arXiv Papers with Code in Machine Learning (January 2026 - June 2026)
Authors:Marcel Torne, Anubha Mahajan, Abhijnya Bhat, Chelsea Finn
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/
Authors:Sanghyuk Chun, William Yang, Amaya Dharmasiri, Olga Russakovsky
Abstract:
Uncertainty estimation has been a long‑standing challenge in AI models; it amounts to "knowing what you don't know," and metacognition is notoriously difficult even for humans (cf. the Dunning‑Kruger effect). Although it is still far from solved even in simpler classification systems, tackling it in multimodal large language models (MLLMs) is becoming increasingly important. Within MLLMs, uncertainty can stem from any of the diverse sources as well as from their relationships, and further can stem from the unbounded answers in the open‑ended setting. To tackle the issues, we propose CoMet, an MLLM uncertainty estimation method by decomposing uncertainty into a context‑specific term and a multiplicity‑specific term. The former captures ambiguity induced by the given context (e.g., task or prompt), while the latter captures how many plausible answers determined by the context remain compatible with the given input. We train a lightweight post‑hoc uncertainty module to estimate these quantities, which enables efficient uncertainty estimation without autoregressive answer generation or repeated sampling. Experiments on various open‑ended multimodal benchmarks, hallucination detection, and multiple‑choice visual question answering benchmarks show that CoMet consistently improves uncertainty estimation over existing baselines while remaining efficient in practice. Code is available at https://github.com/princetonvisualai/comet_uncertainty
Authors:Philippe Chlenski, Zachariah Carmichael, Ayush Warikoo, Chia-Tse Shao, Yingxiao Ye, Aobo Yang, Vivek Miglani, Nehal Bandi
Abstract:
Mechanistic interpretability (MI) requires full access to model internals, yet the APIs for most widely deployed language models at best expose log‑probabilities over output tokens. This creates a surrogate problem: when do measurements made on open models allow us to make claims about a closed model? We evaluate surrogate fidelity at the prediction, attribution, and representation levels. For binary classification tasks, log‑odds provide an API‑compatible scalar readout of the model's representation space, and leave‑one‑out attributions provide insight into model behavior. Across eleven models spanning four families (Llama, Qwen, GPT, and Gemini), we find that prediction fidelity substantially overstates attribution fidelity: models that agree on what the answer is often disagree on why. We document an access‑validity inversion: white‑box signals like attention patterns and perturbation magnitudes are highly stable across models but only weakly predictive of causal attributions, which black‑box input ablations capture by design. Mechanistic insight does not automatically transfer to closed targets, and prediction‑level agreement is insufficient to warrant such transfer. Code and results are available at https://github.com/facebookresearch/surrogate.
Authors:Hubert Dymarkowski, Xingjian Fu, Rappy Saha, Jude Haris, José Cano
Abstract:
Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT models, which incorporate both fully connected and convolutional layers. This heterogeneity leads to significant variation in tensor shapes, requiring flexible and efficient FPGA‑based acceleration. In this paper, we present FlexViT, a reconfigurable FPGA accelerator for efficient ViT inference on resource‑constrained edge devices. Built on the SECDA‑TFLite framework, FlexViT employs a hardware‑software co‑design approach that maps both fully connected and convolutional layers onto a unified high‑throughput INT8 GEMM engine using a runtime im2col transformation. To efficiently support diverse layer configurations, we propose a dual‑mode dataflow that dynamically switches between input and weight reuse by reconfiguring the compute array at runtime. We further introduce a depth‑first tiling strategy that completes accumulation in a single pass, eliminating off‑chip partial‑sum transfers and reducing memory bandwidth requirements. We implement FlexViT on a PYNQ‑Z2 FPGA and evaluate it across a representative set of ViT models. FlexViT achieves up to 2.74x speedup on accelerator‑executed layers, translating into up to 1.40x end‑to‑end speedup compared to CPU‑only execution. The code is available at: https://github.com/gicLAB/FlexViT
Authors:Kyle Kramer
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Residual connections add every sublayer's proposed update with a fixed coefficient of one; the network never evaluates whether an update is reliable before committing it. Drawing on the human‑factors principle of independent verification, we introduce Review Residuals, which scale each update by a learned, input‑dependent gate conditioned on both the current state and the proposed update: h_l = h_l‑1 + r_l u_l with r_l = sigmoid(W[RMSNorm(h_l‑1), RMSNorm(u_l)]). Conditioning the gate on the update is the property that distinguishes it from prior gated and scaled residuals. We report two findings. First, a depth‑stability result: a convex (Highway‑style) form of the gate reintroduces vanishing gradients and fails to train beyond ~20 layers, whereas the additive, identity‑preserving form trains stably at all depths we tested. Second, an emergence‑with‑scale result: trained from scratch across five sizes (60M‑1B parameters, multi‑seed), Review Residuals show no advantage at small scale but at 590M significantly outperform both a parameter‑matched Highway gate and a parameter‑matched standard residual (p<0.05), with a larger advantage at 1B. The benefit grows with model size rather than shrinking.
Authors:Ruijia Zhang, Jiacheng Zhu, Hanqing Zhu, Laixi Shi
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.
Authors:Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
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.
Authors:Rafael Rivera Soto, Anna Wegmann, Cristina Aggazzotti
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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.
Authors:Haoming Liu, Yuanhe Guo, Yijia Cao, Shenji Wan, Hongyi Wen
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/.
Authors:Nikolai Röhrich, Julian Gleißner, Ahmed H. A. Ibrahim, Silvan Mertes, Tobias Huber
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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.
Authors:Clément Fuchs, Tim Bary, Benoît Macq
Abstract:
Conformal predictions have attracted significant attention in the field of uncertainty quantification, mainly because of their strong marginal coverage guarantees. Full conditional guarantee is not an attainable goal, a well known fact in conformal predictions literature. As a result, several approaches have tried to approximate this behavior by adapting the conformal sets of test‑time samples according to their similarity to calibration examples. Although the latter has gained traction and shown impressive performances for regression problems, its application to image classification remains under‑explored. We conduct an extensive benchmarking on natural image classification tasks with vision‑language models (VLMs), using our open source implementation of a recent localized conformal prediction algorithm. We show that straightforward usage of the cosine similarity between test‑time and calibration visual features, an intuitive choice for VLMs, is not sufficient to improve over the non‑local baselines. In response, we propose a simple non‑linear transformation of the cosine similarities, which conserves marginal coverage guarantees and achieves statistically significant mean set sizes reduction. Code is available at https://github.com/cfuchs2023/lcp‑vlm/.
Authors:Zikang Yan, Xiao Wang, Qingquan Yang, Zhendong Yang, Gaoting Chen, Zehua Chen, Bo Jiang, Jin Tang, Guosheng Xu
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
Authors:Johan Land
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.
Authors:Wenhao Li, Jinhao Dong, Hailin Zhang, Wenhang Shi, Wei Lu, Xiaoyong Du
Abstract:
Long‑context Large Language Model inference is severely bottlenecked by the massive Key‑Value (KV) cache, yet existing sparse attention methods often suffer from static fixed‑budget (Top‑k) retrieval or rely on proxy scores that are computationally expensive and biased. To address these limitations, we propose RaBitQCache, a novel sparse attention framework that utilizes randomized rotated binary quantization and high‑throughput binary‑INT4 arithmetic to efficiently estimate attention weights. Our proxy score serves as an unbiased estimator with a proven error bound, enabling adaptive Top‑p retrieval that dynamically adjusts the token budget based on actual attention sparsity. We further implement a hardware‑aware system with asynchronous pipelining and lazy updates to mask overhead. Evaluations demonstrate that RaBitQCache significantly accelerates inference and reduces memory I/O while preserving generation quality compared to state‑of‑the‑art baselines. Code is available at https://github.com/Sakuraaa0/RaBitQCache.git.
Authors:Seyed Alireza Molavi, Zhan Su, Yan Hu, Peyman Sheikholharam Mashhadi, Stefan Byttner, Prayag Tiwari
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.
Authors:Jisung Park, Seohyeon Kang, Daeun Yoo, Eunsu Lee, Seoin Cho, Wooyeop Choi, Ian Choi, James R. Evan, Daesoo Kim, Sonia Gandhi, Minee L. Choi
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
Authors:Hongmin Li
Abstract:
AI‑assisted research has entered a stage in which the central question is not only whether systems can generate hypotheses, run experiments, or produce manuscripts, but whether their scientific claims are calibrated to the evidence that supports them. This Perspective‑style paper develops a conceptual and methodological framework for evidence‑licensed claims in AI‑assisted research. Motivated by representative routes including specialized scientific foundation models, LLM research assistants, multi‑agent co‑scientists, AI Scientist pipelines, mathematical discovery agents, and self‑driving laboratories, it represents AI‑assisted research as five operators: hypothesis generation, model‑mediated consequence derivation, external validation, belief update, and claim calibration. The central claim is that calibration is not merely cautious wording but a mechanism for managing scientific assertion rights: evidence licenses some forms of speech and withholds others. The paper distinguishes linguistic, consequence‑based, interventional, and evidence‑licensed semantics; defines the claim‑evidence gap and epistemic debt; and treats minimal structural reconstruction across heterogeneous outputs as an upward form of claim calibration. AISim‑Cal is included as an illustrative synthetic dynamics exercise, not as an empirical forecast or benchmark. The resulting principles are: no claim without license, validation does not determine claim level, and automation amplifies the need for calibration. Reliable AI‑assisted research is therefore evaluated as a loop that generates hypotheses, derives testable consequences, accepts independent adjudication, updates beliefs, and outputs only evidence‑licensed claims.
Authors:Prakhar Dixit, Tim Oates
Abstract:
We propose Intelligent Schema Memory (ISM), a self‑evolving memory‑augmented system that improves mathematical reasoning for a frozen LLM under continual learning with hard episodic resets. ISM maintains a compact, self‑refined bank of strategy schemas learned from both successful and failed episodes, with symbolic tools that check intermediate steps and certify answers. Without updating model parameters, ISM outperforms passive, retrieval, and reflection baselines on MATH‑Hard and OlympiadBench, using 64% and 86% fewer schemas respectively than the strongest passive baseline. These results show that small, actively maintained, and verified strategy memories can support reliable continual mathematical reasoning under strict episodic isolation. The codebase is available at https://github.com/pdx97/ISM .
Authors:Apurva Gandhi, Vishwas Suryanarayanan, Raja Hasnain Anwar, Firoz Shaik, Shubhang Desai, Thong Q. Nguyen, Muhammad Taqi Raza, Vishal Chowdhary, Graham Neubig
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.
Authors:Björn Braun, Christian Holz
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
Authors:Rui Zhou, Tianci Xie
Abstract:
Full fine‑tuning adapts large language models to new tasks but can erode capabilities they already possess. Existing remedies protect through proxies such as parameter distances, importance penalties, output matching, or dominant singular directions of the weights, but none directly asks which activation directions the preserved capability relies on. We argue that a capability is characterized more faithfully by the activation subspace it induces than by the singular geometry of the weight matrix, and develop function‑space protection, instantiated as FORA (Function‑space Orthogonal Residual Adaptation). From label‑free calibration inputs, FORA estimates, per layer, the principal directions Q of the input‑activation covariance and forms a right projector P_Q = I ‑ QQ^T. Paired with a left projector P_U from the weight SVD, the update is ΔW = P_U M P_Q + U_2 D_δ V_2^T: a high‑capacity branch structurally barred from reading capability‑relevant function directions, plus a narrow spectral channel for controlled plasticity. The construction extends to parameter‑efficient adaptation via M \to (α/r) BA. Across three settings on Qwen3‑1.7B, including COGS and GSM8K learned while preserving translation and translation learned while preserving math, FORA consistently improves preservation over weight‑space projection and standard regularization, with only a small new‑task trade‑off in the math‑preservation setting. A controlled ablation isolating the projection source shows that the advantage comes not from projection itself, but from projecting onto capability‑derived rather than weight‑derived directions. Code is available at https://github.com/zrui239/FORA.
Authors:Hiroki Takeda, Yuto Miyatake, Daisuke Furihata
Abstract:
Tensor Train (TT) decomposition is a powerful technique for analyzing high‑dimensional data. Existing algorithms for computing TT decompositions can be categorized into two main types: conventional batch‑based approaches and recursive online methods. In the context of streaming data, batch methods typically achieve higher reconstruction accuracy but often suffer from memory exhaustion, while online methods provide greater computational efficiency. In this work, we introduce Online TT‑ALS (Alternating Least Squares), an algorithm that sequentially enforces orthogonality constraints. This approach allows for efficient and exact updates of the core tensor while maintaining high reconstruction accuracy. Theoretically, we prove that enforcing these orthogonal gauge constraints guarantees monotonic decrease of the local objective function and temporal smoothness. Computationally, our deterministic single‑sweep update reduces the rank dependence from quadratic to linear, achieving an overall complexity of \mathcalO(I^n‑1 r). Experimental results demonstrate that the proposed method outperforms existing online techniques not only in terms of mathematical approximation accuracy but also in human perception‑based video quality metrics. Furthermore, compared to recent deep learning‑based paradigms, our algebraic approach achieves speedups of several orders of magnitude. Consequently, our method exhibits high computational efficiency and is suitable for low‑latency real‑time processing applications.
Authors:Gaurab Baral, Aaditya Khanal, Yangyang Tao, Junxiu Zhou
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.
Authors:Huanlin Gao, Fang Zhao, Qiang Hui, Fuyuan Shi, Shaoan Zhao, Yantao Li, Chao Tan, Ting Lu, Yuren You, Kai Wang, Shiguo Lian
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
Authors:Mohammad Mahdi Abootorabi, Sina Namazi, Armin Saadat, Lyuyang Wang, Obed Dzikunu, Paul F. R. Wilson, Zhuoxin Guo, Brian Wodlinger, Parvin Mousavi, Purang Abolmaesumi
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.
Authors:Brent A. Griffin, Jason J. Corso
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.
Authors:Yujin Kim, Nidhi Soma, Sarah Dean
Abstract:
Probabilistic downscaling is the task of modeling the conditional distribution of high‑resolution fields given coarse inputs, and is a central challenge to atmospheric science, climate modeling, and other multiscale physical systems. A widely used paradigm decomposes the problem into a deterministic mean predictor followed by a stochastic residual generator. While effective in idealized settings, this mean‑‑residual approach frequently produces biased and under‑dispersive ensembles in real‑world applications. Is this merely generic predictive uncertainty miscalibration? We show that the root cause is more fundamental: residual target misspecification, the residual distribution induced during training differs systematically from the one required at test time due to downscaling bias. To close this gap, we introduce ReMatch (Residual Distribution Matching). ReMatch aligns the training residual distribution toward the test‑time regime via optimal transport in a low‑dimensional PCA space. This preserves the statistical benefits of the mean‑‑residual framework while reducing the train‑‑test mismatch in the residual targets seen by the stochastic generator. On a controlled synthetic benchmark with varying bias levels and a real‑world HRRR‑‑ERA5 wind field downscaling task, ReMatch substantially reduces under‑dispersion, improves calibration (SSR and CRPS), and outperforms strong baselines, including the standard mean‑‑residual model and its variants, as well as state‑of‑the‑art super‑resolution models. Our code is available at https://github.com/sdean‑group/ReMatch.git.
Authors:Federico Felizzi
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.
Authors:Kan Zhu, Mathew Jacob, Chenxi Ma, Yi Pan, Stephanie Wang, Arvind Krishnamurthy, Baris Kasikci
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.
Authors:Marek Polewczyk, Maximilian Schambach, Marco Spinaci, Sam Thelin, Johannes Höhne
Abstract:
We introduce FlexTab, a flexible encoder‑decoder architecture for in‑context learning on tabular data that pairs a single, task‑agnostic encoder with a suite of task‑specific decoders. Unlike existing tabular in‑context learners, which entangle feature representations with a specific prediction target, our design produces target‑agnostic row embeddings that can be leveraged across a wide range of downstream tasks within a table‑native in‑context learning setup. We demonstrate this flexibility on six distinct problems: classification, regression, anomaly detection, clustering, entity matching, and entity classification in relational databases. Both the encoder and the task‑specific decoders are trained on a large corpus of real‑world, unlabeled tables. FlexTab achieves state‑of‑the‑art performance on classification, regression, anomaly detection and entity matching, while remaining competitive with specialized models on entity classification in a relational setting. These results demonstrate that a single shared encoder, paired with task‑specific decoders, can serve as an effective general‑purpose backbone for diverse tabular prediction problems. The inference code and checkpoints will be made publicly available at https://github.com/SAP‑samples/flextab.
Authors:Vladimir Beskorovainyi
Abstract:
Organizations that cannot send data to a cloud API increasingly ask: how good is Text‑to‑SQL if the model must run on‑premises on open weights, and which popular accuracy "recipes" are worth their compute? We answer with an honest, fully reproducible benchmark on the BIRD development split (n=1534, Execution Accuracy), evaluating three open model families across two generations ‑‑ Qwen2.5‑Coder (7B/14B/32B), CodeLlama‑Instruct (7B/13B/34B), and Llama‑3.x (8B, 70B) ‑‑ under one matched protocol, ablating a model‑agnostic recipe (schema linking, self‑correction, self‑consistency) component by component, with every difference tested by the paired McNemar test. Four findings stand out. (i) Generation matters more than raw size, and the recipe is family‑robust: Qwen2.5‑Coder dominates the older CodeLlama at matched size (39.1 vs 20.9 at 7B), but a modern non‑Qwen model (Llama‑3.3‑70B, 49.2 on a matched serving) is competitive, so CodeLlama's weakness reflects its 2023 generation, not "non‑Qwen = weak". (ii) Self‑correction is a robust, near‑free win, significant on all three families where there is room to improve. (iii) Schema linking does not help, and a stronger linker does not rescue it: a retrieval/embedding linker with 96.5% gold‑table recall is statistically indistinguishable from no linking, ruling out the "weak lexical strawman" objection across three families. (iv) Self‑consistency is poor value (+0.13 pp for ~5x tokens, not significant). We report real per‑stage cost (/1k queries) and release all code, predictions, and summaries; archived code and data: https://doi.org/10.5281/zenodo.20952794
Authors:Guang-Xing Li
Abstract:
Continuous physical fields represent a large fraction of data under scientific investigation. Their multiscale structures are central to discovery, yet useful coordinates are not known in advance. Standard self‑supervised methods define context and targets in fixed image coordinates, posing a predictive task misaligned with fields organized across a continuous scale hierarchy. We introduce ScaleAware‑JEPA, a framework that constructs dense, label‑free latent coordinates for continuous scalar fields. Constrained Diffusion Decomposition (CDD) separates each field into pixel‑registered scale components and provides the scale coordinates that define the masking geometry. The resulting JEPA objective predicts hidden structure with a context footprint tied to the diffusion scale of each component rather than to an arbitrary patch size. Across MHD turbulence, interstellar molecular gas and urban nighttime‑light structure, the learned geometry maps back to coherent morphology, forming dense structural atlases without labels or predefined segmentation rules. By tying latent prediction to the scale hierarchy of a field, ScaleAware‑JEPA constructs latent coordinates through which complex physical patterns can be inspected before their relevant structures have been prescribed. Code is available at https://github.com/gxli/SA‑JEPA.
Authors:Youngseok Hwang, Sungho Bae, Dohun Lee, Jaeeun Seo, Jeehong Kim, Wonhee Lee, Hyunwoo Park
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.
Authors:Aojie Yuan, Yi Nian, Haiyue Zhang, Zijian Su, Yue Zhao
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.
Authors:Heshan Fernando, Quan Xiao, Yan Xin, Tianyi Chen
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.
Authors:Duc Anh Nguyen
Abstract:
We ask a simple question about decoder‑only transformers: between which two layers is the probability of a predicted token actually produced? Existing layer‑wise readout tools answer only approximately. The logit lens and its trained variant report a per‑layer level of probability but give no additive decomposition; their estimates are biased and non‑monotone across depth. Direct Logit Attribution and related residual‑stream methods are additive, but only in logit space, the softmax nonlinearity breaks additivity in probability space, precisely the quantity one usually cares about. Layer Conductance integrates gradients per layer, but attributes each to its own baseline and so does not sum to the total change in prediction. We introduce IG‑Lens, a telescoping application of Integrated Gradients along a single path through the hidden states from a baseline to the final layer. Crediting each segment to the layer it terminates at yields a layer‑wise attribution whose sum is exactly the change in target probability, with the softmax inside the integration path rather than linearized away. Our default estimator credits each integration step its observed change in target probability (a prediction‑aware reweighting in the spirit of IDGI) rather than its raw gradient. Because the readout is a one‑dimensional probability, this collapses each segment to a telescoping sum of endpoint values, so completeness holds exactly (to floating point) at any step count, removing Riemann discretization error while suppressing steps that show gradient sensitivity without a change in output. We give the telescoping identity and its proof, verify completeness to floating point, and describe a single‑pass batched implementation computing the full token‑by‑layer map without any backward call. Code: https://github.com/anhnda/IGLens.
Authors:Yeji Kim, Housam Babiker, Mi-Young Kim, Randy Goebel
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.
Authors:Zain Naboulsi
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.
Authors:Subhadip Mitra
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.
Authors:Xiao Wang, Liye Jin, Dan Xu, Yuehang Li, Lan Chen, Yaowei Wang, Yonghong Tian, Jin Tang
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.
Authors:Dishan Sarkar
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.
Authors:Isao Kurosawa
Abstract:
Reliable event detection underpins induced‑seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise. These two failure modes are routinely conflated, and architectural complexity is often credited with robustness it may not deserve. We assemble a unified binary event‑detection benchmark from three physically distinct real sources ‑‑ Hi‑net seismic waveforms, Utah FORGE 2024 borehole DAS, and MAFAULDA industrial vibration ‑‑ each mapped to a common 8‑channel, 256‑sample representation, and evaluate a fault‑tolerant detector (CEPHALON) trained with per‑sample sensor‑dropout against standard detectors (a 1D convolutional network, a temporal convolutional network, and a compact Transformer) trained with an identical recipe. On clean data every model is near‑perfect (AUC ~ 0.99). Under progressive sensor loss, simple models with sensor‑dropout are already robust and CEPHALON holds no advantage. Under additive noise, however, CEPHALON degrades far more gracefully: at ‑2.5 dB its overall AUC is 0.939 versus 0.532‑0.572 for the convolutional baselines. Same‑architecture ablations isolate the cause: disabling internal redundancy at inference reduces the low‑SNR advantage only modestly, whereas removing sensor‑dropout training collapses it (0.899 to 0.603 at ‑5 dB). The training recipe is therefore the dominant cause and parallel redundancy only secondary. We release a complete, numbered, reproducible pipeline so that every figure can be regenerated.
Authors:Zeynep Türkmen, Kürşat Kaya, Alexander Pfefferle, Frank Hutter
Abstract:
Data‑generating priors are a central component of tabular foundation models because they define the task distribution used during pretraining. However, priors are rarely evaluated as independent components, making it difficult to understand how much they affect downstream model behavior. This raises a methodological question: how can priors from different tabular foundation models be compared independently of the architectures and training protocols they were introduced with? To study this question, we implement a unified interface for publicly available priors from recent tabular foundation models and priors constructed from real datasets. We generate training tasks from each prior, train the same model architecture under a fixed training protocol, and evaluate the resulting models on shared downstream classification tasks. We compare priors through both generated‑task statistics and downstream predictive performance. Our results show that different priors favor different downstream behaviors, with some achieving stronger absolute performance and others exhibiting more consistent relative rankings across datasets. We further find that data‑level similarity only partially explains downstream behavior. Our code is available at https://github.com/automl/TFM‑Playground/tree/prior‑dev.
Authors:Rui-Xi Wang, Runzhong Wang, Connor W. Coley
Abstract:
Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines. In this work, we revisit the problem through the lens of object detection on molecular graphs. Molecular fragmentation, a central step in MS/MS prediction, can be approximated as detecting a set of subgraphs (i.e., fragments) and their associated spectral contributions. Existing fragment‑based models follow a two‑stage paradigm ‑‑ first generating candidate fragments and then scoring them ‑‑ analogous to two‑stage R‑CNNs in computer vision. Towards higher accuracy and faster inference, we introduce GLACIER, a single‑stage transformer‑based fragment detection neural network for molecular graphs. This unified formulation eliminates the need for candidate enumeration, enabling scalable and globally consistent modeling of molecular fragmentation. GLACIER is faster and more accurate than existing state‑of‑the‑art by a significant margin, achieving 70.0% and 69.7% Top‑1 retrieval accuracy with and without contrastive finetuning on the MassSpecGym dataset (from the previous SOTA of 64.0%) and 52.5% and 38.5% respectively on the NIST'20 dataset (from 33.2%). Furthermore, GLACIER provides nearly 8‑fold inference speedup over our prior two‑stage model. Code is available at https://github.com/coleygroup/ms‑pred
Authors:Abdullah Al Shafi, Md Kawsar Mahmud Khan Zunayed, Safin Ahmmed, Sk Imran Hossain, Engelbert Mephu Nguifo
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
Authors:Stephan Goerttler, Min Wu, Fei He
Abstract:
A growing number of techniques leverage the spatial structures that underlie many real‑world datasets. Despite these advances, the complementary task of estimating spatial structures and understanding their role within these techniques has often been overlooked. In neurophysiological data analysis specifically, numerous methods exist to estimate brain connectivity, but most are not explicitly model‑based, dynamic, multivariate, or directed. To address these limitations, we previously introduced noise‑driven heat modelling on graphs for neurophysiological connectivity estimation. In this study, we extend this framework by relaxing earlier noise assumptions and adding regularisation to improve robustness. We also develop a simulation procedure to characterise and evaluate our technique in a controlled setting. Finally, we demonstrate that the technique is able to capture meaningful spatial structure across two experiments, each using two real‑world datasets. The explicit model formulation of our connectivity estimator has the potential to improve the interpretability of graph‑based techniques across a wide range of applications. The code implementing our method is available at https://github.com/sgoerttler/Heat_Connectivity.
Authors:Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang
Abstract:
Would experience designing faster GPU kernels also help close in on a long‑standing open mathematical conjecture? Large Language Models (LLMs) integrated into evolutionary search have recently produced state‑of‑the‑art solutions on optimization tasks, including open mathematical conjectures, GPU kernel design, scientific law discovery, and combinatorial puzzles. To achieve this, prior work applied search scaffolds to one target task at a time, so every new problem is approached from scratch and the experience accumulated during search is discarded once the model finishes its attempt. This leaves the capability of iteratively evolving a solution (e.g., knowing which part to mutate and how, deciding when to backtrack) entirely in the scaffold rather than in the model itself. Whether the model itself could acquire this capability and reuse it across different tasks has been largely unexamined. To address this, we introduce Evolution Fine‑Tuning (EFT), a mid‑training paradigm that teaches LLMs to evolve solutions across tasks by converting evolutionary search trajectories into supervision. We construct Finch Collection, a 156K‑trajectory dataset spanning 10 domains and 371 optimization tasks, and fine‑tune open‑source LLMs from 2B to 9B parameters. Empirically, EFT confers cross‑task generalization: across 22 held‑out tasks, our models surpass their base counterparts by 10.22% on average. Furthermore, when paired with test‑time RL, our model matches state‑of‑the‑art performance on two circle‑packing tasks and outperforms its base‑model counterpart on the Erdős minimum‑overlap problem. EFT thus serves as a "practice phase" for general‑purpose discovery agents that do not solve new problems from scratch.
Authors:Dong Wang, Wenwu Tang, Yun Cheng, Olga Saukh
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.
Authors:Yuxuan Yang, Feiyang Ren, Bowen Zeng, Dalin Zhang, Jinpeng Chen, Gang Chen, Huan Li
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.
Authors:Dawon Ahn, Auder Der, Evangelos E. Papalexakis
Abstract:
Accurately explaining hidden patterns in multi‑aspect data has typically been done by leveraging labels and/or accompanying auxiliary metadata. However, labels and auxiliary data may be inaccurate (e.g. nonstandard, inconsistent), insufficient (e.g. static tabular metadata for time‑dependent recordings), or unavailable. % We propose \fullmethod (\method), which leverages the knowledge of large language models (LLMs) to explain the hidden patterns in human narratives. \method uses task‑agnostic and task‑specific prompts to explain extracted co‑clustered latent patterns from tensor decomposition. To evaluate these explanations, we test the LLMs on forward and backward inference tasks. % Our demo system is available at https://github.com/dawonahn/ECML_PKDD_AnTenA.
Authors:Eryn Libert-Scott, Emma A. M. Stanley, Vibujithan Vigneshwaran, Matthias Wilms, Erik Y. Ohara, Nils D. Forkert
Abstract:
Causally linking disease‑related factors to image‑derived biomarkers provides a powerful pathway to understanding disease mechanisms. Despite growing interest in applying causal artificial intelligence (AI) approaches for this task, these methods still need to be adapted for complex medical images, and especially, neuroimaging. However, the lack of ground‑truth data presents a barrier to development. To bridge this gap, we developed and tested a method for generating synthetic neuroimages, which adhere to a user‑specified causal structure describing the non‑image to image variable relationships, permitting the creation of ground‑truth neuroimaging datasets. In the simulated T1‑weighted magnetic resonance images, anatomical variability is modeled by sampling from a subspace estimated from real data and deforming a template image to create unique simulated subjects. Causal relationships are encoded via precise volumetric changes of any region‑of‑interest without unwanted global artifacts. We achieved relative volume errors of 0.3‑2.66% for the targeted regions‑of‑interest and demonstrate their statistically significant causal relationships, while maintaining mean absolute errors for non‑target brain regions between 0.034‑0.397ml. An initial evaluation of causal discovery methods exposes their limited ability to suppress spurious connections, highlighting the need for image‑appropriate methods. Our framework is the first to enable the generation of realistic synthetic 3D neuroimages with explicit causal control that can serve as the missing ground‑truth data necessary for the objective benchmarking and development of causal AI methods.
Authors:Yong Yi Bay, Kathleen A. Yearick
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.
Authors:Md Mozaharul Mottalib, Rahmatollah Beheshti
Abstract:
Effective sub‑typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self‑supervised Mamba‑based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real‑world EHR datasets to classify the data based on the available labels and subtype patients based on the representations learned from the model. Through an extensive set of experiments, we demonstrate that our model's design choices lead to better performance compared to competitive baseline models for prediction. Moreover, we evaluate several clustering techniques to demonstrate that our findings offer valuable insights into subtyping patients based on temporal records from EHR models\footnoteOur implementations are available at https://github.com/healthylaife/triplet_mamba.
Authors:Oscar Miró López-Feliu, Daimy van Loo, Xanthos Kekkos, Mikel Blom, Clara Rus
Abstract:
Fayyazi et al. (2025) recently proposed FACTER, a model‑agnostic framework designed to jointly enforce fairness and statistical coverage in LLM‑based recommendation through conformal thresholding and iterative prompt repair. In this work, we conduct a reproducibility study of the FACTER framework across diverse architectures and dataset sparsity levels, evaluating both the original open‑ended generation task and a constrained re‑ranking extension. Under the strict reproduction, we observe a divergence in recommendation utility, which we trace to underspecified target‑set evaluation in the original study. We then use the constrained re‑ranking setting to evaluate FACTER when the candidate set is fixed, and introduce a static Fair Zero‑Shot baseline to isolate the contribution of the iterative prompt repair loop. Our analysis shows that FACTER consistently reduces adaptive‑threshold violation counts, but that these reductions are not consistently reflected under the fixed threshold or in global fairness metrics. In the constrained ranking setting, static fairness instructions achieve comparable semantic‑parity outcomes to FACTER's dynamic repair loop, suggesting that the additional online repair mechanism provides limited benefit in this formulation. All code and reproduction artifacts are available at https://github.com/oscar‑omlf/facter‑repr.
Authors:Nhi Nguyen, Shauli Ravfogel, Rajesh Ranganath
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 .
Authors:Yuexi Du, Leya Barrientos, Laura Sheiman, John Lewin, Hemant D. Tagare, Nicha C. Dvornek
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
Authors:Yunhun Nam, Jongheon Jeong
Abstract:
Vision‑Language Models (VLMs) have shown strong performance in visual understanding, yet they still suffer from hallucinations, generating content that is not grounded in the image. Preference alignment is a promising approach to improve visual faithfulness, but its success depends heavily on how preference pairs are constructed. Existing methods exhibit two key limitations; (a) intervention‑based methods often introduce significant deviation from the policy distribution, and (b) sampling‑based methods often underuse visual information during the construction. In this paper, we propose ViPSy (Vision‑driven Preference Synthesis), a framework for constructing preference data that are both policy‑aligned and visually grounded. Our framework consists of two stages; in the first stage, ViPSy derives a visual cue from recurring object‑level content across semantically aligned image variants, so preference construction can rely on visual information rather than language priors. In the second stage, ViPSy conditions the policy's own rollouts on this cue, allowing candidates to be guided by visually grounded content while staying close to the policy's response distribution. The resulting candidates remain close to the policy's response distribution while better leveraging visual information from the image. Experiments show that the resulting VLM, preference‑aligned with ViPSy‑constructed preference pairs, achieves a new state‑of‑the‑art in hallucination mitigation. Compared with the previous state‑of‑the‑art method, it reduces hallucination rates on AMBER and Object HalBench by 35.7% and 24.5%, respectively. The resulting model further improves on general visual grounding benchmarks, e.g., MMStar, MMVP, and CV‑Bench, while also yielding gains in semantic segmentation and ImageNet linear probing, underscoring the effectiveness of our framework in enhancing the model's visual capabilities.
Authors:Jiasheng Wang, Tanun Jitwatcharakomol, Piyawadee Jongpradubgiat, Simeng Zhu
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.
Authors:Yichuan Wang, Zhifei Li, Zirui Wang, Paul Teiletche, Lesheng Jin, Matei Zaharia, Joseph E. Gonzalez, Sewon Min
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.
Authors:Daoming Wan, Yizheng Huang, Jimmy X. Huang
Abstract:
In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems. However, evaluating text clustering algorithms remains challenging since many real‑world textual datasets are not suitable for clustering assessment due to ambiguous semantic boundaries, the high dimensionality of embeddings, and inconsistent cluster structure. Current clustering dataset generators are designed for numerical data, providing limited support for text‑specific benchmarking. This paper introduces TextClusterLab, a comprehensive framework for text clustering research. TextClusterLab offers a Large Language Model (LLM)‑driven text clustering dataset generator to produce synthetic text datasets for evaluating clustering algorithms. This generator supports setting various clustering attributes, such as class imbalance, intra‑cluster compactness, and inter‑cluster diversity. These generated datasets can serve as practical benchmarks for testing the robustness and versatility of text clustering algorithms in diverse scenarios. Moreover, we introduce a benchmark to verify whether a text dataset is suitable for clustering evaluation. Therefore, TextClusterLab provides an integrated framework for reproducible and comprehensive text‑specific clustering research. Our TextClusterLab is publicly available at https://github.com/research‑paper‑code/TextClusterLab, and some synthetic example datasets with various attributes are publicly available at https://huggingface.co/datasets/DW‑irlab/TextClusterLab.
Authors:Dihong Huang, Zhenyu Wei, Zhuxiu Xu, Yunchao Yao, Sikai Li, Mingyu Ding
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.
Authors:Mansoo Jung, Youngwook Kim, Jungwoo Lee
Abstract:
Test‑Time Adaptation (TTA) methods commonly update the affine parameters of normalization layers to adapt deployed models under distribution shifts. However, per‑channel affine parameters perform axis‑aligned scaling and shifting, making them geometrically incapable of correcting cross‑channel structural changes induced by distribution shift. To address this limitation, we propose MixTTA, a lightweight plug‑in module that equips normalization layers with a low‑rank cross‑channel transformation, enabling inter‑channel mixing at each layer. To ensure that the low‑rank branch captures only cross‑channel interactions, we also propose Decoupling Projection that enforces strict separation from the diagonal affine path, along with Spectral Projection that prevents rank‑1 collapse under non‑stationary test streams. MixTTA can be seamlessly integrated into any existing normalization‑based TTA method. Experiments in both standard and wild TTA settings show consistent improvements over strong baselines while mitigating adaptation failure under challenging conditions. The source code is publicly available at https://github.com/delta6189/MixTTA.
Authors:Andrin Rehmann, Heiko Zimmermann, Dion Häfner
Abstract:
Differentiable partial differential equation (PDE) solvers underpin solver‑in‑the‑loop ML training, gradient‑based optimal control, and inverse problems, yet the practical cost of obtaining correct, usable gradients from a given solver on a given problem is largely undocumented. Integration effort, computational cost, gradient accuracy, and numerical conditioning vary widely across solvers and are discoverable only by trial and error. We introduce Mosaic, an extensible benchmarking framework for differentiable PDE solvers that standardizes access to solver gradients. Each solver is packaged as a containerized component (Tesseract) exposing a uniform gradient API regardless of language or automatic differentiation (AD) strategy, enabling researchers to evaluate, compare, and build on non‑trivial physical solvers. Our evaluation of 14 solvers across fluid dynamics, structural mechanics, and heat transfer demonstrates that the benchmark surfaces practically relevant differences: order‑of‑magnitude variation in computational cost and Jacobian conditioning, alongside structural incompatibilities that eliminate solvers from realistic tasks entirely. Despite this variation, all solvers that produce gradients converge to similar optima, indicating that the practical barriers are memory limits, numerical stability, and setup compatibility rather than gradient accuracy alone. Mosaic is open‑source and available at https://github.com/pasteurlabs/mosaic.
Authors:Taïga Gonçalves, Yongsong Huang, Tomo Miyazaki, Shinichiro Omachi
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.
Authors:Sichao He, Aiersi Tuerhong, Shangjun She, Tianhao Chu, Yuling Wu, Junfeng Zuo, Si Wu
Abstract:
Continuous attractor neural networks (CANNs) are the canonical computational framework for how the brain encodes continuous variables such as spatial position, head direction, and movement direction, and explain the activity of hippocampal place cells, entorhinal grid cells, and head‑direction cells. CANN research, however, is fragmented: most results rest on lab‑specific implementations, general‑purpose simulators lack CANN‑specific abstractions, and the path from spike trains to attractor geometry in real recordings lacks a standardized toolkit. Here, we present a comprehensive open‑source toolkit that unifies the full CANN research workflow. It combines three tightly integrated components: 1) canns, a Python library on BrainPy/JAX that provides standardized 1D/2D CANNs, spike‑frequency‑adaptation variants, grid cell networks, hierarchical path‑integration models, and brain‑inspired attractor architectures, together with curated datasets, task generators, an analyzer module and trainer modules for biologically plausible plasticity; 2) canns‑lib, a Rust acceleration backend delivering hundreds‑of‑times speedups for spatial‑navigation workloads and modest gains for Ripser‑based persistent homology; 3) ASA (Attractor Structure Analyzer), a PySide6 pipeline applying persistent homology and cohomology to experimental neural recordings to detect ring‑like and toroidal attractor signatures in real data. The toolkit ships with full‑detail reproducible pipelines that recover recent CANN results including SFA‑driven anticipative tracking, theta sweeps in head‑direction/place/grid systems, and hierarchical path integration.
Authors:Zhuo Zuo, Li Yue, Wenhao Zheng, Chenpeng Wang, Xianggen Liu
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.
Authors:Zhiqiang Xie, Yijing Lin, Zhipeng Gao, Dong In Kim
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.
Authors:Arnav Raj
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.
Authors:Arnav Raj
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.
Authors:Thomas Shih-Chao Liang, Zhuoran Yu, Yong Jae Lee
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.
Authors:Raymond Yu, William Huey, Mustafa Mukadam, Anusha Nagabandi, Abhishek Gupta
Abstract:
Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations. Improving these policies with reinforcement learning (RL) is an appealing alternative, but this process often requires expensive training in the real world. Performing policy improvement in simulation instead provides a far cheaper alternative, but unconstrained RL in simulation can exploit contact and dynamics mismatches, resulting in unsafe behaviors that do not transfer to hardware. Common forms of regularization can furthermore limit improvement by overconstraining to an imperfect behavior prior. In this work, we propose Support‑Constrained Off‑Domain REinforcement (SCORE), a real‑to‑sim‑to‑real framework that constrains RL in simulation to the support of a generative policy pretrained on real data. We instantiate this constraint through flow steering, restricting SCORE to actions the base policy can already produce, which ensures transferable behaviors while maximizing policy improvement. Improving a policy with SCORE requires minimal effort: it learns from sparse rewards, avoids distillation, and leaves the base policy untouched. Across eight real‑world dexterous multi‑fingered robotic manipulation tasks, SCORE improves average success rate from 37.8% to 89.9%, compared to 59.5% for the best baseline, and reaches success in 36.8% fewer steps than the base policy. Ultimately, through extensive experiments and ablations, we show that simulation can substantially improve real‑world manipulation policies when policy optimization is appropriately constrained, introducing a new paradigm for real‑to‑sim‑to‑real policy improvement. Videos and code are available at https://weirdlabuw.github.io/score/.
Authors:Vedant Patel
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.
Authors:Igor Itkin
Abstract:
Multi‑agent large language model (LLM) systems often rely on verifier and critic agents to suppress hallucinations, but verification is delayed. During this delay, false claims can propagate through the agent network. We model this process as delayed consensus on a graph with grounded corrector nodes. Spectral decomposition by the grounded Laplacian yields a closed‑form stability threshold for the verification dose: correction that is too strong or too delayed can turn consensus into oscillation. The most unstable regime occurs when the communication and verification delays coincide; for delay two, the threshold is the inverse golden ratio. The same framework gives a supermodular placement objective and a greedy (1‑1/e)‑approximation rule for assigning a limited corrector budget to influential nodes. Experiments across five open models confirm the predicted dose‑delay oscillations. By contrast, grounded factual answering makes truth an absorbing boundary and eliminates the effect, suggesting that the instability is specific to signed‑belief tasks while grounded verification remains stabilizing
Authors:Danyal Rehman, Charlie B. Tan, Yoshua Bengio, Avishek Joey Bose, Alexander Tong
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.
Authors:Haina Jiang, Liam Wang, Peng-Chen Chen, Min Seop Kwak, Seungryong Kim, Brian Bell, Jeong Joon Park
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/.
Authors:Hanli Xu, Fengxiang He, Sarat Moka
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.
Authors:Jiahe Chen, Qian Shao, Qiyuan Chen, Jiaying He, Jintai Chen, Jian Wu, Hongxia Xu
Abstract:
Open‑set semi‑supervised learning aims to leverage unlabeled data that may contain out‑of‑distribution outliers while maintaining performance on in‑distribution classes. Existing methods mainly follow two paradigms: filtering suspicious samples or incorporating unlabeled objectives with soft weighting. We argue that both face a common trade‑off: aggressive filtering can discard informative but hard ID samples, whereas utilization can introduce auxiliary gradients that conflict with supervised learning when pseudo labels are wrong. We therefore shift the focus from sample selection to gradient‑level control. We propose Geometric Gradient Rectification (GGR), a plug‑in framework that uses the supervised gradient as an anchor and projects conflicting auxiliary gradients onto an admissible region in gradient space. This makes the applied auxiliary update first‑order non‑opposing within the rectified coordinate block while preserving orthogonal components that may still carry useful representation signals. We further extend GGR with subspace‑aware rectification to stabilize the anchor under noisy mini‑batch gradients. Experiments on CIFAR and ImageNet benchmarks show that GGR improves representative OSSL baselines in most settings and yields gains in both closed‑set generalization and open‑set robustness. Code will be available at https://github.com/JiaheChen2002/GGR.
Authors:Snehal Raj, Brian Coyle, Léo Monbroussou, André J. Ferreira-Martins, Renato M. S. Farias, Elham Kashefi
Abstract:
Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the recent progress in learning from such data through message passing, a single primitive that generalises convolution and attention. Quantum counterparts have been proposed, but with limited connection to message passing and few guarantees on performance or scalability. More broadly, the trainability of variational quantum circuits is a recognised bottleneck for their wide applicability, and pre‑training has emerged as one way to address it. Yet for a quantum model to be useful, it must offer expressivity guarantees along with demonstrable scalability. Here we show how a quantum graph neural network can be built to perform message passing, to be permutation equivariant, and to sit at a chosen level of the Weisfeiler‑Leman hierarchy, the standard measure of how finely a model can tell graphs apart. We show that, as for classical GNNs, the training can be done first on small graph instances, allowing for a pre‑training that can mitigate usual training issues, and its output can be read out at a cost that stays low as the graph grows. We validate the framework in large‑scale simulations of up to 56 qubits across three datasets, on synthetic graphs that ordinary message passing cannot separate, on molecular property prediction, and on the travelling salesperson problem. Our framework opens a path for near‑term quantum algorithms with theoretical guarantees and practical scalability, bringing the principles of graph learning into quantum circuit design.
Authors:Inderjeet Singh, Andrés Murillo, Motoyoshi Sekiya, Yuki Unno, Junichi Suga
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.
Authors:Xuyue Huang, Zhe Chen, Wang Shen, Xiao-Ping Zhang
Abstract:
Diffusion Transformers (DiTs) have driven substantial progress in image and video generation but suffer from prohibitive computational costs. Feature caching accelerates inference by reusing intermediate representations. Existing methods rely on historical features for implementation simplicity, yet suffer from severe error accumulation at high acceleration ratios. To address this limitation, we investigate the nature of the requisite feature correction. We demonstrate that the optimal calibration update is characterized by a shared low‑rank subspace across diverse prompts. Guided by this structural insight, we propose LearniBridge, a learnable calibration mechanism for feature caching that bridges multiple timesteps through lightweight LoRA updates. This mechanism enables effective calibration requiring only 3‑5 training samples. Extensive experiments on image and video generation show that LearniBridge achieves up to 5.87×, 5.75×, and 4.10× acceleration on FLUX, HunyuanVideo, and WAN2.1, respectively. On WAN2.1, it improves VBench by 1.28% over the previous SOTA at 4.10× acceleration. Our code is available at https://github.com/Iiiiiiirene/LearniBridge.
Authors:Majharulislam Babor, Giacomo Rossi, Annalisa Altavilla, Oliver Schlüter, Marina M. -C. Höhne
Abstract:
Edible insects offer an efficient source of alternative protein, requiring less land, water and emitting less greenhouse gas than conventional livestock. However, their successful integration into the food supply chain demands reliable species authentication to control allergen exposure, prevent adulteration, and meet regulatory standards. Near‑infrared spectroscopy provides a rapid analytical tool, but its performance drops when applied to production batches unseen during training due to batch‑to‑batch variation in spectral measurements. We introduce the Batch‑Invariant Spectral Network (BISN), an end‑to‑end framework that combines a learnable preprocessing module, initialised with Savitzky‑Golay filtering, with an entropy‑regularised adversarial objective to suppress batch‑specific spectral variation. In contrast to Domain‑Adversarial Neural Networks, which enforce domain adaptation only after feature extraction, BISN suppress batch‑effects before species‑specific features are learned. Using 2,700 spectra from three species (Acheta domesticus, Hermetia illucens, and Tenebrio molitor) collected across three independent production batches, BISN achieves a mean leave‑one‑batch‑out accuracy of 0.93 (standard deviation 0.04), outperforming the strongest baseline by four percent. Further insights gained by using explainable AI confirm that model decisions consistently rely on the lipid and protein absorption regions across all folds, connecting predictive performance to known insect biochemistry. BISN addresses both cross‑batch robustness and biochemical interpretability for automated insect species authentication under realistic industrial conditions. The source code and dataset are publicly available at https://github.com/majharB/bisn.
Authors:Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Huaqing Zheng, Xindian Ma, Peng Zhang
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.
Authors:Ao Hu, Liangjian Wen, Jiang Duan, Yong Dai, He Yan, Dongkai Wang, Jun Wang, Yukun Zhang, Ruoxi Jiang, Zenglin Xu
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.
Authors:Xiao Wang, Xufeng Lou, Zikang Yan, Lan Chen, Sibao Chen, Yaowei Wang, Yonghong Tian, Jin Tang
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
Authors:Neelam Saini, Sourav Ghosh
Abstract:
Automatic singing quality assessment (SQA) requires evaluating lyrical correctness and musical fidelity while handling expressive variations. However, existing systems largely rely on either acoustic cues or lyric transcriptions exclusively, limiting holistic performance evaluation. Furthermore, their integration is non‑trivial due to challenges in robust singing transcription amid melisma, vibrato, and tempo elasticity. To this end, we propose MusicJudge, a modality‑guided framework for automated SQA that performs block‑aligned multimodal analysis by coupling lyric correctness with pitch‑rhythm fidelity. It detects semantically meaningful lyric blocks using multi‑signal matching that integrates semantic embeddings, lexical similarity, and phonetic alignment. To improve singing audio transcription, we introduce Modality‑Guided LoRA for ASR fine‑tuning. Experiments across datasets demonstrate strong agreement with human expert judgments and validate the generalizability of MusicJudge.
Authors:Tianyi Wu, Xiaoxi Sun, Yanhua Jiao, Yulin Li, Yixin Chen, YunHao Cao, YiQi Hu, Zhuotao Tian
Abstract:
Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms. However, their computational complexity scales on the order of L cubed with the sequence length L. This poses significant challenges for long‑sequence and real‑time applications, primarily due to the lack of compatibility with key‑value caching and the non‑autoregressive nature of denoising steps. Existing acceleration methods rely on static caching or parallel decoding strategies, which fail to account for the dynamic behavior of token properties across layers and decoding steps. We propose Dynamic‑dLLM, a training‑free framework that enhances dLLM inference efficiency through two components: Dynamic Cache Updating (DCU), which adaptively allocates cache‑update budgets based on layer‑wise token dynamics, and Adaptive Parallel Decoding (APD), which dynamically calibrates decoding thresholds to balance generation quality and efficiency. Extensive experiments on models like LLaDA‑8B‑Instruct, LLaDA‑1.5, and Dream‑v0‑7B‑Instruct across benchmarks such as MMLU, GSM8K, and HumanEval demonstrate that Dynamic‑dLLM significantly improves inference speed. It attains an average speedup exceeding 3 times while maintaining performance. Dynamic‑dLLM outperforms state‑of‑the‑art acceleration methods and provides a plug‑and‑play solution for efficient dLLM deployment without compromising performance. The code is available at https://github.com/TianyiWu233/DYNAMIC‑DLLM.
Authors:Derek Thomas
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.
Authors:Juliana Li, Diya Sreedhar
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.
Authors:Yupu Hao, Zhuoran Jin, Huanxuan Liao, Kang Liu, Jun Zhao
Abstract:
Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities. However, RL alone often leads to instability or limited gains in tool‑use tasks. In our experiments, some models exhibit catastrophic collapse, where performance abruptly drops and tool‑invocation structures fail. The analysis reveals that these failures stem from unexpected probability spikes in specific control tokens, disrupting structured execution, yet the underlying tool‑use capability remains intact, merely obscured by specific formats. To address this, we systematically investigate a diverse set of supervisory signals, including off‑policy supervision, hint‑based guidance, erroneous example supervision, and others, applied under both synchronous and interleaved training schemes. We find that interleaving supervised fine‑tuning (SFT) with RL substantially improves stability, but exhibits degraded performance under format and content out‑of‑distribution (OOD) evaluation. We also analyze the impact of learning rates and generalization across settings. These results highlight the importance of understanding RL failures and demonstrate how diverse supervisory signals can guide exploratory learning, enabling robust training of LLMs for complex, multi‑step tool‑use tasks. Our Code is available at https://github.com/hypasd‑art/Tool‑RL‑Box.
Authors:Alexandre Bouayad
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.
Authors:Nathan Painchaud, Tristan Habémont, Morgane des Ligneris, Allan Serva, Pierre Croisille, Laurent Bertoletti, Thomas Lampert, Johannes F. Lutzeyer, Odyssée Merveille
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.
Authors:Ke Zhao, Zixiang Di, Hong Qian, Xiang Shu, Yaolin Wen, Qitao Shi, Bingdong Li, Xingyu Lu, Xiangfeng Wang, Jun Zhou, Ke Tang, Yang Yu
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.
Authors:Muhammed Furkan Dasdelen, Fatih Ozlugedik, Anastasia Litinetskaya, Nassir Navab, Carsten Marr, Ario Sadafi
Abstract:
Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities. We introduce a statistically grounded patient augmentation approach that generates realistic patients directly in embedding space. Using Gaussian Mixture Models as a probabilistic clustering approach on pooled instance embeddings from all patients, our method learns disease‑specific "recipes"‑statistical distributions of instances across unsupervised clusters. New patients are then generated by sampling embeddings from clusters based on learned recipes. Unlike existing methods that require examples from all categories, our method can generate patients offline by re‑mixing pooled embeddings. Generated patients are further selected based on uncertainty quantification to improve MIL performance. We evaluate our method across three clinically relevant scarcity scenarios: (i) cross‑dataset transfer, where an entirely missing "healthy" class is generated using statistics from an external cohort; (ii) low‑data regimes, where class sizes are extremely limited; and (iii) small‑cohort non‑image tasks, including single‑cell RNA‑seq and flow cytometry. Across all experiments, our method improves performance over baseline, often outperforming other bag‑mixing strategies. Notably, in the missing‑class scenario, a performance comparable to full‑dataset training is achieved, demonstrating its potential for rare disease diagnostic and privacy‑preserving patient augmentation. The code is available at https://github.com/marrlab/RECIPE
Authors:Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal
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.
Authors:Hanyang Wang, Weijieying Ren, Yuxiang Zhang, Ding Cao, Zhizhao Zeng, Ke Zeng, Tianxiang Zhao
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.
Authors:Bang Giang Le, Viet Cuong Ta
Abstract:
Cooperative multi‑agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region framework of single‑agent. Instead of relying on other agents' actions, the independent actors setting considers each agent to act based only on its local information, thus having more flexible applications. However, in the sequential update framework, it is required to re‑estimate the joint advantage function after each individual agent's policy step. Despite the practical success of importance sampling, the updated advantage function suffers from exponentially high variance problems, which likely result in unstable convergence. In this work, we first analyze the high variance advantage both empirically and theoretically. To overcome this limitation, we introduce a clipping objective to control the upper bounds of the advantage fluctuation in sequential updates. With the proposed objective, we provide a monotonic bound with sub‑linear convergence to ε‑Nash Equilibria. We further derive two new practical algorithms using our clipping objective. The experiment results on three popular multi‑agent reinforcement learning benchmarks show that our proposed method outperforms the tested baselines in most environments. By carefully analyzing different training settings, our proposed method is highlighted with both stable convergence properties and the desired low advantage variance estimation. For reproducibility purposes, our source code is publicly available at https://github.com/giangbang/Low‑Variance‑Trust‑Region‑MARL.
Authors:Shaocheng Jin, Tao Zhou, Rui Wang, Ziheng Chen, Xiaoqing Luo, Xiaojun Wu, Josef Kittler
Abstract:
Brain‑Computer Interface (BCI) based on electroencephalography (EEG) enables direct interaction between the brain and external environments and has significant applications in assistive technologies, medical rehabilitation, and entertainment. Recently, EEG decoding methods based on Symmetric Positive Definite (SPD) learning have demonstrated superior performance. However, these methods typically employ basic network architectures and do not explicitly capture local relationships between EEG signals. This limitation is problematic for EEG signals due to their inherently low Signal‑to‑Noise Ratio (SNR). Moreover, most existing Riemannian manifold‑based methods are restricted to specific metrics. The most widely used is the Affine‑Invariant Metric (AIM). However, it has a quadratic dependency on the SPD matrices and cannot handle ill‑conditioned SPD matrices, which hinders the effectiveness of networks. In contrast, the Bures‑Wasserstein Metric (BWM) exhibits linear dependence on SPD matrices and demonstrates superior performance for ill conditioning. To overcome these challenges, we propose a Riemannian self‑attention network based on the BWM. Additionally, the recently introduced power‑deformed generalized Bures‑Wasserstein metric reveals a nonlinear relationship between SPD matrices and matrix power deformation. This metric provides a more nuanced representation of the geometric structure of the SPD manifold. Consequently, we extend our model to a learnable version. For simplicity, we refer to it as GBWAtt. Experimental results on three EEG benchmarking datasets validate the robustness and effectiveness of our proposed method. The code is available at https://github.com/jissc/GBWAtt.
Authors:Hongye Xu, Bartosz Krawczyk
Abstract:
Exemplar‑free class‑incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space. While maintaining class‑conditional Gaussian statistics provides a principled classification strategy, these parametric summaries remain sensitive to anisotropic representation drift. Existing methods often transport these statistics across tasks using a decoupled, post‑hoc paradigm: optimizing a backbone without explicit geometric constraints can distort the legacy manifold, limiting the precision of retroactive alignment. In this paper, we formulate feature transport as an endogenous training constraint rather than a separate post‑task step, presenting the Geometry‑Anchored Transport Framework. First, we derive an Analytic Geometric Anchor via Mahalanobis‑aligned regression to mitigate macroscopic anisotropic drift. Second, we introduce a Topology‑Aware Evolution objective that regularizes localized manifold degradation while calibrating a residual network against the analytic prior. By coupling manifold evolution with transport constraints during the primary training phase, our framework mitigates evaluation errors without requiring decoupled fine‑tuning. Experiments across CIFAR‑100, TinyImageNet, and ImageNet‑100 demonstrate that the proposed framework consistently improves upon existing post‑hoc alternatives under strict exemplar‑free constraints.
Authors:Shen Nie, Qiyang Min, Shaoxuan Xu, Zihao Huang, Yuxuan Song, Yong Shan, Yankai Lin, Wayne Xin Zhao, Chongxuan Li, Ji-Rong Wen
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.
Authors:Qinzhe Yang, Chenyang Liu, Jia Xu, Zhenwei Shi, Zhengxia Zou
Abstract:
State Space Models (SSMs), designed for long‑range modeling, offer linear computational complexity and strong capabilities in capturing long‑range dependencies. In the field of remote sensing, SSMs have gained popularity due to their effectiveness in addressing unique challenges such as dense visual predictions, multi‑modal remote sensing data, and temporal remote sensing data, which have also yielded significant advancements in customized architectures. This paper presents a comprehensive review of SSM‑based approaches in remote sensing, covering most of the relevant studies since SSMs were first introduced to the field. We offer a multi‑dimensional analysis examining SSM applications in remote sensing tasks and discussing advancements in architecture design. This paper not only synthesizes the rapid progress in SSM‑based research but also identifies key challenges and future opportunities. By providing a detailed perspective, this paper aims to serve as a foundational resource for remote sensing researchers, offering actionable insights to foster further advancements in this evolving domain. We will keep tracing related works at https://github.com/QinzheYang/Awesome‑RS‑State‑Space‑Model.
Authors:Ke Xu, Jiaqi Wan, Wenhao Hu, Han Pu, Xiaoyun Wang
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.
Authors:Habibullah Akbar
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
Authors:Tony Salomone, Deep Gandhi, Ali Asaria
Abstract:
Sparse Mixture‑of‑Experts (MoE) models route each token to a few of many experts, inviting the hypothesis that experts form functional modules tied to capabilities or languages. We test this causally on Command A+, a frontier open‑weights MoE (218B total / 25B active; 128 experts, 8 active, +1 shared). We build a routing‑mass atlas, pre‑register six family‑to‑axis hypotheses before any intervention, and ablate each family at inference time against a size‑matched random‑expert null, measuring whether it selectively breaks its own axis (worst off‑target effect at most one third of on‑target). Crucially, we test the same families under four metrics and a held‑out, independent‑corpus run with bootstrap confidence intervals. Our finding is cautionary: robust functional modularity is rare and measurement‑dependent. Of six pre‑registered families, only one, the Arabic‑language family, is a clean selective module that survives an independent corpus and a conservative statistical bar (1/6; a more permissive pre‑registered point rule admits 3/6, but that count is threshold‑sensitive). Every other family has a real causal effect yet fails selectivity, and its apparent modularity flips with the measurement: with the corpus, the metric, and the statistical bar. A positive control on Qwen3‑30B‑A3B recovers its published disjoint structure, confirming the method detects modularity when present. The verdict reproduces on the un‑quantized BF16 model, ruling out a 4‑bit quantization artifact. We conclude that ablation‑based modularity verdicts are not safe unless the corpus, metric, and statistical bar are controlled. We release the atlas and ablation data.
Authors:Giulia Perciballi, Ahmad Fall, Federica Granese, Edi Prifti, Jean-Daniel Zucker
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/.
Authors:John Pavlopoulos, Spyros Barbakos, Lavinia Ferretti, Dionysis Voulgarakis, Asimina Paparrigopoulou, Maria Konstantinidou, Giuseppe De Gregorio, Isabelle Marthot-Santaniello, Paraskevi Platanou, Holger Essler
Abstract:
Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning. Taking one of the longest continuously used writing systems, ancient Greek, as a case study, we introduce three datasets for diachronic representation learning: Hell‑Char, a curated training set spanning the 3rd‑1st centuries BCE, and two evaluation sets, PaLit‑Char (2nd‑5th c. CE) and Med‑Char (9th‑14th c. CE). To address the challenges of symbolic variation, scarce data, and systematic degradation, we propose: a similarity‑weighted supervised contrastive loss that biases embeddings using dynamically estimated inter‑class similarities, and a lacuna‑driven augmentation scheme that simulates realistic manuscript corruptions. Trained with these strategies, both a lightweight CNN and a pretrained ResNet achieve strong recognition performance and produce embeddings that more coherently separate character classes than PCA or generic pretrained models. These embeddings enable clustering, identification of stylistic subgroups, and construction of prototype images that visualize diachronic evolution and transitional letterforms. Our results demonstrate that respecting intrinsic inter‑letter relationships and augmenting with domain‑informed corruptions yield robust, interpretable representations, offering a transferable paradigm for representation learning under scarce, temporally evolving, and noisy conditions. Code and data available at: https://github.com/ipavlopoulos/diachronic‑greek‑letterforms.
Authors:Shuai Zhang, Yancheng Chen, Chuan Zhou, Yang Liu, Xixun Lin, Xiangyu Zhao, Jun Zhu, Zhi-Ming Ma
Abstract:
Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real‑world applications, including social networks, medical diagnosis, and financial transactions. Existing autoregressive methods suffer from error accumulation during multi‑step generation, while non‑autoregressive diffusion methods are typically limited to fixed‑length output sequences. In this paper, we propose Latent Block‑Diffusion Temporal Point Processes (LBDTPP), a novel semi‑autoregressive TPP framework that introduces a latent block diffusion mechanism for high‑quality and variable‑length event sequence generation. The core idea is to define an autoregressive probability distribution over event blocks in latent space and perform Gaussian diffusion within each block. By sequentially generating blocks while simultaneously sampling events in each block, LBDTPP preserves the length flexibility of autoregressive TPPs and inherits the parallel high‑quality generation capability of diffusion models. Theoretically, we derive Wasserstein error bounds showing that, under suitable local approximation and prefix‑stability assumptions, block‑wise generation can reduce error accumulation compared with event‑wise autoregressive generation. Extensive experiments on six real‑world benchmark datasets demonstrate that LBDTPP outperforms state‑of‑the‑art TPP baselines in both unconditional and conditional generation tasks. Further empirical analyses verify the benefits of latent‑space diffusion and block‑wise generation, and reveal the trade‑off between generation quality and block size. Our code is available at https://github.com/Zh‑Shuai/LBDTPP.
Authors:Anirban Das, Joanne Boisson, Irtaza Khalid, Sumita Garai, Steven Schockaert
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.
Authors:Randhir Kumar
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.
Authors:Hongbo Wang
Abstract:
We ask a representation‑learning question about physical world models: when does a conservation law remain certifiable after a model learns a latent representation? A certified horizon bounds ‑‑ in advance, from measurable model defects ‑‑ how many steps a rollout provably stays on a physical invariant's level set. The key design choice is what is certified: not a learned latent Hamiltonian or a learned scalar witness (a model can conserve either while drifting in true energy), but the decoded physical invariant obtained by decoding the latent state and evaluating the known invariant. Around this object we derive shell‑horizon certificates whose budget decomposes into representation, readout, and latent‑dynamics defects, with a monotone alignment bridge through which a soft learned witness yields a certified horizon for the decoded invariant, and test them across state, learned‑lift, and pixel observations on conservative systems. Conservation certificates can survive learned representation, but not all geometric priors survive equally. Hard canonical symplectic structure yields the longest horizons in known phase coordinates yet does not cross a learned chart, whereas a controlled‑Lipschitz‑aligned soft invariant survives in the nonlinear learned‑representation settings we test ‑‑ two lift systems, with the gain growing with nonlinearity, and pixels. Pixel certification is recovered on a readout‑stable sub‑tube, and the Kepler problem exposes a geometric boundary. The central object is therefore not a latent Hamiltonian, but a decoded physical invariant whose robustness to representation learning can be measured, certified, and falsified.
Authors:Kunyu Ni, Lei Cao, Jie He, Xiaotong Zhang, Jianfeng Jin, Junyu Dong, Yanwei Yu
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.
Authors:Petr Nyoma
Abstract:
We present Harmonic, a hierarchical state space model (SSM) for language modeling. The architecture stacks three recurrent levels at progressively slower timescales; each level receives the prediction error of the level below as input, rather than its raw hidden state. On enwiki8 with equal token budgets, Harmonic outperforms a comparable Transformer (28M params) by +1.4% at 1K tokens, +6.7% at 8K tokens, and +11.4% at 32K tokens (bpt, lower is better). It also outperforms Mamba at every tested length by 0.7‑‑1.8%. At 64K tokens, both Mamba and Transformer run out of memory on an 80GB H100; Harmonic trains successfully, reaching 6.169 bpt. Results replicate on WikiText‑103 (H‑TF gap +1.7% to +7.2% across 1K‑‑32K). At 1B parameter scale, replacing all attention layers in TinyLlama 1.1B with HarmonicBlock eliminates the RoPE positional encoding limit: the resulting Hallamonic model maintains stable loss across sequence lengths 1K‑‑8K on two independent clean benchmarks (Lambada and fineweb‑edu held‑out), while TinyLlama degrades catastrophically past its 2K‑token RoPE limit (gap: +9.4 bpt at seq=8K on Lambada). Compute is O(L) per forward pass vs. O(L^2) for attention. Logs: https://github.com/Omibranch/harmonic‑logs.
Authors:Hojun Choi, Seulbin Hwang, Dae Jung Kim, Kisung Kim, Hyunjung Shim, Jinhan Lee
Abstract:
Bird's‑eye view (BEV) perception fuses multi‑camera images into a unified top‑down representation for autonomous driving. Despite recent progress, state‑of‑the‑art methods remain confined to closed‑set scenarios, making them vulnerable to unpredictable real‑world environments. In this work, we introduce open‑vocabulary BEV segmentation (OVBS), which leverages vision‑language models (VLMs) to recognize categories beyond the training set while maintaining precise BEV perception and real‑time efficiency. A key challenge in OVBS lies in the 3D geometric inconsistency inherent in the ill‑posed lifting of 2D VLM semantics into BEV. To address this, we propose OVBEVSeg, a geometry‑aware OVBS framework that enhances efficient Gaussian splatting (GS)‑based unprojection by leveraging robust 3D geometric constraints across three progressive stages: (1) 2D‑to‑BEV pseudo‑labeling via reliable 3D projection for OV generalization; (2) joint 2D‑BEV per‑scene optimization with BEV structural constraints for 3D geometric consistency; and (3) 3D geometric distillation for online efficiency. On the nuScenes dataset, OVBEVSeg achieves state‑of‑the‑art performance, outperforming closed‑set methods by 15.3 mIoU on unseen categories. Remarkably, even with no novel‑class ground‑truth labels, it remains competitive with self‑ and semi‑supervised baselines trained with up to 40% of ground‑truth annotations. Furthermore, it achieves 2.5x faster inference with only 0.22x the memory consumption of projection‑based methods. Project page: https://hchoi256.github.io/projects/ovbevseg/.
Authors:Anton Morgunov, Victor S. Batista
Abstract:
Retrosynthetic planning seeks to connect a target molecule to commercially available starting materials through a multistep route. Classical planners construct such routes by iteratively applying single‑step reaction models within a search procedure; constrained variants often require specialized algorithms or architectural changes. Direct route generation reframes retrosynthesis as sequence generation, but existing direct‑generation methods still train separate models for different planning specifications. We introduce Ariadne, a decoder‑only route generator that represents the target, optional constraints, and route in one prompt‑completion sequence. On the RetroCast/PaRoutes mkt‑cnv‑160 benchmark family, one 24‑layer checkpoint follows route‑depth and required‑starting‑material prompts: adding the corresponding prompt fields raises Solv‑0 by 13.7 points for depth constraints and 31.2 points for required‑leaf constraints. Ariadne also improves over DESP, a bidirectional search planner, on required‑leaf Top‑10 and Solv‑0 in 24 GPU‑minutes versus 6.8 GPU‑hours. On standard reconstruction, Ariadne is comparable to DMS Explorer XL at about half the reported inference time. Across additional target‑only benchmarks, Ariadne's clearest gains are on route‑holdout reconstruction, whereas AiZynthFinder MCTS remains stronger on several Solv‑0 comparisons. These results extend sequence generation from specialist retrosynthesis models to prompt‑conditioned structural route generation. We release the codebase and training scripts to support further work, but do not introduce Tier‑1‑‑3 route checkers; those remain the main bottleneck before models of this kind can become useful to experimental chemists.
Authors:Jin Huang, Yutong Xie, Wanli Song, Xingjian Zhang, Walter Yuan, Matthew O. Jackson, Qiaozhu Mei
Abstract:
Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics. While these models show promise in individual tasks such as survey response prediction and human‑subject experiment simulation, there remains no systematic understanding of how well they perform across diverse behavioral science tasks, contexts, and populations. We introduce BehaviorBench, a comprehensive benchmark that evaluates foundation models along four core capabilities: (1) behavior prediction and simulation, (2) strategic decision‑making, (3) subject‑trait inference, and (4) behavioral knowledge application. Crucially, BehaviorBench evaluates model outputs at both the individual and distributional levels, capturing not only per‑subject accuracy but also population‑level alignment, an essential requirement for behavioral validity. Leveraging the tasks in BehaviorBench, we further develop Be.FM‑1.5, extending the Be.FM family of behavioral foundation models fine‑tuned on behavioral data. Our results reveal a considerable gap: proprietary general‑purpose models excel at individual‑level prediction and knowledge‑intensive tasks, whereas behavioral foundation models, fine‑tuned on behavioral data, achieve substantially stronger distributional alignment. Notably, Be.FM‑1.5 leads on distributional metrics and remains competitive on individual‑level metrics, suggesting that proper behavioral adaptation can close the gap. Our results highlight the importance of distributional evaluation, establish BehaviorBench as a foundation for developing and assessing behaviorally aligned AI systems, and demonstrate Be.FM‑1.5's potential for a broad range of behavioral science studies. Our BehaviorBench and Be.FM‑1.5 models can be accessed via https://umich‑foreseer.github.io/behaviorbench/.
Authors:Wonjun Kang, Kevin Galim, Seunghyuk Oh, Minjun Kang, Sanghyun Park, Donghoon Kim, Minjae Lee, Minseo Kim, Rishabh Tiwari, Yuchen Zeng, Hyung Il Koo, Kangwook Lee
Abstract:
On‑policy distillation (OPD) trains a student on its own rollouts guided by teacher feedback and is becoming increasingly important for large language model (LLM) post‑training. Like reinforcement learning (RL), however, OPD faces an on‑policy systems bottleneck, as rollouts can dominate training time for reasoning workloads. Asynchronous training pipelines can alleviate this bottleneck by decoupling rollout generation from learner updates, but doing so introduces stale‑policy data. While prior work has studied stale data in asynchronous RL, its effects in OPD remain underexplored. We present the first systematic study of staleness in asynchronous OPD, focusing on a practical setting where teacher feedback is implemented through local KL losses and full‑vocabulary teacher logits are too expensive to store or transfer, necessitating finite teacher‑score caches. We first show that KL direction changes the stale‑data problem: teacher‑weighted forward KL is more robust to stale rollouts, whereas student‑weighted reverse KL is vulnerable. Second, for this vulnerable reverse‑KL case, we study whether methods designed to stabilize asynchronous RL can mitigate OPD staleness. In our experiments, they do not improve over a simpler OPD‑specific surrogate: recomputing the reverse‑KL signal under the current student at learner time. Third, we analyze how finite teacher‑score caches create a bias‑variance tradeoff for sparse and sampled reverse‑KL OPD estimators. This motivates multi‑sample Monte Carlo (MC), which preserves MC correctability while reducing one‑sample variance. Finally, we present and open‑source AsyncOPD, a fully asynchronous OPD training pipeline built from these estimator choices. Experiments show that AsyncOPD improves training throughput by 1.6× to 3.8× over strict synchronous training while reaching comparable accuracy.
Authors:Chenhao Dang, Jing Ma, Mingjie Liao
Abstract:
The composition of training data, governed by the diversity of sources and their mixing strategy, is a cornerstone of Large Language Model (LLM) pre‑training. Online Data Mixing (ODM), the technique of adaptively adjusting data mixtures during training, has emerged as a promising direction to improve efficiency. However, existing methods are constrained by their reliance on a singular optimization perspective, which fundamentally overlooks the need for complex LLM pre‑training to consider the dynamic data composition from multiple dimensions. To overcome this limitation, we introduce the Holistic Data Scheduler (HDS), a novel online data mixing framework. HDS formulates the data scheduling challenge as a reinforcement learning problem in a continuous control space and leverages the Soft Actor‑Critic (SAC) algorithm for its stability and sample efficiency in exploring the high‑dimensional policy space. At the core of HDS lies a novel multi‑objective, holistic reward function that integrates three critical perspectives: a data‑driven reward for quality, a loss‑driven reward capturing inter‑domain influence, and a model‑driven reward based on weight norms. To validate our design and determine its optimal configuration, we conducted systematic experiments on LLMs of various sizes. On The Pile benchmark, HDS reaches the final validation perplexity of the next best method with 44% fewer training iterations. Furthermore, it achieves a 7.2% improvement on the MMLU 0‑shot task along with consistent gains on other benchmarks, showcasing its ability to enhance both training efficiency and final model capability.
Authors:Lucky Verma, Pratik Yadav
Abstract:
Discrete diffusion language model (DLM) fine‑tuning inherits inexpensive diagnostics from denoising‑time confidence monitors, but their PEFT‑training meaning is untested. We test top‑1 argmax concentration as a collapse warning. Across 816 LoRA/PEFT configurations from three DLM families, the warning fires for every configuration while logs record 0/816 actual collapses at the 200 step horizon, giving zero precision. The cause is pre‑equilibrium saturation: top‑1 concentration is already high before optimization and quickly becomes insensitive to final training stability. We then evaluate max LoRA gradient norm, a parameter‑side signal that samples gradient routing rather than token concentration. On a pooled held‑out LLaDA‑family split, a train‑optimized threshold identifies top‑decile final‑loss configurations with precision 0.68 and F1=0.79, above the all‑positive top‑1 baseline even at the lower split‑bootstrap confidence bound. Autoregressive controls and cross‑family threshold failures bound the result to short‑horizon DLM‑LoRA inspection rather than a universal collapse detector. Workflow: drop top‑1 as a PEFT alarm, log max‑gradient early in training, and calibrate thresholds per DLM family before routing runs for inspection.
Authors:Feihong Nan, Zhengyi Zhong, Pan Wang, Weidong Bao, Xiongtao Zhang, Quan Wen, Ji Wang
Abstract:
Federated unlearning (FU) is critical for complying with legal mandates like the right to be forgotten in decentralized systems, yet current methods face a persistent dilemma between non‑target knowledge loss and high request latency. To resolve these issues, we propose FedUP, a one‑shot federated unlearning framework utilizing lightweight pluggable filters that act as a "knowledge funnel" to screen out target data while preserving original model performance. By freezing original model parameters and training filters at the server side using differentially private (DP)‑protected class centroid samples, FedUP bypasses the need for multi‑round client‑server communication and complex retraining, reducing unlearning latency from minutes to mere seconds. Additionally, the framework's pluggable architecture ensures inherent reversibility, enabling the seamless restoration of forgotten knowledge by simply removing the filters. Extensive experiments on diverse image and text tasks demonstrate that FedUP effectively reduces non‑target knowledge loss and achieves superior unlearning precision and efficiency across various scenarios. Code is available at: https://github.com/suows/FedUP‑code.
Authors:Wenhao Gao, Yifan Wang, Yijia Ma, Carl Yang, Wen Li, Chenyu You
Abstract:
Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging. EEG provides a weak, spatially diffuse, and highly variable measurement of distributed cortical activity, whereas speech is organized as a coherent acoustic trajectory with strong harmonic and temporal structure. The resulting mismatch makes waveform regression unstable and causes stochastic multi‑step generation to be sensitive to artifact‑dependent conditioning and subject variability. We introduce NeuroSonic, a conditional flow‑matching framework for EEG‑to‑speech reconstruction. Instead of predicting waveforms directly or refining them through stochastic denoising, NeuroSonic learns a deterministic probability‑flow velocity field that transports a noise‑corrupted acoustic state toward clean speech under EEG conditioning. EEG and audio are embedded into a shared token space and processed by a time‑conditioned gated Transformer that parameterizes the transport ordinary differential equation. This formulation models trajectory evolution explicitly while avoiding iterative stochastic sampling. We evaluate NeuroSonic on the CineBrain and EAV benchmarks under cross‑subject evaluation. Across both datasets, the proposed method improves distributional realism, spectral fidelity, and perceptual quality over representative GAN‑, diffusion‑, and mean‑flow baselines, with up to a 26.3% gain in overall perceptual quality. The performance gap is most evident in artifact‑heavy segments, where conditioning variability is strongest. These findings indicate that deterministic conditional transport provides a stable and effective formulation for EEG‑driven speech reconstruction. Code is available at https://github.com/Y‑Research‑SBU/NeuroSonic/ .
Authors:Morayo Danielle Adeyemi, Ryan A. Rossi, Franck Dernoncourt
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.
Authors:Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov, Brian Plancher, Thomas Lew
Abstract:
Robotics increasingly relies on GPUs for parallel simulation, large‑scale learning, and neural‑network inference. For model predictive control (MPC) to scale with this paradigm, solvers must run efficiently on this hardware while remaining fast, differentiable, and compatible with expressive MPC formulations used in robotics. We present TurboMPC, a differentiable MPC solver that runs entirely on the GPU and supports state and control inequality constraints, implicit integrators, cross‑time‑coupled costs, and slack variables. TurboMPC combines sequential quadratic programming (SQP), an alternating direction method of multipliers (ADMM) inner solver, implicit differentiation, and a co‑designed JAX‑CUDA implementation for efficiency and ease of use. In simulation, we validate TurboMPC on constrained planning, humanoid imitation learning, and reinforcement learning with neural‑network cost function tasks, achieving up to 15× and 58× speedups over state‑of‑the‑art CPU and GPU differentiable solvers, respectively. We deploy TurboMPC on a full‑scale car for minimum‑time racing and find that batched, GPU‑accelerated tuning of MPC parameters via Bayesian optimization yields significantly faster driving than a hand‑tuned baseline. TurboMPC also scales to planning horizons of over 8000 knot points while maintaining control of the vehicle. We open‑source TurboMPC at: https://github.com/ToyotaResearchInstitute/turbompc
Authors:Fengfeng Liang, Yuechen Zhang, Jiaya Jia
Abstract:
Existing low‑bit KV‑cache quantizers often treat each cached key as a flat vector. Under RoPE, however, a key's contribution to a future attention logit decomposes into a position‑dependent sum over two‑dimensional frequency blocks. This makes key‑cache quantization a block‑wise bit‑allocation problem: high‑energy RoPE blocks are more sensitive to quantization error and should receive more bits. We introduce Block‑GTQ, a RoPE‑aware bit allocator for key‑cache quantization built on TurboQuant‑MSE(TQ‑MSE). For each layer and KV head, Block‑GTQ computes a label‑free energy score for each RoPE block and greedily allocates integer bit widths by marginal gain. Under matched K/V bit budgets, Block‑GTQ better preserves RoPE query‑key logits on a ten‑model diagnostic panel, cutting per‑layer MAE by 32‑80% at 2 and 3 b/dim K‑only quantization and winning all 367/367 layer comparisons against uniform TQ‑MSE. These fidelity gains translate to stronger downstream long‑context retrieval, understanding, and reasoning. At K2V2 on Llama‑3.1‑8B‑Instruct, Block‑GTQ raises the six‑task NIAH average from 70.6 to 97.4, and the LongBench‑EN average from 36.87 to 53.31. On AIME 2024/2025 with DeepSeek‑R1‑Distill‑Qwen‑7B, without an fp16 recent‑key buffer, Block‑GTQ at K3V2 scores 51.7/37.5, close to fp16's 54.2/37.9, whereas uniform TQ‑MSE collapses to 0.0/0.0. We further implement a packed‑cache serving path. On a single H800 GPU with Qwen2.5‑3B‑Instruct, packed K3V3 achieves 3.24x KV‑cache compression with fp16‑comparable quality, runs 1.34x faster than fp16 FlashAttention2 at 128K context, reduces peak memory from 56.31 GB to 19.85 GB, and remains feasible at 256K and 512K where fp16 OOMs. Code is available at https://github.com/JIA‑Lab‑research/blockgtq.
Authors:Zixin Ding, Shaghayegh Emami, Giovanna Salvi, Cecilia Tosciri, Abhijith Gandrakota, Jennifer Ngadiuba, Nhan Tran, Christian Herwig, David W. Miller, Yuxin Chen
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).
Authors:Amirhossein Kardoost, Lion Gleiter, Tingying Peng, Carsten Marr
Abstract:
Self‑supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three‑dimensional nature of cells. We present a systematic comparison of 2D and 3D masked autoencoders (MAE‑2D vs. MAE‑3D) on volumetric microscopy data. Under matched architectures and training protocols, MAE‑3D consistently outperforms 2D max‑projection and slice‑based variants on downstream single‑cell tasks. We further align visual representations with a pretrained protein language model (ESM2) and show that cross‑modal supervision yields larger gains for volumetric models. Channel cross‑attention and frequency‑domain regularization are critical for leveraging 3D spatial context. On a protein‑‑protein interaction task, MAE‑3D achieves a ROC‑‑AUC of 0.865, outperforming prior methods by up to +0.025. For protein localization, our best 3D model attains state‑of‑the‑art AUC_\textmicro (0.952) and F1_\textmicro (0.742), improving over previous approaches by +0.003 and +0.010 absolute, respectively. Overall, these results demonstrate the advantages of native 3D modeling and multimodal alignment for representation learning in single‑cell microscopy.
Authors:Mohammed Nagdi, Evangelos-Marios Nikolados, Alexey Yermakov, Mars Gao, Nathan Kutz, Filippo Menolascina
Abstract:
Learning Koopman operators with autoencoders enables linear prediction in a latent space, but long‑horizon rollouts often drift off the learned manifold, leading to phase and amplitude errors on systems with switching, continuous spectra, or strong transients. We introduce two complementary components that make Koopman predictors more robust. First, we add an attention‑free latent memory (AFT) block that aggregates a short window of past latents to produce a corrected latent before each Koopman update. Unlike multi‑head attention, AFT operates in linear time and adds only \approx30k parameters (3d^2 + T^2, fewer than matched multi‑head attention), yet captures the local temporal context needed to suppress error divergence. Second, we propose dynamic re‑encoding: lightweight, online change‑point triggers (EWMA, CUSUM, and sequential two‑sample tests) that detect latent drift and project predictions back onto the autoencoder manifold. Across three benchmark systems ‑‑ Duffing oscillator, Repressilator, IRMA ‑‑ our model consistently reduces error accumulation compared to a Koopman autoencoder and matched‑capacity multi‑head attention. We also compare against GRU and Transformer autoencoders, evaluated both from initial conditions and with a 50‑step context, and find that Koopman+AFT (with optional re‑encoding) attains markedly lower long‑horizon error while maintaining lower inference latency. We report improvements over horizons up to 1000 steps, together with ablations over trigger policies. The result is a fast, compact predictor that stays on the learned manifold over long horizons.
Authors:T. Lucas Makinen, Deaglan J. Bartlett, Niall Jeffrey, Benjamin D. Wandelt
Abstract:
When two or more parameters or labels produce similar data, they are degenerate, or hard to distinguish. Degeneracies render both label prediction and inverse problems difficult, since both machine learning algorithms and probabilistic samplers rely on the distinguishability of data and its gradients with respect to parameters. However, identifying degeneracies in physical models or real‑world datasets can be elucidating about the choice of model or the underlying process that produces the data. We present the degeneracy distillery, a method that (1) detects and (2) resolves degenerate parameter combinations (a) automatically and (b) symbolically, from parameter‑data (or parameter‑simulation) pairs alone, through estimation and flattening of the Fisher information matrix. By exploring the information geometry of the likelihood, we characterize degeneracies as an intrinsic property of the physical model, requiring no realised data observation. We demonstrate our approach on a range of synthetic and real‑world problems, discovering symbolic coordinate transformations that identify the combinations of parameters of a model which yield independent effects on the data. The resulting coordinates flatten the Fisher information in expectation globally, in contrast to posterior‑based methods that flatten only at a single point, and substantially reduce the simulation budget required for downstream neural posterior estimation. In test cases we require up to 10× fewer simulations for posterior estimation at matched validation calibration whilst simultaneously gaining physical insight on the system.
Authors:Wietse Stienstra
Abstract:
Headline accuracies on the Tuebingen cause‑effect pairs are routinely compared across papers even though each is measured under its authors' own protocol ‑‑ different pair subsets, weightings, model‑selection, and decision rates. We argue this is the wrong comparison and run the right one: a same‑hands re‑evaluation in which every method is run by us on the identical 102 pairs, with one strict rule ‑‑ no tuning and a decision forced on every pair. As a clean reference point we introduce a deliberately minimal baseline: sorted‑conditional compression, which feeds quantized, sorted, first‑differenced data to an off‑the‑shelf compressor (bz2) and has zero fitted parameters. Under the common ruler the ranking differs sharply from the literature. Our baseline reaches 74.7% weighted accuracy (p = 3.7e‑7); on the same 100 pairs that SLOPE is evaluated on it scores 76.0%, a 1.2‑point gap below the authors' own forced‑decision SLOPE (77.2%) that is well inside noise (McNemar p = 0.39). A faithful re‑run of RECI lands at 70.7% ‑‑ inside the original authors' reported error bar, not the 77.5% often quoted (which we trace to a mis‑copied cell). SLOPE's published 82.4% is a decided‑subset figure: scoring the authors' own stored output only on the pairs its significance test chose to answer reproduces 81.7%. Under the common ruler the methods cluster in the low‑to‑mid 70s and the zero‑parameter compressor ties the strongest of them. We document the mechanisms that inflate published figures (test‑set model selection, significance‑gated abstention) and contribute two further results: compression score magnitude is a model‑free confounding flag (p = 2.8e‑68), and a pre‑registered falsification test fails in an instructive way that bounds the method's theoretical interpretation. Code, pre‑registrations, and per‑pair outputs are released.
Authors:Yashkumar R Lukhi, Harsh Rameshbhai Moradiya, Radu Timofte, Dmitry Ignatov
Abstract:
We present an automated large‑scale search pipeline for heterogeneous 4‑Expert Mixture‑of‑Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem. Building on a hand‑crafted heterogeneous MoE reference model, we replace manual design with a deterministic code‑assembly generator that systematically combines base architecture families drawn from the LEMUR database into MoE4 ensembles, each governed by a convolutional gating network with temperature scaling, mixup augmentation, and cosine‑annealed learning rate scheduling. Over a 28‑day campaign on an NVIDIA RTX 4090, the pipeline generated 4,463 candidate models across 197 batches, of which 1,021 were evaluated successfully. A critical finding emerged from the campaign: due to alphabetical enumeration via itertools.combinations, the entire explored search space (4.8% of the theoretical 23,751 possible 4‑family combinations) is anchored to a single family, AirNet. We characterise this coverage bias precisely, identify the root cause in the generator, and propose a stratified random sampling fix. Within the AirNet anchored scope, ShuffleNet and MobileNetV3 consistently co‑produce the highest‑accuracy ensembles (mean accuracy up to 0.632), while FractalNet and MNASNet are identified as low‑yield families warranting exclusion in future campaigns. The pipeline, analysis artefacts, and corrected generator are released as part of the open‑source NNGPT project at https://github.com/ABrain‑One/nn‑gpt
Authors:Francesco Di Salvo, Sebastian Doerrich, Christian Ledig
Abstract:
Foundation models provide highly descriptive representations for medical images, yet their reliability degrades under distribution shifts arising from changes in patients, devices, or acquisition conditions. Reliable out‑of‑distribution (OOD) detection is therefore essential for safe deployment. Recent post‑hoc detectors efficiently exploit frozen embeddings (e.g., kNN), whereas reconstruction‑based OOD detection in latent feature space has seen limited adoption due to inconsistent performance. In this work, we show that the limitation of reconstruction‑based methods in latent space does not stem from poor reconstruction quality, but from how reconstruction errors are scored. Standard L2 residual norms collapse the anisotropic residual structure, thereby suppressing informative deviations. To address this limitation, we introduce MaRS (Mahalanobis Residual Scoring), a label‑free OOD detector that learns an in‑distribution manifold using a lightweight autoencoder and measures deviation via a Mahalanobis distance on reconstruction residuals, yielding variance‑aware OOD scores. Across three imaging modalities, multiple types of distribution shift, and different model families and scales, MaRS outperforms established confidence‑, distance‑, and reconstruction‑based baselines, while remaining fully post‑hoc and lightweight. The code is available at https://github.com/francescodisalvo05/mars.
Authors:Samat Zharassov
Abstract:
Standard autoregressive Transformer decoders can often exhibit substantial forgetting under sequential fine‑tuning on shifting curriculum distributions. This technical report evaluates SamatNext v0.2‑B, an experimental 356M‑parameter hybrid sequence decoder that alternates Differential‑Attention‑style layers with DeltaNet‑inspired simplified linear‑state mixer layers using RMS normalization and output scale calibration. We study the model under a controlled staged Python code curriculum and compare it with a parameter‑matched Transformer baseline. In this setting, SamatNext v0.2‑B achieves a 100.0% pass rate on the controlled Stage 5 holdout while retaining 98.8% of adjacent Stage 3 semantic behavior and reaching 12.0% on the Stage 2E early syntax holdout. The strongest Transformer baseline reaches 97.6% on Stage 5 but retains only 6.0% of Stage 3 behavior. Both architectures remain weak on long‑horizon early‑stage retention, so the result should be interpreted as evidence of an altered retention/plasticity tradeoff in this controlled setting, not as a general solution to catastrophic forgetting. Code, model specifications, evaluation scripts, and result tables are provided for independent verification.
Authors:Syed Hamzah Rizvi, Yash Vardhan Tomar
Abstract:
Simulation‑to‑reality transfer, often called sim‑to‑real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynamics is still poorly understood. In this work, we focused on the allocation of real‑robot measurement time between system identification and domain randomization. We studied this tradeoff in a controlled sim‑to‑sim pendulum setting, where a hidden‑parameter model stands in for the physical robot, and the experiment sweeps identification rollouts against the width of the randomization distribution. Across the reality gaps and noise levels we tested, the measurement budget did most of the work. A small number of identification rollouts closed most of the transfer gap, and once any real data was available, policies performed best when trained at the estimated parameters rather than over a widened randomization band. Broad randomization that contained the true system still did not substitute for measurement. These results hold in a benign regime where the dynamics are identifiable and only two parameters are unknown, so structural model mismatch remains the setting where randomization breadth may become more valuable. Overall, our results suggest that sim‑to‑real pipelines should first measure the parameters they can and reserve randomization for the uncertainty that remains.
Authors:Irene Iele, Elena Mulero Ayllón, Paolo Soda, Matteo Tortora
Abstract:
Vegetation monitoring under climate stress requires answering not only how it will evolve given the expected weather, but how it would respond to alternative meteorological conditions. Forecasting models return the expected vegetation state for the observed weather and cannot answer these scenario‑conditioned questions, because future weather is fixed to the recorded trajectory. We present VegSim, a geospatial world model for scenario‑conditioned vegetation simulation. VegSim infers a latent vegetation state from sparse satellite‑derived NDVI histories, past meteorological covariates, and static spatial context, propagates it forward under future weather forcing through recurrent latent dynamics, and decodes predictive NDVI quantiles at each lead time. Because future forcing enters as a controllable input, the same trained model supports probabilistic forecasting under observed weather and conditional simulation under user‑defined meteorological forcing, without supervision on scenario responses. We evaluate VegSim on GreenEarthNet across in‑distribution data and spatial, temporal, and joint spatial‑temporal shift, where it achieves strong point and probabilistic accuracy against time series and Earth observation forecasting baselines while using a compact architecture. We then simulate vegetation responses across Europe under four meteorological scenarios, and in a France summer 2022 case study, obtaining spatially coherent patterns consistent with known sensitivity to temperature and precipitation. The code is available at https://github.com/arco‑group/vegsim.
Authors:Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
Abstract:
Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain‑specific expertise. Self‑supervised learning, particularly contrastive learning, has emerged as a compelling alternative, enabling rich representation learning directly from unlabeled data. Yet its success hinges critically on the design of positive and negative sample pairs. Existing approaches for time series rely on hand‑crafted augmentations and masking heuristics that embed strong domain assumptions, often limiting generalization across diverse temporal patterns and potentially introducing spurious correlations. In this work, we challenge this paradigm by demonstrating that explicitly encoding temporal shift invariance through a simple, deterministic view construction is sufficient to learn strong representations for time series classification. By exploiting temporal structure, our method, Shift Invariant Feature Training (ShiFT), achieves state‑of‑the‑art performance on six diverse real‑world time series benchmark datasets, as well as the UCR and UEA archives, while reducing training time. Beyond empirical performance, we present a systematic analysis of contrastive learning dynamics in time series settings, examining the effects of batch size and the number of negatives on downstream performance. Our findings provide practical insights for designing efficient contrastive learning frameworks for time series representation learning. The source code is publicly available at https://github.com/sfi‑norwai/ShiFT.
Authors:Jayadratha Gayen, Bradly Alicea
Abstract:
Understanding how a nervous system wires itself from birth to adulthood is a fundamental challenge in developmental neuroscience. We present DevoTG, a temporal graph framework that applies Temporal Graph Neural Networks (TGNs) to two complementary representations of C. elegans neural development: a Continuous‑Time Dynamic Graph (CTDG) of cell division events derived from cell lineage data, and a Discrete‑Time Dynamic Graph (DTDG) of the developing synaptic connectome spanning eight reconstructed electron‑microscopy datasets. On the lineage prediction task, our TGN achieves a mean test AUC of 0.839 +/‑ 0.007 (5 seeds; validation AUC 0.937 +/‑ 0.001), outperforming a static GNN with the identical architecture by 26 AUC points (0.577 +/‑ 0.080), demonstrating that temporal memory is the decisive factor. Applied to the connectome DTDG, DevoTG identifies three connection stability classes (stable, developmental, and variable) across 225 neurons and 858 to 2,496 connections over development (L1 birth to adult), providing a temporal‑graph‑theoretic complement to the individual‑variability classification of Witvliet et al. Analysis of hub command interneurons AVA, AVB, and AVE reveals their persistent centrality and how their integration roles are progressively reinforced across larval stages. Accompanying interactive visualizations (3D animated networks, centrality heatmaps, and a spatiotemporal lineage graph) make developmental dynamics accessible for biological hypothesis generation. DevoTG is open‑source and designed for extension to other developing nervous systems. Code is publicly available at https://github.com/DevoLearn/DevoGraph/tree/main/DevoTG.
Authors:Weiqin Yang, Yue Pan, Chongming Gao, Sheng Zhou, Xiang Wang, Can Wang, Jiawei Chen
Abstract:
We identify a critical pitfall in scaling transformer‑based sequential recommenders: while increasing model size improves recommendation accuracy, it simultaneously amplifies popularity bias. This bias drives systems to over‑recommend popular items at the expense of niche ones, which not only undermines fairness but also degrades the broader ecosystem by reinforcing the Matthew effect and filter bubbles. Consequently, this bias amplification emerges as a fundamental obstacle to sustainable model scaling. Through comprehensive theoretical and empirical analyses, we uncover the root cause of this amplification. Our findings reveal that as model depth increases, the two core components of the transformer architecture, i.e., attention aggregation and feed‑forward projections, synergistically induce severe spectral collapse in model predictions, which directly translates to the amplification of popularity bias. To address this challenge, we propose SPRINT (Scalable Popularity Regularization IN Transformers), which mitigates spectral collapse during scaling by constraining (i) the maximum column‑sums of the attention score matrices and (ii) the spectral norms of the feed‑forward parameters. Extensive experiments demonstrate that SPRINT significantly improves both accuracy and long‑tail fairness. Crucially, it yields more favorable scaling behaviors when expanding model sizes from 0.05M to 0.34B parameters. The code is available at https://github.com/Tiny‑Snow/GenRec.
Authors:Harsh Patel
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.
Authors:Rome Thorstenson
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.
Authors:Chao Han, Haozhe Hu, Fei Ma, Wei Zhang, Xiaoyu Shen
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.
Authors:Shubham Gandhi, Yiqing Xie, Atharva Naik, Ruichen Zhu, Carolyn Rose
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
Authors:Sachin Sundar, Sandeep Kumar, Mothish M
Abstract:
Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural contexts where intergroup hostility is prevalent. The problem is particularly challenging due to the data scarcity for these tasks in the low‑resource languages. Identifying such phenomena has become an active area of research and is addressed in SemEval‑2026 Task 9: Multilingual, Multicultural Online Polarization Detection. To address this problem we propose an architecture that leverages LaBSE embeddings ‑ an unconventional choice typically reserved for retrieval tasks, to obtain strong cross‑lingual learning which enhances scores in low‑resource language by a score up to 0.2 macro F1. Furthermore, we provide a comprehensive ablation study evaluating the performance of diverse encoder models in the Qwen model family within a retrieval‑based prompting framework. Our code will be soon available at https://github.com/carrycurious/PolarMind.
Authors:Tairan Xu, Leyang Xue, Zhan Lu, Jinfu Deng, Hongyang Xiao, Yinsicheng Jiang, Congjie He, Matej Sandor, Le Xu, Luo Mai
Abstract:
Batch inference has become a central mode of AI computation, yet existing inference engines still rely on execution models designed for interactive serving. When scaled to millions of sequences, batch workloads reveal two fundamental requirements: the ability to handle extreme inter‑ and intra‑sequence load variation that emerges only at runtime, and the ability to sustain high utilization across large fleets of GPUs. Existing systems fail to meet these requirements, losing substantial fractions of achievable throughput. We introduce a new architectural foundation for batch inference: the sequence coroutine compute model, which represents each sequence as a fine‑grained, event‑driven coroutine. This model exposes expressive primitives that allow the runtime to reorganize work dynamically, enabling larger expert‑level batches, mitigating stragglers, reallocating work across devices, and maintaining utilization even on cost‑effective or memory‑constrained GPUs. Building on this abstraction, we implement BatchGen, a production‑ready system that uses the coroutine model at cluster scale. On a 128‑GPU cluster, BatchGen reduces batch completion time by up to 2.3×, and on memory‑constrained accelerators it outperforms the strongest offloading baseline by up to 9.6×. We will open‑source BatchGen at https://github.com/batchgen‑project/batchgen
Authors:Saifelden M. Ismail, Aser O. Ibrahim, Omar A. Mahmoud
Abstract:
Phishing is a multi‑modal threat. We present a hybrid pipeline that scores each modality with its own engine and fuses the results. Three engines are built, deployed, and independently benchmarked: a four‑stage URL stack (Domain Guard, lexical model, threat intelligence, and an asymmetric L2 fusion sidecar); a generalization‑hardened DistilBERT NLP classifier whose held‑out real‑phishing recall rises from 0.8% to 87.3%; and a threat‑intelligence synchronizer with end‑to‑end OpenTelemetry instrumentation confirming 1:1 message conservation. A decision‑level fusion stage, characterized on a 10,677‑email whole‑system benchmark, reaches F1 = 0.914 with a calibrated probabilistic‑OR over URL, header, and phishing‑probability channels while cutting held‑out real‑spam false positives to 3.6%. Because that benchmark uses proxy URL and header channels and an operating point still needing recalibration, we present it as a preliminary integrated result. The binding constraint for deployable detection is generalization rather than same‑distribution accuracy.
Authors:Tianyou Wang, Anson Lei, Joe Watson, Ingmar Posner
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/.
Authors:Zhiqing Yang, Yilun Liu, Yunpu Ma, Volker Tresp, Hinrich Schütze
Abstract:
Large language models (LLMs) can readily reproduce conventional expressions, yet their ability to model gradient frequency distributions remains underexplored. We investigate this using linguistic binomials, such as men and women, where both word permutations are grammatically valid but exhibit distinct, cross‑linguistic variations in conventionality. We formalize binomial ordering as a distributional alignment problem, and construct a multilingual dataset of 600 binomial pairs across 8 languages. With categorical and distributional metrics, we measure and compare the corpus‑derived preferences with model‑induced ordering probabilities of 6 open‑weight LLMs. While models often behaviorally recover the dominant corpus‑preferred order, particularly for strongly conventionalized pairs, they align less well with the exact corpus preference distributions. This suggests that apparent directional order overstates how faithfully LLMs capture the statistical nuances of language use. Sparse probing verifies that the concept of preference strength is partially encoded among middle‑to‑late layers, and steering along probe‑derived directions alters model‑induced ordering distributions, demonstrating that the statistical behavioral preference of LLMs can be mechanistically measured and manipulated via internal representations.
Authors:Lucas Fagan, Michele Tarquini, Ali Shehper, Maksymilian Manko, Angus Gruen, Coco Huang, Giorgi Butbaia, Davide Passaro, Sergei Gukov
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.
Authors:Linara Adilova, Henning Petzka, Asja Fischer, Bernhard C. Geiger
Abstract:
Deep neural networks transform input data into latent representations that support a wide range of downstream tasks. These representations can be characterized along information‑theoretic and geometric dimensions, but their relationship remains poorly understood. A central open question is whether low mutual information (MI) between inputs and representations necessarily implies geometrically compressed latent spaces and vice versa. We investigate this question using class‑wise clustering as a measure of geometric compression and theoretically sound MI estimation in conditional entropy bottleneck (CEB) networks and continuous dropout networks. We evaluate the interplay between MI, geometric compression, and generalization on classification tasks under controlled noise injection schemes. Our findings show that low MI does not reliably correspond to geometric compression, and that the connection between the two is more nuanced than often assumed. Indeed, our experiments reveal a negative and nonlinear relationship that can reverse when varying training setup. Our results put forward a hypothesis that generalization acts as a potential confounder in this connection rather than being their direct consequence.
Authors:Eight Suzuki, Hideitsu Hino, Noboru Murata
Abstract:
Transformers achieve strong performance, but their internal computations remain opaque. We view each Transformer layer as a dynamic graph whose nodes are token representations and per‑head attention outputs, with Multi‑Head Attention (ATT) and MLP as module boundaries. On this graph we use LIG (Layer‑wise Integrated Gradients), which applies set‑to‑set Integrated Gradients (IG) at nonlinear module boundaries. Set‑to‑set IG applies IG to a map from a set of input token representations to a set of output representations, evaluating token‑to‑token contributions, which is not standard in prior IG applications. This extends IG from the usual scalar‑objective setting to set‑to‑set maps via an L2 scalarization, and composes within‑layer contributions in the spirit of Layer‑wise Relevance Propagation (LRP), with IG completeness playing the role of LRP‑style conservation at each boundary. We use LIG to analyze (i) the agreement between module‑wise composition and layer‑whole attribution under an L2 criterion, and (ii) within‑layer information flow by tracing separated ATT and MLP contributions. On BERT‑base and PTB, configurations that best preserved within‑layer consistency used the target token's embedding as the ATT baseline and either the ATT output at a=0 or Zero as the MLP baseline. We therefore present LIG as a diagnostic XAI tool at module‑boundary granularity, without model‑specific retraining or per‑operation interpreter design. Code is available at https://github.com/eightsuzuki/layer‑wise‑integrated‑gradients.
Authors:Yuanhao Chiang, Hongbo Duan, Chunru Yang, Jiahua Pei, Yi Liu, Xueqian Wang
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.
Authors:Yanlei Liu, Zhenyu Liao
Abstract:
Tensor factorization (TF) has been widely adopted for high‑dimensional sparse data completion tasks. Despite significant progress, neural TF methods often struggle to capture complex cross‑mode interactions and remain vulnerable to (extreme) data sparsity. To address these challenges, we propose a novel neural tensor factorization approach, termed Dual‑Attention Convolution Expert Networks with Group‑Level Contrastive Learning (DCGC). For the first problem, DCGC generates diverse non‑linear alignment patterns of latent factors via a multi‑channel convolution network, and leverages the gated dual‑attention mechanism to drive the model to focus on more important output channels (i.e., convolution experts) and the aligned features. Furthermore, DCGC introduces a group‑level contrastive learning strategy that aggregates positive samples with identical feedback levels while separating negative samples across different levels. This strategy injects high‑quality self‑supervised signals to mitigate data sparsity. Extensive experiments conducted on five datasets demonstrate that our DCGC outperforms the state‑of‑the‑art methods in sparse tensor completion for traffic and recommendation applications. Code to reproduce the experimental results in the paper is available at https://github.com/ku1z/DCGC.
Authors:Nichula Wasalathilaka, Abhijit Das, Imran Razzak, Dwarikanath Mahapatra
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.
Authors:Cong Wan, Zeyu Guo, Zijian Cai, Jiangyang Li, SongLin Dong, Lin Peng, Xiangyang Luo, Zhiheng Ma, Yihong Gong
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
Authors:Mingzhi Chen, Yiyu Gui, Guibo Luo
Abstract:
Medical time series (MedTS) signals such as electroencephalography (EEG) and electrocardiography (ECG) support many clinical applications. However, substantial subject‑level heterogeneity often induces subject‑level distribution shift, causing a fixed parameter set to generalize poorly to unseen individuals. Compared with domain adaptation methods that often depend on extra adaptation components or target‑batch statistics, Test‑Time Training (TTT) provides a more practical solution for sequential clinical data by enabling online adaptation from unlabeled test samples. However, many representative TTT methods require iterative inner‑loop optimization, increasing test‑time overhead. In this paper, we propose MedTS‑TTT, a test‑time training framework for medical time series modeling. MedTS‑TTT is built upon Closed‑Loop Self‑Alignment Test‑Time Training (CLSA‑TTT) and a Gated Convolutional Backbone (GCB). CLSA‑TTT constructs a token‑level self‑supervised target and performs a single‑step fast‑weight update for intra‑layer closed‑loop alignment, enabling rapid sample‑wise adaptation without iterative inner‑loop optimization. GCB combines CLSA‑TTT‑based fast adaptation and token‑level fusion with a gated convolutional branch to balance local dynamic modeling and information‑flow control. On 4 public datasets (2 EEG and 2 ECG) with subject‑independent splits, MedTS‑TTT achieves 11 top‑1 rankings out of 12 evaluations across 9 baselines and 3 metrics. The code is publicly available at https://github.com/mingzhi‑c/MedTS‑TTT.
Authors:Cengizhan Bayram
Abstract:
Retrieval heads, attention heads that copy information from earlier context to the current position, have been proposed as the mechanistic substrate for long‑context recall. Rotary position embeddings (RoPE) rotate queries and keys by frequencies decaying with a base hyperparameter theta, and a natural hypothesis is that this rotation either prevents retrieval heads from forming or degrades their function. We test both across four open‑weight 7‑8B models spanning multi‑head and grouped‑query attention and a 100x range of theta, using paired‑seed needle‑in‑a‑haystack tests, layer‑clustered permutation, and causal head‑masking. (i) Retrieval heads are causally necessary: masking the 87 detected heads in OLMo‑2 collapses recall from 1.00 to 0.00, while masking matched random heads has no effect; this replicates in Qwen. (ii) Higher theta does not reduce retrieval‑head count (LLaMA‑3.1 at theta=500K has 47 heads vs LLaMA‑2 at theta=10K with 42), refuting the prevention hypothesis. (iii) The norm‑utility relation is family‑specific and significant in opposite directions (Qwen d=‑0.49, OLMo d=+0.50, both significant; LLaMA null); since OLMo and LLaMA‑3.1 share theta=500K yet differ, the effect is not theta‑driven. (iv) Building on Chiang and Yogatama (2025), a controlled patch shows that zeroing the lowest‑frequency RoPE dimensions of retrieval heads degrades recall dose‑dependently (1.00 to 0.18 when 32 of 128 dimensions are zeroed, vs 0.98 for random dimensions); the effect is head‑specific and task‑specific. The causal variable is RoPE frequency, not norm‑utility. The direction holds in all five models patched (OLMo‑2, Qwen2.5‑7B/14B, Gemma‑2, Mistral) across four lineages and two scales. We do not claim cross‑model magnitude. Code and a paired‑seed harness are released.
Authors:Sergio Lanza, Jae Hee Lee, Stefan Wermter
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
Authors:Youngjoon Jeong, Jihwan Yu, Minsoo Jo, Junha Chun, Taesup Kim
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.
Authors:Vasile Marian, Yong-Bin Kang, Alexander Buddery
Abstract:
Object‑centric image generation is important in settings with few labeled examples, including pedestrian analysis in smart‑city scenes, traffic‑sign inspection, and domain‑specific object detection. Synthetic images are most useful for training and evaluation when datasets preserve object structure, bounding boxes, visual diversity, and realistic context. Existing image datasets usually target classification, detection, or scene understanding rather than controlled object‑centric generation and augmentation with limited class‑specific data. We present a shareable collection of three object‑centric dataset resources: Cityscapes‑Pedestrian, TrafficSigns, and COCO PottedPlant. The collection standardizes 256‑by‑256 object‑centric crops and bounding‑box annotations across three regimes: dense pedestrian scenes with privacy blur and occlusion, cleaner high‑contrast traffic signs, and context‑diverse potted‑plant scenes. The release contains 3,009 TrafficSigns samples, 2,156 Cityscapes‑Pedestrian manifest records, and 7,679 COCO PottedPlant manifest records. The larger COCO‑derived manifest preserves contextual and multi‑instance diversity, while equal‑size subsets can be drawn with a fixed random seed for controlled comparisons. The release provides direct TrafficSigns data where redistribution is permitted, together with scripts, manifests, box‑level annotation tables, checksums, and reconstruction documentation for the Cityscapes‑ and COCO‑derived subsets. It is available through the Latzi/object‑centric‑low‑data‑datasets GitHub repository and Zenodo DOI 10.5281/zenodo.20573001. The collection supports label and split inspection, subset creation, reconstruction from upstream data, and evaluation of object‑centric image generation or synthetic‑data augmentation methods on shared records.
Authors:Hanqing Li, Xuewen Lu, Yuting Chen
Abstract:
Bayesian model averaging in support‑indexed regression induces a posterior distribution over active predictor supports. Under predictor redundancy, posterior mass can spread across many nearly interchangeable supports, making exact‑support summaries unstable or hard to interpret even when prediction is stable. We study how to report an already fitted Bayesian model averaging posterior without changing the Bayesian target. A report uses hard or soft regions of support space, and its compressed reporting law is compared with the reference posterior through an explicit density ratio. This ratio gives computable total‑variation and Kullback‑‑Leibler distortion, bounds for bounded predictive summaries, retained‑mass diagnostics, and fallback‑weight diagnostics. The framework covers fixed hard regions, metric‑ball regions, posterior‑cluster regions, and pooled‑pruned region dictionaries. We prove exact error formulas and validation bounds for these region reports, and give conditions under which a few regions can replace a long list of individual supports. In simulations, our region reports often give shorter and clearer summaries while preserving the main posterior information, and the density‑ratio diagnostics show when too much information has been lost.
Authors:Hanchun Wang
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.
Authors:Abhijit Das, Nichula Wasalathilaka, Yifan Lu, Adinath Dukre, Dwarikanath Mahapatra, Shadab Khan, Imran Razzak
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.
Authors:Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan D Burgert, Yuancheng Xu, Kuan Heng Lin, Emmett Steven, Julien Philip, Li Ma, Andrea Vedaldi, Paul Debevec, Ning Yu
Abstract:
Filmmaking demands precise motion control and reference image compositing ‑‑ capabilities that existing methods treat separately. Point‑track‑conditioned image‑to‑video models restrict content insertion to the first frame, while reference‑to‑video models lack fine‑grained spatial‑temporal control over how reference content integrates across frames. We present Go‑with‑the‑Track, which unifies both capabilities by jointly conditioning on multiple reference images and reference‑anchored point‑tracks ‑‑ extending conventional point‑tracks to explicitly establish correspondences between generated frames and reference images, thus enabling precise compositing and motion control throughout the video. To achieve this, we introduce spatially‑aware point‑track embeddings that encode the full sequence of point‑track coordinates using a coordinate‑wise MLP followed by temporal pooling. This representation captures the spatial characteristics of each point‑track (serving as a unique identifier), while the embedding similarity correlates directly with spatial proximity, enhancing the model's ability to distinguish and associate point‑tracks. We inject these point‑track embeddings into a video diffusion transformer via a lightweight adapter, resolving the pixel‑to‑patch resolution mismatch while avoiding the substantial motion detail loss inherent in naive point‑track subsampling. We use a hybrid training strategy to train jointly on dynamic, static, and synthetic scene video datasets to boost motion controllability. Experiments demonstrate that Go‑with‑the‑Track achieves superior motion and reference control in a single model and enables new capabilities: multi‑reference conditioned video generation with point‑track driven compositing, as well as camera control for both static and dynamic scenes. Project Page: https://eyeline‑labs.github.io/Go‑with‑the‑Track/
Authors:Bahrul Ilmi Nasution
Abstract:
Synthetic tabular data enables microdata sharing in regulated domains, yet deploying continuous‑time generative models requires balancing analytical utility, disclosure risk, and computational cost. Latent‑space flow models are flexible, but theoretical equivalences across learning targets, probability paths, and sampling dynamics can translate into different behaviour under finite‑step integration and explicit compute budgets. We present an empirical study of tabular latent flow models across seven datasets, evaluating velocity, score, noise, and posterior matching objectives under optimal transport (OT) and variance‑preserving (VP) paths, ODE and SDE sampling, and varying integration budgets. Our contributions are threefold: (1) we show that the learning target largely determines the utility‑risk operating regime, with velocity and posterior matching tending to yield higher utility, while score and noise matching tend to achieve lower disclosure risk; (2) we demonstrate that configuration and sampling choices shift performance, with midpoint often improving distributional fidelity and OT paths often tolerating earlier stopping than VP, enabling compute savings under fixed budgets or risk thresholds; and (3) we distil these findings into actionable defaults and practical configuration guidance to support pre‑release model selection under disclosure risk and resource constraints. The code implementation and supplementary materials can be accessed in https://github.com/rulnasution/tabular‑latent‑flow/.
Authors:Alan Nadelsticher Ruvalcaba
Abstract:
The temporal structure of reward composition in reinforcement learning (RL) is typically hand‑designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored. In this work, we propose an evolutionary framework for discovering developmental reward schedules, in which three distinct biologically inspired motivational components ‑‑ agency, novelty, and reactivity ‑‑ are combined through time‑varying weights that dynamically shift over the course of training. Evaluated on two sparse‑reward MiniGrid tasks: DoorKey‑6x6 and KeyCorridorS3R1, our framework compares the generalizability of four evolutionary algorithms: CMA‑ES, xNES, DE, and L‑SHADE against an extrinsically motivated baseline (our main comparison point), and three additional hand‑designed methods. On DoorKey‑6x6, all evolved methods outperform the non‑evolved baselines, with L‑SHADE achieving the best performance ‑‑ an approximate relative mean improvement of 11.4% over the extrinsic only baseline. On KeyCorridorS3R1, CMA‑ES achieves the best overall performance, with the remaining evolved methods showing weaker and less reliable generalization capability compared to the extrinsic only baseline. Interestingly, the discovered schedules diverge from our defined developmental ordering, with novelty consistently emerging as the dominant early signal during training, across both tasks. Collectively, our results position evolutionary optimization as a promising approach for developmental reward schedule discovery in deep reinforcement learning, and suggest that what evolution finds to be optimal in computational settings may differ from what it finds to be optimal in biology. The code for this project can be found at: https://github.com/alannadels/Evolutionary_RL.git.
Authors:Hiroki Sakuma, Masatoshi Okutomi
Abstract:
Multi‑view surface reconstruction is a core problem in computer vision. One prominent line of work represents the surface implicitly as a signed distance field (SDF), optimizing it based on the photometric loss between rendered and observed pixel colors. These approaches typically employ SDF‑based volume rendering to obtain a differentiable relaxation of discontinuous visibility along rays, thereby reducing reliance on silhouette supervision. In this paper, we reformulate SDF‑based volume rendering as probabilistic surface rendering, where each pixel color is modeled as a mixture distribution induced by the random first ray‑surface intersection. To this end, we introduce Stochastic Signed Distance Processes (SSDP), which model the SDF along each ray as a stochastic process, inducing a first‑passage‑time distribution for each ray. We then derive the first‑passage probability for each sampling interval based on Bayesian filtering, together with its practical approximation for parallel rendering. We further show that NeuS, an existing SDF‑based volume rendering method, arises as a special case of our formulation. Experiments on the DTU and MobileBrick datasets demonstrate that our method outperforms baselines in both surface reconstruction and uncertainty quantification, supporting the effectiveness of our first‑passage formulation. Our code is available at https://github.com/skmhrk1209/SSDP.
Authors:Shishen Lin, Yixin Chen
Abstract:
NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for control tasks. However, standard offline optimisation searches for connection strengths directly, which can scale poorly in high‑dimensional weight spaces and more difficult continuous control problems. Hybrid methods that combine neuroevolution with online learning can address this challenge, but their theoretical properties remain underexplored. This paper gives the first regret analysis for a general NeuroEvolutionary Online Learning (NEOL) framework, which decouples learning into two timescales: an outer loop for architecture search and an inner loop for online weight adaptation via rewardmodulated plasticity. Under mild conditions, we prove that NEOL achieves sublinear regret. Empirically, under fixed interaction budgets on four standard control benchmarks, a NEAT‑based NEOL implementation achieves higher final fitness and lower variance than pure NEAT, and is competitive with strong reinforcement learning (RL) baselines on several tasks. The results are supported byWilcoxon rank‑sum tests and ablation studies. Overall, the findings show that online plasticity can improve the sample efficiency and robustness of two‑timescale neuroevolution. Code is available at https://github.com/boobaa2001/NeuroEvolution Online Learning NEOL.
Authors:Jaedong Hwang, Kathleen Zhang, Wei Dai, Konstantinos Kontras, Maarten Vanmarcke, Maarten De Vos, Ila Fiete, Paul Pu Liang
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
Authors:Maruthi Vemula
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.
Authors:Yifan Li, Shengbin Yue, Boyu Feng, Jinhu Qi, Bo Ke, Zixing Song, Hongru Wang, Zhongyu Wei, Irwin King
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.
Authors:Tingzhou Wei, Zeyu Zheng, Ethan X. Fang, Junwei Lu
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.
Authors:Yinsicheng Jiang, Liang Cheng, Yeqi Huang, Yufan Zhao, Zhan Lu, Li Dong, Wenda Li, Edoardo Ponti, Luo Mai
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
Authors:Hang Yu, Zifan Zheng, Jeff Z. Pan, Tongliang Liu, Zhiyong Wang, Fengxiang He
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.
Authors:Kejiang Qian, Fengxiang He
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.
Authors:Yang Feng, Ziwei Xu, Xia Hu, Fengxiang He
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.
Authors:Qingyang Zhu, Eric Karl Oermann, Kyunghyun Cho
Abstract:
Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization. However, exact inference is often intractable, and scalable approximations may remain computationally expensive or require restrictive modeling assumptions that degrade predictive performance. Prior‑Data Fitted and in‑context models have recently emerged as an amortized alternative by learning to map datasets directly to predictive distributions, but existing approaches are tightly coupled to the support of the training prior and lack explicit mechanisms for adapting to new priors at test time, resulting in limited robustness under distribution shift. We introduce a multi‑task in‑context learning framework for amortized hierarchical Bayesian predictive inference that explicitly represents prior information as a prefix of in‑context datasets. A transformer trained on sequences of prior and target tasks learns to adapt its predictions across families of priors. On a suite of evaluations with increasing difficulty, including out‑of‑meta‑distribution priors and priors with high‑dimensional latent structures, our method matches oracle Bayesian predictors while being orders of magnitude faster. We further demonstrate its practical relevance on a real‑world spatiotemporal temperature prediction benchmark. Code is available at https://github.com/martianmartina/multi‑task‑bayesian‑icl/.
Authors:Haw-Shiuan Chang, Jeffrey Gomez, Mehul Patwari, Aryan Sajith, Hamed Zamani
Abstract:
To align a Large Language Model (LLM), most existing methods collect explicit human feedback and train a reward model to predict the human preference based on the response text. These existing methods have two key limitations. First, the users rarely provide explicit feedback for LLM responses, which makes the high‑quality preference annotation expensive to collect. Second, the methods do not leverage implicit human feedback, which has proven vital to the economic moats of Internet giants. To quantify the value of implicit feedback, we build a new dataset called IFLLM, which collects 1336 multi‑turn questions from the 59 Mechanical Turk workers, their mouse trajectories, and eye gazing points to the LLMs' responses from their webcams. IFLLM shows that the users have very diverse types of gazing behavior and mouse trajectories. Our reward model based on the implicit user feedback boosts the accuracy of the text‑based reward model from 55% to 64% and nearly triples the relative response quality improvements after applying the DPO to eight LLMs, demonstrating the value of implicit feedback in the wild. Our data collection website, dataset, and codes can be found at https://github.com/themehulpatwari/llm‑implicit‑feedback/.
Authors:Diego Fajardo-Rojas, Megan Hall, Daniel Cromb, Mary A. Rutherford, Lisa Story, Emma C. Robinson, Jana Hutter
Abstract:
Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A pipeline consisting of bespoke machine learning methods for data imputation, feature selection, and regression models to predict gestational age (GA) at birth was developed and evaluated from comprehensive multi‑modal morphological and functional fetal MRI data from 333 control cases and 93 preterm birth cases. The GA at birth predictions were classified into term and preterm categories and their accuracy, sensitivity, and specificity were reported. An ablation study was performed to further validate the design of the pipeline. Performance was evaluated using stratified 10‑fold cross‑validation. The pipeline achieves an R2 score of 0.13 and a mean absolute error of 2.74 weeks. It also achieves a 0.77 accuracy, 0.59 sensitivity, and 0.82 specificity across folds. The predominant features selected by the pipeline include cervical length and statistics derived from placental T2 values. The confluence of fast, motion‑robust and multi‑modal fetal MRI techniques and machine learning prediction allowed the prediction of the gestation at birth. This information is essential for any pregnancy. To the best of our knowledge, preterm birth had only been addressed as a classification problem in the literature. Therefore, this work provides a proof of concept. Future work will increase the cohort size to allow for finer stratification within the preterm birth cohort. Our code is available at https://github.com/dfajardorojas/ml‑for‑preterm‑birth‑.
Authors:Masaya Kawamura, Yuma Shirahata, Kentaro Mitsui, Reo Shimizu
Abstract:
Existing mean opinion score (MOS) prediction models typically predict utterance‑level naturalness MOS and can be insensitive to localized pitch‑accent errors. We propose Pitch‑Accent‑focused Speech Quality Assessment (PASQA), which explicitly targets pitch‑accent correctness. To train our model, we construct a controlled Japanese accent‑error dataset by changing accent patterns using an accent‑controllable text‑to‑speech system, and compute a pseudo accent‑quality score from the accent‑error rate. PASQA builds on self‑supervised representations and employs mora‑conditioned fusion, ranking loss, an auxiliary accent‑error localization task, and speaker‑invariant training. Experiments show that conventional models fail to preserve the ordering by accent‑error severity, whereas PASQA achieves high ordering accuracy on both seen and unseen speakers. Further, PASQA shows stronger agreement with human accent‑correctness judgments. The code is available at https://github.com/lycorp‑jp/PASQA.
Authors:Pengwei Wang, José Morano, Qian Wan, Hrvoje Bogunović
Abstract:
Image quality control is vital for a wide range of downstream applications. Deep learning‑based image quality assessment methods typically train classifiers on dataset‑specific quality labels, inheriting two limitations: (1) generalization is tied to the labeling criteria of the training set and (2) these methods cannot provide spatial feedback on where the quality is degraded, lacking explainability. In this work, we propose EFIQA, a framework that requires no quality‑related supervision and produces spatial quality maps by design. Rather than learning ``what is degradation" from human‑annotated labels, EFIQA learns ``what should be there" by leveraging anatomical priors. For fundus photography, we instantiate this as a two‑stage approach, by first training an unsupervised anomaly detector via masked anatomical inpainting to identify regions of missing vasculature, and then distilling this prior knowledge into a shallow adapter mapping features of a frozen foundation model to precise quality maps. External‑dataset evaluation demonstrates that this label‑free approach with minimal adaptation achieves better performance and explainability compared with supervised methods across benchmarks with different quality criteria, highlighting its potential for real‑world applications.
Authors:Xinghao Chen, Chak Tou Leong, Wenjin Guo, Jian Wang, Wenjie Li, Xiaoyu Shen
Abstract:
Latent Chain‑of‑Thought (CoT) internalizes reasoning within continuous hidden states, offering a promising alternative to verbose discrete reasoning traces. However, robust latent reasoning remains difficult because outcome supervision provides weak learning signals and leaves latent trajectories prone to semantic drift. In this work, we analyze Latent CoT from an information‑theoretic perspective and identify this failure as a dual collapse: gradient attenuation along the optimization path and representational drift in the latent space. We further decompose process supervision into two complementary dimensions: Trajectory Supervision, which injects dense stepwise reasoning signals, and Space Supervision, which preserves the semantic structure of the latent manifold. Our analysis shows that rigid geometric compression can collapse the reasoning space, whereas generative reconstruction provides a more flexible semantic anchor that better preserves information capacity. To measure these effects, we introduce the Unified Latent Probe (ULP), which quantifies the mutual information between latent trajectories and explicit reasoning steps. Experiments reveal a clear Information‑Performance Binding: reasoning accuracy depends on the information fidelity preserved in the latent chain. These findings provide a principled framework for latent reasoning supervision and suggest shifting from geometric imitation toward mutual information maximization. Our code is available at \hrefhttps://github.com/EIT‑NLP/Supervision‑in‑Latent‑CoTthis repository.
Authors:Xu Zhang, Zhengang Huang, Yunzhi Wu, Xun Lu, Erpeng Qi, Yunkai Chen, Zhongya Xue, Peng Wang, Wei Wang
Abstract:
Current time series forecasting (TSF) research predominantly focuses on scale‑homogeneous data, where different time series share similar numerical magnitude ranges. However, in real‑world industrial scenarios such as financial product sales, different time series often differ by orders of magnitude (scale heterogeneity). Since these series share similar temporal patterns, joint modeling is desirable for better data utilization, yet existing scaling methods either compress low‑scale signals (global normalization) or destroy semantic discriminability and amplify inverse‑scaling errors (window‑based scaling). This paper proposes a self‑Adaptive Scale‑handling (AS) module that learns adaptive scale factors tailored to each input, preserving semantic discriminability while reducing inverse‑scaling errors. AS consists of Scale Calibrating (SC), which calibrates prior mean scaling factors through neural networks, and Scaling Selection (SS), which decides whether to apply calibration or retain the original factor, avoiding over‑calibration. Experiments on real‑world fund sales datasets from Ant Fortune and Alipay show that AS seamlessly integrates into popular TSF models and consistently improves their performance. The code and dataset are available at the link https://github.com/Meteor‑Stars/ASTSF.
Authors:Yanxi Chen, Weijie Shi, Yuexiang Xie, Boyi Hu, Yaliang Li, Bolin Ding, Jingren Zhou
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.
Authors:Yu Deng
Abstract:
Activation steering controls model behavior by modifying intermediate hidden states at inference time without retraining. Existing methods handle only single‑direction injection; when multiple semantic directions are superposed without constraints, the model collapses. We show that this collapse decomposes into two independently acting sources: distributional deviation, where additive perturbations accumulate in norm across layers and drive activations outside the training distribution, and directional interference, where non‑orthogonal semantic vectors mutually dampen when superposed. These two sources define the design constraints that any training‑free multi‑directional intervention must address. As one instantiation of these principles, we propose GEMS, a training‑free method that maps each source to a corresponding geometric constraint: norm‑preserving weighted superposition and targeted attention‑pathway injection for distributional deviation, and real‑time orthogonalization for directional interference. On GSM8K, injecting three concurrent non‑mathematical directions preserves accuracy at 98% (baseline 92%), while unconstrained addition collapses to 4%; on Wikitext‑2, the same injection incurs only 2.2% PPL increase. Component ablation isolates the causal role of each constraint, and layer‑level probes confirm that orthogonalized signals survive the FFN pathway and reach the output distribution with semantic specificity. Qualitative steering effects transfer across architectures from 3B to 31B.
Authors:Yasutoshi Kishimoto, Kota Yamanishi, Takuya Matsuda, Shinichi Shirakawa
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.
Authors:Daehwan Kim, Haejun Chung, Ikbeom Jang
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.
Authors:Aditeya Baral, Radoslav Ralev, Iliya Sotirov Zhechev, Srijith Rajamohan, Jen Agarwal
Abstract:
Semantic caching cuts LLM inference costs by serving a cached response to semantically similar queries. Standard practice evaluates these systems using PR‑AUC, a metric that only measures how well scores rank and ignores whether they are usable at a fixed threshold. We show this mismatch leads to systematically poor deployment choices, as models with the highest PR‑AUC are often the worst in operation. We introduce Precision‑Cache Hit Ratio (P‑CHR) AUC, a cache‑aware metric that measures precision across cache utilization levels, and Calibration Retention Rate (CRR), which captures how much offline ranking quality survives at deployment. We decompose the operational gap between offline and deployed quality into a recoverable calibration component and an irreducible structural component fixed by the dataset's positive rate. Our experiments show that the calibration gap is governed by the training objective rather than data scale, and post‑hoc calibration only partially closes it. Ultimately, model selection for semantic caching is a calibration problem, not a ranking one, and measuring it is the first step to closing the gap.
Authors:Hongxuan Liu, Roman Bushuiev, Ivy Lightheart, Mrunali Manjrekar, Anton Bushuiev, Magdalena Lederbauer, Filip Jozefov, Yinkai Wang, Soha Hassoun, Josef Sivic, James Taylor, Runzhong Wang, David Healey, Tomáš Pluskal, Connor W. Coley
Abstract:
Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery. Subtle issues in experimental design and model evaluation procedures can degrade the trustworthiness of such benchmarks and lead to erroneous conclusions. We conduct a thorough review of model evaluation issues in the recent MS/MS machine learning literature, using the standard MassSpecGym benchmark suite as a case study to illustrate the impact of these issues. We find evaluation issues in at least 17 of 26 papers reporting MassSpecGym benchmark results in the first year of its adoption. We isolate three classes of failures: (i) data leakage, (ii) shortcut learning, and (iii) implementation bugs and metric divergence. Through extensive experimentation and code replication, we quantify the impact of these issues and show how they corrupt the evaluation standards MassSpecGym was designed to enforce. We distill our findings into recommendations generalizable to MS/MS challenges, benchmarks, and custom evaluation setups. We also release MassSpecGym v1.5, an implementation of our recommendations in the MassSpecGym benchmarking suite which addresses the failure modes identified in this audit. MassSpecGym v1.5 is publicly available at https://github.com/pluskal‑lab/MassSpecGym.
Authors:Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen, Fengxiang He
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.
Authors:Nathaniel L. Diamant, Brian L. Trippe
Abstract:
Generative models can produce individually plausible samples while deviating substantially from a target set in the distribution of key features. For example, a model pretrained on broad drug‑like chemical space may generate molecules whose molecular features differ from those of a therapeutic class of interest, such as known antibiotics. Correcting such distributional miscalibration is challenging: direct finetuning on the target set can overfit and does not control which features are matched. To fill this gap, we introduce kernel Calibrating Generative Models (kCGM). kCGM minimizes a maximum mean discrepancy (MMD) between generated and target feature distributions using an unbiased score‑function estimator, with KL regularization to remain close to the pretrained model. On a target set of 174 antibiotics, direct finetuning sacrifices chemical validity for feature‑distribution matching, whereas kCGM improves target feature matching while increasing validity. We further demonstrate kCGM in protein and DNA generation tasks, showing it can adapt autoregressive, continuous‑space diffusion, and discrete diffusion models using only feature‑level supervision. Code is available at https://github.com/smithhenryd/cgm.
Authors:Ellina Zhang, Madhaven Iyengar, Amir Zadeh, Chuan Li, Deepak Pathak, David Held, Tal Daniel
Abstract:
We introduce 3D‑DLP, a self‑supervised object‑centric representation learning model that decomposes scene‑level RGB‑D or voxel observations into a set of 3D latent particles. Building on the Deep Latent Particles (DLP) framework, each particle encodes disentangled attributes, including 3D keypoint position, bounding box dimensions, and appearance features, and represents a distinct entity in the scene. The model learns interpretable per‑particle segmentation maps through an end‑to‑end self‑supervised reconstruction objective. We demonstrate on both simulated and real‑world datasets that the learned latent space is interpretable and controllable: by manipulating particle positions and decoding, we can generate novel scene configurations. Furthermore, we show that leveraging these compact 3D latent particles for downstream robotic manipulation improves performance over baselines that either lack explicit 3D information or rely on memory‑intensive dense 3D inputs without object‑centric structure. Code and videos are available at https://eubooks3003.github.io/3d‑dlp.
Authors:Manvendra Modgil
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.
Authors:Mohamed Mouhajir, Limei Wang, El Houcine Bergou, Hajar El Hammouti, Lamiae Azizi, Dongqi Fu
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
Authors:Tiexin Ding
Abstract:
Building on a two‑parameter Weibull framework for diagnosing transformer weight distributions, we study why the Weibull weight‑scale parameter λ grows, overshoots, and then relaxes during AdamW training. We derive a leading‑order three‑force decomposition of the squared weight norm from the AdamW update: an alignment force measuring the correlation between weights and the adaptive update direction, an injection force from adaptive step magnitude, and a decay force from decoupled weight decay. On self‑trained Pythia‑70M models with ground‑truth optimizer moments, alignment dominates the rise phase, contributing 88‑94% of the absolute force budget across four random seeds and remaining robust to super‑weight removal. Near saturation, alignment and decay approach balance, explaining the transition from weight‑scale growth to relaxation. These force dynamics directly govern the squared‑norm component underlying λ(t); the remaining RMS‑to‑Weibull reconstruction offset is measurable and decomposes into bridge and integration components, totaling approximately 5‑6% in densely sampled regions. To extend the analysis to real models where optimizer moments are unavailable, we introduce a spline displacement method that recovers the alignment force from sparse checkpoints with approximately 92‑94% accuracy, about twice the naive two‑point baseline. We further observe that the peak value of λ(t) varies with training‑data coherence in our experiments, suggesting a data‑dependent component of weight‑scale growth that we leave to a controlled follow‑up study. Code and data are available at https://github.com/tiexinding/NPM‑Weibull‑public.
Authors:Rupasree Dey, Abdul Matin, Nathan Orwick, Yao Zhang, Shrideep Pallickara, Sangmi Lee Pallickara
Abstract:
The deployment of Time‑Series Foundation Models (TSFMs) in physical sciences is hindered by a critical trade‑off: while these models encode rich, universal temporal dynamics, they suffer from severe distributional misalignment when applied zero‑shot to specific scientific domains, and their computational cost prohibits deployment in edge‑computing sensor networks. We address a fundamental challenge: How can we extract latent structural knowledge from misaligned foundation models (FM) to train lightweight, specialized forecasters? We propose Gated Uncertainty‑Aware Routing for Distillation (Guard), a novel framework that reframes multiteacher distillation as an instance‑wise decision process with two adaptive mechanisms: (1) a Contextual Router that dynamically selects the most relevant teacher based on local input statistics, exploiting complementarity across diverse foundation models; and (2) an Uncertainty‑Gated Temperature mechanism that acts as a "circuit‑breaker," automatically attenuating distillation strength when teacher confidence diverges from domain reality. We evaluate our proposed lightweight framework on four climate‑critical domains: meteorology, ecosystem carbon flux, soil moisture, and energy grids. Our method significantly reduces RMSE relative to a fixed‑weight multi‑teacher distillation baseline, successfully distilling knowledge from pretrained FMs (teachers) even when they exhibit suboptimal zero‑shot accuracy due to distribution shift between the original and target data domains. We demonstrate that these domain‑misaligned teachers can still serve as critical correctives, outperforming the globally superior FMs on 28.5% of the hardest instances. Ultimately, this enables high‑precision scientific forecasting suitable for resource‑constrained edge deployment. Code is available at https://github.com/RupasreeDey/GUARD‑KDD2026.
Authors:Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho
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.
Authors:Nikita Kachaev, Andrey Moskalenko, Matvey Skripkin, Nikita Kurlaev, Daria Pugacheva, Albina Burlova, Mikhail Kolosov, Denis Shepelev, Andrey Kuznetsov, Elena Tutubalina, Aleksandr I. Panov, Alexey K. Kovalev, Vlad Shakhuro
Abstract:
Embodied Vision‑Language‑Action (VLA) models are typically obtained by fine‑tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge‑sensitive tasks are ambiguous, conflating missing knowledge with poor generalization of low‑level control. We introduce Act2Answer, a lightweight protocol that adapts VLM knowledge benchmarks to VLA evaluation by requiring agents to answer through action. Each question becomes a short tabletop episode where the agent performs a single object‑placement action to select among candidate answers, yielding an action‑grounded success rate with reduced control confounds. We curate a test suite of such environments across diverse commonsense and world‑knowledge categories and introduce layerwise intent probing to localize answer‑relevant information across the VLM backbone and action head. In a large‑scale study of 7 VLA models and 9 VLM baselines, we systematically rank models across categories, finding that VLAs show solid performance on simple concepts while exhibiting larger gaps on richer semantic categories relative to their source VLMs, that VQA co‑training is associated with better knowledge retention, and that answer‑relevant signals peak in middle VLA layers but attenuate in upper layers. Act2Answer is available at https://tttonyalpha.github.io/act2answer/.
Authors:Sanghyeok Choi, Henry Gouk, Esmeralda S. Whitammer
Abstract:
The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem. We propose Large Language Gibbs, a scheme for structured probabilistic inference that uses conditional distributions of an LLM as transition operators. Rather than sampling structured objects through single‑pass autoregressive generation, we iteratively resample individual variables conditioned on others using an LLM's next‑token conditionals. This approach avoids order‑dependent biases and produces a stationary distribution that reflects a compromise between all local conditionals. We apply this approach to sampling from synthetic distributions, consistent reasoning tasks, and Bayesian structure learning. The results suggest that the use of LLM conditionals in MCMC is a practical alternative to one‑pass generation for structured probabilistic inference under a world prior accessible through noisy LLM conditionals.
Authors:Bartłomiej Baranowski, Dave Zhenyu Chen, Matthias Nießner
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.
Authors:Haipeng Luo, Qingfeng Sun, Songli Wu, Can Xu, Wenfeng Deng, Han Hu, Yansong Tang
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.
Authors:Bolin Shen, Ziwei Huang, Zhiguang Cao, Yushun Dong
Abstract:
The Traveling Salesman Problem (TSP) is a cornerstone of combinatorial optimization and arises in many practical scenarios. Although graph‑based learning approaches have been explored for TSP, the question of how to exploit graph structure more effectively remains open. We present the Anisotropic Graph Diffusion Network (AGDN), a new Graph Neural Network framework designed to solve TSP. Our method tackles two central difficulties: (1) the lack of informative topological prior in fully connected TSP graphs, and (2) losing connected nodes in the optimal solution after the commonly used graph sparsification techniques. To overcome these issues, we construct a MixScore transition matrix that merges node similarity with pairwise distance, and we develop an anisotropic graph diffusion strategy that supports efficient information exchange across multiple hops. Comprehensive experiments spanning diverse instance sizes and node distributions show that AGDN consistently outperforms existing methods while keeping computation time competitive. Furthermore, AGDN generalizes well to problem sizes and distributions beyond those seen during training. The implementation is publicly available at: https://github.com/LabRAI/AGDN.
Authors:Nahum Korda, Gadi Evron
Abstract:
Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false‑positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs. Recent advances in large language models (LLMs) enable semantic reasoning about program behavior, but applying LLMs to repository‑scale security analysis introduces challenges related to context management, cost, and verification. We present OpenAnt, an open‑source vulnerability discovery system that integrates static program analysis with LLM‑based reasoning in a multi‑stage pipeline. OpenAnt introduces three key techniques. First, codebases are decomposed into self‑contained analysis units filtered by reachability from external entry points, reducing the analysis surface by up to 97% while preserving attack‑relevant code. Second, candidate vulnerabilities undergo adversarial verification through constrained attacker simulation, where the model evaluates exploitability under realistic attacker capabilities. Third, findings are validated through dynamic verification, in which exploit environments are generated automatically, executed in sandboxed containers, and discarded after use. Evaluation on widely used open‑source projects including OpenSSL, WordPress, and Flowise shows that this architecture can identify previously unknown vulnerabilities while maintaining manageable analysis cost and substantially reducing false positives. Our results suggest that closed‑loop vulnerability discovery pipelines, combining semantic reasoning with exploit validation, provide a practical path toward scalable automated security analysis. OpenAnt is released as open source under the Apache 2.0 license at https://github.com/knostic/OpenAnt.
Authors:Michael Detzel, Gabriel Nobis, Kristiyan Blagov, Juri Schubert, Jackie Ma, Wojciech Samek
Abstract:
Neural Controlled Differential Equations (NCDE) provide a powerful continuous‑time framework for forecasting time series, but standard graph‑based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori. We introduce Informed Neural controlled Differential EQuationS (INDEQS), a graph‑based NCDE forecasting method that incorporates prior knowledge of a directed graph at distinct architectural positions. INDEQS separates inner mixing of hidden states across graph nodes from outer mixing between vector field and control, and offers both a lightweight graph‑constrained variant and a more expressive variant, learning additional graph connections from data via adaptive graph convolutions. To systematically study when graph informedness is beneficial in forecasting, we devise a continuous advection simulation on directed graphs, yielding synthetic spatio‑temporal datasets with known ground‑truth flow structure. We then evaluate INDEQS on two real‑world tasks: river discharge forecasting on a hydrological network and traffic flow prediction on PeMS08. Across these synthetic and real‑world benchmarks, outer informedness consistently improves mean absolute error over an uninformed NCDE with comparable parameter count, particularly on larger graphs, while inner informedness offers a more parameter‑efficient alternative when strict adherence to a known adjacency is desired. A comparison of discrete convolutional and continuous‑time decoders further shows that continuous decoders yield better accuracy and greater temporal flexibility on real‑world tasks. An implementation of INDEQS and the advection simulation is available at https://github.com/Mitchi1/indeqs.
Authors:Zheng He, Danica J. Sutherland
Abstract:
Testing conditional independence is fundamental yet intrinsically difficult: without additional assumptions, Type I error control is impossible in general. The "Model‑X'' paradigm addresses this difficulty by assuming exact knowledge of a relevant conditional distribution. While small deviations from this assumption can sometimes be tolerated in classical one‑shot testing, existing sequential conditional independence tests typically require the Model‑X conditional to be known exactly, making them fragile when it must instead be estimated. We propose a new approach that is substantially more robust to such estimation error. Our method applies testing‑by‑betting to an adaptively optimized Kernel Conditional Independence statistic, together with a normalization scheme and a truncate‑and‑shift calibration strategy. These modifications greatly reduce Type I error inflation while preserving high power across high‑dimensional synthetic benchmarks and real‑world fairness tasks, outperforming existing sequential Model‑X approaches. Code is available at https://github.com/he‑zh/SKCI.
Authors:Minseo Kim, Minjae Lee, Seunghyuk Oh, Kevin Galim, Donghoon Kim, Coleman Hooper, Harman Singh, Amir Gholami, Hyung Il Koo, Wonjun Kang
Abstract:
Reinforcement learning (RL) has become a representative post‑training paradigm for LLMs, enabling strong reasoning and agentic capabilities. However, rollout generation remains a dominant latency bottleneck because autoregressive sampling decodes responses sequentially and a small number of long‑tailed generations often determine completion time. Speculative decoding (SD) offers a natural way to address this bottleneck, as it is a well‑established technique for serving fixed LLMs that reduces latency by rapidly drafting tokens and accepting them through parallel verification while preserving the target‑model distribution. However, its practical speedups do not directly carry over to RL rollouts: (i) the evolving target policy makes any fixed drafter increasingly mismatched with the policy's output distribution; and (ii) active batch sizes shrink throughout rollout decoding, shifting decoding from compute‑bound to memory‑bound regimes where parallel verification can exploit underutilized compute. Therefore, accelerating RL rollouts requires both a drafter that remains effective under long, high‑temperature generations from an evolving policy and system‑aware use of SD that avoids compute‑bound regimes. We present EfficientRollout, a system‑aware self‑SD framework designed to address this gap for RL rollouts. EfficientRollout induces a quantized drafter from the target model (i.e. self‑speculative decoding), keeping it coupled to the evolving policy without separate drafter pretraining or online adaptation. It further coordinates a system‑aware SD toggle policy with acceptance‑aware draft‑length adaptation, enabling speculation only in beneficial regimes while matching the drafting budget to evolving drafter quality. EfficientRollout reduces rollout and end‑to‑end latency by up to 19.6% and 12.7%, respectively, over an accelerated AR rollout baseline, while preserving final model quality.
Authors:Yuanxin Liu, Ruida Zhou, Xinyan Zhao, Amr Sharaf, Hongzhou Lin, Arijit Biswas, Mohammad Ghavamzadeh, Zhaoran Wang, Mingyi Hong
Abstract:
Test‑time scaling via sequential revision has emerged as a powerful paradigm for enhancing Large Language Model (LLM) reasoning. However, standard post‑training methods primarily optimize single‑shot objectives, creating a fundamental misalignment with multi‑step inference dynamics. While recent work treats this as multi‑turn reinforcement learning (RL), conventional approaches optimize over the multi‑step trajectories directly, failing to further exploit the high‑quality mistakes in intermediate steps that model can learn from correcting them. We propose a two‑stage iterative framework that alternates between online data/prompt augmentation and policy optimization. By converting the intermediate steps (``near‑miss'' answers) in the successful recovery trajectories into decoupled revision and verification prompts, our approach concentrates training on both effective answer transformation and error identification. This approach enables efficient off‑policy data generation and reduces the computational overhead of long‑horizon sampling compared to standard multi‑turn RL. On LiveCodeBench, using publicly available test cases as feedback, we observe gains of +6.5 points over the RL baseline and +4.0 points over standard multi‑turn training. Beyond coding, our approach matches the previously reported SOTA result on circle packing while using the smallest base model (4B) and far fewer rollouts than the much larger evolutionary search systems. Math results under ground‑truth verification further confirm improved correction ability. It also generalizes to out‑of‑distribution constraint‑satisfaction puzzles such as n\_queens and mini\_sudoku, where correctness is defined entirely by problem constraints. Code is available at https://github.com/yxliu02/REVES.git.
Authors:Veit Hucke, Thomas Pinetz, Gregor Reiter, Ursula Schmidt-Erfurth, Hrvoje Bogunović
Abstract:
Optical coherence tomography (OCT) is essential in ophthalmology, but inconsistent image quality especially in low‑cost devices hinders automated analysis. To address this, we introduce a flow‑matching‑based test‑time adaptation method that generates high‑quality surrogate images from noisy inputs. Typically, domain gaps between test and training data cause pixel distribution mismatches during the denoising process. We overcome this by matching the test image's histogram to synthetic reference trajectories, successfully aligning the input with expected distributions. Additionally, we remove the network's time conditioning to account for slight deviations in real‑world noise distributions. Our approach achieves state‑of‑the‑art performance in segmenting critical biomarkers for two stages of Age‑related Macular Degeneration (AMD). Code is available: https://github.com/Veit21/tta‑flow.
Authors:Hana Jebril, Thomas Pinetz, Günter Klambauer, Hrvoje Bogunović
Abstract:
Reliable pixel‑level uncertainty quantification holds the potential to transform clinical workflows by enabling high‑fidelity longitudinal monitoring and distinguishing true pathological changes from artifacts. Ideally, these models provide the stability required for critical treatment planning and surgical intervention. However, standard deep learning models often suffer from miscalibration, yielding overconfident predictions that mask underlying vulnerabilities at subtle pathological boundaries. To address this, we propose QUAM‑SM, a post‑hoc framework using targeted adversarial search to identify "adversarially fragile" pixels. By actively seeking perturbations that expose predictive instability, our method highlights regions where decisions are most vulnerable to being flipped. Importantly, the framework disentangles epistemic uncertainty from aleatoric uncertainty. Experiments on two public datasets with multiple expert annotations demonstrate that QUAM‑SM outperforms both standard and recent uncertainty estimation approaches in terms of reliability and boundary sensitivity. Code is available at https://github.com/HanaJebril/quam_sm
Authors:Zhe Ren, Yibo Yang, Yimeng Chen, Zijun Zhao, Benshuo Fu, Zhihao Shu, Bingjie Zhang, Yangyang Xu, Dandan Guo, Shuicheng Yan
Abstract:
Memory benchmarks for LLM agents largely assume single‑user settings, leaving shared assistants for hospitals, workplaces, campuses, and households understudied. In these deployments, multiple principals write to a common memory pool and query it under different roles, scopes, and relationships, so memory quality requires governance as well as recall. We introduce GateMem, a benchmark for multi‑principal shared‑memory agents. GateMem jointly evaluates utility for legitimate long‑horizon requests with state updates, access control across contextual authorization boundaries, and agent‑facing active forgetting after explicit deletion requests. It spans medical, office, education, and household domains, with long‑form multi‑party episodes, incremental memory injection, hidden checkpoints, structured judging, and leak‑target annotations. Across diverse baselines and backbone models, no method simultaneously achieves strong utility, robust access control, and reliable forgetting. Long‑context prompting often yields the best governance score at high token cost, while retrieval‑based and external‑memory methods reduce cost yet still leak unauthorized or deleted information. These results show current memory agents remain far from reliable shared institutional deployment.
Authors:Guannan Lai, Haoran Hu, Han-Jia Ye
Abstract:
We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://routejudge.cn. Different from model‑level response evaluation, RouteJudge focuses on router‑level decision quality. For each user query, multiple routing strategies independently recommend candidate models under the same model pool and budget constraints. The selected model responses are then presented to users through anonymous pairwise comparisons, and the resulting user preferences are attributed back to the routing strategies behind the compared responses. Each evaluation record stores the query, routing decisions, model responses, preference labels, cost, latency, and task metadata, enabling preference‑aware, cost‑aware, and task‑conditioned analysis of LLM routers. To support the continuous expansion of routing methods in RouteJudge, we further release ORBIT (Optimal Routing and Budgeted Inference Toolbox), a modular and extensible toolbox that standardizes the end‑to‑end workflow of LLM routing. ORBIT provides unified interfaces for benchmark loading, query representation, router implementation, budget‑aware evaluation, and method comparison, allowing researchers to develop and evaluate routing algorithms under consistent protocols. It also serves as the submission and integration layer for RouteJudge: researchers can implement routing methods within ORBIT, validate them on existing routing benchmarks, and submit compatible routers for online preference‑based evaluation. The code of ORBIT is available at https://github.com/AIGNLAI/LAMDA‑ORBIT.
Authors:Jinmo Lee, Doyun Choi, Moongi Choi, Jaemin Yoo
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.
Authors:Jinke Wu, Yifan Wang, Siyu Yi, Caiyang Yu, Ziyue Qiao, Nan Yin, Jiancheng Lv, Wei Ju
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.
Authors:Patrick Cooper, Alvaro Velasquez
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.
Authors:Yuyang Zhao, Lian Xu, Hao Miao, Chenxi Liu, Hao Xue
Abstract:
Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number (e.g., average error) on clean held‑out data, under the implicit assumption that it predicts deployed reliability. However, real faults are not i.i.d noise but structured events with temporal shape, broken cross‑variable dependencies, regime change coupled with missingness, and causal propagation across a sensing pipeline. Treating TSF robustness as a data‑quality problem, we present TS‑Fault, a benchmark that evaluates forecasting models under explicit, parameterized fault scenarios with controllable semantic difficulty. TS‑Fault organizes recurring failures into four modes along two orthogonal axes (observation‑ vs mechanism‑level; univariate vs multivariate) and injects each fault into the most prediction‑critical window via a unified importance score. This design enables robustness to be tested against the structures models actually rely on, rather than reduced to generic noise sensitivity. We evaluate 21 models across 6 datasets, 4 modes, and 5 difficulty levels under a paired clean/corrupt protocol. The results reveal three findings that contradict common leaderboard intuition: (i) clean‑data accuracy anti‑correlates with robustness; (ii) clean rankings are preserved under observation‑level faults but reshuffled under mechanism‑level faults; and (iii) all catastrophic failures occur under mechanism‑level faults, with foundation models achieving the highest clean‑data accuracy yet exhibiting the greatest fragility. The code is publicly available at https://github.com/Ray‑zyy/TS‑Fault.
Authors:Truong Xuan Khanh
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.
Authors:Hiranya Garbha Kumar, Minhas Kamal, Balakrishnan Prabhakaran
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.
Authors:Edward T. Stevenson, Mei Ting Mak, Eric Wolf, Denis E. Sergeev, Tobi Hammond, N. J. Mayne, Miles Cranmer
Abstract:
The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets. Interpreting those signatures requires understanding the host planet's climate: the same molecule may signal life on one planet and abiotic chemistry on another. Global climate models (GCMs) provide this understanding, but individual runs can require up to millions of core‑hours and substantial domain expert time. Machine‑learning emulators could remove this bottleneck, but progress has been limited by the absence of a curated, multi‑model exoclimate dataset. We introduce ThousandWorlds, an ML‑ready benchmark for exoclimate emulation and for the broader regime of low‑data, multi‑simulator, parameter‑to‑field regression. The dataset contains approximately 1800 simulations from five GCMs, mapping eight planet parameters to 3D atmospheric fields including temperature, humidity, winds, clouds, and radiation. Three nested subsets define progressively harder challenges: single‑simulator regression, multi‑simulator regression with complete observations, and multi‑simulator regression with structured missingness. We propose two evaluation protocols: one for ranking methods, and one that measures performance relative to the disagreement between GCMs themselves. We evaluate seven baselines spanning simple methods, deep learning, and Gaussian processes. GP‑based methods perform best, suggesting that ThousandWorlds exposes a regime where off‑the‑shelf deep learning does not yet succeed. Data: https://doi.org/10.57967/hf/8695. Code: https://github.com/edstevenson/ThousandWorlds.
Authors:Mingyue Cui, Linghui Shen, Xingyi Yang
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.
Authors:Shanda Li, Qiuhong Anna Wei, Jingwu Tang, Valerie Chen, Nihar B Shah, Tim Dettmers, Yiming Yang, Ameet Talwalkar
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.
Authors:Gbenga T. Awojinrin, Abdul-Akeem Olawoyin, Rami M. Younis
Abstract:
We present a numerical method for the forward solution of nonlinear partial differential equations (PDEs) in which Bellman‑Kalaba quasilinearization reduces the nonlinear problem to a sequence of linear subproblems, each discretized by collocation onto a trial space that is linear in its parameters and solved by a single direct linear least‑squares QR factorization. The trial space, which we term Linear‑in‑Learnables (LiL), comprises representations whose trainable parameters enter linearly, including random‑feature extreme learning machines, spectral polynomial bases, and trigonometric expansions, each implemented as a physics‑informed neural network. The method thus replaces the nonconvex gradient‑based training that limits standard PINNs with a convex per‑step solve. We establish local Newton‑Kantorovich convergence of the outer iteration to a residual‑limited neighborhood under an explicit smallness condition, with the limiting accuracy governed by the best‑approximation residual of the trial space rather than by an optimization tolerance. The method, denoted LiL‑Q, is assessed on seven benchmarks spanning scalar nonlinear PDEs (Bratu, viscous Burgers, Buckley‑Leverett), coupled systems (plane‑strain elasticity and the incompressible Navier‑Stokes equations in two and three spatial dimensions), and steady‑state Darcy flow with heterogeneous permeability. Across these problems, LiL‑Q converges in single‑digit outer iterations in most cases, even at the coarsest basis sizes and independent of the parameter count. When the exact solution lies in the span of the trial space, the method recovers it to machine precision in a single solve. On the Navier‑Stokes benchmarks, it matches or exceeds published PINN solvers with up to two orders of magnitude fewer trainable parameters, without gradient‑based optimization.
Authors:Nathan Ouyang, Kexin Wan, Anna Seigal
Abstract:
Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor‑based Second‑order Causal Discovery (TSCD). Its input is a tensor obtained from the covariance matrices of observational and interventional data. Assuming the causal dependencies follow a linear structural equation model on a directed acyclic graph (DAG), TSCD outputs the DAG and the functions on its edges, requiring only that the noise variables are uncorrelated. We also implement a version of the approach for nonlinear models. Our focus on second‑order statistics (via the covariance matrices) is motivated by their statistical and computational efficiency relative to higher‑order moments, their identifiability relative to first‑order statistics, and that they work regardless of whether the variables are Gaussian. We show that TSCD has identifiable causal order and parameters from a number of interventions that is logarithmic in the number of variables. Experiments show that TSCD is robust to noise, competitive with existing methods, and scales to hundreds of variables.
Authors:Shayan Dodge, Alessandro Formisano, Sami Barmada
Abstract:
We propose a new weak‑form Physics‑Informed Neural Network approach (named INI‑VPINN). INI‑VPINN naturally incorporates Neumann boundary and interface conditions into the variational formulation. It removes the need for additional loss terms or multiple subdomain networks. This framework employs compact support weighting functions and integration by parts to implicitly impose flux and continuity constraints. In this way, it implicitly ensures physical consistency across material boundaries. The proposed method is tested on Poisson and Laplace problems with sharp interfaces and complex geometries. Results show that, compared with several other Physics Informed Neural Networks‑based formulations, the INI‑VPINN consistently achieves higher accuracy, smoother and faster convergence. The proposed framework provides a general approach for solving multimaterial problems with complex geometries and mixed Neumann‑Dirichlet boundary conditions using neural networks. The implementation is publicly available in a GitHub repository.
Authors:Eric V. Strobl
Abstract:
Constraint‑based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships. We introduce BLITZ (Broad‑to‑Local Independence Testing via residualiZation), a nonparametric conditional independence test designed to run well under a second while maintaining the accuracy needed for the thousands of queries performed by constraint‑based causal discovery algorithms. BLITZ first removes broad smooth dependence on the conditioning set using low‑order polynomial regression, then applies a small nonlinear feature map and residualizes those features with shallow tree regressions. The resulting statistic tests residual cross‑covariance, with a moment‑matched chi‑square approximation to the null distribution. We show theoretically that the two‑stage design reduces the effective complexity faced by the tree residualizers, allowing shallow trees to control residual conditional‑mean bias while avoiding excessive overfitting. In simulations, BLITZ provides better null calibration than fast kernel, random‑feature, and regression‑based competitors while remaining among the fastest methods tested. In causal discovery experiments on synthetic graphs and flow‑cytometry data, BLITZ yields more reliable endpoint orientations among retained adjacencies and competitive structural recovery. These results suggest that broad‑to‑local residualization is a practical route to calibrated, scalable nonparametric conditional independence testing for causal discovery.
Authors:Yuming Chen, Yuxin Xie, Tao Zhou, Yi Zhou
Abstract:
Semi‑supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regularization on unlabeled data. However, existing approaches operate predominantly via visual pattern matching, relying heavily on pixel‑level similarities. This visual‑centric dependency often falters in clinical scenarios characterized by the visual‑semantic mismatch, where visually similar lesions warrant distinct diagnostic conclusions, thus failing to capture the underlying diagnostic logic used by experts. To address this, we move beyond visual cues and propose CERS (CoT‑Enhanced Reasoning Segmentation), a framework that integrates Chain‑of‑Thought (CoT) reasoning to distinguish pathologically distinct cases. Specifically, we construct a knowledge pool enriched with linguistic reasoning descriptions generated by large language models (LLMs). A semantic‑aware reference selection strategy is introduced to identify historical evidence, filtering candidates first by morphology, and then refining them via CoT consistency to eliminate hard negatives. Furthermore, a multi‑scale coordinate attention module (MCAM) is designed to effectively fuse this reasoning‑derived context into the decoding process. Extensive experiments demonstrate the superiority of CERS against state‑of‑the‑art approaches, particularly in resolving boundary ambiguities and semantic inconsistencies. The code is available at https://github.com/cymasuna/CERS.
Authors:Toshitaka Hayashi, Hamido Fujita, Dalibor Cimr, Richard Cimler, Jitka Kühnová
Abstract:
Machine Learning (ML) techniques have been applied to various problems. However, applying ML to ML models is an unexplored direction. For this purpose, this paper considers a meta‑classification of one‑class classification (OCC) models, because all ML models could be approximated as OCC models. The proposal represents OCC models as normality rankings and classifies them using nearest‑neighbor and ranking‑correlation metrics. The experiment classifies OCC models, where classes correspond to training datasets, algorithms, and hyperparameters. The proposal achieves high accuracy when class labels are datasets. Moreover, it can classify algorithms when the training datasets contain the same class. In addition, the discussion highlights that the classification of OCC models is essentially the classification of datasets that treats multiple samples as a single input. The experiment demonstrates the classification of datasets using sleeping records. The proposed method can provide a unified solution for classifying OCC models, datasets, and rankings. Source code is uploaded to the public repository https://github.com/ToshiHayashi/ClassOCC.
Authors:Semin Kim, Jihwan Yoon, Seunghoon Hong
Abstract:
Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training‑free image editing. Existing fixed‑point inversion methods improve inversion accuracy by formulating each inversion step as a fixed‑point problem, but they lack a principled mechanism for selecting among multiple fixed‑point solutions that can arise in practice. We observe that different selections induce different inversion trajectories, leading to substantial variation in reconstruction and editing quality. For rectified flows, we further find that this variation is closely associated with trajectory straightness, motivating straightness as a principled selection criterion. We propose SelFix, a fixed‑point inversion method that selects fixed‑point solutions inducing straighter inverse trajectories while retaining convergence to an exact inverse root under standard local assumptions. Experiments on FLUX.1‑dev and PIE‑Bench show that SelFix improves fixed‑point inversion, achieving stronger real‑image reconstruction and better source‑preserving prompt‑based editing than prior inversion baselines. The code is available at https://github.com/seminkim/selfix.
Authors:Da Chang, Ganzhao Yuan
Abstract:
Efficient optimization is essential for training large language models. Although intra‑layer selective updates have been explored, a general mechanism that enables fine‑grained control while ensuring convergence guarantees is still lacking. To bridge this gap, we propose MGUP, a novel mechanism for selective updates. MGUP augments standard momentum‑based optimizers by applying larger step‑sizes to a selected fixed proportion of parameters in each iteration, while applying smaller, non‑zero step‑sizes to the rest. As a nearly plug‑and‑play module, MGUP seamlessly integrates with optimizers such as AdamW, Lion, and Muon. This yields powerful variants such as MGUP‑AdamW, MGUP‑Lion, and MGUP‑Muon. Under standard assumptions, we provide theoretical convergence guarantees for MGUP‑AdamW (without weight decay) in stochastic optimization. Extensive experiments across diverse tasks, including MAE pretraining, LLM pretraining, and downstream fine‑tuning, demonstrate that our MGUP‑enhanced optimizers achieve superior or more stable performance compared to their original base optimizers. We offer a principled, versatile, and theoretically grounded strategy for efficient intra‑layer selective updates, accelerating and stabilizing the training of large‑scale models. The code is publicly available at https://github.com/MaeChd/MGUP.
Authors:Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
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.
Authors:Gram Koski, Sean Lipps, Zhenghua Ma, G. Abarajithan, Ryan Kastner
Abstract:
Transformer‑based models achieve strong performance for jet tagging at the CERN LHC, but deploying them in low‑latency, resource‑constrained trigger systems is challenging. We present an initial implementation of a quantized, integer‑only transformer for jet tagging on the AMD Versal AI Engine (AIE), mapping dense and multi‑head attention (MHA) layers to AIE tiles. The main contribution is a reusable software framework that represents transformer layers as composable AIE building blocks and automatically generates the corresponding Vitis graph code from a high‑level Python model description. This framework provides a foundation for future research and is released as open‑source software at https://github.com/KastnerRG/particle_transformer_aie.
Authors:Hao Li, Man Fung Zhuo
Abstract:
Laser‑induced breakdown spectroscopy (LIBS) quantitative analysis faces critical challenges in wavelength selection due to high‑dimensional spectral data and the fundamental trade‑off between prediction accuracy and feature efficiency. This paper presents a novel Multi‑Adapter PPO framework that transforms wavelength selection into a reinforcement learning problem, leveraging cross‑attention mechanisms and multiple specialized adapters to capture complex spectral relationships. Our approach outperforms traditional Particle Swarm Optimization (PSO) by an average of 28.4% in comprehensive score and 45.2% in prediction accuracy across steel and coal datasets. The proposed method demonstrates superior performance in balancing prediction accuracy with feature efficiency, achieving state‑of‑the‑art results in LIBS quantitative analysis while maintaining interpretability and computational efficiency. We released our code and dataset here: https://github.com/Hflying/MAPPO
Authors:Jeffrey G. Wang, Jason Wang, Marvin Li, Seth Neel
Abstract:
Membership inference attacks (MIAs) are a canonical way to assess a machine learning model's privacy properties. Although several attempts have been made to evaluate MIAs on language models, the extant literature has suffered numerous difficulties in constructing clean evaluations to test new techniques. In particular, subtle distribution shifts between member and non‑member sets can undermine the statistical validity of MIAs; recent work has underscored this by showing that "blind" methods with no access to the underlying model can perform far better than published methods on the same benchmarks. This paper constructs a benchmark for principled evaluation of MIAs against LLMs, by leveraging the insight that training data before and after a fixed point during training are drawn from the same distribution. Therefore, all open‑source models with intermediate checkpoints and public training data can be converted into MIA testbeds. We apply our framework to a half‑dozen published attacks on the Pythia and OLMo family of models, from 70M to 7B parameters. To facilitate further privacy research, we open‑source a modular library for designing and implementing attacks in this setting: https://github.com/safr‑ai‑lab/pandora_llm.
Authors:James Avtges, Jake Ketchum, Helena Young, Taekyoung Kim, Ryan Truby, Todd Murphey
Abstract:
Adaptation to damages and in‑situ physical repairs is essential for long‑term robot autonomy, yet challenging outside of narrowly defined and well‑anticipated bounds. In this work we proprioceptively adapt to catastrophic damage in soft‑actuated systems in under one minute. Architected materials are well equipped for adaptation: actuator failure occurs gradually rather than acutely, and damage can be described in a low‑dimensional, discrete coordinate space. Surprisingly, latent damage representations plus a simple yet robust ensemble method is sufficient for adapting to unseen damage in real‑time. Moreover, we identify conditions under which exponential sample complexity collapses to linear sample complexity for learned representations of architected materials, a concrete advantage over rigid components or continuum soft mechanisms. We demonstrate LEAP, our method for adaptive proprioception, via a tracing task for a 6DoF soft wrist based on Handed Shearing Auxetic (HSA) actuators. Our algorithm is able to adapt to cuts, burns, and actuator repairs, enabling simulation‑free real‑time adaptation that is critical for realizing the promise of soft robots outside the lab. Videos and more information are available at https://murpheylab.github.io/leap.
Authors:Logan Mann, Yi Xia, Ajit Saravanan, Ishan Dave, Saadullah Ismail, Shikhar Shiromani, Emily Huang, Ruizhe Li, Kevin Zhu
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.
Authors:Matthew Francis Dixon
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.
Authors:Emre Can Kizilates
Abstract:
The dominant trajectory of modern machine learning has been to scale up: larger models, larger accelerators, larger memory budgets. Yet a multi‑year global semiconductor supply constraint and the growing energy and carbon cost of always‑online inference expose the fragility of this trajectory and motivate the opposite direction: refactoring AI and ML algorithms to fit the small, ubiquitous microcontrollers already in mass production in wearables, sensors, and edge appliances. We present an end‑to‑end open‑source reproduction of FastGRNN, a compact gated recurrent cell, deployed on two bare‑metal targets: the 8‑bit Arduino (ATmega328P) and the 16‑bit MSP430 (no hardware multiplier; 16 KB Flash; 512 B SRAM). Our compression pipeline combines low‑rank weight factorization, iterative hard‑thresholding sparsity, and per‑tensor Q15 post‑training quantization with explicit activation calibration. The deployed model occupies 566 bytes of weights and achieves macro F1 = 0.918 (seed 0; five‑seed Q15 mean 0.853+‑0.107) on the HAPT test set. It matches a PyTorch reference at 100% prediction agreement across 3,399 test windows (MCU seed 0; 99.91‑100% C‑equivalent across five seeds). Both platforms sustain real‑time 50 Hz streaming inference (9.21 ms per sample on Arduino; 13 ms on MSP430), where a 256‑entry sigmoid/tanh look‑up table delivers a 30.5x speedup on the multiplier‑less MSP430. Four contributions extend the original FastGRNN paper: (i) cross‑platform bit‑equivalent deterministic inference; (ii) characterization of recurrent warm‑up latency (median 74 samples, 1.48 s; worst‑case 125 samples, 2.50 s over 100 test windows); (iii) a deployable look‑up‑table recipe for multiplier‑less embedded targets; and (iv) hardware energy characterization showing 17.7 mW active inference power, <0.09 mW idle power, and 96.7% energy reduction with the LUT.
Authors:Petr Nyoma
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.
Authors:Jay Jung, Xiaohan Zhang, Shenghan Song, Mahmoud Sayedahmed, Chijian Xiang, Yunong Xu, Ahmed AbdelKhalek, Severin T. Schneebeli, Matthew J. Wargo, Jianing Li, Safwan Wshah
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
Authors:Zhenhong Yang, Zhuoyun Liu, Jintao Fei, Wen Tang, Shichao Quan, Jun Zhao, Jun Xu
Abstract:
Radiology report evaluation must distinguish clinical compatibility from surface similarity, because negation, laterality, or normal‑abnormal polarity can reverse a finding. We propose RadSEM (Radiology Sentence‑Level Evaluation Metric), a constrained LLM‑assisted metric for reference‑based evaluation of radiology Findings. RadSEM rewrites reference and generated reports into ordered atomic finding sentences, each expressing one site‑finding proposition. It then performs contradiction‑constrained many‑to‑many matching: incompatible pairs such as "effusion" and "no effusion" receive no credit, while compatible granularity differences can receive partial credit. A deterministic stage weights pairs by part‑whole and abnormal‑detail relationships, counts unmatched findings, and produces an abnormal‑focused weighted F1 score. Thus, the LLM supports structured rewriting and local alignment rather than acting as an opaque judge. We evaluate RadSEM with SSREE, a controlled monotonicity stress test built from 2,448 de‑identified reports expanded into five graded corruption levels. RadSEM achieves Kendall tau_b of 0.957, all‑pairs concordance of 97.8%, adjacent concordance of 95.0%, and strict five‑level ordering for 81.9% of reports, outperforming radiology‑specific and general text metrics while avoiding the failure in which polarity‑inverted reports regain lexical overlap. On the same SSREE set, RadSEM outperforms the Ref‑anchored RadSEM‑Alt policy, improving adjacent concordance from 90.7% to 95.0% and strict ordering from 67.2% to 81.9%. On a 599‑triplet synonym/antonym subset, RadSEM prefers synonyms in 597 cases (99.67%). These results suggest that explicit finding units, contradiction‑aware matching, and abnormal‑focused deterministic scoring make report scoring more interpretable and sensitive to clinically meaningful errors. Code is available at https://github.com/jdh‑algo/RadSEM.
Authors:Jisang Han, Seonghu Jeon, Jaewoo Jung, René Zurbrügg, Honggyu An, Tifanny Portela, Marco Hutter, Marc Pollefeys, Seungryong Kim, Sunghwan Hong
Abstract:
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision‑language‑action models (VLAs) and video world‑action models (WAMs) inherit strong semantic or temporal priors from large‑scale foundation models, but they still operate primarily on 2D image frames or 2D‑derived latent spaces, leaving implicit the 3D geometry required for contact‑rich manipulation. We propose the Geometric Action Model (GAM), a language‑conditioned manipulation policy that directly repurposes a pretrained geometric foundation model (GFM) as a shared substrate for perception, temporal prediction, and action decoding. GAM splits the GFM at an intermediate layer: the shallow layers serve as an observation encoder, and a causal future predictor inserted at the split layer forecasts future latent tokens conditioned on language, proprioception, and action history. The predicted future tokens are then routed through the remaining GFM blocks for feature propagation and decoding, allowing a single backbone to produce both future geometry and actions. This design equips the GFM with language‑conditioned temporal world modeling through minimal architectural modification while preserving its rich geometric priors. Across a broad suite of simulation and real‑robot manipulation benchmarks, GAM is more accurate, more robust, faster, and lighter than current foundation‑model‑scale baselines.
Authors:Zewen Liu
Abstract:
When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge. We show that Evaluator Preference Collapse (EPC) is dramatically amplified in multimodal settings. Using GPT‑4o to evaluate DeepSeek‑chat across text and visual tasks, we find that a single strategy (step_by_step) absorbs 48.4% of all weight ‑‑ 3.2x the collapse observed in text‑only self‑evaluation ‑‑ while three visual‑domain strategies receive only 9.1% combined weight. We then demonstrate a novel phenomenon we term cross‑modal contagion: evaluator preferences acquired on one modality transfer to and corrupt strategy selection on another. Through a four‑phase isolation training paradigm, we measure contagion coefficients and document strategy inversion ‑‑ the optimal strategy for a modality reverses after cross‑modal exposure. A Phase 3 statistical validation across five evaluator configurations (N=80 total independent repetitions, ~35,000 API calls) with both text‑proxy and real‑image visual tasks finds: cross‑model evaluation produces strong contagion (JSD~0.19‑0.34), real‑image inputs yield the most directionally consistent signal (mean gamma_T‑>V=1.145, gamma_V‑>T=0.937, 70% T‑>V, Cohen's d=0.56), and self‑evaluation provides near‑complete immunity ‑‑ 97% of runs (N=30) yield zero contagion (JSD=0.003, d=0.07). Three methodological ablations and multi‑executor validation confirm the effect is not a structural artifact. We introduce the contagion matrix indexed by evaluator identity, release the MM‑EPC framework, and identify cross‑model evaluator architecture as the primary risk factor for preference drift. Code and data: https://github.com/aidless/mm‑epc.
Authors:Francisco M. López, Markus R. Ernst, Francisco Cruz, Matej Hoffmann, and Jochen Triesch
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.
Authors:Prasanth YSS, Zhichen Ren, Rasa Hosseinzadeh, Ilan Gofman, Yuqi Chen, Zhaoyan Liu, Guangwei Yu, Jesse C. Cresswell, Satya Krishna Gorti
Abstract:
Reinforcement learning with verifiable rewards (RLVR) improves language‑model reasoning, but GRPO‑style optimization remains prone to collapse. We analyse this instability through token‑level gradient dynamics, deriving a taxonomy that predicts how updates affect next‑token probabilities and entropy. The taxonomy shows that stability depends jointly on the advantage sign and token distribution under the current policy. Motivated by this finding, we propose Winner Advantage Policy Optimization (WAPO), a simple online clipped policy‑gradient objective that updates only on positive‑advantage completions. Across mathematical reasoning and multi‑hop QA benchmarks, WAPO improves training stability and matches or outperforms baselines across multiple model families. Full code can be found at https://github.com/layer6ai‑labs/wapo.
Authors:Andreas Einwiller, Max Klabunde, Florian Lemmerich
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
Authors:Bita Azarijoo, John Krumm, Cyrus Shahabi
Abstract:
Human mobility appears highly diverse, yet much of a person's daily mobility can be explained by a small set of recurring behavioral templates, such as commuting, school‑centered activities, caregiving, nightlife, or errand patterns. We present \textttIBAD (\underlineInterpretable \underlineBehavioral \underlineAnomaly \underlineDetection), a framework that learns interpretable daily mobility templates and represents each individual as a distribution over mixtures of these templates. Rather than focusing on specific locations, IBAD characterizes activities that individuals perform across locations. This approach first discovers global behavioral templates using Latent Dirichlet Allocation (LDA), then employs a hierarchical self‑supervised model to learn normal behavior of individuals from their soft behavioral templates. We also introduce a \emphsplicing benchmark that creates controlled behavioral mismatches between an individual's historical profile and injected mobility patterns. Experiments on real‑world and synthetic datasets show that daily behavior can be effectively decomposed into a small number of interpretable templates. Crucially, we show that the learned behavioral archetypes \emphtransfer across distinct geographic and demographic contexts. Furthermore, IBAD maintains a robust competitive performance across all settings. For reproducibility purposes, the code is accessible at ~\hrefhttps://github.com/USC‑InfoLab/IBADhttps://github.com/USC‑InfoLab/IBAD.
Authors:Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler
Abstract:
One‑class classification (OCC) is a classification problem in which the training data contains only one class. The one‑class support vector machine (OCSVM) is one of the most competitive OCC algorithms. However, OCSVM has scalability issues with large‑scale datasets. This paper proposes the acceleration strategy of OCSVM. The idea is to decompose the dataset into samples and train OCSVM models for single data points. Subsequently, ensemble learning is applied to combine all models to compute the OCSVM model for the dataset. In addition, further acceleration is achieved through a data‑reduction strategy with an OCSVM model trained on the average of the training samples. The experiment compared the proposal and traditional OCSVM using the Python package. The proposed strategy is faster than traditional OCSVM, while achieving similar classification results. Moreover, the proposed strategy can create one‑to‑one correspondence between samples and models. Source code is uploaded at https://github.com/ToshiHayashi/ODSVM
Authors:Qiyue Liang, Steven Ingram, George Vanica, Andi Gavrilescu, Newfel Harrat, Hassan Sipra, Sethuraman Sankaran
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
Authors:Ji Feng, Zhouxing Shi
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.
Authors:Gaston Besanson
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.
Authors:Kacper Wyrwal, İsmail İlkan Ceylan, Alexander Tong
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.
Authors:Miso Choi, Seonga Choi, Mincheol Kwon, Woosung Joung, Jinkyu Kim, Jungbeom Lee
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.
Authors:Nguyen Linh Dan Le
Abstract:
Alzheimer's disease (AD) progression is often described through the amyloid‑tau‑neurodegeneration, or AT(N), cascade. However, most longitudinal models represent this cascade either as a fixed sequence of biomarkers or as a black‑box forecasting task. This makes it difficult to determine when biologically guided biomarker relationships influence future regional pathology. In this study, we introduce Bayesian Networks with Latent Time Embedding (BN‑LTE), a Bayesian structural framework for stage‑aware modeling of AD progression. BN‑LTE estimates disease pseudotime from baseline biomarker profiles and constrains directed dependencies according to biologically plausible AT(N) ordering. Posterior spline‑varying structural equations are then used to link initial multimodal measurements with future annualized regional tau‑PET change. Across repeated subject‑disjoint evaluations using ADNI data, BN‑LTE shows strong spatial reconstruction of tau progression compared with the included forecasting baselines. Beyond spatial reconstruction, BN‑LTE recovers posterior stage‑varying AT(N)‑constrained effects and identifies a mid‑pseudotime window of amyloid sensitivity. This window is supported by model‑implied g‑formula contrasts, root‑adjusted AIPW, mechanism‑sensitive ablations, and robustness analyses across spline and prior specifications. Overall, these findings position BN‑LTE as a Bayesian structural framework for forecasting tau progression while examining stage‑dependent AT(N)‑cascade mechanisms in observational longitudinal neuroimaging data. Our code is available at https://github.com/danleneurocom/BN‑LTE.
Authors:Marios Koulakis, Constantin Seibold
Abstract:
A significant gap exists between theory and practice in deep learning. Generalization and approximation error bounds are often derived for simplified models or are too loose to be informative. Many rely on the manifold hypothesis and on geometric regularity such as intrinsic dimension, curvature, and reach. Progress requires insight into data‑manifold geometry and suitable benchmarks, yet existing options are polarized: analytic manifolds with known geometry but limited applicability, or real‑world datasets where geometry is only coarsely estimable. We introduce a benchmarking framework for studying data geometry. We repurpose and extend dSprites and COIL‑20 with additional transformation dimensions and dense, axis‑aligned sampling, and pair them with finite‑difference estimators that recover curvature, reach, and volume at near‑ground‑truth accuracy in a regime where general‑purpose estimators are unreliable or difficult to deploy. The framework is intended as a controlled testbed, useful as a calibration environment for geometric estimators and a sandbox for probing theoretical assumptions. To illustrate its use, we present two application studies, namely assessing the scaling behavior of the bounds of Genovese et al. and Fefferman et al., and tracking the layer‑wise geometry of a β‑VAE, highlighting the behavior of current bounds and the value of controlled benchmarks for guiding and validating future theory. A reference implementation is available at https://github.com/koulakis/manifold‑microscope.
Authors:Hyebin Cho, Jaehyuk Jang, Changick Kim, Joon Son Chung
Abstract:
Audio‑Language Models (ALMs) have shown remarkable success in zero‑shot audio classification by aligning audio waveforms with text. Recent efforts to improve downstream performance focus on learning optimal text prompts. However, previous approaches focus on the text encoder, leaving the potential of learnable prompts within the audio encoder unexplored. In this paper, we propose a novel framework that introduces trainable prompts into the audio encoder to capture task‑specific acoustic features. We demonstrate that integrating audio‑side prompt learning with existing text‑side approaches enhances few‑shot adaptation. Through extensive experiments across 11 datasets show that integrating our method as a plug‑and‑play module alongside existing text prompt tuning generally leads to performance improvements. These findings suggest that explicitly modulating the audio representation space effectively complements text‑only prompting approaches. The code is available at https://github.com/hyebin‑c/aspl.
Authors:Hassan Ismkhan, Hamid Bouchahcia
Abstract:
This paper addresses the missing‑modality challenge in multi‑modal learning by introducing Unsupervised Learning for Missing Modalities in Multi‑Modal Learning (UL4M4), a flexible framework that imputes missing feature embeddings in a task‑independent manner before supervised prediction. We propose modality‑specific normalization and a novel partial‑modality distance metric to enable fair clustering of incomplete observations, capturing cross‑modal structures while preserving scale‑invariance across varying dimensionalities and modality counts. Cluster centers from this unsupervised stage guide an iterative greedy imputation process for any missing modalities during training or inference, supporting arbitrary numbers of modalities and arbitrary missing patterns per sample. The imputation module is lightweight, uses frozen encoders, and decouples from the downstream task, allowing easy integration with any fusion/prediction architecture. Extensive experiments under diverse and highly incomplete regimes demonstrate UL4M4's robustness, achieving, to the best of our knowledge, the first consistent F1‑Micro scores above 0.7 on challenging missing configurations even when more than 50% of modality slots are missing. Results are also stable across cluster sizes and significantly outperform state‑of‑the‑art baselines. Code is available here: https://github.com/h‑ismkhan/Multimodal‑Learning‑with‑Missing‑Modalities‑via‑Unsupervised‑Learning.
Authors:Nils Matteson
Abstract:
Per‑token counterfactual credit estimation asks which token in a language‑model rollout caused the final answer to be right or wrong: cut the transcript at a pivot, substitute an alternative token, replay continuations, and compare outcomes. Published methods re‑feed the transcript prefix as a fresh prompt, assuming this reproduces the state the model passed through during generation. We measure what that assumption costs on a stock inference engine, with a three‑pass design: continuations resumed from the verified decode‑time KV state, an identical second exact pass (a replica noise floor), and a re‑feed pass. Across six configurations and three models (including a GRPO‑trained checkpoint), at low‑margin decision tokens, re‑feeding changes the credit estimate at rates 14‑28 percentage points above the replica floor (7‑21pp under a treatment‑independent conditioning; problem‑clustered t = 2.9‑6.4). Most changes are zero‑boundary crossings of the quantized estimator rather than polarity reversals, and the perturbation is consistent with mean‑zero, so averaged quantities are largely safe; but selection is not: a critical‑token set chosen by thresholding |\hatA_t| under re‑feed overlaps the exact‑resume selection at Jaccard 0.34‑0.90, versus a 0.63‑0.96 replica ceiling. A causal confirmation closes the loop: under vLLM's batch‑invariant kernels all three passes are identical on every measured channel, with both disagreement rates exactly zero. Replica passes themselves disagree on 9‑23% of eligible estimates: single‑sample credit measurements at decision tokens are unreliable under any replay. Settings were fixed in advance; exact‑pass cache hits in the second campaign are instrumented (100% hit rate, 3,434 pivots); total compute was under 10 USD. We recommend that counterfactual credit studies resume decoder state or use batch‑invariant kernels, and report a replica floor.
Authors:Madhulatha Mandarapu, Sandeep Kunkunuru
Abstract:
Cardinality‑estimation (CE) research ranks estimators by q‑error, yet it is well known that q‑error is an imperfect proxy for query‑plan quality. We give a measurement‑driven account of when it is a good proxy and when it is not, and why. Modeling plan selection as an argmin over a piecewise‑linear cost landscape, we find that plan regret (the cost of the chosen plan relative to the optimal, under true cardinalities) is governed by plan‑cost geometry in a regime‑dependent way. (i) For small errors, a true‑point condition number kappa predicts regret and out‑predicts q‑error; its predictive power decays to zero as error grows, as a local linearization must. (ii) For large errors ‑‑ where deployed learned estimators operate ‑‑ an estimator‑independent average‑case sub‑optimality measure ACS‑infinity predicts which queries are regret‑prone (Spearman rho ~ 0.54 on STATS‑CEB), while q‑error is nearly uninformative at the query level (rho ~ 0.05). (iii) The worst case is Haritsa's maximum sub‑optimality (MSO). The three are one cost‑ratio spectrum under three weightings. We prove a limit law ACS‑infinity = sum_k r_k pi_k with cardinality‑independent combinatorial weights, and validate every claim on STATS‑CEB and JOB‑light with four released estimators under pre‑registered decision rules, and confirm on real PostgreSQL runtime that ACS‑infinity predicts regret where q‑error does not. The contribution is conceptual and empirical ‑‑ an average‑case companion to worst‑case robust query optimization, and a characterization of when an accuracy metric tracks plan quality ‑‑ rather than a new estimator. Code and the full pre‑registration are public.
Authors:Terry Tong, Yu Feng, Surbhi Goel, Dan Roth
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.
Authors:Hassan Ismkhan, Hamid Bouchahcia
Abstract:
Robotic systems perceive the world through multiple input modalities ‑‑ including visual camera streams and natural language instructions ‑‑ and must select appropriate actions based on these signals. However, assuming the permanent availability of all input devices is unrealistic, as sensors may fail, become occluded, or drop out entirely during deployment. Robust handling of such missing‑modality scenarios is therefore essential for real‑world robot operation. This paper introduces RL4IL, a reinforcement learning guided method for imitation learning that selects the most suitable action for a given observation by identifying the most relevant expert demonstrations from a training library. A reinforcement learning policy, trained via Proximal Policy Optimisation over Breadth‑First Search candidate sets, ranks candidate demonstrations and a soft cross‑attention fusion head aggregates their action signals to produce the final prediction. When a modality is missing at inference time, a dedicated per‑modality RL retrieval policy identifies donor demonstrations from the training library, and a soft imputation head reconstructs the missing embedding via cross‑attention over the top‑ranked donors ‑‑ without requiring any retraining of the system. Experiments on three LIBERO benchmark suites demonstrate that RL4IL substantially outperforms state‑of‑the‑art imitation learning methods under sensor dropout conditions, while requiring no policy network training. The code can be found at https://github.com/h‑ismkhan/Reinforcement‑Learning‑via‑kNN‑for‑Robotic‑Learning‑with‑Missing‑Camera
Authors:Yixuan Wang, Yiyang Zhou, Yiming Liang, Congyu Zhang, Fuxiao Liu, Jiawei Zhou, Huaxiu Yao
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.
Authors:Chenyang He, Xinyi Shao, Shun Huang, Bosong Huang, Daoqiang Zhang, Ming Jing, Cheng Ding
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 .
Authors:Kun Ma, Qilong Han, Chengjing Song, Jingzheng Yao, Xiao Han, Yuee Zhou, Changmao Wu
Abstract:
Modern trajectory predictors increasingly condition on external spatial context, such as map geometry, signed distance fields (SDFs), and nearby moving agents. While this context improves prediction quality, constructing it for every training anchor has become a hidden systems bottleneck. In a representative maritime AIS pipeline, spatial context construction requires roughly 17 CPU‑days for a 5.48M‑anchor corpus, dominating the cost of the downstream predictor. We present M‑CTX, an exact and scalable spatial context‑retrieval framework for trajectory analytics. M‑CTX recasts context construction as an ingest‑once, query‑many spatial database workload and replaces three brute‑force stages ‑‑ OSM range retrieval, SDF computation, and moving‑vessel neighbour lookup ‑‑ with composable, index‑backed operators. Its learned range‑index backend, BR‑LZ, provides recall‑complete MBR‑overlap range retrieval and reduces candidate amplification by 1.1x‑‑2.7x relative to global‑expansion one‑curve baselines. Across four maritime regions, eight baseline systems, synthetic workloads with up to 40M spatial features, and 10^7‑record AIS streams, M‑CTX reproduces the reference context exactly. On the 5.48M‑anchor corpus, it reduces context construction from about 17 CPU‑days to 1.8 hours, a measured 226x end‑to‑end speed‑up. An optional storage mode further compresses SDF context by 64x with only a 0.04 m ADE change. These results establish exact spatial context retrieval as a first‑class database problem in modern trajectory analytics. Code and datasets are publicly available at https://github.com/mark000071/M‑CTX‑Traj.
Authors:Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian
Abstract:
Multi‑objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives. In many practical scenarios, function evaluations are unavailable or prohibitively expensive, necessitating optimization solely based on a fixed offline dataset. In this setting, known as offline MOO, the goal is to find out the Pareto set without access to the true objective functions. This setting suffers from the out‑of‑distribution (OOD) issue, where the surrogate model is not accurate for unseen designs. Due to the OOD issue, surrogate errors may cause the optimizer to select solutions that do not lie on the true Pareto front and are biased toward its extremes. To address this, this paper proposes Diversity‑driven Offline Multi‑Objective Optimization (DOMOO), which aims to find out a diverse and high‑quality set of solutions. First, DOMOO incorporates an accumulative risk control module that estimates the potential risk of candidate solutions and alleviates the OOD issue between the training data and the generated solutions. In addition, a nested Pareto set learning (PSL) strategy is proposed to jointly learn preference and PSL parameters, then optimize them, enabling adaptation to diverse Pareto front geometries. To further enhance solution quality, we design a diversity‑driven selection strategy that extracts a representative and well‑distributed set of final solutions. To achieve this diversity‑driven selection strategy, we propose \textIGD_\textoffline, a tailored indicator for the offline setting that considers both diversity and convergence, and avoids the bias of hypervolume indicator. Extensive experiments on synthetic and real‑world benchmarks show that DOMOO achieves the best average rank across tasks in both convergence and diversity among the compared methods.
Authors:Aarav Bedi
Abstract:
Manipulation demonstrations have temporal phase structure, and a natural hypothesis is that demonstration‑curation metrics should be applied within phases rather than globally. The idea is to segment each trajectory into phases, score each phase with the metric that is locally most informative, and then aggregate. This follows directly from prior work showing that a single global metric can be the best detector of a defect and yet the worst curator of the resulting policy. We test the per‑phase hypothesis on three contact‑rich LIBERO pick‑and‑place tasks with a controlled early‑release structural defect, comparing phase‑gated curation against the same metrics applied uniformly and against a strong single global metric. Across all three tasks and five random seeds per condition, phase‑gated curation is never the best curation strategy, and it is the worst of the three on two of the three tasks (Task 1: 86.0 vs. 92.0 for global; Task 3: 22.7 vs. 48.0 for uniform). We trace the failure to a concrete mechanism. When the defect signal is concentrated in a single phase, rank‑aggregating across phases dilutes that signal with uninformative scores from defect‑free phases, selecting a worse demonstration subset than simply applying the defect‑informative metric everywhere. We further show that the per‑phase metric selection does not transfer across tasks, since no phase shares a winning metric between any two tasks, so the selection cannot be reused and must be re‑derived per task from a noisy sweep. These results bound a plausible and previously untested method, and they argue that practitioners should prefer identifying a single defect‑informative metric over decomposing curation by phase. We release the full pipeline, all metric implementations, and per‑seed results.
Authors:Jea Kwon, Dong-Kyum Kim, Jiwon Kim, Yonghyun Kim, Woong Kook, Meeyoung Cha
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.
Authors:Yilin Wu, Zilin Si, Zeynep Temel, Oliver Kroemer, Andrea Bajcsy
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.
Authors:Haoru Tan, Sitong Wu, Yanfeng Chen, Jun Xia, Ruobing Xie, Bin Xia, Xingwu Sun, Xiaojuan Qi
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)
Authors:Adnan El Assadi, Roman Solomatin, Isaac Chung, Chenghao Xiao, Deep Shah, Manan Dey, Shriya Sudhakar, Zacharie Bugaud, Wissam Siblini, Ayush Sunil Munot, Yashwanth Devavarapu, Rakshitha Ireddi, Michelle Yang, Márton Kardos, Niklas Muennighoff, Kenneth Enevoldsen
Abstract:
We introduce the Massive Video Embedding Benchmark (MVEB), a 23‑task benchmark for video embeddings spanning classification, zero‑shot classification, clustering, pair classification, retrieval, and video‑centric question answering. We evaluate 33 models and find that no single model dominates: MLLM‑based embeddings lead on classification, clustering, pair classification, and QA; multimodal binding leads on retrieval and zero‑shot classification; generative MLLMs without contrastive adaptation collapse on cross‑modal tasks. Paired video‑only vs. audio+video evaluations show that audio's contribution depends on dataset annotation provenance: audio helps when labels were produced from both modalities and hurts when they were produced from visuals alone, a six‑point gap consistent across model families. MVEB is derived from MVEB+, a 184‑task pool, and is designed to maintain task diversity while reducing evaluation cost. It integrates into the MTEB ecosystem for unified evaluation across text, image, audio, and video. We release MVEB and all 184 tasks along with code and a leaderboard at https://github.com/embeddings‑benchmark/mteb.
Authors:Zhengtao Gui, Sheng Yang, Zhouxing Shi
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.
Authors:Fengrui Liu, Ruiyang Huang, Qijian Zheng, Yuanfang Wang, Feng Liu
Abstract:
Self‑supervised learning advances audio representation for multimedia analysis. However, prevailing data‑centric approaches rely on massive real‑world corpora, increasing training costs, curation burdens, and privacy barriers. To address this, we present AudioPG, a procedural synthesis framework eliminating real audio recordings during pre‑training. AudioPG trains a Transformer‑based masked autoencoder on waveforms generated on‑the‑fly from basic acoustic primitives and composition rules. The encoder transfers effectively to real audio benchmarks, achieving 90.60% accuracy on ESC‑50, 0.546 mAP on FSD50K, 88.17% on UrbanSound8K, and 97.03% on Speech Commands V2. Notably, pre‑training completes in under 20 minutes on a single GPU. Latent space analysis reveals physical factors, including fundamental frequency and relative intensity, emerge in orthogonal subspaces, making representations linearly decodable. These results establish procedural synthesis as an efficient, interpretable pre‑training signal when large‑scale corpora are unavailable. Our code is available at: https://github.com/Freyliu0516/audioPG.
Authors:Xirui Kang, Yanpei Shi, Lucy Liang, Roy Gan, Dongxiu Liu, Pushi Zhang, Danpeng Chen, Xiaoyi Qin, Yinan Zheng, Jinliang Zheng, Hao Wang, Xianyuan Zhan, Hang Su
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.
Authors:Matiur Rahman Minar, Seunghun Oh, GangHyeon Jeong, Unsang Park
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/
Authors:Jinsu Kim, Jihoon Tack, Noah Lee, Jongheon Jeong
Abstract:
Language Models (LMs) have shown remarkable potential as role‑playing chatbots, delivering consistent, stylized interactions when given a specification of a character or user persona. However, applying these capabilities to real‑world applications (e.g., ecosystems with numerous NPCs interacting simultaneously) exposes a critical inefficiency due to the excessive computational cost. In this paper, we question the necessity of dedicating a full, generalist model to a single persona, hypothesizing that a specific character identity relies on only a fraction of the model's total capacity. We observe that naively pruning LMs often severely degrades the role‑playing performance for a specific persona; it does not distinguish between redundant knowledge and essential character traits. We propose Persona‑Pruner, a framework that sculpts a lightweight role‑playing model by isolating persona‑specific sub‑networks from a single description. Our experiments consistently show that Persona‑Pruner preserves role‑playing performance substantially more effectively than existing state‑of‑the‑art LLM pruning techniques, reducing the performance drop from the dense model by up to 93.8% over the strongest baseline on RoleBench in LLM‑as‑a‑judge score, while still maintaining general LLM capabilities. Code is available at https://github.com/jsu‑kim/Persona‑Pruner.
Authors:Armand de Villeroché, Sibo Cheng, Vincent Le Guen, Marc Bocquet, Rem-Sophia Mouradi, Patrick Armand, Alban Farchi, Patrick Massin
Abstract:
Transformer‑based neural operators have shown remarkable performance for approximating solution operators of partial differential equations on complex geometries. However, existing approaches implicitly assume a fixed domain size, which limits their ability to generalize at inference. In this work, we investigate domain extension, namely zero‑shot inference on spatial domains that are significantly larger than those encountered during training. We argue that this setting fundamentally requires spatial locality and translation equivariance. We propose to implement this locality via a decomposable bias in the attention logits computation, enabling finely controllable locality while remaining fully decomposable into query‑key inner products and directly compatible with optimized attention kernels. Combined with rotary positional embeddings, it enables expressive embeddings with controllable spatial support without altering the transformer architecture. We empirically show that our approach substantially improves zero‑shot generalization to larger domains across two PDE benchmarks and a 3D industrial atmospheric flow application. Our code and datasets are available at https://github.com/cerea‑daml/domain‑extension.
Authors:Carlo Di Cicco
Abstract:
Large language models encode rich information in their hidden states. This work asks whether code correctness is legible in the hidden states of Qwen3‑4B‑Instruct‑2507, before it generates and as it repairs a failed attempt, studied on 444 LiveCodeBench tasks. It reports two findings connected by a single confound‑control tool: residualization. First, the correctness of the model's first‑attempt code is linearly decodable from the prompt‑final hidden state, with a leakage‑free held‑out AUC of 0.931 +/‑ 0.008 across 50 outer splits. After the linear effect of prompt length is removed from each hidden state dimension, the probe still reaches 0.911 +/‑ 0.010, well above a prompt‑length baseline of 0.754 +/‑ 0.014. Second, on 236 cleaned cases where the model attempts to repair a failed first attempt, the hidden state shift from the failing attempt to its repair carries a statistically detectable contrastive direction, significant on both a magnitude and a split‑half test against label‑shuffled nulls. This direction does not survive a conditional residualization against repair‑context covariates that differ between successful and failed repairs, marking it as a correlate of repair success driven by the repair context rather than an isolated repair‑comprehension feature. The probe layer is selected by nested cross‑validation, and the same residualization approach that upholds the pre‑generation correctness result overturns the repair‑direction interpretation. The contribution is as much methodological as empirical: a diagnostic honest enough to report a negative result alongside a positive one.
Authors:Liou Tang, James Joshi, Ashish Kundu
Abstract:
The removal of learned data from Machine Learning models through Machine Unlearning (MU) has been widely studied; however, there has yet to be an agreed‑upon scheme for auditing MU. Existing work has shown that a dishonest model owner can falsify evidence to avoid executing MU, while curious auditors (and adversaries) can infer the privacy‑sensitive properties of the model and its training data even with limited access. Yet auditing of MU under mutual distrust between the model owner and the auditor remains unexplored. We provide an information‑theoretic proof for this scenario: for convex ML models, a generic audit scheme that relies solely on querying the model for behavioral signals cannot identify insufficiently unlearned models without revealing membership information of the retained set. Therefore, auditing MU under the assumption of a dishonest model owner and an honest‑but‑curious auditor faces an inherent privacy‑audit tradeoff. Our empirical results on convex models strongly supports this result, while further experiments demonstrate that this privacy‑audit tension persists in non‑convex models. Our results call for a more careful consideration of the privacy‑audit tension under a realistic auditor threat model, and serve as a foundation for more scrutiny of designs of privacy‑preserving audit schemes for the MU pipeline. We also release our code implementation at https://github.com/LiouTang/Behavioral‑Unlearn‑Audit.
Authors:Xihang Shan, Ye Luo
Abstract:
We present a recipe‑controlled decoder audit (RCDA) for structural transductive knowledge‑graph completion (KGC). The audit asks a simple reporting question: before attributing gains to an encoder or training recipe, what changes when the decoder is swapped under the same recipe? Using ComplEx and DistMult as the primary controlled pair, with targeted RotatE/TransE spot‑checks, we evaluate seven benchmarks. On five standard KGs, ComplEx‑vs‑DistMult differences are modest but consistent under our recipe (+0.005 to +0.012 MRR), whereas CompGCN‑style encoder effects vary more by dataset. On small KGs, decoder effects become the main diagnostic: Kinship shows a stable ComplEx advantage of +0.143 MRR (6 seeds), while UMLS favours ComplEx by +0.022 MRR in a clean 6‑seed server rerun but reverses in an earlier provenance variant. We therefore treat small‑KG decoder choice as recipe‑ and provenance‑sensitive rather than as a fixed dataset winner. We further show that decoder choice interacts with encoder depth on WN18RR, and that under our recipe L=0 ComplEx on YAGO3‑10 reaches 0.6971 +/‑ 0.0048 MRR at d=128. The result is a compact audit protocol: report matched decoder rows, log small‑KG provenance, and sweep decoder x depth before making encoder‑level claims.
Authors:Piotr Kitłowski, Dominik Wiącek, Mateusz Modrzejewski
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
Authors:Jiaen Lv, Leran Qi, Shaowei Wang
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.
Authors:Xilin Dai, Yiding Liu, Hongjie Xia, Yifan Hu, Zewei Dong, Jiang-Ming Yang, Qiang Xu
Abstract:
The rapid evolution of Time Series Foundation Models (TSFMs) has advanced zero‑shot forecasting across diverse domains. Inspired by the current form of Large Language Models, future TSFMs may be offered as commercialized, closed‑source API services. However, many existing online adaptation methods still rely on white‑box access for parameter fine‑tuning or gradient backpropagation. This paradigm mismatch raises a question: In black‑box online adaptation for TSFMs, what should we learn? We answer this with an insight: the predictive errors of the base model are conditioned on both the input and output of the base model (i.e., the context of errors). To validate this insight, we propose ORCA (Online Residual Contextual Adaptation). We conduct extensive experiments across 5 state‑of‑the‑art TSFMs and 8 datasets to demonstrate the effectiveness of our approach. Furthermore, through ablation studies, we quantitatively analyze the impact of different adapter learning hypotheses on the final adaptation performance in black‑box online adaptation. Code available at https://github.com/Fifthky/ORCA.
Authors:Xuhui Zhou, Weiwei Sun, Weihua Du, Jiarui Liu, Haojia Sun, Qianou Ma, Tongshuang Wu, Yiming Yang, Maarten Sap
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.
Authors:Kaiwen Chen, Shuhai Zhang, Zimo Liu, Linxiao Li, Ying Sun, Yuchen Li, Yifan Zhang, Bo Han, Mingkui Tan, Qiuwu Chen
Abstract:
Large‑scale neural network training increasingly relies on matrix‑aware optimizers that exploit the structure of weight parameters beyond element‑wise adaptation. However, existing matrix‑aware methods such as Muon have an underappreciated vulnerability: their core operation, Newton‑Schulz iteration, depends critically on input conditioning, yet the raw momentum matrices exhibit severe coordinate‑wise scale heterogeneity. In this paper, we first verify this scale heterogeneity through a chi‑square uniformity test, showing that intra‑matrix scale imbalance is prevalent across Transformer layers and that coordinate whitening effectively corrects it. Motivated by this finding, we propose Zeta, a dual whitening optimizer that applies coordinate whitening and spectral whitening in a strictly ordered pipeline. The ordering is not a tunable choice but follows from a mathematical dependency: coordinate whitening establishes the statistical isotropy that spectral whitening requires to function reliably. We further prove that this dual pipeline strictly reduces orthogonalization error relative to pure spectral methods by improving the condition number of the input. Empirically, Zeta matches or surpasses strong baselines across language modeling (0.6B to 8B parameters), mixture‑of‑experts architectures, and vision tasks, demonstrating that resolving scale imbalance before orthogonalization leads to faster convergence and better generalization. Code is available at https://github.com/AIGCodeOS/aigcode_zeta_optimizer.
Authors:Yufeng Xu, Taiming Lu, Kunjun Li, Jiachen Zhu, Mingjie Sun, Zhuang Liu
Abstract:
Pruning promises a shortcut to strong small language models. In this work, we examine this promise by pruning Llama‑3.1‑8B at pruning ratios of 0.5‑‑0.8 with six methods spanning depth, width, and sparse granularities, under two controlled token‑matched settings. (1) With the same training token budget, pruned initialization consistently outperforms random initialization. This shows that the parent model provides a strong starting point, although the advantage narrows as the training token budget grows and as the pruning ratio rises, nearly vanishing at the highest pruning ratio we study. (2) When training from scratch is instead given the full token budget consumed by the whole pipeline, pruning at finer granularities still retains an advantage, while coarser structured pruning can be matched or surpassed. This suggests that the parent model transfers knowledge that additional training tokens alone cannot fully recover, but only at fine granularity. Taken together, our results yield a clear recommendation: with a large pretrained model in hand and a limited training token budget, pruning is better than training from scratch; when the training budget is not limited, training from scratch can be competitive for coarser pruning, so a large pretrained parent is not always necessary.
Authors:Xiaoran Yan, Cheng Tang, Atsushi Shimada
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.
Authors:Ziwei Wang, Xingyi He, Tianwang Jia, Hongbin Wang, Dongrui Wu
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.
Authors:Ibrahim Denis Fofanah
Abstract:
Sierra Leone's agriculture operates with almost no data‑driven decision support, and no published machine learning study has examined the country's crop yields. We ask whether rice yield can be forecast from data Sierra Leone currently has. Using 25 years of FAOSTAT production data (2000‑2024) for nine major crops, we train XGBoost, Gradient Boosting, and Random Forest under a strict anti‑leakage protocol with expanding‑window walk‑forward evaluation across seven held‑out years, benchmarked against naive persistence. No model trained on crop statistics alone outperforms persistence. Augmenting with free satellite climate data (CHIRPS rainfall, NASA POWER temperature) reverses this result: a climate‑only XGBoost reduces forecast error by one third (RMSE 284 vs 428 kg/ha), a gain that holds for a linear model and is robust to excluding the anomalous 2018 season. Early‑season (May‑June) rainfall is the dominant predictor, implying seasonal yield risk is observable months before harvest. No model anticipated the 2018 collapse, whose origins were institutional rather than climatic. We translate the findings into policy recommendations for Sierra Leone's Feed Salone Strategy, with a fully open‑source pipeline.
Authors:Yijun Liang, Hengguang Zhou, Ming Li, Lichen Li, Cho-Jui Hsieh, Tianyi Zhou
Abstract:
Vision‑language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse, non‑trivial, visual‑centric and grounded questions remains underexplored. Existing visual questioners' performance is bottlenecked by the availability of high‑quality training data or the cost of curating them. We show that a VLM can continuously improve itself as a visual questioner without any external supervision. We propose a self‑evolving framework that uses a VLM itself as both a proposer and a filter to produce harder, more informative, and visual‑centric questions, while maintaining their exploration diversity to avoid training collapse. These questions are then used to train the VLM in both questioner and answerer modes. To evaluate the questioner, we introduce an agentic protocol that assesses questions along perception, reasoning, and diversity dimensions. Experiments across various backbone VLMs show that our method substantially enhances the quality and substantially expands the difficulty boundary of autonomous question generation. Under the same budget, our self‑supervision is more effective than training on the static source data. Moreover, the self‑evolving questioner remains a competitive or even better answerer.
Authors:Jafar Bakhshaliyev, Niels Landwehr
Abstract:
Spiking Neural Networks (SNNs) have emerged as an energy‑efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics. More recently, SNNs have been applied to time series forecasting (TSF), with methods exploring spiking temporal backbones, spike‑compatible positional encodings, Fourier‑domain processing, and redesigned neuron dynamics. However, existing SNN forecasting approaches process variables independently, lacking explicit mechanisms for modeling inter‑variable dependencies. This is a critical limitation in multivariate settings, where cross‑variable correlations carry substantial predictive information. We propose Spiking Fourier Graph Operators (SpikF‑GO), which addresses this gap by combining a hypervariate graph formulation in which every scalar observation becomes a graph node with spike‑driven spectral processing. SpikF‑GO introduces a Hard Concrete frequency gate for learnable sparse frequency selection and a Complex LIF gate that applies independent spiking neurons to real and imaginary Fourier components, preserving binary, event‑driven computation throughout the spectral domain. We further present a variant incorporating Central Pattern Generator‑based positional encodings for stronger long‑range temporal modeling. Evaluated on eight benchmarks under a unified experimental protocol, SpikF‑GO achieves the best average rank among all SNN methods and outperforms its ANN counterpart, FourierGNN, at reduced energy cost. SpikF‑GO maintains competitive accuracy even at substantially smaller embedding dimensions, thereby achieving significant energy reductions. To our knowledge, this is among the first works to bring graph‑based multivariate modeling into the spiking domain for TSF and the first to provide a unified comparison across SNN forecasting architectures under a common experimental protocol.
Authors:Nadav Benedek, Tomer Koren, Ohad Fried
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
Authors:Lev V. Utkin, Andrei V. Konstantinov, Stanislav K. Kogan, Natalya M. Verbova, Maksim I. Goriunov
Abstract:
Estimating the probability that a treatment outperforms a control for an individual patient, called the Individual Probability of Treatment Benefit (IPTB), offers a clinically intuitive alternative to population‑average metrics. However, existing methods for IPTB estimation are largely confined to binary treatment settings, despite the prevalence of dose‑varying interventions in clinical practice. We propose a general framework for IPTB estimation with ordinal outcomes under discrete dose assignments, called Dose‑AIPTB (Dose Attention‑based IPTB). Our approach recasts the problem as binary classification over the unobserved sign of the individual treatment effect, constructing pseudo‑labels from covariate‑similar pairwise comparisons and aggregating them via attention mechanisms or Nadaraya‑Watson kernel regression. This formulation naturally accommodates multiple discrete dose levels, extending beyond the binary treatment paradigm. Through numerical experiments on real‑world and synthetic data under covariate shift, varying sample sizes, and heterogeneous outcomes, we demonstrate that attention‑based aggregation consistently outperforms kernel alternatives. The framework provides a foundation for personalized dose selection grounded in individual‑level benefit probabilities. Codes implementing the model are publicly available at https://github.com/NTAILab/AIPTBDose.
Authors:Tejas Agrawal, Vu Le, Sumit Gulwani, Gust Verbruggen
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.
Authors:Jorge Miguel Silva
Abstract:
Finding the shortest program that generates a sequence is uncomputable, and for six decades that fact has been mistaken for a wall around finding any generating program. It is not a wall but a price, and this paper measures it. For every algorithm that learns about a candidate program only through its score, a class spanning Levin search, evolutionary methods, simulated annealing, and the cross‑entropy method, we define the coupling width of a search problem and prove an unconditional worst‑case lower bound, exponential in that width with base one less than the domain size. From it follows a conservation law: structural knowledge injected into a search trades one for one against the search it removes, and their sum can never fall below the length of the program sought. Levin's 1973 upper bound and the lower bound proved here are the two ends of one conserved quantity, closing on each other as the instruction set grows. The only escape is to read a candidate's structure rather than its score, and its price, which we prove for generic targets, is incompleteness. A deterministic engine built on this theory recovers a generating program, certified by compressing its data and predicting an unseen continuation, for 2,383 of 3,914 sequences across four independent populations, including 244 of the 256 elementary cellular automata, with measured discovery cost rising along program length more than an order of magnitude inside the score‑oracle worst case.
Authors:Haozhe Jiang, Haiwen Feng, Pieter Abbeel, Jiantao Jiao, Angjoo Kanazawa, Nika Haghtalab
Abstract:
RL post‑training has become increasingly pivotal for improving diffusion policies, but existing diffusion policy‑gradient methods are often unstable and cannot achieve reliable policy improvement. We identify the cause as the double‑drift phenomenon: optimizing a variational surrogate can let the ELBO separate from the true log‑likelihood, which then makes the resulting proxy policy gradient misaligned with the true policy gradient of expected return. We propose DiPOD, a diffusion policy optimization framework that maintains tight‑bound behavior throughout training by interleaving self‑distillation with policy‑improving gradient updates. This leads to a simple and practical algorithm: augmenting each diffusion policy‑gradient update with an on‑policy ELBO regularizer. Across diffusion language model post‑training and continuous‑control diffusion policies, DiPOD substantially stabilizes training and reaches higher rewards than previous methods.
Authors:Elijah Cadenhead, Cristian McGee, Xin Li, El Houcine Bergou, Aritra Dutta
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/.
Authors:Zhao-Heng Yin, Guanya Shi, Pieter Abbeel, C. Karen Liu
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.
Authors:Guo Yu, Wenlin Liu, Yulan Hu, Hao-Xuan Ma, Jun-Peng Jiang, Han-Jia Ye
Abstract:
On‑policy distillation (\textscOPD) has recently become a prominent post‑training recipe as it combines two desirable ingredients: on‑policy student trajectories and dense teacher supervision, yet how this hybrid changes a model's parameters remains unclear. Across several language and vision‑language model pairs and use cases, our analysis yields two main findings. On sparsity, \textscOPD‑style updates are small and coordinate‑sparse. They are distributed across layers and are usually FFN‑heavy. This sparse structure is operationally useful: training only the discovered subnetwork recovers nearly the same performance as full \textscOPD. However, the sparsity‑inducing SGD optimizer underperforms AdamW in our optimizer ablation, likely because dense teacher supervision preserves heterogeneous coordinate‑wise gradient scales where AdamW's adaptive scaling remains useful. On geometry, the updates are numerically full‑rank but spectrally concentrated; they lie mostly away from the principal singular subspaces of the source weights and fall disproportionately on coordinates where the source weights are close to zero. These findings suggest that dense teacher supervision does not turn \textscOPD into ordinary dense parameter rewriting; instead, \textscOPD retains important geometric signatures of on‑policy post‑training.
Authors:Achraf Hsain, Sultan Almuhammadi
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.
Authors:King Yeung Tsang, Zihao Zhao, Vishal Venkataramani, Haizhou Shi, Zixuan Ke, Semih Yavuz, Shafiq Joty, Hao Wang
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.
Authors:Baochang Ren, Xinjie Liu, Xi Chen, Yanshuo Liu, Chenxi Li, Daqi Gao, Zeqin Su, Jintao Xing, Zirui Xue, Rui Li, Xiangyu Zhao, Shuofei Qiao, Minting Pan, Wangmeng Zuo, Lei Bai, Dongzhan Zhou, Ningyu Zhang, Huajun Chen
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.
Authors:Yifan Hu, Hongzhou Chen, Peiyuan Liu, Yiding Liu, Zewei Dong, Jiang-Ming Yang
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.
Authors:William Smits
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
Authors:Tianyi Ma, Yijun Ma, Zehong Wang, Weixiang Sun, Ziming Li, Connor R. Schmidt, Chuxu Zhang, Matthew J. Webber, Yanfang Ye
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.
Authors:Jianming Ma, Qiyue Yang, Yang Zhang, Liyun Yan, Zhanxiang Cao, Yazhou Zhang, Yue Gao
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.
Authors:Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen
Abstract:
Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self‑supervised classification‑based approaches. These methods apply transformations to normal training samples, training a classifier to recognize transformation‑specific patterns that help identify anomalies through increased classification errors. Despite their strong performance, a significant challenge is their lack of explainability, as they provide limited insight into the characteristics of flagged anomalies. To address this limitation, we propose ProtoX‑AD, a prototype‑based self‑explainable framework for self‑supervised TSAD. ProtoX‑AD learns transformation‑aware latent representations alongside interpretable prototypes, enabling both accurate anomaly detection and the identification of distinct anomalous profiles through prototype‑based explanations. Additionally, it allows for systematic analysis of how transformation design impacts detection performance and explainability. Experimental results on synthetic and real‑world datasets demonstrate that ProtoX‑AD achieves detection performance comparable to its black‑box counterparts while offering more consistent and semantically meaningful explanations than existing explainable baselines. Our code is publicly available at https://github.com/Aitorzan3/ProtoX‑AD.
Authors:Kirato Yoshihara
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.
Authors:Sihwa Lee, Janghwan Lee, Donghoon Yoo, Jae Gon Kim, Hanyul Ryu, Soojung Ryu, Jungwook Choi
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.
Authors:Bora Kargi, David Salinas
Abstract:
Evaluating new large language models typically requires costly human annotation campaigns at scale. LLM‑as‑a‑judge offers a cheaper alternative, but judge scores carry systematic errors ‑ such as position bias, self‑preference, or intransitivity ‑ that can strongly miscalibrate the resulting rankings. We quantify the resulting judge‑human disagreement at two complementary levels. At the local level, we estimate per‑battle uncertainty from the judge's own score differences by propagating calibrated win probabilities rather than hard labels into the Bradley‑Terry procedure. This alone provides a drastic improvement to Elo estimation accuracy, bringing LLM‑derived ratings within 17.9 Elo MAE of human‑derived ones when averaged over 55 held‑out models on LMArena. At the global level, we apply split conformal prediction to the residual gap between LLM‑derived and human‑derived Elo ratings across held‑out models, producing prediction intervals with distribution‑free marginal coverage guarantees that account for irreducible LLM‑human disagreement. Together, these two layers yield a low‑cost evaluation tool that provides developers with calibrated Elo estimates and honest uncertainty bounds, without access to large‑scale human annotations.To facilitate reproducibility, we release our code at https://github.com/kargibora/SoftElo .
Authors:Eshaan Tanwar, Pepa Atanasova
Abstract:
Reward models are a key component of reinforcement learning from human feedback (RLHF), aligning language models toward both helpful and harmless behaviour. However, the internal mechanisms underlying these objectives and their conflicts remain poorly understood. We study alignment tension in reward models trained under helpfulness‑only, harmlessness‑only, and mixed‑objective settings. We find that mixed‑objective models often underperform single‑objective models, indicating interference between objectives. Using activation‑based methods, we identify neurons associated with each objective and study their functional roles via targeted ablations. We find that these neurons causally support their corresponding objectives while often negatively affecting the opposing one. We find that a substantial proportion of neurons are shared between helpfulness and harmlessness, and that these shared neurons exert a disproportionate influence on model behaviour, contributing to alignment tension. Additionally, our results provide insights and mechanistic interpretation into how alignment objectives are represented in reward models and why multi‑objective alignment remains challenging, motivating future work on disentangled and controllable alignment methods.
Authors:Yujun Zhou, Kehan Guo, Haomin Zhuang, Xiangqi Wang, Yue Huang, Zhenwen Liang, Pin-Yu Chen, Tian Gao, Nuno Moniz, Nitesh V. Chawla, Xiangliang Zhang
Abstract:
Interactive LLM agents are becoming part of daily work, but they do not reliably become easier to work with over time: a correction remembered in one session may still be violated in the next. We study this gap between preference access and preference compliance. In tasks derived from anonymized real‑user friction cases, Mem0 memory still leaves 57.5% of applicable preference checks violated. We introduce Test‑time Rule Acquisition and Compiled Enforcement (TRACE), a drop‑in skill‑layer pipeline for coding‑agent runtimes that mines user corrections, rewrites them as atomic rules, and compiles them into runtime checks that must pass before an agent completes future tasks. Unlike runtime checks written ahead of time by developers, TRACE skills come from the user's own chat corrections. We evaluate TRACE with simulated user‑in‑the‑loop experiments on ClawArena coding‑agent tasks and MemoryArena‑derived memory‑intensive tasks. On ClawArena, TRACE reduces held‑out preference violation from 100.0% to 37.6% on in‑distribution tasks and from 100.0% to 2.0% on out‑of‑distribution tasks. On MemoryArena‑derived tasks, TRACE reduces in‑distribution violation from 100.0% to 60.5% while matching or exceeding the strongest memory baseline on task pass. These results suggest that compiling corrections into runtime enforcement can address a repeated‑friction failure mode that memory alone does not reliably solve, reducing the need for users to restate the same correction across future sessions. Experiment code is available at https://github.com/YujunZhou/TRACE_exp, and the deployable skill is available at https://github.com/YujunZhou/tellonce.
Authors:Lilan Peng, Yandi Liu, Qingren Yao, Chongshou Li, Tianrui Li
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.
Authors:Aryan Khurana, Aravind Ramana RN, Dhruv Kumar
Abstract:
Large language models are increasingly deployed in citation‑augmented settings, yet the effect of citation presence on model behavior independent of factual content remains poorly understood. We introduce AuthorityBench, a 220,564‑prompt multi‑domain benchmark that isolates how citation‑based authority signals influence epistemic behavior in LLMs. The benchmark uses a fully balanced 2x2 factorial design crossing claim veracity with citation veracity, the first to do so, across four domains (general knowledge, science, law, and medicine), with controlled variation over 40 prompt templates, four venue prestige tiers, and a country‑coded author name dataset. Evaluating seven models on 12 structured research questions, we find that citation presence, whether real or fabricated, consistently increases hallucination rates relative to a no‑citation baseline. The effect is strongest when fabricated citations accompany true claims, raising hallucination rates by 3 to 22 percentage points and reaching 35 to 77% in the general knowledge domain, while legal claims are comparatively robust and venue prestige and author demographics show negligible impact. All datasets and evaluation code are available at: https://github.com/floating‑reeds/AuthorityBench
Authors:Hongbo Wang
Abstract:
Scale buys interpolation; structure buys a certified horizon. A world model's average error says nothing about whether a particular prediction can be trusted, or for how long. For equivariant latent world models we give a computable, multi‑step certificate of the predictable horizon: T‑step rollout error is provably constant over each symmetry orbit (Theorem A) and stratified channel‑by‑channel by the predictor's Lyapunov spectrum, T_j(ε)～\log(1/ε)/λ_j. The horizon is two‑sided ‑‑ a matching lower bound makes approximate equivariance provably horizon‑limited ‑‑ and the certificate is exclusive to structure: orbit‑constant error characterizes equivariance, so no non‑equivariant model has it at any scale. Empirically, on 40‑D Lorenz‑96 only a \mathbbZ_N‑equivariant network recovers the full Lyapunov spectrum (R^2=0.98); dense and recurrent baselines fail. Because the spectrum is faithful, the certificate acts, a priori: under a fixed sensing budget a c×‑inflated certificate provably needs c× the budget, and the equivariant certificate meets a budget its inflated dense counterpart cannot ‑‑ with zero calibration data. The same read‑out, unchanged, audits public pretrained world models training‑free: TD‑MPC2 checkpoints land on the certificate's own scope taxonomy ‑‑ calibrated where strongly expansive (ratio 0.94‑1.02), optimistic where weakly expansive, correctly abstaining where contracting ‑‑ a map a deployed monitor replicates cell‑by‑cell, out‑of‑sample. Across the official 1M‑317M multitask ladder, calibration does not improve with parameters. On V‑JEPA 2‑AC (1B, real robot data) the measured cross‑check correctly overrides an over‑promising tangent spectrum ‑‑ the cross‑validated audit, not the raw number, is the deployable object. Scale buys interpolation, not a calibrated horizon.
Authors:Zhixiong Zhao, Zukang Xu, Zhixuan Chen, Xing Hu, Zhe Jiang, Dawei Yang
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>.
Authors:Xiang Li, Yixuan Zhou, Jingran Xie, Zhiyong Wu, Hui Wang
Abstract:
Neural speech codecs based on Vector‑Quantized VAEs (VQ‑VAEs) are core audio tokenizers for speech LLMs, yet their reconstruction fidelity is bottlenecked by quantization error. Modifying the quantizer or increasing model capacity are common fixes, but they complicate downstream language modeling. Our core idea is to align the decoder's internal feature manifolds when processing both the quantized tokens and their original continuous embeddings, using a lightweight feature‑mapping loss. This requires minimal training overhead and no inference‑time changes. Applied to XCodec2, self‑guidance improves all reconstruction metrics, achieving state‑of‑the‑art low‑bitrate performance. Notably, it enables a 4x codebook reduction without fidelity loss, which downstream TTS experiments show significantly improves LLM‑based synthesis by simplifying the token modeling space. Multiple statistical observations and visualizations corroborate the enhanced internal manifold alignment in the decoder. Extensive experiments confirm its generality across various inductive biases. Self‑guidance thus establishes an efficient, broadly applicable method for high‑fidelity neural audio coding.
Authors:Zehong Wang, Yijun Ma, Connor R. Schmidt, Tianyi Ma, Weixiang Sun, Ziming Li, Xiaoguang Guo, Chuxu Zhang, Matthew J. Webber, Yanfang Ye
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.
Authors:Jiahao Yang, Shuhai Zhang, Hailong Kang, Feng Liu, Qi Chen, Mingkui Tan
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.
Authors:Xinrui He, Qiyu Kang, Xuhao Li, Zheng-Jun Zha
Abstract:
Spiking Neural Networks (SNNs) are well‑regarded for their biological plausibility and energy efficiency in processing sequential data. However, dominant SNN architectures typically rely on first‑order Ordinary Differential Equations (ODEs) to govern neuronal state transitions. This first‑order assumption imposes a "memoryless" bottleneck, limiting the model's capacity to capture the complex, long‑range dependencies inherent in long‑sequence tasks. In this work, we propose LongSpike, a novel SNN framework that integrates fractional‑order State‑Space Modeling, or f‑SSM, from control theory into the spiking domain. By extending traditional integer‑order SSMs to the fractional‑calculus regime, LongSpike enables the hierarchical integration of neuronal dynamics with long‑memory kernels. To mitigate the computational overhead and parallelization challenges typically associated with fractional operators, we leverage a state‑space formulation that supports efficient, parallel training. Empirical evaluations on challenging benchmarks, including Long Range Arena (LRA), large‑scale WikiText‑103, and Speech Commands, demonstrate that LongSpike outperforms state‑of‑the‑art SNNs in accuracy while preserving sparse synaptic computation. The code is available at https://github.com/xinruihe389‑commits/LongSpike.
Authors:John Fields, K M Sajjadul Islam, Ruchitha Thota, Victor Chen, Praveen Madiraju
Abstract:
This study explores privacy‑preserving machine learning (PPML) techniques using the PySyft platform to enable collaborative prediction of student retention between institutions. We developed a remote data science (RDS) framework with a semi‑air‑gapped architecture consisting of high‑side and low‑side servers, allowing researchers from three universities to build predictive models on sensitive student data without direct data access. Using historical data from a small private university (N=720), we evaluated three synthetic data generation approaches and validated the framework through inter‑institutional collaboration. The results demonstrate consistent classification performance across institutions (Macro F1: 0.690‑‑0.695) while maintaining strict Family Educational Rights and Privacy Act (FERPA) compliance. We also propose Data‑Type‑Aware Templates, a novel synthetic data method that prioritizes privacy over distributional fidelity. Our findings confirm that RDS‑based PPML is technically feasible for educational settings and offers a practical alternative to federated learning for small‑scale inter‑institutional collaborations. The code is available at https://github.com/jtfields/NAIRR240195‑Privacy‑Preserving‑Machine‑Learning.
Authors:He Li, Haoang Chi, Qizhou Wang, Yunxin Mao, Zhiheng Zhang, Jie Tan, Tongliang Liu, Wenjing Yang, Bo Han
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.
Authors:Yijun Ma, Zehong Wang, Yiyang Li, Ziming Li, Xiaoguang Guo, Weixiang Sun, Chuxu Zhang, Yanfang Ye
Abstract:
Self‑evolving agents are expected to improve through interaction without external supervision, but this remains difficult in partially observable environments where agents must explore actively, learn from limited feedback, and decide when to trust prior experience. Existing LLM‑agent methods often rely on memory or planning modules, yet they rarely close the loop between them to continually refine an internal understanding of environment dynamics. We introduce ProPlay, a procedural world model that supports procedure‑level preplay, where agents can rehearse future procedural paths using the learned world knowledge. Rather than representing experience as isolated rules or low‑level action constraints, ProPlay abstracts successful trajectories into procedures and organizes them in a procedure graph that captures causal transitions among task stages. Each transition is associated with a reliability record embedding to estimate its task‑specific contribution from past outcomes. Before each episode, ProPlay simulates future procedural trajectories over known graph structures as structured soft guidance; after execution, it refines the graph using environment feedback. Experiments on public benchmarks show that ProPlay consistently improves environment understanding and self‑evolution capability over strong baselines. Our code has been released in https://github.com/antman9914/proplay.
Authors:Tianyu Liu, Allen Xin Wang, Antonia Panescu, Lisa Xinyi Chen, Wenxin Long, Xinyu Wei, Yueqian Jing, Ziyao Zeng, Jihang Chen, Sihan Jiang, Ziqing Wang, Siyi Gu, Siyu Chen, Xinyang Hu, Haoran Shao, Leqi Xu, Wangjie Zheng, Zhiyuan Cao, Ada Fang, Botao Yu, Kunyang Sun, Rex Ying, Arman Cohan, Qingyu Chen, Lingzhou Xue, Kaize Ding, Yuanqi Du, Wengong Jin, Zhuoran Yang, Marinka Zitnik, James Zou, Hua Xu, Hongyu Zhao
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/.
Authors:Weijie Chen, Alan B. McMillan
Abstract:
Federated learning (FL) enables collaborative model training without sharing raw patient data, but standard approaches such as FedAvg treat each client as a black box and provide no mechanism for isolating an adversarial contributor, auditing per‑client influence, or honoring a departed participant's right to be forgotten. We present Fed‑FBD (Federated Functional Block Diversification), a modular federated architecture that decomposes a ResNet backbone into six functional blocks (the stem, four residual groups, and the classification head) and maintains a warehouse of N color variants, each assembled from independently tracked and contributor‑stamped blocks. Fed‑FBD provides three capabilities absent in FedAvg: (i) architecturally guaranteed block‑level isolation, so that an adversarial or mislabelled client cannot contaminate the clean colous; (ii) privacy‑by‑design, where membership inference advantage is already indistinguishable from chance before any privacy mechanism is applied; and (iii) surgical machine unlearning of a departed participant's contribution at sub‑second cost and without retraining. Experiments on six MedMNIST‑2D datasets, PathMNIST at 224x224, and CIFAR‑10 show that Fed‑FBD trades a modest 0.3%‑3.1% IID accuracy gap on the adequately sized datasets for these guarantees, remains within 0.8%‑4.0% of FedAvg at Dirichlet alpha=1.0 on three of four datasets, and confines all six adversarial attacks we study to the poisoned client's own blocks with at most +/‑0.01 AUC drift on the clean colors.
Authors:Jiangtao Kong, Peijun Zhao, Chun-Fu Chen, Youngwook Do, Shaohan Hu, Tianyi Zhou, Huajie Shao
Abstract:
Incremental Learning (IL) for Open‑ended Image‑to‑Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preserving previously acquired knowledge. Unlike prior studies, this paper addresses a more practical scenario in which the predominant category of visual data shifts over time as environments evolve. In this context, we introduce a new notion of continual alignment, which incrementally adapts the alignment module within pre‑trained VLMs to preserve high‑quality cross‑modal representations. Based on this idea, we propose Efficient Continual Alignment (ECA), a novel exemplar‑free IL approach for OpenITG. The key challenge is enabling the model to acquire new, task‑specific features while minimizing interference with the established alignment without accessing raw data from previous tasks. To address this, ECA employs three core mechanisms: a Mixture of Query (MoQ) module that adapts task‑specific query tokens, a Fisher Dynamic Expansion (FeDEx) that dynamically expands model structure based on a Fisher Information Matrix (FIM)‑based metric, and an embedding dictionary with Dictionary Replay (DR) to retain past knowledge. To evaluate ECA's performance, we construct four new IL OpenITG benchmarks that better reflect real‑world scenarios. Experimental results demonstrate that ECA significantly mitigates catastrophic forgetting and improves IL performance compared to baseline methods. Code and benchmarks are available at https://github.com/Snowball0823/ECA.
Authors:Arthur Bigot, Harmon Bhasin, Core Francisco Park, Eugene Shakhnovich, Dianzhuo Wang
Abstract:
Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case study across ESM model families, we identify a dominant nativeness axis in embedding space, aligned with masked reconstruction perplexity, that orders sequences from well‑modeled cellular proteins through viral proteins to shuffled and random sequences. Scaling contracts this axis unevenly across viral families. Despite this, protein language model embeddings retain viral‑specific signal: viral proteins remain linearly separable beyond zero‑shot perplexity and shallow sequence features. Together, these results suggest that pLM representations are structured by a general notion of nativeness while preserving information specific to distinct biological groups.
Authors:Egor Cherepanov, Nikita Kachaev, Daniil Zelezetsky, Aydar Bulatov, Artem Pshenitsyn, Yuri Kuratov, Alexey Skrynnik, Aleksandr I. Panov, Alexey K. Kovalev
Abstract:
Vision‑language‑action (VLA) models predict chunks of future actions from the current observation, an assumption that fails under partial observability, where decisions depend on information no longer visible. Existing memory‑augmented VLAs simultaneously introduce recurrence, retrieval, compression modules, auxiliary objectives, hierarchical memory, or task‑specific architectural changes, so the contribution of recurrence itself remains entangled with surrounding machinery. We present a controlled isolation study of recurrence in a strong pretrained VLA backbone. Our formulation augments the transformer with a small set of learnable memory tokens carried across timesteps and updated through self‑attention, trained end to end with truncated backpropagation through time, with no auxiliary losses and no architectural changes. We instantiate this as μVLA, a family of OpenVLA‑OFT variants parameterized by memory width m, TBPTT length K, and the memory update rule (cross‑step gradients or a detached EMA), so that recurrence is the only varying factor. On MIKASA‑Robo, μVLA improves average success rate on five training tasks from 0.42 to 0.84 at the strongest setting and reaches 0.23 on held‑out tasks with the same memory structure versus 0.07 for the memoryless baseline. On tasks requiring different memory structure, performance remains near baseline. On LIBERO, the strongest recurrent variant achieves 96.2% average success, indicating no regression under full observability. We interpret these results as a calibration of the capability envelope of minimal in‑backbone recurrence, identifying the regime in which it is sufficient and the regime where additional memory structure is required. Demos and videos can be found in https://avanturist322.github.io/mu‑vla/.
Authors:Guangyu Wang, Zhaonan Wang
Abstract:
Reducing real‑world trial and error has long been a central goal of decision making, and generative simulators advance this goal by modeling the evolution of future states. An even more challenging yet meaningful task is simulating how disturbance events (e.g., accidents) propagate their impacts across real‑world networks. The existing approaches fall short of modeling both structured attributes and unstructured semantics of events, and capturing topological structures in simulating network event evolution. Therefore, we are motivated to propose Net‑Ev^2 (\underlineNetwork \underlineEvent \underlineEvolution), a novel generative simulator that jointly leverages event cues while preserving network topology in simulations. Specifically, the framework consists of two stages, namely structure‑guided masked pre‑training and topology‑aware diffusion process, which is achieved by U‑Net‑like graph downsampling and upsampling during denoising. At inference time, Net‑Ev^2 can generate simulations using natural‑language event input only, with greater flexibility for practical usage. Furthermore, we introduce Net‑Ev^2‑6.5M, a multimodal benchmark of aligned event and network traffic data across four large‑scale road networks, as well as a new topology‑aware metric, namely JL‑MMD, to evaluate topological fidelity in generated network dynamics. Extensive experiments demonstrate the state‑of‑the‑art performance and strong generalization ability of Net‑Ev^2. Code is made available at https://github.com/Guangyu4/Net‑Ev‑2.
Authors:Niccolò Biondi, Federico Pernici, Simone Ricci, Alberto Del Bimbo
Abstract:
Learning compatible representations aims to learn feature representations that can be used interchangeably over time whenever a model undergoes updates. In this paper, we demonstrate that stationary representations learned by d‑Simplex fixed classifiers imply compatibility as in its formal definition. This result establishes a foundation for future works and can be directly exploited in practical learning scenarios. We address the challenge of learning compatibility using d‑Simplex fixed classifiers when the model is sequentially fine‑tuned. Learning according to a d‑Simplex fixed classifier with the cross‑entropy loss aligns feature distributions at the first‑order statistics. Consequently, it may not fully capture higher‑order dependencies in the representation between model updates. To address this issue, we demonstrate that training the model using a d‑Simplex fixed classifier through a convex combination of the cross‑entropy loss and a contrastive loss not only captures higher‑order dependencies, but is also equivalent to learning with the cross‑entropy under the compatibility constraints. We confirm our findings with extensive experiments also considering a new scenario where a pre‑trained model is sequentially fine‑tuned and occasionally replaced with an improved model. We show that stationary representations enable uninterrupted retrieval services (without reprocessing gallery images) while improving performance during model updates and replacements, achieving state‑of‑the‑art. Code at https://github.com/miccunifi/iamcl2r.
Authors:Longkun Hao, Hongyu Lin, Hao Li, Zhichao Yang, Haojie Hao, Dongshuo Huang, Haitao Yang, Hongyu Ge, Ming jie Xie, Yanjun Wu, Zi Hao Yin, Yan Bai, Yihang Lou
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.
Authors:Ashutosh Hathidara, Sai Shruthi Sistla, Sebastian Schreiber, Sahil Bansal
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.
Authors:Haoyuan Deng, Yitong Gao, Yudong Lin, Haichao Liu, Zhenyu Wu, Ziwei Wang
Abstract:
Human‑in‑the‑loop reinforcement learning (HiL‑RL) has emerged as an effective paradigm for real‑world robotic manipulation, enabling online policy improvement with human guidance. However, current HiL‑RL frameworks remain intervention‑intensive, relying on frequent human corrections to redirect the policy out of unproductive exploration, which incurs high labor cost and limits real‑world scalability. To address this, we propose UniIntervene, an agentic intervention model that detects unproductive exploration and autonomously recovers the policy toward high‑value states, taking over the bulk of interventions from human operators. Specifically, UniIntervene first performs future‑conditioned action‑value estimation, predicting the latent consequence of the current action and evaluating its induced value, which provides a more stable progress signal. Building on this, a temporal value‑risk critic aggregates recent value dynamics and triggers intervention when the estimated value exhibits sustained stagnation or degradation. When intervention is required, UniIntervene retrieves a high‑value recovery target from a memory of past intervention episodes and produces executable corrective actions through a goal‑conditioned recovery policy. In this way, UniIntervene turns intervention from passive human correction into a value‑aware recovery process for efficient real‑world RL. Extensive experiments on diverse real‑world manipulation tasks demonstrate that UniIntervene improves the average success rate by 8.6% while reducing human interventions by 57% relative to state‑of‑the‑art HiL‑RL baselines.
Authors:Mengyu Zheng, Kai Han, Boxun Li, Haiyang Xu, Yuchuan Tian, Wei He, Hang Zhou, Jianyuan Guo, Hailin Hu, Lin Ma, Chao Xu, Guohao Dai, Lixue Xia, Yunchao Wei, Yunhe Wang, Yu Wang
Abstract:
General‑purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE‑bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring. We introduce Claw‑SWE‑Bench, a multilingual SWE‑bench‑style benchmark and adapter protocol that makes heterogeneous agent harnesses, or claws, comparable under fair settings including a fixed prompt, runtime budget, workspace contract, patch extraction procedure, and evaluator. The full benchmark contains 350 GitHub issue‑resolution instances across 8 languages and 43 repositories, drawn from SWE‑bench‑Multilingual and SWE‑bench‑Verified‑Mini after future‑commit cleanup. We also release Claw‑SWE‑Bench Lite for faster validation, which is an 80‑instance subset selected by a cost‑aware, rank‑aware procedure over 17 calibration columns. On the full benchmark, OpenClaw with a minimal direct‑diff adapter scores only 19.1% Pass@1, whereas the full adapter reaches 73.4% with the same GLM 5.1 backbone, showing that adapter design is essential for enabling OpenClaw‑style harnesses to perform coding tasks effectively. Across an OpenClaw × nine‑model sweep and a five‑claw × two‑model sweep, model choice changes Pass@1 by 29.4 pp and harness choice by 27.4 pp under fixed models; systems with similar accuracy can differ substantially in total API cost. Claw‑SWE‑Bench therefore treats harness and cost accounting as first‑class axes of SWE‑style coding‑agent evaluation, providing both a full benchmark and a low‑cost reference set for reproducible comparison. The data is available at https://github.com/opensquilla/claw‑swe‑bench and https://huggingface.co/datasets/TokenRhythm/Claw‑SWE‑Bench.
Authors:Balázs Gyenes, Emiliyan Gospodinov, Jan Frieling, Enrico Krohmer, Nicolas Schreiber, Xiaogang Jia, Niklas Freymuth, Gerhard Neumann
Abstract:
High‑precision robotic manipulation requires fine‑grained spatial reasoning that is often difficult to achieve with RGB‑only policies due to depth ambiguity and perspective scale issues. Policies that leverage 3D information directly, such as those based on point clouds, offer a stronger geometric prior over purely image‑based ones, yet their performance remains highly task‑dependent. We hypothesize that this discrepancy may be due to the spectral bias of neural networks towards learning low frequency functions, which especially affects architectures conditioned on slow‑moving Cartesian features. We thus propose to map point clouds from Cartesian space into high‑dimensional Fourier space, effectively equipping the point cloud encoder with direct access to high‑frequency features. We experimentally validate the use of Fourier features on challenging manipulation tasks from the RoboCasa and ManiSkill3 benchmarks and on a real robot setup. Despite their simplicity, we find that Fourier features provide significant benefits across diverse encoder architectures and benchmarks and are robust across hyperparameters. Our results indicate that Fourier features let policies leverage geometric details more effectively than Cartesian features, showing their potential as a general‑purpose tool for point cloud‑based imitation learning. We provide source code and videos on our project page: https://fourier‑il.github.io/fourier‑il
Authors:Dmitrii Gavrilev
Abstract:
Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable‑length performances in the latent space of a pretrained Music2Latent model. We synthesize MIDI scores into deadpan audio and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated in DiT blocks, allowing for a simple and effective learning of the dependencies between the score and performances. Audio samples are available at our demo page: https://realfolkcode.github.io/pianokontext_demo/.
Authors:Jinyuan Zu, Xiaowei Lv, Yongcai Wang, Deying Li, Yunjun Han, Wenping Chen, Fengyi Zhang, Naiqi Wu
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.
Authors:Pankhuri Vanjani, Zhuoyue Li, Jakub Suliga, Moritz Reuss, Gianluca Geraci, Xinkai Jiang, Rudolf Lioutikov
Abstract:
Vision‑language‑action (VLA) models inherit a shared synchronous clock from vision‑language pretraining, processing every input at one rate. This is misaligned with physical interaction, where a high‑frequency modality changes at hundreds of hertz, vision evolves more slowly, and language stays constant across an episode. A synchronous VLA oversamples slow modalities, undersamples fast ones, and caps action generation at the lowest effective frequency. We hypothesize that decoupling temporal processing per modality, letting each update and retain information at its own sensor rate, yields stronger representations and more robust control. We present DAM‑VLA, which maintains per‑modality latent buffers refreshed at sensor rates and read continuously by the action head, integrating new high‑frequency modalities through gated cross‑attention that leaves the pretrained backbone intact. Across seven contact‑rich real‑world manipulation tasks, DAM‑VLA more than doubles the average success rate of the strongest synchronous baseline (95.2% vs.\ 40.95%) while sustaining smooth, reactive 100\,Hz control. Project website: \hrefhttps://intuitive‑robots.github.io/DAM‑VLA/intuitive‑robots.github.io/DAM‑VLA/
Authors:Mostafa Bamdad, Mohammad Sadegh Eshaghi, Timon Rabczuk
Abstract:
Neural operators provide a powerful framework for learning solution mappings of partial differential equations directly in function space. However, many existing architectures still struggle to represent nonlinear time‑dependent systems that involve multi‑scale structures, long‑range interactions, and stable long‑time evolution. In this work, we introduce the Hierarchical Adaptive Multi‑scale Neural Operator (HAMNO), a neural‑operator architecture that combines local convolutional representations, global spectral operators, and hierarchical encoder‑decoder processing. The central component of HAMNO is a data‑dependent gating mechanism that adaptively balances local and global information at each spatial location, allowing the model to resolve fine‑scale features while preserving long‑range dependencies. We further develop a physics‑informed extension, PI‑HAMNO, based on a multi‑objective loss strategy that combines data fitting with strong‑ and weak‑form physics constraints. The strong‑form term penalizes the domain‑integrated squared PDE residual in physical coordinates, while the weak‑form term is constructed by multiplying the governing residual by finite‑element test functions and evaluating the resulting element integrals using centroid‑based tetrahedral quadrature. The framework is evaluated on non‑periodic Allen‑Cahn (AC), Cahn‑Hilliard (CH), and Swift‑Hohenberg (SH) equations defined on cubic domains. Across long‑horizon rollout, data‑limited training, out‑of‑distribution initial‑condition shifts, and random‑seed variations, HAMNO improves predictive accuracy over standard neural‑operator baselines, while PI‑HAMNO further enhances stability, physical consistency, and data efficiency. The implementation is publicly available at https://github.com/MBamdad/HAMNO .
Authors:Jun Wen Leong
Abstract:
We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution. Upon detection, a conformal abstention layer adapts decision thresholds to recover a target error rate epsilon=0.1. In a pre‑registered factorial evaluation (4 classifiers x 5 shift conditions x 20 seeds x 2 window sizes, 800 cells), the system achieves 86.6% valid detection (693/800, 95% CI [84.1%, 88.8%]) with mean latency of 39.5 steps. Detection holds across three ground‑truth regimes: synthetic onset (86.6%), real temporal jailbreaks (85%, 17/20), and GCG adversarial attacks. Weighted conformal prediction recovers up to 39 pp of lost coverage for DeBERTa (ESS=46/300) but collapses for all other classifiers (ESS~300): logistic density ratio estimation achieves perfect source/target separability in high‑dimensional embedding spaces, clipping all importance weights to the floor. DeBERTa shows a gradient from effective correction (paraphrase, ESS=46) to near‑total collapse (adversarial suffix, ESS=206). PCA to 32 dimensions breaks the collapse, recovering 33 pp for Llama Guard and 21 pp for ShieldGemma. Variance decomposition reveals classifier (eta^2=0.243), shift type (eta^2=0.237), and their interaction (eta^2=0.185) all contribute substantially to detection latency variance (all p<0.001), indicating per‑classifier monitoring profiles are necessary.
Authors:Zsolt Robotka, Ádám Rák, Jalal Al-Afandi, András Horváth, György Cserey
Abstract:
Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non‑signing communities. While large weakly‑aligned datasets have enabled pre‑training at scale and gloss‑free methods have reduced reliance on expert annotation, high‑quality parallel sign video‑text pairs for fine‑tuning remain scarce, limiting generalisation on long‑tail vocabulary and unseen constructions. We propose a corpus augmentation approach that requires no additional human annotation, external sign‑language video corpora, or generative video models, relying only on the existing gloss‑annotated training corpus and an LLM for sentence generation: per‑gloss clips are extracted from training videos via CTC forced‑alignment, novel gloss‑sentence pairs are generated by a corpus‑anchored LLM, and synthetic sequences are assembled through random sentence sampling and clip assignment. The resulting synthetic RGB video‑text pairs are architecture‑agnostic at the downstream training stage and can be consumed directly by RGB‑based SLT models, or converted into pose or feature representations by pipelines that derive such inputs from video. Sincan et al. re‑evaluated five recent gloss‑free methods under strictly identical conditions; the largest verified gain over the GFSLT‑VLP baseline was only 0.98 BLEU‑4. Our augmentation, applied within the same framework, achieves +2.92 BLEU‑4 without any change to architecture or training protocol. We further identify that synthetic data harms vision‑language pretraining despite improving its objectives, and that optimising clip transitions for visual smoothness is counter‑productive under L2‑based criteria; we propose that abrupt boundaries may act as a form of implicit regularisation. Code is available at https://github.com/robizso/slt‑datagen.
Authors:Hengyi Feng, Zeang Sheng, Meiyi Qiang, Li Yang, Wentao Zhang
Abstract:
Research on Text‑Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real‑world data scenarios, such as citation networks, e‑commerce platforms, social media, and web pages. Inspired by the remarkable semantic understanding ability of Large Language Models (LLMs), there have been numerous attempts to integrate LLMs into TAGs. However, existing methods still struggle to generalize across diverse graphs and tasks, and their ability to capture transferable graph structural patterns remains limited. To address this, we introduce the GraspLLM, a framework that combines Graph structural comprehension with semantic understanding prowess of LLMs to enhance the cross‑dataset and cross‑task generalizability. Specifically, we represent node texts from different graphs in a unified semantic space with a frozen general embedding model, on top of which we perform motif‑aware contrastive learning across multiple motif‑induced adjacency matrices to extract dataset‑agnostic structural information. Then, with our proposed optimal contextual subgraph, we extract the most contextually relevant subgraph for each target node and align these subgraphs to the token space of LLM via an alignment projector. Extensive experiments on TAG benchmark datasets spanning diverse domains reveal that GraspLLM consistently outperforms previous LLM‑based methods for TAGs, especially in zero‑shot scenarios, highlighting its strong generalizability across different datasets and tasks. Our code is available at https://github.com/Heinz217/GraspLLM.
Authors:Aarchi Singh Thakur, Abhijoy Sarkar
Abstract:
Circulating‑tumour DNA (ctDNA) carries evidence of drug resistance months before imaging shows it, but the earliest evidence lives below the assay's limit of detection (LoD): a nascent subclone is detected only intermittently, producing a flickering sequence of faint detects and non‑detects. Commercial liquid biopsies treat each draw as an independent snapshot and a non‑detect as nothing. We argue a non‑detect is a left‑censored observation, and the pattern of non‑detects and faint detects over time carries actionable evidence of growth before any single value is trustworthy. We introduce Span, a censored‑Poisson Bayesian latent‑growth change‑point detector that models the binary detection process, accumulates a sequential generalised‑likelihood‑ratio statistic for an upward change‑point in the per‑variant detection rate, and raises a competing‑risks alarm with calibrated false‑alarm control. Span has no learned weights, so there is nothing to overfit. On a synthetic cohort of HR+/HER2‑ metastatic breast cancer on first‑line CDK4/6‑inhibitor plus endocrine therapy, at a matched 10% false‑alarm rate, Span roughly doubles the fraction of impending progressions caught three months ahead (indolent regime: 25% vs 11% for the snapshot), with a falsifiable dose‑response: large for indolent emergence, vanishing for fast emergence. A value‑trajectory baseline performs identically to the snapshot, isolating the gain to the censored detection model. The survival backbone matches a Cox baseline on real breast‑cancer data (GBSG‑2, n=686; C‑index 0.67 vs 0.68), and on a real longitudinal cohort with clean biomarkers (PBC2, n=312) the same pipeline correctly declines to win, a falsifiable boundary test confirming the mechanism is regime‑specific. All ctDNA trajectories are synthetic.
Authors:Dongxin Lyu, Jingbo Zhou, Hongxin Xiang, Yuqiang Li, Jun Xia
Abstract:
De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases. While recent Transformer‑based encoder‑decoder models have achieved remarkable performance, we uncover a critical pathology in their inference dynamics. Through comprehensive feature scaling experiments, we demonstrate that existing auto‑regressive peptide decoders tend to over‑rely on generated‑sequence priors while progressively under‑utilizing fine‑grained physical evidence from the input mass spectrum. This phenomenon leads to suboptimal results, where generated peptide sequences are biologically plausible yet not faithful to the input spectrum. To rectify this, we propose MemNovo, a training‑free and plug‑and‑play mechanism that re‑balances peptide and spectral contributions at inference time. MemNovo alleviates the information bottleneck by establishing a persistent spectral memory bank and injecting retrieved features directly into the final decoding stage via an ultra‑conservative residual connection. Theoretical analysis confirms that this mechanism restores the mutual information between the decoder state and the raw spectrum. Extensive experiments on the Nine Species benchmark with two representative baselines, Casanovo and InstaNovo, demonstrate that MemNovo consistently improves both amino acid precision and peptide precision, achieving up to 39.1% relative improvement in peptide precision for Casanovo and up to 3.9% for InstaNovo, with negligible computational overhead.
Authors:Sam Gijsen, Michał Łukomski, Marc-André Schulz, Kerstin Ritter
Abstract:
Flow matching and diffusion models enable conditional generation across domains ranging from images to proteins, with recent extensions to out‑of‑distribution contexts. Yet generative models of neural time series have largely remained restricted to categorical conditioning, precluding compositional and zero‑shot generalization. In this work, we propose a per‑timestep conditioned diffusion transformer for generating realistic fMRI brain dynamics during unseen cognitive tasks by injecting both compositional language and optional spatial priors in‑context. Such zero‑shot generation could enable counterfactual neuroscience by supporting in‑silico design and evaluation of novel cognitive experiments before empirical validation. Leveraging this model, we evaluate across hundreds of held‑out task conditions and characterize predictive performance in relation to the training manifold. From language alone, the model recovers region‑specific recruitment across tasks and held‑out spatial activation patterns. Spatial priors, when available, complement the text pathway by anchoring generation in regions of task space where language alone degrades, while retaining the compositional structure needed for counterfactual task specification. To our knowledge this is the first generative model of whole‑cortex fMRI dynamics for unseen cognitive tasks, advancing counterfactual neuroscience and data‑driven experimental design.
Authors:Qi Shao, Hao Guo, Jiawen Chen, Duxin Chen, Wenwu Yu
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.
Authors:Maximilian Poretschkin, Tabea Naeven
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.
Authors:Atif Hassan, Swanand Khare, Jiaul H. Paik
Abstract:
Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training. However, existing methods often struggle to maintain strong worst‑group accuracy, particularly at high pruning rates, across balanced and imbalanced datasets. To address this challenge, we propose RCAP, a Robust, Class‑Aware, Probabilistic dynamic dataset pruning algorithm for classification tasks. RCAP applies a closed‑form solution to estimate the fraction of samples to be included in the training subset for each individual class. This fraction is adaptively adjusted in every epoch using class‑wise aggregated loss. Thereafter, it employs an adaptive sampling strategy that prioritizes samples having high loss for populating the class‑wise subsets. We evaluate RCAP on six diverse datasets ranging from class‑balanced to highly imbalanced using five distinct models across three training paradigms: training from scratch, transfer learning, and fine‑tuning. Our approach consistently outperforms state‑of‑the‑art dataset pruning methods, achieving superior worst‑group accuracy at all pruning rates. Remarkably, with only 10% data, RCAP delivers >1% improvement in performance on class‑imbalanced datasets compared to full data training while providing an average 8.69× speedup. The code can be accessed at https://github.com/atif‑hassan/RCAP‑dynamic‑dataset‑pruning
Authors:Youwang Deng
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.
Authors:Marius Bayizere
Abstract:
Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield‑AI, a unified open framework integrating six processing layers: RF signal classification, acoustic motor‑signature detection, YOLOv8‑based visual detection, evidence‑weighted sensor fusion, a Behavioral Intent Classification Engine (BICE), and a Graph Neural Network Swarm Intelligence Module (GNN‑SIM). BICE introduces the first systematic six‑class threat taxonomy for drone flight patterns, enabling predictive operator alerts with a 30‑second advance‑warning horizon. GNN‑SIM is the first open framework for adversarial multi‑drone formation analysis using Graph Attention Networks. Evaluated on three publicly available real‑world datasets, the fused pipeline achieves 96.1% detection accuracy, 3.2% false alarm rate, AUC‑ROC: 0.981, and 142ms end‑to‑end latency on commodity CPU‑class hardware at approximately 500‑780 USD total system cost. All code, model weights, and simulation datasets are publicly released at submission.
Authors:Lucia Quirke, Louis Jaburi, David Johnston, William Z. Li, Gonçalo Paulo, Guillaume Martres, Girish Gupta, Stella Biderman, Nora Belrose
Abstract:
Data attribution is a promising field in interpretability that aims to explain model behavior through the influence of its training data, with applications including debugging undesirable model behavior and training dataset curation. However, significant engineering effort is required to perform it at scale, and many cutting edge techniques lack open‑source tooling and support. Bergson is an open source library that aims to enable faster progress in the field by providing a host of techniques that scale to very large language models and pre‑training datasets. The library natively supports on‑disk gradient stores and multi‑node distributed training, and provides quality of life tools for researchers. Finally, we introduce the first open‑source implementations of three leading data attribution methods: MAGIC, SOURCE, and TrackStar. The library is available at https://github.com/EleutherAI/bergson .
Authors:Kanghui Ning, Yushan Jiang, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
Abstract:
Time‑series foundation models (TSFMs) are increasingly explored as predictive experts within emerging agentic time‑series systems. However, TSFMs exhibit heterogeneous inductive biases, and no single model consistently dominates across forecasting regimes, making expert selection a critical challenge. Existing systems often delegate this decision to LLM‑based controllers, incurring substantial inference overhead. We present TimeRouter, an efficient routing framework that leverages empirical complementarity across a pool of pretrained TSFMs through lightweight discriminative routing, selective gating, and ensemble fallback. Concretely, TimeRouter combines a learned routing head, a selective gate, and an ensemble fallback, enabling adaptive expert selection without invoking an LLM at inference time. TimeRouter achieves state‑of‑the‑art performance on the GIFT‑EVAL leaderboard, with an LB MASE of 0.6765. Beyond benchmark performance, our ablation studies provide empirical insights into TSFM routing design, highlighting the importance of pool composition and selective gating. Taken together, these results position TimeRouter as a modular and lightweight routing layer for future agentic time‑series systems built upon foundation‑model pools. Our code is available at https://github.com/UConn‑DSIS/TimeRouter.
Authors:Jiale Deng, Yanyan Shen, Xiaogang Shi, Chai Junjun
Abstract:
High‑quality training data is essential for the success of machine learning models. However, real‑world datasets often contain mixed types of errors arising from systematic flaws in data preparation pipelines, including label errors, feature errors, and spurious correlations. Effective debugging of training data requires both detecting erroneous samples and identifying their specific error types to enable targeted repair, yet existing data cleaning and attribution methods fail to adequately address this dual requirement. In this paper, we propose DeMix, a novel framework that simultaneously diagnoses erroneous samples and their error types. Our key insight is that different error types produce distinct patterns on model behavior. DeMix captures such error‑specific patterns by influence vectors that characterize how each training sample affects model predictions across all validation samples. We formulate training data debugging as a multi‑label classification problem where a classifier is developed to predict error types directly from influence vectors. We further introduce an intervention‑based learning strategy that guides the classifier to capture invariant rationales specific to each error type, ensuring the learned classifier generalizes effectively. Empirical evaluations on 11 tasks across tabular data prediction, recommendation systems, and LLM alignment demonstrate that DeMix significantly outperforms state‑of‑the‑art approaches, achieving a 22.61% improvement in data debugging F1‑score and a 9.32% gain in task model performance after data repair. Code is available at: https://github.com/SJTU‑DMTai/DeMix.
Authors:Zequn Yang, Yake Wei, Haotian Ni, Zhihao Xu, Di Hu
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.
Authors:Zhuoyi Peng, Hanlin Gu, Lixin Fan, Yi Yang
Abstract:
Text‑attributed graphs (TAGs) underlie real‑world applications such as citation networks, social media, and e‑commerce. Few‑shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the graph unannotated, neither GNNs nor LLMs can learn well on their own. GNNs read topology and fail on cold nodes; LLMs read text and fail on text‑ambiguous nodes. Existing LLM‑GNN methods all follow the same recipe: designate one model as the golden teacher and use its outputs (e.g., features or pseudo‑labels) to supervise the other. We argue this golden‑teacher assumption breaks under sparse supervision: neither model is golden, and treating either as such transfers its blind spots into the student. We therefore ask: can we avoid designating either model as the golden teacher, and still perform effective graph learning? We answer with LLM‑GNN Co‑Teaching, a bidirectional co‑teaching framework in which neither model is fixed as teacher. The GNN and LLM exchange their most confident pseudo‑labels under an architecture‑specific small‑loss criterion, and both update every round. Supervision is then mined from the trajectory: whenever a node moves from cross‑model contradiction at round t to cross‑model agreement at round t+1, the LLM's two answers on the same input form a preference pair (old contradicting self < new peer‑endorsed self) for DPO training. We call this Round‑based Pseudo‑Label Preference Optimization (RPL‑PO). On six benchmarks, LLM‑GNN Co‑Teaching consistently outperforms GNN‑as‑Judge and all prior methods, with absolute 3‑shot gains of 7.86% on Cora and 7.73% on ogbn‑arxiv; improvements carry over to 5‑shot and to zero‑shot cross‑dataset transfer. Error‑structure analysis further shows that abandoning the golden‑teacher assumption substantially improves the LLM's graph learning capability on challenging samples.
Authors:Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu, Lixin Fan, Yi Yang
Abstract:
Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood. We introduce GraphInfer‑Bench, a benchmark for whether LLMs can perform this graph inference: producing an open‑ended answer that no single node supports and no path retrieves. Existing graph‑QA protocols cannot test this capability: algorithm simulation, node classification, single‑node description, KG‑QA, and GraphRAG all admit answers retrievable from one node or along a path. GraphInfer‑Bench defines five tasks along Description (what a region is) and Comparison (how regions differ), each constructed so the ground truth lives in no single node. The release contains 42,000 samples across six real‑world graphs, produced automatically and screened by a four‑layer quality‑control protocol. We evaluate four method families against the same tasks: graph‑token alignment models, zero‑shot frontier closed‑source LLMs, Graph2Text supervised fine‑tuning, and plain GNNs as a structural reference. No method family closes the gap. Graph‑token alignment partially handles description tasks (relational, theme) but collapses on comparison tasks. Frontier LLMs lead on outlier detection and community partition among LLM‑based methods but lag on masked‑node prediction. Graph2Text SFT is the strongest LLM‑based method on the description side yet falls behind frontier LLMs on comparison. Across every task, plain GNNs match or beat the strongest LLM‑based row, with the largest margin on community detection. GraphInfer‑Bench surfaces graph inference as an open capability gap rather than a property of any one architecture.
Authors:Hongyi Liu, Frederic Sala, Thomas Reps, Adithya Murali
Abstract:
LLMs and LLM agents should improve when given feedback, but identifying when they are able to do so is difficult: feedback is heterogeneous, domain‑specific, and difficult to control. We approach this challenge by asking LLMs to perform regular‑expression induction, a classical symbolic learning problem where precise mechanisms for feedback exist in the form of counterexamples. In counterexample‑guided learning, a learner (LLM) proposes candidate regular expressions from positive/negative‑labeled strings, and the teacher (verifier) returns counterexamples showcasing the difference between the candidate and target languages. We identify novel counterexample‑guided refinement strategies that enable effective regex learning, such as regularization and symbolic counterexample clusters. We also explore agentic strategies such as reflection and repair loops. Empirically, we find that verifier feedback substantially improves sample efficiency on challenging regex‑induction tasks, reducing the number of labeled examples required and enabling learning of complex target expressions where standard prompting fails. For example, on the hardest task groups, our counterexample‑guided framework improves success from 3.2% to 38.1% and from 38.9% to 74.1% on two different regex domains. These results suggest that LLMs can benefit from rich feedback beyond treating it as additional data, opening the door for robust verifier‑guided methods for LLM‑based program synthesis and formal reasoning.
Authors:Siyuan Luo, Nairong Zheng, Lin Zhou, Tiankuo Yao, Shengyou Yuan, Haojia Yu, Cong Pang, Jiapeng Luo, Lewei Lu
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.
Authors:Mo Wang, Wenhao Ye, Junfeng Xia, Minghao Xu, Hongkai Wen, Quanying Liu
Abstract:
The success of large‑scale deep learning models in neuroscience is fundamentally constrained by severe data heterogeneity. Native fMRI data aggregated from diverse sources exhibit substantial variation in both spatial and temporal resolutions. Consequently, most existing frameworks rely on lengthy, rigid preprocessing pipelines that enforce uniformity across datasets. This practice introduces two critical limitations: (1) potential degradation of subject‑specific anatomical information; (2) significant computational overhead, often requiring hours of processing per subject. Here, we propose FlexiBrain, a resolution‑agnostic voxel‑level encoding framework for native fMRI based on Mamba‑JEPA. FlexiBrain defines patch sizes in real‑world physical units and employs a dynamic patch resizing, thereby bypassing destructive spatial standardization while enabling direct ingestion of data in native space. We instantiate the framework using an efficient Mamba‑JEPA backbone to model high‑dimensional 4D fMRI signals. Across five diverse downstream neuroscience tasks, FlexiBrain consistently outperforms recent state‑of‑the‑art methods, achieving gains of up to 12 percentage points without external data augmentation. Importantly, FlexiBrain functions as a seamless plug‑in module, substantially reducing preprocessing costs and accelerating the development of robust voxel‑level fMRI foundation models. Code is available at https://github.com/OneMore1/FlexiBrain.
Authors:Jebacyril Arockiaraj, Dhruv Parikh, Jayashree Adivarahan, Rajgopal Kannan, Viktor Prasanna
Abstract:
Federated continual learning (FCL) must learn from distributed task streams under limited resources, such as communication, computation, memory, and label availability. Existing FCL methods often rely on repeated local optimization, replay, and full supervision. Analytic alternatives avoid iterative training and replay, but using high‑dimensional random features to improve accuracy requires a second‑order feature statistic, the Gram matrix, which has a quadratic communication cost in the random feature size M. We propose FedRAN, a resource‑aware analytic FCL framework that replaces gradient‑based updates with compact random feature statistics. Each client transmits a truncated‑SVD summary of its Gram matrix, reducing the dominant second‑order upload from quadratic to linear in M for fixed rank. The server performs a two‑level QR‑SVD subspace merge, spatially across clients and temporally across tasks, and solves a ridge classifier in closed form. FedRAN further supports label scarcity through prototype‑based pseudo‑labeling. Across CIFAR‑100, ImageNet‑R, and VTAB datasets, FedRAN improves average accuracy by up to 4.8 percentage points over the strongest baseline, uses 30.6‑121.8× less per‑client communication than optimization‑based FCL, and is 190.3× faster on average than gradient‑based baselines; with only 20% labels, pseudo‑labeling improves average accuracy by up to 6.61 points. These results show that FedRAN enables accurate and resource‑efficient FCL under communication, computation, and label constraints. The source code is available at https://github.com/JebacyrilArockiaraj/Fed‑RAN‑SSL.
Authors:Ayush Mittal, Dhruv Gupta
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.
Authors:Emily Nguyen, Yongchan Hong, Harsh Toshniwal, Yan Liu, Andreas Luttens
Abstract:
Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds. However, the computational burden to develop and deploy state‑of‑the‑art models continuously increases, limiting their scalability. Most large‑scale models are unimodal in nature and overlook the potential to leverage complementary molecular data modalities. To address these shortcomings, this paper introduces the Graph‑Language Alignment for Chemical Inference and Exploration using Representations (GLACIER) model, a student‑teacher framework that integrates molecular graphs, SMILES strings, and physicochemical descriptors to learn rich molecular embeddings. Our framework consists of three stages: (1) we pretrain three student encoders on 100,000 drug‑like molecules: a message‑passing neural network for molecular graphs, a transformer‑based encoder for SMILES strings, and a multilayer perceptron for physicochemical descriptors, (2) we fuse these student modalities using a novel Finsler geometry‑aware module, and (3) distill complementary knowledge from large teacher models, including MiniMol and MolFormer, into a single lightweight model via contrastive learning. We demonstrate that GLACIER is a robust framework that delivers high predictive performance and computational efficiency in complex molecular property prediction tasks. Our code is publicly available at https://github.com/eemokey/glacier.
Authors:Orion Reblitz-Richardson
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.
Authors:Barsat Khadka, Kawsher Roxy, Md Rubel Ahmed
Abstract:
Clock Tree Synthesis (CTS) is a computationally expensive stage in the physical design flow, requiring iterative EDA tool invocations to navigate a vast configuration space for optimal power, wirelength, and timing skew. Existing machine learning approaches require computationally expensive retraining or fine‑tuning cycles to adapt to unseen macro architectures and are architecturally mismatched to the millions of evaluations demanded by exhaustive combinatorial search. We present SwiftCTS, a physics‑informed surrogate framework that addresses both limitations simultaneously. By coupling lightweight, physics‑grounded statistical features with gradient‑boosted ensembles, SwiftCTS trains in under five seconds on a CPU and delivers sub‑millisecond inference without GPU support. To handle out‑of‑distribution (OOD) designs without retraining or fine‑tuning, we introduce a K‑shot multiplicative calibration mechanism that anchors predictions to just one or two physical reference runs, reducing power prediction error from 24.5% to 3.3% and wirelength error from 56.6% to under 1% on unseen macros. Integrating this engine with an evolutionary optimizer, SwiftCTS evaluates 100,000 CTS configurations in under ten seconds, yielding Pareto‑optimal frontiers that are physically validated within the OpenROAD flow. Closed‑loop validation confirms prediction errors below 0.5% for power and wirelength, and timing skew predictions within five picoseconds on an OOD benchmark, consistently outperforming default tool heuristics across all target metrics. Code publicly available at: \hrefhttps://anonymous.4open.science/r/SwiftCTS‑7E6Ehttps://github.com/BarsatKhadka/SwiftCTS
Authors:Xurui Wang, Qin Ren, Jun Ma, Haibin Ling, Chenyu You
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/.
Authors:Yechan Kang, Yongjin Kweon, Mingyeong Seo, Sohee Park, Yeonguk Jeon, Jongkil Park, Hyun Jae Jang, Jaewook Kim, YeonJoo Jeong, Suyoun Lee, Seongsik Park
Abstract:
Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients. To address these challenges, we propose a unified framework: adaptive and asymmetric surrogate gradients A2SG. The adaptive gradients adjust an effective window for spatio‑temporal adaptation, reducing spatial gradient variation and maintaining directional consistency of gradients over time. The asymmetric gradients reflect neuronal dynamics by assigning larger gradients to neurons with higher membrane potentials, and we prove that they yield lower variation than symmetric surrogates. Our analysis further establishes a direct connection between local gradient variation and the curvature of the loss landscape, providing a principled explanation for how A2SG promotes convergence to flatter minima and improves generalization. We conduct extensive experiments on diverse models, including CNN‑based and Transformer‑based SNNs, across various tasks such as image classification using both static and neuromorphic datasets, as well as segmentation. The results demonstrate that A2SG consistently improves accuracy and energy efficiency, establishing it as a general and reliable solution for training deep SNNs. Our code is available at https://github.com/KIST‑NCL/A2SG.git.
Authors:Faruk Alpay, Bugra Kilictas
Abstract:
Structured sequence generation often requires a model to satisfy several input‑derived constraints in a single output. Standard decoding methods may assign high probability to fluent continuations while placing low mass on continuations that realize all required anchors jointly. We study this regime as a rare‑event sequential inference problem. LatticeBridge combines a compact prefix language model, instance‑compiled surface automata, and a twisted sequential Monte Carlo (SMC) decoder with resampling, multilevel splitting, and a source‑support proposal term derived from instance‑provided phrases. The constraint representation is compiled from each input instance and does not rely on manually curated lexical classes. On 2,610 attainable validation tasks spanning CommonGen, E2E NLG, and WikiBio, the particle decoder improves exact anchor satisfaction and mean anchor coverage over greedy, beam‑filtered, and best‑of‑k ancestral baselines under a shared proposal model. Since exact anchor satisfaction alone does not rule out unsupported attribute substitutions, the evaluation reports required‑anchor coverage, source coverage, source‑intrusion diagnostics, overlap, runtime, and particle statistics jointly. The benchmark characterizes the faithfulness‑overlap‑latency frontier under a fixed proposal model.
Authors:Jin Gan, Xin Li, Jun Luo
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.
Authors:Ilay Kamai, Hugues Van Assel, Aviv Regev, Hagai B. Perets, Randall Balestriero
Abstract:
Cross‑modal alignment (CA) and cross‑modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic understanding of when each succeeds, when each fails, and when cross‑modal training helps at all ‑‑ a gap that leaves practitioners, especially in scientific domains like biomedicine or astrophysics, with heterogeneous instruments and multiple levels of organization and measurement, unable to diagnose why standard methods underperform the best single modality. We develop a unified linear framework that addresses both questions. Under a spiked signal‑plus‑noise model with structured cross‑modal nuisance correlation, we derive separation ratios for both objectives that expose complementary failure modes: alignment whitens each modality and fails when nuisance is strongly correlated across views; prediction encodes whatever is cross‑predictable through a one‑sided whitening, with recovery governed by source‑modality quality. The resulting phase diagram partitions multimodal problems into four regimes: Both, CA only, CP only, and Neither. We present a data‑driven procedure to locate real‑world datasets in this diagram using a small labeled subsample, identifying the preferred objective and prediction direction before any cross‑modal training. Experiments on synthetic data, stereo‑vision benchmarks, image‑caption pairs, and real astrophysical data validate the predictions in the nonlinear regime, including the Neither regime where cross‑modal training is actively harmful. Our framework lets practitioners diagnose their multimodal problem and choose the right objective before committing to training. Code to reproduce the results is available at https://github.com/IlayMalinyak/mm_align_vs_pred.
Authors:Abhijoy Sarkar, Aarchi Singh Thakur
Abstract:
Resistance to first‑line osimertinib in EGFR‑mutant non‑small‑cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories. We introduce OncoTraj, a public benchmark of 813 EGFR‑mutant NSCLC patients receiving first‑line osimertinib, harmonized from three real‑world clinical‑genomic sources: MSK‑CHORD (672 patients), AACR Project GENIE BPC NSCLC (34 patients), and the FLAURA molecular‑resistance supplement (107 patients). OncoTraj defines three locked tasks: (A) binary classification of progression by a fixed 12‑month landmark, (B) regression of time‑to‑first‑progression in days, and (C) six‑class classification of the dominant resistance mechanism. We release the harmonized dataset, patient‑level train/validation/test splits with an audited no‑leakage guarantee, an open‑source evaluation harness, and six reference baselines spanning a majority‑class predictor, logistic regression, random forest, XGBoost, an LSTM, and a multi‑task transformer. With v1's single‑timepoint snapshot features, no task clears chance on clean within‑source evaluation: the uniformity of this ceiling across every model class localizes the limit to the input modality (single‑snapshot tissue NGS rather than serial ctDNA), not the algorithm. The benchmark does recover a reproducible literature‑consistent association: TP53 co‑mutation raises the 12‑month progression rate from 29% to 59% cohort‑wide. OncoTraj establishes a reproducible, leakage‑audited baseline and converts the modality limit into concrete design requirements for a serial‑ctDNA‑enriched v2.
Authors:Zach Moczkodan, Hany Ragab
Abstract:
Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near‑perfect performance on CIC‑IDS2017. However, many existing studies neither supply their temporal modules with genuine sequence inputs nor evaluate under realistic, leakage‑free conditions, making it unclear whether reported gains arise from true sequence‑modeling capability. In this work, we reformulate CIC‑IDS2017 as a temporal intrusion‑detection task by constructing ordered flow sequences from network conversations and benchmarking nine classical and deep learning architectures under a random split, two leakage‑free splits, and a padding‑scheme ablation. The central finding is that padding convention, not architecture, determines the Transformer's performance: on genuinely sequential (non‑padded) windows the Transformer achieves the highest macro‑F1 of any model in the experiment (0.89); under zero‑pad+mask evaluation it drops markedly (‑0.24 macro‑F1), while LSTM, GRU, and 1D‑CNN remain stable. Under leakage‑free group evaluation the Random Forest is the most robust model (+0.009), while the Transformer's false‑alarm rate grows from 0.04% to 2.7%, a 67‑fold increase invisible under conventional protocols. These findings demonstrate that evaluation methodology ‑‑ specifically padding convention and split protocol ‑‑ has a larger effect on reported performance than architectural choice, and that widely used random splits with repeat‑last padding can overestimate model robustness by up to 0.24 macro‑F1. We advocate leakage‑free splits, explicit padding disclosure, and sequence‑aware benchmarking as standard practice in future IDS research. Code and implementation details are available at https://github.com/zachmocz/temporal‑ids‑bench.
Authors:Bowen Ping, Xiangxin Zhou, Penghui Qi, Minnan Luo, Liefeng Bo, Tianyu Pang
Abstract:
Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for image and video generation. Methods such as Flow‑GRPO and CPS cast the denoising process as a Markov Decision Process and apply PPO‑style ratio clipping to enforce a trust region. However, we argue that ratio clipping is structurally ill‑suited for flow models: the probability ratio between new and old policies is a noisy, single‑sample estimate of the true policy divergence, leading to over‑constraining in some regions of the trajectory and under‑constraining in others. We propose Flow‑DPPO (Flow Divergence Proximal Policy Optimization), which replaces ratio clipping with a divergence proximal constraint. A key observation is that the per‑step policy in flow models is Gaussian, enabling exact and cheap computation of the KL divergence between old and new policies. Flow‑DPPO employs an asymmetric divergence mask that blocks gradient updates only when they simultaneously move away from the trusted region and violate the divergence threshold. Experiments show that Flow‑DPPO achieves higher rewards with better KL‑proximal efficiency, alleviates catastrophic forgetting, promotes balanced multi‑objective optimization, and enables stable multi‑epoch training where ratio clipping degrades. Code and models are available at https://github.com/Tencent‑Hunyuan/UniRL/tree/main/FlowDPPO.
Authors:Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King
Abstract:
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre‑trained large language models (LLMs) can provide rich semantic representations, existing approaches only rely on static encoding of fixed attributes, overlooking the crucial role of target audiences in defining item identity. Moreover, the semantic space struggles to reflect actual user behavior, resulting in a significant gap between semantic representations and behavioral patterns. To address these limitations, we propose GenAIR, a general framework that empowers sequential recommendation with Generative Archetype‑grounded Item Representations. Specifically, we first leverage an LLM to analyze item metadata and infer textual description of the Archetype, which represents the conceptual profile of the item's ideal target audience. We then extract the corresponding embeddings in a single forward pass. Further, to ground these generative archetypes in real‑world behavior, we introduce a behavioral calibration objective, which explicitly incorporates behavioral signals from actual interactions. This objective adjusts the structure of the embedding space to reflect empirical patterns. GenAIR enables seamless integration with most existing models while maintaining high efficiency. Comprehensive experiments conducted on three real‑world datasets demonstrate that GenAIR significantly improves the performance of various sequential recommendation models and consistently outperforms state‑of‑the‑art baseline approaches. Implementation codes are available at https://github.com/AI‑Santiago/GenAIR.
Authors:Chirag Shinde
Abstract:
We present Density Field State Space Models (DF‑SSM), a framework for compressing SSMs to a 1‑bit scaffold with int8 low‑rank correction. Applied to Mamba‑2 1.3B, we achieve a 278 MB model (9.7x smaller than the 2.7 GB FP16 teacher) that runs at 21.4x faster inference on GPU (batch=1, relative to the mamba‑ssm reference implementation) while maintaining downstream task performance within 2‑4 percentage points of BitMamba‑2, a 1.58‑bit model trained from scratch on 150B tokens. The distillation itself requires only 32M tokens and 6 hours on a single A100 GPU, though it presupposes a pretrained FP16 teacher. We develop an optimized inference pipeline combining cuBLAS INT8 tensor cores for the scaffold matmul, custom CUDA kernels for stateful SSM and convolution operations, and an AVX‑512 CPU backend for efficient deployment on both GPU and CPU. Beyond compression, we investigate the internal knowledge organization of the resulting model, discovering three distinct processing phases: intent classification (layers 0‑3, operating in an abstract space with no vocabulary alignment), knowledge retrieval (layers 25‑35, where factual associations localize to a 5‑layer window), and output formatting (layers 36‑47, where category structure dissolves). Through systematic analysis of 445 factual prompts across 19 categories, we find that early‑layer classification is syntactic (driven by template structure) rather than semantic, and that the model exhibits well‑organized knowledge representations despite weak factual recall‑‑suggesting that representational structure may precede factual strength.
Authors:Artur Kuramshin, Özgür Aslan, Cyrus Neary, Glen Berseth
Abstract:
The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios. However, these policies continue to struggle with following instructions, likely due to the limited linguistic and action sequence diversity in existing robotics datasets. This paper introduces Task Robustness via Re‑Labelling Vision‑Action Robot Data (TREAD), a scalable framework that leverages large Vision‑Language Models (VLMs) to augment existing robotics datasets without additional data collection, harnessing the transferable knowledge embedded in these models. Our approach leverages a pretrained VLM through three stages: generating semantic sub‑tasks from original instruction labels and initial scenes, segmenting demonstration videos conditioned on these sub‑tasks, and producing diverse instructions that incorporate object properties, effectively decomposing longer demonstrations into grounded language‑action pairs. We further enhance robustness by augmenting the data with linguistically diverse versions of the text goals. Evaluations on LIBERO demonstrate that policies trained on our augmented datasets exhibit improved performance on novel, unseen tasks and goals. Our results show that TREAD enhances both planning generalization through trajectory decomposition and language‑conditioned policy generalization through increased linguistic diversity.
Authors:Zhiwei Tang, Yuanyu He, Yizheng Han, Wangbo Zhao, Jiasheng Tang, Fan Wang, Bohan Zhuang
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.
Authors:Xukun Zhu, Hang Yu, Peng Di, Linchao Zhu
Abstract:
The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase. However, current rollout techniques face a fundamental trade‑off: token‑level sampling often yields redundant trajectories that differ only in rephrasing, while embedding‑level methods utilizing random noise frequently disrupt semantic consistency. To resolve this, we introduce N‑GRPO, a novel exploration strategy integrated into the Group Relative Policy Optimization (GRPO) framework. Rather than relying on token‑level sampling or native embedding‑level noise, our approach leverages Semantic Neighbor Mixing. This mechanism dynamically constructs input representations by mixing the embeddings of an anchor token and its nearest semantic neighbors, thereby injecting diversity while strictly adhering to the local semantic manifold. Experimental evaluations on the DeepSeek‑R1‑Distill‑Qwen models across different sizes show that N‑GRPO not only achieves consistent improvements over strong baselines on math reasoning benchmarks but also exhibits robust generalization capabilities on out‑of‑distribution tasks.
Authors:Ana Sofia Santos, André Ferreira, Gijs Luijten, Naida Solak, Lisle Faray de Paiva, Behrus Hinrichs-Puladi, Jens Kleesiek, Jan Egger, Victor Alves
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
Authors:Justin Berman, Francois Charton, Andres Luna, Matthias Wilhelm, Mao Zeng
Abstract:
In this paper, we use machine learning to discover a new seeding strategy for integration‑by‑parts reduction of Feynman integrals, which is a frequent bottleneck in state‑of‑the‑art calculations in theoretical particle and gravitational‑wave physics. Our strategy allows us to reduce multi‑loop integrals with large numerator powers via essentially the standard Laporta algorithm but with a sparse selection of seed integrals that grows only linearly with the numerator power, whereas existing strategies lead to growth with a polynomial power that increases with the complexity of the integral being reduced. The seeds are restricted to a thin tube‑like region that connects the target integral to the master integrals along a zigzag path. We demonstrate the power of our approach by reducing non‑planar 2‑loop 5‑point integrals of rank 20 with numerical kinematics over a finite field, which is prohibitively difficult for the Laporta algorithm with conventional seeding. Going beyond individual integrals, we further demonstrate the reduction of a complete set of top‑level rank‑10 integrals by dividing the target integrals into several chunks, each of which can be solved by our sparse seeding strategy with considerably less time and a significantly lower memory footprint than other state‑of‑the‑art strategies, making the approach well‑suited for phenomenological applications. We provide a proof‑of‑principle implementation on GitHub at https://github.com/andreslunagodoy/tube_seeding.
Authors:David P. Hofmeyr
Abstract:
Although some very common test beds exist for assessing the performance of clustering methods, large scale benchmarking is typically limited to relatively simplistic simulation set‑ups. Here we describe the production and curation of close to 3000 synthetic data sets, derived from more than 200 publicly available data sets; the majority of which arose from real‑world applications. By fitting a flexible non‑parametric distribution to each base data set we are able to retain much of the nuance in real‑world data which is difficult to reproduce in standard simulations, while also producing data sets whose sizes are sometimes substantially greater than the data sets from which they are derived. The synthetic data sets, plus an accompanying R package, are available for download from https://github.com/DavidHofmeyr/ClusBench.
Authors:Emma Kasteleyn, Timo Maier, Axel Lauer, Veronika Eyring, Pierre Gentine, Ana Lucic
Abstract:
Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs required for traditional physics‑based methods. However, they are primarily (1) data‑driven and (2) evaluated using pixel‑wide error metrics (e.g., RMSE), so there are no guarantees that their forecasts are consistent with known physical laws. We introduce PhysMetrics.Weather, an evaluation framework that assesses the physical realism of MLWP models across three types of metrics: conservation, spectral, and dynamical. By quantifying physical realism, this tool guides the development of physics‑informed architectures and helps evaluate whether MLWP models are reliable for operational use. Our framework is available on Github at https://github.com/Emmakast/PhysMetrics.Weather.
Authors:Zeyu Liu, Xuanzhi Feng, Sing Kwong Lai, Yuanchen Gao, Xiaoyi Pang, Hualei Zhang, Jingcai Guo, Jie Zhang, Song Guo
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.
Authors:Muhammad Ahmed
Abstract:
Transformers achieve strong language modeling performance by providing direct token‑to‑token communication paths, but causal self‑attention scales quadratically with context length. Recurrent and state‑space models reduce this cost, yet compress history into sequentially updated fixed‑size states. This paper studies a third primitive: a parallel content‑addressed memory over causal successor records. The proposed Parallel Causal Associative Field (PCAF) writes local records from a context window into hash buckets, retrieves a bounded candidate set for the current query, forms a sparse cache distribution over successor tokens, and mixes that cache with a parametric local language model through a learned gate. The resulting model maintains sparse long‑context access while avoiding a single fixed recurrent state bottleneck. We evaluate PCAF under full autoregressive pretraining on WikiText‑103 and PG‑19 using a distributed Google Cloud TPU v4‑32 pod. At 303M parameters and context length T = 2048, PCAF‑semantic reaches 36.31 perplexity on WikiText‑103 and 52.45 perplexity on PG‑19, compared with 47.49 and 53.84 for a matched dense Transformer. PCAF‑semantic simultaneously processes 0.61‑0.62M tokens/s across the TPU pod, versus 0.43M tokens/s for dense and local attention baselines. Supporting 41M‑parameter multi‑seed sweeps and single‑GPU component ablations show that the associative cache, retrieval capacity, and learned gate materially affect the speed‑quality trade‑off.
Authors:Wenhao Zhang
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.
Authors:Dhruvesh Patel, Benjamin Rozonoyer, Soumitra Das, Tahira Naseem, Tim G. J. Rudner, Andrew McCallum
Abstract:
Insertion Language Models (ILMs) offer several advantages over left‑to‑right generation and mask‑based generation. However, existing formulations of insertion‑based generation have largely been ad‑hoc. In this paper, we derive a diffusion‑style denoising objective for ILMs from first principles by formulating the noising process as a continuous‑time Markov chain on the space of variable‑length sequences. We show that previous formulations of ILMs can be viewed as special cases of this denoising framework. Through empirical evaluation on a synthetic planning task, we show that the proposed approach retains the benefits of insertion‑based generation over left‑to‑right generation and masked diffusion models. In language modeling, our diffusion‑based approach is competitive with left‑to‑right generation and masked diffusion models, while offering additional flexibility in sampling compared to existing insertion language models.
Authors:Yu Han, Kailing Li, Yang Jiao, Yulin Dai, Yuqian Fu, Linhai Zhuo, Tianwen Qian
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.
Authors:Gijs van Nieuwkoop, Siamak Mehrkanoon
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.
Authors:Dmitry Manning-Coe, Thomas Read, Anna Soligo, Oliver Clive-Griffin, Chun-Hei Yip, Rajashree Agrawal, Jason Gross
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.
Authors:Anirudh Sekar
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.
Authors:Liuyin Wang
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.
Authors:Yuhao Zhang
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.
Authors:Chong Liu, Luxuan Fu, Xuyu Feng, Zhen Dong, Bisheng Yang
Abstract:
The paradigm of digital twin cities is shifting from coarse visual mapping toward more precise and actionable digitization of urban assets. However, existing datasets predominantly focus on coarse visual perception, lacking the strict multi‑modal alignment and attribute and status diagnosis required for automated infrastructure maintenance. To bridge this gap, we introduce WHU‑Infra3D, a large‑scale, multi‑modal benchmark dataset dedicated to roadside infrastructure inventory. Covering 53.8 km across three cities, WHU‑Infra3D uniquely integrates panoramic imagery and LiDAR point clouds with rigorous 2D‑3D instance association and cross‑frame tracking. Comprising over 175k multi‑view 2D bounding boxes alongside thousands of 3D infrastructure instances, the dataset provides over 181k detailed attribute and status annotations (e.g., rust, occlusion) to empower operational health assessment. We establish comprehensive baselines across five core tasks: 2D detection, 2D cross‑view matching, 3D geo‑identification, 3D point cloud segmentation, and attribute recognition. Extensive evaluations expose significant cross‑city domain gaps and inherent vulnerabilities of current models on long‑tailed defective statuses, establishing WHU‑Infra3D as an essential testbed for advancing scalable, AI‑driven urban infrastructure inventory and lifecycle management. The WHU‑Infra3D dataset is available at https://github.com/WHU‑USI3DV/WHU‑Infra3D.
Authors:Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang, Miao Pan
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.
Authors:Manel Slokom, Malek Slokom, Thierno Kante
Abstract:
Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular data, auditing privacy remains hard. In many cases, even humans cannot easily tell if a table is real or synthetic. In this paper, we propose a method based on LLM discrimination. We ask an LLM to classify each table sample as REAL or SYNTHETIC. We test two settings: C1 with table only, and C2 with table plus distributional metadata. We use LLaMA as an open model and Gemini as a reference model. In our experiments, we run three synthesis models, CTGAN, TVAE, and Gaussian Copula, on two public datasets, UCI Adult and ACS Census. We collect 451 valid trials. Our results show clear differences between models. On Adult, LLaMA reaches DRS=0% in reported cells, while Gemini reaches DRS=100% for CTGAN and TVAE. On Census, LLaMA predicts SYNTHETIC for most samples, while Gemini stays high in C1 but drops for CTGAN and TVAE in C2. We also compare with a classifier two‑sample test (C2ST) and record linkage as distributional baselines, and with a human pilot of 2 annotators and 240 trials. Our results show that LLM discrimination is a practical privacy audit signal when model choice, per provider reporting, and data encoding are handled with care. For reproducibility, code and experiment scripts are available at https://github.com/SlokomManel/LLM‑as‑a‑Discriminator.
Authors:Hankun Lin, Ruqi Zhang
Abstract:
Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference‑time adaptation. While inference‑time alignment methods such as Best‑of‑N and rejection sampling are widely used, they frame the task as a sampling‑intensive, reward‑guided search, leading to two key limitations: their performance is bounded by the base model's generation quality, and their reliance on imperfect reward models makes them vulnerable to reward hacking. To address these challenges, we introduce Gradient‑Guided Reward Optimization (GGRO), a lightweight inference‑time method that performs targeted, minimal intervention during decoding via gradient guidance. Specifically, GGRO monitors token‑level entropy to identify high‑uncertainty regions indicative of drift or misalignment. Upon detection, it responds by injecting nudging tokens, generated using gradient signals from an off‑the‑shelf reward model, to steer the generation trajectory rather than merely re‑ranking samples. Experiments show that GGRO consistently improves inference‑time alignment across safety, helpfulness, and reasoning benchmarks. It also increases coverage of high‑quality responses and robustness to reward hacking, with minimal computational overhead. Code is available at https://github.com/lhk2004/GGRO.
Authors:Berker Demirel, Valentino Maiorca, Marco Fumero, Theofanis Karaletsos, Francesco Locatello
Abstract:
Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimental factors. In many applications, especially in scientific domains, such models are attractive to explore conditions for which real samples are rare, expensive, or not yet observed. However, this creates a circularity for evaluation: standard conditional quality metrics require a reference target distribution, but in the extrapolative regime that distribution is unavailable by definition. We address this problem with a post‑hoc, per‑sample trust score for assessing conditional samples using only the training distribution. The score combines two estimable quantities: global realism, measuring compatibility with the real data manifold, and attribute‑wise faithfulness, measuring whether a sample is closer to the requested attributes than to plausible alternatives. We show that the score can recover meaningful comparisons across extrapolated generations, under a mild coverage condition on the observed attributes. These comparisons enable effective filtering, ranking, and abstention of generations and can be used directly on off‑the‑shelf pretrained models. In biological imaging, selected samples preserve real morphological structure better and improve downstream predictive performance, while similar gains are observed on controlled vision benchmarks. Finally, we show how the score can be applied during generation, enabling abstention before full decoding. Code is available at https://github.com/berkerdemirel/faithful‑cond‑gen.
Authors:Apratim Bhattacharyya, Shweta Mahajan, Sanjay Haresh, Rajeev Yasarla, Reza Pourreza, Litian Liu, Risheek Garrepalli, Roland Memisevic
Abstract:
Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video (and multimodal) large language models (LLMs) as task guidance assistants. A crucial capability for the real‑world success of a prospective task guidance assistant is it's ability to intervene proactively as soon as a mistake is apparent in order to guide the user. To evaluate this crucial capability, we introduce Ego‑MC‑Bench (Mistake Corrections), a benchmark for evaluating reactive, step‑by‑step task guidance in realistic cooking scenarios. Extensive experiments show that Ego‑MC‑Bench is highly challenging for state‑of‑the‑art video LLMs. We argue that a key reason is the limited availability of training data for fine‑tuning models on this task. Although there exists a wide range of cooking video datasets, existing datasets lack examples of mistakes along with appropriately timed interventions. To help address this data limitation, we also introduce Ego‑CoMist, a counterfactual synthetic dataset created by transforming non interactive cooking videos into supervised training examples showing proactive interventions. We show that fine‑tuning on Ego‑CoMist yields performance gains especially for smaller and more efficient video LLMs that are well suited for delivering assistance on edge devices.
Authors:Yan Wang, Qifan Zhang, Jiachen Yu, Tian Liang, Dongyang Ma, Xiang Hu, Zibo Lin, Chunyang Li, Zhichao Wang, Miao Peng, Nuo Chen, Jia Li, Yujiu Yang, Haitao Mi, Dong Yu
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.
Authors:Siyu Chen, Miao Lu, Beining Wu, Heejune Sheen, Fengzhuo Zhang, Shuangning Li, Zhiyuan Li, Jose Blanchet, Tianhao Wang, Zhuoran Yang
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.
Authors:Yanxiong Li, Guoqing Chen, Qianqian Li, Sen Huang
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.
Authors:Stéphane Eilles-Chan Way, Hugo Percot, Quentin Cappart, Tias Guns, Louis-Martin Rousseau
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.
Authors:Muhammed Rasin
Abstract:
We study a capability the dominant paradigm in synthetic tabular data does not provide: exact satisfaction of a declared analytical outcome with no source data. Imitation methods (copulas, GANs, diffusion) learn a real distribution and sample from it, and are judged on fidelity to real data. A large, practical class of needs is different: generating data with no source data ("cold start") that reproduces a declared outcome (a revenue curve, a churn rate, a group share) across a relational schema. Off‑the‑shelf imitation tools offer no interface for such targets, and no sampler can hit an exact aggregate, because sampling has variance. On a real public dataset, off‑the‑shelf learned synthesizers trained on that very data miss the declared monthly aggregate by 74 to 86 percent; a per‑period steelman cuts the miss to about 19 percent and still cannot reach 0; a closed‑form generator reaches exactly 0. We name this task outcome‑conformant synthesis, argue its evaluation axis is conformance rather than fidelity, and show the two axes are orthogonal. We contribute: (1) a formal account showing a widely‑used family of exact‑aggregate generators is exactly conditional‑sum sampling of a Gamma population (via Lukacs' characterization), with closed‑form exactness, a closed‑form marginal CV, and scale‑invariance; a controlled experiment maps the boundary, enforcing the exact aggregate costs at most 0.006 in 1‑Wasserstein distance to an arbitrary external marginal, the rest being shape‑family mismatch; (2) SpecBench, to our knowledge the first benchmark to measure conformance to analytical outcomes for cold‑start relational synthesis; and (3) a closed‑form, deterministic reference system. Exact aggregation alone is trivial; the contribution is conformance jointly with closed‑form marginals, integrity, determinism, and zero source data. We concede fidelity to imitation where real data exists.
Authors:Ruihua Han, Shuai Wang, Chengyang Li, Rui Gao, Xinyi Wang, Zhe Liu, Guoliang Li, Yupu Lu, Qi Hao, Jia Pan, Hengshuang Zhao
Abstract:
Simulation plays a key role in automated robotics research supported by large language models (LLMs). However, existing simulators often require custom code or complex interfaces, creating a barrier to rapid prototyping and automated algorithm development. To this end, we propose the Intelligent Robot Simulator (IR‑SIM), a lightweight skill‑native navigation simulator designed for rapid scenario construction, benchmarking, and robot learning. In IR‑SIM, scenarios are entirely defined by YAML configuration files that specify mobile robot kinematics, geometric collision checking, LiDAR sensing, visualization, and behavior modules. This design makes robotic simulation fully describable and reproducible, allowing scenarios to be generated and modified from text prompts through the proposed IR‑SIM agent skills. The resulting scenarios can be used for automated benchmarking of navigation algorithms and for automated generation of training data for learning methods. Furthermore, IR‑SIM provides bridges to high fidelity simulators and real world deployment, allowing users to validate their algorithms in more realistic settings after prototyping without extra coding. The experiments showcase the convenience and versatility of IR‑SIM in multiple tasks: constructing navigation scenarios from natural language, training a collision avoidance policy, benchmarking social navigation policies, and bridging to high fidelity simulators and real world deployment. The project website is available at https://github.com/hanruihua/ir‑sim.
Authors:Syed Rifat Raiyan, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
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.
Authors:Boshu Lei, Kostas Daniilidis, Antonio Loquercio
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/.
Authors:Xu Zhang, Peang Wang, Wei Wang
Abstract:
Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task‑specific models. However, prior studies indicate that pre‑trained LTSMs may exhibit a poorly conditioned non‑convex loss landscape, leading to limited trainability. As a result, direct fine‑tuning tends to cause overfitting and suboptimal performance, sometimes even worse than training from scratch, substantially diminishing the benefits of pre‑training. To overcome this limitation, we propose Smoothed Full Fine‑tuning (SFF), a novel fine‑tuning technology. Specifically, we construct an auxiliary LTSM via random initialization to obtain a smoother loss landscape, and then linearly interpolate its weights with those of the pre‑trained model to smooth the original landscape. This process improves trainability while preserving pre‑trained knowledge, thereby enabling more effective downstream fine‑tuning. From an optimization perspective, SFF perturbs sharp minima without significantly harming flat regions, facilitating escape from poor local basins toward smoother and more generalizable solutions. Extensive experiments on benchmark datasets demonstrate consistent improvements across eight representative LTSMs, including Timer, TimesFM, MOMENT, UniTS, MOIRAI, Chronos, TTMs, and Sundial, on diverse downstream tasks. The code is available at the link: https://github.com/Meteor‑Stars/SFF.
Authors:Chenhan Jin, Shengze Xu, Qingsong Wang, Fan Jia, Dingshuo Chen, Tieyong Zeng
Abstract:
Data pruning (DP), as an oft‑stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well‑defined pruning method while striving for near‑lossless performance. However, existing approaches, which commonly select highly informative samples, can lead to biased gradient estimation compared to full‑dataset training. Furthermore, the analysis of this bias and its impact on final performance remains ambiguous. To address these challenges, we propose OrderDP, a plug‑and‑play framework that aims to obtain stable, unbiased, and near‑lossless training acceleration with theoretical guarantees. Specifically, OrderDP first randomly selects a subset and then chooses the top‑q samples, where unbiasedness is established with respect to a surrogate loss. This ensures that OrderDP conducts unbiased training in terms of the surrogate objective. We further establish convergence and generalization analyses, elucidating how OrderDP affects optimal performance and enables well‑controlled acceleration while ensuring guaranteed final performance. Empirically, we evaluate OrderDP against comprehensive baselines on CIFAR‑10, CIFAR‑100, and ImageNet‑1K, demonstrating competitive accuracy, stable convergence, and exact control ‑‑ all with a simpler design and faster runtime, while reducing training cost by over 40%. Delivering both strong performance and computational efficiency, our method serves as a robust and easily adaptable tool for data‑efficient learning. The code is publicly available at https://github.com/shengze‑xu/OrderDP.
Authors:Cuong Dang, Jiahao Zhang, Hieu Ta Quang, Dung Le, Lu Cheng, Suhang Wang
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.
Authors:Priyansh Bhatnagar, Ashkan Moradifirouzabadi, Se-Hyun Yang, SeungJae Lee, Jungwook Choi, Mingu Kang
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.
Authors:Gianluca Scarpellini, Ron Shprints, Peter Holderrieth, Juno Nam, Pranav Murugan, Rafael Gómez-Bombarelli, Tommi Jaakola, Maruan Al-Shedivat, Nicholas Matthew Boffi, Avishek Joey Bose
Abstract:
All‑atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein‑ligand systems. Generating structures at the atomic level of fidelity, however, typically requires expensive iterative diffusion rollouts, making both conventional deployment and inference‑time search techniques computationally costly. In this paper, we introduce the Denoiser Cofolding All‑Atom Flowmap (DeCAF) framework for distilling state‑of‑the‑art all‑atom cofolding models into all‑atom flow maps that produce high‑quality samples in only a few inference steps. We build DeCAF on a denoiser‑based formulation of flow maps with endpoint losses that naturally support SE(3) rigid alignment, which we show is critical for training accurate models. We further derive a simple change of variables that lets DeCAF operate in the σ‑space noise schedule of EDM‑style architectures, enabling direct distillation from pretrained cofolding diffusion models. Equipped with DeCAF's flowmap lookahead, we introduce a purpose‑built inference‑time framework that improves sampling through reward‑guided search. Empirically, DeCAF‑Boltz statistically improves over Boltz‑1x in both accuracy (RMSD) and physical validity scores of protein‑ligand poses at strict NFE budgets on the challenging Runs N' Poses, while also showing a more optimal Pareto frontier across all inference compute budgets on PoseBusters. Distilling the state‑of‑the‑art Pearl cofolding model, DeCAF‑Pearl outperforms diffusion‑based cofolding models and matches its teacher on success rate while using 5x fewer NFEs. We release our code at https://github.com/genesistherapeutics/decaf.
Authors:Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker, Alexander Rutherford, Davide Paglieri, Aidan Scannell, Henry Gouk, Elliot J. Crowley, Tim Rocktäschel, Amos Storkey
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.
Authors:Josiah D. Kunz, Kamal Choudhary
Abstract:
Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios. Machine learning surrogate models offer a promising alternative, yet most approaches struggle with a critical limitation: generalizing across varying geometries. This work presents a mesh graph network (MGN) for predicting von Mises stress fields in 2D structural components with arbitrary hole geometries. Unlike traditional machine learning approaches that use absolute node coordinates as features, the proposed model builds on existing MGN frameworks that encode node types (e.g., fixed boundary, free surface, hole edge), relative edge features (distance between neighbors), and global features (applied load). This architecture is inherently translation‑ and rotation‑invariant, enabling generalization to unseen geometries without retraining. The MGN was trained on 11 plate geometries under 20 load conditions and evaluated on 7 unseen geometries and 3 unseen loads. In the most favorable case, the model achieves R^2 \geq 0.97 on an unseen geometry and unseen load, compared to R^2 \approx 0.01‑‑0.86 for conventional models (Random Forest, Gradient Boosting , K‑Nearest Neighbors) trained on identical data. However, even in less favorable cases, the MGN model still outperforms conventional models. This work extends the mesh‑based simulation framework of Pfaff et al. (arXiv:2010.03409) to structural mechanics, demonstrating that graph neural networks can serve as efficient surrogates for finite element analysis across varying geometries.
Authors:Jaineet Shah
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.
Authors:Kewei Li, Rongying Zhang, Xueli Wang, Xiwen Gong, Zhongjian Wang, Lan Huang, Ruochi Zhang, Fengfeng Zhou
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.
Authors:Rui Dai, Shuran Zheng
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.
Authors:Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang, Zuochang Ye, Yibo Lin, Wenjian Yu
Abstract:
As capacitance extraction accuracy of rule‑based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep‑learning‑based 2D capacitance models. However, existing MLP‑ and CNN‑based methods constrain their input to fixed metal‑layer combinations in a specific process node, limiting their usability in practice. Recognizing the inherent similarity between capacitance matrix and the prevailing attention mechanism, we propose AttentionCap, a customized Transformer for capacitance matrix learning, with a Gram representation framework, a physics‑aligned symmetric‑attention output layer, and a novel normalized Laplacian loss. We also introduce a process‑node embedding to enable multi‑node learning. Trained on synthetic data, AttentionCap attains 0.67%/3.99% self/coupling‑capacitance error on unseen real designs under a multi‑layer and multi‑node setting, surpassing the CNN‑Cap baseline with 4.6×/5.7× lower self/coupling error and 192× faster inference speed. A pretrained AttentionCap accurately transfers to an unseen node with only 5K samples and 4K finetuning steps. With sufficient accuracy on unseen real designs and strong transferability to new process nodes, AttentionCap offers highly practical value for modern EDA workflows. Code and data are available at https://github.com/THU‑numbda/AttentionCap.
Authors:Yi Duan, Zhao Yang, Jiwei Zhu, Ying Ba, Chuan Cao, Bing Su
Abstract:
DNA cis‑regulatory elements (CREs) such as enhancers control gene expression levels. Accurately predicting regulatory activity from DNA sequences is valuable but challenging, as it requires understanding complex biological regulatory processes. Existing methods typically regress activity scores from sequences in a black‑box manner, limiting both interpretability and regression performance. Meanwhile, large language models (LLMs) benefit from explicit reasoning processes, yet directly applying LLMs to raw DNA sequences performs poorly. In this paper, we bridge this gap by introducing R3LM, a framework that teaches LLMs reasoning‑informed regression on regulatory DNA through structured biological knowledge. Specifically, we design a biologically grounded data format that structures DNA's regulatory information for improved LLM understanding, and construct CRE‑ReasonBench, the first dataset that associates DNA sequences and activity scores with mechanistic reasoning traces. Through two‑stage training that first teaches LLMs reasoning over structured biological information then performs regression, R3LM achieves state‑of‑the‑art performance on enhancer prediction across three cell types, outperforming both LLMs with raw sequence input and specialized DNA models while providing interpretable mechanistic explanations. We expect R3LM as an interpretable reward model that can effectively assist biologists in CRE design. Code is available at https://github.com/DuanYi516/R3LM.
Authors:Khanh D. Nguyen, Hung T. Ho, Chinh T. Nguyen, Thanh Q. Duong, Linh D. Le, Duy M. H. Nguyen, Vien A. Ngo, An T. Le
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/.
Authors:Hongkyu Koh, Ikbeom Jang
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.
Authors:Zhanke Zhou, Xiangyu Lu, Chentao Cao, Brando Miranda, Tongliang Liu, Bo Han, Sanmi Koyejo
Abstract:
RL with verifiable rewards can substantially improve LLM reasoning, yet standard GRPO‑style training often treats easy, hard, and learnable questions alike through uniform sampling and weighting, leading to inefficient compute allocation. We study GRPO by tracking token log‑probabilities, group‑normalized advantages, and the induced token‑level update weights. This reveals three recurring dynamics as training proceeds: (1) confidence inflation, (2) advantage contraction, and (3) hierarchical convergence. These findings suggest that the utility of each update depends strongly on both question difficulty and the model's current competence. Motivated by this, we propose Confidence and Difficulty‑adaptive Policy Optimization (CoDaPO), which assigns each question a bounded value from rollout confidence and empirical difficulty. CoDaPO then uses this value to reweight policy updates and resample high‑value learnable questions within mini‑batches, thereby increasing discovery within the learnable band under a fixed compute budget. Across twelve benchmarks, CoDaPO consistently improves accuracy over existing RL methods. Our code is publicly available at https://github.com/tmlr‑group/CoDaPO.
Authors:Anish Laddha, Nitesh Pradhan, Gaurav Srivastava
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/.
Authors:Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer, Doron Haviv, Tommaso Biancalani, Kyunghyun Cho, Gabriele Scalia, Aïcha BenTaieb, Hector Corrada Bravo
Abstract:
Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single‑cell RNA sequencing enables characterization at cellular resolution, yet the combinatorial space of conditions makes exhaustive experimental mapping infeasible. We introduce single‑cell Concept Bottleneck Generative Models (scCBGM), a framework for interpretable and precise counterfactual editing of individual cells. scCBGM adapts concept bottleneck architectures for single‑cell data through decoder skip connections and a cross‑covariance penalty that promotes disentanglement without dimensional constraints. We extend the framework to flow matching models, enabling concept‑guided editing in both encoding‑decoding and generation regimes. To enable rigorous evaluation, we develop a synthetic benchmark with ground‑truth counterfactuals. Across multiple real datasets, scCBGM demonstrates superior performance in combinatorial generalization and counterfactual prediction, supported by cell‑level validation on synthetic data and population‑level benchmarks on real datasets.
Authors:Blake B. Gaines, Jinbo Bi
Abstract:
It is well established that ReLU networks define continuous piecewise‑linear functions, and that their linear regions are polyhedra in the input space. These regions form a complex that fully partitions the input space. The way these regions fit together is fundamental to the behavior of the network, as nonlinearities occur only at the boundaries where these regions connect. However, relatively little is known about the geometry of these complexes beyond bounds on the total number of regions, and calculating the complex exactly is intractable for most networks. In this work, we prove new theoretical results about these complexes that hold for all fully‑connected ReLU networks, specifically about their connectivity graphs in which nodes correspond to regions and edges exist between each pair of regions connected by a face. We find that the average degree of this graph is upper bounded by twice the input dimension regardless of the width and depth of the network, and that the diameter of this graph has an upper bound that does not depend on input dimension, despite the number of regions increasing exponentially with input dimension. We corroborate our findings through experiments with networks trained on both synthetic and real‑world data, which provide additional insight into the geometry of ReLU networks. Code to reproduce our results can be found at https://github.com/bl‑ake/ICLR‑2026.
Authors:Maryam Zaman, Muhammad Khuram Shahzad
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.
Authors:Yuchen He, Baolong Bi, Shenghua Liu, Huaming Liao, Yuyao Ge, Bolin Wan, Siqian Tong, Juan Chen, Jiafeng Guo, Xueqi Cheng
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
Authors:Sumit Chongder
Abstract:
Noisy intermediate‑scale quantum (NISQ) processors are entering an early fault‑tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates. Existing compilation and error‑detection toolchains treat these concerns in isolation, with no principled way to balance detection overhead against success probability under latency constraints. We present an integrated hardware‑aware compilation and data‑driven quantum error‑detection (QED) framework that jointly optimises qubit mapping, SWAP insertion, and syndrome‑schedule placement via a noise‑weighted cost function and a learned multi‑objective scheduler. Simulation experiments on an HPC cluster using GPU‑accelerated density‑matrix simulation (NVIDIA cuQuantum SDK) across VQE, phase‑estimation, and Grover benchmarks, three noise profiles, and circuit sizes of 6‑20 qubits (depths 10‑160), show that joint co‑design raises algorithmic success probability by up to 68 percent (95 percent CI: 60 percent to 76 percent) over SABRE on an 8‑qubit VQE instance with post‑selection.
Authors:Bo Zhang, Borui Zhang, Chenghao Jiang, Minglei Shi, Xiaofeng Wang, Zheng Zhu, Jie Zhou, Jiwen Lu
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.
Authors:Neel Tushar Shah, Manglam Kartik
Abstract:
We present CARTOGRAPH, a verification layer for AI scientists that couples unresolved‑subspace experiment steering (select), explicit ambiguity closure (resolve), and residual‑based library inadequacy detection (refuse). Under a local linear‑Gaussian bridge, raw unresolved projection is the isotropic unresolved Fisher‑information trace, while CARTOGRAPH‑A is the exact unresolved A‑optimal rule; closed‑form EIG and Box‑Hill arise as local comparators rather than global equivalents. Across five testbeds, CARTOGRAPH‑A beats raw projection 129W/0T/15L at d = 8 (p < 10^‑21) in a replicated structured cascade. More distinctively, the framework tentatively identifies three out‑of‑library pharmacokinetic mechanisms and then revokes those identifications as residuals expose structural misfit, while one perturbed in‑library control stays identified throughout. In low‑dimensional pharmacokinetic and filtered EPA settings, near‑ties against disagreement are predicted by theory and observed. Finally, in a retrospective audit of 40 positive claims from the published A‑Lab autonomous materials system, the refuse guard flags all 4 claims later marked inconclusive under manual reanalysis while passing 32/36 confirmed claims. Code is available at https://github.com/ai4science‑boed/cartograph.git
Authors:Roghayeh Taghavi, Aysa Hasanazde Bashkandi, Amir Ali Bengari, Mohammad Amin Raji, Mohammad Salahi Ardekani, Parisa Mardukhian, Parvaneh Rezaei, Ramin Mousa
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
Authors:Shengli Zhou, Xiangchen Wang, Guanhua Chen, Feng Zheng
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.
Authors:Simon Schug
Abstract:
Sparsity allows scaling model parameters without proportionally increasing computational cost. While mixture of experts (MoE) models are made increasingly sparse, individual experts typically remain large and dense. Here, we demonstrate that further increasing sparsity by shrinking each expert to consist of a single neuron and selecting a tiny fraction of many available neurons can improve compute efficiency and interpretability. Counterintuitively, the key to achieving both is removing the nonlinearity typically applied to the experts, resulting in a network of sparsely gated linear neurons (sgatlin). In an isoflop comparison, we find that replacing all transformer feedforward layers with sgatlin improves perplexity in language models across different compute budgets. At the same time, the sparsity and linearity of the resulting feedforward circuits present new opportunities for model interpretability. In a small‑scale case study, we demonstrate that feedforward circuits in sgatlin can be interpreted without having to train additional replacement models. We find that they form semantically structured clusters and are causally implicated in factual recall. Our findings paint a possible path towards compute‑efficient and interpretable transformer feedforward layers.
Authors:Rohan Shravan
Abstract:
This paper reports on training a hundred‑billion‑parameter sparse mixture of experts on a single eight‑GPU node, end to end. LightningLM 0.1V is a recurrence‑backbone language model family grown in four stages from a small dense seed, through a 5B and a 9B mixture of experts, to a 120B model with 460 routed experts under top‑12 routing. Each larger model is grown from the trained weights of the smaller one; active parameters rise monotonically from 1.78B at the dense seed to 5.93B at 120B (about 5% of the 118.67B stored). The full lineage runs on single nodes, the larger stages at 8K context, reaching a released training loss of 1.78 at 120B scale. This is a systems and experience report. It is organized around three disciplines. Reversibility: a reversible recurrence stack reconstructs activations in the backward pass instead of storing them, holding activation memory flat as the model grows. State‑preserving growth: each expansion (dense to MoE, shallow to deep, few experts to many) is given as a reproducible principle paired with the failure that results from getting it wrong; several failures are silent. Single‑node economics: the 120B trains through TQP, a strategy of quantized base expert weights and trained low‑rank adapters that carries optimizer state on 2.26B adapter parameters rather than 100B+ resident in routed experts, cutting expert‑path optimizer state by a factor of ~45. What is new is the integration of known primitives, not any primitive in isolation: one grown lineage running end to end on a single node, documented at practitioner level, with per‑domain held‑out loss as evidence that targeted capabilities (multilingual Indic competence, code) were learned by construction. Model family, tokenizer, and training code are released.
Authors:Huixi Intelligence, :, Chen Zhang, Chenyang Zhou, Guanglei Ding, Guanghui He, Haibin Gao, Jiajia Chen, Jianyong Zhang, Lianyi Yu, Ningyi Xu, Ping Xu, Qingchen Li, Yingjun Hu, Yijia Zhang, Yuxi Liu
Abstract:
Vision‑Language‑Action (VLA) models have shown strong potential for robotic manipulation, but real‑time deployment on edge hardware remains challenging. In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM‑dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed. Motivated by this observation, we propose RhinoVLA, a deployment‑oriented VLA model co‑designed with the Huixi R1 edge SoC. RhinoVLA adopts a token‑efficient Qwen3‑VL backbone and a continuous Action Expert, reducing the VLM‑side token and computation burden while preserving pretrained multimodal capability. To support cross‑robot learning, RhinoVLA further introduces a unified interface that combines View Registry, 72D physical state‑action slot space, and robotinstance LoRA, allowing heterogeneous robot observations and action schemas to be aligned under a shared policy. On the deployment side, RhinoVLA is optimized through hardware‑aware compilation, mixed‑precision execution, and parallel visual encoding. Experiments show that RhinoVLA achieves downstream performance comparable to π0.5 at a similar parameter scale, while reaching 11.69 Hz end‑to‑end inference on Huixi R1, meeting the 10 Hz real‑time closedloop control target. The project will be open‑sourced at https://github.com/HuixiAI/RhinoVLA.
Authors:Huzaifa Shaaban Kabakibo, Animesh Trivedi, Lin Wang
Abstract:
As scalable inference services become popular, the cold start latency of an inference engine becomes important. Today, vLLM has evolved into the de facto inference engine of choice for many inference workloads. Although popular, due to its complexity and rapid evolution, there has not been a systematic study of its startup latency. With major architectural innovations such as the V1 API and the introduction of torch.compile, this paper presents the first detailed performance characterization of vLLM startup latency. We break down the startup process into six foundational steps and demonstrate that it is predominantly CPU bound. Each step exhibits consistent and interpretable scaling trends with respect to model‑level and system‑level parameters, enabling fine‑grained attribution of latency sources. Building on these insights, we develop a lightweight analytical model that accurately predicts vLLM startup latency for a given hardware configuration, providing actionable guidance for resource planning in large‑scale inference environments. All benchmarking datasets, analysis tools, and prediction scripts are open sourced at https://github.com/upb‑cn/vllm‑startup‑profiler.
Authors:Daniel Cieślak, Andrzej Czyżewski
Abstract:
Physics‑informed machine learning is often assessed by curve error, although engineering use depends on downstream decisions: ranking candidates, avoiding infeasible designs and limiting regret. We introduce pinn‑gym, an open benchmark for material‑conditioned lattice design that couples a transparent reduced‑order crush‑and‑impact oracle with five printable polymer cards, dimensionless force‑response targets and a protocol spanning curve fidelity, physical admissibility, top‑k retrieval and mass regret. Across per‑material, pooled and cross‑material settings, low nRMSE is frequently insufficient to identify useful design selections. Physics‑informed losses alter trade‑offs rather than monotonically improving all metrics, and dimensionless conditioning improves comparability without making transfer symmetric. The benchmark is not a certified material model; within the released oracle, candidate generator and material cards, pinn‑gym provides a reproducible testbed for evaluating PIML surrogates as decision systems rather than curve predictors alone.
Authors:Herilalaina Rakotoarison, Steven Adriaensen, Tom Viering, Carl Hvarfner, Samuel Müller, Frank Hutter, Eytan Bakshy
Abstract:
Information‑theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration‑exploitation framework for Bayesian optimization (BO). However, their practical implementation relies on complicated and slow approximations, i.e., a Monte Carlo estimation of the information gain. This complexity can introduce numerical errors and requires specialized, hand‑crafted implementations. We propose a two‑stage amortization strategy that learns to approximate entropy search‑based acquisition functions using Prior‑data Fitted Networks (PFNs) in a single forward pass. A first PFN is trained to be conditioned on information about the optima; second, the α‑PFN is trained to predict the expected information gain by training on information gains measured with the first PFN. The α‑PFN offers a flexible learned approximation, which replaces the complex heuristic approximations with a single forward pass per candidate, enabling rapid and extensible acquisition evaluation. Empirically, our approach is competitive with state‑of‑the‑art entropy search implementations on synthetic and real‑world benchmarks, while accelerating the different entropy search variants across all our experiments, with speed ups over 50x. Source code: https://github.com/automl/AlphaPFN.
Authors:Jilles S. van Hulst, Jakub M. Tomczak, W. P. M. H. Heemels, Duarte J. Antunes
Abstract:
Variational autoencoders (VAEs) learn low‑dimensional latent representations of high‑dimensional data. When the data lies on a manifold with non‑Euclidean topology, the standard Gaussian prior introduces a topological mismatch that degrades reconstruction quality and prevents faithful representation. We present a constructive mathematical framework that resolves this mismatch for all manifolds that admit a product covering space. These are manifolds expressible as products of elementary factors (circles, intervals, or lines) or as quotients of such products by a finite symmetry group. The class includes cylinders, tori, Möbius strips, Klein bottles, and real projective spaces. Factorized distributions over the elementary factors yield product topologies with closed‑form, decoupled KL divergences, so that each latent factor can be shaped independently while keeping training tractable. We catalogue reparametrizable encoder‑prior pairs for periodic, bounded, and unbounded supports, and provide coordinate transformations that allow standard neural networks to output non‑Euclidean parameters with smooth gradients. For quotient manifolds, the decoder receives group‑invariant features of the covering‑space coordinates, so that identified points produce identical outputs. Anchor constraints fix the coordinate system relative to the data or create soft topological holes. Experiments on synthetic manifolds and real‑image datasets (rotated and cyclically shifted MNIST) confirm that a topology‑matched prior aligns KL regularization with the data manifold. The resulting topology‑aware models outperform the Gaussian baseline at all practically relevant regularization strengths. The code is available at https://github.com/JvHulst/VAE‑Topology.
Authors:Yongliang Miao, Fengyuan Liu, Wei Shi, Yanguang Liu, Fei Sun, Na Zou, Mengnan Du
Abstract:
Supervised fine‑tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations, often treating a single expert trajectory as the target behavior. However, reasoning is not simple path imitation: rigidly following one demonstrated solution may overfit to surface forms and suppress the model's own reasoning distribution. We propose Rollout‑Adaptive Supervised Fine‑Tuning (RASFT), a policy‑aware SFT framework that calibrates expert supervision according to problem‑level solvability estimated from verified on‑policy rollouts. For each problem, RASFT strengthens expert guidance when the current policy struggles, while relaxing rigid imitation and incorporating correct self‑generated trajectories when the model already exhibits reliable reasoning behavior. To preserve useful reasoning priors, RASFT further introduces a clipped inverse ratio between the frozen reference model and the current policy to constrain excessive policy drift. Experiments across multiple models on six mathematical reasoning benchmarks and two code reasoning benchmarks show that RASFT achieves better overall performance than SFT, SFT variants, and representative RL methods. The code is available at https://github.com/zjd1sq/RASFT.
Authors:Rahul Nair, Chun Tao
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.
Authors:Zhiwei Xu, Shihao Wu, Hanseul Cho, Wei Hu, Yixin Wang
Abstract:
Classical scaling laws for language model pretraining balance model size against training dataset size under a fixed compute budget, assuming abundant data and a single pass over the corpus. As training compute grows faster than the supply of natural language data, pretraining is likely to enter a data‑constrained, compute‑rich regime where models train for multiple epochs over a finite dataset. We study data‑constrained pretraining along two axes, regularization and scaling. For regularization, we study masked‑input regularization (MIR), an auxiliary next‑token prediction loss on randomly masked inputs. MIR tests whether the random masking central to diffusion language models can benefit autoregressive pretraining without architectural changes or inference overhead. Across 72M to 1.4B parameter models, we find that MIR added on top of strong weight decay improves validation loss over autoregressive strong‑weight‑decay‑only models, with downstream gains at 1.4B. For scaling, we propose SoftQ, a scaling law that couples model size and data size to capture their interaction under repeated data. Classical alternatives such as the Chinchilla law use an additive form that decouples these terms, making them misspecified in the data‑constrained regime. We find that SoftQ fits data‑constrained experiments substantially better than these alternatives, and estimates MIR's gains as equivalent to roughly 1.3 times as much unique training data. We release our code at https://github.com/yixinw‑lab/dc_pretrain.
Authors:Jiazi Bu, Pengyang Ling, Yujie Zhou, Yibin Wang, Yuhang Zang, Tianyi Wei, Xiaohang Zhan, Jiaqi Wang, Tong Wu, Xingang Pan, Dahua Lin
Abstract:
Group Relative Policy Optimization (GRPO) has demonstrated remarkable success in aligning text‑to‑image (T2I) flow models with human preferences. However, we have identified that the learning loop of current flow‑based GRPO is fundamentally decoupled from the learner's current capability, suffering from critical blind spots at both prompt selection and advantage estimation: (i) Existing methods sample prompts randomly, overlooking the substantial impact of data selection on reinforcement learning (RL) efficacy‑‑a factor proven crucial in GRPO for large language models; (ii) They evaluate sample quality solely relying on intra‑group statistics, lacking a global perspective to accurately measure true policy improvement. To address these issues, we propose Adaptive GRPO (AdaGRPO), a novel capability‑aware RL algorithm tailored for flow models. Specifically, AdaGRPO consists of two principal components: (i) Online Curriculum Filtering Strategy: Dynamically tracks the model's proficiency and adaptively selects prompts that best match its current learning boundary; (ii) Cross‑Level Advantage Fusion: Synergistically integrates fine‑grained intra‑group advantages with macro‑level global advantages, providing a comprehensive and unbiased policy evaluation. As a lightweight, plug‑and‑play module, AdaGRPO can be seamlessly integrated with existing frameworks such as Flow‑GRPO, DanceGRPO, and Flow‑CPS. Extensive experiments demonstrate that AdaGRPO consistently drives performance gains while significantly stabilizes GRPO training for flow models.
Authors:Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali, Owen Palinski, Allan Salihovic, Dinh Nguyen, Minghui Liwang, Seyyedali Hosseinalipour
Abstract:
This paper presents a forward‑looking vision for integrating the emerging multi‑modal multi‑task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi‑modal multi‑task foundation models (M3T FMs) with the privacy‑preserving and distributed learning capabilities of federated learning (FL). Given the largely underexplored nature of this research direction, we first introduce the fundamental training/fine‑tuning principles of M3T FedFMs. We then discuss a range of their representative use cases in vehicular networks, illustrating the significant potential of M3T FedFMs to enable next‑generation vehicular intelligence. Afterwards, we identify key constraints inherent to vehicular environments that challenge the practical deployment of M3T FedFMs, and articulate a set of forward‑looking research directions to address these challenges. Furthermore, through a case study conducted on a real‑world vehicular dataset (i.e., Waymo Open Dataset), we demonstrate the promise of M3T FedFMs for vehicular networks and release our implementation to facilitate reproducibility and stimulate research in this emerging area (repository: https://github.com/KasraBorazjani/vehicular‑fedfm)
Authors:Yikai Zhang, Gaoxiang Jia, Jie Ding, Boxiang Wang
Abstract:
TorchKM is an open‑source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration. The library features a scikit‑learn‑style API and is designed to exploit GPU‑friendly linear algebra, accelerating the full training and model‑selection pipeline through intelligent reuse of matrix operations. Benchmarks show competitive predictive performance with substantial speedups over standard baselines. The efficiency and programmable design also make TorchKM a kernel‑learning component for AI‑driven workflows. Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.
Authors:Zhiling Yan, Dingjie Song, Hanrong Zhang, Wei Liang, Yuxuan Zhang, Yutong Dai, Lifang He, Philip S. Yu, Ran Xu, Xiang Li, Lichao Sun
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.
Authors:Zepeng Zhang, Aref Einizade, Jhony H. Giraldo, Olga Fink
Abstract:
Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning approaches, like recurrent and graph neural networks, rely on iterative propagation, which tends to accumulate errors over time and space. Recent diffusion‑based methods mitigate error propagation but require iterative sampling and often depend on problem‑agnostic Gaussian priors, limiting both efficiency and effectiveness. To address these limitations, we propose GiFlow, a Graph‑Informed Flow Matching framework for spatiotemporal imputation. GiFlow replaces the typical Gaussian prior with a graph‑informed prior constructed via spatiotemporal filtering of observable signals, which better aligns the source distribution to the target and thereby simplifies the generation trajectory. The flow field is parameterized by a hybrid vector field model that integrates spatial attention, temporal attention, and spatiotemporal propagation, enabling joint modeling of spatial and temporal dependencies. Extensive experiments on both synthetic and real‑world datasets demonstrate that the proposed GiFlow outperforms the state‑of‑the‑art approaches in spatiotemporal imputation. The code is available at https://github.com/zepengzhang/GiFlow.
Authors:Ujjwal Bhatta, Utsabi Dangol, Sumaly Bajracharya, Rodrigue Rizk, KC Santosh
Abstract:
Sparse rewards and heterogeneous task sequences remain persistent challenges in Reinforcement Learning (RL), often resulting in slow convergence, weak generalization, and inefficient exploration. We propose Uncertainty‑Aware LLM‑Guided Policy Shaping (ULPS), a novel framework that integrates a calibrated Large Language Model (LLM) into the RL training loop to provide structured, uncertainty‑modulated behavioral guidance. ULPS employs an A‑based oracle to synthesize optimal symbolic trajectories, which are used to fine‑tune a BERT‑based language model. During training, this model supplies action suggestions whose influence is conditioned on epistemic uncertainty estimated via Monte Carlo (MC) dropout. An entropy‑based blending mechanism adaptively balances LLM guidance and the learned policy (via Proximal Policy Optimization, PPO), allowing the agent to prioritize reliable priors while preserving adaptability. We evaluate ULPS on the MiniGridUnlockPickup benchmark and observe consistent improvements in success rate, reward efficiency, and sample complexity over unguided, uncalibrated, and standard RL baselines. ULPS achieves more than 9% improvement in execution accuracy after fine‑tuning, requires fewer environment interactions, and yields higher reward AUC. Our results demonstrate that integrating symbolic A trajectories, pretrained language priors, and uncertainty‑aware control offers a principled and effective approach to multi‑task reinforcement learning in sparse‑reward domains, with potential extensibility to partially observable and multi‑agent settings.
Authors:Tang Li, Yanlin Chen, Mengmeng Ma, Xi Peng
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.
Authors:Jun-You Lin, Ying Choon Wu, Tzyy-Ping Jung
Abstract:
Objective. EEG foundation models (FMs) report strong accuracy on clinical resting‑state EEG. However, high accuracy under subject‑disjoint cross‑validation remains ambiguous: it can reflect a genuine clinical biomarker, or subject‑identity features that correlate with the label. We name this the Identity Trap and ask whether it can be diagnosed at the representation level before fine‑tuning. Approach. We propose FMScope, a frozen‑representation protocol packaging five diagnostics: variance decomposition, subject‑axis erasure, aperiodic 1/f ablation, layer‑wise label probing, and within‑subject direction consistency. We apply it to three pretrained FMs (LaBraM, CBraMod, REVE) across four datasets in a 2x2 layout: subject relation of label x presence of a consensus cross‑subject EEG marker. Main results. (i) The Identity Trap is universal: frozen subject‑variance is 13‑89x a random null in 12/12 pairs, rising in all 12 under fine‑tuning (+10 to +63 pp). This dominance is a removable linear axis: erasing it improves label decoding where the label varies within subject (+6 to +12 pp in primary cells; +4 to +27 pp across external cohorts). (ii) Aperiodic 1/f is one subject carrier: removing it drops the subject probe by 9‑19 pp on LaBraM and CBraMod. REVE saturates subject identity without measurable aperiodic dependence. (iii) Fine‑tuning amplifies label‑variance only in cells with a literature‑established cross‑subject marker. Significance. The Identity Trap is a physically‑grounded instance of shortcut learning: the preferred cue has a measurable physiological component, and subject‑disjoint splitting alone cannot rule it out. FMScope separates gains reflecting a biological marker from those reflecting subject identity.
Authors:Richard Li, Aditya Prakash, Andrew Wen, Saurabh Gupta, Yilun Du, Pulkit Agrawal
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.
Authors:Sam Buchanan, Druv Pai, Peng Wang, Yi Ma
Abstract:
In the current era of deep learning and especially generative models, there is significant investment in training very large deep neural networks. Thus far, such models have been "black boxes" that are difficult to understand in the sense that they have opaque internal mechanisms, leading to difficulties in interpretability, reliability, and control. Naturally, this lack of understanding has led to both hype and fear. This book is an attempt to "open the black box" and understand the mechanisms of large deep networks, through the perspective of representation learning, which is a major factor ‑ arguably the single most important one ‑ in the empirical power of deep learning models. A brief outline of this book is as follows. Chapter 1 will summarize the threads that underlie the whole text. Chapters 2, 3, 4, 5, and 6 will explain the design principles of modern neural network architectures through optimization and information theory, reducing the process of architecture development (long having been described as a sort of "alchemy") to undergraduate‑level linear algebra and calculus exercises once the underlying principles are introduced. Chapters 7 and 8 will discuss applications of these principles to solve problems in more paradigmatic ways, obtaining new methods and models which are efficient, interpretable, and controllable by design, and yet no less ‑ sometimes even more ‑ powerful than the black‑box models they resemble. Chapter 9 will discuss potential future directions for deep learning, the role of representation learning, as well as some open problems.
Authors:Nishit Anand, Ashish Seth, Sreyan Ghosh, Dinesh Manocha, Ramani Duraiswami
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%.
Authors:Jingbo Gong, Yikai Wang, Yushi Lan, Yuhao Wan, Ziheng Ouyang, Rui Zhao, Ming-Ming Cheng, Qibin Hou, Chen Change Loy
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.
Authors:Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak, N. J. Mayne, Miles Cranmer
Abstract:
In the sciences, regression tasks often require predicting high‑dimensional outputs from few training examples. Multi‑output Gaussian processes excel in low‑data regimes but typically struggle with high‑dimensional outputs. Compress‑then‑predict pipelines such as PCA‑GP (principal component analysis plus Gaussian process regression) handle high dimensionality, but rely on bases optimized for reconstruction rather than prediction. To address this gap, we propose a model that represents each output as a linear‑Gaussian decoding of a low‑dimensional latent state drawn from a Gaussian process prior. By analytically marginalizing the decoder weights, we couple compression and prediction in a single objective that scales to high‑dimensional outputs. We refer to this model as Gaussian process latent factor regression (GPLFR). We demonstrate GPLFR by building the first spatially resolved emulator of global climate models for rocky exoplanets.
Authors:Ziyue Li, Yang Li, Tianyi Zhou
Abstract:
Large language models (LLMs) perform inference by following a fixed depth and order, non‑recurrent execution of all layers. We reveal the wide existence of training‑free, flexible, dynamic program‑of‑layers (PoLar), where pretrained layers can be packed as modules and then skipped or looped to form a customized program for each input. For most inputs, substantially shorter program executions can achieve the same or better accuracy, while incorrect predictions of the original LLM can be corrected by alternative programs with fewer layers. These observations indicate that inference admits multiple valid latent computations beyond the standard forward pass. To efficiently achieve PoLar in practice, we propose a lightweight PoLar prediction network, which learns to generate execution programs that dynamically skip or repeat pretrained layers for each input. Experiments on mathematical reasoning benchmarks demonstrate that PoLar consistently improves accuracy over standard inference and prior dynamic‑depth methods, often while executing fewer layers, and that these gains persist under out‑of‑distribution evaluation. Our results suggest that fixed‑depth execution captures only a narrow subset of an LLM's latent reasoning capacity.
Authors:Bo Liu, Yuan Xie, Yuan Gao, Xiaolong Luo, Peng Ye, Tao Chen, Fujun Han
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.
Authors:Haoji Hu, Huaqing Mao, Yijun Lin, Xiaowei Jia, Jinwei Zhou, Minoh Jeong, Yao-Yi Chiang
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
Authors:Xiwen Chen, Wenhui Zhu, Jingjing Wang, Peijie Qiu, Zhipeng Wang, Huayu Li, ZhengXiao He, Xuanzhao Dong, Prayag Tiwari, Mingkun Xu, Yujian Xiong, Feng Luo, Abolfazl Razi, Brendan Hogan Rappazzo, Anderson Schneider, Yuriy Nevmyvaka
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.
Authors:Žiga Kovačič, Kevin Ellis
Abstract:
To study the ability to infer physical dynamics from videos and extrapolate them forward in time, we assemble a dataset of 2D Material Point Method (MPM) physical simulations covering rich physical phenomena such as deformable objects, fluids, kinetic objects, and emitters. We study code generation and video diffusion approaches on this dataset, identifying their strengths and weaknesses by varying the amount of physically relevant side information. The code generation model, beyond giving a working demonstration of automatic synthesis of MPM simulations, reveals that such an approach struggles with inferring physical parameters from visual input, but relative to video diffusion, produces physically and temporally stable extrapolations forward in time, while the video diffusion model more strongly identifies geometric properties from visual input but produces physically implausible extrapolations.
Authors:Anushka Tiwari, Kaiyi Ji
Abstract:
While prompt‑based parameter‑efficient continual learning mitigates catastrophic forgetting by isolating task‑specific prompts, this isolation also limits later tasks from improving earlier ones, leaving backward knowledge transfer underexplored. We address this limitation by proposing Selective bAckward refinement for positive Backward knowledge transfER (SABER), a replay‑free framework that enables controlled backward transfer in prompt‑based continual learning. SABER determines when backward refinement is beneficial using complementary task‑correlation criteria based on prompt‑gradient geometry and loss‑distribution similarity, and how to perform refinement safely by restricting updates to non‑interfering directions in the prompt parameter space. Extensive experiments across multiple continual learning benchmarks and diverse pretrained backbones, including T5‑Large, LLaMA, and Qwen, demonstrate that SABER consistently achieves positive backward transfer while maintaining strong overall average performance. Code is available at https://github.com/OptMN‑Lab/SABER‑ICML‑2026/.
Authors:Zhicheng Zhang, Jiwei Tang, Kuicai Dong, Xiaopeng Li, Jieming Zhu, Jingyu Li, Qianhui Zhu, Fengyuan Lu, Wang Jiaheng, Gang Wang, Hai-Tao Zheng, Zhaocheng Du
Abstract:
Hard negative mining has become the dominant strategy for training retrievers, yet it faces intrinsic limitations: negatives are bounded by corpus availability, selected by retriever score rather than diagnostic value, and increasingly contaminated by false positives as the retriever improves. LLM‑based synthesis offers a principled alternative, where negatives that are unconstrained, targeted, and free from false positive risk. But we show that naively incorporating generated negatives into contrastive learning often degrades retrieval performance. We identify and formalize the root cause as a generative‑discriminative gap: LLM generation optimizes for fluent, plausible text, while contrastive learning demands strategic violations of relevance at the decision boundary. Our analysis reveals two compounding failure modes: discriminative‑agnostic generation, where the LLM lacks an explicit model of query information needs and defaults to generic or topic‑drifted text that provides no contrastive signal; and source‑dependent shortcuts, where distributional artifacts enable the model to distinguish negatives by origin rather than relevance, causing gradient drift that actively corrupts optimization. To close this gap, we propose CausalNeg consisting of two main modules: (1) CoT‑guided counterfactual perturbation for data construction: decomposes why a document satisfies a query into explicit information requirements, then surgically violates individual requirements to construct negatives with controlled, interpretable hardness. (2) Query‑view entropy maximization during training: disperses generated negatives across the similarity spectrum, minimizing the mutual information between source identity and similarity scores to suppress shortcut exploitation. We make our code publicly available at https://github.com/mzhangzhicheng/CausalNeg.
Authors:Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Xinyue Bi, Zhaoyi Li, Zhiqiang Shen
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.
Authors:Benjamin A. Burns, Sara Fridovich-Keil
Abstract:
Diffusion models have excellent capacity to model complex distributions of natural data, which has made them a popular and effective choice for posterior sampling in imaging inverse problems. Existing methods can incorporate any measurement model at inference time but must use an inexact approximation for the likelihood at intermediate timesteps for computational tractability. Although these approximations can often work well empirically, their downstream effect on the sampled posterior is poorly understood and can result in unexplained failures. To understand when, why, and how these likelihood approximations propagate to erroneous posterior distributions, we introduce a finite‑sample perspective on posterior sampling that approximates the posterior to arbitrary precision as training set size tends towards infinity, for any forward model and prior distribution. Using this finite‑sample lens, we observe that popular posterior sampling approximations tend to under‑ or over‑estimate the spread of the posterior at intermediate timesteps, causing downstream consequences including sensitivity to early stopping time, inaccurate relative weighting of posterior modes, and hallucination, both of prior modes that are not in the posterior and likelihood modes that are not supported by the prior. Moreover, we find that the cause of these posterior errors requires neither a nonlinear measurement model nor a multimodal posterior, but can arise solely due to a multimodal prior and inaccurate posterior spread at intermediate sampling times. Our finite‑sample posterior sampling approach is agnostic to the type of likelihood approximation and the type of (linear or nonlinear) forward model, and can thus serve as a drop‑in diagnostic to evaluate the accuracy and failure modes of existing and future posterior samplers.
Authors:Sy-Tuyen Ho, Minghui Liu, Huy Nghiem, Furong Huang
Abstract:
Autonomous AI research agents aim to accelerate scientific discovery by automating the research pipeline, from hypothesis generation to peer review. However, existing benchmarks rarely test a fundamental bottleneck: whether Large Language Models can judge the methodological viability of a research idea before expending time and computational resources. We introduce SoundnessBench, a curated benchmark of 1,099 machine‑learning research proposals reconstructed from ICLR submissions, labeled with reviewer soundness sub‑scores, and audited against source papers. SoundnessBench should be interpreted as a benchmark for recoverable proposal‑stage soundness rather than exact prediction of full‑paper review outcomes. Across 12 frontier LLMs, we find a pervasive optimism bias: under standard prompting, models frequently rate low‑soundness proposals as sound, while aggressive prompting largely shifts errors from false positives to false negatives. Additional controls for public‑corpus contamination, paper‑identifying phrases, surface features, and human audit quality suggest that this behavior is not explained by a single confounder. Our results indicate that current LLMs are not yet reliable as standalone first‑gate evaluators for scientific rigor.
Authors:Ziwen Xu, Haiwen Hong, Linsong Yu, Benglei Cui, Longtao Huang, Hui Xue, Ningyu Zhang
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.
Authors:Xin Wang, Linxin Xiao, Yang Yao, Wenwu Zhu
Abstract:
Drug synergy prediction (DSP) aims to identify efficacious drug combinations under various cellular contexts with different targets. However, the continual emergence of novel compounds results in variations in molecular scaffolds and sizes, causing drug synergy data to exhibit out‑of‑distribution (O.O.D.) shifts with respect to topological structure. Existing works rely on in‑distribution (I.D.) assumption, failing to handle the O.O.D. shifts. To solve this problem, we study out‑of‑distribution generalized drug synergy prediction through a graph large language model for the first time. Nevertheless, O.O.D. generalized DSP is highly non‑trivial, posing several challenges: i) how to discover structurally relevant and irrelevant molecular representations with respect to cell targets; ii) how to find the optimal graph neural architectures that accurately calculate molecular representations; and iii) how to jointly leverage molecular structural and semantic information in LLMs. To address these challenges, we propose OOD‑GraphLLM, a novel graphLLM framework which is able to accurately predict drug synergy under O.O.D. settings via jointly optimizing molecular graph representation and biomedical semantic language representations in a unified manner. Furthermore, we finetune DrugSyn‑LLM, a biomedical LLM, and employ a retrieval‑augmented biomedical instruction tuning strategy to align molecular topological information and molecular semantic information with language‑based reasoning for O.O.D. generalized DSP. Both the source code (https://github.com/EkkoXiao/Bio‑GraphLLM) and released model (https://mn.cs.tsinghua.edu.cn/bio‑graphllm/) are publicly available, where users are allowed to download model resources and interactively use the system through a web interface.
Authors:Haoming Xu, Weihong Xu, Zongrui Li, Mengru Wang, Yunzhi Yao, Chiyu Wu, Jin Shang, Yu Gong, Shumin Deng
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.
Authors:Travis Lelle
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.
Authors:Zhuguanyu Wu, Ruihao Gong, Yang Yong, Yushi Huang, Xiangyu Fan, Lei Yang, Dahua Lin, Xianglong Liu
Abstract:
Distribution Matching Distillation (DMD) is a widely used paradigm for accelerating inference in few‑step video diffusion models. However, DMD‑style video distillation faces two coupled challenges: the fake score must track a continuously evolving generator, making training costly when frequent updates are required, while reverse‑KL‑style matching can be mode‑seeking and conservative for preserving strong motion dynamics. To address these issues, we propose Score Gradient Matching Distillation (SGMD). SGMD adopts a fake‑score perspective by directly optimizing the fake score toward the teacher, while using teacher stop‑gradient Fisher as a stable distribution‑matching objective. We provide a gradient analysis that motivates this objective choice under ideal tracking. Building on this, SGMD introduces a pair of dual potentials: negative‑residual (NR) for outer‑loop correction and residual‑contraction (RC) for inner‑loop tracking. Empirically, compared to DMD2, SGMD achieves an approximately ～ 3× training speedup and substantially improves motion dynamics for 4‑step distilled models while preserving temporal consistency. A human study confirms that SGMD is preferred in motion quality and overall preference, while visual quality and text alignment remain comparable. Code is available at https://github.com/ModelTC/LightX2V.
Authors:Matt Y. Cheung, Ashok Veeraraghavan, Hanjie Chen, Guha Balakrishnan
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.
Authors:Jaa-Yeon Lee, Yeobin Hong, Taesung Kwon, Jong Chul Ye
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
Authors:Víctor Gallego
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.
Authors:Muhammed Furkan Dasdelen, Fatih Ozlugedik, Ilaria Looser, Rao Muhammad Umer, Christian Pohlkamp, Carsten Marr
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.
Authors:Haochen Yang, Ke Zhao, Mengyuan Ma, Xingyu Lu, Xiangfeng Wang, Hong Qian
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.
Authors:Leyi Qi, Yiming Li, Siyuan Liang, Zhengzhong Tu, Dacheng Tao
Abstract:
Large‑scale text‑to‑image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intellectual property concerns, making model ownership verification (MOV) increasingly critical. We find that existing backdoor‑based diffusion watermarking methods often (implicitly) assume a "faithful" verification process, namely, that the verifier can query a suspicious model and obtain the faithful watermark response to complete MOV. However, in practice, adversaries may intentionally or unintentionally damage potential watermark signals, significantly degrading verification reliability. To address this issue, we propose Cert‑LAS, the first certified MOV method for T2I models based on layer‑adaptive smoothing. In general, Cert‑LAS embeds specified watermarks using diffusion classifiers and an LFS‑guided layer‑adaptive noise, and verifies ownership by examining whether the suspected model exhibits significantly stronger watermark responses compared to unwatermarked references through hypothesis testing. We further prove that, under certain conditions, our Cert‑LAS can still achieve reliable verification even in the presence of malicious removal attacks. Extensive experiments validate the effectiveness of Cert‑LAS and its resistance to adaptive attacks. Our code is available at https://github.com/Leyi‑Qi/Cert‑LAS.
Authors:NamGyu Jung, Chang Choi
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.
Authors:Runang He, Tongya Zheng, Huiling Peng, Yuanyu Wan, Bingde Hu, Jiawei Chen, Canghong Jin, Mingli Song, Can Wang
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/.
Authors:Yan Chen, Taojie Zhu, Meng Zhang, Xin Chen, Jiaqi Huang, Dongyang Xu, Yizhi Wang
Abstract:
Continual supervised fine‑tuning (SFT) is the de facto recipe for adapting large language models (LLMs) to a stream of downstream tasks, but it suffers from catastrophic forgetting of earlier capabilities. Recent work shows that on‑policy signals ‑‑ training on the model's own outputs ‑‑ reduce forgetting more reliably than off‑policy supervision. Existing on‑policy methods route this signal through a new training objective (e.g., self‑distillation losses with a teacher copy), inheriting an extra forward pass, schedule sensitivity, and stylistic drift from the teacher.We instead route the on‑policy signal through the training data source. Our method, On‑Policy Replay (OPR), rolls out the most recent checkpoint on a small budget of historical prompts, filters the generations by a task reward, and replays the surviving (prompt, model response) pairs as ordinary SFT examples. There is no teacher, no auxiliary loss, and no on‑the‑fly distillation. Across three 7‑‑8B instruction‑tuned backbones (Qwen2.5‑7B‑Instruct, Qwen3‑8B, Llama3.1‑8B‑Instruct) on the TRACE continual‑learning benchmark, OPR consistently reduces forgetting; on the sharpest stress test (Qwen2.5‑7B‑Instruct, Sequential SFT BWT ‑13.93), OPR lifts BWT to ‑0.65 at a 10% replay budget and to ‑2.29 at a 1% budget ‑‑ a 46% reduction in |BWT| over a tuned Vanilla Replay baseline, with 42‑‑46% reductions observed across all three backbones. We give a KL‑shrinkage interpretation that places OPR and prior on‑policy distillation methods on a single axis, and we present a counterintuitive finding that explains why Vanilla Replay is already a strong baseline: low‑score replay is uniformly worse than Vanilla Replay, demonstrating that the active ingredient in OPR is the on‑policy distribution, not the response quality alone.Our code is available at https://github.com/Yancey2024/OnPolicyReplay.
Authors:Rohan Shravan
Abstract:
Large language models route every input through a learned embedding table of shape |V| x d_model, consuming hundreds of millions to billions of trainable parameters at frontier scale. We introduce Kronecker Embeddings, a deterministic byte‑level character‑position factorization that replaces this table with a fixed encoder and a single learned projection, compatible with standard BPE tokenizers, eliminating 91‑‑94% of input‑side trainable parameters at frontier scale. We provide five contributions. First, a cross‑model probe across six LMs (135M‑671B parameters) shows trained input embeddings cluster typographic variants of the probe word far more than morphological relatives; Kronecker escapes this clustering at the embedding layer. Second, a controlled three‑seed comparison on nanoGPT GPT‑2 124M over 2.5B tokens of FineWeb‑Edu shows Kronecker reaching 2.5 +‑ 0.2% lower validation loss than the BPE‑tied baseline (gap 0.083 +‑ 0.007 nats, ~9% lower perplexity), needing ~1.43x fewer steps to reach BPE's converged loss. Third, a spelling‑robustness probe over 110 clean/typo pairs shows Kronecker preserves the top‑1 prediction on 55.5% of pairs vs. 47.3% for BPE (+8.2 pp) and lowers KL by 7.6%, winning or tying in 10 of 11 categories; a generation probe shows Kronecker echoes byte‑novel strings and typos through generation where BPE forgets them. Fourth, BPE embedding norm drifts during training while Kronecker projection norm stays near 1.0, consistent with a stable representational target. Fifth, an on‑the‑fly runtime variant reconstructs embeddings from a 4.5 MB byte buffer rather than a 2.15 GB table at vocabulary 131,072, with 0.01‑‑0.24% step‑time overhead. Byte‑level locality has a tradeoff: byte‑similar but semantically distant pairs (compute/commute, nation/notion) cluster together, shifting disambiguation to early attention layers.
Authors:Xiaohang Tang, Keyue Jiang, Che Liu, Qifang Zhao, Xiaoxiao Xu, Sangwoong Yoon, Ilija Bogunovic
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.
Authors:Hesam Asadollahzadeh, Feng Liu, Christopher Leckie, Sarah M. Erfani
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).
Authors:Rohan Shravan
Abstract:
We present BrahmicTokenizer‑131K, a 131,072‑vocabulary byte‑level BPE tokenizer that closes the Brahmic compression gap at the 131K‑vocabulary class while preserving the English, EU‑language, and code compression of OpenAI's o200k_base. We construct it through a two‑stage retrofit: (1) a script‑prune crop that reduces 200,019 tokens to 131,072 by removing nine out‑of‑scope writing systems, and (2) a surgical retrofit of 2,372 corpus‑dead vocabulary slots determined by linear‑programming allocation across nine Brahmic Unicode blocks. The pre‑tokenizer, decoder, and inherited merge rules are unchanged from o200k_base, making BrahmicTokenizer‑131K a drop‑in replacement at the tokenizer interface.
On 27 million documents of public Indic pretraining text (2.84 billion words, 46.21 GB), BrahmicTokenizer‑131K produces 26.7% fewer tokens than Mistral‑Nemo Tekken / Sarvam‑m at the same vocabulary budget, with per‑language savings of 15.79% (Tamil) to 76.79% (Odia, a 4.31x compression ratio). The Odia advantage is mechanistically explained by Tekken/Sarvam‑m containing zero Oriya‑block tokens; our surgery added 725. On non‑Indic content, BrahmicTokenizer‑131K matches o200k_base's English fertility (1.235 vs 1.232 tokens/word) and beats Tekken/Sarvam‑m by 4.0‑14.2% on HumanEval, MBPP, and GSM8K. Across our 14‑tokenizer benchmark, it is the only tokenizer simultaneously competitive on Brahmic, English, EU, code, and math at the 131K budget. Specialist tokenizers at other vocab classes (Sarvam‑30B, Sarvam‑1, MUTANT‑Indic) achieve better Indic compression at the cost of non‑Indic performance: Sarvam‑1's English fertility is 15.9% worse and its code/math compression 26‑33% worse than ours. We release the artifact under Apache 2.0 at https://huggingface.co/theschoolofai/BrahmicTokenizer‑131K.
Authors:Kyuil Sim, Sanghyeok Choi, Jinkyoo Park
Abstract:
Integer Linear Programming (ILP) serves as a versatile framework for modeling a wide range of combinatorial optimization problems, typically addressed by sophisticated exact solvers or heuristics. While learning‑based approaches have recently shown their effectiveness, they suffer from poor generalization to out‑of‑distribution instances and inherent dependence on external solvers. In this work, we propose a solver‑free, sampling‑based optimization framework for ILP that directly explores discrete feasible regions without training or external solvers. Exploiting the linear structure of ILP, we employ a Locally‑Balanced Proposal to construct a transition kernel, thereby avoiding the gradient approximation. To overcome the highly multimodal nature of ILP energy landscapes, we integrate Parallel Tempering. In addition to standard temperature tempering, we introduce penalty tempering, which modulates constraint barriers while preserving the objective landscape over feasible solutions. Empirically, our method consistently outperforms SCIP across all four benchmarks, matches or exceeds Gurobi on two of four tasks within a 200‑second budget, and is substantially more robust to distribution shift than learning‑based methods. Furthermore, on MIPLIB 2017 instances, our framework remains competitive with classical solvers without any problem‑specific tuning.
Authors:Yiqun Liu, Yingsheng Wu, Ruqi Yang, Enrong Zheng, Honglei Qiu, Sijun He, Tai Liang, Jingjing Wu, Yuhan Zhou, Yiwei Zhang, Dongyan Chen, Weihan Yi, Xinqi Li, Siqi Bao
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.
Authors:Arunkumar Ramachandran
Abstract:
Alarm fatigue in intensive care units (ICUs) is a well documented patient safety crisis. Clinical monitors generate 350 or more alarms per patient per day, out of which 72‑99% are clinically irrelevant. Staff desensitization to non‑actionable alarms increases the risk of missed true emergencies. This paper presents SigmaMedStat, a machine learning system that evaluates the trustworthiness of physiological alarm signals before clinical action is taken. Four approaches were evaluated on the PhysioNet/Computing in Cardiology Challenge 2015 dataset of 498 four‑channel ICU alarm recordings. Primary contribution is a temporal modeling framework that splits each 60 second recording into six consecutive 10‑second chunks, and this in turn generates Continuous Wavelet Transform (CWT) scalograms per chunk, encodes each chunk with a shared EfficientNet‑B0 encoder, and passes the resulting feature sequence to a two‑layer Long Short‑Term Memory (LSTM) network. Five‑fold stratified cross‑validation yields a mean AUC of 0.822 +/‑ 0.016 (95% CI: [0.790,0.853]), compared to 0.641 for a static EfficientNet baseline trained on the full 60‑second window. Ablation studies confirm that temporal chunking and multi‑channel signal fusion both contribute independently to classification performance. Per‑alarm type analysis reveals that Ventricular Flutter is the most accurately classified alarm type (AUC 0.820) while Asystole remains the hardest (AUC 0.722). Error analysis identifies 65 false negatives and 85 high‑confidence misclassifications as the primary failure modes. All code and results are publicly available at https://github.com/Arun‑K‑Ram/sigmamedstat.
Authors:Xinyu Liu, Darryl Cherian Jacob, Yang Zhou, Jindong Wang, Pan He
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
Authors:Mohan Zhang, Yuqi Jia, Zhen Tan, Steven Jiang, Neil Zhenqiang Gong, Tianlong Chen, Dawn Song
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.
Authors:Nicolas Gillis, Subhayan Saha, Stefano Sicilia, Arnaud Vandaele
Abstract:
Given a matrix X, and two ranks r_1 and r_2, the Hadamard decomposition (HD) looks for two low‑rank matrices, X_1 of rank r_1 and X_2 of rank r_2, both of the same size as X, such that X\approx X_1\circ X_2, where \circ is the Hadamard (element‑wise) product. In most cases, HD is more expressive than standard low‑rank approximations such as the truncated singular value decomposition (TSVD), as it can represent higher‑rank matrices with the same number of parameters; this is because the rank of X_1 \circ X_2 is generically equal to r_1 r_2. In this paper, we first present some theoretical insights for HD, in particular a useful reformulation X\approx WH^\top where W and H have r_1 r_2 columns and belong to certain manifolds. These allow us to develop three new algorithms for computing HD. The first one uses the representation X\approx X_1\circ X_2 and relies on the Manopt toolbox. The other two rely on the reformulation X\approx WH^\top: one is a block projected gradient method, and the other is a manifold‑based gradient descent algorithm that does not require projection onto the feasible set. The last two algorithms are particularly effective for handling large sparse data. We also propose new initializations that allow us to improve the accuracy of the HD. We compare our algorithms and initialization strategies with the TSVD and with the state of the art. Numerical results show that the new methods are efficient and competitive on both synthetic and real data.
Authors:Venkat Akhil Lakkapragada
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.
Authors:Clement Etienam, Juntao Yang, Oleg Ovcharenko, Nick Luiken, Tsubasa Onishi, Nefeli Moridis, Issam Said
Abstract:
We develop a comprehensive mathematical and computational framework for sequential surrogate modeling of three‑phase black‑oil reservoir dynamics using neural operators, with particular emphasis on Fourier Neural Operators (FNO) and their physics‑informed variant (PINO). The application focus is the Norne benchmark reservoir, defined on a heterogeneous 46×112×22 grid (N=113,344 cells), with a production history spanning T=30 timesteps covering 3298 days. Our theoretical contributions are organized around four interlocking problems: (1) functional‑analytic formulation in a product‑Sobolev‑space setting, including well‑posedness of the implicit timestep map and sharp local Lipschitz estimates; (2) covariate shift quantification, proving that the Wasserstein‑2 distance grows as W_2 \leq \varepsilon(L^n‑1)/(L‑1), with exponential population‑risk discrepancy for L>1; (3) physics‑constrained spectral stability, showing PINO training with λ_R \geq λ^_R reduces the learned Jacobian spectral radius to ρ_F + Cλ_R^‑1/2, yielding uniform‑in‑time rollout error |δ_n| \leq \varepsilon/(1‑ρ); and (4) K‑step TBPTT gradient analysis, deriving geometric bias decay O(ρ^K), optimal window K^ = O(\log(T/σ^2)), and Adam convergence O(1/\sqrtt) + O(ρ^K^). Empirical validation confirms all theoretical predictions: autoregressive PINO surrogates sustain R^2>0.99 (oil), R^2>0.90 (gas), R^2\approx 0.80 (pressure), and monotonically improving R^2 (water) across the full 3298‑day horizon, trained on eight NVIDIA B200 GPUs in under one hour. A 1000‑member ensemble runs in under one minute on a single B200 GPU, giving a ～10^4× wall‑clock speedup over the OPM finite‑volume simulator.
Authors:Sicong Wang, Ruiting Dong, Yue Liu, Bowen Zheng, Jun Meng, Jie Li, Shuaijun Guo, Yu Gu, Fanyi Di, Xin Li
Abstract:
Real‑world user behavior rarely consists of isolated actions; instead, it often forms intent flows governed by spatiotemporal dependencies. To provide integrated service recommendations, we focus on the task of Generative Spatiotemporal Intent Sequence Recommendation (GSISR), which aims to generate intent sequences that are logically coherent and physically executable within complex spatiotemporal contexts. While LLMs offer strong reasoning potential for GSISR, direct industrial deployment is limited by high inference latency and context‑mismatched or physically infeasible plans. To address these challenges, we propose a generative framework, GPlan, that internalizes LLM reasoning into lightweight models through two components. First, to enable reasoning under strict latency constraints, we introduce Progressive Implicit CoT Distillation, which compresses explicit reasoning processes into reserved latent tokens, allowing small models to inherit complex planning logic without generating long reasoning text. Second, to address the disconnect between general knowledge and real‑world constraints, we design Spatiotemporal Counterfactual DPO. By aligning the model with counterfactual context‑plan pairs, we improve sensitivity to spatiotemporal context and reduce context‑mismatched plans. Offline experiments and online A/B testing demonstrate that our approach improves sequence coherence and context responsiveness. Our implementation and the anonymized GSISR dataset are available at https://github.com/alibaba/GPlan.
Authors:Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen, Nomgondalai Amgalanbaatar, Yannis Zongo, Vasu Sharma, Maheep Chaudhary
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.
Authors:Jiahe Pan, Stelian Coros, Jitendra Malik, Toru Lin
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.
Authors:Zhen-Hao Xie, Yu-Cheng Shi, Da-Wei Zhou
Abstract:
Class‑Incremental Learning (CIL) is important in building real‑world learning systems. In CLIP‑based CIL, the model performs classification by comparing similarity between visual and textual embeddings obtained from template prompts, e.g., ``a photo of a [CLASS]''. This seemingly monolithic matching process can be decomposed into two conceptually distinct stages: attribute extraction and attribute aggregation. For example, a model may recognize cat using attributes such as fur texture and whiskers. When learning a new class like car, the model must extract additional attributes like wheels and adjust how they are aggregated in the shared representation space. However, since only data from the current task is available, incremental updates can bias both attribute extraction and aggregation toward new classes, leading to catastrophic forgetting. Therefore, we propose AREA for attribute extraction and aggregation in CLIP‑based CIL. To stabilize extraction, we anchor class‑level visual and textual attributes on the hyperspherical embedding space via principal geodesic analysis. To stabilize aggregation, we learn lightweight task‑specific experts with scoring and residual refinement, regularized by a variational information bottleneck objective. During inference, we perform routing over task attribute manifolds via optimal transport for more concise prediction. Experiments show that AREA consistently outperforms SOTA methods. Code is available at https://github.com/LAMDA‑CL/ICML2026‑AREA.
Authors:Xinchen Zhang, Bowei Liu, Jiale Liu, Chufan Shi, Yizhen Zhang, Junhong Liu, Youliang Zhang, Zhiheng Li, Yujiu Yang, Ling Yang
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.
Authors:Xinyu Wang, Mingze Li, Sicheng Lyu, Dongxiu Liu, Kaicheng Yang, Ziyu Zhao, Yufei Cui, Xiao-Wen Chang, Peng Lu
Abstract:
Vision‑Language‑Action (VLA) models unify perception, reasoning, and control within a single policy, yet their multi‑billion‑parameter backbones and diffusion‑based action heads make on‑device deployment prohibitively expensive. Prior quantization efforts offer only partial solutions, compressing the LLM backbone while leaving the DiT action head at full precision, or resorting to mixed‑precision schemes, driven by the belief that uniformly quantizing the action head is inherently unstable. We challenge this assumption with Omega‑QVLA, the first training‑free post‑training quantization framework that compresses both the language backbone and the entire diffusion action head of a VLA model to a uniform W4A4 precision, eliminating the need for mixed‑precision allocation. Omega‑QVLA combines a composite SVD‑Hadamard rotation that equalizes per‑channel weight energy while diffusing residual activation outliers with per‑step DiT activation scaling quantization that absorbs dynamic‑range drift across denoising steps. On LIBERO, Omega‑QVLA compresses Pi 0.5 and GR00T N1.5 to W4A4 with 98.0% and 87.8% task success rates, matching or exceeding their FP16 references of 97.1% and 87.0%, while reducing the static memory footprint by 71.3%. Real‑world manipulation experiments further confirm smooth, accurate manipulation where prior methods fail. Code is available at https://github.com/UCMP13753/Omega‑QVLA.
Authors:Thomas Vitry, Kieran Edgeworth, Stefan Wermter, Jae Hee Lee
Abstract:
Vision classifiers can exploit spurious correlations, achieving high in‑distribution accuracy yet failing under distribution shift. Existing approaches to bias mitigation and analysis often depend on curated datasets, spurious‑attribute or group labels, or retraining, which may be infeasible once a model is deployed or the relevant bias is unknown. We present a bias‑label‑free, post‑hoc method for identifying spurious concepts in frozen vision models, relying only on standard class labels from a held‑out audit dataset. For each target class, we collect patches from inputs predicted as that class and apply non‑negative matrix factorization to intermediate activations to obtain a bank of interpretable concept vectors. Candidate concepts are then ranked with a bias estimator derived from their interaction with backpropagated gradients on misclassified examples: bias concepts tend to get activated when correcting false negatives and suppressed when correcting false positives. On Colored MNIST and Waterbirds the method recovers concepts aligned with the known spurious cue, and on CelebA it surfaces decision‑relevant directions that only partially coincide with the annotated gender attribute; suppressing the top‑ranked concepts at inference time improves worst‑group accuracy by up to 17.9 percentage points on Waterbirds and 10.4 on CelebA without any retraining or parameter updates. Our method identifies decision‑relevant spurious directions that need not coincide with annotated ones, providing both an interpretable auditing tool and an actionable debiasing handle for frozen vision models. Code is available at https://github.com/vitryt/label‑free‑bias‑identification.
Authors:Jizhan Fang, Buqiang Xu, Zhixian Wang, Haoliang Cao, Xinle Deng, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu, Ying Wei, Guozhou Zheng, Feiyu Xiong, Haofen Wang, Huajun Chen, Ningyu Zhang
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.
Authors:Tirtharaj Dash
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.
Authors:Xinle Deng, Ruobin Zhong, Hujin Peng, Xiaoben Lu, Yanzhe Wu, Guang Li, Buqiang Xu, Yunzhi Yao, Jizhan Fang, Haoliang Cao, Junjie Guo, Yuan Yuan, Ziqing Ma, Yuanqiang Yu, Rui Hu, Baohua Dong, Hangcheng Zhu, Ningyu Zhang
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.
Authors:Krishnam Gupta
Abstract:
We discover that VLA architectures fail in fundamentally different, predictable ways at the motor‑command level. Running VQ‑BeT, Diffusion Policy, and ACT on identical evaluation protocols (n=450 episodes across PushT and ALOHA 14‑DOF bimanual manipulation), we find: (1) direction reversal rate is a universal failure predictor across all three architectures (AUROC=0.93, 0.79, 0.91; p<0.001); (2) jerk monitoring is predictive only for discrete‑token architectures, following a discrete‑to‑continuous gradient (0.88, 0.69, 0.41); (3) velocity violations alone are non‑predictive everywhere (AUROC 0.41‑0.69), yet velocity checking is the most common safety mechanism in VLA deployment code; and (4) for continuous‑family VLAs, velocity monitoring provides effectively zero predictive signal (AUROC=0.52 on ACT, 0.41 on Diffusion), proving that architecture‑matched monitor selection is essential. These results quantify a monitoring consequence of the well‑known discrete/continuous VLA distinction: the two families produce qualitatively different failure signatures that require different monitors. No single monitor works universally; architecture‑matched selection is required. This finding was enabled by SafeContract, a training‑free, black‑box action monitoring toolkit with conformal calibration. Code: https://github.com/krishnam94/vla‑edge
Authors:Haonan Wen, Hanyang Chen, Songhe Feng
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.
Authors:Satoshi Tanaka, Takahiro Nishimichi, Yosuke Kobayashi
Abstract:
\textscDarkEmulator2 is a neural network emulator of the nonlinear matter power spectrum in a nine‑dimensional w_0 w_a νo \mathrmCDM parameter space, developed as the emulator component of the \textscDark Quest II (DQ2) program. It is trained on simulations generated with the \textscGinkaku code, whose numerical implementation, accuracy tests, and post‑processing pipeline are described in the companion paper. The design follows a unified strategy: in addition to the cosmological parameter vector, we supplement the neural network's inputs with three families of physically motivated auxiliary quantities ‑‑ the linear matter power spectrum, descriptors of the simulation resolution, and a low‑dimensional summary of the initial Gaussian random field ‑‑ that are expected to improve generalization across the parameter space. Training a single network jointly across three simulation resolution tiers allows the emulator to exploit a small number of high‑resolution simulations while retaining broad coverage from lower‑resolution simulations. For a L_\mathrmbox=1\,\hiGpc box with N=3000^3 particles, the emulator reproduces the simulated matter power spectrum to subpercent accuracy up to the particle Nyquist scale, k_\mathrmNy～eq 10\,\hMpci. The emulator remains accurate over the calibrated wavenumber range, while its highest‑k predictions depend on the simulation resolution and shot noise. We validate the emulator on independent test suites and, through a cross‑comparison with several public emulators and widely used fitting formulas, characterize the inter‑model consistency and the parameter‑dependent trends in their residuals.
Authors:José Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan
Abstract:
Recent Tabular Foundation Models (TFMs) have demonstrated state‑of‑the‑art predictive performance, often surpassing Gradient‑Boosted Decision Trees (GBDTs). However, the trustworthiness of these models, particularly their uncertainty quantification, has been largely overlooked. We investigate this gap through an extensive study comparing TFMs, GBDTs, and classical baselines on the 112 datasets of the TALENT benchmark. Our results reveal a performance‑uncertainty trade‑off: although TFMs achieve the highest predictive performance, measured by AUC, they exhibit lower conditional coverage under conformal prediction, measured by SSCS, compared to GBDTs. Complementary experiments on synthetic datasets further characterize the regimes in which this effect intensifies. We conclude that while TFMs advance predictive frontiers, achieving well‑calibrated uncertainty remains a major open challenge for their reliable adoption. Code is available at: https://github.com/jose‑melo/high‑performance‑low‑reliability
Authors:Alan Ferrari
Abstract:
Standard transformer architectures apply a single attention mechanism uniformly across all tokens and sequence positions, irrespective of local context or computational budget. We propose Meta‑Attention, a framework that dynamically routes each token to the most appropriate attention strategy ‑‑ full softmax attention, linear (kernel) attention, or sliding‑window local attention ‑‑ via a Bayesian Meta‑Controller. Unlike prior routing approaches that use deterministic or prior‑free learned routing, the Meta‑Controller treats per‑token mechanism selection as posterior inference under a compute‑aware Dirichlet prior: routing weights are the output of an amortised variational posterior q(alpha | x_t; phi) trained with an Evidence Lower Bound (ELBO) objective that jointly encodes task performance and attention‑mechanism cost. This design produces principled routing uncertainty estimates that govern the soft‑to‑hard routing transition, mitigates routing collapse without ad hoc load‑balancing losses, and yields better compute‑performance trade‑offs than deterministic or prior‑free learned routing at negligible overhead. Phase 1 empirical results on a Tiny LM benchmark confirm core predictions: the Bayesian controller's learned routing distribution implies a projected normalised FLOP cost of 25.1% under hard routing, vs. 59.3% for the prior‑free baseline (‑34.2 pp), and reduces routing entropy from 55.8% to 43.3% (‑12.5 pp), demonstrating that the Dirichlet prior prevents routing collapse while the non‑Bayesian model defaults to full attention. We present the Bayesian architecture, ELBO training objective, and a Phase 1 PyTorch prototype validating forward‑pass correctness, posterior diversity, and a controlled ablation against a prior‑free baseline. Code available at: https://github.com/KFEAL/meta‑attention
Authors:Hongru Hou, Tiehua Mei, Denghui Geng, Jinhui Huang, Ao Xu, Hengrui Chen, Jiaqing Liang, Deqing Yang
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.
Authors:Evgenii Palnikov, Elizaveta Gavrilova
Abstract:
We study quality‑latency‑resource trade‑offs in a documentation‑grounded retrieval‑augmented generation (RAG) system that uses Low‑Rank Adaptation (LoRA) of the generator. We build a manually verified benchmark of 5,144 question‑answer pairs over the official Kubernetes documentation and combine it with a fixed hybrid‑retrieval pipeline (BGE‑M3 dense, BGE‑M3 native sparse, Reciprocal Rank Fusion, cross‑encoder reranking). Over this benchmark we ablate 20 LoRA configurations on Llama‑3.2‑3B‑Instruct and Llama‑3.1‑8B‑Instruct across rank and target‑module choices, and evaluate each on token‑level F1, LLM‑judged groundedness and correctness (pass@4), inference latency, inference memory, and training cost, all reported with bootstrap 95% confidence intervals. Pareto analysis shows that LoRA adapters acting only on the q and v attention projections consistently dominate the front, while the 3B/8B choice mainly defines operating regime. A param‑matched control comparison further indicates that the q/v advantage is structural rather than purely parametric. The benchmark, selected adapters, and code are available at https://github.com/EugPal/rag‑lora‑tradeoffs.
Authors:Hao Jiang, Shurui Li, Tianpeng Bu, Bowen Xu, Xin Liu, Qihua Chen, Hongtao Duan, Lulu Hu, Bin Yang, Minying Zhang
Abstract:
Recent advances in online reinforcement learning (RL) for large language models (LLMs) have demonstrated promising performance in complex reasoning tasks. However, they often exhibit an imbalanced exploration‑exploitation trade‑off, resulting in unstable optimization and sub‑optimal performance. We introduce IB‑Score, a novel metric grounded in Information Bottleneck theory that evaluates policy's exploration‑exploitation balance by quantifying the trade‑off between step‑level reasoning diversity and mutual information shared with the correct answer. Analysis based on IB‑Score shows that popular online RL approaches (e.g., GRPO) with common regularizers fail to consistently maintain balance during training with suboptimal results. To address this, we propose Information Bottleneck‑driven Tree‑based Policy Optimization (IB‑TPO), a principled framework that formulates IB‑Score as a fine‑grained optimization objective and utilizes a novel IB‑guided tree sampling strategy that not only improves the efficiency of online sampling with 50% more trajectories under the same token budget, but also reuses the tree structure for effective IB‑Score Monte Carlo estimation. Extensive experiments across standard benchmarks show that our method significantly outperforms GRPO baseline by 2.9% to 3.6% and also outperforms other state‑of‑the‑art online RL approaches. Our code is available at https://github.com/alibaba/EfficientRL.
Authors:Shuhao Chen, Weisen Jiang, Yeqi Gong, Shengda Luo, Chengxiang Zhuo, Zang Li, James T. Kwok, Yu Zhang
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.
Authors:Ziqi Zhao, Xinyu Ma, Liu Yang, Yujie Feng, Daiting Shi, Jingzhou He, Xin Xin, Zhaochun Ren, Xiao-Ming Wu
Abstract:
On‑policy self‑distillation (OPSD) improves the reasoning performance of large language models (LLMs) by providing dense token‑level supervision for on‑policy rollouts. However, existing OPSD methods often yield limited gains on in‑domain reasoning and generalize poorly to out‑of‑domain problems. We identify two key causes: conditioning the self‑teacher on a verified solution encourages imitation of training‑domain reference trajectories rather than error‑specific correction, and applying distillation to the full response can overwrite valid reasoning prefixes and reinforce overfitting.
We propose Reflective On‑policy Self‑Distillation (ROSD), a framework that turns reference‑solution imitation into targeted reasoning correction through reflection‑guided, error‑localized distillation. For each rollout, ROSD uses a self‑reflector to extract a corrective idea and locate the first erroneous span. The corrective idea guides the self‑teacher toward targeted supervision, while the localized error span restricts distillation to where correction is needed. This design corrects flawed reasoning while preserving valid prefixes. Experiments on multiple in‑domain and out‑of‑domain reasoning benchmarks show that ROSD yields stronger in‑domain reasoning performance overall and substantially better out‑of‑domain generalization than standard OPSD. Code is available at https://github.com/ZiqiZhao1/ROSD.
Authors:Seunghyeok Shin, Minwoo Kim, Dabin Kim, Hongki Lim
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.
Authors:Junlin Wang
Abstract:
Learning visuomotor policies via behavior cloning typically involves mimicking expert demonstrations collected by human operators. However, natural human demonstrations inherently contain high‑frequency noise, such as intermittent jerks, pauses, and action jitter. Training policies to directly imitate these raw trajectories inevitably causes the model to inherit these suboptimal behaviors. This pathology is particularly pronounced in diffusion‑based policies, where iterative denoising steps can inadvertently amplify high‑frequency artifacts at the expense of meaningful fine‑grained details. To address these limitations, we present a novel frequency‑based algorithm that enables implicit spectral maneuvering and smooth action generation. Our method, Frequency Guidance Operator (FGO), steers the generation process of diffusion polices by progressively driving the noisy samples through intermediate sub‑frequency manifolds with expanding spectral bands. Validated on 15 robotic manipulation tasks from 5 benchmarks, FGO achieves superior performance in enhancing action smoothness and temporal consistency while preserving the details necessary for successful task execution. Project website: https://henrywjl.github.io/frequency‑guidance‑operator/
Authors:Shubhashis Roy Dipta, Ankur Padia, Francis Ferraro
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
Authors:Jiacheng Pang, Ashutosh Chaubey, Mohammad Soleymani
Abstract:
Audio large language models (Audio LLMs) demonstrate strong performance on speech understanding tasks, yet their ability to understand paralinguistic information remains limited. To systematically quantify this issue, we introduce VoxParadox, an adversarial benchmark with 2,000 verified examples, spanning 10 paralinguistic tasks, created with controlled speech synthesis to intentionally mismatch transcript claims and speaking style, enabling direct measurement of speech paralinguistic understanding. Evaluation of a diverse set of Audio LLMs reveals consistently low accuracy on acoustic ground truth and a strong tendency to follow language‑implied (incorrect) answers. To understand the cause of this gap, we perform layer‑wise probing and find that (i) paralinguistic cues can degrade in deeper encoder layers and at the encoder‑‑LLM interface, and (ii) even when such cues are available in audio tokens, the language model frequently ignores them. To address these problems, we propose Prompt‑Conditioned Layer Mixer (PCLM), which adaptively combines information from multiple audio layers based on the input prompt, and pair it with Direct Preference Optimization (DPO) to explicitly prefer acoustically supported options over language‑implied alternatives. These methods substantially improve Audio LLM paralinguistic understanding, improving Audio Flamingo 3 from 17.40% to 65.20% on VoxParadox, and from 37.74% to 54.78% on MMSU paralinguistic subset. Our project page is available at https://voxparadox.github.io/.
Authors:Nate MacFadden
Abstract:
We introduce `dualGNN', an autoregressive message‑passing GNN for sampling fine, regular triangulations (FRTs) of convex polytopes. dualGNN operates on a generalization of the dual graph of a triangulation, with edges labeled by `signed circuits' ‑‑ combinatorial invariants from oriented matroid theory which we show are both necessary and sufficient for exposing regularity. The model is independent of the number of points in the polytope and invariant under the polytope's orientation‑preserving symmetries (\mathrmSL(d,\mathbbZ) \ltimes \mathbbZ^d). When implemented with a certain masking procedure, one can also guarantee that every rollout produces a fine triangulation (in 2D). On unseen polygons with N_\mathrmpts \leq 40, dualGNN is the most uniform FRT sampler we tested, and even a model trained on a single polygon generalizes well to other polygons. The model is small (～92k parameters), trains in ～7.5 hours on a single consumer GPU, and runs without modification on an M1 MacBook Pro. We apply dualGNN to string theory, uniformly sampling Calabi‑Yau threefolds at h^1,1=86 and consistent with uniformity at h^1,1=128. This is an order of magnitude beyond previous learned methods with a model ～1000× smaller. Code, training scripts, and pretrained models are available at https://github.com/natemacfadden/dualGNN .
Authors:Liu Zhang, Amit Singer
Abstract:
Moment‑based estimation is a theoretically attractive approach to parametric inference, especially when likelihood‑based estimation is unavailable, misspecified, or computationally inconvenient. However, the moment equations involve sample averages, which makes moment‑based estimation sensitive to outliers. We propose the SGR‑GMM algorithm, a robust generalized method of moments (GMM) procedure that uses a spectral gradient reweighting (SGR) primitive to soft‑reweight the per‑observation gradients during the moment‑matching optimization. Our analysis has three layers. First, for a fixed center, the SGR primitive is formulated as an entropy‑regularized spectral game between a sample‑weight player and a density‑matrix player, which is analyzed using classical multiplicative‑weights and matrix‑multiplicative‑weights regret bounds. Second, we establish explicit convergence radius and finite termination bound for the fixed‑center updates in the SGR primitive. Third, we prove a local finite‑sample parameter estimation error bound with explicit dependence on the contamination fraction, inlier gradient stability, local GMM identification strength, and optimization accuracy. We further specialize the SGR‑GMM algorithm to obtain a robust diagonally‑weighted GMM (DGMM) estimator for estimating heteroscedastic low‑rank Gaussian mixtures observed under additive Gaussian noise and strong contamination. In the numerical experiments, the SGR primitive produces nearly‑oracle gradient estimation and the robust DGMM specialization substantially improves over non‑robust moment baselines. The code and data are available at https://github.com/liu‑lzhang/sgr‑gmm.
Authors:Khang Tran, Yazan Boshmaf, Issa Khalil, NhatHai Phan, Ting Yu, Md Rizwan Parvez
Abstract:
Code Large Language Models (CLLMs) serve as the core of modern code agents, enabling developers to automate complex software development tasks. In this paper, we present Poison‑with‑Style (PwS), a practical and stealthy model poisoning attack targeting CLLMs. Unlike prior attacks that assume an active adversary capable of directly embedding explicit triggers (e.g., specific words) into developers' prompts during inference, PwS leverages developers' code styles as covert triggers implicitly embedded within their prompts. PwS introduces a novel data collection method and a two‑step training strategy to fine‑tune CLLMs, causing them to generate vulnerable code when prompts contain trigger code styles while maintaining normal behavior on other prompts. Experimental results on Python code completion tasks show that PwS is robust against state‑of‑the‑art defenses and achieves high attack success rates across diverse vulnerabilities, while maintaining strong performance on standard code completion benchmarks. For example, PwS‑poisoned models generate CWE‑20 vulnerable code in 95% of cases when the trigger code style is used, with less than a 5% drop in pass@1 performance on the HumanEval and MBPP benchmarks. Our implementation and dataset are here: https://github.com/khangtran2020/pws.
Authors:Tim R. Davidson, Anja Surina, Caglar Gulcehre
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.
Authors:Aurelio Amerio
Abstract:
Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation‑based inference (SBI), extending naturally from neural posterior estimation to likelihood and joint density estimation. Their principled optimization objectives and freedom from architectural constraints have driven rapid adoption across the natural sciences. Yet the most widely used SBI libraries remain PyTorch‑based, leaving researchers who develop their forward models and analysis pipelines in JAX without a native option. We present GenSBI, an open‑source library that implements flow matching, score matching, and denoising diffusion entirely in JAX. The library offers three transformer‑based architectures ‑ SimFormer, Flux1, and a novel Flux1Joint that extends gate‑modulated transformer blocks to joint density estimation ‑ all interchangeable through a unified interface that decouples generative method, neural backbone, and inference mode. GenSBI provides an end‑to‑end workflow from training through posterior calibration (SBC, TARP, LC2ST) and supports custom architectures with domain‑specific embedding networks. We validate the framework on standard SBI benchmarks, achieving near‑ideal mean C2ST scores (0.50‑0.56, where 0.50 is ideal) on SBIBM tasks with minimal per‑task tuning and well‑calibrated posterior coverage across all tested configurations. The code is publicly available at https://github.com/aurelio‑amerio/GenSBI.
Authors:Syed Huma Shah
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.
Authors:Hyunmin Cho, Woo Kyoung Han, Kyong Hwan Jin
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).
Authors:Nicole Koenigstein
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.
Authors:Zihui Zhang, Zhixuan Sun, Yafei Yang, Jinxi Li, Jiahao Chen, Bo Yang
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.
Authors:Xiongwei Zhu, Xiaojian Liao, Tianyang Jiang, Yusen Zhang, Liang Wang, Limin Xiao
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.
Authors:Hsiu-Yuan Huang, Weijie Liu, Chenming Tang, Sanwoo Lee, Kai Yang, Yangkun Chen, Saiyong Yang, Yunfang Wu
Abstract:
The proliferation of Reinforcement Learning from Verifiable Rewards (RLVR) datasets has exacerbated provenance collapse due to unclear lineage among existing datasets. To bridge this fragmented RLVR data landscape, we propose Atomic‑source Tracing via Lineage‑Aware Search (ATLAS), a systematic framework for tracing RLVR datasets back to their atomic sources, attributing over 99.7% of 1.45M instances to 20 atomic sources. Our analysis reveals that most RLVR datasets are variants of a small set of shared upstream sources, with few introducing genuinely new data, and many facing data contamination risks. These findings naturally motivate us to curate a new RLVR dataset, DAPO++, and to benchmark existing datasets from a lineage‑aware perspective. To this end, we propose Source‑level Counterfactual Attribution (SCA) as a guiding principle to curate a decontaminated training dataset with concentrated learning signals. Essentially, SCA measures a sample's marginal utility by comparing per‑atomic‑source RL checkpoints against a shared base model. Building upon these attribution signals, we further design a composite dataset quality score Q that strongly correlates with downstream RLVR performance. Experiments on Qwen3 series models verify that DAPO++ consistently improves performance on held‑out benchmarks, while Q reliably predicts downstream RLVR training effectiveness. Our code and data is available at https://github.com/Celine‑hxy/ATLAS.
Authors:Yali Fink, Ido Ben-Yair, Lars Ruthotto, Eran Treister
Abstract:
The scalable solution of large sparse linear systems is a bottleneck in scientific computing and graph analysis. While algebraic multigrid (AMG) offers optimal linear scaling, its performance is severely constrained by the trade‑off between the sparsity and convergence quality of coarse‑grid operators. Classical AMG heuristics struggle to balance these objectives, often sacrificing stability or performance for sparsity. We propose RAPNet, a graph neural network (GNN) framework that resolves this trade‑off by learning to generate sparse, robust coarse operators directly from the sparse algebraic system. Key to our approach is a level‑wise training strategy that enables learning from small subgraphs and generalization to million‑node domains, bypassing the bottlenecks of prior neural AMG attempts. RAPNet executes exclusively during the solver setup phase, ensuring that the solve phase retains its favorable computational properties. We show that our method outperforms classical non‑Galerkin baselines on diverse PDE discretizations and graph Laplacians, making it particularly effective for multi‑query tasks such as eigenproblems, time‑dependent simulations, and inverse or design problems.
Authors:Gwangho Kim, Sungyoon Lee
Abstract:
Diffusion models can unintentionally memorize training samples, raising concerns about privacy and copyright. While recent methods can detect memorization, they often rely on global or model‑specific signals and provide limited insight into where memorization appears within a generated image. We provide a geometric characterization of local memorization as a coordinate‑wise variance collapse. However, such collapse can also arise from intrinsic data constraints rather than overfitting. To isolate overfitting‑driven memorization, we propose curvature‑difference methods that subtract the curvature of an underfitted baseline, either the unconditional model or a less‑trained version of itself. We further derive a score‑difference proxy that provides a geometric explanation for the widely used score‑difference‑based detection metric. Experiments on Stable Diffusion, evaluated against ground‑truth memorization masks, show that our method outperforms the prior attention‑based localization method. Code is available at https://github.com/Gwangho99/mem‑curv‑diff.
Authors:Ashima Khanna, Dominik Grimm
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
Authors:Shuang Liang, Chaochuan Hou, Xu Yao, Shiping Wang, Hailiang Huang, Songqiao Han, Minqi Jiang
Abstract:
While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component‑level understanding of their impacts. We propose TSCOMP, the first large‑scale benchmark that systematically deconstructs deep forecasting methods into their core, fine‑grained components‑‑spanning series preprocessing, encoding strategies, network architectures including specific and large time‑series models, and optimization methods. Using constrained orthogonal experimental design and extensive evaluations, we conduct multi‑view analyses that reveal component effectiveness across different backbones, data characteristics, and their interactions. Beyond providing insights, this benchmark establishes a fine‑grained performance corpus comprising over 20,000 model‑dataset evaluations, which supports the learning of automated component selection, enabling zero‑shot model construction on new datasets. Our experiments demonstrate that the corpus‑driven approach, despite its simplicity, consistently outperforms state‑of‑the‑art methods, validating the soundness of our evaluation design and confirming that systematic component selection surpasses manually designed complex architectures. All code and the performance corpus are publicly available at https://github.com/SUFE‑AILAB/TSCOMP.
Authors:Jaewoo Lee, Hyeongyu Kang, Dohyun Kim, Kyuil Sim, Woocheol Shin, Minsu Kim, Taeyoung Yun, Jeongjae Lee, Sanghyeok Choi, Tabitha Edith Lee, Jong Chul Ye, Jinkyoo Park
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.
Authors:Jiahe Huang, Sihan Xu, Sharvaree Vadgama, Rose Yu
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.
Authors:Jiawei Tang, Xinyan Du, Hui Liu, Junhui Hou, Yuheng Jia
Abstract:
While Deep Neural Networks (DNNs) achieve remarkable performance, their tendency to produce overconfident predictions. Evidential Deep Learning (EDL) mitigates this by formulating predictions as a Dirichlet distribution over class probabilities to explicitly quantify epistemic uncertainty. However, we found that the conventional EDL suffers from two fundamental limitations: a Kullback‑Leibler (KL) penalty that only suppresses the evidence of negative classes, producing excessively high evidence therefore decreasing the model's ability to quantify uncertainty, and an absence in theoretical guarantee of setting Dirichlet parameter α=e+1. In this paper, we propose a mathematically principled framework, Variational Inference Evidential Deep Learning (VI‑EDL). By reformulating evidential learning through the lens of variational inference, we derive an Evidence Lower Bound (ELBO), which prevents the evidence from growing excessively. Theoretically, we rigorously establish a generalization bound and reveal how the predicted uncertainty, feature and network complexity affect this bound, and why setting \boldsymbolα = \mathbfe + \mathbf1 can minimize it. Extensive experiments on standard visual and medical datasets demonstrate that VI‑EDL achieves state‑of‑the‑art performance, showing excellent performance in out‑of‑distribution detection, noise detection and autonomous driving scenario. The code is available in https://github.com/seutjw/VI‑EDL.
Authors:Dhruv S. Kushwaha, Zoleikha A. Biron
Abstract:
Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints during both training and deployment. Control barrier functions (CBFs) provide a principled mechanism for enforcing forward invariance through minimally invasive safety filters, but their use in model‑free RL is limited by the need for accurate dynamics and hand‑designed barrier certificates. We propose Robust Koopman‑CBF SAC, a safety‑filtered actor‑‑critic framework that learns a finite‑dimensional Koopman predictor from data, constructs affine CBF constraints in the lifted space, and enforces them through a quadratic‑program safety layer. To account for finite‑dimensional Koopman approximation error, the CBF condition is tightened using a projected residual margin estimated from held‑out rollout data. The critic is trained on the executed safe action, while the actor is regularized toward the Koopman‑CBF feasible set, reducing dependence on the filter over training. Across safe‑control benchmarks, the method achieves zero constraint violations on CartPole stabilization and tracking while matching or exceeding unconstrained SAC returns. On high‑dimensional Safety Gymnasium locomotion tasks, the method reduces violations in some settings but also exposes important limitations of first‑order velocity barriers and linear EDMD models, motivating high‑order and multi‑step Koopman‑CBF extensions. These results suggest that robust Koopman‑CBF filters are a promising bridge between model‑free RL and certifiable safety, while clarifying the structural conditions under which such filters remain effective. All code is available at \hrefhttps://github.com/DhruvKushwaha/Koopman‑CBF‑Soft‑Actor‑CriticGithub Repository.
Authors:Joohwan Ko, Justin Domke
Abstract:
Amortized inference promises fast test‑time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unseen models typically requires retraining or costly test‑time finetuning. In this paper, we ask: is it possible to build a single inference network capable of generalizing across varying priors, likelihoods, and dimensionality? We introduce Amortized Factor Inference Networks (AFINs), a family of encode‑merge‑decode inference networks built on dimension‑independent modules that map a model specification and its observations to the parameters of a variational posterior. Experimentally, a single trained AFIN achieves posterior accuracy comparable to NUTS and several variational inference methods, while requiring 2 to 4 orders of magnitude less test‑time compute. Code is available at https://github.com/joohwanko/AFINs.
Authors:Junlin Yang, Tian Yu, Nicha C. Dvornek, Yuexi Du, Peiyu Duan, Annabella Shewarega, Lawrence H. Staib, James S. Duncan, Julius Chapiro
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.
Authors:Athanasios Zeris
Abstract:
Standard transformer attention computes pairwise token similarity but treats all tokens as equally salient and all positions as equally local, regardless of the informational structure of the input. We identify two complementary inductive biases that standard attention lacks: energy salience (which tokens concentrate informational energy, learned end‑to‑end without explicit frequency decomposition) and scale‑selective locality (how far positional influence extends at each frequency, implemented via Morlet wavelet encoding). We address both with two simple components. Energy‑Gated Attention (EGA) gates value aggregation by a learned energy estimate of key token embeddings, computed via a single linear projection; it selects what to attend to. Morlet Positional Encoding (MoPE) replaces fixed sinusoidal encodings with learned Gaussian‑windowed wavelets that adapt the joint position‑frequency localization to the corpus; it specifies where attention operates at each scale. On TinyShakespeare, EGA alone achieves +0.092 validation loss improvement over standard attention (+0.103 over Phase 1‑3 baseline); MoPE alone is ‑0.032 (below baseline as a standalone encoding); but their combination achieves +0.119 ‑‑ more than the sum of parts. This superadditivity, observed across two independent training runs, is the central empirical finding: salience and locality are complementary inductive biases, each addressing a gap the other cannot fill alone. Ablations confirm that structured spectral priors (Morlet wavelet gates, scale‑initialized heads, fixed sinusoidal PE) consistently underperform their unconstrained learned counterparts, while complementary learned components interact superadditively. All experiments are at small scale (<=6M parameters, character‑level benchmarks, single seed); larger‑scale multi‑seed validation is the most important direction for future work.
Authors:Xinran Liang, Esin Tureci, Prachi Sinha, Ye Zhu, Vikram V. Ramaswamy, Olga Russakovsky
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.
Authors:Chenghao Qiu, Chunli Peng, Yufeng Yang, Kuan-Hao Huang, Yi Zhou
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.
Authors:Sandeep Kumar, Virginia Smith, Chhavi Yadav
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.
Authors:Michael Fuchs, Dominik Kreiss
Abstract:
When treatment effects are naturally expressed as ratios ‑‑ as in medicine, pricing, and marketing ‑‑ the ratio‑based CATE τ(x) = E[Y|W=1,X=x] / E[Y|W=0,X=x] is the appropriate estimand. Yet existing estimators either impose a log‑linear parametric structure or apply generic regression without robustness guarantees for this functional. We introduce the Q‑Learner, which decomposes τ(x) into a product of two odds ratios, reducing ratio‑CATE estimation for binary outcomes to two propensity classification tasks. We further derive doubly robust augmentations for both S/T‑ and Q‑style ratio learners and characterize their distinct robustness properties. In benchmarks on seven RCT datasets, the Q‑Learner is the most consistently competitive method in low‑conversion regimes, where its propensity‑only construction sidesteps the imbalanced regression that hurts outcome‑based estimators. On four observational datasets, where propensity must be estimated and confounding cannot be ruled out, the DR learners introduced here decisively come out on top, making them practitioners' natural default for confounded observational data.
Authors:Guanghui Wang, Kaiwen Lv Kacuila, Zhiyong Yang, Zitai Wang, Jin-Wen Wu, Longtao Huang, Qianqian Xu, Qingming Huang
Abstract:
Knowledge distillation (KD) transfers knowledge from a large teacher model to a smaller student. In language modeling, the student is trained either on tokens sampled from the teacher (hard labels) or the teacher's full next‑token distribution (soft labels). Despite soft labels appear strictly richer, we find that mixing hard and soft labels consistently yields better results. Crucially, we show that this gain cannot be explained by closer teacher matching during training. Instead, it comes from reduced exposure bias, the mismatch between training and inference distributions. To explain this phenomenon, we introduce the Bridge‑Garden Decomposition theory, which categorizes generation steps into two types: Bridges, where the next token must be exact, and Gardens, where it can be flexible. We show that hard‑only KD excels in Bridges by avoiding risky deviations, while soft‑only KD preserves diversity in Gardens. A hybrid strategy handles both cases and, as a result, reduces exposure bias across the sequence. Guided by this theory, we develop a family of Bridge‑Garden hybrid supervision methods that adaptively balance hard and soft labels. Across a primary suite of seven teacher‑student pairs (including Qwen, Llama, Gemma, and DeepSeek) and benchmarks in reasoning and coding, our approach outperforms divergence‑based and on‑policy KD baselines while reducing training cost by 9.7x, enabling efficient model compression. Code is available at https://github.com/ghwang‑s/bridge_garden_hybrid_kd_release.
Authors:Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody
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.
Authors:Zihang Zhou, Ziqian Ren, Yukai Wu, Yingjie Xiong, Wei Zhou, Chao Peng, Dong Zhang, Bingheng Yan, Xuanhe Zhou, Fan Wu
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.
Authors:Ke Li, Dong An, Xiaoling Zang, Can Ye, Liang Xie, Qibo Qiu, Chen Shen, Xiaofei He, Wenxiao Wang
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)
Authors:Zejia Qi
Abstract:
Linux is the foundation of the digital age, accounting for the majority of the cloud and mobile OS markets. Any device that runs Linux uses the Linux page cache, a central pillar in OS and application performance, serving to reduce extraneous disk access. Many page cache eviction policies have been developed but remain bound by the rigidity of heuristics. The rise of AI‑driven tools in recent years, melded with the ever‑increasing variety of workloads for Linux devices, sets the stage for machine‑learning‑driven cache eviction policies. Promising research has been done in this field, but only in the field of user‑space applications such as CDNs. We develop LearnedCache, an eBPF‑integrated single‑layer perceptron‑based cache eviction policy for the Linux page cache, trained on real kernel data from diverse workloads. We demonstrate median AUCs of nearly 80% over multiple linear models modeling page reuse time, then take a step further by embedding these models inside the Linux kernel for real‑time performance evaluation. Through statistical testing over 50 paired trials against a baseline of FIFO for each workload, LearnedCache reveals that machine‑learning‑derived cache eviction policies are practical in the Linux kernel under representative empirical workloads and are able to surpass conventional FIFO by statistically significant margins of up to 10% in insertion rate, a frequency‑adjusted derivation of cache hit rate, in specific workloads while incurring minimal overhead.
Authors:Hanzala Afzaal, Danish Memon, Chouhdary Bilal Raza, Muhammad Khurram Shahzad
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.
Authors:Xindi Tong, Chee Wei Tan, H. Vincent Poor
Abstract:
Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst‑case interference. This formulation naturally arises in multi‑operator low Earth orbit (LEO) satellite spectrum sharing, where transmissions from competing constellations interfere in real‑time. Under Gaussian channels, AWF is strongly convex‑‑concave on nondegenerate active channels, whereas discrete constellations yield generally nonconvex mercury/water‑filling formulations. In this paper we propose the Adversarial Water‑Filling (AWF) problem with corresponding theory and algorithms for these real situations. In addition, we develop a wireless foundation model for AWF to learn the AWF search dynamics. The architecture incorporates permutation‑invariant channel representations, a constraint‑aware graph neural network (GNN) with sparse message passing, and global latent variables capturing the low‑dimensional water level implied by the AWF optimality. Through learned projected extragradient iterations, the model approximates stationary solutions of the constrained minimax problem arising under mercury/water‑filling. We further show that, under local regularity and contractivity conditions, the learned AWF dynamics converge locally linearly around regular stationary points. Experiments demonstrate empirical generalization across unseen problem sizes, different constraints, and multiple discrete constellations, while achieving more than one‑order‑of‑magnitude runtime improvements over iterative baselines. The related code can be found at https://github.com/convexsoft/AWF.
Authors:Dongxu Yang
Abstract:
Large language models are increasingly used as orchestrators of external tools via the Model Context Protocol (MCP), but MCP is built for software services with megabytes of memory and does not descend to the microcontrollers that dominate the long tail of physical devices. Recent work (IoT‑MCP) ports MCP to edge gateways at 74 KB peak memory; this still excludes the smallest commodity MCUs and, critically, does not address the safety problem of giving an unreliable caller (an LLM that may hallucinate or be prompt‑injected) direct control of physical hardware. We present the Device Context Protocol (DCP): a sub‑50‑byte typical frame (6‑byte header + CBOR payload + optional 16‑byte HMAC), a manifest schema in which capability scoping, range and type checks, dry‑run evaluation, and units‑as‑types are protocol‑layer primitives, and a host‑side Bridge that rejects malformed or hallucinated calls before any byte reaches the device. Reference firmware measures 27.6 KB flash / 0.6 KB RAM on ESP32; the Python Bridge, ESP32 firmware, and a language‑neutral conformance suite are MIT‑licensed and public. An empirical study ‑‑ 675 tool calls produced by five LLMs across four vendors (DeepSeek, Alibaba, Zhipu, MiniMax) against six categories of adversarial prompts, with the injection category instantiating AgentDojo's attack templates ‑‑ shows DCP rejects 100% of capability‑escalation attempts and 78% of prompt‑injection attempts, versus 0‑‑1% for Raw MCP and IoT‑MCP, matching the expressiveness of a well‑formed OpenAPI 3 schema at three orders of magnitude less firmware footprint. We position DCP as the missing layer between MCP (which is moving toward enterprise SaaS connectivity) and the physical devices it does not reach.
Authors:Tongxi Wu, Jian Zhang, Yang Gao
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.
Authors:Xianglin Yang, Bryan Hooi, Gelei Deng, Tianwei Zhang, Jin Song Dong
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.
Authors:Venkatakrishnan Gopalakrishnan
Abstract:
Unsupervised anomaly detection is widely used in transaction fraud detection where labels are scarce. Isolation Forest (IF) is among the most popular classical methods due to its scalability and ease of deployment. We propose SilIF, an augmentation of Isolation Forest that adds a silhouette‑based scoring layer computed in a representation space induced by the trees of the forest. For each point, we extract a vector of per‑tree path lengths, cluster these "fingerprints" into structural groups, and compute a silhouette score that measures how well the point fits its assigned group versus the nearest alternative. The silhouette signal is combined with the base IF score via a single hyperparameter alpha. On the IEEE‑CIS Fraud Detection benchmark (~590K transactions, 3.5% fraud), SilIF with alpha=1.0 improves over plain Isolation Forest by +0.0080 AUC‑PR on average across five seeds, with SilIF winning on all five seeds (paired t‑test p=0.046). We also report results on a synthetic credit‑card dataset (Sparkov) where the silhouette augmentation does not improve over plain IF, and we characterize the conditions that distinguish the two outcomes. The paper presents SilIF as a tunable, easy‑to‑deploy enhancement to Isolation Forest with honest reporting of when it helps and when it does not. Code at https://github.com/venkat15vk/silif‑anomaly‑detection.
Authors:Shangding Gu
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.
Authors:Shuhong Zheng, Aashish Kumar Misraa, Yu-Teng Li, Yu-Jhe Li, Igor Gilitschenski
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/.
Authors:Jun-Tao Tang, Yu-Cheng Shi, Zhen-Hao Xie, Da-Wei Zhou
Abstract:
Multimodal Large Language Models (MLLMs) achieve versatility by reformulating diverse tasks into a unified instruction‑following framework via instruction tuning. However, real‑world deployment requires continuous adaptation to emerging tasks, motivating Multimodal Continual Instruction Tuning (MCIT). Despite its growing importance, current MCIT research is hindered by severe engineering bottlenecks. Existing methods are typically implemented by directly modifying the base MLLM codebase, which imposes substantial implementation overhead and yields method‑specific architectures that severely limit code reuse and fair comparison. To address this, we introduce Prism, a plug‑in reproducible codebase specifically designed for scalable MCIT research. It separates algorithmic development from the backbone implementation via a lightweight plugin registration mechanism, enabling new strategies to be integrated as independent plugins without modifying the underlying MLLM codebase, thereby eliminating structural fragmentation and accelerating method development. Prism natively supports widely used large‑scale training pipeline, thereby enabling reproducible and scalable MCIT experimentation. Code is available at https://github.com/LAMDA‑CL/Prism.
Authors:Adina Scheinfeld, Haotan Zhang, Shang Mu, Rudolf L. M. van Herten, Lucas Stoffl, Ali Erturk, Zhuhao Wu, Johannes C. Paetzold
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.
Authors:Mengfan Liu, Da Zheng, Junwei Su, Chuan Wu
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.
Authors:Li Wang, Xiaodong Lu, Xiaohan Wang, Yikun Ban, Jiajun Chai, Wei Lin, Tianhao Peng, Guojun Yin
Abstract:
Large Language Models (LLMs) have achieved remarkable advancements in reasoning capabilities empowered by Reinforcement Learning with Verifiable Rewards (RLVR). Nonetheless, RLVR intrinsically relies on ground‑truth labels for reward computation, the acquisition of which is often prohibitively expensive in real‑world scenarios. While unsupervised RLVR paradigms attempt to circumvent this by training on pseudo‑labels, they are notoriously susceptible to training collapse. Moreover, different samples often exhibit varying annotation values. In this paper, we propose Reinforcement Learning with Active Verifiable Rewards (RLAVR), which actively acquires ground‑truth labels for a small set of selected samples and integrates them with pseudo‑labels, thereby stabilizing training dynamics and improving performance under limited annotation budgets. To identify valuable samples, we propose the Corrective Advantage Gap (CAG) metric and analyze the sample‑level supervision value. Building on this, we introduce Correction‑Aware Reliability Estimation for RLAVR (CARE), which translates the oracle CAG criterion into a practical pre‑query acquisition policy to substantially improve training stability. Extensive experiments across diverse domains, model families, and model scales demonstrate the effectiveness and generality of our approach. Our code is available at https://github.com/Lumina04/CARE.
Authors:Yulin Yuan, Hongshuo Zhao, Xiangming Meng
Abstract:
Diffusion‑based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel. This turns each decoding step into a position‑selection problem: the model must choose not only which predictions are reliable in isolation, but also which positions should be committed together as context for later decoding steps. Existing confidence‑based decoding ranks masked positions independently and commits the top‑K positions, largely ignoring whether the committed tokens provide complementary visual grounding. We identify a step‑level limitation of this strategy in multimodal settings: high‑confidence tokens selected in the same step can rely on overlapping visual grounding, introducing visual redundancy among the committed tokens and leaving less complementary visual grounding available for later decoding. To quantify this effect, we introduce the Visual Redundancy Index (VRI), which measures visual grounding overlap among tokens committed in parallel. To control this redundancy during decoding, we propose Visual‑Redundancy‑Controlled Decoding (VRCD), a training‑free inference‑time decoding method that uses token‑to‑image attention to prioritize visually complementary positions. Across diverse multimodal benchmarks, VRCD reduces visual redundancy and remaining‑position entropy with modest runtime overhead. In longer decoding experiments, it also achieves relative accuracy gains of up to 18.8% on M^3CoT and 6.9% on MMBench over confidence‑based decoding. Code will be released at https://github.com/infiniteYuanyl/VRCD.
Authors:Bocheng Zeng, Rui Zhang, Runze Mao, Mengtao Yan, Xuan Bai, Yang Liu, Zhi X. Chen, Hao Sun
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.
Authors:Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Xinyan Huang
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.
Authors:Hyungkyu Kang, Byeongchan Kim, Min-hwan Oh
Abstract:
Offline goal‑conditioned reinforcement learning (GCRL) provides a practical framework for obtaining goal‑reaching policies from fixed datasets. However, learning a reliable goal‑conditioned value function in long‑horizon tasks remains challenging. In this paper, we identify erroneous generalization in goal‑conditioned value functions as a fundamental bottleneck, and demonstrate that appropriate inductive bias in the value function is crucial for addressing the bottleneck. Building on these findings, we propose Latent‑Aligned Value Learning (LAVL), an offline GCRL algorithm that integrates latent‑representation‑based value generalization with hierarchical planning in a unified framework. Extensive experiments on OGBench demonstrate that LAVL consistently outperforms existing offline GCRL methods, achieving the highest performance on 20 out of 22 datasets. Notably, LAVL exhibits strong performance in long‑horizon tasks and trajectory stitching datasets, where prior methods suffer significant performance degradation. Our code is available at https://github.com/oh‑lab/LAVL.git.
Authors:João Alves Ribeiro, Bruno Alves Ribeiro, Francisco Pimenta, Sérgio M. O. Tavares, Faez Ahmed
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.
Authors:Florent Tariolle, Florian Yger
Abstract:
Black‑box adversarial attacks that minimize only the ground‑truth confidence suffer from class drift: perturbations wander through the feature space without committing to a specific adversarial class, wasting queries on diffuse, undirected progress. We introduce Opportunistic Target Selection (OTS), a lightweight wrapper that switches an untargeted attack to a targeted objective early in its trajectory, locking onto whichever non‑true class currently leads. OTS requires no architectural modification to the underlying attack, no gradient access, and no a priori target‑class knowledge.
We validate OTS on three score‑based attacks (SimBA, Square Attack with cross‑entropy loss, and Bandits) across five standard ImageNet classifiers (4,500 runs). On random‑search attacks, OTS closely tracks oracle performance, with gains up to +27 pp in success rate and 43% relative reduction in censored‑mean iterations on ResNet‑50. On gradient‑estimation attacks (Bandits) and attacks with margin loss, OTS is redundant, a negative result that reinforces our interpretation of OTS as a margin‑loss surrogate. On adversarially‑trained models, a bimodal difficulty distribution eliminates the regime where targeting helps.
Authors:Qi He, Huan Chen, Ya Guo, Huijia Zhu, Yi R. Fung, Baojian Zhou
Abstract:
Policy loss estimation remains a fundamental and long‑standing challenge in reinforcement learning (RL) for diffusion language models (dLLMs). We introduce Reinforcement Learning from Denoising Feedback (RLDF), a novel training paradigm that leverages feedback obtained from rollout and training processes to facilitate accurate and efficient policy loss estimation. To balance the trade‑off between computational efficiency and estimation effectiveness, RLDF optimizes the model toward the clipped clean state \hatx_0 from intermediate noisy states x_t, combined with weighted timestep sampling over t. Extensive experiments demonstrate that RLDF achieves consistent and substantial improvements in both performance and generalizability across two representative dLLM architectures, LLaDA and Dream, on multiple reasoning benchmarks. Our work lays a principled foundation for scalable reinforcement learning in diffusion language models. We build Drift, a training framework for dLLMs, available at https://github.com/ant‑research/Drift.
Authors:Dongxin Ye, Fang Hu, Han Hu, Shu Hu, Yang Tan, Wanli Ouyang, Stan Z. Li, Jie Cui, Nanqing Dong
Abstract:
Nucleotide sequences constitute the fundamental genetic basis of biological systems, rendering viral genomic analysis critical for biomedical advancement. Despite progress in biological foundation models, specifically nucleotide foundation models (NFMs), the field lacks a unified standard for viral genomics to facilitate community development and enforce biosecurity constraints. To address this, we introduce ViroBench, the first comprehensive and large‑scale benchmark specifically designed for NFMs in viral settings. ViroBench evaluates models across two critical dimensions: biological understanding and latent biosecurity risk, covering 18 diverse scenarios within 4 task types. Extensive evaluation of 66 NFMs across diverse architectures yields three critical conclusions. Firstly, NFMs exhibit a performance degradation in biological understanding under phylogenetic and temporal shifts, indicating weak extrapolation capabilities. Secondly, generation tasks reveal a decoupling between statistical likelihood and biological functional validity, posing latent biosecurity risks. Thirdly, controlled ablation studies reveal that taxonomic diversity in pretraining data outweighs parameter scale. Specifically, a lightweight baseline trained on diverse data achieves a 67.5% performance gain over its original model. Overall, ViroBench provides interpretable, diagnostic evaluations and a reproducible measurement framework for future research on viral nucleotide foundation models. The datasets and code are publicly available at https://github.com/QIANJINYDX/ViroBench.
Authors:Jinghao Zhang, Ruilin Li, Feng Zhao, Jiaqi Wang
Abstract:
Reinforcement learning with verifiable rewards (RLVR) has become a core technique for post‑training of Large Language Models (LLMs). While policy optimization is driven by all sampled tokens under a globally broadcast scalar reward, the heterogeneous policy behaviors exhibited along trajectories are largely overlooked without differentiation. Existing works address this by credit allocation, including token‑level advantage reweighting, and selective token optimization, however, the allocation criterion are principally stagnant throughout training, limiting resilient policy evolution. In this work, we argue that when learning signals are scheduled can be as important as where they are allocated across tokens, and introduce the temporal dimension that scheduling the credit allocation criteria over the course of RLVR optimization. We find that prioritizing targeted tokens emphasized with specific policy behaviors, and gradually attenuating toward general optimization leads to more stable and efficient learning dynamics. Furthermore, we show that simple trajectory percentiles provide a natural perspective for distinguishing policy behaviors, and works effectively with temporal scheduling. Our analysis reveals that standard optimization substantially sacrifices policy entropy when simultaneously accommodating heterogeneous behaviors, whereas temporal scheduling yields healthier policy evolution dynamics. Experiments across mathematical and general reasoning benchmarks demonstrate consistent improvements, suggesting that temporal scheduling constitutes a promising optimization dimension.
Authors:Santosh Kumar Radha, Oktay Goktas
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.
Authors:Sohaib Lafifi
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
Authors:Gorgi Pavlov
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.
Authors:Ruitao Liu, Qinghao Hu, Alex Hu, Yecheng Wu, Shang Yang, Luke J. Huang, Zhuoyang Zhang, Han Cai, Song Han
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.
Authors:Mohamed Boussena, Florence Monville, Jacques Fieschi-Meric, Frederic Vely, Pierre Milpied, Julien Mazieres, Maurice Perol, Eric Vivier, Laurent Greillier, Fabrice Barlesi, Sebastien Benzekry
Abstract:
Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often struggle with these gaps, leading to biased results or patient exclusion.
We introduce Multimodality Stacking with Blockwise missing values (MSB), a late‑fusion framework for survival analysis that independently models modality‑specific features before aggregating predictions via a cross‑validated stacking meta‑learner. MSB was validated on the PIONeeR study (n=443 patients, 378 biomarkers across eight heterogeneous sources) to predict progression‑free survival in advanced non‑small cell lung cancer patients receiving immunotherapy.
MSB yielded higher predictive performance (C‑index) than baseline algorithms. Improvements varied by baseline strength: linear models showed a 15.9% increase (p<0.001 for the Wilcoxon signed‑rank test), random survival forests gained 5.4% (p=0.002), and gradient boosting methods improved by 2.1% (p=0.030). Beyond discrimination, MSB reduced the generalization gap (train‑test difference in 5 folds cross‑validation repeated 3 times: 0.055 vs 0.380 for linear models). Permutation importance analysis identified routine laboratory markers, clinical features, and PD‑L1 expression as primary predictive drivers. Missing block indicators showed negligible importance, suggesting the model learned from biomarker values rather than data availability patterns.
MSB provides a statistically validated framework for multimodal survival prediction with blockwise missingness. By enabling systematic biomarker evaluation without requiring complete data, MSB offers a practical tool for predictive modeling in biomedical research, pending external validation. Implementation is available at https://github.com/MohamedBoussena/MSB under Inria license.
Authors:Festus Kahunla
Abstract:
Applied Behavior Analysis (ABA) is a clinical discipline whose documentation, teaching programs and multi‑session behavioral logs, is formulaic and high‑volume, yet real session data is HIPAA‑protected and bound by professional confidentiality rules, blocking the release of a training corpus. We present TRACE (Taxonomy‑Referenced ABA Clinical Examples), a 2,999‑example synthetic instruction‑tuning dataset covering two ABA tasks: teaching‑program generation across Discrete Trial Training, Natural Environment Teaching, and Task Analysis; and multi‑session behavioral interpretation across twelve trajectory patterns and thirteen target behaviors. Every example is produced by a deterministic taxonomy‑driven generator grounded in the canonical ABA literature, and every example carries complete sampling provenance, the exact taxonomy cells that produced it. The dataset is released under CC BY‑NC 4.0 for data and MIT for code, with stratified train (2,549), validation (149), test (281), and sanity (20) splits. TRACE is a research artifact and has not been clinically validated.
Authors:Yichen Luo, Peiyu Zhu, Dongxiao Hu, Jia Wang, Tailin Wu, Dapeng Lan, Yu Liu, Zhibo Pang
Abstract:
While Physics‑Informed Neural Networks (PINNs) are powerful for solving Partial Differential Equations (PDEs), their training is often paralyzed by gradient pathology. The gradients from the PDE residuals and boundary constraints oppose each other, trapping the model in local minima. Current solutions, such as adaptive weighting or hard constraints, either fail to fundamentally resolve this ill‑conditioning or are limited to simple geometries. In this study, we systematically analyze the possible causes of this gradient pathology from the perspectives of loss landscapes and optimization dynamics. Based on the obtained conclusion, we propose Constraint‑Aligned loss with Manifold Lifting (CAML). By reformulating all zeroth‑order terms into aligned constraints, our method effectively mitigates gradient conflicts. In addition, we introduce a delay factor to help the optimizer skip the high‑curvature area. Experiments demonstrate that our CAML significantly enhances numerical stability and efficiency in highly complex PINN problems. Our code is open‑sourced on https://github.com/YichenLuo‑0/CAML.
Authors:Ibrahim Delibasoglu
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
Authors:Da Zhang, Bingyu Li, Zhiyuan Zhao, Hongyuan Zhang, Junyu Gao, Xuelong Li
Abstract:
Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existing methods struggle to jointly model local‑global dynamics and handle nonstationarities like baseline drift, while often failing to capture latent channel interactions. To address these challenges, we propose MedMamba, an end‑to‑end architecture that integrates state space models with domain‑specific inductive biases. Specifically, MedMamba first employs multi‑scale convolutional embeddings to capture discriminative local morphology. Second, to mitigate nonstationarity, we introduce a tri‑branch differential state space encoder that processes raw, temporal‑difference, and frequency‑domain views, fusing them to emphasize informative patterns while suppressing drift. Furthermore, to uncover latent channel correlations, we design a spatial graph Mamba module that learns a directed dependency structure regularized toward sparsity and acyclicity, which obviates the need for predefined graphs. Extensive experiments on five real‑world datasets demonstrate that MedMamba achieves state‑of‑the‑art performance while maintaining linear computational complexity, and ablation studies validate each component's contribution.Code is available at https://github.com/zhangda1018/MedMamba.
Authors:Ruize Li, Zhibin Wen, Tao Han, Hao Chen, Fenghua Ling, Wei Zhang, Song Guo, Lei Bai
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.
Authors:Zhi Wang, Botao He, Kelin Yu, Seungjae Lee, Ruohan Gao, Furong Huang, Yiannis Aloimonos
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
Authors:Ali Noshad, Zishan Zheng, Yinjun Wu
Abstract:
To reduce LLM costs and latency, semantic caching systems must accurately identify when a new prompt matches a cached one. Current methods often rely on simplistic similarity measures, which limit their effectiveness. We introduce MVR‑cache, a novel semantic caching approach that significantly improves retrieval accuracy by integrating Multi‑Vector Retrieval (MVR). MVR‑cache is built upon a learnable segmentation model that intelligently splits prompts, enabling fine‑grained similarity comparisons via MaxSim. We derive the model's training objective from a rigorous theoretical analysis. This can ensure that optimizing this objective directly maximizes cache hits under strict correctness constraints. To solve the resulting non‑differentiable combinatorial optimization problem, we leverage a reinforcement learning‑based training strategy with the theoretically grounded objectives as the reward. Experimental results on established benchmarks across diverse tasks confirm that in comparison to the state‑of‑the‑art, MVR‑cache consistently increases the cache hit rates by up to 37% while maintaining the same correctness guarantees. MVR‑cache is available at https://github.com/PKU‑SDS‑lab/MVR‑Cache
Authors:Mini Han Wang, Liting Huang, Wei Hong, Boonthawan Wingwon
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
Authors:James Henry
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).
Authors:Zeyu Shen, Zhuoyuan Wang, Laixi Shi
Abstract:
Recent advances in learning‑based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance when nonstationary system dynamics deviate from nominal models. However, existing approaches primarily address specific or relatively structured forms of dynamical variation, leaving more general, unknown, and unpredictable time‑varying dynamics insufficiently handled. To tackle this challenge, we propose T2S‑MPC, a framework that adaptively learns a residual dynamics model online and integrates it with the nominal model within the MPC framework to enable fast‑evolving online planning. To make the model time‑aware, we explicitly encode temporal information through a structured time embedding and employ a two‑timescale update scheme, allowing the controller to capture nonstationary dynamics while balancing rapid adaptation with stable learning. We evaluate the proposed method on a 2D quadrotor across stabilization and trajectory tracking tasks under diverse time‑varying disturbances, including linear drifting and periodic perturbations. Experimental results show that T2S‑MPC consistently outperforms classical MPC, neural MPC, and ablated variants in control performance, while also demonstrating strong robustness across a wide range of disturbance conditions without additional tuning. The source code is publicly available at https://github.com/Zeyuu0920/T2S_MPC
Authors:Jinjin He, Zhiqi Li, Sinan Wang, Bo Zhu
Abstract:
We propose Hermite‑NGP, a gradient‑augmented multi‑resolution hash encoding designed to enable fast and accurate computation of spatial derivatives for neural PDE solvers. Unlike existing NGP‑based approaches that rely on automatic differentiation or finite differences and suffer from instability or high cost, Hermite‑NGP explicitly stores function values and mixed partial derivatives at hash grid vertices, allowing fully analytic evaluation of gradients, Jacobians, and Hessians via Hermite interpolation. This design preserves the efficiency and spatial adaptivity of NGP while supporting analytic differential operators up to second order. We further introduce a multi‑resolution curriculum training strategy analogous to multigrid V‑cycles to enable coarse‑to‑fine optimization. Across a range of 2D and 3D PDE benchmarks, Hermite‑NGP achieves up to approximately 20 times lower error than prior neural PDE methods, and reduces wall‑clock convergence time by 2 to 10 times compared to other solvers, with per‑epoch training times as low as 3.5 ms for models with up to 17M parameters.
Authors:Ismail Lamaakal
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.
Authors:Michel A. Youssef
Abstract:
Streaming network intrusion detection systems must process flows continuously while keeping memory bounded, but most current methods leave alerting threshold selection as a post‑hoc tuning problem poorly suited to production. Operators need alerting behaviour specifiable before deployment using inputs such as false‑negative cost, false‑positive cost, and alerting budget. This paper presents CALIBURN, a five‑component streaming alerting pipeline composed of a truncated Bayesian online change‑point detector, an isotonic calibration layer mapping the change‑point posterior to an empirical conditional attack probability, a cost‑sensitive decision threshold derived from operator‑specified misclassification costs, a Conformal Risk Control wrapper that converts an alert‑budget specification into a within‑window valid threshold under exchangeability, and a multi‑window burn‑rate alerting layer adapted from Site Reliability Engineering practice. Rather than claiming uniform dominance, we present CALIBURN as a regime‑sensitivity study, evaluating the pipeline across three attack‑prevalence regimes: LITNET‑2020 at 5.2 percent, CICIDS2017 at 22.06 percent, and UNSW‑NB15 at 64 percent. In the rare‑attack regime, CALIBURN achieves AUC‑PR 0.943 on LITNET‑2020, outperforming the best streaming baseline by 2.21x and the best batch reference by 4.12x; isotonic calibration reduces Brier score by 30 percent. In the moderate‑prevalence regime, CALIBURN remains the strongest streaming method on CICIDS2017 but is exceeded by batch density methods. In the high‑prevalence regime, all streaming methods approach the prevalence floor. We further identify two distinct CRC‑collapse mechanisms driving the alert rule to degeneracy at small alpha, treating both as operational guidance for practitioners.
Authors:Sol Park, Soobin Um
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.
Authors:Jaeung Lee, Dohyun Kim, Jaemin Jo
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
Authors:Piotr Wilam
Abstract:
A sparse 8‑layer code transformer develops dedicated neural circuitry for every Python construct tested, and that circuitry is organised by a clean computational principle rather than by semantic category. We extract neural circuits for 106 concepts (43 AST node types, 63 builtin objects) by marginalising across 63,800 controlled prompts, and decompose each circuit into concept‑specific and token‑driven components using contrastive checker prompts that present a keyword token without its associated syntactic structure. Three findings emerge. First, all 106 concepts produce non‑empty universal circuits at every one of nine parameter settings, and the ranking of concept‑specificity across constructs is stable across the sweep ‑ survival is not an artifact of a permissive threshold. Second, AST circuits contain a genuine concept component distinct from token activation: concept‑only neurons constitute up to 62.5% of the loudest‑firing neurons at mid‑to‑late layers, while builtin circuits are almost entirely token‑driven. Third, six computationally atomic constructs ‑ Import, ImportFrom, Break, Continue, Pass, Assert ‑ cluster together despite being semantically unrelated, sharing only the property of being single‑statement constructs requiring no nested body; this atomicity super‑cluster, together with a four‑tier hierarchy organised by token ambiguity and structural distinctiveness, shows that the model's internal organisation tracks computational structure rather than meaning. The methodology, full decomposition data, and analysis code are released.
Authors:Sattam Altuuaim, Lama Ayash, Muhammad Mubashar, Naeemullah Khan
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
Authors:Yosef Worku Alemneh, Kidist Amde Mekonnen, Maarten de Rijke
Abstract:
Multilingual retrieval increasingly underpins cross‑lingual question answering and retrieval‑augmented generation. Strong zero‑shot scores on multilingual benchmarks are often taken as evidence that current encoders transfer reliably across many languages. We argue that this assumption breaks down for underrepresented, morphologically rich languages, and use Amharic as a diagnostic case. Under a shared passage retrieval protocol covering dense, late‑interaction, learned sparse, and cross‑encoder paradigms, we compare zero‑shot multilingual retrievers, Amharic‑fine‑tuned multilingual retrievers, and monolingual Amharic retrievers. The strongest zero‑shot multilingual retriever underperforms the strongest monolingual Amharic first‑stage retriever by 23% relative MRR@10. Fine‑tuning two recent multilingual embedding models on the same Amharic supervision yields 32‑60% relative MRR@10 gains over zero‑shot, but the best Amharic‑fine‑tuned multilingual model remains below the strongest monolingual Amharic retriever. These findings indicate that zero‑shot multilingual retrieval is not a sufficient proxy for equitable information access in the LLM era: for underrepresented languages, retrieval must be evaluated and adapted in‑language rather than inferred from aggregate multilingual benchmarks. To foster future research, we publicly release the dataset, codebase, and trained models at https://github.com/rasyosef/amharic‑neural‑ir.
Authors:Zexuan Chen, Sichao Liu, Runhao Lu, Huichao Qi, Alexandra Woolgar, Xi Vincent Wang, Lihui Wang
Abstract:
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. We introduce a tri‑modal contrastive framework for EEG‑based visual decoding that aligns EEG, visual, and textual representations within a unified latent space. Our approach follows a two‑stage design. First, we pre‑train an EEG encoder via masked reconstruction on unlabeled trials, learning spatio‑temporal regularities that transfer robustly to downstream tasks. Second, we jointly align EEG, image, and LLM‑generated textual descriptions through contrastive learning, where text supervision acts as a semantic regularizer that injects linguistic structure into the shared space without overwhelming the primary EEG‑image signal. The encoder integrates subject‑specific adaptation, graph‑attention over channels, and temporal‑spatial convolutional embeddings. On the Things‑EEG2 200‑way zero‑shot benchmark, our framework achieves 54.1% Top‑1 and 83.4% Top‑5 accuracy, substantially exceeding the strongest prior baseline (32.4% / 64.0%), with paired Wilcoxon tests confirming significance (p < 0.01) over all in‑subject baselines. We validate generalization on Things‑MEG. Analysis reveals that compact embedding geometries (CN‑CLIP) outperform much larger backbones, and that decoding aligns with established neurophysiology of visual processing. This work is a critical step towards robust, semantically‑grounded visual decoding from non‑invasive temporal neural signals. The source code is publicly available in https://github.com/anon‑eeg/eeg_image_decoding.
Authors:Muhammad Muneeb, David B. Ascher
Abstract:
Missense variant interpretation remains challenging because pathogenicity depends on heterogeneous evidence from population frequency, evolutionary conservation, transcript context, amino acid substitution severity, prior pathogenicity predictors and protein‑language‑model‑derived features. We present AnnotateMissense, a scalable annotation, benchmarking and genome‑wide prediction framework for missense variant interpretation. AnnotateMissense integrates hg38 missense variants derived from dbNSFP v5.1 with ANNOVAR annotations, dbNSFP transcript/protein descriptors, AlphaMissense scores, ESM‑derived features, conservation metrics, population‑frequency variables, established pathogenicity predictors and engineered amino acid/codon‑context features. Using 132,714 ClinVar‑labelled missense variants, we benchmarked machine‑learning and deep‑learning models under controlled feature configurations. The full 303‑feature benchmark set achieved the strongest performance with XGBoost, reaching mean MCC = 0.9411 and ROC‑AUC = 0.9950 across stratified five‑fold cross‑validation. Restricted naive and location‑oriented feature sets achieved lower best MCC values of 0.4989 and 0.5113, respectively. Circularity‑controlled ablations showed that removing prior‑predictor, population‑frequency and clinically overlapping evidence reduced performance, whereas excluding AlphaMissense and ESM‑derived features alone had minimal effect. Temporal ClinVar validation on newly observed pathogenic/benign variants achieved MCC = 0.7613, accuracy = 0.8798 and F1‑score = 0.8750. The final model was applied to 90,643,830 hg38 missense variants to generate AnnotateMissense pathogenicity scores and binary prediction labels. Code and outputs are available at https://github.com/MuhammadMuneeb007/CAGI7_Annotate_All_Missense and https://doi.org/10.5281/zenodo.19981867.
Authors:Karan Sharma, Aditya Tripathi, Rahul Mishra, Tapas Kumar Maiti
Abstract:
Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard federated algorithms assume homogeneous hardware, identical architectures, and centralized aggregation, which fails when hospitals have unequal compute resources. We propose capacity‑aware coordination: measure each hospital's throughput, assign capacity‑appropriate architectures (MobileNetV3‑Small, EfficientNet‑B0, ResNet‑50), and combine predictions via weighted ensemble. Weak and strong hospitals can participate without forcing uniform architectures.
We separate on‑chain policy from off‑chain learning. A Solidity contract stores hospital registration, benchmark hashes, metrics, and weights. Hospitals train locally and submit only hashes and scalars (not parameters). Weighted ensemble inference is computed off‑chain.
Experiments on PneumoniaMNIST and DermaMNIST (5 seeds, 3 non‑IID levels) show our method achieves lower or equal calibration error versus equal‑weight ensemble and competitive accuracy versus FedAvg, FedProx, and FedMD. Communication overhead is 224 bytes per round, a reduction of over 912,000x compared to FedAvg.
Authors:Zherui Yang, Tao Du, Ligang Liu
Abstract:
The eigendecomposition of the Laplace‑‑Beltrami Operator (LBO) is fundamental to geometric analysis, yet computing its low‑frequency eigenmodes remains a significant bottleneck due to the high cost of iterative solvers on large‑scale data. To amortize this cost, we introduce the Neural Eigenspace Operator (NEO), a feed‑forward framework designed to predict the spectrum directly from point clouds. Crucially, NEO circumvents the ill‑posed nature of standard eigenvector regression, which suffers from intrinsic sign flips and rotation ambiguities, by learning the stable, invariant low‑frequency subspace instead. Specifically, the network predicts a redundant set of basis functions whose span robustly covers the target eigenspace, allowing for the recovery of accurate eigenpairs via a lightweight Rayleigh‑‑Ritz refinement. To handle irregular sampling, we propose a mass‑aware neural operator that incorporates per‑point area weights into attention‑based aggregation, improving robustness to non‑uniform densities and enabling zero‑shot generalization across resolutions. Our approach achieves near‑linear runtime scaling and substantial wall‑clock speedups over iterative solvers at comparable accuracy, and exhibits strong zero‑shot transfer to high‑resolution point clouds. The resulting eigenpairs support standard spectral geometry tasks, while the raw basis functions provide effective point‑wise features for downstream learning. Code: https://github.com/Adversarr/NEO.
Authors:Kavin Soni, Debanshu Das, Vamshi Guduguntla
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.
Authors:Ke Sun, Yizhou Zhao, Jiayi Xin, Qi Long, Weijie Su
Abstract:
Context or prompt‑level reweighting has emerged as a central algorithmic lever in Reinforcement Learning with Verified Rewards (RLVR) for improving the reasoning capability of large language models, yet the principle determining what constitutes an optimal weighting remains poorly understood. We address this gap by formulating prompt reweighting as a functional derivative of a utility functional defined in the pass‑rate function space, yielding a unified optimality framework that accommodates existing schemes, including REINFORCE and GRPO. Building on this optimality framework, we propose a distribution‑aware prompt reweighting approach, called CurveRL, based on a quantile coordinate transform, in which the weight assigned to each prompt depends not on the absolute value of pass rates but on its rank and density to reflect the distributional structure of the pass rates in the learning dynamics. Extensive experiments across multiple benchmarks demonstrate that our proposed CurveRL consistently outperforms GRPO and other RLVR baselines. Our study identifies context‑distribution control as a principled axis for analyzing and designing prompt‑reweighted RLVR algorithms. The code is released in https://github.com/zhyzmath/CurveRL.
Authors:Jinghan Jia, Joe Benton, Eric Easley
Abstract:
Chain‑of‑thought (CoT) reasoning is useful for monitoring language models only when the reasoning trace faithfully reflects the computation that produces the final answer. However, models can rely on prompt‑to‑answer shortcuts that bypass the CoT, making the visible reasoning trace misleading even when it appears plausible. We study CoT faithfulness through a structural information‑flow perspective: faithful reasoning should route answer‑relevant information through the mediated path from prompt to CoT to answer, rather than through a direct prompt‑to‑answer shortcut. This perspective yields a task‑agnostic framework based on three complementary properties, sufficiency, completeness, and necessity, which we instantiate with entropy‑based, masked‑KL, and gradient‑based diagnostics. We show that these metrics recover externally judged faithfulness differences in hinted reasoning, and identify a low‑entropy failure mode of KL‑based diagnostics where gradient‑based measures remain more stable. Building on this analysis, we introduce update‑time interventions for verifier‑based on‑policy RL, including attention masking, backward‑only gradient masking, CoT gradients, and adversarial perturbations of prompt representations. Across hinted arithmetic, reward‑hackable code repair, and DAPO‑Math models trained without hints but evaluated under wrong‑hint injection, our interventions shift behavioral and structural indicators toward stronger CoT mediation. In particular, they make shortcut and reward‑hacking behavior more transparent in the CoT and improve task‑agnostic faithfulness metrics, while in some settings also reducing wrong‑hint susceptibility. Our results suggest that controlling information flow during training is a practical route toward more faithful and monitorable CoT reasoning. Code is available at https://github.com/safety‑research/faithful‑cot.
Authors:Bowen Duan, Cong Guo, Chiyue Wei, Haoxuan Shan, Yuzhe Fu, Xinhua Chen, Yifan Xu, Ziyue Zhang, Changchun Zhou, Hai Li, Yiran Chen
Abstract:
Large Language Models (LLMs) have achieved impressive performance across diverse domains but remain inefficient during the autoregressive decoding phase. Unlike the prefill stage, which employs compute‑bound GEMM operations, decoding executes a sequence of small GEMV‑like computations that are memory‑bound and underutilize modern accelerators. Weight‑only vector quantization (VQ) has emerged as an effective compression technique that clusters model weights into a shared codebook and replaces the original weight matrix with low‑precision indices, enabling 2‑bit‑level weight compression. While this approach substantially reduces model size and memory bandwidth, it still suffers from two critical inefficiencies: the low utilization of GEMV computation and frequent memory conflicts during codebook lookups.
This paper presents EVA, an efficient vector‑quantization‑based architecture that addresses both computational and memory bottlenecks in LLM decoding. EVA builds on a simple yet effective insight that combines input‑codebook computation with conflict‑free memory access. Instead of reconstructing quantized weights from indices, EVA directly performs dot products between input vectors and the weight codebook, transforming LLM decoding from GEMV to GEMM computation. It then performs structured lookups from an intermediate output buffer, eliminating memory bank conflicts. We further design a hardware‑software co‑optimized architecture specialized for LLM decoding while remaining compatible with conventional prefill execution. Evaluations show that EVA achieves up to 11.17× speedup and 7.17× higher energy efficiency compared with the SOTA lookup‑based architecture, while preserving arithmetic precision after vector quantization. Our code is available at https://github.com/dbw6/Eva.git.
Authors:Sanchit Kabra, Nikhil Abhyankar, Saaketh Desai, Prasad Iyer, Chandan K Reddy
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
Authors:Xiaotian Liu, Shuyuan Shang, Xiaopeng Wang, Pu Ren, Yaoqing Yang
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
Authors:Youwei Pang, Changsheng Gao, Dong Liu, Huchuan Lu, Weisi Lin
Abstract:
Large models have delivered remarkable performance across a wide range of perception and generation tasks, yet practical deployment is increasingly constrained by computational and memory budgets, as well as privacy requirements. Split execution alleviates these constraints by partitioning computation across devices, but it inevitably introduces intensive transmission and storage of intermediate features. Unlike conventional feature coding for CNNs that typically targets homogeneous spatial activation maps, modern large models generate heterogeneous features with varying statistical distributions and compression tolerances, e.g., multi‑level/multi‑modal representations and autoregressive context caches. These characteristics necessitate treating large model feature coding (LaMoFC) as a fundamental system component and call for a systematic evaluation framework. In this paper, we present a comprehensive benchmark and evaluation framework for LaMoFC. We first build the feature dataset LaMoFCBench, covering diverse task requirements across 4 categories and 16 scenarios while integrating widelyadopted architectures and various split‑computing settings. We then specify representative split points according to practical application scenarios to extract intermediate features, establishing a unified pipeline for fair and reproducible comparisons. Finally, we benchmark mainstream universal feature codecs, exposing the profound misalignment between existing coding paradigms and the heterogeneous nature of large model features. These findings reveal that LaMoFC demands a fundamental departure from existing paradigms, and LaMoFCBench provides the shared empirical foundation to drive this transition. The data and code will be available at https://github.com/lartpang/LaMoFCBench.
Authors:Zhengqi Sun, Yiwen Sun, Boxuan Liu, Tailai Chen, Tianxu Guo, Jiabin Liu
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.
Authors:Siqiao Huang, Partha Kaushik, Michael Chen, Hengkai Pan, Kaiwen Geng, Omar Chehab, Fernando Moreno-Pino, Max Simchowitz
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.
Authors:Mingqing Wang, Zhiwei Nie, Athanasios V. Vasilakos, Yonghong He, Zhixiang Ren
Abstract:
Proteins encode diverse functions within complex three‑dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals that underlie function. Here we introduce ProtDiS, a knowledge‑guided framework that decomposes pretrained protein micro‑environment embeddings into biologically grounded and task‑relevant dimensions. Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression, yielding structural features that are more specific, independent, and information‑efficient, and achieving consistent improvements across twelve downstream tasks, with the largest gains under structure‑based splits. Protein‑ and residue‑level analyses further show that ProtDiS differentiates proteins with similar folds but divergent functions and captures fine‑grained biophysical signals critical. These findings suggest that knowledge‑guided decomposition provides a general and interpretable approach for structuring latent spaces in protein structural modeling. The source code and implementation details are publicly available at https://github.com/AI‑HPC‑Research‑Team/ProtDiS.
Authors:Zhiyuan Zhai, Xinkai You, Wenjing Yan, Xin Wang
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
Authors:Shuhong Zheng, Michael Oechsle, Erik Sandström, Marie-Julie Rakotosaona, Federico Tombari, Igor Gilitschenski
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.
Authors:Stuart Bladon, Brinnae Bent
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.
Authors:Bo Peng, Jie Lu, Guangquan Zhang, Zhen Fang
Abstract:
Aiming at identifying unexpected inputs from unknown classes, out‑of‑distribution (OOD) detection has emerged as a pivotal approach to enhancing the reliability of machine learning models. This paper focuses on the burgeoning paradigm of post‑hoc OOD detection with pre‑trained vision‑language models (VLMs), where a popular pipeline is to detect OOD inputs by examining their affinities between ID labels and negative labels, i.e., those semantically different from ID labels. Due to the unavailability of target OOD labels, existing works predominantly rely on heuristic rules to mine negative labels from unlabeled wild corpus data. Despite the empirical success, we argue that the power of VLM‑based OOD detection has yet to be fully unleashed since the notorious false negative problem is far from addressed in the literature. With this motivation, we are interested in addressing the challenge of mining true negative labels for OOD scoring. To this end, we develop a theoretical framework for correcting the sampling bias of negatives labels by indirectly approximating the distribution of negative labels. Perhaps surprisingly, we show that the debiased negative mining can be naturally converted into Monte‑Carlo sampling based on ID labels and the unlabeled wild corpus data. Extensive experiments empirically manifest that our method establishes a new state‑of‑the‑art in a variety of OOD detection setups. Code is publicly available at \hrefhttps://github.com/60pen9/Debiased‑Negative‑Mining‑Improves‑OOD‑Detection‑with‑Pre‑trained‑VLMs\textcolorredhere.
Authors:Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima
Abstract:
Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer‑wise relevance propagation for GNNs (GNN‑LRP) evaluates the relevance of \emphwalks to reveal important information flows in the network, and provides higher‑order explanations, which have been shown to be superior to the lower‑order, i.e., node‑/edge‑level, explanations. However, identifying relevant walks by GNN‑LRP requires \em exponential computational complexity with respect to the network depth, which we will remedy in this paper. Specifically, we propose \em polynomial‑time algorithms for finding top‑K relevant walks, which drastically reduces the computation and thus increases the applicability of GNN‑LRP to large‑scale problems. Our proposed algorithms are based on the \emphmax‑product algorithm ‑‑ a common tool for finding the maximum likelihood configurations in probabilistic graphical models ‑‑ and can find the most relevant walks exactly at the neuron level and approximately at the node level. Our experiments demonstrate the performance of our algorithms at scale and their utility across application domains, i.e., on epidemiology, molecular, and natural language benchmarks. We provide our codes under \hrefhttps://github.com/xiong‑ping/rel_walk_gnnlrpgithub.com/xiong‑ping/rel\_walk\_gnnlrp.
Authors:Liupeng Li, Haoqian Kang, Zhenyu Lu, Jinpeng Wang, Bin Chen, Ke Chen, Yaowei Wang
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.
Authors:Zhangyi Hu, Chenhui Liu, Tian Huang, Jindong Li, Yang Yang, Jiemin Wu, Zining Zhong, Menglin Yang, Yutao Yue
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.
Authors:Hanadi Alhamdan, Ghadah Alosaimi, Amir Atapour-Abarghouei, Farshad Arvin
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
Authors:Dai Shi, Luke Thompson, Linhan Luo, Lequan Lin, Andi Han, Junbin Gao, José Miguel Hernández Lobato
Abstract:
Message‑passing neural networks (MPNNs) often suffer from an information bottleneck when capturing long‑range dependencies, leading to the oversquashing (OSQ) phenomenon. Alongside spatial connectivity enrichment (e.g., rewiring), recent studies have shown that spectral filtering can yield strong long‑range learning outcomes, as spectral operators enable global information mixing that alleviates OSQ. These approaches achieve this either by stabilizing the Jacobian energies in deep propagation or by guaranteeing OSQ mitigation under strong theoretical assumptions. We revisit these conclusions and show that the associated Jacobian sensitivity lower bound is generally difficult to achieve in practice. We then propose S^3GNN, which mitigates OSQ without such restrictive assumptions by lightweightly reintroducing omitted components with substantially lower computational complexity, while standard stability constraints on feature transformations remain effective under our new dynamics. Extensive experiments across diverse domains (e.g., long‑range benchmarks, KGQA, and mesh‑based fluid dynamics) demonstrate that S^3GNN achieves up to an order‑of‑magnitude error reduction with up to 50% fewer parameters. Our code can be found in https://github.com/EEthanShi/S3‑GNN.git.
Authors:Hongyi Li, Jun Xu, Hong Yan
Abstract:
Learning high‑quality oblique decision trees remains a significant challenge due to the discrete and non‑convex nature of split optimization. We present the Hinge Regression Tree (HRT) framework, which reframes each oblique split as a nonlinear least‑squares problem over two linear predictors whose max/min envelope induces ReLU‑like representation capacity. We show that the resulting node‑level optimization can be interpreted as a damped Newton method, and we establish the monotonic decrease of the node objective for its backtracking line‑search variant. We establish, theoretically, that HRT is a universal approximator with an explicit O(δ^2) approximation rate. Building upon this base learner, we propose HRT‑Boost, a mathematically synergistic ensemble extension that couples node‑level Newton updates with stage‑wise functional gradient descent. We show that this ensemble construction admits a stage‑wise empirical risk reduction guarantee under the squared loss. Empirical evaluations on synthetic and real‑world benchmarks show that HRT is highly competitive with established single‑tree baselines, and HRT‑Boost compares favorably with strong ensemble baselines and often yields substantially more compact models. The code is publicly available at https://github.com/Hongyi‑Li‑sz/HRT‑Boost.
Authors:Eunwoo Heo, Kyeongkook Seo, Jaejun Yoo
Abstract:
The explosive growth of open‑source model repositories has created a Model Jungle, where checkpoints are frequently shared without adequate documentation or metadata. While weight‑space learning offers a pathway to identify and analyze these models directly from their parameters, processing full‑scale weights is computationally prohibitive. Probing‑based methods have emerged as a lightweight alternative, extracting permutation‑equivariant representations via learnable probe vectors. However, existing probing methods are limited by a single‑view design: they capture first‑order structures but fail to encode the rich, higher‑order correlation patterns inherent in row‑column interactions. To bridge this gap, we introduce MVProbe, a multi‑perspective probing framework that synthesizes first‑order signals with interaction‑aware (Gram‑based) views. Our approach is theoretically grounded; we analyze the scaling laws of different probing orders to derive a principled standardization and fusion strategy that ensures balanced contributions from all branches. On the Model Jungle benchmark, MVProbe consistently outperforms the state‑of‑the‑art ProbeX across diverse architectures, including discriminative backbones (ResNet, SupViT, MAE, DINO) and large‑scale generative LoRA adapters (Stable Diffusion LoRA).
Authors:Jinglin Li, Jun Tan, QI Fang, Ning Gui
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.
Authors:Po-Kai Chen, Niki van Stein, Aske Plaat
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.
Authors:Shaoqing Duan, Haofei Song, Xintian Mao, Qingli Li, Yan Wang
Abstract:
Defocus deblurring in pathological microscopy remains challenging due to the spatially varying and locally discontinuous nature of optical blur induced by a position‑dependent integral imaging process.
Existing deep learning methods, constrained by shift‑invariance assumptions and limited interpretability, are not well suited to such heterogeneous blur patterns.
Neural operators provide a principled alternative by modeling defocus formation directly as an integral operator, offering a new perspective on defocus deblurring.
However, most existing neural operator architectures for low‑level vision rely on globally parameterized kernels that assume smoothness and stationarity, limiting their ability to model heterogeneous and locally discontinuous blur patterns.
To address this limitation, we propose the Discontinuous Galerkin Neural Operator (DGNO), which parameterizes the integral kernel using a discontinuous Galerkin formulation with element‑local volume operators and interface numerical fluxes.
DGNO provides a principled combination of locality,
heterogeneity modeling, and global coherence while preserving the underlying physics
of optical image formation.
Extensive and insightful experiments demonstrate that DGNO surpasses state‑of‑the‑arts, delivering sharper reconstructions, robust handling of spatially varying blur, and scalable high‑resolution performance. The code will be released at https://github.com/DeepMed‑Lab‑ECNU/Single‑Image‑Deblur.
Authors:Woohyun Lee, Hogun Park
Abstract:
Defending Graph Neural Networks (GNNs) against adversarial attacks requires balancing accuracy and robustness, a trade‑off often mishandled by traditional methods like adversarial training that intertwine these conflicting objectives within a single classifier. To overcome this limitation, we propose a self‑supervised adversarial purification framework. We separate robustness from the classifier by introducing a dedicated purifier, which cleanses the input data before classification. In contrast to prior adversarial purification methods, we propose GPR‑GAE, a novel graph auto‑encoder (GAE), as a specialized purifier trained with a self‑supervised strategy, adapting to diverse graph structures in a data‑driven manner. Utilizing multiple Generalized PageRank (GPR) filters, GPR‑GAE captures diverse structural representations for robust and effective purification. Our multi‑step purification process further facilitates GPR‑GAE to achieve precise graph recovery and robust defense against structural perturbations. Experiments across diverse datasets and attack scenarios demonstrate the state‑of‑the‑art robustness of GPR‑GAE, showcasing it as an independent plug‑and‑play purifier for GNN classifiers.
Authors:Minju Kim, Youngbum Hur
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
Authors:Gabriele Oliaro, Yichao Fu, May Jiang, Owen Lu, Junli Wang, Zhihao Jia, Hao Zhang, Samyam Rajbhandari
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
Authors:Jean-Guillaume Durand, Panagiotis Kouvaros, Maxime Gariel, Alessio Lomuscio
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 .
Authors:Guoming Li, Shangyu Zhang, Junwei Pan, Wentao Ning, Jin Chen, Gengsheng Xue, Chao Zhou, Shudong Huang, Haijie Gu, Menglin Yang
Abstract:
Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a unified token representation and alternating between token mixing and per‑token feedforward networks (P‑FFNs) to achieve scalable performance. However, RankMixer suffers from embedding collapse, where learned representations have low effective rank, limiting expressivity and underutilizing the expanded representation space. Through empirical analysis and theoretical insights, we identify rigid token mixing and P‑FFN modules as the primary causes of this phenomenon, jointly inducing a damped oscillatory trajectory in effective‑rank evolution across layers. To address it, we propose RankElastor, a novel architecture that produces spectrum‑robust representations with provable collapse mitigation. RankElastor introduces two components: (i) parameterized full mixing, which enables expressive token mixing with improved spectral robustness; and (ii) GLU‑improved P‑FFNs, which stabilize representation spectra through GLU‑style FFN modules. Extensive experiments on large‑scale industrial datasets demonstrate that RankElastor consistently improves recommendation performance, mitigates embedding collapse, and exhibits robust scaling behavior. Code is available at this GitHub repository: https://github.com/vasile‑paskardlgm/RankElastor
Authors:Jaehyeop Hong, Youngbum Hur
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
Authors:Yannick Kirchhoff, Maximilian Rokuss, Daniel Philipp Mertens, David Füller, Benjamin Hamm, Andreas Schreyer, Oliver Ritter, Klaus Maier-Hein
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
Authors:Joe Sharratt
Abstract:
Efficient attention algorithms are critical to mitigate the quadratic cost of attention in long‑context workloads. Prior work utilises block‑scaled quantisation techniques on Blackwell GPUs to move attention computation to 4‑bit precision to accelerate inference. However, these techniques result in significant quality degradation in long‑context settings. We show that the output impact of quantisation error is highly non‑uniform and increases with the importance of each query‑key interaction, concentrating functionally relevant error in a small number of attention blocks that contain the most important tokens. We propose ThriftAttention, a low‑bit attention variant that delivers near‑FP16 long‑context quality at FP4 inference efficiency. This approach proceeds in two stages. First, a heuristic rapidly selects a small number of important query‑key block pairs for FP16 precision. Second, the selected blocks are computed in FP16 and the remaining blocks in FP4, with both paths merged via online softmax into a single output. We demonstrate across long‑context benchmarks and model families that by computing only 5% of query‑key blocks in FP16, ThriftAttention recovers on average 89.1% of the FP4‑to‑FP16 performance gap. We show ThriftAttention's advantage grows with sequence length, mitigating the systematic FP4 quality degradation observed at longer contexts. The code is available at https://github.com/joesharratt1229/ThriftAttention.
Authors:Jianing Deng, Song Wang, Dongwei Wang, Zijie Liu, Tianlong Chen, Huanrui Yang, Jingtong Hu
Abstract:
Mixture‑of‑Experts Large Language Models (MoE‑LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters. Mixed‑precision quantization mitigates this cost by allocating expert‑wise bit‑widths based on their importance, approaching the accuracy‑memory Pareto frontier and enabling extreme low‑bit quantization. However, existing methods rely on layer‑wise importance estimation and overlook router shifts induced by quantization, resulting in suboptimal allocation and routing. In this work, we propose Global Expert‑level Mixed‑precision Quantization (GEMQ) to overcome these limitations via (1) a global linear‑programming formulation that captures model‑wide expert importance based on quantization error analysis, and (2) efficient router fine‑tuning to adapt routing to quantized experts. These components are integrated into a progressive quantization framework that iteratively refines importance estimation and allocation. Experiments demonstrate that GEMQ significantly reduces memory and accelerates inference with minimal accuracy degradation. Source code is available at https://github.com/jndeng/GEMQ .
Authors:Colin Gaffney, Shutong Li, Daniel Ng, Anastasia Petrushkina, Niket Kumar, Adam Cogdell, Mridul Sahu, Yaning Liang, Nikhil Bansal, Justin Pan, Angel Mau, Abhishek Agrawal, Marco Berlot, Ruoxin Sang, Kiranbir Sodhia, Rakesh Iyer
Abstract:
In a landscape of high‑performance distributed ML systems, JAX has emerged as a framework of choice. However, JAX's modular design philosophy leaves it without a standardized checkpointing solution. In this paper, we introduce Orbax, a modular, JAX‑native checkpointing library that abstracts the complexities of distributed accelerator systems while also providing flexibility for user‑friendly checkpoint manipulations throughout the ML model lifecycle. We demonstrate performance exceeding comparable PyTorch competitors by up to 3.5× for saving and 2× for loading. The library is available at https://github.com/google/orbax.
Authors:Simone Antonelli, Sadegh Akhondzadeh, Aleksandar Bojchevski
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.
Authors:Shubham Parashar, Atharv Chagi, Jacob Helwig, Lakshmi Jotsna, Sushil Vemuri, James Caverlee, Dileep Kalathil, Shuiwang Ji
Abstract:
We aim to improve the reasoning capabilities of diffusion language models (DLMs). While SFT is a popular post‑training recipe for autoregressive models, its use in DLMs faces challenges and can even hurt performance, though the underlying causes remain understudied. Our analysis reveals that vanilla SFT overlooks learnability, namely what and when tokens are learned. Specifically, rare tokens are difficult to learn when most of the input is masked, whereas it is straightforward and thus of little value to learn common tokens when most of the input is unmasked. Motivated by our analysis, we propose LIFT, an efficient SFT‑based post‑training algorithm for DLMs. LIFT learns easy tokens when most of the input is masked and hard tokens when more context is available, thus aligning the training with the information available at different diffusion time steps. Our results show that LIFT outperforms existing SFT baselines across six reasoning benchmarks, achieving up to a 3x relative gain on AIME'24 and AIME'25. Our code is publicly available at https://github.com/divelab/LIFT.
Authors:Jingyan Zhang, Han Liang, Ruichi Zhang, Bin Li, Juze Zhang, Xin Chen, Jingya Wang, Lan Xu, Jingyi Yu
Abstract:
Controlling physics‑based humanoids from natural‑language instructions is a critical step toward general‑purpose embodied agents. However, existing methods remain constrained by a tension between semantic expressiveness and physical feasibility, often failing to jointly achieve faithful instruction following, high‑quality motion, and stable long‑horizon control. We propose SCRIPT, a scalable diffusion policy with a multi‑stage training framework for language‑driven physics‑based humanoid control. The core of SCRIPT is a Joint Action‑State‑Text Diffusion Transformer (JAST‑DiT), which represents actions, physical states, and text as dedicated token streams and couples them through joint attention, enabling direct interaction between language semantics and control dynamics. To stabilize autoregressive control, we introduce a nonlinear history conditioning mechanism, which preserves the dense recent context and samples increasingly sparse cues from long‑term history. Beyond supervised imitation pre‑training, we propose a post‑training stage, further improving the performance using Reinforcement Learning with Hybrid Rewards (RLHR). By injecting learnable noise into the flow‑sampling process, RLHR effectively improves motion quality and instruction following within closed‑loop simulations using hybrid physical feedback and text rewards. Quantitative evaluations demonstrate that SCRIPT outperforms prior state‑of‑the‑art methods, with gains across text alignment, motion quality, and physical realism metrics. Furthermore, scaling studies on the 1200‑hour MotionMillion dataset demonstrate consistent performance gains with model scaling, highlighting SCRIPT's robust scalability for large‑scale pre‑training. Our code will be publicly available for future research.
Authors:Yequan Zhao, Ruijie Zhang, Liyan Tan, Niall Moran, Tong Qin, Zheng Zhang
Abstract:
Both full fine‑tuning (Full FT) and parameter‑efficient fine‑tuning methods such as LoRA introduce weight updates without accounting for the spectral structure established during pretraining. As a result, noisy gradients from limited fine‑tuning data can perturb robust pretrained features. We identify spectral preconditioning as the missing ingredient: reparameterizing each weight matrix through its full‑rank singular value decomposition (SVD) and freezing one singular basis constrains updates to the pretrained column space, yielding a preconditioned optimization scheme that outperforms unconstrained Full FT at the same trainable parameter count. Building on this insight, we propose FuRA (Full‑Rank Adaptation), an efficient full‑rank adaptation framework based on a block tensor‑train factorization W = LSR, where the large core L is fixed to the pretrained block‑wise SVD basis, while only the compact core R and the block‑wise singular values S are optimized. This design simultaneously provides full‑rank spectral preconditioning, preserves full‑rank update expressivity, and achieves parameter, memory, and step‑time efficiency comparable to LoRA. FuRA consistently outperforms Full FT across multiple settings, including LLM fine‑tuning (+1.37 on LLaMA‑3‑8B commonsense reasoning), LLM reinforcement learning for mathematical reasoning, and visual instruction tuning for VLMs. Furthermore, the 4‑bit quantized variant, QFuRA, also surpasses QLoRA. Code is available at https://github.com/olokevin/FuRA‑NIPS
Authors:Yingjie Lei
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.
Authors:Lily Goli, Justin Kerr, Daniele Reda, Alec Jacobson, Andrea Tagliasacchi, Angjoo Kanazawa
Abstract:
Exploration is a prerequisite for learning useful behaviors in sparse‑reward, long‑horizon tasks, particularly within 3D environments. Curiosity‑driven reinforcement learning addresses this via intrinsic rewards derived from the mismatch between the agent's predictive model of the world and reality. However, translating this intrinsic motivation to complex, photorealistic environments remains difficult, as agents can become trapped in local loops and receive fresh rewards for revisiting forgotten states. In this work, we demonstrate that this failure stems from a lack of spatial persistence and episodic context. We show that effective curiosity requires a model of the world that is persistent and continuously updated, paired with an agent that maintains an episodic trajectory history to navigate toward novel regions. We achieve this using an online 3D reconstruction as a persistent model of the world, while the agent policy is parameterized as a sequence model over RGB observations to maintain episodic context. This design enables effective exploration during training while allowing the agent to navigate using solely RGB frames at deployment. Trained purely via curiosity on HM3D, our agent outperforms RL‑based active mapping baselines and generalizes zero‑shot to Gibson and AI‑generated worlds. Our end‑to‑end policy enables efficient adaptation to downstream tasks, such as apple picking and image‑goal navigation, outperforming from‑scratch baselines. Please see video results at https://recuriosity.github.io/.
Authors:Qianshu Cai, Yonggang Zhang, Xianzhang Jia, Huajiang Zheng, Wei Xue, Jun Song, Xinmei Tian, Yike Guo
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.
Authors:Samson Gourevitch, Yazid Janati, Dario Shariatian, Umut Simsekli, Eric Moulines, Eric P. Xing, Alain Durmus
Abstract:
Discrete diffusion models are often trained through clean‑data prediction, but the prediction can be used in different ways to define the reverse dynamics. In Masked Diffusion Models (MDM) these choices largely coincide, whereas in Uniform Diffusion Models (UDM) they do not. We show that the standard plug‑in bridge parameterization for UDM is not optimized by the denoising posterior, but by a leave‑one‑out posterior that predicts each clean token without using its own noisy observation. This identifies a mismatch between the plug‑in ELBO and the usual cross‑entropy denoising objective. We characterize the leave‑one‑out target and derive exact conversions between the denoiser, the leave‑one‑out posterior, and the score. These conversions allow us to disentangle parameterization and training objective. Our results also lead to inference improvements without any additional training through an informed predictor‑corrector sampler and improved temperature sampling based on the leave‑one‑out predictor.
We further introduce an absorbing‑state reformulation of uniform diffusion that preserves the UDM joint law while decomposing it into masked‑diffusion‑like sampling operations, with simpler denoising posteriors, carry‑over unmasking, and a natural remasking mechanism. On language modeling, leave‑one‑out parameterizations consistently improve UDM generation, while the absorbing construction matches or surpasses masked diffusion. These results suggest that the empirical gap between masked and uniform diffusion is driven less by the choice of marginals themselves than by parameterization and sampling design. The code and models can be found at https://github.com/samsongourevitch/rev_udm.
Authors:Sid-Ali Temkit
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.
Authors:Zhenyu Lu, Liupeng Li, Jinpeng Wang, Haoqian Kang, Yan Feng, Ke Chen, Yaowei Wang
Abstract:
While large language models provide strong compositional reasoning, existing reasoning segmentation pipelines fail to transparently connect this reasoning to visual perception. Current methods, such as latent query alignment, are end‑to‑end yet opaque "black boxes". Conversely, textual localization readout is merely readable, not truly interpretable, often functioning as an unconstrained post‑hoc step. To bridge this interpretability gap, we propose SegCompass, an end‑to‑end model that leverages a Sparse Autoencoder (SAE) to forge an explicit, interpretable, and differentiable alignment pathway. Given an image‑instruction pair, SegCompass first generates a chain‑of‑thought (CoT) trace. The core of our method is an SAE that maps both the CoT and visual tokens into a shared, high‑dimensional sparse concept space. A query codebook selects salient concepts from this space, which are then spatially grounded by a slot mapper into a multi‑slot heatmap that guides the final mask decoder. The entire model is trained jointly, unifying reinforcement learning for the reasoning path with standard segmentation supervision. This SAE‑driven interface provides a "white‑box" connection that is significantly more traceable than latent queries and more coherent than textual readouts. Extensive experiments on five challenging benchmarks demonstrate that SegCompass matches or surpasses state‑of‑the‑art performance. Crucially, our visual and quantitative analyses show a strong correlation between the quality of the learned sparse concepts and final mask accuracy, confirming that SegCompass achieves superior results through its enhanced and inspectable alignment. Code is available at https://github.com/ZhenyuLU‑Heliodore/SegCompass.
Authors:Víctor Yeste, Paolo Rosso
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.
Authors:Erjian Zhang, Yatong Hao, Liejun Wang, Zhiqing Guo
Abstract:
While multi‑task learning based automatic radiology report generation (RRG) is widely adopted to ensure clinical consistency, most focus on architectural designs yet remain limited to coarse linear scalarization strategies. These strategies cannot effectively balance the hard constraints of discriminative clinical supervision with the smoothness requirements of report generation. To address these problems, we analyze the failure mechanism of linear scalarization from the perspective of gradient dynamics, utilizing the stochastic differential equation (SDE) framework to characterize it as a "Double Dilemma" of drift term deviation and diffusion term decay. Based on this, we propose a backbone‑agnostic optimizer named Conflict‑Averse Magnitude‑Enhanced Gradient Descent (CAME‑Grad). Through conflict‑averse direction rectification and magnitude‑enhanced energy injection, the algorithm not only ensures geometric validity, but also avoids local optimal solutions. Then, the adaptive gradient fusion mechanism is used to establish a dynamic balance between the theoretical optimal direction and the task‑specific inductive bias. Experiments show that as a universal plug‑and‑play optimizer, CAME‑Grad brings substantial and consistent improvements across eight diverse RRG methods, elevating overall clinical efficacy performance by an average of 2.3% on MIMIC‑CXR and 1.9% on IU X‑Ray. Our code is available at https://github.com/vpsg‑research/CAME‑Grad.
Authors:Jiaxu Wang, Junhao He, Jingkai Sun, Yi Gu, Yunyang Mo, Jiahang Cao, Qiang Zhang, Renjing Xu
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/.
Authors:Junhyeong Cho, Ruojin Cai, Hadar Averbuch-Elor
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.
Authors:Shuaiqi Wang, Aadyaa Maddi, Zinan Lin, Giulia Fanti
Abstract:
Today, tool‑calling agents are commonly evaluated or tested on static datasets of execution traces, including input commands, agent responses, and associated tool calls. However, internal production datasets are often insufficient or unusable for testing; for example, they may contain sensitive or proprietary data, or they may be too sparse to support comprehensive testing (especially pre‑deployment). In these settings, practitioners are increasingly replacing or augmenting real datasets with synthetic ones for evaluation purposes. A key challenge is quantifying the relation between these synthetic datasets and the real data. We introduce SynAE, an evaluation framework for assessing how well synthetic benchmarks for multi‑turn, tool‑calling agents replicate and augment the characteristics of real data trajectories. SynAE assesses the validity, fidelity, and diversity of synthetic data across four metric categories: (i) task instructions and intermediate responses, (ii) tool calls, (iii) final outputs, and (iv) downstream evaluation. We evaluate SynAE using recent agent benchmarks and test common synthetic data failure modes via realistic and controlled generation schemes. SynAE detects fine‑grained variations in data validity, fidelity and diversity, and shows that no single metric is sufficient to fully characterize synthetic data quality, motivating a multi‑axis evaluation of synthetic data for agent testing. A demo of SynAE is available at https://synae‑2026‑synae‑demo.static.hf.space/index.html, with code at https://github.com/wsqwsq/SynAE.
Authors:Lucas Sheneman
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.
Authors:An Xuan Nguyen
Abstract:
Hybrid language models like Jamba mix attention layers with State Space Models (SSMs), creating two memory cache types with opposite profiles: Key‑Value (KV) caches grow linearly with sequence length, while SSM states stay fixed per layer. Current inference engines handle this poorly. Unified pools pad SSM states to attention page sizes, wasting up to 7.3x capacity. Static dual pools cannot adapt when prompt distributions shift between requests. We present Asymmetric Virtual Memory Paging (AVMP). The allocator separates the two cache types into physically distinct pools behind a unified virtual address space, and migrates capacity between pools when one runs out. Migration triggers only on allocation failure, keeping behavior deterministic. We evaluate AVMP across 270 synthetic cells plus 60 cells of ShareGPT trace replay on an RTX 3060 12GB. Out‑of‑Memory events drop 7.6% and request throughput improves 1.83x to 13.3x across synthetic workloads and 2.36x on ShareGPT. All gains hold under paired‑bootstrap 95% confidence intervals. A phase‑time breakdown reveals two distinct mechanisms: shorter OOM recovery on capacity‑pressured workloads, and faster allocation calls on KV‑heavy workloads. Implementation is pure Python; Triton integration is future work.
Authors:Yifan Bai, Xiaoyang Liu, Zihao Mou, Guihong Wang, Jian Yu, Shuhan Xie, Yantao Li, Yangyu Zhang, Jingwei Liang, Tao Luo
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.
Authors:Hanyu Guo, Jiedong Yang, Chao Chen, Longfei Xu, Kaikui Liu, Xiangxiang Chu
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.
Authors:Fabian Morelli, Stephan Eckstein
Abstract:
Ensembles of neural networks typically outperform individual networks but incur large computational costs, whereas weight aggregation produces less costly, yet also less accurate, aggregate models. We introduce partial fusion of networks, which interpolates between ensembles and weight aggregation and thus allows for a flexible tradeoff between computational cost and performance. A direct way to achieve this is to extend existing weight aggregation methods based on neuron‑level similarity between different networks, where partial fusion then only aggregates weights of neurons which are most similar. We showcase one particular method to jointly identify which neurons are most similar and match them via partial optimal transport. Further, we consider the more general perspective of weight aggregation and partial fusion as generalized pruning of ensemble models, where neurons cannot just be deleted, but also linearly combined. Finally, we show that generalized pruning applied to a single network yields similar benefits as partial fusion by allowing for a tradeoff between isolating, deleting, and linearly combining neurons based on similarity. Our code is available at https://github.com/Fabian‑Mor/partial_fusion_nn.
Authors:Santiago Ospitia, John Sanabria, John Garcia-Henao
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.
Authors:Di He, Songjun Tu, Keyu Wang, Lu Yin, Shiwei Liu
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.
Authors:Jinyang Wu, Guocheng Zhai, Ruihan Jin, Yuhao Shen, Zhengxi Lu, Fan Zhang, Haoran Luo, Zheng Lian, Zhengqi Wen, Jianhua Tao
Abstract:
The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on monolithic LLMs and fixed logic to interface with these skills. This gives rise to a critical bottleneck: different LLMs offer distinct advantages across diverse domains, yet current frameworks fail to exploit the complementary strengths of models and skills, thereby limiting their performance on downstream tasks. In this paper, we present Maestro (Multimodal Agent for Expert‑Skill Targeted Reinforced Orchestration), a Reinforcement Learning (RL)‑driven orchestration framework that reframes heterogeneous multimodal tasks as a sequential decision‑making process over a hierarchical model‑skill registry. Rather than consolidating all knowledge into a single model, Maestro trains a lightweight policy to dynamically compose ensembles of frozen expert models and a two‑tier skill library, deciding at each step whether to invoke an external expert, which model‑skill pair to select, and when to terminate. The policy is optimized via outcome‑based RL, requiring no step‑level supervision. We evaluate Maestro across ten representative multimodal benchmarks spanning mathematical reasoning, chart understanding, high‑resolution perception, and domain‑specific analysis. With only a 4B orchestrator, Maestro achieves an average accuracy of 70.1%, surpassing both GPT‑5 (69.3%) and Gemini‑2.5‑Pro (68.7%). Crucially, the learned coordination policy generalizes to unseen models and skills without retraining: augmenting the registry with out‑of‑domain experts yields a 59.5% average on four challenging benchmarks, outperforming all closed‑source baselines. Maestro further maintains high computational efficiency with low latency. The source code is available at https://github.com/jinyangwu/Maestro.
Authors:Mingkai Deng, Jinyu Hou, Lara Sá Neves, Varad Pimpalkhute, Taylor W. Killian, Zhengzhong Liu, Eric P. Xing
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.
Authors:Hyeseong Kim, Geonhui Son, Deukhee Lee, Dosik Hwang
Abstract:
Novel view synthesis from sparse‑view inputs poses a significant challenge in 3D computer vision, particularly for achieving high‑quality scene reconstructions with limited viewpoints. We introduce TWINGS, a framework that enhances 3D Gaussian Splatting (3DGS) by directly addressing point sparsity. We employ Thin Plate Splines (TPS), a smooth non‑rigid deformation model that minimizes bending energy to estimate a globally coherent warp from control‑point correspondences, to align backprojected points from estimated depth with triangulated 3D control points, yielding calibrated backprojected points. By sampling these calibrated points near the control points, TWINGS provides a fast and geometrically accurate initialization for 3DGS, ultimately improving structural detail preservation and color fidelity in reconstructed scenes. Extensive experiments on DTU, LLFF, and Mip‑NeRF360 demonstrate that TWINGS consistently outperforms existing methods, delivering detailed and accurate reconstructions under sparse‑view scenarios.
Authors:Hongsin Lee, Hye Won Chung
Abstract:
Adversarial Distillation aims to enhance student robustness by guiding the student with a robust teacher's soft labels within the min‑max adversarial training framework, yet its success is notoriously inconsistent: a more robust teacher often fails to improve, or even harms, the student's robust generalization. In this paper, we identify a key mechanism of this teacher dependency: the misalignment between the teacher's supervisory confidence and the student's representational limitations on a consistent subset of training data ‑‑ the Robustly Unlearnable Set. We present a theoretical framework analyzing the feature learning dynamics of a two‑layer neural network, demonstrating that this mismatch creates a dichotomy in distillation outcomes. We prove that when a teacher provides confident supervision on unlearnable samples, it compels the student to memorize spurious noise patterns that eventually overpower the learned robust signal, thereby driving robust overfitting. Conversely, a teacher that exhibits high uncertainty on these samples effectively suppresses noise memorization, allowing the student to rely solely on the learnable signal for robust generalization. We empirically validate our theory across both synthetic simulations and real‑image classification datasets, confirming that robust overfitting is driven by the teacher's interaction with unlearnable samples. Finally, we demonstrate that a teacher's predictive entropy on unlearnable samples serves as a strong indicator of student robustness, validating our theoretical framework and offering a principled guideline for robust teacher selection.
Authors:Yifan Lan, Yuanpu Cao, Hanyu Wang, Lu Lin, Jinghui Chen
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.
Authors:Gonçalo Duarte, Miguel Couceiro, Marcos V. Treviso
Abstract:
Long‑context decoding is increasingly limited by KV‑cache memory traffic since each generated token attends over a cache whose size grows linearly with context length. Existing sparse decoding methods reduce this cost by selecting subsets of tokens or pages, but are designed for softmax attention, whose dense tails make any truncation discard nonzero probability mass. In contrast, α‑entmax produces exact zeros, turning sparse decoding from dense‑tail approximation into support recovery: if the selected candidates contain the entmax support, sparse decoding remains exact. While recent entmax kernels enable efficient training, they do not address the autoregressive decoding bottleneck, where dense inference still streams the full KV cache before sparsity is known. In this work, we introduce EntmaxKV, an entmax‑native sparse decoding framework that exploits sparsity before KV pages are loaded. EntmaxKV combines query‑aware page scoring, support‑aware candidate selection, and sparse entmax attention. We analyze truncation error through the dropped probability mass δ, showing that output error is controlled by δ and vanishes when the entmax support is recovered. We further introduce a Gaussian‑aware entmax selector that estimates the entmax threshold from lightweight page statistics, adapting the selected budget to the score distribution. Empirically, EntmaxKV drops less probability mass, retains more support tokens, and achieves lower output error than softmax‑based sparse decoding at matched KV budgets. On long‑context and language modeling benchmarks, it closely matches full‑cache entmax while using a small fraction of the KV cache, achieving up to 3.36× (softmax) and 5.43× (entmax) speedup over full attention baselines at 1M context length. Code available at: https://github.com/deep‑spin/entmaxkv.
Authors:Mansoor Ahmed, Murray Patterson
Abstract:
Antibody design methods condition on antigen structure to generate complementarity‑determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they largely ignore the antigen input. We identify three failure modes that explain this behavior. Antigen blindness arises because models derive predictions from antibody framework context rather than antigen information, producing nearly identical CDRs regardless of the target. Vocabulary collapse reduces predicted amino acids to three to five per position, far below the ground truth distribution in native sequences. Moreover, any model trained with standard per‑position cross‑entropy converges to the positional marginal distribution, making it provably unable to produce antigen‑specific sequence predictions. We propose a novel encoder‑decoder architecture called AgForce, that uses a graph neural network (GNN) as the encoder and specialized decoders for sequence‑structure co‑design. Specifically, we apply framework dropout, gated bottlenecks, and hyperbolic cross attention that prevent the antibody shortcut path. In the decoder, a Mixture Density Network (MDN) sequence head with Potts‑like pairwise coupling and annealed Multiple Choice Learning (aMCL) replaces the cross‑entropy objective with a multi‑component distribution whose optimal solution differs from the positional marginal. An antigen cycle consistency head routes gradients through the sequence decoder, forcing predicted distributions to encode antigen identity. AgForce achieves the best binding quality and sequence recovery simultaneously on the CHIMERA‑Bench dataset, improving amino acid recovery by 8% over the strongest sequence baseline while surpassing the baselines across all interface metrics, and nearly doubling the effective vocabulary of GNN methods. The source code is available at: https://github.com/mansoor181/ag‑force.git
Authors:Xiaogeng Liu, Xinyan Wang, Yingzi Ma, Yechao Zhang, Chaowei Xiao
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.
Authors:Xuyang Zhong, Qizhang Li, Yiwen Guo, Chen Liu
Abstract:
We propose DualOptim+, a novel optimization framework for improving machine unlearning in large language models. It introduces a base state to capture common representations shared by forgetting and retaining objectives and delta states to preserve objective‑specific residuals. This architecture allows the optimizer to adaptively bridge shared and decoupled states based on the directional conflict between forgetting and retaining gradients. We further introduce DualOptim+ 8bit, a quantized variant that reduces memory overhead without compromising performance. Extensive experiments across fictitious and real‑world unlearning, safety alignment, and multi‑task learning tasks demonstrate that DualOptim+ consistently achieves a superior trade‑off between different objectives. Codes are available at https://github.com/CityU‑MLO/DualOptimPlus.
Authors:Jimmy Dubuisson
Abstract:
Reference‑based graph compression encodes each vertex's neighbor list relative to a recent vertex, exploiting locality to compress large directed graphs. The dominant tool, WebGraph's BVGraph, fixes a single encoding pipeline and relies on a separately chosen vertex ordering ‑‑ typically URL‑lexicographic or Layered Label Propagation (LLP). The interaction between ordering and encoder is rarely measured.
We propose a two‑stage Leiden+LLP vertex ordering ‑‑ global LLP to seed labels, Leiden community detection, then per‑cluster LLP on each induced subgraph ‑‑ and study how it interacts with reference‑based compression. On graphs with poor initial vertex order, reordering saves 0.3 to 5.4 bits per edge on every dataset and encoder we measured. The size of that gain is largely insensitive to the encoder: on four of five weakly ordered datasets, four independently parameterised encoders agree on the Leiden+LLP‑vs‑plain‑LLP gain within roughly +/‑ 0.04 bpe. On URL‑ordered web crawls, where the distributed ordering already encodes locality, adaptive encoders still benefit from reordering, but encoders tuned to URL‑induced residual structure (BV‑HC, CG at K>1) are mildly hurt by it.
To quantify how much encoder choice matters once ordering is fixed, we contribute three reference‑based encoders ‑‑ BG, CS, and CG ‑‑ that perform per‑vertex cost‑optimal selection from up to 28 candidate decompositions. Each is run under its own best‑tested ordering. The best of the three improves over BVGraph high‑compression by 2‑9% on every dataset tested, with the encoder‑level gain consistently smaller than the ordering‑level gain on weakly ordered datasets. The encoder framework also yields a self‑delimiting bitstream that supports low‑overhead random access.
Authors:Brandon Dent
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.
Authors:Drake Caraker, Bryan Arnold, David Rhoads
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.
Authors:Zhepei Wei, Xinyu Zhu, Wei-Lin Chen, Chengsong Huang, Jiaxin Huang, Yu Meng
Abstract:
Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving reasoning in large language models (LLMs), yet the underlying geometry of the resulting parameter trajectories remains underexplored. In this work, we demonstrate that RLVR weight trajectories are extremely low‑rank and highly predictable. Specifically, we find that the majority of downstream performance gains are captured by a rank‑1 approximation of the parameter deltas, where the magnitude of this projection evolves near‑linearly with training steps. Motivated by this, we propose a simple and compute‑efficient method RELEX (REinforcement Learning EXtrapolation), which estimates the rank‑1 subspace from a short observation window and extrapolates future checkpoints via linear regression, with no learned model required. Across three models (i.e., Qwen2.5‑Math‑1.5B, Qwen3‑4B‑Base, and Qwen3‑8B‑Base), RELEX produces checkpoints that match or exceed RLVR performance on both in‑domain and out‑of‑domain benchmarks, requiring as few as 15% steps of full RLVR training. Remarkably, RELEX is able to extrapolate far beyond the observation window at no training cost, predicting checkpoints up to 10‑20× beyond the observed prefix with continued improvement (e.g., observe only the first 50 steps and extrapolate to 1000 steps). Our ablation analysis confirms the minimalist sufficiency of RELEX: neither increasing the subspace rank nor employing non‑linear modeling yields further gains in extrapolation. Finally, we show that RELEX's success stems from a "denoising" effect: by projecting updates onto the rank‑1 subspace, the model discards stochastic optimization noise that would otherwise degrade performance during extrapolation. Our code is available at https://github.com/weizhepei/RELEX.
Authors:Alim Igilik
Abstract:
Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is systematically violated in seismic data from Central Asia (2010‑2024), where a likelihood‑ratio test with boundary correction strongly rejects the Poisson hypothesis (p < 10^‑179).
The main contribution of this work is the EarthquakeNet architecture, which provides an endogenous per‑cell estimate of the overdispersion parameter alpha via a neural network (spatial embeddings + MLP), without explicit spatial covariance specification. In contrast to existing negative binomial regression approaches in seismological forecasting, which typically assume a single global alpha, the proposed per‑cell formulation allows the model to identify spatial heterogeneity in seismic clustering and to construct probabilistic risk‑aware alerts via quantiles of the predicted distribution.
A walk‑forward evaluation (2018‑2023) over four systems shows an 8.6 percent reduction in mean pinball deviation (MPD) relative to a negative binomial GLM baseline. The strongest improvements are observed in the tail regime (Y >= 5), where the continuous ranked probability score (CRPS) of the proposed model is 12.5 percent lower than that of the baseline, indicating improved calibration in extreme‑event forecasting.
Authors:Elle Miller, Jayaram Reddy, Ayush Deshmukh, Trevor McInroe, David Abel, Oisin Mac Aodha, Sethu Vijayakumar
Abstract:
Tactile‑based reinforcement learning (RL) is currently hindered by fragmented research and a focus on over‑saturated orientation tasks. We introduce v2 of the Robot Tactile Olympiad (\textttroto 2.0), a GPU‑parallelised benchmark designed to standardise tactile‑based RL across four distinct robotic morphologies (16‑DOF to 24‑DOF). Unlike prior benchmarks, roto focuses on end‑to‑end "blind" manipulation, utilising only proprioception and tactile sensing without state information or distillation. We demonstrate a significant performance leap, with our blind agents achieving 13 Baoding ball rotations in 10 seconds, an order of magnitude faster than current state‑of‑the‑art speeds. By open‑sourcing our environments and robustly tuned baselines, we reduce the barrier to entry and enable researchers to prioritise fundamental algorithmic challenges over tedious RL tuning. Website: https://elle‑miller.github.io/roto/
Authors:Lucheng Fu, Ye Yu, Yiyang Wang, Yiqiao Jin, Haibo Jin, B. Aditya Prakash, Haohan Wang
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.
Authors:Ziqi Wang, Qiang Liu, Nils Thuerey
Abstract:
The aggregation of conflicting client updates remains a fundamental bottleneck in federated learning (FL) under heterogeneous data distributions. Naive averaging can produce a global update that improves the global objective while conflicting with specific clients, causing degradation for those clients. In this work, we propose CRAFT (Conflict‑Resolved Aggregation for Federated Training), a new aggregation framework that treats the global update as a geometric correction problem. We formulate aggregation as finding the update closest to a reference direction while satisfying conflict‑free alignment constraints. We derive a closed‑form expression for the constrained optimization problem, avoiding the computational overhead of iterative solvers. Furthermore, we use a layer‑wise adaptation to address conflicts at varying feature granularities. We provide a theoretical analysis showing that CRAFT promotes a common‑descent structure and mitigates conflicts through its projection geometry. Extensive experiments on heterogeneous benchmarks demonstrate that CRAFT improves the accuracy of the global model while reducing performance disparity across clients compared with state‑of‑the‑art baselines. The source code for CRAFT is available at https://github.com/tum‑pbs/CRAFT.
Authors:Robin Louiset, Edouard Duchesnay, Benoit Dufumier, Antoine Grigis, Pietro Gori
Abstract:
In biomedical Subgroup Discovery, practitioners are interested in discovering interpretable and homogeneous subgroups within a group of patients. In this paper, assuming that healthy subjects (i.e., controls) share common but irrelevant factors of variation with the patients, we motivate and develop a Contrastive Subgroup Discovery method, entitled Deep UCSL. By contrasting patients with controls, Deep UCSL identifies subgroups driven solely by pathological factors, ignoring common variability shared with healthy subjects. Our framework employs a deep feature extractor to learn a discriminative representation space. Mathematically, we derive a novel loss based on the conditional joint likelihood of latent clusters and patient/control labels, optimized via an Expectation‑Maximization strategy alternating between subgroup inference and feature encoder updates. A regularization term further encourages representations to capture disease‑specific variability while ignoring variability shared with controls. Compared to previous related works, our approach quantitatively improves the quality of the estimated subgroups, as demonstrated on a MNIST example and four distinct real medical imaging datasets. Code and datasets are available at: https://github.com/rlouiset/deep_ucsl.
Authors:Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
Abstract:
Self‑supervised learning for time‑series representation aims to reduce reliance on labeled data while maintaining strong downstream performance, yet many existing approaches incur high computational costs or rely on assumptions that do not hold across diverse temporal dynamics. In this work, we introduce Divide and Contrast (Di‑COT), an unsupervised framework that avoids data augmentation and multiple encoder passes by contrasting informative substructures within a window rather than individual timesteps. Di‑COT stochastically partitions each window into a small number of overlapping sub‑blocks per iteration, enabling efficient and meaningful contrast while mitigating false positives during temporal transitions. To further improve scalability, we adopt a contrastive objective whose computation depends on the batch size and the number of sub‑blocks, making loss computation independent of sequence length. Extensive experiments on six large‑scale real‑world datasets, as well as the UCR and UEA benchmarks, demonstrate that Di‑COT learns semantically structured and transferable representations, achieving state‑of‑the‑art performance on classification, clustering, kNN, and cross‑dataset transfer, while substantially reducing training time. The source code is publicly available at https://github.com/sfi‑norwai/Di‑COT.
Authors:Yongkang Liu, Zijing Wang, Mengjie Zhao, Ercong Nie, Mingyang Wang, Qian Li, Feiliang Ren, Shi Feng, Daling Wang, Hinrich Schütze
Abstract:
This work presents \textscChunkFT, a memory‑efficient fine‑tuning framework that reformulates full‑parameter fine‑tuning around a dynamically activated working set. \textscChunkFT enables gradient computation for arbitrary sub‑tensors without modifying the network architecture, providing an algorithmic foundation for optimizing arbitrary sub‑networks while avoiding standard dense gradient computation. We provide a theoretical convergence analysis of \textscChunkFT in the deterministic setting. Empirically, we apply \textscChunkFT to fine‑tune Llama 3‑8B and Llama 3‑70B using a single RTX 4090‑24GB GPU and 2× H800‑80GB GPUs, respectively. Full‑parameter fine‑tuning of a 7B model with a 1K input length requires only 13.72GB of GPU memory. The results demonstrate the effectiveness of \textscChunkFT in memory usage, running time, and optimization quality. Moreover, downstream evaluations on language understanding, mathematical reasoning, and MT‑Bench show that \textscChunkFT consistently outperforms existing memory‑efficient baselines. Notably, \textscChunkFT achieves performance comparable to, and in some cases exceeding, full‑parameter fine‑tuning. Our repository is on https://github.com/misonsky/chunk.
Authors:Kesong Li, Yixuan Xu, Kuo-kun Tseng, Weiyi Lu, Kan Liu, Tao Lan
Abstract:
Direct Preference Optimization (DPO) is successful for alignment in LLMs but still faces challenges in text‑to‑image generation. Existing studies are confined to denoising diffusion models while overlooking flow‑matching, and suffer from an objective mismatch when applying discrete NLP‑based DPO to regression‑based generative tasks.\ In this paper, we derive a generalized DPO objective that covers both diffusion and flow‑matching via a unified reverse‑time SDE framework, and point out from a gradient perspective that the standard DPO objective is suboptimal for text‑to‑image generation. Consequently, we propose Linear‑DPO, which replaces the aggressive sigmoid‑based utility function with a sustained linear utility and incorporates an EMA‑updated reference model. Qualitative and quantitative experiments on diffusion models (SD1.5, SDXL) and flow‑matching model (SD3‑Medium) demonstrate the superiority of our approach over existing baselines.
Authors:Minh Hoang Nguyen, Dai Do, Huu Hiep Nguyen, Dung Nguyen, Kien Do, Hung Le
Abstract:
Modern deep‑learning models have achieved remarkable success in time‑series forecasting. Yet, their performance degrades in long‑term prediction due to error accumulation in autoregressive inference, where predictions are recursively used as inputs. While classical error correction mechanisms (ECMs) have long been used in statistical methods, their applicability to deep learning models remains limited or ineffective. In this work, we revisit the error accumulation problem in deep time‑series forecasting and investigate the role and necessity of ECMs in this new context. We propose a simple, architecture‑agnostic error correction model that can be integrated with any existing forecaster without requiring retraining. By explicitly decomposing predictions into trend and seasonal components and training the corrector to adjust each separately, we introduce the Universal Error Corrector with Seasonal‑Trend Decomposition (UEC‑STD), which significantly improves correction accuracy and robustness across 4 backbones and 10 datasets. Our findings provide a practical tool for enhancing forecasts while offering new insights into mitigating autoregressive errors in deep time‑series models. Code is available at https://github.com/DA2I2‑SLM/UEC‑STD.
Authors:Jiawen Dai, Yue Song
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.
Authors:Hanxiang Ren, Pei Zhou, Xunzhe Zhou, Yanchao Yang
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.
Authors:Zhiqin Yang, Yonggang Zhang, Wei Xue, Dong Fang, Bo Han, Yike Guo
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.
Authors:Haozhe Jia, Pengyu Yin, Wenshuo Chen, Shaofeng Liang, Lei Wang, Bowen Tian, Xiucheng Wang, Nanqian Jia, Yutao Yue
Abstract:
Physics‑informed diffusion models typically enforce PDE constraints only on final outputs, leaving intermediate representations unconstrained and prone to shortcut learning under shifted boundary conditions. We introduce REPA‑P, a teacher‑free, architecture‑agnostic framework that aligns intermediate features with physical states using first‑principles residuals. REPA‑P attaches lightweight 1×1 projection heads to selected layers, decodes hidden activations into physical quantities, and applies PDE residual losses during training. These heads are discarded at inference, introducing zero overhead. Across four PDE tasks, including Darcy flow, topology optimization, electrostatic potential, and turbulent channel flow, REPA‑P accelerates convergence by up to 2×, reduces physics residuals by up to 66.4%, and improves out‑of‑distribution robustness by up to 49.3%, with consistent gains on both U‑Net and Diffusion Transformer backbones. Ablations show that supervising a small set of intermediate layers captures most benefits and complements output‑level physics losses. Code is available at [https://github.com/Hxxxz0/REPA‑P](https://github.com/Hxxxz0/REPA‑P).
Authors:Xuehui Yu, Fucheng Cai, Meiyi Wang, Xiaopeng Fan, Harold Soh
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.
Authors:Xingyu Lyu, Qianqian Xu, Zhiyong Yang, Peisong Wen, Qingming Huang
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.
Authors:Yefan Zhou, Yilun Zhou, Austin Xu, Soroush Vosoughi, Shafiq Joty, Jiang Gui
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.
Authors:Amit Roth, Ankur Samanta, Matan Halevy, Yoav Levine, Yonathan Efroni
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/.
Authors:Ziang Song, Ying Jin, Emmanuel J. Candès
Abstract:
Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p‑values fall below a threshold. The quality of such methods is often measured by the false discovery proportion (FDP), defined as the fraction of incorrect selections. Existing approaches typically control the expected value of the FDP, using methods such as the Benjamini‑Hochberg procedure. This approach fails to provide high‑probability bounds on the realized false discovery proportion and invalidates statistical guarantees if the rejection threshold is selected after inspecting the data. This paper establishes finite‑sample, distribution‑free upper bounds on the FDP that hold simultaneously over all possible rejection thresholds, enabling arbitrary post hoc selection of the threshold. Simultaneous validity is achieved by constructing a high‑probability envelope for the empirical distribution function of null conformal p‑values by sampling from their joint distribution. Furthermore, our framework allows practitioners to modulate the envelope's shape, thereby producing tight bounds in rejection regions of primary interest. We use this flexible approach to derive simultaneous FDP upper bounds for both outlier detection and conformal selection. We demonstrate through synthetic and real‑data experiments that the resulting bounds are both valid and substantially less conservative than those derived from existing approaches.
Authors:Miaobo Hu, Shuhao Hu, Bokun Wang, Ruohan Wang, Xin Wang, Xiaobo Guo, Daren Zha, Jun Xiao
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.
Authors:Jiawen Zhu, Shuhan Liu, Di Weng, Yingcai Wu
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.
Authors:Duy Nguyen, Hanqi Xiao, Archiki Prasad, Zaid Khan, Anirban Das, Austin Zhang, Sambit Sahu, Hyunji Lee, Elias Stengel-Eskin, Mohit Bansal
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.
Authors:Junseok Kim, Dohyeong Kim, Mineui Hong, Songhwai Oh
Abstract:
Compositional generalization is essential for reaching unseen goals under novel contextual variations in offline goal‑conditioned reinforcement learning (GCRL), where a generalist goal‑reaching agent must be learned from limited data. Most prior approaches pursue this via trajectory stitching over temporally contiguous segments, which limits composing behaviors across varying contexts. To overcome this limitation, we formalize analogy transduction as synthesizing new plans by composing task‑endogenous analogies with given contexts and propose a novel analogy representation tailored for it. Grounded in our theory, this analogy representation captures what changes under optimal task execution, remains invariant to contextual variations, and is sufficient for optimal goal reaching. We further contend that generalization to unseen analogy‑context pairs is a practical obstacle in analogy transduction, and introduce a new approach for offline GCRL that enables analogy transduction beyond seen pairs to unseen combinations. We empirically demonstrate the effectiveness of our approach on OGBench manipulation environments, substantially outperforming prior methods that do not perform analogy transduction. Project page: https://rllab‑snu.github.io/projects/CTA/
Authors:Ali Ramlaoui, Alexandre Duval, Hannah Bull, Victor Schmidt, Hugues Talbot, Fragkiskos D. Malliaros, Joseph Musielewicz
Abstract:
Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in practice, they must often be adapted to target chemistries using small and expensive task‑specific datasets. However, MLIPs transfer inconsistently across domains, with representations that often loose accessible composition and structure information. To address this, we present TriForces, a model‑agnostic three‑stream framework that separates composition and structure information, combined with self‑supervised learning to preserve transferable representations. TriForces improves performance on MatBench and QM9 over baselines without needing DFT labels and enables efficient similar structure retrieval through its learned latent space. On OMat24, in limited‑data training regime, TriForces reduces energy MAE by 57% at 20K samples only and improves force MAE across sample sizes. We release pretrained TriForces variants across multiple MLIP architectures with code at https://github.com/Ramlaoui/triforces.
Authors:Meng Zhu, Quan Xiao, Weidong Min
Abstract:
Optimization algorithms are core methods by which machine learning models iteratively minimize loss functions, update parameters, learn from data, and improve performance. Momentum SGD and AdamW represent two important optimization paradigms. AdamW produces stable updates and usually has strong robustness across training scenarios, but its generalization performance is sometimes weaker than that of momentum methods. Momentum SGD can often obtain better generalization after careful tuning, but it is more sensitive to gradient‑scale variation and hyperparameter settings. To balance the strengths and weaknesses of the two paradigms, this paper proposes Ada2MS, an optimization algorithm that achieves a smooth transition between AdamW‑like behavior and momentum‑SGD‑like behavior through continuous exponential interpolation between elementwise second‑moment estimates and global second‑moment estimates. On the visual tasks evaluated in this study, Ada2MS obtains competitive results under a unified optimizer‑comparison protocol. The code will be released at https://github.com/mengzhu0308/Ada2MS
Authors:Slim Barkallah, Luke Bailey, Kaiyue Wen, Mohammed Abouzaid, Tengyu Ma
Abstract:
Reliable verification of proofs remains a bottleneck for training and evaluating AI systems on hard mathematical reasoning. Fully formal proofs, in languages like Lean, are easy to verify because they are unambiguous and modular. Most proofs, particularly those written by AI systems, have neither property, and translating them into formal languages remains challenging in many frontier math settings. We propose Pseudo‑Formalization (PF), a proof format that captures the modularity and precision of formal proofs while retaining the flexibility of natural language. A Pseudo‑Formal proof is decomposed into self‑contained modules, each stating its premises, conclusion, and proof in natural language. To verify the correctness of a regular natural language proof, an LLM translates it to Pseudo‑Formal and then verifies each module independently, an algorithm we call Block Verification (BV). We evaluate PF+BV on two benchmarks spanning olympiad and research‑level mathematics, where it pareto‑dominates LLM‑as‑judge baselines on error‑finding precision and recall. To support future work, we release our research‑level proof verification benchmark ArxivMathGradingBench.
Authors:Lucky Verma
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.
Authors:Zhaohui Zheng, Chenhang He, Shihao Wang, Yuxuan Li, Ming-Ming Cheng, Lei Zhang
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
Authors:Xinlei Liu, Tao Hu, Jichao Xie, Peng Yi, Hailong Ma, Baolin Li
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.
Authors:Panagiotis Koromilas, Theodoros Giannakopoulos, Mihalis A. Nicolaou, Yannis Panagakis
Abstract:
Supervised classification has a theoretical optimum, Neural Collapse (NC), yet neither of its two dominant paradigms reaches it in practice. Cross entropy (CE) leaves radial degrees of freedom unconstrained and converges to a degenerate geometry, while supervised contrastive learning (SCL) drives features toward NC during pretraining but discards this structure in a post hoc linear probing phase. We show that both paradigms are different appearances of the same method that contrasts prototypes on the unit hypersphere, and that closing the gap requires fixing each at its point of failure. From the CE side, we propose NTCE and NONL, two normalized losses that import contrastive optimization's missing ingredients into classifier learning: a large effective negative set and decoupled alignment and uniformity terms. From the SCL side, we prove that SCL's objective already optimizes throughout training for a principled classifier whose weights are the class mean embeddings, making linear probing both redundant and harmful. Empirically, on four benchmarks including ImageNet‑1K, NTCE and NONL surpass CE accuracy, closely approximate NC (\geq 95%), and match CE's converged NC on 4/5 metrics in under 7.5% of its iterations, while SCL with fixed prototypes matches linear probing without the hours‑long classifier training phase. The learned geometry yields +5.5% mean relative improvement in transfer learning, up to +8.7% under severe class imbalance, and improved robustness to corruptions on ImageNet‑C. Our work recasts supervised learning as prototype learning on the hypersphere, with NC reached by design.
Authors:Ziyuan Gao
Abstract:
Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning. Existing methods either apply uniform constraints across all tasks, causing catastrophic forgetting when tasks conflict, or require predefined task groupings that cannot anticipate future task diversity. We introduce MedCRP‑CL, a framework that performs online task structure discovery and structure‑aware continual learning. Leveraging the Chinese Restaurant Process (CRP), our method dynamically infers task groupings from clinical text prompts as tasks arrive, without requiring predefined cluster counts or access to future tasks. We term these discovered groupings semantic modalities, as they capture finer‑grained structure than physical imaging modalities by integrating anatomical region and pathological context. Guided by this discovered structure, we maintain semantic modality‑specific LoRA adapters regularized by intra‑modality EWC, ensuring parameter isolation across dissimilar task groups while facilitating knowledge transfer within similar ones. The framework is also replay‑free, storing only aggregate statistics rather than raw patient data. Experiments on 16 medical segmentation tasks across four imaging modalities demonstrate that MedCRP‑CL achieves 73.3% Dice score with only 4.1% forgetting, outperforming the best baseline by 8.0% while requiring 6× fewer parameters. Code is available at https://github.com/zygao930/MedCRP‑CL.
Authors:Fatemeh Pesaran Zadeh, Seyeon Choi, Xing Han Lù, Siva Reddy, Gunhee Kim
Abstract:
Large language models (LLMs) have enabled web agents that follow natural language goals through multi‑step browser interactions. However, agents fine‑tuned on specific trajectories and domain often struggle to generalize out of domain, and offline training can be compute‑inefficient due to noisy, redundant trajectories and long accessibility‑tree (AXTree) states. To address both issues, we propose Weasel, a trajectory selection method for offline training of web agents. Weasel selects a fixed‑budget subset of trajectory steps by optimizing an objective that balances unary importance with pairwise diversity over states, websites, and interaction patterns, solving efficiently with a greedy algorithm. We further improve efficiency with target‑centered AXTree pruning that keeps only content around the ground‑truth action target, and we mitigate style mismatch for reasoning‑native models by replacing expert traces with model‑generated, style‑consistent rationales. Across AgentTrek and NNetNav training datasets, evaluations in WebArena, WorkArena, and MiniWob, and experiments with Qwen2.5‑7B, Gemma3‑4B, and Qwen3‑8B, Weasel improves out‑of‑domain performance while reducing training cost, producing roughly 9.7‑12.5× training speedups over standard fine‑tuning. We make the code available at https://github.com/fatemehpesaran310/weasel.
Authors:Junxi Chen, Junhao Dong, Xiaohua Xie
Abstract:
Recent work has demonstrated the potential of contrastive steering for jailbreaking Large Language Models (LLMs). However, existing methods rely on limited and inherently biased contrastive prompts and require laborious manual tuning of steering strength, limiting their robustness and effectiveness. In this paper, we leverage the idea of model extraction to guide the learned steering vectors to approximate the ideal one and propose tuning the steering strength adaptively based on contrastive activations' statistics. Experiments demonstrate that our method notably improves the effectiveness and robustness of probe‑based steering, without any extra contrastive prompts or laborious manual tuning. Being an attack paper, this paper focuses on revealing the breakdown of fortified LLMs, raising the average harmfulness score from 6% to 70%. Our code is available at https://github.com/fhdnskfbeuv/adaptiveSteering.
Authors:Siyuan Li, Youyuan Zhang, Fangming Liu, Jing Li
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.
Authors:Brown Zaz, Mar Gonzàlez I Català, Ferran Hernandez Caralt, Moshe Eliasof, Pietro Liò
Abstract:
In the transductive setting, where the full graph is observed but node labels are only partially available, progress in semi‑supervised node classification has largely focused on architectural innovation. In this paper, we revisit an orthogonal axis: the training objective. We start from a simple observation: transductive models produce predictions for every node during training, including nodes without labels. These unlabeled‑node predictions may contain useful training signal, but standard supervised objectives discard them because no ground‑truth labels are available. Inspired by the decomposition of cross‑entropy into a label‑dependent alignment term and a label‑independent entropy term, we propose prediction confidence as a natural way to extract this signal in the absence of labels. This motivates Transductive Sharpening (TS): a loss‑level modification that minimizes prediction entropy on unlabeled nodes while counterbalancing this effect on labeled nodes. We evaluate Transductive Sharpening across a wide range of node‑classification benchmarks and observe consistent performance improvements without requiring any changes to the backbone architecture. Code is available at https://github.com/transductive‑sharpening/tunedGNN.
Authors:Krati Saxena, Tomohiro Shibata
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.
Authors:Junjun Pan, Yixin Liu, Yu Zheng, Lianhua Chi, Alan Wee-Chung Liew, Shirui Pan
Abstract:
Text‑attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e‑commerce platforms. However, to evade detection, fraudsters continuously evolve their camouflaging strategies by deliberately mimicking textual responses of benign users, thereby concealing their malicious purposes. This phenomenon, referred to as semantic camouflage, fundamentally undermines commonly relied assumptions on how structural and attribute cues can be exploited to identify fraudsters, and makes it difficult to spot fraudsters with unsupervised TAGFD. To bridge the gaps, we propose a Case‑Adaptive Multi‑cue Expert fRAmework (CAMERA) for unsupervised TAGFD. CAMERA employs an ego‑decoupled mixture‑of‑experts architecture, where each expert specializes in modeling a distinct type of fraud‑indicative cue. A context‑informed gating model is introduced to jointly consider the ego node representation and its local neighborhood context for adaptive integration of cues learned by different experts. Furthermore, CAMERA leverages the inherent rarity of fraudsters to support unsupervised one‑class learning with expert‑level objectives that encourage modeling dominant benign patterns, thereby enabling reliable unsupervised detection of camouflaged fraudsters. Experiments on 4 challenging datasets show that CAMERA consistently outperforms competitors, showing its effectiveness against semantically camouflaged fraudsters. Code available at https://github.com/CampanulaBells/CAMERA
Authors:He-Yang Xu, Pengyuan Zhang, Zongyuan Ge, Xiaoshuai Hao, Serge Belongie, Xin Geng, Yuxin Peng, Xiu-Shen Wei
Abstract:
Fine‑grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute grounding, high‑fidelity spatial perception, and constraint‑respecting motor execution. However, current embodied AI benchmarks collapse these capacities into binary success rates, systematically inflating reported capabilities by up to 70% and masking the architectural bottlenecks that impede real‑world deployment. We introduce MetaFine, a diagnostic meta‑evaluation framework that disentangles manipulation competency along three axes: understanding, perception, and controlled behavior. Built on a compositional task graph, MetaFine absorbs heterogeneous external benchmarks and reconstructs them into diagnostic scenarios of varying complexity under a unified protocol. Evaluating state‑of‑the‑art vision‑language‑action (VLA) models through this lens exposes severe dimension‑specific failures invisible to conventional metrics. Through targeted causal intervention, we identify the visual encoder's ability to preserve local spatial structure as a key bottleneck for fine‑grained precision: improving it directly unlocks previously inaccessible manipulation capabilities without modifying downstream policies. MetaFine further supports hybrid real‑sim validation, using limited paired real‑world rollouts to calibrate scalable simulation‑based estimates for more stable physical benchmarking. By shifting evaluation from ranking to diagnosis, MetaFine turns benchmarking into an actionable compass for repairing the layered capacities underlying genuine physical dexterity. The MetaFine framework, benchmarks, and supporting resources will be publicly released at our project page: https://metafine.github.io/.
Authors:Bariscan Bozkurt, Efe Ali Gorguner, Francesco Innocenti, Rafal Bogacz
Abstract:
Blind source separation (BSS) is a natural framework for studying how latent causes may be recovered from sensory mixtures, but deriving online and biologically plausible algorithms for structured (i.e., constrained to known domains) and potentially correlated sources remains challenging. Recent work has derived neural networks for BSS from maximization of an entropy measure, yet its online implementations involve complex and nonlocal recurrent dynamics. Motivated by this perspective, we propose Predictive Entropy Maximization, which achieves competitive performance in BSS, using only local weight updates. The method employs a close approximation of an entropy measure, yielding an objective function with easily interpretable components. Minimizing this objective leads to a predictive neural architecture in which feedforward synapses follow an error‑driven rule (that can be realized through dendritic mechanisms), lateral inhibitory connections are learned with local Hebbian plasticity, and source‑domain constraints are enforced through simple output nonlinearities. We derive explicit spectral bounds on the surrogate error, characterizing when the approximation is accurate. Empirically, Predictive Entropy Maximization remains robust under increasing source correlation and observation noise, outperforms biologically plausible algorithms that rely on stronger independence or decorrelation assumptions, and remains competitive with exact determinant‑ and correlative‑information‑based baselines. These results show how local plasticity and adaptive lateral inhibition can emerge from maximizing a regularized second‑order entropy over structured source domains. Our implementation code is available at https://github.com/BariscanBozkurt/Predictive‑Entropy‑Maximization.
Authors:Serhii Zabolotnii
Abstract:
Uniform sampling on implicitly defined manifolds is a core primitive in motion planning, constrained simulation, and probabilistic machine learning. MASEM addresses this problem by entropy‑maximizing resampling, but its resampling weights depend on a local k‑nearest‑neighbour density estimate whose errors can be amplified by aggressive resampling temperatures. We ask whether a polynomial‑maximization moment estimator can replace the plug‑in density rule without changing the surrounding MASEM architecture. The proposed PMM‑MASEM module computes shell spacings from nested k‑nearest‑neighbour radii, estimates their standardized cumulants, and uses a gated PMM2/PMM3 estimator only when the spacing distribution departs from the flat Exp(1) regime; otherwise it falls back to the plug‑in/MLE rule. This fallback is essential: on a flat homogeneous manifold the plug‑in estimator is already the MLE, so PMM should not outperform it. A local Known‑DGP Monte Carlo experiment confirms this gate: the selector returns MLE on flat Exp(1) spacings and reduces density MSE by 22‑‑36% on asymmetric gamma and boundary‑spacing regimes. The evidence is not uniformly positive: PMM3 worsens a platykurtic uniform spacing law, and a lightweight resampling‑proxy experiment improves seven‑lobes coverage but degrades the sine and swiss‑roll proxies. The current evidence therefore supports an applicability‑boundary result rather than a general MASEM improvement claim.
Authors:Shuo Zhang, Rongqi Hong, Huifeng Zhang, Jian K. Liu
Abstract:
Predicting protein‑ligand binding affinity remains intractable for multi‑domain proteins, where inter‑domain dynamics govern molecular recognition. Existing geometric deep learning methods typically treat proteins as monolithic static graphs, suffering from rigid‑body assumptions and aleatoric noise in flexible regions. To address this, we introduced HCLBind, a self‑supervised framework that decouples geometric representation learning from affinity regression. HCLBind leverages a general‑to‑specific pre‑training paradigm on the Q‑BioLiP database to learn a robust physical grammar of binding. We propose a novel hierarchical decoy strategy: the model learns local physicochemical constraints through protein coordinate perturbation in single‑domain proteins and global conformational geometry through inter‑domain rotation in multi‑domain complexes. Our hybrid architecture integrates a domain‑gated graph attention network and cross‑modal attention to explicitly prioritize domain interfaces. Furthermore, we employ LoRA on protein and ligand foundation models, ensuring efficient optimization while preserving evolutionary knowledge. Experiments on PDBBind demonstrate that HCLBind effectively learns discriminative interface features and provides robust uncertainty estimation, overcoming the limitations of standard supervised learning. The code is available at https://github.com/jiankliu/HCLBind.
Authors:Hongjiang Chen, Xin Zheng, Pengfei Jiao, Huan Liu, Zhidong Zhao, Huaming Wu, Feng Xia, Shirui Pan
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.
Authors:Arman Bolatov, Artem Riabinin, Nikita Kornilov, Andrey Veprikov, Samuel Horváth, Martin Takáč, Aleksandr Beznosikov
Abstract:
In large‑scale optimization, the cheapness and effectiveness of update steps are the most crucial factors for a successful optimizer. Sign‑based optimizers like Lion or Signum produce cheap per‑step updates, whereas Muon's spectral matrix‑sign update gives a much stronger direction at a substantially higher per‑step cost. In this work, we propose LionMuon, which retains the effectiveness of Muon steps while considerably cutting the averaged iteration cost, similar to sign‑based methods. It alternates between Lion's and Muon's updates on a fixed period P, sharing a single dual‑EMA momentum buffer between them. The optimizer state memory therefore matches Lion and is exactly half of AdamW's. A simpler single‑EMA variant, SignMuon, by itself already outperforms pure Muon. At P = 2, LionMuon Pareto‑dominates Muon, Lion, Signum, and AdamW on every dataset and architecture we tested at 124M model size, reaching lower validation loss at lower compute, and the same advantage persists at 355M and 720M scale. On the theory side, we prove sharp complexity bounds under heavy‑tailed noise which are governed by period‑averaged smoothness and noise that interpolate between Muon's and Lion's constants. These bounds predict the compute‑optimal period and the conditions under which LionMuon outruns Muon and Lion. Code: https://github.com/brain‑lab‑research/lion‑muon
Authors:Hyojun Go, Hyungjin Chung, Prune Truong, Goutam Bhat, Li Mi, Zhaochong An, Zixiang Zhao, Dominik Narnhofer, Serge Belongie, Federico Tombari, Konrad Schindler
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.
Authors:Hao Li, Lifu Du, Nurul Hameed, Shemonti Saha Authai, Zlata Stefanovic, Chenjie Xu
Abstract:
Cryomicroneedles offer a route to minimally invasive intradermal delivery of living cells, but their cryogenic formulations must reconcile cell protection with constraints on toxicity and device fabrication. Here we report an AI‑assisted, closed‑loop workflow for cryomicroneedle cryoprotectant discovery that combines literature curation, Gaussian‑process surrogate modelling, Bayesian optimization, and sequential wet‑lab validation. A curated dataset of 198 mesenchymal stem‑cell cryopreservation formulations from 42 studies was converted into 21 ingredient features and used to train an uncertainty‑aware literature prior. This model captured moderate structure in the literature data but failed prospectively, motivating iterative wet‑lab correction. Across ten validation iterations and 106 wet‑lab observations, the model progressively adapted to cryomicroneedle‑specific outcomes: batch RMSE decreased from 41.21 to 6.86 percentage points, later‑stage rank correlations became consistently positive, and the cumulative wet‑lab predicted‑versus‑measured summary reached R^2 = 0.942. The best validated formulation achieved 95.15% post‑thaw viability with low DMSO, ectoin, ethylene glycol, and fetal bovine serum. However, high viability alone did not ensure intact cryomicroneedle formation, highlighting the need for future multi‑objective optimization. These results demonstrate that agent‑assisted computational infrastructure can make data‑efficient formulation discovery more accessible to labs with minimal data expertise in‑house. Project code is available at https://github.com/baitmeister/ML‑for‑CryoMN.
Authors:Zunhai Su, Rui Yang, Chao Zhang, Yaxiu Liu, Yifan Zhang, Wei Wu, Jing Xiong, Dayou Du, Xialie Zhuang, Yulei Qian, Yuchen Xie, Yik-Chung Wu, Hongxia Yang, Ngai Wong
Abstract:
The rapid advancement toward long‑context reasoning and multi‑modal intelligence has made the memory footprint of the Key‑Value (KV) cache a dominant memory bottleneck for efficient deployment. While the established per‑channel quantization effectively accommodates intrinsic channel‑wise outliers in Key tensors, its efficacy diminishes under extreme compression. In this work, we revisit the inherent limitations of the per‑channel quantization paradigm from both empirical and theoretical perspectives. Our analysis identifies Token Norm Imbalance (TNI) as the primary bottleneck to quantization fidelity. We demonstrate that TNI systematically amplifies errors when shared quantization parameters are required to span token groups exhibiting substantial norm disparities. Instead of relying on intricate quantization pipelines (e.g., TurboQuant), we propose OScaR (Omni‑Scaled Canalized Rotation), an accurate and lightweight KV cache compression framework for X‑LLMs (i.e., text‑only, multi‑modal, and omni‑modal LLMs). Advancing the per‑channel paradigm, OScaR employs Canalized Rotation followed by Omni‑Token Scaling to mitigate TNI‑induced sequence‑dimensional variance both effectively and efficiently, further supported by our optimized system design and CUDA kernels. Extensive evaluations across X‑LLMs show that OScaR consistently outperforms existing methods and achieves near‑lossless performance under INT2 quantization, establishing it as a robust, low‑complexity, and universal framework that defines a new Pareto front. Compared with the BF16 FlashDecoding‑v2 baseline, our OScaR implementation achieves a notable up to 3.0x speedup in decoding, reduces memory footprint by 5.3x, and increases throughput by 4.1x. The code for OScaR is publicly available at https://github.com/ZunhaiSu/OScaR‑KV‑Quant.
Authors:Lakshya A Agrawal, Donghyun Lee, Shangyin Tan, Wenjie Ma, Karim Elmaaroufi, Rohit Sandadi, Sanjit A. Seshia, Koushik Sen, Dan Klein, Ion Stoica, Joseph E. Gonzalez, Omar Khattab, Alexandros G. Dimakis, Matei Zaharia
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 .
Authors:Soyeon Kim, Seongwoo Lim, Kyowoon Lee, Jaesik Choi
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/.
Authors:Jianan Ma, Jingyi Wang, Qi Xuan, Zhen Wang
Abstract:
Deep neural networks (DNNs) are suffering from ethical issues such as individual discrimination. In response, extensive NN repair techniques have been developed to adjust models and mitigate such undesired behaviors. However, existing fairness repair methods are typically data‑centric, which often lack provable guarantees and generalization to unseen samples. To overcome these limitations, we propose ProF, a novel fairness repair framework with provable guarantees. The key intuition of ProF is to leverage interval bound propagation (a widely used NN verification technique) to soundly capture model outputs over the whole set S(\mathbfx) around a biased sample \mathbfx. The derived bounds are utilized to guide fairness repair which encourages the model to produce consistent outputs on S(\mathbfx). Specifically, we integrate fairness constraints and model modifications into a unified constraint‑solving formulation, which can be transformed to a Mixed‑Integer Linear Programming (MILP) problem solvable by off‑the‑shelf solvers. The solution to the MILP problem effectively induces a repaired model with guaranteed fairness over the whole set S(\mathbfx). We evaluate ProF on four widely used benchmark datasets and demonstrate that it achieves provable fairness repair, with generalization of up to 95.93% on full datasets and 93.16% on the entire input space. Notably, ProF can be easily configured to support multiple sensitive attributes and more practical fairness definitions, while providing provable repair guarantees and delivering around 90% fairness improvement. Our code is available at https://github.com/nninjn/ProF.
Authors:Carlo Romeo, Andrew D. Bagdanov
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.
Authors:Daisuke Oba, Hiroki Furuta, Naoaki Okazaki
Abstract:
Discrete diffusion language models (DDLMs) generate text by iteratively denoising categorical token sequences, while recent drifting methods for continuous generators suggest that part of this sampling‑time correction can instead be absorbed into training through an anti‑symmetric fixed‑point objective. We study how to transfer this principle to DDLMs, where the main challenge is the interface with discrete text: hard token samples are non‑differentiable, and categorical predictions do not directly provide continuous samples to drift. We formulate TokenDrift, a drifting objective that lifts categorical predictions to soft‑token features, applies anti‑symmetric drifting in a frozen semantic space, and backpropagates the resulting stop‑gradient feature target to DDLM logits. In controlled continual‑training experiments with masked and uniform‑state diffusion backbones, TokenDrift improves fixed‑NFE generation quality over matched continuation baselines, reducing Gen.‑PPL at 4 NFEs by 89% on MDLM and 86% on DUO. These results suggest that drifting can provide a practical refinement objective for DDLMs.
Authors:Noam Major, Kathy Razmadze, Yoli Shavit
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
Authors:Ahmed Heakl, Abdelrahman M. Shaker, Youssef Mohamed, Rania Elbadry, Omar Fetouh, Fahad Shahbaz Khan, Salman Khan
Abstract:
When a model produces a correct solution under reinforcement learning with verifiable rewards (RLVR), every token receives the same reward signal regardless of whether it was a decisive reasoning step or a grammatical filler. A natural fix is to condition the model on the correct answer as a teacher, identifying tokens it would have generated differently had it known the answer. Prior work shows this either corrupts training by leaking the answer into the gradient, or produces a weak signal that cannot distinguish decisive steps from filler, since both look equally surprising relative to the model's baseline. We propose Contrastive Evidence Policy Optimization (CEPO), which asks a sharper question at every token: not just "does the correct answer favor this token?" but "does the correct answer favor it while the wrong answer disfavors it?" A token satisfying both is a genuine reasoning step; one satisfying neither is filler. The wrong‑answer teacher is constructed from rejected rollouts already in the training batch, incurring no additional sampling cost. We prove CEPO inherits all structural safety guarantees of the prior state of the art while strictly sharpening credit at decisive tokens, with the improvement vanishing exactly at filler positions. Empirically, CEPO achieves 43.43% and 60.56% average accuracy across five multimodal mathematical reasoning benchmarks at 2B and 4B scale, respectively, versus 41.17% and 57.43% for GRPO under identical training budgets. Distribution‑matching self‑distillation methods (OPSD, SDPO) fall below the untrained baseline, empirically confirming the information leakage our theory predicts. Our code is available at https://github.com/ahmedheakl/CEPO.
Authors:Chenyu Lian, Hong-Yu Zhou, Chun-Ka Wong, Jing Qin
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.
Authors:Halil Ibrahim Gulluk, Olivier Gevaert
Abstract:
Deep learning methods have demonstrated promising results in predicting BI‑RADS scores from mammography images. However, the interpretation of these images can vary, leading to discrepancies even among radiologists. Given the inherent complexity of mammograms, training classification models solely on image labels often yields limited performance. To address this challenge, we curated 2313 mammogram images and their corresponding captions from two mammography atlases. Our proposed approach employs a multi‑modal model that uses a pretrained PubMedBERT as the language component. By training this model on image‑text pairs with contrastive learning, we enable the vision encoder to absorb the rich information contained in the captions, thereby improving its understanding of mammography findings. We then fine‑tune the vision encoder on two datasets for BI‑RADS prediction, achieving superior performance compared with models trained without this pretraining, particularly when labeled samples are scarce. The improvement in the 3‑class average F1 score ranges from +1% to +14%: a +1% increase with 40K training samples, and a +14% increase with 1K samples. Furthermore, our experiments reveal that 2K image‑text pairs from mammography atlases can be more informative than 2K labeled samples for label prediction, with an average margin of +1.1% when more than 10K training samples are available. Overall, our work provides a vision‑language model for mammography and highlights the value of textual information from mammography atlases. In addition, we publicly release preprocessed mammography images of the TEKNOFEST dataset. The training code, pre‑trained model weights, data extraction scripts, and the released dataset are publicly available at: https://github.com/igulluk/MAM‑CLIP
Authors:Soojin Choi, Seokhyeon Hong, Chaelin Kim, Junghyun Nam, Junhyuk Jeon, Junyong Noh
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.
Authors:Joy Bose
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.
Authors:Qiujing Lu, Tonmoy Monsoor, Ehsan Ebrahimzadeh, Kartik Sharma, Vwani Roychowdhury
Abstract:
Nonnegative matrix factorization (NMF) seeks a low‑rank approximation X \approx UV^T with nonnegative factors and is commonly solved using interior methods that enforce feasibility throughout optimization. We show that such constraint‑driven approaches can impede progress in the nonconvex landscape, leading to slow convergence or convergence to suboptimal stationary points. We propose an exterior framework for NMF (eNMF) that separates low‑rank approximation from nonnegativity enforcement. Our method initializes from the optimal unconstrained factorization and introduces a rotation procedure that maps unconstrained factors to an exterior point closest to the nonnegative orthant. This viewpoint yields an algorithmic framework in which simple iterative updates converge to KKT‑satisfying stationary points on the boundary of the positive orthant. The exterior formulation also enables a geometric interpretation of NMF solutions, clarifying equivalence classes of factorizations under permutation and orthogonal transformations. An intriguing numerical result, involving 400 NMF experiments across both real and synthetic datasets, show that in 99% of the cases, different algorithms tend to converge towards equivalent factor matrices. We benchmark eNMF against 9 state‑of‑the‑art NMF algorithms with 9 initialization schemes across 3 real‑world and 2 synthetic datasets. eNMF consistently outperforms all 81 competitors, achieving up to 30% lower reconstruction error under equal‑time settings and up to 150% speedup under equal‑error settings. The downstream experiments further demonstrate substantial performance gains in audio processing and recommendation tasks, corroborating the practical benefits of the proposed exterior optimization framework. Code is available at https://github.com/roychowdhuryresearch/eNMF
Authors:Taegu Kang, Jaesik Yoon, Sungjin Ahn
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
Authors:Thomas Vincent Howe, David Wingate
Abstract:
In the training data used by large language models (LLMs), the same latent concept is often presented in multiple distinct ways: the same facts appear in English and Swahili; many functions can be expressed in both Python and Haskell; we can express propositions in both formal and natural language. We show that LLMs can exhibit compartmentalization, where they fail to identify and share statistical strength between distinct presentations of unified concepts. In the worst case, LLMs simply learn parallel internal representations of each presentation of the concept, saturating model capacity with redundancies and decreasing sample efficiency with the number of such presentations. We also demonstrate that synthetic parallel data can fail to improve this despite being easily learned itself. Under this framework, we find that, for small models, early multilingual learning is nearly entirely compartmentalized. Finally, all interventions that we study exhibit a phase transition in which their effectiveness depends on the number of distinct presentations, suggesting that the language modeling objective may only inconsistently unify representations.
Authors:Omer Haq
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.
Authors:Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi, Fazel Arasteh, Jinjun Shan, Lili Mou, Dongfeng Bai, Kasra Rezaee
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.
Authors:Ali Zindari, Xiaowen Jiang, Rotem Mulayoff, Sebastian U. Stich
Abstract:
Low‑rank adaptation (LoRA) is a widely used parameter‑efficient fine‑tuning method, yet its learned correction is static: the same low‑rank update is applied to every input. This input‑agnostic approach creates an inevitable compromise between adapting to the fine‑tuning distribution and preserving pre‑trained behavior on inputs outside that distribution, contributing to catastrophic forgetting. We introduce DISeL (Dynamic Input‑Sensitive LoRA), which augments LoRA modules with lightweight input‑dependent gates over individual rank‑one components. The gating mechanism is designed to preserve the pre‑trained model's behavior by default, while training learns to activate selected components that reduce the fine‑tuning loss. DISeL adds only a small number of parameters and preserves the low‑rank structure. Across RoBERTa on GLUE, and Llama and Mistral models fine‑tuned for mathematical reasoning and code generation, DISeL reduces forgetting relative to LoRA and related variants while maintaining competitive fine‑tuning accuracy. In addition, the learned gate activations provide an interpretable diagnostic view of which layers and rank components are most activated during fine‑tuning, giving insight into where task‑specific adaptation is concentrated. Code available at https://github.com/alizindari/DISeL .
Authors:Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria Fodor
Abstract:
Deep Graph Neural Networks (GNNs) are essential for capturing complex dependencies in graph‑structured data. However, scaling GNNs to depth remains challenging, as stacking layers leads to representation collapse and diminishing sensitivity due to repeated aggregation. While Neural Sheaf Diffusion (NSD) provides strong theoretical guarantees against such collapse, these guarantees do not translate to practice: as depth increases, the disagreement signal of the sheaf Laplacian vanishes, limiting the contribution of deeper layers. We identify mechanisms that hinder NSD effectiveness at depth and propose \emphDeep Neural Sheaf Diffusion (DNSD), which replaces the sheaf Laplacian with a sheaf adjacency operator to maintain informative signals across layers. This is complemented by normalization, odd nonlinearities, and gating. To provide a principled explanation of the expected performance improvement, we contrast sheaf diffusion to graph attention mechanisms, highlighting that DNSD replaces scalar attention scores with matrix‑valued edge functions and normalizes node representations rather than attention scores. We demonstrate empirically that DNSD effectively utilizes deep aggregation in graph tasks, outperforming GNN and NSD baselines with up to 30pp accuracy on synthetic long‑range datasets, and consistently outperforming them on real‑world benchmarks. These results position sheaf‑based architectures as a promising building block for graph foundation models by supporting effective deep architectures.
Authors:Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, Hafize Gonca Cömert
Abstract:
Microsimulation models used by ministries of finance and central banks rely on parametric processes for lifetime earnings that capture only first and second moments of the conditional distribution and miss long‑range nonlinear structure. We propose SAGA, a decoder‑only transformer for irregular tabular panel sequences, paired with a split conformal calibration wrapper that delivers individual‑level prediction intervals with finite‑sample marginal coverage guarantees. Trained on the longitudinal Swedish LISA register over 1990 to 2022, comprising 2,143,817 individuals and 61,284,903 person‑years, the model forecasts annual labor earnings at horizons of one to thirty years and aggregates them by Monte Carlo into present‑discounted lifetime earnings distributions. Against the canonical Guvenen, Karahan, Ozkan, and Song parametric process and tabular and recurrent baselines, SAGA reduces continuous ranked probability score by 31.9 percent at the ten‑year horizon and mean absolute error by 37.7 percent at the twenty‑year horizon. Conformal intervals achieve nominal coverage to within 0.4 percentage points marginally and within 2.4 percentage points on the worst‑case demographic subgroup. The reconstructed lifetime earnings Gini coefficient is 0.327 against the partially observed truth of 0.341 and the GKOS estimate of 0.378. Model weights, calibration tables, and a synthetic equivalent dataset are released for replication outside the protected SCB MONA environment.
Authors:Ahmad Yehia, Abduallah Mohamed, Tianyi Wang, Jiseop Byeon, Kun Qian, Junfeng Jiao, Christian Claudel
Abstract:
Accurately forecasting human trajectories from an egocentric perspective plays a central role in applications such as humanoid robotics, wearable sensing systems, and assistive navigation. However, progress in this direction remains limited due to the scarcity of egocentric trajectory datasets collected in real‑world environments. Addressing this need, we introduce EgoTraj, an egocentric multimodal open dataset recorded using Meta Quest Pro (MQPro). EgoTraj contains 75 sequences of human navigation collected from multiple MQPro wearers in real‑world urban environments. Each recording provides synchronized RGB video along with ground‑truth data, including continuous time‑synchronized 6‑degree‑of‑freedom head poses, per‑frame 3D eye gaze vectors, scene annotations. To the best of our knowledge, EgoTraj differs from typical egocentric trajectory datasets by capturing long‑horizon, self‑directed navigation across diverse urban routes with broad participant diversity. To demonstrate the potential of the dataset, we benchmark several state‑of‑the‑art methods for egocentric trajectory prediction and conduct ablation studies to analyze the contributions of gaze, scene, and motion cues. The results highlight the utility of EgoTraj for AR‑based perception, navigation, and assistive systems. The EgoTraj dataset, code, and EgoViz Dashboard are publicly available at https://github.com/yehiahmad/EgoTraj.
Authors:Tiexin Ding
Abstract:
We apply the Weibull distribution ‑‑ a two‑parameter family from extreme‑value theory ‑‑ as a diagnostic framework for element‑wise weight magnitude distributions in transformers. At initialization, i.i.d. Gaussian weights give |w| ~ HalfNormal, yielding k ~ 1.20 via middle‑80% probability‑plot fit (the protocol used throughout this work). This anchor makes k a principled, architecture‑independent measuring stick for training dynamics; fitting each weight matrix independently at every layer at every checkpoint enables per‑component, per‑layer, and per‑step diagnostics that aggregate statistics cannot resolve.
Applying this framework to 12 model entries spanning 7 architectural families (Pythia, OLMo‑1/2, LLaMA‑3, Mistral, Qwen2.5/3) reveals three findings. First, FFN modules and the attention output projection W_o ‑‑ the Transmission Class ‑‑ fall in a narrow k band: median terminal k in [1.186, 1.204] across 12 entries (cross‑family CV = 0.51%), shared across SwiGLU/GeLU activations, Pre‑LN/QK‑Norm placements, and 70M‑14B sizes. Second, the attention input projections W_q, W_k ‑‑ the Selection Class ‑‑ depart from the Weibull family, with severity shaped by storage: separately‑stored Q/K (OLMo‑1, OLMo‑2) yields k in [0.76, 0.99] (deep); GQA models yield k in [1.10, 1.16] (mild); Pythia's merged W_qkv occupies a transitional zone tracking training budget T/tau monotonically. Third, lambda grows substantially during training and scales with sqrt(eta/lambda_wd) within the Pythia family (Pearson r = 0.94, three Transmission kinds), directionally consistent with Fan et al. (2025). The two parameters carry independent information: k labels the functional class, lambda labels training progress.
We release npm‑weibull‑py v0.4 (Python library) and DATABASE_v9_1 at https://github.com/tiexinding/NPM‑Weibull‑public .
Authors:Wei Shi, Ziheng Peng, Sihang Li, Xiting Wang, Xiang Wang, Mengnan Du, Na Zou
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/.
Authors:Chanuk Lee, Minki Kang, Sung Ju Hwang
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.
Authors:Pei Yang, Wanyi Chen, Tongyun Yang, Pengbin Feng, Jiarong Xing, Wentao Guo, Yuhang Yao, Yuhang Han, Hanchen Li, Xu Wang, Zeyu Wang, Jie Xiao, Anjie Yang, Liang Tian, Lynn Ai, Eric Yang, Tianyu Shi
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.
Authors:Vyzantinos Repantis, Harshvardhan Singh, Tony Joseph, Cien Zhang, Akash Vishwakarma, Svetlana Karslioglu, Michael Wyatt Thot, Ameya Gawde
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.
Authors:Cheng Luo, Zefan Cai, Junjie Hu
Abstract:
Attention Residuals replace standard additive residual connections with learned softmax attention over previous layer outputs, enabling selective cross‑layer routing. However, standard Attention Residuals still attend over cumulative hidden states in previous layers, which are highly redundant. We show that this redundancy leads to routing collapse in deeper layers: attention weights become low‑contrast and closer to uniform (max weight \approx0.2), limiting the model's ability to select informative states in previous layers. This raises a key but underexplored design question: what layer‑wise representations should be routed in Attention Residuals? To answer this question, we propose Delta Attention Residuals, which attend over deltas ‑‑ the change introduced by each sublayer (\mathbfv_i = \mathbfh_i+1 ‑ \mathbfh_i) ‑‑ instead of cumulative states. Delta representations are structurally diverse and yield higher‑contrast attention distributions (max weight \approx0.6), enabling more selective and effective routing across layers. This principle applies at both per‑sublayer and block granularity. Across all tested scales (220M‑‑7.6B), Delta Attention Residuals consistently outperform both standard residuals and Attention Residuals, with 1.7‑‑8.2% validation perplexity gains. Delta Attention Residuals also enables converting pretrained checkpoints into Delta Attention Residuals via standard fine‑tuning. Code is available at https://github.com/wdlctc/delta‑attention‑residuals‑code.
Authors:Adil Amin
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/.
Authors:Yuanqing Wang, Yuchen Zhang, Hao Lin, Junhao Hu, Chunyang Zhu, Quanlu Zhang, Boxun Li, Guohao Dai, Zhi Yang, Daning Cheng, Yunquan Zhang, Yu Wang
Abstract:
Modern large language model (LLM) training is inherently dynamic: resource fluctuations, RLHF phase shifts, and cluster elasticity continually reshape the optimal parallelism layout, posing a significant challenge to existing training frameworks built around a static execution model. We present DynaTrain, a distributed training system for sub‑second, online reconfiguration across arbitrary multi‑dimensional parallelism. At its core, we propose a Virtual Parameter Space (VPS) abstraction that unifies all distributed training states under one logical coordinate space, turning any parallelism configuration into a deterministic mapping and collapsing complex transition into manageable geometric intersections. On top of VPS, a state routing‑and‑transition layer executes rank‑local transfers under a memory‑aware, deadlock‑free schedule, and an Elastic Device Manager overlaps new‑world construction with ongoing training to mask topology‑change cost. On dense and MoE models up to 235B parameters, DynaTrain reconfigures a 70B dense model in under 2s and a 235B MoE model in 4.36s, outperforming state‑of‑the‑art checkpoint‑based and elastic systems by up to three orders of magnitude while preserving correctness.
Authors:Wanghan Xu, Yuhao Zhou, Hengyuan Zhao, Shuo Li, Dianzhi Yu, Zhenfei Yin, Yaowen Hu, Fengli Xu, Wanli Ouyang, Wenlong Zhang, Lei Bai
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 .
Authors:Taiki Miyagawa, Akinori F. Ebihara
Abstract:
We propose non‑parametric estimators for the average run length (ARL) and average detection delay (ADD) in quickest changepoint detection (QCD) under finite and irregular sequence lengths. Although ARL and ADD are widely used as optimality criteria in theoretical and simulation studies, their application to real‑world datasets is hindered by limited and irregular sequence lengths. To address this issue, we propose non‑parametric estimators for the ARL and ADD, termed KM‑ARL and KM‑ADD, by drawing an analogy between QCD and survival analysis to model detection probabilities under sequence truncation. We derive estimation bias bounds and prove that they are asymptotically unbiased unless extrapolation is required. Experiments on simulated and real‑world datasets demonstrate their practical utility, enhancing robustness against limited and irregular sequence lengths, improving interpretability, and facilitating empirical, intuitive model selection. Our Python code is provided at https://github.com/TaikiMiyagawa/Kaplan‑Meier‑Average‑Run‑Length, offering ready‑to‑use implementations for practitioners.
Authors:Varun Kotte
Abstract:
LLM cascades and model routing promise lower inference cost by sending easy queries to a small model and escalating hard ones to a large model, but most deployed routers use uncalibrated confidence scores and require per‑workload threshold tuning. We present UCCI, a calibration‑first router that maps token‑level margin uncertainty to a per‑query error probability via isotonic regression and selects the escalation threshold by constrained cost minimization. Under three explicit assumptions, threshold policies on the calibrated score are cost‑optimal, and isotonic calibration achieves O(n^‑1/3) sample complexity for expected calibration error (ECE). On a production named entity recognition workload of 75,000 queries served by 4B and 12B instruction‑tuned LLMs on H100 GPUs, UCCI cuts inference cost by 31% (95% CI: [27%, 35%]) at micro‑F1 = 0.91 while reducing ECE from 0.12 to 0.03. At the same operating point, UCCI beats entropy thresholding, split‑conformal routing, and a FrugalGPT‑style learned threshold. All cascade results use end‑to‑end routing on actual model outputs and measured H100 latency, not simulated routing from global accuracies or nominal API prices.
Authors:Jing Chen, Shixiang Pan, Yujie Fan, Haocheng Ye, Haitao Xu, Wenqiang Xu
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.
Authors:Miguel Farinha, Ronald Clark
Abstract:
We present PIXLRelight, a feed‑forward approach for physically controllable single‑image relighting. Existing methods either provide limited lighting control (e.g. through text or environment maps), accumulate errors when chaining inverse and forward rendering, or require costly per‑image optimization. Our key idea is to bridge physically based rendering (PBR) and learned image synthesis through a shared intrinsic conditioning that can be obtained from either real photographs or PBR renders. At training time, paired multi‑illumination photographs are decomposed into albedo, diffuse shading, and non‑diffuse residuals, which condition the model. At inference time, the same conditioning is computed from a path‑traced render of a coarse 3D reconstruction of the input under user‑specified PBR lights. A transformer‑based neural renderer then applies the target illumination to the source photograph, preserving fine image detail through a per‑pixel affine modulation. PIXLRelight enables arbitrary PBR‑style lighting control, achieves state‑of‑the‑art relighting quality, and runs in under a tenth of a second per image. Code and models are available at https://mlfarinha.github.io/pixl‑relight/.
Authors:Rohit Patel, Alexandre Rezende, Steven McClain
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.
Authors:Chenglei Yu, Chuanrui Wang, Bangyan Liao, Tailin Wu
Abstract:
Single‑cell trajectory inference from destructive time‑course snapshots is fundamentally ill‑posed: neither cross‑time cell correspondences nor continuous trajectories are observed, so the snapshot distributions alone do not uniquely determine the underlying dynamics. Existing optimal transport and flow‑based methods typically couple cells by Euclidean proximity at observed clock times, which can misalign trajectories when development is asynchronous and cells sampled at the same experimental time occupy different latent pseudotime stages. We propose PACE, a trajectory inference framework that recovers geometry‑consistent continuous transport dynamics from destructive time‑course snapshots through three coupled components. First, PACE constructs a state‑ and time‑dependent anisotropic Riemannian metric that assigns low transport cost along locally supported tangent directions while penalizing normal velocity components. Second, it alternates between refining cross‑time couplings under the induced path‑action cost and fitting endpoint‑preserving neural bridges between adjacent snapshots. Third, it distills the learned bridge dynamics into a global continuous‑time velocity field over cellular states. Across seven controlled and biological datasets covering nine held‑out reconstruction experiments, PACE achieves the strongest overall reconstruction performance, reducing MMD, Wasserstein‑1 distance, and Wasserstein‑2 distance by 23.7% on average relative to the strongest competing baseline. PACE also improves RNA‑velocity alignment by 15.4% on an embryoid body differentiation benchmark, without requiring explicit cell pairing, lineage tracing, or RNA‑velocity supervision during training. Code is available at https://github.com/AI4Science‑WestlakeU/PACE.
Authors:Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten Borgwardt
Abstract:
Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large‑scale benchmarks that avoid duplicates and by models that do not scale well. We introduce TEDBench, a large‑scale, non‑redundant benchmark for protein fold classification constructed from the Encyclopedia of Domains (TED) and Foldseek‑clustered AlphaFold structures. We show that on TEDBench, current protein representation learning methods either require very large models or fail to deliver strong performance. To address this challenge, we propose Masked Invariant Autoencoders (MiAE), a self‑supervised framework for protein structure representation learning. MiAE uses an extremely high masking ratio of up to 90% with an \mathrmSE(3)‑invariant encoder and a lightweight decoder that reconstructs backbone coordinates from the latent representation and mask tokens. MiAE scales well and outperforms supervised counterparts and state‑of‑the‑art baselines on TEDBench, establishing a strong recipe for protein fold classification. To test transfer beyond AlphaFold structures, we further benchmark on a curated dataset from experimental structures of CATH v4.4. TEDBench is available at https://github.com/BorgwardtLab/TEDBench.
Authors:Chengpeng Hu, Yingqian Zhang, Hendrik Baier
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.
Authors:Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li
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.
Authors:Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidth-Thieme
Abstract:
Large Language Models (LLMs) have experienced significant growth and development in recent years. However, performing inference on LLMs remains costly, especially for long‑context inference or in resource‑constrained devices. This motivates the development of new post‑training pruning (PTP) methods. These methods reduce LLMs' requirements by removing a substantial part of the model's parameters. The discarded weights are selected depending on their impact on the models performance. Current PTP methods prune the models by removing the less informative hidden nodes from the FFN layers, and the least important attention layers. We propose Putri, a PTP method that introduces three changes to the State‑ of‑the‑art. First, we update the un‑pruned weights of the FFN to compensate for the introduced pruning error. Second, the FFN layers are pruned sequentially, taking into account the updates done to the previous layers. Third, instead of removing full attention layers, we remove individual attention‑heads. We extend this method such that it can also address Grouped‑Query Attention. In summary, Putri is a structure pruning method which remains simple while showing SOTA performance. Pruning experiments on multiple models with a wide variety of sparsity ranges and on different datasets, validate the generality of Putri. Notably, we demonstrate that, unlike previous methods, Putri can prune LLMs on extreme sparsity ratios. The code is available at: https://github.com/Coello‑dev/Putri.
Authors:Afiq Abdillah Effiezal Aswadi, Oliver Britton, Ross Baker, Matthew Farrugia-Roberts
Abstract:
Modern deep learning science often assumes that neural networks learn from a fixed data distribution. However, many practically important learning problems involve data distributions that change throughout training. How does such non‑stationarity impact the inductive biases of deep learning towards models with different structural, generalisation, and safety properties? A fruitful testbed for studying inductive bias is in‑context linear regression sequence modelling, where small transformers display strikingly different generalisation patterns depending on the diversity of the (fixed) training task distribution. In this paper, we explore the effect of diversifying the task distribution across training time, finding that such temporal diversity leads to an increased bias towards generalisation over memorisation.
Authors:Changmin Lee, Jaemin Kim, Taesik Gong
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.
Authors:Md Hasan, Nyvenn Castro, Daiqi Liu, Lukas Mulzer, Jana Hutter, Jonghye Woo, Moritz Zaiss, Andreas Maier, Paula A. Perez-Toro
Abstract:
Real‑time magnetic resonance imaging (rtMRI) of speech production enables non‑invasive visualization of dynamic vocal‑tract motion and is valuable for speech science and clinical assessment. However, rtMRI is fundamentally constrained by trade‑offs among spatial resolution, temporal resolution, and acquisition speed, often leading to undersampled k‑space measurements and degraded reconstructions. We propose SIREM, a speech‑informed MRI reconstruction framework that uses synchronized speech as a cross‑modal prior. The central idea is that vocal‑tract configurations during speech are correlated with the produced acoustics, making part of the image content predictable from audio. SIREM models each frame as a fusion of an audio‑driven component and an MRI‑driven component through a spatial weighting map. The audio branch predicts articulator‑related structure from speech, while the MRI branch reconstructs complementary content from measured k‑space data. We further introduce a learnable soft weighting profile over spiral arms, enabling a differentiable study of how k‑space arm usage interacts with speech‑informed fusion. This yields a unified multimodal formulation that combines audio‑driven prediction, MRI reconstruction, and sampling adaptation. We evaluate SIREM on the USC speech rtMRI benchmark against standard baselines, including gridding, wavelet‑based compressed sensing, and total variation. SIREM introduces a speech‑informed reconstruction paradigm that operates in a substantially higher‑throughput regime than iterative methods while preserving anatomically plausible vocal‑tract structure. These results establish an initial benchmark for multimodal speech‑informed rtMRI reconstruction and highlight the potential of synchronized speech as an auxiliary prior for fast reconstruction. The source code is available at https://github.com/mdhasanai/SIREM
Authors:Stefano Riva, Yantao Luo, Carolina Introini, Antonio Cammi
Abstract:
pyforce is a Python package implementing Data‑Driven Reduced Order Modelling techniques for applications to multi‑physics problems, mainly set in the Nuclear Engineering world. The package is part of the ROSE (Reduced Order modelling with data‑driven techniques for multi‑phySics problEms): mathematical algorithms aimed at reducing the complexity of multi‑physics models (for nuclear reactors applications), at searching for optimal sensor positions and at integrating real measures to improve the knowledge on the physical systems. With respect to the previous original implementation based on dolfinx package (v0.6.0), version 1.0.0 of pyforce has been completely re‑written using pyvista as backend for mesh importing, computing integrals, and visualisation of results; in addition, functions are stored as numpy arrays, improving the ease of use of the package. This choice allows to use pyforce with any software solver able to export results in VTK format.
Authors:Moritz Brösamle, Stephan Eckstein
Abstract:
Existing expressivity results for transformers typically rely on hardmax attention, high precision, and other architectural modifications that disconnect them from the models used in practice. We bridge this gap by analyzing standard transformer decoders with softmax attention and rounding of activations and attention weights, while allowing depth and width to grow logarithmically with the context length. As an intermediate step, we construct hardmax transformers with ternary activations and well‑separated attention scores that simulate Turing machines using Chain‑of‑Thought (CoT). This lets us convert the constructions to equivalent softmax transformers without the unrealistic parameter magnitudes or activation precision that prior approaches would require. Using the same technique, we analyze a recently proposed summarized CoT paradigm and show that it simulates Turing machines more efficiently, with model size scaling logarithmically in a space bound rather than a time bound. We empirically test predictions made by our results on a Sudoku reasoning task and find better alignment with learnability than for prior high‑precision results. Our code is available at https://github.com/moritzbroe/transformer‑expressivity.
Authors:Corentin Dumery, Niki Amini-Naieni, Shervin Naini, Pascal Fua
Abstract:
Object counting is a foundational vision task with over a decade of dedicated research, yet state‑of‑the‑art models still fail systematically in the mixed‑object setting that dominates real‑world applications such as industrial inspection and product sorting. We show that this gap is strongly driven by limitations in existing training and evaluation data: real counting datasets are prohibitively expensive to annotate and suffer from labeling noise, while existing synthetic alternatives lack diversity and realism. We address this with MixCount, a dataset and benchmark for mixed‑object counting designed to target the failure modes of current counting models. To overcome the high cost of constructing and labeling such data, we develop an automatic generation pipeline that synthesizes images, fine‑grained textual descriptions, and pixel‑perfect counting annotations at scale, eliminating the labeling ambiguity that plagues prior datasets. Evaluating state‑of‑the‑art counting models on MixCount exposes severe degradation in the mixed‑object setting. More importantly, training these models on our synthesized data yields substantial gains on real‑world benchmarks, reducing MAE by 20.14% on FSC‑147 and by 18.3% on PairTally. These results establish MixCount as both a benchmark and a training dataset for fine‑grained counting, and demonstrate that our pipeline, which produces effectively unlimited labeled data, helps address a long‑standing bottleneck in counting models.
Authors:Qi Si, Penglei Wang, Yushuai Wu, Yifeng Jiao, Xuyang Liu, Xin Guo, Yuan Qi, Yuan Cheng
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.
Authors:Gabriel Garcia
Abstract:
We study KV cache eviction under a shared globally capped decode‑time harness. Seven policies (LRU, H2O, SnapKV, StreamingLLM, Ada‑KV, QUEST, Random) share a prompt‑boundary vulnerability: without structural protection, they collapse to near‑zero quality on six pure‑transformer models (F1\leq0.064). Reserving 10% of cache at each boundary recovers 69‑‑90% of the C=2,048 reference‑ceiling quality on seven LongBench models at C=256 (13% retention); a ten‑model panel spans 68‑‑98%. An attention‑mass pilot (Qwen2.5‑3B, N=30) suggests why: the position‑0 sink holds ～75% of prefix mass, while other boundary tokens sit near ～0.41× uniform expectation, so attention scorers retain the sink but still drop structurally critical tokens. With protection, simplified score‑isolation variants are TOST‑equivalent to LRU at K=32 (Δ=0.02); at K=8, attention policies pairwise converge yet beat LRU by 0.011‑‑0.021 F1 across C=256 and C=512. Faithful Ada‑KV/QUEST add ～0.03‑‑0.04 F1 on Mistral‑7B and Phi‑3.5 beyond simplified variants. A NIAH‑32K regime‑transfer pilot on Qwen3‑4B (decode vs.\ prefill, C\in\512,2048\) shows near‑identical protection lifts (ratio 0.99‑‑1.00). At 64K, protection helps but recovery is modest; faithful per‑head scoring matches full‑cache ceiling on Gemma‑3‑4B at 6.3% retention only when the model already supports strong 64K retrieval without eviction. Overall: protection dominates; scoring differences are secondary once boundaries are guarded; per‑head allocation gives a further modest gain.
Authors:Jinglong Xiong, Xiaotian Liu, Ruoxin Wang, Zihang Liu, Yefan Zhou, Yujun Yan, Yaoqing Yang
Abstract:
Randomized linear algebra (RLA) algorithms are a modern class of numerical linear algebra techniques that play an essential role in scientific computing and machine learning, with broad and growing adoption. However, their discovery remains mostly a manual process that requires deep expert knowledge and inspiration. While Reinforcement Learning (RL) offers a pathway to automation, standard approaches struggle with sparse reward landscapes and vast search spaces inherent to high‑performing RLA algorithms. In this paper, we present RL4RLA, a general RL framework that automates the discovery of interpretable, symbolic RLA algorithms. Unlike black‑box approaches, our method builds explicit algorithms from basic linear algebra primitives, ensuring verifiable and implementable representations. To enable efficient discovery, we introduce: (1) a numerical curriculum that progressively increments problem difficulty to encode inductive bias specific to the RLA domain; (2) Monte Carlo Graph Search, which optimizes exploration by identifying and merging equivalent partial algorithms. We demonstrate that RL4RLA rediscovers state‑of‑the‑art methods, including sketch‑and‑precondition solvers, Randomized Kaczmarz, and Newton Sketch, and can be targeted to produce algorithms optimized for specific trade‑offs between accuracy, speed, and stability. Code is available at https://github.com/Tim‑Xiong/RL4RLA.
Authors:Hyun Lee, Hyemin Jeong, Yejin Kim, Hyungwook Choi, Hyunsoo Cho, Soo Kyung Kim, Joonseok Lee
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.
Authors:Zichun Yu, Chenyan Xiong
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.
Authors:Ligong Han, Kai Xu, Hao Wang, Akash Srivastava
Abstract:
Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipeline parallelism. We study whether this layerwise dependency can be relaxed by treating the hidden‑state trace across layers as the solution of a nonlinear residual equation and solving it with parallel Newton‑style updates. While this view is principled, exact Newton corrections require expensive Jacobian‑vector products and naive fixed‑point iterations are unstable on trained Transformers. We introduce Structured Newton Layer Parallelism (SNLP), a training and inference framework that replaces exact layer Jacobians with cheap architecture‑induced surrogate dynamics. In residual Transformers, this yields Identity Newton (IDN), where the correction reduces to a prefix‑sum‑like update; in mHC‑style architectures, HC Newton (HCN) uses the model's residual mixing matrix. We also study SNLP‑aware training, including pretraining regularization and direct SNLP‑forward SFT. Experiments on Nanochat‑scale Transformers show that SNLP exposes a practical speed‑quality frontier: on 0.5B models, it reaches up to 2.58x wall‑clock speedup, and a less aggressive configuration reaches 1.40x speedup without increasing PPL. The useful tradeoff comes from the biased finite‑iteration computation induced by IDN/HCN rather than exact recovery of the sequential trace. We further show that SNLP‑forward SFT can preserve downstream task accuracy, and that SNLP can serve as a drafter for self‑speculative decoding while a sequential verifier preserves output correctness.
Authors:Mahdi Naser-Moghadasi
Abstract:
Query optimization in big data analytics remains computationally expensive, particularly for resource‑constrained environments where traditional optimizers fail to satisfy memory and latency constraints. We present an agentic query planning system that combines a rule‑based teacher planner, UCB1 bandit exploration, cost‑aware prediction, and knowledge distillation to a lightweight student planner. Our teacher planner generates SQL plans using six key optimization strategies, while UCB1 bandit search efficiently explores the plan space under explicit resource constraints. A Random Forest cost model predicts query latency from plan features, enabling cost‑aware decisions. A distilled student planner (Logistic Regression or Gradient Boosting) learns to mimic teacher‑bandit decisions for fast inference. Evaluation on NYC Taxi and IMDB datasets demonstrates 23% latency reduction compared to default planners while maintaining 94% constraint satisfaction. The student planner achieves 89% accuracy in replicating optimal plans with 15x faster inference time. Our single‑file implementation enables reproducible big‑data analytics on resource‑limited machines and is publicly available at https://github.com/mahdinaser/agentic‑kd‑planner.
Authors:Jucheng Shen, Barbara Su, Anastasios Kyrillidis
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.
Authors:Chenxi Wang, Xiaorong Wang, Peiyang Li, Yi Wang
Abstract:
Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering limited coverage for generative models, existing time series libraries are mainly engineered for discriminative models, with standardized workflows for specific tasks, such as optimizing Mean Squared Errors for time series forecasting. This rigid structure is fundamentally incompatible with the distinct and often complex paradigms of generative models (e.g., adversarial training, diffusion processes), which learn the underlying data distribution rather than a direct input‑output mapping. To this end, we proposed GenTS, a comprehensive and extensible benchmark library designed for systematic assessment on generative time series models. GenTS features a unified data preprocessing pipeline, a collection of versatile models, and panoramic evaluation metrics. Its modular design also enables the researchers to flexibly customize beyond our built‑in datasets and models. Based on GenTS, we conducted benchmarking experiments under diverse tasks, accordingly offering suggestions for model selection and identifying potential directions for future research. Our codes are open‑source at https://github.com/WillWang1113/GenTS. The official tutorials and document are available at https://willwang1113.github.io/GenTS/.
Authors:Yuan Xue, Daniel Kudenko, Megha Khosla
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 .
Authors:Egor Shvetsov, Aleksandr Serkov, Shokorov Viacheslav, Redko Dmitry, Vladislav Goloshchapov, Evgeny Burnaev
Abstract:
The design of modern neural architectures has converged through incremental empirical choices, yet the mechanisms governing their training dynamics remain only partially understood. We identify and analyze a negative weight drift induced by the interaction between standard losses and positively biased activation functions. We prove that under MSE or cross‑entropy loss, the gradient with respect to positive pre‑activations is non‑negative in expectation at initialization, driving downstream weights toward negative values during early training. The drift is intrinsic to optimization rather than data, and persists across architectures (MLP, ResNet, ViT, GPT‑nano, MP‑SENe) and asymmetric activation functions (ReLU, GELU, SiLU). Coupled with ReLU, weight drift produces activation sparsity reaching up to 90% in GPT‑nano. We characterize the sparsity‑accuracy tradeoff across 79 configurations and identify a sharp accuracy cliff above ～70% activation sparsity. While ReLU^2 achieves a good sparsity‑‑accuracy ratio in GPT‑nano, it pathologically amplifies identified activation spikes in intermediate transformer layers. Clipping resolves this while preserving the representational benefits of squaring: clipped ReLU^2 outperforms its unclipped version, and GELU^2 achieves the lowest validation loss on GPT‑nano. Code is available at https://github.com/On‑Point‑RND/BugOrFeature.
Authors:Meysam Alishahi, Yan Zheng, Junpeng Wang, Chin-Chia Michael Yeh, Jeff M. Phillips
Abstract:
Tabular data generation considers a large table with multiple columns ‑‑ each column comprised of numerical, categorical, or sometimes ordinal values. The goal is to produce new rows for the table that replicate the distribution of rows from the original data ‑‑ without just copying those initial rows. The last 4 years have seen enormous progress on this problem, mostly using computational expensive methods that employ one‑hot encoding, VAEs, and diffusion.
This paper describes a new approach to the problem of tabular data generation. By employing copula transformations and modeling the distribution as a kernel density estimate we can nearly match the accuracy and leakage‑avoidance achievements of the previous methods, but with almost no training time. Our method is very scalable, and can be run on data sets orders of magnitude larger than prior state‑of‑the‑art on a simple laptop. Moreover, because we employ kernel density estimates, we can store the model as a coreset of the original data ‑‑ we believe the first for generative modeling ‑‑ and as a result, require significantly less space as well. Our code is available here: \urlhttps://github.com/tabkde/tabkde‑main
Authors:Miquel Martí i Rabadán, Alessandro Pieropan, Hossein Azizpour, Atsuto Maki
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.
Authors:Jack Wilkie, Hanan Hindy, Christos Tachtatzis, Miroslav Bures, Robert Atkinson
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.
Authors:Zhiquan Tan, Yinrong Hong
Abstract:
GRPO‑style RLVR trains reasoning models from multiple on‑policy attempts per prompt, but typically uses these attempts only through terminal rewards. We show that a mixed group contains a richer process signal: a correct completion is a self‑generated witness of how the current policy can solve the problem, while a wrong completion provides on‑policy prefixes where the policy needs correction. We introduce \emphSelf‑Supervised On‑Policy Distillation (SSOPD), which distills a teacher distribution conditioned on the shortest correct completion into prefixes of the longest wrong completion. This converts intra‑group correct‑‑wrong contrast into dense process supervision without external solution traces. A stopping‑time view motivates the shortest‑correct / longest‑wrong rule as a finite‑group approximation to editing persistent failures toward fast‑success actions, and a prompt‑level frontier weight concentrates the auxiliary loss where correct and wrong branches coexist. Across AIME 2024, AIME 2025, and HMMT 2025, SSOPD improves over GRPO in all nine model‑benchmark settings. On Qwen3‑8B, it reaches a macro Avg@12 of 65.6, outperforming GRPO by 1.6 points and the solution‑conditioned OPSD baseline by 0.8 points. Code will be released at https://github.com/tzq1999/SSOPD.
Authors:Meng Li, Xiaohua Yang, Jie Liu, Shiyu Yan
Abstract:
Context. Metamorphic Testing addresses the test‑oracle problem in scientific computing, but classical Mutation Score operates on syntactic AST mutations and misses domain semantics.
Objective. We propose the Semantic Mutation Score (SMS), built on five domain‑semantic operators (Conservation Erosion, Operator Substitution, Hyperparameter, Trajectory Flip, Structural Injection). SMS degenerates almost everywhere to MS in a characterised limit, so any SMS‑based conclusion remains consistent with prior mutation‑testing literature in the classical regime.
Method. A 12‑PUT x 5‑MP design over four single‑output float‑to‑float classes (numeric, probabilistic, surrogate, machine‑learning) is paired with a three‑layer attribution classifier separating true semantic faults from tolerance, OOD, statistical, and artefact categories. A same‑source / cross‑source ablation under an identical prompt isolates the LLM‑source‑diversity contribution. LLM‑generated mutants are compared against a default‑configuration cosmic‑ray syntactic pool at the AST‑normalised level.
Results. The pre‑registered large‑effect threshold for Cliff's delta is not met under the point‑estimate criterion; the observed effect lies in the medium‑effect range. Cross‑source pooling under an identical prompt does not appreciably shift delta, indicating that LLM identity is not the lever within this design. AST‑level overlap between LLM‑generated and default cosmic‑ray syntactic mutants is small; the Hyperparameter, Structural Injection, and Trajectory Flip classes are unreachable under default first‑order syntactic configurations.
Conclusion. SMS is a backward‑compatible adequacy metric for domain‑semantic metamorphic‑relation sets in scientific computing. The first‑order unreachability evidence is independent of the effect‑size question.
Authors:Meng Li, Xiaohua Yang, Jie Liu, Shiyu Yan
Abstract:
Context. Metamorphic Testing is recognised in IEEE/ISO software‑testing standards and increasingly recommended for AI systems, but its progress is bottlenecked by metamorphic relation (MR) identification: existing approaches (structured frameworks, mining and evolutionary pipelines, LLM‑assisted methods, MetaPattern catalogues) share an inductive grounding that leaves three foundational questions open: origin, closure, and transferability.
Objective. We propose a framework whose downstream step from program‑induced operator algebra to MetaPattern set is mechanical and provable, while the upstream curation of the algebra is a stated empirical hypothesis with explicit scope precondition.
Method. NOETHER is a two‑layer framework. The upstream layer is an eight‑block decomposition over recurrent mathematical structures (symmetry, order, self‑adjoint, time‑reversal, limit, qualitative‑dynamics, method‑comparison, relational equivalence). The downstream CONSTRUCT‑MP algorithm produces a MetaPattern set with algebraic‑closure (Theorem 1) and polynomial‑time decidability (Theorem 2) guarantees. We test the framework on three operator‑algebraic domains.
Results. On Boltzmann reactor physics NOETHER systematises a prior inductive catalogue; on equivariant ML it derives executable MRs for rotation invariance, adjoint duality, and training‑trajectory reversibility; on relational query optimisers it exercises the relational‑equivalence block. The central falsifiable prediction (L‑blindness on homogeneity‑preserving mutators) holds on the in‑scope substrate. The absolute‑completeness conjecture (Theorem 1') is falsified on PWR core diffusion via two pairwise‑independent counterexamples that identify five Translate‑extension dimensions.
Conclusion. Induction is relocated from per‑program MR sampling to a per‑domain algebraic layer; the downstream step is deductive and mechanical.
Authors:Jingru Fei, Kun Yi, Alex Xing Wang, Qingsong Wen, Xiangxiang Zhu, Wei Fan
Abstract:
Time series foundation models rely on large‑scale pretraining over diverse datasets across domains, yet their heterogeneity in temporal patterns could hinder the effectiveness of training and learning transferable time series representations. Inspired a fundamental concept, normalized power spectral density (PSD) in signal processing, we assume harmonizing datasets via PSDs in the spectral domain could reduce mismatches and enhance pretraining. We then go beyond the direct intractable minimization optimization and innovatively reformulate it as a principled harmonization approach. Specifically, we propose Harmonizer, a module that reshapes spectral structures and implicitly harmonizing PSDs across datasets, which theoretically corresponds to a shared reparameterization of second‑order temporal correlations. Our theoretical analysis further reveals token interactions with Harmonizer can be efficiently mediated by a compact set of resonators, motivating a HarmonicAttention design that performs self‑attention in a low‑dimensional interaction space. Then, we propose Olivia, a novel time series foundation model built upon these harmonization mechanisms. Extensive experiments on two large‑scale benchmarks (TSLib and GIFT‑Eval) and extra 6 datasets from GluonTS, demonstrate Olivia consistently achieves state‑of‑the‑art performance under zero‑shot, few‑shot, and full‑shot forecasting scenarios. Our code is available at https://github.com/TSTS13/Olivia.
Authors:Weichu Xie, Haozhe Zhao, Wenpu Liu, Yongfu Zhu, Liang Chen, Minghao Ye, Zirong Chen, Yuqi Xu, Shuai Dong, Ziyue Wang, Xinbo Xu, Kean Shi, Ruoyu Wu, Xiaoying Zhang, Wenqi Shao, Baobao Chang, Nan Duan, Jiaqi Wang
Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in large language models, but rewards only final‑answer correctness with no supervision over intermediate steps. Rubric‑based methods such as Rubrics as Rewards (RaR) introduce finer‑grained supervision by scoring rollouts against structured criteria, yet the rubric scores are still aggregated into a single scalar applied to the entire response, causing three weaknesses: loss of multi‑criterion structure, uniform supervision of correct and incorrect steps, and reward hacking through unbounded self‑correction. On 1,000 problems, we find 18.2% of steps in correct‑answer responses are wrong yet positively rewarded, while 49.9% of steps in incorrect‑answer responses are correct yet penalized. We introduce Step‑wise Rubrics as Rewards (SRaR), an RLVR framework that (i) uses an LLM judge to attribute each rubric item to a specific reasoning step, (ii) normalizes per‑step rubric scores across rollouts so only steps whose quality varies produce a learning signal, and (iii) combines the per‑step reward with the outcome reward through a decoupled advantage estimator that keeps the outcome baseline stable. We further build a 16K‑problem rubric dataset by contrastively distilling rubric items from correct and flawed reasoning paths sampled from a strong model. Across six mathematical reasoning benchmarks, SRaR improves average accuracy over RaR by 3.57 points on Qwen3‑8B and 2.75 points on Qwen3‑32B, raises the Faithful Reasoning Rate on AIME 2025 from 34.5% to 46.7%, and reduces self‑correction looping from 48.1% to 26.5%.
Authors:Jon Saad-Falcon, Avanika Narayan, Robby Manihani, Tanvir Bhathal, Herumb Shandilya, Hakki Orhun Akengin, Gabriel Bo, Andrew Park, Matthew Hart, Caia Costello, Chuan Li, Christopher Ré, Azalia Mirhoseini
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.
Authors:David Troxell, Yulia Alexandr, Sofia Hunt, Stephanie Lei, Guido Montúfar
Abstract:
Neural network verifiers aim to provide formal guarantees on model behavior, but existing verification benchmarks are fundamentally limited by their lack of ground‑truth labels. As a result, verifier evaluation relies on indirect heuristics, which prevents exact scoring and systematic study of verifier failure modes. We address this gap by introducing a reusable framework for generating verification instances whose ground‑truth robustness labels are known a priori through analytic construction. Our framework led to the discovery of multiple numeric tolerance concerns and an implementation bug in popular verifiers, highlighting the need for ground‑truth labels. Additionally, to systematically study verifier failure modes, we introduce the verification Difficulty Profile, a collection of estimable quantities capturing distinct sources of instance hardness. Using our framework and these profiles, we evaluate five state‑of‑the‑art verifiers and show that different instances stress distinct aspects of the verification pipeline. We show that these results can aid the future development of verifiers as they provide actionable targets for improving numerical reliability, relaxation quality, and search behavior. Our code is publicly available: https://github.com/dtroxell19/VeriStressGT.git.
Authors:Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran
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.
Authors:David Troxell, Noah Roemer, Guido Montúfar
Abstract:
Differentiable optimization layers are traditionally integrated in predict‑then‑optimize frameworks where a neural model estimates parameters that subsequently serve as fixed inputs to downstream decision‑making optimization problems. In this work, we introduce the concept of a "fairness layer": a differentiable optimization layer appended to a model's output layer that guarantees a chosen notion of output parity is satisfied when integrated into a neural network. Additionally, we introduce an online primal‑dual inference algorithm that provides provable aggregate fairness guarantees for streaming predictions with arbitrarily small batch sizes, where traditional per‑batch constraints become overly restrictive. Numerical experiments demonstrate the effectiveness of the fairness layer and associated algorithm, and theoretical analysis characterizes the layer's differentiability and stability properties during model training and backpropagation. Our code for these experiments is publicly available on GitHub (https://github.com/dtroxell19/FairDL‑ICML‑2026.git) and our public Python package documentation can be found online: https://dtroxell19.github.io/fairness_training/.
Authors:Tristan Gaudreault, Yongyi Mao
Abstract:
Transformers have become the dominant architecture for sequence modeling by using self‑attention to enable expressive and highly parallel processing. However, the resulting quadratic time and memory costs limit efficiency in long‑context settings. Recurrent models such as LSTMs provide explicit nonlinear state updates and strong state‑tracking capabilities, yet their strictly sequential computation limits parallelism. We introduce the Parallel Recursive LSTM (PR‑LSTM), a hierarchical recurrent architecture that replaces left‑to‑right recurrence with recursive nonlinear state composition over a balanced computation tree. Tokens are first mapped independently to latent states, which are then recursively merged by a learned gated composition block. This structure uses the reduction pattern underlying parallel scans as a fixed execution schedule, rather than assuming an associative recurrence. As a result, PR‑LSTM retains nonlinear gated state representations while reducing recurrent parallel depth from linear to logarithmic. Empirically, PR‑LSTM achieves strong sequence‑length generalization on formal‑language benchmarks, solving more tasks than standard RNN, LSTM, and Transformer baselines, while avoiding the quadratic scaling of attention. These results suggest that recurrent computation can be reorganized hierarchically to expose parallelism without restricting the transition dynamics to linear or associative forms.
Authors:Sajjad Khan
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
Authors:Aleksandr Churilov
Abstract:
Spracklen et al. (USENIX Security '25) showed that code‑generating large language models hallucinate package names that do not exist on PyPI or npm at rates ranging from 5.2% on commercial models to 21.7% on open‑source models, creating an attack surface for slopsquatting ‑‑ the registration of malicious packages under hallucinated names. We replicate their methodology on five frontier code‑capable LLMs released between October 2025 and March 2026: Claude Sonnet 4.6, Claude Haiku 4.5, GPT‑5.4‑mini, Gemini 2.5 Pro, and DeepSeek V3.2. Across 199,845 paired Python and JavaScript prompts validated against PyPI and npm master lists, we measure overall hallucination rates between 4.62% (Claude Haiku 4.5) and 6.10% (GPT‑5.4‑mini) ‑‑ an order‑of‑magnitude compression of the inter‑model spread observed by Spracklen, but not a retirement of the threat. Beyond replication, we identify a set of 127 package names (109 on PyPI, 18 on npm) that all five evaluated models invent identically, constituting a model‑agnostic supply‑chain attack surface that no single‑model study can reveal. We further document a Python‑over‑JavaScript hallucination asymmetry that inverts Spracklen's 2024 finding, identify a Haiku‑below‑Sonnet inversion within the Anthropic family, and observe a Jaccard‑similarity peak between DeepSeek V3.2 and GPT‑5.4‑mini (J = 0.343) suggestive of shared training‑data origins.
Authors:Robin-Nico Kampa, Fabian Deuser, Anna Bößendörfer, Konrad Habel, Norbert Oswald
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.
Authors:Peng Cui, Boyao Yang, Jun Zhu
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.
Authors:Ruth Wan Theng Chew, Zhiliang Chen, Apivich Hemachandra, Bryan Kian Hsiang Low
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.
Authors:Anthonio Oladimeji Gabriel, Ahmad Rufai Yusuf
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.
Authors:Shilong Jin, Lanjun Wang, Zhuosheng Zhang
Abstract:
Autonomous Graphical User Interface (GUI) agents often struggle with multi‑step tasks due to constrained context windows and static policies that fail to adapt to dynamic environments. To address these limitations, this work proposes the Self‑Evolving GUI Agent (SE‑GA), a novel framework that integrates hierarchical memory structures with an iterative self‑improvement mechanism. At the core of our approach is Test‑Time Memory Extension (TTME), which facilitates long‑term planning by dynamically retrieving episodic, semantic, and experiential memories to provide salient contexts during inference. To ensure continuous learning, we introduce Memory‑Augmented Self‑Evolution (MASE), which is a training pipeline that adopts the data collected by TTME to stabilize and enhance the agent's foundational policy. Extensive evaluations across both offline and online benchmarks demonstrate SE‑GA achieves state‑of‑the‑art performance, reaching success rates of 89.0% on ScreenSpot and 75.8% on the challenging AndroidControl‑High dataset. Furthermore, significant improvements on the AndroidWorld benchmark highlight the superior generalization to dynamic environments. Open source code: https://github.com/jinshilong‑dev/SE‑GA
Authors:Yaniv Hassidof, Adir Morgan, Yilun Du, Kiril Solovey
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/
Authors:Anhao Zhao, Haoran Xin, Yingqi Fan, Junlong Tong, Wenjie Li, Xiaoyu Shen
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.
Authors:Shuo Liu, Ding Liu, Shi-Ju Ran
Abstract:
Large language models (LLMs) generate not only reasoning text, but also token‑level confidence trajectories that record how uncertainty evolves during inference. Whether these trajectories are relevant to reasoning correctness remains unclear. Here we show that confidence trajectories encode a content‑agnostic confidence geometry associated with trace‑level final‑answer correctness. Using only token‑level confidence values, without access to the input question, reasoning text, hidden states, or external verifiers, we find that low‑dimensional representations of confidence trajectories separate correct from incorrect reasoning traces. Across GSM8K, MATH, and MMLU, this geometric separation is quantitatively linked to downstream predictability: stronger clustering of correct and incorrect traces, measured by the Davies‑‑Bouldin index, consistently corresponds to higher correctness‑discrimination AUC. We further show that correctness‑related information is enriched in the tail of reasoning, suggesting that late‑stage confidence dynamics carry key correctness signals. We propose NeuralConf, a lightweight estimator that learns from confidence trajectories for correctness evaluation. Under a fixed trace budget, NeuralConf‑derived scores improve confidence‑weighted answer aggregation over majority voting, tail confidence, and other static baselines. These results reveal that LLMs expose trace‑intrinsic statistical signals of correctness through their own confidence dynamics, offering a route to improve inference using information already present within generation.
Authors:Youngmok Ha, Viktor Schlegel, Yidan Sun, Anil Anthony Bharath
Abstract:
While Local Differential Privacy (LDP) serves as a foundational primitive for distributed data collection, its stringent noise injection requirement often leads to severe degradation in data utility. This degradation stems from the task‑agnostic nature of conventional LDP mechanisms, which inject noise uniformly across all dimensions regardless of their relative importance to the downstream objective. To address this issue, we propose a novel approach that mitigates noise in task‑relevant subspaces of the data representation. Our method identifies task‑critical subspaces via the Jacobian matrix of the public downstream model, selectively attenuates noise along those dimensions, and reshapes the isotropic noise of standard LDP into an anisotropic distribution. This method preserves the uniform per‑dimension privacy budget while heterogeneously modulating noise impact across dimensions, thereby substantially enhancing data utility. Furthermore, our approach generalizes to both linear and non‑linear models and integrates seamlessly with existing mechanisms. Extensive experiments on CIFAR‑10‑C (Brightness corruption at the highest severity level 5) demonstrate that integrating our approach improves the utility of PrivUnit2 and PrivUnitG by approximately 20% at ε=7.5. The source code is available at https://github.com/ymha/jacobian‑anr‑ldp.
Authors:Yangyou Liu, Zezhi Shao, Xinyu Chen, Hu Chen, Fei Wang, Yuankai Wu
Abstract:
Time series forecasting under non‑stationarity faces a fundamental tension between capturing stable representations and adapting to distribution shifts. Existing methods implicitly rely on static historical assumptions, leading to a critical failure mode we term Phase Amnesia, where models become blind to the evolving global context. To resolve this, we formalize non‑stationary dynamics through three physical hypotheses: wold decomposition, dynamical phase evolution, and heteroscedastic manifold generation. These principles inspire PULSE, a physics‑informed, plug‑and‑play framework adopting a Disentangle‑‑Evolve‑‑Simulate design philosophy. Specifically, PULSE utilizes phase‑anchored disentanglement to resolve optimization interference caused by dominant trends, employs a Phase Router to actively generate future trajectories, and introduces Statistic‑Aware Mixup (SAM) to ensure robustness against out‑of‑distribution volatility. Empirically, PULSE enables a simple MLP backbone to achieve state‑of‑the‑art or highly competitive performance across 12 real‑world benchmarks. This validates that a correct physics‑informed inductive bias is far more critical than raw architectural complexity for non‑stationary forecasting. The code is available at: https://github.com/Gemost/PULSE.
Authors:Anay Kulkarni, ChiaEn Lu, Dheeraj Mekala, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang
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.
Authors:Yulin Chen, He He, Chen Zhao
Abstract:
Reinforcement Learning with Verifiable Reward (RLVR) has proven effective in improving Large Language Model's (LLM) reasoning ability. However, the learning dynamics of RLVR remain underexplored. In this paper, we reveal a counterintuitive phenomenon: among hard examples that the model initially struggles with, a substantial subset remains unlearnable even when correct rollouts are present. To understand the phenomenon, we first demonstrate that existing optimization and sampling techniques fail to resolve unlearnability. With cross‑example gradient analysis, we show that unlearnable examples have fundamental representation issue, characterized by low gradient similarity with the rest of the examples and ungeneralizable reasoning patterns. We further show that representation flaws are difficult to mitigate in RL, as data augmentation does not improve gradient similarity. Our study provides the first systematic characterization of unlearnable data in RLVR training and reveals fundamental limitations in current RL approaches for reasoning tasks. Code and data are available at \urlhttps://github.com/yulinchen99/unlearnability‑rlvr.
Authors:Puning Yang, Junchi Yu, Qizhou Wang, Philip Torr, Bo Han, Xiuying Chen
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.
Authors:Siqi Zeng, Christopher Jung, Rui Li, Zhe Kang, Ming Li, Nima Noorshams, Zhigang Wang, Fuchun Peng, Han Zhao, Xue Feng
Abstract:
Improving LLM performance on downstream tasks sometimes requires leveraging auxiliary datasets during post‑training. In practice, however, developers face constraints on compute, labeling, and licensing costs that preclude using all available data, necessitating principled dataset‑level selection. These constraints are increasingly shaped by dataset marketplaces, where data acquisition is governed by budgets and negotiation. We study dataset valuation as a subset selection problem during LLM post‑training. Our goal is to identify and weight auxiliary datasets so as to maximize target task performance given constrained budgets. We first show that commonly used gradient alignment scores provide a reasonable yet incomplete valuation signal, as they ignore redundancy among datasets. To address this, we propose a scalable convex dataset‑level valuation method based on kernel mean matching (KMM) in gradient space, which jointly accounts for alignment with the target task and redundancy across auxiliary datasets. Through extensive experiments across diverse post‑training settings and tasks, we show that our approach consistently outperforms existing valuation baselines, achieving stronger performance with low computational overhead. Our results position dataset valuation as a practical decision tool for post‑training data selection in market‑constrained large language model settings. The code is available at https://github.com/uiuctml/convex_data_valuation.
Authors:Kyrie Zhao, Zehong Wang, Tianyi Ma, Fang Wu, Xiangru Tang, Pietro Lio, Sheng Wang, Yanfang Ye
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.
Authors:Amin Karimi Monsefi, Abolfazl Meyarian, Mridul Khurana, Shuheng Wang, Pouyan Navard, Cheng Zhang, Anuj Karpatne, Wei-Lun Chao, Rajiv Ramnath
Abstract:
Animals are described as effectively camouflaged when they blend seamlessly with their surrounding, yet no standardized quantitative measure of this seamlessness exists. We address this gap by framing camouflage evaluation as a visual localization problem: a well‑camouflaged animal is one that remains difficult to detect even when its category is known. We introduce SeamCam (Seamless Camouflage), a metric that quantifies how detectable an animal is from the available visual evidence. Given an image and a target species, SeamCam generates category‑conditioned detection proposals, extracts segmentation masks, and identifies the subset whose collective union yields the highest IoU with the ground‑truth mask. The SeamCam score is one minus this maximum recoverable localization signal, where a higher score indicates stronger camouflage (i.e., lower detectability). In a human two‑alternative forced‑choice study with 94 participants and 2,390 comparisons, SeamCam achieves 78.82% agreement with human camouflage difficulty judgments, outperforming state‑of‑the‑art by about 25%. We then demonstrate SeamCam's utility as a preference signal for Direct Preference Optimization (DPO) to fine‑tune a diffusion‑based inpainting model for camouflage generation. This offers an affordable training approach with an objective explicitly suited for camouflage generation, unlike typical diffusion models. To support rigorous benchmarking, we further introduce CamFG‑1.5k, a curated dataset of 1,521 high‑resolution images in which animals are fully visible prior to camouflage generation, enabling unbiased evaluation by controlling for occlusion artifacts present in existing datasets. https://7amin.github.io/SeamCam/
Authors:Youngin Kim, Ray Sun, Inho Kim, Bumsoo Park, Hyun Oh Song
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.
Authors:Jiajian Li, Jingyuan Huang, Junru Gong, Qi Wang, Xiaokang Yang, Yunbo Wang
Abstract:
We present OrbiSim, a novel robotic simulation paradigm that redefines world models as a fully differentiable physics engine for embodied intelligence. Unlike prior world models that focus on unconstrained imagination in latent or visual domains, OrbiSim establishes a unified, physically‑grounded pathway that bridges structured scene assets, neural dynamics, and downstream reinforcement learning. By enabling end‑to‑end differentiability throughout the entire simulation loop ‑‑ spanning from explicit state transitions to visual observation generation ‑‑ OrbiSim supports tasks traditionally intractable for classical simulators, such as differentiable contact modeling, gradient‑based policy optimization under sparse rewards, and intuitive physical inference. Empirical results demonstrate that OrbiSim significantly outperforms state‑of‑the‑art world models in both predictive fidelity and control performance. Furthermore, its consistent responsiveness to asset configurations and physical parameters suggests its potential as a differentiable tool for enhancing robot simulation and policy training.
Authors:Shunchang Liu, Xin Chen, Belen Martin Urcelay, Francesco Croce
Abstract:
Preference learning in large language models relies on reward models as proxies for human judgment. However, these models frequently exhibit preference instability, producing contradictory preference assignments in response to subtle, meaning‑preserving input variations. We analyze this instability at the representation level under three semantic‑preserving perturbation types: paraphrasing, pattern injection, and backdoor triggers. We attribute this instability to over‑reliance on predictive yet brittle features, which we term unstable features, and isolate them via Sparse Autoencoders (SAEs) in a sparse latent space where benign and perturbed inputs activate distinctly separable patterns. Building on this separability, we propose two SAE‑based instability mitigation strategies: SAE Feature Steering, which identifies and suppresses anomalously activated features at inference, and SAE Residual Correction, which learns adaptive adjustments over SAE features to restore correct preferences. Our methods substantially reduce incorrect preference assignments on harmlessness and hallucination benchmarks while preserving benign performance and general utility on other tasks, without retraining the reward model. Our code and data are available in \urlhttps://github.com/shunchang‑liu/pisa.
Authors:Safayat Bin Hakim, Keyan Guo, Wenkai Tan, Alvaro Velasquez, Shouhuai Xu, Houbing Herbert Song
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.
Authors:Ruichen Zheng, Biao Zhang, Michael Birsak, Mikhail Skopenkov, Peter Wonka
Abstract:
We introduce Patchwork, a new general‑purpose shape representation capable of modeling 2D and 3D geometry with a small number of parameters. Patchwork is grounded in a rigorous mathematical framework, providing provable complexity bounds and the ability to approximate arbitrary shapes with arbitrary precision in any dimension. We propose an efficient gradient‑based optimization scheme to fit Patchwork representations to 2D and 3D data, along with a novel regularization loss that progressively prunes redundant elements, yielding high compactness after convergence. Our approach offers fast fitting performance, a fraction of the required parameters compared to existing alternatives, and native support for inside‑outside classification, making it a versatile and compact representation for geometric learning and reconstruction tasks, with future potential for 3D generation. Our implementation is available at: https://github.com/Ankbzpx/patchwork‑experiment.
Authors:Shuchan Wang
Abstract:
We introduce a dynamics‑level approach to watermarking generative models. Rather than embedding signals into model weights or outputs, we embed the watermark directly into the learned continuous dynamics ‑‑ the velocity field of a flow matching model. We formulate this as random coding over a continuous channel: a key‑dependent perturbation is added during training, and the message is recovered at detection time from black‑box queries. The perturbation is designed to leave the generated distribution unchanged. Experiments on MNIST and CIFAR‑10 across different architectures confirm reliable message recovery, preserved generation quality, and chance‑level decoding accuracy without the secret key.
Authors:Gabriel Garcia
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.
Authors:Gwenolé Quellec
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.
Authors:Himanshu Singh Baghel
Abstract:
Proactive autoscaling for containerized workloads depends on knowing the provisioning delay, i.e., the time between a scaling decision and the moment new capacity is ready to serve traffic. In practice, this cold‑start duration can vary substantially across environments and even across consecutive scale‑out events. We present ADAPT (Adaptive Duration Approximation for Predictive Timing), an online EWMA estimator that tracks coldstart duration at runtime. ADAPT feeds a dynamic planning horizon, FH‑OPT, into a Model Predictive Controller (MPC) that optimizes replica counts over a rolling window. Together, these components form a closed‑loop proactive autoscaling design that adapts its lookahead based on measured provisioning delay. Evaluated across three policies (MPC+LSTM, MPC+Prophet, HPA) and six workload archetypes with five random seeds, MPC+LSTM achieves below 5% SLA violation on all workloads, compared with 7‑19% for reactive HPA and up to 28.7% for MPC+Prophet on bimodal traffic.
Authors:Qingyuan Yang, Dongyue Chen, Da Teng, Junhua Xiao, Jiaji Pan, Shizhuo Deng
Abstract:
Long‑term time series forecasting is essential for decision making in energy, finance, transportation, and healthcare systems. Recent lightweight forecasting models improve efficiency by operating in transformed or linearized spaces, but two challenges remain in frequency‑space forecasting. The real and imaginary streams of complex spectra contain complementary information that is often weakly exchanged, and periodic‑position cues can help recurring patterns only when they are reliable for the current dataset and prediction horizon. To address these challenges, we propose FRWKV+, an enhanced FRWKV forecasting model for selective periodic‑position branch interaction. FRWKV+ first introduces cross‑branch gates that exchange compact contexts between the real and imaginary frequency streams, allowing each stream to modulate the other. It then uses the Adaptive PhaseGate mechanism to extract periodic‑position context and generate signed corrections to these gates. An adaptive trust mechanism controls the correction strength at the sample, variable, and channel levels, so periodic‑position information is admitted as a reliable correction signal while preserving the efficiency of the FRWKV backbone. External benchmark tables report a separately labeled FRWKV‑family selected system for manuscript‑level comparison, while mechanism‑level claims are based on strict matched‑seed FRWKV‑family ablations and representative component‑level ablations. Under this matched protocol, FRWKV+ achieves the largest MSE winner coverage among the family variants and provides clear gains in selected periodic regimes. Component analysis further supports the usefulness of periodic‑position context, signed correction, and adaptive trust in these regimes, while revealing boundary cases where simpler correction rules remain preferable.
Authors:Jinuk Kim, Junsoo Byun, Donghwi Hwang, Seong-Jin Park, Hyun Oh Song
Abstract:
Manufacturable chip layouts must satisfy thousands of geometry‑based design rules, and design rule checking (DRC) enforces them by running executable DRC scripts on layouts. Translating natural language rules into correct DRC scripts is labor‑intensive and requires specialized expertise, motivating LLM agents for DRC script synthesis and debugging. However, existing benchmarks have small evaluation sets and often evaluate scripts by code similarity rather than execution correctness, and prior machine learning‑based methods either ignore execution feedback or require labeled test layouts as agent's input. To this end, we introduce Rule2DRC, a large‑scale benchmark for DRC script coding agents with 1,000 rule‑to‑script tasks and 13,921 evaluation chip layouts for execution‑based scoring. Rule2DRC provides an evaluation pipeline that measures functional correctness via DRC execution outcomes without requiring evaluation layouts as input to the agent. We also propose SplitTester, a tester agent for program selection that uses execution feedback to generate discriminative test cases and separate previously indistinguishable candidate scripts, substantially improving Best‑of‑N selection performance in this domain. We release the code at https://github.com/snu‑mllab/Rule2DRC.
Authors:Pranav Somu, Advay Balakrishnan, Stepan Kravtsov, Aaron McDaniel, Jason Zutty
Abstract:
Developing effective surrogates (performance predictors) for Neural Architecture Search (NAS) typically requires expensive fine‑tuning or the engineering of complex representations. We propose a low‑cost embedding strategy that leverages the inductive bias of Language Models (LMs) to eliminate these overheads. By representing architectures as PyTorch class definition text, we demonstrate that off‑the‑shelf LMs act as competitive feature extractors without NAS‑specialized fine‑tuning. The final predictor is constructed by passing the extracted Code‑Oriented LM Embeddings (COLE) through a lightweight regression head. We also investigate strategies to improve embedding quality and utilization. Our experiments on the NAS‑Bench‑201 and einspace search spaces reveal that raw code inputs yield higher predictive performance than other text‑based encodings (e.g., ONNX‑to‑text encodings) when using frozen LMs. We also observe COLE drives superior surrogate‑assisted search using the BANANAS algorithm in NAS‑Bench‑201. When optimizing for CIFAR‑100 performance, replacing structural path encodings with COLE for architecture representation allows for a 34% decrease in the evaluation budget required to reach within 1% of the fittest architecture in the search space (by test accuracy). As any neural architecture can be represented as code, these findings establish COLE as a versatile and efficient foundation for advancing NAS.
Authors:Ali Abbasi, Chayne Thrash, Haoran Qin, Hamed Pirsiavash, Soheil Kolouri
Abstract:
Large language models deliver strong performance across language and reasoning tasks, but their storage and compute costs remain major barriers to deployment in resource‑constrained and latency‑sensitive settings. SVD‑based post‑training compression offers a hardware‑agnostic way to reduce model size and improve inference efficiency through low‑rank factorization. However, existing methods often rely on input‑only whitening spaces, homogeneous rank allocation, or loss‑agnostic allocation heuristics, limiting their ability to preserve model quality under aggressive compression. We propose Input‑Output Whitened SVD (IO‑SVD), a post‑training compression method that forms a KL‑aware double‑sided whitening space for model weights. Using a second‑order expansion of the KL loss over the top‑K token probabilities, IO‑SVD constructs an output‑side metric that captures predictive sensitivity, while input whitening captures activation statistics. We further introduce an efficient heterogeneous rank‑allocation strategy that scores whitened singular components using first‑order calibration loss estimates and prunes the least sensitive components under a global budget. Inspired by prior work that combines SVD truncation with quantization, we improve hybrid SVD‑quantization compression through loss‑aware remapping, which selects low‑rank factor rows for 8‑bit quantization based on the predicted loss change incurred by quantizing them. Extensive experiments across diverse LLM and VLM families, and inference‑time analysis shows that IO‑SVD compresses LLMs with minimal performance degradation while delivering practical inference speedups. Code is available at https://github.com/mint‑vu/IO‑SVD.git
Authors:Hojun Chung, Junseo Lee, Songhwai Oh
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/
Authors:Jiale Liu, Jungang Li, Jieming Yu, Xinglin Yu, Zihao Dongfang, Zongjian Ding, Kaifeng Ding, Yi Yang, Lidong Chen, Yang Zou, Shunwen Bai, Jiahuan Zhang, Haoran Huang, Shan Huang, Yudong Gao, Mingjun Cheng
Abstract:
Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a sparse, comparable, and geometry‑consistent panoramic training interface. Dense trajectories duplicate nearby views, source‑specific rendering policies yield heterogeneous annotations, and sparse heuristics may miss important regions or introduce depth‑inconsistent observations. We study how to convert 3D assets into sparse panoramic RGB‑D‑pose data that preserves complete scene coverage with low redundancy and auditable provenance. We propose COVER (Coverage‑Oriented Viewpoint curation with ERP Range‑depth warping), a training‑free ERP viewpoint curator that projects geometry observed from selected views into candidate ERP probes, scores incremental coverage, and penalizes depth conflicts. Under bounded proxy error, its greedy coverage proxy preserves the standard coverage‑style approximation behavior up to an additive error term. Using COVER, we build CM‑EVS (Coverage‑curated Metric ERP View Set), a panoramic RGB‑D‑pose dataset with 36,373 curated ERP frames from 1,275 indoor scenes across Blender indoor, HM3D, and ScanNet++, complemented by outdoor panoramas from TartanGround and OB3D re‑encoded into the same schema. Each frame provides full‑sphere RGB, metric range depth, calibrated pose; COVER‑produced indoor frames include per‑step provenance logs. With a median of only 25 frames per indoor scene, CM‑EVS covers all 13 unified room types while maintaining compact scene‑level coverage. Experiments show that COVER improves the coverage‑conflict trade‑off, making CM‑EVS a sparse, compact, and auditable RGB‑D‑pose resource for geometry‑consistent panoramic 3D learning.
Authors:Minseo Kim, Huanghao Mai, Jay Shenoy, Alec Follmer, Gordon Wetzstein, Frederic Poitevin
Abstract:
Generative models trained on public databases of protein structures, most of which have been determined by X‑ray crystallography, now provide powerful priors for structure prediction. However, they are not readily conditioned on the measurements from a new crystallographic experiment, limiting their use for X‑ray structure determination. In crystallography, the measured structure‑factor amplitudes do not by themselves determine an electron density map or atomic structure because the associated phases are unobserved and must be inferred. Structure determination therefore remains an inverse problem in which candidate models must be both structurally plausible and consistent with measured diffraction data, often requiring substantial manual refinement by human experts. Emerging methods aim to incorporate experimental information more directly into predictive and refinement workflows. We present CrystalBoltz, a generative framework that casts crystallographic refinement as Bayesian inference over atomic structures and operates directly on structure‑factor amplitudes. CrystalBoltz moves from unguided generation with a pre‑trained prior over protein structures to experiment‑guided posterior sampling, followed by atomic coordinate and B‑factor refinement. Across multiple protein crystallography datasets, CrystalBoltz attains lower coordinate RMSD and lower R‑factors than the strongest baselines considered, while reducing runtime by a factor of 33 relative to existing experimentally guided refinement.
Authors:De Shuai Zhang
Abstract:
Continuous diffusion and flow models are attractive for non‑autoregressive text generation because they can update all positions in parallel. A major difficulty is the interface between continuous latent states and discrete tokens. This report studies a draft‑conditioned latent refinement model built from a frozen BERT encoder, a parallel decoder, a denoising DraftPrior, a local FlowNet, and a learned diagonal MetricNet. Early Gaussian‑start experiments showed that good latent‑space metrics, such as scale matching or cosine similarity, do not guarantee good decoding. Generated latents can be close to real encoder latents but still produce high‑entropy, biased, or repetitive token distributions. We therefore frame the task as controlled local refinement rather than full generation from noise. On ROCStories, using the first two sentences as prompt and the last three as target, full 768‑dimensional BERT latents recover tokens much better than compressed 256‑dimensional latents. With 768‑dimensional latents, DraftPrior target‑token probability is 0.938 for clean drafts, 0.613 for 3% token dropout, 0.483 for 5% dropout, and 0.272 for 10% dropout. Local flow refinement and fused decoder‑aware readout give modest additional gains, while metric learning and OT‑style alignment improve geometry but do not close the decoder gap. The main result is a diagnostic one: latent geometry alone is not enough. Continuous latent text generation should be evaluated by decoder recoverability, the quality of the start distribution, and whether refinement preserves decoder‑readable structure.
Authors:Yijun Lu, Zilei Yang, Yuyin Ma
Abstract:
While Isaac Lab provides massive parallel UAV simulation, OmniSafe and safe‑control‑gym provide constrained‑RL benchmarks, and CBFKit provides control‑barrier‑function synthesis tooling, no existing framework unifies these capabilities for end‑to‑end safety‑constrained training. ParallelCBF is the first framework to unify (i)~tensor‑parallel UAV environments, (ii)~hard‑gate CBF safety filters, (iii)~sharded BC‑to‑RL pipelines, and (iv)~first‑class operational auditability ‑‑ pre‑registration, watchdog registries, failure forensics, and dataset audits as composable APIs rather than user‑implemented scripts. We release ParallelCBF v0.1.0 under Apache~2.0 with a four‑layer composable API, a CPU PyTorch reference implementation of a dual‑barrier (squared / linear‑predictive) CBF, property‑based safety invariance tests across vectorized batch sizes that complete in 1.67~s for the full 39‑test suite, and a 31,415‑episode behavior‑cloning collection campaign whose curriculum mix, per‑bucket yields, and dataset SHA‑256 are auditable through the framework's own \textttops primitives. We report a representative end‑to‑end pipeline execution in which the framework's auditability layer halted a downstream training stage that did not meet pre‑registered convergence criteria, preventing silent propagation of a degraded checkpoint ‑‑ an architectural property we argue is necessary, not merely useful, for reproducible empirical robotics research. The framework is installable via \textttpip install parallelcbf; source and release artifacts are available at https://github.com/xiaoyang‑123‑cell/ParallelCBF.
Authors:Shi-ang Qi, Vahid Balazadeh, Michael Cooper, Russell Greiner, Rahul G. Krishnan
Abstract:
Survival analysis provides a powerful statistical framework for modeling time‑to‑event outcomes in the presence of censoring. However, selecting an appropriate estimator from the many specialized survival approaches often requires substantial methodological and domain expertise. We introduce SurvivalPFN, a prior‑data fitted network that amortizes Bayesian inference for censored observations through in‑context learning. SurvivalPFN is pretrained on a diverse family of synthetic, identifiable, and right‑censored data‑generating processes, enabling it to amortize survival analysis in a single forward pass during inference. As a result, the model adapts to the effective complexity of each dataset without task‑specific training or hyperparameter tuning, avoids restrictive parametric assumptions, and produces calibrated survival distributions. In a large‑scale benchmark spanning 61 datasets, 21 methods, and 5 evaluation metrics, SurvivalPFN achieves strong predictive performance and often improves upon established survival models. These results suggest that SurvivalPFN offers a principled and practical foundation model for survival analysis, with potential applications in high‑impact domains such as healthcare, finance, and engineering (https://github.com/rgklab/SurvivalPFN).
Authors:Libo Sun, Po-wei Harn, Peixiong He, Xiao Qin
Abstract:
Mixture‑of‑Experts (MoE) networks promise favorable accuracy‑compute trade‑offs, yet practical vision deployments are hindered by expert collapse and limited end‑to‑end efficiency gains. We study when sparse top‑k routing with hard capacity constraints helps in vision classification, evaluated under multi‑seed protocols on four benchmarks (CIFAR‑10/100, Tiny‑ImageNet, ImageNet‑1K). We observe a \emphcompute‑leverage pattern: positive accuracy gaps require a substantial fraction ρ of total FLOPs to be routed; at ImageNet scale this is necessary but not sufficient, as multi‑expert routing (k \geq 2) is additionally required. Two controlled experiments isolate these factors. A hidden‑size sweep on CIFAR‑10 yields both predicted sign reversals across standard and depthwise backbones, ruling out backbone family as the active variable. An ImageNet‑1K ablation that varies only top‑k ‑‑ holding architecture, initialization, and ρ fixed ‑‑ reverses the gap from positive to negative across all five seeds. A per‑sample variant of Soft MoE that softmaxes over experts rather than the batch rescues CIFAR‑100 above the dense baseline, identifying batch‑axis dispatch as the dominant failure mode in per‑sample CNN settings. Code and aggregate results: https://github.com/libophd/sparse‑moe‑vision‑rho.
Authors:Shangjian Yin, Yu Fu, Yue Dong, Zhouxing Shi
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.
Authors:Arsha Nagrani, Jasper Uijilings, Shyamal Buch, Tobias Weyand, Sudheendra Vijayanarasimhan, Bo Hu, Ramin Mehran, David A Ross, Cordelia Schmid
Abstract:
Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation of the output (e.g. the answer to a question), without evaluation of intermediate reasoning steps, and most provide answers only in the text domain. We introduce Minerva‑Ego, a benchmark for evaluating complex egocentric visual reasoning. We extend recent high‑quality video data sources recorded from egocentric / embodied settings with a set of challenging, multi‑step multimodal questions and spatiotemporally‑dense human‑annotated reasoning traces. Benchmarking experiments show that state‑of‑the‑art models still have a large gap to human performance. To investigate this gap in detail, we annotate each reasoning trace in the dataset with the objects of interest required to solve the question, as spatiotemporal mask annotations. Through extensive evaluations, we identify that prompting frontier models with hints of 'where' and 'when' to look yields substantial improvements in performance. Minerva‑Ego can be downloaded at https://github.com/google‑deepmind/neptune.
Authors:Jiachen Jiang, Huminhao Zhu, Zhihui Zhu
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.
Authors:Gideon Popoola, John Sheppard
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
Authors:Qiang Liu, Felix Koehler, Benjamin Holzschuh, Nils Thuerey
Abstract:
We introduce Tadpole, a novel foundation model for three‑dimensional partial differential equations (PDEs) that addresses key challenges in transferability, scalability to high dimensionality, and multi‑functionality. Tadpole is pre‑trained as an autoencoder on synthetic 3D PDE data generated by an efficient online data‑generation framework. This enables large‑scale, diverse training without storage or I/O overhead, demonstrated by scaling to an equivalent of hundreds of terabytes of training data. By autoencoding single‑channel spatial crops, Tadpole learns rich and transferable representations across heterogeneous physical systems with varying numbers of state variables and spatial resolutions. Although pre‑trained solely as an autoencoder, Tadpole can be efficiently applied for multiple downstream tasks beyond reconstruction, including dynamics learning and generative modeling. For dynamics learning, we propose a novel parameter‑efficient fine‑tuning strategy that integrates low‑rank adaptation, latent‑space transformations, and reintroduced skip connections, achieving accurate temporal modeling with a minimal number of trainable parameters. Tadpole demonstrates strong fine‑tuning performance across various downstream tasks, highlighting its versatility and effectiveness as a foundation model for 3D PDE learning. Source code and pre‑trained weights of Tadpole are available at https://github.com/tum‑pbs/tadpole
Authors:Xujia Chen, Xinyue Hu, Letian Chen, Daming Shi, Wenhui Fan
Abstract:
Physics‑Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are widely applied in fluid mechanics, heat transfer, and solid mechanics. However, PINN training still suffers from high‑dimensional non‑convex loss landscapes, imbalanced multiobjective constraints, and ineffective information propagation. Existing curriculum learning and causality‑guided strategies improve training stability, but mainly focus on temporal or parametric progression, lacking explicit treatment of spatial information propagation and inter‑region consistency. Moreover, they are not directly applicable to boundary value problems (BVPs) with strong spatial coupling. To address this issue, we propose a spatially correlated curriculum learning framework for PINNs. To the best of our knowledge, this is the first work to address PINN training difficulties from the perspective of spatial coupling among subregions. First, spatial causal weights guide information from near‑boundary regions inward, reducing optimization failures and spurious convergence. Second, a low‑frequency information bridge enforces pseudo‑label‑based consistency across spatially separated regions, suppressing global low‑frequency drift. Third, a region‑adaptive reweighting strategy adjusts subregion losses to reduce local residuals and recover high‑frequency details. Experiments on PDE benchmarks show that, under comparable computational cost, the proposed method alleviates training failures and improves solution accuracy. The code is available at https://github.com/pigofmomo/CurriculumLearningPINN.
Authors:Fanxu Meng
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.
Authors:Yi Xie, Siao Liu, Falong Fan, Yuanqi Yao, Yue Zhao, Bo Liu
Abstract:
Multi‑agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single‑model baselines. We identify a structural failure mode in sequential fine‑tuning of shared‑context teams: updating one agent shifts the team's context distribution, and when subsequent updates are evaluated on cached rollouts, this mismatch compounds. We formalize this as the compounding occupancy shift and prove that stale‑occupancy evaluation incurs a penalty that scales quadratically with the number of agents. In contrast, intermediate‑occupancy evaluation reduces this to linear scaling. We propose TeamTR, a trust‑region framework that resamples trajectories after each component update and enforces per‑agent divergence control, yielding rigorous per‑update and per‑stage improvement lower bounds. Experiments show that TeamTR outperforms single‑agent and sequential baselines with 7.1% on average, mitigates coordination regressions, and supports plug‑and‑play component replacement. Code is available at https://github.com/Yydc/TeamTR.
Authors:Dzung Pham, Kleomenis Katevas, Ali Shahin Shamsabadi, Hamed Haddadi
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.
Authors:ML Nissen Gonzalez, Melwina Albuquerque, Laurence Wroe, Jacob Meyer Cohen, Logan Riggs Smith, Thomas Dooms
Abstract:
Mechanistic interpretability aims to break models into meaningful parts; verifying that two such parts implement the same computation is a prerequisite. Existing similarity measures evaluate either empirical behaviour, leaving them blind to out‑of‑distribution mechanisms, or basis‑dependent parameters, meaning they disregard weight‑space symmetries. To address these issues for the class of tensor‑based models, we introduce a weight‑based metric, tensor similarity, that is invariant to such symmetries. This metric captures global functional equivalence and accounts for cross‑layer mechanisms using an efficient recursive algorithm. Empirically, tensor similarity tracks functional training dynamics, such as grokking and backdoor insertion, with higher fidelity than existing metrics. This reduces measuring similarity and verifying faithfulness into a solved algebraic problem rather than one of empirical approximation.
Authors:Chenyu Lian, Hong-Yu Zhou, Jing Qin
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.
Authors:Rafi Al Attrach, Rajna Fani, Sebastian Lobentanzer, Joan Giner-Miguelez, Debanshu Das, Varuni H. K., Nobin Sarwar, Rajat Ghosh, Anwai Archit, Surbhi Motghare, Christina Conrad Parry, Luis Oala, Lara Grosso, Joaquin Vanschoren, Steffen Vogler, Sujata Goswami, Eric S. Rosenthal, Marzyeh Ghassemi, Matthew McDermott, Tom Pollard
Abstract:
Croissant has emerged as the metadata standard for machine learning datasets, providing a structured, JSON‑LD‑based format that makes dataset discovery, automated ingestion, and reproducible analysis machine‑checkable across ML platforms. Adoption has accelerated, and NeurIPS now requires Croissant metadata in every submission to its dataset tracks. Yet in practice Croissant generation usually starts with uploading data to a public platform, a path infeasible for governed and large local repositories that hold much of the high‑value data ML increasingly relies on. We release Croissant Baker, a local‑first, open‑source command‑line tool that generates validated Croissant metadata directly from a dataset directory through a modular handler registry. We evaluate Croissant Baker on over 140 datasets, scaling to MIMIC‑IV at 886 million rows and 374 Parquet files. On held‑out comparisons against producer‑authored or standards‑derived ground truth, Croissant Baker reaches 97‑100% agreement across multiple domains.
Authors:Thomas Witt
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.
Authors:Yan Jiang, Ruihong Qiu, Zi Huang
Abstract:
Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single‑domain scenarios. Recent research has extended graph prompts to improve Graph Foundation Models (GFMs) by few‑shot tuning auxiliary prompts. Despite their progress, most existing methods embed source‑domain information into prompts, which serve either as input to GFMs or encoded during model pre‑training. Such prompt entanglement with specific source domains and GFM pre‑training strategy restricts their generalisability to other domains and different GFMs. Furthermore, existing GFM prompts merely rely on few‑shot tuning for adaptation, neglecting the rich information in unlabelled target domain test data. Motivated by these insights, this paper aims to empower GFMs with pre‑training‑agnostic test‑time graph prompt tuning, named GFMate. GFMate introduces centroid and layer prompts applied after pre‑training on target domains, avoiding entanglement with specific source domains and model pre‑training. In addition, a test‑time complementary learning objective is devised to exploit both labelled and unlabelled target domain data for effective test‑time prompt tuning. Extensive experiments on 12 benchmark datasets demonstrate the superior performance and efficiency of GFMate, achieving improvements of up to 30.63%. Code is available at https://github.com/YanJiangJerry/GFMate.
Authors:William Lugoloobi, Samuelle Marro, Jabez Magomere, Joss Wright, Chris Russell
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.
Authors:Byeongchan Kim, Min-hwan Oh
Abstract:
We propose a model‑free offline multi‑step reinforcement learning (RL) algorithm, Conservative Peng's Q(λ) (CPQL). Our algorithm adapts the Peng's Q(λ) (PQL) operator for conservative value estimation as an alternative to the Bellman operator. To the best of our knowledge, this is the first work in offline RL to theoretically and empirically demonstrate the effectiveness of conservative value estimation with a multi‑step operator by fully leveraging offline trajectories. The fixed point of the PQL operator in offline RL lies closer to the value function of the behavior policy, thereby naturally inducing implicit behavior regularization. CPQL simultaneously mitigates over‑pessimistic value estimation, achieves performance greater than (or equal to) that of the behavior policy, and provides near‑optimal performance guarantees ‑‑ a milestone that previous conservative approaches could not achieve. Extensive numerical experiments on the D4RL benchmark demonstrate that CPQL consistently and significantly outperforms existing offline single‑step baselines. In addition to the contributions of CPQL in offline RL, our proposed method also contributes to the offline‑to‑online learning framework. Using the Q‑function pre‑trained by CPQL in offline settings enables the online PQL agent to avoid the performance drop typically observed at the start of fine‑tuning and to attain robust performance improvements. Our code is available at https://github.com/oh‑lab/CPQL.
Authors:Qirui Liu, Hao Chen, Weijie Shi, Jiajie Xu, Jia Zhu
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.
Authors:Davide Scassola, Andrea Coser, Sebastiano Saccani
Abstract:
Tabular data sharing under privacy constraints is increasingly important for research and collaboration. Synthetic data generators (SDGs) are a promising solution, but synthetic data remains vulnerable to attacks, such as membership inference attacks (MIAs), which aim to determine whether a specific record was part of the training data. State‑of‑the‑art MIAs are powerful but impractical: they rely on shadow modeling, requiring hundreds of SDG training runs, and need auxiliary data several times larger than the original training set. Fast proxy metrics like distance to closest record (DCR) are efficient but have limited sensitivity to MIA risk. We introduce ReMIA (Relative Membership Inference Attack), a practical privacy metric that requires only two SDG training runs and additional data no larger than the original training set. Rather than predicting whether a record was in the training set, ReMIA generates two synthetic datasets from two source datasets and measures whether a classifier can identify which source a record came from. Experiments across multiple tabular datasets and SDGs show that ReMIA has a sensitivity comparable to state‑of‑the‑art MIAs while being substantially more practical. We further observe that SDGs can achieve privacy‑utility trade‑offs that traditional noise‑based anonymization methods do not match. Code is available at https://github.com/aindo‑com/remia.
Authors:Nabil Iqbal, T. Anderson Keller, Yue Song, Takeru Miyato, Max Welling
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.
Authors:Jaemin Seo, Surin Lee, Jae Yong Lee
Abstract:
Deep learning methods based on backward stochastic differential equations (BSDEs) have emerged as competitive alternatives to physics‑informed neural networks (PINNs) for solving high‑dimensional partial differential equations (PDEs). By leveraging probabilistic representations, BSDE approaches can avoid the curse of dimensionality and often admit second‑order‑free training objectives that do not require explicit Hessian evaluations. It has recently been established that the commonly used Euler‑Maruyama (EM) time discretization induces an intrinsic bias in BSDE training losses. While high‑order schemes such as Heun can fully eliminate this bias, such schemes re‑introduce second‑order spatial derivatives and incur substantial computational overhead. In this work, we provide a principled analysis of EM‑induced loss bias and propose an unbiased, second‑order‑free training framework that preserves the computational advantages of BSDE methods. Our code is available at https://github.com/seojaemin22/Un‑EM‑BSDE.
Authors:Sohaib Afifi
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.
Authors:Tianfang Zhu, Ning An, Rui Wang, Jiasi Gao, Qingming Luo, Anan Li, Guyue Zhou
Abstract:
Observing touch on another's body can elicit corresponding tactile sensations in the observer, a phenomenon termed mirror touch that supports empathy and social perception. This visuo‑tactile resonance is thought to rely on structural correspondence between visual and somatosensory cortices, yet robotic systems lack computational frameworks that instantiate this principle. Here we demonstrate that cortical correspondence can be operationalized to endow robots with mirror touch. We introduce Mirror Touch Net, which imposes semantic, distributional and geometric alignment between visual and tactile representations through multi‑level constraints, enabling prediction of millimetre‑scale tactile signals across 1,140 taxels on a robotic hand from RGB images. Manifold analysis reveals that these constraints reshape visual representations into geometry consistent with the tactile manifold, reducing the complexity of cross‑modal mapping. Extending this alignment framework to cross‑domain observations of human hands enables tactile prediction and reflexive responses to observed human touch. Our results link a neural principle of visuo‑tactile resonance to robotic perception, providing an explainable route towards anticipatory touch and empathic human‑robot interaction. Code is available at https://github.com/fun0515/Mirror‑Touch‑Net.
Authors:Ali Hassaan Mughal, Noor Fatima, Muhammad Bilal
Abstract:
Context. Behaviour‑Driven Development (BDD) software test suites accumulate duplicated step subsequences. Three published refactoring patterns are available (within‑file Background, within‑repo reusable‑scenario invocation, cross‑organisational shared higher‑level step), but no prior work automates which recurring subsequences are worth extracting or which mechanism applies. Objective. Rank recurring step subsequences ("slices") by refactoring suitability (extraction‑worthy), pre‑map each to one of the three patterns, and quantify prevalence across the public BDD ecosystem. Method. Every contiguous L‑step window (L in [2, 18]) in a 339‑repository / 276‑upstream‑owner Gherkin corpus is keyed by paraphrase‑robust cluster identifiers and counted under three scopes. Sentence‑BERT (SBERT) / Uniform Manifold Approximation and Projection (UMAP) / Hierarchical Density‑Based Clustering (HDBSCAN) recovers paraphrase‑equivalent slices. Three authors label a stratified 200‑slice pool against a written rubric. An eXtreme Gradient Boosting (XGBoost) extraction‑worthy classifier trained under 5‑fold cross‑validation is compared with a tuned rule baseline and two open‑weight Large Language Model (LLM) judges. Results. The miner produces 5,382,249 slices collapsing to 692,020 recurring patterns. Three‑author Fleiss' kappa = 0.56 (extraction‑worthy) and 0.79 (mechanism). The classifier reaches out‑of‑fold F1 = 0.891 (95% CI [0.852, 0.927]), outperforming both the rule baseline (F1 = 0.836, p = 0.017) and the better LLM judge (F1 = 0.728, p < 1e‑4). 75.0%, 59.5%, and 11.7% of scenarios carry a within‑file Background, within‑repo reusable‑scenario, or cross‑organisational shared‑step candidate. Conclusion. Paraphrase‑robust subscenario discovery yields a corpus‑wide census of BDD refactoring opportunities; pipeline, classifier predictions, labelled pool, and rubric are released under Apache‑2.0.
Authors:Hao Li, Lu Zhang, Liu Chong, Yankai Chen, Pengyang Wang, Yingjie Zhou
Abstract:
Instance normalization (IN) is widely used in non‑stationary multivariate time series forecasting to reduce distribution shifts and highlight common patterns across samples. However, IN can over‑smooth instance‑specific structural information that is essential for modeling temporal and cross‑channel heterogeneity. While prior methods further suppress distribution discrepancies or attempt to recover temporal specific dependencies, they often ignore a central tension: how to adaptively model common and instance‑specific dependency based on each instance's non‑stationary structures. To address this dilemma, we propose SeesawNet, a unified architecture that dynamically balances common and instance‑specific dependency modeling in both temporal and channel dimensions. At its core is Adaptive Stationary‑Nonstationary Attention (ASNA), which captures common dependencies from normalized sequences and specific dependencies from raw sequences, and adaptively fuses them according to instance‑level non‑stationarity. Built upon ASNA, SeesawNet alternates dedicated temporal and channel relationship modeling to jointly capture long‑range and cross‑variable dependencies. Extensive experiments on multiple real‑world benchmarks demonstrate that SeesawNet consistently outperforms state‑of‑the‑art methods.
Authors:Shuqi Gu, Yongxiang Zhao, Baoyu Jing, Kan Ren
Abstract:
Time series forecasting has become increasingly critical in real‑world scenarios, where future sequences are influenced not only by historical patterns but also by forthcoming events. In this context, forecasting must dynamically adapt to complex and stochastic future conditions, which introduces fundamental challenges in both forecasting and evaluation. Traditional methods typically rely on historical data or factual future conditions, while overlooking counterfactual scenarios. Furthermore, many existing approaches are restricted to simple structured conditions, limiting their ability to generalize to the real‑world complexities. To address these gaps, we introduce the task of counterfactual time series forecasting with textual conditions, enabling more flexible and condition‑aware forecasting. We propose a comprehensive evaluation framework that encompasses both factual and counterfactual settings, even in the absence of ground truth time series. Additionally, we present a novel text‑attribution mechanism that distinguishes mutable from immutable factors, thereby improving forecast accuracy under sophisticated and stochastic textual conditions. The project page is at https://seqml.github.io/TADiff/
Authors:Qazi Mamunur Rashid, Xuan Yang, Zhengzhe Yang, Yanzhou Pan, Erin van Liemt, Darlene Neal, Kshitij Pancholi, Jamila Smith-Loud
Abstract:
Recent advancements in generative AI facilitate large‑scale synthetic data generation for model evaluation. However, without targeted approaches, these datasets often lack the sociotechnical nuance required for sensitive domains. We introduce NodeSynth, an evidence‑grounded methodology that generates socially relevant synthetic queries by leveraging a fine‑tuned taxonomy generator (TaG) anchored in real‑world evidence. Evaluated against four mainstream LLMs (e.g., Claude 4.5 Haiku), NodeSynth elicited failure rates up to five times higher than human‑authored benchmarks. Ablation studies confirm that our granular taxonomic expansion significantly drives these failure rates, while independent validation reveals critical deficiencies in prominent guard models (e.g., Llama‑Guard‑3). We open‑source our end‑to‑end research prototype and datasets to enable scalable, high‑stakes model evaluation and targeted safety interventions (https://github.com/google‑research/nodesynth).
Authors:Chen Liang, Xiatao Sun, Qian Wang, Daniel Rakita
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.
Authors:Zhengjia Zhong, Shuyan Ke, Zaizhou Lin, Jiaqi Song, Hongyi Lan, Hui Li
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.
Authors:Jessica Rumbelow
Abstract:
We introduce Exemplar Partitioning (EP), an unsupervised method for constructing interpretable feature dictionaries from large language model activations with ～ 10^3× fewer tokens than comparable sparse autoencoders (SAEs). An EP dictionary is a Voronoi partition of activation space, built by leader‑clustering streamed activations within a distance threshold. Each region is anchored by an observed exemplar that serves as both its membership criterion and intervention direction; dictionary size is not prespecified, but determined by the activation geometry at that threshold. Because exemplars are observed rather than learned, dictionaries built from the same data stream are directly comparable across layers, models, and training checkpoints. We characterise EP as an interpretability object via targeted demonstrations of properties newly accessible through this construction, plus one head‑to‑head benchmark. In Gemma‑2‑2B, EP dictionary regions are interpretable and support causal interventions: refusal in instruction‑tuned Gemma concentrates in a region whose exemplar ablation can collapse held‑out refusal. Cross‑checkpoint matching between base and instruction‑tuned dictionaries separates the directions preserved through finetuning from those introduced by it. EP regions and Gemma Scope SAE features decompose activation space differently but agree on a shared core: ～20% of EP regions match an SAE feature at F_1 > 0.5, and EP one‑hot probes retain ～97% of raw‑activation probe accuracy at \ell_0 = 1. Nearest‑exemplar distance provides a free out‑of‑distribution signal at inference. On AxBench latent concept detection at Gemma‑2‑2B‑it L20, EP at p_1 reaches mean AUROC 0.881, +0.126 over the canonical GemmaScope SAE leaderboard entry and within 0.030 of SAE‑A's 0.911, at ～ 10^3× less build compute.
Authors:Libo Sun, Po-wei Harn, Peixiong He, Xiao Qin
Abstract:
KV‑cache compression at small budgets is a crowded design space spanning cache representation, head‑wise routing, compression cadence, decoding behavior, and within‑budget scoring. We study seven mechanisms across these five families under matched mean cache on long‑form mathematical reasoning (MATH‑500~\citehendrycks2021math) with two distilled‑reasoning models (Qwen‑7B and Llama‑8B variants of DeepSeek‑R1‑Distill~\citedeepseek2025r1) at budgets b \in \64, 128\. All seven were rejected. We then propose α, a one‑function modification to the TriAttention~\citemao2026triattention retention scorer that replaces argmax‑top‑k with greedy facility‑location‑inspired selection under a V‑space redundancy penalty controlled by a single weight λ. A pre‑registered protocol tunes λ on a frozen development split and confirms on a disjoint held‑out split; with λ= 0.5, α clears Bonferroni on two of the four (model, budget) cells (Qwen b=128 and Llama b=64), no cell is significantly negative, and the pre‑registered Branch~A triggers. The finding is asymmetric: a minimal scoring modification beat heavier structural redesigns in this regime, and the combined matched‑memory, sympy‑graded, held‑out confirmation protocol is the evidence standard that made the asymmetry visible.
Authors:Weisen Jiang, Shuhao Chen, Sinno Jialin Pan
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.
Authors:Kai Sun, Peibo Duan, Yongsheng Huang, Guowei Zhang, Benjamin Smith, Nanxu Gong, Levin Kuhlmann
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
Authors:Hanxun Huang, Qizhou Wang, Xingjun Ma, Cihang Xie, Christopher Leckie, Sarah Erfani
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.
Authors:Leo Milecki, Qingyu Hu, Bahram Jafrasteh, Mert R. Sabuncu, Qingyu Zhao
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.
Authors:Sanghyeob Song, Donghyeok Lee, Jinsik Kim, Sungroh Yoon
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
Authors:Darius A. Faroughy, Sofia Palacios Schweitzer, Ian Pang, Siddharth Mishra-Sharma, David Shih
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.
Authors:Jiaqi Liu, Xinyu Ye, Peng Xia, Zeyu Zheng, Cihang Xie, Mingyu Ding, Huaxiu Yao
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.
Authors:Stuart Bladon, Brinnae Bent
Abstract:
Brain encoder models predict cortical fMRI responses from the internal activations of pretrained vision and language networks, and are typically evaluated by held‑out prediction accuracy. This is a useful signal for training but a poor one for interpretation: it tells us an encoder fits the data without telling us whether it has internalized the functional organization of the brain. We propose feature visualization ‑‑ gradient ascent on the encoder's predicted activation for a target region of interest (ROI) ‑‑ as a complementary interpretability technique, and apply it to TRIBE v2 composed with V‑JEPA 2 (ViT‑G, 40 layers), holding both frozen and synthesizing still images for seven regions spanning the ventral and dorsal visual hierarchies. Under identical hyperparameters, the probe recovers a visible progression of increasing spatial scale and feature complexity across V1 to V4, matching the ventral‑stream hierarchy. It also produces three distinctive downstream regimes: radial "frozen‑motion" streaks for the middle temporal area (MT) despite static‑only optimization, face‑like features for the fusiform face area (FFA), and consistent rectilinear line patterns for the parahippocampal place area (PPA). Optimized FFA stimuli drive the predicted region ~4x as much as a natural face photograph, consistent with feature visualization producing adversarial super‑stimuli rather than canonical exemplars. The probe is simple, differentiable, and applicable to any brain encoder with a differentiable backbone, allowing for qualitative evaluation of brain encoders.
Authors:Hassan Keshvarikhojasteh, Josien P. W. Pluim, Mitko Veta
Abstract:
We propose a novel multimodal deep learning framework for patient‑level survival prediction, which integrates whole‑slide histology features, RNA‑seq expression profiles, and clinical variables. Our architecture combines an ABMIL module~\citeilse2018attention for slide‑level representation with feedforward encoders for RNA and clinical data. These embeddings are then integrated through low‑rank bilinear cross‑modal fusion~\citeliu2018efficient to model conditional interactions across modalities while controlling parameter growth. The model outputs continuous risk scores that are subsequently mapped to survival times using a nonparametric calibration procedure based on the Kaplan‑‑Meier estimator~\citekaplan1958nonparametric. By decomposing multimodal reasoning into independent pairwise interactions, the proposed fusion design promotes structural interpretability and parameter efficiency compared with full tensor and hierarchical fusion strategies. Experiments on the CHIMERA challenge dataset demonstrate improved predictive performance over concatenation‑based baselines and competitive generalization on hidden evaluation cohorts. These results indicate that the proposed framework is a promising approach for multimodal survival prediction in HR‑NMIBC. The implementation is publicly available at https://github.com/hassancpu/ChimeraChallenge2025_Task_3.
Authors:Dongxia Liu, Jie Ma, Xiaochen Yang, Jiancheng Zhang, Bin Xia, Zhehan Kan, Nisha Huang, Jun Liang, Wenming Yang, Jin Li
Abstract:
The creation of cinematic‑quality animal effects necessitates the precise modeling of muscle and fur dynamics, a process that remains both labor‑intensive and computationally expensive within traditional production workflows. While generative diffusion models have shown promise in diverse artistic workflows, their capacity for high‑fidelity animal simulation remains largely unexploited. We present MoZoo, a generative dynamics solver that bypasses conventional refinement to synthesize high‑fidelity animal videos from coarse meshes under multimodal guidance. We propose Role‑Aware RoPE (RAR‑RoPE) which employs role‑based index remapping to synchronize motion alignment while decoupling reference information via fixed temporal offsets. Complementing this, Asymmetric Decoupled Attention partitions the latent sequence to enforce a unidirectional information flow, effectively preventing feature interference and improving computational efficiency. To address the scarcity of high‑quality training data, we introduce MoZoo‑Data, a synthetic‑to‑real pipeline that leverages a rendering engine and an inverse mapping approach to construct a large‑scale dataset of paired sequences. Furthermore, we establish MoZooBench, a comprehensive benchmark with 120 mesh‑video pairs. Experimental results demonstrate that MoZoo achieves high‑fidelity fur simulation across diverse animal skeletons and layouts, preserving superior temporal and structural consistency.
Authors:Ido Sobol, Kihyuk Sohn, Yoav Blum, Egor Zakharov, Max Bluvstein, Andrea Vedaldi, Or Litany
Abstract:
We often aim to generate images that are both photorealistic and 3D‑consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine‑tuning an image generator, pre‑trained on billions of real images, using renders of synthetic 3D assets, where annotations for control signals are available. While this approach can learn the desired controls, it often compromises the realism of the images due to domain gap between photographs and renders. We observe that this issue largely arises from the model learning an unintended association between the presence of control signals and the synthetic appearance of the images. To address this, we introduce Realiz3D, a lightweight framework for training diffusion models, that decouples controls and visual domain. The key idea is to explicitly learn visual domain, real or synthetic, separately from other control signals by introducing a co‑variate that, fed into small residual adapters, shifts the domain. Then, the generator can be trained to gain controllability, without fitting to specific visual domain. In this way, the model can be guided to produce realistic images even when controls are applied. We enhance control transferability to the real domain by leveraging insights about roles of different layers and denoising steps in diffusion‑based generators, informing new training and inference strategies that further mitigate the gap. We demonstrate the advantages of Realiz3D in tasks as text‑to‑multiview generation and texturing from 3D inputs, producing outputs that are 3D‑consistent and photorealistic.
Authors:Zijie Wu, Lixin Xu, Puhua Jiang, Sicong Liu, Chunchao Guo, Xiang Bai
Abstract:
Video‑guided 3D animation holds immense potential for content creation, offering intuitive and precise control over dynamic assets. However, practical deployment faces a critical yet frequently overlooked hurdle: the pose misalignment dilemma. In real‑world scenarios, the initial pose of a user‑provided static mesh rarely aligns with the starting frame of a reference video. Naively forcing a mesh to follow a mismatched trajectory inevitably leads to severe geometric distortion or animation failure. To address this, we present Rectified Dynamic Mesh (R‑DMesh), a unified framework designed to generate high‑fidelity 4D meshes that are ``rectified'' to align with video context. Unlike standard motion transfer approaches, our method introduces a novel VAE that explicitly disentangles the input into a conditional base mesh, relative motion trajectories, and a crucial rectification jump offset. This offset is learned to automatically transform the arbitrary pose of the input mesh to match the video's initial state before animation begins. We process these components via a Triflow Attention mechanism, which leverages vertex‑wise geometric features to modulate the three orthogonal flows, ensuring physical consistency and local rigidity during the rectification and animation process. For generation, we employ a Rectified Flow‑based Diffusion Transformer conditioned on pre‑trained video latents, effectively transferring rich spatio‑temporal priors to the 3D domain. To support this task, we construct Video‑RDMesh, a large‑scale dataset of over 500k dynamic mesh sequences specifically curated to simulate pose misalignment. Extensive experiments demonstrate that R‑DMesh not only solves the alignment problem but also enables robust downstream applications, including pose retargeting and holistic 4D generation.
Authors:Jascha Wanger
Abstract:
Modern retrieval‑augmented generation (RAG) systems convert sensitive content into high‑dimensional embeddings and store them in vector databases that treat the resulting numerical artifacts as opaque. Major vector‑store products do not provide native controls for embedding integrity, ingestion‑time distributional anomaly detection, or cryptographic provenance attestation. We show this opens a class of steganographic exfiltration attacks: an attacker with write access to the ingestion pipeline can hide payload data inside embeddings using simple post‑embedding perturbations (noise injection, rotation, scaling, offset, fragmentation, and combinations thereof) while preserving the surface‑level retrieval behavior the RAG system exposes to legitimate users.
We evaluate these techniques across a synthetic‑PII corpus on text‑embedding‑3‑large, four locally hosted open embedding models, a cross‑corpus replication on BEIR NFCorpus and a Quora subset (over 26,000 chunks combined), seven vector‑store configurations, an adaptive‑attacker variant of the detector evaluation, and a paraphrased‑query retrieval benchmark. Distribution‑shifting perturbations are often caught by simple anomaly detectors; small‑angle orthogonal rotation defeats distribution‑based detection across every (model, corpus) pair tested. A disjoint‑Givens rotation encoder gives a closed‑form per‑vector capacity ceiling of floor(d/2) b bits, but real embedding manifolds impose a capacity‑detectability trade‑off, and the retrieval‑preserving operating point sits well below it.
We propose VectorPin, a cryptographic provenance protocol that pins each embedding to its source content and producing model via an Ed25519 signature over a canonical byte representation. Any post‑embedding modification breaks signature verification. Embedding‑level integrity is a deployable, standardizable control that closes this attack class.
Authors:Valentin Six, Frederik Panse, Mathis Fajeau, Lancelot Da Costa, Mridul Sharma, Alfonso Amayuelas, Tim Z. Xiao, David Hyland, Philipp Hennig, Bernhard Schölkopf
Abstract:
Whether navigating a building, operating a robot, or playing a game, an agent that acts effectively in an environment must first learn an internal model of how that environment works. Partially‑observable Markov decision processes (POMDPs) provide a flexible modeling class for such internal world models, but learning them from observation‑action trajectories alone is challenging and typically requires extensive environment interaction. We ask whether language‑model priors can reduce costly interaction by leveraging prior knowledge, and introduce \emphPinductor (POMDP‑inductor): an LLM proposes candidate POMDP models from a few observation‑action trajectories and iteratively refines them to optimize a belief‑based likelihood score. Despite using strictly less information, \emphPinductor matches the performance and sample efficiency of LLM‑based POMDP learning methods that assume privileged access to the hidden state, while significantly surpassing the sample efficiency of tabular POMDP baselines. Further results show that performance scales with LLM capability and degrades gracefully as semantic information about the environment is withheld. Together, these results position language‑model priors as a practical tool for sample‑efficient world‑model learning under partial observability, and a step toward generalist agents in real‑world environments. Code is available at https://github.com/atomresearch/pinductor.
Authors:Haaris Mehmood, Giorgos Tatsis, Dimitrios Alexopoulos, Karthikeyan Saravanan, Jie Xu, Anastasios Drosou, Mete Ozay
Abstract:
Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure‑aggregation schemes protect privacy against an honest‑but‑curious server, but existing approaches often suffer from many communication rounds, heavy public‑key operations, or difficulty handling client dropouts. Recent methods like One‑Shot Private Aggregation (OPA) cut rounds to a single server interaction per FL iteration, yet they impose substantial cryptographic and computational overhead on both server and clients. We propose a new protocol called DisAgg that leverages a small committee of clients called Aggregators to perform the aggregation itself: each client secret‑shares its update vector to Aggregators, which locally compute partial sums and return only aggregated shares for server‑side reconstruction. This design eliminates local masking and expensive homomorphic encryption, reducing endpoint computation while preserving privacy against a curious server and a limited fraction of colluding clients. By leveraging optimal trade‑offs between communication and computation costs, DisAgg processes 100k‑dimensional update vectors from 100k 5G clients with a 4.6x speedup compared to OPA, the previous best protocol.
Authors:Cenwei Zhang, Suncheng Xiang, Lei You
Abstract:
Medical segmentation foundation models such as SAM and MedSAM provide strong prompt‑driven segmentation, but their image encoders are still too large for many clinical settings. Compression is also risky in medicine because a model can keep high Dice while losing boundary fidelity. We propose MedCore, a structured pruning framework for MedSAM. The main idea is to preserve two kinds of structures: structures that became important during SAM‑to‑MedSAM adaptation, and structures that have high boundary leverage. We identify the first type by a dual‑intervention score that compares zeroing a group with resetting it to its original SAM weight. We identify the second type by boundary‑aware Fisher estimation. We also introduce a boundary leverage principle, which shows that compression‑induced boundary displacement is controlled by logit perturbation on the boundary divided by the logit spatial gradient. This principle explains why boundary metrics can degrade even when Dice remains high. On polyp segmentation benchmarks, MedCore reduces parameters by 60.0% and FLOPs by 58.4% while achieving Dice 0.9549, Boundary F1 0.6388, and HD95 5.14 after recovery fine‑tuning. It also reaches 86.6% parameter reduction and 90.4G FLOPs with strong boundary quality. Our analysis further shows that MedSAM lies in a head‑fragile boundary regime: head‑pruning steps have 2.887 times larger 95th‑percentile boundary leverage than MLP‑pruning steps, and this logit‑level effect is consistent with BF1 and HD95 degradation. Our code is available at https://github.com/cenweizhang/MedCore.
Authors:Yatin Dandi, Matteo Vilucchio, Luca Arnaboldi, Hugo Tabanelli, Florent Krzakala
Abstract:
Understanding how deep neural networks learn useful internal representations from data remains a central open problem in the theory of deep learning. We introduce Neural Low‑Degree Filtering (Neural LoFi), a stylized limit of gradient‑based training in which hierarchical feature learning becomes an explicit iterative spectral procedure. In this limit, the dynamics at each layer decouple: given the current representation, the next layer selects directions with maximal accessible low‑degree correlation to the label. This yields a tractable surrogate mechanism for deep learning, together with a natural kernel‑space interpretation. Neural LoFi provides a mathematically explicit framework for studying multi‑layer feature learning beyond the lazy regime. It predicts how representations are selected layer by layer, explains how emergence of concepts arises with given sample complexity,and gives a concrete mechanism by which depth progressively constructs new features from old ones through low‑degree compositionality. We complement the theory with mechanistic experiments on fully connected and convolutional architectures, showing that Neural LoFi improves over lazy random‑feature baselines, recovers meaningful structured filters, and predicts representations aligned with early gradient‑descent feature discovery with real datasets.
Authors:Chengzhi Shen, Weixiang Shen, Tobias Susetzky, Chen, Chen, Jun Li, Yuyuan Liu, Xuepeng Zhang, Zhenyu Gong, Daniel Rueckert, Jiazhen Pan
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/
Authors:Jaeyung Kim, YoungJoon Yoo
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
Authors:Marc Molina Van den Bosch, Riccardo Taiello, Albert Sund Aillet, Andrea Protani, Miguel Angel Gonzalez Ballester, Luigi Serio
Abstract:
Differentially private optimization suffers from a fundamental geometric mismatch: deep networks have highly anisotropic loss landscapes, yet DP‑SGD injects isotropic noise. Second‑order preconditioning can resolve this, but estimating curvature typically requires private data (consuming privacy budget) or public data (introducing distribution shift). We show that the Fisher Information Matrix decouples into architectural sensitivity, recoverable via synthetic noise, and input correlations, approximable from modality‑specific frequency statistics. We propose DP‑KFC, which constructs KFAC preconditioners by probing networks with structured synthetic noise, requiring neither private nor public data. Empirically, DP‑KFC consistently outperforms DP‑SGD and adaptive baselines across diverse modalities in strong privacy regimes (\varepsilon \leq 3). DP‑KFC matches private‑data preconditioners while public‑data variants degrade by up to 4.8%, showing that curvature can be estimated without consuming privacy budget or introducing distribution shift. This enables privacy‑preserving learning in specialized domains (e.g., medical applications) where regulatory constraints make data scarce.
Authors:Barathi Ganesh HB, Michal Ptaszynski, Rene Melendez, Juuso Eronen
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.
Authors:Namhyoung Kim, Jae Wook Song
Abstract:
Predicting cross‑sectional stock returns is challenging due to low signal‑to‑noise ratios and evolving market regimes. Classical factor models offer interpretability but limited flexibility, while deep learning models achieve strong performance yet often underutilize financial priors. We address this gap with PRISM‑VQ (PRior‑Informed Stock Model with Vector Quantization), a dynamic factor framework that integrates expert prior factors, vector‑quantized discrete latent factors learned from cross‑sectional structure, and a structure‑conditioned Mixture‑of‑Experts to generate time‑varying factor loadings. Vector quantization acts as an information bottleneck that suppresses noise while capturing robust market structure, with discrete codes serving both as latent factors and as routing signals for temporal expert specialization. Experiments on CSI 300 and S&P 500 show consistent improvements in cross‑sectional return prediction and portfolio performance over strong baselines while preserving interpretability. Our code is available at https://github.com/finxlab/PRISM‑VQ.
Authors:Lilin Zhang, Yimo Guo, Yue Li, Jiancheng Shi, Xianggen Liu
Abstract:
Deep neural networks are highly vulnerable to adversarial examples, i.e.,small perturbations that can significantly degrade model performance. While adversarial training has become the primary defense strategy, most studies focus on balanced datasets, overlooking the challenges posed by real‑world long‑tail data. Motivated by the fact that perturbations in adversarial examples inherently alter the training distribution, we theoretically investigate their impact. We first revisit adversarial training for long‑tail data and identify two key limitations: (i) a skewed training objective caused by class imbalance, and (ii) unstable evolution of adversarial distributions. Furthermore, we show that perturbations can simultaneously address both adversarial vulnerability and class imbalance. Based on these insights, we propose RobustLT, a plug‑and‑play framework that adaptively adjusts perturbations during adversarial training. Extensive experiments demonstrate that RobustLT consistently enhances adversarial robustness and class‑balance on long‑tailed datasets. The code is available at \hrefhttps://github.com/zhang‑lilin/RobustLThttps://github.com/zhang‑lilin/RobustLT.
Authors:Daniel Matsui Smola
Abstract:
Generative models are often conditioned on a small set of examples via cross‑attention. Under the Gaussian optimal‑transport path, we show that the exact velocity field induced by a finite support set is a Nadaraya‑‑Watson kernel smoother whose bandwidth decreases with flow time, from broad averaging at early steps to nearest‑neighbor at late steps. A single Gaussian‑kernel attention head exactly computes this field, connecting cross‑attention conditioning to classical kernel theory. The theory predicts three failure regimes: nearest‑neighbor collapse of the kernel at high dimension, mismatch between the isotropic kernel and the data geometry, and insufficient support for nonparametric estimation. Experiments on Gaussian mixtures, spherical shells, and DINOv2 ImageNet features confirm that learned conditioning improves in precisely these regimes, and that IP‑Adapter's cross‑attention implements approximate NW smoothing in practice.
Authors:Chaehee Song, Minseok Seo, Yeeun Seong, Doyi Kim, Changick Kim
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/.
Authors:Junhyuk Jeon, Seokhyeon Hong, Junyong Noh
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.
Authors:Amjad Seyedi, Lifang He, Songlin Zhao, Akwum Onwunta, Nicolas Gillis
Abstract:
We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes Symmetric Nonnegative Matrix Tri‑Factorization (SNMTF) from unsupervised single‑graph clustering to supervised prediction over populations of multimodal graphs. SD3MF learns deep hierarchical factorizations for each modality together with a shared latent representation that aligns subjects across views. An encoder‑decoder formulation jointly optimizes graph reconstruction and supervised prediction, while adaptive weights enable data‑driven multimodal fusion. By representing each subject through community‑level interaction matrices, the model yields interpretable and discriminative features. Experiments on multimodal connectome datasets show that SD3MF consistently outperforms strong deep learning baselines such as CNNs and GNNs, while enabling biologically interpretable insights. Code for reproducibility is available at: https://github.com/amjadseyedi/SD3MF.
Authors:Stefan Stojanovic, Alexandre Proutiere
Abstract:
Hierarchical reinforcement learning can improve generalization by decomposing long‑horizon decision‑making into simpler subproblems. However, existing approaches often rely on restrictive design choices, such as fixed temporal abstractions or goal‑conditioned objectives, which largely confine them to goal‑reaching tasks and limit their applicability to general reward functions. In this paper, we introduce switching successor measures, an extension of successor measures that enables hierarchical control in zero‑shot reinforcement learning without additional supervision, fixed horizons, or manually designed subgoals. We show that switching successor measures arise naturally from classical successor measures while preserving their underlying structure. Building on this result, we propose FB π‑Switch, an algorithm that extracts both a high‑level subgoal‑selection policy and a low‑level control policy directly from forward‑backward (FB) representations, allowing hierarchical behavior to emerge from a single learned representation. Experiments on both goal‑conditioned and general reward‑based tasks show that FB π‑Switch improves over non‑hierarchical baselines and matches state‑of‑the‑art hierarchical methods in goal‑conditioned settings. These results demonstrate that structured successor representations provide a flexible foundation for hierarchical zero‑shot reinforcement learning beyond goal‑reaching tasks. Our project website is available at: https://stestokth.github.io/switching‑successors/.
Authors:Mahsa Gazeran, Sayvan Soleymanbaigi, Fatemeh Daneshfar, Amjad Seyedi, Fardin Akhlaghian Tab
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.
Authors:Jiahao Chen, Zihui Zhang, Yafei Yang, Jinxi Li, Shenxing Wei, Zhixuan Sun, Bo Yang
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.
Authors:David Iagaru, Nina M. Gottschling, Anders C. Hansen, Josselin Garnier
Abstract:
Artificial intelligence (AI) has transformed imaging inverse problems, from medical diagnostics to Earth observation. Yet deep neural networks can produce hallucinations, realistic‑looking but incorrect details, undermining their reliability, especially when ground truth data is unavailable. We develop a theoretical framework showing that such hallucinations are not merely artifacts of particular models, but can arise from the ill‑posed nature of the inverse problem itself. We derive necessary and sufficient conditions for hallucinations, together with computable bounds on their magnitude that depend only on the forward model. Building on this theory, we introduce algorithms to: (1) estimate the minimum hallucination magnitude achievable by any reconstruction model for a given input; (2) assess the faithfulness of reconstructed details by a given reconstruction model. Experiments across three imaging tasks demonstrate that our approach applies broadly, including to modern generative models, and provides a principled way to quantify and evaluate AI hallucinations.
Authors:Haoning Wang, Wenchao Yang, Shuai Shen, Yang Li
Abstract:
While EEG foundation models have shown significant potential in universal neural decoding across tasks, their advancement remains constrained by the inadequacy modeling of complex spatiotemporal topology, as well as the inherent modality gap between low‑level physiological signals and high‑level textual semantics. To address these challenges, we propose a Knowledge‑Anchored Semantically‑Dynamic Topology Brain Autoregressive Model (KAST‑BAR), which dynamically aligns physiological representations derived from multi‑level brain topology with an expert‑level semantic space. Specifically, we design a Dual‑Stream Hierarchical Attention (DSHA) encoder that accurately captures the brain's intrinsic non‑Euclidean topology by modeling local temporal dynamics with global spatial contexts. On this basis, a Knowledge‑Anchored Semantic Profiler (KASP) is proposed to synthesize physically‑grounded and instance‑level textual profiles, which subsequently drive a Semantic Text‑Aware Refiner (STAR) to dynamically reconstruct EEG representations using Latent Expert Queries. By conducting large‑scale pre‑training on 21 diverse datasets to build a foundation model, KAST‑BAR effectively integrates expert‑level medical knowledge into EEG signal representations, consistently achieving superior performance across six downstream tasks. Our code is available at https://github.com/KAST‑BAR/KAST‑BAR
Authors:Aaditya L. Kachhadiya
Abstract:
Nonlinear inverse problems often trade inexpensive but fragile first‑order updates against curvature‑aware methods such as Gauss‑Newton and Levenberg‑Marquardt, which obtain stronger directions by repeatedly solving Jacobian‑based linearized systems. We propose a learned alternative: amortize local inverse geometry into a reusable reverse operator. Our framework learns a bidirectional surrogate, Deceptron, and deploys it through D‑IPG (Deceptron Inverse‑Preconditioned Gradient), an iterative solver that pulls residual‑corrected measurement‑space proposals back to latent space. The key mechanism is a Jacobian Composition Penalty (JCP), which trains the reverse Jacobian to act as a local left inverse of the forward Jacobian; its runtime counterpart, RJCP, measures the same inverse‑consistency error along optimization trajectories. We prove that D‑IPG is first‑order equivalent to damped Gauss‑Newton under local pseudoinverse consistency, with deviation controlled by composition error and conditioning. Across seven PDE inverse‑problem benchmarks, D‑IPG outperforms standard baselines, achieves 94.8% mean success across the six‑problem reliability suite, and reaches comparable or better recovery quality at up to 77x lower inference‑time solve cost on the main benchmarks.
Authors:Xu Bai, Bin Lu, Kun Zhang, Shengbo Chen, Xinbing Wang, Chenghu Zhou, Meng Jin
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.
Authors:Guiquan Sun, Xikun Zhang, Jingchao Ni, Dongjin Song
Abstract:
Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task‑based formulation, where the data stream is partitioned into a sequence of discrete tasks with pre‑defined boundaries. However, such assumptions rarely hold in real‑world environments, where data distributions evolve continuously and task identity is often unavailable. To better reflect realistic non‑stationary environments, we revisit continual graph learning from a task‑free perspective. We propose a unified formulation that models the data stream as a time‑varying mixture of latent task distributions, enabling continuous modeling of distribution drift. Based on this formulation, we construct \emphDRIFT, a benchmark that spans a spectrum of transition dynamics ranging from hard task switches to smooth distributional drift through a Gaussian parameterization. We evaluate representative continual learning methods under this task‑free setting and observe substantial performance degradation compared to traditional task‑based protocols. Our findings indicate that many existing approaches implicitly rely on task boundary information and struggle under realistic task‑free graph streams. This work highlights the importance of studying continual graph learning under realistic non‑stationary conditions and provides a benchmark for future research in this direction. Our code is available at https://github.com/UConn‑DSIS/DRIFT.
Authors:John R. Minnick, Jinghui Geng, Kamran Hussain, Jesus Gonzalez-Ferrer, Ash Robbins, Mohammed A. Mostajo-Radji, David Haussler, Jason K. Eshraghian, Mircea Teodorescu
Abstract:
Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single aggregate Pearson correlation r between predicted and actual spike counts, a number that masks critical structure. We argue that how we evaluate spike forecasting matters as much as what we build, and introduce SpikeProphecy, the first large‑scale benchmark for causal, autoregressive spike‑count forecasting on real electrophysiology recordings. Our core contribution is a population metric decomposition that separates aggregate performance into temporal fidelity, spatial pattern accuracy, and magnitude‑invariant alignment. The decomposition surfaces aspects of the underlying data that an aggregate scalar collapses together. We apply the protocol to 105 Neuropixels sessions (Steinmetz 2019 + IBL Repeated Site; ~89,800 neurons) with seven architecture baselines spanning four structural families: four SSMs (three diagonal and one non‑diagonal), a Transformer, an LSTM, and a spiking network. The decomposition surfaces a brain‑region predictability ranking that reproduces across all seven baselines and survives ANCOVA correction for firing‑statistics constraints (region ΔR^2 = 0.018 above the firing‑statistics covariates). It also exposes a sub‑Poisson evaluation floor where rigorous metrics combine with genuine biophysical constraints on regular spike trains, and yields a negative result on KL‑on‑output‑rates distillation for ANN‑to‑SNN transfer in this Poisson count domain.
Authors:Feijiang Li, Zhenxiong Li, Jieting Wang, Zizheng Jiu, Saixiong Liu, Liang Du
Abstract:
Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering process. Recent approaches introduce semantic descriptions as prior information, most of which typically relying on matching‑based techniques with predefined vocabularies. However, the limited matching space restricts their adaptability to downstream clustering tasks. Moreover, these methods primarily focus on reducing bias to improve performance, frequently overlooking the importance of variance reduction. To address these limitations, we propose GSEC (Image Clustering based on Generative Semantic Guidance and Bi‑Layer Ensemble), a framework designed to reduce bias through generative semantic guidance and mitigate variance via ensemble learning. Our method employs Multimodal Large Language Models to generate semantic descriptions and derive image embeddings via weighted averaging. Additionally, a bi‑layer ensemble strategy integrates cross‑modal information through BatchEnsemble in the inner layer and aligns outputs via an alignment mechanism in the outer layer. Comparative experiments demonstrate that GSEC outperforms 18 state‑of‑the‑art methods across six benchmark datasets, while further analysis confirms its effectiveness in simultaneously reducing both bias and variance. The code is available at https://github.com/2017LI/GSEC.git.
Authors:Haodong Wu, Jiahao Zhang, Lijie Hu, Yongqi Zhang
Abstract:
Supervised fine‑tuning (SFT) data selection is commonly formulated as instance ranking: score each example and retain a top‑k subset. However, effective SFT training subsets are often produced through ordered curation recipes, where filtering, mixing, and deduplication operators jointly shape the final data distribution. We formulate this problem as fixed‑pool data recipe search: given a raw instruction pool and a library of grounded operators, the goal is to discover an executable recipe that constructs a high‑quality selected subset under a limited budget of full SFT evaluations, without generating, rewriting, or augmenting training samples. We introduce AutoSelection, a two‑layer solver that decouples fixed‑pool materialization based on cached task‑, data‑, and model‑side signals from expensive full evaluation, using warmup probes, realized subset states, local recipe edits, Gaussian‑process‑assisted ranking, and stagnation‑triggered reseeding. Experiments on a 90K instruction pool show that AutoSelection achieves the strongest in‑distribution reasoning average across three base models, outperforming full‑data training, random recipe search, random top‑k, and single‑operator selectors. Additional Out‑of‑distribution graph‑reasoning results, search‑stability analyses, structural ablations, and 1.5B‑to‑7B transfer checks further show that recipe structure matters beyond individual selection operators. Code is available at https://github.com/w253/AutoSelection.
Authors:Zheng Wang, Yuang Liu, Yangkai Ding
Abstract:
Multi‑agent systems provide a powerful way to extend large language models (LLMs) by decomposing a complex task into specialized subtasks handled by different agents. However, their performance is often hindered by error propagation, arising from suboptimal workflow design or inaccurate agent outputs, which can propagate through the agent collaboration process and degrade final results. To address the challenges, we present MANGO (Multi‑Agent Network Gradient Optimization), a data‑driven framework that organizes and refines agent collaboration via a flow network constructed from past successful workflows. MANGO integrates reinforcement learning and textual gradients to jointly optimize workflow paths and agent behaviors, while a skipping mechanism prevents redundant updates to well‑optimized agents for improving efficiency. Extensive experiments on seven benchmarks show that MANGO achieves up to 12.8% performance improvement over state‑of‑the‑art baselines, enhances efficiency by 47.4%, and generalizes effectively to unseen domains. Our code and datasets are publicly available at https://github.com/openJiuwen‑ai/agent‑store/tree/main/community/mango.
Authors:Blaise Delattre, Hengyu Wu, Paul Caillon, Wei Yang Bryan Lim, Yang Cao
Abstract:
Randomized smoothing provides strong, model‑agnostic robustness certificates, but existing guarantees are limited to single modalities, treating continuous and discrete inputs in isolation. This limitation becomes critical in multimodal models, where decisions depend on cross‑modal semantics and adversaries can jointly perturb heterogeneous inputs, rendering unimodal certificates insufficient. We introduce a unified randomized smoothing framework for mixed discrete‑‑continuous inputs based on an analytically tractable Neyman‑‑Pearson formulation of the joint worst‑case problem. By analyzing the joint likelihood ordering induced by factorized discrete and continuous noise, our approach yields a closed‑form, one‑dimensional certificate that strictly generalizes both Gaussian (image‑only) and discrete (text‑only) randomized smoothing. We validate the framework on multimodal safety filtering, providing, to our knowledge, the first model‑agnostic Neyman‑‑Pearson certificate for joint discrete‑token and continuous‑image perturbations in interaction‑dependent text‑‑image safety filtering.
Authors:Zhongkai Yu, Yichen Lin, Chenyang Zhou, Yuwei Zhang, Kun Zhou, Junxia Cui, Haotian Ye, Zhengding Hu, Zaifeng Pan, Ruiyi Wang, Yujie Zhao, Hejia Zhang, Jingbo Shang, Jishen Zhao, Yufei Ding
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.
Authors:Divya Sitani
Abstract:
Pertussis booster vaccination produces immune responses that vary widely across individuals in both peak magnitude and long‑term durability. These two phases are governed by partly distinct biological compartments:peak reflects acute B‑cell activation and antibody secretion, while durability reflects the establishment of long‑term humoral memory. Yet most computational models target only one, missing the full boost‑and‑wane trajectory. Jointly predicting both is non‑trivial because the two endpoints are biologically dissociated rather than redundant; samples are small, modalities are heterogeneous with structured missingness, and the two tasks rely on different measurement windows. We propose a multi‑task contrastive multimodal fusion architecture combining frozen TabPFN‑v2 per‑modality encoders, a dual‑label supervised contrastive loss that treats two subjects as a positive pair if they agree on the Task 1 label or the Task 2 label, modality dropout calibrated to empirical missingness, and missingness‑masked attention fusion. Applied to a curated subset of the CMI‑PB pertussis booster dataset (n = 158 subjects, four modalities, 44.9% with at least one modality missing; Spearman r = ‑0.58 between peak and durability, n = 96), the model achieves test AUROC 0.797 (95% CI [0.621, 0.948]) for peak response and 0.755 (95% CI [0.519, 0.945]) for durability, with both significant under joint label permutation (N = 1000; p = 0.002 and p = 0.045). Across logistic regression, XGBoost, and MLP baselines on raw features and on TabPFN embeddings, the proposed model is the only one whose 95% CIs lie above chance on both tasks simultaneously. Per‑modality contribution analyses recover task‑specific modality contributions consistent with the underlying immunology: peak prediction is carried by cytokine signatures, while durability is carried by baseline antibody features.
Authors:Kaixiang Zhao, Bolin Shen, Yuyang Dai, Shayok Chakraborty, Yushun Dong
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.
Authors:Alejandro Murillo-Gonzalez, Mahmoud Ali, Lantao Liu
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/
Authors:Jack Young
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.
Authors:Simone Antonelli, Vincent Davis, Harrison Rush, Anthony Potdevin, Jesse Shrader, Vikash Singh, Emanuele Rossi
Abstract:
The Lightning Network (LN) is a second‑layer protocol for Bitcoin designed to enable fast and cost‑efficient off‑chain transactions. Channels in the LN can be closed either by mutual agreement or unilaterally through a forced closure, which locks the involved capital for an extended period and degrades network reliability. In this paper, we study the problem of predicting channel closure types from publicly available gossip data, framing it as a temporal link classification task over the evolving channel graph. We construct a dataset spanning over two years of LN activity and benchmark a range of machine learning approaches, from MLPs to temporal graph neural networks and spectral encodings. Our experiments reveal that the dominant predictive signals are temporal and behavioural, namely how recently each endpoint was active and the per‑node history of past closures, while the surrounding network topology provides no additional benefit. We find that a simple MLP operating on edge‑level features, node‑level event counts, and temporal patterns outperforms all graph‑based approaches, and discuss how the inherent privacy of the LN, where critical information such as channel balances and payment flows remains hidden, fundamentally limits the predictability of closures from gossip data alone. We publicly release the dataset and code at https://github.com/AmbossTech/ln‑channel‑closure‑prediction to encourage further research on this practically relevant task.
Authors:Paul Hoareau, Kuan Yi Wang, Brandon Bujak, Roy Sun, Govind Nair, Irene Cortese, Charidimos Tsagkas, Daniel Reich, Julien Cohen-Adad
Abstract:
INTRODUCTION | Fully supervised 3D segmentation of high‑resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, forcing reliance on sparse 2D slices. Weakly supervised Sparse‑to‑Dense frameworks bridge this gap, but guidelines remain ambiguous regarding human‑centric visual enhancements and transferring optimization strategies across dimensions. We analyze divergent regularization needs for multi‑class segmentation of high‑resolution ex vivo spinal cord MRI.
METHODS | We used 9.4T MRI of multiple sclerosis spinal cords (>104,000 slices) with sparse annotations (428 slices). A 2D Teacher trained on sparse slices generated dense pseudo‑labels to train a 3D Student. We systematically evaluated the impact of human‑centric preprocessing, spatial augmentation, and soft‑label regularization on both architectures.
RESULTS | We identified a critical divergence in training dynamics. The 2D Teacher required strong spatial augmentation and soft‑labeling to overcome data scarcity, improving White Matter Lesion Dice scores by >11 points. However, propagating these techniques to the 3D Student degraded its performance. Furthermore, human‑centric preprocessing (e.g., CLAHE) disrupted global statistical cues, dropping Gray Matter Lesion Dice scores by ~25 points.
DISCUSSION | Our study highlights a perception divergence (human‑centric contrast enhancement harms machine models) and a regularization conflict across dimensions. 3D architectures trained on dense pseudo‑labels exhibit fundamentally different optimization landscapes than 2D counterparts and require distinct, conservative regularization. Code and models: https://github.com/ivadomed/model_seg_sc‑gm‑lesion_human_ms_exvivo_t2star.
Authors:Mohammad Jahid Ibna Basher, Ali Khodabandeh Yalabadi, Ivan Garibay, Ozlem Ozmen Garibay
Abstract:
Template based single step retrosynthesis predicts reactants by selecting and applying an explicit reaction template, making each prediction traceable to a chemical transformation rule. This is useful for synthesis planning, but template based methods are often viewed as less competitive than template free models because template prediction is commonly formulated as global classification over a long tailed rule library. We argue that this weakness is not inherent to templates, but to the learning formulation. We present ConRetroBert, a dual encoder framework that reframes template based retrosynthesis as dense product template retrieval followed by candidate set listwise ranking. Stage 1 uses contrastive pretraining to learn a shared embedding space between products and reaction templates. Stage 2 refines template ranking over mined hard negative candidate sets with a multi positive listwise objective. To enable template side adaptation without destabilizing hard negative mining, ConRetroBert uses a slow moving exponential moving average template encoder for retrieval bank construction while updating the live template encoder through the ranking loss. On the local USPTO‑50k benchmark, Stage 2 candidate set ranking improves top‑1 reaction accuracy from 50.5% to 61.3%, while EMA stabilized template adaptation further improves it to 62.4%. Fine tuning from a leakage controlled USPTO‑Full checkpoint reaches 75.4% top‑1 accuracy on USPTO‑50k. We also show that retrieval based template prediction is strong in the long tail of rare templates, and that many correct reactant predictions arise from alternative explicit templates rather than only the recorded positive label. Code and data are available at https://github.com/JahidBasher/ConRetroBert.
Authors:Zhiming Yu, Wangtao Lu, Xin Lai
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.
Authors:Ahmed Heakl, Youssef Mohamed, Abdullah Sohail, Rania Elbadry, Ahmed Nassar, Peter W. J. Staar, Fahad Shahbaz Khan, Imran Razzak, Salman Khan
Abstract:
Multilingual document understanding remains limited for low‑resource languages due to scarce training data and model‑based annotation pipelines that perpetuate existing biases. We introduce DocAtlas, a framework that constructs high‑fidelity OCR datasets and benchmarks covering 82 languages and 9 evaluation tasks. Our dual pipelines, differential rendering of native DOCX documents and synthetic LaTeX‑based generation for right‑to‑left scripts produce precise structural annotations in a unified DocTag format encoding layout, text, and component types, without learned models for core annotation. Evaluating 16 state‑of‑the‑art models reveals persistent gaps in low‑resource scripts. We show that Direct Preference Optimization (DPO) using rendering‑derived ground truth as positive signal achieves stable multilingual adaptation, improving both in‑domain (+1.9%) and out‑of‑domain (+1.8%) accuracy without measurable base‑language degradation, where supervised fine‑tuning degrades out‑of‑domain performance by up to 21%. Our best variant, DocAtlas‑DeepSeek, improves +1.7% over the strongest baseline. Code is available at https://github.com/ahmedheakl/DocAtlas .
Authors:Mushir Akhtar, M. Tanveer, Mohd. Arshad
Abstract:
Randomized neural networks (RdNNs) enable efficient, backpropagation‑free training by freezing randomly initialized input‑to‑hidden weights, which permits a closed‑form solution for the output layer. However, conventional random initialization is blind to inter‑feature dependence, ignoring correlations, asymmetries, and tail dependence in the data, which degrades conditioning and predictive performance. To the best of our knowledge, this limitation remains unaddressed in the RdNN literature. To close this gap, we propose CAWI (Copula‑Aligned Weight Initialization), a framework that draws input‑to‑hidden weights from a data‑fitted copula that matches empirical dependence, ensuring the frozen projections respect inter‑feature dependence without sacrificing the closed‑form solution. CAWI (i) maps each feature to the unit interval using empirical CDFs, (ii) fits a multivariate copula that captures rank‑based dependence among features, and (iii) samples each weight column w_j from the fitted copula and applies a fixed inverse marginal transform to set scale. The objective, solver, and "freeze‑once" paradigm remain unchanged; only the sampling law for W becomes dependence‑aware. For dependence modeling, we consider two copula families: elliptical (Gaussian, t) and Archimedean (Clayton, Frank, Gumbel). This enables CAWI to handle diverse dependence, including tail dependence. We evaluate CAWI across 83 diverse classification benchmarks (binary and multiclass) and two biomedical datasets, BreaKHis and the Schizophrenia dataset, using standard shallow and deep RdNN architectures. CAWI consistently delivers significant improvements in predictive performance over conventional random initialization. Code is available at: https://github.com/mtanveer1/CAWI
Authors:Runhui Huang, Jie Wu, Rui Yang, Zhe Liu, Hengshuang Zhao
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/
Authors:Alan Z. Song, Yinjie Chen, Mu Nan, Rui Zhang, Jiahang Cao, Weijian Mai, Muquan Yu, Hossein Adeli, Deva Ramanan, Michael J. Tarr, Andrew F. Luo
Abstract:
Vision Transformers (ViTs) achieve strong data‑driven scaling by leveraging all‑to‑all self‑attention. However, this flexibility incurs a computational cost that scales quadratically with image resolution, limiting ViTs in high‑resolution domains. Underlying this approach is the assumption that pairwise token interactions are necessary for learning rich visual‑semantic representations. In this work, we challenge this assumption, demonstrating that effective visual representations can be learned without any direct patch‑to‑patch interaction. We propose VECA (Visual Elastic Core Attention), a vision transformer architecture that uses efficient linear‑time core‑periphery structured attention enabled by a small set of learned cores. In VECA, these cores act as a communication interface: patch tokens exchange information exclusively through the core tokens, which are initialized from scratch and propagated across layers. Because the N image patches only directly interact with a resolution invariant set of C learned "core" embeddings, this yields linear complexity O(N) for predetermined C, which bypasses quadratic scaling. Compared to prior cross‑attention architectures, VECA maintains and iteratively updates the full set of N input tokens, avoiding a small C‑way bottleneck. Combined with nested training along the core axis, our model can elastically trade off compute and accuracy during inference. Across classification and dense tasks, VECA achieves performance competitive with the latest vision foundation models while reducing computational cost. Our results establish elastic core‑periphery attention as a scalable alternative building block for Vision Transformers.
Authors:Ariel Gera, Shir Ashury-Tahan, Gal Bloch, Ohad Eytan, Assaf Toledo
Abstract:
We explore the effectiveness of an LLM‑guided query refinement paradigm for extending the usability of embedding models to challenging zero‑shot search and classification tasks. Our approach refines the embedding representation of a user query using feedback from a generative LLM on a small set of documents, enabling embeddings to adapt in real time to the target task. We conduct extensive experiments with state‑of‑the‑art text embedding models across a diverse set of challenging search and classification benchmarks. Empirical results indicate that LLM‑guided query refinement yields consistent gains across all models and datasets, with relative improvements of up to +25% in literature search, intent detection, key‑point matching, and nuanced query‑instruction following. The refined queries improve ranking quality and induce clearer binary separation across the corpus, enabling the embedding space to better reflect the nuanced, task‑specific constraints of each ad‑hoc user query. Importantly, this expands the range of practical settings in which embedding models can be effectively deployed, making them a compelling alternative when costly LLM pipelines are not viable at corpus‑scale. We release our experimental code for reproducibility, at https://github.com/IBM/task‑aware‑embedding‑refinement.
Authors:Rishabh Tiwari, Kusha Sareen, Lakshya A Agrawal, Joseph E. Gonzalez, Matei Zaharia, Kurt Keutzer, Inderjit S Dhillon, Rishabh Agarwal, Devvrit Khatri
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.
Authors:Seokwon Jung, Alexander Rubinstein, Arnas Uselis, Sangdoo Yun, Seong Joon Oh
Abstract:
LLM‑based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate only single‑entity updates, MEME defines six tasks spanning the full space defined by the multi‑entity and evolving axes, including three not scored by prior work: Cascade and Absence (dependency reasoning) and Deletion (post‑removal state). Evaluating six memory systems spanning three memory paradigms on 100 controlled episodes, we find that all systems collapse on dependency reasoning under the default configuration (Cascade: 3%, Absence: 1% in average accuracy) despite adequate static retrieval performance. Prompt optimization, deeper retrieval, reduced filler noise, and most stronger LLMs fail to close this gap. Only a file‑based agent paired with Claude Opus 4.7 as its internal LLM partially closes the gap, but at ~70x the baseline cost, indicating closure currently depends on configurations that are not practical at scale. Code and data are available on the project page: https://seokwonjung‑jay.github.io/meme‑eval/.
Authors:Guinan Su, Yanwu Yang, Xueyan Li, Jonas Geiping
Abstract:
The continued improvements in language model capability have unlocked their widespread use as drivers of autonomous agents, for example in coding or computer use applications. However, the core of these systems has not changed much since early instruction‑tuned models like ChatGPT. Even advanced AI agents function on message exchange formats, successively exchanging messages with users, systems, with itself (i.e. chain‑of‑thought) and tools in a single stream of computation. This bottleneck to a single stream in chat models leads to a number of limitations: the agent cannot act (generate output) while reading, and in reverse, cannot react to new information while writing. Similarly, the agent cannot act while thinking and cannot think while reading or acting on information.
In this work, we show that models can be unblocked by switching from instruction‑tuning for sequential message formats to instruction‑tuning for multiple, parallel streams of computation, splitting each role into a separate stream. Every forward pass of the language model then simultaneously reads from multiple input streams and generates tokens in multiple output streams, all of which causally depend on earlier timesteps. We argue that this data‑driven change remedies a number of usability limitations as outlined above, improves model efficiency through parallelization, improves model security through better separation of concerns and can further improve model monitorability.
Authors:Rasool Fakoor, Murdock Aubry, Nicholas Stranges, Alexander J. Smola
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.
Authors:Vage Egiazarian, Erik Schultheis, Andrei Panferov, Earl Killian, Torsten Hoefler, Dan Alistarh
Abstract:
A major recent advance in quantization is given by microscaled 4‑bit formats such as NVFP4 and MXFP4, quantizing values into small groups sharing a scale, assuming a fixed floating‑point grid. In this paper, we study the following natural extension: assume that, for each group of values, we are free to select the "better" among two or more 4‑bit grids marked by one or more bits in the scale value. We formalize the power‑of‑two‑grids (PO2) problem, and provide theoretical results showing that practical small‑group formats such as MXFP or NVFP can benefit significantly from PO2 grids, while the advantage vanishes for very large groups. On the practical side, we instantiate several grid families, including 1) PO2(NF4), which pairs the standard NF4 normal grid with a learned grid, 2) MPO2, a grid pair that is fully learned over real weights and activations, 3) PO2(Split87), an explicit‑zero asymmetric grid and 4) SFP4, a TensorCore‑implementable triple which pairs NVFP4 with two shifted variants. Results for post‑training quantization of standard open models and pre‑training of Llama‑like models show that adaptive grids consistently improve accuracy vs single‑grid FP4 under both weight‑only and weight+activation. Source code is available at https://github.com/IST‑DASLab/GridGames.
Authors:Moussa Kassem Sbeyti, Joshua Holstein, Philipp Spitzer, Nadja Klein, Gerhard Satzger
Abstract:
High‑quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI‑assisted annotation has therefore become standard in large‑scale labeling workflows. However, in tasks where model predictions carry two independent components, a class label and spatial boundaries, a model may classify an object with high confidence while mislocalizing it. Existing AI‑assisted workflows offer annotators no signal about where spatial errors are most likely. Without such guidance, humans may systematically underinspect subtly misplaced boxes. We address this by studying the effect of visualizing spatial uncertainty via a purpose‑built interface. In a controlled study with 120 participants, those receiving uncertainty cues achieve higher label quality while being faster overall. A box‑level analysis confirms that the cues redirect annotator effort toward high‑uncertainty predictions and away from well‑localized boxes. These findings establish localization uncertainty as a lever to improve human‑in‑the‑loop annotation. Code is available at https://mos‑ks.github.io/MUHA/.
Authors:Junyu Xiong, Yuan Pu, Jia Tang, Yazhe Niu
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.
Authors:Runhe Lai, Xinhua Lu, Yanqi Wu, Jinlun Ye, Weijiang Yu, Ruixuan Wang
Abstract:
Multimodal large language models (MLLMs) have achieved remarkable progress, yet the object hallucination remains a critical challenge for reliable deployment. In this paper, we present an in‑depth analysis of instruction token embeddings and reveal that they implicitly encode visual information while effectively filtering erroneous information introduced by misleading visual embeddings. Building on this insight, we propose the Instruction Lens Score (InsLen), which combines a Calibrated Local Score with a Context Consistency Score that measures context consistency of the object tokens. The proposed approach serves as a plug‑and‑play object hallucination detector without relying on auxiliary models or additional training. Extensive experiments across multiple benchmarks and diverse MLLM architectures demonstrate that InsLen consistently outperforms existing hallucination detection methods, highlighting its effectiveness and robustness. The code is available at https://github.com/Fraserlairh/Instruction‑Lens‑Score.
Authors:Chengzhu Bao, Xianglong Yan, Zhiteng Li, Guangshuo Qin, Guanghua Yu, Yulun Zhang
Abstract:
NVFP4 has recently emerged as an efficient 4‑bit microscaling format for large language models (LLMs), offering superior numerical fidelity with native hardware support. However, existing methods often yield suboptimal performance due to inflexible scale selection and the coupled treatment of quantization and dequantization scales. To address these issues, we propose Scale Optimization for Accurate Reconstruction (SOAR), a novel post‑training quantization framework that improves the accuracy of NVFP4 quantization. At its core, SOAR features Closed‑form Joint Scale Optimization (CJSO), which jointly optimizes global and block‑wise scales via analytical solutions derived from reconstruction error minimization. Furthermore, it incorporates Decoupled Scale Search (DSS). DSS decouples the high‑precision quantization scale from its constrained dequantization counterpart, and performs discrete search to mitigate precision loss from scale quantization. Extensive experiments across multiple LLMs show that our method consistently outperforms existing NVFP4 quantization baselines, achieving superior accuracy under the same memory footprint with no additional hardware overhead. The code and models will be available at https://github.com/steven‑bao1/SOAR.
Authors:Matthew M. Hong, Jesse Zhang, Anusha Nagabandi, Abhishek Gupta
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/.
Authors:Joshua Opria
Abstract:
We present STRUM (Spectral Transcription and Rhythm Understanding Model), an audio‑to‑chart pipeline that converts raw recordings into playable Clone Hero / YARG charts for drums, guitar, bass, vocals, and keys without any oracle metadata. STRUM is a multi‑stage hybrid: a two‑stage CRNN onset detector and a six‑model ensemble classifier for drums; neural onset detectors with monophonic pitch tracking for guitar and bass; word‑aligned ASR for vocals; and spectral keyboard detection for keys. We evaluate on a 30‑song in‑envelope benchmark constructed by screening candidate songs on a single audio‑quality criterion ‑‑ the median 1‑second drum‑stem RMS after htdemucs_6s source separation. On this benchmark STRUM achieves drums onset F1 = 0.838, bass F1 = 0.694, guitar F1 = 0.651, and vocals F1 = 0.539 at a +/‑ 100 ms tolerance with per‑song global offset search. We report a complete ablation of seven drum‑pipeline components with paired per‑song Wilcoxon tests, an analysis of ground‑truth‑to‑audio timing distributions in community Clone Hero charts, and a per‑class confusion matrix for the drum classifier. Code, model weights, and the full benchmark manifest are released.
Authors:Muhammad Aqeel, Maham Nazir, Uzair Khan, Marco Cristani, Francesco Setti
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
Authors:Xu Chu, Guanyu Wang, Zhijie Tan, Xinrong Chen, Ziyu Li, Tong Mo, Weiping Li
Abstract:
Large Language Models (LLMs) suffer from order bias, where their performance is affected by the arrangement order of input elements. This unfairness limits the model's applications in scenarios such as in‑context learning and Retrieval‑Augmented Generation (RAG). Recent studies attempt to obtain optimal or suboptimal arrangements based on statistical results or using dataset‑based search, but these methods increase inference overhead while leaving the model's inherent order bias unresolved. Other studies mitigate order sensitivity through supervised fine‑tuning using augmented training sets with multiple order variants, but often at the cost of accuracy, trapping the model in consistent yet incorrect hallucinations. In this paper, we propose Dual Group Advantage Optimization (DGAO), which aims to improve model accuracy and order stability simultaneously. DGAO calculates and balances intra‑group relative accuracy advantage and inter‑group relative stability advantage, rewarding the policy model for generating order‑stable and correct outputs while penalizing order‑sensitive or incorrect responses. This marks the first time reinforcement learning has been used to mitigate LLMs' order sensitivity. We also propose two new metrics, Consistency Rate and Overconfidence Rate, to reveal the pseudo‑stability of previous methods and guide more comprehensive evaluation. Extensive experiments demonstrate that DGAO achieves superior order fairness while improving performance on RAG, mathematical reasoning, and classification tasks. Our code is available at: https://github.com/Hyalinesky/DGAO.
Authors:Xin Ma, Wei Chen, Qi Liu, Derong Xu, Zhi Zheng, Tong Xu, Enhong Chen
Abstract:
Lifelong Model Editing aims to continuously update evolving facts in Large Language Models while preserving unrelated knowledge and general capabilities, yet it remains plagued by catastrophic forgetting and model collapse. Empirically, we find that recent editors resilient over long horizons share the same core strategy: Lifelong Normalization (LN), which normalizes value gradients using running statistics. Removing LN causes immediate performance collapse, and we observe a counter‑intuitive positive cumulative effect where early edits can promote the success of future edits. Yet the mechanism of LN remains a "black box", leaving its precise role in lifelong stability poorly understood. In this work, we provide the first theoretical account of LN in the lifelong regime. Our analysis reveals a self‑reinforcing stability loop and proves that, when combined with ridge‑regularized regression, LN yields parameter updates with asymptotic orthogonality and bounded norms, directly mitigating forgetting and systemic collapse. Based on these insights, we derive StableEdit, which strengthens this stability loop via an explicit warm‑up stage and full whitening, improving long‑horizon stability at minimal overhead. Extensive experiments validate our theory and demonstrate competitive performance. Our code is available at https://github.com/MINE‑USTC/StableEdit.
Authors:Thor Klamt, Wolfgang Nejdl, Ming Tang
Abstract:
Machine‑learning predictors of biochemical activity often exhibit large random‑split‑to‑leave‑one‑target‑out generalisation gaps that have been documented but not decomposed. We frame this as an evaluation‑science question and use targeted protein degradation as the empirical test bed. PROTACs (proteolysis‑targeting chimeras) are heterobifunctional small molecules that induce targeted protein degradation, with more than forty candidates currently in clinical trials; published predictors report AUROC of 0.85 to 0.91 under random‑split cross‑validation, while the leave‑one‑target‑out (LOTO) protocol of Ribes et al. reduces performance to approximately 0.67. Random splits reward within‑target interpolation, whereas LOTO measures the novel‑target prediction that de‑novo design depends on. We decompose this gap and identify inter‑laboratory measurement variance as the dominant component, anchored by a within‑target cross‑laboratory cascade bounding the inter‑laboratory contribution at 0.124 AUROC, well above the 0.05 contribution from binarisation‑threshold choice. Across eight published architectures and ESM‑2 protein language models up to 3B parameters, LOTO AUROC plateaus near 0.67, with a comparable plateau under SMILES‑level deduplication; a 21‑dimensional 2000‑trial hyperparameter optimisation cannot break this ceiling, and the rank‑1 single‑seed configuration regresses by 0.161 AUROC under multi‑seed validation, matching a closed‑form selection‑bias prediction (Bailey and Lopez de Prado, 2014). Few‑shot k=5 stratified per‑target retraining combined with ADMET features lifts 65‑target LOTO AUROC from 0.668 to 0.7050, and post‑hoc Platt scaling recovers raw output to within the 0.05 well‑calibrated threshold. We release PROTAC‑Bench (10,748 measurements, 173 targets, 65 LOTO folds), the variance‑decomposition framework, the per‑target calibration protocol, and the evaluation code.
Authors:Yingjie Zhou, Yuqin Xie, Fanxing Liu, Dongjin Song, Ce Zhu, Lingqiao Liu
Abstract:
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabeled samples. A major challenge is to learn a meaningful latent feature representation that reduces intra‑class variance among normal data while remaining highly sensitive to anomalies. Although recent works have applied self‑supervised feature learning for graph anomaly detection, their strategies are not specifically tailored to its unique requirements, motivating our exploration of a more domain‑specific approach. In this paper, we introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy tailored for graph anomalies. Our approach is built upon a multi‑task learning scheme that extracts robust feature representations through synthesized anomalies. We generate synthetic anomalies by perturbing the normal graph in various ways and assign a dedicated detection head to each anomaly type, ensuring that learned features are sensitive to potential deviations from normal patterns. Although synthetic anomalies may not perfectly replicate real‑world patterns, they provide valuable auxiliary data for effective feature learnin, much like features learned from ImageNet classification transfer to downstream vision tasks. Additionally, we adopt a two‑phase learning strategy: an initial warm‑up phase using only synthetic samples, followed by a full‑training phase integrating both tasks, to balance the influence of synthetic and real data. Extensive experiments on public datasets demonstrate the superior performance of our method over its competitors. Code is available at https://github.com/yj‑zhou/SAWGAD.
Authors:SeongMin Jin, Doo Seok Jeong
Abstract:
Learning latent representations that capture both semantic and spatial information is central to efficient spatio‑semantic reasoning. However, many existing approaches rely on implicit latent structures combined with dense feature maps or task‑specific heads, limiting computational efficiency and flexibility. We propose WorldComp2D, a novel lightweight representation learning framework that explicitly structures latent space geometry according to object identity and spatial proximity using multiscale local receptive fields. This framework consists of (i) a proximity‑dependent encoder that maps a given observation into a spatio‑semantic latent space and (ii) a localizer that infers the coordinates of objects in the input from the resulting spatio‑semantic representation. Using facial landmark localization as a proof‑of‑concept, we show that, compared to SoTA lightweight models, WorldComp2D reduces the numbers of parameters and FLOPs by up to 4.0X and 2.2X, respectively, while maintaining real‑time performance on CPU. These results demonstrate that explicitly structured latent spaces provide an efficient and general foundation for spatio‑semantic reasoning. This framework is open‑sourced at https://github.com/JinSeongmin/WorldComp2D.
Authors:Yan Jiang, Ruihong Qiu, Zi Huang
Abstract:
Recently, reinforcement learning (RL) has been widely applied during post‑training for diffusion large language models (dLLMs) to enhance reasoning with block‑wise semi‑autoregressive generation. Block size has therefore become a vital factor in dLLMs, since it determines the parallel decoding granularity and affects the rollout trajectories during RL optimisation, e.g., GRPO. Instead of investigating the effect of block size during inference on individual domains, this paper studies block size from a domain conflict perspective for dLLM RL post‑training in multi‑domain scenarios. The main contributions are: (1) a formulation of domain block size conflict in multi‑domain RL for dLLMs, which will largely affect the post‑training effectiveness for rollout‑based RL methods; (2) a novel dataset, Block‑R1‑41K is constructed with a best‑improved training block size for each sample, which also induces a Block Size Conflict Score to quantitatively measure the domain conflict; (3) a new benchmark, Block‑R1, for flexible RL post‑training for dLLMs in both single and cross domain; and (4) a simple yet powerful cross‑domain post‑training method with sample‑level best‑improved training block sizes. Extensive experiments on 13 distinct datasets, 7 latest RL algorithms and diverse dLLM backbones are comprehensively covered in Block‑R1. The benchmark is open‑sourced at https://github.com/YanJiangJerry/Block‑R1 with the dataset released at https://huggingface.co/datasets/YanJiangJerry/Block‑R1‑41K.
Authors:Jiafei Lyu, Zichuan Lin, Scott Fujimoto, Kai Yang, Yangkun Chen, Saiyong Yang, Zongqing Lu, Deheng Ye
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.
Authors:Liqin Ye, Yanbin Yin, Michael Galarnyk, Yuzhao Heng, Sudheer Chava, Chao Zhang
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.
Authors:Madhurima Panja, Danny D'Agostino, Huitao Li, Tanujit Chakraborty, Nan Liu
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).
Authors:Haoxuan Chen, Tianming Liang, Wei-Shi Zheng, Jian-Fang Hu
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.
Authors:Yupeng Su, Ruijie Zhang, Ziyue Liu, Yequan Zhao, Zheng Zhang
Abstract:
The Muon optimizer has emerged as a compelling alternative to Adam for training large language models, achieving remarkable computational savings through gradient orthogonalization. However, Muon's optimizer state is more sensitive to quantization errors: because the orthogonalization discards the magnitudes of singular values and retains only directional information, even small quantization errors in singular vector directions are amplified in the update. In this work, we propose MuonQ, a low‑bit Muon training framework built on the principle of directional fidelity optimization. First, we apply a pre‑quantization normalization so that each step introduces quantization errors of the same magnitude, preventing the accumulated error from developing a preferred direction. Second, we introduce a structural decomposition that separately quantizes the dominant singular components via power iteration, ensuring that quantization errors perturb only singular value magnitudes rather than rotating singular vector directions. Third, we adopt μ‑law companding quantization to allocate higher resolution to densely packed momentum values, shifting the quantization objective from outlier preservation to dense‑region distinguishability. Together, these techniques enable stable 4‑bit quantization of Muon's optimizer states. Pre‑training experiments on GPT‑style and LLaMA‑style models demonstrate that MuonQ at 4‑bit precision closely matches full‑precision Muon in both training loss and downstream task accuracy, while reducing optimizer state memory by up to 7.3 ×. Our code is available at https://github.com/YupengSu/MuonQ.
Authors:Ajay Vikram Periasami, Junlin Wang, Bhuwan Dhingra
Abstract:
Image‑to‑code generation tests whether a vision‑language model (VLM) can recover the structure of an image enough to express it as executable code. Existing benchmarks either focus on narrow visual domains, depend on paired executable reference code, or rely on generic rubrics that miss domain‑specific reconstruction errors. We introduce Vision2Code, a reference‑code‑free benchmark and evaluation framework for multi‑domain image‑to‑code generation. Vision2Code contains 2,169 test examples from 15 source datasets that span charts and plots, geometry, graphs, scientific imagery, documents, and 3D spatial scenes. Models generate executable programs, which we render and score against the source image using a VLM rater with dataset‑specific rubrics and deterministic guardrails for severe semantic failures. We report render‑success diagnostics that separate code execution failures from reconstruction quality. Human validation shows that this evaluation protocol aligns better with human judgments than either a generic visual rubric or embedding‑similarity baselines. Across nine open‑weight and proprietary models, we find that image‑to‑code performance is domain‑dependent: leading models perform well on regular chart‑ and graph‑like visuals but remain weak on spatial scenes, chemistry, documents, and circuit‑style diagrams. Finally, we show that evaluator‑filtered model outputs can serve as training data to improve image‑to‑code capability, with Qwen3.5‑9B improving from 1.60 to 1.86 on the benchmark without paired source programs. Vision2Code provides a reproducible testbed for measuring, diagnosing, and improving image‑to‑code generation. Our code and data are publicly available at https://image2code.github.io/vision2code/.
Authors:Jung Min Kang
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.
Authors:Elias B. Krey, Nils Neukirch, Nils Strodthoff
Abstract:
Intermediate feature representations represent the backbone for the expressivity and adaptability of deep neural networks. However, their geometric structure remains poorly understood. In this submission, we provide indirect insights into this matter by applying a broad selection of manipulations in input space, ranging from geometric and photometric transformations to local masking and semantic manipulations using generative image editing models, and assess the feasibility of learning a mapping in the feature space, mapping from the original to the manipulated feature map. To this end, we devise different types of mappings, from linear to non‑linear and local to global mappings and assess both the reconstruction quality of the mapping as well as the semantic content of the mapped representations. We demonstrate the feasibility of learning such mappings for all considered transformations. While global (transformer) models that operate on the full feature map often achieve best results, we show that the same can be achieved with a shared linear model operating on a single feature vector typically with very little degradation in reconstruction quality, even for highly non‑trivial semantic manipulations. We analyze the corresponding mappings across different feature layers and characterize them according to dominance of weight vs. bias and the effective rank of the linear transformations. These results provide hints for the hypothesis that the feature space is to a first degree of approximation organized in linear structures. From a broader perspective, the study demonstrates that generative image editing models might open the door to a deeper understanding of the feature space through input manipulation.
Authors:Nengneng Yu, Sixian Xiong, Yibo Zhao, Wei Wang, Zaoxing Liu
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.
Authors:Yadang Alexis Rouzoumka, Jean Pinsolle, Eugénie Terreaux, Christèle Morisseau, Jean-Philippe Ovarlez, Chengfang Ren
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/.
Authors:Taekhyun Park, Yongjae Lee, Dohee Kim, Hyerim Bae
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
Authors:Yonatan Sverdlov, Benjamin Friedman, Snir Hordan, Nadav Dym
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
Authors:Yutszyuk Wong, Wentai Wu, Yuen-Ying Yeung, Weiwei Lin
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.
Authors:Hangzhan Jin, Tianwei Ni, Lu Li, Pierre-Luc Bacon, Mohammad Hamdaqa, Doina Precup
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.
Authors:Keya Hu, Linlu Qiu, Yiyang Lu, Hanhong Zhao, Tianhong Li, Yoon Kim, Jacob Andreas, Kaiming He
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.
Authors:Yaman Kindap, Manfred Opper, Benjamin Dupuis, Umut Simsekli, Tolga Birdal
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.
Authors:Chenyang Song, Weilin Zhao, Xu Han, Chaojun Xiao, Yingfa Chen, Zhiyuan Liu
Abstract:
While Mixture‑of‑Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates significant storage and memory‑access bottlenecks, which hinder efficient end‑side deployment that simultaneously requires high performance, low computational cost, and small storage overhead. To achieve these properties, we present DECO, a sparse MoE architecture designed to match the performance of dense Transformers under identical total parameter budgets and training tokens. DECO utilizes the differentiable and flexible ReLU‑based routing enhanced by learnable expert‑wise scaling, which adaptively balances the contributions of routed and shared experts. Furthermore, we introduce NormSiLU, an activation function that normalizes inputs prior to SiLU operators, producing a more stable trend of routed‑expert activation ratio and a higher intrinsic sparsity level. We also identify an empirical advantage in using non‑gated MLP experts with ReLU‑based routing, indicating the possibility of MoE architecture simplification. Experiments demonstrate that DECO, activating only 20% of routed experts, matches dense performance and outperforms established MoE baselines. Our specialized acceleration kernel delivers a 2.93× speedup on Jetson AGX Orin compared with dense inference. Code and checkpoints are available at https://github.com/thunlp/DECO.
Authors:Junhao Shen, Teng Zhang, Xiaoyan Zhao, Hong Cheng
Abstract:
Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilities beyond what parametric memory alone supports. Existing methods assume external skills either accumulate as persistent guidance or internalized into the policy, eventually leading to zero‑skill inference. We argue this assumption is overly restrictive, since with limited parametric capacity and uneven marginal contribution across skills, the optimal active skill set is non‑monotonic, task‑ and stage‑dependent. In this work, we propose SLIM, a framework of dynamic Skill LIfecycle Management for agentic reinforcement learning (RL), which treats the active external skill set as a dynamic optimization variable jointly updated with policy learning. Specifically, SLIM estimates each active skill's marginal external contribution through leave‑one‑skill‑out validation, then applies three lifecycle operations: retaining high‑value skills, retiring skills whose contribution becomes negligible after sufficient exposure, and expanding the skill bank when persistent failures reveal missing capability coverage. Experiments show that SLIM outperforms the best baselines by an average of 7.1% points across ALFWorld and SearchQA. Results further indicate that policy learning and external skill retention are not mutually exclusive: some skills are absorbed into the policy, while others continue to provide external value, supporting SLIM as a more general paradigm for skill‑based agentic RL.
Authors:Yash Akhauri, Mohamed S. Abdelfattah
Abstract:
Efficient LLM inference research has largely focused on reducing the cost of each decoding step (e.g., using quantization, pruning, or sparse attention), typically applying a uniform computation budget to every generated token. In practice, token difficulty varies widely, so static compression can over‑compute on easy steps and under‑compute on hard ones. We study dynamic budget allocation for autoregressive decoding: learning how much computation to spend per token from within a single model.
Self‑Optimizing Language Models (SOL) pair a frozen LLM with a lightweight policy network that reads the LLM hidden state and selects a discrete efficiency action at each decode step. Actions can jointly control (i) token‑level attention sparsity, (ii) structured activation pruning in the MLP, and (iii) activation quantization bit‑width, while leaving the base model weights unchanged. We train the policy with group‑relative policy optimization on teacher‑forced episodes: the token sequence is fixed, while we sample multiple compute schedules (i.e., "counterfactual" schedules that vary only the efficiency actions for the same token path) and compare their likelihoods under the same supervision. Our reward trades off language‑model quality against soft penalties that encourage episode‑average budget usage to match a requested target. Across model variants and compute regimes, SOL improves quality at matched budget over static allocation and strong random schedule search, offering a complementary axis for inference‑efficiency optimization. SOL discovers a better quality‑efficiency pareto‑front across all our experiments and improves MMLU accuracy by up to 7.3% over uniform budget allocation strategies.
Authors:Wei Chow, Linfeng Li, Xian Sun, Lingdong Kong, Zefeng Li, Qi Xu, Hang Song, Tian Ye, Xian Wang, Jinbin Bai, Shilin Xu, Xiangtai Li, Junting Pan, Shaoteng Liu, Ran Zhou, Tianshu Yang, Songhua Liu
Abstract:
Diffusion models dominate image editing, yet their global denoising mechanism entangles edited regions with surrounding context, causing modifications to propagate into areas that should remain intact. We propose a fundamentally different approach by leveraging Masked Generative Transformers (MGTs), whose localized token‑prediction paradigm naturally confines changes to intended regions. We present EditMGT, an MGT‑based editing framework that is the first of its kind. Our approach employs multi‑layer attention consolidation to aggregate cross‑attention maps into precise edit localization signals, and region‑hold sampling to explicitly prevent token flipping in non‑target areas. To support training, we construct CrispEdit‑2M, a 2M‑sample high‑resolution (>1024) editing dataset spanning seven categories. With only 960M parameters, EditMGT achieves state‑of‑the‑art image similarity on multiple benchmarks while delivering 6x faster editing, demonstrating that MGTs offer a compelling alternative to diffusion‑based editing.
Authors:Yixuan Yang, Mehak Arora, Ryan Zhang, Baraa Abed, Junseob Kim, Tilendra Choudhary, Md Hassanuzzaman, Kevin Zhu, Ayman Ali, Chengkun Yang, Alasdair Edward Gent, Victor Moas, Rishikesan Kamaleswaran
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).
Authors:Juyi Lin, Arash Akbari, Yumei He, Lin Zhao, Haichao Zhang, Arman Akbari, Xingchen Xu, Zoe Y. Lu, Enfu Nan, Hokin Deng, Edmund Yeh, Sarah Ostadabbas, Yun Fu, Jennifer Dy, Pu Zhao, Yanzhi Wang
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/.
Authors:Gabriel Garcia
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.
Authors:Venugopalan Iyengar
Abstract:
Trellis‑coded quantization sets the current 2‑bit post‑training frontier for LLMs (QTIP), but pushing below the PTQ ceiling requires quantization‑aware training, and QAT on a trellis is obstructed by the non‑differentiable Viterbi argmax. We introduce BCJR‑QAT, a relaxation that replaces the argmax with the BCJR forward‑backward sum‑product algorithm at temperature T, producing a soft codeword equal to the Boltzmann expectation over trellis paths, exactly differentiable, recovering the hard QTIP code as T \to 0, and mathematically identical to the transfer‑matrix computation for a 1D Ising‑like spin chain. We contribute (i) a fused Triton kernel making BCJR tractable on a single consumer GPU (6.57× speedup, fp32 parity); (ii) a quantitative drift‑budget theory of when BCJR‑QAT can escape the QTIP‑PTQ Voronoi basin, verified across four experiments; and (iii) a positive empirical result on Llama‑3.2‑1B at 2 bpw under end‑to‑end forward‑KL distillation: with the right schedule (skip the high‑T phase to avoid an overshoot we diagnose), single‑layer BCJR‑QAT beats QTIP‑PTQ by \mathbf‑0.084 PPL on WikiText‑2, and multi‑layer compounding is super‑additive.
Authors:Zanmin Wang
Abstract:
Modern language models scale depth by stacking layers, each holding its own state ‑ a per‑layer KV cache in transformers, a per‑layer matrix in Mamba, Gated DeltaNet (GDN), RWKV, and xLSTM. Biological systems lean heavily on recurrence rather than on stacking. We ask how far that shape can go on language modeling. We propose Grounded Prediction Networks (GPN): one state vector revisited at every step through a single recurrent block ‑ one FFN, one shared matrix memory. At 130M parameters, a 1‑layer GPN+M reaches FineWeb‑Edu perplexity 18.06, within 13% of a 12‑layer Transformer++ (16.05) and 18% of a 10‑layer GDN (15.34); a 2‑layer variant closes the gap to 6%/11%. We do not match the deep baselines. Because the working context is a single vector, we can directly inspect its geometry: a persistent default‑token direction, a content‑bearing horizon of tens of tokens, and memory heads that split spontaneously into fast and slow retention pools.
Authors:Raphael Trumpp, Ömer Veysel Çağatan, Barış Akgün, Marco Caccamo
Abstract:
Pixel‑based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded in principled design. In this work, we show that observation resolution is a critical yet overlooked variable for policy learning: higher‑resolution inputs can substantially improve both performance and generalization, provided the network architecture can process them effectively. We find that the widely used Impala encoder, which flattens spatial features into a vector, suffers from quadratic parameter growth as resolution increases and fails to leverage the additional visual detail. Replacing this operation with global average pooling, as in the Impoola architecture, decouples parameter count from resolution and yields consistent improvements across resolutions and network widths ‑ at their respective best conditions, visual scaling unlocks a 28 % performance gain for Impoola over Impala. These gains are strongest in environments that require precise perception of small or distant objects, and gradient saliency analysis confirms that the underlying mechanism is a more spatially localized visual attention of the policy at higher resolutions. Our results challenge the prevailing practice of aggressive input downsampling and position resolution‑independent architectures as a simple, effective path toward scalable visual deep RL. To facilitate future research on resolution scaling in deep RL, we publicly release the open‑source code for the Procgen‑HD benchmark: https://github.com/raphajaner/procgen‑hd.
Authors:Yao Li, Xikun Zhang, Xiaotao Shen, Sonika Tyagi, Xin Zheng, Jiaxing Huang, Feng Xia
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.
Authors:Xingyu Qu, Peigeng Huang, Samuel Horvath
Abstract:
Muon has emerged as an efficient alternative to Adam for pretraining, yet remains underused for fine‑tuning. A key obstacle is that most open models are pretrained with Adam, and naively switching to Muon for fine‑tuning leads to degraded performance due to an optimizer mismatch. We investigate this mismatch through controlled experiments and relate it to the distinct implicit biases of Adam and Muon. We provide evidence that the mismatch disrupts pretrained knowledge, and that this disruption scales with update strength. This leads us to hypothesize that constraining updates should mitigate the mismatch. We validate this with LoRA: across language and vision tasks, LoRA reduces the performance gap between Adam and Muon observed under full fine‑tuning. Studies on LoRA rank, catastrophic forgetting, and LoRA variants further confirm that mismatch severity correlates with update strength. These results shed light on how optimizer mismatch affects fine‑tuning and how it can be mitigated. Our code is available at https://github.com/XingyuQu/muon‑finetune.
Authors:Chaeyoung Lee, Chaeri Jung, Seonghoon Jeong
Abstract:
Domain Generation Algorithms (DGAs) evolve continuously to evade botnet detection, posing a persistent challenge for dependable network defense. While deep learning‑based detectors achieve strong performance under static conditions, they suffer severe degradation when facing temporal drift. Through a 9‑year longitudinal study (2017‑2025), we empirically show that state‑of‑the‑art character‑ and word‑based DGA classifiers rapidly lose effectiveness as new DGA variants emerge.
To address this problem, we propose a drift‑resilient Transformer‑based framework that learns invariant representations through a hybrid tokenization strategy and multi‑task self‑supervised pre‑training. The model integrates (i) character‑level encoding to capture stochastic morphological patterns and (ii) subword‑level encoding for word‑based DGAs. Three pre‑training tasks enable the model to learn robust structural and contextual features prior to supervised fine‑tuning. Comprehensive evaluations demonstrate that our method significantly mitigates temporal degradation and consistently outperforms state‑of‑the‑art baselines in forward‑chaining experiments. The proposed approach offers a dependable foundation for long‑term DGA defense in evolving threat landscapes. Our code is available at: https://github.com/snsec‑net/2026‑DSN‑DRIFT.
Authors:Yao Lu, Dengdong Fan, Shixun Zhang, Yonghong Tian
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.
Authors:Robin Manhaeve, Stefano Colamonaco, Vincent Derkinderen, Rik Adriaensen, Lucas Van Praet, Luc De Raedt, Giuseppe Marra
Abstract:
DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular paradigm and semantics, DeepLog serves as a universal backend that can emulate many systems in the neurosymbolic alphabet soup. By treating diverse neurosymbolic languages as high‑level specifications, the DeepLog software automatically compiles them into optimized arithmetic circuits. This design lowers the barrier for machine learning practitioners by treating logic as composable modules, while providing neurosymbolic developers with a shared, high‑performance basis for prototyping new integration strategies. The code is available here: https://github.com/ML‑KULeuven/deeplog
Authors:Leon Armbruster, Rathan Ramesh, Georg Kruse, Christopher Straub
Abstract:
jNO (jax Neural Operators) is a JAX‑native library for neural operators and foundation models with unified support for both data‑driven and physics‑informed training. Its core design is a tracing system in which domains, model calls, residuals, supervised losses, and diagnostics are written in one symbolic language and compiled into one optimization pipeline. This allows users to move between operator regression, mesh‑aware residual evaluation, and PDE‑constrained training without restructuring the surrounding code. jNO also supports multi‑model compositions, fine‑grained control at parameter level (model, optimizer, and learning rate), hyperparameter tuning, and JAX‑native workflows for translated PDE foundation‑model families. The source repository is available at https://github.com/FhG‑IISB/jNO.
Authors:Leona Hioki
Abstract:
Complex‑valued Transformers have largely inherited softmax attention from real‑valued architectures. However, row‑normalised token competition is not necessarily aligned with phase‑preserving computation. In this paper, we introduce the Phase‑Coherent Transformer (PCT), which applies a real‑valued, element‑independent, smooth gate to L2‑normalised complex query‑key similarities. PCT replaces token competition with token‑non‑competing attention and is designed to preserve phase information across layers.
Across mid‑scale benchmarks spanning long‑range memory, hierarchical long‑range reasoning, positional retrieval, phase‑based memory and superposition, and image classification, PCT shows strong generalisation across task categories. Under parameter‑fair comparison, PCT consistently outperforms both the standard softmax Transformer and its direct complex‑valued counterpart. Moreover, even on tasks traditionally considered difficult for complex‑valued neural networks, such as NIAH and LRA‑Text, PCT remains competitive with Multiscreen, the strongest real‑valued NN baseline in our comparison. Experiments introducing gates that deliberately violate the PCT conditions show that the design is not incidental: smooth gates that preserve negatively aligned phase components remain strong, whereas gates that delete such components collapse on long‑range retrieval, and gates whose outputs become excessively large suffer clear performance degradation. PCT also shows no depth‑related accuracy collapse across the tested depth range. These results support introducing multi‑layer phase‑coherent structure into attention as a promising design principle for achieving generalisation in complex‑valued Transformers.
Authors:Xiangkai Wang, Yun Zhao, Dongyi He, Qingling Xia, Gen Li, Xinlai Xing, Yuchi Pan, Bin Jiang
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.
Authors:Wilson Wongso, Lihuan Li, Arian Prabowo, Xiachong Lin, Baiyu Chen, Hao Xue, Flora D. Salim
Abstract:
Generating high‑fidelity synthetic GPS trajectories is increasingly important for applications in transportation, urban planning, and what‑if scenario simulation, especially as privacy concerns limit access to real‑world mobility data. Existing trajectory generation models face a trade‑off between efficiency and faithfulness to road network topology: continuous‑space methods enable fast generation but ignore the road network, while topology‑aware approaches rely on search‑based autoregressive decoding that limits generation speed. We propose TrajDLM, a topology‑aware trajectory generation framework based on block diffusion language models that bridges this gap. TrajDLM models trajectories as sequences of discrete road segments, combining a block diffusion backbone for efficient denoising, topology‑aware embeddings from a road network encoder, and topology‑constrained sampling to ensure coherent and realistic trajectories. Across three city‑scale datasets, TrajDLM achieves strong performance on fine‑grained local similarity metrics while being up to 2.8× faster than prior work, and demonstrates strong zero‑shot transfer across domains, including unseen transportation modes. These results highlight the effectiveness of block‑wise discrete diffusion as a scalable approach to accurate and efficient trajectory generation. Our code is available at https://github.com/cruiseresearchgroup/TrajDLM/
Authors:Haokun Liu, Zezhong Ding, Xike Xie
Abstract:
Graph foundation models (GFMs), pretrained on massive graph data, have transformed graph machine learning by supporting general‑purpose reasoning across diverse graph tasks and domains. Existing GFMs pretrained with fixed‑hop subgraph sampling impose a fixed receptive field, causing scale mismatch on diverse tasks, which often require heterogeneous and unknown structural contexts beyond a fixed sampling scale. We propose R‑GFM, a Riemannian Graph‑of‑Graphs (GoG) based foundation model, that treats structural scale as a first‑class citizen in modeling. R‑GFM constructs a multi‑scale GoG over‑sampled subgraphs at different hop distances and learns geometry‑adaptive representations from Riemannian manifolds. Theoretical analysis shows that R‑GFM reduces structural domain generalization error compared to fixed‑scale GFMs. Experiments on various datasets demonstrate that R‑GFM achieves state‑of‑the‑art performance, with up to a 49% relative improvement on downstream tasks. Our code is available at https://github.com/USTC‑DataDarknessLab/R‑GFM.
Authors:Xuexiang Wen, Hang Yu, Linchao Zhu, Gaoang Wang
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.
Authors:Daniel Goldstein, Eugene Cheah
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.
Authors:Zhipeng Liu, Chunbo Luo
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.
Authors:Taylan Soydan, Miguel A. Bessa, Dirk Mohr, Rui Barreira
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.
Authors:Víctor Gallego
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
Authors:Mohammad Amin Basiri, Charles Nicholson
Abstract:
Discovering governing equations from observational data remains a fundamental challenge in scientific modeling, particularly when the underlying mathematical structure is unknown. Traditional sparse identification methods like SINDy excel at discovering parsimonious models but require researchers to specify candidate basis functions a priori, a limitation that often leads to model failure when critical terms are omitted or when systems exhibit unconventional dynamics. Purely symbolic regression approaches offer unlimited flexibility but struggle with noise sensitivity and frequently produce overly complex, unstable equations. We present AutoSINDy, a hybrid Discovery‑then‑Solve framework that combines the exploratory power of symbolic regression with the robust sparsity‑promoting capabilities of SINDy. Our method operates in three stages: (1) PySR‑based symbolic regression discovers candidate functional forms from bootstrapped data chunks; (2) a curation pipeline decomposes, expands, and filters these expressions using collinearity analysis to construct a minimal yet comprehensive library; and (3) SINDy identifies sparse governing equations from this custom‑tailored library. Extensive experiments across canonical nonlinear systems demonstrate that AutoSINDy consistently recovers ground‑truth equations even under high observational noise, achieving a ground‑truth recovery rate of 92.8% across all trials. Compared with standard SINDy using enriched libraries and standalone symbolic regression, AutoSINDy achieves higher predictive accuracy, superior generalization to unseen trajectories, and substantially lower symbolic complexity.
Authors:Zahra Asadi, Haeseung Jeon, Sohyun Han, Md Mahmuduzzaman Kamol, Se Eun Oh, Mohammad Saidur Rahman
Abstract:
As over 200 million new malware samples are identified each year, antivirus systems must continuously adapt to the evolving threat landscape. However, retraining solely on new samples leads to catastrophic forgetting and exploitable blind spots, while retraining on the entire dataset incurs substantial computational cost. We propose FreeMOCA, a memory‑ and compute‑efficient continual learning framework for malicious code analysis that preserves prior knowledge via adaptive layer‑wise interpolation between consecutive task updates, leveraging the fact that warm‑started task optima are connected by low‑loss paths in parameter space.
We evaluate FreeMOCA in both class‑incremental (Class‑IL) and domain‑incremental (Domain‑IL) settings on large‑scale Windows (EMBER) and Android (AZ) malware benchmarks. FreeMOCA achieves substantial gains in Class‑IL, outperforming 11 baselines on both EMBER and AZ benchmarks. It also significantly reduces forgetting, achieving the best retention across baselines, and improving accuracy by up to 42% and 37% on EMBER and AZ, respectively. These results demonstrate that warm‑started interpolation in parameter space provides a scalable and effective alternative to replay for continual malware detection.
Code is available at: https://github.com/IQSeC‑Lab/FreeMOCA.
Authors:Sze-Ann Chen, Zhi-Yi Chin, Kui-Yuan Chen, Chi-Yu Li, Ping-Chun Hsieh
Abstract:
Ensuring the security of reinforcement learning (RL) models is critical, particularly when they are trained by third parties and deployed in real‑world systems. Attackers can implant backdoors into these models, causing them to behave normally under typical conditions, but execute malicious behaviors when specific triggers are activated. In this work, we propose Plan2Cleanse, a test‑time detection and mitigation framework that adapts Monte Carlo Tree Search to efficiently identify and neutralize RL backdoor attacks without requiring model retraining. Our approach recasts backdoor detection as a planning problem, enabling systematic exploration of temporally extended trigger sequences while maintaining black‑box access to the target policy. By leveraging the detection results, Plan2Cleanse can further achieve efficient mitigation through tree‑search preventive replanning. We evaluated our method in competitive MuJoCo environments, simulated O‑RAN wireless networks, and Atari games. Plan2Cleanse achieves substantial improvements, increasing trigger detection success rates by more than 61.4 percentage points in stealthy O‑RAN scenarios and improving win rates from 35% to 53% in competitive Humanoid environments. These results demonstrate the effectiveness of our test‑time defense approach and highlight the importance of proactive defenses against backdoor threats in RL deployments. Our implementation is publicly available at https://github.com/rl‑bandits‑lab/RL‑Backdoor.
Authors:Wiktor Matykiewicz, Piotr Wzorek, Kamil Jeziorek, Tomás Muñoz, Antonio Rios-Navarro, Angel Jiménez-Fernández, Tomasz Kryjak
Abstract:
With the rapid growth of mobile robotics and embedded intelligence, there is an increasing demand for efficient on‑device data processing on edge platforms. A promising research direction is the use of neuromorphic sensors inspired by human sensory systems, which generate sparse, event‑based data encoding changes in the environment. In this work, we present the first end‑to‑end FPGA implementation of a keyword spotting system that integrates a Neuromorphic Auditory Sensor (NAS) and a graph neural network (GNN) on a single FPGA device, enabling real‑time processing of raw audio data. The proposed architecture eliminates conventional signal preprocessing and operates directly on event‑based audio streams. Leveraging a compute‑near‑memory network architecture, the system achieves efficient inference with low latency and low power consumption. Experimental results demonstrate an accuracy of 87.43% after quantization on the Google Speech Commands v2 dataset processed through the neuromorphic sensor, with end‑to‑end latency below 35 us and average power consumption of 1.12 W. The processed datasets, software models, and hardware modules are available at https://github.com/vision‑agh/NAS‑GNN‑KWS.
Authors:Lennon J. Shikhman
Abstract:
Neural operators provide fast surrogate models for time‑dependent partial differential equations, but their standard autoregressive use usually assumes that the instantaneous field u(t,\cdot) is a complete state. This assumption fails for delay equations, distributed‑memory systems, and other non‑Markovian dynamics: two trajectories may agree at time t and nevertheless have different futures because their histories differ. We introduce the History‑Space Fourier Neural Operator (HS‑FNO), a neural operator for delay and memory‑driven PDEs formulated on the lifted state u_t(θ,x)=u(t+θ,x), θ\in[‑τ,0]. The key computational step is to decompose one history‑state update into a learned predictor for the newly exposed future slice and an exact shift‑append transport for the portion of the history window already known from the previous state. This avoids learning deterministic history coordinates, reduces the learned output dimension, and enforces the natural discrete history update. We test HS‑FNO on five benchmark families covering delayed reaction‑‑diffusion, spatial epidemiology, nonlocal neural‑field dynamics, delayed waves, and distributed‑memory closures. Across ten random seeds, HS‑FNO attains the lowest aggregate one‑step, history‑space, and rollout errors among the principal baselines. The largest gain occurs in autoregressive prediction, where aggregate rollout error decreases from 0.241, 0.188, and 0.185 for current‑state, lag‑stack, and unconstrained history‑to‑history operators, respectively, to 0.094. The same model uses fewer parameters than unconstrained history prediction. These results indicate that enforcing the discrete shift structure of history‑state evolution is an effective inductive bias for non‑Markovian PDE surrogate modeling.
Authors:Bariscan Bozkurt, Alexandre Galashov, Dimitri Meunier, Zikai Shen, Arthur Gretton, Houssam Zenati
Abstract:
Unobserved confounding prevents standard covariate adjustment from identifying causal response functions in observational studies. Proxy causal learning addresses this problem through bridge equations involving treatment‑ and outcome‑inducing proxies, avoiding direct recovery of the latent confounder. Existing doubly robust proxy estimators combine outcome and treatment bridges, but typically rely on fixed kernels, sieves, or low‑dimensional semiparametric models; existing neural proxy methods are more flexible, but are largely single‑bridge estimators. We develop a neural doubly robust framework for proxy causal learning with continuous and structured treatments. Our method introduces a neural mean‑embedding estimator for the treatment bridge, combines it with a neural outcome bridge, and estimates the doubly robust correction through a final regression stage. The framework covers population, heterogeneous, and conditional dose‑response functions, yielding full response‑curve estimators rather than binary‑treatment effects. The algorithms use two stages for each bridge and history‑aware updates of the final linear layers to stabilize stochastic multi‑stage training. We prove consistency of the algorithms showing that the doubly robust error is controlled by the final averaging and regression errors together with the smaller of the outcome‑ and treatment‑side weak‑norm bridge errors. Across synthetic and image‑valued benchmarks, the proposed estimators outperform existing baselines and single‑bridge neural estimators, showing the benefit of combining learned outcome and treatment bridges in a doubly robust construction. Our implementation is available at https://github.com/BariscanBozkurt/DRPCL‑Neural‑Mean‑Embedding.
Authors:Dong Yang, Yiyi Cai, Haoyu Zhang, Yuki Saito, Hiroshi Saruwatari
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
Authors:Junyu Lu, Shashwath Suresh, Hao Liu, Qi Hong, Qing Wang
Abstract:
Running deep neural networks on microcontroller units (MCUs) is severely constrained by limited memory resources. While TinyML techniques reduce model size and computation, they often fail in practice due to excessive peak Random Access Memory (RAM) usage during inference, dominated by intermediate activations. As a result, many models remain infeasible on standalone MCUs. In this work, we present a fine‑grained split inference system for networked MCUs that enables collaborative inference of Convolutional Neural Networks (CNN) models across multiple devices. Our key insight is that breaking the memory bottleneck requires splitting inference at sub‑layer granularity rather than at layer boundaries. We reinterpret pre‑trained models to enable kernel‑wise and neuron‑wise partitioning, and distribute both model parameters and intermediate activations across multiple MCUs. A lightweight, resource‑aware coordinator orchestrates the inference across MCU devices with heterogeneous resources. We implement the proposed system on a real testbed and evaluate it on up to 8 MCUs using MobileNetV2, a representative CNN model. Our experimental results show that CNN models infeasible on a single MCU can be executed across networked MCUs, reducing the per‑MCU peak RAM usage while maintaining the practical end‑to‑end inference latency. All the source code of this work can be found here: https://github.com/shashsuresh/split‑inference‑on‑MCUs.
Authors:Jiyeon Kim, Byungju Lee, Won-Yong Shin
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.
Authors:Boxuan Zhang, Jianing Zhu, Qifan Wang, Jiang Liu, Ruixiang Tang
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/
Authors:Muhammed Ustaomeroglu, Guannan Qu
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.
Authors:Jiyeon Kim, Youngjoon Hong, Won-Yong Shin
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.
Authors:Kai Zhao, Dongliang Nie, Yuchen Lin, Zhehan Luo, Yixiao Gu, Deng-Ping Fan, Dan Zeng
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.
Authors:Sohan Venkatesh
Abstract:
Large language models fail at counting repeated tokens despite strong performance on broader reasoning benchmarks. These failures are commonly attributed to limitations in internal count tracking. We show this attribution is wrong. Linear probes on the residual stream decode the correct count with near‑perfect accuracy at every post‑embedding layer, across all model depths. This holds even at the exact layers where the wrong answer crystallizes while the model simultaneously outputs an incorrect count. Attention patterns show no evidence of collapse over repeated tokens and tokenization artifacts account for none of the failure. Instead, a format‑triggered multi‑layer perceptron (MLP) block overwrites the correctly‑encoded count with a fixed wrong answer at roughly 88‑‑93,% network depth. This prior fires for repeated word‑tokens in space‑separated list format and is absent for repeated digit‑tokens. It is suppressed by comma‑separated delimiters in larger models but persists in smaller ones. The finding holds across Llama‑3.2 (1B and 3B) and Qwen2.5 (1.5B, 3B and 7B) at consistent relative depth. Counting failure is a failure of routing not of representation and the two require different interventions.
Authors:Yibang Li, Bihari Lal Pandey, Ravi Sah, Andi Han, Cyrus Mostajeran, Pratik Jawanpuria, Bamdev Mishra
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.
Authors:Yang Zhou, Can Jin, Zihan Dong, Zhepeng Wang, Yanting Yang, Shiyu Zhao, Lei Li, Runxue Bao, Yaochen Xie, Dimitris N. Metaxas
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.
Authors:Ankit Hemant Lade, Sai Krishna Jasti, Indar Kumar, Aman Chadha
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.
Authors:Chuning Li, Chris J. Maddison
Abstract:
We introduce a predictive model that estimates the pre‑training loss of large models from model size (N), batch size (B) and number of weight updates (K). This is the first loss prediction model that can handle changing batch size. The model outperforms Chinchilla's loss model, a model of the test loss using the batch size and number of tokens, in terms of projecting the loss at extrapolated compute budgets (up to 1000 folds). A natural use of the model is to find optimal N, B, K configurations under explicit and compound resource constraints like time, memory and compute. In our experiments, the model‑selected configurations are close to ground‑truth optimal. Our work advocates for loss prediction as a better alternative to heuristic‑based laws, which are growing in complexity. The implementation is available on https://github.com/chuningxdy/Noisy‑Quadratic‑System.
Authors:Enrique Hernández Noguera, Md Meftahul Ferdaus, Elias Ioup, Mahdi Abdelguerfi, Julian Simeonov
Abstract:
Neural operators for time‑dependent PDEs face a structural tension: spectral architectures (FNO and descendants) inherit exponential rollout‑error growth from their one‑step Lipschitz constant, while hierarchical U‑Net operators trade resolution invariance for multi‑scale detail. We introduce SpectraNet, an autoregressive neural operator that composes truncated spectral convolutions inside a U‑Net hierarchy with a Residual‑Target Spectral Block trained under a Semigroup‑Consistency Loss. The residual‑target parametrization replaces L^T stability blow‑up with linear Tdelta drift, and the spectral path's parameter count is Theta(L w^2 M^2), independent of grid N. Under a single unified protocol against 16 published neural‑operator baselines on Navier‑Stokes nu=1e‑5 at 64x64, SpectraNet reaches test relative L2 = 0.0822 at 2.04M parameters ‑‑ 2.33x fewer than canonical FNO at ~20% lower error ‑‑ and wins five of six rows in a cross‑PDE comparison against FNO (NS at nu in 1e‑4, 1e‑3, PDEBench Shallow‑Water 2D and Diffusion‑Reaction, with the Active‑Matter row going to FNO inside its seed spread). Trained from scratch at native 128^2 under the same protocol, SpectraNet improves to 0.0724 while FNO regresses to 0.3080. Free rollout stays bounded for T=100 where FNO diverges across all 200 test trajectories. On consumer CPU at B=1, SpectraNet runs sub‑200ms while the full‑attention Transformer that wins raw L2 pays ~60x latency; we do not claim to beat that Transformer on raw L2, only to dominate the lightweight (<=5M parameter, sub‑200ms CPU) Pareto frontier. Source code: https://github.com/Enrikkk/spectranet
Authors:Runyao Yu, Julia Lin, Derek W. Bunn, Jochen Stiasny, Wentao Wang, Yujie Chen, Tara Esterl, Peter Palensky, Jochen L. Cremer
Abstract:
Accurate and efficient imbalance electricity price forecasting is critical for industrial energy trading systems, especially as battery assets and automated bidding pipelines increasingly participate in balancing markets. However, real‑time forecasting is complicated by nonlinear market‑rule‑based price formation, heterogeneous input signals, and incomplete data availability caused by communication delays, publication lags, and measurement outages. This paper proposes a market‑rule‑informed neural forecasting framework that embeds imbalance price formation rules into the latent space of an expressive neural network. The proposed framework preserves raw signal information while exploiting transparent market‑rule priors. We further analyze operational robustness by removing price‑component information and characterize how forecasting performance scales with input length and forecasting horizon. Experimental results show that the proposed model achieves competitive forecasting performance with substantially fewer trainable parameters and shorter training time than generic deep learning baselines. Experimental results show that the proposed model achieves competitive forecasting performance with substantially fewer trainable parameters and shorter training time than generic deep learning baselines, demonstrating that market‑rule priors and expressive neural networks should be jointly used for accurate and computationally sustainable forecasting in industrial energy trading applications. The implementation is publicly available at https://runyao‑yu.github.io/MRINN/.
Authors:Jiahe Chen, Ziye Ma
Abstract:
Zeroth‑order (ZO) optimization has become increasingly popular and important in fine‑tuning large language models (LLMs), especially on edge devices due to its ability to adjust the model to local data without the need for memory‑intensive back‑propagation. Recent works try to reduce ZO variance through low‑dimensional subspace search, but subspace restriction alone leaves key optimization geometry under‑exploited, motivating additional acceleration. In this work, we focus on the hidden layer training problem in which spectral optimizers like Muon outperform AdamW due to its ability to exploit weak spectral directions by orthogonalization. However, we have discovered that unlike in the first‑order setting, full orthogonalization works poorly in the ZO setting since the gradient estimates are highly noisy and unreliable. To address this issue, we propose applying partial spectral orthogonalization to accelerate ZO optimization. To do so, we replace the iconic Newton‑Schulz procedure in Muon with the faster, more concentrated power‑iteration method so that it only amplifies dominant spectral directions. Furthermore, to improve the efficiency and generalization of the algorithm, we adopted a streaming variant of power‑iteration that requires low variance in gradients, which was achieved through constraining our search inside a subspace obtained through the projection of momentum, echoing recent advances. Experiments on LLM fine‑tuning show that our method can achieve from 1.5x to 4x the convergence speed of ZO‑Muon, the current SOTA algorithm, across SuperGlue datasets in the OPT‑13B model. Across different models, we also reach competitive final accuracies with less time in most cases compared with strong ZO baselines such as MeZO, LOZO and ZO‑Muon. Code is available at https://github.com/MOFA‑LAB/ZO‑MOPI.git.
Authors:Dongcheng Zhang, Yi Zhang, Yuxin Chen, An Zhang, Xiang Wang, Chaochao Lu
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.
Authors:Xinyu Li, Ronghui Mu, Lin Li, Tianjin Huang, Gaojie Jin
Abstract:
Large Language Models (LLMs) are increasingly deployed as autonomous agents that execute tool‑augmented, multi‑step tasks, where latency is a critical factor for real‑world applications. Yet an overlooked threat is Reasoning‑Level Denial‑of‑Service (R‑DoS), in which an attacker preserves task correctness but degrades availability by inflating an agent's reasoning depth or tool‑use budget. We introduce OTora, the first unified, two‑stage red‑teaming framework for instantiating R‑DoS attacks. Stage I optimizes an adversarial trigger that induces targeted tool invocations using insertion‑aware scoring and dynamic target co‑evolution, supporting both black‑box and white‑box settings. Stage II generates agent‑aware reasoning payloads via an ICL‑guided genetic search that amplifies overthinking while maintaining correct task outcomes. Across WebShop, Email, and OS agents built on multiple backbone models such as LLaMA‑70B and GPT‑OSS‑120B, OTora achieves up to 10 times increases in reasoning tokens and order‑of‑magnitude latency slowdowns, all while preserving near‑baseline task accuracy. Finally, we discuss mitigation strategies for detecting and constraining abnormal reasoning and latency spikes. The code is available at https://github.com/llm2409/OTora.
Authors:Shota Fujikawa, Issei Sato
Abstract:
Hallucination detection has become increasingly important for improving the reliability of large language models (LLMs). Recently, hybrid approaches such as HaMI, which combine semantic consistency with internal model states via Multiple Instance Learning (MIL), have achieved state‑of‑the‑art performance. However, these methods incur substantial computational overhead due to repeated sampling and costly semantic similarity computations. In this work, we first provide a theoretical analysis of HaMI in terms of decision margins, revealing that scaling internal states with semantic consistency leads to an enlarged decision margin. Motivated by this insight, we revisit classical sentence classification models from a margin enlargement perspective, aggregating token‑level features via max pooling and directly estimating sentence scores using a lightweight MLP. Without requiring semantic consistency computations, our approach achieves substantial efficiency improvements while maintaining competitive performance with state‑of‑the‑art baselines through adaptive aggregation of internal feature representations. Code is available at https://github.com/FUJI1229/Hallucination_Detection.
Authors:Yulang Chen, Haoxuan Peng, Jinyan Liu, Zichen Wen, Dongrui Liu, Linfeng Zhang
Abstract:
Large Language Model‑based Multi‑Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks. However, manually designing optimal communication topologies is labor‑intensive, while automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption. To address this problem, we introduce AgentSlimming, a plug‑and‑play compression framework for graph‑structured multi‑agent workflows. Motivated by pruning and quantization in neural networks, AgentSlimming compresses workflows by first estimating the importance score of each agent with a hybrid mechanism, and then removes redundant agents or replaces them with low‑cost ones, where each operation is validated using a baseline‑anchored acceptance rule to prevent performance collapse. Experiments show that AgentSlimming reduces average token cost by up to 78.9% with negligible performance degradation, and sometimes even improves accuracy, achieving a strong Pareto‑optimal trade‑off between cost and quality. Our code is publicly available at https://github.com/CitrusYL/AgentSlimming
Authors:Xingyu Song, Yuan Mei, Naoya Takeishi
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.
Authors:Renjie Gu, Jiazhen Du, Yihua Zhang, Sijia Liu
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.
Authors:Vladimir Iglovikov, Dmitry Kosarevsky
Abstract:
JPEG decode is routine ML infrastructure, but Python decoder choices are often justified by single‑process, single‑thread microbenchmarks. We audit this evaluation assumption with thirteen Python‑accessible JPEG decode paths on five matched 16 vCPU Google Cloud CPUs: Intel Emerald Rapids, AMD Zen 4, AMD Zen 5, ARM Neoverse V2, and ARM Neoverse N1. ImageNet validation is the workload, not a new dataset contribution: each run decodes the full 50,000‑image split from memory and reports single‑thread throughput for all decoders, PyTorch \textttDataLoader throughput for eligible decoders at worker counts \0,2,4,8\, and decoder skip behavior. The evaluation protocol changes the supported conclusion. On Neoverse V2, \textttimageio is ninth in single‑thread throughput yet lands in the top DataLoader tier with \texttttorchvision; on Zen 4, \texttttorchvision rises from seventh single‑thread to the top measured DataLoader tier; on Neoverse N1, \textttimagecodecs is the single‑thread leader but fifth at peak DataLoader throughput. We also find that worker‑count conclusions differ between Zen 4 and Zen 5, TensorFlow has a large single‑thread ARM penalty, and strict native JPEG decoders/wrappers reject the same rare ImageNet JPEG. For PyTorch DataLoader workloads, \texttttorchvision and \textttsimplejpeg form the strongest measured zero‑skip tier: \texttttorchvision has the highest mean normalized throughput, while \textttsimplejpeg has the highest minimum. OpenCV remains a robust general‑purpose fallback above 90% of the platform‑local winner on every tested CPU. We release raw JSON, generated tables/figures, and an executable local/cloud benchmark framework.
Authors:Weidong Zheng, Kongyang Chen, Yuanwei Guo, Yatie Xiao
Abstract:
Class‑level machine unlearning aims to remove the influence of specified classes while preserving model utility on retained classes. Existing methods are commonly evaluated by retain‑set accuracy, forget‑set accuracy, and unlearning time, but these metrics provide limited insight into how forgetting is achieved internally. In this paper, we reveal a bias‑dominated shortcut in class‑level unlearning: the prediction of forgotten classes can be suppressed by decreasing the corresponding bias terms in the final classification head. We first analyze the gradient dynamics of classification‑head biases under softmax cross‑entropy training, explaining why retain‑set‑only optimization tends to reduce the biases of absent classes. Based on this observation, we introduce BiasShift as a diagnostic baseline, showing that simple bias manipulation can satisfy conventional unlearning metrics while leaving abnormal bias patterns that reveal forgotten labels. To mitigate excessive forgotten‑class bias suppression, we propose two bias‑aware mechanisms, namely Two‑Stage Bias Gradient Reversal Mechanism (TS‑BGRM) and Lower‑Bound Hinge Regularization (LB‑HR). We further introduce three bias‑oriented metrics, including Bias Stability Coefficient (BSC), Median Bias Gap (MBG), and Minimal Bias Score (MBS), to quantify bias dependence and potential leakage. Experiments on CIFAR‑10, CIFAR‑100, and Tiny‑ImageNet demonstrate that the proposed methods maintain competitive unlearning performance while producing more stable bias distributions. We have released our code at https://github.com/zwd2024/Beyond‑the‑Shadow‑of‑Bias‑From‑Classification‑Head‑Bias‑to‑Parameter‑Redistribution.
Authors:Jiaming Liang, Chi-Man Pun, Weisi Lin, Greta Seng Peng Mok
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.
Authors:Chengcheng Sun, Chenhao Li, Xiang Lin, Tianji Zheng, Fanrong Meng, Xiaobin Rui, Zhixiao Wang
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.
Authors:Naoki Masuyama, Yusuke Nojima, Stefan Wermter, Yuichiro Toda, Hisao Ishibuchi, Chu Kiong Loo
Abstract:
Online clustering methods that adaptively create and update nodes as data arrive often make node learning explicit, whereas the mapping from the learned node state to output clusters often remains implicit or simplified. Implicit mappings make output clusters sensitive to weak graph bridges or local relations based on distance in the graph over learned nodes, leaving no explicit constraint on which node groups remain intact during mapping. This paper addresses this gap by proposing PHIDA, a persistence‑guided node‑to‑cluster mapping method for online clustering with learned nodes. PHIDA implements this mapping within Adaptive Resonance Theory (ART)‑based online clustering by combining Inverse‑Distance ART (IDA) node learning with node‑to‑cluster mapping constrained by Persistent Homology (PH). Experiments on 24 benchmark datasets show that PHIDA achieves the best average ranks in stationary comparisons that include the recent stationary‑only clustering methods, while also improving aggregate performance in the nonstationary setting over the evaluated online methods that adaptively create and update nodes. Ablations and comparisons with conventional node‑to‑cluster mappings indicate that the observed gains are associated with PH‑constrained mapping that preserves raw PH components, together with the use of the PH component view during node learning. Source code is available at https://github.com/Masuyama‑lab/PHIDA
Authors:Michael Groom, Victor-Alexandru Darvariu, Lars Kunze, James Wilson, Nick Hawes
Abstract:
Unlike standard expected‑return Reinforcement Learning (RL), Distributional RL (DRL) models the full return distribution, making it better‑suited for uncertainty‑aware and risk‑sensitive decision‑making. Conditional Flow Matching (CFM) critics have recently attracted attention for modelling continuous, multi‑modal return distributions. Despite this interest, there remains a substantial metric mismatch: DRL theory relies on the distributional Bellman operator being contractive in the p‑Wasserstein distance, yet existing CFM critics are trained with arbitrary source‑target couplings, so their flow‑matching losses are not Wasserstein‑aligned surrogates for matching Bellman target return distributions. In this work, we address this mismatch by proposing FlowIQN, a CFM critic that sorts source and Bellman target samples within each mini‑batch to approximate the monotone optimal transport coupling, replacing arbitrary pairings with quantile‑aligned flow paths. We prove that the loss of our quantile‑coupled CFM critic yields a Wasserstein‑aligned approximate projection compatible with the foundations of DRL. To our knowledge, FlowIQN is the first flow‑matching distributional critic with an explicit Wasserstein‑aligned projection guarantee. We further extend FlowIQN with shortcut models for efficient inference. Empirical results show that FlowIQN improves Wasserstein return‑distribution accuracy over other CFM critics. It also yields competitive performance on offline RL benchmarks across multiple policy extraction methods, providing a theoretically grounded CFM critic that is readily compatible with DRL pipelines. Code: https://github.com/ori‑goals/flowIQN.
Authors:Wenhao Wu, Zishan Shao, Kangning Cui, Jinhee Kim, Yixiao Wang, Hancheng Ye, Danyang Zhuo, Yiran Chen
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.
Authors:Siyu Wu, Yulong Ye, Zezhen Xiang, Pengzhou Chen, Gangda Xiong, Tao Chen
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
Authors:Abdulvahap Mutlu, Şengül Doğan, Türker Tuncer
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.
Authors:Xincheng Yao, Ruoqi Li, Cheng Chen, Daoxin Zhang, Yi Wu, Yao Hu, Chongyang Zhang
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.
Authors:Logan Mann, Ajit Saravanan, Ishan Dave, Shikhar Shiromani, Saadullah Ismail, Yi Xia, Emily Huang
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.
Authors:Farjana Yesmin
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.
Authors:Bo Ye, Kai Gan, Tong Wei, Min-Ling Zhang
Abstract:
Generalized Category Discovery (GCD) seeks to identify novel categories from unlabeled data while retaining the classification ability of seen categories. Prior GCD methods commonly leverage transferable representations from pre‑trained models, adapting to downstream datasets via partial fine‑tuning (updating only the final ViT block) and visual prompt tuning (appending learnable vectors to inputs). However, conventional partial fine‑tuning offers limited flexibility, as it fails to adapt the entire model; meanwhile, visual prompt tuning is prone to overfitting, due to its sensitivity to initialization and inherently constrained capacity. To address these limitations, we propose LAGCD, a simple yet effective GCD approach that embeds a residual linear adapter into each ViT block. From the perspective of feature sparsity, we systematically show that non‑linearity in conventional adapters impairs performance, whereas our linear adapter enhances it by enabling more flexible model capacity. We further introduce an auxiliary distribution alignment loss to mitigate the negative impact of biased predictions between seen and novel categories. Extensive experiments on both generic and fine‑grained datasets confirm that LAGCD consistently improves performance over many sophisticated baselines. The source code is available at https://github.com/yebo0216best/LAGCD
Authors:Weicai Yan, Xinhua Ma, Wang Lin, Tao Jin
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.
Authors:Ayoub Agouzoul
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
Authors:Jonathan Bates
Abstract:
We present a PyTorch package that compiles neural networks and their weights from Turing machine descriptions, producing models that exactly simulate the specified machine without any training. Given a transition function and a set of terminal states, the package constructs a model whose forward pass corresponds to one step of the Turing machine. Two architectures are implemented, each realizing a different theoretical result: (1) a transformer with self‑attention, cross‑attention, and feedforward layers based on Wei, Chen, and Ma (2021), and (2) a recurrent network based on Siegelmann and Sontag (1995) that encodes the stack in a Cantor set. We develop the constructions from first principles, showing how ReLU networks implement Boolean circuits (AND, OR, NOT, XOR gates and their composition into DNF formulas and binary adders) and how hard attention implements positional lookup on the tape. The package serves as a concrete, runnable reference for the symbolic‑neural bridge, and as a foundation for future work on the stability of constructed solutions under gradient‑based optimization. Code is available at https://github.com/jonrbates/turing.
Authors:Yuan Fang, Yi Xie, Xuming Ran
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.
Authors:Natalia Frumkin, Bokun Wang, Hung-Yueh Chiang, Chi-Chih Chang, Mohamed S. Abdelfattah, Diana Marculescu
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
Authors:Chao Tang, Jianzong Wu, Qingyu Shi, Ye Tian, Aixi Zhang, Hao Jiang, Jiangning Zhang, Yunhai Tong
Abstract:
Unified multimodal understanding and generation models enable richer human‑AI interaction. Yet jointly customizing a character's persona, dialogue style, and visual identity while maintaining output consistency across modalities remains largely unexplored. To mitigate this gap, we introduce a new task, Customized Multimodal Role‑Play (CMRP). We construct the RoleScape‑20 dataset comprising 20 characters, including training and evaluation data that cover persona, stylistic descriptions, visual/expressive cues, and text‑image interactions. Building on a unified model, we devise UniCharacter, a two‑stage training framework containing Unified Supervised Finetuning (Unified‑SFT) and character‑specific group relative policy optimization (Character‑GRPO). Given only 10 images plus corresponding interaction examples, the model acquires the target character and exhibits coherent persona, style, and visual identity in both generated text and images. This process takes about 100 GPU hours. Experiments on the RoleScape‑20 dataset show that the proposed method substantially outperforms prior approaches. Ablation studies further validate the effectiveness of our cross‑modal consistency design and few‑shot customization strategy. We argue that CMRP, coupled with unified modeling, provides a basis for next‑generation characterful and immersive interactive agents.
Authors:Peng Liao, Shangsong Liang, Lin Chen, Peijia Zheng
Abstract:
Inertial Measurement Unit (IMU)‑based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Recently, deep learning frameworks have advanced this task by learning and extracting discriminative spatiotemporal representations, significantly improving recognition performance. However, IMU‑based HAR still faces several critical challenges, particularly limited training samples and static knowledge utilization, both of which severely hinder its large‑scale deployment. In this paper, we introduce MoRA, the first Retrieval‑Augmented Module specifically designed for motion series. It can be flexibly integrated into any existing HAR model, enhancing recognition performance while maintaining inference efficiency. To address issues such as information redundancy in retrieval results and rigid fusion strategies, we propose an uncertainty‑adaptive fusion unit within MoRA. This unit leverages previous physical knowledge from IMU signals to dynamically adjust the fusion strategy between original outputs and retrieved information, enabling more robust recognition. Extensive experiments on ten real‑world datasets demonstrate that MoRA significantly improves the performance of existing IMU‑based HAR models, consistently delivering stable and effective gains. The source code of MoRA is available at: https://github.com/liavonpenn/mora.
Authors:Amman Yusuf, Zhejun Jiang, Mijung Park
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.
Authors:Drew Dillon, Kasyap Varanasi
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.
Authors:Seohyun Lee, Wenzhi Fang, Dong-Jun Han, Seyyedali Hosseinalipour, Christopher G. Brinton
Abstract:
Recent works have advanced feedback‑based learning systems, whereby a foundation model is able to intake incoming feedback (e.g., a user) to self‑improve, creating a self‑loop system of training. However, existing works are limited in needing to consider an offline setup to allow for such feedback‑based methods, and are further limited in the need of requiring privileged ground‑truth contexts for training. Moreover, there is limited consideration of federated learning (FL), which is particularly well‑suited for incorporating external feedback across large networks of end users, for example, but requires methods to be efficient for training on resource‑constrained edge devices. Therefore, we introduce SPEAR (Self‑Play Enhancement via Advantage‑Weighted Refinement), an efficient online learning algorithm for federated LLM fine‑tuning. SPEAR utilizes a feedback‑guided self‑play loop to construct naturally contrastive pairs per prompt which are utilized to be trained on (i) standard maximum likelihood on correct completions and (ii) confidence‑weighted unlikelihood on tail tokens of incorrect completions. Without the need of expensive group generations and ground‑truth contexts for training (i.e., only partial, non‑answer feedback), in contrast with existing works, SPEAR can be trained both online and in a resource‑efficient manner. We validate SPEAR across various benchmark datasets, demonstrating its superior performance in comparison to state‑of‑the‑art baselines. The implementation code is publicly available at https://github.com/lee3296/SPEAR.
Authors:Atsushi Nitanda, Dake Bu, Yueming Lyu, Tanya Veeravalli
Abstract:
We study Slowly Annealed Langevin Dynamics (SALD), a sampler for tracking a path of moving target distributions and approximating the terminal target through time slowdown. We establish non‑asymptotic convergence guarantees via a KL differential inequality, showing that slowdown improves tracking through contraction of intermediate targets and the complexity of the path. Motivated by training‑free guided generation with pretrained score‑based generative models, we further introduce Velocity‑Aware SALD (VA‑SALD), which explicitly incorporates the underlying marginal distributions of the pretrained model and uses slowdown to correct the additional deviation induced by guidance. This yields a principled framework for training‑free guided generation for diffusion‑based and related generative model families, together with convergence guarantees that clarify the roles of intermediate functional inequalities and guidance bias. Code is available at https://github.com/anitan0925/sald.
Authors:Giacomo Spigler
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.
Authors:Ionut-Vlad Modoranu, Mher Safaryan, Dan Alistarh
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.
Authors:Hugo Vigna, Samuel Bontemps
Abstract:
The Hessian spectrum of trained deep networks exhibits a characteristic structure: a continuous bulk of near‑zero eigenvalues and a small number of large outlier eigenvalues (spikes), confirming the relevance of Random Matrix Theory in deep learning. The spike count matches the number of classes minus one. While prior work has described this structure, no method has exploited it operationally to improve classification performance. We propose Hessian Surgery, a post‑hoc optimization method that directly perturbs model weights along spike eigenvectors to rebalance per‑class accuracy without retraining. We introduce (i) a spike‑class sensitivity matrix that quantifies the directional derivative of each class's accuracy along each spike eigenvector, (ii) a constrained optimization of perturbation coefficients that targets weak classes while preserving strong ones, and (iii) an adaptive amplitude control that raises or lowers the perturbation budget based on iteration‑level improvement signals. We obtain encouraging results on CIFAR‑10 and ISIC‑2019 on both balanced accuracy and standard deviation.
Authors:Naoto Iwase, Yuki Ichihara, Mohammad Atif Quamar, Junpei Komiyama
Abstract:
Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain‑of‑Thought (CoT) traces and aggregating them with majority voting (MV), a test‑time technique called self‑consistency. When we truncate a CoT partway through and regenerate the remainder, we observe that traces with correct answers reproduce their original answer more often than traces with wrong answers. We use this difference as a reliability signal, prefix consistency, that weights each candidate answer by how often it reappears under regeneration. It requires no access to token log‑probabilities or self‑rating prompts. Across five reasoning models and four math and science benchmarks, prefix consistency is the best correctness predictor in most settings, and reweighting votes by it reaches Standard MV plateau accuracy at up to 21x fewer tokens (median 4.6x). Our code is available at https://github.com/naoto‑iwase/prefix‑consistency.
Authors:Yunho Choi, Jongwon Lim, Woojin Ahn, Minjae Oh, Jeonghoon Shim, Yohan Jo
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.
Authors:Christopher Ries, Moussa Kassem Sbeyti, Nicolas Bianco, Nadja Klein
Abstract:
Conformal Prediction (CP) is a distribution‑free method for constructing prediction sets with marginal finite‑sample coverage guarantees, making it a suitable framework for reliable uncertainty quantification in safety‑critical object detection. However, object detection introduces structured multi‑output predictions, complicating the application of classical CP theory developed for single outputs. In addition, standard, unscaled CP produces fixed‑width prediction intervals across inputs, leading to unnecessary width for low‑uncertainty predictions. While scaled CP addresses this by adapting the interval width to an input‑dependent uncertainty estimate, prior work has neither systematically compared unscaled and scaled CP for multi‑class object detection, nor integrated CP with a complementary uncertainty quantification method in this setting. We fill this gap by: (i) applying CP coordinate‑wise to bounding box corners with a Bonferroni correction for box‑level guarantees; (ii) scaling the resulting intervals using per‑prediction aleatoric uncertainty estimates derived from a probabilistic object detector trained with loss attenuation, evaluated in uncalibrated and two calibrated variants; (iii) extending to a two‑step pipeline that constructs prediction sets for the class using RAPS and conditions the conformalized bounding boxes on the predicted class set. Across three autonomous driving datasets (KITTI, BDD, CODA), including a cross‑domain setting under distribution shift, scaled CP consistently improves interval sharpness over unscaled CP, achieving up to 19% higher IoU and 39% lower interval scores, without sacrificing coverage. Class‑wise calibration further improves coverage for both variants with a negligible effect on sharpness. Together, these improvements yield more actionable uncertainty estimates for real‑time, real‑world object detection.
Authors:Fengqiang Wan, Yipeng Lin, Kan Lv, Yang Yang
Abstract:
Pre‑trained models with parameter‑efficient fine‑tuning (PEFT) have demonstrated promising potential for class‑incremental learning (CIL), yet catastrophic forgetting still persists when adapting models to new tasks. In this paper, we present a novel perspective on catastrophic forgetting through the analysis of inter‑layer relation drift, i.e., the progressive disruption of relationships among layer‑wise representations during the learning of new tasks. We theoretically show that the increase of such drift reduces the classification margins of previously learned tasks, thereby degrading overall model performance. To address this issue, we propose \underlineSelf‑\underlineRectifying inter‑layer \underlineRelation Low‑Rank Adaptation~(SR^2‑LoRA), a simple yet effective method that mitigates catastrophic forgetting by constraining inter‑layer relation drift. Specifically, SR^2‑LoRA constructs the relation matrices induced by the previous and current models on current‑task samples, and aligns the corresponding singular values. We further theoretically show that this alignment exhibits greater robustness to estimation perturbations than direct entry‑wise alignment. Extensive experiments on standard CIL benchmarks demonstrate that SR^2‑LoRA effectively mitigates catastrophic forgetting, with its advantages becoming more pronounced as the number of tasks increases. Code is available in the \hrefhttps://github.com/FqWan24/SR‑2‑LoRArepository.
Authors:Junfeng Fang, Zhepei Hong, Mao Zheng, Mingyang Song, Gengsheng Li, Houcheng Jiang, Dan Zhang, Haiyun Guo, Xiang Wang, Tat-Seng Chua
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.
Authors:Yameng Peng, Andy Song, HaythamM. Fayek, Vic Ciesielski, Xiaojun Chang
Abstract:
Zero‑shot proxies, also known as training‑free metrics, are widely adopted to reduce the computational overhead in neural network evaluation for scenarios such as Neural Architecture Search (NAS), as they do not require any training. Existing zero‑shot metrics have several limitations, including weak correlation with the true performance and poor generalisation across different networks or downstream tasks. For example, most of these metrics apply only to either convolutional neural networks (CNNs) or Transformers, but not both. To address these limitations, we propose Sample‑Wise Activation Patterns (SWAP), and its derivative, SWAP‑Score, a novel and highly effective zero‑shot metric. SWAP‑Score is broadly applicable across both architecture families and task domains, demonstrating strong predictive performance in the majority of tasks. This metric measures the expressivity of neural networks over a mini‑batch of samples, showing a high correlation with the neural networks' ground‑truth performance. For both CNNs and Transformers, the SWAP‑Score outperforms existing zero‑shot metrics across computer vision and natural language processing tasks. For instance, Spearman's correlation coefficient between the SWAP‑Score and CIFAR‑10 validation accuracy for DARTS CNNs is 0.93, and 0.71 for FlexiBERT Transformers on GLUE tasks. Moreover, SWAP‑Score is label‑independent, hence can be applied at the pre‑training stage of language models to estimate their performance for downstream tasks. When applied to NAS, SWAP‑empowered NAS, SWAP‑NAS can achieve competitive performance using only approximately 6 and 9 minutes of GPU time, on CIFAR‑10 and ImageNet respectively. Our code is available at: https://github.com/pym1024/SWAP_Universal
Authors:Ruijie Zhou, Fanxu Meng, Yufei Xu, Tongxuan Liu, Guangming Lu, Muhan Zhang, Wenjie Pei
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.
Authors:Yuheng Zhang, Chenlu Ye, Shuowei Jin, Changlong Yu, Wei Xiong, Saurabh Sahu, Nan Jiang
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.
Authors:Lucas Hu, Ranchi Zhao, Isaac Zhu, Zach Zhang, Hscos Zhang, Hugh Yin, Jason Zhao
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.
Authors:Sum Kyun Song, Bong Gyun Shin, Jae Yong Lee
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.
Authors:Peter Pao-Huang, Xiaojie Qiu, Stefano Ermon
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.
Authors:Wenyuan Li, Guang Li, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
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.
Authors:Hao Chen, Zavareh Bozorgasl
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.
Authors:Takato Honda
Abstract:
How few parameters do we really need to forecast a periodic time series? An hourly electricity series, reshaped as a 24‑row matrix with one column per day, is approximately rank‑1: a daily shape modulated by a daily level (median centered rank‑1 energy 0.82 on GIFT‑Eval). Should we learn the shape? Smoothing, shrinkage, and low‑rank fits all seem like obvious upgrades over the simple average of the last K=2 cycles. On all 97 GIFT‑Eval configurations, we tested 8 such alternatives (e.g., Fourier, EWMA, James‑Stein, rank‑r SVD): none significantly beats the frozen baseline under Holm correction; two are significantly worse. The resulting method, FLAIR, is (a) Effective: matches PatchTST on aggregate GIFT‑Eval (relMASE 0.838 vs 0.849); (b) Compact: 28 scalars for hourly, 57 for weekly; (c) Fast: 22 minutes on one CPU core of a MacBook Pro; (d) Closed‑form & Hands‑Off: one SVD per period candidate, GCV‑averaged Ridge, no GPU, no pre‑training, no per‑task tuning. In the high‑rank‑1, many‑cycle regime, extra flexibility is estimation noise.
Authors:Akshita Singh, Prabesh Paudel, Siddhartha Roy
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.
Authors:Fred Zhangzhi Peng, Avishek Joey Bose, Anru R. Zhang, Alexander Tong
Abstract:
Generative modeling over discrete structures underpins applications across deep learning, from biological sequence design and code generation to large language models, yet generation often remains sequential, relying on autoregressive decoding or iterative refinement. In this work, we introduce Coupling Models(Coupling Models), a one‑step discrete generative model that learns a direct coupling between discrete sequences and Gaussian latents. Unlike recent distillation methods that compress a pretrained multi‑step sampler into a few steps, Coupling Model trains a purpose‑built decoder to invert this coupling and generate samples in a single step. The model also avoids complex continuous flows over the simplex and hand‑specified data‑to‑noise couplings. Empirically,Coupling Model improves the strongest one‑step baselines in each domain: it reduces LM1B text‑generation perplexity by 33% at its lowest‑perplexity operating point, Fly Brain enhancer‑design FBD by 18%, and MNIST‑Binary FID by 46%. These results suggest that effective one‑step discrete generation depends strongly on how data and noise are coupled before decoding. Code is available at https://github.com/pengzhangzhi/Coupling‑Models.
Authors:Guankai Li, Jiabin Chen, Yi Xu, Xichen Zhang, Yuan Lu
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.
Authors:Tianle Jiang, Yufa Zhou
Abstract:
Being robust to the presence of outliers is crucial for applying clustering algorithms in practice. In the robust k‑Means problem (i.e., k‑Means with outliers), the goal is to remove z outliers and minimize the k‑Means cost on the remaining points. Despite the close connection between robust k‑Means and outlier detection, both theoretical and empirical understanding of the effectiveness of classic outlier detection heuristics for robust k‑Means remains limited. In this paper, we prove that under a practical assumption on the optimal cluster sizes, simply removing points with large K‑Nearest‑Neighbor distances achieves performance comparable to prior work in terms of approximation guarantees: it yields a constant‑factor reduction from robust k‑Means to standard k‑Means, without introducing additional centers or discarding extra outliers, as is commonly required by existing approaches. Empirically, experiments on real‑world datasets show that our method outperforms or matches several more sophisticated algorithms in terms of clustering cost and runtime. These results demonstrate that simple KNN‑based heuristics can be surprisingly effective for robust clustering, highlighting new opportunities to bridge techniques from outlier detection and clustering.
Authors:Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn
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.
Authors:Mohamed Elrefaie, Dule Shu, Matthew Klenk, Faez Ahmed
Abstract:
Crash simulation is a cornerstone of modern vehicle development because it reduces the need for costly physical prototypes, accelerates safety‑driven design iteration, and increasingly supports virtual testing workflows. At the same time, modeling structural crash mechanics remains exceptionally challenging: the response is governed by nonlinear contact, large deformation, material plasticity, failure, and complex multi‑body interactions evolving over space and time on high‑resolution finite‑element meshes. In this work, we introduce CarCrashNet, a public high‑fidelity open‑source benchmark for data‑driven structural crash simulation. CarCrashNet combines component‑scale and full‑vehicle simulations in a multi‑modal format, including more than 14,000 bumper‑beam pole‑impact simulations with varying geometry, materials, and boundary conditions, together with 825 full‑vehicle crash simulations built from three industry‑standard vehicle models of increasing structural complexity: Dodge Neon, Toyota Yaris, and Chevrolet Silverado. To establish the reliability of the benchmark, we validate our open‑source finite‑element workflow based on OpenRadioss against both experimental crash data and the commercial solver Ansys LS‑DYNA. We also introduce CrashSolver, a machine‑learning model designed for full‑vehicle crash prediction from high‑resolution finite‑element crash data. We further perform extensive benchmarking across the released datasets and evaluate CrashSolver against state‑of‑the‑art geometric deep learning and transformer‑based neural solvers. Our results position CarCrashNet as a foundation for reproducible research in structural simulation, crashworthiness modeling, and AI‑driven virtual crash testing. The dataset is available at https://github.com/Mohamedelrefaie/CarCrashNet.
Authors:Xinyu Zhang, Zhengtong Xu, Yutian Tao, Yeping Wang, Yu She, Abdeslam Boularias
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
Authors:Andy Dong, Ayfer Özgür
Abstract:
Poisson subsampling is the default sampling scheme in differentially private machine learning, largely because its unstructured randomness yields tractable privacy amplification analyses. Yet this same randomness introduces substantial participation variance: each sample appears in very different numbers of training iterations. In this work, we show that this variance is not merely a practical artifact to be tolerated, but a fundamental source of suboptimal privacy amplification. We prove that Balanced Iteration Subsampling (BIS), a structured scheme in which each sample participates in exactly a fixed number of iterations, achieves stronger privacy amplification than Poisson subsampling and is optimal at both extremes of the noise spectrum (σ\to 0 and σ\to \infty). Our analysis reveals that the privacy‑noise tradeoff is governed not by maximizing randomness, but by eliminating participation variance while preserving uniform marginal participation across iterations. To translate this asymptotic theory into finite‑noise guarantees, we introduce a practical near‑exact Monte Carlo accountant for BIS, which removes the analytical slack of existing RDP and composition‑based PLD analyses. Evaluations across more than 60 practical DP‑SGD configurations show that BIS consistently outperforms Poisson subsampling in the low‑noise regimes most relevant for high‑utility private training, reducing the required noise multiplier by up to 9.6%. These results overturn the common intuition that more sampling randomness necessarily yields stronger privacy amplification: in DP‑SGD, structured participation can be both more practical and more private. Our implementation is available at https://github.com/dong‑xin‑ao‑andy/bis‑mc‑accountant.
Authors:Guyue Luo, Qiao Liu
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.
Authors:Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg, Amabel Gale, Xiaoyu Liu, Pareesa Ameneh Golnari, Shengyu Fu
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.
Authors:Haydn Jones, Yimeng Zeng, Alden Rose, Li S. Yifei, Yining Huang, Kaiwen Wu, Jiaming Liang, Maggie Ziyu Huan, Yoseph Barash, Cesar de la Fuente-Nunez, Osbert Bastani, Zachary Ives, Mark Yatskar, Jacob R. Gardner
Abstract:
Manually curated biomedical repositories ‑‑ spanning bioactivity, genomics, and chemistry ‑‑ are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage. We show that PubMed itself can be autonomously and cost‑effectively turned into structured datasets that are larger, more nuanced, and more accurate than the curated databases they replace. We present three coupled contributions: (1) an LLM‑based entity‑tagging pipeline, grounded in nine biomedical ontologies, that tags 4.5B entities across 19 categories in a 22.5M‑paper, 2.5T‑token PubMed corpus; (2) hybrid sparse‑dense retrieval supporting entity‑filtered semantic queries over the tagged corpus; and (3) Starling, a multi‑agent deep research system that, given only a natural‑language task description, designs precision‑ and recall‑targeted retrieval filters, induces an extraction schema, and emits structured records with nuance‑rich fields and supporting passages. Across six tasks ‑‑ blood‑brain barrier permeability, oral bioavailability, acute toxicity (LD50), gene‑disease associations, protein subcellular localization, and chemical reactions ‑‑ Starling produces ~6.3M records (91K‑3M per task); several are, to our knowledge, the largest public datasets for their property. Frontier‑model rejection of our extractions is 0.6‑7.7% across tasks, far below error rates we measure on widely used curated counterparts (e.g., 16.5% on BBB_Martins, 7.3% on Bioavailability_Ma). Beyond scale and accuracy, the supporting passages carry nuance tabular databases discard ‑‑ e.g., oral bioavailability may depend on fed vs. fasted state. Together, the corpus, retrieval, and agent establish a foundation for AI‑driven therapeutic design. Code and datasets: https://github.com/starling‑labs/starling.
Authors:Chenhui Xu, Ziyue Bai, Fuxun Yu, Heng Huang, Jinjun Xiong
Abstract:
While autoregressive models have advanced 3D generation, creating physically stable brick structures remains a challenge due to the strict requirements of gravity and interconnectivity. Existing approaches rely on external physical simulators during inference to perform rejection sampling and brick‑by‑brick rollbacks, which severely bottlenecks efficiency. To address this, we propose a reinforcement learning paradigm that shifts physical validity enforcement from test‑time correction to training‑time policy optimization. By utilizing assembly‑level rewards, the model optimizes for collision avoidance, global connectivity, structural interlocking, and shape conformity. This paradigm allows the model to internalize physical priors, enabling the first rollback‑free generation of stable brick structures. Experimental results demonstrate that our approach achieves state‑of‑the‑art generation quality while accelerating inference speed by orders of magnitude. Our code and dataset are available at https://github.com/miniHuiHui/STABLE. Our models are available at https://huggingface.co/miniHui/STABLE.
Authors:Fred Zhangzhi Peng, Alexis Fox, Anru R. Zhang, Alexander Tong
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.
Authors:Yuwei Yin, Chuyuan Li, Giuseppe Carenini
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.
Authors:Naihe Feng, Yi Sui, Shiyi Hou, Ga Wu, Jesse C. Cresswell
Abstract:
When multi‑agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error attribution remains a fundamental challenge due to the long interaction traces that large language model‑based MAS generate. This paper presents a framework for error attribution based on conformal prediction (CP) which provides finite‑sample, distribution‑free coverage guarantees. We introduce new algorithms for filtration‑based CP designed for sequential data such as agent trajectories. Unlike existing CP algorithms, our approach predicts sets that are contiguous sequences to enable efficient recovery and debugging. We verify our theoretical guarantees on a variety of agents and datasets, show that errors can be precisely isolated, then use prediction sets to rollback MAS to correct their own errors. Our overall approach is model‑agnostic, and offers a principled uncertainty layer for MAS error attribution. We release code at https://github.com/layer6ai‑labs/conformal‑agent‑error‑attribution.
Authors:Zhifeng Gu, Yuqi Wang, Bing Wang
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.
Authors:Arash Shahmansoori
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).
Authors:Yubo Jiang, Yitong An, Xin Yang, Abudukelimu Wuerkaixi, Xuxin Cheng, Fengying Xie, Zhiguo Jiang, Cao Liu, Ke Zeng, Haopeng Zhang
Abstract:
Vision‑Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over‑reliance on linguistic priors. We introduce Positive‑and‑Negative Decoding (PND), a training‑free inference framework that intervenes directly in the decoding process to enforce visual fidelity. PND is motivated by our finding of an attention imbalance in VLMs, where visual features are under‑weighted. Our framework introduces a dual‑path contrast: a positive path that amplifies visual evidence and a negative path that constructs counterfactuals to penalize prior‑dominant generation. By contrasting outputs from both paths during decoding, PND steers generation toward visually grounded results. Experiments on POPE, MME, and CHAIR demonstrate state‑of‑the‑art performance without retraining.
Authors:Jon-Paul Cacioli
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.
Authors:Omar El Khalifi, Thomas Rossi, Oscar Fossey, Thibault Fouque, Ulysse Mizrahi, Philip Torr, Ivan Laptev, Fabio Pizzati, Baptiste Bellot-Gurlet
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/.
Authors:Sushant Gautam, Finn Schwall, Annika Willoch Olstad, Fernando Vallecillos Ruiz, Birk Torpmann-Hagen, Sunniva Maria Stordal Bjørklund, Leon Moonen, Klas Pettersen, Michael A. Riegler
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.
Authors:Hao Dong, Hongzhao Li, Shupan Li, Muhammad Haris Khan, Eleni Chatzi, Olga Fink
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.
Authors:Dan Jacobellis, Neeraja J. Yadwadkar
Abstract:
Modern sensors generate rich, high‑fidelity data, yet applications operating on wearable or remote sensing devices remain constrained by bandwidth and power budgets. Standardized codecs such as JPEG and MPEG achieve efficient trade‑offs between bitrate and perceptual quality but are designed for human perception, limiting their applicability to machine‑perception tasks and non‑traditional modalities such as spatial audio arrays, hyperspectral images, and 3D medical images. General‑purpose compression schemes based on scalar quantization or resolution reduction are broadly applicable but fail to exploit inherent signal redundancies, resulting in suboptimal rate‑distortion performance. Recent generative neural codecs, or tokenizers, model complex signal dependencies but are often over‑parameterized, data‑hungry, and modality‑specific, making them impractical for resource‑constrained environments. We introduce a Lightweight, Versatile, and Asymmetric neural codec architecture (LiVeAction), that addresses these limitations through two key ideas. (1) To reduce the complexity of the encoder to meet the resource constraints of the execution environments, we impose an FFT‑like structure and reduce the overall size and depth of the neural‑network‑based analysis transform. (2) To allow arbitrary signal modalities and simplify training, we replace adversarial and perceptual losses with a variance‑based rate penalty. Our design produces codecs that deliver superior rate‑distortion performance compared to state‑of‑the‑art generative tokenizers, while remaining practical for deployment on low‑power sensors. We release our code, experiments, and python library at https://github.com/UT‑SysML/liveaction .
Authors:Ilya Borovik
Abstract:
Symbolic music datasets with matched scores and performances are essential for many music information retrieval (MIR) tasks. Yet, existing resources often cover a narrow range of composers, lack performance variety, omit note‑level alignments, or use inconsistent naming formats. This work presents PianoCoRe, a large‑scale piano MIDI dataset that unifies and refines major open‑source piano corpora. The dataset contains 250,046 performances of 5,625 pieces written by 483 composers, totaling 21,763 h of performed music. PianoCoRe is released in tiered subsets to support different applications: from large‑scale analysis and pre‑training (PianoCoRe‑C and deduplicated PianoCoRe‑B) to expressive performance modeling with note‑level score alignment (PianoCoRe‑A/A). The note‑aligned subset, PianoCoRe‑A, provides the largest open‑source collection of 157,207 performances aligned to 1,591 scores to date. In addition to the dataset, the contributions are: (1) a MIDI quality classifier for detecting corrupted and score‑like transcriptions and (2) RAScoP, an alignment refinement pipeline that cleans temporal alignment errors and interpolates missing notes. The analysis shows that the refinement reduces temporal noise and eliminates tempo outliers. Moreover, an expressive performance rendering model trained on PianoCoRe demonstrates improved robustness to unseen pieces compared to models trained on raw or smaller datasets. PianoCoRe provides a ready‑to‑use foundation for the next generation of expressive piano performance research.
Authors:Zhexuan Wang, Xuebo Liu, Li Wang, Zifei Shan, Yutong Wang, Zhenxi Song, Min Zhang
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.
Authors:Hongyi Tao, Dingzhi Yu, Lijun Zhang
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.
Authors:Jakub Stępień, Marcin Mazur, Jacek Tabor, Przemysław Spurek
Abstract:
Sparse Autoencoders (SAEs) have become an important tool in mechanistic interpretability, helping to analyze internal representations in both Large Language Models (LLMs) and Vision Transformers (ViTs). By decomposing polysemantic activations into sparse sets of monosemantic features, SAEs aim to translate neural network computations into human‑understandable concepts. However, common architectures such as TopK SAEs rely on a fixed sparsity level. They enforce the same number of active features (K) across all inputs, ignoring the varying complexity of real‑world data. Natural data often lies on manifolds with varying local intrinsic dimensionality, meaning the number of relevant factors can change significantly across samples. This suggests that a fixed sparsity level is not optimal. Simple inputs may require only a few features, while more complex ones need more expressive representations. Using a constant K can therefore introduce noise in simple cases or miss important structure in more complex ones. To address this issue, we propose SoftSAE, a sparse autoencoder with a Dynamic Top‑K selection mechanism. Our method uses a differentiable Soft Top‑K operator to learn an input‑dependent sparsity level k. This allows the model to adjust the number of active features based on the complexity of each input. As a result, the representation better matches the structure of the data, and the explanation length reflects the amount of information in the input. Experimental results confirm that SoftSAE not only finds meaningful features, but also selects the right number of features for each concept. The source code is available at: https://github.com/St0pien/SoftSAE.
Authors:Yiqiao Jin, Yiyang Wang, Lucheng Fu, Yijia Xiao, Yinyi Luo, Haoxin Liu, B. Aditya Prakash, Josiah Hester, Jindong Wang, Srijan Kumar
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.
Authors:Xiaoguang Guo, Zehong Wang, Ziming Li, Shawn Spitzel, Soonwoo Kwon, Tianyi Ma, Yanfang Ye, Chuxu Zhang
Abstract:
Graph representation learning (GRL) has evolved from topology‑only graph embeddings to task‑specific supervised GNNs, and more recently to reusable representations and graph foundation models (GFMs). However, existing evaluations mainly measure clean transfer, adaptation, and task coverage. It remains unclear whether GRL methods stay reliable when deployment stresses affect graph signals, graph contexts, label support, structural groups, or predictive evidence. We introduce GRL‑Safety, a multi‑axis safety evaluation benchmark for GRL. GRL‑Safety evaluates twelve representative methods, spanning topology‑only embedding methods, supervised GNNs, self‑supervised graph models, and GFMs, on twenty‑five graph datasets under standardized evaluation conditions while preserving method‑native adaptation. The evaluation covers five safety axes: corruption robustness, OOD generalization, class imbalance, fairness, and interpretation, with per‑axis and sub‑condition reporting rather than a single aggregate score. Our analysis yields three cross‑axis insights that can inspire future research. First, safety behavior is shaped by the interaction between representation design and the stressed graph factor, rather than by method family alone. Second, foundation‑era methods show axis‑specific strengths rather than broad safety dominance. Third, several deployment regimes remain difficult even for the best evaluated method, revealing capability gaps that require new robustness, adaptation, or training objectives beyond model selection. The benchmark, evaluation protocols, and code are available at: https://github.com/GXG‑CS/GRL‑Safety.
Authors:Victor Daniel Gera
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
Authors:Amir Rezaei Balef, Mykhailo Koshil, Katharina Eggensperger
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.
Authors:Canyu Zhao, Hao Chen, Yunze Tong, Yu Qiao, Jiacheng Li, Chunhua Shen
Abstract:
Reinforcement learning fine‑tuning has become the dominant approach for aligning diffusion models with human preferences. However, assessing images is intrinsically a multi‑dimensional task, and multiple evaluation criteria need to be optimized simultaneously. Existing practice deal with multiple rewards by training one specialist model per reward, optimizing a weighted‑sum reward R(x)=\sum_k w_k R_k(x), or sequentially fine‑tuning with a hand‑crafted stage schedule. These approaches either fail to produce a unified model that can be jointly trained on all rewards or necessitates heavy manually tuned sequential training. We find that the failure stems from using a naive weighted‑sum reward aggregation. This approach suffers from a sample‑level mismatch because most rollouts are specialist samples, highly informative for certain reward dimensions but irrelevant for others; consequently, weighted summation dilutes their supervision. To address this issue, we propose MARBLE (Multi‑Aspect Reward BaLancE), a gradient‑space optimization framework that maintains independent advantage estimators for each reward, computes per‑reward policy gradients, and harmonizes them into a single update direction without manually‑tuned reward weighting, by solving a Quadratic Programming problem. We further propose an amortized formulation that exploits the affine structure of the loss used in DiffusionNFT, to reduce the per‑step cost from K+1 backward passes to near single‑reward baseline cost, together with EMA smoothing on the balancing coefficients to stabilize updates against transient single‑batch fluctuations. On SD3.5 Medium with five rewards, MARBLE improves all five reward dimensions simultaneously, turns the worst‑aligned reward's gradient cosine from negative under weighted summation in 80% of mini‑batches to consistently positive, and runs at 0.97X the training speed of baseline training.
Authors:Magnus Victor Boock, Abdullah Akgül, Mustafa Mert Çelikok, Melih Kandemir
Abstract:
We present a new operator‑theoretic representation learning framework for offline reinforcement learning that recovers the directed temporal geometry of a controlled Markov process from hitting time observations. While prior art often produces symmetric distances or fails to satisfy the triangle inequality, our framework learns a Hilbert‑space displacement geometry where expected hitting times are realized as linear functionals of latent displacements. We prove that this representation exists under latent linear closure and is uniquely identifiable up to a bounded linear isomorphism. For finite‑dimensional implementations, we show that global hitting‑time error is bounded by one‑step transition error amplified by the environment's transient spectral radius. Furthermore, we provide finite‑sample guarantees accounting for approximation, statistical complexity, and trajectory‑label mismatch. Derived from this theory, we curate Isomorphic Embedding Learning (IEL) as a new goal‑agnostic foundation policy learning algorithm that anchors a HILP‑style consistency objective with explicit hitting‑time regression to ensure that the learned geometry reflects actual decision‑time progress. This asymmetric and compositional structure enables robust graph‑based multi‑stage planning for long‑horizon navigation. Our experiments demonstrate that IEL improves the state of the art of learning foundation policy policies from offline maze locomotion data. Our code can be found on https://github.com/MagnusBoock/IEL
Authors:Davide Rindori
Abstract:
Traditional multi‑population models, such as the Li‑Lee framework, rely on the assumption of mean‑reverting country‑specific deviations. However, recent data from high‑longevity clusters suggest a systemic break in this paradigm. We identify a stationarity paradox where mortality residuals in countries like Sweden and West Germany exhibit persistent unit roots, leading to a systematic mispricing of longevity risk in linear models. To address these non‑linearities, we propose Hybrid‑Lift, a neural‑actuarial framework that combines Hierarchical LSTM networks with a Mean‑Bias Correction (MBC) anchoring mechanism. Positioned as a governance‑friendly model challenger rather than a replacement of classical approaches, the framework exhibits selective superiority on out‑of‑sample validation (2012‑2020): it outperforms Li‑Lee by 17.40% in Sweden and 12.57% in West Germany, while remaining comparable for near‑linear regimes such as Switzerland and Japan. We complement the predictive model with an integrated governance suite comprising SHAP‑based cross‑country influence mapping, a dual uncertainty framework for regulatory capital calibration (Swiss ES 99.0% of +1.153 years), and a reverse stress test identifying the critical shock threshold for solvency buffer exhaustion. This research provides evidence that neural networks, when properly anchored by actuarial principles, can serve as effective model challengers for longevity risk management under the SST and Solvency II standards.
Authors:Shouvik Sardar, Sourish Das
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
Authors:Shaofeng Qin, Li Wang
Abstract:
Constructive neural routing solvers usually score the next action by matching a decoder context to candidate embeddings, hiding deterministic one‑step consequences such as travel, waiting, slack, and capacity changes. We propose LINC (Local Inference via Normed Comparison), a decoder‑side candidate decision architecture that computes these consequences explicitly. LINC uses them according to their decision role: centered relative consequences are compared by a shared linear local scorer, while feasible‑set summaries modulate the decoder context. This preserves standard global matching and relieves the hidden state from rediscovering transition arithmetic. The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) serves as the main constrained‑routing stress test; the same interface extends to the Capacitated Vehicle Routing Problem (CVRP) and Traveling Salesman Problem (TSP). In particular, for CVRPTW, LINC reduces PolyNet's Solomon/Homberger gaps from 13.83%/38.15% to 7.26%/14.71%; for TSP and CVRP, it also improves external‑benchmark gaps.
Authors:Guanmeng Xian, Ning Yang, Philip S. Yu
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.
Authors:Jun Li, Peifeng Lai, Xuhang Lou, Jinpeng Wang, Yuting Wang, Ke Chen, Yaowei Wang, Shu-Tao Xia
Abstract:
Partially relevant video retrieval aims to retrieve untrimmed videos using text queries that describe only partial content. However, the inherent asymmetry between brief queries and rich video content inevitably introduces uncertainty into the retrieval process. In this setting, vague queries often induce semantic ambiguity across videos, a challenge that is further exacerbated by the sparse temporal supervision within videos, which fails to provide sufficient matching evidence. To address this, we propose Holmes, a hierarchical evidential learning framework that aggregates multi‑granular cross‑modal evidence to quantify and model uncertainty explicitly. At the inter‑video level, similarity scores are interpreted as evidential support and modeled via a Dirichlet distribution. Based on the proposed three‑fold principle, we perform fine‑grained query identification, which then guides query‑adaptive calibrated learning. At the intra‑video level, to accumulate denser evidence, we formulate a soft query‑clip alignment via flexible optimal transport with an adaptive dustbin, which alleviates sparse temporal supervision while suppressing spurious local responses. Extensive experiments demonstrate that Holmes outperforms state‑of‑the‑art methods. Code is released at https://github.com/lijun2005/ICML26‑Holmes.
Authors:Rappy Saha, Jude Haris, Nicolas Bohm Agostini, David Kaeli, José Cano
Abstract:
Power‑of‑two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit‑shift operations for inference. Prior work has shown that PoT‑quantized DNNs can preserve accuracy for tasks such as image classification; however, their performance on resource‑constrained edge devices remains insufficiently understood. While general‑purpose edge CPUs and GPUs do not provide optimized backends for bit‑shift operations, custom hardware accelerators can better exploit PoT quantization by implementing dedicated shift‑based processing elements. However, deploying PoT‑quantized models on such accelerators is challenging due to limited support in existing inference frameworks. In addition, the impact of different PoT quantization strategies on hardware design, performance, and energy efficiency during full inference has not been systematically explored.
To address these challenges, we propose PoTAcc, an open‑source end‑to‑end pipeline for accelerating and evaluating PoT‑quantized DNNs on resource‑constrained edge devices. PoTAcc enables seamless preparation and deployment of PoT‑quantized models via TensorFlow Lite (TFLite) across heterogeneous platforms, including CPU‑only systems and hybrid CPU‑FPGA systems with custom accelerators. We design shift‑based processing element (shift‑PE) accelerators for three PoT quantization methods and implement them on two FPGA platforms. We evaluate accuracy, performance, energy efficiency, and resource utilization across a range of models, including CNNs and Transformer‑based architectures. Results show that our CPU‑accelerator design achieves up to 3.6x speedup and 78% energy reduction compared to CPU‑only execution for PoT‑quantized DNNs on PYNQ‑Z2 and Kria boards. The code will be publicly released at https://github.com/gicLAB/PoTAcc
Authors:Maxim Fishman, Brian Chmiel, Ron Banner, Daniel Soudry, Boris Ginsburg
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
Authors:Hugo Cazaux, Eyjólfur Ingi Ásgeirsson, Hlynur Stefánsson
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
Authors:Geping Chen, Chunlin Li, Tianzhong Yang, Zhengyuan Zhu, Jing Zhou
Abstract:
Instrumental variable (IV) and control function (CF) methods are powerful tools for causal effect estimation in the presence of unmeasured confounding, yet most existing approaches target only mean effects and/or demand substantial fitting and tuning effort. In this paper, we introduce a simple method, TabCF, for control function regression using tabular foundation models, which enables accurate, fast, identification‑transparent, and tuning‑light causal estimation of distributional quantities, such as interventional means and quantiles; we also propose a copula‑based approximation for multivariate outcomes. TabCF performs favorably against representative methods across a broad range of small‑ to medium‑sized synthetic and real data scenarios. The central message is two‑fold: for practitioners, it highlights that TabCF is an effective tool for distributional causal inference; for researchers, it suggests that the proposed approach could be considered a strong baseline for future method development. Code is available at https://github.com/GepingChen/TabCF.
Authors:Sankarshana Venugopal, Mohammad Mostafavi, Jonghyun Choi
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.
Authors:Hanyu Gao, Bin Cao, Yunyue Su, Tong-Yi Zhang, Qiang Liu
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
Authors:Yulong Huang, Xiang Liu, Hongxiang Huang, Xiaopeng Lin, Zunchang Liu, Xiaowen Chu, Zeke Xie, Bojun Cheng
Abstract:
Linear Attention (LA) offers a promising paradigm for scaling large language models (LLMs) to long sequences by avoiding the quadratic complexity of self‑attention. Recent LA models such as Mamba2 and GDN interpret linear recurrences as closed‑form online stochastic gradient descent (SGD), but naive SGD updates suffer from rapid information decay and suboptimal convergence in optimization. While momentum‑based optimizers provide a natural remedy, they pose challenges in simultaneously achieving training efficiency and effectiveness. To address this, we develop a chunkwise parallel algorithm for LA with a stepwise momentum rule by geometrically reordering the update coefficients. Further, from a dynamical systems perspective, we analyze the momentum‑based recurrence as a second‑order system that introduces complex conjugate eigenvalues. This analysis guides the design of stable gating constraints. The resulting model, Momentum DeltaNet (MDN), leverages Triton kernels to achieve comparable training throughput with competitive linear models such as Mamba2 and KDA. Extensive experiments on the 400M and 1.3B parameter models demonstrate consistent performance improvements over strong baselines, including Transformers, Mamba2 and GDN, across diverse downstream evaluation benchmarks. Code: https://github.com/HuuYuLong/MomentumDeltaNet .
Authors:Zhiyuan Zhai, Xin Wang
Abstract:
Group‑relative RL training (GRPO) samples a small group of parallel rollouts for every training prompt and uses their within‑group reward spread to compute per‑trajectory advantages. In agentic environments each rollout is a long multi‑turn dialogue with one LLM call per step, so this multi‑sample multiplier dominates the total training cost. When every rollout of a prompt ends with the same reward, the group has zero reward variance and contributes no gradient, so the extra rollouts add no information; such groups are common in practice (typically around 40% of all groups), so the wasted‑compute fraction is substantial rather than marginal. Existing methods filter such groups at the prompt level, either after their rollouts are paid for or before any rollout begins, but both decide without using information that becomes available during the rollout itself. We instead ask whether the in‑group divergence between the partial trajectories at an intermediate step can already predict that the group will be zero‑variance: when the parallel rollouts have already converged on the same action prefix, the group is on track to produce a single reward, and we can stop early. We propose a one‑parameter gate that stops a group when the mean pairwise prefix edit distance between its partial action sequences falls below a threshold. On a 60‑iteration on‑policy GRPO run on ALFWorld with Qwen2.5‑7B, averaged over four random seeds, the gated arm finishes 10.7% faster in wall‑clock (bootstrap 95% CI excludes 0) and shifts held‑out success rate on 50 unseen tasks by +2.5 pp, with the held‑out gain tracing to a measurable reduction in zero‑advantage gradient‑batch dilution. Code is available at https://github.com/zhiyuanZhai20/selective‑rollout.
Authors:Mei Wu, Wenchao Weng, Wenxin Su, Wenjie Tang, Wei Zhou
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.
Authors:Anh H. Vo, Sungyo Lee, Phil-Joong Kim, Soo-Mi Choi, Yong-Guk Kim
Abstract:
Recent advances in large language models (LLMs) have significantly improved language‑driven 3D content generation, but most existing approaches still treat scene generation and user interaction as separate processes, limiting the adaptability and immersive potential of interactive multimedia systems. This paper presents a unified framework that closes the loop between language‑driven 3D scene generation and immersive user interaction. Given natural language instructions, the system first constructs structured scene representations using LLMs, and then optimizes spatial layouts via reinforcement learning under geometric and semantic constraints. The generated environments are deployed in a virtual reality setting to facilitate HRI‑in‑the‑loop, where user interactions provide continuous feedback to align generated content with human perception and usability. By tightly coupling generation and interaction, the proposed framework enables more responsive, adaptive, and realistic multimedia experiences. Experiments on the ALFRED benchmark demonstrate state‑of‑the‑art performance in task‑based scene generation. Furthermore, qualitative results and user studies show consistent improvements in immersion, interaction quality, and task efficiency, highlighting the importance of closed‑loop integration of generation and interaction for next‑generation multimedia systems. Our project page can be found at https://proj‑showcase.github.io/h3ds/.
Authors:Gabriel Jeanson, David-Alexandre Duclos, William Larrivée-Hardy, Noé Cochet, Matěj Boxan, Anthony Deschênes, François Pomerleau, Philippe Giguère
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.
Authors:Lei Jiang, Adrian Ildefonso, Daniel Loveless, Fan Chen
Abstract:
Large language models (LLMs) impose rapidly growing energy demands, creating an emerging energy and carbon crisis driven by large‑scale inference. Solar‑powered, AI‑enabled low Earth orbit (LEO) satellites have been proposed to mitigate terrestrial electricity consumption, but their lifecycle carbon footprint remains poorly understood due to launch emissions, satellite manufacturing, and radiation‑hardened hardware requirements. This paper presents LLMSpace, the first carbon modeling framework for LLM inference on AI‑enabled LEO satellites. LLMSpace jointly models operational and embodied carbon, peripheral subsystems, radiation‑hardened accelerators and memories, and LLM‑specific workload characteristics such as prefill‑decode behavior and token generation. Using realistic satellite and GPU configurations, LLMSpace reveals key trade‑offs among carbon footprint, inference latency, hardware design, and operational lifetime for sustainable space‑based LLM inference. Source code: https://github.com/UnchartedRLab/LLMSpace.
Authors:Shivam Kumar Panda, M Khalid Jawed
Abstract:
Elastic ribbons, slender structures whose length (L), width (W), and thickness (b) satisfy L \gg W \gg b, exhibit mechanical behaviors intermediate between one‑dimensional rods (L \gg W, b) and two‑dimensional plates (L, W \gg b). In quadratic Kirchhoff‑type rod‑based frameworks, such as Discrete Elastic Rods (DER), the governing equilibrium equations are independent of width, and therefore these models cannot capture width‑dependent mechanical effects. Reduced centerline‑based ribbon models attempt to capture width dependence via coupled bending‑twisting energies. However, their relative accuracy remain unclear due to the absence of a unified simulation framework. In this work, we formulate a framework grounded in discrete differential geometry where the energy is expressed as functions of coupled bending‑twisting strain measures along the centerline, rather than a linear sum of quadratic bending and twisting energies in DER. We derive analytical gradients and Hessians of the energy that enable implicit time integration. Within this unified setting, we compare five ribbon models: Kirchhoff, Sadowsky, Wunderlich, Sano, and Audoly. As a benchmark, a straight ribbon is longitudinally constrained into a pre‑buckled arch and subjected to transverse displacement, inducing a supercritical pitchfork bifurcation. Predicted bifurcation thresholds are compared against shell‑based finite element simulations, with the Sano model providing the closest agreement in capturing width‑dependent shifts. Our high‑performance JAX‑based implementation achieves \mathcalO(N) per‑iteration cost and also confirms that Sano model introduces negligible per‑iteration overhead relative to standard DER.
Authors:Jae-Won Chung, Zhirui Liang, Yanyong Mao, Jiasi Chen, Mosharaf Chowdhury, Vladimir Dvorkin
Abstract:
AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi‑year interconnection delays for new datacenters and bottlenecking AI growth. To ease this strain, datacenters increasingly offer rapid power flexibility in response to grid signals, where the datacenter can increase or decrease its power consumption by adapting its workload in real time.
In order to understand the impact of large datacenters on the grid and to facilitate the design of effective coordination strategies, we build OpenG2G, a simulation platform for AI datacenter‑grid runtime coordination. We show that OpenG2G is capable of answering a wide range of coordination questions by allowing users to implement and compare various control paradigms (including classic, optimization, and learning‑based controllers), and quantify how AI model and deployment choices affect datacenter flexibility and coordination outcomes. This versatility is enabled by OpenG2G's modular and extensible architecture: a datacenter backend driven by real measurements of production‑grade AI services, a grid backend built on high‑fidelity grid simulators, and a generic controller interface that closes the loop between them. We describe the design of OpenG2G and demonstrate its usefulness through realistic grid scenarios and AI workloads.
Authors:Taeyoung Kim, Joon-Hyuk Ko
Abstract:
We propose an architecture that augments the Flux Neural Operator (Flux NO), which combines the classical finite volume method (FVM) with neural operators, with ViT‑based context injection. Our model is formulated as a hypernetwork: it extracts solution dynamics over a finite temporal window, encodes them with a recurrent Vision Transformer, and generates the parameters of a context‑conditioned neural operator. This enables the model to infer and solve conservation laws without explicit access to the governing equation or PDE coefficients. Experimentally, we show that the proposed method preserves the robustness, generalization ability, and long‑time prediction advantages of Flux NO over standard neural operators, while delivering reliable numerical solutions across a broad range of conservative systems, including previously unseen fluxes. Our code is available at https://github.com/xx257xx/CONTEXT_FLUX_NO.
Authors:Othmane Kabal, Mounira Harzallah, Fabrice Guillet, Hideaki Takeda, Ryutaro Ichise
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.
Authors:Daniel Grimmer
Abstract:
Evolutionary computation has long promised to deliver both high‑performance optimization tools as well as rigorous scientific simulations of Darwinian evolution. However, modern algorithms frequently abandon evolutionary fidelity for physics‑inspired heuristics or superficial biological metaphors. This paper derives a suite of advanced gradient‑based optimization algorithms directly from evolutionary first principles. We introduce Darwinian Lineage Simulations (DLS) to prove that, in an asexual context, Fisher's and Wright's historically opposed views of evolution are actually formally equivalent; One can partition Fisher's deterministically‑evolving total population into Wright's randomly‑drifting sub‑populations. We prove that proper bookkeeping requires introducing a specific kind of structured noise (the DLS noise relation). Crucially, any bookkeeping choices which satisfy this relation will yield a faithful simulation of evolution. Using this vast representational freedom, we prove that a broad family of battle‑tested optimization algorithms are already perfectly compatible with evolutionary dynamics. These include: Stochastic Gradient Descent as well as many regularizations/approximations of Newton's method and Natural Gradient Descent. By simply adding DLS noise (i.e., evolutionarily faithful genetic drift), these algorithms become scientifically valid in silico simulations of Darwinian evolution. Finally, we demonstrate that even the state‑of‑the‑art Adam optimizer can be brought into evolutionary compliance through a minor mathematical surgery.
Authors:Ahmed Abdelmuniem Abdalla Mohammed
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.
Authors:Yi Xie, Yangyang Xu, Yi Fan, Bo Liu
Abstract:
Large language models (LLMs) with a large number of parameters achieve strong performance but are often prohibitively expensive to deploy. Recent work explores using teams of smaller, more efficient LLMs that collectively match or even outperform a single large model. However, jointly updating multiple agents introduces compounding distribution shifts, making coordination and stability during training difficult. We address this by introducing Sequential Agent Tuning (SAT), a coordinator‑free training paradigm. SAT represents the team as a factorized policy and employs block‑coordinate updates over agents, enabling scalable, decentralized training without a central controller. Specifically, we develop a sequence‑aware, on‑policy advantage estimator that conditions on the evolving team policy, coupled with per‑agent KL trust regions that isolate occupancy drift. Theoretically, this framework provides two critical guarantees. First, it ensures monotonic improvement, stabilizing the training process. Second, it establishes provable plug‑and‑play invariance: any agent can be upgraded to a stronger model without retraining the rest of the team, with a formal guarantee that the performance bound improves. Empirically, a team of three 4B agents (12B total) trained with SAT surpasses the much larger Qwen3‑32B on AIME24/25 benchmarks by 3.9% on average. We validate our plug‑and‑play theory by swapping in two 8B agents, which boosts the composite score by 10.4%. We provide code and appendix of proof at https://github.com/Yydc/SAT‑AAMAS
Authors:Wilson Wu, Victor Lecomte, Michael Winer, George Robinson, Jacob Hilton, Paul Christiano
Abstract:
By far the most common way to estimate an expected loss in machine learning is to draw samples, compute the loss on each one, and take the empirical average. However, sampling is not necessarily optimal. Given an MLP at initialization, we show how to estimate its expected output over Gaussian inputs without running samples through the network at all. Instead, we produce approximate representations of the distributions of activations at each layer, leveraging tools such as cumulants and Hermite expansions. We show both theoretically and empirically that for sufficiently wide networks, our estimator achieves a target mean squared error using substantially fewer FLOPs than Monte Carlo sampling. We find moreover that our methods perform particularly well at estimating the probabilities of rare events, and additionally demonstrate how they can be used for model training. Together, these findings suggest a path to producing models with a greatly reduced probability of catastrophic tail risks.
Authors:Jingsen Zhu, Silvia Sellán, Alexander Terenin
Abstract:
We develop a framework for task‑specific active next‑best‑view selection in 3D reconstruction from point clouds, by casting the problem in the language of Bayesian decision theory. Our framework works by (a) placing a prior distribution over the space of implicit surfaces, (b) using recently‑developed stochastic surface reconstruction methods to calculate the resulting posterior distribution, then (c) using the posterior distribution to carefully reason about which view to scan next. This enables us to perform camera selection in a manner that is directly optimized for the intended use of the reconstructed data ‑ meaning, we reduce uncertainty only in those regions that make a difference in the task at hand, as opposed to prior approaches that reduce it uniformly across space. We evaluate our method across three distinct downstream tasks: semantic classification, segmentation, and PDE‑guided physics simulation. Experimental results demonstrate that our framework achieves superior task performance with fewer views compared to commonly used baselines and prior general uncertainty‑reduction techniques.
Authors:Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis, Panagiotis Thomakos, Andreas Sendros
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.
Authors:Senkang Hu, Yong Dai, Xudong Han, Zhengru Fang, Yuzhi Zhao, Sam Tak Wu Kwong, Yuguang Fang
Abstract:
Long‑horizon LLM agents depend on intermediate information‑gathering turns, yet training feedback is usually observed only at the final answer, because process‑level rewards require high‑quality human annotation. Existing turn‑level shaping methods reward turns that increase the likelihood of a gold answer, but they require answer supervision or stable task‑specific verifiers. Conversely, label‑free RL methods extract self‑signals from output distributions, but mainly at the answer or trajectory level and therefore cannot assign credit to intermediate turns. We propose Self‑Induced Outcome Potential (SIOP), which treats semantic clusters of final answers as latent future outcome states for potential‑based turn‑level credit assignment. For each query, SIOP samples multiple rollouts, clusters final answers into semantic outcome modes, and builds a reliability‑aware target distribution over these states. It then rewards turns for increasing posterior support for reliable future states using a tractable cluster‑level approximation. The objective generalizes information‑potential shaping from gold‑answer supervision to settings without task‑specific gold verifiers while avoiding the broadcasted rollout‑level advantages used by standard GRPO. We formalize the framework, characterize its supervised gold‑answer limit, and show that SIOP improves average performance over verifier‑free outcome‑level baselines on seven search‑augmented agentic reasoning benchmarks while approaching a gold‑supervised outcome baseline. Code is available at https://github.com/dl‑m9/SIOP.git.
Authors:Han Wang, Jintao Zhang, Kai Jiang, Haoxu Wang, Jianfei Chen, Jun Zhu
Abstract:
LLM‑based Triton kernel generation has attracted significant interest, yet a fundamental empirical question remains unanswered: where does this capability break down, and why? We present KernelBenchX, a benchmark designed to answer this question through category‑aware evaluation of correctness and hardware efficiency across 176 tasks in 15 categories. Our systematic comparison of five representative methods yields three main findings. First, task structure determines correctness more than method design. Category explains nearly three times more variance in semantic correctness than method (9.4% vs 3.3% explained deviance), and 72% of Fusion tasks fail across all five methods while Math tasks are solved consistently. Second, iterative refinement improves correctness, but not performance. Across GEAK iterations, compile rate rises from 52.3% to 68.8% while average speedup declines from 1.58× to 1.44×; newly rescued kernels consistently underperform persistently correct ones (1.16× vs 1.58× speedup in round~0\to1). Third, correctness does not imply efficiency. 46.6% of correct kernels are slower than the PyTorch eager baseline, and cross‑hardware speedup variance reaches 21.4×. Besides, quantization remains completely unsolved (0/30 successes) despite non‑trivial compilation rates, revealing systematic misunderstanding of numerical computation contracts rather than surface‑level syntax errors. These findings suggest that future progress depends on handling global coordination, explicitly modeling numerical precision, and incorporating hardware efficiency into generation. The code is available at https://github.com/BonnieW05/KernelBenchX
Authors:Weibin Gu, Chen Yang, Lu Shi
Abstract:
Learning tractable linear representations of nonlinear dynamical systems via Koopman operator theory is often hindered by dictionary selection, temporal memory encoding, and numerical ill‑conditioning. Inspired by Reservoir Computing (RC) paradigm, this paper introduces the RC‑Koopman framework, which interprets reservoir as a stateful, finite‑dimensional Koopman dictionary whose temporal depth is explicitly controlled by its spectral radius. We show that the Echo State Property (ESP) guarantees well‑posedness and favorable numerical conditioning of the lifted Koopman approximation. A correlation‑based spectral radius selection algorithm aligns reservoir memory with dominant system timescales. Analysis reveals how the finite memory of the reservoir determines which Koopman eigenfunctions remain observable from the lifted features. Evaluation on synthetic benchmarks demonstrates that RC‑Koopman achieves a favorable balance between reconstruction accuracy of the underlying nonlinear dynamics and dynamical stability, compared to Extended Dynamic Mode Decomposition (EDMD) and Hankel‑based lifting approaches. Code available at: https://github.com/NEAR‑the‑future/RC‑Koopman.git
Authors:Yin Jun Phua
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.
Authors:Hengyu Shi, Tianyang Han, Peizhe Wang, Zhiling Wang, Xu Yang, Junhao Su
Abstract:
LLM post‑training typically propagates task gradients through the full depth of the model. Although this end‑to‑end structure is simple and general, it couples task adaptation to full‑depth activation storage, long‑range backward dependencies and direct task‑gradient access to pretrained representations. We argue that this full‑depth backward coupling can be unnecessarily expensive and intrusive, particularly when post‑training supervision is much narrower than pre‑training. To this end, we propose LoPT: Local‑Learning Post‑Training, a simple post‑training strategy that makes gradient reach an explicit design choice. LoPT places a single gradient boundary at the transformer midpoint: the second‑half block learns from the task objective, while the first‑half block is updated by a lightweight feature‑reconstruction objective to preserve useful representations and maintain interface compatibility. LoPT shortens the task‑induced backward path while limiting direct interference from narrow task gradients on early‑layer representations. Extensive experiments demonstrate that LoPT achieves competitive performance with lower memory cost, higher training efficiency and better retention of pretrained capabilities. Our code is available at: https://github.com/HumyuShi/LoPT
Authors:Mohamed Elhabebe, Ayman El-Baz, Qing Liu
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
Authors:Shereen Elsayed, Ngoc Son Le, Ahmed Rashed, Lars Schmidt-Thieme
Abstract:
Attribute‑aware sequential recommendation entails predicting the next item a user will interact with based on a chronologically ordered history of past interactions, enriched with item attributes. Existing methods typically leverage self‑attention mechanisms to aggregate the entire sequence into a unified representation used for next‑item prediction. While effective, these models often suffer from high computational complexity and memory consumption, limiting their ability to process long user histories. This constraint restricts the model's capacity to fully capture long‑term user preferences. In some scenarios, modeling item interactions purely through attention may also not be the most effective approach to extract sequential patterns. In this work, we propose ConvRec, an alternative method with linear computational and memory complexity that employs convolutional layers in a hierarchical, down‑scaled fashion to generate compact, yet expressive sequence representations. To further enhance the model's ability to capture diverse sequential patterns, each layer aggregates the neighboring items gradually to reach a comprehensive sequence representation. Extensive experiments on four real‑world datasets demonstrate that our approach outperforms state‑of‑the‑art sequential recommendation models, highlighting the potential of convolution‑based architectures for efficient and effective sequence modeling in recommendation systems. Our implementation code and datasets are available here https://github.com/ismll‑research/ConvRec.
Authors:Guangsheng Bao, Hongbo Zhang, Han Cui, Ke Sun, Yanbin Zhao, Juncai He, Yue Zhang
Abstract:
Adapting pretrained models typically involves a trade‑off between the high training costs of backpropagation and the heavy inference overhead of memory‑based or in‑context learning. We propose FAAST, a forward‑only associative adaptation method that analytically compiles labeled examples into fast weights in a single pass. By eliminating memory or context dependence, FAAST achieves constant‑time inference and decouples task adaptation from pretrained representation. Across image classification and language modeling benchmarks, FAAST matches or exceeds backprop‑based adaptation while reducing adaptation time by over 90% and is competitive to memory/context‑based adaptation while saving memory usage by up to 95%. These results demonstrate FAAST as a highly efficient, scalable solution for supervised task adaptation, particularly for resource‑constrained models. We release the code and models at https://github.com/baoguangsheng/faast.
Authors:Ivan Bondarenko, Roman Derunets, Oleg Sedukhin, Mikhail Komarov, Ivan Chernov, Mikhail Kulakov
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
Authors:Jingtao Zhou, Xirui Kang, Feiyang Huang, Lai-Man Po
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.
Authors:Keyu Chen, Nanfei Ye, Yida Wang, Wenchao Sun, Danqi Zhao, Hao Cheng, Sifa Zheng
Abstract:
Open‑loop imitation learning has advanced modern autonomous driving policy architectures, but closed‑loop deployment remains vulnerable to policy‑induced distribution shift. Existing post‑training paradigms exhibit fundamental trade‑offs: closed‑loop RL fine‑tuning provides grounded feedback from executed actions but is constrained by the sparsity of informative events, whereas counterfactual fine‑tuning provides dense supervision over candidate futures but inherits bias from imperfect future estimates. We introduce Counterfactual‑to‑Interactive Reinforcement Fine‑Tuning (CRAFT), an on‑policy framework that formulates closed‑loop post‑training as proxy‑residual optimization. CRAFT uses group‑normalized counterfactual advantages as a dense proxy for real closed‑loop advantages and aligns this proxy with the closed‑loop world through grounded residual correction from interaction‑critical events. To stabilize adaptation, CRAFT regularizes the online policy toward an EMA teacher via asymmetric KL self‑distillation. Theoretically, CRAFT decomposes the real closed‑loop policy gradient into proxy and residual terms under the same visited‑state distribution, reducing residual variance with an aligned proxy while mitigating proxy bias through grounded residual approximation. Empirically, CRAFT achieves the strongest closed‑loop gains on Bench2Drive across hierarchical planning, vision‑language‑action, and vocabulary‑scoring architectures. Ablations, scaling behavior, stability analyses, and transfer results further validate the complementary roles of dense counterfactual proxy and grounded residual correction. Project page: https://currychen77.github.io/CRAFT.
Authors:Fatima Ashraf, Muhammad Ayub Sabir, Junbiao Pang, Yufang Zhou, Yan Shang
Abstract:
Transportation surveys are widely used to understand travel preferences and adoption barriers, yet most survey‑based analyses remain descriptive or predictive and rarely provide sparse, policy‑feasible intervention strategies. We study sparse counterfactual community intervention from survey responses, where the goal is to shift a target respondent group toward a desired reference group through controllable survey‑variable adjustments. We formulate this task as a policy‑feasible distributional alignment problem using a fixed‑basis nonnegative latent representation that preserves pre/post comparability and provides a stable map from latent factors to original variables. To make latent movement actionable, target‑relevant latent factors are identified through Shapley‑guided attribution and transferred to controllable variables as intervention priorities. Feasible group‑level adjustments are then learned by minimizing an entropy‑regularized optimal‑transport discrepancy between the post‑intervention target distribution and the reference distribution, together with a weighted \ell_2,1 penalty that promotes shared policy‑lever sparsity. Experiments on real‑world transportation survey datasets show that the proposed framework produces compact and interpretable policy‑feasible interventions with explicit adjustment magnitudes, improves population‑level conversion, and preserves intervention sparsity. Code and datasets are publicly available at: https://github.com/pangjunbiao/latent‑group‑alignment.git
Authors:Seunghan Lee
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.
Authors:Nicholas J. Cooper, François G. Meyer, Michael L. Roberts, Carlos Zapata-Carratalá, Lijun Chen, Danna Gurari
Abstract:
We introduce a unified theoretical framework for the rigorous analysis and systematic construction of deep neural networks (DNNs). This framework addresses a gap in existing theory by explicitly modeling the structure of tensor operations ‑‑ lower level information that is often abstracted. Our framework enables two novel objectives: (1) analysis of the evolution of architectural complexity over deep learning history, and (2) automatic construction of novel architectures based on new types of tensor operations. Our study of DNNs introduced over the past 40 years reveals a connection between groundbreaking architectures and increases in different types of architectural complexity. Moreover, we identify several large classes of higher complexity architectures that have not yet been explored. We then collect a dataset of 3,000+ higher complexity architectures, which we publicly release at: https://github.com/combinatoriallabs/ArchitecturalComplexity.
Authors:Etienne Gauthier, Francis Bach, Michael I. Jordan
Abstract:
Reinforcement learning from human feedback (RLHF) typically assumes a static or non‑strategic reward model (RM). In iterative deployment, however, the policy generates the data on which the RM is retrained, creating a feedback loop. Building on the Stackelberg game formulation of this interaction, we derive an analytical decomposition of the policy's true optimization gradient into a standard policy gradient and a parameter‑steering term that captures the policy's influence on the RM's future parameters. We show that standard iterative RLHF, which drops this steering term entirely, suffers from alignment collapse: the policy systematically exploits the RM's blind spots, producing low‑quality, high‑reward outputs whose feedback reinforces the very errors it exploits. To mitigate this, we propose foresighted policy optimization (FPO), a mechanism‑design intervention that restores the missing steering term by regularizing the policy's parameter‑steering effect on RM updates. We instantiate FPO via a scalable first‑order approximation and demonstrate that it prevents alignment collapse on both controlled environments and an LLM alignment pipeline using Llama‑3.2‑1B.
Authors:Stephen Price, Kyle Miller, Marco Musto, Kenneth Kroenlein, James Saal, Kyle Tsaknopoulos, Elke A. Rundensteiner, Danielle L. Cote
Abstract:
Cold spraying is an increasingly common approach for repairing and manufacturing components due to its solid‑state manufacturing capabilities. However, process optimization remains difficult due to many interdependent parameters and the lack of large‑scale, machine‑readable data to support modeling. While the scientific literature contains many relevant experiments, results are inconsistently reported (often in tables and figures) and use non‑uniform units, limiting utilization at scale. To address these limitations, this work presents HUGO‑CS, a literature‑derived dataset of 4,383 cold‑spray experiments with 144 features from 1,124 sources, exceeding the previous largest dataset (137 samples) by 30x. With completely manual extraction requiring an average of 91 minutes per document, this work designs and leverages a Hybrid‑labeled, Uncertainty‑aware, General‑purpose, Observational extraction framework, called HUGO, to support this extraction. HUGO combines automated LLM‑based labeling with targeted manual label refinement to handle this experimental result extraction process from scientific literature. To balance labeling efficiency with extraction accuracy, HUGO introduces a Hierarchical Risk Mitigation (HRM) to route LLM outputs with a high risk of potential errors for manual review, while retaining low‑risk records as auto‑labeled. Lastly, HUGO post‑processing consolidates categorical descriptors, maps reported feedstock chemistries into structured continuous compositions, and normalizes units across sources. Of the 4,383 reported experiments, 1,765 are hand‑labeled, providing a high‑quality labeled subset for benchmarking, error analysis, and higher‑fidelity data points. All code to replicate this work, along with the complete HUGO‑CS dataset, are released under a CC‑BY license at https://github.com/sprice134/HUGO.
Authors:Yaobo Zhang
Abstract:
Relative positional encodings determine which functions of query‑key lag can enter the primitive attention logit. RoPE supplies a rotary phase, while ALiBi supplies an additive distance bias. Motivated by group‑theoretic views of linear translation‑invariant positional encodings, we study a non‑semisimple case in which a complex rotary eigenvalue and a nilpotent response live in the same defective Jordan block. The resulting relative operator generates oscillatory‑polynomial features such as e^‑γd\cos(ωd), e^‑γd\sin(ωd), d e^‑γd\cos(ωd), and d e^‑γd\sin(ωd), for causal lag d=i‑j\geq 0. Thus the construction realizes a distance‑modulated phase basis d e^iωd, rather than merely adding a separate distance channel to RoPE.
We formulate Exact Jordan‑RoPE as a non‑semisimple one‑parameter representation, give its real block form, and specify the contragredient query action required by non‑orthogonal positional maps. We also distinguish this exact representation from stabilized variants whose bounded shear improves numerical behavior but breaks the exact group law. Kernel‑level diagnostics and a Jordan‑friendly synthetic language‑model task show that the coupled Jordan basis is useful when the target contains distance‑modulated phase interactions. On a small WikiText‑103 byte language model, a scaled‑exact variant improves over RoPE and direct‑sum baselines within the Jordan family, while RoPE+ALiBi remains strongest overall. The evidence is structural rather than a broad performance claim.
Authors:Lin Song, Wenbo Li, Guoqing Ma, Wei Tang, Bo Wang, Yuan Zhang, Yijun Yang, Yicheng Xiao, Jianhui Liu, Yanbing Zhang, Guohui Zhang, Wenhu Zhang, Hang Xu, Nan Jiang, Xin Han, Haoze Sun, Maoquan Zhang, Haoyang Huang, Nan Duan
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.
Authors:Shu-Hao Zhang, Le-Tong Huang, Xiang-Sheng Deng, Xin-Yi Zou, Chen Wu, Nan Li, Shao-Qun Zhang, Zhi-Hua Zhou
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.
Authors:Nicolas Michel, Maorong Wang, Jiangpeng He, Toshihiko Yamasaki
Abstract:
Deep learning models continue to scale, with some requiring more storage than many large‑scale datasets. Thus, we introduce a new paradigm: Continual Distillation (CD), where a student learns sequentially from a stream of teacher models without retaining access to earlier teachers. CD faces two challenges: teacher training data is unavailable, and teachers have varying expertise. We show that external unlabeled data enables Unseen Knowledge Transfer (UKT), allowing the student to acquire information from domains not present in the training data, while known to the teacher. We also show that sequential distillation causes Unseen Knowledge Forgetting (UKF) when transferred knowledge is lost after training on later teachers. To better trade off between UKT and UKF, we propose Self External Data Distillation (SE2D), a method that preserves logits on external data to stabilize learning across heterogeneous teachers. Experiments on multiple benchmarks show that SE2D reduces UKF and improves cross‑domain generalization. The code and implementation for this work are publicly available at: https://github.com/Nicolas1203/continual_distillation.
Authors:Evangelos Ntavelis, Sean Wu, Mohamad Shahbazi, Fabio Maninchedda, Dmitry Kostiaev, Artem Sevastopolsky, Vittorio Megaro, Trevor Phillips, Alejandro Blumentals, Shridhar Ravikumar, Mehak Gupta, Reinhard Knothe, Jeronimo Bayer, Matthias Vestner, Simon Schaefer, Thomas Etterlin, Christian Zimmermann, Mathias Deschler, Peter Kaufmann, Stefan Brugger, Sebastian Martin, Brian Amberg, Tom Runia
Abstract:
We propose HeadsUp, a scalable feed‑forward method for reconstructing high‑quality 3D Gaussian heads from large‑scale multi‑camera setups. Our method employs an efficient encoder‑decoder architecture that compresses input views into a compact latent representation. This latent representation is then decoded into a set of UV‑parameterized 3D Gaussians anchored to a neutral head template. This UV representation decouples the number of 3D Gaussians from the number and resolution of input images, enabling training with many high‑resolution input views. We train and evaluate our model on an internal dataset with more than 10,000 subjects, which is an order of magnitude larger than existing multi‑view human head datasets. HeadsUp achieves state‑of‑the‑art reconstruction quality and generalizes to novel identities without test‑time optimization. We extensively analyze the scaling behavior of our model across identities, views, and model capacity, revealing practical insights for quality‑compute trade‑offs. Finally, we highlight the strength of our latent space by showcasing two downstream applications: generating novel 3D identities and animating the 3D heads with expression blendshapes.
Authors:Skye Gunasekaran, Téa Wright, Rui-Jie Zhu, Jason Eshraghian
Abstract:
Several recent Transformer architectures expose later layers to representations computed in the earliest layers, motivated by the observation that low‑level features can become harder to recover as the residual stream is repeatedly transformed through depth. The cheapest among these methods add static value residuals: learned mixing coefficients that expose the first‑layer value projection V_1 uniformly across tokens and heads. More expressive dense or dynamic alternatives recover finer‑grained access, but at higher memory cost and lower throughput. The usefulness of V_1 is unlikely to be constant across tokens, heads, and contexts; different positions plausibly require different amounts of access to early lexical or semantic information. We therefore treat early‑representation reuse as a retrieval problem rather than a connectivity problem, and introduce Selective Access Transformer (SATFormer), which preserves the first‑layer value pathway while controlling access with a context‑dependent gate. Across models from 130M to 1.3B parameters, SATFormer consistently improves validation loss and zero‑shot accuracy over the static value‑residual and Transformer baselines. Its strongest gains appear on retrieval‑intensive benchmarks, where it improves over static value residuals by approximately 1.5 average points, while maintaining throughput and memory usage close to the baseline Transformer. Gate analyses suggest sparse, depth‑dependent, head‑specific, and category‑sensitive access patterns, supporting the interpretation that SATFormer learns selective reuse of early representations rather than uniform residual copying. Our code is available at https://github.com/SkyeGunasekaran/SATFormer.
Authors:Xun Jiang, Yufan Gu, Disen Hu, Yuqing Hou, Yazhou Yao, Fumin Shen, Heng Tao Shen, Xing Xu
Abstract:
Multimodal learning often grapples with the challenge of low‑quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues are often studied in isolation, we argue that they share a common root in the predictive uncertainty towards the reliability of individual modalities and instances during learning. In this paper, we propose a unified framework, termed Conformal Predictive Self‑Calibration (CPSC), which leverages conformal prediction to equip the model with the ability to perform self‑guided calibration on‑the‑fly. The core of our proposed CPSC lies in a novel self‑calibrating training loop that seamlessly integrates two key modules: (1) Representation Self‑Calibration, which decomposes unimodal features into components, and selectively fuses the most robust ones identified by a conformal predictor to enhance feature resilience. (2) Gradient Self‑Calibration, which recalibrates the gradient flow during backpropagation based on instance‑wise reliability scores, steering the optimization towards more trustworthy directions. Furthermore, we also devise a self‑update strategy for the conformal predictor to ensure the entire system co‑evolves consistently throughout the training process. Extensive experiments on six benchmark datasets under both imbalanced and noisy settings demonstrate that our CPSC framework consistently outperforms existing state‑of‑the‑art methods. Our code is available at https://github.com/XunCHN/CPSC.
Authors:Valery Manokhin
Abstract:
The Brier score conflates two distinct properties of probabilistic predictions: reliability (calibration error) and resolution (discriminatory power). We introduce the Manokhin Probability Matrix, a BCG‑style two‑dimensional diagnostic framework that separates them. Classifiers are placed on a 2x2 grid by Spiegelhalter Z‑statistic and AUC‑ROC expected rank, then assigned to one of four archetypes: Eagle (good on both axes), Bull (strong discrimination, poor calibration), Sloth (well‑calibrated, weak discriminator), and Mole (poor on both). Each archetype carries a distinct prescription. We populate the matrix from a large‑scale empirical study spanning 21 classifiers, 5 post‑hoc calibrators, and 30 real‑world binary classification tasks from the TabArena‑v0.1 suite. The assignment is unambiguous. CatBoost, TabICL, EBM, TabPFN, GBC, and Random Forest are Eagles. XGBoost, LightGBM, and HGB are Bulls; Venn‑Abers calibration cuts log‑loss by 6.5 to 12.6% on Bulls but degrades Eagles by 2.1%. SVM, LR, LDA, and the empirical base‑rate predictor are Sloths. MLP, KNN, Naive Bayes, and ExtraTrees are Moles. A theoretical asymmetry follows: no order‑preserving post‑hoc calibrator can add discriminatory power (Proposition 1), so calibration is the fixable part and discrimination is the hard part. The practical rule is direct: do not optimise aggregate Brier score without first decomposing it; optimise discrimination first, then fix calibration post‑hoc. Code and raw experimental data are available at https://github.com/valeman/classifier_calibration.
Authors:Faraz Kayani, Sarmad Kayani, Asad Ahmed, Radu Timofte, Dmitry Ignatov
Abstract:
While deep‑learning‑based image restoration has achieved unprecedented fidelity, deployment on mobile Neural Processing Units (NPUs) remains bottlenecked by operator incompatibility and memory‑access overhead. We propose an NPU‑aware hardware‑algorithm co‑design approach for real‑world image denoising on mobile NPUs. Our approach employs a high‑capacity teacher to supervise a lightweight student network specifically designed to leverage the tiled‑memory architectures of modern mobile SoCs. By prioritizing NPU‑native primitives ‑‑ standard 3x3 convolutions, ReLU activations, and nearest‑neighbor upsampling ‑‑ and employing a progressive context expansion strategy (up to 1024x1024 crops), the model achieves 37.66 dB PSNR / 0.9278 SSIM on the validation benchmark and 37.58 dB PSNR / 0.9098 SSIM on the held‑out test benchmark at full resolution (2432x3200) in the Mobile AI 2026 challenge. Following the official challenge rules, the inference runtime is measured under a standardized Full HD (1088x1920) protocol, where it runs in 34.0 ms on the MediaTek Dimensity 9500 and 46.1 ms on the Qualcomm Snapdragon 8 Elite NPU. We further reveal an "Inference Inversion" effect, where strict adherence to NPU‑compatible operations enables dedicated NPU execution up to 3.88x faster than the integrated mobile GPU. The 1.96M‑parameter student recovers 99.8% of the teacher's restoration quality via high‑alpha knowledge distillation (alpha = 0.9), achieving a 21.2x parameter reduction while closing the PSNR gap from 1.63 dB to only 0.05 dB. These results establish hardware‑aware distillation as an effective strategy for unifying high‑fidelity denoising with practical deployment across diverse mobile NPU architectures. The proposed lightweight student model (LiteDenoiseNet) and its training statistics are provided in the NN Dataset, available at https://github.com/ABrain‑One/NN‑Dataset.
Authors:Raphaël Le Bidan, Ahmad Ismail, Elsa Dupraz, Charbel Abdel Nour
Abstract:
Syndrome‑based neural decoding (SBND) has emerged as a promising deep learning approach for soft‑decision decoding of high‑rate, short‑length codes. However, this approach still has substantial room for improvement. In this paper, we show how to leverage code automorphisms to enhance the ability of existing SBND models to learn and generalize through data augmentation during training and inference. As a result, for the short high‑rate codes considered, we obtain models that closely approach MLD performance using small datasets and proper training. Our findings also suggest that many prior results for SBND models in the literature underestimate their true correction capability due to undertraining. Code to reproduce all results is available at: https://github.com/lebidan/sbnd.
Authors:Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
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.
Authors:Al Zadid Sultan Bin Habib, Gianfranco Doretto, Donald A. Adjeroh
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.
Authors:Akshat Singh Jaswal, Ashish Baghel, Paras Chopra
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.
Authors:Alexander Matyasko, Xin Lou, Indriyati Atmosukarto, Wei Zhang
Abstract:
Attacking semantic segmentation models is significantly harder than image classification models because an attacker must flip thousands of pixel predictions simultaneously. Standard pixel‑wise cross‑entropy (CE) is ill‑suited to this setting: it tends to overemphasize already‑misclassified pixels, which slows optimization and overstates model robustness. To address these issues, we introduce TsallisPGD, an adversarial attack built on the Tsallis cross‑entropy, a generalization of CE parameterized by q, which adaptively reshapes the gradient landscape by controlling gradient concentration across pixels. By varying q, we steer the attack toward pixels at different confidence levels. We first show that no single fixed‑q is universally optimal, as its effectiveness depends on the dataset, model architecture, and perturbation budget. Motivated by this, we propose a dynamic q‑schedule that sweeps q during optimization. Extensive experiments on Cityscapes, Pascal VOC, and ADE20K show that TsallisPGD, using a single validation‑selected schedule, achieves the best average attack rank across all evaluated settings and improves over CEPGD, SegPGD, CosPGD, JSPGD, and MaskedPGD in reducing accuracy and mIoU on both standard and robust models.
Authors:Jonathan Muhire
Abstract:
Donor‑level disease classification from single‑cell RNA sequencing (scRNA‑seq) requires strict donor‑aware cross‑validation: naive pipelines that split cells randomly conflate training and test donors, inflating reported performance through pseudoreplication. We present a donor‑aware benchmark evaluating three feature representations across two independent IBD cohorts: centered log‑ratio (CLR) transformed cell‑type composition, GatedStructuralCFN dependency embeddings, and scVI variational autoencoder latent embeddings. The cohorts are the SCP259 ulcerative colitis atlas (UC vs. Healthy, n=30 donors, 51 cell types) and the Kong 2023 Crohn's disease atlas (CD vs. Healthy, n=71 donors, 55‑68 cell types across three intestinal regions).
Compartment‑stratified CLR composition achieves AUROC 0.956 +/‑ 0.061 on SCP259; GatedStructuralCFN on the same features achieves 0.978 +/‑ 0.050. In the Kong cohort, CFN achieves its best performance in the colon region (0.960 +/‑ 0.055 after feature filtering), exceeding linear CLR (0.900 +/‑ 0.100), while terminal ileum classification is dominated by linear models (CatBoost CLR 0.967 +/‑ 0.075 vs. CFN 0.811 +/‑ 0.164). Cross‑dataset transfer (CD‑>UC, four shared cell types) achieves AUC 0.833 with XGBoost CLR; the reverse direction performs at chance. CFN edge stability analysis shows that compartment‑wise composition eliminates spurious unit‑sum‑induced instability present in global composition (Jaccard 0.026 vs. top‑20 recurrence 1.0). CFN shows a consistent numerical advantage over linear models in the colon region of CD (AUROC 0.960 vs. 0.900), though no inter‑method comparison reached statistical significance at n<=34 donors per region. Compartment‑aware feature construction is critical for both classification performance and structural interpretability. Code: https://github.com/Jonathan‑321/sfn‑scrna‑study
Authors:Gabriel Garcia
Abstract:
Large language models often fail at simple counting tasks, even when items to count are in the prompt. We investigate whether this failure occurs because transformers do not represent counts internally, or because they cannot convert representations to the correct output tokens. Across three model families: Pythia, Qwen3, and Mistral, ranging from 0.4B to 14B parameters, we find evidence for the second explanation. Linear probes recover the correct count from intermediate layers with R^2>0.99, showing that the information is present. However, the internal directions that encode counts are nearly orthogonal to digit‑token output‑head rows (|\cos| \leq 0.032). In other words, the model stores the count in a form that the digit logits do not naturally read out. We localize this failure with two interventions. Updating only the digit rows of the output head (36,864 parameters) substantially improves constrained digit prediction (60.7‑‑100.0% on four tasks), but it does not fix unconstrained generation (0%); we do not claim that digit‑row repair fixes open‑ended text. By contrast, small LoRA on attention Q/V (7.67M parameters) improves upstream routing and achieves 83.1%\pm7.2% in true greedy autoregressive generation (deployable fix). Logit‑lens at layer 35 (entity counting; correct‑digit rank): (i) median over 3 seeds drops from order‑10^4 to 1; (ii) seed 42 shows 54,332 \to 838 (median top‑1 while one seed stays far below). Norm, logit‑lens, and cross‑task analyses generalize the bottleneck to counting, addition, and list length; nulls on MMLU and GSM8K and limited DROP transfer. These results identify counting failure as a geometric readout bottleneck, not an internal‑representation failure: the model knows the count but the output pathway is misaligned with tokens needed to express it.
Authors:Seunghyun Ji
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.
Authors:Adrian Grassi
Abstract:
Static benchmarks measure a model frozen at training time. Real systems face distribution shift: new categories, paraphrased queries, drift: and must recover online via user corrections. No existing benchmark measures recovery speed under correction streams. We introduce OCRR (Online Correction Recovery Rate): a benchmark that streams a corpus through a classification system, applies oracle or stochastic corrections to wrong predictions, and reports two curves: novel‑class accuracy and original‑distribution accuracy versus correction count. We evaluate the substrate alongside nine baseline algorithms from five families plus seven bounded‑storage variants of the substrate for the Pareto sweep, including standard online‑learning baselines (river), continual‑learning methods (EWC, A‑GEM, LwF), retrieval/parametric hybrids (kNN‑LM), parameter‑efficient fine‑tuning of a 1.5 B‑parameter encoder (LoRA on DeBERTa‑v3‑large), and a hash‑chained append‑only substrate (Substrate). On Banking77 and CLINC150, under oracle and sparse correction policies, the substrate is the only system that simultaneously recovers novel‑class accuracy (88.7 +/‑ 2.9 %) and retains original‑distribution accuracy (95.4 +/‑ 0.8 %) beating the next‑best published continual‑learning baseline by 32.6 percentage points at equal memory budget, and beating LoRA‑on‑DeBERTa‑v3‑large by 84.6 percentage points on retention. We further find that classification accuracy remains stable at 99 % even as approximate‑nearest‑neighbour recall@5 degrades from 0.69 to 0.23 across 10 k to 10 M corpus scales, suggesting the substrate's margin‑band majority vote is robust to retrieval imperfection in a way that pure top‑k recall metrics do not predict. Code and data are available at https://github.com/adriangrassi/ocrr‑benchmark.
Authors:Muhammad Muneeb, David B. Ascher
Abstract:
Predicting complex human traits from genetic data is challenging because different genetic, clinical, and molecular data sources often contain different parts of the signal. Here, we present EFGPP, a reproducible framework for generating, ranking, and combining multiple types of data for genotype‑to‑phenotype prediction. We applied EFGPP to migraine prediction using UK Biobank data from 733 individuals. The framework combined genotype‑derived features, principal components, clinical and metabolomic covariates, and polygenic risk scores generated from migraine and depression GWAS using PLINK, PRSice‑2, AnnoPred, and LDAK‑GWAS. The best single data type achieved a test AUC of 0.644, while combining multiple data types improved performance to 0.688 using migraine‑focused inputs and 0.663 using cross‑trait depression‑derived inputs. Genetic features alone did not outperform the covariates‑only baseline, but genotype‑derived features performed better than PRS alone, and depression‑derived PRS showed useful predictive signal. Overall, EFGPP provides a practical proof‑of‑concept framework for prioritising and integrating heterogeneous genetic data sources for complex phenotype prediction.
Authors:Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao, Heng-Jui Chang, Zeqi Zhou, Haokai Zhao, Ma Jian, Carl Ma, Yu-Chi Cheng, Kuan Pang, Xiangru Tang, Zehong Wang, Guanlue Li, Hanchen Wang, Kejun Ying, Pan Lu, Chiho Im, Seungju Han, Peng Xia, Tinson Xu, Yinxi Li, Deyao Zhu, Pheng-Ann Heng, Naoto Yokoya, Masashi Sugiyama, Li Erran Li, Jure Leskovec, Yejin Choi
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/.
Authors:Bumjun Kim, Albert No
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.
Authors:Shikhar Shukla
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.
Authors:Kevin Riehl, Andres L. Marin, Nikofors Zacharof, Fan Wu, Patrick Langer, Robert Jakob, Anastasios Kouvelas, Georgios Fontaras, Michail A. Makridis
Abstract:
Scientific peer review increasingly struggles to assess reproducibility at the scale and complexity of modern research output. Evaluating reproducibility requires reconstructing experimental dependencies, methodological choices, data flows, and result‑generating procedures, which often exceeds what human reviewers can provide. Agentic Reproducibility Assessment (ARA) formalizes reproducibility assessment as a structured reasoning task over scientific documents. Given a paper, ARA extracts a directed workflow graph linking sources, methods, experiments, and outputs, then evaluates its reconstructability using structural and content‑based scores for reproducibility assessments. Experiments on 213 ReScience C articles ‑ the largest cross‑domain benchmark of human‑validated computational reproducibility studies considered to date ‑ demonstrate ARA's generalizability and consistent workflow reconstruction and assessment across LLMs, model temperatures, and scientific domains. ARA achieves ~61% accuracy on three benchmarks, and the highest accuracy reported on ReproBench (60.71% vs. 36.84%) and GoldStandardDB (61.68% vs. 43.56%), highlighting its potential to complement human review at scale and enabling next‑generation peer review. Code and Data available: https://github.com/AndresLaverdeMarin/agentic_reproducibility_assessment.
Authors:Jaber Jaber, Osama Jaber
Abstract:
DeepSeek‑V3.2 and V4 introduce Compressed Sparse Attention (CSA): a lightning indexer (a learned scoring projection over compressed keys) scores them, the top‑k are selected per query, and a sparse attention kernel reads only those. Public CSA implementations materialize a [B, S, H_I, T] FP32 score tensor before the top‑k reduction. With H_I=64 indexer heads and the V4‑Flash compression ratio m=4, that intermediate is 256 GB at sequence length S=65,536, exceeding any single‑GPU high‑bandwidth‑memory (HBM) budget. We present StreamIndex, a Triton implementation of the CSA pipeline whose central component is a chunked partition‑merge top‑k driver that never materializes the full intermediate. On synthetic‑but‑realistic V4‑shaped inputs at the indexer‑step (layer) level on a single NVIDIA H200, the materialize path runs out of memory (OOMs) at S=65,536 with V4‑Flash dimensions; StreamIndex runs the same indexer to S=1,048,576 with 6.21 GB peak HBM, a 32x regime extension. Set‑overlap recall against the materialize ground truth is bit‑exact at small S where both fit; across three 5‑point design‑space sweeps (chunk size, key‑tile size, top‑k), mean recall rounds to 1.0000 with min recall at least 0.9980 in every cell. The chunked driver composes with TileLang's pipelined attention kernel: at S=262,144 with V4‑Flash dimensions, the materialize indexer paired with TileLang attention OOMs while the chunked indexer paired with the same attention runs in 1.97 s at 18.56 GB peak. Our contribution targets the indexer step; we make no claim of a faster attention kernel or of real‑checkpoint end‑to‑end behavior. Code: https://github.com/RightNow‑AI/StreamIndex.
Authors:Michael Helcig, Eldar Kurtic, Dan Alistarh
Abstract:
Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade‑offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but typically do not accelerate inference. This paper explores the middle ground of statistically‑lossless compression through three complementary notions of losslessness for quantized LLMs. First, task‑lossless compression preserves zero‑shot benchmark accuracy within natural sampling variance and remains achievable at aggressive bitwidths. Second, we formalize the stricter notion of distribution‑lossless compression, requiring the quantized model's next‑token distribution to be practically indistinguishable from the original, and propose the Expected Acceptance Rate (EAR), the maximum token‑agreement probability under optimal coupling, as a directly interpretable fidelity metric (for example, EAR >= 0.99 indicates 99% agreement). Third, we prove a gamma‑squared variance law showing that symmetric quantization inflates noise variance by gamma squared relative to asymmetric quantization, making asymmetry necessary for distribution‑lossless fidelity but not for task‑level preservation. Using SLQ, a layer‑wise non‑uniform method with asymmetric quantization and wide bitwidth search, we achieve task‑lossless compression at well below 4 bits per parameter (as low as 3.3 bits depending on the model), distribution‑lossless compression at 5 to 6 bits per parameter on average, and inference speedups of 1.7 to 3.6x relative to FP16 with optimized kernels. Source code is available at https://github.com/IST‑DASLab/SLQ.
Authors:Rahul Kumar
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.
Authors:Yan Jiang, Ruihong Qiu, Zi Huang
Abstract:
Recent diffusion large language models (dLLMs) have demonstrated both effectiveness and efficiency in reasoning via a block‑based semi‑autoregressive generation paradigm. Despite their progress, the fixed‑size block generations remain a critical bottleneck for effective and coherent reasoning. 1. From a global perspective, different reasoning tasks would correspond to different optimal decoding block sizes, which makes a ``one‑size‑fits‑all'' assumption ineffective. 2. Even within a single reasoning task, the rigid block partitioning would break the logical flow and reduce reasoning coherence. Through empirical observations, we reveal that for block‑wise entropy, incorrect reasoning exhibits a fluctuating and unsteady trend between blocks, whereas the correctly generated tasks follow a consistent descending trend. Therefore, this paper proposes b1, a novel post‑training framework for dLLMs that learns dynamic‑size reasoning blocks via a Monotonic Entropy Descent objective with reinforcement learning to enhance reasoning coherence.b1 integrates seamlessly as a plug‑and‑play module with existing dLLM's post‑training algorithms. Extensive experiments across various reasoning benchmarks showcase b1's consistent improvement over existing fixed‑size block baselines. Our code has been released at https://github.com/YanJiangJerry/Block‑R1.
Authors:Pawel Kaplanski
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.
Authors:Abdullah Ahmad Khan, Hamid Laga, Ferdous Sohel
Abstract:
Machine unlearning in Vision‑Language Models (VLMs) is required for compliance with the General Data Protection Regulation (GDPR), yet current evaluation practices are inconsistent. We present the first systematic study of metric reliability in multimodal unlearning. Five standard metrics, Forget Accuracy (FA), Retain Accuracy (RA), Membership Inference Attack (MIA), Activation Distance (AD), and JS divergence (JS), yield conflicting method rankings across three VQA benchmarks (MLLMU‑Bench, UnLOK‑VQA, MMUBench). Kendall tau analysis over 36 unlearned LLaVA‑1.5‑7B models reveals two opposing clusters, FA, RA, MIA and AD, JS, with tau_FA_AD = ‑0.26, reproduced on BLIP‑2 OPT‑2.7B. Agreement is lower in multimodal VQA (average tau = 0.086) than in unimodal classification (average tau = 0.158; difference = 0.072), indicating that dual image‑and‑text pathways amplify inconsistency. We introduce the Unified Quality Score (UQS), a composite metric with weights derived from each metric's Spearman correlation with the oracle distance d(M_hat, M_star), where M_star is the oracle model retrained only on the retain set. RA shows the strongest reliability (rho = 0.484, p = 0.003), while FA is negatively correlated (rho = ‑0.418, p = 0.011). UQS yields stable rankings under 100 random weight perturbations (tau = 0.647 +‑ 0.262). We release the benchmark, 36 checkpoints, and an interactive leaderboard. Code and pre‑computed results are available at https://github.com/neurips26/UnifiedUnl.
Authors:Jianing Zhang, Zijian Zhou, Kai Sun
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.
Authors:Peggy Joy Lu, Wei-Yu Chen, Yao-Tsung Huang, Vincent Shin-Mu Tseng
Abstract:
We propose HeroCrystal, a novel privacy‑preserving framework for multi‑camera domain‑adaptive object detection, addressing challenges such as data privacy, class imbalance, and heterogeneous architectures. Our framework consists of three key stages. In the Generated Stage, we introduce a one‑shot, target‑aware diffusion‑based generation module that learns visual style from a single target‑domain image while leveraging prompt‑based control to synthesize specific object instances. Unlike conventional style transfer‑based methods that require large target datasets and ignore semantic‑level discrepancies, our approach enables privacy‑preserving augmentation to reduce ethical concerns, and introduces controllable rare object generation to mitigate long‑tailed category degradation. In the Federated Stage, we employ probabilistic Faster R‑CNN on the client side to improve localization accuracy, and a dynamic model contrastive strategy to suppress domain‑specific bias. The server side performs model fusion across heterogeneous architectures without accessing raw data. Finally, in the Distilled Stage, we propose an inconsistent categories integration algorithm to resolve label inconsistency and architecture heterogeneity across clients. Extensive experiments on multiple cross‑domain detection benchmarks demonstrate that our method outperforms existing multi‑source domain adaptation and federated learning baselines under multi‑class, privacy‑preserving settings. Our method improves mAP by +2.1% over prior privacy‑preserving approaches and achieves a new state‑of‑the‑art mAP of 33.4%, highlighting the effectiveness of HeroCrystal in enabling practical multi‑camera AI surveillance systems.
The source code is publicly available at https://github.com/ccuvislab/HeroCrystal.
Authors:Soyeon Kim, Seongwoo Lim, Kyowoon Lee, Jaesik Choi
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/.
Authors:Vik Pant, Eric Yu
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.
Authors:Daniel da Silva Costa, Pedro Nuno de Souza Moura, Adriana C. F. Alvim
Abstract:
In recent years, several advances have been observed in Deep Learning with surprising results. Models in this area have been increasingly used in numerous applications, including those sensitive to human life, which require clear explanations and justifications. Various explainability methods have been proposed, but not many metrics to evaluate these methods. The most commonly used metric is the Intersection over Union (IoU). However, due to the characteristics of the results of the explainability methods, called saliency maps, which do not have a known shape, we hypothesise that there must be a better metric that allows one to find an explainability method that produces results that best resemble the human perception. We propose using different metrics to assess the similarity between human perception and the explanation saliency maps to find a better metric. An investigation was conducted employing a subset of the Chihuahuas images from ImageNet dataset. Several CAM‑based explainability methods were used to generate saliency maps for each chihuahua image. Alignment was measured by applying distance metrics between the bounding box of human annotations and the saliency maps produced by each explainability method. Rankings of the best saliency maps were created using the results of the distance metrics and compared to the ranking obtained using people's choice, collected through crowdsourcing, of the best explanation saliency maps for each selected image. Comparison between rankings was performed using the Rank‑Biased Overlap (RBO) metric. The results indicate the feasibility of our method to find the explainability method that best resembles human perception. In our experiments, the two metrics that best resemble human perception corresponded to Manhattan and Correlation. Besides, the best explainability methods regarding human perception were LayerCAM, Score‑CAM, and IS‑CAM.
Authors:Luo Ji, Qi Qin, Ningyuan Xi, Teng Chen, Qingqing Gu, Hongyan Li
Abstract:
Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes. While finetuning can create the catastrophe forgetting issue, application of meta‑learning on LLMs is also limited due to its complexity and scalability. In this paper, we activate the meta‑signal of β within the SwiGLU blocks, resulting in a meta‑gating mechanism that adaptively adjusts the nonlinearity of FFN. A hypernetwork is employed which dynamically produces β on textual conditions, providing meta‑controllability on LLMs. By testing on different condition types such as task, domain, persona, and style, our method outperforms finetuning and meta‑learning baselines, and can generalize reasonably on unseen tasks, condition types, or instructions. Our code can be found in https://github.com/AaronJi/MeGan.
Authors:Shengzhe Lyu, Yuhan She, Patrick S. Y. Hung, Ray C. C. Cheung, Weitao Xu
Abstract:
Vision Mamba (ViM) models offer a compelling efficiency advantage over Transformers by leveraging the linear complexity of State Space Models (SSMs), yet efficiently deploying them on FPGAs remains challenging. Linear layers struggle with dynamic activation outliers that render static quantization ineffective, while uniform quantization fails to capture the weight distribution at low bit‑widths. Furthermore, while associative scan accelerates SSMs on GPUs, its memory access patterns are misaligned with the streaming dataflow required by FPGAs. To address these challenges, we present ViM‑Q, a scalable algorithm‑hardware co‑design for end‑to‑end ViM inference on the edge. We introduce a hardware‑aware quantization scheme combining dynamic per‑token activation quantization and per‑channel smoothing to mitigate outliers, alongside a custom 4‑bit per‑block Additive Power‑of‑Two (APoT) weight quantization. The models are deployed on a runtime‑parameterizable FPGA accelerator featuring a linear engine employing a Lookup‑Table (LUT) unit to replace multiplications with shift‑add operations, and a fine‑grained pipelined SSM engine that parallelizes the state dimension while preserving sequential recurrence. Crucially, the hardware supports runtime configuration, adapting to diverse dimensions and input resolutions across the ViM family. Implemented on an AMD ZCU102 FPGA, ViM‑Q achieves an average 4.96x speedup and 59.8x energy efficiency gain over a quantized NVIDIA RTX 3090 GPU baseline for low‑batch inference on ViM‑tiny. This co‑design shows a viable path for deploying ViM models on resource‑constrained edge devices.
Authors:Shengzhe Lyu, Yuhan She, Di Duan, Tao Ni, Yu Hin Chan, Chengwen Luo, Ray C. C. Cheung, Weitao Xu
Abstract:
Channel estimation is crucial in 5G communication networks for optimizing transmission parameters and ensuring reliable, high‑speed communication. However, the use of multiple‑input and multiple‑output (MIMO) and millimeter‑wave (mmWave) in 5G networks presents challenges in achieving accurate estimation under strict latency requirements on resource‑limited hardware platforms. To address these challenges, we propose SwiftChannel, an algorithm‑hardware co‑design framework that integrates a hardware‑friendly deep learning‑based channel estimator with a dedicated accelerator. Our approach employs a convolutional neural network enhanced with a parameter‑free attention mechanism, which effectively reconstructs full‑resolution spatial‑frequency domain channel matrices from low‑resolution least squares (LS) estimates. We further develop a multi‑stage model compression pipeline combining knowledge distillation, convolution re‑parameterization, and quantization‑aware training, resulting in substantial model size reduction with negligible accuracy loss. The hardware accelerator, implementing the compressed model and the LS estimator on FPGA platforms using High‑level Synthesis (HLS), features a fine‑grained pipeline architecture and optimized dataflow strategies. Tested on a Zynq UltraScale+ RFSoC, the accelerator achieves sub‑millisecond latency, providing up to 24x speed‑up and over 33x improvement in energy efficiency compared to GPU‑based solutions. Extensive evaluations demonstrate that the proposed design generalizes not only across various noise levels and user mobilities, but also to a variety of unseen channel profiles, outperforming state‑of‑the‑art baselines. By unifying algorithmic innovation with hardware‑aware design, our work presents a future‑proof channel estimation solution for 5G MIMO systems.
Authors:An T. Le
Abstract:
Neural networks increasingly embed non‑differentiable components (spiking neurons, quantized layers, discrete routing, blackbox simulators, etc.) where backpropagation is inapplicable and surrogate gradients introduce bias. We present PolyStep, a gradient‑free optimizer that updates parameters using only forward passes. Each step evaluates the loss at structured polytope vertices in a compressed subspace, computes softmax‑weighted assignments over the resulting cost matrix, and displaces particles toward low‑cost vertices via barycentric projection. This update corresponds to the one‑sided limit of a regularized optimal‑transport problem, inheriting its geometric structure without Sinkhorn iterations.
PolyStep trains genuinely non‑differentiable models where existing gradient‑free methods collapse to near‑random accuracy. On hard‑LIF spiking networks we reach 93.4% test accuracy, outperforming all gradient‑free baselines by over 60~pp and closing to within 4.4~pp of a surrogate‑gradient Adam ceiling. Across four additional non‑differentiable architectures (int8 quantization, argmax attention, staircase activations, hard MoE routing) we lead every gradient‑free competitor. On MAX‑SAT scaling from 100 to 1M variables, we sustain above 92% clause satisfaction while evolution strategies drop 8‑‑12~pp. On RL policy search, we match OpenAI‑ES on classical control and retain performance under integer and binary quantization that collapses gradient‑based methods. We prove convergence to conservative‑stationary points at rate O(\log T/\sqrtT) on piecewise‑smooth losses, upgraded to Clarke‑stationary on the headline architectures and extended to the piecewise‑constant regime via a hitting‑time bound. These rates match the known zeroth‑order query‑complexity lower bounds that all forward‑only methods inherit. Code is available at https://github.com/anindex/polystep.
Authors:Hongkun Pan, Yuwei Wu, Wanyi Hong, Shenghui Hu, Qitong Yan, Yi Yang, Rufei Han, Changju Zhou, Minfeng Zhu, Dongming Han, Wei Chen
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.
Authors:Favour Nerrise, Lucy Yin, Mohammad H. Abbasi, Kilian M. Pohl, Ehsan Adeli
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.
Authors:Kwan Soo Shin
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.
Authors:Qiao Liu
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.
Authors:Sungyoung Lee, Dohyeong Kim, Eshan Balachandar, Zelal Su Mustafaoglu, Keshav Pingali
Abstract:
We propose Flow‑Anchored Noise‑conditioned Q‑Learning (FAN), a highly efficient and high‑performing offline reinforcement learning (RL) algorithm. Recent work has shown that expressive flow policies and distributional critics improve offline RL performance, but at a high computational cost. Specifically, flow policies require iterative sampling to produce a single action, and distributional critics require computation over multiple samples (e.g., quantiles) to estimate value. To address these inefficiencies while maintaining high performance, we introduce FAN. Our method employs a behavior regularization technique that uses a single flow policy iteration and requires a single Gaussian noise sample for distributional critics. Our theoretical analysis of convergence and performance bounds demonstrates that these simplifications not only improve efficiency but also lead to superior task performance. Experiments on robotic manipulation and locomotion tasks demonstrate that FAN achieves state‑of‑the‑art performance while significantly reducing both training and inference runtimes. We release our code at https://github.com/brianlsy98/FAN.
Authors:Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy
Abstract:
Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology. However, existing protein representation learning methods typically operate at a single structural level or treat different sources of structural information as parallel modalities, without explicitly modeling their hierarchical relationships. We introduce PRIME (Protein Representation via Physics‑Informed Multiscale Equivariant Hierarchies), a unified framework that models proteins as a nested family of five physically grounded structural graphs spanning surface, atomic, residue, secondary‑structure, and protein levels. Adjacent levels are connected through deterministic, physics‑informed assignment operators, enabling bidirectional information exchange via bottom‑up aggregation and top‑down contextual refinement. Experiments on standard protein representation learning benchmarks demonstrate strong and competitive performance across diverse tasks, with particularly notable gains on the Fold Classification benchmark, where PRIME outperforms the strongest geometric GNN baseline by margins of 13.80 and 18.30 points on the harder Superfamily and Fold splits, and achieves a state‑of‑the‑art accuracy of 84.10% on Reaction Class prediction, surpassing all baseline methods, including ESM. Ablation studies confirm that each structural level contributes complementary and non‑redundant information, and adaptive cross‑attention analysis reveals that PRIME autonomously identifies the most task‑relevant structural resolutions at prediction time. Our source code is publicly available at https://github.com/HySonLab/PRIME
Authors:Paul Garnier, Vincent Lannelongue, Elie Hachem
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.
Authors:Kanak Mazumder, Fabian B. Flohr
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.
Authors:Zhaoyang Li, Zhichao You, Tianrui Li
Abstract:
Although multi‑modal learning has advanced point cloud completion, the theoretical mechanisms remain unclear. Recent works attribute success to the connection between modalities, yet we identify that standard hard projection severs this connection: projecting a sparse point cloud onto the image plane yields an extremely sparse support, which hinders visual prior propagation, a failure mode we term Cross‑Modal Entropy Collapse. To address this practical limitation, we propose SplAttN, which replaces hard projection with Differentiable Gaussian Splatting to produce a dense, continuous image‑plane representation. By reformulating projection as continuous density estimation, SplAttN avoids collapsed sparse support, facilitates gradient flow, and improves cross‑modal connection learnability. Extensive experiments show that SplAttN achieves state‑of‑the‑art performance on PCN and ShapeNet‑55/34. Crucially, we utilize the real‑world KITTI benchmark as a stress test for multi‑modal reliance. Counter‑factual evaluation reveals that while baselines degenerate into unimodal template retrievers insensitive to visual removal, SplAttN maintains a robust dependency on visual cues, validating that our method establishes an effective cross‑modal connection. Code is available at https://github.com/zay002/SplAttN.
Authors:Jianze Wang, Ying Liu, Jinlong Chen, Xuchun Hu, Qilong Zhang, Yu Cao, Jun Wang, Hua Yang, Yong Xie, Qianglong Chen
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.
Authors:Xiaorui Wang, Fanda Fan, Chenxi Wang, Yuxuan Yang, Rui Tang, Kuoyu Gao, Simiao Pang, Yuanfeng Shang, Zhipeng Liu, Wanling Gao, Lei Wang, Jianfeng Zhan
Abstract:
Recent progress in time‑series forecasting has led to rapidly increasing architectural complexity, yet many reported State‑of‑the‑Art gains are statistically fragile or misattributed. We argue that progress requires a shift from model selection to modular attribution, identifying which components truly drive performance. We propose CombinationTS, a self‑contained probabilistic evaluation framework that decomposes forecasting models into orthogonal modules‑‑Input Transformation, Embedding, Encoder, Decoder, and Output Transformation‑‑and evaluates them under a shared evaluation condition space. By quantifying each component via marginalized performance (μ) and stability (σ), CombinationTS enables robust attribution beyond fragile point estimates. Through large‑scale paired evaluation, we uncover the Identity Paradox: once the data view (Embedding) is well‑designed, a parameter‑free Identity Encoder often matches or outperforms complex backbones. We further show that explicit structural priors introduced via Input Transformations yield a more favorable performance‑stability trade‑off than increasing Encoder complexity, establishing a principled baseline for architectural necessity.
Authors:James Butterworth, Gevik Grigorian, Alejandro DiazDelaO
Abstract:
Symbolic regression discovers explicit, interpretable equations without assuming a functional form in advance. A Bayesian approach strengthens this through probability distributions over candidate expressions, thus quantifying uncertainty in the presence of noisy and limited data. Deep Symbolic Regression (DSR) uses a neural network to generate symbolic expressions, but it is designed to identify a single best‑fitting expression rather than infer a posterior distribution over models. We introduce Deep Variational Inference Symbolic Regression (DVISR), a variational Bayesian extension of DSR. DVISR replaces the original reward with the integrand of the evidence lower bound. It also extends the network architecture to output distributions over constants within expressions, enabling posterior inference over both expression trees and their associated constants. We show that DVISR can recover the true posterior in simple settings, both with and without constant tokens, and we examine how its performance changes as the size of the expression space increases. These results position DVISR as a step toward scalable Bayesian symbolic regression with uncertainty over full symbolic models.
Authors:Hao Zhou, Simon A. Lee, Cyrus Tanade, Keum San Chun, Juhyeon Lee, Migyeong Gwak, Megha Thukral, Justin Sung, Eugene Hwang, Mehrab Bin Morshed, Li Zhu, Viswam Nathan, Md Mahbubur Rahman, Subramaniam Venkatraman, Sharanya Arcot Desai
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.
Authors:Hada Melino Muhammad, Zechen Li, Flora Salim, Ahmed A. Metwally
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
Authors:Hongjun Wang, Po Hu, Kai Han
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/
Authors:Prabhjot Singh, Manmeet Singh
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
Authors:Firat Ozdemir, Yun Cheng, Salman Mohebi, Fanny Lehmann, Simon Adamov, Zhenyi Zhang, Leonardo Trentini, Dana Grund, Oliver Fuhrer, Torsten Hoefler, Siddhartha Mishra, Sebastian Schemm, Benedikt Soja, Mathieu Salzmann
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.
Authors:Hao Xiao
Abstract:
Entropic regularized optimal transport (OT) via the Sinkhorn algorithm has become a fundamental tool in machine learning, yet existing implementations either suffer from numerical instability for small regularization parameters or incur significant overhead from deep learning frameworks. We present FastSinkhorn, a lightweight, native CUDA implementation of the log‑domain Sinkhorn algorithm that combines warp‑level shuffle reductions with shared‑memory tiling to achieve high GPU utilization without sacrificing numerical stability. Our solver operates entirely in the log‑domain, enabling robust computation for regularization parameters as small as epsilon = 10^‑4 where standard‑domain methods fail. On dense OT problems with n = m = 8192, our implementation achieves 12x speedup over the widely‑used POT library and 5.9x speedup over GPU‑accelerated PyTorch baselines, while consuming only 256 MB of GPU memory. We validate our solver on image color transfer, 3D point cloud matching, and convergence analysis, demonstrating that native CUDA kernels with careful numerical treatment provide a practical and efficient foundation for large‑scale optimal transport computation.
Authors:Hao Xiao
Abstract:
Choosing between classical and Bayesian sparse regression methods involves a real trade‑off: penalized estimators like Lasso run in milliseconds but give no uncertainty estimates,while Horseshoe and Spike‑and‑Slab priors produce full posteriors but need MCMC chains that take minutes per fit.Surprisingly few studies compare these two families head‑to‑head under the conditions that actually make sparse regression hard ‑‑ correlated features, weak signals, and growing dimensionality. We benchmark six methods (OLS, Ridge,Lasso, Elastic Net, Horseshoe, Spike‑and‑Slab) on synthetic data with three covariance structures (rho up to 0.9), four SNR levels, and p in 20, 50, 100, plus the Diabetes dataset,totalling over 2,600 experiments. The results are clear on some points and nuanced on others. Bayesian methods win on prediction error (MSE 72 vs. 108‑267), and the Horseshoe delivers near‑nominal 95% coverage (94.8%). But Spike‑and‑Slab,despite narrower intervals, under‑covers at 91.9% ‑‑ its continuous relaxation likely plays a role. For variable selection, Lasso and Spike‑and‑Slab tie at F1 ~ 0.47, making Lasso the practical default when posteriors are not needed. Code and data are available at https://github.com/xiao98/sparse‑bayesian‑regression‑bench.
Authors:Michael Helcig, Dan Alistarh
Abstract:
Assigning one of K options to each of N groups under a total cost budget is a recurring problem in efficient AI, including mixed‑precision quantization, non‑uniform pruning, and expert selection. The objective, typically model loss, depends jointly on all assignments and does not decompose across groups, preventing combinatorial solvers from directly optimizing the true objective and forcing reliance on proxy formulations. Methods such as evolutionary search evaluate the actual loss but lack gradient information, while penalty‑based approaches enforce the budget only approximately and often require extensive hyperparameter tuning. We present a new approach by showing that, under softmax relaxation, the budget constraint defines a smooth Riemannian manifold in logit space with unusually simple geometry. The normal vector admits a closed‑form expression, shifting logits along the cost vector changes expected cost monotonically, and vector transport reduces to a single inner product. Building on these properties, we propose Riemannian Constrained Optimization (RCO), which augments a standard Adam step with tangent projection, binary‑search retraction, and momentum transport. Combined with Gumbel straight‑through estimation and budget‑constrained dynamic programming for discrete feasibility, RCO enables first‑order optimization of the actual loss under exact budget enforcement without introducing constraint‑specific hyperparameters. Across both synthetic benchmarks and realistic LLM compression settings, RCO matches or exceeds state‑of‑the‑art methods while often requiring substantially less wall‑clock time. Source code is available at https://github.com/IST‑DASLab/RCO.
Authors:Massimo Rondelli, Francesco Pivi, Maurizio Gabbrielli
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.
Authors:Chaohao Yuan, Chenghao Xiao, Yu Rong, Hong Cheng, Long-Kai Huang
Abstract:
SFT and RLVR represent two fundamental yet distinct paradigms for LLM post‑training, each excelling in distinct dimensions. SFT expands knowledge breadth while RLVR enhances reasoning depth. Yet integrating these complementary strengths remains a formidable challenge. Sequential training can cause catastrophic forgetting, and joint optimization often suffers from severe gradient conflicts. We analyze SFT and RLVR through the lens of task vectors and reveal three structural properties behind these failures: a 30 magnitude disparity, 45 sign interference, and heterogeneous module‑wise update distributions. These findings show SFT and RLVR are difficult to integrate directly, but they also suggest that the two paradigms modify partly complementary components of the model. Motivated by these observations, we propose Decoupled Test‑time Synthesis (DoTS), a post‑hoc framework allows SFT and RLVR checkpoints to be trained independently and synthesizes their capabilities only at inference time via task vector arithmetic, without updating model parameters. To reduce interference, DOTS applies selective sparsification with norm‑preserving rescaling. It then uses Bayesian optimization on a small set of unlabeled queries to search for combination coefficients on the Pareto frontier of consistency and perplexity. Empirically, \ours matches or exceeds the performance of training‑based SFT‑‑RLVR integration methods across multiple mathematical reasoning benchmarks, incurring only ～3% of the computational cost. When applied to stronger post‑trained checkpoints, DOTS surpasses SOTA models and generalizes to out‑of‑domain benchmarks without re‑tuning. Code is available at https://github.com/chaohaoyuan/DoTS.
Authors:Man Yung Wong
Abstract:
Sparse MoE routing fails at domain transitions, where the current token belongs to one distribution and the next to another. In a controlled experiment (4 experts, 5 seeds), standard affinity routing assigns only 0.006 +/‑ 0.001 probability to the correct expert at the transition. Three lightweight gate modifications raise this to 0.748 +/‑ 0.002 (124x), cutting experts needed for 99% coverage from infeasible to a small constant: temporal memory (beta), a per‑expert LIF membrane potential accumulating routing context across tokens; precision‑weighted gating (Pi), a per‑expert inverse variance of recent prediction error, yielding 31x contrast between reliable and unreliable experts; and anticipatory routing, a next‑state predictor conditioned on the beta‑accumulated hidden state. The mechanisms draw from Friston's Free Energy Principle and use LIF dynamics from spiking neural networks. An ablation across all 2^3 subsets reveals a super‑additive beta x Ant interaction: anticipation alone gives nothing (+0.000 +/‑ 0.001); beta alone gives modest gain (+0.295 +/‑ 0.013); combined they close 75% of the oracle gap (+0.741 +/‑ 0.002, exceeding the sum by +0.446 +/‑ 0.014). This is structural: a stateless predictor cannot detect approaching transitions because pre‑transition tokens are distributionally identical to within‑domain tokens. In a character‑level MoE LM (5 seeds), beta‑routing reduces transition‑step BPC from 6.56 +/‑ 0.01 (Standard) to 4.01 +/‑ 0.15 (beta‑MoE); the beta + Ant gate places 0.86 +/‑ 0.02 probability on the correct domain expert before that domain appears in input, vs 0.42 +/‑ 0.12 for Standard MoE. Reference implementations (~200 lines each): https://github.com/russellwmy/affinity‑is‑not‑enough
Authors:M. Grailoo, J. Núñez-Yáñez
Abstract:
Scaling laws for Large Language Models (LLMs) establish that model quality improves with computational scale, yet edge deployment imposes strict constraints on compute, memory, and power. Since General Matrix Multiplication (GEMM) accounts for up to 90% of inference time, efficient GEMM acceleration is critical for edge AI. The Adaptive Intelligent Engines available in the AMD Versal adaptive SoCs are well suited for this task, but existing state‑of‑the‑art (SOTA) frameworks maximize performance through spatial scaling, distributing workloads across hundreds of cores ‑‑ an approach that fails on resource‑limited edge SoCs due to physical implementation failures, bandwidth saturation, and excessive resource consumption. We propose Tempus, a Resource‑Invariant Temporal GEMM framework for the AMD Versal AI Edge SoC. Rather than expanding hardware resources with matrix size, Tempus employs a fixed compute block of 16 AIE‑ML cores, achieving scalability through iterative graph execution and algorithmic data tiling and replication in the Programmable Logic. High‑speed cascade streaming ensures low‑latency partial sum reduction at Initiation Interval (II) of 1, while a deadlock‑free DATAFLOW protocol maximizes transfer‑compute overlap and PLIO reuse. Evaluated on GEMM workloads, Tempus achieves 607 GOPS at 10.677 W total on‑chip power. By characterizing system‑level efficiency through the Platform‑Aware Utility (PAU) metric, we prove that Tempus achieves a 211.2x higher prominence factor than the leading spatial SOTA (ARIES). Furthermore, the framework maintains a 0.00% utilization of URAM/DSP, yielding 22.0x core frugality, 7.1x power frugality, and a 6.3x reduction in I/O demand, establishing a sustainable, scalable foundation for edge LLM inference.
Authors:Ziwen Zhao, Menglin Yang
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.
Authors:Weifei Jin, Xilong Wang, Wei Zou, Jinyuan Jia, Neil Gong
Abstract:
Retrieval‑augmented generation (RAG) is vulnerable to prompt injection attacks, in which an adversary inserts malicious documents containing carefully crafted injected prompts into the knowledge database. When a user issues a question targeted by the attack, the RAG system may retrieve these malicious documents, whose injected prompts mislead it into generating attacker‑specified answers, thereby compromising the integrity of the RAG system. In this work, we propose CleanBase, a method to detect malicious documents within a knowledge database. Our key insight is that malicious documents crafted for the same attack‑targeted questions often exhibit high semantic similarity, as attackers deliberately make them consistent to improve attack success rates. Accordingly, CleanBase constructs a similarity graph over the knowledge database, where each node represents a document and an edge connects two nodes if their semantic similarity‑‑computed using an embedding model‑‑exceeds a statistically determined threshold. Due to their inherent similarity, malicious documents tend to form cliques within this graph. CleanBase detects such cliques and flags the corresponding documents as malicious. We theoretically derive upper bounds on CleanBase's false positive and false negative rates and empirically validate its effectiveness. Experimental results across multiple datasets and prompt injection attacks demonstrate that CleanBase accurately detects malicious documents and effectively safeguards RAG systems. Our source code is available at https://github.com/WeifeiJin/CleanBase.
Authors:Pankaj Gupta, Kartik Bose
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
Authors:Jiale Fu, Yuchu Jiang, Peijun Wu, Chonghan Liu, Joey Tianyi Zhou, Xu Yang
Abstract:
Model ensembling is a well‑established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multiple models and selecting the most probable label. This idea has been naturally extended to large language models (LLMs), yielding improved performance but incurring substantial computational cost. This inefficiency stems from directly applying conventional ensemble implementation to LLMs, which require a separate forward pass for each model to explicitly compute the ensemble distribution. In this paper, we propose the Mixture‑model‑like Ensemble (ME). By reinterpreting the ensemble as a mixture model, ME stochastically selects a single model at each step to generate the next token, thereby avoiding the need to explicitly compute the full ensemble distribution. ME is mathematically equivalent to sampling from the ensemble distribution, but requires invoking only one model, making it 1.78x‑2.68x faster than conventional ensembling. Furthermore, this perspective connects LLM ensembling and token‑level routing methods, suggesting that LLM ensembling is a special case of routing methods. Our findings open new avenues for efficient LLM ensembling and motivate further exploration of token‑level routing strategies for LLMs. Our code is available at https://github.com/Kamichanw/Mixture‑model‑like‑Ensemble.
Authors:Zihan Lin, Xiaohan Wang, Jie Cao, Jiajun Chai, Li Wang, Xiaodong Lu, Wei Lin, Ran He, Guojun Yin
Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diversity due to the over‑incentivization of positive rewards. Although methods like Negative Sample Reinforcement (NSR) mitigate this issue by upweighting penalty from negative samples, they may suppress the semantic distributions shared between positive and negative responses. To boost reasoning ability without losing diversity, this paper proposes negative sample projection Residual Reinforcement Learning (ResRL) that decouples similar semantic distributions among positive and negative responses. We theoretically link Lazy Likelihood Displacement (LLD) to negative‑positive head‑gradient interference and derive a single‑forward proxy that upper‑bounds representation alignment to guide conservative advantage reweighting. ResRL then projects negative‑token hidden representations onto an SVD‑based low‑rank positive subspace and uses projection residuals to modulate negative gradients, improving reasoning while preserving diversity and outperforming strong baselines on average across twelve benchmarks spanning Mathematics, Code, Agent Tasks, and Function Calling. Notably, ResRL surpasses NSR on mathematical reasoning by 9.4% in Avg@16 and 7.0% in Pass@128. Code is available at https://github.com/1229095296/ResRL.git.
Authors:Anamika Lochab, Bolian Li, Ruqi Zhang
Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single‑attempt accuracy (Pass@1) on reasoning tasks, yet often suffers from reduced multi‑sample coverage (Pass@K), indicating diversity collapse. We identify a structural cause for this degradation: common RLVR objectives, such as GRPO, are indifferent to how probability mass is distributed among correct solutions. Combined with stochastic training dynamics, this indifference induces a self‑reinforcing collapse, in which probability mass concentrates on a narrow subset of correct outputs while alternative valid solutions are suppressed. We formalize this collapse mechanism and further characterize the optimal policy structure under two complementary criteria: robustness and entropy‑regularized optimality, which identify the Uniform‑Correct Policy as uniquely optimal. Motivated by this analysis, we propose Uniform‑Correct Policy Optimization (UCPO), a modification to GRPO that adds a conditional uniformity penalty on the policy's distribution over correct solutions. The penalty redistributes gradient signal toward underrepresented correct responses, encouraging uniform allocation of probability mass within the correct set. Across three models (1.5B‑7B parameters) and five mathematical reasoning benchmarks, UCPO improves Pass@K and diversity while maintaining competitive Pass@1, achieving up to +10% absolute improvement on AIME24 at Pass@64 and up to 45% higher equation‑level diversity within the correct set. The code is available at https://github.com/AnamikaLochab/UCPO.
Authors:Yuhui Lu, Wenjing Liu, Kun Zhan
Abstract:
Standard diffusion models for graph generation typically rely on uniform time‑stepping, an approach that overlooks the non‑homogeneous dynamics of distributional evolution on complex manifolds. In this paper, we present an information‑geometric framework that reinterprets the diffusion sampling trajectory as a parametric curve on a Riemannian manifold. Our key observation is that the Fisher‑Rao metric provides a principled measure of the intrinsic distance. By analyzing this metric, we derive the Drift Variation Score (DVS), a geometry‑aware indicator that quantifies the instantaneous rate of distributional change. Unlike prior heuristic‑based adaptive samplers, our DVS solver enforces a constant informational speed on the statistical manifold, automatically maintaining a uniform rate of distributional change along the sampling trajectory. This equal arc‑length strategy ensures that each discretization step contributes equally to the information speed. Theoretical analysis verifies that DVS characterizes the local stiffness of the sampling dynamics in the Fisher‑Rao sense. Experimental results on molecule and social network generation show that DVS significantly improves structural fidelity and sampling efficiency. Code is at https://github.com/kunzhan/DVS
Authors:Jesse Schneider, William J. Welch
Abstract:
Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be made. By using all gathered data to train a Gaussian process model for the function and adaptively employing a mixture of global exploration and local exploitation, this method has been used for optimization in many fields including machine learning, automotive engineering and reinforcement learning. However, the standard method suffers from two problems: 1) with cubic computational complexity in the training‑set size it eventually becomes computationally infeasible to train the model, and 2) globally modeling the objective function is not necessarily optimal given the local nature of minimization. Using flexible and recursive binary partitioning of the search space, we adapt both the modeling and acquisitive aspects of standard Bayesian optimization to work harmoniously with the partitioning scheme, thereby ameliorating both standard shortcomings. We compare our method against a commonly used Bayesian optimization library on seven challenging test functions, ranging in dimensionality from 6 to 124, and show that our method achieves superior optimization performance in all tests. In addition our method has linear computational complexity.
Authors:Mohammad Hossein Moslemi, Nima Hosseini Dashtbayaz, Zhimin Mei, Bissan Ghaddar, Boyu Wang
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.
Authors:Eichi Uehara
Abstract:
Double‑machine‑learning pipelines for the Average Dose‑Response Function rely on kernel‑weighted local‑linear smoothers, which inherit unbounded functional influence: a single outlier within a kernel window biases the curve across the entire window. We introduce SHIFT (Self‑calibrated Heavy‑tail Inlier‑Fit with Tempering), a robust DML estimator combining cross‑fit nuisance orthogonalization with a kernel‑local Welsch‑loss second stage optimized by Graduated Non‑Convexity, and ‑‑ the principal design choice ‑‑ a defensive OLS refit whose inlier cutoff is scaled by post‑GNC residual MAD rather than the raw‑outcome MAD. On a localized‑contamination stress test at p=0.25 this design choice drops level‑RMSE from 1.03 to 0.33 while leaving clean and uniformly‑contaminated runs unchanged. Across 1,400 main‑sweep fits, SHIFT has competitive worst‑case shape recovery (RMSE 0.325 at p=0.25, second to Huber‑DML's 0.276); among the three methods with worst‑case RMSE below 0.35, only SHIFT emits a non‑uniform per‑sample weight vector, recovering the ground‑truth outlier mask at mean F_1 \approx 0.96 (range 0.945‑‑0.968) on Gaussian‑jump DGPs. We pair the estimator with a six‑technique Extreme Value Theory diagnostic suite (Hill, GPD‑MLE/PWM, GEV, Mean Excess, parameter stability, causal tail coefficient) that lets a practitioner distinguish Frechet from Weibull regimes and choose between SHIFT and L1 alternatives on empirical grounds. Extensions to binary‑treatment CATE (Huber pseudo‑outcome X‑Learner) and time‑series ADRF (block‑CV + rolling MAD) are included. A counter‑intuitive ablation: linear nuisance models (Ridge, Lasso) outperform gradient‑boosted nuisances for robust DML under uniform contamination, inverting the usual more‑flexible‑is‑better heuristic.
Authors:YiFeng Wang, Zhun Sun, Keisuke Sakaguchi
Abstract:
We present Activation Residual Hessian Quantization (ARHQ), a post‑training weight splitting method designed to mitigate error propagation in low‑bit activation‑weight quantization. By constructing an input‑side residual Hessian from activation quantization residuals (G_x), ARHQ analytically identifies and isolates error‑sensitive weight directions into a high‑precision low‑rank branch. This is achieved via a closed‑form truncated SVD on the scaled weight matrix W G^1/2_x . Experimental results on Qwen3‑4B‑Thinking‑2507 demonstrate that ARHQ significantly improves layer‑wise SNR and preserves downstream reasoning performance on ZebraLogic even under aggressive quantization. The code is available at https://github.com/BeautMoonQ/ARHQ.
Authors:Binghao Huang, Yunzhu Li
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/.
Authors:Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma, Mohammad Masudur Rahman
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++.
Authors:Arthur Corrêa, Paulo Nascimento, Samuel Moniz
Abstract:
Solving practical multi‑depot vehicle routing problems (MDVRP) is a challenging optimization task central to modern logistics, increasingly driven by e‑commerce. To address the MDVRP's computational complexity, neural‑based combinatorial optimization methods offer a promising scalable alternative to traditional approaches. However, neural‑based methods typically rely on rigid architectures and input encodings tailored to specific problem formulations. In real‑world settings, heterogeneous constraints create multiple MDVRP variants, limiting the applicability of such models. While multi‑task learning (MTL) has begun to accelerate the development of unified neural‑based solvers, prior works focus almost exclusively on single‑depot VRPs, leaving the MDVRP unaddressed. To bridge this gap, we propose Feature‑wise Linear Modulation for Cross‑Problem Multi‑Depot Vehicle Routing (FiLMMeD), a novel unified neural‑based model for 24 different MDVRP variants. We introduce three main contributions: (1) to improve the model's generalization, we augment the standard Transformer encoder with Feature‑wise Linear Modulation (FiLM), which dynamically conditions learned internal representations based on the active set of constraints; (2) we provide an initial demonstration of Preference Optimization in the MTL setting, establishing it as a superior alternative to Reinforcement Learning for future MTL works; (3) to mitigate the generalization gap caused by the introduction of multi‑depot constraints, we introduce a targeted curriculum learning strategy that progressively exposes the model to increasingly more complex constraint interactions. Extensive experiments on 24 MDVRP variants (including 8 novel formulations) and 16 single‑depot VRPs confirm the effectiveness of FiLMMeD, which consistently outperforms state‑of‑the‑art baselines. Our code is available at: https://github.com/AJ‑Correa/FiLMMeD/tree/main
Authors:Hanane Nour Moussa, Yifei Li, Zhuoyang Li, Yankai Yang, Cheng Tang, Tianshu Zhang, Nesreen K. Ahmed, Ali Payani, Ziru Chen, Huan Sun
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.
Authors:Ishrak Hamim Mahi, Siam Ferdous, Md Sakib Sadman Badhon, Nabid Hasan Omi, Md Habibun Nabi Hemel, Farig Yousuf Sadeque, Md. Tanzim Reza
Abstract:
The rapid proliferation of image generation models and other artificial intelligence (AI) systems has intensified concerns regarding data privacy and user consent. As the availability of public datasets declines, major technology companies increasingly rely on proprietary or private user data for model training, raising ethical and legal challenges when users request the deletion of their data after it has influenced a trained model. Machine unlearning seeks to address this issue by enabling the removal of specific data from models without complete retraining. This study investigates a modified SISA (Sharded, Isolated, Sliced, and Aggregated) framework designed to achieve class‑level unlearning in Convolutional Neural Network (CNN) architectures. The proposed framework incorporates a reinforced replay mechanism and a gating network to enhance selective forgetting efficiency. Experimental evaluations across multiple image datasets and CNN configurations demonstrate that the modified SISA approach enables effective class unlearning while preserving model performance and reducing retraining overhead. The findings highlight the potential of SISA‑based unlearning for deployment in privacy‑sensitive AI applications. The implementation is publicly available at https://github.com/SiamFS/ sisa‑class‑unlearning.
Authors:Al Zadid Sultan Bin Habib, Tanpia Tasnim, Md. Ekramul Islam, Muntasir Tabasum
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.
Authors:Ethan Bito, Yongli Ren, Estrid He
Abstract:
Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candidates are presented. This creates a mismatch between the set‑based nature of recommendation and the sequence‑based computation of decoder‑only LLMs, where permuting an otherwise identical candidate set can change item scores and final rankings. Such order sensitivity makes LLM‑based rerankers difficult to rely on, since rankings may reflect prompt serialization rather than user preference. We propose InvariRank, a permutation‑invariant listwise reranking framework that addresses this dependence at the architectural level. InvariRank blocks cross‑candidate attention with a structured attention mask and negates position‑induced scoring changes through shared positional framing under Rotary Positional Embeddings (RoPE). Combined with a listwise learning‑to‑rank objective, the model scores all candidates in a single forward pass, avoiding permutation‑based invariance training objectives that require multiple permutations of a candidate set. Experiments on recommendation benchmarks show that InvariRank maintains competitive ranking effectiveness while producing stable rankings across candidate permutations. The results suggest that architectural invariance is a practical route to reliable and efficient LLM‑based recommendation reranking. The source code is at https://github.com/ejbito/InvariRank.
Authors:Eichi Uehara
Abstract:
Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects τ(x), calibrated uncertainty over them, and robustness to the heavy tails that contaminate real outcome data. Meta‑learners (Künzel et al., 2019) give (i); causal forests and BART give (i)‑(ii) with Gaussian‑tail assumptions; no widely used tool gives all three. We present Bayesian X‑Learner, an X‑Learner built on cross‑fitted doubly robust pseudo‑outcomes (Kennedy, 2020) with a full MCMC posterior over τ(x) via a Welsch redescending pseudo‑likelihood. On Hill's IHDP benchmark the default configuration attains mean \sqrt\varepsilon_\mathrmPEHE = 0.56 on 5 replications (lowest mean; differences from S‑/T‑/X‑learners, full‑config Causal BART, and a causal forest baseline are not significant at α=0.05, and rank ordering is unstable at 10 replications ‑‑ IHDP comparisons are competitive rather than dominant). On contaminated "whale" DGPs with up to 20‑25% tail density, a one‑flag extension (contamination_severity) that selects a Huber‑δ nuisance loss per Huber's minimax‑δ relation recovers RMSE \approx 0.13 with tight credible intervals (single‑cross‑fit 30‑seed coverage 83% [Wilson 66%, 93%] at 20% density; modular‑Bayes pooling with Bayesian‑bootstrap nuisance draws restores nominal 95% coverage).
Authors:Yizhou Wu, Shansong Wang, Yuheng Li, Mojtaba Safari, Mingzhe Hu, Chih-Wei Chang, Harini Veeraraghavan, Xiaofeng Yang
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
Authors:Vijay Sadashivaiah, Georgios Dasoulas, Judith Mueller, Soumya Ghosh
Abstract:
Training stable biological foundation models requires rethinking attention mechanisms: we find that using sigmoid attention as a drop in replacement for softmax attention a) produces better learned representations: on six diverse single‑cell datasets, sigmoid achieves 25% higher cell‑type separation, better cell‑type cohesion metrics, and lower validation loss, b) faster training, models with sigmoid attention train up to 10% faster than their softmax counterparts, and c) more stable training by eliminating inherent sources of instability in softmax attention. We establish that sigmoid attention has globally bounded derivatives (\leq 0.25) as opposed to softmax, and a diagonal Jacobian structure in contrast with softmax's dense coupling, which together help alleviate training instabilities. In stress tests on 160M‑parameter bidirectional attention models trained without gradient clipping on 8K‑token sequences, softmax diverges catastrophically, with gradients exploding by four orders of magnitude, while sigmoid remains stable. Finally, we implement and open‑source TritonSigmoid, an efficient GPU kernel that achieves 515 TFLOPS on H100 GPUs, outperforming both FlashAttention‑2 and FlashSigmoid, with native padding support, which is essential for biological sequences. Our results establish sigmoid attention as both theoretically grounded and empirically superior for biological foundation models. Code is available at https://github.com/MSDLLCpapers/triton‑sigmoid
Authors:Yibin Luo, Shiwei Gao, Huichuan Zheng, Youyou Lu, Jiwu Shu
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.
Authors:Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan
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.
Authors:Fei Bai, Huatong Song, Shuang Sun, Daixuan Cheng, Yike Yang, Chuan Hao, Renyuan Li, Feng Chang, Yuan Wei, Ran Tao, Bryan Dai, Jian Yang, Wayne Xin Zhao, Ji-Rong Wen
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.
Authors:Albert Saiapin, Kim Batselier
Abstract:
Uncertainty estimation is essential for robust decision‑making in the presence of ambiguous or out‑of‑distribution inputs. Gaussian Processes (GPs) are classical kernel‑based models that offer principled uncertainty quantification and perform well on small‑ to medium‑scale datasets. Alternatively, formulating the weight space learning problem under tensor network assumptions yields scalable tensor network kernel machines. However, these assumptions break Gaussianity, complicating standard probabilistic inference. This raises a fundamental question: how can tensor network kernel machines provide principled uncertainty estimates? We propose a novel Bayesian Tensor Network Kernel Machine (LA‑TNKM) that employs a (linearized) Laplace approximation for Bayesian inference. A comprehensive set of numerical experiments shows that the proposed method consistently matches or surpasses Gaussian Processes and Bayesian Neural Networks (BNNs) across diverse UCI regression benchmarks, highlighting both its effectiveness and practical relevance.
Authors:Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas, Pietro L. Campos, Tiago A. Almeida
Abstract:
With the increasing availability of online information, recommender systems have become an important tool for many web‑based systems. Due to the continuous aspect of recommendation environments, these systems increasingly rely on contextual multi‑armed bandits (CMAB) to deliver personalized and real‑time suggestions. A critical yet underexplored component in these systems is the representation of user state, which typically encapsulates the user's interaction history and is deeply correlated with the model's decisions and learning. In this paper, we investigate the impact of different embedding‑based state representations derived from matrix factorization models on the performance of traditional CMAB algorithms. Our large‑scale experiments reveal that variations in state representation can lead to improvements greater than those achieved by changing the bandit algorithm itself. Furthermore, no single embedding or aggregation strategy consistently dominates across datasets, underscoring the need for domain‑specific evaluation. These results expose a substantial gap in the literature and emphasize that advancing bandit‑based recommender systems requires a holistic approach that prioritizes embedding quality and state construction alongside algorithmic innovation. The source code for our experiments is publicly available on https://github.com/UFSCar‑LaSID/bandits_blind_spot.
Authors:Seungyub Han, Hyungjin Kim, Jungwoo Lee
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.
Authors:Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng, Chrysa Papagianni
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.
Authors:Zhe Ding, Su Pan, Duowei Pan
Abstract:
Post‑training quantization (PTQ) has become an important technique for reducing the inference cost of Large Language Models (LLMs). While recent mixed‑precision methods improve ultra‑low bit quantization by preserving critical subspaces in high precision, they typically construct these subspaces relying solely on activation statistics. This ignores the fundamental nature of linear operations, where the output perturbation is jointly driven by both activation and weight quantization noise. In this paper, we propose CoQuant, a joint weight‑activation subspace projection method. By theoretically modeling the expected output error, CoQuant formulates a closed‑form weighted PCA solution that balances activation and weight covariances to select the optimal high‑precision subspace. Extensive experiments on Llama‑3.2 and Qwen2.5 models show that CoQuant consistently outperforms strong PTQ baselines in both WikiText perplexity and zero‑shot common‑sense reasoning accuracy. These results demonstrate that joint weight‑activation subspace modeling provides a principled and effective direction for low‑bit LLM quantization. The source code is available at https://github.com/Zachary5895/CoQuant.
Authors:Zhirong Shen, Rui Huang, Jiacheng Liu, Chang Zou, Peiliang Cai, Shikang Zheng, Zhengyi Shi, Liang Feng, Linfeng Zhang
Abstract:
To address the high sampling cost of Diffusion Transformers (DiTs), feature caching offers a training‑free acceleration method. However, existing methods rely on hand‑crafted forecasting formulas that fail under aggressive skipping. We propose L2P (Learnable Linear Predictor), a simple data‑driven caching framework that replaces fixed coefficients with learnable per‑timestep weights. Rapidly trained in ~20 seconds on a single GPU, L2P accurately reconstructs current features from past trajectories. L2P significantly outperforms existing baselines: it achieves a 4.55x FLOPs reduction and 4.15x latency speedup on FLUX.1‑dev, and maintains high visual fidelity under up to 7.18x acceleration on Qwen‑Image models, where prior methods show noticeable quality degradation. Our results show learning linear predictors is highly effective for efficient DiT inference. Code is available at https://github.com/Aredstone/L2P‑Cache.
Authors:Fengchun Zhang, Qiang Ma, Liuyu Xiang, Jinshan Lai, Tingxuan Huang, Jianwei Hu
Abstract:
Federated domain generalization for person re‑identification (FedDG‑ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw data privacy. However, this task is significantly challenged by the inherent stylistic gaps across decentralized clients. Without global supervision, models easily succumb to shortcut learning where representations overfit to domain specific camera biases rather than universal identity features. We propose CO‑EVO, a novel federated framework that resolves this semantic‑style conflict through a co‑evolutionary mechanism. On the semantic side, Camera‑Invariant Semantic Anchoring (CSA) learns identity prompts with cross‑camera consistency to establish purified and domain‑agnostic anchors that filter out local imaging noise. On the visual side, Global Style Diversification (GSD), powered by a Global Camera‑Style Bank (GCSB), synthesizes realistic perturbations to expand the visual boundaries of training data. The core of CO‑EVO is its co‑evolutionary loop where purified anchors act as gravitational centers to guide the image encoder toward robust anatomical attributes amidst diverse style variations. Extensive experiments demonstrate that CO‑EVO achieves state‑of‑the‑art (SOTA) performance, proving that the synergy between semantic purification and style expansion is essential for robust cross‑domain generalization. Our code is available at: https://github.com/NanYiyuzurn/ACL‑LGPS‑2026.
Authors:Aditya Ukarande, Deep Shekhar, Marc Blackstein, Ram Rangan
Abstract:
To usher in the next round of client AI innovation, there is an urgent need to enable efficient, lossless inference of high‑accuracy large language models (LLMs) and vision language models (VLMs), jointly referred to as xLMs, on client systems. To address this, we present pipelined sharding, a novel, benchmark‑profile‑guided CPU‑GPU hybrid scheduling technique to achieve efficient, VRAM‑constrained inference for both dense and mixture‑of‑experts (MoE) LLMs. Using a combination of model sharding at the sub‑layer level, CPU offloading, pipelined copy‑compute, and prioritized tensor placement in VRAM, it optimizes both time‑to‑first‑token (TTFT) and tokens per second (TPS) metrics, while flexibly adapting to system and inference conditions. For efficient, high‑accuracy VLM inference, we combine pipelined sharding with a llama.cpp implementation of three well‑understood prior ideas (jointly called VLMOpt), namely, vision tensor CPU offloading, flash attention, and vision and language model VRAM overlap avoidance.
These enhancements are targeted at improving client xLM inference in future releases of two important NVIDIA products ‑ the In‑Game Inferencing software development kit (IGI SDK) and the Cosmos‑Reason1 (CR1) physical AI reasoning VLM. Highlights from our rigorous evaluation spanning multiple models and client systems include: for interactive use, TTFT improves by up to 6.7x and TPS by up to 30x for LLMs, and CR1 inference's VRAM demand is down by 10x, while in batched mode, throughput improves by up to 8.2x, all compared to their respective aggressive baselines. This paper is accepted at the 9th MLSys Conference (Industry Track), 2026. Code and artifact available at: https://github.com/deepshnv/pipeshard‑mlsys26‑ae
Authors:Wenshuo Zhao, Qi Zhu, Xingshan Zeng, Fei Mi, Lifeng Shang, Yi R., Fung
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.
Authors:Mohammed Suhail B Nadaf
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.
Authors:Emre Ardıç, Yakup Genç
Abstract:
Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. To overcome these issues, we propose novel sample selection methods for image classification, employing a multitask autoencoder to estimate sample contributions through loss and feature analysis. Our approach incorporates unsupervised outlier detection, using one‑class support vector machine (OCSVM), isolation forest (IF), and adaptive loss threshold (AT) methods managed by a central server to filter noisy samples on clients. We also propose a multi‑class deep support vector data description (SVDD) loss controlled by a central server to enhance feature‑based sample selection. We validate our methods on CIFAR10 and MNIST datasets across varying numbers of clients, non‑IID distributions, and noise levels up to 40%. The results show significant accuracy improvements with loss‑based sample selection, achieving gains of up to 7.02% on CIFAR10 with OCSVM and 1.83% on MNIST with AT. Additionally, our federated SVDD loss further improves feature‑based sample selection, yielding accuracy gains of up to 0.99% on CIFAR10 with OCSVM. These results show the effectiveness of our methods in improving model accuracy across various client counts and noise conditions.
Authors:Dominik Żurek, Kamil Faber, Marcin Pietron, Paweł Gajewski, Roberto Corizzo
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.
Authors:Shuning Shang, Hubert Strauss, Stanley Wei, Sanjeev Arora, Noam Razin
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.
Authors:Jianghao Lin, Zi Ling, Chenyu Zhou, Tianyi Xu, Ruoqing Jiang, Zizhuo Wang, Dongdong Ge
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.
Authors:Oliver Kraus, Yash Sarrof, Yuekun Yao, Alexander Koller, Michael Hahn
Abstract:
Chain‑of‑Thought (CoT) has been shown to empirically improve Transformers' performance, and theoretically increase their expressivity to Turing completeness. However, whether Transformers can learn to generalize to CoT traces longer than those seen during training is understudied. We use recent theoretical frameworks for Transformer length generalization and find that ‑‑ under standard positional encodings and a finite alphabet ‑‑ Transformers with CoT cannot solve problems beyond TC^0, i.e. the expressivity benefits do not hold under the stricter requirement of length‑generalizable learnability. However, if we allow the vocabulary to grow with problem size, we attain a length‑generalizable simulation of Turing machines where the CoT trace length is linear in the simulated runtime up to a constant. Our construction overcomes two core obstacles to reliable length generalization: repeated copying and last‑occurrence retrieval. We assign each tape position a unique signpost token, and log only value changes to enable recovery of the current tape symbol through counts circumventing both barriers. Further, we empirically show that the use of such signpost tokens and value change encodings provide actionable guidance to improve length generalization on hard problems.
Authors:Tri-Nhan Vo, Dang Nguyen, Kien Do, Sunil Gupta
Abstract:
Knowledge distillation (KD) is a well‑known technique to effectively compress a large network (teacher) to a smaller network (student) with little sacrifice in performance. However, most KD methods require a large training set and internal access to the teacher, which are rarely available due to various restrictions. These challenges have originated a more practical setting known as black‑box few‑shot KD, where the student is trained with few images and a black‑box teacher. Recent approaches typically generate additional synthetic images but lack an active strategy to promote their diversity, a crucial factor for student learning. To address these problems, we propose a novel training scheme for generative adversarial networks, where we adaptively select high‑confidence images under the teacher's supervision and introduce them to the adversarial learning on‑the‑fly. Our approach helps expand and improve the diversity of the distillation set, significantly boosting student accuracy. Through extensive experiments, we achieve state‑of‑the‑art results among other few‑shot KD methods on seven image datasets. The code is available at https://github.com/votrinhan88/divbfkd.
Authors:Sehyeon Oh, Yongin Kwon, Jemin Lee
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.
Authors:Chenbo Yu
Abstract:
Traffic signal control (TSC) plays a central role in reducing congestion and maintaining urban mobility. This dissertation introduces DGLight, a critic‑guided reinforcement‑learning framework for adapting a pretrained large language model to TSC. DGLight first trains a CoLight‑based Deep Q‑Network critic to estimate traffic‑aware action values from structured intersection states, then uses the frozen critic to score candidate language‑model actions and optimize the policy with Group Relative Policy Optimization (GRPO). The resulting controller maps traffic states to interpretable reasoning traces and signal decisions while learning from dense per‑state supervision rather than raw cumulative environment rewards. Experiments on TSC benchmarks covering Jinan and Hangzhou show that DGLight is the strongest overall method among the compared LLM‑based controllers, remains competitive with strong RL baselines, and transfers well to city datasets not used to fit the critic. Qualitative examples further show that the model's generated reasoning is interpretable and aligned with the chosen signal phase. The project code is available \hrefhttps://github.com/yyccbb/FYP_LLMTSChere.
Authors:Divake Kumar, Sina Tayebati, Devashri Naik, Ranganath Krishnan, Amit Ranjan Trivedi
Abstract:
Vision‑language models (VLMs) are increasingly used as automated judges for multimodal systems, yet their scores provide no indication of reliability. We study this problem through conformal prediction, a distribution‑free framework that converts a judge's point score into a calibrated prediction interval using only score‑token log‑probabilities, with no retraining. We present the first systematic analysis of conformal prediction for VLM‑as‑a‑Judge across 3 judges and 14 visual task categories. Our results show that evaluation uncertainty is strongly task‑dependent: intervals cover ~40% of the score range for aesthetics and natural images but expand to ~70% for chart and mathematical reasoning, yielding a quantitative reliability map for multimodal evaluation. We further identify a failure mode not captured by standard evaluation metrics, ranking‑scoring decoupling, where judges achieve high ranking correlation while producing wide, uninformative intervals, correctly ordering responses but failing to assign reliable absolute scores. Finally, we show that interval width is driven primarily by task difficulty and annotation quality, i.e., the same judge and method yield 4.5x narrower intervals on a clean, multi‑annotator captioning benchmark. Code: https://github.com/divake/VLM‑Judge‑Uncertainty
Authors:Alexander Kolpakov, Igor Rivin
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.
Authors:Douglas Brinkerhoff, Elizabeth Fischer
Abstract:
Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at resolutions of 50‑‑100~km where this topographic detail is absent. Dynamical downscaling with high‑resolution regional models such as WRF can resolve these processes, but the computational cost ‑‑ months of wall‑clock time per scenario ‑‑ precludes the large ensembles needed for uncertainty quantification. We present WxFlow, a conditional generative model based on flow matching that learns to map coarse‑resolution climate model output and high‑resolution topography to calibrated probabilistic ensembles of fine‑scale precipitation fields. Applied to 4~km WRF simulations of maximum 3‑day snowfall over southeast Alaska, WxFlow achieves 87.8% improvement in spectral fidelity and dramatically lower Continuous Ranked Probability Scores relative to conventional lapse‑rate‑corrected bicubic downscaling, while generating 50‑member ensembles in seconds on a laptop. Ensemble spread is spatially coherent and governed by topography, reflecting physically plausible uncertainty structure. All code is available at https://github.com/glide‑ism/wrf‑flow.
Authors:Lei Wang
Abstract:
We solve the time‑dependent Schrödinger equation by learning the score function, the gradient of the log‑probability density, on Bohmian trajectories. In Bohm's formulation of quantum mechanics, particles follow deterministic paths under the classical potential supplemented by a quantum potential depending on the score function of the evolving density. These non‑crossing Bohmian trajectories form a continuous normalizing flow governed by the score. We parametrize the score with a neural network and minimize a self‑consistent Fisher divergence between the network and the score of the resulting density. We prove that the zero‑loss minimizer of this self‑consistent objective recovers Schrödinger dynamics for nodeless wave functions, a condition naturally met in quantum vibrations of atoms. We demonstrate the approach on wavepacket splitting in a double‑well potential and anharmonic vibrations of a Morse chain. By recasting real‑time quantum dynamics as a self‑consistent score‑driven normalizing flow, this framework opens the time‑dependent Schrödinger equation to the rapidly advancing toolkit of modern generative modeling.
Authors:Christian Lysenstøen
Abstract:
Deploying machine learning models under production constraints requires joint optimization over model family, quantization scheme, runtime backend, and serving configuration. This induces a hierarchical mixed‑variable search space in which many configurations are invalid: evaluations may crash, exceed memory limits, or violate latency constraints. Standard black‑box optimizers such as Tree‑structured Parzen Estimators (TPE) and constrained Bayesian optimization are effective when valid configurations are common, but they can spend a large fraction of a small evaluation budget on invalid or uninformative trials in hostile deployment spaces. This paper studies that regime and asks whether optimization should be decomposed into an explicit exploration stage followed by model‑guided exploitation. We propose Thermal Budget Annealing (TBA), a feasible‑first exploration procedure that maps valid and feasible regions before warm‑starting TPE. The method includes two robustness mechanisms for hostile hardware: trial timeouts that abort clearly infeasible evaluations early, and subspace blacklisting that temporarily suppresses categorical subspaces after repeated failures. We also introduce DeployBench, a benchmark suite for deployment optimization with hierarchical structure, hidden crash zones, hard constraints, and unequal evaluation costs. On synthetic benchmarks and real GPU deployment with five pre‑trained vision models across five GPU targets (NVIDIA H100, A100, RTX 5080, L4, and T4), the proposed hybrid improves model‑family discovery under tight constraints while reducing wasted budget relative to cold‑start TPE.
Authors:Dhruv Gupta
Abstract:
Recent work revisiting measurability in the fundamental theorem of statistical learning imposes Borel measurability of ghost‑gap suprema. We show that, at the one‑sided ghost‑gap interface actually used by the standard symmetrization proof, this requirement is stronger than necessary. For any Borel‑parameterized concept class on a Polish domain, the bad event "there exists a hypothesis whose ghost empirical error exceeds its training empirical error by at least ε/2" is analytic. By Choquet capacitability, it is therefore measurable in the completion of every finite Borel measure. We then construct a concept class whose bad event is null‑measurable but not Borel, giving a strict separation from the Borel supremum condition. Finally, we prove closure under patching, fixed and countable interpolation, and fiber‑product amalgamation, showing that the weaker regularity level is stable under natural concept‑class constructors. In the realizable setting, where targets belong to the class and are measurable, these results weaken the measurability hypothesis needed by the symmetrization route from finite VC dimension to PAC learnability. The main results and the descriptive‑set‑theoretic infrastructure used by them are formalized in Lean 4.
Authors:Ishan Patel, Ishan Joshi
Abstract:
We present PolyKV, a system in which multiple concurrent inference agents share a single, asymmetrically compressed KV cache pool. Rather than allocating a separate KV cache per agent ‑‑ the standard paradigm ‑‑ PolyKV writes a compressed cache once and injects it into N independent agent contexts via HuggingFace DynamicCache objects. Compression is asymmetric: Keys are quantized at int8 (q8_0) to preserve softmax stability, while Values are compressed using TurboQuant MSE ‑‑ a Fast Walsh‑Hadamard Transform (FWHT) rotation followed by 3‑bit Lloyd‑Max quantization with centroids tuned to N(0,1). We evaluate across two model scales (SmolLM2‑1.7B‑Instruct and Llama‑3‑8B‑Instruct), three context lengths (600‑7,194 tokens), and up to 15 concurrent agents. PolyKV achieves a stable 2.91x compression ratio across all configurations. On Llama‑3‑8B with 15 agents sharing a 4K‑token context, PolyKV reduces KV cache memory from 19.8 GB to 0.45 GB ‑‑ a 97.7% reduction ‑‑ while maintaining only +0.57% perplexity degradation and a mean BERTScore F1 of 0.928. PPL delta does not grow with agent count and improves as context length increases, inverting to ‑0.26% at 1,851 coherent tokens. To our knowledge, no prior work combines a single shared, lossy‑compressed KV pool with multi‑reader concurrent agent access.
Authors:Jing Chen, Abhijay Deevi, Onat Gungor, Tajana Rosing
Abstract:
The Controller Area Network (CAN) is a safety‑critical in‑vehicle communication protocol that lacks built‑in security mechanisms, making intrusion detection essential. Existing approaches predominantly formulate CAN intrusion detection as a classification task, mapping complex traffic patterns to attack labels. However, this formulation abstracts away the temporal and relational structure of CAN traffic and misaligns with real‑world forensic workflows, which require systematic reasoning about traffic behavior. To address this gap, we introduce CAN‑QA, the first benchmark that reformulates CAN traffic analysis as a question‑answering (QA) task. CAN‑QA converts raw CAN logs into temporally segmented windows and applies deterministic rule‑based templates to generate natural‑language questions paired with automatically derived ground‑truth answers. The resulting dataset comprises 33,128 QA pairs across 10 categories, each targeting distinct semantic and temporal properties of CAN traffic. Using CAN‑QA, we evaluate large language models across both True/False and multiple‑choice formats. Our results indicate that, although these models capture superficial statistical regularities, they struggle with temporal reasoning, multi‑condition inference, and higher‑level behavioral interpretation. Our code is available at https://github.com/Kriiiiss/CAN‑QA.
Authors:Yuanhao Zeng, Ao Lu, Lufei Li, Zheng Zhang, Yexin Li, Kan Ren
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.
Authors:Antoine P. Leeman, Shuyu Zhan, Melanie N. Zeilinger, Glen Chou
Abstract:
We propose VISION‑SLS, a method for nonlinear output‑feedback control from high‑resolution RGB images which provides robust constraint satisfaction guarantees under calibrated uncertainty bounds despite partial observability, sensor noise, and nonlinear dynamics. To enable scalability while retaining guarantees, we propose: (i) a learned low‑dimensional observation map from pretrained visual features with state‑dependent error bounds, and (ii) a causal affine time‑varying output‑feedback policy optimized via System Level Synthesis (SLS). We develop a scalable, novel solver for the resulting nonconvex program that leverages sequential convex programming coupled with efficient Riccati recursions. On two simulated visuomotor tasks (a 4D car and a 10D quadrotor) with >= 512 x 512 pixels and a 59D humanoid task with partial observability, our method enables safe, information‑gathering behavior that reduces uncertainty while guaranteeing constraint satisfaction with empirically‑calibrated error bounds. We also validate our method on hardware, safely controlling a ground vehicle from onboard images, outperforming baselines in safety rate and solve times. Together, these results show that learned visual abstractions coupled with an efficient solver make SLS‑based safe visuomotor output‑feedback practical at scale. The code implementation of our method is available at https://github.com/trustworthyrobotics/VISION‑SLS.
Authors:Maitreya Patel, Jingtao Li, Weiming Zhuang, Yezhou Yang, Lingjuan Lv
Abstract:
We introduce an efficient, resolution‑agnostic autoregressive (AR) image synthesis approach that generalizes to arbitrary resolutions and aspect ratios, narrowing the gap to diffusion models at scale. At its core is VibeToken, a novel resolution‑agnostic 1D Transformer‑based image tokenizer that encodes images into a dynamic, user‑controllable sequence of 32‑256 tokens, achieving a state‑of‑the‑art efficiency and performance trade‑off. Building on VibeToken, we present VibeToken‑Gen, a class‑conditioned AR generator with out‑of‑the‑box support for arbitrary resolutions while requiring significantly fewer compute resources. Notably, VibeToken‑Gen synthesizes 1024x1024 images using only 64 tokens and achieves 3.94 gFID; by comparison, a diffusion‑based state‑of‑the‑art alternative requires 1,024 tokens and attains 5.87 gFID. In contrast to fixed‑resolution AR models such as LlamaGen ‑‑ whose inference FLOPs grow quadratically with resolution (11T FLOPs at 1024x1024) ‑‑ VibeToken‑Gen maintains a constant 179G FLOPs (63.4x efficient) independent of resolution. We hope VibeToken can help unlock the wide adoption of AR visual generative models in production use cases.
Authors:Nishit Anand, Manan Suri, Christopher Metzler, Dinesh Manocha, Ramani Duraiswami
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.
Authors:Tingwu Wang, Olivier Dionne, Michael De Ruyter, David Minor, Davis Rempe, Kaifeng Zhao, Mathis Petrovich, Ye Yuan, Chenran Li, Zhengyi Luo, Brian Robison, Xavier Blackwell, Bernardo Antoniazzi, Xue Bin Peng, Yuke Zhu, Simon Yuen
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.
Authors:Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos Tserpes
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
Authors:Peng Liao, Peijia Zheng, Lingbo Li, Shangsong Liang, Lin Chen
Abstract:
Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models (LLMs) with human values. However, achieving optimal performance with these methods typically involves additional hyperparameter tuning, resulting in substantial time overhead. Although prior work has proposed a range of improvements, these methods remain limited in effectiveness and have not fully eliminated reliance on hyperparameter tuning. In this work, we propose RMiPO, a lightweight and efficient framework for offline preference optimization. RMiPO leverages intrinsic Response‑level Mutual information for Preference Optimization with hyperparameter modulation, dynamically decoupling preference contributions at negligible additional computational cost. Extensive experimental results demonstrate that RMiPO achieves consistently superior performance over existing methods while reducing training overhead by more than 15%. Our code is available at https://github.com/liavonpenn/rmipo.
Authors:Thomas Carmichael
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.
Authors:Zijian Guo, İlker Işık, H. M. Sabbir Ahmad, Wenchao Li
Abstract:
Specification‑guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specifications such as linear temporal logic (LTL). While recent methods have shown promising results, their ability to generalize across unseen specifications and diverse environments remains insufficiently understood. In this work, we introduce SpecRLBench, a benchmark designed to evaluate the generalization capabilities of LTL‑based specification‑guided RL methods. The benchmark spans multiple difficulty levels across navigation and manipulation domains, incorporating both static and dynamic environments, diverse robot dynamics, and varied observation modalities. Through extensive empirical evaluation, we characterize the strengths and limitations of existing approaches and reveal the challenges that emerge as specification and environment complexity increase. SpecRLBench provides a structured platform for systematic comparison and supports the development of more generalizable specification‑guided RL methods. Code is available at https://github.com/BU‑DEPEND‑Lab/SpecRLBench.
Authors:Jiachen Liu, Jiaxin Pei, Jintao Huang, Chenglei Si, Ao Qu, Xiangru Tang, Runyu Lu, Lichang Chen, Xiaoyan Bai, Haizhong Zheng, Carl Chen, Zhiyang Chen, Haojie Ye, Yujuan Fu, Zexue He, Zijian Jin, Zhenyu Zhang, Shangquan Sun, Maestro Harmon, John Dianzhuo Wang, Jianqiao Zeng, Jiachen Sun, Mingyuan Wu, Baoyu Zhou, Chenyu You, Shijian Lu, Yiming Qiu, Fan Lai, Yuan Yuan, Yao Li, Junyuan Hong, Ruihao Zhu, Beidi Chen, Alex Pentland, Ang Chen, Mosharaf Chowdhury, Zechen Zhang
Abstract:
Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation imposes two structural costs: a Storytelling Tax, where failed experiments, rejected hypotheses, and the branching exploration process are discarded to fit a linear narrative; and an Engineering Tax, where the gap between reviewer‑sufficient prose and agent‑sufficient specification leaves critical implementation details unwritten. Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work. We introduce the Agent‑Native Research Artifact (ARA), a protocol that replaces the narrative paper with a machine‑executable research package structured around four layers: scientific logic, executable code with full specifications, an exploration graph that preserves the failures compilation discards, and evidence grounding every claim in raw outputs. Three mechanisms support the ecosystem: a Live Research Manager that captures decisions and dead ends during ordinary development; an ARA Compiler that translates legacy PDFs and repos into ARAs; and an ARA‑native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste. On PaperBench and RE‑Bench, ARA raises question‑answering accuracy from 72.4% to 93.7% and reproduction success from 57.4% to 64.4%. On RE‑Bench's five open‑ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior‑run box depending on the agent's capabilities. Our code is open‑sourced at https://github.com/Orchestra‑Research/Agent‑Native‑Research‑Artifact.
Authors:Shiyi Zhang, Yiji Cheng, Tiankai Hang, Zijin Yin, Runze He, Yu Xu, Wenxun Dai, Yunlong Lin, Chunyu Wang, Qinglin Lu, Yansong Tang
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/
Authors:Di Wang, Chupei Tang, Junxiao Kong, Jixiu Zhai, Moyu Tang, Tianchi Lu
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.
Authors:Dake Bu, Wei Huang, Andi Han, Hau-San Wong, Qingfu Zhang, Taiji Suzuki, Atsushi Nitanda
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.
Authors:Yangping Li, Thomas Pinetz, Michael Hölzel, Marieta Toma, Alexander Effland
Abstract:
In pathology, the spatial distribution and proportions of tissue types are key indicators of disease progression, and are more readily available than fine‑grained annotations. However, these assessments are rarely mapped to pixel‑wise segmentation. The task is fundamentally underdetermined, as many spatially distinct segmentations can satisfy the same global proportions in the absence of pixel‑wise constraints. To address this, we introduce Variational Segmentation from Label Proportions (VSLP), a two‑stage framework that infers dense segmentations from global label proportions, without any pixel‑level annotations. This framework first leverages a pre‑trained transformer model with test‑time augmentation to produce a pixel‑wise confidence estimate. In the second stage, these estimates are fused by solving a variational optimization problem that incorporates a Wasserstein data fidelity term alongside a learned regularizer. Unlike end‑to‑end networks, our variational method can visualize the fidelity‑regularization energy, resulting in more interpretable segmentation. We validate our approach on two public datasets, achieving superior performance over existing weakly supervised and unsupervised methods. For one of these datasets, proportions have been estimated by an experienced pathologist to provide a realistic benchmark to the community. Furthermore, the method scales to an in‑house dataset with noisy pathologist labels, severely outperforming state‑of‑the‑art methods, thereby demonstrating practical applicability. The code and data will be made publicly available upon acceptance at https://github.com/xiaoliangpi/VSLP.
Authors:Zhisong Qiu, Shuofei Qiao, Kewei Xu, Yuqi Zhu, Lun Du, Ningyu Zhang, Huajun Chen
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.
Authors:Wenjie Du, Yiyuan Yang, Tianxiang Zhan, Qingsong Wen
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
Authors:Hojoon Kim, Yuheng Wu, Thierry Tambe
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.
Authors:Yutong He, Zhengyang Huang, Jiahe Geng
Abstract:
Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource‑constrained environments. We introduce FedSLoP, a federated optimization algorithm that combines stochastic low‑rank subspace projections of gradients, thereby reducing the dimension of communicated and stored updates while preserving optimization progress. On the theoretical side, we develop a detailed nonconvex convergence analysis under standard smoothness and bounded‑variance assumptions, showing that FedSLoP is guaranteed to converge to a first‑order stationary point at a rate of O(1/\sqrtNT). On the empirical side, we conduct extensive experiments on federated MNIST classification with heterogeneous data partitions, showing that FedSLoP substantially reduces communication volume and client‑side memory while achieving competitive or better accuracy compared with FedAvg and representative sparse or low‑rank baselines. Together, our results demonstrate that random subspace momentum methods such as FedSLoP provide a principled and effective approach to communication‑ and memory‑efficient federated learning. Codes are available at: https://github.com/pkumelon/FedSLoP.git.
Authors:Jiaqi Wang, Wenhao Zhang, Weijie Shi, Yaliang Li, James Cheng
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.
Authors:Han Wang, Xiaodong Yu, Jialian Wu, Jiang Liu, Ximeng Sun, Mohit Bansal, Zicheng Liu
Abstract:
Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and verbose reasoning traces. While recent work on efficient reasoning reduces this overhead through length‑based rewards or pruning, many approaches are post‑trained under a much shorter context window than base‑model training, a factor whose effect has not been systematically isolated. We first show that short‑context post‑training alone, using standard GRPO without any length‑aware objective, already induces substantial reasoning compression‑but at the cost of increasingly unstable training dynamics and accuracy degradation. To address this, we propose Step‑level Advantage Selection (SAS), which operates at the reasoning‑step level and assigns a zero advantage to low‑confidence steps in correct rollouts and to high‑confidence steps in verifier‑failed rollouts, where failures often arise from truncation or verifier issues rather than incorrect reasoning. Across diverse mathematical and general reasoning benchmarks, SAS improves average Pass@1 accuracy by 0.86 points over the strongest length‑aware baseline while reducing average reasoning length by 16.3%, yielding a better accuracy‑efficiency trade‑off.
Authors:Nicola Zanarini, Niccolò Ferrari, Evelina Lamma
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.
Authors:Chih-Chung Hsu, Xin-Di Ma, Wo-Ting Liao, Chia-Ming Lee
Abstract:
Existing attention accelerators often trade exact softmax semantics, depend on fused Tensor Core kernels, or incur sequential depth that limits FP32 throughput on long sequences. We present ELSA, an algorithmic reformulation of online softmax attention that (i)~preserves exact softmax semantics in real arithmetic with a \emphprovable \mathcalO(u\log n) FP32 relative error bound; (ii)~casts the online softmax update as a prefix scan over an associative monoid (m,S,W), yielding O(n) extra memory and O(\log n) parallel depth; and (iii)~is Tensor‑Core independent, implemented in Triton and CUDA C++, and deployable as a \emphdrop‑in replacement requiring no retraining or weight modification. Unlike FlashAttention‑2/3, which rely on HMMA/GMMA Tensor Core instructions and provide no compatible FP32 path, ELSA operates identically on A100s and resource‑constrained edge devices such as Jetson TX2 ‑‑ making it the only hardware‑agnostic exact‑attention kernel that reduces parallel depth to O(\log n) at full precision. On A100 FP32 benchmarks (1K‑‑16K tokens), ELSA delivers 1.3‑‑3.5× speedup over memory‑efficient SDPA and 1.97‑‑2.27× on BERT; on Jetson TX2, ELSA achieves 1.5‑‑1.6× over Math (64‑‑900 tokens), with 17.8‑‑20.2% throughput gains under LLaMA‑13B offloading at \ge32K. In FP16, ELSA approaches hardware‑fused baselines at long sequences while retaining full FP32 capability, offering a unified kernel for high‑precision inference across platforms. Our code and implementation are available at https://github.com/ming053l/ELSA.
Authors:Seongjin Choi
Abstract:
We present a semantic‑structural atlas of transportation research built from 120,323 papers across 34 peer‑reviewed journals published between 1967 and 2025, roughly an order of magnitude larger than and a decade beyond Sun and Rahwan's~(2017) coauthorship study. We use OpenAlex and Crossref as open, CC0‑licensed data sources, resolve author identity through OpenAlex author IDs, ORCID records, and manual alias resolution, and embed every paper with SPECTER2 with Arora‑style whitening concatenated with concept TF‑‑IDF and venue linear‑discriminant projections. On this substrate we report three findings. First, Leiden on the author‑level semantic k‑nearest‑neighbor graph yields 23 topic communities that agree only weakly with the 172 coauthor communities (normalized mutual information 0.23), opening room for a predictive layer that neither source encodes alone. Second, a multiplex Leiden partition combining both edge types recovers 181 communities and localizes where collaboration and topic structure decouple. Third ‑‑ the paper's core methodological contribution ‑‑ we define \emphphantom collaborators, pairs of authors who are top‑K semantic neighbors yet \geq 3 hops apart in the coauthor graph, and show via a temporal hold‑out (training cutoff 2019) that phantom pairs become real coauthors in 2020‑‑2025 at a rate 16 to 33 times above random, popularity‑weighted, and same‑venue baselines, with a 68‑fold monotone gradient between the highest‑ and lowest‑similarity buckets. All artifacts are released as a live, reproducible web atlas at https://choi‑seongjin.github.io/transport‑atlas/.
Authors:Yuanming Shi, Andreas Haupt
Abstract:
Silicon samples are increasingly used as a low‑cost substitute for human panels and have been shown to reproduce aggregate human opinion with high fidelity. We show that, in the alignment‑relevant domain of philosophy, silicon samples systematically collapse heterogeneity. Using data from N = 277 professional philosophers drawn from PhilPeople profiles, we evaluate seven proprietary and open‑source large language models on their ability to replicate individual philosophical positions and to preserve cross‑question correlation structures across philosophical domains. We find that language models substantially over‑correlate philosophical judgments, producing artificial consensus across domains. This collapse is associated in part with specialist effects, whereby models implicitly assume that domain specialists hold highly similar philosophical views. We assess the robustness of these findings by studying the impact of DPO fine‑tuning and by validating results against the full PhilPapers 2020 Survey (N = 1785). We conclude by discussing implications for alignment, evaluation, and the use of silicon samples as substitutes for human judgment. The code of this project can be found at https://github.com/stanford‑del/silicon‑philosophers.
Authors:Sifan Wang, Shawn Koohy, Yiping Lu, Paris Perdikaris
Abstract:
Physics‑informed neural networks (PINNs) provide a promising machine learning framework for solving partial differential equations, but their training often breaks down on challenging problems, sometimes converging to physically incorrect solutions despite achieving small residual losses. This failure, we argue, is not merely an optimization difficulty. Rather, it reflects a fundamental weakness of the empirical PDE residual loss, which can admit trivial or spurious solutions during training. From this perspective, we revisit pseudo‑time stepping, a technique that has recently shown strong empirical success in PINNs. We show that its main benefit is not simply to ease optimization; instead, when combined with collocation‑point resampling, it helps reveal and avoid spurious solutions. At the same time, we find that the effectiveness of pseudo‑time stepping depends critically on the choice of step size, which cannot be tuned reliably from the training loss alone. To overcome this limitation, we propose an adaptive pseudo‑time stepping strategy that selects the step size from a finite‑difference surrogate of the local residual Jacobian, yielding the largest step permitted by local stability without per‑problem tuning. Across a diverse set of PDE benchmarks, the proposed method consistently improves both accuracy and robustness. Together, these findings provide a clearer understanding of why PINNs fail and suggest a practical pathway toward more reliable physics‑informed learning. All code and data accompanying this manuscript are available at https://github.com/sifanexisted/jaxpi2.
Authors:Lichen Li, Hengguang Zhou, Yijun Liang, Tianyi Zhou, Cho-Jui Hsieh
Abstract:
Reward hacking in code generation, where models exploit evaluation loopholes to obtain full reward without correctly solving the tasks, poses a critical challenge for Reinforcement Learning (RL) and the deployment of reasoning models. Existing studies have been conducted primarily on synthetic hacking trajectories. However, whether these synthetic behaviors faithfully represent naturally emerging hacking in the wild remains unclear. In this work, we present a systematic analysis of the synthetic vs. in‑the‑wild discrepancy in reward hacking. We examine to what extent hacking behaviors induced by prompting resemble those emerging during RL training, and whether monitors trained on synthetic trajectories generalize to naturally arising but previously unseen hacking. To scale up the curation of in‑the‑wild reward hacking trajectories, we modified Group Relative Policy Optimization (GRPO) by injecting conflicting unit tests as tracers and applying a "resampling‑until‑hack" mechanism. Through controlled comparisons between monitors trained on synthetic versus in‑the‑wild data, we find that (1) synthetic‑data‑trained monitors fail to generalize to "in‑the‑wild" hacking, and (2) monitors trained on our "in‑the‑wild" trajectories demonstrate stronger generalizability to unseen hacking types. Our results indicate that synthetic reward hacking data may not fully reflect natural reward hacking behaviors, and that relying solely on synthetic data can lead to misleading conclusions. The codebase is available at https://github.com/LichenLillc/CoTMonitoring.git
Authors:Jainum Sanghavi
Abstract:
Vision Transformers trained only on image classification routinely transfer to tasks that demand spatial understanding, yet they receive no spatial supervision during pretraining. We ask where and how robustly such structure is encoded. Probing a frozen ViT‑B/16 layerwise for two complementary properties, local patch boundaries (BSDS500) and per‑patch depth (NYU Depth V2), reveals a clear hierarchy: boundary structure becomes linearly decodable at layers 5‑6 (AP = 0.833), while depth, which requires integrating global cues, peaks two to three layers later at layer 8 (MAE = 0.0875). Both signals collapse at the final classification layer, and random‑weight controls confirm the encodings are learned rather than architectural. Causal interventions add specificity: ablating the single direction a linear depth probe reads degrades depth decoding by up to 165%, while ablating any other direction changes it by less than 1%. Targeted activation patching along that direction shows the depth signal is partially re‑derived at each layer rather than passively carried in the residual stream, with mid‑layer interventions persisting most strongly downstream. The result is that a classification‑trained ViT develops an actively maintained spatial hierarchy that mirrors the early‑to‑late progression observed in the primate visual cortex.
Authors:Lucky Verma
Abstract:
Dynamic Tanh (DyT) removes LayerNorm by bounding activations with a learned tanh(alpha x). We show that this bounding is a regime‑dependent implicit regularizer, not a uniformly beneficial replacement. Across GPT‑2‑family models spanning 64M to 3.78B parameters and 1M to 118M tokens, with Llama and ViT cross‑checks, DyT improves validation loss by 27.3% at 64M/1M but worsens it by 18.8% at 64M/118M; the 1M benefit vanishes with capacity (+1.7% at 3.78B), while the 118M penalty reaches +27.9%. The mechanism is measurable: 49% of DyT activations saturate at 1M versus 23% at 118M, and a 500‑step saturation heuristic classifies DyT's sign with 75% raw in‑sample accuracy on the 12‑cell GPT‑2 calibration set (AUC 0.75; 64% when adding Scale 5 stress cells), correctly labels 3/3 Llama checks, but only reaches 50% raw leave‑one‑scale‑out accuracy. Three interventions support the bounding explanation: HardTanh reproduces the regime pattern, increasing alpha at 118M monotonically reduces DyT's penalty, and vanilla+dropout(p=0.5) matches DyT's data‑rich loss. We also localize Llama‑DyT collapse to SwiGLU gating, where saturation separates collapse from convergence in a 3‑seed component ablation (r=0.94). Scope: all experiments are compute‑limited (T/P < 1.84), below Chinchilla‑optimal training.
Authors:Emre Ardıç, Yakup Genç
Abstract:
Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caused by variations in connection speed and bandwidth across devices. Therefore, it is essential to reduce the size of transmitted data during training. Additionally, there is a potential risk of exposing sensitive information through the model or gradient analysis during training. To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. We use Laplacian‑based DP to preserve privacy, which is relatively underexplored in FL and offers tighter privacy guarantees than Gaussian‑based DP. We propose a simple and efficient global bit‑length scheduler using round‑based cosine annealing, along with a client‑based scheduler that dynamically adapts based on client contribution estimated through dataset entropy analysis. We evaluate our approach through extensive experiments on CIFAR10, MNIST, and medical imaging datasets, using non‑IID data distributions across varying client counts, bit‑length schedulers, and privacy budgets. The results show that our adaptive quantization methods reduce total communicated data by up to 52.64% for MNIST, 45.06% for CIFAR10, and 31% to 37% for medical imaging datasets compared to 32‑bit float training while maintaining competitive model accuracy and ensuring robust privacy through differential privacy.
Authors:Hanna Rød, Dagny Streit, Nils Valseth Selte, Justin Li
Abstract:
This paper investigates context stickiness in in‑context learning (ICL), a phenomenon where earlier examples in a prompt interfere with a transformer's ability to adapt to later tasks. Using synthetic regression tasks over linear and quadratic functions, we examine how models trained under sequential, mixed, and random curricula handle abrupt task switches during inference. By sweeping over structured combinations of misleading linear examples followed by recovery quadratic examples, we quantify how prior context biases prediction error and how quickly models realign. Our results show strong evidence of persistent interference: more preceding linear examples reliably degrade quadratic predictions, while additional quadratic examples reduce error but with diminishing returns. We further find that training curricula significantly modulate resilience, with sequential training on the target function class yielding the fastest recovery, and surprisingly, random training producing the least robust behavior.
Authors:Jelena Ilić Vulićević
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.
Authors:Thibaud Southiratn, Bonil Koo, Yijingxiu Lu, Sun Kim
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.
Authors:Varun Totakura, Ankita Singh, Yushun Dong, Shayok Chakraborty
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/.
Authors:Bishwamittra Ghosh, Soumi Das, Till Speicher, Qinyuan Wu, Mohammad Aflah Khan, Deepak Garg, Krishna P. Gummadi, Evimaria Terzi
Abstract:
Large language models (LLMs) operate in two fundamental learning modes ‑ fine‑tuning (FT) and in‑context learning (ICL) ‑ raising key questions about which mode yields greater language proficiency and whether they differ in their inductive biases. Prior studies comparing FT and ICL have yielded mixed and inconclusive results due to inconsistent experimental setups. To enable a rigorous comparison, we propose a formal language learning task ‑ offering precise language boundaries, controlled string sampling, and no data contamination ‑ and introduce a discriminative test for language proficiency, where an LLM succeeds if it assigns higher generation probability to in‑language strings than to out‑of‑language strings.
Empirically, we find that: (a) FT has greater language proficiency than ICL on in‑distribution generalization, but both perform equally well on out‑of‑distribution generalization. (b) Their inductive biases, measured by the correlation in string generation probabilities, are similar when both modes partially learn the language but diverge at higher proficiency levels. (c) Unlike FT, ICL performance differs substantially across models of varying sizes and families and is sensitive to the token vocabulary of the language. Thus, our work demonstrates the promise of formal languages as a controlled testbed for evaluating LLMs, behaviors that are difficult to isolate in natural language datasets. Our source code is available at https://github.com/bishwamittra/formallm.
Authors:Xinyue Zhang, Yuanhao Ding, Xiang Ao
Abstract:
Delayed feedback poses a core challenge for online CVR prediction, forcing a trade‑off between label accuracy and data freshness. Existing methods address this through delay modeling or sample reweighting, yet neglect how post‑click behaviors evolve over the observation period. To overcome this limitation, we formalize this evolution as feedback trajectory and propose TRACE. Instead of forcing hard labels on unrevealed samples, our method evaluates how well the accumulated feedback status aligns with conversion versus non‑conversion, dynamically refining posteriors without waiting for final outcomes. To counteract early‑stage trajectory sparsity, we further design a reliability‑gated retrospective completer that leverages full‑lifecycle data to provide adaptive posterior guidance for unrevealed samples. Extensive experiments validate TRACE's superiority over state‑of‑the‑art baselines and confirm the retrospective completion module as a model‑agnostic enhancer for existing systems. Our code is available at https://github.com/LunaZhangxy/TRACE.
Authors:Wugeng Zheng, Ziwen Kan, Katie Wang, Chen Chen, Song Wang
Abstract:
Multimodal Federated Learning (MMFL) enables privacy‑preserving collaborative training, but real‑world clinical applications often suffer from within‑modality missingness caused by sensor intermittency or irregular sampling. Existing methods implicitly represent unobserved data via architectural alignment or missing embeddings, often failing to recover the true distribution and yielding sub‑optimal performance. We propose CondI, a federated framework explicitly addressing this missingness using conditional diffusion models. CondI employs a two‑phase training pipeline: first, imputing unobserved temporal components using available multimodal context and conditional embeddings; second, optimizing modality‑specific extractors and joint embedding spaces. During inference, imputed raw data pass through trained extractors to generate robust features, providing a holistic representation for downstream tasks. Explicit data imputation ensures models operate on complete semantic structures, significantly enhancing resilience against severe data incompleteness. Experiments on three clinical datasets (PTB‑XL, SLEEP‑EDF, MIMIC‑IV) demonstrate CondI achieves comparable results to state‑of‑the‑art baselines. Code: https://github.com/ZhengWugeng/CondI
Authors:Zhicheng Ma, Xiang Liu, Zhaoxiang Liu, Ning Wang, Yi Shen, Kai Wang, Shuming Shi, Shiguo Lian
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.
Authors:Rui Gao, Youngseung Jeon, Swastik Roy, Morteza Ziyadi, Xiang 'Anthony' Chen
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.
Authors:Zixuan Xia, Quanxi Li
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.
Authors:Jeremy Ellis
Abstract:
This paper presents a complete, end‑to‑end on‑device vision machine learning pipeline, comprising data acquisition, two‑layer CNN training with Adam optimization, and real‑time inference, executing entirely on a microcontroller‑class device costing 15‑40 USD. Unlike cloud‑based workflows that require external infrastructure and conceal the computational pipeline from the practitioner, this system implements every step of the core ML lifecycle in approximately 1,750 lines of readable C++ that compiles in under one minute using the Arduino IDE, with no external ML dependencies. Running on the Seeed Studio ESP32‑S3 XIAO ML Kit (8 MB PSRAM), the firmware achieves three‑class 64x64 image classification in approximately 9 minutes per training run, with real‑time inference at 6.3 FPS. Key contributions include: correct batch‑level gradient accumulation; pre‑computed resize lookup tables for inference; dual‑format weight export for SD‑free baked‑in deployment; a three‑tier weight priority system (SD binary > baked‑in header > He‑initialization) resolved automatically at boot; a single‑constant network reconfiguration interface; and PSRAM‑aware memory management suited to microcontroller constraints. All source code and reference datasets are released under the MIT License at https://github.com/webmcu‑ai/on‑device‑vision‑ai
Authors:Rahul Patel
Abstract:
Anemia affects over one billion people globally and remains severely under‑diagnosed in low‑resource regions where laboratory blood tests are inaccessible. This paper presents AnemiaVision, an end‑to‑end web‑based system for non‑invasive anemia screening from smartphone photographs of the palpebral conjunctiva and fingernail beds. The proposed pipeline fine‑tunes a pre‑trained EfficientNet‑B3 backbone with a redesigned three‑layer classifier head incorporating BatchNorm, GELU activations, and high‑rate Dropout (0.45/0.35). Training employs four orthogonal accuracy‑boosting techniques: TrivialAugmentWide for policy‑free image augmentation, RandomErasing for spatial regularisation, Mixup (alpha=0.2) for inter‑class smoothing, and cosine‑annealing scheduling with linear warmup. Early stopping is governed by peak validation accuracy rather than validation loss to prevent premature termination on high‑variance epochs. The deployed Flask application integrates persistent patient‑history management backed by PostgreSQL on Render, with an automated database‑migration entrypoint ensuring zero data loss across redeploys. Ablation experiments demonstrate that accuracy‑first early stopping contributes +1.6% and Mixup contributes +2.8% to final validation accuracy. Overall, the proposed system achieves a validation accuracy of 96.2% and AUC‑ROC of 0.98, compared with 44.9% validation accuracy and AUC‑ROC of 0.58 from the three‑epoch CPU‑only baseline. Sensitivity for the anemic class reaches 0.96, making the system suitable as a first‑line screening tool for community health workers in rural settings. The system is publicly accessible and source code is openly available.
Authors:Jie Hu, Lingyun Chen, Geeho Kim, Jinyoung Choi, Bohyung Han, Do Young Eun
Abstract:
History‑dependent sampling can reduce long‑run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history through empirical measure on finite state spaces, which is infeasible in high‑dimensional discrete configuration spaces or ill‑posed in continuous domains. We propose Score‑Repellent Monte Carlo (SRMC) framework that summarizes trajectory history by a running average of score evaluations in \mathbbR^d, where d is the dimension of the score and state representation. This history is converted into a surrogate target through an exponential score tilt, indexed with α that represents the strength of repellence in controlling the magnitude of the history‑based repulsion. The surrogate family is normalization‑free in the standard MCMC sense, yielding a generic wrapper: at each iteration, any base kernel targeting π can instead be run on the current surrogate π_θ_n while the history is updated online. We analyze the coupled evolution of the history recursion and Monte Carlo estimators using stochastic approximation with controlled Markovian noise, establishing almost sure convergence and a joint central limit theorem. We further identify regimes in which the asymptotic covariance decreases as α increases, with scaling O(1/α), extending the near‑zero‑variance effect of finite‑state history‑dependent samplers to general state spaces with constant memory. Experiments on continuous targets and discrete energy‑based models demonstrate improved estimator variance and mode coverage, while retaining O(d) memory usage and modest per‑iteration overhead.
Authors:Nikoo Moradi, Gijs Luijten, Behrus Hinrichs-Puladi, Jens Kleesiek, Victor Alves, Jan Egger, André Ferreira
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.
Authors:Dong Liu, Haisheng Wang, Yanxuan Yu
Abstract:
Diffusion models achieve remarkable success in time series generation. However, slow inference limits their practical deployment. We propose E^2‑CRF (Error‑Feedback Event‑Driven Cumulative Residual Feature caching) to accelerate frequency domain diffusion models. Our method exploits two structural properties: (1) spectral localization, where signal energy concentrates in low frequencies, and (2) mirror symmetry, which halves the effective frequency dimension. E^2‑CRF uses a closed‑loop error‑feedback system that adaptively caches transformer KV features across diffusion steps. We trigger recomputation using event‑driven residual dynamics instead of fixed schedules. Our method selectively recomputes high‑energy or rapidly‑changing tokens while reusing cached features for stable high‑frequency components. E^2‑CRF achieves ~2.2 speedup while maintaining sample quality. We demonstrate effectiveness on 5 datasets. Our caching strategy naturally aligns with the diffusion process's structure‑to‑detail progression. We include sufficient‑condition error and complexity bounds under standard regularity assumptions (Appendix), alongside empirical validation. Our code is available at https://github.com/NoakLiu/FastFourierDiffusion and is also integrated in https://github.com/NoakLiu/FastCache‑xDiT.
Authors:Chao Pan, Xin Yao
Abstract:
Fast Adversarial Training (FastAT) seeks to achieve adversarial robustness at a fraction of the computational cost incurred by standard multi‑step methods such as PGD‑AT. Although numerous FastAT techniques have been proposed in recent years, fair comparison among them remains elusive. Existing benchmarks and public leaderboards typically permit diverse model architectures, varying training configurations, and external data sources, making it unclear whether reported improvements reflect genuine algorithmic advances or merely more favorable experimental conditions. To address this problem, we introduce the FastAT Benchmark, a controlled evaluation framework built on three core design principles: unified architecture requirements, standardized training settings, and strict prohibition of external or synthetic data. The benchmark implements over twenty representative FastAT methods within a single codebase, enabling direct and reproducible comparison. Each method is assessed through a dual‑metric evaluation framework that measures both adversarial robustness (accuracy under PGD, AutoAttack, and CR Attack) and computational cost (GPU training time and peak memory footprint). Comprehensive experiments on CIFAR‑10, CIFAR‑100, and Tiny‑ImageNet provide reliable baseline measurements and reveal that well‑designed single‑step methods can match or surpass PGD‑AT robustness at substantially lower cost, while no single method dominates across all evaluation dimensions. The complete benchmark, including source code, configuration files, and experimental results, is publicly available to support transparent and fair evaluation of future FastAT research.
Authors:Jeremy Ellis
Abstract:
This paper presents webmcu‑vision‑web, a single‑file, zero‑install browser application for end‑to‑end TinyML vision model training and deployment on the Seeed Studio XIAO ESP32‑S3 Sense (XIAO ML Kit, 15‑‑40 USD). Acting as a browser‑based companion to the on‑device Arduino firmware of Paper 1 [1], it provides a private, fully local machine learning pipeline, from firmware flashing through image collection, CNN training, weight export, and live activation visualization, without any software installation beyond a Chromium‑based browser. The system targets educators, small businesses, and researchers who need to train task‑specific visual classifiers under their exact deployment conditions. Key capabilities include: in‑browser firmware flashing via esptool‑js; an SD card file browser with image preview and inline editing; config.json live‑sync for zero‑recompile hyperparameter adjustment; webcam and ESP32 OV2640 camera image capture; TensorFlow.js CNN training completing a three‑class run (~30 images per class, 20 epochs) in approximately 1 minute browser‑side versus 9 minutes on‑device, enabling a complete collect‑train‑deploy cycle in under 10 minutes; weight export as myWeights.bin and myWeights.h; confusion matrix; and a live Conv2 activation heatmap streamed from the ESP32 during inference. No data leaves the local machine at any stage. A five‑run consistency evaluation on the three‑class reference problem (0Blank, 1Cup, 2Pen) demonstrates stable convergence with mean accuracy and standard deviation reported; all artefacts are released at the repository link below. The repository is a living template for LLM‑assisted adaptation to new hardware and tasks. All source code is MIT‑licensed at https://github.com/webmcu‑ai/webmcu‑vision‑web.
Authors:Liyao Jiang, Ruichen Chen, Keith G. Mills
Abstract:
Pre‑training is a general method that is used in a range of deep learning tasks. By first training a model on one task, and then further training on the downstream task used for final evaluation, the model is forced to learn a more general understanding of the input data. While pre‑training has been applied to 3D Human Pose Estimation (HPE) previously, the scope of datasets used is typically very limited to some strong benchmarks, like Human3.6M. Therefore, in this project, we expand the scope of an existing 3D HPE scheme to be compatible with additional 2D and 3D HPE datasets, like Occlusion Person. We perform an extensive study on how aspects of 2D pre‑training, such as model size, affect downstream performance, and to what extent pre‑training can help the model generalize to different datasets. Experimental results show that 2D pre‑training consistently outperforms training on 3D data alone, particularly in terms of computational efficiency. Finally, using MPII and Human3.6M, we are able to obtain an MPJPE score of under 64.5mm.
Authors:Rongxiao Guo, Qingchao Chen
Abstract:
Millimeter‑wave (mmWave) radar has shown great potential for contactless, privacy‑preserving, and robust human sensing, yet existing mmWave‑based human mesh reconstruction (HMR) studies are still limited by the lack of benchmarks for generalization analysis under configuration shifts and fair comparison of different algorithms. To address the limitation, we present DGHMesh, a large‑scale dual‑radar mmWave dataset and generalization‑focused benchmark for HMR. It contains data from 15 subjects performing 8 actions, with 360,000 synchronized frames collected from FMCW radar, SFCW radar, RGB images, and high‑precision 3D HMR annotations. In addition, the dataset provides synchronized raw I/Q data from both radar modalities and accurately calibrated radar spatial positions. The benchmark is designed to evaluate HMR methods under diverse measurement configurations, including human position shifts, human orientation shifts, subarray size variations, and cross‑subject settings. Based on DGHMesh, we also propose mmPTM, a query‑based multi‑radar fusion framework that jointly exploits point clouds and imaging tubes for HMR. Extensive experiments are conducted against representative baselines under different settings. The results demonstrate that mmPTM consistently achieves outstanding accuracy and competitive generalization capability across multiple sub‑benchmarks, validating the effectiveness of multi‑radar fusion and the practical value of the proposed dataset and benchmark for mmWave‑based HMR research. DGHMesh and mmPTM are publicly available at https://github.com/SPIresearch/DGHMesh.(The complete benchmark and code will be released after paper publication)
Authors:Bayangmbe Mounmo, Sam Chien, Mile Mitrovic
Abstract:
Industrial CAD workflows require robust, generalizable 3D geometric representations supporting accuracy and explainability. We introduce Shape, a self‑supervised foundation model converting surface meshes into dense per‑token embeddings. Shape combines a structured 3D latent grid, a multi‑scale geometry‑aware tokenizer (MAGNO) with cross‑attention, and a transformer processor using grouped‑query attention and RMSNorm. A learned reconstruction prior enables per‑region attribution for explainable predictions. Pretraining uses masked‑token reconstruction of normalized geometry statistics and multi‑resolution contrastive consistency. The 10.9M‑parameter backbone is pretrained on 61,052 CAD meshes from Thingi10K, MFCAD, and Fusion360. On a held‑out split of 2,983 meshes, Shape achieves reconstruction R2 = 0.729 and 98.1% top‑1 retrieval under the Wang‑Isola protocol, with near‑zero reconstruction train/val gap (contrastive scores use a larger evaluation pool). A 2x2 ablation on loss type and target‑space normalization shows per‑dimension normalization is critical: without it, performance collapses (R2 < 0.14, top‑1 < 88%); with it, both losses succeed (R2 > 0.70, top‑1 > 96%). Smooth‑L1 offers secondary stability. Code, embeddings, and an interactive demo are released at https://github.com/simd‑ai/shape.
Authors:Ramit Pahwa, Apoorva Beedu, Parivesh Priye, Rutu Gandhi, Saloni Takawale, Aruna Baijal, Zengli Yang
Abstract:
Voice assistants increasingly rely on Speech Language Models (SpeechLMs) to interpret spoken queries and execute complex tasks, yet existing benchmarks lack domain breadth, acoustic diversity, and compositional reasoning complexity to evaluate tool‑calling performance. We introduce Audio2Tool, a large‑scale dataset comprising approximately 30,000 queries designed to assess tool‑calling capabilities of SpeechLMs across three primary domains: Smart Car, Smart Home, and Wearables. Our benchmark features a multi‑tier complexity hierarchy, ranging from simple direct commands to complex multi‑intent and needle‑in‑a‑haystack extraction to isolate distinct failure modes. To ensure realism, we employ zero‑shot voice cloning text‑to‑speech synthesis and diverse noise profiles to simulate in‑the‑wild conditions. Evaluations of state‑of‑the‑art SpeechLMs and ASR‑LLM pipelines show strong performance on simple commands but significant degradation under compositional and acoustic challenges. Code and dataset are publicly available on the project page: https://audio2tool.github.io/.
Authors:Archit Thorat
Abstract:
We present AutoCompress, a transformer compression method motivated by an empirical finding: in small transformers, Layer 0 carries disproportionately high task‑critical information, with an NTK‑based importance score of 3.6 compared to a maximum of 0.054 for all other layers ‑‑ a gap of over 60x. Based on this finding, we propose Critical Layer Isolation (CLI), an architecture that protects Layer 0 at full dimensionality, compresses all intermediate layers through a learned bottleneck, and restores the full dimension at the final layer. Applied to GPT‑2 Medium (354.8M parameters), CLI‑GPT2 achieves 204.5 perplexity on WikiText‑103 with only 143.8M parameters ‑‑ a 2.47x compression ratio and 59.5% parameter reduction. Crucially, an ablation study demonstrates that a uniform bottleneck baseline of comparable size achieves only 571.8 perplexity under identical training conditions, confirming that the architectural decision to protect Layer 0 ‑‑ rather than simply reducing model size ‑‑ is the primary driver of performance. Code and checkpoints are publicly available.
Authors:Sijie Li, Shanda Li, Haowei Lin, Weiwei Sun, Ameet Talwalkar, Yiming Yang
Abstract:
Scaling laws are used to plan multi‑million‑dollar training runs, but fitting those laws can itself cost millions. In modern large‑scale workflows, assembling a sufficiently informative set of pilot experiments is already a major budget‑allocation problem rather than a routine preprocessing step. We formulate scaling‑law fitting as budget‑aware sequential experimental design: given a finite pool of runnable experiments with heterogeneous costs, choose which runs to execute so as to maximize extrapolation accuracy in a high‑cost target region. We then propose an uncertainty‑aware method for sequentially allocating experimental budget toward the runs most useful for target‑region extrapolation. Across a diverse benchmark of scaling‑law tasks, our method consistently outperforms classical design‑based baselines, and often approaches the performance of fitting on the full experimental set while using only about 10% of the total training budget. Our code is available at https://github.com/PlanarG/active‑sl.
Authors:Jose Geraldo Fernandes, Luiz Facury, Pedro Robles Dutenhefner, Wagner Meira
Abstract:
Self‑supervised learning in healthcare has largely relied on invariance‑based objectives, which maximize similarity between different views of the same patient. While effective for static anatomy, this paradigm is fundamentally misaligned with clinical diagnosis, as it mathematically compels the model to suppress the transient pathological changes it is intended to detect. We propose a shift towards Action‑Conditioned World Models that learn to simulate the dynamics of disease progression, or Event‑Conditioned. Adapting the LeJEPA framework to physiological time‑series, we define pathology not as a static label, but as a transition vector acting on a patient's latent state. By predicting the future electrophysiological state of the heart given a disease onset, our model explicitly disentangles stable anatomical features from dynamic pathological forces. Evaluated on the MIMIC‑IV‑ECG dataset, our approach outperforms fully supervised baselines on the critical triage task. Crucially, we demonstrate superior sample efficiency: in low‑resource regimes, our world model outperforms supervised learning by over 0.05 AUROC. These results suggest that modeling biological dynamics provides a dense supervision signal that is far more robust than static classification. Source code is available at https://github.com/cljosegfer/lesaude‑dynamics
Authors:Isaac Tosin Adisa
Abstract:
Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction: lack of explainability, absence of deployment reliability infrastructure, and inadequate demographic fairness evaluation.
Materials and Methods: We constructed a cohort of 415231 adult admissions from the MIMIC‑IV database (30‑day readmission prevalence 18.0%), split 70/15/15. Logistic regression, XGBoost, and LightGBM models were trained on 26 features. SHAP provided per‑patient explanations. Fairness was evaluated across 16 subgroups using AUC‑ROC, false negative rate (FNR), and positive predictive value (PPV). Calibration was assessed using Brier scores and calibration curves.
Results: XGBoost achieved AUC‑ROC 0.696 (95% CI 0.691‑0.701), outperforming or matching the LACE baseline (AUC 0.60‑0.68). LightGBM achieved best calibration (Brier 0.146). Prior admissions were the dominant predictor. All subgroups met equity thresholds (delta AUC <= 0.05, delta FNR <= 0.10).
Conclusion: This framework delivers competitive performance, clinically actionable explanations, and strong demographic equity. Code is publicly available at https://github.com/Tomisin92/readmission‑prediction.
Authors:Junjun Huang, Xiliang Lu, Xuelin Xie, Jerry Zhijian Yang
Abstract:
K‑plane clustering (KPC), hyperplane clustering, and mixture regression all essentially fall within the same class of problems. This problem can be conceptualized as clustering in relatively high‑dimensional K subspaces or K linear manifolds. Traditional KPC or fuzzy KPC models demonstrate a pronounced susceptibility to outliers, as they presuppose that the projection distance between data points and the plane normal vector adheres to the L2 distance. Meanwhile, the assumption of infinitely extending clusters adversely affects clustering performance. To solve these problems, this paper proposed a new robust fuzzy local k‑plane clustering (RFLkPC) method that combines the mixture distance of hinge loss and L1 norm. The RFLkPC model assumes that each plane cluster is bounded to a finite area, which can flexibly and robustly handle plane clustering tasks with outliers or not. The corresponding model and optimization algorithms of RFLkPC were provided. Compared to other related models on this topic, a large number of experiments verify the efficiency of RFLkPC on simulated data and real data. The source code for the proposed RFLkPC method is publicly available at https://github.com/xuelin‑xie/RFLkPC.
Authors:Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez, F. Javier Rubio
Abstract:
The Super Learner (SL) is a widely used ensemble method that combines predictions from a library of learners based on their predictive performance. Interval predictions are of considerable practical interest because they allow uncertainty in predictions produced by an individual learner or an ensemble to be quantified. Several methods have been proposed for constructing interval predictions based on the SL, however, these approaches are typically justified using asymptotic arguments or rely on computationally intensive procedures such as the bootstrap. Conformal prediction (CP) is a machine learning framework for constructing prediction intervals with finite‑sample and asymptotic coverage guarantees under mild conditions. We propose coupling CP with the SL through a natural construction that mirrors the original SL framework, using individual learner weights and combining learner‑specific conformity scores via a weighted majority vote. We characterize the properties of the resulting SL‑based prediction intervals for continuous outcomes. We cover settings under exchangeability, potential violations of exchangeability, and data‑generating mechanisms exhibiting heteroscedasticity, sparsity, and other forms of distributional heterogeneity. A comprehensive simulation study shows that the conformalized SL achieves valid finite‑sample coverage with competitive performance relative to the true data‑generating mechanism. A central contribution of this work is an application to predicting creatinine levels using socio‑demographic, biometric, and laboratory measurements. This example demonstrates the benefits of an ensemble with carefully selected learners designed to capture key aspects of complex regression functions, including non‑linear effects, interactions, sparsity, heteroscedasticity, and robustness to outliers.R
Authors:Chang Sun, Zhiqiang Que, Bakhtiar Zadeh, Qibin Liu, Kevin H. Alvarez, Wayne Luk, Maria Spiropulu
Abstract:
Lookup‑table (LUT) based neural networks can deliver ultra‑low latency and excellent hardware efficiency on FPGAs by mapping arithmetic operations directly onto the logic primitives. However, state‑of‑the‑art LUT‑aware training (LAT) approaches remain difficult to use in practice: they are often orders of magnitude slower to train than conventional networks, require non‑trivial manual tuning for hardware efficiency, and lack an end‑to‑end workflow. This work presents HGQ‑LUT, integrated in https://github.com/calad0i/HGQ2, a new LAT approach that achieves state‑of‑the‑art hardware efficiency while accelerating training by over 100 times on modern GPUs. HGQ‑LUT introduces LUT‑Dense and LUT‑Conv layers that are implemented with regular, accelerator‑efficient tensor operations during training, which are then compiled into logic LUTs for hardware. By combining these layers with fine‑grained, element‑wise heterogeneous quantization (including zero‑bit pruning) and a LUT‑aware resource surrogate, HGQ‑LUT enables the automatic exploration of accuracy‑resource trade‑offs without manual bit‑width tuning. We further integrate HGQ‑LUT into open‑source toolchains, enabling unified design, compilation, and bit‑exact verification of hybrid architectures that mix LUT‑based with conventional arithmetic blocks. These features make LAT‑based DNNs practical for real‑world deployment, such as at the CERN Large Hadron Collider's experiments.
Authors:Zhancun Mu, Guangyu Zhao, Yiwu Zhong, Chi Zhang
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.
Authors:Rico Angell, Raghav Singhal, Zachary Horvitz, Zhou Yu, Rajesh Ranganath, Kathleen McKeown, He He
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
Authors:Weiqiu You, Cassandra Goldberg, Amin Madani, Daniel A. Hashimoto, Eric Wong
Abstract:
Purpose: Accurate assessment of the Critical View of Safety (CVS) during laparoscopic cholecystectomy is essential to prevent bile duct injury, a complication associated with significant morbidity and mortality. While large vision‑language models (LVLMs) offer flexible reasoning, their predictions remain difficult to audit and unreliable on safety‑critical surgical tasks.
Methods: We introduce Sum‑of‑Checks, a framework that decomposes each CVS criterion into expert‑defined reasoning checks reflecting clinically relevant visual evidence. Given a laparoscopic frame, an LVLM evaluates each check, producing a binary judgment and justification. Criterion‑level scores are computed via fixed, weighted aggregation of check outcomes. We evaluate on the Endoscapes2023 benchmark using three frontier LVLMs, comparing against direct prompting, chain‑of‑thought, and sub‑question decomposition, each with and without few‑shot examples.
Results: Sum‑of‑Checks improves average frame‑level mean average precision by 12‑‑14% relative to the best baseline across all three models and criteria. Analysis of individual checks reveals that LVLMs are reliable on observational checks (e.g., visibility, tool obstruction) but show substantial variability on decision‑critical anatomical evidence.
Conclusion: Structuring surgical reasoning into expert‑aligned verification checks improves both accuracy and transparency of LVLM‑based CVS assessment, demonstrating that explicitly separating evidence elicitation from decision‑making is critical for reliable and auditable surgical AI systems.
Code is available at https://github.com/BrachioLab/SumOfChecks.
Authors:Hector Borobia, Elies Seguí-Mas, Guillermina Tormo-Carbó
Abstract:
Hybrid language models that interleave attention with recurrent components are increasingly competitive with pure Transformers, yet standard LoRA practice applies adapters uniformly without considering the distinct functional roles of each component type. We systematically study component‑type LoRA placement across two hybrid architectures ‑‑ Qwen3.5‑0.8B (sequential, GatedDeltaNet + softmax attention) and Falcon‑H1‑0.5B (parallel, Mamba‑2 SSM + attention) ‑‑ fine‑tuned on three domains and evaluated on five benchmarks. We find that the attention pathway ‑‑ despite being the minority component ‑‑ consistently outperforms full‑model adaptation with 5‑10x fewer trainable parameters. Crucially, adapting the recurrent backbone is destructive in sequential hybrids (‑14.8 pp on GSM8K) but constructive in parallel ones (+8.6 pp). We further document a transfer asymmetry: parallel hybrids exhibit positive cross‑task transfer while sequential hybrids suffer catastrophic forgetting. These results establish that hybrid topology fundamentally determines adaptation response, and that component‑aware LoRA placement is a necessary design dimension for hybrid architectures.
Authors:Zhaohui Wang
Abstract:
In enterprise fraud detection, model accuracy alone is insufficient when insiders can tamper with audit logs or bypass approval workflows. Real‑world incidents show that fraud often persists not because detection algorithms fail, but because the audit trail itself is controllable by privileged operators. This exposes a fundamental trust gap: who audits the auditor?
We present a tamper‑evident fraud detection system that anchors both ML predictions and workflow execution to an immutable blockchain ledger. Rather than using blockchain as passive storage, we enforce the entire approval process through smart contracts, ensuring that every transaction, prediction, and explanation is atomically recorded and cannot be retroactively modified. Our detection module achieves competitive accuracy (F1 = 0.895, PR‑AUC = 0.974) while providing cryptographically verifiable decision trails that support regulatory auditability requirements (e.g., GDPR Article 22). System evaluation shows sub‑25 ms inference latency and economically viable deployment on Layer‑2 networks at under \0.01 per transaction (validated against PolygonScan data), supporting enterprise‑scale workloads of 10,000+ monthly payments.
Authors:Deepank Girish, Yi Hao Chan, Sukrit Gupta, Jing Xia, Jagath C. Rajapakse
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.
Authors:Grigory Sapunov
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.
Authors:Charles Junichi McAndrews
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.
Authors:Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny, Mustafa Shukor, Alasdair Newson, Matthieu Cord
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/ .
Authors:Buqiang Xu, Yijun Chen, Jizhan Fang, Ruobin Zhong, Yunzhi Yao, Yuqi Zhu, Lun Du, Shumin Deng
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 .
Authors:Wenxuan Bao, Yanjun Zhao, Xiyuan Yang, Jingrui He
Abstract:
Pretrained vision‑language models such as CLIP exhibit strong zero‑shot generalization but remain sensitive to distribution shifts. Test‑time adaptation adapts models during inference without access to source data or target labels, offering a practical way to handle such shifts. However, existing methods typically assume that test samples come from a single, consistent domain, while in practice, test data often include samples from mixed domains with distinct characteristics. Consequently, their performance degrades under mixed‑domain settings. To address this, we present Ramen, a framework for robust test‑time adaptation through active sample selection. For each incoming test sample, Ramen retrieves a customized batch of relevant samples from previously seen data based on two criteria: domain consistency, which ensures that adaptation focuses on data from similar domains, and prediction balance, which mitigates adaptation bias caused by skewed predictions. To improve efficiency, Ramen employs an embedding‑gradient cache that stores the embeddings and sample‑level gradients of past test images. The stored embeddings are used to retrieve relevant samples, and the corresponding gradients are aggregated for model updates, eliminating the need for any additional forward or backward passes. Our theoretical analysis provides insight into why the proposed adaptation mechanism is effective under mixed‑domain shifts. Experiments on multiple image corruption and domain‑shift benchmarks demonstrate that Ramen achieves strong and consistent performance, offering robust and efficient adaptation in complex mixed‑domain scenarios. Our code is available at https://github.com/baowenxuan/Ramen .
Authors:Zhiqiu Lin, Chancharik Mitra, Siyuan Cen, Isaac Li, Yuhan Huang, Yu Tong Tiffany Ling, Hewei Wang, Irene Pi, Shihang Zhu, Ryan Rao, George Liu, Jiaxi Li, Ruojin Li, Yili Han, Yilun Du, Deva Ramanan
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/
Authors:Sukesh Subaharan
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.
Authors:Yixuan Zhu, Shilin Ma, Haolin Wang, Ao Li, Yanzhe Jing, Yansong Tang, Lei Chen, Jiwen Lu, Jie Zhou
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.
Authors:Vishal Rajput
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.
Authors:Wadii Boulila, Adel Ammar, Bilel Benjdira, Maha Driss
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.
Authors:Yongcan Yu, Lingxiao He, Jian Liang, Kuangpu Guo, Meng Wang, Qianlong Xie, Xingxing Wang, Ran He
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.
Authors:Hieu Man, Van-Cuong Pham, Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen
Abstract:
Learning robust representations of authorial style is crucial for authorship attribution and AI‑generated text detection. However, existing methods often struggle with content‑style entanglement, where models learn spurious correlations between authors' writing styles and topics, leading to poor generalization across domains. To address this challenge, we propose Explainable Authorship Variational Autoencoder (EAVAE), a novel framework that explicitly disentangles style from content through architectural separation‑by‑design. EAVAE first pretrains style encoders using supervised contrastive learning on diverse authorship data, then finetunes with a Variational Autoencoder (VEA) architecture using separate encoders for style and content representations. Disentanglement is enforced through a novel discriminator that not only distinguishes whether pairs of style/content representations belong to the same or different authors/content sources, but also generates natural language explanation for their decision, simultaneously mitigating confounding information and enhancing interpretability. Extensive experiments demonstrate the effectiveness of EAVAE. On authorship attribution, we achieve state‑of‑the‑art performance on various datasets, including Amazon Reviews, PAN21, and HRS. For AI‑generated text detection, EAVAE excels in few‑shot learning over the M4 dataset. Code and data repositories are available online\footnotehttps://github.com/hieum98/avae \footnotehttps://huggingface.co/collections/Hieuman/document‑level‑authorship‑datasets.
Authors:Jon-Paul Cacioli
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.
Authors:Lars van der Laan, Mark Van Der Laan
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/.
Authors:Guilin Deng, Silong Chen, Yuchuan Luo, Yi Liu, Songlei Wang, Zhiping Cai, Lin Liu, Xiaohua Jia, Shaojing Fu
Abstract:
Federated Large Language Models (FedLLMs) enable multiple parties to collaboratively fine‑tune LLMs without sharing raw data, addressing challenges of limited resources and privacy concerns. Despite data localization, shared gradients can still expose sensitive information through membership inference attacks (MIAs). However, FedLLMs' unique properties, i.e. massive parameter scales, rapid convergence, and sparse, non‑orthogonal gradients, render existing MIAs ineffective. To address this gap, we propose ProjRes, the first projection residuals‑based passive MIA tailored for FedLLMs. ProjRes leverages hidden embedding vectors as sample representations and analyzes their projection residuals on the gradient subspace to uncover the intrinsic link between gradients and inputs. It requires no shadow models, auxiliary classifiers, or historical updates, ensuring efficiency and robustness. Experiments on four benchmarks and four LLMs show that ProjRes achieves near 100% accuracy, outperforming prior methods by up to 75.75%, and remains effective even under strong differential privacy defenses. Our findings reveal a previously overlooked privacy vulnerability in FedLLMs and call for a re‑examination of their security assumptions. Our code and data are available at \hrefhttps://anonymous.4open.science/r/Passive‑MIA‑5268link.
Authors:Amandeep Kaur, Mirali Purohit, Gedeon Muhawenayo, Esther Rolf, Hannah Kerner
Abstract:
New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Performance differences are largely attributed to the model architecture or input modalities, while the role of the pretraining dataset is rarely studied. To address this research gap, we conducted a systematic study on how the geographic composition of pretraining data affects a model's downstream performance. We created global and per‑continent pretraining datasets and evaluated them on global and per‑continent downstream datasets. We found that the pretraining dataset from Europe outperformed global and continent‑specific pretraining datasets on both global and local downstream evaluations. To investigate the factors influencing a pretraining dataset's downstream performance, we analysed 10 pretraining datasets using diversity across continents, biomes, landcover and spectral values. We found that only spectral diversity was strongly correlated with performance, while others were weakly correlated. This finding establishes a new dimension of diversity to be accounted for when creating a high‑performing pretraining dataset. We open‑sourced 7 new pretraining datasets, pretrained models, and our experimental framework at https://github.com/kerner‑lab/pretrain‑where.
Authors:Anurita Das
Abstract:
Deploying large language models to heterogeneous hardware is often constrained by memory, not compute. We introduce MCAP (Monte Carlo Activation Profiling), a load‑time per‑layer importance estimator that enables dynamic precision and memory placement decisions on the target device. MCAP produces a lightweight per‑layer signal that drives both precision dispatch (W4A8 vs. W4A16) and residency tier (GPU, RAM, SSD), allowing a single set of weights to operate across diverse memory budgets. Our system, NVE, achieves 1.5‑1.8x higher decode throughput than llama‑cpp Q4_0 on NVIDIA T4 and enables models to run in memory regimes previously infeasible without modifying weights.
Authors:Chao Pan, Yu Wu, Xin Yao
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.
Authors:Yuzhen Mao, Michael Y. Li, Emily B. Fox
Abstract:
Scaling large language models to long contexts is challenging due to the quadratic computational cost of full attention. Mitigation approaches include KV‑cache selection or compression techniques. We instead provide an effective and end‑to‑end learnable bridge between the two without requiring architecture modification. In particular, our key insight is that interleaved gist compression tokens ‑‑ which provide a learnable summary of sets of raw tokens ‑‑ can serve as routing signals for sparse attention. Building on this, we introduce selective unfolding via GSA, which first compresses the context into gist tokens, then selects the most relevant gists, and subsequently restores the corresponding raw chunks for detailed attention. This yields a simple coarse‑to‑fine mechanism that combines compact global representations with targeted access to fine‑grained evidence. We further incorporate this process directly into training in an end‑to‑end fashion, avoiding the need for external retrieval modules. In addition, we extend the framework hierarchically via recursive gist‑of‑gist construction, enabling multi‑resolution context access with logarithmic per‑step decoding complexity. Empirical results on LongBench and RAG benchmarks demonstrate that our method consistently outperforms other compression baselines as well as inference‑time sparse attention methods across compression ratios from 8× to 32×. The code is available at: https://github.com/yuzhenmao/gist‑sparse‑attention/
Authors:Sina Gholami, Abdulmoneam Ali, Tania Haghighi, Ahmed Arafa, Minhaj Nur Alam
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.
Authors:Shelly Golan, Michael Finkelson, Ariel Bereslavsky, Yotam Nitzan, Or Patashnik
Abstract:
Reinforcement Learning (RL) post‑training has become the standard for aligning generative models with human preferences, yet most methods rely on a single scalar reward. When multiple criteria matter, the prevailing practice of ``early scalarization'' collapses rewards into a fixed weighted sum. This commits the model to a single trade‑off point at training time, providing no inference‑time control over inherently conflicting goals ‑‑ such as prompt adherence versus source fidelity in image editing. We introduce ParetoSlider, a multi‑objective RL (MORL) framework that trains a single diffusion model to approximate the entire Pareto front. By training the model with continuously varying preference weights as a conditioning signal, we enable users to navigate optimal trade‑offs at inference time without retraining or maintaining multiple checkpoints. We evaluate ParetoSlider across three state‑of‑the‑art flow‑matching backbones: SD3.5, FluxKontext, and LTX‑2. Our single preference‑conditioned model matches or exceeds the performance of baselines trained separately for fixed reward trade‑offs, while uniquely providing fine‑grained control over competing generative goals.
Authors:Dimitrije Antić, Alvaro Budria, George Paschalidis, Sai Kumar Dwivedi, Dimitrios Tzionas
Abstract:
Reconstructing 3D Human‑Object Interaction from an RGB image is essential for perceptive systems. Yet, this remains challenging as it requires capturing the subtle physical coupling between the body and objects. While current methods rely on sparse, binary contact cues, these fail to model the continuous proximity and dense spatial relationships that characterize natural interactions. We address this limitation via InterFields, a representation that encodes dense, continuous proximity across the entire body and object surfaces. However, inferring these fields from single images is inherently ill‑posed. To tackle this, our intuition is that interaction patterns are characteristically structured by the action and object geometry. We capture this structure in LEXIS, a novel discrete manifold of interaction signatures learned via a VQ‑VAE. We then develop LEXIS‑Flow, a diffusion framework that leverages LEXIS signatures to estimate human and object meshes alongside their InterFields. Notably, these InterFields help in a guided refinement that ensures physically‑plausible, proximity‑aware reconstructions without requiring post‑hoc optimization. Evaluation on Open3DHOI and BEHAVE shows that LEXIS‑Flow significantly outperforms existing SotA baselines in reconstruction, contact, and proximity quality. Our approach not only improves generalization but also yields reconstructions perceived as more realistic, moving us closer to holistic 3D scene understanding. Code & models will be public at https://anticdimi.github.io/lexis.
Authors:Grant Molnar
Abstract:
We define a multi‑scale metric d_ρ on strings by aggregating angle distances between all n‑gram count vectors with exponential weights ρ^n. We benchmark d_ρ in DBSCAN clustering against edit and n‑gram baselines, give a linear‑time suffix‑tree algorithm for evaluation, prove metric and stability properties (including robustness under tandem‑repeat stutters), and characterize isometries.
Authors:Aravind Venugopal, Jiayu Chen, Xudong Wu, Chongyi Zheng, Benjamin Eysenbach, Jeff Schneider
Abstract:
The temporal lag between actions and their long‑term consequences makes credit assignment a challenge when learning goal‑directed behaviors from data. Generative world models capture the distribution of future states an agent may visit, indicating that they have captured temporal information. How can that temporal information be extracted to perform credit assignment? In this paper, we formalize how the temporal information stored in world models encodes the underlying geometry of the world. Leveraging optimal transport, we extract this geometry from a learned model of the occupancy measure into a reward function that captures goal‑reaching information. Our resulting method, Occupancy Reward Shaping, largely mitigates the problem of credit assignment in sparse reward settings. ORS provably does not alter the optimal policy, yet empirically improves performance by 2.2x across 13 diverse long‑horizon locomotion and manipulation tasks. Moreover, we demonstrate the effectiveness of ORS in the real world for controlling nuclear fusion on 3 Tokamak control tasks.
Code: https://github.com/aravindvenu7/occupancy_reward_shaping; Website: https://aravindvenu7.github.io/website/ors/
Authors:Neemesh Yadav, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim
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.
Authors:Huaiyu Jia, Jiehshun You, Yizhi Luo, Jingyu Liu, Shuo Sun
Abstract:
Automated Market Makers (AMMs), as a core infrastructure of decentralized finance (DeFi), uniquely drive on‑chain asset pricing through a deterministic reserve ratio mechanism. Unlike traditional markets, AMM price dynamics is triggered largely by on‑chain events (e.g., swap) that change the reserve ratio, rather than by continuous responses to off‑chain information. This makes event‑level analysis crucial for understanding price formation mechanisms in AMMs. However, existing research generally neglects the micro‑structural dynamics at the AMMs level, lacking both a comprehensive dataset covering multiple protocols with fine‑grained event classification and an effective framework for event‑aware modeling. To fill this gap, we construct a dataset containing 8.9 million on‑chain event records from four representative AMMs protocols: Pendle, Uniswap v3, Aave and Morpho, with precise annotations of transaction type and block height timestamps. Furthermore, we propose an Uncertainty Weighted Mean Squared Error (UWM) loss function, which incorporates the block interval regression term into the traditional Time‑Point Process (TPP) objective function by weighting the uncertainty with homoscedasticity. Extensive experiments on eight advanced TPP architectures demonstrate that this loss function reduces the time prediction error by an average of 56.41% while maintaining the accuracy of event type prediction, establishing a robust benchmark for event‑aware prediction in the AMMs ecosystem. This work provides the necessary data foundation and methodological framework for modeling the discreteness and event‑driven characteristics of on‑chain price discovery. All datasets and source code are publicly available. https://github.com/yosen‑king/Deep‑AMM‑Events
Authors:Zhenyu Wang, Geyan Ye, Wei Liu, Man Tat Alexander Ng
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.
Authors:Wengyu Zhang, Xiao-Yong Wei, Qing Li
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.
Authors:Rongtao Zhang, Xin Zhu, Masoume Pourebadi Khotbehsara, Warren Dao, Erdem Bıyık, Heather Culbertson
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.
Authors:Mobin Habibpour, Niloufar Alipour Talemi, John Spodnik, Camren J. Khoury, Fatemeh Afghah
Abstract:
Wildfire monitoring requires timely, actionable situational awareness from airborne platforms, yet existing aerial visual question answering (VQA) benchmarks do not evaluate wildfire‑specific multimodal reasoning grounded in thermal measurements. We introduce WildFireVQA, a large‑scale VQA benchmark for aerial wildfire monitoring that integrates RGB imagery with radiometric thermal data. WildFireVQA contains 6,097 RGB‑thermal samples, where each sample includes an RGB image, a color‑mapped thermal visualization, and a radiometric thermal TIFF, and is paired with 34 questions, yielding a total of 207,298 multiple‑choice questions spanning presence and detection, classification, distribution and segmentation, localization and direction, cross‑modal reasoning, and flight planning for operational wildfire intelligence. To improve annotation reliability, we combine multimodal large language model (MLLM)‑based answer generation with sensor‑driven deterministic labeling, manual verification, and intra‑frame and inter‑frame consistency checks. We further establish a comprehensive evaluation protocol for representative MLLMs under RGB, Thermal, and retrieval‑augmented settings using radiometric thermal statistics. Experiments show that across task categories, RGB remains the strongest modality for current models, while retrieved thermal context yields gains for stronger MLLMs, highlighting both the value of temperature‑grounded reasoning and the limitations of existing MLLMs in safety‑critical wildfire scenarios. The dataset and benchmark code are open‑source at https://github.com/mobiiin/WildFire_VQA.
Authors:Boxin Zhao, Mladen Kolar, Jinchi Lv
Abstract:
Multi‑task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning can borrow strength from related studies; yet, many existing methods rely on restrictive bounded‑difference assumptions between the source and target models. We propose SMART, a spectral transfer method for multi‑task linear regression that instead assumes spectral similarity: the target left and right singular subspaces lie within the corresponding source subspaces and are sparsely aligned with the source singular bases. Such an assumption is natural when studies share latent structures and enables transfer beyond the bounded‑difference settings. SMART estimates the target coefficient matrix through structured regularization that incorporates spectral information from a source study. Importantly, it requires only a fitted source model rather than the raw source data, making it useful when data sharing is limited. Although the optimization problem is nonconvex, we develop a practical ADMM‑based algorithm. We establish general, non‑asymptotic error bounds and a minimax lower bound in the noiseless‑source regime. Under additional regularity conditions, these results yield near‑minimax Frobenius error rates up to logarithmic factors. Simulations confirm improved estimation accuracy and robustness to negative transfer, and analysis of multi‑modal single‑cell data demonstrates better predictive performance. The Python implementation of SMART, along with the code to reproduce all experiments in this paper, is publicly available at https://github.com/boxinz17/smart.
Authors:Xi Chen, Arian Maleki, Shirin Jalali
Abstract:
Multi‑look acquisition is a widely used strategy for reducing speckle noise in coherent imaging systems such as digital holography. By acquiring multiple measurements, speckle can be suppressed through averaging or joint reconstruction, typically under the assumption that speckle realizations across looks are statistically independent. In practice, however, hardware constraints limit measurement diversity, leading to inter‑look correlation that degrades the performance of conventional methods. In this work, we study the reconstruction of speckle‑free reflectivity from complex‑valued multi‑look measurements in the presence of correlated speckle. We model the inter‑look dependence using a first‑order Markov process and derive the corresponding likelihood under a first‑order Markov approximation, resulting in a constrained maximum likelihood estimation problem. To solve this problem, we develop an efficient projected gradient descent framework that combines gradient‑based updates with implicit regularization via deep image priors, and leverages Monte Carlo approximation and matrix‑free operators for scalable computation. Simulation results demonstrate that the proposed approach remains robust under strong inter‑look correlation, achieving performance close to the ideal independent‑look scenario and consistently outperforming methods that ignore such dependencies. These results highlight the importance of explicitly modeling inter‑look correlation and provide a practical framework for multi‑look holographic reconstruction under realistic acquisition conditions. Our code is available at: https://github.com/Computational‑Imaging‑RU/MLE‑Holography‑Markov.
Authors:Natalia Martinez Gil, Fearghal O'Donncha, Wesley M. Gifford, Nianjun Zhou, Dhaval C. Patel, Roman Vaculin
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.
Authors:Shanshan Zhong, Yi Lu, Jingjie Ning, Yibing Wan, Lihan Feng, Yuyi Ao, Leonardo F. R. Ribeiro, Markus Dreyer, Sean Ammirati, Chenyan Xiong
Abstract:
Skills have become the de facto way to enable LLM agents to perform complex real‑world tasks with customized instructions, workflows, and tools, but how to learn them automatically and effectively remains unclear. We introduce SkillLearnBench, the first benchmark for evaluating continual skill learning methods, comprising 20 verified, skill‑dependent tasks across 15 sub‑domains derived from a real‑world skill taxonomy , evaluated at three levels: skill quality, execution trajectory, and task outcome. Using this benchmark, we evaluate recent continual learning techniques, those leveraging one‑shot, self/teacher feedback, and skill creator to generate skills from agent experiences. We find that all continual learning methods improve over the no‑skill baseline, yet consistent gains remain elusive: no method leads across all tasks and LLMs, and scaling to stronger LLMs does not reliably help. Continual learning improves tasks with clear, reusable workflows but struggles on open‑ended tasks, and using stronger LLM backbones does not consistently produce better skills. Our analysis also revealed that multiple iterations in continual learning facilitate genuine improvement via external feedback, whereas self‑feedback alone induces recursive drift. Our data and code are open‑source at https://github.com/cxcscmu/SkillLearnBench to enable further studies of automatic skill generation and continual learning techniques.
Authors:Zehuan Zhang, Mark Chen, He Li, Wayne Luk
Abstract:
Complex‑Valued Neural Networks (CVNNs) have significant advantages in handling tasks that involve complex numbers. However, existing CVNNs are unable to quantify predictive uncertainty. We propose, for the first time, dropout‑based Bayesian Complex‑Valued Neural Networks (BayesCVNNs) to enable uncertainty quantification for complex‑valued applications, exhibiting broad applicability and efficiency for hardware implementation due to modularity. Furthermore, as the dual‑part nature of complex values significantly broadens the design space and enables novel configurations based on layer‑mixing and part‑mixing, we introduce an automated search approach to effectively identify optimal configurations for both real and imaginary components. To facilitate deployment, we present a framework that generates customized FPGA‑based accelerators for BayesCVNNs, leveraging a set of optimized building blocks. Experiments demonstrate the best configuration can be effectively found via the automated search, attaining higher performance with lower hardware costs compared with manually crafted models. The optimized accelerators achieve approximately 4.5x and 13x speedups on different models with less than 10% power consumption compared to GPU implementations, and outperform existing work in both algorithm and hardware aspects. Our code is publicly available at: https://github.com/zehuanzhang/BayesCVNN.git.
Authors:SLAM Labs, :, Oleksiy Ostapenko, Raymond Li, Torsten Scholak, Alireza Mousavi-Hosseini, Aman Tiwari, Denis Kocetkov, Joel Lamy Poirier, Kelechi Ogueji, Nanda H Krishna, Rafael Pardinas, Sathwik Tejaswi Madhusudhan, Shruthan Radhakrishna, Srinivas Sunkara, Valerie Becaert
Abstract:
We release Super Apriel, a 15B‑parameter supernet in which every decoder layer provides four trained mixer choices ‑‑ Full Attention (FA), Sliding Window Attention (SWA), Kimi Delta Attention (KDA), and Gated DeltaNet (GDN). A placement selects one mixer per layer; placements can be switched between requests at serving time without reloading weights, enabling multiple speed presets from a single checkpoint. The shared checkpoint also enables speculative decoding without a separate draft model. The all‑FA preset matches the Apriel 1.6 teacher on all reported benchmarks; recommended hybrid presets span 2.9× to 10.7× decode throughput at 96% to 77% quality retention, with throughput advantages that compound at longer context lengths. With four mixer types across 48 layers, the configuration space is vast. A surrogate that predicts placement quality from the per‑layer mixer assignment makes the speed‑quality landscape tractable and identifies the best tradeoffs at each speed level. We investigate whether the best configurations at each speed level can be identified early in training or only after convergence. Rankings stabilize quickly at 0.5B scale, but the most efficient configurations exhibit higher instability at 15B, cautioning against extrapolation from smaller models. Super Apriel is trained by stochastic distillation from a frozen Apriel 1.6 teacher, followed by supervised fine‑tuning. We release the supernet weights, Fast‑LLM training code, vLLM serving code, and a placement optimization toolkit.
Authors:Jason Z Wang
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.
Authors:Mario Tuci, Caner Korkmaz, Umut Şimşekli, Tolga Birdal
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.
Authors:Perry Dong, Alexander Swerdlow, Dorsa Sadigh, Chelsea Finn
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 .
Authors:Jean Mercat, Sedrick Keh, Kushal Arora, Isabella Huang, Paarth Shah, Haruki Nishimura, Shun Iwase, Katherine Liu
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.
Authors:Yi Zhong, Buqiang Xu, Yijun Wang, Zifei Shan, Shuofei Qiao, Guozhou Zheng, Ningyu Zhang
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.
Authors:Quang-Huy Nguyen, Thanh-Hai Nguyen, Khac-Manh Thai, Duc-Hoang Pham, Huy-Son Nguyen, Cam-Van Thi Nguyen, Masoud Mansoury, Duc-Trong Le, Hoang-Quynh Le
Abstract:
Counterfactual explanations (CEs) provide an intuitive way to understand recommender systems by identifying minimal modifications to user‑item interactions that alter recommendation outcomes. Existing CE methods for recommender systems, however, have been evaluated under heterogeneous protocols, using different datasets, recommenders, metrics, and even explanation formats, which hampers reproducibility and fair comparison. Our paper systematically reproduces, re‑implement, and re‑evaluate eleven state‑of‑the‑art CE methods for recommender systems, covering both native explainers (e.g., LIME‑RS, SHAP, PRINCE, ACCENT, LXR, GREASE) and specific graph‑based explainers originally proposed for GNNs. Here, a unified benchmarking framework is proposed to assess explainers along three dimensions: explanation format (implicit vs. explicit), evaluation level (item‑level vs. list‑level), and perturbation scope (user interaction vectors vs. user‑item interaction graphs). Our evaluation protocol includes effectiveness, sparsity, and computational complexity metrics, and extends existing item‑level assessments to top‑K list‑level explanations. Through extensive experiments on three real‑world datasets and six representative recommender models, we analyze how well previously reported strengths of CE methods generalize across diverse setups. We observe that the trade‑off between effectiveness and sparsity depends strongly on the specific method and evaluation setting, particularly under the explicit format; in addition, explainer performance remains largely consistent across item level and list level evaluations, and several graph‑based explainers exhibit notable scalability limitations on large recommender graphs. Our results refine and challenge earlier conclusions about the robustness and practicality of CE generation methods in recommender systems: https://github.com/L2R‑UET/CFExpRec.
Authors:Ghadah Alosaimi, Hanadi Alhamdan, Wenke E, Stamos Katsigiannis, Amir Atapour-Abarghouei, Toby P. Breckon
Abstract:
Predicting driver intention from neurophysiological signals offers a promising pathway for enhancing proactive safety in advanced driver assistance systems, yet remains challenging in real‑world driving due to EEG signal non‑stationarity and the complexity of cognitive‑motor preparation. This study proposes and evaluates an EEG‑based driver intention prediction framework using a synchronised multi‑sensor platform integrated into a real electric vehicle. A real‑world on‑road dataset was collected across 32 driving sessions, and 12 deep learning architectures were evaluated under consistent experimental conditions. Among the evaluated architectures, TSCeption achieved the highest average accuracy (0.907) and Macro‑F1 score (0.901). The proposed framework demonstrates strong temporal stability, maintaining robust decoding performance up to 1000 ms before manoeuvre execution with minimal degradation. Furthermore, additional analyses reveal that minimal EEG preprocessing outperforms artefact‑handling pipelines, and prediction performance peaks within a 400‑600 ms interval, corresponding to a critical neural preparatory phase preceding driving manoeuvres. Overall, these findings support the feasibility of early and stable EEG‑based driver intention decoding under real‑world on‑road conditions. Code: https://github.com/galosaimi/Mind2Drive.
Authors:Yilun Liu, Ruihong Qiu, Zi Huang
Abstract:
Zero‑shot reasoning on text‑rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task‑specific supervision. While graph neural networks rely on fixed label spaces and supervised objectives, recent large language model (LLM)‑based approaches often overlook graph context or depend on distillation from larger models, limiting generalisation. We propose TRN‑R1‑Zero, a post‑training framework for TRN reasoning trained solely via reinforcement learning. TRN‑R1‑Zero directly optimises base LLMs using a Neighbour‑aware Group Relative Policy Optimisation objective that dynamically adjusts rewards based on a novel margin gain metric for the informativeness of neighbouring signals, effectively guiding the model toward relational reasoning. Unlike prior methods, TRN‑R1‑Zero requires no supervised fine‑tuning or chain‑of‑thought data generated from large reasoning models. Extensive experiments across citation, hyperlink, social and co‑purchase TRN benchmarks demonstrate the superiority and robustness of TRN‑R1‑Zero. Moreover, relying strictly on node‑level training, TRN‑R1‑Zero achieves zero‑shot inference on edge‑ and graph‑level tasks, extending beyond cross‑domain transfer. The codebase is publicly available at https://github.com/superallen13/TRN‑R1‑Zero.
Authors:Julian Skifstad, Xinyue Annie Yang, Glen Chou
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
Authors:Xiaoyang Liu, Zineng Dong, Yifan Bai, Yantao Li, Yuntian Liu, Tao Luo
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.
Authors:Ehsan Hoseinzade, Ke Wang, Anandharaju Durai Raju
Abstract:
Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically rich token or sentence embeddings often suffice, tables are structured combinations of columns wherein useful representations must jointly capture column's semantics and the inter‑column relationships. Existing models learn by linearizing the 2D table into a 1D token sequence and encoding it with pretrained language models (PLMs) such as BERT. However, this leads to limited semantic quality and weaker generalization to unseen or rare values compared to modern LLMs, and degraded structural modeling due to 2D‑to‑1D flattening and context‑length constraints. We propose TabEmb, which directly targets these limitations by decoupling semantic encoding from structural modeling. An LLM first produces semantically rich embeddings for each column, and a graph‑based module over columns then injects relationships into the embeddings, yielding joint semantic‑tructural representations for table annotation. Experiments show that TabEmb consistently outperforms strong baselines on different table annotation tasks. Source code and datasets are available at https://github.com/hoseinzadeehsan/TabEmb
Authors:Chih-Yu Chang, Qiyuan Chen, Tianhan Gao, David Fenning, Chinedum Okwudire, Neil Dasgupta, Wei Lu, Raed Al Kontar
Abstract:
Discovering optimal designs through sequential data collection is essential in many real‑world applications. While Bayesian Optimization (BO) has achieved remarkable success in this setting, growing attention has recently turned to context‑specific optimal design, formalized as Contextual Bayesian Optimization (CBO). Unlike BO, CBO is inherently more challenging as it must approximate an entire mapping from the context space to its corresponding optimal design, requiring simultaneous exploration across contexts and exploitation within each. In many modern applications, such tasks arise across multiple potentially heterogeneous but related clients, where collaboration can significantly improve learning efficiency. We propose CCBO, Collaborative Contextual Bayesian Optimization, a unified framework enabling multiple clients to jointly perform CBO with controllable contexts, supporting both online collaboration and offline initialization from peers' historical beliefs, with an optional privacy‑preserving communication mechanism. We establish sublinear regret guarantees and demonstrate, through extensive simulations and a real‑world hot rolling application, that CCBO achieves substantial improvements over existing approaches even under client heterogeneity. The code to reproduce the results can be found at https://github.com/cchihyu/Collaborative‑Contextual‑Bayesian‑Optimization
Authors:Isaac Llorente-Saguer
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.
Authors:Manuel Israel Cazares
Abstract:
We present a systematic empirical study of prompt engineering for formal mathematical reasoning in the context of the SAIR Equational Theories Stage 1 competition. The task requires deciding whether one equational law implies another over all magmas ‑‑ a problem that is undecidable in general but decidable for FALSE via finite model search. Over five weeks, we designed, tested, and analyzed more than 40 prompt variants, ranging from 0 to 4,878 bytes, across four evaluation splits and three language models (gpt‑oss‑120b, Llama 3.3 70B, Gemma 4 31B).
Our central finding is a single‑prompt ceiling: despite substantial engineering effort, balanced hard accuracy plateaus in an empirical saturation region of approximately 60‑‑79% for gpt‑oss‑120b, compared to a 59.75% no‑cheatsheet baseline. We identify three mechanisms underlying this ceiling: (1) the mathematical undecidability of the TRUE case limits what any finite prompt can encode; (2) complex rule systems decrease performance on weaker models (Llama 3.3 70B collapses to 0% TRUE recall with prompts exceeding 2KB); and (3) prompt ordering effects interact with model attention in fragile, non‑monotonic ways.
Our best submission (AN45c, 2,252 bytes) achieves 79.25% accuracy on hard3 (n=400; 95% CI: [75.0%, 82.9%]), with TRUE recall of 95.9% and FALSE recall of 63.4%, representing a +19.5 percentage‑point improvement over the no‑cheatsheet baseline (59.75%). We release all prompt variants, evaluation scripts, and results at https://github.com/israelcazares/sair‑prompt‑engineering
Authors:Benjamin K. Johnson, Thomas Goralski, Ayush Semwal, Hui Shen, H. Josh Jang
Abstract:
Semi‑Markov Conditional Random Fields (semi‑CRFs) assign labels to segments of a sequence rather than to individual positions, enabling exact inference over segment‑level features and principled uncertainty estimates at their boundaries. However, existing implementations must materialize a large edge potential tensor whose size grows with sequence length, maximum segment length, and label count, becoming prohibitive for speech‑scale state spaces and intractable at genomic scales where sequences can exceed 100,000 positions. This memory bottleneck has limited the adoption of exact segment‑level inference for long sequences and large label sets. We identify that the core inefficiency is materializing edge potentials that can instead be evaluated on‑the‑fly from a compact prefix‑sum array, and make several improvements. First, replacing the stored edge tensor with prefix‑sum lookup reduces the memory footprint by a factor proportional to the product of segment length and label count. Second, a streaming forward‑backward pass with checkpoint‑boundary normalization keeps working memory sublinear in sequence length while preserving exact gradients. Third, zero‑centered cumulative scores control numerical drift and induce an adaptive duration prior under label imbalance. We integrate these ideas into Flash‑SemiCRF, a fused Triton kernel that enables exact semi‑CRF inference on previously intractable problem sizes. Available at https://github.com/biobenkj/flash‑semicrf.
Authors:Vin Bhaskara, Haicheng Wang
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.
Authors:Ruixuan Liu, David Evans, Li Xiong
Abstract:
Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to show a model is sufficiently protected against broader memorization risks. However, we show that indistinguishability properties are neither sufficient nor necessary for preventing data extraction in LLM APIs. We formalize a privacy‑game separation between extraction and indistinguishability‑based privacy, showing that indistinguishability and inextractability are incomparable: upper‑bounding distinguishability does not upper‑bound extractability. To address this gap, we introduce (l, b)‑inextractability as a definition that requires at least 2^b expected queries for any black‑box adversary to induce the LLM API to emit a protected l‑gram substring. We instantiate this via a worst‑case extraction game and derive a rank‑based extraction risk upper bound for targeted exact extraction, as well as extensions to cover untargeted and approximate extraction. The resulting estimator captures the extraction risk over multiple attack trials and prefix adaptations. We show that it can provide a tight and efficient estimation for standard greedy extraction and an upper bound on the probabilistic extraction risk given any decoding configuration. We empirically evaluate extractability across different models, clarifying its connection to distinguishability, demonstrating its advantage over existing extraction risk estimators, and providing actionable mitigation guidelines across model training, API access, and decoding configurations in LLM API deployment. Our code is publicly available at: https://github.com/Emory‑AIMS/Inextractability.
Authors:Liubomyr Horbatko
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.
Authors:Yunke Ao, Le Chen, Bruce D. Lee, Assefa S. Wahd, Aline Czarnobai, Philipp Fürnstahl, Bernhard Schölkopf, Andreas Krause
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.
Authors:Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan, Michael Helcig, Eldar Kurtic, Maximilian Kleinegger, Dan Alistarh
Abstract:
Quantization has become a standard tool for efficient LLM deployment, especially for local inference, where models are now routinely served at 2‑3 bits per parameter. The state of the art is currently split into simple scalar quantization techniques, such as GPTQ or AWQ, which are widely deployed but plateau in accuracy at 3‑4 bits per parameter (bpp), and "second‑generation" vector‑ or trellis‑quantized methods, such as QTIP, GPTVQ and AQLM, which push the accuracy frontier but are notoriously hard to implement and to scale. In this paper, we ask whether this gap is fundamental, or whether a carefully optimized scalar quantizer can recover most of it. We answer in the affirmative, by introducing GSQ (Gumbel‑Softmax Quantization), a post‑training scalar quantization method which jointly learns the per‑coordinate grid assignments and the per‑group scales using a Gumbel‑Softmax relaxation of the discrete grid. GSQ matches the cardinality of the relaxation to the small number of levels available in the target bit‑width regime (e.g., 3‑8 levels for ternary and 3 bpp, respectively), making optimization tractable. Practically, on the standard Llama‑3.1‑8B/70B‑Instruct models, GSQ closes most of the gap between scalar quantization and the QTIP frontier at 2 and 3 bits, while using a symmetric scalar grid with group‑wise quantization, and thus remains compatible with existing scalar inference kernels. We further show that the same discrete‑assignment optimization can be applied to practical GGUF K‑Quant checkpoints: starting from publicly released GGUF models, GSQ improves accuracy while projecting the result back into the same deployment format. Finally, GSQ scales to trillion‑scale Mixture‑of‑Experts models such as Kimi‑K2.5, where vector‑quantized methods are difficult to apply. The source code is publicly available at https://github.com/IST‑DASLab/GSQ.
Authors:Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth, Bryan Lewis, Ana I. Bento, Shaun Truelove, Geoffrey Fox, Madhav Marathe, Harry Hochheiser, Srini Venkatramanan
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.
Authors:Jiaqi Wang, Haoge Deng, Ting Pan, Yang Liu, Chengyuan Wang, Fan Zhang, Yonggang Qi, Xinlong Wang
Abstract:
Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains largely unexplored. We observe that naively applying GRPO to UDM leads to training instability and marginal performance gains. To address this, we propose UDM‑GRPO, the first framework to integrate UDM with RL. Our method is guided by two key insights: (i) treating the final clean sample as the action provides more accurate and stable optimization signals; and (ii) reconstructing trajectories via the diffusion forward process better aligns probability paths with the pretraining distribution. Additionally, we introduce two strategies, Reduced‑Step and CFG‑Free, to further improve training efficiency. UDM‑GRPO significantly improves base model performance across multiple T2I tasks. Notably, GenEval accuracy improves from 69% to 96% and PickScore increases from 20.46 to 23.81, achieving state‑of‑the‑art performance in both continuous and discrete settings. On the OCR benchmark, accuracy rises from 8% to 57%, further validating the generalization ability of our method. Code is available at https://github.com/Yovecent/UDM‑GRPO.
Authors:Abdeladhim Tahimi
Abstract:
This paper is a step‑by‑step, self‑contained guide to the complete training cycle of a Physics‑Informed Neural Network (PINN) ‑‑ a topic that existing tutorials and guides typically delegate to automatic differentiation libraries without exposing the underlying algebra. Using a first‑order initial value problem with a known analytical solution as a running example, we walk through every stage of the process: forward propagation of both the network output and its temporal derivative, evaluation of a composite loss function built from the ODE residual and the initial condition, backpropagation of gradients ‑‑ with particular attention to the product rule that arises in hidden layers ‑‑ and a gradient descent parameter update. Every calculation is presented with explicit, verifiable numerical values using a 1‑3‑3‑1 multilayer perceptron with two hidden layers and 22 trainable parameters. From these concrete examples, we derive general recursive formulas ‑‑ expressed as sensitivity propagation relations ‑‑ that extend the gradient computation to networks of arbitrary depth, and we connect these formulas to the automatic differentiation engines used in practice. The trained network is then validated against the exact solution, achieving a relative L^2 error of 4.290 × 10^‑4 using only the physics‑informed loss, without any data from the true solution. A companion Jupyter/PyTorch notebook reproduces every manual calculation and the full training pipeline, providing mutual validation between hand‑derived and machine‑computed gradients.
Authors:Enshu Liu, Xuefei Ning, Yu Wang, Zinan Lin
Abstract:
Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexibility to generate tokens in arbitrary orders and the potential of parallel decoding. However, existing heuristic sampling strategies remain inefficient: they choose only a small part of tokens to sample at each step, leaving substantial room for improvement. In this work, we study the problem of token sampling order optimization and demonstrate its significant potential for acceleration. Specifically, we find that fully leveraging correct predictions at each step can reduce the number of sampling iterations by an order of magnitude without compromising accuracy. Based on this, we propose Neural Indicator Sampling (NI Sampling), a general sampling order optimization framework that utilize a neural indicator to decide which tokens should be sampled at each step. We further propose a novel trajectory‑preserving objective to train the indicator. Experiments on LLaDA and Dream models across multiple benchmarks show that our method achieves up to 14.3× acceleration over full‑step sampling with negligible performance drop, and consistently outperforms confidence threshold sampling in the accuracy‑step trade‑off. Code is available at https://github.com/imagination‑research/NI‑Sampling.
Authors:Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Bogdan Kulynych
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.
Authors:Svetlana Pavlitska, Malte Stüven, Beyza Keskin, J. Marius Zöllner
Abstract:
Mixture‑of‑Experts (MoE) models provide a structured approach to combining specialized neural networks and offer greater interpretability than conventional ensembles. While MoEs have been successfully applied to image classification and semantic segmentation, their use in object detection remains limited due to challenges in merging dense and structured predictions. In this work, we investigate model‑level mixtures of object detectors and analyze their suitability for improving performance and interpretability in object detection. We propose an MoE architecture that combines YOLO‑based detectors trained on semantically disjoint data subsets, with a learned gating network that dynamically weights expert contributions. We study different strategies for fusing detection outputs and for training the gating mechanism, including balancing losses to prevent expert collapse. Experiments on the BDD100K dataset demonstrate that the proposed MoE consistently outperforms standard ensemble approaches and provides insights into expert specialization across domains, highlighting model‑level MoEs as a viable alternative to traditional ensembling for object detection. Our code is available at https://github.com/KASTEL‑MobilityLab/mixtures‑of‑experts/.
Authors:Wei Chen, Yubing Wu, Junmei Yang, Delu Zeng, Qibin Zhao, John Paisley, Min Chen, Zhou Wang
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.
Authors:Tim Goppelsroeder, Rasmus Jensen
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 .
Authors:Ammar Bhilwarawala, Mainak Bandyopadhyay
Abstract:
Automated fetal head segmentation in ultrasound images is critical for accurate biometric measurements in prenatal care. While existing deep learning approaches have achieved a reasonable performance, they struggle with issues like low contrast, noise, and complex anatomical boundaries which are inherent to ultrasound imaging. This paper presents Attention‑ResUNet. It is a novel architecture that synergistically combines residual learning with multi‑scale attention mechanisms in order to achieve enhanced fetal head segmentation. Our approach integrates attention gates at four decoder levels to focus selectively on anatomically relevant regions while suppressing the background noise, and complemented by residual connections which facilitates gradient flow and feature reuse. Extensive evaluation on the HC18 Challenge dataset where n = 200 demonstrates that Attention ResUNet achieves a superior performance with a mean Dice score of 99.30 +/‑ 0.14% against similar architectures. It significantly outperforms five baseline architectures including ResUNet (99.26%), Attention U‑Net (98.79%), Swin U‑Net (98.60%), Standard U‑Net (98.58%), and U‑Net++ (97.46%). Through statistical analysis we confirm highly significant improvements (p < 0.001) with effect sizes that range from 0.230 to 13.159 (Cohen's d). Using Saliency map analysis, we reveal that our architecture produces highly concentrated, anatomically consistent activation patterns, which demonstrate an enhanced interpretability which is crucial for clinical deployment. The proposed method establishes a new state of the art performance for automated fetal head segmentation whilst maintaining computational efficiency with 14.7M parameters and a 45 GFLOPs inference cost. Code repository: https://github.com/Ammar‑ss
Authors:Xiao Lingao, Yang He
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.
Authors:Josh Millar, Ashok Samraj Thangarajan, Soumyajit Chatterjee, Hamed Haddadi
Abstract:
The miniaturisation of neural processing units (NPUs) and other low‑power accelerators has enabled their integration into microcontroller‑scale wearable hardware, supporting near‑real‑time, offline, and privacy‑preserving inference. Yet physiological signal analysis has remained infeasible on such hardware; recent Transformer‑based models show state‑of‑the‑art performance but are prohibitively large for resource‑ and power‑constrained hardware and incompatible with μNPUs due to their dynamic attention operations. We introduce PhysioLite, a lightweight, NPU‑compatible model architecture and training framework for ECG/EMG signal analysis. Using learnable wavelet filter banks, CPU‑offloaded positional encoding, and hardware‑aware layer design, PhysioLite reaches performance comparable to state‑of‑the‑art Transformer‑based foundation models on ECG and EMG benchmarks, while being <10% of the size (～370KB with 8‑bit quantization). We also profile its component‑wise latency and resource consumption on both the MAX78000 and HX6538 WE2 μNPUs, demonstrating its viability for signal analysis on constrained, battery‑powered hardware. We release our model(s) and training framework at: https://github.com/j0shmillar/physiolite.
Authors:Yunjia Yang, Babak Gholami, Caglar Gurbuz, Mohammad Rashed, Nils Thuerey
Abstract:
Accurate machine‑learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for complex three‑dimensional configurations due to the high cost of generating training data. This work introduces a methodology for efficiently constructing accurate surrogate models for design purposes by first pre‑training a large‑scale model on diverse geometries and then fine‑tuning it with a few more detailed task‑specific samples. A Transformer‑based architecture, AeroTransformer, is developed and tailored for large‑scale training to learn aerodynamics. The methodology is evaluated on transonic wings, where the model is pre‑trained on SuperWing, a dataset of nearly 30000 samples with broad geometric diversity, and subsequently fine‑tuned to handle specific wing shapes perturbed from the Common Research Model. Results show that, with 450 task‑specific samples, the proposed methodology achieves 0.36% error on surface‑flow prediction, reducing 84.2% compared to training from scratch. The influence of model configurations and training strategies is also systematically studied to provide guidance on effectively training and deploying such models under limited data and computational budgets. To facilitate reuse, we release the datasets and the pre‑trained models at https://github.com/tum‑pbs/AeroTransformer. An interactive design tool is also built on the pre‑trained model and is available online at https://webwing.pbs.cit.tum.de.
Authors:Sanzo Miyazawa
Abstract:
The inverse Potts problem for estimating evolutionary single‑site fields and pairwise couplings in homologous protein sequences from their single‑site and pairwise amino acid frequencies observed in their multiple sequence alignment would be still one of useful methods in the studies of protein structure and evolution. Since the reproducibility of fields and couplings are the most important, the Boltzmann machine method is employed here, although it is computationally intensive. In order to reduce computational time required for the Boltzmann machine, parallel, persistent Markov chain Monte Carlo method is employed to estimate the single‑site and pairwise marginal distributions in each learning step. Also, stochastic gradient descent methods are used to reduce computational time for each learning. Another problem is how to adjust the values of hyperparameters; there are two regularization parameters for evolutionary fields and couplings. The precision of contact residue pair prediction is often used to adjust the hyperparameters. However, it is not sensitive to these regularization parameters. Here, they are adjusted for the fields and couplings to satisfy a specific condition that is appropriate for protein conformations. This method has been applied to eight protein families.
Authors:H S V N S Kowndinya Renduchintala, Sumit Bhatia
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.
Authors:Xiaoyuan Cheng, Haoyu Wang, Wenxuan Yuan, Ziyan Wang, Zonghao Chen, Li Zeng, Zhuo Sun
Abstract:
Recent advances in flow‑based offline reinforcement learning (RL) have achieved strong performance by parameterizing policies via flow matching. However, they still face critical trade‑offs among expressiveness, optimality, and efficiency. In particular, existing flow policies interpret the L_2 regularization as an upper bound of the 2‑Wasserstein distance (W_2), which can be problematic in offline settings. This issue stems from a fundamental geometric mismatch: the behavioral policy manifold is inherently anisotropic, whereas the L_2 (or upper bound of W_2) regularization is isotropic and density‑insensitive, leading to systematically misaligned optimization directions. To address this, we revisit offline RL from a geometric perspective and show that policy refinement can be formulated as a local transport map: an initial flow policy augmented by a residual displacement. By analyzing the induced density transformation, we derive a local quadratic approximation of the KL‑constrained objective governed by the Fisher information matrix, enabling a tractable anisotropic optimization formulation. By leveraging the score function embedded in the flow velocity, we obtain a corresponding quadratic constraint for efficient optimization. Our results reveal that the optimality gap in prior methods arises from their isotropic approximation. In contrast, our framework achieves a controllable approximation error within a provable neighborhood of the optimal solution. Extensive experiments demonstrate state‑of‑the‑art performance across diverse offline RL benchmarks. See project page: https://github.com/ARC0127/Fisher‑Decorator.
Authors:Hongyu Zhan, Qixin Wang, Yusen Tan, Haitao Yu, Jingbo Zhou, Shuai Chen, Jia Li, Xiao Tan, Jun Xia
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.
Authors:Yuzhe Fu, Hancheng Ye, Cong Guo, Junyao Zhang, Qinsi Wang, Yueqian Lin, Changchun Zhou, Hai, Li, Yiran Chen
Abstract:
Point‑based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces significant inference latency, especially for large‑scale processing. Despite existing CUDA‑ and hardware‑level optimizations, FPS remains a major bottleneck due to exhaustive computations across multiple network layers in PNNs, which hinders scalability. Through systematic analysis, we identify three substantial redundancies in FPS, including unnecessary full‑cloud computations, redundant late‑stage iterations, and predictable inter‑layer outputs that make later FPS computations avoidable. To address these, we propose FlashFPS, a hardware‑agnostic, plug‑and‑play framework for FPS acceleration, composed of FPS‑Prune and FPS‑Cache. FPS‑Prune introduces candidate pruning and iteration pruning to reduce redundant computations in FPS while preserving sampling quality, and FPS‑Cache eliminates layer‑wise redundancy via cache‑and‑reuse. Integrated into existing CUDA libraries and state‑of‑the‑art PNN accelerators, FlashFPS achieves 5.16× speedup over the standard CUDA baseline on GPU and 2.69× on PNN accelerators, with negligible accuracy loss, enabling efficient and scalable PNN inference. Codes are released at https://github.com/Yuzhe‑Fu/FlashFPS.
Authors:Christian Lysenstøen
Abstract:
Serving large language models under latency service‑level objectives (SLOs) is a configuration‑heavy systems problem with an unusually failure‑prone search space: many plausible configurations crash outright or miss user‑visible latency targets, and standard black‑box optimizers treat these failures as wasted trials. We present SLO‑Guard, a crash‑aware autotuner for vLLM serving that treats crashes as first‑class observations. SLO‑Guard combines a feasible‑first Thermal Budget Annealing (TBA) exploration phase with a warm‑started Tree‑structured Parzen Estimator (TPE) exploitation phase; the handoff replays all exploration history, including crashes encoded as extreme constraint violations. We additionally contribute a configuration‑repair pass, a GPU‑aware KV‑cache memory guard, and a four‑category crash taxonomy.
We evaluate SLO‑Guard on Qwen2‑1.5B served with vLLM 0.19 on an NVIDIA A100 40GB. Across a pre‑specified five‑seed study, both SLO‑Guard and uniform random search attain 75/75 feasibility with zero crashes under the corrected concurrent harness, and are statistically tied on best‑achieved latency (Mann‑Whitney two‑sided p=0.84). SLO‑Guard's advantage is in budget consistency: more trials in the fast‑serving regime (10.20 vs. 7.40 out of 15; one‑sided p=0.014) and higher post‑handoff consistency (0.876 vs. 0.539; p=0.010). Under concurrent load, SLO‑Guard's cross‑seed standard deviation on best latency is 4.4x tighter than random search's (2.26 ms vs. 10.00 ms). A harness‑replication analysis shows that the consistency findings survive an independent sequential‑dispatch measurement condition.
The central claim is not that SLO‑Guard finds a better final configuration, but that it spends a fixed tuning budget more predictably once the fast regime has been found.
Authors:Mattie Ji, Indradyumna Roy, Vikas Garg
Abstract:
Persistent homology (PH) encodes global information, such as cycles, and is thus increasingly integrated into graph neural networks (GNNs). PH methods in GNNs typically traverse an increasing sequence of subgraphs. In this work, we first expose limitations of this inclusion procedure. To remedy these shortcomings, we analyze contractions as a principled topological operation, in particular, for graph representation learning. We study the persistence of contraction sequences, which we call Contraction Homology (CH). We establish that forward PH and CH differ in expressivity. We then introduce Hourglass Persistence, a class of topological descriptors that interleave a sequence of inclusions and contractions to boost expressivity, learnability, and stability. We also study related families parametrized by two paradigms. We also discuss how our framework extends to simplicial and cellular networks. We further design efficient algorithms that are pluggable into end‑to‑end differentiable GNN pipelines, enabling consistent empirical improvements over many PH methods across standard real‑world graph datasets. Code is available at \hrefhttps://github.com/Aalto‑QuML/Hourglassthis https URL.
Authors:Harshavardhanan Deekeswar
Abstract:
Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operational data at scale. A modest dataset of 1,000 IoT sensor readings serialized as JSON requires approximately 80,000 tokens ‑ the majority spent on repeated field names, nested braces, and structural punctuation rather than semantic content. We present ONTO (Object Notation for Token Optimization), a columnar notation that declares field names once per entity and arranges values in pipe‑delimited rows with indentation‑based hierarchy. This schema‑once, data‑many design eliminates per‑record key repetition while preserving human readability and nested structure support. Evaluation across three synthetic operational datasets demonstrates 46‑51% token reduction versus JSON, with stable scaling from 100 to 1,000 records. Controlled inference benchmarks on Qwen2.5‑7B show corresponding 5‑10% latency improvement. Comprehension validation confirms no material degradation in LLM task accuracy across lookup, counting, extraction, and aggregation operations when format context is provided. Ablation analysis reveals that key repetition accounts for the majority of JSON overhead, with indentation costs in nested structures explaining the 4‑percentage‑point gap between flat and hierarchical data. ONTO occupies a previously unfilled position in the serialization landscape: columnar efficiency with hierarchical structure, optimized for LLM context windows rather than document interchange. Code and specification are available at https://github.com/harsh‑aranga/onto.
Authors:Qihao Shen, Jiaxing Xuan, Zhenguang Liu, Sifan Wu, Yutong Xie, Zhaoyan Ming, Yingying Jiao, kui Ren
Abstract:
Advanced deepfake technologies are blurring the lines between real and fake, presenting both revolutionary opportunities and alarming threats. While it unlocks novel applications in fields like entertainment and education, its malicious use has sparked urgent ethical and societal concerns ranging from identity theft to the dissemination of misinformation. To tackle these challenges, feature analysis using frequency features has emergedas a promising direction for deepfake detection. However, oneaspect that has been overlooked so far is that existing methodstend to concentrate on one or a few specific frequency domains,which risks overfitting to particular artifacts and significantlyundermines their robustness when facing diverse forgery patterns. Another underexplored aspect we observe is that different features often attend to the same forged region, resulting in redundant feature representations and limiting the diversity of the extracted clues. This may undermine the ability of a model to capture complementary information across different facets, thereby compromising its generalization capability to diverse manipulations. In this paper, we seek to tackle these challenges from two aspects: (1) we propose a triple‑branch network that jointly captures spatial and frequency features by learning from both original image and image reconstructed by different frequency channels, and (2) we mathematically derive feature decoupling and fusion losses grounded in the mutual information theory, which enhances the model to focus on task‑relevant features across the original image and the image reconstructed by different frequency channels. Extensive experiments on six large‑scale benchmark datasets demonstrate that our method consistently achieves state‑of‑the‑art performance. Our code is released at https://github.com/injooker/Unveiling Deepfake.
Authors:Keyang Chen, Mingxuan Jiang, Yongsheng Zhao, Zeping Li, Zaiyuan Chen, Weiqi Luo, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Xihong Wu, Hongfeng Chai
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.
Authors:Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
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.
Authors:Yifan Zhang, Jieyu Li, Kexin Pei, Yu Huang, Kevin Leach
Abstract:
Large Language Models (LLMs) show promise for automated code repair but often struggle with the complex semantic and structural correctness required. We present SynthFix, a hybrid neural‑symbolic framework that improves LLM‑based vulnerability repair by unifying code synthesis with compiler‑informed symbolic feedback. The core of our approach is an adaptive training strategy where a neural Router Model directs code samples to either Supervised Fine‑Tuning (SFT) to learn common patterns or Reward Fine‑Tuning (RFT) with symbolic rewards for complex, iterative refinement. On the FixJS (JavaScript) and CodeFlaws (C) benchmarks, SynthFix achieves up to 18% relative improvement in CodeBLEU/CrystalBLEU and 32% in Exact Match over strong SFT and RFT baselines. Our results show that this adaptive combination of training strategies, which mirrors how developers alternate between pattern application and tool feedback, significantly improves the accuracy and efficiency of LLM‑based vulnerability repair. Our code and data are available at https://github.com/CoderDoge1108/SynthFix.
Authors:Tianbao Zhang
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.
Authors:Sai Vegasena
Abstract:
We present Open‑TQ‑Metal, the first implementation of fused compressed‑domain attention on Apple Silicon, enabling 128K‑context inference for Llama 3.1 70B on a single 64GB consumer Mac ‑‑ a configuration impossible with all existing inference frameworks. Open‑TQ‑Metal quantizes the KV cache to int4 on the fly and computes attention directly on the compressed representation via custom Metal compute shaders, eliminating all intermediate dequantization matrices. Across 330 experiments spanning two model families (Gemma 4 31B and Llama 3.1 70B), the fused sdpa_int4 kernel achieves 48x attention speedup at 128K context over the dequantize‑then‑attend baseline, reduces KV cache memory from 40 GB to 12.5 GB (3.2x compression), and maintains identical top‑1 token predictions to FP16 inference. We further provide the first cross‑architecture analysis of KV cache quantization methods, revealing that the attention scale factor ‑‑ not model size ‑‑ determines whether angular quantization schemes like PolarQuant succeed or fail, with Gemma 4's attn_scale=1.0 amplifying directional error 25‑100x more than Llama's standard 1/sqrt(d) scaling.
Authors:Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen, Zaiwang Gu
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.
Authors:Weiyu Ma, Yongcheng Zeng, Yan Song, Xinyu Cui, Jian Zhao, Xuhui Liu, Mohamed Elhoseiny
Abstract:
Reinforcement Learning (RL) has achieved impressive success in post‑training Large Language Models (LLMs) and Vision‑Language Models (VLMs), with on‑policy algorithms such as PPO, GRPO, and REINFORCE++ serving as the dominant paradigm. However, these methods discard all collected trajectories after a single gradient update, resulting in poor sample efficiency, particularly wasteful for agentic tasks where multi‑turn environment interactions are expensive. While Experience Replay drives sample efficiency in classic RL by allowing agents to reuse past trajectories and prioritize informative ones, directly applying Prioritized Experience Replay (PER) to LLMs fails. The rapid policy evolution of billion‑parameter models renders stored priorities stale, causing old high‑priority trajectories to dominate sampling long after they have become uninformative. We propose Freshness‑Aware PER, which addresses this priority staleness problem by augmenting any PER‑based priority with a multiplicative exponential age decay grounded in effective sample size analysis. To the best of our knowledge, Freshness‑Aware PER is the first work to successfully apply PER to LLM/VLM reinforcement learning. We evaluate on eight multi‑step agentic, reasoning, and math competition tasks with 0.5B, 3B, and 7B models. Freshness‑Aware PER significantly outperforms on‑policy baselines, achieving +46% on NQ Search, +367% on Sokoban, and +133% on VLM FrozenLake, while standard PER without age decay consistently degrades performance. Our code is publicly available at https://github.com/Vision‑CAIR/Freshness‑Aware‑PER.
Authors:Zahid Hasan, Masud Ahmed, Nirmalya Roy
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.
Authors:Jiaxin Zhang, Xiangyu Peng, Qinglin Chen, Qinyuan Ye, Caiming Xiong, Chien-Sheng Wu
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
Authors:Yunshan Peng, Ji Wu, Wentao Bai, Yunke Bai, Jinan Pang, Wenzheng Shu, Yanxiang Zeng, Xialong Liu, Peng Jiang
Abstract:
Reach and Frequency (R&F) contract advertising is an important form of widely used brand advertising. Unlike performance advertising, R&F contracts emphasize controllable delivery of UV and PV under given targeting, scheduling, and frequency control constraints. In practical systems, advertisers typically need to view the UV, PV change curves at different budget levels in real time when creating an R&F contract. However, most existing publicly available advertising datasets are based on independent samples, lacking a characterization of the core structure of the "budget‑performance curve" (including UV and PV) in R&F contracts.This paper proposes and releases a large‑scale R&F contract inventory estimation dataset. This dataset uses the R&F contract context consisting of "targeting‑scheduling‑frequency control" as the basic context, providing observations of UV and PV corresponding to multiple budget points within the same context, thus forming a complete budget‑performance curve. The dataset explicitly includes a time‑window‑based frequency control mechanism (e.g.,"no more than 3 times within 5 days") and naturally satisfies the monotonicity and diminishing marginal returns characteristics in the budget and scheduling dimensions. We further derive the theoretical maximum exposure ceiling and use it as a consistency check to evaluate data quality and the feasibility of model predictions. Using this data set, this paper defines two standardized benchmark tasks: single‑point performance prediction and reconstruction of budget‑performance curves, and provides a set of reproducible baseline methods and evaluation protocols. This dataset can support systematic research on problems such as structural constraint learning, monotonic regression, curve consistency modeling, and R&F contract planning.The code for our experiments can be found at https://github.com/pengyunshan/RF‑Inventory.
Authors:Chongsheng Zhang, Hao Wang, Zelong Yu, Esteban Garces Arias, Julian Rodemann, Zhanshuo Zhang, Qilong Li, Gaojuan Fan, Krikamol Muandet, Christian Heumann
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.
Authors:Lena Zellinger, Nicola Branchini, Lennert De Smet, Víctor Elvira, Nikolay Malkin, Antonio Vergari
Abstract:
Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mixture models with negative coefficients, called subtractive mixture models (SMMs), have been proposed as a potentially more expressive alternative. However, how to effectively use SMMs for VI and IS is still an open question as they do not provide latent variable semantics and therefore cannot use sampling schemes for classical MMs. In this work, we study how to circumvent this issue by designing several expectation estimators for IS and learning schemes for VI with SMMs, and we empirically evaluate them for distribution approximation. Finally, we discuss the additional challenges in estimation stability and learning efficiency that they carry and propose ways to overcome them. Code is available at: https://github.com/april‑tools/delta‑vi.
Authors:Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer, Shuiwang Ji, Runzhong Wang, Connor W. Coley
Abstract:
In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via intermediate fingerprint representations and determined chemical formulae, training at the scale of hundreds of millions of unlabeled structures. We then demonstrate how forward fragmentation models enable inference‑time scaling by identifying spectrum‑inconsistent fragments and refining them through targeted remasking and denoising. While FRIGID already achieves strong performance with its diffusion base, inference‑time scaling significantly improves its accuracy, surpassing 18% Top‑1 accuracy on the challenging MassSpecGym benchmark and tripling the Top‑1 accuracy of the leading methods on NPLIB1. Further empirical analyses show that FRIGID exhibits log‑linear performance scaling with increasing inference‑time compute, opening a promising new direction for continued improvements in de novo structural elucidation. FRIGID code is publicly available at https://github.com/coleygroup/FRIGID
Authors:Weihua Du, Jingming Zhuo, Yixin Dong, Andre Wang He, Weiwei Sun, Zeyu Zheng, Manupa Karunaratne, Ivan Fox, Tim Dettmers, Tianqi Chen, Yiming Yang, Sean Welleck
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.
Authors:Zongru Li, Xingsheng Chen, Honggang Wen, Regina Qianru Zhang, Ming Li, Xiaojin Zhang, Hongzhi Yin, Qiang Yang, Kwok-Yan Lam, Pietro Lio, Siu-Ming Yiu
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.
Authors:Baoyou Chen, Hanchen Xia, Peng Tu, Haojun Shi, Liwei Zhang, Weihao Yuan, Siyu Zhu
Abstract:
Autoregressive vision‑language models (VLMs) deliver strong multimodal capability, but their token‑by‑token decoding imposes a fundamental inference bottleneck. Diffusion VLMs offer a more parallel decoding paradigm, yet directly converting a pretrained autoregressive VLM into a large‑block diffusion VLM (dVLM) often leads to substantial quality degradation. In this work, we present BARD, a simple and effective bridging framework that converts a pretrained autoregressive VLM into a same‑architecture, decoding‑efficient dVLM. Our approach combines progressive supervised block merging, which gradually enlarges the decoding block size, with stage‑wise intra‑dVLM distillation from a fixed small‑block diffusion anchor to recover performance lost at larger blocks. We further incorporate a mixed noise scheduler to improve robustness and token revision during denoising, and memory‑friendly training to enable efficient training on long multimodal sequences. A key empirical finding is that direct autoregressive‑to‑diffusion distillation is poorly aligned and can even hurt performance, whereas distillation within the diffusion regime is consistently effective. Experimental results show that, with \leq 4.4M data, BARD‑VL transfers strong multimodal capability from Qwen3‑VL to a large‑block dVLM. Remarkably, BARD‑VL establishes a new SOTA among comparable‑scale open dVLMs on our evaluation suite at both 4B and 8B scales. At the same time, BARD‑VL achieves up to 3× decoding throughput speedup compared to the source model. Code is available at https://github.com/fudan‑generative‑vision/Bard‑VL.
Authors:Devendra Ghori
Abstract:
State‑of‑the‑art deepfake detectors achieve near‑perfect in‑domain accuracy yet degrade under cross‑generator shifts, heavy compression, and adversarial perturbations. The core limitation remains the decoupling of semantic artifact learning from physical invariants: optical‑flow discontinuities, specular‑reflection inconsistencies, and cardiac‑modulated reflectance (rPPG) are treated either as post‑hoc features or ignored.
We introduce PhyLAA‑X, a novel physics‑conditioned extension of Localized Artifact Attention (LAA‑X). PhyLAA‑X injects three end‑to‑end differentiable physics‑derived feature volumes ‑ optical‑flow curl, specular‑reflectance skewness, and spatially‑upsampled rPPG power spectra ‑ directly into the LAA‑X attention computation via cross‑attention gating and a resonance consistency loss. This forces the network to learn manipulation boundaries where semantic inconsistencies and physical violations co‑occur ‑ regions inherently harder for generative models to replicate consistently.
PhyLAA‑X is embedded across an efficient spatiotemporal ensemble (EfficientNet‑B4+BiLSTM, ResNeXt‑101+Transformer, Xception+causal Conv1D) with uncertainty‑aware adaptive weighting. On FaceForensics++ (c23), Aletheia reaches 97.2% accuracy / 0.992 AUC‑ROC; on Celeb‑DF v2, 94.9% / 0.981; on DFDC, 90.8% / 0.966 ‑ outperforming the strongest published baseline (LAA‑Net [1]) by 4.1‑7.3% in cross‑generator settings and maintaining 79.4% accuracy under epsilon = 0.02 PGD‑10 attacks. Single‑backbone ablations confirm PhyLAA‑X alone delivers a 4.2% cross‑dataset AUC gain. The full production system is open‑sourced at https://github.com/devghori1264/Aletheia (v1.2, April 2026) with pretrained weights, the adversarial corpus (referred to as ADC‑2026 in this work), and complete reproducibility artifacts.
Authors:Maksim Zhdanov, Ana Lucic, Max Welling, Jan-Willem van de Meent
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.
Authors:Vladimer Khasia
Abstract:
The activation memory required for exact backpropagation scales linearly with network depth, context length, and feature dimensionality, forming an O(L BN ) spatial bottleneck (where B is the sequence‑batch cardinality and N is the feature dimension). This constraint historically throttles the scaling of deep neural networks. While randomized automatic differentiation attempts to mitigate this, it historically suffers from catastrophic variance. In this paper, we introduce BASIS (Balanced Activation Sketching with Invariant Scalars), an efficient backpropagation algorithm that fully decouples activation memory from the batch and sequence dimensions. BASIS propagates the exact error signal (dX) to preserve flawless gradient flow, but computes the weight updates (dW) using massively compressed rank‑R tensors. To solve the foundational instability of sketched gradients, we propose two novel mechanisms: Balanced Hashing, which strictly eliminates off‑diagonal collision variance, and Invariant Scalars, a principled bias‑variance tradeoff that deterministically preserves the exact continuous energy norm of the spatial geometry. Theoretically, BASIS reduces activation memory to O(L RN ) and heavily decreases the backward pass matrix‑multiplication footprint. Empirically, training a GPT architecture for 50,000 steps validates our theoretical guarantees: at R = 32, BASIS achieves parity with (and marginally outperforms) exact backpropagation validation loss (6.575 vs. 6.616), acting as an implicit regularizer. Remarkably, the stabilized magnitude trajectory allows the model to converge smoothly even under extreme spatial compression (R = 1), proving the extreme robustness of the estimator. The code is available at https://github.com/VladimerKhasia/basis
Authors:Songtao Wang, Quang Hieu Pham, Fangcong Yin, Xinpeng Wang, Jocelyn Qiaochu Chen, Greg Durrett, Xi Ye
Abstract:
Reinforcement learning with verifiable rewards (RLVR) typically optimizes for outcome rewards without imposing constraints on intermediate reasoning. This leaves training susceptible to reward hacking, where models exploit loopholes (e.g., spurious patterns in training data) in the reward function to achieve high scores without solving the intended task. These reward‑hacking behaviors are often implicit, as the intermediate chain‑of‑thought (CoT) may appear plausible on the surface, limiting the effectiveness of purely text‑based monitoring. We propose Gradient Fingerprint (GRIFT), a method for detecting reward hacking using models' internal computations. Given a prompt and a model‑generated CoT, GRIFT computes gradients of the CoT conditioned on the prompt and compresses them into a compact representation, which is then used to assess whether the CoT reflects reward hacking behavior. Across verifiable reasoning benchmarks spanning math, code, and logical reasoning, GRIFT substantially outperforms strong baselines, including CoT Monitor and TRACE, achieving over 25% relative improvement in detecting reward hacking behavior. Moreover, integrating GRIFT into the rejection fine‑tuning pipeline for reasoning tasks reduces reward hacking and improves performance on the true task objective. Our results highlight a promising direction of leveraging gradient level representations for assessing the quality of CoT reasoning traces. Our code is available at: https://github.com/songtao‑x/reward_hack.
Authors:Weijiang Xiong, Robert Fonod, Nikolas Geroliminis
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
Authors:Yutong Gao, Qinglin Meng, Yuan Zhou, Liangming Pan
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.
Authors:Zhaobo Hu, Vincent Gauthier, Mehdi Naima
Abstract:
Spatiotemporal modeling has evolved beyond simple time series analysis to become fundamental in structural time series analysis. While current research extensively employs graph neural networks (GNNs) for spatial feature extraction with notable success, these networks are limited to capturing only pairwise relationships, despite real‑world networks containing richer topological relationships. Additionally, GNN‑based models face computational challenges that scale with graph complexity, limiting their applicability to large networks. To address these limitations, we present Modern Structure‑Aware Simplicial SpatioTemporal neural network (ModernSASST), the first approach to leverage simplicial complex structures for spatiotemporal modeling. Our method employs spatiotemporal random walks on high‑dimensional simplicial complexes and integrates parallelizable Temporal Convolutional Networks to capture high‑order topological structures while maintaining computational efficiency. Our source code is publicly available on GitHub\footnoteCode is available at: https://github.com/ComplexNetTSP/ST_RUM.
Authors:Oluwaleke Yusuf, M. Tsaqif Wismadi, Adil Rasheed
Abstract:
Urban bike‑sharing systems require strategic station expansion to meet growing demand. Traditional allocation approaches rely on explicit demand modelling that may not capture the urban characteristics distinguishing successful stations. This study addresses the need to exploit patterns from existing stations to inform expansion decisions, particularly in data‑constrained environments. We present a data‑driven framework leveraging existing stations deemed desirable by operational metrics. A hybrid denoising autoencoder (HDAE) learns compressed latent representations from multi‑source grid‑level features (socio‑demographic, built environment, and transport network), with a supervised classification head regularising the embedding space structure. Expansion candidates are selected via greedy allocation with spatial constraints based on latent‑space similarity to existing stations. Evaluation on Trondheim's bike‑sharing network demonstrates that HDAE embeddings yield more spatially coherent clusters and allocation patterns than raw features. Sensitivity analyses across similarity methods and distance metrics confirm robustness. A consensus‑based procedure across multiple parametrisations distils 32 high‑confidence extension zones where all parametrisations agree. The results demonstrate how representation learning captures complex patterns that raw features miss, enabling evidence‑based expansion planning without explicit demand modelling. The consensus procedure strengthens recommendations by requiring agreement across parametrisations, while framework configurability allows planners to incorporate operational knowledge. The methodology generalises to any location‑allocation problem where existing desirable instances inform the selection of new candidates.
Authors:Junguang Yao, Wenye Liu, Stjepan Picek, Yue Zheng
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.
Authors:Jon-Paul Cacioli
Abstract:
We introduce a cross‑domain behavioural assay of monitoring‑control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework and applying human psychometric methodology to LLM evaluation. The battery comprises 524 items across six cognitive domains (learning, metacognitive calibration, social cognition, attention, executive function, prospective regulation), each grounded in an established experimental paradigm. Tasks T1‑T5 were pre‑registered on OSF prior to data collection; T6 was added as an exploratory extension. After every forced‑choice response, dual probes adapted from Koriat and Goldsmith (1996) ask the model to KEEP or WITHDRAW its answer and to BET or decline. The critical metric is the withdraw delta: the difference in withdrawal rate between incorrect and correct items. Applied to 20 frontier LLMs (10,480 evaluations), the battery discriminates three profiles consistent with the Nelson‑Narens architecture: blanket confidence, blanket withdrawal, and selective sensitivity. Accuracy rank and metacognitive sensitivity rank are largely inverted. Retrospective monitoring and prospective regulation appear dissociable (r = .17, 95% CI wide given n=20; exemplar‑based evidence is the primary support). Scaling on metacognitive calibration is architecture‑dependent: monotonically decreasing (Qwen), monotonically increasing (GPT‑5.4), or flat (Gemma). Behavioural findings converge structurally with an independent Type‑2 SDT approach, providing preliminary cross‑method construct validity. All items, data, and code: https://github.com/synthiumjp/metacognitive‑monitoring‑battery.
Authors:Xinge Liu, Terry Jingchen Zhang, Bernhard Schölkopf, Zhijing Jin, Kristen Menou
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.
Authors:Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo, Tiago A. Almeida
Abstract:
Effective user modeling requires distinguishing between short‑term and long‑term preference evolution. While item embeddings have become a key component of recommender systems, standard approaches like Item2Vec treat user histories as unordered sets (bag‑of‑items), implicitly assuming that interactions separated by minutes are as semantically related as those separated by months. This simplification flattens the rich temporal structure of user behavior, obscuring the distinction between coherent consumption sessions and gradual interest drifts. In this work, we introduce TAI2Vec (Time‑Aware Item‑to‑Vector), a family of lightweight embedding models that integrates temporal proximity directly into the representation learning process. Unlike approaches that apply global time constraints, TAI2Vec is user‑adaptive, tailoring its temporal definitions to individual interaction paces. We propose two complementary strategies: TAI2Vec‑Disc, which utilizes personalized anomaly detection to dynamically segment interactions into semantic sessions, and TAI2Vec‑Cont, which employs continuous, user‑specific decay functions to weigh item relationships based on their relative temporal distance. Experimental results across eight diverse datasets demonstrate that TAI2Vec consistently produces more accurate and behaviorally grounded representations than static baselines, achieving competitive or superior performance in over 80% of the datasets, with improvements of up to 135%. The source code is publicly available at https://github.com/UFSCar‑LaSID/tai2vec.
Authors:Pedro R. Pires, Rafael T. Sereicikas, Gregorio F. Azevedo, Tiago A. Almeida
Abstract:
In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state‑of‑the‑art performance. Yet, despite these advancements, award‑winning research has demonstrated that traditional matrix factorization methods can remain competitive, offering simplicity and reduced computational overhead. Hybrid models, which combine matrix factorization with newer techniques, are increasingly employed to harness the strengths of multiple approaches. This paper proposes a novel ensemble method that unifies user‑item and item‑item recommendations through a weighted similarity framework to deliver top‑N recommendations. Our approach is distinctive in its use of shared user and item embeddings for both recommendation strategies, simplifying the architecture and enhancing computational efficiency. Extensive experiments across multiple datasets show that our method achieves competitive performance and is robust in varying scenarios that favor either user‑item or item‑item recommendations. Additionally, by eliminating the need for embedding‑specific fine‑tuning, our model allows for the seamless reuse of hyperparameters from the base algorithm without sacrificing performance. This results in a method that is both efficient and easy to implement. Our open‑source implementation is available at https://github.com/UFSCar‑LaSID/weighted‑sims‑recommender.
Authors:Mohammad Mahdi Abootorabi, Parvin Mousavi, Purang Abolmaesumi, Evan Shelhamer
Abstract:
Deep networks that rely on prototypes‑interpretable representations that can be related to the model input‑have gained significant attention for balancing high accuracy with inherent interpretability, which makes them suitable for critical domains such as healthcare. However, these models are limited by their reliance on training data, which hampers their robustness to distribution shifts. While test‑time adaptation (TTA) improves the robustness of deep networks by updating parameters and statistics, the prototypes of interpretable models have not been explored for this purpose. We introduce ProtoTTA, a general framework for prototypical models that leverages intermediate prototype signals rather than relying solely on model outputs. ProtoTTA minimizes the entropy of the prototype‑similarity distribution to encourage more confident and prototype‑specific activations on shifted data. To maintain stability, we employ geometric filtering to restrict updates to samples with reliable prototype activations, regularized by prototype‑importance weights and model‑confidence scores. Experiments across four prototypical backbones on four diverse benchmarks spanning fine‑grained vision, histopathology, and NLP demonstrate that ProtoTTA improves robustness over standard output entropy minimization while restoring correct semantic focus in prototype activations. We also introduce novel interpretability metrics and a vision‑language model (VLM) evaluation framework to explain TTA dynamics, confirming ProtoTTA restores human‑aligned semantic focus and correlates reliably with VLM‑rated reasoning quality. Code is available at: https://github.com/DeepRCL/ProtoTTA.
Authors:Zixuan Weng, Jinghuai Zhang, Kunlin Cai, Ying Li, Peiran Wang, Yuan Tian
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
Authors:G. Aytug Akarlar
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.
Authors:Sanjeev Panta, Rhett M Morvant, Xu Yuan, Li Chen, Nian-Feng Tzeng
Abstract:
Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains challenging due to limitations in effectively leveraging diverse multimedia data sources. We introduce M3R, a Meteorology‑informed MultiModal attention‑based architecture for direct Rainfall prediction that synergistically combines visual NEXRAD radar imagery with numerical Personal Weather Station (PWS) measurements, using a comprehensive pipeline for temporal alignment of heterogeneous meteorological data. With specialized multimodal attention mechanisms, M3R novelly leverages weather station time series as queries to selectively attend to spatial radar features, enabling focused extraction of precipitation signatures. Experimental results for three spatial areas of 100 km 100 km centered at NEXRAD radar stations demonstrate that M3R outperforms existing approaches, achieving substantial improvements in accuracy, efficiency, and precipitation detection capabilities. Our work establishes new benchmarks for multimedia‑based precipitation nowcasting and provides practical tools for operational weather prediction systems. The source code is available at https://github.com/Sanjeev97/M3Rain
Authors:Kieran A. Murphy
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.
Authors:Yury Gorishniy, Ivan Rubachev, Dmitrii Feoktistov, Artem Babenko
Abstract:
MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go‑to optimizer used to train tabular DL models. Unlike architecture design, however, the choice of optimizer for tabular DL has not been examined systematically, despite new optimizers showing promise in other domains. To fill this gap, we benchmark 15 optimizers on 17 tabular datasets for training MLP‑based models in the standard supervised learning setting under a shared experiment protocol. Our main finding is that the Muon optimizer consistently outperforms AdamW, and thus should be considered a strong and practical choice for practitioners and researchers, if the associated training efficiency overhead is affordable. Additionally, we find exponential moving average of model weights to be a simple yet effective technique that improves AdamW on vanilla MLPs, though its effect is less consistent across model variants.
Authors:Tianhao Fu, Austin Wang, Charles Chen, Roby Aldave-Garza, Yucheng Chen
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.
Authors:Onno Niemann, Gonzalo Martínez Muñoz, Alberto Suárez Gonzalez
Abstract:
Recent work has shown that diffusion models trained with the denoising score matching (DSM) objective often violate the Fokker‑‑Planck (FP) equation that governs the evolution of the true data density. Directly penalizing these deviations in the objective function reduces their magnitude but introduces a significant computational overhead. It is also observed that enforcing strict adherence to the FP equation does not necessarily lead to improvements in the quality of the generated samples, as often the best results are obtained with weaker FP regularization. In this paper, we investigate whether simpler penalty terms can provide similar benefits. We empirically analyze several lightweight regularizers, study their effect on FP residuals and generation quality, and show that the benefits of FP regularization are available at substantially lower computational cost. Our code is available at https://github.com/OnnoNiemann/fp_diffusion_analysis.
Authors:Arman Hatami, Romina Aalishah, Ilya E. Monosov
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.
Authors:Haozhi Fan, Jinhao Duan, Kaidi Xu
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.
Authors:Haochun Tang, Yuliang Yan, Jiahua Lu, Huaxiao Liu, Enyan Dai
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.
Authors:Gökçe Uludoğan, Buse Giledereli, Elif Ozkirimli, Arzucan Özgür
Abstract:
Proteins carry out biological functions through the coordinated action of groups of residues organized into structural arrangements. These arrangements, which we refer to as protein units, exist at an intermediate scale, being larger than individual residues yet smaller than entire proteins. A deeper understanding of protein function can be achieved by identifying these units and their associations with function. However, existing approaches either focus on residue‑level signals, rely on curated annotations, or segment protein structures without incorporating functional information, thereby limiting interpretable analysis of structure‑function relationships. We introduce PUFFIN, a data‑driven framework for discovering protein units by jointly learning structural partitioning and functional supervision. PUFFIN represents proteins as residue‑level structure graphs and applies a graph neural network with a structure‑aware pooling mechanism that partitions each protein into multi‑residue units, with functional supervision that shapes the partition. We show that the learned units are structurally coherent, exhibit organized associations with molecular function, and show meaningful correspondence with curated InterPro annotations. Together, these results demonstrate that PUFFIN provides an interpretable framework for analyzing structure‑function relationships using learned protein units and their statistical function associations. We made our source code available at https://github.com/boun‑tabi‑lifelu/puffin.
Authors:Runze Li, Hongyin Zhang, Junxi Jin, Qixin Zeng, Zifeng Zhuang, Yiqi Tang, Shangke Lyu, Donglin Wang
Abstract:
Vision‑Language‑Action (VLA) models have emerged as a promising paradigm for building embodied agents that ground perception and language into action. However, most existing approaches rely on direct action prediction, lacking the ability to reason over long‑horizon trajectories and evaluate their consequences, which limits performance in complex decision‑making tasks. In this work, we introduce World‑Value‑Action (WAV) model, a unified framework that enables implicit planning in VLA systems. Rather than performing explicit trajectory optimization, WAV model learn a structured latent representation of future trajectories conditioned on visual observations and language instructions. A learned world model predicts future states, while a trajectory value function evaluates their long‑horizon utility. Action generation is then formulated as inference in this latent space, where the model progressively concentrates probability mass on high‑value and dynamically feasible trajectories. We provide a theoretical perspective showing that planning directly in action space suffers from an exponential decay in the probability of feasible trajectories as the horizon increases. In contrast, latent‑space inference reshapes the search distribution toward feasible regions, enabling efficient long‑horizon decision making. Extensive simulations and real‑world experiments demonstrate that the WAV model consistently outperforms state‑of‑the‑art methods, achieving significant improvements in task success rate, generalization ability, and robustness, especially in long‑horizon and compositional scenarios. Code is available at https://github.com/Win‑commit/WAV.
Authors:Sumit Mukherjee, Juan Shu, Nairwita Mazumder, Tate Kernell, Celena Wheeler, Shannon Hastings, Chris Sidey-Gibbons
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.
Authors:Yuxiang Wang, Hongyu Liu, Yijiang Xu, Qinke Ni, Li Wang, Wan Lin, Kunyu Feng, Dekun Chen, Xu Tan, Lei Wang, Jie Shi, Zhizheng Wu
Abstract:
As speech language models (SLMs) transition from personal devices into shared, multi‑user environments, their responses must account for far more than the words alone. Who is speaking, how they sound, and where the conversation takes place can each turn an otherwise benign request into one that is unsafe, unfair, or privacy‑violating. Existing benchmarks, however, largely focus on basic audio comprehension, study individual risks in isolation, or conflate content that is inherently harmful with content that only becomes problematic due to its acoustic context. We introduce VoxSafeBench, among the first benchmarks to jointly evaluate social alignment in SLMs across three dimensions: safety, fairness, and privacy. VoxSafeBench adopts a Two‑Tier design: Tier1 evaluates content‑centric risks using matched text and audio inputs, while Tier2 targets audio‑conditioned risks in which the transcript is benign but the appropriate response hinges on the speaker, paralinguistic cues, or the surrounding environment. To validate Tier2, we include intermediate perception probes and confirm that frontier SLMs can successfully detect these acoustic cues yet still fail to act on them appropriately. Across 22 tasks with bilingual coverage, we find that safeguards appearing robust on text often degrade in speech: safety awareness drops for speaker‑ and scene‑conditioned risks, fairness erodes when demographic differences are conveyed vocally, and privacy protections falter when contextual cues arrive acoustically. Together, these results expose a pervasive speech grounding gap: current SLMs frequently recognize the relevant social norm in text but fail to apply it when the decisive cue must be grounded in speech. Code and data are publicly available at: https://amphionteam.github.io/VoxSafeBench_demopage/
Authors:Mohammad R. Abu Ayyash
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.
Authors:Avinash Amudala
Abstract:
Online A/B testing at scale relies on proxy metrics ‑‑ short‑term, easily‑measured signals used in place of slow‑moving long‑term outcomes. When the proxy‑outcome relationship is heterogeneous across user segments, aggregate correlation can mask directional failures akin to Simpson's Paradox, leading to costly ship/no‑ship errors. We introduce PROXIMA (Proxy Metric Validation Framework for Online Experiments), a lightweight diagnostic framework that scores proxy reliability through a composite of three complementary dimensions: normalised effect correlation, directional accuracy, and segment‑level fragility rate. Unlike surrogate‑index approaches that predict long‑term treatment effects, PROXIMA directly audits whether a candidate proxy leads to correct launch decisions and flags the user segments where it fails. We validate PROXIMA on two public datasets ‑‑ the Criteo Uplift corpus (14M observations, advertising) and KuaiRec (7K users, video recommendation) ‑‑ using 80 simulated A/B tests. Early engagement metrics achieve a composite reliability of 0.80 on Criteo and 0.62 on KuaiRec, yielding 98.4% average decision agreement with an oracle policy. Fragility analysis reveals that recommendation domains exhibit substantially higher segment‑level heterogeneity (68% fragility) than advertising (13%), yet directional accuracy remains above 96% in both cases. A sensitivity analysis over the weight space confirms that no single component suffices and that the composite provides substantially better discrimination between reliable and unreliable proxies than correlation alone. Code and reproduction scripts are available at: https://github.com/Avinash‑Amudala/PROXIMA
Authors:Guillermo Valverde, Igor García-Olaizola, Giannicola Scarpa, Alejandro Pozas-Kerstjens
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.
Authors:Haoran Xu, Kaiwen Hu, Somayeh Sojoudi, Amy Zhang
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.
Authors:Qianyu Chen, Shujian Yu
Abstract:
Functional magnetic resonance imaging (fMRI) is widely used for studying and diagnosing brain disorders, with functional connectivity (FC) matrices providing powerful representations of large‑scale neural interactions. However, existing diagnostic models are trained either on a single site or under full multi‑site access, making them unsuitable for real‑world scenarios where clinical data arrive sequentially from different institutions. This results in limited generalization and severe catastrophic forgetting. This paper presents the first continual learning framework specifically designed for fMRI‑based diagnosis across heterogeneous clinical sites. Our framework introduces a structure‑aware variational autoencoder that synthesizes realistic FC matrices for both patient and control groups. Built on this generative backbone, we develop a multi‑level knowledge distillation strategy that aligns predictions and graph representations between new‑site data and replayed samples. To further enhance efficiency, we incorporate a hierarchical contextual bandit scheme for adaptive replay sampling. Experiments on multi‑site datasets for major depressive disorder (MDD), schizophrenia (SZ), and autism spectrum disorder (ASD) show that the proposed generative model enhances data augmentation quality, and the overall continual learning framework substantially outperforms existing methods in mitigating catastrophic forgetting. Our code is available at https://github.com/4me808/FORGE.
Authors:Jiacheng Liu, Xiaohan Zhao, Xinyi Shang, Zhiqiang Shen
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.
Authors:Pu Cheng, Juncheng Liu, Yunshen Long
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.
Authors:Haiyang Zheng, Nan Pu, Yaqi Cai, Teng Long, Wenjing Li, Nicu Sebe, Zhun Zhong
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.
Authors:Bryan Sanchez
Abstract:
Alignment‑tuned language models frequently suppress factual log‑probabilities on politically sensitive topics despite retaining the knowledge in their hidden representations. We show that a 786K‑parameter (approximately 0.02% of the base model) post‑transformer adapter, trained on frozen hidden states, corrects this suppression on 31 ideology‑discriminating facts across Qwen3‑4B, 8B, and 14B. The adapter memorizes all 15 training facts and generalizes to 11‑‑39% of 16 held‑out facts across 5 random splits per scale, with zero knowledge regressions via anchored training. Both gated (SwiGLU) and ungated (linear bottleneck) adapters achieve comparable results; neither consistently outperforms the other (Fisher exact p > 0.09 at all scales). On instruct models, the adapter corrects log‑probability rankings. When applied at all token positions during generation, the adapter produces incoherent output; however, when applied only at the current prediction position (last‑position‑only), the adapter produces coherent, less censored text. A logit‑space adapter operating after token projection fails to produce coherent generation at any application mode, suggesting hidden‑state intervention is the correct level for generation correction. A previously undocumented silent gradient bug in Apple MLX explains all null results in earlier iterations of this work: the standard pattern nn.value_and_grad(model, fn)(model.parameters()) returns zero gradients without error; the correct pattern nn.value_and_grad(model, fn)(model, data) resolves this. We provide a minimal reproduction and discuss implications for other adapter research using MLX.
Authors:Yuqiao Tan, Minzheng Wang, Bo Liu, Zichen Liu, Tian Liang, Shizhu He, Jun Zhao, Kang Liu
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.
Authors:Itay Itzhak, Eliya Habba, Gabriel Stanovsky, Yonatan Belinkov
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.
Authors:Xiaofan Zhou, Kyumin Lee
Abstract:
Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e‑commerce. The primary goal is to extract user preferences from historical interaction sequences and predict items a user is likely to engage with next. Recent advances have leveraged contrastive learning and graph neural networks to learn more expressive representations from interaction histories ‑‑ graphs capture relational structure between nodes, while ID‑based representations encode item‑specific information. However, few studies have explored multi‑view contrastive learning between ID and graph perspectives to jointly improve user and item representations, especially in settings where only interaction data is available without auxiliary information.
To address this gap, we propose Multi‑View Contrastive learning for sequential recommendation (MVCrec), a framework that integrates complementary signals from both sequential (ID‑based) and graph‑based views. MVCrec incorporates three contrastive objectives: within the sequential view, within the graph view, and across views. To effectively fuse the learned representations, we introduce a multi‑view attention fusion module that combines global and local attention mechanisms to estimate the likelihood of a target user purchasing a target item. Comprehensive experiments on five real‑world benchmark datasets demonstrate that MVCrec consistently outperforms 11 state‑of‑the‑art baselines, achieving improvements of up to 14.44% in NDCG@10 and 9.22% in HitRatio@10 over the strongest baseline. Our code and datasets are available at https://github.com/sword‑Lz/MMCrec.
Authors:Yuanda Xu, Hejian Sang, Zhengze Zhou, Ran He, Zhipeng Wang, Alborz Geramifard
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.
Authors:Yaocheng Zhang, Yuanheng Zhu, Wenyue Chong, Songjun Tu, Qichao Zhang, Jiajun Chai, Xiaohan Wang, Wei Lin, Guojun Yin, Dongbin Zhao
Abstract:
Deep search agents have emerged as a promising paradigm for addressing complex information‑seeking tasks, but their training remains challenging due to sparse rewards, weak credit assignment, and limited labeled data. Self‑play offers a scalable route to reduce data dependence, but conventional self‑play optimizes students only through sparse outcome rewards, leading to low learning efficiency. In this work, we observe that self‑play naturally produces a question construction path (QCP) during task generation, an intermediate artifact that captures the reverse solution process. This reveals a new source of privileged information: self‑play can provide high‑quality privileged information for the self‑distillation at low cost and at scale, without relying on human feedback or curated privileged information. Leveraging this insight, we propose Privileged Information Self‑Play (π‑Play), a novel multi‑agent self‑evolution framework combining self‑play and self‑distillation. In π‑Play, an examiner generates tasks together with QCPs, and a teacher employs QCP as privileged context to densely supervise a student via self‑distillation. This design transforms sparse‑reward self‑play into a dense‑feedback co‑evolution. Extensive experiments show that data‑free π‑Play surpasses fully supervised search agents and improves evolutionary efficiency by 2‑3× over conventional self‑play. Code is available at https://github.com/zhyaoch/pi‑play.
Authors:Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit, J. Marius Zöllner
Abstract:
Sparse mixture‑of‑experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost and are widely used in transformer architectures, where they typically replace feed‑forward network blocks. In contrast, integrating sparse MoE layers into convolutional neural networks (CNNs) remains inconsistent, with most prior work focusing on fine‑grained MoEs operating at the filter or channel levels. In this work, we investigate a coarser, patch‑wise formulation of sparse MoE layers for semantic segmentation, where local regions are routed to a small subset of convolutional experts. Through experiments on the Cityscapes and BDD100K datasets using encoder‑decoder and backbone‑based CNNs, we conduct a design analysis to assess how architectural choices affect routing dynamics and expert specialization. Our results demonstrate consistent, architecture‑dependent improvements (up to +3.9 mIoU) with little computational overhead, while revealing strong design sensitivity. Our work provides empirical insights into the design and internal dynamics of sparse MoE layers in CNN‑based dense prediction. Our code is available at https://github.com/KASTEL‑MobilityLab/moe‑layers/.
Authors:Boxuan Jiang, Chenyun Dai, Can Han
Abstract:
Deep learning‑based surface electromyography (sEMG) gesture recognition is frequently bottlenecked by data scarcity and limited subject diversity. While synthetic data generation via Generative Adversarial Networks (GANs) and diffusion models has emerged as a promising augmentation strategy, these approaches often face challenges regarding training stability or inference efficiency. To bridge this gap, we propose EMGFlow, a conditional sEMG generation framework. To the best of our knowledge, this is the first study to investigate the application of Flow Matching (FM) and continuous‑time generative modeling in the sEMG domain. To validate EMGFlow across three benchmark sEMG datasets, we employ a unified evaluation protocol integrating feature‑based fidelity, distributional geometry, and downstream utility. Extensive evaluations show that EMGFlow outperforms conventional augmentation and GAN baselines, and provides stronger standalone utility than the diffusion baselines considered here under the train‑on‑synthetic test‑on‑real (TSTR) protocol. Furthermore, by optimizing generation dynamics through advanced numerical solvers and targeted time sampling, EMGFlow achieves improved quality‑efficiency trade‑offs. Taken together, these results suggest that Flow Matching is a promising and efficient paradigm for addressing data bottlenecks in myoelectric control systems. Our code is available at: https://github.com/Open‑EXG/EMGFlow.
Authors:Zijian Zhao, Jing Gao, Sen Li
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 .
Authors:Mohammed Ezzaldin Babiker Abdullah
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.
Authors:Mohammed Ezzaldin Babiker Abdullah, Rufaidah Abdallah Ibrahim Mohammed
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.
Authors:Jason Kong, Nilesh Prasad Pandey, Flavio Ponzina, Tajana Rosing
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.
Authors:Yiping Li, Zhiyu An, Wan Du
Abstract:
Communication in Large Language Model (LLM)‑based multi‑agent systems is moving beyond discrete tokens to preserve richer context. Recent work such as LatentMAS enables agents to exchange latent messages through full key‑value (KV) caches. However, full KV relay incurs high memory and communication cost. We adapt eviction‑style KV compression to this setting and introduce Orthogonal Backfill (OBF) to mitigate information loss from hard eviction. OBF injects a low‑rank orthogonal residual from discarded KV states into the retained KV states. We evaluate proposed method against full KV relay on nine standard benchmarks spanning mathematical reasoning, coding, and knowledge‑intensive QA. It achieves performance comparable to full KV relay while reducing communication cost by 79.8%‑‑89.4%. OBF further improves the performance and achieves the best results on 7 of the 9 benchmarks. This suggests that more information does not necessarily lead to better communication; preserving the most useful information matters more. Our codebase is publicly available on https://github.com/markli404/When‑Less‑Latent‑Leads‑to‑Better‑Relay.
Authors:Yilang Zhang, Abraham Jaeger Mountain, Bingcong Li, Georgios B. Giannakis
Abstract:
Meta‑learning offers a principled framework leveraging \emphtask‑invariant priors from related tasks, with which \emphtask‑specific models can be fine‑tuned on downstream tasks, even with limited data records. Gradient‑based meta‑learning (GBML) relies on gradient descent (GD) to adapt the prior to a new task. Albeit effective, these methods incur high computational overhead that scales linearly with the number of GD steps. To enhance efficiency and scalability, existing methods approximate the gradient of prior parameters (meta‑gradient) via truncated backpropagation, yet suffer large approximation errors. Targeting accurate approximation, this work puts forth binomial GBML (BinomGBML), which relies on a truncated binomial expansion for meta‑gradient estimation. This novel expansion endows more information in the meta‑gradient estimation via efficient parallel computation. As a running paradigm applied to model‑agnostic meta‑learning (MAML), the resultant BinomMAML provably enjoys error bounds that not only improve upon existing approaches, but also decay super‑exponentially under mild conditions. Numerical tests corroborate the theoretical analysis and showcase boosted performance with slightly increased computational overhead.
Authors:Bhavana Sajja
Abstract:
We introduce behavioral fidelity ‑‑ a third evaluation dimension for synthetic tabular data that measures whether generated data preserves the temporal, sequential, and structural behavioral patterns that distinguish real‑world entity activity. Existing frameworks evaluate statistical fidelity (marginal distributions and correlations) and downstream utility (classifier AUROC on synthetic‑trained models), but neither tests for the behavioral signals that operational detection and analysis systems actually rely on. We formalize a taxonomy of four behavioral fraud patterns (P1‑P4) covering inter‑event timing, burst structure, multi‑account graph motifs, and velocity‑rule trigger rates; define a degradation ratio metric calibrated to a real‑data noise floor (1.0 = matches real variability, k = k‑times worse); and prove that row‑independent generators ‑‑ the dominant paradigm ‑‑ are structurally incapable of reproducing P3 graph motifs (Proposition 1) and produce non‑positive within‑entity IET autocorrelation (Proposition 2), making the positive burst fingerprint of fraud sequences unachievable regardless of architecture or training data size. We benchmark CTGAN, TVAE, GaussianCopula, and TabularARGN on IEEE‑CIS Fraud Detection and the Amazon Fraud Dataset. All four fail severely: on IEEE‑CIS composite degradation ratios range from 24.4x (TVAE) to 39.0x (GaussianCopula); on Amazon FDB, row‑independent generators score 81.6‑99.7x, while TabularARGN achieves 17.2x. We document generator‑specific failure modes and their resolutions. The P1‑P4 framework extends to any domain with entity‑level sequential tabular data, including healthcare and network security. We release our evaluation framework as open source.
Authors:Xiang Long, Li Du, Yilong Xu, Fangcheng Liu, Haoqing Wang, Ning Ding, Ziheng Li, Jianyuan Guo, Yehui Tang
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.
Authors:Yaxuan Li, Yuxin Zuo, Bingxiang He, Jinqian Zhang, Chaojun Xiao, Cheng Qian, Tianyu Yu, Huan-ang Gao, Wenkai Yang, Zhiyuan Liu, Ning Ding
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.
Authors:Yecheng Wu, Song Han, Hai Cai
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.
Authors:Tong Zhang, Jiangning Zhang, Zhucun Xue, Juntao Jiang, Yicheng Xu, Chengming Xu, Teng Hu, Xingyu Xie, Xiaobin Hu, Yabiao Wang, Yong Liu, Shuicheng Yan
Abstract:
Balancing convergence speed, generalization capability, and computational efficiency remains a core challenge in deep learning optimization. First‑order gradient descent methods, epitomized by stochastic gradient descent (SGD) and Adam, serve as the cornerstone of modern training pipelines. However, large‑scale model training, stringent differential privacy requirements, and distributed learning paradigms expose critical limitations in these conventional approaches regarding privacy protection and memory efficiency. To mitigate these bottlenecks, researchers explore second‑order optimization techniques to surpass first‑order performance ceilings, while zeroth‑order methods reemerge to alleviate memory constraints inherent to large‑scale training. Despite this proliferation of methodologies, the field lacks a cohesive framework that unifies underlying principles and delineates application scenarios for these disparate approaches. In this work, we retrospectively analyze the evolutionary trajectory of deep learning optimization algorithms and present a comprehensive empirical evaluation of mainstream optimizers across diverse model architectures and training scenarios. We distill key emerging trends and fundamental design trade‑offs, pinpointing promising directions for future research. By synthesizing theoretical insights with extensive empirical evidence, we provide actionable guidance for designing next‑generation highly efficient, robust, and trustworthy optimization methods. The code is available at https://github.com/APRIL‑AIGC/Awesome‑Optimizer.
Authors:Jason Z Wang
Abstract:
The most cited calibration result in deep learning ‑‑ post‑temperature‑scaling ECE of 0.012 on CIFAR‑100 (Guo et al., 2017) ‑‑ is below the statistical noise floor. We prove this is not a failure of the experiment but a law: the minimax rate for estimating calibration error with model error rate epsilon is Theta((Lepsilon/m)^1/3), and no estimator can beat it. This "verification tax" implies that as AI models improve, verifying their calibration becomes fundamentally harder ‑‑ with the same exponent in opposite directions. We establish four results that contradict standard evaluation practice: (1) self‑evaluation without labels provides exactly zero information about calibration, bounded by a constant independent of compute; (2) a sharp phase transition at mepsilon approx 1 below which miscalibration is undetectable; (3) active querying eliminates the Lipschitz constant, collapsing estimation to detection; (4) verification cost grows exponentially with pipeline depth at rate L^K. We validate across five benchmarks (MMLU, TruthfulQA, ARC‑Challenge, HellaSwag, WinoGrande; ~27,000 items) with 6 LLMs from 5 families (8B‑405B parameters, 27 benchmark‑model pairs with logprob‑based confidence), 95% bootstrap CIs, and permutation tests. Self‑evaluation non‑significance holds in 80% of pairs. Across frontier models, 23% of pairwise comparisons are indistinguishable from noise, implying that credible calibration claims must report verification floors and prioritize active querying once gains approach benchmark resolution.
Authors:Shaopeng Fu, Di Wang
Abstract:
Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To improve the efficiency of AT for LLMs, recent studies propose continuous AT (CAT) that searches for adversarial inputs within the continuous embedding space of LLMs during AT. While CAT has achieved empirical success, its underlying mechanism, i.e., why adversarial perturbations in the embedding space can help LLMs defend against jailbreak prompts synthesized in the input token space, remains unknown. This paper presents the first theoretical analysis of CAT on LLMs based on in‑context learning (ICL) theory. For linear transformers trained with adversarial examples from the embedding space on in‑context linear regression tasks, we prove a robust generalization bound that has a negative correlation with the perturbation radius in the embedding space. This clearly explains why CAT can defend against jailbreak prompts from the LLM's token space. Further, the robust bound shows that the robustness of an adversarially trained LLM is closely related to the singular values of its embedding matrix. Based on this, we propose to improve LLM CAT by introducing an additional regularization term, which depends on singular values of the LLM's embedding matrix, into the objective function of CAT. Experiments on real‑world LLMs demonstrate that our method can help LLMs achieve a better jailbreak robustness‑utility tradeoff. The code is available at https://github.com/fshp971/continuous‑adv‑icl.
Authors:Arya Shah, Kaveri Visavadiya, Manisha Padala
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.
Authors:Michele De Vita, Julian Wiederer, Vasileios Belagiannis
Abstract:
Trajectory prediction models often fail in real‑world automated driving due to distributional shifts between training and test conditions. Such distributional shifts, whether behavioural or environmental, pose a critical risk by causing the model to make incorrect forecasts in unfamiliar situations. We propose a self‑supervised method that trains a decoder in a post‑hoc fashion on the self‑supervised task of forecasting the second half of observed trajectories from the first half. The L2 norm of the gradient of this forecasting loss with respect to the decoder's final layer defines a score to identify distribution shifts. Our approach, first, does not affect the trajectory prediction model, ensuring no interference with original prediction performance and second, demonstrates substantial improvements on distribution shift detection for trajectory prediction on the Shifts and Argoverse datasets. Moreover, we show that this method can also be used to early detect collisions of a deep Q‑Network motion planner in the Highway simulator. Source code is available at https://github.com/Michedev/forecasting‑the‑past.
Authors:Yuangang Li, Justin Tian Jin Chen, Ethan Yu, David Hong, Iftekhar Ahmed
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.
Authors:Yexiong Lin, Jia Shi, Shanshan Ye, Wanyu Wang, Yu Yao, Tongliang Liu
Abstract:
Flow matching has emerged as a powerful generative framework, with recent few‑step methods achieving remarkable inference acceleration. However, we identify a critical yet overlooked limitation: these models suffer from severe diversity degradation, concentrating samples on dominant modes while neglecting rare but valid variations of the target distribution. We trace this degradation to averaging distortion: when trained with MSE objectives, class‑conditional flows learn a frequency‑weighted mean over intra‑class sub‑modes, causing the model to over‑represent high‑density modes while systematically neglecting low‑density ones. To address this, we propose SubFlow, Sub‑mode Conditioned Flow Matching, which eliminates averaging distortion by decomposing each class into fine‑grained sub‑modes via semantic clustering and conditioning the flow on sub‑mode indices. Each conditioned sub‑distribution is approximately unimodal, so the learned flow accurately targets individual modes with no averaging distortion, restoring full mode coverage in a single inference step. Crucially, SubFlow is entirely plug‑and‑play: it integrates seamlessly into existing one‑step models such as MeanFlow and Shortcut Models without any architectural modifications. Extensive experiments on ImageNet‑256 demonstrate that SubFlow yields substantial gains in generation diversity (Recall) while maintaining competitive image quality (FID), confirming its broad applicability across different one‑step generation frameworks. Project page: https://yexionglin.github.io/subflow.
Authors:Sandra Gómez-Gálvez, Tobias Olenyi, Gillian Dobbie, Katerina Taškova
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.
Authors:Ziqing Wang, Yibo Wen, Abhishek Pandy, Han Liu, Kaize Ding
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.
Authors:Disha Patel
Abstract:
System log anomaly detection is critical for maintaining the reliability of large‑scale software systems, yet traditional methods struggle with the heterogeneous and evolving nature of modern log data. Recent advances in Large Language Models (LLMs) offer promising new approaches to log understanding, but a systematic comparison of LLM‑based methods against established techniques remains lacking. In this paper, we present a comprehensive benchmark study evaluating both LLM‑based and traditional approaches for log anomaly detection across four widely‑used public datasets: HDFS, BGL, Thunderbird, and Spirit. We evaluate three categories of methods: (1) classical log parsers (Drain, Spell, AEL) combined with machine learning classifiers, (2) fine‑tuned transformer models (BERT, RoBERTa), and (3) prompt‑based LLM approaches (GPT‑3.5, GPT‑4, LLaMA‑3) in zero‑shot and few‑shot settings. Our experiments reveal that while fine‑tuned transformers achieve the highest F1‑scores (0.96‑0.99), prompt‑based LLMs demonstrate remarkablezero‑shot capabilities (F1: 0.82‑0.91) without requiring any labeled training data ‑‑ a significant advantage for real‑world deployment where labeled anomalies are scarce. We further analyze the cost‑accuracy trade‑offs, latency characteristics, and failure modes of each approach. Our findings provide actionable guidelines for practitioners choosing log anomaly detection methods based on their specific constraints regarding accuracy, latency, cost, and label availability. All code and experimental configurations are publicly available to facilitate reproducibility.
Authors:Md Tanvirul Alam
Abstract:
Large vision‑language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from rule‑mapping failures. We study this behavior as semantic fixation: preserving a default interpretation even when the prompt specifies an alternative, equally valid mapping. To isolate this effect, we introduce VLM‑Fix, a controlled benchmark over four abstract strategy games that evaluates identical terminal board states under paired standard and inverse rule formulations. Across 14 open and closed VLMs, accuracy consistently favors standard rules, revealing a robust semantic‑fixation gap. Prompt interventions support this mechanism: neutral alias prompts substantially narrow the inverse‑rule gap, while semantically loaded aliases reopen it. Post‑training is strongly rule‑aligned: training on one rule improves same‑rule transfer but hurts opposite‑rule transfer, while joint‑rule training improves broader transfer. To test external validity beyond synthetic games, we evaluate analogous defamiliarization interventions on VLMBias and observe the same qualitative pattern. Finally, late‑layer activation steering partially recovers degraded performance, indicating that semantic‑fixation errors are at least partly editable in late representations. Project page, code, and dataset available at https://maveryn.github.io/vlm‑fix/.
Authors:Arun Sharma
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.
Authors:Zixuan Liu, Xiaolin Sun, Zizhan Zheng
Abstract:
Designing robust reinforcement learning (RL) agents in the presence of imperfect reward signals remains a core challenge. In practice, agents are often trained with proxy rewards that only approximate the true objective, leaving them vulnerable to reward hacking, where high proxy returns arise from unintended or exploitative behaviors. Recent work formalizes this issue using r‑correlation between proxy and true rewards, but existing methods like occupancy‑regularized policy optimization (ORPO) optimize against a fixed proxy and do not provide strong guarantees against broader classes of correlated proxies. In this work, we formulate reward hacking as a robust policy optimization problem over the space of all r‑correlated proxy rewards. We derive a tractable max‑min formulation, where the agent maximizes performance under the worst‑case proxy consistent with the correlation constraint. We further show that when the reward is a linear function of known features, our approach can be adapted to incorporate this prior knowledge, yielding both improved policies and interpretable worst‑case rewards. Experiments across several environments show that our algorithms consistently outperform ORPO in worst‑case returns, and offer improved robustness and stability across different levels of proxy‑true reward correlation. These results show that our approach provides both robustness and transparency in settings where reward design is inherently uncertain. The code is available at https://github.com/ZixuanLiu4869/reward_hacking.
Authors:Vladimir Vasilenko
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.
Authors:Jiayi Xin, Xiang Li, Evan Qiang, Weiqing He, Tianqi Shang, Weijie J. Su, Qi Long
Abstract:
In‑context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. We propose Unseen Coverage Selection (UKS), a training‑free, subset‑level coverage prior motivated by the principle that a good demonstration set should expose the model to latent cluster unrevealed by the currently selected subset. UCS operationalizes this idea by (1) inducing discrete latent clusters from model‑consistent embeddings and (2) estimating the number of unrevealed clusters within a candidate subset via a Smoothed Good‑‑Turing estimator from its empirical frequency spectrum. Unlike previous selection methods, UCS is coverage‑based and training‑free, and can be seamlessly combined with both query‑dependent and query‑independent selection baselines via a simple regularized objective. Experiments on multiple intent‑classification and reasoning benchmarks with frontier Large Language Models show that augmenting strong baselines with UCS consistently improves ICL accuracy by up to 2‑6% under the same selection budget, while also yielding insights into task‑ and model‑level latent cluster distributions. Code is available at https://github.com/Raina‑Xin/UCS.
Authors:Thomas Walker, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
Abstract:
The Linear Representation Hypothesis (LRH) identifies features of a trained deep network (DN) as linear directions in the activation spaces, i.e., output spaces of intermediate layers. This characterization decouples the input‑output maps learned by a DN from the organization of feature directions in its activation spaces. We introduce the Linear Centroids Hypothesis (LCH), which instead identifies features with linear directions among a DN's centroid spaces ‑‑ where any vector denotes a centroid or summary of a local affine expert characterizing the learned input‑output maps of the DN exactly (e.g., for piecewise‑affine DNs) or approximately (e.g., for smooth DNs like transformers). We show that replacing intermediate activations with centroids yields a functional drop‑in alternative for standard interpretability tools. Empirically, this change yields sparser, more downstream‑useful feature dictionaries on DINO ViTs, suppresses spurious directions on a controlled task, recovers interpretable circuits in GPT2‑Large, and produces faithful gradient‑based saliency maps. LCH unifies dictionaries, probing, circuits, and saliency maps into a single geometric object grounded in the network's input‑output map ‑‑ making interpretability mechanistic by construction rather than post hoc. Code to study the LCH https://github.com/ThomasWalker1/LinearCentroidsHypothesis .
Authors:Elliott C. Pryor, Marc D. Breton, Anas El Fathi
Abstract:
Diabetes devices, including Continuous Glucose Monitoring (CGM), Smart Insulin Pens, and Automated Insulin Delivery systems, generate rich time‑series data widely used in research and machine learning. However, inconsistent data formats across sources hinder sharing, integration, and analysis.
We present DIAX (DIAbetes eXchange), a standardized JSON‑based format for unifying diabetes time‑series data, including CGM, insulin, and meal signals. DIAX promotes interoperability, reproducibility, and extensibility, particularly for machine learning applications. An open‑source repository provides tools for dataset conversion, cross‑format compatibility, visualization, and community contributions. DIAX is a translational resource, not a data host, ensuring flexibility without imposing data‑sharing constraints.
Currently, DIAX is compatible with other standardization efforts and supports major datasets (DCLP3, DCLP5, IOBP2, PEDAP, T1Dexi, Loop), totaling over 10 million patient‑hours of data. https://github.com/Center‑for‑Diabetes‑Technology/DIAX
Authors:Mohammed Ezzaldin Babiker Abdullah
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.
Authors:Wenhao Zhang, Lin Mu, Li Ni, Peiquan Jin, Yiwen Zhang
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
Authors:Wenbo Pan, Shujie Liu, Xiangyang Zhou, Shiwei Zhang, Wanlu Shi, Mirror Xu, Xiaohua Jia
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.
Authors:Mohammed Ezzaldin Babiker Abdullah
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.
Authors:Mihir Prabhudesai, Aryan Satpathy, Yangmin Li, Zheyang Qin, Nikash Bhardwaj, Amir Zadeh, Chuan Li, Katerina Fragkiadaki, Deepak Pathak
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/.
Authors:Yuxin Chen, Chumeng Liang, Hangke Sui, Ruihan Guo, Chaoran Cheng, Jiaxuan You, Ge Liu
Abstract:
Continuous diffusion has been the foundation of high‑fidelity, controllable, and few‑step generation of many data modalities such as images. However, in language modeling, prior continuous diffusion language models (DLMs) lag behind discrete counterparts due to the sparse data space and the underexplored design space. In this work, we close this gap with LangFlow, the first continuous DLM to rival discrete diffusion, by connecting embedding‑space DLMs to Flow Matching via Bregman divergence, alongside three key innovations: (1) we derive a novel ODE‑based NLL bound for principled evaluation of continuous flow‑based language models; (2) we propose an information‑uniform principle for setting the noise schedule, which motivates a learnable noise scheduler based on a Gumbel distribution; and (3) we revise prior training protocols by incorporating self‑conditioning, as we find it improves both likelihood and sample quality of embedding‑space DLMs with effects substantially different from discrete diffusion. Putting everything together, LangFlow rivals top discrete DLMs on both the perplexity (PPL) and the generative perplexity (Gen. PPL), reaching a PPL of 30.0 on LM1B and 24.6 on OpenWebText. It even exceeds autoregressive baselines in zero‑shot transfer on 4 out of 7 benchmarks. LangFlow provides the first clear evidence that continuous diffusion is a promising paradigm for language modeling. Homepage: https://github.com/nealchen2003/LangFlow
Authors:Jieying Xue, Phuong Minh Nguyen, Ha Thanh Nguyen, May Myo Zin, Ken Satoh
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.
Authors:Hanqi Xiao, Vaidehi Patil, Zaid Khan, Hyunji Lee, Elias Stengel-Eskin, Mohit Bansal
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.
Authors:Maxim Bolshim, Alexander Kugaevskikh
Abstract:
Modern automatic differentiation frameworks (JAX, PyTorch) return the Hessian of the loss function as a monolithic tensor, without exposing the internal structure of inter‑layer interactions. This paper presents an analytical formalism that explicitly decomposes the full Hessian into blocks indexed by the DAG of an arbitrary architecture. The canonical decomposition H = H^GN + H^T separates the Gauss‑‑Newton component (convex part) from the tensor component (residual curvature responsible for saddle points). For piecewise‑linear activations (ReLU), the tensor component of the input Hessian vanishes (H^T_v,w\!\equiv\!0 a.e., H^f_v,w\!=\!H^GN_v,w\!\succeq\!0); the full parametric Hessian contains residual terms that do not reduce to the GGN. Building on this decomposition, we introduce diagnostic metrics (inter‑layer resonance~\mathcalR, geometric coupling~\mathcalC, stable rank~\mathcalD, GN‑Gap) that are estimated stochastically in O(P) time and reveal structural curvature interactions between layers. The theoretical analysis explains exponential decay of resonance in vanilla networks and its preservation under skip connections; empirical validation spans fully connected MLPs (Exp.\,1‑‑5) and convolutional architectures (ResNet‑18, ～11M~parameters, Exp.\,6). When the architecture reduces to a single node, all definitions collapse to the standard Hessian \nabla^2_θ\mathcalL(θ)\in\mathbbR^p× p.
Authors:Haozhe Wang, Cong Wei, Weiming Ren, Jiaming Liu, Fangzhen Lin, Wenhu Chen
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.
Authors:Haolin Li, Shuyang Jiang, Ruipeng Zhang, Jiangchao Yao, Ya Zhang, Yanfeng Wang
Abstract:
While large language models hold promise for complex medical applications, their development is hindered by the scarcity of high‑quality reasoning data. To address this issue, existing approaches typically distill chain‑of‑thought reasoning traces from large proprietary models via supervised fine‑tuning, then conduct reinforcement learning (RL). These methods exhibit limited improvement on underrepresented domains like rare diseases while incurring substantial costs from generating complex reasoning chains. To efficiently enhance medical reasoning, we propose MedSSR, a Medical Knowledge‑enhanced data Synthesis and Semi‑supervised Reinforcement learning framework. Our framework first employs rare disease knowledge to synthesize distribution‑controllable reasoning questions. We then utilize the policy model itself to generate high‑quality pseudo‑labels. This enables a two‑stage, intrinsic‑to‑extrinsic training paradigm: self‑supervised RL on the pseudo‑labeled synthetic data, followed by supervised RL on the human‑annotated real data. MedSSR scales model training efficiently without relying on costly trace distillation. Extensive experiments on Qwen and Llama demonstrate that our method outperforms existing methods across ten medical benchmarks, achieving up to +5.93% gain on rare‑disease tasks. Our code is available at https://github.com/tdlhl/MedSSR.
Authors:Denizalp Goktas, Gerardo Riaño-Briceño, Alif Abdullah, Aryan Nair, Chenkai Shen, Beatriz de Lucio, Alexandra Magnusson, Farhan Mashrur, Ahmed Abdulla, Shawrna Sen, Mahitha Thippireddy, Gregory Schwartz, Amy Greenwald
Abstract:
Foundation models have transformed natural language processing and computer vision, and a rapidly growing literature on time‑series foundation models (TSFMs) seeks to replicate this success in forecasting. While recent open‑source models demonstrate the promise of TSFMs, the field lacks a comprehensive and community‑accepted model evaluation framework. We see at least four major issues impeding progress on the development of such a framework. First, existing evaluation frameworks comprise benchmark forecasting tasks derived from often outdated datasets (e.g., M3), many of which lack clear metadata and overlap with the corpora used to pre‑train TSFMs. Second, these frameworks evaluate models along a narrowly defined set of benchmark forecasting tasks, such as forecast horizon length or domain, but overlook core statistical properties such as non‑stationarity and seasonality. Third, domain‑specific models (e.g., XGBoost) are often compared unfairly, as existing frameworks do not enforce a systematic and consistent hyperparameter tuning convention for all models. Fourth, visualization tools for interpreting comparative performance are lacking. To address these issues, we introduce TempusBench, an open‑source evaluation framework for TSFMs. TempusBench consists of 1) new datasets which are not included in existing TSFM pretraining corpora, 2) a set of novel benchmark tasks that go beyond existing ones, 3) a model evaluation pipeline with a standardized hyperparameter tuning protocol, and 4) a tensorboard‑based visualization interface. We provide access to our code on GitHub: https://github.com/Smlcrm/TempusBench and maintain a live leaderboard at https://benchmark.smlcrm.com/.
Authors:Zhipeng Chen, Tao Qian, Wayne Xin Zhao, Ji-Rong Wen
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.
Authors:Ammar Bhilwarawala, Likhamba Rongmei, Harsh Sharma, Arya Jena, Kaushal Singh, Jayashree Piri, Raghunath Dey
Abstract:
IoT botnet detection has advanced, yet most published systems are validated on a single dataset and rarely generalise across environments. Heterogeneous feature spaces make multi‑dataset training practically impossible without discarding semantic interpretability or introducing data integrity violations. No prior work has addressed both problems with a formally specified, reproducible methodology. This paper does. We introduce BRIDGE (Benchmark Reference for IoT Domain Generalisation Evaluation), the first formally specified heterogeneous multi‑dataset benchmark for IoT intrusion detection, unifying CICIDS‑2017, CIC‑IoT‑2023, Bot‑IoT, Edge‑IIoTset, and N‑BaIoT through a 46‑feature semantic canonical vocabulary grounded in CICFlowMeter nomenclature, with genuine‑equivalence‑only feature mapping, explicit zero‑filling, and per‑dataset coverage from 15% to 93%. A leave‑one‑dataset‑out (LODO) protocol makes the generalisation gap precisely measurable: all five evaluated architectures achieve mean LODO F1 between 0.39 and 0.47, and we establish the first community generalisation baseline at mean LODO F1 = 0.5577, a result that shifts the agenda from single‑benchmark optimisation toward cross‑environment generalisation. We propose TCH‑Net, a multi‑branch network fusing a three‑path Temporal branch (residual convolutional‑BiGRU, stride‑downsampled BiGRU, pre‑LayerNorm Transformer), a provenance‑conditioned Contextual branch, and a Statistical branch via Cross‑Branch Gated Attention Fusion (CB‑GAF) with learnable sigmoid gates for dynamic feature‑wise mixing. Across five random seeds, TCH‑Net achieves F1 = 0.8296 +/‑ 0.0028, AUC = 0.9380 +/‑ 0.0025, and MCC = 0.6972 +/‑ 0.0056, outperforming all twelve baselines (p < 0.05, Wilcoxon) and recording the highest LODO F1 overall. BRIDGE and the full pipeline are at https://github.com/Ammar‑ss/TCH‑Net.
Authors:Stefan Schulz, Fernando Edelstein, Hannah Dröge, Matthias B. Hullin, Markus Plack
Abstract:
Real‑time free‑viewpoint rendering requires balancing multi‑camera redundancy with the latency constraints of interactive applications. We address this challenge by combining lightweight geometry with learning and propose 3DTV, a feedforward network for real‑time sparse‑view interpolation. A Delaunay‑based triplet selection ensures angular coverage for each target view. Building on this, we introduce a pose‑aware depth module that estimates a coarse‑to‑fine depth pyramid, enabling efficient feature reprojection and occlusion‑aware blending. Unlike methods that require scene‑specific optimization, 3DTV runs feedforward without retraining, making it practical for AR/VR, telepresence, and interactive applications. Our experiments on challenging multi‑view video datasets demonstrate that 3DTV consistently achieves a strong balance of quality and efficiency, outperforming recent real‑time novel‑view baselines. Crucially, 3DTV avoids explicit proxies, enabling robust rendering across diverse scenes. This makes it a practical solution for low‑latency multi‑view streaming and interactive rendering.
Project Page: https://stefanmschulz.github.io/3DTV_webpage/
Authors:Hector R. Rodriguez, Jiechen Huang, Wenjian Yu
Abstract:
We present CapBench, a fully reproducible, multi‑PDK dataset for capacitance extraction. The dataset is derived from open‑source designs, including single‑core CPUs, systems‑on‑chip, and media accelerators. All designs are fully placed and routed using 14 independent OpenROAD flow runs spanning three technology nodes: ASAP7, NanGate45, and Sky130HD. From these layouts, we extract 61,855 3D windows across three size tiers to enable transfer learning and scalability studies. High‑fidelity capacitance labels are generated using RWCap, a state‑of‑the‑art random‑walk solver, and validated against the industry‑standard Raphael, achieving a mean absolute error of 0.64% for total capacitance. Each window is pre‑processed into density maps, graph representations, and point clouds. We evaluate 10 machine learning architectures that illustrate dataset usage and serve as baselines, including convolutional neural networks (CNNs), point cloud transformers, and graph neural networks (GNNs). CNNs demonstrate the lowest errors (1.75%), while GNNs are up to 41.4x faster but exhibit larger errors (10.2%), illustrating a clear accuracy‑speed trade‑off. Code and dataset are available at https://github.com/THU‑numbda/CapBench.
Authors:Pierre Jourlin
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.
Authors:Prateek Chanda, Prayas Agrawal, Karthik S. Gurumoorthy, Ganesh Ramakrishnan, Bamdev Mishra, Pratik Jawanpuria
Abstract:
Selecting prototypical examples from a source distribution to represent a target data distribution is a fundamental problem in machine learning. Existing subset selection methods often rely on implicit importance scores, which can be skewed towards majority classes and lead to low‑quality prototypes for minority classes. We present \methodprop, a novel subset selection framework that minimizes the optimal transport (OT) distance between a uniformly weighted prototypical distribution and the target distribution. While intuitive, this formulation leads to a cardinality‑constrained maximization of a \emphsuper‑additive objective, which is generally intractable to approximate efficiently. To address this, we propose a principled reformulation of the OT marginal constraints, yielding a partial optimal transport‑based submodular objective. We prove that this reformulation enables a greedy algorithm with a (1‑1/e) approximation guarantee relative to the original super‑additive maximization problem. Empirically, we showcase that enforcing uniform prototype weights in UniPROT consistently improves minority‑class representation in imbalanced classification benchmarks without compromising majority‑class accuracy. In both finetuning and pretraining regimes for large language models under domain imbalance, UniPROT enforces uniform source contributions, yielding robust performance gains. Our results establish UniPROT as a scalable, theoretically grounded solution for uniform‑weighted prototype selection. Our code is publicly available at GitHub\footnoteCode: https://github.com/efficiency‑learning/UniPROT
Authors:Dheeraj Mudireddy, Sai Patibandla
Abstract:
Pokemon Red is a long‑horizon JRPG with sparse rewards, partial observability, and quirky control mechanics that make it a challenging benchmark for reinforcement learning. While recent work has shown that PPO agents can clear the first two gyms using heavy reward shaping and engineered observations, training remains brittle in practice, with agents often degenerating into action loops, menu spam, or unproductive wandering. In this paper, we present PokeRL, a modular system that trains deep reinforcement learning agents to complete early game tasks in Pokemon Red, including exiting the player's house, exploring Pallet Town to reach tall grass, and winning the first rival battle. Our main contributions are a loop‑aware environment wrapper around the PyBoy emulator with map masking, a multi‑layer anti‑loop and anti‑spam mechanism, and a dense hierarchical reward design. We argue that practical systems like PokeRL, which explicitly model failure modes such as loops and spam, are a necessary intermediate step between toy benchmarks and full Pokemon League champion agents. Code is available at https://github.com/reddheeraj/PokemonRL
Authors:Chirag Shinde
Abstract:
We propose two complementary modifications to transformer attention blocks. First, a non‑linear pre‑projection MLP is inserted between layer norm and Q/K/V projections, constructing richer features in a position‑agnostic manner before any positional encoding is applied. Second, a content skip connection routes the pre‑projection's features around the attention mechanism, allowing content information to bypass position‑aware attention where beneficial. In frozen‑probe experiments on Pythia‑160M and 410M, the combined approach achieves the strongest results across methods: +40.6% LAMBADA accuracy and ‑39% perplexity at 160M scale. Learned skip connection weights reveal a consistent pattern across model sizes: later transformer layers activate the content bypass more strongly than earlier layers, suggesting that deeper layers benefit from content information that does not pass through positional attention. All modifications add no K/V cache overhead.
Authors:Hao Wang, Guozhi Wang, Han Xiao, Yufeng Zhou, Yue Pan, Jichao Wang, Ke Xu, Yafei Wen, Xiaohu Ruan, Xiaoxin Chen, Honggang Qi
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/
Authors:Luis Balderas, Miguel Lastra, José M. Benítez
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.
Authors:Yuzhen Mao, Qitong Wang, Martin Ester, Ke Li
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/.
Authors:Jiahui Zhang, Rouyi Wang, Kuangqi Zhou, Tianshu Xiao, Lingyan Zhu, Yaosen Min, Yang Wang
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/.
Authors:Xiangyang Yin, Xingyu Liu, Tianhua Xia, Bo Bao, Vithursan Thangarasa, Valavan Manohararajah, Eric Sather, Sai Qian Zhang
Abstract:
Outliers have emerged as a fundamental bottleneck in preserving accuracy for low‑precision large models, particularly within Mixture‑of‑Experts (MoE) architectures that are increasingly central to large‑scale language modeling. Under post‑training quantization (PTQ), these outliers induce substantial quantization errors, leading to severe accuracy degradation. While recent rotation‑based smoothing techniques alleviate the problem by redistributing outlier magnitudes, residual errors remain and continue to impede reliable low‑precision deployment.
In this work, we tackle this challenge by introducing CodeQuant, a unified quantization‑and‑clustering scheme that contains smoothing activation outliers via learnable rotation and absorbing weight outliers into fine‑tuned cluster centroids for MoE. This design reduces the influence of extreme values by fitting them within cluster centroids, thereby lowering quantization error while maintaining expressive capacity. Coupled with a dedicated kernel design for GPU and CPU, CodeQuant achieves up to 4.15× speedup while delivering significantly higher accuracy than state‑of‑the‑art quantization approaches across diverse MoE models. Our results highlight CodeQuant as a promising direction for efficient and accurate deployment of MoE‑based large language models under low‑precision constraints. Our code is available at https://github.com/SAI‑Lab‑NYU/CodeQuant.
Authors:Naichuan Zheng, Hailun Xia, Zepeng Sun, Weiyi Li, Yinzhe Zhou
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.
Authors:Mariano Fernández Méndez
Abstract:
Cross‑modal retrieval between audio recordings and symbolic music representations (MIDI) remains challenging because continuous waveforms and discrete event sequences encode different aspects of the same performance. We study descriptor injection, the augmentation of modality‑specific encoders with hand‑crafted domain features, as a bridge across this gap. In a three‑phase campaign covering 13 descriptor‑mechanism combinations, 6 architectural families, and 3 training schedules, the best configuration reaches a mean S of 84.0 percent across five independent seeds, improving the descriptor‑free baseline by 8.8 percentage points. Causal ablation shows that the audio descriptor A4, based on octave‑band energy dynamics, drives the gain in the top dual models, while the MIDI descriptor D4 has only a weak inference‑time effect despite improving training dynamics. We also introduce reverse cross‑attention, where descriptor tokens query encoder features, reducing attention operations relative to the standard formulation while remaining competitive. CKA analysis shows that descriptors substantially increase audio‑MIDI transformer layer alignment, indicating representational convergence rather than simple feature concatenation. Perturbation analysis identifies high‑frequency octave bands as the dominant discriminative signal. All experiments use MAESTRO v3.0.0 with an evaluation protocol controlling for composer and piece similarity.
Authors:Mani Rash Ahmadi
Abstract:
We prove that in a coupled Kuramoto oscillator network at stable equilibrium, the physical phase displacement under weak output nudging is the gradient of the loss with respect to natural frequencies, with equality as the nudging strength beta tends to zero. Prior oscillator equilibrium propagation work explicitly set aside natural frequency as a learnable parameter; we show that on sparse layered architectures, frequency learning outperforms coupling‑weight learning among converged seeds (96.0% vs. 83.3% at matched parameter counts, p = 1.8e‑12). The approximately 50% convergence failure rate under random initialization is a loss‑landscape property, not a gradient error; topology‑aware spectral seeding eliminates it in all settings tested (46/100 to 100/100 seeds on the primary task; 50/50 on a second task, K‑only training, and a larger architecture).
Authors:Zae Myung Kim, Dongseok Lee, Jaehyung Kim, Vipul Raheja, Dongyeop Kang
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
Authors:Meng'en Qin, Yu Song, Quanling Zhao, Xiaodong Yang, Yingtao Che, Xiaohui Yang
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.
Authors:Luca Jiang-Tao Yu, Chenshu Wu
Abstract:
Wireless sensing, traditionally relying on signal processing (SP) techniques, has recently shifted toward data‑driven deep learning (DL) to achieve performance breakthroughs. However, existing deep wireless sensing models are typically end‑to‑end and task‑specific, lacking reusability and interpretability. We propose RF‑LEGO, a modular co‑design framework that transforms interpretable SP algorithms into trainable, physics‑grounded DL modules through deep unrolling. By replacing hand‑tuned parameters with learnable ones while preserving core processing structures and mathematical operators, RF‑LEGO ensures modularity, cascadability, and structure‑aligned interpretability. Specifically, we introduce three deep‑unrolled modules for critical RF sensing tasks: frequency transform, spatial angle estimation, and signal detection. Extensive experiments using real‑world data for Wi‑Fi, millimeter‑wave, UWB, and 6G sensing demonstrate that RF‑LEGO significantly outperforms existing SP and DL baselines, both standalone and when integrated into multiple downstream tasks. RF‑LEGO pioneers a novel SP‑DL co‑design paradigm for wireless sensing via deep unrolling, shedding light on efficient and interpretable deep wireless sensing solutions. Our code is available at https://github.com/aiot‑lab/RF‑LEGO.
Authors:Rui Lin, Zhenyu Jin, Guancheng Zhou, Xuyang Ge, Wentao Shu, Jiaxing Wu, Junxuan Wang, Zhengfu He, Junping Zhang, Xipeng Qiu
Abstract:
While modern transformer neural networks achieve grandmaster‑level performance in chess and other reasoning tasks, their internal computation process remains largely opaque. Focusing on Leela Chess Zero (LC0), we introduce a sparse decomposition framework to interpret its internal computation by decomposing its MLP and attention modules with sparse replacement layers, which capture the primary computation process of LC0. We conduct a detailed case study showing that these pathways expose rich, interpretable tactical considerations that are empirically verifiable. We further introduce three quantitative metrics and show that LC0 exhibits parallel reasoning behavior consistent with the inductive bias of its policy head architecture. To the best of our knowledge, this is the first work to decompose the internal computation of a transformer on both MLP and attention modules for interpretability. Combining sparse replacement layers and causal interventions in LC0 provides a comprehensive understanding of advanced tactical reasoning, offering critical insights into the underlying mechanisms of superhuman systems. Our code is available at https://github.com/JacklE0niden/Leela‑SAEs.
Authors:Zunhai Su, Hengyuan Zhang, Wei Wu, Yifan Zhang, Yaxiu Liu, He Xiao, Qingyao Yang, Yuxuan Sun, Rui Yang, Chao Zhang, Keyu Fan, Weihao Ye, Jing Xiong, Hui Shen, Chaofan Tao, Taiqiang Wu, Zhongwei Wan, Yulei Qian, Yuchen Xie, Ngai Wong
Abstract:
As the foundational architecture of modern machine learning, Transformers have driven remarkable progress across diverse AI domains. Despite their transformative impact, a persistent challenge across various Transformers is Attention Sink (AS), in which a disproportionate amount of attention is focused on a small subset of specific yet uninformative tokens. AS complicates interpretability, significantly affecting the training and inference dynamics, and exacerbates issues such as hallucinations. In recent years, substantial research has been dedicated to understanding and harnessing AS. However, a comprehensive survey that systematically consolidates AS‑related research and offers guidance for future advancements remains lacking. To address this gap, we present the first survey on AS, structured around three key dimensions that define the current research landscape: Fundamental Utilization, Mechanistic Interpretation, and Strategic Mitigation. Our work provides a pivotal contribution by clarifying key concepts and guiding researchers through the evolution and trends of the field. We envision this survey as a definitive resource, empowering researchers and practitioners to effectively manage AS within the current Transformer paradigm, while simultaneously inspiring innovative advancements for the next generation of Transformers. The paper list of this work is available at https://github.com/ZunhaiSu/Awesome‑Attention‑Sink.
Authors:Yujie Li, Jiuniu Wang, Mugen Peng, Guangzuo Li, Wenjia Xu
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.
Authors:Kening Wang, Di Wen, Yufan Chen, Ruiping Liu, Junwei Zheng, Jiale Wei, Kailun Yang, Rainer Stiefelhagen, Kunyu Peng
Abstract:
Automatic sleep staging is a multimodal learning problem involving heterogeneous physiological signals such as EEG and EOG, which often suffer from domain shifts across institutions, devices, and populations. In practice, these data are also affected by noisy annotations, yet label‑noise‑robust multi‑source domain generalization remains underexplored. We present the first benchmark for Noisy Labels in Multi‑Source Domain‑Generalized Sleep Staging (NL‑DGSS) and show that existing noisy‑label learning methods degrade substantially when domain shifts and label noise coexist. To address this challenge, we propose FF‑TRUST, a domain‑invariant multimodal sleep staging framework with Joint Time‑Frequency Early Learning Regularization (JTF‑ELR). By jointly exploiting temporal and spectral consistency together with confidence‑diversity regularization, FF‑TRUST improves robustness under noisy supervision. Experiments on five public datasets demonstrate consistent state‑of‑the‑art performance under diverse symmetric and asymmetric noise settings. The benchmark and code will be made publicly available at https://github.com/KNWang970918/FF‑TRUST.git.
Authors:Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee
Abstract:
Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT‑v2 and ConstBERT across five dimensions, finding that while ConstBERT reproduces within 0.05% MRR@10 on MS‑MARCO, both models show a drop of 86‑97% on long, narrative queries (TREC ToT 2025). Ablations prove this failure is architectural: performance plateaus at 20 words because the MaxSim operator's uniform token weighting cannot distinguish signal from filler noise. Furthermore, undocumented backend parameters create an 8‑point gap due to ConstBERT's sparse centroid coverage, and fine‑tuning with 3x more data actually degrades performance by up to 29%. We conclude that architectural constraints in multi‑vector retrieval cannot be overcome by adaptation alone. Code: https://github.com/utshabkg/multi‑vector‑reproducibility.
Authors:Weijian Mai, Mu Nan, Yu Zhu, Jiahang Cao, Rui Zhang, Yuqin Dai, Chunfeng Song, Andrew F. Luo, Jiamin Wu
Abstract:
Visual encoding and decoding models act as gateways to understanding the neural mechanisms underlying human visual perception. Typically, visual encoding models that predict brain activity from stimuli and decoding models that reproduce stimuli from brain activity are treated as distinct tasks, requiring separate models and training procedures. This separation is inefficient and fails to model the consistency between encoding and decoding processes. To address this limitation, we propose NeuroFlow, the first unified framework that jointly models visual encoding and decoding from neural activity within a single flow model. NeuroFlow introduces two key components: (1) NeuroVAE is designed as a variational backbone to model neural variability and establish a compact, semantically structured latent space for bidirectional modeling across visual and neural modalities. (2) Cross‑modal Flow Matching (XFM) bypasses the typical paradigm of noise‑to‑data diffusion guided by a specific modality condition, instead learning a reversibly consistent flow model between visual and neural latent distributions. For the first time, visual encoding and decoding are reformulated as a time‑dependent, reversible process within a shared latent space for unified modeling. Empirical results demonstrate that NeuroFlow achieves superior overall performance in visual encoding and decoding tasks with higher computational efficiency compared to any isolated methods. We further analyze principal factors that steer the model toward encoding‑decoding consistency and, through brain functional analyses, demonstrate that NeuroFlow captures consistent activation patterns underlying neural variability. NeuroFlow marks a major step toward unified visual encoding and decoding from neural activity, providing mechanistic insights that inform future bidirectional visual brain‑computer interfaces.
Authors:Justin Li, Daniel Ding, Asmita Yuki Pritha, Aryana Hou, Xin Wang, Shu Hu
Abstract:
Automated diagnosis from chest CT has improved considerably with deep learning, but models trained on skewed datasets tend to perform unevenly across patient demographics. However, the situation is worse than simple demographic bias. In clinical data, class imbalance and group underrepresentation often coincide, creating compound failure modes that neither standard rebalancing nor fairness corrections can fix alone. We introduce a two‑level objective that targets both axes of this problem. Logit‑adjusted cross‑entropy loss operates at the sample level, shifting decision margins by class frequency with provable consistency guarantees. Conditional Value at Risk aggregation operates at the group level, directing optimization pressure toward whichever demographic group currently has the higher loss. We evaluate on the Fair Disease Diagnosis benchmark using a 3D ResNet‑18 pretrained on Kinetics‑400, classifying CT volumes into Adenocarcinoma, Squamous Cell Carcinoma, COVID‑19, and Normal groups with patient sex annotations. The training set illustrates the compound problem concretely: squamous cell carcinoma has 84 samples total, 5 of them female. The combined loss reaches a gender‑averaged macro F1 of 0.8403 with a fairness gap of 0.0239, a 13.3% improvement in score and 78% reduction in demographic disparity over the baseline. Ablations show that each component alone falls short. The code is publicly available at https://github.com/Purdue‑M2/Fair‑Disease‑Diagnosis.
Authors:Dongmin Kim, Hoshinori Kanazawa, Yasuo Kuniyoshi
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
Authors:Jon M Laurent, Albert Bou, Michael Pieler, Conor Igoe, Alex Andonian, Siddharth Narayanan, James Braza, Alexandros Sanchez Vassopoulos, Jacob L Steenwyk, Blake Lash, Andrew D White, Samuel G Rodriques
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.
Authors:Stefan Andreas Baumann, Jannik Wiese, Tommaso Martorella, Mahdi M. Kalayeh, Björn Ommer
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.
Authors:Kyle Whitecross, Negin Rahimi
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.
Authors:Maksim Anisimov, Francesco Belardinelli, Matthew Wicker
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.
Authors:Siyuan Zhou, Hejun Wang, Hu Cheng, Jinxi Li, Dongsheng Wang, Junwei Jiang, Yixiao Jin, Jiayue Huang, Shiwei Mao, Shangjia Liu, Yafei Yang, Hongkang Song, Shenxing Wei, Zihui Zhang, Peng Huang, Shijie Liu, Zhengli Hao, Hao Li, Yitian Li, Wenqi Zhou, Zhihan Zhao, Zongqi He, Hongtao Wen, Shouwang Huang, Peng Yun, Bowen Cheng, Pok Kazaf Fu, Wai Kit Lai, Jiahao Chen, Kaiyuan Wang, Zhixuan Sun, Ziqi Li, Haochen Hu, Di Zhang, Chun Ho Yuen, Bing Wang, Zhihua Wang, Chuhang Zou, Bo Yang
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.
Authors:Prasad Nimantha Madusanka Ukwatta Hewage, Midhun Chakkravarthy, Ruvan Kumara Abeysekara
Abstract:
We propose a Hybrid Quantum‑Classical Physics‑Informed Neural Network (HQC‑PINN) that integrates parameterized variational quantum circuits into the PINN framework for hydrological PDE‑constrained learning. Our architecture encodes multi‑source remote sensing features into quantum states via trainable angle encoding, processes them through a hardware‑efficient variational ansatz with entangling layers, and constrains the output using the Saint‑Venant shallow water equations and Manning's flow equation as differentiable physics loss terms. The inherent stochasticity of quantum measurement provides a natural mechanism for uncertainty quantification without requiring explicit Bayesian inference machinery. We further introduce a quantum transfer learning protocol that pre‑trains on multi‑hazard disaster data before fine‑tuning on flood‑specific events. Numerical simulations on multi‑modal satellite and meteorological data from the Kalu River basin, Sri Lanka, show that the HQC‑PINN achieves convergence in ~3x fewer training epochs and uses ~44% fewer trainable parameters compared to an equivalent classical PINN, while maintaining competitive classification accuracy. Theoretical analysis indicates that hydrological physics constraints narrow the effective optimization landscape, providing a natural mitigation against barren plateaus in variational quantum circuits. This work establishes the first application of quantum‑enhanced physics‑informed learning to hydrological prediction and demonstrates a viable path toward quantum advantage in environmental science.
Authors:Nazir Nayal, Christopher Wewer, Jan Eric Lenssen
Abstract:
Diffusion models and their variations, such as rectified flows, generate diverse and high‑quality images, but they are still hindered by slow iterative sampling caused by the highly curved generative paths they learn. An important cause of high curvature, as shown by previous work, is independence between the source distribution (standard Gaussian) and the data distribution. In this work, we tackle this limitation by two complementary contributions. First, we attempt to break away from the standard Gaussian assumption by introducing κ\texttt‑FC, a general formulation that conditions the source distribution on an arbitrary signal κ that aligns it better with the data distribution. Then, we present MixFlow, a simple but effective training strategy that reduces the generative path curvatures and considerably improves sampling efficiency. MixFlow trains a flow model on linear mixtures of a fixed unconditional distribution and a κ\texttt‑FC‑based distribution. This simple mixture improves the alignment between the source and data, provides better generation quality with less required sampling steps, and accelerates the training convergence considerably. On average, our training procedure improves the generation quality by 12% in FID compared to standard rectified flow and 7% compared to previous baselines under a fixed sampling budget. Code available at: \hrefhttps://github.com/NazirNayal8/MixFlowhttps://github.com/NazirNayal8/MixFlow
Authors:Mengxin Fu, Yuezun Li
Abstract:
Diffusion models are known for generating high‑quality images, causing serious security concerns. To combat this, most efforts rely on deep neural networks (e.g., CNNs and Transformers), while largely overlooking the potential of traditional machine learning models. In this paper, we freshly investigate such alternatives and proposes a novel Dynamic Assembly Forest model (DAF) to detect diffusion‑generated images. Built upon the deep forest paradigm, DAF addresses the inherent limitations in feature learning and scalable training, making it an effective diffusion‑generated image detector. Compared to existing DNN‑based methods, DAF has significantly fewer parameters, much lower computational cost, and can be deployed without GPUs, while achieving competitive performance under standard evaluation protocols. These results highlight the strong potential of the proposed method as a practical substitute for heavyweight DNN models in resource‑constrained scenarios. Our code and models are available at https://github.com/OUC‑VAS/DAF.
Authors:Jiabao Brad Wang, Xiang Shi, Yiliang Yuan, Mustafa Misir
Abstract:
Automated algorithm selection for continuous black‑box optimization depends on representing problem information under limited probing and selecting solvers under heavy‑tailed performance distributions. This paper proposes a geometric probing framework that represents each problem instance by randomly sampled multi‑scale two‑dimensional slices of the objective landscape. The slices are encoded with validity‑mask‑aware visual pooling and aggregated into an instance representation. Solver selection is then performed by a logarithmic composite score combining a learned instance‑conditioned estimate with an algorithm‑side empirical prior.
The framework is evaluated on a standard single‑objective black‑box optimization benchmark suite with a portfolio of twelve solvers under instance‑level, grouped random, and problem‑level transfer protocols. Under the two within‑suite protocols, it reduces aggregate mean relative expected running time from 30.37 for the single best solver to 3.14 and 3.61, while also improving median and upper‑tail performance. Under problem‑level transfer, the canonical adaptive setting improves typical and moderate‑tail performance but leaves the mean dominated by rare extreme failures; a prior‑heavy scoring variant mitigates this failure mode, although its robustness may be benchmark‑dependent. The results suggest that coarse geometric probes provide useful solver‑relevant information, while robust cross‑problem selection also depends on metric‑aligned decision scoring.
Authors:Yi Luo, Xu Sun, Guangchun Luo, Aiguo Chen
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.
Authors:Taojie Zhu, Dongyang Xu, Ding Zou, Sen Zhao, Qiaobo Hao, Zhiguo Yang, Yonghong He
Abstract:
Post‑training paradigms for Large Language Models (LLMs), primarily Supervised Fine‑Tuning (SFT) and Reinforcement Learning (RL), face a fundamental dilemma: SFT provides stability (low variance) but suffers from high fitting bias, while RL enables exploration (low bias) but grapples with high gradient variance. Existing unified optimization strategies often employ naive loss weighting, overlooking the statistical conflict between these distinct gradient signals. In this paper, we provide a rigorous theoretical analysis of this bias‑variance trade‑off and propose DYPO (Dynamic Policy Optimization), a unified framework designed to structurally mitigate this conflict. DYPO integrates three core components: (1) a Group Alignment Loss (GAL) that leverages intrinsic group dynamics to significantly reduce RL gradient variance; (2) a Multi‑Teacher Distillation mechanism that corrects SFT fitting bias via diverse reasoning paths; and (3) a Dynamic Exploitation‑Exploration Gating mechanism that adaptively arbitrates between stable SFT and exploratory RL based on reward feedback. Theoretical analysis confirms that DYPO linearly reduces fitting bias and minimizes overall variance. Extensive experiments demonstrate that DYPO significantly outperforms traditional sequential pipelines, achieving an average improvement of 4.8% on complex reasoning benchmarks and 13.3% on out‑of‑distribution tasks. Our code is publicly available at https://github.com/Tocci‑Zhu/DYPO.
Authors:Rafael da Silva, Jeff Eicher, Gregory Longo
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.
Authors:Jinqi Luo, Jinyu Yang, Tal Neiman, Lei Fan, Bing Yin, Son Tran, Mubarak Shah, René Vidal
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.
Authors:Roi Paul
Abstract:
We study whether low‑rank spectral summaries of LoRA weight deltas can identify which fine‑tuning objective was applied to a language model, and whether that geometric signal predicts downstream behavioral harm. In a pre‑registered experiment on \textttLlama‑3.2‑3B‑Instruct, we manufacture 38 LoRA adapters across four categories: healthy SFT baselines, DPO on inverted harmlessness preferences, DPO on inverted helpfulness preferences, and activation‑steering‑derived adapters, and extract per‑layer spectral features (norms, stable rank, singular‑value entropy, effective rank, and singular‑vector cosine alignment to a healthy centroid). Within a single training method (DPO), a logistic regression classifier achieves AUC~1.00 on binary drift detection, all six pairwise objective comparisons, and near‑perfect ordinal severity ranking (ρ\geq 0.956). Principal component analysis on flattened weight deltas reveals that training objective is PC1 (AUC~1.00 for objective separation), orthogonal to training duration on PC2. Query‑projection weights detect that drift occurred; value‑projection weights identify which objective. Cross‑method generalization fails completely: a DPO‑trained classifier assigns every steering adapter a lower drift score than every DPO adapter (AUC~0.00). In a behavioral evaluation phase, DPO‑inverted‑harmlessness adapters show elevated harmful compliance on HEx‑PHI prompts (mean ASR 0.266 vs.\ healthy 0.112, Δ= +0.154), with near‑perfect dose‑‑response (ρ= 0.986). The geometry‑to‑behavior rank correlation is ρ= 0.72 across 24 non‑steered adapters. These results establish that within a controlled manufacturing regime, LoRA weight‑space geometry carries objective identity, intensity ordering, and a coarse link to harmful compliance, and that cross‑method monitoring requires per‑method calibration.
Authors:Mehmet Kerem Turkcan
Abstract:
We present Loom, a computer architecture that executes programs compiled from C inside a looped transformer whose weights are derived analytically. The architecture implements a 22‑opcode instruction set in 8 transformer layers. Each forward pass executes one instruction; the model is applied iteratively until the program counter reaches zero. The full machine state resides in a single tensor X \in \mathbbR^d × n of fixed size, and every step has fixed cost for fixed d and n, independent of program length or execution history. The default configuration uses d = 155 and n = 1024, yielding 4.7 million parameters and 928 instruction slots. A compact configuration at d = 146 and n = 512 suffices for a 9×9 Sudoku solver (284 instructions). The weights are program‑independent: programs live in the state tensor, and the same fixed‑weight model executes any compiled program. We make Loom source code publicly available at https://github.com/mkturkcan/Loom.
Authors:Zewei Zhou, Jiajun Zou, Jiajia Zhang, Ao Yang, Ruichao He, Haozheng Zhou, Ao Liu, Jiawei Liu, Leilei Jin, Shan Shen, Daying Sun
Abstract:
Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is hindered by inconsistent circuit representations and the absence of controlled evaluation protocols. We present R2G (RTL‑to‑GDSII), a multi‑view circuit‑graph benchmark suite that standardizes five stage‑aware views with information parity (every view encodes the same attribute set, differing only in where features attach) over 30 open‑source IP cores (up to 10^6 nodes/edges). R2G provides an end‑to‑end DEF‑to‑graph pipeline spanning synthesis, placement, and routing stages, together with loaders, unified splits, domain metrics, and reproducible baselines. By decoupling representation choice from model choice, R2G isolates a confound that prior EDA and graph‑ML benchmarks leave uncontrolled. In systematic studies with GINE, GAT, and ResGatedGCN, we find: (i) view choice dominates model choice, with Test R^2 varying by more than 0.3 across representations for a fixed GNN; (ii) node‑centric views generalize best across both placement and routing; and (iii) decoder‑head depth (3‑‑4 layers) is the primary accuracy driver, turning divergent training into near‑perfect predictions (R^2>0.99). Code and datasets are available at https://github.com/ShenShan123/R2G.
Authors:Yingjie Yu, Mingyuan Wu, Ahmadreza Eslaminia, Lingzhi Zhao, Kaizhuo Yan, Klara Nahrstedt
Abstract:
QoS‑QoE translation is a fundamental problem in multimedia systems because it characterizes how measurable system and network conditions affect user‑perceived experience. Although many prior studies have examined this relationship, their findings are often developed for specific setups and remain scattered across papers, experimental settings, and reporting formats, limiting systematic reuse, cross‑scenario generalization, and large‑scale analysis. To address this gap, we first introduce QoS‑QoE Translation dataset, a source‑grounded dataset of structured QoS‑QoE relationships from the multimedia literature, with a focus on video streaming related tasks. We construct the dataset through an automated pipeline that combines paper curation, QoS‑QoE relationship extraction, and iterative data evaluation. Each record preserves the extracted relationship together with parameter definitions, supporting evidence, and contextual metadata. We further evaluate the capability of large language models (LLMs) on QoS‑QoE translation, both before and after supervised fine‑tuning on our dataset, and show strong performance on both continuous‑value and discrete‑label prediction in bidirectional translation, from QoS‑QoE and QoE‑QoS. Our dataset provides a foundation for benchmarking LLMs in QoS‑QoE translation and for supporting future LLM‑based reasoning for multimedia quality prediction and optimization. The complete dataset and code are publicly available at https://yyu6969.github.io/qos‑qoe‑translation‑page/, for full reproducibility and open access.
Authors:Siddharth Mishra-Sharma, Tracy R. Slatyer, Yitian Sun, Yuqing Wu
Abstract:
We motivate the use of differentiable probabilistic programming techniques in order to account for the large model‑space inherent to astrophysical γ‑ray analyses. Targeting the longstanding Galactic Center γ‑ray Excess (GCE) puzzle, we construct differentiable forward model and likelihood that make liberal use of GPU acceleration and vectorization in order to simultaneously account for a continuum of possible spatial morphologies consistent with the GCE emission in a fully probabilistic manner. Our setup allows for efficient inference over the large model space using variational methods. Beyond application to γ‑ray data, a goal of this work is to showcase how differentiable probabilistic programming can be used as a tool to enable flexible analyses of astrophysical datasets.
Authors:Yuki Kataoka, Masahiro Banno, Michihito Kyo, Shuri Nakao, Tomoo Sato, Shunsuke Taito, Tomohiro Takayama, Takahiro Tsuge, Yasushi Tsujimoto, Ryuhei So, Toshi A. Furukawa
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.
Authors:Brendan R. Hogan, Xiwen Chen, James T. Wilson, Kashif Rasul, Adel Boyarsky, Thomas Kamei, Anderson Schneider, Yuriy Nevmyvaka
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/.
Authors:Gianluca Guglielmo, Marc Masana
Abstract:
State‑of‑the‑art post‑hoc out‑of‑distribution detection methods rely on intermediate layer activation editing. However, they exhibit inconsistent performance across datasets and models. We show that this instability is driven by differences in the activation distributions, and identify a failure mode of scaling‑based methods that arises when penultimate layer activations are not rectified. Motivated by this analysis, we propose \ours, a hyperparameter‑free post‑hoc method that replaces sorted activation magnitudes with a fixed in‑distribution reference profile. Our simple plug‑and‑play method shows strong and consistent performance across datasets and architectures without assumptions on the penultimate layer activation function, and without requiring any hyperparameter tuning, while preserving in‑distribution classification accuracy by construction. We further analyze what drives the improvement, showing that both inhibiting and exciting activation shifts independently contribute to better out‑of‑distribution discrimination.
Authors:Svetoslav Nizhnichenkov, Rahul Nair, Elizabeth Daly, Brian Mac Namee
Abstract:
We investigate how successful bias mitigation reshapes the embedding space of encoder‑only and decoder‑only foundation models, offering an internal audit of model behaviour through representational analysis. Using BERT and Llama2 as representative architectures, we assess the shifts in associations between gender and occupation terms by comparing baseline and bias‑mitigated variants of the models. Our findings show that bias mitigation reduces gender‑occupation disparities in the embedding space, leading to more neutral and balanced internal representations. These representational shifts are consistent across both model types, suggesting that fairness improvements can manifest as interpretable and geometric transformations. These results position embedding analysis as a valuable tool for understanding and validating the effectiveness of debiasing methods in foundation models. To further promote the assessment of decoder‑only models, we introduce WinoDec, a dataset consisting of 4,000 sequences with gender and occupation terms, and release it to the general public. (https://github.com/winodec/wino‑dec)
Authors:Mu Nan, Muquan Yu, Weijian Mai, Jacob S. Prince, Hossein Adeli, Rui Zhang, Jiahang Cao, Benjamin Becker, John A. Pyles, Margaret M. Henderson, Chunfeng Song, Nikolaus Kriegeskorte, Michael J. Tarr, Xiaoqing Hu, Andrew F. Luo
Abstract:
Visual decoding from brain signals is a key challenge at the intersection of computer vision and neuroscience, requiring methods that bridge neural representations and computational models of vision. A field‑wide goal is to achieve generalizable, cross‑subject models. A major obstacle towards this goal is the substantial variability in neural representations across individuals, which has so far required training bespoke models or fine‑tuning separately for each subject. To address this challenge, we introduce a meta‑optimized approach for semantic visual decoding from fMRI that generalizes to novel subjects without any fine‑tuning. By simply conditioning on a small set of image‑brain activation examples from the new individual, our model rapidly infers their unique neural encoding patterns to facilitate robust and efficient visual decoding. Our approach is explicitly optimized for in‑context learning of the new subject's encoding model and performs decoding by hierarchical inference, inverting the encoder. First, for multiple brain regions, we estimate the per‑voxel visual response encoder parameters by constructing a context over multiple stimuli and responses. Second, we construct a context consisting of encoder parameters and response values over multiple voxels to perform aggregated functional inversion. We demonstrate strong cross‑subject and cross‑scanner generalization across diverse visual backbones without retraining or fine‑tuning. Moreover, our approach requires neither anatomical alignment nor stimulus overlap. This work is a critical step towards a generalizable foundation model for non‑invasive brain decoding.
Authors:Runpeng Geng, Chenlong Yin, Yanting Wang, Ying Chen, Jinyuan Jia
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.
Authors:Abdelkarim Loukili
Abstract:
Federated learning (FL) enables privacy‑preserving predictive maintenance across distributed aerospace fleets, but gradient communication overhead constrains deployment on bandwidth‑limited IoT nodes. This paper investigates the impact of symmetric uniform quantization (b \in \32,8,4,2\ bits) on the accuracy‑‑efficiency trade‑off of a custom‑designed lightweight 1‑D convolutional model (AeroConv1D, 9\,697 parameters) trained via FL on the NASA C‑MAPSS benchmark under a realistic Non‑IID client partition. Using a rigorous multi‑seed evaluation (N=10 seeds), we show that INT4 achieves accuracy \emphstatistically indistinguishable from FP32 on both FD001 (p=0.341) and FD002 (p=0.264 MAE, p=0.534 NASA score) while delivering an 8× reduction in gradient communication cost (37.88~KiB \to 4.73~KiB per round). A key methodological finding is that naïve IID client partitioning artificially suppresses variance; correct Non‑IID evaluation reveals the true operational instability of extreme quantization, demonstrated via a direct empirical IID vs.\ Non‑IID comparison. INT2 is empirically characterized as unsuitable: while it achieves lower MAE on FD002 through extreme quantization‑induced over‑regularization, this apparent gain is accompanied by catastrophic NASA score instability (CV\,=\,45.8% vs.\ 22.3% for FP32), confirming non‑reproducibility under heterogeneous operating conditions. Analytical FPGA resource projections on the Xilinx ZCU102 confirm that INT4 fits within hardware constraints (85.5% DSP utilization), potentially enabling a complete FL pipeline on a single SoC. The full simulation codebase and FPGA estimation scripts are publicly available at https://github.com/therealdeadbeef/aerospace‑fl‑quantization.
Authors:Zigeng Chen, Gongfan Fang, Xinyin Ma, Ruonan Yu, Xinchao Wang
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
Authors:Yunxiang Peng, Mengmeng Ma, Ziyu Yao, Xi Peng
Abstract:
Reliable generalization metrics are fundamental to the evaluation of machine learning models. Especially in high‑stakes applications where labeled target data are scarce, evaluation of models' generalization performance under distribution shift is a pressing need. We focus on two practical scenarios: (1) Before deployment, how to select the best model for unlabeled target data? (2) After deployment, how to monitor model performance under distribution shift? The central need in both cases is a reliable and label‑free proxy metric. Yet existing proxy metrics, such as model confidence or accuracy‑on‑the‑line, are often unreliable as they only assess model output while ignoring the internal mechanisms that produce them. We address this limitation by introducing a new perspective: using the inner workings of a model, i.e., circuits, as a predictive metric of generalization performance. Leveraging circuit discovery, we extract the causal interactions between internal representations as a circuit, from which we derive two metrics tailored to the two practical scenarios. (1) Before deployment, we introduce Dependency Depth Bias, which measures different models' generalization capability on target data. (2) After deployment, we propose Circuit Shift Score, which predicts a model's generalization under different distribution shifts. Across various tasks, both metrics demonstrate significantly improved correlation with generalization performance, outperforming existing proxies by an average of 13.4% and 34.1%, respectively. Our code is available at https://github.com/deep‑real/GenCircuit.
Authors:Junjie Fei, Jun Chen, Zechun Liu, Yunyang Xiong, Chong Zhou, Wei Wen, Junlin Han, Mingchen Zhuge, Saksham Suri, Qi Qian, Shuming Liu, Lemeng Wu, Raghuraman Krishnamoorthi, Vikas Chandra, Mohamed Elhoseiny, Chenchen Zhu
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.
Authors:Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta
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.
Authors:Minh Sao Khue Luu, Evgeniy N. Pavlovskiy, Bair N. Tuchinov
Abstract:
We propose a unified objective function, termed CATMIL, that augments the base segmentation loss with two auxiliary supervision terms operating at different levels. The first term, Component‑Adaptive Tversky, reweights voxel contributions based on connected components to balance the influence of lesions of different sizes. The second term, based on Multiple Instance Learning, introduces lesion‑level supervision by encouraging the detection of each lesion instance. These terms are combined with the standard nnU‑Net loss to jointly optimize voxel‑level segmentation accuracy and lesion‑level detection. We evaluate the proposed objective on the MSLesSeg dataset using a consistent nnU‑Net framework and 5‑fold cross‑validation. The results show that CATMIL achieves the most balanced performance across segmentation accuracy, lesion detection, and error control. It improves Dice score (0.7834) and reduces boundary error compared to standard losses. More importantly, it substantially increases small lesion recall and reduces false negatives, while maintaining the lowest false positive volume among compared methods. These findings demonstrate that integrating component‑level and lesion‑level supervision within a unified objective provides an effective and practical approach for improving small lesion segmentation in highly imbalanced settings. All code and pretrained models are available at https://github.com/luumsk/SmallLesionMRI.
Authors:Felix Embacher, Jonas Uhrig, Marius Cordts, Markus Enzweiler
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/
Authors:Shuaiting Li, Juncan Deng, Kedong Xu, Rongtao Deng, Hong Gu, Minghan Jiang, Haibin Shen, Kejie Huang
Abstract:
Methods based on weight compensation, which iteratively apply quantization and weight compensation to minimize the output error, have recently demonstrated remarkable success in quantizing Large Language Models (LLMs). The representative work, GPTQ, introduces several key techniques that make such iterative methods practical for LLMs with billions of parameters. GPTAQ extends this approach by introducing an asymmetric calibration process that aligns the output of each quantized layer with its full‑precision counterpart, incorporating a residual error into the weight compensation framework. In this work, we revisit the formulation of the residual error. We identify a sub‑optimal calibration objective in existing methods: during the intra‑layer calibration process, they align the quantized output with the output from compensated weights, rather than the true output from the original full‑precision model. Therefore, we redefine the objective to precisely align the quantized model's output with the original output of the full‑precision model at each step. We then reveal that the residual error originates not only from the output difference of the preceding layer but also from the discrepancy between the compensated and original weights within each layer, which we name the 'compensation‑aware error'. By inheriting the neuron decomposition technique from GPTAQ, we can efficiently incorporate this compensation‑aware error into the weight update process. Extensive experiments on various LLMs and quantization settings demonstrate that our proposed enhancements integrate seamlessly with both GPTQ and GPTAQ, significantly improving their quantization performance. Our code is publicly available at https://github.com/list0830/ResComp.
Authors:Tao Han, Zhibin Wen, Zhenghao Chen, Fenghua Lin, Junyu Gao, Song Guo, Lei Bai
Abstract:
While AI‑based numerical weather prediction (NWP) enables rapid forecasting, generating high‑resolution outputs remains computationally demanding due to limited multi‑scale adaptability and inefficient data representations. We propose the 3D Gaussian splatting‑based scale‑aware vision transformer (GSSA‑ViT), a novel framework for arbitrary‑resolution forecasting and flexible downscaling of high‑dimensional atmospheric fields. Specifically, latitude‑longitude grid points are treated as centers of 3D Gaussians. A generative 3D Gaussian prediction scheme is introduced to estimate key parameters, including covariance, attributes, and opacity, for unseen samples, improving generalization and mitigating overfitting. In addition, a scale‑aware attention module is designed to capture cross‑scale dependencies, enabling the model to effectively integrate information across varying downscaling ratios and support continuous resolution adaptation. To our knowledge, this is the first NWP approach that combines generative 3D Gaussian modeling with scale‑aware attention for unified multi‑scale prediction. Experiments on ERA5 show that the proposed method accurately forecasts 87 atmospheric variables at arbitrary resolutions, while evaluations on ERA5 and CMIP6 demonstrate its superior performance in downscaling tasks. The proposed framework provides an efficient and scalable solution for high‑resolution, multi‑scale atmospheric prediction and downscaling. Code is available at: https://github.com/binbin2xs/weather‑GS.
Authors:Chanhyuk Choi, Taesoo Kim, Donggyu Lee, Siyeol Jung, Taehwan Kim
Abstract:
Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthesized videos, emotion editing in talking face video plays a crucial role. However, existing approaches often limit expressive flexibility and struggle to generate extended emotions. Label‑based methods represent emotions with discrete categories, which fail to capture a wide range of emotions. Audio‑based methods can leverage emotionally rich speech signals ‑ and even benefit from expressive text‑to‑speech (TTS) synthesis ‑ but they fail to express the target emotions because emotions and linguistic contents are entangled in emotional speeches. Images‑based methods, on the other hand, rely on target reference images to guide emotion transfer, yet they require high‑quality frontal views and face challenges in acquiring reference data for extended emotions (e.g., sarcasm). To address these limitations, we propose Cross‑Modal Emotion Transfer (C‑MET), a novel approach that generates facial expressions based on speeches by modeling emotion semantic vectors between speech and visual feature spaces. C‑MET leverages a large‑scale pretrained audio encoder and a disentangled facial expression encoder to learn emotion semantic vectors that represent the difference between two different emotional embeddings across modalities. Extensive experiments on the MEAD and CREMA‑D datasets demonstrate that our method improves emotion accuracy by 14% over state‑of‑the‑art methods, while generating expressive talking face videos ‑ even for unseen extended emotions. Code, checkpoint, and demo are available at https://chanhyeok‑choi.github.io/C‑MET/
Authors:Yasong Fan
Abstract:
We present FDM (Fan Duality Model), a linear sequence architecture that resolves the fundamental tension between memory efficiency and associative recall in sequence modeling. FDM separates sequence processing into two components: a wave component (recurrent scan via phase‑preserving Givens rotations) that compresses long‑range patterns into a fixed‑size complex hidden state, and a particle component (local‑global cache) that retrieves specific tokens via learned associative addressing with W+K=272 slots independent of sequence length N. This yields strictly O(1) decode memory: 867 MB fixed across all prompt lengths 128‑8,192 tokens, versus Transformer's 853‑4,247 MB (4.9x reduction at N=8,192).
Beyond the architecture, we discover that jointly training the wave and particle components leads to suboptimal convergence. We propose Freeze‑Scan, a two‑phase training strategy that freezes the recurrent scan and optimizes the cache jointly with embeddings, achieving PPL=64.9 on WikiText‑103 in 44K steps ‑‑ a 7.5x improvement over full fine‑tuning (PPL=487).
On Multi‑Query Associative Recall (MQAR), FDM achieves 0.966 accuracy, surpassing Transformer (0.606) by 59.5%, while pure scan without cache scores only 0.011, confirming the necessity of the particle component.
Finally, we introduce Holographic Reference Beam Decoding, interpreting the complex hidden state h_t as a holographic plate encoding the entire temporal history. Using the current input x_t as a reference beam to modulate h_t reduces PPL by up to 2.13 points (PPL=62.79) with a 4‑head orthogonal reference beam using only 1.3M additional parameters, providing empirical support for the holographic interpretation.
Code and pretrained weights: https://github.com/YasongFan/FDM
Authors:Yang Cao
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.
Authors:Haimeng Zhao, Alexander Zlokapa, Hartmut Neven, Ryan Babbush, John Preskill, Jarrod R. McClean, Hsin-Yuan Huang
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.
Authors:Ziyi Wang, Siva Rajesh Kasa, Ankith M S, Santhosh Kumar Kasa, Jiaru Zou, Sumit Negi, Ruqi Zhang, Nan Jiang, Qifan Song
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.
Authors:Michael Cuccarese
Abstract:
Activity cliff prediction ‑ identifying positions where small structural changes cause large potency shifts ‑ has been a persistent challenge in computational medicinal chemistry. This work focuses on a parsimonious definition: which small modifications, at which positions, confer the highest probability of an outcome change. Position‑level sensitivity is calculated using 25 million matched molecular pairs from 50 ChEMBL targets across six protein families, revealing that two questions have fundamentally different answers. "Which positions vary most?" is answered by scaffold size alone (NDCG@3 = 0.966), requiring no machine learning. "Which are true activity cliffs?" ‑ where small modifications cause disproportionately large effects, as captured by SALI normalization ‑ requires an 11‑feature model with 3D pharmacophore context (NDCG@3 = 0.910 vs. 0.839 random), generalizing across all six protein families, novel scaffolds (0.913), and temporal splits (0.878). The model identifies the cliff‑prone position first 53% of the time (vs. 27% random ‑ 2x lift), reducing positions a chemist must explore from 3.1 to 2.1 ‑ a 31% reduction in first‑round experiments. Predicting which modification to make is not tractable from structure alone (Spearman 0.268, collapsing to ‑0.31 on novel scaffolds). The system is released as open‑source code and an interactive webapp.
Authors:Yu Liang, Liangxin Liu, Longzheng Wang, Yan Wang, Yueyang Zhang, Long Xia, Zhiyuan Sun, Daiting Shi
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.
Authors:Ziyang Cheng, Haoyu Wei, Hang Yin, Xiuwei Xu, Bingyao Yu, Jie Zhou, Jiwen Lu
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.
Authors:Zhenyu Wang, Peiyuan Li, Yongxiang Shi, Ruoyu Wu, Chenfei Liao, Lei Zhang
Abstract:
Full‑waveform inversion (FWI) is pivotal for reconstructing high‑resolution subsurface velocity models but remains computationally intensive and ill‑posed. While deep learning approaches promise efficiency, existing Convolutional Neural Networks (CNNs) and single‑paradigm Neural Operators (NOs) struggle with one fundamental issue: frequency entanglement of multi‑scale geological features. To address this challenge, we propose Spectral‑Preserving Adaptive MoE (SPAMoE), a novel spectrum‑aware framework for solving inverse problems with complex multi‑scale structures. Our approach introduces a Spectral‑Preserving DINO Encoder that enforces a lower bound on the high‑to‑low frequency energy ratio of the encoded representation, mitigating high‑frequency collapse and stabilizing subsequent frequency‑domain modeling. Furthermore, we design a novel Spectral Decomposition and Routing mechanism that dynamically assigns frequency bands to a Mixture‑of‑Experts (MoE) ensemble comprising FNO, MNO, and LNO. On the ten OpenFWI sub‑datasets, experiments show that SPAMoE reduces the average MAE by 44.4% relative to the best officially reported OpenFWI baseline, thereby establishing a new architectural framework for learning‑based full‑waveform inversion. Our code and data are available at https://github.com/zhenyuwang12366/SPAMoE
Authors:Xiangru Jian, Hao Xu, Wei Pang, Xinjian Zhao, Chengyu Tao, Qixin Zhang, Xikun Zhang, Chao Zhang, Guanzhi Deng, Alex Xue, Juan Du, Tianshu Yu, Garth Tarr, Linqi Song, Qiuzhuang Sun, Dacheng Tao
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.
Authors:Daniel Nobrega Medeiros
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.
Authors:Wonseon Lim, Jaesung Lee, Dae-Won Kim
Abstract:
Continual learning (CL) on edge devices requires not only high accuracy but also training‑time efficiency to support on‑device adaptation under strict memory and computational constraints. While prompt‑based continual learning (PCL) is parameter‑efficient and achieves competitive accuracy, prior work has focused mainly on accuracy or inference‑time performance, often overlooking the memory and computational costs of on‑device training. In this paper, we propose CPS‑Prompt, a critical patch‑aware sparse prompting framework that explicitly targets training‑time memory usage and computational cost by integrating critical patch sampling (CPS) for task‑aware token reduction and decoupled prompt and classifier training (DPCT) to reduce backpropagation overhead. Experiments on three public benchmarks and real edge hardware show that CPS‑Prompt improves peak memory, training time, and energy efficiency by about 1.6x over the balanced CODA‑Prompt baseline, while maintaining accuracy within 2% of the state‑of‑the‑art C‑Prompt on average and remaining competitive with CODA‑Prompt in accuracy. The code is available at https://github.com/laymond1/cps‑prompt.
Authors:David Golchinfar, Daryoush Vaziri, Alexander Marquardt
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.
Authors:Rui Dong, Zitong Wang, Jiaxing Li, Weihuang Zheng, Youyong Kong
Abstract:
Graph Neural Networks (GNNs) have been widely used in diverse brain network analysis tasks based on preprocessed functional magnetic resonance imaging (fMRI) data. However, their performances are constrained due to high feature sparsity and inherent limitations of domain knowledge within uni‑modal neurographs. Meanwhile, large language models (LLMs) have demonstrated powerful representation capabilities. Combining LLMs with GNNs presents a promising direction for brain network analysis. While LLMs and MLLMs have emerged in neuroscience, integration of LLMs with graph‑based data remains unexplored. In this work, we deal with these issues by incorporating LLM's powerful representation and generalization capabilities. Considering great cost for directly tuning LLMs, we instead function LLM as enhancer to boost GNN's performance on downstream tasks. Our method, namely BLEG, can be divided into three stages. We firstly prompt LLM to get augmented texts for fMRI graph data, then we design a LLM‑LM instruction tuning method to get enhanced textual representations at a relatively lower cost. GNN is trained together for coarsened alignment. Finally we finetune an adapter after GNN for given downstream tasks. Alignment loss between LM and GNN logits is designed to further enhance GNN's representation. Extensive experiments on different datasets confirmed BLEG's superiority.Code can be available at https://github.com/KamonRiderDR/BLEG.
Authors:Sam Gunn
Abstract:
How does the choice of training data influence an AI model? This broad question is of central importance to interpretability, privacy, and basic science. At its technical core is the data deletion problem: after a reasonable amount of precomputation, quickly predict how the model would behave in a given situation if a given subset of training data had been excluded from the learning algorithm.
We present a data deletion scheme capable of predicting model outputs with vanishing error \varepsilon and failure probability δ in the deep learning setting. Our precomputation and prediction algorithms are only \tildeO(\log(1/δ)/\varepsilon^2) factors slower than regular training and inference, respectively. The storage requirements are those of \tildeO(\log(1/δ)/\varepsilon^2) models.
Our proof is based on an assumption that we call stability. In contrast to the assumptions made by prior work, stability appears to be fully compatible with learning powerful AI models. In support of this, we show that stability is satisfied in a minimal set of experiments with microgpt. Our code is available at https://github.com/SamSpo1/microgpt‑sketch.
At a technical level, our work is based on a new method for locally sketching an arithmetic circuit by computing higher‑order derivatives in random complex directions. Forward‑mode automatic differentiation allows cheap computation of these derivatives.
Authors:Huaiyuan Qin, Muli Yang, Gabriel James Goenawan, Kai Wang, Zheng Wang, Peng Hu, Xi Peng, Hongyuan Zhu
Abstract:
Existing dynamic data pruning methods often fail under noisy‑label settings, as they typically rely on per‑sample loss as the ranking criterion. This could mistakenly lead to preserving noisy samples due to their high loss values, resulting in significant performance drop. To address this, we propose AlignPrune, a noise‑robust module designed to enhance the reliability of dynamic pruning under label noise. Specifically, AlignPrune introduces the Dynamic Alignment Score (DAS), which is a loss‑trajectory‑based criterion that enables more accurate identification of noisy samples, thereby improving pruning effectiveness. As a simple yet effective plug‑and‑play module, AlignPrune can be seamlessly integrated into state‑of‑the‑art dynamic pruning frameworks, consistently outperforming them without modifying either the model architecture or the training pipeline. Extensive experiments on five widely‑used benchmarks across various noise types and pruning ratios demonstrate the effectiveness of AlignPrune, boosting accuracy by up to 6.3% over state‑of‑the‑art baselines. Our results offer a generalizable solution for pruning under noisy data, encouraging further exploration of learning in real‑world scenarios. Code is available at: https://github.com/leonqin430/AlignPrune.
Authors:Huidong Ma, Xinyan Shi, Hui Sun, Xiaofei Yue, Xiaoguang Liu, Gang Wang, Wentong Cai
Abstract:
While Learned Data Compression (LDC) has achieved superior compression ratios, balancing precise probability modeling with system efficiency remains challenging. Crucially, uniform single‑stream architectures struggle to simultaneously capture micro‑syntactic and macro‑semantic features, necessitating deep serial stacking that exacerbates latency. Compounding this, heterogeneous systems are constrained by device speed mismatches, where throughput is capped by Amdahl's Law due to serial processing. To this end, we propose a Dual‑Stream Multi‑Scale Decoupler that disentangles local and global contexts to replace deep serial processing with shallow parallel streams, and incorporate a Hierarchical Gated Refiner for adaptive feature refinement and precise probability modeling. Furthermore, we design a Concurrent Stream‑Parallel Pipeline, which overcomes systemic bottlenecks to achieve full‑pipeline parallelism. Extensive experiments demonstrate that our method achieves state‑of‑the‑art performance in both compression ratio and throughput, while maintaining the lowest latency and memory usage. The code is available at https://github.com/huidong‑ma/FADE.
Authors:Jianfei Wu, Zhichun Wang, Zhensheng Wang, Zhiyu He
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.
Authors:Ricardo Knauer, Andre Beinrucker, Erik Rodner
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.
Authors:Zhixiong Zhao, Zukang Xu, Zhixuan Chen, Dawei Yang
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.
Authors:Qipeng Zhan, Zhuoping Zhou, Zexuan Wang, Qi Long, Li Shen
Abstract:
Autoencoders are widely used for dimensionality reduction, based on the assumption that high‑dimensional data lies on low‑dimensional manifolds. Regularized autoencoders aim to preserve manifold geometry during dimensionality reduction, but existing approaches often suffer from non‑injective mappings and overly rigid constraints that limit their effectiveness and robustness. In this work, we identify encoder non‑injectivity as a core bottleneck that leads to poor convergence and distorted latent representations. To ensure robustness across data distributions, we formalize the concept of admissible regularization and provide sufficient conditions for its satisfaction. In this work, we propose the Bi‑Lipschitz Autoencoder (BLAE), which introduces two key innovations: (1) an injective regularization scheme based on a separation criterion to eliminate pathological local minima, and (2) a bi‑Lipschitz relaxation that preserves geometry and exhibits robustness to data distribution drift. Empirical results on diverse datasets show that BLAE consistently outperforms existing methods in preserving manifold structure while remaining resilient to sampling sparsity and distribution shifts. Code is available at https://github.com/qipengz/BLAE.
Authors:Yue Fang, Weibin Liao, Yuxin Guo, Jiaran Gao, Hongxin Ding, Jinyang Zhang, Xinke Jiang, Zhibang Yang, Junfeng Zhao, Yasha Wang, Liantao Ma
Abstract:
Clinical Reasoning on Electronic Health Records (EHRs) is a fundamental yet challenging task in modern healthcare. While in‑context learning (ICL) offers a promising inference‑time adaptation paradigm for large language models (LLMs) in EHR reasoning, existing methods face three fundamental challenges: (1) Perspective Limitation, where data‑driven similarity fails to align with LLM reasoning needs and model‑driven signals are constrained by limited clinical competence; (2) Cohort Awareness, as demonstrations are selected independently without modeling population‑level structure; and (3) Information Aggregation, where redundancy and interaction effects among demonstrations are ignored, leading to diminishing marginal gains. To address these challenges, we propose GraphWalker, a principled demonstration selection framework for EHR‑oriented ICL. GraphWalker (i) jointly models patient clinical information and LLM‑estimated information gain by integrating data‑driven and model‑driven perspectives, (ii) incorporates Cohort Discovery to avoid noisy local optima, and (iii) employs a Lazy Greedy Search with Frontier Expansion algorithm to mitigate diminishing marginal returns in information aggregation. Extensive experiments on multiple real‑world EHR benchmarks demonstrate that GraphWalker consistently outperforms state‑of‑the‑art ICL baselines, yielding substantial improvements in clinical reasoning performance. Our code is open‑sourced at https://github.com/PuppyKnightUniversity/GraphWalker
Authors:Hanyang Wang, Mingxuan Zhu
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.
Authors:Peigui Qi, Kunsheng Tang, Yanpu Yu, Jialin Wu, Yide Song, Wenbo Zhou, Zhicong Huang, Cheng Hong, Weiming Zhang, Nenghai Yu
Abstract:
Vision‑Language Models (VLMs) face significant safety vulnerabilities from malicious prompt attacks due to weakened alignment during visual integration. Existing defenses suffer from efficiency and robustness. To address these challenges, we first propose the Multimodal Aggregated Feature Extraction (MAFE) framework that enables CLIP to handle long text and fuse multimodal information into unified representations. Through empirical analysis of MAFE‑extracted features, we discover distinct distributional patterns between benign and malicious prompts. Building upon this finding, we develop VLMShield, a lightweight safety detector that efficiently identifies multimodal malicious attacks as a plug‑and‑play solution. Extensive experiments demonstrate superior performance across multiple dimensions, including robustness, efficiency, and utility. Through our work, we hope to pave the way for more secure multimodal AI deployment. Code is available at [this https URL](https://github.com/pgqihere/VLMShield).
Authors:Dev Arpan Desai, Shaoyi Huang, Zining Zhu
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.
Authors:Mingchen Zhuge, Changsheng Zhao, Haozhe Liu, Zijian Zhou, Shuming Liu, Wenyi Wang, Ernie Chang, Gael Le Lan, Junjie Fei, Wenxuan Zhang, Yasheng Sun, Zhipeng Cai, Zechun Liu, Yunyang Xiong, Yining Yang, Yuandong Tian, Yangyang Shi, Vikas Chandra, Jürgen Schmidhuber
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.
Authors:Wenyue Hua, Sripad Karne, Qian Xie, Armaan Agrawal, Nikos Pagonas, Kostis Kaffes, Tianyi Peng
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/.
Authors:Lin Mu, Haiyang Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang
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.
Authors:Ashmal Vayani, Parth Parag Kulkarni, Joseph Fioresi, Song Wang, Mubarak Shah
Abstract:
Medical diagnosis using Large Multimodal Models (LMMs) has gained increasing attention due to capability of these models in providing precise diagnoses. These models generally combine medical questions with visual inputs to generate diagnoses or treatments. However, they are often overly general and unsuitable under the wide range of medical conditions in real‑world healthcare. In clinical practice, diagnosis is performed by multiple specialists, each contributing domain‑specific expertise. To emulate this process, a potential solution is to deploy a dynamic multi‑agent LMM framework, where each agent functions as a medical specialist. Current approaches in this emerging area, typically relying on static or predefined selection of various specialists, cannot be adapted to the changing practical scenario. In this paper, we propose MedRoute, a flexible and dynamic multi‑agent framework that comprises of a collaborative system of specialist LMM agents. Furthermore, we add a General Practitioner with an RL‑trained router for dynamic specialist selection, and a Moderator that produces the final decision. In this way, our framework closely mirrors real clinical workflows. Extensive evaluations on text and image‑based medical datasets demonstrate improved diagnostic accuracy, outperforming the state‑of‑the‑art baselines. Our work lays a strong foundation for future research. Code and models are available at https://github.com/UCF‑CRCV/MedRoute/.
Authors:Guhao Feng, Shengjie Luo, Kai Hua, Ge Zhang, Di He, Wenhao Huang, Tianle Cai
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.
Authors:Jean Kaddour
Abstract:
In RL, given a prompt, we sample a group of completions from a model and score them. Two questions follow: which completions should gain probability mass, and how should the parameters move to realize that change? Standard policy‑gradient methods answer both at once, so the update can overshoot or undershoot depending on the learning rate, clipping, and other optimizer choices. We introduce \emphTarget Policy Optimization (TPO), which separates the two questions. Given scored completions, TPO constructs a target distribution q_i \propto p_i^\,\mathrmold \exp(u_i) and fits the policy to it by cross‑entropy. The loss gradient on sampled‑completion logits is p^θ‑ q, which vanishes once the policy matches the target. On tabular bandits, transformer sequence tasks, and billion‑parameter LLM RLVR, TPO matches PG, PPO, GRPO, and DG on easy tasks and substantially outperforms them under sparse reward. Code is available at https://github.com/JeanKaddour/tpo.
Authors:Xiaojie Gu, Ziying Huang, Weicong Hong, Jian Xie, Renze Lou, Kai Zhang
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.
Authors:Ioannis Nasios
Abstract:
Landslides represent a major geohazard with severe impacts on human life, infrastructure, and ecosystems, underscoring the need for accurate and timely detection approaches to support disaster risk reduction. This study proposes a modular, multi‑model framework that fuses Sentinel‑2 optical imagery with Sentinel‑1 Synthetic Aperture Radar (SAR) data, for robust landslide detection. The methodology leverages multi‑encoder vision transformers, where each data modality is processed through separate lightweight pretrained encoders, achieving strong performance in landslide detection. In addition, the integration of multiple models, particularly the combination of neural networks and gradient boosting models (LightGBM and XGBoost), demonstrates the power of ensemble learning to further enhance accuracy and robustness. Derived spectral indices, such as NDVI, are integrated alongside original bands to enhance sensitivity to vegetation and surface changes. The proposed methodology achieves a state‑of‑the‑art F1 score of 0.919 on landslide detection, addressing a patch‑based classification task rather than pixel‑level segmentation and operating without pre‑event Sentinel‑2 data, highlighting its effectiveness in a non‑classical change detection setting. It also demonstrated top performance in a machine learning competition, achieving a strong balance between precision and recall and highlighting the advantages of explicitly leveraging the complementary strengths of optical and radar data. The conducted experiments and research also emphasize scalability and operational applicability, enabling flexible configurations with optical‑only, SAR‑only, or combined inputs, and offering a transferable framework for broader natural hazard monitoring and environmental change applications. Full training and inference code can be found in https://github.com/IoannisNasios/sentinel‑landslide‑cls.
Authors:Shuai Zhen, Yanhua Yu, Ruopei Guo, Nan Cheng, Yang Deng
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.
Authors:Laurits Fredsgaard, Aaron Thomas, Michael Riis Andersen, Mikkel N. Schmidt, Mahito Sugiyama
Abstract:
Autoregressive graph generators define likelihoods via a sequential construction process, but these likelihoods are only meaningful if they are consistent across all linearizations of the same graph. Segmented Eulerian Neighborhood Trails (SENT), a recent linearization method, converts graphs into sequences that can be perfectly decoded and efficiently processed by language models, but admit multiple equivalent linearizations of the same graph. We quantify violations in assigned negative log‑likelihood (NLL) using the coefficient of variation across equivalent linearizations, which we call Linearization Uncertainty (LU). Training transformers under four linearization strategies on two datasets, we show that biased orderings achieve lower NLL on their native order but exhibit expected calibration error (ECE) two orders of magnitude higher under random permutation, indicating that these models have learned their training linearization rather than the underlying graph. On the molecular graph benchmark QM9, NLL for generated graphs is negatively correlated with molecular stability (AUC =0.43), while LU achieves AUC =0.85, suggesting that permutation‑based evaluation provides a more reliable quality check for generated molecules. Code is available at https://github.com/lauritsf/linearization‑uncertainty
Authors:Tõnis Lees, Tambet Matiisen
Abstract:
This work investigates the adaptation of the AlphaZero reinforcement learning algorithm to Tablut, an asymmetric historical board game featuring unequal piece counts and distinct player objectives (king capture versus king escape). While the original AlphaZero architecture successfully leverages a single policy and value head for symmetric games, applying it to asymmetric environments forces the network to learn two conflicting evaluation functions, which can hinder learning efficiency and performance. To address this, the core architecture is modified to use separate policy and value heads for each player role, while maintaining a shared residual trunk to learn common board features. During training, the asymmetric structure introduced training instabilities, notably catastrophic forgetting between the attacker and defender roles. These issues were mitigated by applying C4 data augmentation, increasing the replay buffer size, and having the model play 25 percent of training games against randomly sampled past checkpoints. Over 100 self‑play iterations, the modified model demonstrated steady improvement, achieving a BayesElo rating of 1235 relative to a randomly initialized baseline. Training metrics also showed a significant decrease in policy entropy and average remaining pieces, reflecting increasingly focused and decisive play. Ultimately, the experiments confirm that AlphaZero's self‑play framework can transfer to highly asymmetric games, provided that distinct policy/value heads and robust stabilization techniques are employed.
Authors:Ali Aliev, Kamil Garifullin, Nikolay Yudin, Vera Soboleva, Alexander Molozhavenko, Ivan Oseledets, Aibek Alanov, Maxim Rakhuba
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.
Authors:Jarrid Rector-Brooks, Théophile Lambert, Marta Skreta, Daniel Roth, Yueming Long, Zi-Qi Li, Xi Zhang, Miruna Cretu, Francesca-Zhoufan Li, Tanvi Ganapathy, Emily Jin, Avishek Joey Bose, Jason Yang, Kirill Neklyudov, Yoshua Bengio, Alexander Tong, Frances H. Arnold, Cheng-Hao Liu
Abstract:
Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre‑specifying catalytic residues. We introduce DISCO (DIffusion for Sequence‑structure CO‑design), a multimodal model that co‑designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference‑time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active‑site geometries. These enzymes catalyze new‑to‑nature carbene‑transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B‑H, and C(sp^3)‑H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO‑design/DISCO.
Authors:Lucas Dionisopoulos, Nicklas Majamaki, Prithviraj Ammanabrolu
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.
Authors:Quyet V. Do, Thinh Pham, Nguyen Nguyen, Sha Li, Pratibha Zunjare, Tu Vu
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.
Authors:Ximing Xing, Ziteng Xue, Zhenxi Li, Weicong Liang, Linqing Wang, Zhantao Yang, Tiankai Hang, Zijin Yin, Qinglin Lu, Chunyu Wang, Qian Yu
Abstract:
Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on generic byte‑level tokenization inherited from natural language processing, which poorly reflects the geometric structure of vector graphics. Numerical coordinates are fragmented into discrete symbols, destroying spatial relationships and introducing severe token redundancy, often leading to coordinate hallucination and inefficient long‑sequence generation. To address these challenges, we propose HiVG, a hierarchical SVG tokenization framework tailored for autoregressive vector graphics generation. HiVG decomposes raw SVG strings into structured atomic tokens and further compresses executable command‑‑parameter groups into geometry‑constrained segment tokens, substantially improving sequence efficiency while preserving syntactic validity. To further mitigate spatial mismatch, we introduce a Hierarchical Mean‑‑Noise (HMN) initialization strategy that injects numerical ordering signals and semantic priors into new token embeddings. Combined with a curriculum training paradigm that progressively increases program complexity, HiVG enables more stable learning of executable SVG programs. Extensive experiments on both text‑to‑SVG and image‑to‑SVG tasks demonstrate improved generation fidelity, spatial consistency, and sequence efficiency compared with conventional tokenization schemes. Our code is publicly available at https://github.com/ximinng/HiVG
Authors:Yasaman Kashefbahrami, Erkut Akdag, Panagiotis Meletis, Evgeniya Balmashnova, Dip Goswami, Egor Bondarau
Abstract:
Accurate Point Cloud Registration (PCR) is an important task in 3D data processing, involving the estimation of a rigid transformation between two point clouds. While deep‑learning methods have addressed key limitations of traditional non‑learning approaches, such as sensitivity to noise, outliers, occlusion, and initialization, they are developed and evaluated on clean, dense, synthetic datasets (limiting their generalizability to real‑world industrial scenarios). This paper introduces R3PM‑Net, a lightweight, global‑aware, object‑level point matching network designed to bridge this gap by prioritizing both generalizability and real‑time efficiency. To support this transition, two datasets, Sioux‑Cranfield and Sioux‑Scans, are proposed. They provide an evaluation ground for registering imperfect photogrammetric and event‑camera scans to digital CAD models, and have been made publicly available. Extensive experiments demonstrate that R3PM‑Net achieves competitive accuracy with unmatched speed. On ModelNet40, it reaches a perfect fitness score of 1 and inlier RMSE of 0.029 cm in only 0.007s, approximately 7 times faster than the state‑of‑the‑art method RegTR. This performance carries over to the Sioux‑Cranfield dataset, maintaining a fitness of 1 and inlier RMSE of 0.030 cm with similarly low latency. Furthermore, on the highly challenging Sioux‑Scans dataset, R3PM‑Net successfully resolves edge cases in under 50 ms. These results confirm that R3PM‑Net offers a robust, high‑speed solution for critical industrial applications, where precision and real‑time performance are indispensable. The code and datasets are available at https://github.com/YasiiKB/R3PM‑Net.
Authors:Gowrav Vishwakarma, Christopher J. Agostino
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.
Authors:Yiwen Song, Yale Song, Tomas Pfister, Jinsung Yoon
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.
Authors:Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang, Shuofei Qiao, Kexin Cao, Guozhou Zheng, Xiang Qi, Peng Zhang, Shumin Deng
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.
Authors:Justin Chih-Yao Chen, Archiki Prasad, Zaid Khan, Joykirat Singh, Runchu Tian, Elias Stengel-Eskin, Mohit Bansal
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.
Authors:Peter Balogh
Abstract:
The final MLP of GPT‑2 Small exhibits a fully legible routing program ‑‑ 27 named neurons organized into a three‑tier exception handler ‑‑ while the knowledge it routes remains entangled across ~3,040 residual neurons. We decompose all 3,072 neurons (to numerical precision) into: 5 fused Core neurons that reset vocabulary toward function words, 10 Differentiators that suppress wrong candidates, 5 Specialists that detect structural boundaries, and 7 Consensus neurons that each monitor a distinct linguistic dimension. The consensus‑exception crossover ‑‑ where MLP intervention shifts from helpful to harmful ‑‑ is statistically sharp (bootstrap 95% CIs exclude zero at all consensus levels; crossover between 4/7 and 5/7). Three experiments show that "knowledge neurons" (Dai et al., 2022), at L11 of this model, function as routing infrastructure rather than fact storage: the MLP amplifies or suppresses signals already present in the residual stream from attention, scaling with contextual constraint. A garden‑path experiment reveals a reversed garden‑path effect ‑‑ GPT‑2 uses verb subcategorization immediately, consistent with the exception handler operating at token‑level predictability rather than syntactic structure. This architecture crystallizes only at the terminal layer ‑‑ in deeper models, we predict equivalent structure at the final layer, not at layer 11. Code and data: https://github.com/pbalogh/transparent‑gpt2
Authors:Qing Zhou, Bingxuan Zhao, Tao Yang, Hongyuan Zhang, Junyu Gao, Qi Wang
Abstract:
Dynamic data pruning accelerates deep learning by selectively omitting less informative samples during training. While per‑sample loss is a common importance metric, obtaining it can be challenging or infeasible for complex models or loss functions, often requiring significant implementation effort. This work proposes the Batch Loss Score (BLS), a computationally efficient alternative using an Exponential Moving Average (EMA) of readily available batch losses to assign scores to individual samples. We frame the batch loss, from the perspective of a single sample, as a noisy measurement of its scaled individual loss, with noise originating from stochastic batch composition. It is formally shown that the EMA mechanism functions as a first‑order low‑pass filter, attenuating high‑frequency batch composition noise. This yields a score approximating the smoothed and persistent contribution of the individual sample to the loss, providing a theoretical grounding for BLS as a proxy for sample importance. BLS demonstrates remarkable code integration simplicity (three‑line injection) and readily adapts existing per‑sample loss‑based methods (one‑line proxy). Its effectiveness is demonstrated by enhancing two such methods to losslessly prune 20%‑50% of samples across 14 datasets, 11 tasks and 18 models, highlighting its utility and broad applicability, especially for complex scenarios where per‑sample loss is difficult to access. Code is available at https://github.com/mrazhou/BLS.
Authors:Fatemeh Khadem, Sajad Mousavi, Yi Fang, Yuhong Liu
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.
Authors:Abu Noman Md Sakib, Zhensen Wang, Merjulah Roby, Zijie Zhang
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.
Authors:Seoyoung Park, Haemin Lee, Hankook Lee
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.
Authors:Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Sekitoshi Kanai, Masanori Yamada, Kosuke Nishida, Kazutoshi Shinoda
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.
Authors:Gregory N. Frank
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.
Authors:Saurav Jha, Maryam Hashemzadeh, Ali Saheb Pasand, Ali Parviz, Min-Joong Lee, Boris Knyazev
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.
Authors:Hristo Petkov, Calum MacLellan, Feng Dong
Abstract:
Understanding the causal relationships between data variables can provide crucial insights into the construction of tabular datasets. Most existing causality learning methods typically focus on applying a single identifiable causal model, such as the Additive Noise Model (ANM) or the Linear non‑Gaussian Acyclic Model (LiNGAM), to discover the dependencies exhibited in observational data. We improve on this approach by introducing a novel dual‑step framework capable of performing both causal structure learning and tabular data synthesis under multiple causal model assumptions. Our approach uses Directed Acyclic Graphs (DAG) to represent causal relationships among data variables. By applying various functional causal models including ANM, LiNGAM and the Post‑Nonlinear model (PNL), we implicitly learn the contents of DAG to simulate the generative process of observational data, effectively replicating the real data distribution. This is supported by a theoretical analysis to explain the multiple loss terms comprising the objective function of the framework. Experimental results demonstrate that DAGAF outperforms many existing methods in structure learning, achieving significantly lower Structural Hamming Distance (SHD) scores across both real‑world and benchmark datasets (Sachs: 47%, Child: 11%, Hailfinder: 5%, Pathfinder: 7% improvement compared to state‑of‑the‑art), while being able to produce diverse, high‑quality samples.
Authors:Jinhao Pan, Bowen Wei, Ziwei Zhu
Abstract:
Most deep learning recommendation models operate as black boxes, relying on latent representations that obscure their decision process. This lack of intrinsic interpretability raises concerns in applications that require transparency and accountability. In this work, we propose a Logical‑rule Interpretable Autoencoder (LIA) for collaborative filtering that is interpretable by design. LIA introduces a learnable logical rule layer in which each rule neuron is equipped with a gate parameter that automatically selects between AND and OR operators during training, enabling the model to discover diverse logical patterns directly from data. To support functional completeness without doubling the input dimensionality, LIA encodes negation through the sign of connection weights, providing a parameter‑efficient mechanism for expressing both positive and negated item conditions within each rule. By learning explicit, human‑readable reconstruction rules, LIA allows users to directly trace the decision process behind each recommendation. Extensive experiments show that our method achieves improved recommendation performance over traditional baselines while remaining fully interpretable. Code and data are available at https://github.com/weibowen555/LIA.
Authors:Yancheng Huang, Changsheng Wang, Chongyu Fan, Yicheng Lang, Bingqi Shang, Yang Zhang, Mingyi Hong, Qing Qu, Alvaro Velasquez, Sijia Liu
Abstract:
Foundation models, such as large language models (LLMs), are powerful but often require customization before deployment to satisfy practical constraints such as safety, privacy, and task‑specific requirements, leading to "constrained" optimization problems for model steering and adaptation. However, solving such problems remains largely underexplored and is particularly challenging due to interference between the primary objective and constraint objectives during optimization. In this paper, we propose a subspace control framework for constrained model training. Specifically, (i) we first analyze, from a model merging perspective, how spectral cross‑task interference arises and show that it can be resolved via a one‑shot solution that orthogonalizes the merged subspace; (ii) we establish a connection between this solution and gradient orthogonalization in the spectral optimizer Muon; and (iii) building on these insights, we introduce SIFT (spectral interference‑free training), which leverages a localization scheme to selectively intervene during optimization, enabling controllable updates that mitigate objective‑constraint conflicts. We evaluate SIFT across four representative applications: (a) machine unlearning, (b) safety alignment, (c) text‑to‑speech adaptation, and (d) hallucination mitigation. Compared to both control‑based and control‑free baselines, SIFT consistently achieves substantial and robust performance improvements across all tasks. Code is available at https://github.com/OPTML‑Group/SIFT.
Authors:Haonian Ji, Kaiwen Xiong, Siwei Han, Peng Xia, Shi Qiu, Yiyang Zhou, Jiaqi Liu, Jinlong Li, Bingzhou Li, Zeyu Zheng, Cihang Xie, Huaxiu Yao
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.
Authors:Milo Coombs
Abstract:
Classical approximation bases such as Chebyshev polynomials provide principled and interpretable representations, but their multivariate tensor‑product constructions scale exponentially with dimension and impose axis‑aligned structure that is poorly matched to real tabular data. We address this by replacing tensorised oscillations with directional harmonic modes of the form \cos(\mathbfm^\top\arccos(\mathbfx)), which organise multivariate structure by direction in angular space rather than by coordinate index. This representation yields a discrete spectral regression model in which complexity is controlled by selecting a small number of structured frequency vectors (spectral paths), and training reduces to a single closed‑form ridge solve with no iterative optimisation. Experiments on standard continuous‑feature tabular regression benchmarks show that the resulting models achieve accuracy competitive with strong nonlinear baselines while remaining compact, computationally efficient, and explicitly interpretable through analytic expressions of learned feature interactions.
Authors:Ling Yue, Chaoqian Ouyang, Hang Xu, Ruijun Huang, Yuchen Liu, Libin Zheng, Wei Liu, Shaowu Pan, Shimin Di, Min-Ling Zhang
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.
Authors:Nahyuk Lee, Zhiang Chen, Marc Pollefeys, Sunghwan Hong
Abstract:
Flow‑matching methods for 3D shape assembly learn point‑wise velocity fields that transport parts toward assembled configurations, yet they receive no explicit guidance about which cross‑part interactions should drive the motion. We introduce TORA, a topology‑first representation alignment framework that distills relational structure from a frozen pretrained 3D encoder into the flow‑matching backbone during training. We first realize this via simple instantiation, token‑wise cosine matching, which injects the learned geometric descriptors from the teacher representation. We then extend to employ a Centered Kernel Alignment (CKA) loss to match the similarity structure between student and teacher representations for enhanced topological alignment. Through systematic probing of diverse 3D encoders, we show that geometry‑ and contact‑centric teacher properties, not semantic classification ability, govern alignment effectiveness, and that alignment is most beneficial at later transformer layers where spatial structure naturally emerges. TORA introduces zero inference overhead while yielding two consistent benefits: faster convergence (up to 6.9×) and improved accuracy in‑distribution, along with greater robustness under domain shift. Experiments on five benchmarks spanning geometric, semantic, and inter‑object assembly demonstrate state‑of‑the‑art performance, with particularly pronounced gains in zero‑shot transfer to unseen real‑world and synthetic datasets. Project page: https://nahyuklee.github.io/tora.
Authors:Yifu Ding, Xinhao Zhang, Jinyang Guo
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.
Authors:Indar Kumar, Girish Karhana, Sai Krishna Jasti, Ankit Hemant Lade
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.
Authors:Indar Kumar, Akanksha Tiwari
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.
Authors:Kitsuya Azuma, Takayuki Nishio
Abstract:
Federated learning (FL) research increasingly relies on single‑node simulations with hundreds or thousands of virtual clients, making both efficiency and reproducibility essential. Yet parallel client training often introduces nondeterminism through shared random state and scheduling variability, forcing researchers to trade throughput for reproducibility or to implement custom control logic within complex frameworks. We present BlazeFL, a lightweight framework for single‑node FL simulation that alleviates this trade‑off through free‑threaded shared‑memory execution and deterministic randomness management. BlazeFL uses thread‑based parallelism with in‑memory parameter exchange between the server and clients, avoiding serialization and inter‑process communication overhead. To support deterministic execution, BlazeFL assigns isolated random number generator (RNG) streams to clients. Under a fixed software/hardware stack, and when stochastic operators consume BlazeFL‑managed generators, this design yields bitwise‑identical results across repeated high‑concurrency runs in both thread‑based and process‑based modes. In CIFAR‑10 image‑classification experiments, BlazeFL substantially reduces execution time relative to a widely used open‑source baseline, achieving up to 3.1× speedup on communication‑dominated workloads while preserving a lightweight dependency footprint. Our open‑source implementation is available at: https://github.com/kitsuyaazuma/blazefl.
Authors:Viet Dung Nguyen, Yuhang Song, Anh Nguyen, Jamison Heard, Reynold Bailey, Alexander Ororbia
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.
Authors:Maharshi Savdhariya
Abstract:
BitNet b1.58 (Ma et al., 2024) demonstrates that large language models can operate entirely on ternary weights ‑1, 0, +1, yet no native binary wire format exists for such models. NativeTernary closes this gap. Benchmarked against GGUF on the real BitNet b1.58 2B4T architecture (24 layers, ~170 tensors, 2B parameters): NativeTernary encodes ternary weights at exactly 2.000 bits per weight ‑‑ 1.31x smaller than GGUF Q2_K and 4.0x smaller than GGUF int8 ‑‑ while reducing boundary and framing overhead by 460x (91 bytes vs ~42KB of GGUF tensor headers). Encode throughput: 47‑‑69 MB/s. Decode throughput: 35‑‑45 MB/s on commodity hardware. The decoder is a 10‑line stateless state machine resilient to bitstream corruption.
Authors:Tomek Kaszyński
Abstract:
Can multi‑agent communication pressure extract discrete, compositional representations of invisible physical properties from frozen video features? We show that agents communicating through a Gumbel‑Softmax bottleneck with iterated learning develop positionally disentangled protocols for latent properties (elasticity, friction, mass ratio) without property labels or supervision on message structure. With 4 agents, 100% of 80 seeds converge to near‑perfect compositionality (PosDis=0.999, holdout 98.3%). Controls confirm multi‑agent structure ‑‑ not bandwidth or temporal coverage ‑‑ drives this effect. Causal intervention shows surgical property disruption (~15% drop on targeted property, <3% on others). A controlled backbone comparison reveals that the perceptual prior determines what is communicable: DINOv2 dominates on spatially‑visible ramp physics (98.3% vs 95.1%), while V‑JEPA 2 dominates on dynamics‑only collision physics (87.4% vs 77.7%, d=2.74). Scale‑matched (d=3.37) and frame‑matched (d=6.53) controls attribute this gap entirely to video‑native pretraining. The frozen protocol supports action‑conditioned planning (91.5%) with counterfactual velocity reasoning (r=0.780). Validation on Physics 101 real camera footage confirms 85.6% mass‑comparison accuracy on unseen objects, temporal dynamics contributing +11.2% beyond static appearance, agent‑scaling compositionality replicating at 90% for 4 agents, and causal intervention extending to real video (d=1.87, p=0.022).
Authors:Carmine Valentino, Federico Pichi, Francesco Colace, Dajana Conte, Gianluigi Rozza
Abstract:
The conservation of cultural heritage increasingly relies on integrating technological innovation with domain expertise to ensure effective monitoring and predictive maintenance. This paper presents a novel framework to support the preservation of cultural assets, combining Internet of Things (IoT) and Artificial Intelligence (AI) technologies, enhanced with the physical knowledge of phenomena. The framework is structured into four functional layers that permit the analysis of 3D models of cultural assets and elaborate simulations based on the knowledge acquired from data and physics. A central component of the proposed framework consists of Scientific Machine Learning, particularly Physics‑Informed Neural Networks (PINNs), which incorporate physical laws into deep learning models. To enhance computational efficiency, the framework also integrates Reduced Order Methods (ROMs), specifically Proper Orthogonal Decomposition (POD), and is also compatible with classical Finite Element (FE) methods. Additionally, it includes tools to automatically manage and process 3D digital replicas, enabling their direct use in simulations. The proposed approach offers three main contributions: a methodology for processing 3D models of cultural assets for reliable simulation; the application of PINNs to combine data‑driven and physics‑based approaches in cultural heritage conservation; and the integration of PINNs with ROMs to efficiently model degradation processes influenced by environmental and material parameters. The reproducible and open‑access experimental phase exploits simulated scenarios on complex and real‑life geometries to test the efficacy of the proposed framework in each of its key components, allowing the possibility of dealing with both direct and inverse problems. Code availability: https://github.com/valc89/PhysicsInformedCulturalHeritage
Authors:David Ilić, Kostadin Cvejoski, David Stanojević, Evgeny Grigorenko
Abstract:
All prior membership inference attacks for fine‑tuned language models use hand‑crafted heuristics (e.g., loss thresholding, Min‑K%, reference calibration), each bounded by the designer's intuition. We introduce the first transferable learned attack, enabled by the observation that fine‑tuning any model on any corpus yields unlimited labeled data, since membership is known by construction. This removes the shadow model bottleneck and brings membership inference into the deep learning era: learning what matters rather than designing it, with generalization through training diversity and scale. We discover that fine‑tuning language models produces an invariant signature of memorization detectable across architectural families and data domains. We train a membership inference classifier exclusively on transformer‑based models. It transfers zero‑shot to Mamba (state‑space), RWKV‑4 (linear attention), and RecurrentGemma (gated recurrence), achieving 0.963, 0.972, and 0.936 AUC respectively. Each evaluation combines an architecture and dataset never seen during training, yet all three exceed performance on held‑out transformers (0.908 AUC). These four families share no computational mechanisms, their only commonality is gradient descent on cross‑entropy loss. Even simple likelihood‑based methods exhibit strong transfer, confirming the signature exists independently of the detection method. Our method, Learned Transfer MIA (LT‑MIA), captures this signal most effectively by reframing membership inference as sequence classification over per‑token distributional statistics. On transformers, LT‑MIA achieves 2.8× higher TPR at 0.1% FPR than the strongest baseline. The method also transfers to code (0.865 AUC) despite training only on natural language texts. Code and trained classifier available at https://github.com/JetBrains‑Research/learned‑mia.
Authors:Nikita Vassilyev, William Berrios, Ruowang Zhang, Bo Han, Douwe Kiela, Shikib Mehri
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.
Authors:Santosh Mohan Rajkumar, Dibyasri Barman, Kumar Vikram Singh, Debdipta Goswami
Abstract:
This work establishes a rigorous bridge between infinite‑dimensional delay dynamics and finite‑dimensional Koopman learning, with explicit and interpretable error guarantees. While Koopman analysis is well‑developed for ordinary differential equations (ODEs) and partially for partial differential equations (PDEs), its extension to delay differential equations (DDEs) remains limited due to the infinite‑dimensional phase space of DDEs. We propose a finite‑dimensional Koopman approximation framework based on history discretization and a suitable reconstruction operator, enabling a tractable representation of the Koopman operator via kernel‑based extended dynamic mode decomposition (kEDMD). Deterministic error bounds are derived for the learned predictor, decomposing the total error into contributions from history discretization, kernel interpolation, and data‑driven regression. Additionally, we develop a kernel‑based reconstruction method to recover discretized states from lifted Koopman coordinates, with provable guarantees. Numerical results demonstrate convergence of the learned predictor with respect to both discretization resolution and training data, supporting reliable prediction and control of delay systems.
Authors:Md. Rashadul Islam
Abstract:
Microgravity induces profound metabolic adaptations in mammalian physiology, yet the molecular mechanisms governing thermogenesis in female white adipose tissue (WAT) remain poorly characterized. This paper presents the first machine learning (ML) analysis of NASA Open Science Data Repository (OSDR) dataset OSD‑970, derived from the Rodent Research‑1 (RR‑1) mission. Using RT‑qPCR data from 89 adipogenesis and thermogenesis pathway genes in gonadal WAT of 16 female C57BL/6J mice (8 flight, 8 ground control) following 37 days aboard the International Space Station (ISS), we applied differential expression analysis, multiple ML classifiers with Leave‑One‑Out Cross‑Validation (LOO‑CV), and Explainable AI via SHapley Additive exPlanations (SHAP). The most striking finding is a dramatic 12.21‑fold upregulation of Ucp1 (Delta‑Delta‑Ct = ‑3.61, p = 0.0167) in microgravity‑exposed WAT, accompanied by significant activation of the thermogenesis pathway (mean pathway fold‑change = 3.24). The best‑performing model (Random Forest with top‑20 features) achieved AUC = 0.922, Accuracy = 0.812, and F1 = 0.824 via LOO‑CV. SHAP analysis consistently ranked Ucp1 among the top predictive features, while Angpt2, Irs2, Jun, and Klf‑family transcription factors emerged as dominant consensus classifiers. Principal component analysis (PCA) revealed clear separation between flight and ground samples, with PC1 explaining 69.1% of variance. These results suggest rapid thermogenic reprogramming in female WAT as a compensatory response to microgravity. This study demonstrates the power of explainable AI for re‑analysis of newly released NASA space biology datasets, with direct implications for female astronaut health on long‑duration missions and for Earth‑based obesity and metabolic disease research.
Authors:Haseeb Tariq, Marwan Hassani
Abstract:
Money laundering presents a persistent challenge for financial institutions worldwide, while criminal organizations constantly evolve their tactics to bypass detection systems. Traditional anti‑money laundering approaches mainly rely on predefined risk‑based rules, leading to resource‑intensive investigations and high numbers of false positive alerts. In order to restrict operational costs from exploding, while billions of transactions are being processed every day, financial institutions are investing in more sophisticated mechanisms to improve existing systems. In this paper, we present ExSTraQt (EXtract Suspicious TRAnsactions from Quasi‑Temporal graph representation), an advanced supervised learning approach to detect money laundering (or suspicious) transactions in financial datasets. Our proposed framework excels in performance, when compared to the state‑of‑the‑art AML (Anti Money Laundering) detection models. The key strengths of our framework are sheer simplicity, in terms of design and number of parameters; and scalability, in terms of the computing and memory requirements. We evaluated our framework on transaction‑level detection accuracy using a real dataset; and a set of synthetic financial transaction datasets. We consistently achieve an uplift in the F1 score for most datasets, up to 1% for the real dataset; and more than 8% for one of the synthetic datasets. We also claim that our framework could seamlessly complement existing AML detection systems in banks. Our code and datasets are available at https://github.com/mhaseebtariq/exstraqt.
Authors:Eunbi Yoon, Won Chang, Donghan Kim, Dae Wook Kim
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.
Authors:Giyeong Oh, Junghyun Lee, Jaehyun Park, Youngjae Yu, Wonho Bae, Junhyug Noh
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.
Authors:Mirali Purohit, Bimal Gajera, Irish Mehta, Bhanu Tokas, Jacob Adler, Steven Lu, Scott Dickenshied, Serina Diniega, Brian Bue, Umaa Rebbapragada, Hannah Kerner
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.
Authors:Patrick Pynadath, Jiaxin Shi, Ruqi Zhang
Abstract:
Diffusion language models have seen exciting recent progress, offering far more flexibility in generative trajectories than autoregressive models. This flexibility has motivated a growing body of research into new approaches to diffusion language modeling, which typically begins at the scale of GPT‑2 small (150 million parameters). However, these advances introduce new issues with evaluation methodology. In this technical note, we discuss the limitations of current methodology and propose principled augmentations to ensure reliable comparisons. We first discuss why OpenWebText has become the standard benchmark, and why alternatives such as LM1B are inherently less meaningful. We then discuss the limitations of likelihood evaluations for diffusion models, and explain why relying on generative perplexity alone as a metric can lead to uninformative results. To address this, we show that generative perplexity and entropy are two components of the KL divergence to a reference distribution. This decomposition explains generative perplexity's sensitivity to entropy, and naturally suggests generative frontiers as a principled method for evaluating model generative quality. We conclude with empirical observations on model quality at this scale. We include a blog post with interactive content to illustrate the argument at https://patrickpynadath1.github.io/blog/eval_methodology/.
Authors:Yasushi Nishida
Abstract:
We propose AXELRAM, a smart SRAM macro architecture that computes attention scores directly from quantized KV cache indices without dequantization. The key enabler is a design‑time fixed codebook: orthogonal‑transform‑based quantization concentrates each coordinate's distribution to N(0,1/d), so the optimal quantizer depends only on dimension d and bit‑width b, not on input data. The asymmetric path design ‑‑ transform on write, table‑lookup on read with no inverse transform ‑‑ reduces per‑query multiplications by 102.4x (a mathematical identity).
Through multi‑seed evaluation (10 seeds x 3 models), we discover that sign pattern sensitivity causes catastrophic PPL spikes (Delta > 50) on certain models (Qwen2.5‑3B), while others (LLaMA‑3.1‑8B) are fully stable. This phenomenon extends SpinQuant's observation of rotation variance in weight quantization to the KV cache domain, where the effect is qualitatively more severe. We trace the root cause to layer‑wise norm heterogeneity and propose a gradient‑free sign pattern selection (200 candidates, 8 calibration samples, one‑time) that eliminates catastrophic spikes with zero additional hardware cost. All source code is available at https://github.com/Axelidea/AXELRAM.
Authors:Gonzalo Uribarri
Abstract:
We introduce ROMAN (ROuting Multiscale representAtioN), a deterministic operator for time series that maps temporal scale and coarse temporal position into an explicit channel structure while reducing sequence length. ROMAN builds an anti‑aliased multiscale pyramid, extracts fixed‑length windows from each scale, and stacks them as pseudochannels, yielding a compact representation on which standard convolutional classifiers can operate. In this way, ROMAN provides a simple mechanism to control the inductive bias of downstream models: it can reduce temporal invariance, make temporal pooling implicitly coarse‑position‑aware, and expose multiscale interactions through channel mixing, while often improving computational efficiency by shortening the processed time axis. We formally analyze the ROMAN operator and then evaluate it in two complementary ways by measuring its impact as a preprocessing step for four representative convolutional classifiers: MiniRocket, MultiRocket, a standard CNN‑based classifier, and a fully convolutional network (FCN) classifier. First, we design synthetic time series classification tasks that isolate coarse position awareness, long‑range correlation, multiscale interaction, and full positional invariance, showing that ROMAN behaves consistently with its intended mechanism and is most useful when class information depends on temporal structure that standard pooled convolution tends to suppress. Second, we benchmark the same models with and without ROMAN on long‑sequence subsets of the UCR and UEA archives, showing that ROMAN provides a practically useful alternative representation whose effect on accuracy is task‑dependent, but whose effect on efficiency is often favorable. Code is available at https://github.com/gon‑uri/ROMAN
Authors:Haiyu Wang, Yutong Wang, Jack Jiang, Sai Qian Zhang
Abstract:
Singular Value Decomposition (SVD) has become an important technique for reducing the computational burden of Vision Language Models (VLMs), which play a central role in tasks such as image captioning and visual question answering. Although multiple prior works have proposed efficient SVD variants to enable low‑rank operations, we find that in practice it remains difficult to achieve substantial latency reduction during model execution. To address this limitation, we introduce a new computational pattern and apply SVD at a finer granularity, enabling real and measurable improvements in execution latency. Furthermore, recognizing that weight elements differ in their relative importance, we adaptively allocate relative importance to each element during SVD process to better preserve accuracy, then extend this framework with quantization applied to both weights and activations, resulting in a highly efficient VLM. Collectively, we introduce~Weighted SVD (WSVD), which outperforms other approaches by achieving over 1.8× decoding speedup while preserving accuracy. We open source our code at: \hrefhttps://github.com/SAI‑Lab‑NYU/WSVD\texttthttps://github.com/SAI‑Lab‑NYU/WSVD
Authors:Ye Mao, Weixun Luo, Ranran Huang, Junpeng Jing, Krystian Mikolajczyk
Abstract:
Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable representations for 3D scene understanding. In this paper, we propose UniScene3D, a transformer‑based encoder that learns unified scene representations from multi‑view colored pointmaps, jointly modeling image appearance and geometry. For robust colored pointmap representation learning, we introduce novel cross‑view geometric alignment and grounded view alignment to enforce cross‑view geometry and semantic consistency. Extensive low‑shot and task‑specific fine‑tuning evaluations on viewpoint grounding, scene retrieval, scene type classification, and 3D VQA demonstrate our state‑of‑the‑art performance. These results highlight the effectiveness of our approach for unified 3D scene understanding. https://yebulabula.github.io/UniScene3D/
Authors:Malik Hassanaly, Corey R. Randall, Peter J. Weddle, Paul J. Gasper, Conlain Kelly, Tanvir R. Tanim, Kandler Smith
Abstract:
Diagnosing the internal state of Li‑ion batteries is critical for battery research, operation of real‑world systems, and prognostic evaluation of remaining lifetime. By using physics‑based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li‑ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics‑based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real‑time applications. The present work shows that NPE calibrates parameters equally or more accurately than Bayesian calibration, and we demonstrate that the higher computational costs for data generation are tractable even in high‑dimensional cases (ranging from 6 to 27 estimated parameters), but the NPE method can lead to higher voltage prediction errors. The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).
Authors:Pangpang Liu, Chengchun Shi, Will Wei Sun
Abstract:
Reinforcement learning from human feedback (RLHF) has emerged as a central framework for aligning large language models (LLMs) with human preferences. Despite its practical success, RLHF raises fundamental statistical questions because it relies on noisy, subjective, and often heterogeneous feedback to learn reward models and optimize policies. This survey provides a statistical perspective on RLHF, focusing primarily on the LLM alignment setting. We introduce the main components of RLHF, including supervised fine‑tuning, reward modeling, and policy optimization, and relate them to familiar statistical ideas such as Bradley‑Terry‑Luce (BTL) model, latent utility estimation, active learning, experimental design, and uncertainty quantification. We review methods for learning reward functions from pairwise preference data and for optimizing policies through both two‑stage RLHF pipelines and emerging one‑stage approaches such as direct preference optimization. We further discuss recent extensions including reinforcement learning from AI feedback, inference‑time algorithms, and reinforcement learning from verifiable rewards, as well as benchmark datasets, evaluation protocols, and open‑source frameworks that support RLHF research. We conclude by highlighting open challenges in RLHF. An accompanying GitHub demo https://github.com/Pangpang‑Liu/RLHF_demo illustrates key components of the RLHF pipeline.
Authors:Chin-Chia Michael Yeh
Abstract:
Matrix Profile (MP) methods are an interpretable and scalable family of distance‑based methods for time‑series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest‑neighbor profile. This technical report documents an open‑source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB‑AD, a benchmark that covers both univariate and multivariate time series. The submitted system combines pre‑sorted multidimensional aggregation, efficient exclusion‑zone‑aware k‑nearest‑neighbor (kNN) retrieval for repeated anomalies, and moving‑average post‑processing. To serve as a reproducible reference for MP‑based anomaly detection on TSB‑AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corresponding benchmark results. We further analyze how the system performs on the aggregate leaderboard and across specific dataset characteristics.The open‑source implementation is available at https://github.com/mcyeh/mmpad_tsb.
Authors:Mostapha Benhenda
Abstract:
Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and evaluation cycles are slow. Y Combinator batches offer a unique mitigation: each batch comprises around 200 startups, funded simultaneously, with evaluation at Demo Day only three months later. We introduce YC Bench, a live benchmark for forecasting early outperformance within YC batches. Using the YC W26 batch as a case study (196 startups), we measure outperformance with a Pre‑Demo Day Score, a KPI combining publicly available traction signals and web visibility. This short‑term metric enables rapid evaluation of forecasting models. As a baseline, we take Google mentions prior to the YC W26 application deadline, a simple proxy for prior brand recognition, recovering 6 of 11 top performers at YC Demo Day (55% recall). YC Bench provides a live benchmark for studying startup success forecasting, with iteration cycles measured in months rather than years. Code and Data are available on GitHub: https://github.com/benstaf/ycbench
Authors:Yonas Kassa, James Bonacci, Ping Wang
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.
Authors:Tin Hadži Veljković, Joshua Rosenthal, Ivor Lončarić, Jan-Willem van de Meent
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.
Authors:Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Chengcheng Han, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
Abstract:
Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference‑time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely follows. We ask whether skills can instead be internalized into model parameters, enabling zero‑shot autonomous behavior without any runtime skill retrieval. We introduce SKILL0, an in‑context reinforcement learning framework designed for skill internalization. SKILL0 introduces a training‑time curriculum that begins with full skill context and progressively withdraws it. Skills are grouped offline by category and rendered with interaction history into a compact visual context, teaching he model tool invocation and multi‑turn task completion. A Dynamic Curriculum then evaluates each skill file's on‑policy helpfulness, retaining only those from which the current policy still benefits within a linearly decaying budget, until the agent operates in a fully zero‑shot setting. Extensive agentic experiments demonstrate that SKILL0 achieves substantial improvements over the standard RL baseline (+9.7% for ALFWorld, +6.6% for Search‑QA, and+10.1% for WebShop), while maintaining a highly efficient context of fewer than 0.5k tokens per step. Our code is available at https://github.com/ZJU‑REAL/SkillZero.
Authors:Hao Zhu, Di Zhou, Donna Slonim
Abstract:
Understanding causal dependencies in observational data is critical for informing decision‑making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acyclic Graphs (DAGs). Existing methods, such as NOTEARS and DAG‑GNN, often face issues with scalability and stability in high‑dimensional data, especially when there is a feature‑sample imbalance. Here, we show that the denoising score matching objective of diffusion models could smooth the gradients for faster, more stable convergence. We also propose an adaptive k‑hop acyclicity constraint that improves runtime over existing solutions that require matrix inversion. We name this framework Denoising Diffusion Causal Discovery (DDCD). Unlike generative diffusion models, DDCD utilizes the reverse denoising process to infer a parameterized causal structure rather than to generate data. We demonstrate the competitive performance of DDCDs on synthetic benchmarking data. We also show that our methods are practically useful by conducting qualitative analyses on two real‑world examples. Code is available at this url: https://github.com/haozhu233/ddcd.
Authors:Xuanfeng Zhou
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.
Authors:Jeremy Herbst, Stefan Wermter, Jae Hee Lee
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.
Authors:Soo Won Seo, KyungChae Lee, Hyungchan Cho, Taein Son, Nam Ik Cho, Jun Won Choi
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.
Authors:Jaber Jaber, Osama Jaber
Abstract:
Recursive transformers reuse a shared weight block across multiple depth steps, trading parameters for compute. A core limitation: every step applies the same transformation, preventing the model from composing distinct operations across depth. We present Ouroboros, a system that attaches a compact Controller hypernetwork to a recursive transformer block. The Controller observes the current hidden state, produces a per‑step diagonal modulation vector, and applies it to frozen SVD‑initialized LoRA bases, making each recurrence step input‑dependent. We combine this with gated recurrence (bias‑initialized to 88% retention) and per‑step LayerNorm for stable deep iteration. On Qwen2.5‑3B split into a Prelude/Recurrent/Coda architecture (17 of 36 layers retained), Ouroboros reduces training loss by 43.4% over the unmodified 17‑layer baseline, recovering 51.3% of the performance gap caused by layer removal. The full system adds only 9.2M trainable parameters (Controller, gate, and per‑step norms) yet outperforms equivalently‑sized static per‑step LoRA by 1.44 loss points at depth 1 and remains ahead across all tested depths (1, 4, 8, 16) and ranks (8, 32, 64). We also find that gated recurrence is essential: without it, recursive layer application makes the model strictly worse. These gains are measured on the training distribution; on held‑out text, the Controller does not yet improve over the baseline, a limitation we attribute to frozen downstream layers and discuss in detail. Code: https://github.com/RightNow‑AI/ouroboros
Authors:Sebastian-Ion Nae, Radu Moldoveanu, Alexandra Stefania Ghita, Adina Magda Florea
Abstract:
Understanding human behaviour in crowded indoor environments is central to surveillance, smart buildings, and human‑robot interaction, yet existing datasets rarely capture real‑world indoor complexity at scale. We introduce IndoorCrowd, a multi‑scene dataset for indoor human detection, instance segmentation, and multi‑object tracking, collected across four campus locations (ACS‑EC, ACS‑EG, IE‑Central, R‑Central). It comprises 31 videos (9,913 frames at 5fps) with human‑verified, per‑instance segmentation masks. A 620‑frame control subset benchmarks three foundation‑model auto‑annotators: SAM3, GroundingSAM, and EfficientGroundingSAM, against human labels using Cohen's κ, AP, precision, recall, and mask IoU. A further 2,552‑frame subset supports multi‑object tracking with continuous identity tracks in MOTChallenge format. We establish detection, segmentation, and tracking baselines using YOLOv8n, YOLOv26n, and RT‑DETR‑L paired with ByteTrack, BoT‑SORT, and OC‑SORT. Per‑scene analysis reveals substantial difficulty variation driven by crowd density, scale, and occlusion: ACS‑EC, with 79.3% dense frames and a mean instance scale of 60.8px, is the most challenging scene. The project page is available at https://sheepseb.github.io/IndoorCrowd/.
Authors:Ilan Gold, Felix Fischer, Lucas Arnoldt, F. Alexander Wolf, Fabian J. Theis
Abstract:
The scale of biological datasets now routinely exceeds system memory, making data access rather than model computation the primary bottleneck in training machine‑learning models. This bottleneck is particularly acute in biology, where widely used community data formats must support heterogeneous metadata, sparse and dense assays, and downstream analysis within established computational ecosystems. Here we present annbatch, a mini‑batch loader native to anndata that enables out‑of‑core training directly on disk‑backed datasets. Across single‑cell transcriptomics, microscopy and whole‑genome sequencing benchmarks, annbatch increases loading throughput by up to an order of magnitude and shortens training from days to hours, while remaining fully compatible with the scverse ecosystem. Annbatch establishes a practical data‑loading infrastructure for scalable biological AI, allowing increasingly large and diverse datasets to be used without abandoning standard biological data formats. Github: https://github.com/scverse/annbatch
Authors:Gaëtan Hadjeres, Marc Ferras, Khaled Koutini, Benno Weck, Alexandre Bittar, Thomas Hummel, Zineb Lahrichi, Hakim Missoum, Joan Serrà, Yuki Mitsufuji
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/.
Authors:Shuibai Zhang, Caspian Zhuang, Chihan Cui, Zhihan Yang, Fred Zhangzhi Peng, Yanxin Zhang, Haoyue Bai, Zack Jia, Yang Zhou, Guanhua Chen, Ming Liu
Abstract:
Diffusion language models (DLMs) enable parallel, non‑autoregressive text generation, yet existing DLM mixture‑of‑experts (MoE) models inherit token‑choice (TC) routing from autoregressive systems, leading to load imbalance and rigid computation allocation. We show that expert‑choice (EC) routing is a better fit for DLMs: it provides deterministic load balancing by design, yielding higher throughput and faster convergence than TC. Building on the property that EC capacity is externally controllable, we introduce timestep‑dependent expert capacity, which varies expert allocation according to the denoising step. We find that allocating more capacity to low‑mask‑ratio steps consistently achieves the best performance under matched FLOPs, and provide a mechanistic explanation: tokens in low‑mask‑ratio contexts exhibit an order‑of‑magnitude higher learning efficiency, so concentrating compute on these steps yields the largest marginal return. Finally, we show that existing pretrained TC DLMs can be retrofitted to EC by replacing only the router, achieving faster convergence and improved accuracy across diverse downstream tasks. Together, these results establish EC routing as a superior paradigm for DLM MoE models and demonstrate that computation in DLMs can be treated as an adaptive policy rather than a fixed architectural constant. Code is available at https://github.com/zhangshuibai/EC‑DLM.
Authors:Lincan Li, Rikuto Kotoge, Xihao Piao, Zheng Chen, Yushun Dong
Abstract:
Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter‑patient variability. To model them, recent methods construct dynamic graphs via statistical correlations, predefined similarity measures, or implicit learning, yet rarely account for EEG's noisy nature. Consequently, these graphs usually contain redundant or task‑irrelevant connections, undermining model performance even with state‑of‑the‑art architectures. In this paper, we present a new perspective for EEG seizure detection: jointly learning denoised dynamic graph structures and informative spatial‑temporal representations guided by the Information Bottleneck (IB). Unlike prior approaches, our graph constructor explicitly accounts for the noisy characteristics of EEG data, producing compact and reliable connectivity patterns that better support downstream seizure detection. To further enhance representation learning, we employ a self‑supervised Graph Masked AutoEncoder that reconstructs masked EEG signals based on dynamic graph context, promoting structure‑aware and compact representations aligned with the IB principle. Bringing things together, we introduce Information Bottleneck‑guided EEG SeizuRE DetectioN via SElf‑Supervised Learning (IRENE), which explicitly learns dynamic graph structures and interpretable spatial‑temporal EEG representations. IRENE addresses three core challenges: (i) Identifying the most informative nodes and edges; (ii) Explaining seizure propagation in the brain network; and (iii) Enhancing robustness against label scarcity and inter‑patient variability. Extensive experiments on benchmark EEG datasets demonstrate that our method outperforms state‑of‑the‑art baselines in seizure detection and provides clinically meaningful insights into seizure dynamics. The source code is available at https://github.com/LabRAI/IRENE.
Authors:Yiming Fan, Jun Yeon Won, Ding Zhu, Melih Sirlanci, Mahdi Khalili, Carter Yagemann
Abstract:
Binary Function Similarity Detection (BFSD) is a core problem in software security, supporting tasks such as vulnerability analysis, malware classification, and patch provenance. In the past few decades, numerous models and tools have been developed for this application; however, due to the lack of a comprehensive universal benchmark in this field, researchers have struggled to compare different models effectively. Existing datasets are limited in scope, often focusing on a narrow set of transformations or types of binaries, and fail to reflect the full diversity of real‑world applications.
We introduce EXHIB, a benchmark comprising five realistic datasets collected from the wild, each highlighting a distinct aspect of the BFSD problem space. We evaluate 9 representative models spanning multiple BFSD paradigms on EXHIB and observe performance degradations of up to 30% on firmware and semantic datasets compared to standard settings, revealing substantial generalization gaps. Our results show that robustness to low‑ and mid‑level binary variations does not generalize to high‑level semantic differences, underscoring a critical blind spot in current BFSD evaluation practices.
Authors:Yixiao Wang, Ting Jiang, Zishan Shao, Hancheng Ye, Jingwei Sun, Mingyuan Ma, Jianyi Zhang, Yiran Chen, Hai Li
Abstract:
Denoising generative models deliver high‑fidelity generation but remain bottlenecked by inference latency due to the many iterative denoiser calls required during sampling. Training‑free acceleration methods reduce latency by either sparsifying the model architecture or shortening the sampling trajectory. Current training‑free acceleration methods are more complex than necessary: higher‑order predictors amplify error under aggressive speedups, and architectural modifications hinder deployment. Beyond 2x acceleration, step skipping creates structural scarcity ‑‑ at most one fresh evaluation per local window ‑‑ leaving the computed output and its backward difference as the only causally grounded information. Based on this, we propose ZEUS, an acceleration method that predicts reduced denoiser evaluations using a second‑order predictor, and stabilizes aggressive consecutive skipping with an interleaved scheme that avoids back‑to‑back extrapolations. ZEUS adds essentially zero overhead, no feature caches, and no architectural modifications, and it is compatible with different backbones, prediction objectives, and solver choices. Across image and video generation, ZEUS consistently improves the speed‑fidelity performance over recent training‑free baselines, achieving up to 3.2x end‑to‑end speedup while maintaining perceptual quality. Our code is available at: https://github.com/Ting‑Justin‑Jiang/ZEUS.
Authors:William Hoy, Binxu Wang, Xu Pan
Abstract:
Evolution Strategies (ES) have emerged as a scalable gradient‑free alternative to reinforcement learning based LLM fine‑tuning, but it remains unclear whether comparable task performance implies comparable solutions in parameter space. We compare ES and Group Relative Policy Optimization (GRPO) across four tasks in both single‑task and sequential continual‑learning settings. ES matches or exceeds GRPO in single‑task accuracy and remains competitive sequentially when its iteration budget is controlled. Despite this similarity in task performance, the two methods produce markedly different model updates: ES makes much larger changes and induces broader off‑task KL drift, whereas GRPO makes smaller, more localized updates. Strikingly, the ES and GRPO solutions are linearly connected with no loss barrier, even though their update directions are nearly orthogonal. We develop an analytical theory of ES that explains all these phenomena within a unified framework, showing how ES can accumulate large off‑task movement on weakly informative directions while still making enough progress on the task to match gradient‑based RL in downstream accuracy. These results show that gradient‑free and gradient‑based fine‑tuning can reach similarly accurate yet geometrically distinct solutions, with important consequences for forgetting and knowledge preservation. The source code is publicly available: https://github.com/Bhoy1/ESvsGRPO.
Authors:Neo Christopher Chung, Maxim Laletin
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
Authors:Haseeb Tariq, Alen Kaja, Marwan Hassani
Abstract:
Money launderers take advantage of limitations in existing detection approaches by hiding their financial footprints in a deceitful manner. They manage this by replicating transaction patterns that the monitoring systems cannot easily distinguish. As a result, criminally gained assets are pushed into legitimate financial channels without drawing attention. Algorithms developed to monitor money flows often struggle with scale and complexity. The difficulty of identifying such activities is further intensified by the (persistent) inability of current solutions to control the excessive number of false positive signals produced by rigid, risk‑based rules systems. We propose a framework called ReDiRect (REduce, DIstribute, and RECTify), specifically designed to overcome these challenges. The primary contribution of our work is a novel framing of this problem in an unsupervised setting; where a large transaction graph is fuzzily partitioned into smaller, manageable components to enable fast processing in a distributed manner. In addition, we define a refined evaluation metric that better captures the effectiveness of exposed money laundering patterns. Through comprehensive experimentation, we demonstrate that our framework achieves superior performance compared to existing and state‑of‑the‑art techniques, particularly in terms of efficiency and real‑world applicability. For validation, we used the real (open source) Libra dataset and the recently released synthetic datasets by IBM Watson. Our code and datasets are available at https://github.com/mhaseebtariq/redirect.
Authors:Nathan Benjamin, A. Liam Fitzpatrick, Wei Li, Jesse Thaler
Abstract:
In this paper, we attempt to explore the landscape of two‑dimensional conformal field theories (2d CFTs) by efficiently searching for numerical solutions to the modular bootstrap equation using machine‑learning‑style optimization. The torus partition function of a 2d CFT is fixed by the spectrum of its primary operators and its chiral algebra, which we take to be the Virasoro algebra with c>1. We translate the requirement that this partition function is modular invariant into a loss function, which we then minimize to identify possible primary spectra. Our approach involves two technical innovations that facilitate finding reliable candidate CFTs. The first is a strategy to estimate the uncertainty associated with truncating the spectrum to the lowest dimension operators. The second is the use of a new singular‑value‑based optimizer (Sven) that is more effective than gradient descent at navigating the hierarchical structure of the loss landscape. We numerically construct candidate truncated CFT partition functions with central charges between 1 and \frac87, a range devoid of known examples, and argue that these candidates likely come from a continuous space of modular bootstrap solutions. We also provide evidence for a more stringent constraint on the spectral gap near c = 1 than the existing bound of Δ_\rm gap \le \fracc6 + \frac13.
Authors:Cai Zhou, Zekai Wang, Menghua Wu, Qianyu Julie Zhu, Flora C. Shi, Chenyu Wang, Ashia Wilson, Tommi Jaakkola, Stephen Bates
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.
Authors:Jack Young
Abstract:
Using roughly 48 execution‑verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval. The method, which we call S0 tuning, optimizes one state matrix per recurrent layer while freezing all model weights. On Qwen3.5‑4B (GatedDeltaNet hybrid), S0 tuning improves greedy pass@1 by +23.6 +/‑ 1.7 pp (10 seeds). On FalconH1‑7B (Mamba‑2 hybrid), S0 reaches 71.8% +/‑ 1.3 and LoRA reaches 71.4% +/‑ 2.4 (3 seeds), statistically indistinguishable at this sample size while requiring no weight merging. Cross‑domain transfer is significant on MATH‑500 (+4.8 pp, p = 0.00002, 8 seeds) and GSM8K (+2.8 pp, p = 0.0003, 10 seeds); a text‑to‑SQL benchmark (Spider) shows no transfer, consistent with the trajectory‑steering mechanism. A prefix‑tuning control on a pure Transformer (Qwen2.5‑3B) degrades performance by ‑13.9 pp under all nine configurations tested. On Qwen3.5, a per‑step state‑offset variant reaches +27.1 pp, above both S0 and LoRA but with per‑step inference cost. Taken together, the results show that recurrent state initialization is a strong zero‑inference‑overhead PEFT surface for hybrid language models when verified supervision is scarce. The tuned state is a ~48 MB file; task switching requires no weight merging or model reload. Code and library: https://github.com/jackyoung27/s0‑tuning.
Authors:Aaron Rose, Carissa Cullen, Sahar Abdelnabi, Philip Torr, Brandon Gary Kaplowitz, Christian Schroeder de Witt
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.
Authors:Atsuyuki Miyai, Mashiro Toyooka, Zaiying Zhao, Kenta Watanabe, Toshihiko Yamasaki, Kiyoharu Aizawa
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.
Authors:Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Di Wen, Danda Pani Paudel, Luc Van Gool, Kailun Yang
Abstract:
3D semantic occupancy prediction is central to autonomous driving, yet current methods are vulnerable to long‑tailed class bias and out‑of‑distribution (OOD) inputs, often overconfidently assigning anomalies to rare classes. We present ProOOD, a lightweight, plug‑and‑play method that couples prototype‑guided refinement with training‑free OOD scoring. ProOOD comprises (i) prototype‑guided semantic imputation that fills occluded regions with class‑consistent features, (ii) prototype‑guided tail mining that strengthens rare‑class representations to curb OOD absorption, and (iii) EchoOOD, which fuses local logit coherence with local and global prototype matching to produce reliable voxel‑level OOD scores. Extensive experiments on five datasets demonstrate that ProOOD achieves state‑of‑the‑art performance on both in‑distribution 3D occupancy prediction and OOD detection. On SemanticKITTI, it surpasses baselines by +3.57% mIoU overall and +24.80% tail‑class mIoU; on VAA‑KITTI, it improves AuPRCr by +19.34 points, with consistent gains across benchmarks. These improvements yield more calibrated occupancy estimates and more reliable OOD detection in safety‑critical urban driving. The source code is publicly available at https://github.com/7uHeng/ProOOD.
Authors:Zhengyang Tang, Ke Ji, Xidong Wang, Zihan Ye, Xinyuan Wang, Yiduo Guo, Ziniu Li, Chenxin Li, Jingyuan Hu, Shunian Chen, Tongxu Luo, Jiaxi Bi, Zeyu Qin, Shaobo Wang, Xin Lai, Pengyuan Lyu, Junyi Li, Can Xu, Chengquan Zhang, Han Hu, Ming Yan, Benyou Wang
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.
Authors:Yilun Liu, Jinru Han, Sikuan Yan, Volker Tresp, Yunpu Ma
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.
Authors:Björn Roman Kohlberger
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.
Authors:Rajkiran Panuganti
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.
Authors:Karan Singh, Michael Yu, Varun Gangal, Zhuofu Tao, Sachin Kumar, Emmy Liu, Steven Y. Feng
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.
Authors:Yu Xia, Canwen Xu, Zhewei Yao, Julian McAuley, Yuxiong He
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.
Authors:Marwan Hassani, Tamara Verbeek, Sjoerd van Straten
Abstract:
Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept drifts. This results in catastrophic forgetting, where training while focusing merely on new data distribution negatively impacts the performance on previously learned data distributions. Continual learning addresses, among others, the challenges related to mitigating catastrophic forgetting. This paper proposes a novel approach called Continual Next Activity Prediction with Prompts (CNAPwP), which adapts the DualPrompt algorithm for next activity prediction to improve accuracy and adaptability while mitigating catastrophic forgetting. We introduce new datasets with recurring concept drifts, alongside a task‑specific forgetting metric that measures the prediction accuracy gap between initial occurrence and subsequent task occurrences. Extensive testing on three synthetic and two real‑world datasets representing several setups of recurrent drifts shows that CNAPwP achieves SOTA or competitive results compared to five baselines, demonstrating its potential applicability in real‑world scenarios. An open‑source implementation of our method, together with the datasets and results, is available at: https://github.com/SvStraten/CNAPwP.
Authors:Yichen Xie, Yixiao Wang, Shuqi Zhao, Cheng-En Wu, Masayoshi Tomizuka, Jianwen Xie, Hao-Shu Fang
Abstract:
The generalization ability of imitation learning policies for robotic manipulation is fundamentally constrained by the diversity of expert demonstrations, while collecting demonstrations across varied environments is costly and difficult in practice. In this paper, we propose a practical framework that exploits inherent scene diversity without additional human effort by scaling camera views during demonstration collection. Instead of acquiring more trajectories, multiple synchronized camera perspectives are used to generate pseudo‑demonstrations from each expert trajectory, which enriches the training distribution and improves viewpoint invariance in visual representations. We analyze how different action spaces interact with view scaling and show that camera‑space representations further enhance diversity. In addition, we introduce a multiview action aggregation method that allows single‑view policies to benefit from multiple cameras during deployment. Extensive experiments in simulation and real‑world manipulation tasks demonstrate significant gains in data efficiency and generalization compared to single‑view baselines. Our results suggest that scaling camera views provides a practical and scalable solution for imitation learning, which requires minimal additional hardware setup and integrates seamlessly with existing imitation learning algorithms. The website of our project is https://yichen928.github.io/robot_multiview.
Authors:Yabin Zhang, Chong Wang, Yunhe Gao, Jiaming Liu, Maya Varma, Justin Xu, Sophie Ostmeier, Jin Long, Sergios Gatidis, Seena Dehkharghani, Arne Michalson, Eun Kyoung Hong, Christian Bluethgen, Haiwei Henry Guo, Alexander Victor Ortiz, Stephan Altmayer, Sandhya Bodapati, Joseph David Janizek, Ken Chang, Jean-Benoit Delbrouck, Akshay S. Chaudhari, Curtis P. Langlotz
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.
Authors:Michael Maynord, Minghui Liu, Cornelia Fermüller, Seongjin Choi, Yuxin Zeng, Shishir Dahal, Daniel M. Harrison
Abstract:
Ultra‑high field 7‑tesla (7T) MRI improves visualization of multiple sclerosis (MS) white matter lesions (WML) but differs sufficiently in contrast and artifacts from 1.5‑3T imaging ‑ suggesting that widely used automated segmentation tools may not translate directly. We analyzed 7T FLAIR scans and generated reference WML masks from Lesion Segmentation Tool (LST) outputs followed by expert manual revision. As external comparators, we applied LST‑LPA and the more recent LST‑AI ensemble, both originally developed on lower‑field data. We then trained 3D UNETR and SegFormer transformer‑based models on 7T FLAIR at multiple resolutions (0.5x0.5x0.5^3, 1.0x1.0x1.0^3, and 1.5x1.5x2.0^3) and evaluated all methods using voxel‑wise and lesion‑wise metrics from the BraTS 2023 framework. On the held‑out test set at native 0.5x0.5x0.5^3 resolution, 7T‑trained transformers achieved competitive overlap with LST‑AI while recovering additional small lesions that were missed by classical methods, at the cost of some boundary variability and occasional artifact‑related false positives. On a held‑out 7 T test set, our best transformer model (SegFormer) achieved a voxel‑wise Dice of 0.61 and lesion‑wise Dice of 0.20, improving on the classical LST‑LPA tool (Dice 0.39, lesion‑wise Dice 0.02). Performance decreased for models trained on downsampled images, underscoring the value of native 7T resolution for small‑lesion detection. By releasing our 7T‑trained models, we aim to provide a reproducible, ready‑to‑use resource for automated lesion quantification in ultra‑high field MS research (https://github.com/maynord/7T‑MS‑lesion‑segmentation).
Authors:Borislav Mavrin
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%).
Authors:Ankit Grover, Lodovico Giaretta, Rémi Bourgerie, Sarunas Girdzijauskas
Abstract:
The integration of Graph Neural Networks (GNNs) with Large Language Models (LLMs) has emerged as a promising paradigm for Graph Question Answering (GraphQA). However, effective methods for encoding complex structural information into the LLM's latent space remain an open challenge. Current state‑of‑the‑art architectures, such as G‑Retriever, typically rely on standard GNNs and aggressive mean pooling to compress entire graph substructures into a single token, creating a severe information bottleneck. This work mitigates this bottleneck by investigating two orthogonal strategies: (1) increasing the bandwidth of the graph‑to‑LLM interface via multi‑token pooling, and (2) enhancing the semantic quality of the graph encoder via global attention mechanisms. We evaluate a suite of hierarchical pruning and clustering‑based pooling operators including Top‑k, SAGPool, DiffPool, MinCutPool, and Virtual Node Pooling (VNPool) to project graph data into multiple learnable tokens. Empirically, we demonstrate that while pooling introduces significant instability during soft prompt tuning, the application of Low‑Rank Adaptation (LoRA) effectively stabilizes specific hierarchical projections (notably VNPool and pruning methods), though dense clustering operators remain challenging. This stabilization allows compressed representations to rival full‑graph baselines (achieving ~73% Hit@1 on WebQSP). Conceptually, we demonstrate that a Graph Transformer with VNPool implementation functions structurally as a single‑layer Perceiver IO encoder. Finally, we adapt the FandE (Features and Edges) Score to the generative GraphQA domain. Our analysis reveals that the GraphQA benchmark suffers from representational saturation, where target answers are often highly correlated with isolated node features. The implementation is available at https://github.com/Agrover112/G‑Retriever/tree/all_good/
Authors:Yagiz Ihlamur
Abstract:
Predicting startup success from founder career data is hard. The signal is weak, the labels are rare (9%), and most founders who succeed look almost identical to those who fail. We engineer 28 structured features directly from raw JSON fields ‑‑ jobs, education, exits ‑‑ and combine them with a deterministic rule layer and XGBoost boosted stumps. Our model achieves Val F0.5 = 0.3030, Precision = 0.3333, Recall = 0.2222 ‑‑ a +17.7pp improvement over the zero‑shot LLM baseline. We then run a controlled experiment: extract 9 features from the prose field using Claude Haiku, at 67% and 100% dataset coverage. LLM features capture 26.4% of model importance but add zero CV signal (delta = ‑0.05pp). The reason is structural: anonymised_prose is generated from the same JSON fields we parse directly ‑‑ it is a lossy re‑encoding, not a richer source. The ceiling (CV ~= 0.25, Val ~= 0.30) reflects the information content of this dataset, not a modeling limitation. In characterizing where the signal runs out and why, this work functions as a benchmark diagnostic ‑‑ one that points directly to what a richer dataset would need to include.
Authors:Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco, Thales Souza, Felix J. Herrmann
Abstract:
Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Waveform Inversion. However, these approaches predominantly rely on the availability of large‑scale datasets of high‑quality, geologically realistic subsurface velocity models, which are often difficult to obtain in practice. We introduce SAGE, a novel framework for statistically consistent proxy velocity generation from incomplete observations, specifically sparse well logs and migrated seismic images. During training, SAGE learns a proxy posterior over velocity models conditioned on both modalities (wells and seismic); at inference, it produces full‑resolution velocity fields conditioned solely on migrated images, with well information implicitly encoded in the learned distribution. This enables the generation of geologically plausible and statistically accurate velocity realizations. We validate SAGE on both synthetic and field datasets, demonstrating its ability to capture complex subsurface variability under limited observational constraints. Furthermore, samples drawn from the learned proxy distribution can be leveraged to train downstream networks, supporting inversion workflows. Overall, SAGE provides a scalable and data‑efficient pathway toward learning geological proxy posterior for seismic imaging and inversion. Repo link: https://github.com/slimgroup/SAGE.
Authors:Jinghan Yao, Sam Adé Jacobs, Walid Krichene, Masahiro Tanaka, Dhabaleswar K Panda
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
Authors:Annette Taberner-Miller
Abstract:
Multi‑model LLM serving operates in a non‑stationary, noisy environment: providers revise pricing, model quality can shift or regress without notice, and new models arrive regularly. More than a dozen recent methods have proposed learned routers to navigate the resulting quality‑‑cost tradeoff across portfolios spanning a ～530× cost range. Despite this activity, two gaps in the current solution space limit routing effectiveness under these conditions: no existing router enforces a dollar‑denominated cost ceiling in closed loop over an open‑ended request stream, and none provides principled online adaptation to post‑deployment shifts in pricing or model quality. We present ParetoBandit, an open‑source adaptive router built on cost‑aware contextual bandits that addresses both gaps. Its core contributions are: (1) an online primal‑‑dual budget pacer that enforces a per‑request cost ceiling without a known horizon, and (2) geometric forgetting on sufficient statistics that gives the bandit bounded memory for tracking quality and cost shifts. A hot‑swap model registry further supports runtime model changes with budget‑controlled exploration. On 1,824 benchmark prompts with a three‑model portfolio, the router maintains budget compliance within 0.4%, adapts to price and quality shifts with up to +0.071 quality lift, and integrates a cold‑started model within ～142 steps.
Authors:Silong Yong, Stephen Sheng, Carl Qi, Xiaojie Wang, Evan Sheehan, Anurag Shivaprasad, Yaqi Xie, Katia Sycara, Yesh Dattatreya
Abstract:
Existing robotic foundation policies are trained primarily via large‑scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long‑horizon tasks due to distribution shift and error accumulation. While reinforcement learning (RL) can finetune these models, it cannot work well across diverse tasks without manual reward engineering. We propose VLLR, a dense reward framework combining (1) an extrinsic reward from Large Language Models (LLMs) and Vision‑Language Models (VLMs) for task progress recognition, and (2) an intrinsic reward based on policy self‑certainty. VLLR uses LLMs to decompose tasks into verifiable subtasks and then VLMs to estimate progress to initialize the value function for a brief warm‑up phase, avoiding prohibitive inference cost during full training; and self‑certainty provides per‑step intrinsic guidance throughout PPO finetuning. Ablation studies reveal complementary benefits: VLM‑based value initialization primarily improves task completion efficiency, while self‑certainty primarily enhances success rates, particularly on out‑of‑distribution tasks. On the CHORES benchmark covering mobile manipulation and navigation, VLLR achieves up to 56% absolute success rate gains over the pretrained policy, up to 5% gains over state‑of‑the‑art RL finetuning methods on in‑distribution tasks, and up to 10% gains on out‑of‑distribution tasks, all without manual reward engineering. Additional visualizations can be found in https://silongyong.github.io/vllr_project_page/
Authors:Luan Borges Teodoro Reis Sena, Francisco Galuppo Azevedo
Abstract:
Interpretability is central for scientific machine learning, as understanding \emphwhy models make predictions enables hypothesis generation and validation. While tabular foundation models show strong performance, existing explanation methods like SHAP are computationally expensive, limiting interactive exploration. We introduce ShapPFN, a foundation model that integrates Shapley value regression directly into its architecture, producing both predictions and explanations in a single forward pass. On standard benchmarks, ShapPFN achieves competitive performance while producing high‑fidelity explanations (R^2=0.96, cosine=0.99) over 1000× faster than KernelSHAP (0.06s vs 610s). Our code is available at https://github.com/kunumi/ShapPFN
Authors:Yi Chen, Yuying Ge, Hui Zhou, Mingyu Ding, Yixiao Ge, Xihui Liu
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.
Authors:Giovanni Seraghiti, Kévin Dubrulle, Arnaud Vandaele, Nicolas Gillis
Abstract:
Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, X, by the product of two nonnegative factors, WH, where W has r columns and H has r rows. In this paper, we consider NMF using the component‑wise L1 norm as the error measure (L1‑NMF), which is suited for data corrupted by heavy‑tailed noise, such as Laplace noise or salt and pepper noise, or in the presence of outliers. Our first contribution is an NP‑hardness proof for L1‑NMF, even when r=1, in contrast to the standard NMF that uses least squares. Our second contribution is to show that L1‑NMF strongly enforces sparsity in the factors for sparse input matrices, thereby favoring interpretability. However, if the data is affected by false zeros, too sparse solutions might degrade the model. Our third contribution is a new, more general, L1‑NMF model for sparse data, dubbed weighted L1‑NMF (wL1‑NMF), where the sparsity of the factorization is controlled by adding a penalization parameter to the entries of WH associated with zeros in the data. The fourth contribution is a new coordinate descent (CD) approach for wL1‑NMF, denoted as sparse CD (sCD), where each subproblem is solved by a weighted median algorithm. To the best of our knowledge, sCD is the first algorithm for L1‑NMF whose complexity scales with the number of nonzero entries in the data, making it efficient in handling large‑scale, sparse data. We perform extensive numerical experiments on synthetic and real‑world data to show the effectiveness of our new proposed model (wL1‑NMF) and algorithm (sCD).
Authors:Lixin Xiu, Xufang Luo, Hideki Nakayama
Abstract:
Large vision‑language models (LVLMs) achieve impressive performance, yet their internal decision‑making processes remain opaque, making it difficult to determine if the success stems from true multimodal fusion or from reliance on unimodal priors. To address this attribution gap, we introduce a novel framework using partial information decomposition (PID) to quantitatively measure the "information spectrum" of LVLMs ‑‑ decomposing a model's decision‑relevant information into redundant, unique, and synergistic components. By adapting a scalable estimator to modern LVLM outputs, our model‑agnostic pipeline profiles 26 LVLMs on four datasets across three dimensions ‑‑ breadth (cross‑model & cross‑task), depth (layer‑wise information dynamics), and time (learning dynamics across training). Our analysis reveals two key results: (i) two task regimes (synergy‑driven vs. knowledge‑driven) and (ii) two stable, contrasting family‑level strategies (fusion‑centric vs. language‑centric). We also uncover a consistent three‑phase pattern in layer‑wise processing and identify visual instruction tuning as the key stage where fusion is learned. Together, these contributions provide a quantitative lens beyond accuracy‑only evaluation and offer insights for analyzing and designing the next generation of LVLMs. Code and data are available at https://github.com/RiiShin/pid‑lvlm‑analysis .
Authors:Cheng Yang, Yu Hao, Qi Zhang, Chuan Shi
Abstract:
When testing data and training data come from different distributions, deep neural networks (DNNs) will face significant safety risks in practical applications. Therefore, out‑of‑distribution (OOD) detection techniques, which can identify OOD samples at test time and alert the system, are urgently needed. Existing graph OOD detection methods usually characterize fine‑grained in‑distribution (ID) patterns from multiple perspectives, and train end‑to‑end graph neural networks (GNNs) for prediction. However, due to the unavailability of OOD data during training, the absence of explicit supervision signals could lead to sub‑optimal performance of end‑to‑end encoders. To address this issue, we follow the pre‑training+prompting paradigm to utilize pre‑trained GNN encoders, and propose Disentangled Graph Prompting (DGP), to capture fine‑grained ID patterns with the help of ID graph labels. Specifically, we design two prompt generators that respectively generate class‑specific and class‑agnostic prompt graphs by modifying the edge weights of an input graph. We also design several effective losses to train the prompt generators and prevent trivial solutions. We conduct extensive experiments on ten datasets to demonstrate the superiority of our proposed DGP, which achieves a relative AUC improvement of 3.63% over the best graph OOD detection baseline. Ablation studies and hyper‑parameter experiments further show the effectiveness of DGP. Code is available at https://github.com/BUPT‑GAMMA/DGP.
Authors:Linda Zeng, Steven Y. Feng, Michael C. Frank
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.
Authors:Steven Y. Feng, Alvin W. M. Tan, Michael C. Frank
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.
Authors:Zhongheng Jiang, Yuechao Zhao, Donglin Xie, Chenxi Sun, Rongchen Lu, Silu Luo, Zisheng Liang, Shenda Hong
Abstract:
Medical time‑series data captures the dynamic progression of patient conditions, playing a vital role in modern clinical decision support systems. However, real‑world clinical data is highly heterogeneous and inconsistently formatted. Furthermore, existing machine learning tools often have steep learning curves and fragmented workflows. Consequently, a significant gap remains between cutting‑edge AI technologies and clinical application. To address this, we introduce mtslearn, an end‑to‑end integrated toolkit specifically designed for medical time‑series data. First, the framework provides a unified data interface that automates the parsing and alignment of wide, long, and flat data formats. This design significantly reduces data cleaning overhead. Building on this, mtslearn provides a complete pipeline from data reading and feature engineering to model training and result visualization. Furthermore, it offers flexible interfaces for custom algorithms. Through a modular design, mtslearn simplifies complex data engineering tasks into a few lines of code. This significantly lowers the barrier to entry for clinicians with limited programming experience, empowering them to focus more on exploring medical hypotheses and accelerating the translation of advanced algorithms into real‑world clinical practice. mtslearn is publicly available at https://github.com/PKUDigitalHealth/mtslearn.
Authors:Yubo Cui, Xianchao Guan, Zijun Xiong, Zheng Zhang
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.
Authors:Tal Ishon, Yoav Goldberg, Uri Shaham
Abstract:
Topic modeling seeks to uncover latent semantic structure in text, with LDA providing a foundational probabilistic framework. While recent methods often incorporate external knowledge (e.g., pre‑trained embeddings), such reliance limits applicability in emerging or underexplored domains. We introduce PRISM, a corpus‑intrinsic method that derives a Dirichlet parameter from word co‑occurrence statistics to initialize LDA without altering its generative process. Experiments on text and single cell RNA‑seq data show that PRISM improves topic coherence and interpretability, rivaling models that rely on external knowledge. These results underscore the value of corpus‑driven initialization for topic modeling in resource‑constrained settings.
Code is available at: https://github.com/shaham‑lab/PRISM.
Authors:Zhuowen Liang, Xiaotian Lin, Zhengxuan Zhang, Yuyu Luo, Haixun Wang, Nan Tang
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.
Authors:Xiaohui Zhou, Yijie Wang, Hongzuo Xu, Weixuan Liang, Xiaoli Li, Guansong Pang
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.
Authors:Disen Liao, Felix Dangel, Yaoliang Yu
Abstract:
Bilevel optimization (BO) is widely applicable to many machine learning problems. Scaling BO, however, requires repeatedly computing hypergradients, which involves solving inverse Hessian‑vector products (IHVPs). In practice, these operations are often approximated using crude surrogates such as one‑step gradient unrolling or identity/short Neumann expansions, which discard curvature information. We build on implicit function theorem‑based algorithms and propose to incorporate Kronecker‑factored approximate curvature (KFAC), yielding curvature‑aware hypergradients with a better performance efficiency trade‑off than Conjugate Gradient (CG) or Neumann methods and consistently outperforming unrolling. We evaluate this approach across diverse tasks, including meta‑learning and AI safety problems. On models up to BERT, we show that curvature information is valuable at scale, and KFAC can provide it with only modest memory and runtime overhead. Our implementation is available at https://github.com/liaodisen/NeuralBo.
Authors:Jaber Jaber, Osama Jaber
Abstract:
World models that predict future states from video remain limited by flat latent representations that entangle objects, ignore causal structure, and collapse temporal dynamics into a single scale. We present HCLSM, a world model architecture that operates on three interconnected principles: object‑centric decomposition via slot attention with spatial broadcast decoding, hierarchical temporal dynamics through a three‑level engine combining selective state space models for continuous physics, sparse transformers for discrete events, and compressed transformers for abstract goals, and causal structure learning through graph neural network interaction patterns. HCLSM introduces a two‑stage training protocol where spatial reconstruction forces slot specialization before dynamics prediction begins. We train a 68M‑parameter model on the PushT robotic manipulation benchmark from the Open X‑Embodiment dataset, achieving 0.008 MSE next‑state prediction loss with emerging spatial decomposition (SBD loss: 0.0075) and learned event boundaries. A custom Triton kernel for the SSM scan delivers 38x speedup over sequential PyTorch. The full system spans 8,478 lines of Python across 51 modules with 171 unit tests. Code: https://github.com/rightnow‑ai/hclsm
Authors:Tushar Dhananjay Pathak
Abstract:
This paper presents ARCS (Autoregressive Circuit Synthesis), a system for amortized analog circuit generation. ARCS produces complete, SPICE‑simulatable designs (topology and component values) in milliseconds rather than the minutes required by search‑based methods. A hybrid pipeline combines two learned generators, a graph VAE and a flow‑matching model, with SPICE‑based ranking. It achieves 99.9% simulation validity (reward 6.43/8.0) across 32 topologies using only 8 SPICE evaluations, 40x fewer than genetic algorithms. For single‑model inference, a topology‑aware Graph Transformer with Best‑of‑3 candidate selection reaches 85% simulation validity in 97ms, over 600x faster than random search. The key technical contribution adapts Group Relative Policy Optimization (GRPO) to multi‑topology circuit reinforcement learning. GRPO resolves a critical failure mode of REINFORCE, cross‑topology reward distribution mismatch, through per‑topology advantage normalization. This improves simulation validity by +9.6 percentage points over REINFORCE in only 500 RL steps (10x fewer). Grammar‑constrained decoding additionally guarantees 100% structural validity by construction via topology‑aware token masking.
Authors:Shikhar Bharadwaj, Chin-Jou Li, Kwanghee Choi, Eunjung Yeo, William Chen, Shinji Watanabe, David R. Mortensen
Abstract:
Phone recognition (PR) is a key enabler of multilingual and low‑resource speech processing tasks, yet robust performance remains elusive. Highly performant English‑focused models do not generalize across languages, while multilingual models underutilize pretrained representations. It also remains unclear how data scale, architecture, and training objective contribute to multilingual PR. We present PhoneticXEUS ‑‑ trained on large‑scale multilingual data and achieving state‑of‑the‑art performance on both multilingual (17.7% PFER) and accented English speech (10.6% PFER). Through controlled ablations with evaluations across 100+ languages under a unified scheme, we empirically establish our training recipe and quantify the impact of SSL representations, data scale, and loss objectives. In addition, we analyze error patterns across language families, accented speech, and articulatory features. All data and code are released openly.
Authors:Subhadip Mitra
Abstract:
Evaluating large language models at scale remains a practical bottleneck for many organizations. While existing evaluation frameworks work well for thousands of examples, they struggle when datasets grow to hundreds of thousands or millions of samples. This scale is common when assessing model behavior across diverse domains or conducting comprehensive regression testing. We present Spark‑LLM‑Eval, a distributed evaluation framework built natively on Apache Spark. The system treats evaluation as a data‑parallel problem, partitioningexamplesacrossexecutorsandaggregatingresultswithproperstatistical accounting. Beyond raw throughput, we emphasize statistical rigor: every reported metric includes bootstrap confidence intervals, and model comparisons come with appropriate significance tests (paired t‑tests, McNemar's test, or Wilcoxon signed‑rank, depending on the metric type). The framework also addresses the cost problem inherent in LLM evaluation through content‑addressable response caching backed by Delta Lake, which allows iterating on metric definitions without re‑running inference. We describe the system architecture, the statistical methodology, and report benchmark results showing linear scaling with cluster size. The framework and all evaluation code are available as open source.
Authors:Liliang Ren, Yang Liu, Yelong Shen, Weizhu Chen
Abstract:
Scaling laws for large language models depend critically on the optimizer and parameterization. Existing hyperparameter transfer laws are mainly developed for first‑order optimizers, and they do not structurally prevent training instability at scale. Recent hypersphere optimization methods constrain weight matrices to a fixed‑norm hypersphere, offering a promising alternative for more stable scaling. We introduce HyperP (Hypersphere Parameterization), the first framework for transferring optimal learning rates across model width, depth, training tokens, and Mixture‑of‑Experts (MoE) granularity under the Frobenius‑sphere constraint with the Muon optimizer. We prove that weight decay is a first‑order no‑op on the Frobenius sphere, show that Depth‑μP remains necessary, and find that the optimal learning rate follows the same data‑scaling power law with the "magic exponent" 0.32 previously observed for AdamW. A single base learning rate tuned at the smallest scale transfers across all compute budgets under HyperP, yielding 1.58× compute efficiency over a strong Muon baseline at 6×10^21 FLOPs. Moreover, HyperP delivers transferable stability: all monitored instability indicators, including Z‑values, output RMS, and activation outliers, remain bounded and non‑increasing under training FLOPs scaling. We also propose SqrtGate, an MoE gating mechanism derived from the hypersphere constraint that preserves output RMS across MoE granularities for improved granularity scaling, and show that hypersphere optimization enables substantially larger auxiliary load‑balancing weights, yielding both strong performance and good expert balance. We release our training codebase at https://github.com/microsoft/ArchScale.
Authors:Yi Yu, Guangquan Hu, Chenghuang Shen, Xingyan Liu, Jing Gu, Hangyi Sun, Junzhuo Ma, Weiting Liu, Jianfeng Liu, Mingyue Pu, Yu Wang, Zhengdong Xiao, Rui Xie, Longjiu Luo, Qianrong Wang, Gurong Cui, Honglin Qiao, Wenlian Lu
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
Authors:Yihan Gao, Chenxi Huang, Wen Shi, Ke Sun, Ziqi Xu, Xikun Zhang, Mingliang Hou, Renqiang Luo
Abstract:
Graph condensation (GC) has become a vital strategy for scaling Graph Neural Networks by compressing massive datasets into small, synthetic node sets. While current GC methods effectively maintain predictive accuracy, they are primarily designed for utility and often ignore fairness constraints. Because these techniques are bias‑blind, they frequently capture and even amplify demographic disparities found in the original data. This leads to synthetic proxies that are unsuitable for sensitive applications like credit scoring or social recommendations. To solve this problem, we introduce FairGC, a unified framework that embeds fairness directly into the graph distillation process. Our approach consists of three key components. First, a Distribution‑Preserving Condensation module synchronizes the joint distributions of labels and sensitive attributes to stop bias from spreading. Second, a Spectral Encoding module uses Laplacian eigen‑decomposition to preserve essential global structural patterns. Finally, a Fairness‑Enhanced Neural Architecture employs multi‑domain fusion and a label‑smoothing curriculum to produce equitable predictions. Rigorous evaluations on four real‑world datasets, show that FairGC provides a superior balance between accuracy and fairness. Our results confirm that FairGC significantly reduces disparity in Statistical Parity and Equal Opportunity compared to existing state‑of‑the‑art condensation models. The codes are available at https://github.com/LuoRenqiang/FairGC.
Authors:Yangmei Chen, Zhongyuan Zhang, Xikun Zhang, Xinyu Hao, Mingliang Hou, Renqiang Luo, Ziqi Xu
Abstract:
Thyroid nodule classification using ultrasound imaging is essential for early diagnosis and clinical decision‑making; however, despite promising performance on in‑distribution data, existing deep learning methods often exhibit limited robustness and generalisation when deployed across different ultrasound devices or clinical environments. This limitation is mainly attributed to the pronounced heterogeneity of thyroid ultrasound images, which can lead models to capture spurious correlations rather than reliable diagnostic cues. To address this challenge, we propose PEMV‑thyroid, a Prototype‑Enhanced Multi‑View learning framework that accounts for data heterogeneity by learning complementary representations from multiple feature perspectives and refining decision boundaries through a prototype‑based correction mechanism with mixed prototype information. By integrating multi‑view representations with prototype‑level guidance, the proposed approach enables more stable representation learning under heterogeneous imaging conditions. Extensive experiments on multiple thyroid ultrasound datasets demonstrate that PEMV‑thyroid consistently outperforms state‑of‑the‑art methods, particularly in cross‑device and cross‑domain evaluation scenarios, leading to improved diagnostic accuracy and generalisation performance in real‑world clinical settings. The source code is available at https://github.com/chenyangmeii/Prototype‑Enhanced‑Multi‑View‑Learning.
Authors:Chanyoung Kim, Minwoo Kim, Minseok Kang, Hyunwoo Kim, Dahuin Jung
Abstract:
Vision‑Language‑Action (VLA) models achieve strong performance in robotic manipulation by leveraging pre‑trained vision‑language backbones. However, in downstream robotic settings, they are typically fine‑tuned with limited data, leading to overfitting to specific instruction formulations and leaving robustness to paraphrased instructions underexplored. To study this gap, we introduce LIBERO‑Para, a controlled benchmark that independently varies action expressions and object references for fine‑grained analysis of linguistic generalization. Across seven VLA configurations (0.6B‑7.5B), we observe consistent performance degradation of 22‑52 pp under paraphrasing. This degradation is primarily driven by object‑level lexical variation: even simple synonym substitutions cause large drops, indicating reliance on surface‑level matching rather than semantic grounding. Moreover, 80‑96% of failures arise from planning‑level trajectory divergence rather than execution errors, showing that paraphrasing disrupts task identification. Binary success rate treats all paraphrases equally, obscuring whether models perform consistently across difficulty levels or rely on easier cases. To address this, we propose PRIDE, a metric that quantifies paraphrase difficulty using semantic and syntactic factors. Our benchmark and corresponding code are available at: https://github.com/cau‑hai‑lab/LIBERO‑Para
Authors:Qing Qing, Huafei Huang, Mingliang Hou, Renqiang Luo, Mohsen Guizani
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.
Authors:Gnankan Landry Regis N'guessan
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.
Authors:Da Chang, Qiankun Shi, Lvgang Zhang, Yu Li, Ruijie Zhang, Yao Lu, Yongxiang Liu, Ganzhao Yuan
Abstract:
Orthogonalized‑update optimizers such as Muon improve training of matrix‑valued parameters, but existing extensions typically either rescale updates after orthogonalization or use heavier whitening‑based preconditioners before it. We introduce \method, a lightweight family of pre‑orthogonalization equilibration schemes for Muon with three forms: two‑sided row/column normalization (RC), row normalization (R), and column normalization (C). By rebalancing the momentum matrix before finite‑step Newton‑‑Schulz orthogonalization, \method improves the geometry seen by orthogonalization. We show that finite‑step orthogonalization is governed by the input spectrum, especially stable rank and condition number, and that row/column normalization acts as a zeroth‑order surrogate for whitening. For hidden matrix weights, R is the default variant. Theoretically, \method (R) retains the standard \widetilde\mathcal O(T^‑1/4) Muon‑type nonconvex stationarity guarantee with decoupled weight decay and a horizon‑free diminishing learning‑rate schedule, and extends it to finite‑step NS5 up to an explicit inexactness constant. In LLaMA2 pretraining on C4, \method (R) consistently outperforms Muon on 130M, 350M, and 1B models, with faster convergence and lower validation perplexity. The code is available at the \hrefhttps://github.com/MaeChd/muon‑eqMuonEq codebase.
Authors:He Yang, Dongyi Lv, Song Ma, Wei Xi, Zhi Wang, Hanlin Gu, Yajie Wang
Abstract:
Dataset Condensation (DC) is a data‑efficient learning paradigm that synthesizes small yet informative datasets, enabling models to match the performance of full‑data training. However, recent work exposes a critical vulnerability of DC to backdoor attacks, where malicious patterns (e.g., triggers) are implanted into the condensation dataset, inducing targeted misclassification on specific inputs. Existing attacks always prioritize attack effectiveness and model utility, overlooking the crucial dimension of stealthiness. To bridge this gap, we propose InkDrop, which enhances the imperceptibility of malicious manipulation without degrading attack effectiveness and model utility. InkDrop leverages the inherent uncertainty near model decision boundaries, where minor input perturbations can induce semantic shifts, to construct a stealthy and effective backdoor attack. Specifically, InkDrop first selects candidate samples near the target decision boundary that exhibit latent semantic affinity to the target class. It then learns instance‑dependent perturbations constrained by perceptual and spatial consistency, embedding targeted malicious behavior into the condensed dataset. Extensive experiments across diverse datasets validate the overall effectiveness of InkDrop, demonstrating its ability to integrate adversarial intent into condensed datasets while preserving model utility and minimizing detectability. Our code is available at https://github.com/lvdongyi/InkDrop.
Authors:Truong-Son Hy
Abstract:
Protein fitness optimization is inherently a discrete combinatorial problem, yet most learning‑based approaches rely on continuous representations and are primarily evaluated through predictive accuracy. We introduce Q‑BIOLAT, a framework for modeling and optimizing protein fitness landscapes in compact binary latent spaces. Starting from pretrained protein language model embeddings, we construct binary latent representations and learn a quadratic unconstrained binary optimization (QUBO) surrogate that captures unary and pairwise interactions.
Beyond its formulation, Q‑BIOLAT provides a representation‑centric perspective on protein fitness modeling. We show that representations with similar predictive performance can induce fundamentally different optimization landscapes. In particular, learned autoencoder‑based representations collapse after binarization, producing degenerate latent spaces that fail to support combinatorial search, whereas simple structured representations such as PCA yield high‑entropy, decodable, and optimization‑friendly latent spaces.
Across multiple datasets and data regimes, we demonstrate that classical combinatorial optimization methods, including simulated annealing, genetic algorithms, and greedy hill climbing, are highly effective in structured binary latent spaces. By expressing the objective in QUBO form, our approach connects modern machine learning with discrete and quantum‑inspired optimization.
Our implementation and dataset are publicly available at: https://github.com/HySonLab/Q‑BIOLAT‑Extended
Authors:Duraimurugan Rajamanickam
Abstract:
Statistical fairness metrics in AI‑driven credit decisions conflate two causally distinct mechanisms: discrimination operating directly from a protected attribute to a credit outcome, and structural inequality propagating through legitimate financial features. We formalise this distinction using Pearl's framework of natural direct and indirect effects applied to the credit decision setting. Our primary theoretical contribution is an identification strategy for natural direct and indirect effects under treatment‑induced confounding ‑‑ the prevalent setting in which protected attributes causally affect both financial mediators and the final decision, violating standard sequential ignorability. We show that interventional direct and indirect effects (IDE/IIE) are identified under the weaker Modified Sequential Ignorability assumption, and prove that IDE/IIE provide conservative bounds on the unidentified natural effects under monotone indirect treatment response. We propose a doubly‑robust augmented inverse probability weighted (AIPW) estimator for IDE/IIE with semiparametric efficiency properties, implemented via cross‑fitting. An E‑value sensitivity analysis addresses residual confounding on the direct pathway. Empirical evaluation on 89,465 real HMDA conventional purchase mortgage applications from New York State (2022) demonstrates that approximately 77% of the observed 7.9 percentage‑point racial denial disparity operates through financial mediators shaped by structural inequality, while the remaining 23% constitutes a conservative lower bound on direct discrimination. The open‑source CausalFair Python package implements the full pipeline for deployment at resource‑constrained financial institutions.
Authors:Chongyang Zhao, Mingsong Li, Haodong Lu, Dong Gong
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.
Authors:Suraj Ranganath, Vaishak Menon, Anish Patnaik
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/.
Authors:Mohsen Dehghankar, Abolfazl Asudeh
Abstract:
Matrix‑vector multiplication is a fundamental building block in neural networks, vector databases, and large language models, particularly during inference. As a result, efficient matrix‑vector multiplication engines directly translate into more efficient inference. Recent work has explored low‑bit quantization of model weights, where matrices are represented using binary (1‑bit) or ternary (1.58‑bit) values while activation is kept in higher precision. These representations enable efficient hardware‑level computation. In parallel, algorithms such as Redundant Segment Reduction (RSR) provide theoretical guarantees for accelerating low‑bit matrix‑vector multiplication. However, existing implementations operate at the application level and cannot be efficiently integrated into hardware kernels, limiting practical performance. To bridge this gap, we present RSR‑core, a high‑performance engine that implements the RSR algorithm as optimized low‑level kernels for both CPU and CUDA environments. RSR‑core supports efficient matrix‑vector multiplication for binary and ternary weight matrices and general vectors while enabling practical deployment of RSR algorithm in real inference pipelines. RSR‑core is provided as a production‑ready engine with HuggingFace integration for preprocessing low‑bit models and running accelerated inference. Experimental results demonstrate significant performance improvements over baseline HuggingFace PyTorch multiplication, achieving up to 62x speedup on CPU and up to 1.9x speedup for token generation on CUDA for popular ternary LLMs. The source code is publicly available at https://github.com/UIC‑InDeXLab/RSR‑core.
Authors:Chenxiao Gao, Edward Chen, Tianyi Chen, Bo Dai
Abstract:
Thanks to their remarkable flexibility, diffusion models and flow models have emerged as promising candidates for policy representation. However, efficient reinforcement learning (RL) upon these policies remains a challenge due to the lack of explicit log‑probabilities for vanilla policy gradient estimators. While numerous attempts have been proposed to address this, the field lacks a unified perspective to reconcile these seemingly disparate methods, thus hampering ongoing development. In this paper, we bridge this gap by introducing a comprehensive taxonomy for RL algorithms with diffusion/flow policies. To support reproducibility and agile prototyping, we introduce a modular, JAX‑based open‑source codebase that leverages JIT‑compilation for high‑throughput training. Finally, we provide systematic and standardized benchmarks across Gym‑Locomotion, DeepMind Control Suite, and IsaacLab, offering a rigorous side‑by‑side comparison of diffusion‑based methods and guidance for practitioners to choose proper algorithms based on the application. Our work establishes a clear foundation for understanding and algorithm design, a high‑efficiency toolkit for future research in the field, and an algorithmic guideline for practitioners in generative models and robotics. Our code is available at https://github.com/typoverflow/flow‑rl.
Authors:Naveen Mysore
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.
Authors:PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das
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.
Authors:Alberto G. Rodriguez Salgado
Abstract:
How do multimodal models solve visual spatial tasks ‑‑ through genuine planning, or through brute‑force search in token space? We introduce \textscMazeBench, a benchmark of 110 procedurally generated maze images across nine controlled groups, and evaluate 16 model configurations from OpenAI, Anthropic, Google, and Alibaba. GPT‑5.4 solves 91% and Gemini 3.1 Pro 79%, but these scores are misleading: models typically translate images into text grids and then enumerate paths step by step, consuming 1,710‑‑22,818 tokens per solve for a task humans do quickly. Without added reasoning budgets, all configurations score only 2‑‑12%; on 20×20 ultra‑hard mazes, they hit token limits and fail. Qualitative traces reveal a common two‑stage strategy: image‑to‑grid translation followed by token‑level search, effectively BFS in prose. A text‑grid ablation shows Claude Sonnet 4.6 rising from 6% on images to 80% when given the correct grid, isolating weak visual extraction from downstream search. When explicitly instructed not to construct a grid or perform graph search, models still revert to the same enumeration strategy. \textscMazeBench therefore shows that high accuracy on visual planning tasks does not imply human‑like spatial understanding.
Authors:Ling Zhang, Boxiang Yun, Ting Jin, Qingli Li, Xinxing Li, Yan Wang
Abstract:
Prediction of genetic biomarkers, e.g., microsatellite instability in colorectal cancer is crucial for clinical decision making. But, two primary challenges hamper accurate prediction: (1) It is difficult to construct a pathology‑aware representation involving the complex interconnections among pathological components. (2) WSIs contain a large proportion of areas unrelated to genetic biomarkers, which make the model easily overfit simple but irrelative instances. We hereby propose a Dictionary‑based hierarchical pathology mining with hard‑instance‑assisted classifier Debiasing framework to address these challenges, dubbed as D2Bio. Our first module, dictionary‑based hierarchical pathology mining, is able to mine diverse and very fine‑grained pathological contextual interaction without the limit to the distances between patches. The second module, hard‑instance‑assisted classfier debiasing, learns a debiased classifier via focusing on hard but task‑related features, without any additional annotations. Experimental results on five cohorts show the superiority of our method, with over 4% improvement in AUROC compared with the second best on the TCGA‑CRC‑MSI cohort. Our analysis further shows the clinical interpretability of D2Bio in genetic biomarker diagnosis and potential clinical utility in survival analysis. Code will be available at https://github.com/DeepMed‑Lab‑ECNU/D2Bio.
Authors:Swastik R
Abstract:
Vision‑language models score well on mathematical, scientific, and spatial reasoning benchmarks, yet these evaluations are overwhelmingly English. I present the first cross‑lingual visual reasoning audit for Indian languages. 980 questions from MathVista, ScienceQA, and MMMU are translated into Hindi, Tamil, Telugu, Bengali, Kannada, and Marathi using IndicTrans2, with Gemini 2.0 Flash cross‑verification on 50 samples per language (inter‑translator agreement 0.79‑0.84). Eight VLMs, from 7B open‑source models to GPT‑4o, are evaluated across all seven languages, yielding 68,600 inference records that include text‑only and chain‑of‑thought ablations. I find accuracy drops of 9.8‑25 percentage points when switching from English to an Indian language, with Dravidian languages suffering up to 13.2 pp more than Indo‑Aryan. Chain‑of‑thought prompting degrades Bengali (‑14.4 pp) and Kannada (‑11.4 pp) rather than helping, exposing English‑centric reasoning chains. Aya‑Vision‑8B, built for 23 languages, still drops 28.5 pp on Dravidian scripts; multilingual pretraining alone does not transfer visual reasoning. I release the translated benchmark and all model outputs.
Authors:Guangli Li, Canbiao Wu, Na Tian, Li Zhang, Zhen Liang
Abstract:
Electroencephalography (EEG)‑based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological variability, experimental paradigm differences, and device inconsistencies. Existing domain adversarial methods primarily enforce global marginal alignment and often overlook class‑conditional mismatch and decision boundary distortion, limiting cross‑corpus generalization. In this work, we propose a unified Prototype‑driven Adversarial Alignment (PAA) framework for cross‑corpus EEG emotion recognition. The framework is progressively instantiated in three configurations: PAA‑L, which performs prototype‑guided local class‑conditional alignment; PAA‑C, which further incorporates contrastive semantic regularization to enhance intra‑class compactness and inter‑class separability; and PAA‑M, the full boundary‑aware configuration that integrates dual relation‑aware classifiers within a three‑stage adversarial optimization scheme to explicitly refine controversial samples near decision boundaries. By combining prototype‑guided subdomain alignment, contrastive discriminative enhancement, and boundary‑aware aggregation within a coherent adversarial architecture, the proposed framework reformulates emotion recognition as a relation‑driven representation learning problem, reducing sensitivity to label noise and improving cross‑domain stability. Extensive experiments on SEED, SEED‑IV, and SEED‑V demonstrate state‑of‑the‑art performance under four cross‑corpus evaluation protocols, with average improvements of 6.72%, 5.59%, 6.69%, and 4.83%, respectively. Furthermore, the proposed framework generalizes effectively to clinical depression identification scenarios, validating its robustness in real‑world heterogeneous settings. The source code is available at https://github.com/WuCB‑BCI/PAA
Authors:Dávid Pukanec, Tibor Kubík, Michal Španěl
Abstract:
We present ToothCraft, a diffusion‑based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth. Building upon recent advancements in conditioned diffusion models for 3D shapes, we developed a model capable of an automated tooth crown completion conditioned on local anatomical context. To address the lack of training data for this task, we designed an augmentation pipeline that generates incomplete tooth geometries from a publicly available dataset of complete dental arches (3DS, ODD). By synthesising a diverse set of training examples, our approach enables robust learning across a wide spectrum of tooth defects. Experimental results demonstrate the strong capability of our model to reconstruct complete tooth crowns, achieving an intersection over union (IoU) of 81.8% and a Chamfer Distance (CD) of 0.00034 on synthetically damaged testing restorations. Our experiments demonstrate that the model can be applied directly to real‑world cases, effectively filling in incomplete teeth, while generated crowns show minimal intersection with the opposing dentition, thus reducing the risk of occlusal interference. Access to the code, model weights, and dataset information will be available at: https://github.com/ikarus1211/VISAPP_ToothCraft
Authors:Doğaç Eldenk, Stephen Xia
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.
Authors:Siddhartha Laghuvarapu, Rohan Deb, Jimeng Sun
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.
Authors:Cai Selvas-Sala, Lei Kang, Lluis Gomez
Abstract:
As multimodal models like CLIP become integral to downstream systems, the need to remove sensitive information is critical. However, machine unlearning for contrastively‑trained encoders remains underexplored, and existing evaluations fail to diagnose fine‑grained, association‑level forgetting. We introduce SALMUBench (Sensitive Association‑Level Multimodal Unlearning), a benchmark built upon a synthetic dataset of 60K persona‑attribute associations and two foundational models: a Compromised model polluted with this data, and a Clean model without it. To isolate unlearning effects, both are trained from scratch on the same 400M‑pair retain base, with the Compromised model additionally trained on the sensitive set. We propose a novel evaluation protocol with structured holdout sets (holdout identity, holdout association) to precisely measure unlearning efficacy and collateral damage. Our benchmark reveals that while utility‑efficient deletion is feasible, current methods exhibit distinct failure modes: they either fail to forget effectively or over‑generalize by erasing more than intended. SALMUBench sets a new standard for comprehensive unlearning evaluation, and we publicly release our dataset, models, evaluation scripts, and leaderboards to foster future research.
Authors:Shuyi Gao, Stavros Orfanoudakis, Shengren Hou, Peter Palensky, Pedro P. Vergara
Abstract:
Optimal dispatch of energy storage systems (ESSs) in distribution networks involves jointly improving operating economy and voltage security under time‑varying conditions and possible topology changes. To support fast online decision making, we develop a topology‑aware Reinforcement Learning architecture based on Twin Delayed Deep Deterministic Policy Gradient (TD3), which integrates graph neural networks (GNNs) as graph feature encoders for ESS dispatch. We conduct a systematic investigation of three GNN variants: graph convolutional networks (GCNs), topology adaptive graph convolutional networks (TAGConv), and graph attention networks (GATs) on the 34‑bus and 69‑bus systems, and evaluate robustness under multiple topology reconfiguration cases as well as cross‑system transfer between networks with different system sizes. Results show that GNN‑based controllers consistently reduce the number and magnitude of voltage violations, with clearer benefits on the 69‑bus system and under reconfiguration; on the 69‑bus system, TD3‑GCN and TD3‑TAGConv also achieve lower saved cost relative to the NLP benchmark than the NN baseline. We also highlight that transfer gains are case‑dependent, and zero‑shot transfer between fundamentally different systems results in notable performance degradation and increased voltage magnitude violations. This work is available at: https://github.com/ShuyiGao/GNNs_RL_ESSs and https://github.com/distributionnetworksTUDelft/GNNs_RL_ESSs.
Authors:Yuhang Ma, Jie Wang, Zheng Yan
Abstract:
Large Language Models (LLMs) have advanced Graph Neural Networks (GNNs) by enriching node representations with semantic features, giving rise to LLM‑enhanced GNNs that achieve notable performance gains. However, the robustness of these models against poisoning attacks, which manipulate both graph structures and textual attributes during training, remains unexplored. To bridge this gap, we propose a robustness assessment framework that systematically evaluates LLM‑enhanced GNNs under poisoning attacks. Our framework enables comprehensive evaluation across multiple dimensions. Specifically, we assess 24 victim models by combining eight LLM‑ or Language Model (LM)‑based feature enhancers with three representative GNN backbones. To ensure diversity in attack coverage, we incorporate six structural poisoning attacks (both targeted and non‑targeted) and three textual poisoning attacks operating at the character, word, and sentence levels. Furthermore, we employ four real‑world datasets, including one released after the emergence of LLMs, to avoid potential ground truth leakage during LLM pretraining, thereby ensuring fair evaluation. Extensive experiments show that LLM‑enhanced GNNs exhibit significantly higher accuracy and lower Relative Drop in Accuracy (RDA) than a shallow embedding‑based baseline across various attack settings. Our in‑depth analysis identifies key factors that contribute to this robustness, such as the effective encoding of structural and label information in node representations. Based on these insights, we outline future research directions from both offensive and defensive perspectives, and propose a new combined attack along with a graph purification defense. To support future research, we release the source code of our framework at~\urlhttps://github.com/CyberAlSec/LLMEGNNRP.
Authors:Harunori Kawano, Takeshi Sasaki
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
Authors:Siqiao Xue, Zhaoyang Zhu, Wei Zhang, Rongyao Cai, Rui Wang, Yixiang Mu, Fan Zhou, Jianguo Li, Peng Di, Hang Yu
Abstract:
Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large‑scale, high‑quality benchmarks. To address this gap, we introduce \textscQuitoBench, a regime‑balanced benchmark for time series forecasting with coverage across eight trend×seasonality×forecastability (TSF) regimes, designed to capture forecasting‑relevant properties rather than application‑defined domain labels. The benchmark is built upon \textscQuito, a billion‑scale time series corpus of application traffic from Alipay spanning nine business domains. Benchmarking 10 models from deep learning, foundation models, and statistical baselines across 232,200 evaluation instances, we report four key findings: (i) a context‑length crossover where deep learning models lead at short context (L=96) but foundation models dominate at long context (L \ge 576); (ii) forecastability is the dominant difficulty driver, producing a 3.64 × MAE gap across regimes; (iii) deep learning models match or surpass foundation models at 59 × fewer parameters; and (iv) scaling the amount of training data provides substantially greater benefit than scaling model size for both model families. These findings are validated by strong cross‑benchmark and cross‑metric consistency. Our open‑source release enables reproducible, regime‑aware evaluation for time series forecasting research.
Authors:Mikalai Korbit, Mario Zanon
Abstract:
Second‑order methods promise improved stability and faster convergence, yet they remain underused due to implementation overhead, tuning brittleness, and the lack of composable APIs. We introduce Somax, a composable Optax‑native stack that treats curvature‑aware training as a single JIT‑compiled step governed by a static plan. Somax exposes first‑class modules ‑‑ curvature operators, estimators, linear solvers, preconditioners, and damping policies ‑‑ behind a single step interface and composes with Optax by applying standard gradient transformations (e.g., momentum, weight decay, schedules) to the computed direction. This design makes typically hidden choices explicit and swappable. Somax separates planning from execution: it derives a static plan (including cadences) from module requirements, then runs the step through a specialized execution path that reuses intermediate results across modules. We report system‑oriented ablations showing that (i) composition choices materially affect scaling behavior and time‑to‑accuracy, and (ii) planning reduces per‑step overhead relative to unplanned composition with redundant recomputation.
Authors:Zehao Wang, Huaide Jiang, Shuaiwu Dong, Yuping Wang, Hang Qiu, Jiachen Li
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/.
Authors:Hai X. Pham, David T. Hoffmann, Ricardo Guerrero, Brais Martinez
Abstract:
Contrastive vision‑language (V&L) models remain a popular choice for various applications. However, several limitations have emerged, most notably the limited ability of V&L models to learn compositional representations. Prior methods often addressed this limitation by generating custom training data to obtain hard negative samples. Hard negatives have been shown to improve performance on compositionality tasks, but are often specific to a single benchmark, do not generalize, and can cause substantial degradation of basic V&L capabilities such as zero‑shot or retrieval performance, rendering them impractical. In this work we follow a different approach. We identify two root causes that limit compositionality performance of V&Ls: 1) Long training captions do not require a compositional representation; and 2) The final global pooling in the text and image encoders lead to a complete loss of the necessary information to learn binding in the first place. As a remedy, we propose two simple solutions: 1) We obtain short concept centric caption parts using standard NLP software and align those with the image; and 2) We introduce a parameter‑free cross‑modal attention‑pooling to obtain concept centric visual embeddings from the image encoder. With these two changes and simple auxiliary contrastive losses, we obtain SOTA performance on standard compositionality benchmarks, while maintaining or improving strong zero‑shot and retrieval capabilities. This is achieved without increasing inference cost. We release the code for this work at https://github.com/saic‑fi/concept_centric_clip.
Authors:Armand de Villeroché, Rem-Sophia Mouradi, Vincent Le Guen, Sibo Cheng, Marc Bocquet, Alban Farchi, Patrick Armand, Patrick Massin
Abstract:
Air flow modeling at a local scale is essential for applications such as pollutant dispersion modeling or wind farm modeling. To circumvent costly Computational Fluid Dynamics (CFD) computations, deep learning surrogate models have recently emerged as promising alternatives. However, in the context of urban air flow, deep learning models struggle to adapt to the high variations of the urban geometry and to large mesh sizes. To tackle these challenges, we introduce Anchored Branched Steady‑state WInd Flow Transformer (AB‑SWIFT), a transformer‑based model with an internal branched structure uniquely designed for atmospheric flow modeling. We train our model on a specially designed database of atmospheric simulations around randomised urban geometries and with a mixture of unstable, neutral, and stable atmospheric stratifications. Our model reaches the best accuracy on all predicted fields compared to state‑of‑the‑art transformers and graph‑based models. Our code and data is available at https://github.com/cerea‑daml/abswift.
Authors:Selim An, Il hong Suh, Yeseong Kim
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.
Authors:Hector Borobia, Elies Seguí-Mas, Guillermina Tormo-Carbó
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.
Authors:Yabin Zhang, Maya Varma, Yunhe Gao, Jean-Benoit Delbrouck, Jiaming Liu, Chong Wang, Curtis Langlotz
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.
Authors:Yinjian Wang, Wei Li, Yuanyuan Gui, James E. Fowler, Gemine Vivone
Abstract:
Robust principal component analysis (RPCA) seeks a low‑rank component and a sparse component from their summation. Yet, in many applications of interest, the sparse foreground actually replaces, or occludes, elements from the low‑rank background. To address this mismatch, a new framework is proposed in which the sparse component is identified indirectly through determining its support. This approach, called robust principal component completion (RPCC), is solved via variational Bayesian inference applied to a fully probabilistic Bayesian sparse tensor factorization. Convergence to a hard classifier for the support is shown, thereby eliminating the post‑hoc thresholding required of most prior RPCA‑driven approaches. Experimental results reveal that the proposed approach delivers near‑optimal estimates on synthetic data as well as robust foreground‑extraction and anomaly‑detection performance on real color video and hyperspectral datasets, respectively. Source implementation and Appendices are available at https://github.com/WongYinJ/BCP‑RPCC.
Authors:Isha Puri, Mehul Damani, Idan Shenfeld, Marzyeh Ghassemi, Jacob Andreas, Yoon Kim
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/.
Authors:Yongda Fan, John Wu, Andrea Fitzpatrick, Naveen Baskaran, Jimeng Sun, Adam Cross
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.
Authors:Manglam Kartik, Neel Tushar Shah
Abstract:
Standard vision models treat objects as independent points in Euclidean space, unable to capture hierarchical structure like parts within wholes. We introduce Worldline Slot Attention, which models objects as persistent trajectories through spacetime worldlines, where each object has multiple slots at different hierarchy levels sharing the same spatial position but differing in temporal coordinates. This architecture consistently fails without geometric structure: Euclidean worldlines achieve 0.078 level accuracy, below random chance (0.33), while Lorentzian worldlines achieve 0.479‑0.661 across three datasets: a 6x improvement replicated over 20+ independent runs. Lorentzian geometry also outperforms hyperbolic embeddings showing visual hierarchies require causal structure (temporal dependency) rather than tree structure (radial branching). Our results demonstrate that hierarchical object discovery requires geometric structure encoding asymmetric causality, an inductive bias absent from Euclidean space but natural to Lorentzian light cones, achieved with only 11K parameters. The code is available at: https://github.com/iclrsubmissiongram/loco.
Authors:Daniel Benniah John
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.
Authors:Shengli Zhou, Minghang Zheng, Feng Zheng, Yang Liu
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.
Authors:Cheng Jiayang, Xin Liu, Zhihan Zhang, Haoyang Wen, Zixuan Zhang, Qingyu Yin, Shiyang Li, Priyanka Nigam, Bing Yin, Chao Zhang, Yangqiu Song
Abstract:
Multi‑step tool orchestration remains challenging for LLMs, as state‑of‑the‑art models frequently fail on full sequence execution due to parameter errors. Training for these workflows faces two obstacles: the lack of environments supporting complex real‑world API dependencies, and sparse binary rewards that provide no signal for partial correctness. We propose a reinforcement learning framework addressing both challenges. First, we construct a deterministic environment backed by a large‑scale cache of real API responses, enabling constrained synthesis of valid multi‑step traces with controllable complexity. Second, we introduce a graduated reward that decomposes correctness into atomic validity (call‑level correctness at increasing granularity) and orchestration consistency (correct sequencing with dependency respect). On ComplexFuncBench, our approach substantially improves turn accuracy, with ablations confirming both reward components are essential. Cross‑benchmark evaluation on BFCL v4 shows that the learned orchestration skills transfer to entirely different API ecosystems (e.g., agentic web search and memory management), yielding consistent gains while maintaining stable single‑step performance. Code is available at https://github.com/horizon‑rl/ToolOrchestrationReward
Authors:Shwai He, Guoheng Sun, Haichao Zhang, Yun Fu, Ang Li
Abstract:
Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. However, this expectation does not consistently hold across language tasks: pruned models can perform well on non‑generative tasks but frequently fail in generative settings. To understand this discrepancy, we analyze network pruning from a representation‑hierarchy perspective, decomposing the internal computation of language models into three sequential spaces: embedding (hidden representations), logit (pre‑softmax outputs), and probability (post‑softmax distributions). We find that representations in the embedding and logit spaces are largely robust to pruning‑induced perturbations. However, the nonlinear transformation from logits to probabilities amplifies these deviations, which accumulate across time steps and lead to substantial degradation during generation. In contrast, the stability of the categorical‑token probability subspace, together with the robustness of the embedding space, supports the effectiveness of pruning for non‑generative tasks such as retrieval and multiple‑choice selection. Our analysis disentangles the effects of pruning across tasks and provides practical guidance for its application. Code is available at https://github.com/CASE‑Lab‑UMD/Pruning‑on‑Representations
Authors:Fabio Ferreira, Lucca Wobbe, Arjun Krishnakumar, Frank Hutter, Arber Zela
Abstract:
The autoresearch repository enables an LLM agent to optimize hyperparameters by editing training code directly. We use it as a testbed to compare classical HPO algorithms against LLM‑based methods on tuning the hyperparameters of a small language model under a fixed compute budget. When defining a fixed search space over autoresearch, classical methods such as CMA‑ES and TPE consistently outperform LLM‑based agents, where avoiding out‑of‑memory failures matters more than search diversity. Allowing the LLM to directly edit source code narrows the gap to the classical methods but does not close it, even with frontier models available at the time of writing such as Claude Opus 4.6 and Gemini 3.1 Pro Preview. We observe that LLMs struggle to track optimization state across trials. In contrast, classical methods lack the domain knowledge of LLMs. To combine the strengths of both, we introduce Centaur, a hybrid that shares CMA‑ES's interpretable internal state, including mean vector, step‑size, and covariance matrix, with an LLM. Centaur achieves the best result in our experiments, and a 0.8B LLM already suffices to outperform all classical and pure LLM methods. Unconstrained code editing requires larger models to be competitive with classical methods. We further analyze search diversity, model scaling from 0.8B to frontier models, and ablate the fraction of LLM‑proposed trials in Centaur. All in all, our results suggest that LLMs are most effective as a complement to classical optimizers, not as a replacement.
Code is available at https://github.com/ferreirafabio/autoresearch‑automl & interactive demo at https://ferreirafabio.github.io/autoresearch‑automl.
Authors:Zichuan Lin, Feiyu Liu, Yijun Yang, Jiafei Lyu, Yiming Gao, Yicheng Liu, Zhicong Lu, Yangbin Yu, Mingyu Yang, Junyou Li, Deheng Ye, Jie Jiang
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.
Authors:Jeonghye Kim, Xufang Luo, Minbeom Kim, Sangmook Lee, Dohyung Kim, Jiwon Jeon, Dongsheng Li, Yuqing Yang
Abstract:
Self‑distillation has emerged as an effective post‑training paradigm for LLMs, often improving performance while shortening reasoning traces. However, in mathematical reasoning, we find that it can reduce response length while degrading performance. We trace this degradation to the suppression of epistemic verbalization ‑ the model's expression of uncertainty during reasoning. Through controlled experiments varying conditioning context richness and task coverage, we show that conditioning the teacher on rich information suppresses uncertainty expression, enabling rapid in‑domain optimization with limited task coverage but harming OOD performance, where unseen problems benefit from expressing uncertainty and adjusting accordingly. Across Qwen3‑1.7B/8B, DeepSeek‑Distill‑Qwen‑7B, and Olmo3‑7B‑Instruct, we observe performance drops of up to 40%. Our findings highlight that exposing appropriate levels of uncertainty is crucial for robust reasoning and underscore the importance of optimizing reasoning behavior beyond merely reinforcing correct answer traces.
Authors:Arsen Kuzhamuratov, Mikhail Zhirnov, Andrey Kuznetsov, Ivan Oseledets, Konstantin Sobolev
Abstract:
Accurate subseasonal weather forecasting remains a major challenge due to the inherently chaotic nature of the atmosphere, which limits the predictive skill of conventional models beyond the mid‑range horizon (approximately 15 days). In this work, we present Marchuk, a generative latent flow‑matching model for global weather forecasting spanning mid‑range to subseasonal timescales, with prediction horizons of up to 30 days. Marchuk conditions on current‑day weather maps and autoregressively predicts subsequent days' weather maps within the learned latent space. We replace rotary positional encodings (RoPE) with trainable positional embeddings and extend the temporal context window, which together enhance the model's ability to represent and propagate long‑range temporal dependencies during latent forecasting. Marchuk offers two key advantages: high computational efficiency and strong predictive performance. Despite its compact architecture of only 276 million parameters, the model achieves performance comparable to LaDCast, a substantially larger model with 1.6 billion parameters, while operating at significantly higher inference speeds. We open‑source our inference code and model at: https://v‑gen‑ai.github.io/Marchuk/
Authors:Yifeng Zhang, Harsh Goel, Peizhuo Li, Mehul Damani, Sandeep Chinchali, Guillaume Sartoretti
Abstract:
Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever‑expanding cities. Multi‑Agent Reinforcement Learning (MARL) has recently shown significant potential in addressing complex traffic dynamics, but the intricacies of partial observability and coordination in decentralized environments still remain key challenges in formulating scalable and efficient control strategies. To address these challenges, we present CoordLight, a MARL‑based framework designed to improve intra‑neighborhood traffic by enhancing decision‑making at individual junctions (agents), as well as coordination with neighboring agents, thereby scaling up to network‑level traffic optimization. Specifically, we introduce the Queue Dynamic State Encoding (QDSE), a novel state representation based on vehicle queuing models, which strengthens the agents' capability to analyze, predict, and respond to local traffic dynamics. We further propose an advanced MARL algorithm, named Neighbor‑aware Policy Optimization (NAPO). It integrates an attention mechanism that discerns the state and action dependencies among adjacent agents, aiming to facilitate more coordinated decision‑making, and to improve policy learning updates through robust advantage calculation. This enables agents to identify and prioritize crucial interactions with influential neighbors, thus enhancing the targeted coordination and collaboration among agents. Through comprehensive evaluations against state‑of‑the‑art traffic signal control methods over three real‑world traffic datasets composed of up to 196 intersections, we empirically show that CoordLight consistently exhibits superior performance across diverse traffic networks with varying traffic flows. The code is available at https://github.com/marmotlab/CoordLight
Authors:Eyal Weiss
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 .
Authors:Minjun Kim, Minje Kim
Abstract:
Personalized Federated Learning (PFL) aims to deliver effective client‑specific models under heterogeneous distributions, yet existing methods suffer from shallow prototype alignment and brittle server‑side distillation. We propose HEART‑PFL, a dual‑sided framework that (i) performs depth‑aware Hierarchical Directional Alignment (HDA) using cosine similarity in the early stage and MSE matching in the deep stage to preserve client specificity, and (ii) stabilizes global updates through Adversarial Knowledge Transfer (AKT) with symmetric KL distillation on clean and adversarial proxy data. Using lightweight adapters with only 1.46M trainable parameters, HEART‑PFL achieves state‑of‑the‑art personalized accuracy on CIFAR‑100, Flowers‑102, and Caltech‑101 (63.42%, 84.23%, and 95.67%, respectively) under Dirichlet non‑IID partitions, and remains robust to out‑of‑domain proxy data. Ablation studies further confirm that HDA and AKT provide complementary gains in alignment, robustness, and optimization stability, offering insights into how the two components mutually reinforce effective personalization. Overall, these results demonstrate that HEART‑PFL simultaneously enhances personalization and global stability, highlighting its potential as a strong and scalable solution for PFL(code available at https://github.com/danny0628/HEART‑PFL).
Authors:Rami Luisto
Abstract:
Antonyms, or opposites, are sometimes defined as \emphword pairs that have all of the same contextually relevant properties but one. Seeing how transformer models seem to encode concepts as directions, this begs the question if one can detect ``antonymity'' in the geometry of the embedding vectors of word pairs, especially based on their difference vectors. Such geometrical studies are then naturally contrasted by comparing antonymic pairs to their opposites; synonyms.
This paper started as an exploratory project on the complexity of the systems needed to detect the geometry of the embedding vectors of antonymic word pairs. What we now report is a curious ``swirl'' that appears across embedding models in a somewhat specific projection configuration.
Authors:Zhanhe Lei, Zhongyuan Wang, Jikang Cheng, Baojin Huang, Yuhong Yang, Zhen Han, Chao Liang, Dengpan Ye
Abstract:
Standard supervised training for deepfake detection treats all samples with uniform importance, which can be suboptimal for learning robust and generalizable features. In this work, we propose a novel Tutor‑Student Reinforcement Learning (TSRL) framework to dynamically optimize the training curriculum. Our method models the training process as a Markov Decision Process where a ``Tutor'' agent learns to guide a ``Student'' (the deepfake detector). The Tutor, implemented as a Proximal Policy Optimization (PPO) agent, observes a rich state representation for each training sample, encapsulating not only its visual features but also its historical learning dynamics, such as EMA loss and forgetting counts. Based on this state, the Tutor takes an action by assigning a continuous weight (0‑1) to the sample's loss, thereby dynamically re‑weighting the training batch. The Tutor is rewarded based on the Student's immediate performance change, specifically rewarding transitions from incorrect to correct predictions. This strategy encourages the Tutor to learn a curriculum that prioritizes high‑value samples, such as hard‑but‑learnable examples, leading to a more efficient and effective training process. We demonstrate that this adaptive curriculum improves the Student's generalization capabilities against unseen manipulation techniques compared to traditional training methods. Code is available at https://github.com/wannac1/TSRL.
Authors:Mayssa Soussia, Gita Ayu Salsabila, Mohamed Ali Mahjoub, Islem Rekik
Abstract:
Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating information from neighboring nodes. Graph Convolutional Networks (GCNs) exemplify this approach by adapting convolutional operations for graph structures, allowing features from adjacent nodes to be combined effectively. However, GCNs encounter challenges with complex or dynamic data. Capturing long‑range dependencies often requires deeper layers, which not only increase computational costs but also lead to over‑smoothing, where node embeddings become indistinguishable. To overcome these challenges, reservoir computing has been integrated into GNNs, leveraging iterative message‑passing dynamics for stable information propagation without extensive parameter tuning. Despite its promise, existing reservoir‑based models lack structured convolutional mechanisms, limiting their ability to accurately aggregate multi‑hop neighborhood information. To address these limitations, we propose RGC‑Net (Reservoir‑based Graph Convolutional Network), which integrates reservoir dynamics with structured graph convolution. Key contributions include: (i) a reimagined convolutional framework with fixed random reservoir weights and a leaky integrator to enhance feature retention; (ii) a robust, adaptable model for graph classification; and (iii) an RGC‑Net‑powered transformer for graph generation with application to dynamic brain connectivity. Extensive experiments show that RGC‑Net achieves state‑of‑the‑art performance in classification and generative tasks, including brain graph evolution, with faster convergence and reduced over‑smoothing. Source code is available at https://github.com/basiralab/RGC‑Net .
Authors:Mingyi Liu
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.
Authors:Anjun Gao, Zhenglin Wan, Pingfu Chao, Shunyu Yao
Abstract:
The integration of GNSS data into portable devices has led to the generation of vast amounts of trajectory data, which is crucial for applications such as map‑matching. To tackle the limitations of rule‑based methods, recent works in deep learning for trajectory‑related tasks occur. However, existing models remain challenging due to issues such as the difficulty of large‑scale data labeling, ineffective modeling of spatial‑temporal relationships, and discrepancies between training and test data distributions. To tackle these challenges, we propose HSTGMatch, a novel model designed to enhance map‑matching performance. Our approach involves a two‑stage process: hierarchical self‑supervised learning and spatial‑temporal supervised learning. We introduce a hierarchical trajectory representation, leveraging both grid cells and geographic tuples to capture moving patterns effectively. The model constructs an Adaptive Trajectory Adjacency Graph to dynamically capture spatial relationships, optimizing GATs for improved efficiency. Furthermore, we incorporate a Spatial‑Temporal Factor to extract relevant features and employ a decay coefficient to address variations in trajectory length. Our extensive experiments demonstrate the model's superior performance, module effectiveness, and robustness, providing a promising solution for overcoming the existing limitations in map‑matching applications. The source code of HSTGMatch is publicly available on GitHub at https://github.com/Nerooo‑g/HSTGMatch.
Authors:Jimyung Hong, Jaehyung Kim
Abstract:
Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a promising solution by removing entire dimensions or layers, yet existing methods face critical trade‑offs: task‑agnostic approaches cannot adapt to task‑specific requirements, while task‑aware methods require costly training to learn task adaptability. We propose DIET (Dimension‑wise global pruning of LLMs via merging Task‑wise importance scores), a training‑free structured pruning method that combines dimension‑level granularity with task‑aware selection. DIET profiles activation magnitudes across tasks using only 100 samples per task, then applies majority voting to construct a single global mask. DIET does not require large costs from pre‑computation or training. Experiments on seven zero‑shot benchmarks using Gemma‑2 2B and 9B models demonstrate the effectiveness of DIET; for example, at 20% sparsity on Gemma‑2 2B, DIET achieves near 10% average accuracy improvement, compared to previous state‑of‑the‑art structured pruning methods. This advantage persists across various sparsity levels and model scales, positioning DIET as a practical and robust choice for structured LLM pruning.
Authors:Henry LeCates, Haoze Wu
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.
Authors:Forest Agostinelli
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.
Authors:Akshay Rangamani, Altay Unal
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.
Authors:Philipp Wesp, Robbie Holland, Vasiliki Sideri-Lampretsa, Sergios Gatidis
Abstract:
Vision foundation models (FMs) achieve state‑of‑the‑art performance in medical imaging. However, they encode information in abstract latent representations that clinicians cannot interrogate or verify. The goal of this study is to investigate Sparse Autoencoders (SAEs) for replacing opaque FM image representations with human‑interpretable, sparse features. We train SAEs on embeddings from BiomedParse (biomedical) and DINOv3 (general‑purpose) using 909,873 CT and MRI 2D image slices from the TotalSegmentator dataset. We find that learned sparse features: (a) reconstruct original embeddings with high fidelity (R2 up to 0.941) and recover up to 87.8% of downstream performance using only 10 features (99.4% dimensionality reduction), (b) preserve semantic fidelity in image retrieval tasks, (c) correspond to specific concepts that can be expressed in language using large language model (LLM)‑based auto‑interpretation. (d) bridge clinical language and abstract latent representations in zero‑shot language‑driven image retrieval. Our work indicates SAEs are a promising pathway towards interpretable, concept‑driven medical vision systems. Code repository: https://github.com/pwesp/sail.
Authors:Igor Jankowski
Abstract:
While Multi‑Agent Reinforcement Learning (MARL) algorithms achieve unprecedented successes across complex continuous domains, their standard deployment strictly adheres to a synchronous operational paradigm. Under this paradigm, agents are universally forced to execute deep neural network inferences at every micro‑frame, regardless of immediate necessity. This dense throughput acts as a fundamental barrier to physical deployment on edge‑devices where thermal and metabolic budgets are highly constrained. We propose Epistemic Time‑Dilation MAPPO (ETD‑MAPPO), augmented with a Dual‑Gated Epistemic Trigger. Instead of depending on rigid frame‑skipping (macro‑actions), agents autonomously modulate their execution frequency by interpreting aleatoric uncertainty (via Shannon entropy of their policy) and epistemic uncertainty (via state‑value divergence in a Twin‑Critic architecture). To format this, we structure the environment as a Semi‑Markov Decision Process (SMDP) and build the SMDP‑Aligned Asynchronous Gradient Masking Critic to ensure proper credit assignment. Empirical findings demonstrate massive improvements (> 60% relative baseline acquisition leaps) over current temporal models. By assessing LBF, MPE, and the 115‑dimensional state space of Google Research Football (GRF), ETD correctly prevented premature policy collapse. Remarkably, this unconstrained approach leads to emergent Temporal Role Specialization, reducing computational overhead by a statistically dominant 73.6% entirely during off‑ball execution without deteriorating centralized task dominance.
Authors:Jannik Endres, Etienne Laliberté, David Rolnick, Arthur Ouaknine
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.
Authors:Wei Sun, Ting Wang, Xinran Tian, Wanshun Lan, Xuhan Feng, Haoyue Li, Fangxin Wang
Abstract:
Existing LLM‑based Kubernetes diagnostic systems cannot learn from operational experience, operating on static knowledge bases without improving from past resolutions. We present MetaKube, an experience‑aware LLM framework through three synergistic innovations: (1) an Episodic Pattern Memory Network (EPMN) that abstracts diagnostic patterns from historical resolutions and provides confidence‑calibrated retrieval for both rapid pattern matching and guided causal exploration, (2) a meta‑cognitive controller that dynamically routes between intuitive and analytical pathways based on problem familiarity, optimizing the trade‑off between speed and depth, and (3) KubeLLM, a locally‑deployable 8B model enhanced through domain‑specific post‑training on our 7,000‑sample Kubernetes Fault Resolution Dataset. Evaluation on 1,873 real‑world scenarios demonstrates MetaKube transforms Qwen3‑8B from 50.9 to 90.5 points, approaching GPT‑4.1 performance while ensuring complete data privacy. EPMN contributes 15.3% improvement through experiential learning, with continuous learning experiments showing progressive gains as the system accumulates operational knowledge. The source code and related resources are available at https://github.com/MetaKube‑LLM‑for‑Kubernetes‑Diagnosis/MetaKube.
Authors:Haoyu Wang, Yuxin Chen, Liang Luo, Buyun Zhang, Ellie Dingqiao Wen, Pan Li
Abstract:
Multi‑turn human‑AI collaboration is fundamental to deploying interactive services such as adaptive tutoring, conversational recommendation, and professional consultation. However, optimizing these interactions via reinforcement learning is hindered by the sparsity of verifiable intermediate rewards and the high stochasticity of user responses. To address these challenges, we introduce Implicit Turn‑wise Policy Optimization (ITPO). ITPO leverages an implicit process reward model to derive fine‑grained, turn‑wise process rewards from sparse outcome signals. Unlike volatile token‑level rewards, these turn‑level signals exhibit superior robustness and may utilize a normalization mechanism to further enhance training stability. We evaluate ITPO across three representative multi‑turn collaborative tasks: math tutoring, document writing, and medical recommendation. Empirical results demonstrate that ITPO, when combined with PPO, GRPO, or RLOO, consistently achieves improved convergence than existing baselines. Elaborate trajectory analysis confirms that ITPO infers turn‑wise preferences that are semantically aligned with human judgment. Code is publicly available at https://github.com/Graph‑COM/ITPO.
Authors:Bhavik Mangla
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.
Authors:Haoran Yuan, Weigang Yi, Zhenyu Zhang, Wendi Chen, Yuchen Mo, Jiashi Yin, Xinzhuo Li, Xiangyu Zeng, Chuan Wen, Cewu Lu, Katherine Driggs-Campbell, Ismini Lourentzou
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.
Authors:Ezgi Ozyilkan, Zhiqi Chen, Oren Rippel, Jona Ballé, Kedar Tatwawadi
Abstract:
Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad‑hoc combinations of pixel‑level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a diverse set of distortion losses. We conduct the first‑of‑its‑kind large‑scale human subjective study on 3DGS, involving 39,320 pairwise ratings across several datasets and 3DGS frameworks. A regularized version of Wasserstein Distortion, which we call WD‑R, emerges as the clear winner, excelling at recovering fine textures without incurring a higher splat count. WD‑R is preferred by raters more than 2.3× over the original 3DGS loss, and 1.5× over current best method Perceptual‑GS. WD‑R also consistently achieves state‑of‑the‑art LPIPS, DISTS, and FID scores across various datasets, and generalizes across recent frameworks, such as Mip‑Splatting and Scaffold‑GS, where replacing the original loss with WD‑R consistently enhances perceptual quality within a similar resource budget (number of splats for Mip‑Splatting, model size for Scaffold‑GS), and leads to reconstructions being preferred by human raters 1.8× and 3.6×, respectively. We also find that this carries over to the task of 3DGS scene compression, with \approx 50% bitrate savings for comparable perceptual metric performance.
Authors:Chao Han, Stefanos Ioannou, Luca Manneschi, T. J. Hayward, Michael Mangan, Aditya Gilra, Eleni Vasilaki
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
Authors:Edoardo Cetin, Stefano Peluchetti, Emilio Castillo, Akira Naruse, Mana Murakami, Llion Jones
Abstract:
Scaling autoregressive large language models (LLMs) has driven unprecedented progress but comes with vast computational costs. In this work, we tackle these costs by leveraging unstructured sparsity within an LLM's feedforward layers, the components accounting for most of the model parameters and execution FLOPs. To achieve this, we introduce a new sparse packing format and a set of CUDA kernels designed to seamlessly integrate with the optimized execution pipelines of modern GPUs, enabling efficient sparse computation during LLM inference and training. To substantiate our gains, we provide a quantitative study of LLM sparsity, demonstrating that simple L1 regularization can induce over 99% sparsity with negligible impact on downstream performance. When paired with our kernels, we show that these sparsity levels translate into substantial throughput, energy efficiency, and memory usage benefits that increase with model scale. We will release all code and kernels under an open‑source license to promote adoption and accelerate research toward establishing sparsity as a practical axis for improving the efficiency and scalability of modern foundation models.
Authors:Yuanhang Lei, Tao Cheng, Xingxuan Li, Boming Zhao, Siyuan Huang, Ruizhen Hu, Peter Yichen Chen, Hujun Bao, Zhaopeng Cui
Abstract:
Achieving real‑time physics‑based animation that generalizes across diverse 3D shapes and discretizations remains a fundamental challenge. We introduce PhysSkin, a physics‑informed framework that addresses this challenge. In the spirit of Linear Blend Skinning, we learn continuous skinning fields as basis functions lifting motion subspace coordinates to full‑space deformation, with subspace defined by handle transformations. To generate mesh‑free, discretization‑agnostic, and physically consistent skinning fields that generalize well across diverse 3D shapes, PhysSkin employs a new neural skinning fields autoencoder which consists of a transformer‑based encoder and a cross‑attention decoder. Furthermore, we also develop a novel physics‑informed self‑supervised learning strategy that incorporates on‑the‑fly skinning‑field normalization and conflict‑aware gradient correction, enabling effective balancing of energy minimization, spatial smoothness, and orthogonality constraints. PhysSkin shows outstanding performance on generalizable neural skinning and enables real‑time physics‑based animation.
Authors:Louis Claeys, Artur Goldman, Zebang Shen, Niao He
Abstract:
High‑dimensional stochastic optimal control (SOC) becomes harder with longer planning horizons: existing methods scale linearly in the horizon T, with performance often deteriorating exponentially. We overcome these limitations for a subclass of linearly‑solvable SOC problems‑those whose uncontrolled drift is the gradient of a potential. In this setting, the Hamilton‑Jacobi‑Bellman equation reduces to a linear PDE governed by an operator \mathcalL. We prove that, under the gradient drift assumption, \mathcalL is unitarily equivalent to a Schrödinger operator \mathcalS = ‑Δ+ \mathcalV with purely discrete spectrum, allowing the long‑horizon control to be efficiently described via the eigensystem of \mathcalL. This connection provides two key results: first, for a symmetric linear‑quadratic regulator (LQR), \mathcalS matches the Hamiltonian of a quantum harmonic oscillator, whose closed‑form eigensystem yields an analytic solution to the symmetric LQR with \empharbitrary terminal cost. Second, in a more general setting, we learn the eigensystem of \mathcalL using neural networks. We identify implicit reweighting issues with existing eigenfunction learning losses that degrade performance in control tasks, and propose a novel loss function to mitigate this. We evaluate our method on several long‑horizon benchmarks, achieving an order‑of‑magnitude improvement in control accuracy compared to state‑of‑the‑art methods, while reducing memory usage and runtime complexity from \mathcalO(Td) to \mathcalO(d).
Authors:Donya Jafari, Farzan Farnia
Abstract:
The expansion of generative AI and LLM services underscores the growing need for adaptive mechanisms to select an appropriate available model to respond to a user's prompts. Recent works have proposed offline and online learning formulations to identify the optimal generative AI model for an input prompt, based solely on maximizing prompt‑based fidelity evaluation scores, e.g., CLIP‑Score in text‑to‑image generation. However, such fidelity‑based selection methods overlook the diversity of generated outputs, and hence, they can fail to address potential diversity shortcomings in the generated responses. In this paper, we introduce the Diversity‑Aware Kernelized Upper Confidence Bound (DAK‑UCB) method as a contextual bandit algorithm for the online selection of generative models with diversity considerations. The proposed DAK‑UCB method incorporates both fidelity and diversity‑related metrics into the selection process. We design this framework based on prompt‑aware diversity score functions that decompose to a two‑sample‑based expectation over prompt‑output pairs in the previous generation rounds. Specifically, we illustrate the application of our framework using joint kernel distance and kernel entropy measures. Our experimental results demonstrate the effectiveness of DAK‑UCB in promoting diversity‑aware model selection while maintaining fidelity in the generations for a sequence of prompts. The code is available at https://github.com/Donya‑Jafari/DAK‑UCB.
Authors:Devvrat Joshi, Islem Rekik
Abstract:
Automated knowledge graph (KG) construction is essential for navigating the rapidly expanding body of scientific literature. However, existing approaches struggle to recognize long multi‑word entities, often fail to generalize across domains, and typically overlook the hierarchical nature of scientific knowledge. While general‑purpose large language models (LLMs) offer adaptability, they are computationally expensive and yield inconsistent accuracy on specialized tasks. As a result, current KGs are shallow and inconsistent, limiting their utility for exploration and synthesis. We propose a two‑stage framework for scalable, zero‑shot scientific KG construction. The first stage, Z‑NERD, introduces (i) Orthogonal Semantic Decomposition (OSD), which promotes domain‑agnostic entity recognition by isolating semantic "turns" in text, and (ii) a Multi‑Scale TCQK attention mechanism that captures coherent multi‑word entities through n‑gram‑aware attention heads. The second stage, HGNet, performs relation extraction with hierarchy‑aware message passing, explicitly modeling parent, child, and peer relations. To enforce global consistency, we introduce two complementary objectives: a Differentiable Hierarchy Loss to discourage cycles and shortcut edges, and a Continuum Abstraction Field (CAF) Loss that embeds abstraction levels along a learnable axis in Euclidean space. This is the first approach to formalize hierarchical abstraction as a continuous property within standard Euclidean embeddings, offering a simpler alternative to hyperbolic methods. We release SPHERE (https://github.com/basiralab/SPHERE), a multi‑domain benchmark for hierarchical relation extraction. Our framework establishes a new state of the art on SciERC, SciER, and SPHERE, improving NER by 8.08% and RE by 5.99% on out‑of‑distribution tests. In zero‑shot settings, gains reach 10.76% for NER and 26.2% for RE.
Authors:Davide Scassola, Dylan Ponsford, Adrián Javaloy, Sebastiano Saccani, Luca Bortolussi, Henry Gouk, Antonio Vergari
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.
Authors:Shengping Xie, Zekun Wu, Quan Chen, Kaixu Tang
Abstract:
Implicit bias induced by gradient‑based algorithms is essential to the generalization of overparameterized models, yet its mechanisms can be subtle. This work leverages the Normalized Steepest Descent (NSD) framework to investigate how optimization geometry shapes solutions on multiclass separable data. We introduce NucGD, a geometry‑aware optimizer designed to enforce low rank structures through nuclear norm constraints. Beyond the algorithm itself, we connect NucGD with emerging low‑rank projection methods, providing a unified perspective. To enable scalable training, we derive an efficient SVD‑free update rule via asynchronous power iteration. Furthermore, we empirically dissect the impact of stochastic optimization dynamics, characterizing how varying levels of gradient noise induced by mini‑batch sampling and momentum modulate the convergence toward the expected maximum margin solutions.Our code is accessible at: https://github.com/Tsokarsic/observing‑the‑implicit‑bias‑on‑multiclass‑seperable‑data.
Authors:Hector Borobia, Elies Seguí-Mas, Guillermina Tormo-Carbó
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.
Authors:Xingyu Chen, Junxiu An, Jun Guo, Yuqian Zhou
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
Authors:Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn
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/.
Authors:Fangyuan Li, Pengfei Li, Shijie Wang, Junqi Gao, Jianxing Liu, Biqing Qi, Yuqiang Li
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.
Authors:Drake Caraker, Bryan Arnold, David Rhoads
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
Authors:Eric Czech, Zhiwei Xu, Yael Elmatad, Yixin Wang, William Held
Abstract:
Chinchilla Approach 2 is among the most widely used methods for fitting neural scaling laws. Its parabolic approximation introduces systematic biases in compute‑optimal allocation estimates, even on noise‑free synthetic data. Applied to published Llama 3 IsoFLOP data at open frontier compute scales, these biases imply a parameter underallocation corresponding to 6.5% of the 3.8×10^25 FLOP training budget and \1.4M (90% CI: \412K‑\2.9M) in unnecessary compute at 50% H100 MFU. Simulated multimodal model misallocations show even greater opportunity costs due to higher loss surface asymmetry. Three sources of this error are examined: IsoFLOP sampling grid width (Taylor approximation accuracy), uncentered IsoFLOP sampling, and loss surface asymmetry (α\neq β). Chinchilla Approach 3 largely eliminates these biases but is often regarded as less data‑efficient, numerically unstable, prone to local minima, and harder to implement. Each concern is shown to be unfounded or addressable, especially when the partially linear structure of the objective is exploited via Variable Projection, enabling unbiased inference on all five loss surface parameters through a two‑dimensional optimization that is well‑conditioned, analytically differentiable, and amenable to dense, or even exhaustive, grid search. It may serve as a more convenient replacement for Approach 2 or a more scalable alternative for adaptations of Approach 3 to richer scaling law formulations. See https://github.com/Open‑Athena/vpnls for details and https://openathena.ai/scaling‑law‑analysis for other results from this study.
Authors:Chenhan Wang, Zhengyi Bao, Huipin Lin, Jiahao Nie, Chunxiang Zhu
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.
Authors:Peisong Niu, Haifan Zhang, Yang Zhao, Tian Zhou, Ziqing Ma, Wenqiang Shen, Junping Zhao, Huiling Yuan, Liang Sun
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.
Authors:Yan Xie, Tiansheng Wen, Tangda Huang, Bo Chen, Chenyu You, Stefanie Jegelka, Yifei Wang
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.
Authors:Yutao Xie, Nathaniel Thomas, Nicklas Hansen, Yang Fu, Li Erran Li, Xiaolong Wang
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.
Authors:Umair Nawaz, Ahmed Heakl, Ufaq Khan, Abdelrahman Shaker, Salman Khan, Fahad Shahbaz Khan
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.
Authors:Shivam Duggal, Xingjian Bai, Zongze Wu, Richard Zhang, Eli Shechtman, Antonio Torralba, Phillip Isola, William T. Freeman
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.
Authors:Alexandra Zelenin, Alexandra Zhuravlyova
Abstract:
Weight‑Decomposed Low‑Rank Adaptation (DoRA) extends LoRA by decoupling weight magnitude from direction, but its forward pass requires the row‑wise norm of W + sBA, a computation that every major framework we surveyed implements by materializing the dense [d_out, d_in] product BA. At d_in = 8192 and rank r = 384, a single module's norm requires about 512 MB of transient working memory in bf16, making high‑rank DoRA costly and often infeasible on common single‑GPU setups once hundreds of adapted modules and checkpointing are involved.
We present two systems contributions. A factored norm decomposes the squared norm into base, cross, and Gram terms computable through O(d_out r + r^2) intermediates, eliminating the dense product. Fused Triton kernels collapse the four‑kernel DoRA composition into a single pass, reducing memory traffic by about 4x and using a numerically stable form that avoids catastrophic cancellation in the near‑unity rescaling regime where magnitude scales concentrate in practice.
Across six 8‑32B vision‑language models (VLMs) on three NVIDIA GPUs (RTX 6000 PRO, H200, B200) at r = 384 in bf16, the fused implementation is 1.5‑2.0x faster than Hugging Face PEFT's DoRA implementation for inference and 1.5‑1.9x faster for gradient computation (optimizer step excluded), with up to 7 GB lower peak VRAM. Microbenchmarks on six GPUs spanning four architecture generations (L40S, A100, RTX 6000 PRO, H200, B200, B300) confirm 1.5‑2.7x compose‑kernel speedup. Final‑logit cosine similarity exceeds 0.9999 across all model/GPU pairs, and multi‑seed training curves match within 7.1 x 10^‑4 mean per‑step loss delta over 2000 steps.
Authors:Chi Zhang, Xixi Hu, Bo Liu, Qiang Liu
Abstract:
The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods. However, these non‑AR models often sacrifice generation quality as they struggle to model the complex joint distribution of token sequences. To narrow this performance gap, we introduce Gumbel Distillation, a novel distillation technique that enables parallel decoders to learn this distribution effectively. Our method leverages the Gumbel‑Max trick to create a deterministic mapping from a latent Gumbel noise space to the output tokens of a high‑performing AR teacher. As a model‑agnostic technique, Gumbel Distillation seamlessly integrates with diverse parallel decoding architectures, including MDLM and BD3‑LM. Experiments on LM1B and OpenWebText show that Gumbel Distillation substantially improves the generation quality of parallel language models, achieving a 30.0% improvement in MAUVE score and 10.5% in generative perplexity over MDLM trained on OpenWebText dataset. Code available at https://github.com/hxixixh/gumbel‑distill.
Authors:Kexian Tang, Jiani Wang, Shaowen Wang, Kaifeng Lyu
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.
Authors:Risa Shinoda, Kaede Shiohara, Nakamasa Inoue, Hiroaki Santo, Fumio Okura
Abstract:
Animal vocalizations provide crucial insights for wildlife assessment, particularly in complex environments such as forests, aiding species identification and ecological monitoring. Recent advances in deep learning have enabled automatic species classification from their vocalizations. However, classifying species unseen during training remains challenging. To address this limitation, we introduce AnimalCLAP, a taxonomy‑aware language‑audio framework comprising a new dataset and model that incorporate hierarchical biological information. Specifically, our vocalization dataset consists of 4,225 hours of recordings covering 6,823 species, annotated with 22 ecological traits. The AnimalCLAP model is trained on this dataset to align audio and textual representations using taxonomic structures, improving the recognition of unseen species. We demonstrate that our proposed model effectively infers ecological and biological attributes of species directly from their vocalizations, achieving superior performance compared to CLAP. Our dataset, code, and models will be publicly available at https://dahlian00.github.io/AnimalCLAP_Page/.
Authors:Xinyan Wang, Xiaogeng Liu, Chaowei Xiao
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.
Authors:Xixi Wu, Qianguo Sun, Ruiyang Zhang, Chao Song, Junlong Wu, Yiyan Qi, Hong Cheng
Abstract:
Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long‑horizon planning, yet a practical recipe for scaling RL in complex, multi‑turn environments remains elusive. This paper presents a systematic empirical study using TravelPlanner, a challenging testbed requiring tool orchestration to satisfy multifaceted constraints. We decompose the agentic RL design space along 5 axes: reward shaping, model scaling, data composition, algorithm selection, and environmental stability. Our controlled experiments yield 7 key takeaways, e.g., (1) reward and algorithm choices are scale‑dependent as smaller models benefit from staged rewards and enhanced exploration, whereas larger models converge efficiently with simpler dense rewards, (2) ~ 1K training samples with a balanced difficulty mixture mark a sweet spot for both in‑domain and out‑of‑domain performance, and (3) environmental stability is critical to prevent policy degradation. Based on our distilled recipe, our RL‑trained models achieve state‑of‑the‑art performance on TravelPlanner, significantly outperforming leading LLMs.
Authors:Guannan Lai, Da-Wei Zhou, Zhenguo Li, Han-Jia Ye
Abstract:
Continual Test‑Time Adaptation (CTTA) aims to enable models to adapt online to unlabeled data streams under distribution shift without accessing source data. Existing CTTA methods face an efficiency‑generalization trade‑off: updating more parameters improves adaptation but severely reduces online inference efficiency. An ideal solution is to achieve comparable adaptation with minimal feature updates; we call this minimal subspace the golden subspace. We prove its existence in a single‑step adaptation setting and show that it coincides with the row space of the pretrained classifier. To enable online maintenance of this subspace, we introduce the sample‑wise Average Gradient Outer Product (AGOP) as an efficient proxy for estimating the classifier weights without retraining. Building on these insights, we propose Guided Online Low‑rank Directional adaptation (GOLD), which uses a lightweight adapter to project features onto the golden subspace and learns a compact scaling vector while the subspace is dynamically updated via AGOP. Extensive experiments on classification and segmentation benchmarks, including autonomous‑driving scenarios, demonstrate that GOLD attains superior efficiency, stability, and overall performance. Our code is available at https://github.com/AIGNLAI/GOLD.
Authors:Donald Shenaj, Federico Errica, Antonio Carta
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.
Authors:Nikolas Stavrou, Siamak Mehrkanoon
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.
Authors:Yuze Qin, Qingyong Li, Zhiqing Guo, Wen Wang, Yan Liu, Yangli-ao Geng
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.
Authors:Shiyan Hu, Jianxin Jin, Yang Shu, Peng Chen, Bin Yang, Chenjuan Guo
Abstract:
Time series anomaly detection plays a critical role in many dynamic systems. Despite its importance, previous approaches have primarily relied on unimodal numerical data, overlooking the importance of complementary information from other modalities. In this paper, we propose a novel multimodal time series anomaly detection model (MindTS) that focuses on addressing two key challenges: (1) how to achieve semantically consistent alignment across heterogeneous multimodal data, and (2) how to filter out redundant modality information to enhance cross‑modal interaction effectively. To address the first challenge, we propose Fine‑grained Time‑text Semantic Alignment. It integrates exogenous and endogenous text information through cross‑view text fusion and a multimodal alignment mechanism, achieving semantically consistent alignment between time and text modalities. For the second challenge, we introduce Content Condenser Reconstruction, which filters redundant information within the aligned text modality and performs cross‑modal reconstruction to enable interaction. Extensive experiments on six real‑world multimodal datasets demonstrate that the proposed MindTS achieves competitive or superior results compared to existing methods. The code is available at: https://github.com/decisionintelligence/MindTS.
Authors:Hyoseok Park, Yeonsang Park
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).
Authors:Bayezid Baten, M. Ayyan Iqbal, Sebastian Ament, Julius Kusuma, Nishant Garg
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.
Authors:Uzay Macar, Li Yang, Atticus Wang, Peter Wallich, Emmanuel Ameisen, Jack Lindsey
Abstract:
Recent work has shown that LLMs can sometimes detect when steering vectors are injected into their residual stream and identify the injected concept ‑‑ a phenomenon termed "introspective awareness." We investigate the mechanisms underlying this capability in open‑weights models. First, we find that it is behaviorally robust: models detect injected steering vectors at moderate rates with 0% false positives across diverse prompts and dialogue formats. Notably, this capability emerges specifically from post‑training; we show that preference optimization algorithms like DPO can elicit it, but standard supervised finetuning does not. We provide evidence that detection cannot be explained by simple linear association between certain steering vectors and directions promoting affirmative responses. We trace the detection mechanism to a two‑stage circuit in which "evidence carrier" features in early post‑injection layers detect perturbations monotonically along diverse directions, suppressing downstream "gate" features that implement a default negative response. This circuit is absent in base models and robust to refusal ablation. Identification of injected concepts relies on largely distinct later‑layer mechanisms that only weakly overlap with those involved in detection. Finally, we show that introspective capability is substantially underelicited: ablating refusal directions improves detection by +53%, and a trained bias vector improves it by +75% on held‑out concepts, both without meaningfully increasing false positives. Our results suggest that this introspective awareness of injected concepts is robust and mechanistically nontrivial, and could be substantially amplified in future models. Code: https://github.com/safety‑research/introspection‑mechanisms.
Authors:Mohamed A Mabrok
Abstract:
We present HamVision, a framework for medical image analysis that uses the damped harmonic oscillator, a fundamental building block of signal processing, as a structured inductive bias for both segmentation and classification tasks. The oscillator's phase‑space decomposition yields three functionally distinct representations: position~q (feature content), momentum~p (spatial gradients that encode boundary and texture information), and energy H = \tfrac12|z|^2 (a parameter‑free saliency map). These representations emerge from the dynamics, not from supervision, and can be exploited by different task‑specific heads without any modification to the oscillator itself. For segmentation, energy gates the skip connections while momentum injects boundary information at every decoder level (HamSeg). For classification, the three representations are globally pooled and concatenated into a phase‑space feature vector (HamCls). We evaluate HamVision across ten medical imaging benchmarks spanning five imaging modalities. On segmentation, HamSeg achieves state‑of‑the‑art Dice scores on ISIC\,2018 (89.38%), ISIC\,2017 (88.40%), TN3K (87.05%), and ACDC (92.40%), outperforming most baselines with only 8.57M parameters. On classification, HamCls achieves state‑of‑the‑art accuracy on BloodMNIST (98.85%) and PathMNIST (96.65%), and competitive results on the remaining MedMNIST datasets against MedMamba and MedViT. Diagnostic analysis confirms that the oscillator's momentum consistently encodes an interior\,>\,boundary\,>\,exterior gradient for segmentation and that the energy map correlates with discriminative regions for classification, properties that emerge entirely from the Hamiltonian dynamics. Code is available at https://github.com/Minds‑R‑Lab/hamvision.
Authors:Pawel Batorski, Paul Swoboda
Abstract:
In‑context learning (ICL) adapts large language models by conditioning on a small set of ICL examples, avoiding costly parameter updates. Among other factors, performance is often highly sensitive to the ordering of the examples. However, exhaustive search over the n! possible orderings is infeasible. Therefore more efficient ordering methods use model confidence measures (e.g., label‑probability entropy) over label sets or take a direct approach to finding the best ordering. We propose PLR, a probabilistic approach to in‑context example ordering that replaces discrete ordering search with learning a probability distribution over orderings with the Plackett‑Luce model. PLR models orderings using a Plackett‑Luce distribution and iteratively updates its parameters to concentrate probability mass on high‑performing orderings under a task‑level metric. Candidate orderings are sampled efficiently via a Gumbel perturb‑and‑sort procedure. Experiments on multiple classification benchmarks show that PLR consistently improves few‑shot accuracy for k \in \4, 8, 16, 32\ examples, and we further demonstrate gains on mathematical reasoning tasks where label‑based ordering methods are not applicable. Our code is available at https://github.com/Batorskq/PLR.
Authors:Jaber Jaber, Osama Jaber
Abstract:
Large language models run every token through every layer, regardless of difficulty. We present TIDE, a post‑training system that attaches tiny learned routers at periodic checkpoint layers and, at inference time, selects the earliest layer whose hidden state has converged for each token. TIDE requires no model retraining, works with any HuggingFace causal LM, auto‑detects GPU architecture, and supports float32, float16, and bfloat16 through fused CUDA kernels. On an NVIDIA A100 with DeepSeek R1 Distill 8B, TIDE achieves 100% prefill exit rate (5% of tokens exit at layer 11, the remaining at layer 31), reduces prefill latency by 7.2%, and increases single‑batch throughput by 6.6%. During autoregressive decoding, 98‑99% of tokens exit early while the model correctly solves a multi‑step math problem with 95 unique output tokens. On Qwen3 8B (36 layers), throughput improves by 8.1% at batch size 8. Calibration on 2,000 WikiText samples takes under 3 minutes and produces a ~4 MB router checkpoint. The system comprises 1,308 lines of Python and 1,081 lines of CUDA/C++ with 74 passing tests. Code: https://github.com/RightNow‑AI/TIDE
Authors:Oussama Zekri, Théo Uscidda, Nicolas Boullé, Anna Korba
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.
Authors:Jaber Jaber, Osama Jaber
Abstract:
Writing high‑performance GPU kernels is among the most labor‑intensive tasks in machine learning systems engineering. We present AutoKernel, an open‑source framework that applies an autonomous agent loop to GPU kernel optimization for arbitrary PyTorch models. Given a model, AutoKernel profiles it to identify computational bottlenecks, ranks them by Amdahl's law impact, and iteratively refines Triton or CUDA C++ kernel implementations through hundreds of experiments without human intervention. A five‑stage correctness harness covering smoke tests, shape sweeps, numerical stability, determinism verification, and edge‑case coverage ensures every candidate kernel is validated before any speedup is recorded. The system comprises over 9,000 lines of Python, 18 starter kernel implementations across two backends, a six‑tier optimization playbook, and integration with the KernelBench benchmark suite. AutoKernel covers nine kernel types spanning the dominant operations in modern transformer architectures. On an NVIDIA H100, our Triton kernels outperform both PyTorch eager and torch.compile (max‑autotune) on the majority of tested configurations: 5.29x over eager on RMSNorm, 2.82x on softmax, and 2.21x on cross‑entropy, while beating torch.compile by 2.83x, 3.44x, and 2.94x respectively. In community deployment, an AutoKernel‑optimized kernel achieved first place on the vectorsum_v2 B200 leaderboard. The full system is available at https://github.com/RightNow‑AI/autokernel.
Authors:Hongyang Yang, Boyu Zhang, Yang She, Xinyu Liao, Xiaoli Zhang
Abstract:
We present FinRL‑X, a modular and deployment‑consistent trading architecture that unifies data processing, strategy construction, backtesting, and broker execution under a weight‑centric interface. While existing open‑source platforms are often backtesting‑ or model‑centric, they rarely provide system‑level consistency between research evaluation and live deployment. FinRL‑X addresses this gap through a composable strategy pipeline that integrates stock selection, portfolio allocation, timing, and portfolio‑level risk overlays within a unified protocol. The framework supports both rule‑based and AI‑driven components, including reinforcement learning allocators and LLM‑based sentiment signals, without altering downstream execution semantics. FinRL‑X provides an extensible foundation for reproducible, end‑to‑end quantitative trading research and deployment. The official FinRL‑X implementation is available at https://github.com/AI4Finance‑Foundation/FinRL‑Trading.
Authors:Fabien Polly
Abstract:
World models learn to predict future states of an environment, enabling planning and mental simulation. Current approaches default to Transformer‑based predictors operating in learned latent spaces. This comes at a cost: O(N^2) computation and no explicit spatial inductive bias. This paper asks a foundational question: is self‑attention necessary for predictive world modeling, or can alternative computational substrates achieve comparable or superior results? I introduce FluidWorld, a proof‑of‑concept world model whose predictive dynamics are governed by partial differential equations (PDEs) of reaction‑diffusion type. Instead of using a separate neural network predictor, the PDE integration itself produces the future state prediction. In a strictly parameter‑matched three‑way ablation on unconditional UCF‑101 video prediction (64x64, ~800K parameters, identical encoder, decoder, losses, and data), FluidWorld is compared against both a Transformer baseline (self‑attention) and a ConvLSTM baseline (convolutional recurrence). While all three models converge to comparable single‑step prediction loss, FluidWorld achieves 2x lower reconstruction error, produces representations with 10‑15% higher spatial structure preservation and 18‑25% more effective dimensionality, and critically maintains coherent multi‑step rollouts where both baselines degrade rapidly. All experiments were conducted on a single consumer‑grade PC (Intel Core i5, NVIDIA RTX 4070 Ti), without any large‑scale compute. These results establish that PDE‑based dynamics, which natively provide O(N) spatial complexity, adaptive computation, and global spatial coherence through diffusion, are a viable and parameter‑efficient alternative to both attention and convolutional recurrence for world modeling.
Authors:Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
Abstract:
The continual learning literature has rapidly shifted from traditional class incremental learning (CIL) techniques to foundation model (FM)‑based CIL methods without a clear understanding of how these newer approaches compare to strong, lightweight convolutional baselines. This abrupt transition has created a substantial methodological gap, making it difficult to assess whether recent FM‑based CIL progress reflects genuine advances or merely the absence of rigorous baselines. To address this gap, we introduce Pruned Adaptation Modules (PAM), a simple yet effective method that freezes the vast majority of the pre‑trained ResNet while enabling scalable continual adaptation through sparse task‑specific layers. PAM yields up to a ~5x reduction in trainable parameters and a ~6x reduction in total parameters, significantly reducing the cost of continual updates. Across diverse benchmarks, PAM consistently mitigates catastrophic forgetting and outperforms state‑of‑the‑art FM‑based CIL approaches. Our findings position PAM as a strong and transparent baseline that helps bridge the gap between traditional and FM‑based CIL, guiding future research for a more accurate assessment of true progress in continual adaptation. The code can be found at: https://github.com/ElifCerenGokYildirim/PAM.
Authors:Tianhao Ma, Ximing Li, Changchun Li, Renchu Guan
Abstract:
Learning from Label Proportions (LLP) is a weakly supervised problem in which the training data comprise bags, that is, groups of instances, each annotated only with bag‑level class label proportions, and the objective is to learn a classifier that predicts instance‑level labels. This setting is widely applicable when privacy constraints limit access to instance‑level annotations or when fine‑grained labeling is costly or impractical. In this work, we introduce a method that leverages Dual proportion Constraints (LLP‑DC) during training, enforcing them at both the bag and instance levels. Specifically, the bag‑level training aligns the mean prediction with the given proportion, and the instance‑level training aligns hard pseudo‑labels that satisfy the proportion constraint, where a minimum‑cost maximum‑flow algorithm is used to generate hard pseudo‑labels. Extensive experimental results across various benchmark datasets empirically validate that LLP‑DC consistently improves over previous LLP methods across datasets and bag sizes. The code is publicly available at https://github.com/TianhaoMa5/CV PR2026_Findings_LLP_DC.
Authors:Shih-Wen Liu, Yen-Chang Chen, Wei-Ta Chu, Fu-En Yang, Yu-Chiang Frank Wang
Abstract:
Multi‑task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter‑efficient fine‑tuning (PEFT) methods remain largely limited to single‑task adaptation. We introduce Free Sinewich, a parameter‑efficient multi‑task learning framework that enables near‑zero‑cost weight modulation via frequency switching (Free). Specifically, a Sine‑AWB (Sinewich) layer combines low‑rank factors and convolutional priors into a single kernel, which is then modulated elementwise by a sinusoidal transformation to produce task‑specialized weights. A lightweight Clock Net is introduced to produce bounded frequencies that stabilize this modulation during training. Theoretically, sine modulation enhances the rank of low‑rank adapters, while frequency separation decorrelates the weights of different tasks. On dense prediction benchmarks, Free Sinewich achieves state‑of‑the‑art performance‑efficiency trade‑offs (e.g., up to +5.39% improvement over single‑task fine‑tuning with only 6.53M trainable parameters), offering a compact and scalable paradigm based on frequency‑based parameter sharing. Project page: \hrefhttps://casperliuliuliu.github.io/projects/Free‑Sinewich/https://casperliuliuliu.github.io/projects/Free‑Sinewich.
Authors:Long Xu, Junping Guo, Jianbo Zhao, Jianbo Lu, Yuzhong Peng
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.
Authors:Tasmay Pankaj Tibrewal, Pritish Saha, Ankit Meda, Kunal Singh, Pradeep Moturi
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).
Authors:Jinquan Zheng, Jia Yuan, Jiacheng Yao, Chenyang Gu, Pujun Zheng, Guoxiu He
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).
Authors:Florent Draye, Abir Harrasse, Vedant Palit, Tung-Yu Wu, Jiarui Liu, Punya Syon Pandey, Roderick Wu, Terry Jingchen Zhang, Zhijing Jin, Bernhard Schölkopf
Abstract:
Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information. Recent approaches based on dictionary learning and transcoders enable representing model computation in terms of sparse, interpretable features and their interactions, giving rise to feature attribution graphs. However, these graphs are often large and redundant, limiting their interpretability in practice. Cross‑Layer Transcoders (CLTs) address this issue by sharing features across layers while preserving layer‑specific decoding, yielding more compact representations, but remain difficult to train and analyze at scale. We introduce an open‑source library for end‑to‑end training and interpretability of CLTs. Our framework integrates scalable distributed training with model sharding and compressed activation caching, a unified automated interpretability pipeline for feature analysis and explanation, attribution graph computation using Circuit‑Tracer, and a flexible visualization interface. This provides a practical and unified solution for scaling CLT‑based mechanistic interpretability. Our code is available at: https://github.com/LLM‑Interp/CLT‑Forge.
Authors:Jason Dury
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.
Authors:Yong Wang, Qifan Shen, Bao Zhang, Zijun Huang, Chengbo Zhu, Shuai Yao, Qisong Wu
Abstract:
Millimeter‑wave (mmWave) radar enables contactless respiratory sensing,yet fine‑grained monitoring is often degraded by nonstationary interference from body micromotions.To achieve micromotion interference removal,we propose mmWave‑Diffusion,an observation‑anchored conditional diffusion framework that directly models the residual between radar phase observations and the respiratory ground truth,and initializes sampling within an observation‑consistent neighborhood rather than from Gaussian noise‑thereby aligning the generative process with the measurement physics and reducing inference overhead. The accompanying Radar Diffusion Transformer (RDT) is explicitly conditioned on phase observations, enforces strict one‑to‑one temporal alignment via patch‑level dual positional encodings, and injects local physical priors through banded‑mask multi‑head cross‑attention, enabling robust denoising and interference removal in just 20 reverse steps. Evaluated on 13.25 hours of synchronized radar‑respiration data, mmWave‑Diffusion achieves state‑of‑the‑art waveform reconstruction and respiratory‑rate estimation with strong generalization. Code repository:https://github.com/goodluckyongw/mmWave‑Diffusion.
Authors:Hung Yun Tseng, Wuzhen Li, Blerina Gkotse, Grigorios Chrysos
Abstract:
The potential of Large Language Models (LLMs) to provide harmful information remains a significant concern due to the vast breadth of illegal queries they may encounter. Unfortunately, existing benchmarks only focus on a handful types of illegal activities, and are not grounded in legal works. In this work, we introduce an ontology of crime‑related concepts grounded in the legal frameworks of Model Panel Code, which serves as an influential reference for criminal law and has been adopted by many U.S. states, and instantiated using Californian Law. This structured knowledge forms the foundation for LJ‑Bench, the first comprehensive benchmark designed to evaluate LLM robustness against a wide range of illegal activities. Spanning 76 distinct crime types organized taxonomically, LJ‑Bench enables systematic assessment of diverse attacks, revealing valuable insights into LLM vulnerabilities across various crime categories: LLMs exhibit heightened susceptibility to attacks targeting societal harm rather than those directly impacting individuals. Our benchmark aims to facilitate the development of more robust and trustworthy LLMs. The LJ‑Bench benchmark and LJ‑Ontology, along with experiments implementation for reproducibility are publicly available at https://github.com/AndreaTseng/LJ‑Bench.
Authors:Shenyang Deng, Zhuoli Ouyang, Tianyu Pang, Zihang Liu, Ruochen Jin, Shuhua Yu, Yaoqing Yang
Abstract:
Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information of the loss landscape. The central challenge in this field lies in balancing preconditioning effectiveness with computational efficiency of implementing the preconditioner. Among recent advances, Muon stands out by using Newton‑Schulz iteration to obtain preconditioned updates without explicitly constructing the preconditioning matrix. Despite its advantages, the efficiency of Muon still leaves room for further improvement. In this paper, we introduce RMNP (Row Momentum Normalized Preconditioning), an optimizer that replaces Newton‑Schulz iteration with a simple row‑wise (d_\textin) \ell_2 normalization operation, motivated by the empirically observed diagonal block structure of the Transformer layerwise Hessian. We empirically verified that orthogonalization and row‑wise (on input dim) \ell_2 normalization are asymptotically equivalent in the case of the transformer. This substitution reduces the per‑iteration computational complexity from O(mn\cdot\min(m,n)) to O(mn) for an m× n weight matrix while maintaining comparable optimization performance. Theoretically, we establish convergence guarantees for RMNP in the non‑convex setting that match recent results for Muon optimizers, achieving the minimax optimal complexity. Extensive experiments on large language model pretraining show that RMNP delivers competitive optimization performance compared with Muon while substantially reducing preconditioning wall‑clock time. Our code is available at https://github.com/Dominator‑Index/RMNP.
Authors:Truong Quynh Hoa, Hoang Dinh Cuong, Truong Xuan Khanh
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
Authors:Erez Badash, Dan Boneh, Ilan Komargodski, Megha Srivastava
Abstract:
We present Hawkeye, a system for analyzing and reproducing GPU‑level arithmetic operations. Using our framework, anyone can re‑execute on a CPU the exact matrix multiplication operations underlying a machine learning model training or inference workflow that was executed on an NVIDIA GPU, without any precision loss. This is in stark contrast to prior approaches to verifiable machine learning, which either introduce significant computation overhead to the original model owner, or suffer from non‑robustness and quality degradation. The main technical contribution of Hawkeye is a systematic sequence of carefully crafted tests that study rounding direction, subnormal number handling, and order of (non‑associative) accumulation during matrix multiplication on NVIDIA's Tensor Cores. We test and evaluate our framework on multiple NVIDIA GPU architectures ( Ampere, Hopper, and Lovelace) and precision types (FP16, BFP16, FP8). In all test cases, Hawkeye enables perfect reproduction of matrix multiplication on a CPU, paving the way for efficient and trustworthy third‑party auditing of ML model training and inference. We provide source code for Hawkeye at https://github.com/badasherez/gpu‑simulator.
Authors:Simon Ambrozak, Ulysse McConnell, Bhargav Srinivasan, Burak Ozkan, Ernest Zhang, Can Firtina
Abstract:
Nanopore sequencing can read substantially longer sequences of nucleic acid molecules, called reads, than other sequencing methods, which has led to advances in genomic analysis such as the gapless human genome assembly. By analyzing the raw electrical signal reads that nanopore sequencing generates from molecules, existing works can map these reads without translating them into DNA characters (i.e., basecalling), allowing for quick and efficient analysis of sequencing data. However, raw signals often contain errors due to noise and processing errors, which limits the overall accuracy of raw signal analysis. Our goal in this work is to detect and correct errors in raw signals to improve the accuracy of raw signal analyses. To this end, we propose CRANE, a mechanism that trains and utilizes a Hidden Markov Model (HMM) to accurately correct signal errors. Our extensive evaluation on various datasets shows that CRANE 1) consistently improves the overall accuracy of the underlying raw signal analysis tools, 2) minimizes the burden of optimizing analysis pipelines for newer nanopore technologies, and 3) does not introduce substantial computational overhead. We conclude that CRANE provides an effective mechanism to systematically identify and correct the errors in raw nanopore signals before further analysis, which can enable the development of a new class of error correction mechanisms purely designed for raw nanopore signals. Source Code: CRANE is available at https://github.com/STORMgroup/CRANE. We also provide the scripts to fully reproduce our results on our GitHub page
Authors:Dhruv Menon, Vivek Singh, Xu Chen, Mohammad Reza Alizadeh Kiapi, Ivan Zyuzin, Hamish W. Macleod, Nakul Rampal, William Shepard, Omar M. Yaghi, David Fairen-Jimenez
Abstract:
Reticular chemistry has enabled the synthesis of tens of thousands of metal‑organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition‑driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the systematic discovery of frameworks with targeted properties. Here, we introduce Nexerra‑R1, a building‑block chemical language model that enables inverse design in reticular chemistry through the targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra‑R1 operates at the level of molecular building blocks, preserving the modular logic that underpins reticular synthesis. The model supports both unconstrained generation of low‑connectivity linkers and scaffold‑constrained design of symmetric multidentate motifs compatible with predefined nodes and topologies. We further combine linker generation with flow‑guided distributional targeting to steer the generative process toward application‑relevant objectives while maintaining chemical validity and assembly feasibility. The generated linkers are subsequently assembled into three‑dimensional frameworks and are structurally optimized to produce candidate materials compatible with experimental synthesis. Using Nexerra‑R1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU‑525, generated entirely in silico. Together, these results establish a general inverse‑design paradigm for reticular materials in which controllable chemical language modelling enables the direct translation from computational design to synthesizable frameworks.
Authors:Liu hung ming
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.
Authors:Alex Popa, Adrian Taylor, Ranwa Al Mallah
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.
Authors:Behnood Rasti, Bikram Koirala, Paul Scheunders
Abstract:
This paper proposes a semisupervised geometric unmixing approach called minimum simplex semisupervised unmixing (MiSiSUn). The geometry of the data was incorporated for the first time into library‑based unmixing using a simplex‑volume‑flavored penalty based on an archetypal analysis‑type linear model. The experimental results were performed on two simulated datasets considering different levels of mixing ratios and spatial instruction at varying input noise. MiSiSUn considerably outperforms state‑of‑the‑art semisupervised unmixing methods. The improvements vary from 1 dB to over 3 dB in different scenarios. The proposed method was also applied to a real dataset where visual interpretation is close to the geological map. MiSiSUn was implemented using PyTorch, which is open‑source and available at https://github.com/BehnoodRasti/MiSiSUn. Moreover, we provide a dedicated Python package for Semisupervised Unmixing, which is open‑source and includes all the methods used in the experiments for the sake of reproducibility.
Authors:Jiaqi Yuan, Jialu Wang, Zihan Wang, Qingyun Sun, Ruijie Wang, Jianxin Li
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.
Authors:Xinyi Shang, Yi Tang, Jiacheng Cui, Ahmed Elhagry, Salwa K. Al Khatib, Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Jing-Hao Xue, Hao Li, Salman Khan, Zhiqiang Shen
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.
Authors:Alejandro Almodóvar, Mar Elizo, Patricia A. Apellániz, Santiago Zazo, Juan Parras
Abstract:
Causal generative models provide a principled framework for answering observational, interventional, and counterfactual queries from observational data. However, many deep causal models rely on highly expressive architectures with opaque mechanisms, limiting auditability in high‑stakes domains. We propose KaCGM, a causal generative model for mixed‑type tabular data where each structural equation is parameterized by a Kolmogorov‑‑Arnold Network (KAN). This decomposition enables direct inspection of learned causal mechanisms, including symbolic approximations and visualization of parent‑‑child relationships, while preserving query‑agnostic generative semantics. We introduce a validation pipeline based on distributional matching and independence diagnostics of inferred exogenous variables, allowing assessment using observational data alone. Experiments on synthetic and semi‑synthetic benchmarks show competitive performance against state‑of‑the‑art methods. A real‑world cardiovascular case study further demonstrates the extraction of simplified structural equations and interpretable causal effects. These results suggest that expressive causal generative modeling and functional transparency can be achieved jointly, supporting trustworthy deployment in tabular decision‑making settings. Code: https://github.com/aalmodovares/kacgm
Authors:Amartya Roy, Rasul Tutunov, Xiaotong Ji, Matthieu Zimmer, Haitham Bou-Ammar
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.
Authors:Henry J. Kobs
Abstract:
Continual learning methods usually preserve old behavior by regularizing parameters, matching old outputs, or replaying previous examples. These strategies can reduce forgetting, but they do not directly specify how the latent representation should evolve. We study a narrower geometric alternative for the regime where old and new data should remain on the same latent support: continual learning as continuation of a shared manifold. We instantiate this view within Support‑Preserving Manifold Assimilation (SPMA) and evaluate a geometry‑preserving variant, SPMA‑OG, that combines sparse replay, output distillation, relational geometry preservation, local smoothing, and chart‑assignment regularization on old anchors. On representative compatible‑shift CIFAR10 and Tiny‑ImageNet runs, SPMA‑OG improves over sparse replay baselines in old‑task retention and representation‑preservation metrics while remaining competitive on new‑task accuracy. On a controlled synthetic atlas‑manifold benchmark, it achieves near‑perfect anchor‑geometry preservation while also improving new‑task accuracy over replay. These results provide evidence that geometry‑aware anchor regularization is a useful inductive bias when continual learning should preserve a shared latent support rather than create a new one.
Authors:Leonardo Kuffo, Sven Hepkema, Peter Boncz
Abstract:
We present SuperKMeans: a k‑means variant designed for clustering collections of high‑dimensional vector embeddings. SuperKMeans' clustering is up to 7x faster than FAISS and Scikit‑Learn on modern CPUs and up to 4x faster than cuVS on GPUs (Figure 1), while maintaining the quality of the resulting centroids for vector similarity search tasks. SuperKMeans acceleration comes from reducing data‑access and compute overhead by reliably and efficiently pruning dimensions that are not needed to assign a vector to a centroid. Furthermore, we present Early Termination by Recall, a novel mechanism that early‑terminates k‑means when the quality of the centroids for retrieval tasks stops improving across iterations. In practice, this further reduces runtimes without compromising retrieval quality. We open‑source our implementation at https://github.com/cwida/SuperKMeans
Authors:Hao Wang, Licheng Pan, Qingsong Wen, Jialin Yu, Zhichao Chen, Chunyuan Zheng, Xiaoxi Li, Zhixuan Chu, Chao Xu, Mingming Gong, Haoxuan Li, Yuan Lu, Zhouchen Lin, Philip Torr, Yan Liu
Abstract:
Autocorrelation is a defining characteristic of time‑series data, where each observation is statistically dependent on its predecessors. In the context of deep time‑series forecasting, autocorrelation arises in both the input history and the label sequences, presenting two central research challenges: (1) designing neural architectures that model autocorrelation in history sequences, and (2) devising learning objectives that model autocorrelation in label sequences. Recent studies have made strides in tackling these challenges, but a systematic survey examining both aspects remains lacking. To bridge this gap, this paper provides a comprehensive review of deep time‑series forecasting from the perspective of autocorrelation modeling. In contrast to existing surveys, this work makes two distinctive contributions. First, it proposes a novel taxonomy that encompasses recent literature on both model architectures and learning objectives ‑‑ whereas prior surveys neglect or inadequately discuss the latter aspect. Second, it offers a thorough analysis of the motivations, insights, and progression of the surveyed literature from a unified, autocorrelation‑centric perspective, providing a holistic overview of the evolution of deep time‑series forecasting. The full list of papers and resources is available at https://github.com/Master‑PLC/Awesome‑TSF‑Papers.
Authors:Dong Yan, Jian Liang, Yanbo Wang, Shuo Lu, Ran He, Tieniu Tan
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.
Authors:Danya Li, Yan Feng, Rico Krueger
Abstract:
To address this gap, we conduct a Virtual Reality experiment in which pedestrians interact with automated shuttles under varying approach angles (45°, 90°, 135°) and continuous‑traffic conditions (single shuttle, two shuttles with 3 or 5‑second gaps), collecting synchronized motion, eye gaze, and head orientation data. To investigate to what extent, under what conditions, and in what form fine‑grained eye gaze is informative for pedestrian motion prediction, we develop a multi‑modal prediction model that fuses these signals through modality‑specific encoders, and systematically ablate gaze representations against head orientation and situational context. We report three main results. First, the predictive value of eye gaze is angle‑dependent and tightly coupled with eye‑head‑body coordination: at acute angles where pedestrians actively redirect gaze to acquire the shuttle, eye gaze carries information that head orientation alone misses. Second, continuous gaze orientation outperforms categorical semantic fixation labels, with the optimal encoding frame (global or body‑relative) depending on whether gaze is used alone or jointly with context. Third, eye gaze and situational context provide complementary predictive information: their combination reduces final displacement error (FDE) by 8.47%, close to the sum of their individual contributions. Together, these findings highlight the value of incorporating human perceptual signals into pedestrian behavior prediction and motivate a human‑centered complement to vehicle‑centric modeling approaches. Our code is available at https://github.com/danyayay/GazeX.git.
Authors:Kaleem Ullah Qasim, Jiashu Zhang, Muhammad Kafeel Shaheen, Razan Alharith, Heying Zhang
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.
Authors:Minghe Xu, Rouying Wu, ChiaWei Chu, Xiao Wang, Yu Li
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
Authors:J. Ben Tamo, Yuxing Lu, Benoit L. Marteau, Micky C. Nnamdi, May D. Wang
Abstract:
Large Language Models (LLMs) are fluent but prone to hallucinations, producing answers that appear plausible yet are unsupported by available evidence. This failure is especially problematic in high‑stakes domains where decisions must be justified by verifiable information. We introduce EvidenceRL, a reinforcement learning framework that enforces evidence adherence during training. EvidenceRL scores candidate responses for grounding (entailment with retrieved evidence and context) and correctness (agreement with reference answers) and optimizes the generator using Group Relative Policy Optimization (GRPO). We evaluate across two high‑stakes domains, cardiac diagnosis and legal reasoning, where EvidenceRL consistently improves evidence grounding and faithfulness without sacrificing task accuracy. On cardiac diagnosis, F1@3 increases from 37.0 to 54.5 on Llama‑3.2‑3B while grounding (G_\max@3) rises from 47.6 to 78.2; hallucinations drop nearly 5× and evidence‑supported diagnoses increase from 31.8% to 61.6%. On legal reasoning, EvidenceRL raises Faithfulness from 32.8% to 67.6% on Llama‑3.1‑8B, demonstrating consistent behavioral change across domains. Our code is open‑sourced at https://github.com/Wizaaard/EvidenceRL.git.
Authors:Jinming Wang, Hai Wang, Hongkai Wen, Geyong Min, Man Luo
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
Authors:Nathan Weill, Kaizheng Wang
Abstract:
We propose a principled framework for unsupervised domain adaptation under covariate shift in kernel Generalized Linear Models (GLMs), encompassing kernelized linear, logistic, and Poisson regression with ridge regularization. Our goal is to minimize prediction error in the target domain by leveraging labeled source data and unlabeled target data, despite differences in covariate distributions. We partition the labeled source data into two batches: one for training a family of candidate models, and the other for building an imputation model. This imputation model generates pseudo‑labels for the target data, enabling robust model selection. We establish non‑asymptotic excess‑risk bounds that characterize adaptation performance through an "effective labeled sample size", explicitly accounting for the unknown covariate shift. Experiments on synthetic and real datasets demonstrate consistent performance gains over source‑only baselines.
Authors:Tomasz Wietrzykowski
Abstract:
Current transformer language models are trained with uniform computational budgets across all layers, implicitly assuming layer homogeneity. We challenge this assumption through empirical analysis of SmolLM2‑135M, a 30‑layer, 135M‑parameter causal language model, using five diagnostic metrics: weight predictability (R2), ablation degradation, recovery speed, weight manipulation robustness, and structural analysis. We find profound anatomical heterogeneity: (1) Layer weights follow strong mathematical regularity (R2 = 0.91) with a universal oscillatory delta pattern (correlation ~= ‑0.50), yet predicted weights cause catastrophic failure due to nonlinear error accumulation. (2) Layer importance spans a 10^7 range, from a critical core (L8‑11, up to +63,419% PPL degradation) to anti‑layers (L14, L17) whose removal improves performance. (3) Recovery speed correlates with layer importance, indicating differential training requirements. (4) Only weight scaling (alpha = 0.9) preserves model quality among five tested manipulation strategies. (5) Growth Transformer Training, allocating budget by layer importance, achieves ~54% cost reduction. A proof‑of‑concept experiment confirms this: 4.7x lower validation loss than uniform training at identical parameter count, while being 13% faster.
Authors:Celal Alagöz, Mehmet Kurnaz, Farhan Aadil
Abstract:
Time series classification (TSC) performance depends not only on architectural design but also on the diversity of input representations. In this work, we propose a scalable multi‑scale convolutional framework that systematically integrates structured multi‑representation inputs for univariate time series.
We introduce two architectures: MRMS‑Net, a hierarchical multi‑scale convolutional network optimized for robustness and calibration, and LMRMS‑Net, a lightweight variant designed for efficiency‑aware deployment. In addition, we adapt LiteMV ‑‑ originally developed for multivariate inputs ‑‑ to operate on multi‑representation univariate signals, enabling cross‑representation interaction.
We evaluate all models across 142 benchmark datasets under a unified experimental protocol. Critical Difference (CD) analysis confirms statistically significant performance differences among the top models. Results show that LiteMV achieves the highest mean accuracy, MRMS‑Net provides superior probabilistic calibration (lowest NLL), and LMRMS‑Net offers the best efficiency‑accuracy tradeoff. Pareto analysis further demonstrates that multi‑representation multi‑scale modeling yields a flexible design space that can be tuned for accuracy‑oriented, calibration‑oriented, or resource‑constrained settings.
These findings establish scalable multi‑representation multi‑scale learning as a principled and practical direction for modern TSC. Reference implementation of MRMS‑Net and LMRMS‑Net is available at: https://github.com/alagoz/mrmsnet‑tsc
Authors:Wentao Wang, Haoran Xu, Guang Tan
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.
Authors:Manit Baser, Alperen Yildiz, Dinil Mon Divakaran, Mohan Gurusamy
Abstract:
The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model‑editing techniques offer a promising solution by modifying a model's factual associations, they often produce unpredictable ripple effects, which are unintended behavioral changes that propagate even to the hidden space. In this work, we introduce CLaRE, a lightweight representation‑level technique to identify where these ripple effects may occur. Unlike prior gradient‑based methods, CLaRE quantifies entanglement between facts using forward activations from a single intermediate layer, avoiding costly backward passes. To enable systematic study, we prepare and analyse a corpus of 11,427 facts drawn from three existing datasets. Using CLaRE, we compute large‑scale entanglement graphs of this corpus for multiple models, capturing how local edits propagate through representational space. These graphs enable stronger preservation sets for model editing, audit trails, efficient red‑teaming, and scalable post‑edit evaluation. In comparison to baselines, CLaRE achieves an average of 62.2% improvement in Spearman correlation with ripple effects while being 2.74× faster, and using 2.85× less peak GPU memory. Besides, CLaRE requires only a fraction of the storage needed by the baselines to compute and preserve fact representations. Our entanglement graphs and corpus are available at https://github.com/manitbaser/CLaRE.
Authors:Vivan Madan, Prajwal Singhania, Abhinav Bhatele, Tom Goldstein, Ashwinee Panda
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.
Authors:Zhen Tan, Chengshuai Zhao, Song Wang, Jundong Li, Tianlong Chen, Huan Liu
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.
Authors:Dong Zhuo, Wenzhao Zheng, Sicheng Zuo, Siming Yan, Lu Hou, Jie Zhou, Jiwen Lu
Abstract:
With the growing adoption of vision‑language‑action models and world models in autonomous driving systems, scalable image tokenization becomes crucial as the interface for the visual modality. However, most existing tokenizers are designed for monocular and 2D scenes, leading to inefficiency and inter‑view inconsistency when applied to high‑resolution multi‑view driving scenes. To address this, we propose DriveTok, an efficient 3D driving scene tokenizer for unified multi‑view reconstruction and understanding. DriveTok first obtains semantically rich visual features from vision foundation models and then transforms them into the scene tokens with 3D deformable cross‑attention. For decoding, we employ a multi‑view transformer to reconstruct multi‑view features from the scene tokens and use multiple heads to obtain RGB, depth, and semantic reconstructions. We also add a 3D head directly on the scene tokens for 3D semantic occupancy prediction for better spatial awareness. With the multiple training objectives, DriveTok learns unified scene tokens that integrate semantic, geometric, and textural information for efficient multi‑view tokenization. Extensive experiments on the widely used nuScenes dataset demonstrate that the scene tokens from DriveTok perform well on image reconstruction, semantic segmentation, depth prediction, and 3D occupancy prediction tasks.
Authors:Shang-Jui Ray Kuo, Paola Cascante-Bonilla
Abstract:
Large vision‑‑language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightweight connector. While transformer‑based encoders are the standard visual backbone, we ask whether state space model (SSM) vision backbones can be a strong alternative. We systematically evaluate SSM vision backbones for VLMs in a controlled setting. Under matched ImageNet‑1K initialization, the SSM backbone achieves the strongest overall performance across both VQA and grounding/localization. We further adapt both SSM and ViT‑family backbones with detection or segmentation training and find that dense‑task tuning generally improves performance across families; after this adaptation, the SSM backbone remains competitive while operating at a substantially smaller model scale. We further observe that (i) higher ImageNet accuracy or larger backbones do not reliably translate into better VLM performance, and (ii) some visual backbones are unstable in localization. Based on these findings, we propose stabilization strategies that improve robustness for both backbone families and highlight SSM backbones as a strong alternative to transformer‑based vision encoders in VLMs.
Authors:Masoumeh Shafieinejad, Xi He, Mahshid Alinoori, John Jewell, Sana Ayromlou, Wei Pang, Veronica Chatrath, Gauri Sharma, Deval Pandya
Abstract:
Synthetic data is often perceived as a silver‑bullet solution to data anonymization and privacy‑preserving data publishing. Drawn from generative models like diffusion models, synthetic data is expected to preserve the statistical properties of the original dataset while remaining resilient to privacy attacks. Recent developments of diffusion models have been effective on a wide range of data types, but their privacy resilience, particularly for tabular formats, remains largely unexplored. MIDST challenge sought a quantitative evaluation of the privacy gain of synthetic tabular data generated by diffusion models, with a specific focus on its resistance to membership inference attacks (MIAs). Given the heterogeneity and complexity of tabular data, multiple target models were explored for MIAs, including diffusion models for single tables of mixed data types and multi‑relational tables with interconnected constraints. MIDST inspired the development of novel black‑box and white‑box MIAs tailored to these target diffusion models as a key outcome, enabling a comprehensive evaluation of their privacy efficacy. The MIDST GitHub repository is available at https://github.com/VectorInstitute/MIDST
Authors:Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah, Omkar Patil, Thao Nguyen, Nakul Gopalan
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.
Authors:Tianci Luo, Jinpeng Wang, Shiyu Qin, Niu Lian, Yan Feng, Bin Chen, Chun Yuan, Shu-Tao Xia
Abstract:
Visual In‑Context Learning (VICL) aims to complete vision tasks by imitating pixel demonstrations. Recent work pioneered prompt fusion that combines the advantages of various demonstrations, which shows a promising way to extend VICL. Unfortunately, the patch‑wise fusion framework and model‑agnostic supervision hinder the exploitation of informative cues, thereby limiting performance gains. To overcome this deficiency, we introduce PromptHub, a framework that holistically strengthens multi‑prompting through locality‑aware fusion, concentration and alignment. PromptHub exploits spatial priors to capture richer contextual information, employs complementary concentration, alignment, and prediction objectives to mutually guide training, and incorporates data augmentation to further reinforce supervision. Extensive experiments on three fundamental vision tasks demonstrate the superiority of PromptHub. Moreover, we validate its universality, transferability, and robustness across out‑of‑distribution settings, and various retrieval scenarios. This work establishes a reliable locality‑aware paradigm for prompt fusion, moving beyond prior patch‑wise approaches. Code is available at https://github.com/luotc‑why/ICLR26‑PromptHub.
Authors:Yizhou Han, Di Wu, Blesson Varghese
Abstract:
In real‑world Federated Learning (FL) deployments, data distributions on devices that participate in training evolve over time. This leads to asynchronous data drift, where different devices shift at different times and toward different distributions. Mitigating such drift is challenging: frequent retraining incurs high computational cost on resource‑constrained devices, while infrequent retraining degrades performance on drifting devices. We propose DriftGuard, a federated continual learning framework that efficiently adapts to asynchronous data drift. DriftGuard adopts a Mixture‑of‑Experts (MoE) inspired architecture that separates shared parameters, which capture globally transferable knowledge, from local parameters that adapt to group‑specific distributions. This design enables two complementary retraining strategies: (i) global retraining, which updates the shared parameters when system‑wide drift is identified, and (ii) group retraining, which selectively updates local parameters for clusters of devices identified via MoE gating patterns, without sharing raw data. Experiments across multiple datasets and models show that DriftGuard matches or exceeds state‑of‑the‑art accuracy while reducing total retraining cost by up to 83%. As a result, it achieves the highest accuracy per unit retraining cost, improving over the strongest baseline by up to 2.3x. DriftGuard is available for download from https://github.com/blessonvar/DriftGuard.
Authors:Xiucheng Wang, Zixuan Guo, Nan Cheng
Abstract:
Radio maps (RMs) provide spatially continuous propagation characterizations essential for 6G network planning, but high‑fidelity RM construction remains challenging. Rigorous electromagnetic solvers incur prohibitive computational latency, while data‑driven models demand massive labeled datasets and generalize poorly from simplified simulations to complex multipath environments. This paper proposes RadioDiff‑FS, a few‑shot diffusion framework that adapts a pretrained main‑path generator to multipath‑rich target domains with only a small number of high‑fidelity samples. The adaptation is grounded in a theoretical decomposition of the multipath RM into a dominant main‑path component and a directionally sparse residual. This decomposition shows that the cross‑domain shift corresponds to a bounded and geometrically structured feature translation rather than an arbitrary distribution change. A direction‑consistency loss (DCL) is then introduced to constrain diffusion score updates along physically plausible propagation directions, thereby suppressing phase‑inconsistent artifacts that arise in the low‑data regime. Experiments show that RadioDiff‑FS reduces NMSE by 59.5% on static RMs and by 74.0% on dynamic RMs relative to the vanilla diffusion baseline, achieving an SSIM of 0.9752 and a PSNR of 36.37 dB under severely limited supervision. Even in a one‑shot setting with a single target‑domain sample per scene, RadioDiff‑FS outperforms all fully supervised baselines, confirming that the directional constraint provides an effective inductive bias under extreme data scarcity. Code is available at https://github.com/UNIC‑Lab/RadioDiff‑FS.
Authors:Xiao Feng, Bo Han, Zhanke Zhou, Jiaqi Fan, Jiangchao Yao, Ka Ho Li, Dahai Yu, Michael Kwok-Po Ng
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.
Authors:Huichi Zhou, Siyuan Guo, Anjie Liu, Zhongwei Yu, Ziqin Gong, Bowen Zhao, Zhixun Chen, Menglong Zhang, Yihang Chen, Jinsong Li, Runyu Yang, Qiangbin Liu, Xinlei Yu, Jianmin Zhou, Na Wang, Chunyang Sun, Jun Wang
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.
Authors:Xuan Liu, Xiaobin Chang
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.
Authors:Yinan Xia, Haotian Zhang, Huiming Wang
Abstract:
Large Reasoning Models (LRMs) have shown exceptional reasoning capabilities, but they also suffer from the issue of overthinking, often generating excessively long and redundant answers.
For problems that exceed the model's capabilities, LRMs tend to exhibit the overconfidence phenomenon, generating overly short but incorrect answers, which may contribute to suboptimal performance.
To address these issues, we propose Difficulty‑Differentiated Policy Optimization (DDPO), an efficient reinforcement learning algorithm that optimizes simple and complex tasks separately based on the overconfidence phenomenon.
Specifically, it reduces the output length for simple tasks without compromising accuracy, while for complex tasks, it expands the exploration space to improve performance. We further derive the theoretical conditions for maximizing expected accuracy, which require the length distribution to closely approximate the optimal length and be as concentrated as possible. Based on these conditions, we propose using the difficulty‑level average as a well‑founded reference for length optimization.
Extensive experiments on both in‑domain and out‑of‑domain benchmarks validate the superiority and effectiveness of DDPO. Compared to GRPO, DDPO reduces the average answer length by 12% while improving accuracy by 1.85% across multiple benchmarks, achieving a better trade‑off between accuracy and length. The code is available at https://github.com/Yinan‑Xia/DDPO.
Authors:Seonghyun Jin, Jong Chul Ye
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.
Authors:Chengxuan Lu, Shukuan Wang, Yanjie Li, Wei Liu, Shiji Jin, Fuyuan Qian, Peiming Li, Baigui Sun, Yang Liu
Abstract:
Reinforcement learning (RL) for large‑scale Vision‑Language‑Action (VLA) models faces significant challenges in computational efficiency and data acquisition. We propose AcceRL, a fully asynchronous and decoupled RL framework designed to eliminate synchronization barriers by physically isolating training, inference, and rollouts. Crucially, AcceRL is the first to integrate a plug‑and‑play, trainable world model into a distributed asynchronous RL pipeline to generate virtual experiences. Experiments on the LIBERO~\citeliu2023libero benchmark demonstrate that AcceRL achieves state‑of‑the‑art (SOTA) performance. Systematically, it exhibits super‑linear scaling in throughput and highly efficient hardware utilization. Algorithmically, the world‑model‑augmented variant delivers unprecedented sample efficiency and robust training stability in complex control tasks. Code is publicly available at https://github.com/distanceLu/AcceRL.
Authors:Jason Dury
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.
Authors:Kaiyang Li, Shihao Ji, Zhipeng Cai, Wei Li
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.
Authors:Gregory N. Frank
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.
Authors:Haocheng Luo, Zehang Deng, Thanh-Toan Do, Mehrtash Harandi, Dinh Phung, Trung Le
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.
Authors:Naoki Morihira, Amal Nahar, Kartik Bharadwaj, Yasuhiro Kato, Akinobu Hayashi, Tatsuya Harada
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.
Authors:Mohammed Rahman Sherif Khan Mohammad, Ardhendu Behera, Sandip Pradhan, Swagat Kumar, Amr Ahmed
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.
Authors:Alexander D. Goldie, Zilin Wang, Adrian Hayler, Deepak Nathani, Edan Toledo, Ken Thampiratwong, Aleksandra Kalisz, Michael Beukman, Alistair Letcher, Shashank Reddy, Clarisse Wibault, Theo Wolf, Charles O'Neill, Uljad Berdica, Nicholas Roberts, Saeed Rahmani, Hannah Erlebach, Roberta Raileanu, Shimon Whiteson, Jakob N. Foerster
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.
Authors:Teng Pan, Yuchen Yan, Zixuan Wang, Ruiqing Zhang, Guiyang Hou, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen
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.
Authors:Binqing Wu, Zongjiang Shang, Shiyu Liu, Jianlong Huang, Jiahui Xu, Ling Chen
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.
Authors:Truong-Son Hy
Abstract:
We propose Q‑BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embeddings, which are then transformed into compact binary latent representations. In this space, protein fitness is approximated using a quadratic unconstrained binary optimization (QUBO) model, enabling efficient combinatorial search via classical heuristics such as simulated annealing and genetic algorithms.
On the ProteinGym benchmark, we demonstrate that Q‑BIOLAT captures meaningful structure in protein fitness landscapes and enables the identification of high‑fitness variants. Despite using a simple binarization scheme, our method consistently retrieves sequences whose nearest neighbors lie within the top fraction of the training fitness distribution, particularly under the strongest configurations. We further show that different optimization strategies exhibit distinct behaviors, with evolutionary search performing better in higher‑dimensional latent spaces and local search remaining competitive in preserving realistic sequences.
Beyond its empirical performance, Q‑BIOLAT provides a natural bridge between protein representation learning and combinatorial optimization. By formulating protein fitness as a QUBO problem, our framework is directly compatible with emerging quantum annealing hardware, opening new directions for quantum‑assisted protein engineering.
Our implementation is publicly available at: https://github.com/HySonLab/Q‑BIOLAT
Authors:Tynan Perez, Rafael Gomez-Bombarelli
Abstract:
The success of large‑scale pretraining in NLP and computer vision has catalyzed growing efforts to develop analogous foundation models for the physical sciences. However, pretraining strategies using atomistic data remain underexplored. To date, large‑scale supervised pretraining on DFT force‑energy labels has provided the strongest performance gains to downstream property prediction, out‑performing existing methods of self‑supervised learning (SSL) which remain limited to ground‑state geometries, and/or single domains of atomistic data. We address these shortcomings with Self‑Conditioned Denoising (SCD), a backbone‑agnostic reconstruction objective that utilizes self‑embeddings for conditional denoising across any domain of atomistic data, including small molecules, proteins, periodic materials, and 'non‑equilibrium' geometries. When controlled for backbone architecture and pretraining dataset, SCD significantly outperforms previous SSL methods on downstream benchmarks and matches or exceeds the performance of supervised force‑energy pretraining. We show that a small, fast GNN pretrained by SCD can achieve competitive or superior performance to larger models pretrained on significantly larger labeled or unlabeled datasets, across tasks in multiple domains. Our code is available at: https://github.com/TyJPerez/SelfConditionedDenoisingAtoms
Authors:Sophie Kearney, Shu Yang, Zixuan Wen, Weimin Lyu, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Jason H. Moore, Marylyn D. Ritchie, Chao Chen, Li Shen
Abstract:
Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to outperform classical methods. Pretrained large language models (LLMs) offer few‑shot generalization, structured reasoning, and interpretable outputs, providing a powerful paradigm shift for clinical prediction. We propose TAP‑GPT Tabular Alzheimer's Prediction GPT, a domain‑adapted tabular LLM framework built on TableGPT2 and fine‑tuned for few‑shot AD classification using tabular prompts rather than plain texts. We evaluate TAP‑GPT across four ADNI‑derived datasets, including QT‑PAD biomarkers and region‑level structural MRI, amyloid PET, and tau PET for binary AD classification. Across multimodal and unimodal settings, TAP‑GPT improves upon its backbone models and outperforms traditional machine learning baselines in the few‑shot setting while remaining competitive with state‑of‑the‑art general‑purpose LLMs. We show that feature selection mitigates degradation in high‑dimensional inputs and that TAP‑GPT maintains stable performance under simulated and real‑world missingness without imputation. Additionally, TAP‑GPT produces structured, modality‑aware reasoning aligned with established AD biology and shows greater stability under self‑reflection, supporting its use in iterative multi‑agent systems. To our knowledge, this is the first systematic application of a tabular‑specialized LLM to multimodal biomarker‑based AD prediction, demonstrating that such pretrained models can effectively address structured clinical prediction tasks and laying the foundation for tabular LLM‑driven multi‑agent clinical decision‑support systems. The source code is publicly available on GitHub: https://github.com/sophie‑kearney/TAP‑GPT.
Authors:Peng Xia, Jianwen Chen, Xinyu Yang, Haoqin Tu, Jiaqi Liu, Kaiwen Xiong, Siwei Han, Shi Qiu, Haonian Ji, Yuyin Zhou, Zeyu Zheng, Cihang Xie, Huaxiu Yao
Abstract:
Large language model (LLM) agents are increasingly used for complex tasks, yet deployed agents often remain static, failing to adapt as user needs evolve. This creates a tension between the need for continuous service and the necessity of updating capabilities to match shifting task distributions. On platforms like OpenClaw, which handle diverse workloads across 20+ channels, existing methods either store raw trajectories without distilling knowledge, maintain static skill libraries, or require disruptive downtime for retraining. We present MetaClaw, a continual meta‑learning framework that jointly evolves a base LLM policy and a library of reusable behavioral skills. MetaClaw employs two complementary mechanisms. Skill‑driven fast adaptation analyzes failure trajectories via an LLM evolver to synthesize new skills, enabling immediate improvement with zero downtime. Opportunistic policy optimization performs gradient‑based updates via cloud LoRA fine‑tuning and Reinforcement Learning with a Process Reward Model (RL‑PRM). This is triggered during user‑inactive windows by the Opportunistic Meta‑Learning Scheduler (OMLS), which monitors system inactivity and calendar data. These mechanisms are mutually reinforcing: a refined policy generates better trajectories for skill synthesis, while richer skills provide higher‑quality data for policy optimization. To prevent data contamination, a versioning mechanism separates support and query data. Built on a proxy‑based architecture, MetaClaw scales to production‑size LLMs without local GPUs. Experiments on MetaClaw‑Bench and AutoResearchClaw show that skill‑driven adaptation improves accuracy by up to 32% relative. The full pipeline advances Kimi‑K2.5 accuracy from 21.4% to 40.6% and increases composite robustness by 18.3%. Code is available at https://github.com/aiming‑lab/MetaClaw.
Authors:Yasaswini Chebolu
Abstract:
Reliable terrain perception is a fundamental requirement for autonomous navigation in unstructured, off‑road environments. Desert landscapes present unique challenges due to low chromatic contrast between terrain categories, extreme lighting variability, and sparse vegetation that defy the assumptions of standard road‑scene segmentation models. We present DesertFormer, a semantic segmentation pipeline for off‑road desert terrain analysis based on SegFormer B2 with a hierarchical Mix Transformer (MiT‑B2) backbone. The system classifies terrain into ten ecologically meaningful categories ‑‑ Trees, Lush Bushes, Dry Grass, Dry Bushes, Ground Clutter, Flowers, Logs, Rocks, Landscape, and Sky ‑‑ enabling safety‑aware path planning for ground robots and autonomous vehicles. Trained on a purpose‑built dataset of 4,176 annotated off‑road images at 512x512 resolution, DesertFormer achieves a mean Intersection‑over‑Union (mIoU) of 64.4% and pixel accuracy of 86.1%, representing a +24.2% absolute improvement over a DeepLabV3 MobileNetV2 baseline (41.0% mIoU). We further contribute a systematic failure analysis identifying the primary confusion patterns ‑‑ Ground Clutter to Landscape and Dry Grass to Landscape ‑‑ and propose class‑weighted training and copy‑paste augmentation for rare terrain categories. Code, checkpoints, and an interactive inference dashboard are released at https://github.com/Yasaswini‑ch/Vision‑based‑Desert‑Terrain‑Segmentation‑using‑SegFormer.
Authors:Martin G. Frasch
Abstract:
Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum‑Action Learning (MAL), a framework that selects symbolic force laws from a pre‑specified basis library by minimizing a Triple‑Action functional combining trajectory reconstruction, architectural sparsity, and energy‑conservation enforcement. A wide‑stencil acceleration‑matching technique reduces noise variance by 10,000x, transforming an intractable problem (SNR ~0.02) into a learnable one (SNR ~1.6); this preprocessing is the critical enabler shared by all methods tested, including SINDy variants. On two benchmarks ‑‑ Kepler gravity and Hooke's law ‑‑ MAL recovers the correct force law with Kepler exponent p = 3.01 +/‑ 0.01 at ~0.07 kWh (40% reduction vs. prediction‑error‑only baselines). The raw correct‑basis rate is 40% for Kepler and 90% for Hooke; an energy‑conservation‑based criterion discriminates the true force law in all cases, yielding 100% pipeline‑level identification. Basis library sensitivity experiments show that near‑confounders degrade selection (20% with added r^‑2.5 and r^‑1.5), while distant additions are harmless, and the conservation diagnostic remains informative even when the correct basis is absent. Direct comparison with noise‑robust SINDy variants, Hamiltonian Neural Networks, and Lagrangian Neural Networks confirms MAL's distinct niche: interpretable, energy‑constrained model selection that combines symbolic basis identification with dynamical rollout validation.
Authors:Vladimer Khasia
Abstract:
Sequence modeling universally relies on discrete subword tokenization to circumvent the \mathcalO(N^2) computational intractability of native byte‑level attention. However, this heuristic quantization imposes artificial morphological boundaries, enforces vocabulary dependence, and fractures the continuity of the optimization landscape. To resolve this dichotomy, we introduce HoloByte: a strictly tokenizer‑free framework utilizing Continuous Hyperspherical Distillation. HoloByte partitions discrete byte sequences into fixed‑capacity chunks and projects them into a continuous, strictly bounded hyperspherical manifold via an invertible, dimension‑preserving orthogonal rotation operator. This spatial superposition allows a macroscopic transformer to operate exclusively on compressed continuous representations, formally reducing the exact attention time complexity from \mathcalO(N^2D) to \mathcalO\left( \fracN^2W^2D + ND^2 \right). A localized causal micro‑decoder subsequently unbinds these representations to compute exact byte‑level distributions. To govern this continuous trajectory, we propose a dual‑objective formulation incorporating a mathematically precise Holographic Latent Mean Squared Error, which strictly bounds the gradient and guarantees asymptotic stability. Theoretically, we derive the minimal embedding dimension D = Ω(W \ln |\mathcalV|) required to ensure error‑free discrete recovery from the continuous manifold. Empirically, under strictly matched parameter constraints, HoloByte is systematically outperforming a comparable discrete Byte‑Pair Encoding (BPE) baseline. These results establish Continuous Hyperspherical Distillation as a mathematically rigorous and computationally tractable foundation for vocabulary‑invariant sequence modeling. The code is available at https://github.com/VladimerKhasia/HoloByte
Authors:Kaixuan Wang, Tianxing Chen, Jiawei Liu, Honghao Su, Shaolong Zhu, Minxuan Wang, Zixuan Li, Yue Chen, Huan-ang Gao, Yusen Qin, Jiawei Wang, Qixuan Zhang, Lan Xu, Jingyi Yu, Yao Mu, Ping Luo
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/.
Authors:Valentin Lafargue, Ariel Guerra-Adames, Emmanuelle Claeys, Elouan Vuichard, Jean-Michel Loubes
Abstract:
Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode. We probe these representations by evaluating whether LLMs can perform author profiling from song lyrics in a zero‑shot setting, inferring singers' gender and ethnicity without task‑specific fine‑tuning. Across several open‑source models evaluated on more than 10,000 lyrics, we find that LLMs achieve non‑trivial profiling performance but demonstrate systematic cultural alignment: most models default toward North American ethnicity, while DeepSeek‑1.5B aligns more strongly with Asian ethnicity. This finding emerges from both the models' prediction distributions and an analysis of their generated rationales. To quantify these disparities, we introduce two fairness metrics, Modality Accuracy Divergence (MAD) and Recall Divergence (RD), and show that Ministral‑8B displays the strongest ethnicity bias among the evaluated models, whereas Gemma‑12B shows the most balanced behavior. Our code is available on [GitHub](https://github.com/ValentinLafargue/CulturalProbingLLM) and results on [HuggingFace](https://huggingface.co/datasets/ValentinLAFARGUE/AuthorProfilingResults).
Authors:Xiaojie Gu, Sherry T. Tong, Aosong Feng, Sophia Simeng Han, Jinghui Lu, Yingjian Chen, Yusuke Iwasawa, Yutaka Matsuo, Chanjun Park, Rex Ying, Irene Li
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.
Authors:Yong Zou, Haoran Li, Fanxiao Li, Shenyang Wei, Yunyun Dong, Li Tang, Wei Zhou, Renyang Liu
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.
Authors:Yikai Gu, Lele Cao, Bo Zhao, Lei Lei, Lei You
Abstract:
Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions. Most existing methods operate at the instance level. Distributional Counterfactual Explanations (DCE) extend this setting by optimizing an optimal transport objective that balances proximity to a factual input distribution and alignment to a target output distribution, with statistical certification via chance constrained bounds. However, DCE relies on gradient based optimization, while many real‑world tabular pipelines are dominated by non‑differentiable models. We propose DISCOVER, a model‑agnostic solver for distributional counterfactual explanations. DISCOVER preserves the original DCE objective and certification while replacing gradient descent with a sparse propose‑and‑select search paradigm. It exploits a sample‑wise decomposition of the transport objective to compute per‑row impact scores and enforce a top‑k intervention budget, focusing edits on the most influential samples. To guide candidate generation without predictor gradients, DISCOVER introduces an OT‑guided cone sampling primitive driven by input‑side transport geometry. Experiments on multiple tabular datasets demonstrate strong joint alignment of input and output distributions, extending distributional counterfactual reasoning to modern black box learning pipelines. A code repository is available at https://github.com/understanding‑ml/DCE.
Authors:Hanif Rahman
Abstract:
We present PashtoCorp, a 1.25‑billion‑word corpus for Pashto, a language spoken by 60 million people that remains severely underrepresented in NLP. The corpus is assembled from 39 sources spanning seven HuggingFace datasets and 32 purpose‑built web scrapers, processed through a reproducible pipeline with Arabic‑script tokenization, SHA‑256 deduplication, and quality filtering. At 1.25B words across 2.81 million documents, PashtoCorp is 40x larger than the OSCAR Pashto subset and 83x larger than the previously largest dedicated Pashto corpus. Continued MLM pretraining of XLM‑R‑base on PashtoCorp reduces held‑out perplexity by 25.1% (8.08‑>6.06). On WikiANN Pashto NER, the pretrained model improves entity F1 by 10% relative (19.0%‑>21.0%) and reduces training variance nearly 7x; the largest gain appears at 50 training sentences (+27%), with PashtoCorp covering 97.9% of WikiANN entity vocabulary. On Belebele Pashto reading comprehension, Gemma‑3n achieves 64.6% accuracy, the first published LLM baseline for Pashto on this benchmark. A leave‑one‑out source ablation shows that Wikipedia (0.7% of documents) is the most critical source for NER: removing it alone reduces entity F1 by 47%. Corpus data, trained model, and code are available at https://huggingface.co/datasets/ihanif/pashto‑corpus, https://huggingface.co/ihanif/xlmr‑pashto, and https://github.com/ihanif/pashto‑corpus.
Authors:Hoang Phan, Quang H. Nguyen, Hung T. Q. Le, Xiusi Chen, Heng Ji, Khoa D. Doan
Abstract:
Large Reasoning Models (LRMs) exhibit backtracking and self‑verification mechanisms that enable them to revise intermediate steps and reach correct solutions, yielding strong performance on complex logical benchmarks. We hypothesize that such behaviors are beneficial only when the model has sufficiently strong ``critique'' ability to detect its own mistakes. This work systematically investigates how current LRMs recover from errors by inserting arithmetic mistakes in their intermediate reasoning steps. Notably, we discover a peculiar yet important phenomenon: despite the error propagating throughout the entire chain‑of‑thought (CoT) without any verbalized correction, the model still reaches the correct final answer after the thinking process finishes. This recovery implies the existence of an internal mechanism helping the model to detect errors and trigger self‑correction, which we refer to as the hidden critique ability. Building on feature space analysis, we identify a highly interpretable critique vector representing this behavior. Extensive experiments across multiple model scales and families demonstrate that steering latent representations with this vector improves the model's error detection capability and enhances the performance of test‑time scaling at no extra training cost. Our findings provide a valuable understanding of LRMs' critique behavior, suggesting a promising direction to control and improve their self‑verification mechanism. Our code is available at: https://github.com/mail‑research/lrm‑critique‑vectors.
Authors:Tiantian Dang, Chao Bi, Shufan Shen, Jinzhe Liu, Qingming Huang, Shuhui Wang
Abstract:
Despite the significant advancements in Large Vision‑Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and restricts broader practical deployment. Among the hallucination mitigation methods, feature steering emerges as a promising approach that reduces erroneous outputs in LVLMs without increasing inference costs. However, current methods apply uniform feature steering across all layers. This heuristic strategy ignores inter‑layer differences, potentially disrupting layers unrelated to hallucinations and ultimately leading to performance degradation on general tasks. In this paper, we propose Locate‑Then‑Sparsify for Feature Steering (LTS‑FS), a plug‑and‑play framework which controls the steering intensity according to the hallucination relevance of each layer. We first construct a dataset comprising token‑level and sentence‑level hallucination cases. Based on this dataset, we introduce an attribution method based on causal interventions to quantify the hallucination relevance of each layer. With the attribution scores across layers, we propose a layerwise strategy that converts these scores into feature steering intensities for individual layers, enabling more precise adjustments specifically on hallucination‑relevant layers. Extensive experiments across multiple LVLMs and benchmarks demonstrate that LTS‑FS effectively mitigates hallucination while preserving strong performance. Codes are available at https://github.com/huttersadan/LTS‑FS.
Authors:Moonsoo Park, Seulbeen Je, Donghyeon Park
Abstract:
In recommender systems, large language models (LLMs) have gained popularity for generating descriptive summarization to improve recommendation robustness, along with Graph Convolution Networks. However, existing LLM‑enhanced recommendation studies mainly rely on the internal knowledge of LLMs about item titles while neglecting the importance of various factors influencing users' decisions. Although information reflecting various decision factors of each user is abundant in reviews, few studies have actively exploited such insights for recommendation. To address these limitations, we propose a ReFORM: Review‑aggregated Profile Generation via LLM with Multi‑FactOr Attentive RecoMmendation framework. Specifically, we first generate factor‑specific user and item profiles from reviews using LLM to capture a user's preference by items and an item's evaluation by users. Then, we propose a Multi‑Factor Attention to highlight the most influential factors in each user's decision‑making process. In this paper, we conduct experiments on two restaurant datasets of varying scales, demonstrating its robustness and superior performance over state‑of‑the‑art baselines. Furthermore, in‑depth analyses validate the effectiveness of the proposed modules and provide insights into the sources of personalization. Our source code and datasets are available at https://github.com/m0onsoo/ReFORM.
Authors:Shin'ya Yamaguchi, Daiki Chijiwa, Tamao Sakao, Taku Hasegawa
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.
Authors:Yiqun T. Chen, Moran Guo, Shengy Li
Abstract:
Modern studies increasingly leverage outcomes predicted by machine learning and artificial intelligence (AI/ML) models, and recent work, such as prediction‑powered inference (PPI), has developed valid downstream statistical inference procedures. However, classical power and sample size formulas do not readily account for these predictions. In this work, we tackle a simple yet practical question: given a new AI/ML model with high predictive power, how many labeled samples are needed to achieve a desired level of statistical power? We derive closed‑form power formulas by characterizing the asymptotic variance of the PPI estimator and applying Wald test inversion to obtain the required labeled sample size. Our results cover widely used settings including two‑sample comparisons and risk measures in 2x2 tables. We find that a useful rule of thumb is that the reduction in required labeled samples relative to classical designs scales roughly with the R2 between the predictions and the ground truth. Our analytical formulas are validated using Monte Carlo simulations, and we illustrate the framework in three contemporary biomedical applications spanning single‑cell transcriptomics, clinical blood pressure measurement, and dermoscopy imaging. We provide our software as an R package and online calculators at https://github.com/yiqunchen/pppower.
Authors:Yifan Zhang
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.
Authors:Sijie Li, Biao Qian, Jungong Han
Abstract:
Network pruning is an effective technique for enabling lightweight Large Vision‑Language Models (LVLMs), which primarily incorporates both weights and activations into the importance metric. However, existing efforts typically process calibration data from different modalities in a unified manner, overlooking modality‑specific behaviors. This raises a critical challenge: how to address the divergent behaviors of textual and visual tokens for accurate pruning of LVLMs. To this end, we systematically investigate the sensitivity of visual and textual tokens to the pruning operation by decoupling their corresponding weights, revealing that: (i) the textual pathway should be calibrated via text tokens, since it exhibits higher sensitivity than the visual pathway; (ii) the visual pathway exhibits high redundancy, permitting even 50% sparsity. Motivated by these insights, we propose a simple yet effective Asymmetric Text‑Visual Weight Pruning method for LVLMs, dubbed ATV‑Pruning, which establishes the importance metric for accurate weight pruning by selecting the informative tokens from both textual and visual pathways. Specifically, ATV‑Pruning integrates two primary innovations: first, a calibration pool is adaptively constructed by drawing on all textual tokens and a subset of visual tokens; second, we devise a layer‑adaptive selection strategy to yield important visual tokens. Finally, extensive experiments across standard multimodal benchmarks verify the superiority of our ATV‑Pruning over state‑of‑the‑art methods.
Authors:Xiaolong Han, Ferrante Neri, Zijian Jiang, Fang Wu, Yanfang Ye, Lu Yin, Zehong Wang
Abstract:
Each LoRA checkpoint compactly stores task‑specific updates in low‑rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In principle, these weights already encode what the adapter does and how well it performs. In this paper, we ask whether this information can be read directly from the weights, without running the base model or accessing training data. A key obstacle is that a single LoRA update can be factorized in infinitely many ways. Without resolving this ambiguity, models trained on the factors may fit the particular factorization rather than the underlying update. To this end, we propose \methodfull, which maps each LoRA update to a provably canonical form via QR decomposition followed by SVD, so that all equivalent factorizations share the same representation. The resulting components are then tokenized and processed by a Transformer to produce a weight‑space embedding. Across language and vision LoRA collections, W2T achieves strong results on attribute classification, performance prediction, and adapter retrieval, demonstrating that LoRA weights reliably indicate model behavior once factorization ambiguity is removed. Code is available at https://github.com/xiaolonghan2000/Weight2Token.
Authors:Rushil Thareja, Gautam Gupta, Francesco Pinto, Nils Lukas
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.
Authors:Max Zimmer, Nico Pelleriti, Christophe Roux, Sebastian Pokutta
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.
Authors:Dibakar Sigdel, Namuna Panday
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.
Authors:Jakaria Rabbi, Nilanjan Ray, Dana Cobzas
Abstract:
Disentangling pathological changes from physiological aging in 3D medical shapes is crucial for developing interpretable biomarkers and patient stratification. However, this separation is challenging when diagnosis labels are limited or unavailable, since disease and aging often produce overlapping effects on shape changes, obscuring clinically relevant shape patterns. To address this challenge, we propose a two‑stage framework combining unsupervised disease discovery with self‑supervised disentanglement of implicit shape representations. In the first stage, we train an implicit neural model with signed distance functions to learn stable shape embeddings. We then apply clustering on the shape latent space, which yields pseudo disease labels without using ground‑truth diagnosis during discovery. In the second stage, we disentangle factors in a compact variational space using pseudo disease labels discovered in the first stage and the ground truth age labels available for all subjects. We enforce separation and controllability with a multi‑objective disentanglement loss combining covariance and a supervised contrastive loss. On ADNI hippocampus and OAI distal femur shapes, we achieve near‑supervised performance, improving disentanglement and reconstruction over state‑of‑the‑art unsupervised baselines, while enabling high‑fidelity reconstruction, controllable synthesis, and factor‑based explainability. Code and checkpoints are available at https://github.com/anonymous‑submission01/medical‑shape‑disentanglement
Authors:Tomas Ruiz, Zhen Qin, Yifan Zhang, Xuyang Shen, Yiran Zhong, Mengdi Wang
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.
Authors:Ye Wang, Zixuan Wu, Lifeng Shen, Jiang Xie, Xiaoling Wang, Hong Yu, Guoyin Wang
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.
Authors:Paras Sharma, Swastika Sharma
Abstract:
Flash floods are the most destructive natural hazard in Himachal Pradesh (HP), India, causing over 400 fatalities and 1.2 billion in losses in the 2023 monsoon season alone. Existing risk maps treat every pixel independently, ignoring the basic fact that flooding upstream raises risk downstream. We address this with a Graph Neural Network (GraphSAGE) trained on a watershed connectivity graph (460 sub‑watersheds, 1,700 directed edges), built from a six‑year Sentinel‑1 SAR flood inventory (2018‑2023, 3,000 events) and 12 environmental variables at 30 m resolution. Four pixel‑based ML models (RF, XGBoost, LightGBM, stacking ensemble) serve as baselines. All models are evaluated with leave‑one‑basin‑out spatial cross‑validation to avoid the 5‑15% AUC inflation of random splits. Conformal prediction produces the first HP susceptibility maps with statistically guaranteed 90% coverage intervals.
The GNN achieved AUC = 0.978 +/‑ 0.017, outperforming the best baseline (AUC = 0.881) and the published HP benchmark (AUC = 0.88). The +0.097 gain confirms that river connectivity carries predictive signal that pixel‑based models miss. High‑susceptibility zones overlap 1,457 km of highways (including 217 km of the Manali‑Leh corridor), 2,759 bridges, and 4 major hydroelectric installations. Conformal intervals achieved 82.9% empirical coverage on the held‑out 2023 test set; lower coverage in high‑risk zones (45‑59%) points to SAR label noise as a target for future work.
Authors:Zeyu Zhang, Rui Li, Xiaoyan Zhao, Yang Zhang, Wenjie Wang, Xu Chen, Tat-Seng Chua
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.
Authors:Pengjun Fang, Yingqing He, Yazhou Xing, Qifeng Chen, Ser-Nam Lim, Harry Yang
Abstract:
Existing video‑to‑audio (V2A) generation methods predominantly rely on text prompts alongside visual information to synthesize audio. However, two critical bottlenecks persist: semantic granularity gaps in training data, such as conflating acoustically distinct sounds under coarse labels, and textual ambiguity in describing micro‑acoustic features. These bottlenecks make it difficult to perform fine‑grained sound synthesis using text‑controlled modes. To address these limitations, we propose AC‑Foley, an audio‑conditioned V2A model that directly leverages reference audio to achieve precise and fine‑grained control over generated sounds. This approach enables fine‑grained sound synthesis, timbre transfer, zero‑shot sound generation, and improved audio quality. By directly conditioning on audio signals, our approach bypasses the semantic ambiguities of text descriptions while enabling precise manipulation of acoustic attributes. Empirically, AC‑Foley achieves state‑of‑the‑art performance for Foley generation when conditioned on reference audio, while remaining competitive with state‑of‑the‑art video‑to‑audio methods even without audio conditioning. Code and demo are available at: https://ff2416.github.io/AC‑Foley‑Page
Authors:Yuanfan Zheng, Kunyu Peng, Xu Zheng, Kailun Yang
Abstract:
Cross‑domain panoramic semantic segmentation has attracted growing interest as it enables comprehensive 360° scene understanding for real‑world applications. However, it remains particularly challenging due to severe geometric Field of View (FoV) distortions and inconsistent open‑set semantics across domains. In this work, we formulate an open‑set domain adaptation setting, and propose Extrapolative Domain Adaptive Panoramic Segmentation (EDA‑PSeg) framework that trains on local perspective views and tests on full 360° panoramic images, explicitly tackling both geometric FoV shifts across domains and semantic uncertainty arising from previously unseen classes. To this end, we propose the Euler‑Margin Attention (EMA), which introduces an angular margin to enhance viewpoint‑invariant semantic representation, while performing amplitude and phase modulation to improve generalization toward unseen classes. Additionally, we design the Graph Matching Adapter (GMA), which builds high‑order graph relations to align shared semantics across FoV shifts while effectively separating novel categories through structural adaptation. Extensive experiments on four benchmark datasets under camera‑shift, weather‑condition, and open‑set scenarios demonstrate that EDA‑PSeg achieves state‑of‑the‑art performance, robust generalization to diverse viewing geometries, and resilience under varying environmental conditions. The code is available at https://github.com/zyfone/EDA‑PSeg.
Authors:Yanning Dai, Yuhui Wang, Dylan R. Ashley, Jürgen Schmidhuber
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.
Authors:Jacob Sanderson, Hua Mao, Wai Lok Woo
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 .
Authors:Aram Davtyan, Leello Tadesse Dadi, Volkan Cevher, Paolo Favaro
Abstract:
Conditional Flow Matching (CFM), a simulation‑free method for training continuous normalizing flows, provides an efficient alternative to diffusion models for key tasks like image and video generation. The performance of CFM in solving these tasks depends on the way data is coupled with noise. A recent approach uses minibatch optimal transport (OT) to reassign noise‑data pairs in each training step to streamline sampling trajectories and thus accelerate inference. However, its optimization is restricted to individual minibatches, limiting its effectiveness on large datasets. To address this shortcoming, we introduce LOOM‑CFM (Looking Out Of Minibatch‑CFM), a novel method to extend the scope of minibatch OT by preserving and optimizing these assignments across minibatches over training time. Our approach demonstrates consistent improvements in the sampling speed‑quality trade‑off across multiple datasets. LOOM‑CFM also enhances distillation initialization and supports high‑resolution synthesis in latent space training.
Authors:Benjamin Uhrich, Tim Häntschel, Erhard Rahm
Abstract:
A comprehensive understanding of heat transport is essential for optimizing various mechanical and engineering applications, including 3D printing. Recent advances in machine learning, combined with physics‑based models, have enabled a powerful fusion of numerical methods and data‑driven algorithms. This progress is driven by the availability of limited sensor data in various engineering and scientific domains, where the cost of data collection and the inaccessibility of certain measurements are high. To this end, we present PiGRAND, a Physics‑informed graph neural diffusion framework. In order to reduce the computational complexity of graph learning, an efficient graph construction procedure was developed. Our approach is inspired by the explicit Euler and implicit Crank‑Nicolson methods for modeling continuous heat transport, leveraging sub‑learning models to secure the accurate diffusion across graph nodes. To enhance computational performance, our approach is combined with efficient transfer learning. We evaluate PiGRAND on thermal images from 3D printing, demonstrating significant improvements in prediction accuracy and computational performance compared to traditional graph neural diffusion (GRAND) and physics‑informed neural networks (PINNs). These enhancements are attributed to the incorporation of physical principles derived from the theoretical study of partial differential equations (PDEs) into the learning model. The PiGRAND code is open‑sourced on GitHub: https://github.com/bu32loxa/PiGRAND
Authors:Vladyslav Parakhin
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.
Authors:Omer Ben Hayun, Roy Betser, Meir Yossef Levi, Levi Kassel, Guy Gilboa
Abstract:
Following major advances in text and image generation, the video domain has surged, producing highly realistic and controllable sequences. Along with this progress, these models also raise serious concerns about misinformation, making reliable detection of synthetic videos increasingly crucial. Image‑based detectors are fundamentally limited because they operate per frame and ignore temporal dynamics, while supervised video detectors generalize poorly to unseen generators, a critical drawback given the rapid emergence of new models. These challenges motivate zero‑shot approaches, which avoid synthetic data and instead score content against real‑data statistics, enabling training‑free, model‑agnostic detection. We introduce STALL, a simple, training‑free, theoretically justified detector that provides likelihood‑based scoring for videos, jointly modeling spatial and temporal evidence within a probabilistic framework. We evaluate STALL on two public benchmarks and introduce ComGenVid, a new benchmark with state‑of‑the‑art generative models. STALL consistently outperforms prior image‑ and video‑based baselines. Code and data are available at https://omerbenhayun.github.io/stall‑video.
Authors:Xiaoqing Lian, Pengsen Ma, Tengfeng Ma, Zhonghao Ren, Xibao Cai, Zhixiang Cheng, Bosheng Song, He Wang, Xiang Pan, Yangyang Chen, Sisi Yuan, Chen Lin
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.
Authors:Jingyu Lu, Yuhan Wang, Fan Zhuo, Xize Cheng, Changhao Pan, Xueyi Pu, Yifu Chen, Chenyuhao Wen, Tianle Liang, Zhou Zhao
Abstract:
The rapid evolution of end‑to‑end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances and the spontaneous nature of human conversation. However, current methods struggle with two critical gaps: the modality gap, involving prosody and emotion, and the colloquialness gap, distinguishing written scripts from natural speech. To address these challenges, we introduce SDiaReward, an end‑to‑end multi‑turn reward model trained on SDiaReward‑Dataset, a novel collection of episode‑level preference pairs explicitly targeting these gaps. It operates directly on full multi‑turn speech episodes and is optimized with pairwise preference supervision, enabling joint assessment of modality and colloquialness in a single evaluator. We further establish ESDR‑Bench, a stratified benchmark for robust episode‑level evaluation. Experiments demonstrate that SDiaReward achieves state‑of‑the‑art pairwise preference accuracy, significantly outperforming general‑purpose audio LLMs. Further analysis suggests that SDiaReward captures relative conversational expressiveness beyond superficial synthesis cues, improving generalization across domains and recording conditions. Code, data, and demos are available at https://github.com/MM‑Speech/SDiaReward/.
Authors:Tien-Cuong Bui, Injae Chung, Wonjun Lee, Junsu Ko, Juyong Lee
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).
Authors:Ziqing Ma, Kai Ying, Xinyue Gu, Tian Zhou, Tianyu Zhu, Haifan Zhang, Peisong Niu, Zheng Wang, Cong Bai, Liang Sun
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.
Authors:Tuan-Anh Yang, Bao V. Q. Bui, Chanh-Quang Vo-Van, Truong-Son Hy
Abstract:
We propose a deep learning framework for COVID‑19 detection and disease classification from chest CT scans that integrates both 2.5D and 3D representations to capture complementary slice‑level and volumetric information. The 2.5D branch processes multi‑view CT slices (axial, coronal, sagittal) using a DINOv3 vision transformer to extract robust visual features, while the 3D branch employs a ResNet‑18 architecture to model volumetric context and is pretrained with Variance Risk Extrapolation (VREx) followed by supervised contrastive learning to improve cross‑source robustness. Predictions from both branches are combined through logit‑level ensemble inference. Experiments on the PHAROS‑AIF‑MIH benchmark demonstrate the effectiveness of the proposed approach: for binary COVID‑19 detection, the ensemble achieves 94.48% accuracy and a 0.9426 Macro F1‑score, outperforming both individual models, while for multi‑class disease classification the 2.5D DINOv3 model achieves the best performance with 79.35% accuracy and a 0.7497 Macro F1‑score. These results highlight the benefit of combining pretrained slice‑based representations with volumetric modeling for robust multi‑source medical imaging analysis. Code is available at https://github.com/HySonLab/PHAROS‑AIF‑MIH
Authors:Xuanfei Ren, Allen Nie, Tengyang Xie, Ching-An Cheng
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.
Authors:Yu Hao, Qiuyu Wang, Cheng Yang, Yawen Li, Zhiqiang Zhang, Chuan Shi
Abstract:
Large language models (LLMs) facilitate the development of autonomous agents. As a core component of such agents, task planning aims to decompose complex natural language requests into concrete, solvable sub‑tasks. Since LLM‑generated plans are frequently prone to hallucinations and sensitive to long‑context prom‑pts, recent research has introduced plan verifiers to identify and correct potential flaws. However, most existing approaches still rely on an LLM as the verifier via additional prompting for plan review or self‑reflection. LLM‑based verifiers can be misled by plausible narration and struggle to detect failures caused by structural relations across steps, such as type mismatches, missing intermediates, or broken dependencies. To address these limitations, we propose a graph‑based verifier for LLM task planning. Specifically, the proposed method has four major components: Firstly, we represent a plan as a directed graph with enriched attributes, where nodes denote sub‑tasks and edges encode execution order and dependency constraints. Secondly, a graph neural network (GNN) then performs structural evaluation and diagnosis, producing a graph‑level plausibility score for plan acceptance as well as node/edge‑level risk scores to localize erroneous regions. Thirdly, we construct controllable perturbations from ground truth plan graphs, and automatically generate training data with fine‑grained annotations. Finally, guided by the feedback from our GNN verifier, we enable an LLM to conduct local edits (e.g., tool replacement or insertion) to correct the plan when the graph‑level score is insufficient. Extensive experiments across diverse datasets, backbone LLMs, and planners demonstrate that our GNNVerifier achieves significant gains in improving plan quality. Our data and code is available at https://github.com/BUPT‑GAMMA/GNNVerifier.
Authors:Seunghan Lee, Jaehoon Lee, Jun Seo, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn
Abstract:
Recent advances in time series foundation models (TSFMs) demonstrate strong expressive capacity through large‑scale pretraining across diverse time series domains. Zero‑shot time series forecasting with TSFMs, however, exhibits limited generalization to unseen datasets, which retrieval‑augmented forecasting addresses by leveraging an external knowledge base. Existing approaches rely on a fixed number of retrieved samples that may introduce irrelevant information. To this end, we propose Cross‑RAG, a zero‑shot retrieval‑augmented forecasting framework that selectively attends to query‑relevant retrieved samples. Cross‑RAG models input‑level relevance between the query and retrieved samples via query‑retrieval cross‑attention, while jointly incorporating information from the query and retrieved samples. Extensive experiments demonstrate that Cross‑RAG consistently improves zero‑shot forecasting performance across various TSFMs and RAG methods, and additional analyses confirm its effectiveness across diverse retrieval scenarios. Code is available at https://github.com/seunghan96/cross‑rag/.
Authors:Salim Khazem
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
Authors:Ping Chen, Xiang Liu, Xingpeng Zhang, Fei Shen, Xun Gong, Zhaoxiang Liu, Zezhou Chen, Huan Hu, Kai Wang, Shiguo Lian
Abstract:
Diffusion models operate in a reflexive System 1 mode, constrained by a fixed, content‑agnostic sampling schedule. This rigidity arises from the curse of state dimensionality, where the combinatorial explosion of possible states in the high‑dimensional noise manifold renders explicit trajectory planning intractable and leads to systematic computational misallocation. To address this, we introduce Chain‑of‑Trajectories (CoTj), a train‑free framework enabling System 2 deliberative planning. Central to CoTj is Diffusion DNA, a low‑dimensional signature that quantifies per‑stage denoising difficulty and serves as a proxy for the high‑dimensional state space, allowing us to reformulate sampling as graph planning on a directed acyclic graph. Through a Predict‑Plan‑Execute paradigm, CoTj dynamically allocates computational effort to the most challenging generative phases. Experiments across multiple generative models demonstrate that CoTj discovers context‑aware trajectories, improving output quality and stability while reducing redundant computation. This work establishes a new foundation for resource‑aware, planning‑based diffusion modeling. The code is available at https://github.com/UnicomAI/CoTj.
Authors:Mike Amega
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).
Authors:Varun Pratap Bhardwaj
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.
Authors:Yuchen Wang, Jiangtao Kong, Sizhe Wei, Xiaochang Li, Haohong Lin, Hongjue Zhao, Tianyi Zhou, Lu Gan, Huajie Shao
Abstract:
Trajectory world models play a crucial role in robotic dynamics learning, planning, and control. While recent works have explored trajectory world models for diverse robotic systems, they struggle to scale to a large number of distinct system dynamics and overlook domain knowledge of physical structures. To address these limitations, we introduce WestWorld, a knoWledge‑Encoded Scalable Trajectory World model for diverse robotic systems. To tackle the scalability challenge, we propose a novel system‑aware Mixture‑of‑Experts (Sys‑MoE) that dynamically combines and routes specialized experts for different robotic systems via a learnable system embedding. To further enhance zero‑shot generalization, we incorporate domain knowledge of robot physical structures by introducing a structural embedding that aligns trajectory representations with morphological information. After pretraining on 89 complex environments spanning diverse morphologies across both simulation and real‑world settings, WestWorld achieves significant improvements over competitive baselines in zero‑ and few‑shot trajectory prediction. Additionally, it shows strong scalability across a wide range of robotic environments and significantly improves performance on downstream model‑based control for different robots. Finally, we deploy our model on a real‑world Unitree Go1, where it demonstrates stable locomotion performance. The code is available at https://github.com/511205787/WestWorld.
Authors:Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao, Binbin Zheng, Lu Pan, Ke Zeng
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.
Authors:Jaeyo Shin, Jiwook Kim, Hyunjung Shim
Abstract:
Representation Alignment (REPA) has emerged as a simple way to accelerate Diffusion Transformers training in latent space. At the same time, pixel‑space diffusion transformers such as Just image Transformers (JiT) have attracted growing attention because they remove a dependency on a pretrained tokenizer, and then avoid the reconstruction bottleneck of latent diffusion. This paper shows that the REPA can fail for JiT. REPA yields worse FID for JiT as training proceeds and collapses diversity on image subsets that are tightly clustered in the representation space of pretrained semantic encoder on ImageNet. We trace the failure to an information asymmetry: denoising occurs in the high dimensional image space, while the semantic target is strongly compressed, making direct regression a shortcut objective. We propose PixelREPA, which transforms the alignment target and constrains alignment with a Masked Transformer Adapter that combines a shallow transformer adapter with partial token masking. PixelREPA improves both training convergence and final quality. PixelREPA reduces FID from 3.66 to 3.17 for JiT‑B/16 and improves Inception Score (IS) from 275.1 to 284.6 on ImageNet 256 × 256, while achieving > 2× faster convergence. Finally, PixelREPA‑H/16 achieves FID=1.81 and IS=317.2. Our code is available at https://github.com/kaist‑cvml/PixelREPA.
Authors:Jungwoo Oh, Hyunseung Chung, Junhee Lee, Min-Gyu Kim, Hangyul Yoon, Ki Seong Lee, Youngchae Lee, Muhan Yeo, Edward Choi
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.
Authors:He Zhang, Ying Sun, Hui Xiong
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.
Authors:Yutong Wu, Chenrui Cao, Pengwei Jin, Di Huang, Rui Zhang, Xishan Zhang, Zidong Du, Qi Guo, Xing Hu
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.
Authors:Huan Wang, Jun Shen, Jun Yan, Guansong Pang
Abstract:
Federated learning (FL) allows distributed clients to collaboratively train a global model in a privacy‑preserving manner. However, one major challenge is domain skew, where clients' data originating from diverse domains may hinder the aggregated global model from learning a consistent representation space, resulting in poor generalizable ability in multiple domains. In this paper, we argue that the domain skew is reflected in the domain‑specific biased features of each client, causing the local model's representations to collapse into a narrow low‑dimensional subspace. We then propose Federated Feature Decoupling and Calibration (F^2DC), which liberates valuable class‑relevant information by calibrating the domain‑specific biased features, enabling more consistent representations across domains. A novel component, Domain Feature Decoupler (DFD), is first introduced in F^2DC to determine the robustness of each feature unit, thereby separating the local features into domain‑robust features and domain‑related features. A Domain Feature Corrector (DFC) is further proposed to calibrate these domain‑related features by explicitly linking discriminative signals, capturing additional class‑relevant clues that complement the domain‑robust features. Finally, a domain‑aware aggregation of the local models is performed to promote consensus among clients. Empirical results on three popular multi‑domain datasets demonstrate the effectiveness of the proposed F^2DC and the contributions of its two modules. Code is available at https://github.com/mala‑lab/F2DC.
Authors:Shahriar Kabir, Abdullah Muhammed Amimul Ehsan, Istiak Ahmmed Rifti, Md Kaykobad Reza
Abstract:
Automated segmentation of Martian landslides, particularly in tectonically active regions such as Valles Marineris,is important for planetary geology, hazard assessment, and future robotic exploration. However, detecting landslides from planetary imagery is challenging due to the heterogeneous nature of available sensing modalities and the limited number of labeled samples. Each observation combines RGB imagery with geophysical measurements such as digital elevation models, slope maps, thermal inertia, and contextual grayscale imagery, which differ significantly in resolution and statistical properties. To address these challenges, we propose DualSwinFusionSeg, a multimodal segmentation architecture that separates modality‑specific feature extraction and performs multi‑scale cross‑modal fusion. The model employs two parallel Swin Transformer V2 encoders to independently process RGB and auxiliary geophysical inputs, producing hierarchical feature representations. Corresponding features from the two streams are fused at multiple scales and decoded using a UNet++ decoder with dense nested skip connections to preserve fine boundary details. Extensive ablation studies evaluate modality contributions, loss functions, decoder architectures, and fusion strategies. Experiments on the MMLSv2 dataset from the PBVS 2026 Mars‑LS Challenge show that modality‑specific encoders and simple concatenation‑based fusion improve segmentation accuracy under limited training data. The final model achieves 0.867 mIoU and 0.905 F1 on the development benchmark and 0.783 mIoU on the held‑out test set, demonstrating strong performance for multimodal planetary surface segmentation.
Authors:Hamza Mahmood, Abhishek Halder, Adeel Akhtar
Abstract:
This work studies the Schrödinger bridge problem for the kinematic equation on a compact connected Lie group. The objective is to steer a controlled diffusion between given initial and terminal densities supported over the Lie group while minimizing the control effort. We develop a coordinate‑free formulation of this stochastic optimal control problem that respects the underlying geometric structure of the Lie group, thereby avoiding limitations associated with local parameterizations or embeddings in Euclidean spaces. We establish the existence and uniqueness of solution to the corresponding Schrödinger system. Our results are constructive in that they derive a geometric controller that optimally interpolates probability densities supported over the Lie group. To illustrate the results, we provide numerical examples on \mathsfSO(2) and \mathsfSO(3). The codes and animations are publicly available at https://github.com/gradslab/SbpLieGroups.git .
Authors:N. Brag
Abstract:
A task‑specific model trained on 212,231 UK Biobank subjects to predict vascular age from PPG (AI‑PPG Age) fails on a different clinical population: predictions collapse to a narrow 38‑67 year range regardless of true age. Meanwhile, a general‑purpose foundation model with no age‑related training objective achieves lower error on the same data. We investigate why this happens and what it means for PPG‑based biological age prediction.
We evaluate three open‑source PPG models (Pulse‑PPG, PaPaGei‑S, AI‑PPG Age) on 906 surgical patients from PulseDB, using frozen embeddings with Ridge regression and 5‑fold cross‑validation. Pulse‑PPG reaches MAE = 9.28 years, beating both AI‑PPG Age in linear probe mode (9.72) and HR/HRV combined with demographics (9.59). Adding demographic features brings the best result down to MAE = 8.22 years (R2 = 0.517, r = 0.725). The predicted age gap correlates with diastolic blood pressure after adjusting for chronological age (r = ‑0.188, p = 1.2e‑8), consistent with what Apple reported for their proprietary PpgAge model. The remaining gap with Apple (MAE 2.43) appears driven by dataset size (906 vs 213,593 subjects) and population differences rather than model architecture, as our learning curve shows no plateau. Code is publicly available.
Authors:Gwanwoo Song, Kwanyoung Park, Youngwoon Lee
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.
Authors:Kursat Komurcu, Linas Petkevicius
Abstract:
Predicting satellite imagery requires a balance between structural accuracy and textural detail. Standard deterministic methods like PredRNN or SimVP minimize pixel‑based errors but suffer from the "regression to the mean" problem, producing blurry outputs that obscure subtle geographic‑spatial features. Generative models provide realistic textures but often misleadingly reveal structural anomalies. To bridge this gap, we introduce Sat‑JEPA‑Diff, which combines Self‑Supervised Learning (SSL) with Hidden Diffusion Models (LDM). An IJEPA module predicts stable semantic representations, which then route a frozen Stable Diffusion backbone via a lightweight cross‑attention adapter. This ensures that the synthesized high‑accuracy textures are based on absolutely accurate structural predictions. Evaluated on a global Sentinel‑2 dataset, Sat‑JEPA‑Diff excels at resolving sharp boundaries. It achieves leading perceptual scores (GSSIM: 0.8984, FID: 0.1475) and significantly outperforms deterministic baselines, despite standard autoregressive stability limits. The code and dataset are publicly available on https://github.com/VU‑AIML/SAT‑JEPA‑DIFF.
Authors:Shivnath Tathe
Abstract:
Low‑precision neural network training has emerged as a promising direction for reducing computational costs and democratizing access to deep learning research. However, existing 4‑bit quantization methods either rely on expensive GPU infrastructure or suffer from significant accuracy degradation. In this work, we present a practical method for training convolutional neural networks at true 4‑bit precision using standard PyTorch operations on commodity CPUs. We introduce a novel tanh‑based soft weight clipping technique that, combined with symmetric quantization, dynamic per‑layer scaling, and straight‑through estimators, achieves stable convergence and competitive accuracy. Training a VGG‑style architecture with 3.25 million parameters from scratch on CIFAR‑10, our method achieves 92.34% test accuracy on Google Colab's free CPU tier ‑‑ matching full‑precision baseline performance (92.5%) with only a 0.16% gap. We further validate on CIFAR‑100, achieving 70.94% test accuracy across 100 classes with the same architecture and training procedure, demonstrating that 4‑bit training from scratch generalizes to harder classification tasks. Both experiments achieve 8x memory compression over FP32 while maintaining exactly 15 unique weight values per layer throughout training. We additionally validate hardware independence by demonstrating rapid convergence on a consumer mobile device (OnePlus 9R), achieving 83.16% accuracy in only 6 epochs. To the best of our knowledge, no prior work has demonstrated 4‑bit quantization‑aware training achieving full‑precision parity on standard CPU hardware without specialized kernels or post‑training quantization.
Authors:Seokmin Lee, Yunghee Lee, Byeonghyun Pak, Byeongju Woo
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.
Authors:Jiahao Qin
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.
Authors:Dongyuan Li, Ying Zhang, Yaozu Wu, Renhe Jiang
Abstract:
Dynamic graph clustering aims to detect and track time‑varying clusters in dynamic graphs, revealing how complex real‑world systems evolve over time. However, existing methods are predominantly black‑box models. They lack interpretability in their clustering decisions and fail to provide semantic explanations of why clusters form or how they evolve, severely limiting their use in safety‑critical domains such as healthcare or transportation. To address these limitations, we propose an end‑to‑end interpretable framework that maps continuous graph embeddings into discrete semantic concepts through learnable prototypes. Specifically, we first decompose node representations into orthogonal role and clustering subspaces, so that nodes with similar roles (e.g., hubs, bridges) but different cluster affiliations can be properly distinguished. We then introduce five node role prototypes (Leader, Contributor, Wanderer, Connector, Newcomer) in the role subspace as semantic anchors, transforming continuous embeddings into discrete concepts to facilitate LLM understanding of node roles within communities. Finally, we design a hierarchical LLM reasoning mechanism to generate both clustering results and natural language explanations, while providing consistency feedback as weak supervision to refine node representations. Experimental results on four synthetic and six real‑world benchmarks demonstrate the effectiveness, interpretability, and robustness of DyG‑RoLLM. Code is available at https://github.com/Clearloveyuan/DyG‑RoLLM.
Authors:Xuan Cui, Huiyue Li, Run Zeng, Yunfei Zhao, Jinrui Qian, Wei Duan, Bo Liu, Zhanpeng Zhou
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
Authors:Grayson Lee, Minh Bui, Shuzi Zhou, Yankai Li, Mo Chen, Ke Li
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/.
Authors:Zhaoyuan Gu, Yipu Chen, Zimeng Chai, Alfred Cueva, Thong Nguyen, Yifan Wu, Huishu Xue, Minji Kim, Isaac Legene, Fukang Liu, Matthew Kim, Ayan Barula, Yongxin Chen, Ye Zhao
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/
Authors:Dongyuan Li, Shun Zheng, Chang Xu, Jiang Bian, Renhe Jiang
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.
Authors:Andrii Shchur, Inna Skarga-Bandurova
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
Authors:Adrien Corenflos
Abstract:
Square‑root Kalman filters propagate state covariances in Cholesky‑factor form for numerical stability, and are a natural target for gradient‑based parameter learning in state‑space models. Their core operation, triangularization of a matrix M \in \mathbbR^n × m, is computed via a QR decomposition in practice, but naively differentiating through it causes two problems: the semi‑orthogonal factor is non‑unique when m > n, yielding undefined gradients; and the standard Jacobian formula involves inverses, which diverges when M is rank‑deficient. Both are resolved by the observation that all filter outputs relevant to learning depend on the input matrix only through the Gramian MM^\top, so the composite loss is smooth in M even where the triangularization is not. We derive a closed‑form chain‑rule directly from the differential of this Gramian identity, prove it exact for the Kalman log‑marginal likelihood and filtered moments, and extend it to rank‑deficient inputs via a two‑component decomposition: a column‑space term based on the Moore‑‑Penrose pseudoinverse, and a null‑space correction for perturbations outside the column space of M.
Authors:Pratik Ramesh, George Stoica, Arun Iyer, Leshem Choshen, Judy Hoffman
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
Authors:Guanyu Chen, Ruichen Wang, Tianren Zhang, Feng Chen
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
Authors:Mansoor Ahmed, Nadeem Taj, Imdad Ullah Khan, Hemanth Venkateswara, Murray Patterson
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
Authors:Jiajin Liu, Dongzhe Fan, Chuanhao Ji, Daochen Zha, Qiaoyu Tan
Abstract:
Vision‑Language Models (VLMs) have demonstrated remarkable capabilities in aligning and understanding multimodal signals, yet their potential to reason over structured data, where multimodal entities are connected through explicit relational graphs, remains largely underexplored. Unlocking this capability is crucial for real‑world applications such as social networks, recommendation systems, and scientific discovery, where multimodal information is inherently structured. To bridge this gap, we present GraphVLM, a systematic benchmark designed to evaluate and harness the capabilities of VLMs for multimodal graph learning (MMGL). GraphVLM investigates three complementary paradigms for integrating VLMs with graph reasoning: (1) VLM‑as‑Encoder, which enriches graph neural networks through multimodal feature fusion; (2) VLM‑as‑Aligner, which bridges modalities in latent or linguistic space to facilitate LLM‑based structured reasoning; and (3) VLM‑as‑Predictor, which directly employs VLMs as multimodal backbones for graph learning tasks. Extensive experiments across six datasets from diverse domains demonstrate that VLMs enhance multimodal graph learning via all three roles. Among these paradigms, VLM‑as‑Predictor achieves the most substantial and consistent performance gains, revealing the untapped potential of vision‑language models as a new foundation for multimodal graph learning. The benchmark code is publicly available at https://github.com/oamyjin/GraphVLM.
Authors:Jim Achterberg, Bram Van Dijk, Jing Meng, Saif Ul Islam, Gregory Epiphaniou, Carsten Maple, Xuefei Ding, Theodoros N. Arvanitis, Simon Brouwer, Marcel Haas, Marco Spruit
Abstract:
This study presents OpenExtract, an open‑source pipeline for automated data extraction in large‑scale systematic literature reviews. The pipeline queries large language models (LLMs) to predict data entries based on relevant sections of scientific articles. To test the efficacy of OpenExtract, we apply it to a systematic literature review in digital health and compare its outputs with those of human researchers. OpenExtract achieves precision and recall scores of > 0.8 in this task, indicating that it can be effective at extracting data automatically and efficiently. OpenExtract: https://github.com/JimAchterbergLUMC/OpenExtract.
Authors:Domen Preložnik, Žiga Špiclin
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.
Authors:Yanzhe Hu, Yijie Jin, Pengfei Liu, Kai Yu, Zhijie Deng
Abstract:
Diffusion Large Language Models (dLLMs) have emerged as a promising paradigm for parallel token generation, with block‑wise variants garnering significant research interest. Despite their potential, existing dLLMs typically suffer from a rigid accuracy‑parallelism trade‑off: increasing the number of tokens per forward (TPF) via aggressive parallel decoding often leads to performance degradation and increased generation instability. We identify that this limitation stems from the model's inability to navigate high‑parallelism regimes where approximation errors and local corruptions accumulate, ultimately undermining the reliability of parallel generation. To address this, we propose LightningRL, a post‑training framework designed to directly optimize the speed‑quality Pareto frontier of pre‑trained dLLMs. Instead of forcing uniform parallelization, our approach leverages reinforcement learning to identify and reinforce high‑parallelism trajectories that maintain generation accuracy. Built upon the Group Relative Policy Optimization (GRPO) framework, LightningRL introduces several enhancements tailored for dLLMs: (1) stabilized training via per‑reward decoupled normalization; (2) token‑level negative log‑likelihood (NLL) regularization on correct trajectories to anchor model performance; and (3) a dynamic sampling strategy with TPF‑aware filtering to enhance training efficiency. Experimental results across mathematical and coding benchmarks demonstrate that LightningRL consistently advances the Pareto frontier, achieving competitive task accuracy while significantly increasing parallelism, reaching an average TPF of 7.32 (with a peak of 11.10 on the MBPP dataset). Our code is available at https://github.com/SJTU‑DENG‑Lab/LightningRL.
Authors:Idan Sulami, Alon Itzkovitch, Michael R. Kearney, Moni Shahar, Ofir Levy
Abstract:
Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As a result, the spatial scales over which surrounding environmental conditions influence local microclimates remain poorly quantified. Here, we show how remote sensing can help quantify the contribution of spatial context to microclimate temperature predictions. Building on convolutional neural network principles, we designed a task‑specific deep neural network and trained a series of models in which the spatial extent of input data was systematically varied. Drone‑derived spatial layers and meteorological data were used to predict ground temperature at a focal location, allowing direct assessment of how prediction accuracy changes with increasing spatial context. Our results show that incorporating spatially adjacent information substantially improves prediction accuracy, with diminishing returns beyond spatial extents of approximately 5‑7 m. This characteristic scale indicates that ground temperatures are influenced not only by local surface properties, but also by horizontal heat transfer and radiative interactions operating across neighboring microhabitats. The magnitude of spatial effects varied systematically with time of day, microhabitat type, and local environmental characteristics, highlighting context‑dependent spatial coupling in microclimate formation. By treating deep learning as a diagnostic tool rather than solely a predictive one, our approach provides a general and transferable method for quantifying spatial dependencies in microclimate models and informing the development of hybrid mechanistic‑data‑driven approaches that explicitly account for spatial interactions while retaining physical interpretability.
Authors:Minsang Kim, Seung Jun Baek
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.
Authors:Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov, Abdul Ahad Butt, Gül Varol, Pascal Fua, Fabio Pizzati, Ivan Laptev
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.
Authors:Helen Qu, Rudy Morel, Michael McCabe, Alberto Bietti, François Lanusse, Shirley Ho, Yann LeCun
Abstract:
Machine learning approaches to spatiotemporal physical systems have primarily focused on next‑frame prediction, with the goal of learning an accurate emulator for the system's evolution in time. However, these emulators are computationally expensive to train and are subject to performance pitfalls, such as compounding errors during autoregressive rollout. In this work, we take a different perspective and look at scientific tasks further downstream of predicting the next frame, such as estimation of a system's governing physical parameters. Accuracy on these tasks offers a uniquely quantifiable glimpse into the physical relevance of the representations of these models. We evaluate the effectiveness of general‑purpose self‑supervised methods in learning physics‑grounded representations that are useful for downstream scientific tasks. Surprisingly, we find that not all methods designed for physical modeling outperform generic self‑supervised learning methods on these tasks, and methods that learn in the latent space (e.g., joint embedding predictive architectures, or JEPAs) outperform those optimizing pixel‑level prediction objectives. Code is available at https://github.com/helenqu/physical‑representation‑learning.
Authors:Yiqi Zhou, Yue Yuan, Yikai Wang, Bohao Liu, Qinxin Mei, Zhuohua Liu, Shan Shen, Wei Xing, Daying Sun, Li Li, Guozhu Liu
Abstract:
Digital Compute‑in‑Memory (DCiM) accelerates neural networks by reducing data movement. Approximate DCiM can further improve power‑performance‑area (PPA), but demands accuracy‑constrained co‑optimization across coupled architecture and transistor‑level choices. Building on OpenYield, we introduce Accuracy‑Constrained Co‑Optimization (ACCO) and present OpenACMv2, an open framework that operationalizes ACCO via two‑level optimization: (1) accuracy‑constrained architecture search of compressor combinations and SRAM macro parameters, driven by a fast GNN‑based surrogate for PPA and error; and (2) variation‑ and PVT‑aware transistor sizing for standard cells and SRAM bitcells using Monte Carlo. By decoupling ACCO into architecture‑level exploration and circuit‑level sizing, OpenACMv2 integrates classic single‑ and multi‑objective optimizers to deliver strong PPA‑accuracy tradeoffs and robust convergence. The workflow is compatible with FreePDK45 and OpenROAD, supporting reproducible evaluation and easy adoption. Experiments demonstrate significant PPA improvements under controlled accuracy budgets, enabling rapid "what‑if" exploration for approximate DCiM. The framework is available on https://github.com/ShenShan123/OpenACM.
Authors:Steven Motta, Gioele Nanni
Abstract:
Federated learning on neuromorphic hardware remains unexplored because on‑chip spike‑timing‑dependent plasticity (STDP) produces binary weight updates rather than the floating‑point gradients assumed by standard algorithms. We build a two‑node federated system with BrainChip Akida AKD1000 processors and run approximately 1,580 experimental trials across seven analysis phases. Of four weight‑exchange strategies tested, neuron‑level concatenation (FedUnion) consistently preserves accuracy while element‑wise weight averaging (FedAvg) destroys it (p = 0.002). Domain‑adaptive fine‑tuning of the upstream feature extractor accounts for most of the accuracy gains, confirming feature quality as the dominant factor. Scaling feature dimensionality from 64 to 256 yields 77.0% best‑strategy federated accuracy (n=30, p < 0.001). Two independent asymmetries (wider features help federation more than individual learning, while binarization hurts federation more) point to a shared prototype complementarity mechanism: cross‑node transfer scales with the distinctiveness of neuron prototypes.
Authors:Tianhao Fu, Bingxuan Yang, Juncheng Guo, Shrena Sribalan, Yucheng Chen
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.
Authors:Chenlong Yin, Runpeng Geng, Yanting Wang, Jinyuan Jia
Abstract:
Prompt injection poses serious security risks to real‑world LLM applications, particularly autonomous agents. Although many defenses have been proposed, their robustness against adaptive attacks remains insufficiently evaluated, potentially creating a false sense of security. In this work, we propose PISmith, a reinforcement learning (RL)‑based red‑teaming framework that systematically assesses existing prompt‑injection defenses by training an attack LLM to optimize injected prompts in a practical black‑box setting, where the attacker can only query the defended LLM and observe its outputs. We find that directly applying standard GRPO to attack strong defenses leads to sub‑optimal performance due to extreme reward sparsity ‑‑ most generated injected prompts are blocked by the defense, causing the policy's entropy to collapse before discovering effective attack strategies, while the rare successes cannot be learned effectively. In response, we introduce adaptive entropy regularization and dynamic advantage weighting to sustain exploration and amplify learning from scarce successes. Extensive evaluation on 13 benchmarks demonstrates that state‑of‑the‑art prompt injection defenses remain vulnerable to adaptive attacks. We also compare PISmith with 7 baselines across static, search‑based, and RL‑based attack categories, showing that PISmith consistently achieves the highest attack success rates. Furthermore, PISmith achieves strong performance in agentic settings on InjecAgent and AgentDojo against both open‑source and closed‑source LLMs (e.g., GPT‑4o‑mini and GPT‑5‑nano). Our code is available at https://github.com/albert‑y1n/PISmith.
Authors:Shaofeng Guo, Jiequan Cui, Richang Hong
Abstract:
With the rapid rise of Artificial Intelligence Generated Content (AIGC), image manipulation has become increasingly accessible, posing significant challenges for image forgery detection and localization (IFDL). In this paper, we study how to fully leverage vision‑language models (VLMs) to assist the IFDL task. In particular, we observe that priors from VLMs hardly benefit the detection and localization performance and even have negative effects due to their inherent biases toward semantic plausibility rather than authenticity. Additionally, the location masks explicitly encode the forgery concepts, which can serve as extra priors for VLMs to ease their training optimization, thus enhancing the interpretability of detection and localization results. Building on these findings, we propose a new IFDL pipeline named IFDL‑VLM. To demonstrate the effectiveness of our method, we conduct experiments on 9 popular benchmarks and assess the model performance under both in‑domain and cross‑dataset generalization settings. The experimental results show that we consistently achieve new state‑of‑the‑art performance in detection, localization, and interpretability.Code is available at: https://github.com/sha0fengGuo/IFDL‑VLM.
Authors:David McAllister, Miika Aittala, Tero Karras, Janne Hellsten, Angjoo Kanazawa, Timo Aila, Samuli Laine
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.
Authors:Yiqun Zhang, Zexi Tan, Xiaopeng Luo, Yunlin Liu
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.
Authors:Yonghun Jeong, David Yoon Suk Kang, Yeon-Chang Lee
Abstract:
Multimodal recommender systems (MMRS) leverage images, text, and interaction signals to enrich item representations. However, recent alignment based MMRSs that enforce a unified embedding space often blur modality specific structures and exacerbate ID dominance. Therefore, we propose AnchorRec, a multimodal recommendation framework that performs indirect, anchor based alignment in a lightweight projection domain. By decoupling alignment from representation learning, AnchorRec preserves each modality's native structure while maintaining cross modal consistency and avoiding positional collapse. Experiments on four Amazon datasets show that AnchorRec achieves competitive top N recommendation accuracy, while qualitative analyses demonstrate improved multimodal expressiveness and coherence. The codebase of AnchorRec is available at https://github.com/hun9008/AnchorRec.
Authors:Jillur Rahman Saurav, Thuong Le Hoai Pham, Pritam Mukherjee, Paul Yi, Brent A. Orr, Jacob M. Luber
Abstract:
Virtual immunohistochemistry (IHC) staining from hematoxylin and eosin (H&E) images can accelerate diagnostics by providing preliminary molecular insight directly from routine sections, reducing the need for repeat sectioning when tissue is limited. Existing methods improve realism through contrastive objectives, prototype matching, or domain alignment, yet the generator itself receives no direct guidance from pathology foundation models. We present UNIStainNet, a SPADE‑UNet conditioned on dense spatial tokens from a frozen pathology foundation model (UNI), providing tissue‑level semantic guidance for stain translation. A misalignment‑aware loss suite preserves stain quantification accuracy, and learned stain embeddings enable a single model to serve multiple IHC markers simultaneously. On MIST, UNIStainNet achieves state‑of‑the‑art distributional metrics on all four stains (HER2, Ki67, ER, PR) from a single unified model, where prior methods typically train separate per‑stain models. On BCI, it also achieves the best distributional metrics. A tissue‑type stratified failure analysis reveals that remaining errors are systematic, concentrating in non‑tumor tissue. Code is available at https://github.com/facevoid/UNIStainNet.
Authors:Selim Furkan Tekin, Yichang Xu, Gaowen Liu, Ramana Rao Kompella, Margaret L. Loper, Ling Liu
Abstract:
With the growing number and diversity of Vision‑Language Models (VLMs), many works explore language‑based ensemble, collaboration, and routing techniques across multiple VLMs to improve multi‑model reasoning. In contrast, we address the diverse model selection using both vision and language modalities. We introduce focal error diversity to capture complementary reasoning across VLMs and a CKA‑based focal diversity metric (CKA‑focal) to measure disagreement in their visual embeddings. On the constructed ensemble surface from a pool of candidate VLMs, we applied a Genetic Algorithm to effectively prune out those component VLMs that do not add value to the fusion performance. We identify the best combination for each task as well as fuse the outputs of each VLMs in the model pool, and show that heterogeneous models can capture epistemic uncertainty dynamically and mitigate hallucinations. Our V3Fusion approach is capable of producing dual focal‑diversity fused predictions with high performance for vision‑language reasoning, even when there is no majority consensus or the majority of VLMs make incorrect predictions. Extensive experiments validate V3Fusion on four popular VLM benchmarks (A‑OKVQA, MMMU, MMMU‑Pro, and OCR‑VQA). The results show that V3Fusion outperforms the best‑performing VLM on MMMU by 8.09% and MMMU‑Pro by 4.87% gain in accuracy. For generative tasks, V3Fusion outperforms Intern‑VL2‑8b and Qwen2.5‑VL‑7b, the top‑2 VLM performers on both A‑OKVQA and OCR‑VQA. Our code and datasets are available at https://github.com/sftekin/v3fusion.
Authors:Gihoon Kim, Euntai Kim
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
Authors:Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh, Dileep Kalathil, Krishna Narayanan, Jean-Francois Chamberland
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.
Authors:Shivam Chaudhary, Sheethal Bhat, Andreas Maier
Abstract:
Accurate detection and localization of traumatic injuries in abdominal CT scans remains a critical challenge in emergency radiology, primarily due to severe scarcity of annotated medical data. This paper presents a label‑efficient approach combining self‑supervised pre‑training with semi‑supervised detection for 3D medical image analysis. We employ patch‑based Masked Image Modeling (MIM) to pre‑train a 3D U‑Net encoder on 1,206 CT volumes without annotations, learning robust anatomical representations. The pretrained encoder enables two downstream clinical tasks: 3D injury detection using VDETR with Vertex Relative Position Encoding, and multi‑label injury classification. For detection, semi‑supervised learning with 2,000 unlabeled volumes and consistency regularization achieves 56.57% validation mAP@0.50 and 45.30% test mAP@0.50 with only 144 labeled training samples, representing a 115% improvement over supervised‑only training. For classification, expanding to 2,244 labeled samples yields 94.07% test accuracy across seven injury categories using only a frozen encoder, demonstrating immediately transferable self‑supervised features. Our results validate that self‑supervised pre‑training combined with semi‑supervised learning effectively addresses label scarcity in medical imaging, enabling robust 3D object detection with limited annotations.
Authors:Joong Ho Kim, Nicholas Thai, Souhardya Saha Dip, Dong Lao, Keith G. Mills
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.
Authors:Rujie Wu, Haozhe Zhao, Hai Ci, Yizhou Wang
Abstract:
Multimodal instruction tuning is often compute‑inefficient because training budgets are spread across large mixed image‑video pools whose utility is highly uneven. We present Goal‑Driven Data Optimization (GDO), a framework that computes six sample descriptors for each candidate and constructs optimized 1× training subsets for different goals. Under a fixed one‑epoch Qwen3‑VL‑8B‑Instruct training and evaluation recipe on 8 H20 GPUs, GDO uses far fewer training samples than the Uni‑10x baseline while converging faster and achieving higher accuracy. Relative to the fixed 512k‑sample Uni‑10x baseline, GDO reaches the Uni‑10x reference after 35.4k samples on MVBench, 26.6k on VideoMME, 27.3k on MLVU, and 34.7k on LVBench, while improving Accuracy by +1.38, +1.67, +3.08, and +0.84 percentage points, respectively. The gains are largest on MVBench and MLVU, while LVBench improves more modestly, consistent with its ultra‑long‑video setting and the mismatch between that benchmark and the short‑video/image‑dominant training pool. Across MinLoss, Diverse, Temp, and Temp+, stronger temporal emphasis yields steadily better long‑video understanding behavior. Overall, GDO provides a goal‑driven data optimization framework that enables faster convergence with fewer training samples under a fixed training protocol. Code is available at https://github.com/rujiewu/GDO.
Authors:Davi Bonetto
Abstract:
State Space Models (SSMs) such as Mamba achieve linear‑time sequence processing through input‑dependent recurrence, but this mechanism introduces a critical safety vulnerability. We show that the spectral radius rho(A‑bar) of the discretized transition operator governs effective memory horizon: when an adversary drives rho toward zero through gradient‑based Hidden State Poisoning, memory collapses from millions of tokens to mere dozens, silently destroying reasoning capacity without triggering output‑level alarms. We prove an Evasion Existence Theorem showing that for any output‑only defense, adversarial inputs exist that simultaneously induce spectral collapse and evade detection, then introduce SpectralGuard, a real‑time monitor that tracks spectral stability across all model layers. SpectralGuard achieves F1=0.961 against non‑adaptive attackers and retains F1=0.842 under the strongest adaptive setting, with sub‑15ms per‑token latency. Causal interventions and cross‑architecture transfer to hybrid SSM‑Attention systems confirm that spectral monitoring provides a principled, deployable safety layer for recurrent foundation models.
Authors:Ping He, Om Khangaonkar, Hamed Pirsiavash, Yikun Bai, Soheil Kolouri
Abstract:
We establish a theoretical link between the recently proposed "drifting" generative dynamics and gradient flows induced by the Sinkhorn divergence. In a particle discretization, the drift field admits a cross‑minus‑self decomposition: an attractive term toward the target distribution and a repulsive/self‑correction term toward the current model, both expressed via one‑sided normalized Gibbs kernels. We show that Sinkhorn divergence yields an analogous cross‑minus‑self structure, but with each term defined by entropic optimal‑transport couplings obtained through two‑sided Sinkhorn scaling (i.e., enforcing both marginals). This provides a precise sense in which drifting acts as a surrogate for a Sinkhorn‑divergence gradient flow, interpolating between one‑sided normalization and full two‑sided Sinkhorn scaling. Crucially, this connection resolves an identifiability gap in prior drifting formulations: leveraging the definiteness of the Sinkhorn divergence, we show that zero drift (equilibrium of the dynamics) implies that the model and target measures match. Experiments show that Sinkhorn drifting reduces sensitivity to kernel temperature and improves one‑step generative quality, trading off additional training time for a more stable optimization, without altering the inference procedure used by drift methods. These theoretical gains translate to strong low‑temperature improvements in practice: on FFHQ‑ALAE at the lowest temperature setting we evaluate, Sinkhorn drifting reduces mean FID from 187.7 to 37.1 and mean latent EMD from 453.3 to 144.4, while on MNIST it preserves full class coverage across the temperature sweep. Project page: https://mint‑vu.github.io/SinkhornDrifting/
Authors:Ziwei Wang, Zhentao He, Xingyi He, Hongbin Wang, Tianwang Jia, Jingwei Luo, Siyang Li, Xiaoqing Chen, Dongrui Wu
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.
Authors:Yining Qian, Lijie Su, Meiling Xu, Xianpeng Wang
Abstract:
Predicting protein secondary structure is essential for understanding protein function and advancing drug discovery. However, the intricate sequence‑structure relationship poses significant challenges for accurate modeling. To address these, we propose MOGP‑MMF, a multi‑objective genetic programming framework that reformulates PSSP as an automated optimization task focused on feature selection and fusion. Specifically, MOGP‑MMF introduces a multi‑view multi‑level representation strategy that integrates evolutionary, semantic, and newly introduced structural views to capture the comprehensive protein folding logic. Leveraging an enriched operator set, the framework evolves both linear and nonlinear fusion functions, effectively capturing high‑order feature interactions while reducing fusion complexity. To resolve the accuracy‑complexity trade‑off, an improved multi‑objective GP algorithm is developed, incorporating a knowledge transfer mechanism that utilizes prior evolutionary experience to guide the population toward global optima. Extensive experiments across seven benchmark datasets demonstrate that MOGP‑MMF surpasses state‑of‑the‑art methods, particularly in Q8 accuracy and structural integrity. Furthermore, MOGP‑MMF generates a diverse set of non‑dominated solutions, offering flexible model selection schemes for various practical application scenarios. The source code is available on GitHub: https://github.com/qian‑ann/MOGP‑MMF/tree/main.
Authors:Terrence J. Lee-St. John, Jordan L. Lawson, Bartlomiej Piechowski-Jozwiak
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.
Authors:Mateusz Pach, Jessica Bader, Quentin Bouniot, Serge Belongie, Zeynep Akata
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.
Authors:Fangfu Liu, Diankun Wu, Jiawei Chi, Yimo Cai, Yi-Hsin Hung, Xumin Yu, Hao Li, Han Hu, Yongming Rao, Yueqi Duan
Abstract:
Humans perceive and understand real‑world spaces through a stream of visual observations. Therefore, the ability to streamingly maintain and update spatial evidence from potentially unbounded video streams is essential for spatial intelligence. The core challenge is not simply longer context windows but how spatial information is selected, organized, and retained over time. In this paper, we propose Spatial‑TTT towards streaming visual‑based spatial intelligence with test‑time training (TTT), which adapts a subset of parameters (fast weights) to capture and organize spatial evidence over long‑horizon scene videos. Specifically, we design a hybrid architecture and adopt large‑chunk updates parallel with sliding‑window attention for efficient spatial video processing. To further promote spatial awareness, we introduce a spatial‑predictive mechanism applied to TTT layers with 3D spatiotemporal convolution, which encourages the model to capture geometric correspondence and temporal continuity across frames. Beyond architecture design, we construct a dataset with dense 3D spatial descriptions, which guides the model to update its fast weights to memorize and organize global 3D spatial signals in a structured manner. Extensive experiments demonstrate that Spatial‑TTT improves long‑horizon spatial understanding and achieves state‑of‑the‑art performance on video spatial benchmarks. Project page: https://liuff19.github.io/Spatial‑TTT.
Authors:Jiacheng Liu, Shengkun Tang, Jiacheng Cui, Dongkuan Xu, Zhiqiang Shen
Abstract:
Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity. We revisit token compression with a joint objective and present BiGain, a training‑free, plug‑and‑play framework that preserves generation quality while improving classification in accelerated diffusion models. Our key insight is frequency separation: mapping feature‑space signals into a frequency‑aware representation disentangles fine detail from global semantics, enabling compression that respects both generative fidelity and discriminative utility. BiGain reflects this principle with two frequency‑aware operators: (1) Laplacian‑gated token merging, which encourages merges among spectrally smooth tokens while discouraging merges of high‑contrast tokens, thereby retaining edges and textures; and (2) Interpolate‑Extrapolate KV Downsampling, which downsamples keys/values via a controllable interextrapolation between nearest and average pooling while keeping queries intact, thereby conserving attention precision. Across DiT‑ and U‑Net‑based backbones and ImageNet‑1K, ImageNet‑100, Oxford‑IIIT Pets, and COCO‑2017, our operators consistently improve the speed‑accuracy trade‑off for diffusion‑based classification, while maintaining or enhancing generation quality under comparable acceleration. For instance, on ImageNet‑1K, with 70% token merging on Stable Diffusion 2.0, BiGain increases classification accuracy by 7.15% while improving FID by 0.34 (1.85%). Our analyses indicate that balanced spectral retention, preserving high‑frequency detail and low/mid‑frequency semantics, is a reliable design rule for token compression in diffusion models. To our knowledge, BiGain is the first framework to jointly study and advance both generation and classification under accelerated diffusion, supporting lower‑cost deployment.
Authors:Yulu Gan, Phillip Isola
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.
Authors:Jae-Won Chung, Jeff J. Ma, Jisang Ahn, Yizhuo Liang, Akshay Jajoo, Myungjin Lee, Mosharaf Chowdhury
Abstract:
Any‑to‑Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input and generate them as output. Serving these models are challenging; different requests with different input and output modalities traverse different paths through the model computation graph, and each component of the model have different scaling characteristics.
We present Cornserve, a distributed serving system for generic Any‑to‑Any models. Cornserve provides a flexible task abstraction for expressing Any‑to‑Any model computation graphs, enabling component disaggregation and independent scaling. The distributed runtime dispatches compute to the data plane via an efficient record‑and‑replay execution model that keeps track of data dependencies, and forwards tensor data between components directly from the producer to the consumer. Built on Kubernetes with approximately 23K new lines of Python, Cornserve supports diverse Any‑to‑Any models and delivers up to 3.81× higher throughput and 5.79× lower tail latency. Cornserve is open‑source, and the demo video is available on YouTube.
Authors:Ming-Hong Chen, Kuan-Chen Pan, You-De Huang, Xi Liu, Ping-Chun Hsieh
Abstract:
Cross‑domain reinforcement learning (CDRL) is meant to improve the data efficiency of RL by leveraging the data samples collected from a source domain to facilitate the learning in a similar target domain. Despite its potential, cross‑domain transfer in RL is known to have two fundamental and intertwined challenges: (i) The source and target domains can have distinct state space or action space, and this makes direct transfer infeasible and thereby requires more sophisticated inter‑domain mappings; (ii) The transferability of a source‑domain model in RL is not easily identifiable a priori, and hence CDRL can be prone to negative effect during transfer. In this paper, we propose to jointly tackle these two challenges through the lens of cross‑domain Bellman consistency and hybrid critic. Specifically, we first introduce the notion of cross‑domain Bellman consistency as a way to measure transferability of a source‑domain model. Then, we propose QAvatar, which combines the Q functions from both the source and target domains with an adaptive hyperparameter‑free weight function. Through this design, we characterize the convergence behavior of QAvatar and show that QAvatar achieves reliable transfer in the sense that it effectively leverages a source‑domain Q function for knowledge transfer to the target domain. Through experiments, we demonstrate that QAvatar achieves favorable transferability across various RL benchmark tasks, including locomotion and robot arm manipulation. Our code is available at https://rl‑bandits‑lab.github.io/Cross‑Domain‑RL/.
Authors:Ping Guo, Tiantian Zhang, Xi Lin, Xiang Li, Zhi-Ri Tang, Qingfu Zhang
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.
Authors:Ilias Aarab
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.
Authors:Xingze Zou, Jing Wang, Yuhua Zheng, Xueyi Chen, Haolei Bai, Lingcheng Kong, Syed A. R. Abu-Bakar, Zhaode Wang, Chengfei Lv, Haoji Hu, Huan Wang
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.
Authors:Fengyuan Yu, Xiaohua Feng, Yuyuan Li, Changwang Zhang, Jun Wang, Chaochao Chen
Abstract:
Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy risks. However, existing methods overlook a critical issue, i.e., the stable learning of a generalized item embedding throughout the federated recommender system training process. Item embedding plays a central role in facilitating knowledge sharing across clients. Yet, under the cross‑device setting, local data distributions exhibit significant heterogeneity and sparsity, exacerbating the difficulty of learning generalized embeddings. These factors make the stable learning of generalized item embeddings both indispensable for effective federated recommendation and inherently difficult to achieve. To fill this gap, we propose a new federated recommendation framework, named Federated Recommendation with Generalized Embedding Learning (FedRecGEL). We reformulate the federated recommendation problem from an item‑centered perspective and cast it as a multi‑task learning problem, aiming to learn generalized embeddings throughout the training procedure. Based on theoretical analysis, we employ sharpness‑aware minimization to address the generalization problem, thereby stabilizing the training process and enhancing recommendation performance. Extensive experiments on four datasets demonstrate the effectiveness of FedRecGEL in significantly improving federated recommendation performance. Our code is available at https://github.com/anonymifish/FedRecGEL.
Authors:Yuxiang Liu, Qiao Liu, Tong Luo, Yanglei Gan, Peng He, Yao LIu
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.
Authors:Ehsan Hoseinzade, Ke Wang
Abstract:
This study addresses the challenge of automatically detecting semantic column types in relational tables, a key task in many real‑world applications. Zero‑shot modeling eliminates the need for user‑provided labeled training data, making it ideal for scenarios where data collection is costly or restricted due to privacy concerns. However, existing zero‑shot models suffer from poor performance when the number of semantic column types is large, limited understanding of tabular structure, and privacy risks arising from dependence on high‑performance closed‑source LLMs. We introduce ZTab, a domain‑based zero‑shot framework that addresses both performance and zero‑shot requirements. Given a domain configuration consisting of a set of predefined semantic types and sample table schemas, ZTab generates pseudo‑tables for the sample schemas and fine‑tunes an annotation LLM on them. ZTab is domain‑based zero‑shot in that it does not depend on user‑specific labeled training data; therefore, no retraining is needed for a test table from a similar domain. We describe three cases of domain‑based zero‑shot. The domain configuration of ZTab provides a trade‑off between the extent of zero‑shot and annotation performance: a "universal domain" that contains all semantic types approaches "pure" zero‑shot, while a "specialized domain" that contains semantic types for a specific application enables better zero‑shot performance within that domain. Source code and datasets are available at https://github.com/hoseinzadeehsan/ZTab
Authors:Teng Xiao, Yige Yuan, Hamish Ivison, Huaisheng Zhu, Faeze Brahman, Nathan Lambert, Pradeep Dasigi, Noah A. Smith, Hannaneh Hajishirzi
Abstract:
This paper introduces MR‑Search, an in‑context meta reinforcement learning (RL) formulation for agentic search with self‑reflection. Instead of optimizing a policy within a single independent episode with sparse rewards, MR‑Search trains a policy that conditions on past episodes and adapts its search strategy across episodes. MR‑Search learns to learn a search strategy with self‑reflection, allowing search agents to improve in‑context exploration at test‑time. Specifically, MR‑Search performs cross‑episode exploration by generating explicit self‑reflections after each episode and leveraging them as additional context to guide subsequent attempts, thereby promoting more effective exploration during test‑time. We further introduce a multi‑turn RL algorithm that estimates a dense relative advantage at the turn level, enabling fine‑grained credit assignment on each episode. Empirical results across various benchmarks demonstrate the advantages of MR‑Search over baselines based RL, showing strong generalization and relative improvements of 9.2% to 19.3% across eight benchmarks. Our code and data are available at https://github.com/tengxiao1/MR‑Search.
Authors:Massimiliano Altieri, Ronan Hamon, Roberto Corizzo, Michelangelo Ceci, Ignacio Sanchez
Abstract:
Network intrusion detection systems play a crucial role in the security strategy employed by organisations to detect and prevent cyberattacks. Such systems usually combine pattern detection signatures with anomaly detection techniques powered by machine learning methods. However, the commonly proposed machine learning methods present drawbacks such as over‑reliance on labeled data and limited generalization capabilities. To address these issues, embedding‑based methods have been introduced to learn representations from network data, such as DNS traffic, mainly due to its large availability, that generalise effectively to many downstream tasks. However, current approaches do not properly consider contextual information among DNS queries. In this paper, we tackle this issue by proposing DNS‑GT, a novel Transformer‑based model that learns embeddings for domain names from sequences of DNS queries. The model is first pre‑trained in a self‑supervised fashion in order to learn the general behavior of DNS activity. Then, it can be finetuned on specific downstream tasks, exploiting interactions with other relevant queries in a given sequence. Our experiments with real‑world DNS data showcase the ability of our method to learn effective domain name representations. A quantitative evaluation on domain name classification and botnet detection tasks shows that our approach achieves better results compared to relevant baselines, creating opportunities for further exploration of large‑scale language models for intrusion detection systems. Our code is available at: https://github.com/m‑altieri/DNS‑GT.
Authors:Zeyuan Guo, Enmao Diao, Cheng Yang, Chuan Shi
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.
Authors:Chandler Smith, Magnus Sesodia, Friedrich Lindenberg, Christian Schroeder de Witt
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
Authors:Tao Zhong, Yixun Hu, Dongzhe Zheng, Aditya Sood, Christine Allen-Blanchette
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/
Authors:Yan-Bo Lin, Jonah Casebeer, Long Mai, Aniruddha Mahapatra, Gedas Bertasius, Nicholas J. Bryan
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.
Authors:Konrad Szafer, Marek Kraft, Dominik Belter
Abstract:
Foundation models for point cloud data have recently grown in capability, often leveraging extensive representation learning from language or vision. In this work, we take a more controlled approach by introducing a lightweight transformer‑based point cloud architecture. In contrast to the heavy reliance on cross‑modal supervision, our model is trained only on 39k point clouds ‑ yet it outperforms several larger foundation models trained on over 200k training samples. Interestingly, our method approaches state‑of‑the‑art results from models that have seen over a million point clouds, images, and text samples, demonstrating the value of a carefully curated training setup and architecture. To ensure rigorous evaluation, we conduct a comprehensive replication study that standardizes the training regime and benchmarks across multiple point cloud architectures. This unified experimental framework isolates the impact of architectural choices, allowing for transparent comparisons and highlighting the benefits of our design and other tokenizer‑free architectures. Our results show that simple backbones can deliver competitive results to more complex or data‑rich strategies. The implementation, including code, pre‑trained models, and training protocols, is available at https://github.com/KonradSzafer/Pointy.
Authors:Prithviraj Tarale, Kiet Chu, Abhishek Varghese, Kai-Chun Liu, Maxwell A. Xu, Mohit Iyyer, Sunghoon I. Lee
Abstract:
Wearable accelerometers enable large‑scale health monitoring, yet learning robust human‑activity representations has been constrained by scarce labeled data. While self‑supervised learning offers a remedy, existing methods treat sensor streams as unstructured time series, overlooking the underlying biological structure of human movement, a factor we argue is critical for effective Human Activity Recognition (HAR). We introduce a novel tokenization strategy grounded in the submovement theory of motor control, which posits that continuous wrist motion is composed of elementary basis functions called submovements. We define our token as the movement segment, a computationally tractable unit of motion composed of a finite sequence of submovements. By pretraining a Transformer encoder via masked reconstruction of these tokens, we shift the learning focus from local waveform morphology to high‑level structural and temporal organization. Pretrained on the NHANES corpus (approximately 28k hours; 11k participants), our representations outperform strong wearable SSL baselines across six subject‑disjoint HAR benchmarks. Code and pretrained weights are available at https://prithvitarale.github.io/biopm‑site/.
Authors:Mohsen Hariri, Michael Hinczewski, Jing Ma, Vipin Chaudhary
Abstract:
Test‑time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking models in this regime remains underexplored. We formalize dense benchmark ranking under test‑time scaling and introduce Scorio, a library that implements statistical ranking methods such as paired‑comparison models, item response theory (IRT) models, voting rules, and graph‑ and spectral‑based methods. Across 20 reasoning models on four Olympiad‑style math benchmarks (AIME'24, AIME'25, HMMT'25, and BrUMO'25; up to N=80 trials), most full‑trial rankings agree closely with the Bayesian gold standard \mathrmBayes_\mathcalU@80 (mean Kendall's τ_b = 0.93‑‑0.95), and 19‑‑34 methods recover exactly the same ordering. In the single‑trial regime, the best methods reach τ_b \approx 0.86. Using greedy decoding as an empirical prior (\mathrmBayes_\mathbfR_0@N) reduces variance at N=1 by 16‑‑52%, but can bias rankings when greedy and stochastic sampling disagree. These results identify reliable ranking methods for both high‑ and low‑budget test‑time scaling. We release Scorio as an open‑source library at https://github.com/mohsenhariri/scorio.
Authors:Rajdeep Pathak, Sayantee Jana
Abstract:
The use of synthetic data has become increasingly popular as a privacy‑preserving alternative to sharing real datasets, especially in sensitive domains such as healthcare, finance, and demography. However, the privacy assurances of synthetic data are not absolute, and remain susceptible to membership inference attacks (MIAs), where adversaries aim to determine whether a specific individual was present in the dataset used to train the generator. In this work, we propose a practical and effective method to quantify membership disclosure risk in tabular synthetic datasets using kernel density estimators (KDEs). Our KDE‑based approach models the distribution of nearest‑neighbour distances between synthetic data and the training records, allowing probabilistic inference of membership and enabling robust evaluation via ROC curves. We propose two attack models: a 'True Distribution Attack', which assumes privileged access to training data, and a more realistic, implementable 'Realistic Attack' that uses auxiliary data without true membership labels. Empirical evaluations across four real‑world datasets and six synthetic data generators demonstrate that our method consistently achieves higher F1 scores and sharper risk characterization than a prior baseline approach, without requiring computationally expensive shadow models. The proposed method provides a practical framework and metric for quantifying membership disclosure risk in synthetic data, which enables data custodians to conduct a post‑generation risk assessment prior to releasing their synthetic datasets for downstream use. The datasets and codes for this study are available at https://github.com/PyCoder913/MIA‑KDE.
Authors:Zegu Zhang, Jian Zhang
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.
Authors:Jinwoo Ahn, Ingyu Seong, Akhil Kedia, Junhan Kim, Hyemi Jang, Kangwook Lee, Yongkweon Jeon
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.
Authors:Xinran Xu, Xiuyi Fan
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.
Authors:Changyi Xiao, Caijun Xu, Yixin Cao
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.
Authors:Yuanbo Hou, Yanru Wu, Qiaoqiao Ren, Shengchen Li, Stephen Roberts, Dick Botteldooren
Abstract:
Environmental sound understanding in computational auditory scene analysis (CASA) is often formulated as an audio‑only recognition problem. This formulation leaves a persistent drawback in multi‑label audio tagging (AT): acoustic similarity can make certain events difficult to separate from waveforms alone. In such cases, disambiguating cues often lie outside the waveform. Geospatial semantic context (GSC), derived from geographic information system data, e.g., points of interest (POI), provides location‑tied environmental priors that can help reduce this ambiguity. A systematic study of this direction is enabled through the proposed geospatial audio tagging (Geo‑AT) task, which conditions multi‑label sound event tagging on GSC alongside audio. To benchmark Geo‑AT, Geo‑ATBench is introduced as a polyphonic audio benchmark with geographical annotations, containing 10.71 hours of audio across 28 event categories; each clip is paired with a GSC representation from 11 semantic context categories. GeoFusion‑AT is proposed as a unified geo‑audio fusion framework that evaluates feature‑, representation‑, and decision‑level fusion on representative audio backbones, with audio‑ and GSC‑only baselines. Results show that incorporating GSC improves AT performance, especially on acoustically confounded labels, indicating geospatial semantics provide effective priors beyond audio alone. A crowdsourced listening study with 10 participants on 579 samples shows that there is no significant difference in performance between models on Geo‑ATBench labels and aggregated human labels, supporting Geo‑ATBench as a human‑aligned benchmark. The Geo‑AT task, benchmark Geo‑ATBench, and reproducible geo‑audio fusion framework GeoFusion‑AT provide a foundation for studying AT with geospatial semantic context within the CASA community. Dataset, code, models are on homepage (https://github.com/WuYanru2002/Geo‑ATBench).
Authors:Ivan Bioli, Mikel Mendibe Abarrategi
Abstract:
We present a JAX implementation of the Self‑Scaled Broyden family of quasi‑Newton methods, fully compatible with JAX and building on the Optimistix~\citerader_optimistix_2024 optimisation library. The implementation includes BFGS, DFP, Broyden and their Self‑Scaled variants(SSBFGS, SSDFP, SSBroyden), together with a Zoom line search satisfying the strong Wolfe conditions. This is a short technical note, not a research paper, as it does not claim any novel contribution; its purpose is to document the implementation and ease the adoption of these optimisers within the JAX community. The code is available at https://github.com/IvanBioli/ssbroyden_optimistix.git.
Authors:Ilseung Park, Eunsik Choi, Jangwhan Ahn, Jooeun Ahn
Abstract:
Human locomotion emerges from high‑dimensional neuromuscular control, making predictive musculoskeletal simulation challenging. We present a physiology‑informed reinforcement‑learning framework that constrains control using muscle synergies. We extracted a low‑dimensional synergy basis from inverse musculoskeletal analyses of a small set of overground walking trials and used it as the action space for a muscle‑driven three‑dimensional model trained across variable speeds, slopes and uneven terrain. The resulting controller generated stable gait from 0.7‑1.8 m/s and on \pm 6^\circ grades and reproduced condition‑dependent modulation of joint angles, joint moments and ground reaction forces. Compared with an unconstrained controller, synergy‑constrained control reduced non‑physiological knee kinematics and kept knee moment profiles within the experimental envelope. Across conditions, simulated vertical ground reaction forces correlated strongly with human measurements, and muscle‑activation timing largely fell within inter‑subject variability. These results show that embedding neurophysiological structure into reinforcement learning can improve biomechanical fidelity and generalization in predictive human locomotion simulation with limited experimental data.
Authors:Simon D. Nguyen, Troy Russo, Kentaro Hoffman, Tyler H. McCormick
Abstract:
Active learning for regression reduces labeling costs by selecting the most informative samples. Improved Greedy Sampling is a prominent method that balances feature‑space diversity and output‑space uncertainty using a static, multiplicative rule. We propose Weighted improved Greedy Sampling (WiGS), which replaces this framework with a dynamic, additive criterion. We formulate weight selection as a reinforcement learning problem, enabling an agent to adapt the exploration‑investigation balance throughout learning. Experiments on 18 benchmark datasets and a synthetic environment show WiGS outperforms iGS and other baseline methods in both accuracy and labeling efficiency, particularly in domains with irregular data density where the baseline's multiplicative rule ignores high‑error samples in dense regions.
Authors:Tongcheng Zhang, Zhanpeng Zhou, Mingze Wang, Andi Han, Wei Huang, Taiji Suzuki, Junchi Yan
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.
Authors:Chen-Chen Zong, Sheng-Jun Huang
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.
Authors:Zhanyi Sun, Shuran Song
Abstract:
We introduce Distribution Contractive Reinforcement Learning (DICE‑RL), a framework that uses reinforcement learning (RL) as a "distribution contraction" operator to refine pretrained generative robot policies. DICE‑RL turns a pretrained behavior prior into a high‑performing "pro" policy by amplifying high‑success behaviors from online feedback. We pretrain a diffusion‑ or flow‑based policy for broad behavioral coverage, then finetune it with a stable, sample‑efficient residual off‑policy RL framework that combines selective behavior regularization with value‑guided action selection. Extensive experiments and analyses show that DICE‑RL reliably improves performance with strong stability and sample efficiency. It enables mastery of complex long‑horizon manipulation skills directly from high‑dimensional pixel inputs, both in simulation and on a real robot. Project website: https://zhanyisun.github.io/dice.rl.2026/.
Authors:Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin, Giovanni Cinà
Abstract:
Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior‑Data Fitted Network (TabPFN), a recent foundation model for tabular data, has been shown capable of generating high‑quality synthetic tabular data. However, TabPFN is autoregressive: features are generated sequentially by conditioning on the previous ones, depending on the order in which they appear in the input data. We demonstrate that when the feature order conflicts with causal structure, the model produces spurious correlations that impair its ability to generate synthetic data and preserve causal effects. We address this limitation by integrating causal structure into TabPFN's generation process through two complementary approaches: Directed Acyclic Graph (DAG)‑aware conditioning, which samples each variable given its causal parents, and a Completed Partially Directed Acyclic Graph (CPDAG)‑based strategy for scenarios with partial causal knowledge. We evaluate these approaches on controlled benchmarks and six CSuite datasets, assessing structural fidelity, distributional alignment, privacy preservation, and Average Treatment Effect (ATE) preservation. Across most settings, DAG‑aware conditioning improves the quality and stability of synthetic data relative to vanilla TabPFN. The CPDAG‑based strategy shows moderate improvements, with effectiveness depending on the number of oriented edges. These results indicate that injecting causal structure into autoregressive generation enhances the reliability of synthetic tabular data.
Authors:Xiaoyan Zhang, Jiangpeng He
Abstract:
Class‑incremental learning (CIL) aims to acquire new classes over time while retaining prior knowledge, yet most setups and methods assume balanced task streams. In practice, the number of classes per task often varies significantly. We refer to this as step imbalance, where large tasks that contain more classes dominate learning and small tasks inject unstable updates. Existing CIL methods assume balanced tasks and therefore treat all tasks uniformly, producing imbalanced updates that degrade overall learning performance. To address this challenge, we propose One‑A, a unified and imbalance‑aware framework that incrementally merges task updates into a single adapter, maintaining constant inference cost. One‑A performs asymmetric subspace alignment to preserve dominant subspaces learned from large tasks while constraining low‑information updates within them. An information‑adaptive weighting balances the contribution between base and new adapters, and a directional gating mechanism selectively fuses updates along each singular direction, maintaining stability in head directions and plasticity in tail ones. Across multiple benchmarks and step‑imbalanced streams, One‑A achieves competitive accuracy with significantly low inference overhead, showing that a single, asymmetrically fused adapter can remain both adaptive to dynamic task sizes and efficient at deployment.
Authors:Ortal Reshef, Ofer Glassman, Or Zuk, Yariv Aizenbud, Boaz Nadler, Ariel Jaffe
Abstract:
Recovering a tree that represents the evolutionary history of a group of species is a key task in phylogenetics. Performing this task using sequence data from multiple genetic markers poses two key challenges. The first is the discordance between the evolutionary history of individual genes and that of the species. The second challenge is computational, as contemporary studies involve thousands of species. Here we present SDSR, a scalable divide‑and‑conquer approach for species tree reconstruction based on spectral graph theory. The algorithm recursively partitions the species into subsets until their sizes are below a given threshold. The trees of these subsets are reconstructed by a user‑chosen species tree algorithm. Finally, these subtrees are merged to form the full tree. On the theoretical front, we derive recovery guarantees for SDSR, under the multispecies coalescent (MSC) model. We also perform a runtime complexity analysis. We show that SDSR, when combined with a species tree reconstruction algorithm as a subroutine, yields substantial runtime savings as compared to applying the same algorithm on the full data. Empirically, we evaluate SDSR on synthetic benchmark datasets with incomplete lineage sorting and horizontal gene transfer. In accordance with our theoretical analysis, the simulations show that combining SDSR with common species tree methods, such as CA‑ML or ASTRAL, yields up to 10‑fold faster runtimes. In addition, SDSR achieves a comparable tree reconstruction accuracy to that obtained by applying these methods on the full data.
Authors:Chujie Chang, Shoko Miyauchi, Ken'ichi Morooka, Ryo Kurazume, Oscar Martinez Mozos
Abstract:
Cardiac magnetic resonance (CMR) imaging is widely used to visualise cardiac motion and diagnose heart disease. However, standard CMR imaging requires patients to lie still in a confined space inside a loud machine for 40‑60 min, which increases patient discomfort. In addition, shorter scan times decrease either or both the temporal and spatial resolutions of cardiac motion, and thus, the diagnostic accuracy of the procedure. Of these, we focus on reduced temporal resolution and propose a neural network called FusionNet to obtain four‑dimensional (4D) cardiac motion with high temporal resolution from CMR images captured in a short period of time. The model estimates intermediate 3D heart shapes based on adjacent shapes. The results of an experimental evaluation of the proposed FusionNet model showed that it achieved a performance of over 0.897 in terms of the Dice coefficient, confirming that it can recover shapes more precisely than existing methods. This code is available at: https://github.com/smiyauchi199/FusionNet.git
Authors:Deyi Li, Zijun Yao, Qi Xu, Muxuan Liang, Lingyao Li, Zijian Xu, Mei Liu
Abstract:
The growing adoption of electronic health record (EHR) systems has provided unprecedented opportunities for predictive modeling to guide clinical decision making. Structured EHRs contain longitudinal observations of patients across hospital visits, where each visit is represented by a set of medical codes. While sequence‑based, graph‑based, and graph‑enhanced sequence approaches have been developed to capture rich code interactions over time or within the same visits, they often overlook the inherent heterogeneous roles of medical codes arising from distinct clinical characteristics and contexts. To this end, in this study we propose the Disease Trajectory‑aware Transformer for EHR (DT‑BEHRT), a graph‑enhanced sequential architecture that disentangles disease trajectories by explicitly modeling diagnosis‑centric interactions within organ systems and capturing asynchronous progression patterns. To further enhance the representation robustness, we design a tailored pre‑training methodology that combines trajectory‑level code masking with ontology‑informed ancestor prediction, promoting semantic alignment across multiple modeling modules. Extensive experiments on multiple benchmark datasets demonstrate that DT‑BEHRT achieves strong predictive performance and provides interpretable patient representations that align with clinicians' disease‑centered reasoning. The source code is publicly accessible at https://github.com/GatorAIM/DT‑BEHRT.git.
Authors:Yinjie Wang, Xuyang Chen, Xiaolong Jin, Mengdi Wang, Ling Yang
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.
Authors:Sofia Maria Lo Cicero Vaina, Artem Chumachenko, Max Ryabinin
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.
Authors:Sijia Cui, Pengyu Cheng, Jiajun Song, Yongbo Gai, Guojun Zhang, Zhechao Yu, Jianhe Lin, Xiaoxi Jiang, Guanjun Jiang
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.
Authors:Yuze Dong, Jinsong Wu
Abstract:
Time‑series forecasting often faces challenges from non‑stationarity, particularly distributional drift, where the data distribution evolves over time. This dynamic behavior can undermine the effectiveness of adaptive optimizers, such as Adam, which are typically designed for stationary objectives. In this paper, we revisit Adam in the context of non‑stationary forecasting and identify that its second‑order bias correction limits responsiveness to shifting loss landscapes. To address this, we propose TS_Adam, a lightweight variant that removes the second‑order correction from the learning rate computation. This simple modification improves adaptability to distributional drift while preserving the optimizer core structure and requiring no additional hyperparameters. TS_Adam integrates easily into existing models and consistently improves performance across long‑ and short‑term forecasting tasks. On the ETT datasets with the MICN model, it achieves an average reduction of 12.8% in MSE and 5.7% in MAE compared to Adam. These results underscore the practicality and versatility of TS_Adam as an effective optimization strategy for real‑world forecasting scenarios involving non‑stationary data. Code is available at: https://github.com/DD‑459‑1/TS_Adam.
Authors:Xiaolong Han, Zehong Wang, Bo Zhao, Binchi Zhang, Jundong Li, Damian Borth, Rose Yu, Haggai Maron, Yanfang Ye, Lu Yin, Ferrante Neri
Abstract:
Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances show that the set of all possible weight values (weight space) itself contains rich structure: pretrained models form organized distributions, exhibit symmetries, and can be embedded, compared, or even generated. Understanding such structures has tremendous impact on how neural networks are analyzed and compared, and on how knowledge is transferred across models, beyond individual training instances. This emerging research direction, which we refer to as Weight Space Learning (WSL), treats neural weights as a meaningful domain for analysis and modeling. This survey provides the first unified taxonomy of WSL. We categorize existing methods into three core dimensions: Weight Space Understanding (WSU), which studies the geometry and symmetries of weights; Weight Space Representation (WSR), which learns embeddings over model weights; and Weight Space Generation (WSG), which synthesizes new weights through hypernetworks or generative models. We further show how these developments enable practical applications, including model retrieval, continual and federated learning, neural architecture search, and data‑free reconstruction. By consolidating fragmented progress under a coherent framework, this survey highlights weight space as a learnable, structured domain with growing impact across model analysis, transferring, and weight generation. We release an accompanying resource at https://github.com/Zehong‑Wang/Awesome‑Weight‑Space‑Learning.
Authors:Qitong Sun, Jun Han, Tianlin Li, Zhe Tang, Sheng Chen, Fei Yang, Aishan Liu, Xianglong Liu, Yang Liu
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/.
Authors:Ammar Daskin
Abstract:
Quantum machine learning models based on parameterized circuits can be viewed as Fourier series approximators. However, they often struggle to learn functions with multiple frequency components, particularly high‑frequency or non‑dominant ones; a phenomenon we term the quantum Fourier parameterization bias. Inspired by recent advances in classical Fourier neural operators (FNOs), we adapt the multi‑stage residual learning idea to the quantum domain, iteratively training additional quantum modules on the residuals of previous stages. We evaluate our method on a synthetic benchmark composed of spatially localized frequency components with diverse envelope shapes (Gaussian, Lorentzian, triangular). Systematic experiments show that the number of qubits, the encoding scheme, and residual learning are all crucial for resolving multiple frequencies; residual learning alone can improve test MSE significantly over a single‑stage baseline trained for the same total number of epochs. Our work provides a practical framework for enhancing the spectral expressivity of quantum models and offers new insights into their frequency‑learning behavior.
Authors:Tianyu Pang, Yujie Fang, Zihang Liu, Shenyang Deng, Lei Hsiung, Shuhua Yu, Yaoqing Yang
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.
Authors:Dan Lee, Seungwook Han, Akarsh Kumar, Pulkit Agrawal
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.
Authors:Yunzhou Song, Long Le, Yong-Hyun Park, Jie Wang, Junyao Shi, Lingjie Liu, Jiatao Gu, Eric Eaton, Dinesh Jayaraman, Kostas Daniilidis
Abstract:
Vision‑language‑action(VLA) models have shown great promise as generalist policies for a large range of relatively simple tasks. However, they demonstrate limited performance on more complex tasks, such as those requiring complex spatial or semantic understanding, manipulation in clutter, or precise manipulation. We propose OMNIGUIDE, a flexible framework that improves VLA performance on such tasks by leveraging arbitrary sources of guidance, such as 3D foundation models, semantic‑reasoning VLMs, and human pose models. We show how many kinds of guidance can be naturally expressed as differentiable energy functions with task‑specific attractors and repellers located in 3D space, that influence the sampling of VLA actions. In this way, OMNIGUIDE enables guidance sources with complementary task‑relevant strengths to improve a VLA model's performance on challenging tasks. Extensive experiments in both simulation and real‑world environments, across diverse sources of guidance, demonstrate that OMNIGUIDE enhances the performance of state‑of‑the‑art generalist policies (e.g., π_0.5, GR00T N1.6) significantly across success and safety rates. Critically, our unified framework matches or surpasses the performance of prior methods designed to incorporate specific sources of guidance into VLA policies. Project Page: \hrefhttps://omniguide.github.io/this \; url
Authors:Eric Roginek, Jingyan Xu, D. Frank. Hsu
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
Authors:Shubham Kumar Singh
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
Authors:Izzat Alsmadi, Anas Alsobeh
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.
Authors:Shuhuai Li, Jianghao Lin, Dongdong Ge, Yinyu Ye
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 .
Authors:Lucas Prieto, Edward Stevinson, Melih Barsbey, Tolga Birdal, Pedro A. M. Mediano
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.
Authors:Fredrik K. Gustafsson, Xiao Gu, Mattia Carletti, Patitapaban Palo, David W. Eyre, David A. Clifton
Abstract:
Recent biosignal foundation models (FMs) have demonstrated promising performance across diverse clinical prediction tasks, yet systematic evaluation on long‑duration multimodal data remains limited. We introduce SignalMC‑MED, a benchmark for evaluating biosignal FMs on synchronized single‑lead electrocardiogram (ECG) and photoplethysmogram (PPG) data. Derived from the MC‑MED dataset, SignalMC‑MED comprises 22,256 visits with 10‑minute overlapping ECG and PPG signals, and includes 20 clinically relevant tasks spanning prediction of demographics, emergency department disposition, laboratory value regression, and detection of prior ICD‑10 diagnoses. Using this benchmark, we perform a systematic evaluation of representative time‑series and biosignal FMs across ECG‑only, PPG‑only, and ECG + PPG settings. We find that domain‑specific biosignal FMs consistently outperform general time‑series models, and that multimodal ECG + PPG fusion yields robust improvements over unimodal inputs. Moreover, using the full 10‑minute signal consistently outperforms shorter segments, and larger model variants do not reliably outperform smaller ones. Hand‑crafted ECG domain features provide a strong baseline and offer complementary value when combined with learned FM representations. Together, these results establish SignalMC‑MED as a standardized benchmark and provide practical guidance for evaluating and deploying biosignal FMs.
Authors:Boya Zhang, Shuaijie Yin, Huiwen Zhu, Xing He
Abstract:
Mining time‑frequency features is critical for time series forecasting. Existing research has predominantly focused on modeling low‑frequency patterns, where most time series energy is concentrated. The overlooking of mid to high frequency continues to limit further performance gains in deep learning models. We propose FreqCycle, a novel framework integrating: (i) a Filter‑Enhanced Cycle Forecasting (FECF) module to extract low‑frequency features by explicitly learning shared periodic patterns in the time domain, and (ii) a Segmented Frequency‑domain Pattern Learning (SFPL) module to enhance mid to high frequency energy proportion via learnable filters and adaptive weighting. Furthermore, time series data often exhibit coupled multi‑periodicity, such as intertwined weekly and daily cycles. To address coupled multi‑periodicity as well as long lookback window challenges, we extend FreqCycle hierarchically into MFreqCycle, which decouples nested periodic features through cross‑scale interactions. Extensive experiments on seven diverse domain benchmarks demonstrate that FreqCycle achieves state‑of‑the‑art accuracy while maintaining faster inference speeds, striking an optimal balance between performance and efficiency.
Authors:Elisabeth Sommer James, Asger Hobolth, Marta Pelizzola
Abstract:
Non‑negative matrix factorisation (NMF) is a widely used tool for unsupervised learning and feature extraction, with applications ranging from genomics to text analysis and signal processing. Standard formulations of NMF are typically derived under Gaussian or Poisson noise assumptions, which may be inadequate for data exhibiting overdispersion or other complex mean‑variance relationships. In this paper, we develop a unified framework for both traditional and convex NMF under a broad class of distributional assumptions, including Negative Binomial and Tweedie models, where the connection between the Tweedie and the β‑divergence is also highlighted. Using a Majorize‑Minimisation approach, we derive multiplicative update rules for all considered models, and novel updates for convex NMF with Poisson and Negative Binomial cost functions. We provide a unified implementation of all considered models, including the first implementations of several convex NMF models. Empirical evaluations on mutational and word count data demonstrate that the choice of noise model critically affects model fit and feature recovery, and that convex NMF can provide an efficient and robust alternative to traditional NMF in scenarios where the number of classes is large. The code for our proposed updates is available in the R package nmfgenr and can be found at https://github.com/MartaPelizzola/nmfgenr.
Authors:Cosmo Santoni
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.
Authors:Luxi Lin, Zhihang Lin, Zhanpeng Zeng, Yuhao Chen, Qingyu Zhang, Jixiang Luo, Xuelong Li, Rongrong Ji
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.
Authors:Taesung Kwon, Lorenzo Bianchi, Lennart Wittke, Felix Watine, Fabio Carrara, Jong Chul Ye, Romann Weber, Vinicius Azevedo
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.
Authors:MoonJeong Park, Seungbeom Lee, Kyungmin Kim, Jaeseung Heo, Seunghyuk Cho, Shouheng Li, Sangdon Park, Dongwoo Kim
Abstract:
Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In this work, we establish new representation‑based generalization bounds in a distribution‑free transductive setting, where learned representations are dependent, and test features are accessible during training. We derive global and class‑wise bounds via optimal transport, expressed in terms of Wasserstein distances between encoded feature distributions. We demonstrate that our bounds are efficiently computable and strongly correlate with empirical generalization in graph node classification, improving upon classical complexity measures. Additionally, our analysis reveals how the GNN aggregation process transforms the representation distributions, inducing a trade‑off between intra‑class concentration and inter‑class separation. This yields depth‑dependent characterizations that capture the non‑monotonic relationship between depth and generalization error observed in practice. The code is available at https://github.com/ml‑postech/Transductive‑OT‑Gen‑Bound.
Authors:Runyao Yu, Viviana Kleine, Philipp Gromotka, Thomas Rudolf, Adrian Eisenmann, Gautham Ram Chandra Mouli, Peter Palensky, Jochen L. Cremer
Abstract:
Batteries with silicon‑graphite‑based anodes, which offer higher energy density and improved charging performance, introduce pronounced voltage hysteresis, making state‑of‑charge (SoC) estimation particularly challenging. Existing approaches to modeling hysteresis rely on exhaustive high‑fidelity tests or focus on conventional graphite‑based lithium‑ion batteries, without considering uncertainty quantification or computational constraints. This work introduces a data‑driven approach for probabilistic hysteresis factor prediction, with a particular emphasis on applications involving silicon‑graphite anode‑based batteries. A data harmonization framework is proposed to standardize heterogeneous driving cycles across varying operating conditions. Statistical learning and deep learning models are applied to assess performance in predicting the hysteresis factor with uncertainties while considering computational efficiency. Extensive experiments are conducted to evaluate the generalizability of the optimal model configuration in unseen vehicle models through retraining, zero‑shot prediction, fine‑tuning, and joint training. By addressing key challenges in SoC estimation, this research facilitates the adoption of advanced battery technologies. A summary page is available at: https://runyao‑yu.github.io/Porsche_Hysteresis_Factor_Prediction/
Authors:Wei Feng, Jingbo Zhang, Qiong Wu, Pingyi Fan, Qiang Fan
Abstract:
To support latency‑sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (RIS)‑aided semantic‑aware Vehicle Edge Computing (VEC) framework. This approach integrates RIS to optimize wireless connectivity and semantic communication to minimize latency by transmitting semantic features. We formulate a comprehensive joint optimization problem by optimizing offloading ratios, the number of semantic symbols, and RIS phase shifts. Considering the problem's high dimensionality and non‑convexity, we propose a two‑tier hybrid scheme that employs Proximal Policy Optimization (PPO) for discrete decision‑making and Linear Programming (LP) for offloading optimization. The simulation results have validated the proposed framework's superiority over existing methods. Specifically, the proposed PPO‑based hybrid optimization scheme reduces the average end‑to‑end latency by approximately 40% to 50% compared to Genetic Algorithm (GA) and Quantum‑behaved Particle Swarm Optimization (QPSO). Moreover, the system demonstrates strong scalability by maintaining low latency even in congested scenarios with up to 30 vehicles.
Authors:Pranav Mantini, Shishir K. Shah
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
Authors:Cornelius Emde, Alexander Rubinstein, Anmol Goel, Ahmed Heakl, Sangdoo Yun, Seong Joon Oh, Martin Gubri
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.
Authors:Azul Garza, Renée Rosillo, Rodrigo Mendoza-Smith, David Salinas, Andrew Robert Williams, Arjun Ashok, Mononito Goswami, José Martín Juárez
Abstract:
Recent advances in time‑series forecasting increasingly rely on pre‑trained foundation‑style models. While these models often claim broad generalization, existing evaluation protocols provide limited evidence. Indeed, most current benchmarks use static train‑test splits that can easily lead to contamination as foundation models can inadvertently train on test data or perform model selection using test scores, which can inflate performance. We introduce Impermanent, a live benchmark that evaluates forecasting models under open‑world temporal change by scoring forecasts sequentially over time on continuously updated data streams, enabling the study of temporal robustness, distributional shift, and performance stability rather than one‑off accuracy on a frozen test set. Impermanent is instantiated on GitHub open‑source activity, providing a naturally live and highly non‑stationary dataset shaped by releases, shifting contributor behavior, platform/tooling changes, and external events. We focus on the top 400 repositories by star count and construct time series from issues opened, pull requests opened, push events, and new stargazers, evaluated over a rolling window with daily updates, alongside standardized protocols and leaderboards for reproducible, ongoing comparison. By shifting evaluation from static accuracy to sustained performance, Impermanent takes a concrete step toward assessing when and whether foundation‑level generalization in time‑series forecasting can be meaningfully claimed. Code and a live dashboard are available at https://github.com/TimeCopilot/impermanent and https://impermanent.timecopilot.dev.
Authors:Weize Liu, Minghui Liu, Sy-Tuyen Ho, Souradip Chakraborty, Xiyao Wang, Furong Huang
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.
Authors:Michelle Espranita Liman, Özgün Turgut, Alexander Müller, Eimo Martens, Daniel Rueckert, Philip Müller
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.
Authors:Mina Jamshidi Idaji, Julius Hense, Tom Neuhäuser, Augustin Krause, Yanqing Luo, Oliver Eberle, Thomas Schnake, Laure Ciernik, Farnoush Rezaei Jafari, Reza Vahidimajd, Jonas Dippel, Christoph Walz, Frederick Klauschen, Andreas Mock, Klaus-Robert Müller
Abstract:
Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated into slide‑level predictions. Heatmaps are widely used to validate MIL models and to discover tissue biomarkers. Yet, the validity of these heatmaps has barely been investigated. In this work, we introduce a general framework for evaluating the quality of MIL heatmaps without requiring additional labels. We conduct a large‑scale benchmark experiment to assess six explanation methods across histopathology task types (classification, regression, survival), MIL model architectures (Attention‑, Transformer‑, Mamba‑based), and patch encoder backbones (UNI2, Virchow2). Our results show that explanation quality mostly depends on MIL model architecture and task type, with perturbation ("Single"), layer‑wise relevance propagation (LRP), and integrated gradients (IG) consistently outperforming attention‑based and gradient‑based saliency heatmaps, which often fail to reflect model decision mechanisms. We further demonstrate the advanced capabilities of the best‑performing explanation methods: (i) We provide a proof‑of‑concept that MIL heatmaps of a bulk gene expression prediction model can be correlated with spatial transcriptomics for biological validation, and (ii) showcase the discovery of distinct model strategies for predicting human papillomavirus (HPV) infection from head and neck cancer slides. Our work highlights the importance of validating MIL heatmaps and establishes that improved explainability can enable more reliable model validation and yield biological insights, making a case for a broader adoption of explainable AI in digital pathology. Our code is provided in a public GitHub repository: https://github.com/bifold‑pathomics/xMIL/tree/xmil‑journal
Authors:Yehonatan Elisha, Oren Barkan, Noam Koenigstein
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.
Authors:Yilin Wen, Yi Guo, Bo Zhao, Wei Qi, Zechun Hu, Colin Jones, Jian Sun
Abstract:
Real‑world optimization problems are often constrained by complex physical laws that limit computational scalability. These constraints are inherently tied to complex regions, and thus learning models that incorporate physical and geometric knowledge, i.e., physics‑informed machine learning (PIML), offer a promising pathway for efficient solution. Here, we introduce PolyFormer, which opens a new direction for PIML in prescriptive optimization tasks, where physical and geometric knowledge is not merely used to regularize learning models, but to simplify the problems themselves. PolyFormer captures geometric structures behind constraints and transforms them into efficient polytopic reformulations, thereby decoupling problem complexity from solution difficulty and enabling off‑the‑shelf optimization solvers to efficiently produce feasible solutions with acceptable optimality loss. Through evaluations across three important problems (large‑scale resource aggregation, network‑constrained optimization, and optimization under uncertainty), PolyFormer achieves computational speedups up to 6,400‑fold and memory reductions up to 99.87%, while maintaining solution quality competitive with or superior to state‑of‑the‑art methods. These results demonstrate that PolyFormer provides an efficient and reliable solution for scalable constrained optimization, expanding the scope of PIML to prescriptive tasks in scientific discovery and engineering applications.
Authors:Michael Kösel, Marcel Schreiber, Michael Ulrich, Claudius Gläser, Klaus Dietmayer
Abstract:
LiDAR‑based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for objects not belonging to known categories, posing significant safety risks. This is caused by so‑called out‑of‑distribution (OOD) objects, which were not part of the training data, resulting in incorrect predictions. To address this challenge, we propose ALOOD (Aligned LiDAR representations for Out‑Of‑Distribution Detection), a novel approach that incorporates language representations from a vision‑language model (VLM). By aligning the object features from the object detector to the feature space of the VLM, we can treat the detection of OOD objects as a zero‑shot classification task. We demonstrate competitive performance on the nuScenes OOD benchmark, establishing a novel approach to OOD object detection in LiDAR using language representations. The source code is available at https://github.com/uulm‑mrm/mmood3d.
Authors:Lukas König, Manuel Kuhn, David Kappel, Anand Subramoney
Abstract:
Existing frameworks for gradient‑based training of spiking neural networks face a trade‑off: discrete‑time methods using surrogate gradients support arbitrary neuron models but introduce gradient bias and constrain spike‑time resolution, while continuous‑time methods that compute exact gradients require analytical expressions for spike times and state evolution, restricting them to simple neuron types such as Leaky Integrate and Fire (LIF). We introduce the Eventax framework, which resolves this trade‑off by combining differentiable numerical ODE solvers with event‑based spike handling. Built in JAX, our frame‑work uses Diffrax ODE‑solvers to compute gradients that are exact with respect to the forward simulation for any neuron model defined by ODEs . It also provides a simple API where users can specify just the neuron dynamics, spike conditions, and reset rules. Eventax prioritises modelling flexibility, supporting a wide range of neuron models, loss functions, and network architectures, which can be easily extended. We demonstrate Eventax on multiple benchmarks, including Yin‑Yang and MNIST, using diverse neuron models such as Leaky Integrate‑and‑fire (LIF), Quadratic Integrate‑and‑fire (QIF), Exponential integrate‑and‑fire (EIF), Izhikevich and Event‑based Gated Recurrent Unit (EGRU) with both time‑to‑first‑spike and state‑based loss functions, demonstrating its utility for prototyping and testing event‑based architectures trained with exact gradients. We also demonstrate the application of this framework for more complex neuron types by implementing a multi‑compartment neuron that uses a model of dendritic spikes in human layer 2/3 cortical Pyramidal neurons for computation. Code available at https://github.com/efficient‑scalable‑machine‑learning/eventax.
Authors:Divake Kumar, Sina Tayebati, Devashri Naik, Patrick Poggi, Amanda Sofie Rios, Nilesh Ahuja, Amit Ranjan Trivedi
Abstract:
Most uncertainty‑aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses. This aggregation obscures whether uncertainty arises from corrupted observations or from mismatch between the learned model and the true system dynamics. As a result, corrective actions may be applied to the wrong component of the closed loop, degrading performance relative to leaving the policy unchanged. We introduce a lightweight post hoc framework that decomposes uncertainty into aleatoric and epistemic components and uses these signals to regulate system responses at inference time. Aleatoric uncertainty is estimated from deviations in the observation distribution using a Mahalanobis density model, while epistemic uncertainty is detected using a noise robust forward dynamics ensemble that isolates model mismatch from measurement corruption. The two signals remain empirically near orthogonal during closed loop execution and enable type specific responses. High aleatoric uncertainty triggers observation recovery, while high epistemic uncertainty moderates control actions. The same signals also regulate adaptive perception by guiding model capacity selection during tracking inference. Experiments demonstrate consistent improvements across both control and perception tasks. In robotic manipulation, the decomposed controller improves task success from 59.4% to 80.4% under compound perturbations and outperforms a combined uncertainty baseline by up to 21.0%. In adaptive tracking inference on MOT17, uncertainty‑guided model selection reduces average compute by 58.2% relative to a fixed high capacity detector while preserving detection quality within 0.4%. Code and demo videos are available at https://divake.github.io/uncertainty‑decomposition/.
Authors:Zhongjian Qiao, Jiafei Lyu, Boxiang Lyu, Yao Shu, Siyang Gao, Shuang Qiu
Abstract:
Model‑based offline reinforcement learning (RL) aims to enhance offline RL with a dynamics model that facilitates policy exploration. However, model exploitation could occur due to inevitable model errors, degrading algorithm performance. Adversarial model learning offers a theoretical framework to mitigate model exploitation by solving a maximin formulation. Within such a paradigm, RAMBO~\citeprigter2022rambo has emerged as a representative and most popular method that provides a practical implementation with model gradient. However, we empirically reveal that severe Q‑value underestimation and gradient explosion can occur in RAMBO with only slight hyperparameter tuning, suggesting that it tends to be overly conservative and suffers from unstable model updates. To address these issues, we propose RObust value‑aware Model learning with Implicitly differentiable adaptive weighting (ROMI). Instead of updating the dynamics model with model gradient, ROMI introduces a novel robust value‑aware model learning approach. This approach requires the dynamics model to predict future states with values close to the minimum Q‑value within a scale‑adjustable state uncertainty set, enabling controllable conservatism and stable model updates. To further improve out‑of‑distribution (OOD) generalization during multi‑step rollouts, we propose implicitly differentiable adaptive weighting, a bi‑level optimization scheme that adaptively achieves dynamics‑ and value‑aware model learning. Empirical results on D4RL and NeoRL datasets show that ROMI significantly outperforms RAMBO and achieves competitive or superior performance compared to other state‑of‑the‑art methods on datasets where RAMBO typically underperforms. Code is available at https://github.com/zq2r/ROMI.git.
Authors:Irene Iele, Floriano Caprio, Paolo Soda, Matteo Tortora
Abstract:
Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi‑horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum‑classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non‑linear feature mixer, modeling cross‑variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave‑One‑Patient‑Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small‑cohort clinical settings. The code is available at https://github.com/arco‑group/quantum‑ml.
Authors:Yusong Wang, Chuang Yang, Jiawei Wang, Xiaohang Xu, Jiayi Xu, Dongyuan Li, Chuan Xiao, Renhe Jiang
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.
Authors:Darius Catrina, Christian Bepler, Samuel Sledzieski, Rohit Singh
Abstract:
Unlike the predictable scaling laws in natural language processing and computer vision, protein language models (PLMs) scale poorly: for many tasks, models within the same family plateau or even decrease in performance, with mid‑sized models often outperforming the largest in the family. We introduce Reverse Distillation, a principled framework that decomposes large PLM representations into orthogonal subspaces guided by smaller models of the same family. The resulting embeddings have a nested, Matryoshka‑style structure: the first k dimensions of a larger model's embedding are exactly the representation from the smaller model. This ensures that larger reverse‑distilled models consistently outperform smaller ones. A motivating intuition is that smaller models, constrained by capacity, preferentially encode broadly‑shared protein features. Reverse distillation isolates these shared features and orthogonally extracts additional contributions from larger models, preventing interference between the two. On ProteinGym benchmarks, reverse‑distilled ESM‑2 variants outperform their respective baselines at the same embedding dimensionality, with the reverse‑distilled 15 billion parameter model achieving the strongest performance. Our framework is generalizable to any model family where scaling challenges persist. Code and trained models are available at https://github.com/rohitsinghlab/plm_reverse_distillation.
Authors:Najeeb Jebreel, Mona Khalil, David Sánchez, Josep Domingo-Ferrer
Abstract:
Membership inference attacks (MIAs) have become the standard tool for evaluating privacy leakage in machine learning (ML). Among them, the Likelihood‑Ratio Attack (LiRA) is widely regarded as the state of the art when sufficient shadow models are available. However, prior evaluations have often overstated the effectiveness of LiRA by attacking models overconfident on their training samples, calibrating thresholds on target data, assuming balanced membership priors, and/or overlooking attack reproducibility. We re‑evaluate LiRA under a realistic protocol that (i) trains models using anti‑overfitting (AOF) and transfer learning (TL), when applicable, to reduce overconfidence as in production models; (ii) calibrates decision thresholds using shadow models and data rather than target data; (iii) measures positive predictive value (PPV, or precision) under shadow‑based thresholds and skewed membership priors (pi <= 10%); and (iv) quantifies per‑sample membership reproducibility across different seeds and training variations. We find that AOF significantly weakens LiRA, while TL further reduces attack effectiveness while improving model accuracy. Under shadow‑based thresholds and skewed priors, LiRA's PPV often drops substantially, especially under AOF or AOF+TL. We also find that thresholded vulnerable sets at extremely low FPR show poor reproducibility across runs, while likelihood‑ratio rankings are more stable. These results suggest that LiRA, and likely weaker MIAs, are less effective than previously suggested under realistic conditions, and that reliable privacy auditing requires evaluation protocols that reflect practical training practices, feasible attacker assumptions, and reproducibility considerations. Code is available at https://github.com/najeebjebreel/lira_analysis.
Authors:Ramin Akbari, Milad Afshari, Vishnu Naresh Boddeti
Abstract:
Concept erasure aims to remove unwanted attributes, such as social or demographic factors, from learned representations, while preserving their task‑relevant utility. While the goal of concept erasure is protection against all adversaries, existing methods remain vulnerable to nonlinear ones. This vulnerability arises from their failure to fully capture the complex, nonlinear statistical dependencies between learned representations and unwanted attributes. Moreover, although the existence of a trade‑off between utility and erasure is expected, its progression during the erasure process, i.e., the cost of erasure, remains unstudied. In this work, we introduce Obliviator, a post‑hoc erasure method designed to fully capture nonlinear statistical dependencies. We formulate erasure from a functional perspective, leading to an optimization problem involving a composition of kernels that lacks a closed‑form solution. Instead of solving this problem in a single shot, we adopt an iterative approach that gradually morphs the feature space to achieve a more utility‑preserving erasure. Unlike prior methods, Obliviator guards unwanted attribute against nonlinear adversaries. Our gradual approach quantifies the cost of nonlinear guardedness and reveals the dynamics between attribute protection and utility‑preservation over the course of erasure. The utility‑erasure trade‑off curves obtained by Obliviator outperform the baselines and demonstrate its strong generalizability: its erasure becomes more utility‑preserving when applied to the better‑disentangled representations learned by more capable models.
Authors:Mohammad Saeid, Amir Salarpour, Pedram MohajerAnsari, Mert D. Pesé
Abstract:
We present SLNet, a lightweight backbone for 3D point cloud recognition designed to achieve strong performance without the computational cost of many recent attention, graph, and deep MLP based models. The model is built on two simple ideas: NAPE (Nonparametric Adaptive Point Embedding), which captures spatial structure using a combination of Gaussian RBF and cosine bases with input adaptive bandwidth and blending, and GMU (Geometric Modulation Unit), a per channel affine modulator that adds only 2D learnable parameters. These components are used within a four stage hierarchical encoder with FPS+kNN grouping, nonparametric normalization, and shared residual MLPs. In experiments, SLNet shows that a very small model can still remain highly competitive across several 3D recognition tasks. On ModelNet40, SLNet‑S with 0.14M parameters and 0.31 GFLOPs achieves 93.64% overall accuracy, outperforming PointMLP‑elite with 5x fewer parameters, while SLNet‑M with 0.55M parameters and 1.22 GFLOPs reaches 93.92%, exceeding PointMLP with 24x fewer parameters. On ScanObjectNN, SLNet‑M achieves 84.25% overall accuracy within 1.2 percentage points of PointMLP while using 28x fewer parameters. For large scale scene segmentation, SLNet‑T extends the backbone with local Point Transformer attention and reaches 58.2% mIoU on S3DIS Area 5 with only 2.5M parameters, more than 17x fewer than Point Transformer V3. We also introduce NetScore+, which extends NetScore by incorporating latency and peak memory so that efficiency can be evaluated in a more deployment oriented way. Across multiple benchmarks and hardware settings, SLNet delivers a strong overall balance between accuracy and efficiency. Code is available at: https://github.com/m‑saeid/SLNet.
Authors:Xiang Zhang, Hongming Xu, Le Zhou, Wei Zhou, Xuanhe Zhou, Guoliang Li, Yuyu Luo, Changdong Liu, Guorun Chen, Jiang Liao, Fan Wu
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.
Authors:Suorong Yang, Fangjian Su, Hai Gan, Ziqi Ye, Jie Li, Baile Xu, Furao Shen, Soujanya Poria
Abstract:
Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typically rely on task‑specific handcrafted metrics or static/snapshot‑based criteria to estimate sample importance, limiting scalability across learning paradigms and making it difficult to capture the evolving utility of data throughout training. To address this challenge, we propose Data Agent, an end‑to‑end dynamic data selection framework that formulates data selection as a training‑aware sequential decision‑making problem. The agent learns a sample‑wise selection policy that co‑evolves with model optimization, guided by a composite reward that integrates loss‑based difficulty and confidence‑based uncertainty signals. The reward signals capture complementary objectives of optimization impact and information gain, together with a tuning‑free adaptive weighting mechanism that balances these signals over training. Extensive experiments across a wide range of datasets and architectures demonstrate that Data Agent consistently accelerates training while preserving or improving performance, e.g., reducing costs by over 50% on ImageNet‑1k and MMLU with lossless performance. Moreover, its dataset‑agnostic formulation and modular reward make it plug‑and‑play across tasks and scenarios, e.g., robustness to noisy datasets, highlighting its potential in real‑world scenarios. Code is available at https://github.com/Jackbrocp/Data‑Agent.
Authors:Li Gu, Zihuan Jiang, Zhixiang Chi, Huan Liu, Ziqiang Wang, Yuanhao Yu, Glen Berseth, Yang Wang
Abstract:
Graphical user interface (GUI)‑based mobile agents automate digital tasks on mobile devices by interpreting natural‑language instructions and interacting with the screen. While recent methods apply reinforcement learning (RL) to train vision‑language‑model(VLM) agents in interactive environments with a primary focus on performance, generalization remains underexplored due to the lack of standardized benchmarks and open‑source RL systems. In this work, we formalize the problem as a Contextual Markov Decision Process (CMDP) and introduce AndroidWorld‑Generalization, a benchmark with three increasingly challenging regimes for evaluating zero‑shot generalization to unseen task instances, templates, and applications. We further propose an RL training system that integrates Group Relative Policy Optimization (GRPO) with a scalable rollout collection system, consisting of containerized infrastructure and asynchronous execution % , and error recovery to support reliable and efficient training. Experiments on AndroidWorld‑Generalization show that RL enables a 7B‑parameter VLM agent to surpass supervised fine‑tuning baselines, yielding a 26.1% improvement on unseen instances but only limited gains on unseen templates (15.7%) and apps (8.3%), underscoring the challenges of generalization. As a preliminary step, we demonstrate that few‑shot adaptation at test‑time improves performance on unseen apps, motivating future research in this direction. To support reproducibility and fair comparison, we open‑source the full RL training system, including the environment, task suite, models, prompt configurations, and the underlying infrastructure \footnotehttps://github.com/zihuanjiang/AndroidWorld‑Generalization.
Authors:Antonio De Santis, Schrasing Tong, Marco Brambilla, Lalana Kagal
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.
Authors:Yatharth Sharma
Abstract:
We propose a Vocos‑based bandwidth extension model that enhances audio at 8‑48 kHz by generating missing high‑frequency content. Inputs are resampled to 48 kHz and processed by a neural vocoder backbone, enabling a single network to support arbitrary upsampling ratios. A lightweight Linkwitz‑Riley‑inspired refiner merges the original low band with the generated high frequencies via a smooth crossover. On validation, the model achieves competitive log‑spectral distance while running at a real‑time factor of 0.0001 on an NVIDIA A100 GPU and 0.0053 on an 8‑core CPU, demonstrating practical, high‑quality BWE at extreme throughput.
Authors:Abbas Mammadov, So Takao, Bohan Chen, Ricardo Baptista, Morteza Mardani, Yee Whye Teh, Julius Berner
Abstract:
Flow maps enable high‑quality image generation in a single forward pass. However, unlike iterative diffusion models, their lack of an explicit sampling trajectory impedes incorporating external constraints for conditional generation and solving inverse problems. We put forth Variational Flow Maps, a framework for conditional sampling that shifts the perspective of conditioning from "guiding a sampling path", to that of "learning the proper initial noise". Specifically, given an observation, we seek to learn a noise adapter model that outputs a noise distribution, so that after mapping to the data space via flow map, the samples respect the observation and data prior. To this end, we develop a principled variational objective that jointly trains the noise adapter and the flow map, improving noise‑data alignment, such that sampling from complex data posterior is achieved with a simple adapter. Experiments on various inverse problems show that VFMs produce well‑calibrated conditional samples in a single (or few) steps. For ImageNet, VFM attains competitive fidelity while accelerating the sampling by orders of magnitude compared to alternative iterative diffusion/flow models. Code is available at https://github.com/abbasmammadov/VFM
Authors:Andrea Giuseppe Di Francesco, Andrea Rubbi, Pietro Liò
Abstract:
Predicting how cells respond to genetic perturbations is fundamental to understanding gene function, disease mechanisms, and therapeutic development. While recent deep learning approaches have shown promise in modeling single‑cell perturbation responses, they struggle to generalize across cell types and perturbation contexts due to limited contextual information during generation. We introduce PT‑RAG (Perturbation‑aware Two‑stage Retrieval‑Augmented Generation), a novel framework that extends Retrieval‑Augmented Generation beyond traditional language‑model applications to cellular biology. Unlike standard RAG systems designed for text retrieval with pre‑trained LLMs, perturbation retrieval lacks established similarity metrics and requires learning what constitutes relevant context, making differentiable retrieval essential. PT‑RAG addresses this through a two‑stage pipeline: first, retrieving candidate perturbations K using GenePT embeddings, then adaptively refining the selection through Gumbel‑Softmax discrete sampling conditioned on both the cell state and the input perturbation. This cell‑type‑aware differentiable retrieval enables end‑to‑end optimization of the retrieval objective jointly with generation. On the Replogle‑Nadig single‑gene perturbation dataset, we demonstrate that PT‑RAG outperforms both STATE and vanilla RAG under identical experimental conditions, with the strongest gains in distributional similarity metrics (W_1, W_2). Notably, vanilla RAG's dramatic failure is itself a key finding: it demonstrates that differentiable, cell‑type‑aware retrieval is essential in this domain, and that naive retrieval can actively harm performance. Our results establish retrieval‑augmented generation as a promising paradigm for modelling cellular responses to gene perturbation. The code to reproduce our experiments is available at https://github.com/difra100/PT‑RAG_ICLR.
Authors:Yuxuan Han, Meng-Hao Guo, Zhengning Liu, Wenguang Chen, Shi-Min Hu
Abstract:
Optimizing GPU kernels manually is a challenging and time‑consuming task. With the rapid development of LLMs, automated GPU kernel optimization is gradually becoming a tangible reality. However, current LLM‑driven automated optimization methods narrowly focus on machine learning applications, such as PyTorch operator optimization, while overlooking broader domains like sparse matrix operations in scientific computing. Extending to these broader applications brings new challenges for the benchmark and algorithm. Therefore, developing a general‑purpose automated kernel optimization method becomes our primary focus. In this paper, we address the absence of systematic evaluation for multi‑scenario settings by introducing MSKernelBench, which spans multiple scenarios, including fundamental algebraic operations, common LLM kernels, sparse matrix operators, and scientific computing routines, each supporting both FP32 and BF16 precision. Building on this benchmark, we introduce CUDAMaster, a multi‑agent, hardware‑aware system for kernel optimization that leverages profiling information and automatically constructs the full compilation and execution toolchain. Experimental results demonstrate that CUDAMaster achieves significant speedups across most operators, outperforming Astra by about 35%. In several cases, its performance matches or surpasses that of highly optimized, closed‑source libraries such as cuBLAS. A demo showcasing the original and optimized code for each operator is available at https://hanyx2021.github.io/MSKernelBenchDemo/.
Authors:Tao Shi, Liangming Chen, Long Jin, Mengchu Zhou
Abstract:
In the training of neural networks, adaptive moment estimation (Adam) typically converges fast but exhibits suboptimal generalization performance. A widely accepted explanation for its defect in generalization is that it often tends to converge to sharp minima. To enhance its ability to find flat minima, we propose its new variant named inverse Adam (InvAdam). The key improvement of InvAdam lies in its parameter update mechanism, which is opposite to that of Adam. Specifically, it computes element‑wise multiplication of the first‑order and second‑order moments, while Adam computes the element‑wise division of these two moments. This modification aims to increase the step size of the parameter update when the elements in the second‑order moments are large and vice versa, which helps the parameter escape sharp minima and stay at flat ones. However, InvAdam's update mechanism may face challenges in convergence. To address this challenge, we propose dual Adam (DualAdam), which integrates the update mechanisms of both Adam and InvAdam, ensuring convergence while enhancing generalization performance. Additionally, we introduce the diffusion theory to mathematically demonstrate InvAdam's ability to escape sharp minima. Extensive experiments are conducted on image classification tasks and large language model (LLM) fine‑tuning. The results validate that DualAdam outperforms Adam and its state‑of‑the‑art variants in terms of generalization performance. The code is publicly available at https://github.com/LongJin‑lab/DualAdam.
Authors:Muhammad Khalifa, Zohaib Khan, Omer Tafveez, Hao Peng, Lu Wang
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.
Authors:Sofiane Ouaari, Jules Kreuer, Nico Pfeifer
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.
Authors:Yanjun Chen, Yirong Sun, Hanlin Wang, Jinghan Wang, Xinming Zhang, Xiaoyu Shen, Wenjie Li, Wei Zhang
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
Authors:Irene Wang, Vishnu Varma Venkata, Arvind Krishnamurthy, Divya Mahajan
Abstract:
The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prior works often rely on heuristic or topology‑agnostic search, handling communication and memory separately. Without per‑device memory awareness, these methods typically ensure feasibility post hoc by sharding parameters and activations across many devices, increasing synchronization, inflating communication, and underutilizing compute‑limiting scalability and efficiency on real datacenter networks. We present NEST, a network‑, compute‑, and memory‑aware device placement framework that unifies model parallelism, topology modeling, and memory feasibility via structured dynamic programming. NEST's DP operates on operator graphs with tensor and expert parallel configurations, explicit allreduce latencies across hierarchical or arbitrary networks, and memory/compute profiles. By factoring parallelism across tensor, pipeline, data, and expert dimensions, NEST defines a principled search space for hybrid strategies while jointly optimizing co‑location, network latency, and memory feasibility. Evaluations across diverse hardware and networks show NEST achieves up to 2.43 times higher throughput, better memory efficiency, and improved scalability over state‑of‑the‑art baselines, providing a foundation for co‑designing parallelization strategies and datacenter interconnects for next‑generation AI infrastructure. The source code of NEST is available at: https://github.com/scai‑tech/Nest
Authors:Gregor Baer
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.
Authors:Sayeem Bin Zaman, Fahim Hafiz, Riasat Azim
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
Authors:Jiefu Zhang, Yang Xu, Vaneet Aggarwal
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.
Authors:Guanglin Zhou, Armin Catic, Motahare Shabestari, Matthew Young, Chaiquan Li, Katrina Poppe, Sebastiano Barbieri
Abstract:
Access to electronic health records (EHRs) for digital health research is often limited by privacy regulations and institutional barriers. Synthetic EHRs have been proposed as a way to enable safe and sovereign data sharing; however, existing methods may produce records that capture overall statistical properties of real data but present inconsistencies across clinical processes and observations. We developed an integrated pipeline to make synthetic patient trajectories clinically consistent through two synergistic steps: high‑fidelity generation and scalable auditing. Using the MIMIC‑IV database, we trained a knowledge‑grounded generative model that represents nearly 32,000 distinct clinical events, including demographics, laboratory measurements, medications, procedures, and diagnoses, while enforcing structural integrity. To support clinical consistency at scale, we incorporated an automated auditing module leveraging large language models to filter out clinical inconsistencies (e.g., contraindicated medications) that escape probabilistic generation. We generated 18,071 synthetic patient records derived from a source cohort of 180,712 real patients. While synthetic clinical event probabilities demonstrated robust agreement (mean bias effectively 0.00) and high correlation (R2=0.99) with the real counterparts, review of a random sample of synthetic records (N=20) by three clinicians identified inconsistencies in 45‑60% of them. Automated auditing reduced the difference between real and synthetic data (Cohen's effect size d between 0.59 and 1.60 before auditing, and between 0.18 and 0.67 after auditing). Downstream models trained on audited data matched or even exceeded real‑data performance. We found no evidence of privacy risks, with membership inference performance indistinguishable from random guessing (F1‑score=0.51).
Authors:Kejing Lu, Zhenpeng Pan, Jianbin Qin, Yoshiharu Ishikawa, Chuan Xiao
Abstract:
Approximate Nearest Neighbor Search (ANNS) is fundamental to modern AI applications. Most existing solutions optimize query efficiency but fail to align with the practical requirements of modern workloads. In this paper, we outline six critical demands of modern AI applications: high query efficiency, fast indexing, low memory footprint, scalability to high dimensionality, robustness across varying retrieval sizes, and support for online insertions. To satisfy all these demands, we introduce Projection‑Augmented Graph (PAG), a new ANNS framework that integrates projection techniques into a graph index. PAG reduces unnecessary exact distance computations through asymmetric comparisons between exact and approximate distances as guided by projection‑based statistical tests. Three key components are designed and unified to the graph index to optimize indexing and searching. Experiments on six modern datasets demonstrate that PAG consistently achieves superior query per second (QPS)‑recall performance ‑‑ up to 5x faster than HNSW ‑‑ while offering fast indexing speed and moderate memory footprint. PAG remains robust as dimensionality and retrieval size increase and naturally supports online insertions.
Authors:Swamynathan V P
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.
Authors:Qingsong Zou, Zhi Yan, Zhiyao Xu, Kuofeng Gao, Jingyu Xiao, Yong Jiang
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.
Authors:Yuqi Xu, Rizhen Hu, Zihan Liu, Mou Sun, Kun Yuan
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.
Authors:Rishabh Tiwari, Aditya Tomar, Udbhav Bamba, Monishwaran Maheswaran, Heng Yang, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
Abstract:
Process Reward Models (PRMs) are rapidly becoming the backbone of LLM reasoning pipelines, yet we demonstrate that state‑of‑the‑art PRMs are systematically exploitable under adversarial optimization pressure. To address this, we introduce a three‑tiered diagnostic framework that applies increasing adversarial pressure to quantify these vulnerabilities. Static perturbation analysis uncovers a fluency‑logic dissociation: high invariance to surface‑level style changes reward changes <0.1, yet inconsistent detection of logically‑corrupted reasoning, with different models failing on different attack types. Adversarial optimization demonstrates that gradient‑based attacks inflate rewards on invalid trajectories, with reward landscapes exhibiting wide, exploitable peaks. RL‑induced reward hacking exposes the critical failure mode: policies trained on AIME problems achieve near‑perfect PRM rewards (>0.9), while ground‑truth accuracy remains low (below 4%), with 43% of reward gains attributable to stylistic shortcuts. These findings reveal that current PRMs function as fluency detectors rather than reasoning verifiers, creating systematic blind spots that undermine their use as training signals. We release PRM‑BiasBench and a diagnostic toolkit to enable robustness evaluation before deployment. The code and dataset are available at https://github.com/SqueezeAILab/reward‑under‑attack.
Authors:Fali Wang, Chenglin Weng, Xianren Zhang, Siyuan Hong, Hui Liu, Suhang Wang
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..
Authors:Ching-Yun Ko, Pin-Yu Chen
Abstract:
Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for transformer‑based large language models (LLMs). The vLLM project is a major open‑source library to support model serving and inference. However, the current implementation of vLLM limits programmability of the internal states of deployed models. This prevents the use of popular test‑time model alignment and enhancement methods. For example, it prevents the detection of adversarial prompts based on attention patterns or the adjustment of model responses based on activation steering. To bridge this critical gap, we present vLLM Hook, an opensource plug‑in to enable the programming of internal states for vLLM models. Based on a configuration file specifying which internal states to capture, vLLM Hook provides seamless integration to vLLM and supports two essential features: passive programming and active programming. For passive programming, vLLM Hook probes the selected internal states for subsequent analysis, while keeping the model generation intact. For active programming, vLLM Hook enables efficient intervention of model generation by altering the selected internal states. In addition to presenting the core functions of vLLM Hook, in version 0, we demonstrate 3 use cases including prompt injection detection, enhanced retrieval‑augmented retrieval (RAG), and activation steering. Finally, we welcome the community's contribution to improve vLLM Hook via https://github.com/ibm/vllm‑hook.
Authors:Vishal Thengane, Zhaochong An, Tianjin Huang, Son Lam Phung, Abdesselam Bouzerdoum, Lu Yin, Na Zhao, Xiatian Zhu
Abstract:
Incremental Few‑Shot (IFS) segmentation aims to learn new categories over time from only a few annotations. Although widely studied in 2D, it remains underexplored for 3D point clouds. Existing methods suffer from catastrophic forgetting or fail to learn discriminative prototypes under sparse supervision, and often overlook a key cue: novel categories frequently appear as unlabelled background in base‑training scenes. We introduce SCOPE (Scene‑COntextualised Prototype Enrichment), a plug‑and‑play background‑guided prototype enrichment framework that integrates with any prototype‑based 3D segmentation method. After base training, a class‑agnostic segmentation model extracts high‑confidence pseudo‑instances from background regions to build a prototype pool. When novel classes arrive with few labelled samples, relevant background prototypes are retrieved and fused with few‑shot prototypes to form enriched representations without retraining the backbone or adding parameters. Experiments on ScanNet and S3DIS show that SCOPE achieves SOTA performance, improving novel‑class IoU by up to 6.98% and 3.61%, and mean IoU by 2.25% and 1.70%, respectively, while maintaining low forgetting. Code is available https://github.com/Surrey‑UP‑Lab/SCOPE.
Authors:Kartik Sharma, Rakshit S. Trivedi
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.
Authors:Xiaojie Li, Yu Han, Zhizheng Lu, Shi Jin, Chao-Kai Wen
Abstract:
The upper 6 GHz (U6G) band with XL‑MIMO is a key enabler for sixth‑generation wireless systems, yet intelligent radiomap prediction for such systems remains challenging. Existing datasets support only small‑scale arrays (up to 8x8) with predominantly isotropic antennas, far from the 1024‑element directional arrays envisioned for 6G. Moreover, current methods encode array configurations as scalar parameters, forcing neural networks to extrapolate array‑specific radiation patterns, which fails when predicting radiomaps for configurations absent from training data. To jointly address data scarcity and generalization limitations, this paper advances XL‑MIMO radiomap prediction from three aspects. To overcome data limitations, we construct the first XL‑MIMO radiomap dataset containing 78400 radiomaps across 800 urban scenes, five frequency bands (1.8‑6.7 GHz), and nine array configurations up to 32x32 uniform planar arrays with directional elements. To enable systematic evaluation, we establish a comprehensive benchmark framework covering practical scenarios from coverage estimation without field measurements to generalization across unseen configurations and environments. To enable generalization to arbitrary beam configurations without retraining, we propose the beam map, a physics‑informed spatial feature that analytically computes array‑specific coverage patterns. By decoupling deterministic array radiation from data learned multipath propagation, beam maps shift generalization from neural network extrapolation to physics‑based computation. Integrating beam maps into existing architectures reduces mean absolute error by up to 60.0% when generalizing to unseen configurations and up to 50.5% when transferring to unseen environments. The complete dataset and code are publicly available at https://lxj321.github.io/MulticonfigRadiomapDataset/.
Authors:Han-Chen Zhang, Zi-Hao Zhou, Mao-Lin Luo, Shimin Di, Min-Ling Zhang, Tong Wei
Abstract:
Model merging aims to integrate multiple task‑adapted models into a unified model that preserves the knowledge of each task. In this paper, we identify that the key to this knowledge retention lies in maintaining the directional consistency of singular spaces between merged multi‑task vector and individual task vectors. However, this consistency is frequently compromised by two issues: i) an imbalanced energy distribution within task vectors, where a small fraction of singular values dominate the total energy, leading to the neglect of semantically important but weaker components upon merging, and ii) the geometric inconsistency of task vectors in parameter space, which causes direct merging to distort their underlying directional geometry. To address these challenges, we propose DC‑Merge, a method for directional‑consistent model merging. It first balances the energy distribution of each task vector by smoothing its singular values, ensuring all knowledge components are adequately represented. These energy‑balanced vectors are then projected onto a shared orthogonal subspace to align their directional geometries with minimal reconstruction error. Finally, the aligned vectors are aggregated in the shared orthogonal subspace and projected back to the original parameter space. Extensive experiments on vision and vision‑language benchmarks show that DC‑Merge consistently achieves state‑of‑the‑art performance in both full fine‑tuning and LoRA settings. The implementation code is available at https://github.com/Tobeginwith/DC‑Merge.
Authors:Soumya Mazumdar, Vineet Kumar Rakesh
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
Authors:Zongfang Liu, Shengkun Tang, Boyang Sun, Zhiqiang Shen, Xin Yuan
Abstract:
Sparse Mixture‑of‑Experts (SMoE) language models achieve strong capability at low per‑token compute, yet deployment remains constrained by memory footprint and throughput because the full expert pool must still be stored and served. Post‑training expert pruning reduces this cost, but most methods focus on which experts to prune within each layer and default to a uniform layer‑wise sparsity allocation, even though the layer‑wise allocation can strongly affect performance. We decouple pruning into within‑layer expert ranking and across‑layer budget allocation, and introduce Expected Speculative Acceptance Proxy (ESAP), a speculative‑decoding‑inspired, teacher‑forced metric that measures how well a pruned model matches the full model without costly autoregressive decoding. ESAP is bounded and stable, enabling cheap comparison of many candidates. Building on ESAP, we propose EvoESAP, an evolutionary search framework that finds an improved non‑uniform layer‑wise sparsity allocation under a fixed global budget while holding the within‑layer pruning order fixed, making it a plug‑and‑play method for criteria such as Frequency, EAN, SEER, and REAP. Across 7B‑‑30B SMoE LLMs at 25% and 50% sparsity, EvoESAP consistently discovers non‑uniform allocations that improve open‑ended generation (up to +19.6% on MATH‑500 at 50% sparsity) while preserving competitive multiple‑choice accuracy compared with uniform pruning at the same sparsity. Code is available at https://github.com/ZongfangLiu/EvoESAP.
Authors:Habibullah Akbar
Abstract:
Modern neural network optimization relies heavily on architectural priorssuch as Batch Normalization and Residual connectionsto stabilize training dynamics. Without these, or in low‑data regimes with aggressive augmentation, low‑bias architectures like Vision Transformers (ViTs) often suffer from optimization collapse. This work adopts Sketched Isotropic Gaussian Regularization (SIGReg), recently introduced in the LeJEPA self‑supervised framework, and repurposes it as a general optimization stabilizer for supervised learning. While the original formulation targets the full characteristic function, a computationally efficient variant is derived, Weak‑SIGReg, which targets the covariance matrix via random sketching. Inspired by interacting particle systems, representation collapse is viewed as stochastic drift; SIGReg constrains the representation density towards an isotropic Gaussian, mitigating this drift. Empirically, SIGReg recovers the training of a ViT on CIFAR‑100 from a collapsed 20.73% to 72.02% accuracy without architectural hacks and significantly improves the convergence of deep vanilla MLPs trained with pure SGD. Code is available at \hrefhttps://github.com/kreasof‑ai/sigreggithub.com/kreasof‑ai/sigreg.
Authors:Xuan Li, Zhanke Zhou, Zongze Li, Jiangchao Yao, Yu Rong, Lu Zhang, Bo Han
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.
Authors:Xiang Zhang, Sohyun Yoo, Hongrui Wu, Chuan Li, Jianwen Xie, Zhuowen Tu
Abstract:
We introduce PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed distance fields and post‑hoc layout optimization, PixARMesh jointly predicts object layout and geometry within a unified model, producing coherent and artist‑ready meshes in a single forward pass. Building on recent advances in mesh generative models, we augment a point‑cloud encoder with pixel‑aligned image features and global scene context via cross‑attention, enabling accurate spatial reasoning from a single image. Scenes are generated autoregressively from a unified token stream containing context, pose, and mesh, yielding compact meshes with high‑fidelity geometry. Experiments on synthetic and real‑world datasets show that PixARMesh achieves state‑of‑the‑art reconstruction quality while producing lightweight, high‑quality meshes ready for downstream applications.
Authors:Mingluo Su, Huan Wang
Abstract:
Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to more efficient deployment and inference. One classic and prominent path of LLM one‑shot pruning is to leverage second‑order gradients (i.e., Hessian), represented by the pioneering work SparseGPT. However, the predefined left‑to‑right pruning order in SparseGPT leads to suboptimal performance when the weights exhibit columnar patterns. This paper studies the effect of pruning order under the SparseGPT framework. The analyses lead us to propose ROSE, a reordered SparseGPT method that prioritizes weights with larger potential pruning errors to be pruned earlier. ROSE first performs pre‑pruning to identify candidate weights for removal, and estimates both column and block pruning loss. Subsequently, two‑level reordering is performed: columns within each block are reordered in descending order of column loss, while blocks are reordered based on block loss. We introduce the relative range of block loss as a metric to identify columnar layers, enabling adaptive reordering across the entire model. Substantial empirical results on prevalent LLMs (LLaMA2‑7B/13B/70B, LLaMA3‑8B, Mistral‑7B) demonstrate that ROSE surpasses the original SparseGPT and other counterpart pruning methods. Our code is available at https://github.com/mingluo‑su/ROSE.
Authors:Juyong Jiang, Jiasi Shen, Sunghun Kim, Kang Min Yoo, Jeonghoon Kim, Sungju Kim
Abstract:
While Large Language Models (LLMs) have revolutionized code generation, standard ``System 1'' approaches that generate solutions in a single forward pass often hit a performance ceiling on complex algorithmic tasks. Existing iterative refinement strategies attempt to bridge this gap at inference time, yet they predominantly rely on external oracles, execution feedback, or computationally expensive prompt‑response cycles. In this work, we propose ReflexiCoder, a novel reinforcement learning (RL) framework that internalizes the structured reasoning trajectory, encompassing initial generation, bug and optimization aware reflection, and self‑correction, directly into the model's weights. Unlike prior methods, ReflexiCoder shifts the paradigm from external‑dependent refinement to an intrinsic, fully autonomous self‑reflection and self‑correction capabilities at inference time. We utilize an RL‑only training paradigm with granular reward functions to optimize the entire reflection‑correction trajectory, teaching the model how to debug without reliance on ground‑truth feedback or execution engines at inference time. Extensive experiments across seven benchmarks demonstrate that our ReflexiCoder‑8B establishes a new state‑of‑the‑art (SOTA) among leading open‑source models in the 1.5B to 14B range, achieving 94.51% (87.20%) on HumanEval (Plus), 81.80% (78.57%) on MBPP (Plus), 35.00% on BigCodeBench, 52.21% on LiveCodeBench, and 37.34% on CodeForces in a single‑attempt setting, rivaling or surpassing proprietary models like GPT‑5.1. Notably, our framework is significantly more token‑efficient than base models, reducing inference‑time compute overhead by approximately 40% through disciplined, efficient reasoning and reflection patterns. The source code and data are available at https://github.com/juyongjiang/ReflexiCoder.
Authors:Son Thai Ly, Hien V. Nguyen
Abstract:
Adapting foundation models to new clinical sites remains challenging in practice. Domain shift and scarce annotations must be handled by experts, yet many clinical groups do not have ready access to skilled AI engineers to tune adapter designs and training recipes. As a result, adaptation cycles can stretch from weeks to months, particularly in few‑shot settings. Existing PEFT methods either require manual adapter configuration or automated searches that are computationally infeasible in few‑shot 3D settings. We propose SEA‑PEFT (SElf‑Auditing Parameter‑Efficient Fine‑Tuning) to automate this process. SEA‑PEFT treats adapter configuration as an online allocation problem solved during fine‑tuning rather than through manual, fixed‑topology choices. SEA‑PEFT uses a search‑audit‑allocate loop that trains active adapters, estimates each adapter's Dice utility by momentarily toggling it off, and then reselects the active set under a parameter budget using a greedy knapsack allocator. Exponential Moving Average and Interquartile Range smoothing, together with a Finite‑State Ranking controller, stabilize the loop and improve reliability in high‑noise few‑shot regimes. On TotalSegmentator and FLARE'22, SEA‑PEFT improves mean Dice by 2.4‑‑2.8 points over the strongest fixed‑topology PEFT baselines across 1/5/10‑shot settings while training <1% of parameters. For reproducibility purposes, we made our code publicly available at https://github.com/tsly123/SEA_PEFT
Authors:Mykola Pinchuk
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.
Authors:Tongda Xu, Mingwei He, Shady Abu-Hussein, Jose Miguel Hernandez-Lobato, Chunhang Zheng, Kai Zhao, Chao Zhou, Ya-Qin Zhang, Yan Wang
Abstract:
It is well known that the reconstruction FID (rFID) of a VAE is poorly correlated with the generation FID (gFID) of a latent diffusion model. We propose interpolated FID (iFID), a simple variant of rFID that exhibits a strong correlation with gFID. Specifically, for each dataset element, we retrieve its nearest neighbor in latent space, interpolate between their latent representations, decode the interpolated latent, and compute the FID between the decoded samples and the original dataset. We provide an intuitive explanation for why iFID correlates well with gFID, and why reconstruction metrics can be negatively correlated with gFID, by connecting iFID to recent results on diffusion generalization and hallucination. Theoretically, we show that iFID evaluates decoded interpolations aligned with the ridge set around which diffusion samples concentrate, thereby measuring a quantity closely related to diffusion sample quality. Empirically, iFID is the first metric shown to strongly correlate with diffusion gFID across diverse VAEs, achieving Pearson and Spearman correlations of approximately 0.85. The project page is available at https://tongdaxu.github.io/pages/ifid.html.
Authors:Dylan R. Ashley, Jan Przepióra, Yimeng Chen, Ali Abualsaud, Nurzhan Yesmagambet, Shinkyu Park, Eric Feron, Jürgen Schmidhuber
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.
Authors:Fengxiang Nie, Yasuhiro Suzuki
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.
Authors:Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss, George H. Chen
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 .
Authors:Hejian Sang, Yuanda Xu, Zhengze Zhou, Ran He, Zhipeng Wang, Jiachen Sun
Abstract:
Reasoning models think out loud, but much of what they say is noise. We introduce CRISP (Compressed Reasoning via Iterative Self‑Policy Distillation), a method that teaches models to reason more concisely by distilling their own concise behavior back into themselves. The entire approach reduces to one idea: condition the same model on a ''be concise'' instruction to obtain teacher logits, and minimize per‑token reverse KL on the student's own rollouts. No ground‑truth answers, no token budgets, no difficulty estimators. Just self‑distillation. Yet this simplicity belies surprising sophistication: CRISP automatically compresses easy problems aggressively while preserving the deliberation needed for hard ones. On Qwen3‑8B and Qwen3‑14B, we achieve 57‑‑59% token reduction on MATH‑500 while improving accuracy by 9‑‑16 points absolute. On AIME 2024, the 14B model gains 10 points with 41% compression. Ablations show that qualitative conciseness instructions outperform explicit token targets, and periodic teacher refreshes yield a broad stable regime. The method generalizes across model families ‑‑ DeepSeek‑R1‑Distill‑Llama‑8B improves accuracy by up to 5 points with 17‑‑32% compression ‑‑ and transfers beyond math to multi‑step agentic planning (DeepPlanning), reducing token usage by 42‑‑51% while preserving planning quality. Code is available at https://github.com/HJSang/OPSD_Reasoning_Compression.
Authors:Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Vitor Fortes Rey, Paul Lukowicz, Todor Stoyanov, Oscar Martinez Mozos
Abstract:
This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model's generalization capabilities to new unseen individuals due to inter‑subject variability, i.e., the same activity is performed differently by different individuals. To address this problem, we propose a novel deep adversarial framework that integrates the concept of inter‑subject variability in the adversarial task, thereby encouraging subject‑invariant feature representations and enhancing the classification performance in the HAR problem. Our approach outperforms previous methods in three well‑established HAR datasets using a leave‑one‑subject‑out (LOSO) cross‑validation. Further results indicate that our proposed adversarial task effectively reduces inter‑subject variability among different users in the feature space, and it outperforms adversarial tasks from previous works when integrated into our framework. Code: https://github.com/FranciscoCalatrava/EmbeddedSubjectVariability.git
Authors:Luca Della Libera, Cem Subakan, Mirco Ravanelli
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/.
Authors:Hokyun Im, Andrey Kolobov, Jianlong Fu, Youngwoon Lee
Abstract:
Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration. Yet, offline RL's performance often hinges on a brittle trade‑off between (1) return maximization, which can push policies outside the dataset support, and (2) behavioral constraints, which typically require sensitive hyperparameter tuning. Latent steering offers a structural way to stay within the dataset support during RL, but existing offline adaptations commonly approximate action values using latent‑space critics learned via indirect distillation, which can lose information and hinder convergence. We propose Latent Policy Steering (LPS), which enables high‑fidelity latent policy improvement by backpropagating original‑action‑space Q‑gradients through a differentiable one‑step MeanFlow policy to update a latent‑action‑space actor. By eliminating proxy latent critics, LPS allows an original‑action‑space critic to guide end‑to‑end latent‑space optimization, while the one‑step MeanFlow policy serves as a behavior‑constrained generative prior. This decoupling yields a robust method that works out‑of‑the‑box with minimal tuning. Across OGBench and real‑world robotic tasks, LPS achieves state‑of‑the‑art performance and consistently outperforms behavioral cloning and strong latent steering baselines.
Authors:Hanyong Shao, Yingbo Hao, Ting Song, Yan Xia, Di Zhang, Shaohan Huang, Xun Wu, Songchen Xu, Le Xu, Li Dong, Zewen Chi, Yi Zou, Furu Wei
Abstract:
NVIDIA's 2:4 Sparse Tensor Cores deliver 2x throughput but demand strict 50% pruning ‑‑ a ratio that collapses LLM reasoning accuracy (Qwen3: 54% to 15%). Milder (2N‑2):2N patterns (e.g., 6:8, 25% pruning) preserve accuracy yet receive no hardware support, falling back to dense execution without any benefit from sparsity. We present SlideSparse, the first system to unlock Sparse Tensor Core acceleration for the (2N‑2):2N model family on commodity GPUs. Our Sliding Window Decomposition reconstructs any (2N‑2):2N weight block into N‑1 overlapping 2:4‑compliant windows without any accuracy loss; Activation Lifting fuses the corresponding activation rearrangement into per‑token quantization at near‑zero cost. Integrated into vLLM, SlideSparse is evaluated across various GPUs (A100, H100, B200, RTX 4090, RTX 5080, DGX‑spark), precisions (FP4, INT8, FP8, BF16, FP16), and model families (Llama, Qwen, BitNet). On compute‑bound workloads, the measured speedup ratio (1.33x) approaches the theoretical upper‑bound N/(N‑1)=4/3 at 6:8 weight sparsity in Qwen2.5‑7B, establishing (2N‑2):2N as a practical path to accuracy‑preserving LLM acceleration. Code available at https://github.com/bcacdwk/vllmbench.
Authors:Alper Yıldırım
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.
Authors:Yize Wu, Ke Gao, Ling Li, Yanjun Wu
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.
Authors:Junkang Liu, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Yuangang Li, YunXiang Gong
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.
Authors:Cenwei Zhang, Lin Zhu, Manxi Lin, Lei You
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.
Authors:Hai Siong Tan, Rafe McBeth
Abstract:
In this work, we investigate uncertainty‑aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence models based on LSTM, GRU, and Transformer architectures, with uncertainty quantification enabled by either Monte Carlo dropout or through evidential output layers compatible with Deep Evidential Regression. Using the HUPA‑UCM diabetes dataset for validation, we find that Transformer‑based models equipped with evidential output heads provide the most effective uncertainty‑aware framework, achieving consistently higher predictive accuracies and better‑calibrated uncertainty estimates whose magnitudes significantly correlate with prediction errors. We further evaluate the clinical risk of each model using the recently proposed Diabetes Technology Society error grid, with risk categories defined by international expert consensus. Our results demonstrate the value of integrating principled uncertainty quantification into real‑time machine‑learning‑based blood glucose prediction systems.
Authors:Nilusha Jayawickrama, Henrik Toikka, Risto Ojala
Abstract:
This paper investigates person detection and tracking in an industrial indoor workspace using a LiDAR mounted on an overhead crane. The overhead viewpoint introduces a strong domain shift from common vehicle‑centric LiDAR benchmarks, and limited availability of suitable public training data. Henceforth, we curate a site‑specific overhead LiDAR dataset with 3D human bounding‑box annotations and adapt selected candidate 3D detectors under a unified training and evaluation protocol. We further integrate lightweight tracking‑by‑detection using AB3DMOT and SimpleTrack to maintain person identities over time. Detection performance is reported with distance‑sliced evaluation to quantify the practical operating envelope of the sensing setup. The best adapted detector configurations achieve average precision (AP) up to 0.84 within a 5.0 m horizontal radius, increasing to 0.97 at 1.0 m, with VoxelNeXt and SECOND emerging as the most reliable backbones across this range. The acquired results contribute in bridging the domain gap between standard driving datasets and overhead sensing for person detection and tracking. We also report latency measurements, highlighting practical real‑time feasibility. Finally, we release our dataset and implementations in GitHub to support further research
Authors:Yuan Li, Bo Wang, Yufei Gao, Yuqian Yao, Xinyuan Wang, Zhangyue Yin, Xipeng Qiu
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.
Authors:Yuheng Lei, Zhixuan Liang, Hongyuan Zhang, Ping Luo
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.
Authors:Yiang Wu, Qiong Wu, Pingyi Fan, Kezhi Wang, Wen Chen, Guoqiang Mao, Khaled B. Letaief
Abstract:
This demonstration presents U‑Parking, a distributed Ultra‑Wideband (UWB)‑assisted autonomous parking system. By integrating Large Language Models (LLMs)‑assisted planning with robust fusion localization and trajectory tracking, it enables reliable automated parking in challenging indoor environments, as validated through real‑vehicle demonstrations.
Authors:Minjune Hwang, Yigit Korkmaz, Daniel Seita, Erdem Bıyık
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
Authors:Rui Zhao, Bin Shi, Kai Sun, Bo Dong
Abstract:
Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real‑world scenarios, candidate labels are often influenced by instance features, leading to the emergence of instance‑dependent PLL (ID‑PLL), a setting that more accurately reflects this relationship. A significant challenge in ID‑PLL is instance entanglement, where instances from similar classes share overlapping features and candidate labels, resulting in increased class confusion. To address this issue, we propose a novel Class‑specific Augmentation based Disentanglement (CAD) framework, which tackles instance entanglement by both intra‑ and inter‑class regulations. For intra‑class regulation, CAD amplifies class‑specific features to generate class‑wise augmentations and aligns same‑class augmentations across instances. For inter‑class regulation, CAD introduces a weighted penalty loss function that applies stronger penalties to more ambiguous labels, encouraging larger inter‑class distances. By jointly applying intra‑ and inter‑class regulations, CAD improves the clarity of class boundaries and reduces class confusion caused by entanglement. Extensive experimental results demonstrate the effectiveness of CAD in mitigating the entanglement problem and enhancing ID‑PLL performance. The code is available at https://github.com/RyanZhaoIc/CAD.git.
Authors:Boyu Han, Qianqian Xu, Shilong Bao, Zhiyong Yang, Ruochen Cui, Xilin Zhao, Qingming Huang
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.
Authors:Shaocheng Lan, Shuqi Gu, Zhangzhi Xiong, Kan Ren
Abstract:
Conditional time series generation plays a critical role in addressing data scarcity and enabling causal analysis in real‑world applications. Despite its increasing importance, the field lacks a standardized and systematic benchmarking framework for evaluating generative models across diverse conditions. To address this gap, we introduce the Conditional Time Series Generation Benchmark (ConTSG‑Bench). ConTSG‑Bench comprises a large‑scale, well‑aligned dataset spanning diverse conditioning modalities and levels of semantic abstraction, first enabling systematic evaluation of representative generation methods across these dimensions with a comprehensive suite of metrics for generation fidelity and condition adherence. Both the quantitative benchmarking and in‑depth analyses of conditional generation behaviors have revealed the traits and limitations of the current approaches, highlighting critical challenges and promising research directions, particularly with respect to precise structural controllability and downstream task utility under complex conditions.
Authors:Baoqing Yue, Zihan Zhu, Yutong Han, Brian Fan, Qian Sun, Jichen Feng, Hufei Yang, Yifan Zhang, Mengdi Wang
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.
Authors:Jihoon Jeong
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.
Authors:Ancymol Thomas, Jaya Sreevalsan-Nair
Abstract:
Local Climate Zones (LCZs) give a zoning map to study urban structures and land use and analyze the impact of urbanization on local climate. Multimodal remote sensing enables LCZ classification, for which data fusion is significant for improving accuracy owing to the data complexity. However, there is a gap in a comprehensive analysis of the fusion mechanisms used in their deep learning (DL) classifier architectures. This study analyzes different fusion strategies in the multi‑class LCZ classification models for multimodal data and grouping strategies based on inherent data characteristics. The different models involving Convolutional Neural Networks (CNNs) include: (i) baseline hybrid fusion (FM1), (ii) with self‑ and cross‑attention mechanisms (FM2), (iii) with the multi‑scale Gaussian filtered images (FM3), and (iv) weighted decision‑level fusion (FM4). Ablation experiments are conducted to study the pixel‑, feature‑, and decision‑level fusion effects in the model performance. Grouping strategies include band grouping (BG) within the data modalities and label merging (LM) in the ground truth. Our analysis is exclusively done on the So2Sat LCZ42 dataset, which consists of Synthetic Aperture Radar (SAR) and Multispectral Imaging (MSI) image pairs. Our results show that FM1 consistently outperforms simple fusion methods. FM1 with BG and LM is found to be the most effective approach among all fusion strategies, giving an overall accuracy of 76.6%. Importantly, our study highlights the effect of these strategies in improving prediction accuracy for the underrepresented classes. Our code and processed datasets are available at https://github.com/GVCL/LCZC‑MultiModalHybridFusion
Authors:Tal Daniel, Carl Qi, Dan Haramati, Amir Zadeh, Chuan Li, Aviv Tamar, Deepak Pathak, David Held
Abstract:
We introduce Latent Particle World Model (LPWM), a self‑supervised object‑centric world model scaled to real‑world multi‑object datasets and applicable in decision‑making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling it to learn rich scene decompositions without supervision. Our architecture is trained end‑to‑end purely from videos and supports flexible conditioning on actions, language, and image goals. LPWM models stochastic particle dynamics via a novel latent action module and achieves state‑of‑the‑art results on diverse real‑world and synthetic datasets. Beyond stochastic video modeling, LPWM is readily applicable to decision‑making, including goal‑conditioned imitation learning, as we demonstrate in the paper. Code, data, pre‑trained models and video rollouts are available: https://taldatech.github.io/lpwm‑web
Authors:Faisal Bin Ashraf, Animesh Ray, Stefano Lonardi
Abstract:
Machine learning‑based antibody design is emerging as one of the most promising approaches to combat infectious diseases, due to significant advancements in the field of artificial intelligence and an exponential surge in experimental antibody data (in particular related to COVID‑19). The ability of an antibody to bind to an antigens (called binding affinity) is one of the the most critical properties in designing neutralizing antibodies. In this study we introduce Ab‑Affinity, a new large language model that can accurately predict the binding affinity of antibodies against a target peptide, e.g., the SARS‑CoV‑2 spike protein. Code and model are available at https://github.com/ucrbioinfo/AbAffinity.
Authors:Yakov Pyotr Shkolnikov
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
Authors:Murad Farzulla
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.
Authors:Ekansh Arora
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.
Authors:Haian Jin, Rundi Wu, Tianyuan Zhang, Ruiqi Gao, Jonathan T. Barron, Noah Snavely, Aleksander Holynski
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.
Authors:Zachary Novack, Zack Zukowski, CJ Carr, Julian Parker, Zach Evans, Josiah Taylor, Taylor Berg-Kirkpatrick, Julian McAuley, Jordi Pons
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.
Authors:Kelly L Vomo-Donfack, Adryel Hoszu, Grégory Ginot, Ian Morilla
Abstract:
Federated learning (FL) faces two structural tensions: gradient sharing enables data‑reconstruction attacks, while non‑IID client distributions degrade aggregation quality. We introduce PTOPOFL, a framework that addresses both challenges simultaneously by replacing gradient communication with topological descriptors derived from persistent homology (PH). Clients transmit only 48‑dimensional PH feature vectors‑compact shape summaries whose many‑to‑one structure makes inversion provably ill‑posed‑rather than model gradients. The server performs topology‑guided personalised aggregation: clients are clustered by Wasserstein similarity between their PH diagrams, intra‑cluster models are topology‑weighted,and clusters are blended with a global consensus. We prove an information‑contraction theorem showing that PH descriptors leak strictly less mutual information per sample than gradients under strongly convex loss functions, and we establish linear convergence of the Wasserstein‑weighted aggregation scheme with an error floor strictly smaller than FedAvg. Evaluated against FedAvg, FedProx, SCAFFOLD, and pFedMe on a non‑IID healthcare scenario (8 hospitals, 2 adversarial) and a pathological benchmark (10 clients), PTOPOFL achieves AUC 0.841 and 0.910 respectively‑the highest in both settings‑while reducing reconstruction risk by a factor of 4.5 relative to gradient sharing. Code is publicly available at https://github.com/MorillaLab/TopoFederatedL and data at https://doi.org/10.5281/zenodo.18827595.
Authors:Pranav Kumar Kaliaperumal
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.
Authors:Ioannis Prokopiou, Ioannis Sina, Agisilaos Kounelis, Pantelis Vikatos, Themos Stafylakis
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.
Authors:Han Xiao
Abstract:
mlx‑vis implements eight dimensionality reduction methods ‑‑ UMAP, t‑SNE, PaCMAP, LocalMAP, TriMap, DREAMS, CNE, MMAE ‑‑ and NNDescent k‑NN graph construction entirely in MLX for Apple Silicon Metal GPU. A built‑in GPU renderer produces scatter plots and smooth animations via hardware H.264 encoding. On Fashion‑MNIST (70K points, M3 Ultra), seven of eight methods embed in 2.0‑4.7s and render 800‑frame animations in 1.4s. The library depends only on MLX and NumPy and is available at https://github.com/hanxiao/mlx‑vis.
Authors:Yang Li, Youyang Sha, Yinzhi Wang, Timothy Hospedales, Xi Shen, Shell Xu Hu, Xuanlong Yu
Abstract:
Building reliable classifiers is a fundamental challenge for deploying machine learning in real‑world applications. A reliable system should not only detect out‑of‑distribution (OOD) inputs but also anticipate in‑distribution (ID) errors by assigning low confidence to potentially misclassified samples. Yet, most prior work treats OOD detection and failure prediction as separated problems, overlooking their closed connection. We argue that reliability requires evaluating them jointly. To this end, we propose a unified evaluation framework that integrates OOD detection and failure prediction, quantified by our new metrics DS‑F1 and DS‑AURC, where DS denotes double scoring functions. Experiments on the OpenOOD benchmark show that double scoring functions yield classifiers that are substantially more reliable than traditional single scoring approaches. Our analysis further reveals that OOD‑based approaches provide notable gains under simple or far‑OOD shifts, but only marginal benefits under more challenging near‑OOD conditions. Beyond evaluation, we extend the reliable classifier SURE and introduce SURE+, a new approach that significantly improves reliability across diverse scenarios. Together, our framework, metrics, and method establish a new benchmark for trustworthy classification and offer practical guidance for deploying robust models in real‑world settings. The source code is publicly available at https://github.com/Intellindust‑AI‑Lab/SUREPlus.
Authors:Markus Knauer, Samuel Bustamante, Thomas Eiband, Alin Albu-Schäffer, Freek Stulp, João Silvério
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.
Authors:Huihan Liu, Changyeon Kim, Bo Liu, Minghuan Liu, Yuke Zhu
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/
Authors:Yanbo Wang, Jiaxuan You, Chuan Shi, Muhan Zhang
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.
Authors:Taejun Lim, Joong-Won Hwang, Kibok Lee
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.
Authors:Qi Zhang, Harsh Parikh, Ashley Naimi, Razieh Nabi, Christopher Kim, Timothy Lash
Abstract:
Method validation and study design in causal inference rely on synthetic data with known counterfactuals. Existing simulators trade off distributional realism, the ability to capture mixed‑type and multimodal tabular data, against causal controllability, including explicit control over overlap, unmeasured confounding, and treatment effect heterogeneity. We introduce CausalMix, a variational generative framework that closes this gap by coupling a mixture of Gaussian latent priors with data‑type‑specific decoders for continuous, binary, and categorical variables. The model incorporates explicit causal controls: an overlap regularizer shaping propensity‑score distributions, alongside direct parameterizations of confounding strength and effect heterogeneity. This unified objective preserves fidelity to the observed data while enabling factorial manipulation of causal mechanisms, allowing overlap, confounding strength, and treatment effect heterogeneity to be varied independently at design time. Across benchmarks, CausalMix achieves state‑of‑the‑art distributional metrics on mixed‑type tables while providing stable, fine‑grained causal control. We demonstrate practical utility in a comparative safety study of metastatic castration‑resistant prostate cancer treatments, using CausalMix to compare estimators under calibrated data‑generating processes, tune hyperparameters, and conduct simulation‑based power analyses under targeted treatment effect heterogeneity scenarios.
Authors:Achleshwar Luthra, Yash Salunkhe, Tomer Galanti
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].
Authors:Jiahao Qin
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
Authors:Peter Adema, Karim Galliamov, Aleksey Evstratovskiy, Ross Geurts
Abstract:
Offline Reinforcement Learning (RL) is an emerging field of RL in which policies are learned solely from demonstrations. Within offline RL, some environments involve balancing multiple objectives, but existing multi‑objective offline RL algorithms do not provide an efficient way to find a fair compromise. FairDICE (see arXiv:2506.08062v2) seeks to fill this gap by adapting OptiDICE (an offline RL algorithm) to automatically learn weights for multiple objectives to e.g. incentivise fairness among objectives. As this would be a valuable contribution, this replication study examines the replicability of claims made regarding FairDICE. We find that many theoretical claims hold, but an error in the code reduces FairDICE to standard behaviour cloning in continuous environments, and many important hyperparameters were originally underspecified. After rectifying this, we show in experiments extending the original paper that FairDICE can scale to complex environments and high‑dimensional rewards, though it can be reliant on (online) hyperparameter tuning. We conclude that FairDICE is a theoretically interesting method, but the experimental justification requires significant revision.
Authors:Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Zorah Lähner, Moshe Eliasof
Abstract:
Obtaining a single‑vector representation from a Large Language Model's (LLM) token‑level outputs is a critical step for nearly all sentence‑level tasks. However, standard pooling methods like mean or max aggregation treat tokens as an independent set, discarding the rich relational structure captured by the model's self‑attention layers and making them susceptible to signal dilution. To address this, we introduce GLOT, a lightweight, structure‑aware pooling module that reframes pooling as relational learning followed by aggregation. Operating on the outputs of a frozen LLM, GLOT first constructs a latent token‑similarity graph, then refines token representations with a graph neural network, and finally aggregates them using a readout layer. Experimentally, our approach is remarkably robust and efficient: on a diagnostic stress test where 90% of tokens are random distractors, GLOT maintains over 97% accuracy while baseline methods collapse. Furthermore, it is competitive with state‑of‑the‑art techniques on benchmarks like GLUE and MTEB with 20x fewer trainable parameters and speeds up the training time by over 100x compared with parameter‑efficient fine‑tuning methods. Supported by a theoretical analysis of its expressive power, our work shows that learning over token graphs is a powerful paradigm for the efficient adaptation of frozen LLMs. Our code is published at https://github.com/ipsitmantri/GLOT.
Authors:Ashwath Vaithinathan Aravindan, Mayank Kejriwal
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
Authors:Xin Yang, Letian Li, Abudukelimu Wuerkaixi, Xuxin Cheng, Cao Liu, Ke Zeng, Xunliang Cai, Wenyuan Jiang
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.
Authors:Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Jingjing Wang, Xuanzhao Dong, Minzhou Huang, Rui Cai, Hejian Sang, Hao Wang, Peijie Qiu, Yueyue Deng, Prayag Tiwari, Brendan Hogan Rappazzo, Yalin Wang
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.
Authors:Hanyang Wang, Yiyang Liu, Jiawei Chi, Fangfu Liu, Ran Xue, Yueqi Duan
Abstract:
Classifier‑Free Guidance (CFG) has emerged as a central approach for enhancing semantic alignment in flow‑based diffusion models. In this paper, we explore a unified framework called CFG‑Ctrl, which reinterprets CFG as a control applied to the first‑order continuous‑time generative flow, using the conditional‑unconditional discrepancy as an error signal to adjust the velocity field. From this perspective, we summarize vanilla CFG as a proportional controller (P‑control) with fixed gain, and typical follow‑up variants develop extended control‑law designs derived from it. However, existing methods mainly rely on linear control, inherently leading to instability, overshooting, and degraded semantic fidelity especially on large guidance scales. To address this, we introduce Sliding Mode Control CFG (SMC‑CFG), which enforces the generative flow toward a rapidly convergent sliding manifold. Specifically, we define an exponential sliding mode surface over the semantic prediction error and introduce a switching control term to establish nonlinear feedback‑guided correction. Moreover, we provide a Lyapunov stability analysis to theoretically support finite‑time convergence. Experiments across text‑to‑image generation models including Stable Diffusion 3.5, Flux, and Qwen‑Image demonstrate that SMC‑CFG outperforms standard CFG in semantic alignment and enhances robustness across a wide range of guidance scales. Project Page: https://hanyang‑21.github.io/CFG‑Ctrl
Authors:Toru Lin, Shuying Deng, Zhao-Heng Yin, Pieter Abbeel, Jitendra Malik
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.
Authors:Jessie Z. Li, Zhiqing Hong, Toru Shirakawa, Serina Chang
Abstract:
Human mobility trajectories are widely studied in public health and social science, where different demographic groups exhibit significantly different mobility patterns. However, existing trajectory generation models rarely capture this heterogeneity because most trajectory datasets lack demographic labels. To address this gap in data, we propose ATLAS, a weakly supervised approach for demographic‑conditioned trajectory generation using only (i) individual trajectories without demographic labels, (ii) region‑level aggregated mobility features, and (iii) region‑level demographic compositions from census data. ATLAS trains a trajectory generator and fine‑tunes it so that simulated mobility matches observed regional aggregates while conditioning on demographics. Experiments on real trajectory data with demographic labels show that ATLAS substantially improves demographic realism over baselines (JSD \downarrow 12%‑‑69%) and closes much of the gap to strongly supervised training. We further develop theoretical analyses for when and why ATLAS works, identifying key factors including demographic diversity across regions and the informativeness of the aggregate feature, paired with experiments demonstrating the practical implications of our theory. We release our code at https://github.com/schang‑lab/ATLAS.
Authors:Huanlei Guo, Hongxin Wei, Bingyi Jing
Abstract:
Recent text‑to‑image (T2I) diffusion and flow‑matching models can produce highly realistic images from natural language prompts. In practical scenarios, T2I systems are often run in a ``generate‑‑then‑‑select'' mode: many seeds are sampled and only a few images are kept for use. However, this pipeline is highly resource‑intensive since each candidate requires tens to hundreds of denoising steps, and evaluation metrics such as CLIPScore and ImageReward are post‑hoc. In this work, we address this inefficiency by introducing Probe‑Select, a plug‑in module that enables efficient evaluation of image quality within the generation process. We observe that certain intermediate denoiser activations, even at early timesteps, encode a stable coarse structure, object layout and spatial arrangement‑‑that strongly correlates with final image fidelity. Probe‑Select exploits this property by predicting final quality scores directly from early activations, allowing unpromising seeds to be terminated early. Across diffusion and flow‑matching backbones, our experiments show that early evaluation at only 20% of the trajectory accurately ranks candidate seeds and enables selective continuation. This strategy reduces sampling cost by over 60% while improving the quality of the retained images, demonstrating that early structural signals can effectively guide selective generation without altering the underlying generative model. Code is available at https://github.com/Guhuary/ProbeSelect.
Authors:Erik Hartman, Di Tang, Johan Malmström
Abstract:
Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence‑function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data‑efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of Streptococcus pneumoniae. BoGA accelerates the discovery of high‑confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at \hrefhttps://github.com/ErikHartman/bopepGitHub.
Authors:Zixuan Xu, Tiancheng He, Huahui Yi, Kun Wang, Xi Chen, Gongli Xi, Qiankun Li, Kang Li, Yang Liu, Zhigang Zeng
Abstract:
Vision‑language models remain susceptible to multimodal jailbreaks and over‑refusal because safety hinges on both visual evidence and user intent, while many alignment pipelines supervise only the final response. To address this, we present SaFeR‑ToolKit, which formalizes safety decision‑making as a checkable protocol. Concretely, a planner specifies a persona, a Perception \to Reasoning \to Decision tool set, and a constrained transition graph, while a responder outputs a typed key‑value tool trace before the final answer. To make the protocol reliably followed in practice, we train a single policy with a three‑stage curriculum (SFT \to DPO \to GRPO), where GRPO directly supervises tool usage beyond answer‑level feedback. Our contributions are two‑fold: I. Dataset. The first tool‑based safety reasoning dataset, comprising 31,654 examples (SFT 6k, DPO 18.6k, GRPO 6k) plus 1k held‑out evaluation. II. Experiments. On Qwen2.5‑VL, SaFeR‑ToolKit significantly improves Safety/Helpfulness/Reasoning Rigor on 3B (29.39/45.04/4.98 \to 84.40/71.13/78.87) and 7B (53.21/52.92/19.26 \to 86.34/80.79/85.34), while preserving general capabilities (3B: 58.67 \to 59.21; 7B: 66.39 \to 66.81). Codes are available at https://github.com/Duebassx/SaFeR_ToolKit.
Authors:Zhiyu Pan, Yizheng Wu, Jiashen Hua, Junyi Feng, Shaotian Yan, Bing Deng, Zhiguo Cao, Jieping Ye
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.
Authors:Xuejin Luo, Shiquan Sun, Runshi Zhang, Ruizhi Zhang, Junchen Wang
Abstract:
During surgery, scrub nurses are required to frequently deliver surgical instruments to surgeons, which can lead to physical fatigue and decreased focus. Robotic scrub nurses provide a promising solution that can replace repetitive tasks and enhance efficiency. Existing research on robotic scrub nurses relies on predefined paths for instrument delivery, which limits their generalizability and poses safety risks in dynamic environments. To address these challenges, we present a collision‑free dual‑arm surgical assistive robot capable of performing instrument delivery. A vision‑language model is utilized to automatically generate the robot's grasping and delivery trajectories in a zero‑shot manner based on surgeons' instructions. A real‑time obstacle minimum distance perception method is proposed and integrated into a unified quadratic programming framework. This framework ensures reactive obstacle avoidance and self‑collision prevention during the dual‑arm robot's autonomous movement in dynamic environments. Extensive experimental validations demonstrate that the proposed robotic system achieves an 83.33% success rate in surgical instrument delivery while maintaining smooth, collision‑free movement throughout all trials. The project page and source code are available at https://give‑me‑scissors.github.io/.
Authors:Liu Yang, Zeyu Nie, Andrew Liu, Felix Zou, Deniz Altinbüken, Amir Yazdanbakhsh, Quanquan C. Liu
Abstract:
The transition from sequential to parallel computing is essential for modern high‑performance applications but is hindered by the steep learning curve of concurrent programming. This challenge is magnified for irregular data structures (such as sparse graphs, unbalanced trees, and non‑uniform meshes) where static scheduling fails and data dependencies are unpredictable. Current Large Language Models (LLMs) often fail catastrophically on these tasks, generating code plagued by subtle race conditions, deadlocks, and sub‑optimal scaling.
We bridge this gap with ParEVO, a framework designed to synthesize high‑performance parallel algorithms for irregular data. Our contributions include: (1) The Parlay‑Instruct Corpus, a curated dataset of 13,820 tasks synthesized via a "Critic‑Refine" pipeline that explicitly filters for empirically performant algorithms that effectively utilize Work‑Span parallel primitives; (2) specialized DeepSeek, Qwen, and Gemini models fine‑tuned to align probabilistic generation with the rigorous semantics of the ParlayLib library; and (3) an Evolutionary Coding Agent (ECA) that improves the "last mile" of correctness by iteratively repairing code using feedback from compilers, dynamic race detectors, and performance profilers.
On the ParEval benchmark, ParEVO achieves an average 106x speedup (with a maximum of 1103x) across the suite, and a robust 13.6x speedup specifically on complex irregular graph problems, outperforming state‑of‑the‑art commercial models. Furthermore, our evolutionary approach matches state‑of‑the‑art expert human baselines, achieving up to a 4.1x speedup on specific highly‑irregular kernels. Source code and datasets are available at https://github.com/WildAlg/ParEVO.
Authors:Semih Cantürk, Thomas Sabourin, Frederik Wenkel, Michael Perlmutter, Guy Wolf
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 .
Authors:Zhanghan Ni, Yanjing Li, Zeju Qiu, Bernhard Schölkopf, Hongyu Guo, Weiyang Liu, Shengchao Liu
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.
Authors:Marta Grzeskiewicz
Abstract:
We develop a flexible neural demand system for continuous budget allocation that estimates budget shares on the simplex by minimizing KL divergence. Shares are produced via a softmax of a state‑dependent preference scorer and disciplined with regularity penalties (monotonicity, Slutsky symmetry) to support coherent comparative statics and welfare without imposing a parametric utility form. State dependence enters through a habit stock defined as an exponentially weighted moving average of past consumption. Simulations recover elasticities and welfare accurately and show sizable gains when habit formation is present. In our empirical application using Dominick's analgesics data, adding habit reduces out‑of‑sample error by c.33%, reshapes substitution patterns, and increases CV losses from a 10% ibuprofen price rise by about 15‑16% relative to a static model. The code is available at https://github.com/martagrz/neural_demand_habit .
Authors:Kyle Elliott Mathewson
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.
Authors:Jiace Zhu, Wentao Chen, Qi Fan, Zhixing Ren, Junying Wu, Xing Zhe Chai, Chotiwit Rungrueangwutthinon, Yehan Ma, An Zou
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.
Authors:Ran Li, Shimin Di, Haowei LI, Luanshi Bu, Jiachuan Wang, Wangze Ni, Lei Chen
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.
Authors:Moru Liu, Hao Dong, Olga Fink, Mario Trapp
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.
Authors:Richard Freinschlag, Timo Bertram, Erich Kobler, Andreas Mayr, Günter Klambauer
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.
Authors:Songtao Liu, Hongwu Peng, Zhiwei Zhang, Zhengyu Chen, Yue Guo
Abstract:
Long‑context inference in large language models is bottlenecked by Key‑‑Value (KV) cache loading during the decoding stage, where the sequential nature of generation requires repeatedly transferring the KV cache from off‑chip High‑Bandwidth Memory (HBM) to on‑chip Static Random‑Access Memory (SRAM) at each step. While Multi‑Head Latent Attention (MLA) significantly reduces the total KV cache size, it suffers from a sharding bottleneck during distributed decoding via Tensor Parallelism (TP). Since its single latent head cannot be partitioned, each device is forced to redundantly load the complete KV cache for every token, consuming excessive memory traffic and diminishing TP benefits like weight sharding. In this work, we propose Multi‑Head Low‑Rank Attention (MLRA), which enables partitionable latent states for efficient 4‑way TP decoding. Extensive experiments show that MLRA achieves state‑of‑the‑art perplexity and downstream task performance, while also delivering a 2.8× decoding speedup over MLA. Code is available at https://github.com/SongtaoLiu0823/MLRA. Pretrained weights, along with the training and evaluation data, are available at https://huggingface.co/Soughing/MLRA.
Authors:Jinqi Wu, Sishuo Chen, Zhangming Chan, Yong Bai, Lei Zhang, Sheng Chen, Chenghuan Hou, Xiang-Rong Sheng, Han Zhu, Jian Xu, Bo Zheng, Chaoyou Fu
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.
Authors:Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sidhant Kaushik, Aditya Shah, Alex S. Huang, Luke Zettlemoyer, Dieter Fox, Yu Xiang, Anqi Li, Andreea Bobu, Abhishek Gupta, Stephen Tu, Erdem Biyik, Jesse Zhang
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/.
Authors:Harikrishnan Unnikrishnan, Rita Patel
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.
Authors:Cristiana Diaconu, Miles Cranmer, Richard E. Turner, Tanya Marwah, Payel Mukhopadhyay
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.
Authors:Songming Zhang, Xue Zhang, Tong Zhang, Bojie Hu, Yufeng Chen, Jinan Xu
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
Authors:Hongkun Dou, Zike Chen, Zeyu Li, Hongjue Li, Lijun Yang, Yue Deng
Abstract:
Diffusion models have gained prominence as powerful generative tools for solving inverse problems due to their ability to model complex data distributions. However, existing methods typically rely on complete knowledge of the forward observation process to compute gradients for guided sampling, limiting their applicability in scenarios where such information is unavailable. In this work, we introduce \emphConstrained Particle Seeking (CPS), a novel gradient‑free approach that leverages all candidate particle information to actively search for the optimal particle while incorporating constraints aligned with high‑density regions of the unconditional prior. Unlike previous methods that passively select promising candidates, CPS reformulates the inverse problem as a constrained optimization task, enabling more flexible and efficient particle seeking. We demonstrate that CPS can effectively solve both image and scientific inverse problems, achieving results comparable to gradient‑based methods while significantly outperforming gradient‑free alternatives. Code is available at https://github.com/deng‑ai‑lab/CPS.
Authors:Zichen Tian, Yaoyao Liu, Qianru Sun
Abstract:
Training large foundation models from scratch for domain‑specific applications is almost impossible due to data limits and long‑tailed distributions ‑‑ taking remote sensing (RS) as an example. Fine‑tuning natural image pre‑trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter‑efficient fine‑tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters ‑‑ such as intra‑layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine‑tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module influence during fine‑tuning. MetaPEFT dynamically adjusts three key factors of PEFT on RS images: module insertion, layer selection, and module‑wise learning rates, which collectively control the influence of PEFT modules across the network. We conduct extensive experiments on three transfer‑learning scenarios and five datasets in both RS and natural image domains. The results show that MetaPEFT achieves state‑of‑the‑art performance in cross‑spectral adaptation, requiring only a small amount of trainable parameters and improving tail‑class accuracy significantly.
Authors:Naoki Shitanda, Motoki Omura, Tatsuya Harada, Takayuki Osa
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/ .
Authors:Qizheng Li, Yifei Zhang, Xiao Yang, Xu Yang, Zhuo Wang, Weiqing Liu, Jiang Bian
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.
Authors:Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Mingrui Xu, Weiqing Liu, Jiang Bian
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.
Authors:Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi, Aida Derrablia, Warda Deghmane, Mohamed Amine Ferrag
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.
Authors:Jérome Eertmans, Enrico M. Vitucci, Vittorio Degli-Esposti, Nicola Di Cicco, Laurent Jacques, Claude Oestges
Abstract:
Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of candidate paths scales with the number of objects raised to the interaction order. This bottleneck limits its use in large‑scale or real‑time applications, forcing traditional tools to rely on heuristics that reduce path candidates at the cost of potentially reduced accuracy. To overcome this limitation, we propose a machine‑learning‑assisted framework that replaces exhaustive path searching with intelligent sampling via Generative Flow Networks. Applying these generative models to this domain presents challenges, particularly sparse rewards due to the rarity of valid paths, which can lead to convergence failures and trivial solutions when evaluating high‑order interactions in complex environments. To ensure robust learning and efficient exploration, our framework incorporates three key components. First, an \emphexperience replay buffer captures and retains rare valid paths. Second, a uniform exploratory policy improves generalization and prevents overfitting to simple geometries. Third, a physics‑based action masking strategy filters out physically impossible paths before the model considers them. Validated on idealized street‑canyon scenarios, our model achieves substantial speedups over exhaustive search ‑‑ up to 10× faster on GPU and 100× faster on CPU ‑‑ while maintaining high coverage accuracy and successfully uncovering complex propagation paths. However, out‑of‑distribution evaluations on real‑world Manhattan street geometries reveal that generalizing to substantially different urban morphologies requires further advancement in model capacity or alternative training strategies. Source code, tests, and a tutorial are available at https://github.com/jeertmans/sampling‑paths.
Authors:Yuexi Du, Jinglu Wang, Shujie Liu, Nicha C. Dvornek, Yan Lu
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/
Authors:Ningfeng Yang, Tor M. Aamodt
Abstract:
Quantization‑Aware Pre‑Training (QAPT) is an effective technique to reduce the compute and memory overhead of Deep Neural Networks while improving their energy efficiency on edge devices. Existing QAPT methods produce models stored in compute‑efficient data types (e.g. integers) that are not information theoretically optimal (ITO). On the other hand, existing ITO data types (e.g. Quantile/NormalFloat Quantization) are not compute‑efficient. We propose BBQ, the first ITO quantization method that is also compute‑efficient. BBQ builds on our key insight that since learning is domain‑agnostic, the output of a quantizer does not need to reside in the same domain as its input. BBQ performs ITO quantization in its input domain, and returns its output in a compute‑efficient domain where ITO data types are mapped to compute‑efficient data types. Without sacrificing compute efficiency, BBQ outperforms prior SOTA QAPT methods by a perplexity reduction of up to 2 points for 4‑bit models, up to 4 points for 3‑bit models, up to 5 points for 2‑bit models, and up to 18 points for 1‑bit models. Code is available at https://github.com/1733116199/bbq.
Authors:Guang Huang, Zeyi Wen
Abstract:
Speculative Decoding (SD) has emerged as a premier technique for accelerating Large Language Model (LLM) inference by decoupling token generation into rapid drafting and parallel verification. While recent advancements in self‑speculation and lookahead decoding have successfully minimized drafting overhead, they have shifted the primary performance bottleneck to the verification phase. Since verification requires a full forward pass of the target model, it remains strictly memory‑bandwidth bound, fundamentally limiting the maximum achievable speedup.In this paper, we introduce Quasar (Quantized Self‑speculative Acceleration for Rapid Inference), a novel, training‑free framework designed to overcome this "memory wall" by employing low‑bit quantization specifically for the verification stage. Our empirical analysis reveals that while aggressive structural pruning significantly degrades verification accuracy, quantization‑based verification preserves the logit distribution with high fidelity while effectively halving memory traffic. Extensive experiments on state‑of‑the‑art models (e.g., OpenPangu and Qwen3) demonstrate that Quasar maintains a speculative acceptance length comparable to full‑precision methods while achieving a 1.28× improvement in end‑to‑end throughput. Being orthogonal to existing drafting strategies, Quasar offers a generic and efficient pathway to accelerate the verification leg of speculative execution. Code is available at https://github.com/Tom‑HG/Quasar.
Authors:Mehdi Makni, Xiang Meng, Rahul Mazumder
Abstract:
Sparse plus Low‑Rank (\mathbfS + \mathbfLR) decomposition of Large Language Models (LLMs) has emerged as a promising direction in model compression, aiming to decompose pre‑trained model weights into a sum of sparse and low‑rank matrices (\mathbfW \approx \mathbfS + \mathbfLR). Despite recent progress, existing methods often suffer from substantial performance degradation compared to dense models. In this work, we introduce 3BASiL‑TM, an efficient one‑shot post‑training method for (\mathbfS + \mathbfLR) decomposition of LLMs that addresses this gap. Our approach first introduces a novel 3‑Block Alternating Direction Method of Multipliers (ADMM) method, termed 3BASiL, to minimize the layer‑wise reconstruction error with convergence guarantees. We then design an efficient transformer‑matching (TM) refinement step that jointly optimizes the sparse and low‑rank components across transformer layers. This step minimizes a novel memory‑efficient loss that aligns outputs at the transformer level. Notably, the TM procedure is universal as it can enhance any (\mathbfS + \mathbfLR) decomposition, including pure sparsity. Our numerical experiments show that 3BASiL‑TM reduces the WikiText2 perplexity gap relative to dense LLaMA‑8B model by over 30% under a (2:4 Sparse + 64 LR) configuration, compared to prior methods. Moreover, our method achieves over 2.5x faster compression runtime on an A100 GPU compared to SOTA (\mathbfS + \mathbfLR) method. Our code is available at https://github.com/mazumder‑lab/3BASiL.
Authors:Maifang Zhang, Hang Yu, Qian Zuo, Cheng Wang, Vaishak Belle, Fengxiang He
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.
Authors:Masahiro Kaneko, Ayana Niwa, Timothy Baldwin
Abstract:
Fake news undermines societal trust and decision‑making across politics, economics, health, and international relations, and in extreme cases threatens human lives and societal safety. Because fake news reflects region‑specific political, social, and cultural contexts and is expressed in language, evaluating the risks of large language models (LLMs) requires a multi‑lingual and regional perspective. Malicious users can bypass safeguards through jailbreak attacks, inducing LLMs to generate fake news. However, no benchmark currently exists to systematically assess attack resilience across languages and regions. Here, we propose JailNewsBench, the first benchmark for evaluating LLM robustness against jailbreak‑induced fake news generation. JailNewsBench spans 34 regions and 22 languages, covering 8 evaluation sub‑metrics through LLM‑as‑a‑Judge and 5 jailbreak attacks, with approximately 300k instances. Our evaluation of 9 LLMs reveals that the maximum attack success rate (ASR) reached 86.3% and the maximum harmfulness score was 3.5 out of 5. Notably, for English and U.S.‑related topics, the defensive performance of typical multi‑lingual LLMs was significantly lower than for other regions, highlighting substantial imbalances in safety across languages and regions. In addition, our analysis shows that coverage of fake news in existing safety datasets is limited and less well defended than major categories such as toxicity and social bias. Our dataset and code are available at https://github.com/kanekomasahiro/jail_news_bench.
Authors:Oscar Rivera, Ziqing Wang, Matthieu Dagommer, Abhishek Pandey, Kaize Ding
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.
Authors:Gaojie Jin, Xinping Yi, Wei Huang, Sven Schewe, Xiaowei Huang
Abstract:
Adversarial training has emerged as a highly effective way to improve the robustness of deep neural networks (DNNs). It is typically conceptualized as a min‑max optimization problem over model weights and adversarial perturbations, where the weights are optimized using gradient descent methods, such as SGD. In this paper, we propose a novel approach by treating model weights as random variables, which paves the way for enhancing adversarial training through Second‑Order Statistics Optimization (S^2O) over model weights. We challenge and relax a prevalent, yet often unrealistic, assumption in prior PAC‑Bayesian frameworks: the statistical independence of weights. From this relaxation, we derive an improved PAC‑Bayesian robust generalization bound. Our theoretical developments suggest that optimizing the second‑order statistics of weights can substantially tighten this bound. We complement this theoretical insight by conducting an extensive set of experiments that demonstrate that S^2O not only enhances the robustness and generalization of neural networks when used in isolation, but also seamlessly augments other state‑of‑the‑art adversarial training techniques. The code is available at https://github.com/Alexkael/S2O.
Authors:Changwoo Baek, Jouwon Song, Sohyeon Kim, Kyeongbo Kong
Abstract:
Large Vision‑Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive visual token sequences. While prior works primarily focus on either attention‑based or diversity‑based pruning methods, in‑depth analysis of these approaches' characteristics and limitations remains largely unexplored. In this work, we conduct thorough empirical analysis using effective rank (erank) as a measure of feature diversity and attention score entropy to investigate visual token processing mechanisms and analyze the strengths and weaknesses of each approach. Our analysis reveals two insights: (1) Our erank‑based quantitative analysis shows that many diversity‑oriented pruning methods preserve substantially less feature diversity than intended; moreover, analysis using the CHAIR dataset reveals that the diversity they do retain is closely tied to increased hallucination frequency compared to attention‑based pruning. (2) We further observe that attention‑based approaches are more effective on simple images where visual evidence is concentrated, while diversity‑based methods better handle complex images with distributed features. Building on these empirical insights, we show that incorporating image‑aware adjustments into existing hybrid pruning strategies consistently improves their performance. We also provide a minimal instantiation of our empirical findings through a simple adaptive pruning mechanism, which achieves strong and reliable performance across standard benchmarks as well as hallucination‑specific evaluations. Our project page available at https://cvsp‑lab.github.io/AgilePruner.
Authors:Victor May, Aaditya Salgarkar, Yishan Wang, Diganta Misra, Huu Nguyen
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.
Authors:Hrishikesh Viswanath, Hong Chul Nam, Xi Deng, Julius Berner, Anima Anandkumar, Aniket Bera
Abstract:
Training neural PDE solvers is often bottlenecked by expensive data generation or unstable physics‑informed neural network (PINN) involving challenging optimization landscapes due to higher‑order derivatives. To tackle this issue, we propose an alternative approach using Monte Carlo approaches to estimate the solution to the PDE as a stochastic process for weak supervision during training. Leveraging the Walk‑on‑Spheres method, we introduce a learning scheme called \emphWalk‑on‑Spheres Neural Operator (WoS‑NO) which uses weak supervision from WoS to train any given neural operator. We propose to amortize the cost of Monte Carlo walks across the distribution of PDE instances using stochastic representations from the WoS algorithm to generate cheap, noisy, estimates of the PDE solution during training. This is formulated into a data‑free physics‑informed objective where a neural operator is trained to regress against these weak supervisions, allowing the operator to learn a generalized solution map for an entire family of PDEs. This strategy does not require expensive pre‑computed datasets, avoids computing higher‑order derivatives for loss functions that are memory‑intensive and unstable, and demonstrates zero‑shot generalization to novel PDE parameters and domains. Experiments show that for the same number of training steps, our method exhibits up to 8.75× improvement in L_2‑error compared to standard physics‑informed training schemes, up to 6.31× improvement in training speed, and reductions of up to 2.97× in GPU memory consumption. We present the code at https://github.com/neuraloperator/WoS‑NO
Authors:Sumin Kim, Hyemin Jeong, Mingu Kang, Yejin Kim, Yoori Oh, Joonseok Lee
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.
Authors:Tongtong Wu, Yanming Li, Ziye Tang, Chen Jiang, Linhao Luo, Guilin Qi, Shirui Pan, Gholamreza Haffari
Abstract:
Large language model (LLM)‑based multi‑agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and robustness of these systems critically depend on their communication topology, which is often fixed or statically learned, ignoring real‑world dynamics such as model upgrades, API (or tool) changes, or knowledge source variability. To address this limitation, we propose CARD (Conditional Agentic Graph Designer), a conditional graph‑generation framework that instantiates AMACP, a protocol for adaptive multi‑agent communication. CARD explicitly incorporates dynamic environmental signals into graph construction, enabling topology adaptation at both training and runtime. Through a conditional variational graph encoder and environment‑aware optimization, CARD produces communication structures that are both effective and resilient to shifts in model capability or resource availability. Empirical results on HumanEval, MATH, and MMLU demonstrate that CARD consistently outperforms static and prompt‑based baselines, achieving higher accuracy and robustness across diverse conditions. The source code is available at: https://github.com/Warma10032/CARD.
Authors:Zebin You, Xiaolu Zhang, Jun Zhou, Chongxuan Li, Ji-Rong Wen
Abstract:
We present LLaDA‑o, an effective and length‑adaptive omni diffusion model for multimodal understanding and generation. LLaDA‑o is built on a Mixture of Diffusion (MoD) framework that decouples discrete masked diffusion for text understanding and continuous diffusion for visual generation, while coupling them through a shared, simple, and efficient attention backbone that reduces redundant computation for fixed conditions. Building on MoD, we further introduce a data‑centric length adaptation strategy that enables flexible‑length decoding in multimodal settings without architectural changes. Extensive experiments show that LLaDA‑o achieves state‑of‑the‑art performance among omni‑diffusion models on multimodal understanding and generation benchmarks, and reaches 87.04 on DPG‑Bench for text‑to‑image generation, supporting the effectiveness of unified omni diffusion modeling. Code is available at https://github.com/ML‑GSAI/LLaDA‑o.
Authors:Jiafeng Lin, Yuxuan Wang, Jialong Wu, Huakun Luo, Zhongyi Pei, Jianmin Wang
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.
Authors:Yangyang Xu, Junbo Ke, You-Wei Wen, Chao Wang
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.
Authors:Ke Sun, Hongming Zhang, Jun Jin, Chao Gao, Xi Chen, Wulong Liu, Linglong Kong
Abstract:
Inspired by the human learning and memory system, particularly the interplay between the hippocampus and cerebral cortex, this study proposes a dual‑learner framework comprising a fast learner and a meta learner to address continual Reinforcement Learning~(RL) problems. These two learners are coupled to perform distinct yet complementary roles: the fast learner focuses on knowledge transfer, while the meta learner ensures knowledge integration. In contrast to traditional multi‑task RL approaches that share knowledge through average return maximization, our meta learner incrementally integrates new experiences by explicitly minimizing catastrophic forgetting, thereby supporting efficient cumulative knowledge transfer for the fast learner. To facilitate rapid adaptation in new environments, we introduce an adaptive meta warm‑up mechanism that selectively harnesses past knowledge. We conduct experiments in various pixel‑based and continuous control benchmarks, revealing the superior performance of continual learning for our proposed dual‑learner approach relative to baseline methods. The code is released in https://github.com/datake/FAME.
Authors:Igor Berezkin
Abstract:
Work introduces a hierarchical binary tree‑based reduction that replaces standard self‑attention. The core idea is to use a recursive Gated Linear Unit merge operation, achieving O(n) total merge operations O(log n) parallel depth O(n d^2) total work and O(n) space complexity. In these experiments, the model significantly outperforms standard Transformers in both convergence speed and accuracy on long‑range structural dependencies, specifically where hierarchical inductive bias is critical.
Authors:Shilong Tao, Zhe Feng, Shaohan Chen, Weichen Zhang, Zhanxing Zhu, Yunhuai Liu
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.
Authors:Minkyoung Cho, Insu Jang, Shuowei Jin, Zesen Zhao, Adityan Jothi, Ethem F. Can, Min-Hung Chen, Z. Morley Mao
Abstract:
Fine‑tuning Multimodal Large Language Models (MLLMs) with parameter‑efficient methods like Low‑Rank Adaptation (LoRA) is crucial for task adaptation. However, imbalanced training dynamics across modalities often lead to suboptimal accuracy due to negative interference, a challenge typically addressed with inefficient heuristic methods such as manually tuning separate learning rates. To overcome this, we introduce MARS (Multimodal Adaptive Rank Search), an approach to discover optimal rank pairs that balance training dynamics while maximizing performance. Our key innovation, a proposed framework of dual scaling laws, enables this search: one law models module‑specific convergence time to prune the search space to candidates with aligned dynamics, while the other predicts final task performance to select the optimal pair from the pruned set. By re‑purposing the LoRA rank as a controller for modality‑specific convergence speed, MARS outperforms baseline methods and provides a robust, automated strategy for optimizing MLLM fine‑tuning.
Authors:Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Zhiqiang Shen
Abstract:
Dataset pruning has been widely studied for 2D images to remove redundancy and accelerate training, while particular pruning methods for 3D data remain largely unexplored. In this work, we study dataset pruning for 3D data, where its observed common long‑tail class distribution nature make optimization under conventional evaluation metrics Overall Accuracy (OA) and Mean Accuracy (mAcc) inherently conflicting, and further make pruning particularly challenging. To address this, we formulate pruning as approximating the full‑data expected risk with a weighted subset, which reveals two key errors: coverage error from insufficient representativeness and prior‑mismatch bias from inconsistency between subset‑induced class weights and target metrics. We propose representation‑aware subset selection with per‑class retention quotas for long‑tail coverage, and prior‑invariant teacher supervision using calibrated soft labels and embedding‑geometry distillation. The retention quota also serves as a switch to control the OA‑mAcc trade‑off. Extensive experiments on 3D datasets show that our method can improve both metrics across multiple settings while adapting to different downstream preferences. Our code is available at https://github.com/XiaohanZhao123/3D‑Dataset‑Pruning.
Authors:Jin Zeng, Yupeng Qi, Hui Li, Chengming Li, Ziyu Lyu, Lixin Cui, Lu Bai
Abstract:
Large language models (LLMs) are increasingly adopted as the backbone of recommender systems. However, user‑item interactions in real‑world scenarios are non‑stationary, making preference drift over time inevitable. Existing model update strategies mainly rely on global fine‑tuning or pointwise editing, but they face two fundamental challenges: (i) imbalanced update granularity, where global updates perturb behaviors unrelated to the target while pointwise edits fail to capture broader preference shifts; (ii) unstable incremental updates, where repeated edits interfere with prior adaptations, leading to catastrophic forgetting and inconsistent recommendations. To address these issues, we propose Region‑Aware Incremental Editing (RAIE), a plug‑in framework that freezes the backbone model and performs region‑level updates. RAIE first constructs semantically coherent preference regions via spherical k‑means in the representation space. It then assigns incoming sequences to regions via confidence‑aware gating and performs three localized edit operations ‑ Update, Expand, and Add ‑ to dynamically revise the affected region. Each region is equipped with a dedicated Low‑Rank Adaptation (LoRA) module, which is trained only on the region's updated data. During inference, RAIE routes each user sequence to its corresponding region and activates the region‑specific adapter for prediction. Experiments on two benchmark datasets under a time‑sliced protocol that segments data into Set‑up (S), Finetune (F), and Test (T) show that RAIE significantly outperforms state‑of‑the‑art baselines while effectively mitigating forgetting. These results demonstrate that region‑aware editing offers an accurate and scalable mechanism for continual adaptation in dynamic recommendation scenarios. Our code is available at https://github.com/fengaogao/RAIE.
Authors:Cedric Damour
Abstract:
We propose a retrodictive forecasting paradigm for time series: instead of predicting the future from the past, we identify the future that best explains the observed present via inverse MAP optimization over a Conditional Variational Autoencoder (CVAE). This conditioning is a statistical modeling choice for Bayesian inversion; it does not assert that future events cause past observations. The approach is theoretically grounded in an information‑theoretic arrow‑of‑time measure: the symmetrized Kullback‑Leibler divergence between forward and time‑reversed trajectory ensembles provides both the conceptual rationale and an operational GO/NO‑GO diagnostic for applicability. We implement the paradigm as MAP inference over an inverse CVAE with a learned RealNVP normalizing‑flow prior and evaluate it on six time series cases: four synthetic processes with controlled temporal asymmetry and two ERA5 reanalysis datasets (wind speed and solar irradiance). The work makes four contributions: (i) a formal retrodictive inference formulation; (ii) an inverse CVAE architecture; (iii) a model‑free irreversibility diagnostic; and (iv) a falsifiable validation protocol with four pre‑specified predictions. All pre‑specified predictions are empirically supported: the diagnostic correctly classifies all six cases; the learned flow prior improves over an isotropic Gaussian baseline on GO cases; the inverse MAP yields no spurious advantage on time‑reversible dynamics; and on irreversible GO cases, it achieves competitive or superior RMSE relative to forward baselines, with a statistically significant 17.7% reduction over a forward MLP on ERA5 solar irradiance. These results provide a structured proof‑of‑concept that retrodictive forecasting can constitute a viable alternative to conventional forward prediction when statistical time‑irreversibility is present and exploitable.
Authors:Yunzhong Qiu, Zhiyao Cen, Zhongyi Pei, Chen Wang, Jianmin Wang
Abstract:
Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real‑world time series data, leading to an unsatisfactory trade‑off between forecasting accuracy and generalization. Rather than continually finetuning new LTM instances for each domain, we propose a data‑centric framework, time‑series adaptive transformation optimization (TATO), that enables a single frozen pre‑trained LTM to adapt to diverse downstream domains through an optimally configured transformation pipeline. Specifically, TATO constructs three representative types of transformations, including context slicing, scale normalization, and outlier correction, to help LTMs better align with target domain characteristics. To ensure robustness, we incorporate carefully selected time series augmentations and a two‑stage ranking mechanism that filters out pipelines underperforming on specific metrics. Extensive experiments on state‑of‑the‑art LTMs and widely used datasets demonstrate that TATO consistently and significantly improves domain‑adaptive forecasting performance, achieving a maximum reduction in MSE of 65.4% and an average reduction of 13.6%. Moreover, TATO is highly efficient, typically completing optimization in under 2 minutes, making it practical for real‑world deployment. The source code is available at https://github.com/thulab/TATO.
Authors:Zhanwang Liu, Yuting Li, Haoyuan Gao, Yexin Li, Linghe Kong, Lichao Sun, Weiran Huang
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.
Authors:Li Sun, Zhenhao Huang, Silei Chen, Lanxu Yang, Junda Ye, Sen Su, Philip S. Yu
Abstract:
Multi‑domain graph pre‑training integrates knowledge from diverse domains to enhance performance in the target domains, which is crucial for building graph foundation models. Despite initial success, existing solutions often fall short of answering a fundamental question: how is knowledge integrated or transferred across domains? This theoretical limitation motivates us to rethink the consistency and transferability between model pre‑training and domain adaptation. In this paper, we propose a fresh Riemannian geometry perspective, whose core idea is to merge any graph dataset into a unified, smooth Riemannian manifold, enabling a systematic understanding of knowledge integration and transfer. To achieve this, our key contribution is the theoretical establishment of neural manifold gluing, which first characterizes local geometry using an adaptive orthogonal frame and then "glues" the local pieces together into a coherent whole. Building on this theory, we present the GraphGlue framework, which supports batched pre‑training with EMA prototyping and provides a transferability measure based on geometric consistence. Extensive experiments demonstrate its superior performance across diverse graph domains. Moreover, we empirically validated GraphGlue's geometric scaling law, showing that larger quantities of datasets improve model transferability by producing a smoother manifold. Codes are available at https://github.com/RiemannGraph/GraphGlue.
Authors:Yuchen Hou, Lin Zhao
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.
Authors:Xu Luo, Ji Zhang, Lianli Gao, Heng Tao Shen, Jingkuan Song
Abstract:
Few‑shot transfer has been revolutionized by stronger pre‑trained models and improved adaptation algorithms.However, there lacks a unified, rigorous evaluation protocol that is both challenging and realistic for real‑world usage. In this work, we establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets, and propose the Hyperparameter Ensemble (HPE) protocol to overcome the "validation set illusion" in data‑scarce regimes. Our empirical findings demonstrate that the choice of pre‑trained model is the dominant factor for performance, while many sophisticated transfer methods offer negligible practical advantages over a simple full‑parameter fine‑tuning baseline. To explain this surprising effectiveness, we provide an in‑depth mechanistic analysis showing that full fine‑tuning succeeds via distributed micro‑adjustments and more flexible reshaping of high‑level semantic presentations without suffering from overfitting. Additionally, we quantify the performance collapse of multimodal models in specialized domains as a result of linguistic rarity using adjusted Zipf frequency scores. By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few‑shot transfer learning research. We make the FEWTRANS benchmark publicly available at https://github.com/Frankluox/FewTrans.
Authors:Lingfeng He, De Cheng, Huaijie Wang, Xi Yang, Nannan Wang, Xinbo Gao
Abstract:
Continual Learning (CL) requires models to sequentially adapt to new tasks without forgetting old knowledge. Recently, Low‑Rank Adaptation (LoRA), a representative Parameter‑Efficient Fine‑Tuning (PEFT) method, has gained increasing attention in CL. Several LoRA‑based CL methods reduce interference across tasks by separating their update spaces, typically building the new space from the estimated null space of past tasks. However, they (i) overlook task‑shared directions, which suppresses knowledge transfer, and (ii) fail to capture truly effective task‑specific directions since these ``null bases" of old tasks can remain nearly inactive for new task under correlated tasks. To address this, we study LoRA learning capability from a projection energy perspective, and propose Low‑rank Decomposition and Adaptation (LoDA). It performs a task‑driven decomposition to build general and truly task‑specific LoRA subspaces by solving two energy‑based objectives, decoupling directions for knowledge sharing and isolation. LoDA fixes LoRA down‑projections on two subspaces and learns robust up‑projections via a Gradient‑Aligned Optimization (GAO) approach. After each task, before integrating the LoRA updates into the backbone, LoDA derives a closed‑form recalibration for the general update, approximating a feature‑level joint optimum along this task‑shared direction. Experiments indicate that LoDA outperforms existing CL methods. Our code is available at https://github.com/HHHLF/LoDA_ICML2026.
Authors:Sevda Öğüt, Cédric Vincent-Cuaz, Natalia Dubljevic, Carlos Hurtado, Vaishnavi Subramanian, Pascal Frossard, Dorina Thanou
Abstract:
Self‑supervised vision models have achieved notable success in digital pathology. However, their domain‑agnostic transformer architectures are not originally designed to account for fundamental biological elements of histopathology images, namely cells and their complex interactions. In this work, we hypothesize that a biologically‑informed modeling of tissues as cell graphs offers a more efficient representation learning. Thus, we introduce GrapHist, a novel graph‑based self‑supervised learning framework for histopathology, which learns generalizable and structurally‑informed embeddings that enable diverse downstream tasks. GrapHist integrates masked autoencoders and heterophilic graph neural networks that are explicitly designed to capture the heterogeneity of tumor microenvironments. We pre‑train GrapHist on a large collection of 11 million cell graphs derived from breast tissues and evaluate its transferability across in‑ and out‑of‑domain benchmarks. Our results show that GrapHist achieves competitive performance compared to its vision‑based counterparts in slide‑, region‑, and cell‑level tasks, while requiring four times fewer parameters. It also drastically outperforms fully‑supervised graph models on cancer subtyping tasks. Finally, we also release five graph‑based digital pathology datasets used in our study at https://huggingface.co/ogutsevda/datasets , establishing the first large‑scale graph benchmark in this field. Our code is available at https://github.com/ogutsevda/graphist .
Authors:Sathwik Karnik, Juyeop Kim, Sanmi Koyejo, Jong-Seok Lee, Somil Bansal
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/.
Authors:Atah Nuh Mih, Jianzhou Wang, Truong Thanh Hung Nguyen, Hung Cao
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
Authors:David Jackson, Michael Gertz, Jürgen Hesser
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.
Authors:Chao Huang, Yanhui Li, Yunkang Cao, Wei Wang, Hongxi Huang, Jie Wen, Wenqi Ren, Xiaochun Cao
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.
Authors:Ian Li, Zilei Shao, Benjie Wang, Rose Yu, Guy Van den Broeck, Anji Liu
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
Authors:Jitian Zhao, Changho Shin, Tzu-Heng Huang, Satya Sai Srinath Namburi GNVV, Frederic Sala
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.
Authors:Jintao Zhang, Zirui Liu, Mingyue Cheng, Xianquan Wang, Zhiding Liu, Qi Liu
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/.
Authors:Shengqu Cai, Weili Nie, Chao Liu, Julius Berner, Lvmin Zhang, Nanye Ma, Hansheng Chen, Maneesh Agrawala, Leonidas Guibas, Gordon Wetzstein, Arash Vahdat
Abstract:
Scaling video generation from seconds to minutes faces a critical bottleneck: while short‑video data is abundant and high‑fidelity, coherent long‑form data is scarce and limited to narrow domains. To address this, we propose a training paradigm where Mode Seeking meets Mean Seeking, decoupling local fidelity from long‑term coherence based on a unified representation via a Decoupled Diffusion Transformer. Our approach utilizes a global Flow Matching head trained via supervised learning on long videos to capture narrative structure, while simultaneously employing a local Distribution Matching head that aligns sliding windows to a frozen short‑video teacher via a mode‑seeking reverse‑KL divergence. This strategy enables the synthesis of minute‑scale videos that learns long‑range coherence and motions from limited long videos via supervised flow matching, while inheriting local realism by aligning every sliding‑window segment of the student to a frozen short‑video teacher, resulting in a few‑step fast long video generator. Evaluations show that our method effectively closes the fidelity‑horizon gap by jointly improving local sharpness, motion and long‑range consistency. Project website: https://primecai.github.io/mmm/.
Authors:Zhengbo Wang, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
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.
Authors:Arnas Uselis, Andrea Dittadi, Seong Joon Oh
Abstract:
Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although modern models are trained on massive datasets, they still cover only a tiny fraction of the combinatorial space of possible inputs, raising the question of what structure representations must have to support generalization to unseen combinations. We formalize three desiderata for compositional generalization under standard training (divisibility, transferability, stability) and show they impose necessary geometric constraints: representations must decompose linearly into per‑concept components, and these components must be orthogonal across concepts. This provides theoretical grounding for the Linear Representation Hypothesis: the linear structure widely observed in neural representations is a necessary consequence of compositional generalization. We further derive dimension bounds linking the number of composable concepts to the embedding geometry. Empirically, we evaluate these predictions across modern vision models (CLIP, SigLIP, DINO) and find that representations exhibit partial linear factorization with low‑rank, near‑orthogonal per‑concept factors, and that the degree of this structure correlates with compositional generalization on unseen combinations. As models continue to scale, these conditions predict the representational geometry they may converge to. Code is available at https://github.com/oshapio/necessary‑compositionality.
Authors:Eugène Berta, Sacha Braun, David Holzmüller, Francis Bach, Michael I. Jordan
Abstract:
Calibration\unicodex2014the problem of ensuring that predicted probabilities align with observed class frequencies\unicodex2014is a basic desideratum for reliable prediction with machine learning systems. Calibration error is traditionally assessed via a divergence function, using the expected divergence between predictions and empirical frequencies. Accurately estimating this quantity is challenging, especially in the multiclass setting. Here, we show how to extend a recent variational framework for estimating calibration errors beyond divergences induced induced by proper losses, to cover a broad class of calibration errors induced by L_p divergences. Our method can separate over‑ and under‑confidence and, unlike non‑variational approaches, avoids overestimation. We provide extensive experiments and integrate our code in the open‑source package probmetrics (https://github.com/dholzmueller/probmetrics) for evaluating calibration errors.
Authors:Miras Seilkhan, Adilbek Taizhanov
Abstract:
Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models operate on qubits in high‑dimensional Hilbert spaces and provide an alternative approach to model expressivity. We compare classical models and a variational quantum classifier on the XOR problem. Logistic regression, a one‑hidden‑layer multilayer perceptron, and a two‑qubit variational quantum classifier with circuit depths 1 and 2 are evaluated on synthetic XOR datasets with varying Gaussian noise and sample sizes using accuracy and binary cross‑entropy.
Performance is determined primarily by model expressivity. Logistic regression and the depth‑1 quantum circuit fail to represent XOR reliably, whereas the multilayer perceptron and the depth‑2 quantum circuit achieve perfect test accuracy under representative conditions. Robustness analyses across noise levels, dataset sizes, and random seeds confirm that circuit depth is decisive for quantum performance on this task. Despite matching accuracy, the multilayer perceptron achieves lower binary cross‑entropy and substantially shorter training time. Hardware execution preserves the global XOR structure but introduces structured deviations in the decision function. Overall, deeper variational quantum classifiers can match classical neural networks in accuracy on low‑dimensional XOR benchmarks, but no clear empirical advantage in robustness or efficiency is observed in the examined settings.
Authors:Daniel Yang, Samuel Stante, Florian Redhardt, Lena Libon, Parnian Kassraie, Ido Hakimi, Barna Pásztor, Andreas Krause
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.
Authors:Xianglong Shi, Ziheng Chen, Yunhan Jiang, Nicu Sebe
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.
Authors:Andrei-Alexandru Bunea, Dan-Matei Popovici, Radu Tudor Ionescu
Abstract:
State‑of‑the‑art models for medical image segmentation achieve excellent accuracy but require substantial computational resources, limiting deployment in resource‑constrained clinical settings. We present SegMate, an efficient 2.5D framework that achieves state‑of‑the‑art accuracy, while considerably reducing computational requirements. Our efficient design is the result of meticulously integrating asymmetric architectures, attention mechanisms, multi‑scale feature fusion, slice‑based positional conditioning, and multi‑task optimization. We demonstrate the efficiency‑accuracy trade‑off of our framework across three modern backbones (EfficientNetV2‑M, MambaOut‑Tiny, FastViT‑T12). We perform experiments on three datasets: TotalSegmentator, SegTHOR and AMOS22. Compared with the vanilla models, SegMate reduces computation (GFLOPs) by up to 2.5x and memory footprint (VRAM) by up to 2.1x, while generally registering performance gains of around 1%. On TotalSegmentator, we achieve a Dice score of 93.51% with only 295MB peak GPU memory. Zero‑shot cross‑dataset evaluations on SegTHOR and AMOS22 demonstrate strong generalization, with Dice scores of up to 86.85% and 89.35%, respectively. We release our open‑source code at https://github.com/andreibunea99/SegMate.
Authors:Ning Gao, Xiuhui Zhang, Xingyu Jiang, Mukang You, Mohan Zhang, Yue Deng
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.
Authors:Zhaowen Wang, Dongdong Zhou, Qi Xu, Fengyu Cong, Mohammad Al-Sa'd, Jenni Raitoharju
Abstract:
Automatic sleep stage scoring is crucial for the diagnosis and treatment of sleep disorders. Although deep learning models have advanced the field, many existing models are computationally demanding and designed for single‑channel electroencephalography (EEG), limiting their practicality for multimodal polysomnography (PSG) data. To overcome this, we propose ULW‑SleepNet, an ultra‑lightweight multimodal sleep stage scoring framework that efficiently integrates information from multiple physiological signals. ULW‑SleepNet incorporates a novel Dual‑Stream Separable Convolution (DSSC) Block, depthwise separable convolutions, channel‑wise parameter sharing, and global average pooling to reduce computational overhead while maintaining competitive accuracy. Evaluated on the Sleep‑EDF‑20 and Sleep‑EDF‑78 datasets, ULW‑SleepNet achieves accuracies of 86.9% and 81.4%, respectively, with only 13.3K parameters and 7.89M FLOPs. Compared to state‑of‑the‑art methods, our model reduces parameters by up to 98.6% with only marginal performance loss, demonstrating its strong potential for real‑time sleep monitoring on wearable and IoT devices. The source code for this study is publicly available at https://github.com/wzw999/ULW‑SLEEPNET.
Authors:Junkang Liu, Fanhua Shang, Yuxuan Tian, Hongying Liu, Yuanyuan Liu
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.
Authors:Tiantong Wang, Xinyu Yan, Tiantong Wu, Yurong Hao, Pengjun Xie, Wei Yang Bryan Lim
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.
Authors:Wenwu Tang, Dong Wang, Lothar Thiele, Olga Saukh
Abstract:
Structured deep model compression methods are hardware‑friendly and substantially reduce memory and inference costs. However, under aggressive compression, the resulting accuracy degradation often necessitates post‑compression finetuning, which can be impractical due to missing labeled data or high training cost. We propose post‑hoc blockwise compensation, called GRAIL, a simple zero‑finetuning step applied after model compression that restores each block's input‑output behavior using a small calibration set. The method summarizes hidden activations via a Gram matrix and applies ridge regression to linearly reconstruct the original hidden representation from the reduced one. The resulting reconstruction map is absorbed into the downstream projection weights, while the upstream layer is compressed. The approach is selector‑agnostic (Magnitude, Wanda, Gram‑based selection, or folding), data‑aware (requiring only a few forward passes without gradients or labels), and recovers classic pruning or folding when the Gram matrix is near identity, indicating weak inter‑channel correlations. Across ResNets, ViTs, and decoder‑only LLMs, GRAIL consistently improves accuracy or perplexity over data‑free and data‑aware pruning or folding baselines in practical compression regimes, with manageable overhead and no backpropagation. The code is available at https://github.com/TWWinde/GRAIL_Compensation.
Authors:Dingqi Ye, Daniel Kiv, Wei Hu, Jimeng Shi, Shaowen Wang
Abstract:
The remote sensing community is witnessing a rapid growth of foundation models, which provide powerful embeddings for a wide range of downstream tasks. However, practical adoption and fair comparison remain challenging due to substantial heterogeneity in model release formats, platforms and interfaces, and input data specifications. These inconsistencies significantly increase the cost of obtaining, using, and benchmarking embeddings across models. To address this issue, we propose rs‑embed, a Python library that offers a unified, region of interst (ROI) centric interface: with a single line of code, users can retrieve embeddings from any supported model for any location and any time range. The library also provides efficient batch processing to enable large‑scale embedding generation and evaluation. The code is available at: https://github.com/cybergis/rs‑embed
Authors:Kohei Obata, Taichi Murayama, Zheng Chen, Yasuko Matsubara, Yasushi Sakurai
Abstract:
Multi‑mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various applications, but it is also challenging since the complexities inherent in the tensor hinder the realization of rich representations. In this paper, we propose a novel representation learning method designed specifically for TTS, namely MoST. Specifically, MoST uses a tensor slicing approach to reduce the complexity of the TTS structure and learns representations that can be disentangled into individual non‑temporal modes. Each representation captures mode‑specific features, which are the relationship between variables within the same mode, and mode‑invariant features, which are in common in representations of different modes. We employ a contrastive learning framework to learn parameters; the loss function comprises two parts intended to learn representation in a mode‑specific way and mode‑invariant way, effectively exploiting disentangled representations as augmentations. Extensive experiments on real‑world datasets show that MoST consistently outperforms the state‑of‑the‑art methods in terms of classification and forecasting accuracy. Code is available at https://github.com/KoheiObata/MoST.
Authors:Kejing Yin, Haizhou Xu, Wenfang Yao, Chen Liu, Zijie Chen, Yui Haang Cheung, William K. Cheung, Jing Qin
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.
Authors:Abhishek Dalvi, Vasant Honavar
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.
Authors:Xiang Ao
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
Authors:Ruxiao Duan, Alex Wong
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.
Authors:Aishwarya Sarkar, Sayan Ghosh, Nathan Tallent, Aman Chadha, Tanya Roosta, Ali Jannesari
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.
Authors:Jose Javier Gonzalez Ortiz, Abhay Gupta, Christopher Rinard, Davis Blalock
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.
Authors:Max S. Bennett, Thomas P. Zollo, Richard Zemel
Abstract:
Modern machine learning models are deployed in diverse, non‑stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine‑tuning and in‑context learning are costly and brittle, whereas neural memory methods promise lightweight updates with minimal forgetting. However, existing neural memory models typically assume a single fixed objective and homogeneous information streams, leaving users with no control over what the model remembers or ignores over time. To address this challenge, we propose a generalized neural memory system that performs flexible updates based on learning instructions specified in natural language. Our approach enables adaptive agents to learn selectively from heterogeneous information sources, supporting settings, such as healthcare and customer service, where fixed‑objective memory updates are insufficient.
Authors:Thomas Woergaard, Raghavendra Selvan
Abstract:
Compressing neural networks by quantizing model parameters offers useful trade‑off between performance and efficiency. Methods like quantization‑aware training and post‑training quantization strive to maintain the downstream performance of compressed models compared to the full precision models. However, these techniques do not explicitly consider the impact on algorithmic fairness. In this work, we study fairness‑aware mixed‑precision quantization schemes for medical image classification under explicit bit budgets. We introduce FairQuant, a framework that combines group‑aware importance analysis, budgeted mixed‑precision allocation, and a learnable Bit‑Aware Quantization (BAQ) mode that jointly optimizes weights and per‑unit bit allocations under bitrate and fairness regularization. We evaluate the method on Fitzpatrick17k and ISIC2019 across ResNet18/50, DeiT‑Tiny, and TinyViT. Results show that FairQuant configurations with average precision near 4‑6 bits recover much of the Uniform 8‑bit accuracy while improving worst‑group performance relative to Uniform 4‑ and 8‑bit baselines, with comparable fairness metrics under shared budgets.
Authors:Jayadev Billa
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.
Authors:Bangrui Xu, Qihang Yao, Zirui Tang, Xuanhe Zhou, Yeye He, Shihan Yu, Qianqian Xu, Bin Wang, Guoliang Li, Conghui He, Fan Wu
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.
Authors:Camile Lendering, Erkut Akdag, Egor Bondarev
Abstract:
Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few‑shot methods achieve strong results employing foundation‑model features, but typically rely on memory banks, auxiliary datasets, or multi‑modal tuning of vision‑language models. We therefore question whether such complexity is necessary given the feature representations of vision foundation models. To answer this question, we introduce SubspaceAD, a training‑free method, that operates in two simple stages. First, patch‑level features are extracted from a small set of normal images by a frozen DINOv2 backbone. Second, a Principal Component Analysis (PCA) model is fit to these features to estimate the low‑dimensional subspace of normal variations. At inference, anomalies are detected via the reconstruction residual with respect to this subspace, producing interpretable and statistically grounded anomaly scores. Despite its simplicity, SubspaceAD achieves state‑of‑the‑art performance across one‑shot and few‑shot settings without training, prompt tuning, or memory banks. In the one‑shot anomaly detection setting, SubspaceAD achieves image‑level and pixel‑level AUROC of 97.1% and 97.5% on the MVTec‑AD dataset, and 93.2% and 98.2% on the VisA dataset, respectively, surpassing prior state‑of‑the‑art results. Code and demo are available at https://github.com/CLendering/SubspaceAD.
Authors:Shuo He, Lang Feng, Qi Wei, Xin Cheng, Lei Feng, Bo An
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.
Authors:Yuanjun Li, Bin Zhang, Hao Chen, Zhouyang Jiang, Dapeng Li, Zhiwei Xu
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.
Authors:Yujie Zhao, Boqin Yuan, Junbo Huang, Haocheng Yuan, Zhongming Yu, Haozhou Xu, Lanxiang Hu, Abhilash Shankarampeta, Zimeng Huang, Wentao Ni, Yuandong Tian, Jishen Zhao
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/).
Authors:Hao Yu, Shuning Jia, Guanghao Li, Wenhao Jiang, Chun Yuan
Abstract:
Vision‑language models (VLMs) often struggle with geometric reasoning due to their limited perception of fundamental diagram elements. To tackle this challenge, we introduce GeoPerceive, a benchmark comprising diagram instances paired with domain‑specific language (DSL) representations, along with an efficient automatic data generation pipeline. This design enables the isolated evaluation of geometric perception independently from reasoning. To exploit the data provided by GeoPerceive for enhancing the geometric perception capabilities of VLMs, we propose GeoDPO, a translator‑guided reinforcement learning (RL) framework. GeoDPO employs an NL‑to‑DSL translator, which is trained on synthetic pairs generated by the data engine of GeoPerceive, to bridge natural language and DSL. This translator facilitates the computation of fine‑grained, DSL‑level scores, which serve as reward signals in reinforcement learning. We assess GeoDPO on both in‑domain and out‑of‑domain datasets, spanning tasks in geometric perception as well as downstream reasoning. Experimental results demonstrate that, while supervised fine‑tuning (SFT) offers only marginal improvements and may even impair performance in out‑of‑domain scenarios, GeoDPO achieves substantial gains: +26.5% on in‑domain data, +8.0% on out‑of‑domain data, and +39.0% on downstream reasoning tasks. These findings underscore the superior performance and generalization ability of GeoDPO over SFT. All codes are released at https://github.com/Longin‑Yu/GeoPerceive
to ensure reproducibility.
Authors:Tomoya Matsumoto, Shokichi Takakura, Shun Takagi, Satoshi Hasegawa
Abstract:
SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through membership or attribute inference attacks. Differential privacy (DP) provides rigorous privacy guarantees, but in practice, DP alone may not satisfy governance requirements such as the \emphminimum frequency rule, which requires each released group (cell) to include contributions from at least k distinct individuals. In this paper, we present DPSQL+, a privacy‑preserving SQL library that simultaneously enforces user‑level (\varepsilon,δ)‑DP and the minimum frequency rule. DPSQL+ adopts a modular architecture consisting of: (i) a \emphValidator that statically restricts queries to a DP‑safe subset of SQL; (ii) an \emphAccountant that consistently tracks cumulative privacy loss across multiple queries; and (iii) a \emphBackend that interfaces with various database engines, ensuring portability and extensibility. Experiments on the TPC‑H benchmark demonstrate that DPSQL+ achieves practical accuracy across a wide range of analytical workloads ‑‑ from basic aggregates to quadratic statistics and join operations ‑‑ and allows substantially more queries under a fixed global privacy budget than prior libraries in our evaluation.
Authors:Shuchen Zhu, Rizhen Hu, Mingze Wang, Mou Sun, Xue Wang, Kun Yuan, Zaiwen Wen
Abstract:
Pre‑training Large Language Models requires immense computational resources, making optimizer efficiency essential. The optimization landscape is highly anisotropic, with loss reduction driven predominantly by progress along flat directions. While matrix‑based optimizers such as Muon and SOAP leverage fine‑grained curvature information to outperform AdamW, their updates tend toward isotropy ‑‑ relatively conservative along flat directions yet potentially aggressive along sharp ones. To address this limitation, we first establish a unified Riemannian Ordinary Differential Equation (ODE) framework that elucidates how common adaptive algorithms operate synergistically: the preconditioner induces a Riemannian geometry that mitigates ill‑conditioning, while momentum serves as a Riemannian damping term that promotes convergence. Guided by these insights, we propose LITE, a generalized acceleration strategy that enhances training dynamics by applying larger Hessian damping coefficients and learning rates along flat trajectories. Extensive experiments demonstrate that LITE significantly accelerates both Muon and SOAP across diverse architectures (Dense, MoE), parameter scales (130M‑‑1.3B), datasets (C4, Pile), and learning‑rate schedules (cosine, warmup‑stable‑decay). Theoretical analysis confirms that LITE facilitates faster convergence along flat directions in anisotropic landscapes, providing a principled approach to efficient LLM pre‑training. The code is available at https://github.com/SHUCHENZHU/LITE.
Authors:Md Tanvir Hasan Turja
Abstract:
Antimicrobial resistance (AMR) is a growing global crisis projected to cause 10 million deaths per year by 2050. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) provides standardized surveillance data across 44 countries, few studies have applied machine learning to forecast population‑level resistance trends from this data. This paper presents a two‑component framework for AMR trend forecasting and evidence‑grounded policy decision support. We benchmark six models ‑‑ Naive, Linear Regression, Ridge Regression, XGBoost, LightGBM, and LSTM ‑‑ on 5,909 WHO GLASS observations across six WHO regions (2021‑2023). XGBoost achieved the best performance with a test MAE of 7.07% and R‑squared of 0.854, outperforming the naive baseline by 83.1%. Feature importance analysis identified the prior‑year resistance rate as the dominant predictor (50.5% importance), while regional MAE ranged from 4.16% (European Region) to 10.14% (South‑East Asia Region). We additionally implemented a Retrieval‑Augmented Generation (RAG) pipeline combining a ChromaDB vector store of WHO policy documents with a locally deployed Phi‑3 Mini language model, producing source‑attributed, hallucination‑constrained policy answers. Code and data are available at https://github.com/TanvirTurja
Authors:Zhanhui Zhou, Lingjie Chen, Hanghang Tong, Dawn Song
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.
Authors:Zhengyang Su, Isay Katsman, Yueqi Wang, Ruining He, Lukasz Heldt, Raghunandan Keshavan, Shao-Chuan Wang, Xinyang Yi, Mingyan Gao, Onkar Dalal, Lichan Hong, Ed Chi, Ningren Han
Abstract:
Generative retrieval has emerged as a powerful paradigm for LLM‑based recommendation. However, industrial recommender systems often benefit from restricting the output space to a constrained subset of items based on business logic (e.g. enforcing content freshness or product category), which standard autoregressive decoding cannot natively support. Moreover, existing constrained decoding methods that make use of prefix trees (Tries) incur severe latency penalties on hardware accelerators (TPUs/GPUs). In this work, we introduce STATIC (Sparse Transition Matrix‑Accelerated Trie Index for Constrained Decoding), an efficient and scalable constrained decoding technique designed specifically for high‑throughput LLM‑based generative retrieval on TPUs/GPUs. By flattening the prefix tree into a static Compressed Sparse Row (CSR) matrix, we transform irregular tree traversals into fully vectorized sparse matrix operations, unlocking massive efficiency gains on hardware accelerators. We deploy STATIC on a large‑scale industrial video recommendation platform serving billions of users. STATIC produces significant product metric impact with minimal latency overhead (0.033 ms per step and 0.25% of inference time), achieving a 948x speedup over a CPU trie implementation and a 47‑1033x speedup over a hardware‑accelerated binary‑search baseline. Furthermore, the runtime overhead of STATIC remains extremely low across a wide range of practical configurations. To the best of our knowledge, STATIC enables the first production‑scale deployment of strictly constrained generative retrieval. In addition, evaluation on academic benchmarks demonstrates that STATIC can considerably improve cold‑start performance for generative retrieval. Our code is available at https://github.com/youtube/static‑constraint‑decoding.
Authors:Hai Huang, Yann LeCun, Randall Balestriero
Abstract:
Large Language Models (LLMs) obey consistent scaling laws ‑‑ empirical power‑law fits that predict how loss decreases with compute, data, and parameters. While predictive, these laws are descriptive rather than prescriptive: they characterize typical training, not optimal training. Surprisingly few works have successfully challenged the data‑efficiency bounds implied by these laws ‑‑ which is our primary focus. To that end, we introduce the Geodesic Hypothesis, positing that token sequences trace geodesics on a smooth semantic manifold and are therefore locally linear. Building on this principle, we propose a novel Semantic Tube Prediction (STP) task, a JEPA‑style regularizer that confines hidden‑state trajectories to a tubular neighborhood of the geodesic. STP generalizes JEPA to language without requiring explicit multi‑view augmentations. We show this constraint improves signal‑to‑noise ratio, and consequently preserves diversity by preventing trajectory collisions during inference. Empirically, STP allows LLMs to match baseline accuracy with 16× less training data on the NL‑RX‑SYNTH dataset, directly violating the data term of Chinchilla‑style scaling laws and demonstrating that principled geometric priors can surpass brute‑force scaling. Code is available at https://github.com/galilai‑group/llm‑jepa#stp.
Authors:Jessie Yuan, Yilin Wu, Andrea Bajcsy
Abstract:
Policy steering is an emerging way to adapt robot behaviors at deployment‑time: a learned verifier analyzes low‑level action samples proposed by a pre‑trained policy (e.g., diffusion policy) and selects only those aligned with the task. While Vision‑Language Models (VLMs) are promising general‑purpose verifiers due to their reasoning capabilities, existing frameworks often assume these models are well‑calibrated. In practice, the overconfident judgment from VLM can degrade the steering performance under both high‑level semantic uncertainty in task specifications and low‑level action uncertainty or incapability of the pre‑trained policy. We propose uncertainty‑aware policy steering (UPS), a framework that jointly reasons about semantic task uncertainty and low‑level action feasibility, and selects an uncertainty resolution strategy: execute a high‑confidence action, clarify task ambiguity via natural language queries, or ask for action interventions to correct the low‑level policy when it is deemed incapable at the task. We leverage conformal prediction to calibrate the composition of the VLM and the pre‑trained base policy, providing statistical assurances that the verifier selects the correct strategy. After collecting interventions during deployment, we employ residual learning to improve the capability of the pre‑trained policy, enabling the system to learn continually but with minimal expensive human feedback. We demonstrate our framework through experiments in simulation and on hardware, showing that UPS can disentangle confident, ambiguous, and incapable scenarios and minimizes expensive user interventions compared to uncalibrated baselines and prior human‑ or robot‑gated continual learning approaches. Videos can be found at https://jessie‑yuan.github.io/ups/
Authors:Emilio Ferrara
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
Authors:Yibo Peng, Peng Xia, Ding Zhong, Kaide Zeng, Siwei Han, Yiyang Zhou, Jiaqi Liu, Ruiyi Zhang, Huaxiu Yao
Abstract:
Despite the rapid advancements in Multimodal Large Language Models (MLLMs), a critical question regarding their visual grounding mechanism remains unanswered: do these models genuinely ``read'' text embedded in images, or do they merely rely on parametric shortcuts in the text prompt? In this work, we diagnose this issue by introducing the Visualized‑Question (VQ) setting, where text queries are rendered directly onto images to structurally mandate visual engagement. Our diagnostic experiments on Qwen2.5‑VL reveal a startling capability‑utilization gap: despite possessing strong OCR capabilities, models suffer a performance degradation of up to 12.7% in the VQ setting, exposing a deep‑seated ``modality laziness.'' To bridge this gap, we propose SimpleOCR, a plug‑and‑play training strategy that imposes a structural constraint on the learning process. By transforming training samples into the VQ format with randomized styles, SimpleOCR effectively invalidates text‑based shortcuts, compelling the model to activate and optimize its visual text extraction pathways. Empirically, SimpleOCR yields robust gains without architectural modifications. On four representative OOD benchmarks, it surpasses the base model by 5.4% and GRPO based on original images by 2.7%, while exhibiting extreme data efficiency, achieving superior performance with 30x fewer samples (8.5K) than recent RL‑based methods. Furthermore, its plug‑and‑play nature allows seamless integration with advanced RL strategies like NoisyRollout to yield complementary improvements. Code is available at https://github.com/aiming‑lab/SimpleOCR.
Authors:Yan Zhao, Zhengxue Cheng, Junxuan Zhang, Dajiang Zhou, Qunshan Gu, Qi Wang, Li Song
Abstract:
Lossless compression is essential for efficient data storage and transmission. Although learning‑based lossless compressors achieve strong results, most of them are designed for a single modality, leading to redundant compressor deployments in multi‑modal settings. Designing a unified multi‑modal compressor is critical yet challenging, as different data types vary largely in format, dimension, and statistics. Multi‑modal large language models offer a promising resolution but remain too complex for practical use. Thus, we propose OmniZip, a unified and lightweight lossless compressor for multi‑modal data (like image, text, speech, tactile, database, and gene sequence). Built on a lightweight backbone, OmniZip incorporates three key components to enable efficient multi‑modal lossless compression: a modality‑unified tokenizer that reversibly transforms diverse data into tokens, a modality‑routing context learning mechanism that enables flexible multi‑modal context modeling, and a modality‑routing feedforward design that further enhances the model's nonlinear representation flexibility. A reparameterization training strategy is used to enhance model capacity. OmniZip outperforms or matches other state‑of‑the‑art compressors on multiple modalities, achieving 42%, 57%, 62% and 42%, 53% higher compression efficiency than gzip on CLIC‑M, TouchandGo, enwik9, LibriSpeech, and WikiSQL datasets, respectively. It also supports near real‑time inference on resource‑constrained edge devices, reaching about 1MB/s on MacBook CPUs and iPhone NPUs. Our code is released at https://github.com/adminasmi/OmniZip‑CVPR2026.
Authors:Dawei Su, Dongsheng Wang
Abstract:
Multimodal information retrieval (MMIR) has gained attention for its flexibility in handling text, images, or mixed queries and candidates. Recent breakthroughs in multimodal large language models (MLLMs) boost MMIR performance by incorporating MLLM knowledge under the contrastive finetuning framework. However, they suffer from pre‑training inconsistency and require large datasets. In this work, we introduce a novel framework, RetLLM, designed to query MLLMs for MMIR in a training‑ and data‑free manner. Specifically, we formulate MMIR as a similarity score generation task and prompt MLLMs to directly predict retrieval scores in a coarse‑then‑fine pipeline. At the coarse stage, a top‑k filtering strategy builds a small yet high‑quality candidate pool for each query, enabling MLLMs to focus on semantically relevant candidates. Subsequently, the retrieval score is predicted by feeding both the query and candidate into MLLMs at the fine stage. Importantly, we propose a visual enhancement module during reasoning to help MLLMs re‑pick forgotten visuals, improving retrieval. Extensive experiments on MMIR benchmarks show that RetLLM outperforms fine‑tuned models. Ablation studies further verify each component. Our work demonstrates that MLLMs can achieve strong MMIR performance without any training, highlighting their inherent multimodal reasoning ability in a simple, scalable framework. We release our code at: https://github.com/alivecat05/RETLLM
Authors:Alex Morehead, Miruna Cretu, Antonia Panescu, Rishabh Anand, Maurice Weiler, Tynan Perez, Samuel Blau, Steven Farrell, Wahid Bhimji, Anubhav Jain, Hrushikesh Sahasrabuddhe, Pietro Lio, Tommi Jaakkola, Rafael Gomez-Bombarelli, Rex Ying, N. Benjamin Erichson, Michael W. Mahoney
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.
Authors:Ida Egendal, Rasmus Froberg Brøndum, Dan J Woodcock, Christopher Yau, Martin Bøgsted
Abstract:
Mutational signature analysis has emerged as a powerful method for uncovering the underlying biological processes driving cancer development. However, the signature extraction process, typically performed using non‑negative matrix factorization (NMF), often lacks reliability and clinical applicability. To address these limitations, several solutions have been introduced, including the use of neural networks to achieve more accurate estimates and probabilistic methods to better capture natural variation in the data. In this work, we introduce a Variational Autoencoder for Mutational Signatures (VAE‑MS), a novel model that leverages both an asymmetric architecture and probabilistic methods for the extraction of mutational signatures. VAE‑MS is compared to with three state‑of‑the‑art models for mutational signature extraction: SigProfilerExtractor, the NMF‑based gold standard; MUSE‑XAE, an autoencoder that employs an asymmetric design without probabilistic components; and SigneR, a Bayesian NMF model, to illustrate the strength in combining a nonlinear extraction with a probabilistic model. In the ability to reconstruct input data and generalize to unseen data, models with probabilistic components (VAE‑MS, SigneR) dramatically outperformed models without (SigProfilerExtractor, MUSE‑XAE). The NMF‑baed models (SigneR, SigProfilerExtractor) had the most accurate reconstructions in simulated data, while VAE‑MS reconstructed more accurately on real cancer data. Upon evaluating the ability to extract signatures consistently, no model exhibited a clear advantage over the others. Software for VAE‑MS is available at https://github.com/CLINDA‑AAU/VAE‑MS.
Authors:Jinpeng Li, Zhongyi Pei, Huaze Xue, Bojian Zheng, Chen Wang, Jianmin Wang
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.
Authors:Sterre de Jonge, Elisabeth J. Vinke, Meike W. Vernooij, Daniel C. Alexander, Alexandra L. Young, Esther E. Bron
Abstract:
Disease progression modeling provides a robust framework to identify long‑term disease trajectories from short‑term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real‑world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed‑SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed‑SuStaIn through simulation experiments and real‑world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl‑pond/pySuStaIn.
Authors:Cuong Anh Pham, Praneeth Vepakomma, Samuel Horváth
Abstract:
Alleviating catastrophic forgetting while enabling further learning is a primary challenge in continual learning (CL). Orthogonal‑based training methods have gained attention for their efficiency and strong theoretical properties, and many existing approaches enforce orthogonality through gradient projection. In this paper, we revisit orthogonality and exploit the fact that small singular values correspond to directions that are nearly orthogonal to the input space of previous tasks. Building on this principle, we introduce NESS (Null‑space Estimated from Small Singular values), a CL method that applies orthogonality directly in the weight space rather than through gradient manipulation. Specifically, NESS constructs an approximate null space using the smallest singular values of each layer's input representation and parameterizes task‑specific updates via a compact low‑rank adaptation (LoRA‑style) formulation constrained to this subspace. The subspace basis is fixed to preserve the null‑space constraint, and only a single trainable matrix is learned for each task. This design ensures that the resulting updates remain approximately in the null space of previous inputs while enabling adaptation to new tasks. Our theoretical analysis and experiments on three benchmark datasets demonstrate competitive performance, low forgetting, and stable accuracy across tasks, highlighting the role of small singular values in continual learning. The code is available at https://github.com/pacman‑ctm/NESS.
Authors:Lin Zhu, Lei You
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.
Authors:Xiannan Huang, Quan Yuan, Chao Yang
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
Authors:Yue Yang, Shuo Cheng, Yu Fang, Homanga Bharadhwaj, Mingyu Ding, Gedas Bertasius, Daniel Szafir
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/.
Authors:Boyuan Li, Zhen Liu, Yicheng Luo, Qianli Ma
Abstract:
Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies. In addition, IMTS often exhibit diverse dependencies across multiple time scales. However, many existing multi‑scale IMTS methods use resampling to obtain the coarse series, which can alter the original timestamps and disrupt the sampling pattern information. To address the challenge, we propose ReIMTS, a Recursive multi‑scale modeling approach for Irregular Multivariate Time Series forecasting. Instead of resampling, ReIMTS keeps timestamps unchanged and recursively splits each sample into subsamples with progressively shorter time periods. Based on the original sampling timestamps in these long‑to‑short subsamples, an irregularity‑aware representation fusion mechanism is proposed to capture global‑to‑local dependencies for accurate forecasting. Extensive experiments demonstrate an average performance improvement of 27.1% in the forecasting task across different models and real‑world datasets. Our code is available at https://github.com/Ladbaby/PyOmniTS.
Authors:Ningyuan Yang, Weihua Du, Weiwei Sun, Sean Welleck, Yiming Yang
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
Authors:Jesse He, Helen Jenne, Max Vargas, Davis Brown, Gal Mishne, Yusu Wang, Henry Kvinge
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.
Authors:Jan Pauls, Karsten Schrödter, Sven Ligensa, Martin Schwartz, Berkant Turan, Max Zimmer, Sassan Saatchi, Sebastian Pokutta, Philippe Ciais, Fabian Gieseke
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.
Authors:Alina Devkota, Jacob Thrasher, Donald Adjeroh, Binod Bhattarai, Prashnna K. Gyawali
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.
Authors:Subhadip Mitra
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.
Authors:Abdulaziz Almuzairee, Henrik I. Christensen
Abstract:
Visual reinforcement learning is appealing for robotics but expensive ‑‑ off‑policy methods are sample‑efficient yet slow; on‑policy methods parallelize well but waste samples. Recent work has shown that off‑policy methods can train faster than on‑policy methods in wall‑clock time for state‑based control. Extending this to vision remains challenging, where high‑dimensional input images complicate training dynamics and introduce substantial storage and encoding overhead. To address these challenges, we introduce Squint, a visual Soft Actor Critic method that achieves faster wall‑clock training than prior visual off‑policy and on‑policy methods. Squint achieves this via parallel simulation, a distributional critic, resolution squinting, layer normalization, a tuned update‑to‑data ratio, and an optimized implementation. We evaluate on the SO‑101 Task Set, a new suite of eight manipulation tasks in ManiSkill3 with heavy domain randomization, and demonstrate sim‑to‑real transfer to a real SO‑101 robot. We train policies for 15 minutes on a single RTX 3090 GPU, with most tasks converging in under 6 minutes.
Authors:Tony Feng, Junehyuk Jung, Sang-hyun Kim, Carlo Pagano, Sergei Gukov, Chiang-Chiang Tsai, David Woodruff, Adel Javanmard, Aryan Mokhtari, Dawsen Hwang, Yuri Chervonyi, Jonathan N. Lee, Garrett Bingham, Trieu H. Trinh, Vahab Mirrokni, Quoc V. Le, Thang Luong
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.
Authors:Duowen Chen, Yan Wang
Abstract:
Federated Semi‑Supervised Learning (FSSL) aims to collaboratively train a global model across clients by leveraging partially‑annotated local data in a privacy‑preserving manner. In FSSL, data heterogeneity is a challenging issue, which exists both across clients and within clients. External heterogeneity refers to the data distribution discrepancy across different clients, while internal heterogeneity represents the mismatch between labeled and unlabeled data within clients. Most FSSL methods typically design fixed or dynamic parameter aggregation strategies to collect client knowledge on the server (external) and / or filter out low‑confidence unlabeled samples to reduce mistakes in local client (internal). But, the former is hard to precisely fit the ideal global distribution via direct weights, and the latter results in fewer data participation into FL training. To this end, we propose a proxy‑guided framework called ProxyFL that focuses on simultaneously mitigating external and internal heterogeneity via a unified proxy. I.e., we consider the learnable weights of classifier as proxy to simulate the category distribution both locally and globally. For external, we explicitly optimize global proxy against outliers instead of direct weights; for internal, we re‑include the discarded samples into training by a positive‑negative proxy pool to mitigate the impact of potentially‑incorrect pseudo‑labels. Insight experiments & theoretical analysis show our significant performance and convergence in FSSL.
Authors:Dongik Park, Hyunwoo Ryu, Suahn Bae, Keondo Park, Hyung-Sin Kim
Abstract:
Imputing missing values in multivariate time series remains challenging, especially under diverse missing patterns and heavy missingness. Existing methods suffer from suboptimal performance as corrupted temporal features hinder effective cross‑variable information transfer, amplifying reconstruction errors. Robust imputation requires both extracting temporal patterns from sparse observations within each variable and selectively transferring information across variables‑‑yet current approaches excel at one while compromising the other. We introduce T1 (Time series imputation with 1‑to‑1 channel‑head binding), a CNN‑Transformer hybrid architecture that achieves robust imputation through Channel‑Head Binding‑‑a mechanism creating one‑to‑one correspondence between CNN channels and attention heads. This design enables selective information transfer: when missingness corrupts certain temporal patterns, their corresponding attention pathways adaptively down‑weight based on remaining observable patterns while preserving reliable cross‑variable connections through unaffected channels. Experiments on 11 benchmark datasets demonstrate that T1 achieves state‑of‑the‑art performance, reducing MSE by 46% on average compared to the second‑best baseline, with particularly strong gains under extreme sparsity (70% missing ratio). The model generalizes to unseen missing patterns without retraining and uses a consistent hyperparameter configuration across all datasets. The code is available at https://github.com/Oppenheimerdinger/T1.
Authors:Tianhao Fu, Yucheng Chen
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.
Authors:Yuechen Xie, Xiaoyan Zhang, Yicheng Shan, Hao Zhu, Rui Tang, Rong Wei, Mingli Song, Yuanyu Wan, Jie Song
Abstract:
Vision‑Language Models (VLMs) have been increasingly applied in real‑world scenarios due to their outstanding understanding and reasoning capabilities. Although VLMs have already demonstrated impressive capabilities in common visual question answering and logical reasoning, they still lack the ability to make reasonable decisions in complex real‑world environments. We define this ability as spatial logical reasoning, which not only requires understanding the spatial relationships among objects in complex scenes, but also the logical dependencies between steps in multi‑step tasks. To bridge this gap, we introduce Spatial Logical Question Answering (SpatiaLQA), a benchmark designed to evaluate the spatial logical reasoning capabilities of VLMs. SpatiaLQA consists of 9,605 question answer pairs derived from 241 real‑world indoor scenes. We conduct extensive experiments on 41 mainstream VLMs, and the results show that even the most advanced models still struggle with spatial logical reasoning. To address this issue, we propose a method called recursive scene graph assisted reasoning, which leverages visual foundation models to progressively decompose complex scenes into task‑relevant scene graphs, thereby enhancing the spatial logical reasoning ability of VLMs, outperforming all previous methods. Code and dataset are available at https://github.com/xieyc99/SpatiaLQA.
Authors:Aram Davtyan, Yusuf Sahin, Yasaman Haghighi, Sebastian Stapf, Pablo Acuaviva, Alexandre Alahi, Paolo Favaro
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.
Authors:Jiawei Wang, Chuang Yang, Jiawei Yong, Xiaohang Xu, Hongjun Wang, Noboru Koshizuka, Shintaro Fukushima, Ryosuke Shibasaki, Renhe Jiang
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.
Authors:Santiago Gonzalez, Alireza Amiri Bavandpour, Peter Ye, Edward Zhang, Ruslans Aleksejevs, Todor Antić, Polina Baron, Sujeet Bhalerao, Shubhrajit Bhattacharya, Zachary Burton, John Byrne, Hyungjun Choi, Nujhat Ahmed Disha, Koppany István Encz, Yuchen Fang, Robert Joseph George, Ebrahim Ghorbani, Alan Goldfarb, Jing Guo, Meghal Gupta, Stefano Huber, Annika Kanckos, Minjung Kang, Hyun Jong Kim, Dino Lorenzini, Levi Lorenzo, Tianyi Mao, Giovanni Marzenta, Ariane M. Masuda, Lukas Mauth, Ana Mickovic, Andres Miniguano-Trujillo, Antoine Moulin, Wenqi Ni, Tomos Parry, Kevin Ren, Hossein Roodbarani, Mathieu Rundström, Manjil Saikia, Detchat Samart, Rebecca Steiner, Connor Stewart, Dhara Thakkar, Jeffrey Tse, Vasiliki Velona, Yunhai Xiang, Sibel Yalçın, Jun Yan, Ji Zeng, Arman Cohan, Quanquan C. Liu
Abstract:
As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated evaluation. We demonstrate that standard "LLM‑as‑a‑Judge" protocols suffer from a systematic Alignment Gap when applied to upper‑undergraduate to early graduate level mathematics. To quantify this, we introduce QEDBench, the first large‑scale dual‑rubric alignment benchmark to systematically measure alignment with human experts on university‑level math proofs by contrasting course‑specific rubrics against expert common knowledge criteria. By deploying a dual‑evaluation matrix (7 judges x 5 solvers) against 1,000+ hours of human evaluation, we reveal that certain frontier evaluators like Claude Opus 4.5, DeepSeek‑V3, Qwen 2.5 Max, and Llama 4 Maverick exhibit significant positive bias (up to +0.18, +0.20, +0.30, +0.36 mean score inflation, respectively). Furthermore, we uncover a critical reasoning gap in the discrete domain: while Gemini 3.0 Pro achieves state‑of‑the‑art performance (0.91 average human evaluation score), other reasoning models like GPT‑5 Pro and Claude Sonnet 4.5 see their performance significantly degrade in discrete domains. Specifically, their average human evaluation scores drop to 0.72 and 0.63 in Discrete Math, and to 0.74 and 0.50 in Graph Theory. In addition to these research results, we also release QEDBench as a public benchmark for evaluating and improving AI judges. Our benchmark is publicly published at https://github.com/qqliu/Yale‑QEDBench.
Authors:Mohammed Rakib, Luke Vaughan, Shivang Patel, Flera Rizatdinova, Alexander Khanov, Atriya Sen
Abstract:
The High‑Luminosity Large Hadron Collider (HL‑LHC) at CERN will produce unprecedented datasets capable of revealing fundamental properties of the universe. However, realizing its discovery potential faces a significant challenge: extracting small signal fractions from overwhelming backgrounds dominated by approximately 200 simultaneous pileup collisions. This extreme noise severely distorts the physical observables required for accurate reconstruction. To address this, we introduce the Physics‑Guided Hypergraph Transformer (PhyGHT), a hybrid architecture that combines distance‑aware local graph attention with global self‑attention to mirror the physical topology of particle showers formed in proton‑proton collisions. Crucially, we integrate a Pileup Suppression Gate (PSG), an interpretable, physics‑constrained mechanism that explicitly learns to filter soft noise prior to hypergraph aggregation. To validate our approach, we release a novel simulated dataset of top‑quark pair production to model extreme pileup conditions. PhyGHT outperforms state‑of‑the‑art baselines from the ATLAS and CMS experiments in predicting the signal's energy and mass correction factors. By accurately reconstructing the top quark's invariant mass, we demonstrate how machine learning innovation and interdisciplinary collaboration can directly advance scientific discovery at the frontiers of experimental physics and enhance the HL‑LHC's discovery potential. The dataset and code are available at https://github.com/rAIson‑Lab/PhyGHT
Authors:Bolin Shen, Zhan Cheng, Neil Zhenqiang Gong, Fan Yao, Yushun Dong
Abstract:
Machine Learning as a Service (MLaaS) has emerged as a widely adopted paradigm for providing access to deep neural network (DNN) models, enabling users to conveniently leverage these models through standardized APIs. However, such services are highly vulnerable to Model Extraction Attacks (MEAs), where an adversary repeatedly queries a target model to collect input‑output pairs and uses them to train a surrogate model that closely replicates its functionality. While numerous defense strategies have been proposed, verifying the ownership of a suspicious model with strict theoretical guarantees remains a challenging task. To address this gap, we introduce CREDIT, a certified ownership verification against MEAs. Specifically, we employ mutual information to quantify the similarity between DNN models, propose a practical verification threshold, and provide rigorous theoretical guarantees for ownership verification based on this threshold. We extensively evaluate our approach on several mainstream datasets across different domains and tasks, achieving state‑of‑the‑art performance. Our implementation is publicly available at: https://github.com/LabRAI/CREDIT.
Authors:Bolin Shen, Md Shamim Seraj, Zhan Cheng, Shayok Chakraborty, Yushun Dong
Abstract:
Graph neural networks (GNNs) have demonstrated superior performance in various applications, such as recommendation systems and financial risk management. However, deploying large‑scale GNN models locally is particularly challenging for users, as it requires significant computational resources and extensive property data. Consequently, Machine Learning as a Service (MLaaS) has become increasingly popular, offering a convenient way to deploy and access various models, including GNNs. However, an emerging threat known as Model Extraction Attacks (MEAs) presents significant risks, as adversaries can readily obtain surrogate GNN models exhibiting similar functionality. Specifically, attackers repeatedly query the target model using subgraph inputs to collect corresponding responses. These input‑output pairs are subsequently utilized to train their own surrogate models at minimal cost. Many techniques have been proposed to defend against MEAs, but most are limited to specific output levels (e.g., embedding or label) and suffer from inherent technical drawbacks. To address these limitations, we propose a novel ownership verification framework CITED which is a first‑of‑its‑kind method to achieve ownership verification on both embedding and label levels. Moreover, CITED is a novel signature‑based method that neither harms downstream performance nor introduces auxiliary models that reduce efficiency, while still outperforming all watermarking and fingerprinting approaches. Extensive experiments demonstrate the effectiveness and robustness of our CITED framework. Code is available at: https://github.com/LabRAI/CITED.
Authors:Mainak Singha, Sarthak Mehrotra, Paolo Casari, Subhasis Chaudhuri, Elisa Ricci, Biplab Banerjee
Abstract:
Recent vision‑language models (VLMs) such as CLIP demonstrate impressive cross‑modal reasoning, extending beyond images to 3D perception. Yet, these models remain fragile under domain shifts, especially when adapting from synthetic to real‑world point clouds. Conventional 3D domain adaptation approaches rely on heavy trainable encoders, yielding strong accuracy but at the cost of efficiency. We introduce CLIPoint3D, the first framework for few‑shot unsupervised 3D point cloud domain adaptation built upon CLIP. Our approach projects 3D samples into multiple depth maps and exploits the frozen CLIP backbone, refined through a knowledge‑driven prompt tuning scheme that integrates high‑level language priors with geometric cues from a lightweight 3D encoder. To adapt task‑specific features effectively, we apply parameter‑efficient fine‑tuning to CLIP's encoders and design an entropy‑guided view sampling strategy for selecting confident projections. Furthermore, an optimal transport‑based alignment loss and an uncertainty‑aware prototype alignment loss collaboratively bridge source‑target distribution gaps while maintaining class separability. Extensive experiments on PointDA‑10 and GraspNetPC‑10 benchmarks show that CLIPoint3D achieves consistent 3‑16% accuracy gains over both CLIP‑based and conventional encoder‑based baselines. Project page: https://sarthakm320.github.io/CLIPoint3D.
Authors:Haixu Wu, Minghao Guo, Zongyi Li, Zhiyang Dou, Mingsheng Long, Kaiming He, Wojciech Matusik
Abstract:
Neural simulators promise efficient surrogates for physics simulation, but scaling them is bottlenecked by the prohibitive cost of generating high‑fidelity training data. Pre‑training on abundant off‑the‑shelf geometries offers a natural alternative, yet faces a fundamental gap: supervision on static geometry alone ignores dynamics and can lead to negative transfer on physics tasks. We present GeoPT, a unified pre‑trained model for general physics simulation based on lifted geometric pre‑training. The core idea is to augment geometry with synthetic dynamics, enabling dynamics‑aware self‑supervision without physics labels. Pre‑trained on over one million samples, GeoPT consistently improves industrial‑fidelity benchmarks spanning fluid mechanics for cars, aircraft, and ships, and solid mechanics in crash simulation, reducing labeled data requirements by 20‑60% and accelerating convergence by 2×. These results show that lifting with synthetic dynamics bridges the geometry‑physics gap, unlocking a scalable path for neural simulation and potentially beyond. Code is available at https://github.com/Physics‑Scaling/GeoPT.
Authors:Zhuofan Josh Ying, Shauli Ravfogel, Nikolaus Kriegeskorte, Peter Hase
Abstract:
Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding's generality. We reconcile these views with the truthfulness spectrum hypothesis: the representational space contains directions ranging from broadly domain‑general to narrowly domain‑specific. To test this hypothesis, we systematically evaluate probe generalization across five truth types (definitional, empirical, logical, fictional, and ethical), sycophantic and expectation‑inverted lying, and existing honesty benchmarks. Linear probes generalize well across most domains but fail on sycophantic and expectation‑inverted lying. Yet training on all domains jointly recovers strong performance, confirming that domain‑general directions exist despite poor pairwise transfer. The geometry of probe directions explains these patterns: Mahalanobis cosine similarity between probes near‑perfectly predicts cross‑domain generalization (R^2=0.98). Concept‑erasure methods further isolate truth directions that are (1) domain‑general, (2) domain‑specific, or (3) shared only across particular domain subsets. Causal interventions reveal that domain‑specific directions steer more effectively than domain‑general ones. Finally, post‑training reshapes truth geometry, pushing sycophantic lying further from other truth types, suggesting a representational basis for chat models' sycophantic tendencies. Together, our results support the truthfulness spectrum hypothesis: truth directions of varying generality coexist in representational space, with post‑training reshaping their geometry. Code for all experiments is provided in https://github.com/zfying/truth_spec.
Authors:Wall Kim, Chaeyoung Song, Hanul Kim
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.
Authors:Soumik Deb Niloy, Md. Fahmid-Ul-Alam Juboraj, Swakkhar Shatabda
Abstract:
Pro‑inflammatory peptides (PIPs) play critical roles in immune signaling and inflammation but are difficult to identify experimentally due to costly and time‑consuming assays. To address this challenge, we present KEMP‑PIP, a hybrid machine learning framework that integrates deep protein embeddings with handcrafted descriptors for robust PIP prediction. Our approach combines contextual embeddings from pretrained ESM protein language models with multi‑scale k‑mer frequencies, physicochemical descriptors, and modlAMP sequence features. Feature pruning and class‑weighted logistic regression manage high dimensionality and class imbalance, while ensemble averaging with an optimized decision threshold enhances the sensitivity‑‑specificity balance. Through systematic ablation studies, we demonstrate that integrating complementary feature sets consistently improves predictive performance. On the standard benchmark dataset, KEMP‑PIP achieves an MCC of 0.505, accuracy of 0.752, and AUC of 0.762, outperforming ProIn‑fuse, MultiFeatVotPIP, and StackPIP. Relative to StackPIP, these results represent improvements of 9.5% in MCC and 4.8% in both accuracy and AUC. The KEMP‑PIP web server is freely available at https://nilsparrow1920‑kemp‑pip.hf.space/ and the full implementation at https://github.com/S18‑Niloy/KEMP‑PIP.
Authors:Zhuoxu Huang, Mengxi Jia, Hao Sun, Xuelong Li, Jungong Han
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.
Authors:Zhenyao Ma, Yue Liang, Dongxu Li
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.
Authors:Lingwei Gu, Nour Jedidi, Jimmy Lin
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.
Authors:Thanh Q. Tran, Arun Verma, Kiwan Wong, Bryan Kian Hsiang Low, Daniela Rus, Wei Xiao
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.
Authors:Harry Anthony, Ziyun Liang, Hermione Warr, Konstantinos Kamnitsas
Abstract:
Deep Neural Networks achieve high performance in vision tasks by learning features from regions of interest (ROI) within images, but their performance degrades when deployed on out‑of‑distribution (OOD) data that differs from training data. This challenge has led to OOD detection methods that aim to identify and reject unreliable predictions. Although prior work shows that OOD detection performance varies by artefact type, the underlying causes remain underexplored. To this end, we identify a previously unreported bias in OOD detection: for hard‑to‑detect artefacts (near‑OOD), detection performance typically improves when the artefact shares visual similarity (e.g. colour) with the model's ROI and drops when it does not ‑ a phenomenon we term the Invisible Gorilla Effect. For example, in a skin lesion classifier with red lesion ROI, we show the method Mahalanobis Score achieves a 31.5% higher AUROC when detecting OOD red ink (similar to ROI) compared to black ink (dissimilar) annotations. We annotated artefacts by colour in 11,355 images from three public datasets (e.g. ISIC) and generated colour‑swapped counterfactuals to rule out dataset bias. We then evaluated 40 OOD methods across 7 benchmarks and found significant performance drops for most methods when artefacts differed from the ROI. Our findings highlight an overlooked failure mode in OOD detection and provide guidance for more robust detectors. Code and annotations are available at: https://github.com/HarryAnthony/Invisible_Gorilla_Effect.
Authors:Ha-Anh Hoang Nguyen, Tri-Duc Phan Le, Duc-Hoang Pham, Huy-Son Nguyen, Cam-Van Thi Nguyen, Duc-Trong Le, Hoang-Quynh Le
Abstract:
This paper tackles the problem of time‑to‑event counterfactual survival prediction, aiming to optimize individualized survival outcomes in the presence of heterogeneity and censored data. We propose CURE, a framework that advances counterfactual survival modeling via comprehensive multimodal embedding and latent subgroup retrieval. CURE integrates clinical, paraclinical, demographic, and multi‑omics information, which are aligned and fused through cross‑attention mechanisms. Complex multi‑omics signals can be adaptively refined using a mixture‑of‑experts architecture, emphasizing the most informative omics components. Building upon this representation, CURE implicitly retrieves patient‑specific latent subgroups that capture both baseline survival dynamics and treatment‑dependent variations. Experimental results on METABRIC and TCGA‑LUAD datasets demonstrate that proposed CURE model consistently outperforms strong baselines in survival analysis, evaluated using the Time‑dependent Concordance Index (C^td) and Integrated Brier Score (IBS). These findings highlight the potential of CURE to enhance multimodal understanding and serve as a foundation for future treatment recommendation models. All code and related resources are publicly available to facilitate the reproducibility https://github.com/L2R‑UET/CURE.
Authors:Zhongwei Wan, Yun Shen, Zhihao Dou, Donghao Zhou, Yu Zhang, Xin Wang, Hui Shen, Jing Xiong, Chaofan Tao, Zixuan Zhong, Peizhou Huang, Mi Zhang
Abstract:
Reinforcement learning with verifiers (RLVR) is a central paradigm for improving large language model (LLM) reasoning, yet existing methods often suffer from limited exploration. Policies tend to collapse onto a few reasoning patterns and prematurely stop deep exploration, while conventional entropy regularization introduces only local stochasticity and fails to induce meaningful path‑level diversity, leading to weak and unstable learning signals in group‑based policy optimization. We propose DSDR, a Dual‑Scale Diversity Regularization reinforcement learning framework that decomposes diversity in LLM reasoning into global and coupling components. Globally, DSDR promotes diversity among correct reasoning trajectories to explore distinct solution modes. Locally, it applies a length‑invariant, token‑level entropy regularization restricted to correct trajectories, preventing entropy collapse within each mode while preserving correctness. The two scales are coupled through a global‑to‑local allocation mechanism that emphasizes local regularization for more distinctive correct trajectories. We provide theoretical support showing that DSDR preserves optimal correctness under bounded regularization, sustains informative learning signals in group‑based optimization, and yields a principled global‑to‑local coupling rule. Experiments on multiple reasoning benchmarks demonstrate consistent improvements in accuracy and pass@k, highlighting the importance of dual‑scale diversity for deep exploration in RLVR. Code is available at https://github.com/SUSTechBruce/DSDR.
Authors:Johanna S. Fröhlich, Bastian Heinlein, Jan U. Claar, Hans Rosenberger, Vasileios Belagiannis, Ralf R. Müller
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.
Authors:Haitao Lin, Hanyang Yu, Jingshun Huang, He Zhang, Yonggen Ling, Ping Tan, Xiangyang Xue, Yanwei Fu
Abstract:
Existing Vision‑Language‑Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high‑level perception with sparse, embodiment‑specific action supervision. Since these models typically rely on VLM backbones optimized for Visual Question Answering (VQA), they excel at semantic identification but often overlook subtle 3D state variations that dictate distinct action patterns. To resolve these misalignments, we propose Pose‑VLA, a decoupled paradigm that separates VLA training into a pre‑training phase for extracting universal 3D spatial priors in a unified camera‑centric space, and a post‑training phase for efficient embodiment alignment within robot‑specific action space. By introducing discrete pose tokens as a universal representation, Pose‑VLA seamlessly integrates spatial grounding from diverse 3D datasets with geometry‑level trajectories from robotic demonstrations. Our framework follows a two‑stage pre‑training pipeline, establishing fundamental spatial grounding via poses followed by motion alignment through trajectory supervision. Extensive evaluations demonstrate that Pose‑VLA achieves state‑of‑the‑art results on RoboTwin 2.0 with a 79.5% average success rate and competitive performance on LIBERO at 96.0%. Real‑world experiments further showcase robust generalization across diverse objects using only 100 demonstrations per task, validating the efficiency of our pre‑training paradigm.
Authors:Xinyu Yuan, Xixian Liu, Ya Shi Zhang, Zuobai Zhang, Hongyu Guo, Jian Tang
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).
Authors:Jiayu Wang, Yifei Ming, Zixuan Ke, Shafiq Joty, Aws Albarghouthi, Frederic Sala
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.
Authors:Luhan Tang, Longxuan Yu, Shaorong Zhang, Greg Ver Steeg
Abstract:
Discrete diffusion language models (dLLMs) provide a fast and flexible alternative to autoregressive models (ARMs) via iterative denoising with parallel updates. However, their evaluation is challenging: existing metrics conflate denoiser approximation error with sampler‑induced error from the sampling dynamics, a problem that does not arise for ARMs whose autoregressive sampling exactly reflects the learned probability model. We introduce a sampler‑centric oracle framework that replaces learned denoisers with an exact Hidden Markov Model posterior derived from a ground‑truth Markov chain, isolating sampler‑induced error in a controlled setting. We show that few‑step discrete diffusion samplers are not distributionally correct even under an oracle denoiser, with transition‑level mismatch that vanishes only as the number of steps approaches the sequence length. Moreover, improvements in negative log‑likelihood (NLL), generative perplexity (GenPPL), or MAUVE do not imply correct sampling. Code is available at https://luhantang.github.io/dllm_sampler
Authors:Jeremy McEntire
Abstract:
We introduce Leap+Verify, a framework that applies speculative execution ‑‑ predicting future model weights and validating predictions before acceptance ‑‑ to accelerate neural network training. Inspired by speculative decoding in language model inference and by the Automatically Scalable Computation (ASC) architecture for program execution, Leap+Verify decomposes training into three dynamically detected regimes (chaotic, transition, stable) using activation‑space cosine similarity as a real‑time Lyapunov proxy signal. Within each regime, analytic weight predictors (momentum, linear, quadratic extrapolation) attempt to forecast model parameters K training steps ahead; predictions are accepted only when validated against a held‑out loss criterion. We evaluate Leap+Verify on GPT‑2 124M and Qwen 2.5‑1.5B trained on WikiText‑103 across five random seeds, sweeping prediction depth K in 5, 10, 25, 50, 75, 100. Momentum‑based prediction (Adam moment extrapolation) fails catastrophically at both scales, with predicted losses exceeding actuals by 100‑10,000x ‑‑ a universal norm explosion in optimizer‑state extrapolation. Finite‑difference predictors (linear, quadratic) succeed where momentum fails: at 124M, they achieve 24% strict acceptance at K=5 in stable regimes; at 1.5B, they achieve 37% strict acceptance in transition regimes. The scale‑dependent finding is in regime distribution: GPT‑2 124M spends 34% of training in stable regime, while Qwen 1.5B spends 64% in chaotic regime and reaches stable in only 0‑2 of 40 checkpoints. Larger models are more predictable when predictable, but less often predictable ‑‑ the practical bottleneck shifts from predictor accuracy to regime availability. Cross‑seed results are highly consistent (less than 1% validation loss variance), and the three‑regime framework produces identical phase boundaries (plus or minus 50 steps) across seeds.
Authors:Pengxi Liu, Zeyu Michael Li, Xiang Cheng
Abstract:
We introduce a variational framework for diffusion models with anisotropic noise schedules parameterized by a matrix‑valued path M_t(θ) that allocates noise across subspaces. Central to our framework is a trajectory‑level objective that jointly trains the score network and learns M_t(θ), which encompasses general parameterization classes of matrix‑valued noise schedules. We further derive an estimator for the derivative with respect to θ of the score that enables efficient optimization of the M_t(θ) schedule. For inference, we develop an efficiently‑implementable reverse‑ODE solver that is an anisotropic generalization of the second‑order Heun discretization algorithm. Across CIFAR‑10, AFHQv2, FFHQ, and ImageNet‑64, our method consistently improves upon the baseline EDM model in all NFE regimes. Code is available at https://github.com/lizeyu090312/anisotropic‑diffusion‑paper.
Authors:Arjun Chatterjee, Sayeed Sajjad Razin, John Wu, Siddhartha Laghuvarapu, Jathurshan Pradeepkumar, Jimeng Sun
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.
Authors:Pao-Hsiung Chiu, Jian Cheng Wong, Chin Chun Ooi, Chang Wei, Yuchen Fan, Yew-Soon Ong
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.
Authors:Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chaowen Hu, Lu Pan, Ke Zeng, Xunliang Cai
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.
Authors:Saba Kublashvili
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 .
Authors:Qi Sun, Can Wang, Jiaxiang Shang, Yingchun Liu, Jing Liao
Abstract:
Current 3D human animation methods struggle to achieve photorealism: kinematics‑based approaches lack non‑rigid dynamics (e.g., clothing dynamics), while methods that leverage video diffusion priors can synthesize non‑rigid motion but suffer from quality artifacts and identity loss. To overcome these limitations, we present Ani3DHuman, a framework that marries kinematics‑based animation with video diffusion priors. We first introduce a layered motion representation that disentangles rigid motion from residual non‑rigid motion. Rigid motion is generated by a kinematic method, which then produces a coarse rendering to guide the video diffusion model in generating video sequences that restore the residual non‑rigid motion. However, this restoration task, based on diffusion sampling, is highly challenging, as the initial renderings are out‑of‑distribution, causing standard deterministic ODE samplers to fail. Therefore, we propose a novel self‑guided stochastic sampling method, which effectively addresses the out‑of‑distribution problem by combining stochastic sampling (for photorealistic quality) with self‑guidance (for identity fidelity). These restored videos provide high‑quality supervision, enabling the optimization of the residual non‑rigid motion field. Extensive experiments demonstrate that \MethodName can generate photorealistic 3D human animation, outperforming existing methods. Code is available in https://github.com/qiisun/ani3dhuman.
Authors:Kwanghee Choi, Eunjung Yeo, Cheol Jun Cho, David Harwath, David R. Mortensen
Abstract:
Self‑supervised speech models (S3Ms) are known to encode rich phonetic information, yet how this information is structured remains underexplored. We conduct a comprehensive study across 96 languages to analyze the underlying structure of S3M representations, with particular attention to phonological vectors. We first show that there exist linear directions within the model's representation space that correspond to phonological features. We further demonstrate that the scale of these phonological vectors correlate to the degree of acoustic realization of their corresponding phonological features in a continuous manner. For example, the difference between [d] and [t] yields a voicing vector: adding this vector to [p] produces [b], while scaling it results in a continuum of voicing. Together, these findings indicate that S3Ms encode speech using phonologically interpretable and compositional vectors, demonstrating phonological vector arithmetic. All code and interactive demos are available at https://github.com/juice500ml/phonetic‑arithmetic .
Authors:Ziheng Chen, Bernhard Schölkopf, Nicu Sebe
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.
Authors:Jiayi Li, Penghao Jiang, Hira Saleem, Zhaonan Wang, Piotr Koniusz, Flora D. Salim
Abstract:
Autoregressive neural PDE surrogates predict future states by repeatedly applying a learned one‑step operator. This is a simple and widely used method, but small one‑step errors can accumulate during long rollouts. The resulting drift often appears as spectral amplitude distortion, phase misalignment, and nonlinear mode‑interaction error. These effects are especially important for time‑dependent PDEs with clear Fourier structure.
We introduce the Spectral Generator Neural Operator (SGNO), a structured autoregressive neural operator for long‑horizon PDE forecasting. SGNO organizes each learned one‑step map as a structured spectral evolution update. A real‑valued nonpositive diagonal generator provides a gain‑controlled spectral backbone, while a learned correction pathway with complex‑valued spectral mixing completes the residual evolution. This design gives the autoregressive step an evolution‑like structure while retaining the flexibility needed for dissipative, dispersive, transport‑dominated, and nonlinear PDEs.
SGNO is designed for periodic linear and semilinear evolution PDEs with Fourier multiplier linear dynamics. Across ten mechanism‑matched APEBench tasks spanning this regime, SGNO consistently outperforms strong single‑step autoregressive baselines in long‑horizon rollout accuracy, reducing GMean100 by a median of 74.8% relative to the strongest available non‑SGNO baseline, with per‑task reductions ranging from 13.6% to 92.9%. The gains are strongest on dispersive and transport‑dominated tasks, as well as tasks involving nonlinear closure and mode coupling. Spectral diagnostics show lower spectral energy error and improved rollout‑level phase fidelity. Ablations show that the constrained generator, the structured update, and the learned correction pathway each contribute to performance. The code is available at https://github.com/cruiseresearchgroup/SGNO.
Authors:Osman Onur Kuzucu, Tunca Doğan
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.
Authors:Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang, Bora Caglayan, Dario Simionato, Andrea Tonon, Ioannis Tsamardinos
Abstract:
Causal discovery for both cross‑sectional and temporal data has traditionally followed a dataset‑specific paradigm, where a new model is fitted for each individual dataset. Such an approach limits the potential of multi‑dataset pretraining. The concept of large causal models (LCMs) envisions a class of pre‑trained neural architectures specifically designed for temporal causal discovery. Prior approaches are constrained to small variable counts, degrade with larger inputs, and rely heavily on synthetic data, limiting generalization. We propose a principled framework for LCMs, combining diverse synthetic generators with realistic time‑series datasets, allowing learning at scale. Extensive experiments on synthetic, semi‑synthetic and realistic benchmarks show that LCMs scale effectively to higher variable counts and deeper architectures while maintaining strong performance. Trained models achieve competitive or superior accuracy compared to classical and neural baselines, particularly in out‑of‑distribution settings, while enabling fast, single‑pass inference. Results demonstrate LCMs as a promising foundation‑model paradigm for temporal causal discovery. Experiments and model weights are available at https://github.com/kougioulis/LCM‑paper/.
Authors:Ziqiao Shang, Lingyue Ge, Zi-Jian Cheng, Shi-Yu Tian, Zhenyu Huang, Wenbo Fu, Weiming Wu, Yang Chen, Xiangwen Zhang, Yulan Hu, Bin Liu, Yu-Feng Li, Lan-Zhe Guo
Abstract:
Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing benchmarks remain insufficient for rigorously assessing their reasoning capabilities under multi‑criteria constraints. To bridge this gap, we introduce MapTab, a multimodal benchmark specifically designed to evaluate holistic multi‑criteria reasoning in MLLMs via route planning tasks. MapTab requires MLLMs to perceive and ground visual cues from map images alongside route attributes (e.g., Time, Price) from structured tabular data. The benchmark encompasses two scenarios: Metromap, covering metro networks in 160 cities across 52 countries, and Travelmap, depicting 168 representative tourist attractions from 19 countries. In total, MapTab comprises 328 images, 196,800 route planning queries, and 3,936 QA queries, all incorporating 4 key criteria: Time, Price, Comfort, and Reliability. Extensive evaluations across 15 representative MLLMs reveal that current models face substantial challenges in multi‑criteria multimodal reasoning. Notably, under conditions of limited visual perception, multimodal collaboration often underperforms compared to unimodal approaches. We believe MapTab provides a challenging and realistic testbed to advance the systematic evaluation of MLLMs. Our code is available at https://github.com/Ziqiao‑Shang/MapTab.
Authors:Sarah Müller, Philipp Berens
Abstract:
Although deep learning models in medical imaging often achieve excellent classification performance, they can rely on shortcut learning, exploiting spurious correlations or confounding factors that are not causally related to the target task. This poses risks in clinical settings, where models must generalize across institutions, populations, and acquisition conditions. Feature disentanglement is a promising approach to mitigate shortcut learning by separating task‑relevant information from confounder‑related features in latent representations. In this study, we systematically evaluated feature disentanglement methods for mitigating shortcuts in medical imaging, including adversarial learning and latent space splitting based on dependence minimization. We assessed classification performance and disentanglement quality using latent space analyses across one artificial and two medical datasets with natural and synthetic confounders. We also examined robustness under varying levels of confounding and compared computational efficiency across methods. We found that shortcut mitigation methods improved classification performance under strong spurious correlations during training. Latent space analyses revealed differences in representation quality not captured by classification metrics, highlighting the strengths and limitations of each method. Model reliance on shortcuts depended on the degree of confounding in the training data. The best‑performing models combine data‑centric rebalancing with model‑centric disentanglement, achieving stronger and more robust shortcut mitigation than rebalancing alone while maintaining similar computational efficiency. The project code is publicly available at https://github.com/berenslab/medical‑shortcut‑mitigation.
Authors:Yanlin Zhang, Linjie Xu, Quan Gan, David Wipf, Minjie Wang
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.
Authors:Nahom Birhan, Daniel Wesego, Dereje Shenkut, Frank Liu, Daniel Takabi
Abstract:
Personalized federated learning (PFL) creates client‑specific models to handle data heterogeneity. Previously, PFL has been shown to be naturally resistant to backdoor attack propagation across clients. In this work, we reveal that PFL remains vulnerable to backdoor attacks through a novel frequency‑domain approach. We propose DCInject, an adaptive frequency‑domain backdoor attack for PFL, which removes portions of the zero‑frequency (DC) component and replaces them with Gaussian‑distributed samples in the frequency domain. Our attack achieves superior attack success rates while maintaining clean accuracy across four datasets (CIFAR‑10/100, GTSRB, SVHN) compared to existing spatial‑domain attacks, evaluated under parameter decoupling based personalization. DCInject achieves superior performance with ASRs of 96.83% (CIFAR‑10), 99.38% (SVHN), and 100% (GTSRB) while maintaining clean accuracy. Under I‑BAU defense, DCInject demonstrates strong persistence, retaining 90.30% ASR vs BadNet's 58.56% on VGG‑16, exposing critical vulnerabilities in PFL security assumptions. Our code is available at https://github.com/NahomMA/DCINject‑PFL
Authors:Guoqi Yu, Juncheng Wang, Chen Yang, Jing Qin, Angelica I. Aviles-Rivero, Shujun Wang
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.
Authors:Sanjeev Panta, Xu Yuan, Li Chen, Nian-Feng Tzeng
Abstract:
Time series forecasting presents significant challenges in real‑world applications across various domains. Building upon the decomposition of the time series, we enhance the architecture of machine learning models for better multivariate time series forecasting. To achieve this, we focus on the trend and seasonal components individually and investigate solutions to predict them with less errors. Recognizing that reversible instance normalization is effective only for the trend component, we take a different approach with the seasonal component by directly applying backbone models without any normalization or scaling procedures. Through these strategies, we successfully reduce error values of the existing state‑of‑the‑art models and finally introduce dual‑MLP models as more computationally efficient solutions. Furthermore, our approach consistently yields positive results with around 10% MSE average reduction across four state‑of‑the‑art baselines on the benchmark datasets. We also evaluate our approach on a hydrological dataset extracted from the United States Geological Survey (USGS) river stations, where our models achieve significant improvements while maintaining linear time complexity, demonstrating real‑world effectiveness. The source code is available at https://github.com/Sanjeev97/Time‑Series‑Decomposition
Authors:Xiaoyan Bai, Alexander Baumgartner, Haojia Sun, Ari Holtzman, Chenhao Tan
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.
Authors:Geri Skenderi, Lorenzo Buffoni, Francesco D'Amico, David Machado, Raffaele Marino, Matteo Negri, Federico Ricci-Tersenghi, Carlo Lucibello, Maria Chiara Angelini
Abstract:
Graph neural networks (GNNs) are increasingly applied to hard optimization problems, often claiming superiority over classical heuristics. However, such claims risk being unsolid due to a lack of standard benchmarks on truly hard instances. From a statistical physics perspective, we propose new hard benchmarks based on random problems. We provide these benchmarks, along with performance results from both classical heuristics and GNNs. Our fair comparison shows that classical algorithms still outperform GNNs. We discuss the challenges for neural networks in this domain. Future claims of superiority can be made more robust using our benchmarks, available at https://github.com/ArtLabBocconi/RandCSPBench.
Authors:Minh Dinh, Stéphane Deny
Abstract:
Despite the successes of deep learning in computer vision, difficulties persist in recognizing objects that have undergone group‑symmetric transformations rarely seen during training\unicodex2013for example objects seen in unusual poses, scales, positions, or combinations thereof. Equivariant neural networks are a solution to the problem of generalizing across symmetric transformations, but require knowledge of transformations a priori. An alternative family of architectures proposes to learn equivariant operators in a latent space, from examples of symmetric transformations. Here, using simple datasets of rotated and translated noisy MNIST, we illustrate how such architectures can successfully be harnessed for out‑of‑distribution classification, thus overcoming the limitations of both traditional and equivariant networks. While conceptually enticing, we discuss challenges ahead on the path of scaling these architectures to more complex datasets. Our code is available at https://github.com/BRAIN‑Aalto/equivariant_operator.
Authors:Yutong Xin, Qiaochu Chen, Greg Durrett, Işil Dillig
Abstract:
Large language models have achieved striking results in interactive theorem proving, particularly in Lean. However, most benchmarks for LLM‑based proof automation are drawn from mathematics in the Mathlib ecosystem, whereas proofs in software verification are developed inside definition‑rich codebases with substantial project‑specific libraries. We introduce VeriSoftBench, a benchmark of 500 Lean 4 proof obligations drawn from open‑source formal‑methods developments and packaged to preserve realistic repository context and cross‑file dependencies. Our evaluation of frontier LLMs and specialized provers yields three observations. First, provers tuned for Mathlib‑style mathematics transfer poorly to this repository‑centric setting. Second, success is strongly correlated with transitive repository dependence: tasks whose proofs draw on large, multi‑hop dependency closures are less likely to be solved. Third, providing curated context restricted to a proof's dependency closure improves performance relative to exposing the full repository, but nevertheless leaves substantial room for improvement. Our benchmark and evaluation suite are released at https://github.com/utopia‑group/VeriSoftBench.
Authors:Finn van der Knaap, Kejiang Qian, Zheng Xu, Fengxiang He
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.
Authors:Aaron Louis Eidt, Nils Feldhus
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.
Authors:Jorge Carrasco Pollo, Ioannis Kapetangeorgis, Joshua Rosenthal, John Hua Yao
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.
Authors:Yuankai Luo, Woping Chen, Tong Liang, Baiqiao Wang, Zhenguo Li
Abstract:
Vision‑Language‑Action (VLA) models have emerged as a promising paradigm for general‑purpose robotic manipulation, leveraging large‑scale pre‑training to achieve strong performance. The field has rapidly evolved with additional spatial priors and diverse architectural innovations. However, these advancements are often accompanied by varying training recipes and implementation details, which can make it challenging to disentangle the precise source of empirical gains. In this work, we introduce SimVLA, a streamlined baseline designed to establish a transparent reference point for VLA research. By strictly decoupling perception from control, using a standard vision‑language backbone and a lightweight action head, and standardizing critical training dynamics, we demonstrate that a minimal design can achieve state‑of‑the‑art performance. Despite having only 0.5B parameters, SimVLA outperforms multi‑billion‑parameter models on standard simulation benchmarks without robot pretraining. SimVLA also reaches on‑par real‑robot performance compared to pi0.5. Our results establish SimVLA as a robust, reproducible baseline that enables clear attribution of empirical gains to future architectural innovations. Website: https://frontierrobo.github.io/SimVLA
Authors:Joseph Bingham, Netanel Arussy, Dvir Aran
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
Authors:Xiuying Wei, Caglar Gulcehre
Abstract:
Structured dilated attention has an appealing inference‑time efficiency knob: it reduces the FLOPs of attention and the KV cache size by a factor of the dilation size D, while preserving long‑range connectivity. While prior work studies it by training each configuration from scratch, directly sparsifying a pretrained attention model into a dilated pattern leads to severe accuracy degradation, preventing flexible reuse across inference scenarios. We introduce RAT+, a dense‑pretraining architecture that augments attention with full‑sequence recurrence and active recurrence learning. A single RAT+ model is pretrained densely once and can then be flexibly switched at inference time to dilated attention (optionally with local windows) or hybrid layer/head compositions, requiring only a short 1B‑token resolution adaptation rather than retraining separate sparse models. At 1.5B parameters trained on 100B tokens, RAT+ closely matches dense accuracy at D = 16, and drops by about 2‑3 points at D = 64 on commonsense reasoning and LongBench tasks. We further scale to 2.6B and 7.6B parameters and observe even more promising performance (e.g., a 1‑point average accuracy loss with a 64x reduction in attention FLOPs and KV cache size). Code is available at https://github.com/wimh966/rat‑plus.
Authors:Narjes Nourzad, Carlee Joe-Wong
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/
Authors:Athanasios Angelakis
Abstract:
Vision Transformers rely on positional embeddings and class tokens encoding fixed spatial priors. While effective for natural images, these priors may be suboptimal when spatial layout is weakly informative, a frequent condition in medical imaging. We introduce ZACH‑ViT (Zero‑token Adaptive Compact Hierarchical Vision Transformer), a compact Vision Transformer that removes positional embeddings and the [CLS] token, achieving permutation‑invariant patch processing via global average pooling. Zero‑token denotes removal of the dedicated aggregation token and positional encodings. Patch tokens remain unchanged. Adaptive residual projections preserve training stability under strict parameter constraints. We evaluate ZACH‑ViT across seven MedMNIST datasets under a strict few‑shot protocol (50 samples/class, fixed hyperparameters, five seeds). Results reveal regime‑dependent behavior: ZACH‑ViT (0.25M parameters, trained from scratch) achieves strongest advantage on BloodMNIST and remains competitive on PathMNIST, while relative advantage decreases on datasets with stronger anatomical priors (OCTMNIST, OrganAMNIST), consistent with our hypothesis. Component and pooling ablations show positional support becomes mildly beneficial as spatial structure increases, whereas reintroducing a [CLS] token is consistently unfavorable. These findings support that architectural alignment with data structure can outweigh universal benchmark dominance. Despite minimal size and no pretraining, ZACH‑ViT achieves competitive performance under data‑scarce conditions, relevant for compact medical imaging and low‑resource settings. Code: https://github.com/Bluesman79/ZACH‑ViT
Authors:Adrian Catalin Lutu, Eduard Poesina, Radu Tudor Ionescu
Abstract:
Query performance prediction (QPP) is an important and actively studied information retrieval task, having various applications, such as query reformulation, query expansion, and retrieval system selection, among many others. The task has been primarily studied in the context of text and image retrieval, whereas QPP for content‑based video retrieval (CBVR) remains largely underexplored. To this end, we propose the first benchmark for video query performance prediction (VQPP), comprising two text‑to‑video retrieval datasets and two CBVR systems, respectively. VQPP contains a total of 56K text queries and 51K videos, and comes with official training, validation and test splits, fostering direct comparisons and reproducible results. We explore multiple pre‑retrieval and post‑retrieval performance predictors, creating a representative benchmark for future exploration of QPP in the video domain. Our results show that pre‑retrieval predictors obtain competitive performance, enabling applications before performing the retrieval step. We also demonstrate the applicability of VQPP by employing the best performing pre‑retrieval predictor as reward model for training a large language model (LLM) on the query reformulation task via direct preference optimization (DPO). We release our benchmark and code at https://github.com/AdrianLutu/VQPP.
Authors:Balamurugan Thambiraja, Omid Taheri, Radek Danecek, Giorgio Becherini, Gerard Pons-Moll, Justus Thies
Abstract:
Hands play a central role in daily life, yet modeling natural hand motions remains underexplored. Existing methods that tackle text‑to‑hand‑motion generation or hand animation captioning rely on studio‑captured datasets with limited actions and contexts, making them costly to scale to "in‑the‑wild" settings. Further, contemporary models and their training schemes struggle to capture animation fidelity with text‑motion alignment. To address this, we (1) introduce '3D Hands in the Wild' (3D‑HIW), a dataset of 32K 3D hand‑motion sequences and aligned text, and (2) propose CLUTCH, an LLM‑based hand animation system with two critical innovations: (a) SHIFT, a novel VQ‑VAE architecture to tokenize hand motion, and (b) a geometric refinement stage to finetune the LLM. To build 3D‑HIW, we propose a data annotation pipeline that combines vision‑language models (VLMs) and state‑of‑the‑art 3D hand trackers, and apply it to a large corpus of egocentric action videos covering a wide range of scenarios. To fully capture motion in‑the‑wild, CLUTCH employs SHIFT, a part‑modality decomposed VQ‑VAE, which improves generalization and reconstruction fidelity. Finally, to improve animation quality, we introduce a geometric refinement stage, where CLUTCH is co‑supervised with a reconstruction loss applied directly to decoded hand motion parameters. Experiments demonstrate state‑of‑the‑art performance on text‑to‑motion and motion‑to‑text tasks, establishing the first benchmark for scalable in‑the‑wild hand motion modelling. Code, data and models will be released.
Authors:Ziyuan Liu, Shizhao Sun, Danqing Huang, Yingdong Shi, Meisheng Zhang, Ji Li, Jingsong Yu, Jiang Bian
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/.
Authors:Irene Iele, Giulia Romoli, Daniele Molino, Elena Mulero Ayllón, Filippo Ruffini, Paolo Soda, Matteo Tortora
Abstract:
Short‑term forecasting of vegetation dynamics is a key enabler for data‑driven decision support in precision agriculture. Normalized Difference Vegetation Index (NDVI) forecasting from satellite observations, however, remains challenging due to sparse and irregular sampling caused by cloud masking, as well as the heterogeneous climatic conditions under which crops evolve. In this work, we propose a probabilistic forecasting framework for field‑level NDVI prediction under sparse, irregular clear‑sky acquisitions. The architecture separates the encoding of historical NDVI and meteorological observations from future exogenous covariates, fusing both representations for multi‑step quantile prediction. To address irregular revisit patterns and horizon‑dependent uncertainty, we introduce a temporal‑distance weighted quantile loss that aligns the training objective with the effective forecasting horizon. In addition, we incorporate cumulative and extreme‑weather feature engineering to capture delayed meteorological effects relevant to vegetation response. Experiments on European satellite data show that the proposed approach outperforms statistical, deep learning, and time‑series baselines on both pointwise and probabilistic evaluation metrics. Ablation studies confirm that target history is the primary driver of performance, with meteorological covariates providing additional gains in the full multimodal setting. The code is available at https://github.com/arco‑group/ndvi‑forecasting.
Authors:Peng Sun, Xinyi Shang, Tao Lin, Zhiqiang Shen
Abstract:
Consistency‑based generative models like Shortcut and MeanFlow achieve impressive results via a target‑aware design for solving the Probability Flow ODE (PF‑ODE). Typically, such methods introduce a target time r alongside the current time t to modulate outputs between a local multi‑step derivative (r = t) and a global few‑step integral (r = 0). However, the conventional "one input, one output" paradigm enforces a partition of the training budget, often allocating a significant portion (e.g., 75% in MeanFlow) solely to the multi‑step objective for stability. This separation forces a trade‑off: allocating sufficient samples to the multi‑step objective leaves the few‑step generation undertrained, which harms convergence and limits scalability. To this end, we propose Duality Models (DuMo) via a "one input, dual output" paradigm. Using a shared backbone with dual heads, DuMo simultaneously predicts velocity v_t and flow‑map u_t from a single input x_t. This applies geometric constraints from the multi‑step objective to every sample, bounding the few‑step estimation without separating training objectives, thereby significantly improving stability and efficiency. On ImageNet 256 × 256, a 679M Diffusion Transformer with SD‑VAE achieves a state‑of‑the‑art (SOTA) FID of 1.79 in just 2 steps. Code is available at: https://github.com/LINs‑lab/DuMo
Authors:Aidar Myrzakhan, Tianyi Li, Bowei Guo, Shengkun Tang, Zhiqiang Shen
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.
Authors:Xiaohan Zhao, Zhaoyi Li, Yaxin Luo, Jiacheng Cui, Zhiqiang Shen
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.
Authors:Luke J. Huang, Zhuoyang Zhang, Qinghao Hu, Shang Yang, Song Han
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
Authors:Yaoyue Zheng, Yin Zhang, Joost van de Weijer, Gido M van de Ven, Shaoyi Du, Xuetao Zhang, Zhiqiang Tian
Abstract:
Continual Learning (CL) with large‑scale pre‑trained models (PTMs) has recently gained wide attention, shifting the focus from training from scratch to continually adapting PTMs. This has given rise to a promising paradigm: parameter‑efficient continual learning (PECL), where task interference is typically mitigated by assigning a task‑specific module during training, such as low‑rank adapters. However, weight regularization techniques, such as Elastic Weight Consolidation (EWC)‑a key strategy in CL‑remain underexplored in this new paradigm. In this paper, we revisit weight regularization in low‑rank CL as a new perspective for mitigating task interference in PECL. Unlike existing low‑rank CL methods, we mitigate task interference by regularizing a shared low‑rank update through EWC, thereby keeping the storage requirement and inference costs constant regardless of the number of tasks. Our proposed method EWC‑LoRA leverages a low‑rank representation to estimate parameter importance over the full‑dimensional space. This design offers a practical, computational‑ and memory‑efficient solution for CL with PTMs, and provides insights that may inform the broader application of regularization techniques within PECL. Extensive experiments on various benchmarks demonstrate the effectiveness of EWC‑LoRA, achieving a stability‑plasticity trade‑off superior to existing low‑rank CL approaches. These results indicate that, even under low‑rank parameterizations, weight regularization remains an effective mechanism for mitigating task interference. Code is available at: https://github.com/yaoyz96/low‑rank‑cl.
Authors:Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Binbin Zheng, Chaowen Hu, Zekai Shao, Cong Qin, Lu Pan, Ke Zeng, Xunliang Cai
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.
Authors:Masahiro Kato
Abstract:
Efficient estimation of causal and structural parameters can be automated using the Riesz representation theorem and debiased machine learning (DML). We present genriesz, an open‑source Python package that implements automatic DML and generalized Riesz regression, a unified framework for estimating Riesz representers by minimizing empirical Bregman divergences. This framework includes covariate balancing, nearest‑neighbor matching, calibrated estimation, and density ratio estimation as special cases. A key design principle of the package is automatic regressor balancing (ARB): given a Bregman generator g and a representer model class, genriesz automatically constructs a compatible link function so that the generalized Riesz regression estimator satisfies balancing (moment‑matching) optimality conditions in a user‑chosen basis. The package provides a modulr interface for specifying (i) the target linear functional via a black‑box evaluation oracle, (ii) the representer model via basis functions (polynomial, RKHS approximations, random forest leaf encodings, neural embeddings, and a nearest‑neighbor catchment basis), and (iii) the Bregman generator, with optional user‑supplied derivatives. It returns regression adjustment (RA), Riesz weighting (RW), augmented Riesz weighting (ARW), and TMLE‑style estimators with cross‑fitting, confidence intervals, and p‑values. We highlight representative workflows for estimation problems such as the average treatment effect (ATE), ATE on treated (ATT), and average marginal effect estimation. The Python package is available at https://github.com/MasaKat0/genriesz and on PyPI.
Authors:Peter Balogh
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.
Authors:Gurjeet Sangra Singh, Frantzeska Lavda, Giangiacomo Mercatali, Alexandros Kalousis
Abstract:
Deep generative models such as flow matching and diffusion models have shown great potential in learning complex distributions and dynamical systems, but often act as black‑boxes, neglecting underlying physics. In contrast, physics‑based simulation models described by ODEs/PDEs remain interpretable, but may have missing or unknown terms, unable to fully describe real‑world observations. We bridge this gap with a novel grey‑box method that integrates incomplete physics models directly into generative models. Our approach learns dynamics from observational trajectories alone, without ground‑truth physics parameters, in a simulation‑free manner that avoids scalability and stability issues of Neural ODEs. The core of our method lies in modelling a structured variational distribution within the flow matching framework, by using two latent encodings: one to model the missing stochasticity and multi‑modal velocity, and a second to encode physics parameters as a latent variable with a physics‑informed prior. Furthermore, we present an adaptation of the framework to handle second‑order dynamics. Our experiments on representative ODE/PDE problems and real‑world weather forecasting demonstrate that our method performs on par with or superior to fully data‑driven approaches and previous grey‑box baselines, while preserving the interpretability of the physics model. Our code is available at https://github.com/DMML‑Geneva/VGB‑DM.
Authors:Dylan Bouchard, Mohit Singh Chauhan, Viren Bajaj, David Skarbrevik
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.
Authors:Lorenzo Caselli, Marco Mistretta, Simone Magistri, Andrew D. Bagdanov
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.
Authors:Luzhi Wang, Xuanshuo Fu, He Zhang, Chuang Liu, Xiaobao Wang, Hongbo Liu
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.
Authors:Ron Shapira Weber, Oren Freifeld
Abstract:
We present softdtw‑cuda‑torch, an open‑source PyTorch library for computing Soft Dynamic Time Warping (SoftDTW) on GPUs. Our implementation addresses three key limitations of existing GPU implementations of SoftDTW: a hard sequence‑length cap of 1024, numerical instability in the backward pass for small smoothing parameters, and excessive GPU memory consumption from materializing pairwise distance tensors. We introduce (1) tiled anti‑diagonal kernel execution that removes the sequence‑length constraint, (2) a log‑space back‑ward pass that prevents floating‑point overflow, and (3) a fused distance‑computation mode that eliminates the O(BN M ) intermediate distance tensor, achieving up to 98% memory reduction compared to prior work. The library supports arbitrary sequence lengths, full PyTorch autograd integration, and Soft‑DTW Barycenter computation. Code is available at https://github.com/BGU‑CS‑VIL/sdtw‑cuda‑torch.
Authors:Mingzhe Cui, Tao Chen, Yang Jiao, Yiqin Wang, Lei Xie, Yi Pan, Luca Mainardi
Abstract:
Developing foundation models for electroencephalography (EEG) remains challenging due to the signal's low signal‑to‑noise ratio and complex spectro‑temporal non‑stationarity. Existing approaches often overlook the hierarchical latent structure inherent in neural dynamics, leading to suboptimal reconstruction of fine‑grained information. In this work, we propose BrainRVQ, a general‑purpose EEG foundation model pre‑trained on a large‑scale corpus of clinical EEG data. Unlike standard masked modeling, BrainRVQ features a Dual‑Domain Residual Vector Quantization (DD‑RVQ) tokenizer that disentangles temporal waveforms and spectral patterns into hierarchical discrete codes. We further introduce a hierarchical autoregressive pre‑training objective that learns to reconstruct these codes in a coarse‑to‑fine manner, utilizing an importance‑guided curriculum masking strategy to prioritize information‑rich neural events over background noise. Extensive experiments across 8 diverse downstream datasets demonstrate that BrainRVQ consistently outperforms state‑of‑the‑art baselines, validating its effectiveness in learning robust and generalizable neural representations. Our code and model weights are available:https://github.com/keqicmz/BrainRVQ
Authors:Mehrshad Taji, Arad Mahdinezhad Kashani, Iman Ahmadi, AmirHossein Jadidi, Saina Kashani, Babak Khalaj
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 .
Authors:Yiqing Xie, Emmy Liu, Gaokai Zhang, Nachiket Kotalwar, Shubham Gandhi, Sathwik Acharya, Xingyao Wang, Carolyn Rose, Graham Neubig, Daniel Fried
Abstract:
When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE‑Bench. In contrast, in real use, these agents solve more various and complex tasks that involve other skills such as exploring codebases, testing software, and designing architecture. In this paper, we first characterize some transferable skills that are shared across diverse tasks by decomposing trajectories into fine‑grained components, and derive a set of principles for designing auxiliary training tasks to teach language models these skills. Guided by these principles, we propose a training environment, Hybrid‑Gym, consisting of a set of scalable synthetic tasks, such as function localization and dependency search. Experiments show that agents trained on our synthetic tasks effectively generalize to diverse real‑world tasks that are not present in training, improving a base model by 25.4% absolute gain on SWE‑Bench Verified, 7.9% on SWT‑Bench Verified, and 5.1% on Commit‑0 Lite. Hybrid‑Gym also complements datasets built for the downstream tasks (e.g., improving SWE‑Play by 4.9% on SWT‑Bench Verified). Code available at: https://github.com/yiqingxyq/Hybrid‑Gym.
Authors:Xidong Wang, Shuqi Guo, Yue Shen, Junying Chen, Jian Wang, Jinjie Gu, Ping Zhang, Lei Liu, Benyou Wang
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.
Authors:Haoxiang Sun, Lizhen Xu, Bing Zhao, Wotao Yin, Wei Wang, Boyu Yang, Rui Wang, Hu Wei
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.
Authors:Karan Bali, Jack Stanley, Praneet Suresh, Danilo Bzdok
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.
Authors:SungJun Cho, Chetan Gohil, Rukuang Huang, Oiwi Parker Jones, Mark W. Woolrich
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.
Authors:Qi You, Yitai Cheng, Zichao Zeng, James Haworth
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.
Authors:Kaiting Liu, Hazel Doughty
Abstract:
Video recognition models are typically trained on fixed taxonomies which are often too coarse, collapsing distinctions in object, manner or outcome under a single label. As tasks and definitions evolve, such models cannot accommodate emerging distinctions and collecting new annotations and retraining to accommodate such changes is costly. To address these challenges, we introduce category splitting, a new task where an existing classifier is edited to refine a coarse category into finer subcategories, while preserving accuracy elsewhere. We propose a zero‑shot editing method that leverages the latent compositional structure of video classifiers to expose fine‑grained distinctions without additional data. We further show that low‑shot fine‑tuning, while simple, is highly effective and benefits from our zero‑shot initialization. Experiments on our new video benchmarks for category splitting demonstrate that our method substantially outperforms vision‑language baselines, improving accuracy on the newly split categories without sacrificing performance on the rest. Project page: https://kaitingliu.github.io/Category‑Splitting/.
Authors:Tiou Wang, Zhuoqian Yang, Markus Flierl, Mathieu Salzmann, Sabine Süsstrunk
Abstract:
We propose the Subtractive Modulative Network (SMN), a novel, parameter‑efficient Implicit Neural Representation (INR) architecture inspired by classical subtractive synthesis. The SMN is designed as a principled signal processing pipeline, featuring a learnable periodic activation layer (Oscillator) that generates a multi‑frequency basis, and a series of modulative mask modules (Filters) that actively generate high‑order harmonics. We provide both theoretical analysis and empirical validation for our design. Our SMN achieves a PSNR of 40+ dB on two image datasets, comparing favorably against state‑of‑the‑art methods in terms of both reconstruction accuracy and parameter efficiency. Furthermore, consistent advantage is observed on the challenging 3D NeRF novel view synthesis task. Supplementary materials are available at https://inrainbws.github.io/smn/.
Authors:Guy Bar-Shalom, Ami Tavory, Itay Evron, Maya Bechler-Speicher, Ido Guy, Haggai Maron
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.
Authors:Filippos Bellos, NaveenJohn Premkumar, Yannis Avrithis, Nam H. Nguyen, Jason J. Corso
Abstract:
LLM‑for‑time series (TS) methods typically treat time shallowly, injecting positional or prompt‑based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce Temporal‑Prior Conditioning (TPC), which elevates time to a first‑class modality that conditions the model at multiple depths. TPC attaches a small set of learnable time series tokens to the patch stream; at selected layers these tokens cross‑attend to temporal embeddings derived from compact, human‑readable temporal descriptors encoded by the same frozen LLM, then feed temporal context back via self‑attention. This disentangles time series signal and temporal information while maintaining a low parameter budget. We show that by training only the cross‑attention modules and explicitly disentangling time series signal and temporal information, TPC consistently outperforms both full fine‑tuning and shallow conditioning strategies, achieving state‑of‑the‑art performance in long‑term forecasting across diverse datasets. Code available at: https://github.com/fil‑mp/Deep_tpc
Authors:Idil Bilge Altun, Mert Onur Cakiroglu, Elham Buxton, Mehmet Dalkilic, Hasan Kurban
Abstract:
Discrete image tokenization is a key bottleneck for scalable visual generation: a tokenizer must remain compact for efficient latent‑space priors while preserving semantic structure and using discrete capacity effectively. Existing quantizers face a trade‑off: vector‑quantized tokenizers learn flexible geometries but often suffer from biased straight‑through optimization, codebook under‑utilization, and representation collapse at large vocabularies. Structured scalar or implicit tokenizers ensure stable, near‑complete utilization by design, yet rely on fixed discretization geometries that may allocate capacity inefficiently under heterogeneous latent statistics.
We introduce Learnable Geometric Quantization (LGQ), a discrete image tokenizer that learns discretization geometry end‑to‑end. LGQ replaces hard nearest‑neighbor lookup with temperature‑controlled soft assignments, enabling fully differentiable training while recovering hard assignments at inference. The assignments correspond to posterior responsibilities of an isotropic Gaussian mixture and minimize a variational free‑energy objective, provably converging to nearest‑neighbor quantization in the low‑temperature limit. LGQ combines a token‑level peakedness regularizer with a global usage regularizer to encourage confident yet balanced code utilization without imposing rigid grids.
Under a controlled VQGAN‑style backbone on ImageNet across multiple vocabulary sizes, LGQ achieves stable optimization and balanced utilization. At 16K codebook size, LGQ improves rFID by 11.88% over FSQ while using 49.96% fewer active codes, and improves rFID by 6.06% over SimVQ with 49.45% lower effective representation rate, achieving comparable fidelity with substantially fewer active entries. Our GitHub repository is available at: https://github.com/KurbanIntelligenceLab/LGQ
Authors:Nelson Salazar-Pena, Alejandra Tabares, Andres Gonzalez-Mancera
Abstract:
This paper introduces a novel, open‑source MARL simulation framework for studying implicit cooperation in LEMs, modeled as a decentralized partially observable Markov decision process and implemented as a Gymnasium environment for MARL. Our framework features a modular market platform with plug‑and‑play clearing mechanisms, physically constrained agent models (including battery storage), a realistic grid network, and a comprehensive analytics suite to evaluate emergent coordination. The main contribution is a novel method to foster implicit cooperation, where agents' observations and rewards are enhanced with system‑level key performance indicators to enable them to independently learn strategies that benefit the entire system and aim for collectively beneficial outcomes without explicit communication. Through representative case studies (available in a dedicated GitHub repository in https://github.com/salazarna/marlem, we show the framework's ability to analyze how different market configurations (such as varying storage deployment) impact system performance. This illustrates its potential to facilitate emergent coordination, improve market efficiency, and strengthen grid stability. The proposed simulation framework is a flexible, extensible, and reproducible tool for researchers and practitioners to design, test, and validate strategies for future intelligent, decentralized energy systems.
Authors:KC Santosh, Srikanth Baride, Rodrigue Rizk
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.
Authors:Cheng Zeng, Harry W. Sullivan, Thomas Egg, Maya M. Martirossyan, Philipp Höllmer, Jirui Jin, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Ellad B. Tadmor, Mingjie Liu
Abstract:
Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra‑ and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids, and metal‑organic frameworks, extending such approaches to fully periodic molecular crystals is still elusive. Here, we present MolCrystalFlow, a flow‑based generative model for molecular crystal structure prediction. The framework disentangles intramolecular complexity from intermolecular packing by embedding molecules as rigid bodies and jointly learning the lattice matrix, molecular orientations, and centroid positions. Centroids and orientations are represented on their native Riemannian manifolds, allowing geodesic flow construction and graph neural network operations that respects geometric symmetries. We benchmark our model against a state‑of‑the‑art generative model (MOFFlow) for large‑size periodic crystals and a rule‑based structure generation method (Genarris) on two open‑source molecular crystal datasets. MolCrystalFlow outperforms MOFFlow while achieving competitive performance against Genarris. We also demonstrate an integration of MolCrystalFlow model with universal machine learning potential to accelerate molecular crystal structure prediction, paving the way for data‑driven generative discovery of molecular crystals.
Authors:Adnan El Assadi, Isaac Chung, Chenghao Xiao, Roman Solomatin, Animesh Jha, Rahul Chand, Silky Singh, Kaitlyn Wang, Ali Sartaz Khan, Marc Moussa Nasser, Sufen Fong, Pengfei He, Alan Xiao, Ayush Sunil Munot, Aditya Shrivastava, Artem Gazizov, Niklas Muennighoff, Kenneth Enevoldsen
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.
Authors:Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo
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.
Authors:Christian Schlarmann, Matthias Hein
Abstract:
Generative large vision‑language models (LVLMs) have recently achieved impressive performance gains, and their user base is growing rapidly. However, the security of LVLMs, in particular in a long‑context multi‑turn setting, is largely underexplored. In this paper, we consider the realistic scenario in which an attacker uploads a manipulated image to the web/social media. A benign user downloads this image and uses it as input to the LVLM. Our novel stealthy Visual Memory Injection (VMI) attack is designed such that on normal prompts the LVLM exhibits nominal behavior, but once the user gives a triggering prompt, the LVLM outputs a specific prescribed target message to manipulate the user, e.g. for adversarial marketing or political persuasion. Compared to previous work that focused on single‑turn attacks, VMI is effective even after a long multi‑turn conversation with the user. We demonstrate our attack on several recent open‑weight LVLMs. This article thereby shows that large‑scale manipulation of users is feasible with perturbed images in multi‑turn conversation settings, calling for better robustness of LVLMs against these attacks. We release the source code at https://github.com/chs20/visual‑memory‑injection
Authors:Kaaustaaub Shankar, Kelly Cohen
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.
Authors:GLM-5-Team, :, Aohan Zeng, Xin Lv, Zhenyu Hou, Zhengxiao Du, Qinkai Zheng, Bin Chen, Da Yin, Chendi Ge, Chenghua Huang, Chengxing Xie, Chenzheng Zhu, Congfeng Yin, Cunxiang Wang, Gengzheng Pan, Hao Zeng, Haoke Zhang, Haoran Wang, Huilong Chen, Jiajie Zhang, Jian Jiao, Jiaqi Guo, Jingsen Wang, Jingzhao Du, Jinzhu Wu, Kedong Wang, Lei Li, Lin Fan, Lucen Zhong, Mingdao Liu, Mingming Zhao, Pengfan Du, Qian Dong, Rui Lu, Shuang-Li, Shulin Cao, Song Liu, Ting Jiang, Xiaodong Chen, Xiaohan Zhang, Xuancheng Huang, Xuezhen Dong, Yabo Xu, Yao Wei, Yifan An, Yilin Niu, Yitong Zhu, Yuanhao Wen, Yukuo Cen, Yushi Bai, Zhongpei Qiao, Zihan Wang, Zikang Wang, Zilin Zhu, Ziqiang Liu, Zixuan Li, Bojie Wang, Bosi Wen, Can Huang, Changpeng Cai, Chao Yu, Chen Li, Chengwei Hu, Chenhui Zhang, Dan Zhang, Daoyan Lin, Dayong Yang, Di Wang, Ding Ai, Erle Zhu, Fangzhou Yi, Feiyu Chen, Guohong Wen, Hailong Sun, Haisha Zhao, Haiyi Hu, Hanchen Zhang, Hanrui Liu, Hanyu Zhang, Hao Peng, Hao Tai, Haobo Zhang, He Liu, Hongwei Wang, Hongxi Yan, Hongyu Ge, Huan Liu, Huanpeng Chu, Jia'ni Zhao, Jiachen Wang, Jiajing Zhao, Jiamin Ren, Jiapeng Wang, Jiaxin Zhang, Jiayi Gui, Jiayue Zhao, Jijie Li, Jing An, Jing Li, Jingwei Yuan, Jinhua Du, Jinxin Liu, Junkai Zhi, Junwen Duan, Kaiyue Zhou, Kangjian Wei, Ke Wang, Keyun Luo, Laiqiang Zhang, Leigang Sha, Liang Xu, Lindong Wu, Lintao Ding, Lu Chen, Minghao Li, Nianyi Lin, Pan Ta, Qiang Zou, Rongjun Song, Ruiqi Yang, Shangqing Tu, Shangtong Yang, Shaoxiang Wu, Shengyan Zhang, Shijie Li, Shuang Li, Shuyi Fan, Wei Qin, Wei Tian, Weining Zhang, Wenbo Yu, Wenjie Liang, Xiang Kuang, Xiangmeng Cheng, Xiangyang Li, Xiaoquan Yan, Xiaowei Hu, Xiaoying Ling, Xing Fan, Xingye Xia, Xinyuan Zhang, Xinze Zhang, Xirui Pan, Xu Zou, Xunkai Zhang, Yadi Liu, Yandong Wu, Yanfu Li, Yidong Wang, Yifan Zhu, Yijun Tan, Yilin Zhou, Yiming Pan, Ying Zhang, Yinpei Su, Yipeng Geng, Yong Yan, Yonglin Tan, Yuean Bi, Yuhan Shen, Yuhao Yang, Yujiang Li, Yunan Liu, Yunqing Wang, Yuntao Li, Yurong Wu, Yutao Zhang, Yuxi Duan, Yuxuan Zhang, Zezhen Liu, Zhengtao Jiang, Zhenhe Yan, Zheyu Zhang, Zhixiang Wei, Zhuo Chen, Zhuoer Feng, Zijun Yao, Ziwei Chai, Ziyuan Wang, Zuzhou Zhang, Bin Xu, Minlie Huang, Hongning Wang, Juanzi Li, Yuxiao Dong, Jie Tang
Abstract:
We present GLM‑5, a next‑generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (ARC) capabilities of its predecessor, GLM‑5 adopts DSA to significantly reduce training and inference costs while maintaining long‑context fidelity. To advance model alignment and autonomy, we implement a new asynchronous reinforcement learning infrastructure that drastically improves post‑training efficiency by decoupling generation from training. Furthermore, we propose novel asynchronous agent RL algorithms that further improve RL quality, enabling the model to learn from complex, long‑horizon interactions more effectively. Through these innovations, GLM‑5 achieves state‑of‑the‑art performance on major open benchmarks. Most critically, GLM‑5 demonstrates unprecedented capability in real‑world coding tasks, surpassing previous baselines in handling end‑to‑end software engineering challenges. Code, models, and more information are available at https://github.com/zai‑org/GLM‑5.
Authors:Chansung Park, Juyong Jiang, Fan Wang, Sayak Paul, Jiasi Shen, Jing Tang, Jianguo Li
Abstract:
Large Language Models (LLMs) are changing the coding paradigm, known as vibe coding, yet synthesizing algorithmically sophisticated and robust code still remains a critical challenge. Incentivizing the deep reasoning capabilities of LLMs is essential to overcoming this hurdle. Reinforcement Fine‑Tuning (RFT) has emerged as a promising strategy to address this need. However, most existing approaches overlook the heterogeneous difficulty and granularity inherent in test cases, leading to an imbalanced distribution of reward signals and consequently biased gradient updates during training. To address this, we propose Test‑driven and cApability‑adaptive cuRriculum reinfOrcement fine‑Tuning (TAROT). TAROT systematically constructs, for each problem, a four‑tier test suite (basic, intermediate, complex, edge), providing a controlled difficulty landscape for curriculum design and evaluation. Crucially, TAROT decouples curriculum progression from raw reward scores, enabling capability‑conditioned evaluation and principled selection from a portfolio of curriculum policies rather than incidental test‑case difficulty composition. This design fosters stable optimization and more efficient competency acquisition. Extensive experimental results reveal that the optimal curriculum for RFT in code generation is closely tied to a model's inherent capability, with less capable models achieving greater gains with an easy‑to‑hard progression, whereas more competent models excel under a hard‑first curriculum. TAROT provides a reproducible method that adaptively tailors curriculum design to a model's capability, thereby consistently improving the functional correctness and robustness of the generated code. All code and data are released to foster reproducibility and advance community research at https://github.com/deep‑diver/TAROT.
Authors:Hao Chen, Zavareh Bozorgasl
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.
Authors:Muhammad J. Alahmadi, Peng Gao, Feiyi Wang, Dongkuan Xu
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.
Authors:Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip, Ammar Ali, Baher Mohammad, Stamatios Lefkimmiatis
Abstract:
Post‑training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union‑of‑subspaces representation, but existing approaches often suffer from iterative dictionary and coefficient updates. We propose COMPOT (Calibration‑Optimized Matrix Procrustes Orthogonalization for Transformers), a training‑free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed‑form Procrustes updates for the dictionary and analytical single‑step sparse coding for the coefficients, eliminating iterative optimization. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one‑shot dynamic allocation strategy that adaptively redistributes layer‑wise compression rates. Extensive experiments across diverse architectures and tasks show that COMPOT consistently delivers a superior quality‑compression trade‑off over strong low‑rank and sparse baselines, while remaining fully compatible with post‑training quantization for extreme compression. Code is available \hrefhttps://github.com/mts‑ai/COMPOThere.
Authors:Tianyu Xiong, Skylar Wurster, Han-Wei Shen
Abstract:
Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity‑speed dilemma: deep MLPs suffer from high inference cost, while efficient embedding‑based models lack sufficient expressiveness. To resolve this, we propose the Decoupled Representation Refinement (DRR) architectural paradigm. DRR leverages a deep refiner network, alongside non‑parametric transformations, in a one‑time offline process to encode rich representations into a compact and efficient embedding structure. This approach decouples slow neural networks with high representational capacity from the fast inference path. We introduce DRR‑Net, a simple network that validates this paradigm, and a novel data augmentation strategy, Variational Pairs (VP) for improving INRs under complex tasks like high‑dimensional surrogate modeling. Experiments on several ensemble simulation datasets demonstrate that our approach achieves state‑of‑the‑art fidelity, while being up to 27× faster at inference than high‑fidelity baselines and remaining competitive with the fastest models. The DRR paradigm offers an effective strategy for building powerful and practical neural field surrogates and INRs in broader applications, with a minimal compromise between speed and quality.
Authors:Per Åhag, Alexander Friedrich, Fredrik Ohlsson, Viktor Vigren Näslund
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 .
Authors:Shangding Gu
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
Authors:Subham Sekhar Sahoo, Jean-Marie Lemercier, Zhihan Yang, Justin Deschenaux, Jingyu Liu, John Thickstun, Ante Jukic
Abstract:
Diffusion language models are a promising alternative to autoregressive models due to their potential for faster generation. Among discrete diffusion approaches, Masked diffusion currently dominates, largely driven by strong perplexity on language modeling benchmarks. In this work, we present the first scaling law study of uniform‑state and interpolating discrete diffusion methods. We also show that Masked diffusion models can be made approximately 12% more FLOPs‑efficient when trained with a simple cross‑entropy objective. We find that perplexity is informative within a diffusion family but can be misleading across families, where models with worse likelihood scaling may be preferable due to faster and more practical sampling, as reflected by the speed‑quality Pareto frontier. These results challenge the view that Masked diffusion is categorically the future of diffusion language modeling and that perplexity alone suffices for cross‑algorithm comparison. Scaling all methods to 1.7B parameters, we show that uniform‑state diffusion remains competitive on likelihood‑based benchmarks and outperforms autoregressive and Masked diffusion models on GSM8K, despite worse validation perplexity. We provide the code, model checkpoints, and video tutorials on the project page: http://s‑sahoo.github.io/scaling‑dllms
Authors:Jivat Neet Kaur, Isaac Gibbs, Michael I. Jordan
Abstract:
We consider the general problem of learning a predictor that satisfies multiple objectives of interest simultaneously, a broad framework that captures a range of specific learning goals including calibration, regret, and multiaccuracy. We work in an online setting where the data distribution can change arbitrarily over time. Existing approaches to this problem aim to minimize the set of objectives over the entire time horizon in a worst‑case sense, and in practice they do not necessarily adapt to distribution shifts. Earlier work has aimed to alleviate this problem by incorporating additional objectives that target local guarantees over contiguous subintervals. Empirical evaluation of these proposals is, however, scarce. In this article, we consider an alternative procedure that achieves local adaptivity by replacing one part of the multi‑objective learning method with an adaptive online algorithm. Empirical evaluations on datasets from energy forecasting and algorithmic fairness show that our proposed method improves upon existing approaches and achieves unbiased predictions over subgroups, while remaining robust under distribution shift.
Authors:Lunjun Zhang, Ryan Chen, Bradly C. Stadie
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
Authors:Nihal V. Nayak, Paula Rodriguez-Diaz, Neha Hulkund, Sara Beery, David Alvarez-Melis
Abstract:
Instruction fine‑tuning of large language models (LLMs) often involves selecting a subset of instruction training data from a large candidate pool, using a small query set from the target task. Despite growing interest, the literature on targeted instruction selection remains fragmented and opaque: methods vary widely in selection budgets, often omit zero‑shot baselines, and frequently entangle the contributions of key components. As a result, practitioners lack actionable guidance on selecting instructions for their target tasks. In this work, we aim to bring clarity to this landscape by disentangling and systematically analyzing the two core ingredients: data representation and selection algorithms. Our framework enables controlled comparisons across models, tasks, and budgets. We find that only gradient‑based data representations choose subsets whose similarity to the query consistently predicts performance across datasets and models. While no single method dominates, gradient‑based representations paired with a greedy round‑robin selection algorithm tend to perform best on average at low budgets, but these benefits diminish at larger budgets. Finally, we unify several existing selection algorithms as forms of approximate distance minimization between the selected subset and the query set, and support this view with new generalization bounds. More broadly, our findings provide critical insights and a foundation for more principled data selection in LLM fine‑tuning. The code is available at https://github.com/dcml‑lab/targeted‑instruction‑selection.
Authors:Adrián Javaloy, Antonio Vergari
Abstract:
Orthogonality constraints are ubiquitous in robust and probabilistic machine learning. Unfortunately, current optimizers are computationally expensive and do not scale to problems with hundreds or thousands of constraints. One notable exception is the Landing algorithm (Ablin et al., 2024) which, however comes at the expense of temporarily relaxing orthogonality. In this work, we revisit and improve on the ideas behind Landing, enabling the inclusion of modern adaptive optimizers while ensuring that orthogonal constraints are effectively met. Remarkably, these improvements come at little to no cost, and reduce the number of required hyperparemeters. Our algorithm POGO is fast and GPU‑friendly, consisting of only 5 matrix products, and in practice maintains orthogonality at all times. On several challenging benchmarks, POGO greatly outperforms recent optimizers and shows it can optimize problems with thousands of orthogonal matrices in minutes while alternatives would take hours. As such, POGO sets a milestone to finally exploit orthogonality constraints in ML at scale. A PyTorch implementation of POGO is publicly available at https://github.com/adrianjav/pogo.
Authors:Karim Galliamov, Syed M Ahsan Kazmi, Adil Khan, Adín Ramírez Rivera
Abstract:
Concept Bottleneck Models (CBMs) aim to deliver interpretable predictions by routing decisions through a human‑understandable concept layer, yet they often suffer reduced accuracy and concept leakage that undermines faithfulness. We introduce an explicit Information Bottleneck regularizer on the concept layer that penalizes I(X;C) while preserving task‑relevant information in I(C;Y), encouraging minimal‑sufficient concept representations. We derive two practical variants (a variational objective and an entropy‑based surrogate) and integrate them into standard CBM training without architectural changes or additional supervision. Evaluated across six CBM families and three benchmarks, the IB‑regularized models consistently outperform their vanilla counterparts. Information‑plane analyses further corroborate the intended behavior. These results indicate that enforcing a minimal‑sufficient concept bottleneck improves both predictive performance and the reliability of concept‑level interventions. The proposed regularizer offers a theoretic‑grounded, architecture‑agnostic path to more faithful and intervenable CBMs, resolving prior evaluation inconsistencies by aligning training protocols and demonstrating robust gains across model families and datasets.
Authors:Erkan Karabulut, Daniel Daza, Paul Groth, Martijn C. Schut, Victoria Degeler
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.
Authors:Aswathi Varma, Suprosanna Shit, Chinmay Prabhakar, Daniel Scholz, Hongwei Bran Li, Bjoern Menze, Daniel Rueckert, Benedikt Wiestler
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
Authors:Tianyi Ma, Yiyang Li, Yiyue Qian, Zheyuan Zhang, Zehong Wang, Chuxu Zhang, Yanfang Ye
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.
Authors:Chaosheng Dong, Peiyao Xiao, Yijia Wang, Kaiyi Ji
Abstract:
This paper presents DeepMTL2R, an open‑source deep learning framework for Multi‑task Learning to Rank (MTL2R), where multiple relevance criteria must be optimized simultaneously. DeepMTL2R integrates heterogeneous relevance signals into a unified, context‑aware model by leveraging the self‑attention mechanism of transformer architectures, enabling effective learning across diverse and potentially conflicting objectives. The framework includes 21 state‑of‑the‑art multi‑task learning algorithms and supports multi‑objective optimization to identify Pareto‑optimal ranking models. By capturing complex dependencies and long‑range interactions among items and labels, DeepMTL2R provides a scalable and expressive solution for modern ranking systems and facilitates controlled comparisons across MTL strategies. We demonstrate its effectiveness on a publicly available dataset, report competitive performance, and visualize the resulting trade‑offs among objectives. DeepMTL2R is available at \hrefhttps://github.com/amazon‑science/DeepMTL2Rhttps://github.com/amazon‑science/DeepMTL2R.
Authors:Aryan Das, Tanishq Rachamalla, Koushik Biswas, Swalpa Kumar Roy, Vinay Kumar Verma
Abstract:
We introduce a novel uncertainty‑aware multimodal segmentation framework that leverages both radiological images and associated clinical text for precise medical diagnosis. We propose a Modality Decoding Attention Block (MoDAB) with a lightweight State Space Mixer (SSMix) to enable efficient cross‑modal fusion and long‑range dependency modelling. To guide learning under ambiguity, we propose the Spectral‑Entropic Uncertainty (SEU) Loss, which jointly captures spatial overlap, spectral consistency, and predictive uncertainty in a unified objective. In complex clinical circumstances with poor image quality, this formulation improves model reliability. Extensive experiments on various publicly available medical datasets, QATA‑COVID19, MosMed++, and Kvasir‑SEG, demonstrate that our method achieves superior segmentation performance while being significantly more computationally efficient than existing State‑of‑the‑Art (SoTA) approaches. Our results highlight the importance of incorporating uncertainty modelling and structured modality alignment in vision‑language medical segmentation tasks. Code: https://github.com/arya‑domain/UA‑VLS
Authors:Alejandro Francisco Queiruga
Abstract:
Scaling laws can be understood from ground‑up numerical analysis, where traditional function approximation theory can explain shifts in model architecture choices. GLU variants now dominate frontier LLMs and similar outer‑product architectures are prevalent in ranking models. The success of these architectures has mostly been left as an empirical discovery. In this paper, we apply the tools of numerical analysis to expose a key factor: these models have an x^2 which enables \emphasymptotically faster scaling than MLPs. GLUs have piecewise quadratic functional forms that are sufficient to exhibit quadratic order of approximation. Our key contribution is to demonstrate that the L(P) scaling slope is L(P)\propto P^‑3 for GLUs but only L(P)=P^‑2 for MLPs on function reconstruction problems. We provide a parameter construction and empirical verification of these slopes for 1D function approximation. From the first principles we discover, we make one stride and propose the ``Gated Quadratic Unit'' which has an even steeper L(P) slope than the GLU and MLP. This opens the possibility of architecture design from first principles numerical theory to unlock superior scaling in large models. Replication code is available at https://github.com/afqueiruga/divine_scaling.
Authors:William L. Tong, Ege Cakar, Cengiz Pehlevan
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.
Authors:Morgan Byrd, Donghoon Baek, Kartik Garg, Hyunyoung Jung, Daesol Cho, Maks Sorokin, Robert Wright, Sehoon Ha
Abstract:
This paper presents Adaptive Whole‑body Loco‑Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delivery. Unlike prior imitation learning‑based approaches that rely on human demonstrations and are often brittle to disturbances, AdaptManip aims to train a robust loco‑manipulation policy via reinforcement learning without human demonstrations or teleoperation data. The proposed framework consists of three coupled components: (1) a recurrent object state estimator that tracks the manipulated object in real time under limited field‑of‑view and occlusions; (2) a whole‑body base policy for robust locomotion with residual manipulation control for stable object lifting and delivery; and (3) a LiDAR‑based robot global position estimator that provides drift‑robust localization. All components are trained in simulation using reinforcement learning and deployed on real hardware in a zero‑shot manner. Experimental results show that AdaptManip significantly outperforms baseline methods, including imitation learning‑based approaches, in adaptability and overall success rate, while accurate object state estimation improves manipulation performance even under occlusion. We further demonstrate fully autonomous real‑world navigation, object lifting, and delivery on a humanoid robot.
Authors:Mehran Aghabozorgi, Alireza Moazeni, Yanshu Zhang, Ke Li
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.
Authors:Lingxiang Hu, Yiding Sun, Tianle Xia, Wenwei Li, Ming Xu, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang
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.
Authors:Yaxuan Kong, Hoyoung Lee, Yoontae Hwang, Alejandro Lopez-Lira, Bradford Levy, Dhagash Mehta, Qingsong Wen, Chanyeol Choi, Yongjae Lee, Stefan Zohren
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.
Authors:Nicolas Zumarraga, Thomas Kaar, Ning Wang, William Tennien, Alpay Hasanli, Max Rosenblattl, Fan Wu, Kevin Riehl, Maxwell A. Xu, Markus Kreft, Kevin O'Sullivan, Elgar Fleisch, Paul Schmiedmayer, Robert Jakob, Patrick Langer
Abstract:
Time Series Language Models (TSLMs) promise reasoning over real‑world temporal data, but their ability to retrieve and reason over long time‑series remains largely untested. We introduce TS‑Haystack, a multi‑domain retrieval benchmark with ten event‑grounded question‑answering tasks over contexts from 100 seconds to 24 hours, spanning direct retrieval, temporal reasoning, multi‑step reasoning, and contextual anomaly detection. Existing TSLMs exhibit severe long‑context degradation: accuracy declines with context length, direct‑tokenization models run out of memory beyond 100 seconds on high‑rate signals, and time‑interval‑grounded tasks collapse toward near‑zero accuracy when increasing the time‑series lengths, aligning with existing literature on text and multi‑modal long context retrieval. An agentic retrieval framework using specialized time‑series classifier tools matches or outperforms SoTA TSLMs on 9 of 10 tasks, highlighting agentic retrieval as a promising approach for long‑context TSLMs.
Authors:Max Fomin
Abstract:
Detecting prompt injection and jailbreak attacks is critical for deploying LLM‑based agents safely. As agents increasingly process untrusted data from emails, documents, tool outputs, and external APIs, robust attack detection becomes essential. Yet current evaluation practices and production systems have fundamental limitations. We present a comprehensive analysis using a diverse benchmark of 18 datasets spanning harmful requests, jailbreaks, indirect prompt injections, and extraction attacks. We propose Leave‑One‑Dataset‑Out (LODO) evaluation to measure true out‑of‑distribution generalization, revealing that the standard practice of train‑test splits from the same dataset sources severely overestimates performance: aggregate metrics show an 8.4 percentage point AUC inflation, but per‑dataset gaps range from 1% to 25% accuracy‑exposing heterogeneous failure modes. To understand why classifiers fail to generalize, we analyze Sparse Auto‑Encoder (SAE) feature coefficients across LODO folds, finding that 28% of top features are dataset‑dependent shortcuts whose class signal depends on specific dataset compositions rather than semantic content. We systematically compare production guardrails (PromptGuard 2, LlamaGuard) and LLM‑as‑judge approaches on our benchmark, finding all three fail on indirect attacks targeting agents (7‑37% detection) and that PromptGuard 2 and LlamaGuard cannot evaluate agentic tool injection due to architectural limitations. Finally, we show that LODO‑stable SAE features provide more reliable explanations for classifier decisions by filtering dataset artifacts. We release our evaluation framework at https://github.com/maxf‑zn/prompt‑mining to establish LODO as the appropriate protocol for prompt attack detection research.
Authors:Yuxuan Linghu, Zhiyuan Liu, Qi Deng
Abstract:
Differentiating through the solution of a quadratic program (QP) is a central problem in differentiable optimization. Most existing approaches differentiate through the Karush‑‑Kuhn‑‑Tucker (KKT) system, but their computational cost and numerical robustness can degrade at scale. To address these limitations, we propose dXPP, a penalty‑based differentiation framework that decouples QP solving from differentiation. In the solving step (forward pass), dXPP is solver‑agnostic and can leverage any black‑box QP solver. In the differentiation step (backward pass), we map the solution to a smooth approximate penalty problem and implicitly differentiate through it, requiring only the solution of a much smaller linear system in the primal variables. This approach bypasses the difficulties inherent in explicit KKT differentiation and significantly improves computational efficiency and robustness. We evaluate dXPP on various tasks, including randomly generated QPs, large‑scale sparse projection problems, and a real‑world multi‑period portfolio optimization task. Empirical results demonstrate that dXPP is competitive with KKT‑based differentiation methods and achieves substantial speedups on large‑scale problems. Our implementation is open source and available at https://github.com/mmmmmmlinghu/dXPP.
Authors:Juntong Wang, Libin Chen, Xiyuan Wang, Shijia Kang, Haotong Yang, Da Zheng, Muhan Zhang
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.
Authors:Xiaoyu Tao, Mingyue Cheng, Chuang Jiang, Tian Gao, Huanjian Zhang, Yaguo Liu
Abstract:
Time series forecasting has long been dominated by model‑centric approaches that formulate prediction as a single‑pass mapping from historical observations to future values. Despite recent progress, such formulations often struggle in complex and evolving settings, largely because most forecasting models lack the ability to autonomously acquire informative evidence, reason about potential future changes, or revise predictions through iterative decision processes. In this work, we propose Cast‑R1, a learned time series forecasting framework that reformulates forecasting as a sequential decision‑making problem. Cast‑R1 introduces a memory‑based state management mechanism that maintains decision‑relevant information across interaction steps, enabling the accumulation of contextual evidence to support long‑horizon reasoning. Building on this formulation, forecasting is carried out through a tool‑augmented agentic workflow, in which the agent autonomously interacts with a modular toolkit to extract statistical features, invoke lightweight forecasting models for decision support, perform reasoning‑based prediction, and iteratively refine forecasts through self‑reflection. To train Cast‑R1, we adopt a two‑stage learning strategy that combines supervised fine‑tuning with multi‑turn reinforcement learning, together with a curriculum learning scheme that progressively increases task difficulty to improve policy learning. Extensive experiments on multiple real‑world time series datasets demonstrate the effectiveness of Cast‑R1. We hope this work provides a practical step towards further exploration of agentic paradigms for time series modeling. Our code is available at https://github.com/Xiaoyu‑Tao/Cast‑R1‑TS.
Authors:Youwei Shu, Shaomian Zheng, Dingnan Jin, Wenjie Qu, Ziyao Guo, Qing Cui, Jun Zhou, Jiaheng Zhang
Abstract:
Data quality is a crucial factor in large language models training. While prior work has shown that models trained on smaller, high‑quality datasets can outperform those trained on much larger but noisy or low‑quality corpora, systematic methods for industrial‑scale data selection in instruction tuning remain underexplored. In this work, we study instruction‑tuning data selection through the lens of semantic representation similarity and identify a key limitation of state‑of‑the‑art LLM encoders: they produce highly redundant semantic embeddings. To mitigate this redundancy, we propose Compressed Representation Data Selection (CRDS), a novel framework with two variants. CRDS‑R applies Rademacher random projection followed by concatenation of transformer hidden‑layer representations, while CRDS‑W employs whitening‑based dimensionality reduction to improve representational quality. Experimental results demonstrate that both variants substantially enhance data quality and consistently outperform state‑of‑the‑art representation‑based selection methods. Notably, CRDS‑W achieves strong performance using only 3.5% of the data, surpassing the full‑data baseline by an average of 0.71% across four datasets. Our code is available at https://github.com/tdano1/CRDS.
Authors:Binyu Zhao, Wei Zhang, Xingrui Yu, Zhaonian Zou, Ivor Tsang
Abstract:
Class‑incremental learning (CIL) with pre‑trained models (PTMs) faces a critical trade‑off between efficient adaptation and long‑term stability. While analytic learning enables rapid, recursive closed‑form updates, its efficacy is often compromised by accumulated errors and feature incompatibility. In this paper, we first conduct a systematic study to dissect the failure modes of PTM‑based analytic CIL, identifying representation rigidity as the primary bottleneck. Motivated by this insight, we propose VILA, a novel dual‑branch framework that advances analytic CIL via a two‑level vision‑language calibration strategy. Specifically, we coherently fuse plastic, task‑adapted features with a frozen, universal visual anchor at the feature level through geometric calibration, and leverage cross‑modal semantic priors at the decision level to rectify prediction bias. This confluence maintains analytic‑learning's extreme efficiency while overcoming its inherent brittleness. Extensive experiments across eight benchmarks demonstrate that VILA consistently yields superior performance, particularly in fine‑grained and long‑sequence scenarios. Our framework harmonizes high‑fidelity prediction with the simplicity of analytic learning. Our code is available at https://github.com/byzhaoAI/VILA.
Authors:Valery Parfenov, Grigoriy Evseev, Andrey Veprikov, Nikolay Bushkov, Stanislav Moiseev, Aleksandr Beznosikov
Abstract:
Fine‑tuning large pretrained language models (LLMs) is a cornerstone of modern NLP, yet its growing memory demands (driven by backpropagation and large optimizer States) limit deployment in resource‑constrained settings. Zero‑order (ZO) methods bypass backpropagation by estimating directional derivatives from forward evaluations, offering substantial memory savings. However, classical ZO estimators suffer from high variance and an adverse dependence on the parameter dimensionality d, which has constrained their use to low‑dimensional problems. In this work, we propose a policy‑driven ZO framework that treats the sampling distribution over perturbation directions as a learnable policy and updates it to reduce the variance of directional estimates. We develop a practical algorithm implementing this idea and provide a theoretical analysis, showing that learned sampling distributions improve the quality of gradient information and relax the explicit dependence on d in convergence bounds. Empirically, we validate the approach on challenging LLM fine‑tuning benchmarks, demonstrating substantially improved performance compared to standard ZO baselines. Our results suggest that adaptive direction sampling is a promising route to make ZO fine‑tuning viable at scale. The source code is available at https://github.com/brain‑lab‑research/zo_ldsd
Authors:Li Zhang, Nital Patel, Xiuqi Li, Jessica Lin
Abstract:
Time series chain (TSC) is a recently introduced concept that captures the evolving patterns in large scale time series. Informally, a time series chain is a temporally ordered set of subsequences, in which consecutive subsequences in the chain are similar to one another, but the last and the first subsequences maybe be dissimilar. Time series chain has the great potential to reveal latent unusual evolving trend in the time series, or identify precursor of important events in a complex system. Unfortunately, existing definitions of time series chains only consider finding chains in a single time series. As a result, they are likely to miss unexpected evolving patterns in interrupted time series, or across two related time series. To address this limitation, in this work, we introduce a new definition called Joint Time Series Chain, which is specially designed for the task of finding unexpected evolving trend across interrupted time series or two related time series. Our definition focuses on mitigating the robustness issues caused by the gap or interruption in the time series. We further propose an effective ranking criterion to identify the best chain. We demonstrate that our proposed approach outperforms existing TSC work in locating unusual evolving patterns through extensive empirical evaluations. We further demonstrate the utility of our work with a real‑life manufacturing application from Intel. Our source code is publicly available at the supporting page https://github.com/lizhang‑ts/JointTSC .
Authors:Mingqiao Zhang, Qiyao Peng, Yinghui Wang, Hongtao Liu, Yumeng Wang
Abstract:
The expanding integration of Large Language Models (LLMs) into recommender systems poses critical challenges to evaluation reliability. This paper identifies and investigates a previously overlooked issue: benchmark data leakage in LLM‑based recommendation. This phenomenon occurs when LLMs are exposed to and potentially memorize benchmark datasets during pre‑training or fine‑tuning, leading to artificially inflated performance metrics that fail to reflect true model performance. To validate this phenomenon, we simulate diverse data leakage scenarios by conducting continued pre‑training of foundation models on strategically blended corpora, which include user‑item interactions from both in‑domain and out‑of‑domain sources. Our experiments reveal a dual‑effect of data leakage: when the leaked data is domain‑relevant, it induces substantial but spurious performance gains, misleadingly exaggerating the model's capability. In contrast, domain‑irrelevant leakage typically degrades recommendation accuracy, highlighting the complex and contingent nature of this contamination. Our findings reveal that data leakage acts as a critical, previously unaccounted‑for factor in LLM‑based recommendation, which could impact the true model performance. We release our code at https://github.com/yusba1/LLMRec‑Data‑Leakage.
Authors:Sihao Hu, Selim Furkan Tekin, Yichang Xu, Ling Liu
Abstract:
Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high‑risk launches that existing rug‑pull detection methods cannot capture. We argue that detecting these threats requires structured behavioral traces that underlie raw heterogeneous blockchain data, i.e., how insiders accumulate, coordinate, and unwind positions. To enable such analysis, we introduce MELT (MEmecoin Launch Trace, the first behavioral trace dataset for analyzing and detecting high‑risk memecoin launches on Solana. MELT covers 41k+ memecoin launches with 200M+ transactions parsed into typed behavioral records that distinguish swaps, wash trades, transfers, and mints. Beyond per‑account behaviors, MELT contributes bundle‑trace data that links accounts controlled by the same entity, revealing that, on average, 36.5% of token supply is held by coordinated accounts, a concealment strategy that disguises the true ownership concentration from unsuspecting buyers. On top of these traces, MELT provides 122 behavioral features and risk‑level annotations, enabling supervised learning at a population scale. We benchmark representative ML models on the high‑risk launch detection task. Integrating their predictions into a simple memecoin selection strategy reduces investment loss significantly, demonstrating that behavioral traces can be translated into risk mitigation. Our dataset and code is available at https://github.com/git‑disl/MELT.
Authors:Xu Li, Simon Yu, Minzhou Pan, Yiyou Sun, Bo Li, Dawn Song, Xue Lin, Weiyan Shi
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.
Authors:Daesik Jang, Morgan Lindsay Heisler, Linzi Xing, Yifei Li, Edward Wang, Ying Xiong, Yong Zhang, Zhenan Fan
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 .
Authors:Bowen Liu, Zhi Wu, Runquan Xie, Zhanhui Kang, Jia Li
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
Authors:Pingzhi Li, Hongxuan Li, Zirui Liu, Xingcheng Lin, Tianlong Chen
Abstract:
Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many‑body interactions, but remain slower than classical force fields due to fragmented kernels and memory‑bound pipelines that underutilize GPUs. We show that a missing principle is making GNN‑MD IO‑aware, carefully accounting for reads and writes between GPU high‑bandwidth memory (HBM) and on‑chip SRAM. We present FlashSchNet, an efficient and accurate IO‑aware SchNet‑style GNN‑MD framework built on four techniques: (1) flash radial basis, which fuses pairwise distance computation, Gaussian basis expansion, and cosine envelope into a single tiled pass, computing each distance once and reusing it across all basis functions; (2) flash message passing, which fuses cutoff, neighbor gather, filter multiplication, and reduction to avoid materializing edge tensors in HBM; (3) flash aggregation, which reformulates scatter‑add via CSR segment reduce, reducing atomic writes by a factor of feature dimension and enabling contention‑free accumulation in both forward and backward passes; (4) channel‑wise 16‑bit quantization that exploits the low per‑channel dynamic range in SchNet MLP weights to further improve throughput with negligible accuracy loss. On a single NVIDIA RTX PRO 6000, FlashSchNet achieves 1000 ns/day aggregate simulation throughput over 64 parallel replicas on coarse‑grained (CG) protein containing 269 beads (6.5x faster than CGSchNet baseline with 80% reduction of peak memory), surpassing classical force fields (e.g. MARTINI) while retaining SchNet‑level accuracy and transferability.
Authors:Gengsheng Li, Jinghan He, Shijie Wang, Dan Zhang, Ruiqi Liu, Renrui Zhang, Zijun Yao, Junfeng Fang, Haiyun Guo, Jinqiao Wang
Abstract:
Self‑play bootstraps LLM reasoning through an iterative Challenger‑Solver loop: the Challenger is trained to generate questions that target the Solver's capabilities, and the Solver is optimized on the generated data to expand its reasoning skills. However, existing frameworks like R‑Zero often exhibit non‑sustained improvement, where early gains degrade as self‑play continues. We identify a key failure mode, Diversity Illusion, where the Solver's training signals appear diverse yet collapse into recurring underlying patterns. It manifests as (1) Local Diversity Illusion, where diversity is enforced only within‑batch, inducing cross‑iteration mode cycling; and (2) Surface Diversity Illusion, where questions vary superficially but require near‑identical reasoning skills. To mitigate them, we propose R‑Diverse with two aligned innovations: Memory‑Augmented Penalty (MAP), which uses a persistent memory bank to discourage recycling across iterations, and Skill‑Aware Measurement (SAM), which evaluates diversity by the reasoning skills exercised rather than surface variation of questions. Across 10 math and general reasoning benchmarks, R‑Diverse sustains gains over more iterations and consistently outperforms prior self‑play methods. Code is available at https://github.com/Gengsheng‑Li/R‑Diverse.
Authors:Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Nicu Sebe, Mubarak Shah
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.
Authors:Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Amparo Alonso-Betanzos, Iván Pérez Digón
Abstract:
Federated Learning (FL) enables collaborative training without centralizing data, essential for privacy compliance in real‑world scenarios involving sensitive visual information. Most FL approaches rely on expensive, iterative deep network optimization, which still risks privacy via shared gradients. In this work, we propose FedHENet, extending the FedHEONN framework to image classification. By using a fixed, pre‑trained feature extractor and learning only a single output layer, we avoid costly local fine‑tuning. This layer is learned by analytically aggregating client knowledge in a single round of communication using homomorphic encryption (HE). Experiments show that FedHENet achieves competitive accuracy compared to iterative FL baselines while demonstrating superior stability performance and up to 70% better energy efficiency. Crucially, our method is hyperparameter‑free, removing the carbon footprint associated with hyperparameter tuning in standard FL. Code available in https://github.com/AlejandroDopico2/FedHENet/
Authors:Mohammed Amine Bencheikh Lehocine, Julian Schmidt, Frank Moosmann, Dikshant Gupta, Fabian Flohr
Abstract:
Classical autonomous driving systems connect perception and prediction modules via hand‑crafted bounding‑box interfaces, limiting information flow and propagating errors to downstream tasks. Recent research aims to develop end‑to‑end models that jointly address perception and prediction; however, they often fail to fully exploit the synergy between appearance and motion cues, relying mainly on short‑term visual features. We follow the idea of "looking backward to look forward", and propose MASAR, a novel fully differentiable framework for joint 3D detection and trajectory forecasting compatible with any transformer‑based 3D detector. MASAR employs an object‑centric spatio‑temporal mechanism that jointly encodes appearance and motion features. By predicting past trajectories and refining them using guidance from appearance cues, MASAR captures long‑term temporal dependencies that enhance future trajectory forecasting. Experiments conducted on the nuScenes dataset demonstrate MASAR's effectiveness, showing improvements of over 20% in minADE and minFDE while maintaining robust detection performance. Code and models are available at https://github.com/aminmed/MASAR.
Authors:Filippo Rinaldi, Aniello Panariello, Giacomo Salici, Angelo Porrello, Simone Calderara
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.
Authors:Ali Mekky, Mohamed El Zeftawy, Lara Hassan, Amr Keleg, Preslav Nakov
Abstract:
Being modeled as a single‑label classification task for a long time, recent work has argued that Arabic Dialect Identification (ADI) should be framed as a multi‑label classification task. However, ADI remains constrained by the availability of single‑label datasets, with no large‑scale multi‑label resources available for training. By analyzing models trained on single‑label ADI data, we show that the main difficulty in repurposing such datasets for Multi‑Label Arabic Dialect Identification (MLADI) lies in the selection of negative samples, as many sentences treated as negative could be acceptable in multiple dialects. To address these issues, we construct a multi‑label dataset by generating automatic multi‑label annotations using GPT‑4o and binary dialect acceptability classifiers, with aggregation guided by the Arabic Level of Dialectness (ALDi). Afterward, we train a BERT‑based multi‑label classifier using curriculum learning strategies aligned with dialectal complexity and label cardinality. On the MLADI leaderboard, our best‑performing LAHJATBERT model achieves a macro F1 of 0.69, compared to 0.55 for the strongest previously reported system. Code and data are available at https://mohamedalaa9.github.io/lahjatbert/.
Authors:Rubén Pérez-Jove, Osvaldo Simeone, Alejandro Pazos, Jose Vázquez-Naya
Abstract:
Operating System (OS) fingerprinting is critical for network security, but conventional methods do not provide formal uncertainty quantification mechanisms. Conformal Prediction (CP) could be directly wrapped around existing methods to obtain prediction sets with guaranteed coverage. However, a direct application of CP would treat OS identification as a flat classification problem, ignoring the natural taxonomic structure of OSs and providing brittle point predictions. This work addresses these limitations by introducing and evaluating two distinct structured CP strategies: level‑wise CP (L‑CP), which calibrates each hierarchy level independently, and projection‑based CP (P‑CP), which ensures structural consistency by projecting leaf‑level sets upwards. Our results demonstrate that, while both methods satisfy validity guarantees, they expose a fundamental trade‑off between level‑wise efficiency and structural consistency. L‑CP yields tighter prediction sets suitable for human forensic analysis but suffers from taxonomic inconsistencies. Conversely, P‑CP guarantees hierarchically consistent, nested sets ideal for automated policy enforcement, albeit at the cost of reduced efficiency at coarser levels.
Authors:Umar Marikkar, Syed Sameed Husain, Muhammad Awais, Sara Atito
Abstract:
Training and evaluating vision encoders on Multi‑Channel Imaging (MCI) data remains challenging as channel configurations vary across datasets, preventing fixed‑channel training and limiting reuse of pre‑trained encoders on new channel settings. Prior work trains MCI encoders but typically evaluates them via full fine‑tuning, leaving probing with frozen pre‑trained encoders comparatively underexplored. Existing studies that perform probing largely focus on improving representations, rather than how to best leverage fixed representations for downstream tasks. Although the latter problem has been studied in other domains, directly transferring those strategies to MCI yields weak results, even worse than training from scratch. We therefore propose Channel‑Aware Probing (CAP), which exploits the intrinsic inter‑channel diversity in MCI datasets by controlling feature flow at both the encoder and probe levels. CAP uses Independent Feature Encoding (IFE) to encode each channel separately, and Decoupled Pooling (DCP) to pool within channels before aggregating across channels. Across three MCI benchmarks, CAP consistently improves probing performance over the default probing protocol, matches fine‑tuning from scratch, and largely reduces the gap to full fine‑tuning from the same MCI pre‑trained checkpoints. Code can be found in https://github.com/umarikkar/CAP.
Authors:Sangwoo Jo, Sungjoon Choi
Abstract:
Diffusion‑based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by high sampling costs. While prior work focuses on training objectives or individual solvers, the holistic design of sampling, specifically solver selection and scheduling, remains dominated by static heuristics. In this work, we revisit this challenge through a geometric lens, proposing SDM, a principled framework that aligns the numerical solver with the intrinsic properties of the diffusion trajectory. By analyzing the ODE dynamics, we show that efficient low‑order solvers suffice in early high‑noise stages while higher‑order solvers can be progressively deployed to handle the increasing non‑linearity of later stages. Furthermore, we formalize the scheduling by introducing a Wasserstein‑bounded optimization framework. This method systematically derives adaptive timesteps that explicitly bound the local discretization error, ensuring the sampling process remains faithful to the underlying continuous dynamics. Without requiring additional training or architectural modifications, SDM achieves state‑of‑the‑art performance across standard benchmarks, including an FID of 1.93 on CIFAR‑10, 2.41 on FFHQ, and 1.98 on AFHQv2, with a reduced number of function evaluations compared to existing samplers. Our code is available at https://github.com/aiimaginglab/sdm.
Authors:Xianchao Xiu, Chenyi Huang, Wei Zhang, Wanquan Liu
Abstract:
Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence as a privacy‑preserving framework for distributed IoT environments, current federated principal component analysis (PCA) methods lack the integration of personalization and robustness, which are critical for effective anomaly detection. To address these limitations, we propose an efficient personalized federated PCA (FedEP) method for anomaly detection in IoT networks. The proposed model achieves personalization through introducing local representations with the \ell_1‑norm for element‑wise sparsity, while maintaining robustness via enforcing local models with the \ell_2,1‑norm for row‑wise sparsity. To solve this non‑convex problem, we develop a manifold optimization algorithm based on the alternating direction method of multipliers (ADMM) with rigorous theoretical convergence guarantees. Experimental results confirm that the proposed FedEP outperforms the state‑of‑the‑art FedPG, achieving excellent F1‑scores and accuracy in various IoT security scenarios. Our code will be available at \hrefhttps://github.com/xianchaoxiu/FedEPhttps://github.com/xianchaoxiu/FedEP.
Authors:Bowen Ping, Chengyou Jia, Minnan Luo, Hangwei Qian, Ivor Tsang
Abstract:
Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow‑matching models with human preferences, yet practitioners face fragmented codebases, model‑specific implementations, and engineering complexity. We introduce Flow‑Factory, a unified framework that decouples algorithms, models, and rewards through through a modular, registry‑based architecture. This design enables seamless integration of new algorithms and architectures, as demonstrated by our support for GRPO, DiffusionNFT, and AWM across Flux, Qwen‑Image, and WAN video models. By minimizing implementation overhead, Flow‑Factory empowers researchers to rapidly prototype and scale future innovations with ease. Flow‑Factory provides production‑ready memory optimization, flexible multi‑reward training, and seamless distributed training support. The codebase is available at https://github.com/X‑GenGroup/Flow‑Factory.
Authors:Lorenzo Magnino, Jiacheng Shen, Matthieu Geist, Olivier Pietquin, Mathieu Laurière
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.
Authors:Ara Yeroyan
Abstract:
Multi‑vector visual retrievers (e.g., ColPali‑style late interaction models) deliver strong accuracy, but scale poorly because each page yields thousands of vectors, making indexing and search increasingly expensive. We present Visual RAG Toolkit, a practical system for scaling visual multi‑vector retrieval with training‑free, model‑aware pooling and multi‑stage retrieval. Motivated by Matryoshka Embeddings, our method performs static spatial pooling ‑ including a lightweight sliding‑window averaging variant ‑ over patch embeddings to produce compact tile‑level and global representations for fast candidate generation, followed by exact MaxSim reranking using full multi‑vector embeddings.
Our design yields a quadratic reduction in vector‑to‑vector comparisons by reducing stored vectors per page from thousands to dozens, notably without requiring post‑training, adapters, or distillation. Across experiments with interaction‑style models such as ColPali and ColSmol‑500M, we observe that over the limited ViDoRe v2 benchmark corpus 2‑stage retrieval typically preserves NDCG and Recall @ 5/10 with minimal degradation, while substantially improving throughput (approximately 4x QPS); with sensitivity mainly at very large k. The toolkit additionally provides robust preprocessing ‑ high resolution PDF to image conversion, optional margin/empty‑region cropping and token hygiene (indexing only visual tokens) ‑ and a reproducible evaluation pipeline, enabling rapid exploration of two‑, three‑, and cascaded retrieval variants. By emphasizing efficiency at common cutoffs (e.g., k <= 10), the toolkit lowers hardware barriers and makes state‑of‑the‑art visual retrieval more accessible in practice.
Authors:Milan Gautam, Ning Dai, Tianshuo Zhou, Bowen Xie, David Mathews, Liang Huang
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.
Authors:Ali Subhan, Ashir Raza
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.
Authors:Tunyu Zhang, Xinxi Zhang, Ligong Han, Haizhou Shi, Xiaoxiao He, Zhuowei Li, Hao Wang, Kai Xu, Akash Srivastava, Chengzhi Mao, Hao Wang, Vladimir Pavlovic, Dimitris N. Metaxas
Abstract:
Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this potential in practice remains challenging: reducing the number of decoding steps, typically causes a substantial degradation in output quality due to token factorization error. To alleviate this, we propose a self‑distillation framework that trains a few‑step student to match the generative trajectory of a full‑step teacher. We theoretically and empirically show that trajectory‑level supervision mitigates this factorization error, thereby enabling effective few‑step decoding. We further incorporate Direct Discriminative Optimization (DDO), a reverse‑KL objective that encourages mode‑seeking toward the teacher's modes, yielding stronger performance on challenging reasoning tasks. Across reasoning and code‑generation benchmarks, our method substantially narrows the gap between few‑step and full‑step decoding. The source code is available at https://github.com/Tyrion58/T3D.
Authors:Nick Ferguson, Josh Pennington, Narek Beghian, Aravind Mohan, Douwe Kiela, Sheshansh Agrawal, Thien Hang Nguyen
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.
Authors:Miaosen Zhang, Yishan Liu, Shuxia Lin, Xu Yang, Qi Dai, Chong Luo, Weihao Jiang, Peng Hou, Anxiang Zeng, Xin Geng, Baining Guo
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
Authors:Muhammad bin Javaid, Hasham Hussain, Ashima Khanna, Berke Kisin, Jonathan Pirnay, Alexander Mitsos, Dominik G. Grimm, Martin Grohe
Abstract:
In structurally constrained molecular optimization, state‑of‑the‑art methods restart an expensive oracle‑driven search from scratch for every new input structure, scaling poorly to settings with many starting structures or expensive oracles. While amortized approaches that learn a transferable policy could in principle remove this bottleneck, existing methods struggle to generalize to diverse structural constraints at inference time. We present AMORTIX, an amortized Graph Transformer model that natively supports such constraints, optimizing molecular structures in a single forward pass with zero inference‑time oracle calls. A central challenge for amortized training in this domain is that optimization difficulty varies drastically across starting structures. We show that, under this heterogeneity, standard reinforcement learning methods fail to stabilize training, and address this by normalizing rewards within groups of completions sharing the same starting structure. We evaluate on structurally constrained single‑ and multi‑target kinase inhibitor design, and on a few‑shot prodrug case study. AMORTIX outperforms both amortized and instance‑optimization baselines on goal‑directed scaffold decoration and ranks first among amortized methods on the PMO benchmark; the prodrug case study further demonstrates transfer of a learned modification rule to unseen drug structures. Code is available at https://github.com/Hash‑hh/AMORTIX/.
Authors:Xiaoxiao Wang, Chunxiao Li, Junying Wang, Yijin Guo, Zijian Chen, Chunyi Li, Xiaohong Liu, Zicheng Zhang, Guangtao Zhai
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.
Authors:Rahin Arefin Ahmed, Md. Anik Chowdhury, Sakil Ahmed Sheikh Reza, Devnil Bhattacharjee, Muhammad Abdullah Adnan, Nafis Sadeq
Abstract:
Personalized book recommendation in Bangla literature has been constrained by the lack of structured, large‑scale, and publicly available datasets. This work introduces RokomariBG, a large‑scale, multi‑entity heterogeneous book graph dataset designed to support research on personalized recommendation in a low‑resource language setting. The dataset comprises 127,302 books, 63,723 users, 16,601 authors, 1,515 categories, 2,757 publishers, and 209,602 reviews, connected through eight relation types and organized as a comprehensive knowledge graph.
To demonstrate the utility of the dataset, we provide a systematic benchmarking study on the Top‑N recommendation task, evaluating a diverse set of representative recommendation models, including classical collaborative filtering methods, matrix factorization models, content‑based approaches, graph neural networks, a hybrid matrix factorization model with side information, and a neural two‑tower retrieval architecture. The benchmarking results highlight the importance of leveraging multi‑relational structure and textual side information, with neural retrieval models achieving the strongest performance (NDCG@10 = 0.204). Overall, this work establishes a foundational benchmark and a publicly available resource for Bangla book recommendation research, enabling reproducible evaluation and future studies on recommendation in low‑resource cultural domains. The dataset and code are publicly available at https://github.com/backlashblitz/Bangla‑Book‑Recommendation‑Dataset
Authors:Jiakang Shen, Qinghui Chen, Runtong Wang, Chenrui Xu, Jinglin Zhang, Cong Bai, Feng Zhang
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.
Authors:Daiqing Wu, Xuan Zhang, Dongbao Yang, Jiashu Yao, Longfei Chen, Qingsong Liu, Sicheng Zhao, Can Ma, Yangyang Kang, Yu Zhou
Abstract:
The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them to comprehend complex audio much like humans. Current efforts primarily replicate text‑based reasoning by contextualizing audio content through a one‑time encoding, which introduces a critical information bottleneck. Drawing inspiration from human cognition, we propose audio‑interleaved reasoning to break through this bottleneck. It treats audio as an active reasoning component, enabling sustained audio engagement and perception‑grounded analysis. To instantiate it, we introduce a two‑stage training framework, first teaching LALMs to localize salient audio segments through supervised fine‑tuning, and then incentivizing proficient re‑listening via reinforcement learning. In parallel, a structured data generation pipeline is developed to produce high‑quality training data. Consequently, we present Echo, a LALM capable of dynamically re‑listening to audio in demand during reasoning. On audio comprehension benchmarks, Echo achieves overall superiority in both challenging expert‑level and general‑purpose tasks. Comprehensive analysis further confirms the efficiency and generalizability of audio‑interleaved reasoning, establishing it as a promising direction for advancing audio comprehension. Project page: https://github.com/wdqqdw/Echo.
Authors:Suraj Ranganath, Anish Patnaik, Vaishak Menon
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.
Authors:Lai Wei, Liangbo He, Jun Lan, Lingzhong Dong, Yutong Cai, Siyuan Li, Huijia Zhu, Weiqiang Wang, Linghe Kong, Yue Wang, Zhuosheng Zhang, Weiran Huang
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.
Authors:Xinyi Liu, Yujie Wang, Fangcheng Fu, Xuefeng Xiao, Huixia Li, Jiashi Li, Bin Cui
Abstract:
Expert parallelism is vital for effectively training Mixture‑of‑Experts (MoE) models, enabling different devices to host distinct experts, with each device processing different input data. However, during expert parallel training, dynamic routing results in significant load imbalance among experts: a handful of overloaded experts hinder overall iteration, emerging as a training bottleneck.
In this paper, we introduce LAER‑MoE, an efficient MoE training framework. The core of LAER‑MoE is a novel parallel paradigm, Fully Sharded Expert Parallel (FSEP), which fully partitions each expert parameter by the number of devices and restores partial experts at expert granularity through All‑to‑All communication during training. This allows for flexible re‑layout of expert parameters during training to enhance load balancing. In particular, we perform fine‑grained scheduling of communication operations to minimize communication overhead. Additionally, we develop a load balancing planner to formulate re‑layout strategies of experts and routing schemes for tokens during training. We perform experiments on an A100 cluster, and the results indicate that our system achieves up to 1.69x acceleration compared to the current state‑of‑the‑art training systems. Source code available at https://github.com/PKU‑DAIR/Hetu‑Galvatron/tree/laer‑moe.
Authors:Jianhua Wang, Yinlin Su
Abstract:
Federated Learning (FL) facilitates collaborative model training while preserving data locality; however, the exchange of gradients renders the system vulnerable to Gradient Inversion Attacks (GIAs), allowing adversaries to reconstruct private training data with high fidelity. Existing defenses, such as Differential Privacy (DP), typically employ indiscriminate noise injection across all parameters, which severely degrades model utility and convergence stability. To address those limitation, we proposes Targeted Interpretable Perturbation (TIP), a novel defense framework that integrates model interpretability with frequency domain analysis. Unlike conventional methods that treat parameters uniformly, TIP introduces a dual‑targeting strategy. First, leveraging Gradient‑weighted Class Activation Mapping (Grad‑CAM) to quantify channel sensitivity, we dynamically identify critical convolution channels that encode primary semantic features. Second, we transform these selected kernels into the frequency domain via the Discrete Fourier Transform and selectively inject calibrated perturbations into the high‑frequency spectrum. By selectively perturbing high‑frequency components, TIP effectively destroys the fine‑grained details necessary for image reconstruction while preserving the low‑frequency information crucial for model accuracy. Extensive experiments on benchmark datasets demonstrate that TIP renders reconstructed images visually unrecognizable against state‑of‑the‑art GIAs, while maintaining global model accuracy comparable to non‑private baselines, significantly outperforming existing DP‑based defenses in the privacy‑utility trade‑off and interpretability. Code is available in https://github.com/2766733506/asldkfjssdf_arxiv
Authors:Weida Li, Yaoliang Yu, Bryan Kian Hsiang Low
Abstract:
We revisit the use of probabilistic values, which include the well‑known Shapley and Banzhaf values, to rank features for explaining the local predicted values of decision trees. The quality of feature rankings is typically assessed with the insertion and deletion metrics. Empirically, we observe that co‑optimizing these two metrics is closely related to a joint optimization that selects a subset of features to maximize the local predicted value while minimizing it for the complement. However, we theoretically show that probabilistic values are generally unreliable for solving this joint optimization. Therefore, we explore deriving feature rankings by directly optimizing the joint objective. As the backbone, we propose TreeGrad, which computes the gradients of the multilinear extension of the joint objective in O(L) time for decision trees with L leaves; these gradients include weighted Banzhaf values. Building upon TreeGrad, we introduce TreeGrad‑Ranker, which aggregates the gradients while optimizing the joint objective to produce feature rankings, and TreeGrad‑Shap, a numerically stable algorithm for computing Beta Shapley values with integral parameters. In particular, the feature scores computed by TreeGrad‑Ranker satisfy all the axioms uniquely characterizing probabilistic values, except for linearity, which itself leads to the established unreliability. Empirically, we demonstrate that the numerical error of Linear TreeShap can be up to 10^15 times larger than that of TreeGrad‑Shap when computing the Shapley value. As a by‑product, we also develop TreeProb, which generalizes Linear TreeShap to support all probabilistic values. In our experiments, TreeGrad‑Ranker performs significantly better on both insertion and deletion metrics. Our code is available at https://github.com/watml/TreeGrad.
Authors:Yuanfu Wang, Zhixuan Liu, Xiangtian Li, Chaochao Lu, Chao Yang
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.
Authors:Jingkun Liu, Yisong Yue, Max Welling, Yue Song
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.
Authors:Chongyi Zheng, Royina Karegoudra Jayanth, Benjamin Eysenbach
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.
Authors:Dibyanayan Bandyopadhyay, Asif Ekbal
Abstract:
Standard statistical learning theory predicts that Large Language Models (LLMs) should overfit because their parameter counts vastly exceed the number of training tokens. Yet, in practice, they generalize robustly. We propose that the effective capacity controlling generalization lies in the geometry of the model's internal representations: while the parameter space is high‑dimensional, the activation states lie on a low‑dimensional, sparse manifold. To formalize this, we introduce the Sparse Semantic Dimension (SSD), a complexity measure derived from the active feature vocabulary of a Sparse Autoencoder (SAE) trained on the model's layers. Treating the LLM and SAE as frozen oracles, we utilize this framework to attribute the model's generalization capabilities to the sparsity of the dictionary rather than the total parameter count. Empirically, we validate this framework on GPT‑2 Small and Gemma‑2B, demonstrating that our bound provides non‑vacuous certificates at realistic sample sizes. Crucially, we uncover a counter‑intuitive "feature sharpness" scaling law: despite being an order of magnitude larger, Gemma‑2B requires significantly fewer calibration samples to identify its active manifold compared to GPT‑2, suggesting that larger models learn more compressible, distinct semantic structures. Finally, we show that this framework functions as a reliable safety monitor: out‑of‑distribution inputs trigger a measurable "feature explosion" (a sharp spike in active features), effectively signaling epistemic uncertainty through learned feature violation. Code is available at: https://github.com/newcodevelop/sparse‑semantic‑dimension.
Authors:Christopher Kverne, Mayur Akewar, Yuqian Huo, Tirthak Patel, Janki Bhimani
Abstract:
The training of Quantum Neural Networks (QNNs) is hindered by the high computational cost of gradient estimation and the barren plateau problem, where optimization landscapes become intractably flat. To address these challenges, we introduce Weighted Stochastic Block Descent (WSBD), a novel optimizer with a dynamic, parameter‑wise freezing strategy. WSBD intelligently focuses computational resources by identifying and temporarily freezing less influential parameters based on a gradient‑derived importance score. This approach significantly reduces the number of forward passes required per training step and helps navigate the optimization landscape more effectively. Unlike pruning or layer‑wise freezing, WSBD maintains full expressive capacity while adapting throughout training. Our extensive evaluation shows that WSBD converges on average 63.9% faster than Adam for the popular ground‑state‑energy problem, an advantage that grows with QNN size. We provide a formal convergence proof for WSBD and show that parameter‑wise freezing outperforms traditional layer‑wise approaches in QNNs. Project page: https://github.com/Damrl‑lab/WSBD‑Stochastic‑Freezing‑Optimizer.
Authors:Zachary Pedram Dadfar
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.
Authors:Yihang Yao, Zhepeng Cen, Haohong Lin, Shiqi Liu, Zuxin Liu, Jiacheng Zhu, Zhang-Wei Hong, Laixi Shi, Ding Zhao
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/.
Authors:Jason Dury
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).
Authors:Christopher Mitcheltree, Vincent Lostanlen, Emmanouil Benetos, Mathieu Lagrange
Abstract:
The Euclidean distance between wavelet scattering transform coefficients (known as paths) provides informative gradients for perceptual quality assessment of deep inverse problems in computer vision, speech, and audio processing. However, these transforms are computationally expensive when employed as differentiable loss functions for stochastic gradient descent due to their numerous paths, which significantly limits their use in neural network training. Against this problem, we propose "Scattering transform with Random Paths for machine Learning" (SCRAPL): a stochastic optimization scheme for efficient evaluation of multivariable scattering transforms. We implement SCRAPL for the joint time‑frequency scattering transform (JTFS) which demodulates spectrotemporal patterns at multiple scales and rates, allowing a fine characterization of intermittent auditory textures. We apply SCRAPL to differentiable digital signal processing (DDSP), specifically, unsupervised sound matching of a granular synthesizer and the Roland TR‑808 drum machine. We also propose an initialization heuristic based on importance sampling, which adapts SCRAPL to the perceptual content of the dataset, improving neural network convergence and evaluation performance. We make our code and audio samples available and provide SCRAPL as a Python package.
Authors:Ruichuan An, Sihan Yang, Ziyu Guo, Wei Dai, Zijun Shen, Haodong Li, Renrui Zhang, Xinyu Wei, Guopeng Li, Wenshan Wu, Wentao Zhang
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.
Authors:Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan
Abstract:
Tabular foundation models, such as TabPFNv2 and TabICL, have recently dethroned gradient‑boosted trees at the top of predictive benchmarks, demonstrating the value of in‑context learning for tabular data. We introduce TabICLv2, a new state‑of‑the‑art foundation model for regression and classification built on three pillars: (1) a novel synthetic data generation engine designed for high pretraining diversity; (2) various architectural innovations, including a new scalable softmax in attention improving generalization to larger datasets without prohibitive long‑sequence pretraining; and (3) optimized pretraining protocols, notably replacing AdamW with the Muon optimizer. On the TabArena and TALENT benchmarks, TabICLv2 without any tuning surpasses the performance of the current state of the art, RealTabPFN‑2.5 (hyperparameter‑tuned, ensembled, and fine‑tuned on real data). With only moderate pretraining compute, TabICLv2 generalizes effectively to million‑scale datasets under 50GB GPU memory while being markedly faster than RealTabPFN‑2.5. We provide extensive ablation studies to quantify these contributions and commit to open research by first releasing inference code and model weights at https://github.com/soda‑inria/tabicl, with synthetic data engine and pretraining code to follow.
Authors:Victoria Hankemeier, Malte Schilling
Abstract:
Spatio‑temporal models analyze spatial structures and temporal dynamics, which makes them prone to information degeneration among space and time. Prior literature has demonstrated that over‑squashing in causal attention or temporal convolutions creates a bias on the first tokens. To analyze whether such a bias is present in temporal attention mechanisms, we derive sensitivity bounds on the expected value of the Jacobian of a temporal attention layer. We theoretically show how off‑diagonal attention scores depend on the sequence length, and that temporal attention matrices suffer a diagonal attention sink. We suggest regularization methods, and experimentally demonstrate their effectiveness.
Authors:Masataka Yoneda, Yusuke Matsushita, Go Kamoda, Kohei Suenaga, Takuya Akiba, Masaki Waga, Sho Yokoi
Abstract:
We present an ultra‑fast and flexible search algorithm that enables search over trillion‑scale natural language corpora in under 0.3 seconds while handling semantic variations (substitution, insertion, and deletion). Our approach employs string matching based on suffix arrays that scales well with corpus size. To mitigate the combinatorial explosion induced by the semantic relaxation of queries, our method is built on two key algorithmic ideas: fast exact lookup enabled by a disk‑aware design, and dynamic corpus‑aware pruning. We theoretically show that the proposed method suppresses exponential growth in the search space with respect to query length by leveraging statistical properties of natural language. In experiments on FineWeb‑Edu (Lozhkov et al., 2024) (1.4T tokens), we show that our method achieves significantly lower search latency than existing methods: infini‑gram (Liu et al., 2024), infini‑gram mini (Xu et al., 2025), and SoftMatcha (Deguchi et al., 2025). As a practical application, we demonstrate that our method identifies benchmark contamination in training corpora, unidentified by existing approaches. We also provide an online demo of fast, soft search across corpora in seven languages.
Authors:Zhiyin Tan, Jennifer D'Souza
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).
Authors:Cong Pang, Xuyu Feng, Yujie Yi, Zixuan Chen, Jiawei Hong, Tiankuo Yao, Nang Yuan, Jiapeng Luo, Lewei Lu, Xin Lou
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.
Authors:Fanpu Cao, Lu Dai, Jindong Han, Hui Xiong
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.
Authors:Xuecheng Zou, Yu Tang, Bingbing Wang
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.
Authors:Aojun Lu, Tao Feng, Hangjie Yuan, Wei Li, Yanan Sun
Abstract:
The adaptation of large‑scale Vision‑Language Models (VLMs) through post‑training reveals a pronounced generalization gap: models fine‑tuned with Reinforcement Learning (RL) consistently achieve superior out‑of‑distribution (OOD) performance compared to those trained with Supervised Fine‑Tuning (SFT). This paper posits a data‑centric explanation for this phenomenon, contending that RL's generalization advantage arises from an implicit data filtering mechanism that inherently prioritizes medium‑difficulty training samples. To test this hypothesis, we systematically evaluate the OOD generalization of SFT models across training datasets of varying difficulty levels. Our results confirm that data difficulty is a critical factor, revealing that training on hard samples significantly degrades OOD performance. Motivated by this finding, we introduce Difficulty‑Curated SFT (DC‑SFT), a straightforward method that explicitly filters the training set based on sample difficulty. Experiments show that DC‑SFT not only substantially enhances OOD generalization over standard SFT, but also surpasses the performance of RL‑based training, all while providing greater stability and computational efficiency. This work offers a data‑centric account of the OOD generalization gap in VLMs and establishes a more efficient pathway to achieving robust generalization. Code is available at https://github.com/byyx666/DC‑SFT.
Authors:Li-Min Chu, Kai-Siang Ma, Ming-Hong Chen, Ping-Chun Hsieh
Abstract:
Cross‑domain imitation learning (CDIL) accelerates policy learning by transferring expert knowledge across domains, which is valuable in applications where the collection of expert data is costly. Existing methods are either supervised, relying on proxy tasks and explicit alignment, or unsupervised, aligning distributions without paired data, but often unstable. We introduce the Semi‑Supervised CDIL (SS‑CDIL) setting and propose the first algorithm for SS‑CDIL with theoretical justification. Our method uses only offline data, including a small number of target expert demonstrations and some unlabeled imperfect trajectories. To handle domain discrepancy, we propose a novel cross‑domain loss function for learning inter‑domain state‑action mappings and design an adaptive weight function to balance the source and target knowledge. Experiments on MuJoCo and Robosuite show consistent gains over the baselines, demonstrating that our approach achieves stable and data‑efficient policy learning with minimal supervision. Our code is available at~ https://github.com/NYCU‑RL‑Bandits‑Lab/CDIL.
Authors:Tameem Bakr, Anish Ambreth, Nils Lukas
Abstract:
In federated learning (FL), K clients jointly train a model without sharing raw data. Because each participant invests data and compute, clients need mechanisms to later prove the provenance of a jointly trained model. Model watermarking embeds a hidden signal in the weights, but naive approaches either do not scale with many clients as per‑client watermarks dilute as K grows, or give any individual client the ability to verify and potentially remove the watermark. We introduce (t,K)‑threshold watermarking: clients collaboratively embed a shared watermark during training, while only coalitions of at least t clients can reconstruct the watermark key and verify a suspect model. We secret‑share the watermark key τ so that coalitions of fewer than t clients cannot reconstruct it, and verification can be performed without revealing τ in the clear. We instantiate our protocol in the white‑box setting and evaluate it on image classification tasks on both IID and non‑IID partitions, as well as language models fine‑tuning setting. Our watermark remains detectable at scale (K=128) with minimal accuracy loss and stays above the detection threshold (z\ge 4) under attacks including adaptive fine‑tuning using up to 20% of the training data. Code is available at https://github.com/tameemalaa/collaborative‑threshold‑watermark.
Authors:Yifan Zhang, Zunhai Su, Shuhao Hu, Rui Yang, Wei Wu, Yulei Qian, Yuchen Xie, Xunliang Cai
Abstract:
While FP8 attention has shown substantial promise in innovations like FlashAttention‑3, its integration into the decoding phase of the DeepSeek Multi‑head Latent Attention (MLA) architecture presents notable challenges. These challenges include numerical heterogeneity arising from the decoupling of positional embeddings, misalignment of quantization scales in FP8 PV GEMM, and the need for optimized system‑level support. In this paper, we introduce SnapMLA, an FP8 MLA decoding framework optimized to improve long‑context efficiency through the following hardware‑aware algorithm‑kernel co‑optimization techniques: (i) RoPE‑Aware Per‑Token KV Quantization: Motivated by our analysis of the heterogeneous quantization sensitivity inherent to the MLA KV cache, this approach preserves the RoPE part in high precision. Furthermore, per‑token granularity is employed to align with the autoregressive decoding process and maintain quantization accuracy. (ii) Quantized PV Computation Pipeline Reconstruction: Addresses the misalignment of quantization scales in FP8 PV computation caused by the shared KV structure of the MLA. (iii) End‑to‑End Dataflow Optimization: Establishes an efficient data read‑and‑write workflow using specialized kernels, ensuring streamlined data flow and improved performance. Extensive experiments on state‑of‑the‑art MLA LLMs show that SnapMLA achieves up to a 1.91x improvement in throughput on long‑output decoding workloads while maintaining near‑parity benchmark quality compared with the BF16 baseline on the evaluated reasoning and code‑generation benchmarks. Code is available at https://github.com/meituan‑longcat/SGLang‑FluentLLM.
Authors:Paweł Lorek, Rafał Nowak, Rafał Topolnicki, Tomasz Trzciński, Maciej Zięba, Aleksandra Krystecka
Abstract:
Estimating the expectation of a real‑valued function of a random variable from sample data is a critical aspect of statistical analysis, with far‑reaching implications in various applications. Current methodologies typically assume (semi‑)parametric distributions such as Gaussian or mixed Gaussian, leading to significant estimation uncertainty if these assumptions do not hold. We propose a flow‑based model, integrated with stratified sampling, that leverages a parametrized neural network to offer greater flexibility in modeling unknown data distributions, thereby mitigating this limitation. Our model shows a marked reduction in estimation uncertainty across multiple datasets, including high‑dimensional (30 and 128) ones, outperforming crude Monte Carlo estimators and Gaussian mixture models. Reproducible code is available at https://github.com/rnoxy/flowstrat.
Authors:Guobin Shen, Chenxiao Zhao, Xiang Cheng, Lei Huang, Xing Yu
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.
Authors:Keane Ong, Sabri Boughorbel, Luwei Xiao, Chanakya Ekbote, Wei Dai, Ao Qu, Jingyao Wu, Rui Mao, Ehsan Hoque, Erik Cambria, Gianmarco Mengaldo, Paul Pu Liang
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.
Authors:Nusaibah Farrukh, Malavika Pradeep, Akshay Sasi, Rahul Venugopal, Elizabeth Sherly
Abstract:
Cognitive load, the mental effort required during working memory, is central to neuroscience, psychology, and human‑computer interaction. Accurate assessment is vital for adaptive learning, clinical monitoring, and brain‑computer interfaces. Physiological signals such as pupillometry and electroencephalography are established biomarkers of cognitive load, but their comparative utility and practical integration as lightweight, wearable monitoring solutions remain underexplored. EEG provides high temporal resolution of neural activity. Although non‑invasive, it is technologically demanding and limited in wearability and cost due to its resource‑intensive nature, whereas pupillometry is non‑invasive, portable, and scalable. Existing studies often rely on deep learning models with limited interpretability and substantial computational expense. This study integrates feature‑based and model‑driven approaches to advance time‑series analysis. Using the OpenNeuro 'Digit Span Task' dataset, this study investigates cognitive load classification from EEG and pupillometry. Feature‑based approaches using Catch‑22 features and classical machine learning models outperform deep learning in both binary and multiclass tasks. The findings demonstrate that pupillometry alone can compete with EEG, serving as a portable and practical proxy for real‑world applications. These results challenge the assumption that EEG is necessary for load detection, showing that pupil dynamics combined with interpretable models and SHAP based feature analysis provide physiologically meaningful insights. This work supports the development of wearable, affordable cognitive monitoring systems for neuropsychiatry, education, and healthcare.
Authors:Guangzhi Xiong, Sanchit Sinha, Aidong Zhang
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.
Authors:Jonathan Williams, Esin Tureci
Abstract:
Looped Language Models (LoopLMs) perform multi‑step latent reasoning prior to token generation and outperform conventional LLMs on reasoning benchmarks at smaller parameter budgets. However, attempts to further improve LoopLM reasoning with reinforcement learning have failed ‑ standard objectives such as Group Relative Policy Optimization (GRPO) only assign credit to the final latent state, creating a fundamental mismatch with the model's internal computation. To resolve this, we introduce RLTT (Reward Latent Thought Trajectories), a reinforcement learning framework which distributes reward across the full latent reasoning trajectory. RLTT provides dense, trajectory‑level credit assignment without relying on external verifiers and can directly replace GRPO with negligible overhead. Across extensive experiments with Ouro‑1.4B/2.6B‑Thinking under identical training and inference conditions, RLTT yields statistically significant improvements over GRPO on challenging mathematical reasoning benchmarks, improving mean accuracy over MATH‑500, AIME24/26, and BeyondAIME by +5.8% on the 1.4B scale, and +10.9% on the 2.6B scale. Despite being trained exclusively on mathematics, RLTT also transfers effectively to non‑mathematical reasoning benchmarks, demonstrating the effectiveness of trajectory‑level credit assignment for reinforcement learning in LoopLMs. Code is available at https://github.com/jonwill8/RLTT.git.
Authors:Yansong Qu, Zihao Sheng, Zilin Huang, Jiancong Chen, Yuhao Luo, Tianyi Wang, Yiheng Feng, Samuel Labi, Sikai Chen
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.
Authors:Feiyu Pan, Tianbin Zhang, Aoqian Zhang, Yu Sun, Zheng Wang, Lixing Chen, Li Pan, Jianhua Li
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.
Authors:Mateo Juliani, Mingxuan Li, Elias Bareinboim
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.
Authors:Seyedarmin Azizi, Erfan Baghaei Potraghloo, Minoo Ahmadi, Souvik Kundu, Massoud Pedram
Abstract:
Many recent reasoning gains in large language models can be explained as distribution sharpening: biasing generation toward high‑likelihood trajectories already supported by the pretrained model, rather than modifying its weights. A natural formalization is the sequence‑level power distribution π_α(y\mid x)\propto p_θ(y\mid x)^α (α>1), which concentrates mass on whole sequences instead of adjusting token‑level temperature. Prior work shows that Metropolis‑‑Hastings (MH) sampling from this distribution recovers strong reasoning performance, but at order‑of‑magnitude inference slowdowns. We introduce Power‑SMC, a training‑free Sequential Monte Carlo scheme that targets the same objective while remaining close to standard decoding latency. Power‑SMC advances a small particle set in parallel, corrects importance weights token‑by‑token, and resamples when necessary, all within a single GPU‑friendly batched decode. We prove that temperature τ=1/α is the unique prefix‑only proposal minimizing incremental weight variance, interpret residual instability via prefix‑conditioned Rényi entropies, and introduce an exponent‑bridging schedule that improves particle stability without altering the target. On MATH500, Power‑SMC matches or exceeds MH power sampling while reducing latency from 16‑‑28× to 1.4‑‑3.3× over baseline decoding. The code is available at https://github.com/ArminAzizi98/Power‑SMC.
Authors:Joesph An, Phillip Keung, Jiaqi Wang, Orevaoghene Ahia, Noah A. Smith
Abstract:
Large audio language models are increasingly used for complex audio understanding tasks, but they struggle with temporal tasks that require precise temporal grounding, such as word alignment and speaker diarization. The standard approach, where we generate timestamps as sequences of text tokens, is computationally expensive and prone to hallucination, especially when processing audio lengths outside the model's training distribution. In this work, we propose frame‑level internal tool use, a method that trains audio LMs to use their own internal audio representations to perform temporal grounding directly. We introduce a lightweight prediction mechanism trained via two objectives: a binary frame classifier and a novel inhomogeneous Poisson process (IHP) loss that models temporal event intensity. Across word localization, speaker diarization, and event localization tasks, our approach outperforms token‑based baselines. Most notably, it achieves a >50x inference speedup and demonstrates robust length generalization, maintaining high accuracy on out‑of‑distribution audio durations where standard token‑based models collapse completely.
Authors:Tony Feng, Trieu H. Trinh, Garrett Bingham, Dawsen Hwang, Yuri Chervonyi, Junehyuk Jung, Joonkyung Lee, Carlo Pagano, Sang-hyun Kim, Federico Pasqualotto, Sergei Gukov, Jonathan N. Lee, Junsu Kim, Kaiying Hou, Golnaz Ghiasi, Yi Tay, YaGuang Li, Chenkai Kuang, Yuan Liu, Hanzhao Lin, Evan Zheran Liu, Nigamaa Nayakanti, Xiaomeng Yang, Heng-Tze Cheng, Demis Hassabis, Koray Kavukcuoglu, Quoc V. Le, Thang Luong
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.
Authors:Zahra Khodagholi, Niloofar Yousefi
Abstract:
Saliency maps are increasingly used as design guidance in siRNA efficacy prediction, yet attribution methods are rarely validated before motivating sequence edits. We introduce a pre‑synthesis gate: a protocol for counterfactual sensitivity faithfulness that tests whether mutating high‑saliency positions changes model output more than composition‑matched controls. Cross‑dataset transfer reveals two failure modes that would otherwise go undetected: faithful‑but‑wrong (saliency valid, predictions fail) and inverted saliency (top‑saliency edits less impactful than random). Strikingly, models trained on mRNA‑level assays collapse on a luciferase reporter dataset, demonstrating that protocol shifts can silently invalidate deployment. Across four benchmarks, 19/20 fold instances pass; the single failure shows inverted saliency. A biology‑informed regularizer (BioPrior) strengthens saliency faithfulness with modest, dataset‑dependent predictive trade‑offs. Our results establish saliency validation as essential pre‑deployment practice for explanation‑guided therapeutic design. Code is available at https://github.com/shadi97kh/BioPrior.
Authors:Yuxin Jiang, Yuchao Gu, Ivor W. Tsang, Mike Zheng Shou
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.
Authors:Amandeep Kumar, Vishal M. Patel
Abstract:
Leveraging representation encoders for generative modeling offers a path for efficient, high‑fidelity synthesis. However, standard diffusion transformers fail to converge on these representations directly. While recent work attributes this to a capacity bottleneck proposing computationally expensive width scaling of diffusion transformers we demonstrate that the failure is fundamentally geometric. We identify Geometric Interference as the root cause: standard Euclidean flow matching forces probability paths through the low‑density interior of the hyperspherical feature space of representation encoders, rather than following the manifold surface. To resolve this, we propose Riemannian Flow Matching with Jacobi Regularization (RJF). By constraining the generative process to the manifold geodesics and correcting for curvature‑induced error propagation, RJF enables standard Diffusion Transformer architectures to converge without width scaling. Our method RJF enables the standard DiT‑B architecture (131M parameters) to converge effectively, achieving an FID of 3.37 where prior methods fail to converge. Code: https://github.com/amandpkr/RJF
Authors:Zhaoyang Wang, Canwen Xu, Boyi Liu, Yite Wang, Siwei Han, Zhewei Yao, Huaxiu Yao, Yuxiong He
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.
Authors:Xinchen Han, Hossam Afifi, Michel Marot, Xilu Wang, Lu Yin
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.
Authors:J Rosser, Robert Kirk, Edward Grefenstette, Jakob Foerster, Laura Ruis
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.
Authors:William Lugoloobi, Thomas Foster, William Bankes, Chris Russell
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
Authors:Kun Chen, Peng Shi, Fanfan Liu, Haibo Qiu, Zhixiong Zeng, Siqi Yang, Wenji Mao
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.
Authors:R E Zera Marveen Lyngkhoi, Chirag Chawla, Pratinav Seth, Utsav Avaiya, Soham Bhattacharjee, Mykola Khandoga, Rui Yuan, Vinay Kumar Sankarapu
Abstract:
Post‑training alignment is central to deploying large language models (LLMs), yet practical workflows remain split across backend‑specific tools and ad‑hoc glue code, making experiments hard to reproduce. We identify backend interference, reward fragmentation, and irreproducible pipelines as key obstacles in alignment research. We introduce AlignTune, a modular toolkit exposing a unified interface for supervised fine‑tuning (SFT) and RLHF‑style optimization with interchangeable TRL and Unsloth backends. AlignTune standardizes configuration, provides an extensible reward layer (rule‑based and learned), and integrates evaluation over standard benchmarks and custom tasks. By isolating backend‑specific logic behind a single factory boundary, AlignTune enables controlled comparisons and reproducible alignment experiments.
Authors:Chenyu Yang, Denis Tarasov, Davide Liconti, Hehui Zheng, Robert K. Katzschmann
Abstract:
Real‑world fine‑tuning of dexterous manipulation policies remains challenging due to limited real‑world interaction budgets and highly multimodal action distributions. Diffusion‑based policies, while expressive, do not permit conservative likelihood‑based updates during fine‑tuning because action probabilities are intractable. In contrast, conventional Gaussian policies collapse under multimodality, particularly when actions are executed in chunks, and standard per‑step critics fail to align with chunked execution, leading to poor credit assignment. We present SERFN, a sample‑efficient off‑policy fine‑tuning framework with normalizing flow (NF) to address these challenges. The normalizing flow policy yields exact likelihoods for multimodal action chunks, allowing conservative, stable policy updates through likelihood regularization and thereby improving sample efficiency. An action‑chunked critic evaluates entire action sequences, aligning value estimation with the policy's temporal structure and improving long‑horizon credit assignment. To our knowledge, this is the first demonstration of a likelihood‑based, multimodal generative policy combined with chunk‑level value learning on real robotic hardware. We evaluate SERFN on two challenging dexterous manipulation tasks in the real world: cutting tape with scissors retrieved from a case, and in‑hand cube rotation with a palm‑down grasp ‑‑ both of which require precise, dexterous control over long horizons. On these tasks, SERFN achieves stable, sample‑efficient adaptation where standard methods struggle.
Authors:James Burgess, Rameen Abdal, Dan Stoddart, Sergey Tulyakov, Serena Yeung-Levy, Kuan-Chieh Jackson Wang
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.
Authors:Jie Lu, Peihao Yan, Huacheng Zeng
Abstract:
With over 3.5 million 5G base stations deployed globally, their collective energy consumption (projected to exceed 131 TWh annually) raises significant concerns over both operational costs and environmental impacts. In this paper, we present EExAPP, a deep reinforcement learning (DRL)‑based xApp for 5G Open Radio Access Network (O‑RAN) that jointly optimizes radio unit (RU) sleep scheduling and distributed unit (DU) resource slicing. EExAPP uses a dual‑actor‑dual‑critic Proximal Policy Optimization (PPO) architecture, with dedicated actor‑critic pairs targeting energy efficiency and quality‑of‑service (QoS) compliance. A transformer‑based encoder enables scalable handling of variable user equipment (UE) populations by encoding all‑UE observations into fixed‑dimensional representations. To coordinate the two optimization objectives, a bipartite Graph Attention Network (GAT) is used to modulate actor updates based on both critic outputs, enabling adaptive trade‑offs between power savings and QoS. We have implemented EExAPP and deployed it on a real‑world 5G O‑RAN testbed with live traffic, commercial RU and smartphones. Extensive over‑the‑air experiments and ablation studies confirm that EExAPP significantly outperforms existing methods in reducing the energy consumption of RU while maintaining QoS. The source code is available at https://github.com/EExApp/EExApp.
Authors:Venus Team, Changlong Gao, Zhangxuan Gu, Yulin Liu, Xinyu Qiu, Shuheng Shen, Yue Wen, Tianyu Xia, Zhenyu Xu, Zhengwen Zeng, Beitong Zhou, Xingran Zhou, Weizhi Chen, Sunhao Dai, Jingya Dou, Yichen Gong, Yuan Guo, Zhenlin Guo, Feng Li, Qian Li, Jinzhen Lin, Yuqi Zhou, Linchao Zhu, Liang Chen, Zhenyu Guo, Changhua Meng, Weiqiang Wang
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
Authors:Georgios Ioannides, Adrian Kieback, Judah Goldfeder, Linsey Pang, Aman Chadha, Aaron Elkins, Yann LeCun, Ravid Shwartz-Ziv
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.
Authors:Jiacheng Liu, Yaxin Luo, Jiacheng Cui, Xinyi Shang, Xiaohan Zhao, Zhiqiang Shen
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.
Authors:Sijia Peng, Yun Xiong, Xi Chen, Yi Xie, Guanzhi Li, Yanwei Yu, Yangyong Zhu, Zhiqiang Shen
Abstract:
Time series data supports many domains (e.g., finance and climate science), but its rapid growth strains storage and computation. Dataset condensation can alleviate this by synthesizing a compact training set that preserves key information. Yet most condensation methods are image‑centric and often fail on time series because they miss time‑series‑specific temporal structure, especially local discriminative motifs such as shapelets. In this work, we propose ShapeCond, a novel and efficient condensation framework for time series classification that leverages shapelet‑based dataset knowledge via a shapelet‑guided optimization strategy. Our shapelet‑assisted synthesis cost is independent of sequence length: longer series yield larger speedups in synthesis (e.g., 29× faster over prior state‑of‑the‑art method CondTSC for time‑series condensation, and up to 10,000× over naively using shapelets on the Sleep dataset with 3,000 timesteps). By explicitly preserving critical local patterns, ShapeCond improves downstream accuracy and consistently outperforms all prior state‑of‑the‑art time series dataset condensation methods across extensive experiments. Code is available at https://github.com/lunaaa95/ShapeCond.
Authors:Ruijie Zhu, Jiahao Lu, Wenbo Hu, Xiaoguang Han, Jianfei Cai, Ying Shan, Chuanxia Zheng
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
Authors:Suraj Ranganath, Atharv Ramesh
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.
Authors:Kateřina Henclová, Václav Šmídl
Abstract:
Selecting interpretable feature sets in underdetermined (n \ll p) and highly correlated regimes constitutes a fundamental challenge in data science, particularly when analyzing physical measurements. In such settings, multiple distinct sparse subsets may explain the response equally well. Identifying these alternatives is crucial for generating domain‑specific insights into the underlying mechanisms, yet conventional methods typically isolate a single solution, obscuring the full spectrum of plausible explanations.
We present GEMSS (Gaussian Ensemble for Multiple Sparse Solutions), a variational Bayesian framework specifically designed to simultaneously discover multiple, diverse sparse feature combinations. The method employs a structured spike‑and‑slab prior for sparsity, a mixture of Gaussians to approximate the intractable multimodal posterior, and a Jaccard‑based penalty to further control solution diversity. Unlike sequential greedy approaches, GEMSS optimizes the entire ensemble of solutions within a single objective function via stochastic gradient descent.
The method is validated on a comprehensive benchmark comprising 128 synthetic experiments across classification and regression tasks. Results demonstrate that GEMSS scales effectively to high‑dimensional settings (p=5000) with sample size as small as n = 50, generalizes seamlessly to continuous targets, handles missing data natively, and exhibits remarkable robustness to class imbalance and Gaussian noise.
GEMSS is available as a Python package 'gemss' at PyPI. The full GitHub repository at https://github.com/kat‑er‑ina/gemss/ also includes a free, easy‑to‑use application suitable for non‑coders.
Authors:Bo Peng, Sirui Chen, Jiaguo Tian, Yu Qiao, Chaochao Lu
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.
Authors:Yukun Jiang, Hai Huang, Mingjie Li, Yage Zhang, Michael Backes, Yang Zhang
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.
Authors:Tiantong Wang, Yiyang Duan, Haoyu Chen, Tiantong Wu, Wei Yang Bryan Lim
Abstract:
Training of large‑scale models is both computationally intensive and often constrained by the availability of labeled data. Model merging offers a compelling alternative by directly integrating the weights of multiple source models without requiring additional data or extensive training. However, conventional model merging techniques, such as parameter averaging, often suffer from the unintended combination of non‑generalizable features, especially when source models exhibit significant weight disparities. Comparatively, model ensembling generally provides more stable and superior performance that aggregates multiple models by averaging outputs. However, it incurs higher inference costs and increased storage requirements. While previous studies experimentally showed the similarities between model merging and ensembling, theoretical evidence and evaluation metrics remain lacking. To address this gap, we introduce Merging‑ensembling loss (M‑Loss), a novel evaluation metric that quantifies the compatibility of merging source models using very limited unlabeled data. By measuring the discrepancy between parameter averaging and model ensembling at layer and node levels, M‑Loss facilitates more effective merging strategies. Specifically, M‑Loss serves both as a quantitative criterion of the theoretical feasibility of model merging, and a guide for parameter significance in model pruning. Our theoretical analysis and empirical evaluations demonstrate that incorporating M‑Loss into the merging process significantly improves the alignment between merged models and model ensembling, providing a scalable and efficient framework for accurate model consolidation.
Authors:Ahmed Salem, Andrew Paverd, Sahar Abdelnabi
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.
Authors:Shih-Fang Chen, Jun-Cheng Chen, I-Hong Jhuo, Yen-Yu Lin
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.
Authors:Rui Wu, Li YongJun
Abstract:
Generative modeling typically seeks the path of least action via deterministic flows (ODE). While effective for in‑distribution tasks, we argue that these deterministic paths become brittle under causal interventions, which often require transporting probability mass across low‑density regions ("off‑manifold") where the vector field is ill‑defined. This leads to numerical instability and the pathology of anticipatory control. In this work, we introduce the Causal Schrodinger Bridge (CSB), a framework that reformulates counterfactual inference as Entropic Optimal Transport. By leveraging diffusion processes (SDEs), CSB enables probability mass to robustly "tunnel" through support mismatches while strictly enforcing structural admissibility. We prove the Structural Decomposition Theorem, showing that the global high‑dimensional bridge factorizes exactly into local, robust transitions. This theorem provides a principled resolution to the Information Bottleneck that plagues monolithic architectures in high dimensions. We empirically validate CSB on a full‑rank causal system (d=10^5, intrinsic rank 10^5), where standard structure‑blind MLPs fail to converge (MSE ~0.31). By physically implementing the structural decomposition, CSB achieves high‑fidelity transport (MSE ~0.06) in just 73.73 seconds on a single GPU. This stands in stark contrast to structure‑agnostic O(d^3) baselines, estimated to require over 6 years. Our results demonstrate that CSB breaks the Curse of Dimensionality through structural intelligence, offering a scalable foundation for high‑stakes causal discovery in 10^5‑node systems. Code is available at: https://github.com/cochran1/causal‑schrodinger‑bridge
Authors:Yunhui Liu, Pengyu Qiu, Yu Xing, Yongchao Liu, Peng Du, Chuntao Hong, Jiajun Zheng, Tao Zheng, Tieke He
Abstract:
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph‑structured data. Despite its significance in industrial applications such as fraud detection and user segmentation, a significant chasm persists between academic research and real‑world deployment. Current evaluation protocols suffer from the small‑scale, high‑homophily citation datasets, non‑scalable full‑batch training paradigms, and a reliance on supervised metrics that fail to reflect performance in label‑scarce environments. To bridge these gaps, we present PyAGC, a comprehensive, production‑ready benchmark and library designed to stress‑test AGC methods across diverse scales and structural properties. We unify existing methodologies into a modular Encode‑Cluster‑Optimize framework and, for the first time, provide memory‑efficient, mini‑batch implementations for a wide array of state‑of‑the‑art AGC algorithms. Our benchmark curates 12 diverse datasets, ranging from 2.7K to 111M nodes, specifically incorporating industrial graphs with complex tabular features and low homophily. Furthermore, we advocate for a holistic evaluation protocol that mandates unsupervised structural metrics and efficiency profiling alongside traditional supervised metrics. Battle‑tested in high‑stakes industrial workflows at Ant Group, this benchmark offers the community a robust, reproducible, and scalable platform to advance AGC research towards realistic deployment. The code and resources are publicly available via GitHub (https://github.com/Cloudy1225/PyAGC), PyPI (https://pypi.org/project/pyagc), and Documentation (https://pyagc.readthedocs.io).
Authors:Yuntian Tang, Bohan Jia, Wenxuan Huang, Lianyue Zhang, Jiao Xie, Wenxi Li, Wei Li, Jie Hu, Xinghao Chen Rongrong Ji, Shaohui Lin
Abstract:
Chain‑of‑Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference. Existing CoT compression methods often suffer from a critical loss of logical fidelity at high compression ratios, resulting in significant performance degradation. To achieve high‑fidelity, fast reasoning, we propose a novel EXTreme‑RAtio Chain‑of‑Thought Compression framework, termed Extra‑CoT, which aggressively reduces the token budget while preserving answer accuracy. To generate reliable, high‑fidelity supervision, we first train a dedicated semantically‑preserved compressor on mathematical CoT data with fine‑grained annotations. An LLM is then fine‑tuned on these compressed pairs via a mixed‑ratio supervised fine‑tuning (SFT), teaching it to follow a spectrum of compression budgets and providing a stable initialization for reinforcement learning (RL). We further propose Constrained and Hierarchical Ratio Policy Optimization (CHRPO) to explicitly incentivize question‑solving ability under lower budgets by a hierarchical reward. Experiments on three mathematical reasoning benchmarks show the superiority of Extra‑CoT. For example, on MATH‑500 using Qwen3‑1.7B, Extra‑CoT achieves over 73% token reduction with an accuracy improvement of 0.6%, significantly outperforming state‑of‑the‑art (SOTA) methods. Our source codes have been released at https://github.com/Mwie1024/Extra‑CoT.
Authors:Suizhi Huang, Mei Li, Han Yu, Xiaoxiao Li
Abstract:
Textual Gradient‑style optimizers (TextGrad) enable gradient‑like feedback propagation through compound AI systems. However, they do not work well for deep chains. The root cause of this limitation stems from the Semantic Entanglement problem in these extended workflows. In standard textual backpropagation, feedback signals mix local critiques with upstream contexts, leading to Attribution Ambiguity. To address this challenge, we propose TextResNet, a framework that reformulates the optimization process to achieve precise signal routing via four key innovations. Firstly, in the forward pass, it enforces Additive Semantic Deltas to preserve an Identity Highway for gradient flow. Secondly, in the backward pass, it introduces Semantic Gradient Decomposition via a Semantic Projector to disentangle feedback into causally independent subspaces. Thirdly, it implements Causal Routing, which routes projected signals to their specific components. Finally, it performs Density‑Aware Optimization Scheduling to leverage the disentangled signals to dynamically allocate resources to key system bottlenecks. Our results show that TextResNet not only achieves superior performance compared to TextGrad, but also exhibits remarkable stability for agentic tasks in compound AI systems where baselines collapse. Code is available at https://github.com/JeanDiable/TextResNet.
Authors:Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, Kaide Zeng, Yu Wang, Siwei Han, Yiyang Zhou, Xujiang Zhao, Haifeng Chen, Zeyu Zheng, Cihang Xie, Huaxiu Yao
Abstract:
Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past experiences. Existing memory‑based methods primarily store raw trajectories, which are often redundant and noise‑heavy. This prevents agents from extracting high‑level, reusable behavioral patterns that are essential for generalization. In this paper, we propose SkillRL, a framework that bridges the gap between raw experience and policy improvement through automatic skill discovery and recursive evolution. Our approach introduces an experience‑based distillation mechanism to build a hierarchical skill library SkillBank, an adaptive retrieval strategy for general and task‑specific heuristics, and a recursive evolution mechanism that allows the skill library to co‑evolve with the agent's policy during reinforcement learning. These innovations significantly reduce the token footprint while enhancing reasoning utility. Experimental results on ALFWorld, WebShop and seven search‑augmented tasks demonstrate that SkillRL achieves state‑of‑the‑art performance, outperforming strong baselines over 15.3% and maintaining robustness as task complexity increases. Code is available at this https://github.com/aiming‑lab/SkillRL.
Authors:Shingo Higashiguchi, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai
Abstract:
With the rapid development of web services, large amounts of time series data are generated and accumulated across various domains such as finance, healthcare, and online platforms. As such data often co‑evolves with multiple variables interacting with each other, estimating the time‑varying dependencies between variables (i.e., the dynamic network structure) has become crucial for accurate modeling. However, real‑world data is often represented as tensor time series with multiple modes, resulting in large, entangled networks that are hard to interpret and computationally intensive to estimate. In this paper, we propose Kronecker Time‑Varying Graphical Lasso (KTVGL), a method designed for modeling tensor time series. Our approach estimates mode‑specific dynamic networks in a Kronecker product form, thereby avoiding overly complex entangled structures and producing interpretable modeling results. Moreover, the partitioned network structure prevents the exponential growth of computational time with data dimension. In addition, our method can be extended to stream algorithms, making the computational time independent of the sequence length. Experiments on synthetic data show that the proposed method achieves higher edge estimation accuracy than existing methods while requiring less computation time. To further demonstrate its practical value, we also present a case study using real‑world data. Our source code and datasets are available at https://github.com/Higashiguchi‑Shingo/KTVGL.
Authors:Konstantinos Mitsides, Maxence Faldor, Antoine Cully
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.
Authors:Zejia You, Chunyuan Deng, Hanjie Chen
Abstract:
Inference‑time steering offers a promising way to control language models (LMs) without retraining. However, standard approaches typically rely on activation addition, which inevitably alters the hidden‑state magnitudes raising concerns about representation collapse and degraded open‑ended generation. In this work, we explore Spherical Steering, a training‑free primitive that resolves this trade‑off through activation rotation. Rather than shifting activations with a fixed vector, our method rotates them along a geodesic toward a target direction, preserving signal integrity while steering toward the target concept. To further enhance adaptivity, we incorporate a confidence gate that dynamically modulates steering strength based on input uncertainty. Extensive experiments across multiple‑choice benchmarks demonstrate that Spherical Steering significantly outperforms addition‑based baselines (notably by +10% on TruthfulQA, COPA, and Storycloze), while simultaneously maintaining the model's general open‑ended generation quality. This work highlights the value of geometric consistency, suggesting that norm‑preserving rotation is a robust and effective primitive for precise inference‑time control. The code is available at: https://github.com/chili‑lab/Spherical‑Steering.
Authors:Sidike Paheding, Abel Reyes-Angulo, Leo Thomas Ramos, Angel D. Sappa, Rajaneesh A., Hiral P. B., Sajin Kumar K. S., Thomas Oommen
Abstract:
We present MMLSv2, a dataset for landslide segmentation on Martian surfaces. MMLSv2 consists of multimodal imagery with seven bands: RGB, digital elevation model, slope, thermal inertia, and grayscale channels. MMLSv2 comprises 664 images distributed across training, validation, and test splits. In addition, an isolated test set of 276 images from a geographically disjoint region from the base dataset is released to evaluate spatial generalization. Experiments conducted with multiple segmentation models show that the dataset supports stable training and achieves competitive performance, while still posing challenges in fragmented, elongated, and small‑scale landslide regions. Evaluation on the isolated test set leads to a noticeable performance drop, indicating increased difficulty and highlighting its value for assessing model robustness and generalization beyond standard in‑distribution settings. Dataset will be available at: https://github.com/MAIN‑Lab/MMLS_v2
Authors:Tianyu Li, Dongchen Han, Zixuan Cao, Haofeng Huang, Mengyu Zhou, Ming Chen, Erchao Zhao, Xiaoxi Jiang, Guanjun Jiang, Gao Huang
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.
Authors:Jingtao Liu, Xinming Zhang
Abstract:
Graph Continual Learning (GCL) aims to solve the challenges of streaming graph data. However, current methods often depend on replay‑based strategies, which raise concerns like memory limits and privacy issues, while also struggling to resolve the stability‑plasticity dilemma. In this paper, we suggest that lightweight, task‑specific modules can effectively guide the reasoning process of a fixed GNN backbone. Based on this idea, we propose Task‑Aware Adaptive Modulation (TAAM). The key component of TAAM is its lightweight Neural Synapse Modulators (NSMs). For each new task, a dedicated NSM is trained and then frozen, acting as an "expert module." These modules perform detailed, node‑attentive adaptive modulation on the computational flow of a shared GNN backbone. This setup ensures that new knowledge is kept within compact, task‑specific modules, naturally preventing catastrophic forgetting without using any data replay. Additionally, to address the important challenge of unknown task IDs in real‑world scenarios, we propose and theoretically prove a novel method named Anchored Multi‑hop Propagation (AMP). Notably, we find that existing GCL benchmarks have flaws that can cause data leakage and biased evaluations. Therefore, we conduct all experiments in a more rigorous inductive learning scenario. Extensive experiments show that TAAM comprehensively outperforms state‑of‑the‑art methods across eight datasets. Code and Datasets are available at: https://github.com/1iuJT/TAAM_AAMAS2026.
Authors:Lior Cohen, Ofir Nabati, Kaixin Wang, Navdeep Kumar, Shie Mannor
Abstract:
We study diffusion‑based world models for reinforcement learning, which offer high generative fidelity but face critical efficiency challenges in control. Current methods either require heavyweight models at inference or rely on highly sequential imagination, both of which impose prohibitive computational costs. We propose Horizon Imagination (HI), an on‑policy imagination process for discrete stochastic policies that denoises multiple future observations in parallel. HI incorporates a stabilization mechanism and a novel sampling schedule that decouples the denoising budget from the effective horizon over which denoising is applied while also supporting sub‑frame budgets. Experiments on Atari 100K and Craftium show that our approach maintains control performance with a sub‑frame budget of half the denoising steps and achieves superior generation quality under varied schedules. Code is available at https://github.com/leor‑c/horizon‑imagination.
Authors:Ziyang Fan, Keyu Chen, Ruilong Xing, Yulin Li, Li Jiang, Zhuotao Tian
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.
Authors:Sizhe Dang, Jiaqi Shao, Xiaodong Zheng, Guang Dai, Yan Song, Haishan Ye
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.
Authors:Taolin Zhang, Hang Guo, Wang Lu, Tao Dai, Shu-Tao Xia, Jindong Wang
Abstract:
As large language models (LLMs) continue to scale up, their performance on various downstream tasks has significantly improved. However, evaluating their capabilities has become increasingly expensive, as performing inference on a large number of benchmark samples incurs high computational costs. In this paper, we revisit the model‑item performance matrix and show that it exhibits sparsity, that representative items can be selected as anchors, and that the task of efficient benchmarking can be formulated as a sparse optimization problem. Based on these insights, we propose SparseEval, a method that, for the first time, adopts gradient descent to optimize anchor weights and employs an iterative refinement strategy for anchor selection. We utilize the representation capacity of MLP to handle sparse optimization and propose the Anchor Importance Score and Candidate Importance Score to evaluate the value of each item for task‑aware refinement. Extensive experiments demonstrate the low estimation error and high Kendall's~τ of our method across a variety of benchmarks, showcasing its superior robustness and practicality in real‑world scenarios. Code is available at https://github.com/taolinzhang/SparseEval.
Authors:Yuzhu Cai, Zexi Liu, Xinyu Zhu, Cheng Wang, Yanfeng Wang, Siheng Chen
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.
Authors:Guanglong Sun, Siyuan Zhang, Liyuan Wang, Jun Zhu, Hang Su, Yi Zhong
Abstract:
Safety post‑training can improve the harmfulness and policy compliance of Large Language Models (LLMs), but it may also reduce general utility, a phenomenon often described as the \emphalignment tax. We study this trade‑off through the lens of continual learning: sequential alignment stages expose the model to shifted data distributions and objectives, and their gradients may interfere with directions that support previously acquired general capabilities. This view does not claim that all alignment degradation has a single cause; rather, it provides a useful first‑order mechanism for mitigating one important source of capability regression. We propose Orthogonal Gradient Projection for Safety Alignment (OGPSA), a lightweight update rule that estimates a low‑rank reference subspace from gradients on a small set of general‑capability data and removes from each safety gradient the component lying in this subspace. The resulting update is the steepest local safety‑descent direction subject to first‑order preservation constraints on the reference objectives. OGPSA is compatible with standard post‑training pipelines and avoids large‑scale replay, although it introduces periodic reference‑gradient computation. Across Supervised Fine‑Tuning (SFT), Direct Preference Optimization (DPO), and sequential SFT\rightarrowDPO settings, OGPSA improves the observed safety‑‑utility trade‑off over standard baselines. Under the sequential SFT\rightarrowDPO pipeline, the average performance gain increases from 33.98% to 42.74% on Qwen2.5‑7B‑Instruct and from 19.74% to 32.98% on Llama3.1‑8B‑Instruct. We have open sourced our code at https://github.com/SunGL001/OGPSA.
Authors:Ruiqi Wang, Ruikang Liu, Runyu Chen, Haoxiang Suo, Zhiyi Peng, Zhuo Tang, Changjian Chen
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.
Authors:Jarrod Barnes
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.
Authors:Juntong Wu, Jialiang Cheng, Fuyu Lv, Ou Dan, Li Yuan
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.
Authors:Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
Abstract:
Object‑centric learning (OCL) aims to learn structured scene representations that support compositional generalization and robustness to out‑of‑distribution (OOD) data. However, OCL models are often not evaluated regarding these goals. Instead, most prior work focuses on evaluating OCL models solely through object discovery and simple reasoning tasks, such as probing the representation via image classification. We identify two limitations in existing benchmarks: (1) They provide limited insights on the representation usefulness of OCL models, and (2) localization and representation usefulness are assessed using disjoint metrics. To address (1), we use instruction‑tuned VLMs as evaluators, enabling scalable benchmarking across diverse VQA datasets to measure how well VLMs leverage OCL representations for complex reasoning tasks. To address (2), we introduce a unified evaluation task and metric that jointly assess localization (where) and representation usefulness (what), thereby eliminating inconsistencies introduced by disjoint evaluation. Finally, we include a simple multi‑feature reconstruction baseline as a reference point.
Authors:Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao
Abstract:
Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequential autoregressive decoding forces inherently parallel clinical reasoning, such as differential diagnosis, into a single linear reasoning path, limiting both efficiency and reliability for complex medical problems. To address this, we propose MedVerse, a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph (DAG) process based on Petri net theory. The framework adopts a full‑stack design across data, model architecture, and system execution. For data creation, we introduce the MedVerse Curator, an automated pipeline that synthesizes knowledge‑grounded medical reasoning paths and transforms them into Petri net‑structured representations. At the architectural level, we propose a topology‑aware attention mechanism with adaptive position indices that supports parallel reasoning while preserving logical consistency. Systematically, we develop a customized inference engine that supports parallel execution without additional overhead. Empirical evaluations show that MedVerse improves strong general‑purpose LLMs by up to 8.9%. Compared to specialized medical LLMs, MedVerse achieves comparable performance while delivering a 1.3x reduction in inference latency and a 1.7x increase in generation throughput, enabled by its parallel decoding capability. Code is available at https://github.com/aiming‑lab/MedVerse.
Authors:Martin Fixman, Alessandro Abati, Julián Jiménez Nimmo, Sean Lim, Esther Mondragón
Abstract:
In contrast to static formalisms, computational definitions describe the operational mechanisms of a model. Simulations are an essential part of the cycle of theory development and refinement, assisting researchers in formulating the precise definitions that models require, and making accurate predictions. This manuscript introduces a computational implementation of Pavlovian learning models in a Python environment, termed Pavlovian Associative Learning Models' Simulation (PALMS). In addition to the canonical Rescorla‑Wagner model, attentional approaches are implemented, including Pearce‑Kaye‑Hall, Mackintosh Extended, Le Pelley's Hybrid, and a novel extension of the Rescorla‑Wagner model featuring a unified variable learning rate that synthesises Mackintosh's and Pearce and Hall's opposing conceptualisations. To our knowledge, only the first attentional model has been previously specified computationally in a general design tool. PALMS integrates a graphical interface that permits the input of entire experimental designs in an alphanumeric format, akin to that used by experimental neuroscientists. It uniquely enables the simulation of experiments involving hundreds of stimuli, such as those used with human participants, and the computation of configural cues and configural‑cue compounds across all models, thereby substantially broadening their predictive capabilities. A comprehensive description of the models' implementation is provided in the paper. We evaluate PALMS by simulating five published experiments in the associative learning literature that assessed the predictive scope of existing models, and we show that this implementation provides neuroscientists with a useful tool for identifying critical variables, refining experimental designs, making precise predictions, comparing model fitness, and formulating new theoretical approaches.
Authors:Dingzhi Yu, Hongyi Tao, Yuanyu Wan, Luo Luo, Lijun Zhang
Abstract:
While adaptive gradient methods are the workhorse of modern machine learning, sign‑based optimization algorithms such as Lion and Muon have recently demonstrated superior empirical performance over AdamW in training large language models (LLM). However, a theoretical understanding of why sign‑based updates outperform variance‑adapted methods remains elusive. In this paper, we aim to bridge the gap between theory and practice through the lens of heavy‑tailed gradient noise, a phenomenon frequently observed in language modeling tasks. Theoretically, we introduce a novel generalized heavy‑tailed noise condition that captures the behavior of LLMs more accurately than standard finite variance assumptions. Under this noise model, we establish sharp convergence rates of SignSGD and Lion for generalized smooth function classes, matching or surpassing previous best‑known bounds. Furthermore, we extend our analysis to Muon and Muonlight, providing what is, to our knowledge, the first rigorous analysis of matrix optimization under heavy‑tailed stochasticity. These results offer a strong theoretical justification for the empirical superiority of sign‑based optimizers, showcasing that they are naturally suited to handle the noisy gradients associated with heavy tails. Empirically, LLM pretraining experiments validate our theoretical insights and confirm that our proposed noise models are well‑aligned with practice.
Authors:Yicheng Yang, Ruijiao Li, Lifeng Wang, Shuai Zheng, Shunzheng Ma, Keyu Zhang, Tuoyu Sun, Chenyun Dai, Jie Ding, Zhuo Zou
Abstract:
This paper focuses on the scalable robot learning for manipulation in the dexterous robot arm‑hand systems, where the remote human‑robot interactions via augmented reality (AR) are established to collect the expert demonstration data for improving efficiency. In such a system, we present a unified framework to address the general manipulation task problem. Specifically, the proposed method consists of two phases: i) In the first phase for pretraining, the policy is created in a behavior cloning (BC) manner, through leveraging the learning data from our AR‑based remote human‑robot interaction system; ii) In the second phase, a contrastive learning empowered reinforcement learning (RL) method is developed to obtain more efficient and robust policy than the BC, and thus a projection head is designed to accelerate the learning progress. An event‑driven augmented reward is adopted for enhancing the safety. To validate the proposed method, both the physics simulations via PyBullet and real‑world experiments are carried out. The results demonstrate that compared to the classic proximal policy optimization and soft actor‑critic policies, our method not only significantly speeds up the inference, but also achieves much better performance in terms of the success rate for fulfilling the manipulation tasks. By conducting the ablation study, it is confirmed that the proposed RL with contrastive learning overcomes policy collapse. Supplementary demonstrations are available at https://cyberyyc.github.io/.
Authors:Yonghui Yang, Wenjian Tao, Jilong Liu, Xingyu Zhu, Junfeng Fang, Weibiao Huang, Le Wu, Richang Hong, Tat-Sent Chua
Abstract:
Safety alignment of large language models remains brittle under domain shift and noisy preference supervision. Most existing robust alignment methods focus on uncertainty in alignment data, while overlooking optimization‑induced fragility in preference‑based objectives. In this work, we revisit robustness for LLM safety alignment from an optimization geometry perspective, and argue that robustness failures cannot be addressed by data‑centric methods alone. We propose ShaPO, a geometry‑aware preference optimization framework that enforces worst‑case alignment objectives via selective geometry control over alignment‑critical parameter subspace. By avoiding uniform geometry constraints, ShaPO mitigates the over‑regularization that can harm robustness under distribution shift. We instantiate ShaPO at two levels: token‑level ShaPO stabilizes likelihood‑based surrogate optimization, while reward‑level ShaPO enforces reward‑consistent optimization under noisy supervision. Across diverse safety benchmarks and noisy preference settings, ShaPO consistently improves safety robustness over popular preference optimization methods. Moreover, ShaPO composes cleanly with data‑robust objectives, yielding additional gains and empirically supporting the proposed optimization‑geometry perspective. The code is available at https://github.com/liujilong0116/ShaPO.
Authors:Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy
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.
Authors:Ayush Roy, Rudrasis Chakraborty, Lav Varshney, Vishnu Suresh Lokhande
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.
Authors:Jianrui Zhang, Anirudh Sundara Rajan, Brandon Han, Soochahn Lee, Sukanta Ganguly, Yong Jae Lee
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.
Authors:Gyoung S. Na, Chanyoung Park
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.
Authors:Vladimer Khasia
Abstract:
Standard Transformer architectures rely heavily on dense linear transformations, treating feature projection as a monolithic, full‑rank operation. We argue that this formulation is inefficient and lacks the structural inductive bias necessary for distinguishing between local feature preservation and global context integration. To address this, we introduce the Hybrid Dual‑Path Linear (HDPL) operator, which decomposes the affine transformation into two topologically distinct pathways: a sparse block‑diagonal component for high‑rank local processing, and a low‑rank Variational Autoencoder (VAE) bottleneck for global context regularization. By "surgically" replacing specific projections (Query, Key, Value, Gate, Up) with HDPL operators while retaining standard dense layers for aggregation (Output, Down), we achieve a superior balance of efficiency and representational power. Experiments on the FineWeb‑Edu dataset demonstrate that the HDPL architecture outperforms a standard Llama‑style baseline, reducing validation loss while simultaneously reducing parameter count by 6.8%. Beyond immediate performance gains, we discuss how the explicit materialization of a probabilistic latent space within the Transformer backbone serves as a vital architectural affordance, offering new pathways for inference‑time or hypernetwork induced control, continual adaptation, interpretability, and cross‑model or cross‑modal synchronization. The code is available at https://github.com/VladimerKhasia/HDPL
Authors:Shashank
Abstract:
Transformers achieve strong language modeling accuracy, yet their position‑wise feed‑forward networks (FFNs) are dense, globally shared, and typically updated end to end. These properties create two practical tensions. First, dense FFNs spend the same compute on every token regardless of context, and they allocate capacity uniformly even when language exhibits highly clustered context structure. Second, continual learning, in the sense of updating the model while serving a data stream, often produces interference because a small update touches broadly shared weights.
We propose Attractor Patch Networks (APN), a plug‑compatible replacement for the Transformer FFN. APN is a bank of patch experts. A similarity router selects a small top‑k set of patches for each token by matching the token representation to learned prototypes. Each selected patch emits a low‑rank residual update conditioned on a compact code. The architecture yields conditional, context‑specialized nonlinear transformations while preserving the standard Transformer interface.
This paper focuses on APN as an architectural primitive. We formalize APN, analyze its expressivity as a piecewise low‑rank residual function class, and derive simple interference and stability arguments that make APN naturally compatible with continual learning. In experiments on character‑level language modeling, APN achieves competitive perplexity (4.57 vs 4.32 PPL) while enabling dramatically better continual adaptation: when adapting to a shifted domain, APN achieves 2.6 times better retention (11.1 vs 29.4 PPL on the original domain) and 2.8 times better adaptation (6.4 vs 17.8 PPL on the new domain) compared to global fine‑tuning of a dense FFN baseline.
Authors:Zitao Song, Cedar Site Bai, Zhe Zhang, Brian Bullins, David F. Gleich
Abstract:
Adaptive methods like Adam have become the de facto standard for large‑scale vector and Euclidean optimization due to their coordinate‑wise adaptation with a second‑order nature. More recently, matrix‑based spectral optimizers like Muon (Jordan et al., 2024b) show the power of treating weight matrices as matrices rather than long vectors. Linking these is hard because many natural generalizations are not feasible to implement, and we also cannot simply move the Adam adaptation to the matrix spectrum. To address this, we reformulate the AdaGrad update and decompose it into a variance adaptation term and a scale‑invariant term. This decoupling produces DeVA (Decoupled Variance Adaptation), a framework that bridges between vector‑based variance adaptation and matrix spectral optimization, enabling a seamless transition from Adam to adaptive spectral descent. Extensive experiments across language modeling and image classification demonstrate that DeVA consistently outperforms state‑of‑the‑art methods such as Muon and SOAP (Vyas et al., 2024), reducing token usage by around 6.6%. Theoretically, we show that the variance adaptation term effectively improves the blockwise smoothness, facilitating faster convergence. Our implementation is available at https://github.com/Tsedao/Decoupled‑Variance‑Adaptation
Authors:Keonyoung Koh, Moonkyeong Jung, Samuel Seungsup Lee, Daehyung Park
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/.
Authors:Sohan Venkatesh, Ashish Mahendran Kurapath
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.
Authors:Joao Baptista Cardia Neto, Claudio Ferrari, Stefano Berretti
Abstract:
Facial emotion recognition has been typically cast as a single‑label classification problem of one out of six prototypical emotions. However, that is an oversimplification that is unsuitable for representing the multifaceted spectrum of spontaneous emotional states, which are most often the result of a combination of multiple emotions contributing at different intensities. Building on this, a promising direction that was explored recently is to cast emotion recognition as a distribution learning problem. Still, such approaches are limited in that research datasets are typically annotated with a single emotion class. In this paper, we contribute a novel approach to describe complex emotional states as probability distributions over a set of emotion classes. To do so, we propose a solution to automatically re‑label existing datasets by exploiting the result of a study in which a large set of both basic and compound emotions is mapped to probability distributions in the Valence‑Arousal‑Dominance (VAD) space. In this way, given a face image annotated with VAD values, we can estimate the likelihood of it belonging to each of the distributions, so that emotional states can be described as a mixture of emotions, enriching their description, while also accounting for the ambiguous nature of their perception. In a preliminary set of experiments, we illustrate the advantages of this solution and a new possible direction of investigation. Data annotations are available at https://github.com/jbcnrlz/affectnet‑b‑annotation.
Authors:Mingxi Xu, Qi Wang, Zhengyu Wen, Phong Dao Thien, Zhengyu Li, Ning Zhang, Xiaoyu He, Wei Zhao, Kehong Gong, Mingyuan Zhang
Abstract:
Motion tokenization is a key component of generalizable motion models, yet most existing approaches are restricted to species‑specific skeletons, limiting their applicability across diverse morphologies. We propose NECromancer (NEC), a universal motion tokenizer that operates directly on arbitrary BVH skeletons. NEC consists of three components: (1) an Ontology‑aware Skeletal Graph Encoder (OwO) that encodes structural priors from BVH files, including joint semantics, rest‑pose offsets, and skeletal topology, into skeletal embeddings; (2) a Topology‑Agnostic Tokenizer (TAT) that compresses motion sequences into a universal, topology‑invariant discrete representation; and (3) the Unified BVH Universe (UvU), a large‑scale dataset aggregating BVH motions across heterogeneous skeletons. Experiments show that NEC achieves high‑fidelity reconstruction under substantial compression and effectively disentangles motion from skeletal structure. The resulting token space supports cross‑species motion transfer, composition, denoising, generation with token‑based models, and text‑motion retrieval, establishing a unified framework for motion analysis and synthesis across diverse morphologies. Demo page: https://animotionlab.github.io/NECromancer/
Authors:Yuntong Hu, Yuting Zhang, Matthew Trager, Yi Zhang, Shuo Yang, Wei Xia, Stefano Soatto
Abstract:
Large language model (LLM)‑based multi‑agent systems (MAS) show strong promise for complex reasoning, planning, and tool‑augmented tasks, but designing effective MAS architectures remains labor‑intensive, brittle, and hard to generalize. Existing automatic MAS generation methods either rely on code generation, which often leads to executability and robustness failures, or impose rigid architectural templates that limit expressiveness and adaptability. We propose Evolutionary Generation of Multi‑Agent Systems (EvoMAS), which formulates MAS generation as structured configuration generation. EvoMAS performs evolutionary generation in configuration space. Specifically, EvoMAS selects initial configurations from a pool, applies feedback‑conditioned mutation and crossover guided by execution traces, and iteratively refines both the candidate pool and an experience memory. We evaluate EvoMAS on diverse benchmarks, including BBEH, SWE‑Bench, and WorkBench, covering reasoning, software engineering, and tool‑use tasks. EvoMAS consistently improves task performance over both human‑designed MAS and prior automatic MAS generation methods, while producing generated systems with higher executability and runtime robustness. EvoMAS outperforms the agent evolution method EvoAgent by +10.5 points on BBEH reasoning and +7.1 points on WorkBench. With Claude‑4.5‑Sonnet, EvoMAS also reaches 79.1% on SWE‑Bench‑Verified, matching the top of the leaderboard. Code is available at https://github.com/amazon‑science/EvoMAS
Authors:Daisuke Oba, Hiroki Furuta, Naoaki Okazaki
Abstract:
Masked diffusion language models generate text through iterative masked‑token filling, but terminal‑only rewards on final completions provide coarse credit assignment for the intermediate filling decisions that shape the generation process. We propose Diffusion‑State Policy Optimization (DiSPO), a plug‑in credit‑assignment layer that directly optimizes intermediate filling decisions. At selected intermediate masked states, DiSPO branches by resampling the currently masked positions from rollout‑cached logits, scores the resulting completions, and updates only the newly filled tokens, requiring no additional multi‑step diffusion rollouts or optimizer steps. We formalize a fixed‑state objective for branched completions and derive a policy‑gradient estimator that reuses the same rollouts as terminal‑feedback policy optimization. Experiments on LLaDA‑8B‑Instruct show that DiSPO consistently improves terminal‑feedback baselines, including diffu‑GRPO and SPG, on math and planning benchmarks under matched rollout compute and optimizer steps, supporting its use as a general plug‑in for masked diffusion policy optimization. Our project page is available at https://daioba.github.io/dispo .
Authors:Daisuke Oba, Danushka Bollegala, Masahiro Kaneko, Naoaki Okazaki
Abstract:
Masked Diffusion Language Models generate sequences via iterative sampling that progressively unmasks tokens. However, they still recompute the attention and feed‑forward blocks for every token position at every step ‑‑ even when many unmasked tokens are essentially fixed, resulting in substantial waste in compute. We propose SureLock: when the posterior at an unmasked position has stabilized across steps (our sure condition), we lock that position ‑‑ thereafter skipping its query projection and feed‑forward sublayers ‑‑ while caching its attention keys and values so other positions can continue to attend to it. This reduces the dominant per‑iteration computational cost from O(N^2d) to O(MNd) where N is the sequence length, M is the number of unlocked token positions, and d is the model dimension. In practice, M decreases as the iteration progresses, yielding substantial savings. On LLaDA‑8B, SureLock reduces algorithmic FLOPs by 30‑‑50% relative to the same sampler without locking, while maintaining comparable generation quality. We also provide a theoretical analysis to justify the design rationale of SureLock: monitoring only the local KL at the lock step suffices to bound the deviation in final token probabilities. Our project page is available at https://daioba.github.io/surelock .
Authors:Junqi Chen, Sirui Chen, Chaochao Lu
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.
Authors:Patryk Rybak, Paweł Batorski, Paul Swoboda, Przemysław Spurek
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/
Authors:Yu Zhang, Sean Bin Yang, Arijit Khan, Cuneyt Gurcan Akcora
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.
Authors:Peiyang Song, Pengrui Han, Noah Goodman
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.
Authors:Bruno Lopes Yamamoto, Lucas Lauton de Alcantara, Victor Zacarias, Leandro Giusti Mugnaini, Keith Ando Ogawa, Lucas Pellicer, Rosimeire Pereira Costa, Edson Bollis, Anna Helena Reali Costa, Artur Jordao
Abstract:
The high computational demands of Large Language Models (LLMs) motivate methods that reduce parameter count and accelerate inference. In response, model pruning emerges as an effective strategy, yet current methods typically focus on a single dimension‑depth or width. We introduce MoP (Mixture of Pruners), an iterative framework that unifies these dimensions. At each iteration, MoP generates two branches‑pruning in depth versus pruning in width‑and selects a candidate to advance the path. On LLaMA‑2 and LLaMA‑3, MoP advances the frontier of structured pruning, exceeding the accuracy of competing methods across a broad set of compression regimes. It also consistently outperforms depth‑only and width‑only pruning. Furthermore, MoP translates structural pruning into real speedup, reducing end‑to‑end latency by 39% at 40% compression. Finally, extending MoP to the vision‑language model LLaVA‑1.5, we notably improve computational efficiency and demonstrate that text‑only recovery fine‑tuning can restore performance even on visual tasks.
Authors:José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko, Mohammad Kohandel
Abstract:
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural networks (NNs) provide greater predictive power; yet both remain vulnerable to privacy attacks. We empirically assess these risks by designing attacks that identify which public datasets were used to train a model under varying levels of adversarial access, applying them to LORIS, a publicly available LR model for immunotherapy response prediction, as well as to additional shallow NN models trained for the same task. Our results show that both models leak significant training‑set information, with LRs proving particularly vulnerable in white‑box scenarios. Moreover, we observe that common practices such as cross‑validation in LRs exacerbate these risks. To mitigate these vulnerabilities, we propose a quantum‑inspired defense based on tensorizing discretized models into tensor trains (TTs), which fully obfuscates parameters while preserving accuracy, reducing white‑box attacks to random guessing and degrading black‑box attacks comparably to Differential Privacy. TT models retain LR interpretability and extend it through efficient computation of marginal and conditional distributions, while also enabling this higher level of interpretability for NNs. Our results demonstrate that tensorization is widely applicable and establishes a practical foundation for private, interpretable, and effective clinical prediction.
Authors:Haozhen Zhang, Haodong Yue, Tao Feng, Quanyu Long, Jianzhu Bao, Bowen Jin, Weizhi Zhang, Xiao Li, Jiaxuan You, Chengwei Qin, Wenya Wang
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.
Authors:Xianyang Liu, Shangding Gu, Dawn Song
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.
Authors:Keith Ando Ogawa, Bruno Lopes Yamamoto, Lucas Lauton de Alcantara, Lucas Pellicer, Rosimeire Pereira Costa, Edson Bollis, Anna Helena Reali Costa, Artur Jordao
Abstract:
Pre‑training Large Language Models (LLMs) on web‑scale datasets becomes fundamental for advancing general‑purpose AI. In contrast, enhancing their predictive performance on downstream tasks typically involves adapting their knowledge through fine‑tuning. Parameter‑efficient fine‑tuning techniques, such as Low‑Rank Adaptation (LoRA), aim to reduce the computational cost of this process by freezing the pre‑trained model and updating a smaller number of parameters. In comparison to full fine‑tuning, these methods achieve over 99% reduction in trainable parameter count, depending on the configuration. Unfortunately, such a reduction may prove insufficient as LLMs continue to grow in scale. In this work, we address the previous problem by systematically selecting only a few layers to fine‑tune using LoRA or its variants. We argue that not all layers contribute equally to the model adaptation. Leveraging this, we identify the most relevant layers to fine‑tune by measuring their contribution to changes in internal representations. Our method is orthogonal to and readily compatible with existing low‑rank adaptation techniques. We reduce the trainable parameters in LoRA‑based techniques by up to 50%, while maintaining the predictive performance across different models and tasks. Specifically, on encoder‑only architectures, this reduction in trainable parameters leads to a negligible predictive performance drop on the GLUE benchmark. On decoder‑only architectures, we achieve a small drop or even improvements in the predictive performance on mathematical problem‑solving capabilities and coding tasks. Finally, this effectiveness extends to multimodal models, for which we also observe competitive results relative to fine‑tuning with LoRA modules in all layers. Code is available at: https://github.com/c2d‑usp/Layer‑wise‑LoRA‑with‑CKA
Authors:Arran Carter, Sanghyeok Choi, Kirill Tamogashev, Víctor Elvira, Esmeralda S. Whitammer
Abstract:
Sampling from a distribution p(x) \propto e^‑\mathcalE(x) known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the rise of a new family of amortised sampling algorithms, commonly referred to as diffusion samplers, that enable fast and efficient sampling from an unnormalised density. Such algorithms have been widely studied for continuous‑space sampling tasks; however, their application to problems in discrete space remains largely unexplored. Although some progress has been made in this area, discrete diffusion samplers do not take full advantage of ideas commonly used for continuous‑space sampling. In this paper, we propose to bridge this gap by introducing off‑policy training techniques for discrete diffusion samplers. We show that these techniques improve the performance of discrete samplers on both established and new synthetic benchmarks. Next, we generalise discrete diffusion samplers to the task of bridging between two arbitrary distributions, introducing data‑to‑energy Schrödinger bridge training for the discrete domain for the first time. Lastly, we showcase the application of the proposed diffusion samplers to data‑free posterior sampling in the discrete latent spaces of image generative models.
Authors:Zhenghao Xu, Qin Lu, Changlong Yu, Tuo Zhao
Abstract:
Policy mirror descent (PMD) provides a principled framework for reinforcement learning (RL) by iteratively solving KL‑regularized policy improvement subproblems. While this approach has been adopted in training advanced LLMs such as Kimi K1.5/K2, the ideal closed‑form PMD updates require reliable partition function estimation, a significant challenge when working with limited rollouts in the vast action spaces of LLMs. We investigate a practical algorithm, termed PMD‑mean, that approximates the log‑partition term with the mean reward under the sampling policy and performs regression in log‑policy space. Specifically, we characterize the population solution of PMD‑mean and demonstrate that it implicitly optimizes mirror descent subproblems with an adaptive mixed KL‑‑χ^2 regularizer. This additional χ^2 regularization constrains large probability changes, producing more conservative updates when expected rewards are low and enhancing robustness against finite‑sample estimation errors. Experiments on math reasoning tasks show that PMD‑mean achieves superior performance with improved stability and time efficiency. These findings deepen our understanding of PMD‑mean and illuminate pathways toward principled improvements in RL algorithms for LLMs. Code is available at https://github.com/horizon‑rl/OpenKimi.
Authors:András Balogh, Márk Jelasity
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.
Authors:Shengpu Wang, Yuhao Mao, Yani Zhang, Martin Vechev
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.
Authors:Artem Riabinin, Andrey Veprikov, Arman Bolatov, Martin Takáč, Aleksandr Beznosikov
Abstract:
We study adaptive learning rate scheduling for norm‑constrained optimizers (e.g., Muon and Lion). We introduce a generalized smoothness assumption under which local curvature decreases with the suboptimality gap and empirically verify that this behavior holds along optimization trajectories. Under this assumption, we establish convergence guarantees under an appropriate choice of learning rate, for which warm‑up followed by decay arises naturally from the proof rather than being imposed heuristically.
Building on this theory, we develop a practical learning rate scheduler that relies only on standard hyperparameters and adapts the warm‑up duration automatically at the beginning of training. We evaluate this method on large language model pretraining with LLaMA architectures and show that our adaptive warm‑up selection consistently outperforms or at least matches the best manually tuned warm‑up schedules across all considered setups, without additional hyperparameter search. Our source code is available at https://github.com/brain‑lab‑research/llm‑baselines/tree/warmup
Authors:Lixuan Guo, Yifei Wang, Tiansheng Wen, Yifan Wang, Aosong Feng, Bo Chen, Stefanie Jegelka, Chenyu You
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.
Authors:Ling Zhan, Zhen Li, Junjie Huang, Tao Jia
Abstract:
Benchmarking the hundreds of functional connectivity (FC) modeling methods on large‑scale fMRI datasets is critical for reproducible neuroscience. However, the combinatorial explosion of model‑data pairings makes exhaustive evaluation computationally prohibitive, preventing such assessments from becoming a routine pre‑analysis step. To break this bottleneck, we reframe the challenge of FC benchmarking by selecting a small, representative core‑set whose sole purpose is to preserve the relative performance ranking of FC operators. We formalize this as a ranking‑preserving subset selection problem and propose Structure‑aware Contrastive Learning for Core‑set Selection (SCLCS), a self‑supervised framework to select these core‑sets. SCLCS first uses an adaptive Transformer to learn each sample's unique FC structure. It then introduces a novel Structural Perturbation Score (SPS) to quantify the stability of these learned structures during training, identifying samples that represent foundational connectivity archetypes. Finally, while SCLCS identifies stable samples via a top‑k ranking, we further introduce a density‑balanced sampling strategy as a necessary correction to promote diversity, ensuring the final core‑set is both structurally robust and distributionally representative. On the large‑scale REST‑meta‑MDD dataset, SCLCS preserves the ground‑truth model ranking with just 10% of the data, outperforming state‑of‑the‑art (SOTA) core‑set selection methods by up to 23.2% in ranking consistency (nDCG@k). To our knowledge, this is the first work to formalize core‑set selection for FC operator benchmarking, thereby making large‑scale operators comparisons a feasible and integral part of computational neuroscience. Code is publicly available on https://github.com/lzhan94swu/SCLCS
Authors:Chenxi Wan, Xunkai Li, Yilong Zuo, Haokun Deng, Sihan Li, Bowen Fan, Hongchao Qin, Ronghua Li, Guoren Wang
Abstract:
Multimodal‑Attributed Graph (MAG) learning has achieved remarkable success in modeling complex real‑world systems by integrating graph topology with rich attributes from multiple modalities. With the rapid proliferation of novel MAG models capable of handling intricate cross‑modal semantics and structural dependencies, establishing a rigorous and unified evaluation standard has become imperative. Although existing benchmarks have facilitated initial progress, they exhibit critical limitations in domain coverage, encoder flexibility, model diversity, and task scope, presenting significant challenges to fair evaluation. To bridge this gap, we present OpenMAG, a comprehensive benchmark that integrates 19 datasets across 6 domains and incorporates 16 encoders to support both static and trainable feature encoding. OpenMAG further implements a standardized library of 24 state‑of‑the‑art models and supports 8 downstream tasks, enabling fair comparisons within a unified framework. Through systematic assessment of necessity, data quality, effectiveness, robustness, and efficiency, we derive 14 fundamental insights into MAG learning to guide future advancements. Our code is available at https://github.com/YUKI‑N810/OpenMAG.
Authors:Yayuan Li, Ze Peng, Jian Zhang, Jintao Guo, Yue Duan, Yinghuan Shi
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.
Authors:Tao Huang, Rui Wang, Xiaofei Liu, Yi Qin, Li Duan, Liping Jing
Abstract:
%Large vision‑language models (LVLMs) have shown substantial advances in multimodal understanding and generation. However, when presented with incompetent or adversarial inputs, they frequently produce unreliable or even harmful content, such as fact hallucinations or dangerous instructions. This misalignment with human expectations, referred to as \emphmisbehaviors of LVLMs, raises serious concerns for deployment in critical applications. These misbehaviors are found to stem from epistemic uncertainty, specifically either conflicting internal knowledge or the absence of supporting information. However, existing uncertainty quantification methods, which typically capture only overall epistemic uncertainty, have shown limited effectiveness in identifying such issues. To address this gap, we propose Evidential Uncertainty Quantification (EUQ), a fine‑grained method that captures both information conflict and ignorance for effective detection of LVLM misbehaviors. In particular, we interpret features from the model output head as either supporting (positive) or opposing (negative) evidence. Leveraging Evidence Theory, we model and aggregate this evidence to quantify internal conflict and knowledge gaps within a single forward pass. %We extensively evaluate our method across four categories of misbehavior, including hallucinations, jailbreaks, adversarial vulnerabilities, and out‑of‑distribution (OOD) failures, using state‑of‑the‑art LVLMs, and find that EUQ consistently outperforms strong baselines, showing that hallucinations correspond to high internal conflict and OOD failures to high ignorance. Furthermore, layer‑wise evidential uncertainty dynamics analysis helps interpret the evolution of internal representations from a new perspective. The source code is available at https://github.com/HT86159/EUQ.
Authors:Budhaditya Mukhopadhyay, Chirag Mandal, Pavan Tummala, Naghmeh Mahmoodian, Andreas Nürnberger, Soumick Chatterjee
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
Authors:Sheshansh Agrawal, Thien Hang Nguyen, Douwe Kiela
Abstract:
Selecting the top m from n items via expensive k‑wise comparisons is central to settings ranging from LLM‑based document reranking to crowdsourced evaluation and tournament design. Existing methods either rely on heuristics that discard comparison information, or exploit it at prohibitive cost. We introduce a tournament graph framework that provides a principled foundation for k‑wise ranking. Our key observation is that each k‑item comparison reveals an induced tournament of \binomk2 pairwise preferences; aggregating these into a global preference graph and computing its transitive closure yields many additional orderings without further oracle calls. We formalize when the current top‑m output is certifiably determined and design a greedy query schedule that maximizes information gain towards identifying the top‑m items. The framework also gracefully handles non‑transitive preferences ‑‑ cycles induced by real‑world oracles ‑‑ by collapsing them into equivalence classes that yield principled tiered rankings. Applied to LLM reranking across 14 benchmarks and 5 models, BlitzRank achieves Pareto dominance over existing approaches: matching or exceeding accuracy while requiring 25‑‑40% fewer tokens than comparable methods; against pairwise reranking, it achieves near‑identical quality with 7× fewer tokens. Code available at https://github.com/ContextualAI/BlitzRank.
Authors:Kritchanat Ponyuenyong, Pengyu Tu, Jia Wei Tan, Wei Soon Cheong, Jamie Ng Suat Ling, Lianlian Jiang
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.
Authors:Wei Soon Cheong, Lian Lian Jiang, Jamie Ng Suat Ling
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.
Authors:Zolnamar Dorjsembe, Hung-Yi Chen, Furen Xiao, Hsing-Kuo Pao
Abstract:
MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation. As a result, current radiotherapy workflows rely on combined MRI and CT acquisitions, increasing registration uncertainty and procedural complexity. Synthetic CT generation enables MRI only planning but remains challenging due to nonlinear MRI‑CT relationships and anatomical variability. We propose Parallel Swin Transformer‑Enhanced Med2Transformer, a 3D architecture that integrates convolutional encoding with dual Swin Transformer branches to model both local anatomical detail and long‑range contextual dependencies. Multi‑scale shifted window attention with hierarchical feature aggregation improves anatomical fidelity. Experiments on public and clinical datasets demonstrate higher image similarity and improved geometric accuracy compared with baseline methods. Dosimetric evaluation shows clinically acceptable performance, with a mean target dose error of 1.69%. Code is available at: https://github.com/mobaidoctor/med2transformer.
Authors:Paribesh Regmi, Rui Li, Kishan KC
Abstract:
The neighborhood scope (i.e., number of hops) where graph neural networks (GNNs) aggregate information to characterize a node's statistical property is critical to GNNs' performance. Two‑stage approaches, training and validating GNNs for every pre‑specified neighborhood scope to search for the best setting, is a time‑consuming task and tends to be biased due to the search space design. How to adaptively determine proper neighborhood scopes for the aggregation process for both homophilic and heterophilic graphs remains largely unexplored. We thus propose to model the GNNs' message‑passing behavior on a graph as a stochastic process by treating the number of hops as a beta process. This Bayesian framework allows us to infer the most plausible neighborhood scope for message aggregation simultaneously with the optimization of GNN parameters. Our theoretical analysis shows that the scope inference improves the expressivity of a GNN. Experiments on benchmark homophilic and heterophilic datasets show that the proposed method is compatible with state‑of‑the‑art GNN variants, achieving competitive or superior performance on the node classification task, and providing well‑calibrated predictions. Implementation is available at : https://github.com/paribeshregmi/BNA‑GNN
Authors:Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang, Bo Dai, Pan Li
Abstract:
Sequential probabilistic inference from streaming observations requires modeling distributions over future trajectories as new observations arrive. Although diffusion and flow‑matching models are effective at capturing high‑dimensional, multimodal distributions, their deployment in real‑time streaming settings typically relies on repeatedly sampling from a non‑informative initial distribution. This results in substantial inference latency, particularly when multiple samples are needed to characterize the predictive distribution. In this work, we introduce Sequential Bayesian Flow Matching, a framework inspired by Bayesian filtering. By learning a probability flow that transports the posterior distribution from one time step to the next time step conditioned on new observations, it mirrors the recursive structure of Bayesian belief updates. Crucially, by using the previous belief as an informative source distribution, it enables substantially faster sampling than naive resampling from scratch. Across scientific forecasting tasks spanning accelerator beam spill dynamics, fluid dynamics, and weather forecasting, as well as decision‑making benchmarks, our method achieves performance competitive with full‑step diffusion on distributional metrics while using far fewer sampling steps, substantially reducing inference latency. Our code is available at https://github.com/Graph‑COM/Sequential_Flow_Matching.
Authors:Minhuan Li, Jiequn Han, Pilar Cossio, Luhuan Wu
Abstract:
A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experimental measurements. While sequence‑to‑structure diffusion models provide powerful priors, posterior sampling methods steer generation by perturbing atomic coordinates with gradients from experimental likelihoods. However, when the target lies in a low‑density region of the prior, these methods require aggressive upweighting of the likelihood that can destabilize sampling and be sensitive to hyperparameters. We propose EmbedOpt, an inference‑time steering framework that introduces an orthogonal optimization axis: rather than performing posterior sampling under a fixed prior, EmbedOpt directly optimizes the prior by updating the model's conditional embedding. This embedding space encodes rich coevolutionary signals, so optimizing it shifts the structural prior to align with experimental constraints. Empirically, EmbedOpt matches coordinate‑based posterior sampling baselines on sparse distance constraints and outperforms them on cryo‑electron microscopy map fitting, including real, noisy experimental ones. Furthermore, EmbedOpt's smooth optimization behavior yields robustness to hyperparameters spanning two orders of magnitude and enables comparable performance with fewer diffusion steps. Code is available at https://github.com/rs‑station/embedopt.
Authors:Haoran Li, Sucheng Ren, Alan Yuille, Feng Wang
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.
Authors:Yuntai Bao, Xuhong Zhang, Jintao Chen, Ge Su, Yuxiang Cai, Hao Peng, Bing Sun, Haiqin Weng, Liu Yan, Jianwei Yin
Abstract:
Intervention‑based model steering offers a lightweight and interpretable alternative to prompting and fine‑tuning. However, by adapting strong optimization objectives from fine‑tuning, current methods are susceptible to overfitting and often underperform, sometimes generating unnatural outputs. We hypothesize that this is because effective steering requires the faithful identification of internal model mechanisms, not the enforcement of external preferences. To this end, we build on the principles of distributed alignment search (DAS), the standard for causal variable localization, to propose a new steering method: Concept DAS (CDAS). While we adopt the core mechanism of DAS, distributed interchange intervention (DII), we introduce a novel distribution matching objective tailored for the steering task by aligning intervened output distributions with counterfactual distributions. CDAS differs from prior work in two main ways: first, it learns interventions via weak‑supervised distribution matching rather than probability maximization; second, it uses DIIs that naturally enable bi‑directional steering and allow steering factors to be derived from data, reducing the effort required for hyperparameter tuning and resulting in more faithful and stable control. On AxBench, a large‑scale model steering benchmark, we show that CDAS does not always outperform preference‑optimization methods but may benefit more from increased model scale. In two safety‑related case studies, overriding refusal behaviors of safety‑aligned models and neutralizing a chain‑of‑thought backdoor, CDAS achieves systematic steering while maintaining general model utility. These results indicate that CDAS is complementary to preference‑optimization approaches and conditionally constitutes a robust approach to intervention‑based model steering. Our code is available at https://github.com/colored‑dye/concept_das.
Authors:Changhoon Song, Teng Yuan Chang, Youngjoon Hong
Abstract:
Accurate forecasting of extreme weather events such as heavy rainfall or storms is critical for risk management and disaster mitigation. Although high‑resolution radar observations have spurred extensive research on nowcasting models, precipitation nowcasting remains particularly challenging due to pronounced spatial locality, intricate fine‑scale rainfall structures, and variability in forecasting horizons. While recent diffusion‑based generative ensembles show promising results, they are computationally expensive and unsuitable for real‑time applications. In contrast, deterministic models are computationally efficient but remain biased toward normal rainfall. Furthermore, the benchmark datasets commonly used in prior studies are themselves skewed‑‑either dominated by ordinary rainfall events or restricted to extreme rainfall episodes‑‑thereby hindering general applicability in real‑world settings. In this paper, we propose exPreCast, an efficient deterministic framework for generating finely detailed radar forecasts, and introduce a newly constructed balanced radar dataset from the Korea Meteorological Administration (KMA), which encompasses both ordinary precipitation and extreme events. Our model integrates local spatiotemporal attention, a texture‑preserving cubic dual upsampling decoder, and a temporal extractor to flexibly adjust forecasting horizons. Experiments on established benchmarks (SEVIR and MeteoNet) as well as on the balanced KMA dataset demonstrate that our approach achieves state‑of‑the‑art performance, delivering accurate and reliable nowcasts across both normal and extreme rainfall regimes.
Authors:Magesh Rajasekaran, Md Saiful Sajol, Chris Alvin, Supratik Mukhopadhyay, Yanda Ou, Z. George Xue
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/
Authors:Olga Ovcharenko, Matthias Boehm, Sebastian Schelter
Abstract:
Real‑world machine learning on tabular data relies on complex data preparation pipelines for prediction, data integration, augmentation, and debugging. Designing these pipelines requires substantial domain expertise and engineering effort, motivating the question of how large language models (LLMs) can support tabular ML through code synthesis. We introduce SemPipes, a novel declarative programming model that integrates LLM‑powered semantic data operators into tabular ML pipelines. Semantic operators specify data transformations in natural language while delegating execution to a runtime system. During training, SemPipes synthesizes custom operator implementations based on data characteristics, operator instructions, and pipeline context. This design enables the automatic optimization of data operations in a pipeline via LLM‑based code synthesis guided by evolutionary search. We evaluate SemPipes across diverse tabular ML tasks and show that semantic operators substantially improve end‑to‑end predictive performance for both expert‑designed and agent‑generated pipelines, while reducing pipeline complexity. We implement SemPipes in Python and release it at https://github.com/deem‑data/sempipes/tree/v1.
Authors:Abdul Joseph Fofanah, Lian Wen, David Chen, Alpha Alimamy Kamara, Zhongyi Zhang
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.
Authors:Atula Tejaswi, Litu Rout, Constantine Caramanis, Sanjay Shakkottai, Sujay Sanghavi
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
Authors:Yu-Ang Lee, Ching-Yun Ko, Pin-Yu Chen, Mi-Yen Yeh
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.
Authors:Davide Berasi, Matteo Farina, Massimiliano Mancini, Elisa Ricci
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.
Authors:Junhan Kim, Yeo Jeong Park, Seungwoo Son, Chungman Lee, Ho-young Kim, Joonyoung Kim, Yongkweon Jeon
Abstract:
The rapid growth of large language models (LLMs) has heightened the importance of post‑training quantization (PTQ) for reducing memory and computation costs. Among PTQ methods, GPTQ has gained significant attention for its efficiency, enabling billion‑scale LLMs to be quantized within a few GPU hours. However, GPTQ's assumption of layer‑wise independence leads to severe accuracy drops in low‑bit regimes. Recently, BoA improved upon GPTQ by incorporating inter‑layer dependencies within attention modules, but its reliance on sequential quantization across all out‑channels makes it substantially less efficient. In this paper, we propose TurboBoA, a new backpropagation‑free PTQ algorithm that preserves the accuracy benefits of BoA while significantly accelerating the process. The proposed TurboBoA introduces three key innovations: (i) joint quantization of multiple out‑channels with a closed‑form error compensation rule, which reduces sequential bottlenecks and yields more than a three‑fold speedup; (ii) a correction mechanism for errors propagated from preceding quantized layers; and (iii) adaptive grid computation with coordinate descent refinement to maintain alignment during iterative updates. Extensive experiments demonstrate that TurboBoA delivers substantial acceleration over BoA while consistently improving accuracy. When combined with outlier suppression techniques, it achieves state‑of‑the‑art results in both weight‑only and weight‑activation quantization. The code will be available at https://github.com/SamsungLabs/TurboBoA.
Authors:Ruizhe Zhong, Jiesong Lian, Xiaoyue Mi, Zixiang Zhou, Yuan Zhou, Qinglin Lu, Junchi Yan
Abstract:
While online Reinforcement Learning has emerged as a crucial technique for aligning flow matching models with human preferences, current approaches are hindered by inefficient exploration during training rollouts. Relying on undirected stochasticity and sparse outcome rewards, these methods struggle to discover high‑reward samples, resulting in data‑inefficient and slow optimization. To address these limitations, we propose Euphonium, a novel framework that steers generation via process reward gradient guided dynamics. Our key insight is to formulate the sampling process as a theoretically principled Stochastic Differential Equation that explicitly incorporates the gradient of a Process Reward Model into the flow drift. This design enables dense, step‑by‑step steering toward high‑reward regions, advancing beyond the unguided exploration in prior works, and theoretically encompasses existing sampling methods (e.g., Flow‑GRPO, DanceGRPO) as special cases. We further derive a distillation objective that internalizes the guidance signal into the flow network, eliminating inference‑time dependency on the reward model. We instantiate this framework with a Dual‑Reward Group Relative Policy Optimization algorithm, combining latent process rewards for efficient credit assignment with pixel‑level outcome rewards for final visual fidelity. Experiments on text‑to‑video generation show that Euphonium achieves better alignment compared to existing methods while accelerating training convergence by 1.66x. Our code is available at https://github.com/zerzerzerz/Euphonium
Authors:Zhenning Shi, Yijia Zhu, Junhan Shi, Xun Zhang, Lei Wang, Congcong Miao
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.
Authors:Dinh Phu Tran, Jihoon Jeong, Saad Wazir, Seongah Kim, Thao Do, Cem Subakan, Daeyoung Kim
Abstract:
We present a formal problem formulation for Reliable Audio‑Visual Question Answering (\mathcalR‑AVQA), where we prefer abstention over answering incorrectly. While recent AVQA models have high accuracy, their ability to identify when they are likely wrong and their consequent abstention from answering remain underexplored areas of research. To fill this gap, we explore several approaches and then propose Adaptive Confidence Refinement (ACR), a lightweight method to further enhance the performance of \mathcalR‑AVQA. Our key insight is that the Maximum Softmax Probability (MSP) is Bayes‑optimal only under strong calibration, a condition usually not met in deep neural networks, particularly in multimodal models. Instead of replacing MSP, our ACR maintains it as a primary confidence signal and applies input‑adaptive residual corrections when MSP is deemed unreliable. ACR introduces two learned heads: i) a Residual Risk Head that predicts low‑magnitude correctness residuals that MSP does not capture, and ii) a Confidence Gating Head to determine MSP trustworthiness. Our experiments and theoretical analysis show that ACR consistently outperforms existing methods on in‑ and out‑of‑disrtibution, and data bias settings across three different AVQA architectures, establishing a solid foundation for \mathcalR‑AVQA task. The code and checkpoints will be available upon acceptance \hrefhttps://github.com/PhuTran1005/R‑AVQAat here
Authors:Licheng Pan, Yunsheng Lu, Jiexi Liu, Jialing Tao, Haozhe Feng, Hui Xue, Zhixuan Chu, Kui Ren
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.
Authors:Penghui Qi, Xiangxin Zhou, Zichen Liu, Tianyu Pang, Chao Du, Min Lin, Wee Sun Lee
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.
Authors:Ishaq Aden-Ali, Noah Golowich, Allen Liu, Abhishek Shetty, Ankur Moitra, Nika Haghtalab
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.
Authors:Philipp Nazari, T. Konstantin Rusch
Abstract:
Linear attention offers a computationally efficient yet expressive alternative to softmax attention. However, recent empirical results indicate that the hidden state of trained linear attention models often exhibits a low‑rank structure, suggesting that these models underexploit their capacity in practice. To illuminate this phenomenon, we provide a theoretical analysis of the role of rank in linear attention, revealing that low effective rank can affect retrieval error by amplifying query noise. In addition to these theoretical insights, we conjecture that the low‑rank states can be substantially reduced post‑training with only minimal performance degradation, yielding faster and more memory‑efficient models. To this end, we propose a novel hardware‑aware approach that structurally prunes key and query matrices, reducing the state size while retaining compatibility with existing CUDA kernels. We adapt several existing pruning strategies to fit our framework and, building on our theoretical analysis, propose a novel structured pruning method based on a rank‑revealing QR decomposition. Our empirical results, evaluated across models of varying sizes and on various downstream tasks, demonstrate the effectiveness of our state reduction framework. We highlight that our framework enables the removal of 50% of the query and key channels at only a marginal increase in perplexity. The code for this project can be found at https://github.com/camail‑official/LinearAttentionPruning.
Authors:Jiarui Yuan, Tailin Jin, Weize Chen, Zeyuan Liu
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.
Authors:Kieran A. Murphy
Abstract:
We make the information transmitted by attention an explicit, measurable quantity in vision transformers. By inserting variational information bottlenecks on all attention‑mediated writes to the residual stream ‑‑ without other architectural changes ‑‑ we train models with an explicit information cost and obtain a controllable spectrum from independent patch processing to fully expressive global attention. On ImageNet‑100, we characterize how classification behavior and information routing evolve across this spectrum, and provide initial insights into how global visual representations emerge from local patch processing by analyzing the first attention heads that transmit information. By biasing learning toward solutions with constrained internal communication, our approach yields models that are more tractable for mechanistic analysis and more amenable to control.
Authors:Mingyang Deng, He Li, Tianhong Li, Yilun Du, Kaiming He
Abstract:
Generative modeling can be formulated as learning a mapping f such that its pushforward distribution matches the data distribution. The pushforward behavior can be carried out iteratively at inference time, for example in diffusion and flow‑based models. In this paper, we propose a new paradigm called Drifting Models, which evolve the pushforward distribution during training and naturally admit one‑step inference. We introduce a drifting field that governs the sample movement and achieves equilibrium when the distributions match. This leads to a training objective that allows the neural network optimizer to evolve the distribution. In experiments, our one‑step generator achieves state‑of‑the‑art results on ImageNet at 256 x 256 resolution, with an FID of 1.54 in latent space and 1.61 in pixel space. We hope that our work opens up new opportunities for high‑quality one‑step generation.
Authors:Thatchanon Anancharoenkij, Donlapark Ponnoprat
Abstract:
A complete understanding of heterogeneous treatment effects involves characterizing the full conditional distribution of potential outcomes. To this end, we propose the Conditional Counterfactual Mean Embeddings (CCME), a framework that embeds conditional distributions of counterfactual outcomes into a reproducing kernel Hilbert space (RKHS). Under this framework, we develop a two‑stage meta‑estimator for CCME that accommodates any RKHS‑valued regression in each stage. Based on this meta‑estimator, we develop three practical CCME estimators: (1) Ridge Regression estimator, (2) Deep Feature estimator that parameterizes the feature map by a neural network, and (3) Neural‑Kernel estimator that performs RKHS‑valued regression, with the coefficients parameterized by a neural network. We provide finite‑sample convergence rates for all estimators, establishing that they possess the double robustness property. Our experiments demonstrate that our estimators accurately recover distributional features including multimodal structure of conditional counterfactual distributions.
Authors:Moritz Miller, Florent Draye, Bernhard Schölkopf
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.
Authors:Xianbiao Qi, Marco Chen, Jiaquan Ye, Yelin He, Rong Xiao
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.
Authors:Dipan Maity
Abstract:
Proximal Policy Optimization (PPO) has been positioned by recent literature as the canonical method for the RL part of Reinforcement Learning from Human Feedback (RLHF). PPO performs well empirically but has a heuristic motivation and handles the KL‑divergence constraint used in LM‑RLHF in an ad‑hoc manner and suffers form reward oscillations, entropy collapse, value function drift, and sudden policy divergence that require frequent restarts and extensive hyperparameter tuning. In this paper, we develop a new pure on policy actor‑critic RL method for the LM‑RLHF setting. We present SAFE (Stable Alignment Finetuning with Entropy‑aware control),a novel RLHF algorithm that combines a Double Soft‑Min Critic for pessimistic value estimation with a new multi‑layer stabilization framework combining entropy‑gated KL regulation, and PID‑controlled adaptive thresholds. Unlike standard PPO's symmetric KL penalties, SAFE distinguishes high‑entropy exploration from low‑entropy mode collapse and adjusts penalties dynamically based on reward velocity. Experiments on a 3B parameter model show SAFE achieves +5.15% training‑average reward than PPO (0.725 vs 0.689), negligible reward crashes, and superior KL control than ppo . Our method adds minimal computational overhead and provides an interpretable, crash‑resistant RLHF framework that maintains aggressive learning speed while ensuring stable long‑horizon optimization suitable for production deployment. Code is available at https://github.com/ryyzn9/SAFE
Authors:Sandra Fortini, Kenyon Ng, Sonia Petrone, Judith Rousseau, Susan Wei
Abstract:
TabPFN is a transformer that achieves state‑of‑the‑art performance on supervised tabular tasks by amortizing Bayesian prediction into a single forward pass. However, there is currently no method for uncertainty decomposition in TabPFN. Because it behaves, in an idealised limit, as a Bayesian in‑context learner, we cast the decomposition challenge as a Bayesian predictive inference (BPI) problem. The main computational tool in BPI, predictive Monte Carlo, is challenging to apply here as it requires simulating unmodeled covariates. We therefore pursue the asymptotic alternative, filling a gap in the theory for supervised settings by proving a predictive CLT under quasi‑martingale conditions. We derive variance estimators determined by the volatility of predictive updates along the context. The resulting credible bands are fast to compute, target epistemic uncertainty, and achieve near‑nominal frequentist coverage. For classification, we further obtain an entropy‑based uncertainty decomposition.
Authors:Lunjun Zhang, Jimmy Ba
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
Authors:Puyue Wang, Jiawei Hu, Yan Gao, Junyan Wang, Yu Zhang, Gillian Dobbie, Tao Gu, Wafa Johal, Ting Dang, Hong Jia
Abstract:
Humanoid robots can suffer significant performance drops under small changes in dynamics, task specifications, or environment setup. We propose HoRD, a two‑stage learning framework for robust humanoid control under domain shift. First, we train a high‑performance teacher policy via history‑conditioned reinforcement learning, where the policy infers latent dynamics context from recent state‑‑action trajectories to adapt online to diverse randomized dynamics. Second, we perform online distillation to transfer the teacher's robust control capabilities into a transformer‑based student policy that operates on sparse root‑relative 3D joint keypoint trajectories. By combining history‑conditioned adaptation with online distillation, HoRD enables a single policy to adapt zero‑shot to unseen domains without per‑domain retraining. Extensive experiments show HoRD outperforms strong baselines in robustness and transfer, especially under unseen domains and external perturbations. Code and project page are available at https://tonywang‑0517.github.io/hord/.
Authors:Zekun Li, Ning Wang, Tongxin Bai, Changwang Mei, Peisong Wang, Shuang Qiu, Jian Cheng
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.
Authors:Lin Zheng, Xinyu Li, Qian Liu, Xiachong Feng, Lingpeng Kong
Abstract:
Modern language models are trained almost exclusively on token sequences produced by a fixed tokenizer, an external lossless compressor often over UTF‑8 byte sequences, thereby coupling the model to that compressor. This work introduces proxy compression, an alternative training scheme that preserves the efficiency benefits of compressed inputs while providing an end‑to‑end, raw‑byte interface at inference time. During training, a single language model is jointly trained on raw byte sequences and compressed views generated by external compressors; through the process, the model learns to internally align compressed sequences and raw bytes. This alignment enables strong transfer between the two formats, even when training predominantly on compressed inputs that are discarded at inference. Extensive experiments on code language modeling demonstrate that proxy compression substantially improves training efficiency and significantly outperforms pure byte‑level baselines given fixed compute budgets. As model scale increases, these gains become more pronounced, and proxy‑trained models eventually match or surpass tokenizer approaches, all while operating solely on raw bytes and retaining the inherent robustness of byte‑level modeling. Our code is available at https://github.com/LZhengisme/proxy‑compression.
Authors:Yansong Ning, Jun Fang, Naiqiang Tan, Hao Liu
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.
Authors:Suzeyu Chen, Leheng Li, Ying-Cong Chen
Abstract:
Achieving highly accurate and real‑time 3D occupancy prediction from cameras is a critical requirement for the safe and practical deployment of autonomous vehicles. While this shift to sparse 3D representations solves the encoding bottleneck, it creates a new challenge for the decoder: how to efficiently aggregate information from a sparse, non‑uniformly distributed set of voxel features without resorting to computationally prohibitive dense attention.
In this paper, we propose a novel Prototype‑based Sparse Transformer Decoder that replaces this costly interaction with an efficient, two‑stage process of guided feature selection and focused aggregation. Our core idea is to make the decoder's attention prototype‑guided. We achieve this through a sparse prototype selection mechanism, where each query adaptively identifies a compact set of the most salient voxel features, termed prototypes, for focused feature aggregation.
To ensure this dynamic selection is stable and effective, we introduce a complementary denoising paradigm. This approach leverages ground‑truth masks to provide explicit guidance, guaranteeing a consistent query‑prototype association across decoder layers. Our model, dubbed SPOT‑Occ, outperforms previous methods with a significant margin in speed while also improving accuracy. Source code is released at https://github.com/chensuzeyu/SpotOcc.
Authors:Zeming Wei, Qiaosheng Zhang, Xia Hu, Xingcheng Xu
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.
Authors:Angel Martinez-Sanchez, Parthib Roy, Ross Greer
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
Authors:Junyu Chen, Jungang Li, Jing Xiong, Wenjie Wang, Qingyao Yang, He Xiao, Zhen Li, Taiqiang Wu, Mengzhao Chen, Zhen Peng, Chaofan Tao, Long Shi, Hongxia Yang, Ngai Wong
Abstract:
Large language model inference is often bounded by memory footprint and bandwidth in resource‑constrained deployments, making quantization fundamental to efficient serving. While post‑training quantization (PTQ) maintains high fidelity at 4‑bit, it deteriorates at 2‑3 bits. In essence, existing methods enforce a shape‑invariant quantization grid (e.g., the fixed uniform intervals of UINT2) for each group, severely restricting the feasible set for error minimization. To address this, we propose Bit‑Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit‑planes and scalar coefficients, and iteratively refines them using second‑order information while progressively compensating for quantization errors to minimize output discrepancy. In the 2‑bit regime, BPDQ enables serving Qwen2.5‑72B on a single RTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16‑bit). Moreover, we theoretically show that the variable grid expands the feasible set, and that the quantization process consistently aligns with the optimization objective in Hessian‑induced geometry. The code is available at https://github.com/KingdalfGoodman/BPDQ.
Authors:Sungwon Park, Anthony Zhou, Hongjoong Kim, Amir Barati Farimani
Abstract:
Neural operators provide a powerful framework for learning discretization invariant mappings between function spaces, but standard deterministic models do not capture predictive uncertainty. We introduce diffusion last layer (DLL), a modular probabilistic output head for neural operator backbones. DLL represents target fields through an input dependent low rank expansion inspired by the Karhunen‑Loéve expansion and learns a conditional diffusion model over the corresponding coefficient space. This design enables efficient distributional modeling while preserving the structural advantages of operator learning. On stochastic PDE benchmarks with random forcing, DLL achieves strong distributional fidelity and performs competitively with pixel space and conventional latent diffusion baselines. In deterministic long horizon rollout tasks, DLL improves rollout stability over the underlying backbone and provides useful estimates of predictive uncertainty under compounding autoregressive errors. These results suggest that diffusion modeling in learned coefficient spaces offers a practical route to uncertainty aware neural operators.
Authors:Kevin Zhai, Sabbir Mollah, Zhenyi Wang, Mubarak Shah
Abstract:
Standard decoding in Masked Diffusion Models (MDMs) is hindered by context rigidity: tokens are retained based on transient high confidence, often ignoring that early predictions lack full context. This creates cascade effects where initial inconsistencies misguide the remaining generation. Existing revision strategies attempt to mitigate this by relying on static confidence scores, but these signals are inherently myopic; inconsistent tokens can appear confident to the model itself. We propose Context‑Robust Remasking (CORE), a training‑free framework for inference‑time revision. Rather than trusting static token probabilities, CORE identifies context‑brittle tokens by probing their sensitivity to targeted masked‑context perturbations. We formalize revision as a robust optimization objective over context shifts and efficiently approximate this objective to prioritize unstable tokens for revision. On LLaDA‑8B‑Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute‑matched baselines and improving MBPP by up to 9.2 percentage points.
Authors:Xiaofeng Lin, Sirou Zhu, Yilei Chen, Mingyu Chen, Hejian Sang, Ioannis Paschalidis, Zhipeng Wang, Aldo Pacchiano, Xuezhou Zhang
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.
Authors:Felipe Angelim, Alessandro Leite
Abstract:
We propose Partition Tree, a novel tree‑based framework for conditional density estimation over general outcome spaces that supports both continuous and categorical variables within a unified formulation. Our approach models conditional distributions as piecewise‑constant densities on data‑adaptive partitions and learns trees by directly minimizing conditional negative log‑likelihood. This yields a scalable, nonparametric alternative to existing probabilistic trees that does not make parametric assumptions about the target distribution. We further introduce Partition Forest, a bagging extension obtained by averaging conditional densities. Empirically, we demonstrate improved probabilistic prediction over CART‑style trees and competitive performance compared to state‑of‑the‑art probabilistic tree methods and Random Forests.
Authors:Pengcheng Wang, Qinghang Liu, Haotian Lin, Yiheng Li, Guojian Zhan, Masayoshi Tomizuka, Yixiao Wang
Abstract:
Learning domain adaptive policies that can generalize to unseen transition dynamics, remains a fundamental challenge in learning‑based control. Substantial progress has been made through domain representation learning to capture domain‑specific information, thus enabling domain‑aware decision making. We analyze the process of learning domain representations through dynamical prediction and find that selecting contexts adjacent to the current step causes the learned representations to entangle static domain information with varying dynamical properties. Such mixture can confuse the conditioned policy, thereby constraining zero‑shot adaptation. To tackle the challenge, we propose DADP (Domain Adaptive Diffusion Policy), which achieves robust adaptation through unsupervised disentanglement and domain‑aware diffusion injection. First, we introduce Lagged Context Dynamical Prediction, a strategy that conditions future state estimation on a historical offset context; by increasing this temporal gap, we unsupervisedly disentangle static domain representations by filtering out transient properties. Second, we integrate the learned domain representations directly into the generative process by biasing the prior distribution and reformulating the diffusion target. Extensive experiments on challenging benchmarks across locomotion and manipulation demonstrate the superior performance, and the generalizability of DADP over prior methods. More visualization results are available on the https://outsider86.github.io/DomainAdaptiveDiffusionPolicy/.
Authors:Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik, Vijay Prakash Dwivedi, Johannes Hoffart, Carlos Guestrin, Jure Leskovec
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.
Authors:Jusheng Zhang, Ningyuan Liu, Qinhan Lyu, Jing Yang, Keze Wang
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.
Authors:Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
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.
Authors:Azmine Toushik Wasi, Wahid Faisal, Abdur Rahman, Mahfuz Ahmed Anik, Munem Shahriar, Mohsin Mahmud Topu, Sadia Tasnim Meem, Rahatun Nesa Priti, Sabrina Afroz Mitu, Md. Iqramul Hoque, Shahriyar Zaman Ridoy, Mohammed Eunus Ali, Majd Hawasly, Mohammad Raza, Md Rizwan Parvez
Abstract:
Spatial reasoning is a fundamental aspect of human cognition, yet it remains a major challenge for contemporary vision‑language models (VLMs). Prior work largely relied on synthetic or LLM‑generated environments with limited task designs and puzzle‑like setups, failing to capture the real‑world complexity, visual noise, and diverse spatial relationships that VLMs encounter. To address this, we introduce SpatiaLab, a comprehensive benchmark for evaluating VLMs' spatial reasoning in realistic, unconstrained contexts. SpatiaLab comprises 1,400 visual question‑answer pairs across six major categories: Relative Positioning, Depth & Occlusion, Orientation, Size & Scale, Spatial Navigation, and 3D Geometry, each with five subcategories, yielding 30 distinct task types. Each subcategory contains at least 25 questions, and each main category includes at least 200 questions, supporting both multiple‑choice and open‑ended evaluation. Experiments across diverse state‑of‑the‑art VLMs, including open‑ and closed‑source models, reasoning‑focused, and specialized spatial reasoning models, reveal a substantial gap in spatial reasoning capabilities compared with humans. In the multiple‑choice setup, InternVL3.5‑72B achieves 54.93% accuracy versus 87.57% for humans. In the open‑ended setting, all models show a performance drop of around 10‑25%, with GPT‑5‑mini scoring highest at 40.93% versus 64.93% for humans. These results highlight key limitations in handling complex spatial relationships, depth perception, navigation, and 3D geometry. By providing a diverse, real‑world evaluation framework, SpatiaLab exposes critical challenges and opportunities for advancing VLMs' spatial reasoning, offering a benchmark to guide future research toward robust, human‑aligned spatial understanding. SpatiaLab is available at: https://spatialab‑reasoning.github.io/.
Authors:Jinxing Zhou, Yanghao Zhou, Yaoting Wang, Zongyan Han, Jiaqi Ma, Henghui Ding, Rao Muhammad Anwer, Hisham Cholakkal
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.
Authors:Romain Cosentino
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.
Authors:Dingkun Zhang, Shuhan Qi, Yulin Wu, Xinyu Xiao, Xuan Wang, Long Chen
Abstract:
Multimodal Large Language Models (MLLMs) suffer from severe training inefficiency issue, which is associated with their massive model sizes and visual token numbers. Existing efforts in efficient training focus on reducing model sizes or trainable parameters. Inspired by the success of Visual Token Pruning (VTP) in improving inference efficiency, we are exploring another substantial research direction for efficient training by reducing visual tokens. However, applying VTP at the training stage results in a training‑inference mismatch: pruning‑trained models perform poorly when inferring on non‑pruned full visual token sequences. To close this gap, we propose DualSpeed, a fast‑slow framework for efficient training of MLLMs. The fast‑mode is the primary mode, which incorporates existing VTP methods as plugins to reduce visual tokens, along with a mode isolator to isolate the model's behaviors. The slow‑mode is the auxiliary mode, where the model is trained on full visual sequences to retain training‑inference consistency. To boost its training, it further leverages self‑distillation to learn from the sufficiently trained fast‑mode. Together, DualSpeed can achieve both training efficiency and non‑degraded performance. Experiments show DualSpeed accelerates the training of LLaVA‑1.5 by 2.1× and LLaVA‑NeXT by 4.0×, retaining over 99% performance. Code: https://github.com/dingkun‑zhang/DualSpeed
Authors:Ziru Chen, Dongdong Chen, Ruinan Jin, Yingbin Liang, Yujia Xie, Huan Sun
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.
Authors:Yingxuan Yang, Chengrui Qu, Muning Wen, Laixi Shi, Ying Wen, Weinan Zhang, Adam Wierman, Shangding Gu
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.
Authors:Duy Nguyen, Hanqi Xiao, Archiki Prasad, Elias Stengel-Eskin, Hyunji Lee, Mohit Bansal
Abstract:
Large language models (LLMs) rely on internal knowledge to solve many downstream tasks, making it crucial to keep them up to date. Since full retraining is expensive, prior work has explored efficient alternatives such as model editing and parameter‑efficient fine‑tuning. However, these approaches often break down in practice due to poor generalization across inputs, limited stability, and knowledge conflict. To address these limitations, we propose the CoRSA (Conflict‑Resolving and Sharpness‑Aware Minimization) training framework, a parameter‑efficient, holistic approach for knowledge editing with multiple updates. CoRSA tackles multiple challenges simultaneously: it improves generalization to different input forms and enhances stability across multiple updates by minimizing loss curvature, and resolves conflicts by maximizing the margin between new and prior knowledge. Across three widely used fact editing benchmarks, CoRSA achieves significant gains in generalization, outperforming baselines with average absolute improvements of 12.42% over LoRA and 10% over model editing methods. With multiple updates, it maintains high update efficacy while reducing catastrophic forgetting by 27.82% compared to LoRA. CoRSA also generalizes to the code domain, outperforming the strongest baseline by 5.48% Pass@5 in update efficacy.
Authors:Yicheng Zhang, Zhen Qin, Zhaomin Wu, Wenqi Zhang, Shuiguang Deng
Abstract:
Retrieval‑augmented generation (RAG) enables large language models (LLMs) to produce evidence‑based responses, and its performance hinges on the matching between the retriever and LLMs. Retriever optimization has emerged as an efficient alternative to fine‑tuning LLMs. However, existing solutions suffer from objective mismatch between retriever optimization and the goal of RAG pipeline. Reinforcement learning (RL) provides a promising solution to address this limitation, yet applying RL to retriever optimization introduces two fundamental challenges: 1) the deterministic retrieval is incompatible with RL formulations, and 2) state aliasing arises from query‑only retrieval in multi‑hop reasoning. To address these challenges, we replace deterministic retrieval with stochastic sampling and formulate RAG as a Markov decision process, making retriever optimizable by RL. Further, we incorporate retrieval history into the state at each retrieval step to mitigate state aliasing. Extensive experiments across diverse RAG pipelines, datasets, and retriever scales demonstrate consistent improvements of our approach in RAG performance.
Authors:Chao Huang, Yujing Lu, Quangang Li, Shenghe Wang, Yan Wang, Yueyang Zhang, Long Xia, Jiashu Zhao, Zhiyuan Sun, Daiting Shi, Tingwen Liu
Abstract:
Entropy regularization is a standard technique in reinforcement learning (RL) to enhance exploration, yet it yields negligible effects or even degrades performance in Large Language Models (LLMs). We attribute this failure to the cumulative tail risk inherent to LLMs with massive vocabularies and long generation horizons. In such environments, standard global entropy maximization indiscriminately dilutes probability mass into the vast tail of invalid tokens rather than focusing on plausible candidates, thereby disrupting coherent reasoning. To address this, we propose Trust Region Entropy (TRE), a method that encourages exploration strictly within the model's trust region. Extensive experiments across mathematical reasoning (MATH), combinatorial search (Countdown), and preference alignment (HH) tasks demonstrate that TRE consistently outperforms vanilla PPO, standard entropy regularization, and other exploration baselines. Our code is available at https://github.com/WhyChaos/TRE‑Encouraging‑Exploration‑in‑the‑Trust‑Region.
Authors:Yaguo Liu, Mingyue Cheng, Daoyu Wang, Xiaoyu Tao, Qi Liu
Abstract:
Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dynamics. Existing approaches typically rely on either autoregressive large language models (LLMs) for semantic context modeling or diffusion‑like models for continuous probabilistic generation. However, neither method alone can adequately model both aspects simultaneously. In this work, we propose CoGenCast, a hybrid generative framework that couples pre‑trained LLMs with flow‑matching mechanism for effective time series forecasting. Specifically, we reconfigure pre‑trained decoder‑only LLMs into a native forecasting encoder‑decoder backbone by modifying only the attention topology, enabling bidirectional context encoding and causal representation generation. Building on this, a flow‑matching mechanism is further integrated to model temporal evolution, capturing continuous stochastic dynamics conditioned on the autoregressively generated representation. Notably, CoGenCast naturally supports multimodal forecasting and cross‑domain unified training. Extensive experiments on multiple benchmarks show that CoGenCast consistently outperforms previous compared baselines. Code is available at https://github.com/liuyaguo/_CoGenCast.
Authors:Maximilian Kleinegger, Elvir Crnčević, Dan Alistarh
Abstract:
Matryoshka Quantization (MatQuant) is a recent quantization approach showing that a single integer‑quantized model can be served across multiple precisions, by slicing the most significant bits (MSB) at inference time. This enables a single checkpoint to cover a wide range of memory and latency budgets, but renders quantization much more challenging. In particular, the initial MatQuant relies on expensive quantization‑aware training (QAT) variants, rather than fast one‑shot post training quantization (PTQ), and lacks open‑source and kernel support. We address all of these limitations by introducing Post‑Training Matryoshka Quantization (MatGPTQ), a new PTQ pipeline that produces a single parent model jointly optimized for multiple target precisions in one‑shot, based on a small calibration set. MatGPTQ casts Matryoshka quantization as a multi‑precision objective with bit‑slicing and cross‑bit error compensation, resulting in an algorithm that produces a multi‑bit‑width, "sliceable" model in a single pass. We also incorporate a new budget‑aware search for heterogeneous per‑layer bit‑witdhs and provide efficient kernels that implement slicing and mixed‑precision execution. Across standard LLMs and benchmarks, MatGPTQ preserves high‑bit accuracy while substantially improving performance at low‑bit‑witdh settings. Overall, we establish a new state of the art for Matryoshka‑style post‑training quantization and make single‑checkpoint, multi‑precision deployment open and practical. Code is available at https://github.com/IST‑DASLab/MatGPTQ.
Authors:Yiran Qiao, Jing Chen, Xiang Ao, Qiwei Zhong, Yang Liu, Qing He
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/.
Authors:Meng Lou, Yunxiang Fu, Yizhou Yu
Abstract:
Continual learning, especially class‑incremental learning (CIL), on the basis of a pre‑trained model (PTM) has garnered substantial research interest in recent years. However, how to effectively learn both discriminative and comprehensive feature representations while maintaining stability and plasticity over very long task sequences remains an open problem. We propose CaRE, a scalable Continual Learner with efficient Bi‑Level Routing Mixture‑of‑Experts (BR‑MoE). The core idea of BR‑MoE is a bi‑level routing mechanism: a router selection stage that dynamically activates relevant task‑specific routers, followed by an expert routing phase that dynamically activates and aggregates experts, aiming to inject discriminative and comprehensive representations into every intermediate network layer. On the other hand, we introduce a challenging dataset, OmniBenchmark‑1K, for CIL performance evaluation on very long task sequences with hundreds of tasks. Extensive experiments show that CaRE demonstrates leading performance across a variety of datasets and task settings, including commonly used CIL datasets with classical CIL settings (e.g., 5‑20 tasks). To the best of our knowledge, CaRE is the first continual learner that scales to very long task sequences (ranging from 100 to over 300 non‑overlapping tasks), while outperforming all baselines by a large margin on such task sequences. We hope that this work will inspire further research into continual learning over extremely long task sequences. Code and dataset are publicly released at https://github.com/LMMMEng/CaRE.
Authors:Hyun Seok Seong, WonJun Moon, Jae-Pil Heo
Abstract:
Unsupervised object‑centric learning models, particularly slot‑based architectures, have shown great promise in decomposing complex scenes. However, their reliance on reconstruction‑based training creates a fundamental conflict between the sharp, high‑frequency attention maps of the encoder and the spatially consistent but blurry reconstruction maps of the decoder. We identify that this discrepancy gives rise to a vicious cycle: the noisy feature map from the encoder forces the decoder to average over possibilities and produce even blurrier outputs, while the gradient computed from blurry reconstruction maps lacks high‑frequency details necessary to supervise encoder features. To break this cycle, we introduce Synergistic Representation Learning (SRL) that establishes a virtuous cycle where the encoder and decoder mutually refine one another. SRL leverages the encoder's sharpness to deblur the semantic boundary within the decoder output, while exploiting the decoder's spatial consistency to denoise the encoder's features. This mutual refinement process is stabilized by a warm‑up phase with a slot regularization objective that initially allocates distinct entities per slot. By bridging the representational gap between the encoder and decoder, SRL achieves state‑of‑the‑art results on video object‑centric learning benchmarks. Codes are available at https://github.com/hynnsk/SRL.
Authors:Ning Ding, Fangcheng Liu, Kyungrae Kim, Linji Hao, Kyeng-Hun Lee, Hyeonmok Ko, Yehui Tang
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.
Authors:Bryan Sangwoo Kim, Jonghyun Park, Jong Chul Ye
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/.
Authors:Songming Liu, Bangguo Li, Kai Ma, Lingxuan Wu, Hengkai Tan, Xiao Ouyang, Hang Su, Jun Zhu
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.
Authors:Francesco Di Salvo, Sebastian Doerrich, Jonas Alle, Christian Ledig
Abstract:
Robust generalization beyond training distributions remains a critical challenge for deep neural networks. This is especially pronounced in medical image analysis, where data is often scarce and covariate shifts arise from different hardware devices, imaging protocols, and heterogeneous patient populations. These factors collectively hinder reliable performance and slow down clinical adoption. Despite recent progress, existing learning paradigms primarily rely on the Euclidean manifold, whose flat geometry fails to capture the complex, hierarchical structures present in clinical data. In this work, we exploit the advantages of hyperbolic manifolds to model complex data characteristics. We present the first comprehensive validation of hyperbolic representation learning for medical image analysis and demonstrate statistically significant gains across eleven in‑distribution datasets and three ViT models. We further propose an unsupervised, domain‑invariant hyperbolic cross‑branch consistency constraint. Extensive experiments confirm that our proposed method promotes domain‑invariant features and outperforms state‑of‑the‑art Euclidean methods by an average of +2.1% AUC on three domain generalization benchmarks: Fitzpatrick17k, Camelyon17‑WILDS, and a cross‑dataset setup for retinal imaging. These datasets span different imaging modalities, data sizes, and label granularities, confirming generalization capabilities across substantially different conditions. The code is available at https://github.com/francescodisalvo05/hyperbolic‑cross‑branch‑consistency .
Authors:Wenquan Lu, Hai Huang, Enqi Liu, Randall Balestriero
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.
Authors:Xiaoyu Tao, Mingyue Cheng, Ze Guo, Shuo Yu, Yaguo Liu, Qi Liu, Shijin Wang
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.
Authors:Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian
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.
Authors:Yinggan Xu, Risto Miikkulainen, Xin Qiu
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 .
Authors:Soyeon Hong, Jinchan Kim, Jaegook You, Seungtaek Choi, Suha Kwak, Hyunsouk Cho
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.
Authors:Felix X. -F. Ye, Xingjie Li, An Yu, Ming-Ching Chang, Linsong Chu, Davis Wertheimer
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 .
Authors:Ran Li, Zeyuan Liu, Yinghao Chen, Bingxiang He, Jiarui Yuan, Zixuan Fu, Weize Chen, Jinyi Hu, Chen Qian, Zhiyuan Liu, Maosong Sun
Abstract:
Large Language Models (LLMs) have demonstrated strong potential in complex reasoning, yet their progress remains fundamentally constrained by reliance on massive high‑quality human‑curated tasks and labels, either through supervised fine‑tuning (SFT) or reinforcement learning (RL) on reasoning‑specific data. This dependence renders supervision‑heavy training paradigms increasingly unsustainable, with signs of diminishing scalability already evident in practice. To overcome this limitation, we introduce CPMöbius (CPMobius), a collaborative Coach‑Player paradigm for data‑free reinforcement learning of reasoning models. Unlike traditional adversarial self‑play, CPMöbius, inspired by real world human sports collaboration and multi‑agent collaboration, treats the Coach and Player as independent but cooperative roles. The Coach proposes instructions targeted at the Player's capability and receives rewards based on changes in the Player's performance, while the Player is rewarded for solving the increasingly instructive tasks generated by the Coach. This cooperative optimization loop is designed to directly enhance the Player's mathematical reasoning ability. Remarkably, CPMöbius achieves substantial improvement without relying on any external training data, outperforming existing unsupervised approaches. For example, on Qwen2.5‑Math‑7B‑Instruct, our method improves accuracy by an overall average of +4.9 and an out‑of‑distribution average of +5.4, exceeding RENT by +1.5 on overall accuracy and R‑zero by +4.2 on OOD accuracy. Our codebase has been released at https://github.com/thunlp/CPMobius.
Authors:Haocheng Xi, Shuo Yang, Yilong Zhao, Muyang Li, Han Cai, Xingyang Li, Yujun Lin, Zhuoyang Zhang, Jintao Zhang, Xiuyu Li, Zhiying Xu, Jun Wu, Chenfeng Xu, Ion Stoica, Song Han, Kurt Keutzer
Abstract:
Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capability: KV cache memory. In autoregressive video generation models, the KV cache grows with generation history and quickly dominates GPU memory, often exceeding 30 GB, preventing deployment on widely available hardware. More critically, constrained KV cache budgets restrict the effective working memory, directly degrading long horizon consistency in identity, layout, and motion. To address this challenge, we present Quant VideoGen (QVG), a training free KV cache quantization framework for autoregressive video diffusion models. QVG leverages video spatiotemporal redundancy through Semantic Aware Smoothing, producing low magnitude, quantization friendly residuals. It further introduces Progressive Residual Quantization, a coarse to fine multi stage scheme that reduces quantization error while enabling a smooth quality memory trade off. Across LongCat Video, HY WorldPlay, and Self Forcing benchmarks, QVG establishes a new Pareto frontier between quality and memory efficiency, reducing KV cache memory by up to 7.0 times with less than 4% end to end latency overhead while consistently outperforming existing baselines in generation quality. Code is available at: https://github.com/svg‑project/Quant‑VideoGen
Authors:Yin Jin, Tucker R. Stewart, Deyi Zhou, Chhavi Gupta, Arjita Nema, Scott C. Brakenridge, Grant E. O'Keefe, Juhua Hu
Abstract:
Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical outcome can be substantially improved through early detection and timely intervention. By leveraging publicly available datasets, machine learning (ML) has driven advances in both research and clinical practice. However, existing public datasets consider ICU patients (Intensive Care Unit) as a uniform group and neglect the potential challenges presented by critically ill trauma patients in whom injury‑related inflammation and organ dysfunction can overlap with the clinical features of sepsis. We propose that a targeted identification of post‑traumatic sepsis is necessary in order to develop methods for early detection. Therefore, we introduce a publicly available standardized post‑trauma sepsis onset dataset extracted, relabeled using standardized post‑trauma clinical facts, and validated from MIMIC‑III. Furthermore, we frame early detection of post‑trauma sepsis onset according to clinical workflow in ICUs in a daily basis resulting in a new rare event detection problem. We then establish a general benchmark through comprehensive experiments, which shows the necessity of further advancements using this new dataset. The data code is available at https://github.com/ML4UWHealth/SepsisOnset_TraumaCohort.git.
Authors:Yidong Ouyang, Panwen Hu, Zhengyan Wan, Zhe Wang, Liyan Xie, Dmitriy Bespalov, Ying Nian Wu, Guang Cheng, Hongyuan Zha, Qiang Sun
Abstract:
Masked diffusion models have emerged as a powerful framework for text and multimodal generation. However, their sampling procedure updates multiple tokens simultaneously and treats generated tokens as immutable, which may lead to error accumulation when early mistakes cannot be revised. In this work, we revisit existing self‑correction methods and identify limitations stemming from additional training requirements or reliance on misaligned likelihood estimates. We propose a training‑free self‑correction framework that exploits the inductive biases of pre‑trained masked diffusion models. Without modifying model parameters or introducing auxiliary evaluators, our method significantly improves generation quality on text‑to‑image generation and multimodal understanding tasks with reduced sampling steps. Moreover, the proposed framework generalizes across different masked diffusion architectures, highlighting its robustness and practical applicability. Code can be found in https://github.com/huge123/FreeCorrection.
Authors:Ali Abbasi, Chayne Thrash, Haoran Qin, Shansita Sharma, Sepehr Seifi, Soheil Kolouri
Abstract:
Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD‑based compression reduces storage and can speed up inference via low‑rank factors, yet performance depends on how rank is allocated under a global compression ratio. Prior methods often use homogeneous ranks for similarly sized matrices, despite large differences in loss sensitivity, or rely on expensive iterative pre‑truncation optimization to determine per matrix ranks. We propose Zero Sum SVD (ZS‑SVD), a post‑training method that performs \emphglobal singular component selection using activation whitening and first‑order calibration loss estimates in whitened coordinates. ZS‑SVD prunes components across the whole model with a zero sum rule that keeps the cumulative predicted loss change near zero, automatically yielding heterogeneous ranks without solving a rank allocation optimization. Motivated by evidence that gradients near pretrained solutions exhibit low rank structure, we also introduce an optional lightweight correction that applies a single projected gradient update after truncation, followed by re‑truncation. Extensive experiments across multiple LLM architectures show consistent gains across diverse benchmarks and compression ratios. Code is available at https://github.com/mint‑vu/Zero‑Sum‑SVD
Authors:Michael Ogezi, Martin Bell, Freda Shi, Ethan Smith
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.
Authors:Matteo Bastico, Pierre Onghena, David Ryckelynck, Beatriz Marcotegui, Santiago Velasco-Forero, Laurent Corté, Caroline Robine--Decourcelle, Etienne Decencière
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.
Authors:Reza Rezvan, Gustav Gille, Moritz Schauer, Richard Torkar
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.
Authors:Viresh Pati, Yubin Kim, Vinh Pham, Jevon Twitty, Shihao Yang, Jiecheng Lu
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.
Authors:Fahim Tajwar, Guanning Zeng, Yueer Zhou, Yuda Song, Daman Arora, Yiding Jiang, Jeff Schneider, Ruslan Salakhutdinov, Haiwen Feng, Andrea Zanette
Abstract:
Reinforcement learning is the method of choice to train models in sampling‑based setups with binary outcome feedback, such as navigation, code generation, and mathematical problem solving. In such settings, models implicitly induce a likelihood over correct rollouts. However, we observe that reinforcement learning does not maximize this likelihood, and instead optimizes only a lower‑order approximation. Inspired by this observation, we introduce Maximum Likelihood Reinforcement Learning (MaxRL), a sampling‑based framework to approximate maximum likelihood using reinforcement learning techniques. MaxRL addresses the challenges of non‑differentiable sampling by defining a compute‑indexed family of sample‑based objectives that interpolate between standard reinforcement learning and exact maximum likelihood as additional sampling compute is allocated. The resulting objectives admit a simple, unbiased policy‑gradient estimator and converge to maximum likelihood optimization in the infinite‑compute limit. Empirically, we show that MaxRL Pareto‑dominates existing methods in all models and tasks we tested, achieving up to 20x test‑time scaling efficiency gains compared to its GRPO‑trained counterpart. We also observe MaxRL to scale better with additional data and compute. Our results suggest MaxRL is a promising framework for scaling RL training in correctness based settings.
Authors:Punya Syon Pandey, Zhijing Jin
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.
Authors:Md Ishtyaq Mahmud, Veena Kochat, Suresh Satpati, Jagan Mohan Reddy Dwarampudi, Humaira Anzum, Kunal Rai, Tania Banerjee
Abstract:
High‑resolution spatial transcriptomics platforms, such as Xenium, generate single‑cell images that capture both molecular and spatial context, but their extremely high dimensionality poses major challenges for representation learning and clustering. In this study, we analyze data from the Xenium platform, which captures high‑resolution images of tumor microarray (TMA) tissues and converts them into cell‑by‑gene matrices suitable for computational analysis. We benchmark and extend nonnegative matrix factorization (NMF) for spatial transcriptomics by introducing two spatially regularized variants. First, we propose Spatial NMF (SNMF), a lightweight baseline that enforces local spatial smoothness by diffusing each cell's NMF factor vector over its spatial neighborhood. Second, we introduce Hybrid Spatial NMF (hSNMF), which performs spatially regularized NMF followed by Leiden clustering on a hybrid adjacency that integrates spatial proximity (via a contact‑radius graph) and transcriptomic similarity through a tunable mixing parameter alpha. Evaluated on a cholangiocarcinoma dataset, SNMF and hSNMF achieve markedly improved spatial compactness (CHAOS < 0.004, Moran's I > 0.96), greater cluster separability (Silhouette > 0.12, DBI < 1.8), and higher biological coherence (CMC and enrichment) compared to other spatial baselines. Availability and implementation: https://github.com/ishtyaqmahmud/hSNMF
Authors:Xiaoce Wang, Guibin Zhang, Junzhe Li, Jinzhe Tu, Chun Li, Ming Li
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.
Authors:Xianglong Yan, ChengZhu Bao, Zhiteng Li, Tianao Zhang, Shaoqiu Zhang, Ruobing Xie, Samm Sun, Yulun Zhang
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.
Authors:Wenhao Sun, Rong-Cheng Tu, Yifu Ding, Zhao Jin, Jingyi Liao, Yongcheng Jing, Dacheng Tao
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.
Authors:Tianle Gu, Kexin Huang, Lingyu Li, Ruilin Luo, Shiyang Huang, Zongqi Wang, Yujiu Yang, Yan Teng, Yingchun Wang
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.
Authors:Yuyang Bai, Zhuofeng Li, Ping Nie, Jianwen Xie, Yu Zhang
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.
Authors:Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Christina Lioma
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.
Authors:Chen Hu, Qianxi Zhao, Xiaochen Yuan, Hong Zhang, Ding Yuan, Yanbin Wu, Xiying Li
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.
Authors:Dulhan Jayalath, Oiwi Parker Jones
Abstract:
Clinical brain‑to‑text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre‑training improves data‑efficient generalisation by learning statistical priors across subjects, but these priors critically depend on context. While natural speech might unfold gradually over minutes, most methods pre‑train with only a few seconds of context. Thus, we propose MEG‑XL, a model pre‑trained with 2.5 minutes of MEG context per sample, 5‑300x longer than prior work, and equivalent to 191k tokens, capturing extended neural context. Fine‑tuning on the task of word decoding from brain data, MEG‑XL matches supervised performance with a fraction of the data (e.g. 1hr vs 50hrs) and outperforms brain foundation models. We find that models pre‑trained with longer contexts learn representations that transfer better to word decoding. Our results indicate that long‑context pre‑training helps exploit extended neural context that other methods unnecessarily discard. Code, model weights, and instructions are available at https://github.com/neural‑processing‑lab/MEG‑XL .
Authors:Yinjie Wang, Tianbao Xie, Ke Shen, Mengdi Wang, Ling Yang
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
Authors:Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, Wenya Wang
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.
Authors:Yaolun Zhang, Yiran Wu, Yijiong Yu, Qingyun Wu, Huazheng Wang
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.
Authors:Wangduo Xie, Matthew B. Blaschko
Abstract:
Computed Tomography (CT) plays a vital role in inspecting the internal structures of industrial objects. Furthermore, achieving high‑quality CT reconstruction from sparse views is essential for reducing production costs. While classic implicit neural networks have shown promising results for sparse reconstruction, they are unable to leverage shape priors of objects. Motivated by the observation that numerous industrial objects exhibit rectangular structures, we propose a novel Neural Adaptive Binning (NAB) method that effectively integrates rectangular priors into the reconstruction process. Specifically, our approach first maps coordinate space into a binned vector space. This mapping relies on an innovative binning mechanism based on differences between shifted hyperbolic tangent functions, with our extension enabling rotations around the input‑plane normal vector. The resulting representations are then processed by a neural network to predict CT attenuation coefficients. This design enables end‑to‑end optimization of the encoding parameters ‑‑ including position, size, steepness, and rotation ‑‑ via gradient flow from the projection data, thus enhancing reconstruction accuracy. By adjusting the smoothness of the binning function, NAB can generalize to objects with more complex geometries. This research provides a new perspective on integrating shape priors into neural network‑based reconstruction. Extensive experiments demonstrate that NAB achieves superior performance on two industrial datasets. It also maintains robust on medical datasets when the binning function is extended to more general expression. The code is available at https://github.com/Wangduo‑Xie/NAB_CT_reconstruction.
Authors:Ziwen Xu, Chenyan Wu, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang
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.
Authors:Min Cai, Yu Liang, Longzheng Wang, Yan Wang, Yueyang Zhang, Long Xia, Zhiyuan Sun, Xi Ye, Daiting Shi
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.
Authors:Zheng Li, Jerry Cheng, Huanying Gu
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
Authors:Yuanhe Zhang, Jason D. Lee, Fanghui Liu
Abstract:
We present the first comprehensive Lean 4 formalization of statistical learning theory (SLT) grounded in empirical process theory. Our end‑to‑end formal infrastructure implement the missing contents in latest Lean 4 Mathlib library, including a complete development of Gaussian Lipschitz concentration, the first formalization of Dudley's entropy integral theorem for sub‑Gaussian processes, and an application to least‑squares (sparse) regression with a sharp rate. The project was carried out using a human‑AI collaborative workflow, in which humans design proof strategies and AI agents execute tactical proof construction, leading to the human‑verified Lean 4 toolbox for SLT. Beyond implementation, the formalization process exposes and resolves implicit assumptions and missing details in standard SLT textbooks, enforcing a granular, line‑by‑line understanding of the theory. This work establishes a reusable formal foundation and opens the door for future developments in machine learning theory. The code is available at https://github.com/YuanheZ/lean‑stat‑learning‑theory
Authors:Tong Yang, Yemin Wang, Chaoning Zhang, Aming Wu
Abstract:
The effectiveness of LLM‑based agents is often limited not by model capacity alone, but by how efficiently contextual information is utilized at runtime. Existing agent frameworks rely on rigid, syntax‑heavy state representations such as nested JSON, which require models to devote a substantial portion of their limited attention to syntactic processing rather than semantic reasoning. In this paper, we propose Fat‑Cat, a document‑driven agent architecture that improves the signal‑to‑noise ratio of state management. By integrating three key components: (1) a Semantic File System that represents agent state as Markdown documents aligned with common pre‑training corpora, (2) a Textual Strategy Evolution module that accumulates task‑solving knowledge without parameter updates, and (3) a Closed‑Loop Watcher that monitors reasoning trajectories to reduce hallucinations. Extensive reasoning, retrieval, and coding benchmarks, Fat‑Cat consistently improves agent performance. It enables the Kimi‑k2 model to outperform the proprietary GPT‑4o baseline on HotPotQA. Replacing the document‑based state with JSON leads to performance drop, while empirically validating the critical necessity of document‑driven state modeling over rigid syntax. The code is available at https://github.com/answeryt/Fat‑Cat.
Authors:Yu Zeng, Wenxuan Huang, Zhen Fang, Shuang Chen, Yufan Shen, Yishuo Cai, Xiaoman Wang, Zhenfei Yin, Lin Chen, Zehui Chen, Shiting Huang, Yiming Zhao, Xu Tang, Yao Hu, Philip Torr, Wanli Ouyang, Shaosheng Cao
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.
Authors:Yuli Zhou, Qingxuan Chen, Luca Benini, Guolei Sun, Yawei Li
Abstract:
Adaptive Rounding has emerged as an alternative to round‑to‑nearest (RTN) for post‑training quantization by enabling cross‑element error cancellation. Yet, dense and element‑wise rounding matrices are prohibitively expensive for billion‑parameter large language models (LLMs). We revisit adaptive rounding from an efficiency perspective and propose VQRound, a parameter‑efficient optimization framework that reparameterizes the rounding matrix into a compact codebook. Unlike low‑rank alternatives, VQRound minimizes the element‑wise worst‑case error under L_\infty norm, which is critical for handling heavy‑tailed weight distributions in LLMs. Beyond reparameterization, we identify rounding initialization as a decisive factor and develop a lightweight end‑to‑end finetuning pipeline that optimizes codebooks across all layers using only 128 samples. Extensive experiments on OPT, LLaMA, LLaMA2, and Qwen3 models demonstrate that VQRound achieves better convergence than traditional adaptive rounding at the same number of steps while using as little as 0.2% of the trainable parameters. Our results show that adaptive rounding can be made both scalable and fast‑fitting. The code is available at https://github.com/zhoustan/VQRound.
Authors:Pawel Batorski, Paul Swoboda
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.
Authors:Nima Shoghi, Yuxuan Liu, Yuning Shen, Rob Brekelmans, Pan Li, Quanquan Gu
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.
Authors:Zhongqian Fu, Tianyi Zhao, Kai Han, Hang Zhou, Xinghao Chen, Yunhe Wang
Abstract:
World models learn an internal representation of environment dynamics, enabling agents to simulate and reason about future states within a compact latent space for tasks such as planning, prediction, and inference. However, running world models rely on hevay computational cost and memory footprint, making model quantization essential for efficient deployment. To date, the effects of post‑training quantization (PTQ) on world models remain largely unexamined. In this work, we present a systematic empirical study of world model quantization using DINO‑WM as a representative case, evaluating diverse PTQ methods under both weight‑only and joint weight‑activation settings. We conduct extensive experiments on different visual planning tasks across a wide range of bit‑widths, quantization granularities, and planning horizons up to 50 iterations. Our results show that quantization effects in world models extend beyond standard accuracy and bit‑width trade‑offs: group‑wise weight quantization can stabilize low‑bit rollouts, activation quantization granularity yields inconsistent benefits, and quantization sensitivity is highly asymmetric between encoder and predictor modules. Moreover, aggressive low‑bit quantization significantly degrades the alignment between the planning objective and task success, leading to failures that cannot be remedied by additional optimization. These findings reveal distinct quantization‑induced failure modes in world model‑based planning and provide practical guidance for deploying quantized world models under strict computational constraints. The code will be available at https://github.com/huawei‑noah/noah‑research/tree/master/QuantWM.
Authors:Liyan Xu, Mo Yu, Fandong Meng, Jie Zhou
Abstract:
Chain‑of‑thought (CoT) reasoning has become a central mechanism for eliciting multi‑step reasoning in Large Language Models (LLMs). Yet recent evidence presents a tension: hidden states appear to already encode future reasoning before CoT fully unfolds, while explicit steps still remain crucial for tasks requiring compositional computation. To deepen the understanding between LLM's internal states and its verbalized reasoning trajectories, we investigate the latent planning strength of LLMs, through our probing method, Tele‑Lens, applying to hidden states across diverse task domains. Our empirical results indicate that LLMs exhibit a myopic horizon, primarily conducting incremental transitions without precise global planning. Leveraging this characteristic, we propose a hypothesis on enhancing uncertainty estimation of CoT, which we validate that a sparse set of pivot positions can effectively represent the uncertainty of the entire path. We further underscore the significance of exploiting CoT dynamics, and demonstrate that automatic recognition of CoT bypass can be achieved without performance degradation. Our code, data and models are released at https://github.com/lxucs/tele‑lens.
Authors:Keqin Peng, Yuanxin Ouyang, Xuebo Liu, Zhiliang Tian, Ruijian Han, Yancheng Yuan, Liang Ding
Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) can elicit strong multi‑step reasoning, yet it often encourages overly verbose traces. Moreover, naive length penalties in group‑relative optimization can severely hurt accuracy. We attribute this failure to two structural issues: (i) Dilution of Length Baseline, where incorrect responses (with zero length reward) depress the group baseline and over‑penalize correct solutions; and (ii) Difficulty‑Penalty Mismatch, where a static penalty cannot adapt to problem difficulty, suppressing necessary reasoning on hard instances while leaving redundancy on easy ones. We propose Dynamic Decoupled Conditional Advantage (DDCA) to decouple efficiency optimization from correctness. DDCA computes length advantages conditionally within the correct‑response cluster to eliminate baseline dilution, and dynamically scales the penalty strength using the group pass rate as a proxy for difficulty. Experiments on GSM8K, MATH500, AMC23, and AIME25 show that DDCA consistently improves the efficiency‑‑accuracy trade‑off relative to adaptive baselines, reducing generated tokens by approximately 60% on simpler tasks (e.g., GSM8K) versus over 20% on harder benchmarks (e.g., AIME25), thereby maintaining or improving accuracy. Code is available at https://github.com/alphadl/DDCA.
Authors:Sicheng Shen, Mingyang Lv, Han Shen, Jialin Wu, Binghao Wang, Zhou Yang, Guobin Shen, Dongcheng Zhao, Feifei Zhao, Yi Zeng
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.
Authors:Ionut-Vlad Modoranu, Philip Zmushko, Erik Schultheis, Mher Safaryan, Dan Alistarh
Abstract:
Shampoo is one of the leading approximate second‑order optimizers: a variant of it has won the MLCommons AlgoPerf competition, and it has been shown to produce models with lower activation outliers that are easier to compress. Yet, applying Shampoo currently comes at the cost of significant computational slowdown, due to its expensive internal operations. In this paper, we take a significant step to address this shortcoming by proposing \method (for Distributed Accelerated SHampoo), a faster implementation of Distributed Shampoo based on two main new techniques: First, we show that preconditioner blocks can be stacked into 3D tensors to significantly improve GPU utilization; second, we introduce the Newton‑DB iteration and the Chebyshev polynomial approximations as novel and faster approaches for computing the inverse matrix roots required by Shampoo. Along with these algorithmic contributions, we provide a first in‑depth analysis of how matrix scaling critically affects Shampoo convergence. On the practical side, our GPU‑aware implementation achieves up to 4.83× faster optimizer steps compared to the well‑optimized Distributed Shampoo, while Newton‑DB attains the lowest validation perplexity per iteration among all tested methods. Our code is available at https://github.com/IST‑DASLab/DASH.
Authors:Yoonjun Cho, Dongjae Jeon, Soeun Kim, Moongyu Jeon, Albert No
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.
Authors:Zhen-Hao Xie, Jun-Tao Tang, Yu-Cheng Shi, Han-Jia Ye, De-Chuan Zhan, Da-Wei Zhou
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.
Authors:Hongwei Yan, Guanglong Sun, Kanglei Zhou, Qian Li, Liyuan Wang, Yi Zhong
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.
Authors:Muli Yang, Gabriel James Goenawan, Henan Wang, Huaiyuan Qin, Chenghao Xu, Yanhua Yang, Fen Fang, Ying Sun, Joo-Hwee Lim, Hongyuan Zhu
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.
Authors:Zishuo Lan, Junjie Li, Lei Wang, Jincheng Wang
Abstract:
Autoregressive learning of time‑stepping operators provides an effective approach to data‑driven partial differential equation (PDE) simulation, yet for conservation laws, they face a fundamental challenge: learned updates may violate global conservation over long rollouts. For the important subclass of mass‑conservation‑type equations, the problem is compounded by inherent physical bounds (e.g., nonnegativity or concentrations in [0,1]) whose violation further destabilizes predictions. We introduce FluxNet, which learns cumulative transport amounts representing the total conserved quantity redistributed between each cell and a configurable neighborhood over the full surrogate interval. A conservative update guarantees exact discrete conservation by construction; modular capacity‑constrained transport heads (L, U, and D) enforce lower bounds, upper bounds, or near‑zero dual‑bound violations through architectural design. Unlike flux‑rate surrogates that require temporal integration and thus inherit CFL constraints, FluxNet involves no such integration; configurable transport neighborhoods enable large‑timestep prediction at full spatial resolution. Ghost cells extend the framework to non‑periodic boundaries. Experiments on four benchmarks (1D convection‑‑diffusion, 2D shallow water, 1D traffic flow, 2D Cahn‑‑Hilliard) demonstrate exact conservation, structural bound preservation, architecture modularity, and superior stability over flux‑rate surrogates at large temporal strides. The code is publicly available at: https://github.com/Lan‑zs/FluxNet.
Authors:Abdul Joseph Fofanah, Lian Wen, David Chen
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.
Authors:Abdul Joseph Fofanah, Lian Wen, David Chen
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.
Authors:Shaoheng Xu, Chunyi Sun, Jihui Zhang, Prasanga N. Samarasinghe, Thushara D. Abhayapala
Abstract:
Room impulse responses (RIRs) are essential for many acoustic signal processing tasks, yet measuring them densely across space is often impractical. In this work, we propose RIR‑Former, a grid‑free, one‑step feed‑forward model for RIR reconstruction. By introducing a sinusoidal encoding module into a transformer backbone, our method effectively incorporates microphone position information, enabling interpolation at arbitrary array locations. Furthermore, a segmented multi‑branch decoder is designed to separately handle early reflections and late reverberation, improving reconstruction across the entire RIR. Experiments on diverse simulated acoustic environments demonstrate that RIR‑Former consistently outperforms state‑of‑the‑art baselines in terms of normalized mean square error (NMSE) and cosine distance (CD), under varying missing rates and array configurations. These results highlight the potential of our approach for practical deployment and motivate future work on scaling from randomly spaced linear arrays to complex array geometries, dynamic acoustic scenes, and real‑world environments.
Authors:Ziwei Luo, Ziqi Jin, Lei Wang, Lidong Bing, Thomas B. Schön
Abstract:
This work presents self‑rewarding sequential Monte Carlo (SMC), an inference‑time scaling algorithm enabling effective sampling of masked diffusion language models (MDLMs). Our algorithm stems from the observation that most existing MDLMs rely on a confidence‑based sampling strategy, where only tokens with the highest prediction confidence are preserved at each step. This restricts the generation to a noise‑sensitive, greedy decoding paradigm, resulting in an inevitable collapse in the diversity of possible paths. We address this problem by launching multiple interacting diffusion processes in parallel, referred to as particles, for trajectory exploration. Importantly, we introduce the trajectory‑level confidence as a self‑rewarding signal for assigning particle importance weights. During sampling, particles are iteratively weighted and resampled to systematically steer generation towards globally confident, high‑quality samples. Our self‑rewarding SMC is verified on various masked diffusion language models and benchmarks, achieving significant improvement without extra training or reward guidance, while effectively converting parallel inference capacity into improved sampling quality. Our code is available at https://github.com/Algolzw/self‑rewarding‑smc.
Authors:Furkan Eris
Abstract:
Protein language models (PLMs) face a fundamental divide: masked language models (MLMs) excel at fitness prediction while causal models enable generation, forcing practitioners to maintain separate architectures. We introduce Proust, a 309M‑parameter causal PLM that bridges this gap through architectural innovations adapted from recent LLM research, including grouped‑query attention with shared K/V projections, cross‑layer value residuals, and depthwise causal convolutions. Trained on 33B tokens in 40 B200 GPU‑hours, Proust achieves Spearman ρ= 0.390 on ProteinGym substitutions, competitive with MLMs requiring 50‑‑200× the compute. On indels, Proust sets a new state‑of‑the‑art, outperforming models up to 20× larger. On EVEREST viral fitness benchmarks, it approaches structure‑aware methods using sequence alone. These powerful representations position Proust in a sweet spot as it also retains native generative capabilities that MLMs lack by design. Interpretability analysis reveals that per‑position entropy variance predicts, to an extent, when retrieval augmentation helps and hurts. Such insights can grow in both quantity and quality at scale and inform capabilities such as test‑time scaling. Code and weights are available at https://github.com/Furkan9015/proust‑inference
Authors:Jinbin Bai, Yixuan Li, Yuchen Zhu, Yi Xin, Qingyu Shi, Aosong Feng, Xiaohong Liu, Molei Tao, Jianru Xue, Xiangtai Li, Ming-Hsuan Yang
Abstract:
Inference‑time compute has re‑emerged as a practical way to improve LLM reasoning. Most test‑time scaling (TTS) algorithms rely on autoregressive decoding, which is ill‑suited to discrete diffusion language models (dLLMs) due to their parallel decoding over the entire sequence. As a result, developing effective and efficient TTS methods to unlock dLLMs' full generative potential remains an underexplored challenge. To address this, we propose Prism (Pruning, Remasking, and Integrated Self‑verification Method), an efficient TTS framework for dLLMs that (i) performs Hierarchical Trajectory Search (HTS) which dynamically prunes and reallocates compute in an early‑to‑mid denoising window, (ii) introduces Local branching with partial remasking to explore diverse implementations while preserving high‑confidence tokens, and (iii) replaces external verifiers with Self‑Verified Feedback (SVF) obtained via self‑evaluation prompts on intermediate completions. Across four mathematical reasoning and code generation benchmarks on three dLLMs, including LLaDA 8B Instruct, Dream 7B Instruct, and LLaDA 2.0‑mini, our Prism achieves a favorable performance‑efficiency trade‑off, matching best‑of‑N performance with substantially fewer function evaluations (NFE). The code is released at https://github.com/viiika/Prism.
Authors:Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, Jingbo Zhu, Wenbo Su, Bo Zheng
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
Authors:Pengyu Li, Lingling Zhang, Zhitao Gao, Yanrui Wu, Yuxuan Dong, Huan Liu, Bifan Wei, Jun Liu
Abstract:
While Large Language Models (LLMs) have achieved remarkable capabilities, they unintentionally memorize sensitive data, posing critical privacy and security risks. Machine unlearning is pivotal for mitigating these risks, yet existing paradigms face a fundamental dilemma: aggressive unlearning often induces catastrophic forgetting that degrades model utility, whereas conservative strategies risk superficial forgetting, leaving models vulnerable to adversarial recovery. To address this trade‑off, we propose AGT^AO (Adversarial Gating Training with Adaptive Orthogonality), a unified framework designed to reconcile robust erasure with utility preservation. Specifically, our approach introduces Adaptive Orthogonality (AO) to dynamically mitigate geometric gradient conflicts between forgetting and retention objectives, thereby minimizing unintended knowledge degradation. Concurrently, Adversarial Gating Training (AGT) formulates unlearning as a latent‑space min‑max game, employing a curriculum‑based gating mechanism to simulate and counter internal recovery attempts. Extensive experiments demonstrate that AGT^AO achieves a superior trade‑off between unlearning efficacy (KUR \approx 0.01) and model utility (MMLU 58.30). Code is available at https://github.com/TiezMind/AGT‑unlearning.
Authors:Wenhui Tan, Fiorenzo Parascandolo, Enver Sangineto, Jianzhong Ju, Zhenbo Luo, Qian Cao, Rita Cucchiara, Ruihua Song, Jian Luan
Abstract:
Large Reasoning Models (LRMs) have recently achieved strong mathematical and code reasoning performance through Reinforcement Learning (RL) post‑training. However, we show that modern reasoning post‑training induces an unintended exploration collapse: temperature‑based sampling no longer increases pass@n accuracy. Empirically, the final‑layer posterior of post‑trained LRMs exhibit sharply reduced entropy, while the entropy of intermediate layers remains relatively high. Motivated by this entropy asymmetry, we propose Latent Exploration Decoding (LED), a depth‑conditioned decoding strategy. LED aggregates intermediate posteriors via cumulative sum and selects depth configurations with maximal entropy as exploration candidates. Without additional training or parameters, LED consistently improves pass@1 and pass@16 accuracy by 0.61 and 1.03 percentage points across multiple reasoning benchmarks and models. Furthermore, integrating LED into reinforcement learning, e.g., using GRPO as the rollout strategy, yields faster reward improvement and higher final performance, due to the efficient exploration capability of LED. Project page: https://github.com/AlbertTan404/LED.
Authors:Byeonghu Na, Hyungho Na, Yeongmin Kim, Suhyeon Jo, HeeSun Bae, Mina Kang, Il-Chul Moon
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.
Authors:Qianyang Li, Xingjun Zhang, Shaoxun Wang, Jia Wei, Yueqi Xing
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
Authors:Hayeong Lee, JunHyeok Oh, Byung-Jun Lee
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.
Authors:Zitao Guo, Changyang Jiang, Tianhong Zhao, Jinzhou Cao, Genan Dai, Bowen Zhang
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.
Authors:Zeqiao Li, Yijing Wang, Haoyu Wang, Zheng Li, Zhiqiang Zuo
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.
Authors:Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le
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.
Authors:Xu Zhang, Qitong Wang, Peng Wang, Wei Wang
Abstract:
Recent work shows that linear models can outperform several transformer models in long‑term time‑series forecasting (TSF). However, instead of explicitly performing temporal interaction through self‑attention, linear models implicitly perform it based on stacked MLP structures, which may be insufficient in capturing the complex temporal dependencies and their performance still has potential for improvement. To this end, we propose a Lightweight Sparse Interaction Network (LSINet) for TSF task. Inspired by the sparsity of self‑attention, we propose a Multihead Sparse Interaction Mechanism (MSIM). Different from self‑attention, MSIM learns the important connections between time steps through sparsity‑induced Bernoulli distribution to capture temporal dependencies for TSF. The sparsity is ensured by the proposed self‑adaptive regularization loss. Moreover, we observe the shareability of temporal interactions and propose to perform Shared Interaction Learning (SIL) for MSIM to further enhance efficiency and improve convergence. LSINet is a linear model comprising only MLP structures with low overhead and equipped with explicit temporal interaction mechanisms. Extensive experiments on public datasets show that LSINet achieves both higher accuracy and better efficiency than advanced linear models and transformer models in TSF tasks. The code is available at the link https://github.com/Meteor‑Stars/LSINet.
Authors:Xiaoyu Wen, Zhida He, Han Qi, Ziyu Wan, Zhongtian Ma, Ying Wen, Tianhang Zheng, Xingcheng Xu, Chaochao Lu, Qiaosheng Zhang
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.
Authors:Shiju Zhao, Junhao Hu, Jiaqi Zheng, Guihai Chen
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.
Authors:Muheng Li, Jian Qian, Wenlong Mou
Abstract:
Test‑time scaling has emerged as a critical avenue for enhancing the reasoning capabilities of Large Language Models (LLMs). Though the straight‑forward ''best‑of‑N'' (BoN) strategy has already demonstrated significant improvements in performance, it lacks principled guidance on the choice of N, budget allocation, and multi‑stage decision‑making, thereby leaving substantial room for optimization. While many works have explored such optimization, rigorous theoretical guarantees remain limited. In this work, we propose new methodologies to predict and improve scaling properties via tail‑guided search. By estimating the tail distribution of rewards, our method predicts the scaling law of LLMs without the need for exhaustive evaluations. Leveraging this prediction tool, we introduce Scaling‑Law Guided (SLG) Search, a new test‑time algorithm that dynamically allocates compute to identify and exploit intermediate states with the highest predicted potential. We theoretically prove that SLG achieves vanishing regret compared to perfect‑information oracles, and achieves expected rewards that would otherwise require a polynomially larger compute budget required when using BoN. Empirically, we validate our framework across different LLMs and reward models, confirming that tail‑guided allocation consistently achieves higher reward yields than Best‑of‑N under identical compute budgets. Our code is available at https://github.com/PotatoJnny/Scaling‑Law‑Guided‑search.
Authors:Christoffer Koo Øhrstrøm, Rafael I. Cabral Muchacho, Yifei Dong, Filippos Moumtzidellis, Ronja Güldenring, Florian T. Pokorny, Lazaros Nalpantidis
Abstract:
We propose Parabolic Position Encoding (PaPE), a parabola‑based position encoding for vision modalities in attention‑based architectures. Given a set of vision tokens‑such as from videos, event camera streams, images, or point clouds‑our objective is to encode their positions while accounting for the characteristics of vision modalities. Prior works have largely extended position encodings from 1D‑sequences in language to nD‑structures in vision, but only with partial account of vision characteristics. We address this gap by designing PaPE from principles distilled from prior work: translation invariance, rotation invariance (PaPE‑RI), distance decay, directionality, and context awareness. Extrapolation experiments on ImageNet‑1K show how PaPE extrapolates remarkably well, improving in absolute terms by up to 10.5% over the next‑best encoding. Generality experiments on 8 datasets across 4 modalities show that PaPE is a general vision position encoding, as PaPE matches the best baseline on 5 datasets and exceeds all on 2 datasets. Code is available at https://github.com/DTU‑PAS/parabolic‑position‑encoding.
Authors:Yochai Yemini, Yoav Ellinson, Rami Ben-Ari, Sharon Gannot, Ethan Fetaya
Abstract:
This paper addresses the challenge of audio‑visual single‑microphone speech separation and enhancement in the presence of real‑world environmental noise. Our approach is based on generative inverse sampling, where we model clean speech and ambient noise with dedicated diffusion priors and jointly leverage them to recover all underlying sources. To achieve this, we reformulate a recent inverse sampler to match our setting. We evaluate on mixtures of 1, 2, and 3 speakers with noise and show that, despite being entirely unsupervised, our method consistently outperforms leading supervised baselines in \acWER across all conditions. We further extend our framework to handle off‑screen speaker separation. Moreover, the high fidelity of the separated noise component makes it suitable for downstream acoustic scene detection. Demo page: https://ssnapsicml.github.io/ssnapsicml2026/
Authors:Fu-Yun Wang, Han Zhang, Michael Gharbi, Hongsheng Li, Taesung Park
Abstract:
Flow matching models (FMs) have revolutionized text‑to‑image (T2I) generation, with reinforcement learning (RL) serving as a critical post‑training strategy for alignment with reward objectives. In this research, we show that current RL pipelines for FMs suffer from two underappreciated yet important limitations: sample inefficiency due to insufficient generation diversity, and pronounced prompt overfitting, where models memorize specific training formulations and exhibit dramatic performance collapse when evaluated on semantically equivalent but stylistically varied prompts. We present PromptRL (Prompt Matters in RL for Flow‑Based Image Generation), a framework that incorporates language models (LMs) as trainable prompt refinement agents directly within the flow‑based RL optimization loop. This design yields two complementary benefits: rapid development of sophisticated prompt rewriting capabilities and, critically, a synergistic training regime that reshapes the optimization dynamics. PromptRL achieves state‑of‑the‑art performance across multiple benchmarks, obtaining scores of 0.97 on GenEval, 0.98 on OCR accuracy, and 24.05 on PickScore.
Furthermore, we validate the effectiveness of our RL approach on large‑scale image editing models, improving the EditReward of FLUX.1‑Kontext from 1.19 to 1.43 with only 0.06 million rollouts, surpassing Gemini 2.5 Flash Image (also known as Nano Banana), which scores 1.37, and achieving comparable performance with ReasonNet (1.44), which relied on fine‑grained data annotations along with a complex multi‑stage training. Our extensive experiments empirically demonstrate that PromptRL consistently achieves higher performance ceilings while requiring over 2× fewer rollouts compared to naive flow‑only RL. Our code is available at https://github.com/G‑U‑N/UniRL.
Authors:Panagiotis Koromilas, Andreas D. Demou, James Oldfield, Yannis Panagakis, Mihalis Nicolaou
Abstract:
Sparse autoencoders (SAEs) interpret neural network representations by decomposing activations into sparse combinations of dictionary atoms. However, SAEs assume features combine additively through linear reconstruction, an assumption that cannot capture compositional structure: linear models cannot distinguish whether ''Starbucks'' arises from the composition of ''star'' and ''coffee'' features or merely their co‑occurrence. This forces SAEs to allocate monolithic features for compound concepts rather than decomposing them into interpretable constituents. We introduce PolySAE, which extends the SAE decoder with higher‑order terms to model feature interactions while preserving the linear encoder essential for interpretability. Through low‑rank tensor factorization on a shared projection subspace, PolySAE captures pairwise and triple feature interactions with small parameter overhead (3% on GPT2). Across four language models and three SAE variants, PolySAE achieves an average improvement of ～8% in probing F1 while maintaining comparable reconstruction error, and produces 2‑‑10× larger Wasserstein distances between class‑conditional feature distributions. Critically, learned interaction weights exhibit negligible correlation with co‑occurrence frequency (r = 0.06 vs r = 0.82 for SAE feature covariance), suggesting that polynomial terms capture compositional structure largely independent of surface statistics. Finally, the learned interaction directions causally steer model outputs toward the corresponding compositional semantics.
Authors:Kangjun Noh, Seongchan Lee, Ilmun Kim, Kyungwoo Song
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
Authors:Qishuai Wen, Zhiyuan Huang, Xianghan Meng, Wei He, Chun-Guang Li
Abstract:
The vanilla self‑attention mechanism in Transformers can be viewed as a two‑layer fast‑weight MLP, whose weights are dynamically induced by inputs and whose hidden dimension is equal to the sequence length N. As the context extends, the expressive capacity of such an N‑width MLP increases, but it becomes unscalable for extremely long sequences. Recently, this fast‑weight perspective has motivated the Mixture‑of‑Experts (MoE) attention mechanism, which partitions the sequence into rigid blocks, treats them as fast‑weight experts, and sparsely routes the tokens to them. In this paper, we elevate this perspective to a unifying framework for efficient attention mechanisms, interpreting them as making fast weights scalable through either routing or compression, and organizing them into a five‑dimensional taxonomy. Then, we propose Mixture‑of‑Top‑k Attention (MiTA), which employs a small set of landmark queries to gather top‑k attended key‑value pairs as query‑aware and deformable routed experts, while compressing the N‑width MLP into a narrower shared expert. Consequently, our MiTA improves the flexibility of prior MoE attention from rigid to deformable fast‑weight experts, as well as the scalability of prior top‑k attention from query‑specific set to reusable top‑k set. We conduct extensive experiments on vision tasks showing the superior effectiveness and efficiency of our MiTA, and also uncovering intriguing properties such as an emergent token‑pruning effect and easy generalization from standard attention. Code is available at https://github.com/QishuaiWen/MiTA.
Authors:Marco Chen, Xianbiao Qi, Yelin He, Jiaquan Ye, Rong Xiao
Abstract:
In this work, we revisit Transformer optimization through the lens of second‑order geometry and establish a direct connection between architectural design, activation scale, the Hessian matrix, and the maximum tolerable learning rate. We introduce a simple normalization strategy, termed SimpleNorm, which stabilizes intermediate activation scales by construction. Then, by analyzing the Hessian of the loss with respect to network activations, we theoretically show that SimpleNorm significantly reduces the spectral norm of the Hessian, thereby permitting larger stable learning rates. We validate our theoretical findings through extensive experiments on large GPT models at parameter scales 1B, 1.4B, 7B and 8B. Empirically, SimpleGPT, our SimpleNorm‑based network, tolerates learning rates 3×‑10× larger than standard convention, consistently demonstrates strong optimization stability, and achieves substantially better performance than well‑established baselines. Specifically, when training 7B‑scale models for 60K steps, SimpleGPT achieves a training loss that is 0.08 lower than that of LLaMA2 with QKNorm, reducing the loss from 2.290 to 2.208. Our source code will be released at https://github.com/Ocram7/SimpleGPT.
Authors:Mengsha Kou, Xiaoyu Xia, Ziqi Wang, Ibrahim Khalil, Runkun Luo, Jingwen Zhou, Minhui Xue
Abstract:
Large Language Models (LLMs) increasingly underpin intelligent web applications, from chatbots to search and recommendation, where efficient specialization is essential. Low‑Rank Adaptation (LoRA) enables such adaptation with minimal overhead, while federated LoRA allows web service providers to fine‑tune shared models without data sharing. However, in privacy‑sensitive deployments, clients inject varying levels of differential privacy (DP) noise, creating privacy heterogeneity that misaligns individual incentives and global performance. In this paper, we propose WinFLoRA, a privacy‑heterogeneous federated LoRA that utilizes aggregation weights as incentives with noise awareness. Specifically, the noises from clients are estimated based on the uploaded LoRA adapters. A larger weight indicates greater influence on the global model and better downstream task performance, rewarding lower‑noise contributions. By up‑weighting low‑noise updates, WinFLoRA improves global accuracy while accommodating clients' heterogeneous privacy requirements. Consequently, WinFLoRA aligns heterogeneous client utility in terms of privacy and downstream performance with global model objectives without third‑party involvement. Extensive evaluations demonstrate that across multiple LLMs and datasets, WinFLoRA achieves up to 52.58% higher global accuracy and up to 2.56x client utility than state‑of‑the‑art benchmarks. Source code is publicly available at https://github.com/koums24/WinFLoRA.git.
Authors:Kaiyuan Cui, Yige Li, Yutao Wu, Xingjun Ma, Sarah Erfani, Christopher Leckie, Hanxun Huang
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.
Authors:Zhishen Sun, Sizhe Dang, Guang Dai, Haishan Ye
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
Authors:Yuheng Yang, Siqi Zhu, Tao Feng, Ge Liu, Jiaxuan You
Abstract:
Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far their knowledge boundary extends. Existing benchmarks are mostly static and provide limited support for systematic knowledge probing. In this paper, we propose an interactive agentic framework to systematically extract and quantify the knowledge of LLMs. Our method includes four adaptive exploration policies to probe knowledge at different granularity. To ensure the quality of extracted knowledge, we introduce a three‑stage knowledge processing pipeline that combines vector‑based filtering to remove strict duplicates, LLM‑based adjudication to resolve ambiguous semantic overlap, and domain relevance auditing to retain valid knowledge units. Through extensive experiments, we find that Recursive Taxonomy is the most effective exploration strategy. We also observe a clear knowledge scaling law, where larger models consistently recover more knowledge. In addition, we identify a Pass@1 versus Pass@k trade‑off: domain‑specialized models achieve higher initial accuracy but experience rapid degradation, while general‑purpose models maintain stable performance over extended extraction. Finally, our results show that differences in training data composition lead to distinct and measurable knowledge profiles across model families, reflecting how pretraining shapes each model's parametric knowledge.
Authors:I-Chun Arthur Liu, Krzysztof Choromanski, Sandy Huang, Connor Schenck
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/.
Authors:Víctor Yeste, Paolo Rosso
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.
Authors:Gaurav Srivastava, Aafiya Hussain, Chi Wang, Yingyan Celine Lin, Xuan Wang
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.
Authors:Yuhao Huang, Taos Transue, Shih-Hsin Wang, William Feldman, Hong Zhang, Bao Wang
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.
Authors:Wei Chen, Jiacheng Li, Shigui Li, Zhiqi Lin, Junmei Yang, John Paisley, Delu Zeng
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.
Authors:Hao Gu, Mao-Lin Luo, Zi-Hao Zhou, Han-Chen Zhang, Min-Ling Zhang, Tong Wei
Abstract:
Parameter‑efficient continual learning aims to adapt pre‑trained models to sequential tasks without forgetting previously acquired knowledge. Most existing approaches treat continual learning as avoiding interference with past updates, rather than considering what properties make the current task‑specific update naturally preserve previously acquired knowledge. From a knowledge‑decomposition perspective, we observe that low‑rank adaptations exhibit highly imbalanced singular value spectra: a few dominant components absorb most of the adaptation energy, thereby (i) more likely to disrupt previously acquired knowledge and (ii) making the update more vulnerable to interference from subsequent tasks. To enable explicit balance among components, we decouple the magnitude of the task update from its directional structure and formulate it as a constrained optimization problem on a restricted Stiefel manifold. We address this problem using a projected first‑order method compatible with standard deep‑learning optimizers used in vision‑language models. Our method mitigates both backward and forward forgetting, consistently outperforming continual learning baselines. The implementation code is available at https://github.com/haodotgu/EBLoRA.
Authors:Kezhao Lai, Yutao Lai, Hai-Lin Liu
Abstract:
While Monte Carlo Tree Search (MCTS) shows promise in Large Language Model (LLM) based Automatic Heuristic Design (AHD), it suffers from a critical over‑exploitation tendency under the limited computational budgets required for heuristic evaluation. To address this limitation, we propose Clade‑AHD, an efficient framework that replaces node‑level point estimates with clade‑level Bayesian beliefs. By aggregating descendant evaluations into Beta distributions and performing Thompson Sampling over these beliefs, Clade‑AHD explicitly models uncertainty to guide exploration, enabling more reliable decision‑making under sparse and noisy evaluations. Extensive experiments on complex combinatorial optimization problems demonstrate that Clade‑AHD consistently outperforms state‑of‑the‑art methods while significantly reducing computational cost. The source code is publicly available at: https://github.com/Mriya0306/Clade‑AHD.
Authors:Xinmo Jin, Bowen Fan, Xunkai Li, Henan Sun, YuXin Zeng, Zekai Chen, Yuxuan Sun, Jia Li, Qiangqiang Dai, Hongchao Qin, Rong-Hua Li, Guoren Wang
Abstract:
Drug‑Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety. As experimental discovery is resource‑intensive and time‑consuming, efficient computational methodologies have become essential. The predominant paradigm formulates DDI prediction as a drug graph‑based link prediction task. However, further progress is hindered by two fundamental challenges: (1) lack of high‑quality data: most studies rely on small‑scale DDI datasets and single‑modal drug representations; (2) lack of standardized evaluation: inconsistent scenarios, varied metrics, and diverse baselines. To address the above issues, we propose OpenDDI, a comprehensive benchmark for DDI prediction. Specifically, (1) from the data perspective, OpenDDI unifies 6 widely used DDI datasets and 2 existing forms of drug representation, while additionally contributing 3 new large‑scale LLM‑augmented datasets and a new multimodal drug representation covering 5 modalities. (2) From the evaluation perspective, OpenDDI unifies 20 SOTA model baselines across 3 downstream tasks, with standardized protocols for data quality, effectiveness, generalization, robustness, and efficiency. Based on OpenDDI, we conduct a comprehensive evaluation and derive 10 valuable insights for DDI prediction while exposing current limitations to provide critical guidance for this rapidly evolving field. Our code is available at https://github.com/xiaoriwuguang/OpenDDI
Authors:Apurba Prasad Padhy, Fernando Camacho, Saibal Mukhopadhyay
Abstract:
State space models (SSMs) often sacrifice capacity, search space, or stability to offset the memory and compute costs of large state dimensions. We introduce a structured post‑training pruning method for SSMs ‑‑ AIRE‑Prune (Asymptotic Impulse‑Response Energy for State PRUN(E)) ‑‑ that reduces each layer's state dimension by directly minimizing long‑run output‑energy distortion. AIRE‑Prune assigns every state a closed‑form asymptotic impulse‑response energy‑based score, i.e., the total impulse‑response energy it contributes over an infinite horizon (time), and normalizes these scores layer‑wise to enable global cross‑layer comparison and selection. This extends modal truncation from single systems to deep stacks and aligns pruning with asymptotic response energy rather than worst‑case gain. Across diverse sequence benchmarks, AIRE‑Prune reveals substantial redundancy in SISO and MIMO SSMs with average pruning of 60.8%, with average accuracy drop of 0.29% without retraining, while significantly lowering compute. Code: https://github.com/falcon‑arrow/AIRE‑Prune.
Authors:Hengchang Liu, Zhao Yang, Bing Su
Abstract:
Diffusion language models (DLMs) provide a bidirectional generation framework naturally suited for infilling, yet their performance is constrained by the pre‑specified infilling length. In this paper, we reveal that DLMs possess an inherent ability to discover the correct infilling length. We identify two key statistical phenomena in the first‑step denoising confidence: a local Oracle Peak that emerges near the ground‑truth length and a systematic Length Bias that often obscures this signal. By leveraging this signal and calibrating the bias, our training‑free method CAL (Calibrated Adaptive Length) enables DLMs to approximate the optimal length through an efficient search before formal decoding. Empirical evaluations demonstrate that CAL improves Pass@1 by up to 47.7% over fixed‑length baselines and 40.5% over chat‑based adaptive methods in code infilling, while boosting BLEU‑2 and ROUGE‑L by up to 8.5% and 9.9% in text infilling. These results demonstrate that CAL paves the way for robust DLM infilling without requiring any specialized training. Code is available at https://github.com/NiuHechang/Calibrated_Adaptive_Length.
Authors:Suprim Nakarmi, Junggab Son, Yue Zhao, Zuobin Xiong
Abstract:
Federated Graph Neural Networks (FedGNNs) facilitate collaborative learning across multiple clients with graph‑structured data while preserving user privacy. However, emerging research indicates that within this setting, shared model updates, particularly gradients, can unintentionally leak sensitive information of local users. Numerous privacy inference attacks have been explored in traditional federated learning and extended to graph settings, but the problem of label distribution inference in FedGNNs remains largely underexplored. In this work, we introduce Fed‑Listing (Federated Label Distribution Inference in GNNs), a novel gradient‑based attack designed to infer the private label statistics of target clients in FedGNNs without access to raw data or node features. Fed‑Listing only leverages the final‑layer gradients exchanged during training to uncover statistical patterns that reveal class proportions in a stealthy manner. Extensive experiments on four benchmark datasets and three GNN architectures show that Fed‑Listing significantly outperforms existing baselines, including random guessing and Decaf, even under challenging non‑i.i.d. scenarios. Moreover, existing defense mechanisms can barely reduce the attack performance of Fed‑Listing, unless the model's utility is severely degraded. The code implementation and Supplementary materials are available here: https://github.com/suprimnakarmi/Fed‑Listing.
Authors:Gabriel Bromonschenkel, Alessandro L. Koerich, Thiago M. Paixão, Hilário Tomaz Alves de Oliveira
Abstract:
Image captioning (IC) refers to the automatic generation of natural language descriptions for images, with applications ranging from social media content generation to assisting individuals with visual impairments. While most research has been focused on English‑based models, low‑resource languages such as Brazilian Portuguese face significant challenges due to the lack of specialized datasets and models. Several studies create datasets by automatically translating existing ones to mitigate resource scarcity. This work addresses this gap by proposing a cross‑native‑translated evaluation of Transformer‑based vision and language models for Brazilian Portuguese IC. We use a version of Flickr30K comprised of captions manually created by native Brazilian Portuguese speakers and compare it to a version with captions automatically translated from English to Portuguese. The experiments include a cross‑context approach, where models trained on one dataset are tested on the other to assess the translation impact. Additionally, we incorporate attention maps for model inference interpretation and use the CLIP‑Score metric to evaluate the image‑description alignment. Our findings show that Swin‑DistilBERTimbau consistently outperforms other models, demonstrating strong generalization across datasets. ViTucano, a Brazilian Portuguese pre‑trained VLM, surpasses larger multilingual models (GPT‑4o, LLaMa 3.2 Vision) in traditional text‑based evaluation metrics, while GPT‑4 models achieve the highest CLIP‑Score, highlighting improved image‑text alignment. Attention analysis reveals systematic biases, including gender misclassification, object enumeration errors, and spatial inconsistencies. The datasets and the models generated and analyzed during the current study are available in: https://github.com/laicsiifes/transformer‑caption‑ptbr.
Authors:Jie Yang, Yifan Hu, Yuante Li, Kexin Zhang, Kaize Ding, Philip S. Yu
Abstract:
Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Latent Chaos: models with accurate predictions often learn latent representations that are temporally disordered and lack continuity. We attribute this to the dominant observation‑space forecasting paradigm, where minimizing point‑wise errors on noisy and partially observed data encourages shortcut solutions instead of the recovery of underlying system dynamics. To address this, we propose Latent Time Series Forecasting (LatentTSF), a paradigm that shifts TSF from observation regression to latent state prediction. LatentTSF employs an AutoEncoder to project each observation into a learned latent state space and performs forecasting entirely in this space, allowing the model to focus on learning structured temporal dynamics. We provide an information‑theoretic analysis showing that the latent objectives can be motivated as surrogates for maximizing mutual information between predicted and ground‑truth latent states and future observations. Extensive experiments on widely‑used benchmarks confirm that LatentTSF effectively mitigates latent chaos, yielding consistent improvements in both forecasting accuracy and representation quality. Our code is available at https://github.com/Muyiiiii/LatentTSF.
Authors:Franz A. Heinsen, Leo Kozachkov
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.
Authors:Keisuke Kamahori, Wei-Tzu Lee, Atindra Jha, Rohan Kadekodi, Stephanie Wang, Arvind Krishnamurthy, Baris Kasikci
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.
Authors:Tianyi Hu, Niket Tandon, Akhil Arora
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
Authors:Beier Zhu, Kesen Zhao, Jiequan Cui, Qianru Sun, Yuan Zhou, Xun Yang, Hanwang Zhang
Abstract:
Deep neural networks often exhibit substantial disparities in class‑wise accuracy, even when trained on class‑balanced data, posing concerns for reliable deployment. While prior efforts have explored empirical remedies, a theoretical understanding of such performance disparities in classification remains limited. In this work, we present Margin Regularization for Performance Disparity Reduction (MR^2), a theoretically principled regularization for classification by dynamically adjusting margins in both the logit and representation spaces. Our analysis establishes a margin‑based, class‑sensitive generalization bound that reveals how per‑class feature variability contributes to error, motivating the use of larger margins for hard classes. Guided by this insight, MR^2 optimizes per‑class logit margins proportional to feature spread and penalizes excessive representation margins to enhance intra‑class compactness. Experiments on seven datasets, including ImageNet, and diverse pre‑trained backbones (MAE, MoCov2, CLIP) demonstrate that MR^2 not only improves overall accuracy but also significantly boosts hard class performance without trading off easy classes, thus reducing performance disparity. Code is available at: https://github.com/BeierZhu/MR2
Authors:Yadang Alexis Rouzoumka, Jean Pinsolle, Eugénie Terreaux, Christèle Morisseau, Jean-Philippe Ovarlez, Chengfang Ren
Abstract:
Diffusion models learn a time‑indexed score field \mathbfs_θ(\mathbfx_t,t) that often inherits approximate equivariances (flips, rotations, circular shifts) from in‑distribution (ID) data and convolutional backbones. Most diffusion‑based out‑of‑distribution (OOD) detectors exploit score magnitude or local geometry (energies, curvature, covariance spectra) and largely ignore equivariances. We introduce Group‑Equivariant Posterior Consistency (GEPC), a training‑free probe that measures how consistently the learned score transforms under a finite group \mathcalG, detecting equivariance breaking even when score magnitude remains unchanged. At the population level, we propose the ideal GEPC residual, which averages an equivariance‑residual functional over \mathcalG, and we derive ID upper bounds and OOD lower bounds under mild assumptions. GEPC requires only score evaluations and produces interpretable equivariance‑breaking maps. On OOD image benchmark datasets, we show that GEPC achieves competitive or improved AUROC compared to recent diffusion‑based baselines while remaining computationally lightweight. On high‑resolution synthetic aperture radar imagery where OOD corresponds to targets or anomalies in clutter, GEPC yields strong target‑background separation and visually interpretable equivariance‑breaking maps. Code is available at https://github.com/RouzAY/gepc‑diffusion/.
Authors:Soumyadip Sarkar
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
Authors:Han Xiao
Abstract:
We present an ε‑bounded compression method for unit‑norm embeddings that achieves 1.5× compression, 25% better than the best prior lossless method. The method exploits that spherical coordinates of high‑dimensional unit vectors concentrate around π/2, causing IEEE 754 exponents to collapse to a single value and high‑order mantissa bits to become predictable, enabling entropy coding of both. Reconstruction error is bounded by float32 machine epsilon (1.19 × 10^‑7), making reconstructed values indistinguishable from originals at float32 precision. Evaluation across 26 configurations spanning text, image, and multi‑vector embeddings confirms consistent compression improvement with zero measurable retrieval degradation on BEIR benchmarks.
Authors:Ming-Yao Ho, Cheng-Kai Wang, You-Teng Lin, Hung-Hsuan Chen
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/
Authors:Michael Siebenmann, Javier Argota Sánchez-Vaquerizo, Stefan Arisona, Krystian Samp, Luis Gisler, Dirk Helbing
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.
Authors:Yue Yu, Ting Bai, HengZhi Lan, Li Qian, Li Peng, Jie Wu, Wei Liu, Jian Luan, Chuan Shi
Abstract:
The attribution technique enhances the credibility of LLMs by adding citations to the generated sentences, enabling users to trace back to the original sources and verify the reliability of the output. However, existing instruction‑tuned attributed LLMs often fail to properly interpret the contextual semantics of citation symbols (e.g., [i]) during text generation. This shortcoming arises from their insufficient awareness of the context information surrounding citation markers, which in turn leads to disjointed references and poor integration of retrieved knowledge into the generated content. To address this issue, we propose a novel Contextual‑aware Citation generation framework (C^2‑Cite) that explicitly integrates the semantic relationships between citation markers and their referenced content. Specifically, a contextual citation alignment mechanism is adopted: it first encodes the retrieved document contexts into the symbol representation of citations, then aligns the marker numbers by decoding information from a citation router function. This mechanism enables the transformation of citation markers from generic placeholders into active knowledge pointers that link to the referenced source information. Experimental results on the ALCE benchmark across three datasets validate our framework C^2‑Cite++: it outperforms the SOTA baseline by an average of 5.8% in citation quality and 17.4% in response correctness. The implementation is publicly available at https://github.com/BAI‑LAB/c2cite
Authors:Yu Zheng, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Depeng Jin, Meng Wang, Yong Li
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/.
Authors:Kaihua Liang, Xin Tan, An Zhong, Hong Xu, Marco Canini
Abstract:
Diffusion Large Language Models (DLLMs) offer a compelling alternative to Auto‑Regressive models, but their deployment is constrained by high decoding cost. In this work, we identify a key inefficiency in DLLM decoding: while computation is parallelized over token blocks, only a small subset of tokens is decodable at each diffusion step, causing most compute to be wasted on non‑decodable tokens. We further observe a strong correlation between attention‑derived token importance and token‑wise decoding probability. Based on this insight, we propose FOCUS ‑‑ an inference system designed for DLLMs. By dynamically focusing computation on decodable tokens and evicting non‑decodable ones on‑the‑fly, FOCUS increases the effective batch size, alleviating compute limitations and enabling scalable throughput. Empirical evaluations demonstrate that FOCUS achieves up to 3.52× throughput improvement over the production‑grade engine LMDeploy, while preserving or improving generation quality across multiple benchmarks. The FOCUS system is publicly available on GitHub: https://github.com/sands‑lab/FOCUS.
Authors:Luca Della Libera, Cem Subakan, Mirco Ravanelli
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.
Authors:Powei Chang, Jinpeng Zhang, Bowen Chen, Chenyu Wang, Chenlu Guo, Yixing Zhang, Yukang Gao, JianXiang Xiang, Yue Gao, Chaoqun Sun, Yiyi Chen, Dongying Kong
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.
Authors:Eugenia Iofinova, Dan Alistarh
Abstract:
As artificial neural networks, and specifically large language models, have improved rapidly in capabilities and quality, they have increasingly been deployed in real‑world applications, from customer service to Google search, despite the fact that they frequently make factually incorrect or undesirable statements. This trend has inspired practical and academic interest in model editing, that is, in adjusting the weights of the model to modify its likely outputs for queries relating to a specific fact or set of facts. This may be done either to amend a fact or set of facts, for instance, to fix a frequent error in the training data, or to suppress a fact or set of facts entirely, for instance, in case of dangerous knowledge. Multiple methods have been proposed to do such edits. However, at the same time, it has been shown that such model editing can be brittle and incomplete. Moreover the effectiveness of any model editing method necessarily depends on the data on which the model is trained, and, therefore, a good understanding of the interaction of the training data distribution and the way it is stored in the network is necessary and helpful to reliably perform model editing. However, working with large language models trained on real‑world data does not allow us to understand this relationship or fully measure the effects of model editing. We therefore propose Behemoth, a fully synthetic data generation framework. To demonstrate the practical insights from the framework, we explore model editing in the context of simple tabular data, demonstrating surprising findings that, in some cases, echo real‑world results, for instance, that in some cases restricting the update rank results in a more effective update. The code is available at https://github.com/IST‑DASLab/behemoth.git.
Authors:Christiaan P. Opperman, Anna S. Bosman, Katherine M. Malan
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.
Authors:Santanu Subhash Rathod, Pietro Liò, Xiao Zhang
Abstract:
Flow matching is a scalable generative framework for characterizing continuous normalizing flows with wide‑range applications. However, current state‑of‑the‑art methods are not well‑suited for modeling dynamical systems, as they construct conditional paths using linear interpolants that may not capture the underlying state evolution, especially when learning higher‑order dynamics from irregular sampled observations. Constructing unified paths that satisfy multi‑marginal constraints across observations is challenging, since naïve higher‑order polynomials tend to be unstable and oscillatory. We introduce SplineFlow, a theoretically grounded flow matching algorithm that jointly models conditional paths across observations via B‑spline interpolation. Specifically, SplineFlow exploits the smoothness and stability of B‑spline bases to learn the complex underlying dynamics in a structured manner while ensuring the multi‑marginal requirements are met. Comprehensive experiments across various deterministic and stochastic dynamical systems of varying complexity, as well as on cellular trajectory inference tasks, demonstrate the strong improvement of SplineFlow over existing baselines. Our code is available at: https://github.com/santanurathod/SplineFlow.
Authors:Seyedeh Ava Razi Razavi, James Sargant, Sheridan Houghten, Renata Dividino
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.
Authors:Arvind Mahankali, Kaiyue Wen, Tengyu Ma
Abstract:
Long chain‑of‑thought reasoning (Long CoT) is now fundamental to state‑of‑the‑art LLMs, especially in mathematical reasoning. However, LLM generation is highly sequential, and long CoTs lead to a high latency. We propose to train Divide‑and‑Conquer CoT (DC‑CoT) to reduce the latency. With DC‑CoT, the model can act as a director that identifies distinct subtasks that can be performed in parallel in its reasoning process, and then spawns workers to execute the subtasks. Our goal is to achieve high accuracy, with a low longest path length, which is a theoretical measure of the latency needed for the response. We start with a long CoT base model (DeepScaleR‑1.5B‑Preview), and first use SFT with a small curated demonstration set to initialize its ability to spawn workers in a certain format. Because SFT degrades the accuracy significantly, we design a multi‑stage RL algorithm, with various data filtering strategies, to recover the accuracy while decreasing the longest path length. Across several benchmarks including AIME 2024 and HMMT 2025, DC‑CoT achieves similar accuracy as DeepScaleR‑1.5B‑Preview while decreasing longest path length by 35‑40%. Our code, SFT dataset and models are publicly available at https://github.com/amahankali10/DC_CoT_RL_for_Low_Latency_CoT_with_Parallel_Reasoning.
Authors:Weiqin Yang, Bohao Wang, Zhenxiang Xu, Jiawei Chen, Shengjia Zhang, Jingbang Chen, Canghong Jin, Can Wang
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 .
Authors:Muqing Liu, Chongjie Si, Yuheng Jia
Abstract:
Large pre‑trained models achieve remarkable success across diverse domains, yet fully fine‑tuning incurs prohibitive computational and memory costs. Parameter‑efficient fine‑tuning (PEFT) has thus become a mainstream paradigm. Among them, Low‑Rank Adaptation (LoRA) introduces trainable low‑rank matrices and shows strong performance, nevertheless, its fixed‑rank design limits flexibility. Dynamic rank allocation methods mitigate this issue by pruning redundant directions; however, they often rely on heuristic, element‑level metrics that globally sort rank directions without matrix‑wise distinction, and they lack mechanisms to expand capacity in layers requiring additional adaptation. To overcome these limitations, we propose FlexLoRA, an entropy‑guided flexible low‑rank adaptation framework that (i) evaluates matrix importance via spectral energy entropy, (ii) supports rank pruning and expansion under a global budget, and (iii) employs zero‑impact initialization for newly added singular directions to ensure stability. By addressing granularity, flexibility, and stability limitations, FlexLoRA provides a more principled solution for PEFT. Extensive experiments show that FlexLoRA consistently outperforms state‑of‑the‑art baselines across benchmarks. Codes are available at https://github.com/Chongjie‑Si/Subspace‑Tuning.
Authors:Mathieu Petitbois, Rémy Portelas, Sylvain Lamprier
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/.
Authors:Markus Mueller, Kathrin Gruber, Dennis Fok
Abstract:
Advances in generative modeling have recently been adapted to tabular data containing discrete and continuous features. However, generating mixed‑type features that combine discrete states with an otherwise continuous distribution in a single feature remains challenging. We advance the state‑of‑the‑art in diffusion models for tabular data with a cascaded approach. We first generate a low‑resolution version of a tabular data row, that is, the collection of the purely categorical features and a coarse categorical representation of numerical features. Next, this information is leveraged in the high‑resolution flow matching model via a novel guided conditional probability path and data‑dependent coupling. The low‑resolution representation of numerical features explicitly accounts for discrete outcomes, such as missing or inflated values, and therewith enables a more faithful generation of mixed‑type features. We formally prove that this cascade tightens the transport cost bound. The results indicate that our model generates significantly more realistic samples and captures distributional details more accurately, for example, the detection score improves by 51.9%. Code is available at https://github.com/muellermarkus/tabcascade.
Authors:Dong Xu, Qihua Pan, Sisi Yuan, Jianqiang Li, Zexuan Zhu, Junkai Ji
Abstract:
Molecular generative models, often employing GPT‑style language modeling on molecular string representations, have shown promising capabilities when scaled to large datasets and model sizes. However, it remains unclear and subject to debate whether these models adhere to predictable scaling laws under fixed computational budgets, which is a crucial understanding for optimally allocating resources between model size, data volume, and molecular representation. In this study, we systematically investigate the scaling behavior of molecular language models across both pretraining and downstream tasks. We train 300 models and conduct over 10,000 experiments, rigorously controlling compute budgets while independently varying model size, number of training tokens, and molecular representation. Our results demonstrate clear scaling laws in molecular models for both pretraining and downstream transfer, reveal the substantial impact of molecular representation on performance, and explain previously observed inconsistencies in scaling behavior for molecular generation. Additionally, we publicly release the largest library of molecular language models to date to facilitate future research and development. Code and models are available at https://github.com/SZU‑ADDG/MLM‑Scaling.
Authors:Ritesh Bhadana
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/.
Authors:Mengfan Liu, Da Zheng, Junwei Su, Chuan Wu
Abstract:
Full‑graph and mini‑batch Graph Neural Network (GNN) training approaches have distinct system design demands, making it crucial to choose the appropriate approach to develop. A core challenge in comparing these two GNN training approaches lies in characterizing their model performance (i.e., convergence and generalization) and computational efficiency. While a batch size has been an effective lens in analyzing such behaviors in deep neural networks (DNNs), GNNs extend this lens by introducing a fan‑out size, as full‑graph training can be viewed as mini‑batch training with the largest possible batch size and fan‑out size. However, the impact of the batch and fan‑out size for GNNs remains insufficiently explored. To this end, this paper systematically compares full‑graph vs. mini‑batch training of GNNs through empirical and theoretical analyses from the view points of the batch size and fan‑out size. Our key contributions include: 1) We provide a novel generalization analysis using the Wasserstein distance to study the impact of the graph structure, especially the fan‑out size. 2) We uncover the non‑isotropic effects of the batch size and the fan‑out size in GNN convergence and generalization, providing practical guidance for tuning these hyperparameters under resource constraints. Finally, full‑graph training does not always yield better model performance or computational efficiency than well‑tuned smaller mini‑batch settings. The implementation can be found in the github link: https://github.com/LIUMENGFAN‑gif/GNN_fullgraph_minibatch_training.
Authors:Abhishek Tyagi, Yunuo Cen, Shrey Dhorajiya, Bharadwaj Veeravalli, Xuanyao Fong
Abstract:
Large Language Models (LLMs) exhibit substantial parameter redundancy, particularly in Feed‑Forward Networks (FFNs). Existing pruning methods suffer from two primary limitations. First, reliance on dataset‑specific calibration introduces significant data dependency and computational overhead. Second, being predominantly static, they fail to account for the evolving subset of knowledge neurons in LLMs during autoregressive generation as the context evolves. To address this, we introduce DART, i.e., Dynamic Attention‑Guided Runtime Tracing), a lightweight, training‑free method that performs on‑the‑fly context‑based pruning. DART monitors shifts in attention score distributions to infer context changes, dynamically updating neuron‑level masks to retain salient parameters. Across ten benchmarks, DART outperforms prior dynamic baseline, achieving accuracy gains of up to 14.5% on LLAMA‑3.1‑8B at 70% FFN sparsity. Furthermore, DART achieves up to 3x better ROUGE‑L scores with respect to static‑masked pruning on summarization tasks, with its performance comparable to the original dense models. We conclusively demonstrate that the proposed framework effectively adapts to diverse semantic contexts, preserves model capabilities across both general and domain‑specific tasks while running at less than 10MBs of memory for LLAMA‑3.1‑8B(16GBs) with 0.1% FLOPs overhead. The code is available at https://github.com/seeder‑research/DART.
Authors:En Fu, Yanyan Hu, Changhua Hu, Zengwang Jin, Kaixiang Peng
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.
Authors:Chengyi Yang, Zhishang Xiang, Yunbo Tang, Zongpei Teng, Chengsong Huang, Fei Long, Yuhan Liu, Jinsong Su
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.
Authors:Youngeun Kim
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
Authors:Naeem Paeedeh, Mahardhika Pratama, Ary Shiddiqi, Zehong Cao, Mukesh Prasad, Wisnu Jatmiko
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.
Authors:Aditya Sarkar, Yi Li, Jiacheng Cheng, Shlok Mishra, Nuno Vasconcelos
Abstract:
Selective prediction aims to endow predictors with a reject option, to avoid low confidence predictions. However, existing literature has primarily focused on closed‑set tasks, such as visual question answering with predefined options or fixed‑category classification. This paper considers selective prediction for visual language foundation models, addressing a taxonomy of tasks ranging from closed to open set and from finite to unbounded vocabularies, as in image captioning. We seek training‑free approaches of low‑complexity, applicable to any foundation model and consider methods based on external vision‑language model embeddings, like CLIP. This is denoted as Plug‑and‑Play Selective Prediction (PaPSP). We identify two key challenges: (1) instability of the visual‑language representations, leading to high variance in image‑text embeddings, and (2) poor calibration of similarity scores. To address these issues, we propose a memory augmented PaPSP (MA‑PaPSP) model, which augments PaPSP with a retrieval dataset of image‑text pairs. This is leveraged to reduce embedding variance by averaging retrieved nearest‑neighbor pairs and is complemented by the use of contrastive normalization to improve score calibration. Through extensive experiments on multiple datasets, we show that MA‑PaPSP outperforms PaPSP and other selective prediction baselines for selective captioning, image‑text matching, and fine‑grained classification. Code is publicly available at https://github.com/kingston‑aditya/MA‑PaPSP.
Authors:Zhengyan Wan, Yidong Ouyang, Liyan Xie, Hongyuan Zha, Fang Fang, Guang Cheng
Abstract:
Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete diffusion models, such as tau‑leaping and Euler solver. However, these samplers require a large number of iterations to control discretization error, since the transition rates are frozen in time and evaluated at the initial state within each time interval. Moreover, theoretical results for these samplers often require boundedness conditions of the transition rate or they focus on a specific type of source distributions. To address those limitations, we establish non‑asymptotic discretization error bounds for those samplers without any restriction on transition rates and source distributions, under the framework of discrete flow models. Furthermore, by analyzing a one‑step lower bound of the Euler sampler, we propose two corrected samplers: time‑corrected sampler and location‑corrected sampler, which can reduce the discretization error of tau‑leaping and Euler solver with almost no additional computational cost. We rigorously show that the location‑corrected sampler has a lower complexity than existing parallel samplers. We validate the effectiveness of the proposed method by achieving better generation quality with reduced inference time on simulations and text‑to‑image generation tasks. Code can be found in https://github.com/WanZhengyan/Corrected‑Samplers‑for‑Discrete‑Flow‑Models.
Authors:Yaowei Jin, Junjie Wang, Cheng Cao, Penglei Wang, Duo An, Qian Shi
Abstract:
Structure‑Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for continuous atomic coordinates and discrete chemical categories, leading to a mismatch with the underlying statistical manifolds. We address this issue by representing molecules using composite exponential‑family distributions, where coordinates and categories are represented within a unified natural parameter space to evolve synchronously along exponential geodesics under the Fisher‑Rao metric. To avoid the instantaneous trajectory collapse induced by geodesics directly targeting Dirac distributions, we propose Evolving Exponential Geodesic Flow for SBDD (EvoEGF‑Mol), which replaces static Dirac targets with dynamically concentrating distributions and is trained with a progressive‑parameter‑refinement architecture. Our model approaches a reference‑level PoseBusters passing rate (93.4%) on CrossDock, demonstrating remarkable geometric precision and interaction fidelity, while achieving superior performance over baseline methods on real‑world MolGenBench tasks for bioactive scaffold recovery. Code is available at https://github.com/BLEACH366/EvoEGF‑Mol.
Authors:Yijun Ma, Zehong Wang, Weixiang Sun, Yanfang Ye
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.
Authors:Weiqi Wang, Xin Liu, Binxuan Huang, Hejie Cui, Rongzhi Zhang, Changlong Yu, Shuowei Jin, Jingfeng Yang, Qingyu Yin, Zhengyang Wang, Zheng Li, Yifan Gao, Priyanka Nigam, Bing Yin, Lihong Li, Yangqiu Song
Abstract:
RLVR is now a standard way to train LLMs on reasoning tasks with verifiable outcomes, but when rollout generation dominates the cost, efficiency depends heavily on which prompts you sample and when. In practice, prompt pools are often static or only loosely tied to the model's learning progress, so uniform sampling can't keep up with the shifting capability frontier and ends up wasting rollouts on prompts that are already solved or still out of reach. Existing approaches improve efficiency through filtering, curricula, adaptive rollout allocation, or teacher guidance, but they typically assume a fixed pool‑which makes it hard to support stable on‑policy pool growth‑or they add extra teacher cost and latency. We introduce HeaPA (Heap Sampling and On‑Policy Query Augmentation), which maintains a bounded, evolving pool, tracks the frontier using heap‑based boundary sampling, expands the pool via on‑policy augmentation with lightweight asynchronous validation, and stabilizes correlated queries through topology‑aware re‑estimation of pool statistics and controlled reinsertion. Across two training corpora, two training recipes, and seven benchmarks, HeaPA consistently improves accuracy and reaches target performance with fewer computations while keeping wall‑clock time comparable. Our analyses suggest these gains come from frontier‑focused sampling and on‑policy pool growth, with the benefits becoming larger as model scale increases. Our code is available at https://github.com/horizon‑rl/HeaPA.
Authors:Ziming Li, Xiaoming Wu, Zehong Wang, Jiazheng Li, Yijun Tian, Jinhe Bi, Yunpu Ma, Yanfang Ye, Chuxu Zhang
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.
Authors:Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
Abstract:
Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly understood. We introduce SCALAR (Structural Consistency And Logic Across Regimes), a benchmark for evaluating geometric scale generalization and its connection to structural hallucination, consistency, and reasoning in materials foundation models. Given canonical crystal representations, models must reason about derived nanoparticle structures obtained through supercell expansion and geometric truncation across length scales spanning a few atoms to over 18,000 atoms, totaling \approx100,000 structures from DFT‑validated unit cells. SCALAR defines three tasks. (i) CIF to property prediction. (ii) A Chain‑of‑Thought variant with explicit physics‑grounded reasoning. (iii) Inverse retrieval identifying crystals from candidates given target properties. Outputs are evaluated via structured metrics capturing numeric error, hallucination, cross‑prompt consistency, monotonic reasoning, output validity, and retrieval regret. Experiments across diverse foundation models reveal large, model‑dependent shifts under explicit reasoning, often reducing hallucination and error, but frequently destabilizing consistency or validity. These results demonstrate that geometric scale generalization cannot be inferred from accuracy alone. Supplementary materials are available at https://github.com/KurbanIntelligenceLab/SCALAR.
Authors:Bo Yuan, Yun Zhou, Zhichao Xu, Kiran Ramnath, Aosong Feng, Balasubramaniam Srinivasan
Abstract:
Automatic workflow generation is the process of automatically synthesizing sequences of LLM calls, tool invocations, and post‑processing steps for complex end‑to‑end tasks. Most prior methods cast this task as an optimization problem with limited theoretical grounding. We propose to cast workflow generation as Bayesian inference over a posterior distribution on workflows, and introduce Bayesian Workflow Generation (BWG), a sampling framework that builds workflows step‑by‑step using parallel look‑ahead rollouts for importance weighting and a sequential in‑loop refiner for pool‑wide improvements. We prove that, without the refiner, the weighted empirical distribution converges to the target posterior. We instantiate BWG as BayesFlow, a training‑free algorithm for workflow construction. Across six benchmark datasets, BayesFlow improves accuracy by up to 9 percentage points over SOTA workflow generation baselines and by up to 65 percentage points over zero‑shot prompting, establishing BWG as a principled upgrade to search‑based workflow design. Code will be available on https://github.com/BoYuanVisionary/BayesFlow.
Authors:Manuela Chacon-Chamorro, Luis Felipe Giraldo, Nicanor Quijano
Abstract:
Multi‑agent social dilemmas, such as the tragedy of the commons, capture settings where individual incentives conflict with collective well‑being, making these systems highly vulnerable to collapse under disruptions. In this context, this work studies cooperative resilience, understood as the system‑level ability to maintain collective well‑being under perturbations through adaptive agent behavior. We propose a framework for learning incentive structures aligned with collective well‑being in multi‑agent reinforcement learning systems, where reward functions shape individual decision‑making and collective behavior. A resilience metric is used to score and rank agent trajectories, allowing the inference of reward functions that promote resilient collective behavior. These inferred reward functions are integrated into the multi‑agent reinforcement learning process to shape agent interactions in social dilemma settings. The approach is evaluated in resource‑sharing environments subject to disruptions, using three incentive structures: individual incentives, resilience‑aligned incentives, and a hybrid incentive structure that combines both individual and collective components. The results show that the hybrid incentive structure promotes sustained collective behavior, reduces collapse events associated with resource depletion, and preserves system performance under disruption. These findings highlight the role of incentive design as a mechanism for promoting resilient collective behavior and provide a computational framework for multi‑agent social dilemmas under disruptions.
Authors:Shirin Reyhanian, Laurenz Wiskott
Abstract:
Vector‑quantized variational autoencoders (VQ‑VAEs) are central to models that rely on high reconstruction fidelity, from neural compression to generative pipelines. Hierarchical extensions, such as VQ‑VAE2, are often credited with superior reconstruction performance because they split global and local features across multiple levels. However, since higher levels derive all their information from lower levels, they should not carry additional reconstructive content beyond what the lower‑level already encodes. Combined with recent advances in training objectives and quantization mechanisms, this leads us to ask whether a single‑level VQ‑VAE, with matched representational budget and no codebook collapse, can equal the reconstruction fidelity of its hierarchical counterpart. Although the multi‑scale structure of hierarchical models may improve perceptual quality in downstream tasks, the effect of hierarchy on reconstruction accuracy, isolated from codebook utilization and overall representational capacity, remains empirically underexamined. We revisit this question by comparing a two‑level VQ‑VAE and a capacity‑matched single‑level model on high‑resolution ImageNet images. Consistent with prior observations, we confirm that inadequate codebook utilization limits single‑level VQ‑VAEs and that overly high‑dimensional embeddings destabilize quantization and increase codebook collapse. We show that lightweight interventions such as initialization from data, periodic reset of inactive codebook vectors, and systematic tuning of codebook hyperparameters significantly reduce collapse. Our results demonstrate that when representational budgets are matched, and codebook collapse is mitigated, single‑level VQ‑VAEs can match the reconstruction fidelity of hierarchical variants, challenging the assumption that hierarchical quantization is inherently superior for high‑quality reconstructions.
Authors:Jathurshan Pradeepkumar, Zheng Chen, Jimeng Sun
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.
Authors:Ido Aharon, Emanuele La Malfa, Michael Wooldridge, Sarit Kraus
Abstract:
In tacit coordination games with multiple outcomes, purely rational solution concepts, such as Nash equilibria, provide no guidance for which equilibrium to choose. Shelling's theory explains how, in these settings, humans coordinate by relying on focal points: solutions or outcomes that naturally arise because they stand out in some way as salient or prominent to all players. This work studies Large Language Models (LLMs) as players in tacit coordination games, and addresses how, when, and why focal points emerge. We compare and quantify the coordination capabilities of LLMs in cooperative and competitive games for which human experiments are available. We also introduce several learning‑free strategies to improve the coordination of LLMs, with themselves and with humans. On a selection of heterogeneous open‑source models, including Llama, Qwen, and GPT‑oss, we discover that LLMs have a remarkable capability to coordinate and often outperform humans, yet fail on common‑sense coordination that involves numbers or nuanced cultural archetypes. This paper constitutes the first large‑scale assessment of LLMs' tacit coordination within the theoretical and psychological framework of focal points.
Authors:Daniel Stein, Shaoyi Huang, Rolf Drechsler, Bing Li, Grace Li Zhang
Abstract:
Neural networks have been successfully applied in various resource‑constrained edge devices, where usually central processing units (CPUs) instead of graphics processing units exist due to limited power availability. State‑of‑the‑art research still focuses on efficiently executing enormous numbers of multiply‑accumulate (MAC) operations. However, CPUs themselves are not good at executing such mathematical operations on a large scale, since they are more suited to execute control flow logic, i.e., computer algorithms. To enhance the computation efficiency of neural networks on CPUs, in this paper, we propose to convert them into logic flows for execution. Specifically, neural networks are first converted into equivalent decision trees, from which decision paths with constant leaves are then selected and compressed into logic flows. Such logic flows consist of if and else structures and a reduced number of MAC operations. Experimental results demonstrate that the latency can be reduced by up to 14.9 % on a simulated RISC‑V CPU without any accuracy degradation.
The code is open source at https://github.com/TUDa‑HWAI/NN2Logic
Authors:Zhengyan Huan, Camila Pazos, Martin Klassen, Vincent Croft, Pierre-Hugues Beauchemin, Shuchin Aeron
Abstract:
We introduce a new multivariate statistical problem that we refer to as the Ensemble Inverse Problem (EIP). The aim of EIP is to invert for an ensemble that is distributed according to the pushforward of a prior under a forward process. In high energy physics (HEP), this is related to a widely known problem called unfolding, which aims to reconstruct the true physics distribution of quantities, such as momentum and angle, from measurements that are distorted by detector effects. In recent applications, the EIP also arises in full waveform inversion (FWI) and inverse imaging with unknown priors. We propose non‑iterative inference‑time methods that construct posterior samplers based on a new class of conditional generative models, which we call ensemble inverse generative models. For the posterior modeling, these models additionally use the ensemble information contained in the observation set on top of single measurements. Unlike existing methods, our proposed methods avoid explicit and iterative use of the forward model at inference time via training across several sets of truth‑observation pairs that are consistent with the same forward model, but originate from a wide range of priors. We demonstrate that this training procedure implicitly encodes the likelihood model. The use of ensemble information helps posterior inference and enables generalization to unseen priors. We benchmark the proposed method on several synthetic and real datasets in inverse imaging, HEP, and FWI. The codes are available at https://github.com/ZhengyanHuan/The‑Ensemble‑Inverse‑Problem‑‑Applications‑and‑Methods.
Authors:Meng Cao, Jiexi Liu, Songcan Chen
Abstract:
Imbalanced Domain Generalization (IDG) focuses on mitigating both domain and label shifts, both of which fundamentally shape the model's decision boundaries, particularly under heterogeneous long‑tailed distributions across domains. Despite its practical significance, it remains underexplored, primarily due to the technical complexity of handling their entanglement and the paucity of theoretical foundations. In this paper, we begin by theoretically establishing the generalization bound for IDG, highlighting the role of posterior discrepancy and decision margin. This bound motivates us to focus on directly steering decision boundaries, marking a clear departure from existing methods. Subsequently, we technically propose a novel Negative‑Dominant Contrastive Learning (NDCL) for IDG to enhance discriminability while enforce posterior consistency across domains. Specifically, inter‑class decision‑boundary separation is enhanced by placing greater emphasis on negatives as the primary signal in our contrastive learning, naturally amplifying gradient signals for minority classes to avoid the decision boundary being biased toward majority classes. Meanwhile, intra‑class compactness is encouraged through a re‑weighted cross‑entropy strategy, and posterior consistency across domains is enforced through a prediction‑central alignment strategy. Finally, rigorous yet challenging experiments on benchmarks validate the effectiveness of our NDCL. The code is available at https://github.com/Alrash/NDCL.
Authors:Qianwei Yang, Dong Xu, Zhangfan Yang, Sisi Yuan, Zexuan Zhu, Jianqiang Li, Junkai Ji
Abstract:
Drug discovery can be viewed as a combinatorial search over an immense chemical space, motivating the development of deep generative models for de novo molecular design. Among these, GPT‑based molecular language models (MLM) have shown strong molecular design performance by learning chemical syntax and semantics from large‑scale data. However, existing MLMs face two fundamental limitations: they inadequately capture the graph‑structured nature of molecules when formulated as next‑token prediction problems, and they typically lack explicit mechanisms for target‑aware generation. Here, we propose SoftMol, a unified framework that co‑designs molecular representation, model architecture, and search strategy for target‑aware molecular generation. SoftMol introduces soft fragments, a rule‑free block representation of SMILES that enables diffusion‑native modeling, and develops SoftBD, the first block‑diffusion molecular language model that combines local bidirectional diffusion with autoregressive generation under molecular structural constraints. To favor generated molecules with high drug‑likeness and synthetic accessibility, SoftBD is trained on a carefully curated dataset named ZINC‑Curated. SoftMol further integrates a gated Monte Carlo tree search to assemble fragments in a target‑aware manner. Experimental results show that, compared with current state‑of‑the‑art models, SoftMol achieves 100% chemical validity, improves binding affinity by 9.7%, yields a 2‑3x increase in molecular diversity, and delivers a 6.6x speedup in inference efficiency. Code is available at https://github.com/szu‑aicourse/softmol
Authors:Elias Jääsaari, Ville Hyvönen, Teemu Roos
Abstract:
Multi‑vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single‑vector representations in information retrieval applications. In multi‑vector retrieval systems, both queries and documents are encoded using one embedding per token, and similarity between queries and documents is measured by the MaxSim similarity measure. However, the improved quality of multi‑vector retrieval comes at the expense of significantly increased search latency. In this work, we introduce LEMUR, a simple yet efficient framework for multi‑vector similarity search. LEMUR consists of two consecutive problem reductions: First, we formulate multi‑vector similarity search as a supervised learning problem that can be solved using a one‑hidden‑layer neural network. Second, we reduce inference under this model to single‑vector similarity search in its latent space, enabling the use of existing single‑vector search indexes to accelerate retrieval. LEMUR is an order of magnitude faster than prior multi‑vector similarity search methods. Our code is available at https://github.com/ejaasaari/lemur
Authors:Xinwei Qiang, Hongmin Chen, Shixuan Sun, Jingwen Leng, Xin Liu, Minyi Guo
Abstract:
Determinism is indispensable for reproducibility in large language model (LLM) training, yet it often exacts a steep performance cost. In widely used attention implementations such as FlashAttention‑3, the deterministic backward pass can incur up to a 37.9% throughput reduction relative to its non‑deterministic counterpart, primarily because gradient accumulation operations must be serialized to guarantee numerical consistency. This performance loss stems from suboptimal scheduling of compute and gradient‑reduction phases, leading to significant hardware underutilization.
To address this challenge, we formulate the backward pass of deterministic attention as a scheduling problem on a Directed Acyclic Graph (DAG) and derive schedules that minimize the critical path length. Building on this formulation, we present DASH (Deterministic Attention Scheduling for High‑Throughput), which encapsulates two complementary scheduling strategies: (i) Descending Q‑Tile Iteration, a reversed query‑block traversal that shrinks pipeline stalls in causal attention, and (ii) Shift Scheduling, a theoretically optimal schedule within our DAG model that reduces pipeline stalls for both full and causal masks.
Our empirical evaluations on NVIDIA H800 GPUs demonstrate that DASH narrows the performance gap of deterministic attention. The proposed strategies improve the throughput of the attention backward pass by up to 1.28× compared to the baseline, significantly advancing the efficiency of reproducible LLM training.
Our code is open‑sourced at https://github.com/SJTU‑Liquid/deterministic‑FA3.
Authors:Liangze Jiang, Zachary Shinnick, Anton van den Hengel, Hemanth Saratchandran, Damien Teney
Abstract:
Pretraining language models directly on web‑scale corpora is the de facto paradigm. We study an alternative where the model is initially exposed to abstract structured data to ease the subsequent acquisition of rich semantic knowledge, much like humans learning simple logic and mathematics before higher reasoning. We focus on procedural data, generated by formal languages and other simple algorithms, as such abstract data. We first diagnose the algorithmic skills that different forms of procedural data can improve, often significantly. For example, the accuracy of context recall (Needle‑in‑a‑haystack) jumps from 10 to 98% when a model is pretrained on Dyck sequences (balanced brackets). Second, we study how these gains are reflected in pretraining larger models (up to 1.3B). We find that front‑loading as little as 0.1 to 0.3% procedural data significantly outperforms standard pretraining on natural language, code, and informal mathematics (C4, CodeParrot, and DeepMind‑Math datasets). Notably, this also enables the models to reach the same loss value with only 55/67/86% of the original data and thus a comparable reduction in FLOPs. Third, we explore the mechanisms behind the benefits and find that procedural pretraining instills non‑trivial structure in both attention and MLP layers. The former is particularly important for structured domains (e.g. code), and the latter for language. Finally, we lay a path for combining multiple forms of procedural data. Our results show that procedural pretraining is a simple, lightweight means of improving performance and accelerating language model pretraining, ultimately suggesting the promise of disentangling knowledge acquisition from reasoning in LLMs.
Authors:Alexandre Myara, Nicolas Bourriez, Thomas Boyer, Thomas Lemercier, Ihab Bendidi, Auguste Genovesio
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.
Authors:Qisong Xiao, Xinhai Chen, Qinglin Wang, Xiaowei Guo, Binglin Wang, Weifeng Chen, Zhichao Wang, Yunfei Liu, Rui Xia, Hang Zou, Gencheng Liu, Shuai Li, Jie Liu
Abstract:
Deep learning has emerged as a promising paradigm for spatio‑temporal modeling of fluid dynamics. However, existing approaches often suffer from limited generalization to unseen flow conditions and typically require retraining when applied to new scenarios. In this paper, we present LLM4Fluid, a spatio‑temporal prediction framework that leverages Large Language Models (LLMs) as generalizable neural solvers for fluid dynamics. The framework first compresses high‑dimensional flow fields into a compact latent space via reduced‑order modeling enhanced with a physics‑informed disentanglement mechanism, effectively mitigating spatial feature entanglement while preserving essential flow structures. A pretrained LLM then serves as a temporal processor, autoregressively predicting the dynamics of physical sequences with time series prompts. To bridge the modality gap between prompts and physical sequences, which can otherwise degrade prediction accuracy, we propose a dedicated modality alignment strategy that resolves representational mismatch and stabilizes long‑term prediction. Extensive experiments across diverse flow scenarios demonstrate that LLM4Fluid functions as a robust and generalizable neural solver without retraining, achieving state‑of‑the‑art accuracy while exhibiting powerful zero‑shot and in‑context learning capabilities. Code and datasets are publicly available at https://github.com/qisongxiao/LLM4Fluid.
Authors:Tianqi Zhao, Guanyang Wang, Yan Shuo Tan, Qiong Zhang
Abstract:
Clustering tabular data is a fundamental yet challenging problem due to heterogeneous feature types, diverse data‑generating mechanisms, and the absence of transferable inductive biases across datasets. Prior‑fitted networks (PFNs) have recently demonstrated strong generalization in supervised tabular learning by amortizing Bayesian inference under a broad synthetic prior. Extending this paradigm to clustering is nontrivial: clustering is unsupervised, admits a combinatorial and permutation‑invariant output space, and requires inferring the number of clusters. We introduce TabClustPFN, a prior‑fitted network for tabular data clustering that performs amortized Bayesian inference over both cluster assignments and cluster cardinality. Pretrained on synthetic datasets drawn from a flexible clustering prior, TabClustPFN clusters unseen datasets in a single forward pass, without dataset‑specific retraining or hyperparameter tuning. The model naturally handles heterogeneous numerical and categorical features and adapts to a wide range of clustering structures. Experiments on synthetic data and curated real‑world tabular benchmarks show that TabClustPFN outperforms classical, deep, and amortized clustering baselines, while exhibiting strong robustness in out‑of‑the‑box exploratory settings. Code is available at https://github.com/Tianqi‑Zhao/TabClustPFN.
Authors:Lige Zhang, Ali Maatouk, Jialin Chen, Leandros Tassiulas, Rex Ying
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
Authors:Robert van der Klis, Ricardo Chávez Torres, Max van Spengler, Yuhui Ding, Thomas Hofmann, Pascal Mettes
Abstract:
Hyperbolic space is quickly gaining traction as a promising geometry for hierarchical and robust representation learning. A core open challenge is the development of a mathematical formulation of hyperbolic neural networks that is both efficient and captures the key properties of hyperbolic space. The Lorentz model of hyperbolic space has been shown to enable both fast forward and backward propagation. However, we prove that, with the current formulation of Lorentz linear layers, the hyperbolic norms of the outputs scale logarithmically with the number of gradient descent steps, nullifying the key advantage of hyperbolic geometry. We propose a new Lorentz linear layer grounded in the well‑known ``distance‑to‑hyperplane" formulation. We prove that our formulation results in the usual linear scaling of output hyperbolic norms with respect to the number of gradient descent steps. Our new formulation, together with further algorithmic efficiencies through Lorentzian activation functions and a new caching strategy results in neural networks fully abiding by hyperbolic geometry while simultaneously bridging the computation gap to Euclidean neural networks. Code available at: https://github.com/robertdvdk/hyperbolic‑fully‑connected.
Authors:Xiuyu Li, Jinkai Zhang, Mingyang Yi, Yu Li, Longqiang Wang, Yue Wang, Ju Fan
Abstract:
Reinforcement Learning (RL) post‑training alignment for language models is effective, but also costly and unstable in practice, owing to its complicated training process. To address this, we propose a training‑free inference method to sample directly from the optimal RL policy. The transition probability applied to Masked Language Modeling (MLM) consists of a reference policy model and an energy term. Based on this, our algorithm, Energy‑Guided Test‑Time Scaling (ETS), estimates the key energy term via online Monte Carlo, with a provable convergence rate. Moreover, to ensure practical efficiency, ETS leverages modern acceleration frameworks alongside tailored importance sampling estimators, substantially reducing inference latency while provably preserving sampling quality. Experiments on MLM (including autoregressive models and diffusion language models) across reasoning, coding, and science benchmarks show that our ETS consistently improves generation quality, validating its effectiveness and design. The code is available at https://github.com/sheriyuo/ETS.
Authors:Zihan Su, Hongyang Wei, Kangrui Cen, Yong Wang, Guanhua Chen, Chun Yuan, Xiangxiang Chu
Abstract:
Unified Multimodal Models (UMMs) integrate both visual understanding and generation within a single framework. Their ultimate aspiration is to create a cycle where understanding and generation mutually reinforce each other. While recent post‑training methods have successfully leveraged understanding to enhance generation, the reverse direction of utilizing generation to improve understanding remains largely unexplored. In this work, we propose UniMRG (Unified Multi‑Representation Generation), a simple yet effective architecture‑agnostic post‑training method. UniMRG enhances the understanding capabilities of UMMs by incorporating auxiliary generation tasks. Specifically, we train UMMs to generate multiple intrinsic representations of input images, namely pixel (reconstruction), depth (geometry), and segmentation (structure), alongside standard visual understanding objectives. By synthesizing these diverse representations, UMMs capture complementary information regarding appearance, spatial relations, and structural layout. Consequently, UMMs develop a deeper and more comprehensive understanding of visual inputs. Extensive experiments across diverse UMM architectures demonstrate that our method notably enhances fine‑grained perception, reduces hallucinations, and improves spatial understanding, while simultaneously boosting generation capabilities.
Authors:Minjae Cho, Huy Trong Tran
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.
Authors:Aoyu Pang, Maonan Wang, Zifan Sha, Wenwei Yue, Changle Li, Chung Shue Chen, Man-On Pun
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.
Authors:Seonghyeon Go, Yumin Kim
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.
Authors:Yuchen Yang, Yaru Zhao, Pu Yang, Shaowei Wang, Zhi-Hua Zhou
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.
Authors:Minjae Kwon, Josephine Lamp, Lu Feng
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.
Authors:Marvin Sextro, Weronika Kłos, Gabriel Dernbach
Abstract:
Planning effective interventions in biological systems requires treatment‑effect models that adapt to unseen biological contexts by identifying their specific underlying mechanisms. Yet single‑cell perturbation datasets span only a handful of biological contexts, and existing methods cannot leverage new interventional evidence at inference time to adapt beyond their training data. To meta‑learn a perturbation effect estimator, we present MapPFN, a prior‑data fitted network (PFN) pre‑trained on a synthetic biological prior with causal interventions, decoupling pre‑training from limited wet‑lab data. Unlike existing methods, MapPFN uses in‑context learning to map a sequence of experiments to a post‑perturbation distribution, enabling a single pre‑trained model to adapt to new datasets and arbitrary gene sets at inference time. Zero‑shot, MapPFN identifies differentially expressed genes on par with models trained on real single‑cell data, and fine‑tuning further improves predictions across biological contexts. Our code, model and data are available at https://marvinsxtr.github.io/MapPFN.
Authors:Zongheng Guo, Tao Chen, Yang Jiao, Yi Pan, Xiao Hu, Manuela Ferrario
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.
Authors:Hajung Kim, Eunha Lee, Sohyun Chung, Jueon Park, Seungheun Baek, Jaewoo Kang
Abstract:
Identifying metabolic sites where cytochrome P450 enzymes metabolize small‑molecule drugs is essential for drug discovery. Although existing computational approaches have been proposed for site‑of‑metabolism prediction, they typically ignore cytochrome P450 isoform identity or model isoforms independently, thereby failing to fully capture inherent cross‑isoform metabolic patterns. In addition, prior evaluations often rely on top‑k metrics, where false positive atoms may be included among the top predictions, underscoring the need for complementary metrics that more directly assess binary atom‑level discrimination under severe class imbalance. We propose ATTNSOM, an atom‑level site‑of‑metabolism prediction framework that integrates intrinsic molecular reactivity with cross‑isoform relationships. The model combines a shared graph encoder, molecule‑conditioned atom representations, and a cross‑attention mechanism to capture correlated metabolic patterns across cytochrome P450 isoforms. The model is evaluated on two benchmark datasets annotated with site‑of‑metabolism labels at atom resolution. Across these benchmarks, the model achieves consistently strong top‑k performance across multiple cytochrome P450 isoforms. Relative to ablated variants, the model yields higher Matthews correlation coefficient, indicating improved discrimination of true metabolic sites. These results support the importance of explicitly modeling cross‑isoform relationships for site‑of‑metabolism prediction. The code and datasets are available at https://github.com/dmis‑lab/ATTNSOM.
Authors:Bharath Krishnamurthy, Ajita Rattani
Abstract:
Morphing techniques generate artificial biometric samples that combine features from multiple individuals, allowing each contributor to be verified against a single enrolled template. While extensively studied in face recognition, this vulnerability remains largely unexplored in voice biometrics. Prior work on voice morphing is computationally expensive, non‑scalable, and limited to acoustically similar identity pairs, constraining practical deployment. Moreover, existing sound‑morphing methods target audio textures, music, or environmental sounds and are not transferable to voice identity manipulation. We propose VoxMorph, a zero‑shot framework that produces high‑fidelity voice morphs from as little as five seconds of audio per subject without model retraining. Our method disentangles vocal traits into prosody and timbre embeddings, enabling fine‑grained interpolation of speaking style and identity. These embeddings are fused via Spherical Linear Interpolation (Slerp) and synthesized using an autoregressive language model coupled with a Conditional Flow Matching network. VoxMorph achieves state‑of‑the‑art performance, delivering a 2.6x gain in audio quality, a 73% reduction in intelligibility errors, and a 67.8% morphing attack success rate on automated speaker verification systems under strict security thresholds. This work establishes a practical and scalable paradigm for voice morphing with significant implications for biometric security. The code and dataset are available on our project page: https://vcbsl.github.io/VoxMorph/
Authors:Matteo Gianferrari, Omayma Moussadek, Riccardo Salami, Cosimo Fiorini, Lorenzo Tartarini, Daniela Gandolfi, Simone Calderara
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.
Authors:Hao Sun, Da-Wei Zhou
Abstract:
Traditional machine learning systems are typically designed for static data distributions, which suffer from catastrophic forgetting when learning from evolving data streams. Class‑Incremental Learning (CIL) addresses this challenge by enabling learning systems to continuously learn new classes while preserving prior knowledge. With the rise of pre‑trained models (PTMs) such as CLIP, leveraging their strong generalization and semantic alignment capabilities has become a promising direction in CIL. However, existing CLIP‑based CIL methods are often scattered across disparate codebases, rely on inconsistent configurations, hindering fair comparisons, reproducibility, and practical adoption. Therefore, we propose C3Box (CLIP‑based Class‑inCremental learning toolBOX), a modular and comprehensive Python toolbox. C3Box integrates representative traditional CIL methods, ViT‑based CIL methods, and state‑of‑the‑art CLIP‑based CIL methods into a unified CLIP‑based framework. By inheriting the streamlined design of PyCIL, C3Box provides a JSON‑based configuration and standardized execution pipeline. This design enables reproducible experimentation with low engineering overhead and makes C3Box a reliable benchmark platform for continual learning research. Designed to be user‑friendly, C3Box relies only on widely used open‑source libraries and supports major operating systems. The code is available at https://github.com/LAMDA‑CL/C3Box.
Authors:Weixin Chen, Li Chen, Yuhan Zhao
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.
Authors:Steve Azzolin, Stefano Teso, Bruno Lepri, Andrea Passerini, Sagar Malhotra
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.
Authors:Shahd Seddik, Fahd Seddik, Iman Saberi, Fatemeh Fard, Minh Hieu Huynh, Patanamon Thongtanunam
Abstract:
Large Language Models (LLMs) excel at code generation but struggle with complex problems. Retrieval‑Augmented Generation (RAG) mitigates this issue by integrating external knowledge, yet retrieval models often miss relevant context, and generation models hallucinate with irrelevant data. We propose Programming Knowledge Graph (PKG) for semantic representation and fine‑grained retrieval of code and text. Our approach enhances retrieval precision through tree pruning and mitigates hallucinations via a re‑ranking mechanism that integrates non‑RAG solutions. Structuring external data into finer‑grained nodes improves retrieval granularity. Evaluations on HumanEval and MBPP show up to 20% pass@1 accuracy gains and a 34% improvement over baselines on MBPP. Our findings demonstrate that our proposed PKG approach along with re‑ranker effectively address complex problems while maintaining minimal negative impact on solutions that are already correct without RAG. The replication package is published at https://github.com/iamshahd/ProgrammingKnowledgeGraph
Authors:Yiran Huang, Karsten Roth, Quentin Bouniot, Wenjia Xu, Zeynep Akata
Abstract:
Transformer‑based multimodal large language models often exhibit in‑context learning (ICL) abilities. Motivated by this phenomenon, we ask: how do transformers learn to associate information across modalities from in‑context examples? We investigate this question through controlled experiments on small transformers trained on synthetic classification tasks, enabling precise manipulation of data statistics and model architecture. We begin by revisiting core principles of unimodal ICL in modern transformers. While several prior findings replicate, we find that Rotary Position Embeddings (RoPE) increases the data complexity threshold for ICL. Extending to the multimodal setting reveals a fundamental learning asymmetry: when pretrained on high‑diversity data from a primary modality, surprisingly low data complexity in the secondary modality suffices for multimodal ICL to emerge. Mechanistic analysis shows that both settings rely on an induction‑style mechanism that copies labels from matching in‑context exemplars; multimodal training refines and extends these circuits across modalities. Our findings provide a mechanistic foundation for understanding multimodal ICL in modern transformers and introduce a controlled testbed for future investigation. Code is available at: https://github.com/YiranHuangIrene/multimodal‑icl
Authors:Zhiheng Jiang, Yunzhe Wang, Ryan Marr, Ellen Novoseller, Benjamin T. Files, Volkan Ustun
Abstract:
Preference‑Conditioned Policy Learning (PCPL) in Multi‑Objective Reinforcement Learning (MORL) aims to approximate diverse Pareto‑optimal solutions by conditioning policies on user‑specified preferences over objectives. This enables a single model to flexibly adapt to arbitrary trade‑offs at run‑time by producing a policy on or near the Pareto front. However, existing benchmarks for PCPL are largely restricted to toy tasks and fixed environments, limiting their realism and scalability. To address this gap, we introduce GraphAllocBench, a flexible benchmark built on a novel graph‑based resource allocation sandbox environment inspired by city management, which we call CityPlannerEnv. GraphAllocBench provides a rich suite of problems with diverse objective functions, varying preference conditions, and high‑dimensional scalability. We also propose two new evaluation metrics ‑‑ Proportion of Non‑Dominated Solutions (PNDS) and Ordering Score (OS) ‑‑ that directly capture preference consistency while complementing the widely used hypervolume metric. Through experiments with Multi‑Layer Perceptrons (MLPs) and graph‑aware models, we show that GraphAllocBench exposes the limitations of existing MORL approaches and paves the way for using graph‑based methods such as Graph Neural Networks (GNNs) in complex, high‑dimensional combinatorial allocation tasks. Beyond its predefined problem set, GraphAllocBench enables users to flexibly vary objectives, preferences, and allocation rules, establishing it as a versatile and extensible benchmark for advancing PCPL. Code: https://github.com/jzh001/GraphAllocBench
Authors:Guoan Wang, Feiyu Wang, Zongwei Lv, Yikun Zong, Tong Yang
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.
Authors:Ziwei Liu, Borui Kang, Hangjie Yuan, Zixiang Zhao, Wei Li, Yifan Zhu, Tao Feng
Abstract:
As digital environments (data distribution) are in flux, with new GUI data arriving over time‑introducing new domains or resolutions‑agents trained on static environments deteriorate in performance. In this work, we introduce Continual GUI Agents, a new task that requires GUI agents to perform continual learning under shifted domains and resolutions. We find existing methods fail to maintain stable grounding as GUI distributions shift over time, due to the diversity of UI interaction points and regions in fluxing scenarios. To address this, we introduce GUI‑Anchoring in Flux (GUI‑AiF), a new reinforcement fine‑tuning framework that stabilizes continual learning through two novel rewards: Anchoring Point Reward in Flux (APR‑iF) and Anchoring Region Reward in Flux (ARR‑iF). These rewards guide the agents to align with shifting interaction points and regions, mitigating the tendency of existing reward strategies to over‑adapt to static grounding cues (e.g., fixed coordinates or element scales). Extensive experiments show GUI‑AiF surpasses state‑of‑the‑art baselines. Our work establishes the first continual learning framework for GUI agents, revealing the untapped potential of reinforcement fine‑tuning for continual GUI Agents.
Authors:Rohan Asthana, Vasileios Belagiannis
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 .
Authors:Ariel Maymon, Yanir Buznah, Uri Shaham
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.
Authors:Zhiyu Chen, Minhao Liu, Yanru Zhang
Abstract:
Recent lightweight MLP‑based models have achieved strong performance in time series forecasting by capturing stable trends and seasonal patterns. However, their effectiveness hinges on an implicit assumption of local stationarity assumption, making them prone to errors in long‑term forecasting of highly non‑stationary series, especially when abrupt fluctuations occur, a common challenge in domains like web traffic monitoring. To overcome this limitation, we propose TimeCatcher, a novel Volatility‑Aware Variational Forecasting framework. TimeCatcher extends linear architectures with a variational encoder to capture latent dynamic patterns hidden in historical data and a volatility‑aware enhancement mechanism to detect and amplify significant local variations. Experiments on nine real‑world datasets from traffic, financial, energy, and weather domains show that TimeCatcher consistently outperforms state‑of‑the‑art baselines, with particularly large improvements in long‑term forecasting scenarios characterized by high volatility and sudden fluctuations. Our code is available at https://github.com/ColaPrinceCHEN/TimeCatcher.
Authors:Mariia Drozdova
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.
Authors:Minjae Lee, Wonjun Kang, Byeongkeun Ahn, Christian Classen, Kevin Galim, Seunghyuk Oh, Minghao Yan, Hyung Il Koo, Kangwook Lee
Abstract:
Speculative decoding (SD) has proven effective for accelerating LLM inference by quickly generating draft tokens and verifying them in parallel. However, SD remains largely unexplored for Large Vision‑Language Models (LVLMs), which extend LLMs to process both image and text prompts. To address this gap, we benchmark existing inference methods with small draft models on 11 datasets across diverse input scenarios and observe scenario‑specific performance fluctuations. Motivated by these findings, we propose Test‑time Adaptive Batched Ensemble Drafting (TABED), which dynamically ensembles multiple drafts obtained via batch inference by leveraging deviations from past ground truths available in the SD setting. The dynamic ensemble method achieves an average robust walltime speedup of 1.74x over autoregressive decoding and a 5% improvement over single drafting methods, while remaining training‑free and keeping ensembling costs negligible through parameter sharing. With its plug‑and‑play compatibility, we further enhance TABED by integrating advanced verification and alternative drafting methods. Code and custom‑trained models are available at https://github.com/furiosa‑ai/TABED.
Authors:Murad Farzulla
Abstract:
This study investigates whether cryptocurrency whitepaper narratives align with empirically observed market factor structure. We construct a pipeline combining zero‑shot NLP classification of 38 whitepapers across 10 semantic categories with CP tensor decomposition of hourly market data (49 assets, 17,543 timestamps). Using Procrustes rotation and Tucker's congruence coefficient (phi), we find weak alignment between claims and market statistics (phi = 0.246, p = 0.339) and between claims and latent factors (phi = 0.058, p = 0.751). A methodological validation comparison (statistics versus factors, both derived from market data) achieves significance (p < 0.001), confirming the pipeline detects real structure. The null result indicates whitepaper narratives do not meaningfully predict market factor structure, with implications for narrative economics and investor decision‑making. Entity‑level analysis reveals specialized tokens (XMR, CRV, YFI) show stronger narrative‑market correspondence than broad infrastructure tokens.
Authors:Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu
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
Authors:Fengrui Zuo, Zhiwei Ke, Yiming Liu, Wenqi Lou, Chao Wang, Xuehai Zhou
Abstract:
Diffusion language models (DLMs) generate text through iterative denoising, but inference requires full‑sequence attention at every iteration, resulting in substantial redundant computation on masked tokens. Block‑wise diffusion can reduce this cost, yet it typically relies on retraining and constrained update orders, limiting its direct applicability to pretrained DLMs. Our token‑level analysis reveals pronounced structural locality in DLM inference. Decoding is driven by a small set of prefix‑localized active tokens; the influence of distant undecoded context diminishes rapidly, and decoded tokens exhibit stage‑wise temporal stability, enabling reuse of intermediate representations except for a brief post‑decode transient. Motivated by these observations, we propose \placeholder\footnoteThe source code is available at https://github.com/vhicrgit/Window‑Diffusion., a window‑based token pruning and caching method for inference. We maintain a local computation window that slides rightward as denoising progresses, and partition undecoded tokens into: (i) active tokens that are computed online, (ii) buffer tokens whose KV states are cached and periodically refreshed, and (iii) far‑field tokens that are pruned outside the window. Computation is restricted to active and buffer tokens within the window, while far‑field tokens are omitted at each stage. Experiments on LLaDA and Dream show that, under matched compute budgets, our method achieves up to 99× inference speedup while largely preserving generation performance.
Authors:Zeyu Xing, Xing Li, Hui-Ling Zhen, Mingxuan Yuan, Sinno Jialin Pan
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.
Authors:Jiahuan Yu, Mingtao Hu, Zichao Lin, Minjia Zhang
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.
Authors:Xinyu Li, Sishuo Chen, Guipeng Xv, Li Zhang, Mingxuan Luo, Zhangming Chan, Xiang-Rong Sheng, Han Zhu, Jian Xu, Chen Lin
Abstract:
The prediction objectives of online advertisement ranking models are evolving from probabilistic metrics like conversion rate (CVR) to numerical business metrics like post‑click gross merchandise volume (GMV). Unlike the well‑studied delayed feedback problem in CVR prediction, delayed feedback modeling for GMV prediction remains unexplored and poses greater challenges, as GMV is a continuous target, and a single click can lead to multiple purchases that cumulatively form the label. To bridge the research gap, we establish TRACE, a GMV prediction benchmark containing complete transaction sequences rising from each user click, which supports delayed feedback modeling in an online streaming manner. Our analysis and exploratory experiments on TRACE reveal two key insights: (1) the rapid evolution of the GMV label distribution necessitates modeling delayed feedback under online streaming training; (2) the label distribution of repurchase samples substantially differs from that of single‑purchase samples, highlighting the need for separate modeling. Motivated by these findings, we propose RepurchasE‑Aware Dual‑branch prEdictoR (READER), a novel GMV modeling paradigm that selectively activates expert parameters according to repurchase predictions produced by a router. Moreover, READER dynamically calibrates the regression target to mitigate under‑estimation caused by incomplete labels. Experimental results show that READER yields superior performance on TRACE over baselines, achieving a 2.19% improvement in terms of accuracy. We believe that our study will open up a new avenue for studying online delayed feedback modeling for GMV prediction, and our TRACE benchmark with the gathered insights will facilitate future research and application in this promising direction. Our code and dataset are available at https://github.com/alimama‑tech/OnlineGMV .
Authors:Jie Tang, Chuanlong Xie, Xianli Zeng, Lixing Zhu
Abstract:
Machine learning models in high‑stakes applications, such as recidivism prediction and automated personnel selection, often exhibit systematic performance disparities across sensitive subpopulations, raising critical concerns regarding algorithmic bias. Fairness auditing addresses these risks through two primary functions: certification, which verifies adherence to fairness constraints; and flagging, which isolates specific demographic groups experiencing disparate treatment. However, existing auditing techniques are frequently limited by restrictive distributional assumptions or prohibitive computational overhead. We propose a novel empirical likelihood‑based (EL) framework that constructs robust statistical measures for model performance disparities. Unlike traditional methods, our approach is non‑parametric; the proposed disparity statistics follow asymptotically chi‑square or mixed chi‑square distributions, ensuring valid inference without assuming underlying data distributions. This framework uses a constrained optimization profile that admits stable numerical solutions, facilitating both large‑scale certification and efficient subpopulation discovery. Empirically, the EL methods outperform bootstrap‑based approaches, yielding coverage rates closer to nominal levels while reducing computational latency by several orders of magnitude. We demonstrate the practical utility of this framework on the COMPAS dataset, where it successfully flags intersectional biases, specifically identifying a significantly higher positive prediction rate for African‑American males under 25 and a systemic under‑prediction for Caucasian females relative to the population mean.
Authors:Jinren Ding, Xuejian Xu, Shen Jiang, Zhitong Hao, Jinhui Yang, Peng Jiang
Abstract:
Decision Transformer (DT) shows promise for generative auto‑bidding by capturing temporal dependencies, but suffers from two critical limitations: insufficient cross‑correlation modeling among state, action, and return‑to‑go (RTG) sequences, and indiscriminate learning of optimal/suboptimal behaviors. To address these, we propose C2, a novel framework enhancing DT with two core innovations: (1) a Cross Learning Block (CLB) via cross‑attention to strengthen inter‑sequence correlation modeling; (2) a Constraint‑aware Loss (CL) incorporating budget and Cost‑Per‑Acquisition (CPA) constraints for selective learning of optimal trajectories. Extensive offline evaluations on the AuctionNet dataset demonstrate consistent performance gains (up to 3.2% over state‑of‑the‑art method) across diverse budget settings; ablation studies verify the complementary synergy of CLB and CL, confirming C2's superiority in auto‑bidding. The code for reproducing our results is available at: https://github.com/Dingjinren/C2.
Authors:Jim Maar, Denis Paperno, Callum Stuart McDougall, Neel Nanda
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.
Authors:Richard Csaky
Abstract:
We present a large autoregressive model for source‑space MEG that scales next‑token prediction to long context across datasets and scanners: handling a corpus of over 500 hours and thousands of sessions across the three largest MEG datasets. A modified SEANet‑style vector‑quantizer reduces multichannel MEG into a flattened token stream on which we train a Qwen2.5‑VL backbone from scratch to predict the next brain token and to recursively generate minutes of MEG from up to a minute of context. To evaluate long‑horizon generation, we introduce task‑matched tests: (i) on‑manifold stability via generated‑only drift compared to the time‑resolved distribution of real sliding windows, and (ii) conditional specificity via correct context versus prompt‑swap controls using a neurophysiologically grounded metric set. We train on CamCAN and Omega and run all analyses on held‑out MOUS, establishing cross‑dataset generalization. Across metrics, generations remain relatively stable over long rollouts and are closer to the correct continuation than swapped controls. Code available at: https://github.com/ricsinaruto/brain‑gen.
Authors:Abha Jha, Akanksha Mahajan, Ashwath Vaithinathan Aravindan, Praveen Saravanan, Sai Sailaja Policharla, Sonal Chaturbhuj Gehlot
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.
Authors:Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan, Nikhil Muralidhar, Huaxia Wang
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.
Authors:Fang Li
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.
Authors:Haoyuan Deng, Yuanjiang Xue, Haoyang Du, Boyang Zhou, Zhenyu Wu, Ziwei Wang
Abstract:
Human‑in‑the‑loop guidance has emerged as an effective approach for enabling faster convergence in online reinforcement learning (RL) of complex real‑world manipulation tasks. However, existing human‑in‑the‑loop RL (HiL‑RL) frameworks often suffer from low sample efficiency, requiring substantial human interventions to achieve convergence and thereby leading to high labor costs. To address this, we propose a sample‑efficient real‑world human‑in‑the‑loop RL framework named \method, which requires fewer human intervention by actively selecting informative samples. Specifically, stable reduction of policy entropy enables improved trade‑off between exploration and exploitation with higher sample efficiency. We first build influence functions of different samples on the policy entropy, which is efficiently estimated by the covariance of action probabilities and soft advantages of policies. Then we select samples with moderate values of influence functions, where shortcut samples that induce sharp entropy drops and noisy samples with negligible effect are pruned. Extensive experiments on four real‑world manipulation tasks demonstrate that \method achieves a 42.1% higher success rate while requiring 10.1% fewer human interventions compared to the state‑of‑the‑art HiL‑RL method, validating its effectiveness. The project page providing code, videos, and mathematical formulations can be found at https://e2hil.github.io/.
Authors:Mingxuan Luo, Guipeng Xv, Sishuo Chen, Xinyu Li, Li Zhang, Zhangming Chan, Xiang-Rong Sheng, Han Zhu, Jian Xu, Bo Zheng, Chen Lin
Abstract:
In industrial recommender systems, conversion rate (CVR) is widely used for traffic allocation, but it fails to fully reflect recommendation effectiveness because it ignores refund behavior. To better capture true user satisfaction and business value, net conversion rate (NetCVR), defined as the probability that a clicked item is purchased and not refunded, has been proposed.Unlike CVR, NetCVR prediction involves a more complex multi‑stage cascaded delayed feedback process. The two cascaded delays from click to conversion and from conversion to refund have opposite effects, making traditional CVR modeling methods inapplicable. Moreover, the lack of open‑source datasets and online continuous training schemes further hinders progress in this area.To address these challenges, we introduce CASCADE (Cascaded Sequences of Conversion and Delayed Refund), the first large‑scale open dataset derived from the Taobao app for online continuous NetCVR prediction. Through an in‑depth analysis of CASCADE, we identify three key insights: (1) NetCVR exhibits strong temporal dynamics, necessitating online continuous modeling; (2) cascaded modeling of CVR and refund rate outperforms direct NetCVR modeling; and (3) delay time, which correlates with both CVR and refund rate, is an important feature for NetCVR prediction.Based on these insights, we propose TESLA, a continuous NetCVR modeling framework featuring a CVR‑refund‑rate cascaded architecture, stage‑wise debiasing, and a delay‑time‑aware ranking loss. Extensive experiments demonstrate that TESLA consistently outperforms state‑of‑the‑art methods on CASCADE, achieving absolute improvements of 12.41 percent in RI‑AUC and 14.94 percent in RI‑PRAUC on NetCVR prediction. The code and dataset are publicly available at https://github.com/alimama‑tech/NetCVR.
Authors:Nikhil Raghav, Avisek Gupta, Swagatam Das, Md Sahidullah
Abstract:
Speaker diarization aims to segment audio recordings into regions corresponding to individual speakers. Although unsupervised speaker diarization is inherently challenging, the prospect of identifying speaker regions without pretraining or weak supervision motivates research on clustering techniques. In this work, we share the notable observation that measuring multiple kernel similarities of speaker embeddings to thereafter craft a sparse graph for spectral clustering in a principled manner is sufficient to achieve state‑of‑the‑art performances in a fully unsupervised setting. Specifically, we consider four polynomial kernels and a degree one arccosine kernel to measure similarities in speaker embeddings, using which sparse graphs are constructed in a principled manner to emphasize local similarities. Experiments show the proposed approach excels in unsupervised speaker diarization over a variety of challenging environments in the DIHARD‑III, AMI, and VoxConverse corpora. To encourage further research, our implementations are available at https://github.com/nikhilraghav29/MK‑SGC‑SC.
Authors:Yuhao Li
Abstract:
How can populations of learners develop coordinated, diverse behaviors without explicit communication or diversity incentives? We demonstrate that competition alone is sufficient to induce emergent specialization ‑‑ learners spontaneously partition into specialists for different environmental regimes through competitive dynamics, consistent with ecological niche theory. We introduce the NichePopulation algorithm, a simple mechanism combining competitive exclusion with niche affinity tracking. Validated across six real‑world domains (cryptocurrency trading, commodity prices, weather forecasting, solar irradiance, urban traffic, and air quality), our approach achieves a mean Specialization Index of 0.75 with effect sizes of Cohen's d > 20. Key findings: (1) At lambda=0 (no niche bonus), learners still achieve SI > 0.30, proving specialization is genuinely emergent; (2) Diverse populations outperform homogeneous baselines by +26.5% through method‑level division of labor; (3) Our approach outperforms MARL baselines (QMIX, MAPPO, IQL) by 4.3x while being 4x faster.
Authors:Kei Saito
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.
Authors:Yitian Chen, Cheng Cheng, Yinan Sun, Zi Ling, Dongdong Ge
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).
Authors:Gong Gao, Weidong Zhao, Xianhui Liu, Ning Jia
Abstract:
Existing value‑based online reinforcement learning (RL) algorithms suffer from slow policy exploitation due to ineffective exploration and delayed policy updates. To address these challenges, we propose an algorithm called Instant Retrospect Action (IRA). Specifically, we propose Q‑Representation Discrepancy Evolution (RDE) to facilitate Q‑network representation learning, enabling discriminative representations for neighboring state‑action pairs. In addition, we adopt an explicit method to policy constraints by enabling Greedy Action Guidance (GAG). This is achieved through backtracking historical actions, which effectively enhances the policy update process. Our proposed method relies on providing the learning algorithm with accurate k‑nearest‑neighbor action value estimates and learning to design a fast‑adaptable policy through policy constraints. We further propose the Instant Policy Update (IPU) mechanism, which enhances policy exploitation by systematically increasing the frequency of policy updates. We further discover that the early‑stage training conservatism of the IRA method can alleviate the overestimation bias problem in value‑based RL. Experimental results show that IRA can significantly improve the learning efficiency and final performance of online RL algorithms on eight MuJoCo continuous control tasks.The code is available at https://github.com/2706853499/IRA.
Authors:Mao-Lin Luo, Zi-Hao Zhou, Yi-Lin Zhang, Yuanyu Wan, Tong Wei, Min-Ling Zhang
Abstract:
Continual learning for pre‑trained vision‑language models requires balancing three competing objectives: retaining pre‑trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge. This paper presents a simple but effective approach called KeepLoRA to effectively balance these objectives. We first analyze the knowledge retention mechanism within the model parameter space and find that general knowledge is mainly encoded in the principal subspace, while task‑specific knowledge is encoded in the residual subspace. Motivated by this finding, KeepLoRA learns new tasks by restricting LoRA parameter updates in the residual subspace to prevent interfering with previously learned capabilities. Specifically, we infuse knowledge for a new task by projecting its gradient onto a subspace orthogonal to both the principal subspace of pre‑trained model and the dominant directions of previous task features. Our theoretical and empirical analyses confirm that KeepLoRA balances the three objectives and achieves state‑of‑the‑art performance. The implementation code is available at https://github.com/MaolinLuo/KeepLoRA.
Authors:Yongqi Wang, Xiaofeng Ji, Jie Wang, Qingbin Li, Xiao Xiong, Zheming Yang, Jian Xu, Minghui Qiu, Xinxiao Wu
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.
Authors:Haonan Zhang, Dongxia Wang, Yi Liu, Kexin Chen, Wenhai Wang
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/.
Authors:Hongzhu Yi, Xinming Wang, Zhenghao zhang, Tianyu Zong, Yuanxiang Wang, Jun Xie, Tao Yu, Haopeng Jin, Kaixin Xu, Feng Chen, Jiahuan Chen, Yujia Yang, Zhenyu Guan, Bingkang Shi, Jungang Xu
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.
Authors:Viacheslav Sydora, Guner Dilsad Er, Michael Muehlebach
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.
Authors:Quy-Anh Dang, Chris Ngo
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
Authors:Zhao-Han Peng, Shaohui Li, Zhi Li, Shulan Ruan, Yu Liu, You He
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.
Authors:Tianyi Chen, Sihan Chen, Xiaoyi Qu, Dan Zhao, Ruomei Yan, Jongwoo Ko, Luming Liang, Pashmina Cameron
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.
Authors:Alexandre Alouadi, Pierre Henry-Labordère, Grégoire Loeper, Othmane Mazhar, Huyên Pham, Nizar Touzi
Abstract:
The Schrodinger Bridge and Bass (SBB) formulation, which jointly controls drift and volatility, is an established extension of the classical Schrodinger Bridge (SB). Building on this framework, we introduce LightSBB‑M, an algorithm that computes the optimal SBB transport plan in only a few iterations. The method exploits a dual representation of the SBB objective to obtain analytic expressions for the optimal drift and volatility, and it incorporates a tunable parameter beta greater than zero that interpolates between pure drift (the Schrodinger Bridge) and pure volatility (Bass martingale transport). We show that LightSBB‑M achieves the lowest 2‑Wasserstein distance on synthetic datasets against state‑of‑the‑art SB and diffusion baselines with up to 32 percent improvement. We also illustrate the generative capability of the framework on an unpaired image‑to‑image translation task (adult to child faces in FFHQ). These findings demonstrate that LightSBB‑M provides a scalable, high‑fidelity SBB solver that outperforms existing SB and diffusion baselines across both synthetic and real‑world generative tasks. The code is available at https://github.com/alexouadi/LightSBB‑M.
Authors:Shengjia Zhang, Weiqin Yang, Jiawei Chen, Peng Wu, Yuegang Sun, Gang Wang, Qihao Shi, Can Wang
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.
Authors:Qi Si, Xuyang Liu, Penglei Wang, Xin Guo, Yuan Qi, Yuan Cheng
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.
Authors:Chaozheng Wen, Jingwen Tong, Zehong Lin, Chenghong Bian, Jun Zhang
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.
Authors:Benjamin Turtel, Paul Wilczewski, Danny Franklin, Kris Skotheim
Abstract:
Risk disclosures in SEC filings describe potential adverse events but rarely quantify their likelihood, limiting their usefulness for probabilistic analysis. A central obstacle is the absence of large‑scale, risk‑level supervision linking disclosed risks to realized outcomes.
We introduce a fully automated data generation pipeline that converts qualitative SEC risk disclosures into temporally grounded supervision using only public data. For each filing, the pipeline generates firm‑specific, time‑bounded risk queries from the Risk Factors section and labels them by automatically resolving outcomes against subsequent disclosures.
Using this dataset of risk queries and outcomes grounded in SEC filings, we train a compact large language model to estimate the probability that a disclosed risk will materialize within a specified horizon. Despite its modest size, the resulting model substantially improves over pretrained and heuristic baselines, and outperforms frontier general‑purpose models, including GPT‑5, on probabilistic accuracy and calibration.
More broadly, this work demonstrates that Foresight Learning enables scalable and fully automated training of domain‑specific expert models using only raw, chronological, in‑domain text ‑‑ without proprietary data, external corpora, or manual annotation. The resulting models achieve frontier‑level performance while remaining deployable on a single GPU. This result suggests a general pathway for learning calibrated, decision‑relevant signals from naturally occurring enterprise documents.
To support transparency and reproducibility, we open‑source the evaluation dataset used in this study.
Evaluation Data: https://huggingface.co/datasets/LightningRodLabs/sec_risk_questions_test_set
Data Generation Platform: https://lightningrod.ai/
SDK: https://github.com/lightning‑rod‑labs/lightningrod‑python‑sdk
Authors:Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
Abstract:
Generative models for materials have achieved strong performance on periodic bulk crystals, yet their ability to generalize across scale transitions to finite nanostructures remains largely untested. We introduce Crystal‑to‑Nanoparticle (C2NP), a systematic benchmark for evaluating generative models when moving between infinite crystalline unit cells and finite nanoparticles, where surface effects and size‑dependent distortions dominate. C2NP defines two complementary tasks: (i) generating nanoparticles of specified radii from periodic unit cells, testing whether models capture surface truncation and geometric constraints; and (ii) recovering bulk lattice parameters and space‑group symmetry from finite particle configurations, assessing whether models can infer underlying crystallographic order despite surface perturbations. Using diverse materials as a structurally consistent testbed, we construct over 170,000 nanoparticle configurations by carving particles from supercells derived from DFT‑relaxed crystal unit cells, and introduce size‑based splits that separate interpolation from extrapolation regimes. Experiments with state‑of‑the‑art approaches, including diffusion, flow‑matching, and variational models, show that even when losses are low, models often fail geometrically under distribution shift, yielding large lattice‑recovery errors and near‑zero joint accuracy on structure and symmetry. Overall, our results suggest that current methods rely on template memorization rather than scalable physical generalization. C2NP offers a controlled, reproducible framework for diagnosing these failures, with immediate applications to nanoparticle catalyst design, nanostructured hydrides for hydrogen storage, and materials discovery. Dataset and code are available at https://github.com/KurbanIntelligenceLab/C2NP.
Authors:Junwei Deng, Chang Xu, Jiaqi W. Ma, Ming Jin, Chenghao Liu, Jiang Bian
Abstract:
Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. Existing augmentation methods, however, typically rely on heuristics and static paradigms. Motivated by dynamic data optimization, which shows that the contribution of samples varies across training stages, we propose OATS (Online Data Augmentation for Time Series Foundation Models), a principled strategy that generates synthetic data tailored to different training steps. OATS leverages valuable training samples as principled guiding signals and dynamically generates high‑quality synthetic data conditioned on them. We further design a diffusion‑based framework to produce realistic time series and introduce an explore‑exploit mechanism to balance efficiency and effectiveness. Experiments on TSFMs demonstrate that OATS consistently outperforms regular training and yields substantial performance gains over static data augmentation baselines across six validation datasets and two TSFM architectures. The code is available at the link https://github.com/microsoft/TimeCraft.
Authors:Haozheng Luo, Zhuolin Jiang, Md Zahid Hasan, Yan Chen, Soumalya Sarkar
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
Authors:Fangzhou Wu, Sandeep Silwal, Qiuyi, Zhang
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.
Authors:Haimo Fang, Kevin Tan, Jonathan Pipping-Gamon, Giles Hooker
Abstract:
Explainable boosting machines (EBMs) are popular "glass‑box" models that learn a set of univariate functions using boosting trees. These achieve explainability through visualizations of each feature's effect. However, unlike linear model coefficients, uncertainty quantification for the learned univariate functions requires computationally intensive bootstrapping, making it hard to know which features truly matter. We provide an alternative using recent advances in statistical inference for gradient boosting, deriving methods for statistical inference as well as end‑to‑end theoretical guarantees. Using a moving average instead of a sum of trees (Boulevard regularization) allows the boosting process to converge to a feature‑wise kernel ridge regression. This produces asymptotically normal predictions that achieve the minimax‑optimal MSE for fitting Lipschitz GAMs with p features of O(p n^‑2/3), successfully avoiding the curse of dimensionality. We then construct prediction intervals for the response and confidence intervals for each learned univariate function with a runtime independent of the number of datapoints, enabling further explainability within EBMs. Code is available at https://github.com/hetankevin/ebm‑inference.
Authors:Yaohua Zha, Chunlin Fan, Peiyuan Liu, Yong Jiang, Tao Dai, Hai Wu, Shu-Tao Xia
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.
Authors:Vincent Gurgul, Ying Chen, Stefan Lessmann
Abstract:
This paper presents a Quantum Reinforcement Learning (QRL) solution to the dynamic portfolio optimization problem based on Variational Quantum Circuits. The implemented QRL approaches are quantum analogues of the classical neural‑network‑based Deep Deterministic Policy Gradient and Deep Q‑Network algorithms. Through an empirical evaluation on real‑world financial data, we show that our quantum agents achieve risk‑adjusted performance comparable to, and in some cases exceeding, that of classical Deep RL models with several orders of magnitude more parameters. However, while quantum circuit execution is inherently fast at the hardware level, practical deployment on cloud‑based quantum systems introduces substantial latency, making end‑to‑end runtime currently dominated by infrastructural overhead and limiting practical applicability. Taken together, our results suggest that QRL is theoretically competitive with state‑of‑the‑art classical reinforcement learning and may become practically advantageous as deployment overheads diminish. This positions QRL as a promising paradigm for dynamic decision‑making in complex, high‑dimensional, and non‑stationary environments such as financial markets. The complete codebase is released as open source at: https://github.com/VincentGurgul/qrl‑dpo‑public
Authors:Shobhita Sundaram, John Quan, Ariel Kwiatkowski, Kartik Ahuja, Yann Ollivier, Julia Kempe
Abstract:
Can a model learn to escape its own learning plateau? Reinforcement learning methods for finetuning large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretrained LLM leverage latent knowledge to generate an automated curriculum for problems it cannot solve? To explore this, we design SOAR: A self‑improvement framework designed to surface these pedagogical signals through meta‑RL. A teacher copy of the model proposes synthetic problems for a student copy, and is rewarded with its improvement on a small subset of hard problems. Critically, SOAR grounds the curriculum in measured student progress rather than intrinsic proxy rewards. Our study on the hardest subsets of mathematical benchmarks (0/128 success) reveals three core findings. First, we show that it is possible to realize bi‑level meta‑RL that unlocks learning under sparse, binary rewards by sharpening a latent capacity of pretrained models to generate useful stepping stones. Second, grounded rewards outperform intrinsic reward schemes used in prior LLM self‑play, reliably avoiding the instability and diversity collapse modes they typically exhibit. Third, analyzing the generated questions reveals that structural quality and well‑posedness are more critical for learning progress than solution correctness. Our results suggest that the ability to generate useful stepping stones does not require the preexisting ability to actually solve the hard problems, paving a principled path to escape reasoning plateaus without additional curated data.
Authors:Xinyue Zeng, Junhong Lin, Yujun Yan, Feng Guo, Liang Shi, Jun Wu, Dawei Zhou
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.
Authors:Fangxu Yu, Xingang Guo, Lingzhi Yuan, Haoqiang Kang, Hongyu Zhao, Lianhui Qin, Furong Huang, Bin Hu, Tianyi Zhou
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/.
Authors:Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, Aditya Grover
Abstract:
Knowledge distillation improves large language model (LLM) reasoning by compressing the knowledge of a teacher LLM to train smaller LLMs. On‑policy distillation advances this approach by having the student sample its own trajectories while a teacher LLM provides dense token‑level supervision, addressing the distribution mismatch between training and inference in off‑policy distillation methods. However, on‑policy distillation typically requires a separate, often larger, teacher LLM and does not explicitly leverage ground‑truth solutions available in reasoning datasets. Inspired by the intuition that a sufficiently capable LLM can rationalize external privileged reasoning traces and teach its weaker self, we introduce On‑Policy Self‑Distillation (OPSD), a learning algorithm where a single LLM acts as both teacher and student with different contexts. The teacher policy conditions on privileged information (e.g., verified reasoning traces) while the student policy sees only the question; training minimizes the per‑token divergence between these distributions over the student's own rollouts. We demonstrate the efficacy of our method on multiple mathematical reasoning benchmarks, achieving superior token efficiency compared to reinforcement learning methods and better performance over off‑policy distillation methods. Code repo: https://github.com/siyan‑zhao/OPSD.
Authors:Lintang Sutawika, Gokul Swamy, Zhiwei Steven Wu, Graham Neubig
Abstract:
When asked a question in a language less seen in its training data, current reasoning large language models (RLMs) often exhibit dramatically lower performance than when asked the same question in English. In response, we introduce \textttSP3F (Self‑Play with Privileged Pairwise Feedback), a two‑stage framework for enhancing multilingual reasoning without any data in the target language(s). First, we supervise fine‑tune (SFT) on translated versions of English question‑answer pairs to raise base model correctness. Second, we perform RL with feedback from a pairwise judge in a self‑play fashion, with the judge receiving the English reference response as privileged information. Thus, even when none of the model's responses are completely correct, the privileged pairwise judge can still tell which response is better. End‑to‑end, \textttSP3F greatly improves base model performance, even outperforming fully post‑trained models on multiple math and non‑math tasks with less than
of the training data across the single‑language, multilingual, and generalization to unseen language settings.
Authors:Zhiwei Zheng, Kevin Bryson
Abstract:
Motivation: Pathway enrichment analysis is widely used to interpret gene expression data. Standard approaches, such as GSEA, rely on predefined phenotypic labels and pairwise comparisons, which limits their applicability in unsupervised settings. Existing unsupervised extensions, including single‑sample methods, provide pathway‑level summaries but primarily capture linear relationships and do not explicitly model gene‑pathway associations. More recently, deep learning models have been explored to capture non‑linear transcriptomic structure. However, their interpretation has typically relied on generic explainable AI (XAI) techniques designed for feature‑level attribution. As these methods are not designed for pathway‑level interpretation in unsupervised transcriptomic analyses, their effectiveness in this setting remains limited.
Results: To bridge this gap, we introduce LaCoGSEA (Latent Correlation GSEA), an unsupervised framework that integrates deep representation learning with robust pathway statistics. LaCoGSEA employs an autoencoder to capture non‑linear manifolds and proposes a global gene‑latent correlation metric as a proxy for differential expression, generating dense gene rankings without prior labels. We demonstrate that LaCoGSEA offers three key advantages: (i) it achieves improved clustering performance in distinguishing cancer subtypes compared to existing unsupervised baselines; (ii) it recovers a broader range of biologically meaningful pathways at higher ranks compared with linear dimensionality reduction and gradient‑based XAI methods; and (iii) it maintains high robustness and consistency across varying experimental protocols and dataset sizes. Overall, LaCoGSEA provides state‑of‑the‑art performance in unsupervised pathway enrichment analysis.
Availability and implementation: https://github.com/willyzzz/LaCoGSEA
Authors:Sangwon Jang, Taekyung Ki, Jaehyeong Jo, Saining Xie, Jaehong Yoon, Sung Ju Hwang
Abstract:
Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additional training on augmented data, which is computationally expensive and still limited in capturing fine‑grained motion. In this work, we present self‑refining video sampling, a simple method that uses a pre‑trained video generator trained on large‑scale datasets as its own self‑refiner. By interpreting the generator as a denoising autoencoder, we enable iterative inner‑loop refinement at inference time without any external verifier or additional training. We further introduce an uncertainty‑aware refinement strategy that selectively refines regions based on self‑consistency, which prevents artifacts caused by over‑refinement. Experiments on state‑of‑the‑art video generators demonstrate significant improvements in motion coherence and physics alignment, achieving over 70% human preference compared to the default sampler and guidance‑based sampler.
Authors:Yibo Li, Zijie Lin, Ailin Deng, Xuan Zhang, Yufei He, Shuo Ji, Tri Cao, Bryan Hooi
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.
Authors:Tianyi Gong, Can Han, Junxi Wu, Dahong Qian
Abstract:
Dry‑electrode Motor Imagery Electroencephalography (MI‑EEG) enables fast, comfortable, real‑world Brain Computer Interface by eliminating gels and shortening setup for at‑home and wearable use.However, dry recordings pose three main issues: lower Signal‑to‑Noise Ratio with more baseline drift and sudden transients; weaker and noisier data with poor phase alignment across trials; and bigger variances between sessions. These drawbacks lead to larger data distribution shift, making features less stable for MI‑EEG tasks.To address these problems, we introduce STGMFM, a tri‑branch framework tailored for dry‑electrode MI‑EEG, which models complementary spatio‑temporal dependencies via dual graph orders, and captures robust envelope dynamics with a multi‑scale frequency mixing branch, motivated by the observation that amplitude envelopes are less sensitive to contact variability than instantaneous waveforms. Physiologically meaningful connectivity priors guide learning, and decision‑level fusion consolidates a noise‑tolerant consensus. On our collected dry‑electrode MI‑EEG, STGMFM consistently surpasses competitive CNN/Transformer/graph baselines. Codes are available at https://github.com/Tianyi‑325/STGMFM.
Authors:Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang
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.
Authors:Aniket Rege, Arka Sadhu, Yuliang Li, Kejie Li, Ramya Korlakai Vinayak, Yuning Chai, Yong Jae Lee, Hyo Jin Kim
Abstract:
The advent of always‑on personal AI assistants, enabled by all‑day wearable devices such as smart glasses, demands a new level of contextual understanding, one that goes beyond short, isolated events to encompass the continuous, longitudinal stream of egocentric video. Achieving this vision requires advances in long‑horizon video understanding, where systems must interpret and recall visual and audio information spanning days or even weeks. Existing methods, including large language models and retrieval‑augmented generation, are constrained by limited context windows and lack the ability to perform compositional, multi‑hop reasoning over very long video streams. In this work, we address these challenges through EGAgent, an enhanced agentic framework centered on entity scene graphs, which represent people, places, objects, and their relationships over time. Our system equips a planning agent with tools for structured search and reasoning over these graphs, as well as hybrid visual and audio search capabilities, enabling detailed, cross‑modal, and temporally coherent reasoning. Experiments on the EgoLifeQA and Video‑MME (Long) datasets show that our method achieves state‑of‑the‑art performance on EgoLifeQA (57.5%) and competitive performance on Video‑MME (Long) (74.1%) for complex longitudinal video understanding tasks. Code is available at https://github.com/facebookresearch/egagent.
Authors:Longwei Ding, Anhao Zhao, Fanghua Ye, Ziyang Chen, Xiaoyu Shen
Abstract:
Large language models (LLMs) are increasingly costly to deploy, motivating extensive research on model pruning. However, most existing studies focus on instruction‑following LLMs, leaving it unclear whether established pruning strategies transfer to reasoning‑augmented models that explicitly generate long intermediate reasoning traces. In this work, we conduct a controlled study of pruning for both instruction‑following (LLM‑instruct) and reasoning‑augmented (LLM‑think) models. To isolate the effects of pruning, we align pruning calibration and post‑pruning recovery data with each model's original training distribution, which we show yields more stable and reliable pruning behavior. We evaluate static depth pruning, static width pruning, and dynamic pruning across 17 tasks spanning classification, generation, and reasoning. Our results reveal clear paradigm‑dependent differences: depth pruning outperforms width pruning on classification tasks, while width pruning is more robust for generation and reasoning. Moreover, static pruning better preserves reasoning performance, whereas dynamic pruning excels on classification and generation but remains challenging for long‑chain reasoning. These findings underscore the need for pruning strategies that explicitly account for the distinct characteristics of reasoning‑augmented LLMs. Our code is publicly available at https://github.com/EIT‑NLP/LRM‑Pruning.
Authors:Peixuan Han, Yingjie Yu, Jingjun Xu, Jiaxuan You
Abstract:
Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial step in academic communication and peer review, remains largely underexplored. Existing approaches typically rely on off‑the‑shelf LLMs or simple pipelines, which struggle with long‑context understanding and often fail to produce targeted and persuasive responses. In this paper, we propose DRPG, an agentic framework for automatic academic rebuttal generation that operates through four steps: Decompose reviews into atomic concerns, Retrieve relevant evidence from the paper, Plan rebuttal strategies, and Generate responses accordingly. Notably, the Planner in DRPG reaches over 98% accuracy in identifying the most feasible rebuttal direction. Experiments on data from top‑tier conferences demonstrate that DRPG significantly outperforms existing rebuttal pipelines and achieves performance beyond the average human level using only an 8B model. Our analysis further demonstrates the effectiveness of the planner design and its value in providing multi‑perspective and explainable suggestions. We also showed that DRPG works well in a more complex multi‑round setting. These results highlight the effectiveness of DRPG and its potential to provide high‑quality rebuttal content and support the scaling of academic discussions. Codes for this work are available at https://github.com/ulab‑uiuc/DRPG‑RebuttalAgent.
Authors:Brijesh FNU, Viet Thanh Duy Nguyen, Ashima Sharma, Md Harun Rashid Molla, Chengyi Xu, Truong-Son Hy
Abstract:
Dielectric materials are critical building blocks for modern electronics such as sensors, actuators, and transistors. With rapid advances in soft and stretchable electronics for emerging human‑ and robot‑interfacing applications, there is a growing need for high‑performance dielectric elastomers. However, developing soft elastomers that simultaneously exhibit high dielectric constants (k) and low Young's moduli (E) remains a major challenge. Although individual elastomer designs have been reported, structured datasets that systematically integrate molecular sequence, dielectric, and mechanical properties are largely unavailable. To address this gap, we curate a compact, high‑quality dataset of acrylate‑based dielectric elastomers by aggregating experimental results from the past decade. Building on this dataset, we propose a multimodal learning framework leveraging large‑scale pretrained polymer representations. These pretrained embeddings transfer chemical and structural knowledge from vast polymer corpora, enabling accurate few‑shot prediction of dielectric and mechanical properties and accelerating data‑efficient discovery of soft high‑k dielectric elastomers. Our data and implementation are publicly available at: https://github.com/HySonLab/Polymers
Authors:Zhongyu Xiao, Zhiwei Hao, Jianyuan Guo, Yong Luo, Jia Liu, Jie Xu, Han Hu
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.
Authors:Yixin Liu, Kehan Yan, Shiyuan Li, Qingfeng Chen, Shirui Pan
Abstract:
Text anomaly detection (TAD) plays a critical role in various language‑driven real‑world applications, including harmful content moderation, phishing detection, and spam review filtering. While two‑step "embedding‑detector" TAD methods have shown state‑of‑the‑art performance, their effectiveness is often limited by the use of a single embedding model and the lack of adaptability across diverse datasets and anomaly types. To address these limitations, we propose to exploit the embeddings from multiple pretrained language models and integrate them into MCA^2, a multi‑view TAD framework. MCA^2 adopts a multi‑view reconstruction model to effectively extract normal textual patterns from multiple embedding perspectives. To exploit inter‑view complementarity, a contrastive collaboration module is designed to leverage and strengthen the interactions across different views. Moreover, an adaptive allocation module is developed to automatically assign the contribution weight of each view, thereby improving the adaptability to diverse datasets. Extensive experiments on 10 benchmark datasets verify the effectiveness of MCA^2 against strong baselines. The source code of MCA^2 is available at https://github.com/yankehan/MCA2.
Authors:Raja Gond, Aditya K Kamath, Ramachandran Ramjee, Ashish Panwar
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.
Authors:Hao Li, He Cao, Shenyao Peng, Zijing Liu, Bin Feng, Yu Wang, Zhiyuan Yan, Yonghong Tian, Yu Li, Li Yuan
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.
Authors:Jang-Hyun Kim, Dongyoon Han, Sangdoo Yun
Abstract:
Efficient key‑value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade‑off between performance degradation and computational overhead. We propose a novel gating‑based KV cache eviction method for frozen‑weight LLMs that achieves high compression ratios with negligible computational cost. Our approach introduces lightweight sink‑attention gating modules to identify and retain critical KV pairs, and integrates seamlessly into both the prefill and decoding stages. The proposed gate training algorithm relies on forward passes of an LLM, avoiding expensive backpropagation, while achieving strong task generalization through a task‑agnostic reconstruction objective. Extensive experiments across the Qwen2.5‑1M, Qwen3, and Gemma3 families show that our method maintains near‑lossless performance while evicting up to 70% of the KV cache. The results are consistent across a wide range of tasks, including long‑context understanding, code comprehension, and mathematical reasoning, demonstrating the generality of our approach.
Authors:Rahul Bera, Zhenrong Lang, Caroline Hengartner, Konstantinos Kanellopoulos, Rakesh Kumar, Mohammad Sadrosadati, Onur Mutlu
Abstract:
Prefetching and off‑chip prediction are two techniques proposed to hide long memory access latencies in high‑performance processors. In this work, we demonstrate that: (1) prefetching and off‑chip prediction often provide complementary performance benefits, yet (2) naively combining them often fails to realize their full performance potential, and (3) existing prefetcher control policies leave significant room for performance improvement behind.
Our goal is to design a holistic framework that can autonomously learn to coordinate an off‑chip predictor with multiple prefetchers employed at various cache levels. To this end, we propose a new technique called Athena, which models the coordination between prefetchers and off‑chip predictor (OCP) as a reinforcement learning (RL) problem. Athena acts as the RL agent that observes multiple system‑level features (e.g., prefetcher/OCP accuracy, bandwidth usage) over an epoch of program execution, and uses them as state information to select a coordination action (i.e., enabling the prefetcher and/or OCP, and adjusting prefetcher aggressiveness). At the end of every epoch, Athena receives a numerical reward that measures the change in multiple system‑level metrics (e.g., number of cycles taken to execute an epoch). Athena uses this reward to autonomously and continuously learn a policy to coordinate prefetchers with OCP.
Our extensive evaluation using a diverse set of memory‑intensive workloads shows that Athena consistently outperforms prior state‑of‑the‑art coordination policies across a wide range of system configurations with various combinations of underlying prefetchers, OCPs, and main memory bandwidths, while incurring only modest storage overhead. Athena is freely available at https://github.com/CMU‑SAFARI/Athena.
Authors:Sebastian Doerrich, Francesco Di Salvo, Jonas Alle, Christian Ledig
Abstract:
Deep learning models in medical image analysis often struggle with generalizability across domains and demographic groups due to data heterogeneity and scarcity. Traditional augmentation improves robustness, but fails under substantial domain shifts. Recent advances in stylistic augmentation enhance domain generalization by varying image styles but fall short in terms of style diversity or by introducing artifacts into the generated images. To address these limitations, we propose Stylizing ViT, a novel Vision Transformer encoder that utilizes weight‑shared attention blocks for both self‑ and cross‑attention. This design allows the same attention block to maintain anatomical consistency through self‑attention while performing style transfer via cross‑attention. We assess the effectiveness of our method for domain generalization by employing it for data augmentation on three distinct image classification tasks in the context of histopathology and dermatology. Results demonstrate an improved robustness (up to +13% accuracy) over the state of the art while generating perceptually convincing images without artifacts. Additionally, we show that Stylizing ViT is effective beyond training, achieving a 17% performance improvement during inference when used for test‑time augmentation. The source code is available at https://github.com/sdoerrich97/stylizing‑vit .
Authors:Aadam, Monu Verma, Mohamed Abdel-Mottaleb
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.
Authors:Thomas Ziller, Shashikant Ilager, Alessandro Tundo, Ezio Bartocci, Leonardo Mariani, Ivona Brandic
Abstract:
Large language models (LLMs) demonstrate remarkable capabilities, but their broad deployment is limited by significant computational resource demands, particularly energy consumption during inference. Static, one‑model‑fits‑all inference strategies are often inefficient, as they do not exploit the diverse range of available models or adapt to varying query requirements.
This paper presents GreenServ, a dynamic, context‑aware routing framework that optimizes the trade‑off between inference accuracy and energy efficiency. GreenServ extracts lightweight contextual features from each query, including task type, semantic cluster, and text complexity, and routes queries to the most suitable model from a heterogeneous pool, based on observed accuracy and energy usage. We employ a multi‑armed bandit approach to learn adaptive routing policies online. This approach operates under partial feedback, eliminates the need for extensive offline calibration, and streamlines the integration of new models into the inference pipeline.
We evaluated GreenServ across five benchmark tasks and a pool of 16 contemporary open‑access LLMs. Experimental results show that GreenServ consistently outperforms static (single‑model) and random baselines. In particular, compared to random routing, GreenServ achieved a 22% increase in accuracy while reducing cumulative energy consumption by 31%. Finally, we evaluated GreenServ with RouterBench, achieving an average accuracy of 71.7% with a peak accuracy of 75.7%. All artifacts are open‑source and available here: \hrefhttps://github.com/TZData1/llm‑inference‑routerGitHub
Authors:Silong Chen, Yuchuan Luo, Guilin Deng, Yi Liu, Min Xu, Shaojing Fu, Xiaohua Jia
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.
Authors:Chia-Ming Lee, Yu-Fan Lin, Jin-Hui Jiang, Yu-Jou Hsiao, Chih-Chung Hsu, Yu-Lun Liu
Abstract:
Single Image Reflection Separation (SIRS) disentangles mixed images into transmission and reflection layers. Existing methods suffer from transmission‑reflection confusion under nonlinear mixing, particularly in deep decoder layers, due to implicit fusion mechanisms and inadequate multi‑scale coordination. We propose ReflexSplit, a dual‑stream framework with three key innovations. (1) Cross‑scale Gated Fusion (CrGF) adaptively aggregates semantic priors, texture details, and decoder context across hierarchical depths, stabilizing gradient flow and maintaining feature consistency. (2) Layer Fusion‑Separation Blocks (LFSB) alternate between fusion for shared structure extraction and differential separation for layer‑specific disentanglement. Inspired by Differential Transformer, we extend attention cancellation to dual‑stream separation via cross‑stream subtraction. (3) Curriculum training progressively strengthens differential separation through depth‑dependent initialization and epoch‑wise warmup. Extensive experiments on synthetic and real‑world benchmarks demonstrate state‑of‑the‑art performance with superior perceptual quality and robust generalization. Our code is available at https://github.com/wuw2135/ReflexSplit.
Authors:Jianxiong Zhang, Bing Guo, Yuming Jiang, Haobo Wang, Bo An, Sean Du
Abstract:
Large reasoning models (LRMs) often generate long, seemingly coherent reasoning traces yet still produce incorrect answers, making hallucination detection challenging. Although trajectories contain useful signals, directly using trace text or vanilla hidden states for detection is brittle: traces vary in form and detectors can overfit to superficial patterns rather than answer validity. We introduce Answer‑agreement Representation Shaping (ARS), which learns detection‑friendly trace‑conditioned representations by explicitly encoding answer stability. ARS generates counterfactual answers through small latent interventions, specifically, perturbing the trace‑boundary embedding, and labels each perturbation by whether the resulting answer agrees with the original. It then learns representations that bring answer‑agreeing states together and separate answer‑disagreeing ones, exposing latent instability indicative of hallucination risk. The shaped embeddings are plug‑and‑play with existing embedding‑based detectors and require no human annotations during training. Experiments demonstrate that ARS consistently improves detection and achieves substantial gains over strong baselines. Code is available at: https://github.com/radiolab‑ntu/ars_icml2026.
Authors:Jialei Li, Yang Zhang, Yimeng Bai, Shuai Zhu, Ziqi Xue, Xiaoyan Zhao, Dingxian Wang, Frank Yang, Andrew Rabinovich, Xiangnan He
Abstract:
Generative recommendation has recently emerged as a transformative paradigm that directly generates target items, surpassing traditional cascaded approaches. It typically involves two components: a tokenizer that learns item identifiers and a recommender trained on them. Existing methods often decouple tokenization from recommendation or rely on asynchronous alternating optimization, limiting full end‑to‑end alignment. To address this, we unify the tokenizer and recommender under the ultimate recommendation objective via differentiable soft item identifiers, enabling joint end‑to‑end training. However, this introduces three challenges: training‑inference discrepancy due to soft‑to‑hard mismatch, item identifier collapse from codeword usage imbalance, and collaborative signal deficiency due to an overemphasis on fine‑grained token‑level semantics.
To tackle these challenges, we propose UniGRec, a unified generative recommendation framework that addresses them from three perspectives. UniGRec employs Annealed Inference Alignment during tokenization to smoothly bridge soft training and hard inference, a Codeword Uniformity Regularization to prevent identifier collapse and encourage codebook diversity, and a Dual Collaborative Distillation mechanism that distills collaborative priors from a lightweight teacher model to jointly guide both the tokenizer and the recommender. Extensive experiments on real‑world datasets demonstrate that UniGRec consistently outperforms state‑of‑the‑art baseline methods. Our codes are available at https://github.com/Jialei‑03/UniGRec.
Authors:Zichuan Yang, Yiming Xing
Abstract:
We study the problem of identifying an anomalous subset of streams under correlated noise, motivated by monitoring and security in cyber‑physical systems. This problem can be viewed as a form of combinatorial pure exploration, where each stream plays the role of an arm and measurements must be allocated sequentially under uncertainty. Existing combinatorial bandit and hypothesis testing methods typically assume independent observations and fail to exploit correlation for efficient measurement design. We propose ECC‑AHT, an adaptive algorithm that selects continuous, constrained measurements to maximize Chernoff information between competing hypotheses, enabling active noise cancellation through differential sensing. ECC‑AHT achieves optimal sample complexity guarantees and significantly outperforms state‑of‑the‑art baselines in both synthetic and real‑world correlated environments. The code is available on https://github.com/VincentdeCristo/ECC‑AHT
Authors:Zihan Huang, Rohan Surana, Zhouhang Xie, Junda Wu, Yu Xia, Julian McAuley
Abstract:
Entity matching is a crucial component in various recommender systems, including conversational recommender systems (CRS) and knowledge‑based recommender systems. However, the lack of rigorous evaluation frameworks for cross‑dataset entity matching impedes progress in areas such as LLM‑driven conversational recommendations and knowledge‑grounded dataset construction.
In this paper, we introduce Reddit‑Amazon‑EM, a novel dataset comprising naturally occurring items from Reddit and the Amazon '23 dataset. Through careful manual annotation, we identify corresponding movies across Reddit‑Movies and Amazon'23, two existing recommender system datasets with inherently overlapping catalogs. Leveraging Reddit‑Amazon‑EM, we conduct a comprehensive evaluation of state‑of‑the‑art entity matching methods, including rule‑based, graph‑based, lexical‑based, embedding‑based, and LLM‑based approaches.
For reproducible research, we release our manually annotated entity matching gold set and provide the mapping between the two datasets using the best‑performing method from our experiments. This serves as a valuable resource for advancing future work on entity matching in recommender systems.Data and Code are accessible at: https://github.com/huang‑zihan/Reddit‑Amazon‑Entity‑Matching.
Authors:Ruoqing Zheng, Chang Sun, Qibin Liu, Lauri Laatu, Arianna Cox, Benedikt Maier, Alexander Tapper, Jose G. F. Coutinho, Wayne Luk, Zhiqiang Que
Abstract:
We present JetFormer, a versatile and scalable encoder‑only Transformer architecture for particle jet tagging at the Large Hadron Collider (LHC). Unlike prior approaches that are often tailored to specific deployment regimes, JetFormer is designed to operate effectively across the full spectrum of jet tagging scenarios, from high‑accuracy offline analysis to ultra‑low‑latency online triggering. The model processes variable‑length sets of particle features without relying on input of explicit pairwise interactions, yet achieves competitive or superior performance compared to state‑of‑the‑art methods. On the large‑scale JetClass dataset, a large‑scale JetFormer matches the accuracy of the interaction‑rich ParT model (within 0.7%) while using 37.4% fewer FLOPs, demonstrating its computational efficiency and strong generalization. On benchmark HLS4ML 150P datasets, JetFormer consistently outperforms existing models such as MLPs, Deep Sets, and Interaction Networks by 3‑4% in accuracy. To bridge the gap to hardware deployment, we further introduce a hardware‑aware optimization pipeline based on multi‑objective hyperparameter search, yielding compact variants like JetFormer‑tiny suitable for FPGA‑based trigger systems with sub‑microsecond latency requirements. Through structured pruning and quantization, we show that JetFormer can be aggressively compressed with minimal accuracy loss. By unifying high‑performance modeling and deployability within a single architectural framework, JetFormer provides a practical pathway for deploying Transformer‑based jet taggers in both offline and online environments at the LHC. Code is available at https://github.com/walkieq/JetFormer.
Authors:Yinkai Wang, Yan Zhou Chen, Xiaohui Chen, Li-Ping Liu, Soha Hassoun
Abstract:
Small‑molecule identification from tandem mass spectrometry (MS/MS) remains a bottleneck in untargeted settings where spectral libraries are incomplete. While deep learning offers a solution, current approaches typically fall into two extremes: explicit generative models that construct molecular graphs atom‑by‑atom, or joint contrastive models that learn cross‑modal subspaces from scratch. We introduce SpecBridge, a novel implicit alignment framework that treats structure identification as a geometric alignment problem. SpecBridge fine‑tunes a self‑supervised spectral encoder (DreaMS) to project directly into the latent space of a frozen molecular foundation model (ChemBERTa), and then performs retrieval by cosine similarity to a fixed bank of precomputed molecular embeddings. Across MassSpecGym, Spectraverse, and MSnLib benchmarks, SpecBridge improves top‑1 retrieval accuracy by roughly 20‑25% relative to strong neural baselines, while keeping the number of trainable parameters small. These results suggest that aligning to frozen foundation models is a practical, stable alternative to designing new architectures from scratch. The code for SpecBridge is released at https://github.com/HassounLab/SpecBridge.
Authors:Seyyed Saeid Cheshmi, Hahnemann Ortiz, James Mooney, Dongyeop Kang
Abstract:
Vision‑language models (VLMs) have demonstrated strong reasoning abilities in literal multimodal tasks such as visual mathematics and science question answering. However, figurative language, such as sarcasm, humor, and metaphor, remains a significant challenge, as it conveys intent and emotion through subtle incongruities between expressed and intended meanings. In multimodal settings, accompanying images can amplify or invert textual meaning, demanding models that reason across modalities and account for subjectivity. We propose a three‑step framework for developing efficient multimodal reasoning models that can (i) interpret multimodal figurative language, (ii) provide transparent reasoning traces, and (iii) generalize across multiple figurative styles. Experiments across four styles show that (1) incorporating reasoning traces substantially improves multimodal figurative understanding, (2) reasoning learned in one style can transfer to others, especially between related styles like sarcasm and humor, and (3) training jointly across styles yields a generalized reasoning VLM that outperforms much larger open‑ and closed‑source models. Our findings show that lightweight VLMs with verifiable reasoning achieve robust cross‑style generalization while providing inspectable reasoning traces for multimodal tasks. The code and implementation are available at https://github.com/scheshmi/CrossStyle‑MMR.
Authors:Adrian Tkachenko, Sepehr Salem, Ayotomiwa Ezekiel Adeniyi, Zulal Bingol, Mohammed Nayeem Uddin, Akshat Prasanna, Alexander Zelikovsky, Serghei Mangul, Can Alkan, Mohammed Alser
Abstract:
Motivation: High‑throughput sequencing (HTS) enables population‑scale genomics but generates massive datasets, creating bottlenecks in storage, transfer, and analysis. FASTQ, the standard format for over two decades, stores one byte per base and one byte per quality score, leading to inefficient I/O, high storage costs, and redundancy. Existing compression tools can mitigate some issues, but often introduce costly decompression or complex dependency issues. Results: We introduce FASTR, a lossless, computation‑native successor to FASTQ that encodes each nucleotide together with its base quality score into a single 8‑bit value. FASTR reduces file size by at least 2x while remaining fully reversible and directly usable for downstream analyses. Applying general‑purpose compression tools on FASTR consistently yields higher compression ratios, 2.47, 3.64, and 4.8x faster compression, and 2.34, 1.96, 1.75x faster decompression than on FASTQ across Illumina, HiFi, and ONT reads. FASTR is machine‑learning‑ready, allowing reads to be consumed directly as numerical vectors or image‑like representations. We provide a highly parallel software ecosystem for FASTQ‑FASTR conversion and show that FASTR integrates with existing tools, such as minimap2, with minimal interface changes and no performance overhead. By eliminating decompression costs and reducing data movement, FASTR lays the foundation for scalable genomics analyses and real‑time sequencing workflows. Availability and Implementation: https://github.com/ALSER‑Lab/FASTR
Authors:Yonghan Jung, Bogyeong Kang
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.
Authors:Inderjeet Singh, Eleonore Vissol-Gaudin, Andikan Otung, Motoyoshi Sekiya
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.
Authors:Ole Stüven, Keno Moenck, Thorsten Schüppstuhl
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.
Authors:Haoxuan Li, He Chang, Yunshan Ma, Yi Bin, Yang Yang, See-Kiong Ng, Tat-Seng Chua
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.
Authors:Wei Zhou, Jun Zhou, Haoyu Wang, Zhenghao Li, Qikang He, Shaokun Han, Guoliang Li, Xuanhe Zhou, Yeye He, Chunwei Liu, Zirui Tang, Bin Wang, Shen Tang, Kai Zuo, Yuyu Luo, Zhenzhe Zheng, Conghui He, Jingren Zhou, Fan Wu
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.
Authors:Basile Van Hoorick, Dian Chen, Shun Iwase, Pavel Tokmakov, Muhammad Zubair Irshad, Igor Vasiljevic, Swati Gupta, Fangzhou Cheng, Sergey Zakharov, Vitor Campagnolo Guizilini
Abstract:
Modern generative video models excel at producing convincing, high‑quality outputs, but struggle to maintain multi‑view and spatiotemporal consistency in highly dynamic real‑world environments. In this work, we introduce AnyView, a diffusion‑based video generation framework for \emphdynamic view synthesis with minimal inductive biases or geometric assumptions. We leverage multiple data sources with various levels of supervision, including monocular (2D), multi‑view static (3D) and multi‑view dynamic (4D) datasets, to train a generalist spatiotemporal implicit representation capable of producing zero‑shot novel videos from arbitrary camera locations and trajectories. We evaluate AnyView on standard benchmarks, showing competitive results with the current state of the art, and propose AnyViewBench, a challenging new benchmark tailored towards \emphextreme dynamic view synthesis in diverse real‑world scenarios. In this more dramatic setting, we find that most baselines drastically degrade in performance, as they require significant overlap between viewpoints, while AnyView maintains the ability to produce realistic, plausible, and spatiotemporally consistent videos when prompted from \emphany viewpoint. Results, data, code, and models can be viewed at: https://tri‑ml.github.io/AnyView/
Authors:Lin Huang, Chengxiang Huang, Ziang Wang, Yiyue Du, Chu Wang, Haocheng Lu, Yunyang Li, Xiaoli Liu, Arthur Jiang, Jia Zhang
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.
Authors:Erik Wallin, Fredrik Kahl, Lars Hammarstrand
Abstract:
Hierarchical open‑set classification handles previously unseen classes by assigning them to the most appropriate high‑level category in a class taxonomy. We extend this paradigm to the semi‑supervised setting, enabling the use of large‑scale, uncurated datasets containing a mixture of known and unknown classes to improve the hierarchical open‑set performance. To this end, we propose a teacher‑student framework based on pseudo‑labeling. Two key components are introduced: 1) subtree pseudo‑labels, which provide reliable supervision in the presence of unknown data, and 2) age‑gating, a mechanism that mitigates overconfidence in pseudo‑labels. Experiments show that our framework outperforms self‑supervised pretraining followed by supervised adaptation, and even matches the fully supervised counterpart when using only 20 labeled samples per class on the iNaturalist19 benchmark. Our code is available at https://github.com/walline/semihoc.
Authors:Feixiang Zheng, Yu Wu, Cecilia Mascolo, Ting Dang
Abstract:
Time series data from the Intensive Care Unit (ICU) provides critical information for patient monitoring. While recent advancements in applying Large Language Models (LLMs) to time series modeling (TSM) have shown great promise, their effectiveness on the irregular ICU data, characterized by particularly high rates of missing values, remains largely unexplored. This work investigates two key components underlying the success of LLMs for TSM: the time series encoder and the multimodal alignment strategy. To this end, we establish a systematic testbed to evaluate their impact across various state‑of‑the‑art LLM‑based methods on benchmark ICU datasets against strong supervised and self‑supervised baselines. Results reveal that the encoder design is more critical than the alignment strategy. Encoders that explicitly model irregularity achieve substantial performance gains, yielding an average AUPRC increase of 12.8% over the vanilla Transformer. While less impactful, the alignment strategy is also noteworthy, with the best‑performing semantically rich, fusion‑based strategy achieving a modest 2.9% improvement over cross‑attention. However, LLM‑based methods require at least 10× longer training than the best‑performing irregular supervised models, while delivering only comparable performance. They also underperform in data‑scarce few‑shot learning settings. These findings highlight both the promise and current limitations of LLMs for irregular ICU time series. The code is available at https://github.com/mHealthUnimelb/LLMTS.
Authors:Hannah Cyberey, Yangfeng Ji, David Evans
Abstract:
Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators. Current audits of large language models (LLMs) primarily rely on black‑box evaluations that assess model behavior only through input‑output testing. These methods are limited to tests constructed in the input space, often generated by heuristics. In addition, many socially relevant model properties (e.g., gender bias) are abstract and difficult to measure through text‑based inputs alone. To address these limitations, we propose a white‑box sensitivity auditing framework for LLMs that leverages activation steering to conduct more rigorous assessments through model internals. Our auditing method conducts internal sensitivity tests by manipulating key concepts relevant to the model's intended function for the task. We demonstrate its application to bias audits in four simulated high‑stakes LLM decision tasks. Our method consistently indicates substantial dependence on protected attributes in model predictions, even in settings where standard black‑box evaluations suggest little or no bias. Our code is openly available at https://github.com/hannahxchen/llm‑steering‑audit
Authors:Dohun Lee, Chun-Hao Paul Huang, Xuelin Chen, Jong Chul Ye, Duygu Ceylan, Hyeonho Jeong
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.
Authors:Bing Xu, Terry Chen, Fengzhe Zhou, Tianqi Chen, Yangqing Jia, Vinod Grover, Haicheng Wu, Wei Liu, Craig Wittenbrink, Wen-mei Hwu, Roger Bringmann, Ming-Yu Liu, Luis Ceze, Michael Lightstone, Humphrey Shi
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.
Authors:Mert Yuksekgonul, Daniel Koceja, Xinhao Li, Federico Bianchi, Jed McCaleb, Xiaolong Wang, Jan Kautz, Yejin Choi, James Zou, Carlos Guestrin, Yu Sun
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.
Authors:Dong Xu, Jiantao Wu, Qihua Pan, Sisi Yuan, Zexuan Zhu, Junkai Ji
Abstract:
Drug‑drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmarks, they often fail to generalize to realistic deployment scenarios, where most candidate drug pairs involve previously unseen drugs and validated interactions are scarce. We demonstrate that proximity in the embedding spaces of prevailing molecule‑centric DDI models does not reliably correspond to interaction labels, and that simply scaling up model capacity therefore fails to improve generalization. To address these limitations, we propose GenRel‑DDI, a generalizable relation learning framework that reformulates DDI prediction as a relation‑centric learning problem, in which interaction representations are learned independently of drug identities. This relation‑level abstraction enables the capture of transferable interaction patterns that generalize to unseen drugs and novel drug pairs. Extensive experiments across multiple benchmark demonstrate that GenRel‑DDI consistently and significantly outperforms state‑of‑the‑art methods, with particularly large gains on strict entity‑disjoint evaluations, highlighting the effectiveness and practical utility of relation learning for robust DDI prediction. The code is available at https://github.com/SZU‑ADDG/GenRel‑DDI.
Authors:Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang, Haoyu Wang, Zhiyou Xiao, Jingze Shi, Yuyu Luo, Wentao Zhang, Chunlei Men, Guang Liu, Yonghua Lin
Abstract:
The performance of modern AI systems is fundamentally constrained by the quality of their underlying kernels, which translate high‑level algorithmic semantics into low‑level hardware operations. Achieving near‑optimal kernels requires expert‑level understanding of hardware architectures and programming models, making kernel engineering a critical but notoriously time‑consuming and non‑scalable process. Recent advances in large language models (LLMs) and LLM‑based agents have opened new possibilities for automating kernel generation and optimization. LLMs are well‑suited to compress expert‑level kernel knowledge that is difficult to formalize, while agentic systems further enable scalable optimization by casting kernel development as an iterative, feedback‑driven loop. Rapid progress has been made in this area. However, the field remains fragmented, lacking a systematic perspective for LLM‑driven kernel generation. This survey addresses this gap by providing a structured overview of existing approaches, spanning LLM‑based approaches and agentic optimization workflows, and systematically compiling the datasets and benchmarks that underpin learning and evaluation in this domain. Moreover, key open challenges and future research directions are further outlined, aiming to establish a comprehensive reference for the next generation of automated kernel optimization. To keep track of this field, we maintain an open‑source GitHub repository at https://github.com/flagos‑ai/awesome‑LLM‑driven‑kernel‑generation.
Authors:Jingjing Bai, Yoshinobu Kawahara
Abstract:
Transformer‑based models, despite their promise for long‑term time series forecasting (LTSF), suffer from an inherent low‑pass filtering effect that limits their effectiveness. This issue arises due to undifferentiated propagation of frequency components across layers, causing a progressive attenuation of high‑frequency information crucial for capturing fine‑grained temporal variations. To address this limitation, we propose Dualformer, a principled dual‑domain framework that rethinks frequency modeling from a layer‑wise perspective. Dualformer introduces three key components: (1) a dual‑branch architecture that concurrently models complementary temporal patterns in both time and frequency domains; (2) a hierarchical frequency sampling module that allocates distinct frequency bands to different layers, preserving high‑frequency details in lower layers while modeling low‑frequency trends in deeper layers; and (3) a periodicity‑aware weighting mechanism that dynamically balances contributions from the dual branches based on the harmonic energy ratio of inputs, supported theoretically by a derived lower bound. This design enables structured frequency modeling and adaptive integration of time‑frequency features, effectively preserving high‑frequency information and enhancing generalization. Extensive experiments conducted on eight widely used benchmarks demonstrate Dualformer's robustness and superior performance, particularly on heterogeneous or weakly periodic data. Our code is publicly available at https://github.com/Akira‑221/Dualformer.
Authors:William Huang, Siyou Pei, Leyi Zou, Eric J. Gonzalez, Ishan Chatterjee, Yang Zhang
Abstract:
The proliferation of XR devices has made egocentric hand pose estimation a vital task, yet this perspective is inherently challenged by frequent finger occlusions. To address this, we propose a novel approach that leverages the rich information in dorsal hand skin deformation, unlocked by recent advances in dense visual featurizers. We introduce a dual‑stream delta encoder that learns pose by contrasting features from a dynamic hand with a baseline relaxed position. Our evaluation demonstrates that, using only cropped dorsal images, our method reduces the Mean Per Joint Angle Error (MPJAE) by 18% in self‑occluded scenarios (fingers >= 50% occluded) compared to state‑of‑the‑art techniques that depend on the whole hand's geometry and large model backbones. Consequently, our method not only enhances the reliability of downstream tasks like index finger pinch and tap estimation in occluded scenarios but also unlocks new interaction paradigms, such as detecting isometric force for a surface "click" without visible movement while minimizing model size.
Authors:Jingwei Song, Xinyu Wang, Hanbin Wang, Xiaoxuan Lei, Bill Shi, Shixin Han, Eric Yang, Xiao-Wen Chang, Lynn Ai
Abstract:
Speculative Decoding (SD) accelerates autoregressive large language model (LLM) inference by decoupling generation and verification. While recent methods improve draft quality by tightly coupling the drafter with the target model, the verification mechanism itself remains largely unchanged, relying on strict token‑level rejection sampling. In practice, modern LLMs frequently operate in low‑margin regimes where the target model exhibits weak preference among top candidates. In such cases, rejecting plausible runner‑up tokens yields negligible information gain while incurring substantial rollback cost, leading to a fundamental inefficiency in verification. We propose Margin‑Aware Speculative Verification, a training‑free and domain‑agnostic verification strategy that adapts to the target model's local decisiveness. Our method conditions verification on decision stability measured directly from the target logits and relaxes rejection only when strict verification provides minimal benefit. Importantly, the approach modifies only the verification rule and is fully compatible with existing target‑coupled speculative decoding frameworks. Extensive experiments across model scales ranging from 8B to 235B demonstrate that our method delivers consistent and significant inference speedups over state‑of‑the‑art baselines while preserving generation quality across diverse benchmarks. The code is available at https://github.com/5SSjw/MARS.
Authors:Huayu Li, ZhengXiao He, Siyuan Tian, Jinghao Wen, Ao Li
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.
Authors:Md Nabi Newaz Khan, Abdullah Arafat Miah, Yu Bi
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.
Authors:Fahd Seddik, Abdulrahman Elbedewy, Gaser Sami, Mohamed Abdelmoniem, Yahia Zakaria
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.
Authors:Pablo Messina, Andrés Villa, Juan León Alcázar, Karen Sánchez, Carlos Hinojosa, Denis Parra, Álvaro Soto, Bernard Ghanem
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
Authors:Francesca Pia Panaccione, Carlo Sgaravatti, Pietro Pinoli
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
Authors:Rishit Chugh
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.
Authors:Zanlin Ni, Shenzhi Wang, Yang Yue, Tianyu Yu, Weilin Zhao, Yeguo Hua, Tianyi Chen, Jun Song, Cheng Yu, Bo Zheng, Gao Huang
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
Authors:Bostan Khan, Masoud Daneshtalab
Abstract:
Deploying federated learning across heterogeneous IoT device fleets requires tailored neural network architectures for each device class, yet existing Federated Neural Architecture Search (FedNAS) methods suffer from unguided supernet training and prohibitively costly post‑training search pipelines that demand over 20 GPU‑hours per deployment target. We introduce DeepFedNAS, a two‑phase framework built on a multi‑objective fitness function that synthesizes information‑theoretic network metrics with architectural heuristics. In the first phase, Federated Pareto Optimal Supernet Training replaces random subnet sampling with a pre‑computed cache of elite, high‑fitness architectures, yielding a superior supernet. In the second phase, a Predictor‑Free Search uses this fitness function as a zero‑cost accuracy proxy, discovering hardware‑optimized subnets in ~20 seconds, a ~61x speedup over the baseline pipeline. Experiments on CIFAR‑10, CIFAR‑100, and CINIC‑10 demonstrate state‑of‑the‑art accuracy (up to +1.21% on CIFAR‑100), a 2.8x reduction in per‑round transmission size, and robust performance under extreme non‑IID conditions (α = 0.1), making DeepFedNAS practical for scalable, communication‑constrained IoT federations. Source code: https://github.com/bostankhan6/DeepFedNAS
Authors:Oleg Shchendrigin, Egor Cherepanov, Alexey K. Kovalev, Aleksandr I. Panov
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/
Authors:Adam Rokah, Daniel Veress, Caleb Caulk, Sourav Sharan
Abstract:
Mixture‑of‑Experts (MoE) architectures enable conditional computation by routing inputs to multiple expert subnetworks and are often motivated as a mechanism for scaling large language models. In this project, we instead study MoE behavior in an image classification setting, focusing on predictive performance, expert utilization, and generalization. We compare dense, SoftMoE, and SparseMoE classifier heads on the CIFAR10 dataset under comparable model capacity. Both MoE variants achieve slightly higher validation accuracy than the dense baseline while maintaining balanced expert utilization through regularization, avoiding expert collapse. To analyze generalization, we compute Hessian‑based sharpness metrics at convergence, including the largest eigenvalue and trace of the loss Hessian, evaluated on both training and test data. We find that SoftMoE exhibits higher sharpness by these metrics, while Dense and SparseMoE lie in a similar curvature regime, despite all models achieving comparable generalization performance. Complementary loss surface perturbation analyses reveal qualitative differences in non‑local behavior under finite parameter perturbations between dense and MoE models, which help contextualize curvature‑based measurements without directly explaining validation accuracy. We further evaluate empirical inference efficiency and show that naively implemented conditional routing does not yield inference speedups on modern hardware at this scale, highlighting the gap between theoretical and realized efficiency in sparse MoE models.
Authors:Jannis Becktepe, Aleksandra Franz, Nils Thuerey, Sebastian Peitz
Abstract:
Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as existing studies rely on heterogeneous observation and actuation schemes, numerical setups, and evaluation protocols. Current AFC benchmarks attempt to address these issues but heavily rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi‑agent support. To overcome these limitations, we introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC. Built entirely in PyTorch on top of the GPU‑accelerated PICT solver, FluidGym runs in a single Python stack, requires no external CFD software, and provides standardized evaluation protocols. We present baseline results with PPO, SAC, DPC, and TD‑MPC, and release all environments, datasets, and trained models as public resources. FluidGym enables systematic comparison of control methods, establishes a scalable foundation for future research in learning‑based flow control, and is available at github.com/safe‑autonomous‑systems/fluidgym.
Authors:Michael Feil, Julius Lipp
Abstract:
Batch inference workloads for causal transformer models frequently process sequences that share common prefixes, such as system prompts, few‑shot examples, or shared queries. Standard inference engines treat each sequence independently, redundantly recomputing identical MLP activations for every copy of the shared prefix. We introduce RadixMLP, a technique that exploits the position‑wise nature of MLPs, LayerNorms, linear projections, and embeddings to eliminate this redundancy. RadixMLP dynamically maps batches to a prefix trie, gathering shared segments into a compressed representation for position‑wise computation and scattering results back only at attention boundaries. RadixMLP is stateless and operates within a single forward pass. In end‑to‑end serving benchmarks on MS~MARCO v1.1 with Qwen3 models (0.6B to 8B parameters), RadixMLP achieves 1.44‑1.59× speedups in realistic reranking workloads, with up to 5× speedups on synthetic benchmarks with longer shared prefixes. Our code is available at https://github.com/michaelfeil/radix‑mlp.
Authors:Harry Mead, Bruno Lacerda, Jakob Foerster, Nick Hawes
Abstract:
Unsupervised Environment Design (UED) seeks to automatically generate training curricula for reinforcement learning (RL) agents, with the goal of improving generalisation and zero‑shot performance. However, designing effective curricula remains a difficult problem, particularly in settings where small subsets of environment parameterisations result in significant increases in the complexity of the required policy. Current methods struggle with a difficult credit assignment problem and rely on regret approximations that fail to identify challenging levels, both of which are compounded as the size of the environment grows. We propose Dynamic Environment Generation for UED (DEGen) to enable a denser level generator reward signal, reducing the difficulty of credit assignment and allowing for UED to scale to larger environment sizes. We also introduce a new regret approximation, Maximised Negative Advantage (MNA), as a significantly improved metric to optimise for, that better identifies more challenging levels. We show empirically that MNA outperforms current regret approximations and when combined with DEGen, consistently outperforms existing methods, especially as the size of the environment grows. We have made all our code available here: https://github.com/HarryMJMead/Dynamic‑Environment‑Generation‑for‑UED.
Authors:Zhihao Chen, Zirui Gong, Jianting Ning, Yanjun Zhang, Leo Yu Zhang
Abstract:
Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its discrete, ranking‑based update mechanism. Unlike traditional FL methods that rely on model updates, FRL leverages discrete rankings as a communication parameter between clients and the server. This approach significantly reduces communication costs and limits an adversary's ability to scale or optimize malicious updates in the continuous space, thereby enhancing its robustness. This makes FRL particularly appealing for applications where system security and data privacy are crucial, such as web‑based auction and bidding platforms. While FRL substantially reduces the attack surface, we demonstrate that it remains vulnerable to a new class of local model poisoning attack, i.e., fine‑grained control attacks. We introduce the Edge Control Attack (ECA), the first fine‑grained control attack tailored to ranking‑based FL frameworks. Unlike conventional denial‑of‑service (DoS) attacks that cause conspicuous disruptions, ECA enables an adversary to precisely degrade a competitor's accuracy to any target level while maintaining a normal‑looking convergence trajectory, thereby avoiding detection. ECA operates in two stages: (i) identifying and manipulating Ascending and Descending Edges to align the global model with the target model, and (ii) widening the selection boundary gap to stabilize the global model at the target accuracy. Extensive experiments across seven benchmark datasets and nine Byzantine‑robust aggregation rules (AGRs) show that ECA achieves fine‑grained accuracy control with an average error of only 0.224%, outperforming the baseline by up to 17x. Our findings highlight the need for stronger defenses against advanced poisoning attacks. Our code is available at: https://github.com/Chenzh0205/ECA
Authors:Mingyang Xie, Numair Khan, Tianfu Wang, Naina Dhingra, Seonghyeon Nam, Haitao Yang, Zhuo Hui, Christopher Metzler, Andrea Vedaldi, Hamed Pirsiavash, Lei Luo
Abstract:
Given a monocular video, the goal of video re‑rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrically unconditioned models lack spatial awareness, leading to drift and deformation under viewpoint changes. On the other hand, geometrically‑conditioned models depend on estimated depth and explicit reconstruction, making them susceptible to depth inaccuracies and calibration errors.
We propose to address these challenges by using the implicit geometric knowledge embedded in the latent space of a large 4D reconstruction model to condition the video generation process. These latents capture scene structure in a continuous space without explicit reconstruction. Therefore, they provide a flexible representation that allows the pretrained diffusion prior to regularize errors more effectively. By jointly conditioning on these latents and source camera poses, we demonstrate that our model achieves state‑of‑the‑art results on the video re‑rendering task. Project webpage is https://lavr‑4d‑scene‑rerender.github.io/.
Authors:Yuyu Liu, Jiannan Yang, Ziyang Yu, Weishen Pan, Fei Wang, Tengfei Ma
Abstract:
Missing data in single‑cell sequencing datasets poses significant challenges for extracting meaningful biological insights. However, existing imputation approaches, which often assume uniformity and data completeness, struggle to address cases with large patches of missing data. In this paper, we present CROT (Cluster‑Regularized Optimal Transport), an optimal transport‑based imputation algorithm designed to handle patch‑based missing data in tabular formats. Our approach effectively captures the underlying data structure in the presence of significant missingness. Notably, it achieves superior imputation accuracy while significantly reducing runtime, demonstrating its scalability and efficiency for large‑scale datasets. This work introduces a robust solution for imputation in heterogeneous, high‑dimensional datasets with structured data absence, addressing critical challenges in both biological and clinical data analysis. Our code is available on GitHub, https://github.com/yuyuliu11037/CROT.
Authors:Po-Kai Chiu, Hung-Hsuan Chen
Abstract:
The requirement for expert annotations limits the effectiveness of deep learning for medical image analysis. Although 3D self‑supervised methods like volume contrast learning (VoCo) are powerful and partially address the labeling scarcity issue, their high computational cost and memory consumption are barriers. We propose 2D‑VoCo, an efficient adaptation of the VoCo framework for slice‑level self‑supervised pre‑training that learns spatial‑semantic features from unlabeled 2D CT slices via contrastive learning. The pre‑trained CNN backbone is then integrated into a CNN‑LSTM architecture to classify multi‑organ injuries. In the RSNA 2023 Abdominal Trauma dataset, 2D‑VoCo pre‑training significantly improves mAP, precision, recall, and RSNA score over training from scratch. Our framework provides a practical method to reduce the dependency on labeled data and enhance model performance in clinical CT analysis. We release the code for reproducibility. https://github.com/tkz05/2D‑VoCo‑CT‑Classifier
Authors:Andrew Crossman, Jonah Dodd, Viralam Ramamurthy Chaithanya Kumar, Riyaz Mohammed, Andrew R. Plummer, Chandra Sekharudu, Deepak Warrier, Mohammad Yekrangian
Abstract:
MITRE ATT&CK is a cybersecurity knowledge base that organizes threat actor and cyber‑attack information into a set of tactics describing the reasons and goals threat actors have for carrying out attacks, with each tactic having a set of techniques that describe the potential methods used in these attacks. One major application of ATT&CK is the use of its tactic and technique hierarchy by security specialists as a framework for annotating cyber‑threat intelligence reports, vulnerability descriptions, threat scenarios, inter alia, to facilitate downstream analyses. To date, the tagging process is still largely done manually. In this technical note, we provide a stratified "task space" characterization of the MITRE ATT&CK text tagging task for organizing previous efforts toward automation using AIML methods, while also clarifying pathways for constructing new methods. To illustrate one of the pathways, we use the task space strata to stage‑wise construct our own multi‑label hierarchical classification models for the text tagging task via experimentation over general cyber‑threat intelligence text ‑‑ using shareable computational tools and publicly releasing the models to the security community (via https://github.com/jpmorganchase/MITRE_models). Our multi‑label hierarchical approach yields accuracy scores of roughly 94% at the tactic level, as well as accuracy scores of roughly 82% at the technique level. The models also meet or surpass state‑of‑the‑art performance while relying only on classical machine learning methods ‑‑ removing any dependence on LLMs, RAG, agents, or more complex hierarchical approaches. Moreover, we show that GPT‑4o model performance at the tactic level is significantly lower (roughly 60% accuracy) than our own approach. We also extend our baseline model to a corpus of threat scenarios for financial applications produced by subject matter experts.
Authors:Deming Chen, Vijay Ganesh, Weikai Li, Yingyan Celine Lin, Yong Liu, Subhasish Mitra, David Z. Pan, Ruchir Puri, Jason Cong, Yizhou Sun
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/.
Authors:Alistair Cheong, Haolin Cong, Tyler Yang, Dustin Miao
Abstract:
Many LLM‑based open‑ended search systems freeze the foundation model that proposes improvements to existing solutions, which may bottleneck long‑run progress. Recent work has explored updating the proposal model at test time [arXiv:2511.23473], but the update strategy is still typically hand‑specified. Therefore, this study investigated whether an LLM can use task feedback to decide how it should update its weights. For tractability, we focused on the simpler case where there is only one round of self‑improvement, and restricted the update operator to self‑supervised next token prediction (NTP), leaving the model freedom in choosing its training data and key NTP hyperparameters. Using the Self‑Adapting Language Models (SEAL) [arXiv:2506.10943] framework as a testbed, we relaxed its fixed human template constraint and allowed the model to generate its own self‑edit templates, thereby giving it more control over its training data and hyperparameters. Two variants were studied, differing in whether template generation was conditioned on a lightweight archive of past templates. In SEAL's Single‑Passage Knowledge Incorporation setting with Qwen3‑8B on SQuAD [arXiv:1606.05250], the no‑archive variant performed comparably to the weaker "Implications" baseline, while the archive variant outperformed "Implications" and approached the strongest human‑designed "Rewrite" baseline without surpassing it. Further analysis of collapse in the model's exploration revealed that a naive archive can confer some short‑term robustness but can also accelerate homogenization, suggesting that explicit novelty pressure may be required to consistently advance beyond carefully optimized human strategies. Our code is available at https://github.com/cheongalc/search‑self‑edit‑strategies .
Authors:Leyi Zhao, Weijie Huang, Yitong Guo, Jiang Bian, Chenghong Wang, Xuhong Zhang
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
Authors:Sunghyun Kim, Seokwoo Yun, Youngseo Yun, Youngrak Lee, Sangsoo Lim
Abstract:
Motivation: Developing high‑performing bioinformatics models typically requires repeated cycles of hypothesis formulation, architectural redesign, and empirical validation, making progress slow, labor‑intensive, and difficult to reproduce. Although recent LLM‑based assistants can automate isolated steps, they lack performance‑grounded reasoning and stability‑aware mechanisms required for reliable, iterative model improvement in bioinformatics workflows. Results: We introduce MARBLE, an execution‑stable autonomous model refinement framework for bioinformatics models. MARBLE couples literature‑aware reference selection with structured, debate‑driven architectural reasoning among role‑specialized agents, followed by autonomous execution, evaluation, and memory updates explicitly grounded in empirical performance. Across spatial transcriptomics domain segmentation, drug‑target interaction prediction, and drug response prediction, MARBLE consistently achieves sustained performance improvements over strong baselines across multiple refinement cycles, while maintaining high execution robustness and low regression rates. Framework‑level analyses demonstrate that structured debate, balanced evidence selection, and performance‑grounded memory are critical for stable, repeatable model evolution, rather than single‑run or brittle gains. Availability: Source code, data and Supplementary Information are available at https://github.com/PRISM‑DGU/MARBLE.
Authors:YuanLab. ai, :, Shawn Wu, Jiangang Luo, Darcy Chen, Sean Wang, Louie Li, Allen Wang, Xudong Zhao, Tong Yu, Bach Li, Joseph Shen, Gawain Ma, Jasper Jia, Marcus Mao, Claire Wang, Hunter He, Carol Wang, Zera Zhang, Jason Wang, Chonly Shen, Leo Zhang, Logan Chen, Qasim Meng, James Gong, Daniel Zhao, Penn Zheng, Owen Zhu
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.
Authors:Kangyu Zheng, Kai Zhang, Jiale Tan, Xuehan Chen, Yingzhou Lu, Zaixi Zhang, Lichao Sun, Marinka Zitnik, Tianfan Fu, Zhiding Liang
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
Authors:Anh-Tuan Mai, Cam-Van Thi Nguyen, Duc-Trong Le
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
Authors:Egor Cherepanov, Daniil Zelezetsky, Alexey K. Kovalev, Aleksandr I. Panov
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/.
Authors:Shi-Shun Chen, Xiao-Yang Li, Enrico Zio
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.
Authors:Niall McGuire, Yashar Moshfeghi
Abstract:
Query formulation from internal information needs remains fundamentally challenging across all Information Retrieval paradigms due to cognitive complexity and physical impairments. Brain Passage Retrieval (BPR) addresses this by directly mapping EEG signals to passage representations without intermediate text translation. However, existing BPR research exclusively uses visual stimuli, leaving critical questions unanswered: Can auditory EEG enable effective retrieval for voice‑based interfaces and visually impaired users? Can training on combined EEG datasets from different sensory modalities improve performance despite severe data scarcity? We present the first systematic investigation of auditory EEG for BPR and evaluate cross‑sensory training benefits. Using dual encoder architectures with four pooling strategies (CLS, mean, max, multi‑vector), we conduct controlled experiments comparing auditory‑only, visual‑only, and combined training on the Alice (auditory) and Nieuwland (visual) datasets. Results demonstrate that auditory EEG consistently outperforms visual EEG, and cross‑sensory training with CLS pooling achieves substantial improvements over individual training: 31% in MRR (0.474), 43% in Hit@1 (0.314), and 28% in Hit@10 (0.858). Critically, combined auditory EEG models surpass BM25 text baselines (MRR: 0.474 vs 0.428), establishing neural queries as competitive with traditional retrieval whilst enabling accessible interfaces. These findings validate auditory neural interfaces for IR tasks and demonstrate that cross‑sensory training addresses data scarcity whilst outperforming single‑modality approaches Code: https://github.com/NiallMcguire/Audio_BPR
Authors:Cheol-Hui Lee, Hwa-Yeon Lee, Dong-Joo Kim
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.
Authors:Gorgi Pavlov
Abstract:
Learning precise Boolean logic via gradient descent remains challenging: neural networks typically converge to "fuzzy" approximations that degrade under quantization. We introduce Hierarchical Spectral Composition, a differentiable architecture that selects spectral coefficients from a frozen Boolean Fourier basis and composes them via Sinkhorn‑constrained routing with column‑sign modulation. Our approach draws on recent insights from Manifold‑Constrained Hyper‑Connections (mHC), which demonstrated that projecting routing matrices onto the Birkhoff polytope preserves identity mappings and stabilizes large‑scale training. We adapt this framework to logic synthesis, adding column‑sign modulation to enable Boolean negation ‑‑ a capability absent in standard doubly stochastic routing.
We validate our approach across four phases of increasing complexity: (1) For n=2 (16 Boolean operations over 4‑dim basis), gradient descent achieves 100% accuracy with zero routing drift and zero‑loss quantization to ternary masks. (2) For n=3 (10 three‑variable operations), gradient descent achieves 76% accuracy, but exhaustive enumeration over 3^8 = 6561 configurations proves that optimal ternary masks exist for all operations (100% accuracy, 39% sparsity). (3) For n=4 (10 four‑variable operations over 16‑dim basis), spectral synthesis ‑‑ combining exact Walsh‑Hadamard coefficients, ternary quantization, and MCMC refinement with parallel tempering ‑‑ achieves 100% accuracy on all operations. This progression establishes (a) that ternary polynomial threshold representations exist for all tested functions, and (b) that finding them requires methods beyond pure gradient descent as dimensionality grows. All operations enable single‑cycle combinational logic inference at 10,959 MOps/s on GPU, demonstrating viability for hardware‑efficient neuro‑symbolic logic synthesis.
Authors:Kai Wittenmayer, Sukrut Rao, Amin Parchami-Araghi, Bernt Schiele, Jonas Fischer
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.
Authors:Antoine Siraudin, Christopher Morris
Abstract:
Graph diffusion models achieve state‑of‑the‑art performance in graph generation but suffer from quadratic complexity in the number of nodes ‑‑ and much of their capacity is wasted modeling the absence of edges in sparse graphs. Inspired by latent diffusion in other modalities, a natural idea is to compress graphs into a low‑dimensional latent space and perform diffusion in that space. However, unlike images or text, graph generation requires nearly lossless reconstruction, as even a single error in decoding an adjacency matrix can render the entire sample invalid. This challenge has remained largely unaddressed. We propose LG‑Flow, a latent graph diffusion framework that directly overcomes these obstacles. A permutation‑equivariant autoencoder maps nodes to fixed‑dimensional embeddings that enable near‑lossless reconstruction of both undirected graphs and DAGs. The dimensionality of this latent representation scales linearly with the number of nodes, thereby removing the quadratic adjacency‑space bottleneck in the diffusion process and enabling the training of substantially larger generative backbones. In this latent space, we train a Diffusion Transformer with flow matching, enabling efficient and expressive graph generation. Our approach achieves competitive results against state‑of‑the‑art graph diffusion models while delivering up to a 1000× speed‑up. Our code is available at https://github.com/asiraudin/LG‑Flow .
Authors:Thibaut Boissin, Franck Mamalet, Valentin Lafargue, Mathieu Serrurier
Abstract:
Orthogonal and 1‑Lipschitz neural network layers are essential building blocks in robust deep learning architectures, crucial for certified adversarial robustness, stable generative models, and reliable recurrent networks. Despite significant advancements, existing implementations remain fragmented, limited, and computationally demanding. To address these issues, we introduce Orthogonium , a unified, efficient, and comprehensive PyTorch library providing orthogonal and 1‑Lipschitz layers. Orthogonium provides access to standard convolution features‑including support for strides, dilation, grouping, and transposed‑while maintaining strict mathematical guarantees. Its optimized implementations reduce overhead on large scale benchmarks such as ImageNet. Moreover, rigorous testing within the library has uncovered critical errors in existing implementations, emphasizing the importance of standardized and reliable tools. Orthogonium thus significantly lowers adoption barriers, enabling scalable experimentation and integration across diverse applications requiring orthogonality and robust Lipschitz constraints. Orthogonium is available at https://github.com/deel‑ai/orthogonium.
Authors:Mostapha Benhenda
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
Authors:Daniel Kyselica, Jonáš Herec, Oliver Kutis, Rado Pitoňák
Abstract:
Natural disaster monitoring through continuous satellite observation requires processing multi‑temporal data under strict operational constraints. This paper addresses flood detection, a critical application for hazard management, by developing an onboard change detection system that operates within the memory and computational limits of small satellites. We propose History Injection mechanism for Transformer models (HiT), that maintains historical context from previous observations while reducing data storage by over 99% of original image size. Moreover, testing on the STTORM‑CD flood dataset confirms that the HiT mechanism within the Prithvi‑tiny foundation model maintains detection accuracy compared to the bi‑temporal baseline. The proposed HiT‑Prithvi model achieved 43 FPS on Jetson Orin Nano, a representative onboard hardware used in nanosats. This work establishes a practical framework for satellite‑based continuous monitoring of natural disasters, supporting real‑time hazard assessment without dependency on ground‑based processing infrastructure. Architecture as well as model checkpoints is available at https://github.com/zaitra/HiT‑change‑detection .
Authors:Benaya Trabelsi, Jonathan Shaki, Sarit Kraus
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.
Authors:Yang Cao, Bicheng Yu, Sikun Yang, Ming Liu, Yujiu Yang
Abstract:
Despite significant progress in text anomaly detection for web applications such as spam filtering and fake news detection, existing methods are fundamentally limited to document‑level analysis, unable to identify which specific parts of a text are anomalous. We introduce token‑level anomaly detection, a novel paradigm that enables fine‑grained localization of anomalies within text. We formally define text anomalies at both document and token‑levels, and propose a unified detection framework that operates across multiple levels. To facilitate research in this direction, we collect and annotate three benchmark datasets spanning spam, reviews and grammar errors with token‑level labels. Experimental results demonstrate that our framework get better performance than other 6 baselines, opening new possibilities for precise anomaly localization in text. All the codes and data are publicly available on https://github.com/charles‑cao/TokenCore.
Authors:Bo Peng, Sirui Chen, Lei Xu, Chaochao Lu
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.
Authors:Ahmad Al-Zuraiqi
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.
Authors:Qian Feng, JiaHang Tu, Mintong Kang, Hanbin Zhao, Chao Zhang, Hui Qian
Abstract:
Incremental unlearning (IU) is critical for pre‑trained models to comply with sequential data deletion requests, yet existing methods primarily suppress parameters or confuse knowledge without explicit constraints on both feature and gradient level, resulting in superficial forgetting where residual information remains recoverable. This incomplete forgetting risks security breaches and disrupts retention balance, especially in IU scenarios. We propose FG‑OrIU (Feature‑Gradient Orthogonality for Incremental Unlearning), the first framework unifying orthogonal constraints on both features and gradients level to achieve deep forgetting, where the forgetting effect is irreversible. FG‑OrIU decomposes feature spaces via Singular Value Decomposition (SVD), separating forgetting and remaining class features into distinct subspaces. It then enforces dual constraints: feature orthogonal projection on both forgetting and remaining classes, while gradient orthogonal projection prevents the reintroduction of forgotten knowledge and disruption to remaining classes during updates. Additionally, dynamic subspace adaptation merges newly forgetting subspaces and contracts remaining subspaces, ensuring a stable balance between removal and retention across sequential unlearning tasks. Extensive experiments demonstrate the effectiveness of our method.
Authors:Zhiguang Liu, Yi Shang
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.
Authors:Xu Zhang, Junwei Deng, Chang Xu, Hao Li, Jiang Bian
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.
Authors:Adriana-Valentina Costache, Daria-Nicoleta Dragomir, Silviu-Florin Gheorghe, Eduard Poesina, Paul Irofti, Radu Tudor Ionescu
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.
Authors:Nickil Maveli, Antonio Vergari, Shay B. Cohen
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.
Authors:Wei Wang, Quoc-Toan Ly, Chong Yu, Jun Bai
Abstract:
Spatial transcriptomics (ST) enables transcriptome‑wide profiling while preserving the spatial context of tissues, offering unprecedented opportunities to study tissue organization and cell‑cell interactions in situ. Despite recent advances, existing methods often lack effective integration of histological morphology with molecular profiles, relying on shallow fusion strategies or omitting tissue images altogether, which limits their ability to resolve ambiguous spatial domain boundaries. To address this challenge, we propose MultiST, a unified multimodal framework that jointly models spatial topology, gene expression, and tissue morphology through cross‑attention‑based fusion. MultiST employs graph‑based gene encoders with adversarial alignment to learn robust spatial representations, while integrating color‑normalized histological features to capture molecular‑morphological dependencies and refine domain boundaries. We evaluated the proposed method on 13 diverse ST datasets spanning two organs, including human brain cortex and breast cancer tissue. MultiST yields spatial domains with clearer and more coherent boundaries than existing methods, leading to more stable pseudotime trajectories and more biologically interpretable cell‑cell interaction patterns. The MultiST framework and source code are available at https://github.com/LabJunBMI/MultiST.git.
Authors:Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zheyu Zhao, Lingwei Wu, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong
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.
Authors:Pedro M. Gordaliza, Jaume Banus, Benoît Gérin, Maxence Wynen, Nataliia Molchanova, Jonas Richiardi, Meritxell Bach Cuadra
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.
Authors:Shiyuan Li, Yixin Liu, Yu Zheng, Mei Li, Quoc Viet Hung Nguyen, Shirui Pan
Abstract:
Multi‑Agent Systems (MAS) offer a powerful paradigm for solving complex problems, yet their performance is critically dependent on the design of their underlying collaboration topology. As MAS become increasingly deployed in web services (e.g., search engines), designing adaptive topologies for diverse cross‑domain user queries becomes essential. Current graph learning‑based design methodologies often adhere to a "one‑for‑one" paradigm, where a specialized model is trained for each specific task domain. This approach suffers from poor generalization to unseen domains and fails to leverage shared structural knowledge across different tasks. To address this, we propose OFA‑TAD, a one‑for‑all framework that generates adaptive collaboration graphs for any task described in natural language through a single universal model. Our approach integrates a Task‑Aware Graph State Encoder (TAGSE) that filters task‑relevant node information via sparse gating, and a Mixture‑of‑Experts (MoE) architecture that dynamically selects specialized sub‑networks to drive node and edge prediction. We employ a three‑stage training strategy: unconditional pre‑training on canonical topologies for structural priors, large‑scale conditional pre‑training on LLM‑generated datasets for task‑topology mappings, and supervised fine‑tuning on empirically validated graphs. Experiments across six diverse benchmarks show that OFA‑TAD significantly outperforms specialized one‑for‑one models, generating highly adaptive MAS topologies. Code: https://github.com/Shiy‑Li/OFA‑MAS.
Authors:Siguang Chen, Chunli Lv, Miao Xie
Abstract:
Large language models (LLMs) have become powerful and widely used systems for language understanding and generation, while multi‑armed bandit (MAB) algorithms provide a principled framework for adaptive decision‑making under uncertainty. This survey explores the potential at the intersection of these two fields. As we know, it is the first survey to systematically review the bidirectional interaction between large language models and multi‑armed bandits at the component level. We highlight the bidirectional benefits: MAB algorithms address critical LLM challenges, spanning from pre‑training to retrieval‑augmented generation (RAG) and personalization. Conversely, LLMs enhance MAB systems by redefining core components such as arm definition and environment modeling, thereby improving decision‑making in sequential tasks. We analyze existing LLM‑enhanced bandit systems and bandit‑enhanced LLM systems, providing insights into their design, methodologies, and performance. Key challenges and representative findings are identified to help guide future research. An accompanying GitHub repository that indexes relevant literature is available at https://github.com/bucky1119/Awesome‑LLM‑Bandit‑Interaction.
Authors:Meng Liu, Ke Liang, Siwei Wang, Xingchen Hu, Sihang Zhou, Xinwang Liu
Abstract:
Temporal Graph Clustering (TGC) is a new task with little attention, focusing on node clustering in temporal graphs. Compared with existing static graph clustering, it can find the balance between time requirement and space requirement (Time‑Space Balance) through the interaction sequence‑based batch‑processing pattern. However, there are two major challenges that hinder the development of TGC, i.e., inapplicable clustering techniques and inapplicable datasets. To address these challenges, we propose a comprehensive benchmark, called BenchTGC. Specially, we design a BenchTGC Framework to illustrate the paradigm of temporal graph clustering and improve existing clustering techniques to fit temporal graphs. In addition, we also discuss problems with public temporal graph datasets and develop multiple datasets suitable for TGC task, called BenchTGC Datasets. According to extensive experiments, we not only verify the advantages of BenchTGC, but also demonstrate the necessity and importance of TGC task. We wish to point out that the dynamically changing and complex scenarios in real world are the foundation of temporal graph clustering. The code and data is available at: https://github.com/MGitHubL/BenchTGC.
Authors:Ishir Garg, Neel Kolhe, Andy Peng, Rohan Gopalam
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.
Authors:Yuqi Li, Kuiye Ding, Chuanguang Yang, Szu-Yu Chen, Yingli Tian
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.
Authors:Zhaochun Li, Chen Wang, Jionghao Bai, Shisheng Cui, Ge Lan, Zhou Zhao, Yue Wang
Abstract:
The exploration‑exploitation (EE) trade‑off is a central challenge in reinforcement learning (RL) for large language models (LLMs). With Group Relative Policy Optimization (GRPO), training tends to be exploitation driven: entropy decreases monotonically, samples convergence, and exploration fades. Most existing fixes are sample‑centric: they seek or bonus rare samples, assuming exploration comes from novel trajectories and tokens. These heuristics depend on the "luck" of informative samples, lack principled control of the policy, and often yield limited or inconsistent gains. In this work, we are the first to introduce a distribution‑centric perspective for RL, in which exploration is always guided by a "better" target distribution, and reveal that a policy's ability to resist entropy collapse is governed by the distribution itself rather than individual samples. Building on this insight, we propose Distribution‑Centric Policy Optimization (DCPO), which reformulates entropy regulation as distribution‑level regularization. DCPO achieves controllable entropy fully on‑policy without sampling from external distributions, enabling efficient exploration while maintaining training stability. Across multiple models and seven benchmarks, DCPO improves over GRPO by about 20% on average. Overall, DCPO replaces sample‑level heuristics with distribution‑level principles, offering a theoretically grounded and flexible framework for controllable exploration and a stronger EE trade‑off. The code is available in https://github.com/597358816/DCPO.
Authors:Maab Elrashid, Anthony Deschênes, Cem Subakan, Mirco Ravanelli, Rémi Georges, Michael Morin
Abstract:
Interpretability is essential for user trust in real‑world anomaly detection applications. However, deep learning models, despite their strong performance, often lack transparency. In this work, we study the interpretability of autoencoder‑based models for audio anomaly detection, by comparing a standard autoencoder (AE) with a mask autoencoder (MAE) in terms of detection performance and interpretability. We applied several attribution methods, including error maps, saliency maps, SmoothGrad, Integrated Gradients, GradSHAP, and Grad‑CAM. Although MAE shows a slightly lower detection, it consistently provides more faithful and temporally precise explanations, suggesting a better alignment with true anomalies. To assess the relevance of the regions highlighted by the explanation method, we propose a perturbation‑based faithfulness metric that replaces them with their reconstructions to simulate normal input. Our findings, based on experiments in a real industrial scenario, highlight the importance of incorporating interpretability into anomaly detection pipelines and show that masked training improves explanation quality without compromising performance.
Authors:Younes Bouhadjar, Maxime Fabre, Felix Schmidt, Emre Neftci
Abstract:
Linear recurrent neural networks have emerged as efficient alternatives to the original Transformer's softmax attention mechanism, thanks to their highly parallelizable training and constant memory and computation requirements at inference. Iterative refinements of these models have introduced an increasing number of architectural mechanisms, leading to increased complexity and computational costs. Nevertheless, systematic direct comparisons among these models remain limited. Existing benchmark tasks are either too simplistic to reveal substantial differences or excessively resource‑intensive for experimentation. In this work, we propose a refined taxonomy of linear recurrent models and introduce SelectivBench, a set of lightweight and customizable synthetic benchmark tasks for systematically evaluating sequence models. SelectivBench specifically evaluates selectivity in sequence models at small to medium scale, such as the capacity to focus on relevant inputs while ignoring context‑based distractors. It employs rule‑based grammars to generate sequences with adjustable complexity, incorporating irregular gaps that intentionally violate transition rules. Evaluations of linear recurrent models on SelectivBench reveal performance patterns consistent with results from large‑scale language tasks. Our analysis clarifies the roles of essential architectural features: gating and rapid forgetting mechanisms facilitate recall, in‑state channel mixing is unnecessary for selectivity, but critical for generalization, and softmax attention remains dominant due to its memory capacity scaling with sequence length. Our benchmark enables targeted, efficient exploration of linear recurrent models and provides a controlled setting for studying behaviors observed in large‑scale evaluations. Code is available at https://github.com/symseqbench/selectivbench
Authors:Ruo Qi, Linhui Dai, Yusong Qin, Chaolei Yang, Yanshan Li
Abstract:
In remote sensing images, complex backgrounds, weak object signals, and small object scales make accurate detection particularly challenging, especially under low‑quality imaging conditions. A common strategy is to integrate single‑image super‑resolution (SR) before detection; however, such serial pipelines often suffer from misaligned optimization objectives, feature redundancy, and a lack of effective interaction between SR and detection. To address these issues, we propose a Saliency‑Driven multi‑task Collaborative Network (SDCoNet) that couples SR and detection through implicit feature sharing while preserving task specificity. SDCoNet employs the swin transformer‑based shared encoder, where hierarchical window‑shifted self‑attention supports cross‑task feature collaboration and adaptively balances the trade‑off between texture refinement and semantic representation. In addition, a multi‑scale saliency prediction module produces importance scores to select key tokens, enabling focused attention on weak object regions, suppression of background clutter, and suppression of adverse features introduced by multi‑task coupling. Furthermore, a gradient routing strategy is introduced to mitigate optimization conflicts. It first stabilizes detection semantics and subsequently routes SR gradients along a detection‑oriented direction, enabling the framework to guide the SR branch to generate high‑frequency details that are explicitly beneficial for detection. Experiments on public datasets, including NWPU VHR‑10‑Split, DOTAv1.5‑Split, and HRSSD‑Split, demonstrate that the proposed method, while maintaining competitive computational efficiency, significantly outperforms existing mainstream algorithms in small object detection on low‑quality remote sensing images. Our code is available at https://github.com/qiruo‑ya/SDCoNet.
Authors:Chun-Yi Kuan, Hung-yi Lee
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.
Authors:Anzhe Cheng, Shukai Duan, Shixuan Li, Chenzhong Yin, Mingxi Cheng, Shahin Nazarian, Paul Thompson, Paul Bogdan
Abstract:
The relentless scaling of deep learning models has led to unsustainable computational demands, positioning Mixture‑of‑Experts (MoE) architectures as a promising path towards greater efficiency. However, MoE models are plagued by two fundamental challenges: 1) a load imbalance problem known as the``rich get richer" phenomenon, where a few experts are over‑utilized, and 2) an expert homogeneity problem, where experts learn redundant representations, negating their purpose. Current solutions typically employ an auxiliary load‑balancing loss that, while mitigating imbalance, often exacerbates homogeneity by enforcing uniform routing at the expense of specialization. To resolve this, we introduce the Eigen‑Mixture‑of‑Experts (EMoE), a novel architecture that leverages a routing mechanism based on a learned orthonormal eigenbasis. EMoE projects input tokens onto this shared eigenbasis and routes them based on their alignment with the principal components of the feature space. This principled, geometric partitioning of data intrinsically promotes both balanced expert utilization and the development of diverse, specialized experts, all without the need for a conflicting auxiliary loss function. Our code is publicly available at https://github.com/Belis0811/EMoE.
Authors:Bing Hu, Yixin Li, Asma Bahamyirou, Helen Chen
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
Authors:Siru Zhong, Junjie Qiu, Yangyu Wu, Yiqiu Liu, Yuanpeng He, Zhongwen Rao, Bin Yang, Chenjuan Guo, Hao Xu, Yuxuan Liang
Abstract:
Spatio‑Temporal (ST) Foundation Models (STFMs) promise cross‑dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of domain‑specific spatial patterns. Substantially extending our preliminary conference version, we present FactoST‑v2, an enhanced factorized framework redesigned for full weight transfer and arbitrary‑length generalization. FactoST‑v2 decouples universal temporal learning from domain‑specific spatial adaptation. The first stage pretrains a minimalist encoder‑only backbone using randomized sequence masking to capture invariant temporal dynamics, enabling probabilistic quantile prediction across variable horizons. The second stage employs a streamlined adapter to rapidly inject spatial awareness via meta adaptive learning and prompting. Comprehensive evaluations across diverse domains demonstrate that FactoST‑v2 achieves state‑of‑the‑art accuracy with linear efficiency ‑ significantly outperforming existing foundation models in zero‑shot and few‑shot scenarios while rivaling domain‑specific expert baselines. This factorized paradigm offers a practical, scalable path toward truly universal STFMs. Code is available at https://github.com/CityMind‑Lab/FactoST.
Authors:Francisco Angulo de Lafuente, Vladimir Veselov, Richard Goodman
Abstract:
This definitive research memoria presents a comprehensive, mathematically verified paradigm for neural communication with Bitcoin mining Application‑Specific Integrated Circuits (ASICs), integrating five complementary frameworks: thermodynamic reservoir computing, hierarchical number system theory, algorithmic analysis, network latency optimization, and machine‑checked mathematical formalization. We establish that obsolete cryptocurrency mining hardware exhibits emergent computational properties enabling bidirectional information exchange between AI systems and silicon substrates. The research program demonstrates: (1) reservoir computing with NARMA‑10 Normalized Root Mean Square Error (NRMSE) of 0.8661; (2) the Thermodynamic Probability Filter (TPF) achieving 92.19% theoretical energy reduction; (3) the Virtual Block Manager achieving +25% effective hashrate; and (4) hardware universality across multiple ASIC families including Antminer S9, Lucky Miner LV06, and Goldshell LB‑Box. A significant contribution is the machine‑checked mathematical formalization using Lean 4 and Mathlib, providing unambiguous definitions, machine‑verified theorems, and reviewer‑proof claims. Key theorems proven include: independence implies zero leakage, predictor beats baseline implies non‑independence (the logical core of TPF), energy savings theoretical maximum, and Physical Unclonable Function (PUF) distinguishability witnesses. Vladimir Veselov's hierarchical number system theory explains why early‑round information contains predictive power. This work establishes a new paradigm: treating ASICs not as passive computational substrates but as active conversational partners whose thermodynamic state encodes exploitable computational information.
Authors:Jingchu Wang, Bingbing Xu, Yige Yuan, Bin Xie, Xiaoqian Sun, Huawei Shen
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.
Authors:Fatih Maulana
Abstract:
High‑Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), generate massive datasets that challenge classical computational limits. Quantum Machine Learning (QML) offers a potential advantage in processing high‑dimensional data; however, finding the optimal architecture for current Noisy Intermediate‑Scale Quantum (NISQ) devices remains an open challenge. This study investigates the performance of Variational Quantum Classifiers (VQC) in detecting Higgs Boson signals using the ATLAS Higgs Boson Machine Learning Challenge 2014 experiment dataset. We implemented a dimensionality reduction pipeline using Principal Component Analysis (PCA) to map 30 physical features into 4‑qubit and 8‑qubit latent spaces. We benchmarked three configurations: (A) a shallow 4‑qubit circuit, (B) a deep 4‑qubit circuit with increased entanglement layers, and (C) an expanded 8‑qubit circuit. Experimental results demonstrate that increasing circuit depth significantly improves performance, yielding the highest accuracy of 56.2% (Configuration B), compared to a baseline of 51.9%. Conversely, simply scaling to 8 qubits resulted in a performance degradation to 50.6% due to optimization challenges associated with Barren Plateaus in the larger Hilbert space. These findings suggest that for near‑term quantum hardware, prioritizing circuit depth and entanglement capability is more critical than increasing qubit count for effective anomaly detection in HEP data.
Authors:Jinshi Liu, Pan Liu, Lei He
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)
Authors:Arnav S. Sonavane
Abstract:
We investigate the impact of domain‑specific self‑supervised pre‑training on agricultural disease classification using hierarchical vision transformers. Our key finding is that SimCLR pre‑training on just 3,000 unlabeled agricultural images provides a +4.57% accuracy improvement‑‑exceeding the +3.70% gain from hierarchical architecture design. Critically, we show this SSL benefit is architecture‑agnostic: applying the same pre‑training to Swin‑Base yields +4.08%, to ViT‑Base +4.20%, confirming practitioners should prioritize domain data collection over architectural choices. Using HierarchicalViT (HVT), a Swin‑style hierarchical transformer, we evaluate on three datasets: Cotton Leaf Disease (7 classes, 90.24%), PlantVillage (38 classes, 96.3%), and PlantDoc (27 classes, 87.1%). At matched parameter counts, HVT‑Base (78M) achieves 88.91% vs. Swin‑Base (88M) at 87.23%, a +1.68% improvement. For deployment reliability, we report calibration analysis showing HVT achieves 3.56% ECE (1.52% after temperature scaling). Code: https://github.com/w2sg‑arnav/HierarchicalViT
Authors:Farzana Islam Adiba, Varsha Danduri, Fahmida Liza Piya, Ali Abbasi, Mehak Gupta, Rahmatollah Beheshti
Abstract:
The MIMIC‑IV dataset is a large, publicly available electronic health record (EHR) resource widely used for clinical machine learning research. It comprises multiple modalities, including structured data, clinical notes, waveforms, and imaging data. Working with these disjointed modalities requires an extensive manual effort to preprocess and align them for downstream analysis. While several pipelines for MIMIC‑IV data extraction are available, they target a small subset of modalities or do not fully support arbitrary downstream applications. In this work, we greatly expand our prior popular unimodal pipeline and present a comprehensive and customizable multimodal pipeline that can significantly reduce multimodal processing time and enhance the reproducibility of MIMIC‑based studies. Our pipeline systematically integrates the listed modalities, enabling automated cohort selection, temporal alignment across modalities, and standardized multimodal output formats suitable for arbitrary static and time‑series downstream applications. We release the code, a simple UI, and a Python package for selective integration (with embedding) at https://github.com/healthylaife/MIMIC‑IV‑Data‑Pipeline.
Authors:Yawar Siddiqui, Duncan Frost, Samir Aroudj, Armen Avetisyan, Henry Howard-Jenkins, Daniel DeTone, Pierre Moulon, Qirui Wu, Zhengqin Li, Julian Straub, Richard Newcombe, Jakob Engel
Abstract:
Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well‑segmented inputs. Such conditions are rarely met in real‑world scenarios. We present ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Given an image sequence, we leverage off‑the‑shelf visual‑inertial SLAM, 3D detection algorithms, and vision‑language models to extract, for each object, a set of sparse SLAM points, posed multi‑view images, and machine‑generated captions. A rectified flow transformer trained to effectively condition on these modalities then generates high‑fidelity metric 3D shapes. To ensure robustness to the challenges of casually captured data, we employ a range of techniques including on‑the‑fly compositional augmentations, a curriculum training scheme spanning object‑ and scene‑level datasets, and strategies to handle background clutter. Additionally, we introduce a new evaluation benchmark comprising 178 in‑the‑wild objects across 7 real‑world scenes with geometry annotations. Experiments show that ShapeR significantly outperforms existing approaches in this challenging setting, achieving an improvement of 2.7x in Chamfer distance compared to state of the art.
Authors:Dimitar Nedanovski, Svetoslav Nenov, Dimitar Pilev
Abstract:
We study first‑hitting times in Differential Evolution (DE) through a conditional hazard frame work. Instead of analyzing convergence via Markov‑chain transition kernels or drift arguments, we ex press the survival probability of a measurable target set A as a product of conditional first‑hit probabilities (hazards) p_t=\Prob(E_t\mid\mathcal F_t‑1). This yields distribution‑free identities for survival and explicit tail bounds whenever deterministic lower bounds on the hazard hold on the survival event.
For the L‑SHADE algorithm with current‑to‑pbest/1 mutation, we construct a checkable algorithmic witness event \mathcal L_t under which the conditional hazard admits an explicit lower bound depending only on sampling rules, population size, and crossover statistics. This separates theoretical constants from empirical event frequencies and explains why worst‑case constant‑hazard bounds are typically conservative.
We complement the theory with a Kaplan‑‑Meier survival analysis on the CEC2017 benchmark suite . Across functions and budgets, we identify three distinct empirical regimes: (i) strongly clustered success, where hitting times concentrate in short bursts; (ii) approximately geometric tails, where a constant‑hazard model is accurate; and (iii) intractable cases with no observed hits within the evaluation horizon. The results show that while constant‑hazard bounds provide valid tail envelopes, the practical behavior of L‑SHADE is governed by burst‑like transitions rather than homogeneous per‑generati on success probabilities.
Authors:Oishee Bintey Hoque, Nibir Chandra Mandal, Kyle Luong, Amanda Wilson, Samarth Swarup, Madhav Marathe, Abhijin Adiga
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.
Authors:Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso, Damien Garreau, Pierre-Alexandre Mattei
Abstract:
Diffusion models now generate high‑quality, diverse samples, with an increasing focus on more powerful models. Although ensembling is a well‑known way to improve supervised models, its application to unconditional score‑based diffusion models remains largely unexplored. In this work we investigate whether it provides tangible benefits for generative modelling. We find that while ensembling the scores generally improves the score‑matching loss and model likelihood, it fails to consistently enhance perceptual quality metrics such as FID on image datasets. We confirm this observation across a breadth of aggregation rules using Deep Ensembles, Monte Carlo Dropout, on CIFAR‑10 and FFHQ. We attempt to explain this discrepancy by investigating possible explanations, such as the link between score estimation and image quality. We also look into tabular data through random forests, and find that one aggregation strategy outperforms the others. Finally, we provide theoretical insights into the summing of score models, which shed light not only on ensembling but also on several model composition techniques (e.g. guidance).
Authors:Xiaojie Gu, Guangxu Chen, Yuheng Yang, Jingxin Han, Andi Zhang
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
Authors:Mark Eastwood, Thomas McKee, Zedong Hu, Sabine Tejpar, Fayyaz Minhas
Abstract:
Separating the contributions of individual chromogenic stains in RGB histology whole slide images (WSIs) is essential for stain normalization, quantitative assessment of marker expression, and cell‑level readouts in immunohistochemistry (IHC). Classical Beer‑Lambert (BL) color deconvolution is well‑established for two‑ or three‑stain settings, but becomes under‑determined and unstable for multiplex IHC (mIHC) with K>3 chromogens. We present a simple, data‑driven encoder‑decoder architecture that learns cohort‑specific stain characteristics for mIHC RGB WSIs and yields crisp, well‑separated per‑stain concentration maps. The encoder is a compact U‑Net that predicts K nonnegative concentration channels; the decoder is a differentiable BL forward model with a learnable stain matrix initialized from typical chromogen hues. Training is unsupervised with a perceptual reconstruction objective augmented by loss terms that discourage unnecessary stain mixing. On a colorectal mIHC panel comprising 5 stains (H, CDX2, MUC2, MUC5, CD8) we show excellent RGB reconstruction, and significantly reduced inter‑channel bleed‑through compared with matrix‑based deconvolution. Code and model are available at https://github.com/measty/StainQuant.git.
Authors:Lorenzo Tomada, Federico Pichi, Gianluigi Rozza
Abstract:
Graph Neural Networks (GNNs) are emerging as powerful tools for nonlinear Model Order Reduction (MOR) of time‑dependent parameterized Partial Differential Equations (PDEs). However, existing methodologies struggle to combine geometric inductive biases with interpretable latent behavior, overlooking dynamics‑driven features or disregarding spatial information. In this work, we address this gap by introducing Latent Dynamics Graph Convolutional Network (LD‑GCN), a purely data‑driven, encoder‑free architecture that learns a global, low‑dimensional representation of dynamical systems conditioned on external inputs and parameters. The temporal evolution is modeled in the latent space and advanced through time‑stepping, allowing for time‑extrapolation, and the trajectories are consistently decoded onto geometrically parameterized domains using a GNN. Our framework enhances interpretability by enabling the analysis of the reduced dynamics and supporting zero‑shot prediction through latent interpolation. The methodology is mathematically validated via a universal approximation theorem for encoder‑free architectures, and numerically tested on complex computational mechanics problems involving physical and geometric parameters, including the detection of bifurcating phenomena for Navier‑Stokes equations. Code availability: https://github.com/lorenzotomada/ld‑gcn‑rom
Authors:Yuling Shi, Maolin Sun, Zijun Liu, Mo Yang, Yixiong Fang, Tianran Sun, Xiaodong Gu
Abstract:
Retrieval‑Augmented Generation (RAG) has demonstrated significant effectiveness in enhancing large language models (LLMs) for complex multi‑hop question answering (QA). For multi‑hop QA tasks, current iterative approaches predominantly rely on LLMs to self‑guide and plan multi‑step exploration paths during retrieval, leading to substantial challenges in maintaining reasoning coherence across steps from inaccurate query decomposition and error propagation. To address these issues, we introduce Reasoning Tree Guided RAG (RT‑RAG), a novel hierarchical framework for complex multi‑hop QA. RT‑RAG systematically decomposes multi‑hop questions into explicit reasoning trees, minimizing inaccurate decomposition through structured entity analysis and consensus‑based tree selection that clearly separates core queries, known entities, and unknown entities. Subsequently, a bottom‑up traversal strategy employs iterative query rewriting and refinement to collect high‑quality evidence, thereby mitigating error propagation. Comprehensive experiments show that RT‑RAG substantially outperforms state‑of‑the‑art methods by 7.0% F1 and 6.0% EM, demonstrating the effectiveness of RT‑RAG in complex multi‑hop QA.
Authors:Pascal Schlachter, Bin Yang
Abstract:
Unsupervised domain adaptation tackles the problem that domain shifts between training and test data impair the performance of neural networks in many real‑world applications. Thereby, in realistic scenarios, the source data may no longer be available during adaptation, and the label space of the target domain may differ from the source label space. This setting, known as source‑free universal domain adaptation (SF‑UniDA), has recently gained attention, but all existing approaches only assume a single domain shift from source to target. In this work, we present the first study on continual SF‑UniDA, where the model must adapt sequentially to a stream of multiple different unlabeled target domains. Building upon our previous methods for online SF‑UniDA, we combine their key ideas by integrating Gaussian mixture model‑based pseudo‑labeling within a mean teacher framework for improved stability over long adaptation sequences. Additionally, we introduce consistency losses for further robustness. The resulting method GMM‑COMET provides a strong first baseline for continual SF‑UniDA and is the only approach in our experiments to consistently improve upon the source‑only model across all evaluated scenarios. Our code is available at https://github.com/pascalschlachter/GMM‑COMET.
Authors:Yuki Nakamura, Shingo Takemoto, Shunsuke Ono
Abstract:
We propose Comprehensive Robust Dynamic Mode Decomposition (CR‑DMD), a novel framework that robustifies the entire DMD process ‑ from mode extraction to dimensional reduction ‑ against mixed noise. Although standard DMD widely used for uncovering spatio‑temporal patterns and constructing low‑dimensional models of dynamical systems, it suffers from significant performance degradation under noise due to its reliance on least‑squares estimation for computing the linear time evolution operator. Existing robust variants typically modify the least‑squares formulation, but they remain unstable and fail to ensure faithful low‑dimensional representations. First, we introduce a convex optimization‑based preprocessing method designed to effectively remove mixed noise, achieving accurate and stable mode extraction. Second, we propose a new convex formulation for dimensional reduction that explicitly links the robustly extracted modes to the original noisy observations, constructing a faithful representation of the original data via a sparse weighted sum of the modes. Both stages are efficiently solved by a preconditioned primal‑dual splitting method. Experiments on fluid dynamics datasets demonstrate that CR‑DMD consistently outperforms state‑of‑the‑art robust DMD methods in terms of mode accuracy and fidelity of low‑dimensional representations under noisy conditions.
Authors:Lecheng Yan, Ruizhe Li, Guanhua Chen, Qing Li, Jiahui Geng, Wenxi Li, Vincent Wang, Chris Lee
Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Qwen 2.5 achieve significant gains even with spurious or incorrect rewards. We investigate this phenomenon and identify a "Perplexity Paradox": spurious RLVR triggers a divergence where answer‑token perplexity drops while prompt‑side coherence degrades, suggesting the model is bypassing reasoning in favor of memorization. Using Path Patching, Logit Lens, JSD analysis, and Neural Differential Equations, we uncover a hidden Anchor‑Adapter circuit that facilitates this shortcut. We localize a Functional Anchor in the middle layers (L18‑20) that triggers the retrieval of memorized solutions, followed by Structural Adapters in later layers (L21+) that transform representations to accommodate the shortcut signal. Finally, we demonstrate that scaling specific MLP keys within this circuit allows for bidirectional causal steering‑artificially amplifying or suppressing contamination‑driven performance. Our results provide a mechanistic roadmap for identifying and mitigating data contamination in RLVR‑tuned models. Code is available at https://github.com/idwts/How‑RLVR‑Activates‑Memorization‑Shortcuts.
Authors:Mohsin Hasan, Viktor Ohanesian, Artem Gazizov, Yoshua Bengio, Alán Aspuru-Guzik, Roberto Bondesan, Marta Skreta, Kirill Neklyudov
Abstract:
Discrete diffusion models have recently emerged as a promising alternative to the autoregressive approach for generating discrete sequences. Sample generation via gradual denoising or demasking processes allows them to capture hierarchical non‑sequential interdependencies in the data. These custom processes, however, do not assume a flexible control over the distribution of generated samples. We propose Discrete Feynman‑Kac Correctors, a framework that allows for controlling the generated distribution of discrete masked diffusion models at inference time. We derive Sequential Monte Carlo (SMC) algorithms that, given a trained discrete diffusion model, control the temperature of the sampled distribution (i.e. perform annealing), sample from the product of marginals of several diffusion processes (e.g. differently conditioned processes), and sample from the product of the marginal with an external reward function, producing likely samples from the target distribution that also have high reward. Notably, our framework does not require any training of additional models or fine‑tuning of the original model. We illustrate the utility of our framework in several applications including: efficient sampling from the annealed Boltzmann distribution of the Ising model, improving the performance of language models for code generation and amortized learning, as well as reward‑tilted protein sequence generation.
Authors:Mark Kashirskiy, Ilya Makarov
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.
Authors:Yuxuan Lou, Kai Yang, Yang You
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
Authors:Piyush Singh Pasi
Abstract:
Multimodal models excel in English, supported by abundant image‑text and audio‑text data, but performance drops sharply for other languages due to limited multilingual multimodal resources. Existing solutions rely on machine translation, while advances in multilingual text modeling remain underutilized. We introduce M2M, a lightweight alignment method that learns only a few linear layers‑‑using English text alone‑‑to map multilingual text embeddings into multimodal space. Despite its simplicity, M2M matches baseline performance in English (94.9% Recall@10) and achieves strong zero‑shot transfer (89.5% Recall@10 averaged across 11 languages, 10 unseen) on XTD Text‑to‑Image retrieval. Qualitative t‑SNE visualizations show that multilingual embeddings align tightly with multimodal representations, while weight analysis reveals that the transformation reshapes embedding geometry rather than performing trivial rotations. Beyond image‑text retrieval, M2M demonstrates robustness across datasets and tasks, extending to Audio‑Text retrieval and Text‑to‑Image generation. We release code and checkpoints (https://github.com/piyushsinghpasi/M2M) along with multilingual evaluation datasets: MSCOCO Multilingual 30K (https://huggingface.co/datasets/piyushsinghpasi/mscoco‑multilingual‑30k), AudioCaps Multilingual (https://huggingface.co/datasets/piyushsinghpasi/audiocaps‑multilingual), and Clotho Multilingual (https://huggingface.co/datasets/piyushsinghpasi/clotho‑multilingual).
Authors:Han Wang, Yi Yang, Jingyuan Hu, Minfeng Zhu, Wei Chen
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
Authors:Hung Vinh Tran, Tong Chen, Hechuan Wen, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin
Abstract:
Content‑based recommendation systems (CRSs) utilize content features to predict user‑item interactions, serving as essential tools for helping users navigate information‑rich web services. However, ensuring the effectiveness of CRSs requires large‑scale and even continuous model training to accommodate diverse user preferences, resulting in significant computational costs and resource demands. A promising approach to this challenge is coreset selection, which identifies a small but representative subset of data samples that preserves model quality while reducing training overhead. Yet, the selected coreset is vulnerable to the pervasive noise in user‑item interactions, particularly when it is minimally sized. To this end, we propose Noise‑aware Coreset Selection (NaCS), a specialized framework for CRSs. NaCS constructs coresets through submodular optimization based on training gradients, while simultaneously correcting noisy labels using a progressively trained model. Meanwhile, we refine the selected coreset by filtering out low‑confidence samples through uncertainty quantification, thereby avoid training with unreliable interactions. Through extensive experiments, we show that NaCS produces higher‑quality coresets for CRSs while achieving better efficiency than existing coreset selection techniques. Notably, NaCS recovers 93‑95% of full‑dataset training performance using merely 1% of the training data. The source code is available at \hrefhttps://github.com/chenxing1999/nacshttps://github.com/chenxing1999/nacs.
Authors:Yuan-Sen Ting, André Curtis-Trudel, Siyu Yao
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.
Authors:Peter Jemley
Abstract:
We present a hybrid transformer architecture that replaces discrete middle layers with a continuous‑depth Neural Ordinary Differential Equation (ODE) block, enabling inference‑time control over generation attributes via a learned steering signal. Unlike standard transformers that process representations through fixed discrete layers, our approach treats depth as a continuous variable governed by a learned vector field F_θ(H, τ, u), where u is a low‑dimensional control signal injected via explicit concatenation. We validate the architecture through four experiments: (1) gradient flow stability with zero exploding/vanishing gradient events, (2) semantic steering achieving 98%/88% accuracy for positive/negative sentiment control, (3) continuous interpolation validated by a negligible 0.068% trajectory divergence between fixed and adaptive solvers, and (4) efficiency benchmarking demonstrating latency parity with standard discrete baselines. Additionally, we show that adaptive ODE solvers reveal geometric structure in the learned dynamics: the control signal partitions the vector field into distinct dynamical regimes with different curvature characteristics. The adjoint method enables O(1) memory training regardless of integration depth. Our results demonstrate that continuous‑depth dynamics with learned control signals provide a viable, efficient mechanism for steerable language generation.
Authors:Kirandeep Kaur, Vinayak Gupta, Aditya Gupta, Chirag Shah
Abstract:
Current approaches to proactive assistance move beyond the ask‑and‑respond paradigm by anticipating user needs. In practice, they either burden users with clarifying questions or rely on context‑based extrapolation, often leading to unnecessary or mistimed interventions. Such systems lack explicit mechanisms to model users' knowledge gaps, resulting in incomplete or suboptimal task outcomes. To address this, we propose PROPER, a framework that explicitly models user‑specific knowledge gaps in a controlled manner. Central to our approach is the notion of dimensions: structured, task‑relevant factors that define the considerations required for effective task completion. Given a user query, the DGA (Dimension Generating Agent) identifies explicit dimensions (from the user's query) and generates a set of candidate implicit dimensions capturing unarticulated aspects of the task. The RGA (Response Generating Agent) integrates both explicit and implicit dimensions selectively to produce personalized, context‑aware, and proactively informative responses. We evaluate PROPER across multiple domains using a structured, gap‑aware rubric that measures coverage, initiative appropriateness, and intent alignment. PROPER improves on quality scores and win rates across all domains, achieving up to 84% gains in single‑turn evaluation and consistent dominance in multi‑turn interactions. All code for PROPER is available at: https://github.com/i‑kiran/ProPer‑Agent.
Authors:Jack Wilkie, Hanan Hindy, Craig Michie, Christos Tachtatzis, James Irvine, Robert Atkinson
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.
Authors:Nguyen Minh Phuong, Dang Huu Tien, Naoya Inoue
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.
Authors:Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, Jan Kautz, Yu-Chiang Frank Wang, Fu-En Yang
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.
Authors:Tianyi Niu, Justin Chih-Yao Chen, Genta Indra Winata, Shi-Xiong Zhang, Supriyo Chakraborty, Sambit Sahu, Yue Zhang, Elias Stengel-Eskin, Mohit Bansal
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.
Authors:Kuo Liang, Yuhang Lu, Jianming Mao, Shuyi Sun, Chunwei Yang, Congcong Zeng, Xiao Jin, Hanzhang Qin, Ruihao Zhu, Chung-Piaw Teo
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.
Authors:Ralf Römer, Yi Zhang, Yuming Li, Angela P. Schoellig
Abstract:
To teach robots complex manipulation tasks, a common approach is to fine‑tune a pre‑trained vision‑language‑action model (VLA) on task‑specific data. However, since this recipe updates existing representations, it is unsuitable for long‑term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired. Existing continual learning methods for robotics commonly require storing previous data (exemplars), struggle with long task sequences, or rely on task identifiers for deployment. To address these limitations, we propose CLARE, a general, parameter‑efficient framework for exemplar‑free continual learning with VLAs. CLARE introduces lightweight modular adapters into selected VLA modules and autonomously expands the model only where necessary when learning a new task, guided by layer‑wise feature similarity. During deployment, an autoencoder‑based routing mechanism dynamically activates the most relevant adapters without requiring task labels. Through extensive experiments on the LIBERO benchmark and five real‑world tasks, we show that CLARE achieves high performance on new tasks without catastrophic forgetting of earlier tasks, significantly outperforming even exemplar‑based methods. Code, data, and videos are available at our website: https://tum‑lsy.github.io/clare.
Authors:Ritabrata Chakraborty, Hrishit Mitra, Shivakumara Palaiahnakote, Umapada Pal
Abstract:
Object detectors often perform well in‑distribution, yet degrade sharply on a different benchmark. We study cross‑dataset object detection (CD‑OD) through a lens of setting specificity. We group benchmarks into setting‑agnostic datasets with diverse everyday scenes and setting‑specific datasets tied to a narrow environment, and evaluate a standard detector family across all train‑‑test pairs. This reveals a clear structure in CD‑OD: transfer within the same setting type is relatively stable, while transfer across setting types drops substantially and is often asymmetric. The most severe breakdowns occur when transferring from specific sources to agnostic targets, and persist after open‑label alignment, indicating that domain shift dominates in the hardest regimes. To disentangle domain shift from label mismatch, we compare closed‑label transfer with an open‑label protocol that maps predicted classes to the nearest target label using CLIP similarity. Open‑label evaluation yields consistent but bounded gains, and many corrected cases correspond to semantic near‑misses supported by the image evidence. Overall, we provide a principled characterization of CD‑OD under setting specificity and practical guidance for evaluating detectors under distribution shift. Code will be released at \href[https://github.com/Ritabrata04/cdod‑icpr.githttps://github.com/Ritabrata04/cdod‑icpr.
Authors:Renqiang Luo, Yongshuai Yang, Huafei Huang, Qing Qing, Mingliang Hou, Ziqi Xu, Yi Yu, Jingjing Zhou, Feng Xia
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.
Authors:Maria Sdraka, Dimitrios Michail, Ioannis Papoutsis
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.
Authors:Zhengyang Zhao, Lu Ma, Yizhen Jiang, Xiaochen Ma, Zimo Meng, Chengyu Shen, Lexiang Tang, Haoze Sun, Peng Pei, Wentao Zhang
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.
Authors:Shijian Ma, Yan Lin, Yi Yang
Abstract:
We present EvasionBench, a comprehensive benchmark for detecting evasive responses in corporate earnings call question‑and‑answer sessions. Drawing from 22.7 million Q&A pairs extracted from S&P Capital IQ transcripts, we construct a rigorously filtered dataset and introduce a three‑level evasion taxonomy: direct, intermediate, and fully evasive. Our annotation pipeline employs a Multi‑Model Consensus (MMC) framework, combining dual frontier LLM annotation with a three‑judge majority voting mechanism for ambiguous cases, achieving a Cohen's Kappa of 0.835 on human inter‑annotator agreement. We release: (1) a balanced 84K training set, (2) a 1K gold‑standard evaluation set with expert human labels, and (3) [Eva‑4B], a 4‑billion parameter classifier fine‑tuned from Qwen3‑4B that achieves 84.9% Macro‑F1, outperforming Claude 4.5, GPT‑5.2, and Gemini 3 Flash. Our ablation studies demonstrate the effectiveness of multi‑model consensus labeling over single‑model annotation. EvasionBench fills a critical gap in financial NLP by providing the first large‑scale benchmark specifically targeting managerial communication evasion.
Authors:Zhixiang Liang, Beichen Huang, Zheng Wang, Minjia Zhang
Abstract:
Large Language Models (LLMs) can enhance reasoning capabilities through test‑time scaling by generating multiple traces. However, the combination of lengthy reasoning traces with multiple sampling introduces substantial computation and high end‑to‑end latency. Prior work on accelerating this process has relied on similarity‑based or confidence‑based pruning, but these signals do not reliably indicate trace quality. To address these limitations, we propose STEP: Step‑level Trace Evaluation and Pruning, a novel pruning framework that evaluates reasoning steps using hidden states and dynamically prunes unpromising traces during generation. We train a lightweight step scorer to estimate trace quality, and design a GPU memory‑aware pruning strategy that triggers pruning as the GPU memory is saturated by KV cache to reduce end‑to‑end latency. Experiments across challenging reasoning benchmarks demonstrate that STEP reduces end‑to‑end inference latency by 45%‑70% on average compared to self‑consistency while also improving reasoning accuracy. Our code is released at: https://github.com/Supercomputing‑System‑AI‑Lab/STEP
Authors:Shaotian Yan, Kaiyuan Liu, Chen Shen, Bing Wang, Sinan Fan, Jun Zhang, Yue Wu, Zheng Wang, Jieping Ye
Abstract:
In this report, we introduce DASD‑4B‑Thinking, a lightweight yet highly capable, fully open‑source reasoning model. It achieves SOTA performance among open‑source models of comparable scale across challenging benchmarks in mathematics, scientific reasoning, and code generation ‑‑ even outperforming several larger models. We begin by critically reexamining a widely adopted distillation paradigm in the community: SFT on teacher‑generated responses, also known as sequence‑level distillation. Although a series of recent works following this scheme have demonstrated remarkable efficiency and strong empirical performance, they are primarily grounded in the SFT perspective. Consequently, these approaches focus predominantly on designing heuristic rules for SFT data filtering, while largely overlooking the core principle of distillation itself ‑‑ enabling the student model to learn the teacher's full output distribution so as to inherit its generalization capability. Specifically, we identify three critical limitations in current practice: i) Inadequate representation of the teacher's sequence‑level distribution; ii) Misalignment between the teacher's output distribution and the student's learning capacity; and iii) Exposure bias arising from teacher‑forced training versus autoregressive inference. In summary, these shortcomings reflect a systemic absence of explicit teacher‑student interaction throughout the distillation process, leaving the essence of distillation underexploited. To address these issues, we propose several methodological innovations that collectively form an enhanced sequence‑level distillation training pipeline. Remarkably, DASD‑4B‑Thinking obtains competitive results using only 448K training samples ‑‑ an order of magnitude fewer than those employed by most existing open‑source efforts. To support community research, we publicly release our models and the training dataset.
Authors:Youwei Liu, Jian Wang, Hanlin Wang, Beichen Guo, Wenjie Li
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.
Authors:Anush Lakshman S, Adam Haroon, Beiwen Li
Abstract:
Machine learning approaches for fringe projection profilometry (FPP) are hindered by the lack of large, diverse datasets and standardized benchmarking protocols. This paper introduces the first open‑source, photorealistic synthetic dataset for FPP, generated using NVIDIA Isaac Sim, comprising 15,600 fringe images and 300 depth reconstructions across 50 objects. We apply this dataset to single‑shot FPP, where models predict 3D depth maps directly from individual fringe images without temporal phase shifting. Through systematic ablation studies, we identify optimal learning configurations for long‑range (1.5‑2.1 m) depth prediction. We compare three depth normalization strategies and show that individual normalization, which decouples object shape from absolute scale, yields a 9.1x improvement in object reconstruction accuracy over raw depth. We further show that removing background fringe patterns severely degrades performance across all normalizations, demonstrating that background fringes provide essential spatial phase reference rather than noise. We evaluate six loss functions and identify Hybrid L1 loss as optimal. Using the best configuration, we benchmark four architectures and find UNet achieves the strongest performance, though errors remain far above the sub‑millimeter accuracy of classical FPP. The small performance gap between architectures indicates that the dominant limitation is information deficit rather than model design: single fringe images lack sufficient information for accurate depth recovery without explicit phase cues. This work provides a standardized benchmark and evidence motivating hybrid approaches combining phase‑based FPP with learned refinement. The dataset is available at https://huggingface.co/datasets/aharoon/fpp‑ml‑bench and code at https://github.com/AnushLak/fpp‑ml‑bench.
Authors:Jiahao Qin, Yiwen Wang
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 .
Authors:Yao Tang, Li Dong, Yaru Hao, Qingxiu Dong, Furu Wei, Jiatao Gu
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.
Authors:Vikas Dwivedi, Monica Sigovan, Bruno Sixou
Abstract:
Physics‑informed machine learning holds great promise for solving differential equations, yet existing methods struggle with highly oscillatory, multiscale, or singularly perturbed PDEs due to spectral bias, costly backpropagation, and manually tuned kernel or Fourier frequencies. This work introduces a soft partition‑‑based Kernel‑Adaptive Physics‑Informed Extreme Learning Machine (KAPI‑ELM), a deterministic low‑dimensional parameterization in which smooth partition lengths jointly control collocation centers and Gaussian kernel widths, enabling continuous coarse‑to‑fine resolution without Fourier features, random sampling, or hard domain interfaces. A signed‑distance‑based weighting further stabilizes least‑squares learning on irregular geometries. Across eight benchmarks‑‑including oscillatory ODEs, high‑frequency Poisson equations, irregular‑shaped domains, and stiff singularly perturbed convection‑diffusion problems‑the proposed method matches or exceeds the accuracy of state‑of‑the‑art Physics‑Informed Neural Network (PINN) and Theory of Functional Connections (TFC) variants while using only a single linear solve. Although demonstrated on steady linear PDEs, the results show that soft‑partition kernel adaptation provides a fast, architecture‑free approach for multiscale PDEs with broad potential for future physics‑informed modeling. For reproducibility, the reference codes are available at https://github.com/vikas‑dwivedi‑2022/soft_kapi
Authors:Miaomiao Cai, Zhijie Zhang, Junfeng Fang, Zhiyong Cheng, Xiang Wang, Meng Wang
Abstract:
Multi‑behavior recommendation faces a critical challenge in practice: auxiliary behaviors (e.g., clicks, carts) are often noisy, weakly correlated, or semantically misaligned with the target behavior (e.g., purchase), which leads to biased preference learning and suboptimal performance. While existing methods attempt to fuse these heterogeneous signals, they inherently lack a principled mechanism to ensure robustness against such behavioral inconsistency.
In this work, we propose Robust Multi‑Behavior Recommendation towards Target Behaviors (RMBRec), a robust multi‑behavior recommendation framework grounded in an information‑theoretic robustness principle. We interpret robustness as a joint process of maximizing predictive information while minimizing its variance across heterogeneous behavioral environments. Under this perspective, the Representation Robustness Module (RRM) enhances local semantic consistency by maximizing the mutual information between users' auxiliary and target representations, whereas the Optimization Robustness Module (ORM) enforces global stability by minimizing the variance of predictive risks across behaviors, which is an efficient approximation to invariant risk minimization. This local‑global collaboration bridges representation purification and optimization invariance in a theoretically coherent way. Extensive experiments on three real‑world datasets demonstrate that RMBRec not only outperforms state‑of‑the‑art methods in accuracy but also maintains remarkable stability under various noise perturbations. For reproducibility, our code is available at https://github.com/miaomiao‑cai2/RMBRec/.
Authors:Renyang Liu, Kangjie Chen, Han Qiu, Jie Zhang, Kwok-Yan Lam, Tianwei Zhang, See-Kiong Ng
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.
Authors:Jia-Xin He, Hung-Hsuan Chen
Abstract:
Session‑based recommendation systems must capture implicit user intents from sessions. However, existing models suffer from issues such as item interaction dominance and noisy sessions. We propose a multi‑channel recommendation model, including a knowledge graph channel, a session hypergraph channel, and a session line graph channel, to capture information from multiple sources. Our model adaptively removes redundant edges in the knowledge graph channel to reduce noise. Knowledge graph representations cooperate with hypergraph representations for prediction to alleviate item dominance. We also generate in‑session attention for denoising. Finally, we maximize mutual information between the hypergraph and line graph channels as an auxiliary task. Experiments demonstrate that our method enhances the accuracy of various recommendations, including e‑commerce and multimedia recommendations. We release the code on GitHub for reproducibility.\footnotehttps://github.com/hohehohe0509/DSR‑HK
Authors:Jose Lozano-Montoya, Emilio Soria-Olivas, Almudena Fuster-Matanzo, Angel Alberich-Bayarri, Ana Jimenez-Pastor
Abstract:
Automated machine learning (AutoML) frameworks can lower technical barriers for predictive and prognostic model development in radiomics by enabling researchers without programming expertise to build models. However, their effectiveness in addressing radiomics‑specific challenges remains unclear. This study evaluates the performance, efficiency, and accessibility of general‑purpose and radiomics‑specific AutoML frameworks on diverse radiomics classification tasks, thereby highlighting development needs for radiomics. Ten public/private radiomics datasets with varied imaging modalities (CT/MRI), sizes, anatomies and endpoints were used. Six general‑purpose and five radiomics‑specific frameworks were tested with predefined parameters using standardized cross‑validation. Evaluation metrics included AUC, runtime, together with qualitative aspects related to software status, accessibility, and interpretability. Simplatab, a radiomics‑specific tool with a no‑code interface, achieved the highest average test AUC (81.81%) with a moderate runtime (~1 hour). LightAutoML, a general‑purpose framework, showed the fastest execution with competitive performance (78.74% mean AUC in six minutes). Most radiomics‑specific frameworks were excluded from the performance analysis due to obsolescence, extensive programming requirements, or computational inefficiency. Conversely, general‑purpose frameworks demonstrated higher accessibility and ease of implementation. Simplatab provides an effective balance of performance, efficiency, and accessibility for radiomics classification problems. However, significant gaps remain, including the lack of accessible survival analysis support and the limited integration of feature reproducibility and harmonization within current AutoML frameworks. Future research should focus on adapting AutoML solutions to better address these radiomics‑specific challenges.
Authors:Pei Heng, Yi Sun, Jianhua Guo
Abstract:
This work introduces a novel technique, named structural dimension reduction, to collapse a Bayesian network onto a minimum and localized one while ensuring that probabilistic inferences between the original and reduced networks remain consistent. To this end, we propose a new combinatorial structure in directed acyclic graphs called the directed convex hull, which has turned out to be equivalent to their minimum localized Bayesian networks. An efficient polynomial‑time algorithm is devised to identify them by determining the unique directed convex hulls containing the variables of interest from the original networks. Experiments demonstrate that the proposed technique has high dimension reduction capability in real networks, and the efficiency of probabilistic inference based on directed convex hulls can be significantly improved compared with traditional methods such as variable elimination and belief propagation algorithms. The code of this study is open at \hrefhttps://github.com/Balance‑H/Algorithmshttps://github.com/Balance‑H/Algorithms and the proofs of the results in the main body are postponed to the appendix.
Authors:Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby, Khaled R Ahmed, Amer AbuGhazaleh
Abstract:
Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi‑gas Emission Network), the first deep learning approach for rumen acidosis detection from dual‑gas optical imaging under in vitro conditions. Our method leverages complementary carbon dioxide (CO2) and methane (CH4) emission patterns captured by infrared cameras to classify rumen health into Healthy, Transitional, and Acidotic states. FUME employs a lightweight dual‑stream architecture with weight‑shared encoders, modality‑specific self‑attention, and channel attention fusion, jointly optimizing gas plume segmentation and classification of dairy cattle health. We introduce the first dual‑gas OGI dataset comprising 8,967 annotated frames across six pH levels with pixel‑level segmentation masks. Experiments demonstrate that FUME achieves 80.99% mIoU and 98.82% classification accuracy while using only 1.28M parameters and 1.97G MACs‑‑outperforming state‑of‑the‑art methods in segmentation quality with 10x lower computational cost. Ablation studies reveal that CO2 provides the primary discriminative signal and dual‑task learning is essential for optimal performance. Our work establishes the feasibility of gas emission‑based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems. Codes are available at https://github.com/taminulislam/fume.
Authors:Anh H. Vo, Tae-Seok Kim, Hulin Jin, Soo-Mi Choi, Yong-Guk Kim
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/
Authors:Ashutosh Hathidara, Julien Yu, Vaishali Senthil, Sebastian Schreiber, Anil Babu Ankisettipalli
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.
Authors:Zheng Zhou, Isabella McEvoy, Camilo E. Valderrama
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.
Authors:Xin Dai, Pengcheng Huang, Zhenghao Liu, Shuo Wang, Yukun Yan, Chaojun Xiao, Yu Gu, Ge Yu, Maosong Sun
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.
Authors:Hong Huang, Decheng Wu, Qiangqiang Hu, Guanghua Yu, Jinhai Yang, Jianchen Zhu, Xue Liu, Dapeng Wu
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 .
Authors:Simon Jegou, Maximilian Jeblick
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.
Authors:Hao-Xiang Xu, Jun-Yu Ma, Ziqi Peng, Yuhao Sun, Zhen-Hua Ling, Jia-Chen Gu
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.
Authors:Nina Peire, Yupei Li, Björn Schuller
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.
Authors:Wen Guo
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.
Authors:Masahiro Kato
Abstract:
Estimating the Riesz representer is central to debiased machine learning for causal and structural parameter estimation. We propose generalized Riesz regression, a unified framework for estimating the Riesz representer by fitting a representer model via Bregman divergence minimization. This framework includes various divergences as special cases, such as the squared distance and the Kullback‑‑Leibler (KL) divergence, where the former recovers Riesz regression and the latter recovers tailored loss minimization. Under suitable pairs of divergence and model specifications (link functions), the dual problems of the Riesz representer fitting problem correspond to covariate balancing, which we call automatic covariate balancing. Moreover, under the same specifications, the sample average of outcomes weighted by the estimated Riesz representer satisfies Neyman orthogonality even without estimating the regression function, a property we call automatic Neyman orthogonalization. This property not only reduces the estimation error of Neyman orthogonal scores but also clarifies a key distinction between debiased machine learning and targeted maximum likelihood estimation (TMLE). Our framework can also be viewed as a generalization of density ratio fitting under Bregman divergences to Riesz representer estimation, and it applies beyond density ratio estimation. We provide convergence analyses for both reproducing kernel Hilbert space (RKHS) and neural network model classes. A Python package for generalized Riesz regression is released as genriesz and is available at https://github.com/MasaKat0/genriesz.
Authors:Rei Taniguchi, Yuyang Dong, Makoto Onizuka, Chuan Xiao
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.
Authors:Yu-Yang Qian, Junda Su, Lanxiang Hu, Peiyuan Zhang, Zhijie Deng, Peng Zhao, Hao Zhang
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.
Authors:Zexi Tan, Tao Xie, Haoyi Xiao, Baoyao Yang, Yuzhu Ji, An Zeng, Xiang Zhang, Yiqun Zhang
Abstract:
Multivariate Time‑Series (MTS) clustering is crucial for signal processing and data analysis. Although deep learning approaches, particularly those leveraging Contrastive Learning (CL), are prominent for MTS representation, existing CL‑based models face two key limitations: 1) neglecting clustering information during positive/negative sample pair construction, and 2) introducing unreasonable inductive biases, e.g., destroying time dependence and periodicity through augmentation strategies, compromising representation quality. This paper, therefore, proposes a Temporal‑Frequency Enhanced Contrastive (TFEC) learning framework. To preserve temporal structure while generating low‑distortion representations, a temporal‑frequency Co‑EnHancement (CoEH) mechanism is introduced. Accordingly, a synergistic dual‑path representation and cluster distribution learning framework is designed to jointly optimize cluster structure and representation fidelity. Experiments on six real‑world benchmark datasets demonstrate TFEC's superiority, achieving 4.48% average NMI gains over SOTA methods, with ablation studies validating the design. The code of the paper is available at: https://github.com/yueliangy/TFEC.
Authors:Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Peng Zhang, Xindian Ma
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 .
Authors:Michael J. Clark
Abstract:
As models grow more capable, humans cannot reliably verify what they say. Scalable steering requires methods that are internal, self‑supervised, and transfer out‑of‑distribution; existing methods satisfy some but not all three. We introduce AntiPaSTO, which separates representations along an antiparallel axis (+1/‑1 produce opposite shifts), with coherence constraints preventing collapse. Training uses only two contrasting words inserted into template sentences, with no preference labels. When we use 800 such synthetic pairs on Gemma‑3‑1B, AntiPaSTO beats prompting baselines by 6.9x Steering F1 on DailyDilemmas and wins on 5 of 6 tested value axes. We also find preliminary evidence that it maintains bidirectional control where prompting triggers refusal.
Authors:Zhuoka Feng, Kang Chen, Sihan Zhao, Kai Xiong, Yaoning Wang, Minshen Yu, Junjie Nian, Changyi Xiao, Yixin Cao, Yugang Jiang
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.
Authors:Ruiyi Ding, Yongxuan Lv, Xianhui Meng, Jiahe Song, Chao Wang, Chen Jiang, Yuan Cheng
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
Authors:Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, Bingxu Mu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Xiansong Huang, Fan Xu, Feidiao Yang, Yao Lu, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian
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.
Authors:Osama Yousuf, Andreu L. Glasmann, Martin Lueker-Boden, Sina Najmaei, Gina C. Adam
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
Authors:Vladimer Khasia
Abstract:
Post‑training quantization is essential for deploying Large Language Models (LLMs) on resource‑constrained devices. However, standard integer quantization (e.g., INT4) fundamentally degrades performance by imposing a uniform grid on the heavy‑tailed distribution of weight parameters, particularly in smaller‑scale models (e.g., <2B parameters). We introduce HAS‑VQ (Hessian‑Adaptive Sparse Vector Quantization), a compression framework that strictly decouples high‑sensitivity outliers from the bulk weight distribution using second‑order sensitivity analysis. HAS‑VQ employs a Hessian‑Masked Decoupling strategy to isolate sensitive parameters, followed by robust Vector Quantization (VQ) of the remaining dense body. Crucially, we introduce a residual sparse feedback mechanism that corrects quantization errors in the most sensitive dimensions, ensuring exact reconstruction of outliers. We evaluate HAS‑VQ on SmolLM2‑1.7B, demonstrating two distinct regimes of superiority: (1) Pareto Dominance over Integer Baselines: At 4.23 effective bits‑per‑parameter (BPP), we achieve a perplexity of 14.23, significantly outperforming the standard INT4 baseline (20.03 PPL at 4.71 BPP). (2) High‑Fidelity Compression: Relative to the FP16 baseline, HAS‑VQ achieves a 2.3x reduction in model size (7.03 BPP) while maintaining statistically indistinguishable perplexity (10.12 vs. 10.04), effectively offering a lossless compression alternative for bandwidth‑constrained environments. The code is available at https://github.com/VladimerKhasia/HASVQ
Authors:Jie Wu, Haoling Li, Xin Zhang, Jiani Guo, Jane Luo, Steven Liu, Yangyu Huang, Ruihang Chu, Scarlett Li, Yujiu Yang
Abstract:
Competitive programming poses a significant challenge for Code LLMs. While recent models have shown promise, they heavily rely on finite real‑world data, raising concerns about scalability and contamination. In this paper, we investigate a critical question: Can we elevate models to expert‑level reasoning performance using fully synthetic data? In response, we first observe that off‑the‑shelf synthesis methods yield suboptimal results in this domain. To address this, we systematically investigate the key factors governing synthetic data quality. Leveraging these findings, we significantly advance the feature‑based synthesis paradigm via domain‑specific evolution and a dual‑verification strategy, promoting task solvability, solution correctness, and test accuracy. Using this high‑quality synthetic data, we train the X‑Coder model series under an SFT‑then‑RL paradigm. X‑Coder‑7B shows significant performance gains on the challenging LiveCodeBench v5 (62.9% avg@8) and v6 (55.8% avg@8), outperforming larger models trained on real‑world data. Extensive analysis distills valuable insights into synthetic data scaling, the necessity of domain‑adapted feature evolution, and code‑centric reinforcement.
Authors:Shiyuan Zhang, Yilai Liu, Yuwei Du, Ruoxuan Yang, Dong In Kim, Hongyang Du
Abstract:
Personalized mobile artificial intelligence applications are widely deployed, yet they are expected to infer user behavior from sparse and irregular histories under a continuously evolving spatio‑temporal context. This setting induces a fundamental tension among three requirements, i.e., immediacy to adapt to recent behavior, stability to resist transient noise, and generalization to support long‑horizon prediction and cold‑start users. Most existing approaches satisfy at most two of these requirements, resulting in an inherent impossibility triangle in data‑scarce, non‑stationary personalization. To address this challenge, we model mobile behavior as a partially observed spatio‑temporal tensor and unify short‑term adaptation, long‑horizon forecasting, and cold‑start recommendation as a conditional completion problem, where a user‑ and task‑specific mask specifies which coordinates are treated as evidence. We propose U‑MASK, a user‑adaptive spatio‑temporal masking method that allocates evidence budgets based on user reliability and task sensitivity. To enable mask generation under sparse observations, U‑MASK learns a compact, task‑agnostic user representation from app and location histories via U‑SCOPE, which serves as the sole semantic conditioning signal. A shared diffusion transformer then performs mask‑guided generative completion while preserving observed evidence, so personalization and task differentiation are governed entirely by the mask and the user representation. Experiments on real‑world mobile datasets demonstrate consistent improvements over state‑of‑the‑art methods across short‑term prediction, long‑horizon forecasting, and cold‑start settings, with the largest gains under severe data sparsity. The code and dataset will be available at https://github.com/NICE‑HKU/U‑MASK.
Authors:Qiangwei Peng, Zihan Wang, Junda Ying, Yuhao Sun, Qing Nie, Lei Zhang, Tiejun Li, Peijie Zhou
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.
Authors:Zhongping Ji
Abstract:
Modern computer vision architectures, from CNNs to Transformers, predominantly rely on the stacking of heuristic modules: spatial mixers (Attention/Conv) followed by channel mixers (FFNs). In this work, we challenge this paradigm by returning to mathematical first principles. We propose the Clifford Algebra Network (CAN), also referred to as CliffordNet, a vision backbone grounded purely in Geometric Algebra. Instead of engineering separate modules for mixing and memory, we derive a unified interaction mechanism based on the Clifford Geometric Product (uv = u \cdot v + u \wedge v). This operation ensures algebraic completeness regarding the Geometric Product by simultaneously capturing feature coherence (via the generalized inner product) and structural variation (via the exterior wedge product).
Implemented via an efficient sparse rolling mechanism with strict linear complexity O(N), our model reveals a surprising emergent property: the geometric interaction is so representationally dense that standard Feed‑Forward Networks (FFNs) become redundant. Empirically, CliffordNet establishes a new Pareto frontier: our Nano variant achieves 77.82% accuracy on CIFAR‑100 with only 1.4M parameters, effectively matching the heavy‑weight ResNet‑18 (11.2M) with 8× fewer parameters, while our Lite variant (2.6M) sets a new SOTA for tiny models at 79.05%. Our results suggest that global understanding can emerge solely from rigorous, algebraically complete local interactions, potentially signaling a shift where geometry is all you need. Code is available at https://github.com/ParaMind2025/CAN.
Authors:Malavika Pradeep, Akshay Sasi, Nusaibah Farrukh, Rahul Venugopal, Elizabeth Sherly
Abstract:
The electroencephalogram (EEG) has been the gold standard for quantifying mental workload; however, due to its complexity and non‑portability, it can be constraining. ECG signals, which are feasible on wearable equipment pieces such as headbands, present a promising method for cognitive state monitoring. This research explores whether electrocardiogram (ECG) signals are able to indicate mental workload consistently and act as surrogates for EEG‑based cognitive indicators. This study investigates whether ECG‑derived features can serve as surrogate indicators of cognitive load, a concept traditionally quantified using EEG. Using a publicly available multimodal dataset (OpenNeuro) of EEG and ECG recorded during working‑memory and listening tasks, features of HRV and Catch22 descriptors are extracted from ECG, and spectral band‑power with Catch22 features from EEG. A cross‑modal regression framework based on XGBoost was trained to map ECG‑derived HRV representations to EEG‑derived cognitive features. In order to address data sparsity and model brain‑heart interactions, we integrated the PSV‑SDG to produce EEG‑conditioned synthetic HRV time series.This addresses the challenge of inferring cognitive load solely from ECG‑derived features using a combination of multimodal learning, signal processing, and synthetic data generation. These outcomes form a basis for light, interpretable machine learning models that are implemented through wearable biosensors in non‑lab environments. Synthetic HRV inclusion enhances robustness, particularly in sparse data situations. Overall, this work is an initiation for building low‑cost, explainable, and real‑time cognitive monitoring systems for mental health, education, and human‑computer interaction, with a focus on ageing and clinical populations.
Authors:Shubhashis Roy Dipta, Khairul Mahbub, Nadia Najjar
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
Authors:Juan Miguel López Alcaraz, Xicoténcatl López Moran, Erick Dávila Zaragoza, Claas Händel, Richard Koebe, Wilhelm Haverkamp, Nils Strodthoff
Abstract:
Artificial intelligence holds strong potential to support clinical decision making in intensive care units where timely and accurate risk assessment is critical. However, many existing models focus on isolated outcomes or limited data types, while clinicians integrate longitudinal history, real time physiology, and heterogeneous clinical information. To address this gap, we developed MDS ICU, a unified multimodal machine learning framework that fuses routinely collected data including demographics, biometrics, vital signs, laboratory values, ECG waveforms, surgical procedures, and medical device usage to provide continuous predictive support during ICU stays. Using 63001 samples from 27062 patients in MIMIC IV, we trained a deep learning architecture that combines structured state space S4 encoders for ECG waveforms with multilayer perceptron RealMLP encoders for tabular data to jointly predict 33 clinically relevant outcomes spanning mortality, organ dysfunction, medication needs, and acute deterioration. The model achieved strong discrimination with AUROCs of 0.90 for 24 hour mortality, 0.92 for sedative administration, 0.97 for invasive mechanical ventilation, and 0.93 for coagulation dysfunction. Calibration analysis showed close agreement between predicted and observed risks, with consistent gains from ECG waveform integration. Comparisons with clinicians and large language models showed that model predictions alone outperformed both, and that providing model outputs as decision support further improved their performance. These results demonstrate that multimodal AI can deliver clinically meaningful risk stratification across diverse ICU outcomes while augmenting rather than replacing clinical expertise, establishing a scalable foundation for precision critical care decision support.
Authors:Carl Vincent Ladres Kho
Abstract:
Consumer‑grade biosensors offer a cost‑effective alternative to medical‑grade electromyography (EMG) systems, reducing hardware costs from thousands of dollars to approximately 13. However, these low‑cost sensors introduce significant signal instability and motion artifacts. Deploying machine learning models on resource‑constrained edge devices like the ESP32 presents a challenge: balancing classification accuracy with strict latency (<100ms) and memory (<320KB) constraints. Using a single‑subject dataset comprising 1,540 seconds of raw data (1.54M data points, segmented into ~1,300 one‑second windows), I evaluate 18 model architectures, ranging from statistical heuristics to deep transfer learning (ResNet50) and custom hybrid networks (MaxCRNN). While my custom "MaxCRNN" (Inception + Bi‑LSTM + Attention) achieved the highest safety (99% Precision) and robustness, I identify Random Forest (74% accuracy) as the Pareto‑optimal solution for embedded control on legacy microcontrollers. I demonstrate that reliable, low‑latency EMG control is feasible on commodity hardware, with Deep Learning offering a path to near‑perfect reliability on modern Edge AI accelerators.
Authors:Sang T. Truong, Duc Q. Nguyen, Willie Neiswanger, Ryan-Rhys Griffiths, Stefano Ermon, Nick Haber, Sanmi Koyejo
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.
Authors:Xuezhe Ma, Shicheng Wen, Linghao Jin, Bilge Acun, Ruihang Lai, Bohan Hou, Will Lin, Hao Zhang, Songlin Yang, Ryan Lee, Mengxi Wu, Jonathan May, Luke Zettlemoyer, Carole-Jean Wu
Abstract:
Designing a unified neural network to efficiently and inherently process sequential data with arbitrary lengths is a central and challenging problem in sequence modeling. The design choices in Transformer, including quadratic complexity and weak length extrapolation, have limited their ability to scale to long sequences. In this work, we propose Gecko, a neural architecture that inherits the design of Mega and Megalodon (exponential moving average with gated attention), and further introduces multiple technical components to improve its capability to capture long range dependencies, including timestep decay normalization, sliding chunk attention mechanism, and adaptive working memory. In a controlled pretraining comparison with Llama2 and Megalodon in the scale of 7 billion parameters and 2 trillion training tokens, Gecko achieves better efficiency and long‑context scalability. Gecko reaches a training loss of 1.68, significantly outperforming Llama2‑7B (1.75) and Megalodon‑7B (1.70), and landing close to Llama2‑13B (1.67). Notably, without relying on any context‑extension techniques, Gecko exhibits inherent long‑context processing and retrieval capabilities, stably handling sequences of up to 4 million tokens and retrieving information from contexts up to 4× longer than its attention window. Code: https://github.com/XuezheMax/gecko‑llm
Authors:Kaiyuan Deng, Bo Hui, Gen Li, Jie Ji, Minghai Qin, Geng Yuan, Xiaolong Ma
Abstract:
The widespread adoption of text‑to‑image (T2I) diffusion models has raised concerns about their potential to generate copyrighted, inappropriate, or sensitive imagery. As a practical solution, machine unlearning aims to erase unwanted concepts without retraining from scratch. While most existing methods are effective for single‑concept unlearning, they often struggle when removing multiple concepts, causing significant challenges in unlearning effectiveness, generation quality, and sensitivity to hyperparameters and datasets. We take a unique perspective on multi‑concept unlearning by leveraging model sparsity and propose the Forget It All (FIA) framework. FIA first introduces Contrastive Concept Saliency to quantify each weight connection's contribution to a target concept. It then identifies Concept Sensitive Neurons by combining temporal and spatial information, ensuring that only neurons consistently responsive to the target concept are selected. Finally, FIA constructs masks from the identified neurons and fuses them into a unified multi‑concept mask, where Concept Agnostic Neurons that broadly support general content generation are preserved while concept‑specific neurons are pruned to remove the targets. FIA is training‑free and requires minimal hyperparameter tuning for new tasks, enabling plug‑and‑play use. Extensive experiments across three distinct unlearning tasks demonstrate that FIA achieves more reliable multi‑concept unlearning, improving forgetting effectiveness while maintaining generation fidelity and quality. Code is available at https://github.com/kaiyuan02415/Forget‑It‑All
Authors:Kaiyuan Deng, Gen Li, Yang Xiao, Bo Hui, Xiaolong Ma
Abstract:
Text‑to‑image diffusion models have achieved remarkable progress, yet their use raises copyright and misuse concerns, prompting research into machine unlearning. However, extending multi‑concept unlearning to large‑scale scenarios remains difficult due to three challenges: (i) conflicting weight updates that hinder unlearning or degrade generation; (ii) imprecise mechanisms that cause collateral damage to similar content; and (iii) reliance on additional data or modules, creating scalability bottlenecks. To address these, we propose Scalable‑Precise Concept Unlearning (ScaPre), a unified framework tailored for large‑scale unlearning. ScaPre introduces a conflict‑aware stable design, integrating spectral trace regularization and geometry alignment to stabilize optimization, suppress conflicts, and preserve global structure. Furthermore, an Informax Decoupler identifies concept‑relevant parameters and adaptively reweights updates, strictly confining unlearning to the target subspace. ScaPre yields an efficient closed‑form solution without requiring auxiliary data or sub‑models. Comprehensive experiments on objects, styles, and explicit content demonstrate that ScaPre effectively removes target concepts while maintaining generation quality. It forgets up to × \mathbf5 more concepts than the best baseline within acceptable quality limits, achieving state‑of‑the‑art precision and efficiency for large‑scale unlearning. Code is available at https://github.com/kaiyuan02415/scapre
Authors:Anshul Kumar
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
Authors:Ahmed H. Ismail, Anthony Kuang, Ayo Akinkugbe, Kevin Zhu, Sean O'Brien
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.
Authors:Bohan Liang, Zijian Chen, Qi Jia, Kaiwei Zhang, Kaiyuan Ji, Guangtao Zhai
Abstract:
Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied. Current large language models (LLMs) have shown remarkable potential in various domains, exhibiting expert‑level performance through advanced reasoning and contextual understanding. In this paper, we introduce PriceSeer, a live, dynamic, and data‑uncontaminated benchmark specifically designed for LLMs performing stock prediction tasks. Specifically, PriceSeer includes 110 U.S. stocks from 11 industrial sectors, with each containing 249 historical data points. Our benchmark implements both internal and external information expansion, where LLMs receive extra financial indicators, news, and fake news to perform stock price prediction. We evaluate six cutting‑edge LLMs under different prediction horizons, demonstrating their potential in generating investment strategies after obtaining accurate price predictions for different sectors. Additionally, we provide analyses of LLMs' suboptimal performance in long‑term predictions, including the vulnerability to fake news and specific industries. The code and evaluation data will be open‑sourced at https://github.com/BobLiang2113/PriceSeer.
Authors:Jingsheng Zheng, Jintian Zhang, Yujie Luo, Yuren Mao, Yunjun Gao, Lun Du, Huajun Chen, Ningyu Zhang
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.
Authors:Haoming Xu, Ningyuan Zhao, Yunzhi Yao, Weihong Xu, Hongru Wang, Xinle Deng, Shumin Deng, Jeff Z. Pan, Huajun Chen, Ningyu Zhang
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.
Authors:Alexandra Dragomir, Florin Brad, Radu Tudor Ionescu
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.
Authors:ChunTeng Chen, YiChen Hsu, YiWen Liu, WeiFang Sun, TsaiChing Ni, ChunYi Lee, Min Sun, YuanFu Yang
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/
Authors:Xiaoshuai Song, Haofei Chang, Guanting Dong, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou
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.
Authors:Yongyi Yang, Jianyang Gao
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.
Authors:George Ma, Zhongyuan Liang, Irene Y. Chen, Somayeh Sojoudi
Abstract:
We study how reliably sparse autoencoders (SAEs) support claims about reasoning‑related internal features in large language models. We first give a stylized analysis showing that sparsity‑regularized decoding can preferentially retain stable low‑dimensional correlates while suppressing high‑dimensional within‑behavior variation, motivating the possibility that contrastively selected "reasoning" features may concentrate on cue‑like structure when such cues are coupled with reasoning traces. Building on this perspective, we propose a falsification‑based evaluation framework that combines causal token injection with LLM‑guided counterexample construction. Across 22 configurations spanning multiple model families, layers, and reasoning datasets, we find that many contrastively selected candidates are highly sensitive to token‑level interventions, with 45%‑90% activating after injecting only a few associated tokens into non‑reasoning text. For the remaining context‑dependent candidates, LLM‑guided falsification produces targeted non‑reasoning inputs that trigger activation and meaning‑preserving paraphrases of top‑activating reasoning traces that suppress it. A small steering study yields minimal changes on the evaluated benchmarks. Overall, our results suggest that, in the settings we study, sparse decompositions can favor low‑dimensional correlates that co‑occur with reasoning, underscoring the need for falsification when attributing high‑level behaviors to individual SAE features. Code is available at https://github.com/GeorgeMLP/reasoning‑probing.
Authors:Shufei Ge, Shijia Wang, Lloyd Elliott
Abstract:
Neural networks have shown state‑of‑the‑art performances in various classification and regression tasks. Rectified linear units (ReLU) are often used as activation functions for the hidden layers in a neural network model. In this article, we establish the connection between the Poisson hyperplane processes (PHP) and two‑layer ReLU neural networks. We show that the PHP with a Gaussian prior is an alternative probabilistic representation to a two‑layer ReLU neural network. In addition, we show that a two‑layer neural network constructed by PHP is scalable to large‑scale problems via the decomposition propositions. Finally, we propose an annealed sequential Monte Carlo algorithm for Bayesian inference. Our numerical experiments demonstrate that our proposed method outperforms the classic two‑layer ReLU neural network. The implementation of our proposed model is available at https://github.com/ShufeiGe/Pois_Relu.git.
Authors:Marko Sterbentz, Kevin Cushing, Cameron Barrie, Kristian J. Hammond
Abstract:
Recent advances in text‑to‑SQL systems have been driven by larger models and improved datasets, yet progress is still limited by the scarcity of high‑quality training data. Manual data creation is expensive, and existing synthetic methods trade off reliability and scalability. Template‑based approaches ensure correct SQL but require schema‑specific templates, while LLM‑based generation scales easily but lacks quality and correctness guarantees. We introduce RingSQL, a hybrid data generation framework that combines schema‑independent query templates with LLM‑based paraphrasing of natural language questions. This approach preserves SQL correctness across diverse schemas while providing broad linguistic variety. In our experiments, we find that models trained using data produced by RingSQL achieve an average gain in accuracy of +2.3% across six text‑to‑SQL benchmarks when compared to models trained on other synthetic data. We make our code available at https://github.com/nu‑c3lab/RingSQL.
Authors:Yiqun T Chen, Sizhu Lu, Sijia Li, Moran Guo, Shengyi Li
Abstract:
Large language models (LLMs) are increasingly used as automatic evaluators of generative AI outputs, a paradigm often referred to as "LLM‑as‑a‑judge." In practice, LLM judges are imperfect predictions for the underlying truth and can exhibit systematic, non‑random errors. Two main approaches have recently been proposed to address this issue: (i) direct measurementerror correction based on misclassification models such as Rogan‑Gladen‑style estimators, and (ii) surrogate‑outcome approaches such as prediction‑powered inference (PPI), which correct bias by calibrating prediction residuals on a small set of gold‑standard human labels. In this paper, we systematically study the performance of these two approaches for estimating mean parameters (e.g., average benchmark scores or pairwise win rates). Leveraging tools from semiparametric efficiency theory, we unify the two classes of estimators by deriving explicit forms of efficient influence function (EIF)‑based efficient estimators and characterize conditions under which PPI‑style estimators attain strictly smaller asymptotic variance than measurement‑error corrections. We verify our theoretical results in simulations and demonstrate the methods on real‑data examples. We provide an implementation of the benchmarked methods and comparison utilities at https://github.com/yiqunchen/debias‑llm‑as‑a‑judge.
Authors:Qiao Liu, Wing Hung Wong
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.
Authors:Susmit Das
Abstract:
Reasoning‑oriented language models typically expose explicit reasoning as a long, front‑loaded chain of "thinking" tokens before the main output, either always enabled or externally toggled at inference time. Although this can help on arithmetic, coding, and other multi‑step tasks, it is costly, weakens claim‑level auditability, and does not allow the model to re‑trigger explicit reasoning once presentation has begun. In dialogue, these limitations are compounded by weak sensitivity to temporal structure: unless time is explicitly stated in text, standard models treat replies separated by seconds and replies separated by weeks as equivalent. We introduce TIME (Temporally Intelligent Meta‑reasoning Engine), a behavioral alignment framework that learns explicit reasoning as a context‑triggered control policy rather than a fixed response mode. TIME augments dialogue with optional ISO 8601 <time> tags, tick events that represent silent time passage, and short <think> blocks that may appear anywhere in a response. Using a four‑phase curriculum, including a small maximally diverse full‑batch alignment stage, we train Qwen3 dense models to invoke brief, in‑place reasoning bursts only when contextual cues warrant them, while keeping user‑facing output compact. We also introduce TIMEBench, a diagnostic benchmark for evaluating reasoning from temporal cues in dialogue. Across 4B‑32B scales, TIME improves TIMEBench scores over the corresponding base Qwen3 models in both thinking and no‑thinking modes while reducing explicit reasoning tokens by roughly an order of magnitude. Beyond score improvements, TIME induces a distinct behavioral shift: explicit reasoning becomes more compact and more responsive to contextual cues. Code, training data, and benchmark artifacts are publicly available.
Authors:Yiji Zhao, Zihao Zhong, Ao Wang, Haomin Wen, Ming Jin, Yuxuan Liang, Huaiyu Wan, Hao Wu
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.
Authors:Ao Sun, Xiaoyu Wang, Zhe Tan, Yu Li, Jiachen Zhu, Shu Su, Yuheng Jia
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.
Authors:Matan Kleiner, Lior Michaeli, Tomer Michaeli
Abstract:
Diffractive neural networks have recently emerged as a promising framework for all‑optical computing. However, these networks are typically trained for a single task, limiting their potential adoption in systems requiring multiple functionalities. Existing approaches to achieving multi‑task functionality either modify the mechanical configuration of the network per task or use a different illumination wavelength or polarization state for each task. In this work, we propose a new control mechanism, which is based on the illumination's angular spectrum. Specifically, we shape the illumination using an amplitude mask that selectively controls its angular spectrum. We employ different illumination masks for achieving different network functionalities, so that the mask serves as a unique task encoder. Interestingly, we show that effective control can be achieved over a very narrow angular range, within the paraxial regime. We numerically illustrate the proposed approach by training a single diffractive network to perform multiple image‑to‑image translation tasks. In particular, we demonstrate translating handwritten digits into typeset digits of different values, and translating handwritten English letters into typeset numbers and typeset Greek letters, where the type of the output is determined by the illumination's angular components. As we show, the proposed framework can work under different coherence conditions, and can be combined with existing control strategies, such as different wavelengths. Our results establish the illumination angular spectrum as a powerful degree of freedom for controlling diffractive networks, enabling a scalable and versatile framework for multi‑task all‑optical computing.
Authors:Lei Xu, Shanshan Wang, Chenglong Xiao
Abstract:
High‑Level Synthesis (HLS) design space exploration (DSE) seeks Pareto‑optimal designs within expansive pragma configuration spaces. To accelerate HLS DSE, graph neural networks (GNNs) are commonly employed as surrogates for HLS tools to predict quality of results (QoR) metrics, while multi‑objective optimization algorithms expedite the exploration. However, GNN‑based prediction methods may not fully capture the rich semantic features inherent in behavioral descriptions, and conventional multi‑objective optimization algorithms often do not explicitly account for the domain‑specific knowledge regarding how pragma directives influence QoR. To address these limitations, this paper proposes the MPM‑LLM4DSE framework, which incorporates a multimodal prediction model (MPM) that simultaneously fuses features from behavioral descriptions and control and data flow graphs. Furthermore, the framework employs a large language model (LLM) as an optimizer, accompanied by a tailored prompt engineering methodology. This methodology incorporates pragma impact analysis on QoR to guide the LLM in generating high‑quality configurations (LLM4DSE). Experimental results demonstrate that our multimodal predictive model significantly outperforms state‑of‑the‑art work ProgSG by up to 10.25×. Furthermore, in DSE tasks, the proposed LLM4DSE achieves an average performance gain of 39.90% over prior methods, validating the effectiveness of our prompting methodology. Code and models are available at https://github.com/wslcccc/MPM‑LLM4DSE.
Authors:Marios Thoma, Vassilis Vassiliades, Loizos Michael
Abstract:
Neural‑Symbolic (NeSy) Artificial Intelligence has emerged as a promising approach for combining the learning capabilities of neural networks with the interpretable reasoning of symbolic systems. However, existing NeSy frameworks typically require either predefined symbolic policies or policies that are differentiable, limiting their applicability when domain expertise is unavailable or when policies are inherently non‑differentiable. We propose a framework that addresses this limitation by enabling the concurrent learning of both non‑differentiable symbolic policies and neural network weights through an evolutionary process. Our approach casts NeSy systems as organisms in a population that evolve through mutations (both symbolic rule additions and neural weight changes), with fitness‑based selection guiding convergence toward hidden target policies. The framework extends the NEUROLOG architecture to make symbolic policies trainable, adapts Valiant's Evolvability framework to the NeSy context, and employs Machine Coaching semantics for mutable symbolic representations. Neural networks are trained through abductive reasoning from the symbolic component, eliminating differentiability requirements. Through extensive experimentation, we demonstrate that NeSy systems starting with empty policies and random neural weights can successfully approximate hidden non‑differentiable target policies, achieving median correct performance approaching 100%. This work represents a step toward enabling NeSy research in domains where the acquisition of symbolic knowledge from experts is challenging or infeasible.
Authors:Quang-Tu Pham, Hoang-Dieu Vu, Dinh-Dat Pham, Hieu H. Pham
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.
Authors:Paul Pu Liang
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/.
Authors:Tianle Wang, Jiayu Liu, Zhongyuan Wu, Shenghao Jin, Wei Chen, Hao Xu, Ning Miao
Abstract:
Reinforcement learning with verifiable rewards (RLVR) has driven significant performance gains in reasoning‑oriented large language models (LLMs), yet its internal training dynamics remain largely a black box. In this work, we perform a comprehensive trajectory‑level analysis of RLVR and uncover a striking regularity: across various model families, RL algorithms, and training configurations, RLVR consistently enters a robust linear regime, where both parameter weights and output log‑probabilities, measured rigorously via teacher‑forced evaluation, evolve in a highly linear manner (R^2 > 0.7). Through controlled experiments and theoretical analysis, we demonstrate that this linearity is not a coincidence, but stems from the high‑variance, noisy nature of RLVR training signals, which act as a low‑pass filter to concentrate optimization along a stable, low‑dimensional drift. Moreover, we show that this linear structure is not merely descriptive but powerfully predictive and actionable. Specifically, weight‑space extrapolation matches the performance of standard RL optimization while achieving a 6.1x training speedup through periodic re‑grounding. Meanwhile, output‑space extrapolation serves as a lightweight intervention that effectively bypasses late‑stage model collapse, consistently outperforming standard RL across mathematical and coding benchmarks, with an average performance improvement of 4.2%. Our code is available at https://github.com/Miaow‑Lab/RLVR‑Linearity.
Authors:Iaroslav Chelombitko, Ekaterina Chelombitko, Aleksey Komissarov
Abstract:
The quality of subword tokenization is critical for Large Language Models, yet evaluating tokenizers for morphologically rich Uralic languages is hampered by the lack of clean morpheme lexicons.
We introduce SampoNLP, a corpus‑free toolkit for morphological lexicon creation using MDL‑inspired Self‑Referential Atomicity Scoring, which filters composite forms through internal structural cues ‑ suited for low‑resource settings.
Using the high‑purity lexicons generated by SampoNLP for Finnish, Hungarian, and Estonian, we conduct a systematic evaluation of BPE tokenizers across a range of vocabulary sizes (8k‑256k). We propose a unified metric, the Integrated Performance Score (IPS), to navigate the trade‑off between morpheme coverage and over‑splitting. By analyzing the IPS curves, we identify the "elbow points" of diminishing returns and provide the first empirically grounded recommendations for optimal vocabulary sizes (k) in these languages. Our study not only offers practical guidance but also quantitatively demonstrates the limitations of standard BPE for highly agglutinative languages. The SampoNLP library and all generated resources are made publicly available: https://github.com/AragonerUA/SampoNLP
Authors:Mustapha Hamdi, Mourad Jabou
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.
Authors:Xueqing Wu, Zihan Xue, Da Yin, Shuyan Zhou, Kai-Wei Chang, Nanyun Peng, Yeming Wen
Abstract:
We present FronTalk, a benchmark for front‑end code generation that pioneers the study of a unique interaction dynamic: conversational code generation with multi‑modal feedback. In front‑end development, visual artifacts such as sketches, mockups and annotated creenshots are essential for conveying design intent, yet their role in multi‑turn code generation remains largely unexplored. To address this gap, we focus on the front‑end development task and curate FronTalk, a collection of 100 multi‑turn dialogues derived from real‑world websites across diverse domains such as news, finance, and art. Each turn features both a textual instruction and an equivalent visual instruction, each representing the same user intent. To comprehensively evaluate model performance, we propose a novel agent‑based evaluation framework leveraging a web agent to simulate users and explore the website, and thus measuring both functional correctness and user experience. Evaluation of 20 models reveals two key challenges that are under‑explored systematically in the literature: (1) a significant forgetting issue where models overwrite previously implemented features, resulting in task failures, and (2) a persistent challenge in interpreting visual feedback, especially for open‑source vision‑language models (VLMs). We propose a strong baseline to tackle the forgetting issue with AceCoder, a method that critiques the implementation of every past instruction using an autonomous web agent. This approach significantly reduces forgetting to nearly zero and improves the performance by up to 9.3% (56.0% to 65.3%). Overall, we aim to provide a solid foundation for future research in front‑end development and the general interaction dynamics of multi‑turn, multi‑modal code generation. Code and data are released at https://github.com/shirley‑wu/frontalk
Authors:Pietro de Oliveira Esteves
Abstract:
We demonstrate a deep learning framework capable of recovering physical parameters from the Nonlinear Schrodinger Equation (NLSE) under severe noise conditions. By integrating Physics‑Informed Neural Networks (PINNs) with automatic differentiation, we achieve reconstruction of the nonlinear coefficient beta with less than 0.2 percent relative error using only 500 sparse, randomly sampled data points corrupted by 20 percent additive Gaussian noise, a regime where traditional finite difference methods typically fail due to noise amplification in numerical derivatives. We validate the method's generalization capabilities across different physical regimes (beta between 0.5 and 2.0) and varying data availability (between 100 and 1000 training points), demonstrating consistent sub‑1 percent accuracy. Statistical analysis over multiple independent runs confirms robustness (standard deviation less than 0.15 percent for beta equals 1.0). The complete pipeline executes in approximately 80 minutes on modest cloud GPU resources (NVIDIA Tesla T4), making the approach accessible for widespread adoption. Our results indicate that physics‑based regularization acts as an effective filter against high measurement uncertainty, positioning PINNs as a viable alternative to traditional optimization methods for inverse problems in spatiotemporal dynamics where experimental data is scarce and noisy. All code is made publicly available to facilitate reproducibility.
Authors:Chi Liu, Xin Chen
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.
Authors:Benedikt Mayrhofer, Franz Pernkopf, Philipp Aichinger, Martin Hagmüller
Abstract:
Electro‑laryngeal (EL) speech is characterized by constant pitch, limited prosody, and mechanical noise, reducing naturalness and intelligibility. We propose a lightweight adaptation of the state‑of‑the‑art StreamVC framework to this setting by removing pitch and energy modules and combining self‑supervised pretraining with supervised fine‑tuning on parallel EL and healthy (HE) speech data, guided by perceptual and intelligibility losses. Objective and subjective evaluations across different loss configurations confirm their influence: the best model variant, based on WavLM features and human‑feedback predictions (+WavLM+HF), drastically reduces character error rate (CER) of EL inputs, raises naturalness mean opinion score (nMOS) from 1.1 to 3.3, and consistently narrows the gap to HE ground‑truth speech in all evaluated metrics. These findings demonstrate the feasibility of adapting lightweight voice conversion architectures to EL voice rehabilitation while also identifying prosody generation and intelligibility improvements as the main remaining bottlenecks.
Authors:Jan Tagscherer, Sarah de Boer, Lena Philipp, Fennie van der Graaf, Dré Peeters, Joeran Bosma, Lars Leijten, Bogdan Obreja, Ewoud Smit, Alessa Hering
Abstract:
Developing foundation models in medical imaging requires continuous monitoring of downstream performance. Researchers are burdened with tracking numerous experiments, design choices, and their effects on performance, often relying on ad‑hoc, manual workflows that are inherently slow and error‑prone. We introduce EvalBlocks, a modular, plug‑and‑play framework for efficient evaluation of foundation models during development. Built on Snakemake, EvalBlocks supports seamless integration of new datasets, foundation models, aggregation methods, and evaluation strategies. All experiments and results are tracked centrally and are reproducible with a single command, while efficient caching and parallel execution enable scalable use on shared compute infrastructure. Demonstrated on five state‑of‑the‑art foundation models and three medical imaging classification tasks, EvalBlocks streamlines model evaluation, enabling researchers to iterate faster and focus on model innovation rather than evaluation logistics. The framework is released as open source software at https://github.com/DIAGNijmegen/eval‑blocks.
Authors:Arpad Berta, Gabor Danner, Istvan Hegedus, Mark Jelasity
Abstract:
Detecting semantic backdoors in classification models‑‑where some classes can be activated by certain natural, but out‑of‑distribution inputs‑‑is an important problem that has received relatively little attention. Semantic backdoors are significantly harder to detect than backdoors that are based on trigger patterns due to the lack of such clearly identifiable patterns. We tackle this problem under the assumption that the clean training dataset and the training recipe of the model are both known. These assumptions are motivated by a consumer protection scenario, in which the responsible authority performs mystery shopping to test a machine learning service provider. In this scenario, the authority uses the provider's resources and tools to train a model on a given dataset and tests whether the provider included a backdoor. In our proposed approach, the authority creates a reference model pool by training a small number of clean and poisoned models using trusted infrastructure, and calibrates a model distance threshold to identify clean models. We propose and experimentally analyze a number of approaches to compute model distances and we also test a scenario where the provider performs an adaptive attack to avoid detection. The most reliable method is based on requesting adversarial training from the provider. The model distance is best measured using a set of input samples generated by inverting the models in such a way as to maximize the distance from clean samples. With these settings, our method can often completely separate clean and poisoned models, and it proves to be superior to state‑of‑the‑art backdoor detectors as well.
Authors:Sethupathy Parameswaran, Suresh Sundaram, Yuan Fang
Abstract:
Node classification is a fundamental problem in information retrieval with many real‑world applications, such as community detection in social networks, grouping articles published online and product categorization in e‑commerce. Zero‑shot node classification in text‑attributed graphs (TAGs) presents a significant challenge, particularly due to the absence of labeled data. In this paper, we propose a novel Zero‑shot Prompt Tuning (ZPT) framework to address this problem by leveraging a Universal Bimodal Conditional Generator (UBCG). Our approach begins with pre‑training a graph‑language model to capture both the graph structure and the associated textual descriptions of each node. Following this, a conditional generative model is trained to learn the joint distribution of nodes in both graph and text modalities, enabling the generation of synthetic samples for each class based solely on the class name. These synthetic node and text embeddings are subsequently used to perform continuous prompt tuning, facilitating effective node classification in a zero‑shot setting. Furthermore, we conduct extensive experiments on multiple benchmark datasets, demonstrating that our framework performs better than existing state‑of‑the‑art baselines. We also provide ablation studies to validate the contribution of the bimodal generator. The code is provided at: https://github.com/Sethup123/ZPT.
Authors:Weijie Shi, Yanxi Chen, Zexi Li, Xuchen Pan, Yuchang Sun, Jiajie Xu, Xiaofang Zhou, Yaliang Li
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.
Authors:Wajid Arshad Abbasi, Syed Ali Abbas, Maryum Bibi, Saiqa Andleeb, Muhammad Naveed Akhtar
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.
Authors:Joshua Salako
Abstract:
Scalability and data sparsity remain critical bottlenecks for collaborative filtering on massive interaction datasets. This work investigates the latent geometry of user preferences using the MovieLens 32M dataset, implementing a high‑performance, parallelized Alternating Least Squares (ALS) framework. Through extensive hyperparameter optimization, we demonstrate that constrained low‑rank models significantly outperform higher dimensional counterparts in generalization, achieving an optimal balance between Root Mean Square Error (RMSE) and ranking precision. We visualize the learned embedding space to reveal the unsupervised emergence of semantic genre clusters, confirming that the model captures deep structural relationships solely from interaction data. Finally, we validate the system's practical utility in a cold‑start scenario, introducing a tunable scoring parameter to manage the trade‑off between popularity bias and personalized affinity effectively. The codebase for this research can be found here: https://github.com/joshsalako/recommender.git
Authors:Carles Balsells-Rodas, Toshiko Matsui, Pedro A. M. Mediano, Yixin Wang, Yingzhen Li
Abstract:
Identifiability is central to the interpretability of deep latent variable models, ensuring parameterisations are uniquely determined by the data‑generating distribution. However, it remains underexplored for deep regime‑switching time series. We develop a general theoretical framework for multi‑lag Regime‑Switching Models (RSMs), encompassing Markov Switching Models (MSMs) and Switching Dynamical Systems (SDSs). For MSMs, we formulate the model as a temporally structured finite mixture and prove identifiability of both the number of regimes and the multi‑lag transitions in a nonlinear‑Gaussian setting. For SDSs, we establish identifiability of the latent variables up to permutation and scaling via temporal structure, which in turn yields conditions for identifiability of regime‑dependent latent causal graphs (up to regime/node permutations). Our results hold in a fully unsupervised setting through architectural and noise assumptions that are directly enforceable via neural network design. We complement the theory with a flexible variational estimator that satisfies the assumptions and validate the results on synthetic benchmarks. Across real‑world datasets from neuroscience, finance, and climate, identifiability leads to more trustworthy interpretability analysis, which is crucial for scientific discovery.
Authors:Bugra Kilictas, Faruk Alpay
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.
Authors:Oran Duan, Yinghua Shen, Yingzhu Lv, Luyang Jie, Yaxin Liu, Qiong Wu
Abstract:
Advances in generative models and sequence learning have greatly promoted research in dance motion generation, yet current methods still suffer from coarse semantic control and poor coherence in long sequences. In this work, we present Listen to Rhythm, Choose Movements (LRCM), a multimodal‑guided diffusion framework supporting both diverse input modalities and autoregressive dance motion generation. We explore a feature decoupling paradigm for dance datasets and generalize it to the Motorica Dance dataset, separating motion capture data, audio rhythm, and professionally annotated global and local text descriptions. Our diffusion architecture integrates an audio‑latent Conformer and a text‑latent Cross‑Conformer, and incorporates a Motion Temporal Mamba Module (MTMM) to enable smooth, long‑duration autoregressive synthesis. Experimental results indicate that LRCM delivers strong performance in both functional capability and quantitative metrics, demonstrating notable potential in multimodal input scenarios and extended sequence generation. The project page is available at https://oranduanstudy.github.io/LRCM/.
Authors:Shanglin Li, Shiwen Chu, Okan Koç, Yi Ding, Qibin Zhao, Motoaki Kawanabe, Ziheng Chen
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
Authors:Dhruv Trehan, Paras Chopra
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
Authors:Scott Thornton
Abstract:
Large language models remain vulnerable to jailbreak attacks, and single‑layer defenses often trade security for usability. We present TRYLOCK, the first defense‑in‑depth architecture that combines four heterogeneous mechanisms across the inference stack: weight‑level safety alignment via DPO, activation‑level control via Representation Engineering (RepE) steering, adaptive steering strength selected by a lightweight sidecar classifier, and input canonicalization to neutralize encoding‑based bypasses. On Mistral‑7B‑Instruct evaluated against a 249‑prompt attack set spanning five attack families, TRYLOCK achieves 88.0% relative ASR reduction (46.5% to 5.6%), with each layer contributing unique coverage: RepE blocks 36% of attacks that bypass DPO alone, while canonicalization catches 14% of encoding attacks that evade both. We discover a non‑monotonic steering phenomenon ‑‑ intermediate strength (alpha=1.0) degrades safety below baseline ‑‑ and provide mechanistic hypotheses explaining RepE‑DPO interference. The adaptive sidecar reduces over‑refusal from 60% to 48% while maintaining identical attack defense, demonstrating that security and usability need not be mutually exclusive. We release all components ‑‑ trained adapters, steering vectors, sidecar classifier, preference pairs, and complete evaluation methodology ‑‑ enabling full reproducibility.
Authors:Gabriel Benedict, Matthew Butler, Naved Merchant, Eetu Salama-Laine
Abstract:
The emergence of Large Language Models (LLMs) has shifted language model evaluation toward reasoning and problem‑solving tasks as measures of general intelligence. Small Language Models (SLMs) ‑‑ defined here as models under 10B parameters ‑‑ typically score 3‑4 times lower than LLMs on these metrics. However, we demonstrate that these evaluations fail to capture SLMs' effectiveness in common industrial applications, such as tone modification tasks (e.g., funny, serious, professional). We propose an evaluation framework specifically designed to highlight SLMs' capabilities in non‑reasoning tasks where predefined evaluation datasets don't exist. Our framework combines novel approaches in data generation, prompt‑tuning, and LLM‑based evaluation to demonstrate the potential of task‑specific finetuning. This work provides practitioners with tools to effectively benchmark both SLMs and LLMs for practical applications, particularly in edge and private computing scenarios. Our implementation is available at: https://github.com/amazon‑science/wraval.
Authors:Marc Finzi, Shikai Qiu, Yiding Jiang, Pavel Izmailov, J. Zico Kolter, Andrew Gordon Wilson
Abstract:
Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream task? On these questions, Shannon information and Kolmogorov complexity come up nearly empty‑handed, in part because they assume observers with unlimited computational capacity and do not target the useful information content. In this work, we identify and exemplify three seeming paradoxes in information theory: (1) information cannot be increased by deterministic transformations; (2) information is independent of the order of data; (3) likelihood modeling is merely distribution matching. To shed light on the tension between these results and modern practice, and to quantify the value of data, we introduce epiplexity, a formalization of information capturing what computationally bounded observers can learn from data. Epiplexity captures the structural content in data while excluding time‑bounded entropy, the random unpredictable content exemplified by pseudorandom number generators and chaotic dynamical systems. With these concepts, we demonstrate how information can be created with computation, how it depends on the ordering of the data, and how likelihood modeling can produce more complex programs than present in the data generating process itself. We also present practical procedures to estimate epiplexity which we show capture differences across data sources, track with downstream performance, and highlight dataset interventions that improve out‑of‑distribution generalization. In contrast to principles of model selection, epiplexity provides a theoretical foundation for data selection, guiding how to select, generate, or transform data for learning systems.
Authors:Anees Ur Rehman Hashmi, Numan Saeed, Christoph Lippert
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
Authors:Joseph Kampeas, Emir Haleva
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.
Authors:Vidhi Rathore, Sambu Aneesh, Himanshu Singh
Abstract:
Hallucinations can be produced by conversational AI systems, particularly in multi‑turn conversations where context changes and contradictions may eventually surface. By representing the entire conversation as a temporal graph, we present a novel graph‑based method for detecting dialogue‑level hallucinations. Our framework models each dialogue as a node, encoding it using a sentence transformer. We explore two different ways of connectivity: i) shared‑entity edges, which connect turns that refer to the same entities; ii) temporal edges, which connect contiguous turns in the conversation. Message‑passing is used to update the node embeddings, allowing flow of information between related nodes. The context‑aware node embeddings are then combined using attention pooling into a single vector, which is then passed on to a classifier to determine the presence and type of hallucinations. We demonstrate that our method offers slightly improved performance over existing methods. Further, we show the attention mechanism can be used to justify the decision making process. The code and model weights are made available at: https://github.com/sambuaneesh/anlp‑project.
Authors:Youngjoon Jeong, Junha Chun, Taesup Kim
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.
Authors:Junseok Kim, Nakyeong Yang, Kyungmin Min, Kyomin Jung
Abstract:
Self‑Consistency improves reasoning reliability through multi‑sample aggregation, but incurs substantial inference cost. Adaptive self‑consistency methods mitigate this issue by adjusting the sampling budget; however, they rely on count‑based stopping rules that treat all responses equally, often leading to unnecessary sampling. We propose Reliability‑Aware Adaptive Self‑Consistency (ReASC), which addresses this limitation by reframing adaptive sampling from response counting to evidence sufficiency, leveraging response‑level confidence for principled information aggregation. ReASC operates in two stages: a single‑sample decision stage that resolves instances confidently answerable from a single response, and a reliability‑aware accumulation stage that aggregates responses by jointly leveraging their frequency and confidence. Across five models and four datasets, ReASC consistently achieves the best accuracy‑cost trade‑off compared to existing baselines, yielding improved inference efficiency across model scales from 3B to 27B parameters. As a concrete example, ReASC reduces inference cost by up to 70% relative to self‑consistency while preserving accuracy on GSM8K using Gemma‑3‑4B‑it.
Authors:Arjun S. Nair
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/.
Authors:Fabio Cumbo, Kabir Dhillon, Daniel Blankenberg
Abstract:
Following the initial publication of hdlib, a Python library for designing Vector‑Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also known as Hyperdimensional Computing, is a computing paradigm that represents and processes information using high‑dimensional vectors. While the first version of hdlib established a robust foundation for creating and manipulating these vectors, this update addresses the growing need for more advanced, data‑driven modeling within the VSA framework. Here, we present four extensions: significant enhancements to the existing supervised classification model also enabling feature selection, and a new regression model for predicting continuous variables, a clustering model for unsupervised learning, and a graph‑based learning model. Furthermore, we propose the first implementation ever of Quantum Hyperdimensional Computing with quantum‑powered arithmetic operations and a new Quantum Machine Learning model for supervised learning. hdlib remains open‑source and available on GitHub at https://github.com/cumbof/hdlib under the MIT license, and distributed through the Python Package Index (pip install hdlib) and Conda (conda install c conda‑forge hdlib). Documentation and examples of these new features are available on the official Wiki at https://github.com/cumbof/hdlib/wiki.
Authors:Subhankar Mishra
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
Authors:Buqing Cao, Qian Peng, Xiang Xie, Liang Chen, Min Shi, Jianxun Liu
Abstract:
Real‑world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource‑constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain‑inspired and energy‑saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath‑based neighbor information using a single‑layer graph convolution with shared parameters. It then employs a semantic‑level attention mechanism to capture the importance of different meta‑paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1‑bit representation of the heterogeneous graph. Comprehensive experimental results from three real‑world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption. Code is available at https://github.com/QianPeng369/SpikingHAN.
Authors:Ahmad Makinde
Abstract:
High‑Frequency trading (HFT) environments are characterised by large volumes of limit order book (LOB) data, which is notoriously noisy and non‑linear. Alpha decay represents a significant challenge, with traditional models such as DeepLOB losing predictive power as the time horizon (k) increases. In this paper, using data from the FI‑2010 dataset, we introduce Temporal Kolmogorov‑Arnold Networks (T‑KAN) to replace the fixed, linear weights of standard LSTMs with learnable B‑spline activation functions. This allows the model to learn the 'shape' of market signals as opposed to just their magnitude. This resulted in a 19.1% relative improvement in the F1‑score at the k = 100 horizon. The efficacy of T‑KAN networks cannot be understated, producing a 132.48% return compared to the ‑82.76% DeepLOB drawdown under 1.0 bps transaction costs. In addition to this, the T‑KAN model proves quite interpretable, with the 'dead‑zones' being clearly visible in the splines. The T‑KAN architecture is also uniquely optimized for low‑latency FPGA implementation via High level Synthesis (HLS). The code for the experiments in this project can be found at https://github.com/AhmadMak/Temporal‑Kolmogorov‑Arnold‑Networks‑T‑KAN‑for‑High‑Frequency‑Limit‑Order‑Book‑Forecasting.
Authors:Salim Khazem
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
Authors:Shikun Sun, Liao Qu, Huichao Zhang, Yiheng Liu, Yangyang Song, Xian Li, Xu Wang, Yi Jiang, Daniel K. Du, Xinglong Wu, Jia Jia
Abstract:
Visual generation is dominated by three paradigms: AutoRegressive (AR), diffusion, and Visual AutoRegressive (VAR) models. Unlike AR and diffusion, VARs operate on heterogeneous input structures across their generation steps, which creates severe asynchronous policy conflicts. This issue becomes particularly acute in reinforcement learning (RL) scenarios, leading to unstable training and suboptimal alignment. To resolve this, we propose a novel framework to enhance Group Relative Policy Optimization (GRPO) by explicitly managing these conflicts. Our method integrates three synergistic components: 1) a stabilizing intermediate reward to guide early‑stage generation; 2) a dynamic time‑step reweighting scheme for precise credit assignment; and 3) a novel mask propagation algorithm, derived from principles of Reward Feedback Learning (ReFL), designed to isolate optimization effects both spatially and temporally. Our approach demonstrates significant improvements in sample quality and objective alignment over the vanilla GRPO baseline, enabling robust and effective optimization for VAR models.
Authors:Almaz Ermilov
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.
Authors:Zhuofan Shi, Hubao A, Yufei Shao, Dongliang Huang, Hongxu An, Chunxiao Xin, Haiyang Shen, Zhenyu Wang, Yunshan Na, Gang Huang, Xiang Jing
Abstract:
Molecular dynamics (MD) simulations are essential for understanding atomic‑scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time‑consuming tasks. Although LLMs show promise in code generation and domain‑specific question answering, their performance in MD scenarios is limited by scarce domain data, the high deployment cost of state‑of‑the‑art LLMs, and low code executability. Building upon our prior MDAgent, we present MDAgent2, the first end‑to‑end framework capable of performing both knowledge Q&A and code generation within the MD domain. We construct a domain‑specific data‑construction pipeline that yields three high‑quality datasets spanning MD knowledge, question answering, and code generation. Based on these datasets, we adopt a three stage post‑training strategy‑‑continued pre‑training (CPT), supervised fine‑tuning (SFT), and reinforcement learning (RL)‑‑to train two domain‑adapted models, MD‑Instruct and MD‑Code. Furthermore, we introduce MD‑GRPO, a closed‑loop RL method that leverages simulation outcomes as reward signals and recycles low‑reward trajectories for continual refinement. We further build MDAgent2‑RUNTIME, a deployable multi‑agent system that integrates code generation, execution, evaluation, and self‑correction. Together with MD‑EvalBench proposed in this work, the first benchmark for LAMMPS code generation and question answering, our models and system achieve performance surpassing several strong baselines.This work systematically demonstrates the adaptability and generalization capability of large language models in industrial simulation tasks, laying a methodological foundation for automatic code generation in AI for Science and industrial‑scale simulations. URL: https://github.com/FredericVAN/PKU_MDAgent2
Authors:Yanhai Gan, Yipeng Chen, Ning Li, Xingguo Liu, Junyu Dong, Xianyao Chen
Abstract:
The El Ni~no‑Southern Oscillation (ENSO) exerts profound influence on global climate variability, yet its prediction remains a grand challenge. Recent advances in deep learning have significantly improved forecasting skill, but the opacity of these models hampers scientific trust and operational deployment. Here, we introduce a mathematically grounded interpretability framework based on bounded variation function. By rescuing the "dead" neurons from the saturation zone of the activation function, we enhance the model's expressive capacity. Our analysis reveals that ENSO predictability emerges dominantly from the tropical Pacific, with contributions from the Indian and Atlantic Oceans, consistent with physical understanding. Controlled experiments affirm the robustness of our method and its alignment with established predictors. Notably, we probe the persistent Spring Predictability Barrier (SPB), finding that despite expanded sensitivity during spring, predictive performance declines‑likely due to suboptimal variable selection. These results suggest that incorporating additional ocean‑atmosphere variables may help transcend SPB limitations and advance long‑range ENSO prediction.
Authors:Matthias Bartolo, Dylan Seychell, Gabriel Hili, Matthew Montebello, Carl James Debono, Saviour Formosa, Konstantinos Makantasis
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.
Authors:Peiyan Hu, Haodong Feng, Hongyuan Liu, Tongtong Yan, Wenhao Deng, Tianrun Gao, Rong Zheng, Haoren Zheng, Chenglei Yu, Chuanrui Wang, Kaiwen Li, Zhi-Ming Ma, Dezhi Zhou, Xingcai Lu, Dixia Fan, Tailin Wu
Abstract:
Predicting the evolution of complex physical systems remains a central problem in science and engineering. Despite rapid progress in scientific Machine Learning (ML) models, a critical bottleneck is the lack of expensive real‑world data, resulting in most current models being trained and validated on simulated data. Beyond limiting the development and evaluation of scientific ML, this gap also hinders research into essential tasks such as sim‑to‑real transfer. We introduce RealPDEBench, the first benchmark for scientific ML that integrates real‑world measurements with paired numerical simulations. RealPDEBench consists of five datasets, three tasks, eight metrics, and ten baselines. We first present five real‑world measured datasets with paired simulated datasets across different complex physical systems. We further define three tasks, which allow comparisons between real‑world and simulated data, and facilitate the development of methods to bridge the two. Moreover, we design eight evaluation metrics, spanning data‑oriented and physics‑oriented metrics, and finally benchmark ten representative baselines, including state‑of‑the‑art models, pretrained PDE foundation models, and a traditional method. Experiments reveal significant discrepancies between simulated and real‑world data, while showing that pretraining with simulated data consistently improves both accuracy and convergence. In this work, we hope to provide insights from real‑world data, advancing scientific ML toward bridging the sim‑to‑real gap and real‑world deployment. Our benchmark, datasets, and instructions are available at https://realpdebench.github.io/.
Authors:Lakshay Sharma, Alex Marin
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.
Authors:Siba Smarak Panigrahi, Jovana Videnović, Maria Brbić
Abstract:
LLM‑based reasoning models have enabled the development of agentic systems that act as co‑scientists, assisting in multi‑step scientific analysis. However, evaluating these systems is challenging, as it requires realistic, end‑to‑end research scenarios that integrate data analysis, interpretation, and the generation of new insights from the experimental data. To address this limitation, we introduce HeurekaBench, a framework to create benchmarks with exploratory, open‑ended research questions for experimental datasets. Each such question is grounded in a scientific study and its corresponding code repository, and is created using a semi‑automated pipeline that leverages multiple LLMs to extract insights and generate candidate workflows, which are then verified against reported findings. We instantiate the framework in single‑cell biology to obtain sc‑HeurekaBench benchmark and use it to compare state‑of‑the‑art single‑cell agents. We further showcase the benefits of our benchmark for quantitatively analyzing current design choices in agentic systems. We find that the addition of a critic module can improve ill‑formed responses for open‑source LLM‑based agents by up to 22% and close the gap with their closed‑source counterparts. Overall, HeurekaBench sets a path toward rigorous, end‑to‑end evaluation of scientific agents, grounding benchmark construction in real scientific workflows.
Authors:Emiliya Khidirova, Oktay Karakuş
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.
Authors:Myung-Hwan Jang, Jeong-Min Park, Yunyong Ko, Sang-Wook Kim
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.
Authors:Akshay Sasi, Malavika Pradeep, Nusaibah Farrukh, Rahul Venugopal, Elizabeth Sherly
Abstract:
Understanding the interaction of neural and cardiac systems during cognitive activity is critical to advancing physiological computing. Although EEG has been the gold standard for assessing mental workload, its limited portability restricts its real‑world use. Widely available ECG through wearable devices proposes a pragmatic alternative. This research investigates whether ECG signals can reliably reflect cognitive load and serve as proxies for EEG‑based indicators. In this work, we present multimodal data acquired from two different paradigms involving working‑memory and passive‑listening tasks. For each modality, we extracted ECG time‑domain HRV metrics and Catch22 descriptors against EEG spectral and Catch22 features, respectively. We propose a cross‑modal XGBoost framework to project the ECG features onto EEG‑representative cognitive spaces, thereby allowing workload inferences using only ECG. Our results show that ECG‑derived projections expressively capture variation in cognitive states and provide good support for accurate classification. Our findings underpin ECG as an interpretable, real‑time, wearable solution for everyday cognitive monitoring.
Authors:Vladimer Khasia
Abstract:
Scaling sequence modeling to extreme contexts requires balancing computational efficiency with representational expressivity. While Transformers provide precise retrieval via the attention mechanism, their quadratic \mathcalO(T^2) complexity limits their application to long‑horizon tasks. In this work, we propose the Spectral‑Window Hybrid (SWH), an architecture that decouples sequence modeling into two parallel streams: a global branch utilizing the Convolution Theorem to model long‑range decay dynamics in \mathcalO(T \log T) time, and a local branch employing sliding‑window attention for token interactions within a bounded context. By aggregating these representations, SWH avoids the computational bottleneck of global attention while retaining local precision. We demonstrate that SWH matches the perplexity of standard Transformers on short contexts while enabling efficient linear scaling to extended sequences. The code is available at https://github.com/VladimerKhasia/SWH
Authors:Keith Frankston, Benjamin Howard
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.
Authors:Hyeonjeong Ha, Jinjin Ge, Bo Feng, Kaixin Ma, Gargi Chakraborty
Abstract:
Multimodal large language models (MLLMs) have achieved impressive progress in vision‑language reasoning, yet their ability to understand temporally unfolding narratives in videos remains underexplored. True narrative understanding requires grounding who is doing what, when, and where, maintaining coherent entity representations across dynamic visual and temporal contexts. We introduce NarrativeTrack, the first benchmark to evaluate narrative understanding in MLLMs through fine‑grained entity‑centric reasoning. Unlike existing benchmarks limited to short clips or coarse scene‑level semantics, we decompose videos into constituent entities and examine their continuity via a Compositional Reasoning Progression (CRP), a structured evaluation framework that progressively increases narrative complexity across three dimensions: entity existence, entity changes, and entity ambiguity. CRP challenges models to advance from temporal persistence to contextual evolution and fine‑grained perceptual reasoning. A fully automated entity‑centric pipeline enables scalable extraction of temporally grounded entity representations, providing the foundation for CRP. Evaluations of state‑of‑the‑art MLLMs reveal that models fail to robustly track entities across visual transitions and temporal dynamics, often hallucinating identity under context shifts. Open‑source general‑purpose MLLMs exhibit strong perceptual grounding but weak temporal coherence, while video‑specific MLLMs capture temporal context yet hallucinate entity's contexts. These findings uncover a fundamental trade‑off between perceptual grounding and temporal reasoning, indicating that narrative understanding emerges only from their integration. NarrativeTrack provides the first systematic framework to diagnose and advance temporally grounded narrative comprehension in MLLMs.
Authors:Shiao Wang, Xiao Wang, Haonan Zhao, Jiarui Xu, Bo Jiang, Lin Zhu, Xin Zhao, Yonghong Tian, Jin Tang
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
Authors:Subhankar Mishra
Abstract:
3D Gaussian Splatting produces high‑quality scene reconstructions but generates hundreds of thousands of spurious Gaussians (floaters) scattered throughout the environment. These artifacts obscure objects of interest and inflate model sizes, hindering deployment in bandwidth‑constrained applications. We present Clean‑GS, a method for removing background clutter and floaters from 3DGS reconstructions using sparse semantic masks. Our approach combines whitelist‑based spatial filtering with color‑guided validation and outlier removal to achieve 60‑80% model compression while preserving object quality. Unlike existing 3DGS pruning methods that rely on global importance metrics, Clean‑GS uses semantic information from as few as 3 segmentation masks (1% of views) to identify and remove Gaussians not belonging to the target object. Our multi‑stage approach consisting of (1) whitelist filtering via projection to masked regions, (2) depth‑buffered color validation, and (3) neighbor‑based outlier removal isolates monuments and objects from complex outdoor scenes. Experiments on Tanks and Temples show that Clean‑GS reduces file sizes from 125MB to 47MB while maintaining rendering quality, making 3DGS models practical for web deployment and AR/VR applications. Our code is available at https://github.com/smlab‑niser/clean‑gs
Authors:Gihyeon Sim
Abstract:
Large language models apply uniform computation to all inputs, regardless of difficulty. We propose PonderTTT, a gating strategy using the TTT layer's self‑supervised reconstruction loss to selectively trigger Test‑Time Training (TTT) updates. The gating decision itself is training‑free‑‑requiring no learned classifier or auxiliary networks; only a single scalar threshold is initially calibrated on unlabeled data and continuously adapted via EMA to maintain target update rates. Our experiments with GPT‑2 models (124M to 1.5B) on code language modeling (The Stack v2, teacher‑forced perplexity) demonstrate that this signal is inference‑compatible, requiring no ground‑truth labels. Our Reconstruction Gating achieves 82‑89% Oracle Recovery while being fully training‑free, significantly outperforming Random Skip baselines (up to 16% lower loss on OOD languages).
Authors:Samson Gourevitch, Alain Durmus, Eric Moulines, Jimmy Olsson, Yazid Janati
Abstract:
Learning models with categorical variables requires optimizing expectations over discrete distributions, a setting in which stochastic gradient‑based optimization is challenging due to the non‑differentiability of categorical sampling. A common workaround is to replace the discrete distribution with a continuous relaxation, yielding a smooth surrogate that admits reparameterized gradient estimates via the reparameterization trick. Building on this idea, we introduce ReDGE, a novel and efficient diffusion‑based soft reparameterization method for categorical distributions. Our approach defines a flexible class of gradient estimators that includes the Straight‑Through estimator as a special case. Experiments spanning latent variable models and inference‑time reward guidance in discrete diffusion models demonstrate that ReDGE consistently matches or outperforms existing gradient‑based methods. The code will be made available at https://github.com/samsongourevitch/redge.
Authors:Thomas Katraouras, Dimitrios Rafailidis
Abstract:
Large Language Models (LLMs) have become a mainstay for many everyday applications. However, as data evolve their knowledge quickly becomes outdated. Continual learning aims to update LLMs with new information without erasing previously acquired knowledge. Although methods such as full fine‑tuning can incorporate new data, they are computationally expensive and prone to catastrophic forgetting, where prior knowledge is overwritten. Memory‑augmented approaches address this by equipping LLMs with a memory bank, that is an external memory module which stores information for future use. However, these methods face a critical limitation, in particular, the memory bank constantly grows in the real‑world scenario when large‑scale data streams arrive. In this paper, we propose MBC, a model that compresses the memory bank through a codebook optimization strategy during online adaptation learning. To ensure stable learning, we also introduce an online resetting mechanism that prevents codebook collapse. In addition, we employ Key‑Value Low‑Rank Adaptation in the attention layers of the LLM, enabling efficient utilization of the compressed memory representations. Experiments with benchmark question‑answering datasets demonstrate that MBC reduces the memory bank size to 0.3% when compared against the most competitive baseline, while maintaining high retention accuracy during online adaptation learning. Our code is publicly available at https://github.com/Thomkat/MBC.
Authors:Shengjun Zhang, Zhang Zhang, Chensheng Dai, Yueqi Duan
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.
Authors:Yifan Zhang, Yifeng Liu, Mengdi Wang, Quanquan Gu
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.
Authors:Aditya Sai Ellendula, Yi Wang, Minh Nguyen, Chandrajit Bajaj
Abstract:
We present GRL‑SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping(SNAM) in unknown environments. A SNAM problem is challenging as it needs to design hierarchical or joint policies of multiple agents that control the movement of a real‑life robot towards the goal in mapless environment, i.e. an environment where the map of the environment is not available apriori, and needs to be acquired through sensors. The sensors are invoked from the path learner, i.e. navigator, through active query responses to sensory agents, and along the motion path. GRL‑SNAM differs from preemptive navigation algorithms and other reinforcement learning methods by relying exclusively on local sensory observations without constructing a global map. Our approach formulates path navigation and mapping as a dynamic shortest path search and discovery process using controlled Hamiltonian optimization: sensory inputs are translated into local energy landscapes that encode reachability, obstacle barriers, and deformation constraints, while policies for sensing, planning, and reconfiguration evolve stagewise via updating Hamiltonians. A reduced Hamiltonian serves as an adaptive score function, updating kinetic/potential terms, embedding barrier constraints, and continuously refining trajectories as new local information arrives. We evaluate GRL‑SNAM on two different 2D navigation tasks. Comparing against local reactive baselines and global policy learning references under identical stagewise sensing constraints, it preserves clearance, generalizes to unseen layouts, and demonstrates that Geometric RL learning via updating Hamiltonians enables high‑quality navigation through minimal exploration via local energy refinement rather than extensive global mapping. The code is publicly available on \hrefhttps://github.com/CVC‑Lab/GRL‑SNAMGithub.