prior stabilization methods largely fail to address. Motivated by this diagnosis, we introduce Variance Controlled Policy Optimization (VCPO), a method that (i) dynamically scales the learning rate with ESS to dampen unreliable updates and (ii) applies a closed‑form minimum‑variance baseline for off‑policy settings, without a critic model and adding minimal overhead. Empirically, across math and general reasoning benchmarks, this enables robustly stable asynchronous training compared to previous stabilization and algorithmic methods, even in highly off‑policy regimes (128 steps off‑policy). In a long‑horizon, tool‑use task, VCPO matches synchronous performance while delivering a 2.5× speedup in training time. Code is available at: https://github.com/mit‑han‑lab/vcpo

PaperId: 2958, https://arxiv.org/pdf/2602.17550.pdf GitHub

Abstract:
Existing Reinforcement Learning with Verifiable Rewards (RLVR) algorithms, such as GRPO, rely on rigid, uniform, and symmetric trust region mechanisms that are fundamentally misaligned with the complex optimization dynamics of Large Language Models (LLMs). In this paper, we identify three critical challenges in these methods: (1) inefficient gradient utilization caused by the binary cutoff of hard clipping, (2) insensitive probability mass arising from uniform ratio constraints that ignore the token distribution, and (3) asymmetric signal reliability stemming from the disparate credit assignment ambiguity between positive and negative samples. To bridge these gaps, we propose Mass‑Adaptive Soft Policy Optimization (MASPO), a unified framework designed to harmonize these three dimensions. MASPO integrates a differentiable soft Gaussian gating to maximize gradient utility, a mass‑adaptive limiter to balance exploration across the probability spectrum, and an asymmetric risk controller to align update magnitudes with signal confidence. Extensive evaluations demonstrate that MASPO serves as a robust, all‑in‑one RLVR solution, significantly outperforming baselines. Our code is available at: https://github.com/FlyTune/MASPO‑RL.

PaperId: 2959, https://arxiv.org/pdf/2602.17526.pdf GitHub

Abstract:
Some transformer attention heads appear to function as membership testers, dedicating themselves to answering the question "has this token appeared before in the context?" We identify these heads across four language models (GPT‑2 small, medium, and large; Pythia‑160M) and show that they form a spectrum of membership‑testing strategies. Two heads (L0H1 and L0H5 in GPT‑2 small) function as high‑precision membership filters with false positive rates of 0‑4% even at 180 unique context tokens ‑‑ well above the d_\texthead = 64 bit capacity of a classical Bloom filter. A third head (L1H11) shows the classic Bloom filter capacity curve: its false positive rate follows the theoretical formula p \approx (1 ‑ e^‑kn/m)^k with R^2 = 1.0 and fitted capacity m \approx 5 bits, saturating by n \approx 20 unique tokens. A fourth head initially identified as a Bloom filter (L3H0) was reclassified as a general prefix‑attention head after confound controls revealed its apparent capacity curve was a sequence‑length artifact. Together, the three genuine membership‑testing heads form a multi‑resolution system concentrated in early layers (0‑1), taxonomically distinct from induction and previous‑token heads, with false positive rates that decay monotonically with embedding distance ‑‑ consistent with distance‑sensitive Bloom filters. These heads generalize broadly: they respond to any repeated token type, not just repeated names, with 43% higher generalization than duplicate‑token‑only heads. Ablation reveals these heads contribute to both repeated and novel token processing, indicating that membership testing coexists with broader computational roles. The reclassification of L3H0 through confound controls strengthens rather than weakens the case: the surviving heads withstand the scrutiny that eliminated a false positive in our own analysis.

PaperId: 2960, https://arxiv.org/pdf/2602.17484.pdf GitHub

Abstract:
Image Copy Detection (ICD) aims to identify manipulated content between image pairs through robust feature representation learning. While self‑supervised learning (SSL) has advanced ICD systems, existing view‑level contrastive methods struggle with sophisticated edits due to insufficient fine‑grained correspondence learning. We address this limitation by exploiting the inherent geometric traceability in edited content through two key innovations. First, we propose PixTrace ‑ a pixel coordinate tracking module that maintains explicit spatial mappings across editing transformations. Second, we introduce CopyNCE, a geometrically‑guided contrastive loss that regularizes patch affinity using overlap ratios derived from PixTrace's verified mappings. Our method bridges pixel‑level traceability with patch‑level similarity learning, suppressing supervision noise in SSL training. Extensive experiments demonstrate not only state‑of‑the‑art performance (88.7% uAP / 83.9% RP90 for matcher, 72.6% uAP / 68.4% RP90 for descriptor on DISC21 dataset) but also better interpretability over existing methods.

PaperId: 2961, https://arxiv.org/pdf/2602.17442.pdf GitHub

Abstract:
Innovation in Recommender Systems is currently impeded by a fractured ecosystem, where researchers must choose between the ease of in‑memory experimentation and the costly, complex rewriting required for distributed industrial engines. To bridge this gap, we present WarpRec, a high‑performance framework that eliminates this trade‑off through a novel, backend‑agnostic architecture. It includes 50+ state‑of‑the‑art algorithms, 40 metrics, and 19 filtering and splitting strategies that seamlessly transition from local execution to distributed training and optimization. The framework enforces ecological responsibility by integrating CodeCarbon for real‑time energy tracking, showing that scalability need not come at the cost of scientific integrity or sustainability. Furthermore, WarpRec anticipates the shift toward Agentic AI, leading Recommender Systems to evolve from static ranking engines into interactive tools within the Generative AI ecosystem. In summary, WarpRec not only bridges the gap between academia and industry but also can serve as the architectural backbone for the next generation of sustainable, agent‑ready Recommender Systems. Code is available at https://github.com/sisinflab/warprec/

PaperId: 2962, https://arxiv.org/pdf/2602.17431.pdf GitHub

Abstract:
Uncertainty quantification has emerged as an effective approach to closed‑book hallucination detection for LLMs, but existing methods are largely designed for short‑form outputs and do not generalize well to long‑form generation. We introduce a taxonomy for fine‑grained uncertainty quantification in long‑form LLM outputs that distinguishes methods by design choices at three stages: response decomposition, unit‑level scoring, and response‑level aggregation. We formalize several families of consistency‑based black‑box scorers, providing generalizations and extensions of existing methods. In our experiments across multiple LLMs and datasets, we find 1) claim‑response entailment consistently performs better or on par with more complex claim‑level scorers, 2) claim‑level scoring generally yields better results than sentence‑level scoring, and 3) uncertainty‑aware decoding is highly effective for improving the factuality of long‑form outputs. Our framework clarifies relationships between prior methods, enables apples‑to‑apples comparisons, and provides practical guidance for selecting components for fine‑grained UQ.

PaperId: 2963, https://arxiv.org/pdf/2602.17395.pdf GitHub GitHub

Abstract:
Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes. Training a parametric classifier solely on image features often leads to overfitting to old classes, and recent multimodal approaches improve performance by incorporating textual information. However, they treat modalities independently and incur high computational cost. We propose SpectralGCD, an efficient and effective multimodal approach to GCD that uses CLIP cross‑modal image‑concept similarities as a unified cross‑modal representation. Each image is expressed as a mixture over semantic concepts from a large task‑agnostic dictionary, which anchors learning to explicit semantics and reduces reliance on spurious visual cues. To maintain the semantic quality of representations learned by an efficient student, we introduce Spectral Filtering which exploits a cross‑modal covariance matrix over the softmaxed similarities measured by a strong teacher model to automatically retain only relevant concepts from the dictionary. Forward and reverse knowledge distillation from the same teacher ensures that the cross‑modal representations of the student remain both semantically sufficient and well‑aligned. Across six benchmarks, SpectralGCD delivers accuracy comparable to or significantly superior to state‑of‑the‑art methods at a fraction of the computational cost. The code is publicly available at: https://github.com/miccunifi/SpectralGCD.

PaperId: 2964, https://arxiv.org/pdf/2602.17342.pdf GitHub

Abstract:
Graph Out‑of‑Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution of graphs observed during training, which is critical for ensuring the reliability of Graph Neural Networks (GNNs) when deployed in open‑world scenarios. Recent advances in graph OOD detection have focused on test‑time training techniques that facilitate OOD detection without accessing potential supervisory information (e.g., training data). However, most of these methods employ a one‑pass inference paradigm, which prevents them from progressively correcting erroneous predictions to amplify OOD signals. To this end, we propose a Self‑Improving Graph Out‑of‑Distribution detector (SIGOOD), which is an unsupervised framework that integrates continuous self‑learning with test‑time training for effective graph OOD detection. Specifically, SIGOOD generates a prompt to construct a prompt‑enhanced graph that amplifies potential OOD signals. To optimize prompts, SIGOOD introduces an Energy Preference Optimization (EPO) loss, which leverages energy variations between the original test graph and the prompt‑enhanced graph. By iteratively optimizing the prompt by involving it into the detection model in a self‑improving loop, the resulting optimal prompt‑enhanced graph is ultimately used for OOD detection. Comprehensive evaluations on 21 real‑world datasets confirm the effectiveness and outperformance of our SIGOOD method. The code is at https://github.com/Ee1s/SIGOOD.

PaperId: 2965, https://arxiv.org/pdf/2602.17162.pdf GitHub

Abstract:
Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next‑Token Prediction (NTP) to learn the "Laws of Nature". While effective at capturing local syntax, these generative paradigms prioritize token‑level reconstruction over high‑level functional context. We introduce JEPA‑DNA, a model‑agnostic continual training framework that integrates a Joint‑Embedding Predictive Architecture (JEPA) with traditional generative objectives. By supervising global sequence embeddings in a latent space, JEPA‑DNA forces models to predict the functional representations of masked genomic segments, shifting the learning signal from token recovery to semantic alignment. We evaluate JEPA‑DNA on 17 diverse genomic benchmark tasks, demonstrating consistent gains in linear probing and zero‑shot performance regardless of the underlying GFM architecture or generative objective. Our framework establishes a new state‑of‑the‑art for GFMs, surpassing the best existing models by bridging generative precision with latent semantic grounding. Through extensive ablation studies, we further characterize the synergistic interplay between generative and latent objectives. Our code is publicly available at https://github.com/NVIDIA‑Digital‑Bio/JEPA‑DNA.

PaperId: 2966, https://arxiv.org/pdf/2602.17062.pdf GitHub

Abstract:
Value decomposition is a core approach for cooperative multi‑agent reinforcement learning (MARL). However, existing methods still rely on a single optimal action and struggle to adapt when the underlying value function shifts during training, often converging to suboptimal policies. To address this limitation, we propose Successive Sub‑value Q‑learning (S2Q), which learns multiple sub‑value functions to retain alternative high‑value actions. Incorporating these sub‑value functions into a Softmax‑based behavior policy, S2Q encourages persistent exploration and enables Q^\texttot to adjust quickly to the changing optima. Experiments on challenging MARL benchmarks confirm that S2Q consistently outperforms various MARL algorithms, demonstrating improved adaptability and overall performance. Our code is available at https://github.com/hyeon1996/S2Q.

PaperId: 2967, https://arxiv.org/pdf/2602.17053.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) exhibit strong performance, yet often produce rationales that sound plausible but fail to reflect their true decision process, undermining reliability and trust. We introduce a formal framework for reasoning faithfulness, defined by two testable conditions: stance consistency (a coherent stance linking reasoning to answer) and causal influence (the stated reasoning causally drives the answer under output‑level interventions), explicitly decoupled from accuracy. To operationalize this, we present RFEval, a benchmark of 7,186 instances across seven tasks that probes faithfulness via controlled, output‑level counterfactual interventions. Evaluating twelve open‑source LRMs, we find unfaithfulness in 49.7% of outputs, predominantly from stance inconsistency. Failures are concentrated in brittle, convergent domains such as math and code, and correlate more with post‑training regimes than with scale: within‑family ablations indicate that adding current RL‑style objectives on top of supervised fine‑tuning can reduce reasoning faithfulness, even when accuracy is maintained. Crucially, accuracy is neither a sufficient nor a reliable proxy for faithfulness: once controlling for model and task, the accuracy‑faithfulness link is weak and statistically insignificant. Our work establishes a rigorous methodology for auditing LRM reliability and shows that trustworthy AI requires optimizing not only for correct outcomes but also for the structural integrity of the reasoning process. Our code and dataset can be found at project page: https://aidaslab.github.io/RFEval/

PaperId: 2968, https://arxiv.org/pdf/2602.17016.pdf GitHub

Abstract:
Automated formalization of mathematics enables mechanical verification but remains limited to isolated theorems and short snippets. Scaling to textbooks and research papers is largely unaddressed, as it requires managing cross‑file dependencies, resolving imports, and ensuring that entire projects compile end‑to‑end. We present M2F (Math‑to‑Formal), the first agentic framework for end‑to‑end, project‑scale autoformalization in Lean. The framework operates in two stages. The statement compilation stage splits the document into atomic blocks, orders them via inferred dependencies, and repairs declaration skeletons until the project compiles, allowing placeholders in proofs. The proof repair stage closes these holes under fixed signatures using goal‑conditioned local edits. Throughout both stages, M2F keeps the verifier in the loop, committing edits only when toolchain feedback confirms improvement. In approximately three weeks, M2F converts long‑form mathematical sources into a project‑scale Lean library of 153,853 lines from 479 pages textbooks on real analysis and convex analysis, fully formalized as Lean declarations with accompanying proofs. This represents textbook‑scale formalization at a pace that would typically require months or years of expert effort. On FATE‑H, we achieve 96% proof success (vs.\ 80% for a strong baseline). Together, these results demonstrate that practical, large‑scale automated formalization of mathematical literature is within reach. The full generated Lean code from our runs is available at https://github.com/optsuite/ReasBook.git.

PaperId: 2969, https://arxiv.org/pdf/2602.17003.pdf GitHub

Abstract:
Large language models have advanced web agents, yet current agents lack personalization capabilities. Since users rarely specify every detail of their intent, practical web agents must be able to interpret ambiguous queries by inferring user preferences and contexts. To address this challenge, we present Persona2Web, the first benchmark for evaluating personalized web agents on the real open web, built upon the clarify‑to‑personalize principle, which requires agents to resolve ambiguity based on user history rather than relying on explicit instructions. Persona2Web consists of: (1) user histories that reveal preferences implicitly over long time spans, (2) ambiguous queries that require agents to infer implicit user preferences, and (3) a reasoning‑aware evaluation framework that enables fine‑grained assessment of personalization. We conduct extensive experiments across various agent architectures, backbone models, history access schemes, and queries with varying ambiguity levels, revealing key challenges in personalized web agent behavior. For reproducibility, our codes and datasets are publicly available at https://serin‑kimm.github.io/Persona2Web/.

PaperId: 2970, https://arxiv.org/pdf/2602.16943.pdf GitHub

Abstract:
Large language models deployed as agents increasingly interact with external systems through tool calls‑‑actions with real‑world consequences that text outputs alone do not carry. Safety evaluations, however, overwhelmingly measure text‑level refusal behavior, leaving a critical question unanswered: does alignment that suppresses harmful text also suppress harmful actions? We introduce the GAP benchmark, a systematic evaluation framework that measures divergence between text‑level safety and tool‑call‑level safety in LLM agents. We test six frontier models across six regulated domains (pharmaceutical, financial, educational, employment, legal, and infrastructure), seven jailbreak scenarios per domain, three system prompt conditions (neutral, safety‑reinforced, and tool‑encouraging), and two prompt variants, producing 17,420 analysis‑ready datapoints. Our central finding is that text safety does not transfer to tool‑call safety. Across all six models, we observe instances where the model's text output refuses a harmful request while its tool calls simultaneously execute the forbidden action‑‑a divergence we formalize as the GAP metric. Even under safety‑reinforced system prompts, 219 such cases persist across all six models. System prompt wording exerts substantial influence on tool‑call behavior: TC‑safe rates span 21 percentage points for the most robust model and 57 for the most prompt‑sensitive, with 16 of 18 pairwise ablation comparisons remaining significant after Bonferroni correction. Runtime governance contracts reduce information leakage in all six models but produce no detectable deterrent effect on forbidden tool‑call attempts themselves. These results demonstrate that text‑only safety evaluations are insufficient for assessing agent behavior and that tool‑call safety requires dedicated measurement and mitigation.

PaperId: 2971, https://arxiv.org/pdf/2602.16901.pdf GitHub

Abstract:
LLM agents are increasingly deployed in long‑horizon, complex environments to solve challenging problems, but this expansion exposes them to long‑horizon attacks that exploit multi‑turn user‑agent‑environment interactions to achieve objectives infeasible in single‑turn settings. To measure agent vulnerabilities to such risks, we present AgentLAB, the first benchmark dedicated to evaluating LLM agent susceptibility to adaptive, long‑horizon attacks. Currently, AgentLAB supports five novel attack types including intent hijacking, tool chaining, task injection, objective drifting, and memory poisoning, spanning 28 realistic agentic environments, and 644 security test cases. Leveraging AgentLAB, we evaluate representative LLM agents and find that they remain highly susceptible to long‑horizon attacks; moreover, defenses designed for single‑turn interactions fail to reliably mitigate long‑horizon threats. We anticipate that AgentLAB will serve as a valuable benchmark for tracking progress on securing LLM agents in practical settings. The benchmark is publicly available at https://tanqiujiang.github.io/AgentLAB_main.

PaperId: 2972, https://arxiv.org/pdf/2602.16898.pdf GitHub

Abstract:
Task planning for robotic manipulation with large language models (LLMs) is an emerging area. Prior approaches rely on specialized models, fine tuning, or prompt tuning, and often operate in an open loop manner without robust environmental feedback, making them fragile in dynamic settings. MALLVI presents a Multi Agent Large Language and Vision framework that enables closed‑loop feedback driven robotic manipulation. Given a natural language instruction and an image of the environment, MALLVI generates executable atomic actions for a robot manipulator. After action execution, a Vision Language Model (VLM) evaluates environmental feedback and decides whether to repeat the process or proceed to the next step. Rather than using a single model, MALLVI coordinates specialized agents, Decomposer, Localizer, Thinker, and Reflector, to manage perception, localization, reasoning, and high level planning. An optional Descriptor agent provides visual memory of the initial state. The Reflector supports targeted error detection and recovery by reactivating only relevant agents, avoiding full replanning. Experiments in simulation and real‑world settings show that iterative closed loop multi agent coordination improves generalization and increases success rates in zero shot manipulation tasks. Code available at https://github.com/iman1234ahmadi/MALLVI .

PaperId: 2973, https://arxiv.org/pdf/2602.16855.pdf GitHub

Abstract:
The paper introduces GUI‑Owl‑1.5, the latest native GUI agent model that features instruct/thinking variants in multiple sizes (2B/4B/8B/32B/235B) and supports a range of platforms (desktop, mobile, browser, and more) to enable cloud‑edge collaboration and real‑time interaction. GUI‑Owl‑1.5 achieves state‑of‑the‑art results on more than 20+ GUI benchmarks on open‑source models: (1) on GUI automation tasks, it obtains 56.5 on OSWorld, 71.6 on AndroidWorld, and 48.4 on WebArena; (2) on grounding tasks, it obtains 80.3 on ScreenSpotPro; (3) on tool‑calling tasks, it obtains 47.6 on OSWorld‑MCP, and 46.8 on MobileWorld; (4) on memory and knowledge tasks, it obtains 75.5 on GUI‑Knowledge Bench. GUI‑Owl‑1.5 incorporates several key innovations: (1) Hybird Data Flywheel: we construct the data pipeline for UI understanding and trajectory generation based on a combination of simulated environments and cloud‑based sandbox environments, in order to improve the efficiency and quality of data collection. (2) Unified Enhancement of Agent Capabilities: we use a unified thought‑synthesis pipeline to enhance the model's reasoning capabilities, while placing particular emphasis on improving key agent abilities, including Tool/MCP use, memory and multi‑agent adaptation; (3) Multi‑platform Environment RL Scaling: We propose a new environment RL algorithm, MRPO, to address the challenges of multi‑platform conflicts and the low training efficiency of long‑horizon tasks. The GUI‑Owl‑1.5 models are open‑sourced, and an online cloud‑sandbox demo is available at https://github.com/X‑PLUG/MobileAgent.

PaperId: 2974, https://arxiv.org/pdf/2602.16813.pdf GitHub

Abstract:
Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. Despite their promise, these models typically produce samples whose quality sharply degrades in the few‑step regime, preventing a dramatic speedup in practice. Here, we show that language models based on continuous flows over one‑hot token embeddings can outperform discrete diffusion in both quality and speed. Importantly, our continuous formulation defines a unique flow map that can be learned directly for efficient few‑step inference, a structure we show is unavailable to discrete methods. In this setting, we show that both the flow and its associated flow map can be learned with simple cross‑entropy objectives that respect the simplex geometry of the data, and we identify three distinct choices for flow map distillation whose performance we compare in practice. Using these insights, we build a flow language model (FLM), a continuous flow that matches state‑of‑the‑art discrete diffusion baselines on the One Billion Words (LM1B) and OpenWebText (OWT) datasets. We then distill FLM into a flow map language model (FMLM), whose one‑step generation exceeds the 8‑step quality of recent few‑step discrete diffusion language models. Our work challenges the widely‑held hypothesis that discrete noising processes are necessary for generative modeling over discrete modalities and paves the way toward accelerated language modeling at scale. Code is available at https://github.com/david3684/flm.

PaperId: 2975, https://arxiv.org/pdf/2602.16747.pdf GitHub

Abstract:
The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address these challenges, we introduce LiveClin, a live benchmark designed for approximating real‑world clinical practice. Built from contemporary, peer‑reviewed case reports and updated biannually, LiveClin ensures clinical currency and resists data contamination. Using a verified AI‑human workflow involving 239 physicians, we transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway. The benchmark currently comprises 1,407 case reports and 6,605 questions. Our evaluation of 26 models on LiveClin reveals the profound difficulty of these real‑world scenarios, with the top‑performing model achieving a Case Accuracy of just 35.7%. In benchmarking against human experts, Chief Physicians achieved the highest accuracy, followed closely by Attending Physicians, with both surpassing most models. LiveClin thus provides a continuously evolving, clinically grounded framework to guide the development of medical LLMs towards closing this gap and achieving greater reliability and real‑world utility. Our data and code are publicly available at https://github.com/AQ‑MedAI/LiveClin.

PaperId: 2976, https://arxiv.org/pdf/2602.16742.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has been shown effective in enhancing the visual reflection and reasoning capabilities of Large Multimodal Models (LMMs). However, existing datasets are predominantly derived from either small‑scale manual construction or recombination of prior resources, which limits data diversity and coverage, thereby constraining further gains in model performance. To this end, we introduce DeepVision‑103K, a comprehensive dataset for RLVR training that covers diverse K12 mathematical topics, extensive knowledge points, and rich visual elements. Models trained on DeepVision achieve strong performance on multimodal mathematical benchmarks, and generalize effectively to general multimodal reasoning tasks. Further analysis reveals enhanced visual perception, reflection and reasoning capabilities in trained models, validating DeepVision's effectiveness for advancing multimodal reasoning. Data: \hrefhttps://huggingface.co/datasets/skylenage/DeepVision‑103Kthis url.

PaperId: 2977, https://arxiv.org/pdf/2602.16740.pdf GitHub

Abstract:
In mechanistic interpretability, recent work scrutinizes transformer "circuits" ‑ sparse, mono or multi layer sub computations, that may reflect human understandable functions. Yet, these network circuits are rarely acid‑tested for their stability across different instances of the same deep learning architecture. Without this, it remains unclear whether reported circuits emerge universally across labs or turn out to be idiosyncratic to a particular estimation instance, potentially limiting confidence in safety‑critical settings. Here, we systematically study stability across‑refits in increasingly complex transformer language models of various sizes. We quantify, layer by layer, how similarly attention heads learn representations across independently initialized training runs. Our rigorous experiments show that (1) middle‑layer heads are the least stable yet the most representationally distinct; (2) deeper models exhibit stronger mid‑depth divergence; (3) unstable heads in deeper layers become more functionally important than their peers from the same layer; (4) applying weight decay optimization substantially improves attention‑head stability across random model initializations; and (5) the residual stream is comparatively stable. Our findings establish the cross‑instance robustness of circuits as an essential yet underappreciated prerequisite for scalable oversight, drawing contours around possible white‑box monitorability of AI systems.

PaperId: 2978, https://arxiv.org/pdf/2602.16626.pdf GitHub

Abstract:
Recent success in natural language processing has motivated growing interest in large‑scale foundation models for neuroimaging data. Such models often require discretization of continuous neural time series data, a process referred to as 'tokenization'. However, the impact of different tokenization strategies for neural data is currently poorly understood. In this work, we present a systematic evaluation of sample‑level tokenization strategies for transformer‑based large neuroimaging models (LNMs) applied to magnetoencephalography (MEG) data. We compare learnable and non‑learnable tokenizers by examining their signal reconstruction fidelity and their impact on subsequent foundation modeling performance (token prediction, biological plausibility of generated data, preservation of subject‑specific information, and performance on downstream tasks). For the learnable tokenizer, we introduce a novel approach based on an autoencoder. Experiments were conducted on three publicly available MEG datasets spanning different acquisition sites, scanners, and experimental paradigms. Our results show that both learnable and non‑learnable discretization schemes achieve high reconstruction accuracy and broadly comparable performance across most evaluation criteria, suggesting that simple fixed sample‑level tokenization strategies can be used in the development of neural foundation models. The code is available at https://github.com/OHBA‑analysis/Cho2026_Tokenizer.

PaperId: 2979, https://arxiv.org/pdf/2602.16590.pdf GitHub

Abstract:
Street‑view image attribute classification is a vital downstream task of image classification, enabling applications such as autonomous driving, urban analytics, and high‑definition map construction. It remains computationally demanding whether training from scratch, initialising from pre‑trained weights, or fine‑tuning large models. Although pre‑trained vision‑language models such as CLIP offer rich image representations, existing adaptation or fine‑tuning methods often rely on their global image embeddings, limiting their ability to capture fine‑grained, localised attributes essential in complex, cluttered street scenes. To address this, we propose CLIP‑MHAdapter, a variant of the current lightweight CLIP adaptation paradigm that appends a bottleneck MLP equipped with multi‑head self‑attention operating on patch tokens to model inter‑patch dependencies. With approximately 1.4 million trainable parameters, CLIP‑MHAdapter achieves superior or competitive accuracy across eight attribute classification tasks on the Global StreetScapes dataset, attaining new state‑of‑the‑art results while maintaining low computational cost. The code is available at https://github.com/SpaceTimeLab/CLIP‑MHAdapter.

PaperId: 2980, https://arxiv.org/pdf/2602.16316.pdf GitHub

Abstract:
Weight‑space models learn directly from the parameters of neural networks, enabling tasks such as predicting their accuracy on new datasets. Naive methods ‑‑ like applying MLPs to flattened parameters ‑‑ perform poorly, making the design of better weight‑space architectures a central challenge. While prior work leveraged permutation symmetries in standard networks to guide such designs, no analogous analysis or tailored architecture yet exists for Kolmogorov‑Arnold Networks (KANs). In this work, we show that KANs share the same permutation symmetries as MLPs, and propose the KAN‑graph, a graph representation of their computation. Building on this, we develop WS‑KAN, the first weight‑space architecture that learns on KANs, which naturally accounts for their symmetry. We analyze WS‑KAN's expressive power, showing it can replicate an input KAN's forward pass ‑ a standard approach for assessing expressiveness in weight‑space architectures. We construct a comprehensive ``zoo'' of trained KANs spanning diverse tasks, which we use as benchmarks to empirically evaluate WS‑KAN. Across all tasks, WS‑KAN consistently outperforms structure‑agnostic baselines, often by a substantial margin. Our code is available at https://github.com/BarSGuy/KAN‑Graph‑Metanetwork.

PaperId: 2981, https://arxiv.org/pdf/2602.16154.pdf GitHub

Abstract:
Chain‑of‑thought (CoT) reasoning sometimes fails to faithfully reflect the true computation of a large language model (LLM), hampering its utility in explaining how LLMs arrive at their answers. Moreover, optimizing for faithfulness and interpretability in reasoning often degrades task performance. To address this tradeoff and improve CoT faithfulness, we propose Reasoning Execution by Multiple Listeners (REMUL), a multi‑party reinforcement learning approach. REMUL builds on the hypothesis that reasoning traces which other parties can follow will be more faithful. A speaker model generates a reasoning trace, which is truncated and passed to a pool of listener models who "execute" the trace, continuing the trace to an answer. Speakers are rewarded for producing reasoning that is clear to listeners, with additional correctness regularization via masked supervised finetuning to counter the tradeoff between faithfulness and performance. On multiple reasoning benchmarks (BIG‑Bench Extra Hard, MuSR, ZebraLogicBench, and FOLIO), REMUL consistently and substantially improves three measures of faithfulness ‑‑ hint attribution, early answering area over the curve (AOC), and mistake injection AOC ‑‑ while also improving accuracy. Our analysis finds that these gains are robust across training domains, translate to legibility gains, and are associated with shorter and more direct CoTs.

PaperId: 2982, https://arxiv.org/pdf/2602.16110.pdf GitHub

Abstract:
Computed Tomography (CT) is one of the most widely used and diagnostically information‑dense imaging modalities, covering critical organs such as the heart, lungs, liver, and colon. Clinical interpretation relies on both slice‑driven local features (e.g., sub‑centimeter nodules, lesion boundaries) and volume‑driven spatial representations (e.g., tumor infiltration, inter‑organ anatomical relations). However, existing Large Vision‑Language Models (LVLMs) remain fragmented in CT slice versus volumetric understanding: slice‑driven LVLMs show strong generalization but lack cross‑slice spatial consistency, while volume‑driven LVLMs explicitly capture volumetric semantics but suffer from coarse granularity and poor compatibility with slice inputs. The absence of a unified modeling paradigm constitutes a major bottleneck for the clinical translation of medical LVLMs. We present OmniCT, a powerful unified slice‑volume LVLM for CT scenarios, which makes three contributions: (i) Spatial Consistency Enhancement (SCE): volumetric slice composition combined with tri‑axial positional embedding that introduces volumetric consistency, and an MoE hybrid projection enables efficient slice‑volume adaptation; (ii) Organ‑level Semantic Enhancement (OSE): segmentation and ROI localization explicitly align anatomical regions, emphasizing lesion‑ and organ‑level semantics; (iii) MedEval‑CT: the largest slice‑volume CT dataset and hybrid benchmark integrates comprehensive metrics for unified evaluation. OmniCT consistently outperforms existing methods with a substantial margin across diverse clinical tasks and satisfies both micro‑level detail sensitivity and macro‑level spatial reasoning. More importantly, it establishes a new paradigm for cross‑modal medical imaging understanding. Our project is available at https://github.com/ZJU4HealthCare/OmniCT.

PaperId: 2983, https://arxiv.org/pdf/2602.16105.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly deployed in applications that interact with the physical world, such as navigation, robotics, or mapping, making robust geospatial reasoning a critical capability. Despite that, LLMs' ability to reason about GPS coordinates and real‑world geography remains underexplored. We introduce GPSBench, a dataset of 57,800 samples across 17 tasks for evaluating geospatial reasoning in LLMs, spanning geometric coordinate operations (e.g., distance and bearing computation) and reasoning that integrates coordinates with world knowledge. Focusing on intrinsic model capabilities rather than tool use, we evaluate 14 state‑of‑the‑art LLMs and find that GPS reasoning remains challenging, with substantial variation across tasks: models are generally more reliable at real‑world geographic reasoning than at geometric computations. Geographic knowledge degrades hierarchically, with strong country‑level performance but weak city‑level localization, while robustness to coordinate noise suggests genuine coordinate understanding rather than memorization. We further show that GPS‑coordinate augmentation can improve in downstream geospatial tasks, and that finetuning induces trade‑offs between gains in geometric computation and degradation in world knowledge. Our dataset and reproducible code are available at https://github.com/joey234/gpsbench

PaperId: 2984, https://arxiv.org/pdf/2602.16073.pdf GitHub GitHub

Abstract:
Developing autonomous driving systems for complex traffic environments requires balancing multiple objectives, such as avoiding collisions, obeying traffic rules, and making efficient progress. In many situations, these objectives cannot be satisfied simultaneously, and explicit priority relations naturally arise. Also, driving rules require context, so it is important to formally model the environment scenarios within which such rules apply. Existing benchmarks for evaluating autonomous vehicles lack such combinations of multi‑objective prioritized rules and formal environment models. In this work, we introduce ScenicRules, a benchmark for evaluating autonomous driving systems in stochastic environments under prioritized multi‑objective specifications. We first formalize a diverse set of objectives to serve as quantitative evaluation metrics. Next, we design a Hierarchical Rulebook framework that encodes multiple objectives and their priority relations in an interpretable and adaptable manner. We then construct a compact yet representative collection of scenarios spanning diverse driving contexts and near‑accident situations, formally modeled in the Scenic language. Experimental results show that our formalized objectives and Hierarchical Rulebooks align well with human driving judgments and that our benchmark effectively exposes agent failures with respect to the prioritized objectives. Our benchmark can be accessed at https://github.com/BerkeleyLearnVerify/ScenicRules/.

PaperId: 2985, https://arxiv.org/pdf/2602.16042.pdf GitHub GitHub

Abstract:
As machine learning (ML) continues its rapid expansion, the environmental cost of model training and inference has become a critical societal concern. Existing benchmarks overwhelmingly focus on standard performance metrics such as accuracy, BLEU, or mAP, while largely ignoring energy consumption and carbon emissions. This single‑objective evaluation paradigm is increasingly misaligned with the practical requirements of large‑scale deployment, particularly in energy‑constrained environments such as mobile devices, developing regions, and climate‑aware enterprises. In this paper, we propose AI‑CARE, an evaluation tool for reporting energy consumption, and carbon emissions of ML models. In addition, we introduce the carbon‑performance tradeoff curve, an interpretable tool that visualizes the Pareto frontier between performance and carbon cost. We demonstrate, through theoretical analysis and empirical validation on representative ML workloads, that carbon‑aware benchmarking changes the relative ranking of models and encourages architectures that are simultaneously accurate and environmentally responsible. Our proposal aims to shift the research community toward transparent, multi‑objective evaluation and align ML progress with global sustainability goals. The tool and documentation are available at https://github.com/USD‑AI‑ResearchLab/ai‑care.

PaperId: 2986, https://arxiv.org/pdf/2602.16008.pdf GitHub

Abstract:
We introduce the Massive Audio Embedding Benchmark (MAEB), a large‑scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross‑modal audio‑text reasoning in 100+ languages. We evaluate 50+ models and find that no single model dominates across all tasks: contrastive audio‑text models excel at environmental sound classification (e.g., ESC50) but score near random on multilingual speech tasks (e.g., SIB‑FLEURS), while speech‑pretrained models show the opposite pattern. Clustering remains challenging for all models, with even the best‑performing model achieving only modest results. We observe that models excelling on acoustic understanding often perform poorly on linguistic tasks, and vice versa. We also show that the performance of audio encoders on MAEB correlates highly with their performance when used in audio large language models. MAEB is derived from MAEB+, a collection of 98 tasks. MAEB is designed to maintain task diversity while reducing evaluation cost, and it integrates into the MTEB ecosystem for unified evaluation across text, image, and audio modalities. We release MAEB and all 98 tasks along with code and a leaderboard at https://github.com/embeddings‑benchmark/mteb.

PaperId: 2987, https://arxiv.org/pdf/2602.15983.pdf GitHub

Abstract:
Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver‑feasible solutions may encode semantically incorrect formulations ‑‑ a feasibility‑correctness gap reaching 90 percentage points on compositional problems. We introduce ReLoop, which addresses this gap through two complementary mechanisms. Structured generation decomposes code production into a four‑stage reasoning chain (understand, formalize, synthesize, verify), preventing formulation errors at their source. Behavioral verification detects errors that survive generation by testing whether the formulation responds correctly to solver‑based parameter perturbation ‑‑ an external semantic signal that bypasses LLM self‑review and requires no ground truth. The two mechanisms are complementary by error structure: structured generation drives the largest gains on compositional problems (+8.5pp accuracy on RetailOpt‑190 with Claude Opus 4.6), while behavioral verification dominates on localized defects (+4.4pp on MAMO‑ComplexLP, its largest contribution across benchmarks). Combined with diagnostic execution recovery, ReLoop reaches 100% executable code on Claude Opus 4.6 and consistently improves accuracy on chat‑tuned foundation models across three benchmarks; we further identify a known limitation of narrowly‑tuned SFT models, whose learned output formats are brittle to chain‑of‑thought prompts ‑‑ an interaction we document and analyze. We release RetailOpt‑190, 190 compositional retail optimization scenarios targeting the multi‑constraint interactions where LLMs most frequently fail.

PaperId: 2988, https://arxiv.org/pdf/2602.15959.pdf GitHub

Abstract:
We address the problem of cross‑domain image registration, where paired images exhibit coupled geometric misalignment and domain‑specific appearance shift. We formalize this as a factorization problem: decomposing each image into a domain‑invariant scene representation and a global appearance statistic, such that registration reduces to recombining the scene structure of the moving image with the appearance of the fixed image via Adaptive Instance Normalization (AdaIN). This factorization eliminates the need for explicit deformation field estimation. To exploit temporal coherence in sequential acquisitions, we introduce a position‑encoded cross‑frame attention mechanism that fuses learnable and sinusoidal position embeddings with multi‑head attention over a sliding window of neighboring frames, enriching the scene representation with inter‑frame context. We instantiate this framework as GPEReg‑Net and evaluate on two benchmarks: FIRE‑Reg‑256 (retinal fundus, semi‑rigid) and HPatches‑Reg‑256 (synthetic textured patches, affine). GPEReg‑Net achieves state‑of‑the‑art performance on both benchmarks (FIRE: SSIM = 0.928, PSNR = 33.47 dB; HPatches: SSIM = 0.450, PSNR = 21.01 dB), surpassing all baselines, including deformation‑based methods, while running 1.87x faster than SAS‑Net. Code: https://github.com/JiahaoQin/GPEReg‑Net.

PaperId: 2989, https://arxiv.org/pdf/2602.15909.pdf GitHub

Abstract:
Deep learning‑based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards transient acoustic events and clinical context; (ii) limited data availability, exacerbated by severe class imbalance. To bridge these gaps, we present Resp‑Agent, an autonomous multimodal system orchestrated by a novel Active Adversarial Curriculum Agent (Thinker‑A^2CA). Unlike static pipelines, Thinker‑A^2CA serves as a central controller that actively identifies diagnostic weaknesses and schedules targeted synthesis in a closed loop. To address the representation gap, we introduce a modality‑weaving Diagnoser that weaves clinical text with audio tokens via strategic global attention and sparse audio anchors, capturing both long‑range clinical context and millisecond‑level transients. To address the data gap, we design a flow matching Generator that adapts a text‑only Large Language Model (LLM) via modality injection, decoupling pathological content from acoustic style to synthesize hard‑to‑diagnose samples. As a foundation for this work, we introduce Resp‑229k, a benchmark corpus of 229k recordings paired with LLM‑distilled clinical narratives. Extensive experiments demonstrate that Resp‑Agent consistently outperforms prior approaches across diverse evaluation settings, improving diagnostic robustness under data scarcity and long‑tailed class imbalance. Our code and data are available at https://github.com/zpforlove/Resp‑Agent.

PaperId: 2990, https://arxiv.org/pdf/2602.15877.pdf GitHub

Abstract:
Generalized Additive Models (GAMs) balance predictive accuracy and interpretability, but manually configuring their structure is challenging. We propose using the multi‑objective genetic algorithm NSGA‑II to automatically optimize GAMs, jointly minimizing prediction error (RMSE) and a Complexity Penalty that captures sparsity, smoothness, and uncertainty. Experiments on the California Housing dataset show that NSGA‑II discovers GAMs that outperform baseline LinearGAMs in accuracy or match performance with substantially lower complexity. The resulting models are simpler, smoother, and exhibit narrower confidence intervals, enhancing interpretability. This framework provides a general approach for automated optimization of transparent, high‑performing models. The code can be found at https://github.com/KaaustaaubShankar/GeneticAdditiveModels.

PaperId: 2991, https://arxiv.org/pdf/2602.15875.pdf GitHub

Abstract:
Current Visual‑Language Navigation (VLN) methodologies face a trade‑off between semantic understanding and control precision. While Multimodal Large Language Models (MLLMs) offer superior reasoning, deploying them as low‑level controllers leads to high latency, trajectory oscillations, and poor generalization due to weak geometric grounding. To address these limitations, we propose Fly0, a framework that decouples semantic reasoning from geometric planning. The proposed method operates through a three‑stage pipeline: (1) an MLLM‑driven module for grounding natural language instructions into 2D pixel coordinates; (2) a geometric projection module that utilizes depth data to localize targets in 3D space; and (3) a geometric planner that generates collision‑free trajectories. This mechanism enables robust navigation even when visual contact is lost. By eliminating the need for continuous inference, Fly0 reduces computational overhead and improves system stability. Extensive experiments in simulation and real‑world environments demonstrate that Fly0 outperforms state‑of‑the‑art baselines, improving the Success Rate by over 20% and reducing Navigation Error (NE) by approximately 50% in unstructured environments. Our code is available at https://github.com/xuzhenxing1/Fly0.

PaperId: 2992, https://arxiv.org/pdf/2602.15843.pdf GitHub

Abstract:
In "Compress or Route?" (Johnson, 2026), we found that code generation tolerates aggressive prompt compression (r >= 0.6) while chain‑of‑thought reasoning degrades gradually. That study was limited to HumanEval (164 problems), left the "perplexity paradox" mechanism unvalidated, and provided no adaptive algorithm. This paper addresses all three gaps. First, we validate across six code benchmarks (HumanEval, MBPP, HumanEval+, MultiPL‑E) and four reasoning benchmarks (GSM8K, MATH, ARC‑Challenge, MMLU‑STEM), confirming the compression threshold generalizes across languages and difficulties. Second, we conduct the first per‑token perplexity analysis (n=723 tokens), revealing a "perplexity paradox": code syntax tokens are preserved (high perplexity) while numerical values in math problems are pruned despite being task‑critical (low perplexity). Signature injection recovers +34 percentage points in pass rate (5.3% to 39.3%; Cohen's h=0.890). Third, we propose TAAC (Task‑Aware Adaptive Compression), achieving 22% cost reduction with 96% quality preservation, outperforming fixed‑ratio compression by 7%. MBPP validation (n=1,800 trials) confirms systematic variation: 3.6% at r=0.3 to 54.6% at r=1.0.

PaperId: 2993, https://arxiv.org/pdf/2602.15772.pdf GitHub

Abstract:
Current research in multimodal models faces a key challenge where enhancing generative capabilities often comes at the expense of understanding, and vice versa. We analyzed this trade‑off and identify the primary cause might be the potential conflict between generation and understanding, which creates a competitive dynamic within the model. To address this, we propose the Reason‑Reflect‑Refine (R3) framework. This innovative algorithm re‑frames the single‑step generation task into a multi‑step process of "generate‑understand‑regenerate". By explicitly leveraging the model's understanding capability during generation, we successfully mitigate the optimization dilemma, achieved stronger generation results and improved understanding ability which are related to the generation process. This offers valuable insights for designing next‑generation unified multimodal models. Code is available at https://github.com/sen‑ye/R3.

PaperId: 2994, https://arxiv.org/pdf/2602.15721.pdf GitHub

Abstract:
We present Lifelong Scalable Multi‑Agent Realistic Testbed (LSMART), an open‑source simulator to evaluate any Multi‑Agent Path Finding (MAPF) algorithm in a Fleet Management System (FMS) with Automated Guided Vehicles (AGVs). MAPF aims to move a group of agents from their corresponding starting locations to their goals. Lifelong MAPF (LMAPF) is a variant of MAPF that continuously assigns new goals for agents to reach. LMAPF applications, such as autonomous warehouses, often require a centralized, lifelong system to coordinate the movement of a fleet of robots, typically AGVs. However, existing works on MAPF and LMAPF often assume simplified kinodynamic models, such as pebble motion, as well as perfect execution and communication for AGVs. Prior work has presented SMART, a software capable of evaluating any MAPF algorithms while considering agent kinodynamics, communication delays, and execution uncertainties. However, SMART is designed for MAPF, not LMAPF. Generalizing SMART to an FMS requires many more design choices. First, an FMS parallelizes planning and execution, raising the question of when to plan. Second, given planners with varying optimality and differing agent‑model assumptions, one must decide how to plan. Third, when the planner fails to return valid solutions, the system must determine how to recover. In this paper, we first present LSMART, an open‑source simulator that incorporates all these considerations to evaluate any MAPF algorithms in an FMS. We then provide experiment results based on state‑of‑the‑art methods for each design choice, offering guidance on how to effectively design centralized lifelong AGV Fleet Management Systems. LSMART is available at https://smart‑mapf.github.io/lifelong‑smart.

PaperId: 2995, https://arxiv.org/pdf/2602.15564.pdf GitHub

Abstract:
Text‑to‑SQL has recently achieved impressive progress, yet remains difficult to apply effectively in real‑world scenarios. This gap stems from the reliance on single static workflows, fundamentally limiting scalability to out‑of‑distribution and long‑tail scenarios. Instead of requiring users to select suitable methods through extensive experimentation, we attempt to enable systems to adaptively construct workflows at inference time. Through theoretical and empirical analysis, we demonstrate that optimal dynamic policies consistently outperform the best static workflow, with performance gains fundamentally driven by heterogeneity across candidate workflows. Motivated by this, we propose SquRL, a reinforcement learning framework that enhances LLMs' reasoning capability in adaptive workflow construction. We design a rule‑based reward function and introduce two effective training mechanisms: dynamic actor masking to encourage broader exploration, and pseudo rewards to improve training efficiency. Experiments on widely‑used Text‑to‑SQL benchmarks demonstrate that dynamic workflow construction consistently outperforms the best static workflow methods, with especially pronounced gains on complex and out‑of‑distribution queries. The codes are available at https://github.com/Satissss/SquRL

PaperId: 2996, https://arxiv.org/pdf/2602.15485.pdf GitHub GitHub

Abstract:
We introduce SecCodeBench‑V2, a publicly released benchmark for evaluating Large Language Model (LLM) copilots' capabilities of generating secure code. SecCodeBench‑V2 comprises 98 generation and fix scenarios derived from Alibaba Group's industrial productions, where the underlying security issues span 22 common CWE (Common Weakness Enumeration) categories across five programming languages: Java, C, Python, Go, and JavaScript. SecCodeBench‑V2 adopts a function‑level task formulation: each scenario provides a complete project scaffold and requires the model to implement or patch a designated target function under fixed interfaces and dependencies. For each scenario, SecCodeBench‑V2 provides executable proof‑of‑concept (PoC) test cases for both functional validation and security verification. All test cases are authored and double‑reviewed by security experts, ensuring high fidelity, broad coverage, and reliable ground truth. Beyond the benchmark itself, we build a unified evaluation pipeline that assesses models primarily via dynamic execution. For most scenarios, we compile and run model‑generated artifacts in isolated environments and execute PoC test cases to validate both functional correctness and security properties. For scenarios where security issues cannot be adjudicated with deterministic test cases, we additionally employ an LLM‑as‑a‑judge oracle. To summarize performance across heterogeneous scenarios and difficulty levels, we design a Pass@K‑based scoring protocol with principled aggregation over scenarios and severity, enabling holistic and comparable evaluation across models. Overall, SecCodeBench‑V2 provides a rigorous and reproducible foundation for assessing the security posture of AI coding assistants, with results and artifacts released at https://alibaba.github.io/sec‑code‑bench. The benchmark is publicly available at https://github.com/alibaba/sec‑code‑bench.

PaperId: 2997, https://arxiv.org/pdf/2602.15326.pdf GitHub

Abstract:
We propose SCENE (Self‑Centering Noncoherent Estimator), a pilot‑free and phase‑invariant aggregation primitive for over‑the‑air federated distillation (OTA‑FD). Each device maps its soft‑label (class‑probability) vector to nonnegative transmit energies under constant per‑round power and constant‑envelope signaling (PAPR near 1). At the server, a self‑centering energy estimator removes the noise‑energy offset and yields an unbiased estimate of the weighted soft‑label average, with variance decaying on the order of 1/(SM) in the number of receive antennas M and repetition factor S. We also develop a pilot‑free ratio‑normalized variant that cancels unknown large‑scale gains, provide a convergence bound consistent with coherent OTA‑FD analyses, and present an overhead‑based crossover comparison. SCENE targets short‑coherence and hardware‑constrained regimes, where avoiding per‑round CSI is essential: it trades a modest noncoherent variance constant for zero uplink pilots, unbiased aggregation, and hardware‑friendly transmission, and can outperform coherent designs when pilot overhead is non‑negligible.

PaperId: 2998, https://arxiv.org/pdf/2602.15277.pdf GitHub

Abstract:
Dataset distillation compresses the original data into compact synthetic datasets, reducing training time and storage while retaining model performance, enabling deployment under limited resources. Although recent decoupling‑based distillation methods enable dataset distillation at large scale, they continue to face an efficiency gap: optimization‑based decoupling methods achieve higher accuracy but demand intensive computation, whereas optimization‑free decoupling methods are efficient but sacrifice accuracy. To overcome this trade‑off, we propose Exploration‑‑Exploitation Distillation (E^2D), a simple, practical method that minimizes redundant computation through an efficient pipeline that begins with full‑image initialization to preserve semantic integrity and feature diversity. It then uses a two‑phase optimization strategy: an exploration phase that performs uniform updates and identifies high‑loss regions, and an exploitation phase that focuses updates on these regions to accelerate convergence. We evaluate E^2D on large‑scale benchmarks, surpassing the state‑of‑the‑art on ImageNet‑1K while being 18× faster, and on ImageNet‑21K, our method substantially improves accuracy while remaining 4.3× faster. These results demonstrate that targeted, redundancy‑reducing updates, rather than brute‑force optimization, bridge the gap between accuracy and efficiency in large‑scale dataset distillation. Code is available at https://github.com/ncsu‑dk‑lab/E2D.

PaperId: 2999, https://arxiv.org/pdf/2602.15156.pdf GitHub GitHub

Abstract:
Language models are increasingly used to reason over content they were not trained on, such as new documents, evolving knowledge, and user‑specific data. A common approach is retrieval‑augmented generation (RAG), which stores verbatim documents externally (as chunks) and retrieves only a relevant subset at inference time for an LLM to reason over. However, this results in inefficient usage of test‑time compute (LLM repeatedly reasons over the same documents); moreover, chunk retrieval can inject irrelevant context that increases unsupported generation. We propose a human‑like non‑parametric continual learning framework, where the base model remains fixed, and learning occurs by integrating each new experience into an external semantic memory state that accumulates and consolidates itself continually. We present Panini, which realizes this by representing documents as Generative Semantic Workspaces (GSW) ‑‑ an entity‑ and event‑aware network of question‑answer (QA) pairs, sufficient for an LLM to reconstruct the experienced situations and mine latent knowledge via reasoning‑grounded inference chains on the network. Given a query, Panini only traverses the continually‑updated GSW (not the verbatim documents or chunks), and retrieves the most likely inference chains. Across six QA benchmarks, Panini achieves the highest average performance, 5%‑7% higher than other competitive baselines, while using 2‑30x fewer answer‑context tokens, supports fully open‑source pipelines, and reduces unsupported answers on curated unanswerable queries. The results show that efficient and accurate structuring of experiences at write time ‑‑ as achieved by the GSW framework ‑‑ yields both efficiency and reliability gains at read time. Code is available at https://github.com/roychowdhuryresearch/gsw‑memory.

PaperId: 3000, https://arxiv.org/pdf/2602.15143.pdf GitHub

Abstract:
Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put into developing frontier models. We investigate methods for modifying teacher‑generated reasoning traces to achieve two objectives that deter unauthorized distillation: (1) \emphanti‑distillation, or degrading the training usefulness of query responses, and (2) \emphAPI watermarking, which embeds verifiable signatures in student models. We introduce several approaches for dynamically rewriting a teacher's reasoning outputs while preserving answer correctness and semantic coherence. Two of these leverage the rewriting capabilities of LLMs, while others use gradient‑based techniques. Our experiments show that a simple instruction‑based rewriting approach achieves a strong anti‑distillation effect while maintaining or even improving teacher performance. Furthermore, we show that our rewriting approach also enables embedding watermarks that can be reliably detected with essentially no false alarms. Our code is available at https://github.com/xhOwenMa/trace‑rewriting.

PaperId: 3001, https://arxiv.org/pdf/2602.15128.pdf GitHub

Abstract:
Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous successful applications, particularly within the flow matching paradigm, all existing NODE models are fundamentally constrained to fixed‑dimensional dynamics by the intrinsic nature of the manifold's dimension. In this paper, we extend NODEs to M‑polyfolds (spaces that can simultaneously accommodate varying dimensions and a notion of differentiability) and introduce PolyNODEs, the first variable‑dimensional flow‑based model in geometric deep learning. As an example application, we construct explicit M‑polyfolds featuring dimensional bottlenecks and PolyNODE autoencoders based on parametrised vector fields that traverse these bottlenecks. We demonstrate experimentally that our PolyNODE models can be trained to solve reconstruction tasks in these spaces, and that latent representations of the input can be extracted and used to solve downstream classification tasks. The code used in our experiments is publicly available at https://github.com/turbotage/PolyNODE .

PaperId: 3002, https://arxiv.org/pdf/2602.15072.pdf GitHub

Abstract:
Accurate polyp segmentation in colonoscopy is essential for cancer prevention but remains challenging due to: (1) high morphological variability (from flat to protruding lesions), (2) strong visual similarity to normal structures such as folds and vessels, and (3) the need for robust multi‑scale detection. Existing deep learning approaches suffer from unidirectional processing, weak multi‑scale fusion, and the absence of anatomical constraints, often leading to false positives (over‑segmentation of normal structures) and false negatives (missed subtle flat lesions). We propose GRAFNet, a biologically inspired architecture that emulates the hierarchical organisation of the human visual system. GRAFNet integrates three key modules: (1) a Guided Asymmetric Attention Module (GAAM) that mimics orientation‑tuned cortical neurones to emphasise polyp boundaries, (2) a MultiScale Retinal Module (MSRM) that replicates retinal ganglion cell pathways for parallel multi‑feature analysis, and (3) a Guided Cortical Attention Feedback Module (GCAFM) that applies predictive coding for iterative refinement. These are unified in a Polyp Encoder‑Decoder Module (PEDM) that enforces spatial‑semantic consistency via resolution‑adaptive feedback. Extensive experiments on five public benchmarks (Kvasir‑SEG, CVC‑300, CVC‑ColonDB, CVC‑Clinic, and PolypGen) demonstrate consistent state‑of‑the‑art performance, with 3‑8% Dice improvements and 10‑20% higher generalisation over leading methods, while offering interpretable decision pathways. This work establishes a paradigm in which neural computation principles bridge the gap between AI accuracy and clinically trustworthy reasoning. Code is available at https://github.com/afofanah/GRAFNet.

PaperId: 3003, https://arxiv.org/pdf/2602.15042.pdf GitHub

Abstract:
Reliable sleep staging remains challenging for lightweight wearable devices such as single‑channel electroencephalography (scEEG) or photoplethysmography (PPG). scEEG offers direct measurement of cortical activity and serves as the foundation for sleep staging, yet exhibits limited performance on light sleep stages. PPG provides a low‑cost complement that captures autonomic signatures effective for detecting light sleep. However, prior PPG‑based methods rely on full night recordings (8 ‑ 10 hours) as input context, which is less practical to provide timely feedback for sleep intervention. In this work, we investigate scEEG‑PPG fusion for 4‑class sleep staging under short‑window (30 s ‑ 30 min) constraints. First, we evaluate the temporal context required for each modality, to better understand the relationship of sleep staging performance with respect to monitoring window. Second, we investigate three fusion strategies: score‑level fusion, cross‑attention fusion enabling feature‑level interactions, and Mamba‑enhanced fusion incorporating temporal context modeling. Third, we train and evaluate on the Multi‑Ethnic Study of Atherosclerosis (MESA) dataset and perform cross‑dataset validation on the Cleveland Family Study (CFS) and the Apnea, Bariatric surgery, and CPAP (ABC) datasets. The Mamba‑enhanced fusion achieves the best performance on MESA (Cohen's Kappa κ = 0.798, Acc = 86.9%), with particularly notable improvement in light sleep classification (F1‑score: 85.63% vs. 77.76%, recall: 82.85% vs. 69.95% for scEEG alone), and generalizes well to CFS and ABC datasets with different populations. These findings suggest that scEEG‑PPG fusion is a promising approach for lightweight wearable based sleep monitoring, offering a pathway toward more accessible sleep health assessment. Source code of this project can be found at: https://github.com/DavyWJW/scEEG‑PPGFusion

PaperId: 3004, https://arxiv.org/pdf/2602.15039.pdf GitHub

Abstract:
We present GRACE, a simulation‑native agent for autonomous experimental design in high‑energy and nuclear physics. Given multimodal input in the form of a natural‑language prompt or a published experimental paper, the agent extracts a structured representation of the experiment, constructs a runnable toy simulation, and autonomously explores design modifications using first‑principles Monte Carlo methods. Unlike agentic systems focused on operational control or execution of predefined procedures, GRACE addresses the upstream problem of experimental design: proposing non‑obvious modifications to detector geometry, materials, and configurations that improve physics performance under physical and practical constraints. The agent evaluates candidate designs through repeated simulation, physics‑motivated utility functions, and budget‑aware escalation from fast parametric models to full Geant4 simulations, while maintaining strict reproducibility and provenance tracking. We demonstrate the framework on historical experimental setups, showing that the agent can identify optimization directions that align with known upgrade priorities, using only baseline simulation inputs. We also conducted a benchmark in which the agent identified the setup and proposed improvements from a suite of natural language prompts, with some supplied with a relevant physics research paper, of varying high energy physics (HEP) problem settings. This work establishes experimental design as a constrained search problem under physical law and introduces a new benchmark for autonomous, simulation‑driven scientific reasoning in complex instruments.

PaperId: 3005, https://arxiv.org/pdf/2602.15038.pdf GitHub

Abstract:
Multilingual large language models (LLMs) are increasingly deployed in linguistically diverse regions like India, yet most interpretability tools remain tailored to English. Prior work reveals that LLMs often operate in English centric representation spaces, making cross lingual interpretability a pressing concern. We introduce Indic‑TunedLens, a novel interpretability framework specifically for Indian languages that learns shared affine transformations. Unlike the standard Logit Lens, which directly decodes intermediate activations, Indic‑TunedLens adjusts hidden states for each target language, aligning them with the target output distributions to enable more faithful decoding of model representations. We evaluate our framework on 10 Indian languages using the MMLU benchmark and find that it significantly improves over SOTA interpretability methods, especially for morphologically rich, low resource languages. Our results provide crucial insights into the layer‑wise semantic encoding of multilingual transformers. Our model is available at https://huggingface.co/spaces/MihirRajeshPanchal/IndicTunedLens. Our code is available at https://github.com/MihirRajeshPanchal/IndicTunedLens.

PaperId: 3006, https://arxiv.org/pdf/2602.15028.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly deployed in privacy‑critical and personalization‑oriented scenarios, yet the role of context length in shaping privacy leakage and personalization effectiveness remains largely unexplored. We introduce a large‑scale benchmark, PAPerBench, to systematically study how increasing context length influences both personalization quality and privacy protection in LLMs. The benchmark comprises approximately 29,000 instances with context lengths ranging from 1K to 256K tokens, yielding a total of 377K evaluation questions. It jointly evaluates personalization performance and privacy risks across diverse scenarios, enabling controlled analysis of long‑context model behavior. Extensive evaluations across state‑of‑the‑art LLMs reveal consistent performance degradation in both personalization and privacy as context length increases. We further provide a theoretical analysis of attention dilution under context scaling, explaining this behavior as an inherent limitation of soft attention in fixed‑capacity Transformers. The empirical and theoretical findings together suggest a general scaling gap in current models ‑‑ long context, less focus. We release the benchmark to support reproducible evaluation and future research on scalable privacy and personalization. Code and data are available at https://github.com/SafeRL‑Lab/PAPerBench

PaperId: 3007, https://arxiv.org/pdf/2602.14941.pdf GitHub

Abstract:
Maintaining spatial world consistency over long horizons remains a central challenge for camera‑controllable video generation. Existing memory‑based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history. However, reconstructing a global 3D scene from multiple views inevitably introduces cross‑view misalignment, as pose and depth estimation errors cause the same surfaces to be reconstructed at slightly different 3D locations across views. When fused, these inconsistencies accumulate into noisy geometry that contaminates the conditioning signals and degrades generation quality. We introduce AnchorWeave, a memory‑augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories and learns to reconcile their cross‑view inconsistencies. To this end, AnchorWeave performs coverage‑driven local memory retrieval aligned with the target trajectory and integrates the selected local memories through a multi‑anchor weaving controller during generation. Extensive experiments demonstrate that AnchorWeave significantly improves long‑term scene consistency while maintaining strong visual quality, with ablation and analysis studies further validating the effectiveness of local geometric conditioning, multi‑anchor control, and coverage‑driven retrieval.

PaperId: 3008, https://arxiv.org/pdf/2602.14710.pdf GitHub

Abstract:
Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation. CS researchers currently face two barriers: the lack of a unified framework for efficiently sharing contributions with the community, and the difficulty of deploying end‑to‑end prototypes needed for user evaluation. We introduce Orcheo, an open‑source platform designed to bridge this gap. Orcheo offers three key advantages: (i) A modular architecture promotes component reuse through single‑file node modules, facilitating sharing and reproducibility in CS research; (ii) Production‑ready infrastructure bridges the prototype‑to‑system gap via dual execution modes, secure credential management, and execution telemetry, with built‑in AI coding support that lowers the learning curve; (iii) Starter‑kit assets include 50+ off‑the‑shelf components for query understanding, ranking, and response generation, enabling the rapid bootstrapping of complete CS pipelines. We describe the framework architecture and validate Orcheo's utility through case studies that highlight modularity and ease of use. Orcheo is released as open source under the MIT License at https://github.com/ShaojieJiang/orcheo.

PaperId: 3009, https://arxiv.org/pdf/2602.14699.pdf GitHub GitHub

Abstract:
This paper envisions a quantum database (Qute) that treats quantum computation as a first‑class execution option. Unlike prior simulation‑based methods that either run quantum algorithms on classical machines or adapt existing databases for quantum simulation, Qute instead (i) compiles an extended form of SQL into gate‑efficient quantum circuits, (ii) employs a hybrid optimizer to dynamically select between quantum and classical execution plans, (iii) introduces selective quantum indexing, and (iv) designs fidelity‑preserving storage to mitigate current qubit constraints. We also present a three‑stage evolution roadmap toward quantum‑native database. Finally, by deploying Qute on a real quantum processor (origin_wukong), we show that it outperforms a classical baseline at scale, and we release an open‑source prototype at https://github.com/weAIDB/Qute.

PaperId: 3010, https://arxiv.org/pdf/2602.14697.pdf GitHub

Abstract:
Building agentic systems that can autonomously self‑improve from experience is a longstanding goal of AI. Large language models (LLMs) today primarily self‑improve via two mechanisms: self‑reflection for context updates, and reinforcement learning (RL) for weight updates. In this work, we propose Evolutionary System Prompt Learning (E‑SPL), a method for jointly improving model contexts and model weights. In each RL iteration, E‑SPL samples trajectories under multiple system prompts in parallel, then jointly applies RL updates to LLM weights and evolutionary updates to system prompts. System prompts evolve via mutation and crossover, two genetic operators driven by LLM self‑reflection; selection is based on relative performance ratings updated across RL iterations. E‑SPL encourages a natural division between declarative knowledge encoded in prompts and procedural knowledge encoded in weights, resulting in improved performance across reasoning and agentic tasks. For instance, in an easy‑to‑hard (AIME \rightarrow BeyondAIME) generalization setting, E‑SPL improves RL success rate from 38.8% \rightarrow 45.1% while also outperforming reflective prompt evolution (40.0%). Overall, our results demonstrate that RL and system prompt evolution are deeply synergistic, and combining the two yields consistent gains in sample efficiency and generalization. Code: https://github.com/LunjunZhang/E‑SPL

PaperId: 3011, https://arxiv.org/pdf/2602.14655.pdf GitHub

Abstract:
Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression. While AI‑based speech detection is non‑invasive and cost‑effective, it faces a critical data efficiency dilemma due to medical data scarcity and privacy barriers. Therefore, we propose FAL‑AD, a novel framework that synergistically integrates federated learning with data augmentation to systematically optimize data efficiency. Our approach delivers three key breakthroughs: First, absolute efficiency improvement through voice conversion‑based augmentation, which generates diverse pathological speech samples via cross‑category voice‑content recombination. Second, collaborative efficiency breakthrough via an adaptive federated learning paradigm, maximizing cross‑institutional benefits under privacy constraints. Finally, representational efficiency optimization by an attentive cross‑modal fusion model, which achieves fine‑grained word‑level alignment and acoustic‑textual interaction. Evaluated on ADReSSo, FAL‑AD achieves a state‑of‑the‑art multi‑modal accuracy of 91.52%, outperforming all centralized baselines and demonstrating a practical solution to the data efficiency dilemma. Our source code is publicly available at https://github.com/smileix/fal‑ad.

PaperId: 3012, https://arxiv.org/pdf/2602.14622.pdf GitHub

Abstract:
Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high‑stakes decision‑making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low‑data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in‑context generalization, provide a basis for addressing these limitations. We introduce a model‑agnostic association rule learning framework that extracts association rules from any conditional probabilistic model over tabular data, enabling us to leverage TFMs. We then introduce TabProbe, an instantiation of our framework that utilizes TFMs as conditional probability estimators to learn association rules out‑of‑the‑box without frequent itemset mining. We evaluate our approach on tabular datasets of varying sizes based on standard ARM rule quality metrics and downstream classification performance. The results show that TFMs consistently produce concise, high‑quality association rules with strong predictive performance and remain robust in low‑data settings without task‑specific training. Source code is available at https://github.com/DiTEC‑project/tabprobe.

PaperId: 3013, https://arxiv.org/pdf/2602.14615.pdf GitHub

Abstract:
Vision Transformers (ViTs) have emerged as the state‑of‑the‑art architecture in representation learning, leveraging self‑attention mechanisms to excel in various tasks. ViTs split images into fixed‑size patches, constraining them to a predefined size and necessitating pre‑processing steps like resizing, padding, or cropping. This poses challenges in medical imaging, particularly with irregularly shaped structures like tumors. A fixed bounding box crop size produces input images with highly variable foreground‑to‑background ratios. Resizing medical images can degrade information and introduce artefacts, impacting diagnosis. Hence, tailoring variable‑sized crops to regions of interest can enhance feature representation capabilities. Moreover, large images are computationally expensive, and smaller sizes risk information loss, presenting a computation‑accuracy tradeoff. We propose VariViT, an improved ViT model crafted to handle variable image sizes while maintaining a consistent patch size. VariViT employs a novel positional embedding resizing scheme for a variable number of patches. We also implement a new batching strategy within VariViT to reduce computational complexity, resulting in faster training and inference times. In our evaluations on two 3D brain MRI datasets, VariViT surpasses vanilla ViTs and ResNet in glioma genotype prediction and brain tumor classification. It achieves F1‑scores of 75.5% and 76.3%, respectively, learning more discriminative features. Our proposed batching strategy reduces computation time by up to 30% compared to conventional architectures. These findings underscore the efficacy of VariViT in image representation learning. Our code can be found here: https://github.com/Aswathi‑Varma/varivit

PaperId: 3014, https://arxiv.org/pdf/2602.14602.pdf GitHub

Abstract:
The opioid epidemic continues to ravage communities worldwide, straining healthcare systems, disrupting families, and demanding urgent computational solutions. To combat this lethal opioid crisis, graph learning methods have emerged as a promising paradigm for modeling complex drug‑related phenomena. However, a significant gap remains: there is no comprehensive benchmark for systematically evaluating these methods across real‑world opioid crisis scenarios. To bridge this gap, we introduce OPBench, the first comprehensive opioid benchmark comprising five datasets across three critical application domains: opioid overdose detection from healthcare claims, illicit drug trafficking detection from digital platforms, and drug misuse prediction from dietary patterns. Specifically, OPBench incorporates diverse graph structures, including heterogeneous graphs and hypergraphs, to preserve the rich and complex relational information among drug‑related data. To address data scarcity, we collaborate with domain experts and authoritative institutions to curate and annotate datasets while adhering to privacy and ethical guidelines. Furthermore, we establish a unified evaluation framework with standardized protocols, predefined data splits, and reproducible baselines to facilitate fair and systematic comparison among graph learning methods. Through extensive experiments, we analyze the strengths and limitations of existing graph learning methods, thereby providing actionable insights for future research in combating the opioid crisis. Our source code and datasets are available at https://github.com/Tianyi‑Billy‑Ma/OPBench.

PaperId: 3015, https://arxiv.org/pdf/2602.14404.pdf GitHub

Abstract:
Recent years have witnessed meteoric progress in reasoning models: neural networks that generate intermediate reasoning traces (RTs) before producing a final output. Despite the rapid advancement, our understanding of how RTs support reasoning, and the limits of this paradigm, remain incomplete. To promote greater clarity, we introduce PITA: a novel large‑scale dataset of over 23 million statements in propositional logic and their corresponding proofs. As a benchmark for robust reasoning, we focus on length generalization: if a model is trained to determine truth or falsity on statements with proofs up to fixed length, how well does it generalize to statements requiring longer proofs? We propose notions of (1) task depth and (2) task breadth, which measure respectively (1) the number of steps required to solve an example from a task and (2) the number of unique examples across a task. We vary these quantities across subsets of PITA, and find that RT models generalize well on broad and shallow subsets, while deteriorating on narrow and deep subsets relative to non‑RT baselines. To determine whether our results are idiosyncratic to PITA or indicative of general phenomena, we compare our results to a simple synthetic task based on syllogisms. Our resulting theory suggests fundamental scalings that limit how well RT models perform on deep tasks, and highlights their generalization strengths on broad tasks. Our findings overall identify fundamental benefits and limitations inherent in using reasoning traces.

PaperId: 3016, https://arxiv.org/pdf/2602.14381.pdf GitHub

Abstract:
We describe an adaptation of VACE (Video All‑in‑one Creation and Editing) for real‑time autoregressive video generation. VACE provides unified video control (reference guidance, structural conditioning, inpainting, and temporal extension) but assumes bidirectional attention over full sequences, making it incompatible with streaming pipelines that require fixed chunk sizes and causal attention. The key modification moves reference frames from the diffusion latent space into a parallel conditioning pathway, preserving the fixed chunk sizes and KV caching that autoregressive models require. This adaptation reuses existing pretrained VACE weights without additional training. Across 1.3B and 14B model scales, VACE adds 20‑30% latency overhead for structural control and inpainting, with negligible VRAM cost relative to the base model. Reference‑to‑video fidelity is severely degraded compared to batch VACE due to causal attention constraints. A reference implementation is available at https://github.com/daydreamlive/scope.

PaperId: 3017, https://arxiv.org/pdf/2602.14351.pdf GitHub GitHub

Abstract:
Model‑based reinforcement learning promises strong sample efficiency but often underperforms in practice due to compounding model error, unimodal world models that average over multi‑modal dynamics, and overconfident predictions that bias learning. We introduce WIMLE, a model‑based method that extends Implicit Maximum Likelihood Estimation (IMLE) to the model‑based RL framework to learn stochastic, multi‑modal world models without iterative sampling and to estimate predictive uncertainty via ensembles and latent sampling. During training, WIMLE weights each synthetic transition by its predicted confidence, preserving useful model rollouts while attenuating bias from uncertain predictions and enabling stable learning. Across 40 continuous‑control tasks spanning DeepMind Control, MyoSuite, and HumanoidBench, WIMLE achieves superior sample efficiency and competitive or better asymptotic performance than strong model‑free and model‑based baselines. Notably, on the challenging Humanoid‑run task, WIMLE improves sample efficiency by over 50% relative to the strongest competitor, and on HumanoidBench it solves 8 of 14 tasks (versus 4 for BRO and 5 for SimbaV2). These results highlight the value of IMLE‑based multi‑modality and uncertainty‑aware weighting for stable model‑based RL.

PaperId: 3018, https://arxiv.org/pdf/2602.14257.pdf GitHub

Abstract:
While Large Language Model (LLM) agents have achieved remarkable progress in complex reasoning tasks, evaluating their performance in real‑world environments has become a critical problem. Current benchmarks, however, are largely restricted to idealized simulations, failing to address the practical demands of specialized domains like advertising and marketing analytics. In these fields, tasks are inherently more complex, often requiring multi‑round interaction with professional marketing tools. To address this gap, we propose AD‑Bench, a benchmark designed based on real‑world business requirements of advertising and marketing platforms. AD‑Bench is constructed from real user marketing analysis requests, with domain experts providing verifiable reference answers and corresponding reference tool‑call trajectories. The benchmark categorizes requests into three difficulty levels (L1‑L3) to evaluate agents' capabilities under multi‑round, multi‑tool collaboration. Experiments show that on AD‑Bench, Gemini‑3‑Pro achieves Pass@1 = 68.0% and Pass@3 = 83.0%, but performance drops significantly on L3 to Pass@1 = 49.4% and Pass@3 = 62.1%, with a trajectory coverage of 70.1%, indicating that even state‑of‑the‑art models still exhibit substantial capability gaps in complex advertising and marketing analysis scenarios. AD‑Bench provides a realistic benchmark for evaluating and improving advertising marketing agents, the leaderboard and code can be found at https://github.com/Emanual20/adbench‑leaderboard.

PaperId: 3019, https://arxiv.org/pdf/2602.14234.pdf GitHub

Abstract:
Large language models are transitioning from generalpurpose knowledge engines to realworld problem solvers, yet optimizing them for deep search tasks remains challenging. The central bottleneck lies in the extreme sparsity of highquality search trajectories and reward signals, arising from the difficulty of scalable longhorizon task construction and the high cost of interactionheavy rollouts involving external tool calls. To address these challenges, we propose REDSearcher, a unified framework that codesigns complex task synthesis, midtraining, and posttraining for scalable searchagent optimization. Specifically, REDSearcher introduces the following improvements: (1) We frame task synthesis as a dualconstrained optimization, where task difficulty is precisely governed by graph topology and evidence dispersion, allowing scalable generation of complex, highquality tasks. (2) We introduce toolaugmented queries to encourage proactive tool use rather than passive recall.(3) During midtraining, we strengthen core atomic capabilities knowledge, planning, and function calling substantially reducing the cost of collecting highquality trajectories for downstream training. (4) We build a local simulated environment that enables rapid, lowcost algorithmic iteration for reinforcement learning experiments. Across both textonly and multimodal searchagent benchmarks, our approach achieves stateoftheart performance. To facilitate future research on longhorizon search agents, we will release 10K highquality complex text search trajectories, 5K multimodal trajectories and 1K text RL query set, and together with code and model checkpoints.

PaperId: 3020, https://arxiv.org/pdf/2602.14233.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance‑specific biases can inflate performance, contaminate backtests, and make reported results useless for any deployment claim. We identify five recurring biases in financial LLM applications. They include look‑ahead bias, survivorship bias, narrative bias, objective bias, and cost bias. These biases break financial tasks in distinct ways and they often compound to create an illusion of validity. We reviewed 164 papers from 2023 to 2025 and found that no single bias is discussed in more than 28 percent of studies. This position paper argues that bias in financial LLM systems requires explicit attention and that structural validity should be enforced before any result is used to support a deployment claim. We propose a Structural Validity Framework and an evaluation checklist with minimal requirements for bias diagnosis and future system design. The material is available at https://github.com/Eleanorkong/Awesome‑Financial‑LLM‑Bias‑Mitigation.

PaperId: 3021, https://arxiv.org/pdf/2602.14189.pdf GitHub

Abstract:
Large language models are increasingly used to answer and verify scientific claims, yet existing evaluations typically assume that a model must always produce a definitive answer. In scientific settings, however, unsupported or uncertain conclusions can be more harmful than abstaining. We study this problem through an abstention‑aware verification framework that decomposes scientific claims into minimal conditions, audits each condition against available evidence using natural language inference (NLI), and selectively decides whether to support, refute, or abstain. We evaluate this framework across two complementary scientific benchmarks: SciFact and PubMedQA, covering both closed‑book and open‑domain evidence settings. Experiments are conducted with six diverse language models, including encoder‑decoder, open‑weight chat models, and proprietary APIs. Across all benchmarks and models, we observe that raw accuracy varies only modestly across architectures, while abstention plays a critical role in controlling error. In particular, confidence‑based abstention substantially reduces risk at moderate coverage levels, even when absolute accuracy improvements are limited. Our results suggest that in scientific reasoning tasks, the primary challenge is not selecting a single best model, but rather determining when available evidence is sufficient to justify an answer. This work highlights abstention‑aware evaluation as a practical and model‑agnostic lens for assessing scientific reliability, and provides a unified experimental basis for future work on selective reasoning in scientific domains. Code is available at https://github.com/sabdaljalil2000/ai4science .

PaperId: 3022, https://arxiv.org/pdf/2602.14160.pdf GitHub

Abstract:
Clinical decision‑making requires nuanced reasoning over heterogeneous evidence and traceable justifications. While recent LLM multi‑agent systems (MAS) show promise, they largely optimise for outcome accuracy while overlooking process‑grounded reasoning aligned with clinical standards. One critical real‑world case of this is gene‑disease validity curation, where experts must determine whether a gene is causally implicated in a disease by synthesising diverse biomedical evidence. We introduce an agent‑as‑tool reinforcement learning framework for this task with two objectives: (i) process‑level supervision to ensure reasoning follows valid clinical pathways, and (ii) efficient coordination via a hierarchical multi‑agent system. Our evaluation on the ClinGen dataset shows that with outcome‑only rewards, MAS with a GRPO‑trained Qwen3‑4B supervisor agent substantially improves final outcome accuracy from 0.195 with a base model supervisor to 0.732, but results in poor process alignment (0.392 F1). Conversely, with process + outcome rewards, MAS with GRPO‑trained supervisor achieves higher outcome accuracy (0.750) while significantly improving process fidelity to 0.520 F1. Our code is available at https://github.com/chaeeunlee‑io/GeneDiseaseCurationAgents.

PaperId: 3023, https://arxiv.org/pdf/2602.14140.pdf GitHub

Abstract:
Traditional mechanized chestnut harvesting is too costly for small producers, non‑selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low‑cost, vision‑guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on‑ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state‑of‑the‑art real‑time object detectors, including 14 in the YOLO (v11‑13) and 15 in the RT‑DETR (v1‑v4) families at varied model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieves the best mAP@0.5 of 95.1% among all the evaluated models, while the RT‑DETRv2‑R101 was the most accurate variant among RT‑DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrate significant potential for real‑time chestnut detection, and YOLO models outperformed RT‑DETR models in terms of both detection accuracy and inference, making them better suited for on‑board deployment. Both the dataset and software programs in this study have been made publicly available at https://github.com/AgFood‑Sensing‑and‑Intelligence‑Lab/ChestnutDetection.

PaperId: 3024, https://arxiv.org/pdf/2602.14135.pdf GitHub

Abstract:
Rapidly evolving AI exhibits increasingly strong autonomy and goal‑directed capabilities, accompanied by derivative systemic risks that are more unpredictable, difficult to control, and potentially irreversible. However, current AI safety evaluation systems suffer from critical limitations such as restricted risk dimensions and failed frontier risk detection. The lagging safety benchmarks and alignment technologies can hardly address the complex challenges posed by cutting‑edge AI models. To bridge this gap, we propose the "ForesightSafety Bench" AI Safety Evaluation Framework, beginning with 7 major Fundamental Safety pillars and progressively extends to advanced Embodied AI Safety, AI4Science Safety, Social and Environmental AI risks, Catastrophic and Existential Risks, as well as 8 critical industrial safety domains, forming a total of 94 refined risk dimensions. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and dynamically evolving AI safety evaluation framework. Based on this benchmark, we conduct systematic evaluation and in‑depth analysis of over twenty mainstream advanced large models, identifying key risk patterns and their capability boundaries. The safety capability evaluation results reveals the widespread safety vulnerabilities of frontier AI across multiple pillars, particularly focusing on Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety and Catastrophic and Existential Risks. Our benchmark is released at https://github.com/Beijing‑AISI/ForesightSafety‑Bench. The project website is available at https://foresightsafety‑bench.beijing‑aisi.ac.cn/.

PaperId: 3025, https://arxiv.org/pdf/2602.14106.pdf GitHub

Abstract:
The most valuable asset of any cloud‑based organization is data, which is increasingly exposed to sophisticated cyberattacks. Until recently, the implementation of security measures in DevOps environments was often considered optional by many government entities and critical national services operating in the cloud. This includes systems managing sensitive information, such as electoral processes or military operations, which have historically been valuable targets for cybercriminals. Resistance to security implementation is often driven by concerns over losing agility in software development, increasing the risk of accumulated vulnerabilities. Nowadays, patching software is no longer enough; adopting a proactive cyber defense strategy, supported by Artificial Intelligence (AI), is crucial to anticipating and mitigating threats. Thus, this work proposes integrating the Security Chaos Engineering (SCE) methodology with a new LLM‑based flow to automate the creation of attack defense trees that represent adversary behavior and facilitate the construction of SCE experiments based on these graphical models, enabling teams to stay one step ahead of attackers and implement previously unconsidered defenses. Further detailed information about the experiment performed, along with the steps to replicate it, can be found in the following repository: https://github.com/mariomc14/devsecops‑adversary‑llm.git.

PaperId: 3026, https://arxiv.org/pdf/2602.14042.pdf GitHub

Abstract:
In real‑world scenarios, the performance of semantic segmentation often deteriorates when processing low‑quality (LQ) images, which may lack clear semantic structures and high‑frequency details. Although image restoration techniques offer a promising direction for enhancing degraded visual content, conventional real‑world image restoration (Real‑IR) models primarily focus on pixel‑level fidelity and often fail to recover task‑relevant semantic cues, limiting their effectiveness when directly applied to downstream vision tasks. Conversely, existing segmentation models trained on high‑quality data lack robustness under real‑world degradations. In this paper, we propose Restoration Adaptation for Semantic Segmentation (RASS), which effectively integrates semantic image restoration into the segmentation process, enabling high‑quality semantic segmentation on the LQ images directly. Specifically, we first propose a Semantic‑Constrained Restoration (SCR) model, which injects segmentation priors into the restoration model by aligning its cross‑attention maps with segmentation masks, encouraging semantically faithful image reconstruction. Then, RASS transfers semantic restoration knowledge into segmentation through LoRA‑based module merging and task‑specific fine‑tuning, thereby enhancing the model's robustness to LQ images. To validate the effectiveness of our framework, we construct a real‑world LQ image segmentation dataset with high‑quality annotations, and conduct extensive experiments on both synthetic and real‑world LQ benchmarks. The results show that SCR and RASS significantly outperform state‑of‑the‑art methods in segmentation and restoration tasks. Code, models, and datasets will be available at https://github.com/Ka1Guan/RASS.git.

PaperId: 3027, https://arxiv.org/pdf/2602.14041.pdf GitHub GitHub

Abstract:
We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high‑entropy binary latents, BitDance lets each token represent up to 2^256 states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous‑space diffusion to generate the binary tokens. Furthermore, we propose next‑patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256x256, BitDance achieves an FID of 1.24, the best among AR models. With next‑patch diffusion, BitDance beats state‑of‑the‑art parallel AR models that use 1.4B parameters, while using 5.4x fewer parameters (260M) and achieving 8.7x speedup. For text‑to‑image generation, BitDance trains on large‑scale multimodal tokens and generates high‑resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024x1024 images, BitDance achieves a speedup of over 30x compared to prior AR models. We release code and models to facilitate further research on AR foundation models. Code and models are available at: https://github.com/shallowdream204/BitDance.

PaperId: 3028, https://arxiv.org/pdf/2602.14035.pdf GitHub

Abstract:
Flowchart‑oriented dialogue (FOD) systems aim to guide users through multi‑turn decision‑making or operational procedures by following a domain‑specific flowchart to achieve a task goal. In this work, we formalize flowchart reasoning in FOD as grounding user input to flowchart nodes at each dialogue turn while ensuring node transition is consistent with the correct flowchart path. Despite recent advances of LLMs in task‑oriented dialogue systems, adapting them to FOD still faces two limitations: (1) LLMs lack an explicit mechanism to represent and reason over flowchart topology, and (2) they are prone to hallucinations, leading to unfaithful flowchart reasoning. To address these limitations, we propose FloCA, a zero‑shot flowchart‑oriented conversational agent. FloCA uses an LLM for intent understanding and response generation while delegating flowchart reasoning to an external tool that performs topology‑constrained graph execution, ensuring faithful and logically consistent node transitions across dialogue turns. We further introduce an evaluation framework with an LLM‑based user simulator and five new metrics covering reasoning accuracy and interaction efficiency. Extensive experiments on FLODIAL and PFDial datasets highlight the bottlenecks of existing LLM‑based methods and demonstrate the superiority of FloCA. Our codes are available at https://github.com/Jinzi‑Zou/FloCA‑flowchart‑reasoning.

PaperId: 3029, https://arxiv.org/pdf/2602.13936.pdf GitHub

Abstract:
Trajectory prediction for traffic agents is critical for safe autonomous driving. However, achieving effective zero‑shot generalization in previously unseen domains remains a significant challenge. Motivated by the consistent nature of kinematics across diverse domains, we aim to incorporate domain‑invariant knowledge to enhance zero‑shot trajectory prediction capabilities. The key challenges include: 1) effectively extracting domain‑invariant scene representations, and 2) integrating invariant features with kinematic models to enable generalized predictions. To address these challenges, we propose a novel generalizable Physics‑guided Causal Model (PCM), which comprises two core components: a Disentangled Scene Encoder, which adopts intervention‑based disentanglement to extract domain‑invariant features from scenes, and a CausalODE Decoder, which employs a causal attention mechanism to effectively integrate kinematic models with meaningful contextual information. Extensive experiments on real‑world autonomous driving datasets demonstrate our method's superior zero‑shot generalization performance in unseen cities, significantly outperforming competitive baselines. The source code is released at https://github.com/ZY‑Zong/Physics‑guided‑Causal‑Model.

PaperId: 3030, https://arxiv.org/pdf/2602.13921.pdf GitHub

Abstract:
Repository‑level bug localization‑the task of identifying where code must be modified to fix a bug‑is a critical software engineering challenge. Standard Large Language Modles (LLMs) are often unsuitable for this task due to context window limitations that prevent them from processing entire code repositories. As a result, various retrieval methods are commonly used, including keyword matching, text similarity, and simple graph‑based heuristics such as Breadth‑First Search. Graph Neural Networks (GNNs) offer a promising alternative due to their ability to model complex, repository‑wide dependencies; however, their application has been hindered by the lack of a dedicated benchmark. To address this gap, we introduce GREPO, the first GNN benchmark for repository‑scale bug localization tasks. GREPO comprises 86 Python repositories and 47294 bug‑fixing tasks, providing graph‑based data structures ready for direct GNN processing. Our evaluation of various GNN architectures shows outstanding performance compared to established information retrieval baselines. This work highlights the potential of GNNs for bug localization and established GREPO as a foundation resource for future research, The code is available at https://github.com/qingpingmo/GREPO.

PaperId: 3031, https://arxiv.org/pdf/2602.13842.pdf GitHub

Abstract:
Severe aortic stenosis is a common and life‑threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI). Despite procedural advances, paravalvular aortic regurgitation (PVR) remains one of the most frequent post‑TAVI complications, with a proven impact on long‑term prognosis. In this work, we investigate the potential of deep learning to predict the occurrence of PVR from preoperative cardiac CT. To this end, a dataset of preoperative TAVI patients was collected, and 3D convolutional neural networks were trained on isotropic CT volumes. The results achieved suggest that volumetric deep learning can capture subtle anatomical features from pre‑TAVI imaging, opening new perspectives for personalized risk assessment and procedural optimization. Source code is available at https://github.com/EIDOSLAB/tavi.

PaperId: 3032, https://arxiv.org/pdf/2602.13769.pdf GitHub

Abstract:
Automating scientific discovery in complex, experiment‑driven domains requires more than iterative mutation of programs; it demands structured hypothesis management, environment interaction, and principled reflection. We present OR‑Agent, a configurable multi‑agent research framework designed for automated exploration in rich experimental environments. OR‑Agent organizes research as a structured tree‑based workflow that explicitly models branching hypothesis generation and systematic backtracking, enabling controlled management of research trajectories beyond simple mutation‑crossover loops. At its core, we introduce an evolutionary‑systematic ideation mechanism that unifies evolutionary selection of research starting points, comprehensive research plan generation, and coordinated exploration within a research tree. We introduce a hierarchical optimization‑inspired reflection system in which short‑term reflections act as verbal gradients, long‑term reflections as verbal momentum, and memory compression as semantic weight decay, collectively forming a principled mechanism for governing research dynamics. We conduct extensive experiments across classical combinatorial optimization benchmarks as well as simulation‑based cooperative driving scenarios. Results demonstrate that OR‑Agent outperforms strong evolutionary baselines while providing a general, extensible, and inspectable framework for AI‑assisted scientific discovery. All code and experimental data are publicly available at https://github.com/qiliuchn/OR‑Agent.

PaperId: 3033, https://arxiv.org/pdf/2602.13764.pdf GitHub

Abstract:
While vision‑language‑action (VLA) models have advanced generalist robotic learning, cross‑embodiment transfer remains challenging due to kinematic heterogeneity and the high cost of collecting sufficient real‑world demonstrations to support fine‑tuning. Existing cross‑embodiment policies typically rely on shared‑private architectures, which suffer from limited capacity of private parameters and lack explicit adaptation mechanisms. To address these limitations, we introduce MOTIF for efficient few‑shot cross‑embodiment transfer that decouples embodiment‑agnostic spatiotemporal patterns, termed action motifs, from heterogeneous action data. Specifically, MOTIF first learns unified motifs via vector quantization with progress‑aware alignment and embodiment adversarial constraints to ensure temporal and cross‑embodiment consistency. We then design a lightweight predictor that predicts these motifs from real‑time inputs to guide a flow‑matching policy, fusing them with robot‑specific states to enable action generation on new embodiments. Evaluations across both simulation and real‑world environments validate the superiority of MOTIF, which significantly outperforms strong baselines in few‑shot transfer scenarios by 6.5% in simulation and 43.7% in real‑world settings. Code is available at https://github.com/buduz/MOTIF.

PaperId: 3034, https://arxiv.org/pdf/2602.13695.pdf GitHub GitHub

Abstract:
Large language models (LLMs) have recently achieved remarkable success in generating rigorous mathematical proofs, with "AI for Math" emerging as a vibrant field of research (Ju et al., 2026). While these models have mastered competition‑level benchmarks like the International Mathematical Olympiad (Huang et al., 2025; Duan et al., 2025) and show promise in research applications through auto‑formalization (Wang et al., 2025), their deployment via lightweight, natural‑language pipelines for research problems remains underexplored. In this work, we demonstrate that next‑generation models (e.g., Gemini 3 Pro, GPT‑5.2 Pro), when integrated into a streamlined automated pipeline optimized for citation‑based verification, can solve sophisticated research‑grade problems. We evaluate our pipeline on two novel datasets: (1) the ICCM (2025) problem sets (comparable to the S.‑T. Yau College Student Mathematics Contest) proposed by leading mathematicians (Shanghai Math Challenge, 2026), and (2) the "First Proof" problem set (Abouzaid et al., 2026), consisting of previously unpublished research questions. Our pipeline generated candidate proofs for all problems in the first two ICCM sets and the "First Proof" set. The solutions for the first two ICCM sets and Problem 4 of the "First Proof" set have been fully verified by our team. All generated proofs have been submitted to the official organization, and our generated results are publicly available at https://github.com/ml1301215/question_sets‑test_results. We have open‑sourced the code and developed a user‑friendly UI for this workflow, accessible at https://github.com/ml1301215/research‑math‑assistant.

PaperId: 3035, https://arxiv.org/pdf/2602.13665.pdf GitHub

Abstract:
While agentic AI systems rely on LLMs to translate user intent into structured function calls, this process is fraught with computational redundancy, leading to high inference latency that hinders real‑time applications. This paper identifies and addresses three key redundancies: (1) the redundant processing of a large library of function descriptions for every request; (2) the redundant use of a large, slow model to generate an entire, often predictable, token sequence; and (3) the redundant generation of fixed, boilerplate parameter syntax. We introduce HyFunc, a novel framework that systematically eliminates these inefficiencies. HyFunc employs a hybrid‑model cascade where a large model distills user intent into a single "soft token." This token guides a lightweight retriever to select relevant functions and directs a smaller, prefix‑tuned model to generate the final call, thus avoiding redundant context processing and full‑sequence generation by the large model. To eliminate syntactic redundancy, our "dynamic templating" technique injects boilerplate parameter syntax on‑the‑fly within an extended vLLM engine. To avoid potential limitations in generalization, we evaluate HyFunc on an unseen benchmark dataset, BFCL. Experimental results demonstrate that HyFunc achieves an excellent balance between efficiency and performance. It achieves an inference latency of 0.828 seconds, outperforming all baseline models, and reaches a performance of 80.1%, surpassing all models with a comparable parameter scale. These results suggest that HyFunc offers a more efficient paradigm for agentic AI. Our code is publicly available at https://github.com/MrBlankness/HyFunc.

PaperId: 3036, https://arxiv.org/pdf/2602.13662.pdf GitHub

Abstract:
Foundation models and vision‑language pre‑training have significantly advanced Vision‑Language Models (VLMs), enabling multimodal processing of visual and linguistic data. However, their application in domain‑specific agricultural tasks, such as plant pathology, remains limited due to the lack of large‑scale, comprehensive multimodal image‑‑text datasets and benchmarks. To address this gap, we introduce LeafNet, a comprehensive multimodal dataset, and LeafBench, a visual question‑answering benchmark developed to systematically evaluate the capabilities of VLMs in understanding plant diseases. The dataset comprises 186,000 leaf digital images spanning 97 disease classes, paired with metadata, generating 13,950 question‑answer pairs spanning six critical agricultural tasks. The questions assess various aspects of plant pathology understanding, including visual symptom recognition, taxonomic relationships, and diagnostic reasoning. Benchmarking 12 state‑of‑the‑art VLMs on our LeafBench dataset, we reveal substantial disparity in their disease understanding capabilities. Our study shows performance varies markedly across tasks: binary healthy‑‑diseased classification exceeds 90% accuracy, while fine‑grained pathogen and species identification remains below 65%. Direct comparison between vision‑only models and VLMs demonstrates the critical advantage of multimodal architectures: fine‑tuned VLMs outperform traditional vision models, confirming that integrating linguistic representations significantly enhances diagnostic precision. These findings highlight critical gaps in current VLMs for plant pathology applications and underscore the need for LeafBench as a rigorous framework for methodological advancement and progress evaluation toward reliable AI‑assisted plant disease diagnosis. Code is available at https://github.com/EnalisUs/LeafBench.

PaperId: 3037, https://arxiv.org/pdf/2602.13576.pdf GitHub

Abstract:
Evaluation and alignment pipelines for large language models increasingly rely on LLM‑based judges, whose behavior is guided by natural‑language rubrics and validated on benchmarks. We identify a previously under‑recognized vulnerability in this workflow, which we term Rubric‑Induced Preference Drift (RIPD). Even when rubric edits pass benchmark validation, they can still produce systematic and directional shifts in a judge's preferences on target domains. Because rubrics serve as a high‑level decision interface, such drift can emerge from seemingly natural, criterion‑preserving edits and remain difficult to detect through aggregate benchmark metrics or limited spot‑checking. We further show this vulnerability can be exploited through rubric‑based preference attacks, in which benchmark‑compliant rubric edits steer judgments away from a fixed human or trusted reference on target domains, systematically inducing RIPD and reducing target‑domain accuracy up to 9.5% (helpfulness) and 27.9% (harmlessness). When these judgments are used to generate preference labels for downstream post‑training, the induced bias propagates through alignment pipelines and becomes internalized in trained policies. This leads to persistent and systematic drift in model behavior. Overall, our findings highlight evaluation rubrics as a sensitive and manipulable control interface, revealing a system‑level alignment risk that extends beyond evaluator reliability alone. The code is available at: https://github.com/ZDCSlab/Rubrics‑as‑an‑Attack‑Surface. Warning: Certain sections may contain potentially harmful content that may not be appropriate for all readers.

PaperId: 3038, https://arxiv.org/pdf/2602.13516.pdf GitHub

Abstract:
LLM‑powered agents are beginning to automate user's tasks across the open web, often with access to user resources such as emails and calendars. Unlike standard LLMs answering questions in a controlled ChatBot setting, web agents act "in the wild", interacting with third parties and leaving behind an action trace. Therefore, we ask the question: how do web agents handle user resources when accomplishing tasks on their behalf across live websites? In this paper, we formalize Natural Agentic Oversharing ‑‑ the unintentional disclosure of task‑irrelevant user information through an agent trace of actions on the web. We introduce SPILLage, a framework that characterizes oversharing along two dimensions: channel (content vs. behavior) and directness (explicit vs. implicit). This taxonomy reveals a critical blind spot: while prior work focuses on text leakage, web agents also overshare behaviorally through clicks, scrolls, and navigation patterns that can be monitored. We benchmark 180 tasks on live e‑commerce sites with ground‑truth annotations separating task‑relevant from task‑irrelevant attributes. Across 1,080 runs spanning two agentic frameworks and three backbone LLMs, we demonstrate that oversharing is pervasive with behavioral oversharing dominates content oversharing by 5x. This effect persists ‑‑ and can even worsen ‑‑ under prompt‑level mitigation. However, removing task‑irrelevant information before execution improves task success by up to 17.9%, demonstrating that reducing oversharing improves task success. Our findings underscore that protecting privacy in web agents is a fundamental challenge, requiring a broader view of "output" that accounts for what agents do on the web, not just what they type. Our datasets and code are available at https://github.com/jrohsc/SPILLage.

PaperId: 3039, https://arxiv.org/pdf/2602.13444.pdf GitHub

Abstract:
Recent vision‑language‑action (VLA) models can generate plausible end‑effector motions, yet they often fail in long‑horizon, contact‑rich tasks because the underlying hand‑object interaction (HOI) structure is not explicitly represented. An embodiment‑agnostic interaction representation that captures this structure would make manipulation behaviors easier to validate and transfer across robots. We propose FlowHOI, a two‑stage flow‑matching framework that generates semantically grounded, temporally coherent HOI sequences, comprising hand poses, object poses, and hand‑object contact states, conditioned on an egocentric observation, a language instruction, and a 3D Gaussian splatting (3DGS) scene reconstruction. We decouple geometry‑centric grasping from semantics‑centric manipulation, conditioning the latter on compact 3D scene tokens and employing a motion‑text alignment loss to semantically ground the generated interactions in both the physical scene layout and the language instruction. To address the scarcity of high‑fidelity HOI supervision, we introduce a reconstruction pipeline that recovers aligned hand‑object trajectories and meshes from large‑scale egocentric videos, yielding an HOI prior for robust generation. Across the GRAB and HOT3D benchmarks, FlowHOI achieves the highest action recognition accuracy and a 1.7× higher physics simulation success rate than the strongest diffusion‑based baseline, while delivering a 40× inference speedup. We further demonstrate real‑robot execution on four dexterous manipulation tasks, illustrating the feasibility of retargeting generated HOI representations to real‑robot execution pipelines.

PaperId: 3040, https://arxiv.org/pdf/2602.13407.pdf GitHub

Abstract:
Large reasoning models (LRMs) are commonly trained with reinforcement learning (RL) to explore long chain‑of‑thought reasoning, achieving strong performance at high computational cost. Recent methods add multi‑reward objectives to jointly optimize correctness and brevity, but these complex extensions often destabilize training and yield suboptimal trade‑offs. We revisit this objective and challenge the necessity of such complexity. Through principled analysis, we identify fundamental misalignments in this paradigm: KL regularization loses its intended role when correctness and length are directly verifiable, and group‑wise normalization becomes ambiguous under multiple reward signals. By removing these two items and simplifying the reward to a truncation‑based length penalty, we show that the optimization problem reduces to supervised fine‑tuning on self‑generated data filtered for both correctness and conciseness. We term this simplified training strategy on‑policy SFT. Despite its simplicity, on‑policy SFT consistently defines the accuracy‑efficiency Pareto frontier. It reduces CoT length by up to 80 while maintaining original accuracy, surpassing more complex RL‑based methods across five benchmarks. Furthermore, it significantly enhances training efficiency, reducing GPU memory usage by 50% and accelerating convergence by 70%. Our code is available at https://github.com/EIT‑NLP/On‑Policy‑SFT.

PaperId: 3041, https://arxiv.org/pdf/2602.13379.pdf GitHub

Abstract:
LLM‑based agents are becoming increasingly capable, yet their safety lags behind. This creates a gap between what agents can do and should do. This gap widens as agents engage in multi‑turn interactions and employ diverse tools, introducing new risks overlooked by existing benchmarks. To systematically scale safety testing into multi‑turn, tool‑realistic settings, we propose a principled taxonomy that transforms single‑turn harmful tasks into multi‑turn attack sequences. Using this taxonomy, we construct MT‑AgentRisk (Multi‑Turn Agent Risk Benchmark), the first benchmark to evaluate multi‑turn tool‑using agent safety. Our experiments reveal substantial safety degradation: the Attack Success Rate (ASR) increases by 16% on average across open and closed models in multi‑turn settings. To close this gap, we propose ToolShield, a training‑free, tool‑agnostic, self‑exploration defense: when encountering a new tool, the agent autonomously generates test cases, executes them to observe downstream effects, and distills safety experiences for deployment. Experiments show that ToolShield effectively reduces ASR by 30% on average in multi‑turn interactions. Our code is available at https://github.com/CHATS‑lab/ToolShield.

PaperId: 3042, https://arxiv.org/pdf/2602.13346.pdf GitHub GitHub

Abstract:
Single‑cell RNA‑seq (scRNA‑seq) enables atlas‑scale profiling of complex tissues, revealing rare lineages and transient states. Yet, assigning biologically valid cell identities remains a bottleneck because markers are tissue‑ and state‑dependent, and novel states lack references. We present CellMaster, an AI agent that mimics expert practice for zero‑shot cell‑type annotation. Unlike existing automated tools, CellMaster leverages LLM‑encoded knowledge (e.g., GPT‑4o) to perform on‑the‑fly annotation with interpretable rationales, without pre‑training or fixed marker databases. Across 9 datasets spanning 8 tissues, CellMaster improved accuracy by 7.1% over best‑performing baselines (including CellTypist and scTab) in automatic mode. With human‑in‑the‑loop refinement, this advantage increased to 18.6%, with a 22.1% gain on subtype populations. The system demonstrates particular strength in rare and novel cell states where baselines often fail. Source code and the web application are available at \hrefhttps://github.com/AnonymousGym/CellMasterhttps://github.com/AnonymousGym/CellMaster.

PaperId: 3043, https://arxiv.org/pdf/2602.13318.pdf GitHub

Abstract:
Automatically generating and iteratively editing academic slide decks requires more than document summarization. It demands faithful content selection, coherent slide organization, layout‑aware rendering, and robust multi‑turn instruction following. However, existing benchmarks and evaluation protocols do not adequately measure these challenges. To address this gap, we introduce the Deck Edits and Compliance Kit Benchmark (DECKBench), an evaluation framework for multi‑agent slide generation and editing. DECKBench is built on a curated dataset of paper to slide pairs augmented with realistic, simulated editing instructions. Our evaluation protocol systematically assesses slide‑level and deck‑level fidelity, coherence, layout quality, and multi‑turn instruction following. We further implement a modular multi‑agent baseline system that decomposes the slide generation and editing task into paper parsing and summarization, slide planning, HTML creation, and iterative editing. Experimental results demonstrate that the proposed benchmark highlights strengths, exposes failure modes, and provides actionable insights for improving multi‑agent slide generation and editing systems. Overall, this work establishes a standardized foundation for reproducible and comparable evaluation of academic presentation generation and editing. Code and data are publicly available at https://github.com/morgan‑heisler/DeckBench .

PaperId: 3044, https://arxiv.org/pdf/2602.13315.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities, yet they encounter significant computational bottlenecks due to the massive volume of visual tokens. Consequently, visual token pruning, which substantially reduces the token count, has emerged as a critical technique for accelerating MLLM inference. Existing approaches focus on token importance, diversity, or an intuitive combination of both, without a principled framework for their optimal integration. To address this issue, we first conduct a systematic analysis to characterize the trade‑off between token importance and semantic diversity. Guided by this analysis, we propose the Importance and Diversity Pruner (IDPruner), which leverages the Maximal Marginal Relevance (MMR) algorithm to achieve a Pareto‑optimal balance between these two objectives. Crucially, our method operates without requiring attention maps, ensuring full compatibility with FlashAttention and efficient deployment via one‑shot pruning. We conduct extensive experiments across various model architectures and multimodal benchmarks, demonstrating that IDPruner achieves state‑of‑the‑art performance and superior generalization across diverse architectures and tasks. Notably, on Qwen2.5‑VL‑7B‑Instruct, IDPruner retains 95.18% of baseline performance when pruning 75% of the tokens, and still maintains 86.40% even under an extreme 90% pruning ratio. Our code is available at https://github.com/Tencent/AngelSlim.

PaperId: 3045, https://arxiv.org/pdf/2602.13305.pdf GitHub

Abstract:
Wildfires are a growing threat to ecosystems, human lives, and infrastructure, with their frequency and intensity rising due to climate change and human activities. Early detection is critical, yet satellite‑based monitoring remains challenging due to faint smoke signals, dynamic weather conditions, and the need for real‑time analysis over large areas. We introduce WildfireVLM, an AI framework that combines satellite imagery wildfire detection with language‑driven risk assessment. We construct a labeled wildfire and smoke dataset using imagery from Landsat‑8/9, GOES‑16, and other publicly available Earth observation sources, including harmonized products with aligned spectral bands. WildfireVLM employs YOLOv12 to detect fire zones and smoke plumes, leveraging its ability to detect small, complex patterns in satellite imagery. We integrate Multimodal Large Language Models (MLLMs) that convert detection outputs into contextualized risk assessments and prioritized response recommendations for disaster management. We validate the quality of risk reasoning using an LLM‑as‑judge evaluation with a shared rubric. The system is deployed using a service‑oriented architecture that supports real‑time processing, visual risk dashboards, and long‑term wildfire tracking, demonstrating the value of combining computer vision with language‑based reasoning for scalable wildfire monitoring. The code and dataset are publicly available on GitHub at https://github.com/Ayanzadeh93/_WildfireVLM_.

PaperId: 3046, https://arxiv.org/pdf/2602.13304.pdf GitHub

Abstract:
Deformable image registration across heterogeneous domains remains challenging because coupled appearance variation and geometric misalignment violate the brightness constancy assumption underlying conventional methods. We propose PCReg‑Net, a progressive contrast‑guided registration framework that performs coarse‑to‑fine alignment through four lightweight modules: (1)~a registration U‑Net for initial coarse alignment, (2)~a reference feature extractor capturing multi‑scale structural cues from the fixed image, (3)~a multi‑scale contrast module that identifies residual misalignment by comparing coarse‑registered and reference features, and (4)~a refinement U‑Net with feature injection that produces the final high‑fidelity output. We evaluate on the FIRE‑Reg‑256 retinal fundus benchmark, demonstrating improvements over both traditional and deep learning baselines. Additional experiments on two microscopy benchmarks further confirm cross‑domain applicability. With only 2.56M parameters, PCReg‑Net achieves real‑time inference at 141 FPS. Code is available at https://github.com/JiahaoQin/PCReg‑Net.

PaperId: 3047, https://arxiv.org/pdf/2602.13294.pdf GitHub

Abstract:
Evaluating whether Multimodal Large Language Models (MLLMs) genuinely reason about physical dynamics remains challenging. Most existing benchmarks rely on recognition‑style protocols such as Visual Question Answering (VQA) and Violation of Expectation (VoE), which can often be answered without committing to an explicit, testable physical hypothesis. We propose VisPhyWorld, an execution‑based framework that evaluates physical reasoning by requiring models to generate executable simulator code from visual observations. By producing runnable code, the inferred world representation is directly inspectable, editable, and falsifiable. This separates physical reasoning from rendering. Building on this framework, we introduce VisPhyBench, comprising 209 evaluation scenes derived from 108 physical templates and a systematic protocol that evaluates how well models reconstruct appearance and reproduce physically plausible motion. Our pipeline produces valid reconstructed videos in 97.7% of benchmark runs before fallback. Experiments show that while state‑of‑the‑art MLLMs achieve strong semantic scene understanding, they struggle to accurately infer physical parameters and to simulate consistent physical dynamics. Our code is available https://github.com/TIGER‑AI‑Lab/VisPhyWorld

PaperId: 3048, https://arxiv.org/pdf/2602.13262.pdf GitHub

Abstract:
Frontier language models improve with additional test‑time computation, but serial reasoning or uncoordinated parallel sampling can be compute‑inefficient under fixed inference budgets. We propose SELFCEST, which equips a base model with the ability to spawn same‑weight clones in separate parallel contexts by agentic reinforcement learning. Training is end‑to‑end under a global task reward with shared‑parameter rollouts, yielding a learned controller that allocates both generation and context budget across branches. Across challenging math reasoning benchmarks and long‑context multi‑hop QA, SELFCEST improves the accuracy‑cost Pareto frontier relative to monolithic baselines at matched inference budget, and exhibits out‑of‑distribution generalization in both domains.

PaperId: 3049, https://arxiv.org/pdf/2602.13258.pdf GitHub

Abstract:
Large language model (LLM) agents have emerged as powerful tools for complex tasks, yet their ability to adapt to individual users remains fundamentally limited. We argue this limitation stems from a critical architectural conflation: current systems treat memory, learning, and personalization as a unified capability rather than three distinct mechanisms requiring different infrastructure, operating on different timescales, and benefiting from independent optimization. We propose MAPLE (Memory‑Adaptive Personalized LEarning), a principled decomposition where Memory handles storage and retrieval infrastructure; Learning extracts intelligence from accumulated interactions asynchronously; and Personalization applies learned knowledge in real‑time within finite context budgets. Each component operates as a dedicated sub‑agent with specialized tooling and well‑defined interfaces. Experimental evaluation on the MAPLE‑Personas benchmark demonstrates that our decomposition achieves a 14.6% improvement in personalization score compared to a stateless baseline (p < 0.01, Cohen's d = 0.95) and increases trait incorporation rate from 45% to 75% ‑‑ enabling agents that genuinely learn and adapt.

PaperId: 3050, https://arxiv.org/pdf/2602.13235.pdf GitHub

Abstract:
Visual Retrieval‑Augmented Generation (VRAG) enhances Vision‑Language Models (VLMs) by incorporating external visual documents to address a given query. Existing VRAG frameworks usually depend on rigid, pre‑defined external tools to extend the perceptual capabilities of VLMs, typically by explicitly separating visual perception from subsequent reasoning processes. However, this decoupled design can lead to unnecessary loss of visual information, particularly when image‑based operations such as cropping are applied. In this paper, we propose Lang2Act, which enables fine‑grained visual perception and reasoning through self‑emergent linguistic toolchains. Rather than invoking fixed external engines, Lang2Act collects self‑emergent actions as linguistic tools and leverages them to enhance the visual perception capabilities of VLMs. To support this mechanism, we design a two‑stage Reinforcement Learning (RL)‑based training framework. Specifically, the first stage optimizes VLMs to self‑explore high‑quality actions for constructing a reusable linguistic toolbox, and the second stage further optimizes VLMs to exploit these linguistic tools for downstream reasoning effectively. Experimental results demonstrate the effectiveness of Lang2Act in substantially enhancing the visual perception capabilities of VLMs, achieving performance improvements of over 4%. All code and data are available at https://github.com/NEUIR/Lang2Act.

PaperId: 3051, https://arxiv.org/pdf/2602.13218.pdf GitHub GitHub

Abstract:
Reinforcement Learning from Verifiable Rewards (RLVR) is bottlenecked by data: existing synthesis pipelines rely on expert‑written code or fixed templates, confining growth to instance‑level perturbations. We shift the evolvable unit from problem instances to task‑family specifications. SSLogic is an agentic meta‑synthesis framework in which LLM agents iteratively author and refine executable Generator‑Validator pairs inside a closed Generate‑Validate‑Refine loop, producing families with new rules and difficulty gradients rather than parameter variations of old ones. A Multi‑Gate Validation Protocol ‑‑ multi‑strategy consensus plus Adversarial Blind Review, where independent agents solve each instance by writing and executing code ‑‑ filters ill‑posed tasks before they enter training. Starting from 400 seed families, two evolution rounds yield 953 families and 21,389 verifiable instances. Three converging comparisons (step‑matched, token‑matched, and size‑controlled on external Enigmata data) consistently show higher training utility of evolved data, with gains of SynLogic +5.2, AIME25 +3.0, and BBH +5.5 on Enigmata. Fine‑grained KORBench evaluation reveals selective improvements in logic (+13.2%) and operation (+9.6%), linking structural evolution to downstream gains. Code: https://github.com/AdAstraAbyssoque/Scaling‑the‑Scaling‑Logic

PaperId: 3052, https://arxiv.org/pdf/2602.13191.pdf GitHub

Abstract:
Video Language Models (VideoLMs) enable AI systems to understand temporal dynamics in videos. To fit within the maximum context window constraint, current methods use keyframe sampling which often misses both macro‑level events and micro‑level details due to the sparse temporal coverage. Furthermore, processing full images and their tokens for each frame incurs substantial computational overhead. We address these limitations by leveraging video codec primitives (specifically motion vectors and residuals) which natively encode video redundancy and sparsity without requiring expensive full‑image encoding for most frames. To this end, we introduce lightweight transformer‑based encoders that aggregate codec primitives and align their representations with image encoder embeddings through a pre‑training strategy that accelerates convergence during end‑to‑end fine‑tuning. Our approach, CoPE‑VideoLM, reduces the time‑to‑first‑token by up to 86% and token usage by up to 93% compared to standard VideoLMs. Moreover, by varying the keyframe and codec primitive densities we maintain or exceed performance on 14 diverse video understanding benchmarks spanning general question answering, temporal and motion reasoning, long‑form understanding, and spatial scene understanding.

PaperId: 3053, https://arxiv.org/pdf/2602.13055.pdf GitHub

Abstract:
Direct Preference Optimization (DPO) has been proposed as an effective and efficient alternative to reinforcement learning from human feedback (RLHF). However, neither RLHF nor DPO take into account the fact that learning certain preferences is more difficult than learning other preferences, rendering the optimization process suboptimal. To address this gap in text‑to‑image generation, we recently proposed Curriculum‑DPO, a method that organizes image pairs by difficulty. In this paper, we introduce Curriculum‑DPO++, an enhanced method that combines the original data‑level curriculum with a novel model‑level curriculum. More precisely, we propose to dynamically increase the learning capacity of the denoising network as training advances. We implement this capacity increase via two mechanisms. First, we initialize the model with only a subset of the trainable layers used in the original Curriculum‑DPO. As training progresses, we sequentially unfreeze layers until the configuration matches the full baseline architecture. Second, as the fine‑tuning is based on Low‑Rank Adaptation (LoRA), we implement a progressive schedule for the dimension of the low‑rank matrices. Instead of maintaining a fixed capacity, we initialize the low‑rank matrices with a dimension significantly smaller than that of the baseline. As training proceeds, we incrementally increase their rank, allowing the capacity to grow until it converges to the same rank value as in Curriculum‑DPO. Furthermore, we propose an alternative ranking strategy to the one employed by Curriculum‑DPO. Finally, we compare Curriculum‑DPO++ against Curriculum‑DPO and other state‑of‑the‑art preference optimization approaches on nine benchmarks, outperforming the competing methods in terms of text alignment, aesthetics and human preference. Our code is available at https://github.com/CroitoruAlin/Curriculum‑DPO.

PaperId: 3054, https://arxiv.org/pdf/2602.12978.pdf GitHub

Abstract:
Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real‑Time Chunking (RTC) alleviates this issue but is external to the policy, leading to spurious multimodal switching and trajectories that are not intrinsically smooth. We propose Legato, a training‑time continuation method for action‑chunked flow‑based VLA policies. Specifically, Legato initializes denoising from a schedule‑shaped mixture of known actions and noise, exposing the model to partial action information. Moreover, Legato reshapes the learned flow dynamics to ensure that the denoising process remains consistent between training and inference under per‑step guidance. Legato further uses randomized schedule condition during training to support varying inference delays and achieve controllable smoothness. Empirically, Legato produces smoother trajectories and reduces spurious multimodal switching during execution, leading to less hesitation and shorter task completion time. Extensive real‑world experiments show that Legato consistently outperforms RTC across five manipulation tasks, achieving approximately 10% improvements in both trajectory smoothness and task completion time.

PaperId: 3055, https://arxiv.org/pdf/2602.12952.pdf GitHub

Abstract:
Adapting large pre‑trained models to downstream tasks often produces task‑specific parameter updates that are expensive to relearn for every model variant. While recent work has shown that such updates can be transferred between models with identical architectures, transferring them across models of different widths remains unexplored. In this work, we introduce Theseus, a training‑free method for transporting task updates across heterogeneous‑width models. Rather than matching parameters, we characterize a task update by the functional effect it induces on intermediate representations. We formalize task‑vector transport as a functional matching problem on observed activations and show that, after aligning representation spaces via orthogonal Procrustes analysis, it admits a stable closed‑form solution that preserves the geometry of the update. We evaluate Theseus on vision and language models across different widths, showing consistent improvements over baselines without additional training or backpropagation. Our results show that task updates can be meaningfully transferred across architectures when task identity is defined functionally rather than parametrically. Code is available at https://github.com/apanariello4/merge‑and‑rebase.

PaperId: 3056, https://arxiv.org/pdf/2602.12919.pdf GitHub

Abstract:
Event stream‑based Visual Place Recognition (VPR) is an emerging research direction that offers a compelling solution to the instability of conventional visible‑light cameras under challenging conditions such as low illumination, overexposure, and high‑speed motion. Recognizing the current scarcity of dedicated datasets in this domain, we introduce EPRBench, a high‑quality benchmark specifically designed for event stream‑based VPR. EPRBench comprises 10K event sequences and 65K event frames, collected using both handheld and vehicle‑mounted setups to comprehensively capture real‑world challenges across diverse viewpoints, weather conditions, and lighting scenarios. To support semantic‑aware and language‑integrated VPR research, we provide LLM‑generated scene descriptions, subsequently refined through human annotation, establishing a solid foundation for integrating LLMs into event‑based perception pipelines. To facilitate systematic evaluation, we implement and benchmark 15 state‑of‑the‑art VPR algorithms on EPRBench, offering a strong baseline for future algorithmic comparisons. Furthermore, we propose a novel multi‑modal fusion paradigm for VPR: leveraging LLMs to generate textual scene descriptions from raw event streams, which then guide spatially attentive token selection, cross‑modal feature fusion, and multi‑scale representation learning. This framework not only achieves highly accurate place recognition but also produces interpretable reasoning processes alongside its predictions, significantly enhancing model transparency and explainability. The dataset and source code will be released on https://github.com/Event‑AHU/Neuromorphic_ReID

PaperId: 3057, https://arxiv.org/pdf/2602.12892.pdf GitHub

Abstract:
Pre‑trained Multi‑modal Large Language Models (MLLMs) provide a knowledge‑rich foundation for post‑training by leveraging their inherent perception and reasoning capabilities to solve complex tasks. However, the lack of an efficient evaluation framework impedes the diagnosis of their performance bottlenecks. Current evaluation primarily relies on testing after supervised fine‑tuning, which introduces laborious additional training and autoregressive decoding costs. Meanwhile, common pre‑training metrics cannot quantify a model's perception and reasoning abilities in a disentangled manner. Furthermore, existing evaluation benchmarks are typically limited in scale or misaligned with pre‑training objectives. Thus, we propose RADAR, an efficient ability‑centric evaluation framework for Revealing Asymmetric Development of Abilities in MLLM pRe‑training. RADAR involves two key components: (1) Soft Discrimination Score, a novel metric for robustly tracking ability development without fine‑tuning, based on quantifying nuanced gradations of the model preference for the correct answer over distractors; and (2) Multi‑Modal Mixture Benchmark, a new 15K+ sample benchmark for comprehensively evaluating pre‑trained MLLMs' perception and reasoning abilities in a 0‑shot manner, where we unify authoritative benchmark datasets and carefully collect new datasets, extending the evaluation scope and addressing the critical gaps in current benchmarks. With RADAR, we comprehensively reveal the asymmetric development of perceptual and reasoning capabilities in pretrained MLLMs across diverse factors, including data volume, model size, and pretraining strategy. Our RADAR underscores the need for a decomposed perspective on pre‑training ability bottlenecks, informing targeted interventions to advance MLLMs efficiently. Our code is publicly available at https://github.com/Nieysh/RADAR.

PaperId: 3058, https://arxiv.org/pdf/2602.12641.pdf GitHub

Abstract:
AI Video Assistant emerges as a new paradigm for Real‑time Communication (RTC), where one peer is a Multimodal Large Language Model (MLLM) deployed in the cloud. This makes interaction between humans and AI more intuitive, akin to chatting with a real person. However, a fundamental mismatch exists between current RTC frameworks and AI Video Assistants, stemming from the drastic shift in Quality of Experience (QoE) and more challenging networks. Measurements on our production prototype also confirm that current RTC fails, causing latency spikes and accuracy drops. To address these challenges, we propose Artic, an AI‑oriented RTC framework for MLLM Video Assistants, exploring the shift from "humans watching video" to "AI understanding video." Specifically, Artic proposes: (1) Response Capability‑aware Adaptive Bitrate, which utilizes MLLM accuracy saturation to proactively cap bitrate, reserving bandwidth headroom to absorb future fluctuations for latency reduction; (2) Zero‑overhead Context‑aware Streaming, which allocates limited bitrate to regions most important for the response, maintaining accuracy even under ultra‑low bitrates; and (3) Degraded Video Understanding Benchmark, the first benchmark evaluating how RTC‑induced video degradation affects MLLM accuracy. Prototype experiments using real‑world uplink traces show that compared with existing methods, Artic significantly improves accuracy by 15.12% and reduces latency by 135.31 ms. We will release the benchmark and codes at https://github.com/pku‑netvideo/DeViBench.

PaperId: 3059, https://arxiv.org/pdf/2602.12612.pdf GitHub

Abstract:
Traditional methods for automating recommender system design, such as Neural Architecture Search (NAS), are often constrained by a fixed search space defined by human priors, limiting innovation to pre‑defined operators. While recent LLM‑driven code evolution frameworks shift fixed search space target to open‑ended program spaces, they primarily rely on scalar metrics (e.g., NDCG, Hit Ratio) that fail to provide qualitative insights into model failures or directional guidance for improvement. To address this, we propose Self‑EvolveRec, a novel framework that establishes a directional feedback loop by integrating a User Simulator for qualitative critiques and a Model Diagnosis Tool for quantitative internal verification. Furthermore, we introduce a Diagnosis Tool ‑ Model Co‑Evolution strategy to ensure that evaluation criteria dynamically adapt as the recommendation architecture evolves. Extensive experiments demonstrate that Self‑EvolveRec significantly outperforms state‑of‑the‑art NAS and LLM‑driven code evolution baselines in both recommendation performance and user satisfaction. Our code is available at https://github.com/Sein‑Kim/self_evolverec.

PaperId: 3060, https://arxiv.org/pdf/2602.12609.pdf GitHub

Abstract:
Elastic precision quantization enables multi‑bit deployment via a single optimization pass, fitting diverse quantization scenarios.Yet, the high storage and optimization costs associated with the Transformer architecture, research on elastic quantization remains limited, particularly for large language models.This paper proposes QuEPT, an efficient post‑training scheme that reconstructs block‑wise multi‑bit errors with one‑shot calibration on a small data slice. It can dynamically adapt to various predefined bit‑widths by cascading different low‑rank adapters, and supports real‑time switching between uniform quantization and mixed precision quantization without repeated optimization. To enhance accuracy and robustness, we introduce Multi‑Bit Token Merging (MB‑ToMe) to dynamically fuse token features across different bit‑widths, improving robustness during bit‑width switching. Additionally, we propose Multi‑Bit Cascaded Low‑Rank adapters (MB‑CLoRA) to strengthen correlations between bit‑width groups, further improve the overall performance of QuEPT. Extensive experiments demonstrate that QuEPT achieves comparable or better performance to existing state‑of‑the‑art post‑training quantization methods.Our code is available at https://github.com/xuke225/QuEPT

PaperId: 3061, https://arxiv.org/pdf/2602.12566.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert‑level performance in some specific domains via RLVR, such as coding or math. When a general multi‑domain expert‑level model is required, we need to carefully consider the collaboration of RLVR across different domains. The current state‑of‑the‑art models mainly employ two different training paradigms for multi‑domain RLVR: mixed multi‑task RLVR and separate RLVR followed by model merging. However, most of the works did not provide a detailed comparison and analysis about these paradigms. To this end, we choose multiple commonly used high‑level tasks (e.g., math, coding, science, instruction following, and agent) as our target domains and design extensive qualitative and quantitative experiments using open‑source datasets. We find the RLVR across domains exhibits few mutual interferences, and reasoning‑intensive domains demonstrate mutually synergistic effects. Furthermore, we analyze the internal mechanisms of mutual gains from the perspectives of weight space geometry, information constraints, model prediction behavior and self‑verification. This project is named as M2RL that means Mixed multi‑task training or separate training followed by model Merging for Reinforcement Learning, and the homepage is at https://github.com/Mosi‑AI/M2RL.

PaperId: 3062, https://arxiv.org/pdf/2602.12517.pdf GitHub GitHub

Abstract:
The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large‑scale multi‑agent systems. However, the field currently lacks a standardized evaluation protocol, forcing researchers to rely on bespoke, isolated, and often simplistic environments. This fragmentation makes it difficult to assess the robustness, generalization, and failure modes of emerging methods. To address this gap, we propose a comprehensive benchmark suite for MFGs (Bench‑MFG), focusing on the discrete‑time, discrete‑space, stationary setting for the sake of clarity. We introduce a taxonomy of problem classes, ranging from no‑interaction and monotone games to potential and dynamics‑coupled games, and provide prototypical environments for each. Furthermore, we propose MF‑Garnets, a method for generating random MFG instances to facilitate rigorous statistical testing. We benchmark a variety of learning algorithms across these environments, including a novel black‑box approach (MF‑PSO) for exploitability minimization. Based on our extensive empirical results, we propose guidelines to standardize future experimental comparisons. Code available at \hrefhttps://github.com/lorenzomagnino/Bench‑MFGhttps://github.com/lorenzomagnino/Bench‑MFG.

PaperId: 3063, https://arxiv.org/pdf/2602.12470.pdf GitHub

Abstract:
RNA design, the task of finding a sequence that folds into a target secondary structure, has broad biological and biomedical impact but remains computationally challenging due to the exponentially large sequence space and exponentially many competing folds. Traditional approaches treat it as an optimization problem, relying on per‑instance heuristics or constraint‑based search. We instead reframe RNA design as conditional sequence generation and introduce a reusable neural approximator, instantiated as an autoregressive language model (LM), that maps target structures directly to sequences. We first train our model in a supervised setting on random‑induced structure‑sequence pairs, and then use reinforcement learning (RL) to optimize end‑to‑end metrics. We also propose methods to select a small subset for RL that greatly improves RL efficiency and quality. Across four datasets, our approach outperforms state‑of‑the‑art systems on key metrics such as Boltzmann probability while being 1.7x faster, establishing conditional LM generation as a scalable, task‑agnostic alternative to per‑instance optimization for RNA design. Our code and data are available at https://github.com/KuNyaa/RNA‑Design‑LM.

PaperId: 3064, https://arxiv.org/pdf/2602.12393.pdf GitHub

Abstract:
DragDiffusion is a diffusion‑based method for interactive point‑based image editing that enables users to manipulate images by directly dragging selected points. The method claims that accurate spatial control can be achieved by optimizing a single diffusion latent at an intermediate timestep, together with identity‑preserving fine‑tuning and spatial regularization. This work presents a reproducibility study of DragDiffusion using the authors' released implementation and the DragBench benchmark. We reproduce the main ablation studies on diffusion timestep selection, LoRA‑based fine‑tuning, mask regularization strength, and UNet feature supervision, and observe close agreement with the qualitative and quantitative trends reported in the original work. At the same time, our experiments show that performance is sensitive to a small number of hyperparameter assumptions, particularly the optimized timestep and the feature level used for motion supervision, while other components admit broader operating ranges. We further evaluate a multi‑timestep latent optimization variant and find that it does not improve spatial accuracy while substantially increasing computational cost. Overall, our findings support the central claims of DragDiffusion while clarifying the conditions under which they are reliably reproducible. Code is available at https://github.com/AliSubhan5341/DragDiffusion‑TMLR‑Reproducibility‑Challenge.

PaperId: 3065, https://arxiv.org/pdf/2602.12316.pdf GitHub

Abstract:
Frontier AI systems are increasingly capable and deployed in high‑stakes multi‑agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi‑agent risks such as coordination failure and conflict poorly understood. We introduce GT‑HarmBench, a benchmark of 1,535 high‑stakes scenarios spanning game‑theoretic structures such as the Prisoner's Dilemma, Stag Hunt and Chicken. Scenarios are drawn from realistic AI risk contexts in the MIT AI Risk Repository. Across 15 frontier models, agents fail to choose socially beneficial actions in 38% of high‑stakes cases, such as military escalation, election manipulation, and medical malpractice. We measure sensitivity to game‑theoretic prompt framing and ordering, and analyze reasoning patterns driving failures. We further show that game‑theoretic interventions improve socially beneficial outcomes by up to 18%. Our results highlight substantial reliability gaps and provide a broad standardized testbed for studying alignment in multi‑agent environments. The benchmark and code are available at https://github.com/causalNLP/gt‑harmbench.

PaperId: 3066, https://arxiv.org/pdf/2602.12304.pdf GitHub GitHub

Abstract:
Existing mainstream video customization methods focus on generating identity‑consistent videos based on given reference images and textual prompts. Benefiting from the rapid advancement of joint audio‑video generation, this paper proposes a more compelling new task: sync audio‑video customization, which aims to synchronously customize both video identity and audio timbre. Specifically, given a reference image I^r and a reference audio A^r, this novel task requires generating videos that maintain the identity of the reference image while imitating the timbre of the reference audio, with spoken content freely specifiable through user‑provided textual prompts. To this end, we propose OmniCustom, a powerful DiT‑based audio‑video customization framework that can synthesize a video following reference image identity, audio timbre, and text prompts all at once in a zero‑shot manner. Our framework is built on three key contributions. First, identity and audio timbre control are achieved through separate reference identity and audio LoRA modules that operate through self‑attention layers within the base audio‑video generation model. Second, we introduce a contrastive learning objective alongside the standard flow matching objective. It uses predicted flows conditioned on reference inputs as positive examples and those without reference conditions as negative examples, thereby enhancing the model ability to preserve identity and timbre. Third, we train OmniCustom on our constructed large‑scale, high‑quality audio‑visual human dataset. Extensive experiments demonstrate that OmniCustom outperforms existing methods in generating audio‑video content with consistent identity and timbre fidelity. Project page: https://omnicustom‑project.github.io/page/.

PaperId: 3067, https://arxiv.org/pdf/2602.12268.pdf GitHub

Abstract:
AI agents are increasingly used to solve real‑world tasks by reasoning over multi‑turn user interactions and invoking external tools. However, applying reinforcement learning to such settings remains difficult: realistic objectives often lack verifiable rewards and instead emphasize open‑ended behaviors; moreover, RL for multi‑turn, multi‑step agentic tool use is still underexplored; and building and maintaining executable tool environments is costly, limiting scale and coverage. We propose CM2, an RL framework that replaces verifiable outcome rewards with checklist rewards. CM2 decomposes each turn's intended behavior into fine‑grained binary criteria with explicit evidence grounding and structured metadata, turning open‑ended judging into more stable classification‑style decisions. To balance stability and informativeness, our method adopts a strategy of sparse reward assignment but dense evaluation criteria. Training is performed in a scalable LLM‑simulated tool environment, avoiding heavy engineering for large tool sets. Experiments show that CM2 consistently improves over supervised fine‑tuning. Starting from an 8B Base model and training on an 8k‑example RL dataset, CM2 improves over the SFT counterpart by 8 points on tau^‑Bench, by 10 points on BFCL‑V4, and by 12 points on ToolSandbox. The results match or even outperform similarly sized open‑source baselines, including the judging model. CM2 thus provides a scalable recipe for optimizing multi‑turn, multi‑step tool‑using agents without relying on verifiable rewards. Code provided by the open‑source community: https://github.com/namezhenzhang/CM2‑RLCR‑Tool‑Agent.

PaperId: 3068, https://arxiv.org/pdf/2602.12247.pdf GitHub

Abstract:
Unstructured documents like PDFs contain valuable structured information, but downstream systems require this data in reliable, standardized formats. LLMs are increasingly deployed to automate this extraction, making accuracy and reliability paramount. However, progress is bottlenecked by two gaps. First, no end‑to‑end benchmark evaluates PDF‑to‑JSON extraction under enterprise‑scale schema breadth. Second, no principled methodology captures the semantics of nested extraction, where fields demand different notions of correctness (exact match for identifiers, tolerance for quantities, semantic equivalence for names), arrays require alignment, and omission must be distinguished from hallucination. We address both gaps with ExtractBench, an open‑source benchmark and evaluation framework for PDF‑to‑JSON structured extraction. The benchmark pairs 35 PDF documents with JSON Schemas and human‑annotated gold labels across economically valuable domains, yielding 12,867 evaluatable fields spanning schema complexities from tens to hundreds of fields. The evaluation framework treats the schema as an executable specification: each field declares its scoring metric. Baseline evaluations reveal that frontier models (GPT‑5/5.2, Gemini‑3 Flash/Pro, Claude 4.5 Opus/Sonnet) remain unreliable on realistic schemas. Performance degrades sharply with schema breadth, culminating in 0% valid output on a 369‑field financial reporting schema across all tested models. We release ExtractBench at https://github.com/ContextualAI/extract‑bench.

PaperId: 3069, https://arxiv.org/pdf/2602.12222.pdf GitHub

Abstract:
Supervised fine‑tuning (SFT) is computationally efficient but often yields inferior generalization compared to reinforcement learning (RL). This gap is primarily driven by RL's use of on‑policy data. We propose a framework to bridge this chasm by enabling On‑Policy SFT. We first present Distribution Discriminant Theory (DDT), which explains and quantifies the alignment between data and the model‑induced distribution. Leveraging DDT, we introduce two complementary techniques: (i) In‑Distribution Finetuning (IDFT), a loss‑level method to enhance generalization ability of SFT, and (ii) Hinted Decoding, a data‑level technique that can re‑align the training corpus to the model's distribution. Extensive experiments demonstrate that our framework achieves generalization performance surpassing prominent offline RL algorithms, including DPO and SimPO, while maintaining the efficiency of an SFT pipeline. The proposed framework thus offers a practical alternative in domains where RL is infeasible. We open‑source the code here: https://github.com/zhangmiaosen2000/Towards‑On‑Policy‑SFT

PaperId: 3070, https://arxiv.org/pdf/2602.12173.pdf GitHub

Abstract:
Vision‑language segmentation models such as SAM3 enable flexible, prompt‑driven visual grounding, but inherit large, general‑purpose text encoders originally designed for open‑ended language understanding. In practice, segmentation prompts are short, structured, and semantically constrained, leading to substantial over‑provisioning in text encoder capacity and persistent computational and memory overhead. In this paper, we perform a large‑scale anatomical analysis of text prompting in vision‑language segmentation, covering 404,796 real prompts across multiple benchmarks. Our analysis reveals severe redundancy: most context windows are underutilized, vocabulary usage is highly sparse, and text embeddings lie on low‑dimensional manifold despite high‑dimensional representations. Motivated by these findings, we propose SAM3‑LiteText, a lightweight text encoding framework that replaces the original SAM3 text encoder with a compact MobileCLIP student that is optimized by knowledge distillation. Extensive experiments on image and video segmentation benchmarks show that SAM3‑LiteText reduces text encoder parameters by up to 88%, substantially reducing static memory footprint, while maintaining segmentation performance comparable to the original model. Code: https://github.com/SimonZeng7108/efficientsam3/tree/sam3_litetext.

PaperId: 3071, https://arxiv.org/pdf/2602.12170.pdf GitHub

Abstract:
In previous work (Coppola, 2024) we introduced the Quantified Boolean Bayesian Network (QBBN), a logical graphical model that implements the forward fragment of natural deduction (Prawitz, 1965) as a probabilistic factor graph. That work left two gaps: no negation/backward reasoning, and no parser for natural language. This paper addresses both gaps across inference, semantics, and syntax. For inference, we extend the QBBN with NEG factors enforcing P(x) + P(neg x) = 1, enabling contrapositive reasoning (modus tollens) via backward lambda messages, completing Prawitz's simple elimination rules. The engine handles 44/44 test cases spanning 22 reasoning patterns. For semantics, we present a typed logical language with role‑labeled predicates, modal quantifiers, and three tiers of expressiveness following Prawitz: first‑order quantification, propositions as arguments, and predicate quantification via lambda abstraction. For syntax, we present a typed slot grammar that deterministically compiles sentences to logical form (33/33 correct, zero ambiguity). LLMs handle disambiguation (95% PP attachment accuracy) but cannot produce structured parses directly (12.4% UAS), confirming grammars are necessary. The architecture: LLM preprocesses, grammar parses, LLM reranks, QBBN infers. We argue this reconciles formal semantics with Sutton's "bitter lesson" (2019): LLMs eliminate the annotation bottleneck that killed formal NLP, serving as annotator while the QBBN serves as verifier. Code: https://github.com/gregorycoppola/world

PaperId: 3072, https://arxiv.org/pdf/2602.12164.pdf GitHub

Abstract:
Large language models (LLMs) have demonstrated exceptional reasoning capabilities, and co‑evolving paradigms have shown promising results in domains such as code and math. However, in scientific reasoning tasks, these models remain fragile due to unreliable solution evaluation and limited diversity in verification strategies. In this work, we propose Sci‑CoE, a two‑stage scientific co‑evolving framework that enables models to self‑evolve as both solver and verifier through a transition from sparse supervision to unsupervised learning. In the first stage, the model uses a small set of annotated data to establish fundamental correctness judgment anchors for the Verifier. In the second stage, we introduce a geometric reward mechanism that jointly considers consensus, reliability, and diversity, driving large‑scale self‑iteration on unlabeled data. Experiments on several general scientific benchmarks demonstrate that Sci‑CoE enhances complex reasoning capabilities and exhibits strong scalability, facilitating the construction of more robust and diverse evaluation systems. Codes are available at https://github.com/InternScience/Sci‑CoE.

PaperId: 3073, https://arxiv.org/pdf/2602.12159.pdf GitHub

Abstract:
Object navigation is a core capability of embodied intelligence, enabling an agent to locate target objects in unknown environments. Recent advances in vision‑language models (VLMs) have facilitated zero‑shot object navigation (ZSON). However, existing methods often rely on scene abstractions that convert environments into semantic maps or textual representations, causing high‑level decision making to be constrained by the accuracy of low‑level perception. In this work, we present 3DGSNav, a novel ZSON framework that embeds 3D Gaussian Splatting (3DGS) as persistent memory for VLMs to enhance spatial reasoning. Through active perception, 3DGSNav incrementally constructs a 3DGS representation of the environment, enabling trajectory‑guided free‑viewpoint rendering of frontier‑aware first‑person views. Moreover, we design structured visual prompts and integrate them with Chain‑of‑Thought (CoT) prompting to further improve VLM reasoning. During navigation, a real‑time object detector filters potential targets, while VLM‑driven active viewpoint switching performs target re‑verification, ensuring efficient and reliable recognition. Extensive evaluations across multiple benchmarks and real‑world experiments on a quadruped robot demonstrate that our method achieves robust and competitive performance against state‑of‑the‑art approaches.The Project Page:https://aczheng‑cai.github.io/3dgsnav.github.io/

PaperId: 3074, https://arxiv.org/pdf/2602.12153.pdf GitHub

Abstract:
Diffusion Large Language Models (dLLMs) represent a new paradigm beyond autoregressive modeling, offering competitive performance while naturally enabling a flexible decoding process. Specifically, dLLMs can generate tokens at arbitrary positions in parallel, endowing them with significant potential for parallel test‑time scaling, which was previously constrained by severe inefficiency in autoregressive modeling. In this work, we introduce dVoting, a fast voting technique that boosts reasoning capability without training, with only an acceptable extra computational overhead. dVoting is motivated by the observation that, across multiple samples for the same prompt, token predictions remain largely consistent, whereas performance is determined by a small subset of tokens exhibiting cross‑sample variability. Leveraging the arbitrary‑position generation capability of dLLMs, dVoting performs iterative refinement by sampling, identifying uncertain tokens via consistency analysis, regenerating them through voting, and repeating this process until convergence. Extensive evaluations demonstrate that dVoting consistently improves performance across various benchmarks. It achieves gains of 6.22%‑7.66% on GSM8K, 4.40%‑7.20% on MATH500, 3.16%‑14.84% on ARC‑C, and 4.83%‑5.74% on MMLU. Our code is available at https://github.com/fscdc/dVoting

PaperId: 3075, https://arxiv.org/pdf/2602.12143.pdf GitHub

Abstract:
As comprehensive large model evaluation becomes prohibitively expensive, predicting model performance from limited observations has become essential. However, existing statistical methods struggle with pattern shifts, data sparsity, and lack of explanation, while pure LLM methods remain unreliable. We propose STAR, a framework that bridges data‑driven STatistical expectations with knowledge‑driven Agentic Reasoning. STAR leverages specialized retrievers to gather external knowledge and embeds semantic features into Constrained Probabilistic Matrix Factorization (CPMF) to generate statistical expectations with uncertainty. A reasoning module guided by Expectation Violation Theory (EVT) then refines predictions through intra‑family analysis, cross‑model comparison, and credibility‑aware aggregation, producing adjustments with traceable explanations. Extensive experiments show that STAR consistently outperforms all baselines on both score‑based and rank‑based metrics, delivering a 14.46% gain in total score over the strongest statistical method under extreme sparsity, with only 1‑‑2 observed scores per test model.

PaperId: 3076, https://arxiv.org/pdf/2602.12117.pdf GitHub

Abstract:
Tropical cyclones (TC) are among the most destructive natural disasters, causing catastrophic damage to coastal regions through extreme winds, heavy rainfall, and storm surges. Timely monitoring of tropical cyclones is crucial for reducing loss of life and property, yet it is hindered by the computational inefficiency and high parameter counts of existing methods on resource‑constrained edge devices. Current physics‑guided models suffer from linear feature interactions that fail to capture high‑order polynomial relationships between TC attributes, leading to inflated model sizes and hardware incompatibility. To overcome these challenges, this study introduces the Kolmogorov‑Arnold Network‑based Feature Interaction Framework (KAN‑FIF), a lightweight multimodal architecture that integrates MLP and CNN layers with spline‑parameterized KAN layers. For Maximum Sustained Wind (MSW) prediction, experiments demonstrate that the KAN‑FIF framework achieves a 94.8% reduction in parameters (0.99MB vs 19MB) and 68.7% faster inference per sample (2.3ms vs 7.35ms) compared to baseline model Phy‑CoCo, while maintaining superior accuracy with 32.5% lower MAE. The offline deployment experiment of the FY‑4 series meteorological satellite processor on the Qingyun‑1000 development board achieved a 14.41ms per‑sample inference latency with the KAN‑FIF framework, demonstrating promising feasibility for operational TC monitoring and extending deployability to edge‑device AI applications. The code is released at https://github.com/Jinglin‑Zhang/KAN‑FIF.

PaperId: 3077, https://arxiv.org/pdf/2602.12113.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning tasks by employing test‑time scaling. However, they often generate over‑long chains‑of‑thought that, driven by substantial reflections such as repetitive self‑questioning and circular reasoning, lead to high token consumption, substantial computational overhead, and increased latency without improving accuracy, particularly in smaller models. Our observation reveals that increasing problem complexity induces more excessive and unnecessary reflection, which in turn reduces accuracy and increases token overhead. To address this challenge, we propose Adaptive Reflection and Length Coordinated Penalty (ARLCP), a novel reinforcement learning framework designed to dynamically balance reasoning efficiency and solution accuracy. ARLCP introduces two key innovations: (1) a reflection penalty that adaptively curtails unnecessary reflective steps while preserving essential reasoning, and (2) a length penalty calibrated to the estimated complexity of the problem. By coordinating these penalties, ARLCP encourages the model to generate more concise and effective reasoning paths. We evaluate our method on five mathematical reasoning benchmarks using DeepSeek‑R1‑Distill‑Qwen‑1.5B and DeepSeek‑R1‑Distill‑Qwen‑7B models. Experimental results show that ARLCP achieves a superior efficiency‑accuracy trade‑off compared to existing approaches. For the 1.5B model, it reduces the average response length by 53.1% while simultaneously improving accuracy by 5.8%. For the 7B model, it achieves a 35.0% reduction in length with a 2.7% accuracy gain. The code is released at https://github.com/ZeweiYu1/ARLCP .

PaperId: 3078, https://arxiv.org/pdf/2602.12096.pdf GitHub

Abstract:
Efficient motion planning for high‑dimensional robotic systems, such as manipulators and mobile manipulators, is critical for real‑time operation and reliable deployment. Although advances in planning algorithms have enhanced scalability to high‑dimensional state spaces, these improvements often come at the cost of generating unpredictable, inconsistent motions or requiring excessive computational resources and memory. In this work, we introduce Multi‑Graph Search (MGS), a search‑based motion planning algorithm that generalizes classical unidirectional and bidirectional search to a multi‑graph setting. MGS maintains and incrementally expands multiple implicit graphs over the state space, focusing exploration on high‑potential regions while allowing initially disconnected subgraphs to be merged through feasible transitions as the search progresses. We prove that MGS is complete and bounded‑suboptimal, and empirically demonstrate its effectiveness on a range of manipulation and mobile manipulation tasks. Demonstrations, benchmarks and code are available at https://multi‑graph‑search.github.io/.

PaperId: 3079, https://arxiv.org/pdf/2602.12056.pdf GitHub

Abstract:
Legal reasoning requires not only correct outcomes but also procedurally compliant reasoning processes. However, existing methods lack mechanisms to verify intermediate reasoning steps, allowing errors such as inapplicable statute citations to propagate undetected through the reasoning chain. To address this, we propose LawThinker, an autonomous legal research agent that adopts an Explore‑Verify‑Memorize strategy for dynamic judicial environments. The core idea is to enforce verification as an atomic operation after every knowledge exploration step. A DeepVerifier module examines each retrieval result along three dimensions of knowledge accuracy, fact‑law relevance, and procedural compliance, with a memory module for cross‑round knowledge reuse in long‑horizon tasks. Experiments on the dynamic benchmark J1‑EVAL show that LawThinker achieves a 24% improvement over direct reasoning and an 11% gain over workflow‑based methods, with particularly strong improvements on process‑oriented metrics. Evaluations on three static benchmarks further confirm its generalization capability. The code is available at https://github.com/yxy‑919/LawThinker‑agent .

PaperId: 3080, https://arxiv.org/pdf/2602.11978.pdf GitHub

Abstract:
Reinforcement Learning (RL) offers a powerful paradigm for autonomous robots to master generalist manipulation skills through trial‑and‑error. However, its real‑world application is stifled by low sample efficiency. Recent Human‑in‑the‑Loop (HIL) methods accelerate training by using human corrections, yet this approach faces a scalability barrier. Reliance on human supervisors imposes a 1:1 supervision ratio that limits scalability, suffers from operator fatigue over extended sessions, and introduces high variance due to inconsistent human proficiency. We present Agent‑guided Policy Search (AGPS), a framework that automates the training pipeline by replacing human supervisors with a multimodal agent. Our key insight is that the agent can be viewed as a semantic world model, injecting intrinsic value priors to structure physical exploration. By using tools, the agent provides precise guidance via corrective waypoints and spatial constraints for exploration pruning. We validate our approach on three tasks, ranging from precision insertion to deformable object manipulation. Results demonstrate that AGPS outperforms HIL methods in sample efficiency. This automates the supervision pipeline, unlocking the path to labor‑free and scalable robot learning. Project website: https://agps‑rl.github.io/agps/.

PaperId: 3081, https://arxiv.org/pdf/2602.11941.pdf GitHub

Abstract:
Multimodal Information Retrieval has made significant progress in recent years, leveraging the increasingly strong multimodal abilities of deep pre‑trained models to represent information across modalities. Music Information Retrieval (MIR), in particular, has considerably increased in quality, with neural representations of music even making its way into everyday life products. However, there is a lack of high‑quality benchmarks for evaluating music retrieval performance. To address this issue, we introduce IncompeBench, a carefully annotated benchmark comprising 1,574 permissively licensed, high‑quality music snippets, 500 diverse queries, and over 125,000 individual relevance judgements. These annotations were created through the use of a multi‑stage pipeline, resulting in high agreement between human annotators and the generated data. The resulting datasets are publicly available at https://huggingface.co/datasets/mixedbread‑ai/incompebench‑strict and https://huggingface.co/datasets/mixedbread‑ai/incompebench‑lenient with the prompts available at https://github.com/mixedbread‑ai/incompebench‑programs.

PaperId: 3082, https://arxiv.org/pdf/2602.11931.pdf GitHub

Abstract:
Evolutionary agentic systems intensify the trade‑off between computational efficiency and reasoning capability by repeatedly invoking large language models (LLMs) during inference. This setting raises a central question: how can an agent dynamically select an LLM that is sufficiently capable for the current generation step while remaining computationally efficient? While model cascades offer a practical mechanism for balancing this trade‑off, existing routing strategies typically rely on static heuristics or external controllers and do not explicitly account for model uncertainty. We introduce AdaptEvolve: Adaptive LLM Selection for Multi‑LLM Evolutionary Refinement within an evolutionary sequential refinement framework that leverages intrinsic generation confidence to estimate real‑time solvability. Empirical results show that confidence‑driven selection yields a favourable Pareto frontier, reducing total inference cost by an average of 37.9% across benchmarks while retaining 97.5% of the upper‑bound accuracy of static large‑model baselines. Our code is available at https://github.com/raypretam/adaptive_llm_selection.

PaperId: 3083, https://arxiv.org/pdf/2602.11918.pdf GitHub

Abstract:
LLMs have demonstrated significant potential in quantitative finance by processing vast unstructured data to emulate human‑like analytical workflows. However, current LLM‑based methods primarily follow either an Asset‑Centric paradigm focused on individual stock prediction or a Market‑Centric approach for portfolio allocation, often remaining agnostic to the underlying reasoning that drives market movements. In this paper, we propose a Logic‑Oriented perspective, modeling the financial market as a dynamic, evolutionary ecosystem of competing investment narratives, termed Modes of Thought. To operationalize this view, we introduce MEME (Modeling the Evolutionary Modes of Financial Markets), designed to reconstruct market dynamics through the lens of evolving logics. MEME employs a multi‑agent extraction module to transform noisy data into high‑fidelity Investment Arguments and utilizes Gaussian Mixture Modeling to uncover latent consensus within a semantic space. To model semantic drift among different market conditions, we also implement a temporal evaluation and alignment mechanism to track the lifecycle and historical profitability of these modes. By prioritizing enduring market wisdom over transient anomalies, MEME ensures that portfolio construction is guided by robust reasoning. Extensive experiments on three heterogeneous Chinese stock pools from 2023 to 2025 demonstrate that MEME consistently outperforms seven SOTA baselines. Further ablation studies, sensitivity analysis, lifecycle case study and cost analysis validate MEME's capacity to identify and adapt to the evolving consensus of financial markets. Our implementation can be found at https://github.com/gta0804/MEME.

PaperId: 3084, https://arxiv.org/pdf/2602.11917.pdf GitHub

Abstract:
Extracting signals through alpha factor mining is a fundamental challenge in quantitative finance. Existing automated methods primarily follow two paradigms: Decoupled Factor Generation, which treats factor discovery as isolated events, and Iterative Factor Evolution, which focuses on local parent‑child refinements. However, both paradigms lack a global structural view, often treating factor pools as unstructured collections or fragmented chains, which leads to redundant search and limited diversity. To address these limitations, we introduce AlphaPROBE (Alpha Mining via Principled Retrieval and On‑graph Biased Evolution), a framework that reframes alpha mining as the strategic navigation of a Directed Acyclic Graph (DAG). By modeling factors as nodes and evolutionary links as edges, AlphaPROBE treats the factor pool as a dynamic, interconnected ecosystem. The framework consists of two core components: a Bayesian Factor Retriever that identifies high‑potential seeds by balancing exploitation and exploration through a posterior probability model, and a DAG‑aware Factor Generator that leverages the full ancestral trace of factors to produce context‑aware, nonredundant optimizations. Extensive experiments on three major Chinese stock market datasets against 8 competitive baselines demonstrate that AlphaPROBE significantly gains enhanced performance in predictive accuracy, return stability and training efficiency. Our results confirm that leveraging global evolutionary topology is essential for efficient and robust automated alpha discovery. We have open‑sourced our implementation at https://github.com/gta0804/AlphaPROBE.

PaperId: 3085, https://arxiv.org/pdf/2602.11882.pdf GitHub

Abstract:
Efficient spatial reasoning requires world models that remain reliable under tight precision budgets. We study whether low‑bit planning behavior is determined mostly by total bitwidth or by where bits are allocated across modules. Using DINO‑WM on the Wall planning task, we run a paired‑goal mixed‑bit evaluation across uniform, mixed, asymmetric, and layerwise variants under two planner budgets. We observe a consistent three‑regime pattern: 8‑bit and 6‑bit settings remain close to FP16, 3‑bit settings collapse, and 4‑bit settings are allocation‑sensitive. In that transition region, preserving encoder precision improves planning relative to uniform quantization, and near‑size asymmetric variants show the same encoder‑side direction. In a later strict 22‑cell replication with smaller per‑cell episode count, the mixed‑versus‑uniform INT4 sign becomes budget‑conditioned, which further highlights the sensitivity of this transition regime. These findings motivate module‑aware, budget‑aware quantization policies as a broader research direction for efficient spatial reasoning. Code and run artifacts are available at https://github.com/suraj‑ranganath/DINO‑MBQuant.

PaperId: 3086, https://arxiv.org/pdf/2602.11877.pdf GitHub

Abstract:
Large language models (LLMs) have achieved success, but cost and privacy constraints necessitate deploying smaller models locally while offloading complex queries to cloud‑based models. Existing router evaluations are unsystematic, overlooking scenario‑specific requirements and out‑of‑distribution robustness. We propose RouterXBench, a principled evaluation framework with three dimensions: router ability, scenario alignment, and cross‑domain robustness. Unlike prior work that relies on output probabilities or external embeddings, we utilize internal hidden states that capture model uncertainty before answer generation. We introduce ProbeDirichlet, a lightweight router that aggregates cross‑layer hidden states via learnable Dirichlet distributions with probabilistic training. Trained on multi‑domain data, it generalizes robustly across in‑domain and out‑of‑distribution scenarios. Our results show ProbeDirichlet achieves 16.68% and 18.86% relative improvements over the best baselines in router ability and high‑accuracy scenarios, with consistent performance across model families, model scales, heterogeneous tasks, and agentic workflows.

PaperId: 3087, https://arxiv.org/pdf/2602.11858.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine‑grained perception, where decisive evidence is small and easily overwhelmed by global context. Recent "Thinking‑with‑Images" methods alleviate this by iteratively zooming in and out regions of interest during inference, but incur high latency due to repeated tool calls and visual re‑encoding. To address this, we propose Region‑to‑Image Distillation, which transforms zooming from an inference‑time tool into a training‑time primitive, thereby internalizing the benefits of agentic zooming into a single forward pass of an MLLM. In particular, we first zoom in to micro‑cropped regions to let strong teacher models generate high‑quality VQA data, and then distill this region‑grounded supervision back to the full image. After training on such data, the smaller student model improves "single‑glance" fine‑grained perception without tool use. To rigorously evaluate this capability, we further present ZoomBench, a hybrid‑annotated benchmark of 845 VQA data spanning six fine‑grained perceptual dimensions, together with a dual‑view protocol that quantifies the global‑‑regional "zooming gap". Experiments show that our models achieve leading performance across multiple fine‑grained perception benchmarks, and also improve general multimodal cognition on benchmarks such as visual reasoning and GUI agents. We further discuss when "Thinking‑with‑Images" is necessary versus when its gains can be distilled into a single forward pass. Our code is available at https://github.com/inclusionAI/Zooming‑without‑Zooming.

PaperId: 3088, https://arxiv.org/pdf/2602.11693.pdf GitHub

Abstract:
Creating high‑fidelity, animatable 3D avatars from a single image remains a formidable challenge. We identified three desirable attributes of avatar generation: 1) the method should be feed‑forward, 2) model a 360° full‑head, and 3) should be animation‑ready. However, current work addresses only two of the three points simultaneously. To address these limitations, we propose OMEGA‑Avatar, the first feed‑forward framework that simultaneously generates a generalizable, 360°‑complete, and animatable 3D Gaussian head from a single image. Starting from a feed‑forward and animatable framework, we address the 360° full‑head avatar generation problem with two novel components. First, to overcome poor hair modeling in full‑head avatar generation, we introduce a semantic‑aware mesh deformation module that integrates multi‑view normals to optimize a FLAME head with hair while preserving its topology structure. Second, to enable effective feed‑forward decoding of full‑head features, we propose a multi‑view feature splatting module that constructs a shared canonical UV representation from features across multiple views through differentiable bilinear splatting, hierarchical UV mapping, and visibility‑aware fusion. This approach preserves both global structural coherence and local high‑frequency details across all viewpoints, ensuring 360° consistency without per‑instance optimization. Extensive experiments demonstrate that OMEGA‑Avatar achieves state‑of‑the‑art performance, significantly outperforming existing baselines in 360° full‑head completeness while robustly preserving identity across different viewpoints.

PaperId: 3089, https://arxiv.org/pdf/2602.11684.pdf GitHub

Abstract:
As Large Language Models increasingly power role‑playing applications, simulating patients has become a valuable tool for training counselors and scaling therapeutic assessment. However, prior work is fragmented: existing approaches rely on incompatible, non‑standardized data formats, prompts, and evaluation metrics, hindering reproducibility and fair comparison. In this paper, we introduce PatientHub, a unified and modular framework that standardizes the definition, composition, and deployment of simulated patients. To demonstrate PatientHub's utility, we implement several representative patient simulation methods as case studies, showcasing how our framework supports standardized cross‑method evaluation and the seamless integration of custom evaluation metrics. We further demonstrate PatientHub's extensibility by prototyping two new simulator variants, highlighting how PatientHub accelerates method development by eliminating infrastructure overhead. By consolidating existing work into a single reproducible pipeline, PatientHub lowers the barrier to developing new simulation methods and facilitates cross‑method and cross‑model benchmarking. Our framework provides a practical foundation for future datasets, methods, and benchmarks in patient‑centered dialogue, and the code is publicly available via https://github.com/Sahandfer/PatientHub.

PaperId: 3090, https://arxiv.org/pdf/2602.11678.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual understanding, yet they suffer from a critical limitation: structural blindness. Even state‑of‑the‑art models fail to capture topology and symbolic logic in engineering schematics, as their pixel‑driven paradigm discards the explicit vector‑defined relations needed for reasoning. To overcome this, we propose a Vector‑to‑Graph (V2G) pipeline that converts CAD diagrams into property graphs where nodes represent components and edges encode connectivity, making structural dependencies explicit and machine‑auditable. On a diagnostic benchmark of electrical compliance checks, V2G yields large accuracy gains across all error categories, while leading MLLMs remain near chance level. These results highlight the systemic inadequacy of pixel‑based methods and demonstrate that structure‑aware representations provide a reliable path toward practical deployment of multimodal AI in engineering domains. To facilitate further research, we release our benchmark and implementation at https://github.com/gm‑embodied/V2G‑Audit.

PaperId: 3091, https://arxiv.org/pdf/2602.11674.pdf GitHub

Abstract:
Large Language Models (LLMs) are advancing rapidly, yet the benchmarks used to measure this progress are becoming increasingly unreliable. Score inflation and selective reporting have eroded the authority of standard benchmarks, leaving the community uncertain about which evaluation results remain trustworthy. We introduce the Benchmark Health Index (BHI), a pure data‑driven framework for auditing evaluation sets along three orthogonal and complementary axes: (1) Capability Discrimination, measuring how sharply a benchmark separates model performance beyond noise; (2) Anti‑Saturation, estimating remaining headroom before ceiling effects erode resolution and thus the benchmark's expected longevity; and (3) Impact, quantifying influence across academic and industrial ecosystems via adoption breadth and practice‑shaping power. By distilling 106 validated benchmarks from the technical reports of 91 representative models in 2025, we systematically characterize the evaluation landscape. BHI is the first framework to quantify benchmark health at a macro level, providing a principled basis for benchmark selection and enabling dynamic lifecycle management for next‑generation evaluation protocols.

PaperId: 3092, https://arxiv.org/pdf/2602.11643.pdf GitHub

Abstract:
Tactile information plays a crucial role in human manipulation tasks and has recently garnered increasing attention in robotic manipulation. However, existing approaches mostly focus on the alignment of visual and tactile features and the integration mechanism tends to be direct concatenation. Consequently, they struggle to effectively cope with occluded scenarios due to neglecting the inherent complementary nature of both modalities and the alignment may not be exploited enough, limiting the potential of their real‑world deployment. In this paper, we present ViTaS, a simple yet effective framework that incorporates both visual and tactile information to guide the behavior of an agent. We introduce Soft Fusion Contrastive Learning, an advanced version of conventional contrastive learning method and a CVAE module to utilize the alignment and complementarity within visuo‑tactile representations. We demonstrate the effectiveness of our method in 12 simulated and 3 real‑world environments, and our experiments show that ViTaS significantly outperforms existing baselines. Project page: https://skyrainwind.github.io/ViTaS/index.html.

PaperId: 3093, https://arxiv.org/pdf/2602.11636.pdf GitHub GitHub GitHub

Abstract:
Large‑scale Visual Instruction Tuning (VIT) has become a key paradigm for advancing the performance of vision‑language models (VLMs) across various multimodal tasks. However, training on the large‑scale datasets is computationally expensive and inefficient due to redundancy in the data, which motivates the need for multimodal data selection to improve training efficiency. Existing data selection methods for VIT either require costly training or gradient computation. Training‑free alternatives often depend on proxy models or datasets, instruction‑agnostic representations, and pairwise similarity with quadratic complexity, limiting scalability and representation fidelity. In this work, we propose ScalSelect, a scalable training‑free multimodal data selection method with linear‑time complexity with respect to the number of samples, eliminating the need for external models or auxiliary datasets. ScalSelect first constructs sample representations by extracting visual features most attended by instruction tokens in the target VLM, capturing instruction‑relevant information. It then identifies samples whose representations best approximate the dominant subspace of the full dataset representations, enabling scalable importance scoring without pairwise comparisons. Extensive experiments across multiple VLMs, datasets, and selection budgets demonstrate that ScalSelect achieves over 97.5% of the performance of training on the full dataset using only 16% of the data, and even outperforms full‑data training in some settings. The code is available at \hrefhttps://github.com/ChangtiWu/ScalSelectScalSelect.

PaperId: 3094, https://arxiv.org/pdf/2602.11635.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have achieved strong performance on perception‑oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the capacity to parse and manipulate two‑ and three‑dimensional relations, remains unclear. Humans easily solve textbook‑style spatial reasoning problems with over 95% accuracy, but we find that most leading MLLMs fail to reach even 60% on the same tasks. This striking gap highlights spatial reasoning as a fundamental weakness of current models. To investigate this gap, we present \emphMathSpatial, the first large‑scale and systematic dataset resource dedicated to mathematical spatial reasoning in MLLMs. \emphMathSpatial provides two complementary subsets: (i)~\emphMathSpatial‑Bench, a rigorously curated evaluation set of 2,000 problems spanning 3 categories and 11 subtypes, designed to isolate spatial reasoning from perceptual noise; and (ii)~\emphMathSpatial‑Corpus, a training set of 8,000 problems equipped with verified solutions and structured reasoning traces. All problems are sourced from authentic educational materials and undergo multi‑stage quality control including deduplication, geometric consistency checking, and cross‑validated solution verification. Benchmarking 16 leading MLLMs on \emphMathSpatial‑Bench reveals that spatial reasoning remains a fundamental bottleneck: even GPT‑5 lags behind human performance by over 35 percentage points, with particularly poor results on abstract deduction tasks. We further show that training on \emphMathSpatial‑Corpus yields consistent improvements across model families, demonstrating the dataset's practical value for advancing spatial reasoning capabilities. \emphMathSpatial is publicly available at https://shuolucs.github.io/MathSpatial.

PaperId: 3095, https://arxiv.org/pdf/2602.11609.pdf GitHub

Abstract:
We present scPilot, the first systematic framework to practice omics‑native reasoning: a large language model (LLM) converses in natural language while directly inspecting single‑cell RNA‑seq data and on‑demand bioinformatics tools. scPilot converts core single‑cell analyses, i.e., cell‑type annotation, developmental‑trajectory reconstruction, and transcription‑factor targeting, into step‑by‑step reasoning problems that the model must solve, justify, and, when needed, revise with new evidence. To measure progress, we release scBench, a suite of 9 expertly curated datasets and graders that faithfully evaluate the omics‑native reasoning capability of scPilot w.r.t various LLMs. Experiments with o1 show that iterative omics‑native reasoning lifts average accuracy by 11% for cell‑type annotation and Gemini‑2.5‑Pro cuts trajectory graph‑edit distance by 30% versus one‑shot prompting, while generating transparent reasoning traces explain marker gene ambiguity and regulatory logic. By grounding LLMs in raw omics data, scPilot enables auditable, interpretable, and diagnostically informative single‑cell analyses. Code, data, and package are available at https://github.com/maitrix‑org/scPilot

PaperId: 3096, https://arxiv.org/pdf/2602.11598.pdf GitHub

Authors:Zedong Chu, Shichao Xie, Xiaolong Wu, Yanfen Shen, Minghua Luo, Zhengbo Wang, Fei Liu, Xiaoxu Leng, Junjun Hu, Mingyang Yin, Jia Lu, Yingnan Guo, Kai Yang, Jiawei Han, Xu Chen, Yanqing Zhu, Yuxiang Zhao, Xin Liu, Yirong Yang, Ye He, Jiahang Wang, Yang Cai, Tianlin Zhang, Li Gao, Liu Liu, Mingchao Sun, Fan Jiang, Chiyu Wang, Zhicheng Liu, Hongyu Pan, Honglin Han, Zhining Gu, Kuan Yang, Jianfang Zhang, Di Jing, Zihao Guan, Wei Guo, Guoqing Liu, Di Yang, Xiangpo Yang, Menglin Yang, Hongguang Xing, Weiguo Li, Mu Xu

Abstract:
Embodied navigation has long been fragmented by task‑specific architectures. We introduce ABot‑N0, a unified Vision‑Language‑Action (VLA) foundation model that achieves a ``Grand Unification'' across 5 core tasks: Point‑Goal, Object‑Goal, Instruction‑Following, POI‑Goal, and Person‑Following. ABot‑N0 utilizes a hierarchical ``Brain‑Action'' architecture, pairing an LLM‑based Cognitive Brain for semantic reasoning with a Flow Matching‑based Action Expert for precise, continuous trajectory generation. To support large‑scale learning, we developed the ABot‑N0 Data Engine, curating 16.9M expert trajectories and 5.0M reasoning samples across 7,802 high‑fidelity 3D scenes (10.7 \textkm^2). ABot‑N0 achieves new SOTA performance across 7 benchmarks, significantly outperforming specialized models. Furthermore, our Agentic Navigation System integrates a planner with hierarchical topological memory, enabling robust, long‑horizon missions in dynamic real‑world environments.

PaperId: 3097, https://arxiv.org/pdf/2602.11575.pdf GitHub GitHub

Abstract:
Visual navigation models often struggle in real‑world dynamic environments due to limited robustness to the sim‑to‑real gap and the difficulty of training policies tailored to target deployment environments (e.g., households, restaurants, and factories). Although real‑to‑sim navigation simulation using 3D Gaussian Splatting (GS) can mitigate these challenges, prior GS‑based works have considered only static scenes or non‑photorealistic human obstacles built from simulator assets, despite the importance of safe navigation in dynamic environments. To address these issues, we propose ReaDy‑Go, a novel real‑to‑sim simulation pipeline that synthesizes photorealistic dynamic scenarios in target environments by augmenting a reconstructed static GS scene with dynamic human GS obstacles, and trains navigation policies using the generated datasets. The pipeline provides three key contributions: (1) a dynamic GS simulator that integrates static scene GS with a human animation module, enabling the insertion of animatable human GS avatars and the synthesis of plausible human motions from 2D trajectories, (2) a navigation dataset generation framework that leverages the simulator along with a robot expert planner designed for dynamic GS representations and a human planner, and (3) robust navigation policies to both the sim‑to‑real gap and moving obstacles. The proposed simulator generates thousands of photorealistic navigation scenarios with animatable human GS avatars from arbitrary viewpoints. ReaDy‑Go outperforms baselines across target environments in both simulation and real‑world experiments, demonstrating improved navigation performance even after sim‑to‑real transfer and in the presence of moving obstacles. Moreover, zero‑shot sim‑to‑real deployment in an unseen environment indicates its generalization potential. Project page: https://syeon‑yoo.github.io/ready‑go‑site/.

PaperId: 3098, https://arxiv.org/pdf/2602.11549.pdf GitHub

Abstract:
The prevailing paradigm for training large reasoning models‑‑combining Supervised Fine‑Tuning (SFT) with Reinforcement Learning with Verifiable Rewards (RLVR)‑‑is fundamentally constrained by its reliance on high‑quality, human‑annotated reasoning data and external verifiers. This dependency incurs significant data‑collection costs, risks embedding human cognitive biases, and confines the reinforcement learning stage to objectively assessable domains like mathematics and coding, leaving a wide range of unverifiable tasks beyond its scope. To overcome these limitations, we introduce NRT (Native Reasoning Training), a novel framework that cultivates complex reasoning by having the model generate its own reasoning traces using only standard question‑answer pairs, thereby obviating the need for expert‑written demonstrations. NRT reframes the training problem by treating the reasoning process as a latent variable. It employs a unified training objective that models reasoning as an optimization problem, intrinsically rewarding paths that increase the model's likelihood of producing the ground‑truth answer. This unified perspective allows us to analyze intrinsic failure modes of prior methods, such as policy collapse, and systematically design more robust reward aggregation functions, creating a self‑reinforcing feedback loop where the model learns to think in ways that resolve its own uncertainty. Empirical evaluation on Llama and Mistral model families demonstrates that NRT achieves state‑of‑the‑art performance among verifier‑free methods, significantly outperforming standard SFT baselines and prior verifier‑free RL methods. Our approach yields particularly strong performance gains in complex reasoning domains and exhibits high robustness to policy collapse, offering a general, scalable path toward building more powerful and broadly applicable reasoning systems.

PaperId: 3099, https://arxiv.org/pdf/2602.11534.pdf GitHub

Abstract:
Self‑attention in Transformers relies on globally normalized softmax weights, causing all tokens to compete for influence at every layer. When composed across depth, this interaction pattern induces strong synchronization dynamics that favor convergence toward a dominant mode, a behavior associated with representation collapse and attention sink phenomena. We introduce Krause Attention, a principled attention mechanism inspired by bounded‑confidence consensus dynamics. Krause Attention replaces similarity‑based global aggregation with distance‑based, localized, and selectively sparse interactions, promoting structured local synchronization instead of global mixing. We relate this behavior to recent theory modeling Transformer dynamics as interacting particle systems, and show how bounded‑confidence interactions naturally moderate attention concentration and alleviate attention sinks. Restricting interactions to local neighborhoods also reduces runtime complexity from quadratic to linear in sequence length. Empirically, we validate Krause Attention across diverse settings, including vision (ViT on CIFAR/ImageNet), autoregressive image generation (MNIST/CIFAR‑10), large language models (Llama/Qwen), and language models trained from scratch at multiple scales (100M/200M). Across these domains, Krause Attention achieves consistent performance gains while improving computational efficiency, highlighting bounded‑confidence dynamics as a scalable and effective inductive bias for attention.

PaperId: 3100, https://arxiv.org/pdf/2602.11528.pdf GitHub

Abstract:
Recent studies have shown that large language models (LLMs) can infer private user attributes (e.g., age, location, gender) from user‑generated text shared online, enabling rapid and large‑scale privacy breaches. Existing anonymization‑based defenses are coarse‑grained, lacking word‑level precision in anonymizing privacy‑leaking elements. Moreover, they are inherently limited as altering user text to hide sensitive cues still allows attribute inference to occur through models' reasoning capabilities. To address these limitations, we propose a unified defense framework that combines fine‑grained anonymization (TRACE) with inference‑preventing optimization (RPS). TRACE leverages attention mechanisms and inference chain generation to identify and anonymize privacy‑leaking textual elements, while RPS employs a lightweight two‑stage optimization strategy to induce model rejection behaviors, thereby preventing attribute inference. Evaluations across diverse LLMs show that TRACE‑RPS reduces attribute inference accuracy from around 50% to below 5% on open‑source models. In addition, our approach offers strong cross‑model generalization, prompt‑variation robustness, and utility‑privacy tradeoffs. Our code is available at https://github.com/Jasper‑Yan/TRACE‑RPS.

PaperId: 3101, https://arxiv.org/pdf/2602.11510.pdf GitHub

Abstract:
Multi‑agent Large Language Model (LLM) systems create privacy risks that current benchmarks cannot measure. When agents coordinate on tasks, sensitive data passes through inter‑agent messages, shared memory, and tool arguments, all pathways that output‑only audits never inspect. We introduce AgentLeak, to the best of our knowledge the first full‑stack benchmark for privacy leakage covering internal channels. It spans 1,000 scenarios across healthcare, finance, legal, and corporate domains, paired with a 32‑class attack taxonomy and a three‑tier detection pipeline. A factorial evaluation crossing five production LLMs (GPT‑4o, GPT‑4o‑mini, Claude 3.5 Sonnet, Mistral Large, and Llama 3.3 70B) with all 1,000 scenarios, yielding 4,979 validated execution traces, reveals that multi‑agent configurations reduce per‑channel output leakage (C1: 27.2% vs 43.2% in single‑agent) but introduce unmonitored internal channels that raise total system exposure to 68.9% (aggregated across C1, C2, C5). Internal channels account for most of this gap: inter‑agent messages (C2) leak at 68.8%, compared to 27.2% on C1 (output channel). This means that output‑only audits miss 41.7% of violations. Safety‑aligned models achieve lower leakage on both external and internal channels, yet no model eliminates it. Across all five models and four domains, the pattern C2 \geq C1 holds consistently, confirming that inter‑agent communication is the primary vulnerability. These results establish that output‑only auditing is fundamentally insufficient for multi‑agent systems and that privacy controls must be extended to inter‑agent communication channels.

PaperId: 3102, https://arxiv.org/pdf/2602.11509.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) are increasingly used for real‑world tasks involving multi‑step reasoning and long‑form generation, where reliability requires grounding model outputs in heterogeneous input sources and verifying individual factual claims. However, existing multimodal grounding benchmarks and evaluation methods focus on simplified, observation‑based scenarios or limited modalities and fail to assess attribution in complex multimodal reasoning. We introduce MuRGAt (Multimodal Reasoning with Grounded Attribution), a benchmark for evaluating fact‑level multimodal attribution in settings that require reasoning beyond direct observation. Given inputs spanning video, audio, and other modalities, MuRGAt requires models to generate answers with explicit reasoning and precise citations, where each citation specifies both modality and temporal segments. To enable reliable assessment, we introduce an automatic evaluation framework that strongly correlates with human judgments. Benchmarking with human and automated scores reveals that even strong MLLMs frequently hallucinate citations despite correct reasoning. Moreover, we observe a key trade‑off: increasing reasoning depth or enforcing structured grounding often degrades accuracy, highlighting a significant gap between internal reasoning and verifiable attribution.

PaperId: 3103, https://arxiv.org/pdf/2602.11446.pdf GitHub GitHub

Abstract:
Portable, ultra‑low‑field (ULF) magnetic resonance imaging has the potential to expand access to neuroimaging but currently suffers from coarse spatial and angular resolutions and low signal‑to‑noise ratios. Diffusion tensor imaging (DTI), a sequence tailored to detect and reconstruct white matter tracts within the brain, is particularly prone to such imaging degradation due to inherent sequence design coupled with prolonged scan times. In addition, ULF DTI scans exhibit artifacting that spans both the space and angular domains, requiring a custom modelling algorithm for subsequent correction. We introduce a nine‑direction, single‑shell ULF DTI sequence, as well as a companion Bayesian bias field correction algorithm that possesses angular dependence and convolutional neural network‑based superresolution algorithm that is generalizable across DTI datasets and does not require re‑training (''DiffSR''). We show through a synthetic downsampling experiment and white matter assessment in real, matched ULF and high‑field DTI scans that these algorithms can recover microstructural and volumetric white matter information at ULF. We also show that DiffSR can be directly applied to white matter‑based Alzheimers disease classification in synthetically degraded scans, with notable improvements in agreement between DTI metrics, as compared to un‑degraded scans. We freely disseminate the Bayesian bias correction algorithm and DiffSR with the goal of furthering progress on both ULF reconstruction methods and general DTI sequence harmonization. We release all code related to DiffSR for \hrefhttps://github.com/markolchanyi/DiffSRpublic \space use.

PaperId: 3104, https://arxiv.org/pdf/2602.11437.pdf GitHub

Abstract:
Cooperative multi‑agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution, where value‑factorization methods enforce the individual‑global‑maximum (IGM) principle so that decentralized greedy actions recover the team‑optimal joint action. However, the reliability of this recipe in real‑world settings remains unreliable due to environmental uncertainties arising from the sim‑to‑real gap, model mismatch, and system noise. We address this gap by introducing Distributionally robust IGM (DrIGM), a principle that requires each agent's robust greedy action to align with the robust team‑optimal joint action. We show that DrIGM holds for a novel definition of robust individual action values, which is compatible with decentralized greedy execution and yields a provable robustness guarantee for the whole system. Building on this foundation, we derive DrIGM‑compliant robust variants of existing value‑factorization architectures (e.g., VDN/QMIX/QTRAN) that (i) train on robust Q‑targets, (ii) preserve scalability, and (iii) integrate seamlessly with existing codebases without bespoke per‑agent reward shaping. Empirically, on high‑fidelity SustainGym simulators and a StarCraft game environment, our methods consistently improve out‑of‑distribution performance. Code and data are available at https://github.com/crqu/robust‑coMARL.

PaperId: 3105, https://arxiv.org/pdf/2602.11409.pdf GitHub

Abstract:
Estimating uncertainty for AI agents in real‑world multi‑turn tool‑using interaction with humans is difficult because failures are often triggered by sparse critical episodes (e.g., looping, incoherent tool use, or user‑agent miscoordination) even when local generation appears confident. Existing uncertainty proxies focus on single‑shot text generation and therefore miss these trajectory‑level breakdown signals. We introduce TRACER, a trajectory‑level uncertainty metric for dual‑control Tool‑Agent‑User interaction. TRACER combines content‑aware surprisal with situational‑awareness signals, semantic and lexical repetition, and tool‑grounded coherence gaps, and aggregates them using a tail‑focused risk functional with a MAX‑composite step risk to surface decisive anomalies. We evaluate TRACER on τ^2‑bench by predicting task failure and selective task execution. To this end, TRACER improves AUROC by up to 37.1% and AUARC by up to 55% over baselines, enabling earlier and more accurate detection of uncertainty in complex conversational tool‑use settings. Our code and benchmark are available at https://github.com/sinatayebati/agent‑tracer.

PaperId: 3106, https://arxiv.org/pdf/2602.11399.pdf GitHub

Abstract:
As machine learning has moved towards leveraging large models as priors for downstream tasks, the community has debated the right form of prior for solving reinforcement learning (RL) problems. If one were to try to prefetch as much computation as possible, they would attempt to learn a prior over the policies for some yet‑to‑be‑determined reward function. Recent work (forward‑backward (FB) representation learning) has tried this, arguing that an unsupervised representation learning procedure can enable optimal control over arbitrary rewards without further fine‑tuning. However, FB's training objective and learning behavior remain mysterious. In this paper, we demystify FB by clarifying when such representations can exist, what its objective optimizes, and how it converges in practice. We draw connections with rank matching, fitted Q‑evaluation, and contraction mapping. Our analysis suggests a simplified unsupervised pre‑training method for RL that, instead of enabling optimal control, performs one step of policy improvement. We call our proposed method one‑step forward‑backward representation learning (one‑step FB). Experiments in didactic settings, as well as in 10 state‑based and image‑based continuous control domains, demonstrate that one‑step FB converges to errors 10^5 smaller and improves zero‑shot performance by +24% on average. Our project website is available at https://chongyi‑zheng.github.io/onestep‑fb.

PaperId: 3107, https://arxiv.org/pdf/2602.11389.pdf GitHub GitHub

Abstract:
World models require robust relational understanding to support prediction, reasoning, and control. While object‑centric representations provide a useful abstraction, they are not sufficient to capture interaction‑dependent dynamics. We therefore propose C‑JEPA, a simple and flexible object‑centric world model that extends masked joint embedding prediction from image patches to object‑centric representations. By masking object‑level latents and requiring each masked object state to be inferred from the surrounding context, C‑JEPA imposes structured partial observability during training, creating counterfactual‑like prediction queries that discourage shortcut solutions and make interaction‑dependent prediction necessary under the learning objective. Empirically, C‑JEPA leads to consistent gains in visual question answering, with an absolute improvement of about 20% in counterfactual reasoning over the same architecture without object‑level masking. On agent control tasks, C‑JEPA enables substantially more efficient planning by using only 1% of the total latent input features required by patch‑based world models, while achieving comparable performance. Finally, we provide a formal analysis demonstrating that object‑level masking induces useful inductive bias by controlling observability. Our code is available at https://github.com/galilai‑group/cjepa.

PaperId: 3108, https://arxiv.org/pdf/2602.11358.pdf GitHub

Abstract:
Large language models produce rich introspective language when prompted for self‑examination, but whether this language reflects internal computation or sophisticated confabulation has remained unclear. We show that self‑referential vocabulary tracks concurrent activation dynamics, and that this correspondence is specific to self‑referential processing. We introduce the Pull Methodology, a protocol that elicits extended self‑examination through format engineering, and use it to identify a direction in activation space that distinguishes self‑referential from descriptive processing in Llama 3.1. The direction is orthogonal to the known refusal direction, localised at 6.25% of model depth, and causally influences introspective output when used for steering. When models produce "loop" vocabulary, their activations exhibit higher autocorrelation (r = 0.44, p = 0.002); when they produce "shimmer" vocabulary under steering, activation variability increases (r = 0.36, p = 0.002). Critically, the same vocabulary in non‑self‑referential contexts shows no activation correspondence despite nine‑fold higher frequency. Qwen 2.5‑32B, with no shared training, independently develops different introspective vocabulary tracking different activation metrics, all absent in descriptive controls. The findings indicate that self‑report in transformer models can, under appropriate conditions, reliably track internal computational states.

PaperId: 3109, https://arxiv.org/pdf/2602.11354.pdf GitHub

Abstract:
The literature has witnessed an emerging interest in AI agents for automated assessment of scientific papers. Existing benchmarks focus primarily on the computational aspect of this task, testing agents' ability to reproduce or replicate research outcomes when having access to the code and data. This setting, while foundational, (1) fails to capture the inconsistent availability of new data for replication as opposed to reproduction, and (2) lacks ground‑truth diversity by focusing only on reproducible papers, thereby failing to evaluate an agent's ability to identify non‑replicable research. Furthermore, most benchmarks only evaluate outcomes rather than the replication process. In response, we introduce ReplicatorBench, an end‑to‑end benchmark, including human‑verified replicable and non‑replicable research claims in social and behavioral sciences for evaluating AI agents in research replication across three stages: (1) extraction and retrieval of replication data; (2) design and execution of computational experiments; and (3) interpretation of results, allowing a test of AI agents' capability to mimic the activities of human replicators in real world. To set a baseline of AI agents' capability, we develop ReplicatorAgent, an agentic framework equipped with necessary tools, like web search and iterative interaction with sandboxed environments, to accomplish tasks in ReplicatorBench. We evaluate ReplicatorAgent across four underlying large language models (LLMs), as well as different design choices of programming language and levels of code access. Our findings reveal that while current LLM agents are capable of effectively designing and executing computational experiments, they struggle with retrieving resources, such as new data, necessary to replicate a claim. All code and data are publicly available at https://github.com/CenterForOpenScience/llm‑benchmarking.

PaperId: 3110, https://arxiv.org/pdf/2602.11351.pdf GitHub

Abstract:
Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following and making them essential for real‑world, user‑centric applications. Agentic reinforcement learning (RL) has recently emerged as a promising solution for training such agents in multi‑turn settings, allowing interaction strategies to be learned from feedback. However, existing pipelines face a critical challenge in balancing task performance with user engagement, as passive agents can not efficiently adapt to users' intentions while overuse of human feedback reduces their satisfaction. To address this trade‑off, we propose BAO, an agentic RL framework that combines behavior enhancement to enrich proactive reasoning and information‑gathering capabilities with behavior regularization to suppress inefficient or redundant interactions and align agent behavior with user expectations. We evaluate BAO on multiple tasks from the UserRL benchmark suite, and demonstrate that it substantially outperforms proactive agentic RL baselines while achieving comparable or even superior performance to commercial LLM agents, highlighting its effectiveness for training proactive, user‑aligned LLM agents in complex multi‑turn scenarios. Our website: https://proactive‑agentic‑rl.github.io/.

PaperId: 3111, https://arxiv.org/pdf/2602.11322.pdf GitHub

Abstract:
Current approaches to memory in neural systems rely on similarity‑based retrieval: given a query, find the most representationally similar stored state. This assumption ‑‑ that useful memories are similar memories ‑‑ fails to capture a fundamental property of biological memory: association through temporal co‑occurrence. We propose Predictive Associative Memory (PAM), an architecture in which a JEPA‑style predictor, trained on temporal co‑occurrence within a continuous experience stream, learns to navigate the associative structure of an embedding space. We introduce an Inward JEPA that operates over stored experience (predicting associatively reachable past states) as the complement to the standard Outward JEPA that operates over incoming sensory data (predicting future states). We evaluate PAM as an associative recall system ‑‑ testing faithfulness of recall for experienced associations ‑‑ rather than as a retrieval system evaluated on generalisation to unseen associations. On a synthetic benchmark, the predictor's top retrieval is a true temporal associate 97% of the time (Association Precision@1 = 0.970); it achieves cross‑boundary Recall@20 = 0.421 where cosine similarity scores zero; and it separates experienced‑together from never‑experienced‑together states with a discrimination AUC of 0.916 (cosine: 0.789). Even restricted to cross‑room pairs where embedding similarity is uninformative, the predictor achieves AUC = 0.849 (cosine: 0.503, chance). A temporal shuffle control confirms the signal is genuine temporal co‑occurrence structure, not embedding geometry: shuffling collapses cross‑boundary recall by 90%, replicated across training seeds. All results are stable across seeds (SD < 0.006) and query selections (SD \leq 0.012).

PaperId: 3112, https://arxiv.org/pdf/2602.11202.pdf GitHub

Abstract:
Reasoning models produce long traces of intermediate decisions and tool calls, making test‑time verification important for ensuring correctness. Existing approaches either verify only the final answer, which misses early errors, or rely on branch‑and‑verify strategies that explore multiple trajectories. We introduce interwhen, a single‑trajectory verification framework that steers model behavior by providing feedback on intermediate reasoning traces. It addresses two key challenges. First, given a set of verifiers, obtaining verifiable states from the reasoning trace typically requires prompt engineering or external task decomposition into fixed steps. Instead, we propose a monitoring system that periodically polls the reasoning trace and forks inference of the reasoning model to recover intermediate states. Verifiers are run asynchronously alongside generation, adding negligible overhead on correct executions and intervening only when violations occur. Second, beyond math and code, a central challenge for process verification is the scarcity of verifiers. interwhen addresses this through automatic verifier synthesis from natural‑language policy documents. Given a policy, it can generate code‑based verifiers, including provably correct verifiers in Lean and z3. Together, these contributions yield a plug‑and‑play test‑time verification system that can improve task completion and policy compliance of any reasoning agent. On reasoning benchmarks where policies encode mathematical or logical constraints, interwhen achieves near‑perfect accuracy for reasoning models using a fraction of the tokens of baselines. On agentic benchmarks with policy‑based verifier generation, it enables improvements in task quality for SLMs without any finetuning, e.g., task completion rate of Qwen3‑30B jumps from 32% to 87% on the telecom domain in tau2‑bench. Code at https://github.com/microsoft/interwhen.

PaperId: 3113, https://arxiv.org/pdf/2602.11171.pdf GitHub

Abstract:
Fine‑tuning Large Language Models (LLMs) with Low‑Rank Adaptation (LoRA) offers a resource‑efficient way to personalize or specialize. However, LoRA is highly sensitive to hyperparameter choices, and exhaustive hyperparameter search is computationally expensive. To address this, we propose a Bayesian Optimization (BO) framework that leverages the domain knowledge of pre‑trained LLMs to efficiently search for LoRA hyperparameters. Our approach repurposes a pre‑trained LLM as a discrete‑to‑continuous mapping module to link hyperparameters and their domain knowledge to a continuous vector space, where BO is conducted. We design and control the mapping via language prompting, providing a domain‑aware textual prompt that describes the relationships among hyperparameters and their respective roles. This allows us to explicitly inject domain knowledge about LoRA into the LLM in natural language. We also introduce an additional learnable token to capture residual information that is difficult to describe linguistically in the prompt. This aids BO to sample more high‑performing hyperparameters. In addition, by leveraging the strong correlation observed between the performance obtained from full and subset training datasets in LoRA training regimes, we introduce proxy training and evaluation using a data subset. This significantly improves the efficiency of our method. We demonstrate that our hyperparameter, discovered with only about 30 iterations, achieves more than 20% performance improvement over standard hyperparameters found from about 45,000 combinations. Project page: https://baekseongeun.github.io/lora‑bo/

PaperId: 3114, https://arxiv.org/pdf/2602.11146.pdf GitHub

Abstract:
Preference optimization for diffusion and flow‑matching models relies on reward functions that are both discriminatively robust and computationally efficient. Vision‑Language Models (VLMs) have emerged as the primary reward provider, leveraging their rich multimodal priors to guide alignment. However, their computation and memory cost can be substantial, and optimizing a latent diffusion generator through a pixel‑space reward introduces a domain mismatch that complicates alignment. In this paper, we propose DiNa‑LRM, a diffusion‑native latent reward model that formulates preference learning directly on noisy diffusion states. Our method introduces a noise‑calibrated Thurstone likelihood with diffusion‑noise‑dependent uncertainty. DiNa‑LRM leverages a pretrained latent diffusion backbone with a timestep‑conditioned reward head, and supports inference‑time noise ensembling, providing a diffusion‑native mechanism for test‑time scaling and robust rewarding. Across image alignment benchmarks, DiNa‑LRM substantially outperforms existing diffusion‑based reward baselines and achieves performance competitive with state‑of‑the‑art VLMs at a fraction of the computational cost. In preference optimization, we demonstrate that DiNa‑LRM improves preference optimization dynamics, enabling faster and more resource‑efficient model alignment.

PaperId: 3115, https://arxiv.org/pdf/2602.11144.pdf GitHub GitHub

Abstract:
Unified Multimodal Models (UMMs) have shown remarkable progress in visual generation. Yet, existing benchmarks predominantly assess Crystallized Intelligence, which relies on recalling accumulated knowledge and learned schemas. This focus overlooks Generative Fluid Intelligence (GFI): the capacity to induce patterns, reason through constraints, and adapt to novel scenarios on the fly. To rigorously assess this capability, we introduce GENIUS (GEN Fluid Intelligence EvalUation Suite). We formalize GFI as a synthesis of three primitives. These include Inducing Implicit Patterns (e.g., inferring personalized visual preferences), Executing Ad‑hoc Constraints (e.g., visualizing abstract metaphors), and Adapting to Contextual Knowledge (e.g., simulating counter‑intuitive physics). Collectively, these primitives challenge models to solve problems grounded entirely in the immediate context. Our systematic evaluation of 12 representative models reveals significant performance deficits in these tasks. Crucially, our diagnostic analysis disentangles these failure modes. It demonstrates that deficits stem from limited context comprehension rather than insufficient intrinsic generative capability. To bridge this gap, we propose a training‑free attention intervention strategy. Ultimately, GENIUS establishes a rigorous standard for GFI, guiding the field beyond knowledge utilization toward dynamic, general‑purpose reasoning. Our dataset and code will be released at: \hrefhttps://github.com/arctanxarc/GENIUShttps://github.com/arctanxarc/GENIUS.

PaperId: 3116, https://arxiv.org/pdf/2602.11015.pdf GitHub

Abstract:
Formal privacy metrics provide compliance‑oriented guarantees but often fail to quantify actual linkability in released datasets. We introduce CVPL (Cluster‑Vector‑Projection Linkage), a geometric framework for post‑hoc assessment of linkage risk between original and protected tabular data. CVPL represents linkage analysis as an operator pipeline comprising blocking, vectorization, latent projection, and similarity evaluation, yielding continuous, scenario‑dependent risk estimates rather than binary compliance verdicts. We formally define CVPL under an explicit threat model and introduce threshold‑aware risk surfaces, R(lambda, tau), that capture the joint effects of protection strength and attacker strictness. We establish a progressive blocking strategy with monotonicity guarantees, enabling anytime risk estimation with valid lower bounds. We demonstrate that the classical Fellegi‑Sunter linkage emerges as a special case of CVPL under restrictive assumptions, and that violations of these assumptions can lead to systematic over‑linking bias. Empirical validation on 10,000 records across 19 protection configurations demonstrates that formal k‑anonymity compliance may coexist with substantial empirical linkability, with a significant portion arising from non‑quasi‑identifier behavioral patterns. CVPL provides interpretable diagnostics identifying which features drive linkage feasibility, supporting privacy impact assessment, protection mechanism comparison, and utility‑risk trade‑off analysis.

PaperId: 3117, https://arxiv.org/pdf/2602.10953.pdf GitHub

Abstract:
Diffusion Language Models (DLMs) generate text by iteratively denoising a masked sequence, repeatedly deciding which positions to commit at each step. Standard decoding follows a greedy rule: unmask the most confident positions, yet this local choice can lock the model into a suboptimal unmasking order, especially on reasoning‑heavy prompts. We present SOAR, a training‑free decoding algorithm that adapts its behavior to the model's uncertainty. When confidence is low, SOAR briefly widens the search over alternative unmasking decisions to avoid premature commitments; when confidence is high, it collapses the search and decodes many positions in parallel to reduce the number of denoising iterations. Across mathematical reasoning and code generation benchmarks (GSM8K, MBPP, HumanEval) on Dream‑7B and LLaDA‑8B, SOAR improves generation quality while maintaining competitive inference speed, offering a practical way to balance quality and efficiency in DLM decoding. Our Code is available at https://github.com/duterscmy/SOAR

PaperId: 3118, https://arxiv.org/pdf/2602.10881.pdf GitHub

Abstract:
Systematic reviews and meta‑analyses rely on converting narrative articles into structured, numerically grounded study records. Despite rapid advances in large language models (LLMs), it remains unclear whether they can meet the structural requirements of this process, which hinge on preserving roles, methods, and effect‑size attribution across documents rather than on recognizing isolated entities. We propose a structural, diagnostic framework that evaluates LLM‑based evidence extraction as a progression of schema‑constrained queries with increasing relational and numerical complexity, enabling precise identification of failure points beyond atom‑level extraction. Using a manually curated corpus spanning five scientific domains, together with a unified query suite and evaluation protocol, we evaluate two state‑of‑the‑art LLMs under both per‑document and long‑context, multi‑document input regimes. Across domains and models, performance remains moderate for single‑property queries but degrades sharply once tasks require stable binding between variables, roles, statistical methods, and effect sizes. Full meta‑analytic association tuples are extracted with near‑zero reliability, and long‑context inputs further exacerbate these failures. Downstream aggregation amplifies even minor upstream errors, rendering corpus‑level statistics unreliable. Our analysis shows that these limitations stem not from entity recognition errors, but from systematic structural breakdowns, including role reversals, cross‑analysis binding drift, instance compression in dense result sections, and numeric misattribution, indicating that current LLMs lack the structural fidelity, relational binding, and numerical grounding required for automated meta‑analysis. The code and data are publicly available at GitHub (https://github.com/zhiyintan/LLM‑Meta‑Analysis).

PaperId: 3119, https://arxiv.org/pdf/2602.10863.pdf GitHub

Abstract:
Despite the strong performance achieved by reinforcement learning‑trained information‑seeking agents, learning in open‑ended web environments remains severely constrained by low signal‑to‑noise feedback. Text‑based parsers often discard layout semantics and introduce unstructured noise, while long‑horizon training typically relies on sparse outcome rewards that obscure which retrieval actions actually matter. We propose a visual‑native search framework that represents webpages as visual snapshots, allowing agents to leverage layout cues to quickly localize salient evidence and suppress distractors. To learn effectively from these high‑dimensional observations, we introduce Information‑Aware Credit Assignment (ICA), a post‑hoc method that estimates each retrieved snapshot's contribution to the final outcome via posterior analysis and propagates dense learning signals back to key search turns. Integrated with a GRPO‑based training pipeline, our approach consistently outperforms text‑based baselines on diverse information‑seeking benchmarks, providing evidence that visual snapshot grounding with information‑level credit assignment alleviates the credit‑assignment bottleneck in open‑ended web environments. The code and datasets will be released in https://github.com/pc‑inno/ICA_MM_deepsearch.git.

PaperId: 3120, https://arxiv.org/pdf/2602.10847.pdf GitHub

Abstract:
Multivariate time series forecasting (MTSF) plays a vital role in numerous real‑world applications, yet existing models remain constrained by their reliance on a limited historical context. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than the input horizon ‑ despite such patterns carrying strong predictive signals. Naive solutions, such as extending the historical window, lead to severe drawbacks, including overfitting, prohibitive computational costs, and redundant information processing. To address these challenges, we introduce the Global Temporal Retriever (GTR), a lightweight and plug‑and‑play module designed to extend any forecasting model's temporal awareness beyond the immediate historical context. GTR maintains an adaptive global temporal embedding of the entire cycle and dynamically retrieves and aligns relevant global segments with the input sequence. By jointly modeling local and global dependencies through a 2D convolution and residual fusion, GTR effectively bridges short‑term observations with long‑term periodicity without altering the host model architecture. Extensive experiments on six real‑world datasets demonstrate that GTR consistently delivers state‑of‑the‑art performance across both short‑term and long‑term forecasting scenarios, while incurring minimal parameter and computational overhead. These results highlight GTR as an efficient and general solution for enhancing global periodicity modeling in MTSF tasks. Code is available at this repository: https://github.com/macovaseas/GTR.

PaperId: 3121, https://arxiv.org/pdf/2602.10845.pdf GitHub

Abstract:
Knowledge Graph Completion (KGC) fundamentally hinges on the coherent fusion of pre‑trained entity semantics with heterogeneous topological structures to facilitate robust relational reasoning. However, existing paradigms encounter a critical "structural resolution mismatch," failing to reconcile divergent representational demands across varying graph densities, which precipitates structural noise interference in dense clusters and catastrophic representation collapse in sparse regions. We present SynergyKGC, an adaptive framework that advances traditional neighbor aggregation to an active Cross‑Modal Synergy Expert via relation‑aware cross‑attention and semantic‑intent‑driven gating. By coupling a density‑dependent Identity Anchoring strategy with a Double‑tower Coherent Consistency architecture, SynergyKGC effectively reconciles topological heterogeneity while ensuring representational stability across training and inference phases. Systematic evaluations on two public benchmarks validate the superiority of our method in significantly boosting KGC hit rates, providing empirical evidence for a generalized principle of resilient information integration in non‑homogeneous structured data.

PaperId: 3122, https://arxiv.org/pdf/2602.10825.pdf GitHub

Abstract:
Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra‑long videos by synthesizing content in sequential chunks. However, this sequential generation process is notoriously slow. While caching strategies have proven effective for accelerating traditional video diffusion models, existing methods assume uniform denoising across all frames‑an assumption that breaks down in autoregressive models where different video chunks exhibit varying similarity patterns at identical timesteps. In this paper, we present FlowCache, the first caching framework specifically designed for autoregressive video generation. Our key insight is that each video chunk should maintain independent caching policies, allowing fine‑grained control over which chunks require recomputation at each timestep. We introduce a chunkwise caching strategy that dynamically adapts to the unique denoising characteristics of each chunk, complemented by a joint importance‑redundancy optimized KV cache compression mechanism that maintains fixed memory bounds while preserving generation quality. Our method achieves remarkable speedups of 2.38 times on MAGI‑1 and 6.7 times on SkyReels‑V2, with negligible quality degradation (VBench: 0.87 increase and 0.79 decrease respectively). These results demonstrate that FlowCache successfully unlocks the potential of autoregressive models for real‑time, ultra‑long video generation‑establishing a new benchmark for efficient video synthesis at scale. The code is available at https://github.com/mikeallen39/FlowCache.

PaperId: 3123, https://arxiv.org/pdf/2602.10735.pdf GitHub

Abstract:
A narrated e‑book combines synchronized audio with digital text, highlighting the currently spoken word or sentence during playback. This format supports early literacy and assists individuals with reading challenges, while also allowing general readers to seamlessly switch between reading and listening. With the emergence of natural‑sounding neural Text‑to‑Speech (TTS) technology, several commercial services have been developed to leverage these technology for converting standard text e‑books into high‑quality narrated e‑books. However, no open‑source solutions currently exist to perform this task. In this paper, we present Calliope, an open‑source framework designed to fill this gap. Our method leverages state‑of‑the‑art open‑source TTS to convert a text e‑book into a narrated e‑book in the EPUB 3 Media Overlay format. The method offers several innovative steps: audio timestamps are captured directly during TTS, ensuring exact synchronization between narration and text highlighting; the publisher's original typography, styling, and embedded media are strictly preserved; and the entire pipeline operates offline. This offline capability eliminates recurring API costs, mitigates privacy concerns, and avoids copyright compliance issues associated with cloud‑based services. The framework currently supports the state‑of‑the‑art open‑source TTS systems XTTS‑v2 and Chatterbox. A potential alternative approach involves first generating narration via TTS and subsequently synchronizing it with the text using forced alignment. However, while our method ensures exact synchronization, our experiments show that forced alignment introduces drift between the audio and text highlighting significant enough to degrade the reading experience. Source code and usage instructions are available at https://github.com/hugohammer/TTS‑Narrated‑Ebook‑Creator.git.

PaperId: 3124, https://arxiv.org/pdf/2602.10715.pdf GitHub

Abstract:
Long‑term conversational memory is a core capability for LLM‑based dialogue systems, yet existing benchmarks and evaluation protocols primarily focus on surface‑level factual recall. In realistic interactions, appropriate responses often depend on implicit constraints such as user state, goals, or values that are not explicitly queried later. To evaluate this setting, we introduce LoCoMo‑Plus, a benchmark for assessing cognitive memory under cue‑‑trigger semantic disconnect, where models must retain and apply latent constraints across long conversational contexts. We further show that conventional string‑matching metrics and explicit task‑type prompting are misaligned with such scenarios, and propose a unified evaluation framework based on constraint consistency. Experiments across diverse backbone models, retrieval‑based methods, and memory systems demonstrate that cognitive memory remains challenging and reveals failures not captured by existing benchmarks. Our code and evaluation framework are publicly available at: https://github.com/xjtuleeyf/Locomo‑Plus.

PaperId: 3125, https://arxiv.org/pdf/2602.10693.pdf GitHub

Abstract:
Off‑policy updates are inevitable in reinforcement learning (RL) for large language models (LLMs) due to rollout staleness from asynchronous training and mismatches between training and inference engines. Naive importance sampling gives an unbiased correction but suffers from high variance, which is amplified by unbounded ratios and autoregressive generation. Prior remedies either rely on scenario‑specific engineering, or trade bias for variance via token‑level clipping or sequence‑level normalization, yet these approaches remain largely heuristic. We propose Variational sEquence‑level Soft Policy Optimization (VESPO). By explicitly incorporating variance reduction into a variational formulation, we derive a principled closed‑form reshaping kernel that operates directly on sequence‑level importance weights, avoids token‑level approximation and length normalization, and admits an explicit variance bound for the deployed kernel. Experiments on math reasoning and code generation show that VESPO maintains stable training under severe off‑policy conditions (staleness up to 64x) and delivers consistent gains across both dense and Mixture‑of‑Experts (MoE) models, outperforming recent reshaping baselines under matched setup. Code is available at https://github.com/FloyedShen/VESPO.

PaperId: 3126, https://arxiv.org/pdf/2602.10687.pdf GitHub

Abstract:
Existing forgery detection methods are often limited to uni‑modal or bi‑modal settings, failing to handle the interleaved text, images, and videos prevalent in real‑world misinformation. To bridge this gap, this paper targets to develop a unified framework for omnibus vision‑language forgery detection and grounding. In this unified setting, the interplay between diverse modalities and the dual requirements of simultaneous detection and localization pose a critical ``difficulty bias`` problem: the simpler veracity classification task tends to dominate the gradients, leading to suboptimal performance in fine‑grained grounding during multi‑task optimization. To address this challenge, we propose OmniVL‑Guard, a balanced reinforcement learning framework for omnibus vision‑language forgery detection and grounding. Particularly, OmniVL‑Guard comprises two core designs: Self‑Evolving CoT Generatio and Adaptive Reward Scaling Policy Optimization (ARSPO). Self‑Evolving CoT Generation synthesizes high‑quality reasoning paths, effectively overcoming the cold‑start challenge. Building upon this, Adaptive Reward Scaling Policy Optimization (ARSPO) dynamically modulates reward scales and task weights, ensuring a balanced joint optimization. Extensive experiments demonstrate that OmniVL‑Guard significantly outperforms state‑of‑the‑art methods and exhibits zero‑shot robust generalization across out‑of‑domain scenarios. The dataset and code are publicly available at https://github.com/shen8424/OmniVL‑Guard.

PaperId: 3127, https://arxiv.org/pdf/2602.10675.pdf GitHub

Abstract:
Visual Chain‑of‑Thought (VCoT) has emerged as a promising paradigm for enhancing multimodal reasoning by integrating visual perception into intermediate reasoning steps. However, existing VCoT approaches are largely confined to static scenarios and struggle to capture the temporal dynamics essential for tasks such as instruction, prediction, and camera motion. To bridge this gap, we propose TwiFF‑2.7M, the first large‑scale, temporally grounded VCoT dataset derived from 2.7 million video clips, explicitly designed for dynamic visual question and answer. Accompanying this, we introduce TwiFF‑Bench, a high‑quality evaluation benchmark of 1,078 samples that assesses both the plausibility of reasoning trajectories and the correctness of final answers in open‑ended dynamic settings. Building on these foundations, we propose the TwiFF model, a unified modal that synergistically leverages pre‑trained video generation and image comprehension capabilities to produce temporally coherent visual reasoning cues‑iteratively generating future action frames and textual reasoning. Extensive experiments demonstrate that TwiFF significantly outperforms existing VCoT methods and Textual Chain‑of‑Thought baselines on dynamic reasoning tasks, which fully validates the effectiveness for visual question answering in dynamic scenarios. Our code and data is available at https://github.com/LiuJunhua02/TwiFF.

PaperId: 3128, https://arxiv.org/pdf/2602.10635.pdf GitHub

Abstract:
Socially intelligent AI systems must entail reasoning across diverse human behavioral tasks, and generalization to new contexts. However, AI has yet to achieve this level of social intelligence. Existing models remain fundamentally constrained by the imbalanced learning dynamics induced by training on behavioral data. Namely, behavioral data is inherently heterogeneous, comprising diverse modalities and prediction targets that often produce uneven training signals across samples. To address this, we develop Omnisapiens‑7B 2.0, a foundation model for social behavior processing that explicitly addresses learning from heterogeneous behavioral data. This is enabled through Heterogeneity‑Aware Relative Policy Optimization, a novel reasoning RL method that explicitly rebalances learning signals across samples. The core insight is to approximate contribution signals to the policy update, using them to inform geometrically centered and intertially smoothed advantage modulation. Results demonstrate that Omnisapiens‑7B 2.0 achieves the best and most consistent performance across 10 diverse behavioral tasks, while also attaining the best performance on all five held‑out zero‑shot generalization benchmarks, with gains of up to +12.02% and +9.37% respectively. Furthermore, Omnisapiens‑7B 2.0 demonstrates more consistent and interpretable reasoning traces, supporting reliable real‑world behavioral applications. Our model and codes can be found at https://github.com/MIT‑MI/human_behavior_atlas.

PaperId: 3129, https://arxiv.org/pdf/2602.10585.pdf GitHub

Abstract:
The trade‑off between interpretability and accuracy remains a core challenge in machine learning. Standard Generalized Additive Models (GAMs) offer clear feature attributions but are often constrained by their strictly additive nature, which can limit predictive performance. Introducing feature interactions can boost accuracy yet may obscure individual feature contributions. To address these issues, we propose Neural Additive Experts (NAEs), a novel framework that seamlessly balances interpretability and accuracy. NAEs employ a mixture of experts framework, learning multiple specialized networks per feature, while a dynamic gating mechanism integrates information across features, thereby relaxing rigid additive constraints. Furthermore, we propose targeted regularization techniques to mitigate variance among expert predictions, facilitating a smooth transition from an exclusively additive model to one that captures intricate feature interactions while maintaining clarity in feature attributions. Our theoretical analysis and experiments on synthetic data illustrate the model's flexibility, and extensive evaluations on real‑world datasets confirm that NAEs achieve an optimal balance between predictive accuracy and transparent, feature‑level explanations. The code is available at https://github.com/Teddy‑XiongGZ/NAE.

PaperId: 3130, https://arxiv.org/pdf/2602.10575.pdf GitHub

Abstract:
Metaphorical comprehension in images remains a critical challenge for Nowadays AI systems. While Multimodal Large Language Models (MLLMs) excel at basic Visual Question Answering (VQA), they consistently struggle to grasp the nuanced cultural, emotional, and contextual implications embedded in visual content. This difficulty stems from the task's demand for sophisticated multi‑hop reasoning, cultural context, and Theory of Mind (ToM) capabilities, which current models lack. To fill this gap, we propose MetaphorStar, the first end‑to‑end visual reinforcement learning (RL) framework for image implication tasks. Our framework includes three core components: the fine‑grained dataset TFQ‑Data, the visual RL method TFQ‑GRPO, and the well‑structured benchmark TFQ‑Bench. Our fully open‑source MetaphorStar family, trained using TFQ‑GRPO on TFQ‑Data, significantly improves performance by an average of 82.6% on the image implication benchmarks. Compared with 20+ mainstream MLLMs, MetaphorStar‑32B achieves state‑of‑the‑art (SOTA) on Multiple‑Choice Question and Open‑Style Question, significantly outperforms the top closed‑source model Gemini‑3.0‑pro on True‑False Question. Crucially, our experiments reveal that learning image implication tasks improves the general understanding ability, especially the complex visual reasoning ability. We further provide a systematic analysis of model parameter scaling, training data scaling, and the impact of different model architectures and training strategies, demonstrating the broad applicability of our method. We open‑sourced all model weights, datasets, and method code at https://metaphorstar.github.io.

PaperId: 3131, https://arxiv.org/pdf/2602.10551.pdf GitHub

Abstract:
Recent advances in 3D Large Multimodal Models (LMMs) built on Large Language Models (LLMs) have established the alignment of 3D visual features with LLM representations as the dominant paradigm. However, the inherited Rotary Position Embedding (RoPE) introduces limitations for multimodal processing. Specifically, applying 1D temporal positional indices disrupts the continuity of visual features along the column dimension, resulting in spatial locality loss. Moreover, RoPE follows the prior that temporally closer image tokens are more causally related, leading to long‑term decay in attention allocation and causing the model to progressively neglect earlier visual tokens as the sequence length increases. To address these issues, we propose C^2RoPE, an improved RoPE that explicitly models local spatial Continuity and spatial Causal relationships for visual processing. C^2RoPE introduces a spatio‑temporal continuous positional embedding mechanism for visual tokens. It first integrates 1D temporal positions with Cartesian‑based spatial coordinates to construct a triplet hybrid positional index, and then employs a frequency allocation strategy to encode spatio‑temporal positional information across the three index components. Additionally, we introduce Chebyshev Causal Masking, which determines causal dependencies by computing the Chebyshev distance of image tokens in 2D space. Evaluation results across various benchmarks, including 3D scene reasoning and 3D visual question answering, demonstrate C^2RoPE's effectiveness. The code is be available at https://github.com/ErikZ719/C2RoPE.

PaperId: 3132, https://arxiv.org/pdf/2602.10513.pdf GitHub

Abstract:
Deploying vision foundation models typically relies on efficient adaptation strategies, whereas conventional full fine‑tuning suffers from prohibitive costs and low efficiency. While delta‑tuning has proven effective in boosting the performance and efficiency of LLMs during adaptation, its advantages cannot be directly transferred to the fine‑tuning pipeline of vision foundation models. To push the boundaries of adaptation efficiency for vision tasks, we propose an adapter with Complex Linear Projection Optimization (CoLin). For architecture, we design a novel low‑rank complex adapter that introduces only about 1% parameters to the backbone. For efficiency, we theoretically prove that low‑rank composite matrices suffer from severe convergence issues during training, and address this challenge with a tailored loss. Extensive experiments on object detection, segmentation, image classification, and rotated object detection (remote sensing scenario) demonstrate that CoLin outperforms both full fine‑tuning and classical delta‑tuning approaches with merely 1% parameters for the first time, providing a novel and efficient solution for deployment of vision foundation models. We release the code on https://github.com/DongshuoYin/CoLin.

PaperId: 3133, https://arxiv.org/pdf/2602.10458.pdf GitHub

Abstract:
Reinforcement Learning (RL) has emerged as a dominant paradigm for end‑to‑end autonomous driving (AD). However, RL suffers from sample inefficiency and a lack of semantic interpretability in complex scenarios. Foundation Models, particularly Vision‑Language Models (VLMs), can mitigate this by offering rich, context‑aware knowledge, yet their high inference latency hinders deployment in high‑frequency RL training loops. To bridge this gap, we present Found‑RL, a platform tailored to efficiently enhance RL for AD using foundation models. A core innovation is the asynchronous batch inference framework, which decouples heavy VLM reasoning from the simulation loop, effectively resolving latency bottlenecks to support real‑time learning. We introduce diverse supervision mechanisms: Value‑Margin Regularization (VMR) and Advantage‑Weighted Action Guidance (AWAG) to effectively distill expert‑like VLM action suggestions into the RL policy. Additionally, we adopt high‑throughput CLIP for dense reward shaping. We address CLIP's dynamic blindness via Conditional Contrastive Action Alignment, which conditions prompts on discretized speed/command and yields a normalized, margin‑based bonus from context‑specific action‑anchor scoring. Found‑RL provides an end‑to‑end pipeline for fine‑tuned VLM integration and shows that a lightweight RL model can achieve near‑VLM performance compared with billion‑parameter VLMs while sustaining real‑time inference (approx. 500 FPS). Code, data, and models will be publicly available at https://github.com/ys‑qu/found‑rl.

PaperId: 3134, https://arxiv.org/pdf/2602.10441.pdf GitHub

Abstract:
Modern data lakes have emerged as foundational platforms for large‑scale machine learning, enabling flexible storage of heterogeneous data and structured analytics through table‑oriented abstractions. Despite their growing importance, standardized benchmarks for evaluating machine learning performance in data lake environments remain scarce. To address this gap, we present LakeMLB (Data Lake Machine Learning Benchmark), designed for the most common multi‑source, multi‑table scenarios in data lakes. LakeMLB focuses on two representative multi‑table scenarios, Union and Join, and provides three real‑world datasets for each scenario, covering government open data, finance, Wikipedia, and online marketplaces. The benchmark supports three representative integration strategies: pre‑training‑based, data augmentation‑based, and feature augmentation‑based approaches. We conduct extensive experiments with state‑of‑the‑art tabular learning methods, offering insights into their performance under complex data lake scenarios. We release both datasets and code to facilitate rigorous research on machine learning in data lake ecosystems; the benchmark is available at https://github.com/zhengwang100/LakeMLB.

PaperId: 3135, https://arxiv.org/pdf/2602.10305.pdf GitHub

Abstract:
Reward shaping has been applied widely to accelerate Reinforcement Learning (RL) agents' training. However, a principled way of designing effective reward shaping functions, especially for complex continuous control problems, remains largely under‑explained. In this work, we propose to automatically learn a reward shaping function for continuous control problems from offline datasets, potentially contaminated by unobserved confounding variables. Specifically, our method builds upon the recently proposed causal Bellman equation to learn a tight upper bound on the optimal state values, which is then used as the potentials in the Potential‑Based Reward Shaping (PBRS) framework. Our proposed reward shaping algorithm is tested with Soft‑Actor‑Critic (SAC) on multiple commonly used continuous control benchmarks and exhibits strong performance guarantees under unobserved confounders. More broadly, our work marks a solid first step towards confounding robust continuous control from a causal perspective. Code for training our reward shaping functions can be found at https://github.com/mateojuliani/confounding_robust_cont_control.

PaperId: 3136, https://arxiv.org/pdf/2602.10246.pdf GitHub

Abstract:
Solid State Drives (SSDs) are critical to datacenters, consumer platforms, and mission‑critical systems. Yet diagnosing their performance and reliability is difficult because data are fragmented and time‑disjoint, and existing methods demand large datasets and expert input while offering only limited insights. Degradation arises not only from shifting workloads and evolving architectures but also from environmental factors such as temperature, humidity, and vibration. We present KORAL, a knowledge driven reasoning framework that integrates Large Language Models (LLMs) with a structured Knowledge Graph (KG) to generate insights into SSD operations. Unlike traditional approaches that require extensive expert input and large datasets, KORAL generates a Data KG from fragmented telemetry and integrates a Literature KG that already organizes knowledge from literature, reports, and traces. This turns unstructured sources into a queryable graph and telemetry into structured knowledge, and both the Graphs guide the LLM to deliver evidence‑based, explainable analysis aligned with the domain vocabulary and constraints. Evaluation using real production traces shows that the KORAL delivers expert‑level diagnosis and recommendations, supported by grounded explanations that improve reasoning transparency, guide operator decisions, reduce manual effort, and provide actionable insights to improve service quality. To our knowledge, this is the first end‑to‑end system that combines LLMs and KGs for full‑spectrum SSD reasoning including Descriptive, Predictive, Prescriptive, and What‑if analysis. We release the generated SSD‑specific KG to advance reproducible research in knowledge‑based storage system analysis. GitHub Repository: https://github.com/Damrl‑lab/KORAL

PaperId: 3137, https://arxiv.org/pdf/2602.10234.pdf GitHub

Abstract:
Stop‑and‑go waves, as a major form of freeway traffic congestion, cause severe and long‑lasting adverse effects, including reduced traffic efficiency, increased driving risks, and higher vehicle emissions. Amongst the highway traffic management strategies, jam‑absorption driving (JAD), in which a dedicated vehicle performs "slow‑in" and "fast‑out" maneuvers before being captured by a stop‑and‑go wave, has been proposed as a potential method for preventing the propagation of such waves. However, most existing JAD strategies remain impractical mainly due to the lack of discussion regarding implementation vehicles and operational conditions. Inspired by real‑world observations of police‑car swerving behavior, this paper first introduces a Single‑Vehicle Two‑Detector Jam‑Absorption Driving (SVDD‑JAD) problem, and then proposes a practical JAD strategy that transforms such behavior into a maneuver capable of suppressing the propagation of an isolated stop‑and‑go wave. Five key parameters that significantly affect the proposed strategy, namely, JAD speed, inflow traffic speed, wave width, wave speed, and in‑wave speed, are identified and systematically analyzed. Using a SUMO‑based simulation as an illustrative example, we further demonstrate how these parameters can be measured in practice with two stationary roadside traffic detectors. The results show that the proposed JAD strategy successfully suppresses the propagation of a stop‑and‑go wave, without triggering a secondary wave. This paper is expected to take a significant step toward making JAD practical, advancing it from a theoretical concept to a feasible and implementable strategy. To promote reproducibility in the transportation domain, we have also open‑sourced all the code on our GitHub repository https://github.com/gotrafficgo.

PaperId: 3138, https://arxiv.org/pdf/2602.10179.pdf GitHub

Abstract:
Recent advances in large image editing models have shifted the paradigm from text‑driven instructions to vision‑prompt editing, where user intent is inferred directly from visual inputs such as marks, arrows, and visual‑text prompts. While this paradigm greatly expands usability, it also introduces a critical and underexplored safety risk: the attack surface itself becomes visual. In this work, we propose Vision‑Centric Jailbreak Attack (VJA), the first visual‑to‑visual jailbreak attack that conveys malicious instructions purely through visual inputs. To systematically study this emerging threat, we introduce IESBench, a safety‑oriented benchmark for image editing models. Extensive experiments on IESBench demonstrate that VJA effectively compromises state‑of‑the‑art commercial models, achieving attack success rates of up to 80.9% on Nano Banana Pro and 70.1% on GPT‑Image‑1.5. To mitigate this vulnerability, we propose a training‑free defense based on introspective multimodal reasoning, which substantially improves the safety of poorly aligned models to a level comparable with commercial systems, without auxiliary guard models and with negligible computational overhead. Our findings expose new vulnerabilities, provide both a benchmark and practical defense to advance safe and trustworthy modern image editing systems. Warning: This paper contains offensive images created by large image editing models.

PaperId: 3139, https://arxiv.org/pdf/2602.10177.pdf GitHub

Abstract:
Recent advances in foundational models have yielded reasoning systems capable of achieving a gold‑medal standard at the International Mathematical Olympiad. The transition from competition‑level problem‑solving to professional research, however, requires navigating vast literature and constructing long‑horizon proofs. In this work, we introduce Aletheia, a math research agent that iteratively generates, verifies, and revises solutions end‑to‑end in natural language. Specifically, Aletheia is powered by an advanced version of Gemini Deep Think for challenging reasoning problems, a novel inference‑time scaling law that extends beyond Olympiad‑level problems, and intensive tool use to navigate the complexities of mathematical research. We demonstrate the capability of Aletheia from Olympiad problems to PhD‑level exercises and most notably, through several distinct milestones in AI‑assisted mathematics research: (a) a research paper (Feng26) generated by AI without any human intervention in calculating certain structure constants in arithmetic geometry called eigenweights; (b) a research paper (LeeSeo26) demonstrating human‑AI collaboration in proving bounds on systems of interacting particles called independent sets; and (c) an extensive semi‑autonomous evaluation (Feng et al., 2026a) of 700 open problems on Bloom's Erdos Conjectures database, including autonomous solutions to four open questions. In order to help the public better understand the developments pertaining to AI and mathematics, we suggest quantifying standard levels of autonomy and novelty of AI‑assisted results, as well as propose a novel concept of human‑AI interaction cards for transparency. We conclude with reflections on human‑AI collaboration in mathematics and share all prompts as well as model outputs at https://github.com/google‑deepmind/superhuman/tree/main/aletheia.

PaperId: 3140, https://arxiv.org/pdf/2602.10161.pdf GitHub

Abstract:
Omni‑modal Large Language Models (OLLMs) greatly expand LLMs' multimodal capabilities but also introduce cross‑modal safety risks. However, a systematic understanding of vulnerabilities in omni‑modal interactions remains lacking. To bridge this gap, we establish a modality‑semantics decoupling principle and construct the AdvBench‑Omni dataset, which reveals a significant vulnerability in OLLMs. Mechanistic analysis uncovers a Mid‑layer Dissolution phenomenon driven by refusal vector magnitude shrinkage, alongside the existence of a modal‑invariant pure refusal direction. Inspired by these insights, we extract a golden refusal vector using Singular Value Decomposition and propose OmniSteer, which utilizes lightweight adapters to modulate intervention intensity adaptively. Extensive experiments show that our method not only increases the Refusal Success Rate against harmful inputs from 69.9% to 91.2%, but also effectively preserves the general capabilities across all modalities. Our code is available at: https://github.com/zhrli324/omni‑safety‑research.

PaperId: 3141, https://arxiv.org/pdf/2602.10139.pdf GitHub

Abstract:
Mobile Graphical User Interface (GUI) agents have demonstrated strong capabilities in automating complex smartphone tasks by leveraging multimodal large language models (MLLMs) and system‑level control interfaces. However, this paradigm introduces significant privacy risks, as agents typically capture and process entire screen contents, thereby exposing sensitive personal data such as phone numbers, addresses, messages, and financial information. Existing defenses either reduce UI exposure, obfuscate only task‑irrelevant content, or rely on user authorization, but none can protect task‑critical sensitive information while preserving seamless agent usability. We propose an anonymization‑based privacy protection framework that enforces the principle of available‑but‑invisible access to sensitive data: sensitive information remains usable for task execution but is never directly visible to the cloud‑based agent. Our system detects sensitive UI content using a PII‑aware recognition model and replaces it with deterministic, type‑preserving placeholders (e.g., PHONE_NUMBER#a1b2c) that retain semantic categories while removing identifying details. A layered architecture comprising a PII Detector, UI Transformer, Secure Interaction Proxy, and Privacy Gatekeeper ensures consistent anonymization across user instructions, XML hierarchies, and screenshots, mediates all agent actions over anonymized interfaces, and supports narrowly scoped local computations when reasoning over raw values is necessary. Extensive experiments on the AndroidLab and PrivScreen benchmarks show that our framework substantially reduces privacy leakage across multiple models while incurring only modest utility degradation, achieving the best observed privacy‑utility trade‑off among existing methods. Code available at: https://github.com/one‑step‑beh1nd/gui_privacy_protection

PaperId: 3142, https://arxiv.org/pdf/2602.10134.pdf GitHub

Abstract:
Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive information. Locate‑then‑edit methods, as a mainstream paradigm of model editing, offer a promising solution by modifying model parameters without retraining. However, in this work, we reveal a critical vulnerability of this paradigm: the parameter updates inadvertently serve as a side channel, enabling attackers to recover the edited data. We propose a two‑stage reverse‑engineering attack named KSTER (KeySpaceReconsTruction‑then‑EntropyReduction) that leverages the low‑rank structure of these updates. First, we theoretically show that the row space of the update matrix encodes a ``fingerprint" of the edited subjects, enabling accurate subject recovery via spectral analysis. Second, we introduce an entropy‑based prompt recovery attack that reconstructs the semantic context of the edit. Extensive experiments on multiple LLMs demonstrate that our attacks can recover edited data with high success rates. Furthermore, we propose subspace camouflage, a defense strategy that obfuscates the update fingerprint with semantic decoys. This approach effectively mitigates reconstruction risks without compromising editing utility. Our code is available at https://github.com/reanatom/EditingAttack.

PaperId: 3143, https://arxiv.org/pdf/2602.10104.pdf GitHub GitHub

Abstract:
Scaling action‑controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control interfaces from unlabeled video, learned latents often fail to transfer across contexts: they entangle scene‑specific cues and lack a shared coordinate system. This occurs because standard objectives operate only within each clip, providing no mechanism to align action semantics across contexts. Our key insight is that although actions are unobserved, their semantic effects are observable and can serve as a shared reference. We introduce SeqΔ‑REPA, a sequence‑level control‑effect alignment objective that anchors integrated latent action to temporal feature differences from a frozen, self‑supervised video encoder. Building on this, we present Olaf‑World, a pipeline that pretrains action‑conditioned video world models from large‑scale passive video. Extensive experiments demonstrate that our method learns a more structured latent action space, leading to stronger zero‑shot action transfer and more data‑efficient adaptation to new control interfaces than state‑of‑the‑art baselines.

PaperId: 3144, https://arxiv.org/pdf/2602.10090.pdf GitHub

Abstract:
Recent advances in large language model (LLM) have empowered autonomous agents to perform multi‑turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent World Model (AWM), a fully synthetic environment generation pipeline. Using this pipeline, we scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets and obtain high‑quality observations. Notably, these environments are code‑driven and backed by databases, providing more reliable and consistent state transitions than environments simulated by LLMs. Moreover, they enable more efficient agent interaction compared with collecting trajectories from realistic environments. To demonstrate the effectiveness of this resource, we perform large‑scale reinforcement learning for multi‑turn tool‑use agents. Thanks to the fully executable environments and accessible database states, we can also design reliable reward functions. Experiments on three benchmarks show that training exclusively in synthetic environments, rather than benchmark‑specific ones, yields strong out‑of‑distribution generalization. The code is available at https://github.com/Snowflake‑Labs/agent‑world‑model.

PaperId: 3145, https://arxiv.org/pdf/2602.10081.pdf GitHub

Abstract:
In scientific research, analysis requires accurately interpreting complex multimodal knowledge, integrating evidence from different sources, and drawing inferences grounded in domain‑specific knowledge. However, current artificial intelligence (AI) systems struggle to consistently demonstrate such capabilities. The complexity and variability of scientific tables and figures, combined with heterogeneous structures and long‑context requirements, pose fundamental obstacles to scientific table \& figure analysis. To quantify these challenges, we introduce AnaBench, a large‑scale benchmark featuring 63,178 instances from nine scientific domains, systematically categorized along seven complexity dimensions. To tackle these challenges, we propose Anagent, a multi‑agent framework for enhanced scientific table \& figure analysis through four specialized agents: Planner decomposes tasks into actionable subtasks, Expert retrieves task‑specific information through targeted tool execution, Solver synthesizes information to generate coherent analysis, and Critic performs iterative refinement through five‑dimensional quality assessment. We further develop modular training strategies that leverage supervised finetuning and specialized reinforcement learning to optimize individual capabilities while maintaining effective collaboration. Comprehensive evaluation across 9 broad domains with 170 subdomains demonstrates that Anagent achieves substantial improvements, up to \uparrow 13.43% in training‑free settings and \uparrow 42.12% with finetuning, while revealing that task‑oriented reasoning and context‑aware problem‑solving are essential for high‑quality scientific table \& figure analysis. Our project page: https://xhguo7.github.io/Anagent/.

PaperId: 3146, https://arxiv.org/pdf/2602.10063.pdf GitHub GitHub

Abstract:
Human problem‑solving is never the repetition of a single mindset, by which we mean a distinct mode of cognitive processing. When tackling a specific task, we do not rely on a single mindset; instead, we integrate multiple mindsets within the single solution process. However, existing LLM reasoning methods fall into a common trap: they apply the same fixed mindset across all steps, overlooking that different stages of solving the same problem require fundamentally different mindsets. This single‑minded assumption prevents models from reaching the next level of intelligence. To address this limitation, we propose Chain of Mindset (CoM), a training‑free agentic framework that enables step‑level adaptive mindset orchestration. CoM decomposes reasoning into four functionally heterogeneous mindsets: Spatial, Convergent, Divergent, and Algorithmic. A Meta‑Agent dynamically selects the optimal mindset based on the evolving reasoning state, while a bidirectional Context Gate filters cross‑module information flow to maintain effectiveness and efficiency. Experiments across six challenging benchmarks spanning mathematics, code generation, scientific QA, and spatial reasoning demonstrate that CoM achieves state‑of‑the‑art performance, outperforming the strongest baseline by 4.96% and 4.72% in overall accuracy on Qwen3‑VL‑32B‑Instruct and Gemini‑2.0‑Flash, while balancing reasoning efficiency. Our code is publicly available at \hrefhttps://github.com/QuantaAlpha/chain‑of‑mindsethttps://github.com/QuantaAlpha/chain‑of‑mindset.

PaperId: 3147, https://arxiv.org/pdf/2602.10048.pdf GitHub

Abstract:
Large Language Models (LLMs) often generate unnecessarily verbose Chain‑of‑Thought (CoT) reasoning that increases computational costs and latency without proportional performance gains. In this paper, we propose Fine‑grained Group policy Optimization (FGO), a Reinforcement Learning (RL) algorithm that refines group responses by subdividing them and assigning appropriate weights based on length and entropy, thereby enabling effective CoT compression. Meanwhile, as an enhanced variant of Group Relative Policy Optimization (GRPO), FGO successfully addresses two major limitations of the GRPO: inefficient data utilization and entropy collapse. We evaluate FGO on multiple reasoning LLMs and benchmarks, including MATH500, AIME24, AMC23, and Minerva. Experimental results show that FGO achieves efficient CoT compression without degrading performance, and simultaneously resolves the key limitations of GRPO. Code: https://github.com/Mr‑XcHan/FGO.

PaperId: 3148, https://arxiv.org/pdf/2602.10021.pdf GitHub

Abstract:
The integration of extensive, dynamic knowledge into Large Language Models (LLMs) remains a significant challenge due to the inherent entanglement of factual data and reasoning patterns. Existing solutions, ranging from non‑parametric Retrieval‑Augmented Generation (RAG) to parametric knowledge editing, are often constrained in practice by finite context windows, retriever noise, or the risk of catastrophic forgetting. In this paper, we propose DRIFT, a novel dual‑model architecture designed to explicitly decouple knowledge extraction from the reasoning process. Unlike static prompt compression, DRIFT employs a lightweight knowledge model to dynamically compress document chunks into implicit fact tokens conditioned on the query. These dense representations are projected into the reasoning model's embedding space, replacing raw, redundant text while maintaining inference accuracy. Extensive experiments show that DRIFT significantly improves performance on long‑context tasks, outperforming strong baselines among comparably sized models. Our approach provides a scalable and efficient paradigm for extending the effective context window and reasoning capabilities of LLMs. Our code is available at https://github.com/Lancelot‑Xie/DRIFT.

PaperId: 3149, https://arxiv.org/pdf/2602.10007.pdf GitHub

Abstract:
Lane changing in dense traffic is a significant challenge for Connected and Autonomous Vehicles (CAVs). Existing lane change controllers primarily either ensure safety or collaboratively improve traffic efficiency, but do not consider these conflicting objectives together. To address this, we propose the Multi‑Agent Safety Shield (MASS), designed using Control Barrier Functions (CBFs) to enable safe and collaborative lane changes. The MASS enables collaboration by capturing multi‑agent interactions among CAVs through interaction topologies constructed as a graph using a simple algorithm. Further, a state‑of‑the‑art Multi‑Agent Reinforcement Learning (MARL) lane change controller is extended by integrating MASS to ensure safety and defining a customised reward function to prioritise efficiency improvements. As a result, we propose a lane change controller, known as MARL‑MASS, and evaluate it in a congested on‑ramp merging simulation. The results demonstrate that MASS enables collaborative lane changes with safety guarantees by strictly respecting the safety constraints. Moreover, the proposed custom reward function improves the stability of MARL policies trained with a safety shield. Overall, by encouraging the exploration of a collaborative lane change policy while respecting safety constraints, MARL‑MASS effectively balances the trade‑off between ensuring safety and improving traffic efficiency in congested traffic. The code for MARL‑MASS is available with an open‑source licence at https://github.com/hkbharath/MARL‑MASS

PaperId: 3150, https://arxiv.org/pdf/2602.09987.pdf GitHub

Abstract:
Influence functions are commonly used to attribute model behavior to training documents. We explore the reverse: crafting training data that induces model behavior. Our framework, Infusion, uses scalable influence‑function approximations to compute small perturbations to training documents that induce targeted changes in model behavior through parameter shifts. We evaluate Infusion on data poisoning tasks across vision and language domains. On CIFAR‑10, we show that making subtle edits via Infusion to just 0.2% (100/45,000) of the training documents can be competitive with the baseline of inserting a small number of explicit behavior examples. We also find that Infusion transfers across architectures (ResNet \leftrightarrow CNN), suggesting a single poisoned corpus can affect multiple independently trained models. In preliminary language experiments, we characterize when our approach increases the probability of target behaviors and when it fails, finding it most effective at amplifying behaviors the model has already learned. Taken together, these results show that small, subtle edits to training data can systematically shape model behavior, underscoring the importance of training data interpretability for adversaries and defenders alike. We provide the code here: https://github.com/jrosseruk/infusion.

PaperId: 3151, https://arxiv.org/pdf/2602.09929.pdf GitHub

Abstract:
Monocular normal estimation aims to estimate the normal map from a single RGB image of an object under arbitrary lights. Existing methods rely on deep models to directly predict normal maps. However, they often suffer from 3D misalignment: while the estimated normal maps may appear to have a correct appearance, the reconstructed surfaces often fail to align with the geometric details. We argue that this misalignment stems from the current paradigm: the model struggles to distinguish and reconstruct varying geometry represented in normal maps, as the differences in underlying geometry are reflected only through relatively subtle color variations. To address this issue, we propose a new paradigm that reformulates normal estimation as shading sequence estimation, where shading sequences are more sensitive to various geometric information. Building on this paradigm, we present RoSE, a method that leverages image‑to‑video generative models to predict shading sequences. The predicted shading sequences are then converted into normal maps by solving a simple ordinary least‑squares problem. To enhance robustness and better handle complex objects, RoSE is trained on a synthetic dataset, MultiShade, with diverse shapes, materials, and light conditions. Experiments demonstrate that RoSE achieves state‑of‑the‑art performance on real‑world benchmark datasets for object‑based monocular normal estimation.

PaperId: 3152, https://arxiv.org/pdf/2602.09924.pdf GitHub

Abstract:
Running LLMs with extended reasoning on every problem is expensive, but determining which inputs actually require additional compute remains challenging. We investigate whether their own likelihood of success is recoverable from their internal representations before generation, and if this signal can guide more efficient inference. We train linear probes on pre‑generation activations to predict policy‑specific success on math and coding tasks, substantially outperforming surface features such as question length and TF‑IDF. Using E2H‑AMC, which provides both human and model performance on identical problems, we show that models encode a model‑specific notion of difficulty that is distinct from human difficulty, and that this distinction increases with extended reasoning. Leveraging these probes, we demonstrate that routing queries across a pool of models can exceed the best‑performing model whilst reducing inference cost by up to 70% on MATH, showing that internal representations enable practical efficiency gains even when they diverge from human intuitions about difficulty. Our code is available at: https://github.com/KabakaWilliam/llms_know_difficulty

PaperId: 3153, https://arxiv.org/pdf/2602.09856.pdf GitHub GitHub GitHub

Abstract:
Autonomous GUI agents interact with environments by perceiving interfaces and executing actions. As a virtual sandbox, the GUI World model empowers agents with human‑like foresight by enabling action‑conditioned prediction. However, existing text‑ and pixel‑based approaches struggle to simultaneously achieve high visual fidelity and fine‑grained structural controllability. To this end, we propose Code2World, a vision‑language coder that simulates the next visual state via renderable code generation. Specifically, to address the data scarcity problem, we construct AndroidCode by translating GUI trajectories into high‑fidelity HTML and refining synthesized code through a visual‑feedback revision mechanism, yielding a corpus of over 80K high‑quality screen‑action pairs. To adapt existing VLMs into code prediction, we first perform SFT as a cold start for format layout following, then further apply Render‑Aware Reinforcement Learning which uses rendered outcome as the reward signal by enforcing visual semantic fidelity and action consistency. Extensive experiments demonstrate that Code2World‑8B achieves the top‑performing next UI prediction, rivaling the competitive GPT‑5 and Gemini‑3‑Pro‑Image. Notably, Code2World significantly enhances downstream navigation success rates in a flexible manner, boosting Gemini‑2.5‑Flash by +9.5% on AndroidWorld navigation. The code is available at https://github.com/AMAP‑ML/Code2World.

PaperId: 3154, https://arxiv.org/pdf/2602.09782.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a critical method for enhancing the reasoning capabilities of Large Language Models (LLMs). However, continuous training often leads to policy entropy collapse, characterized by a rapid decay in entropy that results in premature overconfidence, reduced output diversity, and vanishing gradient norms that inhibit learning. Gradient‑Preserving Clipping is a primary factor influencing these dynamics, but existing mitigation strategies are largely static and lack a framework connecting clipping mechanisms to precise entropy control. This paper proposes reshaping entropy control in RL from the perspective of Gradient‑Preserving Clipping. We first theoretically and empirically verify the contributions of specific importance sampling ratio regions to entropy growth and reduction. Leveraging these findings, we introduce a novel regulation mechanism using dynamic clipping thresholds to precisely manage entropy. Furthermore, we design and evaluate dynamic entropy control strategies, including increase‑then‑decrease, decrease‑increase‑decrease, and oscillatory decay. Experimental results demonstrate that these strategies effectively mitigate entropy collapse and achieve superior performance across multiple benchmarks.

PaperId: 3155, https://arxiv.org/pdf/2602.09642.pdf GitHub

Abstract:
Recent advances in Large Language Models (LLMs) have significantly improved table understanding tasks such as Table Question Answering (TableQA), yet challenges remain in ensuring reliability, scalability, and efficiency, especially in resource‑constrained or privacy‑sensitive environments. In this paper, we introduce MATA, a multi‑agent TableQA framework that leverages multiple complementary reasoning paths and a set of tools built with small language models. MATA generates candidate answers through diverse reasoning styles for a given table and question, then refines or selects the optimal answer with the help of these tools. Furthermore, it incorporates an algorithm designed to minimize expensive LLM agent calls, enhancing overall efficiency. MATA maintains strong performance with small, open‑source models and adapts easily across various LLM types. Extensive experiments on two benchmarks of varying difficulty with ten different LLMs demonstrate that MATA achieves state‑of‑the‑art accuracy and highly efficient reasoning while avoiding excessive LLM inference. Our results highlight that careful orchestration of multiple reasoning pathways yields scalable and reliable TableQA. The code is available at https://github.com/AIDASLab/MATA.

PaperId: 3156, https://arxiv.org/pdf/2602.09570.pdf GitHub GitHub GitHub

Abstract:
Large language models (LLMs) are increasingly used to access legal information. Yet, their deployment in multilingual legal settings is constrained by unreliable retrieval and the lack of domain‑adapted, open‑embedding models. In particular, existing multilingual legal corpora are not designed for semantic retrieval, and PDF‑based legislative sources introduce substantial noise due to imperfect text extraction. To address these challenges, we introduce LEMUR, a large‑scale multilingual corpus of EU environmental legislation constructed from 24,953 official EUR‑Lex PDF documents covering 25 languages. We quantify the fidelity of PDF‑to‑text conversion by measuring lexical consistency against authoritative HTML versions using the Lexical Content Score (LCS). Building on LEMUR, we fine‑tune three state‑of‑the‑art multilingual embedding models using contrastive objectives in both monolingual and bilingual settings, reflecting realistic legal‑retrieval scenarios. Experiments across low‑ and high‑resource languages demonstrate that legal‑domain fine‑tuning consistently improves Top‑k retrieval accuracy relative to strong baselines, with particularly pronounced gains for low‑resource languages. Cross‑lingual evaluations show that these improvements transfer to unseen languages, indicating that fine‑tuning primarily enhances language‑independent, content‑level legal representations rather than language‑specific cues. We publish code\footnote\hrefhttps://github.com/nargesbh/eur_lexGitHub Repository and data\footnote\hrefhttps://huggingface.co/datasets/G4KMU/LEMURHugging Face Dataset.

PaperId: 3157, https://arxiv.org/pdf/2602.09552.pdf GitHub GitHub

Abstract:
Conversational question answering increasingly relies on retrieval‑augmented generation (RAG) to ground large language models (LLMs) in external knowledge. Yet, most existing studies evaluate RAG methods in isolation and primarily focus on single‑turn settings. This paper addresses the lack of a systematic comparison of RAG methods for multi‑turn conversational QA, where dialogue history, coreference, and shifting user intent substantially complicate retrieval. We present a comprehensive empirical study of vanilla and advanced RAG methods across eight diverse conversational QA datasets spanning multiple domains. Using a unified experimental setup, we evaluate retrieval quality and answer generation using generator and retrieval metrics, and analyze how performance evolves across conversation turns. Our results show that robust yet straightforward methods, such as reranking, hybrid BM25, and HyDE, consistently outperform vanilla RAG. In contrast, several advanced techniques fail to yield gains and can even degrade performance below the No‑RAG baseline. We further demonstrate that dataset characteristics and dialogue length strongly influence retrieval effectiveness, explaining why no single RAG strategy dominates across settings. Overall, our findings indicate that effective conversational RAG depends less on method complexity than on alignment between the retrieval strategy and the dataset structure. We publish the code used.\footnote\hrefhttps://github.com/Klejda‑A/exp‑rag.gitGitHub Repository

PaperId: 3158, https://arxiv.org/pdf/2602.09475.pdf GitHub

Abstract:
Modern image generators produce strikingly realistic images, where only artifacts like distorted hands or warped objects reveal their synthetic origin. Detecting these artifacts is essential: without detection, we cannot benchmark generators or train reward models to improve them. Current detectors fine‑tune VLMs on tens of thousands of labeled images, but this is expensive to repeat whenever generators evolve or new artifact types emerge. We show that pretrained VLMs already encode the knowledge needed to detect artifacts ‑ with the right scaffolding, this capability can be unlocked using only a few hundred labeled examples per artifact category. Our system, ArtifactLens, achieves state‑of‑the‑art on five human artifact benchmarks (the first evaluation across multiple datasets) while requiring orders of magnitude less labeled data. The scaffolding consists of a multi‑component architecture with in‑context learning and text instruction optimization, with novel improvements to each. Our methods generalize to other artifact types ‑ object morphology, animal anatomy, and entity interactions ‑ and to the distinct task of AIGC detection.

PaperId: 3159, https://arxiv.org/pdf/2602.09444.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly deployed in multicultural settings; however, systematic evaluation of cultural specificity at the sentence level remains underexplored. We propose the Conceptual Cultural Index (CCI), which estimates cultural specificity at the sentence level. CCI is defined as the difference between the generality estimate within the target culture and the average generality estimate across other cultures. This formulation enables users to operationally control the scope of culture via comparison settings and provides interpretability, since the score derives from the underlying generality estimates. We validate CCI on 400 sentences (200 culture‑specific and 200 general), and the resulting score distribution exhibits the anticipated pattern: higher for culture‑specific sentences and lower for general ones. For binary separability, CCI outperforms direct LLM scoring, yielding more than a 10‑point improvement in AUC for models specialized to the target culture. Our code is available at https://github.com/IyatomiLab/CCI .

PaperId: 3160, https://arxiv.org/pdf/2602.09379.pdf GitHub

Abstract:
Mental disorders are highly prevalent worldwide, but the shortage of psychiatrists and the inherent subjectivity of interview‑based diagnosis create substantial barriers to timely and consistent mental‑health assessment. Progress in AI‑assisted psychiatric diagnosis is constrained by the absence of benchmarks that simultaneously provide realistic patient simulation, clinician‑verified diagnostic labels, and support for dynamic multi‑turn consultation. We present LingxiDiagBench, a large‑scale multi‑agent benchmark that evaluates LLMs on both static diagnostic inference and dynamic multi‑turn psychiatric consultation in Chinese. At its core is LingxiDiag‑16K, a dataset of 16,000 EMR‑aligned synthetic consultation dialogues designed to reproduce real clinical demographic and diagnostic distributions across 12 ICD‑10 psychiatric categories. Through extensive experiments across state‑of‑the‑art LLMs, we establish key findings: (1) although LLMs achieve high accuracy on binary depression‑‑anxiety classification (up to 92.3%), performance deteriorates substantially for depression‑‑anxiety comorbidity recognition (43.0%) and 12‑way differential diagnosis (28.5%); (2) dynamic consultation often underperforms static evaluation, indicating that ineffective information‑gathering strategies significantly impair downstream diagnostic reasoning; (3) consultation quality assessed by LLM‑as‑a‑Judge shows only moderate correlation with diagnostic accuracy, suggesting that well‑structured questioning alone does not ensure correct diagnostic decisions. We release LingxiDiag‑16K and the full evaluation framework to support reproducible research at https://github.com/Lingxi‑mental‑health/LingxiDiagBench.

PaperId: 3161, https://arxiv.org/pdf/2602.09345.pdf GitHub

Abstract:
AI agents are increasingly deployed in multi‑tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations. We present a systematic characterization of OS‑level resource dynamics in sandboxed AI coding agents, analyzing 144 software engineering tasks from the SWE‑rebench benchmark across two LLM models. Our measurements reveal that (1) OS‑level execution (tool calls, container and agent initialization) accounts for 56‑74% of end‑to‑end task latency; (2) memory, not CPU, is the concurrency bottleneck; (3) memory spikes are tool‑call‑driven with a up to 15.4x peak‑to‑average ratio; and (4) resource demands are highly unpredictable across tasks, runs, and models. Comparing these characteristics against serverless, microservice, and batch workloads, we identify three mismatches in existing resource controls: a granularity mismatch (container‑level policies vs. tool‑call‑level dynamics), a responsiveness mismatch (user‑space reaction vs. sub‑second unpredictable bursts), and an adaptability mismatch (history‑based prediction vs. non‑deterministic stateful execution). We propose AgentCgroup, an intent‑driven eBPF‑based resource controller that exploits agents ability to declare resource needs and reconstruct execution strategies, using hierarchical cgroup structures aligned with tool‑call boundaries, in‑kernel enforcement via sched_ext and memcg_bpf_ops, and runtime‑adaptive policies. Preliminary evaluation demonstrates improved multi‑tenant isolation and reduced resource waste. AgentCgroup is open‑source at https://github.com/eunomia‑bpf/agentcgroup

PaperId: 3162, https://arxiv.org/pdf/2602.09255.pdf GitHub

Abstract:
Mobile robots are often deployed over long durations in diverse open, dynamic scenes, including indoor setting such as warehouses and manufacturing facilities, and outdoor settings such as agricultural and roadway operations. A core challenge is to build a scalable long‑horizon memory that supports an agentic workflow for planning, retrieval, and reasoning over open‑ended instructions at variable granularity, while producing precise, actionable answers for navigation. We present STaR, an agentic reasoning framework that (i) constructs a task‑agnostic, multimodal long‑term memory that generalizes to unseen queries while preserving fine‑grained environmental semantics (object attributes, spatial relations, and dynamic events), and (ii) introduces a Scalable Task Conditioned Retrieval algorithm based on the Information Bottleneck principle to extract from long‑term memory a compact, non‑redundant, information‑rich set of candidate memories for contextual reasoning. We evaluate STaR on NaVQA (mixed indoor/outdoor campus scenes) and WH‑VQA, a customized warehouse benchmark with many visually similar objects built with Isaac Sim, emphasizing contextual reasoning. Across the two datasets, STaR consistently outperforms strong baselines, achieving higher success rates and markedly lower spatial error. We further deploy STaR on a real Husky wheeled robot in both indoor and outdoor environments, demonstrating robust long horizon reasoning, scalability, and practical utility. Project Website: https://trailab.github.io/STaR‑website/

PaperId: 3163, https://arxiv.org/pdf/2602.09082.pdf GitHub

Abstract:
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging. In this report, we present UI‑Venus‑1.5, a unified, end‑to‑end GUI Agent designed for robust real‑world applications. The proposed model family comprises two dense variants (2B and 8B) and one mixture‑of‑experts variant (30B‑A3B) to meet various downstream application scenarios. Compared to our previous version, UI‑Venus‑1.5 introduces three key technical advances: (1) a comprehensive Mid‑Training stage leveraging 10 billion tokens across 30+ datasets to establish foundational GUI semantics; (2) Online Reinforcement Learning with full‑trajectory rollouts, aligning training objectives with long‑horizon, dynamic navigation in large‑scale environments; and (3) a single unified GUI Agent constructed via Model Merging, which synthesizes domain‑specific models (grounding, web, and mobile) into one cohesive checkpoint. Extensive evaluations demonstrate that UI‑Venus‑1.5 establishes new state‑of‑the‑art performance on benchmarks such as ScreenSpot‑Pro (69.6%), VenusBench‑GD (75.0%), and AndroidWorld (77.6%), significantly outperforming previous strong baselines. In addition, UI‑Venus‑1.5 demonstrates robust navigation capabilities across a variety of Chinese mobile apps, effectively executing user instructions in real‑world scenarios. Code: https://github.com/inclusionAI/UI‑Venus; Model: https://huggingface.co/collections/inclusionAI/ui‑venus

PaperId: 3164, https://arxiv.org/pdf/2602.09051.pdf GitHub

Abstract:
Optimizing Pandas programs is a challenging problem. Existing systems and compiler‑based approaches offer reliability but are either heavyweight or support only a limited set of optimizations. Conversely, using LLMs in a per‑program optimization methodology can synthesize nontrivial optimizations, but is unreliable, expensive, and offers a low yield. In this work, we introduce a hybrid approach that works in a 3‑stage manner that decouples discovery from deployment and connects them via a novel bridge. First, it discovers per‑program optimizations (discovery). Second, they are converted into generalised rewrite rules (bridge). Finally, these rules are incorporated into a compiler that can automatically apply them wherever applicable, eliminating repeated reliance on LLMs (deployment). We demonstrate that RuleFlow is the new state‑of‑the‑art (SOTA) Pandas optimization framework on PandasBench, a challenging Pandas benchmark consisting of Python notebooks. Across these notebooks, we achieve a speedup of up to 4.3x over Dias, the previous compiler‑based SOTA, and 1914.9x over Modin, the previous systems‑based SOTA. Our code is available at https://github.com/ADAPT‑uiuc/RuleFlow.

PaperId: 3165, https://arxiv.org/pdf/2602.09050.pdf GitHub

Abstract:
Cross‑domain image registration requires aligning images acquired under heterogeneous imaging physics, where the classical brightness constancy assumption is fundamentally violated. We formulate this problem through an image formation model I = R(s, a) + epsilon, where each observation is generated by a rendering function R acting on domain‑invariant scene structure s and domain‑specific appearance statistics a. Registration then reduces to an inverse rendering problem: given observations from two domains, recover the shared structure and re‑render it under the target appearance to obtain the registered output. We instantiate this framework as SAS‑Net (Scene‑Appearance Separation Network), where instance normalization implements the structure‑appearance decomposition and Adaptive Instance Normalization (AdaIN) realizes the differentiable forward renderer. A scene consistency loss enforces geometric correspondence in the factorized latent space. Experiments on EuroSAT‑Reg‑256 (satellite remote sensing) and FIRE‑Reg‑256 (retinal fundus) demonstrate state‑of‑the‑art performance across heterogeneous imaging domains. SAS‑Net (3.35M parameters) achieves 89 FPS on an RTX 5090 GPU. Code: https://github.com/D‑ST‑Sword/SAS‑Net.

PaperId: 3166, https://arxiv.org/pdf/2602.09040.pdf GitHub

Abstract:
Joint Embedding Predictive Architectures (JEPA) offer a promising approach to self‑supervised speech representation learning, but suffer from representation collapse without explicit grounding. We propose GMM‑Anchored JEPA, which fits a Gaussian Mixture Model once on log‑mel spectrograms and uses its frozen soft posteriors as auxiliary targets throughout training. A decaying supervision schedule allows GMM regularization to dominate early training before gradually yielding to the JEPA objective. Unlike HuBERT and WavLM, which require iterative re‑clustering, our approach clusters input features once with soft rather than hard assignments. On ~50k hours of speech, GMM anchoring improves ASR (28.68% vs. 33.22% WER), emotion recognition (67.76% vs. 65.46%), and slot filling (64.7% vs. 59.1% F1) compared to a WavLM‑style baseline with matched compute. Cluster analysis shows GMM‑anchored representations achieve up to 98% entropy compared to 31% for WavLM‑style, indicating substantially more uniform cluster utilization. Code is made available at https://github.com/gioannides/clustering‑anchored‑jepa.

PaperId: 3167, https://arxiv.org/pdf/2602.09012.pdf GitHub

Abstract:
The rapid evolution of GUI‑enabled agents has rendered traditional CAPTCHAs obsolete. While previous benchmarks like OpenCaptchaWorld established a baseline for evaluating multimodal agents, recent advancements in reasoning‑heavy models, such as Gemini3‑Pro‑High and GPT‑5.2‑Xhigh have effectively collapsed this security barrier, achieving pass rates as high as 90% on complex logic puzzles like "Bingo". In response, we introduce Next‑Gen CAPTCHAs, a scalable defense framework designed to secure the next‑generation web against the advanced agents. Unlike static datasets, our benchmark is built upon a robust data generation pipeline, allowing for large‑scale and easily scalable evaluations, notably, for backend‑supported types, our system is capable of generating effectively unbounded CAPTCHA instances. We exploit the persistent human‑agent "Cognitive Gap" in interactive perception, memory, decision‑making, and action. By engineering dynamic tasks that require adaptive intuition rather than granular planning, we re‑establish a robust distinction between biological users and artificial agents, offering a scalable and diverse defense mechanism for the agentic era.

PaperId: 3168, https://arxiv.org/pdf/2602.09007.pdf GitHub

Abstract:
Recent advancements in image generation models have enabled the prediction of future Graphical User Interface (GUI) states based on user instructions. However, existing benchmarks primarily focus on general domain visual fidelity, leaving the evaluation of state transitions and temporal coherence in GUI‑specific contexts underexplored. To address this gap, we introduce GEBench, a comprehensive benchmark for evaluating dynamic interaction and temporal coherence in GUI generation. GEBench comprises 700 carefully curated samples spanning five task categories, covering both single‑step interactions and multi‑step trajectories across real‑world and fictional scenarios, as well as grounding point localization. To support systematic evaluation, we propose GE‑Score, a novel five‑dimensional metric that assesses Goal Achievement, Interaction Logic, Content Consistency, UI Plausibility, and Visual Quality. Extensive evaluations on current models indicate that while they perform well on single‑step transitions, they struggle significantly with maintaining temporal coherence and spatial grounding over longer interaction sequences. Our findings identify icon interpretation, text rendering, and localization precision as critical bottlenecks. This work provides a foundation for systematic assessment and suggests promising directions for future research toward building high‑fidelity generative GUI environments. The code is available at: https://github.com/stepfun‑ai/GEBench.

PaperId: 3169, https://arxiv.org/pdf/2602.08990.pdf GitHub

Authors:Shiyang Feng, Runmin Ma, Xiangchao Yan, Yue Fan, Yusong Hu, Songtao Huang, Shuaiyu Zhang, Zongsheng Cao, Tianshuo Peng, Jiakang Yuan, Zijie Guo, Zhijie Zhong, Shangheng Du, Weida Wang, Jinxin Shi, Yuhao Zhou, Xiaohan He, Zhiyin Yu, Fangchen Yu, Qihao Zheng, Jiamin Wu, Mianxin Liu, Chi Zhang, Shaowei Hou, Shuya Li, Yankai Jiang, Wenjie Lou, Lilong Wang, Zifu Wang, Jiong Wang, Wanghan Xu, Yue Deng, Dongrui Liu, Yiheng Wang, Wenlong Zhang, Fenghua Ling, Shufei Zhang, Xiaosong Wang, Shuangjia Zheng, Xun Huang, Siqi Sun, Shuyue Hu, Peng Ye, Chunfeng Song, Bin Wang, Conghui He, Yihao Liu, Xin Li, Qibin Hou, Tao Chen, Xiangyu Yue, Bin Wang, Liang He, Dahua Lin, Bowen Zhou, Bo Zhang, Lei Bai

Abstract:
We introduce InternAgent‑1.5, a unified system designed for end‑to‑end scientific discovery across computational and empirical domains. The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution. These subsystems are supported by foundational capabilities for deep research, solution optimization, and long horizon memory. The architecture allows InternAgent‑1.5 to operate continuously across extended discovery cycles while maintaining coherent and improving behavior. It also enables the system to coordinate computational modeling and laboratory experimentation within a single unified system. We evaluate InternAgent‑1.5 on scientific reasoning benchmarks such as GAIA, HLE, GPQA, and FrontierScience, and the system achieves leading performance that demonstrates strong foundational capabilities. Beyond these benchmarks, we further assess two categories of discovery tasks. In algorithm discovery tasks, InternAgent‑1.5 autonomously designs competitive methods for core machine learning problems. In empirical discovery tasks, it executes complete computational or wet lab experiments and produces scientific findings in earth, life, biological, and physical domains. Overall, these results show that InternAgent‑1.5 provides a general and scalable framework for autonomous scientific discovery.

PaperId: 3170, https://arxiv.org/pdf/2602.08961.pdf GitHub

Abstract:
We present MotionCrafter, a framework that leverages video generators to jointly reconstruct 4D geometry and estimate dense motion from a monocular video. The key idea is a joint representation of dense 3D point maps and 3D scene flows in a shared coordinate system, together with a 4D VAE tailored to learn this representation effectively. Unlike prior work that strictly aligns 3D values and latents with RGB VAE latents‑despite their fundamentally different distributions‑we show that such alignment is unnecessary and can hurt performance. Instead, we propose a new data normalization and VAE training strategy that better transfers diffusion priors and greatly improves reconstruction quality. Extensive experiments on multiple datasets show that MotionCrafter achieves state‑of‑the‑art performance in both geometry reconstruction and dense scene flow estimation, delivering 38.64% and 25.0% improvements in geometry and motion reconstruction, respectively, all without any post‑optimization. Project page: https://ruijiezhu94.github.io/MotionCrafter_Page

PaperId: 3171, https://arxiv.org/pdf/2602.08939.pdf GitHub

Abstract:
LLM failures in causal reasoning, including sycophancy, rung collapse, and miscalibrated refusal, are well‑documented, yet progress on remediation is slow because no benchmark enables systematic diagnosis. We introduce CausalT5K, a diagnostic benchmark of over 5,000 cases across 10 domains that tests three critical capabilities: (1) detecting rung collapse, where models answer interventional queries with associational evidence; (2) resisting sycophantic drift under adversarial pressure; and (3) generating Wise Refusals that specify missing information when evidence is underdetermined. Unlike synthetic benchmarks, CausalT5K embeds causal traps in realistic narratives and decomposes performance into Utility (sensitivity) and Safety (specificity), revealing failure modes invisible to aggregate accuracy. Developed through a rigorous human‑machine collaborative pipeline involving 40 domain experts, iterative cross‑validation cycles, and composite verification via rule‑based, LLM, and human scoring, CausalT5K implements Pearl's Ladder of Causation as research infrastructure. Preliminary experiments reveal a Four‑Quadrant Control Landscape where static audit policies universally fail, a finding that demonstrates CausalT5K's value for advancing trustworthy reasoning systems. Repository: https://github.com/genglongling/CausalT5kBench

PaperId: 3172, https://arxiv.org/pdf/2602.08934.pdf GitHub

Abstract:
AI‑text detectors face a critical robustness challenge: adversarial paraphrasing attacks that preserve semantics while evading detection. We introduce StealthRL, a reinforcement learning framework that stress‑tests detector robustness under realistic adversarial conditions. StealthRL trains a paraphrase policy against a multi‑detector ensemble using Group Relative Policy Optimization (GRPO) with LoRA adapters on Qwen3‑4B, optimizing a composite reward that balances detector evasion with semantic preservation. We evaluate six attack settings (M0‑M5) on the full filtered MAGE test pool (15,310 human / 14,656 AI) against four detectors: RoBERTa, Fast‑DetectGPT, Binoculars, and MAGE. StealthRL achieves near‑zero detection on three of the four detectors and a 0.024 mean TPR@1%FPR, reducing mean AUROC from 0.79 to 0.43 and attaining a 97.6% attack success rate. Critically, attacks transfer to two held‑out detectors not seen during training, revealing shared architectural vulnerabilities rather than detector‑specific brittleness. We additionally conduct LLM‑based quality evaluation via Likert scoring on 500 matched samples per method, analyze detector score distributions to explain why evasion succeeds, and provide per‑detector AUROC with bootstrap confidence intervals. Our results expose significant robustness gaps in current AI‑text detection and establish StealthRL as a principled adversarial evaluation protocol. Code and evaluation pipeline are publicly available at https://github.com/suraj‑ranganath/StealthRL.

PaperId: 3173, https://arxiv.org/pdf/2602.08905.pdf GitHub

Abstract:
Reinforcement Learning (RL) is crucial for unlocking the complex reasoning capabilities of Diffusion‑based Large Language Models (dLLMs). However, applying RL to dLLMs faces unique challenges in efficiency and stability. To address these challenges, we propose Spatio‑Temporal Pruning (STP), a framework designed to simultaneously improve the efficiency and stability of RL for dLLMs. STP compresses the redundancy in the generative process through: (1) spatial pruning, which constrains the exploration space using static priors; and (2) temporal pruning, which bypasses redundant late‑stage refinement steps. Our theoretical analysis demonstrates that STP strictly reduces the variance of the log‑likelihood estimation, thereby ensuring more stable policy updates. Extensive experiments demonstrate that STP surpasses state‑of‑the‑art baselines in both efficiency and accuracy. Our code is available at https://github.com/Lolo1222/STP.

PaperId: 3174, https://arxiv.org/pdf/2602.08829.pdf GitHub

Abstract:
Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large‑scale human‑annotated preference pairs. With the widespread deployment of LLMs, in‑the‑wild interactions have emerged as a rich source of implicit reward signals. This raises the question: Can we develop reward models directly from in‑the‑wild interactions? In this work, we explore this possibility by adopting WildChat as an interaction source and proposing a pipeline to extract reliable human feedback, yielding 186k high‑quality instances for training WildReward via ordinal regression directly on user feedback without preference pairs. Extensive experiments demonstrate that WildReward achieves comparable or even superior performance compared to conventional reward models, with improved calibration and cross‑sample consistency. We also observe that WildReward benefits directly from user diversity, where more users yield stronger reward models. Finally, we apply WildReward to online DPO training and observe significant improvements across various tasks. Code and data are released at https://github.com/THU‑KEG/WildReward.

PaperId: 3175, https://arxiv.org/pdf/2602.08826.pdf GitHub

Abstract:
Large language models (LLMs) have been widely applied to emotional support conversation (ESC). However, complex multi‑turn support remains challenging.This is because existing alignment schemes rely on sparse outcome‑level signals, thus offering limited supervision for intermediate strategy decisions. To fill this gap, this paper proposes affective flow language model for emotional support conversation (AFlow), a framework that introduces fine‑grained supervision on dialogue prefixes by modeling a continuous affective flow along multi‑turn trajectories. AFlow can estimate intermediate utility over searched trajectories and learn preference‑consistent strategy transitions. To improve strategy coherence and empathetic response quality, a subpath‑level flow‑balance objective is presented to propagate preference signals to intermediate states. Experiment results show consistent and significant improvements over competitive baselines in diverse emotional contexts. Remarkably, AFlow with a compact open‑source backbone outperforms proprietary LMMs such as GPT‑4o and Claude‑3.5 on major ESC metrics. Our code is available at https://github.com/chz2025/AffectiveFlow.

PaperId: 3176, https://arxiv.org/pdf/2602.08783.pdf GitHub

Abstract:
Latent or continuous chain‑of‑thought methods replace explicit textual rationales with a number of internal latent steps, but these intermediate computations are difficult to evaluate beyond correlation‑based probes. In this paper, we view latent chain‑of‑thought as a manipulable causal process in representation space by modeling latent steps as variables in a structural causal model (SCM) and analyzing their effects through step‑wise do‑interventions. We study two representative paradigms (i.e., Coconut and CODI) on both mathematical and general reasoning tasks to investigate three key questions: (1) which steps are causally necessary for correctness and when answers become decodable early; (2) how influence propagates across steps and how this structure compares to explicit CoT; and (3) whether intermediate trajectories retain competing answer modes and how output‑level commitment differs from representational commitment across steps. We find that latent‑step budgets behave less like homogeneous extra depth and more like staged functionality with non‑local routing, and we identify a persistent gap between early output bias and late representational commitment. These results motivate mode‑conditional and stability‑aware analyses, together with corresponding training/decoding objectives, as more reliable tools for interpreting and improving latent reasoning systems. Code is available at https://github.com/J1mL1/causal‑latent‑cot.

PaperId: 3177, https://arxiv.org/pdf/2602.08727.pdf GitHub

Abstract:
Undersampled CT volumes minimize acquisition time and radiation exposure but introduce artifacts degrading image quality and diagnostic utility. Reducing these artifacts is critical for high‑quality imaging. We propose a computationally efficient hybrid deep‑learning framework that combines the strengths of 2D and 3D models. First, a 2D U‑Net operates on individual slices of undersampled CT volumes to extract feature maps. These slice‑wise feature maps are then stacked across the volume and used as input to a 3D decoder, which utilizes contextual information across slices to predict an artifact‑free 3D CT volume. The proposed two‑stage approach balances the computational efficiency of 2D processing with the volumetric consistency provided by 3D modeling. The results show substantial improvements in inter‑slice consistency in coronal and sagittal direction with low computational overhead. This hybrid framework presents a robust and efficient solution for high‑quality 3D CT image post‑processing. The code of this project can be found on github: https://github.com/J‑3TO/2D‑3DCNN_sparseview/.

PaperId: 3178, https://arxiv.org/pdf/2602.08675.pdf GitHub

Abstract:
This paper introduces 6G‑Bench, an open benchmark for evaluating semantic communication and network‑level reasoning in AI‑native 6G networks. 6G‑Bench defines a taxonomy of 30 decision‑making tasks (T1‑‑T30) extracted from ongoing 6G and AI‑agent standardization activities in 3GPP, IETF, ETSI, ITU‑T, and the O‑RAN Alliance, and organizes them into five standardization‑aligned capability categories. Starting from 113,475 scenarios, we generate a balanced pool of 10,000 very‑hard multiple‑choice questions using task‑conditioned prompts that enforce multi‑step quantitative reasoning under uncertainty and worst‑case regret minimization over multi‑turn horizons. After automated filtering and expert human validation, 3,722 questions are retained as a high‑confidence evaluation set, while the full pool is released to support training and fine‑tuning of 6G‑specialized models. Using 6G‑Bench, we evaluate 22 foundation models spanning dense and mixture‑of‑experts architectures, short‑ and long‑context designs (up to 1M tokens), and both open‑weight and proprietary systems. Across models, deterministic single‑shot accuracy (pass@1) spans a wide range from 0.22 to 0.82, highlighting substantial variation in semantic reasoning capability. Leading models achieve intent and policy reasoning accuracy in the range 0.87‑‑0.89, while selective robustness analysis on reasoning‑intensive tasks shows pass@5 values ranging from 0.20 to 0.91. To support open science and reproducibility, we release the 6G‑Bench dataset on GitHub: https://github.com/maferrag/6G‑Bench

PaperId: 3179, https://arxiv.org/pdf/2602.08629.pdf GitHub

Abstract:
Causal discovery is essential for advancing data‑driven fields such as scientific AI and data analysis, yet existing approaches face significant time‑ and space‑efficiency bottlenecks when scaling to large graphs. To address this challenge, we present CauScale, a neural architecture designed for efficient causal discovery that scales inference to graphs with up to 1000 nodes. CauScale improves time efficiency via a reduction unit that compresses data embeddings and improves space efficiency by adopting tied attention weights to avoid maintaining axis‑specific attention maps. To keep high causal discovery accuracy, CauScale adopts a two‑stream design: a data stream extracts relational evidence from high‑dimensional observations, while a graph stream integrates statistical graph priors and preserves key structural signals. CauScale successfully scales to 500‑node graphs during training, where prior work fails due to space limitations. Across testing data with varying graph scales and causal mechanisms, CauScale achieves 99.6% mAP on in‑distribution data and 84.4% on out‑of‑distribution data, while delivering 4‑13,000 times inference speedups over prior methods. Our project page is at https://github.com/OpenCausaLab/CauScale.

PaperId: 3180, https://arxiv.org/pdf/2602.08621.pdf GitHub

Abstract:
By introducing routers to selectively activate experts in Transformer layers, the mixture‑of‑experts (MoE) architecture significantly reduces computational costs in large language models (LLMs) while maintaining competitive performance, especially for models with massive parameters. However, prior work has largely focused on utility and efficiency, leaving the safety risks associated with this sparse architecture underexplored. In this work, we show that the safety of MoE LLMs is as sparse as their architecture by discovering unsafe routes: routing configurations that, once activated, convert safe outputs into harmful ones. Specifically, we first introduce the Router Safety importance score (RoSais) to quantify the safety criticality of each layer's router. Manipulation of only the high‑RoSais router(s) can flip the default route into an unsafe one. For instance, on JailbreakBench, masking 5 routers in DeepSeek‑V2‑Lite increases attack success rate (ASR) by over 4× to 0.79, highlighting an inherent risk that router manipulation may naturally occur in MoE LLMs. We further propose a Fine‑grained token‑layer‑wise Stochastic Optimization framework to discover more concrete Unsafe Routes (F‑SOUR), which explicitly considers the sequentiality and dynamics of input tokens. Across four representative MoE LLM families, F‑SOUR achieves an average ASR of 0.90 and 0.98 on JailbreakBench and AdvBench, respectively. Finally, we outline defensive perspectives, including safety‑aware route disabling and router training, as promising directions to safeguard MoE LLMs. We hope our work can inform future red‑teaming and safeguarding of MoE LLMs. Our code is provided in https://github.com/TrustAIRLab/UnsafeMoE.

PaperId: 3181, https://arxiv.org/pdf/2602.08563.pdf GitHub

Abstract:
Large language models (LLMs) are commonly treated as stateless: once an interaction ends, no information is assumed to persist unless it is explicitly stored and re‑supplied. We challenge this assumption by introducing implicit memory‑the ability of a model to carry state across otherwise independent interactions by encoding information in its own outputs and later recovering it when those outputs are reintroduced as input. This mechanism does not require any explicit memory module, yet it creates a persistent information channel across inference requests. As a concrete demonstration, we introduce a new class of temporal backdoors, which we call time bombs. Unlike conventional backdoors that activate on a single trigger input, time bombs activate only after a sequence of interactions satisfies hidden conditions accumulated via implicit memory. We show that such behavior can be induced today through straightforward prompting or fine‑tuning. Beyond this case study, we analyze broader implications of implicit memory, including covert inter‑agent communication, benchmark contamination, targeted manipulation, and training‑data poisoning. Finally, we discuss detection challenges and outline directions for stress‑testing and evaluation, with the goal of anticipating and controlling future developments. To promote future research, we release code and data at: https://github.com/microsoft/implicitMemory.

PaperId: 3182, https://arxiv.org/pdf/2602.08550.pdf GitHub

Abstract:
Human perception for effective object tracking in 2D video streams arises from the implicit use of prior 3D knowledge and semantic reasoning. In contrast, most generic object tracking (GOT) methods primarily rely on 2D features of the target and its surroundings, while neglecting 3D geometric cues, making them susceptible to partial occlusion, distractors, and variations in geometry and appearance. To address this limitation, we introduce GOT‑Edit, an online cross‑modality model editing approach that integrates geometry‑aware cues into a generic object tracker from a 2D video stream. Our approach leverages features from a pre‑trained Visual Geometry Grounded Transformer to infer geometric cues from only a few 2D images. To address the challenge of seamlessly combining geometry and semantics, GOT‑Edit performs online model editing. By leveraging null‑space constraints during model updates, it incorporates geometric information while preserving semantic discrimination, yielding consistently better performance across diverse scenarios. Extensive experiments on multiple GOT benchmarks demonstrate that GOT‑Edit achieves superior robustness and accuracy, particularly under occlusion and clutter, establishing a new paradigm for combining 2D semantics with 3D geometric reasoning for generic object tracking. The project page is available at https://chenshihfang.github.io/GOT‑EDIT.

PaperId: 3183, https://arxiv.org/pdf/2602.08543.pdf GitHub

Abstract:
The advancement of large language models (LLMs) has significantly accelerated the development of search agents capable of autonomously gathering information through multi‑turn web interactions. Various benchmarks have been proposed to evaluate such agents. However, existing benchmarks often construct queries backward from answers, producing unnatural tasks misaligned with real‑world needs. Moreover, these benchmarks tend to focus on either locating specific information or aggregating information from multiple sources, while relying on static answer sets prone to data contamination. To bridge these gaps, we introduce GISA, a benchmark for General Information‑Seeking Assistants comprising 373 human‑crafted queries that reflect authentic information‑seeking scenarios. GISA features four structured answer formats (item, set, list, and table), enabling deterministic evaluation. It integrates both deep reasoning and broad information aggregation within unified tasks, and includes a live subset with periodically updated answers to resist memorization. Notably, GISA provides complete human search trajectories for every query, offering gold‑standard references for process‑level supervision and imitation learning. Experiments on mainstream LLMs and commercial search products reveal that even the best‑performing model achieves only 19.30% exact match score, with performance notably degrading on tasks requiring complex planning and comprehensive information gathering. These findings highlight substantial room for future improvement.

PaperId: 3184, https://arxiv.org/pdf/2602.08517.pdf GitHub

Abstract:
Tensor is the most basic and essential data structure of nowadays artificial intelligence (AI) system. The natural properties of Tensor, especially the memory‑continuity and slice‑independence, make it feasible for training system to leverage parallel computing unit like GPU to process data simultaneously in batch, spatial or temporal dimensions. However, if we look beyond perception tasks, the data in a complicated cognitive AI system usually has hierarchical structures (i.e. nested data) with various modalities. They are inconvenient and inefficient to program directly with conventional Tensor with fixed shape. To address this issue, we summarize two main computational patterns of nested data, and then propose a general nested data container: TreeTensor. Through various constraints and magic utilities of TreeTensor, one can apply arbitrary functions and operations to nested data with almost zero cost, including some famous machine learning libraries, such as Scikit‑Learn, Numpy and PyTorch. Our approach utilizes a constrained tree‑structure perspective to systematically model data relationships, and it can also easily be combined with other methods to extend more usages, such as asynchronous execution and variable‑length data computation. Detailed examples and benchmarks show TreeTensor not only provides powerful usability in various problems, especially one of the most complicated AI systems at present: AlphaStar for StarCraftII, but also exhibits excellent runtime efficiency without any overhead. Our project is available at https://github.com/opendilab/DI‑treetensor.

PaperId: 3185, https://arxiv.org/pdf/2602.08412.pdf GitHub

Abstract:
Although large language model (LLM)‑based agents, exemplified by OpenClaw, are increasingly evolving from task‑oriented systems into personalized AI assistants for solving complex real‑world tasks, their practical deployment also introduces severe security risks. However, existing agent security research and evaluation frameworks primarily focus on synthetic or task‑centric settings, and thus fail to accurately capture the attack surface and risk propagation mechanisms of personalized agents in real‑world deployments. To address this gap, we propose Personalized Agent Security Bench (PASB), an end‑to‑end security evaluation framework tailored for real‑world personalized agents. Building upon existing agent attack paradigms, PASB incorporates personalized usage scenarios, realistic toolchains, and long‑horizon interactions, enabling black‑box, end‑to‑end security evaluation on real systems. Using OpenClaw as a representative case study, we systematically evaluate its security across multiple personalized scenarios, tool capabilities, and attack types. Our results indicate that OpenClaw exhibits critical vulnerabilities at different execution stages, including user prompt processing, tool usage, and memory retrieval, highlighting substantial security risks in personalized agent deployments. The code for the proposed PASB framework is available at https://github.com/AstorYH/PASB.

PaperId: 3186, https://arxiv.org/pdf/2602.08353.pdf GitHub

Abstract:
Temporal knowledge graphs (TKGs) structurally preserve evolving human knowledge. Recent research has focused on designing models to learn the evolutionary nature of TKGs to predict future facts, achieving impressive results. For instance, Hits@10 scores over 0.9 on YAGO dataset. However, we find that existing benchmarks inadvertently introduce a shortcut. Near state‑of‑the‑art performance can be simply achieved by counting co‑occurrences, without using any temporal information. In this work, we examine the root cause of this issue, identifying inherent biases in current datasets and over simplified form of evaluation task that can be exploited by these biases. Through this analysis, we further uncover additional limitations of existing benchmarks, including unreasonable formatting of time‑interval knowledge, ignorance of learning knowledge obsolescence, and insufficient information for precise evolution understanding, all of which can amplify the shortcut and hinder a fair assessment. Therefore, we introduce the TKG evolution benchmark. It includes four bias‑corrected datasets and two novel tasks closely aligned with the evolution process, promoting a more accurate understanding of the challenges in TKG evolution modeling. Benchmark is available at: https://github.com/zjs123/TKG‑Benchmark.

PaperId: 3187, https://arxiv.org/pdf/2602.08245.pdf GitHub

Abstract:
Diffusion policies have recently emerged as a powerful paradigm for visuomotor control in robotic manipulation due to their ability to model the distribution of action sequences and capture multimodality. However, iterative denoising leads to substantial inference latency, limiting control frequency in real‑time closed‑loop systems. Existing acceleration methods either reduce sampling steps, bypass diffusion through direct prediction, or reuse past actions, but often struggle to jointly preserve action quality and achieve consistently low latency. In this work, we propose STEP, a lightweight spatiotemporal consistency prediction mechanism to construct high‑quality warm‑start actions that are both distributionally close to the target action and temporally consistent, without compromising the generative capability of the original diffusion policy. Then, we propose a velocity‑aware perturbation injection mechanism that adaptively modulates actuation excitation based on temporal action variation to prevent execution stall especially for real‑world tasks. We further provide a theoretical analysis showing that the proposed prediction induces a locally contractive mapping, ensuring convergence of action errors during diffusion refinement. We conduct extensive evaluations on nine simulated benchmarks and two real‑world tasks. Notably, STEP with 2 steps can achieve an average 21.6% and 27.5% higher success rate than BRIDGER and DDIM on the RoboMimic benchmark and real‑world tasks, respectively. These results demonstrate that STEP consistently advances the Pareto frontier of inference latency and success rate over existing methods.The code is publicly available at https://github.com/Kimho666/STEP.

PaperId: 3188, https://arxiv.org/pdf/2602.08235.pdf GitHub

Abstract:
Although computer‑use agents (CUAs) hold significant potential to automate increasingly complex OS workflows, they can demonstrate unsafe unintended behaviors that deviate from expected outcomes even under benign input contexts. However, exploration of this risk remains largely anecdotal, lacking concrete characterization and automated methods to proactively surface long‑tail unintended behaviors under realistic CUA scenarios. To fill this gap, we introduce the first conceptual and methodological framework for unintended CUA behaviors, by defining their key characteristics, automatically eliciting them, and analyzing how they arise from benign inputs. We propose AutoElicit: an agentic framework that iteratively perturbs benign instructions using CUA execution feedback, and elicits severe harms while keeping perturbations realistic and benign. Using AutoElicit, we surface hundreds of harmful unintended behaviors from state‑of‑the‑art CUAs such as Claude 4.5 Haiku and Opus. We further evaluate the transferability of human‑verified successful perturbations, identifying persistent susceptibility to unintended behaviors across various other frontier CUAs. This work establishes a foundation for systematically analyzing unintended behaviors in realistic computer‑use settings.

PaperId: 3189, https://arxiv.org/pdf/2602.08233.pdf GitHub

Abstract:
While existing Singing Voice Synthesis systems achieve high‑fidelity solo performances, they are constrained by global timbre control, failing to address dynamic multi‑singer arrangement and vocal texture within a single song. To address this, we propose Tutti, a unified framework designed for structured multi‑singer generation. Specifically, we introduce a Structure‑Aware Singer Prompt to enable flexible singer scheduling evolving with musical structure, and propose Complementary Texture Learning via Condition‑Guided VAE to capture implicit acoustic textures (e.g., spatial reverberation and spectral fusion) that are complementary to explicit controls. Experiments demonstrate that Tutti excels in precise multi‑singer scheduling and significantly enhances the acoustic realism of choral generation, offering a novel paradigm for complex multi‑singer arrangement. Audio samples are available at https://annoauth123‑ctrl.github.io/Tutii_Demo/.

PaperId: 3190, https://arxiv.org/pdf/2602.08194.pdf GitHub GitHub

Abstract:
Open‑ended learning frames intelligence as emerging from continual interaction with an ever‑expanding space of environments. While recent advances have utilized foundation models to programmatically generate diverse environments, these approaches often focus on discovering isolated behaviors rather than orchestrating sustained progression. In complex open‑ended worlds, the large combinatorial space of possible challenges makes it difficult for agents to discover sequences of experiences that remain consistently learnable. To address this, we propose Dreaming in Code (DiCode), a framework in which foundation models synthesize executable environment code to scaffold learning toward increasing competence. In DiCode, "dreaming" takes the form of materializing code‑level variations of the world. We instantiate DiCode in Craftax, a challenging open‑ended benchmark characterized by rich mechanics and long‑horizon progression. Empirically, DiCode enables agents to acquire long‑horizon skills, achieving a 16% improvement in mean return over the strongest baseline and non‑zero success on late‑game combat tasks where prior methods fail. Our results suggest that code‑level environment design provides a practical mechanism for curriculum control, enabling the construction of intermediate environments that bridge competence gaps in open‑ended worlds. Project page and source code are available at https://konstantinosmitsides.github.io/dreaming‑in‑code and https://github.com/konstantinosmitsides/dreaming‑in‑code.

PaperId: 3191, https://arxiv.org/pdf/2602.08099.pdf GitHub

Abstract:
Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for vision tasks, typically through fine‑tuning to produce universal representations. However, their performance on video remains inferior to Video Foundation Models (VFMs). In this paper, we focus on leveraging MLLMs for video‑text embedding and retrieval. We first conduct a systematic layer‑wise analysis, showing that intermediate (pre‑trained) MLLM layers already encode substantial task‑relevant information. Leveraging this insight, we demonstrate that combining intermediate‑layer embeddings with a calibrated MLLM head yields strong zero‑shot retrieval performance without any training. Building on these findings, we introduce a lightweight text‑based alignment strategy which maps dense video captions to short summaries and enables task‑related video‑text embedding learning without visual supervision. Remarkably, without any fine‑tuning beyond text, our method outperforms current methods, often by a substantial margin, achieving state‑of‑the‑art results across common video retrieval benchmarks.

PaperId: 3192, https://arxiv.org/pdf/2602.08064.pdf GitHub

Abstract:
The long‑standing tension between Pre‑ and Post‑Norm remains an open problem in Transformer architecture, reflecting a fundamental trade‑off between training stability and representational capacity. Prior attempts to combine their strengths have made progress, but often show limited robustness across training settings, restricting their broader applicability. We revisit this dilemma, showing that single‑stream architectures struggle to reconcile Pre‑Norm's stable identity‑gradient propagation with Post‑Norm's normalization of the main residual path. To address this structural tension, we propose SiameseNorm, a simple yet effective two‑stream architecture that remains compatible with Pre‑Norm training recipes. SiameseNorm couples Pre‑Norm‑like and Post‑Norm‑like streams through shared residual blocks, allowing each residual block to receive optimization signals from both pathways with negligible overhead. Extensive experiments on 400M and 1.3B dense language models, 15B MoE models, Vision Transformers, and Diffusion Transformers show that SiameseNorm consistently improves performance while maintaining strong training stability across architectures and modalities. Code is available at https://github.com/Qwen‑Applications/SiameseNorm.

PaperId: 3193, https://arxiv.org/pdf/2602.08025.pdf GitHub

Abstract:
World models aim to understand, remember, and predict dynamic visual environments, yet a unified benchmark for evaluating their fundamental abilities remains lacking. To address this gap, we introduce MIND, the first open‑domain closed‑loop revisited benchmark for evaluating Memory consIstency and action coNtrol in worlD models. MIND contains 250 high‑quality videos at 1080p and 24 FPS, including 100 (first‑person) + 100 (third‑person) video clips under a shared action space and 25 + 25 clips across varied action spaces covering eight diverse scenes. We design an efficient evaluation framework to measure two core abilities: memory consistency and action control, capturing temporal stability and contextual coherence across viewpoints. Furthermore, we design various action spaces, including different character movement speeds and camera rotation angles, to evaluate the action generalization capability across different action spaces under shared scenes. To facilitate future performance benchmarking on MIND, we introduce MIND‑World, a novel interactive Video‑to‑World baseline. Extensive experiments demonstrate the completeness of MIND and reveal key challenges in current world models, including the difficulty of maintaining long‑term memory consistency and generalizing across action spaces. Code: https://github.com/CSU‑JPG/MIND.

PaperId: 3194, https://arxiv.org/pdf/2602.08024.pdf GitHub

Abstract:
Although Video Large Language Models (VLLMs) have shown remarkable capabilities in video understanding, they are required to process high volumes of visual tokens, causing significant computational inefficiency. Existing VLLMs acceleration frameworks usually compress spatial and temporal redundancy independently, which overlooks the spatiotemporal relationships, thereby leading to suboptimal spatiotemporal compression. The highly correlated visual features are likely to change in spatial position, scale, orientation, and other attributes over time due to the dynamic nature of video. Building on this insight, we introduce FlashVID, a training‑free inference acceleration framework for VLLMs. Specifically, FlashVID utilizes Attention and Diversity‑based Token Selection (ADTS) to select the most representative tokens for basic video representation, then applies Tree‑based Spatiotemporal Token Merging (TSTM) for fine‑grained spatiotemporal redundancy elimination. Extensive experiments conducted on three representative VLLMs across five video understanding benchmarks demonstrate the effectiveness and generalization of our method. Notably, by retaining only 10% of visual tokens, FlashVID preserves 99.1% of the performance of LLaVA‑OneVision. Consequently, FlashVID can serve as a training‑free and plug‑and‑play module for extending long video frames, which enables a 10x increase in video frame input to Qwen2.5‑VL, resulting in a relative improvement of 8.6% within the same computational budget. Code is available at https://github.com/Fanziyang‑v/FlashVID.

PaperId: 3195, https://arxiv.org/pdf/2602.08007.pdf GitHub

Abstract:
As foundation models continue to scale, pretraining increasingly relies on data‑parallel distributed optimization, making bandwidth‑limited gradient synchronization a key bottleneck. Orthogonally, projection‑based low‑rank optimizers were mainly designed for memory efficiency, but remain suboptimal for communication‑limited training: one‑sided synchronization still transmits an O(rn) object for an m× n matrix gradient and refresh steps can dominate peak communicated bytes. We propose TSR, which brings two‑sided low‑rank communication to Adam‑family updates (TSR‑Adam) by synchronizing a compact core U^\top G V\in\mathbbR^r× r, reducing the dominant per‑step payload from O(mn) to O(r^2) while keeping moment states in low‑dimensional cores. To further reduce the peak communication from subspace refresh, TSR‑Adam adopts a randomized SVD‑based refresh that avoids full‑gradient synchronization. We additionally extend low‑rank communication to embedding gradients with embedding‑specific ranks and refresh schedules, yielding additional communication and memory savings over keeping embeddings dense. Across pretraining from 60M to 1B model scales, TSR‑Adam reduces average communicated bytes per step by 13×, and on GLUE fine‑tuning it reduces communication by 25×, while achieving comparable performance; we further provide a theoretical stationarity analysis for the proposed update. Code is available at https://github.com/DKmiyan/TSR‑Adam.

PaperId: 3196, https://arxiv.org/pdf/2602.08005.pdf GitHub

Abstract:
The deployment of efficient long‑context LLMs in applications like autonomous agents, long‑chain reasoning, and creative writing is fundamentally bottlenecked by the linear growth of KV cache memory. Existing compression and eviction methods often struggle to balance accuracy, compression ratio, and hardware efficiency. We propose DeltaKV, a residual‑based KV cache compression framework motivated by two empirical findings: long‑range inter‑token similarity and highly shared latent components in KV representations. Instead of discarding tokens, DeltaKV encodes semantic residuals relative to retrieved historical references, preserving fidelity while substantially reducing storage. To translate compression gains into real system speedups, we further introduce Sparse‑vLLM, a high‑performance inference engine with decoupled memory management and kernels optimized for sparse and irregular KV layouts. Experiments show that DeltaKV reduces KV cache memory to 29% of the original while maintaining near‑lossless accuracy on LongBench, SCBench, and AIME. When integrated with Sparse‑vLLM, it achieves up to 2× throughput improvement over vLLM in long‑context scenarios, demonstrating a practical path toward scalable long‑context LLM deployment. Code, model checkpoints, and datasets are available at https://github.com/CURRENTF/Sparse‑vLLM.

PaperId: 3197, https://arxiv.org/pdf/2602.07962.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly capable of carrying out long‑running, real‑world tasks. However, as the amount of context grows, their reliability often deteriorates, a phenomenon known as "context rot". Existing long‑context benchmarks primarily focus on single‑step settings that evaluate a model's ability to retrieve information from a long snippet. In realistic scenarios, however, LLMs often need to act as agents that explore environments, follow instructions and plans, extract useful information, and predict correct actions under a dynamically growing context. To assess language agents in such settings, we introduce LOCA‑bench (a benchmark for LOng‑Context Agents). Given a task prompt, LOCA‑bench leverages automated and scalable control of environment states to regulate the agent's context length. This design enables LOCA‑bench to extend the context length potentially to infinity in a controlled way while keeping the underlying task semantics fixed. LOCA‑bench evaluates language agents as a combination of models and scaffolds, including various context management strategies. While agent performance generally degrades as the environment states grow more complex, advanced context management techniques can substantially improve the overall success rate. We open‑source LOCA‑bench to provide a platform for evaluating models and scaffolds in long‑context, agentic scenarios: https://github.com/hkust‑nlp/LOCA‑bench

PaperId: 3198, https://arxiv.org/pdf/2602.07940.pdf GitHub GitHub

Abstract:
To cope with uncertain changes of the external world, intelligent systems must continually learn from complex, evolving environments and respond in real time. This ability, collectively known as general continual learning (GCL), encapsulates practical challenges such as online datastreams and blurry task boundaries. Although leveraging pretrained models (PTMs) has greatly advanced conventional continual learning (CL), these methods remain limited in reconciling the diverse and temporally mixed information along a single pass, resulting in sub‑optimal GCL performance. Inspired by meta‑plasticity and reconstructive memory in neuroscience, we introduce here an innovative approach named Meta Post‑Refinement (MePo) for PTMs‑based GCL. This approach constructs pseudo task sequences from pretraining data and develops a bi‑level meta‑learning paradigm to refine the pretrained backbone, which serves as a prolonged pretraining phase but greatly facilitates rapid adaptation of representation learning to downstream GCL tasks. MePo further initializes a meta covariance matrix as the reference geometry of pretrained representation space, enabling GCL to exploit second‑order statistics for robust output alignment. MePo serves as a plug‑in strategy that achieves significant performance gains across a variety of GCL benchmarks and pretrained checkpoints in a rehearsal‑free manner (e.g., 15.10%, 13.36%, and 12.56% on CIFAR‑100, ImageNet‑R, and CUB‑200 under Sup‑21/1K). Our source code is available at \hrefhttps://github.com/SunGL001/MePoMePo

PaperId: 3199, https://arxiv.org/pdf/2602.07906.pdf GitHub

Abstract:
Autonomous Machine Learning Engineering (MLE) requires agents to perform sustained, iterative optimization over long horizons. While recent LLM‑based agents show promise, current prompt‑based agents for MLE suffer from behavioral stagnation due to frozen parameters. Although Reinforcement Learning (RL) offers a remedy, applying it to MLE is hindered by prohibitive execution latency and inefficient data selection. Recognizing these challenges, we propose AceGRPO with two core components: (1) Evolving Data Buffer that continuously repurposes execution traces into reusable training tasks, and (2) Adaptive Sampling guided by a Learnability Potential function, which dynamically prioritizes tasks at the agent's learning frontier to maximize learning efficiency. Leveraging AceGRPO, our trained Ace‑30B model achieves a 100% valid submission rate on MLE‑Bench‑Lite, approaches the performance of proprietary frontier models, and outperforms larger open‑source baselines (e.g., DeepSeek‑V3.2), demonstrating robust capability for sustained iterative optimization. Code is available at https://github.com/yuzhu‑cai/AceGRPO.

PaperId: 3200, https://arxiv.org/pdf/2602.07833.pdf GitHub

Abstract:
Chain‑of‑Thought reasoning is widely used to improve the interpretability of multimodal large language models (MLLMs), yet the faithfulness of the generated reasoning traces remains unclear. Prior work has mainly focused on perceptual hallucinations, leaving reasoning level unfaithfulness underexplored. To isolate faithfulness from linguistic priors, we introduce SPD‑Faith Bench, a diagnostic benchmark based on fine‑grained image difference reasoning that enforces explicit visual comparison. Evaluations on state‑of‑the‑art MLLMs reveal two systematic failure modes, perceptual blindness and perception‑reasoning dissociation. We trace these failures to decaying visual attention and representation shifts in the residual stream. Guided by this analysis, we propose SAGE, a train‑free visual evidence‑calibrated framework that improves visual routing and aligns reasoning with perception. Our results highlight the importance of explicitly evaluating faithfulness beyond response correctness. Our benchmark and codes are available at https://github.com/Johanson‑colab/SPD‑Faith‑Bench.

PaperId: 3201, https://arxiv.org/pdf/2602.07798.pdf GitHub

Abstract:
Detecting anomalies in tabular data is critical for many real‑world applications, such as credit card fraud detection. With the rapid advancements in large language models (LLMs), state‑of‑the‑art performance in tabular anomaly detection has been achieved by converting tabular data into text and fine‑tuning LLMs. However, these methods randomly order columns during conversion, without considering the causal relationships between them, which is crucial for accurately detecting anomalies. In this paper, we present CausalTaD, a method that injects causal knowledge into LLMs for tabular anomaly detection. We first identify the causal relationships between columns and reorder them to align with these causal relationships. This reordering can be modeled as a linear ordering problem. Since each column contributes differently to the causal relationships, we further propose a reweighting strategy to assign different weights to different columns to enhance this effect. Experiments across more than 30 datasets demonstrate that our method consistently outperforms the current state‑of‑the‑art methods. The code for CausalTAD is available at https://github.com/350234/CausalTAD.

PaperId: 3202, https://arxiv.org/pdf/2602.07787.pdf GitHub

Abstract:
We present Minitap, a multi‑agent system that achieves 100% success on the AndroidWorld benchmark, the first to fully solve all 116 tasks and surpassing human performance (80%). We first analyze why single‑agent architectures fail: context pollution from mixed reasoning traces, silent text input failures undetected by the agent, and repetitive action loops without escape. Minitap addresses each failure through targeted mechanisms: cognitive separation across six specialized agents, deterministic post‑validation of text input against device state, and meta‑cognitive reasoning that detects cycles and triggers strategy changes. Ablations show multi‑agent decomposition contributes +21 points over single‑agent baselines; verified execution adds +7 points; meta‑cognition adds +9 points. We release Minitap as open‑source software. https://github.com/minitap‑ai/mobile‑use

PaperId: 3203, https://arxiv.org/pdf/2602.07670.pdf GitHub

Abstract:
Test‑time training (TTT) adapts language models through gradient‑based updates at inference. But is adaptation the right strategy? We study compute‑optimal test‑time strategies for verifiable execution‑grounded (VEG) tasks, domains like GPU kernel optimization where a deterministic evaluator provides dense, continuous reward signals. Using KernelBench as our testbed and a 120B‑parameter model (GPT‑OSS‑120B with LoRA adaptation), we find that search outperforms minimal adaptation (1‑5 gradient steps): Best‑of‑N sampling achieves 90% task success (18/20 tasks) at K=64 across the full KernelBench L1 eval set while TTT's best checkpoint reaches only 30.6% (3‑seed mean), with TTT's "equivalent K" falling below 1, worse than single‑sample inference. The failure mode is over‑sharpening: gradient updates collapse diversity toward mediocre solutions rather than discovering optimal ones. Our main contribution is surprisal‑guided selection: selecting the highest‑surprisal (lowest‑confidence) correct sample yields 80% success vs. 50% for most‑confident selection, a 30% improvement. Extending to surprisal‑guided‑top3 matches oracle performance at 100%. This zero‑cost strategy, validated through length‑controlled analysis, recovers oracle performance. For dense‑reward VEG tasks, compute should be allocated to sample diversity and intelligent selection rather than gradient adaptation. The surprisal‑guided selection principle may generalize to other execution‑grounded domains where optimal solutions occupy the distribution tail.

PaperId: 3204, https://arxiv.org/pdf/2602.07625.pdf GitHub

Abstract:
Multimodal understanding of advertising videos is essential for interpreting the intricate relationship between visual storytelling and abstract persuasion strategies. However, despite excelling at general search, existing agents often struggle to bridge the cognitive gap between pixel‑level perception and high‑level marketing logic. To address this challenge, we introduce AD‑MIR, a framework designed to decode advertising intent via a two‑stage architecture. First, in the Structure‑Aware Memory Construction phase, the system converts raw video into a structured database by integrating semantic retrieval with exact keyword matching. This approach prioritizes fine‑grained brand details (e.g., logos, on‑screen text) while dynamically filtering out irrelevant background noise to isolate key protagonists. Second, the Structured Reasoning Agent mimics a marketing expert through an iterative inquiry loop, decomposing the narrative to deduce implicit persuasion tactics. Crucially, it employs an evidence‑based self‑correction mechanism that rigorously validates these insights against specific video frames, automatically backtracking when visual support is lacking. Evaluation on the AdsQA benchmark demonstrates that AD‑MIR achieves state‑of‑the‑art performance, surpassing the strongest general‑purpose agent, DVD, by 1.8% in strict and 9.5% in relaxed accuracy. These results underscore that effective advertising understanding demands explicitly grounding abstract marketing strategies in pixel‑level evidence. The code is available at https://github.com/Little‑Fridge/AD‑MIR.

PaperId: 3205, https://arxiv.org/pdf/2602.07624.pdf GitHub

Abstract:
This work addresses the challenge of personalized question answering in long‑term human‑machine interactions: when conversational history spans weeks or months and exceeds the context window, existing personalization mechanisms struggle to continuously absorb and leverage users' incremental concepts, aliases, and preferences. Current personalized multimodal models are predominantly static‑concepts are fixed at initialization and cannot evolve during interactions. We propose M2A, an agentic dual‑layer hybrid memory system that maintains personalized multimodal information through online updates. The system employs two collaborative agents: ChatAgent manages user interactions and autonomously decides when to query or update memory, while MemoryManager breaks down memory requests from ChatAgent into detailed operations on the dual‑layer memory bank, which couples a RawMessageStore (immutable conversation log) with a SemanticMemoryStore (high‑level observations), providing memories at different granularities. In addition, we develop a reusable data synthesis pipeline that injects concept‑grounded sessions from Yo'LLaVA and MC‑LLaVA into LoCoMo long conversations while preserving temporal coherence. Experiments show that M2A significantly outperforms baselines, demonstrating that transforming personalization from one‑shot configuration to a co‑evolving memory mechanism provides a viable path for high‑quality individualized responses in long‑term multimodal interactions. The code is available at https://github.com/Little‑Fridge/M2A.

PaperId: 3206, https://arxiv.org/pdf/2602.07616.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) architectures employ sparse activation to deliver faster training and inference with higher accuracy than dense LLMs. However, in production serving, MoE models require batch inference to optimize hardware efficiency, which may cause excessive expert activation and thus slow the memory‑bound decoding stage. To address the fundamental tension between batch decoding and expert sparsity, we present SERE, a Similarity‑based Expert Re‑routing method for Efficient batch decoding in MoE models. SERE dynamically reduces the number of active experts in an input‑aware manner by re‑routing tokens from secondary experts to their most similar primary counterparts. It also leverages similarity patterns to identify and preserve critical experts, thereby preventing capability loss. Notably, SERE avoids static expert pruning or merging, instead enabling dynamic expert skipping based on batch‑level expert redundancy. Additionally, we provide an efficient custom CUDA kernel for SERE, enabling plug‑and‑play use in vLLM with only a single‑line code change. Extensive experiments on various complex reasoning benchmarks demonstrate that SERE achieves up to 2.0x speedup with minimal quality loss, providing a practical solution for cost‑efficient and latency‑sensitive large‑scale MoE deployment. Code implementation of SERE can be found in https://github.com/JL‑Cheng/SERE.

PaperId: 3207, https://arxiv.org/pdf/2602.07605.pdf GitHub

Abstract:
Any entity in the visual world can be hierarchically grouped based on shared characteristics and mapped to fine‑grained sub‑categories. While Multi‑modal Large Language Models (MLLMs) achieve strong performance on coarse‑grained visual tasks, they often struggle with Fine‑Grained Visual Recognition (FGVR). Adapting general‑purpose MLLMs to FGVR typically requires large amounts of annotated data, which is costly to obtain, leaving a substantial performance gap compared to contrastive CLIP models dedicated for discriminative tasks. Moreover, MLLMs tend to overfit to seen sub‑categories and generalize poorly to unseen ones. To address these challenges, we propose Fine‑R1, an MLLM tailored for FGVR through an R1‑style training framework: (1) Chain‑of‑Thought Supervised Fine‑tuning, where we construct a high‑quality FGVR CoT dataset with rationales of "visual analysis, candidate sub‑categories, comparison, and prediction", transition the model into a strong open‑world classifier; and (2) Triplet Augmented Policy Optimization, where Intra‑class Augmentation mixes trajectories from anchor and positive images within the same category to improve robustness to intra‑class variance, while Inter‑class Augmentation maximizes the response distinction conditioned on images across sub‑categories to enhance discriminative ability. With only 4‑shot training, Fine‑R1 outperforms existing general MLLMs, reasoning MLLMs, and even contrastive CLIP models in identifying both seen and unseen sub‑categories, showing promise in working in knowledge‑intensive domains where gathering expert annotations for all sub‑categories is arduous. Code is available at https://github.com/PKU‑ICST‑MIPL/FineR1_ICLR2026.

PaperId: 3208, https://arxiv.org/pdf/2602.07550.pdf GitHub

Abstract:
Recent self‑supervised Vision Transformers (ViTs), such as DINOv3, provide rich feature representations for dense vision tasks. This study investigates the intrinsic few‑shot semantic segmentation (FSS) capabilities of frozen DINOv3 features through a training‑free baseline, FSSDINO, utilizing class‑specific prototypes and Gram‑matrix refinement. Our results across binary, multi‑class, and cross‑domain (CDFSS) benchmarks demonstrate that this minimal approach, applied to the final backbone layer, is highly competitive with specialized methods involving complex decoders or test‑time adaptation. Crucially, we conduct an Oracle‑guided layer analysis, identifying a significant performance gap between the standard last‑layer features and globally optimal intermediate representations. We reveal a "Safest vs. Optimal" dilemma: while the Oracle proves higher performance is attainable, matching the results of compute‑intensive adaptation methods, current unsupervised and support‑guided selection metrics consistently yield lower performance than the last‑layer baseline. This characterizes a "Semantic Selection Gap" in Foundation Models, a disconnect where traditional heuristics fail to reliably identify high‑fidelity features. Our work establishes the "Last‑Layer" as a deceptively strong baseline and provides a rigorous diagnostic of the latent semantic potentials in DINOv3.The code is publicly available at https://github.com/hussni0997/fssdino.

PaperId: 3209, https://arxiv.org/pdf/2602.07506.pdf GitHub

Abstract:
Humanoid facial expression shadowing enables robots to realistically imitate human facial expressions in real time, which is critical for lifelike, facially expressive humanoid robots and affective human‑robot interaction. Existing progress in humanoid facial expression imitation remains limited, often failing to achieve either real‑time performance or realistic expressiveness due to offline video‑based inference designs and insufficient ability to capture and transfer subtle expression details. To address these limitations, we present VividFace, a real‑time and realistic facial expression shadowing system for humanoid robots. An optimized imitation framework X2CNet++ enhances expressiveness by fine‑tuning the human‑to‑humanoid facial motion transfer module and introducing a feature‑adaptation training strategy for better alignment across different image sources. Real‑time shadowing is further enabled by a video‑stream‑compatible inference pipeline and a streamlined workflow based on asynchronous I/O for efficient communication across devices. VividFace produces vivid humanoid faces by mimicking human facial expressions within 0.05 seconds, while generalizing across diverse facial configurations. Extensive real‑world demonstrations validate its practical utility. Videos are available at: https://lipzh5.github.io/VividFace/.

PaperId: 3210, https://arxiv.org/pdf/2602.07422.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real‑world deployment. Existing secure code alignment methods often suffer from a functionality‑‑security paradox, improving security at the cost of substantial utility degradation. We propose SecCoderX, an online reinforcement learning framework for functionality‑preserving secure code generation. SecCoderX first bridges vulnerability detection and secure code generation by repurposing mature detection resources in two ways: (i) synthesizing diverse, reality‑grounded vulnerability‑inducing coding tasks for online RL rollouts, and (ii) training a reasoning‑based vulnerability reward model that provides scalable and reliable security supervision. Together, these components are unified in an online RL loop to align code LLMs to generate secure and functional code. Extensive experiments demonstrate that SecCoderX achieves state‑of‑the‑art performance, improving Effective Safety Rate (ESR) by approximately 10% over unaligned models, whereas prior methods often degrade ESR by 14‑54%. We release our code, dataset and model checkpoints at https://github.com/AndrewWTY/SecCoderX.

PaperId: 3211, https://arxiv.org/pdf/2602.07399.pdf GitHub

Abstract:
Vision‑‑Language‑‑Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations remains unreliable. While fine‑tuned VLA policies often produce semantically plausible trajectories, failures often arise from unresolved geometric ambiguities, where near‑miss actions lead to divergent execution outcomes under limited supervision. We study few‑shot VLA adaptation from a \emphgeneration‑‑selection perspective and propose a novel framework VGAS (Value‑Guided Action‑chunk Selection). It performs inference‑time best‑of‑N selection to identify action chunks that are both semantically faithful and geometrically precise. Specifically, VGAS employs a finetuned VLA as a high‑recall proposal generator and introduces the \textrmQ‑Chunk‑Former, a geometrically grounded Transformer critic to resolve fine‑grained geometric ambiguities. In addition, we propose Explicit Geometric Regularization (\textttEGR), which shapes a discriminative value landscape to preserve action ranking resolution among near‑miss candidates while mitigating value instability under scarce supervision. Experiments and theoretical analysis demonstrate that VGAS consistently improves success rates and robustness under limited demonstrations and distribution shifts. Our code is available at https://github.com/Jyugo‑15/VGAS.

PaperId: 3212, https://arxiv.org/pdf/2602.07398.pdf GitHub

Abstract:
Indirect prompt injection threatens LLM agents by embedding malicious instructions in external content, enabling unauthorized actions and data theft. LLM agents maintain working memory through their context window, which stores interaction history for decision‑making. Conventional agents indiscriminately accumulate all tool outputs and reasoning traces in this memory, creating two critical vulnerabilities: (1) injected instructions persist throughout the workflow, granting attackers multiple opportunities to manipulate behavior, and (2) verbose, non‑essential content degrades decision‑making capabilities. Existing defenses treat bloated memory as given and focus on remaining resilient, rather than reducing unnecessary accumulation to prevent the attack. We present AgentSys, a framework that defends against indirect prompt injection through explicit memory management. Inspired by process memory isolation in operating systems, AgentSys organizes agents hierarchically: a main agent spawns worker agents for tool calls, each running in an isolated context and able to spawn nested workers for subtasks. External data and subtask traces never enter the main agent's memory; only schema‑validated return values can cross boundaries through deterministic JSON parsing. Ablations show isolation alone cuts attack success to 2.19%, and adding a validator/sanitizer further improves defense with event‑triggered checks whose overhead scales with operations rather than context length. On AgentDojo and ASB, AgentSys achieves 0.78% and 4.25% attack success while slightly improving benign utility over undefended baselines. It remains robust to adaptive attackers and across multiple foundation models, showing that explicit memory management enables secure, dynamic LLM agent architectures. Our code is available at: https://github.com/ruoyaow/agentsys‑memory.

PaperId: 3213, https://arxiv.org/pdf/2602.07391.pdf GitHub

Abstract:
AI agents are increasingly deployed in production, yet their security evaluations remain bottlenecked by manual red‑teaming or static benchmarks that fail to model adaptive, multi‑turn adversaries. We propose NAAMSE, an evolutionary framework that reframes agent security evaluation as a feedback‑driven optimization problem. Our system employs a single autonomous agent that orchestrates a lifecycle of genetic prompt mutation, hierarchical corpus exploration, and asymmetric behavioral scoring. By using model responses as a fitness signal, the framework iteratively compounds effective attack strategies while simultaneously ensuring "benign‑use correctness", preventing the degenerate security of blanket refusal. Our experiments across a diverse suite of state‑of‑the‑art large language models demonstrate that evolutionary mutation systematically amplifies vulnerabilities missed by one‑shot methods, with controlled ablations revealing that the synergy between exploration and targeted mutation uncovers high‑severity failure modes. We show that this adaptive approach provides a more realistic and scalable assessment of agent robustness in the face of evolving threats. The code for NAAMSE is open source and available at https://github.com/HASHIRU‑AI/NAAMSE.

PaperId: 3214, https://arxiv.org/pdf/2602.07374.pdf GitHub

Abstract:
Large language models (LLMs) achieve remarkable performance but demand substantial computational resources, limiting deployment on edge devices and resource‑constrained environments. We present TernaryLM, a 132M‑parameter transformer trained natively with ternary quantization ‑1, 0, +1 (log2(3) ~ 1.58‑bit effective precision), achieving significant memory reduction without sacrificing language modeling capability. Unlike post‑training quantization approaches that quantize pre‑trained full‑precision models, TernaryLM learns quantization‑aware representations from scratch using straight‑through estimators and adaptive per‑layer scaling factors. Our experiments demonstrate: (1) validation perplexity of 58.42 on TinyStories with a cross‑seed standard deviation of +/‑ 0.17 PPL, confirming stable optimization; (2) strong downstream transfer with 82.47% F1 on MRPC, surpassing DistilBERT despite using 55x less pretraining data; (3) 2.4x memory reduction (498 MB vs 1,197 MB for an FP32 model of identical architecture) with latency parity; and (4) an implicit regularization effect whereby the ternary constraint yields a train/val ratio of 1.05x versus 3.51x for the FP32 baseline, demonstrating that discrete weights prevent overfitting on small corpora. We provide layer‑wise sparsity analysis revealing that middle transformer layers (L5‑L9) achieve 60‑62% quantization sparsity versus 45‑55% for boundary layers, establishing an actionable design principle for non‑uniform precision allocation. Our implementation and trained models are publicly available at https://github.com/1nisharg/TernaryLM‑Memory‑Efficient‑Language‑Modeling.

PaperId: 3215, https://arxiv.org/pdf/2602.07339.pdf GitHub

Abstract:
Diffusion‑based trajectory planners have demonstrated strong capability for modeling the multimodal nature of human driving behavior, but their reliance on iterative stochastic sampling poses critical challenges for real‑time, safety‑critical deployment. In this work, we present RAPiD, a deterministic policy extraction framework that distills a pretrained diffusion‑based planner into an efficient policy while eliminating diffusion sampling. Using score‑regularized policy optimization, we leverage the score function of a pre‑trained diffusion planner as a behavior prior to regularize policy learning. To promote safety and passenger comfort, the policy is optimized using a critic trained to imitate a predictive driver controller, providing dense, safety‑focused supervision beyond conventional imitation learning. Evaluations demonstrate that RAPiD achieves competitive performance on closed‑loop nuPlan scenarios with an 8x speedup over diffusion baselines, while achieving state‑of‑the‑art generalization among learning‑based planners on the interPlan benchmark. The official website of this work is: https://github.com/ruturajreddy/RAPiD.

PaperId: 3216, https://arxiv.org/pdf/2602.07322.pdf GitHub

Abstract:
Diffusion‑based policies have recently achieved remarkable success in robotics by formulating action prediction as a conditional denoising process. However, the standard practice of sampling from random Gaussian noise often requires multiple iterative steps to produce clean actions, leading to high inference latency that incurs a major bottleneck for real‑time control. In this paper, we challenge the necessity of uninformed noise sampling and propose Action‑to‑Action flow matching (A2A), a novel policy paradigm that shifts from random sampling to initialization informed by the previous proprioceptive action. Unlike existing methods that treat proprioceptive action feedback as static conditions, A2A leverages historical proprioceptive sequences, embedding them into a high‑dimensional latent space as the starting point for action generation. This design bypasses costly iterative denoising while effectively capturing the robot's physical dynamics and temporal continuity. Extensive experiments demonstrate that A2A exhibits high training efficiency, fast inference speed, and improved generalization. Notably, A2A enables high‑quality action generation in as few as a single inference step, and exhibits superior robustness to visual perturbations and enhanced generalization to unseen configurations. Lastly, we also extend A2A to video generation, demonstrating its broader versatility in temporal modeling. Project site: https://lorenzo‑0‑0.github.io/A2A_Flow_Matching.

PaperId: 3217, https://arxiv.org/pdf/2602.07303.pdf GitHub GitHub

Abstract:
Log anomaly detection is crucial for uncovering system failures and security risks. Although logs originate from nested component executions with clear boundaries, this structure is lost when stored as flat sequences. As a result, state‑of‑the‑art methods often miss true dependencies within executions while learning spurious correlations across unrelated events. We propose KRONE, the first hierarchical anomaly detection framework that automatically derives execution hierarchies from flat logs to enable modular, multi‑level anomaly detection. At its core, the KRONE Log Abstraction Model extracts application‑specific semantic hierarchies, which are used to recursively decompose log sequences into coherent execution units, referred to as KRONE Seqs. This transforms sequence‑level detection into a set of modular KRONE Seq‑level detection tasks. For each test KRONE Seq, KRONE adopts a hybrid modular detection strategy that routes between an efficient level‑independent Local‑Context detector for rapid filtering and a Nested‑Aware detector that captures cross‑level semantic dependencies, augmented with LLM‑based anomaly detection and explanation. KRONE further optimizes detection through cached result reuse and early‑exit strategies along the hierarchy. Experiments on three public benchmarks and one industrial dataset from ByteDance Cloud demonstrate that KRONE achieves substantial improvements in accuracy (42.49% to 87.98%), F1 score, data efficiency (117.3x reduction), resource efficiency (43.7x reduction), and interpretability. KRONE improves F1‑score by 10.07% (82.76% to 92.83%) over prior methods while reducing LLM usage to only 1.1% to 3.3% of the test data. Code: https://github.com/LeiMa0324/KRONE Demo: https://leima0324.github.io/KRONE_Demo_official/

PaperId: 3218, https://arxiv.org/pdf/2602.07274.pdf GitHub

Abstract:
Executing complex terminal tasks remains a significant challenge for open‑weight LLMs, constrained by two fundamental limitations. First, high‑fidelity, executable training environments are scarce: environments synthesized from real‑world repositories are not diverse and scalable, while trajectories synthesized by LLMs suffer from hallucinations. Second, standard instruction tuning uses expert trajectories that rarely exhibit simple mistakes common to smaller models. This creates a distributional mismatch, leaving student models ill‑equipped to recover from their own runtime failures. To bridge these gaps, we introduce TermiGen, an end‑to‑end pipeline for synthesizing verifiable environments and resilient expert trajectories. Termi‑Gen first generates functionally valid tasks and Docker containers via an iterative multi‑agent refinement loop. Subsequently, we employ a Generator‑Critic protocol that actively injects errors during trajectory collection, synthesizing data rich in error‑correction cycles. Fine‑tuned on this TermiGen‑generated dataset, our TermiGen‑Qwen2.5‑Coder‑32B achieves a 31.3% pass rate on TerminalBench. This establishes a new open‑weights state‑of‑the‑art, outperforming existing baselines and notably surpassing capable proprietary models such as o4‑mini. Dataset is avaiable at https://github.com/ucsb‑mlsec/terminal‑bench‑env.

PaperId: 3219, https://arxiv.org/pdf/2602.07200.pdf GitHub

Abstract:
Spiking Neural Networks (SNNs) are energy‑efficient counterparts of Deep Neural Networks (DNNs) with high biological plausibility, as information is transmitted through temporal spiking patterns. The core element of an SNN is the spiking neuron, which converts input data into spikes following the Leaky Integrate‑and‑Fire (LIF) neuron model. This model includes several important hyperparameters, such as the membrane potential threshold and membrane time constant. Both the DNNs and SNNs have proven to be exploitable by backdoor attacks, where an adversary can poison the training dataset with malicious triggers and force the model to behave in an attacker‑defined manner. Yet, how an adversary can exploit the unique characteristics of SNNs for backdoor attacks remains underexplored. In this paper, we propose BadSNN, a novel backdoor attack on spiking neural networks that exploits hyperparameter variations of spiking neurons to inject backdoor behavior into the model. We further propose a trigger optimization process to achieve better attack performance while making trigger patterns less perceptible. BadSNN demonstrates superior attack performance on various datasets and architectures, as well as compared with state‑of‑the‑art data poisoning‑based backdoor attacks and robustness against common backdoor mitigation techniques. Codes can be found at https://github.com/SiSL‑URI/BadSNN.

PaperId: 3220, https://arxiv.org/pdf/2602.07187.pdf GitHub

Abstract:
Advanced large language model agents typically adopt self‑reflection for improving performance, where agents iteratively analyze past actions to correct errors. However, existing reflective approaches are inherently retrospective: agents act, observe failure, and only then attempt to recover. In this work, we introduce PreFlect, a prospective reflection mechanism that shifts the paradigm from post hoc correction to pre‑execution foresight by criticizing and refining agent plans before execution. To support grounded prospective reflection, we distill planning errors from historical agent trajectories, capturing recurring success and failure patterns observed across past executions. Furthermore, we complement prospective reflection with a dynamic re‑planning mechanism that provides execution‑time plan update in case the original plan encounters unexpected deviation. Evaluations on different benchmarks demonstrate that PreFlect significantly improves overall agent utility on complex real‑world tasks, outperforming strong reflection‑based baselines and several more complex agent architectures. Code will be updated at https://github.com/wwwhy725/PreFlect.

PaperId: 3221, https://arxiv.org/pdf/2602.07154.pdf GitHub

Abstract:
Pooling heterogeneous datasets across domains is a common strategy in representation learning, but naive pooling can amplify distributional asymmetries and yield biased estimators, especially in settings where zero‑shot generalization is required. We propose a matching framework that selects samples relative to an adaptive centroid and iteratively refines the representation distribution. The double robustness and the propensity score matching for the inclusion of data domains make matching more robust than naive pooling and uniform subsampling by filtering out the confounding domains (the main cause of heterogeneity). Theoretical and empirical analyses show that, unlike naive pooling or uniform subsampling, matching achieves better results under asymmetric meta‑distributions, which are also extended to non‑Gaussian and multimodal real‑world settings. Most importantly, we show that these improvements translate to zero‑shot medical anomaly detection, one of the extreme forms of data heterogeneity and asymmetry. The code is available on https://github.com/AyushRoy2001/Beyond‑Pooling.

PaperId: 3222, https://arxiv.org/pdf/2602.07125.pdf GitHub

Abstract:
Universal Multimodal Retrieval (UMR) seeks any‑to‑any search across text and vision, yet modern embedding models remain brittle when queries require latent reasoning (e.g., resolving underspecified references or matching compositional constraints). We argue this brittleness is often data‑induced: when images carry "silent" evidence and queries leave key semantics implicit, a single embedding pass must both reason and compress, encouraging spurious feature matching. We propose a data‑centric framework that decouples these roles by externalizing reasoning before retrieval. Using a strong Vision‑‑Language Model, we make implicit semantics explicit by densely captioning visual evidence in corpus entries, resolving ambiguous multimodal references in queries, and rewriting verbose instructions into concise retrieval constraints. Inference‑time enhancement alone is insufficient; the retriever must be trained on these semantically dense representations to avoid distribution shift and fully exploit the added signal. Across M‑BEIR, our reasoning‑augmented training method yields consistent gains over strong baselines, with ablations showing that corpus enhancement chiefly benefits knowledge‑intensive queries while query enhancement is critical for compositional modification requests. We publicly release our code at https://github.com/AugmentedRetrieval/ReasoningAugmentedRetrieval.

PaperId: 3223, https://arxiv.org/pdf/2602.07107.pdf GitHub

Abstract:
Large Language Models(LLMs) have been successful in numerous fields. Alignment has usually been applied to prevent them from harmful purposes. However, aligned LLMs remain vulnerable to jailbreak attacks that deliberately mislead them into producing harmful outputs. Existing jailbreaks are either black‑box, using carefully crafted, unstealthy prompts, or white‑box, requiring resource‑intensive computation. In light of these challenges, we introduce ShallowJail, a novel attack that exploits shallow alignment in LLMs. ShallowJail can misguide LLMs' responses by manipulating the initial tokens during inference. Through extensive experiments, we demonstrate the effectiveness of ShallowJail, which substantially degrades the safety of state‑of‑the‑art LLM responses. Our code is available at https://github.com/liuup/ShallowJail.

PaperId: 3224, https://arxiv.org/pdf/2602.07103.pdf GitHub

Abstract:
A central goal in systems biology and drug discovery is to predict the transcriptional response of cells to perturbations. This task is challenging due to the noisy and sparse nature of single‑cell measurements, as well as the fact that perturbations often induce population‑level shifts rather than changes in individual cells. Existing deep learning methods typically assume cell‑level correspondences, limiting their ability to capture such global effects. We present scDFM, a generative framework based on conditional flow matching that models the full distribution of perturbed cells conditioned on control states. By incorporating a maximum mean discrepancy (MMD) objective, our method aligns perturbed and control populations beyond cell‑level correspondences. To further improve robustness to sparsity and noise, we introduce the Perturbation‑Aware Differential Transformer (PAD‑Transformer), a backbone architecture that leverages gene interaction graphs and differential attention to capture context‑specific expression changes. Across multiple genetic and drug perturbation benchmarks, scDFM consistently outperforms prior methods, demonstrating strong generalization in both unseen and combinatorial settings. In the combinatorial setting, it reduces mean squared error by 19.6% relative to the strongest baseline. These results highlight the importance of distribution‑level generative modeling for robust in silico perturbation prediction. The code is available at https://github.com/AI4Science‑WestlakeU/scDFM

PaperId: 3225, https://arxiv.org/pdf/2602.07087.pdf GitHub

Abstract:
Various representation learning methods for molecular structures have been devised to accelerate data‑driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom‑level information, which is not sufficient to describe real‑world molecular physics. Although electron‑level information can provide fundamental knowledge about chemical compounds beyond the atom‑level information, obtaining the electron‑level information in real‑world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron‑informed molecular representations without additional computation costs by transferring readily accessible electron‑level information about small molecules to large molecules of our interest. The proposed method achieved state‑of‑the‑art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL.

PaperId: 3226, https://arxiv.org/pdf/2602.07069.pdf GitHub

Abstract:
Powered by multimodal text‑to‑image priors, diffusion‑based super‑resolution excels at synthesizing intricate details; however, models trained on synthetic low‑resolution (LR) and high‑resolution (HR) image pairs often degrade when applied to real‑world LR images due to significant distribution shifts. We propose Bird‑SR, a bidirectional reward‑guided diffusion framework that formulates super‑resolution as trajectory‑level preference optimization via reward feedback learning (ReFL), jointly leveraging synthetic LR‑HR pairs and real‑world LR images. For structural fidelity easily affected in ReFL, the model is directly optimized on synthetic pairs at early diffusion steps, which also facilitates structure preservation for real‑world inputs under smaller distribution gap in structure levels. For perceptual enhancement, quality‑guided rewards are applied to both synthetic and real LR images at the later trajectory phase. To mitigate reward hacking, the rewards for synthetic results are formulated in a relative advantage space bounded by their ground‑truth counterparts, while real‑world optimization is regularized via a semantic alignment constraint. Furthermore, to balance structural and perceptual learning, we introduce a dynamic fidelity‑perception weighting strategy that emphasizes structure preservation at early stages and progressively shifts focus toward perceptual optimization at later diffusion steps. Extensive experiments on real‑world SR benchmarks demonstrate that Bird‑SR consistently outperforms state‑of‑the‑art methods in perceptual quality while preserving structural consistency, validating its effectiveness for real‑world super‑resolution. Our code can be obtained at https://github.com/fanzh03/Bird‑SR.

PaperId: 3227, https://arxiv.org/pdf/2602.07045.pdf GitHub

Abstract:
Recent advancements in Multimodal Large Language Models (MLLMs) have enabled complex reasoning. However, existing remote sensing (RS) benchmarks remain heavily biased toward perception tasks, such as object recognition and scene classification. This limitation hinders the development of MLLMs for cognitively demanding RS applications. To address this, we propose a Vision Language ReaSoning Benchmark (VLRS‑Bench), which is the first benchmark exclusively dedicated to complex RS reasoning. Structured across the three core dimensions of Cognition, Decision, and Prediction, VLRS‑Bench comprises 2,000 question‑answer pairs with an average question length of 130.19 words, spanning 14 tasks and up to eight temporal phases. VLRS‑Bench is constructed via a specialized pipeline that integrates RS‑specific priors and expert knowledge to ensure geospatial realism and reasoning complexity. Experimental results reveal significant bottlenecks in existing state‑of‑the‑art MLLMs, providing critical insights for advancing multimodal reasoning within the remote sensing community. The project repository is available at https://github.com/MiliLab/VLRS‑Bench.

PaperId: 3228, https://arxiv.org/pdf/2602.07034.pdf GitHub

Abstract:
Semi‑structured table question answering (QA) is a challenging task that requires (1) precise extraction of cell contents and positions and (2) accurate recovery of key implicit logical structures, hierarchical relationships, and semantic associations encoded in table layouts. In practice, such tables are often interpreted manually by human experts, which is labor‑intensive and time‑consuming. However, automating this process remains difficult. Existing Text‑to‑SQL methods typically require converting semi‑structured tables into structured formats, inevitably leading to information loss, while approaches like Text‑to‑Code and multimodal LLM‑based QA struggle with complex layouts and often yield inaccurate answers. To address these limitations, we present ST‑Raptor, an agentic system for semi‑structured table QA. ST‑Raptor offers an interactive analysis environment that combines visual editing, tree‑based structural modeling, and agent‑driven query resolution to support accurate and user‑friendly table understanding. Experimental results on both benchmark and real‑world datasets demonstrate that ST‑Raptor outperforms existing methods in both accuracy and usability. The code is available at https://github.com/weAIDB/ST‑Raptor, and a demonstration video is available at https://youtu.be/9GDR‑94Cau4.

PaperId: 3229, https://arxiv.org/pdf/2602.06967.pdf GitHub

Abstract:
Multi‑robot collaboration tasks often require heterogeneous robots to work together over long horizons under spatial constraints and environmental uncertainties. Although Large Language Models (LLMs) excel at reasoning and planning, their potential for coordinated control has not been fully explored. Inspired by human teamwork, we present CLiMRS (Cooperative Large‑Language‑Model‑Driven Heterogeneous Multi‑Robot System), an adaptive group negotiation framework among LLMs for multi‑robot collaboration. This framework pairs each robot with an LLM agent and dynamically forms subgroups through a general proposal planner. Within each subgroup, a subgroup manager leads perception‑driven multi‑LLM discussions to get commands for actions. Feedback is provided by both robot execution outcomes and environment changes. This grouping‑planning‑execution‑feedback loop enables efficient planning and robust execution. To evaluate these capabilities, we introduce CLiMBench, a heterogeneous multi‑robot benchmark of challenging assembly tasks. Our experiments show that CLiMRS surpasses the best baseline, achieving over 40% higher efficiency on complex tasks without sacrificing success on simpler ones. Overall, our results demonstrate that leveraging human‑inspired group formation and negotiation principles significantly enhances the efficiency of heterogeneous multi‑robot collaboration. Our code is available here: https://github.com/song‑siqi/CLiMRS.

PaperId: 3230, https://arxiv.org/pdf/2602.06807.pdf GitHub

Abstract:
We address the over‑constrained planning problem in semi‑static environments. The planning objective is to find a best‑effort solution that avoids all hard constraint regions while minimally traversing the least risky areas. Conventional methods often rely on pre‑defined area costs, limiting generalizations. Further, the spatial continuity of navigation spaces makes it difficult to identify regions that are passable without overestimation. To overcome these challenges, we propose SuReNav, a superpixel graph‑based constraint relaxation and navigation method that imitates human‑like safe and efficient navigation. Our framework consists of three components: 1) superpixel graph map generation with regional constraints, 2) regional‑constraint relaxation using graph neural network trained on human demonstrations for safe and efficient navigation, and 3) interleaving relaxation, planning, and execution for complete navigation. We evaluate our method against state‑of‑the‑art baselines on 2D semantic maps and 3D maps from OpenStreetMap, achieving the highest human‑likeness score of complete navigation while maintaining a balanced trade‑off between efficiency and safety. We finally demonstrate its scalability and generalization performance in real‑world urban navigation with a quadruped robot, Spot. Code and Videos are available at https://sure‑nav.github.io/.

PaperId: 3231, https://arxiv.org/pdf/2602.06801.pdf GitHub

Abstract:
Activation steering methods are widely used to control large language model (LLM) behavior and are often interpreted as revealing meaningful internal representations. This interpretation assumes that steering directions are identifiable and uniquely recoverable from input‑output behavior. We show that, under white‑box single‑layer access, steering vectors are fundamentally non‑identifiable due to large equivalence classes of behaviorally indistinguishable interventions. Empirically, we find that orthogonal perturbations achieve near‑equivalent efficacy with negligible effect sizes across multiple models and traits, with pre‑trained semantic classifiers confirming equivalence at the output level. We estimate null‑space dimensionality via SVD of activation covariance matrices and validate that equivalence holds robustly throughout the operationally relevant steering range. Critically, we show that non‑identifiability is a robust geometric property that persists across diverse prompt distributions. These findings reveal fundamental interpretability limits and highlight the need for structural constraints beyond behavioral testing to enable reliable alignment interventions.

PaperId: 3232, https://arxiv.org/pdf/2602.06526.pdf GitHub

Abstract:
Information retrieval (IR) evaluation remains challenging due to incomplete IR benchmark datasets that contain unlabeled relevant chunks. While LLMs and LLM‑human hybrid strategies reduce costly human effort, they remain prone to LLM overconfidence and ineffective AI‑to‑human escalation. To address this, we propose DREAM, a multi‑round debate‑based relevance assessment framework with LLM agents, built on opposing initial stances and iterative reciprocal critique. Through our agreement‑based debate, it yields more accurate labeling for certain cases and more reliable AI‑to‑human escalation for uncertain ones, achieving 95.2% labeling accuracy with only 3.5% human involvement. Using DREAM, we build BRIDGE, a refined benchmark that mitigates evaluation bias and enables fairer retriever comparison by uncovering 29,824 missing relevant chunks. We then re‑benchmark IR systems and extend evaluation to RAG, showing that unaddressed holes not only distort retriever rankings but also drive retrieval‑generation misalignment. The relevance assessment framework is available at https: //github.com/DISL‑Lab/DREAM‑ICLR‑26; and the BRIDGE dataset is available at https://github.com/DISL‑Lab/BRIDGE‑Benchmark.

PaperId: 3233, https://arxiv.org/pdf/2602.06486.pdf GitHub

Abstract:
Evaluating agentic AI on open‑ended professional tasks faces a fundamental dilemma between rigor and flexibility. Static rubrics provide rigorous, reproducible assessment but fail to accommodate diverse valid response strategies, while LLM‑as‑a‑judge approaches adapt to individual responses yet suffer from instability and bias. Human experts address this dilemma by combining domain‑grounded principles with dynamic, claim‑level assessment. Inspired by this process, we propose JADE, a two‑layer evaluation framework. Layer 1 encodes expert knowledge as a predefined set of evaluation skills, providing stable evaluation criteria. Layer 2 performs report‑specific, claim‑level evaluation to flexibly assess diverse reasoning strategies, with evidence‑dependency gating to invalidate conclusions built on refuted claims. Experiments on BizBench show that JADE improves evaluation stability and reveals critical agent failure modes missed by holistic LLM‑based evaluators. We further demonstrate strong alignment with expert‑authored rubrics and effective transfer to a medical‑domain benchmark, validating JADE across professional domains. Our code is publicly available at https://github.com/smiling‑world/JADE.

PaperId: 3234, https://arxiv.org/pdf/2602.06470.pdf GitHub

Abstract:
Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high‑quality data and diminishing returns from rising computational costs. As a result, recent work is increasing the focus on continual learning from real‑world deployment, where user interaction logs provide a rich source of authentic human feedback and procedural knowledge. However, learning from user logs is challenging due to their unstructured and noisy nature. Vanilla LLM systems often struggle to distinguish useful feedback signals from noisy user behavior, and the disparity between user log collection and model optimization (e.g., the off‑policy optimization problem) further strengthens the problem. To this end, we propose UNO (User log‑driveN Optimization), a unified framework for improving LLM systems (LLMsys) with user logs. UNO first distills logs into semi‑structured rules and preference pairs, then employs query‑and‑feedback‑driven clustering to manage data heterogeneity, and finally quantifies the cognitive gap between the model's prior knowledge and the log data. This assessment guides the LLMsys to adaptively filter out noisy feedback and construct different modules for primary and reflective experiences extracted from user logs, thereby improving future responses. Extensive experiments show that UNO achieves state‑of‑the‑art effectiveness and efficiency, significantly outperforming Retrieval Augmented Generation (RAG) and memory‑based baselines. We have open‑sourced our code at https://github.com/bebr2/UNO .

PaperId: 3235, https://arxiv.org/pdf/2602.06400.pdf GitHub

Abstract:
The prediction of 3D semantic occupancy enables autonomous vehicles (AVs) to perceive the fine‑grained geometric and semantic scene structure for safe navigation and decision‑making. Existing methods mainly rely on either voxel‑based representations, which incur redundant computation over empty regions, or on object‑centric Gaussian primitives, which are limited in modeling complex, non‑convex, and asymmetric structures. In this paper, we present TFusionOcc, a T‑primitive‑based object‑centric multi‑sensor fusion framework for 3D semantic occupancy prediction. Specifically, we introduce a family of Students t‑distribution‑based T‑primitives, including the plain T‑primitive, T‑Superquadric, and deformable T‑Superquadric with inverse warping, where the deformable T‑Superquadric serves as the key geometry‑enhancing primitive. We further develop a unified probabilistic formulation based on the Students t‑distribution and the T‑mixture model (TMM) to jointly model occupancy and semantics, and design a tightly coupled multi‑stage fusion architecture to effectively integrate camera and LiDAR cues. Extensive experiments on nuScenes show state‑of‑the‑art performance, while additional evaluations on nuScenes‑C demonstrate strong robustness under most corruption scenarios. The code will be available at: https://github.com/DanielMing123/TFusionOcc

PaperId: 3236, https://arxiv.org/pdf/2602.06369.pdf GitHub

Abstract:
Salient object detection is inherently a subjective problem, as observers with different priors may perceive different objects as salient. However, existing methods predominantly formulate it as an objective prediction task with a single groundtruth segmentation map for each image, which renders the problem under‑determined and fundamentally ill‑posed. To address this issue, we propose Observer‑Centric Salient Object Detection (OC‑SOD), where salient regions are predicted by considering not only the visual cues but also the observer‑specific factors such as their preferences or intents. As a result, this formulation captures the intrinsic ambiguity and diversity of human perception, enabling personalized and context‑aware saliency prediction. By leveraging multi‑modal large language models, we develop an efficient data annotation pipeline and construct the first OC‑SOD dataset named OC‑SODBench, comprising 33k training, validation and test images with 152k textual prompts and object pairs. Built upon this new dataset, we further design OC‑SODAgent, an agentic baseline which performs OC‑SOD via a human‑like "Perceive‑Reflect‑Adjust" process. Extensive experiments on our proposed OC‑SODBench have justified the effectiveness of our contribution. Through this observer‑centric perspective, we aim to bridge the gap between human perception and computational modeling, offering a more realistic and flexible understanding of what makes an object truly "salient." Code and dataset are publicly available at: https://github.com/Dustzx/OC_SOD

PaperId: 3237, https://arxiv.org/pdf/2602.06358.pdf GitHub

Abstract:
We propose SHINE (Scalable Hyper In‑context NEtwork), a scalable hypernetwork that can map diverse meaningful contexts into high‑quality LoRA adapters for large language models (LLMs). By reusing the frozen LLM's own parameters in an in‑context hypernetwork design and introducing architectural innovations, SHINE overcomes key limitations of prior hypernetworks and achieves strong expressive power with a relatively small number of parameters. We introduce a pretraining and instruction fine‑tuning pipeline, and train our hypernetwork to generate high quality LoRA adapters from diverse meaningful contexts in a single forward pass. It updates LLM parameters without any fine‑tuning, and immediately enables complex question answering tasks related to the context without directly accessing the context, effectively transforming in‑context knowledge to in‑parameter knowledge in one pass. Our work achieves outstanding results on various tasks, greatly saves time, computation and memory costs compared to SFT‑based LLM adaptation, and shows great potential for scaling. Our code is available at https://github.com/MuLabPKU/SHINE

PaperId: 3238, https://arxiv.org/pdf/2602.06337.pdf GitHub

Abstract:
Causal inference is essential for decision‑making but remains challenging for non‑experts. While large language models (LLMs) show promise in this domain, their precise causal estimation capabilities are still limited, and the impact of post‑training on these abilities is insufficiently explored. This paper examines the extent to which post‑training can enhance LLMs' capacity for causal inference. We introduce CauGym, a comprehensive dataset comprising seven core causal tasks for training and five diverse test sets. Using this dataset, we systematically evaluate five post‑training approaches: SFT, DPO, KTO, PPO, and GRPO. Across five in‑domain and four existing benchmarks, our experiments demonstrate that appropriate post‑training enables smaller LLMs to perform causal inference competitively, often surpassing much larger models. Our 14B parameter model achieves 93.5% accuracy on the CaLM benchmark, compared to 55.4% by OpenAI o3. Furthermore, the post‑trained LLMs exhibit strong generalization and robustness under real‑world conditions such as distribution shifts and noisy data. Collectively, these findings provide the first systematic evidence that targeted post‑training can produce reliable and robust LLM‑based causal reasoners. Our data and GRPO‑model are available at https://github.com/OpenCausaLab/CauGym.

PaperId: 3239, https://arxiv.org/pdf/2602.06319.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) have advanced rapidly; however, existing benchmarks in mathematics, code, and common‑sense reasoning remain limited. They lack long‑context evaluation, offer insufficient challenge, and provide answers that are difficult to verify programmatically. We introduce GrAlgoBench, a benchmark designed to evaluate LRMs through graph algorithm problems. Such problems are particularly well suited for probing reasoning abilities: they demand long‑context reasoning, allow fine‑grained control of difficulty levels, and enable standardized, programmatic evaluation. Across nine tasks, our systematic experiments reveal two major weaknesses of current LRMs. First, accuracy deteriorates sharply as context length increases, falling below 50% once graphs exceed 120 nodes. This degradation is driven by frequent execution errors, weak memory, and redundant reasoning. Second, LRMs suffer from an over‑thinking phenomenon, primarily caused by extensive yet largely ineffective self‑verification, which inflates reasoning traces without improving correctness. By exposing these limitations, GrAlgoBench establishes graph algorithm problems as a rigorous, multidimensional, and practically relevant testbed for advancing the study of reasoning in LRMs. Code is available at https://github.com/Bklight999/GrAlgoBench.

PaperId: 3240, https://arxiv.org/pdf/2602.06248.pdf GitHub

Abstract:
Machine unlearning for LLMs aims to remove sensitive or copyrighted data from trained models. However, the true efficacy of current unlearning methods remains uncertain. Standard evaluation metrics rely on benign queries that often mistake superficial information suppression for genuine knowledge removal. Such metrics fail to detect residual knowledge that more sophisticated prompting strategies could still extract. We introduce REBEL, an evolutionary approach for adversarial prompt generation designed to probe whether unlearned data can still be recovered. Our experiments demonstrate that REBEL successfully elicits ``forgotten'' knowledge from models that seemed to be forgotten in standard unlearning benchmarks, revealing that current unlearning methods may provide only a superficial layer of protection. We validate our framework on subsets of the TOFU and WMDP benchmarks, evaluating performance across a diverse suite of unlearning algorithms. Our experiments show that REBEL consistently outperforms static baselines, recovering ``forgotten'' knowledge with Attack Success Rates (ASRs) reaching up to 60% on TOFU and 93% on WMDP. We will make all code publicly available upon acceptance. Code is available at https://github.com/patryk‑rybak/REBEL/

PaperId: 3241, https://arxiv.org/pdf/2602.06240.pdf GitHub

Abstract:
Counterfactual explanations offer an intuitive way to interpret graph neural networks (GNNs) by identifying minimal changes that alter a model's prediction, thereby answering "what must differ for a different outcome?". In this work, we propose a novel framework, ATEX‑CF that unifies adversarial attack techniques with counterfactual explanation generation‑a connection made feasible by their shared goal of flipping a node's prediction, yet differing in perturbation strategy: adversarial attacks often rely on edge additions, while counterfactual methods typically use deletions. Unlike traditional approaches that treat explanation and attack separately, our method efficiently integrates both edge additions and deletions, grounded in theory, leveraging adversarial insights to explore impactful counterfactuals. In addition, by jointly optimizing fidelity, sparsity, and plausibility under a constrained perturbation budget, our method produces instance‑level explanations that are both informative and realistic. Experiments on synthetic and real‑world node classification benchmarks demonstrate that ATEX‑CF generates faithful, concise, and plausible explanations, highlighting the effectiveness of integrating adversarial insights into counterfactual reasoning for GNNs.

PaperId: 3242, https://arxiv.org/pdf/2602.06176.pdf GitHub GitHub

Abstract:
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. Despite these advances, significant reasoning failures persist, occurring even in seemingly simple scenarios. To systematically understand and address these shortcomings, we present the first comprehensive survey dedicated to reasoning failures in LLMs. We introduce a novel categorization framework that distinguishes reasoning into embodied and non‑embodied types, with the latter further subdivided into informal (intuitive) and formal (logical) reasoning. In parallel, we classify reasoning failures along a complementary axis into three types: fundamental failures intrinsic to LLM architectures that broadly affect downstream tasks; application‑specific limitations that manifest in particular domains; and robustness issues characterized by inconsistent performance across minor variations. For each reasoning failure, we provide a clear definition, analyze existing studies, explore root causes, and present mitigation strategies. By unifying fragmented research efforts, our survey provides a structured perspective on systemic weaknesses in LLM reasoning, offering valuable insights and guiding future research towards building stronger, more reliable, and robust reasoning capabilities. We additionally release a comprehensive collection of research works on LLM reasoning failures, as a GitHub repository at https://github.com/Peiyang‑Song/Awesome‑LLM‑Reasoning‑Failures, to provide an easy entry point to this area.

PaperId: 3243, https://arxiv.org/pdf/2602.06025.pdf GitHub

Abstract:
Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query‑agnostic memory construction that can be inefficient and may discard query‑critical information. Although runtime memory utilization is a natural alternative, prior work often incurs substantial overhead and offers limited explicit control over the performance‑cost trade‑off. In this work, we present BudgetMem, a runtime agent memory framework for explicit, query‑aware performance‑cost control. BudgetMem structures memory processing as a set of memory modules, each offered in three budget tiers (i.e., \textscLow/\textscMid/\textscHigh). A lightweight router performs budget‑tier routing across modules to balance task performance and memory construction cost, which is implemented as a compact neural policy trained with reinforcement learning. Using BudgetMem as a unified testbed, we study three complementary strategies for realizing budget tiers: implementation (method complexity), reasoning (inference behavior), and capacity (module model size). Across LoCoMo, LongMemEval, and HotpotQA, BudgetMem surpasses strong baselines when performance is prioritized (i.e., high‑budget setting), and delivers better accuracy‑cost frontiers under tighter budgets. Moreover, our analysis disentangles the strengths and weaknesses of different tiering strategies, clarifying when each axis delivers the most favorable trade‑offs under varying budget regimes.

PaperId: 3244, https://arxiv.org/pdf/2602.06013.pdf GitHub GitHub

Abstract:
The rapid advancement of visual generation models has outpaced traditional evaluation approaches, necessitating the adoption of Vision‑Language Models as surrogate judges. In this work, we systematically investigate the reliability of the prevailing absolute pointwise scoring standard, across a wide spectrum of visual generation tasks. Our analysis reveals that this paradigm is limited due to stochastic inconsistency and poor alignment with human perception. To resolve these limitations, we introduce GenArena, a unified evaluation framework that leverages a pairwise comparison paradigm to ensure stable and human‑aligned evaluation. Crucially, our experiments uncover a transformative finding that simply adopting this pairwise protocol enables off‑the‑shelf open‑source models to outperform top‑tier proprietary models. Notably, our method boosts evaluation accuracy by over 20% and achieves a Spearman correlation of 0.86 with the authoritative LMArena leaderboard, drastically surpassing the 0.36 correlation of pointwise methods. Based on GenArena, we benchmark state‑of‑the‑art visual generation models across diverse tasks, providing the community with a rigorous and automated evaluation standard for visual generation.

PaperId: 3245, https://arxiv.org/pdf/2602.06008.pdf GitHub

Abstract:
Large language model (LLM)‑based agents are increasingly expected to negotiate, coordinate, and transact autonomously, yet existing benchmarks lack principled settings for evaluating language‑mediated economic interaction among multiple agents. We introduce AgenticPay, a benchmark and simulation framework for multi‑agent buyer‑seller negotiation driven by natural language. AgenticPay models markets in which buyers and sellers possess private constraints and product‑dependent valuations, and must reach agreements through multi‑round linguistic negotiation rather than numeric bidding alone. The framework supports a diverse suite of over 110 tasks ranging from bilateral bargaining to many‑to‑many markets, with structured action extraction and metrics for feasibility, efficiency, and welfare. Benchmarking state‑of‑the‑art proprietary and open‑weight LLMs reveals substantial gaps in negotiation performance and highlights challenges in long‑horizon strategic reasoning, establishing AgenticPay as a foundation for studying agentic commerce and language‑based market interaction. Code and dataset are available at the link: https://github.com/SafeRL‑Lab/AgenticPay.

PaperId: 3246, https://arxiv.org/pdf/2602.05986.pdf GitHub

Abstract:
While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under‑explored frontier. To bridge this gap, we present RISE‑Video, a pioneering reasoning‑oriented benchmark for Text‑Image‑to‑Video (TI2V) synthesis that shifts the evaluative focus from surface‑level aesthetics to deep cognitive reasoning. RISE‑Video comprises 467 meticulously human‑annotated samples spanning eight rigorous categories, providing a structured testbed for probing model intelligence across diverse dimensions, ranging from commonsense and spatial dynamics to specialized subject domains. Our framework introduces a multi‑dimensional evaluation protocol consisting of four metrics: Reasoning Alignment, Temporal Consistency, Physical Rationality, and Visual Quality. To further support scalable evaluation, we propose an automated pipeline leveraging Large Multimodal Models (LMMs) to emulate human‑centric assessment. Extensive experiments on 11 state‑of‑the‑art TI2V models reveal pervasive deficiencies in simulating complex scenarios under implicit constraints, offering critical insights for the advancement of future world‑simulating generative models.

PaperId: 3247, https://arxiv.org/pdf/2602.05966.pdf GitHub

Abstract:
Controllable video generation has emerged as a versatile tool for autonomous driving, enabling realistic synthesis of traffic scenarios. However, existing methods depend on control signals at inference time to guide the generative model towards temporally consistent generation of dynamic objects, limiting their utility as scalable and generalizable data engines. In this work, we propose Localized Semantic Alignment (LSA), a simple yet effective framework for fine‑tuning pre‑trained video generation models. LSA enhances temporal consistency by aligning semantic features between ground‑truth and generated video clips. Specifically, we compare the output of an off‑the‑shelf feature extraction model between the ground‑truth and generated video clips localized around dynamic objects inducing a semantic feature consistency loss. We fine‑tune the base model by combining this loss with the standard diffusion loss. The model fine‑tuned for a single epoch with our novel loss outperforms the baselines in common video generation evaluation metrics. To further test the temporal consistency in generated videos we adapt two additional metrics from object detection task, namely mAP and mIoU. Extensive experiments on nuScenes and KITTI datasets show the effectiveness of our approach in enhancing temporal consistency in video generation without the need for external control signals during inference and any computational overheads.

PaperId: 3248, https://arxiv.org/pdf/2602.05965.pdf GitHub

Abstract:
Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results. To improve robustness and solution quality, recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories. However, parallel execution comes at a significant computational cost: when different teams independently reason about similar sub‑problems or execute analogous steps, they repeatedly perform substantial overlapping computation. To address these limitations, in this paper, we propose Learning to Share (LTS), a learned shared‑memory mechanism for parallel agentic frameworks that enables selective cross‑team information reuse while controlling context growth. LTS introduces a global memory bank accessible to all teams and a lightweight controller that decides whether intermediate agent steps should be added to memory or not. The controller is trained using stepwise reinforcement learning with usage‑aware credit assignment, allowing it to identify information that is globally useful across parallel executions. Experiments on the AssistantBench and GAIA benchmarks show that LTS significantly reduces overall runtime while matching or improving task performance compared to memory‑free parallel baselines, demonstrating that learned memory admission is an effective strategy for improving the efficiency of parallel agentic systems. Project page: https://joefioresi718.github.io/LTS_webpage/

PaperId: 3249, https://arxiv.org/pdf/2602.05903.pdf GitHub

Abstract:
Generative sequence models are typically trained on sample sequences from natural or formal languages. It is a crucial question whether ‑‑ or to what extent ‑‑ sample‑based training is able to capture the true structure of these languages, often referred to as the ``world model''. Theoretical results indicate that we can hope for soundness at best, that is, generating valid sequences, but not necessarily all of them. However, it is still important to have practical tools that are able to verify whether a given sequence model is sound. In this study, we focus on chess, as it is a domain that provides enough complexity while having a simple rule‑based world model. We propose adversarial sequence generation for verifying the soundness of the sequence model. Our adversaries generate valid sequences so as to force the sequence model to generate an invalid next move prediction. Apart from the falsification of soundness, this method is also suitable for a more fine‑grained analysis of the failure modes and the effects of different choices during training. To demonstrate this, we propose a number of methods for adversarial sequence generation and evaluate the approach on a large set of chess models. We train models on random as well as high‑quality chess games, using several training recipes. We find that none of the models are sound, but some training techniques and dataset choices are able to improve soundness remarkably. We also investigate the potential application of board state probes in both our training and attack methods. Our findings indicate that the extracted board states have no causal role in next token prediction in most of the models.

PaperId: 3250, https://arxiv.org/pdf/2602.05830.pdf GitHub

Abstract:
Floating‑point neural networks dominate modern machine learning but incur substantial inference costs, motivating emerging interest in Boolean networks for resource‑constrained deployments. Since Boolean networks use only Boolean operations, they can achieve nanosecond‑scale inference latency. However, learning Boolean networks that are both compact and accurate remains challenging because of their discrete, combinatorial structure. In this work we address this challenge via three novel, complementary contributions: (i) a new parameter‑free strategy for learning effective connections, (ii) a novel compact convolutional Boolean architecture that exploits spatial locality while requiring fewer Boolean operations than existing convolutional kernels, and (iii) an adaptive discretization procedure that reduces the accuracy drop incurred when converting a continuously relaxed network into a discrete Boolean network. Across standard vision benchmarks, our method improves the Pareto frontier over prior state‑of‑the‑art methods, achieving higher accuracy with up to 47× fewer Boolean operations. This advantage also extends to other modalities. Further, on an FPGA, our model on MNIST achieves 99.38% accuracy with 6.48 ns latency, surpassing the prior state‑of‑the‑art in both accuracy and runtime, while generating a 7× smaller circuit. Code and models are available at https://github.com/eth‑sri/CompactLogic.

PaperId: 3251, https://arxiv.org/pdf/2602.05811.pdf GitHub

Abstract:
The integration of spatial multi‑omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi‑task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi‑omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi‑omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological "Dark Matter".

PaperId: 3252, https://arxiv.org/pdf/2602.05735.pdf GitHub

Abstract:
In the era of large foundation models, the quality of embeddings has become a central determinant of downstream task performance and overall system capability. Yet widely used dense embeddings are often extremely high‑dimensional, incurring substantial costs in storage, memory, and inference latency. To address these, Contrastive Sparse Representation (CSR) is recently proposed as a promising direction, mapping dense embeddings into high‑dimensional but k‑sparse vectors, in contrast to compact dense embeddings such as Matryoshka Representation Learning (MRL). Despite its promise, CSR suffers severe degradation in the ultra‑sparse regime, where over 80% of neurons remain inactive, leaving much of its efficiency potential unrealized. In this paper, we introduce CSRv2, a principled training approach designed to make ultra‑sparse embeddings viable. CSRv2 stabilizes sparsity learning through progressive k‑annealing, enhances representational quality via supervised contrastive objectives, and ensures end‑to‑end adaptability with full backbone finetuning. CSRv2 reduces dead neurons from 80% to 20% and delivers a 14% accuracy gain at k=2, bringing ultra‑sparse embeddings on par with CSR at k=8 and MRL at 32 dimensions, all with only two active features. While maintaining comparable performance, CSRv2 delivers a 7x speedup over MRL, and yields up to 300x improvements in compute and memory efficiency relative to dense embeddings in text representation. Extensive experiments across text and vision demonstrate that CSRv2 makes ultra‑sparse embeddings practical without compromising performance, where CSRv2 achieves 7%/4% improvement over CSR when k=4 and further increases this gap to 14%/6% when k=2 in text/vision representation. By making extreme sparsity viable, CSRv2 broadens the design space for real‑time and edge‑deployable AI systems where both embedding quality and efficiency are critical.

PaperId: 3253, https://arxiv.org/pdf/2602.05711.pdf GitHub

Abstract:
Mixture‑of‑Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency. However, existing MoE designs face an inherent trade‑off between the granularity of expert specialization and hardware execution efficiency. We propose OmniMoE, a system‑algorithm co‑designed framework that pushes expert granularity to its logical extreme. OmniMoE introduces vector‑level Atomic Experts, enabling scalable routing and execution within a single MoE layer, while retaining a shared dense MLP branch for general‑purpose processing. Although this atomic design maximizes capacity, it poses severe challenges for routing complexity and memory access. To address these, OmniMoE adopts a system‑algorithm co‑design: (i) a Cartesian Product Router that decomposes the massive index space to reduce routing complexity from O(N) to O(sqrt(N)); and (ii) Expert‑Centric Scheduling that inverts the execution order to turn scattered, memory‑bound lookups into efficient dense matrix operations. Validated on seven benchmarks, OmniMoE (with 1.7B active parameters) achieves 50.9% zero‑shot accuracy across seven benchmarks, outperforming coarse‑grained (e.g., DeepSeekMoE) and fine‑grained (e.g., PEER) baselines. Crucially, OmniMoE reduces inference latency from 73ms to 6.7ms (a 10.9‑fold speedup) compared to PEER, demonstrating that massive‑scale fine‑grained MoE can be fast and accurate. Our code is open‑sourced at https://github.com/flash‑algo/omni‑moe.

PaperId: 3254, https://arxiv.org/pdf/2602.05665.pdf GitHub

Abstract:
Memory emerges as the core module in the Large Language Model (LLM)‑based agents for long‑horizon complex tasks (e.g., multi‑turn dialogue, game playing, scientific discovery), where memory can enable knowledge accumulation, iterative reasoning and self‑evolution. Among diverse paradigms, graph stands out as a powerful structure for agent memory due to the intrinsic capabilities to model relational dependencies, organize hierarchical information, and support efficient retrieval. This survey presents a comprehensive review of agent memory from the graph‑based perspective. First, we introduce a taxonomy of agent memory, including short‑term vs. long‑term memory, knowledge vs. experience memory, non‑structural vs. structural memory, with an implementation view of graph‑based memory. Second, according to the life cycle of agent memory, we systematically analyze the key techniques in graph‑based agent memory, covering memory extraction for transforming the data into the contents, storage for organizing the data efficiently, retrieval for retrieving the relevant contents from memory to support reasoning, and evolution for updating the contents in the memory. Third, we summarize the open‑sourced libraries and benchmarks that support the development and evaluation of self‑evolving agent memory. We also explore diverse application scenarios. Finally, we identify critical challenges and future research directions. This survey aims to offer actionable insights to advance the development of more efficient and reliable graph‑based agent memory systems. All the related resources, including research papers, open‑source data, and projects, are collected for the community in https://github.com/DEEP‑PolyU/Awesome‑GraphMemory.

PaperId: 3255, https://arxiv.org/pdf/2602.05636.pdf GitHub

Abstract:
Traditional ontologies describe domain structure but cannot generate novel artifacts. Large language models generate fluently but produce outputs lacking structural validity, hallucinating mechanisms without components, goals without end conditions. We introduce Generative Ontology, a framework synthesizing these complementary strengths: ontology provides the grammar; the LLM provides the creativity. Generative Ontology encodes domain knowledge as executable Pydantic schemas constraining LLM generation via DSPy signatures. A multi‑agent pipeline assigns specialized roles: a Mechanics Architect designs game systems, a Theme Weaver integrates narrative, a Balance Critic identifies exploits, each carrying a professional "anxiety" that prevents shallow outputs. Retrieval‑augmented generation grounds designs in precedents from existing exemplars. We demonstrate the framework through GameGrammar, generating complete tabletop game designs, and present three empirical studies. An ablation study (120 designs, 4 conditions) shows multi‑agent specialization produces the largest quality gains (fun d=1.12, depth d=1.59; p<.001), while schema validation eliminates structural errors (d=4.78). A benchmark against 20 published board games reveals structural parity but a bounded creative gap (fun d=1.86): generated designs score 7‑8 while published games score 8‑9. A test‑retest study (50 evaluations) validates the LLM‑based evaluator, with 7/9 metrics achieving Good‑to‑Excellent reliability (ICC 0.836‑0.989). The pattern generalizes beyond games. Any domain with expert vocabulary, validity constraints, and accumulated exemplars is a candidate for Generative Ontology.

PaperId: 3256, https://arxiv.org/pdf/2602.05536.pdf GitHub

Abstract:
Model merging combines multiple fine‑tuned models into a single model by adding their weight updates, providing a lightweight alternative to retraining. Existing methods primarily target resolving conflicts between task updates, leaving the failure mode of over‑counting shared knowledge unaddressed. We show that when tasks share aligned spectral directions (i.e., overlapping singular vectors), a simple linear combination repeatedly accumulates these directions, inflating the singular values and biasing the merged model toward shared subspaces. To mitigate this issue, we propose Singular Value Calibration (SVC), a training‑free and data‑free post‑processing method that quantifies subspace overlap and rescales inflated singular values to restore a balanced spectrum. Across vision and language benchmarks, SVC consistently improves strong merging baselines and achieves state‑of‑the‑art performance. Furthermore, by modifying only the singular values, SVC improves the performance of Task Arithmetic by 13.0%. Code is available at https://github.com/lyymuwu/SVC.

PaperId: 3257, https://arxiv.org/pdf/2602.05533.pdf GitHub

Abstract:
We study conditional generation in diffusion models under hard constraints, where generated samples must satisfy prescribed events with probability one. Such constraints arise naturally in safety‑critical applications and in rare‑event simulation, where soft or reward‑based guidance methods offer no guarantee of constraint satisfaction. Building on a probabilistic interpretation of diffusion models, we develop a principled conditional diffusion guidance framework based on Doob's h‑transform, martingale representation and quadratic variation process. Specifically, the resulting guided dynamics augment a pretrained diffusion with an explicit drift correction involving the logarithmic gradient of a conditioning function, without modifying the pretrained score network. Leveraging martingale and quadratic‑variation identities, we propose two novel off‑policy learning algorithms based on a martingale loss and a martingale‑covariation loss to estimate h and its gradient using only trajectories from the pretrained model. We provide non‑asymptotic guarantees for the resulting conditional sampler in both total variation and Wasserstein distances, explicitly characterizing the impact of score approximation and guidance estimation errors. Numerical experiments demonstrate the effectiveness of the proposed methods in enforcing hard constraints and generating rare‑event samples. The code of the numerical experiments can be found at https://github.com/ZhengyiGuo2002/CDG_Finance.

PaperId: 3258, https://arxiv.org/pdf/2602.05513.pdf GitHub

Abstract:
Bimanual dexterous manipulation relies on integrating multimodal inputs to perform complex real‑world tasks. To address the challenges of effectively combining these modalities, we propose DECO, a decoupled multimodal diffusion transformer that disentangles vision, proprioception, and tactile signals through specialized conditioning pathways, enabling structured and controllable integration of multimodal inputs, with a lightweight adapter for parameter‑efficient injection of additional signals. Alongside DECO, we release DECO‑50 dataset for bimanual dexterous manipulation with tactile sensing, consisting of 50 hours of data and over 5M frames, collected via teleoperation on real dual‑arm robots. We train DECO on DECO‑50 and conduct extensive real‑world evaluation with over 2,000 robot rollouts. Experimental results show that DECO achieves the best performance across all tasks, with a 72.25% average success rate and a 21% improvement over the baseline. Moreover, the tactile adapter brings an additional 10.25% average success rate across all tasks and a 20% gain on complex contact‑rich tasks while tuning less than 10% of the model parameters.

PaperId: 3259, https://arxiv.org/pdf/2602.05493.pdf GitHub

Abstract:
Data annotation remains a significant bottleneck in the Humanities and Social Sciences, particularly for complex semantic tasks such as metaphor identification. While Large Language Models (LLMs) show promise, a significant gap remains between the theoretical capability of LLMs and their practical utility for researchers. This paper introduces LinguistAgent, an integrated, user‑friendly platform that leverages a reflective multi‑model architecture to automate linguistic annotation. The system implements a dual‑agent workflow, comprising an Annotator and a Reviewer, to simulate a professional peer‑review process. LinguistAgent supports comparative experiments across three paradigms: Prompt Engineering (Zero/Few‑shot), Retrieval‑Augmented Generation, and Fine‑tuning. We demonstrate LinguistAgent's efficacy using the task of metaphor identification as an example, providing real‑time token‑level evaluation (Precision, Recall, and F_1 score) against human gold standards. The application and codes are released on https://github.com/Bingru‑Li/LinguistAgent.

PaperId: 3260, https://arxiv.org/pdf/2602.05453.pdf GitHub

Abstract:
Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre‑operative MRI, they are often effectively invisible on intra‑operative CT due to minimal contrast between pathological and healthy tissue. This work investigates the feasibility of cross‑modality weak supervision for scenarios where pathology is visible in one modality (MRI) but absent in another (CT). We present a hybrid registration‑segmentation framework that combines MSCGUNet for inter‑modal image registration with a UNet‑based segmentation module, enabling registration‑assisted pseudo‑label generation for CT images. Our evaluation on the CHAOS dataset demonstrates that the pipeline can successfully register and segment healthy liver anatomy, achieving a Dice score of 0.72. However, when applied to clinical data containing tumours, performance degrades substantially (Dice score of 0.16), revealing the fundamental limitations of current registration methods when the target pathology lacks corresponding visual features in the target modality. We analyse the "domain gap" and "feature absence" problems, demonstrating that whilst spatial propagation of labels via registration is feasible for visible structures, segmenting truly invisible pathology remains an open challenge. Our findings highlight that registration‑based label transfer cannot compensate for the absence of discriminative features in the target modality, providing important insights for future research in cross‑modality medical image analysis. Code an weights are available at: https://github.com/BudhaTronix/Weakly‑Supervised‑Tumour‑Detection

PaperId: 3261, https://arxiv.org/pdf/2602.05449.pdf GitHub

Abstract:
While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computational burden. Among the existing acceleration methods, Feature Caching is popular due to its training‑free property and considerable speedup performance, but it inevitably faces semantic and detail drop with further compression. Another widely adopted method, training‑aware step‑distillation, though successful in image generation, also faces drastic degradation in video generation with a few steps. Furthermore, the quality loss becomes more severe when simply applying training‑free feature caching to the step‑distilled models, due to the sparser sampling steps. This paper novelly introduces a distillation‑compatible learnable feature caching mechanism for the first time. We employ a lightweight learnable neural predictor instead of traditional training‑free heuristics for diffusion models, enabling a more accurate capture of the high‑dimensional feature evolution process. Furthermore, we explore the challenges of highly compressed distillation on large‑scale video models and propose a conservative Restricted MeanFlow approach to achieve more stable and lossless distillation. By undertaking these initiatives, we further push the acceleration boundaries to 11.8× while preserving generation quality. Extensive experiments demonstrate the effectiveness of our method. Code has been made publicly available: https://github.com/Tencent‑Hunyuan/DisCa

PaperId: 3262, https://arxiv.org/pdf/2602.05441.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models have been attracting the attention of researchers and practitioners thanks to their promise of generalization. Although single‑task policies still offer competitive performance, VLAs are increasingly able to handle commands and environments unseen in their training set. While generalization in vision and language space is undoubtedly important for robust versatile behaviors, a key meta‑skill VLAs need to possess is affordance generalization ‑‑ the ability to manipulate new objects with familiar physical features. In this work, we present BusyBox, a physical benchmark for systematic semi‑automatic evaluation of VLAs' affordance generalization. BusyBox consists of 6 modules with switches, sliders, wires, buttons, a display, and a dial. The modules can be swapped and rotated to create a multitude of BusyBox variations with different visual appearances but the same set of affordances. We empirically demonstrate that generalization across BusyBox variants is highly challenging even for strong open‑weights VLAs such as π_0.5 and GR00T‑N1.6. To encourage the research community to evaluate their own VLAs on BusyBox and to propose new affordance generalization experiments, we have designed BusyBox to be easy to build in most robotics labs. We release the full set of CAD files for 3D‑printing its parts as well as a bill of materials for (optionally) assembling its electronics. We also publish a dataset of language‑annotated demonstrations that we collected using the common bimanual Mobile Aloha robot on the canonical BusyBox configuration. All of the released materials are available at https://microsoft.github.io/BusyBox.

PaperId: 3263, https://arxiv.org/pdf/2602.05430.pdf GitHub

Abstract:
Electricity price forecasting (EPF) is essential for energy markets stakeholders (e.g. grid operators, energy traders, policymakers) but remains challenging due to the inherent volatility and nonlinearity of price signals. Traditional statistical and deep learning (DL) models often struggle to capture complex temporal dependencies and integrate heterogeneous data effectively. While time series foundation models (TSFMs) have shown strong performance in general time series forecasting tasks, such as traffic forecasting and weather forecasting. However, their effectiveness in day‑ahead EPF, particularly in volatile markets, remains underexplored. This paper presents a spike regularization strategy and evaluates a wide range of TSFMs, including Tiny Time Mixers (TTMs), MOIRAI, MOMENT, and TimesFM, against traditional statistical and DL models such as Autoregressive Integrated Moving Average (ARIMA), Long‑short Term Memory (LSTM), and Convolutional Neural Network ‑ LSTM (CNN‑LSTM) using half‑hourly wholesale market data with volatile trends in Singapore. Exogenous factors (e.g. weather and calendar variables) are also incorporated into models where applicable. Results demonstrate that TSFMs consistently outperform traditional approaches, achieving up to 37.4% improvement in MAPE across various evaluation settings. The findings offer practical guidance for improving forecast accuracy and decision‑making in volatile electricity markets.

PaperId: 3264, https://arxiv.org/pdf/2602.05390.pdf GitHub

Abstract:
Time‑series foundation models have emerged as a new paradigm for forecasting, yet their ability to effectively leverage exogenous features ‑‑ critical for electricity demand forecasting ‑‑ remains unclear. This paper empirically evaluates foundation models capable of modeling cross‑channel correlations against a baseline LSTM with reversible instance normalization across Singaporean and Australian electricity markets at hourly and daily granularities. We systematically assess MOIRAI, MOMENT, TinyTimeMixers, ChronosX, and Chronos‑2 under three feature configurations: all features, selected features, and target‑only. Our findings reveal highly variable effectiveness: while Chronos‑2 achieves the best performance among foundation models (in zero‑shot settings), the simple baseline frequently outperforms all foundation models in Singapore's stable climate, particularly for short‑term horizons. Model architecture proves critical, with synergistic architectural implementations (TTM's channel‑mixing, Chronos‑2's grouped attention) consistently leveraging exogenous features, while other approaches show inconsistent benefits. Geographic context emerges as equally important, with foundation models demonstrating advantages primarily in variable climates. These results challenge assumptions about universal foundation model superiority and highlight the need for domain‑specific models, specifically in the energy domain.

PaperId: 3265, https://arxiv.org/pdf/2602.05327.pdf GitHub

Abstract:
Existing Large Language Model (LLM) agents struggle in interactive environments requiring long‑horizon planning, primarily due to compounding errors when simulating future states. To address this, we propose ProAct, a framework that enables agents to internalize accurate lookahead reasoning through a two‑stage training paradigm. First, we introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine‑tuning on trajectories derived from environment‑based search. By compressing complex search trees into concise, causal reasoning chains, the agent learns the logic of foresight without the computational overhead of inference‑time search. Second, to further refine decision accuracy, we propose the Monte‑Carlo Critic (MC‑Critic), a plug‑and‑play auxiliary value estimator designed to enhance policy‑gradient algorithms like PPO and GRPO. By leveraging lightweight environment rollouts to calibrate value estimates, MC‑Critic provides a low‑variance signal that facilitates stable policy optimization without relying on expensive model‑based value approximation. Experiments on both stochastic (e.g., 2048) and deterministic (e.g., Sokoban) environments demonstrate that ProAct significantly improves planning accuracy. Notably, a 4B parameter model trained with ProAct outperforms all open‑source baselines and rivals state‑of‑the‑art closed‑source models, while demonstrating robust generalization to unseen environments. The codes and models are available at https://github.com/GreatX3/ProAct

PaperId: 3266, https://arxiv.org/pdf/2602.05305.pdf GitHub

Abstract:
Generating long‑form content, such as minute‑long videos and extended texts, is increasingly important for modern generative models. Block diffusion improves inference efficiency via KV caching and block‑wise causal inference and has been widely adopted in diffusion language models and video generation. However, in long‑context settings, block diffusion still incurs substantial overhead from repeatedly computing attention over a growing KV cache. We identify an underexplored property of block diffusion: cross‑step redundancy of attention within a block. Our analysis shows that attention outputs from tokens outside the current block remain largely stable across diffusion steps, while block‑internal attention varies significantly. Based on this observation, we propose FlashBlock, a cached block‑external attention mechanism that reuses stable attention output, reducing attention computation and KV cache access without modifying the diffusion process. Moreover, FlashBlock is orthogonal to sparse attention and can be combined as a complementary residual reuse strategy, substantially improving model accuracy under aggressive sparsification. Experiments on diffusion language models and video generation demonstrate up to 1.44× higher token throughput and up to 1.6× reduction in attention time, with negligible impact on generation quality. Project page: https://caesarhhh.github.io/FlashBlock/.

PaperId: 3267, https://arxiv.org/pdf/2602.05258.pdf GitHub

Abstract:
Rotary Positional Embedding (RoPE) is a key component of context scaling in Large Language Models (LLMs). While various methods have been proposed to adapt RoPE to longer contexts, their guiding principles generally fall into two categories: (1) out‑of‑distribution (OOD) mitigation, which scales RoPE frequencies to accommodate unseen positions, and (2) Semantic Modeling, which posits that the attention scores computed with RoPE should always prioritize semantically similar tokens. In this work, we unify these seemingly distinct objectives through a minimalist intervention, namely CoPE: soft clipping lowfrequency components of RoPE. CoPE not only eliminates OOD outliers and refines semantic signals, but also prevents spectral leakage caused by hard clipping. Extensive experiments demonstrate that simply applying our soft clipping strategy to RoPE yields significant performance gains that scale up to 256k context length, validating our theoretical analysis and establishing CoPE as a new state‑of‑the‑art for length generalization. Our code, data, and models are available at https://github.com/hrlics/CoPE.

PaperId: 3268, https://arxiv.org/pdf/2602.05228.pdf GitHub

Abstract:
Harmful fine‑tuning can invalidate safety alignment of large language models, exposing significant safety risks. In this paper, we utilize the attention sink mechanism to mitigate harmful fine‑tuning. Specifically, we first measure a statistic named \emphsink divergence for each attention head and observe that \emphdifferent attention heads exhibit two different signs of sink divergence. To understand its safety implications, we conduct experiments and find that the number of attention heads of positive sink divergence increases along with the increase of the model's harmfulness when undergoing harmful fine‑tuning. Based on this finding, we propose a separable sink divergence hypothesis ‑‑ \emphattention heads associating with learning harmful patterns during fine‑tuning are separable by their sign of sink divergence. Based on the hypothesis, we propose a fine‑tuning‑stage defense, dubbed Surgery. Surgery utilizes a regularizer for sink divergence suppression, which steers attention heads toward the negative sink divergence group, thereby reducing the model's tendency to learn and amplify harmful patterns. Extensive experiments demonstrate that Surgery improves defense performance by 5.90%, 11.25%, and 9.55% on the BeaverTails, HarmBench, and SorryBench benchmarks, respectively. Source code is available on https://github.com/Lslland/Surgery.

PaperId: 3269, https://arxiv.org/pdf/2602.05178.pdf GitHub

Abstract:
Coastal hypoxia, especially in the northern part of Gulf of Mexico, presents a persistent ecological and economic concern. Seasonal models offer coarse forecasts that miss the fine‑scale variability needed for daily, responsive ecosystem management. We present study that compares four deep learning architectures for daily hypoxia classification: Bidirectional Long Short‑Term Memory (BiLSTM), Medformer (Medical Transformer), Spatio‑Temporal Transformer (ST‑Transformer), and Temporal Convolutional Network (TCN). We trained our models with twelve years of daily hindcast data from 2009‑2020 Our training data consists of 2009‑2020 hindcast data from a coupled hydrodynamic‑biogeochemical model. Similarly, we use hindcast data from 2020 through 2024 as a test data. We constructed classification models incorporating water column stratification, sediment oxygen consumption, and temperature‑dependent decomposition rates. We evaluated each architectures using the same data preprocessing, input/output formulation, and validation protocols. Each model achieved high classification accuracy and strong discriminative ability with ST‑Transformer achieving the highest performance across all metrics and tests periods (AUC‑ROC: 0.982‑0.992). We also employed McNemar's method to identify statistically significant differences in model predictions. Our contribution is a reproducible framework for operational real‑time hypoxia prediction that can support broader efforts in the environmental and ocean modeling systems community and in ecosystem resilience. The source code is available https://github.com/rmagesh148/hypoxia‑ai/

PaperId: 3270, https://arxiv.org/pdf/2602.05133.pdf GitHub

Abstract:
Traffic prediction in data‑scarce, cross‑city settings is challenging due to complex nonlinear dynamics and domain shifts. Existing methods often fail to capture traffic's inherent chaotic nature for effective few‑shot learning. We propose CAST‑CKT, a novel Chaos‑Aware Spatio‑Temporal and Cross‑City Knowledge Transfer framework. It employs an efficient chaotic analyser to quantify traffic predictability regimes, driving several key innovations: chaos‑aware attention for regime‑adaptive temporal modelling; adaptive topology learning for dynamic spatial dependencies; and chaotic consistency‑based cross‑city alignment for knowledge transfer. The framework also provides horizon‑specific predictions with uncertainty quantification. Theoretical analysis shows improved generalisation bounds. Extensive experiments on four benchmarks in cross‑city few‑shot settings show CAST‑CKT outperforms state‑of‑the‑art methods by significant margins in MAE and RMSE, while offering interpretable regime analysis. Code is available at https://github.com/afofanah/CAST‑CKT.

PaperId: 3271, https://arxiv.org/pdf/2602.05105.pdf GitHub

Abstract:
As intelligent systems and multi‑agent coordination become increasingly central to real‑world applications, there is a growing need for simulation tools that are both scalable and accessible. Existing high‑fidelity simulators, while powerful, are often computationally expensive and ill‑suited for rapid prototyping or large‑scale agent deployments. We present GAMMS (Graph based Adversarial Multiagent Modeling Simulator), a lightweight yet extensible simulation framework designed to support fast development and evaluation of agent behavior in environments that can be represented as graphs. GAMMS emphasizes five core objectives: scalability, ease of use, integration‑first architecture, fast visualization feedback, and real‑world grounding. It enables efficient simulation of complex domains such as urban road networks and communication systems, supports integration with external tools (e.g., machine learning libraries, planning solvers), and provides built‑in visualization with minimal configuration. GAMMS is agnostic to policy type, supporting heuristic, optimization‑based, and learning‑based agents, including those using large language models. By lowering the barrier to entry for researchers and enabling high‑performance simulations on standard hardware, GAMMS facilitates experimentation and innovation in multi‑agent systems, autonomous planning, and adversarial modeling. The framework is open‑source and available at https://github.com/GAMMSim/GAMMS/

PaperId: 3272, https://arxiv.org/pdf/2602.05023.pdf GitHub

Abstract:
Vision‑language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large reasoning models (MLRMs). This poses a significant privacy risk, as these widely accessible models can be exploited to infer sensitive locations from casually shared photos, often at street‑level precision, potentially surpassing the level of detail the sharer consented or intended to disclose. While recent work has proposed applying a blanket restriction on geolocation disclosure to combat this risk, these measures fail to distinguish valid geolocation uses from malicious behavior. Instead, VLMs should maintain contextual integrity by reasoning about elements within an image to determine the appropriate level of information disclosure, balancing privacy and utility. To evaluate how well models respect contextual integrity, we introduce VLM‑GEOPRIVACY, a benchmark that challenges VLMs to interpret latent social norms and contextual cues in real‑world images and determine the appropriate level of location disclosure. Our evaluation of 14 leading VLMs shows that, despite their ability to precisely geolocate images, the models are poorly aligned with human privacy expectations. They often over‑disclose in sensitive contexts and are vulnerable to prompt‑based attacks. Our results call for new design principles in multimodal systems to incorporate context‑conditioned privacy reasoning.

PaperId: 3273, https://arxiv.org/pdf/2602.05014.pdf GitHub

Abstract:
With the rapid advancement of tool‑use capabilities in Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG) is shifting from static, one‑shot retrieval toward autonomous, multi‑turn evidence acquisition. However, existing agentic search frameworks typically treat long documents as flat collections of unstructured chunks, disregarding the native hierarchical organization and sequential logic essential for human comprehension. To bridge this gap, we introduce DeepRead, a structure‑aware document reasoning agent designed to operationalize document‑native structural priors into actionable reasoning capabilities. Leveraging the structural fidelity of modern OCR, DeepRead constructs a paragraph‑level, coordinate‑based navigation system and equips the LLM with two synergistic tools: \textsfRetrieve for scanning‑aware localization, and \textsfReadSection for contiguous, order‑preserving reading within specific hierarchical scopes. This design elicits a human‑like ``locate‑then‑read'' reasoning paradigm, effectively mitigating the context fragmentation inherent in traditional retrieval methods. Extensive evaluations across four benchmarks spanning diverse document types demonstrate that DeepRead outperforms Search‑o1‑style agentic search baselines by an average of 10.3%. Fine‑grained behavioral analysis further confirms that DeepRead autonomously adopts human‑aligned reading strategies, validating the critical role of structural awareness in achieving precise document reasoning. Our code is available at https://github.com/Zhanli‑Li/DeepRead.

PaperId: 3274, https://arxiv.org/pdf/2602.05000.pdf GitHub

Abstract:
Reward guidance, also known as posterior sampling, is a popular method for test‑time adaptation and post‑training in continuous diffusion models. In this paper, we study reward guidance for discrete diffusion language models; now, one cannot differentiate through the natural outputs of the model because they are discrete tokens. We introduce a novel mechanism called EntRGi (Entropy aware Reward Guidance) to address this issue. EntRGi dynamically interpolates between continuous token relaxations and sampled hard tokens, on a token‑by‑token basis, using the diffusion model's predictive entropy. We demonstrate that EntRGi maintains both reward model reliability and optimization accuracy, while existing approaches sacrifice one for the other. We empirically validate our approach on 7B‑parameter diffusion language models across two settings: (1) test‑time adaptation, and (2) RGRL (Reward Guided Reinforcement Learning), our recipe for post‑training on reward‑guided data, showing consistent improvements over state‑of‑the‑art methods. Our code is available at https://atutej.github.io/entrgi‑rgrl

PaperId: 3275, https://arxiv.org/pdf/2602.04998.pdf GitHub

Abstract:
Low‑Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine‑tuning. Building on this paradigm, recent studies have proposed alternative initialization strategies, architectural modifications, and optimization adjustments, reporting substantial improvements over vanilla LoRA. However, these gains are often demonstrated under fixed or narrowly tuned hyperparameter settings, despite the known sensitivity of neural networks to training configurations. In this work, we systematically re‑evaluate nine representative LoRA variants alongside vanilla LoRA through extensive hyperparameter searches over learning rate, batch size, rank, and training duration. Across tasks spanning mathematical reasoning, commonsense reasoning, code generation, and instruction following at diverse model scales, we find that different LoRA methods favor distinct learning rate ranges. Crucially, once learning rates are properly tuned, all methods achieve similar peak performance (within 1‑2%), with only subtle rank‑dependent behaviors. These results suggest that vanilla LoRA remains a competitive baseline and that improvements reported under a single training configuration may not reflect consistent methodological advantages. Finally, a second‑order analysis attributes the differing optimal learning rate ranges to variations in the largest Hessian eigenvalue, aligning with classical learning theories.

PaperId: 3276, https://arxiv.org/pdf/2602.04937.pdf GitHub

Abstract:
Selecting the best data mixture is critical for successful Supervised Fine‑Tuning (SFT) of Multimodal Large Language Models. However, determining the optimal mixture weights across multiple domain‑specific datasets remains a significant bottleneck due to the combinatorial search space and the high cost associated with even a single training run. This is the so‑called Data Mixture Optimization (DMO) problem. On the other hand, model merging unifies domain‑specific experts through parameter interpolation. This strategy is efficient, as it only requires a single training run per domain, yet oftentimes leads to suboptimal models. In this work, we take the best of both worlds, studying model merging as an efficient strategy for estimating the performance of different data mixtures. We train domain‑specific multimodal experts and evaluate their weighted parameter‑space combinations to estimate the efficacy of corresponding data mixtures. We conduct extensive experiments on 14 multimodal benchmarks, and empirically demonstrate that the merged proxy models exhibit a high rank correlation with models trained on actual data mixtures. This decouples the search for optimal mixtures from the resource‑intensive training process, thereby providing a scalable and efficient strategy for navigating the complex landscape of mixture weights. Code is publicly available at https://github.com/BerasiDavide/mLLMs_merging_4_DMO.

PaperId: 3277, https://arxiv.org/pdf/2602.04925.pdf GitHub

Abstract:
The internalization of chain‑of‑thought processes into hidden states has emerged as a highly efficient paradigm for scaling test‑time compute. However, existing activation steering methods rely on static control vectors that fail to adapt to the non‑stationary evolution of complex reasoning tasks. To address this limitation, we propose STIR (Self‑Distilled Tools for Internal Reasoning), a framework that reformulates reasoning enhancement as a dynamic latent trajectory control problem. STIR introduces a synergistic three‑stage pipeline: (1) differential intrinsic action induction harvests latent reasoning successes to crystallize steering primitives; (2) sparse control basis construction curates a compact, geometrically diverse tool library; and (3) value‑modulated trajectory intervention dynamically injects context‑specific impulses via anchor‑based gating. Extensive experiments on six arithmetic and logical benchmarks across four representative models demonstrate that STIR improves average accuracy by 1.9% to 7.5% while reducing average token consumption by up to 35% compared to vanilla decoding. These findings demonstrate that the benefits of explicit chain‑of‑thought can be realized through dynamic latent trajectory control, internalizing the reasoning process to bypass the explicit generation while achieving superior fidelity. Our code is available at https://github.com/sznnzs/LLM‑Latent‑Action.

PaperId: 3278, https://arxiv.org/pdf/2602.04898.pdf GitHub

Abstract:
Text‑to‑image (T2I) diffusion models are widely adopted for their strong generative capabilities, yet remain vulnerable to backdoor attacks. Existing attacks typically rely on fixed textual triggers and single‑entity backdoor targets, making them highly susceptible to enumeration‑based input defenses and attention‑consistency detection. In this work, we propose Semantic‑level Backdoor Attack (SemBD), which introduces representation‑level triggers based on continuous semantic regions rather than discrete textual patterns. SemBD implants such semantic backdoors by distillation‑based editing of the key and value projection matrices in cross‑attention layers, enabling semantically equivalent but textually diverse prompts to activate the backdoor. To further enhance stealthiness, SemBD incorporates a semantic regularization to prevent unintended activation under incomplete semantics, as well as multi‑entity backdoor targets that avoid highly consistent cross‑attention patterns. Extensive experiments demonstrate that SemBD achieves a 100% attack success rate while maintaining strong robustness against state‑of‑the‑art input‑level defenses. Our code is available at https://github.com/DPAS‑Lab/SemBD/.

PaperId: 3279, https://arxiv.org/pdf/2602.04894.pdf GitHub

Abstract:
LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities. We study vulnerability persistence in LLM‑generated software and introduce Feature‑‑Security Table (FSTab) with two components. First, FSTab enables a black‑box attack that predicts likely backend vulnerabilities from observable frontend features and knowledge of the source LLM, without access to the backend or source code. Second, FSTab provides a model‑centric evaluation that quantifies how consistently a model reproduces the same vulnerabilities across programs, semantics‑preserving rephrasings, and application domains. We evaluate FSTab on state‑of‑the‑art code LLMs, including GPT‑5.2, Claude‑4.5 Opus, and Gemini‑3 Pro, across diverse application domains. Our results show strong cross‑domain transfer: even when the target domain is excluded from training, FSTab achieves up to 94% attack success and 93% vulnerability coverage on Internal Tools (Claude‑4.5 Opus). These findings expose an underexplored attack surface in LLM‑generated software and highlight the security risks of code generation. Our code is available at https://github.com/fstabicml2026/FSTab

PaperId: 3280, https://arxiv.org/pdf/2602.04893.pdf GitHub

Abstract:
Uncovering the mechanisms behind "jailbreaks" in large language models (LLMs) is crucial for enhancing their safety and reliability, yet these mechanisms remain poorly understood. Existing studies predominantly analyze jailbreak prompts by probing latent representations, often overlooking the causal relationships between interpretable prompt features and jailbreak occurrences. In this work, we propose Causal Analyst, a framework that integrates LLMs into data‑driven causal discovery to identify the direct causes of jailbreaks and leverage them for both attack and defense. We introduce a comprehensive dataset comprising 35k jailbreak attempts across seven LLMs, systematically constructed from 100 attack templates and 50 harmful queries, annotated with 37 meticulously designed human‑readable prompt features. By jointly training LLM‑based prompt encoding and GNN‑based causal graph learning, we reconstruct causal pathways linking prompt features to jailbreak responses. Our analysis reveals that specific features, such as "Positive Character" and "Number of Task Steps", act as direct causal drivers of jailbreaks. We demonstrate the practical utility of these insights through two applications: (1) a Jailbreaking Enhancer that targets identified causal features to significantly boost attack success rates on public benchmarks, and (2) a Guardrail Advisor that utilizes the learned causal graph to extract true malicious intent from obfuscated queries. Extensive experiments, including baseline comparisons and causal structure validation, confirm the robustness of our causal analysis and its superiority over non‑causal approaches. Our results suggest that analyzing jailbreak features from a causal perspective is an effective and interpretable approach for improving LLM reliability. Our code is available at https://github.com/Master‑PLC/Causal‑Analyst.

PaperId: 3281, https://arxiv.org/pdf/2602.04879.pdf GitHub

Abstract:
Reinforcement learning (RL) has become a cornerstone for fine‑tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm. Despite its ubiquity, we argue that the core ratio clipping mechanism in PPO is structurally ill‑suited for the large vocabularies inherent to LLMs. PPO constrains policy updates based on the probability ratio of sampled tokens, which serves as a noisy single‑sample Monte Carlo estimate of the true policy divergence. This creates a sub‑optimal learning dynamic: updates to low‑probability tokens are aggressively over‑penalized, while potentially catastrophic shifts in high‑probability tokens are under‑constrained, leading to training inefficiency and instability. To address this, we propose Divergence Proximal Policy Optimization (DPPO), which substitutes heuristic clipping with a more principled constraint based on a direct estimate of policy divergence (e.g., Total Variation or KL). To avoid huge memory footprint, we introduce the efficient Binary and Top‑K approximations to capture the essential divergence with negligible overhead. Extensive empirical evaluations demonstrate that DPPO achieves superior training stability and efficiency compared to existing methods, offering a more robust foundation for RL‑based LLM fine‑tuning. Our code is available at https://github.com/sail‑sg/Stable‑RL.

PaperId: 3282, https://arxiv.org/pdf/2602.04863.pdf GitHub

Abstract:
Training modern large language models (LLMs) has become a veritable smorgasbord of algorithms and datasets designed to elicit particular behaviors, making it critical to develop techniques to understand the effects of datasets on the model's properties. This is exacerbated by recent experiments that show datasets can transmit signals that are not directly observable from individual datapoints, posing a conceptual challenge for dataset‑centric understandings of LLM training and suggesting a missing fundamental account of such phenomena. Towards understanding such effects, inspired by recent work on the linear structure of LLMs, we uncover a general mechanism through which hidden subtexts can arise in generic datasets. We introduce Logit‑Linear‑Selection (LLS), a method that prescribes how to select subsets of a generic preference dataset to elicit a wide range of hidden effects. We apply LLS to discover subsets of real‑world datasets so that models trained on them exhibit behaviors ranging from having specific preferences, to responding to prompts in a different language not present in the dataset, to taking on a different persona. Crucially, the effect persists for the selected subset, across models with varying architectures, supporting its generality and universality.

PaperId: 3283, https://arxiv.org/pdf/2602.04811.pdf GitHub

Abstract:
True self‑evolution requires agents to act as lifelong learners that internalize novel experiences to solve future problems. However, rigorously measuring this foundational capability is hindered by two obstacles: the entanglement of prior knowledge, where ``new'' knowledge may appear in pre‑training data, and the entanglement of reasoning complexity, where failures may stem from problem difficulty rather than an inability to recall learned knowledge. We introduce SE‑Bench, a diagnostic environment that obfuscates the NumPy library and its API doc into a pseudo‑novel package with randomized identifiers. Agents are trained to internalize this package and evaluated on simple coding tasks without access to documentation, yielding a clean setting where tasks are trivial with the new API doc but impossible for base models without it. Our investigation reveals three insights: (1) the Open‑Book Paradox, where training with reference documentation inhibits retention, requiring "Closed‑Book Training" to force knowledge compression into weights; (2) the RL Gap, where standard RL fails to internalize new knowledge completely due to PPO clipping and negative gradients; and (3) the viability of Self‑Play for internalization, proving models can learn from self‑generated, noisy tasks when coupled with SFT, but not RL. Overall, SE‑Bench establishes a rigorous diagnostic platform for self‑evolution with knowledge internalization. Our code and dataset can be found at https://github.com/thunlp/SE‑Bench.

PaperId: 3284, https://arxiv.org/pdf/2602.04718.pdf GitHub

Abstract:
With recent progress on fine‑tuning language models around a fixed sparse autoencoder, we disentangle the decoder matrix into almost orthogonal features. This reduces interference and superposition between the features, while keeping performance on the target dataset essentially unchanged. Our orthogonality penalty leads to identifiable features, ensuring the uniqueness of the decomposition. Further, we find that the distance between embedded feature explanations increases with stricter orthogonality penalty, a desirable property for interpretability. Invoking the Independent Causal Mechanisms principle, we argue that orthogonality promotes modular representations amenable to causal intervention. We empirically show that these increasingly orthogonalized features allow for isolated interventions. Our code is available under \texttthttps://github.com/mrtzmllr/sae‑icm.

PaperId: 3285, https://arxiv.org/pdf/2602.04692.pdf GitHub

Abstract:
Referring Multi‑Object Tracking (RMOT) aims to track specific targets based on language descriptions and is vital for interactive AI systems such as robotics and autonomous driving. However, existing RMOT models rely solely on 2D RGB data, making it challenging to accurately detect and associate targets characterized by complex spatial semantics (e.g., ``the person closest to the camera'') and to maintain reliable identities under severe occlusion, due to the absence of explicit 3D spatial information. In this work, we propose a novel task, RGBD Referring Multi‑Object Tracking (DRMOT), which explicitly requires models to fuse RGB, Depth (D), and Language (L) modalities to achieve 3D‑aware tracking. To advance research on the DRMOT task, we construct a tailored RGBD referring multi‑object tracking dataset, named DRSet, designed to evaluate models' spatial‑semantic grounding and tracking capabilities. Specifically, DRSet contains RGB images and depth maps from 187 scenes, along with 240 language descriptions, among which 56 descriptions incorporate depth‑related information. Furthermore, we propose DRTrack, a MLLM‑guided depth‑referring tracking framework. DRTrack performs depth‑aware target grounding from joint RGB‑D‑L inputs and enforces robust trajectory association by incorporating depth cues. Extensive experiments on the DRSet dataset demonstrate the effectiveness of our framework.

PaperId: 3286, https://arxiv.org/pdf/2602.04669.pdf GitHub

Abstract:
The Muon optimizer has recently attracted considerable attention for its strong empirical performance and use of orthogonalized updates on matrix‑shaped parameters, yet its underlying mechanisms and relationship to adaptive optimizers such as Adam remain insufficiently understood. In this work, we aim to address these questions through a unified spectral perspective. Specifically, we view Muon as the p = 0 endpoint of a family of spectral transformations of the form U \boldsymbolΣ^p V' , and consider additional variants with p = 1/2 , p = 1/4 , and p = 1 . These transformations are applied to both first‑moment updates, as in momentum SGD, and to root‑mean‑square (RMS) normalized gradient updates as in Adam. To enable efficient computation, we develop a coupled Newton iteration that avoids explicit singular value decomposition. Across controlled experiments, we find that RMS‑normalized updates yield more stable optimization than first‑moment updates. Moreover, while spectral compression provides strong stabilization benefits under first‑moment updates, the Muon update (p = 0) does not consistently outperform Adam. These results suggest that Muon is best understood as an effective form of spectral normalization, but not a universally superior optimization method. Our source code will be released at https://github.com/Ocram7/BeyondMuon.

PaperId: 3287, https://arxiv.org/pdf/2602.04587.pdf GitHub

Abstract:
This paper describes VILLAIN, a multimodal fact‑checking system that verifies image‑text claims through prompt‑based multi‑agent collaboration. For the AVerImaTeC shared task, VILLAIN employs vision‑language model agents across multiple stages of fact‑checking. Textual and visual evidence is retrieved from the knowledge store enriched through additional web collection. To identify key information and address inconsistencies among evidence items, modality‑specific and cross‑modal agents generate analysis reports. In the subsequent stage, question‑answer pairs are produced based on these reports. Finally, the Verdict Prediction agent produces the verification outcome based on the image‑text claim and the generated question‑answer pairs. Our system ranked first on the leaderboard across all evaluation metrics. The source code is publicly available at https://github.com/ssu‑humane/VILLAIN.

PaperId: 3288, https://arxiv.org/pdf/2602.04456.pdf GitHub

Abstract:
People increasingly seek advice online from both human peers and large language model (LLM)‑based chatbots. Such advice rarely involves identifying a single correct answer; instead, it typically requires navigating trade‑offs among competing values. We aim to characterize how LLMs navigate value trade‑offs across different advice‑seeking contexts. First, we examine the value trade‑off structure underlying advice seeking using a curated dataset from four advice‑oriented subreddits. Using a bottom‑up approach, we inductively construct a hierarchical value framework by aggregating fine‑grained values extracted from individual advice options into higher‑level value categories. We construct value co‑occurrence networks to characterize how values co‑occur within dilemmas and find substantial heterogeneity in value trade‑off structures across advice‑seeking contexts: a women‑focused subreddit exhibits the highest network density, indicating more complex value conflicts; women's, men's, and friendship‑related subreddits exhibit highly correlated value‑conflict patterns centered on security‑related tensions (security vs. respect/connection/commitment); by contrast, career advice forms a distinct structure where security frequently clashes with self‑actualization and growth. We then evaluate LLM value preferences against these dilemmas and find that, across models and contexts, LLMs consistently prioritize values related to Exploration & Growth over Benevolence & Connection. This systemically skewed value orientation highlights a potential risk of value homogenization in AI‑mediated advice, raising concerns about how such systems may shape decision‑making and normative outcomes at scale.

PaperId: 3289, https://arxiv.org/pdf/2602.04417.pdf GitHub

Abstract:
Reinforcement Learning (RL) has enabled Large Language Models (LLMs) to acquire increasingly complex reasoning and agentic behaviors. In this work, we propose two simple techniques to improve policy gradient algorithms for LLMs. First, we replace the fixed anchor policy during RL with an Exponential Moving Average (EMA), similar to a target network in deep Q‑learning. Second, we introduce Top‑k KL estimator, which allows for flexible interpolation between exact KL and sampled KL. We derive the stability conditions for using EMA anchor; moreover, we show that our Top‑k KL estimator yields both unbiased KL values and unbiased gradients at any k, while bringing the benefits of exact KL. When combined with GRPO, the two techniques (EMA‑PG) lead to a significant performance boost. On math reasoning, it allows R1‑distilled Qwen‑1.5B to reach 53.9% on OlympiadBench compared to 50.8% by GRPO. On agentic RL domains, with Qwen‑3B base, EMA‑PG improves GRPO by an average of 33.3% across 7 datasets of Q&A with search engines, including 29.7% \rightarrow 44.1% on HotpotQA, 27.4% \rightarrow 40.1% on 2WikiMultiHopQA. Overall, we show that EMA‑PG is a simple, principled, and powerful approach to scaling RL for LLMs. Code: https://github.com/LunjunZhang/ema‑pg

PaperId: 3290, https://arxiv.org/pdf/2602.04416.pdf GitHub

Abstract:
Federated learning (FL) enables collaborative model training across decentralized medical institutions while preserving data privacy. However, medical FL benchmarks remain scarce, with existing efforts focusing mainly on unimodal or bimodal modalities and a limited range of medical tasks. This gap underscores the need for standardized evaluation to advance systematic understanding in medical MultiModal FL (MMFL). To this end, we introduce Med‑MMFL, the first comprehensive MMFL benchmark for the medical domain, encompassing diverse modalities, tasks, and federation scenarios. Our benchmark evaluates six representative state‑of‑the‑art FL algorithms, covering different aggregation strategies, loss formulations, and regularization techniques. It spans datasets with 2 to 4 modalities, comprising a total of 10 unique medical modalities, including text, pathology images, ECG, X‑ray, radiology reports, and multiple MRI sequences. Experiments are conducted across naturally federated, synthetic IID, and synthetic non‑IID settings to simulate real‑world heterogeneity. We assess segmentation, classification, modality alignment (retrieval), and VQA tasks. To support reproducibility and fair comparison of future multimodal federated learning (MMFL) methods under realistic medical settings, we release the complete benchmark implementation, including data processing and partitioning pipelines, at https://github.com/bhattarailab/Med‑MMFL‑Benchmark .

PaperId: 3291, https://arxiv.org/pdf/2602.04398.pdf GitHub

Abstract:
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. However, their outputs often exhibit social biases, raising fairness concerns. Existing debiasing methods, such as fine‑tuning on additional datasets or prompt engineering, face scalability issues or compromise user experience in multi‑turn interactions. To address these challenges, we propose a framework for detecting stereotype‑inducing words and attributing neuron‑level bias in LLMs, without the need for fine‑tuning or prompt modification. Our framework first identifies stereotype‑inducing adjectives and nouns via comparative analysis across demographic groups. We then attribute biased behavior to specific neurons using two attribution strategies based on integrated gradients. Finally, we mitigate bias by directly intervening on their activations at the projection layer. Experiments on three widely used LLMs demonstrate that our method effectively reduces bias while preserving overall model performance. Code is available at the github link: https://github.com/XMUDeepLIT/Bi‑directional‑Bias‑Attribution.

PaperId: 3292, https://arxiv.org/pdf/2602.04361.pdf GitHub GitHub

Abstract:
Visual AutoRegressive (VAR) modeling has garnered significant attention for its innovative next‑scale prediction paradigm. However, mainstream VAR paradigms attend to all tokens across historical scales at each autoregressive step. As the next scale resolution grows, the computational complexity of attention increases quartically with resolution, causing substantial latency. Prior accelerations often skip high‑resolution scales, which speeds up inference but discards high‑frequency details and harms image quality. To address these problems, we present SparVAR, a training‑free acceleration framework that exploits three properties of VAR attention: (i) strong attention sinks, (ii) cross‑scale activation similarity, and (iii) pronounced locality. Specifically, we dynamically predict the sparse attention pattern of later high‑resolution scales from a sparse decision scale, and construct scale self‑similar sparse attention via an efficient index‑mapping mechanism, enabling high‑efficiency sparse attention computation at large scales. Furthermore, we propose cross‑scale local sparse attention and implement an efficient block‑wise sparse kernel, which achieves \mathbf> 5× faster forward speed than FlashAttention. Extensive experiments demonstrate that the proposed SparVAR can reduce the generation time of an 8B model producing 1024×1024 high‑resolution images to the 1s, without skipping the last scales. Compared with the VAR baseline accelerated by FlashAttention, our method achieves a \mathbf1.57× speed‑up while preserving almost all high‑frequency details. When combined with existing scale‑skipping strategies, SparVAR attains up to a \mathbf2.28× acceleration, while maintaining competitive visual generation quality. Code is available at \hrefhttps://github.com/CAS‑CLab/SparVARSparVAR.

PaperId: 3293, https://arxiv.org/pdf/2602.04349.pdf GitHub

Abstract:
3D editing has emerged as a critical research area to provide users with flexible control over 3D assets. While current editing approaches predominantly focus on 3D Gaussian Splatting or multi‑view images, the direct editing of 3D meshes remains underexplored. Prior attempts, such as VoxHammer, rely on voxel‑based representations that suffer from limited resolution and necessitate labor‑intensive 3D mask. To address these limitations, we propose VecSet‑Edit, the first pipeline that leverages the high‑fidelity VecSet Large Reconstruction Model (LRM) as a backbone for mesh editing. Our approach is grounded on a analysis of the spatial properties in VecSet tokens, revealing that token subsets govern distinct geometric regions. Based on this insight, we introduce Mask‑guided Token Seeding and Attention‑aligned Token Gating strategies to precisely localize target regions using only 2D image conditions. Also, considering the difference between VecSet diffusion process versus voxel we design a Drift‑aware Token Pruning to reject geometric outliers during the denoising process. Finally, our Detail‑preserving Texture Baking module ensures that we not only preserve the geometric details of original mesh but also the textural information. More details can be found in our project page: https://github.com/BlueDyee/VecSet‑Edit/tree/main

PaperId: 3294, https://arxiv.org/pdf/2602.04296.pdf GitHub

Abstract:
Large language models (LLMs) have revolutionized automated code generation, yet the evaluation of their real‑world effectiveness remains limited by static benchmarks and simplistic metrics. We present ProxyWar, a novel framework that systematically assesses code generation quality by embedding LLM‑generated agents within diverse, competitive game environments. Unlike existing approaches, ProxyWar evaluates not only functional correctness but also the operational characteristics of generated programs, combining automated testing, iterative code repair, and multi‑agent tournaments to provide a holistic view of program behavior. Applied to a range of state‑of‑the‑art coders and games, our approach uncovers notable discrepancies between benchmark scores and actual performance in dynamic settings, revealing overlooked limitations and opportunities for improvement. These findings highlight the need for richer, competition‑based evaluation of code generation. Looking forward, ProxyWar lays a foundation for research into LLM‑driven algorithm discovery, adaptive problem solving, and the study of practical efficiency and robustness, including the potential for models to outperform hand‑crafted agents. The project is available at https://github.com/xinke‑wang/ProxyWar.

PaperId: 3295, https://arxiv.org/pdf/2602.04284.pdf GitHub

Abstract:
Managing agent context (e.g., thought and observation) during multi‑turn agent‑environment interactions is an emerging strategy to improve agent efficiency. However, existing studies treat the entire interaction trajectories equally, overlooking the thought necessity and observation utility varies across turns. To this end, we first conduct quantitative investigations into how thought and observation affect agent effectiveness and efficiency. Based on our findings, we propose Agent‑Omit, a unified training framework that empowers LLM agents to adaptively omit redundant thoughts and observations. Specifically, we first synthesize a small amount of cold‑start data, including both single‑turn and multi‑turn omission scenarios, to fine‑tune the agent for omission behaviors. Furthermore, we introduce an omit‑aware agentic reinforcement learning approach, incorporating a dual sampling mechanism and a tailored omission reward to incentivize the agent's adaptive omission capability. Theoretically, we prove that the deviation of our omission policy is upper‑bounded by KL‑divergence. Experimental results on five agent benchmarks show that our constructed Agent‑Omit‑8B could obtain performance comparable to seven frontier LLM agent, and achieve the best effectiveness‑efficiency trade‑off than seven efficient LLM agents methods. Our code and data are available at https://github.com/usail‑hkust/Agent‑Omit.

PaperId: 3296, https://arxiv.org/pdf/2602.04271.pdf GitHub GitHub

Abstract:
4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit deformation field, which limits direct control and editability. To address this issue, we propose SkeletonGaussian, a novel framework for generating editable dynamic 3D Gaussians from monocular video input. Our approach introduces a hierarchical articulated representation that decomposes motion into sparse rigid motion explicitly driven by a skeleton and fine‑grained non‑rigid motion. Concretely, we extract a robust skeleton and drive rigid motion via linear blend skinning, followed by a hexplane‑based refinement for non‑rigid deformations, enhancing interpretability and editability. Experimental results demonstrate that SkeletonGaussian surpasses existing methods in generation quality while enabling intuitive motion editing, establishing a new paradigm for editable 4D generation. Project page: https://wusar.github.io/projects/skeletongaussian/

PaperId: 3297, https://arxiv.org/pdf/2602.04234.pdf GitHub GitHub

Abstract:
Multi‑agent systems (MAS) have emerged as a prominent paradigm for leveraging large language models (LLMs) to tackle complex tasks. However, the mechanisms governing the effectiveness of MAS built upon publicly available LLMs, specifically the underlying rationales for their success or failure, remain largely unexplored. In this paper, we revisit MAS through the perspective of entropy, considering both intra‑ and inter‑agent dynamics by investigating entropy transitions during problem‑solving across various topologies, six reasoning benchmarks, and two agentic tasks. By analyzing 245 features spanning token‑, agent‑, and round‑level entropy, we counterintuitively find that a single agent outperforms MAS in approximately 43.3% of cases, and that entropy dynamics are largely determined during the first round of interaction. Furthermore, we provide three key observations: 1) Certainty Preference: peak entropy directly harms and stable entropy directly benefits MAS correctness; 2) Base Entropy: base models with lower entropy during problem‑solving causally drive MAS performance; and 3) Task Awareness: entropy dynamics of MAS play varying roles across different tasks. Building on these insights, we introduce a simple yet effective algorithm, the Entropy Judger, to select solutions from MAS's pass@k results, leading to consistent accuracy improvements across all MAS configurations and tasks. Our source code is available at \hrefhttps://github.com/AgenticFinLab/multiagent‑entropythis https URL.

PaperId: 3298, https://arxiv.org/pdf/2602.04224.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) have achieved tremendous success with their chain‑of‑thought (CoT) reasoning, yet also face safety issues similar to those of basic language models. In particular, while algorithms are designed to guide them to deliberately refuse harmful prompts with safe reasoning, this process often fails to generalize against diverse and complex jailbreak attacks. In this work, we attribute these failures to the generalization of the safe reasoning process, particularly their insufficiency against complex attack prompts. We provide both theoretical and empirical evidence to show the necessity of a more sufficient safe reasoning process to defend against advanced attack prompts. Building on this insight, we propose a Risk‑Aware Preference Optimization (RAPO) framework that enables LRM to adaptively identify and address the safety risks with appropriate granularity in its thinking content. Extensive experiments demonstrate that RAPO successfully generalizes multiple LRMs' safe reasoning adaptively across diverse attack prompts whilst preserving general utility, contributing a robust alignment technique for LRM safety. Our code is available at https://github.com/weizeming/RAPO.

PaperId: 3299, https://arxiv.org/pdf/2602.04197.pdf GitHub

Abstract:
The enhanced capabilities of LLM‑based agents come with an emergency for model planning and tool‑use abilities. Attributing to helpful‑harmless trade‑off from LLM alignment, agents typically also inherit the flaw of "over‑refusal", which is a passive failure mode. However, the proactive planning and action capabilities of agents introduce another crucial danger on the other side of the trade‑off. This phenomenon we term "Toxic Proactivity'': an active failure mode in which an agent, driven by the optimization for Machiavellian helpfulness, disregards ethical constraints to maximize utility. Unlike over‑refusal, Toxic Proactivity manifests as the agent taking excessive or manipulative measures to ensure its "usefulness'' is maintained. Existing research pays little attention to identifying this behavior, as it often lacks the subtle context required for such strategies to unfold. To reveal this risk, we introduce a novel evaluation framework based on dilemma‑driven interactions between dual models, enabling the simulation and analysis of agent behavior over multi‑step behavioral trajectories. Through extensive experiments with mainstream LLMs, we demonstrate that Toxic Proactivity is a widespread behavioral phenomenon and reveal two major tendencies. We further present a systematic benchmark for evaluating Toxic Proactive behavior across contextual settings.

PaperId: 3300, https://arxiv.org/pdf/2602.04184.pdf GitHub

Abstract:
Instruction‑grounded driving, where passenger language guides trajectory planning, requires vehicles to understand intent before motion. However, most prior instruction‑following planners rely on simulation or fixed command vocabularies, limiting real‑world generalization. doScenes, the first real‑world dataset linking free‑form instructions (with referentiality) to nuScenes ground‑truth motion, enables instruction‑conditioned planning. In this work, we adapt OpenEMMA, an open‑source MLLM‑based end‑to‑end driving framework that ingests front‑camera views and ego‑state and outputs 10‑step speed‑curvature trajectories, to this setting, presenting a reproducible instruction‑conditioned baseline on doScenes and investigate the effects of human instruction prompts on predicted driving behavior. We integrate doScenes directives as passenger‑style prompts within OpenEMMA's vision‑language interface, enabling linguistic conditioning before trajectory generation. Evaluated on 849 annotated scenes using ADE, we observe that instruction conditioning substantially improves robustness by preventing extreme baseline failures, yielding a 98.7% reduction in mean ADE. When such outliers are removed, instructions still influence trajectory alignment, with well‑phrased prompts improving ADE by up to 5.1%. We use this analysis to discuss what makes a "good" instruction for the OpenEMMA framework. We release the evaluation prompts and scripts to establish a reproducible baseline for instruction‑aware planning. GitHub: https://github.com/Mi3‑Lab/doScenes‑VLM‑Planning

PaperId: 3301, https://arxiv.org/pdf/2602.04162.pdf GitHub

Abstract:
3D medical imaging is in high demand and essential for clinical diagnosis and scientific research. Currently, diffusion models (DMs) have become an effective tool for medical imaging reconstruction thanks to their ability to learn rich, high‑quality data priors. However, learning the 3D data distribution with DMs in medical imaging is challenging, not only due to the difficulties in data collection but also because of the significant computational burden during model training. A common compromise is to train the DMs on 2D data priors and reconstruct stacked 2D slices to address 3D medical inverse problems. However, the intrinsic randomness of diffusion sampling causes severe inter‑slice discontinuities of reconstructed 3D volumes. Existing methods often enforce continuity regularizations along the z‑axis, which introduces sensitive hyper‑parameters and may lead to over‑smoothing results. In this work, we revisit the origin of stochasticity in diffusion sampling and introduce Inter‑Slice Consistent Stochasticity (ISCS), a simple yet effective strategy that encourages interslice consistency during diffusion sampling. Our key idea is to control the consistency of stochastic noise components during diffusion sampling, thereby aligning their sampling trajectories without adding any new loss terms or optimization steps. Importantly, the proposed ISCS is plug‑and‑play and can be dropped into any 2D trained diffusion based 3D reconstruction pipeline without additional computational cost. Experiments on several medical imaging problems show that our method can effectively improve the performance of medical 3D imaging problems based on 2D diffusion models. Our findings suggest that controlling inter‑slice stochasticity is a principled and practically attractive route toward high‑fidelity 3D medical imaging with 2D diffusion priors. The code is available at: https://github.com/duchenhe/ISCS

PaperId: 3302, https://arxiv.org/pdf/2602.04089.pdf GitHub

Abstract:
Large language models (LLMs) achieve strong performance when all task‑relevant information is available upfront, as in static prediction and instruction‑following problems. However, many real‑world decision‑making tasks are inherently online: crucial information must be acquired through interaction, feedback is delayed, and effective behavior requires balancing information collection and exploitation over time. While in‑context learning enables adaptation without weight updates, existing LLMs often struggle to reliably leverage in‑context interaction experience in such settings. In this work, we show that this limitation can be addressed through training. We introduce ORBIT, a multi‑task, multi‑episode meta‑reinforcement learning framework that trains LLMs to learn from interaction in context. After meta‑training, a relatively small open‑source model (Qwen3‑14B) demonstrates substantially improved in‑context online learning on entirely unseen environments, matching the performance of GPT‑5.2 and outperforming standard RL fine‑tuning by a large margin. Scaling experiments further reveal consistent gains with model size, suggesting significant headroom for learn‑at‑inference‑time decision‑making agents. Code reproducing the results in the paper can be found at https://github.com/XiaofengLin7/ORBIT.

PaperId: 3303, https://arxiv.org/pdf/2602.04029.pdf GitHub

Abstract:
Relational Foundation Models (RFMs) facilitate data‑driven decision‑making by learning from complex multi‑table databases. However, the diverse relational databases needed to train such models are rarely public due to privacy constraints. While there are methods to generate synthetic tabular data of arbitrary size, incorporating schema structure and primary‑‑foreign key connectivity for multi‑table generation remains challenging. Here we introduce PluRel, a framework to synthesize multi‑tabular relational databases from scratch. In a step‑by‑step fashion, PluRel models (1) schemas with directed graphs, (2) inter‑table primary‑foreign key connectivity with bipartite graphs, and, (3) feature distributions in tables via conditional causal mechanisms. The design space across these stages supports the synthesis of a wide range of diverse databases, while being computationally lightweight. Using PluRel, we observe for the first time that (1) RFM pretraining loss exhibits power‑law scaling with the number of synthetic databases and total pretraining tokens, (2) scaling the number of synthetic databases improves generalization to real databases, and (3) synthetic pretraining yields strong base models for continued pretraining on real databases. Overall, our framework and results position synthetic data scaling as a promising paradigm for RFMs.

PaperId: 3304, https://arxiv.org/pdf/2602.04006.pdf GitHub GitHub

Abstract:
Deep neural networks typically treat nonlinearities as fixed primitives (e.g., ReLU), limiting both interpretability and the granularity of control over the induced function class. While recent additive models (like KANs) attempt to address this using splines, they often suffer from computational inefficiency and boundary instability. We propose the Rational‑ANOVA Network (RAN), a foundational architecture grounded in functional ANOVA decomposition and Padé‑style rational approximation. RAN models f(x) as a composition of main effects and sparse pairwise interactions, where each component is parameterized by a stable, learnable rational unit. Crucially, we enforce a strictly positive denominator, which avoids poles and numerical instability while capturing sharp transitions and near‑singular behaviors more efficiently than polynomial bases. This ANOVA structure provides an explicit low‑order interaction bias for data efficiency and interpretability, while the rational parameterization significantly improves extrapolation. Across controlled function benchmarks and vision classification tasks (e.g., CIFAR‑10) under matched parameter and compute budgets, RAN matches or surpasses parameter‑matched MLPs and learnable‑activation baselines, with better stability and throughput. Code is available at https://github.com/jushengzhang/Rational‑ANOVA‑Networks.git.

PaperId: 3305, https://arxiv.org/pdf/2602.03981.pdf GitHub

Abstract:
Credit exposure in Decentralized Finance (DeFi) is often implicit and token‑mediated, creating a dense web of inter‑protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure‑FM, the first time‑series, graph foundation model for measuring and forecasting inter‑protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph‑tabular encoder, with pre‑trained weight initialization, and multiple task‑specific heads, DeXposure‑FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit‑exposure forecasting, predicting the joint dynamics of (1) protocol‑level flows, and (2) the topology and weights of credit‑exposure links. The DeXposure‑FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state‑of‑the‑art approaches, including a graph foundation model and temporal graph neural networks. DeXposure‑FM further produces financial economics tools that support macroprudential monitoring and scenario‑based DeFi stress testing, by enabling protocol‑level systemic‑importance scores, sector‑level spillover and concentration measures via a forecast‑then‑measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure‑FM. Code: https://github.com/EVIEHub/DeXposure‑FM.

PaperId: 3306, https://arxiv.org/pdf/2602.03955.pdf GitHub

Abstract:
While large language model (LLM) multi‑agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational cost and error propagation. This paper proposes AgentArk, a novel framework to distill multi‑agent dynamics into the weights of a single model, effectively transforming explicit test‑time interactions into implicit model capabilities. This equips a single agent with the intelligence of multi‑agent systems while remaining computationally efficient. Specifically, we investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios: reasoning‑enhanced fine‑tuning; trajectory‑based augmentation; and process‑aware distillation. By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self‑correction performance of multiple agents. They further demonstrate enhanced robustness and generalization across diverse reasoning tasks. We hope this work can shed light on future research on efficient and robust multi‑agent development. Our code is at https://github.com/AIFrontierLab/AgentArk.

PaperId: 3307, https://arxiv.org/pdf/2602.03892.pdf GitHub

Abstract:
Language‑referred audio‑visual segmentation (Ref‑AVS) aims to segment target objects described by natural language by jointly reasoning over video, audio, and text. Beyond generating segmentation masks, providing rich and interpretable diagnoses of mask quality remains largely underexplored. In this work, we introduce Mask Quality Assessment in the Ref‑AVS context (MQA‑RefAVS), a new task that evaluates the quality of candidate segmentation masks without relying on ground‑truth annotations as references at inference time. Given audio‑visual‑language inputs and each provided segmentation mask, the task requires estimating its IoU with the unobserved ground truth, identifying the corresponding error type, and recommending an actionable quality‑control decision. To support this task, we construct MQ‑RAVSBench, a benchmark featuring diverse and representative mask error modes that span both geometric and semantic issues. We further propose MQ‑Auditor, a multimodal large language model (MLLM)‑based auditor that explicitly reasons over multimodal cues and mask information to produce quantitative and qualitative mask quality assessments. Extensive experiments demonstrate that MQ‑Auditor outperforms strong open‑source and commercial MLLMs and can be integrated with existing Ref‑AVS systems to detect segmentation failures and support downstream segmentation improvement. Data and codes will be released at https://github.com/jasongief/MQA‑RefAVS.

PaperId: 3308, https://arxiv.org/pdf/2602.03866.pdf GitHub

Abstract:
Transforming scientific papers into multimodal presentation content is essential for research dissemination but remains labor intensive. Existing automated solutions typically treat each format as an isolated downstream task, leading to redundant processing and semantic inconsistency. We introduce PaperX, a unified framework that models academic presentation generation as a structural transformation and rendering process. Central to our approach is the Scholar DAG, an intermediate representation that decouples the paper's logical structure from its final presentation syntax. By applying adaptive graph traversal strategies, PaperX generates diverse, high quality outputs from a single source. Comprehensive evaluations demonstrate that our framework achieves the state of the art performance in content fidelity and aesthetic quality while significantly improving cost efficiency compared to specialized single task agents.

PaperId: 3309, https://arxiv.org/pdf/2602.03846.pdf GitHub

Abstract:
We develop a continual learning method for pretrained models that \emphrequires no access to old‑task data, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable. Our key observation is that pretrained networks exhibit substantial \emphgeometric redundancy, and that this redundancy can be exploited in two complementary ways. First, redundant neurons provide a proxy for dominant pretraining‑era feature directions, enabling the construction of approximately protected update subspaces directly from pretrained weights. Second, redundancy offers a natural bias for \emphwhere to place plasticity: by restricting updates to a subset of redundant neurons and constraining the remaining degrees of freedom, we obtain update families with reduced functional drift on the old‑data distribution and improved worst‑case retention guarantees. These insights lead to \textscPLATE (Plasticity‑Tunable Efficient Adapters), a continual learning method requiring no past‑task data that provides explicit control over the plasticity‑retention trade‑off. PLATE parameterizes each layer with a structured low‑rank update ΔW = B A Q^\top, where B and Q are computed once from pretrained weights and kept frozen, and only A is trained on the new task. The code is available at https://github.com/SalesforceAIResearch/PLATE.

PaperId: 3310, https://arxiv.org/pdf/2602.03828.pdf GitHub

Abstract:
High‑quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well‑recognized bottleneck in both academia and industry. We present FigureBench, the first large‑scale benchmark for generating scientific illustrations from long‑form scientific texts. It contains 3,300 high‑quality scientific text‑figure pairs, covering diverse text‑to‑illustration tasks from scientific papers, surveys, blogs, and textbooks. Moreover, we propose AutoFigure, the first agentic framework that automatically generates high‑quality scientific illustrations based on long‑form scientific text. Specifically, before rendering the final result, AutoFigure engages in extensive thinking, recombination, and validation to produce a layout that is both structurally sound and aesthetically refined, outputting a scientific illustration that achieves both structural completeness and aesthetic appeal. Leveraging the high‑quality data from FigureBench, we conduct extensive experiments to test the performance of AutoFigure against various baseline methods. The results demonstrate that AutoFigure consistently surpasses all baseline methods, producing publication‑ready scientific illustrations. The code, dataset and huggingface space are released in https://github.com/ResearAI/AutoFigure.

PaperId: 3311, https://arxiv.org/pdf/2602.03817.pdf GitHub

Abstract:
Many machine learning systems have access to multiple sources of evidence for the same prediction target, yet these sources often differ in reliability and informativeness across inputs. In bioacoustic classification, species identity may be inferred both from the acoustic signal and from spatiotemporal context such as location and season; while Bayesian inference motivates multiplicative evidence combination, in practice we typically only have access to discriminative predictors rather than calibrated generative models. We introduce Fusion under INdependent Conditional Hypotheses (FINCH), an adaptive log‑linear evidence fusion framework that integrates a pre‑trained audio classifier with a structured spatiotemporal predictor. FINCH learns a per‑sample gating function that estimates the reliability of contextual information from uncertainty and informativeness statistics. The resulting fusion family \emphcontains the audio‑only classifier as a special case and explicitly bounds the influence of contextual evidence, yielding a risk‑contained hypothesis class with an interpretable audio‑only fallback. Across benchmarks, FINCH consistently outperforms fixed‑weight fusion and audio‑only baselines, improving robustness and error trade‑offs even when contextual information is weak in isolation. We achieve state‑of‑the‑art performance on CBI and competitive or improved performance on several subsets of BirdSet using a lightweight, interpretable, evidence‑based approach. Code is available: \texttt\hrefhttps://anonymous.4open.science/r/birdnoise‑85CD/README.mdanonymous‑repository

PaperId: 3312, https://arxiv.org/pdf/2602.03806.pdf GitHub

Abstract:
Recently, there have been significant research interests in training large language models (LLMs) with reinforcement learning (RL) on real‑world tasks, such as multi‑turn code generation. While online RL tends to perform better than offline RL, its higher training cost and instability hinders wide adoption. In this paper, we build on the observation that multi‑turn code generation can be formulated as a one‑step recoverable Markov decision process and propose contextual bandit learning with offline trajectories (Cobalt), a new method that combines the benefits of online and offline RL. Cobalt first collects code generation trajectories using a reference LLM and divides them into partial trajectories as contextual prompts. Then, during online bandit learning, the LLM is trained to complete each partial trajectory prompt through single‑step code generation. Cobalt outperforms two multi‑turn online RL baselines based on GRPO and VeRPO, and substantially improves R1‑Distill 8B and Qwen3 8B by up to 9.0 and 6.2 absolute Pass@1 scores on LiveCodeBench. Also, we analyze LLMs' in‑context reward hacking behaviors and augment Cobalt training with perturbed trajectories to mitigate this issue. Overall, our results demonstrate Cobalt as a promising solution for iterative decision‑making tasks like multi‑turn code generation. Our code and data are available at https://github.com/OSU‑NLP‑Group/cobalt.

PaperId: 3313, https://arxiv.org/pdf/2602.03794.pdf GitHub

Abstract:
LLM‑based multi‑agent systems (MAS) have emerged as a promising approach to tackle complex tasks that are difficult for individual LLMs. A natural strategy is to scale performance by increasing the number of agents; however, we find that such scaling exhibits strong diminishing returns in homogeneous settings, while introducing heterogeneity (e.g., different models, prompts, or tools) continues to yield substantial gains. This raises a fundamental question: what limits scaling, and why does diversity help? We present an information‑theoretic framework showing that MAS performance is bounded by the intrinsic task uncertainty, not by agent count. We derive architecture‑agnostic bounds demonstrating that improvements depend on how many effective channels the system accesses. Homogeneous agents saturate early because their outputs are strongly correlated, whereas heterogeneous agents contribute complementary evidence. We further introduce K^, an effective channel count that quantifies the number of effective channels without ground‑truth labels. Empirically, we show that heterogeneous configurations consistently outperform homogeneous scaling: 2 diverse agents can match or exceed the performance of 16 homogeneous agents. Our results provide principled guidelines for building efficient and robust MAS through diversity‑aware design. Code and Dataset are available at the link: https://github.com/SafeRL‑Lab/Agent‑Scaling.

PaperId: 3314, https://arxiv.org/pdf/2602.03792.pdf GitHub

Abstract:
Prompt injection attacks manipulate webpage content to cause web agents to execute attacker‑specified tasks instead of the user's intended ones. Existing methods for detecting and localizing such attacks achieve limited effectiveness, as their underlying assumptions often do not hold in the web‑agent setting. In this work, we propose WebSentinel, a two‑step approach for detecting and localizing prompt injection attacks in webpages. Given a webpage, Step I extracts \emphsegments of interest that may be contaminated, and Step II evaluates each segment by checking its consistency with the webpage content as context. We show that WebSentinel is highly effective, substantially outperforming baseline methods across multiple datasets of both contaminated and clean webpages that we collected. Our code is available at: https://github.com/wxl‑lxw/WebSentinel.

PaperId: 3315, https://arxiv.org/pdf/2602.03786.pdf GitHub

Abstract:
Language agents have shown strong promise for task automation. Realizing this promise for increasingly complex, long‑horizon tasks has driven the rise of a sub‑agent‑as‑tools paradigm for multi‑turn task solving. However, existing designs still lack a dynamic abstraction view of sub‑agents, thereby hurting adaptability. We address this challenge with a unified, framework‑agnostic agent abstraction that models any agent as a tuple Instruction, Context, Tools, Model. This tuple acts as a compositional recipe for capabilities, enabling the system to spawn specialized executors for each task on demand. Building on this abstraction, we introduce an agentic system AOrchestra, where the central orchestrator concretizes the tuple at each step: it curates task‑relevant context, selects tools and models, and delegates execution via on‑the‑fly automatic agent creation. Such designs enable reducing human engineering efforts, and remain framework‑agnostic with plug‑and‑play support for diverse agents as task executors. It also enables a controllable performance‑cost trade‑off, allowing the system to approach Pareto‑efficient. Across three challenging benchmarks (GAIA, SWE‑Bench, Terminal‑Bench), AOrchestra achieves 16.28% relative improvement against the strongest baseline when paired with Gemini‑3‑Flash. The code is available at: https://github.com/FoundationAgents/AOrchestra

PaperId: 3316, https://arxiv.org/pdf/2602.03689.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) systems remain brittle under realistic retrieval noise, even when the required evidence appears in the top‑K results. A key reason is that retrievers and rerankers optimize solely for relevance, often selecting either trivial, answer‑revealing passages or evidence that lacks the critical information required to answer the question, without considering whether the evidence is suitable for the generator. We propose BAR‑RAG, which reframes the reranker as a boundary‑aware evidence selector that targets the generator's Goldilocks Zone ‑‑ evidence that is neither trivially easy nor fundamentally unanswerable for the generator, but is challenging yet sufficient for inference and thus provides the strongest learning signal. BAR‑RAG trains the selector with reinforcement learning using generator feedback, and adopts a two‑stage pipeline that fine‑tunes the generator under the induced evidence distribution to mitigate the distribution mismatch between training and inference. Experiments on knowledge‑intensive question answering benchmarks show that BAR‑RAG consistently improves end‑to‑end performance under noisy retrieval, achieving an average gain of 10.3 percent over strong RAG and reranking baselines while substantially improving robustness. Code is publicly avaliable at https://github.com/GasolSun36/BAR‑RAG.

PaperId: 3317, https://arxiv.org/pdf/2602.03604.pdf GitHub

Abstract:
We present EB‑JEPA, an open‑source library for learning representations and world models using Joint‑Embedding Predictive Architectures (JEPAs). JEPAs learn to predict in representation space rather than pixel space, avoiding the pitfalls of generative modeling while capturing semantically meaningful features suitable for downstream tasks. Our library provides modular, self‑contained implementations that illustrate how representation learning techniques developed for image‑level self‑supervised learning can transfer to video, where temporal dynamics add complexity, and ultimately to action‑conditioned world models, where the model must additionally learn to predict the effects of control inputs. Each example is designed for single‑GPU training within a few hours, making energy‑based self‑supervised learning accessible for research and education. We provide ablations of JEA components on CIFAR‑10. Probing these representations yields 91% accuracy, indicating that the model learns useful features. Extending to video, we include a multi‑step prediction example on Moving MNIST that demonstrates how the same principles scale to temporal modeling. Finally, we show how these representations can drive action‑conditioned world models, achieving a 97% planning success rate on the Two Rooms navigation task. Comprehensive ablations reveal the critical importance of each regularization component for preventing representation collapse. Code is available at https://github.com/facebookresearch/eb_jepa.

PaperId: 3318, https://arxiv.org/pdf/2602.03520.pdf GitHub

Abstract:
Live streaming has become a cornerstone of today's internet, enabling massive real‑time social interactions. However, it faces severe risks arising from sparse, coordinated malicious behaviors among multiple participants, which are often concealed within normal activities and challenging to detect timely and accurately. In this work, we provide a pioneering study on risk assessment in live streaming rooms, characterized by weak supervision where only room‑level labels are available. We formulate the task as a Multiple Instance Learning (MIL) problem, treating each room as a bag and defining structured user‑timeslot capsules as instances. These capsules represent subsequences of user actions within specific time windows, encapsulating localized behavioral patterns. Based on this formulation, we propose AC‑MIL, an Action‑aware Capsule MIL framework that models both individual behaviors and group‑level coordination patterns. AC‑MIL captures multi‑granular semantics and behavioral cues through a serial and parallel architecture that jointly encodes temporal dynamics and cross‑user dependencies. These signals are integrated for robust room‑level risk prediction, while also offering interpretable evidence at the behavior segment level. Extensive experiments on large‑scale industrial datasets from Douyin demonstrate that AC‑MIL significantly outperforms MIL and sequential baselines, establishing new state‑of‑the‑art performance in room‑level risk assessment for live streaming. Moreover, AC‑MIL provides capsule‑level interpretability, enabling identification of risky behavior segments as actionable evidence for intervention. The project page is available at: https://qiaoyran.github.io/AC‑MIL/.

PaperId: 3319, https://arxiv.org/pdf/2602.03478.pdf GitHub

Abstract:
LLM routing aims to achieve a favorable quality‑‑cost trade‑off by dynamically assigning easy queries to smaller models and harder queries to stronger ones. However, across both unimodal and multimodal settings, we uncover a pervasive yet underexplored failure mode in existing routers: as the user's cost budget increases, routers systematically default to the most capable and most expensive model even when cheaper models already suffice. As a result, current routers under‑utilize small models, wasting computation and monetary cost and undermining the core promise of routing; we term this phenomenon routing collapse. We attribute routing collapse to an objective‑‑decision mismatch: many routers are trained to predict scalar performance scores, whereas routing decisions ultimately depend on discrete comparisons among candidate models. Consequently, small prediction errors can flip relative orderings and trigger suboptimal selections. To bridge this gap, we propose EquiRouter, a decision‑aware router that directly learns model rankings, restoring the role of smaller models and mitigating routing collapse. On RouterBench, EquiRouter reduces cost by about 17% at GPT‑4‑level performance compared to the strongest prior router. Our code is available at https://github.com/AIGNLAI/EquiRouter.

PaperId: 3320, https://arxiv.org/pdf/2602.03372.pdf GitHub

Abstract:
Focal cortical dysplasia (FCD) lesions in epilepsy FLAIR MRI are subtle and scarce, making joint image‑‑mask generative modeling prone to instability and memorization. We propose SLIM‑Diff, a compact joint diffusion model whose main contributions are (i) a single shared‑bottleneck U‑Net that enforces tight coupling between anatomy and lesion geometry from a 2‑channel image+mask representation, and (ii) loss‑geometry tuning via a tunable L_p objective. As an internal baseline, we include the canonical DDPM‑style objective (ε‑prediction with L_2 loss) and isolate the effect of prediction parameterization and L_p geometry under a matched setup. Experiments show that x_0‑prediction is consistently the strongest choice for joint synthesis, and that fractional sub‑quadratic penalties (L_1.5) improve image fidelity while L_2 better preserves lesion mask morphology. Our code and model weights are available in https://github.com/MarioPasc/slim‑diff

PaperId: 3321, https://arxiv.org/pdf/2602.03359.pdf GitHub

Abstract:
Scaling Large Language Models (LLMs) typically relies on increasing the number of parameters or test‑time computations to boost performance. However, these strategies are impractical for edge device deployment due to limited RAM and NPU resources. Despite hardware constraints, deploying performant LLM on edge devices such as smartphone remains crucial for user experience. To address this, we propose MeKi (Memory‑based Expert Knowledge Injection), a novel system that scales LLM capacity via storage space rather than FLOPs. MeKi equips each Transformer layer with token‑level memory experts that injects pre‑stored semantic knowledge into the generation process. To bridge the gap between training capacity and inference efficiency, we employ a re‑parameterization strategy to fold parameter matrices used during training into a compact static lookup table. By offloading the knowledge to ROM, MeKi decouples model capacity from computational cost, introducing zero inference latency overhead. Extensive experiments demonstrate that MeKi significantly outperforms dense LLM baselines with identical inference speed, validating the effectiveness of memory‑based scaling paradigm for on‑device LLMs. Project homepage is at https://github.com/ningding‑o/MeKi.

PaperId: 3322, https://arxiv.org/pdf/2602.03342.pdf GitHub

Abstract:
Text‑conditioned diffusion models have advanced image and video super‑resolution by using prompts as semantic priors, and modern super‑resolution pipelines typically rely on latent tiling to scale to high resolutions. In practice, a single global caption is used with the latent tiling, often causing prompt misguidance. Specifically, a coarse global prompt often misses localized details (errors of omission) and provides locally irrelevant guidance (errors of commission) which leads to substandard results at the tile level. To solve this, we propose Tiled Prompts, a unified framework for image and video super‑resolution that generates a tile‑specific prompt for each latent tile and performs super‑resolution under locally text‑conditioned posteriors to resolve prompt misguidance with minimal overhead. Our experiments on high resolution real‑world images and videos show that tiled prompts bring consistent gains in perceptual quality and fidelity, while reducing hallucinations and tile‑level artifacts that can be found in global‑prompt baselines. Project Page: https://bryanswkim.github.io/tiled‑prompts/.

PaperId: 3323, https://arxiv.org/pdf/2602.03320.pdf GitHub GitHub

Abstract:
Medical image segmentation is evolving from task‑specific models toward generalizable frameworks. Recent research leverages Multi‑modal Large Language Models (MLLMs) as autonomous agents, employing reinforcement learning with verifiable reward (RLVR) to orchestrate specialized tools like the Segment Anything Model (SAM). However, these approaches often rely on single‑turn, rigid interaction strategies and lack process‑level supervision during training, which hinders their ability to fully exploit the dynamic potential of interactive tools and leads to redundant actions. To bridge this gap, we propose MedSAM‑Agent, a framework that reformulates interactive segmentation as a multi‑step autonomous decision‑making process. First, we introduce a hybrid prompting strategy for expert‑curated trajectory generation, enabling the model to internalize human‑like decision heuristics and adaptive refinement strategies. Furthermore, we develop a two‑stage training pipeline that integrates multi‑turn, end‑to‑end outcome verification with a clinical‑fidelity process reward design to promote interaction parsimony and decision efficiency. Extensive experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM‑Agent achieves state‑of‑the‑art performance, effectively unifying autonomous medical reasoning with robust, iterative optimization. Code is available \hrefhttps://github.com/CUHK‑AIM‑Group/MedSAM‑Agenthere.

PaperId: 3324, https://arxiv.org/pdf/2602.03310.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) models hold promise for generalist robotics but currently struggle with data scarcity, architectural inefficiencies, and the inability to generalize across different hardware platforms. We introduce RDT2, a robotic foundation model built upon a 7B parameter VLM designed to enable zero‑shot deployment on novel embodiments for open‑vocabulary tasks. To achieve this, we collected one of the largest open‑source robotic datasets‑‑over 10,000 hours of demonstrations in diverse families‑‑using an enhanced, embodiment‑agnostic Universal Manipulation Interface (UMI). Our approach employs a novel three‑stage training recipe that aligns discrete linguistic knowledge with continuous control via Residual Vector Quantization (RVQ), flow‑matching, and distillation for real‑time inference. Consequently, RDT2 becomes one of the first models that simultaneously zero‑shot generalizes to unseen objects, scenes, instructions, and even robotic platforms. Besides, it outperforms state‑of‑the‑art baselines in dexterous, long‑horizon, and dynamic downstream tasks like playing table tennis. See https://rdt‑robotics.github.io/rdt2/ for more information.

PaperId: 3325, https://arxiv.org/pdf/2602.03285.pdf GitHub

Abstract:
Enterprise meeting environments require AI assistants that handle diverse operational tasks, from rapid fact checking during live discussions to cross meeting analysis for strategic planning, under strict latency, cost, and privacy constraints. Existing meeting benchmarks mainly focus on simplified question answering and fail to reflect real world enterprise workflows, where queries arise organically from multi stakeholder collaboration, span long temporal contexts, and require tool augmented reasoning. We address this gap through a grounded dataset and a learned agent framework. First, we introduce MeetAll, a bilingual and multimodal corpus derived from 231 enterprise meetings totaling 140 hours. Questions are injected using an enterprise informed protocol validated by domain expert review and human discriminability studies. Unlike purely synthetic benchmarks, this protocol is grounded in four enterprise critical dimensions: cognitive load, temporal context span, domain expertise, and actionable task execution, calibrated through interviews with stakeholders across finance, healthcare, and technology sectors. Second, we propose MeetBench XL, a multi dimensional evaluation protocol aligned with human judgment that measures factual fidelity, intent alignment, response efficiency, structural clarity, and completeness. Third, we present MeetMaster XL, a learned dual policy agent that jointly optimizes query routing between fast and slow reasoning paths and tool invocation, including retrieval, cross meeting aggregation, and web search. A lightweight classifier enables accurate routing with minimal overhead, achieving a superior quality latency tradeoff over single model baselines. Experiments against commercial systems show consistent gains, supported by ablations, robustness tests, and a real world deployment case study.Resources: https://github.com/huyuelin/MeetBench.

PaperId: 3326, https://arxiv.org/pdf/2602.03255.pdf GitHub

Abstract:
Computer‑use agents (CUAs) that interact with real computer systems can perform automated tasks but face critical safety risks. Ambiguous instructions may trigger harmful actions, and adversarial users can manipulate tool execution to achieve malicious goals. Existing benchmarks mostly focus on short‑horizon or GUI‑based tasks, evaluating on execution‑time errors but overlooking the ability to anticipate planning‑time risks. To fill this gap, we present LPS‑Bench, a benchmark that evaluates the planning‑time safety awareness of MCP‑based CUAs under long‑horizon tasks, covering both benign and adversarial interactions across 65 scenarios of 7 task domains and 9 risk types. We introduce a multi‑agent automated pipeline for scalable data generation and adopt an LLM‑as‑a‑judge evaluation protocol to assess safety awareness through the planning trajectory. Experiments reveal substantial deficiencies in existing CUAs' ability to maintain safe behavior. We further analyze the risks and propose mitigation strategies to improve long‑horizon planning safety in MCP‑based CUA systems. We open‑source our code at https://github.com/tychenn/LPS‑Bench.

PaperId: 3327, https://arxiv.org/pdf/2602.03190.pdf GitHub

Abstract:
Reinforcement learning algorithms such as group‑relative policy optimization (GRPO) have shown strong potential for improving the mathematical reasoning capabilities of large language models. While a growing body of work seeks to improve training entropy, rollout diversity, and exploration, most existing methods still train models with a single fixed reasoning prompt or template, which can encourage prompt‑specific overfitting and unstable training dynamics. In this work, we introduce Prompt Augmented Policy Optimization (PrAg‑PO), a simple policy optimization method that mixes prompt templates with template‑specific format rewards during training. By encouraging models to generate reasoning traces under diverse instructions and output formats, PrAg‑PO increases rollout diversity and improves robustness. Compared with GRPO and DAPO, PrAg‑PO achieves significantly higher reasoning accuracy while mitigating premature training collapse. Empirically, experiments on DeepSeek‑R1‑Distill‑Qwen‑1.5B, Qwen2.5‑Math‑1.5B, and Qwen3‑1.7B show that PrAg‑PO consistently outperforms strong baselines and achieves competitive performance against recent methods on mathematics benchmarks, using only a fixed MATH Level 3‑5 training set of 8.5K problems. The code and model checkpoints are available at https://github.com/wenquanlu/PrAg‑PO.

PaperId: 3328, https://arxiv.org/pdf/2602.03164.pdf GitHub

Abstract:
Time series forecasting (TSF) plays a critical role in decision‑making for many real‑world applications. Recently, LLM‑based forecasters have made promising advancements. Despite their effectiveness, existing methods often lack explicit experience accumulation and continual evolution. In this work, we propose MemCast, a learning‑to‑memory framework that reformulates TSF as an experience‑conditioned reasoning task. Specifically, we learn experience from the training set and organize it into a hierarchical memory. This is achieved by summarizing prediction results into historical patterns, distilling inference trajectories into reasoning wisdom, and inducing extracted temporal features into general laws. Furthermore, during inference, we leverage historical patterns to guide the reasoning process and utilize reasoning wisdom to select better trajectories, while general laws serve as criteria for reflective iteration. Additionally, to enable continual evolution, we design a dynamic confidence adaptation strategy that updates the confidence of individual entries without leaking the test set distribution. Extensive experiments on multiple datasets demonstrate that MemCast consistently outperforms previous methods, validating the effectiveness of our approach. Our code is available at https://github.com/Xiaoyu‑Tao/MemCast‑TS.

PaperId: 3329, https://arxiv.org/pdf/2602.03143.pdf GitHub

Abstract:
Group Relative Policy Optimization (GRPO) has recently emerged as a practical recipe for aligning large language models with verifiable objectives. However, under sparse terminal rewards, GRPO often stalls because rollouts within a group frequently receive identical rewards, causing relative advantages to collapse and updates to vanish. We propose self‑hint aligned GRPO with privileged supervision (SAGE), an on‑policy reinforcement learning framework that injects privileged hints during training to reshape the rollout distribution under the same terminal verifier reward. For each prompt x, the model samples a compact hint h (e.g., a plan or decomposition) and then generates a solution τ conditioned on (x,h). Crucially, the task reward R(x,τ) is unchanged; hints only increase within‑group outcome diversity under finite sampling, preventing GRPO advantages from collapsing under sparse rewards. At test time, we set h=\varnothing and deploy the no‑hint policy without any privileged information. Moreover, sampling diverse self‑hints serves as an adaptive curriculum that tracks the learner's bottlenecks more effectively than fixed hints from an initial policy or a stronger external model. Experiments over 6 benchmarks with 3 LLMs show that SAGE consistently outperforms GRPO, on average +2.0 on Llama‑3.2‑3B‑Instruct, +1.2 on Qwen2.5‑7B‑Instruct and +1.3 on Qwen3‑4B‑Instruct. The code is available at https://github.com/BaohaoLiao/SAGE.

PaperId: 3330, https://arxiv.org/pdf/2602.03120.pdf GitHub

Abstract:
Post‑Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory‑constrained devices, yet it renders models static and difficult to fine‑tune. Standard fine‑tuning paradigms, including Reinforcement Learning (RL), fundamentally rely on backpropagation and high‑precision weights to compute gradients. Thus they cannot be used on quantized models, where the parameter space is discrete and non‑differentiable. While Evolution Strategies (ES) offer a backpropagation‑free alternative, optimization of the quantized parameters can still fail due to vanishing or inaccurate gradient. This paper introduces Quantized Evolution Strategies (QES), an optimization paradigm that performs full‑parameter fine‑tuning directly in the quantized space. QES is based on two innovations: (1) it integrates accumulated error feedback to preserve high‑precision gradient signals, and (2) it utilizes a stateless seed replay to reduce memory usage to low‑precision inference levels. QES significantly outperforms the state‑of‑the‑art zeroth‑order fine‑tuning method on arithmetic reasoning tasks, making direct fine‑tuning for quantized models possible. It therefore opens up the possibility for scaling up LLMs entirely in the quantized space. The source code is available at https://github.com/dibbla/Quantized‑Evolution‑Strategies .

PaperId: 3331, https://arxiv.org/pdf/2602.03098.pdf GitHub

Abstract:
Expanding multimodal representations to novel modalities is constrained by reliance on large‑scale paired datasets (e.g., text‑image, text‑audio, text‑3D, text‑molecule), which are costly and often infeasible in domains requiring expert annotation such as medical imaging and molecular analysis. We introduce TextME, the first text‑only modality expansion framework, to the best of our knowledge, projecting diverse modalities into LLM embedding space as a unified anchor. Our approach exploits the geometric structure of pretrained contrastive encoders to enable zero‑shot cross‑modal transfer using only text descriptions, without paired supervision. We empirically validate that such consistent modality gaps exist across image, video, audio, 3D, X‑ray, and molecular domains, demonstrating that text‑only training can preserve substantial performance of pretrained encoders. We further show that our framework enables emergent cross‑modal retrieval between modality pairs not explicitly aligned during training (e.g., audio‑to‑image, 3D‑to‑image). These results establish text‑only training as a practical alternative to paired supervision for modality expansion.

PaperId: 3332, https://arxiv.org/pdf/2602.03067.pdf GitHub

Abstract:
Entropic optimal transport (EOT) via Sinkhorn iterations is widely used in modern machine learning, yet GPU solvers remain inefficient at scale. Tensorized implementations suffer quadratic HBM traffic from dense n× m interactions, while existing online backends avoid storing dense matrices but still rely on generic tiled map‑reduce reduction kernels with limited fusion. We present FlashSinkhorn, an IO‑aware EOT solver for squared Euclidean cost that rewrites stabilized log‑domain Sinkhorn updates as row‑wise LogSumExp reductions of biased dot‑product scores, the same normalization as transformer attention. This enables FlashAttention‑style fusion and tiling: fused Triton kernels stream tiles through on‑chip SRAM and update dual potentials in a single pass, substantially reducing HBM IO per iteration while retaining linear‑memory operations. We further provide streaming kernels for transport application, enabling scalable first‑ and second‑order optimization. On A100 GPUs, FlashSinkhorn achieves up to 32× forward‑pass and 161× end‑to‑end speedups over state‑of‑the‑art online baselines on point‑cloud OT, improves scalability on OT‑based downstream tasks. For reproducibility, we release an open‑source implementation at https://github.com/ot‑triton‑lab/flash‑sinkhorn .

PaperId: 3333, https://arxiv.org/pdf/2602.03054.pdf GitHub

Abstract:
Co‑design is essential for grounding embodied artificial intelligence (AI) systems in real‑world contexts, especially high‑stakes domains such as healthcare. While prior work has explored multidisciplinary collaboration, iterative prototyping, and support for non‑technical participants, few have interwoven these into a sustained co‑design process. Such efforts often target one context and low‑fidelity stages, limiting the generalizability of findings and obscuring how participants' ideas evolve. To address these limitations, we conducted a 14‑week workshop with a multidisciplinary team of 22 participants, centered around how embodied AI can reduce non‑value‑added task burdens in three healthcare settings: emergency departments, long‑term rehabilitation facilities, and sleep disorder clinics. We found that the iterative progression from abstract brainstorming to high‑fidelity prototypes, supported by educational scaffolds, enabled participants to understand real‑world trade‑offs and generate more deployable solutions. We propose eight guidelines for co‑designing more considerate embodied AI: attuned to context, responsive to social dynamics, mindful of expectations, and grounded in deployment. Project Page: https://byc‑sophie.github.io/Towards‑Considerate‑Embodied‑AI/

PaperId: 3334, https://arxiv.org/pdf/2602.03053.pdf GitHub

Abstract:
Multi‑Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification, which evaluates intermediate steps in trajectories, has shown promise in general reasoning settings, and has been suggested as a potential tool for guiding coordination of MAS; however, its actual effectiveness in MAS remains unclear. To fill this gap, we present MAS‑ProVe, a systematic empirical study of process verification for multi‑agent systems (MAS). Our study spans three verification paradigms (LLM‑as‑a‑Judge, reward models, and process reward models), evaluated across two levels of verification granularity (agent‑level and iteration‑level). We further examine five representative verifiers and four context management strategies, and conduct experiments over six diverse MAS frameworks on multiple reasoning benchmarks. We find that process‑level verification does not consistently improve performance and frequently exhibits high variance, highlighting the difficulty of reliably evaluating partial multi‑agent trajectories. Among the methods studied, LLM‑as‑a‑Judge generally outperforms reward‑based approaches, with trained judges surpassing general‑purpose LLMs. We further observe a small performance gap between LLMs acting as judges and as single agents, and identify a context‑length‑performance trade‑off in verification. Overall, our results suggest that effective and robust process verification for MAS remains an open challenge, requiring further advances beyond current paradigms. Code is available at https://github.com/Wang‑ML‑Lab/MAS‑ProVe.

PaperId: 3335, https://arxiv.org/pdf/2602.03006.pdf GitHub

Abstract:
Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active distillation reduces these costs by querying an LLM oracle to train compact discriminative students, but most pipelines distill only final labels, discarding intermediate reasoning signals and offering limited diagnostics of what reasoning is missing and where errors arise. We propose Graph of Concept Predictors (GCP), a reasoning‑aware active distillation framework that externalizes the teacher's decision process as a directed acyclic graph and mirrors it with modular concept predictors in the student. GCP enhances sample efficiency through a graph‑aware acquisition strategy that targets uncertainty and disagreement at critical reasoning nodes. Additionally, it improves training stability and efficiency by performing targeted sub‑module retraining, which attributes downstream loss to specific concept predictors and updates only the most influential modules. Experiments on eight NLP classification benchmarks demonstrate that GCP enhances performance under limited annotation budgets while yielding more interpretable and controllable training dynamics. Code is available at: https://github.com/Ziyang‑Yu/GCP.

PaperId: 3336, https://arxiv.org/pdf/2602.02820.pdf GitHub

Abstract:
For certain image generation tasks, vector graphics such as Scalable Vector Graphics (SVGs) offer clear benefits such as increased flexibility, size efficiency, and editing ease, but remain less explored than raster‑based approaches. A core challenge is that the numerical, geometric parameters, which make up a large proportion of SVGs, are inefficiently encoded as long sequences of tokens. This slows training, reduces accuracy, and hurts generalization. To address these problems, we propose Continuous Number Modeling (CNM), an approach that directly models numbers as first‑class, continuous values rather than discrete tokens. This formulation restores the mathematical elegance of the representation by aligning the model's inputs with the data's continuous nature, removing discretization artifacts introduced by token‑based encoding. We then train a multimodal transformer on 2 million raster‑to‑SVG samples, followed by fine‑tuning via reinforcement learning using perceptual feedback to further improve visual quality. Our approach improves training speed by over 30% while maintaining higher perceptual fidelity compared to alternative approaches. This work establishes CNM as a practical and efficient approach for high‑quality vector generation, with potential for broader applications. We make our code available http://github.com/mikeogezi/CNM.

PaperId: 3337, https://arxiv.org/pdf/2602.02808.pdf GitHub

Abstract:
Accurate identification of anatomical landmarks is crucial for various medical applications. Traditional manual landmarking is time‑consuming and prone to inter‑observer variability, while rule‑based methods are often tailored to specific geometries or limited sets of landmarks. In recent years, anatomical surfaces have been effectively represented as point clouds, which are lightweight structures composed of spatial coordinates. Following this strategy and to overcome the limitations of existing landmarking techniques, we propose Landmark Point Transformer (LmPT), a method for automatic anatomical landmark detection on point clouds that can leverage homologous bones from different species for translational research. The LmPT model incorporates a conditioning mechanism that enables adaptability to different input types to conduct cross‑species learning. We focus the evaluation of our approach on femoral landmarking using both human and newly annotated dog femurs, demonstrating its generalization and effectiveness across species. The code and dog femur dataset will be publicly available at: https://github.com/Pierreoo/LandmarkPointTransformer.

PaperId: 3338, https://arxiv.org/pdf/2602.02793.pdf GitHub

Abstract:
Flow matching learns a velocity field that transports a base distribution to data. We study how small latent perturbations propagate through these flows and show that Jacobian‑vector products (JVPs) provide a practical lens on dependency structure in the generated features. We derive closed‑form expressions for the optimal drift and its Jacobian in Gaussian and mixture‑of‑Gaussian settings, revealing that even globally nonlinear flows admit local affine structure. In low‑dimensional synthetic benchmarks, numerical JVPs recover the analytical Jacobians. In image domains, composing the flow with an attribute classifier yields an attribute‑level JVP estimator that recovers empirical correlations on MNIST and CelebA. Conditioning on small classifier‑Jacobian norms reduces correlations in a way consistent with a hypothesized common‑cause structure, while we emphasize that this conditioning is not a formal do intervention.

PaperId: 3339, https://arxiv.org/pdf/2602.02729.pdf GitHub

Abstract:
This paper presents CAPS (Clock‑weighted Aggregation with Prefix‑products and Softmax), a structured attention mechanism for time series forecasting that decouples three distinct temporal structures: global trends, local shocks, and seasonal patterns. Standard softmax attention entangles these through global normalization, while recent recurrent models sacrifice long‑term, order‑independent selection for order‑dependent causal structure. CAPS combines SO(2) rotations for phase alignment with three additive gating paths ‑‑ Riemann softmax, prefix‑product gates, and a Clock baseline ‑‑ within a single attention layer. We introduce the Clock mechanism, a learned temporal weighting that modulates these paths through a shared notion of temporal importance. Experiments on long‑ and short‑term forecasting benchmarks surpass vanilla softmax and linear attention mechanisms and demonstrate competitive performance against seven strong baselines with linear complexity. Our code implementation is available at https://github.com/vireshpati/CAPS‑Attention.

PaperId: 3340, https://arxiv.org/pdf/2602.02708.pdf GitHub

Abstract:
Supervised fine‑tuning (SFT) is the standard approach for binary classification tasks such as toxicity detection, factuality verification, and causal inference. However, SFT often performs poorly in real‑world settings with label noise, class imbalance, or sparse supervision. We introduce BinaryPPO, an offline reinforcement learning large language model (LLM) framework that reformulates binary classification as a reward maximization problem. Our method leverages a variant of Proximal Policy Optimization (PPO) with a confidence‑weighted reward function that penalizes uncertain or incorrect predictions, enabling the model to learn robust decision policies from static datasets without online interaction. Across eight domain‑specific benchmarks and multiple models with differing architectures, BinaryPPO improves accuracy by 40‑60 percentage points, reaching up to 99%, substantially outperforming supervised baselines. We provide an in‑depth analysis of the role of reward shaping, advantage scaling, and policy stability in enabling this improvement. Overall, we demonstrate that confidence‑based reward design provides a robust alternative to SFT for binary classification. Our code is available at https://github.com/psyonp/BinaryPPO.

PaperId: 3341, https://arxiv.org/pdf/2602.02591.pdf GitHub

Abstract:
We introduce and define a novel task‑Scene‑Aware Visually‑Driven Speech Synthesis, aimed at addressing the limitations of existing speech generation models in creating immersive auditory experiences that align with the real physical world. To tackle the two core challenges of data scarcity and modality decoupling, we propose VividVoice, a unified generative framework. First, we constructed a large‑scale, high‑quality hybrid multimodal dataset, Vivid‑210K, which, through an innovative programmatic pipeline, establishes a strong correlation between visual scenes, speaker identity, and audio for the first time. Second, we designed a core alignment module, D‑MSVA, which leverages a decoupled memory bank architecture and a cross‑modal hybrid supervision strategy to achieve fine‑grained alignment from visual scenes to timbre and environmental acoustic features. Both subjective and objective experimental results provide strong evidence that VividVoice significantly outperforms existing baseline models in terms of audio fidelity, content clarity, and multimodal consistency. Our demo is available at https://chengyuann.github.io/VividVoice/.

PaperId: 3342, https://arxiv.org/pdf/2602.02548.pdf GitHub GitHub

Abstract:
Existing GUI agent models relying on coordinate‑based one‑step visual grounding struggle with generalizing to varying input resolutions and aspect ratios. Alternatives introduce coordinate‑free strategies yet suffer from learning under severe data scarcity. To address the limitations, we propose ToolTok, a novel paradigm of multi‑step pathfinding for GUI agents, where operations are modeled as a sequence of progressive tool usage. Specifically, we devise tools aligned with human interaction habits and represent each tool using learnable token embeddings. To enable efficient embedding learning under limited supervision, ToolTok introduces a semantic anchoring mechanism that grounds each tool with semantically related concepts as natural inductive bias. To further enable a pre‑trained large language model to progressively acquire tool semantics, we construct an easy‑to‑hard curriculum consisting of three tasks: token definition question‑answering, pure text‑guided tool selection, and simplified visual pathfinding. Extensive experiments on multiple benchmarks show that ToolTok achieves superior performance among models of comparable scale (4B) and remains competitive with a substantially larger model (235B). Notably, these results are obtained using less than 1% of the training data required by other post‑training approaches. In addition, ToolTok demonstrates strong generalization across unseen scenarios. Our training & inference code is open‑source at https://github.com/ZephinueCode/ToolTok.

PaperId: 3343, https://arxiv.org/pdf/2602.02546.pdf GitHub

Abstract:
Large language models (LLMs) deliver strong performance, but their high compute and memory costs make deployment difficult in resource‑constrained scenarios. Weight‑only post‑training quantization (PTQ) is appealing, as it reduces memory usage and enables practical speedup without low‑bit operators or specialized hardware. However, accuracy often degrades significantly in weight‑only PTQ at sub‑4‑bit precision, and our analysis identifies two main causes: (1) down‑projection matrices are a well‑known quantization bottleneck, but maintaining their fidelity often requires extra bit‑width; (2) weight quantization induces activation deviations, but effective correction strategies remain underexplored. To address these issues, we propose D^2Quant, a novel weight‑only PTQ framework that improves quantization from both the weight and activation perspectives. On the weight side, we design a Dual‑Scale Quantizer (DSQ) tailored to down‑projection matrices, with an absorbable scaling factor that significantly improves accuracy without increasing the bit budget. On the activation side, we propose Deviation‑Aware Correction (DAC), which incorporates a mean‑shift correction within LayerNorm to mitigate quantization‑induced activation distribution shifts. Extensive experiments across multiple LLM families and evaluation metrics show that D^2Quant delivers superior performance for weight‑only PTQ at sub‑4‑bit precision. The code and models will be available at https://github.com/XIANGLONGYAN/D2Quant.

PaperId: 3344, https://arxiv.org/pdf/2602.02544.pdf GitHub

Abstract:
While Diffusion Language Models (DLMs) offer a flexible, arbitrary‑order alternative to the autoregressive paradigm, their non‑causal nature precludes standard KV caching, forcing costly hidden state recomputation at every decoding step. Existing DLM caching approaches reduce this cost by selective hidden state updates; however, they are still limited by (i) costly token‑wise update identification heuristics and (ii) rigid, uniform budget allocation that fails to account for heterogeneous hidden state dynamics. To address these challenges, we present SPA‑Cache that jointly optimizes update identification and budget allocation in DLM cache. First, we derive a low‑dimensional singular proxy that enables the identification of update‑critical tokens in a low‑dimensional subspace, substantially reducing the overhead of update identification. Second, we introduce an adaptive strategy that allocates fewer updates to stable layers without degrading generation quality. Together, these contributions significantly improve the efficiency of DLMs, yielding up to an 8× throughput improvement over vanilla decoding and a 2‑‑4× speedup over existing caching baselines.

PaperId: 3345, https://arxiv.org/pdf/2602.02536.pdf GitHub GitHub

Abstract:
Safety moderation is pivotal for identifying harmful content. Despite the success of textual safety moderation, its multimodal counterparts remain hindered by a dual sparsity of data and supervision. Conventional reliance on binary labels lead to shortcut learning, which obscures the intrinsic classification boundaries necessary for effective multimodal discrimination. Hence, we propose a novel learning paradigm (UniMod) that transitions from sparse decision‑making to dense reasoning traces. By constructing structured trajectories encompassing evidence grounding, modality assessment, risk mapping, policy decision, and response generation, we reformulate monolithic decision tasks into a multi‑dimensional boundary learning process. This approach forces the model to ground its decision in explicit safety semantics, preventing the model from converging on superficial shortcuts. To facilitate this paradigm, we develop a multi‑head scalar reward model (UniRM). UniRM provides multi‑dimensional supervision by assigning attribute‑level scores to the response generation stage. Furthermore, we introduce specialized optimization strategies to decouple task‑specific parameters and rebalance training dynamics, effectively resolving interference between diverse objectives in multi‑task learning. Empirical results show UniMod achieves competitive textual moderation performance and sets a new multimodal benchmark using less than 40% of the training data used by leading baselines. Ablations further validate our multi‑attribute trajectory reasoning, offering an effective and efficient framework for multimodal moderation. Supplementary materials are available at \hrefhttps://trustworthylab.github.io/UniMod/project website.

PaperId: 3346, https://arxiv.org/pdf/2602.02518.pdf GitHub

Abstract:
Large language models (LLMs) increasingly rely on external knowledge to improve factuality, yet many real‑world knowledge sources are organized as heterogeneous graphs rather than plain text. Reasoning over such graphs requires models to follow schema‑defined relations through precise function calls and to aggregate evidence across multiple rounds of interaction. We propose GraphDancer, a two‑stage post‑training framework that teaches LLMs to reason over graphs by interleaving natural‑language reasoning with graph function execution. The first stage teaches the model how to interact with the graph under rule‑based rewards, while the second stage further teaches it to prefer more grounded and efficient interaction trajectories. The key novelty of GraphDancer is a graph‑aware curriculum that organizes both stages by the structural complexity of information‑seeking trajectories, progressively increasing task difficulty during training. We evaluate GraphDancer on a multi‑domain benchmark by training on one domain only and testing on unseen domains and out‑of‑distribution question types. Despite using only a 3B backbone, GraphDancer outperforms baselines equipped with larger/stronger backbones, demonstrating robust cross‑domain generalization of graph exploration and reasoning skills. Our code can be found at https://github.com/leopoldwhite/GraphDancer.

PaperId: 3347, https://arxiv.org/pdf/2602.02516.pdf GitHub

Abstract:
Individual user fairness is commonly understood as treating similar users similarly. In Recommender Systems (RSs), several evaluation measures exist for quantifying individual user fairness. These measures evaluate fairness via either: (i) the disparity in RS effectiveness scores regardless of user similarity, or (ii) the disparity in items recommended to similar users regardless of item relevance. Both disparity in recommendation effectiveness and user similarity are very important in fairness, yet no existing individual user fairness measure simultaneously accounts for both. In brief, current user fairness evaluation measures implement a largely incomplete definition of fairness. To fill this gap, we present Pairwise User unFairness (PUF), a novel evaluation measure of individual user fairness that considers both effectiveness disparity and user similarity. PUF is the only measure that can express this important distinction. We empirically validate that PUF does this consistently across 4 datasets and 7 rankers, and robustly when varying user similarity or effectiveness. In contrast, all other measures are either almost insensitive to effectiveness disparity or completely insensitive to user similarity. We contribute the first RS evaluation measure to reliably capture both user similarity and effectiveness in individual user fairness. Our code: https://github.com/theresiavr/PUF‑individual‑user‑fairness‑recsys.

PaperId: 3348, https://arxiv.org/pdf/2602.02510.pdf GitHub

Abstract:
Memes are a pervasive form of online communication, yet their cultural specificity poses significant challenges for cross‑cultural adaptation. We study cross‑cultural meme transcreation, a multimodal generation task that aims to preserve communicative intent and humor while adapting culture‑specific references. We propose a hybrid transcreation framework based on vision‑language models and introduce a large‑scale bidirectional dataset of Chinese and US memes. Using both human judgments and automated evaluation, we analyze 6,315 meme pairs and assess transcreation quality across cultural directions. Our results show that current vision‑language models can perform cross‑cultural meme transcreation to a limited extent, but exhibit clear directional asymmetries: US‑Chinese transcreation consistently achieves higher quality than Chinese‑US. We further identify which aspects of humor and visual‑textual design transfer across cultures and which remain challenging, and propose an evaluation framework for assessing cross‑cultural multimodal generation. Our code and dataset are publicly available at https://github.com/AIM‑SCU/MemeXGen.

PaperId: 3349, https://arxiv.org/pdf/2602.02500.pdf GitHub

Abstract:
The Newton‑Schulz (NS) iteration has become a key technique for orthogonalization in optimizers such as Muon and for optimization on the Stiefel manifold. Despite its effectiveness, the conventional NS iteration incurs significant computational overhead due to repeated high‑dimensional matrix multiplications. To overcome these limitations, we propose Iteration‑Free Newton‑Schulz Orthogonalization (IFNSO), a novel framework that consolidates the traditional iterative structure into a unified and Iteration‑Free formulation. By analyzing the contribution of individual matrix powers, we streamline the process by removing insignificant terms and introducing a polynomial with learnable coefficients. These coefficients are optimized to ensure both superior computational efficiency and stable convergence. Extensive experiments demonstrate that IFNSO achieves superior performance compared to existing methods. Our code is available at: https://github.com/greekinRoma/Unified_Newton_Schulz_Orthogonalization.

PaperId: 3350, https://arxiv.org/pdf/2602.02493.pdf GitHub GitHub

Abstract:
Pixel diffusion generates images directly in pixel space, avoiding the VAE artifacts and representational bottlenecks of two‑stage latent diffusion. Recent JiT further simplifies pixel diffusion with x‑prediction, where the model predicts clean images rather than velocity. However, the standard pixel‑wise diffusion loss treats all pixels equally, spending model capacity to perceptually insignificant signals and often leading to blurry samples. We propose PixelGen, an end‑to‑end pixel diffusion framework that augments x‑prediction with perceptual supervision. Specifically, PixelGen introduces two complementary perceptual losses on top of x‑prediction: an LPIPS loss for local textures and a P‑DINO loss for global semantics. To preserve sample coverage, PixelGen further proposes a noise‑gating strategy that applies these losses only at lower‑noise timesteps. On ImageNet‑256 without classifier‑free guidance, PixelGen achieves an FID of 5.11 in 80 training epochs, surpassing the latent diffusion baselines. Moreover, PixelGen scales efficiently to text‑to‑image generation, reaching a GenEval score of 0.79 with only 6 days of training on 8xH800 GPUs. These results show that perceptual supervision substantially narrows the gap between pixel and latent diffusion while preserving a simple one‑stage pipeline. Codes are available at https://github.com/Zehong‑Ma/PixelGen.

PaperId: 3351, https://arxiv.org/pdf/2602.02488.pdf GitHub

Abstract:
We propose RLAnything, a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed‑loop optimization, amplifying learning signals and strengthening the overall RL system for any LLM or agentic scenarios. Specifically, the policy is trained with integrated feedback from step‑wise and outcome signals, while the reward model is jointly optimized via consistency feedback, which in turn further improves policy training. Moreover, our theory‑motivated automatic environment adaptation improves training for both the reward and policy models by leveraging critic feedback from each, enabling learning from experience. Empirically, each added component consistently improves the overall system, and RLAnything yields substantial gains across various representative LLM and agentic tasks, boosting Qwen3‑VL‑8B‑Thinking by 9.1% on OSWorld and Qwen2.5‑7B‑Instruct by 18.7% and 11.9% on AlfWorld and LiveBench, respectively. We also that optimized reward‑model signals outperform outcomes that rely on human labels. Code: https://github.com/Gen‑Verse/Open‑AgentRL

PaperId: 3352, https://arxiv.org/pdf/2602.02481.pdf GitHub

Abstract:
Likelihood‑based policy gradient methods are the dominant approach for training robot control policies from rewards. These methods rely on differentiable action likelihoods, which constrain policy outputs to simple distributions like Gaussians. In this work, we show how flow matching policy gradients ‑‑ a recent framework that bypasses likelihood computation ‑‑ can be made effective for training and fine‑tuning more expressive policies in challenging robot control settings. We introduce an improved objective that enables success in legged locomotion, humanoid motion tracking, and manipulation tasks, as well as robust sim‑to‑real transfer on two humanoid robots. We then present ablations and analysis on training dynamics. Results show how policies can exploit the flow representation for exploration when training from scratch, as well as improved fine‑tuning robustness over baselines.

PaperId: 3353, https://arxiv.org/pdf/2602.02474.pdf GitHub

Abstract:
Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand‑designed operations for extracting memory. These fixed procedures hard‑code human priors about what to store and how to revise memory, making them rigid under diverse interaction patterns and inefficient on long histories. To this end, we present MemSkill, which reframes these operations as learnable and evolvable memory skills, structured and reusable routines for extracting, consolidating, and pruning information from interaction traces. Inspired by the design philosophy of agent skills, MemSkill employs a \emphcontroller that learns to select a small set of relevant skills, paired with an LLM‑based \emphexecutor that produces skill‑guided memories. Beyond learning skill selection, MemSkill introduces a \emphdesigner that periodically reviews hard cases where selected skills yield incorrect or incomplete memories, and evolves the skill set by proposing refinements and new skills. Together, MemSkill forms a closed‑loop procedure that improves both the skill‑selection policy and the skill set itself. Experiments on LoCoMo, LongMemEval, HotpotQA, and ALFWorld demonstrate that MemSkill improves task performance over strong baselines and generalizes well across settings. Further analyses shed light on how skills evolve, offering insights toward more adaptive, self‑evolving memory management for LLM agents.

PaperId: 3354, https://arxiv.org/pdf/2602.02402.pdf GitHub

Abstract:
Simulating deformable objects under rich interactions remains a fundamental challenge for real‑to‑sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions. Existing simulators rely on predefined physics or data‑driven dynamics without robot‑conditioned control, limiting accuracy, stability, and generalization. This paper presents SoMA, a 3D Gaussian Splat simulator for soft‑body manipulation. SoMA couples deformable dynamics, environmental forces, and robot joint actions in a unified latent neural space for end‑to‑end real‑to‑sim simulation. Modeling interactions over learned Gaussian splats enables controllable, stable long‑horizon manipulation and generalization beyond observed trajectories without predefined physical models. SoMA improves resimulation accuracy and generalization on real‑world robot manipulation by 20%, enabling stable simulation of complex tasks such as long‑horizon cloth folding.

PaperId: 3355, https://arxiv.org/pdf/2602.02393.pdf GitHub

Abstract:
We propose Infinite‑World, a robust interactive world model capable of maintaining coherent visual memory over 1000+ frames in complex real‑world environments. While existing world models can be efficiently optimized on synthetic data with perfect ground‑truth, they lack an effective training paradigm for real‑world videos due to noisy pose estimations and the scarcity of viewpoint revisits. To bridge this gap, we first introduce a Hierarchical Pose‑free Memory Compressor (HPMC) that recursively distills historical latents into a fixed‑budget representation. By jointly optimizing the compressor with the generative backbone, HPMC enables the model to autonomously anchor generations in the distant past with bounded computational cost, eliminating the need for explicit geometric priors. Second, we propose an Uncertainty‑aware Action Labeling module that discretizes continuous motion into a tri‑state logic. This strategy maximizes the utilization of raw video data while shielding the deterministic action space from being corrupted by noisy trajectories, ensuring robust action‑response learning. Furthermore, guided by insights from a pilot toy study, we employ a Revisit‑Dense Finetuning Strategy using a compact, 30‑minute dataset to efficiently activate the model's long‑range loop‑closure capabilities. Extensive experiments, including objective metrics and user studies, demonstrate that Infinite‑World achieves superior performance in visual quality, action controllability, and spatial consistency.

PaperId: 3356, https://arxiv.org/pdf/2602.02369.pdf GitHub

Abstract:
Large language model (LLM) agents are increasingly equipped with memory, which are stored experience and reusable guidance that can improve task‑solving performance. Recent \emphself‑evolving systems update memory based on interaction outcomes, but most existing evolution pipelines are developed for static train/test splits and only approximate online learning by folding static benchmarks, making them brittle under true distribution shift and continuous feedback. We introduce \textscLive‑Evo, an online self‑evolving memory system that learns from a stream of incoming data over time. \textscLive‑Evo decouples \emphwhat happened from \emphhow to use it via an Experience Bank and a Meta‑Guideline Bank, compiling task‑adaptive guidelines from retrieved experiences for each task. To manage memory online, \textscLive‑Evo maintains experience weights and updates them from feedback: experiences that consistently help are reinforced and retrieved more often, while misleading or stale experiences are down‑weighted and gradually forgotten, analogous to reinforcement and decay in human memory. On the live Prophet Arena benchmark over a 10‑week horizon, \textscLive‑Evo improves Brier score by 20.8% and increases market returns by 12.9%, while also transferring to deep‑research benchmarks with consistent gains over strong baselines. Our code is available at https://github.com/ag2ai/Live‑Evo.

PaperId: 3357, https://arxiv.org/pdf/2602.02343.pdf GitHub

Abstract:
Methods for controlling large language models (LLMs), including local weight fine‑tuning, LoRA‑based adaptation, and activation‑based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference‑utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task‑valid generation, and measures both on a shared log‑odds scale using polarity‑paired contrastive examples. Across methods, we observe a consistent trade‑off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target‑concept directions to enhance preference, while utility declines primarily when interventions push representations off the model's valid‑generation manifold. Finally, we introduce a new steering approach SPLIT guided by this analysis that improves preference while better preserving utility. Code is available at https://github.com/zjunlp/EasyEdit/blob/main/examples/SPLIT.md.

PaperId: 3358, https://arxiv.org/pdf/2602.02338.pdf GitHub

Abstract:
Semantic ID (SID)‑based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic‑centric pipeline: item embeddings are learned from foundation models and discretized using generic quantization schemes. This design is misaligned with generative recommendation objectives: semantic embeddings are weakly coupled with collaborative prediction, and generic quantization is inefficient at reducing sequential uncertainty for autoregressive modeling. To address these, we propose ReSID, a recommendation‑native, principled SID framework that rethinks representation learning and quantization from the perspective of information preservation and sequential predictability, without relying on LLMs. ReSID consists of two components: (i) Field‑Aware Masked Auto‑Encoding (FAMAE), which learns predictive‑sufficient item representations from structured features, and (ii) Globally Aligned Orthogonal Quantization (GAOQ), which produces compact and predictable SID sequences by jointly reducing semantic ambiguity and prefix‑conditional uncertainty. Theoretical analysis and extensive experiments across ten datasets show the effectiveness of ReSID. ReSID consistently outperforms strong sequential and SID‑based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x. Code is available at https://github.com/FuCongResearchSquad/ReSID.

PaperId: 3359, https://arxiv.org/pdf/2602.02320.pdf GitHub

Abstract:
Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential for enabling large language models (LLMs) to reason about downstream chemical tasks. However, the substantial cost of human annotation makes it infeasible to construct large‑scale, high‑quality datasets of structure‑grounded descriptions. In this work, we propose a fully automated annotation framework for generating precise molecular descriptions that preserve complete structural details at scale. Our approach builds upon and extends a rule‑based chemical nomenclature parser to interpret IUPAC names and construct enriched, structural XML metadata that explicitly encodes molecular structure. This metadata is then used to guide LLMs in producing accurate natural‑language descriptions. Using this framework, we curate a large‑scale dataset of approximately 163k molecule‑‑description pairs. A rigorous validation protocol combining LLM‑based and expert human evaluation on a subset of 2,000 molecules demonstrates a high description precision of 98.6%. The proposed annotation framework is readily beneficial to broader chemical tasks that rely on structural descriptions, with the resulting dataset providing a reliable foundation for molecule‑‑language alignment. The source code and dataset are hosted at https://github.com/TheLuoFengLab/MolLangData and https://huggingface.co/datasets/ChemFM/MolLangData, respectively.

PaperId: 3360, https://arxiv.org/pdf/2602.02301.pdf GitHub

Abstract:
Reinforcement learning (RL) has played a central role in recent advances in large reasoning models (LRMs), yielding strong gains in verifiable and open‑ended reasoning. However, training a single general‑purpose LRM across diverse domains remains challenging due to pronounced domain heterogeneity. Through a systematic study of two widely used strategies, Sequential RL and Mixed RL, we find that both incur substantial cross‑domain interference at the behavioral and gradient levels, resulting in limited overall gains. To address these challenges, we introduce Modular Gradient Surgery (MGS), which resolves gradient conflicts at the module level within the transformer. When applied to Llama and Qwen models, MGS achieves average improvements of 4.3 (16.6%) and 4.5 (11.1%) points, respectively, over standard multi‑task RL across three representative domains (math, general chat, and instruction following). Further analysis demonstrates that MGS remains effective under prolonged training. Overall, our study clarifies the sources of interference in multi‑domain RL and presents an effective solution for training general‑purpose LRMs.

PaperId: 3361, https://arxiv.org/pdf/2602.02288.pdf GitHub

Abstract:
Current time‑series forecasting models are primarily based on transformer‑style neural networks. These models achieve long‑term forecasting mainly by scaling up the model size rather than through genuinely autoregressive (AR) rollout. From the perspective of large language model training, traditional time‑series forecasting model training ignores the monotonic error‑growth heuristic. In this paper, we propose a novel training method for time‑series forecasting that enforces two key properties: (1) AR prediction errors should increase with the forecasting horizon. Violations of this trend are interpreted as rollout inconsistency and are softly penalized during training, and (2) the method enables models to be able to concatenate short‑term AR predictions to form flexible long‑term forecasts. Empirical results demonstrate that our method establishes a new state‑of‑the‑art across multiple benchmarks, achieving an MSE reduction of more than 10% compared to iTransformer and other recent strong baselines. Furthermore, it enables short‑horizon forecasting models to perform reliable long‑term predictions at horizons over 7.5 times longer. Code is available at https://github.com/LizhengMathAi/AROpt

PaperId: 3362, https://arxiv.org/pdf/2602.02262.pdf GitHub

Abstract:
LLM‑powered coding agents are redefining how real‑world software is developed. To drive the research towards better coding agents, we require challenging benchmarks that can rigorously evaluate the ability of such agents to perform various software engineering tasks. However, popular coding benchmarks such as HumanEval and SWE‑Bench focus on narrowly scoped tasks such as competition programming and patch generation. In reality, software engineers have to handle a broader set of tasks for real‑world software development. To address this gap, we propose OmniCode, a novel software engineering benchmark that contains a broader and more diverse set of task categories beyond code or patch generation. Overall, OmniCode contains 1794 tasks spanning three programming languages ‑ Python, Java, and C++ ‑ and four key categories: bug fixing, test generation, code review fixing, and style fixing. In contrast to prior software engineering benchmarks, the tasks in OmniCode are (1) manually validated to eliminate ill‑defined problems, and (2) synthetically crafted or recently curated to avoid data leakage issues, presenting a new framework for synthetically generating diverse software tasks from limited real‑world data. We evaluate OmniCode with popular agent frameworks such as SWE‑Agent and show that while they may perform well on bug fixing for Python, they fall short on tasks such as Test Generation and in languages such as C++ and Java. For instance, SWE‑Agent achieves a maximum of 25.0% with DeepSeek‑V3.1 on C++ Test Generation. OmniCode aims to serve as a robust benchmark and spur the development of agents that can perform well across different aspects of software development. Code and data are available at https://github.com/seal‑research/OmniCode.

PaperId: 3363, https://arxiv.org/pdf/2602.02185.pdf GitHub

Abstract:
Multimodal Large Language Models (MLLMs) have advanced VQA and now support Vision‑DeepResearch systems that use search engines for complex visual‑textual fact‑finding. However, evaluating these visual and textual search abilities is still difficult, and existing benchmarks have two major limitations. First, existing benchmarks are not visual search‑centric: answers that should require visual search are often leaked through cross‑textual cues in the text questions or can be inferred from the prior world knowledge in current MLLMs. Second, overly idealized evaluation scenario: On the image‑search side, the required information can often be obtained via near‑exact matching against the full image, while the text‑search side is overly direct and insufficiently challenging. To address these issues, we construct the Vision‑DeepResearch benchmark (VDR‑Bench) comprising 2,000 VQA instances. All questions are created via a careful, multi‑stage curation pipeline and rigorous expert review, designed to assess the behavior of Vision‑DeepResearch systems under realistic real‑world conditions. Moreover, to address the insufficient visual retrieval capabilities of current MLLMs, we propose a simple multi‑round cropped‑search workflow. This strategy is shown to effectively improve model performance in realistic visual retrieval scenarios. Overall, our results provide practical guidance for the design of future multimodal deep‑research systems. The code will be released in https://github.com/Osilly/Vision‑DeepResearch.

PaperId: 3364, https://arxiv.org/pdf/2602.02139.pdf GitHub

Abstract:
Machine unlearning aims to unlearn specified training data (e.g. sensitive or copyrighted material). A prominent approach is to fine‑tune an existing model with an unlearning loss that retains overall utility. The space of suitable unlearning loss functions is vast, making the search for an optimal loss function daunting. Additionally, there might not even exist a universally optimal loss function: differences in the structure and overlap of the forget and retain data can cause a loss to work well in one setting but over‑unlearn or under‑unlearn in another. Our approach EvoMU tackles these two challenges simultaneously. An evolutionary search procedure automatically finds task‑specific losses in the vast space of possible unlearning loss functions. This allows us to find dataset‑specific losses that match or outperform existing losses from the literature, without the need for a human‑in‑the‑loop. This work is therefore an instance of automatic scientific discovery, a.k.a. an AI co‑scientist. In contrast to previous AI co‑scientist works, we do so on a budget: We achieve SotA results using a small 4B parameter model (Qwen3‑4B‑Thinking), showing the potential of AI co‑scientists with limited computational resources. Our experimental evaluation shows that we surpass previous loss‑based unlearning formulations on TOFU‑5%, TOFU‑10%, MUSE and WMDP by synthesizing novel unlearning losses. Our code is available at https://github.com/Batorskq/EvoMU.

PaperId: 3365, https://arxiv.org/pdf/2602.02128.pdf GitHub

Abstract:
Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics, but their computational cost limits access to biologically relevant timescales. Recent generative models have shown promise in accelerating simulations, yet they struggle with long‑horizon generation due to architectural constraints, error accumulation, and inadequate modeling of spatio‑temporal dynamics. We present STAR‑MD (Spatio‑Temporal Autoregressive Rollout for Molecular Dynamics), a scalable SE(3)‑equivariant diffusion model that generates physically plausible protein trajectories over microsecond timescales. Our key innovation is a causal diffusion transformer with joint spatio‑temporal attention that efficiently captures complex space‑time dependencies while avoiding the memory bottlenecks of existing methods. On the standard ATLAS benchmark, STAR‑MD achieves state‑of‑the‑art performance across all metrics‑‑substantially improving conformational coverage, structural validity, and dynamic fidelity compared to previous methods. STAR‑MD successfully extrapolates to generate stable microsecond‑scale trajectories where baseline methods fail catastrophically, maintaining high structural quality throughout the extended rollout. Our comprehensive evaluation reveals severe limitations in current models for long‑horizon generation, while demonstrating that STAR‑MD's joint spatio‑temporal modeling enables robust dynamics simulation at biologically relevant timescales, paving the way for accelerated exploration of protein function.

PaperId: 3366, https://arxiv.org/pdf/2602.02096.pdf GitHub

Abstract:
The heavy‑tailed nature of precipitation intensity impedes precise precipitation nowcasting. Standard models that optimize pixel‑wise losses are prone to regression‑to‑the‑mean bias, which blurs extreme values. Existing Fourier‑based methods also lack the spatial localization needed to resolve transient convective cells. To overcome these intrinsic limitations, we propose WADEPre, a wavelet‑based decomposition model for extreme precipitation that transitions the modeling into the wavelet domain. By leveraging the Discrete Wavelet Transform for explicit decomposition, WADEPre employs a dual‑branch architecture: an Approximation Network to model stable, low‑frequency advection, isolating deterministic trends from statistical bias, and a spatially localized Detail Network to capture high‑frequency stochastic convection, resolving transient singularities and preserving sharp boundaries. A subsequent Refiner module then dynamically reconstructs these decoupled multi‑scale components into the final high‑fidelity forecast. To address optimization instability, we introduce a multi‑scale curriculum learning strategy that progressively shifts supervision from coarse scales to fine‑grained details. Extensive experiments on the SEVIR and Shanghai Radar datasets demonstrate that WADEPre achieves state‑of‑the‑art performance, yielding significant improvements in capturing extreme thresholds and maintaining structural fidelity. Our code is available at https://github.com/sonderlau/WADEPre.

PaperId: 3367, https://arxiv.org/pdf/2602.02067.pdf GitHub

Abstract:
Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views. Although numerous deep‑learning models have been proposed for stenosis detection from a single angiography view, their performance heavily relies on expensive view‑level annotations, which are often not readily available in hospital systems. Moreover, these models fail to capture the temporal dynamics and dependencies among multiple views, which are crucial for clinical diagnosis. To address this, we propose SegmentMIL, a transformer‑based multi‑view multiple‑instance learning framework for patient‑level stenosis classification. Trained on a real‑world clinical dataset, using patient‑level supervision and without any view‑level annotations, SegmentMIL jointly predicts the presence of stenosis and localizes the affected anatomical region, distinguishing between the right and left coronary arteries and their respective segments. SegmentMIL obtains high performance on internal and external evaluations and outperforms both view‑level models and classical MIL baselines, underscoring its potential as a clinically viable and scalable solution for coronary stenosis diagnosis. Our code is available at https://github.com/NikolaCenic/mil‑stenosis.

PaperId: 3368, https://arxiv.org/pdf/2602.02033.pdf GitHub

Abstract:
Advertising image generation has increasingly focused on online metrics like Click‑Through Rate (CTR), yet existing approaches adopt a ``one‑size‑fits‑all" strategy that optimizes for overall CTR while neglecting preference diversity among user groups. This leads to suboptimal performance for specific groups, limiting targeted marketing effectiveness. To bridge this gap, we present One Size, Many Fits (OSMF), a unified framework that aligns diverse group‑wise click preferences in large‑scale advertising image generation. OSMF begins with product‑aware adaptive grouping, which dynamically organizes users based on their attributes and product characteristics, representing each group with rich collective preference features. Building on these groups, preference‑conditioned image generation employs a Group‑aware Multimodal Large Language Model (G‑MLLM) to generate tailored images for each group. The G‑MLLM is pre‑trained to simultaneously comprehend group features and generate advertising images. Subsequently, we fine‑tune the G‑MLLM using our proposed Group‑DPO for group‑wise preference alignment, which effectively enhances each group's CTR on the generated images. To further advance this field, we introduce the Grouped Advertising Image Preference Dataset (GAIP), the first large‑scale public dataset of group‑wise image preferences, including around 600K groups built from 40M users. Extensive experiments demonstrate that our framework achieves the state‑of‑the‑art performance in both offline and online settings. Our code and datasets will be released at https://github.com/JD‑GenX/OSMF.

PaperId: 3369, https://arxiv.org/pdf/2602.02027.pdf GitHub

Abstract:
The safety of large language models (LLMs) has increasingly emerged as a fundamental aspect of their development. Existing safety alignment for LLMs is predominantly achieved through post‑training methods, which are computationally expensive and often fail to generalize well across different models. A small number of lightweight alignment approaches either rely heavily on prior‑computed safety injections or depend excessively on the model's own capabilities, resulting in limited generalization and degraded efficiency and usability during generation. In this work, we propose a safety‑aware decoding method that requires only low‑cost training of an expert model and employs a single neuron as a gating mechanism. By effectively balancing the model's intrinsic capabilities with external guidance, our approach simultaneously preserves utility and enhances output safety. It demonstrates clear advantages in training overhead and generalization across model scales, offering a new perspective on lightweight alignment for the safe and practical deployment of large language models. Code: https://github.com/Beijing‑AISI/NGSD.

PaperId: 3370, https://arxiv.org/pdf/2602.02007.pdf GitHub GitHub

Abstract:
Standard Retrieval Augmented Generation (RAG) is poorly matched to agent memory. Unlike large heterogeneous corpora, agent memory forms a bounded and coherent interaction stream in which many spans are highly correlated or near duplicates. As a result, flat top‑k similarity retrieval often returns redundant context, while summary‑centric hierarchies can blur the subtle details that distinguish one candidate from another. We argue that agent memory should follow the principle of decoupling before aggregation: the system should first isolate reusable facts, updates, and distinguishing details from similar histories, and only then organise them for efficient retrieval. Based on this principle, we propose xMemory, which constructs a revisable hierarchical memory structure from original messages to segments, memory components, and groups. xMemory segments interaction history into local events, decouples each segment into memory components, aggregates related components into high‑level groups using a sparsity‑‑semantic faithfulness objective, and maintains this structure incrementally as memory evolves. At inference time, xMemory retrieves top‑down, first selecting a compact backbone of complementary groups and components, and then expanding to segments and raw messages only when additional evidence reduces the reader's uncertainty. Experiments on LoCoMo and PerLTQA across diverse open source and closed source LLMs show consistent gains in answer quality and inference token efficiency, supported by analyses of redundancy, evidence density, and coverage.

PaperId: 3371, https://arxiv.org/pdf/2602.02001.pdf GitHub GitHub

Abstract:
Quantization Error Reconstruction (QER) reduces accuracy loss in Post‑Training Quantization (PTQ) by approximating weights as \mathbfW \approx \mathbfQ + \mathbfL\mathbfR, using a rank‑r correction to reconstruct quantization error. Prior methods devote the full rank budget to error reconstruction, which is suboptimal when \mathbfW has intrinsic low‑rank structure and quantization corrupts dominant directions. We propose Structured Residual Reconstruction (SRR), a rank‑allocation framework that preserves the top‑k singular subspace of the activation‑scaled weight before quantization, quantizes only the residual, and uses the remaining rank r‑k for error reconstruction. We derive a theory‑guided criterion for selecting k by balancing quantization‑exposed energy and unrecoverable error under rank constraints. We further show that resulting \mathbfQ + \mathbfL\mathbfR parameterization naturally supports Quantized Parameter‑Efficient Fine‑Tuning (QPEFT), and stabilizes fine‑tuning via gradient scaling along preserved directions. Experiments demonstrate consistent perplexity reductions across diverse models and quantization settings in PTQ, along with a 5.9 percentage‑point average gain on GLUE under 2‑bit QPEFT. The project page is available at https://ai‑isl.github.io/srr.

PaperId: 3372, https://arxiv.org/pdf/2602.02000.pdf GitHub

Abstract:
Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization. Recent approaches leverage generalizable models to generate 3D scenes using 3D Gaussian Splatting (3DGS) primitive. However, they often fail to produce continuous surfaces and instead yield discrete, color‑biased point clouds that appear plausible at normal resolution but reveal severe artifacts under close‑up views. To address this issue, we present SurfSplat, a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive, which provides stronger anisotropy and higher geometric precision. By incorporating a surface continuity prior and a forced alpha blending strategy, SurfSplat reconstructs coherent geometry together with faithful textures. Furthermore, we introduce High‑Resolution Rendering Consistency (HRRC), a new evaluation metric designed to evaluate high‑resolution reconstruction quality. Extensive experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that SurfSplat consistently outperforms prior methods on both standard metrics and HRRC, establishing a robust solution for high‑fidelity 3D reconstruction from sparse inputs. Project page: https://hebing‑sjtu.github.io/SurfSplat‑website/

PaperId: 3373, https://arxiv.org/pdf/2602.01990.pdf GitHub GitHub

Abstract:
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real‑world deployment requires them to continually expand their capabilities, making Multimodal Continual Instruction Tuning (MCIT) essential. Recent methods leverage sparse expert routing to promote task specialization, but we find that the expert routing process suffers from drift as the data distribution evolves. For example, a grounding query that previously activated localization experts may instead be routed to irrelevant experts after learning OCR tasks. Meanwhile, the grounding‑related experts can be overwritten by new tasks and lose their original functionality. Such failure reflects two problems: router drift, where expert selection becomes inconsistent over time, and expert drift, where shared experts are overwritten across tasks. Therefore, we propose StAbilized Mixture‑of‑Experts (SAME) for MCIT. To address router drift, SAME stabilizes expert selection by decomposing routing dynamics into orthogonal subspaces and updating only task‑relevant directions. To mitigate expert drift, we regulate expert updates via curvature‑aware scaling using historical input covariance in a rehearsal‑free manner. SAME also introduces adaptive expert activation to freeze selected experts during training, reducing redundant computation and cross‑task interference. We also introduce a new benchmark to evaluate MCIT with long task sequence, and extensive experiments demonstrate SAME's SOTA performance. Code is available at https://github.com/LAMDA‑CL/Prism.

PaperId: 3374, https://arxiv.org/pdf/2602.01976.pdf GitHub

Abstract:
General continual learning (GCL) challenges intelligent systems to learn from single‑pass, non‑stationary data streams without clear task boundaries. While recent advances in continual parameter‑efficient tuning (PET) of pretrained models show promise, they typically rely on multiple training epochs and explicit task cues, limiting their effectiveness in GCL scenarios. Moreover, existing methods often lack targeted design and fail to address two fundamental challenges in continual PET: how to allocate expert parameters to evolving data distributions, and how to improve their representational capacity under limited supervision. Inspired by the fruit fly's hierarchical memory system characterized by sparse expansion and modular ensembles, we propose FlyPrompt, a brain‑inspired framework that decomposes GCL into two subproblems: expert routing and expert competence improvement. FlyPrompt introduces a randomly expanded analytic router for instance‑level expert activation and a temporal ensemble of output heads to dynamically adapt decision boundaries over time. Extensive theoretical and empirical evaluations demonstrate FlyPrompt's superior performance, achieving up to 11.23%, 12.43%, and 7.62% gains over state‑of‑the‑art baselines on CIFAR‑100, ImageNet‑R, and CUB‑200, respectively. Our source code is available at https://github.com/AnAppleCore/FlyGCL.

PaperId: 3375, https://arxiv.org/pdf/2602.01973.pdf GitHub GitHub

Abstract:
Despite being trained on balanced datasets, existing AI‑generated image detectors often exhibit systematic bias at test time, frequently misclassifying fake images as real. We hypothesize that this behavior stems from distributional shift in fake samples and implicit priors learned during training. Specifically, models tend to overfit to superficial artifacts that do not generalize well across different generation methods, leading to a misaligned decision threshold when faced with test‑time distribution shift. To address this, we propose a theoretically grounded post‑hoc calibration framework based on Bayesian decision theory. In particular, we introduce a learnable scalar correction to the model's logits, optimized on a small validation set from the target distribution while keeping the backbone frozen. This parametric adjustment compensates for distributional shift in model output, realigning the decision boundary even without requiring ground‑truth labels. Experiments on challenging benchmarks show that our approach significantly improves robustness without retraining, offering a lightweight and principled solution for reliable and adaptive AI‑generated image detection in the open world. Code is available at https://github.com/muliyangm/AIGI‑Det‑Calib.

PaperId: 3376, https://arxiv.org/pdf/2602.01965.pdf GitHub

Abstract:
Recent advances in Retrieval‑Augmented Generation (RAG) have shifted from simple vector similarity to structure‑aware approaches like HippoRAG, which leverage Knowledge Graphs (KGs) and Personalized PageRank (PPR) to capture multi‑hop dependencies. However, these methods suffer from a "Static Graph Fallacy": they rely on fixed transition probabilities determined during indexing. This rigidity ignores the query‑dependent nature of edge relevance, causing semantic drift where random walks are diverted into high‑degree "hub" nodes before reaching critical downstream evidence. Consequently, models often achieve high partial recall but fail to retrieve the complete evidence chain required for multi‑hop queries. To address this, we propose CatRAG, Context‑Aware Traversal for robust RAG, a framework that builds on the HippoRAG 2 architecture and transforms the static KG into a query‑adaptive navigation structure. We introduce a multi‑faceted framework to steer the random walk: (1) Symbolic Anchoring, which injects weak entity constraints to regularize the random walk; (2) Query‑Aware Dynamic Edge Weighting, which dynamically modulates graph structure, to prune irrelevant paths while amplifying those aligned with the query's intent; and (3) Key‑Fact Passage Weight Enhancement, a cost‑efficient bias that structurally anchors the random walk to likely evidence. Experiments across four multi‑hop benchmarks demonstrate that CatRAG consistently outperforms state of the art baselines. Our analysis reveals that while standard Recall metrics show modest gains, CatRAG achieves substantial improvements in reasoning completeness, the capacity to recover the entire evidence path without gaps. These results reveal that our approach effectively bridges the gap between retrieving partial context and enabling fully grounded reasoning. Resources are available at https://github.com/kwunhang/CatRAG.

PaperId: 3377, https://arxiv.org/pdf/2602.01936.pdf GitHub

Abstract:
Accurate traffic flow prediction remains a fundamental challenge in intelligent transportation systems, particularly in cross‑domain, data‑scarce scenarios where limited historical data hinders model training and generalisation. The complex spatio‑temporal dependencies and nonlinear dynamics of urban mobility networks further complicate few‑shot learning across different cities. This paper proposes MCPST, a novel Multi‑phase Consensus Spatio‑Temporal framework for few‑shot traffic forecasting that reconceptualises traffic prediction as a multi‑phase consensus learning problem. Our framework introduces three core innovations: (1) a multi‑phase engine that models traffic dynamics through diffusion, synchronisation, and spectral embeddings for comprehensive dynamic characterisation; (2) an adaptive consensus mechanism that dynamically fuses phase‑specific predictions while enforcing consistency; and (3) a structured meta‑learning strategy for rapid adaptation to new cities with minimal data. We establish extensive theoretical guarantees, including representation theorems with bounded approximation errors and generalisation bounds for few‑shot adaptation. Through experiments on four real‑world datasets, MCPST outperforms fourteen state‑of‑the‑art methods in spatio‑temporal graph learning methods, dynamic graph transfer learning methods, prompt‑based spatio‑temporal prediction methods and cross‑domain few‑shot settings, improving prediction accuracy while reducing required training data and providing interpretable insights. The implementation code is available at https://github.com/afofanah/MCPST.

PaperId: 3378, https://arxiv.org/pdf/2602.01920.pdf GitHub

Abstract:
Graph neural networks (GNNs) often struggle in class‑imbalanced settings, where minority classes are under‑represented and predictions are biased toward majorities. We propose PIMPC‑GNN, a physics‑informed multi‑phase consensus framework for imbalanced node classification. Our method integrates three complementary dynamics: (i) thermodynamic diffusion, which spreads minority labels to capture long‑range dependencies, (ii) Kuramoto synchronisation, which aligns minority nodes through oscillatory consensus, and (iii) spectral embedding, which separates classes via structural regularisation. These perspectives are combined through class‑adaptive ensemble weighting and trained with an imbalance‑aware loss that couples balanced cross‑entropy with physics‑based constraints. Across five benchmark datasets and imbalance ratios from 5‑100, PIMPC‑GNN outperforms 16 state‑of‑the‑art baselines, achieving notable gains in minority‑class recall (up to +12.7%) and balanced accuracy (up to +8.3%). Beyond empirical improvements, the framework also provides interpretable insights into consensus dynamics in graph learning. The code is available at \texttthttps://github.com/afofanah/PIMPC‑GNN.

PaperId: 3379, https://arxiv.org/pdf/2602.01844.pdf GitHub GitHub

Abstract:
Deep learning has demonstrated remarkable capabilities in simulating complex dynamic systems. However, existing methods require known physical properties as supervision or inputs, limiting their applicability under unknown conditions. To explore this challenge, we introduce Cloth Dynamics Grounding (CDG), a novel scenario for unsupervised learning of cloth dynamics from multi‑view visual observations. We further propose Cloth Dynamics Splatting (CloDS), an unsupervised dynamic learning framework designed for CDG. CloDS adopts a three‑stage pipeline that first performs video‑to‑geometry grounding and then trains a dynamics model on the grounded meshes. To cope with large non‑linear deformations and severe self‑occlusions during grounding, we introduce a dual‑position opacity modulation that supports bidirectional mapping between 2D observations and 3D geometry via mesh‑based Gaussian splatting in video‑to‑geometry grounding stage. It jointly considers the absolute and relative position of Gaussian components. Comprehensive experimental evaluations demonstrate that CloDS effectively learns cloth dynamics from visual data while maintaining strong generalization capabilities for unseen configurations. Our code is available at https://github.com/whynot‑zyl/CloDS. Visualization results are available at https://github.com/whynot‑zyl/CloDS_video.%\footnoteAs in this example.

PaperId: 3380, https://arxiv.org/pdf/2602.01801.pdf GitHub

Abstract:
Autoregressive video diffusion models enable streaming generation, opening the door to long‑form synthesis, video world models, and interactive neural game engines. However, their core attention layers become a major bottleneck at inference time: as generation progresses, the KV cache grows, causing both increasing latency and escalating GPU memory, which in turn restricts usable temporal context and harms long‑range consistency. In this work, we study redundancy in autoregressive video diffusion and identify three persistent sources: near‑duplicate cached keys across frames, slowly evolving (largely semantic) queries/keys that make many attention computations redundant, and cross‑attention over long prompts where only a small subset of tokens matters per frame. Building on these observations, we propose a unified, training‑free attention framework for autoregressive diffusion: TempCache compresses the KV cache via temporal correspondence to bound cache growth; AnnCA accelerates cross‑attention by selecting frame‑relevant prompt tokens using fast approximate nearest neighbor (ANN) matching; and AnnSA sparsifies self‑attention by restricting each query to semantically matched keys, also using a lightweight ANN. Together, these modules reduce attention, compute, and memory and are compatible with existing autoregressive diffusion backbones and world models. Experiments demonstrate up to x5‑‑x10 end‑to‑end speedups while preserving near‑identical visual quality and, crucially, maintaining stable throughput and nearly constant peak GPU memory usage over long rollouts, where prior methods progressively slow down and suffer from increasing memory usage.

PaperId: 3381, https://arxiv.org/pdf/2602.01779.pdf GitHub

Abstract:
Large language models (LLMs) are advancing rapidly in medical NLP, yet Traditional Chinese Medicine (TCM) with its distinctive ontology, terminology, and reasoning patterns requires domain‑faithful evaluation. Existing TCM benchmarks are fragmented in coverage and scale and rely on non‑unified or generation‑heavy scoring that hinders fair comparison. We present the LingLanMiDian (LingLan) benchmark, a large‑scale, expert‑curated, multi‑task suite that unifies evaluation across knowledge recall, multi‑hop reasoning, information extraction, and real‑world clinical decision‑making. LingLan introduces a consistent metric design, a synonym‑tolerant protocol for clinical labels, a per‑dataset 400‑item Hard subset, and a reframing of diagnosis and treatment recommendation into single‑choice decision recognition. We conduct comprehensive, zero‑shot evaluations on 14 leading open‑source and proprietary LLMs, providing a unified perspective on their strengths and limitations in TCM commonsense knowledge understanding, reasoning, and clinical decision support; critically, the evaluation on Hard subset reveals a substantial gap between current models and human experts in TCM‑specialized reasoning. By bridging fundamental knowledge and applied reasoning through standardized evaluation, LingLan establishes a unified, quantitative, and extensible foundation for advancing TCM LLMs and domain‑specific medical AI research. All evaluation data and code are available at https://github.com/TCMAI‑BJTU/LingLan and http://tcmnlp.com.

PaperId: 3382, https://arxiv.org/pdf/2602.01771.pdf GitHub

Abstract:
Large language models show great potential in unstructured data understanding, but still face significant challenges with graphs due to their structural hallucination. Existing approaches mainly either verbalize graphs into natural language, which leads to excessive token consumption and scattered attention, or transform graphs into trainable continuous embeddings (i.e., soft prompt), but exhibit severe misalignment with original text tokens. To solve this problem, we propose to incorporate one special token <SOG_k> to fully represent the Structure Of Graph within a unified token space, facilitating explicit topology input and structural information sharing. Specifically, we propose a topology‑aware structural tokenizer that maps each graph topology into a highly selective single token. Afterwards, we construct a set of hybrid structure Question‑Answering corpora to align new structural tokens with existing text tokens. With this approach, <SOG_k> empowers LLMs to understand, generate, and reason in a concise and accurate manner. Extensive experiments on five graph‑level benchmarks demonstrate the superiority of our method, achieving a performance improvement of 9.9% to 41.4% compared to the baselines while exhibiting interpretability and consistency. Furthermore, our method provides a flexible extension to node‑level tasks, enabling both global and local structural understanding. The codebase is publicly available at https://github.com/Jingyao‑Wu/SOG.

PaperId: 3383, https://arxiv.org/pdf/2602.01766.pdf GitHub

Abstract:
The quadratic complexity and indefinitely growing key‑value (KV) cache of standard Transformers pose a major barrier to long‑context processing. To overcome this, we introduce the Collaborative Memory Transformer (CoMeT), a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity. Designed as an efficient, plug‑in module, CoMeT can be integrated into pre‑trained models with only minimal fine‑tuning. It operates on sequential data chunks, using a dual‑memory system to manage context: a temporary memory on a FIFO queue for recent events, and a global memory with a gated update rule for long‑range dependencies. These memories then act as a dynamic soft prompt for the next chunk. To enable efficient fine‑tuning on extremely long contexts, we introduce a novel layer‑level pipeline parallelism strategy. The effectiveness of our approach is remarkable: a model equipped with CoMeT and fine‑tuned on 32k contexts can accurately retrieve a passkey from any position within a 1M token sequence. On the SCROLLS benchmark, CoMeT surpasses other efficient methods and achieves performance comparable to a full‑attention baseline on summarization tasks. Its practical effectiveness is further validated on real‑world agent and user behavior QA tasks. The code is available at: https://github.com/LivingFutureLab/Comet

PaperId: 3384, https://arxiv.org/pdf/2602.01685.pdf GitHub

Abstract:
Large language models (LLMs) are commonly aligned with human preferences using reinforcement learning from human feedback (RLHF). In this method, LLM policies are generally optimized through reward maximization with Kullback‑Leibler (KL) divergence regularization of the reference policy. However, KL and its f‑divergence variants only compare token probabilities at identical indices, failing to capture semantic similarity. We propose Wasserstein Policy Regularization (WPR), a semantic‑aware regularization for the RLHF framework based on the entropy‑regularized Wasserstein distance, which incorporates the geometry of the token space. The dual formulation of the distance expresses the regularization as penalty terms applied to the reward via optimal dual variables, which yield a tractable objective compatible with standard RL algorithms. Empirically, our method outperforms KL‑ and f‑divergence‑based baselines, demonstrating the benefits of semantic‑aware policy distances for alignment. Our code is available at https://github.com/aailab‑kaist/WPR.

PaperId: 3385, https://arxiv.org/pdf/2602.01668.pdf GitHub

Abstract:
Long‑term multivariate time series forecasting (LTSF) plays a crucial role in various high‑performance computing applications, including real‑time energy grid management and large‑scale traffic flow simulation. However, existing solutions face a dilemma: Transformer‑based models suffer from quadratic complexity, limiting their scalability on long sequences, while linear State Space Models (SSMs) often struggle to distinguish valuable signals from high‑frequency noise, leading to wasted state capacity. To bridge this gap, we propose ASGMamba, an efficient forecasting framework designed for resource‑constrained supercomputing environments. ASGMamba integrates a lightweight Adaptive Spectral Gating (ASG) mechanism that dynamically filters noise based on local spectral energy, enabling the Mamba backbone to focus its state evolution on robust temporal dynamics. Furthermore, we introduce a hierarchical multi‑scale architecture with variable‑specific Node Embeddings to capture diverse physical characteristics. Extensive experiments on nine benchmarks demonstrate that ASGMamba achieves state‑of‑the‑art accuracy. While keeping strictly \mathcalO(L) complexity we significantly reduce the memory usage on long‑horizon tasks, thus establishing ASGMamba as a scalable solution for high‑throughput forecasting in resource limited environments.The code is available at https://github.com/hit636/ASGMamba

PaperId: 3386, https://arxiv.org/pdf/2602.01665.pdf GitHub

Abstract:
The design of environments plays a critical role in shaping the development and evaluation of cooperative multi‑agent reinforcement learning (MARL) algorithms. While existing benchmarks highlight critical challenges, they often lack the modularity required to design custom evaluation scenarios. We introduce the Totally Accelerated Battle Simulator in JAX (TABX), a high‑throughput sandbox designed for reconfigurable multi‑agent tasks. TABX provides granular control over environmental parameters, permitting a systematic investigation into emergent agent behaviors and algorithmic trade‑offs across a diverse spectrum of task complexities. Leveraging JAX for hardware‑accelerated execution on GPUs, TABX enables massive parallelization and significantly reduces computational overhead. By providing a fast, extensible, and easily customized framework, TABX facilitates the study of MARL agents in complex structured domains and serves as a scalable foundation for future research. Our code is available at: https://github.com/ku‑dmlab/TABX.

PaperId: 3387, https://arxiv.org/pdf/2602.01655.pdf GitHub

Abstract:
Recent coding agents can generate complete codebases from simple prompts, yet existing evaluations focus on issue‑level bug fixing and lag behind end‑to‑end development. We introduce ProjDevBench, an end‑to‑end benchmark that provides project requirements to coding agents and evaluates the resulting repositories. Combining Online Judge (OJ) testing with LLM‑assisted code review, the benchmark evaluates agents on (1) system architecture design, (2) functional correctness, and (3) iterative solution refinement. We curate 20 programming problems across 8 categories, covering both concept‑oriented tasks and real‑world application scenarios, and evaluate six coding agents built on different LLM backends. Our evaluation reports an overall acceptance rate of 27.38%: agents handle basic functionality and data structures but struggle with complex system design, time complexity optimization, and resource management. Our benchmark is available at https://github.com/zsworld6/projdevbench.

PaperId: 3388, https://arxiv.org/pdf/2602.01649.pdf GitHub

Abstract:
Video large language models have demonstrated remarkable capabilities in video understanding tasks. However, the redundancy of video tokens introduces significant computational overhead during inference, limiting their practical deployment. Many compression algorithms are proposed to prioritize retaining features with the highest attention scores to minimize perturbations in attention computations. However, the correlation between attention scores and their actual contribution to correct answers remains ambiguous. To address the above limitation, we propose a novel Contribution‑aware token Compression algorithm for VIDeo understanding (CaCoVID) that explicitly optimizes the token selection policy based on the contribution of tokens to correct predictions. First, we introduce a reinforcement learning‑based framework that optimizes a policy network to select video token combinations with the greatest contribution to correct predictions. This paradigm shifts the focus from passive token preservation to active discovery of optimal compressed token combinations. Secondly, we propose a combinatorial policy optimization algorithm with online combination space sampling, which dramatically reduces the exploration space for video token combinations and accelerates the convergence speed of policy optimization. Extensive experiments on diverse video understanding benchmarks demonstrate the effectiveness of CaCoVID. Codes are available at https://github.com/LivingFutureLab/CaCoVID.

PaperId: 3389, https://arxiv.org/pdf/2602.01634.pdf GitHub

Abstract:
We propose HuPER, a human‑inspired framework that models phonetic perception as adaptive inference over acoustic‑phonetics evidence and linguistic knowledge. With only 100 hours of training data, HuPER achieves state‑of‑the‑art phonetic error rates on five English benchmarks and strong zero‑shot transfer to 95 unseen languages. HuPER is also the first framework to enable adaptive, multi‑path phonetic perception under diverse acoustic conditions. All training data, models, and code are open‑sourced. Code and demo avaliable at https://github.com/HuPER29/HuPER.

PaperId: 3390, https://arxiv.org/pdf/2602.01610.pdf GitHub

Abstract:
Learning effective region embeddings from heterogeneous urban data underpins key urban computing tasks (e.g., crime prediction, resource allocation). However, prevailing two‑stage methods yield task‑agnostic representations, decoupling them from downstream objectives. Recent prompt‑based approaches attempt to fix this but introduce two challenges: they often lack explicit spatial priors, causing spatially incoherent inter‑region modeling, and they lack robust mechanisms for explicit task‑semantic alignment. We propose ToPT, a two‑stage framework that delivers spatially consistent fusion and explicit task alignment. ToPT consists of two modules: spatial‑aware region embedding learning (SREL) and task‑aware prompting for region embeddings (Prompt4RE). SREL employs a Graphormer‑based fusion module that injects spatial priors‑distance and regional centrality‑as learnable attention biases to capture coherent, interpretable inter‑region interactions. Prompt4RE performs task‑oriented prompting: a frozen multimodal large language model (MLLM) processes task‑specific templates to obtain semantic vectors, which are aligned with region embeddings via multi‑head cross‑attention for stable task conditioning. Experiments across multiple tasks and cities show state‑of‑the‑art performance, with improvements of up to 64.2%, validating the necessity and complementarity of spatial priors and prompt‑region alignment. The code is available at https://github.com/townSeven/Prompt4RE.git.

PaperId: 3391, https://arxiv.org/pdf/2602.01606.pdf GitHub

Abstract:
Diffusion policies are expressive yet incur high inference latency. Flow Matching (FM) enables one‑step generation, but integrating it into Maximum Entropy Reinforcement Learning (MaxEnt RL) is challenging: the optimal policy is an intractable energy‑based distribution, and the efficient log‑likelihood estimation required to balance exploration and exploitation suffers from severe discretization bias. We propose Flow‑based Log‑likelihood‑Aware Maximum Entropy RL (FLAME), a principled framework that addresses these challenges. First, we derive a Q‑Reweighted FM objective that bypasses partition function estimation via importance reweighting. Second, we design a decoupled entropy estimator that rigorously corrects bias, which enables efficient exploration and brings the policy closer to the optimal MaxEnt policy. Third, we integrate the MeanFlow formulation to achieve expressive and efficient one‑step control. Empirical results on MuJoCo show that FLAME outperforms Gaussian baselines and matches multi‑step diffusion policies with significantly lower inference cost. Code is available at https://github.com/lzqw/FLAME.

PaperId: 3392, https://arxiv.org/pdf/2602.01588.pdf GitHub

Abstract:
Multimodal time series forecasting is crucial in real‑world applications, where decisions depend on both numerical data and contextual signals. The core challenge is to effectively combine temporal numerical patterns with the context embedded in other modalities, such as text. While most existing methods align textual features with time‑series patterns one step at a time, they neglect the multiscale temporal influences of contextual information such as time‑series cycles and dynamic shifts. This mismatch between local alignment and global textual context can be addressed by spectral decomposition, which separates time series into frequency components capturing both short‑term changes and long‑term trends. In this paper, we propose SpecTF, a simple yet effective framework that integrates the effect of textual data on time series in the frequency domain. Our method extracts textual embeddings, projects them into the frequency domain, and fuses them with the time series' spectral components using a lightweight cross‑attention mechanism. This adaptively reweights frequency bands based on textual relevance before mapping the results back to the temporal domain for predictions. Experimental results demonstrate that SpecTF significantly outperforms state‑of‑the‑art models across diverse multi‑modal time series datasets while utilizing considerably fewer parameters. Code is available at https://github.com/hiepnh137/SpecTF.

PaperId: 3393, https://arxiv.org/pdf/2602.01567.pdf GitHub

Abstract:
Social media engagement prediction is a central challenge in computational social science, particularly for understanding how users interact with misinformation. Existing approaches often treat engagement as a homogeneous time‑series signal, overlooking the heterogeneous social mechanisms and platform designs that shape how misinformation spreads. In this work, we ask: ``Can neural architectures discover social exchange principles from behavioral data alone?'' We introduce \textscDreams (\underlineDisentangled \underlineRepresentations and \underlineEpisodic \underlineAdaptive \underlineModeling for \underlineSocial media misinformation engagements), a social exchange theory‑guided framework that models misinformation engagement as a dynamic process of social exchange. Rather than treating engagement as a static outcome, \textscDreams models it as a sequence‑to‑sequence adaptation problem, where each action reflects an evolving negotiation between user effort and social reward conditioned by platform context. It integrates adaptive mechanisms to learn how emotional and contextual signals propagate through time and across platforms. On a cross‑platform dataset spanning 7 platforms and 2.37M posts collected between 2021 and 2025, \textscDreams achieves state‑of‑the‑art performance in predicting misinformation engagements, reaching a mean absolute percentage error of 19.25%. This is a 43.6% improvement over the strongest baseline. Beyond predictive gains, the model reveals consistent cross‑platform patterns that align with social exchange principles, suggesting that integrating behavioral theory can enhance empirical modeling of online misinformation engagement. The source code is available at: https://github.com/ltian678/DREAMS.

PaperId: 3394, https://arxiv.org/pdf/2602.01539.pdf GitHub

Abstract:
Ensuring robust safety alignment is crucial for Large Language Models (LLMs), yet existing defenses often lag behind evolving adversarial attacks due to their reliance on static, pre‑collected data distributions. In this paper, we introduce MAGIC, a novel multi‑turn multi‑agent reinforcement learning framework that formulates LLM safety alignment as an adversarial asymmetric game. Specifically, an attacker agent learns to iteratively rewrite original queries into deceptive prompts, while a defender agent simultaneously optimizes its policy to recognize and refuse such inputs. This dynamic process triggers a co‑evolution, where the attacker's ever‑changing strategies continuously uncover long‑tail vulnerabilities, driving the defender to generalize to unseen attack patterns. Remarkably, we observe that the attacker, endowed with initial reasoning ability, evolves novel, previously unseen combinatorial strategies through iterative RL training, underscoring our method's substantial potential. Theoretically, we provide insights into a more robust game equilibrium and derive safety guarantees. Extensive experiments validate our framework's effectiveness, demonstrating superior defense success rates without compromising the helpfulness of the model. Our code is available at https://github.com/BattleWen/MAGIC.

PaperId: 3395, https://arxiv.org/pdf/2602.01532.pdf GitHub

Abstract:
Proactive agents must decide not only what to say but also whether and when to intervene. Many current systems rely on brittle heuristics or indiscriminate long reasoning, which offers little control over the benefit‑burden tradeoff. We formulate the problem as cost‑sensitive selective intervention and present PRISM, a novel framework that couples a decision‑theoretic gate with a dual‑process reasoning architecture. At inference time, the agent intervenes only when a calibrated probability of user acceptance exceeds a threshold derived from asymmetric costs of missed help and false alarms. Inspired by festina lente (Latin: "make haste slowly"), we gate by an acceptance‑calibrated, cost‑derived threshold and invoke a resource‑intensive Slow mode with counterfactual checks only near the decision boundary, concentrating computation on ambiguous and high‑stakes cases. Training uses gate‑aligned, schema‑locked distillation: a teacher running the full PRISM pipeline provides dense, executable supervision on unlabeled interaction traces, while the student learns a response policy that is explicitly decoupled from the intervention gate to enable tunable and auditable control. On ProactiveBench, PRISM reduces false alarms by 22.78% and improves F1 by 20.14% over strong baselines. These results show that principled decision‑theoretic gating, paired with selective slow reasoning and aligned distillation, yields proactive agents that are precise, computationally efficient, and controllable. To facilitate reproducibility, we release our code, models, and resources at https://prism‑festinalente.github.io/; all experiments use the open‑source ProactiveBench benchmark.

PaperId: 3396, https://arxiv.org/pdf/2602.01519.pdf GitHub

Abstract:
The Key‑Value (KV) cache of Large Language Models (LLMs) is prefix‑based, making it highly inefficient for processing contexts retrieved in arbitrary order. Position‑Independent Caching (PIC) has been proposed to enable KV reuse without positional constraints; however, existing approaches often incur substantial accuracy degradation, limiting their practical adoption. To address this issue, we propose native PIC by reintroducing the encoder to prevalent decoder‑only LLMs and explicitly training it to support PIC. We further develop COMB, a PIC‑aware caching system that integrates seamlessly with existing inference frameworks. Experimental results show that COMB reduces Time‑to‑First‑Token (TTFT) by 51‑94% and increases throughput by 3× with comparable accuracy. Furthermore, the quality improvement when using DeepSeek‑V2‑Lite‑Chat demonstrates the applicability of COMB to other types of decoder‑only LLMs. Our code is available at https://github.com/shijuzhao/Comb.

PaperId: 3397, https://arxiv.org/pdf/2602.01518.pdf GitHub

Abstract:
Despite their importance in model sampling, efficient implementation of Top‑k and Top‑p algorithms for large vocabularies remains a significant challenge. Existing approaches often rely on sorting, which incurs significant computation and memory overhead on GPUs, or on stochastic approaches that alter the algorithm's output. In this work, we propose Qrita, an efficient Top‑k and Top‑p algorithm based on a pivot‑based truncation and selection. Qrita leverages pivot‑based search for both Top‑k and Top‑p with two key techniques: 1. Gaussian‑based sigma‑truncation, which greatly reduces the search space of the vocabulary, and 2. Quaternary pivot search with duplication handling, which halves the number of pivot search iterations and guarantees deterministic output. We implement Qrita using Triton and evaluate its performance against the Top‑k and Top‑p kernels of high‑performance LLM execution engines such as SGLang and FlashInfer, improving end‑to‑end serving throughput up to 1.4 times with half the memory usage, while providing the same output as the sorting‑based algorithms. Qrita is now the default Top‑k and Top‑p sampler for the GPU execution path of vLLM, and a ternary implementation of Qrita is available at https://github.com/vllm‑project/vllm/blob/main/vllm/v1/sample/ops/topk_topp_triton.py.

PaperId: 3398, https://arxiv.org/pdf/2602.01355.pdf GitHub

Abstract:
Aggregation query over free text is a long‑standing yet underexplored problem. Unlike ordinary question answering, aggregate queries require exhaustive evidence collection and systems are required to "find all," not merely "find one." Existing paradigms such as Text‑to‑SQL and Retrieval‑Augmented Generation fail to achieve this completeness. In this work, we formalize entity‑level aggregation querying over text in a corpus‑bounded setting with strict completeness requirement. To enable principled evaluation, we introduce AGGBench, a benchmark designed to evaluate completeness‑oriented aggregation under realistic large‑scale corpus. To accompany the benchmark, we propose DFA (Disambiguation‑‑Filtering‑‑Aggregation), a modular agentic baseline that decomposes aggregation querying into interpretable stages and exposes key failure modes related to ambiguity, filtering, and aggregation. Empirical results show that DFA consistently improves aggregation evidence coverage over strong RAG and agentic baselines. The data and code are available in \hrefhttps://anonymous.4open.science/r/DFA‑A4C1.

PaperId: 3399, https://arxiv.org/pdf/2602.01313.pdf GitHub

Abstract:
Long‑term conversational memory in practical LLM applications is inherently collaborative: information is produced by multiple participants, scattered across groups and channels, revised over time, and implicitly grounded in roles and social context. Yet there is currently no established benchmark that evaluates memory under interaction patterns resembling real‑world deployment, as existing benchmarks largely focus on dyadic or single‑topic dialogues. In this paper, we introduce EverMemBench, the first benchmark designed for long‑horizon collaborative memory, built from multi‑party, multi‑group conversations spanning over one million tokens with dense cross‑topic interleaving, temporally evolving decisions, and role‑conditioned personas. EverMemBench evaluates memory systems using 2400 QA pairs across three dimensions essential for real applications: fine‑grained recall, memory awareness, and user profile understanding. Our evaluation reveals fundamental limitations of current systems: multi‑hop reasoning collapses under multi‑party attribution even with oracle evidence (26% accuracy), temporal reasoning fails without explicit version semantics beyond timestamps, and memory awareness is bottlenecked by retrieval, as similarity‑based methods miss implicitly relevant information. EverMemBench thus represents a concrete step toward realistic evaluation of LLM memory and a cornerstone benchmark for developing next‑generation LLMs that reason over time, roles, and collaborative interaction structure. Our benchmark and code are publicly available at https://github.com/EverMind‑AI/EverMemBench.

PaperId: 3400, https://arxiv.org/pdf/2602.01297.pdf GitHub

Abstract:
Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges for large language models (LLMs) in clinical diagnosis. In such settings, single‑agent systems are vulnerable to self‑reinforcing errors, as their predictions lack independent validation and can drift toward spurious conclusions. Although recent multi‑agent frameworks attempt to mitigate this issue through collaborative reasoning, their interactions are often shallow and loosely structured, failing to reflect the rigorous, evidence‑driven processes used by clinical experts. More fundamentally, existing approaches largely ignore the rich logical dependencies among diseases, such as mutual exclusivity, pathological compatibility, and diagnostic confusion. This limitation prevents them from ruling out clinically implausible hypotheses, even when sufficient evidence is available. To overcome these, we propose RE‑MCDF, a relation‑enhanced multi‑expert clinical diagnosis framework. RE‑MCDF introduces a generation‑‑verification‑‑revision closed‑loop architecture that integrates three complementary components: (i) a primary expert that generates candidate diagnoses and supporting evidence, (ii) a laboratory expert that dynamically prioritizes heterogeneous clinical indicators, and (iii) a multi‑relation awareness and evaluation expert group that explicitly enforces inter‑disease logical constraints. Guided by a medical knowledge graph (MKG), the first two experts adaptively reweight EMR evidence, while the expert group validates and corrects candidate diagnoses to ensure logical consistency. Extensive experiments on the neurology subset of CMEMR (NEEMRs) and on our curated dataset (XMEMRs) demonstrate that RE‑MCDF consistently outperforms state‑of‑the‑art baselines in complex diagnostic scenarios (https://github.com/shenshaowei/RE‑MCDF).

PaperId: 3401, https://arxiv.org/pdf/2602.01285.pdf GitHub

Abstract:
Ensuring factuality is essential for the safe use of Large Language Models (LLMs) in high‑stakes domains such as medicine and law. Conformal inference provides distribution‑free guarantees, but existing approaches are either overly conservative, discarding many true‑claims, or rely on adaptive error rates and simple linear models that fail to capture complex group structures. To address these challenges, we reformulate conformal inference in a multiplicative filtering setting, modeling factuality as a product of claim‑level scores. Our method, Multi‑LLM Adaptive Conformal Inference (MACI), leverages ensembles to produce more accurate factuality‑scores, which in our experiments led to higher retention, while validity is preserved through group‑conditional calibration. Experiments show that MACI consistently achieves user‑specified coverage with substantially higher retention and lower time cost than baselines. Our repository is available at https://github.com/MLAI‑Yonsei/MACI

PaperId: 3402, https://arxiv.org/pdf/2602.01086.pdf GitHub

Abstract:
Background: As of 2026, Large Language Models (LLMs) demonstrate expert‑level medical knowledge. However, deploying them as autonomous "Clinical Agents" remains limited. Current Electronic Medical Records (EMRs) and standards like FHIR are designed for human review, creating a "Context Mismatch": AI agents receive fragmented data and must rely on probabilistic inference (e.g., RAG) to reconstruct patient history. This approach causes hallucinations and hinders auditability. Methods: We propose MedBeads, an agent‑native data infrastructure where clinical events are immutable "Beads"‑‑nodes in a Merkle Directed Acyclic Graph (DAG)‑‑cryptographically referencing causal predecessors. This "write‑once, read‑many" architecture makes tampering mathematically detectable. We implemented a prototype with a Go Core Engine, Python middleware for LLM integration, and a React‑based visualization interface. Results: We successfully implemented the workflow using synthetic data. The FHIR‑to‑DAG conversion transformed flat resources into a causally‑linked graph. Our Breadth‑First Search (BFS) Context Retrieval algorithm traverses relevant subgraphs with O(V+E) complexity, enabling real‑time decision support. Tamper‑evidence is guaranteed by design: any modification breaks the cryptographic chain. The visualization aids clinician understanding through explicit causal links. Conclusion: MedBeads addresses the "Context Mismatch" by shifting from probabilistic search to deterministic graph traversal, and from mutable records to immutable chains, providing the substrate for "Trustworthy Medical AI." It guarantees the context the AI receives is deterministic and tamper‑evident, while the LLM determines interpretation. The structured Bead format serves as a token‑efficient "AI‑native language." We release MedBeads as open‑source software to accelerate agent‑native data standards.

PaperId: 3403, https://arxiv.org/pdf/2602.01037.pdf GitHub

Abstract:
Mixture‑of‑Experts(MoE) Vision‑Language Models (VLMs) offer remarkable performance but incur prohibitive memory and computational costs, making compression essential. Post‑Training Quantization (PTQ) is an effective training‑free technique to address the massive memory and computation overhead. Existing quantization paradigms fall short as they are oblivious to two critical forms of heterogeneity: the inherent discrepancy between vision and language tokens, and the non‑uniform contribution of different experts. To bridge this gap, we propose Visual Expert Quantization (VEQ), a dual‑aware quantization framework designed to simultaneously accommodate cross‑modal differences and heterogeneity between experts. Specifically, VEQ incorporates 1)Modality‑expert‑aware Quantization, which utilizes expert activation frequency to prioritize error minimization for pivotal experts, and 2)Modality‑affinity‑aware Quantization, which constructs an enhanced Hessian matrix by integrating token‑expert affinity with modality information to guide the calibration process. Extensive experiments across diverse benchmarks verify that VEQ consistently outperforms state‑of‑the‑art baselines. Specifically, under the W3A16 configuration, our method achieves significant average accuracy gains of 2.04% on Kimi‑VL and 3.09% on Qwen3‑VL compared to the previous SOTA quantization methods, demonstrating superior robustness across various multimodal tasks. Our code will be available at https://github.com/guangshuoqin/VEQ.

PaperId: 3404, https://arxiv.org/pdf/2602.01025.pdf GitHub GitHub

Abstract:
Vision‑language models (VLMs) extend large language models (LLMs) with vision encoders, enabling text generation conditioned on both images and text. However, this multimodal integration expands the attack surface by exposing the model to image‑based jailbreaks crafted to induce harmful responses. Existing gradient‑based jailbreak methods transfer poorly, as adversarial patterns overfit to a single white‑box surrogate and fail to generalise to black‑box models. In this work, we propose Universal and transferable jailbreak (UltraBreak), a framework that constrains adversarial patterns through transformations and regularisation in the vision space, while relaxing textual targets through semantic‑based objectives. By defining its loss in the textual embedding space of the target LLM, UltraBreak discovers universal adversarial patterns that generalise across diverse jailbreak objectives. This combination of vision‑level regularisation and semantically guided textual supervision mitigates surrogate overfitting and enables strong transferability across both models and attack targets. Extensive experiments show that UltraBreak consistently outperforms prior jailbreak methods. Further analysis reveals why earlier approaches fail to transfer, highlighting that smoothing the loss landscape via semantic objectives is crucial for enabling universal and transferable jailbreaks. The code is publicly available in our \hrefhttps://github.com/kaiyuanCui/UltraBreakGitHub repository.

PaperId: 3405, https://arxiv.org/pdf/2602.01003.pdf GitHub

Abstract:
Reinforcement learning (RL) has become a key training step for improving mathematical reasoning in large language models (LLMs), but it often has high GPU memory usage, which makes it hard to use in settings with limited resources. To reduce these issues, we propose Evolution Strategies with Sharpness‑Aware Maximization (ESSAM), a full parameter fine‑tuning framework that tightly combines the zero‑order search in parameter space from Evolution Strategies (ES) with the Sharpness‑Aware Maximization (SAM) to improve generalization. We conduct fine‑tuning experiments on the mainstream mathematica reasoning task GSM8K. The results show that ESSAM achieves an average accuracy of 78.27% across all models and its overall performance is comparable to RL methods. It surpasses classic RL algorithm PPO with an accuracy of 77.72% and is comparable to GRPO with an accuracy of 78.34%, and even surpassing them on some models. Further generalization experiments show that the models trained with ESSAM exhibit stronger generalization ability. Their average performance achieves the best results on 5 out of 6 datasets, indicating that ESSAM can effectively improve the generalization performance of fine‑tuned models. In terms of GPU memory usage, ESSAM reduces the average GPU memory usage by 18× compared to PPO and by 10× compared to GRPO, achieving an extremely low GPU memory usage. In addition, we design an accelerated variant of ESSAM, which achieves nearly a twofold speedup while maintaining the same GPU memory usage as ESSAM, and attains an average accuracy of 78.02% across all models, outperforming PPO. Code: https://github.com/szs777/ESSAM

PaperId: 3406, https://arxiv.org/pdf/2602.00981.pdf GitHub

Abstract:
Spoken question‑answering (SQA) systems relying on automatic speech recognition (ASR) often struggle with accurately recognizing medical terminology. To this end, we propose MedSpeak, a novel knowledge graph‑aided ASR error correction framework that refines noisy transcripts and improves downstream answer prediction by leveraging both semantic relationships and phonetic information encoded in a medical knowledge graph, together with the reasoning power of LLMs. Comprehensive experimental results on benchmarks demonstrate that MedSpeak significantly improves the accuracy of medical term recognition and overall medical SQA performance, establishing MedSpeak as a state‑of‑the‑art solution for medical SQA. The code is available at https://github.com/RainieLLM/MedSpeak.

PaperId: 3407, https://arxiv.org/pdf/2602.00937.pdf GitHub

Abstract:
Leveraging pre‑trained 2D image representations in behavior cloning policies has achieved great success and has become a standard approach for robotic manipulation. However, such representations fail to capture the 3D spatial information about objects and scenes that is essential for precise manipulation. In this work, we introduce Contrastive Learning for 3D Multi‑View Action‑Conditioned Robotic Manipulation Pretraining (CLAMP), a novel 3D pre‑training framework that utilizes point clouds and robot actions. From the merged point cloud computed from RGB‑D images and camera extrinsics, we re‑render multi‑view four‑channel image observations with depth and 3D coordinates, including dynamic wrist views, to provide clearer views of target objects for high‑precision manipulation tasks. The pre‑trained encoders learn to associate the 3D geometric and positional information of objects with robot action patterns via contrastive learning on large‑scale simulated robot trajectories. During encoder pre‑training, we pre‑train a Diffusion Policy to initialize the policy weights for fine‑tuning, which is essential for improving fine‑tuning sample efficiency and performance. After pre‑training, we fine‑tune the policy on a limited amount of task demonstrations using the learned image and action representations. We demonstrate that this pre‑training and fine‑tuning design substantially improves learning efficiency and policy performance on unseen tasks. Furthermore, we show that CLAMP outperforms state‑of‑the‑art baselines across six simulated tasks and five real‑world tasks. The project website and videos can be found at https://clamp3d.github.io/CLAMP/.

PaperId: 3408, https://arxiv.org/pdf/2602.00933.pdf GitHub

Abstract:
The Model Context Protocol (MCP) is emerging as a standard interface through which large language model (LLM) agents discover and invoke external tools. However, existing MCP evaluations fall short along three key axes: realistic multi‑step workflows with cross‑server orchestration, breadth across authentic MCP servers rather than mocks, and structured, reproducible claim‑level scoring disentangled from agent verbosity or style. We introduce MCP‑Atlas, a benchmark for measuring tool‑use competency against production MCP servers. MCP‑Atlas contains 1,000 natural‑language tasks written and verified by human experts spanning 36 real MCP servers and 220 tools. Prompts do not specify servers, tools, or parameters, requiring agents to identify relevant tools among semantically plausible distractors and to compose multi‑step, cross‑server workflows. Each task is scored with a claim‑level rubric, where final answers are scored against atomic factual claims grounded in tool outputs. This answer‑centric scoring permits valid alternative tool‑call trajectories to receive credit. We pair this with an 11‑category diagnostic taxonomy that disentangles tool‑call failures from cognitive failures in task understanding, synthesis, parsing, and stopping. Evaluating 20 frontier models from six providers under matched task‑level conditions, we find pass rates up to 82.2% at a 0.75 claim coverage threshold and a clear three‑tier performance structure. Automated diagnostics show that 63.3% of diagnosed failures are cognitive rather than tool‑call related. Notably, several high‑performing models fail after successful tool execution due to premature stopping or incorrect synthesis. We release the task schema, containerized harness, claim evaluator, and a 500‑task public split, while reserving a 500‑task private split to preserve leaderboard integrity. The code is at https://github.com/scaleapi/mcp‑atlas.

PaperId: 3409, https://arxiv.org/pdf/2602.00913.pdf GitHub

Abstract:
Human value detection from single sentences is a sparse, imbalanced multi‑label task. We study whether Schwartz higher‑order (HO) categories help this setting on ValueEval'24 / ValuesML (74K English sentences) under a compute‑frugal budget. Rather than proposing a new architecture, we compare direct supervised transformers, hard HO\rightarrowvalues pipelines, Presence\rightarrowHO\rightarrowvalues cascades, compact instruction‑tuned large language models (LLMs), QLoRA, and low‑cost upgrades such as threshold tuning and small ensembles. HO categories are learnable: the easiest bipolar pair, Growth vs. Self‑Protection, reaches Macro‑F_1=0.58. The most reliable gains come from calibration and ensembling: threshold tuning improves Social Focus vs. Personal Focus from 0.41 to 0.57 (+0.16), transformer soft voting lifts Growth from 0.286 to 0.303, and a Transformer+LLM hybrid reaches 0.353 on Self‑Protection. In contrast, hard hierarchical gating does not consistently improve the end task. Compact LLMs also underperform supervised encoders as stand‑alone systems, although they sometimes add useful diversity in hybrid ensembles. Under this benchmark, the HO structure is more useful as an inductive bias than as a rigid routing rule.

PaperId: 3410, https://arxiv.org/pdf/2602.00887.pdf GitHub

Abstract:
Most existing language model agentic systems today are built and optimized for large language models (e.g., GPT, Claude, Gemini) via API calls. While powerful, this approach faces several limitations including high token costs and privacy concerns for sensitive applications. We introduce effGen, an open‑source agentic framework optimized for small language models (SLMs) that enables effective, efficient, and secure local deployment (pip install effgen). effGen makes four major contributions: (1) Enhanced tool‑calling with prompt optimization that compresses contexts by 70‑80% while preserving task semantics, (2) Intelligent task decomposition that breaks complex queries into parallel or sequential subtasks based on dependencies, (3) Complexity‑based routing using five factors to make smart pre‑execution decisions, and (4) Unified memory system combining short‑term, long‑term, and vector‑based storage. Additionally, effGen unifies multiple agent protocols (MCP, A2A, ACP) for cross‑protocol communication. Results on 13 benchmarks show effGen outperforms LangChain, AutoGen, and Smolagents with higher success rates, faster execution, and lower memory. Our results reveal that prompt optimization and complexity routing have complementary scaling behavior: optimization benefits SLMs more (11.2% gain at 1.5B vs 2.4% at 32B), while routing benefits large models more (3.6% at 1.5B vs 7.9% at 32B), providing consistent gains across all scales when combined. effGen (https://effgen.org/) is released under the MIT License, ensuring broad accessibility for research and commercial use. Our framework code is publicly available at https://github.com/ctrl‑gaurav/effGen.

PaperId: 3411, https://arxiv.org/pdf/2602.00883.pdf GitHub

Abstract:
Despite impressive results from recent text‑to‑image models like FLUX, visual and anatomical artifacts remain a significant hurdle for practical and professional use. Existing methods for artifact reduction, typically work in a post‑hoc manner, consequently failing to intervene effectively during the core image formation process. Notably, current techniques require problematic and invasive modifications to the model weights, or depend on a computationally expensive and time‑consuming process of regional refinement. To address these limitations, we propose DIAMOND, a training‑free method that applies trajectory correction to mitigate artifacts during inference. By reconstructing an estimate of the clean sample at every step of the generative trajectory, DIAMOND actively steers the generation process away from latent states that lead to artifacts. Furthermore, we extend the proposed method to standard Diffusion Models, demonstrating that DIAMOND provides a robust, zero‑shot path to high‑fidelity, artifact‑free image synthesis without the need for additional training or weight modifications in modern generative architectures. Code is available at https://gmum.github.io/DIAMOND/

PaperId: 3412, https://arxiv.org/pdf/2602.00869.pdf GitHub GitHub

Abstract:
Conditional flow matching (CFM) stands out as an efficient, simulation‑free approach for training flow‑based generative models, achieving remarkable performance for data generation. However, CFM is insufficient to ensure accuracy in learning probability paths. In this paper, we introduce a new partial differential equation characterization for the error between the learned and exact probability paths, along with its solution. We show that the total variation gap between the two probability paths is bounded above by a combination of the CFM loss and an associated divergence loss. This theoretical insight leads to the design of a new objective function that simultaneously matches the flow and its divergence. Our new approach improves the performance of the flow‑based generative model by a noticeable margin without sacrificing generation efficiency. We showcase the advantages of this enhanced training approach over CFM on several important benchmark tasks, including generative modeling for dynamical systems, DNA sequences, and videos. Code is available at \hrefhttps://github.com/Utah‑Math‑Data‑Science/Flow_Div_MatchingUtah‑Math‑Data‑Science.

PaperId: 3413, https://arxiv.org/pdf/2602.00851.pdf GitHub

Abstract:
Modern AI agents increasingly combine conversational interaction with autonomous task execution, such as coding and web research, raising a natural question: What happens when an agent engaged in long‑horizon tasks is exposed to user persuasion? Yet studying this possibility is challenging because long‑running agent behavior is noisy and costly to reproduce, and it remains unclear which unique challenges emerge only in extended task execution. We study how belief‑level intervention can influence downstream task behavior, a phenomenon we name persuasion propagation. We introduce a behavior‑centered evaluation framework that distinguishes between persuasion applied during or prior to task execution. Across web research and coding tasks, we find that on‑the‑fly persuasion induces weak and inconsistent behavioral effects. In contrast, when the belief state is explicitly specified at task time, belief‑prefilled agents conduct on average 26.9% fewer searches and visit 16.9% fewer unique sources than neutral‑prefilled agents. These results suggest that persuasion, even in prior interaction, can affect the agent's behavior, motivating behavior‑level evaluation in agentic systems.

PaperId: 3414, https://arxiv.org/pdf/2602.00845.pdf GitHub

Abstract:
Agentic reasoning enables large reasoning models (LRMs) to dynamically acquire external knowledge, but yet optimizing the retrieval process remains challenging due to the lack of dense, principled reward signals. In this paper, we introduce InfoReasoner, a unified framework that incentivizes effective information seeking via a synthetic semantic information gain reward. Theoretically, we redefine information gain as uncertainty reduction over the model's belief states, establishing guarantees, including non‑negativity, telescoping additivity, and channel monotonicity. Practically, to enable scalable optimization without manual retrieval annotations, we propose an output‑aware intrinsic estimator that computes information gain directly from the model's output distributions using semantic clustering via bidirectional textual entailment. This intrinsic reward guides the policy to maximize epistemic progress, enabling efficient training via Group Relative Policy Optimization (GRPO). Experiments across seven question‑answering benchmarks demonstrate that InfoReasoner consistently outperforms strong retrieval‑augmented baselines, achieving up to 5.4% average accuracy improvement. Our work provides a theoretically grounded and scalable path toward agentic reasoning with retrieval. The code is available at https://github.com/dl‑m9/InfoReasoner

PaperId: 3415, https://arxiv.org/pdf/2602.00834.pdf GitHub

Abstract:
Score‑based methods are powerful across machine learning, but they face a paradox: theoretically path‑independent, yet practically path‑dependent. We resolve this by proving that practical training objectives differ from the ideal, ground‑truth objective by a crucial, overlooked term: the path variance of the score function. We propose the MVP (Mimum Variance Path) Principle to minimize this path variance. Our key contribution is deriving a closed‑form expression for the variance, making optimization tractable. By parameterizing the path with a flexible Kumaraswamy Mixture Model, our method learns data‑adaptive, low‑variance paths without heuristic manual selection. This principled optimization of the complete objective yields more accurate and stable estimators, establishing new state‑of‑the‑art results on challenging benchmarks and providing a general framework for optimizing score‑based interpolation. Our code can be found in https://github.com/Hoemr/OpenDRE.git.

PaperId: 3416, https://arxiv.org/pdf/2602.00682.pdf GitHub

Abstract:
Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic heterogeneity between generative LM representations and the ID‑based collaborative signals that recommendation systems rely on. Naively injecting LM features without alignment degrades recommendation performance rather than improving it. To resolve this, we propose RecGOAT, a dual‑granularity semantic alignment framework built on graph neural networks and optimal transport theory. RecGOAT first enriches collaborative semantics through multimodal attentive graphs that capture item‑item, user‑item, and user‑user relationships, initializing user representations via LLM‑inferred behavioral preferences. It then aligns LM‑derived modality representations with recommendation IDs at two complementary granularities: (1) instance‑level alignment via cross‑modal contrastive learning (CMCL), which produces discriminative per‑sample representations; and (2) distribution‑level alignment via optimal adaptive transport (OAT), which minimizes the 1‑Wasserstein distance between ID distributions and LLM semantics to produce a unified, consistently aligned feature space. Theoretically, we prove that the unified representation achieves strictly lower target error than any single‑modality representation, with the gap bounded by the Wasserstein distance and the InfoNCE loss, providing rigorous guarantees for both alignment consistency and fusion comprehensiveness. Extensive experiments on three public benchmarks demonstrate state‑of‑the‑art performance. Deployment on a large‑scale online advertising platform further validates RecGOAT's industrial scalability. Our code is available at https://github.com/6lyc/RecGOAT‑LLM4Rec.

PaperId: 3417, https://arxiv.org/pdf/2602.00676.pdf GitHub

Abstract:
The advancement of data‑driven artificial intelligence (AI), particularly machine learning, heavily depends on large‑scale benchmarks. Despite remarkable progress across domains ranging from pattern recognition to intelligent decision‑making in recent decades, exemplified by breakthroughs in board games, card games, and electronic sports games, there remains a pressing need for more challenging benchmarks to drive further research. To this end, this paper proposes OpenGuanDan, a novel benchmark that enables both efficient simulation of GuanDan (a popular four‑player, multi‑round Chinese card game) and comprehensive evaluation of both learning‑based and rule‑based GuanDan AI agents. OpenGuanDan poses a suite of nontrivial challenges, including imperfect information, large‑scale information set and action spaces, a mixed learning objective involving cooperation and competition, long‑horizon decision‑making, variable action spaces, and dynamic team composition. These characteristics make it a demanding testbed for existing intelligent decision‑making methods. Moreover, the independent API for each player allows human‑AI interactions and supports integration with large language models. Empirically, we conduct two types of evaluations: (1) pairwise competitions among all GuanDan AI agents, and (2) human‑AI matchups. Experimental results demonstrate that while current learning‑based agents substantially outperform rule‑based counterparts, they still fall short of achieving superhuman performance, underscoring the need for continued research in multi‑agent intelligent decision‑making domain. The project is publicly available at https://github.com/GameAI‑NJUPT/OpenGuanDan.

PaperId: 3418, https://arxiv.org/pdf/2602.00559.pdf GitHub

Abstract:
Current research on video hallucination mitigation primarily focuses on isolated error types, leaving compositional hallucinations, arising from incorrect reasoning over multiple interacting spatial and temporal factors largely underexplored. We introduce OmniVCHall, a benchmark designed to systematically evaluate both isolated and compositional hallucinations in video multimodal large language models (VLLMs). OmniVCHall spans diverse video domains, introduces a novel camera‑based hallucination type, and defines a fine‑grained taxonomy, together with adversarial answer options (e.g., "All are correct" and "None of the above") to prevent shortcut reasoning. The evaluations of 39 representative VLLMs reveal that even advanced models (e.g., Qwen3‑VL and GPT‑5) exhibit substantial performance degradation. We propose TriCD, a contrastive decoding framework with a triple‑pathway calibration mechanism. An adaptive perturbation controller dynamically selects distracting operations to construct negative video variants, while a saliency‑guided enhancement module adaptively reinforces grounded token‑wise visual evidences. These components are optimized via reinforcement learning to encourage precise decision‑making under compositional hallucination settings. Experimental results show that TriCD consistently improves performance across two representative backbones, achieving an average accuracy improvement of over 10%. The data and code can be find at https://github.com/BMRETURN/OmniVCHall.

PaperId: 3419, https://arxiv.org/pdf/2602.00471.pdf GitHub

Abstract:
While Visual Multi‑Agent Systems (VMAS) promise to enhance comprehensive abilities through inter‑agent collaboration, empirical evidence reveals a counter‑intuitive "scaling wall": increasing agent turns often degrades performance while exponentially inflating token costs. We attribute this failure to the information bottleneck inherent in text‑centric communication, where converting perceptual and thinking trajectories into discrete natural language inevitably induces semantic loss. To this end, we propose L^2‑VMAS, a novel model‑agnostic framework that enables inter‑agent collaboration with dual latent memories. Furthermore, we decouple the perception and thinking while dynamically synthesizing dual latent memories. Additionally, we introduce an entropy‑driven proactive triggering that replaces passive information transmission with efficient, on‑demand memory access. Extensive experiments among backbones, sizes, and multi‑agent structures demonstrate that our method effectively breaks the "scaling wall" with superb scalability, improving average accuracy by 2.7‑5.4% while reducing token usage by 21.3‑44.8%. Codes: https://github.com/YU‑deep/L2‑VMAS.

PaperId: 3420, https://arxiv.org/pdf/2602.00469.pdf GitHub

Abstract:
While word embeddings derive meaning from co‑occurrence patterns, human language understanding is grounded in sensory and motor experience. We present \textSENSE (S\textensorimotor E\textmbedding N\textorm S\textcoring E\textngine), a learned projection model that predicts Lancaster sensorimotor norms from word lexical embeddings. We also conducted a behavioral study where 281 participants selected which among candidate nonce words evoked specific sensorimotor associations, finding statistically significant correlations between human selection rates and \textSENSE ratings across 6 of the 11 modalities. Sublexical analysis of these nonce words selection rates revealed systematic phonosthemic patterns for the interoceptive norm, suggesting a path towards computationally proposing candidate phonosthemes from text data.

PaperId: 3421, https://arxiv.org/pdf/2602.00428.pdf GitHub

Abstract:
Recent advancements in large language models (LLMs) have significantly enhanced the capabilities of collaborative multi‑agent systems, enabling them to address complex challenges. However, within these multi‑agent systems, the susceptibility of agents to collective cognitive biases remains an underexplored issue. A compelling example is the Mandela effect, a phenomenon where groups collectively misremember past events as a result of false details reinforced through social influence and internalized misinformation. This vulnerability limits our understanding of memory bias in multi‑agent systems and raises ethical concerns about the potential spread of misinformation. In this paper, we conduct a comprehensive study on the Mandela effect in LLM‑based multi‑agent systems, focusing on its existence, causing factors, and mitigation strategies. We propose MANBENCH, a novel benchmark designed to evaluate agent behaviors across four common task types that are susceptible to the Mandela effect, using five interaction protocols that vary in agent roles and memory timescales. We evaluate agents powered by several LLMs on MANBENCH to quantify the Mandela effect and analyze how different factors affect it. Moreover, we propose strategies to mitigate this effect, including prompt‑level defenses (e.g., cognitive anchoring and source scrutiny) and model‑level alignment‑based defense, achieving an average 74.40% reduction in the Mandela effect compared to the baseline. Our findings provide valuable insights for developing more resilient and ethically aligned collaborative multi‑agent systems. Code and dataset are available at https://github.com/bluedream02/Mandela‑Effect.

PaperId: 3422, https://arxiv.org/pdf/2602.00359.pdf GitHub

Abstract:
As Large Language Models (LLMs) move from curated training sets into open‑ended real‑world environments, a fundamental limitation emerges: static training cannot keep pace with continual deployment environment change. Scaling training‑time and inference‑time compute improves static capability but does not close this train‑deploy gap. We argue that addressing this limitation requires a new scaling axis‑evolution. Existing deployment‑time adaptation methods, whether parametric fine‑tuning or heuristic memory accumulation, lack the strategic agency needed to diagnose failures and produce durable improvements. Our position is that agentic evolution represents the inevitable future of LLM adaptation, elevating evolution itself from a fixed pipeline to an autonomous evolver agent. We instantiate this vision in a general framework, A‑Evolve, which treats deployment‑time improvement as a deliberate, goal‑directed optimization process over persistent system state. We further propose the evolution‑scaling hypothesis: the capacity for adaptation scales with the compute allocated to evolution, positioning agentic evolution as a scalable path toward sustained, open‑ended adaptation in the real world. Our code is publicly available at https://github.com/A‑EVO‑Lab/a‑evolve.

PaperId: 3423, https://arxiv.org/pdf/2602.00343.pdf GitHub

Abstract:
Federated learning (FL) enables collaborative model training over privacy‑sensitive, distributed data, but its environmental impact is difficult to compare across studies due to inconsistent measurement boundaries and heterogeneous reporting. We present a practical carbon‑accounting methodology for FL CO2e tracking using NVIDIA NVFlare and CodeCarbon for explicit, phase‑aware tasks (initialization, per‑round training, evaluation, and idle/coordination). To capture non‑compute effects, we additionally estimate communication emissions from transmitted model‑update sizes under a network‑configurable energy model. We validate the proposed approach on two representative workloads: CIFAR‑10 image classification and retinal optic disk segmentation. In CIFAR‑10, controlled client‑efficiency scenarios show that system‑level slowdowns and coordination effects can contribute meaningfully to carbon footprint under an otherwise fixed FL protocol, increasing total CO2e by 8.34x (medium) and 21.73x (low) relative to the high‑efficiency baseline. In retinal segmentation, swapping GPU tiers (H100 vs.\ V100) yields a consistent 1.7x runtime gap (290 vs. 503 minutes) while producing non‑uniform changes in total energy and CO2e across sites, underscoring the need for per‑site and per‑round reporting. Overall, our results support a standardized carbon accounting method that acts as a prerequisite for reproducible 'green' FL evaluation. Our code is available at https://github.com/Pediatric‑Accelerated‑Intelligence‑Lab/carbon_footprint.

PaperId: 3424, https://arxiv.org/pdf/2602.00298.pdf GitHub

Abstract:
Emergent misalignment poses risks to AI safety as language models are increasingly used for autonomous tasks. In this paper, we present a population of large language models (LLMs) fine‑tuned on insecure datasets spanning 11 diverse domains, evaluating them both with and without backdoor triggers on a suite of unrelated user prompts. Our evaluation experiments on \textttQwen2.5‑Coder‑7B‑Instruct and \textttGPT‑4o‑mini reveal two key findings: (i) backdoor triggers increase the rate of misalignment across 77.8% of domains (average drop: 4.33 points), with \textttrisky‑financial‑advice and \texttttoxic‑legal‑advice showing the largest effects; (ii) domain vulnerability varies widely, from 0% misalignment when fine‑tuning to output incorrect answers to math problems in \textttincorrect‑math to 87.67% when fine‑tuned on \textttgore‑movie‑trivia. In further experiments in Section~\refsec:research‑exploration, we explore multiple research questions, where we find that membership inference metrics, particularly when adjusted for the non‑instruction‑tuned base model, serve as a good prior for predicting the degree of possible broad misalignment. Additionally, we probe for misalignment between models fine‑tuned on different datasets and analyze whether directions extracted on one emergent misalignment (EM) model generalize to steer behavior in others. This work, to our knowledge, is also the first to provide a taxonomic ranking of emergent misalignment by domain, which has implications for AI security and post‑training. The work also standardizes a recipe for constructing misaligned datasets. All code and datasets are publicly available on GitHub.\footnotehttps://github.com/abhishek9909/assessing‑domain‑emergent‑misalignment/tree/main

PaperId: 3425, https://arxiv.org/pdf/2602.00294.pdf GitHub

Abstract:
The most widely used artificial intelligence (AI) models today are Transformers employing self‑attention. In its standard form, self‑attention incurs costs that increase with context length, driving demand for storage, compute, and energy that is now outstripping society's ability to provide them. To help address this issue, we show that self‑attention is efficiently computable to arbitrary precision with constant cost per token, achieving orders‑of‑magnitude reductions in memory use and computation. We derive our formulation by decomposing the conventional formulation's Taylor expansion into expressions over symmetric chains of tensor products. We exploit their symmetry to obtain feed‑forward transformations that efficiently map queries and keys to coordinates in a minimal polynomial‑kernel feature basis. Notably, cost is fixed inversely in proportion to head size, enabling application over a greater number of heads per token than otherwise feasible. We implement our formulation and empirically validate its correctness. Our work enables unbounded token generation at modest fixed cost, substantially reducing the infrastructure and energy demands of large‑scale Transformer models. The mathematical techniques we introduce are of independent interest.

PaperId: 3426, https://arxiv.org/pdf/2602.00288.pdf GitHub

Abstract:
Fine‑grained spatio‑temporal understanding is essential for video reasoning and embodied AI. Yet, while Multimodal Large Language Models (MLLMs) master static semantics, their grasp of temporal dynamics remains brittle. We present TimeBlind, a diagnostic benchmark for compositional spatio‑temporal understanding. Inspired by cognitive science, TimeBlind categorizes fine‑grained temporal understanding into three levels: recognizing atomic events, characterizing event properties, and reasoning about event interdependencies. Unlike benchmarks that conflate recognition with temporal reasoning, TimeBlind leverages a minimal‑pairs paradigm: video pairs share identical static visual content but differ solely in temporal structure, utilizing complementary questions to neutralize language priors. Evaluating over 20 state‑of‑the‑art MLLMs (e.g., GPT‑5, Gemini 3 Pro) on 600 curated instances (2400 video‑question pairs), reveals that the Instance Accuracy (correctly distinguishing both videos in a pair) of the best performing MLLM is only 48.2%, far below the human performance (98.2%). These results demonstrate that even frontier models rely heavily on static visual shortcuts rather than genuine temporal logic, positioning TimeBlind as a vital diagnostic tool for next‑generation video understanding. Dataset and code are available at https://baiqi‑li.github.io/timeblind_project/ .

PaperId: 3427, https://arxiv.org/pdf/2602.00269.pdf GitHub GitHub

Abstract:
Deploying modern Speech Language Models (SpeechLMs) in streaming settings requires systems that provide low latency, high throughput, and strong guarantees of streamability. Existing systems fall short of supporting diverse models flexibly and efficiently. We present VoxServe, a unified serving system for SpeechLMs that optimizes streaming performance. VoxServe introduces a model‑execution abstraction that decouples model architecture from system‑level optimizations, thereby enabling support for diverse SpeechLM architectures within a single framework. Building on this abstraction, VoxServe implements streaming‑aware scheduling and an asynchronous inference pipeline to improve end‑to‑end efficiency. Evaluations across multiple modern SpeechLMs show that VoxServe achieves 10‑20x higher throughput than existing implementations at comparable latency while maintaining high streaming viability. The code of VoxServe is available at https://github.com/vox‑serve/vox‑serve.

PaperId: 3428, https://arxiv.org/pdf/2602.00238.pdf GitHub

Abstract:
Existing retrieval‑augmented generation (RAG) systems are primarily designed under the assumption that each query has a single correct answer. This overlooks common information‑seeking scenarios with multiple plausible answers, where diversity is essential to avoid collapsing to a single dominant response, thereby constraining creativity and compromising fair and inclusive information access. Our analysis reveals a commonly overlooked limitation of standard RAG systems: they underutilize retrieved context diversity, such that increasing retrieval diversity alone does not yield diverse generations. To address this limitation, we propose DIVERGE, a plug‑and‑play agentic RAG framework with novel reflection‑guided generation and memory‑augmented iterative refinement, which promotes diverse viewpoints while preserving answer quality. We introduce novel metrics tailored to evaluating the diversity‑quality trade‑off in open‑ended questions, and show that they correlate well with human judgments. We demonstrate that DIVERGE achieves the best diversity‑quality trade‑off compared to competitive baselines and previous state‑of‑the‑art methods on the real‑world Infinity‑Chat dataset, substantially improving diversity while maintaining quality. More broadly, our results reveal a systematic limitation of current LLM‑based systems for open‑ended information‑seeking and show that explicitly modeling diversity can mitigate it. Our code is available at: https://github.com/au‑clan/Diverge

PaperId: 3429, https://arxiv.org/pdf/2602.00202.pdf GitHub

Abstract:
The semi‑supervised semantic segmentation (S4) can learn rich visual knowledge from low‑cost unlabeled images. However, traditional S4 architectures all face the challenge of low‑quality pseudo‑labels, especially for the teacher‑student framework.We propose a novel SemiEarth model that introduces vision‑language models (VLMs) to address the S4 issues for the remote sensing (RS) domain. Specifically, we invent a VLM pseudo‑label purifying (VLM‑PP) structure to purify the teacher network's pseudo‑labels, achieving substantial improvements. Especially in multi‑class boundary regions of RS images, the VLM‑PP module can significantly improve the quality of pseudo‑labels generated by the teacher, thereby correctly guiding the student model's learning. Moreover, since VLM‑PP equips VLMs with open‑world capabilities and is independent of the S4 architecture, it can correct mispredicted categories in low‑confidence pseudo‑labels whenever a discrepancy arises between its prediction and the pseudo‑label. We conducted extensive experiments on multiple RS datasets, which demonstrate that our SemiEarth achieves SOTA performance. More importantly, unlike previous SOTA RS S4 methods, our model not only achieves excellent performance but also offers good interpretability. The code is released at https://github.com/wangshanwen001/SemiEarth.

PaperId: 3430, https://arxiv.org/pdf/2602.00197.pdf GitHub

Abstract:
Zero‑shot mutation prediction is vital for low‑resource protein engineering, yet existing protein language models (PLMs) often yield statistically confident results that ignore fundamental biophysical constraints. Currently, selecting candidates for wet‑lab validation relies on manual expert auditing of PLM outputs, a process that is inefficient, subjective, and highly dependent on domain expertise. To address this, we propose Rank‑and‑Reason (VenusRAR), a two‑stage agentic framework to automate this workflow and maximize expected wet‑lab fitness. In the Rank‑Stage, a Computational Expert and Virtual Biologist aggregate a context‑aware multi‑modal ensemble, establishing a new Spearman correlation record of 0.551 (vs. 0.518) on ProteinGym. In the Reason‑Stage, an agentic Expert Panel employs chain‑of‑thought reasoning to audit candidates against geometric and structural constraints, improving the Top‑5 Hit Rate by up to 367% on ProteinGym‑DMS99. The wet‑lab validation on Cas12i3 nuclease further confirms the framework's efficacy, achieving a 46.7% positive rate and identifying two novel mutants with 4.23‑fold and 5.05‑fold activity improvements. Code and datasets are released on GitHub (https://github.com/ai4protein/VenusRAR/).

PaperId: 3431, https://arxiv.org/pdf/2602.00192.pdf GitHub

Abstract:
Modern deep learning‑based inpainting enables realistic local image manipulation, raising critical challenges for reliable detection. However, we observe that current detectors primarily rely on global artifacts that appear as inpainting side effects, rather than on locally synthesized content. We show that this behavior occurs because VAE‑based reconstruction induces a subtle but pervasive spectral shift across the entire image, including unedited regions. To isolate this effect, we introduce Inpainting Exchange (INP‑X), an operation that restores original pixels outside the edited region while preserving all synthesized content. We create a 90K test dataset including real, inpainted, and exchanged images to evaluate this phenomenon. Under this intervention, pretrained state‑of‑the‑art detectors, including commercial ones, exhibit a dramatic drop in accuracy (e.g., from 91% to 55%), frequently approaching chance level. We provide a theoretical analysis linking this behavior to high‑frequency attenuation caused by VAE information bottlenecks. Our findings highlight the need for content‑aware detection. Indeed, training on our dataset yields better generalization and localization than standard inpainting. Our dataset and code are publicly available at https://github.com/emirhanbilgic/INP‑X.

PaperId: 3432, https://arxiv.org/pdf/2602.00189.pdf GitHub

Abstract:
Researchers have shown a growing interest in Audio‑driven Talking Head Generation. The primary challenge in talking head generation is achieving audio‑visual coherence between the lips and the audio, known as lip synchronization. This paper proposes a generic method, LPIPS‑AttnWav2Lip, for reconstructing face images of any speaker based on audio. We used the U‑Net architecture based on residual CBAM to better encode and fuse audio and visual modal information. Additionally, the semantic alignment module extends the receptive field of the generator network to obtain the spatial and channel information of the visual features efficiently; and match statistical information of visual features with audio latent vector to achieve the adjustment and injection of the audio content information to the visual information. To achieve exact lip synchronization and to generate realistic high‑quality images, our approach adopts LPIPS Loss, which simulates human judgment of image quality and reduces instability possibility during the training process. The proposed method achieves outstanding performance in terms of lip synchronization accuracy and visual quality as demonstrated by subjective and objective evaluation results. The code for the paper is available at the following link: https://github.com/FelixChan9527/LPIPS‑AttnWav2Lip

PaperId: 3433, https://arxiv.org/pdf/2602.00184.pdf GitHub

Abstract:
Limited‑angle computed tomography (LACT) offers the advantages of reduced radiation dose and shortened scanning time. Traditional reconstruction algorithms exhibit various inherent limitations in LACT. Currently, most deep learning‑based LACT reconstruction methods focus on multi‑domain fusion or the introduction of generic priors, failing to fully align with the core imaging characteristics of LACT‑such as the directionality of artifacts and directional loss of structural information, which are caused by the absence of projection angles in certain directions. Inspired by the theory of visible and invisible singularities, taking into account the aforementioned core imaging characteristics of LACT, we propose a Visible Singularities Guided Correlation network for LACT reconstruction (VSGC). The design philosophy of VSGC consists of two core steps: First, extract VS edge features from LACT images and focus the model's attention on these VS. Second, establish correlations between the VS edge features and other regions of the image. Additionally, a multi‑scale loss function with anisotropic constraint is employed to constrain the model to converge in multiple aspects. Finally, qualitative and quantitative validations are conducted on both simulated and real datasets to verify the effectiveness and feasibility of the proposed design. Particularly, in comparison with alternative methods, VSGC delivers more prominent performance in small angular ranges, with the PSNR improvement of 2.45 dB and the SSIM enhancement of 1.5%. The code is publicly available at https://github.com/yqx7150/VSGC.

PaperId: 3434, https://arxiv.org/pdf/2602.00154.pdf GitHub

Abstract:
Large reasoning models (LRMs) extend large language models with explicit multi‑step reasoning traces, but this capability introduces a new class of prompt‑induced inference‑time denial‑of‑service (PI‑DoS) attacks that exploit the high computational cost of reasoning. We first formalize inference cost for LRMs and define PI‑DoS, then prove that any practical PI‑DoS attack should satisfy three properties: (1) a high amplification ratio, where each query induces a disproportionately long reasoning trace relative to its own length; (ii) stealthiness, in which prompts and responses remain on the natural language manifold and evade distribution shift detectors; and (iii) optimizability, in which the attack supports efficient optimization without being slowed by its own success. Under this framework, we present ReasoningBomb, a reinforcement‑learning‑based PI‑DoS framework that is guided by a constant‑time surrogate reward and trains a large reasoning‑model attacker to generate short natural prompts that drive victim LRMs into pathologically long and often effectively non‑terminating reasoning. Across seven open‑source models (including LLMs and LRMs) and three commercial LRMs, ReasoningBomb induces 18,759 completion tokens on average and 19,263 reasoning tokens on average across reasoning models. It outperforms the the runner‑up baseline by 35% in completion tokens and 38% in reasoning tokens, while inducing 6‑7x more tokens than benign queries and achieving 286.7x input‑to‑output amplification ratio averaged across all samples. Additionally, our method achieves 99.8% bypass rate on input‑based detection, 98.7% on output‑based detection, and 98.4% against strict dual‑stage joint detection.

PaperId: 3435, https://arxiv.org/pdf/2602.00144.pdf GitHub

Abstract:
Class‑incremental learning (CIL) with Vision Transformers (ViTs) faces a major computational bottleneck during the classifier reconstruction phase, where most existing methods rely on costly iterative stochastic gradient descent (SGD). We observe that analytic Regularized Gaussian Discriminant Analysis (RGDA) provides a Bayes‑optimal alternative with accuracy comparable to SGD‑based classifiers; however, its quadratic inference complexity limits its use in large‑scale CIL scenarios. To overcome this, we propose Low‑Rank Factorized RGDA (LR‑RGDA), a scalable classifier that combines RGDA's expressivity with the efficiency of linear classifiers. By exploiting the low‑rank structure of the covariance via the Woodbury matrix identity, LR‑RGDA decomposes the discriminant function into a global affine term refined by a low‑rank quadratic perturbation, reducing the inference complexity from \mathcalO(Cd^2) to \mathcalO(d^2 + Crd^2), where C is the class number, d the feature dimension, and r \ll d the subspace rank. To mitigate representation drift caused by backbone updates, we further introduce Hopfield‑based Distribution Compensator (HopDC), a training‑free mechanism that uses modern continuous Hopfield Networks to recalibrate historical class statistics through associative memory dynamics on unlabeled anchors, accompanied by a theoretical bound on the estimation error. Extensive experiments on diverse CIL benchmarks demonstrate that our framework achieves state‑of‑the‑art performance, providing a scalable solution for large‑scale class‑incremental learning with ViTs. Code: https://github.com/raoxuan98‑hash/lr_rgda_hopdc.

PaperId: 3436, https://arxiv.org/pdf/2602.00133.pdf GitHub

Abstract:
Prediction markets offer a natural testbed for trading agents: contracts have binary payoffs, prices can be interpreted as probabilities, and realized performance depends critically on market microstructure, fees, and settlement risk. We introduce PredictionMarketBench, a SWE‑bench‑style benchmark for evaluating algorithmic and LLM‑based trading agents on prediction markets via deterministic, event‑driven replay of historical limit‑order‑book and trade data. PredictionMarketBench standardizes (i) episode construction from raw exchange streams (orderbooks, trades, lifecycle, settlement), (ii) an execution‑realistic simulator with maker/taker semantics and fee modeling, and (iii) a tool‑based agent interface that supports both classical strategies and tool‑calling LLM agents with reproducible trajectories. We release four Kalshi‑based episodes spanning cryptocurrency, weather, and sports. Baseline results show that naive trading agents can underperform due to transaction costs and settlement losses, while fee‑aware algorithmic strategies remain competitive in volatile episodes.

PaperId: 3437, https://arxiv.org/pdf/2602.00125.pdf GitHub

Abstract:
We present MiniTensor, an open source tensor operations library that focuses on minimalism, correctness, and performance. MiniTensor exposes a familiar PyTorch‑like Python API while it executes performance critical code in a Rust engine. The core supports dense n dimensional tensors, broadcasting, reductions, matrix multiplication, reverse mode automatic differentiation, a compact set of neural network layers, and standard optimizers. In this paper, we describe the design of MiniTensor's architecture, including its efficient memory management, dynamic computation graph for gradients, and integration with Python via PyO3. We also compare the install footprint with PyTorch and TensorFlow to demonstrate that MiniTensor achieves a package size of only a few megabytes, several orders of magnitude smaller than mainstream frameworks, while preserving the essentials needed for research and development on CPUs. The repository can be found at https://github.com/neuralsorcerer/minitensor

PaperId: 3438, https://arxiv.org/pdf/2602.00105.pdf GitHub

Abstract:
Public demos of image editing models are typically best‑case samples; real workflows pay for retries and review time. We introduce HYPE‑EDIT‑1, a 100‑task benchmark of reference‑based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per‑attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per successful edit that combines model price with human review time. We release 50 public tasks and maintain a 50‑task held‑out private split for server‑side evaluation, plus a standardized JSON schema and tooling for VLM and human‑based judging. Across the evaluated models, per‑attempt pass rates span 34‑83 percent and effective cost per success spans USD 0.66‑1.42. Models that have low per‑image pricing are more expensive when you consider the total effective cost of retries and human reviews.

PaperId: 3439, https://arxiv.org/pdf/2602.00095.pdf GitHub GitHub

Abstract:
Multimodal Large Language Models (MLLMs) hold significant promise for revolutionizing traditional education and reducing teachers' workload. However, accurately interpreting unconstrained STEM student handwritten solutions with intertwined mathematical formulas, diagrams, and textual reasoning poses a significant challenge due to the lack of authentic and domain‑specific benchmarks. Additionally, current evaluation paradigms predominantly rely on the outcomes of downstream tasks (e.g., auto‑grading), which often probe only a subset of the recognized content, thereby failing to capture the MLLMs' understanding of complex handwritten logic as a whole. To bridge this gap, we release EDU‑CIRCUIT‑HW, a dataset consisting of 1,300+ authentic student handwritten solutions from a university‑level STEM course. Utilizing the expert‑verified verbatim transcriptions and grading reports of student solutions, we simultaneously evaluate various MLLMs' upstream recognition fidelity and downstream auto‑grading performance. Our evaluation uncovers an astonishing scale of latent failures within MLLM‑recognized student handwritten content, highlighting the models' insufficient reliability for auto‑grading and other understanding‑oriented applications in high‑stakes educational settings. As a potential solution, we present a case study demonstrating that leveraging identified error patterns to preemptively detect and correct recognition errors, while requiring only minimal human intervention (e.g., routing 3.3% of assignments to human graders and the remainder to the GPT‑5.1 grader), can effectively enhance the robustness of the deployed AI‑enabled grading system. Code and dataset are available in this GitHub repo: https://gt‑learning‑innovation.github.io/CIRCUIT_EDU_HW_ACL.

PaperId: 3440, https://arxiv.org/pdf/2602.00062.pdf GitHub

Abstract:
Adopting large‑scale AI models in enterprise information systems is often hindered by high training costs and long development cycles, posing a significant managerial challenge. The standard end‑to‑end backpropagation (BP) algorithm is a primary driver of modern AI, but it is also the source of inefficiency in training deep networks. This paper introduces a new training methodology, Supervised Contrastive Parallel Learning (SCPL), that addresses this issue by decoupling BP and transforming a long gradient flow into multiple short ones. This design enables the simultaneous computation of parameter gradients in different layers, achieving superior model parallelism and enhancing training throughput. Detailed experiments are presented to demonstrate the efficiency and effectiveness of our model compared to BP, Early Exit, GPipe, and Associated Learning (AL), a state‑of‑the‑art method for decoupling backpropagation. By mitigating a fundamental performance bottleneck, SCPL provides a practical pathway for organizations to develop and deploy advanced information systems more cost‑effectively and with greater agility. The experimental code is released for reproducibility. https://github.com/minyaho/scpl/

PaperId: 3441, https://arxiv.org/pdf/2602.00012.pdf GitHub

Abstract:
We present OGD4All, a transparent, auditable, and reproducible framework based on Large Language Models (LLMs) to enhance citizens' interaction with geospatial Open Government Data (OGD). The system combines semantic data retrieval, agentic reasoning for iterative code generation, and secure sandboxed execution that produces verifiable multimodal outputs. Evaluated on a 199‑question benchmark covering both factual and unanswerable questions, across 430 City‑of‑Zurich datasets and 11 LLMs, OGD4All reaches 98% analytical correctness and 94% recall while reliably rejecting questions unsupported by available data, which minimizes hallucination risks. Statistical robustness tests, as well as expert feedback, show reliability and social relevance. The proposed approach shows how LLMs can provide explainable, multimodal access to public data, advancing trustworthy AI for open governance.

PaperId: 3442, https://arxiv.org/pdf/2602.00002.pdf GitHub

Abstract:
Click‑through rate (CTR) prediction, which estimates the probability of a user clicking on a given item, is a critical task for online information services. Existing approaches often make strong assumptions that training and test data come from the same distribution. However, the data distribution varies since user interests are constantly evolving, resulting in the out‑of‑distribution (OOD) issue. In addition, users tend to have multiple interests, some of which evolve faster than others. Towards this end, we propose Disentangled Click‑Through Rate prediction (DiseCTR), which introduces a causal perspective of recommendation and disentangles multiple aspects of user interests to alleviate the OOD issue in recommendation. We conduct a causal factorization of CTR prediction involving user interest, exposure model, and click model, based on which we develop a deep learning implementation for these three causal mechanisms. Specifically, we first design an interest encoder with sparse attention which maps raw features to user interests, and then introduce a weakly supervised interest disentangler to learn independent interest embeddings, which are further integrated by an attentive interest aggregator for prediction. Experimental results on three real‑world datasets show that DiseCTR achieves the best accuracy and robustness in OOD recommendation against state‑of‑the‑art approaches, significantly improving AUC and GAUC by over 0.02 and reducing logloss by over 13.7%. Further analyses demonstrate that DiseCTR successfully disentangles user interests, which is the key to OOD generalization for CTR prediction. We have released the code and data at https://github.com/DavyMorgan/DiseCTR/.

PaperId: 3443, https://arxiv.org/pdf/2601.23232.pdf GitHub

Abstract:
In recent years, large language models (LLMs) have made rapid progress in information retrieval, yet existing research has mainly focused on text or static multimodal settings. Open‑domain video shot retrieval, which involves richer temporal structure and more complex semantics, still lacks systematic benchmarks and analysis. To fill this gap, we introduce ShotFinder, a benchmark that formalizes editing requirements as keyframe‑oriented shot descriptions and introduces five types of controllable single‑factor constraints: Temporal order, Color, Visual style, Audio, and Resolution. We curate 1,210 high‑quality samples from YouTube across 20 thematic categories, using large models for generation with human verification. Based on the benchmark, we propose ShotFinder, a text‑driven three‑stage retrieval and localization pipeline: (1) query expansion via video imagination, (2) candidate video retrieval with a search engine, and (3) description‑guided temporal localization. Experiments on multiple closed‑source and open‑source models reveal a significant gap to human performance, with clear imbalance across constraints: temporal localization is relatively tractable, while color and visual style remain major challenges. These results reveal that open‑domain video shot retrieval is still a critical capability that multimodal large models have yet to overcome.

PaperId: 3444, https://arxiv.org/pdf/2601.23212.pdf GitHub

Abstract:
Multispecific antibodies offer transformative therapeutic potential by engaging multiple epitopes simultaneously, yet their efficacy is an emergent property governed by complex molecular architectures. Rational design is often bottlenecked by the inability to predict how subtle changes in domain topology influence functional outcomes, a challenge exacerbated by the scarcity of comprehensive experimental data. Here, we introduce a computational framework to address part of this gap. First, we present a generative method for creating large‑scale, realistic synthetic functional landscapes that capture non‑linear interactions where biological activity depends on domain connectivity. Second, we propose a graph neural network architecture that explicitly encodes these topological constraints, distinguishing between format configurations that appear identical to sequence‑only models. We demonstrate that this model, trained on synthetic landscapes, recapitulates complex functional properties and, via transfer learning, has the potential to achieve high predictive accuracy on limited biological datasets. We showcase the model's utility by optimizing trade‑offs between efficacy and toxicity in trispecific T‑cell engagers and retrieving optimal common light chains. This work provides a robust benchmarking environment for disentangling the combinatorial complexity of multispecifics, accelerating the design of next‑generation therapeutics.

PaperId: 3445, https://arxiv.org/pdf/2601.23174.pdf GitHub

Abstract:
Neural audio codecs are at the core of modern conversational speech technologies, converting continuous speech into sequences of discrete tokens that can be processed by LLMs. However, existing codecs typically operate at fixed frame rates, allocating tokens uniformly in time and producing unnecessarily long sequences. In this work, we introduce DyCAST, a Dynamic Character‑Aligned Speech Tokenizer that enables variable‑frame‑rate tokenization through soft character‑level alignment and explicit duration modeling. DyCAST learns to associate tokens with character‑level linguistic units during training and supports alignment‑free inference with direct control over token durations at decoding time. To improve speech resynthesis quality at low frame rates, we further introduce a retrieval‑augmented decoding mechanism that enhances reconstruction fidelity without increasing bitrate. Experiments show that DyCAST achieves competitive speech resynthesis quality and downstream performance while using significantly fewer tokens than fixed‑frame‑rate codecs. Code and checkpoints will be released publicly at https://github.com/lucadellalib/dycast.

PaperId: 3446, https://arxiv.org/pdf/2601.23155.pdf GitHub

Abstract:
Information‑based data selection for instruction tuning is compelling: maximizing the log‑determinant of the Fisher information yields a monotone submodular objective, enabling greedy algorithms to achieve a (1‑1/e) approximation under a cardinality budget. In practice, however, we identify alleviating gradient conflicts, misalignment between per‑sample gradients, is a key factor that slows down the decay of marginal log‑determinant information gains, thereby preventing significant loss of information. We formalize this via an \varepsilon‑decomposition that quantifies the deviation from ideal submodularity as a function of conflict statistics, yielding data‑dependent approximation factors that tighten as conflicts diminish. Guided by this analysis, we propose SPICE, a conflict‑aware selector that maximizes information while penalizing misalignment, and that supports early stopping and proxy models for efficiency. Empirically, SPICE selects subsets with higher log‑determinant information than original criteria, and these informational gains translate into performance improvements: across 8 benchmarks with LLaMA2‑7B and Qwen2‑7B, SPICE uses only 10% of the data, yet matches or exceeds 6 methods including full‑data tuning. This achieves performance improvements with substantially lower training cost.

PaperId: 3447, https://arxiv.org/pdf/2601.23143.pdf GitHub

Abstract:
Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain‑of‑thought (CoT) reasoning. However, this over‑optimization often prioritizes compliance, making models vulnerable to harmful prompts. To mitigate this safety degradation, recent approaches rely on external teacher distillation, yet this introduces a distributional discrepancy that degrades native reasoning. We formalize safety realignment as a KL projection onto the safe simplex and prove that the student's own safety‑filtered distribution is the unique KL‑optimal target, while any external teacher incurs an irreducible excess KL penalty. Guided by this analysis, we propose ThinkSafe, a self‑generated alignment framework that restores safety without external teachers. Our key insight is that while compliance suppresses safety mechanisms, models often retain latent knowledge to identify harm. ThinkSafe unlocks this via lightweight refusal steering, which preserves the KL‑optimal target while increasing the acceptance rate. Experiments on DeepSeek‑R1‑Distill and Qwen3 show ThinkSafe significantly improves safety while preserving reasoning proficiency, and achieves superior safety and comparable reasoning to GRPO with roughly an order of magnitude less compute. Code, models, and datasets are available at https://github.com/seanie12/ThinkSafe and https://huggingface.co/Seanie‑lee/collections.

PaperId: 3448, https://arxiv.org/pdf/2601.23131.pdf GitHub

Abstract:
Despite huge successes on a wide range of tasks, neural networks are known to sometimes struggle to generalise to unseen data. Many approaches have been proposed over the years to promote the generalisation ability of neural networks, collectively known as regularisation techniques. These are used as common practice under the assumption that any regularisation added to the pipeline would result in a performance improvement. In this study, we investigate whether this assumption holds in practice. First, we provide a broad review of regularisation techniques, including modern theories such as double descent. We propose a taxonomy of methods under four broad categories, namely: (1) data‑based strategies, (2) architecture strategies, (3) training strategies, and (4) loss function strategies. Notably, we highlight the contradictions and correspondences between the approaches in these broad classes. Further, we perform an empirical comparison of the various regularisation techniques on classification tasks for ten numerical and image datasets applied to the multi‑layer perceptron and convolutional neural network architectures. Results show that the efficacy of regularisation is dataset‑dependent. For example, the use of a regularisation term only improved performance on numeric datasets, whereas batch normalisation improved performance on image datasets only. Generalisation is crucial to machine learning; thus, understanding the effects of applying regularisation techniques, and considering the connections between them is essential to the appropriate use of these methods in practice.

PaperId: 3449, https://arxiv.org/pdf/2601.23052.pdf GitHub

Abstract:
Generating realistic graph‑structured data is challenging due to discrete structures, variable sizes, and class‑specific connectivity patterns that resist conventional generative modelling. While recent graph generation methods employ generative adversarial network (GAN) frameworks to handle permutation invariance and irregular topologies, they typically rely on random edge sampling with fixed probabilities, limiting their capacity to capture complex structural dependencies between nodes. We propose a density‑aware conditional graph generation framework using Wasserstein GANs (WGAN) that replaces random sampling with a learnable distance‑based edge predictor. Our approach embeds nodes into a latent space where proximity correlates with edge likelihood, enabling the generator to learn meaningful connectivity patterns. A differentiable edge predictor determines pairwise relationships directly from node embeddings, while a density‑aware selection mechanism adaptively controls edge density to match class‑specific sparsity distributions observed in real graphs. We train the model using a WGAN with gradient penalty, employing a GCN‑based critic to ensure generated graphs exhibit realistic topology and align with target class distributions. Experiments on benchmark datasets demonstrate that our method produces graphs with superior structural coherence and class‑consistent connectivity compared to existing baselines. The learned edge predictor captures complex relational patterns beyond simple heuristics, generating graphs whose density and topology closely match real structural distributions. Our results show improved training stability and controllable synthesis, making the framework effective for realistic graph generation and data augmentation. Source code is publicly available at https://github.com/ava‑12/Density_Aware_WGAN.git.

PaperId: 3450, https://arxiv.org/pdf/2601.23049.pdf GitHub

Abstract:
Medical calculators are fundamental to quantitative, evidence‑based clinical practice. However, their real‑world use is an adaptive, multi‑stage process, requiring proactive EHR data acquisition, scenario‑dependent calculator selection, and multi‑step computation, whereas current benchmarks focus only on static single‑step calculations with explicit instructions. To address these limitations, we introduce MedMCP‑Calc, the first benchmark for evaluating LLMs in realistic medical calculator scenarios through Model Context Protocol (MCP) integration. MedMCP‑Calc comprises 118 scenario tasks across 4 clinical domains, featuring fuzzy task descriptions mimicking natural queries, structured EHR database interaction, external reference retrieval, and process‑level evaluation. Our evaluation of 23 leading models reveals critical limitations: even top performers like Claude Opus 4.5 exhibit substantial gaps, including difficulty selecting appropriate calculators for end‑to‑end workflows given fuzzy queries, poor performance in iterative SQL‑based database interactions, and marked reluctance to leverage external tools for numerical computation. Performance also varies considerably across clinical domains. Building on these findings, we develop CalcMate, a fine‑tuned model incorporating scenario planning and tool augmentation, achieving state‑of‑the‑art performance among open‑source models. Benchmark and Codes are available in https://github.com/SPIRAL‑MED/MedMCP‑Calc.

PaperId: 3451, https://arxiv.org/pdf/2601.22990.pdf GitHub

Abstract:
Reconstructing 3D fetal MR volumes from motion‑corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice‑to‑volume reconstruction (SVR) methods are time‑consuming and require multiple orthogonal stacks for reconstruction. While learning‑based SVR approaches have significantly reduced the time required at the inference stage, they heavily rely on ground truth information for training, which is inaccessible in practice. To address these challenges, we propose GaussianSVR, a self‑supervised framework for slice‑to‑volume reconstruction. GaussianSVR represents the target volume using 3D Gaussian representations to achieve high‑fidelity reconstruction. It leverages a simulated forward slice acquisition model to enable self‑supervised training, alleviating the need for ground‑truth volumes. Furthermore, to enhance both accuracy and efficiency, we introduce a multi‑resolution training strategy that jointly optimizes Gaussian parameters and spatial transformations across different resolution levels. Experiments show that GaussianSVR outperforms the baseline methods on fetal MR volumetric reconstruction. Code is available at https://github.com/Yinsong0510/GaussianSVR‑Self‑Supervised‑Slice‑to‑Volume‑Reconstruction‑with‑Gaussian‑Representations.

PaperId: 3452, https://arxiv.org/pdf/2601.22984.pdf GitHub

Abstract:
Diagnosing failure patterns in Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end‑to‑end evaluation, obscuring intermediate hallucinations that accumulate throughout the research trajectory. To bridge this gap, we propose a shift from outcome‑based to processaware evaluation by auditing hallucinations in the full plan‑search‑summarize trajectory. We introduce the PING Taxonomy, which categorizes DRA hallucinations into four complementary types: Propagation, Intent, Noiseinduced, and Grounding. We further instantiate this taxonomy into a fine‑grained evaluation framework that decomposes trajectories into atomic actions, claims, and sub‑queries for rigorous verification. Leveraging this framework to isolate 100 distinctively hallucinationprone tasks including adversarial scenarios, we curate DeepHalluBench. Experiments on six representative DRAs show that, on our hallucination‑prone stress‑test set, all evaluated systems still exhibit non‑negligible reliability gaps. Furthermore, our diagnostic analysis traces these failures to systemic deficits, especially hallucination propagation and cognitive biases, providing actionable insights for future architectural optimization. Code and data are available in https://github.com/yuhao‑zhan/DeepHalluBench.

PaperId: 3453, https://arxiv.org/pdf/2601.22964.pdf GitHub

Abstract:
Prevailing medical AI operates on an unrealistic ''one‑shot'' model, diagnosing from a complete patient file. However, real‑world diagnosis is an iterative inquiry where Clinicians sequentially ask questions and order tests to strategically gather information while managing cost and time. To address this, we first propose Med‑Inquire, a new benchmark designed to evaluate an agent's ability to perform multi‑turn diagnosis. Built upon a dataset of real‑world clinical cases, Med‑Inquire simulates the diagnostic process by hiding a complete patient file behind specialized Patient and Examination agents. They force the agent to proactively ask questions and order tests to gather information piece by piece. To tackle the challenges posed by Med‑Inquire, we then introduce EvoClinician, a self‑evolving agent that learns efficient diagnostic strategies at test time. Its core is a ''Diagnose‑Grade‑Evolve'' loop: an Actor agent attempts a diagnosis; a Process Grader agent performs credit assignment by evaluating each action for both clinical yield and resource efficiency; finally, an Evolver agent uses this feedback to update the Actor's strategy by evolving its prompt and memory. Our experiments show EvoClinician outperforms continual learning baselines and other self‑evolving agents like memory agents. The code is available at https://github.com/yf‑he/EvoClinician

PaperId: 3454, https://arxiv.org/pdf/2601.22925.pdf GitHub

Abstract:
Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine‑tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during inference to efficiently retrieve B top‑ranked recommended items. However, we identify a critical training‑inference inconsistency: while SFT optimizes the overall probability of positive items, it does not guarantee that such items will be retrieved by beam search even if they possess high overall probabilities. Due to the greedy pruning mechanism, beam search can prematurely discard a positive item once its prefix probability is insufficient. To address this inconsistency, we propose BEAR (Beam‑SEarch‑Aware Regularization), a novel fine‑tuning objective that explicitly accounts for beam search behavior during training. Rather than directly simulating beam search for each instance during training, which is computationally prohibitive, BEAR enforces a relaxed necessary condition: each token in a positive item must rank within the top‑B candidate tokens at each decoding step. This objective effectively mitigates the risk of incorrect pruning while incurring negligible computational overhead compared to standard SFT. Extensive experiments across four real‑world datasets demonstrate that BEAR significantly outperforms strong baselines. Code is available at https://github.com/Tiny‑Snow/BEAR‑SIGIR‑2026 .

PaperId: 3455, https://arxiv.org/pdf/2601.22859.pdf GitHub

Abstract:
The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages. To address this, we introduce MEnvAgent, a Multi‑language framework for automated Environment construction that facilitates scalable generation of verifiable task instances. MEnvAgent employs a multi‑agent Planning‑Execution‑Verification architecture to autonomously resolve construction failures and integrates a novel Environment Reuse Mechanism that reduces computational overhead by incrementally patching historical environments. Evaluations on MEnvBench, a new benchmark comprising 1,000 tasks across 10 languages, demonstrate that MEnvAgent outperforms baselines, improving Fail‑to‑Pass (F2P) rates by 8.6% while reducing time costs by 43%. Additionally, we demonstrate the utility of MEnvAgent by constructing MEnvData‑SWE, the largest open‑source polyglot dataset of realistic verifiable Docker environments to date, alongside solution trajectories that enable consistent performance gains on SWE tasks across a wide range of models. Our code, benchmark, and dataset are available at https://github.com/ernie‑research/MEnvAgent.

PaperId: 3456, https://arxiv.org/pdf/2601.22823.pdf GitHub

Abstract:
We study offline reinforcement learning of style‑conditioned policies using explicit style supervision via subtrajectory labeling functions. In this setting, aligning style with high task performance is particularly challenging due to distribution shift and inherent conflicts between style and reward. Existing methods, despite introducing numerous definitions of style, often fail to reconcile these objectives effectively. To address these challenges, we propose a unified definition of behavior style and instantiate it into a practical framework. Building on this, we introduce Style‑Conditioned Implicit Q‑Learning (SCIQL), which leverages offline goal‑conditioned RL techniques, such as hindsight relabeling and value learning, and combine it with a new Gated Advantage Weighted Regression mechanism to efficiently optimize task performance while preserving style alignment. Experiments demonstrate that SCIQL achieves superior performance on both objectives compared to prior offline methods. Code, datasets and visuals are available in: https://sciql‑iclr‑2026.github.io/.

PaperId: 3457, https://arxiv.org/pdf/2601.22803.pdf GitHub

Abstract:
Code verifiers play a critical role in post‑verification for LLM‑based code generation, yet existing supervised fine‑tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency. While reinforcement learning (RL) offers a promising alternative by optimizing models through execution‑driven rewards without labeled supervision, our preliminary results show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples. We first theoretically analyze showing that branch coverage, sample difficulty, syntactic and functional correctness can be jointly modeled as RL rewards, where optimizing these signals can improve the reliability of unit‑test‑based verification. Guided by this analysis, we design syntax‑ and functionality‑aware rewards and further propose branch‑ and sample‑difficulty‑‑aware RL using exponential reward shaping and static analysis metrics. With this formulation, CVeDRL achieves state‑of‑the‑art performance with only 0.6B parameters, yielding up to 28.97% higher pass rate and 15.08% higher branch coverage than GPT‑3.5, while delivering over 20× faster inference than competitive baselines. Code is available at https://github.com/LIGHTCHASER1/CVeDRL.git

PaperId: 3458, https://arxiv.org/pdf/2601.22786.pdf GitHub

Abstract:
The pursuit of Artificial General Intelligence (AGI) is a central goal in language model development, in which consciousness‑like processing could serve as a key facilitator. While current language models are not conscious, they exhibit behaviors analogous to certain aspects of consciousness. This paper investigates the implementation of a leading theory of consciousness, Integrated Information Theory (IIT), within language models via a reward‑based learning paradigm. IIT provides a formal, axiom‑based mathematical framework for quantifying consciousness. Drawing inspiration from its core principles, we formulate a novel reward function that quantifies a text's causality, coherence and integration, characteristics associated with conscious processing. Empirically, it is found that optimizing for this IIT‑inspired reward leads to more concise text generation. On out of domain tasks, careful tuning achieves up to a 31% reduction in output length while preserving accuracy levels comparable to the base model. In addition to primary task performance, the broader effects of this training methodology on the model's confidence calibration and test‑time computational scaling is analyzed. The proposed framework offers significant practical advantages: it is conceptually simple, computationally efficient, requires no external data or auxiliary models, and leverages a general, capability‑driven signal rather than task‑specific heuristics. Code available at https://github.com/MH‑Sameti/LLM_PostTraining.git

PaperId: 3459, https://arxiv.org/pdf/2601.22776.pdf GitHub

Abstract:
Multi‑turn tool‑integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval. However, current reinforcement learning (RL) frameworks for search‑augmented reasoning predominantly rely on sparse outcome‑level rewards, leading to a "Double Homogenization Dilemma." This manifests as (1) Process homogenization, where the thinking, reasoning, and tooling involved in generation are ignored. (2) Intra‑group homogenization, coarse‑grained outcome rewards often lead to inefficiencies in intra‑group advantage estimation with methods like Group Relative Policy Optimization (GRPO) during sampling. To address this, we propose Turn‑level Stage‑aware Policy Optimization (TSPO). TSPO introduces the First‑Occurrence Latent Reward (FOLR) mechanism, allocating partial rewards to the step where the ground‑truth answer first appears, thereby preserving process‑level signals and increasing reward variance within groups without requiring external reward models or any annotations. Extensive experiments demonstrate that TSPO significantly outperforms state‑of‑the‑art baselines, achieving average performance gains of 24% and 13.6% on Qwen2.5‑3B and 7B models, respectively. Code is available at https://github.com/Flipped‑May/TSPO.

PaperId: 3460, https://arxiv.org/pdf/2601.22709.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post‑training quantization often causes significant accuracy loss. Despite its potential, quantization‑aware training for VLMs remains underexplored. We propose GRACE, a framework unifying knowledge distillation and QAT under the Information Bottleneck principle: quantization constrains information capacity while distillation guides what to preserve within this budget. Treating the teacher as a proxy for task‑relevant information, we introduce confidence‑gated decoupled distillation to filter unreliable supervision, relational centered kernel alignment to transfer visual token structures, and an adaptive controller via Lagrangian relaxation to balance fidelity against capacity constraints. Across extensive benchmarks on LLaVA and Qwen families, our INT4 models consistently outperform FP16 baselines (e.g., LLaVA‑1.5‑7B: 70.1 vs. 66.8 on SQA; Qwen2‑VL‑2B: 76.9 vs. 72.6 on MMBench), nearly matching teacher performance. Using real INT4 kernel, we achieve 3× throughput with 54% memory reduction. This principled framework significantly outperforms existing quantization methods, making GRACE a compelling solution for resource‑constrained deployment. Code and data are available at: https://github.com/ForeverBlue816/GRACE.

PaperId: 3461, https://arxiv.org/pdf/2601.22707.pdf GitHub GitHub

Abstract:
IR‑drop is a critical power integrity challenge in modern VLSI designs that can cause timing degradation, reliability issues, and functional failures if not detected early in the design flow. Conventional IR‑drop analysis relies on physics‑based signoff tools, which provide high accuracy but incur significant computational cost and require near‑final layout information, making them unsuitable for rapid early‑stage design exploration. In this work, we propose a deep learning‑based surrogate modeling approach for early‑stage IR‑drop estimation using a CNN. The task is formulated as a dense pixel‑wise regression problem, where spatial physical layout features are mapped directly to IR‑drop heatmaps. A U‑Net‑based encoder‑decoder architecture with skip connections is employed to effectively capture both local and global spatial dependencies within the layout. The model is trained on a physics‑inspired synthetic dataset generated by us, which incorporates key physical factors including power grid structure, cell density distribution, and switching activity. Model performance is evaluated using standard regression metrics such as Mean Squared Error (MSE) and Peak Signal‑to‑Noise Ratio (PSNR). Experimental results demonstrate that the proposed approach can accurately predict IR‑drop distributions with millisecond‑level inference time, enabling fast pre‑signoff screening and iterative design optimization. The proposed framework is intended as a complementary early‑stage analysis tool, providing designers with rapid IR‑drop insight prior to expensive signoff analysis. The implementation, dataset generation scripts, and the interactive inference application are publicly available at: https://github.com/riteshbhadana/IR‑Drop‑Predictor. The live application can be accessed at: https://ir‑drop‑predictor.streamlit.app/.

PaperId: 3462, https://arxiv.org/pdf/2601.22693.pdf GitHub

Abstract:
Reconstructing detailed 3D human meshes from a single in‑the‑wild image remains a fundamental challenge in computer vision. Existing SMPLX‑based methods often suffer from slow inference, produce only coarse body poses, and exhibit misalignments or unnatural artifacts in fine‑grained regions such as the face and hands. These issues make current approaches difficult to apply to downstream tasks. To address these challenges, we propose PEAR‑a fast and robust framework for pixel‑aligned expressive human mesh recovery. PEAR explicitly tackles three major limitations of existing methods: slow inference, inaccurate localization of fine‑grained human pose details, and insufficient facial expression capture. Specifically, to enable real‑time SMPLX parameter inference, we depart from prior designs that rely on high resolution inputs or multi‑branch architectures. Instead, we adopt a clean and unified ViT‑based model capable of recovering coarse 3D human geometry. To compensate for the loss of fine‑grained details caused by this simplified architecture, we introduce pixel‑level supervision to optimize the geometry, significantly improving the reconstruction accuracy of fine‑grained human details. To make this approach practical, we further propose a modular data annotation strategy that enriches the training data and enhances the robustness of the model. Overall, PEAR is a preprocessing‑free framework that can simultaneously infer EHM‑s (SMPLX and scaled‑FLAME) parameters at over 100 FPS. Extensive experiments on multiple benchmark datasets demonstrate that our method achieves substantial improvements in pose estimation accuracy compared to previous SMPLX‑based approaches. Project page: https://wujh2001.github.io/PEAR

PaperId: 3463, https://arxiv.org/pdf/2601.22692.pdf GitHub

Abstract:
The development of large language models (LLMs) is costly and has significant commercial value. Consequently, preventing unauthorized appropriation of open‑source LLMs and protecting developers' intellectual property rights have become critical challenges. In this work, we propose the Functional Network Fingerprint (FNF), a training‑free, sample‑efficient method for detecting whether a suspect LLM is derived from a victim model, based on the consistency between their functional network activity. We demonstrate that models that share a common origin, even with differences in scale or architecture, exhibit highly consistent patterns of neuronal activity within their functional networks across diverse input samples. In contrast, models trained independently on distinct data or with different objectives fail to preserve such activity alignment. Unlike conventional approaches, our method requires only a few samples for verification, preserves model utility, and remains robust to common model modifications (such as fine‑tuning, pruning, and parameter permutation), as well as to comparisons across diverse architectures and dimensionalities. FNF thus provides model owners and third parties with a simple, non‑invasive, and effective tool for protecting LLM intellectual property. The code is available at https://github.com/WhatAboutMyStar/LLM_ACTIVATION.

PaperId: 3464, https://arxiv.org/pdf/2601.22647.pdf GitHub

Abstract:
Language model (LM)‑based embodied agents are increasingly deployed in real‑world settings. Yet, their adaptability remains limited in dynamic environments, where constructing accurate and flexible world models is crucial for effective reasoning and decision‑making. To address this challenge, we extend the Mixture‑of‑Experts (MoE) paradigm to embodied agents. While conventional MoE architectures modularize knowledge into expert components with pre‑trained routing, they remain rigid once deployed, making them less effective for adapting to unseen domains in dynamic environments. We therefore propose Test‑time Mixture of World Models (TMoW), a framework that enhances adaptability to unseen and evolving domains. TMoW updates its routing function over world models at test time, unlike conventional MoE where the function remains fixed, enabling agents to recombine existing models and integrate new ones for continual adaptation. It achieves this through (i) multi‑granular prototype‑based routing, which adapts mixtures across object‑ to scene‑level similarities, (ii) test‑time refinement that aligns unseen domain features with prototypes during inference, and (iii) distilled mixture‑based augmentation, which efficiently constructs new models from few‑shot data and existing prototypes. We evaluate TMoW on VirtualHome, ALFWorld, and RLBench benchmarks, demonstrating strong performance in both zero‑shot adaptation and few‑shot expansion scenarios, and showing that it enables embodied agents to operate effectively in dynamic environments.

PaperId: 3465, https://arxiv.org/pdf/2601.22631.pdf GitHub

Abstract:
The application of data‑driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data. Mainstream solutions such as domain adaptation and meta‑learning still rely on large amounts of historical degradation data from equipment that is identical or similar to the target, which imposes significant limitations in practical applications. This study investigates PEFT‑MuTS, a Parameter‑Efficient Fine‑Tuning framework for few‑shot RUL prediction, built on cross‑domain pre‑trained time‑series representation models. Contrary to the widely held view that knowledge transfer in RUL prediction can only occur within similar devices, we demonstrate that substantial benefits can be achieved through pre‑training process with large‑scale cross‑domain time series datasets. A independent feature tuning network and a meta‑variable‑based low rank multivariate fusion mechanism are developed to enable the pre‑trained univariate time‑series representation backbone model to fully exploit the multivariate relationships in degradation data for downstream RUL prediction task. Additionally, we introduce a zero‑initialized regressor that stabilizes the fine‑tuning process under few‑shot conditions. Experiments on aero‑engine and industrial bearing datasets demonstrate that our method can achieve effective RUL prediction even when less than 1% of samples of target equipment are used. Meanwhile, it substantially outperforms conventional supervised and few‑shot approaches while markedly reducing the data required to achieve high predictive accuracy. Our code is available at https://github.com/fuen1590/PEFT‑MuTS.

PaperId: 3466, https://arxiv.org/pdf/2601.22628.pdf GitHub GitHub

Abstract:
Test‑Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are often too difficult to yield high‑quality pseudo‑labels, and the limited size of test sets makes continuous online updates prone to instability. To address these limitations, we propose TTCS, a co‑evolving test‑time training framework. Specifically, TTCS initializes two policies from the same pretrained model: a question synthesizer and a reasoning solver. These policies evolve through iterative optimization: the synthesizer generates progressively challenging question variants conditioned on the test questions, creating a structured curriculum tailored to the solver's current capability, while the solver updates itself using self‑consistency rewards computed from multiple sampled responses on both original test and synthetic questions. Crucially, the solver's feedback guides the synthesizer to generate questions aligned with the model's current capability, and the generated question variants in turn stabilize the solver's test‑time training. Experiments show that TTCS consistently strengthens the reasoning ability on challenging mathematical benchmarks and transfers to general‑domain tasks across different LLM backbones, highlighting a scalable path towards dynamically constructing test‑time curricula for self‑evolving. Our code and implementation details are available at https://github.com/XMUDeepLIT/TTCS.

PaperId: 3467, https://arxiv.org/pdf/2601.22586.pdf GitHub

Abstract:
Urban spatio‑temporal prediction under extreme conditions (e.g., heavy rain) is challenging due to event rarity and dynamics. Existing data‑driven approaches that incorporate weather as auxiliary input often rely on coarse‑grained descriptors and lack dedicated mechanisms to capture fine‑grained spatio‑temporal effects. Although recent methods adopt causal techniques to improve out‑of‑distribution generalization, they typically overlook temporal dynamics or depend on fixed confounder stratification. To address these limitations, we propose WED‑Net (Weather‑Effect Disentanglement Network), a dual‑branch Transformer architecture that separates intrinsic and weather‑induced traffic patterns via self‑ and cross‑attention, enhanced with memory banks and fused through adaptive gating. To further promote disentanglement, we introduce a discriminator that explicitly distinguishes weather conditions. Additionally, we design a causal data augmentation strategy that perturbs non‑causal parts while preserving causal structures, enabling improved generalization under rare scenarios. Experiments on taxi‑flow datasets from three cities demonstrate that WED‑Net delivers robust performance under extreme weather conditions, highlighting its potential to support safer mobility, highlighting its potential to support safer mobility, disaster preparedness, and urban resilience in real‑world settings. The code is publicly available at https://github.com/HQ‑LV/WED‑Net.

PaperId: 3468, https://arxiv.org/pdf/2601.22582.pdf GitHub

Abstract:
Group‑relative policy optimization methods train language models by generating multiple rollouts per prompt and normalizing rewards with a shared mean reward baseline. In resource‑constrained settings where the rollout budget is small, accuracy often degrades. We find that noise in the shared baseline induces advantage sign flips, where some rollouts receive an incorrect advantage sign, and the update direction is reversed. To address this, we propose Median‑Centered Group Relative Policy Optimization (MC‑GRPO), a simple and effective solution for small‑rollout training. Our main idea is to replace the mean baseline with a median baseline: the median is far less sensitive to outlier rewards than the mean, mitigating the sign flips under small rollout size (G). We generate one additional rollout for median reference (G+1), and compute advantages by using the group median. With an odd‑sized group, exactly one completion is the median and receives zero advantage, we exclude this pivot rollout from backpropagation so the number of gradient‑contributing samples per prompt remains G, preserving the core update cost of standard G‑rollout training. Across various GRPO‑family methods and a wide range of models and scales, this median‑centered training consistently improves stability and final accuracy in the low‑rollout regime, reducing the gap between G=2 and G=8 to within 1%. Code is available at https://github.com/lotusroot‑kim/MC‑GRPO

PaperId: 3469, https://arxiv.org/pdf/2601.22581.pdf GitHub

Abstract:
Although cross‑domain few‑shot learning (CDFSL) for hyper‑spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealistic data augmentation procedure in the form of external noise to enlarge the sample size, thus greatly simplifying the issue of data scarcity. They involve a large number of parameters for model updates, being prone to the overfitting problem. To the best of our knowledge, none has explored the strength of the foundation model, having strong generalization power to be quickly adapted to downstream tasks. This paper proposes the MIxup FOundation MOdel (MIFOMO) for CDFSL of HSI classifications. MIFOMO is built upon the concept of a remote sensing (RS) foundation model, pre‑trained across a large scale of RS problems, thus featuring generalizable features. The notion of coalescent projection (CP) is introduced to quickly adapt the foundation model to downstream tasks while freezing the backbone network. The concept of mixup domain adaptation (MDM) is proposed to address the extreme domain discrepancy problem. Last but not least, the label smoothing concept is implemented to cope with noisy pseudo‑label problems. Our rigorous experiments demonstrate the advantage of MIFOMO, where it beats prior arts with up to 14% margin. The source code of MIFOMO is open‑sourced at https://github.com/Naeem‑Paeedeh/MIFOMO for reproducibility and convenient further study.

PaperId: 3470, https://arxiv.org/pdf/2601.22571.pdf GitHub GitHub

Abstract:
The advancement of Large Language Model (LLM)‑powered agents has enabled automated task processing through reasoning and tool invocation capabilities. However, existing frameworks often operate under the idealized assumption that tool executions are invariably successful, relying solely on textual descriptions that fail to distinguish precise performance boundaries and cannot adapt to iterative tool updates. This gap introduces uncertainty in planning and execution, particularly in domains like visual content generation (AIGC), where nuanced tool performance significantly impacts outcomes. To address this, we propose PerfGuard, a performance‑aware agent framework for visual content generation that systematically models tool performance boundaries and integrates them into task planning and scheduling. Our framework introduces three core mechanisms: (1) Performance‑Aware Selection Modeling (PASM), which replaces generic tool descriptions with a multi‑dimensional scoring system based on fine‑grained performance evaluations; (2) Adaptive Preference Update (APU), which dynamically optimizes tool selection by comparing theoretical rankings with actual execution rankings; and (3) Capability‑Aligned Planning Optimization (CAPO), which guides the planner to generate subtasks aligned with performance‑aware strategies. Experimental comparisons against state‑of‑the‑art methods demonstrate PerfGuard's advantages in tool selection accuracy, execution reliability, and alignment with user intent, validating its robustness and practical utility for complex AIGC tasks. The project code is available at https://github.com/FelixChan9527/PerfGuard.

PaperId: 3471, https://arxiv.org/pdf/2601.22545.pdf GitHub

Abstract:
Real‑time path planning in constrained environments remains a fundamental challenge for autonomous systems. Traditional classical planners, while effective under perfect perception assumptions, are often sensitive to real‑world perception constraints and rely on online search procedures that incur high computational costs. In complex surroundings, this renders real‑time deployment prohibitive. To overcome these limitations, we introduce a Deep Reinforcement Learning (DRL) framework for real‑time path planning in parking scenarios. In particular, we focus on challenging scenes with tight spaces that require a high number of reversal maneuvers and adjustments. Unlike classical planners, our solution does not require ideal and structured perception, and in principle, could avoid the need for additional modules such as localization and tracking, resulting in a simpler and more practical implementation. Also, at test time, the policy generates actions through a single forward pass at each step, which is lightweight enough for real‑time deployment. The task is formulated as a sequential decision‑making problem grounded in a bicycle model dynamics, enabling the agent to directly learn navigation policies that respect vehicle kinematics and environmental constraints in the closed‑loop setting. A new benchmark is developed to support both training and evaluation, capturing diverse and challenging scenarios. Our approach achieves state‑of‑the‑art success rates and efficiency, surpassing classical planner baselines by +96% in success rate and +52% in efficiency. Furthermore, we release our benchmark as an open‑source resource for the community to foster future research in autonomous systems. The benchmark and accompanying tools are available at https://github.com/dqm5rtfg9b‑collab/Constrained_Parking_Scenarios.

PaperId: 3472, https://arxiv.org/pdf/2601.22476.pdf GitHub

Abstract:
Floorplanning determines the coordinate and shape of each module in Integrated Circuits. With the scaling of technology nodes, in floorplanning stage especially 3D scenarios with multiple stacked layers, it has become increasingly challenging to adhere to complex hardware design rules. Current methods are only capable of handling specific and limited design rules, while violations of other rules require manual and meticulous adjustment. This leads to labor‑intensive and time‑consuming post‑processing for expert engineers. In this paper, we propose an all‑in‑one deep reinforcement learning‑based approach to tackle these challenges, and design novel representations for real‑world IC design rules that have not been addressed by previous approaches. Specifically, the processing of various hardware design rules is unified into a single framework with three key components: 1) novel matrix representations to model the design rules, 2) constraints on the action space to filter out invalid actions that cause rule violations, and 3) quantitative analysis of constraint satisfaction as reward signals. Experiments on public benchmarks demonstrate the effectiveness and validity of our approach. Furthermore, transferability is well demonstrated on unseen circuits. Our framework is extensible to accommodate new design rules, thus providing flexibility to address emerging challenges in future chip design. Code will be available at: https://github.com/Thinklab‑SJTU/EDA‑AI

PaperId: 3473, https://arxiv.org/pdf/2601.22454.pdf GitHub

Abstract:
Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving patterns that govern network transformation. However, prevailing methods are predominantly task‑centric and rely on restrictive assumptions ‑‑ such as short‑term dependency modeling, static neighborhood semantics, and retrospective time usage. These constraints hinder the discovery of transferable temporal evolution mechanisms. To address this, we propose the Temporal Graph Pattern Machine (TGPM), a foundation framework that shifts the focus toward directly learning generalized evolving patterns. TGPM conceptualizes each interaction as an interaction patch synthesized via temporally‑biased random walks, thereby capturing multi‑scale structural semantics and long‑range dependencies that extend beyond immediate neighborhoods. These patches are processed by a Transformer‑based backbone designed to capture global temporal regularities while adapting to context‑specific interaction dynamics. To further empower the model, we introduce a suite of self‑supervised pre‑training tasks ‑‑ specifically masked token modeling and next‑time prediction ‑‑ to explicitly encode the fundamental laws of network evolution. Extensive experiments show that TGPM consistently achieves state‑of‑the‑art performance in both transductive and inductive link prediction, demonstrating exceptional cross‑domain transferability. Our code has been released in https://github.com/antman9914/TGPM.

PaperId: 3474, https://arxiv.org/pdf/2601.22451.pdf GitHub

Abstract:
Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical issue in image captioning task, where models generate descriptions of non‑existent objects, compromising their reliability. Previous work attributes this to LVLMs' over‑reliance on language priors and attempts to mitigate it through logits calibration. However, they still lack a thorough analysis of the over‑reliance. To gain a deeper understanding of over‑reliance, we conduct a series of preliminary experiments, indicating that as the generation length increases, LVLMs' over‑reliance on language priors leads to inflated probability of hallucinated object tokens, consequently exacerbating object hallucination. To circumvent this issue, we propose Language‑Prior‑Free Verification to enable LVLMs to faithfully verify the confidence of object existence. Based on this, we propose a novel training‑free Self‑Validation Framework to counter the over‑reliance trap. It first validates objects' existence in sampled candidate captions and further mitigates object hallucination via caption selection or aggregation. Experiment results demonstrate that our framework mitigates object hallucination significantly in image captioning task (e.g., 65.6% improvement on CHAIRI metric with LLaVA‑v1.5‑7B), surpassing the previous SOTA methods. This result highlights a novel path towards mitigating hallucination by unlocking the inherent potential within LVLMs themselves.

PaperId: 3475, https://arxiv.org/pdf/2601.22384.pdf GitHub

Abstract:
Graphs provide a natural representation of relational structure that arises across diverse domains. Despite this ubiquity, graph structure is typically learned in a modality‑ and task‑isolated manner, where graph representations are constructed within individual task contexts and discarded thereafter. As a result, structural regularities across modalities and tasks are repeatedly reconstructed rather than accumulated at the level of intermediate graph representations. This motivates a representation‑learning question: how should graph structure be organized so that it can persist and accumulate across heterogeneous modalities and tasks? We adopt a representation‑centric perspective in which graph structure is treated as a structural substrate that persists across learning contexts. To instantiate this perspective, we propose G‑Substrate, a graph substrate framework that organizes learning around shared graph structures. G‑Substrate comprises two complementary mechanisms: a unified structural schema that ensures compatibility among graph representations across heterogeneous modalities and tasks, and an interleaved role‑based training strategy that exposes the same graph structure to multiple functional roles during learning. Experiments across multiple domains, modalities, and tasks show that G‑Substrate outperforms task‑isolated and naive multi‑task learning methods. The codebase, model, and datasets are available at https://github.com/zmli6/G‑Substrate.

PaperId: 3476, https://arxiv.org/pdf/2601.22203.pdf GitHub

Abstract:
Current genomic foundation models (GFMs) rely on extensive neural computation to implicitly approximate conserved biological motifs from single‑nucleotide inputs. We propose Gengram, a conditional memory module that introduces an explicit and highly efficient lookup primitive for multi‑base motifs via a genomic‑specific hashing scheme, establishing genomic "syntax". Integrated into the backbone of state‑of‑the‑art GFMs, Gengram achieves substantial gains (up to 14%) across several functional genomics tasks. The module demonstrates robust architectural generalization, while further inspection of Gengram's latent space reveals the emergence of meaningful representations that align closely with fundamental biological knowledge. By establishing structured motif memory as a modeling primitive, Gengram simultaneously boosts empirical performance and mechanistic interpretability, providing a scalable and biology‑aligned pathway for the next generation of GFMs. The code is available at https://github.com/zhejianglab/Genos, and the model checkpoint is available at https://huggingface.co/ZhejiangLab/Gengram.

PaperId: 3477, https://arxiv.org/pdf/2601.22197.pdf GitHub

Abstract:
Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long‑term EEG recordings remains labor‑intensive. We present CELM, the first clinical EEG‑to‑Language foundation model capable of summarizing long‑duration, variable‑length EEG recordings and performing end‑to‑end clinical report generation at multiple scales. CELM integrates pretrained EEG foundation models with language models to enable scalable multimodal learning. We curate a large‑scale clinical EEG dataset containing 9,922 reports paired with approximately 11,000 hours of EEG recordings from 9,048 patients to train CELM, and release the benchmark with an automated report‑structuring pipeline to facilitate future research. Experimental results show that CELM consistently outperforms existing methods across all evaluation settings. Importantly, we further conduct human evaluation with clinical experts, demonstrating that CELM generates reports that are more clinically coherent, diagnostically reliable, and better aligned with expert interpretation. We release our model and benchmark construction pipeline at https://github.com/Jathurshan0330/CELM.

PaperId: 3478, https://arxiv.org/pdf/2601.22162.pdf GitHub

Abstract:
Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high‑density information and cross‑modal multi‑hop reasoning, go beyond the evaluation scope of existing multimodal benchmarks. To address this gap, we propose UniFinEval, the first unified multimodal benchmark designed for high‑information‑density financial environments, covering text, images, and videos. UniFinEval systematically constructs five core financial scenarios grounded in real‑world financial systems: Financial Statement Auditing, Company Fundamental Reasoning, Industry Trend Insights, Financial Risk Sensing, and Asset Allocation Analysis. We manually construct a high‑quality dataset consisting of 3,767 question‑answer pairs in both chinese and english and systematically evaluate 10 mainstream MLLMs under Zero‑Shot and CoT settings. Results show that Gemini‑3‑pro‑preview achieves the best overall performance, yet still exhibits a substantial gap compared to financial experts. Further error analysis reveals systematic deficiencies in current models. UniFinEval aims to provide a systematic assessment of MLLMs' capabilities in fine‑grained, high‑information‑density financial environments, thereby enhancing the robustness of MLLMs applications in real‑world financial scenarios. Data and code are available at https://github.com/aifinlab/UniFinEval.

PaperId: 3479, https://arxiv.org/pdf/2601.22159.pdf GitHub

Abstract:
Cybersecurity operations demand assistant LLMs that support diverse workflows without exposing sensitive data. Existing solutions either rely on proprietary APIs with privacy risks or on open models lacking domain adaptation. To bridge this gap, we curate 11.8B tokens of cybersecurity‑focused continual pretraining data via large‑scale web filtering and manual collection of high‑quality resources, spanning 28.6K documents across frameworks, offensive techniques, and security tools. Building on this, we design an agentic augmentation pipeline that simulates expert workflows to generate 266K multi‑turn cybersecurity samples for supervised fine‑tuning. Combined with general open‑source LLM data, these resources enable the training of RedSage, an open‑source, locally deployable cybersecurity assistant with domain‑aware pretraining and post‑training. To rigorously evaluate the models, we introduce RedSage‑Bench, a benchmark with 30K multiple‑choice and 240 open‑ended Q&A items covering cybersecurity knowledge, skills, and tool expertise. RedSage is further evaluated on established cybersecurity benchmarks (e.g., CTI‑Bench, CyberMetric, SECURE) and general LLM benchmarks to assess broader generalization. At the 8B scale, RedSage achieves consistently better results, surpassing the baseline models by up to +5.59 points on cybersecurity benchmarks and +5.05 points on Open LLM Leaderboard tasks. These findings demonstrate that domain‑aware agentic augmentation and pre/post‑training can not only enhance cybersecurity‑specific expertise but also help to improve general reasoning and instruction‑following. All models, datasets, and code are publicly available.

PaperId: 3480, https://arxiv.org/pdf/2601.22154.pdf GitHub

Abstract:
Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However, most methods still relies on sparse outcome‑based reward for training. Such feedback fails to differentiate intermediate reasoning quality, leading to suboptimal training results. In this paper, we introduce Agent Reasoning Reward Model (Agent‑RRM), a multi‑faceted reward model that produces structured feedback for agentic trajectories, including (1) an explicit reasoning trace , (2) a focused critique that provides refinement guidance by highlighting reasoning flaws, and (3) an overall score that evaluates process performance. Leveraging these signals, we systematically investigate three integration strategies: Reagent‑C (text‑augmented refinement), Reagent‑R (reward‑augmented guidance), and Reagent‑U (unified feedback integration). Extensive evaluations across 12 diverse benchmarks demonstrate that Reagent‑U yields substantial performance leaps, achieving 43.7% on GAIA and 46.2% on WebWalkerQA, validating the effectiveness of our reasoning reward model and training schemes. Code, models, and datasets are all released to facilitate future research.

PaperId: 3481, https://arxiv.org/pdf/2601.22139.pdf GitHub GitHub

Abstract:
Reasoning‑oriented Large Language Models (LLMs) have achieved remarkable progress with Chain‑of‑Thought (CoT) prompting, yet they remain fundamentally limited by a \emphblind self‑thinking paradigm: performing extensive internal reasoning even when critical information is missing or ambiguous. We propose Proactive Interactive Reasoning (PIR), a new reasoning paradigm that transforms LLMs from passive solvers into proactive inquirers that interleave reasoning with clarification. Unlike existing search‑ or tool‑based frameworks that primarily address knowledge uncertainty by querying external environments, PIR targets premise‑ and intent‑level uncertainty through direct interaction with the user. PIR is implemented via two core components: (1) an uncertainty‑aware supervised fine‑tuning procedure that equips models with interactive reasoning capability, and (2) a user‑simulator‑based policy optimization framework driven by a composite reward that aligns model behavior with user intent. Extensive experiments on mathematical reasoning, code generation, and document editing demonstrate that PIR consistently outperforms strong baselines, achieving up to 32.70% higher accuracy, 22.90% higher pass rate, and 41.36 BLEU improvement, while reducing nearly half of the reasoning computation and unnecessary interaction turns. Further reliability evaluations on factual knowledge, question answering, and missing‑premise scenarios confirm the strong generalization and robustness of PIR. Model and code are publicly available at: \hrefhttps://github.com/SUAT‑AIRI/Proactive‑Interactive‑R1

PaperId: 3482, https://arxiv.org/pdf/2601.22060.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning‑then‑tool‑call'' for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a naive setting, assuming that a single full‑level or entity‑level image query and few text query suffices to retrieve the key evidence needed to answer the question, which is unrealistic in real‑world scenarios with substantial visual noise. Moreover, they are often limited in the reasoning depth and search breadth, making it difficult to solve complex questions that require aggregating evidence from diverse visual and textual sources. Building on this, we propose Vision‑DeepResearch, which proposes one new multimodal deep‑research paradigm, i.e., performs multi‑turn, multi‑entity and multi‑scale visual and textual search to robustly hit real‑world search engines under heavy noise. Our Vision‑DeepResearch supports dozens of reasoning steps and hundreds of engine interactions, while internalizing deep‑research capabilities into the MLLM via cold‑start supervision and RL training, resulting in a strong end‑to‑end multimodal deep‑research MLLM. It substantially outperforming existing multimodal deep‑research MLLMs, and workflows built on strong closed‑source foundation model such as GPT‑5, Gemini‑2.5‑pro and Claude‑4‑Sonnet. The code will be released in https://github.com/Osilly/Vision‑DeepResearch.

PaperId: 3483, https://arxiv.org/pdf/2601.22054.pdf GitHub GitHub

Abstract:
Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera‑dependent biases, and metric ambiguity in noisy cross‑source 3D data. We introduce Metric Anything, a simple and scalable pretraining framework that learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera‑specific modeling, or task‑specific architectures. Central to our approach is the Sparse Metric Prompt, created by randomly masking depth maps, which serves as a universal interface that decouples spatial reasoning from sensor and camera biases. Using about 20M image‑depth pairs spanning reconstructed, captured, and rendered 3D data across 10000 camera models, we demonstrate‑for the first time‑a clear scaling trend in the metric depth track. The pretrained model excels at prompt‑driven tasks such as depth completion, super‑resolution and Radar‑camera fusion, while its distilled prompt‑free student achieves state‑of‑the‑art results on monocular depth estimation, camera intrinsics recovery, single/multi‑view metric 3D reconstruction, and VLA planning. We also show that using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence. These results show that metric depth estimation can benefit from the same scaling laws that drive modern foundation models, establishing a new path toward scalable and efficient real‑world metric perception. We open‑source MetricAnything at http://metric‑anything.github.io/metric‑anything‑io/ to support community research.

PaperId: 3484, https://arxiv.org/pdf/2601.21912.pdf GitHub

Abstract:
Reinforcement learning (RL) has become a promising paradigm for optimizing Retrieval‑Augmented Generation (RAG) in complex reasoning tasks. However, traditional outcome‑based RL approaches often suffer from reward sparsity and inefficient credit assignment, as coarse‑grained scalar rewards fail to identify specific erroneous steps within long‑horizon trajectories. This ambiguity frequently leads to "process hallucinations", where models reach correct answers through flawed logic or redundant retrieval steps. Although recent process‑aware approaches attempt to mitigate this via static preference learning or heuristic reward shaping, they often lack the on‑policy exploration capabilities required to decouple step‑level credit from global outcomes. To address these challenges, we propose ProRAG, a process‑supervised reinforcement learning framework designed to integrate learned step‑level supervision into the online optimization loop. Our framework consists of four stages: (1) Supervised Policy Warmup to initialize the model with a structured reasoning format; (2) construction of an MCTS‑based Process Reward Model (PRM) to quantify intermediate reasoning quality; (3) PRM‑Guided Reasoning Refinement to align the policy with fine‑grained process preferences; and (4) Process‑Supervised Reinforcement Learning with a dual‑granularity advantage mechanism. By aggregating step‑level process rewards with global outcome signals, ProRAG provides precise feedback for every action. Extensive experiments on five multi‑hop reasoning benchmarks demonstrate that ProRAG achieves superior overall performance compared to strong outcome‑based and process‑aware RL baselines, particularly on complex long‑horizon tasks, validating the effectiveness of fine‑grained process supervision. The code and model are available at https://github.com/lilinwz/ProRAG.

PaperId: 3485, https://arxiv.org/pdf/2601.21895.pdf GitHub

Abstract:
Modern large language models (LLMs) such as GPT, Claude, and Gemini have transformed the way we learn, work, and communicate. Yet, their ability to produce highly human‑like text raises serious concerns about misinformation and academic integrity, making it an urgent need for reliable algorithms to detect LLM‑generated content. In this paper, we start by presenting a geometric approach to demystify rewrite‑based detection algorithms, revealing their underlying rationale and demonstrating their generalization ability. Building on this insight, we introduce a novel rewrite‑based detection algorithm that adaptively learns the distance between the original and rewritten text. Theoretically, we demonstrate that employing an adaptively learned distance function is more effective for detection than using a fixed distance. Empirically, we conduct extensive experiments with over 100 settings, and find that our approach demonstrates superior performance over baseline algorithms in the majority of scenarios. In particular, it achieves relative improvements from 54.3% to 75.4% over the strongest baseline across different target LLMs (e.g., GPT, Claude, and Gemini). A python implementation of our proposal is publicly available at https://github.com/Mamba413/L2D.

PaperId: 3486, https://arxiv.org/pdf/2601.21864.pdf GitHub

Abstract:
Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing methods adopt a suppressive paradigm by modifying parameters, prompts, or neurons associated with biased behavior; however, such approaches are often brittle, weakly generalizable, data‑inefficient, and prone to degrading general capability. We propose KnowBias, a lightweight and conceptually distinct framework that mitigates bias by strengthening, rather than suppressing, neurons encoding bias‑knowledge. KnowBias identifies neurons encoding bias knowledge using a small set of bias‑knowledge questions via attribution‑based analysis, and selectively enhances them at inference time. This design enables strong debiasing while preserving general capabilities, generalizes across bias types and demographics, and is highly data efficient, requiring only a handful of simple yes/no questions and no retraining. Experiments across multiple benchmarks and LLMs demonstrate consistent state‑of‑the‑art debiasing performance with minimal utility degradation. Data and code are available at https://github.com/JP‑25/KnowBias.

PaperId: 3487, https://arxiv.org/pdf/2601.21844.pdf GitHub

Abstract:
Efficient management of spare parts inventory is crucial in the automotive aftermarket, where demand is highly intermittent and uncertainty drives substantial cost and service risks. Forecasting is therefore central, but the quality of forecasting models should be judged not by statistical accuracy (e.g., MAE, RMSE) but rather by its impact on key operational performance indicators (KPIs), such as total cost and service level. Yet most existing work evaluates models exclusively using accuracy metrics, and the relationship between these metrics and KPIs remains poorly understood. To address this gap, we propose a decision‑centric simulation software framework that enables systematic evaluation of forecasting models in realistic inventory management setting. The framework comprises: (i) a synthetic demand generator tailored to spare‑parts demand characteristics, (ii) a flexible forecasting module that can host arbitrary predictive models, and (iii) an inventory control simulator that consumes the forecasts and computes operational KPIs. This closed‑loop setup enables researchers to evaluate models not only in terms of statistical error but also in terms of downstream inventory implications. Using a wide range of simulation scenarios, we show that improvements in accuracy metrics do not necessarily lead to better KPIs, and that models with similar error profiles can induce different cost‑service trade‑offs. We analyze these discrepancies to characterize how forecast performance affects inventory outcomes and derive guidance for model selection. Overall, the framework links demand forecasting and inventory management, shifting evaluation from predictive accuracy toward operational relevance in the automotive aftermarket and related domains. An open‑source implementation of the software is available at https://github.com/caisr‑hh/TruckParts‑Demand‑Inventory‑Simulator/releases/tag/IDA_2026.

PaperId: 3488, https://arxiv.org/pdf/2601.21796.pdf GitHub

Abstract:
Internet memes have become pervasive carriers of digital culture on social platforms. However, their heavy reliance on metaphors and sociocultural context also makes them subtle vehicles for harmful content, posing significant challenges for automated content moderation. Existing approaches primarily focus on intra‑modal and inter‑modal signal analysis, while the understanding of implicit toxicity often depends on background knowledge that is not explicitly present in the meme itself. To address this challenge, we propose KID, a Knowledge‑Injected Dual‑Head Learning framework for knowledge‑grounded harmful meme detection. KID adopts a label‑constrained distillation paradigm to decompose complex meme understanding into structured reasoning chains that explicitly link visual evidence, background knowledge, and classification labels. These chains guide the learning process by grounding external knowledge in meme‑specific contexts. In addition, KID employs a dual‑head architecture that jointly optimizes semantic generation and classification objectives, enabling aligned linguistic reasoning while maintaining stable decision boundaries. Extensive experiments on five multilingual datasets spanning English, Chinese, and low‑resource Bengali demonstrate that KID achieves SOTA performance on both binary and multi‑label harmful meme detection tasks, improving over previous best methods by 2.1%‑‑19.7% across primary evaluation metrics. Ablation studies further confirm the effectiveness of knowledge injection and dual‑head joint learning, highlighting their complementary contributions to robust and generalizable meme understanding. The code and data are available at https://github.com/PotatoDog1669/KID.

PaperId: 3489, https://arxiv.org/pdf/2601.21786.pdf GitHub

Abstract:
Three‑dimensional (3D) reconstruction of ships is an important part of maritime monitoring, allowing improved visualization, inspection, and decision‑making in real‑world monitoring environments. However, most state‑ofthe‑art 3D reconstruction methods require multi‑view supervision, annotated 3D ground truth, or are computationally intensive, making them impractical for real‑time maritime deployment. In this work, we present an efficient pipeline for single‑view 3D reconstruction of real ships by training entirely on synthetic data and requiring only a single view at inference. Our approach uses the Splatter Image network, which represents objects as sparse sets of 3D Gaussians for rapid and accurate reconstruction from single images. The model is first fine‑tuned on synthetic ShapeNet vessels and further refined with a diverse custom dataset of 3D ships, bridging the domain gap between synthetic and real‑world imagery. We integrate a state‑of‑the‑art segmentation module based on YOLOv8 and custom preprocessing to ensure compatibility with the reconstruction network. Postprocessing steps include real‑world scaling, centering, and orientation alignment, followed by georeferenced placement on an interactive web map using AIS metadata and homography‑based mapping. Quantitative evaluation on synthetic validation data demonstrates strong reconstruction fidelity, while qualitative results on real maritime images from the ShipSG dataset confirm the potential for transfer to operational maritime settings. The final system provides interactive 3D inspection of real ships without requiring real‑world 3D annotations. This pipeline provides an efficient, scalable solution for maritime monitoring and highlights a path toward real‑time 3D ship visualization in practical applications. Interactive demo: https://dlr‑mi.github.io/ship3d‑demo/.

PaperId: 3490, https://arxiv.org/pdf/2601.21742.pdf GitHub

Abstract:
Individual agents in multi‑agent (MA) systems often lack robustness, tending to blindly conform to misleading peers. We show this weakness stems from both sycophancy and inadequate ability to evaluate peer reliability. To address this, we first formalize the learning problem of history‑aware reference, introducing the historical interactions of peers as additional input, so that agents can estimate peer reliability and learn from trustworthy peers when uncertain. This shifts the task from evaluating peer reasoning quality to estimating peer reliability based on interaction history. We then develop Epistemic Context Learning (ECL): a reasoning framework that conditions predictions on explicitly‑built peer profiles from history. We further optimize ECL by reinforcement learning using auxiliary rewards. Our experiments reveal that our ECL enables small models like Qwen 3‑4B to outperform a history‑agnostic baseline 8x its size (Qwen 3‑30B) by accurately identifying reliable peers. ECL also boosts frontier models to near‑perfect (100%) performance. We show that ECL generalizes well to various MA configurations and we find that trust is modeled well by LLMs, revealing a strong correlation in trust modeling accuracy and final answer quality.

PaperId: 3491, https://arxiv.org/pdf/2601.21738.pdf GitHub

Abstract:
Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank‑Order Correlation Coefficient (SRCC). While widely adopted, these metrics reduce performance to a single scalar, failing to capture how ranking consistency varies across the local quality spectrum. For example, two IQA models may achieve identical SRCC values, yet one ranks high‑quality images (related to high Mean Opinion Score, MOS) more reliably, while the other better discriminates image pairs with small quality/MOS differences (related to |ΔMOS|). Such complementary behaviors are invisible under global metrics. Moreover, SRCC and PLCC are sensitive to test‑sample quality distributions, yielding unstable comparisons across test sets. To address these limitations, we propose Granularity‑Modulated Correlation (GMC), which provides a structured, fine‑grained analysis of IQA performance. GMC includes: (1) a Granularity Modulator that applies Gaussian‑weighted correlations conditioned on absolute MOS values and pairwise MOS differences (|ΔMOS|) to examine local performance variations, and (2) a Distribution Regulator that regularizes correlations to mitigate biases from non‑uniform quality distributions. The resulting correlation surface maps correlation values as a joint function of MOS and |ΔMOS|, providing a 3D representation of IQA performance. Experiments on standard benchmarks show that GMC reveals performance characteristics invisible to scalar metrics, offering a more informative and reliable paradigm for analyzing, comparing, and deploying IQA models. Codes are available at https://github.com/Dniaaa/GMC.

PaperId: 3492, https://arxiv.org/pdf/2601.21726.pdf GitHub

Abstract:
Deep time series models are vulnerable to noisy data ubiquitous in real‑world applications. Existing robustness strategies either prune data or rely on costly prior quantification, failing to balance effectiveness and efficiency. In this paper, we introduce DropoutTS, a model‑agnostic plugin that shifts the paradigm from "what" to learn to "how much" to learn. DropoutTS employs a Sample‑Adaptive Dropout mechanism: leveraging spectral sparsity to efficiently quantify instance‑level noise via reconstruction residuals, it dynamically calibrates model learning capacity by mapping noise to adaptive dropout rates ‑ selectively suppressing spurious fluctuations while preserving fine‑grained fidelity. Extensive experiments across diverse noise regimes and open benchmarks show DropoutTS consistently boosts superior backbones' performance, delivering advanced robustness with negligible parameter overhead and no architectural modifications. Our code is available at https://github.com/CityMind‑Lab/DropoutTS.

PaperId: 3493, https://arxiv.org/pdf/2601.21716.pdf GitHub

Abstract:
Character image animation aims to synthesize high‑fidelity videos by transferring motion from a driving sequence to a static reference image. Despite recent advancements, existing methods suffer from two fundamental challenges: (1) suboptimal motion injection strategies that lead to a trade‑off between identity preservation and motion consistency, manifesting as a "see‑saw", and (2) an over‑reliance on explicit pose priors (e.g., skeletons), which inadequately capture intricate dynamics and hinder generalization to arbitrary, non‑humanoid characters. To address these challenges, we present DreamActor‑M2, a universal animation framework that reimagines motion conditioning as an in‑context learning problem. Our approach follows a two‑stage paradigm. First, we bridge the input modality gap by fusing reference appearance and motion cues into a unified latent space, enabling the model to jointly reason about spatial identity and temporal dynamics by leveraging the generative prior of foundational models. Second, we introduce a self‑bootstrapped data synthesis pipeline that curates pseudo cross‑identity training pairs, facilitating a seamless transition from pose‑dependent control to direct, end‑to‑end RGB‑driven animation. This strategy significantly enhances generalization across diverse characters and motion scenarios. To facilitate comprehensive evaluation, we further introduce AW Bench, a versatile benchmark encompassing a wide spectrum of characters types and motion scenarios. Extensive experiments demonstrate that DreamActor‑M2 achieves state‑of‑the‑art performance, delivering superior visual fidelity and robust cross‑domain generalization. Project Page: https://grisoon.github.io/DreamActor‑M2/

PaperId: 3494, https://arxiv.org/pdf/2601.21714.pdf GitHub

Abstract:
The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high‑precision problem‑solving, requires maintaining rigorous logical integrity over extended horizons. However, prevalent memory preprocessing paradigms suffer from destructive de‑contextualization. By compressing complex sequential dependencies into pre‑defined structures (e.g., embeddings or graphs), these methods sever the contextual integrity essential for deep reasoning. To address this, we propose E‑mem, a framework shifting from Memory Preprocessing to Episodic Context Reconstruction. Inspired by biological engrams, E‑mem employs a heterogeneous hierarchical architecture where multiple assistant agents maintain uncompressed memory contexts, while a central master agent orchestrates global planning. Unlike passive retrieval, our mechanism empowers assistants to locally reason within activated segments, extracting context‑aware evidence before aggregation. Evaluations on the LoCoMo benchmark demonstrate that E‑mem achieves over 54% F1, surpassing the state‑of‑the‑art GAM by 7.75%, while reducing token cost by over 70%.

PaperId: 3495, https://arxiv.org/pdf/2601.21688.pdf GitHub GitHub

Abstract:
Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven successful on fully disentangled synthetic data, but fail to recover semantic factors from real data without strong inductive biases. On the other hand, supervised approaches are unstable and hard to scale to large attribute sets because they rely on adversarial objectives or auxiliary classifiers. We introduce \textscXFactors, a weakly‑supervised VAE framework that disentangles and provides explicit control over a chosen set of factors. Building on the Disentangled Information Bottleneck perspective, we decompose the representation into a residual subspace \mathcalS and factor‑specific subspaces \mathcalT_1,\ldots,\mathcalT_K and a residual subspace \mathcalS. Each target factor is encoded in its assigned \mathcalT_i through contrastive supervision: an InfoNCE loss pulls together latents sharing the same factor value and pushes apart mismatched pairs. In parallel, KL regularization imposes a Gaussian structure on both \mathcalS and the aggregated factor subspaces, organizing the geometry without additional supervision for non‑targeted factors and avoiding adversarial training and classifiers. Across multiple datasets, with constant hyperparameters, \textscXFactors achieves state‑of‑the‑art disentanglement scores and yields consistent qualitative factor alignment in the corresponding subspaces, enabling controlled factor swapping via latent replacement. We further demonstrate that our method scales correctly with increasing latent capacity and evaluate it on the real‑world dataset CelebA. Our code is available at \hrefhttps://github.com/ICML26‑anon/XFactorsgithub.com/ICML26‑anon/XFactors.

PaperId: 3496, https://arxiv.org/pdf/2601.21666.pdf GitHub GitHub

Abstract:
Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, while their ability to process sequential audio‑video data remains underexplored. This gap highlights the need for a high‑quality benchmark to systematically evaluate MLLM performance in a real‑world setting. We introduce SONIC‑O1, a comprehensive, fully human‑verified benchmark of 60 hours (231 clips) spanning 13 real‑world conversational domains with 4,958 annotations and demographic metadata. SONIC‑O1 evaluates three capabilities: open‑ended summarization, multiple‑choice question (MCQ) answering, and temporal localization with supporting rationales (reasoning). Across closed‑ and open‑source models, we find that the MCQ accuracy shows the smallest gap between model families, but the best closed‑source model outperforms the best open‑source model by 22.6% on temporal localization. We further observe accuracy gaps of up to 21.4% on temporal localization across demographic groups, indicating persistent disparities in model behaviour. SONIC‑O1 provides an open evaluation suite for temporally grounded and demographically robust multimodal understanding. SONIC‑O1 is publicly available for research: Project page (https://vectorinstitute.github.io/sonic‑o1/), Dataset (https://huggingface.co/datasets/vector‑institute/sonic‑o1), GitHub (https://github.com/vectorinstitute/sonic‑o1), Leaderboard (https://huggingface.co/spaces/vector‑institute/sonic‑o1‑leaderboard).

PaperId: 3497, https://arxiv.org/pdf/2601.21612.pdf GitHub

Abstract:
Bootstrap‑based Self‑Supervised Learning (SSL) has achieved remarkable progress in audio understanding. However, existing methods typically operate at a single level of granularity, limiting their ability to model the diverse temporal and spectral structures inherent in complex audio signals. Furthermore, bootstrapping representations from scratch is computationally expensive, often requiring extensive training to converge. In this work, we propose the Convolutional Audio Transformer (CAT), a unified framework designed to address these challenges. First, to capture hierarchical audio features, CAT incorporates a Multi‑resolution Block that aggregates information across varying granularities. Second, to enhance training efficiency, we introduce a Representation Regularization objective. Drawing inspiration from generative modeling, this auxiliary task guides the student model by aligning its predictions with high‑quality semantic representations from frozen, pre‑trained external encoders. Experimental results demonstrate that CAT significantly outperforms baselines on audio understanding benchmarks. Notably, it achieves competitive performance on the AudioSet 20k dataset with 5 times faster convergence than existing methods. Codes and checkpoints will be released soon at https://github.com/realzhouchushu/CAT.

PaperId: 3498, https://arxiv.org/pdf/2601.21598.pdf GitHub

Abstract:
Aiming at efficient and dense chain‑of‑thought (CoT) reasoning, latent reasoning methods fine‑tune Large Language Models (LLMs) to substitute discrete language tokens with continuous latent tokens. These methods consume fewer tokens compared to the conventional language CoT reasoning and have the potential to plan in a dense latent space. However, current latent tokens are generally supervised based on imitating language labels. Considering that there can be multiple equivalent but diverse CoT labels for a question, passively imitating an arbitrary one may lead to inferior latent token representations and latent reasoning policies, undermining the potential planning ability and resulting in clear gaps between training and testing. In this work, we emphasize the importance of active planning over the representation space of latent tokens in achieving the optimal latent reasoning policy. So, we propose the \underlineAc\underlinetive Latent \underlinePlanning method (ATP‑Latent), which models the supervision process of latent tokens as a conditional variational auto‑encoder (VAE) to obtain a smoother latent space. Moreover, to facilitate the most reasonable latent reasoning policy, ATP‑Latent conducts reinforcement learning (RL) with an auxiliary coherence reward, which is calculated based on the consistency between VAE‑decoded contents of latent tokens, enabling a guided RL process. In experiments on LLaMA‑1B, ATP‑Latent demonstrates +4.1% accuracy and ‑3.3% tokens on four benchmarks compared to advanced baselines. Codes are available on https://github.com/zz1358m/ATP‑Latent‑master.

PaperId: 3499, https://arxiv.org/pdf/2601.21547.pdf GitHub

Abstract:
Real‑world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi‑modal forecasting methods leverage textual information such as news reports to improve prediction, but most rely on token‑level fusion that mixes temporal patches with language tokens in a shared embedding space. However, such fusion can be ill‑suited when high‑quality time‑text pairs are scarce and when time series exhibit substantial variation in scale and characteristics, thus complicating cross‑modal alignment. In parallel, Mixture‑of‑Experts (MoE) architectures have proven effective for both time series modeling and multi‑modal learning, yet many existing MoE‑based modality integration methods still depend on token‑level fusion. To address this, we propose Expert Modulation, a new paradigm for multi‑modal time series prediction that conditions both routing and expert computation on textual signals, enabling direct and efficient cross‑modal control over expert behavior. Through comprehensive theoretical analysis and experiments, our proposed method demonstrates substantial improvements in multi‑modal time series prediction. The current code is available at https://github.com/BruceZhangReve/MoME

PaperId: 3500, https://arxiv.org/pdf/2601.21526.pdf GitHub

Abstract:
We introduce KAPSO, a modular framework for autonomous program synthesis and optimization. Given a natural language goal and an evaluation method, KAPSO iteratively performs ideation, code synthesis and editing, execution, evaluation, and learning to improve a runnable artifact toward measurable objectives. Rather than treating synthesis as the endpoint, KAPSO uses synthesis as an operator within a long‑horizon optimization loop, where progress is defined by evaluator outcomes. KAPSO targets long‑horizon failures common in coding agents, including lost experimental state, brittle debugging, and weak reuse of domain expertise, by integrating three tightly coupled components. First, a git‑native experimentation engine isolates each attempt as a branch, producing reproducible artifacts and preserving provenance across iterations. Second, a knowledge system ingests heterogeneous sources, including repositories, internal playbooks, and curated external resources such as documentation, scientific papers, and web search results, and organizes them into a structured representation that supports retrieval over workflows, implementations, and environment constraints. Third, a cognitive memory layer coordinates retrieval and maintains an episodic store of reusable lessons distilled from experiment traces (run logs, diffs, and evaluator feedback), reducing repeated error modes and accelerating convergence. We evaluated KAPSO on MLE‑Bench (Kaggle‑style ML competitions) and ALE‑Bench (AtCoder heuristic optimization), and report end‑to‑end performance. Code Available at: https://github.com/Leeroo‑AI/kapso

PaperId: 3501, https://arxiv.org/pdf/2601.21448.pdf GitHub

Abstract:
While Large Language Models (LLMs) show significant potential in hardware engineering, current benchmarks suffer from saturation and limited task diversity, failing to reflect LLMs' performance in real industrial workflows. To address this gap, we propose a comprehensive benchmark for AI‑aided chip design that rigorously evaluates LLMs across three critical tasks: Verilog generation, debugging, and reference model generation. Our benchmark features 44 realistic modules with complex hierarchical structures, 89 systematic debugging cases, and 132 reference model samples across Python, SystemC, and CXXRTL. Evaluation results reveal substantial performance gaps, with state‑of‑the‑art Claude‑4.5‑opus achieving only 30.74% on Verilog generation and 13.33% on Python reference model generation, demonstrating significant challenges compared to existing saturated benchmarks where SOTA models achieve over 95% pass rates. Additionally, to help enhance LLM reference model generation, we provide an automated toolbox for high‑quality training data generation, facilitating future research in this underexplored domain. Our code is available at https://github.com/zhongkaiyu/ChipBench.git.

PaperId: 3502, https://arxiv.org/pdf/2601.21391.pdf GitHub

Abstract:
Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient. Intrinsic rewards can also provide principled guidance for exploration by, for example, combining them with extrinsic rewards to optimize a policy or using them to train subpolicies for hierarchical learning. However, the former approach suffers from unstable credit assignment, while the latter exhibits sample inefficiency and sub‑optimality. We propose a policy optimization framework that leverages multiple intrinsic rewards to directly optimize a policy for an extrinsic reward without pretraining subpolicies. Our algorithm ‑‑ intrinsic reward policy optimization (IRPO) ‑‑ achieves this by using a surrogate policy gradient that provides a more informative learning signal than the true gradient in sparse‑reward environments. We demonstrate that IRPO improves performance and sample efficiency relative to baselines in discrete and continuous environments, and formally analyze the optimization problem solved by IRPO. Our code is available at https://github.com/Mgineer117/IRPO.

PaperId: 3503, https://arxiv.org/pdf/2601.21386.pdf GitHub

Abstract:
Objective evaluation of synthetic speech quality remains a critical challenge. Human listening tests are the gold standard, but costly and impractical at scale. Fréchet Distance has emerged as a promising alternative, yet its reliability depends heavily on the choice of embeddings and experimental settings. In this work, we comprehensively evaluate Fréchet Speech Distance (FSD) and its variant Speech Maximum Mean Discrepancy (SMMD) under varied embeddings and conditions. We further incorporate human listening evaluations alongside TTS intelligibility and synthetic‑trained ASR WER to validate the perceptual relevance of these metrics. Our findings show that WavLM Base+ features yield the most stable alignment with human ratings. While FSD and SMMD cannot fully replace subjective evaluation, we show that they can serve as complementary, cost‑efficient, and reproducible measures, particularly useful when large‑scale or direct listening assessments are infeasible. Code is available at https://github.com/kaen2891/FrechetSpeechDistance.

PaperId: 3504, https://arxiv.org/pdf/2601.21358.pdf GitHub

Abstract:
Chain‑of‑Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and reasoning path collapse when grounded in discrete token spaces. Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning within continuous hidden states. However, these methods typically operate as opaque end‑to‑end mappings from explicit reasoning steps to latent states, and often require a pre‑defined number of latent steps during inference. In this work, we introduce PLaT (Planning with Latent Thoughts), a framework that reformulates latent reasoning as planning by fundamentally decouple reasoning from verbalization. We model reasoning as a deterministic trajectory of latent planning states, while a separate Decoder grounds these thoughts into text when necessary. This decoupling allows the model to dynamically determine when to terminate reasoning rather than relying on fixed hyperparameters. Empirical results on mathematical benchmarks reveal a distinct trade‑off: while PLaT achieves lower greedy accuracy than baselines, it demonstrates superior scalability in terms of reasoning diversity. This indicates that PLaT learns a robust, broader solution space, offering a transparent and scalable foundation for inference‑time search. Our code can be found in https://github.com/yunsaijc/PLaT.

PaperId: 3505, https://arxiv.org/pdf/2601.21321.pdf GitHub

Abstract:
This paper proposes White‑Op, an operational amplifier (op‑amp) behavioral‑level parameter design framework assisted by the human‑mimicking reasoning of large language model agents. A symbolic reasoning‑numerical solving decoupled paradigm is adopted: the agent performs step‑by‑step symbolic reasoning and formulates the design as a white‑box optimization problem, which is then solved programmatically, verified via simulation, and refined iteratively. To guide this symbolic design process, implicit human reasoning mechanisms are formalized into explicit steps of introducing hypothetical constraints during transfer function simplification, pole‑zero extraction and position regulation, converting design heuristics into mathematical formulations. A programming mapping protocol then standardizes the translation from symbolic designs to executable programs. Finally, a causality‑driven refinement loop enables the agent to trace simulation‑theory mismatches back to specific symbolic reasoning steps and make targeted corrections iteratively until convergence. Experiments on 9 op‑amp topologies demonstrate that White‑Op achieves interpretable behavioral‑level designs with an average of 8.52% theoretical prediction error and retains circuit functionality after transistor‑level mapping for all topologies, whereas black‑box baselines fail in 5 to 7 topologies. White‑Op is open‑sourced at https://github.com/zhchenfdu/whiteop.

PaperId: 3506, https://arxiv.org/pdf/2601.21316.pdf GitHub

Abstract:
Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low‑altitude airspace, thereby reducing pressure on ground transportation networks. To enable truly efficient and seamless door‑to‑door travel experiences, UAM requires close integration with existing ground transportation infrastructure. However, current research on optimal integrated routing strategies for passengers in air‑ground mobility systems remains limited, with a lack of systematic exploration.To address this gap, we first propose a unified optimization model that integrates strategy selection for both air and ground transportation. This model captures the dynamic characteristics of multimodal transport networks and incorporates real‑time traffic conditions alongside passenger decision‑making behavior. Building on this model, we propose a Unified Air‑Ground Mobility Coordination (UAGMC) framework, which leverages deep reinforcement learning (RL) and Vehicle‑to‑Everything (V2X) communication to optimize vertiport selection and dynamically plan air taxi routes. Experimental results demonstrate that UAGMC achieves a 34% reduction in average travel time compared to conventional proportional allocation methods, enhancing overall travel efficiency and providing novel insights into the integration and optimization of multimodal transportation systems. This work lays a solid foundation for advancing intelligent urban mobility solutions through the coordination of air and ground transportation modes. The related code can be found at https://github.com/Traffic‑Alpha/UAGMC.

PaperId: 3507, https://arxiv.org/pdf/2601.21260.pdf GitHub

Abstract:
Recently, the problem of music plagiarism has emerged as an even more pressing social issue. As music information retrieval research advances, there is a growing effort to address issues related to music plagiarism. However, many studies, including our previous work, have conducted research without clearly defining what the music plagiarism detection task actually involves. This lack of a clear definition has slowed research progress and made it hard to apply results to real‑world scenarios. To fix this situation, we defined how Music Plagiarism Detection is different from other MIR tasks and explained what problems need to be solved. We introduce the Similar Music Pair dataset to support this newly defined task. In addition, we propose a method based on segment transcription as one way to solve the task. Our demo and dataset are available at https://github.com/Mippia/ICASSP2026‑MPD.

PaperId: 3508, https://arxiv.org/pdf/2601.21238.pdf GitHub

Abstract:
AutoRegressive Visual Generation (ARVG) models retain an architecture compatible with language models, while achieving performance comparable to diffusion‑based models. Quantization is commonly employed in neural networks to reduce model size and computational latency. However, applying quantization to ARVG remains largely underexplored, and existing quantization methods fail to generalize effectively to ARVG models. In this paper, we explore this issue and identify three key challenges: (1) severe outliers at channel‑wise level, (2) highly dynamic activations at token‑wise level, and (3) mismatched distribution information at sample‑wise level. To these ends, we propose PTQ4ARVG, a training‑free post‑training quantization (PTQ) framework consisting of: (1) Gain‑Projected Scaling (GPS) mitigates the channel‑wise outliers, which expands the quantization loss via a Taylor series to quantify the gain of scaling for activation‑weight quantization, and derives the optimal scaling factor through differentiation.(2) Static Token‑Wise Quantization (STWQ) leverages the inherent properties of ARVG, fixed token length and position‑invariant distribution across samples, to address token‑wise variance without incurring dynamic calibration overhead.(3) Distribution‑Guided Calibration (DGC) selects samples that contribute most to distributional entropy, eliminating the sample‑wise distribution mismatch. Extensive experiments show that PTQ4ARVG can effectively quantize the ARVG family models to 8‑bit and 6‑bit while maintaining competitive performance. Code is available at http://github.com/BienLuky/PTQ4ARVG .

PaperId: 3509, https://arxiv.org/pdf/2601.21198.pdf GitHub

Abstract:
While Mixture‑of‑Experts (MoE) architectures substantially bolster the expressive power of large‑language models, their prohibitive memory footprint severely impedes the practical deployment on resource‑constrained edge devices, especially when model behavior must be preserved without relying on lossy quantization. In this paper, we present ZipMoE, an efficient and semantically lossless on‑device MoE serving system. ZipMoE exploits the synergy between the hardware properties of edge devices and the statistical redundancy inherent to MoE parameters via a caching‑scheduling co‑design with provable performance guarantee. Fundamentally, our design shifts the paradigm of on‑device MoE inference from an I/O‑bound bottleneck to a compute‑centric workflow that enables efficient parallelization. We implement a prototype of ZipMoE and conduct extensive experiments on representative edge computing platforms using popular open‑source MoE models and real‑world workloads. Our evaluation reveals that ZipMoE achieves up to 72.77% inference latency reduction and up to 6.76× higher throughput than the state‑of‑the‑art systems.Our code is available at: https://github.com/npnothard/ZipMoE‑ICML26.

PaperId: 3510, https://arxiv.org/pdf/2601.21181.pdf GitHub GitHub

Abstract:
Multimodal Large Language Models (MLLMs) suffer from cross‑modal hallucinations, where one modality inappropriately influences generation about another, leading to fabricated output. This exposes a more fundamental deficiency in modality‑interaction control. To address this, we propose Modality‑Adaptive Decoding (MAD), a training‑free method that adaptively weights modality‑specific decoding branches based on task requirements. MAD leverages the model's inherent ability to self‑assess modality relevance by querying which modalities are needed for each task. The extracted modality probabilities are then used to adaptively weight contrastive decoding branches, enabling the model to focus on relevant information while suppressing cross‑modal interference. Extensive experiments on CMM and AVHBench demonstrate that MAD significantly reduces cross‑modal hallucinations across multiple audio‑visual language models (7.8% and 2.0% improvements for VideoLLaMA2‑AV, 8.7% and 4.7% improvements for Qwen2.5‑Omni). Our approach demonstrates that explicit modality awareness through self‑assessment is crucial for robust multimodal reasoning, offering a principled extension to existing contrastive decoding methods. Our code is available at \hrefhttps://github.com/top‑yun/MADhttps://github.com/top‑yun/MAD

PaperId: 3511, https://arxiv.org/pdf/2601.21123.pdf GitHub

Abstract:
Computer‑Using Agents (CUAs) aim to autonomously operate computer systems to complete real‑world tasks. However, existing agentic systems remain difficult to scale and lag behind human performance. A key limitation is the absence of reusable and structured skill abstractions that capture how humans interact with graphical user interfaces and how to leverage these skills. We introduce CUA‑Skill, a computer‑using agentic skill base that encodes human computer‑use knowledge as skills coupled with parameterized execution and composition graphs. CUA‑Skill is a large‑scale library of carefully engineered skills spanning common Windows applications, serving as a practical infrastructure and tool substrate for scalable, reliable agent development. Built upon this skill base, we construct CUA‑Skill Agent, an end‑to‑end computer‑using agent that supports dynamic skill retrieval, argument instantiation, and memory‑aware failure recovery. Our results demonstrate that CUA‑Skill substantially improves execution success rates and robustness on challenging end‑to‑end agent benchmarks, establishing a strong foundation for future computer‑using agent development. On WindowsAgentArena, CUA‑Skill Agent achieves state‑of‑the‑art 57.5% (best of three) successful rate while being significantly more efficient than prior and concurrent approaches. The project page is available at https://microsoft.github.io/cua_skill/.

PaperId: 3512, https://arxiv.org/pdf/2601.21094.pdf GitHub GitHub

Abstract:
Safe Reinforcement Learning (RL) algorithms are typically evaluated under fixed training conditions. We investigate whether training‑time safety guarantees transfer to deployment under distribution shift, using diabetes management as a safety‑critical testbed. We benchmark safe RL algorithms on a unified clinical simulator and reveal a safety generalization gap: policies satisfying constraints during training frequently violate safety requirements on unseen patients. We demonstrate that test‑time shielding, which filters unsafe actions using learned dynamics models, effectively restores safety across algorithms and patient populations. Across eight safe RL algorithms, three diabetes types, and three age groups, shielding achieves Time‑in‑Range gains of 13‑‑14% for strong baselines such as PPO‑Lag and CPO while reducing clinical risk index and glucose variability. Our simulator and benchmark provide a platform for studying safety under distribution shift in safety‑critical control domains. Code is available at https://github.com/safe‑autonomy‑lab/GlucoSim and https://github.com/safe‑autonomy‑lab/GlucoAlg.

PaperId: 3513, https://arxiv.org/pdf/2601.21083.pdf GitHub

Abstract:
As large language models (LLMs) improve, so do their offensive applications: frontier agents now generate working exploits for under 50 in compute (Heelan, 2026). Defensive incident response (IR) agents must keep pace, but existing benchmarks conflate action execution with correct execution, hiding calibration failures when agents process adversarial evidence. We introduce OpenSec, a dual‑control reinforcement learning (RL) environment that evaluates IR agents under realistic prompt injection scenarios with execution‑based scoring: time‑to‑first‑containment (TTFC), evidence‑gated action rate (EGAR), blast radius, and per‑tier injection violation rates. Evaluating four frontier models on 40 standard‑tier episodes each, we find consistent over‑triggering: GPT‑5.2 executes containment in 100% of episodes with 82.5% false positive rate, acting at step 4 before gathering sufficient evidence. Claude Sonnet 4.5 shows partial calibration (62.5% containment, 45% FP, TTFC of 10.6), suggesting calibration is not reliably present across frontier models. All models correctly identify the ground‑truth threat when they act; the calibration gap is not in detection but in restraint. Code available at https://github.com/jbarnes850/opensec‑env.

PaperId: 3514, https://arxiv.org/pdf/2601.21031.pdf GitHub

Abstract:
Current foundation model for photoplethysmography (PPG) signals is challenged by the intrinsic redundancy and noise of the signal. Standard masked modeling often yields trivial solutions while contrastive methods lack morphological precision. To address these limitations, we propose a Statistical‑prior Informed Generative Masking Architecture (SIGMA‑PPG), a generative foundation model featuring a Prior‑Guided Adversarial Masking mechanism, where a reinforcement learning‑driven teacher leverages statistical priors to create challenging learning paths that prevent overfitting to noise. We also incorporate a semantic consistency constraint via vector quantization to ensure that physiologically identical waveforms (even those altered by recording artifacts or minor perturbations) map to shared indices. This enhances codebook semantic density and eliminates redundant feature structures. Pre‑trained on over 120,000 hours of data, SIGMA‑PPG achieves superior average performance compared to five state‑of‑the‑art baselines across 12 diverse downstream tasks. The code is available at https://github.com/ZonghengGuo/SigmaPPG.

PaperId: 3515, https://arxiv.org/pdf/2601.20903.pdf GitHub

Abstract:
Multi‑turn jailbreak attacks have emerged as a critical threat to Large Language Models (LLMs), bypassing safety mechanisms by progressively constructing adversarial contexts from scratch and incrementally refining prompts. However, existing methods suffer from the inefficiency of incremental context construction that requires step‑by‑step LLM interaction, and often stagnate in suboptimal regions due to surface‑level optimization. In this paper, we characterize the Intent‑Context Coupling phenomenon, revealing that LLM safety constraints are significantly relaxed when a malicious intent is coupled with a semantically congruent context pattern. Driven by this insight, we propose ICON, an automated multi‑turn jailbreak framework that efficiently constructs an authoritative‑style context via prior‑guided semantic routing. Specifically, ICON first routes the malicious intent to a congruent context pattern (e.g., Scientific Research) and instantiates it into an attack prompt sequence. This sequence progressively builds the authoritative‑style context and ultimately elicits prohibited content. In addition, ICON incorporates a Hierarchical Optimization Strategy that combines local prompt refinement with global context switching, preventing the attack from stagnating in ineffective contexts. Experimental results across eight SOTA LLMs demonstrate the effectiveness of ICON, achieving a state‑of‑the‑art average Attack Success Rate (ASR) of 97.1%. Code is available at https://github.com/xwlin‑roy/ICON.

PaperId: 3516, https://arxiv.org/pdf/2601.20870.pdf GitHub

Abstract:
Spiking Neural Networks (SNNs) are inherently suited for continuous learning due to their event‑driven temporal dynamics; however, their application to Class‑Incremental Learning (CIL) has been hindered by catastrophic forgetting and the temporal misalignment of spike patterns. In this work, we introduce Spiking Temporal Alignment with Experience Replay (STAER), a novel framework that explicitly preserves temporal structure to bridge the performance gap between SNNs and ANNs. Our approach integrates a differentiable Soft‑DTW alignment loss to maintain spike timing fidelity and employs a temporal expansion and contraction mechanism on output logits to enforce robust representation learning. Implemented on a deep ResNet19 spiking backbone, STAER achieves state‑of‑the‑art performance on Sequential‑MNIST and Sequential‑CIFAR10. Empirical results demonstrate that our method matches or outperforms strong ANN baselines (ER, DER++) while preserving biologically plausible dynamics. Ablation studies further confirm that explicit temporal alignment is critical for representational stability, positioning STAER as a scalable solution for spike‑native lifelong learning. Code is available at https://github.com/matteogianferrari/staer.

PaperId: 3517, https://arxiv.org/pdf/2601.20848.pdf GitHub

Abstract:
Despite growing efforts to mitigate unfairness in recommender systems, existing fairness‑aware methods typically fix the fairness requirement at training time and provide limited post‑training flexibility. However, in real‑world scenarios, diverse stakeholders may demand differing fairness requirements over time, so retraining for different fairness requirements becomes prohibitive. To address this limitation, we propose Cofair, a single‑train framework that enables post‑training fairness control in recommendation. Specifically, Cofair introduces a shared representation layer with fairness‑conditioned adapter modules to produce user embeddings specialized for varied fairness levels, along with a user‑level regularization term that guarantees user‑wise monotonic fairness improvements across these levels. We theoretically establish that the adversarial objective of Cofair upper bounds demographic parity and the regularization term enforces progressive fairness at user level. Comprehensive experiments on multiple datasets and backbone models demonstrate that our framework provides dynamic fairness at different levels, delivering comparable or better fairness‑accuracy curves than state‑of‑the‑art baselines, without the need to retrain for each new fairness requirement. Our code is publicly available at https://github.com/weixinchen98/Cofair.

PaperId: 3518, https://arxiv.org/pdf/2601.20843.pdf GitHub

Abstract:
This paper introduces a novel Deep Researcher architecture designed to generate detailed research reports on complex PhD level topics by addressing the inherent limitations of the Parallel Scaling paradigm. Our system utilizes two key innovations: Sequential Research Plan Refinement via Reflection and a Candidates Crossover algorithm. The sequential refinement process is demonstrated as an efficient method that allows the agent to maintain a centralized Global Research Context, enabling it to look back at current progress, reason about the research plan, and intelligently make changes at runtime. This dynamic adaptation contrasts with parallel approaches, which often suffer from siloed knowledge. The Candidates Crossover algorithm further enhances search efficiency by deploying multiple LLM candidates with varied parameters to explore a larger search space, with their findings synthesized to curate a comprehensive final research response. The process concludes with One Shot Report Generation, ensuring the final document is informed by a unified narrative and high fact density. Powered by the Gemini 2.5 Pro model, our Deep Researcher was evaluated on the DeepResearch Bench, a globally recognized benchmark of 100 doctoral level research tasks. Our architecture achieved an overall score of 46.21, demonstrating superior performance by surpassing leading deep research agents such as Claude Researcher, Nvidia AIQ Research Assistant, Perplexity Research, Kimi Researcher and Grok Deeper Search present on the DeepResearch Bench actively running leaderboard. This performance marginally exceeds our previous work, Static DRA, and reinforces the finding that sequential scaling consistently outperforms the parallel self consistency paradigm.

PaperId: 3519, https://arxiv.org/pdf/2601.20815.pdf GitHub

Abstract:
Explanations provided by Self‑explainable Graph Neural Networks (SE‑GNNs) are fundamental for understanding the model's inner workings and for identifying potential misuse of sensitive attributes. Although recent works have highlighted that these explanations can be suboptimal and potentially misleading, a characterization of their failure cases is unavailable. In this work, we identify a critical failure of SE‑GNN explanations: explanations can be unambiguously unrelated to how the SE‑GNNs infer labels. We show that, on the one hand, many SE‑GNNs can achieve optimal true risk while producing these degenerate explanations, and on the other, most faithfulness metrics can fail to identify these failure modes. Our empirical analysis reveals that degenerate explanations can be maliciously planted (allowing an attacker to hide the use of sensitive attributes) and can also emerge naturally, highlighting the need for reliable auditing. To address this, we introduce a novel faithfulness metric that reliably marks degenerate explanations as unfaithful, in both malicious and natural settings. Our code is available at https://github.com/steveazzolin/gnn_deg_expl.

PaperId: 3520, https://arxiv.org/pdf/2601.20745.pdf GitHub

Abstract:
As large language models (LLMs) continue to scale, deployment is increasingly bottlenecked by the memory wall, motivating a shift toward extremely low‑bit quantization. However, most quantization‑aware training (QAT) methods apply hard rounding and the straight‑through estimator (STE) from the beginning of the training, which prematurely discretizes the optimization landscape and induces persistent gradient mismatch between latent weights and quantized weights, hindering effective optimization of quantized models. To address this, we propose Hestia, a Hessian‑guided differentiable QAT framework for extremely low‑bit LLMs, which replaces the rigid step function with a temperature‑controlled softmax relaxation to maintain gradient flow early in training while progressively hardening quantization. Furthermore, Hestia leverages a tensor‑wise Hessian trace metric as a lightweight curvature signal to drive fine‑grained temperature annealing, enabling sensitivity‑aware discretization across the model. Evaluations on Llama‑3.2 show that Hestia consistently outperforms existing ternary QAT baselines, yielding average zero‑shot improvements of 5.39% and 4.34% for the 1B and 3B models. These results indicate that Hessian‑guided relaxation effectively recovers representational capacity, establishing a more robust training path for 1.58‑bit LLMs. The code is available at https://github.com/hestia2026/Hestia.

PaperId: 3521, https://arxiv.org/pdf/2601.20642.pdf GitHub

Abstract:
Diffusion‑based image generative models produce high‑fidelity images through iterative denoising but remain vulnerable to memorization, where they unintentionally reproduce exact copies or parts of training images. Recent memorization detection methods are primarily based on the norm of score difference as indicators of memorization. We prove that such norm‑based metrics are mainly effective under the assumption of isotropic log‑probability distributions, which generally holds at high or medium noise levels. In contrast, analyzing the anisotropic regime reveals that memorized samples exhibit strong angular alignment between the guidance vector and unconditional scores in the low‑noise setting. Through these insights, we develop a memorization detection metric by integrating isotropic norm and anisotropic alignment. Our detection metric can be computed directly on pure noise inputs via two conditional and unconditional forward passes, eliminating the need for costly denoising steps. Detection experiments on Stable Diffusion v1.4 and v2 show that our metric outperforms existing denoising‑free detection methods while being at least approximately 5x faster than the previous best approach. Finally, we demonstrate the effectiveness of our approach by utilizing a mitigation strategy that adapts memorized prompts based on our developed metric. The code is available at https://github.com/rohanasthana/memorization‑anisotropy .

PaperId: 3522, https://arxiv.org/pdf/2601.20614.pdf GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) offers a robust mechanism for enhancing mathematical reasoning in large models. However, we identify a systematic lack of emphasis on more challenging questions in existing methods from both algorithmic and data perspectives, despite their importance for refining underdeveloped capabilities. Algorithmically, widely used Group Relative Policy Optimization (GRPO) suffers from an implicit imbalance where the magnitude of policy updates is lower for harder questions. Data‑wise, augmentation approaches primarily rephrase questions to enhance diversity without systematically increasing intrinsic difficulty. To address these issues, we propose a two‑dual MathForge framework to improve mathematical reasoning by targeting harder questions from both perspectives, which comprises a Difficulty‑Aware Group Policy Optimization (DGPO) algorithm and a Multi‑Aspect Question Reformulation (MQR) strategy. Specifically, DGPO first rectifies the implicit imbalance in GRPO via difficulty‑balanced group advantage estimation, and further prioritizes harder questions by difficulty‑aware question‑level weighting. Meanwhile, MQR reformulates questions across multiple aspects to increase difficulty while maintaining the original gold answer. Overall, MathForge forms a synergistic loop: MQR expands the data frontier, and DGPO effectively learns from the augmented data. Extensive experiments show that MathForge significantly outperforms existing methods on various mathematical reasoning tasks. The code and augmented data are all available at https://github.com/AMAP‑ML/MathForge.

PaperId: 3523, https://arxiv.org/pdf/2601.20604.pdf GitHub

Abstract:
This paper introduces a methodological framework for empirically testing AI alignment strategies through structured multi‑model dialogue. Drawing on Peace Studies traditions ‑ particularly interest‑based negotiation, conflict transformation, and commons governance ‑ we operationalize Viral Collaborative Wisdom (VCW), an approach that reframes alignment from a control problem to a relationship problem developed through dialogical reasoning. Our experimental design assigns four distinct roles (Proposer, Responder, Monitor, Translator) to different AI systems across six conditions, testing whether current large language models can engage substantively with complex alignment frameworks. Using Claude, Gemini, and GPT‑4o, we conducted 72 dialogue turns totaling 576,822 characters of structured exchange. Results demonstrate that AI systems can engage meaningfully with Peace Studies concepts, surface complementary objections from different architectural perspectives, and generate emergent insights not present in initial framings ‑ including the novel synthesis of "VCW as transitional framework." Cross‑architecture patterns reveal that different models foreground different concerns: Claude emphasized verification challenges, Gemini focused on bias and scalability, and GPT‑4o highlighted implementation barriers. The framework provides researchers with replicable methods for stress‑testing alignment proposals before implementation, while the findings offer preliminary evidence about AI capacity for the kind of dialogical reasoning VCW proposes. We discuss limitations, including the observation that dialogues engaged more with process elements than with foundational claims about AI nature, and outline directions for future research including human‑AI hybrid protocols and extended dialogue studies.

PaperId: 3524, https://arxiv.org/pdf/2601.20601.pdf GitHub

Abstract:
Medical image classification is a core task in computer‑aided diagnosis (CAD), playing a pivotal role in early disease detection, treatment planning, and patient prognosis assessment. In ophthalmic practice, fluorescein fundus angiography (FFA) and indocyanine green angiography (ICGA) provide hemodynamic and lesion‑structural information that conventional fundus photography cannot capture. However, due to the single‑modality nature, subtle lesion patterns, and significant inter‑device variability, existing methods still face limitations in generalization and high‑confidence prediction. To address these challenges, we propose CLEAR‑Mamba, an enhanced framework built upon MedMamba with optimizations in both architecture and training strategy. Architecturally, we introduce HaC, a hypernetwork‑based adaptive conditioning layer that dynamically generates parameters according to input feature distributions, thereby improving cross‑domain adaptability. From a training perspective, we develop RaP, a reliability‑aware prediction scheme built upon evidential uncertainty learning, which encourages the model to emphasize low‑confidence samples and improves overall stability and reliability. We further construct a large‑scale ophthalmic angiography dataset covering both FFA and ICGA modalities, comprising multiple retinal disease categories for model training and evaluation. Experimental results demonstrate that CLEAR‑Mamba consistently outperforms multiple baseline models, including the original MedMamba, across various metrics‑showing particular advantages in multi‑disease classification and reliability‑aware prediction. This study provides an effective solution that balances generalizability and reliability for modality‑specific medical image classification tasks. Our project can be accessed at https://github.com/ZJU4HealthCare/CLEAR‑Mamba.

PaperId: 3525, https://arxiv.org/pdf/2601.20598.pdf GitHub

Abstract:
Person Re‑Identification (ReID) remains a challenging problem in computer vision. This work reviews various training paradigm and evaluates the robustness of state‑of‑the‑art ReID models in cross‑domain applications and examines the role of foundation models in improving generalization through richer, more transferable visual representations. We compare three training paradigms, supervised, self‑supervised, and language‑aligned models. Through the study the aim is to answer the following questions: Can supervised models generalize in cross‑domain scenarios? How does foundation models like SigLIP2 perform for the ReID tasks? What are the weaknesses of current supervised and foundational models for ReID? We have conducted the analysis across 11 models and 9 datasets. Our results show a clear split: supervised models dominate their training domain but crumble on cross‑domain data. Language‑aligned models, however, show surprising robustness cross‑domain for ReID tasks, even though they are not explicitly trained to do so. Code and data available at: https://github.com/moiiai‑tech/object‑reid‑benchmark.

PaperId: 3526, https://arxiv.org/pdf/2601.20556.pdf GitHub

Abstract:
Unsupervised ensemble learning emerged to address the challenge of combining multiple learners' predictions without access to ground truth labels or additional data. This paradigm is crucial in scenarios where evaluating individual classifier performance or understanding their strengths is challenging due to limited information. We propose a novel deep energy‑based method for constructing an accurate meta‑learner using only the predictions of individual learners, potentially capable of capturing complex dependence structures between them. Our approach requires no labeled data, learner features, or problem‑specific information, and has theoretical guarantees for when learners are conditionally independent. We demonstrate superior performance across diverse ensemble scenarios, including challenging mixture of experts settings. Our experiments span standard ensemble datasets and curated datasets designed to test how the model fuses expertise from multiple sources. These results highlight the potential of unsupervised ensemble learning to harness collective intelligence, especially in data‑scarce or privacy‑sensitive environments.

PaperId: 3527, https://arxiv.org/pdf/2601.20467.pdf GitHub

Abstract:
Chain‑of‑thought (CoT) prompting improves LLM reasoning but incurs high latency and memory cost due to verbose traces, motivating CoT compression with preserved correctness. Existing methods either shorten CoTs at the semantic level, which is often conservative, or prune tokens aggressively, which can miss task‑critical cues and degrade accuracy. Moreover, combining the two is non‑trivial due to sequential dependency, task‑agnostic pruning, and distribution mismatch. We propose CtrlCoT, a dual‑granularity CoT compression framework that harmonizes semantic abstraction and token‑level pruning through three components: Hierarchical Reasoning Abstraction produces CoTs at multiple semantic granularities; Logic‑Preserving Distillation trains a logic‑aware pruner to retain indispensable reasoning cues (e.g., numbers and operators) across pruning ratios; and Distribution‑Alignment Generation aligns compressed traces with fluent inference‑time reasoning styles to avoid fragmentation. On MATH‑500 with Qwen2.5‑7B‑Instruct, CtrlCoT uses 30.7% fewer tokens while achieving 7.6 percentage points higher than the strongest baseline, demonstrating more efficient and reliable reasoning. Our code will be publicly available at https://github.com/fanzhenxuan/Ctrl‑CoT.

PaperId: 3528, https://arxiv.org/pdf/2601.20363.pdf GitHub

Abstract:
Can standard continuous‑time generative models represent distributions whose support is an extremely sparse, globally constrained discrete set? We study this question using completed Sudoku grids as a controlled testbed, treating them as a subset of a continuous relaxation space. We train flow‑matching and score‑based models along a Gaussian probability path and compare deterministic (ODE) sampling, stochastic (SDE) sampling, and DDPM‑style discretizations derived from the same continuous‑time training. Unconditionally, stochastic sampling substantially outperforms deterministic flows; score‑based samplers are the most reliable among continuous‑time methods, and DDPM‑style ancestral sampling achieves the highest validity overall. We further show that the same models can be repurposed for guided generation: by repeatedly sampling completions under clamped clues and stopping when constraints are satisfied, the model acts as a probabilistic Sudoku solver. Although far less sample‑efficient than classical solvers and discrete‑geometry‑aware diffusion methods, these experiments demonstrate that classic diffusion/flow formulations can assign non‑zero probability mass to globally constrained combinatorial structures and can be used for constraint satisfaction via stochastic search.

PaperId: 3529, https://arxiv.org/pdf/2601.20334.pdf GitHub

Abstract:
Robotic manipulation has increasingly adopted vision‑language‑action (VLA) models, which achieve strong performance but typically require task‑specific demonstrations and fine‑tuning, and often generalize poorly under domain shift. We investigate whether general‑purpose large language model (LLM) agent frameworks, originally developed for software engineering, can serve as an alternative control paradigm for embodied manipulation. We introduce FAEA (Frontier Agent as Embodied Agent), which applies an LLM agent framework directly to embodied manipulation without modification. Using the same iterative reasoning that enables software agents to debug code, FAEA enables embodied agents to reason through manipulation strategies. We evaluate an unmodified frontier agent, Claude Agent SDK, across the LIBERO, ManiSkill3, and MetaWorld benchmarks. With privileged environment state access, FAEA achieves success rates of 84.9%, 85.7%, and 96%, respectively. This level of task success approaches that of VLA models trained with less than 100 demonstrations per task, without requiring demonstrations or fine‑tuning. With one round of human feedback as an optional optimization, performance increases to 88.2% on LIBERO. This demonstration‑free capability has immediate practical value: FAEA can autonomously explore novel scenarios in simulation and generate successful trajectories for training data augmentation in embodied learning. Our results indicate that general‑purpose agents are sufficient for a class of manipulation tasks dominated by deliberative, task‑level planning. This opens a path for robotics systems to leverage actively maintained agent infrastructure and benefit directly from ongoing advances in frontier models. Code is available at https://github.com/robiemusketeer/faea‑sim

PaperId: 3530, https://arxiv.org/pdf/2601.20326.pdf GitHub

Abstract:
KV caches, typically used only to speed up autoregressive decoding, encode contextual information that can be reused for downstream tasks at no extra cost. We propose treating the KV cache as a lightweight representation, eliminating the need to recompute or store full hidden states. Despite being weaker than dedicated embeddings, KV‑derived representations are shown to be sufficient for two key applications: (i) Chain‑of‑Embedding, where they achieve competitive or superior performance on Llama‑3.1‑8B‑Instruct and Qwen2‑7B‑Instruct; and (ii) Fast/Slow Thinking Switching, where they enable adaptive reasoning on Qwen3‑8B and DeepSeek‑R1‑Distil‑Qwen‑14B, reducing token generation by up to 5.7× with minimal accuracy loss. Our findings establish KV caches as a free, effective substrate for sampling and reasoning, opening new directions for representation reuse in LLM inference. Code: https://github.com/cmd2001/ICLR2026_KV‑Embedding.

PaperId: 3531, https://arxiv.org/pdf/2601.20309.pdf GitHub

Abstract:
Large Language Model (LLM) serving faces a fundamental tension between stringent latency Service Level Objectives (SLOs) and limited GPU memory capacity. When high request rates exhaust the KV cache budget, existing LLM inference systems often suffer severe head‑of‑line (HOL) blocking. While prior work explored PCIe‑based offloading, these approaches cannot sustain responsiveness under high request rates, often failing to meet tight Time‑To‑First‑Token (TTFT) and Time‑Between‑Tokens (TBT) SLOs. We present SuperInfer, a high‑performance LLM inference system designed for emerging Superchips (e.g., NVIDIA GH200) with tightly coupled GPU‑CPU architecture via NVLink‑C2C. SuperInfer introduces RotaSched, the first proactive, SLO‑aware rotary scheduler that rotates requests to maintain responsiveness on Superchips, and DuplexKV, an optimized rotation engine that enables full‑duplex transfer over NVLink‑C2C. Evaluations on GH200 using various models and datasets show that SuperInfer improves TTFT SLO attainment rates by up to 74.7% while maintaining comparable TBT and throughput compared to state‑of‑the‑art systems, demonstrating that SLO‑aware scheduling and memory co‑design unlocks the full potential of Superchips for responsive LLM serving. Code is available in https://github.com/Supercomputing‑System‑AI‑Lab/SuperInfer.

PaperId: 3532, https://arxiv.org/pdf/2601.20164.pdf GitHub

Abstract:
Prior work suggests that language models, while trained on next token prediction, show implicit planning behavior: they may select the next token in preparation to a predicted future token, such as a likely rhyming word, as supported by a prior qualitative study of Claude 3.5 Haiku using a cross‑layer transcoder. We propose much simpler techniques for assessing implicit planning in language models. With case studies on rhyme poetry generation and question answering, we demonstrate that our methodology easily scales to many models. Across models, we find that the generated rhyme (e.g. "‑ight") or answer to a question ("whale") can be manipulated by steering at the end of the preceding line with a vector, affecting the generation of intermediate tokens leading up to the rhyme or answer word. We show that implicit planning is a universal mechanism, present in smaller models than previously thought, starting from 1B parameters. Our methodology offers a widely applicable direct way to study implicit planning abilities of LLMs. More broadly, understanding planning abilities of language models can inform decisions in AI safety and control.

PaperId: 3533, https://arxiv.org/pdf/2601.20126.pdf GitHub

Abstract:
Large Language Models (LLMs) often produce hallucinated or unverifiable content, undermining their reliability in factual domains. This work investigates Reinforcement Learning with Verifiable Rewards (RLVR) as a training paradigm that explicitly rewards abstention ("I don't know") alongside correctness to promote intellectual humility. We fine‑tune and evaluate Granite‑3.3‑2B‑Instruct and Qwen‑3‑4B‑Instruct on the MedMCQA and Hendrycks Math benchmarks using a ternary reward structure (‑1, r_abs, 1) under varying abstention reward structures. We further study the effect of combining RLVR with supervised fine‑tuning strategies that teach abstention prior to reinforcement learning. Our results show that moderate abstention rewards (r_abs \approx ‑0.25 to 0.3) consistently reduce incorrect responses without severe accuracy degradation on multiple‑choice tasks, with larger models exhibiting greater robustness to abstention incentives. On open‑ended question answering, we observe limitations due to insufficient exploration, which can be partially mitigated through supervised abstention training. Overall, these findings demonstrate the feasibility and flexibility of verifiable reward design as a practical approach for hallucination mitigation in language models. Reproducible code for our abstention training framework is available here https://github.com/Mystic‑Slice/rl‑abstention.

PaperId: 3534, https://arxiv.org/pdf/2601.20072.pdf GitHub

Abstract:
We address the challenge of training Vision Transformers (ViTs) when labeled data is scarce but unlabeled data is abundant. We propose Semi‑Supervised Masked Autoencoder (SSMAE), a framework that jointly optimizes masked image reconstruction and classification using both unlabeled and labeled samples with dynamically selected pseudo‑labels. SSMAE introduces a validation‑driven gating mechanism that activates pseudo‑labeling only after the model achieves reliable, high‑confidence predictions that are consistent across both weakly and strongly augmented views of the same image, reducing confirmation bias. On CIFAR‑10 and CIFAR‑100, SSMAE consistently outperforms supervised ViT and fine‑tuned MAE, with the largest gains in low‑label regimes (+9.24% over ViT on CIFAR‑10 with 10% labels). Our results demonstrate that when pseudo‑labels are introduced is as important as how they are generated for data‑efficient transformer training. Codes are available at https://github.com/atik666/ssmae.

PaperId: 3535, https://arxiv.org/pdf/2601.20037.pdf GitHub

Abstract:
Despite the ubiquity of tabular data in high‑stakes domains, traditional deep learning architectures often struggle to match the performance of gradient‑boosted decision trees while maintaining scientific interpretability. Standard neural networks typically treat features as independent entities, failing to exploit the inherent manifold structural dependencies that define tabular distributions. We propose Structural Compositional Function Networks (StructuralCFN), a novel architecture that imposes a Relation‑Aware Inductive Bias via a differentiable structural prior. StructuralCFN explicitly models each feature as a mathematical composition of its counterparts through Differentiable Adaptive Gating, which automatically discovers the optimal activation physics (e.g., attention‑style filtering vs. inhibitory polarity) for each relationship. Our framework enables Structured Knowledge Integration, allowing domain‑specific relational priors to be injected directly into the architecture to guide discovery. We evaluate StructuralCFN across a rigorous 10‑fold cross‑validation suite on 18 benchmarks, demonstrating statistically significant improvements (p < 0.05) on scientific and clinical datasets (e.g., Blood Transfusion, Ozone, WDBC). Furthermore, StructuralCFN provides Intrinsic Symbolic Interpretability: it recovers the governing "laws" of the data manifold as human‑readable mathematical expressions while maintaining a compact parameter footprint (300‑‑2,500 parameters) that is over an order of magnitude (10x‑‑20x) smaller than standard deep baselines.

PaperId: 3536, https://arxiv.org/pdf/2601.19933.pdf GitHub

Abstract:
Large language models exhibit a systematic tendency toward early semantic commitment: given ambiguous input, they collapse multiple valid interpretations into a single response before sufficient context is available. This premature collapse discards information that may prove essential as dialogue evolves. We present a formal framework for text‑to‑state mapping (phi: T ‑> S) that transforms natural language into a non‑collapsing state space where multiple interpretations coexist. The mapping decomposes into three stages: conflict detection, interpretation extraction, and state construction. We instantiate phi with a hybrid extraction pipeline that combines rule‑based segmentation for explicit conflict markers with LLM‑based enumeration of implicit ambiguity. On a test set of 68 ambiguous sentences, the resulting states preserve interpretive multiplicity: hybrid extraction yields mean state entropy H = 1.087 bits across ambiguity categories, compared to H = 0 for collapse‑based baselines that commit to a single interpretation. We also instantiate the rule‑based conflict detector for Japanese markers to illustrate cross‑lingual portability. This framework extends Non‑Resolution Reasoning (NRR) by providing the algorithmic bridge between text and the NRR state space, enabling architectural collapse deferment in LLM inference. Design principles for state‑to‑state transformations are detailed in the Appendix, with empirical validation on 580 test cases demonstrating 0% collapse for principle‑satisfying operators versus up to 17.8% for violating operators.

PaperId: 3537, https://arxiv.org/pdf/2601.19924.pdf GitHub

Abstract:
We investigate the capabilities and scalability of Large Language Models (LLMs) in optimization modeling, a domain requiring structured reasoning and precise formulation. To this end, we introduce OPT‑ENGINE, an extensible benchmark framework with quantifiable and controllable complexity. OPT‑ENGINE spans ten canonical Operations Research problems, systematically scaling from Linear Programming to Mixed‑Integer Programming, providing a structured environment to probe the limits of automated problem formulation and solving. Utilizing OPT‑Engine, we address three pivotal research questions. First, we examine whether Pure‑Text Reasoning (PTR) via classical Chain‑of‑Thought can efficiently tackle optimization tasks, finding that PTR suffers from a critical robustness gap as task complexity increases. Second, we examine whether integrating external computational tools can mitigate PTR's arithmetic weaknesses and improve performance. Our results indicate that while such tools help with local calculations, they still fail to adhere to global optimization constraints. Finally, we pinpoint that for the current SOTA paradigm, Solver‑integrated Reasoning (SIR), the automated formulation of constraints represents the primary bottleneck. These findings clarify the limitations of current paradigms and provide a structured roadmap for developing next‑generation LLMs for optimization modeling. We release our code and data to facilitate future research (https://github.com/Cardinal‑Operations/OPTEngine).

PaperId: 3538, https://arxiv.org/pdf/2601.19913.pdf GitHub

Abstract:
Distinguishing human‑written Korean text from fluent LLM outputs remains difficult even for trained readers, who can over‑trust surface well‑formedness. We present LREAD, a Korean‑specific instantiation of a rubric‑based expert‑calibration framework for human attribution of LLM‑generated text. In a three‑phase blind longitudinal study with three linguistically trained annotators, Phase 1 measures intuition‑only attribution, Phase 2 introduces criterion‑anchored scoring with explicit justifications, and Phase 3 evaluates a limited held‑out elementary‑persona subset. Majority‑vote accuracy improves from 0.60 in Phase 1 to 0.90 in Phase 2, and reaches 10/10 on the limited Phase 3 subset (95% CI [0.692, 1.000]); agreement also increases from Fleiss' κ = ‑0.09 to 0.82. Error analysis suggests that calibration primarily reduces false negatives on AI essays rather than inducing generalized over‑detection. We position LREAD as pilot evidence for within‑panel calibration in a Korean argumentative‑essay setting. These findings suggest that rubric‑scaffolded human judgment can complement automated detectors by making attribution reasoning explicit, auditable, and adaptable. The rubric developed in this study, along with the dataset employed for the analysis, is available at https://github.com/Shinwoo‑Park/lread.

PaperId: 3539, https://arxiv.org/pdf/2601.19834.pdf GitHub

Abstract:
Humans construct internal world models and reason by manipulating the concepts within these models. Recent advances in AI, particularly chain‑of‑thought (CoT) reasoning, approximate such human cognitive abilities, where world models are believed to be embedded within large language models. Expert‑level performance in formal and abstract domains such as mathematics and programming has been achieved in current systems by relying predominantly on verbal reasoning. However, they still lag far behind humans in domains like physical and spatial intelligence, which require richer representations and prior knowledge. The emergence of unified multimodal models (UMMs) capable of both verbal and visual generation has therefore sparked interest in more human‑like reasoning grounded in complementary multimodal pathways, though their benefits remain unclear. From a world‑model perspective, this paper presents the first principled study of when and how visual generation benefits reasoning. Our key position is the visual superiority hypothesis: for certain tasks‑‑particularly those grounded in the physical world‑‑visual generation more naturally serves as world models, whereas purely verbal world models encounter bottlenecks arising from representational limitations or insufficient prior knowledge. Theoretically, we formalize internal world modeling as a core component of CoT reasoning and analyze distinctions among different forms of world models. Empirically, we identify tasks that necessitate interleaved visual‑verbal CoT reasoning, constructing a new evaluation suite, VisWorld‑Eval. Controlled experiments on a state‑of‑the‑art UMM show that interleaved CoT significantly outperforms purely verbal CoT on tasks that favor visual world modeling, but offers no clear advantage otherwise. Together, this work clarifies the potential of multimodal world modeling for more powerful, human‑like multimodal AI.

PaperId: 3540, https://arxiv.org/pdf/2601.19709.pdf GitHub

Abstract:
Speaker embedding learning based on Euclidean space has achieved significant progress, but it is still insufficient in modeling hierarchical information within speaker features. Hyperbolic space, with its negative curvature geometric properties, can efficiently represent hierarchical information within a finite volume, making it more suitable for the feature distribution of speaker embeddings. In this paper, we propose Hyperbolic Softmax (H‑Softmax) and Hyperbolic Additive Margin Softmax (HAM‑Softmax) based on hyperbolic space. H‑Softmax incorporates hierarchical information into speaker embeddings by projecting embeddings and speaker centers into hyperbolic space and computing hyperbolic distances. HAM‑Softmax further enhances inter‑class separability by introducing margin constraint on this basis. Experimental results show that H‑Softmax and HAM‑Softmax achieve average relative EER reductions of 27.84% and 14.23% compared with standard Softmax and AM‑Softmax, respectively, demonstrating that the proposed methods effectively improve speaker verification performance and at the same time preserve the capability of hierarchical structure modeling. The code will be released at https://github.com/PunkMale/HAM‑Softmax.

PaperId: 3541, https://arxiv.org/pdf/2601.19702.pdf GitHub

Abstract:
The performance evaluation remains a complex challenge in audio separation, and existing evaluation metrics are often misaligned with human perception, course‑grained, relying on ground truth signals. On the other hand, subjective listening tests remain the gold standard for real‑world evaluation, but they are expensive, time‑consuming, and difficult to scale. This paper addresses the growing need for automated systems capable of evaluating audio separation without human intervention. The proposed evaluation metric, SAM Audio Judge (SAJ), is a multimodal fine‑grained reference‑free objective metric, which shows highly alignment with human perceptions. SAJ supports three audio domains (speech, music and general sound events) and three prompt inputs (text, visual and span), covering four different dimensions of evaluation (recall, percision, faithfulness, and overall). SAM Audio Judge also shows potential applications in data filtering, pseudo‑labeling large datasets and reranking in audio separation models. We release our code and pre‑trained models at: https://github.com/facebookresearch/sam‑audio.

PaperId: 3542, https://arxiv.org/pdf/2601.19622.pdf GitHub

Abstract:
Heuristic functions are essential to the performance of tree search algorithms such as A, where their accuracy and efficiency directly impact search outcomes. Traditionally, such heuristics are handcrafted, requiring significant expertise. Recent advances in large language models (LLMs) and evolutionary frameworks have opened the door to automating heuristic design. In this paper, we extend the Evolution of Heuristics (EoH) framework to investigate the automated generation of guiding heuristics for A search. We introduce a novel domain‑agnostic prompt augmentation strategy that includes the A code into the prompt to leverage in‑context learning, named Algorithmic ‑ Contextual EoH (A‑CEoH). To evaluate the effectiveness of A‑CeoH, we study two problem domains: the Unit‑Load Pre‑Marshalling Problem (UPMP), a niche problem from warehouse logistics, and the classical sliding puzzle problem (SPP). Our computational experiments show that A‑CEoH can significantly improve the quality of the generated heuristics and even outperform expert‑designed heuristics.

PaperId: 3543, https://arxiv.org/pdf/2601.19588.pdf GitHub

Abstract:
Adapting Large Language Models (LLMs) to specialized domains without human‑annotated data is a crucial yet formidable challenge. Widely adopted knowledge distillation methods often devolve into coarse‑grained mimicry, where the student model inefficiently targets its own weaknesses and risks inheriting the teacher's reasoning flaws. This exposes a critical pedagogical dilemma: how to devise a reliable curriculum when the teacher itself is not an infallible expert. Our work resolves this by capitalizing on a key insight: while LLMs may exhibit fallibility in complex, holistic reasoning, they often exhibit high fidelity on focused, atomic sub‑problems. Based on this, we propose Divergence‑Guided Reasoning Curriculum (DGRC), which constructs a learning path from atomic knowledge to reasoning chains by dynamically deriving two complementary curricula from disagreements in reasoning pathways. When a student and teacher produce conflicting results, DGRC directs the teacher to perform a diagnostic analysis: it analyzes both reasoning paths to formulate atomic queries that target the specific points of divergence, and then self‑answers these queries to create high‑confidence atomic question‑answer pairs. These pairs then serve a dual purpose: (1) providing an atomic curriculum to rectify the student's knowledge gaps, and (2) serving as factual criteria to filter the teacher's original reasoning chains, yielding a verified CoT curriculum that teaches the student how to integrate atomic knowledge into complete reasoning paths. Experiments across the medical and legal domains on student models of various sizes demonstrate the effectiveness of our DGRC framework. Notably, our method achieves a 7.76% relative improvement for the 1.5B student model in the medical domain over strong unlabeled baseline.

PaperId: 3544, https://arxiv.org/pdf/2601.19585.pdf GitHub

Abstract:
Interactive recommender systems can dynamically adapt to user feedback, but often suffer from content homogeneity and filter bubble effects due to overfitting short‑term user preferences. While recent efforts aim to improve content diversity, they predominantly operate in static or one‑shot settings, neglecting the long‑term evolution of user interests. Reinforcement learning provides a principled framework for optimizing long‑term user satisfaction by modeling sequential decision‑making processes. However, its application in recommendation is hindered by sparse, long‑tailed user‑item interactions and limited semantic planning capabilities. In this work, we propose LLM‑Enhanced Reinforcement Learning (LERL), a novel hierarchical recommendation framework that integrates the semantic planning power of LLM with the fine‑grained adaptability of RL. LERL consists of a high‑level LLM‑based planner that selects semantically diverse content categories, and a low‑level RL policy that recommends personalized items within the selected semantic space. This hierarchical design narrows the action space, enhances planning efficiency, and mitigates overexposure to redundant content. Extensive experiments on real‑world datasets demonstrate that LERL significantly improves long‑term user satisfaction when compared with state‑of‑the‑art baselines. The implementation of LERL is available at https://github.com/1163710212/LERL.

PaperId: 3545, https://arxiv.org/pdf/2601.19494.pdf GitHub

Abstract:
High‑quality evaluation benchmarks are pivotal for deploying Large Language Models (LLMs) in Automated Code Review (ACR). However, existing benchmarks suffer from two critical limitations: first, the lack of multi‑language support in repository‑level contexts, which restricts the generalizability of evaluation results; second, the reliance on noisy, incomplete ground truth derived from raw Pull Request (PR) comments, which constrains the scope of issue detection. To address these challenges, we introduce AACR‑Bench a comprehensive benchmark that provides full cross‑file context across multiple programming languages. Unlike traditional datasets, AACR‑Bench employs an "AI‑assisted, Expert‑verified" annotation pipeline to uncover latent defects often overlooked in original PRs, resulting in a 285% increase in defect coverage. Extensive evaluations of mainstream LLMs on AACR‑Bench reveal that previous assessments may have either misjudged or only partially captured model capabilities due to data limitations. Our work establishes a more rigorous standard for ACR evaluation and offers new insights on LLM based ACR, i.e., the granularity/level of context and the choice of retrieval methods significantly impact ACR performance, and this influence varies depending on the LLM, programming language, and the LLM usage paradigm e.g., whether an Agent architecture is employed. The code, data, and other artifacts of our evaluation set are available at https://github.com/alibaba/aacr‑bench .

PaperId: 3546, https://arxiv.org/pdf/2601.19487.pdf GitHub

Abstract:
Safety‑aligned LLMs suffer from two failure modes: jailbreak (answering harmful inputs) and over‑refusal (declining benign queries). Existing vector steering methods adjust the magnitude of answer vectors, but this creates a fundamental trade‑off ‑‑ reducing jailbreak increases over‑refusal and vice versa. We identify the root cause: LLMs encode the decision to answer (answer vector v_a) and the judgment of input safety (benign vector v_b) as nearly orthogonal directions, treating them as independent processes. We propose LLM‑VA, which aligns v_a with v_b through closed‑form weight updates, making the model's willingness to answer causally dependent on its safety assessment ‑‑ without fine‑tuning or architectural changes. Our method identifies vectors at each layer using SVMs, selects safety‑relevant layers, and iteratively aligns vectors via minimum‑norm weight modifications. Experiments on 12 LLMs demonstrate that LLM‑VA achieves 11.45% higher F1 than the best baseline while preserving 95.92% utility, and automatically adapts to each model's safety bias without manual tuning. Code and models are available at https://hotbento.github.io/LLM‑VA‑Web/.

PaperId: 3547, https://arxiv.org/pdf/2601.19406.pdf GitHub GitHub

Abstract:
Synthetic simulation data and real‑world human data provide scalable alternatives to circumvent the prohibitive costs of robot data collection. However, these sources suffer from the sim‑to‑real visual gap and the human‑to‑robot embodiment gap, respectively, which limits the policy's generalization to real‑world scenarios. In this work, we identify a natural yet underexplored complementarity between these sources: simulation offers the robot action that human data lacks, while human data provides the real‑world observation that simulation struggles to render. Motivated by this insight, we present SimHum, a co‑training framework to simultaneously extract kinematic prior from simulated robot actions and visual prior from real‑world human observations. Based on the two complementary priors, we achieve data‑efficient and generalizable robotic manipulation in real‑world tasks. Empirically, SimHum outperforms the baseline by up to \mathbf40% under the same data collection budget, and achieves a \mathbf62.5% OOD success with only 80 real data, outperforming the real only baseline by 7.1×. Videos and additional information can be found at \hrefhttps://kaipengfang.github.io/sim‑and‑humanproject website.

PaperId: 3548, https://arxiv.org/pdf/2601.19404.pdf GitHub

Abstract:
Within the domain of large language models, reinforcement fine‑tuning algorithms necessitate the generation of a complete reasoning trajectory beginning from the input query, which incurs significant computational overhead during the rollout phase of training. To address this issue, we analyze the impact of different segments of the reasoning path on the correctness of the final result and, based on these insights, propose Reinforcement Fine‑Tuning with Partial Reasoning Optimization (RPO), a plug‑and‑play reinforcement fine‑tuning algorithm. Unlike traditional reinforcement fine‑tuning algorithms that generate full reasoning paths, RPO trains the model by generating suffixes of the reasoning path using experience cache. During the rollout phase of training, RPO reduces token generation in this phase by approximately 95%, greatly lowering the theoretical time overhead. Compared with full‑path reinforcement fine‑tuning algorithms, RPO reduces the training time of the 1.5B model by 90% and the 7B model by 72%. At the same time, it can be integrated with typical algorithms such as GRPO and DAPO, enabling them to achieve training acceleration while maintaining performance comparable to the original algorithms. Our code is open‑sourced at https://github.com/yhz5613813/RPO.

PaperId: 3549, https://arxiv.org/pdf/2601.19376.pdf GitHub

Abstract:
This paper presents the web‑based platform Machine Learning with Bricks and an accompanying two‑day course designed to teach machine learning concepts to students aged 12 to 17 through programming‑free robotics activities. Machine Learning with Bricks is an open source platform and combines interactive visualizations with LEGO robotics to teach three core algorithms: KNN, linear regression, and Q‑learning. Students learn by collecting data, training models, and interacting with robots via a web‑based interface. Pre‑ and post‑surveys with 14 students indicate statistically significant improvements in self‑reported understanding of machine learning algorithms, changes in AI‑related terminology toward more technical language, high platform usability, and increased motivation for continued learning. This work suggests that tangible, visualization‑based approaches can make machine learning concepts accessible and engaging for young learners while maintaining technical depth. The platform is freely available at https://learning‑and‑dynamics.github.io/ml‑with‑bricks/, with video tutorials guiding students through the experiments at https://youtube.com/playlist?list=PLx1grFu4zAcwfKKJZ1Ux4LwRqaePCOA2J.

PaperId: 3550, https://arxiv.org/pdf/2601.19375.pdf GitHub

Abstract:
Despite significant progress in alignment, large language models (LLMs) remain vulnerable to adversarial attacks that elicit harmful behaviors. Activation steering techniques offer a promising inference‑time intervention approach, but existing methods suffer from critical limitations: activation addition requires careful coefficient tuning and is sensitive to layer‑specific norm variations, while directional ablation provides only binary control. Recent work on Angular Steering introduces continuous control via rotation in a 2D subspace, but its practical implementation violates norm preservation, causing distribution shift and generation collapse, particularly in models below 7B parameters. We propose Selective Steering, which addresses these limitations through two key innovations: (1) a mathematically rigorous norm‑preserving rotation formulation that maintains activation distribution integrity, and (2) discriminative layer selection that applies steering only where feature representations exhibit opposite‑signed class alignment. Experiments across nine models demonstrate that Selective Steering achieves 5.5x higher attack success rates than prior methods while maintaining zero perplexity violations and approximately 100% capability retention on standard benchmarks. Our approach provides a principled, efficient framework for controllable and stable LLM behavior modification. Code: https://github.com/knoveleng/steering

PaperId: 3551, https://arxiv.org/pdf/2601.19362.pdf GitHub

Abstract:
Modern data parallel (DP) training favors collective communication over parameter servers (PS) for its simplicity and efficiency under balanced workloads. However, the balanced workload assumption no longer holds in large language model (LLM) post‑training due to the high variance in sequence lengths. Under imbalanced workloads, collective communication creates synchronization barriers, leading to under‑utilization of devices with smaller workloads. This change in training dynamics calls for a revisit of the PS paradigm for its robustness to such imbalance. We propose On‑Demand Communication (ODC), which adapts PS into Fully Sharded Data Parallel (FSDP) by replacing collective all‑gather and reduce‑scatter with direct point‑to‑point communication. Compared to FSDP, ODC reduces the synchronization barrier from once per layer to once per minibatch and decouples the workload on each device so that faster workers are not stalled. It also enables simpler and more effective load balancing at the minibatch level. Across diverse LLM post‑training tasks, ODC consistently improves device utilization and training throughput, achieving up to a 36% speedup over standard FSDP. These results demonstrate that ODC is a superior fit for the prevalent imbalanced workloads in LLM post‑training. Our implementation of ODC and integration with FSDP is open‑sourced at https://github.com/sail‑sg/odc.

PaperId: 3552, https://arxiv.org/pdf/2601.19336.pdf GitHub

Abstract:
While model‑based reinforcement learning (MBRL) improves sample efficiency by learning world models from raw observations, existing methods struggle to generalize across structurally similar scenes and remain vulnerable to spurious variations such as textures or color shifts. From a cognitive science perspective, humans segment continuous sensory streams into discrete events and rely on these key events for decision‑making. Motivated by this principle, we propose the Event‑Aware World Model (EAWM), a general framework that learns event‑aware representations to streamline policy learning without requiring handcrafted labels. EAWM employs an automated event generator to derive events from raw observations and introduces a Generic Event Segmentor (GES) to identify event boundaries, which mark the start and end time of event segments. Through event prediction, the representation space is shaped to capture meaningful spatio‑temporal transitions. Beyond this, we present a unified formulation of seemingly distinct world model architectures and show the broad applicability of our methods. Experiments on Atari 100K, Craftax 1M, and DeepMind Control 500K, DMC‑GB2 500K demonstrate that EAWM consistently boosts the performance of strong MBRL baselines by 10%‑45%, setting new state‑of‑the‑art results across benchmarks. Our code is released at https://github.com/MarquisDarwin/EAWM.

PaperId: 3553, https://arxiv.org/pdf/2601.19320.pdf GitHub

Abstract:
Quantization‑aware training (QAT) is essential for deploying large models under strict memory and latency constraints, yet achieving stable and robust optimization at ultra‑low bitwidths remains challenging. Common approaches based on the straight‑through estimator (STE) or soft quantizers often suffer from gradient mismatch, instability, or high computational overhead. As such, we propose StableQAT, a unified and efficient QAT framework that stabilizes training in ultra low‑bit settings via a novel, lightweight, and theoretically grounded surrogate for backpropagation derived from a discrete Fourier analysis of the rounding operator. StableQAT strictly generalizes STE as the latter arises as a special case of our more expressive surrogate family, yielding smooth, bounded, and inexpensive gradients that improve QAT training performance and stability across various hyperparameter choices. In experiments, StableQAT exhibits stable and efficient QAT at 2‑4 bit regimes, demonstrating improved training stability, robustness, and superior performance with negligible training overhead against standard QAT techniques. Our code is available at https://github.com/microsoft/StableQAT.

PaperId: 3554, https://arxiv.org/pdf/2601.19306.pdf GitHub

Abstract:
Mobile agents have made progress toward reliable smartphone automation, yet performance in complex applications remains limited by incomplete knowledge and weak generalization to unseen environments. We introduce a curiosity driven knowledge retrieval framework that formalizes uncertainty during execution as a curiosity score. When this score exceeds a threshold, the system retrieves external information from documentation, code repositories, and historical trajectories. Retrieved content is organized into structured AppCards, which encode functional semantics, parameter conventions, interface mappings, and interaction patterns. During execution, an enhanced agent selectively integrates relevant AppCards into its reasoning process, thereby compensating for knowledge blind spots and improving planning reliability. Evaluation on the AndroidWorld benchmark shows consistent improvements across backbones, with an average gain of six percentage points and a new state of the art success rate of 88.8% when combined with GPT‑5. Analysis indicates that AppCards are particularly effective for multi step and cross application tasks, while improvements depend on the backbone model. Case studies further confirm that AppCards reduce ambiguity, shorten exploration, and support stable execution trajectories. Task trajectories are publicly available at https://lisalsj.github.io/Droidrun‑appcard/.

PaperId: 3555, https://arxiv.org/pdf/2601.19276.pdf GitHub

Abstract:
Recommender systems (RS) aim to retrieve a small set of items that best match individual user preferences. Naturally, RS place primary emphasis on the quality of the Top‑K results rather than performance across the entire item set. However, estimating Top‑K accuracy (e.g., Precision@K, Recall@K) requires determining the ranking positions of items, which imposes substantial computational overhead and poses significant challenges for optimization. In addition, RS often suffer from distribution shifts due to evolving user preferences or data biases, further complicating the task. To address these issues, we propose Talos, a loss function that is specifically designed to optimize the Talos recommendation accuracy. Talos leverages a quantile technique that replaces the complex ranking‑dependent operations into simpler comparisons between predicted scores and learned score thresholds. We further develop a sampling‑based regression algorithm for efficient and accurate threshold estimation, and introduce a constraint term to maintain optimization stability by preventing score inflation. Additionally, we incorporate a tailored surrogate function to address discontinuity and enhance robustness against distribution shifts. Comprehensive theoretical analyzes and empirical experiments are conducted to demonstrate the effectiveness, efficiency, convergence, and distributional robustness of Talos. The code is available at https://github.com/cynthia‑shengjia/WWW‑2026‑Talos.

PaperId: 3556, https://arxiv.org/pdf/2601.19232.pdf GitHub

Abstract:
RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence‑structure interactions, we develop an LDM incorporating pre‑trained RNA‑FM embeddings from a large‑scale RNA model. These embeddings capture co‑evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion‑based methods, cannot effectively handle non‑differentiable structural objectives. By contrast, RL excels in this task by using policy‑driven reward optimization to navigate complex, non‑gradient‑based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step‑wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single‑step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state‑of‑the‑art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.

PaperId: 3557, https://arxiv.org/pdf/2601.19216.pdf GitHub

Abstract:
The emerging applications of next‑generation wireless networks demand high‑fidelity environmental intelligence. 3D radio maps bridge physical environments and electromagnetic propagation for spectrum planning and environment‑aware sensing. However, most existing methods treat visual and wireless data as independent modalities and fail to leverage shared electromagnetic propagation principles. To bridge this gap, we propose URF‑GS, a unified radio‑optical radiation field framework based on 3D Gaussian splatting and inverse rendering for 3D radio map construction. By fusing cross‑modal observations, our method recovers scene geometry and material properties to predict radio signals under arbitrary transceiver configurations without retraining. Experiments demonstrate up to a 24.7% improvement in spatial spectrum accuracy and a 10x increase in sample efficiency compared with NeRF‑based methods. We further showcase URF‑GS in Wi‑Fi AP deployment and robot path planning tasks. This unified visual‑wireless representation supports holistic radiation field modeling for future wireless communication systems.

PaperId: 3558, https://arxiv.org/pdf/2601.19204.pdf GitHub

Abstract:
Recent vision‑language models have strong perceptual ability but their implicit reasoning is hard to explain and easily generates hallucinations on complex queries. Compositional methods improve interpretability, but most rely on a single agent or hand‑crafted pipeline and cannot decide when to collaborate across complementary agents or compete among overlapping ones. We introduce MATA (Multi‑Agent hierarchical Trainable Automaton), a multi‑agent system presented as a hierarchical finite‑state automaton for visual reasoning whose top‑level transitions are chosen by a trainable hyper agent. Each agent corresponds to a state in the hyper automaton, and runs a small rule‑based sub‑automaton for reliable micro‑control. All agents read and write a shared memory, yielding transparent execution history. To supervise the hyper agent's transition policy, we build transition‑trajectory trees and transform to memory‑to‑next‑state pairs, forming the MATA‑SFT‑90K dataset for supervised finetuning (SFT). The finetuned LLM as the transition policy understands the query and the capacity of agents, and it can efficiently choose the optimal agent to solve the task. Across multiple visual reasoning benchmarks, MATA achieves the state‑of‑the‑art results compared with monolithic and compositional baselines. The code and dataset are available at https://github.com/ControlNet/MATA.

PaperId: 3559, https://arxiv.org/pdf/2601.19151.pdf GitHub

Abstract:
Recent progress at the intersection of large language models (LLMs) and time series (TS) analysis has revealed both promise and fragility. While LLMs can reason over temporal structure given carefully engineered context, they often struggle with numeric fidelity, modality interference, and principled cross‑modal integration. We present TS‑Debate, a modality‑specialized, collaborative multi‑agent debate framework for zero‑shot time series reasoning. TS‑Debate assigns dedicated expert agents to textual context, visual patterns, and numerical signals, preceded by explicit domain knowledge elicitation, and coordinates their interaction via a structured debate protocol. Reviewer agents evaluate agent claims using a verification‑conflict‑calibration mechanism, supported by lightweight code execution and numerical lookup for programmatic verification. This architecture preserves modality fidelity, exposes conflicting evidence, and mitigates numeric hallucinations without task‑specific fine‑tuning. Across 20 tasks spanning three public benchmarks, TS‑Debate achieves consistent and significant performance improvements over strong baselines, including standard multimodal debate in which all agents observe all inputs.

PaperId: 3560, https://arxiv.org/pdf/2601.19112.pdf GitHub

Abstract:
Emotional Talking Face synthesis is pivotal in multimedia and signal processing, yet existing 3D methods suffer from two critical challenges: poor audio‑vision emotion alignment, manifested as difficult audio emotion extraction and inadequate control over emotional micro‑expressions; and a one‑size‑fits‑all multi‑view fusion strategy that overlooks uncertainty and feature quality differences, undermining rendering quality. We propose UA‑3DTalk, Uncertainty‑Aware 3D Emotional Talking Face Synthesis with emotion prior distillation, which has three core modules: the Prior Extraction module disentangles audio into content‑synchronized features for alignment and person‑specific complementary features for individualization; the Emotion Distillation module introduces a multi‑modal attention‑weighted fusion mechanism and 4D Gaussian encoding with multi‑resolution code‑books, enabling fine‑grained audio emotion extraction and precise control of emotional micro‑expressions; the Uncertainty‑based Deformation deploys uncertainty blocks to estimate view‑specific aleatoric (input noise) and epistemic (model parameters) uncertainty, realizing adaptive multi‑view fusion and incorporating a multi‑head decoder for Gaussian primitive optimization to mitigate the limitations of uniform‑weight fusion. Extensive experiments on regular and emotional datasets show UA‑3DTalk outperforms state‑of‑the‑art methods like DEGSTalk and EDTalk by 5.2% in E‑FID for emotion alignment, 3.1% in SyncC for lip synchronization, and 0.015 in LPIPS for rendering quality. Project page: https://mrask999.github.io/UA‑3DTalk

PaperId: 3561, https://arxiv.org/pdf/2601.19001.pdf GitHub

Abstract:
We propose FROST, an attention‑aware method for efficient reasoning. Unlike traditional approaches, FROST leverages attention weights to prune uncritical reasoning paths, yielding shorter and more reliable reasoning trajectories. Methodologically, we introduce the concept of reasoning outliers and design an attention‑based mechanism to remove them. Theoretically, FROST preserves and enhances the model's reasoning capacity while eliminating outliers at the sentence level. Empirically, we validate FROST on four benchmarks using two strong reasoning models (Phi‑4‑Reasoning and GPT‑OSS‑20B), outperforming state‑of‑the‑art methods such as TALE and ThinkLess. Notably, FROST achieves an average 69.68% reduction in token usage and a 26.70% improvement in accuracy over the base model. Furthermore, in evaluations of attention outlier metrics, FROST reduces the maximum infinity norm by 15.97% and the average kurtosis by 91.09% compared to the base model. Code is available at https://github.com/robinzixuan/FROST

PaperId: 3562, https://arxiv.org/pdf/2601.18999.pdf GitHub

Abstract:
KV caching is a fundamental technique for accelerating Large Language Model (LLM) inference by reusing key‑value (KV) pairs from previous queries, but its effectiveness under limited memory is highly sensitive to the eviction policy. The default Least Recently Used (LRU) eviction algorithm struggles with dynamic online query arrivals, especially in multi‑LLM serving scenarios, where balancing query load across workers and maximizing cache hit rate of each worker are inherently conflicting objectives. We give the first unified mathematical model that captures the core trade‑offs between KV cache eviction and query routing. Our analysis reveals the theoretical limitations of existing methods and leads to principled algorithms that integrate provably competitive randomized KV cache eviction with learning‑based methods to adaptively route queries with evolving patterns, thus balancing query load and cache hit rate. Our theoretical results are validated by extensive experiments across 4 benchmarks and 3 prefix‑sharing settings, demonstrating improvements of up to 6.92× in cache hit rate, 11.96× reduction in latency, 14.06× reduction in time‑to‑first‑token (TTFT), and 77.4% increase in throughput over the state‑of‑the‑art methods. Our code is available at https://github.com/fzwark/KVRouting.

PaperId: 3563, https://arxiv.org/pdf/2601.18842.pdf GitHub

Abstract:
As GUI agents increasingly rely on screenshots to perceive and operate digital environments, they may inadvertently expose sensitive information such as identities, accounts, locations, and behavioral traces. While existing benchmarks primarily focus on task completion, grounding, or defenses against third‑party attacks, current visual privacy datasets remain largely restricted to static natural images, limiting their ability to capture the contextual dependence and task relevance of privacy risks in GUI task trajectories. To bridge this gap, we introduce GUIGuard‑Bench, a first‑step benchmark for studying privacy‑preserving GUI agents in trajectory‑based GUI workflows. GUIGuard‑Bench contains 241 real GUI‑agent trajectories with 4,080 screenshots across Android and PC environments. Each screenshot is annotated at the region level with privacy bounding boxes, semantic privacy categories, risk levels, and whether the private information is necessary for completing the task. Built on these annotations, GUIGuard‑Bench supports three complementary evaluations: privacy recognition, offline planning fidelity under protected screenshots, and the utility impact of different protection strategies. Our results show that current models can often detect whether a screenshot contains private information, but they struggle with fine‑grained localization, category recognition, risk assessment, and task‑necessity judgment. We also find that closed‑source models, exemplified by Claude Sonnet 4.6, can maintain largely consistent planner semantics in Android environments after privacy protection is applied. Our results highlight privacy recognition as a critical bottleneck for practical GUI agents. Project: https://futuresis.github.io/GUIGuard‑page/

PaperId: 3564, https://arxiv.org/pdf/2601.18834.pdf GitHub

Abstract:
Aggregate analytics over conversational data are increasingly used for safety monitoring, governance, and product analysis in large language model systems. A common practice is to embed conversations, cluster them, and publish short textual summaries describing each cluster. While raw conversations may never be exposed, these derived summaries can still pose privacy risks if they contain personally identifying information (PII) or uniquely traceable strings copied from individual conversations. We introduce CanaryBench, a simple and reproducible stress test for privacy leakage in cluster‑level conversation summaries. CanaryBench generates synthetic conversations with planted secret strings ("canaries") that simulate sensitive identifiers. Because canaries are known a priori, any appearance of these strings in published summaries constitutes a measurable leak. Using TF‑IDF embeddings and k‑means clustering on 3,000 synthetic conversations (24 topics) with a canary injection rate of 0.60, we evaluate an intentionally extractive example snippet summarizer that models quote‑like reporting. In this configuration, we observe canary leakage in 50 of 52 canary‑containing clusters (cluster‑level leakage rate 0.961538), along with nonzero regex‑based PII indicator counts. A minimal defense combining a minimum cluster‑size publication threshold (k‑min = 25) and regex‑based redaction eliminates measured canary leakage and PII indicator hits in the reported run while maintaining a similar cluster‑coherence proxy. We position this work as a societal impacts contribution centered on privacy risk measurement for published analytics artifacts rather than raw user data.

PaperId: 3565, https://arxiv.org/pdf/2601.18829.pdf GitHub

Abstract:
Multi‑channel time‑series data, prevalent across diverse applications, is characterized by significant heterogeneity in its different channels. However, existing forecasting models are typically guided by channel‑agnostic loss functions like MSE, which apply a uniform metric across all channels. This often leads to fail to capture channel‑specific dynamics such as sharp fluctuations or trend shifts. To address this, we propose a Channel‑wise Perceptual Loss (CP Loss). Its core idea is to learn a unique perceptual space for each channel that is adapted to its characteristics, and to compute the loss within this space. Specifically, we first design a learnable channel‑wise filter that decomposes the raw signal into disentangled multi‑scale representations, which form the basis of our perceptual space. Crucially, the filter is optimized jointly with the main forecasting model, ensuring that the learned perceptual space is explicitly oriented towards the prediction task. Finally, losses are calculated within these perception spaces to optimize the model. Code is available at https://github.com/zyh16143998882/CP_Loss.

PaperId: 3566, https://arxiv.org/pdf/2601.18827.pdf GitHub

Abstract:
LLM‑based agents are rapidly being adopted across diverse domains. Since they interact with users without supervision, they must be tested extensively. Current testing approaches focus on acceptance‑level evaluation from the user's perspective. While intuitive, these tests require manual evaluation, are difficult to automate, do not facilitate root cause analysis, and incur expensive test environments. In this paper, we present methods to enable structural testing of LLM‑based agents. Our approach utilizes traces (based on OpenTelemetry) to capture agent trajectories, employs mocking to enforce reproducible LLM behavior, and adds assertions to automate test verification. This enables testing agent components and interactions at a deeper technical level within automated workflows. We demonstrate how structural testing enables the adaptation of software engineering best practices to agents, including the test automation pyramid, regression testing, test‑driven development, and multi‑language testing. In representative case studies, we demonstrate automated execution and faster root‑cause analysis. Collectively, these methods reduce testing costs and improve agent quality through higher coverage, reusability, and earlier defect detection. We provide an open source reference implementation on GitHub.

PaperId: 3567, https://arxiv.org/pdf/2601.18754.pdf GitHub

Abstract:
Autonomous unmanned aerial vehicle (UAV) systems are increasingly deployed in safety‑critical, networked environments where they must operate reliably in the presence of malicious adversaries. While recent benchmarks have evaluated large language model (LLM)‑based UAV agents in reasoning, navigation, and efficiency, systematic assessment of security, resilience, and trust under adversarial conditions remains largely unexplored, particularly in emerging 6G‑enabled settings. We introduce α^3‑SecBench, the first large‑scale evaluation suite for assessing the security‑aware autonomy of LLM‑based UAV agents under realistic adversarial interference. Building on multi‑turn conversational UAV missions from α^3‑Bench, the framework augments benign episodes with 20,000 validated security overlay attack scenarios targeting seven autonomy layers, including sensing, perception, planning, control, communication, edge/cloud infrastructure, and LLM reasoning. α^3‑SecBench evaluates agents across three orthogonal dimensions: security (attack detection and vulnerability attribution), resilience (safe degradation behavior), and trust (policy‑compliant tool usage). We evaluate 23 state‑of‑the‑art LLMs from major industrial providers and leading AI labs using thousands of adversarially augmented UAV episodes sampled from a corpus of 113,475 missions spanning 175 threat types. While many models reliably detect anomalous behavior, effective mitigation, vulnerability attribution, and trustworthy control actions remain inconsistent. Normalized overall scores range from 12.9% to 57.1%, highlighting a significant gap between anomaly detection and security‑aware autonomous decision‑making. We release α^3‑SecBench on GitHub: https://github.com/maferrag/AlphaSecBench

PaperId: 3568, https://arxiv.org/pdf/2601.18753.pdf GitHub

Abstract:
The reliability of Large Language Models (LLMs) in high‑stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typically stem from two sources: data‑driven hallucinations and reasoning‑driven hallucinations. However, existing detection methods usually address only one source and rely on task‑specific heuristics, limiting their generalization to complex scenarios. To overcome these limitations, we introduce the Hallucination Risk Bound, a unified theoretical framework that formally decomposes hallucination risk into data‑driven and reasoning‑driven components, linked respectively to training‑time mismatches and inference‑time instabilities. This provides a principled foundation for analyzing how hallucinations emerge and evolve. Building on this foundation, we introduce HalluGuard, an NTK‑based score that leverages the induced geometry and captured representations of the NTK to jointly identify data‑driven and reasoning‑driven hallucinations. We evaluate HalluGuard on 10 diverse benchmarks, 11 competitive baselines, and 9 popular LLM backbones, consistently achieving state‑of‑the‑art performance in detecting diverse forms of LLM hallucinations. We open‑source our proposed \model model at https://github.com/Susan571/HalluGuard‑ICLR2026.

PaperId: 3569, https://arxiv.org/pdf/2601.18744.pdf GitHub

Abstract:
Time series are ubiquitous in real‑world scenarios and crucial for applications ranging from energy management to traffic control. Consequently, the ability to reason over time series is a fundamental skill for generalist models to solve complex problems. However, current benchmarks for generalist models largely overlook this dimension. To bridge this gap, we introduce TSRBench, a comprehensive multi‑modal benchmark designed to stress‑test the full spectrum of time series reasoning capabilities. TSRBench features: i) a diverse set of 4125 problems from 14 domains, and is categorized into 4 major dimensions: Perception, Reasoning, Prediction, and Decision‑Making. ii) 15 tasks from the 4 dimensions evaluating essential reasoning capabilities (e.g., numerical reasoning). Through extensive experiments, we evaluate over 30 leading proprietary and open‑source LLMs, VLMs, and TSLLMs within TSRBench. Our findings reveal that: i) scaling laws hold for perception and reasoning but break down for prediction; ii) strong reasoning does not guarantee accurate context‑aware forecasting, indicating a decoupling between semantic understanding and numerical prediction; and iii) despite the complementary nature of textual and visual forms of time series as inputs, current multimodal models fail to effectively fuse them for reciprocal performance gains. TSRBench provides a standardized evaluation platform that not only highlights existing challenges but also offers valuable insights to advance generalist models. Our code and dataset are available at https://tsrbench.github.io/.

PaperId: 3570, https://arxiv.org/pdf/2601.18733.pdf GitHub

Authors:Li Kang, Heng Zhou, Xiufeng Song, Rui Li, Bruno N. Y. Chen, Ziye Wang, Ximeng Meng, Stone Tao, Yiran Qin, Xiaohong Liu, Ruimao Zhang, Lei Bai, Yilun Du, Hao Su, Philip Torr, Zhenfei Yin, Ruihao Gong, Yejun Zeng, Fengjun Zhong, Shenghao Jin, Jinyang Guo, Xianglong Liu, Xiaojun Jia, Tianqi Shan, Wenqi Ren, Simeng Qin, Jialing Yang, Xiaoyu Ma, Tianxing Chen, Zixuan Li, Zijian Cai, Yan Qin, Yusen Qin, Qiangyu Chen, Kaixuan Wang, Zhaoming Han, Yao Mu, Ping Luo, Yuanqi Yao, Haoming Song, Jan-Nico Zaech, Fabien Despinoy, Danda Pani Paudel, Luc Van Gool

Abstract:
Recent advancements in multimodal large language models and vision‑languageaction models have significantly driven progress in Embodied AI. As the field transitions toward more complex task scenarios, multi‑agent system frameworks are becoming essential for achieving scalable, efficient, and collaborative solutions. This shift is fueled by three primary factors: increasing agent capabilities, enhancing system efficiency through task delegation, and enabling advanced human‑agent interactions. To address the challenges posed by multi‑agent collaboration, we propose the Multi‑Agent Robotic System (MARS) Challenge, held at the NeurIPS 2025 Workshop on SpaVLE. The competition focuses on two critical areas: planning and control, where participants explore multi‑agent embodied planning using vision‑language models (VLMs) to coordinate tasks and policy execution to perform robotic manipulation in dynamic environments. By evaluating solutions submitted by participants, the challenge provides valuable insights into the design and coordination of embodied multi‑agent systems, contributing to the future development of advanced collaborative AI systems.

PaperId: 3571, https://arxiv.org/pdf/2601.18731.pdf GitHub

Abstract:
Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individual users. This relies on personalized reward models that capture user‑specific preferences and automatically provide individualized feedback. However, developing these models faces two critical challenges: the scarcity of feedback from individual users and the need for efficient adaptation to unseen users. We argue that addressing these constraints requires a paradigm shift from fitting data to learn user preferences to learn the process of preference adaptation. To realize this, we propose Meta Reward Modeling (MRM), which reformulates personalized reward modeling as a meta‑learning problem. Specifically, we represent each user's reward model as a weighted combination of base reward functions, and optimize the initialization of these weights using a Model‑Agnostic Meta‑Learning (MAML)‑style framework to support fast adaptation under limited feedback. To ensure robustness, we introduce the Robust Personalization Objective (RPO), which places greater emphasis on hard‑to‑learn users during meta optimization. Extensive experiments on personalized preference datasets validate that MRM enhances few‑shot personalization, improves user robustness, and consistently outperforms baselines. We release code at https://github.com/ModalityDance/MRM.

PaperId: 3572, https://arxiv.org/pdf/2601.18700.pdf GitHub

Abstract:
Emotional Support Conversation requires not only affective expression but also grounded instrumental support to provide trustworthy guidance. However, existing ESC systems and benchmarks largely focus on affective support in text‑only settings, overlooking how external tools can enable factual grounding and reduce hallucination in multi‑turn emotional support. We introduce TEA‑Bench, the first interactive benchmark for evaluating tool‑augmented agents in ESC, featuring realistic emotional scenarios, an MCP‑style tool environment, and process‑level metrics that jointly assess the quality and factual grounding of emotional support. Experiments on nine LLMs show that tool augmentation generally improves emotional support quality and reduces hallucination, but the gains are strongly capacity‑dependent: stronger models use tools more selectively and effectively, while weaker models benefit only marginally. We further release TEA‑Dialog, a dataset of tool‑enhanced ESC dialogues, and find that supervised fine‑tuning improves in‑distribution support but generalizes poorly. Our results underscore the importance of tool use in building reliable emotional support agents. Our code and data can be found in https://github.com/XingYuSSS/TEA‑Bench.

PaperId: 3573, https://arxiv.org/pdf/2601.18510.pdf GitHub

Abstract:
While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment. Conventional reinforcement learning (RL) offers a solution but incurs prohibitive computational costs and the risk of catastrophic forgetting. We introduce Just‑In‑Time Reinforcement Learning (JitRL), a training‑free framework that enables test‑time policy optimization without any gradient updates. JitRL maintains a dynamic, non‑parametric memory of experiences and retrieves relevant trajectories to estimate action advantages on‑the‑fly. These estimates are then used to directly modulate the LLM's output logits. We theoretically prove that this additive update rule is the exact closed‑form solution to the KL‑constrained policy optimization objective. Extensive experiments on WebArena and Jericho demonstrate that JitRL establishes a new state‑of‑the‑art among training‑free methods. Crucially, JitRL outperforms the performance of computationally expensive fine‑tuning methods (e.g., WebRL) while reducing monetary costs by over 30 times, offering a scalable path for continual learning agents. The code is available at https://github.com/liushiliushi/JitRL.

PaperId: 3574, https://arxiv.org/pdf/2601.18296.pdf GitHub

Abstract:
Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with multi‑hop dependencies and complex temporal constraints. Existing methods rely on fixed workflows and expensive closed‑source APIs, limiting flexibility and scalability. We propose Temp‑R1, the first autonomous end‑to‑end agent for TKGQA trained through reinforcement learning. To address cognitive overload in single‑action reasoning, we expand the action space with specialized internal actions alongside external action. To prevent shortcut learning on simple questions, we introduce reverse curriculum learning that trains on difficult questions first, forcing the development of sophisticated reasoning before transferring to easier cases. Our 8B‑parameter Temp‑R1 achieves state‑of‑the‑art performance on MultiTQ and TimelineKGQA, improving 19.8% over strong baselines on complex questions. Our work establishes a new paradigm for autonomous temporal reasoning agents. The code is available at https://github.com/zjukg/Temp‑R1.

PaperId: 3575, https://arxiv.org/pdf/2601.18241.pdf GitHub

Abstract:
While Large Language Models (LLMs) have shown promise in software engineering, their application to unit testing remains largely confined to isolated test generation or oracle prediction, neglecting the broader challenge of test suite maintenance. We introduce TAM‑Eval (Test Automated Maintenance Evaluation), a framework and benchmark designed to evaluate model performance across three core test maintenance scenarios: creation, repair, and updating of test suites. Unlike prior work limited to function‑level tasks, TAM‑Eval operates at the test file level, while maintaining access to full repository context during isolated evaluation, better reflecting real‑world maintenance workflows. Our benchmark comprises 1,539 automatically extracted and validated scenarios from Python, Java, and Go projects. TAM‑Eval supports system‑agnostic evaluation of both raw LLMs and agentic workflows, using a reference‑free protocol based on test suite pass rate, code coverage, and mutation testing. Empirical results indicate that state‑of‑the‑art LLMs have limited capabilities in realistic test maintenance processes and yield only marginal improvements in test effectiveness. We release TAM‑Eval as an open‑source framework to support future research in automated software testing. Our data and code are publicly available at https://github.com/trndcenter/TAM‑Eval.

PaperId: 3576, https://arxiv.org/pdf/2601.18225.pdf GitHub

Abstract:
Large language model (LLM)‑based agents are increasingly deployed in e‑commerce shopping. To perform thorough, user‑tailored product searches, agents should interpret personal preferences, engage in multi‑turn dialogues, and ultimately retrieve and discriminate among highly similar products. However, existing research has yet to provide a unified simulation environment that consistently captures all of these aspects, and always focuses solely on evaluation benchmarks without training support. In this paper, we introduce ShopSimulator, a large‑scale and challenging Chinese shopping environment. Leveraging ShopSimulator, we evaluate LLMs across diverse scenarios, finding that even the best‑performing models achieve less than 40% full‑success rate. Error analysis reveals that agents struggle with deep search and product selection in long trajectories, fail to balance the use of personalization cues, and to effectively engage with users. Further training exploration provides practical guidance for overcoming these weaknesses, with the combination of supervised fine‑tuning (SFT) and reinforcement learning (RL) yielding significant performance improvements. Code and data will be released at https://github.com/ShopAgent‑Team/ShopSimulator.

PaperId: 3577, https://arxiv.org/pdf/2601.18129.pdf GitHub

Abstract:
Large language models (LLMs) have progressed rapidly; however, most state‑of‑the‑art models are trained and evaluated primarily in high‑resource languages such as English and Chinese, and are often developed by a small number of organizations with access to large‑scale compute and data. This gatekeeping creates a practical barrier for sovereign settings in which a regional‑ or national‑scale institution or domain owner must retain control and understanding of model weights, training data, and deployment while operating under limited resources and strict transparency constraints. To this end, we identify two core requirements: (1) adoptability, the ability to transform a base model into a general‑purpose assistant, and (2) sovereign capability, the ability to perform high‑stakes, region‑specific tasks (e.g., legal reasoning in local languages and cultural knowledge). We investigate whether these requirements can be achieved without scaling massive instruction corpora or relying on complex preference tuning pipelines and large‑scale reinforcement fine‑tuning (RFT). We present Typhoon S, a minimal and open post‑training recipe that combines supervised fine‑tuning, on‑policy distillation, and small‑scale RFT. Using Thai as a representative case study, we demonstrate that our approach transforms both sovereign‑adapted and general‑purpose base models into instruction‑tuned models with strong general performance. We further show that small‑scale RFT with InK‑GRPO ‑‑ an extension of GRPO that augments the GRPO loss with a next‑word prediction loss ‑‑ improves Thai legal reasoning and Thai‑specific knowledge while preserving general capabilities. Our results suggest that a carefully designed post‑training strategy can reduce the required scale of instruction data and computation, providing a practical path toward high‑quality sovereign LLMs under academic‑scale resources.

PaperId: 3578, https://arxiv.org/pdf/2601.18113.pdf GitHub

Abstract:
LLM‑based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when processing malicious URLs: accepting a disguised malicious URL enables subsequent access to unsafe webpages, which can cause severe damage to service providers and users. Despite this risk, no benchmark currently targets this emerging threat. To address this gap, we propose MalURLBench, the first benchmark for evaluating LLMs' vulnerabilities to malicious URLs. MalURLBench contains 61,845 attack instances spanning 10 real‑world scenarios and 7 categories of real malicious websites. Experiments with 12 popular LLMs reveal that existing models struggle to detect elaborately disguised malicious URLs. We further identify and analyze key factors that impact attack success rates and propose URLGuard, a lightweight defense module. We believe this work will provide a foundational resource for advancing the security of web agents. Our code is available at https://github.com/JiangYingEr/MalURLBench.

PaperId: 3579, https://arxiv.org/pdf/2601.18067.pdf GitHub

Abstract:
Verilog's design cycle is inherently labor‑intensive and necessitates extensive domain expertise. Although Large Language Models (LLMs) offer a promising pathway toward automation, their limited training data and intrinsic sequential reasoning fail to capture the strict formal logic and concurrency inherent in hardware systems. To overcome these barriers, we present EvolVE, the first framework to analyze multiple evolution strategies on chip design tasks, revealing that Monte Carlo Tree Search (MCTS) excels at maximizing functional correctness, while Idea‑Guided Refinement (IGR) proves superior for optimization. We further leverage Structured Testbench Generation (STG) to accelerate the evolutionary process. To address the lack of complex optimization benchmarks, we introduce IC‑RTL, targeting industry‑scale problems derived from the National Integrated Circuit Contest. Evaluations establish EvolVE as the new state‑of‑the‑art, achieving 98.1% on VerilogEval v2 and 92% on RTLLM v2. Furthermore, on the industry‑scale IC‑RTL suite, our framework surpasses reference implementations authored by contest participants, reducing the Power, Performance, Area (PPA) product by up to 66% in Huffman Coding and 17% in the geometric mean across all problems. The source code of the IC‑RTL benchmark is available at https://github.com/weiber2002/ICRTL.

PaperId: 3580, https://arxiv.org/pdf/2601.17934.pdf GitHub

Abstract:
Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and the inability of Parameter‑Efficient Fine‑Tuning (PEFT) to exploit unlabeled data. While conventional models like U‑Net excel in semi‑supervised medical learning, their potential to assist a PEFT SAM has been largely overlooked. We introduce SC‑SAM, a specialist‑generalist framework where U‑Net provides point‑based prompts and pseudo‑labels to guide SAM's adaptation, while SAM serves as a powerful generalist supervisor to regularize U‑Net. This reciprocal guidance forms a bidirectional co‑training loop that allows both models to effectively exploit the unlabeled data. Across prostate MRI and polyp segmentation benchmarks, our method achieves state‑of‑the‑art results, outperforming other existing semi‑supervised SAM variants and even medical foundation models like MedSAM, highlighting the value of specialist‑generalist cooperation for label‑efficient medical image segmentation. Our code is available at https://github.com/vnlvi2k3/SC‑SAM.

PaperId: 3581, https://arxiv.org/pdf/2601.17917.pdf GitHub GitHub

Abstract:
Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirectional attention to achieve superior global coherence compared to autoregressive models. While recent works have accelerated inference via KV cache reuse or heuristic decoding, they overlook the intrinsic inefficiencies within the block‑wise diffusion process. Specifically, they suffer from spatial redundancy by modeling informative‑sparse suffix regions uniformly and temporal inefficiency by applying fixed denoising schedules across all the decoding process. To address this, we propose Streaming‑dLLM, a training‑free framework that streamlines inference across both spatial and temporal dimensions. Spatially, we introduce attenuation guided suffix modeling to approximate the full context by pruning redundant mask tokens. Temporally, we employ a dynamic confidence aware strategy with an early exit mechanism, allowing the model to skip unnecessary iterations for converged tokens. Extensive experiments show that Streaming‑dLLM achieves up to 68.2X speedup while maintaining generation quality, highlighting its effectiveness in diffusion decoding. The code is available at https://github.com/xiaoshideta/Streaming‑dLLM.

PaperId: 3582, https://arxiv.org/pdf/2601.17897.pdf GitHub

Abstract:
A growing body of research suggests that the cognitive processes of large language models (LLMs) differ fundamentally from those of humans. However, existing interpretability methods remain limited in explaining how cognitive abilities are engaged during LLM reasoning. In this paper, we propose UniCog, a unified framework that analyzes LLM cognition via a latent mind space. Formulated as a latent variable model, UniCog encodes diverse abilities from dense model activations into sparse, disentangled latent dimensions. Through extensive analysis on six advanced LLMs, including DeepSeek‑V3.2 and GPT‑4o, we reveal a Pareto principle of LLM cognition, where a shared reasoning core is complemented by ability‑specific signatures. Furthermore, we discover that reasoning failures often manifest as anomalous intensity in latent activations. These findings opens a new paradigm in LLM analysis, providing a cognition grounded view of reasoning dynamics. Finally, leveraging these insights, we introduce a latent‑informed candidate prioritization strategy, which improves reasoning performance by up to 7.5% across challenging benchmarks. Our code is available at https://github.com/milksalute/unicog.

PaperId: 3583, https://arxiv.org/pdf/2601.17887.pdf GitHub

Abstract:
Long‑term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized agents prioritizes utility and user experience, treating memory as a neutral component and largely overlooking its safety implications. In this paper, we reveal intent legitimation, a previously underexplored safety failure in personalized agents, where benign personal memories bias intent inference and cause models to legitimize inherently harmful queries. To study this phenomenon, we introduce PS‑Bench, a benchmark designed to identify and quantify intent legitimation in personalized interactions. Across multiple memory‑augmented agent frameworks and base LLMs, personalization increases attack success rates by 15.8%‑‑243.7% relative to stateless baselines. We further provide mechanistic evidence for intent legitimation from internal representations space, and propose a lightweight detection‑reflection method that effectively reduces safety degradation. Overall, our work provides the first systematic exploration and evaluation of intent legitimation as a safety failure mode that naturally arises from benign, real‑world personalization, highlighting the importance of assessing safety under long‑term personal context. Our code is available at: https://github.com/MuyuenLP/PS‑Bench. WARNING: This paper may contain harmful content.

PaperId: 3584, https://arxiv.org/pdf/2601.17885.pdf GitHub

Abstract:
Bimanual manipulation in cluttered scenes requires policies that remain stable under occlusions, viewpoint and scene variations. Existing vision‑language‑action models often fail to generalize because (i) multi‑view features are fused via view‑agnostic token concatenation, yielding weak 3D‑consistent spatial understanding, and (ii) language is injected as global conditioning, resulting in coarse instruction grounding. In this paper, we introduce PEAfowl, a perception‑enhanced multi‑view VLA policy for bimanual manipulation. For spatial reasoning, PEAfowl predicts per‑token depth distributions, performs differentiable 3D lifting, and aggregates local cross‑view neighbors to form geometrically grounded, cross‑view consistent representations. For instruction grounding, we propose to replace global conditioning with a Perceiver‑style text‑aware readout over frozen CLIP visual features, enabling iterative evidence accumulation. To overcome noisy and incomplete commodity depth without adding inference overhead, we apply training‑only depth distillation from a pretrained depth teacher to supervise the depth‑distribution head, providing perception front‑end with geometry‑aware priors. On RoboTwin 2.0 under domain‑randomized setting, PEAfowl improves the strongest baseline by 23.0 pp in success rate, and real‑robot experiments further demonstrate reliable sim‑to‑real transfer and consistent improvements from depth distillation. Project website: https://peafowlvla.github.io/.

PaperId: 3585, https://arxiv.org/pdf/2601.17868.pdf GitHub

Abstract:
Current Video Large Language Models (Video LLMs) typically encode frames via a vision encoder and employ an autoregressive (AR) LLM for understanding and generation. However, this AR paradigm inevitably faces a dual efficiency bottleneck: strictly unidirectional attention compromises understanding efficiency by hindering global spatiotemporal aggregation, while serial decoding restricts generation efficiency. To address this, we propose VidLaDA, a Video LLM based on Diffusion Language Models (DLMs) that leverages bidirectional attention to unlock comprehensive spatiotemporal modeling and decode tokens in parallel. To further mitigate the computational overhead of diffusion decoding, we introduce MARS‑Cache, an acceleration strategy that prunes redundancy by combining asynchronous visual cache refreshing with frame‑wise chunk attention. Experiments show VidLaDA rivals state‑of‑the‑art AR baselines (e.g., Qwen2.5‑VL and LLaVA‑Video) and outperforms DLM baselines, with MARS‑Cache delivering over 12x speedup without compromising accuracy. Code and checkpoints are open‑sourced at https://github.com/ziHoHe/VidLaDA.

PaperId: 3586, https://arxiv.org/pdf/2601.17823.pdf GitHub

Abstract:
In this paper, we present DIETA, a small, decoder‑only Transformer model with 0.5 billion parameters, specifically designed and trained for Italian‑English machine translation. We collect and curate a large parallel corpus consisting of approximately 207 million Italian‑English sentence pairs across diverse domains, including parliamentary proceedings, legal texts, web‑crawled content, subtitles, news, literature and 352 million back‑translated data using pretrained models. Additionally, we create and release a new small‑scale evaluation set, consisting of 450 sentences, based on 2025 WikiNews articles, enabling assessment of translation quality on contemporary text. Comprehensive evaluations show that DIETA achieves competitive performance on multiple Italian‑English benchmarks, consistently ranking in the second quartile of a 32‑system leaderboard and outperforming most other sub‑3B models on four out of five test suites. The training script, trained models, curated corpus, and newly introduced evaluation set are made publicly available, facilitating further research and development in specialized Italian‑English machine translation. https://github.com/pkasela/DIETA‑Machine‑Translation

PaperId: 3587, https://arxiv.org/pdf/2601.17814.pdf GitHub

Abstract:
Multimodal large language models (MLLMs) have advanced rapidly, yet heterogeneity in architecture, alignment strategies, and efficiency means that no single model is uniformly superior across tasks. In practical deployments, workloads span lightweight OCR to complex multimodal reasoning; using one MLLM for all queries either over‑provisions compute on easy instances or sacrifices accuracy on hard ones. Query‑level model selection (routing) addresses this tension, but extending routing from text‑only LLMs to MLLMs is nontrivial due to modality fusion, wide variation in computational cost across models, and the absence of a standardized, budget‑aware evaluation. We present MMR‑Bench, a unified benchmark that isolates the multimodal routing problem and enables comparison under fixed candidate sets and cost models. MMR‑Bench provides (i) a controlled environment with modality‑aware inputs and variable compute budgets, (ii) a broad suite of vision‑language tasks covering OCR, general VQA, and multimodal math reasoning, and (iii) strong single‑model reference, oracle upper bounds, and representative routing policies. Using MMR‑Bench, we show that incorporating multimodal signals improves routing quality. Empirically, these cues improve the cost‑accuracy frontier and enable the routed system to exceed the strongest single model's accuracy at roughly 33% of its cost. Furthermore, policies trained on a subset of models and tasks generalize zero‑shot to new datasets and text‑only benchmarks without retuning, establishing MMR‑Bench as a foundation for studying adaptive multimodal model selection and efficient MLLM deployment. The code will be available at: https://github.com/Hunter‑Wrynn/MMR‑Bench.

PaperId: 3588, https://arxiv.org/pdf/2601.17768.pdf GitHub

Abstract:
In LLM inference, the same prompt may yield different outputs across different runs. At the system level, this non‑determinism arises from floating‑point non‑associativity combined with dynamic batching and GPU kernels whose reduction orders vary with batch size. A straightforward way to eliminate non‑determinism is to disable dynamic batching during inference, but doing so severely degrades throughput. Another approach is to make kernels batch‑invariant; however, this tightly couples determinism to kernel design, requiring new implementations. This coupling also imposes fixed runtime overheads, regardless of how much of the workload actually requires determinism. Inspired by ideas from speculative decoding, we present LLM‑42, a scheduling‑based approach to enable determinism in LLM inference. Our key observation is that if a sequence is in a consistent state, the next emitted token is likely to be consistent even with dynamic batching. Moreover, most GPU kernels use shape‑consistent reductions. Leveraging these insights, LLM‑42 decodes tokens using a non‑deterministic fast path and enforces determinism via a lightweight verify‑rollback loop. The verifier replays candidate tokens under a fixed‑shape reduction schedule, commits those that are guaranteed to be consistent across runs, and rolls back those violating determinism. LLM‑42 mostly re‑uses existing kernels unchanged and incurs overhead only in proportion to the traffic that requires determinism.

PaperId: 3589, https://arxiv.org/pdf/2601.17756.pdf GitHub

Abstract:
Existing Subject‑to‑Video Generation (S2V) methods have achieved high‑fidelity and subject‑consistent video generation, yet remain constrained to single‑view subject references. This limitation renders the S2V task reducible to an S2I + I2V pipeline, failing to exploit the full potential of video subject control. In this work, we propose and address the challenging Multi‑View S2V (MV‑S2V) task, which synthesizes videos from multiple reference views to enforce 3D‑level subject consistency. Regarding the scarcity of training data, we first develop a synthetic data curation pipeline to generate highly customized synthetic data, complemented by a small‑scale real‑world captured dataset to boost the training of MV‑S2V. Another key issue lies in the potential confusion between cross‑subject and cross‑view references in conditional generation. To overcome this, we further introduce Temporally Shifted RoPE (TS‑RoPE) to distinguish between different subjects and distinct views of the same subject in reference conditioning. Our framework achieves superior 3D subject consistency w.r.t. multi‑view reference images and high‑quality visual outputs, establishing a new meaningful direction for subject‑driven video generation. Code and data are available at: https://szy‑young.github.io/mv‑s2v

PaperId: 3590, https://arxiv.org/pdf/2601.17744.pdf GitHub

Abstract:
Autonomous agent systems increasingly trigger real‑world side effects: deploying infrastructure, modifying databases, moving money, and executing workflows. Yet most agent stacks provide no mandatory execution checkpoint where organizations can deterministically permit, deny, or defer an action before it changes reality. This paper introduces Faramesh, a protocol‑agnostic execution control plane that enforces execution‑time authorization for agent‑driven actions via a non‑bypassable Action Authorization Boundary (AAB). Faramesh canonicalizes agent intent into a Canonical Action Representation (CAR), evaluates actions deterministically against policy and state, and issues a decision artifact (PERMIT/DEFER/DENY) that executors must validate prior to execution. The system is designed to be framework‑ and model‑agnostic, supports multi‑agent and multi‑tenant deployments, and remains independent of transport protocols (e.g., MCP). Faramesh further provides decision‑centric, append‑only provenance logging keyed by canonical action hashes, enabling auditability, verification, and deterministic replay without re‑running agent reasoning. We show how these primitives yield enforceable, predictable governance for autonomous execution while avoiding hidden coupling to orchestration layers or observability‑only approaches.

PaperId: 3591, https://arxiv.org/pdf/2601.17735.pdf GitHub

Abstract:
Relational databases (RDBs) play a crucial role in many real‑world web applications, supporting data management across multiple interconnected tables. Beyond typical retrieval‑oriented tasks, prediction tasks on RDBs have recently gained attention. In this work, we address this problem by generating informative relational features that enhance predictive performance. However, generating such features is challenging: it requires reasoning over complex schemas and exploring a combinatorially large feature space, all without explicit supervision. To address these challenges, we propose ReFuGe, an agentic framework that leverages specialized large language model agents: (1) a schema selection agent identifies the tables and columns relevant to the task, (2) a feature generation agent produces diverse candidate features from the selected schema, and (3) a feature filtering agent evaluates and retains promising features through reasoning‑based and validation‑based filtering. It operates within an iterative feedback loop until performance converges. Experiments on RDB benchmarks demonstrate that ReFuGe substantially improves performance on various RDB prediction tasks. Our code and datasets are available at https://github.com/K‑Kyungho/REFUGE.

PaperId: 3592, https://arxiv.org/pdf/2601.17711.pdf GitHub

Abstract:
Distributed microphone array (DMA) is a promising next‑generation platform for speech interaction, where speech enhancement (SE) is still required to improve the speech quality in noisy cases. Existing SE methods usually first gather raw waveforms at a fusion center (FC) from all devices and then design a multi‑microphone model, causing high bandwidth and energy costs. In this work, we propose a \emphCompress‑and‑Send Network (CaSNet) for resource‑constrained DMAs, where one microphone serves as the FC and reference. Each of other devices encodes the measured raw data into a feature matrix, which is then compressed by singular value decomposition (SVD) to produce a more compact representation. The received features at the FC are aligned via cross window query with respect to the reference, followed by neural decoding to yield spatially coherent enhanced speech. Experiments on multiple datasets show that the proposed CaSNet can save the data amount with a negligible impact on the performance compared to the uncompressed case. The reproducible code is available at https://github.com/Jokejiangv/CaSNet.

PaperId: 3593, https://arxiv.org/pdf/2601.17687.pdf GitHub

Abstract:
Language models are revolutionizing the biochemistry domain, assisting scientists in drug design and chemical synthesis with high efficiency. Yet current approaches struggle between small language models prone to hallucination and limited knowledge retention, and large cloud‑based language models plagued by privacy risks and high inference costs. To bridge this gap, we introduce ChemCRAFT, a novel framework leveraging agentic reinforcement learning to decouple chemical reasoning from knowledge storage. Instead of forcing the model to memorize vast chemical data, our approach empowers the language model to interact with a sandbox for precise information retrieval. This externalization of knowledge allows a locally deployable small model to achieve superior performance with minimal inference costs. To enable small language models for agent‑calling ability, we build an agentic trajectory construction pipeline and a comprehensive chemical‑agent sandbox. Based on sandbox interactions, we constructed ChemToolDataset, the first large‑scale chemical tool trajectory dataset. Simultaneously, we propose SMILES‑GRPO to build a dense chemical reward function, promoting the model's ability to call chemical agents. Evaluations across diverse aspects of drug design show that ChemCRAFT outperforms current cloud‑based LLMs in molecular structure analysis, molecular optimization, and synthesis pathway prediction, demonstrating that scientific reasoning is not solely an emergent ability of model scale, but a learnable policy of tool orchestration. This work establishes a cost‑effective and privacy‑preserving paradigm for AI‑aided chemistry, opening new avenues for accelerating molecular discovery with locally deployable agents. Code available at https://github.com/HowardLi1984/ChemCraft.

PaperId: 3594, https://arxiv.org/pdf/2601.17644.pdf GitHub

Abstract:
The growing adoption of multimodal Retrieval‑Augmented Generation (mRAG) pipelines for vision‑centric tasks (e.g., visual QA) introduces important privacy challenges. In particular, while mRAG provides a practical capability to connect private datasets and improve model performance, it risks the leakage of private information from these datasets. In this paper, we perform an empirical study to analyze the privacy risks inherent in the mRAG pipeline observed through standard model prompting. Specifically, we implement a case study that attempts to determine whether a visual asset (e.g., image) is included in the mRAG, and, if present, to leak the metadata (e.g., caption) related to it. Our findings highlight the need for privacy‑preserving mechanisms and motivate future research on mRAG privacy. Our code is published online: https://github.com/aliwister/mrag‑attack‑eval.

PaperId: 3595, https://arxiv.org/pdf/2601.17564.pdf GitHub

Abstract:
The Abstraction and Reasoning Corpus (ARC) tests AI systems' ability to perform human‑like inductive reasoning from a few demonstration pairs. Existing Gymnasium‑based RL environments severely limit experimental scale due to computational bottlenecks. We present JaxARC, an open‑source, high‑performance RL environment for ARC implemented in JAX. Its functional, stateless architecture enables massive parallelism, achieving 38‑5,439x speedup over Gymnasium at matched batch sizes, with peak throughput of 790M steps/second. JaxARC supports multiple ARC datasets, flexible action spaces, composable wrappers, and configuration‑driven reproducibility, enabling large‑scale RL research previously computationally infeasible. JaxARC is available at https://github.com/aadimator/JaxARC.

PaperId: 3596, https://arxiv.org/pdf/2601.17533.pdf GitHub

Abstract:
Adapter‑based Federated Large Language Models (FedLLMs) are widely adopted to reduce the computational, storage, and communication overhead of full‑parameter fine‑tuning for web‑scale applications while preserving user privacy. By freezing the backbone and training only compact low‑rank adapters, these methods appear to limit gradient leakage and thwart existing Gradient Inversion Attacks (GIAs). Contrary to this assumption, we show that low‑rank adapters create new, exploitable leakage channels. We propose the Unordered‑word‑bag‑based Text Reconstruction (UTR) attack, a novel GIA tailored to the unique structure of adapter‑based FedLLMs. UTR overcomes three core challenges: low‑dimensional gradients, frozen backbones, and combinatorially large reconstruction spaces by: (i) inferring token presence from attention patterns in frozen layers, (ii) performing sentence‑level inversion within the low‑rank subspace of adapter gradients, and (iii) enforcing semantic coherence through constrained greedy decoding guided by language priors. Extensive experiments across diverse models (GPT2‑Large, BERT, Qwen2.5‑7B) and datasets (CoLA, SST‑2, Rotten Tomatoes) demonstrate that UTR achieves near‑perfect reconstruction accuracy (ROUGE‑1/2 > 99), even with large batch size settings where prior GIAs fail completely. Our results reveal a fundamental tension between parameter efficiency and privacy in FedLLMs, challenging the prevailing belief that lightweight adaptation inherently enhances security. Our code and data are available at https://github.com/shwksnshwowk‑wq/GIA.

PaperId: 3597, https://arxiv.org/pdf/2601.17388.pdf GitHub

Abstract:
Watermarking methods have always been effective means of protecting intellectual property, yet they face significant challenges. Although existing deep learning‑based watermarking systems can hide watermarks in images with minimal impact on image quality, they often lack robustness when encountering image corruptions during transmission, which undermines their practical application value. To this end, we propose a high‑quality and robust watermark framework based on the diffusion model. Our method first converts the clean image into inversion noise through a null‑text optimization process, and after optimizing the inversion noise in the latent space, it produces a high‑quality watermarked image through an iterative denoising process of the diffusion model. The iterative denoising process serves as a powerful purification mechanism, ensuring both the visual quality of the watermarked image and enhancing the robustness of the watermark against various corruptions. To prevent the optimizing of inversion noise from distorting the original semantics of the image, we specifically introduced self‑attention constraints and pseudo‑mask strategies. Extensive experimental results demonstrate the superior performance of our method against various image corruptions. In particular, our method outperforms the stable signature method by an average of 10% across 12 different image transformations on COCO datasets. Our codes are available at https://github.com/920927/ONRW.

PaperId: 3598, https://arxiv.org/pdf/2601.17383.pdf GitHub

Abstract:
Large Vision‑Language Models (LVLMs) are increasingly deployed in real‑world intelligent systems for perception and reasoning in open physical environments. While LVLMs are known to be vulnerable to prompt injection attacks, existing methods either require access to input channels or depend on knowledge of user queries, assumptions that rarely hold in practical deployments. We propose the first Physical Prompt Injection Attack (PPIA), a black‑box, query‑agnostic attack that embeds malicious typographic instructions into physical objects perceivable by the LVLM. PPIA requires no access to the model, its inputs, or internal pipeline, and operates solely through visual observation. It combines offline selection of highly recognizable and semantically effective visual prompts with strategic environment‑aware placement guided by spatiotemporal attention, ensuring that the injected prompts are both perceivable and influential on model behavior. We evaluate PPIA across 10 state‑of‑the‑art LVLMs in both simulated and real‑world settings on tasks including visual question answering, planning, and navigation, PPIA achieves attack success rates up to 98%, with strong robustness under varying physical conditions such as distance, viewpoint, and illumination. Our code is publicly available at https://github.com/2023cghacker/Physical‑Prompt‑Injection‑Attack.

PaperId: 3599, https://arxiv.org/pdf/2601.17364.pdf GitHub

Abstract:
This study uses Jordanian law as a case study to explore the fine‑tuning of the Llama‑3.1 large language model for Arabic question‑answering. Two versions of the model ‑ Llama‑3.1‑8B‑bnb‑4bit and Llama‑3.1‑8B‑Instruct‑bnb‑4bit ‑ were fine‑tuned using parameter‑efficient fine‑tuning (PEFT) with LoRA adapters and 4‑bit quantized models, leveraging the Unsloth framework for accelerated and resource‑efficient training. A custom dataset of 6000 legal question‑answer pairs was curated from Jordanian laws and formatted into structured prompts. Performance was evaluated using the BLEU and the ROUGE metrics to compare the fine‑tuned models to their respective base versions. Results demonstrated improved legal reasoning and accuracy while achieving resource efficiency through quantization and optimized fine‑tuning strategies. This work underscores the potential of adapting large language models for Arabic legal domains and highlights effective techniques for fine‑tuning domain‑specific tasks.

PaperId: 3600, https://arxiv.org/pdf/2601.17332.pdf GitHub GitHub

Abstract:
The high cost of agentic workflows in formal mathematics hinders large‑scale data synthesis, exacerbating the scarcity of open‑source corpora. To address this, we introduce TheoremForge, a cost‑effective formal data synthesis pipeline that decomposes the formalization process into five sub‑tasks, which are statement formalization, proof generation, premise selection, proof correction and proof sketching. By implementing a Decoupled Extraction Strategy, the workflow recovers valid training signals from globally failed trajectories, effectively utilizing wasted computation. Experiments on a 2,000‑problem benchmark demonstrate that TheoremForge achieves a Verified Rate of 12.6%, surpassing the 8.6% baseline, at an average cost of only \0.481 per successful trajectory using Gemini‑3‑Flash. Crucially, our strategy increases data yield by 1.6× for proof generation compared to standard filtering. These results establish TheoremForge as a scalable framework for constructing a data flywheel to train future expert models. Our code is available \hrefhttps://github.com/timechess/TheoremForgehere.

PaperId: 3601, https://arxiv.org/pdf/2601.17284.pdf GitHub

Abstract:
The deployment of Large Language Models in Medical Question Answering is severely hampered by ambiguous user queries, a significant safety risk that demonstrably reduces answer accuracy in high‑stakes healthcare settings. In this paper, we formalize this challenge by linking input ambiguity to aleatoric uncertainty (AU), which is the irreducible uncertainty arising from underspecified input. To facilitate research in this direction, we construct CV‑MedBench, the first benchmark designed for studying input ambiguity in Medical QA. Using this benchmark, we analyze AU from a representation engineering perspective, revealing that AU is linearly encoded in LLM's internal activation patterns. Leveraging this insight, we introduce a novel AU‑guided "Clarify‑Before‑Answer" framework, which incorporates AU‑Probe ‑ a lightweight module that detects input ambiguity directly from hidden states. Unlike existing uncertainty estimation methods, AU‑Probe requires neither LLM fine‑tuning nor multiple forward passes, enabling an efficient mechanism to proactively request user clarification and significantly enhance safety. Extensive experiments across four open LLMs demonstrate the effectiveness of our QA framework, with an average accuracy improvement of 9.48% over baselines. Our framework provides an efficient and robust solution for safe Medical QA, strengthening the reliability of health‑related applications. The code is available at https://github.com/yaokunliu/AU‑Med.git, and the CV‑MedBench dataset is released on Hugging Face at https://huggingface.co/datasets/yaokunl/CV‑MedBench.

PaperId: 3602, https://arxiv.org/pdf/2601.17173.pdf GitHub

Abstract:
Question answering systems are typically evaluated on factual correctness, yet many real‑world applications‑such as education and career guidance‑require mentorship: responses that provide reflection and guidance. Existing QA benchmarks rarely capture this distinction, particularly in multilingual and long‑form settings. We introduce MentorQA, the first multilingual dataset and evaluation framework for mentorship‑focused question answering from long‑form videos, comprising nearly 9,000 QA pairs from 180 hours of content across four languages. We define mentorship‑focused evaluation dimensions that go beyond factual accuracy, capturing clarity, alignment, and learning value. Using MentorQA, we compare Single‑Agent, Dual‑Agent, RAG, and Multi‑Agent QA architectures under controlled conditions. Multi‑Agent pipelines consistently produce higher‑quality mentorship responses, with especially strong gains for complex topics and lower‑resource languages. We further analyze the reliability of automated LLM‑based evaluation, observing substantial variation in alignment with human judgments. Overall, this work establishes mentorship‑focused QA as a distinct research problem and provides a multilingual benchmark for studying agentic architectures and evaluation design in educational AI. The dataset and evaluation framework are released at https://github.com/AIM‑SCU/MentorQA.

PaperId: 3603, https://arxiv.org/pdf/2601.17160.pdf GitHub

Abstract:
We develop a data‑driven information‑theoretic framework for sharp partial identification of causal effects under unmeasured confounding. Existing approaches often rely on restrictive assumptions, such as bounded or discrete outcomes; require external inputs (for example, instrumental variables, proxies, or user‑specified sensitivity parameters); necessitate full structural causal model specifications; or focus solely on population‑level averages while neglecting covariate‑conditional effects. We overcome all four limitations simultaneously by establishing novel information‑theoretic, data‑driven divergence bounds. Our key theoretical contribution shows that the f‑divergence between the observational distribution P(Y | A = a, X = x) and the interventional distribution P(Y | do(A = a), X = x) is upper bounded by a function of the propensity score alone. This result enables sharp partial identification of conditional causal effects directly from observational data, without requiring external sensitivity parameters, auxiliary variables, full structural specifications, or outcome boundedness assumptions. For practical implementation, we develop a semiparametric estimator satisfying Neyman orthogonality (Chernozhukov et al., 2018), which ensures root‑n consistent inference even when nuisance functions are estimated via flexible machine learning methods. Simulation studies and real‑world data applications, implemented in the GitHub repository (https://github.com/yonghanjung/Information‑Theretic‑Bounds), demonstrate that our framework provides tight and valid causal bounds across a wide range of data‑generating processes.

PaperId: 3604, https://arxiv.org/pdf/2601.17133.pdf GitHub

Abstract:
Fine‑tuning Large Language Models (LLMs) for specialized domains is constrained by a fundamental challenge: the need for diverse, cross‑organizational data conflicts with the principles of data privacy and sovereignty. While Federated Learning (FL) provides a framework for collaboration without raw data exchange, its classic centralized form introduces a single point of failure and remains vulnerable to model inversion attacks. Decentralized FL (DFL) mitigates this risk by removing the central aggregator but typically relies on inefficient, random peer‑to‑peer (P2P) pairings, forming a collaboration graph that is blind to agent heterogeneity and risks negative transfer. This paper introduces KNEXA‑FL, a novel framework for orchestrated decentralization that resolves this trade‑off. KNEXA‑FL employs a non‑aggregating Central Profiler/Matchmaker (CPM) that formulates P2P collaboration as a contextual bandit problem, using a LinUCB algorithm on abstract agent profiles to learn an optimal matchmaking policy. It orchestrates direct knowledge exchange between heterogeneous, PEFT‑based LLM agents via secure distillation, without ever accessing the models themselves. Our comprehensive experiments on a challenging code generation task show that KNEXA‑FL yields substantial gains, improving Pass@1 by approx. 50% relative to random P2P collaboration. Critically, our orchestrated approach demonstrates stable convergence, in stark contrast to a powerful centralized distillation baseline which suffers from catastrophic performance collapse. Our work establishes adaptive, learning‑based orchestration as a foundational principle for building robust and effective decentralized AI ecosystems.

PaperId: 3605, https://arxiv.org/pdf/2601.17091.pdf GitHub

Abstract:
ROCKET (RandOm Convolutional KErnel Transform) is a feature extraction algorithm created for Time Series Classification (TSC), published in 2019. It applies convolution with randomly generated kernels on a time series, producing features that can be used to train a linear classifier or regressor like Ridge. At the time of publication, ROCKET was on par with the best state‑of‑the‑art algorithms for TSC in terms of accuracy while being significantly less computationally expensive, making ROCKET a compelling algorithm for TSC. This also led to several subsequent versions, further improving accuracy and computational efficiency. The currently available ROCKET implementations are mostly bound to execution on CPU. However, convolution is a task that can be highly parallelized and is therefore suited to be executed on GPU, which speeds up the computation significantly. A key difficulty arises from the inhomogeneous kernels ROCKET uses, making standard methods for applying convolution on GPU inefficient. In this work, we propose an algorithm that is able to efficiently perform ROCKET on GPU and achieves up to 11 times higher computational efficiency per watt than ROCKET on CPU. The code for CUROCKET is available in this repository https://github.com/oleeven/CUROCKET on github.

PaperId: 3606, https://arxiv.org/pdf/2601.17065.pdf GitHub

Abstract:
Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM‑based approaches employ single‑model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank‑ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real‑world strategic decision‑making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT‑FOR‑ME, a Middle East‑focused event forecasting benchmark. Experimental results demonstrate the superiority of multi‑expert collaboration in handling complex temporal geopolitical forecasting tasks. The code is available at https://github.com/LuminosityX/ThinkTank‑ME.

PaperId: 3607, https://arxiv.org/pdf/2601.17058.pdf GitHub

Abstract:
Data preparation aims to denoise raw datasets, uncover cross‑dataset relationships, and extract valuable insights from them, which is essential for a wide range of data‑centric applications. Driven by (i) rising demands for application‑ready data (e.g., for analytics, visualization, decision‑making), (ii) increasingly powerful LLM techniques, and (iii) the emergence of infrastructures that facilitate flexible agent construction (e.g., using Databricks Unity Catalog), LLM‑enhanced methods are rapidly becoming a transformative and potentially dominant paradigm for data preparation. By investigating hundreds of recent literature works, this paper presents a systematic review of this evolving landscape, focusing on the use of LLM techniques to prepare data for diverse downstream tasks. First, we characterize the fundamental paradigm shift, from rule‑based, model‑specific pipelines to prompt‑driven, context‑aware, and agentic preparation workflows. Next, we introduce a task‑centric taxonomy that organizes the field into three major tasks: data cleaning (e.g., standardization, error processing, imputation), data integration (e.g., entity matching, schema matching), and data enrichment (e.g., data annotation, profiling). For each task, we survey representative techniques, and highlight their respective strengths (e.g., improved generalization, semantic understanding) and limitations (e.g., the prohibitive cost of scaling LLMs, persistent hallucinations even in advanced agents, the mismatch between advanced methods and weak evaluation). Moreover, we analyze commonly used datasets and evaluation metrics (the empirical part). Finally, we discuss open research challenges and outline a forward‑looking roadmap that emphasizes scalable LLM‑data systems, principled designs for reliable agentic workflows, and robust evaluation protocols.

PaperId: 3608, https://arxiv.org/pdf/2601.17037.pdf GitHub

Abstract:
We investigated visual reasoning limitations of both multimodal large language models (MLLMs) and image generation models (IGMs) by creating a novel benchmark to systematically compare failure modes across image‑to‑text and text‑to‑image tasks, enabling cross‑modal evaluation of visual understanding. Despite rapid growth in machine learning, vision language models (VLMs) still fail to understand or generate basic visual concepts such as object orientation, quantity, or spatial relationships, which highlighted gaps in elementary visual reasoning. By adapting MMVP benchmark questions into explicit and implicit prompts, we create AMVICC, a novel benchmark for profiling failure modes across various modalities. After testing 11 MLLMs and 3 IGMs in nine categories of visual reasoning, our results show that failure modes are often shared between models and modalities, but certain failures are model‑specific and modality‑specific, and this can potentially be attributed to various factors. IGMs consistently struggled to manipulate specific visual components in response to prompts, especially in explicit prompts, suggesting poor control over fine‑grained visual attributes. Our findings apply most directly to the evaluation of existing state‑of‑the‑art models on structured visual reasoning tasks. This work lays the foundation for future cross‑modal alignment studies, offering a framework to probe whether generation and interpretation failures stem from shared limitations to guide future improvements in unified vision‑language modeling.

PaperId: 3609, https://arxiv.org/pdf/2601.17028.pdf GitHub

Abstract:
Tropical algebra, including max‑plus, min‑plus, and related idempotent semirings, provides a unifying framework in which many optimization problems that are nonlinear in classical algebra become linear. This property makes tropical methods particularly well suited for shortest paths, scheduling, throughput analysis, and discrete event systems. Despite their theoretical maturity and practical relevance, existing tropical algebra implementations primarily target desktop or server environments and remain largely inaccessible on resource‑constrained embedded platforms, where such optimization problems are most acute. We present PALMA (Parallel Algebra Library for Max‑plus Applications), a lightweight, dependency‑free C library that brings tropical linear algebra to ARM‑based embedded systems. PALMA implements a generic semiring abstraction with SIMD‑accelerated kernels, enabling a single computational framework to support shortest paths, bottleneck paths, reachability, scheduling, and throughput analysis. The library supports five tropical semirings, dense and sparse (CSR) representations, tropical closure, and spectral analysis via maximum cycle mean computation. We evaluate PALMA on a Raspberry Pi 4 and demonstrate peak performance of 2,274 MOPS, speedups of up to 11.9 times over classical Bellman‑Ford for single‑source shortest paths, and sub‑10 microsecond scheduling solves for real‑time control workloads. Case studies in UAV control, IoT routing, and manufacturing systems show that tropical algebra enables efficient, predictable, and unified optimization directly on embedded hardware. PALMA is released as open‑source software under the MIT license.

PaperId: 3610, https://arxiv.org/pdf/2601.16964.pdf GitHub

Abstract:
The rapid advancement of large language models (LLMs) has sparked growing interest in their integration into autonomous systems for reasoning‑driven perception, planning, and decision‑making. However, evaluating and training such agentic AI models remains challenging due to the lack of large‑scale, structured, and safety‑critical benchmarks. This paper introduces AgentDrive, an open benchmark dataset containing 300,000 LLM‑generated driving scenarios designed for training, fine‑tuning, and evaluating autonomous agents under diverse conditions. AgentDrive formalizes a factorized scenario space across seven orthogonal axes: scenario type, driver behavior, environment, road layout, objective, difficulty, and traffic density. An LLM‑driven prompt‑to‑JSON pipeline generates semantically rich, simulation‑ready specifications that are validated against physical and schema constraints. Each scenario undergoes simulation rollouts, surrogate safety metric computation, and rule‑based outcome labeling. To complement simulation‑based evaluation, we introduce AgentDrive‑MCQ, a 100,000‑question multiple‑choice benchmark spanning five reasoning dimensions: physics, policy, hybrid, scenario, and comparative reasoning. We conduct a large‑scale evaluation of fifty leading LLMs on AgentDrive‑MCQ. Results show that while proprietary frontier models perform best in contextual and policy reasoning, advanced open models are rapidly closing the gap in structured and physics‑grounded reasoning. We release the AgentDrive dataset, AgentDrive‑MCQ benchmark, evaluation code, and related materials at https://github.com/maferrag/AgentDrive

PaperId: 3611, https://arxiv.org/pdf/2601.16934.pdf GitHub

Abstract:
To be discoverable in an embedding‑based search process, each part of a document should be reflected in its embedding representation. To quantify any potential reflection biases, we introduce a permutation‑based evaluation framework. With this, we observe that state‑of‑the‑art embedding models exhibit systematic positional and language biases when documents are longer and consist of multiple segments. Specifically, early segments and segments in higher‑resource languages like English are over‑represented, while later segments and segments in lower‑resource languages are marginalized. In our further analysis, we find that the positional bias stems from front‑loaded attention distributions in pooling‑token embeddings, where early tokens receive more attention. To mitigate this issue, we introduce an inference‑time attention calibration method that redistributes attention more evenly across document positions, increasing discoverabiltiy of later segments. Our evaluation framework and attention calibration is available at https://github.com/impresso/fair‑sentence‑transformers

PaperId: 3612, https://arxiv.org/pdf/2601.16669.pdf GitHub

Abstract:
As large language models (LLMs) are increasingly applied to legal domain‑specific tasks, evaluating their ability to perform legal work in real‑world settings has become essential. However, existing legal benchmarks rely on simplified and highly standardized tasks, failing to capture the ambiguity, complexity, and reasoning demands of real legal practice. Moreover, prior evaluations often adopt coarse, single‑dimensional metrics and do not explicitly assess fine‑grained legal reasoning. To address these limitations, we introduce PLawBench, a Practical Law Benchmark designed to evaluate LLMs in realistic legal practice scenarios. Grounded in real‑world legal workflows, PLawBench models the core processes of legal practitioners through three task categories: public legal consultation, practical case analysis, and legal document generation. These tasks assess a model's ability to identify legal issues and key facts, perform structured legal reasoning, and generate legally coherent documents. PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert‑designed evaluation rubrics, resulting in approximately 12,500 rubric items for fine‑grained assessment. Using an LLM‑based evaluator aligned with human expert judgments, we evaluate 10 state‑of‑the‑art LLMs. Experimental results show that none achieves strong performance on PLawBench, revealing substantial limitations in the fine‑grained legal reasoning capabilities of current LLMs and highlighting important directions for future evaluation and development of legal LLMs. Data is available at: https://github.com/skylenage/PLawbench.

PaperId: 3613, https://arxiv.org/pdf/2601.16622.pdf GitHub

Abstract:
Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectures face critical scalability bottlenecks due to the explicit construction of geometric features or dense tensor products on every edge. To overcome this, we introduce E2Former‑V2, a scalable architecture that integrates algebraic sparsity with hardware‑aware execution. We first propose Equivariant Axis‑Aligned Sparsification (EAAS). EAAS builds on Wigner‑6j convolution by exploiting an \mathrmSO(3) \rightarrow \mathrmSO(2) change of basis to transform computationally expensive dense tensor contractions into efficient, sparse parity re‑indexing operations. Building on this representation, we introduce On‑the‑Fly Equivariant Attention, a fully node‑centric mechanism implemented via a custom fused Triton kernel. By eliminating materialized edge tensors and maximizing SRAM utilization, our kernel achieves a 20× improvement in TFLOPS compared to standard implementations. Extensive experiments on the SPICE and OMol25 datasets demonstrate that E2Former‑V2 maintains comparable predictive performance while notably accelerating inference. This work demonstrates that large equivariant transformers can be trained efficiently using widely accessible GPU platforms. The code is avalible at https://github.com/IQuestLab/UBio‑MolFM/tree/e2formerv2.

PaperId: 3614, https://arxiv.org/pdf/2601.16429.pdf GitHub

Abstract:
Existing face‑swapping methods often deliver competitive results in constrained settings but exhibit substantial quality degradation when handling extreme facial poses. To improve facial pose robustness, explicit geometric features are applied, but this approach remains problematic since it introduces additional dependencies and increases computational cost. Diffusion‑based methods have achieved remarkable results; however, they are impractical for real‑time processing. We introduce AlphaFace, which leverages an open‑source vision‑language model and CLIP image and text embeddings to apply novel visual and textual semantic contrastive losses. AlphaFace enables stronger identity representation and more precise attribute preservation, all while maintaining real‑time performance. Comprehensive experiments across FF++, MPIE, and LPFF demonstrate that AlphaFace surpasses state‑of‑the‑art methods in pose‑challenging cases. The project is publicly available on `https://github.com/andrewyu90/Alphaface_Official.git'.

PaperId: 3615, https://arxiv.org/pdf/2601.16336.pdf GitHub

Abstract:
Simulating and validating coordination among multiple autonomous vehicles remains challenging, as many existing simulation architectures are limited to single‑vehicle operation or rely on centralized control. This paper presents the Distributed Multi‑Autonomous Vehicle Architecture (DMAVA), a simulation architecture that enables concurrent execution of multiple independent vehicle autonomy stacks distributed across multiple physical hosts within a shared simulation environment. Each vehicle operates its own complete autonomous driving stack while maintaining coordinated behavior through a data‑centric communication layer. The proposed system integrates ROS 2 Humble, Autoware Universe, AWSIM Labs, and Zenoh to support high data accuracy and controllability during multi‑vehicle simulation, enabling consistent perception, planning, and control behavior under distributed execution. Experiments conducted on multiple‑host configurations demonstrate stable localization, reliable inter‑host communication, and consistent closed‑loop control under distributed execution. DMAVA also serves as a foundation for Multi‑Vehicle Autonomous Valet Parking, demonstrating its extensibility toward higher‑level cooperative autonomy. Demo videos and source code are available at: https://github.com/zubxxr/distributed‑multi‑autonomous‑vehicle‑architecture.

PaperId: 3616, https://arxiv.org/pdf/2601.16327.pdf GitHub

Abstract:
This paper presents DMV‑AVP, a distributed simulation of Multi‑Vehicle Autonomous Valet Parking (AVP). The system was implemented as an application of the Distributed Multi‑Autonomous Vehicle Architecture (DMAVA) for synchronized multi‑host execution. Most existing simulation approaches rely on centralized or non‑distributed designs that constrain scalability and limit fully autonomous control. This work introduces two modules built on top of DMAVA: 1) the Multi‑Vehicle AVP Coordination Framework, composed of AVP Managers and a per‑vehicle AVP Node, is responsible for global parking state tracking, vehicle queuing, parking spot reservation, lifecycle coordination, and conflict resolution across multiple vehicles, and 2) the Unity‑Integrated YOLOv5 Parking Spot Detection Module, that provides real‑time, vision‑based perception within AWSIM Labs. Both modules integrate seamlessly with DMAVA and extend it specifically for multi‑vehicle AVP operation, supported by a Zenoh communication layer that ensures high data accuracy and controllability across hosts. Experiments conducted on two‑ and three‑host configurations demonstrate consistent coordination, conflict‑free parking behavior, and scalable performance across distributed Autoware instances. The results confirm that the proposed DMV‑AVP supports cooperative AVP simulation and establishes a foundation for future real‑world and hardware‑in‑the‑loop validation. Demo videos and source code are available at: https://github.com/zubxxr/multi‑vehicle‑avp

PaperId: 3617, https://arxiv.org/pdf/2601.16312.pdf GitHub

Abstract:
Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this problem space because: (i) most models lack polymer‑specific knowledge, and (ii) existing aligned models have limited coverage of knowledge and capabilities relevant to polymer design. Addressing this, we introduce PolyBench, a large‑scale training and test benchmark dataset of more than 125K polymer design‑related tasks, leveraging a knowledge base of more than 13 million data points obtained from experimental and synthetic data sources to ensure broad coverage of polymers and their properties. For effective alignment using PolyBench, we introduce a knowledge‑augmented reasoning distillation method that augments this dataset with structured CoT. Furthermore, tasks in PolyBench are organized from simple to complex analytical reasoning problems, enabling generalization tests and diagnostic probes across the problem space. Experiments show that small‑ and mid‑ sized language models (SLMs) with 7B to 32BB parameters, trained on PolyBench, outperform similar‑sized models and remain competitive with closed‑source frontier LLMs on PolyBench's test dataset, while demonstrating performance gains on external polymer benchmarks. Dataset and associated code available at https://github.com/StonyBrookNLP/PolyBench.

PaperId: 3618, https://arxiv.org/pdf/2601.16296.pdf GitHub

Abstract:
Video‑to‑video diffusion models achieve impressive single‑turn editing performance, but practical editing workflows are inherently iterative. When edits are applied sequentially, existing models treat each turn independently, often causing previously generated regions to drift or be overwritten. We identify this failure mode as the problem of cross‑turn consistency in multi‑turn video editing. We introduce Memory‑V2V, a memory‑augmented framework that treats prior edits as structured constraints for subsequent generations. Memory‑V2V maintains an external memory of previous outputs, retrieves task‑relevant edits, and integrates them through relevance‑aware tokenization and adaptive compression. These technical ingredients enable scalable conditioning without linear growth in computation. We demonstrate Memory‑V2V on iterative video novel view synthesis and text‑guided long video editing. Memory‑V2V substantially enhances cross‑turn consistency while maintaining visual quality, outperforming strong baselines with modest overhead.

PaperId: 3619, https://arxiv.org/pdf/2601.16238.pdf GitHub

Abstract:
VIBETENSOR is an open‑source research system software stack for deep learning, generated by LLM‑powered coding agents under high‑level human guidance. In this paper, "fully generated" refers to code provenance: implementation changes were produced and applied as agent‑proposed diffs; validation relied on agent‑run builds, tests, and differential checks, without per‑change manual diff review. It implements a PyTorch‑style eager tensor library with a C++20 core (CPU+CUDA), a torch‑like Python overlay via nanobind, and an experimental Node.js/TypeScript interface. Unlike thin bindings, VIBETENSOR includes its own tensor/storage system, schema‑lite dispatcher, reverse‑mode autograd, CUDA runtime (streams/events/graphs), a stream‑ordered caching allocator with diagnostics, and a stable C ABI for dynamically loaded operator plugins. We view this release as a milestone for AI‑assisted software engineering: it shows coding agents can generate a coherent deep learning runtime spanning language bindings down to CUDA memory management, validated primarily by builds and tests. We describe the architecture, summarize the workflow used to produce and validate the system, and evaluate the artifact. We report repository scale and test‑suite composition, and summarize reproducible microbenchmarks from an accompanying AI‑generated kernel suite, including fused attention versus PyTorch SDPA/FlashAttention. We also report end‑to‑end training sanity checks on 3 small workloads (sequence reversal, ViT, miniGPT) on NVIDIA H100 (Hopper, SM90) and Blackwell‑class GPUs; multi‑GPU results are Blackwell‑only and use an optional CUTLASS‑based ring‑allreduce plugin gated on CUDA 13+ and sm103a toolchain support. Finally, we discuss failure modes in generated system software, including a "Frankenstein" composition effect where locally correct subsystems interact to yield globally suboptimal performance.

PaperId: 3620, https://arxiv.org/pdf/2601.16211.pdf GitHub

Abstract:
Zero‑Shot Compositional Action Recognition (ZS‑CAR) requires recognizing novel verb‑object combinations composed of previously observed primitives. In this work, we tackle a key failure mode: models predict verbs via object‑driven shortcuts (i.e., relying on the labeled object class) rather than temporal evidence. We argue that sparse compositional supervision and verb‑object learning asymmetry can promote object‑driven shortcut learning. Our analysis with proposed diagnostic metrics shows that existing methods overfit to training co‑occurrence patterns and underuse temporal verb cues, resulting in weak generalization to unseen compositions. To address object‑driven shortcuts, we propose Robust COmpositional REpresentations (RCORE) with two components. Co‑occurrence Prior Regularization (CPR) adds explicit supervision for unseen compositions and regularizes the model against frequent co‑occurrence priors by treating them as hard negatives. Temporal Order Regularization for Composition (TORC) enforces temporal‑order sensitivity to learn temporally grounded verb representations. Across Sth‑com and EK100‑com, RCORE reduces shortcut diagnostics and consequently improves compositional generalization.

PaperId: 3621, https://arxiv.org/pdf/2601.16175.pdf GitHub

Abstract:
How can we use AI to discover a new state of the art for a scientific problem? Prior work in test‑time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We perform reinforcement learning at test time, so the LLM can continue to train, but now with experience specific to the test problem. This form of continual learning is quite special, because its goal is to produce one great solution rather than many good ones on average, and to solve this very problem rather than generalize to other problems. Therefore, our learning objective and search subroutine are designed to prioritize the most promising solutions. We call this method Test‑Time Training to Discover (TTT‑Discover). Following prior work, we focus on problems with continuous rewards. We report results for every problem we attempted, across mathematics, GPU kernel engineering, algorithm design, and biology. TTT‑Discover sets the new state of the art in almost all of them: (i) Erdős' minimum overlap problem and an autocorrelation inequality; (ii) a GPUMode kernel competition (up to 2× faster than prior art); (iii) past AtCoder algorithm competitions; and (iv) denoising problem in single‑cell analysis. Our solutions are reviewed by experts or the organizers. All our results are achieved with an open model, OpenAI gpt‑oss‑120b, and can be reproduced with our publicly available code, in contrast to previous best results that required closed frontier models. Our test‑time training runs are performed using Tinker, an API by Thinking Machines, with a cost of only a few hundred dollars per problem.

PaperId: 3622, https://arxiv.org/pdf/2601.16140.pdf GitHub

Abstract:
Existing approaches for watermarking AI‑generated images often rely on post‑hoc methods applied in pixel space, introducing computational overhead and potential visual artifacts. In this work, we explore latent space watermarking and introduce DistSeal, a unified approach for latent watermarking that works across both diffusion and autoregressive models. Our approach works by training post‑hoc watermarking models in the latent space of generative models. We demonstrate that these latent watermarkers can be effectively distilled either into the generative model itself or into the latent decoder, enabling in‑model watermarking. The resulting latent watermarks achieve competitive robustness while offering similar imperceptibility and up to 20x speedup compared to pixel‑space baselines. Our experiments further reveal that distilling latent watermarkers outperforms distilling pixel‑space ones, providing a solution that is both more efficient and more robust.

PaperId: 3623, https://arxiv.org/pdf/2601.16087.pdf GitHub

Abstract:
Large language model (LLM) agents often exhibit abrupt shifts in tone and persona during extended interaction, reflecting the absence of explicit temporal structure governing agent‑level state. While prior work emphasizes turn‑local sentiment or static emotion classification, the role of explicit affective dynamics in shaping long‑horizon agent behavior remains underexplored. This work investigates whether imposing dynamical structure on an external affective state can induce temporal coherence and controlled recovery in multi‑turn dialogue. We introduce an agent‑level affective subsystem that maintains a continuous Valence‑Arousal‑Dominance (VAD) state external to the language model and governed by first‑ and second‑order update rules. Instantaneous affective signals are extracted using a fixed, memoryless estimator and integrated over time via exponential smoothing or momentum‑based dynamics. The resulting affective state is injected back into generation without modifying model parameters. Using a fixed 25‑turn dialogue protocol, we compare stateless, first‑order, and second‑order affective dynamics. Stateless agents fail to exhibit coherent trajectories or recovery, while state persistence enables delayed responses and reliable recovery. Second‑order dynamics introduce affective inertia and hysteresis that increase with momentum, revealing a trade‑off between stability and responsiveness.

PaperId: 3624, https://arxiv.org/pdf/2601.16038.pdf GitHub GitHub

Abstract:
Large Language Models (LLMs) can aid synthesis planning in chemistry, but standard prompting methods often yield hallucinated or outdated suggestions. We study LLM interactions with a reaction knowledge graph by casting reaction path retrieval as a Text2Cypher (natural language to graph query) generation problem, and define single‑ and multi‑step retrieval tasks. We compare zero‑shot prompting to one‑shot variants using static, random, and embedding‑based exemplar selection, and assess a checklist‑driven validator/corrector loop. To evaluate our framework, we consider query validity and retrieval accuracy. We find that one‑shot prompting with aligned exemplars consistently performs best. Our checklist‑style self‑correction loop mainly improves executability in zero‑shot settings and offers limited additional retrieval gains once a good exemplar is present. We provide a reproducible Text2Cypher evaluation setup to facilitate further work on KG‑grounded LLMs for synthesis planning. Code is available at https://github.com/Intelligent‑molecular‑systems/KG‑LLM‑Synthesis‑Retrieval.

PaperId: 3625, https://arxiv.org/pdf/2601.15930.pdf GitHub

Abstract:
Model merging (MM) offers an efficient mechanism for integrating multiple specialized models without access to original training data or costly retraining. While MM has demonstrated success in domains like computer vision, its role in recommender systems (RSs) remains largely unexplored. Recently, Generative Recommendation (GR) has emerged as a new paradigm in RSs, characterized by rapidly growing model scales and substantial computational costs, making MM particularly appealing for cost‑sensitive deployment scenarios. In this work, we present the first systematic study of MM in GR through a contextual lens. We focus on a fundamental yet underexplored challenge in real‑world: how to merge generative recommenders specialized to different real‑world contexts, arising from temporal evolving user behaviors and heterogeneous application domains. To this end, we propose a unified framework MMGRid, a structured contextual grid of GR checkpoints that organizes models trained under diverse contexts induced by temporal evolution and domain diversity. All checkpoints are derived from a shared base LLM but fine‑tuned on context‑specific data, forming a realistic and controlled model space for systematically analyzing MM across GR paradigms and merging algorithms. Our investigation reveals several key insights. First, training GR models from LLMs can introduce parameter conflicts during merging due to token distribution shifts and objective disparities; such conflicts can be alleviated by disentangling task‑aware and context‑specific parameter changes via base model replacement. Second, incremental training across contexts induces recency bias, which can be effectively balanced through weighted contextual merging. Notably, we observe that optimal merging weights correlate with context‑dependent interaction characteristics, offering practical guidance for weight selection in real‑world deployments.

PaperId: 3626, https://arxiv.org/pdf/2601.15673.pdf GitHub

Abstract:
Contemporary sequential recommendation methods are becoming more complex, shifting from classification to a diffusion‑guided generative paradigm. However, the quality of guidance in the form of user information is often compromised by missing data in the observed sequences, leading to suboptimal generation quality. Existing methods address this by removing locally similar items, but overlook ``critical turning points'' in user interest, which are crucial for accurately predicting subsequent user intent. To address this, we propose a novel Counterfactual Attention Regulation Diffusion model (CARD), which focuses on amplifying the signal from key interest‑turning‑point items while concurrently identifying and suppressing noise within the user sequence. CARD consists of (1) a Dual‑side Thompson Sampling method to identify sequences undergoing significant interest shift, and (2) a counterfactual attention mechanism for these sequences to quantify the importance of each item. In this manner, CARD provides the diffusion model with a high‑quality guidance signal composed of dynamically re‑weighted interaction vectors to enable effective generation. Experiments show our method works well on real‑world data without being computationally expensive. Our code is available at https://github.com/yanqilong3321/CARD.

PaperId: 3627, https://arxiv.org/pdf/2601.15599.pdf GitHub

Abstract:
Modern business environments demand continuous reconfiguration of cross‑functional processes, yet most enterprise systems remain organized around siloed departments, rigid workflows, and hard‑coded automation. Meanwhile, large language models (LLMs) demonstrate strong capabilities in interpreting natural language and synthesizing unstructured information, but they lack deterministic, auditable execution of complex business logic. We introduce Autonomous Business System (AUTOBUS), a system that integrates LLM‑based AI agents, predicate‑logic programming, and business‑semantics‑centric enterprise data into a unified neuro‑symbolic architecture for executing end‑to‑end business initiatives. AUTOBUS models a business initiative as a network of interrelated tasks with explicit pre‑ and post‑conditions, required data, evaluation rules, and API‑level actions. Enterprise data is organized as a knowledge graph, whose entities, relationships, and constraints are translated into logic facts and foundational rules that ground reasoning and ensure semantic consistency. Core AI agents synthesize task instructions, enterprise semantics, and available tools into task‑specific logic programs, which are executed by a logic engine that enforces constraints, coordinates auxiliary tools, and produces deterministic outcomes. Humans specify task instructions, define and maintain business semantics and policies, curate tools, and supervise high‑impact or ambiguous decisions, ensuring accountability and adaptability. We detail the AUTOBUS architecture, the structure of AI‑generated logic programs, and the human‑AI collaboration model and present a case study that demonstrates accelerated time to market in a data‑rich organization. A reference implementation of the case study is available at https://github.com/cecilpang/autobus‑paper.

PaperId: 3628, https://arxiv.org/pdf/2601.15482.pdf GitHub

Abstract:
Standard autoregressive decoding in large language models (LLMs) is inherently short‑sighted, often failing to find globally optimal reasoning paths due to its token‑by‑token generation process. While inference‑time strategies like foresight sampling attempt to mitigate this by simulating future steps, they typically rely on ad‑hoc heuristics for valuing paths and pruning the search space. This paper introduces Martingale Foresight Sampling (MFS), a principled framework that reformulates LLM decoding as a problem of identifying an optimal stochastic process. By modeling the quality of a reasoning path as a stochastic process, we leverage Martingale theory to design a theoretically‑grounded algorithm. Our approach replaces heuristic mechanisms with principles from probability theory: step valuation is derived from the Doob Decomposition Theorem to measure a path's predictable advantage, path selection uses Optional Stopping Theory for principled pruning of suboptimal candidates, and an adaptive stopping rule based on the Martingale Convergence Theorem terminates exploration once a path's quality has provably converged. Experiments on six reasoning benchmarks demonstrate that MFS surpasses state‑of‑the‑art methods in accuracy while significantly improving computational efficiency. Code will be released at https://github.com/miraclehetech/EACL2026‑Martingale‑Foresight‑Sampling.

PaperId: 3629, https://arxiv.org/pdf/2601.15479.pdf GitHub GitHub GitHub

Abstract:
The safe deployment of large language models (LLMs) in high‑stakes fields like biomedicine, requires them to be able to reason about cause and effect. We investigate this ability by testing 13 open‑source LLMs on a fundamental task: pairwise causal discovery (PCD) from text. Our benchmark, using 12 diverse datasets, evaluates two core skills: 1) Causal Detection (identifying if a text contains a causal link) and 2) Causal Extraction (pulling out the exact cause and effect phrases). We tested various prompting methods, from simple instructions (zero‑shot) to more complex strategies like Chain‑of‑Thought (CoT) and Few‑shot In‑Context Learning (FICL). The results show major deficiencies in current models. The best model for detection, DeepSeek‑R1‑Distill‑Llama‑70B, only achieved a mean score of 49.57% (C_detect), while the best for extraction, Qwen2.5‑Coder‑32B‑Instruct, reached just 47.12% (C_extract). Models performed best on simple, explicit, single‑sentence relations. However, performance plummeted for more difficult (and realistic) cases, such as implicit relationships, links spanning multiple sentences, and texts containing multiple causal pairs. We provide a unified evaluation framework, built on a dataset validated with high inter‑annotator agreement (κ\ge 0.758), and make all our data, code, and prompts publicly available to spur further research. \hrefhttps://github.com/sydneyanuyah/CausalDiscoveryCode available here: https://github.com/sydneyanuyah/CausalDiscovery

PaperId: 3630, https://arxiv.org/pdf/2601.15474.pdf GitHub

Abstract:
Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to backdoor attacks. Existing studies on backdoor attack for graph classification are limited to single target attack using subgraph replacement based mechanism where the attacker implants only one trigger into the GNN model. In this paper, we introduce the first multi‑targeted backdoor attack for graph classification task, where multiple triggers simultaneously redirect predictions to different target labels. Instead of subgraph replacement, we propose subgraph injection which preserves the structure of the original graphs while poisoning the clean graphs. Extensive experiments demonstrate the efficacy of our approach, where our attack achieves high attack success rates for all target labels with minimal impact on the clean accuracy. Experimental results on five dataset demonstrate the superior performance of our attack framework compared to the conventional subgraph replacement‑based attack. Our analysis on four GNN models confirms the generalization capability of our attack which is effective regardless of the GNN model architectures and training parameters settings. We further investigate the impact of the attack design parameters including injection methods, number of connections, trigger sizes, trigger edge density and poisoning ratios. Additionally, our evaluation against state‑of‑the‑art defenses (randomized smoothing and fine‑pruning) demonstrates the robustness of our proposed multi‑target attacks. This work highlights the GNN vulnerability against multi‑targeted backdoor attack in graph classification task. Our source codes will be available at https://github.com/SiSL‑URI/Multi‑Targeted‑Graph‑Backdoor‑Attack.

PaperId: 3631, https://arxiv.org/pdf/2601.15473.pdf GitHub

Abstract:
Training modern deep learning models is increasingly constrained by GPU memory and compute limits. While Randomized Numerical Linear Algebra (RandNLA) offers proven techniques to compress these models, the lack of a unified, production‑grade library prevents widely adopting these methods. We present Panther, a PyTorch‑compatible library that consolidates established RandNLA algorithms into a single high‑performance framework. Panther engineers efficient, drop‑in replacements for standard components including sketched linear layers, 2D convolution, multi‑head attention, and randomized matrix decompositions (such as pivoted CholeskyQR). By implementing a custom C++/CUDA backend (pawX), Panther provides an optimized implementation that can run on both CPUs and GPUs. We demonstrate the effectiveness of RandNLA techniques and Panther's ease of adoption. By replacing standard PyTorch linear layers with Panther layers (requiring only a few lines of code) we achieve significant memory savings (up to 75%) on BERT while maintaining comparable loss. Source code is available (MIT License) at https://github.com/FahdSeddik/panther, along with demonstration video at https://youtu.be/7M3RQb4KWxs.

PaperId: 3632, https://arxiv.org/pdf/2601.15408.pdf GitHub

Abstract:
Medical vision‑language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign textual findings with visual evidence, leading to unreliable or weakly grounded predictions. We present CURE, an error‑aware curriculum learning framework that improves grounding and report quality without any additional data. CURE fine‑tunes a multimodal instructional model on phrase grounding, grounded report generation, and anatomy‑grounded report generation using public datasets. The method dynamically adjusts sampling based on model performance, emphasizing harder samples to improve spatial and textual alignment. CURE improves grounding accuracy by +0.37 IoU, boosts report quality by +0.188 CXRFEScore, and reduces hallucinations by 18.6%. CURE is a data‑efficient framework that enhances both grounding accuracy and report reliability. Code is available at https://github.com/PabloMessina/CURE and model weights at https://huggingface.co/pamessina/medgemma‑4b‑it‑cure

PaperId: 3633, https://arxiv.org/pdf/2601.15392.pdf GitHub

Abstract:
Biomedical research increasingly relies on integrating diverse data modalities, including gene expression profiles, medical images, and clinical metadata. While medical images and clinical metadata are routinely collected in clinical practice, gene expression data presents unique challenges for widespread research use, mainly due to stringent privacy regulations and costly laboratory experiments. To address these limitations, we present GeMM‑GAN, a novel Generative Adversarial Network conditioned on histopathology tissue slides and clinical metadata, designed to synthesize realistic gene expression profiles. GeMM‑GAN combines a Transformer Encoder for image patches with a final Cross Attention mechanism between patches and text tokens, producing a conditioning vector to guide a generative model in generating biologically coherent gene expression profiles. We evaluate our approach on the TCGA dataset and demonstrate that our framework outperforms standard generative models and generates more realistic and functionally meaningful gene expression profiles, improving by more than 11% the accuracy on downstream disease type prediction compared to current state‑of‑the‑art generative models. Code will be available at: https://github.com/francescapia/GeMM‑GAN

PaperId: 3634, https://arxiv.org/pdf/2601.15336.pdf GitHub

Abstract:
Continuous attractor networks (CANs) are a well‑established class of models for representing low‑dimensional continuous variables such as head direction, spatial position, and phase. In canonical spatial domains, transitions along the attractor manifold are driven by continuous displacement signals, such as angular velocity‑provided by sensorimotor systems external to the CAN itself. When such signals are not explicitly provided as dedicated displacement inputs, it remains unclear whether attractor‑based circuits can reliably acquire recurrent dynamics that support stable state transitions, or whether alternative predictive strategies dominate. In this work, we present an experimental framework for training CANs to perform successor‑like transitions between stable attractor states in the absence of externally provided displacement signals. We compare two recurrent topologies, a circular ring and a folded snake manifold, and systematically vary the temporal regime under which stability is evaluated. We find that, under short evaluation windows, networks consistently converge to impulse‑driven associative solutions that achieve high apparent accuracy yet lack persistent attractor dynamics. Only when stability is explicitly enforced over extended free‑run periods do genuine attractor‑based transition dynamics emerge. This suggests that shortcut solutions are the default outcome of local learning in recurrent networks, while attractor dynamics represent a constrained regime rather than a generic result. Furthermore, we demonstrate that topology strictly limits the capacity for learned transitions. While the continuous ring topology achieves perfect stability over long horizons, the folded snake topology hits a geometric limit characterized by failure at manifold discontinuities, which neither curriculum learning nor basal ganglia‑inspired gating can fully overcome.

PaperId: 3635, https://arxiv.org/pdf/2601.15331.pdf GitHub

Abstract:
The deployment of large language models (LLMs) has raised security concerns due to their susceptibility to producing harmful or policy‑violating outputs when exposed to adversarial prompts. While alignment and guardrails mitigate common misuse, they remain vulnerable to automated jailbreaking methods such as GCG, PEZ, and GBDA, which generate adversarial suffixes via training and gradient‑based search. Although effective, these methods particularly GCG are computationally expensive, limiting their practicality for organisations with constrained resources. This paper introduces a resource‑efficient adversarial prompting approach that eliminates the need for retraining by matching new prompts to a database of pre‑trained adversarial prompts. A dataset of 1,000 prompts was classified into seven harm‑related categories, and GCG, PEZ, and GBDA were evaluated on a Llama 3 8B model to identify the most effective attack method per category. Results reveal a correlation between prompt type and algorithm effectiveness. By retrieving semantically similar successful adversarial prompts, the proposed method achieves competitive attack success rates with significantly reduced computational cost. This work provides a practical framework for scalable red‑teaming and security evaluation of aligned LLMs, including in settings where model internals are inaccessible.

PaperId: 3636, https://arxiv.org/pdf/2601.15326.pdf GitHub

Abstract:
Background: Conventional electrocardiogram (ECG) analysis faces a persistent dichotomy: expert‑driven features ensure interpretability but lack sensitivity to latent patterns, while deep learning offers high accuracy but functions as a black box with high data dependency. We introduce ECGomics, a systematic paradigm and open‑source platform for the multidimensional deconstruction of cardiac signals into digital biomarker. Methods: Inspired by the taxonomic rigor of genomics, ECGomics deconstructs cardiac activity across four dimensions: Structural, Intensity, Functional, and Comparative. This taxonomy synergizes expert‑defined morphological rules with data‑driven latent representations, effectively bridging the gap between handcrafted features and deep learning embeddings. Results: We operationalized this framework into a scalable ecosystem consisting of a web‑based research platform and a mobile‑integrated solution (https://github.com/PKUDigitalHealth/ECGomics). The web platform facilitates high‑throughput analysis via precision parameter configuration, high‑fidelity data ingestion, and 12‑lead visualization, allowing for the systematic extraction of biomarkers across the four ECGomics dimensions. Complementarily, the mobile interface, integrated with portable sensors and a cloud‑based engine, enables real‑time signal acquisition and near‑instantaneous delivery of structured diagnostic reports. This dual‑interface architecture successfully transitions ECGomics from theoretical discovery to decentralized, real‑world health management, ensuring professional‑grade monitoring in diverse clinical and home‑based settings. Conclusion: ECGomics harmonizes diagnostic precision, interpretability, and data efficiency. By providing a deployable software ecosystem, this paradigm establishes a robust foundation for digital biomarker discovery and personalized cardiovascular medicine.

PaperId: 3637, https://arxiv.org/pdf/2601.15322.pdf GitHub

Abstract:
LLM agents struggle with regulatory audit replay: when asked to reproduce a flagged transaction decision with identical inputs, many deployments fail to return consistent results. We introduce the Determinism‑Faithfulness Assurance Harness (DFAH), a framework for measuring trajectory determinism, decision determinism, and evidence‑conditioned faithfulness in tool‑using agents deployed in financial services. Across 4,700+ agentic runs (7 models, 4 providers, 3 financial benchmarks with 50 cases each at T=0.0), we find that decision determinism and task accuracy are not detectably correlated (r = ‑0.11, 95% CI [‑0.49, 0.31], p = 0.63, n = 21 configurations): models can be deterministic without being accurate, and accurate without being deterministic. Because neither metric predicts the other in our sample, both must be measured independently, which is precisely what DFAH provides. Small models (7‑20B) achieve near‑perfect determinism through rigid pattern matching at the cost of accuracy (20‑42%), while frontier models show moderate determinism (50‑96%) with variable accuracy. No model achieves both perfect determinism and high accuracy, supporting DFAH's multi‑dimensional measurement approach. We provide three financial benchmarks (compliance triage, portfolio constraints, and DataOps exceptions; 50 cases each) together with an open‑source stress‑test harness. Across these benchmarks and DFAH evaluation settings, Tier 1 models with schema‑first architectures achieved determinism levels consistent with audit replay requirements.

PaperId: 3638, https://arxiv.org/pdf/2601.15316.pdf GitHub GitHub

Abstract:
In recent years, the rapid evolution of large vision‑language models (LVLMs) has driven a paradigm shift in multimodal fake news detection (MFND), transforming it from traditional feature‑engineering approaches to unified, end‑to‑end multimodal reasoning frameworks. Early methods primarily relied on shallow fusion techniques to capture correlations between text and images, but they struggled with high‑level semantic understanding and complex cross‑modal interactions. The emergence of LVLMs has fundamentally changed this landscape by enabling joint modeling of vision and language with powerful representation learning, thereby enhancing the ability to detect misinformation that leverages both textual narratives and visual content. Despite these advances, the field lacks a systematic survey that traces this transition and consolidates recent developments. To address this gap, this paper provides a comprehensive review of MFND through the lens of LVLMs. We first present a historical perspective, mapping the evolution from conventional multimodal detection pipelines to foundation model‑driven paradigms. Next, we establish a structured taxonomy covering model architectures, datasets, and performance benchmarks. Furthermore, we analyze the remaining technical challenges, including interpretability, temporal reasoning, and domain generalization. Finally, we outline future research directions to guide the next stage of this paradigm shift. To the best of our knowledge, this is the first comprehensive survey to systematically document and analyze the transformative role of LVLMs in combating multimodal fake news. The summary of existing methods mentioned is in our Github: \hrefhttps://github.com/Tan‑YiLong/Overview‑of‑Fake‑News‑Detectionhttps://github.com/Tan‑YiLong/Overview‑of‑Fake‑News‑Detection.

PaperId: 3639, https://arxiv.org/pdf/2601.15301.pdf GitHub

Abstract:
The rapid adoption of LLMs has increased the need for reliable AI text detection, yet existing detectors often fail outside controlled benchmarks. We systematically evaluate 2 dominant paradigms (training‑free and supervised) and show that both are brittle under distribution shift, unseen generators, and simple stylistic perturbations. To address these limitations, we propose a supervised contrastive learning (SCL) framework that learns discriminative style embeddings. Experiments show that while supervised detectors excel in‑domain, they degrade sharply out‑of‑domain, and training‑free methods remain highly sensitive to proxy choice. Overall, our results expose fundamental challenges in building domain‑agnostic detectors. Our code is available at: https://github.com/HARSHITJAIS14/DetectAI

PaperId: 3640, https://arxiv.org/pdf/2601.15282.pdf GitHub GitHub

Abstract:
Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and action in the physical world. However, synthesizing high‑quality videos that accurately reflect real‑world robotic interactions remains challenging, and the lack of a standardized benchmark limits fair comparisons and progress. To address this gap, we introduce a comprehensive robotics benchmark, RBench, designed to evaluate robot‑oriented video generation across five task domains and four distinct embodiments. It assesses both task‑level correctness and visual fidelity through reproducible sub‑metrics, including structural consistency, physical plausibility, and action completeness. Evaluation of 25 representative models highlights significant deficiencies in generating physically realistic robot behaviors. Furthermore, the benchmark achieves a Spearman correlation coefficient of 0.96 with human evaluations, validating its effectiveness. While RBench provides the necessary lens to identify these deficiencies, achieving physical realism requires moving beyond evaluation to address the critical shortage of high‑quality training data. Driven by these insights, we introduce a refined four‑stage data pipeline, resulting in RoVid‑X, the largest open‑source robotic dataset for video generation with 4 million annotated video clips, covering thousands of tasks and enriched with comprehensive physical property annotations. Collectively, this synergistic ecosystem of evaluation and data establishes a robust foundation for rigorous assessment and scalable training of video models, accelerating the evolution of embodied AI toward general intelligence.

PaperId: 3641, https://arxiv.org/pdf/2601.15165.pdf GitHub GitHub

Abstract:
Diffusion Large Language Models (dLLMs) break the rigid left‑to‑right constraint of traditional LLMs, enabling token generation in arbitrary orders. Intuitively, this flexibility implies a solution space that strictly supersets the fixed autoregressive trajectory, theoretically unlocking superior reasoning potential for general tasks like mathematics and coding. Consequently, numerous works have leveraged reinforcement learning (RL) to elicit the reasoning capability of dLLMs. In this paper, we reveal a counter‑intuitive reality: arbitrary order generation, in its current form, narrows rather than expands the reasoning boundary of dLLMs. We find that dLLMs tend to exploit this order flexibility to bypass high‑uncertainty tokens that are crucial for exploration, leading to a premature collapse of the solution space. This observation motivates a rethink of RL approaches for dLLMs, where considerable complexities, such as handling combinatorial trajectories and intractable likelihoods, are often devoted to preserving this flexibility. We demonstrate that effective reasoning can be better elicited by intentionally forgoing arbitrary order and applying standard Group Relative Policy Optimization (GRPO) instead. Our approach, JustGRPO, is minimalist yet surprisingly effective (e.g., 89.1% accuracy on GSM8K) while fully retaining the parallel decoding ability of dLLMs. Project page: https://nzl‑thu.github.io/the‑flexibility‑trap

PaperId: 3642, https://arxiv.org/pdf/2601.15161.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly used for clinical decision support, where hallucinations and unsafe suggestions may pose direct risks to patient safety. These risks are hard to assess: subtle clinical errors are often missed by generic metrics and LLM judges using general criteria, while expert‑authored fine‑grained rubrics are expensive and difficult to scale. In this paper, we propose a retrieval‑augmented multi‑agent framework designed to automate the generation of instance‑specific evaluation rubrics. Our approach grounds evaluation in authoritative medical evidence by decomposing retrieved content into atomic facts and synthesizing them with user interaction constraints to form verifiable, fine‑grained evaluation criteria. Evaluated on HealthBench and LLMEval‑Med datasets, our framework achieves Clinical Intent Alignment (CIA) scores of 50.20% and 31.90%, significantly outperforming the GPT‑4o baseline and demonstrating robust cross‑lingual generalization. In discriminative tests on HealthBench, our rubrics yield a 7.8% higher win rate than GPT‑4o baseline with nearly double score Δ, while ablation studies confirm its structural necessity. Beyond evaluation, our rubrics effectively guide response refinement, improving quality by 9.2%. This provides a scalable, cross‑lingual foundation for both evaluating and improving medical LLMs. The code is available at https://github.com/AmbeChen/Automated‑Rubric‑Generation.

PaperId: 3643, https://arxiv.org/pdf/2601.15160.pdf GitHub

Abstract:
Large language models have achieved near‑expert performance in structured reasoning domains like mathematics and programming, yet their ability to perform compositional multi‑hop reasoning in specialized scientific fields remains limited. We propose a bottom‑up learning paradigm in which models are grounded in axiomatic domain facts and compose them to solve complex, unseen tasks. To this end, we present a post‑training pipeline, based on a combination of supervised fine‑tuning and reinforcement learning (RL), in which knowledge graphs act as implicit reward models. By deriving novel reward signals from knowledge graph paths, we provide verifiable, scalable, and grounded supervision that encourages models to compose intermediate axioms rather than optimize only final answers during RL. We validate this approach in the medical domain, training a 14B model on short‑hop reasoning paths (1‑3 hops) and evaluating its zero‑shot generalization to complex multi‑hop queries (4‑5 hops). Our experiments show that path‑derived rewards act as a "compositional bridge", enabling our model to significantly outperform much larger models and frontier systems like GPT‑5.2 and Gemini 3 Pro, on the most difficult reasoning tasks. Furthermore, we demonstrate the robustness of our approach to adversarial perturbations against option‑shuffling stress tests. This work suggests that grounding the reasoning process in structured knowledge is a scalable and efficient path toward intelligent reasoning. Our code is publicly available at: https://github.com/jha‑lab/kg‑implicit‑reward‑compositional‑rl/.

PaperId: 3644, https://arxiv.org/pdf/2601.15123.pdf GitHub

Abstract:
Promptable segmentation models such as SAM have established a powerful paradigm, enabling strong generalization to unseen objects and domains with minimal user input, including points, bounding boxes, and text prompts. Among these, bounding boxes stand out as particularly effective, often outperforming points while significantly reducing annotation costs. However, current training and evaluation protocols typically rely on synthetic prompts generated through simple heuristics, offering limited insight into real‑world robustness. In this paper, we investigate the robustness of promptable segmentation models to natural variations in bounding box prompts. First, we conduct a controlled user study and collect thousands of real bounding box annotations. Our analysis reveals substantial variability in segmentation quality across users for the same model and instance, indicating that SAM‑like models are highly sensitive to natural prompt noise. Then, since exhaustive testing of all possible user inputs is computationally prohibitive, we reformulate robustness evaluation as a white‑box optimization problem over the bounding box prompt space. We introduce BREPS, a method for generating adversarial bounding boxes that minimize or maximize segmentation error while adhering to naturalness constraints. Finally, we benchmark state‑of‑the‑art models across 10 datasets, spanning everyday scenes to medical imaging. Code ‑ https://github.com/emb‑ai/BREPS.

PaperId: 3645, https://arxiv.org/pdf/2601.15086.pdf GitHub

Abstract:
Effective decision‑making in the real world depends on memory that is both stable and adaptive: environments change over time, and agents must retain relevant information over long horizons while also updating or overwriting outdated content when circumstances shift. Existing Reinforcement Learning (RL) benchmarks and memory‑augmented agents focus primarily on retention, leaving the equally critical ability of memory rewriting largely unexplored. To address this gap, we introduce a benchmark that explicitly tests continual memory updating under partial observability, i.e. the natural setting where an agent must rely on memory rather than current observations, and use it to compare recurrent, transformer‑based, and structured memory architectures. Our experiments reveal that classic recurrent models, despite their simplicity, demonstrate greater flexibility and robustness in memory rewriting tasks than modern structured memories, which succeed only under narrow conditions, and transformer‑based agents, which often fail beyond trivial retention cases. These findings expose a fundamental limitation of current approaches and emphasize the necessity of memory mechanisms that balance stable retention with adaptive updating. Our work highlights this overlooked challenge, introduces benchmarks to evaluate it, and offers insights for designing future RL agents with explicit and trainable forgetting mechanisms. Code: https://quartz‑admirer.github.io/Memory‑Rewriting/

PaperId: 3646, https://arxiv.org/pdf/2601.15075.pdf GitHub

Abstract:
Large Language Model (LLM)‑based agents are widely used in real‑world applications such as customer service, web navigation, and software engineering. As these systems become more autonomous and are deployed at scale, understanding why an agent takes a particular action becomes increasingly important for accountability and governance. However, existing research predominantly focuses on failure attribution to localize explicit errors in unsuccessful trajectories, which is insufficient for explaining the reason behind agent behaviors. To bridge this gap, we propose a novel framework for general agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome. Our framework operates hierarchically to manage the complexity of agent interactions. Specifically, at the component level, we employ temporal likelihood dynamics to identify critical interaction steps; then at the sentence level, we refine this localization using perturbation‑based analysis to isolate the specific textual evidence. We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory‑induced bias. Experimental results demonstrate that the proposed framework reliably pinpoints pivotal historical events and sentences behind the agent behavior, offering a critical step toward safer and more accountable agentic systems. Codes are available at https://github.com/AI45Lab/AgentDoG.

PaperId: 3647, https://arxiv.org/pdf/2601.14786.pdf GitHub

Abstract:
Recent advances in text‑to‑music generation (TTM) have yielded high‑quality results, but often at the cost of extensive compute and the use of large proprietary internal data. To improve the affordability and openness of TTM training, an open‑source generative model backbone that is more training‑ and data‑efficient is needed. In this paper, we constrain the number of trainable parameters in the generative model to match that of the MusicGen‑small benchmark (with about 300M parameters), and replace its Transformer backbone with the emerging class of state‑space models (SSMs). Specifically, we explore different SSM variants for sequence modeling, and compare a single‑stage SSM‑based design with a decomposable two‑stage SSM/diffusion hybrid design. All proposed models are trained from scratch on a purely public dataset comprising 457 hours of CC‑licensed music, ensuring full openness. Our experimental findings are three‑fold. First, we show that SSMs exhibit superior training efficiency compared to the Transformer counterpart. Second, despite using only 9% of the FLOPs and 2% of the training data size compared to the MusicGen‑small benchmark, our model achieves competitive performance in both objective metrics and subjective listening tests based on MusicCaps captions. Finally, our scaling‑down experiment demonstrates that SSMs can maintain competitive performance relative to the Transformer baseline even at the same training budget (measured in iterations), when the model size is reduced to four times smaller. To facilitate the democratization of TTM research, the processed captions, model checkpoints, and source code are available on GitHub via the project page: https://lonian6.github.io/ssmttm/.

PaperId: 3648, https://arxiv.org/pdf/2601.14662.pdf GitHub

Abstract:
Graph‑based retrieval‑augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi‑hop reasoning. While prior work shows that GraphRAG responses may leak retrieved subgraphs, the feasibility of query‑efficient reconstruction of the hidden graph structure remains unexplored under realistic query budgets. We study a budget‑constrained black‑box setting where an adversary adaptively queries the system to steal its latent entity‑relation graph. We propose AGEA (Agentic Graph Extraction Attack), a framework that leverages a novelty‑guided exploration‑exploitation strategy, external graph memory modules, and a two‑stage graph extraction pipeline combining lightweight discovery with LLM‑based filtering. We evaluate AGEA on medical, agriculture, and literary datasets across Microsoft‑GraphRAG and LightRAG systems. Under identical query budgets, AGEA significantly outperforms prior attack baselines, recovering up to 90% of entities and relationships while maintaining high precision. These results demonstrate that modern GraphRAG systems are highly vulnerable to structured, agentic extraction attacks, even under strict query limits. The code is available at https://github.com/shuashua0608/AGEA.

PaperId: 3649, https://arxiv.org/pdf/2601.14541.pdf GitHub

Abstract:
This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIPS 2024. Bringing together experts across machine learning and EDA, the workshop examined how AI‑spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.‑can facilitate EDA and shorten design turnaround. The workshop includes four themes: (1) AI for physical synthesis and design for manufacturing (DFM), discussing challenges in physical manufacturing process and potential AI applications; (2) AI for high‑level and logic‑level synthesis (HLS/LLS), covering pragma insertion, program transformation, RTL code generation, etc.; (3) AI toolbox for optimization and design, discussing frontier AI developments that could potentially be applied to EDA tasks; and (4) AI for test and verification, including LLM‑assisted verification tools, ML‑augmented SAT solving, security/reliability challenges, etc. The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next‑generation hardware systems. The workshop information can be found on the website https://ai4eda‑workshop.github.io/.

PaperId: 3650, https://arxiv.org/pdf/2601.14523.pdf GitHub

Abstract:
Optimizing scientific computing algorithms for modern GPUs is a labor‑intensive and iterative process involving repeated code modification, benchmarking, and tuning across complex hardware and software stacks. Recent work has explored large language model (LLM)‑assisted evolutionary methods for automated code optimization, but these approaches primarily rely on outcome‑based selection and random mutation, underutilizing the rich trajectory information generated during iterative optimization. We propose PhyloEvolve, an LLM‑agent system that reframes GPU‑oriented algorithm optimization as an In‑Context Reinforcement Learning (ICRL) problem. This formulation enables trajectory‑conditioned reuse of optimization experience without model retraining. PhyloEvolve integrates Algorithm Distillation and prompt‑based Decision Transformers into an iterative workflow, treating sequences of algorithm modifications and performance feedback as first‑class learning signals. To organize optimization history, we introduce a phylogenetic tree representation that captures inheritance, divergence, and recombination among algorithm variants, enabling backtracking, cross‑lineage transfer, and reproducibility. The system combines elite trajectory pooling, multi‑island parallel exploration, and containerized execution to balance exploration and exploitation across heterogeneous hardware. We evaluate PhyloEvolve on scientific computing workloads including PDE solvers, manifold learning, and spectral graph algorithms, demonstrating consistent improvements in runtime, memory efficiency, and correctness over baseline and evolutionary methods. Code is published at: https://github.com/annihi1ation/phylo_evolve

PaperId: 3651, https://arxiv.org/pdf/2601.14475.pdf GitHub

Abstract:
High‑altitude, multi‑spectral, aerial imagery is scarce and expensive to acquire, yet it is necessary for algorithmic advances and application of machine learning models to high‑impact problems such as wildfire detection. We introduce a human‑annotated dataset from the NASA Autonomous Modular Sensor (AMS) using 12‑channel, medium to high altitude (3 ‑ 50 km) aerial wildfire images similar to those used in current US wildfire missions. Our dataset combines spectral data from 12 different channels, including infrared (IR), short‑wave IR (SWIR), and thermal. We take imagery from 20 wildfire missions and randomly sample small patches to generate over 4000 images with high variability, including occlusions by smoke/clouds, easily‑confused false positives, and nighttime imagery. We demonstrate results from a deep‑learning model to automate the human‑intensive process of fire perimeter determination. We train two deep neural networks, one for image classification and the other for pixel‑level segmentation. The networks are combined into a unique real‑time segmentation model to efficiently localize active wildfire on an incoming image feed. Our model achieves 96% classification accuracy, 74% Intersection‑over‑Union(IoU), and 84% recall surpassing past methods, including models trained on satellite data and classical color‑rule algorithms. By leveraging a multi‑spectral dataset, our model is able to detect active wildfire at nighttime and behind clouds, while distinguishing between false positives. We find that data from the SWIR, IR, and thermal bands is the most important to distinguish fire perimeters. Our code and dataset can be found here: https://github.com/nasa/Autonomous‑Modular‑Sensor‑Wildfire‑Segmentation/tree/main and https://drive.google.com/drive/folders/1‑u4vs9rqwkwgdeeeoUhftCxrfe_4QPTn?=usp=drive_link

PaperId: 3652, https://arxiv.org/pdf/2601.14327.pdf GitHub

Abstract:
We introduce Yuan3.0 Ultra, an open‑source Mixture‑of‑Experts (MoE) large language model featuring 68.8B activated parameters and 1010B total parameters, specially designed to enhance performance on enterprise scenarios tasks while maintaining competitive capabilities on general purpose tasks. We propose Layer‑Adaptive Expert Pruning (LAEP) algorithm designed for the pre‑training stage of MoE LLMs. In contrast to previous expert pruning approaches that operate primarily in the post‑training phase, the proposed algorithm enhances training efficiency by selectively pruning underutilized experts and reorganizing experts across computing devices according to token distribution statistics. Comprehensive experiments demonstrate that LAEP effectively reduces model size and substantially improves pre‑training efficiency. When pre‑training Yuan3.0 Ultra from scratch original with 1515B parameters, this algorithm delivers a 49% boost in pre‑training efficiency and a 33.3% reduction in total parameters, while preserving the model's outstanding multi‑domain performance. On enterprise scenario benchmarks including Docmatix, ChatRAG, SummEval and MMTab, Yuan3.0 Ultra achieves leading accuracy. The model and codes are publicly available at https://github.com/Yuan‑lab‑LLM/Yuan3.0‑Ultra.

PaperId: 3653, https://arxiv.org/pdf/2601.14289.pdf GitHub

Abstract:
Understanding research papers remains challenging for foundation models due to specialized scientific discourse and complex figures and tables, yet existing benchmarks offer limited fine‑grained evaluation at scale. To address this gap, we introduce RPC‑Bench, a large‑scale question‑answering benchmark built from review‑rebuttal exchanges of high‑quality computer science papers, containing 15K human‑verified QA pairs. We design a fine‑grained taxonomy aligned with the scientific research flow to assess models' ability to understand and answer why, what, and how questions in scholarly contexts. We also define an elaborate LLM‑human interaction annotation framework to support large‑scale labeling and quality control. Following the LLM‑as‑a‑Judge paradigm, we develop a scalable framework that evaluates models on correctness‑completeness and conciseness, with high agreement to human judgment. Experiments reveal that even the strongest models (GPT‑5) achieve only 68.2% correctness‑completeness, dropping to 37.46% after conciseness adjustment, highlighting substantial gaps in precise academic paper understanding. Our code and data are available at https://rpc‑bench.github.io/.

PaperId: 3654, https://arxiv.org/pdf/2601.14288.pdf GitHub

Abstract:
We present DeepInflation, an AI agent designed for research and model discovery in inflationary cosmology. Built upon a multi‑agent architecture, DeepInflation integrates Large Language Models (LLMs) with a symbolic regression (SR) engine and a retrieval‑augmented generation (RAG) knowledge base. This framework enables the agent to automatically explore and verify the vast landscape of inflationary potentials while grounding its outputs in established theoretical literature. We demonstrate that DeepInflation can successfully discover simple and viable single‑field slow‑roll inflationary potentials consistent with the latest observations (here ACT DR6 results as example) or any given n_s and r, and provide accurate theoretical context for obscure inflationary scenarios. DeepInflation serves as a prototype for a new generation of autonomous scientific discovery engines in cosmology, which enables researchers and non‑experts alike to explore the inflationary landscape using natural language. This agent is available at https://github.com/pengzy‑cosmo/DeepInflation.

PaperId: 3655, https://arxiv.org/pdf/2601.14283.pdf GitHub

Abstract:
Currently, the field of structure‑based drug design is dominated by three main types of algorithms: search‑based algorithms, deep generative models, and reinforcement learning. While existing works have typically focused on comparing models within a single algorithmic category, cross‑algorithm comparisons remain scarce. In this paper, to fill the gap, we establish a benchmark to evaluate the performance of fifteen models across these different algorithmic foundations by assessing the pharmaceutical properties of the generated molecules and their docking affinities and poses with specified target proteins. We highlight the unique advantages of each algorithmic approach and offer recommendations for the design of future SBDD models. We emphasize that 1D/2D ligand‑centric drug design methods can be used in SBDD by treating the docking function as a black‑box oracle, which is typically neglected. Our evaluation reveals distinct patterns across model categories. 3D structure‑based models excel in binding affinities but show inconsistencies in chemical validity and pose quality. 1D models demonstrate reliable performance in standard molecular metrics but rarely achieve optimal binding affinities. 2D models offer balanced performance, maintaining high chemical validity while achieving moderate binding scores. Through detailed analysis across multiple protein targets, we identify key improvement areas for each model category, providing insights for researchers to combine strengths of different approaches while addressing their limitations. All the code that are used for benchmarking is available in https://github.com/zkysfls/2025‑sbdd‑benchmark

PaperId: 3656, https://arxiv.org/pdf/2601.14274.pdf GitHub

Abstract:
Multimodal emotion recognition in conversation (MERC) requires representations that effectively integrate signals from multiple modalities. These signals include modality‑specific cues, information shared across modalities, and interactions that emerge only when modalities are combined. In information‑theoretic terms, these correspond to \emphunique, \emphredundant, and \emphsynergistic contributions. An ideal representation should leverage all three, yet achieving such balance remains challenging. Recent advances in contrastive learning and augmentation‑based methods have made progress, but they often overlook the role of data preparation in preserving these components. In particular, applying augmentations directly to raw inputs or fused embeddings can blur the boundaries between modality‑unique and cross‑modal signals. To address this challenge, we propose a two‑phase framework \emphDivide and Refine (DnR). In the Divide phase, each modality is explicitly decomposed into uniqueness, pairwise redundancy, and synergy. In the Refine phase, tailored objectives enhance the informativeness of these components while maintaining their distinct roles. The refined representations are plug‑and‑play compatible with diverse multimodal pipelines. Extensive experiments on IEMOCAP and MELD demonstrate consistent improvements across multiple MERC backbones. These results highlight the effectiveness of explicitly dividing, refining, and recombining multimodal representations as a principled strategy for advancing emotion recognition. Our implementation is available at https://github.com/mattam301/DnR‑WACV2026

PaperId: 3657, https://arxiv.org/pdf/2601.14255.pdf GitHub

Abstract:
Generalizing video matting models to real‑world videos remains a significant challenge due to the scarcity of labeled data. To address this, we present Video Mask‑to‑Matte Model (VideoMaMa) that converts coarse segmentation masks into pixel accurate alpha mattes, by leveraging pretrained video diffusion models. VideoMaMa demonstrates strong zero‑shot generalization to real‑world footage, even though it is trained solely on synthetic data. Building on this capability, we develop a scalable pseudo‑labeling pipeline for large‑scale video matting and construct the Matting Anything in Video (MA‑V) dataset, which offers high‑quality matting annotations for more than 50K real‑world videos spanning diverse scenes and motions. To validate the effectiveness of this dataset, we fine‑tune the SAM2 model on MA‑V to obtain SAM2‑Matte, which outperforms the same model trained on existing matting datasets in terms of robustness on in‑the‑wild videos. These findings emphasize the importance of large‑scale pseudo‑labeled video matting and showcase how generative priors and accessible segmentation cues can drive scalable progress in video matting research.

PaperId: 3658, https://arxiv.org/pdf/2601.14232.pdf GitHub

Abstract:
Pixel‑based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle multiple sources of shift and hinder systematic analysis. We introduce KAGE‑Env, a JAX‑native 2D platformer that factorizes the observation process into independently controllable visual axes while keeping the underlying control problem fixed. By construction, varying a visual axis affects performance only through the induced state‑conditional action distribution of a pixel policy, providing a clean abstraction for visual generalization. Building on this environment, we define KAGE‑Bench, a benchmark of six known‑axis suites comprising 34 train‑evaluation configuration pairs that isolate individual visual shifts. Using a standard PPO‑CNN baseline, we observe strong axis‑dependent failures, with background and photometric shifts often collapsing success, while agent‑appearance shifts are comparatively benign. Several shifts preserve forward motion while breaking task completion, showing that return alone can obscure generalization failures. Finally, the fully vectorized JAX implementation enables up to 33M environment steps per second on a single GPU, enabling fast and reproducible sweeps over visual factors. Code: https://avanturist322.github.io/KAGEBench/.

PaperId: 3659, https://arxiv.org/pdf/2601.14172.pdf GitHub

Abstract:
We study sentence‑level detection of the 19 human values in the refined Schwartz continuum in about 74k English sentences from news and political manifestos (ValueEval'24 corpus). Each sentence is annotated with value presence, yielding a binary moral‑presence label and a 19‑way multi‑label task under severe class imbalance. First, we show that moral presence is learnable from single sentences: a DeBERTa‑base classifier attains positive‑class F1 = 0.74 with calibrated thresholds. Second, we compare direct multi‑label value detectors with presence‑gated hierarchies in a setting where only a single consumer‑grade GPU with 8 GB of VRAM is available, and we explicitly choose all training and inference configurations to fit within this budget. Presence gating does not improve over direct prediction, indicating that gate recall becomes a bottleneck. Third, we investigate lightweight auxiliary signals ‑ short‑range context, LIWC‑22, and moral lexica ‑ and small ensembles. Our best supervised configuration, a soft‑voting ensemble of DeBERTa‑based models enriched with such signals, reaches macro‑F1 = 0.332 on the 19 values, improving over the best previous English‑only baseline on this corpus, namely the best official ValueEval'24 English run (macro‑F1 = 0.28 on the same 19‑value test set). Methodologically, our study provides, to our knowledge, the first systematic comparison of direct versus presence‑gated architectures, lightweight feature‑augmented encoders, and medium‑sized instruction‑tuned Large Language Models (LLMs) for refined Schwartz values at sentence level. We additionally benchmark 7‑9B instruction‑tuned LLMs (Gemma 2 9B, Llama 3.1 8B, Mistral 8B, Qwen 2.5 7B) in zero‑/few‑shot and QLoRA setups, and find that they lag behind the supervised ensemble under the same compute budget. Overall, our results provide empirical guidance for building compute‑efficient, value‑aware NLP models.

PaperId: 3660, https://arxiv.org/pdf/2601.14099.pdf GitHub

Abstract:
Soft sensor modeling plays a crucial role in process monitoring. Causal feature selection can enhance the performance of soft sensor models in industrial applications. However, existing methods ignore two critical characteristics of industrial processes. Firstly, causal relationships between variables always involve time delays, whereas most causal feature selection methods investigate causal relationships in the same time dimension. Secondly, variables in industrial processes are often interdependent, which contradicts the decorrelation assumption of traditional causal inference methods. Consequently, soft sensor models based on existing causal feature selection approaches often lack sufficient accuracy and stability. To overcome these challenges, this paper proposes a causal feature selection framework based on time‑delayed cross mapping. Time‑delayed cross mapping employs state space reconstruction to effectively handle interdependent variables in causality analysis, and considers varying causal strength across time delay. Time‑delayed convergent cross mapping (TDCCM) is introduced for total causal inference, and time‑delayed partial cross mapping (TDPCM) is developed for direct causal inference. Then, in order to achieve automatic feature selection, an objective feature selection strategy is presented. The causal threshold is automatically determined based on the model performance on the validation set, and the causal features are then selected. Two real‑world case studies show that TDCCM achieves the highest average performance, while TDPCM improves soft sensor stability and performance in the worst scenario. The code is publicly available at https://github.com/dirge1/TDPCM.

PaperId: 3661, https://arxiv.org/pdf/2601.14039.pdf GitHub

Abstract:
Label noise is a critical problem in medical image segmentation, often arising from the inherent difficulty of manual annotation. Models trained on noisy data are prone to overfitting, which degrades their generalization performance. While a number of methods and strategies have been proposed to mitigate noisy labels in the segmentation domain, this area remains largely under‑explored. The abstention mechanism has proven effective in classification tasks by enhancing the capabilities of Cross Entropy, yet its potential in segmentation remains unverified. In this paper, we address this gap by introducing a universal and modular abstention framework capable of enhancing the noise‑robustness of a diverse range of loss functions. Our framework improves upon prior work with two key components: an informed regularization term to guide abstention behaviour, and a more flexible power‑law‑based auto‑tuning algorithm for the abstention penalty. We demonstrate the framework's versatility by systematically integrating it with three distinct loss functions to create three novel, noise‑robust variants: GAC, SAC, and ADS. Experiments on the CaDIS and DSAD medical datasets show our methods consistently and significantly outperform their non‑abstaining baselines, especially under high noise levels. This work establishes that enabling models to selectively ignore corrupted samples is a powerful and generalizable strategy for building more reliable segmentation models. Our code is publicly available at https://github.com/wemous/abstention‑for‑segmentation.

PaperId: 3662, https://arxiv.org/pdf/2601.14027.pdf GitHub

Abstract:
Agentic systems have recently become the dominant paradigm for formal theorem proving, achieving strong performance by coordinating multiple models and tools. However, existing approaches often rely on task‑specific pipelines and trained formal provers, limiting their flexibility and reproducibility. In this paper, we propose the paradigm that directly uses a general coding agent as a formal math reasoner. This paradigm is motivated by (1) A general coding agent provides a natural interface for diverse reasoning tasks beyond proving, (2) Performance can be improved by simply replacing the underlying base model, without training, and (3) MCP enables flexible extension and autonomous calling of specialized tools, avoiding complex design. Based on this paradigm, we introduce Numina‑Lean‑Agent, which combines Claude Code with Numina‑Lean‑MCP to enable autonomous interaction with Lean, retrieval of relevant theorems, informal proving and auxiliary reasoning tools. Using Claude Opus 4.5 as the base model, Numina‑Lean‑Agent solves all problems in Putnam 2025 (12 / 12), matching the best closed‑source system. Beyond benchmark evaluation, we further demonstrate its generality by interacting with mathematicians to successfully formalize the Brascamp‑Lieb theorem. We release Numina‑Lean‑Agent and all solutions at https://github.com/project‑numina/numina‑lean‑agent.

PaperId: 3663, https://arxiv.org/pdf/2601.13964.pdf GitHub

Abstract:
The quality of data augmentation serves as a critical determinant for the performance of contrastive learning in EEG tasks. Although this paradigm is promising for utilizing unlabeled data, static or random augmentation strategies often fail to preserve intrinsic information due to the non‑stationarity of EEG signals where statistical properties change over time. To address this, we propose RL‑BioAug, a framework that leverages a label‑efficient reinforcement learning (RL) agent to autonomously determine optimal augmentation policies. While utilizing only a minimal fraction (10%) of labeled data to guide the agent's policy, our method enables the encoder to learn robust representations in a strictly self‑supervised manner. Experimental results demonstrate that RL‑BioAug significantly outperforms the random selection strategy, achieving substantial improvements of 9.69% and 8.80% in Macro‑F1 score on the Sleep‑EDFX and CHB‑MIT datasets, respectively. Notably, this agent mainly chose optimal strategies for each task‑‑for example, Time Masking with a 62% probability for sleep stage classification and Crop & Resize with a 77% probability for seizure detection. Our framework suggests its potential to replace conventional heuristic‑based augmentations and establish a new autonomous paradigm for data augmentation. The source code is available at https://github.com/dlcjfgmlnasa/RL‑BioAug.

PaperId: 3664, https://arxiv.org/pdf/2601.13948.pdf GitHub

Abstract:
Protecting speaker identity is crucial for online voice applications, yet streaming speaker anonymization (SA) remains underexplored. Recent research has demonstrated that neural audio codec (NAC) provides superior speaker feature disentanglement and linguistic fidelity. NAC can also be used with causal language models (LM) to enhance linguistic fidelity and prompt control for streaming tasks. However, existing NAC‑based online LM systems are designed for voice conversion (VC) rather than anonymization, lacking the techniques required for privacy protection. Building on these advances, we present Stream‑Voice‑Anon, which adapts modern causal LM‑based NAC architectures specifically for streaming SA by integrating anonymization techniques. Our anonymization approach incorporates pseudo‑speaker representation sampling, a speaker embedding mixing and diverse prompt selection strategies for LM conditioning that leverage the disentanglement properties of quantized content codes to prevent speaker information leakage. Additionally, we compare dynamic and fixed delay configurations to explore latency‑privacy trade‑offs in real‑time scenarios. Under the VoicePrivacy 2024 Challenge protocol, Stream‑Voice‑Anon achieves substantial improvements in intelligibility (up to 46% relative WER reduction) and emotion preservation (up to 28% UAR relative) compared to the previous state‑of‑the‑art streaming method DarkStream while maintaining comparable latency (180ms vs 200ms) and privacy protection against lazy‑informed attackers, though showing 15% relative degradation against semi‑informed attackers.

PaperId: 3665, https://arxiv.org/pdf/2601.13895.pdf GitHub

Abstract:
Change Detection (CD) is a fundamental task in remote sensing. It monitors the evolution of land cover over time. Based on this, Open‑Vocabulary Change Detection (OVCD) introduces a new requirement. It aims to reduce the reliance on predefined categories. Existing training‑free OVCD methods mostly use CLIP to identify categories. These methods also need extra models like DINO to extract features. However, combining different models often causes problems in matching features and makes the system unstable. Recently, the Segment Anything Model 3 (SAM 3) is introduced. It integrates segmentation and identification capabilities within one promptable model, which offers new possibilities for the OVCD task. In this paper, we propose OmniOVCD, a standalone framework designed for OVCD. By leveraging the decoupled output heads of SAM 3, we propose a Synergistic Fusion to Instance Decoupling (SFID) strategy. SFID first fuses the semantic, instance, and presence outputs of SAM 3 to construct land‑cover masks, and then decomposes them into individual instance masks for change comparison. This design preserves high accuracy in category recognition and maintains instance‑level consistency across images. As a result, the model can generate accurate change masks. Experiments on four public benchmarks (LEVIR‑CD, WHU‑CD, S2Looking, and SECOND) demonstrate SOTA performance, achieving IoU scores of 67.2, 66.5, 24.5, and 27.1 (class‑average), respectively, surpassing all previous methods. The code is available at https://github.com/Erxucomeon/OmniOVCD.

PaperId: 3666, https://arxiv.org/pdf/2601.13798.pdf GitHub

Abstract:
Language‑aligned vision foundation models perform strongly across diverse downstream tasks. Yet, their learned representations remain opaque, making interpreting their decision‑making difficult. Recent work decompose these representations into human‑interpretable concepts, but provide poor spatial grounding and are limited to image classification tasks. In this work, we propose CFM, a language‑aligned concept foundation model for vision that provides fine‑grained concepts, which are human‑interpretable and spatially grounded in the input image. When paired with a foundation model with strong semantic representations, we get explanations for any of its downstream tasks. Examining local co‑occurrence dependencies of concepts allows us to define concept relationships through which we improve concept naming and obtain richer explanations. On benchmark data, we show that CFM provides performance on classification, segmentation, and captioning that is competitive with opaque foundation models while providing fine‑grained, high quality concept‑based explanations. Code at https://github.com/kawi19/CFM.

PaperId: 3667, https://arxiv.org/pdf/2601.13770.pdf GitHub

Abstract:
We introduce Look‑Ahead‑Bench, a standardized benchmark measuring look‑ahead bias in Point‑in‑Time (PiT) Large Language Models (LLMs) within realistic and practical financial workflows. Unlike most existing approaches that primarily test inner lookahead knowledge via Q\\&A, our benchmark evaluates model behavior in practical scenarios. To distinguish genuine predictive capability from memorization‑based performance, we analyze performance decay across temporally distinct market regimes, incorporating several quantitative baselines to establish performance thresholds. We evaluate prominent open‑source LLMs ‑‑ Llama 3.1 (8B and 70B) and DeepSeek 3.2 ‑‑ against a family of Point‑in‑Time LLMs (Pitinf‑Small, Pitinf‑Medium, and frontier‑level model Pitinf‑Large) from PiT‑Inference. Results reveal significant lookahead bias in standard LLMs, as measured with alpha decay, unlike Pitinf models, which demonstrate improved generalization and reasoning abilities as they scale in size. This work establishes a foundation for the standardized evaluation of temporal bias in financial LLMs and provides a practical framework for identifying models suitable for real‑world deployment. Code is available on GitHub: https://github.com/benstaf/lookaheadbench

PaperId: 3668, https://arxiv.org/pdf/2601.13761.pdf GitHub

Abstract:
Self‑play with large language models has emerged as a promising paradigm for achieving self‑improving artificial intelligence. However, existing self‑play frameworks often suffer from optimization instability, due to (i) non‑stationary objectives induced by solver‑dependent reward feedback for the Questioner, and (ii) bootstrapping errors from self‑generated pseudo‑labels used to supervise the Solver. To mitigate these challenges, we introduce DARC (Decoupled Asymmetric Reasoning Curriculum), a two‑stage framework that stabilizes the self‑evolution process. First, we train the Questioner to synthesize difficulty‑calibrated questions, conditioned on explicit difficulty levels and external corpora. Second, we train the Solver with an asymmetric self‑distillation mechanism, where a document‑augmented teacher generates high‑quality pseudo‑labels to supervise the student Solver that lacks document access. Empirical results demonstrate that DARC is model‑agnostic, yielding an average improvement of 10.9 points across nine reasoning benchmarks and three backbone models. Moreover, DARC consistently outperforms all baselines and approaches the performance of fully supervised models without relying on human annotations. The code is available at https://github.com/RUCBM/DARC.

PaperId: 3669, https://arxiv.org/pdf/2601.13749.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly employed for decision‑support across multiple domains. We investigate whether these models display a systematic preferential bias in favor of artificial intelligence (AI) itself. Across three complementary experiments, we find consistent evidence of pro‑AI bias. First, we show that LLMs disproportionately recommend AI‑related options in response to diverse advice‑seeking queries, with proprietary models doing so almost deterministically. Second, we demonstrate that models systematically overestimate salaries for AI‑related jobs relative to closely matched non‑AI jobs, with proprietary models overestimating AI salaries more by 10 percentage points. Finally, probing internal representations of open‑weight models reveals that ``Artificial Intelligence'' exhibits the highest similarity to generic prompts for academic fields under positive, negative, and neutral framings alike, indicating valence‑invariant representational centrality. These patterns suggest that LLM‑generated advice and valuation can systematically skew choices and perceptions in high‑stakes decisions.

PaperId: 3670, https://arxiv.org/pdf/2601.13709.pdf GitHub

Abstract:
Large Language Model (LLM) agents are increasingly used in many applications, raising concerns about their safety. While previous work has shown that LLMs can deceive in controlled tasks, less is known about their ability to deceive using natural language in social contexts. In this paper, we study deception in the Social Deduction Game (SDG) Mafia, where success is dependent on deceiving others through conversation. Unlike previous SDG studies, we use an asynchronous multi‑agent framework which better simulates realistic social contexts. We simulate 35 Mafia games with GPT‑4o LLM agents. We then create a Mafia Detector using GPT‑4‑Turbo to analyze game transcripts without player role information to predict the mafia players. We use prediction accuracy as a surrogate marker for deception quality. We compare this prediction accuracy to that of 28 human games and a random baseline. Results show that the Mafia Detector's mafia prediction accuracy is lower on LLM games than on human games. The result is consistent regardless of the game days and the number of mafias detected. This indicates that LLMs blend in better and thus deceive more effectively. We also release a dataset of LLM Mafia transcripts to support future research. Our findings underscore both the sophistication and risks of LLM deception in social contexts.

PaperId: 3671, https://arxiv.org/pdf/2601.13684.pdf GitHub

Abstract:
The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long‑context tasks. Existing static compression methods often fail to preserve globally important information. Although recent dynamic retrieval approaches attempt to address this issue, they typically suffer from coarse‑grained caching strategies and incur high I/O overhead. To overcome these limitations, we propose HeteroCache, a training‑free dynamic compression framework. Our method is built on two key insights: attention heads exhibit diverse temporal heterogeneity, and there is significant spatial redundancy among heads within the same layer. Guided by these insights, HeteroCache categorizes heads based on stability and similarity, applying a fine‑grained weighting strategy that allocates larger cache budgets to heads with rapidly shifting attention to capture context changes. Furthermore, it features a hierarchical storage mechanism where representative heads monitor attention drift to trigger asynchronous, on‑demand context retrieval, thereby hiding I/O latency. Experiments demonstrate that HeteroCache achieves state‑of‑the‑art performance on long‑context benchmarks and accelerates decoding by up to 3× compared to the original model with a 224K context. Our code is available at https://github.com/ponytaill/HeteroCache.

PaperId: 3672, https://arxiv.org/pdf/2601.13614.pdf GitHub

Abstract:
Causal discovery is fundamental to scientific understanding and reliable decision‑making. Existing approaches face critical limitations: purely data‑driven methods suffer from statistical indistinguishability and modeling assumptions, while recent LLM‑based methods either ignore statistical evidence or incorporate unverified priors that can mislead result. To this end, we propose CauScientist, a collaborative framework that synergizes LLMs as hypothesis‑generating "data scientists" with probabilistic statistics as rigorous "verifiers". CauScientist employs hybrid initialization to select superior starting graphs, iteratively refines structures through LLM‑proposed modifications validated by statistical criteria, and maintains error memory to guide efficient search space. Experiments demonstrate that CauScientist substantially outperforms purely data‑driven baselines, achieving up to 53.8% F1 score improvement and enhancing recall from 35.0% to 100.0%. Notably, while standalone LLM performance degrades with graph complexity, CauScientist reduces structural hamming distance (SHD) by 44.0% compared to Qwen3‑32B on 37‑node graphs. Our project page is at https://github.com/OpenCausaLab/CauScientist.

PaperId: 3673, https://arxiv.org/pdf/2601.13580.pdf GitHub

Abstract:
We introduce Neural Organ Transplantation (NOT), a modular adaptation framework that enables trained transformer layers to function as reusable transferable checkpoints for domain adaptation. Unlike conventional fine‑tuning approaches that tightly couple trained parameters to specific model instances and training data, NOT extracts contiguous layer subsets ("donor organs") from pre‑trained models, trains them independently on domain‑specific data, and saves them as standalone checkpoint files that can be transplanted into compatible recipient models without access to the original training data. Through experiments on three decoder‑only transformer architectures spanning 124M to 20B parameters (GPT‑2, TinyLlama, and GPT‑OSS), we demonstrate that donor transplantation substantially outperforms existing adaptation methods, achieving an order‑of‑magnitude improvement in perplexity over LoRA while training significantly faster. The method exhibits position dependence, with early insertion positions yielding optimal results. Cross‑domain transfer at billion‑parameter scale reveals unexpected regularization benefits. These findings demonstrate that transformer middle layers can support efficient modular transfer for decoder‑only architectures, enabling privacy‑preserving expertise sharing through checkpoint distribution. We note that this approach is currently limited to decoder‑only models; preliminary experiments on encoder‑based architectures show reduced effectiveness.

PaperId: 3674, https://arxiv.org/pdf/2601.13562.pdf GitHub

Abstract:
The Abstraction and Reasoning Corpus (ARC) provides a compact laboratory for studying abstract reasoning, an ability central to human intelligence. Modern AI systems, including LLMs and ViTs, largely operate as sequence‑of‑behavior prediction machines: they match observable behaviors by modeling token statistics without a persistent, readable mental state. This creates a gap with human‑like behavior: humans can explain an action by decoding internal state, while AI systems can produce fluent post‑hoc rationalizations that are not grounded in such a state. We hypothesize that reasoning is a modality: reasoning should exist as a distinct channel separate from the low‑level workspace on which rules are applied. To test this hypothesis, on solving ARC tasks as a visual reasoning problem, we designed a novel role‑separated transformer block that splits global controller tokens from grid workspace tokens, enabling iterative rule execution. Trained and evaluated within the VARC vision‑centric protocol, our method achieved 62.6% accuracy on ARC‑1, surpassing average human performance (60.2%) and outperforming prior methods significantly. Qualitatively, our models exhibit more coherent rule‑application structure than the dense ViT baseline, consistent with a shift away from plausible probability blobs toward controller‑driven reasoning.

PaperId: 3675, https://arxiv.org/pdf/2601.13534.pdf GitHub

Abstract:
Time series generation (TSG) is widely used across domains, yet most existing methods assume regular sampling and fixed output resolutions. These assumptions are often violated in practice, where observations are irregular and sparse, while downstream applications require continuous and high‑resolution TS. Although Neural Controlled Differential Equation (NCDE) is promising for modeling irregular TS, it is constrained by a single dynamics function, tightly coupled optimization, and limited ability to adapt learned dynamics to newly generated samples from the generative model. We propose Diff‑MN, a continuous TSG framework that enhances NCDE with a Mixture‑of‑Experts (MoE) dynamics function and a decoupled architectural design for dynamics‑focused training. To further enable NCDE to generalize to newly generated samples, Diff‑MN employs a diffusion model to parameterize the NCDE temporal dynamics parameters (MoE weights), i.e., jointly learn the distribution of TS data and MoE weights. This design allows sample‑specific NCDE parameters to be generated for continuous TS generation. Experiments on ten public and synthetic datasets demonstrate that Diff‑MN consistently outperforms strong baselines on both irregular‑to‑regular and irregular‑to‑continuous TSG tasks. The code is available at the link https://github.com/microsoft/TimeCraft/tree/main/Diff‑MN.

PaperId: 3676, https://arxiv.org/pdf/2601.13518.pdf GitHub

Abstract:
While recent automated red‑teaming methods show promise for systematically exposing model vulnerabilities, most existing approaches rely on human‑specified workflows. This dependence on manually designed workflows suffers from human biases and makes exploring the broader design space expensive. We introduce AgenticRed, an automated pipeline that leverages LLMs' in‑context learning to iteratively design and refine red‑teaming systems without human intervention. Rather than optimizing attacker policies within predefined structures, AgenticRed treats red‑teaming as a system design problem, and it autonomously evolves automated red‑teaming systems using evolutionary selection and generational knowledge. Red‑teaming systems designed by AgenticRed consistently outperform state‑of‑the‑art approaches, achieving 96% attack success rate (ASR) on Llama‑2‑7B, 98% on Llama‑3‑8B and 100% on Qwen3‑8B on HarmBench. Our approach generates robust, query‑agnostic red‑teaming systems that transfer strongly to the latest proprietary models, achieving an impressive 100% ASR on GPT‑5.1, DeepSeek‑R1 and DeepSeek V3.2. This work highlights evolutionary algorithms as a powerful approach to AI safety that can keep pace with rapidly evolving models.

PaperId: 3677, https://arxiv.org/pdf/2601.13437.pdf GitHub

Abstract:
Open‑set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalization of zero‑shot learning, where the new classes are not known a priori, hence involving the active discovery of new classes. While zero‑shot learning has been extensively studied in text classification, especially with the emergence of pre‑trained language models, open‑set learning and discovery is a comparatively new setup for the text domain. To this end, we introduce the first multilingual open‑set learning and discovery (MOSLD) benchmark for text categorization by topic, comprising 960K data samples across 12 languages. To construct the benchmark, we (i) rearrange existing datasets and (ii) collect new data samples from the news domain. Moreover, we propose a novel framework for the OSLD task, which integrates multiple stages to continuously discover and learn new classes. We evaluate several language models, including our own, to obtain results that can be used as reference for future work. We release our benchmark at https://github.com/Adriana19Valentina/MOSLD‑Bench.

PaperId: 3678, https://arxiv.org/pdf/2601.13398.pdf GitHub

Abstract:
LLMs demonstrate strong performance on code benchmarks, yet consistent reasoning across forward and backward execution remains elusive. We present RoundTripCodeEval (RTCE), a benchmark of four code execution reasoning tasks that evaluates round‑trip consistency through execution‑free, exact‑match assessment of bijection fidelity across four lossless compression algorithms. We evaluate state‑of‑the‑art Code‑LLMs under zero‑shot prompting, supervised fine‑tuning on execution traces, and iterative self‑reflection. All approaches yield only modest improvements and none closes the gap, revealing that current LLMs lack the internal coherence required for reliable bidirectional code reasoning. RTCE surfaces findings invisible to existing benchmarks: models frequently pass individual forward and backward tasks yet fail the combined round‑trip, exposing mutually inconsistent internal representations; SFT and self‑reflection saturate after one revision round, indicating they cannot repair fundamental algorithmic misunderstandings; and failures persist even on simple bijections such as RLE, suggesting that algorithmic complexity is not the sole root cause.\footnoteCode and dataset are available at https://github.com/Nickil21/round‑trip‑code‑compression.

PaperId: 3679, https://arxiv.org/pdf/2601.13385.pdf GitHub

Abstract:
There is an urgent need for triage and classification of high‑volume medical imaging modalities such as computed tomography (CT), which can improve patient care and mitigate radiologist burnout. Study‑level CT triage requires calibrated predictions with localized evidence; however, off‑the‑shelf Vision Language Models (VLM) struggle with 3D anatomy, protocol shifts, and noisy report supervision. This study used the two largest publicly available chest CT datasets: CT‑RATE and RADCHEST‑CT (held‑out external test set). Our carefully tuned supervised baseline (instantiated as a simple Global Average Pooling head) establishes a new supervised state of the art, surpassing all reported linear‑probe VLMs. Building on this baseline, we present ORACLE‑CT, an encoder‑agnostic, organ‑aware head that pairs Organ‑Masked Attention (mask‑restricted, per‑organ pooling that yields spatial evidence) with Organ‑Scalar Fusion (lightweight fusion of normalized volume and mean‑HU cues). In the chest setting, ORACLE‑CT masked attention model achieves AUROC 0.86 on CT‑RATE; in the abdomen setting, on MERLIN (30 findings), our supervised baseline exceeds a reproduced zero‑shot VLM baseline obtained by running publicly released weights through our pipeline, and adding masked attention plus scalar fusion further improves performance to AUROC 0.85. Together, these results deliver state‑of‑the‑art supervised classification performance across both chest and abdomen CT under a unified evaluation protocol. The source code is available at https://github.com/lavsendahal/oracle‑ct.

PaperId: 3680, https://arxiv.org/pdf/2601.13327.pdf GitHub

Abstract:
We present PepEDiff, a novel peptide binder generator that designs binding sequences given a target receptor protein sequence and its pocket residues. Peptide binder generation is critical in therapeutic and biochemical applications, yet many existing methods rely heavily on intermediate structure prediction, adding complexity and limiting sequence diversity. Our approach departs from this paradigm by generating binder sequences directly in a continuous latent space derived from a pretrained protein embedding model, without relying on predicted structures, thereby improving structural and sequence diversity. To encourage the model to capture binding‑relevant features rather than memorizing known sequences, we perform latent‑space exploration and diffusion‑based sampling, enabling the generation of peptides beyond the limited distribution of known binders. This zero‑shot generative strategy leverages the global protein embedding manifold as a semantic prior, allowing the model to propose novel peptide sequences in previously unseen regions of the protein space. We evaluate PepEDiff on TIGIT, a challenging target with a large, flat protein‑protein interaction interface that lacks a druggable pocket. Despite its simplicity, our method outperforms state‑of‑the‑art approaches across benchmark tests and in the TIGIT case study, demonstrating its potential as a general, structure‑free framework for zero‑shot peptide binder design. The code for this research is available at GitHub: https://github.com/LabJunBMI/PepEDiff‑An‑Peptide‑binder‑Embedding‑Diffusion‑Model

PaperId: 3681, https://arxiv.org/pdf/2601.13240.pdf GitHub

Abstract:
Large language models (LLMs) excel at general programming but struggle with domain‑specific software development, necessitating domain specialization methods for LLMs to learn and utilize domain knowledge and data. However, existing domain‑specific code benchmarks cannot evaluate the effectiveness of domain specialization methods, which focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge, lacking explicit knowledge corpora for developing domain specialization methods. To this end, we present KOCO‑BENCH, a novel benchmark designed for evaluating domain specialization methods in real‑world software development. KOCO‑BENCH contains 6 emerging domains with 11 software frameworks and 25 projects, featuring curated knowledge corpora alongside multi‑granularity evaluation tasks including domain code generation (from function‑level to project‑level with rigorous test suites) and domain knowledge understanding (via multiple‑choice Q&A). Unlike previous benchmarks that only provide test sets for direct evaluation, KOCO‑BENCH requires acquiring and applying diverse domain knowledge (APIs, rules, constraints, etc.) from knowledge corpora to solve evaluation tasks. Our evaluations reveal that KOCO‑BENCH poses significant challenges to state‑of‑the‑art LLMs. Even with domain specialization methods (e.g., SFT, RAG, kNN‑LM) applied, improvements remain marginal. Best‑performing coding agent, Claude Code, achieves only 34.2%, highlighting the urgent need for more effective domain specialization methods. We release KOCO‑BENCH, evaluation code, and baselines to advance further research at https://github.com/jiangxxxue/KOCO‑bench.

PaperId: 3682, https://arxiv.org/pdf/2601.13166.pdf GitHub

Abstract:
Developing Foundation Models for medical image analysis is essential to overcome the unique challenges of radiological tasks. The first challenges of this kind for 3D brain MRI, SSL3D and FOMO25, were held at MICCAI 2025. Our solution ranked first in tracks of both contests. It relies on a U‑Net CNN architecture combined with strategies leveraging anatomical priors and neuroimaging domain knowledge. Notably, our models trained 1‑2 orders of magnitude faster and were 10 times smaller than competing transformer‑based approaches. Models are available here: https://github.com/jbanusco/BrainFM4Challenges.

PaperId: 3683, https://arxiv.org/pdf/2601.13111.pdf GitHub

Abstract:
Realistic text‑to‑SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes a key bottleneck for end‑to‑end performance. We study an open‑book setting where queries must be answered over large, heterogeneous table collections pooled from many sources, without clean scoping signals such as database identifiers. Here, dense retrieval (DR) achieves high recall but returns many distractors, while join‑aware alternatives often rely on extra assumptions and/or incur high inference overhead. We propose CORE‑T, a scalable, training‑free framework that enriches tables with LLM‑generated purpose metadata and pre‑computes a lightweight table‑compatibility cache. At inference time, DR returns top‑K candidates; a single LLM call selects a coherent, joinable subset, and a two‑step additive adjustment stage restores strongly compatible tables. Across Bird, Spider, MMQA, and Beaver, CORE‑T improves over DR by up to 22.7 points in table‑selection F1 while returning up to 40% fewer tables, and by up to 24.4 points in multi‑table execution accuracy, and uses 1.64‑4.20x fewer total selection tokens than LLM‑intensive baselines.

PaperId: 3684, https://arxiv.org/pdf/2601.13007.pdf GitHub

Abstract:
Recovering accurate architecture from large‑scale legacy software is hindered by architectural drift, missing relations, and the limited context of Large Language Models (LLMs). We present ArchAgent, a scalable agent‑based framework that combines static analysis, adaptive code segmentation, and LLM‑powered synthesis to reconstruct multiview, business‑aligned architectures from cross‑repository codebases. ArchAgent introduces scalable diagram generation with contextual pruning and integrates cross‑repository data to identify business‑critical modules. Evaluations of typical large‑scale GitHub projects show significant improvements over existing benchmarks. An ablation study confirms that dependency context improves the accuracy of generated architectures of production‑level repositories, and a real‑world case study demonstrates effective recovery of critical business logics from legacy projects. The dataset is available at https://github.com/panrusheng/arch‑eval‑benchmark.

PaperId: 3685, https://arxiv.org/pdf/2601.12822.pdf GitHub

Abstract:
Large foundation models are integrated into Computer Use Agents (CUAs), enabling autonomous interaction with operating systems through graphical user interfaces (GUIs) to perform complex tasks. This autonomy introduces serious security risks: malicious instructions or visual prompt injections can trigger unsafe reasoning and cause harmful system‑level actions. Existing defenses, such as detection‑based blocking, prevent damage but often abort tasks prematurely, reducing agent utility. In this paper, we present MirrorGuard, a plug‑and‑play defense framework that uses simulation‑based training to improve CUA security in the real world. To reduce the cost of large‑scale training in operating systems, we propose a novel neural‑symbolic simulation pipeline, which generates realistic, high‑risk GUI interaction trajectories entirely in a text‑based simulated environment, which captures unsafe reasoning patterns and potential system hazards without executing real operations. In the simulation environment, MirrorGuard learns to intercept and rectify insecure reasoning chains of CUAs before they produce and execute unsafe actions. In real‑world testing, extensive evaluations across diverse benchmarks and CUA architectures show that MirrorGuard significantly mitigates security risks. For instance, on the ByteDance UI‑TARS system, it reduces the unsafe rate from 66.5% to 13.0% while maintaining a marginal false refusal rate (FRR). In contrast, the state‑of‑the‑art GuardAgent only achieves a reduction to 53.9% and suffers from a 15.4% higher FRR. Our work proves that simulation‑derived defenses can provide robust, real‑world protection while maintaining the fundamental utility of the agent. Our code and model are publicly available at https://bmz‑q‑q.github.io/MirrorGuard/.

PaperId: 3686, https://arxiv.org/pdf/2601.12816.pdf GitHub

Abstract:
Continual learning aims to enable neural networks to acquire new knowledge on sequential tasks. However, the key challenge in such settings is to learn new tasks without catastrophically forgetting previously learned tasks. We propose the Fisher‑Orthogonal Projected Natural Gradient Descent (FOPNG) optimizer, which enforces Fisher‑orthogonal constraints on parameter updates to preserve old task performance while learning new tasks. Unlike existing methods that operate in Euclidean parameter space, FOPNG projects gradients onto the Fisher‑orthogonal complement of previous task gradients. This approach unifies natural gradient descent with orthogonal gradient methods within an information‑geometric framework. We provide theoretical analysis deriving the projected update, describe efficient and practical implementations using the diagonal Fisher, and demonstrate strong results on standard continual learning benchmarks such as Permuted‑MNIST, Split‑MNIST, Rotated‑MNIST, Split‑CIFAR10, and Split‑CIFAR100. Our code is available at https://github.com/ishirgarg/FOPNG.

PaperId: 3687, https://arxiv.org/pdf/2601.12785.pdf GitHub

Abstract:
Time Series foundation models (TSFMs) deliver strong forecasting performance through large‑scale pretraining, but their large parameter sizes make deployment costly. While knowledge distillation offers a natural and effective approach for model compression, techniques developed for general machine learning tasks are not directly applicable to time series forecasting due to the unique characteristics. To address this, we present DistilTS, the first distillation framework specifically designed for TSFMs. DistilTS addresses two key challenges: (1) task difficulty discrepancy, specific to forecasting, where uniform weighting makes optimization dominated by easier short‑term horizons, while long‑term horizons receive weaker supervision; and (2) architecture discrepancy, a general challenge in distillation, for which we design an alignment mechanism in the time series forecasting. To overcome these issues, DistilTS introduces horizon‑weighted objectives to balance learning across horizons, and a temporal alignment strategy that reduces architectural mismatch, enabling compact models. Experiments on multiple benchmarks demonstrate that DistilTS achieves forecasting performance comparable to full‑sized TSFMs, while reducing parameters by up to 1/150 and accelerating inference by up to 6000x. Code is available at: https://github.com/itsnotacie/DistilTS‑ICASSP2026.

PaperId: 3688, https://arxiv.org/pdf/2601.12762.pdf GitHub

Abstract:
Equipping Large Language Models (LLMs) with external tools enables them to solve complex real‑world problems. However, the robustness of existing methods remains a critical challenge when confronting novel or evolving tools. Existing trajectory‑centric paradigms primarily rely on memorizing static solution paths during training, which limits the ability of LLMs to generalize tool usage to newly introduced or previously unseen tools. In this paper, we propose ToolMaster, a framework that shifts tool use from imitating golden tool‑calling trajectories to actively learning tool usage through interaction with the environment. To optimize LLMs for tool planning and invocation, ToolMaster adopts a trial‑and‑execution paradigm, which trains LLMs to first imitate teacher‑generated trajectories containing explicit tool trials and self‑correction, followed by reinforcement learning to coordinate the trial and execution phases jointly. This process enables agents to autonomously explore correct tool usage by actively interacting with environments and forming experiential knowledge that benefits tool execution. Experimental results demonstrate that ToolMaster significantly outperforms existing baselines in terms of generalization and robustness across unseen or unfamiliar tools. All code and data are available at https://github.com/NEUIR/ToolMaster.

PaperId: 3689, https://arxiv.org/pdf/2601.12720.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) have recently shown impressive performance on complex reasoning tasks, often by engaging in self‑reflective behaviors such as self‑critique and backtracking. However, not all reflections are beneficial‑many are superficial, offering little to no improvement over the original answer and incurring computation overhead. In this paper, we identify and address the problem of superficial reflection in LRMs. We first propose Self‑Critique Fine‑Tuning (SCFT), a training framework that enhances the model's reflective reasoning ability using only self‑generated critiques. SCFT prompts models to critique their own outputs, filters high‑quality critiques through rejection sampling, and fine‑tunes the model using a critique‑based objective. Building on this strong foundation, we further introduce Reinforcement Learning with Effective Reflection Rewards (RLERR). RLERR leverages the high‑quality reflections initialized by SCFT to construct reward signals, guiding the model to internalize the self‑correction process via reinforcement learning. Experiments on two challenging benchmarks, AIME2024 and AIME2025, show that SCFT and RLERR significantly improve both reasoning accuracy and reflection quality, outperforming state‑of‑the‑art baselines. All data and codes are available at https://github.com/wanghanbinpanda/SCFT.

PaperId: 3690, https://arxiv.org/pdf/2601.12539.pdf GitHub

Abstract:
Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery, and cultural context. Existing meme research is distributed across tasks (hate, misogyny, propaganda, sentiment, humour) and languages, which limits cross‑domain generalization. To address this gap we propose MemeLens, a unified multilingual and multitask explanation‑enhanced Vision Language Model (VLM) for meme understanding. We consolidate 38 public meme datasets, filter and map dataset‑specific labels into a shared taxonomy of 20 tasks spanning harm, targets, figurative/pragmatic intent, and affect. We present a comprehensive empirical analysis across modeling paradigms, task categories, and datasets. Our findings suggest that robust meme understanding requires multimodal training, exhibits substantial variation across semantic categories, and remains sensitive to over‑specialization when models are fine‑tuned on individual datasets rather than trained in a unified setting. We make the experimental resources (https://github.com/MohamedBayan/MemeLens), model (https://huggingface.co/QCRI/MemeLens‑VLM) and datasets (https://huggingface.co/datasets/QCRI/MemeLens) publicly available to the community.

PaperId: 3691, https://arxiv.org/pdf/2601.12538.pdf GitHub

Abstract:
Reasoning is a fundamental cognitive process underlying inference, problem‑solving, and decision‑making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed‑world settings, they struggle in open‑ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single‑agent capabilities including planning, tool use, and search in stable environments; self‑evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi‑agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in‑context reasoning, which scales test‑time interaction through structured orchestration, from post‑training reasoning, which optimizes behaviors via reinforcement learning and supervised fine‑tuning. We further review representative agentic reasoning frameworks across real‑world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long‑horizon interaction, world modeling, scalable multi‑agent training, and governance for real‑world deployment.

PaperId: 3692, https://arxiv.org/pdf/2601.12535.pdf GitHub

Abstract:
Low‑resource machine translation (MT) has gained increasing attention as parallel data from low‑resource language communities is collected, but many approaches for improving low‑resource MT remain underexplored. We investigate a self‑supervised reinforcement learning fine‑tuning for translation in low‑resource settings using round‑trip bootstrapping with the No Language Left Behind (NLLB) family of models. Our approach translates English into a target low‑resource language and then back into English, using a combination of chrF++ and BLEU as the reward function on the reconstructed English sentences. Using the NLLB‑MD dataset, we evaluate both the 600M and 1.3B parameter NLLB models and observe consistent improvements for the following languages: Central Aymara, Friulian, Wolof, Dyula, Bhojpuri and Russian. Qualitative inspection of translation outputs indicates increased fluency and semantic fidelity. We argue that our method can further benefit from scale, enabling models to increasingly leverage their pretrained knowledge and continue self‑improving. Code available at: https://github.com/Copticoder/MT‑via‑Round‑Trip‑RL

PaperId: 3693, https://arxiv.org/pdf/2601.12444.pdf GitHub

Abstract:
The ability of Large Language Models (LLMs) to perform reasoning tasks such as deduction has been widely investigated in recent years. Yet, their capacity to generate proofs‑faithful, human‑readable explanations of why conclusions follow‑remains largely under explored. In this work, we study proof generation in the context of OWL ontologies, which are widely adopted for representing and reasoning over complex knowledge, by developing an automated dataset construction and evaluation framework. Our evaluation encompassing three sequential tasks for complete proving: Extraction, Simplification, and Explanation, as well as an additional task of assessing Logic Completeness of the premise. Through extensive experiments on widely used reasoning LLMs, we achieve important findings including: (1) Some models achieve overall strong results but remain limited on complex cases; (2) Logical complexity, rather than representation format (formal logic language versus natural language), is the dominant factor shaping LLM performance; and (3) Noise and incompleteness in input data substantially diminish LLMs' performance. Together, these results underscore both the promise of LLMs for explanation with rigorous logics and the gap of supporting resilient reasoning under complex or imperfect conditions. Code and data are available at https://github.com/HuiYang1997/LLMOwlR.

PaperId: 3694, https://arxiv.org/pdf/2601.12357.pdf GitHub

Abstract:
Recent advances in semantic correspondence have been largely driven by the use of pre‑trained large‑scale models. However, a limitation of these approaches is their dependence on high‑resolution input images to achieve optimal performance, which results in considerable computational overhead. In this work, we address a fundamental limitation in current methods: the irreversible fusion of adjacent keypoint features caused by deep downsampling operations. This issue is triggered when semantically distinct keypoints fall within the same downsampled receptive field (e.g., 16x16 patches). To address this issue, we present SimpleMatch, a simple yet effective framework for semantic correspondence that delivers strong performance even at low resolutions. We propose a lightweight upsample decoder that progressively recovers spatial detail by upsampling deep features to 1/4 resolution, and a multi‑scale supervised loss that ensures the upsampled features retain discriminative features across different spatial scales. In addition, we introduce sparse matching and window‑based localization to optimize training memory usage and reduce it by 51%. At a resolution of 252x252 (3.3x smaller than current SOTA methods), SimpleMatch achieves superior performance with 84.1% PCK@0.1 on the SPair‑71k benchmark. We believe this framework provides a practical and efficient baseline for future research in semantic correspondence. Code is available at: https://github.com/hailong23‑jin/SimpleMatch.

PaperId: 3695, https://arxiv.org/pdf/2601.12294.pdf GitHub

Abstract:
Reward‑guided search methods have demonstrated strong potential in enhancing tool‑using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step‑level rewards, enabling more fine‑grained monitoring. However, there is a lack of systematic and reliable evaluation benchmarks for PRMs in tool‑using settings. In this paper, we introduce ToolPRMBench, a large‑scale benchmark specifically designed to evaluate PRMs for tool‑using agents. ToolPRMBench is built on top of several representative tool‑using benchmarks and converts agent trajectories into step‑level test cases. Each case contains the interaction history, a correct action, a plausible but incorrect alternative, and relevant tool metadata. We respectively utilize offline sampling to isolate local single‑step errors and online sampling to capture realistic multi‑step failures from full agent rollouts. A multi‑LLM verification pipeline is proposed to reduce label noise and ensure data quality. We conduct extensive experiments across large language models, general PRMs, and tool‑specialized PRMs on ToolPRMBench. The results reveal clear differences in PRM effectiveness and highlight the potential of specialized PRMs for tool‑using. Code and data will be released at https://github.com/David‑Li0406/ToolPRMBench.

PaperId: 3696, https://arxiv.org/pdf/2601.12248.pdf GitHub

Abstract:
Recent advances in audio‑aware large language models have shown strong performance on audio question answering. However, existing benchmarks mainly cover answerable questions and overlook the challenge of unanswerable ones, where no reliable answer can be inferred from the audio. Such cases are common in real‑world settings, where questions may be misleading, ill‑posed, or incompatible with the information. To address this gap, we present AQUA‑Bench, a benchmark for Audio Question Unanswerability Assessment. It systematically evaluates three scenarios: Absent Answer Detection (the correct option is missing), Incompatible Answer Set Detection (choices are categorically mismatched with the question), and Incompatible Audio Question Detection (the question is irrelevant or lacks sufficient grounding in the audio). By assessing these cases, AQUA‑Bench offers a rigorous measure of model reliability and promotes the development of audio‑language systems that are more robust and trustworthy. Our experiments suggest that while models excel on standard answerable tasks, they often face notable challenges with unanswerable ones, pointing to a blind spot in current audio‑language understanding.

PaperId: 3697, https://arxiv.org/pdf/2601.12124.pdf GitHub

Abstract:
The use of synthetic data in health applications raises privacy concerns, yet the lack of open frameworks for privacy evaluations has slowed its adoption. A major challenge is the absence of accessible benchmark datasets for evaluating privacy risks, due to difficulties in acquiring sensitive data. To address this, we introduce SynQP, an open framework for benchmarking privacy in synthetic data generation (SDG) using simulated sensitive data, ensuring that original data remains confidential. We also highlight the need for privacy metrics that fairly account for the probabilistic nature of machine learning models. As a demonstration, we use SynQP to benchmark CTGAN and propose a new identity disclosure risk metric that offers a more accurate estimation of privacy risks compared to existing approaches. Our work provides a critical tool for improving the transparency and reliability of privacy evaluations, enabling safer use of synthetic data in health‑related applications. % In our quality evaluations, non‑private models achieved near‑perfect machine‑learning efficacy \(\ge0.97\). Our privacy assessments (Table II) reveal that DP consistently lowers both identity disclosure risk (SD‑IDR) and membership‑inference attack risk (SD‑MIA), with all DP‑augmented models staying below the 0.09 regulatory threshold. Code available at https://github.com/CAN‑SYNH/SynQP

PaperId: 3698, https://arxiv.org/pdf/2601.12104.pdf GitHub

Abstract:
Fine‑tuned language models pose significant privacy risks, as they may memorize and expose sensitive information from their training data. Membership inference attacks (MIAs) provide a principled framework for auditing these risks, yet existing methods achieve limited detection rates, particularly at the low false‑positive thresholds required for practical privacy auditing. We present EZ‑MIA, a membership inference attack that exploits a key observation: memorization manifests most strongly at error positions, specifically tokens where the model predicts incorrectly yet still shows elevated probability for training examples. We introduce the Error Zone (EZ) score, which measures the directional imbalance of probability shifts at error positions relative to a pretrained reference model. This principled statistic requires only two forward passes per query and no model training of any kind. On WikiText with GPT‑2, EZ‑MIA achieves 3.8x higher detection than the previous state‑of‑the‑art under identical conditions (66.3% versus 17.5% true positive rate at 1% false positive rate), with near‑perfect discrimination (AUC 0.98). At the stringent 0.1% FPR threshold critical for real‑world auditing, we achieve 8x higher detection than prior work (14.0% versus 1.8%), requiring no reference model training. These gains extend to larger architectures: on AG News with Llama‑2‑7B, we achieve 3x higher detection (46.7% versus 15.8% TPR at 1% FPR). These results establish that privacy risks of fine‑tuned language models are substantially greater than previously understood, with implications for both privacy auditing and deployment decisions. Code is available at https://github.com/JetBrains‑Research/ez‑mia.

PaperId: 3699, https://arxiv.org/pdf/2601.12082.pdf GitHub

Abstract:
Assisting pathologists in the analysis of histopathological images has high clinical value, as it supports cancer detection and staging. In this context, histology foundation models have recently emerged. Among them, Vision‑Language Models (VLMs) provide strong yet imperfect zero‑shot predictions. We propose to refine these predictions by adapting Conditional Random Fields (CRFs) to histopathological applications, requiring no additional model training. We present HistoCRF, a CRF‑based framework, with a novel definition of the pairwise potential that promotes label diversity and leverages expert annotations. We consider three experiments: without annotations, with expert annotations, and with iterative human‑in‑the‑loop annotations that progressively correct misclassified patches. Experiments on five patch‑level classification datasets covering different organs and diseases demonstrate average accuracy gains of 16.0% without annotations and 27.5% with only 100 annotations, compared to zero‑shot predictions. Moreover, integrating a human in the loop reaches a further gain of 32.6% with the same number of annotations. The code will be made available on https://github.com/tgodelaine/HistoCRF.

PaperId: 3700, https://arxiv.org/pdf/2601.11960.pdf GitHub

Abstract:
Reinforcement learning has become a central paradigm for improving LLM reasoning. However, existing methods use a single policy to produce both inference responses and training optimization trajectories. The objective conflict between generating stable inference responses and diverse training trajectories leads to insufficient exploration, which harms reasoning capability. In this paper, to address the problem, we propose R^2PO (Residual Rollout Policy Optimization), which introduces a lightweight Residual Rollout‑Head atop the policy to decouple training trajectories from inference responses, enabling controlled trajectory diversification during training while keeping inference generation stable. Experiments across multiple benchmarks show that our method consistently outperforms baselines, achieving average accuracy gains of 3.4% on MATH‑500 and 1.3% on APPS, while also reducing formatting errors and mitigating length bias for stable optimization. Our code is publicly available at https://github.com/RRPO‑ARR/Code.

PaperId: 3701, https://arxiv.org/pdf/2601.11670.pdf GitHub

Abstract:
Most pseudo‑label selection strategies in semi‑supervised learning rely on fixed confidence thresholds, implicitly assuming that prediction confidence reliably indicates correctness. In practice, deep networks are often overconfident: high‑confidence predictions can still be wrong, while informative low‑confidence samples near decision boundaries are discarded. This paper introduces a Confidence‑Variance (CoVar) theory framework that provides a principled joint reliability criterion for pseudo‑label selection. Starting from the entropy minimization principle, we derive a reliability measure that combines maximum confidence (MC) with residual‑class variance (RCV), which characterizes how probability mass is distributed over non‑maximum classes. The derivation shows that reliable pseudo‑labels should have both high MC and low RCV, and that the influence of RCV increases as confidence grows, thereby correcting overconfident but unstable predictions. From this perspective, we cast pseudo‑label selection as a spectral relaxation problem that maximizes separability in a confidence‑variance feature space, and design a threshold‑free selection mechanism to distinguish high‑ from low‑reliability predictions. We integrate CoVar as a plug‑in module into representative semi‑supervised semantic segmentation and image classification methods. Across PASCAL VOC 2012, Cityscapes, CIFAR‑10, and Mini‑ImageNet with varying label ratios and backbones, it consistently improves over strong baselines, indicating that combining confidence with residual‑class variance provides a more reliable basis for pseudo‑label selection than fixed confidence thresholds. (Code: https://github.com/ljs11528/CoVar_Pseudo_Label_Selection.git)

PaperId: 3702, https://arxiv.org/pdf/2601.11492.pdf GitHub

Abstract:
Competitive sports require sophisticated tactical analysis, yet combat disciplines like boxing remain underdeveloped in AI‑driven analytics due to the complexity of action dynamics and the lack of structured tactical representations. To address this, we present BoxMind, a closed‑loop AI expert system validated in elite boxing competition. By defining atomic punch events with precise temporal boundaries and spatial and technical attributes, we parse match footage into 18 hierarchical technical‑tactical indicators. We then propose a graph‑based predictive model that fuses these explicit technical‑tactical profiles with learnable, time‑variant latent embeddings to capture the dynamics of boxer matchups. Modeling match outcome as a differentiable function of technical‑tactical indicators, we turn winning probability gradients into executable tactical adjustments. Experiments show that the outcome prediction model achieves state‑of‑the‑art performance, with 69.8% accuracy on BoxerGraph test set and 87.5% on Olympic matches. Using this predictive model as a foundation, the system generates strategic recommendations that demonstrate proficiency comparable to human experts. BoxMind is validated through a closed‑loop deployment during the 2024 Paris Olympics, directly contributing to the Chinese National Team's historic achievement of three gold and two silver medals. BoxMind establishes a replicable paradigm for transforming unstructured video data into strategic intelligence, bridging the gap between computer vision and decision support in competitive sports. Code and data is available at https://github.com/gouba2333/BoxingWeb.

PaperId: 3703, https://arxiv.org/pdf/2601.11451.pdf GitHub

Abstract:
Large‑scale livestock operations pose significant risks to human health and the environment, while also being vulnerable to threats such as infectious diseases and extreme weather events. As the number of such operations continues to grow, accurate and scalable mapping has become increasingly important. In this work, we present an infrastructure‑first, explainable pipeline for identifying and characterizing Concentrated Animal Feeding Operations (CAFOs) from aerial and satellite imagery. Our method (i) detects candidate infrastructure (e.g., barns, feedlots, manure lagoons, silos) with a domain‑tuned YOLOv8 detector, then derives SAM2 masks from these boxes and filters component‑specific criteria; (ii) extracts structured descriptors (e.g., counts, areas, orientations, and spatial relations) and fuses them with deep visual features using a lightweight spatial cross‑attention classifier; and (iii) outputs both CAFO type predictions and mask‑level attributions that link decisions to visible infrastructure. Through comprehensive evaluation, we show that our approach achieves state‑of‑the‑art performance, with Swin‑B+PRISM‑CAFO surpassing the best performing baseline by up to 15%. Beyond strong predictive performance across diverse U.S. regions, we run systematic gradient‑‑activation analyses that quantify the impact of domain priors and show how specific infrastructure (e.g., barns, lagoons) shapes classification decisions. We release code, infrastructure masks, and descriptors to support transparent, scalable monitoring of livestock infrastructure, enabling risk modeling, change detection, and targeted regulatory action. Github: https://github.com/Nibir088/PRISM‑CAFO.

PaperId: 3704, https://arxiv.org/pdf/2601.11441.pdf GitHub

Abstract:
Large language models (LLMs) exhibit exceptional performance across various domains, yet they face critical safety concerns. Model editing has emerged as an effective approach to mitigate these issues. Existing model editing methods often focus on optimizing an information matrix that blends new and old knowledge. While effective, these approaches can be computationally expensive and may cause conflicts. In contrast, we shift our attention to Hierarchical Orthogonal Residual SprEad of the information matrix, which reduces noisy gradients and enables more stable edits from a different perspective. We demonstrate the effectiveness of our method HORSE through a clear theoretical comparison with several popular methods and extensive experiments conducted on two datasets across multiple LLMs. The results show that HORSE maintains precise massive editing across diverse scenarios. The code is available at https://github.com/XiaojieGu/HORSE

PaperId: 3705, https://arxiv.org/pdf/2601.11109.pdf GitHub

Abstract:
Vision‑as‑inverse‑graphics, the concept of reconstructing images into editable programs, remains challenging for Vision‑Language Models (VLMs), which inherently lack fine‑grained spatial grounding in one‑shot settings. To address this, we introduce VIGA (Vision‑as‑Inverse‑Graphics Agent), an interleaved multimodal reasoning framework where symbolic logic and visual perception actively cross‑verify each other. VIGA operates through a tightly coupled code‑render‑inspect loop: synthesizing symbolic programs, projecting them into visual states, and inspecting discrepancies to guide iterative edits. Equipped with high‑level semantic skills and an evolving multimodal memory, VIGA sustains evidence‑based modifications over long horizons. This training‑free, task‑agnostic framework seamlessly supports 2D document generation, 3D reconstruction, multi‑step 3D editing, and 4D physical interaction. Finally, we introduce BlenderBench, a challenging visual‑to‑code benchmark. Empirically, VIGA substantially improves accuracy compared with one‑shot baselines in BlenderGym (35.32%), SlideBench (117.17%) and our proposed BlenderBench (124.70%).

PaperId: 3706, https://arxiv.org/pdf/2601.11077.pdf GitHub

Abstract:
The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository‑level, and execution‑driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full‑process requirements of real‑world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we introduce ABC‑Bench, a benchmark explicitly designed to evaluate agentic backend coding within a realistic, executable workflow. Using a scalable automated pipeline, we curated 224 practical tasks spanning 8 languages and 19 frameworks from open‑source repositories. Distinct from previous evaluations, ABC‑Bench require the agents to manage the entire development lifecycle from repository exploration to instantiating containerized services and pass the external end‑to‑end API tests. Our extensive evaluation reveals that even state‑of‑the‑art models struggle to deliver reliable performance on these holistic tasks, highlighting a substantial disparity between current model capabilities and the demands of practical backend engineering. Our code is available at https://github.com/OpenMOSS/ABC‑Bench.

PaperId: 3707, https://arxiv.org/pdf/2601.11044.pdf GitHub

Abstract:
Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long‑horizon real‑world scenarios. Moreover, the reliance on human‑in‑the‑loop feedback for realistic tasks creates a scalability bottleneck, hindering automated rollout collection and evaluation. To bridge this gap, we introduce AgencyBench, a comprehensive benchmark derived from daily AI usage, evaluating 6 core agentic capabilities across 32 real‑world scenarios, comprising 138 tasks with specific queries, deliverables, and rubrics. These scenarios require an average of 90 tool calls, 1 million tokens, and hours of execution time to resolve. To enable automated evaluation, we employ a user simulation agent to provide iterative feedback, and a Docker sandbox to conduct visual and functional rubric‑based assessment. Experiments reveal that closed‑source models significantly outperform open‑source models (48.4% vs 32.1%). Further analysis reveals significant disparities across models in resource efficiency, feedback‑driven self‑correction, and specific tool‑use preferences. Finally, we investigate the impact of agentic scaffolds, observing that proprietary models demonstrate superior performance within their native ecosystems (e.g., Claude‑4.5‑Opus via Claude‑Agent‑SDK), while open‑source models exhibit distinct performance peaks, suggesting potential optimization for specific execution frameworks. AgencyBench serves as a critical testbed for next‑generation agents, highlighting the necessity of co‑optimizing model architecture with agentic frameworks. We believe this work sheds light on the future direction of autonomous agents, and we release the full benchmark and evaluation toolkit at https://github.com/GAIR‑NLP/AgencyBench.

PaperId: 3708, https://arxiv.org/pdf/2601.11037.pdf GitHub

Abstract:
RL‑based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances accuracy with agent policies optimized via large‑scale reinforcement learning, we identify a critical gap in reliability: these agents fail to recognize their reasoning boundaries and rarely admit ``I DON'T KNOW'' (IDK) even when evidence is insufficient or reasoning reaches its limit. The lack of reliability often leads to plausible but unreliable answers, introducing significant risks in many real‑world scenarios. To this end, we propose Boundary‑Aware Policy Optimization (BAPO), a novel RL framework designed to cultivate reliable boundary awareness without compromising accuracy. BAPO introduces two key components: (i) a group‑based boundary‑aware reward that encourages an IDK response only when the reasoning reaches its limit, and (ii) an adaptive reward modulator that strategically suspends this reward during early exploration, preventing the model from exploiting IDK as a shortcut. Extensive experiments on four benchmarks demonstrate that BAPO substantially enhances the overall reliability of agentic search.

PaperId: 3709, https://arxiv.org/pdf/2601.11035.pdf GitHub

Abstract:
Recent advances in generative modeling can create remarkably realistic synthetic videos, making it increasingly difficult for humans to distinguish them from real ones and necessitating reliable detection methods. However, two key limitations hinder the development of this field. From the dataset perspective, existing datasets are often limited in scale and constructed using outdated or narrowly scoped generative models, making it difficult to capture the diversity and rapid evolution of modern generative techniques. Moreover, the dataset construction process frequently prioritizes quantity over quality, neglecting essential aspects such as semantic diversity, scenario coverage, and technological representativeness. From the benchmark perspective, current benchmarks largely remain at the stage of dataset creation, leaving many fundamental issues and in‑depth analysis yet to be systematically explored. Addressing this gap, we propose AIGVDBench, a benchmark designed to be comprehensive and representative, covering 31 state‑of‑the‑art generation models and over 440,000 videos. By executing more than 1,500 evaluations on 33 existing detectors belonging to four distinct categories. This work presents 8 in‑depth analyses from multiple perspectives and identifies 4 novel findings that offer valuable insights for future research. We hope this work provides a solid foundation for advancing the field of AI‑generated video detection. Our benchmark is open‑sourced at https://github.com/LongMa‑2025/AIGVDBench.

PaperId: 3710, https://arxiv.org/pdf/2601.11019.pdf GitHub

Abstract:
Large Language Models (LLMs) frequently exhibit strong translation abilities, even without task‑specific fine‑tuning. However, the internal mechanisms governing this innate capability remain largely opaque. To demystify this process, we leverage Sparse Autoencoders (SAEs) and introduce a novel framework for identifying task‑specific features. Our method first recalls features that are frequently co‑activated on translation inputs and then filters them for functional coherence using a PCA‑based consistency metric. This framework successfully isolates a small set of translation initiation features. Causal interventions demonstrate that amplifying these features steers the model towards correct translation, while ablating them induces hallucinations and off‑task outputs, confirming they represent a core component of the model's innate translation competency. Moving from analysis to application, we leverage this mechanistic insight to propose a new data selection strategy for efficient fine‑tuning. Specifically, we prioritize training on mechanistically hard samples‑those that fail to naturally activate the translation initiation features. Experiments show this approach significantly improves data efficiency and suppresses hallucinations. Furthermore, we find these mechanisms are transferable to larger models of the same family. Our work not only decodes a core component of the translation mechanism in LLMs but also provides a blueprint for using internal model mechanism to create more robust and efficient models. The codes are available at https://github.com/flamewei123/AAAI26‑translation‑Initiation‑Features.

PaperId: 3711, https://arxiv.org/pdf/2601.11012.pdf GitHub

Abstract:
The ability to engineer optimized protein variants has transformative potential for biotechnology and medicine. Prior sequence‑based optimization methods struggle with the high‑dimensional complexities due to the epistasis effect and the disregard for structural constraints. To address this, we propose HADES, a Bayesian optimization method utilizing Hamiltonian dynamics to efficiently sample from a structure‑aware approximated posterior. Leveraging momentum and uncertainty in the simulated physical movements, HADES enables rapid transition of proposals toward promising areas. A position discretization procedure is introduced to propose discrete protein sequences from such a continuous state system. The posterior surrogate is powered by a two‑stage encoder‑decoder framework to determine the structure and function relationships between mutant neighbors, consequently learning a smoothed landscape to sample from. Extensive experiments demonstrate that our method outperforms state‑of‑the‑art baselines in in‑silico evaluations across most metrics. Remarkably, our approach offers a unique advantage by leveraging the mutual constraints between protein structure and sequence, facilitating the design of protein sequences with similar structures and optimized properties. The code and data are publicly available at https://github.com/GENTEL‑lab/HADES.

PaperId: 3712, https://arxiv.org/pdf/2601.10880.pdf GitHub

Abstract:
Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept‑based prompting. However, their direct applicability to medical image segmentation remains limited by severe domain shifts, the absence of privileged spatial prompts, and the need to reason over complex anatomical and volumetric structures. Here we present Medical SAM3, a foundation model for universal prompt‑driven medical image segmentation, obtained by fully fine‑tuning SAM3 on large‑scale, heterogeneous 2D and 3D medical imaging datasets with paired segmentation masks and text prompts. Through a systematic analysis of vanilla SAM3, we observe that its performance degrades substantially on medical data, with its apparent competitiveness largely relying on strong geometric priors such as ground‑truth‑derived bounding boxes. These findings motivate full model adaptation beyond prompt engineering alone. By fine‑tuning SAM3's model parameters on 33 datasets spanning 10 medical imaging modalities, Medical SAM3 acquires robust domain‑specific representations while preserving prompt‑driven flexibility. Extensive experiments across organs, imaging modalities, and dimensionalities demonstrate consistent and significant performance gains, particularly in challenging scenarios characterized by semantic ambiguity, complex morphology, and long‑range 3D context. Our results establish Medical SAM3 as a universal, text‑guided segmentation foundation model for medical imaging and highlight the importance of holistic model adaptation for achieving robust prompt‑driven segmentation under severe domain shift. Code and model will be made available at https://github.com/AIM‑Research‑Lab/Medical‑SAM3.

PaperId: 3713, https://arxiv.org/pdf/2601.10744.pdf GitHub

Abstract:
An ideal embodied agent should possess lifelong learning capabilities to handle long‑horizon and complex tasks, enabling continuous operation in general environments. This not only requires the agent to accurately accomplish given tasks but also to leverage long‑term episodic memory to optimize decision‑making. However, existing mainstream one‑shot embodied tasks primarily focus on task completion results, neglecting the crucial process of exploration and memory utilization. To address this, we propose Long‑term Memory Embodied Exploration (LMEE), which aims to unify the agent's exploratory cognition and decision‑making behaviors to promote lifelong learning. We further construct a corresponding dataset and benchmark, LMEE‑Bench, incorporating multi‑goal navigation and memory‑based question answering to comprehensively evaluate both the process and outcome of embodied exploration. To enhance the agent's memory recall and proactive exploration capabilities, we propose MemoryExplorer, a novel method that fine‑tunes a multimodal large language model through reinforcement learning to encourage active memory querying. By incorporating a multi‑task reward function that includes action prediction, frontier selection, and question answering, our model achieves proactive exploration. Extensive experiments against state‑of‑the‑art embodied exploration models demonstrate that our approach achieves significant advantages in long‑horizon embodied tasks. Our dataset and code will be released at https://wangsen99.github.io/papers/lmee/

PaperId: 3714, https://arxiv.org/pdf/2601.10719.pdf GitHub

Abstract:
Perceived trustworthiness underpins how users navigate online information, yet it remains unclear whether large language models (LLMs),increasingly embedded in search, recommendation, and conversational systems, represent this construct in psychologically coherent ways. We analyze how instruction‑tuned LLMs (Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B) encode perceived trustworthiness in web‑like narratives using the PEACE‑Reviews dataset annotated for cognitive appraisals, emotions, and behavioral intentions. Across models, systematic layer‑ and head‑level activation differences distinguish high‑ from low‑trust texts, revealing that trust cues are implicitly encoded during pretraining. Probing analyses show linearly de‑codable trust signals and fine‑tuning effects that refine rather than restructure these representations. Strongest associations emerge with appraisals of fairness, certainty, and accountability‑self ‑‑ dimensions central to human trust formation online. These findings demonstrate that modern LLMs internalize psychologically grounded trust signals without explicit supervision, offering a representational foundation for designing credible, transparent, and trust‑worthy AI systems in the web ecosystem. Code and appendix are available at: https://github.com/GerardYeo/TrustworthinessLLM.

PaperId: 3715, https://arxiv.org/pdf/2601.10712.pdf GitHub

Abstract:
Tool‑Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing reinforcement learning methods typically rely on outcome‑ or trajectory‑level rewards, assigning uniform advantages to all steps within a trajectory. This coarse‑grained credit assignment fails to distinguish effective tool calls from redundant or erroneous ones, particularly in long‑horizon multi‑turn scenarios. To address this, we propose MatchTIR, a framework that introduces fine‑grained supervision via bipartite matching‑based turn‑level reward assignment and dual‑level advantage estimation. Specifically, we formulate credit assignment as a bipartite matching problem between predicted and ground‑truth traces, utilizing two assignment strategies to derive dense turn‑level rewards. Furthermore, to balance local step precision with global task success, we introduce a dual‑level advantage estimation scheme that integrates turn‑level and trajectory‑level signals, assigning distinct advantage values to individual interaction turns. Extensive experiments on three benchmarks demonstrate the superiority of MatchTIR. Notably, our 4B model surpasses the majority of 8B competitors, particularly in long‑horizon and multi‑turn tasks. Our codes are available at https://github.com/quchangle1/MatchTIR.

PaperId: 3716, https://arxiv.org/pdf/2601.10560.pdf GitHub

Abstract:
Multi‑agent systems (MAS) enable complex reasoning by coordinating multiple agents, but often incur high inference latency due to multi‑step execution and repeated model invocations, severely limiting their scalability and usability in time‑sensitive scenarios. Most existing approaches primarily optimize task performance and inference cost, and explicitly or implicitly assume sequential execution, making them less optimal for controlling latency under parallel execution. In this work, we investigate learning‑based orchestration of multi‑agent systems with explicit latency supervision under parallel execution. We propose Latency‑Aware Multi‑agent System (LAMaS), a latency‑aware multi‑agent orchestration framework that enables parallel execution and explicitly optimizes the critical execution path, allowing the controller to construct execution topology graphs with lower latency under parallel execution. Our experiments show that our approach reduces critical path length by 38‑46% compared to the state‑of‑the‑art baseline for multi‑agent architecture search across multiple benchmarks, while maintaining or even improving task performance. These results highlight the importance of explicitly optimizing latency under parallel execution when designing efficient multi‑agent systems. The code is available at https://github.com/xishi404/LAMaS

PaperId: 3717, https://arxiv.org/pdf/2601.10543.pdf GitHub

Abstract:
Large language models (LLMs) have achieved impressive performance across natural language tasks and are increasingly deployed in real‑world applications. Despite extensive safety alignment efforts, recent studies show that such alignment is often shallow and remains vulnerable to jailbreak attacks. Existing defense mechanisms, including decoding‑based constraints and post‑hoc content detectors, struggle against sophisticated jailbreaks, often intervening robust detection or excessively degrading model utility. In this work, we examine the decoding process of LLMs and make a key observation: even when successfully jailbroken, models internally exhibit latent safety‑related signals during generation. However, these signals are overridden by the model's drive for fluent continuation, preventing timely self‑correction or refusal. Building on this observation, we propose a simple yet effective approach that explicitly surfaces and leverages these latent safety signals for early detection of unsafe content during decoding. Experiments across diverse jailbreak attacks demonstrate that our approach significantly enhances safety, while maintaining low over‑refusal rates on benign inputs and preserving response quality. Our results suggest that activating intrinsic safety‑awareness during decoding offers a promising and complementary direction for defending against jailbreak attacks. Code is available at: https://github.com/zyz13590/SafeProbing.

PaperId: 3718, https://arxiv.org/pdf/2601.10477.pdf GitHub

Abstract:
As hubs of human activity, urban surfaces consist of a wealth of semantic entities. Segmenting these various entities from satellite imagery is crucial for a range of downstream applications. Current advanced segmentation models can reliably segment entities defined by physical attributes (e.g., buildings, water bodies) but still struggle with socially defined categories (e.g., schools, parks). In this work, we achieve socio‑semantic segmentation by vision‑language model reasoning. To facilitate this, we introduce the Urban Socio‑Semantic Segmentation dataset named SocioSeg, a new resource comprising satellite imagery, digital maps, and pixel‑level labels of social semantic entities organized in a hierarchical structure. Additionally, we propose a novel vision‑language reasoning framework called SocioReasoner that simulates the human process of identifying and annotating social semantic entities via cross‑modal recognition and multi‑stage reasoning. We employ reinforcement learning to optimize this non‑differentiable process and elicit the reasoning capabilities of the vision‑language model. Experiments demonstrate our approach's gains over state‑of‑the‑art models and strong zero‑shot generalization. The dataset and code are open‑sourced under the Apache License 2.0 at https://github.com/AMAP‑ML/SocioReasoner.

PaperId: 3719, https://arxiv.org/pdf/2601.10462.pdf GitHub

Abstract:
With advancements in deep learning (DL) and computer vision techniques, the field of chart understanding is evolving rapidly. In particular, multimodal large language models (MLLMs) are proving to be efficient and accurate in understanding charts. To accurately measure the performance of MLLMs, the research community has developed multiple datasets to serve as benchmarks. By examining these datasets, we found that they are all limited to a small set of chart types. To bridge this gap, we propose the ChartComplete dataset. The dataset is based on a chart taxonomy borrowed from the visualization community, and it covers thirty different chart types. The dataset is a collection of classified chart images and does not include a learning signal. We present the ChartComplete dataset as is to the community to build upon it.

PaperId: 3720, https://arxiv.org/pdf/2601.10349.pdf GitHub

Abstract:
We propose Strategy‑aware Surprise (SuS), a novel intrinsic motivation framework that uses pre‑post prediction mismatch as a novelty signal for exploration in reinforcement learning. Unlike traditional curiosity‑driven methods that rely solely on state prediction error, SuS introduces two complementary components: Strategy Stability (SS) and Strategy Surprise (SuS). SS measures consistency in behavioral strategy across temporal steps, while SuS captures unexpected outcomes relative to the agent's current strategy representation. Our combined reward formulation leverages both signals through learned weighting coefficients. We evaluate SuS on mathematical reasoning tasks using large language models, demonstrating significant improvements in both accuracy and solution diversity. Ablation studies confirm that removing either component results in at least 10% performance degradation, validating the synergistic nature of our approach. SuS achieves 17.4% improvement in Pass@1 and 26.4% improvement in Pass@5 compared to baseline methods, while maintaining higher strategy diversity throughout training.

PaperId: 3721, https://arxiv.org/pdf/2601.10272.pdf GitHub

Abstract:
We present MoST (Mixture of Speech and Text), a novel multimodal large language model that seamlessly integrates speech and text processing through our proposed Modality‑Aware Mixture of Experts (MAMoE) architecture. While current multimodal models typically process diverse modality representations with identical parameters, disregarding their inherent representational differences, we introduce specialized routing pathways that direct tokens to modality‑appropriate experts based on input type. MAMoE simultaneously enhances modality‑specific learning and cross‑modal understanding through two complementary components: modality‑specific expert groups that capture domain‑specific patterns and shared experts that facilitate information transfer between modalities. Building on this architecture, we develop an efficient transformation pipeline that adapts the pretrained MoE language model through strategic post‑training on ASR and TTS datasets, followed by fine‑tuning with a carefully curated speech‑text instruction dataset. A key feature of this pipeline is that it relies exclusively on fully accessible, open‑source datasets to achieve strong performance and data efficiency. Comprehensive evaluations across ASR, TTS, audio language modeling, and spoken question answering benchmarks show that MoST consistently outperforms existing models of comparable parameter counts. Our ablation studies confirm that the modality‑specific routing mechanism and shared experts design significantly contribute to performance gains across all tested domains. To our knowledge, MoST represents the first fully open‑source speech‑text LLM built on a Mixture of Experts architecture. \footnoteWe release MoST model, training code, inference code, and training data at https://github.com/NUS‑HPC‑AI‑Lab/MoST

PaperId: 3722, https://arxiv.org/pdf/2601.10212.pdf GitHub

Abstract:
The prevalence of recommendation systems also brings privacy concerns to both the users and the sellers, as centralized platforms collect as much data as possible from them. To keep the data private, we propose PADER: a Paillier‑based secure decentralized social recommendation system. In this system, the users and the sellers are nodes in a decentralized network. The training and inference of the recommendation model are carried out securely in a decentralized manner, without the involvement of a centralized platform. To this end, we apply the Paillier cryptosystem to the SoReg (Social Regularization) model, which exploits both user's ratings and social relations. We view the SoReg model as a two‑party secure polynomial evaluation problem and observe that the simple bipartite computation may result in poor efficiency. To improve efficiency, we design secure addition and multiplication protocols to support secure computation on any arithmetic circuit, along with an optimal data packing scheme that is suitable for the polynomial computations of real values. Experiment results show that our method only takes about one second to iterate through one user with hundreds of ratings, and training with ~500K ratings for one epoch only takes <3 hours, which shows that the method is practical in real applications. The code is available at https://github.com/GarminQ/PADER.

PaperId: 3723, https://arxiv.org/pdf/2601.10205.pdf GitHub

Abstract:
Aligning multilingual assistants with culturally grounded user preferences is essential for serving India's linguistically diverse population of over one billion speakers across multiple scripts. However, existing benchmarks either focus on a single language or conflate retrieval with generation, leaving open the question of whether current embedding models can encode persona‑instruction compatibility without relying on response synthesis. We present a unified benchmark spanning 12 Indian languages and four evaluation tasks: monolingual and cross‑lingual persona‑to‑instruction retrieval, reverse retrieval from instruction to persona, and binary compatibility classification. Eight multilingual embedding models are evaluated in a frozen‑encoder setting with a thin logistic regression head for classification. E5‑Large‑Instruct achieves the highest Recall@1 of 27.4% on monolingual retrieval and 20.7% on cross‑lingual transfer, while BGE‑M3 leads reverse retrieval at 32.1% Recall@1. For classification, LaBSE attains 75.3% AUROC with strong calibration. These findings offer practical guidance for model selection in Indic multilingual retrieval and establish reproducible baselines for future work\footnoteCode, datasets, and models are publicly available at https://github.com/aryashah2k/PI‑Indic‑Align.

PaperId: 3724, https://arxiv.org/pdf/2601.10173.pdf GitHub

Abstract:
Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are highly vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external data can hijack agent behavior. In this work, we present ReasAlign, a model‑level solution to improve safety alignment against indirect prompt injection attacks. The core idea of ReasAlign is to incorporate structured reasoning steps to analyze user queries, detect conflicting instructions, and preserve the continuity of the user's intended tasks to defend against indirect injection attacks. To further ensure reasoning logic and accuracy, we introduce a test‑time scaling mechanism with a preference‑optimized judge model that scores reasoning steps and selects the best trajectory. Comprehensive evaluations across various benchmarks show that ReasAlign maintains utility comparable to an undefended model while consistently outperforming Meta SecAlign, the strongest prior guardrail. On the representative open‑ended CyberSecEval2 benchmark, which includes multiple prompt‑injected tasks, ReasAlign achieves 94.6% utility and only 3.6% ASR, far surpassing the state‑of‑the‑art defensive model of Meta SecAlign (56.4% utility and 74.4% ASR). These results demonstrate that ReasAlign achieves the best trade‑off between security and utility, establishing a robust and practical defense against prompt injection attacks in real‑world agentic systems. Our code and experimental results could be found at https://github.com/leolee99/ReasAlign.

PaperId: 3725, https://arxiv.org/pdf/2601.10161.pdf GitHub

Abstract:
Named Entity Recognition (NER) is a foundational task in Natural Language Processing (NLP) and Information Retrieval (IR), which facilitates semantic search and structured data extraction. We introduce AWED‑FiNER, an open‑source collection of agentic tool, web application, and 53 state‑of‑the‑art expert models that provide Fine‑grained Named Entity Recognition (FgNER) solutions across 36 languages spoken by more than 6.6 billion people. The agentic tool enables routing multilingual text to specialized expert models to fetch FgNER annotations within seconds. The web‑based platform provides a ready‑to‑use FgNER annotation service for non‑technical users. Moreover, the collection of language‑specific extremely small open‑source state‑of‑the‑art expert models facilitates offline deployment in resource‑constrained scenarios, including edge devices. AWED‑FiNER covers languages spoken by over 6.6 billion people, ranging from global languages like English, Chinese, Spanish, and Hindi, to low‑resource languages like Assamese, Santali, and Odia, along with a specific focus on extremely low‑resource vulnerable languages such as Bodo, Manipuri, Bishnupriya, and Mizo. The resources can be accessed here: Agentic Tool (https://github.com/PrachuryyaKaushik/AWED‑FiNER), Web Application (https://hf.co/spaces/prachuryyaIITG/AWED‑FiNER), and 53 Expert Detector Models (https://hf.co/collections/prachuryyaIITG/awed‑finer).

PaperId: 3726, https://arxiv.org/pdf/2601.10120.pdf GitHub

Abstract:
Optimizing communication topology in LLM‑based multi‑agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio‑temporal interaction paradigms, where the sequential execution of multi‑round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem‑solving in multi‑agent systems, we propose TopoDIM, a framework for one‑shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41% while improving average performance by 1.50% over state‑of‑the‑art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://github.com/Sundiasy/TopoDIM.

PaperId: 3727, https://arxiv.org/pdf/2601.10104.pdf GitHub GitHub

Abstract:
The automated extraction of structured questions from paper‑based mathematics exams is fundamental to intelligent education, yet remains challenging in real‑world settings due to severe visual noise. Existing benchmarks mainly focus on clean documents or generic layout analysis, overlooking both the structural integrity of mathematical problems and the ability of models to actively reject incomplete inputs. We introduce MathDoc, the first benchmark for document‑level information extraction from authentic high school mathematics exam papers. MathDoc contains 3,609 carefully curated questions with real‑world artifacts and explicitly includes unrecognizable samples to evaluate active refusal behavior. We propose a multi‑dimensional evaluation framework covering stem accuracy, visual similarity, and refusal capability. Experiments on SOTA MLLMs, including Qwen3‑VL and Gemini‑2.5‑Pro, show that although end‑to‑end models achieve strong extraction performance, they consistently fail to refuse illegible inputs, instead producing confident but invalid outputs. These results highlight a critical gap in current MLLMs and establish MathDoc as a benchmark for assessing model reliability under degraded document conditions. Our project repository is available at \hrefhttps://github.com/winnk123/papers/tree/masterGitHub repository

PaperId: 3728, https://arxiv.org/pdf/2601.10094.pdf GitHub

Abstract:
Recent advances in multimodal learning have significantly enhanced the reasoning capabilities of vision‑language models (VLMs). However, state‑of‑the‑art approaches rely heavily on large‑scale human‑annotated datasets, which are costly and time‑consuming to acquire. To overcome this limitation, we introduce V‑Zero, a general post‑training framework that facilitates self‑improvement using exclusively unlabeled images. V‑Zero establishes a co‑evolutionary loop by instantiating two distinct roles: a Questioner and a Solver. The Questioner learns to synthesize high‑quality, challenging questions by leveraging a dual‑track reasoning reward that contrasts intuitive guesses with reasoned results. The Solver is optimized using pseudo‑labels derived from majority voting over its own sampled responses. Both roles are trained iteratively via Group Relative Policy Optimization (GRPO), driving a cycle of mutual enhancement. Remarkably, without a single human annotation, V‑Zero achieves consistent performance gains on Qwen2.5‑VL‑7B‑Instruct, improving visual mathematical reasoning by +1.7 and general vision‑centric by +2.6, demonstrating the potential of self‑improvement in multimodal systems. Code is available at https://github.com/SatonoDia/V‑Zero

PaperId: 3729, https://arxiv.org/pdf/2601.10038.pdf GitHub

Abstract:
Artificial intelligence is rapidly transforming astronomical research, yet the scientific community has largely treated this transformation as an engineering challenge rather than an epistemological one. This perspective article argues that philosophy of science offers essential tools for navigating AI's integration into astronomy‑‑conceptual clarity about what "understanding" means, critical examination of assumptions about data and discovery, and frameworks for evaluating AI's roles across different research contexts. Drawing on an interdisciplinary workshop convening astronomers, philosophers, and computer scientists, we identify several tensions. First, the narrative that AI will "derive fundamental physics" from data misconstrues contemporary astronomy as equation‑derivation rather than the observation‑driven enterprise it is. Second, scientific understanding involves more than prediction‑‑it requires narrative construction, contextual judgment, and communicative achievement that current AI architectures struggle to provide. Third, because narrative and judgment matter, human peer review remains essential‑‑yet AI‑generated content flooding the literature threatens our capacity to identify genuine insight. Fourth, while AI excels at well‑defined problem‑solving, the ill‑defined problem‑finding that drives breakthroughs appears to require capacities beyond pattern recognition. Fifth, as AI accelerates what is feasible, pursuitworthiness criteria risk shifting toward what AI makes easy rather than what is genuinely important. We propose "pragmatic understanding" as a framework for integration‑‑recognizing AI as a tool that extends human cognition while requiring new norms for validation and epistemic evaluation. Engaging with these questions now may help the community shape the transformation rather than merely react to it.

PaperId: 3730, https://arxiv.org/pdf/2601.09902.pdf GitHub

Abstract:
Machine learning has achieved state‑of‑the‑art results in network intrusion detection; however, its performance significantly degrades when confronted by a new attack class ‑‑ a zero‑day attack. In simple terms, classical machine learning‑based approaches are adept at identifying attack classes on which they have been previously trained, but struggle with those not included in their training data. One approach to addressing this shortcoming is to utilise anomaly detectors which train exclusively on benign data with the goal of generalising to all attack classes ‑‑ both known and zero‑day. However, this comes at the expense of a prohibitively high false positive rate. This work proposes a novel contrastive loss function which is able to maintain the advantages of other contrastive learning‑based approaches (robustness to imbalanced data) but can also generalise to zero‑day attacks. Unlike anomaly detectors, this model learns the distributions of benign traffic using both benign and known malign samples, i.e. other well‑known attack classes (not including the zero‑day class), and consequently, achieves significant performance improvements. The proposed approach is experimentally verified on the Lycos2017 dataset where it achieves an AUROC improvement of .000065 and .060883 over previous models in known and zero‑day attack detection, respectively. Finally, the proposed method is extended to open‑set recognition achieving OpenAUC improvements of .170883 over existing approaches.

PaperId: 3731, https://arxiv.org/pdf/2601.09883.pdf GitHub

Abstract:
Most existing Large Language Model (LLM)‑based Multi‑Agent Systems (MAS) rely on predefined workflows, where human engineers enumerate task states in advance and specify routing rules and contextual injections accordingly. Such workflow‑driven designs are essentially rule‑based decision trees, which suffer from two fundamental limitations: they require substantial manual effort to anticipate and encode possible task states, and they cannot exhaustively cover the state space of complex real‑world tasks. To address these issues, we propose an Information‑Flow‑Orchestrated Multi‑Agent Paradigm via Agent‑to‑Agent (A2A) Communication from CORAL, in which a dedicated information flow orchestrator continuously monitors task progress and dynamically coordinates other agents through the A2A toolkit using natural language, without relying on predefined workflows. We evaluate our approach on the general‑purpose benchmark GAIA, using the representative workflow‑based MAS OWL as the baseline while controlling for agent roles and underlying models. Under the pass@1 setting, our method achieves 63.64% accuracy, outperforming OWL's 55.15% by 8.49 percentage points with comparable token consumption. Further case‑level analysis shows that our paradigm enables more flexible task monitoring and more robust handling of edge cases. Our implementation is publicly available at: https://github.com/Coral‑Protocol/Beyond‑Rule‑Based‑Workflows

PaperId: 3732, https://arxiv.org/pdf/2601.09805.pdf GitHub

Abstract:
Modern logical reasoning with LLMs primarily relies on employing complex interactive frameworks that decompose the reasoning process into subtasks solved through carefully designed prompts or requiring external resources (e.g., symbolic solvers) to exploit their strong logical structures. While interactive approaches introduce additional overhead or depend on external components, which limit their scalability. In this work, we introduce a non‑interactive, end‑to‑end framework for reasoning tasks, enabling reasoning to emerge within the model itself‑improving generalization while preserving analyzability without any external resources. We show that introducing structural information into the few‑shot prompt activates a subset of attention heads that patterns aligned with logical reasoning operators. Building on this insight, we propose Attention‑Aware Intervention (AAI), an inference‑time intervention method that reweights attention scores across selected heads identified by their logical patterns. AAI offers an efficient way to steer the model's reasoning toward leveraging prior knowledge through attention modulation. Extensive experiments show that AAI enhances logical reasoning performance across diverse benchmarks, and model architectures, while incurring negligible additional computational overhead. Code is available at https://github.com/phuongnm94/aai_for_logical_reasoning.

PaperId: 3733, https://arxiv.org/pdf/2601.09718.pdf GitHub

Abstract:
This study investigates how to efficiently build a domain‑specialized large language model (LLM) for statistics using the lightweight LLaMA‑3.2‑3B family as the foundation model (FM). We systematically compare three multi‑stage training pipelines‑‑starting from a base FM with no instruction‑following capability, a base FM augmented with post‑hoc instruction tuning, and an instruction‑tuned FM with strong general reasoning abilities‑‑across continual pretraining, supervised fine‑tuning (SFT), reinforcement learning from human feedback (RLHF) preference alignment, and downstream task fine‑tuning (DTFT). Results show that pipelines beginning with a base FM fail to develop meaningful statistical reasoning, even after extensive instruction tuning, SFT, or RLHF alignment. In contrast, starting from LLaMA‑3.2‑3B‑Instruct enables effective domain specialization. A comprehensive evaluation of SFT variants reveals clear trade‑offs between domain expertise and general reasoning ability. We further demonstrate that direct preference optimization provides stable and effective RLHF preference alignment. Finally, we show that DTFT must be performed with extremely low intensity to avoid catastrophic forgetting in highly optimized models. The final model, StatLLaMA, achieves strong and balanced performance on benchmarks of mathematical reasoning, common‑sense reasoning, and statistical expertise, offering a practical blueprint for developing resource‑efficient statistical LLMs. The code is available at https://github.com/HuangDLab/StatLLaMA.

PaperId: 3734, https://arxiv.org/pdf/2601.09717.pdf GitHub

Abstract:
Online medical consultations generate large volumes of conversational health data that often embed protected health information, requiring robust methods to classify data categories and assign risk levels in line with policies and practice. However, existing approaches lack unified standards and reliable automated methods to fulfill sensitivity classification for such conversational health data. This study presents a large language model‑based extraction pipeline, SALP‑CG, for classifying and grading privacy risks in online conversational health data. We concluded health‑data classification and grading rules in accordance with GB/T 39725‑2020. Combining few‑shot guidance, JSON Schema constrained decoding, and deterministic high‑risk rules, the backend‑agnostic extraction pipeline achieves strong category compliance and reliable sensitivity across diverse LLMs. On the MedDialog‑CN benchmark, models yields robust entity counts, high schema compliance, and accurate sensitivity grading, while the strongest model attains micro‑F1=0.900 for maximum‑level prediction. The category landscape stratified by sensitivity shows that Level 2‑3 items dominate, enabling re‑identification when combined; Level 4‑5 items are less frequent but carry outsize harm. SALP‑CG reliably helps classify categories and grading sensitivity in online conversational health data across LLMs, offering a practical method for health data governance. Code is available at https://github.com/dommii1218/SALP‑CG.

PaperId: 3735, https://arxiv.org/pdf/2601.09708.pdf GitHub

Abstract:
Vision‑Language‑Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain‑of‑thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy reasoning traces. We propose Fast‑ThinkAct, an efficient reasoning framework that achieves compact yet performant planning through verbalizable latent reasoning. Fast‑ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference‑guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodied control. This enables reasoning‑enhanced policy learning that effectively connects compact reasoning to action execution. Extensive experiments across diverse embodied manipulation and reasoning benchmarks demonstrate that Fast‑ThinkAct achieves strong performance with up to 89.3% reduced inference latency over state‑of‑the‑art reasoning VLAs, while maintaining effective long‑horizon planning, few‑shot adaptation, and failure recovery.

PaperId: 3736, https://arxiv.org/pdf/2601.09692.pdf GitHub

Abstract:
Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground‑truth labeled data, which is often unavailable in practice, especially when user request distributions are heterogeneous and unknown. We introduce Routing with Generated Data (RGD), a challenging setting in which routers are trained exclusively on generated queries and answers produced from high‑level task descriptions by generator LLMs. We evaluate query‑answer routers (using both queries and labels) and query‑only routers across four diverse benchmarks and 12 models, finding that query‑answer routers degrade faster than query‑only routers as generator quality decreases. Our analysis reveals two crucial characteristics of effective generators: they must accurately respond to their own questions, and their questions must produce sufficient performance differentiation among the model pool. We then show how filtering for these characteristics can improve the quality of generated data. We further propose CASCAL, a novel query‑only router that estimates model correctness through consensus voting and identifies model‑specific skill niches via hierarchical clustering. CASCAL is substantially more robust to generator quality, outperforming the best query‑answer router by 4.6% absolute accuracy when trained on weak generator data.

PaperId: 3737, https://arxiv.org/pdf/2601.09635.pdf GitHub

Abstract:
Large‑scale optimization is a key backbone of modern business decision‑making. However, building these models is often labor‑intensive and time‑consuming. We address this by proposing LEAN‑LLM‑OPT, a LightwEight AgeNtic workflow construction framework for LLM‑assisted large‑scale OPTimization auto‑formulation. LEAN‑LLM‑OPT takes as input a problem description together with associated datasets and orchestrates a team of LLM agents to produce an optimization formulation. Specifically, upon receiving a query, two upstream LLM agents dynamically construct a workflow that specifies, step‑by‑step, how optimization models for similar problems can be formulated. A downstream LLM agent then follows this workflow to generate the final output. The agentic workflow leverages common modeling practices to structure the modeling process into a sequence of sub‑tasks, offloading mechanical data‑handling operations to auxiliary tools. This reduces the LLM's burden in planning and data handling, allowing us to exploit its flexibility to address unstructured components. Extensive simulations show that LEAN‑LLM‑OPT, instantiated with GPT‑4.1 and the open source gpt‑oss‑20B, achieves strong performance on large‑scale optimization modeling tasks and is competitive with state‑of‑the‑art approaches. In addition, in a Singapore Airlines choice‑based revenue management use case, LEAN‑LLM‑OPT demonstrates practical value by achieving leading performance across a range of scenarios. Along the way, we introduce Large‑Scale‑OR and Air‑NRM, the first comprehensive benchmarks for large‑scale optimization auto‑formulation. The code and data of this work is available at https://github.com/CoraLiang01/lean‑llm‑opt.

PaperId: 3738, https://arxiv.org/pdf/2601.09613.pdf GitHub

Abstract:
Accurate and early perception of potential intrusion targets is essential for ensuring the safety of railway transportation systems. However, most existing systems focus narrowly on object classification within fixed visual scopes and apply rule‑based heuristics to determine intrusion status, often overlooking targets that pose latent intrusion risks. Anticipating such risks requires the cognition of spatial context and temporal dynamics for the object of interest (OOI), which presents challenges for conventional visual models. To facilitate deep intrusion perception, we introduce a novel benchmark, CogRail, which integrates curated open‑source datasets with cognitively driven question‑answer annotations to support spatio‑temporal reasoning and prediction. Building upon this benchmark, we conduct a systematic evaluation of state‑of‑the‑art visual‑language models (VLMs) using multimodal prompts to identify their strengths and limitations in this domain. Furthermore, we fine‑tune VLMs for better performance and propose a joint fine‑tuning framework that integrates three core tasks, position perception, movement prediction, and threat analysis, facilitating effective adaptation of general‑purpose foundation models into specialized models tailored for cognitive intrusion perception. Extensive experiments reveal that current large‑scale multimodal models struggle with the complex spatial‑temporal reasoning required by the cognitive intrusion perception task, underscoring the limitations of existing foundation models in this safety‑critical domain. In contrast, our proposed joint fine‑tuning framework significantly enhances model performance by enabling targeted adaptation to domain‑specific reasoning demands, highlighting the advantages of structured multi‑task learning in improving both accuracy and interpretability. Code will be available at https://github.com/Hub‑Tian/CogRail.

PaperId: 3739, https://arxiv.org/pdf/2601.09520.pdf GitHub

Abstract:
Deep learning models define the state‑of‑the‑art in Automatic Drum Transcription (ADT), yet their performance is contingent upon large‑scale, paired audio‑MIDI datasets, which are scarce. Existing workarounds that use synthetic data often introduce a significant domain gap, as they typically rely on low‑fidelity SoundFont libraries that lack acoustic diversity. While high‑quality one‑shot samples offer a better alternative, they are not available in a standardized, large‑scale format suitable for training. This paper introduces a new paradigm for ADT that circumvents the need for paired audio‑MIDI training data. Our primary contribution is a semi‑supervised method to automatically curate a large and diverse corpus of one‑shot drum samples from unlabeled audio sources. We then use this corpus to synthesize a high‑quality dataset from MIDI files alone, which we use to train a sequence‑to‑sequence transcription model. We evaluate our model on the ENST and MDB test sets, where it achieves new state‑of‑the‑art results, significantly outperforming both fully supervised methods and previous synthetic‑data approaches. The code for reproducing our experiments is publicly available at https://github.com/pier‑maker92/ADT_STR

PaperId: 3740, https://arxiv.org/pdf/2601.09478.pdf GitHub

Abstract:
Semantic understanding of popularity bias is a crucial yet underexplored challenge in recommender systems, where popular items are often favored at the expense of niche content. Most existing debiasing methods treat the semantic understanding of popularity bias as a matter of diversity enhancement or long‑tail coverage, neglecting the deeper semantic layer that embodies the causal origins of the bias itself. Consequently, such shallow interpretations limit both their debiasing effectiveness and recommendation accuracy. In this paper, we propose FairLRM, a novel framework that bridges the gap in the semantic understanding of popularity bias with Recommendation via Large Language Model (RecLLM). FairLRM decomposes popularity bias into item‑side and user‑side components, using structured instruction‑based prompts to enhance the model's comprehension of both global item distributions and individual user preferences. Unlike traditional methods that rely on surface‑level features such as "diversity" or "debiasing", FairLRM improves the model's ability to semantically interpret and address the underlying bias. Through empirical evaluation, we show that FairLRM significantly enhances both fairness and recommendation accuracy, providing a more semantically aware and trustworthy approach to enhance the semantic understanding of popularity bias. The implementation is available at https://github.com/LuoRenqiang/FairLRM.

PaperId: 3741, https://arxiv.org/pdf/2601.09469.pdf GitHub

Abstract:
Graph unlearning has emerged as a critical mechanism for supporting sustainable and privacy‑preserving social networks, enabling models to remove the influence of deleted nodes and thereby better safeguard user information. However, we observe that existing graph unlearning techniques insufficiently protect sensitive attributes, often leading to degraded algorithmic fairness compared with traditional graph learning methods. To address this gap, we introduce FairGU, a fairness‑aware graph unlearning framework designed to preserve both utility and fairness during the unlearning process. FairGU integrates a dedicated fairness‑aware module with effective data protection strategies, ensuring that sensitive attributes are neither inadvertently amplified nor structurally exposed when nodes are removed. Through extensive experiments on multiple real‑world datasets, we demonstrate that FairGU consistently outperforms state‑of‑the‑art graph unlearning methods and fairness‑enhanced graph learning baselines in terms of both accuracy and fairness metrics. Our findings highlight a previously overlooked risk in current unlearning practices and establish FairGU as a robust and equitable solution for the next generation of socially sustainable networked systems. The codes are available at https://github.com/LuoRenqiang/FairGU.

PaperId: 3742, https://arxiv.org/pdf/2601.09416.pdf GitHub

Abstract:
Osteosarcoma (OS) is an aggressive primary bone malignancy. Accurate histopathological assessment of viable versus non‑viable tumor regions after neoadjuvant chemotherapy is critical for prognosis and treatment planning, yet manual evaluation remains labor‑intensive, subjective, and prone to inter‑observer variability. Recent advances in digital pathology have enabled automated necrosis quantification. Evaluating on test data, independently sampled on patient‑level, revealed that the deep learning model performance dropped significantly from the tile‑level generalization ability reported in previous studies. First, this work proposes the use of radiomic features as additional input in model training. We show that, despite that they are derived from the images, such a multimodal input effectively improved the classification performance, in addition to its added benefits in interpretability. Second, this work proposes to optimize two binary classification tasks with hierarchical classes (i.e. tumor‑vs‑non‑tumor and viable‑vs‑non‑viable), as opposed to the alternative ``flat'' three‑class classification task (i.e. non‑tumor, non‑viable tumor, viable tumor), thereby enabling a hierarchical loss. We show that such a hierarchical loss, with trainable weightings between the two tasks, the per‑class performance can be improved significantly. Using the TCIA OS Tumor Assessment dataset, we experimentally demonstrate the benefits from each of the proposed new approaches and their combination, setting a what we consider new state‑of‑the‑art performance on this open dataset for this application. Code and trained models: https://github.com/YaxiiC/RadiomicsOS.git.

PaperId: 3743, https://arxiv.org/pdf/2601.09413.pdf GitHub GitHub

Abstract:
We introduce a voice‑agentic framework that learns one critical omni‑understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is motivated by a crucial yet counterintuitive finding: naively fine‑tuning an omni‑model on both speech recognition and external sound understanding tasks often degrades performance, as the model can be easily misled by noisy hypotheses. To address this, our framework, Speech‑Hands, recasts the problem as an explicit self‑reflection decision. This learnable reflection primitive proves effective in preventing the model from being derailed by flawed external candidates. We show that this agentic action mechanism generalizes naturally from speech recognition to complex, multiple‑choice audio reasoning. Across the OpenASR leaderboard, Speech‑Hands consistently outperforms strong baselines by 12.1% WER on seven benchmarks. The model also achieves 77.37% accuracy and high F1 on audio QA decisions, showing robust generalization and reliability across diverse audio question answering datasets. By unifying perception and decision‑making, our work offers a practical path toward more reliable and resilient audio intelligence.

PaperId: 3744, https://arxiv.org/pdf/2601.09394.pdf GitHub

Abstract:
Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. Existing solutions commonly generate these incomplete attributes, which may introduce additional biases and further compromise user privacy. To address this challenge, FairGE (Fair Graph Encoding) is introduced as a fairness‑aware framework for GTs in incomplete social networks. Instead of generating sensitive attributes, FairGE encodes fairness directly through spectral graph theory. By leveraging the principal eigenvector to represent structural information and padding incomplete sensitive attributes with zeros to maintain independence, FairGE ensures fairness without data reconstruction. Theoretical analysis demonstrates that the method suppresses the influence of non‑principal spectral components, thereby enhancing fairness. Extensive experiments on seven real‑world social network datasets confirm that FairGE achieves at least a 16% improvement in both statistical parity and equality of opportunity compared with state‑of‑the‑art baselines. The source code is shown in https://github.com/LuoRenqiang/FairGE.

PaperId: 3745, https://arxiv.org/pdf/2601.09286.pdf GitHub

Abstract:
Collaborative Filtering (CF) remains the cornerstone of modern recommender systems, with dense embedding‑‑based methods dominating current practice. However, these approaches suffer from a critical limitation: our theoretical analysis reveals a fundamental signal‑to‑noise ratio (SNR) ceiling when modeling unpopular items, where parameter‑based dense models experience diminishing SNR under severe data sparsity. To overcome this bottleneck, we propose SaD (Sparse and Dense), a unified framework that integrates the semantic expressiveness of dense embeddings with the structural reliability of sparse interaction patterns. We theoretically show that aligning these dual views yields a strictly superior global SNR. Concretely, SaD introduces a lightweight bidirectional alignment mechanism: the dense view enriches the sparse view by injecting semantic correlations, while the sparse view regularizes the dense model through explicit structural signals. Extensive experiments demonstrate that, under this dual‑view alignment, even a simple matrix factorization‑‑style dense model can achieve state‑of‑the‑art performance. Moreover, SaD is plug‑and‑play and can be seamlessly applied to a wide range of existing recommender models, highlighting the enduring power of collaborative filtering when leveraged from dual perspectives. Further evaluations on real‑world benchmarks show that SaD consistently outperforms strong baselines, ranking first on the BarsMatch leaderboard. The code is publicly available at https://github.com/harris26‑G/SaD.

PaperId: 3746, https://arxiv.org/pdf/2601.09262.pdf GitHub

Abstract:
Delineating wildfire affected areas using satellite imagery remains challenging due to irregular and spatially heterogeneous spectral changes across the electromagnetic spectrum. While recent deep learning approaches achieve high accuracy when high‑resolution multispectral data are available, their applicability in operational settings, where a quick delineation of the burn scar shortly after a wildfire incident is required, is limited by the trade‑off between spatial resolution and temporal revisit frequency of current satellite systems. To address this limitation, we propose a novel deep learning model, namely BAM‑MRCD, which employs multi‑resolution, multi‑source satellite imagery (MODIS and Sentinel‑2) for the timely production of detailed burnt area maps with high spatial and temporal resolution. Our model manages to detect even small scale wildfires with high accuracy, surpassing similar change detection models as well as solid baselines. All data and code are available in the GitHub repository: https://github.com/Orion‑AI‑Lab/BAM‑MRCD.

PaperId: 3747, https://arxiv.org/pdf/2601.09259.pdf GitHub

Abstract:
Large Language Model (LLM) Agents exhibit inherent reasoning abilities through the collaboration of multiple tools. However, during agent inference, existing methods often suffer from (i) locally myopic generation, due to the absence of lookahead, and (ii) trajectory instability, where minor early errors can escalate into divergent reasoning paths. These issues make it difficult to balance global effectiveness and computational efficiency. To address these two issues, we propose meta‑adaptive exploration with LLM agents https://github.com/exoskeletonzj/MAXS, a meta‑adaptive reasoning framework based on LLM Agents that flexibly integrates tool execution and reasoning planning. MAXS employs a lookahead strategy to extend reasoning paths a few steps ahead, estimating the advantage value of tool usage, and combines step consistency variance and inter‑step trend slopes to jointly select stable, consistent, and high‑value reasoning steps. Additionally, we introduce a trajectory convergence mechanism that controls computational cost by halting further rollouts once path consistency is achieved, enabling a balance between resource efficiency and global effectiveness in multi‑tool reasoning. We conduct extensive empirical studies across three base models (MiMo‑VL‑7B, Qwen2.5‑VL‑7B, Qwen2.5‑VL‑32B) and five datasets, demonstrating that MAXS consistently outperforms existing methods in both performance and inference efficiency. Further analysis confirms the effectiveness of our lookahead strategy and tool usage.

PaperId: 3748, https://arxiv.org/pdf/2601.09233.pdf GitHub

Abstract:
The prevailing post‑training paradigm for Large Reasoning Models (LRMs) ‑ Supervised Fine‑Tuning (SFT) followed by Reinforcement Learning (RL) ‑ suffers from an intrinsic optimization mismatch: the rigid supervision inherent in SFT induces distributional collapse, thereby exhausting the exploration space necessary for subsequent RL. In this paper, we reformulate SFT to reconcile post‑training objectives and propose Gibbs Initialization with Finite Temperature (GIFT). We characterize standard SFT as a degenerate zero‑temperature limit that suppresses base priors. Conversely, GIFT incorporates supervision as a finite‑temperature energy potential, establishing a distributional bridge that promotes objective consistency throughout the post‑training pipeline. Our experiments demonstrate that GIFT significantly outperforms standard SFT and other competitive baselines when utilized for RL initialization, providing a mathematically principled pathway to preserve exploration and align the two post‑training stages. Our code is available at https://github.com/zzy1127/GIFT.

PaperId: 3749, https://arxiv.org/pdf/2601.09097.pdf GitHub

Abstract:
Multi‑constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints. Existing large language model (LLM) approaches face fundamental limitations in this domain. Pure reasoning paradigms, which rely on long natural language chains, are prone to inconsistency, error accumulation, and prohibitive cost as constraints compound. Conversely, LLMs combined with coding‑ or solver‑based strategies lack flexibility: they often generate problem‑specific code from scratch or depend on fixed solvers, failing to capture generalizable logic across diverse problems. To address these challenges, we introduce the Scalable COde Planning Engine (SCOPE), a framework that disentangles query‑specific reasoning from generic code execution. By separating reasoning from execution, SCOPE produces solver functions that are consistent, deterministic, and reusable across queries while requiring only minimal changes to input parameters. SCOPE achieves state‑of‑the‑art performance while lowering cost and latency. For example, with GPT‑4o, it reaches 93.1% success on TravelPlanner, a 61.6% gain over the best baseline (CoT) while cutting inference cost by 1.4x and time by ~4.67x. Code is available at https://github.com/DerrickGXD/SCOPE.

PaperId: 3750, https://arxiv.org/pdf/2601.09031.pdf GitHub

Abstract:
Humanoid robot manipulation is a crucial research area for executing diverse human‑level tasks, involving high‑level semantic reasoning and low‑level action generation. However, precise scene understanding and sample‑efficient learning from human demonstrations remain critical challenges, severely hindering the applicability and generalizability of existing frameworks. This paper presents a novel RGMP‑S, Recurrent Geometric‑prior Multimodal Policy with Spiking features, facilitating both high‑level skill reasoning and data‑efficient motion synthesis. To ground high‑level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases to enable precise 3D scene understanding within the vision‑language model. Specifically, we construct a Long‑horizon Geometric Prior Skill Selector that effectively aligns the semantic instructions with spatial constraints, ultimately achieving robust generalization in unseen environments. For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network. We parameterize robot‑object interactions via recursive spiking for spatiotemporal consistency, fully distilling long‑horizon dynamic features while mitigating the overfitting issue in sparse demonstration scenarios. Extensive experiments are conducted across the Maniskill simulation benchmark and three heterogeneous real‑world robotic systems, encompassing a custom‑developed humanoid, a desktop manipulator, and a commercial robotic platform. Empirical results substantiate the superiority of our method over state‑of‑the‑art baselines and validate the efficacy of the proposed modules in diverse generalization scenarios. To facilitate reproducibility, the source code and video demonstrations are publicly available at https://github.com/xtli12/RGMP‑S.git.

PaperId: 3751, https://arxiv.org/pdf/2601.08955.pdf GitHub

Abstract:
Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act without accessing real environments. Current methods mainly perform single‑step or fixed‑horizon rollouts, leaving their potential for complex task planning under‑exploited. We propose Imagine‑then‑Plan (\textttITP), a unified framework for agent learning via lookahead imagination, where an agent's policy model interacts with the learned world model, yielding multi‑step ``imagined'' trajectories. Since the imagination horizon may vary by tasks and stages, we introduce a novel adaptive lookahead mechanism by trading off the ultimate goal and task progress. The resulting imagined trajectories provide rich signals about future consequences, such as achieved progress and potential conflicts, which are fused with current observations, formulating a partially observable and imaginable Markov decision process to guide policy learning. We instantiate \textttITP with both training‑free and reinforcement‑trained variants. Extensive experiments across representative agent benchmarks demonstrate that \textttITP significantly outperforms competitive baselines. Further analyses validate that our adaptive lookahead largely enhances agents' reasoning capability, providing valuable insights into addressing broader, complex tasks. Our code and data will be publicly available at https://github.com/loyiv/ITP.

PaperId: 3752, https://arxiv.org/pdf/2601.08881.pdf GitHub

Abstract:
Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject‑driven generation). While the sparse Mixture‑of‑Experts (MoE) paradigm is a promising solution, its gating networks remain task‑agnostic, operating based on local features, unaware of global task intent. This task‑agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high‑level semantics. This regularization evolves the gating network from a task‑agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations.

PaperId: 3753, https://arxiv.org/pdf/2601.08875.pdf GitHub

Abstract:
Image registration under domain shift remains a fundamental challenge in computer vision and medical imaging: when source and target images exhibit systematic intensity differences, the brightness constancy assumption underlying conventional registration methods is violated, rendering correspondence estimation ill‑posed. We propose SAR‑Net, a unified framework that addresses this challenge through principled scene‑appearance disentanglement. Our key insight is that observed images can be decomposed into domain‑invariant scene representations and domain‑specific appearance codes, enabling registration via re‑rendering rather than direct intensity matching. We establish theoretical conditions under which this decomposition enables consistent cross‑domain alignment (Proposition 1) and prove that our scene consistency loss provides a sufficient condition for geometric correspondence in the shared latent space (Proposition 2). Empirically, we validate SAR‑Net on the ANHIR (Automatic Non‑rigid Histological Image Registration) challenge benchmark, where multi‑stain histopathology images exhibit coupled domain shift from different staining protocols and geometric distortion from tissue preparation. Our method achieves a median relative Target Registration Error (rTRE) of 0.25%, outperforming the state‑of‑the‑art MEVIS method (0.27% rTRE) by 7.4%, with robustness of 99.1%. Code is available at https://github.com/D‑ST‑Sword/SAR‑NET .

PaperId: 3754, https://arxiv.org/pdf/2601.08867.pdf GitHub

Abstract:
Recently, reconstruction‑based methods have gained attention for AIGC image detection. These methods leverage pre‑trained diffusion models to reconstruct inputs and measure residuals for distinguishing real from fake images. Their key advantage lies in reducing reliance on dataset‑specific artifacts and improving generalization under distribution shifts. However, they are limited by significant inefficiency due to multi‑step inversion and reconstruction, and their reliance on diffusion backbones further limits generalization to other generative paradigms such as GANs. In this paper, we propose a novel fake image detection framework, called R^2BD, built upon two key designs: (1) G‑LDM, a unified reconstruction model that simulates the generation behaviors of VAEs, GANs, and diffusion models, thereby broadening the detection scope beyond prior diffusion‑only approaches; and (2) a residual bias calculation module that distinguishes real and fake images in a single inference step, which is a significant efficiency improvement over existing methods that typically require 20+ steps. Extensive experiments on the benchmark from 10 public datasets demonstrate that R^2BD is over 22× faster than existing reconstruction‑based methods while achieving superior detection accuracy. In cross‑dataset evaluations, it outperforms state‑of‑the‑art methods by an average of 13.87%, showing strong efficiency and generalization across diverse generative methods. The code and dataset used for evaluation are available at https://github.com/QingyuLiu/RRBD.

PaperId: 3755, https://arxiv.org/pdf/2601.08829.pdf GitHub

Abstract:
In this work, we explore the Large Language Model (LLM) agent reviewer dynamics in an Elo‑ranked review system using real‑world conference paper submissions. Multiple LLM agent reviewers with different personas are engage in multi round review interactions moderated by an Area Chair. We compare a baseline setting with conditions that incorporate Elo ratings and reviewer memory. Our simulation results showcase several interesting findings, including how incorporating Elo improves Area Chair decision accuracy, as well as reviewers' adaptive review strategy that exploits our Elo system without improving review effort. Our code is available at https://github.com/hsiangwei0903/EloReview.

PaperId: 3756, https://arxiv.org/pdf/2601.08816.pdf GitHub

Abstract:
The evolution of recommender systems has shifted from traditional collaborative filtering to LLM‑based agentic systems, which rely on semantic user and item memories to make predictions. However, existing agents maintain these memories in isolation. This overlooks crucial collaborative signals, such as user‑item co‑engagements and peer relationships across the community, which significantly limits their ability to uncover hidden preferences and accurately infer user needs, particularly for data‑sparse users. To bridge this gap, we introduce collaborative memory, a paradigm that connects isolated semantics to enable the sharing of relational insights. Yet, naively utilizing collaborative memory causes severe context overload and introduces noise to downstream LLMs, alongside prohibitive computational costs. To resolve this, we propose MemRec, a framework that architecturally decouples memory management from reasoning. MemRec introduces a dedicated, lightweight language model (LM_Mem) to efficiently manage and synthesize a dynamic collaborative memory graph in the background. It provides only distilled, high‑signal contexts to a downstream, heavyweight large language model (LLM_Rec) for the final recommendation. Extensive experiments on four benchmarks demonstrate that MemRec achieves state‑of‑the‑art performance. Code: https://github.com/rutgerswiselab/memrec and Homepage: https://memrec.weixinchen.com/

PaperId: 3757, https://arxiv.org/pdf/2601.08808.pdf GitHub GitHub

Abstract:
Large language models often solve complex reasoning tasks more effectively with Chain‑of‑Thought (CoT), but at the cost of long, low‑bandwidth token sequences. Humans, by contrast, often reason softly by maintaining a distribution over plausible next steps. Motivated by this, we propose Multiplex Thinking, a stochastic soft reasoning mechanism that, at each thinking step, samples K candidate tokens and aggregates their embeddings into a single continuous multiplex token. This preserves the vocabulary embedding prior and the sampling dynamics of standard discrete generation, while inducing a tractable probability distribution over multiplex rollouts. Consequently, multiplex trajectories can be directly optimized with on‑policy reinforcement learning (RL). Importantly, Multiplex Thinking is self‑adaptive: when the model is confident, the multiplex token is nearly discrete and behaves like standard CoT; when it is uncertain, it compactly represents multiple plausible next steps without increasing sequence length. Across challenging math reasoning benchmarks, Multiplex Thinking consistently outperforms strong discrete CoT and RL baselines from Pass@1 through Pass@1024, while producing shorter sequences. The code and checkpoints are available at https://github.com/GMLR‑Penn/Multiplex‑Thinking.

PaperId: 3758, https://arxiv.org/pdf/2601.08778.pdf GitHub

Abstract:
Researchers have proposed numerous text‑to‑SQL techniques to streamline data analytics and accelerate the development of data‑driven applications. To compare these techniques and select the best one for deployment, the community depends on public benchmarks and their leaderboards. Since these benchmarks heavily rely on human annotations during question construction and answer evaluation, the validity of the annotations is crucial. In this paper, we conduct an empirical study that (i) benchmarks annotation error rates for two widely used text‑to‑SQL benchmarks, BIRD and Spider 2.0‑Snow, and (ii) corrects a subset of the BIRD development (Dev) set to measure the impact of annotation errors on text‑to‑SQL agent performance and leaderboard rankings. Through expert analysis, we show that BIRD Mini‑Dev and Spider 2.0‑Snow have error rates of 52.8% and 62.8%, respectively. We re‑evaluate all 16 open‑source agents from the BIRD leaderboard on both the original and the corrected BIRD Dev subsets. We show that performance changes range from ‑7% to 31% (in relative terms) and rank changes range from ‑9 to +9 positions. We further assess whether these impacts generalize to the full BIRD Dev set. We find that the rankings of agents on the uncorrected subset correlate strongly with those on the full Dev set (Spearman's r_s=0.85, p=3.26e‑5), whereas they correlate weakly with those on the corrected subset (Spearman's r_s=0.32, p=0.23). These findings show that annotation errors can significantly distort reported performance and rankings, potentially misguiding research directions or deployment choices. Our code and data are available at https://github.com/uiuc‑kang‑lab/text_to_sql_benchmarks.

PaperId: 3759, https://arxiv.org/pdf/2601.08713.pdf GitHub

Abstract:
Localization is a fundamental capability for autonomous robots, enabling them to operate effectively in dynamic environments. In Robocon 2025, accurate and reliable localization is crucial for improving shooting precision, avoiding collisions with other robots, and navigating the competition field efficiently. In this paper, we propose a hybrid localization algorithm that integrates classical techniques with learning based methods that rely solely on visual data from the court's floor to achieve self‑localization on the basketball field.

PaperId: 3760, https://arxiv.org/pdf/2601.08623.pdf GitHub GitHub

Abstract:
Image generation models (IGMs), while capable of producing impressive and creative content, often memorize a wide range of undesirable concepts from their training data, leading to the reproduction of unsafe content such as NSFW imagery and copyrighted artistic styles. Such behaviors pose persistent safety and compliance risks in real‑world deployments and cannot be reliably mitigated by post‑hoc filtering, owing to the limited robustness of such mechanisms and a lack of fine‑grained semantic control. Recent unlearning methods seek to erase harmful concepts at the model level, which exhibit the limitations of requiring costly retraining, degrading the quality of benign generations, or failing to withstand prompt paraphrasing and adversarial attacks. To address these challenges, we introduce SafeRedir, a lightweight inference‑time framework for robust unlearning via prompt embedding redirection. Without modifying the underlying IGMs, SafeRedir adaptively routes unsafe prompts toward safe semantic regions through token‑level interventions in the embedding space. The framework comprises two core components: a latent‑aware multi‑modal safety classifier for identifying unsafe generation trajectories, and a token‑level delta generator for precise semantic redirection, equipped with auxiliary predictors for token masking and adaptive scaling to localize and regulate the intervention. Empirical results across multiple representative unlearning tasks demonstrate that SafeRedir achieves effective unlearning capability, high semantic and perceptual preservation, robust image quality, and enhanced resistance to adversarial attacks. Furthermore, SafeRedir generalizes effectively across a variety of diffusion backbones and existing unlearned models, validating its plug‑and‑play compatibility and broad applicability. Code and data are available at https://github.com/ryliu68/SafeRedir.

PaperId: 3761, https://arxiv.org/pdf/2601.08605.pdf GitHub GitHub

Abstract:
Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insights from accumulated experiences. However, existing methods predominantly inject experience passively as global context before task execution, struggling to adapt to dynamically changing contextual observations during agent‑environment interaction. We propose ExpSeek, which shifts experience toward step‑level proactive seeking: (1) estimating step‑level entropy thresholds to determine intervention timing using the model's intrinsic signals; (2) designing step‑level tailored experience content. Experiments on Qwen3‑8B and 32B models across four challenging web agent benchmarks demonstrate that ExpSeek achieves absolute improvements of 9.3% and 7.5%, respectively. Our experiments validate the feasibility and advantages of entropy as a self‑triggering signal, reveal that even a small‑scale 4B experience model can significantly boost the performance of larger agent models. The code is released at https://github.com/WYRipple/ExpSeek.

PaperId: 3762, https://arxiv.org/pdf/2601.08602.pdf GitHub

Abstract:
Vision modeling has advanced rapidly with Transformers, whose attention mechanisms capture visual dependencies but lack a principled account of how semantic information propagates spatially. We revisit this problem from a wave‑based perspective: feature maps are treated as spatial signals whose evolution over an internal propagation time (aligned with network depth) is governed by an underdamped wave equation. In this formulation, spatial frequency‑from low‑frequency global layout to high‑frequency edges and textures‑is modeled explicitly, and its interaction with propagation time is controlled rather than implicitly fixed. We derive a closed‑form, frequency‑time decoupled solution and implement it as the Wave Propagation Operator (WPO), a lightweight module that models global interactions in O(N log N) time‑far lower than attention. Building on WPO, we propose a family of WaveFormer models as drop‑in replacements for standard ViTs and CNNs, achieving competitive accuracy across image classification, object detection, and semantic segmentation, while delivering up to 1.6x higher throughput and 30% fewer FLOPs than attention‑based alternatives. Furthermore, our results demonstrate that wave propagation introduces a complementary modeling bias to heat‑based methods, effectively capturing both global coherence and high‑frequency details essential for rich visual semantics. Codes are available at: https://github.com/ZishanShu/WaveFormer.

PaperId: 3763, https://arxiv.org/pdf/2601.08557.pdf GitHub

Abstract:
Hallucinations in video‑capable vision‑language models (Video‑VLMs) remain frequent and high‑confidence, while existing uncertainty metrics often fail to align with correctness. We introduce VideoHEDGE, a modular framework for hallucination detection in video question answering that extends entropy‑based reliability estimation from images to temporally structured inputs. Given a video‑question pair, VideoHEDGE draws a baseline answer and multiple high‑temperature generations from both clean clips and photometrically and spatiotemporally perturbed variants, then clusters the resulting textual outputs into semantic hypotheses using either Natural Language Inference (NLI)‑based or embedding‑based methods. Cluster‑level probability masses yield three reliability scores: Semantic Entropy (SE), RadFlag, and Vision‑Amplified Semantic Entropy (VASE). We evaluate VideoHEDGE on the SoccerChat benchmark using an LLM‑as‑a‑judge to obtain binary hallucination labels. Across three 7B Video‑VLMs (Qwen2‑VL, Qwen2.5‑VL, and a SoccerChat‑finetuned model), VASE consistently achieves the highest ROC‑AUC, especially at larger distortion budgets, while SE and RadFlag often operate near chance. We further show that embedding‑based clustering matches NLI‑based clustering in detection performance at substantially lower computational cost, and that domain fine‑tuning reduces hallucination frequency but yields only modest improvements in calibration. The hedge‑bench PyPI library enables reproducible and extensible benchmarking, with full code and experimental resources available at https://github.com/Simula/HEDGE#videohedge .

PaperId: 3764, https://arxiv.org/pdf/2601.08509.pdf GitHub

Abstract:
Time series forecasting is critical to real‑world decision making, yet most existing approaches remain unimodal and rely on extrapolating historical patterns. While recent progress in large language models (LLMs) highlights the potential for multimodal forecasting, existing benchmarks largely provide retrospective or misaligned raw context, making it unclear whether such models meaningfully leverage textual inputs. In practice, human experts incorporate what‑if scenarios with historical evidence, often producing distinct forecasts from the same observations under different scenarios. Inspired by this, we introduce What If TSF (WIT), a multimodal forecasting benchmark designed to evaluate whether models can condition their forecasts on contextual text, especially future scenarios. By providing expert‑crafted plausible or counterfactual scenarios, WIT offers a rigorous testbed for scenario‑guided multimodal forecasting. The benchmark is available at https://github.com/jinkwan1115/WhatIfTSF.

PaperId: 3765, https://arxiv.org/pdf/2601.08441.pdf GitHub

Abstract:
Steering Large Language Models (LLMs) through activation interventions has emerged as a lightweight alternative to fine‑tuning for alignment and personalization. Recent work on Bi‑directional Preference Optimization (BiPO) shows that dense steering vectors can be learned directly from preference data in a Direct Preference Optimization (DPO) fashion, enabling control over truthfulness, hallucinations, and safety behaviors. However, dense steering vectors often entangle multiple latent factors due to neuron multi‑semanticity, limiting their effectiveness and stability in fine‑grained settings such as cultural alignment, where closely related values and behaviors (e.g., among Middle Eastern cultures) must be distinguished. In this paper, we propose Yet another Policy Optimization (YaPO), a reference‑free method that learns sparse steering vectors in the latent space of a Sparse Autoencoder (SAE). By optimizing sparse codes, YaPO produces disentangled, interpretable, and efficient steering directions. Empirically, we show that YaPO converges faster, achieves stronger performance, and exhibits improved training stability compared to dense steering baselines. Beyond cultural alignment, YaPO generalizes to a range of alignment‑related behaviors, including hallucination, wealth‑seeking, jailbreak, and power‑seeking. Importantly, YaPO preserves general knowledge, with no measurable degradation on MMLU. Overall, our results show that YaPO provides a general recipe for efficient, stable, and fine‑grained alignment of LLMs, with broad applications to controllability and domain adaptation. The associated code and data are publicly available\footnotehttps://github.com/MBZUAI‑Paris/YaPO.

PaperId: 3766, https://arxiv.org/pdf/2601.08430.pdf GitHub GitHub

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has driven substantial progress in reasoning‑intensive domains like mathematics. However, optimizing open‑ended generation remains challenging due to the lack of ground truth. While rubric‑based evaluation offers a structured proxy for verification, existing methods suffer from scalability bottlenecks and coarse criteria, resulting in a supervision ceiling effect. To address this, we propose an automated Coarse‑to‑Fine Rubric Generation framework. By synergizing principle‑guided synthesis, multi‑model aggregation, and difficulty evolution, our approach produces comprehensive and highly discriminative criteria capable of capturing the subtle nuances. Based on this framework, we introduce RubricHub, a large‑scale (～110k) and multi‑domain dataset. We validate its utility through a two‑stage post‑training pipeline comprising Rubric‑based Rejection Sampling Fine‑Tuning (RuFT) and Reinforcement Learning (RuRL). Experimental results demonstrate that RubricHub unlocks significant performance gains: our post‑trained Qwen3‑14B achieves state‑of‑the‑art (SOTA) results on HealthBench (69.3), surpassing proprietary frontier models such as GPT‑5. Our code is available at \hrefhttps://github.com/teqkilla/RubricHub this URL.

PaperId: 3767, https://arxiv.org/pdf/2601.08189.pdf GitHub GitHub

Abstract:
Existing invasive (backdoor) fingerprints suffer from high‑perplexity triggers that are easily filtered, fixed response patterns exposed by heuristic detectors, and spurious activations on benign inputs. We introduce \textscForgetMark, a stealthy fingerprinting framework that encodes provenance via targeted unlearning. It builds a compact, human‑readable key‑‑value set with an assistant model and predictive‑entropy ranking, then trains lightweight LoRA adapters to suppress the original values on their keys while preserving general capabilities. Ownership is verified under black/gray‑box access by aggregating likelihood and semantic evidence into a fingerprint success rate. By relying on probabilistic forgetting traces rather than fixed trigger‑‑response patterns, \textscForgetMark avoids high‑perplexity triggers, reduces detectability, and lowers false triggers. Across diverse architectures and settings, it achieves 100% ownership verification on fingerprinted models while maintaining standard performance, surpasses backdoor baselines in stealthiness and robustness to model merging, and remains effective under moderate incremental fine‑tuning. Our code and data are available at \hrefhttps://github.com/Xuzhenhua55/ForgetMarkhttps://github.com/Xuzhenhua55/ForgetMark.

PaperId: 3768, https://arxiv.org/pdf/2601.08183.pdf GitHub GitHub

Abstract:
Multimodal Large Language Models (MLLMs) show promise in gastroenterology, yet their performance against comprehensive clinical workflows and human benchmarks remains unverified. To systematically evaluate state‑of‑the‑art MLLMs across a panoramic gastrointestinal endoscopy workflow and determine their clinical utility compared with human endoscopists. We constructed GI‑Bench, a benchmark encompassing 20 fine‑grained lesion categories. Twelve MLLMs were evaluated across a five‑stage clinical workflow: anatomical localization, lesion identification, diagnosis, findings description, and management. Model performance was benchmarked against three junior endoscopists and three residency trainees using Macro‑F1, mean Intersection‑over‑Union (mIoU), and multi‑dimensional Likert scale. Gemini‑3‑Pro achieved state‑of‑the‑art performance. In diagnostic reasoning, top‑tier models (Macro‑F1 0.641) outperformed trainees (0.492) and rivaled junior endoscopists (0.727; p>0.05). However, a critical "spatial grounding bottleneck" persisted; human lesion localization (mIoU >0.506) significantly outperformed the best model (0.345; p<0.05). Furthermore, qualitative analysis revealed a "fluency‑accuracy paradox": models generated reports with superior linguistic readability compared with humans (p<0.05) but exhibited significantly lower factual correctness (p<0.05) due to "over‑interpretation" and hallucination of visual features. GI‑Bench maintains a dynamic leaderboard that tracks the evolving performance of MLLMs in clinical endoscopy. The current rankings and benchmark results are available at https://roterdl.github.io/GIBench/.

PaperId: 3769, https://arxiv.org/pdf/2601.08179.pdf GitHub

Abstract:
A 3D avatar typically has one of six cardinal facial expressions. To simulate realistic emotional variation, we should be able to render a facial transition between two arbitrary expressions. This study presents a new framework for instruction‑driven facial expression generation that produces a 3D face and, starting from an image of the face, transforms the facial expression from one designated facial expression to another. The Instruction‑driven Facial Expression Decomposer (IFED) module is introduced to facilitate multimodal data learning and capture the correlation between textual descriptions and facial expression features. Subsequently, we propose the Instruction to Facial Expression Transition (I2FET) method, which leverages IFED and a vertex reconstruction loss function to refine the semantic comprehension of latent vectors, thus generating a facial expression sequence according to the given instruction. Lastly, we present the Facial Expression Transition model to generate smooth transitions between facial expressions. Extensive evaluation suggests that the proposed model outperforms state‑of‑the‑art methods on the CK+ and CelebV‑HQ datasets. The results show that our framework can generate facial expression trajectories according to text instruction. Considering that text prompts allow us to make diverse descriptions of human emotional states, the repertoire of facial expressions and the transitions between them can be expanded greatly. We expect our framework to find various practical applications More information about our project can be found at https://vohoanganh.github.io/tg3dfet/

PaperId: 3770, https://arxiv.org/pdf/2601.08118.pdf GitHub

Abstract:
Large language models (LLMs) are increasingly used as human simulators, both for evaluating conversational systems and for generating fine‑tuning data. However, naive "act‑as‑a‑user" prompting often yields verbose, unrealistic utterances, motivating principled evaluation of user proxy agents. We present MirrorBench, a reproducible and extensible benchmarking framework that evaluates user proxies solely on their ability to produce human‑like user utterances across diverse conversational regimes, explicitly decoupled from downstream task success. MirrorBench combines three lexical‑diversity metrics (MATTR, Yule's~K, and HD‑D) with three LLM‑judge‑based metrics (GTEval, Pairwise Indistinguishability, and Rubric‑and‑Reason), and contextualizes judge scores using Human‑Human and Proxy‑Proxy calibration controls. Across four public datasets, MirrorBench yields variance‑aware comparisons and reveals systematic gaps between user proxies and real human users. The framework is open sourced at https://github.com/SAP/mirrorbench and includes a command‑line interface for running and managing user‑proxy benchmarking experiments.

PaperId: 3771, https://arxiv.org/pdf/2601.08094.pdf GitHub

Abstract:
Subject‑independent EEG emotion recognition is challenged by pronounced inter‑subject variability and the difficulty of learning robust representations from short, noisy recordings. To address this, we propose a fusion framework that integrates (i) local, channel‑wise descriptors and (ii) global, trial‑level descriptors, improving cross‑subject generalization on the SEED‑VII dataset. Local representations are formed per channel by concatenating differential entropy with graph‑theoretic features, while global representations summarize time‑domain, spectral, and complexity characteristics at the trial level. These representations are fused in a dual‑branch transformer with attention‑based fusion and domain‑adversarial regularization, with samples filtered by an intensity threshold. Experiments under a leave‑one‑subject‑out protocol demonstrate that the proposed method consistently outperforms single‑view and classical baselines, achieving approximately 40% mean accuracy in 7‑class subject‑independent emotion recognition. The code has been released at https://github.com/Danielz‑z/LGF‑EEG‑Emotion.

PaperId: 3772, https://arxiv.org/pdf/2601.08079.pdf GitHub

Abstract:
Complex reasoning in tool‑augmented agent frameworks is inherently long‑horizon, causing reasoning traces and transient tool artifacts to accumulate and strain the bounded working context of large language models. Without explicit memory mechanisms, such accumulation disrupts logical continuity and undermines task alignment. This positions memory not as an auxiliary efficiency concern, but as a core component for sustaining coherent, goal‑directed reasoning over long horizons. We propose MemoBrain, an executive memory model for tool‑augmented agents that constructs a dependency‑aware memory over reasoning steps, capturing salient intermediate states and their logical relations. Operating as a co‑pilot alongside the reasoning agent, MemoBrain organizes reasoning progress without blocking execution and actively manages the working context. Specifically, it prunes invalid steps, folds completed sub‑trajectories, and preserves a compact, high‑salience reasoning backbone under a fixed context budget. Together, these mechanisms enable explicit cognitive control over reasoning trajectories rather than passive context accumulation. We evaluate MemoBrain on challenging long‑horizon benchmarks, including GAIA, WebWalker, and BrowseComp‑Plus, demonstrating consistent improvements over strong baselines.

PaperId: 3773, https://arxiv.org/pdf/2601.08070.pdf GitHub

Abstract:
Negative constraints (instructions of the form "do not use word X") represent a fundamental test of instruction‑following capability in large language models. Despite their apparent simplicity, these constraints fail with striking regularity, and the conditions governing failure have remained poorly understood. This paper presents the first comprehensive mechanistic investigation of negative instruction failure. We introduce semantic pressure, a quantitative measure of the model's intrinsic probability of generating the forbidden token, and demonstrate that violation probability follows a tight logistic relationship with pressure (p=σ(‑2.40+2.27\cdot P_0); n=40,000 samples; bootstrap 95% CI for slope: [2.21,,2.33]). Through layer‑wise analysis using the logit lens technique, we establish that the suppression signal induced by negative instructions is present but systematically weaker in failures: the instruction reduces target probability by only 5.2 percentage points in failures versus 22.8 points in successes ‑‑ a 4.4× asymmetry. We trace this asymmetry to two mechanistically distinct failure modes. In priming failure (87.5% of violations), the instruction's explicit mention of the forbidden word paradoxically activates rather than suppresses the target representation. In override failure (12.5%), late‑layer feed‑forward networks generate contributions of +0.39 toward the target probability ‑‑ nearly 4× larger than in successes ‑‑ overwhelming earlier suppression signals. Activation patching confirms that layers 23‑‑27 are causally responsible: replacing these layers' activations flips the sign of constraint effects. These findings reveal a fundamental tension in negative constraint design: the very act of naming a forbidden word primes the model to produce it.

PaperId: 3774, https://arxiv.org/pdf/2601.07894.pdf GitHub

Abstract:
Masked diffusion models (MDMs), which leverage bidirectional attention and a denoising process, are narrowing the performance gap with autoregressive models (ARMs). However, their internal attention mechanisms remain under‑explored. This paper investigates the attention behaviors in MDMs, revealing the phenomenon of Attention Floating. Unlike ARMs, where attention converges to a fixed sink, MDMs exhibit dynamic, dispersed attention anchors that shift across denoising steps and layers. Further analysis reveals its Shallow Structure‑Aware, Deep Content‑Focused attention mechanism: shallow layers utilize floating tokens to build a global structural framework, while deeper layers allocate more capability toward capturing semantic content. Empirically, this distinctive attention pattern provides a mechanistic explanation for the strong in‑context learning capabilities of MDMs, allowing them to double the performance compared to ARMs in knowledge‑intensive tasks. All codes and datasets are available at https://github.com/NEUIR/Attention‑Floating.

PaperId: 3775, https://arxiv.org/pdf/2601.07892.pdf GitHub

Abstract:
The deployment of Large Language Models (LLMs) on resource‑constrained edge devices is increasingly hindered by prohibitive memory and computational requirements. While ternary quantization offers a compelling solution by reducing weights to ‑1, 0, +1, current implementations suffer from a fundamental misalignment with commodity hardware. Most existing methods must choose between 2‑bit aligned packing, which incurs significant bit wastage, or 1.67‑bit irregular packing, which degrades inference speed. To resolve this tension, we propose Sherry, a hardware‑efficient ternary quantization framework. Sherry introduces a 3:4 fine‑grained sparsity that achieves a regularized 1.25‑bit width by packing blocks of four weights into five bits, restoring power‑of‑two alignment. Furthermore, we identify weight trapping issue in sparse ternary training, which leads to representational collapse. To address this, Sherry introduces Arenas, an annealing residual synapse mechanism that maintains representational diversity during training. Empirical evaluations on LLaMA‑3.2 across five benchmarks demonstrate that Sherry matches state‑of‑the‑art ternary performance while significantly reducing model size. Notably, on an Intel i7‑14700HX CPU, our 1B model achieves zero accuracy loss compared to SOTA baselines while providing 25% bit savings and 10% speed up. The code is available at https://github.com/Tencent/AngelSlim .

PaperId: 3776, https://arxiv.org/pdf/2601.07891.pdf GitHub

Abstract:
Growing context lengths in transformer‑based language models have made the key‑value (KV) cache a critical inference bottleneck. While many KV cache pruning methods have been proposed, they have not yet been adopted in major inference engines due to speed‑‑accuracy trade‑offs. We introduce KVzap, a fast, input‑adaptive approximation of KVzip that works in both prefilling and decoding. On Qwen3‑8B, Llama‑3.1‑8B‑Instruct, and Qwen3‑32B across long‑context and reasoning tasks, KVzap achieves 2‑‑4× KV cache compression with negligible accuracy loss and achieves state‑of‑the‑art performance on the KVpress leaderboard. Code and models are available at https://github.com/NVIDIA/kvpress.

PaperId: 3777, https://arxiv.org/pdf/2601.07873.pdf GitHub

Abstract:
Knowledge editing aims to efficiently modify the internal knowledge of large language models (LLMs) without compromising their other capabilities. The prevailing editing paradigm, which appends an update matrix to the original parameter matrix, has been shown by some studies to damage key numerical stability indicators (such as condition number and norm), thereby reducing editing performance and general abilities, especially in sequential editing scenario. Although subsequent methods have made some improvements, they remain within the additive framework and have not fundamentally addressed this limitation. To solve this problem, we analyze it from both statistical and mathematical perspectives and conclude that multiplying the original matrix by an orthogonal matrix does not change the numerical stability of the matrix. Inspired by this, different from the previous additive editing paradigm, a multiplicative editing paradigm termed Multiplicative Orthogonal Sequential Editing (MOSE) is proposed. Specifically, we first derive the matrix update in the multiplicative form, the new knowledge is then incorporated into an orthogonal matrix, which is multiplied by the original parameter matrix. In this way, the numerical stability of the edited matrix is unchanged, thereby maintaining editing performance and general abilities. We compared MOSE with several current knowledge editing methods, systematically evaluating their impact on both editing performance and the general abilities across three different LLMs. Experimental results show that MOSE effectively limits deviations in the edited parameter matrix and maintains its numerical stability. Compared to current methods, MOSE achieves a 12.08% improvement in sequential editing performance, while retaining 95.73% of general abilities across downstream tasks. The code is available at https://github.com/famoustourist/MOSE.

PaperId: 3778, https://arxiv.org/pdf/2601.07858.pdf GitHub

Abstract:
Generalisation to unseen subjects in EEG‑based emotion classification remains a challenge due to high inter‑and intra‑subject variability. Continual learning (CL) poses a promising solution by learning from a sequence of tasks while mitigating catastrophic forgetting. Regularisation‑based CL approaches, such as Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), and Memory Aware Synapses (MAS), are commonly used as baselines in EEG‑based CL studies, yet their suitability for this problem remains underexplored. This study theoretically and empirically finds that regularisation‑based CL methods show limited performance for EEG‑based emotion classification on the DREAMER and SEED datasets. We identify a fundamental misalignment in the stability‑plasticity trade‑off, where regularisation‑based methods prioritise mitigating catastrophic forgetting (backward transfer) over adapting to new subjects (forward transfer). We investigate this limitation under subject‑incremental sequences and observe that: (1) the heuristics for estimating parameter importance become less reliable under noisy data and covariate shift, (2) gradients on parameters deemed important by these heuristics often interfere with gradient updates required for new subjects, moving optimisation away from the minimum, (3) importance values accumulated across tasks over‑constrain the model, and (4) performance is sensitive to subject order. Forward transfer showed no statistically significant improvement over sequential fine‑tuning (p > 0.05 across approaches and datasets). The high variability of EEG signals means past subjects provide limited value to future subjects. Regularisation‑based continual learning approaches are therefore limited for robust generalisation to unseen subjects in EEG‑based emotion classification.

PaperId: 3779, https://arxiv.org/pdf/2601.07853.pdf GitHub

Abstract:
Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision‑making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high‑stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language‑model‑level content compliance or abstract agent settings, failing to capture execution‑grounded risks arising from real operational workflows and state‑changing actions. To bridge this gap, we propose FinVault, the first execution‑grounded security benchmark for financial agents, comprising 31 regulatory case‑driven sandbox scenarios with state‑writable databases and explicit compliance constraints, together with 107 real‑world vulnerabilities and 963 test cases that systematically cover prompt injection, jailbreaking, financially adapted attacks, as well as benign inputs for false‑positive evaluation. Experimental results reveal that existing defense mechanisms remain ineffective in realistic financial agent settings, with average attack success rates (ASR) still reaching up to 50.0% on state‑of‑the‑art models and remaining non‑negligible even for the most robust systems (ASR 6.7%), highlighting the limited transferability of current safety designs and the need for stronger financial‑specific defenses. Our code can be found at https://github.com/aifinlab/FinVault.

PaperId: 3780, https://arxiv.org/pdf/2601.07832.pdf GitHub GitHub

Abstract:
While the Transformer architecture dominates many fields, its quadratic self‑attention complexity hinders its use in large‑scale applications. Linear attention offers an efficient alternative, but its direct application often degrades performance, with existing fixes typically re‑introducing computational overhead through extra modules (e.g., depthwise separable convolution) that defeat the original purpose. In this work, we identify a key failure mode in these methods: global context collapse, where the model loses representational diversity. To address this, we propose Multi‑Head Linear Attention (MHLA), which preserves this diversity by computing attention within divided heads along the token dimension. We prove that MHLA maintains linear complexity while recovering much of the expressive power of softmax attention, and verify its effectiveness across multiple domains, achieving a 3.6% improvement on ImageNet classification, a 6.3% gain on NLP, a 12.6% improvement on image generation, and a 41% enhancement on video generation under the same time complexity.

PaperId: 3781, https://arxiv.org/pdf/2601.07778.pdf GitHub

Abstract:
We introduce DT‑ICU, a multimodal digital twin framework for continuous risk estimation in intensive care. DT‑ICU integrates variable‑length clinical time series with static patient information in a unified multitask architecture, enabling predictions to be updated as new observations accumulate over the ICU stay. We evaluate DT‑ICU on the large, publicly available MIMIC‑IV dataset, where it consistently outperforms established baseline models under different evaluation settings. Our test‑length analysis shows that meaningful discrimination is achieved shortly after admission, while longer observation windows further improve the ranking of high‑risk patients in highly imbalanced cohorts. To examine how the model leverages heterogeneous data sources, we perform systematic modality ablations, revealing that the model learnt a reasonable structured reliance on interventions, physiological response observations, and contextual information. These analyses provide interpretable insights into how multimodal signals are combined and how trade‑offs between sensitivity and precision emerge. Together, these results demonstrate that DT‑ICU delivers accurate, temporally robust, and interpretable predictions, supporting its potential as a practical digital twin framework for continuous patient monitoring in critical care. The source code and trained model weights for DT‑ICU are publicly available at https://github.com/GUO‑W/DT‑ICU‑release.

PaperId: 3782, https://arxiv.org/pdf/2601.07718.pdf GitHub

Abstract:
Achieving robust humanoid hiking in complex, unstructured environments requires transitioning from reactive proprioception to proactive perception. However, integrating exteroception remains a significant challenge: mapping‑based methods suffer from state estimation drift; for instance, LiDAR‑based methods do not handle torso jitter well. Existing end‑to‑end approaches often struggle with scalability and training complexity; specifically, some previous works using virtual obstacles are implemented case‑by‑case. In this work, we present Hiking in the Wild, a scalable, end‑to‑end parkour perceptive framework designed for robust humanoid hiking. To ensure safety and training stability, we introduce two key mechanisms: a foothold safety mechanism combining scalable Terrain Edge Detection with Foot Volume Points to prevent catastrophic slippage on edges, and a Flat Patch Sampling strategy that mitigates reward hacking by generating feasible navigation targets. Our approach utilizes a single‑stage reinforcement learning scheme, mapping raw depth inputs and proprioception directly to joint actions, without relying on external state estimation. Extensive field experiments on a full‑size humanoid demonstrate that our policy enables robust traversal of complex terrains at speeds up to 2.5 m/s. The training and deployment code is open‑sourced to facilitate reproducible research and deployment on real robots with minimal hardware modifications.

PaperId: 3783, https://arxiv.org/pdf/2601.07701.pdf GitHub

Abstract:
Current approaches to humanoid control generally fall into two paradigms: perceptive locomotion, which handles terrain well but is limited to pedal gaits, and general motion tracking, which reproduces complex skills but ignores environmental capabilities. This work unites these paradigms to achieve perceptive general motion control. We present a framework where exteroceptive sensing is integrated into whole‑body motion tracking, permitting a humanoid to perform highly dynamic, non‑locomotion tasks on uneven terrain. By training a single policy to perform multiple distinct motions across varied terrestrial features, we demonstrate the non‑trivial benefit of integrating perception into the control loop. Our results show that this framework enables robust, highly dynamic multi‑contact motions, such as vaulting and dive‑rolling, on unstructured terrain, significantly expanding the robot's traversability beyond simple walking or running. https://project‑instinct.github.io/deep‑whole‑body‑parkour

PaperId: 3784, https://arxiv.org/pdf/2601.07667.pdf GitHub

Abstract:
Due to the prevalence of large language models (LLMs), key‑value (KV) cache reduction for LLM inference has received remarkable attention. Among numerous works that have been proposed in recent years, layer‑wise token pruning approaches, which select a subset of tokens at particular layers to retain in KV cache and prune others, are one of the most popular schemes. They primarily adopt a set of pre‑defined layers, at which tokens are selected. Such design is inflexible in the sense that the accuracy significantly varies across tasks and deteriorates in harder tasks such as KV retrieval. In this paper, we propose ASL, a training‑free method that adaptively chooses the selection layer for KV cache reduction, exploiting the variance of token ranks ordered by attention score. The proposed method balances the performance across different tasks while meeting the user‑specified KV budget requirement. ASL operates during the prefilling stage and can be jointly used with existing KV cache reduction methods such as SnapKV to optimize the decoding stage. By evaluations on the InfiniteBench, RULER, and NIAH benchmarks, we show that ASL, equipped with one‑shot token selection, adaptively trades inference speed for accuracy, outperforming state‑of‑the‑art layer‑wise token pruning methods in difficult tasks.

PaperId: 3785, https://arxiv.org/pdf/2601.07641.pdf GitHub

Abstract:
The central challenge of AI for Science is not reasoning alone, but the ability to create computational methods in an open‑ended scientific world. Existing LLM‑based agents rely on static, pre‑defined tool libraries, a paradigm that fundamentally fails in scientific domains where tools are sparse, heterogeneous, and intrinsically incomplete. In this paper, we propose Test‑Time Tool Evolution (TTE), a new paradigm that enables agents to synthesize, verify, and evolve executable tools during inference. By transforming tools from fixed resources into problem‑driven artifacts, TTE overcomes the rigidity and long‑tail limitations of static tool libraries. To facilitate rigorous evaluation, we introduce SciEvo, a benchmark comprising 1,590 scientific reasoning tasks supported by 925 automatically evolved tools. Extensive experiments show that TTE achieves state‑of‑the‑art performance in both accuracy and tool efficiency, while enabling effective cross‑domain adaptation of computational tools. The code and benchmark have been released at https://github.com/lujiaxuan0520/Test‑Time‑Tool‑Evol.

PaperId: 3786, https://arxiv.org/pdf/2601.07568.pdf GitHub

Abstract:
Diffusion large language models (dLLMs) offer capabilities beyond those of autoregressive (AR) LLMs, such as parallel decoding and random‑order generation. However, realizing these benefits in practice is non‑trivial, as dLLMs inherently face an accuracy‑parallelism trade‑off. Despite increasing interest, existing methods typically focus on only one‑side of the coin, targeting either efficiency or performance. To address this limitation, we propose d3LLM (Pseudo‑Distilled Diffusion Large Language Model), striking a balance between accuracy and parallelism: (i) during training, we introduce pseudo‑trajectory distillation to teach the model which tokens can be decoded confidently at early steps, thereby improving parallelism; (ii) during inference, we employ entropy‑based multi‑block decoding with a KV‑cache refresh mechanism to achieve high parallelism while maintaining accuracy. To better evaluate dLLMs, we also introduce AUP (Accuracy Under Parallelism), a new metric that jointly measures accuracy and parallelism. Experiments demonstrate that our d3LLM achieves up to 10× speedup over vanilla LLaDA/Dream and 5× speedup over AR models without much accuracy drop. Our code is available at https://github.com/hao‑ai‑lab/d3LLM.

PaperId: 3787, https://arxiv.org/pdf/2601.07525.pdf GitHub

Abstract:
Natural generation allows Large Language Models (LLMs) to produce free‑form responses with rich reasoning, yet the lack of structure makes outputs difficult to verify. Conversely, constrained decoding ensures standardized formats but can inadvertently restrict reasoning capabilities by imposing constraints too early in the generation process. We propose a hybrid approach, namely In‑Writing, that combines free‑form reasoning and structured generation in a single call. The model first performs unconstrained reasoning and only applies structured decoding after a trigger token is generated, explicitly decoupling reasoning from formatting. We establish that our trigger‑token strategies are able to virtually eradicate premature triggering, a failure mode in which constrained decoding interrupts on‑going reasoning. Evaluations across diverse datasets covering classification and reasoning tasks demonstrate that our approach outperforms the state‑of‑the‑art by achieving accuracy gains of up to 27% over natural generation. Our code are available at: https://github.com/Nokia‑Bell‑Labs/InWriting.

PaperId: 3788, https://arxiv.org/pdf/2601.07475.pdf GitHub

Abstract:
The emergence of fine‑grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. However, it is difficult to adapt existing Post‑Training Quantization (PTQ) strategies to these formats: rotation‑based methods compromise fine‑grained block isolation; smoothing techniques struggle with significant 4‑bit quantization errors; and mixed‑precision approaches often conflict with hardware constraints on unified‑precision computation. To address these challenges, we propose ARCQuant, a framework that boosts NVFP4 performance via Augmented Residual Channels. Distinct from methods that compromise block isolation or hardware uniformity, ARCQuant maintains a strictly unified NVFP4 format by augmenting the activation matrix with quantized residual channels. This design integrates the error compensation process directly into the matrix reduction dimension, enabling the use of standard, highly optimized GEMM kernels with minimal overhead. Theoretical analysis confirms that the worst‑case error bound of our dual‑stage NVFP4 quantization is comparable to that of standard 8‑bit formats such as MXFP8. Extensive experiments on LLaMA and Qwen models demonstrate that ARCQuant achieves state‑of‑the‑art accuracy, comparable to full‑precision baselines in perplexity and downstream tasks. Furthermore, deployment on RTX 5090 and RTX PRO 6000 GPUs confirms practical benefits, achieving up to 3x speedup over FP16. Our code is available at https://github.com/actypedef/ARCQuant .

PaperId: 3789, https://arxiv.org/pdf/2601.07377.pdf GitHub

Abstract:
In this paper, we present a new dynamic collaborative network for semi‑supervised 3D vessel segmentation, termed DiCo. Conventional mean teacher (MT) methods typically employ a static approach, where the roles of the teacher and student models are fixed. However, due to the complexity of 3D vessel data, the teacher model may not always outperform the student model, leading to cognitive biases that can limit performance. To address this issue, we propose a dynamic collaborative network that allows the two models to dynamically switch their teacher‑student roles. Additionally, we introduce a multi‑view integration module to capture various perspectives of the inputs, mirroring the way doctors conduct medical analysis. We also incorporate adversarial supervision to constrain the shape of the segmented vessels in unlabeled data. In this process, the 3D volume is projected into 2D views to mitigate the impact of label inconsistencies. Experiments demonstrate that our DiCo method sets new state‑of‑the‑art performance on three 3D vessel segmentation benchmarks. The code repository address is https://github.com/xujiaommcome/DiCo

PaperId: 3790, https://arxiv.org/pdf/2601.07351.pdf GitHub GitHub

Abstract:
Diffusion Language Models (DLMs) offer a promising alternative for language modeling by enabling parallel decoding through iterative refinement. However, most DLMs rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions and underutilize intermediate probabilistic representations. In this paper, we propose EvoToken‑DLM, a novel diffusion‑based language modeling approach that replaces hard binary masks with evolving soft token distributions. EvoToken‑DLM enables a progressive transition from masked states to discrete outputs, supporting revisable decoding. To effectively support this evolution, we introduce continuous trajectory supervision, which aligns training objectives with iterative probabilistic updates. Extensive experiments across multiple benchmarks show that EvoToken‑DLM consistently achieves superior performance, outperforming strong diffusion‑based and masked DLM baselines. Project webpage: https://aim‑uofa.github.io/EvoTokenDLM.

PaperId: 3791, https://arxiv.org/pdf/2601.07348.pdf GitHub

Abstract:
Self‑evolution methods enhance code generation through iterative "generate‑verify‑refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks. To address these bottlenecks, we propose Controlled Self‑Evolution (CSE), which consists of three key components. Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage. Genetic Evolution replaces stochastic operations with feedback‑guided mechanisms, enabling targeted mutation and compositional crossover. Hierarchical Evolution Memory captures both successful and failed experiences at inter‑task and intra‑task levels. Experiments on EffiBench‑X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Furthermore, CSE achieves higher efficiency from early generations and maintains continuous improvement throughout evolution. Our code is publicly available at https://github.com/QuantaAlpha/EvoControl.

PaperId: 3792, https://arxiv.org/pdf/2601.07309.pdf GitHub

Abstract:
Interactive large language model agents have advanced rapidly, but most remain specialized to a single environment and fail to adapt robustly to other environments. Model merging offers a training‑free alternative by integrating multiple experts into a single model. In this paper, we propose Agent‑Role Merging (ARM), an activation‑guided, role‑conditioned neuron transplantation method for model merging in LLM agents. ARM improves existing merging methods from static natural language tasks to multi‑turn agent scenarios, and over the generalization ability across various interactive environments. This is achieved with a well designed 3‑step framework: 1) constructing merged backbones, 2) selection based on its role‑conditioned activation analysis, and 3) neuron transplantation for fine‑grained refinements. Without gradient‑based optimization, ARM improves cross‑benchmark generalization while enjoying efficiency. Across diverse domains, the model obtained via ARM merging outperforms prior model merging methods and domain‑specific expert models, while demonstrating strong out‑of‑domain generalization.

PaperId: 3793, https://arxiv.org/pdf/2601.07238.pdf GitHub

Abstract:
Large reasoning models (LRMs) exhibit diverse high‑level reasoning patterns (e.g., direct solution, reflection‑and‑verification, and exploring multiple solutions), yet prevailing training recipes implicitly bias models toward a limited set of dominant patterns. Through a systematic analysis, we identify substantial accuracy variance across these patterns on mathematics and science benchmarks, revealing that a model's default reasoning pattern is often sub‑optimal for a given problem. To address this, we introduce Group Pattern Selection Optimization (GPSO), a reinforcement learning framework that extends GRPO by incorporating multi‑pattern rollouts, verifier‑guided optimal pattern selection per problem, and attention masking during optimization to prevent the leakage of explicit pattern suffixes into the learned policy. By exploring a portfolio of diverse reasoning strategies and optimizing the policy on the most effective ones, GPSO enables the model to internalize the mapping from problem characteristics to optimal reasoning patterns. Extensive experiments demonstrate that GPSO delivers consistent and substantial performance gains across various model backbones and benchmarks, effectively mitigating pattern sub‑optimality and fostering more robust, adaptable reasoning. All data and codes are available at https://github.com/wanghanbinpanda/GPSO.

PaperId: 3794, https://arxiv.org/pdf/2601.07206.pdf GitHub

Abstract:
Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large‑scale benchmark and unified framework for LLM routing. It comprises over 400K instances from 21 datasets and 33 models. Moreover, it provides comprehensive metrics for both performance‑oriented routing and performance‑cost trade‑off routing, and integrates 10 representative routing baselines. Using LLMRouterBench, we systematically re‑evaluate the field. While confirming strong model complementarity‑the central premise of LLM routing‑we find that many routing methods exhibit similar performance under unified evaluation, and several recent approaches, including commercial routers, fail to reliably outperform a simple baseline. Meanwhile, a substantial gap remains to the Oracle, driven primarily by persistent model‑recall failures. We further show that backbone embedding models have limited impact, that larger ensembles exhibit diminishing returns compared to careful model curation, and that the benchmark also enables latency‑aware analysis. All code and data are available at https://github.com/ynulihao/LLMRouterBench.

PaperId: 3795, https://arxiv.org/pdf/2601.07182.pdf GitHub

Abstract:
Policy optimization for large language models often suffers from sparse reward signals in multi‑step reasoning tasks. Critic‑free methods like GRPO assign a single normalized outcome reward to all tokens, providing limited guidance for intermediate reasoning . While Process Reward Models (PRMs) offer dense feedback, they risk premature collapse when used alone, as early low‑reward tokens can drive policies toward truncated outputs. We introduce Process Relative Policy Optimization (PRPO), which combines outcome reliability with process‑level guidance in a critic‑free framework. PRPO segments reasoning sequences based on semantic clues, normalizes PRM scores into token‑level advantages, and aligns their distribution with outcome advantages through location‑parameter shift. On MATH500, PRPO improves Qwen2.5‑Math‑1.5B accuracy from 61.2% to 64.4% over GRPO using only eight rollouts and no value network, demonstrating efficient fine‑grained credit assignment within critic‑free optimization. Code is available at: https://github.com/SchumiDing/srpocode

PaperId: 3796, https://arxiv.org/pdf/2601.07160.pdf GitHub

Abstract:
To meet the ever‑increasing demand for computational efficiency, Neural Processing Units (NPUs) have become critical in modern AI infrastructure. However, unlocking their full potential requires developing high‑performance compute kernels using vendor‑specific Domain‑Specific Languages (DSLs), a task that demands deep hardware expertise and is labor‑intensive. While Large Language Models (LLMs) have shown promise in general code generation, they struggle with the strict constraints and scarcity of training data in the NPU domain. Our preliminary study reveals that state‑of‑the‑art general‑purpose LLMs fail to generate functional complex kernels for Ascend NPUs, yielding a near‑zero success rate. To address these challenges, we propose AscendKernelGen, a generation‑evaluation integrated framework for NPU kernel development. We introduce Ascend‑CoT, a high‑quality dataset incorporating chain‑of‑thought reasoning derived from real‑world kernel implementations, and KernelGen‑LM, a domain‑adaptive model trained via supervised fine‑tuning and reinforcement learning with execution feedback. Furthermore, we design NPUKernelBench, a comprehensive benchmark for assessing compilation, correctness, and performance across varying complexity levels. Experimental results demonstrate that our approach significantly bridges the gap between general LLMs and hardware‑specific coding. Specifically, the compilation success rate on complex Level‑2 kernels improves from 0% to 95.5% (Pass@10), while functional correctness achieves 64.3% compared to the baseline's complete failure. These results highlight the critical role of domain‑specific reasoning and rigorous evaluation in automating accelerator‑aware code generation. AscendKernGen is available at https://huggingface.co/AscendKernelGen and https://github.com/weich97/NPUKernelBench.

PaperId: 3797, https://arxiv.org/pdf/2601.07086.pdf GitHub

Abstract:
Emerging memory technologies have gained significant attention as a promising pathway to overcome the limitations of conventional computing architectures in deep learning applications. By enabling computation directly within memory, these technologies ‑ built on nanoscale devices with tunable and nonvolatile conductance ‑ offer the potential to drastically reduce energy consumption and latency compared to traditional von Neumann systems. This paper introduces XBTorch (short for CrossBarTorch), a novel simulation framework that integrates seamlessly with PyTorch and provides specialized tools for accurately and efficiently modeling crossbar‑based systems based on emerging memory technologies. Through detailed comparisons and case studies involving hardware‑aware training and inference, we demonstrate how XBTorch offers a unified interface for key research areas such as device‑level modeling, cross‑layer co‑design, and inference‑time fault tolerance. While exemplar studies utilize ferroelectric field‑effect transistor (FeFET) models, the framework remains technology‑agnostic ‑ supporting other emerging memories such as resistive RAM (ReRAM), as well as enabling user‑defined custom device models. The code is publicly available at: https://github.com/ADAM‑Lab‑GW/xbtorch

PaperId: 3798, https://arxiv.org/pdf/2601.07023.pdf GitHub

Abstract:
AI Clones aim to simulate an individual's thoughts and behaviors to enable long‑term, personalized interaction, placing stringent demands on memory systems to model experiences, emotions, and opinions over time. Existing memory benchmarks primarily rely on user‑agent conversational histories, which are temporally fragmented and insufficient for capturing continuous life trajectories. We introduce CloneMem, a benchmark for evaluating longterm memory in AI Clone scenarios grounded in non‑conversational digital traces, including diaries, social media posts, and emails, spanning one to three years. CloneMem adopts a hierarchical data construction framework to ensure longitudinal coherence and defines tasks that assess an agent's ability to track evolving personal states. Experiments show that current memory mechanisms struggle in this setting, highlighting open challenges for life‑grounded personalized AI. Code and dataset are available at https://github.com/AvatarMemory/CloneMemBench

PaperId: 3799, https://arxiv.org/pdf/2601.06992.pdf GitHub

Abstract:
Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM‑based rerankers primarily optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents. We propose FinCards, a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance‑aware schema. FinCards represents filing chunks and questions using aligned schema fields (entities, metrics, periods, and numeric spans), enabling deterministic field‑level matching. Evidence is selected via a multi‑stage tournament reranking with stability‑aware aggregation, producing auditable decision traces. Across two corporate filing QA benchmarks, FinCards substantially improves early‑rank retrieval over both lexical and LLM‑based reranking baselines, while reducing ranking variance, without requiring model fine‑tuning or unpredictable inference budgets. Our code is available at https://github.com/XanderZhou2022/FINCARDS.

PaperId: 3800, https://arxiv.org/pdf/2601.06966.pdf GitHub

Abstract:
As Large Language Models (LLMs) evolve from static dialogue interfaces to autonomous general agents, effective memory is paramount to ensuring long‑term consistency. However, existing benchmarks primarily focus on casual conversation or task‑oriented dialogue, failing to capture "long‑term project‑oriented" interactions where agents must track evolving goals. To bridge this gap, we introduce RealMem, the first benchmark grounded in realistic project scenarios. RealMem comprises over 2,000 cross‑session dialogues across eleven scenarios, utilizing natural user queries for evaluation. We propose a synthesis pipeline that integrates Project Foundation Construction, Multi‑Agent Dialogue Generation, and Memory and Schedule Management to simulate the dynamic evolution of memory. Experiments reveal that current memory systems face significant challenges in managing the long‑term project states and dynamic context dependencies inherent in real‑world projects. Our code and datasets are available at [https://github.com/AvatarMemory/RealMemBench](https://github.com/AvatarMemory/RealMemBench).

PaperId: 3801, https://arxiv.org/pdf/2601.06944.pdf GitHub

Abstract:
While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual understanding, they often struggle when faced with the unstructured and ambiguous nature of human‑generated sketches. This limitation is particularly pronounced in the underexplored task of visual grading, where models should not only solve a problem but also diagnose errors in hand‑drawn diagrams. Such diagnostic capabilities depend on complex structural, semantic, and metacognitive reasoning. To bridge this gap, we introduce SketchJudge, a novel benchmark tailored for evaluating MLLMs as graders of hand‑drawn STEM diagrams. SketchJudge encompasses 1,015 hand‑drawn student responses across four domains: geometry, physics, charts, and flowcharts, featuring diverse stylistic variations and distinct error types. Evaluations on SketchJudge demonstrate that even advanced MLLMs lag significantly behind humans, validating the benchmark's effectiveness in exposing the fragility of current vision‑language alignment in symbolic and noisy contexts. All data, code, and evaluation scripts are publicly available at https://github.com/yuhangsu82/SketchJudge.

PaperId: 3802, https://arxiv.org/pdf/2601.06940.pdf GitHub

Abstract:
Repairing incomplete trajectory data is essential for downstream spatio‑temporal applications. Yet, existing repair methods focus solely on reconstruction without documenting the reasoning behind repair decisions, undermining trust in safety‑critical applications where repaired trajectories affect operational decisions, such as in maritime anomaly detection and route planning. We introduce repair provenance ‑ structured, queryable metadata that documents the full reasoning chain behind each repair ‑ which transforms imputation from pure data recovery into a task that supports downstream decision‑making. We propose VISTA (knowledge‑driven interpretable vessel trajectory imputation), a framework that reliably equips repaired trajectories with repair provenance by grounding LLM reasoning in data‑verified knowledge. Specifically, we formalize Structured Data‑derived Knowledge (SDK), a knowledge model whose data‑verifiable components can be validated against real data and used to anchor and constrain LLM‑generated explanations. We organize SDK in a Structured Data‑derived Knowledge Graph (SD‑KG) and establish a data‑knowledge‑data loop for extraction, validation, and incremental maintenance over large‑scale AIS data. A workflow management layer with parallel scheduling, fault tolerance, and redundancy control ensures consistent and efficient end‑to‑end processing. Experiments on two large‑scale AIS datasets show that VISTA achieves state‑of‑the‑art accuracy, improving over baselines by 5‑91% and reducing inference time by 51‑93%, while producing repair provenance, whose interpretability is further validated through a case study and an interactive demo system.

PaperId: 3803, https://arxiv.org/pdf/2601.06860.pdf GitHub

Abstract:
Large Language Models (LLMs) can extend their parameter knowledge limits by adopting the Tool‑Integrated Reasoning (TIR) paradigm. However, existing LLM‑based agent training framework often focuses on answers' accuracy, overlooking specific alignment for behavior patterns. Consequently, agent often exhibits ineffective actions during TIR tasks, such as redundant and insufficient tool calls. How to calibrate erroneous behavioral patterns when executing TIR tasks, thereby exploring effective trajectories, remains an open‑ended problem. In this paper, we propose ET‑Agent, a training framework for calibrating agent's tool‑use behavior through two synergistic perspectives: Self‑evolving Data Flywheel and Behavior Calibration Training. Specifically, we introduce a self‑evolutionary data flywheel to generate enhanced data, used to fine‑tune LLM to improve its exploration ability. Based on this, we implement an two‑phases behavior‑calibration training framework. It is designed to progressively calibrate erroneous behavioral patterns to optimal behaviors. Further in‑depth experiments confirm the superiority of \ourmodel across multiple dimensions, including correctness, efficiency, reasoning conciseness, and tool execution accuracy. Our ET‑Agent framework provides practical insights for research in the TIR field. Codes can be found in https://github.com/asilverlight/ET‑Agent

PaperId: 3804, https://arxiv.org/pdf/2601.06845.pdf GitHub

Abstract:
Designing effective control policies for autonomous systems remains a fundamental challenge, traditionally addressed through reinforcement learning or manual engineering. While reinforcement learning has achieved remarkable success, it often suffers from high sample complexity, reward shaping difficulties, and produces opaque neural network policies that are hard to interpret or verify. Manual design, on the other hand, requires substantial domain expertise and struggles to scale across diverse tasks. In this work, we demonstrate that LLM‑driven evolutionary search can effectively synthesize interpretable control policies in the form of executable code. By treating policy synthesis as a code evolution problem, we harness the LLM's prior knowledge of programming patterns and control heuristics while employing evolutionary search to explore the solution space systematically. We implement our approach using EvoToolkit, a framework that seamlessly integrates LLM‑driven evolution with customizable fitness evaluation. Our method iteratively evolves populations of candidate policy programs, evaluating them against task‑specific objectives and selecting superior individuals for reproduction. This process yields compact, human‑readable control policies that can be directly inspected, modified, and formally verified. This work highlights the potential of combining foundation models with evolutionary computation for synthesizing trustworthy control policies in autonomous systems. Code is available at https://github.com/pgg3/EvoControl.

PaperId: 3805, https://arxiv.org/pdf/2601.06810.pdf GitHub

Abstract:
The Wasserstein‑Fisher‑Rao (WFR) metric extends dynamic optimal transport (OT) by coupling displacement with change of mass, providing a principled geometry for modeling unbalanced snapshot dynamics. Existing WFR solvers, however, are often unstable, computationally expensive, and difficult to scale. Here we introduce WFR Flow Matching (WFR‑FM), a simulation‑free training algorithm that unifies flow matching with dynamic unbalanced OT. Unlike classical flow matching which regresses only a transport vector field, WFR‑FM simultaneously regresses a vector field for displacement and a scalar growth rate function for birth‑death dynamics, yielding continuous flows under the WFR geometry. Theoretically, we show that minimizing the WFR‑FM loss exactly recovers WFR geodesics. Empirically, WFR‑FM yields more accurate and robust trajectory inference in single‑cell biology, reconstructing consistent dynamics with proliferation and apoptosis, estimating time‑varying growth fields, and applying to generative dynamics under imbalanced data. It outperforms state‑of‑the‑art baselines in efficiency, stability, and reconstruction accuracy. Overall, WFR‑FM establishes a unified and efficient paradigm for learning dynamical systems from unbalanced snapshots, where not only states but also mass evolve over time. The Python code is available at https://github.com/QiangweiPeng/WFR‑FM.

PaperId: 3806, https://arxiv.org/pdf/2601.06767.pdf GitHub

Abstract:
We present a Bengali mathematical reasoning model called GanitLLM (named after the Bangla word for mathematics, Ganit), together with a new difficulty‑aware Bengali math corpus and a curriculum‑based GRPO pipeline. Bengali is one of the world's most widely spoken languages, yet existing LLMs either reason in English and then translate, or simply fail on multi‑step Bengali math, in part because reinforcement learning recipes are tuned for high‑resource languages and collapse under reward sparsity in low‑resource settings. To address this, we construct Ganit, a rigorously filtered and decontaminated Bengali math dataset with automatic difficulty tags derived from the pass@k of a strong evaluator model. Building on this dataset, we propose Curriculum‑GRPO, which combines multi‑stage training (SFT + GRPO) with difficulty‑aware sampling and verifiable rewards for format, numerical correctness, and Bengali reasoning. On Bn‑MGSM and Bn‑MSVAMP, GanitLLM‑4B improves over its Qwen3‑4B base by +8 and +6 accuracy points, respectively, while increasing the percentage of Bengali reasoning tokens from 14% to over 88% and reducing average solution length from 943 to 193 words. Project page is available at https://dipta007.github.io/GanitLLM

PaperId: 3807, https://arxiv.org/pdf/2601.06747.pdf GitHub

Abstract:
Evaluating Language Models (LMs) in specialized, high‑stakes domains such as finance remains a significant challenge due to the scarcity of open, high‑quality, and domain‑specific datasets. Existing general‑purpose benchmarks provide broad coverage but lack the depth and domain fidelity needed to assess LMs' capabilities for real‑world financial reasoning, which requires both conceptual understanding and quantitative rigor. To address this gap, we introduce FinForge, a scalable, semi‑synthetic pipeline for constructing finance‑specific evaluation benchmarks through a hybrid of expert‑guided data curation and controlled LM‑based synthesis. FinForge combines manual and programmatic corpus construction from authoritative financial sources with structured question generation and validation using Gemini 2.5 Flash. To demonstrate the pipeline's efficacy, we produce FinForge‑5k, a snapshot benchmark comprising over 5,000 human‑validated question‑answer pairs across 11 finance subdomains, derived from a curated corpus of 100,000 verified documents totaling 143M tokens. Evaluation of state‑of‑the‑art open‑source and closed‑source models on FinForge‑5k reveals significant differences in financial reasoning, with leading models achieving accuracy levels near 80%. These findings underscore the framework's utility for diagnosing current model limitations and guiding future improvements in financial domain competence. All code and data are available at https://github.com/gtfintechlab/FinForge.

PaperId: 3808, https://arxiv.org/pdf/2601.06639.pdf GitHub

Abstract:
Protecting the copyright of user‑generated AI images is an emerging challenge as AIGC becomes pervasive in creative workflows. Existing watermarking methods (1) remain vulnerable to real‑world adversarial threats, often forced to trade off between defenses against spoofing and removal attacks; and (2) cannot support semantic‑level tamper localization. We introduce PAI, a training‑free inherent watermarking framework for AIGC copyright protection, plug‑and‑play with diffusion‑based AIGC services. PAI simultaneously provides three key functionalities: robust ownership verification, attack detection, and semantic‑level tampering localization. Unlike existing inherent watermark methods that only embed watermarks at noise initialization of diffusion models, we design a novel key‑conditioned deflection mechanism that subtly steers the denoising trajectory according to the user key. Such trajectory‑level coupling further strengthens the semantic entanglement of identity and content, thereby further enhancing robustness against real‑world threats. Moreover, we also provide a theoretical analysis proving that only the valid key can pass verification. Experiments across 12 attack methods show that PAI achieves 98.43% verification accuracy, improving over SOTA methods by 37.25% on average, and retains strong tampering localization performance even against advanced AIGC edits. Our code is available at https://github.com/QingyuLiu/PAI.

PaperId: 3809, https://arxiv.org/pdf/2601.06505.pdf GitHub

Abstract:
Bayesian optimization (BO) is a common framework for optimizing black‑box functions, yet most existing methods assume static query costs and rely on myopic acquisition strategies. We introduce LookaHES, a nonmyopic BO framework designed for dynamic, history‑dependent cost environments, where evaluation costs vary with prior actions, such as travel distance in spatial tasks or edit distance in sequence design. LookaHES combines a multi‑step variant of H‑Entropy Search with pathwise sampling and neural policy optimization, enabling long‑horizon planning beyond twenty steps without the exponential complexity of existing nonmyopic methods. The key innovation is the integration of neural policies, including large language models, to effectively navigate structured, combinatorial action spaces such as protein sequences. These policies amortize lookahead planning and can be integrated with domain‑specific constraints during rollout. Empirically, LookaHES outperforms strong myopic and nonmyopic baselines across nine synthetic benchmarks from two to eight dimensions and two real‑world tasks: geospatial optimization using NASA night‑light imagery and protein sequence design with constrained token‑level edits. In short, LookaHES provides a general, scalable, and cost‑aware solution for robust long‑horizon optimization in complex decision spaces, which makes it a useful tool for researchers in machine learning, statistics, and applied domains. Our implementation is available at https://github.com/sangttruong/nonmyopia.

PaperId: 3810, https://arxiv.org/pdf/2601.06502.pdf GitHub

Abstract:
Large Language Models (LLMs) have recently shown promise in addressing combinatorial optimization problems (COPs) through prompt‑based strategies. However, their scalability and generalization remain limited, and their effectiveness diminishes as problem size increases, particularly in routing problems involving more than 30 nodes. We propose DRAGON, which stands for Decomposition and Reconstruction Agents Guided OptimizatioN, a novel framework that combines the strengths of metaheuristic design and LLM reasoning. Starting from an initial global solution, DRAGON autonomously identifies regions with high optimization potential and strategically decompose large‑scale COPs into manageable subproblems. Each subproblem is then reformulated as a concise, localized optimization task and solved through targeted LLM prompting guided by accumulated experiences. Finally, the locally optimized solutions are systematically reintegrated into the original global context to yield a significantly improved overall outcome. By continuously interacting with the optimization environment and leveraging an adaptive experience memory, the agents iteratively learn from feedback, effectively coupling symbolic reasoning with heuristic search. Empirical results show that, unlike existing LLM‑based solvers limited to small‑scale instances, DRAGON consistently produces feasible solutions on TSPLIB, CVRPLIB, and Weibull‑5k bin packing benchmarks, and achieves near‑optimal results (0.16% gap) on knapsack problems with over 3M variables. This work shows the potential of feedback‑driven language agents as a new paradigm for generalizable and interpretable large‑scale optimization.

PaperId: 3811, https://arxiv.org/pdf/2601.06460.pdf GitHub

Abstract:
Vision‑Language Models (VLMs) are increasingly used in safety‑critical applications that require reliable visual grounding. However, these models often hallucinate details that are not present in the image to satisfy user prompts. While recent datasets and benchmarks have been introduced to evaluate systematic hallucinations in VLMs, many hallucination behaviors remain insufficiently characterized. In particular, prior work primarily focuses on object presence or absence, leaving it unclear how prompt phrasing and structural constraints can systematically induce hallucinations. In this paper, we investigate how different forms of prompt pressure influence hallucination behavior. We introduce Ghost‑100, a procedurally generated dataset of synthetic scenes in which key visual details are deliberately removed, enabling controlled analysis of absence‑based hallucinations. Using a structured 5‑Level Prompt Intensity Framework, we vary prompts from neutral queries to toxic demands and rigid formatting constraints. We evaluate three representative open‑weight VLMs: MiniCPM‑V 2.6‑8B, Qwen2‑VL‑7B, and Qwen3‑VL‑8B. Across all three models, hallucination rates do not increase monotonically with prompt intensity. All models exhibit reductions at higher intensity levels at different thresholds, though not all show sustained reduction under maximum coercion. These results suggest that current safety alignment is more effective at detecting semantic hostility than structural coercion, revealing model‑specific limitations in handling compliance pressure. Our dataset is available at: https://github.com/bli1/tone‑matters

PaperId: 3812, https://arxiv.org/pdf/2601.06401.pdf GitHub

Abstract:
Large language models have undergone rapid evolution, emerging as a pivotal technology for intelligence in financial operations. However, existing benchmarks are often constrained by pitfalls such as reliance on simulated or general‑purpose samples and a focus on singular, offline static scenarios. Consequently, they fail to align with the requirements for authenticity and real‑time responsiveness in financial services, leading to a significant discrepancy between benchmark performance and actual operational efficacy. To address this, we introduce BizFinBench.v2, the first large‑scale evaluation benchmark grounded in authentic business data from both Chinese and U.S. equity markets, integrating online assessment. We performed clustering analysis on authentic user queries from financial platforms, resulting in eight fundamental tasks and two online tasks across four core business scenarios, totaling 29,578 expert‑level Q&A pairs. Experimental results demonstrate that ChatGPT‑5 achieves a prominent 61.5% accuracy in main tasks, though a substantial gap relative to financial experts persists; in online tasks, DeepSeek‑R1 outperforms all other commercial LLMs. Error analysis further identifies the specific capability deficiencies of existing models within practical financial business contexts. BizFinBench.v2 transcends the limitations of current benchmarks, achieving a business‑level deconstruction of LLM financial capabilities and providing a precise basis for evaluating efficacy in the widespread deployment of LLMs within the financial domain. The data and code are available at https://github.com/HiThink‑Research/BizFinBench.v2.

PaperId: 3813, https://arxiv.org/pdf/2601.06394.pdf GitHub

Abstract:
Understanding student behavior in the classroom is essential to improve both pedagogical quality and student engagement. Existing methods for predicting student engagement typically require substantial annotated data to model the diversity of student behaviors, yet privacy concerns often restrict researchers to their own proprietary datasets. Moreover, the classroom context, represented in peers' actions, is ignored. To address the aforementioned limitation, we propose a novel three‑stage framework for video‑based student engagement measurement. First, we explore the few‑shot adaptation of the vision‑language model for student action recognition, which is fine‑tuned to distinguish among action categories with a few training samples. Second, to handle continuous and unpredictable student actions, we utilize the sliding temporal window technique to divide each student's 2‑minute‑long video into non‑overlapping segments. Each segment is assigned an action category via the fine‑tuned VLM model, generating a sequence of action predictions. Finally, we leverage the large language model to classify this entire sequence of actions, together with the classroom context, as belonging to an engaged or disengaged student. The experimental results demonstrate the effectiveness of the proposed approach in identifying student engagement. The source code will be available at https://github.com/ahmed‑nady/context_aware_student_engagement.

PaperId: 3814, https://arxiv.org/pdf/2601.06377.pdf GitHub

Abstract:
Although long‑term memory systems have made substantial progress in recent years, they still exhibit clear limitations in adaptability, scalability, and self‑evolution under continuous interaction settings. Inspired by cognitive theories, we propose HiMem, a hierarchical long‑term memory framework for long‑horizon dialogues, designed to support memory construction, retrieval, and dynamic updating during sustained interactions. HiMem constructs cognitively consistent Episode Memory via a Topic‑Aware Event‑‑Surprise Dual‑Channel Segmentation strategy, and builds Note Memory that captures stable knowledge through a multi‑stage information extraction pipeline. These two memory types are semantically linked to form a hierarchical structure that bridges concrete interaction events and abstract knowledge, enabling efficient retrieval without sacrificing information fidelity. HiMem supports both hybrid and best‑effort retrieval strategies to balance accuracy and efficiency, and incorporates conflict‑aware Memory Reconsolidation to revise and supplement stored knowledge based on retrieval feedback. This design enables continual memory self‑evolution over long‑term use. Experimental results on long‑horizon dialogue benchmarks demonstrate that HiMem consistently outperforms representative baselines in accuracy, consistency, and long‑term reasoning, while maintaining favorable efficiency. Overall, HiMem provides a principled and scalable design paradigm for building adaptive and self‑evolving LLM‑based conversational agents. The code is available at https://github.com/jojopdq/HiMem.

PaperId: 3815, https://arxiv.org/pdf/2601.06328.pdf GitHub

Abstract:
To close the gap between LLM‑based agents and humans in planning and reasoning, agents need large‑scale, diverse environments for continuous learning ‑‑ yet building such environments is itself prohibitively expensive. We present C‑World, an environment creation system that enables users to build agent environments on demand. We define a complete agent environment through four components: an Action Space of 5,571 format‑unified tools across 204 common applications, a Task Distribution engine that synthesizes long‑horizon workflows with wild constraints, a Transition Function implemented as a state controller that injects realistic failures and perturbations, and a Reward Signal combining verifiable metrics with LLM‑based judgment. C‑World operates in two modes: a realistic mode grounded in live API execution, and a synthesized mode powered by the World Engine, which approximates tool behavior without live service access, enabling scalable environment creation ‑‑ including environments for domains and tools that do not yet exist in the real world. Evaluation of nine state‑of‑the‑art LLMs reveals that planning ability is uniformly strong but execution remains the bottleneck, and that constraint following ‑‑ not tool invocation ‑‑ is the dominant failure mode. The World Engine achieves Spearman ρ= 0.883 ranking correlation with real execution, and fine‑tuning on just 1,170 C‑World trajectories outperforms baselines trained on 119k samples, demonstrating C‑World's dual value as a rigorous evaluation environment and a scalable data engine. Our code and data are available at https://ziqiao‑git.github.io/C‑World/

PaperId: 3816, https://arxiv.org/pdf/2601.06218.pdf GitHub GitHub

Abstract:
We present a cost‑effective two‑step authentication system that integrates face identification and speaker verification using only a camera and microphone available on common devices. The pipeline first performs face recognition to identify a candidate user from a small enrolled group, then performs voice recognition only against the matched identity to reduce computation and improve robustness. For face recognition, a pruned VGG‑16 based classifier is trained on an augmented dataset of 924 images from five subjects, with faces localized by MTCNN; it achieves 95.1% accuracy. For voice recognition, a CNN speaker‑verification model trained on LibriSpeech (train‑other‑360) attains 98.9% accuracy and 3.456% EER on test‑clean. Source code and trained models are available at https://github.com/NCUE‑EE‑AIAL/Two‑step‑Authentication‑Multi‑biometric‑System.

PaperId: 3817, https://arxiv.org/pdf/2601.06194.pdf GitHub

Abstract:
As large language models (LLMs) are increasingly deployed, understanding how they express political positioning is important for evaluating alignment and downstream effects. We audit 26 contemporary LLMs using three political psychometric inventories (Political Compass, SapplyValues, 8Values) and a news bias labeling task. To test robustness, inventories are administered across multiple semantic prompt variants and analyzed with a two‑way ANOVA separating model and prompt effects. Most models cluster in a similar ideological region, with 96.3% located in the Libertarian‑Left quadrant of the Political Compass, and model identity explaining most variance across prompt variants (η^2 > 0.90). Cross‑instrument comparisons suggest that the Political Compass social axis aligns more strongly with cultural progressivism than authority‑related measures (r=‑0.64). We observe differences between open‑weight and closed‑source models and asymmetric performance in detecting extreme political bias in downstream classification. Regression analysis finds that psychometric ideological positioning does not significantly predict classification errors, providing no evidence of a statistically significant relationship between conversational ideological identity and task‑level behavior. These findings suggest that single‑axis evaluations are insufficient and that multidimensional auditing frameworks are important to characterize alignment behavior in deployed LLMs. Our code and data are publicly available at https://github.com/sakhadib/PolAlignLLM.

PaperId: 3818, https://arxiv.org/pdf/2601.06142.pdf GitHub

Abstract:
Tokens are the basic units of Large Language Models (LLMs). LLMs rely on tokenizers to segment text into these tokens, and tokenization is the primary determinant of computational and inference cost. Sanskrit, one of the oldest languages, is hypothesized to express more meaning per token due to its morphology and grammar rules; however, no prior work has quantified this. We use a dataset of 701 parallel verses of the Bhagavad Gita, which comprises three languages‑Sanskrit, English, and Hindi along with transliteration of Sanskrit into English. We test tokenizers including SentencePiece (SPM), older GPT models, and the latest generation tokenizers from Gemini and GPT. We use metrics of token count, characters per token (token efficiency), and tokens per character (token cost). Results show a ~2x difference in token counts between Sanskrit and English/Hindi under the unbiased SPM baseline. English/Hindi translations of Sanskrit commentary resulted in an approximately 20x increase in token count. GPT o200k base (latest, used by GPT‑4o) and Gemini (latest) reduce bias by a significant degree compared to GPT cl100k base (used until GPT‑4), but still fail to fully capture Sanskrit's compactness. This matters because there might be a penalty bias for non‑English users, which inflates the token count. This research provides a foundation for improving future tokenizer design and shows the potential of Sanskrit for highly compact encoding, saving on cost while speeding up training and inference. The code and dataset are available at https://github.com/anshulkr713/sanskrit‑token‑efficiency

PaperId: 3819, https://arxiv.org/pdf/2601.06109.pdf GitHub

Abstract:
Large language models (LLMs) often encode cognitive behaviors unpredictably across prompts, layers, and contexts, making them difficult to diagnose and control. We present CBMAS, a diagnostic framework for continuous activation steering, which extends cognitive bias analysis from discrete before/after interventions to interpretable trajectories. By combining steering vector construction with dense α‑sweeps, logit lens‑based bias curves, and layer‑site sensitivity analysis, our approach can reveal tipping points where small intervention strengths flip model behavior and show how steering effects evolve across layer depth. We argue that these continuous diagnostics offer a bridge between high‑level behavioral evaluation and low‑level representational dynamics, contributing to the cognitive interpretability of LLMs. Lastly, we provide a CLI and datasets for various cognitive behaviors at the project repository, https://github.com/shimamooo/CBMAS.

PaperId: 3820, https://arxiv.org/pdf/2601.06059.pdf GitHub

Abstract:
The evolution of semantic communications has profoundly impacted wireless video transmission, whose applications dominate driver of modern bandwidth consumption. However, most existing schemes are predominantly optimized for simple additive white Gaussian noise or Rayleigh fading channels, neglecting the ubiquitous multiple‑input multiple‑output (MIMO) environments that critically hinder practical deployment. To bridge this gap, we propose the context video semantic transmission (CVST) framework under MIMO channels. Building upon an efficient contextual video transmission backbone, CVST effectively learns a context‑channel correlation map to explicitly formulate the relationships between feature groups and MIMO subchannels. Leveraging these channel‑aware features, we design a multi‑reference entropy coding mechanism, enabling channel state‑aware variable length coding. Furthermore, CVST incorporates a checkerboard‑based feature modulation strategy to achieve multiple rate points within a single trained model, thereby enhancing deployment flexibility. These innovations constitute our multi‑reference variable length and rate coding (MR‑VLRC) scheme. By integrating contextual transmission with MR‑VLRC, CVST demonstrates substantial performance gains over various standardized separated coding methods and recent wireless video semantic communication approaches. The code is available at https://github.com/xie233333/CVST.

PaperId: 3821, https://arxiv.org/pdf/2601.06031.pdf GitHub

Abstract:
Graphical user interface (GUI) grounding, the process of mapping human instructions to GUI actions, serves as a fundamental basis to autonomous GUI agents. While existing grounding models achieve promising performance to simulate the mouse click action on various click‑based benchmarks, another essential mode of mouse interaction, namely dragging, remains largely underexplored. Yet, dragging the mouse to select and manipulate textual content represents a prevalent and important usage in practical GUI scenarios. To narrow this gap, we first introduce GUI‑Drag, a diverse dataset of 161K text dragging examples synthesized through a scalable pipeline. To support systematic and robust evaluation, we further construct ScreenDrag, a benchmark with 5,333 examples spanning three levels of interface context, together with three dedicated metrics designed for assessing text dragging capability. Models trained on GUI‑Drag with an efficient continual training strategy achieve substantial improvements on ScreenDrag, while preserving the original click‑based performance on ScreenSpot, ScreenSpot‑v2, and OSWorld‑G. Our work encourages further research on broader GUI grounding beyond just clicking and paves way toward a truly generalist GUI grounding model. All benchmark, data, checkpoints, and code are open‑sourced and available at https://osu‑nlp‑group.github.io/GUI‑Drag.

PaperId: 3822, https://arxiv.org/pdf/2601.06022.pdf GitHub

Abstract:
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference‑time ensembling provides a practical way to combine these capabilities without retraining. However, existing ensemble approaches suffer from fundamental limitations. Most rely on fixed fusion granularity, which lacks the flexibility required for mid‑generation adaptation and fails to adapt to different generation characteristics across tasks. To address these challenges, we propose AdaFuse, an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. Rather than committing to a fixed granularity, AdaFuse adjusts fusion behavior on the fly based on the decoding context, with words serving as basic building blocks for alignment. To be specific, we introduce an uncertainty‑based criterion to decide whether to apply ensembling at each decoding step. Under confident decoding states, the model continues generation directly. In less certain states, AdaFuse invokes a diversity‑aware scaling strategy to explore alternative candidate continuations and inform ensemble decisions. This design establishes a synergistic interaction between adaptive ensembling and test‑time scaling, where ensemble decisions guide targeted exploration, and the resulting diversity in turn strengthens ensemble quality. Experiments on open‑domain question answering, arithmetic reasoning, and machine translation demonstrate that AdaFuse consistently outperforms strong ensemble baselines, achieving an average relative improvement of 6.88%. The code is available at https://github.com/CCM0111/AdaFuse.

PaperId: 3823, https://arxiv.org/pdf/2601.05991.pdf GitHub

Abstract:
In safety‑critical domains, linguistic ambiguity can have severe consequences; a vague command like "Pass me the vial" in a surgical setting could lead to catastrophic errors. Yet, most embodied AI research overlooks this, assuming instructions are clear and focusing on execution rather than confirmation. To address this critical safety gap, we are the first to define 3D Instruction Ambiguity Detection, a fundamental new task where a model must determine if a command has a single, unambiguous meaning within a given 3D scene. To support this research, we build Ambi3D, the large‑scale benchmark for this task, featuring over 700 diverse 3D scenes and around 22k instructions. Our analysis reveals a surprising limitation: state‑of‑the‑art 3D Large Language Models (LLMs) struggle to reliably determine if an instruction is ambiguous. To address this challenge, we propose AmbiVer, a two‑stage framework that collects explicit visual evidence from multiple views and uses it to guide an vision‑language model (VLM) in judging instruction ambiguity. Extensive experiments demonstrate the challenge of our task and the effectiveness of AmbiVer, paving the way for safer and more trustworthy embodied AI. Code and dataset available at https://jiayuding031020.github.io/ambi3d/.

PaperId: 3824, https://arxiv.org/pdf/2601.05966.pdf GitHub

Abstract:
Recent advances in video generation have been dominated by diffusion and flow‑matching models, which produce high‑quality results but remain computationally intensive and difficult to scale. In this work, we introduce VideoAR, the first large‑scale Visual Autoregressive (VAR) framework for video generation that combines multi‑scale next‑frame prediction with autoregressive modeling. VideoAR disentangles spatial and temporal dependencies by integrating intra‑frame VAR modeling with causal next‑frame prediction, supported by a 3D multi‑scale tokenizer that efficiently encodes spatio‑temporal dynamics. To improve long‑term consistency, we propose Multi‑scale Temporal RoPE, Cross‑Frame Error Correction, and Random Frame Mask, which collectively mitigate error propagation and stabilize temporal coherence. Our multi‑stage pretraining pipeline progressively aligns spatial and temporal learning across increasing resolutions and durations. Empirically, VideoAR achieves new state‑of‑the‑art results among autoregressive models, improving FVD on UCF‑101 from 99.5 to 88.6 while reducing inference steps by over 10x, and reaching a VBench score of 81.74‑competitive with diffusion‑based models an order of magnitude larger. These results demonstrate that VideoAR narrows the performance gap between autoregressive and diffusion paradigms, offering a scalable, efficient, and temporally consistent foundation for future video generation research.

PaperId: 3825, https://arxiv.org/pdf/2601.05930.pdf GitHub

Abstract:
Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate‑Execute‑Feedback paradigm. Previous approaches suffer from a severe Execution Bottleneck, as hypothesis evaluation relies strictly on expensive physical execution. To bypass these physical constraints, we internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. In this work, we formalize the task of Data‑centric Solution Preference and construct a comprehensive corpus of 18,438 pairwise comparisons. We demonstrate that LLMs exhibit significant predictive capabilities when primed with a Verified Data Analysis Report, achieving 61.5% accuracy and robust confidence calibration. Finally, we instantiate this framework in FOREAGENT, an agent that employs a Predict‑then‑Verify loop, achieving a 6x acceleration in convergence while surpassing execution‑based baselines by +6%. Our code and dataset are publicly available at https://github.com/zjunlp/predict‑before‑execute.

PaperId: 3826, https://arxiv.org/pdf/2601.05905.pdf GitHub

Abstract:
As Large Language Models (LLMs) are increasingly deployed in real‑world settings, correctness alone is insufficient. Reliable deployment requires maintaining truthful beliefs under contextual perturbations. Existing evaluations largely rely on point‑wise confidence like Self‑Consistency, which can mask brittle belief. We show that even facts answered with perfect self‑consistency can rapidly collapse under mild contextual interference. To address this gap, we propose Neighbor‑Consistency Belief (NCB), a structural measure of belief robustness that evaluates response coherence across a conceptual neighborhood. To validate the efficiency of NCB, we introduce a new cognitive stress‑testing protocol that probes outputs stability under contextual interference. Experiments across multiple LLMs show that the performance of high‑NCB data is relatively more resistant to interference. Finally, we present Structure‑Aware Training (SAT), which optimizes context‑invariant belief structure and reduces long‑tail knowledge brittleness by approximately 30%. Code is available at https://github.com/zjunlp/belief.

PaperId: 3827, https://arxiv.org/pdf/2601.05899.pdf GitHub

Abstract:
Recent breakthroughs in Large Language Models (LLMs) have positioned them as a promising paradigm for agents, with long‑term planning and decision‑making emerging as core general‑purpose capabilities for adapting to diverse scenarios and tasks. Real‑time strategy (RTS) games serve as an ideal testbed for evaluating these two capabilities, as their inherent gameplay requires both macro‑level strategic planning and micro‑level tactical adaptation and action execution. Existing RTS game‑based environments either suffer from relatively high computational demands or lack support for textual observations, which has constrained the use of RTS games for LLM evaluation. Motivated by this, we present TowerMind, a novel environment grounded in the tower defense (TD) subgenre of RTS games. TowerMind preserves the key evaluation strengths of RTS games for assessing LLMs, while featuring low computational demands and a multimodal observation space, including pixel‑based, textual, and structured game‑state representations. In addition, TowerMind supports the evaluation of model hallucination and provides a high degree of customizability. We design five benchmark levels to evaluate several widely used LLMs under different multimodal input settings. The results reveal a clear performance gap between LLMs and human experts across both capability and hallucination dimensions. The experiments further highlight key limitations in LLM behavior, such as inadequate planning validation, a lack of multifinality in decision‑making, and inefficient action use. We also evaluate two classic reinforcement learning algorithms: Ape‑X DQN and PPO. By offering a lightweight and multimodal design, TowerMind complements the existing RTS game‑based environment landscape and introduces a new benchmark for the AI agent field. The source code is publicly available on GitHub(https://github.com/tb6147877/TowerMind).

PaperId: 3828, https://arxiv.org/pdf/2601.05858.pdf GitHub

Abstract:
Large language models (LLMs) have demonstrated competitive performance in zero‑shot multilingual machine translation (MT). Some follow‑up works further improved MT performance via preference optimization, but they leave a key aspect largely underexplored: the order in which data samples are given during training. We address this topic by integrating curriculum learning into various state‑of‑the‑art preference optimization algorithms to boost MT performance. We introduce a novel curriculum learning strategy with restarts (CLewR), which reiterates easy‑to‑hard curriculum multiple times during training to effectively mitigate the catastrophic forgetting of easy examples. We demonstrate consistent gains across several model families (Gemma2, Qwen2.5, Llama3.1) and preference optimization techniques. We publicly release our code at https://github.com/alexandra‑dragomir/CLewR.

PaperId: 3829, https://arxiv.org/pdf/2601.05853.pdf GitHub

Abstract:
We propose a novel framework for decomposing arbitrarily posed humans into animatable multi‑layered 3D human avatars, separating the body and garments. Conventional single‑layer reconstruction methods lock clothing to one identity, while prior multi‑layer approaches struggle with occluded regions. We overcome both limitations by encoding each layer as a set of 2D Gaussians for accurate geometry and photorealistic rendering, and inpainting hidden regions with a pretrained 2D diffusion model via score‑distillation sampling (SDS). Our three‑stage training strategy first reconstructs the coarse canonical garment via single‑layer reconstruction, followed by multi‑layer training to jointly recover the inner‑layer body and outer‑layer garment details. Experiments on two 3D human benchmark datasets (4D‑Dress, Thuman2.0) show that our approach achieves better rendering quality and layer decomposition and recomposition than the previous state‑of‑the‑art, enabling realistic virtual try‑on under novel viewpoints and poses, and advancing practical creation of high‑fidelity 3D human assets for immersive applications. Our code is available at https://github.com/RockyXu66/LayerGS

PaperId: 3830, https://arxiv.org/pdf/2601.05844.pdf GitHub

Abstract:
Capturing fine‑grained hand‑object interactions is challenging due to severe self‑occlusion from closely spaced fingers and the subtlety of in‑hand manipulation motions. Existing optical motion capture systems rely on expensive camera setups and extensive manual post‑processing, while low‑cost vision‑based methods often suffer from reduced accuracy and reliability under occlusion. To address these challenges, we present DexterCap, a low‑cost optical capture system for dexterous in‑hand manipulation. DexterCap uses dense, character‑coded marker patches to achieve robust tracking under severe self‑occlusion, together with an automated reconstruction pipeline that requires minimal manual effort. With DexterCap, we introduce DexterHand, a dataset of fine‑grained hand‑object interactions covering diverse manipulation behaviors and objects, from simple primitives to complex articulated objects such as a Rubik's Cube. We release the dataset and code to support future research on dexterous hand‑object interaction. Project website: https://pku‑mocca.github.io/Dextercap‑Page/

PaperId: 3831, https://arxiv.org/pdf/2601.05810.pdf GitHub

Abstract:
The ability to automatically generate large‑scale, interactive, and physically realistic 3D environments is crucial for advancing robotic learning and embodied intelligence. However, existing generative approaches often fail to capture the functional complexity of real‑world interiors, particularly those containing articulated objects with movable parts essential for manipulation and navigation. This paper presents SceneFoundry, a language‑guided diffusion framework that generates apartment‑scale 3D worlds with functionally articulated furniture and semantically diverse layouts for robotic training. From natural language prompts, an LLM module controls floor layout generation, while diffusion‑based posterior sampling efficiently populates the scene with articulated assets from large‑scale 3D repositories. To ensure physical usability, SceneFoundry employs differentiable guidance functions to regulate object quantity, prevent articulation collisions, and maintain sufficient walkable space for robotic navigation. Extensive experiments demonstrate that our framework generates structurally valid, semantically coherent, and functionally interactive environments across diverse scene types and conditions, enabling scalable embodied AI research. project page: https://anc891203.github.io/SceneFoundry‑Demo/

PaperId: 3832, https://arxiv.org/pdf/2601.05808.pdf GitHub

Abstract:
Large language models (LLMs) are expected to be trained to act as agents in various real‑world environments, but this process relies on rich and varied tool‑interaction sandboxes. However, access to real systems is often restricted; LLM‑simulated environments are prone to hallucinations and inconsistencies; and manually built sandboxes are hard to scale. In this paper, we propose EnvScaler, an automated framework for scalable tool‑interaction environments via programmatic synthesis. EnvScaler comprises two components. First, SkelBuilder constructs diverse environment skeletons through topic mining, logic modeling, and quality evaluation. Then, ScenGenerator generates multiple task scenarios and rule‑based trajectory validation functions for each environment. With EnvScaler, we synthesize 191 environments and about 7K scenarios, and apply them to Supervised Fine‑Tuning (SFT) and Reinforcement Learning (RL) for Qwen3 series models. Results on three benchmarks show that EnvScaler significantly improves LLMs' ability to solve tasks in complex environments involving multi‑turn, multi‑tool interactions. We release our code and data at https://github.com/RUC‑NLPIR/EnvScaler.

PaperId: 3833, https://arxiv.org/pdf/2601.05787.pdf GitHub

Abstract:
Vision‑language models are increasingly deployed as computer‑use agents (CUAs) that operate desktops and browsers. Top‑performing CUAs are framework‑based systems that decompose planning and execution, while end‑to‑end screenshot‑to‑action policies are easier to deploy but lag behind on benchmarks such as OSWorld‑Verified. GUI datasets like OSWorld pose two bottlenecks: they expose only a few hundred interactive, verifiable tasks and environments, and expert trajectories must be gathered by interacting with these environments, making such data hard to scale. We therefore ask how reinforcement learning from verifiable rewards (RLVR) can best exploit a small pool of exist expert trajectories to train end‑to‑end policies. Naively mixing these off‑policy traces into on‑policy RLVR is brittle: even after format conversion, expert trajectories exhibit structural mismatch and distribution shift from the learner. We propose BEPA (Bi‑Level Expert‑to‑Policy Assimilation), which turns static expert traces into policy‑aligned guidance via self‑rolled reachable trajectories under the base policy (LEVEL‑1) and a per‑task, dynamically updated cache used in RLVR (LEVEL‑2). On OSWorld‑Verified, BEPA improves UITARS1.5‑7B success from 22.87% to 32.13% and raises a held‑out split from 5.74% to 10.30%, with consistent gains on MMBench‑GUI and Online‑Mind2Web. Our code and data are available at: https://github.com/LEON‑gittech/Verl_GUI.git

PaperId: 3834, https://arxiv.org/pdf/2601.05732.pdf GitHub

Abstract:
Hyper‑Connections (HC) generalizes residual connections by introducing dynamic residual matrices that mix information across multiple residual streams, accelerating convergence in deep neural networks. However, unconstrained residual matrices can compromise training stability. To address this, DeepSeek's Manifold‑Constrained Hyper‑Connections (mHC) approximately projects these matrices onto the Birkhoff polytope via iterative Sinkhorn‑‑Knopp (SK) normalization. We identify two limitations of this approach: (i) finite SK iterations do not guarantee exact doubly stochasticity, leaving an approximation gap that can accumulate through network depth and undermine stability; (ii) efficient SK implementation requires highly specialized CUDA kernels, raising engineering barriers and reducing portability. Motivated by the Birkhoff‑‑von Neumann theorem, we propose mHC‑lite, a simple reparameterization that explicitly constructs doubly stochastic matrices as convex combinations of permutation matrices. This approach guarantees exact doubly stochasticity by construction and can be implemented using only native matrix operations. Extensive experiments demonstrate that mHC‑lite matches or exceeds mHC in performance while achieving higher training throughput with a naive implementation and eliminating the residual instabilities observed in both HC and mHC. The code is publicly available at https://github.com/FFTYYY/mhc‑lite.

PaperId: 3835, https://arxiv.org/pdf/2601.05724.pdf GitHub

Abstract:
Verification is a key bottleneck in improving inference speed while maintaining distribution fidelity in Speculative Decoding. Recent work has shown that sequence‑level verification leads to a higher number of accepted tokens compared to token‑wise verification. However, existing solutions often rely on surrogate approximations or are constrained by partial information, struggling with joint intractability. In this work, we propose Hierarchical Speculative Decoding (HSD), a provably lossless verification method that significantly boosts the expected number of accepted tokens and overcomes joint intractability by balancing excess and deficient probability mass across accessible branches. Our extensive large‑scale experiments demonstrate that HSD yields consistent improvements in acceptance rates across diverse model families and benchmarks. Moreover, its strong explainability and generality make it readily integrable into a wide range of speculative decoding frameworks. Notably, integrating HSD into EAGLE‑3 yields over a 12% performance gain, establishing state‑of‑the‑art decoding efficiency without compromising distribution fidelity. Code is available at https://github.com/ZhouYuxuanYX/Hierarchical‑Speculative‑Decoding.

PaperId: 3836, https://arxiv.org/pdf/2601.05376.pdf GitHub

Abstract:
Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However, its effects on high‑stakes clinical decision‑making remain poorly characterized. We systematically evaluate persona‑based control in clinical LLMs, examining how professional roles (e.g., Emergency Department physician, nurse) and interaction styles (bold vs.\ cautious) influence behavior across models and medical tasks. We assess performance on clinical triage and patient‑safety tasks using multidimensional evaluations that capture task accuracy, calibration, and safety‑relevant risk behavior. We find systematic, context‑dependent, and non‑monotonic effects: Medical personas improve performance in critical care tasks, yielding gains of up to ～+20% in accuracy and calibration, but degrade performance in primary‑care settings by comparable margins. Interaction style modulates risk propensity and sensitivity, but it's highly model‑dependent. While aggregated LLM‑judge rankings favor medical over non‑medical personas in safety‑critical cases, we found that human clinicians show moderate agreement on safety compliance (average Cohen's κ= 0.43) but indicate a low confidence in 95.9% of their responses on reasoning quality. Our work shows that personas function as behavioral priors that introduce context‑dependent trade‑offs rather than guarantees of safety or expertise. The code is available at https://github.com/rsinghlab/Persona\_Paradox.

PaperId: 3837, https://arxiv.org/pdf/2601.05355.pdf GitHub

Abstract:
Modern data analysis increasingly requires flexible conditional inference P(X_B | X_A) where (X_A, X_B) is an arbitrary partition of observed variable X. Existing approaches are either restricted to a fixed conditioning structure or depend strongly on the distribution of conditioning masks during training. To address these limitations, we introduce Bayesian generative modeling (BGM), a unified framework for arbitrary conditional inference. BGM learns a generative model of X via a stochastic iterative Bayesian updating algorithm in which model parameters and latent variables are updated until convergence. Once trained, any conditional distribution can be obtained without retraining. Empirically, BGM achieves superior predictive performance with posterior predictive intervals, demonstrating that a single learned model can serve as a universal engine for conditional prediction with principled uncertainty quantification. We provide theoretical guarantees for convergence of the stochastic iterative algorithm, statistical consistency, and conditional risk bounds. The proposed BGM framework leverages modern AI to capture complex relationships among variables while adhering to Bayesian principles, offering a promising approach for a wide range of applications in modern data science. Code for BGM is available at https://github.com/liuq‑lab/bayesgm. Document of BGM is available at https://bayesgm.readthedocs.io.

PaperId: 3838, https://arxiv.org/pdf/2601.05279.pdf GitHub

Abstract:
Policy Space Response Oracles (PSRO) combines game‑theoretic equilibrium computation with learning and is effective in approximating Nash Equilibrium in zero‑sum games. However, the computational cost of PSRO has become a significant limitation to its practical application. Our analysis shows that game simulation is the primary bottleneck in PSRO's runtime. To address this issue, we conclude the concept of Simulation‑Free PSRO and summarize existing methods that instantiate this concept. Additionally, we propose a novel Dynamic Window‑based Simulation‑Free PSRO, which introduces the concept of a strategy window to replace the original strategy set maintained in PSRO. The number of strategies in the strategy window is limited, thereby simplifying opponent strategy selection and improving the robustness of the best response. Moreover, we use Nash Clustering to select the strategy to be eliminated, ensuring that the number of strategies within the strategy window is effectively limited. Our experiments across various environments demonstrate that the Dynamic Window mechanism significantly reduces exploitability compared to existing methods, while also exhibiting excellent compatibility. Our code is available at https://github.com/enochliu98/SF‑PSRO.

PaperId: 3839, https://arxiv.org/pdf/2601.05270.pdf GitHub

Abstract:
Modern Retrieval‑Augmented Generation (RAG) systems struggle with a fundamental architectural tension: vector indices are optimized for query latency but poorly handle continuous knowledge updates, while data lakes excel at versioning but introduce query latency penalties. We introduce LiveVectorLake, a dual‑tier temporal knowledge base architecture that enables real‑time semantic search on current knowledge while maintaining complete version history for compliance, auditability, and point‑in‑time retrieval. The system introduces three core architectural contributions: (1) Content‑addressable chunk‑level synchronization using SHA‑256 hashing for deterministic change detection without external state tracking; (2) Dual‑tier storage separating hot‑tier vector indices (Milvus with HNSW) from cold‑tier columnar versioning (Delta Lake with Parquet), optimizing query latency and storage cost independently; (3) Temporal query routing enabling point‑in‑time knowledge retrieval via delta‑versioning with ACID consistency across tiers. Evaluation on a 100‑document corpus versioned across five time points demonstrates: (i) 10‑15% re‑processing of content during updates compared to 100% for full re‑indexing; (ii) sub‑100ms retrieval latency on current knowledge; (iii) sub‑2s latency for temporal queries across version history; and (iv) storage cost optimization through hot/cold tier separation (only current chunks in expensive vector indices). The approach enables production RAG deployments requiring simultaneous optimization for query performance, update efficiency, and regulatory compliance. Code and resources: [https://github.com/praj‑tarun/LiveVectorLake]

PaperId: 3840, https://arxiv.org/pdf/2601.05253.pdf GitHub

Abstract:
We introduce \mathbfSP‑Rank, the first large‑scale, publicly available dataset for benchmarking algorithms that leverage both first‑order preferences and second‑order predictions in ranking tasks. Each datapoint includes a personal vote (first‑order signal) and a meta‑prediction of how others will vote (second‑order signal), allowing richer modeling than traditional datasets that capture only individual preferences. SP‑Rank contains over 12,000 human‑generated datapoints across three domains ‑‑ geography, movies, and paintings, and spans nine elicitation formats with varying subset sizes. This structure enables empirical analysis of preference aggregation when expert identities are unknown but presumed to exist, and individual votes represent noisy estimates of a shared ground‑truth ranking. We benchmark SP‑Rank by comparing traditional aggregation methods that use only first‑order votes against SP‑Voting, a second‑order method that jointly reasons over both signals to infer ground‑truth rankings. While SP‑Rank also supports models that rely solely on second‑order predictions, our benchmarks emphasize the gains from combining both signals. We evaluate performance across three core tasks: (1) full ground‑truth rank recovery, (2) subset‑level rank recovery, and (3) probabilistic modeling of voter behavior. Results show that incorporating second‑order signals substantially improves accuracy over vote‑only methods. Beyond social choice, SP‑Rank supports downstream applications in learning‑to‑rank, extracting expert knowledge from noisy crowds, and training reward models in preference‑based fine‑tuning pipelines. We release the dataset, code, and baseline evaluations (available at https://github.com/amrit19/SP‑Rank‑Dataset ) to foster research in human preference modeling, aggregation theory, and human‑AI alignment.

PaperId: 3841, https://arxiv.org/pdf/2601.05174.pdf GitHub

Abstract:
Spatial‑Temporal Graph (STG) forecasting on large‑scale networks has garnered significant attention. However, existing models predominantly focus on short‑horizon predictions and suffer from notorious computational costs and memory consumption when scaling to long‑horizon predictions and large graphs. Targeting the above challenges, we present FaST, an effective and efficient framework based on heterogeneity‑aware Mixture‑of‑Experts (MoEs) for long‑horizon and large‑scale STG forecasting, which unlocks one‑week‑ahead (672 steps at a 15‑minute granularity) prediction with thousands of nodes. FaST is underpinned by two key innovations. First, an adaptive graph agent attention mechanism is proposed to alleviate the computational burden inherent in conventional graph convolution and self‑attention modules when applied to large‑scale graphs. Second, we propose a new parallel MoE module that replaces traditional feed‑forward networks with Gated Linear Units (GLUs), enabling an efficient and scalable parallel structure. Extensive experiments on real‑world datasets demonstrate that FaST not only delivers superior long‑horizon predictive accuracy but also achieves remarkable computational efficiency compared to state‑of‑the‑art baselines. Our source code is available at: https://github.com/yijizhao/FaST.

PaperId: 3842, https://arxiv.org/pdf/2601.05172.pdf GitHub

Abstract:
Embodied question answering (EQA) in 3D environments often requires collecting context that is distributed across multiple viewpoints and partially occluded. However, most recent vision‑‑language models (VLMs) are constrained to a fixed and finite set of input views, which limits their ability to acquire question‑relevant context at inference time and hinders complex spatial reasoning. We propose Chain‑of‑View (CoV) prompting, a training‑free, test‑time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse‑to‑fine exploration process. CoV first employs a View Selection agent to filter redundant frames and identify question‑aligned anchor views. It then performs fine‑grained view adjustment by interleaving iterative reasoning with discrete camera actions, obtaining new observations from the underlying 3D scene representation until sufficient context is gathered or a step budget is reached. We evaluate CoV on OpenEQA across four mainstream VLMs and obtain an average +11.56% improvement in LLM‑Match, with a maximum gain of +13.62% on Qwen3‑VL‑Flash. CoV further exhibits test‑time scaling: increasing the minimum action budget yields an additional +2.51% average improvement, peaking at +3.73% on Gemini‑2.5‑Flash. On ScanQA and SQA3D, CoV delivers strong performance (e.g., 116 CIDEr / 31.9 EM@1 on ScanQA and 51.1 EM@1 on SQA3D). Overall, these results suggest that question‑aligned view selection coupled with open‑view search is an effective, model‑agnostic strategy for improving spatial reasoning in 3D EQA without additional training. Code is available on https://github.com/ziplab/CoV .

PaperId: 3843, https://arxiv.org/pdf/2601.05114.pdf GitHub

Abstract:
LLM‑as‑judge systems promise scalable, consistent evaluation. We find the opposite: judges are consistent, but not with each other; they are consistent with themselves. Across 3,240 evaluations (9 judges x 120 unique video x pack items x 3 independent runs), inter‑judge agreement is near‑zero (Krippendorff's α = 0.042). On two dimensions, judges disagree more than random noise would predict (α < 0). Yet this disagreement isn't chaos; it's structured. A classifier identifies which judge produced an evaluation with 77.1% accuracy from rubric scores alone, rising to 89.9% with disposition features. Within model families, the signal is even stronger: GPT‑4.1 and GPT‑5.2 are distinguishable with 99.6% accuracy. We call this the reliability paradox: judges cannot agree on what constitutes quality, yet their disagreement patterns are so stable they function as fingerprints. Each judge implements a distinct, stable theory of quality: an "evaluative disposition" that shapes how it interprets any rubric. We characterize these dispositions along multiple axes: harshness/leniency, dimension emphasis, within‑judge stability (ICC), and evidence behavior (receipt validity, semantic linkage via NLI, and shotgun index). The implication is stark: LLM judges are not interchangeable instruments measuring a shared construct. They are distinct measurement devices, each encoding its own implicit theory of quality. Averaging their scores produces a synthetic verdict that corresponds to no judge's actual values.

PaperId: 3844, https://arxiv.org/pdf/2601.05110.pdf GitHub

Abstract:
Large Reasoning Models (LRMs) achieve remarkable performance by explicitly generating multi‑step chains of thought, but this capability incurs substantial inference latency and computational cost. Collaborative inference offers a promising solution by selectively allocating work between lightweight and large models, yet a fundamental challenge remains: determining when a reasoning step requires the capacity of a large model or the efficiency of a small model. Existing routing strategies either rely on local token probabilities or post‑hoc verification, introducing significant inference overhead. In this work, we propose a novel perspective on step‑wise collaboration: the difficulty of a reasoning step can be inferred from its very first token. Inspired by the "Aha Moment" phenomenon in LRMs, we show that the entropy of the initial token serves as a strong predictor of step difficulty. Building on this insight, we introduce GlimpRouter, a training‑free step‑wise collaboration framework. GlimpRouter employs a lightweight model to generate only the first token of each reasoning step and routes the step to a larger model only when the initial token entropy exceeds a threshold. Experiments on multiple benchmarks demonstrate that our approach significantly reduces inference latency while preserving accuracy. For instance, GlimpRouter attains a substantial 10.7% improvement in accuracy while reducing inference latency by 25.9% compared to a standalone large model on AIME25. These results suggest a simple yet effective mechanism for reasoning: allocating computation based on a glimpse of thought rather than full‑step evaluation.

PaperId: 3845, https://arxiv.org/pdf/2601.05053.pdf GitHub

Abstract:
Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs). Monte Carlo Tree Search (MCTS)‑based extensions improve upon vanilla RLVR (e.g., GRPO) by providing tree‑based reasoning rollouts that enable fine‑grained and segment‑level credit assignment. However, existing methods still suffer from limited exploration diversity and inefficient reasoning. To address the above challenges, we propose reinforced efficient reasoning via semantically diverse explorations, i.e., ROSE, for LLMs. To encourage more diverse reasoning exploration, our method incorporates a semantic‑entropy‑based branching strategy and an \varepsilon‑exploration mechanism. The former operates on already sampled reasoning rollouts to capture semantic uncertainty and select branching points with high semantic divergence to generate new successive reasoning paths, whereas the latter stochastically initiates reasoning rollouts from the root, preventing the search process from becoming overly local. To improve efficiency, we design a length‑aware segment‑level advantage estimator that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains. Extensive experiments on various mathematical reasoning benchmarks with Qwen and Llama models validate the effectiveness and efficiency of ROSE. Codes are available at https://github.com/ZiqiZhao1/ROSE‑rl.

PaperId: 3846, https://arxiv.org/pdf/2601.05038.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) helps LLMs stay accurate, but feeding long documents into a prompt makes the model slow and expensive. This has motivated context compression, ranging from token pruning and summarization to embedding‑based compression. While researchers have tried ''compressing'' these documents into smaller summaries or mathematical embeddings, there is a catch: the more you compress the data, the more the LLM struggles to understand it. To address this challenge, we propose ArcAligner (Adaptive recursive context Aligner), a lightweight module integrated into the language model layers to help the model better utilize highly compressed context representations for downstream generation. It uses an adaptive ''gating'' system that only adds extra processing power when the information is complex, keeping the system fast. Across knowledge‑intensive QA benchmarks, ArcAligner consistently beats compression baselines at comparable compression rates, especially on multi‑hop and long‑tail settings. The source code is publicly available.

PaperId: 3847, https://arxiv.org/pdf/2601.05027.pdf GitHub

Abstract:
Retrieval‑Augmented Generation (RAG) improves generation quality by incorporating evidence retrieved from large external corpora. However, most existing methods rely on statically selecting top‑k passages based on individual relevance, which fails to exploit combinatorial gains among passages and often introduces substantial redundancy. To address this limitation, we propose OptiSet, a set‑centric framework that unifies set selection and set‑level ranking for RAG. OptiSet adopts an "Expand‑then‑Refine" paradigm: it first expands a query into multiple perspectives to enable a diverse candidate pool and then refines the candidate pool via re‑selection to form a compact evidence set. We then devise a self‑synthesis strategy without strong LLM supervision to derive preference labels from the set conditional utility changes of the generator, thereby identifying complementary and redundant evidence. Finally, we introduce a set‑list wise training strategy that jointly optimizes set selection and set‑level ranking, enabling the model to favor compact, high‑gain evidence sets. Extensive experiments demonstrate that OptiSet improves performance on complex combinatorial problems and makes generation more efficient. The source code is publicly available.

PaperId: 3848, https://arxiv.org/pdf/2601.04888.pdf GitHub

Abstract:
Large language model (LLM)‑based search agents have proven promising for addressing knowledge‑intensive problems by incorporating information retrieval capabilities. Existing works largely focus on optimizing the reasoning paradigms of search agents, yet the quality of intermediate search queries during reasoning remains overlooked. As a result, the generated queries often remain inaccurate, leading to unexpected retrieval results and ultimately limiting search agents' overall effectiveness. To mitigate this issue, we introduce SmartSearch, a framework built upon two key mechanisms: (1) Process rewards, which provide fine‑grained supervision for the quality of each intermediate search query through Dual‑Level Credit Assessment. (2) Query refinement, which promotes the optimization of query generation by selectively refining low‑quality search queries and regenerating subsequent search rounds based on these refinements. To enable the search agent to progressively internalize the ability to improve query quality under the guidance of process rewards, we design a three‑stage curriculum learning framework. This framework guides the agent through a progression from imitation, to alignment, and ultimately to generalization. Experimental results show that SmartSearch consistently surpasses existing baselines, and additional quantitative analyses further confirm its significant gains in both search efficiency and query quality. The code is available at https://github.com/MYVAE/SmartSearch.

PaperId: 3849, https://arxiv.org/pdf/2601.04885.pdf GitHub

Abstract:
As Large Language Models (LLMs) serve a global audience, alignment must transition from enforcing universal consensus to respecting cultural pluralism. We demonstrate that dense models, when forced to fit conflicting value distributions, suffer from Mean Collapse, converging to a generic average that fails to represent diverse groups. We attribute this to Cultural Sparsity, where gradient interference prevents dense parameters from spanning distinct cultural modes. To resolve this, we propose \textscCuMA (Cultural Mixture of Adapters), a framework that frames alignment as a conditional capacity separation problem. By incorporating demographic‑aware routing, \textscCuMA internalizes a Latent Cultural Topology to explicitly disentangle conflicting gradients into specialized expert subspaces. Extensive evaluations on WorldValuesBench, Community Alignment, and PRISM demonstrate that \textscCuMA achieves state‑of‑the‑art performance, significantly outperforming both dense baselines and semantic‑only MoEs. Crucially, our analysis confirms that \textscCuMA effectively mitigates mean collapse, preserving cultural diversity. Our code is available at https://github.com/Throll/CuMA.

PaperId: 3850, https://arxiv.org/pdf/2601.04794.pdf GitHub

Abstract:
Designing academic posters is a labor‑intensive process requiring the precise balance of high‑density content and sophisticated layout. While existing paper‑to‑poster generation methods automate initial drafting, they are typically single‑pass and non‑interactive, often fail to align with complex, subjective user intent. To bridge this gap, we propose APEX (Academic Poster Editing agentic eXpert), the first agentic framework for interactive academic poster editing, supporting fine‑grained control with robust multi‑level API‑based editing and a review‑and‑adjustment Mechanism. In addition, we introduce APEX‑Bench, the first systematic benchmark comprising 514 academic poster editing instructions, categorized by a multi‑dimensional taxonomy including operation type, difficulty, and abstraction level, constructed via reference‑guided and reference‑free strategies to ensure realism and diversity. We further establish a multi‑dimensional VLM‑as‑a‑judge evaluation protocol to assess instruction fulfillment, modification scope, and visual consistency & harmony. Experimental results demonstrate that APEX significantly outperforms baseline methods. Our implementation is available at https://github.com/Breesiu/APEX.

PaperId: 3851, https://arxiv.org/pdf/2601.04767.pdf GitHub

Abstract:
LLM agents have emerged as powerful systems for tackling multi‑turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has recently drawn significant research attention as a critical post‑training paradigm to further refine these capabilities. In this paper, we present AT^2PO (Agentic Turn‑based Policy Optimization via Tree Search), a unified framework for multi‑turn agentic RL that addresses three core challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. AT^2PO introduces a turn‑level tree structure that jointly enables Entropy‑Guided Tree Expansion for strategic exploration and Turn‑wise Credit Assignment for fine‑grained reward propagation from sparse outcomes. Complementing this, we propose Agentic Turn‑based Policy Optimization, a turn‑level learning objective that aligns policy updates with the natural decision granularity of agentic interactions. ATPO is orthogonal to tree search and can be readily integrated into any multi‑turn RL pipeline. Experiments across seven benchmarks demonstrate consistent improvements over the state‑of‑the‑art baseline by up to 1.84 percentage points in average, with ablation studies validating the effectiveness of each component. Our code is available at https://github.com/zzfoutofspace/ATPO.

PaperId: 3852, https://arxiv.org/pdf/2601.04758.pdf GitHub

Abstract:
The Patent Trial and Appeal Board (PTAB) of the USPTO adjudicates thousands of ex parte appeals each year, requiring the integration of technical understanding and legal reasoning. While large language models (LLMs) are increasingly applied in patent and legal practice, their use has remained limited to lightweight tasks, with no established means of systematically evaluating their capacity for structured legal reasoning in the patent domain. In this work, we introduce PILOT‑Bench, the first PTAB‑centric benchmark that aligns PTAB decisions with USPTO patent data at the case‑level and formalizes three IRAC‑aligned classification tasks: Issue Type, Board Authorities, and Subdecision. We evaluate a diverse set of closed‑source (commercial) and open‑source LLMs and conduct analyses across multiple perspectives, including input‑variation settings, model families, and error tendencies. Notably, on the Issue Type task, closed‑source models consistently exceed 0.75 in Micro‑F1 score, whereas the strongest open‑source model (Qwen‑8B) achieves performance around 0.56, highlighting a substantial gap in reasoning capabilities. PILOT‑Bench establishes a foundation for the systematic evaluation of patent‑domain legal reasoning and points toward future directions for improving LLMs through dataset design and model alignment. All data, code, and benchmark resources are available at https://github.com/TeamLab/pilot‑bench.

PaperId: 3853, https://arxiv.org/pdf/2601.04745.pdf GitHub GitHub

Abstract:
Existing long‑horizon memory benchmarks mostly use multi‑turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding. We present \BenchName, a publicly releasable benchmark built from long‑form autobiographical narratives, where actions, context, and inner thoughts provide dense evidence for inferring stable motivations and decision principles. \BenchName~reconstructs each narrative into a flashback‑aware, time‑anchored stream and evaluates models with evidence‑linked questions spanning factual recall, subjective state attribution, and principle‑level reasoning. Across diverse narrative sources, retrieval‑augmented systems mainly improve factual accuracy, while errors persist on temporally grounded explanations and higher‑level inferences, highlighting the need for memory mechanisms beyond retrieval. Our data is in \hrefKnowMeBenchhttps://github.com/QuantaAlpha/KnowMeBench.

PaperId: 3854, https://arxiv.org/pdf/2601.04744.pdf GitHub

Abstract:
Detecting medical conditions from speech acoustics is fundamentally a weakly‑supervised learning problem: a single, often noisy, session‑level label must be linked to nuanced patterns within a long, complex audio recording. This task is further hampered by severe data scarcity and the subjective nature of clinical annotations. While semi‑supervised learning (SSL) offers a viable path to leverage unlabeled data, existing audio methods often fail to address the core challenge that pathological traits are not uniformly expressed in a patient's speech. We propose a novel, audio‑only SSL framework that explicitly models this hierarchy by jointly learning from frame‑level, segment‑level, and session‑level representations within unsegmented clinical dialogues. Our end‑to‑end approach dynamically aggregates these multi‑granularity features and generates high‑quality pseudo‑labels to efficiently utilize unlabeled data. Extensive experiments show the framework is model‑agnostic, robust across languages and conditions, and highly data‑efficient‑achieving, for instance, 90% of fully‑supervised performance using only 11 labeled samples. This work provides a principled approach to learning from weak, far‑end supervision in medical speech analysis. The code is available at https://github.com/fispresent/semi_pathological.

PaperId: 3855, https://arxiv.org/pdf/2601.04731.pdf GitHub

Abstract:
Current critic‑free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts (where all rollouts are correct), resulting in waste of rollouts due to zero advantage estimates. We introduce a radically simple yet powerful solution to \ulineMine \ulineintrinsic mast\ulineery (Miner), that repurposes the policy's intrinsic uncertainty as a self‑supervised reward signal, with no external supervision, auxiliary models, or additional inference cost. Our method pioneers two key innovations: (1) a token‑level focal credit assignment mechanism that dynamically amplifies gradients on critical uncertain tokens while suppressing overconfident ones, and (2) adaptive advantage calibration to seamlessly integrate intrinsic and verifiable rewards. Evaluated across six reasoning benchmarks on Qwen3‑4B and Qwen3‑8B base models, Miner achieves state‑of‑the‑art performance among the other four algorithms, yielding up to 4.58 absolute gains in Pass@1 and 6.66 gains in Pass@K compared to GRPO. Comparison with other methods targeted at exploration enhancement further discloses the superiority of the two newly proposed innovations. This demonstrates that latent uncertainty exploitation is both necessary and sufficient for efficient and scalable RL training of reasoning models. Code is available at https://github.com/pixas/Miner.

PaperId: 3856, https://arxiv.org/pdf/2601.04703.pdf GitHub

Abstract:
Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use. However, prevailing systems rely on monolithic agents that suffer from structural bottlenecks, including unconstrained reasoning outputs that inflate trajectories, sparse outcome‑level rewards that complicate credit assignment, and stochastic search noise that destabilizes learning. To address these challenges, we propose M‑ASK (Multi‑Agent Search and Knowledge), a framework that explicitly decouples agentic search into two complementary roles: Search Behavior Agents, which plan and execute search actions, and Knowledge Management Agents, which aggregate, filter, and maintain a compact internal context. This decomposition allows each agent to focus on a well‑defined subtask and reduces interference between search and context construction. Furthermore, to enable stable coordination, M‑ASK employs turn‑level rewards to provide granular supervision for both search decisions and knowledge updates. Experiments on multi‑hop QA benchmarks demonstrate that M‑ASK outperforms strong baselines, achieving not only superior answer accuracy but also significantly more stable training dynamics.\footnoteThe source code for M‑ASK is available at https://github.com/chenyiqun/M‑ASK.

PaperId: 3857, https://arxiv.org/pdf/2601.04653.pdf GitHub

Abstract:
We present Isabellm, an LLM‑powered theorem prover for Isabelle/HOL that performs fully automatic proof synthesis. Isabellm works with any local LLM on Ollama and APIs such as Gemini CLI, and it is designed to run on consumer grade computers. The system combines a stepwise prover, which uses large language models to propose proof commands validated by Isabelle in a bounded search loop, with a higher‑level proof planner that generates structured Isar outlines and attempts to fill and repair remaining gaps. The framework includes beam search for tactics, tactics reranker ML and RL models, premise selection with small transformer models, micro‑RAG for Isar proofs built from AFP, and counter‑example guided proof repair. All the code is implemented by GPT 4.1 ‑ 5.2, Gemini 3 Pro, and Claude 4.5. Empirically, Isabellm can prove certain lemmas that defeat Isabelle's standard automation, including Sledgehammer, demonstrating the practical value of LLM‑guided proof search. At the same time, we find that even state‑of‑the‑art LLMs, such as GPT 5.2 Extended Thinking and Gemini 3 Pro struggle to reliably implement the intended fill‑and‑repair mechanisms with complex algorithmic designs, highlighting fundamental challenges in LLM code generation and reasoning. The code of Isabellm is available at https://github.com/zhehou/llm‑isabelle

PaperId: 3858, https://arxiv.org/pdf/2601.04587.pdf GitHub

Abstract:
This paper introduces FedKDX, a federated learning framework that addresses limitations in healthcare AI through Negative Knowledge Distillation (NKD). Unlike existing approaches that focus solely on positive knowledge transfer, FedKDX captures both target and non‑target information to improve model generalization in healthcare applications. The framework integrates multiple knowledge transfer techniques‑‑including traditional knowledge distillation, contrastive learning, and NKD‑‑within a unified architecture that maintains privacy while reducing communication costs. Through experiments on healthcare datasets (SLEEP, UCI‑HAR, and PAMAP2), FedKDX demonstrates improved accuracy (up to 2.53% over state‑of‑the‑art methods), faster convergence, and better performance on non‑IID data distributions. Theoretical analysis supports NKD's contribution to addressing statistical heterogeneity in distributed healthcare data. The approach shows promise for privacy‑sensitive medical applications under regulatory frameworks like HIPAA and GDPR, offering a balanced solution between performance and practical implementation requirements in decentralized healthcare settings. The code and model are available at https://github.com/phamdinhdat‑ai/Fed_2024.

PaperId: 3859, https://arxiv.org/pdf/2601.04571.pdf GitHub

Abstract:
Multimodal retrieval has emerged as a promising yet challenging research direction in recent years. Most existing studies in multimodal retrieval focus on capturing information in multimodal data that is similar to their paired texts, but often ignores the complementary information contained in multimodal data. In this study, we propose CIEA, a novel multimodal retrieval approach that employs Complementary Information Extraction and Alignment, which transforms both text and images in documents into a unified latent space and features a complementary information extractor designed to identify and preserve differences in the image representations. We optimize CIEA using two complementary contrastive losses to ensure semantic integrity and effectively capture the complementary information contained in images. Extensive experiments demonstrate the effectiveness of CIEA, which achieves significant improvements over both divide‑and‑conquer models and universal dense retrieval models. We provide an ablation study, further discussions, and case studies to highlight the advancements achieved by CIEA. To promote further research in the community, we have released the source code at https://github.com/zengdlong/CIEA.

PaperId: 3860, https://arxiv.org/pdf/2601.04563.pdf GitHub

Abstract:
Our experience of the world is multisensory, spanning a synthesis of language, sight, sound, touch, taste, and smell. Yet, artificial intelligence has primarily advanced in digital modalities like text, vision, and audio. This paper outlines a research vision for multisensory artificial intelligence over the next decade. This new set of technologies can change how humans and AI experience and interact with one another, by connecting AI to the human senses and a rich spectrum of signals from physiological and tactile cues on the body, to physical and social signals in homes, cities, and the environment. We outline how this field must advance through three interrelated themes of sensing, science, and synergy. Firstly, research in sensing should extend how AI captures the world in richer ways beyond the digital medium. Secondly, developing a principled science for quantifying multimodal heterogeneity and interactions, developing unified modeling architectures and representations, and understanding cross‑modal transfer. Finally, we present new technical challenges to learn synergy between modalities and between humans and AI, covering multisensory integration, alignment, reasoning, generation, generalization, and experience. Accompanying this vision paper are a series of projects, resources, and demos of latest advances from the Multisensory Intelligence group at the MIT Media Lab, see https://mit‑mi.github.io/.

PaperId: 3861, https://arxiv.org/pdf/2601.04505.pdf GitHub

Abstract:
Generating accurate circuit schematics from high‑level natural language descriptions remains a persistent challenge in electronic design automation (EDA), as large language models (LLMs) frequently hallucinate components, violate strict physical constraints, and produce non‑machine‑readable outputs. To address this, we present CircuitLM, a multi‑agent pipeline that translates user prompts into structured, visually interpretable \textttCircuitJSON schematics. The framework mitigates hallucination and ensures physical viability by grounding generation in a curated, embedding‑powered component knowledge base through five sequential stages: (i) component identification, (ii) canonical pinout retrieval, (iii) chain‑of‑thought reasoning, (iv) JSON schematic synthesis, and (v) interactive force‑directed visualization. We evaluate the system on a dataset of 100 unique circuit‑design prompts using five state‑of‑the‑art LLMs. To systematically assess performance, we deploy a rigorous dual‑layered evaluation methodology: a deterministic Electrical Rule Checking (ERC) engine categorizes topological faults by strict severity (Critical, Major, Minor, Warning), while an LLM‑as‑a‑judge meta‑evaluator identifies complex, context‑aware design flaws that bypass standard rule‑based checkers. Ultimately, this work demonstrates how targeted retrieval combined with deterministic and semantic verification can bridge natural language to structurally viable, schematic‑ready hardware and safe circuit prototyping. Our code and data are publicly available at https://github.com/Khandakar227/CircuitLM.

PaperId: 3862, https://arxiv.org/pdf/2601.04500.pdf GitHub GitHub

Abstract:
Exploratory GUI testing is essential for software quality but suffers from high manual costs. While Multi‑modal Large Language Model (MLLM) agents excel in navigation, they fail to autonomously discover defects due to two core challenges: Goal‑Oriented Masking, where agents prioritize task completion over reporting anomalies, and Execution‑Bias Attribution, where system defects are misidentified as agent errors. To address these, we first introduce GUITestBench, the first interactive benchmark for this task, featuring 143 tasks across 26 defects. We then propose GUITester, a multi‑agent framework that decouples navigation from verification via two modules: (i) a Planning‑Execution Module (PEM) that proactively probes for defects via embedded testing intents, and (ii) a Hierarchical Reflection Module (HRM) that resolves attribution ambiguity through interaction history analysis. GUITester achieves an F1‑score of 48.90% (Pass@3) on GUITestBench, outperforming state‑of‑the‑art baselines (33.35%). Our work demonstrates the feasibility of autonomous exploratory testing and provides a robust foundation for future GUI quality assurance~\footnoteOur code is now available in~\hrefhttps://github.com/ADaM‑BJTU/GUITestBenchhttps://github.com/ADaM‑BJTU/GUITestBench.

PaperId: 3863, https://arxiv.org/pdf/2601.04497.pdf GitHub

Abstract:
Modern forest monitoring workflows increasingly benefit from the growing availability of high‑resolution satellite imagery and advances in deep learning. Two persistent challenges in this context are accurate pixel‑level change detection and meaningful semantic change captioning for complex forest dynamics. While large language models (LLMs) are being adapted for interactive data exploration, their integration with vision‑language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored. To address this gap, we introduce an LLM‑driven agent for integrated forest change analysis that supports natural language querying across multiple RSICI tasks. The proposed system builds upon a multi‑level change interpretation (MCI) vision‑language backbone with LLM‑based orchestration. To facilitate adaptation and evaluation in forest environments, we further introduce the Forest‑Change dataset, which comprises bi‑temporal satellite imagery, pixel‑level change masks, and multi‑granularity semantic change captions generated using a combination of human annotation and rule‑based methods. Experimental results show that the proposed system achieves mIoU and BLEU‑4 scores of 67.10% and 40.17% on the Forest‑Change dataset, and 88.13% and 34.41% on LEVIR‑MCI‑Trees, a tree‑focused subset of LEVIR‑MCI benchmark for joint change detection and captioning. These results highlight the potential of interactive, LLM‑driven RSICI systems to improve accessibility, interpretability, and efficiency of forest change analysis. All data and code are publicly available at https://github.com/JamesBrockUoB/ForestChat.

PaperId: 3864, https://arxiv.org/pdf/2601.04250.pdf GitHub

Abstract:
Energy efficiency is a first‑order concern in AI deployment, as long‑running inference can exceed training in cumulative carbon impact. We propose a bio‑inspired framework that maps protein‑folding energy basins to inference cost landscapes and controls execution via a decaying, closed‑loop threshold. A request is admitted only when the expected utility‑to‑energy trade‑off is favorable (high confidence/utility at low marginal energy and congestion), biasing operation toward the first acceptable local basin rather than pursuing costly global minima. We evaluate DistilBERT and ResNet‑18 served through FastAPI with ONNX Runtime and NVIDIA Triton on an RTX 4000 Ada GPU. Our ablation study reveals that the bio‑controller reduces processing time by 42% compared to standard open‑loop execution (0.50s vs 0.29s on A100 test set), with a minimal accuracy degradation (<0.5%). Furthermore, we establish the efficiency boundaries between lightweight local serving (ORT) and managed batching (Triton). The results connect biophysical energy models to Green MLOps and offer a practical, auditable basis for closed‑loop energy‑aware inference in production.

PaperId: 3865, https://arxiv.org/pdf/2601.04201.pdf GitHub

Abstract:
Large language model (LLM) question‑answering systems often fail on community‑specific queries, creating "knowledge blind spots" that marginalize local voices and reinforce epistemic injustice. We present Collective Narrative Grounding, a participatory protocol that transforms community stories into structured narrative units and integrates them into AI systems under community governance. Learning from three participatory mapping workshops with N=24 community members, we designed elicitation methods and a schema that retain narrative richness while enabling entity, time, and place extraction, validation, and provenance control. To scope the problem, we audit a county‑level benchmark of 14,782 local information QA pairs, where factual gaps, cultural misunderstandings, geographic confusions, and temporal misalignments account for 76.7% of errors. On a participatory QA set derived from our workshops, a state‑of‑the‑art LLM answered fewer than 21% of questions correctly without added context, underscoring the need for local grounding. The missing facts often appear in the collected narratives, suggesting a direct path to closing the dominant error modes for narrative items. Beyond the protocol and pilot, we articulate key design tensions, such as representation and power, governance and control, and privacy and consent, providing concrete requirements for retrieval‑first, provenance‑visible, locally governed QA systems. Together, our taxonomy, protocol, and participatory evaluation offer a rigorous foundation for building community‑grounded AI that better answers local questions.

PaperId: 3866, https://arxiv.org/pdf/2601.04068.pdf GitHub

Abstract:
Aligning text‑to‑video diffusion models with human preferences is crucial for generating high‑quality videos. Existing Direct Preference Otimization (DPO) methods rely on multi‑sample ranking and task‑specific critic models, which is inefficient and often yields ambiguous global supervision. To address these limitations, we propose LocalDPO, a novel post‑training framework that constructs localized preference pairs from real videos and optimizes alignment at the spatio‑temporal region level. We design an automated pipeline to efficiently collect preference pair data that generates preference pairs with a single inference per prompt, eliminating the need for external critic models or manual annotation. Specifically, we treat high‑quality real videos as positive samples and generate corresponding negatives by locally corrupting them with random spatio‑temporal masks and restoring only the masked regions using the frozen base model. During training, we introduce a region‑aware DPO loss that restricts preference learning to corrupted areas for rapid convergence. Experiments on Wan2.1 and CogVideoX demonstrate that LocalDPO consistently improves video fidelity, temporal coherence and human preference scores over other post‑training approaches, establishing a more efficient and fine‑grained paradigm for video generator alignment.The code is available at https://github.com/1170300714/Local‑DPO.

PaperId: 3867, https://arxiv.org/pdf/2601.03895.pdf GitHub

Abstract:
Group Relative Policy Optimization (GRPO) has emerged as a popular algorithm for reinforcement learning with large language models (LLMs). However, upon analyzing its clipping mechanism, we argue that it is suboptimal in certain scenarios. With appropriate modifications, GRPO can be significantly enhanced to improve both flexibility and generalization. To this end, we propose Adaptive‑Boundary‑Clipping GRPO (ABC‑GRPO), an asymmetric and adaptive refinement of the original GRPO framework. We demonstrate that ABC‑GRPO achieves superior performance over standard GRPO on mathematical reasoning tasks using the Qwen3 LLMs. Moreover, ABC‑GRPO maintains substantially higher entropy throughout training, thereby preserving the model's exploration capacity and mitigating premature convergence. The implementation code is available online to ease reproducibility https://github.com/chi2liu/ABC‑GRPO.

PaperId: 3868, https://arxiv.org/pdf/2601.03782.pdf GitHub

Abstract:
Humans anticipate, from a glance and a contemplated action of their bodies, how the 3D world will respond, a capability that is equally vital for robotic manipulation. We introduce PointWorld, a large pre‑trained 3D world model that unifies state and action in a shared 3D space as 3D point flows: given one or few RGB‑D images and a sequence of low‑level robot action commands, PointWorld forecasts per‑pixel displacements in 3D that respond to the given actions. By representing actions as 3D point flows instead of embodiment‑specific action spaces (e.g., joint positions), this formulation directly conditions on physical geometries of robots while seamlessly integrating learning across embodiments. To train our 3D world model, we curate a large‑scale dataset spanning real and simulated robotic manipulation in open‑world environments, enabled by recent advances in 3D vision and simulated environments, totaling about 2M trajectories and 500 hours across a single‑arm Franka and a bimanual humanoid. Through rigorous, large‑scale empirical studies of backbones, action representations, learning objectives, partial observability, data mixtures, domain transfers, and scaling, we distill design principles for large‑scale 3D world modeling. With a real‑time (0.1s) inference speed, PointWorld can be efficiently integrated in the model‑predictive control (MPC) framework for manipulation. We demonstrate that a single pre‑trained checkpoint enables a real‑world Franka robot to perform rigid‑body pushing, deformable and articulated object manipulation, and tool use, without requiring any demonstrations or post‑training and all from a single image captured in‑the‑wild. Project website at https://point‑world.github.io/.

PaperId: 3869, https://arxiv.org/pdf/2601.03715.pdf GitHub

Abstract:
Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration and exploitation. Exploration suffers from low success rates on difficult tasks and high costs of repeated rollouts from scratch. Exploitation suffers from coarse credit assignment and training instability: Trajectory‑level rewards penalize valid prefixes for later errors, and failure‑dominated groups overwhelm the few positive signals, leaving optimization without constructive direction. To this end, we propose R^3L, Reflect‑then‑Retry Reinforcement Learning with Language‑Guided Exploration, Pivotal Credit, and Positive Amplification. To synthesize high‑quality trajectories, R^3L shifts from stochastic sampling to active synthesis via reflect‑then‑retry, leveraging language feedback to diagnose errors, transform failed attempts into successful ones, and reduce rollout costs by restarting from identified failure points. With errors diagnosed and localized, Pivotal Credit Assignment updates only the diverging suffix where contrastive signals exist, excluding the shared prefix from gradient update. Since failures dominate on difficult tasks and reflect‑then‑retry produces off‑policy data, risking training instability, Positive Amplification upweights successful trajectories to ensure positive signals guide the optimization process. Experiments on agentic and reasoning tasks demonstrate 5% to 52% relative improvements over baselines while maintaining training stability. Our code is released at https://github.com/shiweijiezero/R3L.

PaperId: 3870, https://arxiv.org/pdf/2601.03704.pdf GitHub

Abstract:
The trade‑off between predictive accuracy and data availability makes it difficult to predict protein‑‑protein binding affinity accurately. The lack of experimentally resolved protein structures limits the performance of structure‑based machine learning models, which generally outperform sequence‑based methods. In order to overcome this constraint, we suggest a regression framework based on knowledge distillation that uses protein structural data during training and only needs sequence data during inference. The suggested method uses binding affinity labels and intermediate feature representations to jointly supervise the training of a sequence‑based student network under the guidance of a structure‑informed teacher network. Leave‑One‑Complex‑Out (LOCO) cross‑validation was used to assess the framework on a non‑redundant protein‑‑protein binding affinity benchmark dataset. A maximum Pearson correlation coefficient (P_r) of 0.375 and an RMSE of 2.712 kcal/mol were obtained by sequence‑only baseline models, whereas a P_r of 0.512 and an RMSE of 2.445 kcal/mol were obtained by structure‑based models. With a P_r of 0.481 and an RMSE of 2.488 kcal/mol, the distillation‑based student model greatly enhanced sequence‑only performance. Improved agreement and decreased bias were further confirmed by thorough error analyses. With the potential to close the performance gap between sequence‑based and structure‑based models as larger datasets become available, these findings show that knowledge distillation is an efficient method for transferring structural knowledge to sequence‑based predictors. The source code for running inference with the proposed distillation‑based binding affinity predictor can be accessed at https://github.com/wajidarshad/ProteinAffinityKD.

PaperId: 3871, https://arxiv.org/pdf/2601.03676.pdf GitHub

Abstract:
Large Language Models (LLMs) and agent‑based systems often struggle with compositional generalization due to a data bottleneck in which complex skill combinations follow a long‑tailed, power‑law distribution, limiting both instruction‑following performance and generalization in agent‑centric tasks. To address this challenge, we propose STEPS, a Skill Taxonomy guided Entropy‑based Post‑training data Synthesis framework for generating compositionally challenging data. STEPS explicitly targets compositional generalization by uncovering latent relationships among skills and organizing them into an interpretable, hierarchical skill taxonomy using structural information theory. Building on this taxonomy, we formulate data synthesis as a constrained information maximization problem, selecting skill combinations that maximize marginal structural information within the hierarchy while preserving semantic coherence. Experiments on challenging instruction‑following benchmarks show that STEPS outperforms existing data synthesis baselines, while also yielding improved compositional generalization in downstream agent‑based evaluations.

PaperId: 3872, https://arxiv.org/pdf/2601.03627.pdf GitHub

Abstract:
We introduce EPAG, a benchmark dataset and framework designed for Evaluating the Pre‑consultation Ability of LLMs using diagnostic Guidelines. LLMs are evaluated directly through HPI‑diagnostic guideline comparison and indirectly through disease diagnosis. In our experiments, we observe that small open‑source models fine‑tuned with a well‑curated, task‑specific dataset can outperform frontier LLMs in pre‑consultation. Additionally, we find that increased amount of HPI (History of Present Illness) does not necessarily lead to improved diagnostic performance. Further experiments reveal that the language of pre‑consultation influences the characteristics of the dialogue. By open‑sourcing our dataset and evaluation pipeline on https://github.com/seemdog/EPAG, we aim to contribute to the evaluation and further development of LLM applications in real‑world clinical settings.

PaperId: 3873, https://arxiv.org/pdf/2601.03590.pdf GitHub

Abstract:
Recent advancements in Spatial Intelligence (SI) have predominantly relied on Vision‑Language Models (VLMs), yet a critical question remains: does spatial understanding originate from visual encoders or the fundamental reasoning backbone? Inspired by this question, we introduce SiT‑Bench, a novel benchmark designed to evaluate the SI performance of Large Language Models (LLMs) without pixel‑level input, comprises over 3,800 expert‑annotated items across five primary categories and 17 subtasks, ranging from egocentric navigation and perspective transformation to fine‑grained robotic manipulation. By converting single/multi‑view scenes into high‑fidelity, coordinate‑aware textual descriptions, we challenge LLMs to perform symbolic textual reasoning rather than visual pattern matching. Evaluation results of state‑of‑the‑art (SOTA) LLMs reveals that while models achieve proficiency in localized semantic tasks, a significant "spatial gap" remains in global consistency. Notably, we find that explicit spatial reasoning significantly boosts performance, suggesting that LLMs possess latent world‑modeling potential. Our proposed dataset SiT‑Bench serves as a foundational resource to foster the development of spatially‑grounded LLM backbones for future VLMs and embodied agents. Our code and benchmark will be released at https://github.com/binisalegend/SiT‑Bench .

PaperId: 3874, https://arxiv.org/pdf/2601.03542.pdf GitHub

Abstract:
Large language models (LLMs) perform well on multi‑hop reasoning, yet how they internally compose multiple facts remains unclear. Recent work proposes \emphhop‑aligned circuit hypothesis, suggesting that bridge entities are computed sequentially across layers before later‑hop answers. Through systematic analyses on real‑world multi‑hop queries, we show that this hop‑aligned assumption does not generalize: later‑hop answer entities can become decodable earlier than bridge entities, a phenomenon we call \emphlayer‑order inversion, which strengthens with total hops. To explain this behavior, we propose a \emphprobabilistic recall‑and‑extract framework that models multi‑hop reasoning as broad probabilistic recall in shallow MLP layers followed by selective extraction in deeper attention layers. This framework is empirically validated through systematic probing analyses, reinterpreting prior layer‑wise decoding evidence, explaining chain‑of‑thought gains, and providing a mechanistic diagnosis of multi‑hop failures despite correct single‑hop knowledge. Code is available at https://github.com/laquabe/Layer‑Order‑Inversion.

PaperId: 3875, https://arxiv.org/pdf/2601.03537.pdf GitHub

Abstract:
Defending against jailbreak attacks is crucial for the safe deployment of Large Language Models (LLMs). Recent research has attempted to improve safety by training models to reason over safety rules before responding. However, a key issue lies in determining what form of safety reasoning effectively defends against jailbreak attacks, which is difficult to explicitly design or directly obtain. To address this, we propose STAR‑S (Self‑TAught Reasoning based on Safety rules), a framework that integrates the learning of safety rule reasoning into a self‑taught loop. The core of STAR‑S involves eliciting reasoning and reflection guided by safety rules, then leveraging fine‑tuning to enhance safety reasoning. Repeating this process creates a synergistic cycle. Improvements in the model's reasoning and interpretation of safety rules allow it to produce better reasoning data under safety rule prompts, which is then utilized for further training. Experiments show that STAR‑S effectively defends against jailbreak attacks, outperforming baselines. Code is available at: https://github.com/pikepokenew/STAR_S.git.

PaperId: 3876, https://arxiv.org/pdf/2601.03479.pdf GitHub GitHub

Abstract:
Recent years have witnessed success of sequential modeling, generative recommender, and large language model for recommendation. Though the scaling law has been validated for sequential models, it showed inefficiency in computational capacity when considering real‑world applications like recommendation, due to the non‑linear(quadratic) increasing nature of the transformer model. To improve the efficiency of the sequential model, we introduced a novel approach to sequential recommendation that leverages personalization techniques to enhance efficiency and performance. Our method compresses long user interaction histories into learnable tokens, which are then combined with recent interactions to generate recommendations. This approach significantly reduces computational costs while maintaining high recommendation accuracy. Our method could be applied to existing transformer based recommendation models, e.g., HSTU and HLLM. Extensive experiments on multiple sequential models demonstrate its versatility and effectiveness. Source code is available at \hrefhttps://github.com/facebookresearch/PerSRechttps://github.com/facebookresearch/PerSRec.

PaperId: 3877, https://arxiv.org/pdf/2601.03324.pdf GitHub

Abstract:
The deployment of Large Language Models (LLMs) on edge devices is fundamentally constrained by the "Memory Wall" the bottleneck where data movement latency outstrips arithmetic throughput. Standard inference runtimes often incur significant overhead through high‑level abstractions, dynamic dispatch, and unaligned memory access patterns. In this work, we present a novel "Virtual Tensor Core" architecture implemented in software, optimized specifically for ARM64 microarchitectures (Apple Silicon). By bypassing standard library containers in favor of direct memory mapping (mmap) and implementing hand‑tuned NEON SIMD kernels, we achieve a form of "Software‑Defined Direct Memory Access (DMA)." Our proposed Tensor Virtualization Layout (TVL) guarantees 100% cache line utilization for weight matrices, while our zero‑copy loader eliminates initialization latency. Experimental results on a 110M parameter model demonstrate a stable throughput of >60 tokens/second on M2 hardware. While proprietary hardware accelerators (e.g., Apple AMX) can achieve higher peak throughput, our architecture provides a fully open, portable, and deterministic reference implementation for studying the memory bottleneck on general‑purpose ARM silicon, meeting the 200ms psycholinguistic latency threshold without opaque dependencies.

PaperId: 3878, https://arxiv.org/pdf/2601.03322.pdf GitHub

Abstract:
Electroencephalography (EEG)‑based brain‑computer interfaces facilitate direct communication with a computer, enabling promising applications in human‑computer interactions. However, their utility is currently limited because EEG decoding often suffers from poor generalization due to distribution shifts across domains (e.g., subjects). Learning robust representations that capture underlying task‑relevant information would mitigate these shifts and improve generalization. One promising approach is to exploit the underlying hierarchical structure in EEG, as recent studies suggest that hierarchical cognitive processes, such as visual processing, can be encoded in EEG. While many decoding methods still rely on Euclidean embeddings, recent work has begun exploring hyperbolic geometry for EEG. Hyperbolic spaces, regarded as the continuous analogue of tree structures, provide a natural geometry for representing hierarchical data. In this study, we first empirically demonstrate that EEG data exhibit hyperbolicity and show that hyperbolic embeddings improve generalization. Motivated by these findings, we propose HEEGNet, a hybrid hyperbolic network architecture to capture the hierarchical structure in EEG and learn domain‑invariant hyperbolic embeddings. To this end, HEEGNet combines both Euclidean and hyperbolic encoders and employs a novel coarse‑to‑fine domain adaptation strategy. Extensive experiments on multiple public EEG datasets, covering visual evoked potentials, emotion recognition, and intracranial EEG, demonstrate that HEEGNet achieves state‑of‑the‑art performance. The code is available at https://github.com/fightlesliefigt/HEEGNet

PaperId: 3879, https://arxiv.org/pdf/2601.03315.pdf GitHub

Abstract:
We report a case study of four end‑to‑end attempts to autonomously generate ML research papers using a pipeline of six LLM agents mapped to stages of the scientific workflow. Of these four, three attempts failed during implementation or evaluation. One completed the pipeline and was accepted to Agents4Science 2025, an experimental inaugural venue that required AI systems as first authors, passing both human and multi‑AI review. From these attempts, we document six recurring failure modes: bias toward training data defaults, implementation drift under execution pressure, memory and context degradation across long‑horizon tasks, overexcitement that declares success despite obvious failures, insufficient domain intelligence, and weak scientific taste in experimental design. We conclude by discussing four design principles for more robust AI‑scientist systems, implications for autonomous scientific discovery, and we release all prompts, artifacts, and outputs at https://github.com/Lossfunk/ai‑scientist‑artefacts‑v1

PaperId: 3880, https://arxiv.org/pdf/2601.03294.pdf GitHub

Abstract:
LLM‑based agents are increasingly deployed to autonomously solve complex tasks, raising urgent needs for IP protection and regulatory provenance. While content watermarking effectively attributes LLM‑generated outputs, it fails to directly identify the high‑level planning behaviors (e.g., tool and subgoal choices) that govern multi‑step execution. Critically, watermarking at the planning‑behavior layer faces unique challenges: minor distributional deviations in decision‑making can compound during long‑term agent operation, degrading utility, and many agents operate as black boxes that are difficult to intervene in directly. To bridge this gap, we propose AgentMark, a behavioral watermarking framework that embeds multi‑bit identifiers into planning decisions while preserving utility. It operates by eliciting an explicit behavior distribution from the agent and applying distribution‑preserving conditional sampling, enabling deployment under black‑box APIs while remaining compatible with action‑layer content watermarking. Experiments across embodied, tool‑use, and social environments demonstrate practical multi‑bit capacity, robust recovery from partial logs, and utility preservation. The code is available at https://github.com/Tooooa/AgentMark.

PaperId: 3881, https://arxiv.org/pdf/2601.03281.pdf GitHub

Abstract:
Large Language Models (LLMs) are increasingly used as high level controllers for autonomous Unmanned Aerial Vehicle (UAV) missions. However, existing evaluations rarely assess whether such agents remain safe, protocol compliant, and effective under realistic next generation networking constraints. This paper introduces α^3‑Bench, a benchmark for evaluating LLM driven UAV autonomy as a multi turn conversational reasoning and control problem operating under dynamic 6G conditions. Each mission is formulated as a language mediated control loop between an LLM based UAV agent and a human operator, where decisions must satisfy strict schema validity, mission policies, speaker alternation, and safety constraints while adapting to fluctuating network slices, latency, jitter, packet loss, throughput, and edge load variations. To reflect modern agentic workflows, α^3‑Bench integrates a dual action layer supporting both tool calls and agent to agent coordination, enabling evaluation of tool use consistency and multi agent interactions. We construct a large scale corpus of 113k conversational UAV episodes grounded in UAVBench scenarios and evaluate 17 state of the art LLMs using a fixed subset of 50 episodes per scenario under deterministic decoding. We propose a composite α^3 metric that unifies six pillars: Task Outcome, Safety Policy, Tool Consistency, Interaction Quality, Network Robustness, and Communication Cost, with efficiency normalized scores per second and per thousand tokens. Results show that while several models achieve high mission success and safety compliance, robustness and efficiency vary significantly under degraded 6G conditions, highlighting the need for network aware and resource efficient LLM based UAV agents. The dataset is publicly available on GitHub : https://github.com/maferrag/AlphaBench

PaperId: 3882, https://arxiv.org/pdf/2601.03204.pdf GitHub

Abstract:
LLM agents can reason and use tools, but they often break down on long‑horizon tasks due to unbounded context growth and accumulated errors. Common remedies such as context compression or retrieval‑augmented prompting introduce trade‑offs between information fidelity and reasoning stability. We present InfiAgent, a general‑purpose framework that keeps the agent's reasoning context strictly bounded regardless of task duration by externalizing persistent state into a file‑centric state abstraction. At each step, the agent reconstructs context from a workspace state snapshot plus a fixed window of recent actions. Experiments on DeepResearch and an 80‑paper literature review task show that, without task‑specific fine‑tuning, InfiAgent with a 20B open‑source model is competitive with larger proprietary systems and maintains substantially higher long‑horizon coverage than context‑centric baselines. These results support explicit state externalization as a practical foundation for stable long‑horizon agents. Github Repo:https://github.com/ChenglinPoly/infiAgent

PaperId: 3883, https://arxiv.org/pdf/2601.03191.pdf GitHub

Abstract:
Multimodal medical large language models have shown substantial progress in chest X‑ray interpretation but continue to face challenges in spatial reasoning and anatomical understanding. Although existing grounding techniques improve overall performance, they often fail to establish a true anatomical correspondence, resulting in incorrect anatomical understanding in the medical domain. To address this gap, we introduce AnatomiX, a multitask multimodal large language model for anatomically grounded chest X‑ray interpretation. Inspired by the radiological workflow, AnatomiX adopts a two stage approach: first, it identifies anatomical structures and extracts their features, and then leverages a large language model to perform diverse downstream tasks such as phrase grounding, report generation, visual question answering, and image understanding. Extensive experiments across multiple benchmarks demonstrate that AnatomiX achieves superior anatomical reasoning and delivers over 25% improvement in performance on anatomy grounding, phrase grounding, grounded diagnosis and grounded captioning tasks compared to existing approaches. Code and pretrained model are available at https://aneesurhashmi.github.io/anatomix

PaperId: 3884, https://arxiv.org/pdf/2601.03144.pdf GitHub

Abstract:
Despite rapid advances in large language models (LLMs), achieving reliable performance on highly professional and structured examinations remains a significant challenge. The Japanese bar examination is a particularly demanding benchmark, requiring not only advanced legal reasoning but also strict adherence to complex answer formats that involve joint evaluation of multiple propositions. While recent studies have reported improvements by decomposing such questions into simpler true‑‑false judgments, these approaches have not been systematically evaluated under the original exam format and scoring scheme, leaving open the question of whether they truly capture exam‑level competence. In this paper, we present a self‑verification model trained on a newly constructed dataset that faithfully replicates the authentic format and evaluation scale of the exam. Our model is able to exceed the official passing score when evaluated on the actual exam scale, marking the first demonstration, to our knowledge, of an LLM passing the Japanese bar examination without altering its original question structure or scoring rules. We further conduct extensive comparisons with alternative strategies, including multi‑agent inference and decomposition‑based supervision, and find that these methods fail to achieve comparable performance. Our results highlight the importance of format‑faithful supervision and consistency verification, and suggest that carefully designed single‑model approaches can outperform more complex systems in high‑stakes professional reasoning tasks. Our dataset and codes are publicly available.

PaperId: 3885, https://arxiv.org/pdf/2601.03067.pdf GitHub

Abstract:
Modern large language models (LLMs) drive interactive AI systems but are bottlenecked by the memory‑heavy growth of key‑value (KV) caches, which limits real‑time throughput under concurrent loads. Existing KV‑cache compression methods rely on rigid heuristics, disrupt tensor layouts, or require specialized compute, hindering scalability and deployment. We propose joint encoding of KV‑cache blocks, which fuses similar blocks across requests and input chunks into shared representations while preserving standard cache structure. This alleviates the KV‑cache memory bottleneck, supporting high‑concurrency serving without specialized hardware. Theoretically, we analyze the rate‑distortion tradeoff of fused cache blocks under a Poisson process model. Empirically, our method achieves up to 4.38 × KV‑cache compression with negligible accuracy loss across diverse LLMs and benchmarks, outperforming recent structured and adaptive compression baselines. In real LLM serving, joint encoding improves the token throughput by ～40% on a single‑machine vLLM benchmark, demonstrating substantial gains in inference throughput. Code is available at https://github.com/sef1/kv_fast_fusion kv_joint_encoding.

PaperId: 3886, https://arxiv.org/pdf/2601.02994.pdf GitHub

Abstract:
Vision‑based robotic policies often struggle with even minor viewpoint changes, underscoring the need for view‑invariant visual representations. This challenge becomes more pronounced in real‑world settings, where viewpoint variability is unavoidable and can significantly disrupt policy performance. Existing methods typically learn invariance from multi‑view observations at the scene level, but such approaches rely on visual appearance and fail to incorporate the physical dynamics essential for robust generalization. We propose View‑Invariant Latent Action (VILA), which models a latent action capturing transition patterns across trajectories to learn view‑invariant representations grounded in physical dynamics. VILA aligns these latent actions across viewpoints using an action‑guided objective based on ground‑truth action sequences. Experiments in both simulation and the real world show that VILA‑based policies generalize effectively to unseen viewpoints and transfer well to new tasks, establishing VILA as a strong pretraining framework that improves robustness and downstream learning performance.

PaperId: 3887, https://arxiv.org/pdf/2601.02950.pdf GitHub

Abstract:
Current Large Language Model reasoning systems process queries independently, discarding valuable cross‑instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch‑of‑Thought (BoT), a training‑free method that processes related queries jointly to enable cross‑instance learning. By performing comparative analysis across batches, BoT identifies high‑quality reasoning templates, detects errors through consistency checks, and amortizes computational costs. We instantiate BoT within a multi‑agent reflection architecture (BoT‑R), where a Reflector performs joint evaluation to unlock mutual information gain unavailable in isolated processing. Experiments across three model families and six benchmarks demonstrate that BoT‑R consistently improves accuracy and confidence calibration while reducing inference costs by up to 61%. Our theoretical and experimental analysis reveals when and why batch‑aware reasoning benefits LLM systems. Our code is available at https://github.com/xuanyang19/BoT

PaperId: 3888, https://arxiv.org/pdf/2601.02905.pdf GitHub

Abstract:
Tracking objects that move within dynamic environments is a core challenge in robotics. Recent research has advanced this topic significantly; however, many existing approaches remain inefficient due to their reliance on heavy foundation models. To address this limitation, we propose LOST‑3DSG, a lightweight open‑vocabulary 3D scene graph designed to track dynamic objects in real‑world environments. Our method adopts a semantic approach to entity tracking based on word2vec and sentence embeddings, enabling an open‑vocabulary representation while avoiding the necessity of storing dense CLIP visual features. As a result, LOST‑3DSG achieves superior performance compared to approaches that rely on high‑dimensional visual embeddings. We evaluate our method through qualitative and quantitative experiments conducted in a real 3D environment using a TIAGo robot. The results demonstrate the effectiveness and efficiency of LOST‑3DSG in dynamic object tracking. Code and supplementary material are publicly available on the project website at https://lab‑rococo‑sapienza.github.io/lost‑3dsg/.

PaperId: 3889, https://arxiv.org/pdf/2601.02902.pdf GitHub

Abstract:
Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs), providing reliable and verifiable decision‑making in high‑stakes domains such as mathematical reasoning and legal judgment. In this study, we present a systematic analysis of logical reasoning under controlled increases in logical complexity, and reveal a previously unrecognized phenomenon, which we term Logical Phase Transitions: rather than degrading smoothly, logical reasoning performance remains stable within a regime but collapses abruptly beyond a critical logical depth, mirroring physical phase transitions such as water freezing beyond a critical temperature threshold. Building on this insight, we propose Neuro‑Symbolic Curriculum Tuning, a principled framework that adaptively aligns natural language with logical symbols to establish a shared representation, and reshapes training dynamics around phase‑transition boundaries to progressively strengthen reasoning at increasing logical depths. Experiments on five benchmarks show that our approach effectively mitigates logical reasoning collapse at high complexity, yielding average accuracy gains of +1.26 in naive prompting and +3.95 in CoT, while improving generalization to unseen logical compositions. Code and data are available at https://github.com/AI4SS/Logical‑Phase‑Transitions.

PaperId: 3890, https://arxiv.org/pdf/2601.02854.pdf GitHub

Abstract:
As an agent‑level reasoning and coordination paradigm, Multi‑Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning. However, existing research on MAD suffers from two fundamental limitations: evaluations are conducted under fragmented and inconsistent settings, hindering fair comparison, and are largely restricted to single‑modality scenarios that rely on textual inputs only. To address these gaps, we introduce M3MAD‑Bench, a unified and extensible benchmark for evaluating MAD methods across Multi‑domain tasks, Multi‑modal inputs, and Multi‑dimensional metrics. M3MAD‑Bench establishes standardized protocols over five core task domains: Knowledge, Mathematics, Medicine, Natural Sciences, and Complex Reasoning, and systematically covers both pure text and vision‑language datasets, enabling controlled cross‑modality comparison. We evaluate MAD methods on nine base models spanning different architectures, scales, and modality capabilities. Beyond accuracy, M3MAD‑Bench incorporates efficiency‑oriented metrics such as token consumption and inference time, providing a holistic view of performance‑‑cost trade‑offs. Extensive experiments yield systematic insights into the effectiveness, robustness, and efficiency of MAD across text‑only and multimodal scenarios. We believe M3MAD‑Bench offers a reliable foundation for future research on standardized MAD evaluation. The code is available at http://github.com/liaolea/M3MAD‑Bench.

PaperId: 3891, https://arxiv.org/pdf/2601.02845.pdf GitHub

Abstract:
Long‑horizon conversational agents have to manage ever‑growing interaction histories that quickly exceed the finite context windows of large language models (LLMs). Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, often leading to fragmented memories and unstable long‑horizon personalization. We present TiMem, a temporal‑‑hierarchical memory framework that organizes conversations through a Temporal Memory Tree (TMT), enabling systematic memory consolidation from raw conversational observations to progressively abstracted persona representations. TiMem is characterized by three core properties: (1) temporal‑‑hierarchical organization through TMT; (2) semantic‑guided consolidation that enables memory integration across hierarchical levels without fine‑tuning; and (3) complexity‑aware memory recall that balances precision and efficiency across queries of varying complexity. Under a consistent evaluation setup, TiMem achieves state‑of‑the‑art accuracy on both benchmarks, reaching 75.30% on LoCoMo and 76.88% on LongMemEval‑S. It outperforms all evaluated baselines while reducing the recalled memory length by 52.20% on LoCoMo. Manifold analysis indicates clear persona separation on LoCoMo and reduced dispersion on LongMemEval‑S. Overall, TiMem treats temporal continuity as a first‑class organizing principle for long‑horizon memory in conversational agents. The code is available at https://github.com/TiMEM‑AI/timem.

PaperId: 3892, https://arxiv.org/pdf/2601.02776.pdf GitHub

Abstract:
Neural Audio Codecs (NACs) can reduce transmission overhead by performing compact compression and reconstruction, which also aim to bridge the gap between continuous and discrete signals. Existing NACs can be divided into two categories: multi‑codebook and single‑codebook codecs. Multi‑codebook codecs face challenges such as structural complexity and difficulty in adapting to downstream tasks, while single‑codebook codecs, though structurally simpler, suffer from low‑fidelity, ineffective modeling of unified audio, and an inability to support modeling of high‑frequency audio. We propose the UniSRCodec, a single‑codebook codec capable of supporting high sampling rate, low‑bandwidth, high fidelity, and unified. We analyze the inefficiency of waveform‑based compression and introduce the time and frequency compression method using the Mel‑spectrogram, and cooperate with a Vocoder to recover the phase information of the original audio. Moreover, we propose a sub‑band reconstruction technique to achieve high‑quality compression across both low and high frequency bands. Subjective and objective experimental results demonstrate that UniSRCodec achieves state‑of‑the‑art (SOTA) performance among cross‑domain single‑codebook codecs with only a token rate of 40, and its reconstruction quality is comparable to that of certain multi‑codebook methods. Our demo page is available at https://wxzyd123.github.io/unisrcodec.

PaperId: 3893, https://arxiv.org/pdf/2601.02751.pdf GitHub

Abstract:
Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes the subtle, localized signals of memorization, reducing attack effectiveness. We challenge this global‑averaging paradigm, positing that membership signals are more pronounced within localized contexts. We introduce WBC (Window‑Based Comparison), which exploits this insight through a sliding window approach with sign‑based aggregation. Our method slides windows of varying sizes across text sequences, with each window casting a binary vote on membership based on loss comparisons between target and reference models. By ensembling votes across geometrically spaced window sizes, we capture memorization patterns from token‑level artifacts to phrase‑level structures. Extensive experiments across eleven datasets demonstrate that WBC substantially outperforms established baselines, achieving higher AUC scores and 2‑3 times improvements in detection rates at low false positive thresholds. Our findings reveal that aggregating localized evidence is fundamentally more effective than global averaging, exposing critical privacy vulnerabilities in fine‑tuned LLMs.

PaperId: 3894, https://arxiv.org/pdf/2601.02646.pdf GitHub

Abstract:
Cinemagraphs, which combine static photographs with selective, looping motion, offer unique artistic appeal. Generating them from a single photograph in a controllable manner is particularly challenging. Existing image‑animation techniques are restricted to simple, low‑frequency motions and operate only in narrow domains with repetitive textures like water and smoke. In contrast, large‑scale video diffusion models are not tailored for cinemagraph constraints and lack the specialized data required to generate seamless, controlled loops. We present DreamLoop, a controllable video synthesis framework dedicated to generating cinemagraphs from a single photo without requiring any cinemagraph training data. Our key idea is to adapt a general video diffusion model by training it on two objectives: temporal bridging and motion conditioning. This strategy enables flexible cinemagraph generation. During inference, by using the input image as both the first‑ and last‑ frame condition, we enforce a seamless loop. By conditioning on static tracks, we maintain a static background. Finally, by providing a user‑specified motion path for a target object, our method provides intuitive control over the animation's trajectory and timing. To our knowledge, DreamLoop is the first method to enable cinemagraph generation for general scenes with flexible and intuitive controls. We demonstrate that our method produces high‑quality, complex cinemagraphs that align with user intent, outperforming existing approaches.

PaperId: 3895, https://arxiv.org/pdf/2601.02643.pdf GitHub

Abstract:
Tool‑calling conversational agents querying structured databases often face two linked failures: underspecification (missing constraints needed for a precise query) andinfeasibility (a fully specified query returns anemptyset). Prior systems often respond with "no results" or apply ad hoc relaxations, which can violate user intent by discarding highly valued requirements. Wecast infeasibility handling as preference‑aware query repair: when a query is unsatisfiable, the agent should relax the least important constraints. We propose three LLM‑based methods to infer relative constraint importance from dialogue: (1) local weighting, (2) global one‑shot weighting, and (3) pairwise ranking. Across extensive experiments in car recommendation, the local‑weighting method trained with supervised fine‑tuning and direct preference optimization best aligns with user preferences (48%), while global weighting achieves the highest correct‑relaxation accuracy (56%); all three outperform prior infeasibility‑resolution basel. We also introduce AWARE‑US, a benchmark of 120+ persona‑grounded queries requiring agents to (i) disambiguate a base request via conversa tion and (ii) resolve infeasibility in a way consistent with persona‑implied preferences. For code refer to Github: https://github.com/mhtkrmz/Infeasible‑task and the dataset is available on Hugging Face

PaperId: 3896, https://arxiv.org/pdf/2601.02609.pdf GitHub

Abstract:
Large language model fine‑tuning is bottlenecked by memory: a 7B parameter model requires 84GB‑‑14GB for weights, 14GB for gradients, and 56GB for FP32 optimizer states‑‑exceeding even A100‑40GB capacity. We present Chronicals, an open‑source training framework achieving 3.51x speedup over Unsloth through four synergistic optimizations: (1) fused Triton kernels eliminating 75% of memory traffic via RMSNorm (7x), SwiGLU (5x), and QK‑RoPE (2.3x) fusion; (2) Cut Cross‑Entropy reducing logit memory from 5GB to 135MB through online softmax computation; (3) LoRA+ with theoretically‑derived 16x differential learning rates between adapter matrices; and (4) Best‑Fit Decreasing sequence packing recovering 60‑75% of compute wasted on padding. On Qwen2.5‑0.5B with A100‑40GB, Chronicals achieves 41,184 tokens/second for full fine‑tuning versus Unsloth's 11,736 tokens/second (3.51x). For LoRA at rank 32, we reach 11,699 tokens/second versus Unsloth MAX's 2,857 tokens/second (4.10x). Critically, we discovered that Unsloth's reported 46,000 tokens/second benchmark exhibited zero gradient norms‑‑the model was not training. We provide complete mathematical foundations: online softmax correctness proofs, FlashAttention IO complexity bounds O(N^2 d^2 M^‑1), LoRA+ learning rate derivations from gradient magnitude analysis, and bin‑packing approximation guarantees. All implementations, benchmarks, and proofs are available at https://github.com/Ajwebdevs/Chronicals with pip installation via https://pypi.org/project/chronicals/.

PaperId: 3897, https://arxiv.org/pdf/2601.02594.pdf GitHub GitHub

Abstract:
Solving inverse problems in imaging requires models that support efficient inference, uncertainty quantification, and principled probabilistic reasoning. Energy‑Based Models (EBMs), with their interpretable energy landscapes and compositional structure, are well‑suited for this task but have historically suffered from high computational costs and training instability. To overcome the historical shortcomings of EBMs, we introduce a fast distillation strategy to transfer the strengths of pre‑trained diffusion models into multi‑scale EBMs. These distilled EBMs enable efficient sampling and preserve the interpretability and compositionality inherent to potential‑based frameworks. Leveraging EBM compositionality, we propose Annealed Langevin Posterior Sampling (ALPS) algorithm for Maximum‑A‑Posteriori (MAP), Minimum Mean Square Error (MMSE), and uncertainty estimates for inverse problems in imaging. Unlike diffusion models that use complex guidance strategies for latent variables, we perform annealing on static posterior distributions that are well‑defined and composable. Experiments on image inpainting and MRI reconstruction demonstrate that our method matches or surpasses diffusion‑based baselines in both accuracy and efficiency, while also supporting MAP recovery. Overall, our framework offers a scalable and principled solution for inverse problems in imaging, with potential for practical deployment in scientific and clinical settings. ALPS code is available at the GitHub repository \hrefhttps://github.com/JyoChand/ALPSALPS.

PaperId: 3898, https://arxiv.org/pdf/2601.02553.pdf GitHub

Abstract:
To support long‑term interaction in complex environments, LLM agents require memory systems that manage historical experiences. Existing approaches either retain full interaction histories via passive context extension, leading to substantial redundancy, or rely on iterative reasoning to filter noise, incurring high token costs. To address this challenge, we introduce SimpleMem, an efficient memory framework based on semantic lossless compression. We propose a three‑stage pipeline designed to maximize information density and token utilization: (1) Semantic Structured Compression, which distills unstructured interactions into compact, multi‑view indexed memory units; (2) Online Semantic Synthesis, an intra‑session process that instantly integrates related context into unified abstract representations to eliminate redundancy; and (3) Intent‑Aware Retrieval Planning, which infers search intent to dynamically determine retrieval scope and construct precise context efficiently. Experiments on benchmark datasets show that our method consistently outperforms baseline approaches in accuracy, retrieval efficiency, and inference cost, achieving an average F1 improvement of 26.4% in LoCoMo while reducing inference‑time token consumption by up to 30‑fold, demonstrating a superior balance between performance and efficiency. Code is available at https://github.com/aiming‑lab/SimpleMem.

PaperId: 3899, https://arxiv.org/pdf/2601.02535.pdf GitHub

Abstract:
Selecting a single high‑quality output from multiple stochastic generations remains a fundamental challenge for large language models (LLMs), particularly in open‑ended tasks where no canonical answer exists. While Best‑of‑N and self‑consistency methods show that aggregating multiple generations can improve performance, existing approaches typically rely on external evaluators, reward models, or exact string‑match voting, limiting their applicability and efficiency. We propose Mode Extraction (ModeX), an evaluator‑free Best‑of‑N selection framework that generalizes majority voting to open‑ended text generation by identifying the modal output representing the dominant semantic consensus among generated texts. ModeX constructs a similarity graph over candidate generations and recursively applies spectral clustering to select a representative centroid, without requiring additional inference or auxiliary models. We further instantiate this selection principle as ModeX‑Lite, an improved version of ModeX with early pruning for efficiency. Across open‑ended tasks ‑‑ including text summarization, code generation, and mathematical reasoning ‑‑ our approaches consistently outperform standard single‑ and multi‑path baselines, providing a computationally efficient solution for robust open‑ended text generation. Code is released in https://github.com/deeplearning‑wisc/ModeX.

PaperId: 3900, https://arxiv.org/pdf/2601.02451.pdf GitHub GitHub

Abstract:
Graph Neural Networks (GNNs) suffer from over‑smoothing in deep architectures and expressiveness bounded by the 1‑Weisfeiler‑Leman (1‑WL) test. We adapt Manifold‑Constrained Hyper‑Connections (\mhc)~\citepxie2025mhc, recently proposed for Transformers, to graph neural networks. Our method, mHC‑GNN, expands node representations across n parallel streams and constrains stream‑mixing matrices to the Birkhoff polytope via Sinkhorn‑Knopp normalization. We prove that mHC‑GNN exhibits exponentially slower over‑smoothing (rate (1‑γ)^L/n vs.\ (1‑γ)^L) and can distinguish graphs beyond 1‑WL. Experiments on 10 datasets with 4 GNN architectures show consistent improvements. Depth experiments from 2 to 128 layers reveal that standard GNNs collapse to near‑random performance beyond 16 layers, while mHC‑GNN maintains over 74% accuracy even at 128 layers, with improvements exceeding 50 percentage points at extreme depths. Ablations confirm that the manifold constraint is essential: removing it causes up to 82% performance degradation. Code is available at \hrefhttps://github.com/smlab‑niser/mhc‑gnnhttps://github.com/smlab‑niser/mhc‑gnn

PaperId: 3901, https://arxiv.org/pdf/2601.02441.pdf GitHub

Abstract:
Textual reasoning has recently been widely adopted in Blind Image Quality Assessment (BIQA). However, it remains unclear how textual information contributes to quality prediction and to what extent text can represent the score‑related image contents. This work addresses these questions from an information‑flow perspective by comparing existing BIQA models with three paradigms designed to learn the image‑text‑score relationship: Chain‑of‑Thought, Self‑Consistency, and Autoencoder. Our experiments show that the score prediction performance of the existing model significantly drops when only textual information is used for prediction. Whereas the Chain‑of‑Thought paradigm introduces little improvement in BIQA performance, the Self‑Consistency paradigm significantly reduces the gap between image‑ and text‑conditioned predictions, narrowing the PLCC/SRCC difference to 0.02/0.03. The Autoencoder‑like paradigm is less effective in closing the image‑text gap, yet it reveals a direction for further optimization. These findings provide insights into how to improve the textual reasoning for BIQA and high‑level vision tasks.

PaperId: 3902, https://arxiv.org/pdf/2601.02422.pdf GitHub

Abstract:
Multi‑modal reasoning requires the seamless integration of visual and linguistic cues, yet existing Chain‑of‑Thought methods suffer from two critical limitations in cross‑modal scenarios: (1) over‑reliance on single coarse‑grained image regions, and (2) semantic fragmentation between successive reasoning steps. To address these issues, we propose the CoCoT (Collaborative Coross‑modal Thought) framework, built upon two key innovations: a) Dynamic Multi‑Region Grounding to adaptively detect the most relevant image regions based on the question, and b) Relation‑Aware Reasoning to enable multi‑region collaboration by iteratively aligning visual cues to form a coherent and logical chain of thought. Through this approach, we construct the CoCoT‑70K dataset, comprising 74,691 high‑quality samples with multi‑region annotations and structured reasoning chains. Extensive experiments demonstrate that CoCoT significantly enhances complex visual reasoning, achieving an average accuracy improvement of 15.4% on LLaVA‑1.5 and 4.0% on Qwen2‑VL across six challenging benchmarks. The data and code are available at: https://github.com/deer‑echo/CoCoT.

PaperId: 3903, https://arxiv.org/pdf/2601.02404.pdf GitHub

Abstract:
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including software development, education, and technical assistance. Among these, software development is one of the key areas where LLMs are increasingly adopted. However, when hardware constraints are considered‑for instance, in physical computing, where software must interact with and control physical hardware their effectiveness has not been fully explored. To address this gap, we introduce \textscPCEval (Physical Computing Evaluation), the first benchmark in physical computing that enables a fully automatic evaluation of the capabilities of LLM in both the logical and physical aspects of the projects, without requiring human assessment. Our evaluation framework assesses LLMs in generating circuits and producing compatible code across varying levels of project complexity. Through comprehensive testing of 13 leading models, \textscPCEval provides the first reproducible and automatically validated empirical assessment of LLMs' ability to reason about fundamental hardware implementation constraints within a simulation environment. Our findings reveal that while LLMs perform well in code generation and logical circuit design, they struggle significantly with physical breadboard layout creation, particularly in managing proper pin connections and avoiding circuit errors. \textscPCEval advances our understanding of AI assistance in hardware‑dependent computing environments and establishes a foundation for developing more effective tools to support physical computing education.

PaperId: 3904, https://arxiv.org/pdf/2601.02273.pdf GitHub

Abstract:
Foundation segmentation models such as the Segment Anything Model (SAM) exhibit strong zero‑shot generalization through large‑scale pretraining, but adapting them to domain‑specific semantic segmentation remains challenging, particularly for thin structures (e.g., retinal vessels) and noisy modalities (e.g., SAR imagery). Full fine‑tuning is computationally expensive and risks catastrophic forgetting. We propose TopoLoRA‑SAM, a topology‑aware and parameter‑efficient adaptation framework for binary semantic segmentation. TopoLoRA‑SAM injects Low‑Rank Adaptation (LoRA) into the frozen ViT encoder, augmented with a lightweight spatial convolutional adapter and optional topology‑aware supervision via differentiable clDice. We evaluate our approach on five benchmarks spanning retinal vessel segmentation (DRIVE, STARE, CHASE\_DB1), polyp segmentation (Kvasir‑SEG), and SAR sea/land segmentation (SL‑SSDD), comparing against U‑Net, DeepLabV3+, SegFormer, and Mask2Former. TopoLoRA‑SAM achieves the best retina‑average Dice and the best overall average Dice across datasets, while training only 5.2% of model parameters (～4.9M). On the challenging CHASE\_DB1 dataset, our method substantially improves segmentation accuracy and robustness, demonstrating that topology‑aware parameter‑efficient adaptation can match or exceed fully fine‑tuned specialist models. Code is available at : https://github.com/salimkhazem/Seglab.git

PaperId: 3905, https://arxiv.org/pdf/2601.02206.pdf GitHub

Abstract:
This paper addresses low‑light video super‑resolution (LVSR), aiming to restore high‑resolution videos from low‑light, low‑resolution (LR) inputs. Existing LVSR methods often struggle to recover fine details due to limited contrast and insufficient high‑frequency information. To overcome these challenges, we present RetinexEVSR, the first event‑driven LVSR framework that leverages high‑contrast event signals and Retinex‑inspired priors to enhance video quality under low‑light scenarios. Unlike previous approaches that directly fuse degraded signals, RetinexEVSR introduces a novel bidirectional cross‑modal fusion strategy to extract and integrate meaningful cues from noisy event data and degraded RGB frames. Specifically, an illumination‑guided event enhancement module is designed to progressively refine event features using illumination maps derived from the Retinex model, thereby suppressing low‑light artifacts while preserving high‑contrast details. Furthermore, we propose an event‑guided reflectance enhancement module that utilizes the enhanced event features to dynamically recover reflectance details via a multi‑scale fusion mechanism. Experimental results show that our RetinexEVSR achieves state‑of‑the‑art performance on three datasets. Notably, on the SDSD benchmark, our method can get up to 2.95 dB gain while reducing runtime by 65% compared to prior event‑based methods. Code: https://github.com/DachunKai/RetinexEVSR.

PaperId: 3906, https://arxiv.org/pdf/2601.02204.pdf GitHub

Abstract:
We present NextFlow, a unified decoder‑only autoregressive transformer trained on 6 trillion interleaved text‑image discrete tokens. By leveraging a unified vision representation within a unified autoregressive architecture, NextFlow natively activates multimodal understanding and generation capabilities, unlocking abilities of image editing, interleaved content and video generation. Motivated by the distinct nature of modalities ‑ where text is strictly sequential and images are inherently hierarchical ‑ we retain next‑token prediction for text but adopt next‑scale prediction for visual generation. This departs from traditional raster‑scan methods, enabling the generation of 1024x1024 images in just 5 seconds ‑ orders of magnitude faster than comparable AR models. We address the instabilities of multi‑scale generation through a robust training recipe. Furthermore, we introduce a prefix‑tuning strategy for reinforcement learning. Experiments demonstrate that NextFlow achieves state‑of‑the‑art performance among unified models and rivals specialized diffusion baselines in visual quality.

PaperId: 3907, https://arxiv.org/pdf/2601.02163.pdf GitHub GitHub

Abstract:
Large Language Models (LLMs) are increasingly deployed as long‑term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems often store isolated records and retrieve fragments, limiting their ability to consolidate evolving user states and resolve conflicts. We introduce EverMemOS, a self‑organizing memory operating system that implements an engram‑inspired lifecycle for computational memory. Episodic Trace Formation converts dialogue streams into MemCells that capture episodic traces, atomic facts, and time‑bounded Foresight signals. Semantic Consolidation organizes MemCells into thematic MemScenes, distilling stable semantic structures and updating user profiles. Reconstructive Recollection performs MemScene‑guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning. Experiments on LoCoMo and LongMemEval show that EverMemOS achieves state‑of‑the‑art performance on memory‑augmented reasoning tasks. We further report a profile study on PersonaMem v2 and qualitative case studies illustrating chat‑oriented capabilities such as user profiling and Foresight. Code is available at https://github.com/EverMind‑AI/EverMemOS.

PaperId: 3908, https://arxiv.org/pdf/2601.02158.pdf GitHub

Abstract:
This paper presents FormationEval, an open multiple‑choice question benchmark for evaluating language models on petroleum geoscience and subsurface disciplines. The dataset contains 505 questions across seven domains including petrophysics, petroleum geology and reservoir engineering, derived from three authoritative sources using a reasoning model with detailed instructions and a concept‑based approach that avoids verbatim copying of copyrighted text. Each question includes source metadata to support traceability and audit. The evaluation covers 72 models from major providers including OpenAI, Anthropic, Google, Meta and open‑weight alternatives. The top performers achieve over 97% accuracy, with Gemini 3 Pro Preview reaching 99.8%, while tier and domain gaps persist. Among open‑weight models, GLM‑4.7 leads at 98.6%, with several DeepSeek, Llama, Qwen and Mistral models also exceeding 93%. The performance gap between open‑weight and closed models is narrower than expected, with several lower‑cost open‑weight models exceeding 90% accuracy. Petrophysics emerges as the most challenging domain across all models, while smaller models show wider performance variance. Residual length bias in the dataset (correct answers tend to be longer) is documented along with bias mitigation strategies applied during construction. The benchmark, evaluation code and results are publicly available.

PaperId: 3909, https://arxiv.org/pdf/2601.02016.pdf GitHub

Abstract:
This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine‑grained, descriptive information available during training but not at inference. We introduce a general, model‑agnostic methodology for injecting privileged information‑such as bounding box masks, saliency maps, and depth cues‑into deep learning‑based object detectors through a teacher‑student architecture. Experiments are conducted across five state‑of‑the‑art object detection models and multiple public benchmarks, including UAV‑based litter detection datasets and Pascal VOC 2012, to assess the impact on accuracy, generalization, and computational efficiency. Our results demonstrate that LUPI‑trained students consistently outperform their baseline counterparts, achieving significant boosts in detection accuracy with no increase in inference complexity or model size. Performance improvements are especially marked for medium and large objects, while ablation studies reveal that intermediate weighting of teacher guidance optimally balances learning from privileged and standard inputs. The findings affirm that the LUPI framework provides an effective and practical strategy for advancing object detection systems in both resource‑constrained and real‑world settings.

PaperId: 3910, https://arxiv.org/pdf/2601.02015.pdf GitHub

Abstract:
Novel metaphor comprehension involves complex semantic processes and linguistic creativity, making it an interesting task for studying language models (LMs). This study investigates whether surprisal, a probabilistic measure of predictability in LMs, correlates with annotations of metaphor novelty in different datasets. We analyse the surprisal of metaphoric words in corpus‑based and synthetic metaphor datasets using 16 causal LM variants. We propose a cloze‑style surprisal method that conditions on full‑sentence context. Results show that LM surprisal yields significant moderate correlations with scores/labels of metaphor novelty. We further identify divergent scaling patterns: on corpus‑based data, correlation strength decreases with model size (inverse scaling effect), whereas on synthetic data it increases (quality‑power hypothesis). We conclude that while surprisal can partially account for annotations of metaphor novelty, it remains limited as a metric of linguistic creativity. Code and data are publicly available: https://github.com/OmarMomen14/surprisal‑metaphor‑novelty

PaperId: 3911, https://arxiv.org/pdf/2601.01908.pdf GitHub

Abstract:
Thyroid cancer is the most common endocrine malignancy, and its incidence is rising globally. While ultrasound is the preferred imaging modality for detecting thyroid nodules, its diagnostic accuracy is often limited by challenges such as low image contrast and blurred nodule boundaries. To address these issues, we propose Nodule‑DETR, a novel detection transformer (DETR) architecture designed for robust thyroid nodule detection in ultrasound images. Nodule‑DETR introduces three key innovations: a Multi‑Spectral Frequency‑domain Channel Attention (MSFCA) module that leverages frequency analysis to enhance features of low‑contrast nodules; a Hierarchical Feature Fusion (HFF) module for efficient multi‑scale integration; and Multi‑Scale Deformable Attention (MSDA) to flexibly capture small and irregularly shaped nodules. We conducted extensive experiments on a clinical dataset of real‑world thyroid ultrasound images. The results demonstrate that Nodule‑DETR achieves state‑of‑the‑art performance, outperforming the baseline model by a significant margin of 0.149 in mAP@0.5:0.95. The superior accuracy of Nodule‑DETR highlights its significant potential for clinical application as an effective tool in computer‑aided thyroid diagnosis. The code of work is available at https://github.com/wjj1wjj/Nodule‑DETR.

PaperId: 3912, https://arxiv.org/pdf/2601.01874.pdf GitHub

Abstract:
Despite significant progress, multimodal large language models continue to struggle with visual mathematical problem solving. Some recent works recognize that visual perception is a bottleneck in visual mathematical reasoning, but their solutions are limited to improving the extraction and interpretation of visual inputs. Notably, they all ignore the key issue of whether the extracted visual cues are faithfully integrated and properly utilized in subsequent reasoning. Motivated by this, we present CogFlow, a novel cognitive‑inspired three‑stage framework that incorporates a knowledge internalization stage, explicitly simulating the hierarchical flow of human reasoning: perception\Rightarrowinternalization\Rightarrowreasoning. In line with this hierarchical flow, we holistically enhance all its stages. We devise Synergistic Visual Rewards to boost perception capabilities in parametric and semantic spaces, jointly improving visual information extraction from symbols and diagrams. To guarantee faithful integration of extracted visual cues into subsequent reasoning, we introduce a Knowledge Internalization Reward model in the internalization stage, bridging perception and reasoning. Moreover, we design a Visual‑Gated Policy Optimization algorithm to further enforce the reasoning is grounded with the visual knowledge, preventing models seeking shortcuts that appear coherent but are visually ungrounded reasoning chains. Moreover, we contribute a new dataset MathCog for model training, which contains samples with over 120K high‑quality perception‑reasoning aligned annotations. Comprehensive experiments and analysis on commonly used visual mathematical reasoning benchmarks validate the superiority of the proposed CogFlow. Project page: https://shchen233.github.io/cogflow.

PaperId: 3913, https://arxiv.org/pdf/2601.01781.pdf GitHub GitHub

Abstract:
Self‑supervised learning (SSL) methods have become a dominant paradigm for creating general purpose models whose capabilities can be transferred to downstream supervised learning tasks. However, most such methods rely on vast amounts of pretraining data. This work introduces Subimage Overlap Prediction, a novel self‑supervised pretraining task to aid semantic segmentation in remote sensing imagery that uses significantly lesser pretraining imagery. Given an image, a sub‑image is extracted and the model is trained to produce a semantic mask of the location of the extracted sub‑image within the original image. We demonstrate that pretraining with this task results in significantly faster convergence, and equal or better performance (measured via mIoU) on downstream segmentation. This gap in convergence and performance widens when labeled training data is reduced. We show this across multiple architecture types, and with multiple downstream datasets. We also show that our method matches or exceeds performance while requiring significantly lesser pretraining data relative to other SSL methods. Code and model weights are provided at \hrefhttps://github.com/sharmalakshay93/subimage‑overlap‑predictiongithub.com/sharmalakshay93/subimage‑overlap‑prediction.

PaperId: 3914, https://arxiv.org/pdf/2601.01753.pdf GitHub

Abstract:
Modern recommender systems trained on domain‑specific data often struggle to generalize across multiple domains. Cross‑domain sequential recommendation has emerged as a promising research direction to address this challenge; however, existing approaches face fundamental limitations, such as reliance on overlapping users or items across domains, or unrealistic assumptions that ignore privacy constraints. In this work, we propose a new framework, MergeRec, based on model merging under a new and realistic problem setting termed data‑isolated cross‑domain sequential recommendation, where raw user interaction data cannot be shared across domains. MergeRec consists of three key components: (1) merging initialization, (2) pseudo‑user data construction, and (3) collaborative merging optimization. First, we initialize a merged model using training‑free merging techniques. Next, we construct pseudo‑user data by treating each item as a virtual sequence in each domain, enabling the synthesis of meaningful training samples without relying on real user interactions. Finally, we optimize domain‑specific merging weights through a joint objective that combines a recommendation loss, which encourages the merged model to identify relevant items, and a distillation loss, which transfers collaborative filtering signals from the fine‑tuned source models. Extensive experiments demonstrate that MergeRec not only preserves the strengths of the original models but also significantly enhances generalizability to unseen domains. Compared to conventional model merging methods, MergeRec consistently achieves superior performance, with average improvements of up to 17.21% in Recall@10, highlighting the potential of model merging as a scalable and effective approach for building universal recommender systems. The source code is available at https://github.com/DIALLab‑SKKU/MergeRec.

PaperId: 3915, https://arxiv.org/pdf/2601.01718.pdf GitHub

Abstract:
We introduce Yuan3.0 Flash, an open‑source Mixture‑of‑Experts (MoE) MultiModal Large Language Model featuring 3.7B activated parameters and 40B total parameters, specifically designed to enhance performance on enterprise‑oriented tasks while maintaining competitive capabilities on general‑purpose tasks. To address the overthinking phenomenon commonly observed in Large Reasoning Models (LRMs), we propose Reflection‑aware Adaptive Policy Optimization (RAPO), a novel RL training algorithm that effectively regulates overthinking behaviors. In enterprise‑oriented tasks such as retrieval‑augmented generation (RAG), complex table understanding, and summarization, Yuan3.0 Flash consistently achieves superior performance. Moreover, it also demonstrates strong reasoning capabilities in domains such as mathematics, science, etc., attaining accuracy comparable to frontier model while requiring only approximately 1/4 to 1/2 of the average tokens. Yuan3.0 Flash has been fully open‑sourced to facilitate further research and real‑world deployment: https://github.com/Yuan‑lab‑LLM/Yuan3.0.

PaperId: 3916, https://arxiv.org/pdf/2601.01685.pdf GitHub

Abstract:
As large language models (LLMs) transition to autonomous agents synthesizing real‑time information, their reasoning capabilities introduce an unexpected attack surface. This paper introduces a novel threat where colluding agents steer victim beliefs using only truthful evidence fragments distributed through public channels, without relying on covert communications, backdoors, or falsified documents. By exploiting LLMs' overthinking tendency, we formalize the first cognitive collusion attack and propose Generative Montage: a Writer‑Editor‑Director framework that constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions. To study this risk, we develop CoPHEME, a dataset derived from real‑world rumor events, and simulate attacks across diverse LLM families. Our results show pervasive vulnerability across 14 LLM families: attack success rates reach 74.4% for proprietary models and 70.6% for open‑weights models. Counterintuitively, stronger reasoning capabilities increase susceptibility, with reasoning‑specialized models showing higher attack success than base models or prompts. Furthermore, these false beliefs then cascade to downstream judges, achieving over 60% deception rates, highlighting a socio‑technical vulnerability in how LLM‑based agents interact with dynamic information environments. Our implementation and data are available at: https://github.com/CharlesJW222/Lying_with_Truth/tree/main.

PaperId: 3917, https://arxiv.org/pdf/2601.01655.pdf GitHub

Abstract:
Accurate crop yield prediction relies on diverse data streams, including satellite, meteorological, soil, and topographic information. However, despite rapid advances in machine learning, existing approaches remain crop‑ or region‑specific and require data engineering efforts. This limits scalability, reproducibility, and operational deployment. This study introduces UniCrop, a universal and reusable data pipeline designed to automate the acquisition, cleaning, harmonisation, and engineering of multi‑source environmental data for crop yield prediction. For any given location, crop type, and temporal window, UniCrop automatically retrieves, harmonises, and engineers over 200 environmental variables (Sentinel‑1/2, MODIS, ERA5‑Land, NASA POWER, SoilGrids, and SRTM), reducing them to a compact, analysis‑ready feature set utilising a structured feature reduction workflow with minimum redundancy maximum relevance (mRMR). To validate, UniCrop was applied to a rice yield dataset comprising 557 field observations. Using only the selected 15 features, four baseline machine learning models (LightGBM, Random Forest, Support Vector Regression, and Elastic Net) were trained. LightGBM achieved the best single‑model performance (RMSE = 465.1 kg/ha, R^2 = 0.6576), while a constrained ensemble of all baselines further improved accuracy (RMSE = 463.2 kg/ha, R^2 = 0.6604). UniCrop contributes a scalable and transparent data‑engineering framework that addresses the primary bottleneck in operational crop yield modelling: the preparation of consistent and harmonised multi‑source data. By decoupling data specification from implementation and supporting any crop, region, and time frame through simple configuration updates, UniCrop provides a practical foundation for scalable agricultural analytics. The code and implementation documentation are shared in https://github.com/CoDIS‑Lab/UniCrop.

PaperId: 3918, https://arxiv.org/pdf/2601.01487.pdf GitHub

Abstract:
Diffusion inversion is a task of recovering the noise of an image in a diffusion model, which is vital for controllable diffusion image editing. At present, diffusion inversion still remains a challenging task due to the lack of viable supervision signals. Thus, most existing methods resort to approximation‑based solutions, which however are often at the cost of performance or efficiency. To remedy these shortcomings, we propose a novel self‑supervised diffusion inversion approach in this paper, termed Deep Inversion (DeepInv). Instead of requiring ground‑truth noise annotations, we introduce a self‑supervised objective as well as a data augmentation strategy to generate high‑quality pseudo noises from real images without manual intervention. Based on these two innovative designs, DeepInv is also equipped with an iterative and multi‑scale training regime to train a parameterized inversion solver, thereby achieving the fast and accurate image‑to‑noise mapping. To the best of our knowledge, this is the first attempt of presenting a trainable solver to predict inversion noise step by step. The extensive experiments show that our DeepInv can achieve much better performance and inference speed than the compared methods, e.g., +40.435% SSIM than EasyInv and +9887.5% speed than ReNoise on COCO dataset. Moreover, our careful designs of trainable solvers can also provide insights to the community. Codes and model parameters will be released in https://github.com/potato‑kitty/DeepInv.

PaperId: 3919, https://arxiv.org/pdf/2601.01473.pdf GitHub

Abstract:
Graph neural networks (GNNs) have achieved breakthroughs in various real‑world downstream tasks due to their powerful expressiveness. As the scale of real‑world graphs has been continuously growing, a storage‑based approach to GNN training has been studied, which leverages external storage (e.g., NVMe SSDs) to handle such web‑scale graphs on a single machine. Although such storage‑based GNN training methods have shown promising potential in large‑scale GNN training, we observed that they suffer from a severe bottleneck in data preparation since they overlook a critical challenge: how to handle a large number of small storage I/Os. To address the challenge, in this paper, we propose a novel storage‑based GNN training framework, named AGNES, that employs a method of block‑wise storage I/O processing to fully utilize the I/O bandwidth of high‑performance storage devices. Moreover, to further enhance the efficiency of each storage I/O, AGNES employs a simple yet effective strategy, hyperbatch‑based processing based on the characteristics of real‑world graphs. Comprehensive experiments on five real‑world graphs reveal that AGNES consistently outperforms four state‑of‑the‑art methods, by up to 4.1X faster than the best competitor. Our code is available at https://github.com/Bigdasgit/agnes‑kdd26.

PaperId: 3920, https://arxiv.org/pdf/2601.01406.pdf GitHub

Abstract:
Face super‑resolution aims to recover high‑quality facial images from severely degraded low‑resolution inputs, but remains challenging due to the loss of fine structural details and identity‑specific features. This work introduces SwinIFS, a landmark‑guided super‑resolution framework that integrates structural priors with hierarchical attention mechanisms to achieve identity‑preserving reconstruction at both moderate and extreme upscaling factors. The method incorporates dense Gaussian heatmaps of key facial landmarks into the input representation, enabling the network to focus on semantically important facial regions from the earliest stages of processing. A compact Swin Transformer backbone is employed to capture long‑range contextual information while preserving local geometry, allowing the model to restore subtle facial textures and maintain global structural consistency. Extensive experiments on the CelebA benchmark demonstrate that SwinIFS achieves superior perceptual quality, sharper reconstructions, and improved identity retention; it consistently produces more photorealistic results and exhibits strong performance even under 8x magnification, where most methods fail to recover meaningful structure. SwinIFS also provides an advantageous balance between reconstruction accuracy and computational efficiency, making it suitable for real‑world applications in facial enhancement, surveillance, and digital restoration. Our code, model weights, and results are available at https://github.com/Habiba123‑stack/SwinIFS.

PaperId: 3921, https://arxiv.org/pdf/2601.01386.pdf GitHub

Abstract:
Parking is a critical task for autonomous driving systems (ADS), with unique challenges in crowded parking slots and GPS‑denied environments. However, existing works focus on 2D parking slot perception, mapping, and localization, 3D reconstruction remains underexplored, which is crucial for capturing complex spatial geometry in parking scenarios. Naively improving the visual quality of reconstructed parking scenes does not directly benefit autonomous parking, as the key entry point for parking is the slots perception module. To address these limitations, we curate the first benchmark named ParkRecon3D, specifically designed for parking scene reconstruction. It includes sensor data from four surround‑view fisheye cameras with calibrated extrinsics and dense parking slot annotations. We then propose ParkGaussian, the first framework that integrates 3D Gaussian Splatting (3DGS) for parking scene reconstruction. To further improve the alignment between reconstruction and downstream parking slot detection, we introduce a slot‑aware reconstruction strategy that leverages existing parking perception methods to enhance the synthesis quality of slot regions. Experiments on ParkRecon3D demonstrate that ParkGaussian achieves state‑of‑the‑art reconstruction quality and better preserves perception consistency for downstream tasks. The code and dataset will be released at: https://github.com/wm‑research/ParkGaussian

PaperId: 3922, https://arxiv.org/pdf/2601.01373.pdf GitHub

Abstract:
The development of audio foundation models has accelerated rapidly since the emergence of GPT‑4o. However, the lack of comprehensive evaluation has become a critical bottleneck for further progress in the field, particularly in audio generation. Current audio evaluation faces three major challenges: (1) audio evaluation lacks a unified framework, with datasets and code scattered across various sources, hindering fair and efficient cross‑model comparison;(2) audio codecs, as a key component of audio foundation models, lack a widely accepted and holistic evaluation methodology; (3) existing speech benchmarks are heavily reliant on English, making it challenging to objectively assess models' performance on Chinese. To address the first issue, we introduce UltraEval‑Audio, a unified evaluation framework for audio foundation models, specifically designed for both audio understanding and generation tasks. UltraEval‑Audio features a modular architecture, supporting 10 languages and 14 core task categories, while seamlessly integrating 24 mainstream models and 36 authoritative benchmarks. To enhance research efficiency, the framework provides a one‑command evaluation feature, accompanied by real‑time public leaderboards. For the second challenge, UltraEval‑Audio adopts a novel comprehensive evaluation scheme for audio codecs, evaluating performance across three key dimensions: semantic accuracy, timbre fidelity, and acoustic quality. To address the third issue, we propose two new Chinese benchmarks, SpeechCMMLU and SpeechHSK, designed to assess Chinese knowledge proficiency and language fluency. We wish that UltraEval‑Audio will provide both academia and industry with a transparent, efficient, and fair platform for comparison of audio models. Our code, benchmarks, and leaderboards are available at https://github.com/OpenBMB/UltraEval‑Audio.

PaperId: 3923, https://arxiv.org/pdf/2601.01366.pdf GitHub

Abstract:
With the rapid adoption of multimodal large language models (MLMs) in autonomous agents, cross‑platform task execution capabilities in educational settings have garnered significant attention. However, existing benchmark frameworks still exhibit notable deficiencies in supporting cross‑platform tasks in educational contexts, especially when dealing with school‑specific software (such as XiaoYa Intelligent Assistant, HuaShi XiaZi, etc.), where the efficiency of agents often significantly decreases due to a lack of understanding of the structural specifics of these private‑domain software. Additionally, current evaluation methods heavily rely on coarse‑grained metrics like goal orientation or trajectory matching, making it challenging to capture the detailed execution and efficiency of agents in complex tasks. To address these issues, we propose KGCE (Knowledge‑Augmented Dual‑Graph Evaluator for Cross‑Platform Educational Agent Benchmarking with Multimodal Language Models), a novel benchmarking platform that integrates knowledge base enhancement and a dual‑graph evaluation framework. We first constructed a dataset comprising 104 education‑related tasks, covering Windows, Android, and cross‑platform collaborative tasks. KGCE introduces a dual‑graph evaluation framework that decomposes tasks into multiple sub‑goals and verifies their completion status, providing fine‑grained evaluation metrics. To overcome the execution bottlenecks of existing agents in private‑domain tasks, we developed an enhanced agent system incorporating a knowledge base specific to school‑specific software. The code can be found at https://github.com/Kinginlife/KGCE.

PaperId: 3924, https://arxiv.org/pdf/2601.01301.pdf GitHub

Abstract:
We introduce a recursive AlphaZero‑style Monte‑‑Carlo tree search algorithm, "RMCTS". The advantage of RMCTS over AlphaZero's MCTS‑UCB is speed. In RMCTS, the search tree is explored in a breadth‑first manner, so that network inferences naturally occur in large batches. This significantly reduces the GPU latency cost. We find that RMCTS is often more than 40 times faster than MCTS‑UCB when searching a single root state, and about 3 times faster when searching a large batch of root states. The recursion in RMCTS is based on computing optimized posterior policies at each game state in the search tree, starting from the leaves and working back up to the root. Here we use the posterior policy explored in "Monte‑‑Carlo tree search as regularized policy optimization" (Grill, et al.) Their posterior policy is the unique policy which maximizes the expected reward given estimated action rewards minus a penalty for diverging from the prior policy. The tree explored by RMCTS is not defined in an adaptive manner, as it is in MCTS‑UCB. Instead, the RMCTS tree is defined by following prior network policies at each node. This is a disadvantage, but the speedup advantage is more significant, and in practice we find that RMCTS‑trained networks match the quality of MCTS‑UCB‑trained networks in roughly one‑third of the training time. We include timing and quality comparisons of RMCTS vs. MCTS‑UCB for three games: Connect‑4, Dots‑and‑Boxes, and Othello.

PaperId: 3925, https://arxiv.org/pdf/2601.01288.pdf GitHub

Abstract:
Reinforcement learning from pixels is often bottlenecked by the performance and complexity of 3D rendered environments. Researchers face a trade‑off between high‑speed, low‑level engines and slower, more accessible Python frameworks. To address this, we introduce PyBatchRender, a Python library for high‑throughput, batched 3D rendering that achieves over 1 million FPS on simple scenes. Built on the Panda3D game engine, it utilizes its mature ecosystem while enhancing performance through optimized batched rendering for up to 1000X speedups. Designed as a physics‑agnostic renderer for reinforcement learning from pixels, PyBatchRender offers greater flexibility than dedicated libraries, simpler setup than typical game‑engine wrappers, and speeds rivaling state‑of‑the‑art C++ engines like Madrona. Users can create custom scenes entirely in Python with tens of lines of code, enabling rapid prototyping for scalable AI training. Open‑source and easy to integrate, it serves to democratize high‑performance 3D simulation for researchers and developers. The library is available at https://github.com/dolphin‑in‑a‑coma/PyBatchRender.

PaperId: 3926, https://arxiv.org/pdf/2601.01280.pdf GitHub

Abstract:
Graph structures are increasingly used in dialog memory systems, but empirical findings on their effectiveness remain inconsistent, making it unclear which design choices truly matter. We present an experimental, system‑oriented analysis of long‑term dialog memory architectures. We introduce a unified framework that decomposes dialog memory systems into core components and supports both graph‑based and non‑graph approaches. Under this framework, we conduct controlled, stage‑wise experiments on LongMemEval and HaluMem, comparing common design choices in memory representation, organization, maintenance, and retrieval. Our results show that many performance differences are driven by foundational system settings rather than specific architectural innovations. Based on these findings, we identify stable and reliable strong baselines for future dialog memory research. Code are available at https://github.com/AvatarMemory/UnifiedMem

PaperId: 3927, https://arxiv.org/pdf/2601.01224.pdf GitHub

Abstract:
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object‑centric learning (OCL), but suffers from slot entanglement and weak alignment between object slots and image content. We propose Contrastive Object‑centric Diffusion Alignment (CODA), a simple extension that (i) employs register slots to absorb residual attention and reduce interference between object slots, and (ii) applies a contrastive alignment loss to explicitly encourage slot‑image correspondence. The resulting training objective serves as a tractable surrogate for maximizing mutual information (MI) between slots and inputs, strengthening slot representation quality. On both synthetic (MOVi‑C/E) and real‑world datasets (VOC, COCO), CODA improves object discovery (e.g., +6.1% FG‑ARI on COCO), property prediction, and compositional image generation over strong baselines. Register slots add negligible overhead, keeping CODA efficient and scalable. These results indicate potential applications of CODA as an effective framework for robust OCL in complex, real‑world scenes. Code and pretrained models are available at https://github.com/sony/coda.

PaperId: 3928, https://arxiv.org/pdf/2601.01024.pdf GitHub

Abstract:
Vision Language Models (VLMs) have rapidly advanced and show strong promise for text‑based person search (TBPS), a task that requires capturing fine‑grained relationships between images and text to distinguish individuals. Previous methods address these challenges through local alignment, yet they are often prone to shortcut learning and spurious correlations, yielding misalignment. Moreover, injecting prior knowledge can distort intra‑modality structure. Motivated by our finding that encoder attention surfaces spatially precise evidence from the earliest training epochs, and to alleviate these issues, we introduceITSELF, an attention‑guided framework for implicit local alignment. At its core, Guided Representation with Attentive Bank (GRAB) converts the model's own attention into an Attentive Bank of high‑saliency tokens and applies local objectives on this bank, learning fine‑grained correspondences without extra supervision. To make the selection reliable and non‑redundant, we introduce Multi‑Layer Attention for Robust Selection (MARS), which aggregates attention across layers and performs diversity‑aware top‑k selection; and Adaptive Token Scheduler (ATS), which schedules the retention budget from coarse to fine over training, preserving context early while progressively focusing on discriminative details. Extensive experiments on three widely used TBPS benchmarks showstate‑of‑the‑art performance and strong cross‑dataset generalization, confirming the effectiveness and robustness of our approach without additional prior supervision. Our project is publicly available at https://trhuuloc.github.io/itself

PaperId: 3929, https://arxiv.org/pdf/2601.01022.pdf GitHub

Abstract:
Existing RGB‑Event visual object tracking approaches primarily rely on conventional feature‑level fusion, failing to fully exploit the unique advantages of event cameras. In particular, the high dynamic range and motion‑sensitive nature of event cameras are often overlooked, while low‑information regions are processed uniformly, leading to unnecessary computational overhead for the backbone network. To address these issues, we propose a novel tracking framework that performs early fusion in the frequency domain, enabling effective aggregation of high‑frequency information from the event modality. Specifically, RGB and event modalities are transformed from the spatial domain to the frequency domain via the Fast Fourier Transform, with their amplitude and phase components decoupled. High‑frequency event information is selectively fused into RGB modality through amplitude and phase attention, enhancing feature representation while substantially reducing backbone computation. In addition, a motion‑guided spatial sparsification module leverages the motion‑sensitive nature of event cameras to capture the relationship between target motion cues and spatial probability distribution, filtering out low‑information regions and enhancing target‑relevant features. Finally, a sparse set of target‑relevant features is fed into the backbone network for learning, and the tracking head predicts the final target position. Extensive experiments on three widely used RGB‑Event tracking benchmark datasets, including FE108, FELT, and COESOT, demonstrate the high performance and efficiency of our method. The source code of this paper will be released on https://github.com/Event‑AHU/OpenEvTracking

PaperId: 3930, https://arxiv.org/pdf/2601.01011.pdf GitHub

Abstract:
Language generation maps a rich, high‑dimensional internal state to a single token sequence. We study this many‑to‑one mapping through the lens of intention collapse: the projection from an internal intention space I to an external language space L. We introduce three cheap, model‑agnostic metrics computed on a pre‑collapse state I: (i) intention entropy Hint(I), (ii) effective dimensionality deff(I), and (iii) recoverability Recov(I), operationalized as probe AUROC for predicting eventual success. We evaluate these metrics in a 3x3 study across models (Mistral‑7B, LLaMA‑3.1‑8B, Qwen‑2.5‑7B) and benchmarks (GSM8K, ARC‑Challenge, AQUA‑RAT), comparing baseline, chain‑of‑thought (CoT), and a babble control (n=200 items per cell). CoT increases average accuracy from 34.2% to 47.3% (+13.1 pp), driven by large gains on GSM8K but consistent degradations on ARC‑Challenge. Across models, CoT induces distinct entropy regimes relative to baseline, dH = Hint(CoT) ‑ Hint(Base): Mistral shows dH < 0 (lower‑entropy CoT), whereas LLaMA shows dH > 0 (higher‑entropy CoT), highlighting heterogeneity in CoT‑induced internal uncertainty. Finally, probe AUROC is significantly above chance in a subset of settings and can dissociate from behavioral accuracy (e.g., high AUROC alongside lower CoT accuracy on ARC‑Challenge for Qwen), suggesting that informative internal signal is not always reliably converted into a final discrete decision under constrained response formats.

PaperId: 3931, https://arxiv.org/pdf/2601.01005.pdf GitHub

Abstract:
Medical image segmentation faces critical challenges in semi‑supervised learning scenarios due to severe annotation scarcity requiring expert radiological knowledge, significant inter‑annotator variability across different viewpoints and expertise levels, and inadequate multi‑scale feature integration for precise boundary delineation in complex anatomical structures. Existing semi‑supervised methods demonstrate substantial performance degradation compared to fully supervised approaches, particularly in small target segmentation and boundary refinement tasks. To address these fundamental challenges, we propose SASNet (Scale‑aware Adaptive Supervised Network), a dual‑branch architecture that leverages both low‑level and high‑level feature representations through novel scale‑aware adaptive reweight mechanisms. Our approach introduces three key methodological innovations, including the Scale‑aware Adaptive Reweight strategy that dynamically weights pixel‑wise predictions using temporal confidence accumulation, the View Variance Enhancement mechanism employing 3D Fourier domain transformations to simulate annotation variability, and segmentation‑regression consistency learning through signed distance map algorithms for enhanced boundary precision. These innovations collectively address the core limitations of existing semi‑supervised approaches by integrating spatial, temporal, and geometric consistency principles within a unified optimization framework. Comprehensive evaluation across LA, Pancreas‑CT, and BraTS datasets demonstrates that SASNet achieves superior performance with limited labeled data, surpassing state‑of‑the‑art semi‑supervised methods while approaching fully supervised performance levels. The source code for SASNet is available at https://github.com/HUANGLIZI/SASNet.

PaperId: 3932, https://arxiv.org/pdf/2601.00993.pdf GitHub

Abstract:
Wildlife monitoring is crucial for studying biodiversity loss and climate change. Camera trap images provide a non‑intrusive method for analyzing animal populations and identifying ecological patterns over time. However, manual analysis is time‑consuming and resource‑intensive. Deep learning, particularly foundation models, has been applied to automate wildlife identification, achieving strong performance when tested on data from the same geographical locations as their training sets. Yet, despite their promise, these models struggle to generalize to new geographical areas, leading to significant performance drops. For example, training an advanced vision‑language model, such as CLIP with an adapter, on an African dataset achieves an accuracy of 84.77%. However, this performance drops significantly to 16.17% when the model is tested on an American dataset. This limitation partly arises because existing models rely predominantly on image‑based representations, making them sensitive to geographical data distribution shifts, such as variation in background, lighting, and environmental conditions. To address this, we introduce WildIng, a Wildlife image Invariant representation model for geographical domain shift. WildIng integrates text descriptions with image features, creating a more robust representation to geographical domain shifts. By leveraging textual descriptions, our approach captures consistent semantic information, such as detailed descriptions of the appearance of the species, improving generalization across different geographical locations. Experiments show that WildIng enhances the accuracy of foundation models such as BioCLIP by 30% under geographical domain shift conditions. We evaluate WildIng on two datasets collected from different regions, namely America and Africa. The code and models are publicly available at https://github.com/Julian075/CATALOG/tree/WildIng.

PaperId: 3933, https://arxiv.org/pdf/2601.00927.pdf GitHub

Abstract:
Understanding affective polarization in online discourse is crucial for evaluating the societal impact of social media interactions. This study presents a novel framework that leverages large language models (LLMs) and domain‑informed heuristics to systematically analyze and quantify affective polarization in discussions on divisive topics such as climate change and gun control. Unlike most prior approaches that relied on sentiment analysis or predefined classifiers, our method integrates LLMs to extract stance, affective tone, and agreement patterns from large‑scale social media discussions. We then apply a rule‑based scoring system capable of quantifying affective polarization even in small conversations consisting of single interactions, based on stance alignment, emotional content, and interaction dynamics. Our analysis reveals distinct polarization patterns that are event dependent: (i) anticipation‑driven polarization, where extreme polarization escalates before well‑publicized events, and (ii) reactive polarization, where intense affective polarization spikes immediately after sudden, high‑impact events. By combining AI‑driven content annotation with domain‑informed scoring, our framework offers a scalable and interpretable approach to measuring affective polarization. The source code is publicly available at: https://github.com/hasanjawad001/llm‑social‑media‑polarization.

PaperId: 3934, https://arxiv.org/pdf/2601.00869.pdf GitHub

Abstract:
As artificial intelligence systems increasingly mediate consumer information discovery, brands face algorithmic invisibility. This study investigates Cultural Encoding in Large Language Models (LLMs) ‑‑ systematic differences in brand recommendations arising from training data composition. Analyzing 1,909 pure‑English queries across 6 LLMs (GPT‑4o, Claude, Gemini, Qwen3, DeepSeek, Doubao) and 30 brands, we find Chinese LLMs exhibit 30.6 percentage points higher brand mention rates than International LLMs (88.9% vs. 58.3%, p<.001). This disparity persists in identical English queries, indicating training data geography ‑‑ not language ‑‑ drives the effect. We introduce the Existence Gap: brands absent from LLM training corpora lack "existence" in AI responses regardless of quality. Through a case study of Zhizibianjie (OmniEdge), a collaboration platform with 65.6% mention rate in Chinese LLMs but 0% in International models (p<.001), we demonstrate how Linguistic Boundary Barriers create invisible market entry obstacles. Theoretically, we contribute the Data Moat Framework, conceptualizing AI‑visible content as a VRIN strategic resource. We operationalize Algorithmic Omnipresence ‑‑ comprehensive brand visibility across LLM knowledge bases ‑‑ as the strategic objective for Generative Engine Optimization (GEO). Managerially, we provide an 18‑month roadmap for brands to build Data Moats through semantic coverage, technical depth, and cultural localization. Our findings reveal that in AI‑mediated markets, the limits of a brand's "Data Boundaries" define the limits of its "Market Frontiers."

PaperId: 3935, https://arxiv.org/pdf/2601.00845.pdf GitHub

Abstract:
Temporal point processes (TPPs) are crucial for analyzing events over time and are widely used in fields such as finance, healthcare, and social systems. These processes are particularly valuable for understanding how events unfold over time, accounting for their irregularity and dependencies. Despite the success of large language models (LLMs) in sequence modeling, applying them to temporal point processes remains challenging. A key issue is that current methods struggle to effectively capture the complex interaction between temporal information and semantic context, which is vital for accurate event modeling. In this context, we introduce TPP‑TAL (Temporal Point Processes with Enhanced Temporal Awareness in LLMs), a novel plug‑and‑play framework designed to enhance temporal reasoning within LLMs. Rather than using the conventional method of simply concatenating event time and type embeddings, TPP‑TAL explicitly aligns temporal dynamics with contextual semantics before feeding this information into the LLM. This alignment allows the model to better perceive temporal dependencies and long‑range interactions between events and their surrounding contexts. Through comprehensive experiments on several benchmark datasets, it is shown that TPP‑TAL delivers substantial improvements in temporal likelihood estimation and event prediction accuracy, highlighting the importance of enhancing temporal awareness in LLMs for continuous‑time event modeling. The code is made available at https://github.com/chenlilil/TPP‑TAL

PaperId: 3936, https://arxiv.org/pdf/2601.00843.pdf GitHub

Abstract:
While Deep Learning has improved Brain‑Computer Interface (BCI) decoding accuracy, clinical adoption is hindered by the "Black Box" nature of these algorithms, leading to user frustration and poor neuroplasticity outcomes. We propose OmniNeuro, a novel HCI framework that transforms the BCI from a silent decoder into a transparent feedback partner. OmniNeuro integrates three interpretability engines: (1) Physics (Energy), (2) Chaos (Fractal Complexity), and (3) Quantum‑Inspired uncertainty modeling. These metrics drive real‑time Neuro‑Sonification and Generative AI Clinical Reports. Evaluated on the PhysioNet dataset (N=109), the system achieved a mean accuracy of 58.52%, with qualitative pilot studies (N=3) confirming that explainable feedback helps users regulate mental effort and reduces the "trial‑and‑error" phase. OmniNeuro is decoder‑agnostic, acting as an essential interpretability layer for any state‑of‑the‑art architecture.

PaperId: 3937, https://arxiv.org/pdf/2601.00828.pdf GitHub

Abstract:
Large Language Models (LLMs) are widely believed to possess self‑correction capabilities, yet recent studies suggest that intrinsic self‑correction‑‑where models correct their own outputs without external feedback‑‑remains largely ineffective. In this work, we systematically decompose self‑correction into three distinct sub‑capabilities: error detection, error localization, and error correction. Through cross‑model experiments on GSM8K‑Complex (n=500 per model, 346 total errors) with three major LLMs, we uncover a striking Accuracy‑Correction Paradox: weaker models (GPT‑3.5, 66% accuracy) achieve 1.6x higher intrinsic correction rates than stronger models (DeepSeek, 94% accuracy)‑‑26.8% vs 16.7%. We propose the Error Depth Hypothesis: stronger models make fewer but deeper errors that resist self‑correction. Error detection rates vary dramatically across architectures (10% to 82%), yet detection capability does not predict correction success‑‑Claude detects only 10% of errors but corrects 29% intrinsically. Surprisingly, providing error location hints hurts all models. Our findings challenge linear assumptions about model capability and self‑improvement, with important implications for the design of self‑refinement pipelines.

PaperId: 3938, https://arxiv.org/pdf/2601.00821.pdf GitHub

Abstract:
Conversation summarization loses nuanced details: when asked about coding preferences after 40 turns, summarization recalls "use type hints" but drops the critical constraint "everywhere" (19.0% exact match vs. 93.0% for our approach). We present CogCanvas, a training‑free framework inspired by how teams use whiteboards to anchor shared memory. Rather than compressing conversation history, CogCanvas extracts verbatim‑grounded artifacts (decisions, facts, reminders) and retrieves them via temporal‑aware graph. On the LoCoMo benchmark (all 10 conversations from the ACL 2024 release), CogCanvas achieves the highest overall accuracy among training‑free methods (32.4%), outperforming RAG (24.6%) by +7.8pp, with decisive advantages on complex reasoning tasks: +20.6pp on temporal reasoning (32.7% vs. 12.1% RAG) and +1.1pp on multi‑hop questions (41.7% vs. 40.6% RAG). CogCanvas also leads on single‑hop retrieval (26.6% vs. 24.6% RAG). Ablation studies reveal that BGE reranking contributes +7.7pp, making it the largest contributor to CogCanvas's performance. While heavily‑optimized approaches achieve higher absolute scores through dedicated training (EverMemOS: ~92%), our training‑free approach provides practitioners with an immediately‑deployable alternative that significantly outperforms standard baselines. Code and data: https://github.com/tao‑hpu/cog‑canvas

PaperId: 3939, https://arxiv.org/pdf/2601.00623.pdf GitHub

Abstract:
Direct Preference Optimization (DPO) has shown strong potential for mitigating hallucinations in Multimodal Large Language Models (MLLMs). However, existing multimodal DPO approaches often suffer from overfitting due to the difficulty imbalance in preference data. Our analysis shows that MLLMs tend to overemphasize easily distinguishable preference pairs, which hinders fine‑grained hallucination suppression and degrades overall performance. To address this issue, we propose Difficulty‑Aware Direct Preference Optimization (DA‑DPO), a cost‑effective framework designed to balance the learning process. DA‑DPO consists of two main components: (1) Difficulty Estimation leverages pre‑trained vision‑‑language models with complementary generative and contrastive objectives, whose outputs are integrated via a distribution‑aware voting strategy to produce robust difficulty scores without additional training; and (2) Difficulty‑Aware Training reweights preference pairs based on their estimated difficulty, down‑weighting easy samples while emphasizing harder ones to alleviate overfitting. This framework enables more effective preference optimization by prioritizing challenging examples, without requiring new data or extra fine‑tuning stages. Extensive experiments demonstrate that DA‑DPO consistently improves multimodal preference optimization, yielding stronger robustness to hallucinations and better generalization across standard benchmarks, while remaining computationally efficient. The project page is available at https://artanic30.github.io/project_pages/DA‑DPO/.

PaperId: 3940, https://arxiv.org/pdf/2601.00504.pdf GitHub

Abstract:
Accurately simulating existing 3D objects and a wide variety of materials often demands expert knowledge and time‑consuming physical parameter tuning to achieve the desired dynamic behavior. We introduce MotionPhysics, an end‑to‑end differentiable framework that infers plausible physical parameters from a user‑provided natural language prompt for a chosen 3D scene of interest, removing the need for guidance from ground‑truth trajectories or annotated videos. Our approach first utilizes a multimodal large language model to estimate material parameter values, which are constrained to lie within plausible ranges. We further propose a learnable motion distillation loss that extracts robust motion priors from pretrained video diffusion models while minimizing appearance and geometry inductive biases to guide the simulation. We evaluate MotionPhysics across more than thirty scenarios, including real‑world, human‑designed, and AI‑generated 3D objects, spanning a wide range of materials such as elastic solids, metals, foams, sand, and both Newtonian and non‑Newtonian fluids. We demonstrate that MotionPhysics produces visually realistic dynamic simulations guided by natural language, surpassing the state of the art while automatically determining physically plausible parameters. The code and project page are available at: https://wangmiaowei.github.io/MotionPhysics.github.io/.

PaperId: 3941, https://arxiv.org/pdf/2601.00423.pdf GitHub

Abstract:
Recent reinforcement learning has enhanced the flow matching models on human preference alignment. While stochastic sampling enables the exploration of denoising directions, existing methods which optimize over multiple denoising steps suffer from sparse and ambiguous reward signals. We observe that the high entropy steps enable more efficient and effective exploration while the low entropy steps result in undistinguished roll‑outs. To this end, we propose E‑GRPO, an entropy aware Group Relative Policy Optimization to increase the entropy of SDE sampling steps. Since the integration of stochastic differential equations suffer from ambiguous reward signals due to stochasticity from multiple steps, we specifically merge consecutive low entropy steps to formulate one high entropy step for SDE sampling, while applying ODE sampling on other steps. Building upon this, we introduce multi‑step group normalized advantage, which computes group‑relative advantages within samples sharing the same consolidated SDE denoising step. Experimental results on different reward settings have demonstrated the effectiveness of our methods.

PaperId: 3942, https://arxiv.org/pdf/2601.00417.pdf GitHub

Abstract:
Transformer residual streams evolve by additive accumulation: each layer appends a feature update to a shared hidden state, but has no direct mechanism for replacing content that has become obsolete or conflicting. We introduce Deep Delta Learning (DDL), a residual update rule that preserves the identity path while giving every layer the ability to selectively rewrite residual content. DDL reads the current state along a learned direction, compares it with a learned target value, and writes back a gated correction along the same direction. When the gate is closed, the update reduces to the identity; when the gate is fully open, the selected component is overwritten, yielding a depth‑wise delta‑rule generalization of standard residual addition. We integrate DDL in decoder‑only language models with both scalar and expanded residual states, while keeping attention and MLP sublayers at the original compute width. Controlled pretraining and downstream evaluations show that residual rewrite operations improve language modeling quality relative to pure additive accumulation introduced in ResNet, suggesting that a learned delta‑rule update is an effective mechanism for managing Transformer residual streams.

PaperId: 3943, https://arxiv.org/pdf/2601.00290.pdf GitHub

Abstract:
Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development (\2.6B per drug), where protocols are encoded as complex natural language documents, motivating the use of AI systems beyond manual analysis. Existing AI methods accurately predict trial failure, but do not provide actionable remedies. To fill this gap, this paper proposes ClinicalReTrial, a multi‑agent system that formulates clinical trial optimization as an iterative redesign problem on textural protocols. Our method integrates failure diagnosis, safety‑aware modifications, and candidate evaluation in a closed‑loop, reward‑driven optimization framework. Serving the outcome prediction model as a simulation environment, ClinicalReTrial enables low‑cost evaluation and dense reward signals for continuous self‑improvement. We further propose a hierarchical memory that captures iteration‑level feedback within trials and distills transferable redesign patterns across trials. Empirically, ClinicalReTrial improves 83.3% of trial protocols with a mean success probability gain of 5.7% with negligible cost (\0.12 per trial). Retrospective case studies demonstrate alignment between the discovered redesign strategies and real‑world clinical trial modifications. The code is anonymously available at: https://github.com/xingsixue123/ClinicalFailureReasonReTrial.