Paperid: 1, https://arxiv.org/pdf/2506.24124.pdf   GitHub GitHub

Authors:Sixun Dong, Wei Fan, Teresa Wu, Yanjie Fu

Title: Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

Abstract:
Time series forecasting traditionally relies on unimodal numerical inputs, which often struggle to capture high-level semantic patterns due to their dense and unstructured nature. While recent approaches have explored representing time series as text using large language models (LLMs), these methods remain limited by the discrete nature of token sequences and lack the perceptual intuition humans typically apply, such as interpreting visual patterns. In this paper, we propose a multimodal contrastive learning framework that transforms raw time series into structured visual and textual perspectives. Rather than using natural language or real-world images, we construct both modalities directly from numerical sequences. We then align these views in a shared semantic space via contrastive learning, enabling the model to capture richer and more complementary representations. Furthermore, we introduce a variate selection module that leverages the aligned representations to identify the most informative variables for multivariate forecasting. Extensive experiments on fifteen short-term and six long-term forecasting benchmarks demonstrate that our approach consistently outperforms strong unimodal and cross-modal baselines, highlighting the effectiveness of multimodal alignment in enhancing time series forecasting. Code is available at: https://github.com/Ironieser/TimesCLIP.
中文摘要：本文提出一种多模态对比学习框架，将时间序列转化为对齐的视觉与文本表征，通过增强语义对齐和变量选择机制，在预测任务中实现了卓越性能。
English Summary: This paper introduces a multimodal contrastive learning framework that transforms time series data into aligned visual and textual representations, achieving superior forecasting performance through enhanced semantic alignment and variate selection.

Paperid: 2, https://arxiv.org/pdf/2506.24120.pdf   GitHub

Authors:Yuqing Wang, Shangding Gu

Title: Data Uniformity Improves Training Efficiency and More, with a Convergence Framework Beyond the NTK Regime

Abstract:
Data selection plays a crucial role in data-driven decision-making, including in large language models (LLMs), and is typically task-dependent. Properties such as data quality and diversity have been extensively studied and are known to enhance model performance. However, it remains unclear whether there exist other quantitative and general principles of data selection that can consistently improve performance, especially for complex tasks with limited prior knowledge. In this paper, we demonstrate that selecting more uniformly distributed data can improve training efficiency while enhancing performance. Specifically, we establish that more uniform (less biased) distribution leads to a larger minimum pairwise distance between data points, denoted by $h_{\min}$, and prove that a smaller $h_{\min}$ can slow down the training dynamics of gradient descent (GD). Moreover, we theoretically show that the approximation error of neural networks decreases as $h_{\min}$ increases. Our analysis introduces a convergence framework for GD beyond the Neural Tangent Kernel (NTK) regime, applicable to a broad class of architectures, including transformers, without requiring Lipschitz smoothness. This framework further provides theoretical justification for the use of residual connections and function compositions in deep neural architectures. In the end, we conduct comprehensive experiments for supervised fine-tuning across various settings, including different optimization strategies, model sizes, and training datasets. The results consistently demonstrate that selecting data by maximizing pairwise distance significantly accelerates training and achieves comparable or better performance in LLMs across diverse datasets. Code and Datasets are available at the link: https://github.com/SafeRL-Lab/data-uniformity.
中文: 本研究证明，通过选择更均匀分布的数据来增加数据点间的最小成对距离，能够提高大型语言模型的训练效率和性能，加速梯度下降收敛并降低近似误差。
English: This research demonstrates that selecting more uniformly distributed data enhances training efficiency and performance in large language models by increasing the minimum pairwise distance between data points, which accelerates gradient descent convergence and reduces approximation error.

Paperid: 3, https://arxiv.org/pdf/2506.24108.pdf   GitHub

Authors:Shai Yehezkel, Omer Dahary, Andrey Voynov, Daniel Cohen-Or

Title: Navigating with Annealing Guidance Scale in Diffusion Space

Abstract:
Denoising diffusion models excel at generating high-quality images conditioned on text prompts, yet their effectiveness heavily relies on careful guidance during the sampling process. Classifier-Free Guidance (CFG) provides a widely used mechanism for steering generation by setting the guidance scale, which balances image quality and prompt alignment. However, the choice of the guidance scale has a critical impact on the convergence toward a visually appealing and prompt-adherent image. In this work, we propose an annealing guidance scheduler which dynamically adjusts the guidance scale over time based on the conditional noisy signal. By learning a scheduling policy, our method addresses the temperamental behavior of CFG. Empirical results demonstrate that our guidance scheduler significantly enhances image quality and alignment with the text prompt, advancing the performance of text-to-image generation. Notably, our novel scheduler requires no additional activations or memory consumption, and can seamlessly replace the common classifier-free guidance, offering an improved trade-off between prompt alignment and quality.

Paperid: 4, https://arxiv.org/pdf/2506.24000.pdf   GitHub

Authors:Lijun Sheng, Jian Liang, Ran He, Zilei Wang, Tieniu Tan

Title: The Illusion of Progress? A Critical Look at Test-Time Adaptation for Vision-Language Models

Abstract:
Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models (VLMs) such as CLIP during inference, without requiring additional labeled data. However, current TTA researches generally suffer from major limitations such as duplication of baseline results, limited evaluation metrics, inconsistent experimental settings, and insufficient analysis. These problems hinder fair comparisons between TTA methods and obscure their practical strengths and weaknesses. To address these challenges, we introduce TTA-VLM, a comprehensive benchmark for evaluating TTA methods on VLMs. Our benchmark implements 8 episodic TTA and 7 online TTA methods within a unified and reproducible framework, and evaluates them across 15 widely used datasets. Unlike prior studies focused solely on CLIP, we extend the evaluation to SigLIP--a model trained with a Sigmoid loss--and include training-time tuning methods such as CoOp, MaPLe, and TeCoA to assess generality. Beyond classification accuracy, TTA-VLM incorporates various evaluation metrics, including robustness, calibration, out-of-distribution detection, and stability, enabling a more holistic assessment of TTA methods. Through extensive experiments, we find that 1) existing TTA methods produce limited gains compared to the previous pioneering work; 2) current TTA methods exhibit poor collaboration with training-time fine-tuning methods; 3) accuracy gains frequently come at the cost of reduced model trustworthiness. We release TTA-VLM to provide fair comparison and comprehensive evaluation of TTA methods for VLMs, and we hope it encourages the community to develop more reliable and generalizable TTA strategies.
中文: TTA-VLM作为一个综合性基准，旨在解决当前视觉语言模型测试时适应研究中的局限性，通过统一框架和多样化评估指标，促进对多种方法、数据集及模型可信度的公平全面比较。
English: TTA-VLM is introduced as a comprehensive benchmark to address limitations in current test-time adaptation research for vision-language models, enabling fair and holistic evaluations across multiple methods, datasets, and metrics beyond just accuracy.

Paperid: 5, https://arxiv.org/pdf/2506.23783.pdf   GitHub

Authors:Shiao Wang, Ju Huang, Qingchuan Ma, Jinfeng Gao, Chunyi Xu, Xiao Wang, Lan Chen, Bo Jiang

Title: Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking

Abstract:
Combining traditional RGB cameras with bio-inspired event cameras for robust object tracking has garnered increasing attention in recent years. However, most existing multimodal tracking algorithms depend heavily on high-complexity Vision Transformer architectures for feature extraction and fusion across modalities. This not only leads to substantial computational overhead but also limits the effectiveness of cross-modal interactions. In this paper, we propose an efficient RGB-Event object tracking framework based on the linear-complexity Vision Mamba network, termed Mamba-FETrack V2. Specifically, we first design a lightweight Prompt Generator that utilizes embedded features from each modality, together with a shared prompt pool, to dynamically generate modality-specific learnable prompt vectors. These prompts, along with the modality-specific embedded features, are then fed into a Vision Mamba-based FEMamba backbone, which facilitates prompt-guided feature extraction, cross-modal interaction, and fusion in a unified manner. Finally, the fused representations are passed to the tracking head for accurate target localization. Extensive experimental evaluations on multiple RGB-Event tracking benchmarks, including short-term COESOT dataset and long-term datasets, i.e., FE108 and FELT V2, demonstrate the superior performance and efficiency of the proposed tracking framework. The source code and pre-trained models will be released on https://github.com/Event-AHU/Mamba_FETrack
Chinese: 本文提出Mamba-FETrack V2，一种基于视觉Mamba网络和提示生成器的高效RGB-事件目标跟踪框架，在降低计算复杂度的同时实现了卓越的跟踪性能。
English: This paper introduces Mamba-FETrack V2, an efficient RGB-Event object tracking framework that uses a Vision Mamba network and a prompt generator to achieve superior performance with reduced computational complexity.

Paperid: 6, https://arxiv.org/pdf/2506.23767.pdf   GitHub

Authors:Xue Wen Tan, Stanley Kok

Title: Explainable AI for Comprehensive Risk Assessment for Financial Reports: A Lightweight Hierarchical Transformer Network Approach

Abstract:
Every publicly traded U.S. company files an annual 10-K report containing critical insights into financial health and risk. We propose Tiny eXplainable Risk Assessor (TinyXRA), a lightweight and explainable transformer-based model that automatically assesses company risk from these reports. Unlike prior work that relies solely on the standard deviation of excess returns (adjusted for the Fama-French model), which indiscriminately penalizes both upside and downside risk, TinyXRA incorporates skewness, kurtosis, and the Sortino ratio for more comprehensive risk assessment. We leverage TinyBERT as our encoder to efficiently process lengthy financial documents, coupled with a novel dynamic, attention-based word cloud mechanism that provides intuitive risk visualization while filtering irrelevant terms. This lightweight design ensures scalable deployment across diverse computing environments with real-time processing capabilities for thousands of financial documents which is essential for production systems with constrained computational resources. We employ triplet loss for risk quartile classification, improving over pairwise loss approaches in existing literature by capturing both the direction and magnitude of risk differences. Our TinyXRA achieves state-of-the-art predictive accuracy across seven test years on a dataset spanning 2013-2024, while providing transparent and interpretable risk assessments. We conduct comprehensive ablation studies to evaluate our contributions and assess model explanations both quantitatively by systematically removing highly attended words and sentences, and qualitatively by examining explanation coherence. The paper concludes with findings, practical implications, limitations, and future research directions. Our code is available at https://github.com/Chen-XueWen/TinyXRA.
Chinese: TinyXRA 是一种轻量级、可解释的变压器模型，它利用高级指标和可视化技术从10-K报告中评估公司风险，实现了最先进的预测精度并具备实时处理能力。
English: TinyXRA is a lightweight, explainable transformer model that assesses company risk from 10-K reports using advanced metrics and visualization, achieving state-of-the-art accuracy with real-time processing capabilities.

Paperid: 7, https://arxiv.org/pdf/2506.23663.pdf   GitHub

Authors:Mario Koddenbrock, Rudolf Hoffmann, David Brodmann, Erik Rodner

Title: On the Domain Robustness of Contrastive Vision-Language Models

Abstract:
In real-world vision-language applications, practitioners increasingly rely on large, pretrained foundation models rather than custom-built solutions, despite limited transparency regarding their training data and processes. While these models achieve impressive performance on general benchmarks, their effectiveness can decline notably under specialized domain shifts, such as unique imaging conditions or environmental variations. In this work, we introduce Deepbench, a framework designed to assess domain-specific robustness of vision-language models (VLMs). Deepbench leverages a large language model (LLM) to generate realistic, context-aware image corruptions tailored to specific deployment domains without requiring labeled data. We evaluate a range of contrastive vision-language architectures and architectural variants across six real-world domains and observe substantial variability in robustness, highlighting the need for targeted, domain-aware evaluation. Deepbench is released as open-source software to support further research into domain-aware robustness assessment.
中文: Deepbench框架利用大语言模型生成针对特定领域的图像干扰，以评估视觉语言模型的鲁棒性，结果发现在不同现实领域中性能存在显著差异，凸显了针对性评估的必要性。
English: Deepbench is a framework that uses a large language model to generate domain-specific image corruptions for evaluating the robustness of vision-language models, revealing significant performance variations across different real-world domains and emphasizing the need for targeted assessments.

Paperid: 8, https://arxiv.org/pdf/2506.23596.pdf   GitHub

Authors:Min-Yeong Park, Won-Jeong Lee, Seong Tae Kim, Gyeong-Moon Park

Title: When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series

Abstract:
Recently, forecasting future abnormal events has emerged as an important scenario to tackle real-world necessities. However, the solution of predicting specific future time points when anomalies will occur, known as Anomaly Prediction (AP), remains under-explored. Existing methods dealing with time series data fail in AP, focusing only on immediate anomalies or failing to provide precise predictions for future anomalies. To address the AP task, we propose a novel framework called Anomaly to Prompt (A2P), comprised of Anomaly-Aware Forecasting (AAF) and Synthetic Anomaly Prompting (SAP). To enable the forecasting model to forecast abnormal time points, we adopt a strategy to learn the relationships of anomalies. For the robust detection of anomalies, our proposed SAP introduces a learnable Anomaly Prompt Pool (APP) that simulates diverse anomaly patterns using signal adaptive prompt. Comprehensive experiments on multiple real-world datasets demonstrate the superiority of A2P over state-of-the-art methods, showcasing its ability to predict future anomalies. Our implementation code is available at https://github.com/KU-VGI/AP.
中文: A2P框架通过异常感知预测和合成异常提示，学习异常关联并模拟多样化模式，在多个真实数据集上展现出超越现有方法的未来异常预测能力。
English: The A2P framework, integrating Anomaly-Aware Forecasting and Synthetic Anomaly Prompting, effectively predicts future anomaly occurrences by learning anomaly relationships and simulating diverse patterns, outperforming existing methods in real-world datasets.

Paperid: 9, https://arxiv.org/pdf/2506.23538.pdf   GitHub

Authors:Yuhao Huang, Yueyue Xu, Haoran Dou, Jiaxiao Deng, Xin Yang, Hongyu Zheng, Dong Ni

Title: Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound

Abstract:
Congenital uterine anomalies (CUAs) can lead to infertility, miscarriage, preterm birth, and an increased risk of pregnancy complications. Compared to traditional 2D ultrasound (US), 3D US can reconstruct the coronal plane, providing a clear visualization of the uterine morphology for assessing CUAs accurately. In this paper, we propose an intelligent system for simultaneous automated plane localization and CUA diagnosis. Our highlights are: 1) we develop a denoising diffusion model with local (plane) and global (volume/text) guidance, using an adaptive weighting strategy to optimize attention allocation to different conditions; 2) we introduce a reinforcement learning-based framework with unsupervised rewards to extract the key slice summary from redundant sequences, fully integrating information across multiple planes to reduce learning difficulty; 3) we provide text-driven uncertainty modeling for coarse prediction, and leverage it to adjust the classification probability for overall performance improvement. Extensive experiments on a large 3D uterine US dataset show the efficacy of our method, in terms of plane localization and CUA diagnosis. Code is available at https://github.com/yuhoo0302/CUA-US.
中文: 本文提出一种智能系统，通过去噪扩散模型和强化学习，自动完成三维超声中的平面定位和先天性子宫异常诊断，实验证明其方法高效可靠。
English: This paper introduces an intelligent system that uses a denoising diffusion model and reinforcement learning to automate both plane localization and diagnosis of congenital uterine anomalies from 3D ultrasound data, demonstrating high effectiveness in experiments.

Paperid: 10, https://arxiv.org/pdf/2506.23424.pdf   GitHub

Authors:Heitor R. Medeiros, Hossein Sharifi-Noghabi, Gabriel L. Oliveira, Saghar Irandoust

Title: Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting

Abstract:
Real-world time series often exhibit a non-stationary nature, degrading the performance of pre-trained forecasting models. Test-Time Adaptation (TTA) addresses this by adjusting models during inference, but existing methods typically update the full model, increasing memory and compute costs. We propose PETSA, a parameter-efficient method that adapts forecasters at test time by only updating small calibration modules on the input and output. PETSA uses low-rank adapters and dynamic gating to adjust representations without retraining. To maintain accuracy despite limited adaptation capacity, we introduce a specialized loss combining three components: (1) a robust term, (2) a frequency-domain term to preserve periodicity, and (3) a patch-wise structural term for structural alignment. PETSA improves the adaptability of various forecasting backbones while requiring fewer parameters than baselines. Experimental results on benchmark datasets show that PETSA achieves competitive or better performance across all horizons. Our code is available at: https://github.com/BorealisAI/PETSA
中文摘要：PETSA提出了一种参数高效的测试时自适应方法，仅通过更新输入输出的微型校准模块，结合低秩适配器和专门设计的损失函数，在减少参数量的同时实现了与现有方法相当或更优的时序预测性能。
English Summary: PETSA introduces a parameter-efficient test-time adaptation method for time series forecasting that updates only small calibration modules using low-rank adapters and a specialized loss function, achieving competitive performance with fewer parameters than existing approaches.

Paperid: 11, https://arxiv.org/pdf/2506.23411.pdf   GitHub

Authors:Jiale Zhang, Zichong Wang, Avash Palikhe, Zhipeng Yin, Wenbin Zhang

Title: Datasets for Fairness in Language Models: An In-Depth Survey

Abstract:
Despite the growing reliance on fairness benchmarks to evaluate language models, the datasets that underpin these benchmarks remain critically underexamined. This survey addresses that overlooked foundation by offering a comprehensive analysis of the most widely used fairness datasets in language model research. To ground this analysis, we characterize each dataset across key dimensions, including provenance, demographic scope, annotation design, and intended use, revealing the assumptions and limitations baked into current evaluation practices. Building on this foundation, we propose a unified evaluation framework that surfaces consistent patterns of demographic disparities across benchmarks and scoring metrics. Applying this framework to sixteen popular datasets, we uncover overlooked biases that may distort conclusions about model fairness and offer guidance on selecting, combining, and interpreting these resources more effectively and responsibly. Our findings highlight an urgent need for new benchmarks that capture a broader range of social contexts and fairness notions. To support future research, we release all data, code, and results at https://github.com/vanbanTruong/Fairness-in-Large-Language-Models/tree/main/datasets, fostering transparency and reproducibility in the evaluation of language model fairness.
中文: 本研究对语言模型公平性评估数据集进行系统性分析，揭示潜在偏见并提出统一评估框架，发现人口统计差异，呼吁建立涵盖更广社会背景的新基准。
English: This survey critically analyzes widely used fairness datasets for language models, revealing embedded biases and proposing a unified evaluation framework that uncovers demographic disparities while advocating for more comprehensive benchmarks.

Paperid: 12, https://arxiv.org/pdf/2506.23382.pdf   GitHub GitHub

Authors:Vikram Rangarajan, Shishira Maiya, Max Ehrlich, Abhinav Shrivastava

Title: SIEDD: Shared-Implicit Encoder with Discrete Decoders

Abstract:
Implicit Neural Representations (INRs) offer exceptional fidelity for video compression by learning per-video optimized functions, but their adoption is crippled by impractically slow encoding times. Existing attempts to accelerate INR encoding often sacrifice reconstruction quality or crucial coordinate-level control essential for adaptive streaming and transcoding. We introduce SIEDD (Shared-Implicit Encoder with Discrete Decoders), a novel architecture that fundamentally accelerates INR encoding without these compromises. SIEDD first rapidly trains a shared, coordinate-based encoder on sparse anchor frames to efficiently capture global, low-frequency video features. This encoder is then frozen, enabling massively parallel training of lightweight, discrete decoders for individual frame groups, further expedited by aggressive coordinate-space sampling. This synergistic design delivers a remarkable 20-30X encoding speed-up over state-of-the-art INR codecs on HD and 4K benchmarks, while maintaining competitive reconstruction quality and compression ratios. Critically, SIEDD retains full coordinate-based control, enabling continuous resolution decoding and eliminating costly transcoding. Our approach significantly advances the practicality of high-fidelity neural video compression, demonstrating a scalable and efficient path towards real-world deployment. Our codebase is available at https://github.com/VikramRangarajan/SIEDD .
中文摘要：SIEDD提出了一种新颖架构，通过共享编码器和并行训练轻量解码器的方法，将隐式神经表示的编码速度提升20-30倍，同时保持重建质量和完整的坐标控制能力，显著推进了神经视频压缩技术的实际应用。
English Summary: SIEDD introduces a novel architecture that accelerates implicit neural representation encoding by 20-30 times while maintaining reconstruction quality and full coordinate-based control, making high-fidelity neural video compression practical for real-world deployment.

Paperid: 13, https://arxiv.org/pdf/2506.23351.pdf   GitHub GitHub

Authors:Tianxing Chen, Kaixuan Wang, Zhaohui Yang, Yuhao Zhang, Zanxin Chen, Baijun Chen, Wanxi Dong, Ziyuan Liu, Dong Chen, Tianshuo Yang, Haibao Yu, Xiaokang Yang, Yusen Qin, Zhiqiang Xie, Yao Mu, Ping Luo, Tian Nian, Weiliang Deng, Yiheng Ge, Yibin Liu, Zixuan Li, Dehui Wang, Zhixuan Liang, Haohui Xie, Rijie Zeng, Yunfei Ge, Peiqing Cong, Guannan He, Zhaoming Han, Ruocheng Yin, Jingxiang Guo, Lunkai Lin, Tianling Xu, Hongzhe Bi, Xuewu Lin, Tianwei Lin, Shujie Luo, Keyu Li, Ziyan Zhao, Ke Fan, Heyang Xu, Bo Peng, Wenlong Gao, Dongjiang Li, Feng Jin, Hui Shen, Jinming Li, Chaowei Cui, Yu Chen, Yaxin Peng, Lingdong Zeng, Wenlong Dong, Tengfei Li, Weijie Ke, Jun Chen, Erdemt Bao, Tian Lan, Tenglong Liu, Jin Yang, Huiping Zhuang, Baozhi Jia, Shuai Zhang, Zhengfeng Zou, Fangheng Guan, Tianyi Jia, Ke Zhou, Hongjiu Zhang, Yating Han, Cheng Fang, Yixian Zou, Chongyang Xu, Qinglun Zhang, Shen Cheng, Xiaohe Wang, Ping Tan, Haoqiang Fan, Shuaicheng Liu, Jiaheng Chen, Chuxuan Huang, Chengliang Lin, Kaijun Luo, Boyu Yue, Yi Liu, Jinyu Chen, Zichang Tan, Liming Deng, Shuo Xu, Zijian Cai, Shilong Yin, Hao Wang, Hongshan Liu, Tianyang Li, Long Shi, Ran Xu, Huilin Xu, Zhengquan Zhang, Congsheng Xu, Jinchang Yang, Feng Xu

Title: Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop

Abstract:
Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical environments. While single-arm systems have shown strong task performance, collaborative dual-arm systems are essential for handling more intricate tasks involving rigid, deformable, and tactile-sensitive objects. To advance this goal, we launched the RoboTwin Dual-Arm Collaboration Challenge at the 2nd MEIS Workshop, CVPR 2025. Built on the RoboTwin Simulation platform (1.0 and 2.0) and the AgileX COBOT-Magic Robot platform, the competition consisted of three stages: Simulation Round 1, Simulation Round 2, and a final Real-World Round. Participants totally tackled 17 dual-arm manipulation tasks, covering rigid, deformable, and tactile-based scenarios. The challenge attracted 64 global teams and over 400 participants, producing top-performing solutions like SEM and AnchorDP3 and generating valuable insights into generalizable bimanual policy learning. This report outlines the competition setup, task design, evaluation methodology, key findings and future direction, aiming to support future research on robust and generalizable bimanual manipulation policies. The Challenge Webpage is available at https://robotwin-benchmark.github.io/cvpr-2025-challenge/.
中文: CVPR 2025的RoboTwin双机械臂协作挑战赛通过仿真和现实阶段，让全球团队完成17项双臂操作任务，为可泛化的双手策略学习提供了重要见解，推动了具身人工智能的发展。
English: The RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 advanced Embodied AI by engaging global teams in 17 dual-arm manipulation tasks, yielding key insights for generalizable bimanual policies through simulation and real-world stages.

Paperid: 14, https://arxiv.org/pdf/2506.23266.pdf   GitHub

Authors:Lujun Li, Zhu Qiyuan, Jiacheng Wang, Wei Li, Hao Gu, Sirui Han, Yike Guo

Title: Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

Abstract:
Mixture of Experts (MoE) LLMs face significant obstacles due to their massive parameter scale, which imposes memory, storage, and deployment challenges. Although recent expert merging methods promise greater efficiency by consolidating multiple experts, they are fundamentally hindered by parameter conflicts arising from expert specialization. In this paper, we present Sub-MoE, a novel MoE compression framework via Subspace Expert Merging. Our key insight is to perform joint Singular Value Decomposition (SVD) on concatenated expert weights, reducing conflicting parameters by extracting shared $U$-matrices while enabling effective merging of the expert-specific $V$ components. Specifically, Sub-MoE consists of two innovative phases: (1) Adaptive Expert Clustering, which groups functionally coherent experts via K-means clustering based on cosine similarity of expert outputs; and (2) Subspace Expert Merging, which first enforces Experts Union Decomposition to derive the shared $U$-matrix across experts in the same group, then pursues frequency-based merging for individual $V$-matrices, and finalizes expert reconstruction using the merged $V$-matrix. In this way, we align and fuse experts in a shared subspace, and can be extended with intra-expert compression for further inference optimization. Extensive experiments on Mixtral, DeepSeek, and Qwen-1.5|3 MoE LLMs demonstrate that our Sub-MoE significantly outperforms existing expert pruning and merging methods. Notably, our Sub-MoE maintains 96\%|86\% of original performance with 25\%|50\% expert reduction on Mixtral-8x7B in zero-shot benchmarks. Code will be released at https://github.com/lliai/MoERazor.
Chinese: Sub-MoE是一种新颖的专家混合模型压缩框架，通过专家聚类和基于联合奇异值分解的子空间专家合并，有效解决参数冲突问题，在保持高性能的同时显著提升模型效率。
English: Sub-MoE is a novel compression framework that addresses parameter conflicts in Mixture of Experts LLMs by clustering experts and merging them in a shared subspace using joint SVD, achieving significant efficiency gains while maintaining high performance.

Paperid: 15, https://arxiv.org/pdf/2506.23201.pdf   GitHub

Authors:Haoran Li, Muhao Guo, Marija Ilic, Yang Weng, Guangchun Ruan

Title: External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting

Abstract:
Accurate residential load forecasting is critical for power system reliability with rising renewable integration and demand-side flexibility. However, most statistical and machine learning models treat external factors, such as weather, calendar effects, and pricing, as extra input, ignoring their heterogeneity, and thus limiting the extraction of useful external information. We propose a paradigm shift: external data should serve as meta-knowledge to dynamically adapt the forecasting model itself. Based on this idea, we design a meta-representation framework using hypernetworks that modulate selected parameters of a base Deep Learning (DL) model in response to external conditions. This provides both expressivity and adaptability. We further integrate a Mixture-of-Experts (MoE) mechanism to enhance efficiency through selective expert activation, while improving robustness by filtering redundant external inputs. The resulting model, dubbed as a Meta Mixture of Experts for External data (M2oE2), achieves substantial improvements in accuracy and robustness with limited additional overhead, outperforming existing state-of-the-art methods in diverse load datasets. The dataset and source code are publicly available at https://github.com/haorandd/M2oE2\_load\_forecast.git.
中文摘要：作者提出创新的M2oE2框架，将外部数据作为元知识，通过超网络和专家混合机制动态调整预测模型，以最小计算开销显著提升了住宅负荷预测的准确性与鲁棒性。
English Summary: The authors propose a novel M2oE2 framework that uses external data as meta-knowledge to dynamically adapt forecasting models through hypernetworks and mixture-of-experts, achieving superior accuracy and robustness in residential load prediction with minimal overhead.

Paperid: 16, https://arxiv.org/pdf/2506.23192.pdf   GitHub

Authors:Gabriel Iturra-Bocaz, Felipe Bravo-Marquez

Title: RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams

Abstract:
Word embeddings have become essential components in various information retrieval and natural language processing tasks, such as ranking, document classification, and question answering. However, despite their widespread use, traditional word embedding models present a limitation in their static nature, which hampers their ability to adapt to the constantly evolving language patterns that emerge in sources such as social media and the web (e.g., new hashtags or brand names). To overcome this problem, incremental word embedding algorithms are introduced, capable of dynamically updating word representations in response to new language patterns and processing continuous data streams. This paper presents RiverText, a Python library for training and evaluating incremental word embeddings from text data streams. Our tool is a resource for the information retrieval and natural language processing communities that work with word embeddings in streaming scenarios, such as analyzing social media. The library implements different incremental word embedding techniques, such as Skip-gram, Continuous Bag of Words, and Word Context Matrix, in a standardized framework. In addition, it uses PyTorch as its backend for neural network training. We have implemented a module that adapts existing intrinsic static word embedding evaluation tasks for word similarity and word categorization to a streaming setting. Finally, we compare the implemented methods with different hyperparameter settings and discuss the results. Our open-source library is available at https://github.com/dccuchile/rivertext.
中文摘要：RiverText是一个Python库，用于从文本数据流中训练和评估增量词嵌入，通过集成Skip-gram和CBOW等动态更新技术，解决了传统静态词嵌入模型无法适应语言演变的问题，并提供了标准化评估框架。
English Summary: RiverText is a Python library designed for training and evaluating incremental word embeddings from text streams, addressing the limitations of static models by dynamically updating word representations using techniques like Skip-gram and CBOW within a PyTorch framework.

Paperid: 17, https://arxiv.org/pdf/2506.23145.pdf   GitHub

Authors:Shahad Hardan, Darya Taratynova, Abdelmajid Essofi, Karthik Nandakumar, Mohammad Yaqub

Title: Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings

Abstract:
Privacy preservation in AI is crucial, especially in healthcare, where models rely on sensitive patient data. In the emerging field of machine unlearning, existing methodologies struggle to remove patient data from trained multimodal architectures, which are widely used in healthcare. We propose Forget-MI, a novel machine unlearning method for multimodal medical data, by establishing loss functions and perturbation techniques. Our approach unlearns unimodal and joint representations of the data requested to be forgotten while preserving knowledge from the remaining data and maintaining comparable performance to the original model. We evaluate our results using performance on the forget dataset, performance on the test dataset, and Membership Inference Attack (MIA), which measures the attacker's ability to distinguish the forget dataset from the training dataset. Our model outperforms the existing approaches that aim to reduce MIA and the performance on the forget dataset while keeping an equivalent performance on the test set. Specifically, our approach reduces MIA by 0.202 and decreases AUC and F1 scores on the forget set by 0.221 and 0.305, respectively. Additionally, our performance on the test set matches that of the retrained model, while allowing forgetting. Code is available at https://github.com/BioMedIA-MBZUAI/Forget-MI.git
Chinese: 提出的Forget-MI方法通过消除特定表征，在保持剩余数据性能的同时，有效从医疗多模态AI模型中移除敏感患者数据，并在降低成员推理攻击和遗忘集指标方面优于现有方法。
English: The proposed Forget-MI method effectively removes sensitive patient data from multimodal AI models in healthcare by unlearning specific representations while maintaining performance on retained data and outperforming existing approaches in reducing membership inference attacks and forget set metrics.

Paperid: 18, https://arxiv.org/pdf/2506.23121.pdf   GitHub

Authors:Xinlei Yu, Changmiao Wang, Hui Jin, Ahmed Elazab, Gangyong Jia, Xiang Wan, Changqing Zou, Ruiquan Ge

Title: CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation

Abstract:
Multi-organ medical segmentation is a crucial component of medical image processing, essential for doctors to make accurate diagnoses and develop effective treatment plans. Despite significant progress in this field, current multi-organ segmentation models often suffer from inaccurate details, dependence on geometric prompts and loss of spatial information. Addressing these challenges, we introduce a novel model named CRISP-SAM2 with CRoss-modal Interaction and Semantic Prompting based on SAM2. This model represents a promising approach to multi-organ medical segmentation guided by textual descriptions of organs. Our method begins by converting visual and textual inputs into cross-modal contextualized semantics using a progressive cross-attention interaction mechanism. These semantics are then injected into the image encoder to enhance the detailed understanding of visual information. To eliminate reliance on geometric prompts, we use a semantic prompting strategy, replacing the original prompt encoder to sharpen the perception of challenging targets. In addition, a similarity-sorting self-updating strategy for memory and a mask-refining process is applied to further adapt to medical imaging and enhance localized details. Comparative experiments conducted on seven public datasets indicate that CRISP-SAM2 outperforms existing models. Extensive analysis also demonstrates the effectiveness of our method, thereby confirming its superior performance, especially in addressing the limitations mentioned earlier. Our code is available at: https://github.com/YU-deep/CRISP_SAM2.git.
中文摘要：CRISP-SAM2是一种新颖的多器官分割模型，通过跨模态交互和语义提示技术提升医学图像分析的细节精度并摆脱对几何提示的依赖，在多个公开数据集上展现出优越性能。
English Summary: CRISP-SAM2 is a novel multi-organ segmentation model that enhances medical image analysis through cross-modal interaction and semantic prompting, achieving superior performance by improving detail accuracy and eliminating reliance on geometric prompts.

Paperid: 19, https://arxiv.org/pdf/2506.23074.pdf   GitHub GitHub

Authors:Yu Zheng, Boyang Gong, Fanye Kong, Yueqi Duan, Bingyao Yu, Wenzhao Zheng, Lei Chen, Jiwen Lu, Jie Zhou

Title: Learning Counterfactually Decoupled Attention for Open-World Model Attribution

Abstract:
In this paper, we propose a Counterfactually Decoupled Attention Learning (CDAL) method for open-world model attribution. Existing methods rely on handcrafted design of region partitioning or feature space, which could be confounded by the spurious statistical correlations and struggle with novel attacks in open-world scenarios. To address this, CDAL explicitly models the causal relationships between the attentional visual traces and source model attribution, and counterfactually decouples the discriminative model-specific artifacts from confounding source biases for comparison. In this way, the resulting causal effect provides a quantification on the quality of learned attention maps, thus encouraging the network to capture essential generation patterns that generalize to unseen source models by maximizing the effect. Extensive experiments on existing open-world model attribution benchmarks show that with minimal computational overhead, our method consistently improves state-of-the-art models by large margins, particularly for unseen novel attacks. Source code: https://github.com/yzheng97/CDAL.
中文摘要：本文提出反事实解耦注意力学习（CDAL）方法，通过因果建模分离判别性模型特征与混杂偏差，在计算开销极小的前提下显著提升开放世界模型溯源性能，尤其针对未知攻击具有卓越泛化能力。
English Summary: This paper introduces Counterfactually Decoupled Attention Learning (CDAL), a novel method that uses causal modeling to separate discriminative model artifacts from confounding biases, significantly improving open-world model attribution performance for unseen attacks with minimal computational cost.

Paperid: 20, https://arxiv.org/pdf/2506.22977.pdf   GitHub

Authors:Asen Dotsinski, Udit Thakur, Marko Ivanov, Mohammad Hafeez Khan, Maria Heuss

Title: On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"

Abstract:
We present a reproduction study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals" (Ortu et al., 2024), which investigates competition of mechanisms in language models between factual recall and counterfactual in-context repetition. Our study successfully reproduces their primary findings regarding the localization of factual and counterfactual information, the dominance of attention blocks in mechanism competition, and the specialization of attention heads in handling competing information. We reproduce their results on both GPT-2 (Radford et al., 2019) and Pythia 6.9B (Biderman et al., 2023). We extend their work in three significant directions. First, we explore the generalizability of these findings to even larger models by replicating the experiments on Llama 3.1 8B (Grattafiori et al., 2024), discovering greatly reduced attention head specialization. Second, we investigate the impact of prompt structure by introducing variations where we avoid repeating the counterfactual statement verbatim or we change the premise word, observing a marked decrease in the logit for the counterfactual token. Finally, we test the validity of the authors' claims for prompts of specific domains, discovering that certain categories of prompts skew the results by providing the factual prediction token as part of the subject of the sentence. Overall, we find that the attention head ablation proposed in Ortu et al. (2024) is ineffective for domains that are underrepresented in their dataset, and that the effectiveness varies based on model architecture, prompt structure, domain and task.
中文: 这项复制研究证实了Ortu等人关于语言模型中注意力机制处理事实与反事实信息的发现，同时扩展研究揭示了更大模型中注意力头专门化程度降低、对提示结构的敏感性增强，以及所提出消融方法在不同领域有效性存在差异的局限性。
English: This reproduction study confirms Ortu et al.'s findings about attention mechanisms handling factual and counterfactual information in language models, while extending the research to reveal limitations in attention head specialization across larger models, sensitivity to prompt structures, and domain-specific effectiveness of their proposed ablation method.

Paperid: 21, https://arxiv.org/pdf/2506.22967.pdf   GitHub

Authors:Amir Aghdam, Vincent Tao Hu, Björn Ommer

Title: ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment

Abstract:
We address the task of zero-shot video classification for extremely fine-grained actions (e.g., Windmill Dunk in basketball), where no video examples or temporal annotations are available for unseen classes. While image-language models (e.g., CLIP, SigLIP) show strong open-set recognition, they lack temporal modeling needed for video understanding. We propose ActAlign, a truly zero-shot, training-free method that formulates video classification as a sequence alignment problem, preserving the generalization strength of pretrained image-language models. For each class, a large language model (LLM) generates an ordered sequence of sub-actions, which we align with video frames using Dynamic Time Warping (DTW) in a shared embedding space. Without any video-text supervision or fine-tuning, ActAlign achieves 30.5% accuracy on ActionAtlas--the most diverse benchmark of fine-grained actions across multiple sports--where human performance is only 61.6%. ActAlign outperforms billion-parameter video-language models while using 8x fewer parameters. Our approach is model-agnostic and domain-general, demonstrating that structured language priors combined with classical alignment methods can unlock the open-set recognition potential of image-language models for fine-grained video understanding.

Paperid: 22, https://arxiv.org/pdf/2506.22935.pdf   GitHub

Authors:Marc Bara Iniesta

Title: Differentiable Radar Ambiguity Functions: Mathematical Formulation and Computational Implementation

Abstract:
The ambiguity function is fundamental to radar waveform design, characterizing range and Doppler resolution capabilities. However, its traditional formulation involves non-differentiable operations, preventing integration with gradient-based optimization methods and modern machine learning frameworks. This paper presents the first complete mathematical framework and computational implementation for differentiable radar ambiguity functions. Our approach addresses the fundamental technical challenges that have prevented the radar community from leveraging automatic differentiation: proper handling of complex-valued gradients using Wirtinger calculus, efficient computation through parallelized FFT operations, numerical stability throughout cascaded operations, and composability with arbitrary differentiable operations. We term this approach GRAF (Gradient-based Radar Ambiguity Functions), which reformulates the ambiguity function computation to maintain mathematical equivalence while enabling gradient flow through the entire pipeline. The resulting implementation provides a general-purpose differentiable ambiguity function compatible with modern automatic differentiation frameworks, enabling new research directions including neural network-based waveform generation with ambiguity constraints, end-to-end optimization of radar systems, and integration of classical radar theory with modern deep learning. We provide complete implementation details and demonstrate computational efficiency suitable for practical applications. This work establishes the mathematical and computational foundation for applying modern machine learning techniques to radar waveform design, bridging classical radar signal processing with automatic differentiation frameworks.
中文摘要：本文提出了GRAF这一新型可微分雷达模糊函数框架，实现了基于梯度的优化方法，并能与机器学习结合用于先进雷达波形设计。
English Summary: This paper introduces GRAF, a novel differentiable radar ambiguity function framework that enables gradient-based optimization and integration with machine learning for advanced radar waveform design.

Paperid: 23, https://arxiv.org/pdf/2506.22901.pdf   GitHub

Authors:Sina Tabakhi, Haiping Lu

Title: Missing-Modality-Aware Graph Neural Network for Cancer Classification

Abstract:
A key challenge in learning from multimodal biological data is missing modalities, where all data from some modalities are missing for some patients. Current fusion methods address this by excluding patients with missing modalities, imputing missing modalities, or making predictions directly with partial modalities. However, they often struggle with diverse missing-modality patterns and the exponential growth of the number of such patterns as the number of modalities increases. To address these limitations, we propose MAGNET (Missing-modality-Aware Graph neural NETwork) for direct prediction with partial modalities, which introduces a patient-modality multi-head attention mechanism to fuse lower-dimensional modality embeddings based on their importance and missingness. MAGNET's complexity increases linearly with the number of modalities while adapting to missing-pattern variability. To generate predictions, MAGNET further constructs a patient graph with fused multimodal embeddings as node features and the connectivity determined by the modality missingness, followed by a conventional graph neural network. Experiments on three public multiomics datasets for cancer classification, with real-world instead of artificial missingness, show that MAGNET outperforms the state-of-the-art fusion methods. The data and code are available at https://github.com/SinaTabakhi/MAGNET.
中文: MAGNET是一种新型图神经网络，通过患者-模态注意力机制和图结构融合有效处理多模态生物数据中的缺失模态问题，在癌症分类任务中以线性增长的复杂度实现了优于现有方法的性能。
English: MAGNET is a novel graph neural network that effectively handles diverse missing-modality patterns in multimodal biological data by using patient-modality attention and graph-based fusion, achieving superior performance in cancer classification with linearly scalable complexity.

Paperid: 24, https://arxiv.org/pdf/2506.22837.pdf   GitHub

Authors:Kamil Faber, Marcin Pietroń, Dominik Żurek, Roberto Corizzo

Title: xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection

Abstract:
The recently proposed xLSTM is a powerful model that leverages expressive multiplicative gating and residual connections, providing the temporal capacity needed for long-horizon forecasting and representation learning. This architecture has demonstrated success in time series forecasting, lossless compression, and even large-scale language modeling tasks, where its linear memory footprint and fast inference make it a viable alternative to Transformers. Despite its growing popularity, no prior work has explored xLSTM for anomaly detection. In this work, we fill this gap by proposing xLSTMAD, the first anomaly detection method that integrates a full encoder-decoder xLSTM architecture, purpose-built for multivariate time series data. Our encoder processes input sequences to capture historical context, while the decoder is devised in two separate variants of the method. In the forecasting approach, the decoder iteratively generates forecasted future values xLSTMAD-F, while the reconstruction approach reconstructs the input time series from its encoded counterpart xLSTMAD-R. We investigate the performance of two loss functions: Mean Squared Error (MSE), and Soft Dynamic Time Warping (SoftDTW) to consider local reconstruction fidelity and global sequence alignment, respectively. We evaluate our method on the comprehensive TSB-AD-M benchmark, which spans 17 real-world datasets, using state-of-the-art challenging metrics such as VUS-PR. In our results, xLSTM showcases state-of-the-art accuracy, outperforming 23 popular anomaly detection baselines. Our paper is the first work revealing the powerful modeling capabilities of xLSTM for anomaly detection, paving the way for exciting new developments on this subject. Our code is available at: https://github.com/Nyderx/xlstmad
中文: 本文提出了xLSTMAD，这是首个采用完整编码器-解码器xLSTM架构的多变量时间序列异常检测方法，在基准测试中实现了最先进的精度，并超越了23种基线方法。
English: This paper introduces xLSTMAD, the first anomaly detection method using a full encoder-decoder xLSTM architecture for multivariate time series, which achieves state-of-the-art accuracy on benchmark datasets and outperforms 23 baseline methods.

Paperid: 25, https://arxiv.org/pdf/2506.22819.pdf   GitHub

Authors:Ramya Hebbalaguppe, Tamoghno Kandar, Abhinav Nagpal, Chetan Arora

Title: Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration

Abstract:
Vision-language models (VLM) have demonstrated impressive performance in image recognition by leveraging self-supervised training on large datasets. Their performance can be further improved by adapting to the test sample using test-time prompt tuning (TPT). Unfortunately, the singular focus of TPT approaches on improving the accuracy suffers from tunnel vision, and leads to degradation in confidence calibration. This limits the applicability of TPT in critical applications. We make three contributions in this work. (1) We posit that random or naive initialization of prompts leads to overfitting on a particular test sample, and is the main reason for miscalibration of the VLM after TPT. To mitigate the problem, we propose careful initialization of test time prompt using prior knowledge about the target label attributes from a large language model (LLM); (2) To further maintain the quality of prompts during \tpt, we propose a novel regularization loss to reduce intraclass distance, and increase inter-class distance between the learnt Through extensive experiments on different CLIP architectures and 15 datasets, we show that our approach can effectively improve the calibration after TPT. We report an average expected calibration error (ECE) of 4.11 with our method, TCA, compared to 11.7 for vanilla TPT, 6.12 for C-TPT (ICLR'24), 6.78 for DiffTPT (CVPR'23), and 8.43 for PromptAlign (NeurIPS'23). The code is publicly accessible at: https://github.com/rhebbalaguppe/TCA_PromptWithoutPanic.
中文: 视觉语言模型通过测试时提示调优可提升性能，但易导致过拟合和置信度校准不佳；我们提出的方法利用大语言模型初始化提示并结合新型正则化损失，有效改善了多个数据集的校准效果。
English: Vision-language models can be enhanced through test-time prompt tuning, but this often leads to overfitting and poor confidence calibration, which our proposed method mitigates by using large language model initialization and a novel regularization loss to significantly improve calibration across multiple datasets.

Paperid: 26, https://arxiv.org/pdf/2506.22806.pdf   GitHub

Authors:Byung Hyun Lee, Sungjin Lim, Seunggyu Lee, Dong Un Kang, Se Young Chun

Title: Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gate

Abstract:
Remarkable progress in text-to-image diffusion models has brought a major concern about potentially generating images on inappropriate or trademarked concepts. Concept erasing has been investigated with the goals of deleting target concepts in diffusion models while preserving other concepts with minimal distortion. To achieve these goals, recent concept erasing methods usually fine-tune the cross-attention layers of diffusion models. In this work, we first show that merely updating the cross-attention layers in diffusion models, which is mathematically equivalent to adding \emph{linear} modules to weights, may not be able to preserve diverse remaining concepts. Then, we propose a novel framework, dubbed Concept Pinpoint Eraser (CPE), by adding \emph{nonlinear} Residual Attention Gates (ResAGs) that selectively erase (or cut) target concepts while safeguarding remaining concepts from broad distributions by employing an attention anchoring loss to prevent the forgetting. Moreover, we adversarially train CPE with ResAG and learnable text embeddings in an iterative manner to maximize erasing performance and enhance robustness against adversarial attacks. Extensive experiments on the erasure of celebrities, artistic styles, and explicit contents demonstrated that the proposed CPE outperforms prior arts by keeping diverse remaining concepts while deleting the target concepts with robustness against attack prompts. Code is available at https://github.com/Hyun1A/CPE
中文摘要：本文提出概念精准擦除器（CPE），通过非线性残差注意力门控和对抗性训练，在扩散模型中有效擦除目标概念的同时保持其余概念的多样性，并具备抗攻击鲁棒性。
English summary: This paper introduces Concept Pinpoint Eraser (CPE), a novel framework that uses nonlinear Residual Attention Gates and adversarial training to effectively erase target concepts from diffusion models while preserving diverse remaining concepts and maintaining robustness against attacks.

Paperid: 27, https://arxiv.org/pdf/2506.22803.pdf   GitHub

Authors:Nuoye Xiong, Anqi Dong, Ning Wang, Cong Hua, Guangming Zhu, Lin Mei, Peiyi Shen, Liang Zhang

Title: Intervening in Black Box: Concept Bottleneck Model for Enhancing Human Neural Network Mutual Understanding

Abstract:
Recent advances in deep learning have led to increasingly complex models with deeper layers and more parameters, reducing interpretability and making their decisions harder to understand. While many methods explain black-box reasoning, most lack effective interventions or only operate at sample-level without modifying the model itself. To address this, we propose the Concept Bottleneck Model for Enhancing Human-Neural Network Mutual Understanding (CBM-HNMU). CBM-HNMU leverages the Concept Bottleneck Model (CBM) as an interpretable framework to approximate black-box reasoning and communicate conceptual understanding. Detrimental concepts are automatically identified and refined (removed/replaced) based on global gradient contributions. The modified CBM then distills corrected knowledge back into the black-box model, enhancing both interpretability and accuracy. We evaluate CBM-HNMU on various CNN and transformer-based models across Flower-102, CIFAR-10, CIFAR-100, FGVC-Aircraft, and CUB-200, achieving a maximum accuracy improvement of 2.64% and a maximum increase in average accuracy across 1.03%. Source code is available at: https://github.com/XiGuaBo/CBM-HNMU.
Chinese: 本文提出了增强人机互理解的概念瓶颈模型（CBM-HNMU），通过自动识别并优化有害概念来提升模型可解释性与准确率，在多个数据集上最高实现了2.64%的精度提升。
English: This paper introduces the Concept Bottleneck Model for Enhancing Human-Neural Network Mutual Understanding (CBM-HNMU), which automatically identifies and refines detrimental concepts to improve both model interpretability and accuracy, achieving up to a 2.64% accuracy gain across multiple datasets.

Paperid: 28, https://arxiv.org/pdf/2506.22799.pdf   GitHub

Authors:Minchao Jiang, Shunyu Jia, Jiaming Gu, Xiaoyuan Lu, Guangming Zhu, Anqi Dong, Liang Zhang

Title: VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding

Abstract:
3D Gaussian Splatting (3DGS) has become horsepower in high-quality, real-time rendering for novel view synthesis of 3D scenes. However, existing methods focus primarily on geometric and appearance modeling, lacking deeper scene understanding while also incurring high training costs that complicate the originally streamlined differentiable rendering pipeline. To this end, we propose VoteSplat, a novel 3D scene understanding framework that integrates Hough voting with 3DGS. Specifically, Segment Anything Model (SAM) is utilized for instance segmentation, extracting objects, and generating 2D vote maps. We then embed spatial offset vectors into Gaussian primitives. These offsets construct 3D spatial votes by associating them with 2D image votes, while depth distortion constraints refine localization along the depth axis. For open-vocabulary object localization, VoteSplat maps 2D image semantics to 3D point clouds via voting points, reducing training costs associated with high-dimensional CLIP features while preserving semantic unambiguity. Extensive experiments demonstrate effectiveness of VoteSplat in open-vocabulary 3D instance localization, 3D point cloud understanding, click-based 3D object localization, hierarchical segmentation, and ablation studies. Our code is available at https://sy-ja.github.io/votesplat/

Paperid: 29, https://arxiv.org/pdf/2506.22773.pdf   GitHub

Authors:Yanran Wu, Inez Hua, Yi Ding

Title: Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing

Abstract:
Water consumption is an increasingly critical dimension of computing sustainability, especially as AI workloads rapidly scale. However, current water impact assessment often overlooks where and when water stress is more severe. To fill in this gap, we present SCARF, the first general framework that evaluates water impact of computing by factoring in both spatial and temporal variations in water stress. SCARF calculates an Adjusted Water Impact (AWI) metric that considers both consumption volume and local water stress over time. Through three case studies on LLM serving, datacenters, and semiconductor fabrication plants, we show the hidden opportunities for reducing water impact by optimizing location and time choices, paving the way for water-sustainable computing. The code is available at https://github.com/jojacola/SCARF.
中文: SCARF作为首个考虑时空水资源压力变化的计算水影响评估框架，通过案例研究揭示了优化时空选择以实现水资源可持续计算的潜力。
English: SCARF is the first framework to assess computing's water impact by incorporating spatial and temporal water stress variations, revealing optimization opportunities for water-sustainable computing through case studies.

Paperid: 30, https://arxiv.org/pdf/2506.22696.pdf   GitHub

Authors:Brian Mak, Jeffrey Flanigan

Title: Residual Matrix Transformers: Scaling the Size of the Residual Stream

Abstract:
The residual stream acts as a memory bus where transformer layers both store and access features (Elhage et al., 2021). We consider changing the mechanism for retrieving and storing information in the residual stream, and replace the residual stream of the transformer with an outer product memory matrix (Kohonen, 1972, Anderson, 1972). We call this model the Residual Matrix Transformer (RMT). We find that the RMT enjoys a number of attractive properties: 1) the size of the residual stream can be scaled independently of compute and model size, improving performance, 2) the RMT can achieve the same loss as the transformer with 58% fewer FLOPS, 25% fewer parameters, and 41% fewer training tokens tokens, and 3) the RMT outperforms the transformer on downstream evaluations. We theoretically analyze the transformer and the RMT, and show that the RMT allows for more efficient scaling of the residual stream, as well as improved variance propagation properties. Code for this project can be found at https://github.com/bmac3/residual-matrix-transformer.
残差矩阵变换器（RMT）用外积记忆矩阵替代了标准残差流，以更少的资源实现了更高的效率和性能，同时允许残差流独立扩展。
The Residual Matrix Transformer (RMT) replaces the standard residual stream with an outer product memory matrix, achieving superior efficiency and performance with fewer resources while enabling independent scaling of the residual stream.

Paperid: 31, https://arxiv.org/pdf/2506.22505.pdf   GitHub

Authors:Hassan Baker, Matthew S. Emigh, Austin J. Brockmeier

Title: Weakly Supervised Object Segmentation by Background Conditional Divergence

Abstract:
As a computer vision task, automatic object segmentation remains challenging in specialized image domains without massive labeled data, such as synthetic aperture sonar images, remote sensing, biomedical imaging, etc. In any domain, obtaining pixel-wise segmentation masks is expensive. In this work, we propose a method for training a masking network to perform binary object segmentation using weak supervision in the form of image-wise presence or absence of an object of interest, which provides less information but may be obtained more quickly from manual or automatic labeling. A key step in our method is that the segmented objects can be placed into background-only images to create realistic, images of the objects with counterfactual backgrounds. To create a contrast between the original and counterfactual background images, we propose to first cluster the background-only images, and then during learning create counterfactual images that blend objects segmented from their original source backgrounds to backgrounds chosen from a targeted cluster. One term in the training loss is the divergence between these counterfactual images and the real object images with backgrounds of the target cluster. The other term is a supervised loss for background-only images. While an adversarial critic could provide the divergence, we use sample-based divergences. We conduct experiments on side-scan and synthetic aperture sonar in which our approach succeeds compared to previous unsupervised segmentation baselines that were only tested on natural images. Furthermore, to show generality we extend our experiments to natural images, obtaining reasonable performance with our method that avoids pretrained networks, generative networks, and adversarial critics. The basecode for this work can be found at \href{GitHub}{https://github.com/bakerhassan/WSOS}.
Chinese: 本文提出了一种弱监督的二元目标分割方法，利用图像级目标存在标签并通过将分割目标与聚类背景融合生成反事实图像，在声纳等专业领域和自然图像上实现了优于无监督基线的性能，且无需预训练网络或对抗训练。
English: This paper introduces a weakly supervised method for binary object segmentation that uses image-level object presence labels and creates counterfactual images by blending segmented objects with clustered backgrounds, achieving improved performance on specialized domains like sonar and natural images without relying on pretrained networks or adversarial training.

Paperid: 32, https://arxiv.org/pdf/2506.22504.pdf   GitHub

Authors:Hassan Baker, Austin J. Brockmeier

Title: Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection

Abstract:
Detecting brain lesions as abnormalities observed in magnetic resonance imaging (MRI) is essential for diagnosis and treatment. In the search of abnormalities, such as tumors and malformations, radiologists may benefit from computer-aided diagnostics that use computer vision systems trained with machine learning to segment normal tissue from abnormal brain tissue. While supervised learning methods require annotated lesions, we propose a new unsupervised approach (Patch2Loc) that learns from normal patches taken from structural MRI. We train a neural network model to map a patch back to its spatial location within a slice of the brain volume. During inference, abnormal patches are detected by the relatively higher error and/or variance of the location prediction. This generates a heatmap that can be integrated into pixel-wise methods to achieve finer-grained segmentation. We demonstrate the ability of our model to segment abnormal brain tissues by applying our approach to the detection of tumor tissues in MRI on T2-weighted images from BraTS2021 and MSLUB datasets and T1-weighted images from ATLAS and WMH datasets. We show that it outperforms the state-of-the art in unsupervised segmentation. The codebase for this work can be found on our \href{https://github.com/bakerhassan/Patch2Loc}{GitHub page}.
中文: 本研究提出Patch2Loc这一无监督方法，通过训练神经网络预测脑部MRI图像中斑块的位置，并利用预测误差识别病变区域，在性能上超越了现有最先进的无监督分割技术。
English: This study introduces Patch2Loc, an unsupervised method that detects brain lesions in MRI by training a neural network to predict patch locations and identifying abnormalities through prediction errors, outperforming current state-of-the-art techniques.

Paperid: 33, https://arxiv.org/pdf/2506.22463.pdf   GitHub

Authors:Weizhi Gao, Zhichao Hou, Junqi Yin, Feiyi Wang, Linyu Peng, Xiaorui Liu

Title: Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization

Abstract:
Diffusion models have emerged as powerful generative models, but their high computation cost in iterative sampling remains a significant bottleneck. In this work, we present an in-depth and insightful study of state-of-the-art acceleration techniques for diffusion models, including caching and quantization, revealing their limitations in computation error and generation quality. To break these limits, this work introduces Modulated Diffusion (MoDiff), an innovative, rigorous, and principled framework that accelerates generative modeling through modulated quantization and error compensation. MoDiff not only inherents the advantages of existing caching and quantization methods but also serves as a general framework to accelerate all diffusion models. The advantages of MoDiff are supported by solid theoretical insight and analysis. In addition, extensive experiments on CIFAR-10 and LSUN demonstrate that MoDiff significant reduces activation quantization from 8 bits to 3 bits without performance degradation in post-training quantization (PTQ). Our code implementation is available at https://github.com/WeizhiGao/MoDiff.
中文: 本文提出MoDiff这一创新框架，通过调制量化和误差补偿加速扩散模型，在训练后量化中将激活位从8位降至3位且不损失性能。
English: This paper introduces MoDiff, a novel framework that accelerates diffusion models through modulated quantization and error compensation, effectively reducing activation bits from 8 to 3 without performance loss in post-training quantization.

Paperid: 34, https://arxiv.org/pdf/2506.22376.pdf   GitHub

Authors:Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei

Title: Probabilistic Optimality for Inference-time Scaling

Abstract:
Inference-time scaling has emerged as a powerful technique for enhancing the reasoning performance of Large Language Models (LLMs). However, existing approaches often rely on heuristic strategies for parallel sampling, lacking a principled foundation. To address this gap, we propose a probabilistic framework that formalizes the optimality of inference-time scaling under the assumption that parallel samples are independently and identically distributed (i.i.d.), and where the Best-of-N selection strategy follows a probability distribution that can be estimated. Within this framework, we derive a theoretical lower bound on the required number of samples to achieve a target performance level, providing the first principled guidance for compute-efficient scaling. Leveraging this insight, we develop OptScale, a practical algorithm that dynamically determines the optimal number of sampled responses. OptScale employs a language model-based predictor to estimate probabilistic prior parameters, enabling the decision of the minimal number of samples needed that satisfy predefined performance thresholds and confidence levels. Extensive experiments on mathematical reasoning benchmarks (including MATH-500, GSM8K, AIME, and AMC) demonstrate that OptScale significantly reduces sampling overhead while remaining better or on par with state-of-the-art reasoning performance. Our work offers both a theoretical foundation and a practical solution for principled inference-time scaling, addressing a critical gap in the efficient deployment of LLMs for complex reasoning. The source code is publicly available at https://github.com/Albertwyk/OptScale.
中文摘要：本文提出了OptScale概率框架，首次从理论上推导出实现目标推理性能所需的最小采样数量，在显著降低计算开销的同时保持最优性能，为LLM的高效推理提供了原理性指导。
English Summary: This paper introduces OptScale, a probabilistic framework that theoretically determines the minimum number of parallel samples needed for efficient inference-time scaling in LLMs, significantly reducing computational overhead while maintaining state-of-the-art reasoning performance.

Paperid: 35, https://arxiv.org/pdf/2506.22331.pdf   GitHub

Authors:Adiba Ejaz, Elias Bareinboim

Title: Less Greedy Equivalence Search

Abstract:
Greedy Equivalence Search (GES) is a classic score-based algorithm for causal discovery from observational data. In the sample limit, it recovers the Markov equivalence class of graphs that describe the data. Still, it faces two challenges in practice: computational cost and finite-sample accuracy. In this paper, we develop Less Greedy Equivalence Search (LGES), a variant of GES that retains its theoretical guarantees while partially addressing these limitations. LGES modifies the greedy step: rather than always applying the highest-scoring insertion, it avoids edge insertions between variables for which the score implies some conditional independence. This more targeted search yields up to a \(10\)-fold speed-up and a substantial reduction in structural error relative to GES. Moreover, LGES can guide the search using prior assumptions, while correcting these assumptions when contradicted by the data. Finally, LGES can exploit interventional data to refine the learned observational equivalence class. We prove that LGES recovers the true equivalence class in the sample limit from observational and interventional data, even with misspecified prior assumptions. Experiments demonstrate that LGES outperforms GES and other baselines in speed, accuracy, and robustness to misspecified assumptions. Our code is available at https://github.com/CausalAILab/lges.
Chinese: 较少贪心等价搜索（LGES）是GES的改进版本，通过避免不必要的边插入来加速因果发现，在保持理论保证的同时实现了高达10倍的速度提升和更高的准确性。
English: Less Greedy Equivalence Search (LGES) is an improved variant of GES that speeds up causal discovery by avoiding unnecessary edge insertions, achieving up to a 10-fold acceleration and higher accuracy while maintaining theoretical guarantees.

Paperid: 36, https://arxiv.org/pdf/2506.22200.pdf   GitHub

Authors:Chen Wang, Lai Wei, Yanzhi Zhang, Chenyang Shao, Zedong Dan, Weiran Huang, Yue Wang, Yuzhi Zhang

Title: EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework

Abstract:
Recent advances in reinforcement learning (RL) have significantly enhanced the reasoning capabilities of large language models (LLMs). Group Relative Policy Optimization (GRPO), an efficient variant of PPO that lowers RL's computational cost, still faces limited exploration, low sample efficiency and instability, constraining its performance on complex reasoning tasks. To address these limitations, we introduce EFRame, an Exploration-Filter-Replay framework that systematically augments GRPO along three critical dimensions. EFRame performs additional rollouts to explore high-quality trajectories, applies online filtering to eliminate low-quality samples that introduce noise and variance, and leverages experience replay to repeatedly exploit rare but informative samples. EFRame establishes a complete and stable learning cycle, guiding the model through a structured transition from exploration to convergence. Our experiments across a variety of reasoning benchmarks demonstrate that EFRame not only improves the robustness and efficiency of training, but also enables access to deeper reasoning capabilities that remain unattainable under vanilla GRPO. Furthermore, EFRame not only enables fine-grained categorization of training samples for deeper insight into their contributions, but also introduces an efficient and precise mechanism for entropy control, which is critical for balancing exploration and convergence in RL training. Our code is available at https://github.com/597358816/EFRame.
中文: EFRame框架通过增强探索、过滤低质量样本和重放关键经验，提升了GRPO在复杂推理任务中的性能，在Geometry3K基准上相对改进37.9%，为语言模型的深度推理提供了稳健解决方案。
English: EFRame, an Exploration-Filter-Replay framework, enhances GRPO by enabling targeted exploration, stabilizing training through sample filtering, and amplifying rare trajectories, achieving a 37.9% improvement on Geometry3K and advancing reasoning in LLMs.

Paperid: 37, https://arxiv.org/pdf/2506.22101.pdf   GitHub

Authors:Hyeongji Kim, Stine Hansen, Michael Kampffmeyer

Title: Tied Prototype Model for Few-Shot Medical Image Segmentation

Abstract:
Common prototype-based medical image few-shot segmentation (FSS) methods model foreground and background classes using class-specific prototypes. However, given the high variability of the background, a more promising direction is to focus solely on foreground modeling, treating the background as an anomaly -- an approach introduced by ADNet. Yet, ADNet faces three key limitations: dependence on a single prototype per class, a focus on binary classification, and fixed thresholds that fail to adapt to patient and organ variability. To address these shortcomings, we propose the Tied Prototype Model (TPM), a principled reformulation of ADNet with tied prototype locations for foreground and background distributions. Building on its probabilistic foundation, TPM naturally extends to multiple prototypes and multi-class segmentation while effectively separating non-typical background features. Notably, both extensions lead to improved segmentation accuracy. Finally, we leverage naturally occurring class priors to define an ideal target for adaptive thresholds, boosting segmentation performance. Taken together, TPM provides a fresh perspective on prototype-based FSS for medical image segmentation. The code can be found at https://github.com/hjk92g/TPM-FSS.
中文: TPM模型通过绑定前景和背景的原型位置，支持多类别分割和自适应阈值，有效解决了医学图像中因患者和器官差异导致的背景多变问题。
English: The Tied Prototype Model (TPM) improves upon ADNet by introducing tied prototypes for foreground and background, enabling multi-class segmentation and adaptive thresholds to address variability in medical images.

Paperid: 38, https://arxiv.org/pdf/2506.22049.pdf   GitHub

Authors:Tianhao Chen, Xin Xu, Zijing Liu, Pengxiang Li, Xinyuan Song, Ajay Kumar Jaiswal, Fan Zhang, Jishan Hu, Yang Wang, Hao Chen, Shizhe Diao, Shiwei Liu, Yu Li, Lu Yin, Can Yang

Title: GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling

Abstract:
Modern Large Language Models, such as the LLaMA, Qwen and DeepSeek series, predominantly adopt the Pre-LayerNorm (Pre-LN) Transformer architecture. While being stable during pretraining and scalable to large model sizes, Pre-LN suffers from an exponential growth in activation variance across layers, causing the shortcut to dominate over sub-layer outputs in the residual connection and limiting the learning capacity of deeper layers. To mitigate this issue, we propose Gradient-Preserving Activation Scaling (GPAS), a simple technique that can be used in combination with existing approaches. GPAS works by scaling down the intermediate activations while keeping their gradients unchanged. This leaves information in the activations intact, and avoids the gradient vanishing problem associated with gradient downscaling. Extensive experiments across various model sizes from 71M to 1B show that GPAS achieves consistent performance gains. Beyond enhancing Pre-LN Transformers, GPAS also shows promise in improving alternative architectures such as Sandwich-LN and DeepNorm, demonstrating its versatility and potential for improving training dynamics in a wide range of settings. Our code is available at https://github.com/dandingsky/GPAS.
中文摘要：本文提出梯度保持激活缩放（GPAS）方法，通过缩放中间激活值同时保持梯度不变，解决Pre-LayerNorm Transformer中激活方差指数增长问题，在不同规模模型中均实现了性能提升。
English Summary: The paper introduces Gradient-Preserving Activation Scaling (GPAS), a technique that addresses the exponential activation variance growth in Pre-LayerNorm Transformers by scaling down intermediate activations while preserving gradients, achieving consistent performance improvements across various model sizes.

Paperid: 39, https://arxiv.org/pdf/2506.22039.pdf   GitHub

Authors:Lu Han, Yu Liu, Qiwen Deng, Jian Jiang, Yinbo Sun, Zhe Yu, Binfeng Wang, Xingyu Lu, Lintao Ma, Han-Jia Ye, De-Chuan Zhan

Title: UniCA: Adapting Time Series Foundation Model to General Covariate-Aware Forecasting

Abstract:
Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their ability to handle general forecasting tasks involving diverse and often heterogeneous covariates--such as categorical variables and multimodal data (e.g., images, text)--which are typically task-specific and difficult to leverage during pretraining. To address this gap, we propose Unified Covariate Adaptation (UniCA), a framework to bridge TSFMs with general covariate-aware forecasting. UniCA first performs covariate homogenization to transform heterogeneous covariates into high-level homogeneous series representations and then fuses them via a unified attention-based fusion mechanism. UniCA is compatible and universal for adaptation with both homogeneous and heterogeneous covariates, incorporating extra covariate information while preserving the generalization ability of TSFMs.Extensive experiments on multiple unimodal and multimodal covariate-aware forecasting benchmarks demonstrate the superiority of UniCA, highlighting the promise of covariate-aware TSFM adaptation in real-world forecasting scenarios. Codes are released on https://github.com/hanlu-nju/UniCA.
中文: 时间序列基础模型虽在大规模预训练中表现出色，但难以处理多样化协变量，为此提出的UniCA框架通过同质化处理和融合机制，有效整合异构数据以提升预测性能，同时保持模型的泛化能力。
English: Time Series Foundation Models (TSFMs) excel in large-scale pretraining but struggle with diverse covariates, prompting the development of UniCA, a framework that homogenizes and fuses heterogeneous data to enhance forecasting while preserving TSFMs' generalization.

Paperid: 40, https://arxiv.org/pdf/2506.21833.pdf   GitHub

Authors:Kunjal Panchal, Sunav Choudhary, Yuriy Brun, Hui Guan

Title: The Cost of Avoiding Backpropagation

Abstract:
Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization have been proposed as memory-efficient alternatives to backpropagation (BP) for gradient computation, especially in low-resource settings. However, their practical benefits remain unclear due to two key gaps: a lack of comparison against memory-efficient BP variants, such as activation checkpointing, and a lack of a unified theoretical analysis. This work presents a comprehensive theoretical and empirical comparison of BP, FmAD, and ZO methods. Our theoretical analysis shows that while FmAD, and ZO can reduce memory usage, they incur significant costs in accuracy, convergence speed, and computation compared to BP with checkpointing. These drawbacks worsen with larger models or constrained perturbation budgets. Empirical experiments on large language and vision-language models show that BP with checkpointing outperforms FmAD and ZO variants, including those enhanced with variance reduction, achieving up to 31.1% higher accuracy, 34.8% faster convergence, and 3.8x fewer computations at comparable memory usage. Our results highlight fundamental limitations of FmAD and ZO, and reaffirm BP with checkpointing as the most effective strategy for model training under memory-constrained settings. Our code is available at https://github.com/Astuary/The_Cost_of_Avoiding_Backpropagation.
中文: 研究表明，尽管前向自动微分和零阶优化能降低内存使用，但与采用检查点的反向传播相比，它们在精度、收敛速度和计算效率上存在显著不足，后者仍是内存受限场景下最优的训练策略。
English: This study demonstrates that while forward-mode automatic differentiation and zero-order optimization reduce memory usage, they significantly compromise accuracy, convergence speed, and computational efficiency compared to backpropagation with checkpointing, which remains superior for memory-constrained model training.

Paperid: 41, https://arxiv.org/pdf/2506.21826.pdf   GitHub

Authors:Rafael Sterzinger, Marco Peer, Robert Sablatnig

Title: Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Models

Abstract:
As rich sources of history, maps provide crucial insights into historical changes, yet their diverse visual representations and limited annotated data pose significant challenges for automated processing. We propose a simple yet effective approach for few-shot segmentation of historical maps, leveraging the rich semantic embeddings of large vision foundation models combined with parameter-efficient fine-tuning. Our method outperforms the state-of-the-art on the Siegfried benchmark dataset in vineyard and railway segmentation, achieving +5% and +13% relative improvements in mIoU in 10-shot scenarios and around +20% in the more challenging 5-shot setting. Additionally, it demonstrates strong performance on the ICDAR 2021 competition dataset, attaining a mean PQ of 67.3% for building block segmentation, despite not being optimized for this shape-sensitive metric, underscoring its generalizability. Notably, our approach maintains high performance even in extremely low-data regimes (10- & 5-shot), while requiring only 689k trainable parameters - just 0.21% of the total model size. Our approach enables precise segmentation of diverse historical maps while drastically reducing the need for manual annotations, advancing automated processing and analysis in the field. Our implementation is publicly available at: https://github.com/RafaelSterzinger/few-shot-map-segmentation.
Chinese: 本研究提出了一种简单有效的历史地图少样本分割方法，通过结合大型视觉基础模型与参数高效微调，在基准数据集上实现了最优性能，同时大幅减少了对标注数据和可训练参数的需求。
English: This study introduces a simple yet effective few-shot segmentation method for historical maps by combining large vision foundation models with parameter-efficient fine-tuning, achieving state-of-the-art performance on benchmark datasets while requiring minimal annotated data and trainable parameters.

Paperid: 42, https://arxiv.org/pdf/2506.21803.pdf   GitHub

Authors:Fuying Wang, Jiacheng Xu, Lequan Yu

Title: From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining

Abstract:
Electrocardiograms (ECGs) play a vital role in monitoring cardiac health and diagnosing heart diseases. However, traditional deep learning approaches for ECG analysis rely heavily on large-scale manual annotations, which are both time-consuming and resource-intensive to obtain. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising alternative, enabling the extraction of robust ECG representations that can be efficiently transferred to various downstream tasks. While previous studies have explored SSL for ECG pretraining and multi-modal ECG-language alignment, they often fail to capture the multi-scale nature of ECG signals. As a result, these methods struggle to learn generalized representations due to their inability to model the hierarchical structure of ECG data. To address this gap, we introduce MELP, a novel Multi-scale ECG-Language Pretraining (MELP) model that fully leverages hierarchical supervision from ECG-text pairs. MELP first pretrains a cardiology-specific language model to enhance its understanding of clinical text. It then applies three levels of cross-modal supervision-at the token, beat, and rhythm levels-to align ECG signals with textual reports, capturing structured information across different time scales. We evaluate MELP on three public ECG datasets across multiple tasks, including zero-shot ECG classification, linear probing, and transfer learning. Experimental results demonstrate that MELP outperforms existing SSL methods, underscoring its effectiveness and adaptability across diverse clinical applications. Our code is available at https://github.com/HKU-MedAI/MELP.
中文：MELP提出了一种新颖的多尺度心电-语言预训练模型，通过分层跨模态监督捕捉结构化心电信号与文本的对齐关系，在多种临床任务中优于现有自监督方法。
English: MELP introduces a novel multi-scale ECG-language pretraining model that leverages hierarchical cross-modal supervision to capture structured ECG-text alignments, outperforming existing self-supervised methods across diverse clinical tasks.

Paperid: 43, https://arxiv.org/pdf/2506.21757.pdf   GitHub

Authors:Tianrong Chen, Huangjie Zheng, David Berthelot, Jiatao Gu, Josh Susskind, Shuangfei Zhai

Title: TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics

Abstract:
Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise scheduling strategies have been proposed to dramatically improve sampling speeds. In this paper, we introduce a new sampling method that is up to $186\%$ faster than the current state of the art solver for comparative FID on ImageNet512. This new sampling method is training-free and uses an ordinary differential equation (ODE) solver. The key to our method resides in using higher-dimensional initial noise, allowing to produce more detailed samples with less function evaluations from existing pretrained diffusion models. In addition, by design our solver allows to control the level of detail through a simple hyper-parameter at no extra computational cost. We present how our approach leverages momentum dynamics by establishing a fundamental equivalence between momentum diffusion models and conventional diffusion models with respect to their training paradigms. Moreover, we observe the use of higher-dimensional noise naturally exhibits characteristics similar to stochastic differential equations (SDEs). Finally, we demonstrate strong performances on a set of representative pretrained diffusion models, including EDM, EDM2, and Stable-Diffusion 3, which cover models in both pixel and latent spaces, as well as class and text conditional settings. The code is available at https://github.com/apple/ml-tada.
中文摘要：本文提出一种无需训练的采样方法，通过使用高维初始噪声和常微分方程求解器，将扩散模型的图像生成速度最高提升186%，并在多种预训练模型上实现优异性能且不增加计算成本。
English Summary: This paper introduces a training-free sampling method that accelerates diffusion model image generation by up to 186% through higher-dimensional initial noise and ODE solver techniques, achieving superior performance across multiple pretrained models without additional computational cost.

Paperid: 44, https://arxiv.org/pdf/2506.21718.pdf   GitHub

Authors:Yash Akhauri, Bryan Lewandowski, Cheng-Hsi Lin, Adrian N. Reyes, Grant C. Forbes, Arissa Wongpanich, Bangding Yang, Mohamed S. Abdelfattah, Sagi Perel, Xingyou Song

Title: Performance Prediction for Large Systems via Text-to-Text Regression

Abstract:
In many industries, predicting metric outcomes of large systems is a fundamental problem, driven largely by traditional tabular regression. However, such methods struggle on complex systems data in the wild such as configuration files or system logs, where feature engineering is often infeasible. We propose text-to-text regression as a general, scalable alternative. For predicting resource efficiency on Borg, Google's massive compute cluster scheduling system, a 60M parameter encoder-decoder, trained from random initialization, achieves up to a near perfect 0.99 (0.9 average) rank correlation across the entire fleet, and 100x lower MSE than tabular approaches. The model also easily adapts to new tasks in only 500 few-shot examples and captures the densities of complex outcome distributions. Ablation studies highlight the importance of using encoders, increasing sequence length, and the model's inherent uncertainty quantification. These findings pave the way for universal simulators of real-world outcomes.
Chinese: 文本到文本回归作为一种可扩展且高效的替代方案，在预测如谷歌Borg等复杂系统的资源效率方面实现了近乎完美的准确性，并能以少量数据轻松适应新任务。
English: Text-to-text regression offers a scalable and effective alternative to traditional tabular methods, achieving near-perfect accuracy in predicting resource efficiency on complex systems like Google's Borg and adapting easily to new tasks with minimal data.

Paperid: 45, https://arxiv.org/pdf/2506.21695.pdf   GitHub

Authors:Oron Nir, Jay Tenenbaum, Ariel Shamir

Title: Unimodal Strategies in Density-Based Clustering

Abstract:
Density-based clustering methods often surpass centroid-based counterparts, when addressing data with noise or arbitrary data distributions common in real-world problems. In this study, we reveal a key property intrinsic to density-based clustering methods regarding the relation between the number of clusters and the neighborhood radius of core points - we empirically show that it is nearly unimodal, and support this claim theoretically in a specific setting. We leverage this property to devise new strategies for finding appropriate values for the radius more efficiently based on the Ternary Search algorithm. This is especially important for large scale data that is high-dimensional, where parameter tuning is computationally intensive. We validate our methodology through extensive applications across a range of high-dimensional, large-scale NLP, Audio, and Computer Vision tasks, demonstrating its practical effectiveness and robustness. This work not only offers a significant advancement in parameter control for density-based clustering but also broadens the understanding regarding the relations between their guiding parameters. Our code is available at https://github.com/oronnir/UnimodalStrategies.
中文摘要：本研究揭示了密度聚类中簇数量与核心点邻域半径存在近似单峰关系，基于此提出利用三分搜索算法高效确定最佳半径参数的方法，并在大规模高维NLP、音频和视觉任务中验证了其有效性与鲁棒性。
English Summary: This study identifies a nearly unimodal relationship between the number of clusters and neighborhood radius in density-based clustering, enabling more efficient parameter tuning via Ternary Search for large-scale high-dimensional data across NLP, audio, and vision tasks.

Paperid: 46, https://arxiv.org/pdf/2506.21655.pdf   GitHub

Authors:Minjie Hong, Zirun Guo, Yan Xia, Zehan Wang, Ziang Zhang, Tao Jin, Zhou Zhao

Title: APO: Enhancing Reasoning Ability of MLLMs via Asymmetric Policy Optimization

Abstract:
Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data, but they often struggle with complex reasoning. While Reinforcement learning (RL) can boost reasoning in LLMs, applying it to MLLMs is tricky. Common issues include a drop in performance on general tasks and the generation of overly detailed or "overthinking" reasoning. Our work investigates how the KL penalty and overthinking affect RL training in MLLMs. We propose Asymmetric Policy Optimization (APO) to address these issues, which divides the sampled responses into positive and negative groups. For positive samples, Difficulty-Adaptive Divergence Shaping (DADS) is introduced to dynamically adjust the KL divergence weight based on their difficulty. This method prevents policy entropy from dropping sharply, improves training stability, utilizes samples better, and preserves the model's existing knowledge. For negative samples, Suboptimal Trajectory Complexity Regularization (STCR) is proposed to penalize overly long responses. This helps mitigate overthinking and encourages more concise reasoning while preserving the model's explorative capacity. We apply our method to Qwen2.5-VL-3B, creating View-R1-3B. View-R1-3B significantly enhances reasoning capabilities, showing an average 7\% gain over the base model and outperforming larger MLLMs (7-11B) on various reasoning benchmarks. Importantly, unlike other reasoning-tuned MLLMs that often degrade on general tasks, View-R1-3B maintains consistent improvement, demonstrating superior generalization. These results highlight the effectiveness and broad applicability of our DADS and STCR techniques for advancing complex multimodal reasoning in MLLMs. The code will be made available at https://github.com/Indolent-Kawhi/View-R1.
中文: 本研究提出非对称策略优化（APO）方法，结合难度自适应散度塑形（DADS）和次优轨迹复杂度正则化（STCR），有效提升了多模态大语言模型的推理能力，在保持泛化性能的同时显著优于基准模型及更大规模模型。
English: This research introduces Asymmetric Policy Optimization (APO) with Difficulty-Adaptive Divergence Shaping (DADS) and Suboptimal Trajectory Complexity Regularization (STCR) to enhance multimodal reasoning in MLLMs, achieving significant performance gains while maintaining generalization across tasks.

Paperid: 47, https://arxiv.org/pdf/2506.21630.pdf   GitHub

Authors:Yixin Sun, Li Li, Wenke E, Amir Atapour-Abarghouei, Toby P. Breckon

Title: TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions

Abstract:
Detecting traversable pathways in unstructured outdoor environments remains a significant challenge for autonomous robots, especially in critical applications such as wide-area search and rescue, as well as incident management scenarios like forest fires. Existing datasets and models primarily target urban settings or wide, vehicle-traversable off-road tracks, leaving a substantial gap in addressing the complexity of narrow, trail-like off-road scenarios. To address this, we introduce the Trail-based Off-road Multimodal Dataset (TOMD), a comprehensive dataset specifically designed for such environments. TOMD features high-fidelity multimodal sensor data -- including 128-channel LiDAR, stereo imagery, GNSS, IMU, and illumination measurements -- collected through repeated traversals under diverse conditions. We also propose a dynamic multiscale data fusion model for accurate traversable pathway prediction. The study analyzes the performance of early, cross, and mixed fusion strategies under varying illumination levels. Results demonstrate the effectiveness of our approach and the relevance of illumination in segmentation performance. We publicly release TOMD at https://github.com/yyyxs1125/TMOD to support future research in trail-based off-road navigation.
中文摘要：本研究针对非结构化户外环境中可通行路径检测的难题，推出了专门设计的越野小径多模态数据集TOMD和动态多尺度融合模型，通过不同光照条件下的实验验证了方法的有效性。
English Summary: The study introduces the Trail-based Off-road Multimodal Dataset (TOMD) and a dynamic multiscale fusion model to address autonomous robots' challenges in detecting traversable pathways in unstructured outdoor environments, demonstrating improved performance under varying illumination conditions.

Paperid: 48, https://arxiv.org/pdf/2506.21604.pdf   GitHub

Authors:Varun Mannam, Fang Wang, Xin Chen

Title: Evaluating VisualRAG: Quantifying Cross-Modal Performance in Enterprise Document Understanding

Abstract:
Current evaluation frameworks for multimodal generative AI struggle to establish trustworthiness, hindering enterprise adoption where reliability is paramount. We introduce a systematic, quantitative benchmarking framework to measure the trustworthiness of progressively integrating cross-modal inputs such as text, images, captions, and OCR within VisualRAG systems for enterprise document intelligence. Our approach establishes quantitative relationships between technical metrics and user-centric trust measures. Evaluation reveals that optimal modality weighting with weights of 30% text, 15% image, 25% caption, and 30% OCR improves performance by 57.3% over text-only baselines while maintaining computational efficiency. We provide comparative assessments of foundation models, demonstrating their differential impact on trustworthiness in caption generation and OCR extraction-a vital consideration for reliable enterprise AI. This work advances responsible AI deployment by providing a rigorous framework for quantifying and enhancing trustworthiness in multimodal RAG for critical enterprise applications.

Paperid: 49, https://arxiv.org/pdf/2506.21567.pdf   GitHub

Authors:Baqer M. Merzah, Tania Taami, Salman Asoudeh, Saeed Mirzaee, Amir reza Hossein pour, Amir Ali Bengari

Title: BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining

Abstract:
Large Language Models (LLMs) have recently gained attention in the life sciences due to their capacity to model, extract, and apply complex biological information. Beyond their classical use as chatbots, these systems are increasingly used for complex analysis and problem-solving in specialized fields, including bioinformatics. First, we introduce BIOPARS-BENCH, a dataset from over 10,000 scientific articles, textbooks, and medical websites. BioParsQA was also introduced to evaluate the proposed model, which consists of 5,231 Persian medical questions and answers. This study then introduces BioPars, a simple but accurate measure designed to assess LLMs for three main abilities: acquiring subject-specific knowledge, interpreting and synthesizing such knowledge, and demonstrating proper evidence. Comparing ChatGPT, Llama, and Galactica, our study highlights their ability to remember and retrieve learned knowledge but also reveals shortcomings in addressing higher-level, real-world questions and fine-grained inferences. These findings indicate the need for further fine-tuning to address the capabilities of LLM in bioinformatics tasks. To our knowledge, BioPars is the first application of LLM in Persian medical QA, especially for generating long answers. Evaluation of four selected medical QA datasets shows that BioPars has achieved remarkable results compared to comparative approaches. The model on BioParsQA achieved a ROUGE-L score of 29.99, which is an improvement over GPT-4 1.0. The model achieved a BERTScore of 90.87 with the MMR method. The MoverScore and BLEURT values were also higher in this model than the other three models. In addition, the reported scores for the model are MoverScore=60.43 and BLEURT=50.78. BioPars is an ongoing project and all resources related to its development will be made available via the following GitHub repository: https://github.com/amirap80/BioPars.
中文摘要：本研究提出BioPars框架用于评估大型语言模型在波斯语医学问答中的表现，结果显示其优于现有模型，同时揭示了模型在处理复杂生物医学推理方面的局限性。
English Summary: This study introduces BioPars, a framework for evaluating large language models in Persian medical question-answering, demonstrating superior performance over existing models while highlighting current limitations in handling complex biomedical reasoning.

Paperid: 50, https://arxiv.org/pdf/2506.21552.pdf   GitHub

Authors:Yutong Bai, Danny Tran, Amir Bar, Yann LeCun, Trevor Darrell, Jitendra Malik

Title: Whole-Body Conditioned Egocentric Video Prediction

Abstract:
We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, our model learns to simulate how physical human actions shape the environment from a first-person point of view. We train an auto-regressive conditional diffusion transformer on Nymeria, a large-scale dataset of real-world egocentric video and body pose capture. We further design a hierarchical evaluation protocol with increasingly challenging tasks, enabling a comprehensive analysis of the model's embodied prediction and control abilities. Our work represents an initial attempt to tackle the challenges of modeling complex real-world environments and embodied agent behaviors with video prediction from the perspective of a human.

Paperid: 51, https://arxiv.org/pdf/2506.21546.pdf   GitHub

Authors:Xinzhuo Li, Adheesh Juvekar, Xingyou Liu, Muntasir Wahed, Kiet A. Nguyen, Ismini Lourentzou

Title: HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation

Abstract:
Recent progress in vision-language segmentation has significantly advanced grounded visual understanding. However, these models often exhibit hallucinations by producing segmentation masks for objects not grounded in the image content or by incorrectly labeling irrelevant regions. Existing evaluation protocols for segmentation hallucination primarily focus on label or textual hallucinations without manipulating the visual context, limiting their capacity to diagnose critical failures. In response, we introduce HalluSegBench, the first benchmark specifically designed to evaluate hallucinations in visual grounding through the lens of counterfactual visual reasoning. Our benchmark consists of a novel dataset of 1340 counterfactual instance pairs spanning 281 unique object classes, and a set of newly introduced metrics that quantify hallucination sensitivity under visually coherent scene edits. Experiments on HalluSegBench with state-of-the-art vision-language segmentation models reveal that vision-driven hallucinations are significantly more prevalent than label-driven ones, with models often persisting in false segmentation, highlighting the need for counterfactual reasoning to diagnose grounding fidelity.

Paperid: 52, https://arxiv.org/pdf/2506.21535.pdf   GitHub

Authors:Mohammed Baharoon, Jun Ma, Congyu Fang, Augustin Toma, Bo Wang

Title: Exploring the Design Space of 3D MLLMs for CT Report Generation

Abstract:
Multimodal Large Language Models (MLLMs) have emerged as a promising way to automate Radiology Report Generation (RRG). In this work, we systematically investigate the design space of 3D MLLMs, including visual input representation, projectors, Large Language Models (LLMs), and fine-tuning techniques for 3D CT report generation. We also introduce two knowledge-based report augmentation methods that improve performance on the GREEN score by up to 10%, achieving the 2nd place on the MICCAI 2024 AMOS-MM challenge. Our results on the 1,687 cases from the AMOS-MM dataset show that RRG is largely independent of the size of LLM under the same training protocol. We also show that larger volume size does not always improve performance if the original ViT was pre-trained on a smaller volume size. Lastly, we show that using a segmentation mask along with the CT volume improves performance. The code is publicly available at https://github.com/bowang-lab/AMOS-MM-Solution
中文摘要：本研究系统探索了用于自动化CT报告生成的3D多模态大语言模型设计，提出的基于知识的报告增强方法在MICCAI 2024挑战赛中荣获第二名，并证明模型性能更取决于训练方案而非大语言模型规模。
English Summary: This study systematically explores the design of 3D multimodal large language models for automated CT report generation, introducing knowledge-based augmentation methods that achieved second place in a MICCAI 2024 challenge while demonstrating that model performance depends more on training protocols than LLM size.

Paperid: 53, https://arxiv.org/pdf/2506.21508.pdf   GitHub

Authors:Marek Šuppa, Andrej Ridzik, Daniel Hládek, Tomáš Javůrek, Viktória Ondrejová, Kristína Sásiková, Martin Tamajka, Marián Šimko

Title: skLEP: A Slovak General Language Understanding Benchmark

Abstract:
In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities. To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources. Within this paper, we also present the first systematic and extensive evaluation of a wide array of Slovak-specific, multilingual, and English pre-trained language models using the skLEP tasks. Finally, we also release the complete benchmark data, an open-source toolkit facilitating both fine-tuning and evaluation of models, and a public leaderboard at https://github.com/slovak-nlp/sklep in the hopes of fostering reproducibility and drive future research in Slovak NLU.
Chinese: 本文介绍了skLEP，这是首个专为评估斯洛伐克自然语言理解模型设计的综合基准，包含九项多样化任务和原始数据集，通过对多种模型进行全面评估并公开所有资源，旨在推动该领域的未来研究。
English: This paper introduces skLEP, the first comprehensive benchmark for evaluating Slovak natural language understanding models, featuring nine diverse tasks and original datasets, along with an extensive evaluation of various models and the release of all resources to promote future research.

Paperid: 54, https://arxiv.org/pdf/2506.21288.pdf   GitHub

Authors:Istabrak Abbes, Gabriele Prato, Quentin Fournier, Fernando Rodriguez, Alaa Boukhary, Adam Elwood, Sarath Chandar

Title: Small Encoders Can Rival Large Decoders in Detecting Groundedness

Abstract:
Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) tasks. However, LLMs struggle to answer queries reliably when the provided context lacks information, often resorting to ungrounded speculation or internal knowledge. Groundedness - generating responses strictly supported by the context - is essential for ensuring factual consistency and trustworthiness. This study focuses on detecting whether a given query is grounded in a document provided in context before the costly answer generation by LLMs. Such a detection mechanism can significantly reduce both inference time and resource consumption. We show that lightweight, task specific encoder models such as RoBERTa and NomicBERT, fine-tuned on curated datasets, can achieve accuracy comparable to state-of-the-art LLMs, such as Llama3 8B and GPT4o, in groundedness detection while reducing inference latency by orders of magnitude. The code is available at : https://github.com/chandarlab/Hallucinate-less
中文摘要：增强大型语言模型的外部上下文可提升其自然语言处理性能，但确保回答严格基于上下文仍具挑战；本研究提出一种轻量级编码器模型，在昂贵的LLM生成答案前检测查询是否基于文档，能以极低延迟和资源消耗达到与先进LLMs相当的检测精度。
English Summary: Augmenting LLMs with external context boosts NLP performance, but ensuring grounded responses remains challenging; this study introduces a lightweight encoder model that detects query grounding in documents before costly LLM processing, achieving comparable accuracy to advanced LLMs while drastically cutting latency and resource use.

Paperid: 55, https://arxiv.org/pdf/2506.21154.pdf   GitHub

Authors:He Li, Haoang Chi, Mingyu Liu, Wanrong Huang, Liyang Xu, Wenjing Yang

Title: Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation

Abstract:
The real world naturally has dimensions of time and space. Therefore, estimating the counterfactual outcomes with spatial-temporal attributes is a crucial problem. However, previous methods are based on classical statistical models, which still have limitations in performance and generalization. This paper proposes a novel framework for estimating counterfactual outcomes with spatial-temporal attributes using the Transformer, exhibiting stronger estimation ability. Under mild assumptions, the proposed estimator within this framework is consistent and asymptotically normal. To validate the effectiveness of our approach, we conduct simulation experiments and real data experiments. Simulation experiments show that our estimator has a stronger estimation capability than baseline methods. Real data experiments provide a valuable conclusion to the causal effect of conflicts on forest loss in Colombia. The source code is available at https://github.com/lihe-maxsize/DeppSTCI_Release_Version-master.
中文: 本文提出了一种基于Transformer的新颖框架，用于估计具有时空特征的反事实结果，通过模拟实验和对哥伦比亚冲突影响的真实数据分析，证明了该方法优于基线模型的性能。
English: This paper introduces a novel Transformer-based framework for estimating counterfactual outcomes with spatial-temporal attributes, demonstrating superior performance over baseline methods through simulations and real-world experiments on conflict impacts in Colombia.

Paperid: 56, https://arxiv.org/pdf/2506.21140.pdf   GitHub

Authors:Ziwei Wang, Hongbin Wang, Tianwang Jia, Xingyi He, Siyang Li, Dongrui Wu

Title: DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding

Abstract:
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolutional neural networks (CNNs) remain the mainstream backbone for EEG decoding, their inherently short receptive field makes it difficult to capture long-range temporal dependencies and global inter-channel relationships. Recent CNN-Transformer (Conformer) hybrids partially address this issue, but most adopt a serial design, resulting in suboptimal integration of local and global features, and often overlook explicit channel-wise modeling. To address these limitations, we propose DBConformer, a dual-branch convolutional Transformer network tailored for EEG decoding. It integrates a temporal Conformer to model long-range temporal dependencies and a spatial Conformer to extract inter-channel interactions, capturing both temporal dynamics and spatial patterns in EEG signals. A lightweight channel attention module further refines spatial representations by assigning data-driven importance to EEG channels. Extensive experiments under four evaluation settings on three paradigms, including motor imagery, seizure detection, and steady-state visual evoked potential, demonstrated that DBConformer consistently outperformed 13 competitive baseline models, with over an eight-fold reduction in parameters than current high-capacity EEG Conformer architecture. Furthermore, the visualization results confirmed that the features extracted by DBConformer are physiologically interpretable and aligned with prior knowledge. The superior performance and interpretability of DBConformer make it reliable for accurate, robust, and explainable EEG decoding. Code is publicized at https://github.com/wzwvv/DBConformer.
中文：提出的DBConformer模型通过结合时序与空间Transformer及通道注意力机制，显著提升了EEG解码性能，在多种实验范式中均表现出优越的准确性和生理可解释性，同时大幅减少了参数量。
English: The proposed DBConformer model enhances EEG decoding by combining temporal and spatial Transformers with channel attention, achieving superior performance and interpretability across multiple paradigms while significantly reducing parameters.

Paperid: 57, https://arxiv.org/pdf/2506.21109.pdf   GitHub

Authors:Luosheng Xu, Dalin Zhang, Zhaohui Song

Title: Pushing Trade-Off Boundaries: Compact yet Effective Remote Sensing Change Detection

Abstract:
Remote sensing change detection is essential for monitoring urban expansion, disaster assessment, and resource management, offering timely, accurate, and large-scale insights into dynamic landscape transformations. While deep learning has revolutionized change detection, the increasing complexity and computational demands of modern models have not necessarily translated into significant accuracy gains. Instead of following this trend, this study explores a more efficient approach, focusing on lightweight models that maintain high accuracy while minimizing resource consumption, which is an essential requirement for on-satellite processing. To this end, we propose FlickCD, which means quick flick then get great results, pushing the boundaries of the performance-resource trade-off. FlickCD introduces an Enhanced Difference Module (EDM) to amplify critical feature differences between temporal phases while suppressing irrelevant variations such as lighting and weather changes, thereby reducing computational costs in the subsequent change decoder. Additionally, the FlickCD decoder incorporates Local-Global Fusion Blocks, leveraging Shifted Window Self-Attention (SWSA) and Efficient Global Self-Attention (EGSA) to effectively capture semantic information at multiple scales, preserving both coarse- and fine-grained changes. Extensive experiments on four benchmark datasets demonstrate that FlickCD reduces computational and storage overheads by more than an order of magnitude while achieving state-of-the-art (SOTA) performance or incurring only a minor (<1% F1) accuracy trade-off. The implementation code is publicly available at https://github.com/xulsh8/FlickCD.
中文: 本研究提出轻量级遥感变化检测模型FlickCD，通过增强差异模块和局部-全局融合块在显著降低计算与存储开销的同时保持高精度，以最小资源消耗实现了最优性能。
English: This study introduces FlickCD, a lightweight remote sensing change detection model that significantly reduces computational and storage demands while maintaining high accuracy through its Enhanced Difference Module and Local-Global Fusion Blocks, achieving state-of-the-art performance with minimal resource consumption.

Paperid: 58, https://arxiv.org/pdf/2506.21103.pdf   GitHub

Authors:Tim Lawson, Laurence Aitchison

Title: Learning to Skip the Middle Layers of Transformers

Abstract:
Conditional computation is a popular strategy to make Transformers more efficient. Existing methods often target individual modules (e.g., mixture-of-experts layers) or skip layers independently of one another. However, interpretability research has demonstrated that the middle layers of Transformers exhibit greater redundancy, and that early layers aggregate information into token positions. Guided by these insights, we propose a novel architecture that dynamically skips a variable number of layers from the middle outward. In particular, a learned gating mechanism determines whether to bypass a symmetric span of central blocks based on the input, and a gated attention mechanism prevents subsequent tokens from attending to skipped token positions. Residual norms are controlled with a 'sandwich' or 'perilayernorm' scheme and gate sparsity with an adaptive regularization loss. We had aimed to reduce compute requirements for 'simpler' tokens and potentially foster an emergent multi-level representational hierarchy but, at the scales investigated, our approach does not achieve improvements in the trade-off between validation cross-entropy and estimated FLOPs compared to dense baselines with fewer layers. We release our code at https://github.com/tim-lawson/skip-middle.
中文: 本文提出了一种新型Transformer架构，通过学习的门控机制动态跳过中间层的可变数量，但与层数更少的基线模型相比，该方法未能在效率与准确性的权衡中取得改进。
English: This paper introduces a novel Transformer architecture that dynamically skips variable numbers of middle layers using a learned gating mechanism, though it fails to improve the efficiency-accuracy trade-off compared to fewer-layer baselines.

Paperid: 59, https://arxiv.org/pdf/2506.21079.pdf   GitHub

Authors:Yann Kerzreho

Title: Homogenization of Multi-agent Learning Dynamics in Finite-state Markov Games

Abstract:
This paper introduces a new approach for approximating the learning dynamics of multiple reinforcement learning (RL) agents interacting in a finite-state Markov game. The idea is to rescale the learning process by simultaneously reducing the learning rate and increasing the update frequency, effectively treating the agent's parameters as a slow-evolving variable influenced by the fast-mixing game state. Under mild assumptions-ergodicity of the state process and continuity of the updates-we prove the convergence of this rescaled process to an ordinary differential equation (ODE). This ODE provides a tractable, deterministic approximation of the agent's learning dynamics. An implementation of the framework is available at\,: https://github.com/yannKerzreho/MarkovGameApproximation
中文: 本文提出一种重标度学习方法，通过将智能体参数视为慢变变量来近似马尔可夫博弈中的多智能体强化学习动态，并在遍历性和连续性假设下证明了该方法会收敛到确定性常微分方程。
English: This paper presents a rescaled learning method that approximates multi-agent reinforcement learning dynamics in Markov games by treating agent parameters as slow variables, with proven convergence to a deterministic ODE under ergodicity and continuity assumptions.

Paperid: 60, https://arxiv.org/pdf/2506.20963.pdf   GitHub

Authors:Fangyuan Zhang, Zhengjun Huang, Yingli Zhou, Qintian Guo, Zhixun Li, Wensheng Luo, Di Jiang, Yixiang Fang, Xiaofang Zhou

Title: EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Abstract:
Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expensive full-graph reconstruction whenever new documents arrive, limiting their scalability in dynamic, evolving environments. To address these limitations, we introduce EraRAG, a novel multi-layered Graph-RAG framework that supports efficient and scalable dynamic updates. Our method leverages hyperplane-based Locality-Sensitive Hashing (LSH) to partition and organize the original corpus into hierarchical graph structures, enabling efficient and localized insertions of new data without disrupting the existing topology. The design eliminates the need for retraining or costly recomputation while preserving high retrieval accuracy and low latency. Experiments on large-scale benchmarks demonstrate that EraRag achieves up to an order of magnitude reduction in update time and token consumption compared to existing Graph-RAG systems, while providing superior accuracy performance. This work offers a practical path forward for RAG systems that must operate over continually growing corpora, bridging the gap between retrieval efficiency and adaptability. Our code and data are available at https://github.com/EverM0re/EraRAG-Official.
中文摘要：EraRAG提出了一种动态图检索增强生成框架，通过分层图结构和局部敏感哈希分区实现对不断更新的文档库的高效维护，在无需全图重建的情况下显著提升了更新速度与检索精度。
English Summary: EraRAG introduces a dynamic Graph-RAG framework that enables efficient updates to evolving document corpora through hierarchical graph structures and LSH partitioning, achieving significant improvements in update speed and accuracy without full-graph reconstruction.

Paperid: 61, https://arxiv.org/pdf/2506.20957.pdf   GitHub

Authors:Jiameng Chen, Xiantao Cai, Jia Wu, Wenbin Hu

Title: Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding

Abstract:
Antibody design remains a critical challenge in therapeutic and diagnostic development, particularly for complex antigens with diverse binding interfaces. Current computational methods face two main limitations: (1) capturing geometric features while preserving symmetries, and (2) generalizing novel antigen interfaces. Despite recent advancements, these methods often fail to accurately capture molecular interactions and maintain structural integrity. To address these challenges, we propose \textbf{AbMEGD}, an end-to-end framework integrating \textbf{M}ulti-scale \textbf{E}quivariant \textbf{G}raph \textbf{D}iffusion for antibody sequence and structure co-design. Leveraging advanced geometric deep learning, AbMEGD combines atomic-level geometric features with residue-level embeddings, capturing local atomic details and global sequence-structure interactions. Its E(3)-equivariant diffusion method ensures geometric precision, computational efficiency, and robust generalizability for complex antigens. Furthermore, experiments using the SAbDab database demonstrate a 10.13\% increase in amino acid recovery, 3.32\% rise in improvement percentage, and a 0.062~Å reduction in root mean square deviation within the critical CDR-H3 region compared to DiffAb, a leading antibody design model. These results highlight AbMEGD's ability to balance structural integrity with improved functionality, establishing a new benchmark for sequence-structure co-design and affinity optimization. The code is available at: https://github.com/Patrick221215/AbMEGD.
中文: 提出的AbMEGD框架通过整合多尺度等变图扩散技术，实现了抗体序列与结构的协同设计，在氨基酸恢复率和结构精度方面均优于现有模型，为复杂抗原的抗体设计确立了新标准。
English: The proposed AbMEGD framework integrates multi-scale equivariant graph diffusion to overcome current limitations in antibody design by co-designing sequences and structures, achieving superior performance in amino acid recovery and structural accuracy compared to existing models.

Paperid: 62, https://arxiv.org/pdf/2506.20790.pdf   GitHub

Authors:Lucius Bushnaq, Dan Braun, Lee Sharkey

Title: Stochastic Parameter Decomposition

Abstract:
A key step in reverse engineering neural networks is to decompose them into simpler parts that can be studied in relative isolation. Linear parameter decomposition -- a framework that has been proposed to resolve several issues with current decomposition methods -- decomposes neural network parameters into a sum of sparsely used vectors in parameter space. However, the current main method in this framework, Attribution-based Parameter Decomposition (APD), is impractical on account of its computational cost and sensitivity to hyperparameters. In this work, we introduce \textit{Stochastic Parameter Decomposition} (SPD), a method that is more scalable and robust to hyperparameters than APD, which we demonstrate by decomposing models that are slightly larger and more complex than was possible to decompose with APD. We also show that SPD avoids other issues, such as shrinkage of the learned parameters, and better identifies ground truth mechanisms in toy models. By bridging causal mediation analysis and network decomposition methods, this demonstration opens up new research possibilities in mechanistic interpretability by removing barriers to scaling linear parameter decomposition methods to larger models. We release a library for running SPD and reproducing our experiments at https://github.com/goodfire-ai/spd/tree/spd-paper.
中文: 本文提出随机参数分解（SPD）方法，相比现有技术更具可扩展性和鲁棒性，能够分解更复杂的神经网络模型，为机制可解释性研究开辟了新途径。
English: This paper introduces Stochastic Parameter Decomposition (SPD), a more scalable and robust method than previous approaches, enabling decomposition of larger neural networks and advancing mechanistic interpretability research.

Paperid: 63, https://arxiv.org/pdf/2506.20752.pdf   GitHub

Authors:Huangyuan Su, Mujin Kwun, Stephanie Gil, Sham Kakade, Nikhil Anand

Title: Characterization and Mitigation of Training Instabilities in Microscaling Formats

Abstract:
Training large language models is an expensive, compute-bound process that must be repeated as models scale, algorithms improve, and new data is collected. To address this, next-generation hardware accelerators increasingly support lower-precision arithmetic formats, such as the Microscaling (MX) formats introduced in NVIDIA's Blackwell architecture. These formats use a shared scale within blocks of parameters to extend representable range and perform forward/backward GEMM operations in reduced precision for efficiency gains. In this work, we investigate the challenges and viability of block-scaled precision formats during model training. Across nearly one thousand language models trained from scratch -- spanning compute budgets from $2 \times 10^{17}$ to $4.8 \times 10^{19}$ FLOPs and sweeping over a broad range of weight-activation precision combinations -- we consistently observe that training in MX formats exhibits sharp, stochastic instabilities in the loss, particularly at larger compute scales. To explain this phenomenon, we conduct controlled experiments and ablations on a smaller proxy model that exhibits similar behavior as the language model, sweeping across architectural settings, hyperparameters, and precision formats. These experiments motivate a simple model in which multiplicative gradient bias introduced by the quantization of layer-norm affine parameters and a small fraction of activations can trigger runaway divergence. Through \emph{in situ} intervention experiments on our proxy model, we demonstrate that instabilities can be averted or delayed by modifying precision schemes mid-training. Guided by these findings, we evaluate stabilization strategies in the LLM setting and show that certain hybrid configurations recover performance competitive with full-precision training. We release our code at https://github.com/Hither1/systems-scaling.
Chinese: 采用块缩放精度格式（如微缩放MX）训练大型语言模型会因量化参数引入梯度偏差，导致损失出现随机不稳定性，但通过混合精度策略可有效稳定训练，实现与全精度方法相媲美的性能。
English: Training large language models with block-scaled precision formats like Microscaling (MX) introduces stochastic instabilities in loss, primarily due to gradient bias from quantized parameters, but hybrid precision strategies can stabilize training and achieve performance comparable to full-precision methods.

Paperid: 64, https://arxiv.org/pdf/2506.20686.pdf   GitHub

Authors:Hoa La, Ahan Gupta, Alex Morehead, Jianlin Cheng, Minjia Zhang

Title: MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction Models

Abstract:
Protein structure prediction models such as AlphaFold3 (AF3) push the frontier of biomolecular modeling by incorporating science-informed architectural changes to the transformer architecture. However, these advances come at a steep system cost, introducing: compute- and memory-intensive operators, 2D attention mechanisms, and retrieval-augmented data pipelines, which collectively hinder the scalability of AF3 training. In this work, we present MegaFold, a cross-platform system to accelerate AF3 training. MegaFold tackles key bottlenecks through ahead-of-time caching to eliminate GPU idle time from the retrieval-augmented data pipeline, Triton-based kernels for memory-efficient EvoAttention on heterogeneous devices, and deep fusion for common and critical small operators in AF3. Evaluation on both NVIDIA H200 and AMD MI250 GPUs shows that MegaFold reduces peak memory usage of AF3 training by up to 1.23$\times$ and improves per-iteration training time by up-to 1.73$\times$ and 1.62$\times$ respectively. More importantly, MegaFold enables training on 1.35$\times$ longer sequence lengths compared to PyTorch baselines without running out-of-memory, significantly improving the scalability of modern protein folding models. We open source our code at https://github.com/Supercomputing-System-AI-Lab/MegaFold/.
中文：MegaFold是一个跨平台系统，通过优化数据管道、注意力机制和算子融合来加速AlphaFold3训练，在英伟达和AMD GPU上显著提升了内存效率和训练速度。
English: MegaFold is a cross-platform system that accelerates AlphaFold3 training by optimizing data pipelines, attention mechanisms, and operator fusion, achieving significant memory and speed improvements on both NVIDIA and AMD GPUs.

Paperid: 65, https://arxiv.org/pdf/2506.20649.pdf   GitHub

Authors:Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti, Francesca Odone

Title: Disentangled representations of microscopy images

Abstract:
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted the possibility to acquire an escalating amount of images, requiring a consequent development of a large collection of deep learning-based automatic image analysis methods. Although deep neural networks have demonstrated great performance in this field, interpretability, an essential requirement for microscopy image analysis, remains an open challenge. This work proposes a Disentangled Representation Learning (DRL) methodology to enhance model interpretability for microscopy image classification. Exploiting benchmark datasets from three different microscopic image domains (plankton, yeast vacuoles, and human cells), we show how a DRL framework, based on transferring a representation learnt from synthetic data, can provide a good trade-off between accuracy and interpretability in this domain.
中文摘要：本研究提出了一种解耦表示学习方法，通过三个显微图像数据集验证了该方法能在保持分类精度的同时有效提升模型可解释性。
English Summary: This study introduces a Disentangled Representation Learning approach to improve interpretability in microscopy image classification, demonstrating through three datasets that it effectively balances accuracy with explainability.

Paperid: 66, https://arxiv.org/pdf/2506.20640.pdf   GitHub

Authors:Sijie Li, Weiwei Sun, Shanda Li, Ameet Talwalkar, Yiming Yang

Title: Towards Community-Driven Agents for Machine Learning Engineering

Abstract:
Large language model-based machine learning (ML) agents have shown great promise in automating ML research. However, existing agents typically operate in isolation on a given research problem, without engaging with the broader research community, where human researchers often gain insights and contribute by sharing knowledge. To bridge this gap, we introduce MLE-Live, a live evaluation framework designed to assess an agent's ability to communicate with and leverage collective knowledge from a simulated Kaggle research community. Building on this framework, we propose CoMind, a novel agent that excels at exchanging insights and developing novel solutions within a community context. CoMind achieves state-of-the-art performance on MLE-Live and outperforms 79.2% human competitors on average across four ongoing Kaggle competitions. Our code is released at https://github.com/comind-ml/CoMind.
中文: MLE-Live是一个评估机器学习代理在模拟研究社区中协作能力的框架，基于该框架开发的CoMind新型代理在Kaggle竞赛中表现出色，平均超越79.2%的人类参赛者。
English: MLE-Live is a framework for evaluating ML agents' ability to collaborate with a simulated research community, and CoMind, a novel agent developed on this framework, demonstrates superior performance by outperforming most human competitors in Kaggle competitions.

Paperid: 67, https://arxiv.org/pdf/2506.20548.pdf   GitHub

Authors:Manyi Li, Renshuai Tao, Yufan Liu, Chuangchuang Tan, Haotong Qin, Bing Li, Yunchao Wei, Yao Zhao

Title: Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks

Abstract:
With the rapid advancement of deep learning, particularly through generative adversarial networks (GANs) and diffusion models (DMs), AI-generated images, or ``deepfakes", have become nearly indistinguishable from real ones. These images are widely shared across Online Social Networks (OSNs), raising concerns about their misuse. Existing deepfake detection methods overlook the ``block effects" introduced by compression in OSNs, which obscure deepfake artifacts, and primarily focus on raw images, rarely encountered in real-world scenarios. To address these challenges, we propose PLADA (Pay Less Attention to Deceptive Artifacts), a novel framework designed to tackle the lack of paired data and the ineffective use of compressed images. PLADA consists of two core modules: Block Effect Eraser (B2E), which uses a dual-stage attention mechanism to handle block effects, and Open Data Aggregation (ODA), which processes both paired and unpaired data to improve detection. Extensive experiments across 26 datasets demonstrate that PLADA achieves a remarkable balance in deepfake detection, outperforming SoTA methods in detecting deepfakes on OSNs, even with limited paired data and compression. More importantly, this work introduces the ``block effect" as a critical factor in deepfake detection, providing a robust solution for open-world scenarios. Our code is available at https://github.com/ManyiLee/PLADA.
中文：提出的PLADA框架通过消除块效应并利用配对与非配对数据，有效解决了在线社交网络中压缩图像深度伪造检测的难题，在多种数据集上实现了卓越性能。
English: The proposed PLADA framework effectively addresses deepfake detection challenges in compressed images from online social networks by eliminating block effects and leveraging both paired and unpaired data, achieving superior performance across diverse datasets.

Paperid: 68, https://arxiv.org/pdf/2506.20535.pdf   GitHub

Authors:Hongzhen Huang, Kunming Zhang, Hanlong Liao, Kui Wu, Guoming Tang

Title: WattsOnAI: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads

Abstract:
The rapid advancement of AI, particularly large language models (LLMs), has raised significant concerns about the energy use and carbon emissions associated with model training and inference. However, existing tools for measuring and reporting such impacts are often fragmented, lacking systematic metric integration and offering limited support for correlation analysis among them. This paper presents WattsOnAI, a comprehensive software toolkit for the measurement, analysis, and visualization of energy use, power draw, hardware performance, and carbon emissions across AI workloads. By seamlessly integrating with existing AI frameworks, WattsOnAI offers standardized reports and exports fine-grained time-series data to support benchmarking and reproducibility in a lightweight manner. It further enables in-depth correlation analysis between hardware metrics and model performance and thus facilitates bottleneck identification and performance enhancement. By addressing critical limitations in existing tools, WattsOnAI encourages the research community to weigh environmental impact alongside raw performance of AI workloads and advances the shift toward more sustainable "Green AI" practices. The code is available at https://github.com/SusCom-Lab/WattsOnAI.
中文: 本文介绍了WattsOnAI工具包，它通过集成测量、分析和可视化AI工作负载的能耗与碳排放，解决了现有工具零散的问题，并借助标准化报告和关联分析推动可持续的绿色AI实践。
English: This paper introduces WattsOnAI, a comprehensive toolkit that addresses the fragmentation in existing tools by providing integrated measurement, analysis, and visualization of energy use and carbon emissions in AI workloads, promoting sustainable practices through standardized reporting and correlation analysis.

Paperid: 69, https://arxiv.org/pdf/2506.20495.pdf   GitHub

Authors:Haoze Wu, Yunzhi Yao, Wenhao Yu, Huajun Chen, Ningyu Zhang

Title: ReCode: Updating Code API Knowledge with Reinforcement Learning

Abstract:
Large Language Models (LLMs) exhibit remarkable code generation capabilities but falter when adapting to frequent updates in external library APIs. This critical limitation, stemming from reliance on outdated API knowledge from their training data, even with access to current documentation, impedes reliable code generation in dynamic environments. To tackle this issue, we propose ReCode (rule-based Reinforcement learning for Code Update), a novel framework that mimics human programmer adaptation to API changes. Specifically, we construct a dataset of approximately 2,000 data entries to train the LLMs to perform version migration based on updated information. Then, we introduce a modified string similarity metric for code evaluation as the reward for reinforcement learning. Our experiments demonstrate that ReCode substantially boosts LLMs' code generation performance in dynamic API scenarios, especially on the unseen CodeUpdateArena task. Crucially, compared to supervised fine-tuning, ReCode has less impact on LLMs' general code generation abilities. We apply ReCode on various LLMs and reinforcement learning algorithms (GRPO and DAPO), all achieving consistent improvements. Notably, after training, Qwen2.5-Coder-7B outperforms that of the 32B parameter code instruction-tuned model and the reasoning model with the same architecture. Code is available at https://github.com/zjunlp/ReCode.
中文摘要：ReCode是一种新颖的强化学习框架，通过基于版本迁移数据训练大语言模型，显著提升其在动态API环境中的代码生成可靠性，同时对其通用编程能力影响较小。
English Summary: ReCode is a novel reinforcement learning framework that significantly enhances large language models' ability to generate reliable code in dynamic API environments by training them on version migration data while minimizing impact on their general coding capabilities.

Paperid: 70, https://arxiv.org/pdf/2506.20354.pdf   GitHub

Authors:Francesco Carzaniga, Michael Hersche, Abu Sebastian, Kaspar Schindler, Abbas Rahimi

Title: A foundation model with multi-variate parallel attention to generate neuronal activity

Abstract:
Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, particularly in clinical domains such as intracranial electroencephalography (iEEG), where channel setups vary widely across subjects. In this work, we introduce multi-variate parallel attention (MVPA), a novel self-attention mechanism that disentangles content, temporal, and spatial attention, enabling flexible, generalizable, and efficient modeling of time-series data with varying channel counts and configurations. We use MVPA to build MVPFormer, a generative foundation model for human electrophysiology, trained to predict the evolution of iEEG signals across diverse subjects. To support this and future efforts by the community, we release the SWEC iEEG dataset, the largest publicly available iEEG dataset to date, comprising nearly 10,000 hours of recordings from heterogeneous clinical sources. MVPFormer leverages MVPA to achieve strong generalization across subjects, demonstrating expert-level performance in several iEEG tasks. MVPFormer surpasses state-of-the-art Transformer baselines in seizure detection across the SWEC, the MAYO, and the FNUSA datasets, while also achieving state-of-the-art performance on four Brain TreeBank iEEG decoding tasks. We further validate MVPA on standard time-series forecasting and classification tasks, where it matches or exceeds the performance of existing attention-based models. Together, our contributions establish MVPA as a general-purpose attention mechanism for heterogeneous time-series and MVPFormer as the first open-source, open-weights, and open-data iEEG foundation model with SOTA clinical performance. The code is available at https://github.com/IBM/multi-variate-parallel-transformer. The SWEC iEEG dataset is available at https://huggingface.co/datasets/NeuroTec/SWEC_iEEG_Dataset.
中文: 本研究提出多元并行注意力机制和MVPFormer模型，能有效处理异构时间序列数据如颅内脑电图，在多项临床任务和数据集上实现顶尖性能，并发布了最大的公开iEEG数据集以推动相关研究。
English: The study introduces a multi-variate parallel attention (MVPA) mechanism and MVPFormer model, which effectively handle heterogeneous time-series data like iEEG, achieving state-of-the-art performance across multiple clinical tasks and datasets while releasing the largest public iEEG dataset to support further research.

Paperid: 71, https://arxiv.org/pdf/2506.20251.pdf   GitHub

Authors:Kejia Chen, Jiawen Zhang, Jiacong Hu, Yu Wang, Jian Lou, Zunlei Feng, Mingli Song

Title: Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models

Abstract:
Quantized large language models (LLMs) have gained increasing attention and significance for enabling deployment in resource-constrained environments. However, emerging studies on a few calibration dataset-free quantization methods suggest that quantization may compromise the safety capabilities of LLMs, underscoring the urgent need for systematic safety evaluations and effective mitigation strategies. In this paper, we present comprehensive safety evaluations across various mainstream quantization techniques and diverse calibration datasets, utilizing widely accepted safety benchmarks. To address the identified safety vulnerabilities, we propose a quantization-aware safety patching framework, Q-resafe, to efficiently restore the safety capabilities of quantized LLMs while minimizing any adverse impact on utility. Extensive experimental results demonstrate that Q-resafe successfully re-aligns the safety of quantized LLMs with their pre-quantization counterparts, even under challenging evaluation scenarios. Project page is available at: https://github.com/Thecommonirin/Qresafe.
中文: 本文对量化大语言模型进行了全面的安全评估，并提出Q-resafe这一量化感知安全补丁框架，能在不影响实用性的前提下有效恢复模型的安全防护能力。
English: This paper introduces comprehensive safety evaluations of quantized large language models (LLMs) and proposes Q-resafe, a quantization-aware safety patching framework that effectively restores safety capabilities without compromising utility.

Paperid: 72, https://arxiv.org/pdf/2506.20046.pdf   GitHub

Authors:Hirad Daneshvar, Reza Samavi

Title: GNN's Uncertainty Quantification using Self-Distillation

Abstract:
Graph Neural Networks (GNNs) have shown remarkable performance in the healthcare domain. However, what remained challenging is quantifying the predictive uncertainty of GNNs, which is an important aspect of trustworthiness in clinical settings. While Bayesian and ensemble methods can be used to quantify uncertainty, they are computationally expensive. Additionally, the disagreement metric used by ensemble methods to compute uncertainty cannot capture the diversity of models in an ensemble network. In this paper, we propose a novel method, based on knowledge distillation, to quantify GNNs' uncertainty more efficiently and with higher precision. We apply self-distillation, where the same network serves as both the teacher and student models, thereby avoiding the need to train several networks independently. To ensure the impact of self-distillation, we develop an uncertainty metric that captures the diverse nature of the network by assigning different weights to each GNN classifier. We experimentally evaluate the precision, performance, and ability of our approach in distinguishing out-of-distribution data on two graph datasets: MIMIC-IV and Enzymes. The evaluation results demonstrate that the proposed method can effectively capture the predictive uncertainty of the model while having performance similar to that of the MC Dropout and ensemble methods. The code is publicly available at https://github.com/tailabTMU/UQ_GNN.
中文: 本文提出了一种基于知识蒸馏的新方法，通过自蒸馏和加权不确定性指标来高效量化图神经网络的预测不确定性，在医疗数据集上取得了与现有方法相当的性能。
English: This paper introduces a knowledge distillation-based method that efficiently quantifies Graph Neural Networks' predictive uncertainty using self-distillation and a weighted uncertainty metric, achieving comparable performance to existing methods on healthcare datasets.

Paperid: 73, https://arxiv.org/pdf/2506.20024.pdf   GitHub

Authors:Salva Rühling Cachay, Miika Aittala, Karsten Kreis, Noah Brenowitz, Arash Vahdat, Morteza Mardani, Rose Yu

Title: Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting

Abstract:
Diffusion models are a powerful tool for probabilistic forecasting, yet most applications in high-dimensional chaotic systems predict future snapshots one-by-one. This common approach struggles to model complex temporal dependencies and fails to explicitly account for the progressive growth of uncertainty inherent to such systems. While rolling diffusion frameworks, which apply increasing noise to forecasts at longer lead times, have been proposed to address this, their integration with state-of-the-art, high-fidelity diffusion techniques remains a significant challenge. We tackle this problem by introducing Elucidated Rolling Diffusion Models (ERDM), the first framework to successfully unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM). To do this, we adapt the core EDM components-its noise schedule, network preconditioning, and Heun sampler-to the rolling forecast setting. The success of this integration is driven by three key contributions: (i) a novel loss weighting scheme that focuses model capacity on the mid-range forecast horizons where determinism gives way to stochasticity; (ii) an efficient initialization strategy using a pre-trained EDM for the initial window; and (iii) a bespoke hybrid sequence architecture for robust spatiotemporal feature extraction under progressive denoising. On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5^\circ resolution, ERDM consistently outperforms key diffusion-based baselines, including conditional autoregressive EDM. ERDM offers a flexible and powerful general framework for tackling diffusion-based sequence generation problems where modeling escalating uncertainty is paramount. Code is available at: https://github.com/salvaRC/erdm
Chinese: 作者提出了Elucidated Rolling Diffusion Models (ERDM)框架，通过将滚动预测结构与精细化扩散模型相结合，有效解决了混沌系统中不确定性递增的建模难题，在流体模拟和全球天气预报任务中均优于现有基线方法。
English: The authors introduce Elucidated Rolling Diffusion Models (ERDM), a novel framework that integrates rolling forecast mechanisms with advanced diffusion techniques to better model escalating uncertainty in chaotic systems, outperforming existing methods in both fluid dynamics simulations and global weather forecasting.

Paperid: 74, https://arxiv.org/pdf/2506.19993.pdf   GitHub

Authors:Haochen Zhang, Tianyi Zhang, Junze Yin, Oren Gal, Anshumali Shrivastava, Vladimir Braverman

Title: CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems

Abstract:
Recommender systems play a pivotal role in providing relevant content to users. With the rapid development of large language models (LLMs), researchers have begun utilizing LLMs to build more powerful recommender systems. However, existing approaches that focus on aligning LLMs with recommendation tasks do not fully leverage their sequential information processing capabilities, leading to suboptimal performance. In this paper, we propose a novel system called compressed vocabulary expansion (CoVE). In CoVE, each item is assigned a unique ID within the expanded vocabulary. Our framework effectively capitalizes on sequence understanding abilities of LLMs, significantly enhancing their performance on recommendation tasks. Additionally, we compress the embedding layer, making CoVE practical for large-scale industrial applications. The effectiveness and performance of CoVE are demonstrated through comprehensive experiments on multiple recommendation datasets and comparisons with prior works. Our code can be found at https://github.com/HaochenZhang717/CoVE-official-Repo.
中文: 本文提出CoVE系统，通过压缩词汇扩展和嵌入层压缩，充分利用大语言模型的序列理解能力来增强推荐系统性能，在多数据集实验中展现出卓越效果。
English: This paper introduces CoVE, a novel system that enhances recommender systems by leveraging large language models' sequential processing capabilities through compressed vocabulary expansion and embedding layer compression, demonstrating superior performance in comprehensive experiments.

Paperid: 75, https://arxiv.org/pdf/2506.19935.pdf   GitHub

Authors:Shuchen Xue, Tianyu Xie, Tianyang Hu, Zijin Feng, Jiacheng Sun, Kenji Kawaguchi, Zhenguo Li, Zhi-Ming Ma

Title: Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture

Abstract:
Large language models (LLMs) predominantly use autoregressive (AR) approaches, but masked diffusion models (MDMs) are emerging as viable alternatives. A key challenge in comparing AR and MDM paradigms is their typical architectural difference: AR models are often decoder-only, while MDMs have largely been encoder-only. This practice of changing both the modeling paradigm and architecture simultaneously makes direct comparisons unfair, as it's hard to distinguish whether observed differences stem from the paradigm itself or the architectural shift. This research evaluates MDMs within a decoder-only framework to: (1) equitably compare MDM (as Any-Order AR, or AO-AR) and standard AR paradigms. Our investigation suggests that the standard AO-AR objective, which averages over all token permutations, may benefit from refinement, as many permutations appear less informative compared to the language's inherent left-to-right structure. (2) Investigate architectural influences (decoder-only vs. encoder-only) within MDMs. We demonstrate that while encoder-only MDMs model a simpler conditional probability space, decoder-only MDMs can achieve dramatic generation speedups ($\sim25\times$) and comparable perplexity with temperature annealing despite modeling a vastly larger space, highlighting key trade-offs. This work thus decouples core paradigm differences from architectural influences, offering insights for future model design. Code is available at https://github.com/scxue/AO-GPT-MDM.
中文摘要：本研究在仅解码器架构下比较掩码扩散模型与自回归模型，发现掩码扩散模型能实现显著生成加速且保持性能，同时强调了区分核心范式差异与架构影响的重要性。
English Summary: This study compares masked diffusion models (MDMs) with autoregressive models using a decoder-only architecture, revealing that MDMs can achieve significant generation speedups while maintaining performance, and highlights the importance of separating paradigm differences from architectural influences.

Paperid: 76, https://arxiv.org/pdf/2506.19852.pdf   GitHub

Authors:Xingyang Li, Muyang Li, Tianle Cai, Haocheng Xi, Shuo Yang, Yujun Lin, Lvmin Zhang, Songlin Yang, Jinbo Hu, Kelly Peng, Maneesh Agrawala, Ion Stoica, Kurt Keutzer, Song Han

Title: Radial Attention: $O(n\log n)$ Sparse Attention with Energy Decay for Long Video Generation

Abstract:
Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training and inference on long videos prohibitively expensive. In this paper, we identify a phenomenon we term Spatiotemporal Energy Decay in video diffusion models: post-softmax attention scores diminish as spatial and temporal distance between tokens increase, akin to the physical decay of signal or waves over space and time in nature. Motivated by this, we propose Radial Attention, a scalable sparse attention mechanism with $O(n \log n)$ complexity that translates energy decay into exponentially decaying compute density, which is significantly more efficient than standard $O(n^2)$ dense attention and more expressive than linear attention. Specifically, Radial Attention employs a simple, static attention mask where each token attends to spatially nearby tokens, with the attention window size shrinking with temporal distance. Moreover, it allows pre-trained video diffusion models to extend their generation length with efficient LoRA-based fine-tuning. Extensive experiments show that Radial Attention maintains video quality across Wan2.1-14B, HunyuanVideo, and Mochi 1, achieving up to a 1.9$\times$ speedup over the original dense attention. With minimal tuning, it enables video generation up to 4$\times$ longer while reducing training costs by up to 4.4$\times$ compared to direct fine-tuning and accelerating inference by up to 3.7$\times$ compared to dense attention inference.
Chinese: 本文提出径向注意力机制，通过利用时空能量衰减现象，在保持视频质量的同时显著降低了视频扩散模型的计算复杂度，实现了高效的训练和推理。
English: This paper introduces Radial Attention, a scalable sparse attention mechanism that reduces computational complexity in video diffusion models by leveraging spatiotemporal energy decay, achieving significant efficiency gains while maintaining video quality.

Paperid: 77, https://arxiv.org/pdf/2506.19830.pdf   GitHub

Authors:Yichao Fu, Rui Ge, Zelei Shao, Zhijie Deng, Hao Zhang

Title: Scaling Speculative Decoding with Lookahead Reasoning

Abstract:
Reasoning models excel by generating long chain-of-thoughts, but decoding the resulting thousands of tokens is slow. Token-level speculative decoding (SD) helps, but its benefit is capped, because the chance that an entire $γ$-token guess is correct falls exponentially as $γ$ grows. This means allocating more compute for longer token drafts faces an algorithmic ceiling -- making the speedup modest and hardware-agnostic. We raise this ceiling with Lookahead Reasoning, which exploits a second, step-level layer of parallelism. Our key insight is that reasoning models generate step-by-step, and each step needs only to be semantically correct, not exact token matching. In Lookahead Reasoning, a lightweight draft model proposes several future steps; the target model expands each proposal in one batched pass, and a verifier keeps semantically correct steps while letting the target regenerate any that fail. Token-level SD still operates within each reasoning step, so the two layers of parallelism multiply. We show Lookahead Reasoning lifts the peak speedup of SD both theoretically and empirically. Across GSM8K, AIME, and other benchmarks, Lookahead Reasoning improves the speedup of SD from 1.4x to 2.1x while preserving answer quality, and its speedup scales better with additional GPU throughput. Our code is available at https://github.com/hao-ai-lab/LookaheadReasoning
Chinese: 前瞻推理通过引入步骤级并行性改进了推测解码，允许草稿模型提出多个未来推理步骤并验证其语义正确性，与令牌级并行性相乘，从而在不影响答案质量的前提下显著提升了解码速度。
English: Lookahead Reasoning enhances speculative decoding by introducing step-level parallelism, allowing a draft model to propose multiple future reasoning steps and verifying their semantic correctness, which multiplies with token-level parallelism to significantly boost decoding speed without compromising answer quality.

Paperid: 78, https://arxiv.org/pdf/2506.19807.pdf   GitHub

Authors:Baochang Ren, Shuofei Qiao, Wenhao Yu, Huajun Chen, Ningyu Zhang

Title: KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

Abstract:
Large Language Models (LLMs), particularly slow-thinking models, often exhibit severe hallucination, outputting incorrect content due to an inability to accurately recognize knowledge boundaries during reasoning. While Reinforcement Learning (RL) can enhance complex reasoning abilities, its outcome-oriented reward mechanism often lacks factual supervision over the thinking process, further exacerbating the hallucination problem. To address the high hallucination in slow-thinking models, we propose Knowledge-enhanced RL, KnowRL. KnowRL guides models to perform fact-based slow thinking by integrating a factuality reward, based on knowledge verification, into the RL training process, helping them recognize their knowledge boundaries. KnowRL guides models to perform fact-based slow thinking by integrating a factuality reward, based on knowledge verification, into the RL training process, helping them recognize their knowledge boundaries. This targeted factual input during RL training enables the model to learn and internalize fact-based reasoning strategies. By directly rewarding adherence to facts within the reasoning steps, KnowRL fosters a more reliable thinking process. Experimental results on three hallucination evaluation datasets and two reasoning evaluation datasets demonstrate that KnowRL effectively mitigates hallucinations in slow-thinking models while maintaining their original strong reasoning capabilities. Our code is available at https://github.com/zjunlp/KnowRL.
Chinese: 针对慢思考大语言模型的严重幻觉问题，我们提出KnowRL方法，通过知识增强的强化学习引入事实性奖励机制，引导模型进行基于事实的慢思考，在保持推理能力的同时有效减少错误输出。
English: To address severe hallucinations in slow-thinking Large Language Models, we propose KnowRL, a knowledge-enhanced reinforcement learning method that integrates factuality rewards to guide fact-based reasoning and reduce incorrect outputs while preserving reasoning capabilities.

Paperid: 79, https://arxiv.org/pdf/2506.19794.pdf   GitHub

Authors:Yuqi Zhu, Yi Zhong, Jintian Zhang, Ziheng Zhang, Shuofei Qiao, Yujie Luo, Lun Du, Da Zheng, Ningyu Zhang, Huajun Chen

Title: Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study

Abstract:
Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs. By curating a seed dataset of diverse, realistic scenarios, we evaluate model behavior across three core dimensions: data understanding, code generation, and strategic planning. Our analysis reveals three key findings: (1) Strategic planning quality serves as the primary determinant of model performance; (2) Interaction design and task complexity significantly influence reasoning capabilities; (3) Data quality demonstrates a greater impact than diversity in achieving optimal performance. We leverage these insights to develop a data synthesis methodology, demonstrating significant improvements in open-source LLMs' analytical reasoning capabilities. Code is available at https://github.com/zjunlp/DataMind.
中文摘要：本研究通过揭示战略规划是性能关键驱动因素，开发了一种数据合成方法，显著提升了开源大语言模型的分析推理能力。
English Summary: This study enhances open-source LLMs' data analysis capabilities by identifying strategic planning as the key performance driver and developing a data synthesis method that significantly improves analytical reasoning.

Paperid: 80, https://arxiv.org/pdf/2506.19780.pdf   GitHub

Authors:Yuhui Sun, Xiyao Wang, Zixi Li, Zhenlong Yuan, Jinman Zhao

Title: Multi-Preference Lambda-weighted Listwise DPO for Small-Scale Model Alignment

Abstract:
Large language models (LLMs) demonstrate strong generalization across a wide range of language tasks, but often generate outputs that misalign with human preferences. Reinforcement Learning from Human Feedback (RLHF) addresses this by optimizing models toward human preferences using a learned reward function and reinforcement learning, yielding improved alignment but suffering from high computational cost and instability. Direct Preference Optimization (DPO) simplifies the process by treating alignment as a classification task over binary preference pairs, reducing training overhead while achieving competitive performance. However, it assumes fixed, single-dimensional preferences and only supports pairwise supervision. To address these limitations, we propose Multi-Preference Lambda-weighted Listwise DPO, which allows the model to learn from more detailed human feedback and flexibly balance multiple goals such as helpfulness, honesty, and fluency. Our method models full-ranked preference distributions rather than binary comparisons, enabling more informative learning signals. The lambda vector controls the relative importance of different alignment goals, allowing the model to generalize across diverse human objectives. During inference, lambda can be adjusted without retraining, providing controllable alignment behavior for downstream use. We also introduce a learned scheduler that dynamically samples performant lambda configurations to improve robustness. Notably, our method requires only 20GB of GPU memory for training, making it suitable for compute-constrained settings such as academic labs, educational tools, or on-device assistants. Experiments on 1B-2B scale models show that our method consistently outperforms standard DPO on alignment benchmarks while enabling efficient, controllable, and fine-grained adaptation suitable for real-world deployment.
中文: 提出的多偏好Lambda加权列表化DPO方法通过列表化偏好建模和可调节的lambda向量，使语言模型能从更细致的人类反馈中学习并灵活平衡多个对齐目标，在低计算需求下实现更优性能。
English: The proposed Multi-Preference Lambda-weighted Listwise DPO method enables language models to learn from detailed human feedback and flexibly balance multiple alignment goals through listwise preference modeling and adjustable lambda vectors, achieving superior performance with low computational requirements.

Paperid: 81, https://arxiv.org/pdf/2506.19741.pdf   GitHub

Authors:Yihong Luo, Shuchen Xue, Tianyang Hu, Jing Tang

Title: Noise Consistency Training: A Native Approach for One-Step Generator in Learning Additional Controls

Abstract:
The pursuit of efficient and controllable high-quality content generation remains a central challenge in artificial intelligence-generated content (AIGC). While one-step generators, enabled by diffusion distillation techniques, offer excellent generation quality and computational efficiency, adapting them to new control conditions--such as structural constraints, semantic guidelines, or external inputs--poses a significant challenge. Conventional approaches often necessitate computationally expensive modifications to the base model and subsequent diffusion distillation. This paper introduces Noise Consistency Training (NCT), a novel and lightweight approach to directly integrate new control signals into pre-trained one-step generators without requiring access to original training images or retraining the base diffusion model. NCT operates by introducing an adapter module and employs a noise consistency loss in the noise space of the generator. This loss aligns the adapted model's generation behavior across noises that are conditionally dependent to varying degrees, implicitly guiding it to adhere to the new control. Theoretically, this training objective can be understood as minimizing the distributional distance between the adapted generator and the conditional distribution induced by the new conditions. NCT is modular, data-efficient, and easily deployable, relying only on the pre-trained one-step generator and a control signal model. Extensive experiments demonstrate that NCT achieves state-of-the-art controllable generation in a single forward pass, surpassing existing multi-step and distillation-based methods in both generation quality and computational efficiency. Code is available at https://github.com/Luo-Yihong/NCT
中文: 本文提出的噪声一致性训练（NCT）是一种轻量级方法，可在无需重新训练的情况下将新控制信号集成到预训练单步生成器中，实现了在生成质量和计算效率方面均超越现有方法的最优可控生成效果。
English: This paper introduces Noise Consistency Training (NCT), a lightweight method that enables pre-trained one-step generators to incorporate new control signals without retraining, achieving state-of-the-art controllable generation with superior quality and efficiency.

Paperid: 82, https://arxiv.org/pdf/2506.19697.pdf   GitHub

Authors:Jungwoo Park, Taewhoo Lee, Chanwoong Yoon, Hyeon Hwang, Jaewoo Kang

Title: Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models

Abstract:
Extreme activation outliers in Large Language Models (LLMs) critically degrade quantization performance, hindering efficient on-device deployment. While channel-wise operations and adaptive gradient scaling are recognized causes, practical mitigation remains challenging. We introduce Outlier-Safe Pre-Training (OSP), a practical guideline that proactively prevents outlier formation rather than relying on post-hoc mitigation. OSP combines three key innovations: (1) the Muon optimizer, eliminating privileged bases while maintaining training efficiency; (2) Single-Scale RMSNorm, preventing channel-wise amplification; and (3) a learnable embedding projection, redistributing activation magnitudes originating from embedding matrices. We validate OSP by training a 1.4B-parameter model on 1 trillion tokens, which is the first production-scale LLM trained without such outliers. Under aggressive 4-bit quantization, our OSP model achieves a 35.7 average score across 10 benchmarks (compared to 26.5 for an Adam-trained model), with only a 2% training overhead. Remarkably, OSP models exhibit near-zero excess kurtosis (0.04) compared to extreme values (1818.56) in standard models, fundamentally altering LLM quantization behavior. Our work demonstrates that outliers are not inherent to LLMs but are consequences of training strategies, paving the way for more efficient LLM deployment. The source code and pretrained checkpoints are available at https://github.com/dmis-lab/Outlier-Safe-Pre-Training.
中文: 离群值安全预训练（OSP）通过三项关键创新主动预防大语言模型中的极端激活离群值，在激进4位量化下实现卓越性能且仅增加2%训练开销，从根本上改变了模型量化行为。
English: Outlier-Safe Pre-Training (OSP) is a novel training strategy that proactively prevents extreme activation outliers in LLMs through three key innovations, enabling superior 4-bit quantization performance with minimal training overhead and fundamentally changing LLM deployment efficiency.

Paperid: 83, https://arxiv.org/pdf/2506.19680.pdf   GitHub

Authors:Mihnea Ghitu, Vihari Piratla, Matthew Wicker

Title: Model Guidance via Robust Feature Attribution

Abstract:
Controlling the patterns a model learns is essential to preventing reliance on irrelevant or misleading features. Such reliance on irrelevant features, often called shortcut features, has been observed across domains, including medical imaging and natural language processing, where it may lead to real-world harms. A common mitigation strategy leverages annotations (provided by humans or machines) indicating which features are relevant or irrelevant. These annotations are compared to model explanations, typically in the form of feature salience, and used to guide the loss function during training. Unfortunately, recent works have demonstrated that feature salience methods are unreliable and therefore offer a poor signal to optimize. In this work, we propose a simplified objective that simultaneously optimizes for explanation robustness and mitigation of shortcut learning. Unlike prior objectives with similar aims, we demonstrate theoretically why our approach ought to be more effective. Across a comprehensive series of experiments, we show that our approach consistently reduces test-time misclassifications by 20% compared to state-of-the-art methods. We also extend prior experimental settings to include natural language processing tasks. Additionally, we conduct novel ablations that yield practical insights, including the relative importance of annotation quality over quantity. Code for our method and experiments is available at: https://github.com/Mihneaghitu/ModelGuidanceViaRobustFeatureAttribution.
Chinese: 本研究提出了一种简化目标，通过增强解释鲁棒性并缓解捷径学习，理论上证明了其有效性，实验表明测试时误分类减少20%，同时扩展至自然语言处理任务，并提供了关于标注质量优于数量的实用见解。
English: This study introduces a streamlined objective that enhances explanation robustness and mitigates shortcut learning, theoretically justifying its effectiveness and demonstrating a 20% reduction in test-time misclassifications across experiments, while also extending evaluations to NLP tasks and providing practical insights on annotation quality.

Paperid: 84, https://arxiv.org/pdf/2506.19598.pdf   GitHub

Authors:Alan N. Amin, Andres Potapczynski, Andrew Gordon Wilson

Title: Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra

Abstract:
To understand how genetic variants in human genomes manifest in phenotypes -- traits like height or diseases like asthma -- geneticists have sequenced and measured hundreds of thousands of individuals. Geneticists use this data to build models that predict how a genetic variant impacts phenotype given genomic features of the variant, like DNA accessibility or the presence of nearby DNA-bound proteins. As more data and features become available, one might expect predictive models to improve. Unfortunately, training these models is bottlenecked by the need to solve expensive linear algebra problems because variants in the genome are correlated with nearby variants, requiring inversion of large matrices. Previous methods have therefore been restricted to fitting small models, and fitting simplified summary statistics, rather than the full likelihood of the statistical model. In this paper, we leverage modern fast linear algebra techniques to develop DeepWAS (Deep genome Wide Association Studies), a method to train large and flexible neural network predictive models to optimize likelihood. Notably, we find that larger models only improve performance when using our full likelihood approach; when trained by fitting traditional summary statistics, larger models perform no better than small ones. We find larger models trained on more features make better predictions, potentially improving disease predictions and therapeutic target identification.
中文摘要：遗传学家利用大规模基因组数据构建预测模型以关联基因变异与表型，但面临矩阵求逆的计算瓶颈；DeepWAS采用快速线性代数方法训练更大的神经网络，通过全似然优化提升预测性能，有望改进疾病预测和治疗靶点识别。
English Summary: Geneticists use large-scale genomic data to build predictive models linking genetic variants to phenotypes, but face computational bottlenecks from matrix inversions, which DeepWAS overcomes using fast linear algebra to train larger neural networks that improve predictions with full likelihood optimization.

Paperid: 85, https://arxiv.org/pdf/2506.19513.pdf   GitHub

Authors:Tao Huang, Zhekun Liu, Rui Wang, Yang Zhang, Liping Jing

Title: Visual hallucination detection in large vision-language models via evidential conflict

Abstract:
Despite the remarkable multimodal capabilities of Large Vision-Language Models (LVLMs), discrepancies often occur between visual inputs and textual outputs--a phenomenon we term visual hallucination. This critical reliability gap poses substantial risks in safety-critical Artificial Intelligence (AI) applications, necessitating a comprehensive evaluation benchmark and effective detection methods. Firstly, we observe that existing visual-centric hallucination benchmarks mainly assess LVLMs from a perception perspective, overlooking hallucinations arising from advanced reasoning capabilities. We develop the Perception-Reasoning Evaluation Hallucination (PRE-HAL) dataset, which enables the systematic evaluation of both perception and reasoning capabilities of LVLMs across multiple visual semantics, such as instances, scenes, and relations. Comprehensive evaluation with this new benchmark exposed more visual vulnerabilities, particularly in the more challenging task of relation reasoning. To address this issue, we propose, to the best of our knowledge, the first Dempster-Shafer theory (DST)-based visual hallucination detection method for LVLMs through uncertainty estimation. This method aims to efficiently capture the degree of conflict in high-level features at the model inference phase. Specifically, our approach employs simple mass functions to mitigate the computational complexity of evidence combination on power sets. We conduct an extensive evaluation of state-of-the-art LVLMs, LLaVA-v1.5, mPLUG-Owl2 and mPLUG-Owl3, with the new PRE-HAL benchmark. Experimental results indicate that our method outperforms five baseline uncertainty metrics, achieving average AUROC improvements of 4%, 10%, and 7% across three LVLMs. Our code is available at https://github.com/HT86159/Evidential-Conflict.
中文摘要：大型视觉语言模型存在视觉幻觉导致可靠性风险，为此开发了PRE-HAL评估基准和基于Dempster-Shafer理论的检测方法，该方法在多项测试中优于现有基准指标。
English Summary: Large Vision-Language Models suffer from visual hallucinations causing reliability risks, prompting the creation of the PRE-HAL benchmark for systematic evaluation and a novel Dempster-Shafer theory-based detection method that outperforms existing metrics.

Paperid: 86, https://arxiv.org/pdf/2506.19502.pdf   GitHub

Authors:Aleksandr Algazinov, Matt Laing, Paul Laban

Title: MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications

Abstract:
Accessibility remains a critical concern in today's society, as many technologies are not developed to support the full range of user needs. Existing multi-agent systems (MAS) often cannot provide comprehensive assistance for users in need due to the lack of customization stemming from closed-source designs. Consequently, individuals with disabilities frequently encounter significant barriers when attempting to interact with digital environments. We introduce MATE, a multimodal accessibility MAS, which performs the modality conversions based on the user's needs. The system is useful for assisting people with disabilities by ensuring that data will be converted to an understandable format. For instance, if the user cannot see well and receives an image, the system converts this image to its audio description. MATE can be applied to a wide range of domains, industries, and areas, such as healthcare, and can become a useful assistant for various groups of users. The system supports multiple types of models, ranging from LLM API calling to using custom machine learning (ML) classifiers. This flexibility ensures that the system can be adapted to various needs and is compatible with a wide variety of hardware. Since the system is expected to run locally, it ensures the privacy and security of sensitive information. In addition, the framework can be effectively integrated with institutional technologies (e.g., digital healthcare service) for real-time user assistance. Furthermore, we introduce ModCon-Task-Identifier, a model that is capable of extracting the precise modality conversion task from the user input. Numerous experiments show that ModCon-Task-Identifier consistently outperforms other LLMs and statistical models on our custom data. Our code and data are publicly available at https://github.com/AlgazinovAleksandr/Multi-Agent-MATE.
中文: MATE是一种多模态无障碍多智能体系统，能根据用户需求进行数据格式转换，通过可定制的模态转换帮助残障人士克服数字障碍，同时本地运行确保隐私安全。
English: MATE is a multimodal accessibility multi-agent system that converts data formats based on user needs, enabling people with disabilities to overcome digital barriers through customizable modality conversions while ensuring local operation for privacy protection.

Paperid: 87, https://arxiv.org/pdf/2506.19482.pdf   GitHub

Authors:Yuelin Zhang, Jiacheng Cen, Jiaqi Han, Wenbing Huang

Title: Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning

Abstract:
Equivariant Graph Neural Networks (GNNs) have achieved remarkable success across diverse scientific applications. However, existing approaches face critical efficiency challenges when scaling to large geometric graphs and suffer significant performance degradation when the input graphs are sparsified for computational tractability. To address these limitations, we introduce FastEGNN and DistEGNN, two novel enhancements to equivariant GNNs for large-scale geometric graphs. FastEGNN employs a key innovation: a small ordered set of virtual nodes that effectively approximates the large unordered graph of real nodes. Specifically, we implement distinct message passing and aggregation mechanisms for different virtual nodes to ensure mutual distinctiveness, and minimize Maximum Mean Discrepancy (MMD) between virtual and real coordinates to achieve global distributedness. This design enables FastEGNN to maintain high accuracy while efficiently processing large-scale sparse graphs. For extremely large-scale geometric graphs, we present DistEGNN, a distributed extension where virtual nodes act as global bridges between subgraphs in different devices, maintaining consistency while dramatically reducing memory and computational overhead. We comprehensively evaluate our models across four challenging domains: N-body systems (100 nodes), protein dynamics (800 nodes), Water-3D (8,000 nodes), and our new Fluid113K benchmark (113,000 nodes). Results demonstrate superior efficiency and performance, establishing new capabilities in large-scale equivariant graph learning. Code is available at https://github.com/GLAD-RUC/DistEGNN.
Chinese: FastEGNN和DistEGNN通过虚拟节点有效近似大型几何图，在保持高精度的同时显著提升处理效率，成功应用于多种大规模科学计算场景。
English: FastEGNN and DistEGNN enhance equivariant GNNs by using virtual nodes to efficiently approximate large geometric graphs, maintaining high accuracy and scalability across diverse large-scale applications.

Paperid: 88, https://arxiv.org/pdf/2506.19343.pdf   GitHub

Authors:Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Weigang Lu

Title: Discrepancy-Aware Graph Mask Auto-Encoder

Abstract:
Masked Graph Auto-Encoder, a powerful graph self-supervised training paradigm, has recently shown superior performance in graph representation learning. Existing works typically rely on node contextual information to recover the masked information. However, they fail to generalize well to heterophilic graphs where connected nodes may be not similar, because they focus only on capturing the neighborhood information and ignoring the discrepancy information between different nodes, resulting in indistinguishable node representations. In this paper, to address this issue, we propose a Discrepancy-Aware Graph Mask Auto-Encoder (DGMAE). It obtains more distinguishable node representations by reconstructing the discrepancy information of neighboring nodes during the masking process. We conduct extensive experiments on 17 widely-used benchmark datasets. The results show that our DGMAE can effectively preserve the discrepancies of nodes in low-dimensional space. Moreover, DGMAE significantly outperforms state-of-the-art graph self-supervised learning methods on three graph analytic including tasks node classification, node clustering, and graph classification, demonstrating its remarkable superiority. The code of DGMAE is available at https://github.com/zhengziyu77/DGMAE.
中文摘要：本文提出的差异感知图掩码自编码器（DGMAE）通过在掩码过程中重构节点差异信息，解决了现有方法在异配图上的局限性，在多项图分析任务中展现出卓越性能。
English Summary: The proposed Discrepancy-Aware Graph Mask Auto-Encoder (DGMAE) addresses limitations in existing graph auto-encoders by reconstructing node discrepancy information during masking, achieving superior performance on heterophilic graphs across multiple graph analytic tasks.

Paperid: 89, https://arxiv.org/pdf/2506.19140.pdf   GitHub

Authors:Barry Wang, Avi Schwarzschild, Alexander Robey, Ali Payani, Charles Fleming, Mingjie Sun, Daphne Ippolito

Title: Command-V: Pasting LLM Behaviors via Activation Profiles

Abstract:
Retrofitting large language models (LLMs) with new behaviors typically requires full finetuning or distillation-costly steps that must be repeated for every architecture. In this work, we introduce Command-V, a backpropagation-free behavior transfer method that copies an existing residual activation adapter from a donor model and pastes its effect into a recipient model. Command-V profiles layer activations on a small prompt set, derives linear converters between corresponding layers, and applies the donor intervention in the recipient's activation space. This process does not require access to the original training data and needs minimal compute. In three case studies-safety-refusal enhancement, jailbreak facilitation, and automatic chain-of-thought reasoning--Command-V matches or exceeds the performance of direct finetuning while using orders of magnitude less compute. Our code and data are accessible at https://github.com/GithuBarry/Command-V/.
中文: Command-V是一种无需反向传播的高效方法，通过复制残差激活适配器并应用线性转换器在大型语言模型间迁移行为，以极低计算成本达到或超越微调效果。
English: Command-V is a computationally efficient, backpropagation-free method that transfers behaviors between large language models by copying residual activation adapters and applying linear converters, matching or surpassing fine-tuning performance with minimal resources.

Paperid: 90, https://arxiv.org/pdf/2506.18959.pdf   GitHub

Authors:Weizhi Zhang, Yangning Li, Yuanchen Bei, Junyu Luo, Guancheng Wan, Liangwei Yang, Chenxuan Xie, Yuyao Yang, Wei-Chieh Huang, Chunyu Miao, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Yankai Chen, Chunkit Chan, Peilin Zhou, Xinyang Zhang, Chenwei Zhang, Jingbo Shang, Ming Zhang, Yangqiu Song, Irwin King, Philip S. Yu

Title: From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents

Abstract:
Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engines are increasingly inadequate for handling complex, multi-step information needs. Our position is that Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research. These systems transcend conventional information search techniques by tightly integrating autonomous reasoning, iterative retrieval, and information synthesis into a dynamic feedback loop. We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn. We also introduce a test-time scaling law to formalize the impact of computational depth on reasoning and search. Supported by benchmark results and the rise of open-source implementations, we demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking. All the related resources, including industry products, research papers, benchmark datasets, and open-source implementations, are collected for the community in https://github.com/DavidZWZ/Awesome-Deep-Research.
中文摘要：具备推理能力的大语言模型正在开创“智能深度研究”新范式，通过自主推理与迭代检索的深度融合，显著超越了传统搜索方法，有望成为未来信息获取的主导模式。
English Summary: Large Language Models with reasoning capabilities are pioneering Agentic Deep Research, a new paradigm that integrates autonomous reasoning and iterative retrieval to significantly outperform traditional search methods and redefine future information seeking.

Paperid: 91, https://arxiv.org/pdf/2506.18945.pdf   GitHub

Authors:Zihan Wang, Rui Pan, Jiarui Yao, Robert Csordas, Linjie Li, Lu Yin, Jiajun Wu, Tong Zhang, Manling Li, Shiwei Liu

Title: Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models

Abstract:
We propose Chain-of-Experts (CoE), a new Mixture-of-Experts (MoE) architecture that introduces sequential expert communication within each layer. Unlike traditional MoE models, where experts operate independently in parallel, CoE processes tokens iteratively across a chain of experts inside a layer. To support dynamic expert selection across iterations, CoE employs a dedicated router at each iteration step within a layer. This design allows tokens to re-evaluate and select different experts during each iteration, rather than being statically assigned. As a result, CoE introduces a flexible routing mechanism that increases the diversity of expert combinations and enriches the model's representational capacity. CoE demonstrates improved performance under fixed compute: on math reasoning tasks, it reduces validation loss from 1.20 to 1.12 compared to a standard MoE. Beyond performance, CoE offers a new scaling axis: depth through expert iteration, which complements conventional width/depth scaling. For example, using 2x iterations matches the performance of 3x expert selections (in width), while reducing memory usage by 17.6-42% relative to other scaling strategies. Our analysis reveals that CoE's benefits stem from its iterative residual structure and enhanced expert specialization empowered by iterative routing, which together unlock more expressive representations. Code is available at https://github.com/ZihanWang314/coe.
中文: Chain-of-Experts (CoE) 提出在层内实现专家顺序通信的新架构，通过动态路由和迭代处理增强模型表达能力，在数学推理任务上将验证损失从1.20降至1.12，相比传统混合专家模型内存使用减少17.6-42%。
English: Chain-of-Experts (CoE) introduces sequential expert communication within layers, enabling dynamic routing and iterative processing that enhances model capacity and reduces validation loss from 1.20 to 1.12 on math tasks while cutting memory usage by 17.6-42% compared to traditional MoE architectures.

Paperid: 92, https://arxiv.org/pdf/2506.18931.pdf   GitHub

Authors:Shuang Ao, Yi Dong, Jinwei Hu, Sarvapali Ramchurn

Title: Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs

Abstract:
Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) enhances adaptability while reducing computational costs. However, fine-tuning can compromise safety alignment, even with benign data, increasing susceptibility to harmful outputs. Existing safety alignment methods struggle to capture complex parameter shifts, leading to suboptimal safety-utility trade-offs. To address this issue, we propose Safe Pruning LoRA (SPLoRA), a novel pruning-based approach that selectively removes LoRA layers that weaken safety alignment, improving safety while preserving performance. At its core, we introduce Empirical-DIEM (E-DIEM), a dimension-insensitive similarity metric that effectively detects safety misalignment in LoRA-adapted models. We conduct extensive experiments on LLMs fine-tuned with mixed of benign and malicious data, and purely benign datasets, evaluating SPLoRA across utility, safety, and reliability metrics. Results demonstrate that SPLoRA outperforms state-of-the-art safety alignment techniques, significantly reducing safety risks while maintaining or improving model performance and reliability. Additionally, SPLoRA reduces inference overhead, making it a scalable and efficient solution for deploying safer and more reliable LLMs. The code is available at https://github.com/AoShuang92/SPLoRA.
中文摘要：SPLoRA是一种基于剪枝的新方法，通过E-DIEM度量选择性移除不安全的LoRA层，在提升微调后大语言模型安全性的同时保持性能优势，并降低推理开销。
English Summary: SPLoRA is a novel pruning-based method that enhances the safety of fine-tuned LLMs by selectively removing unsafe LoRA layers using the E-DIEM metric, achieving superior safety-utility balance with reduced inference overhead.

Paperid: 93, https://arxiv.org/pdf/2506.18791.pdf   GitHub

Authors:Suyash Gaurav, Muhammad Farhan Humayun, Jukka Heikkonen, Jatin Chaudhary

Title: Focus Your Attention: Towards Data-Intuitive Lightweight Vision Transformers

Abstract:
The evolution of Vision Transformers has led to their widespread adaptation to different domains. Despite large-scale success, there remain significant challenges including their reliance on extensive computational and memory resources for pre-training on huge datasets as well as difficulties in task-specific transfer learning. These limitations coupled with energy inefficiencies mainly arise due to the computation-intensive self-attention mechanism. To address these issues, we propose a novel Super-Pixel Based Patch Pooling (SPPP) technique that generates context-aware, semantically rich, patch embeddings to effectively reduce the architectural complexity and improve efficiency. Additionally, we introduce the Light Latent Attention (LLA) module in our pipeline by integrating latent tokens into the attention mechanism allowing cross-attention operations to significantly reduce the time and space complexity of the attention module. By leveraging the data-intuitive patch embeddings coupled with dynamic positional encodings, our approach adaptively modulates the cross-attention process to focus on informative regions while maintaining the global semantic structure. This targeted attention improves training efficiency and accelerates convergence. Notably, the SPPP module is lightweight and can be easily integrated into existing transformer architectures. Extensive experiments demonstrate that our proposed architecture provides significant improvements in terms of computational efficiency while achieving comparable results with the state-of-the-art approaches, highlighting its potential for energy-efficient transformers suitable for edge deployment. (The code is available on our GitHub repository: https://github.com/zser092/Focused-Attention-ViT).
中文摘要：本文提出基于超像素的补丁池化技术和轻量潜在注意力模块，有效降低视觉Transformer的复杂度，在保持与先进方法相当性能的同时显著提升计算效率。
English Summary: This paper introduces a Super-Pixel Based Patch Pooling technique and Light Latent Attention module to reduce Vision Transformer complexity, improving computational efficiency while maintaining competitive performance with state-of-the-art methods.

Paperid: 94, https://arxiv.org/pdf/2506.18716.pdf   GitHub

Authors:Jie Li, Shifei Ding, Lili Guo, Xuan Li

Title: Multi-modal Anchor Gated Transformer with Knowledge Distillation for Emotion Recognition in Conversation

Abstract:
Emotion Recognition in Conversation (ERC) aims to detect the emotions of individual utterances within a conversation. Generating efficient and modality-specific representations for each utterance remains a significant challenge. Previous studies have proposed various models to integrate features extracted using different modality-specific encoders. However, they neglect the varying contributions of modalities to this task and introduce high complexity by aligning modalities at the frame level. To address these challenges, we propose the Multi-modal Anchor Gated Transformer with Knowledge Distillation (MAGTKD) for the ERC task. Specifically, prompt learning is employed to enhance textual modality representations, while knowledge distillation is utilized to strengthen representations of weaker modalities. Furthermore, we introduce a multi-modal anchor gated transformer to effectively integrate utterance-level representations across modalities. Extensive experiments on the IEMOCAP and MELD datasets demonstrate the effectiveness of knowledge distillation in enhancing modality representations and achieve state-of-the-art performance in emotion recognition. Our code is available at: https://github.com/JieLi-dd/MAGTKD.
Chinese: 提出的MAGTKD模型通过提示学习和知识蒸馏增强模态表示，并利用多模态锚点门控变换器有效整合各模态信息，在基准数据集上实现了对话中情感识别的最新性能。
English: The proposed MAGTKD model enhances emotion recognition in conversations by using prompt learning and knowledge distillation to improve modality representations and integrates them effectively with a multi-modal anchor gated transformer, achieving state-of-the-art results on benchmark datasets.

Paperid: 95, https://arxiv.org/pdf/2506.18696.pdf   GitHub

Authors:Yuchang Zhu, Jintang Li, Huizhe Zhang, Liang Chen, Zibin Zheng

Title: SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding

Abstract:
Individual fairness (IF) in graph neural networks (GNNs), which emphasizes the need for similar individuals should receive similar outcomes from GNNs, has been a critical issue. Despite its importance, research in this area has been largely unexplored in terms of (1) a clear understanding of what induces individual unfairness in GNNs and (2) a comprehensive consideration of identifying similar individuals. To bridge these gaps, we conduct a preliminary analysis to explore the underlying reason for individual unfairness and observe correlations between IF and similarity consistency, a concept introduced to evaluate the discrepancy in identifying similar individuals based on graph structure versus node features. Inspired by our observations, we introduce two metrics to assess individual similarity from two distinct perspectives: topology fusion and feature fusion. Building upon these metrics, we propose Similarity-aware GNNs for Individual Fairness, named SaGIF. The key insight behind SaGIF is the integration of individual similarities by independently learning similarity representations, leading to an improvement of IF in GNNs. Our experiments on several real-world datasets validate the effectiveness of our proposed metrics and SaGIF. Specifically, SaGIF consistently outperforms state-of-the-art IF methods while maintaining utility performance. Code is available at: https://github.com/ZzoomD/SaGIF.
中文: 本研究针对图神经网络中的个体公平性问题，提出了两种相似性度量指标和SaGIF方法，通过独立学习相似性表示来提升公平性，同时保持模型性能。
English: This study addresses individual fairness in graph neural networks by introducing two similarity metrics and proposing SaGIF, a method that improves fairness through independent similarity representation learning while maintaining performance.

Paperid: 96, https://arxiv.org/pdf/2506.18630.pdf   GitHub

Authors:Kurt Butler, Guanchao Feng, Tong Chen, Petar Djuric

Title: Trustworthy Prediction with Gaussian Process Knowledge Scores

Abstract:
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are well-informed by previously seen data. In this paper, we propose a knowledge score for predictions from Gaussian process regression (GPR) models that quantifies the extent to which observing data have reduced our uncertainty about a prediction. The knowledge score is interpretable and naturally bounded between 0 and 1. We demonstrate in several experiments that the knowledge score can anticipate when predictions from a GPR model are accurate, and that this anticipation improves performance in tasks such as anomaly detection, extrapolation, and missing data imputation. Source code for this project is available online at https://github.com/KurtButler/GP-knowledge.
Chinese: 本文为高斯过程回归模型提出了一种知识评分，用于量化预测中不确定性的减少程度，并证明该评分在异常检测和数据插补等任务中能有效提升性能。
English: This paper introduces a knowledge score for Gaussian process regression models that measures uncertainty reduction in predictions, demonstrating its effectiveness in improving tasks like anomaly detection and data imputation.

Paperid: 97, https://arxiv.org/pdf/2506.18591.pdf   GitHub

Authors:Mauricio Byrd Victorica, György Dán, Henrik Sandberg

Title: SpaNN: Detecting Multiple Adversarial Patches on CNNs by Spanning Saliency Thresholds

Abstract:
State-of-the-art convolutional neural network models for object detection and image classification are vulnerable to physically realizable adversarial perturbations, such as patch attacks. Existing defenses have focused, implicitly or explicitly, on single-patch attacks, leaving their sensitivity to the number of patches as an open question or rendering them computationally infeasible or inefficient against attacks consisting of multiple patches in the worst cases. In this work, we propose SpaNN, an attack detector whose computational complexity is independent of the expected number of adversarial patches. The key novelty of the proposed detector is that it builds an ensemble of binarized feature maps by applying a set of saliency thresholds to the neural activations of the first convolutional layer of the victim model. It then performs clustering on the ensemble and uses the cluster features as the input to a classifier for attack detection. Contrary to existing detectors, SpaNN does not rely on a fixed saliency threshold for identifying adversarial regions, which makes it robust against white box adversarial attacks. We evaluate SpaNN on four widely used data sets for object detection and classification, and our results show that SpaNN outperforms state-of-the-art defenses by up to 11 and 27 percentage points in the case of object detection and the case of image classification, respectively. Our code is available at https://github.com/gerkbyrd/SpaNN.
中文: 提出的SpaNN检测器通过使用二值化特征图集合和聚类技术，有效识别对抗性补丁攻击，其计算效率与补丁数量无关，性能显著优于现有防御方法。
English: The proposed SpaNN detector effectively identifies adversarial patch attacks by using an ensemble of binarized feature maps and clustering, outperforming existing defenses with computational efficiency independent of patch count.

Paperid: 98, https://arxiv.org/pdf/2506.18495.pdf   GitHub

Authors:Aniss Bessalah, Hatem Mohamed Abdelmoumen, Karima Benatchba, Hadjer Benmeziane

Title: AnalogNAS-Bench: A NAS Benchmark for Analog In-Memory Computing

Abstract:
Analog In-memory Computing (AIMC) has emerged as a highly efficient paradigm for accelerating Deep Neural Networks (DNNs), offering significant energy and latency benefits over conventional digital hardware. However, state-of-the-art neural networks are not inherently designed for AIMC, as they fail to account for its unique non-idealities. Neural Architecture Search (NAS) is thus needed to systematically discover neural architectures optimized explicitly for AIMC constraints. However, comparing NAS methodologies and extracting insights about robust architectures for AIMC requires a dedicated NAS benchmark that explicitly accounts for AIMC-specific hardware non-idealities. To address this, we introduce AnalogNAS-Bench, the first NAS benchmark tailored specifically for AIMC. Our study reveals three key insights: (1) standard quantization techniques fail to capture AIMC-specific noises, (2) robust architectures tend to feature wider and branched blocks, (3) skip connections improve resilience to temporal drift noise. These insights highlight the limitations of current NAS benchmarks for AIMC and pave the way for future analog-aware NAS. All the implementations used in this paper can be found at https://github.com/IBM/analog-nas/tree/main/analognasbench.
模拟内存计算(AIMC)能高效加速深度神经网络，但现有网络和架构搜索基准未考虑其特殊非理想特性，因此推出AnalogNAS-Bench基准来发现稳健架构并揭示关键设计规律。
Analog In-memory Computing (AIMC) accelerates deep neural networks efficiently, but current neural networks and NAS benchmarks overlook AIMC-specific non-idealities, prompting the introduction of AnalogNAS-Bench to identify robust architectures and reveal key design insights.

Paperid: 99, https://arxiv.org/pdf/2506.18330.pdf   GitHub

Authors:Lixin Wu, Na Cai, Qiao Cheng, Jiachen Wang, Yitao Duan

Title: Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning

Abstract:
We introduce Confucius3-Math, an open-source large language model with 14B parameters that (1) runs efficiently on a single consumer-grade GPU; (2) achieves SOTA performances on a range of mathematical reasoning tasks, outperforming many models with significantly larger sizes. In particular, as part of our mission to enhancing education and knowledge dissemination with AI, Confucius3-Math is specifically committed to mathematics learning for Chinese K-12 students and educators. Built via post-training with large-scale reinforcement learning (RL), Confucius3-Math aligns with national curriculum and excels at solving main-stream Chinese K-12 mathematical problems with low cost. In this report we share our development recipe, the challenges we encounter and the techniques we develop to overcome them. In particular, we introduce three technical innovations: Targeted Entropy Regularization, Recent Sample Recovery and Policy-Specific Hardness Weighting. These innovations encompass a new entropy regularization, a novel data scheduling policy, and an improved group-relative advantage estimator. Collectively, they significantly stabilize the RL training, improve data efficiency, and boost performance. Our work demonstrates the feasibility of building strong reasoning models in a particular domain at low cost. We open-source our model and code at https://github.com/netease-youdao/Confucius3-Math.
Chinese: Confucius3-Math是一个开源140亿参数模型，可在消费级GPU上高效运行，在数学推理任务中达到顶尖水平，并针对中国K-12数学教育通过强化学习技术创新实现了低成本高性能。
English: Confucius3-Math is an open-source 14B-parameter model that efficiently runs on consumer GPUs and achieves state-of-the-art performance in mathematical reasoning, specifically tailored for Chinese K-12 education with innovations in reinforcement learning training.

Paperid: 100, https://arxiv.org/pdf/2506.18288.pdf   GitHub

Authors:Muhammad Usama, Hee-Deok Jang, Soham Shanbhag, Yoo-Chang Sung, Seung-Jun Bae, Dong Eui Chang

Title: Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals

Abstract:
This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations by focusing on valid data features. Our approach is evaluated across three anomaly detection algorithms and consistently outperforms two baseline methods. Detailed ablation studies further support these findings. Furthermore, we introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%. The source code and data used in this study are available at https://github.com/Usama1002/learning-latent-representations.
本文提出了一种结合自动编码器和分类器的联合训练框架，用于提升高速动态随机存取存储器信号的异常检测与信号完整性，其性能优于基线方法并实现了11.3%的平均信号改善。
This paper introduces a joint training framework combining an autoencoder and classifier to enhance anomaly detection and signal integrity in high-speed DRAM signals, demonstrating superior performance over baselines and achieving an 11.3% average signal improvement.

Paperid: 101, https://arxiv.org/pdf/2506.18254.pdf   GitHub

Authors:Tianyu Yu, Bo Ji, Shouli Wang, Shu Yao, Zefan Wang, Ganqu Cui, Lifan Yuan, Ning Ding, Yuan Yao, Zhiyuan Liu, Maosong Sun, Tat-Seng Chua

Title: RLPR: Extrapolating RLVR to General Domains without Verifiers

Abstract:
Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates promising potential in advancing the reasoning capabilities of LLMs. However, its success remains largely confined to mathematical and code domains. This primary limitation stems from the heavy reliance on domain-specific verifiers, which results in prohibitive complexity and limited scalability. To address the challenge, our key observation is that LLM's intrinsic probability of generating a correct free-form answer directly indicates its own evaluation of the reasoning reward (i.e., how well the reasoning process leads to the correct answer). Building on this insight, we propose RLPR, a simple verifier-free framework that extrapolates RLVR to broader general domains. RLPR uses the LLM's own token probability scores for reference answers as the reward signal and maximizes the expected reward during training. We find that addressing the high variance of this noisy probability reward is crucial to make it work, and propose prob-to-reward and stabilizing methods to ensure a precise and stable reward from LLM intrinsic probabilities. Comprehensive experiments in four general-domain benchmarks and three mathematical benchmarks show that RLPR consistently improves reasoning capabilities in both areas for Gemma, Llama, and Qwen based models. Notably, RLPR outperforms concurrent VeriFree by 7.6 points on TheoremQA and 7.5 points on Minerva, and even surpasses strong verifier-model-dependent approaches General-Reasoner by 1.6 average points across seven benchmarks.
Chinese: RLPR是一种无需验证器的强化学习框架，它利用大语言模型自身对参考答案的标记概率作为奖励信号，有效提升了在通用领域和数学领域的推理能力，并超越了现有方法的性能。
English: RLPR is a verifier-free reinforcement learning framework that uses an LLM's own token probability scores for reference answers as the reward signal, effectively enhancing reasoning capabilities across both general and mathematical domains while outperforming existing methods.

Paperid: 102, https://arxiv.org/pdf/2506.18193.pdf   GitHub

Authors:Zih-Hao Huang, You-Teng Lin, Hung-Hsuan Chen

Title: DeInfoReg: A Decoupled Learning Framework for Better Training Throughput

Abstract:
This paper introduces Decoupled Supervised Learning with Information Regularization (DeInfoReg), a novel approach that transforms a long gradient flow into multiple shorter ones, thereby mitigating the vanishing gradient problem. Integrating a pipeline strategy, DeInfoReg enables model parallelization across multiple GPUs, significantly improving training throughput. We compare our proposed method with standard backpropagation and other gradient flow decomposition techniques. Extensive experiments on diverse tasks and datasets demonstrate that DeInfoReg achieves superior performance and better noise resistance than traditional BP models and efficiently utilizes parallel computing resources. The code for reproducibility is available at: https://github.com/ianzih/Decoupled-Supervised-Learning-for-Information-Regularization/.
中文: 本文提出的DeInfoReg方法通过将长梯度流分解为短片段解决梯度消失问题，并利用多GPU流水线并行策略显著提升训练效率和模型性能。
English: This paper presents DeInfoReg, a method that breaks long gradient flows into shorter segments to combat vanishing gradients and leverages pipeline parallelism across GPUs for enhanced training efficiency and performance.

Paperid: 103, https://arxiv.org/pdf/2506.18184.pdf   GitHub

Authors:Donghyun Lee, Yuhang Li, Ruokai Yin, Shiting Xiao, Priyadarshini Panda

Title: Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba

Abstract:
State Space Models (SSMs) have emerged as powerful alternatives to attention-based Transformers, with Mamba demonstrating impressive efficiency and scalability. As these models grow increasingly larger, the need for Parameter-Efficient Fine-Tuning (PEFT) methods becomes critical to adapt pre-trained Mamba to downstream tasks without prohibitive computational costs. However, previous approaches simply apply traditional Transformer-tailored PEFT methods without addressing the unique temporal processing dynamics of SSMs. To address this limitation, we propose Memba, a membrane-driven PEFT approach specifically designed for Mamba. Memba introduces Leaky Integrate Membrane (LIM) neurons as bio-inspired gating mechanisms that naturally accumulate membrane potentials over time, enhancing selective information retention. By strategically combining LIM neurons with Low-Rank Adaptations (LoRA) and cross-layer membrane transfer, our approach significantly improves Mamba's temporal modeling capabilities. Extensive experiments across language and vision tasks demonstrate that Memba achieves substantial improvements over existing PEFT methods. The code is available at https://github.com/Intelligent-Computing-Lab-Yale/Memba.
中文: 状态空间模型如Mamba是Transformer的高效替代方案，而提出的Memba方法通过引入具有膜电位的仿生门控机制，显著提升了参数高效微调中的时序建模能力。
English: State Space Models like Mamba offer efficient alternatives to Transformers, and the proposed Memba method introduces bio-inspired gating mechanisms with membrane potentials to enhance temporal modeling through Parameter-Efficient Fine-Tuning.

Paperid: 104, https://arxiv.org/pdf/2506.18046.pdf   GitHub

Authors:Xiangfei Qiu, Zhe Li, Wanghui Qiu, Shiyan Hu, Lekui Zhou, Xingjian Wu, Zhengyu Li, Chenjuan Guo, Aoying Zhou, Zhenli Sheng, Jilin Hu, Christian S. Jensen, Bin Yang

Title: TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

Abstract:
Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time series data will be available, leading also to growing demands for TSAD. While many TSAD methods already exist, new and better methods are still desirable. However, effective progress hinges on the availability of reliable means of evaluating new methods and comparing them with existing methods. We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods. Besides, all datasets and code are available at https://github.com/decisionintelligence/TAB.
中文: 该摘要介绍了TAB这一新型时间序列异常检测基准，它整合了多样化数据集、多种检测方法类型和统一评估流程，旨在解决现有评估缺陷并提供公平的性能比较。
English: The abstract introduces TAB, a new benchmark for time series anomaly detection that includes diverse datasets, multiple method types, and a unified evaluation pipeline to address current evaluation deficiencies and provide fair performance comparisons.

Paperid: 105, https://arxiv.org/pdf/2506.17930.pdf   GitHub

Authors:Jianyu Wang, Zhiqiang Hu, Lidong Bing

Title: Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective

Abstract:
We propose a novel prompt design paradigm that challenges conventional wisdom in large language model (LLM) prompting. While conventional wisdom prioritizes well-crafted instructions and demonstrations for in-context learning (ICL), we show that pruning random demonstrations into seemingly incoherent "gibberish" can remarkably improve performance across diverse tasks. Notably, the "gibberish" always matches or surpasses state-of-the-art automatic prompt optimization techniques, achieving substantial gains regardless of LLM alignment. Nevertheless, discovering an effective pruning strategy is non-trivial, as existing attribution methods and prompt compression algorithms fail to deliver robust results, let alone human intuition. In terms of this, we propose a self-discover prompt optimization framework, PromptQuine, an evolutionary search framework that automatically searches for the pruning strategy by itself using only low-data regimes. Much like the emergent complexity in nature--such as symbiosis and self-organization--arising in response to resource constraints, our framework evolves and refines unconventional yet highly effective prompts by leveraging only the tokens present within the context. We demonstrate its effectiveness across classification, multi-choice question answering, generation and math reasoning tasks across LLMs, while achieving decent runtime efficiency. We hope our findings can guide mechanistic studies on in-context learning, and provide a call to action, to pave the way for more open-ended search algorithms for more effective LLM prompting.
中文: 本研究提出了一种新颖的提示设计范式，证明将随机示例修剪为看似不连贯的"乱码"能在多种任务中超越传统提示方法和最优自动优化技术，同时开发了PromptQuine进化框架，仅需少量数据即可自主发现有效的修剪策略。
English: This study introduces a novel prompt design paradigm that demonstrates pruning random demonstrations into seemingly incoherent "gibberish" can outperform conventional prompting methods and state-of-the-art optimization techniques across various tasks, while also proposing PromptQuine, an evolutionary framework that autonomously discovers effective pruning strategies with minimal data.

Paperid: 106, https://arxiv.org/pdf/2506.17892.pdf   GitHub

Authors:Jianghong Huang, Luping Ji, Xin Ma, Mao Ye

Title: BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learning

Abstract:
Conveyor belts are important equipment in modern industry, widely applied in production and manufacturing. Their health is much critical to operational efficiency and safety. Cracks are a major threat to belt health. Currently, considering safety, how to intelligently detect belt cracks is catching an increasing attention. To implement the intelligent detection with machine learning, real crack samples are believed to be necessary. However, existing crack datasets primarily focus on pavement scenarios or synthetic data, no real-world industrial belt crack datasets at all. Cracks are a major threat to belt health. Furthermore, to validate usability and effectiveness, we propose a special baseline method with triple-domain ($i.e.$, time-space-frequency) feature hierarchical fusion learning for the two whole-new datasets. Experimental results demonstrate the availability and effectiveness of our dataset. Besides, they also show that our baseline is obviously superior to other similar detection methods. Our datasets and source codes are available at https://github.com/UESTC-nnLab/BeltCrack.
中文: 本研究提出了两个全新的工业传送带真实裂纹数据集及基于时-空-频三域特征分层融合的基线方法，实验证明该方法显著优于同类检测方案。
English: This study introduces two novel real-world industrial conveyor belt crack datasets and a baseline method using triple-domain feature fusion learning, which proves more effective than existing detection approaches.

Paperid: 107, https://arxiv.org/pdf/2506.17871.pdf   GitHub GitHub

Authors:Chenghao Yang, Ari Holtzman

Title: How Alignment Shrinks the Generative Horizon

Abstract:
Despite their impressive capabilities, aligned large language models (LLMs) often generate outputs that lack diversity. What drives this stability in the generation? We investigate this phenomenon through the lens of probability concentration in the model's output distribution. To quantify this concentration, we introduce the Branching Factor (BF) -- a token-invariant measure of the effective number of plausible next steps during generation. Our empirical analysis reveals two key findings: (1) BF often decreases as generation progresses, suggesting that LLMs become more predictable as they generate. (2) alignment tuning substantially sharpens the model's output distribution from the outset, reducing BF by nearly an order of magnitude (e.g., from 12 to 1.2) relative to base models. This stark reduction helps explain why aligned models often appear less sensitive to decoding strategies. Building on this insight, we find this stability has surprising implications for complex reasoning. Aligned Chain-of-Thought (CoT) models (e.g., DeepSeek-distilled models), for instance, leverage this effect; by generating longer reasoning chains, they push generation into later, more deterministic (lower BF) stages, resulting in more stable outputs. We hypothesize that alignment tuning does not fundamentally change a model's behavior, but instead steers it toward stylistic tokens (e.g., "Sure") that unlock low-entropy trajectories already present in the base model. This view is supported by nudging experiments, which show that prompting base models with such tokens can similarly reduce BF. Together, our findings establish BF as a powerful diagnostic for understanding and controlling LLM outputs - clarifying how alignment reduces variability, how CoT promotes stable generations, and how base models can be steered away from diversity.
Chinese: 对齐后的大型语言模型输出多样性降低源于概率集中现象，通过分支因子量化发现生成过程中分支因子递减且对齐调优使其大幅降低，这解释了模型稳定性及对解码策略不敏感的原因，并揭示了思维链模型通过延长推理进入确定性阶段实现稳定输出的机制。
English: Aligned large language models exhibit reduced output diversity due to probability concentration, quantified by the Branching Factor which decreases during generation and is substantially lowered by alignment tuning, explaining their stability and insensitivity to decoding strategies.

Paperid: 108, https://arxiv.org/pdf/2506.17870.pdf   GitHub

Authors:Jianhang Xie, Chuntao Ding, Xiaqing Li, Shenyuan Ren, Yidong Li, Zhichao Lu

Title: NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNN

Abstract:
Deploying quantized deep neural network (DNN) models with resource adaptation capabilities on ubiquitous Internet of Things (IoT) devices to provide high-quality AI services can leverage the benefits of compression and meet multi-scenario resource requirements. However, existing dynamic/mixed precision quantization requires retraining or special hardware, whereas post-training quantization (PTQ) has two limitations for resource adaptation: (i) The state-of-the-art PTQ methods only provide one fixed bitwidth model, which makes it challenging to adapt to the dynamic resources of IoT devices; (ii) Deploying multiple PTQ models with diverse bitwidths consumes large storage resources and switching overheads. To this end, this paper introduces a resource-friendly post-training integer-nesting quantization, i.e., NestQuant, for on-device quantized model switching on IoT devices. The proposed NestQuant incorporates the integer weight decomposition, which bit-wise splits quantized weights into higher-bit and lower-bit weights of integer data types. It also contains a decomposed weights nesting mechanism to optimize the higher-bit weights by adaptive rounding and nest them into the original quantized weights. In deployment, we can send and store only one NestQuant model and switch between the full-bit/part-bit model by paging in/out lower-bit weights to adapt to resource changes and reduce consumption. Experimental results on the ImageNet-1K pretrained DNNs demonstrated that the NestQuant model can achieve high performance in top-1 accuracy, and reduce in terms of data transmission, storage consumption, and switching overheads. In particular, the ResNet-101 with INT8 nesting INT6 can achieve 78.1% and 77.9% accuracy for full-bit and part-bit models, respectively, and reduce switching overheads by approximately 78.1% compared with diverse bitwidths PTQ models.
中文: 本文提出NestQuant这一资源友好的训练后量化方法，通过整数权重分解与嵌套机制实现物联网设备上的动态模型切换，在保持高精度的同时显著降低了存储与传输开销。
English: This paper introduces NestQuant, a resource-friendly post-training quantization method that enables dynamic model switching on IoT devices by decomposing weights into integer-nested formats, reducing storage and transmission overhead while maintaining high accuracy.

Paperid: 109, https://arxiv.org/pdf/2506.17848.pdf   GitHub

Authors:Suyash Gaurav, Jukka Heikkonen, Jatin Chaudhary

Title: Pathway-based Progressive Inference (PaPI) for Energy-Efficient Continual Learning

Abstract:
Continual learning systems face the dual challenge of preventing catastrophic forgetting while maintaining energy efficiency, particularly in resource-constrained environments. This paper introduces Pathway-based Progressive Inference (PaPI), a novel theoretical framework that addresses these challenges through a mathematically rigorous approach to pathway selection and adaptation. We formulate continual learning as an energy-constrained optimization problem and provide formal convergence guarantees for our pathway routing mechanisms. Our theoretical analysis demonstrates that PaPI achieves an $\mathcal{O}(K)$ improvement in the stability-plasticity trade-off compared to monolithic architectures, where $K$ is the number of pathways. We derive tight bounds on forgetting rates using Fisher Information Matrix analysis and prove that PaPI's energy consumption scales with the number of active parameters rather than the total model size. Comparative theoretical analysis shows that PaPI provides stronger guarantees against catastrophic forgetting than Elastic Weight Consolidation (EWC) while maintaining better energy efficiency than both EWC and Gradient Episodic Memory (GEM). Our experimental validation confirms these theoretical advantages across multiple benchmarks, demonstrating PaPI's effectiveness for continual learning in energy-constrained settings. Our codes are available at https://github.com/zser092/PAPI_FILES.
中文: 本文提出基于路径的渐进推理（PaPI）理论框架，通过路径选择和适应的数学方法，在持续学习中优化稳定性与可塑性的平衡，并显著提高能源效率，同时降低遗忘率。
English: This paper introduces Pathway-based Progressive Inference (PaPI), a theoretical framework that enhances continual learning by improving the stability-plasticity trade-off and energy efficiency through pathway selection and adaptation, with proven convergence and reduced forgetting rates.

Paperid: 110, https://arxiv.org/pdf/2506.17832.pdf   GitHub

Authors:Pratik Kunapuli, Jake Welde, Dinesh Jayaraman, Vijay Kumar

Title: Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking

Abstract:
Learning-based control approaches like reinforcement learning (RL) have recently produced a slew of impressive results for tasks like quadrotor trajectory tracking and drone racing. Naturally, it is common to demonstrate the advantages of these new controllers against established methods like analytical controllers. We observe, however, that reliably comparing the performance of such very different classes of controllers is more complicated than might appear at first sight. As a case study, we take up the problem of agile tracking of an end-effector for a quadrotor with a fixed arm. We develop a set of best practices for synthesizing the best-in-class RL and geometric controllers (GC) for benchmarking. In the process, we resolve widespread RL-favoring biases in prior studies that provide asymmetric access to: (1) the task definition, in the form of an objective function, (2) representative datasets, for parameter optimization, and (3) feedforward information, describing the desired future trajectory. The resulting findings are the following: our improvements to the experimental protocol for comparing learned and classical controllers are critical, and each of the above asymmetries can yield misleading conclusions. Prior works have claimed that RL outperforms GC, but we find the gaps between the two controller classes are much smaller than previously published when accounting for symmetric comparisons. Geometric control achieves lower steady-state error than RL, while RL has better transient performance, resulting in GC performing better in relatively slow or less agile tasks, but RL performing better when greater agility is required. Finally, we open-source implementations of geometric and RL controllers for these aerial vehicles, implementing best practices for future development. Website and code is available at https://pratikkunapuli.github.io/rl-vs-gc/

Paperid: 111, https://arxiv.org/pdf/2506.17788.pdf   GitHub

Authors:Shahab Rahimirad, Guven Gergerli, Lucia Romero, Angela Qian, Matthew Lyle Olson, Simon Stepputtis, Joseph Campbell

Title: Bayesian Social Deduction with Graph-Informed Language Models

Abstract:
Social reasoning - inferring unobservable beliefs and intentions from partial observations of other agents - remains a challenging task for large language models (LLMs). We evaluate the limits of current reasoning language models in the social deduction game Avalon and find that while the largest models demonstrate strong performance, they require extensive test-time inference and degrade sharply when distilled to smaller, real-time-capable variants. To address this, we introduce a hybrid reasoning framework that externalizes belief inference to a structured probabilistic model, while using an LLM for language understanding and interaction. Our approach achieves competitive performance with much larger models in Agent-Agent play and, notably, is the first language agent to defeat human players in a controlled study - achieving a 67% win rate and receiving higher qualitative ratings than both reasoning baselines and human teammates. We release code, models, and a dataset to support future work on social reasoning in LLM agents, which can be found at https://camp-lab-purdue.github.io/bayesian-social-deduction/
Chinese Summary: 该研究提出了一种混合推理框架，将大型语言模型与结构化概率推理相结合，在社交推理游戏《阿瓦隆》中不仅取得了与更大模型相媲美的表现，还首次在受控研究中击败了人类玩家。
English Summary: The study introduces a hybrid reasoning framework that combines large language models with structured probabilistic inference, achieving competitive performance and even surpassing human players in the social deduction game Avalon.

Paperid: 112, https://arxiv.org/pdf/2506.17768.pdf   GitHub

Authors:Keigo Nishida, Eren Mehmet Kıral, Kenichi Bannai, Mohammad Emtiyaz Khan, Thomas Möllenhoff

Title: Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks

Abstract:
Studies in neuroscience have shown that biological synapses follow a log-normal distribution whose transitioning can be explained by noisy multiplicative dynamics. Biological networks can function stably even under dynamically fluctuating conditions arising due to unreliable synaptic transmissions. Here we ask: Is it possible to design similar multiplicative training in artificial neural networks? To answer this question, we derive a Bayesian learning rule that assumes log-normal posterior distributions over weights which gives rise to a new Log-Normal Multiplicative Dynamics (LMD) algorithm. The algorithm uses multiplicative updates with both noise and regularization applied multiplicatively. The method is as easy to implement as Adam and only requires one additional vector to store. Our results show that LMD achieves stable and accurate training-from-scratch under low-precision forward operations for Vision Transformer and GPT-2. These results suggest that multiplicative dynamics, a biological feature, may enable stable low-precision inference and learning on future energy-efficient hardware.
Chinese: 该研究受生物突触分布启发，提出了一种对数正态乘法动力学（LMD）算法，能在低精度条件下稳定准确地训练视觉Transformer和GPT-2等神经网络，显示出其在未来节能硬件中的应用潜力。
English: The study introduces a Log-Normal Multiplicative Dynamics (LMD) algorithm, inspired by biological synaptic distributions, which enables stable and accurate training of neural networks like Vision Transformer and GPT-2 under low-precision conditions, suggesting its potential for energy-efficient hardware.

Paperid: 113, https://arxiv.org/pdf/2506.17671.pdf   GitHub

Authors:Fabien Furfaro

Title: TPTT: Transforming Pretrained Transformers into Titans

Abstract:
Transformer-based large language models (LLMs) have achieved strong performance across many natural language processing tasks. Nonetheless, their quadratic computational and memory requirements, particularly in self-attention layers, pose challenges for efficient inference on long contexts and for deployment in resource-limited environments. We present TPTT (Transforming Pretrained Transformers into Titans), a framework designed to augment pretrained Transformers with linearized attention (LiZA) and internal memory gating via Memory as Gate (MaG), applied without full retraining. TPTT supports parameter-efficient fine-tuning (LoRA) and integrates with standard toolkits such as Hugging Face Transformers. We evaluated TPTT on several pretrained models, including Llama-1B, OlMoE-1B-7B, Qwen2.5-1.5B, Gemma3-270m, OpenELM-1.3B, and Mistral-7B, in order to assess applicability across architectures of different scales. Experiments on models with approximately 1 billion parameters, evaluated primarily on the MMLU benchmark, suggest potential improvements in both efficiency and accuracy compared to baseline models. For example, Titans-Llama-1B exhibited up to a 20\% relative increase in Exact Match scores in one-shot evaluation. An additional finding is that it is possible to convert a quadratic-attention model into a purely linear-attention model using the DeltaProduct mechanism. All training runs were carried out with modest computational resources. These preliminary findings indicate that TPTT may help adapt pretrained LLMs for long-context tasks with limited overhead. Further studies on larger models and a broader set of benchmarks will be necessary to evaluate the generality and robustness of the framework. Code is available at https://github.com/fabienfrfr/tptt . Python package at https://pypi.org/project/tptt/ .
中文：TPTT框架通过线性化注意力和内存门控增强预训练Transformer，可在有限资源下高效适应长文本任务，同时保持或提升模型性能。
English: The TPTT framework enhances pretrained Transformers with linearized attention and memory gating, enabling efficient adaptation for long-context tasks while maintaining or improving performance with minimal computational resources.

Paperid: 114, https://arxiv.org/pdf/2506.17637.pdf   GitHub

Authors:Yang Wu, Yifan Zhang, Yurong Wu, Yuran Wang, Junkai Zhang, Jian Cheng

Title: Step-Opt: Boosting Optimization Modeling in LLMs through Iterative Data Synthesis and Structured Validation

Abstract:
Large Language Models (LLMs) have revolutionized various domains but encounter substantial challenges in tackling optimization modeling tasks for Operations Research (OR), particularly when dealing with complex problem. In this work, we propose Step-Opt-Instruct, a framework that augments existing datasets and generates high-quality fine-tuning data tailored to optimization modeling. Step-Opt-Instruct employs iterative problem generation to systematically increase problem complexity and stepwise validation to rigorously verify data, preventing error propagation and ensuring the quality of the generated dataset. Leveraging this framework, we fine-tune open-source LLMs, including LLaMA-3-8B and Mistral-7B, to develop Step-Opt--a model that achieves state-of-the-art performance on benchmarks such as NL4OPT, MAMO, and IndustryOR. Extensive experiments demonstrate the superior performance of Step-Opt, especially in addressing complex OR tasks, with a notable 17.01\% improvement in micro average accuracy on difficult problems. These findings highlight the effectiveness of combining structured validation with gradual problem refinement to advance the automation of decision-making processes using LLMs.The code and dataset are available at https://github.com/samwu-learn/Step.
中文摘要：本文提出Step-Opt-Instruct框架，通过迭代式问题生成和逐步验证机制为运筹学优化建模生成高质量训练数据，基于此开发的Step-Opt模型在多项基准测试中实现最优性能，尤其在复杂问题上取得17.01%的显著准确率提升。
English Summary: This paper introduces Step-Opt-Instruct, a framework that enhances optimization modeling for Operations Research by generating high-quality training data through iterative complexity escalation and stepwise validation, resulting in the Step-Opt model which achieves state-of-the-art performance with significant accuracy improvements on complex tasks.

Paperid: 115, https://arxiv.org/pdf/2506.17576.pdf   GitHub

Authors:Furong Peng, Jinzhen Gao, Xuan Lu, Kang Liu, Yifan Huo, Sheng Wang

Title: Towards a deeper GCN: Alleviate over-smoothing with iterative training and fine-tuning

Abstract:
Graph Convolutional Networks (GCNs) suffer from severe performance degradation in deep architectures due to over-smoothing. While existing studies primarily attribute the over-smoothing to repeated applications of graph Laplacian operators, our empirical analysis reveals a critical yet overlooked factor: trainable linear transformations in GCNs significantly exacerbate feature collapse, even at moderate depths (e.g., 8 layers). In contrast, Simplified Graph Convolution (SGC), which removes these transformations, maintains stable feature diversity up to 32 layers, highlighting linear transformations' dual role in facilitating expressive power and inducing over-smoothing. However, completely removing linear transformations weakens the model's expressive capacity. To address this trade-off, we propose Layer-wise Gradual Training (LGT), a novel training strategy that progressively builds deep GCNs while preserving their expressiveness. LGT integrates three complementary components: (1) layer-wise training to stabilize optimization from shallow to deep layers, (2) low-rank adaptation to fine-tune shallow layers and accelerate training, and (3) identity initialization to ensure smooth integration of new layers and accelerate convergence. Extensive experiments on benchmark datasets demonstrate that LGT achieves state-of-the-art performance on vanilla GCN, significantly improving accuracy even in 32-layer settings. Moreover, as a training method, LGT can be seamlessly combined with existing methods such as PairNorm and ContraNorm, further enhancing their performance in deeper networks. LGT offers a general, architecture-agnostic training framework for scalable deep GCNs. The code is available at [https://github.com/jfklasdfj/LGT_GCN].
中文: 深度图卷积网络因可训练线性变换加剧过平滑问题，而提出的逐层渐进训练方法通过渐进构建网络、低秩适应和恒等初始化，在保持表达能力的同时显著提升了深层网络的性能。
English: Deep Graph Convolutional Networks (GCNs) face performance degradation from over-smoothing, which is exacerbated by trainable linear transformations, but the proposed Layer-wise Gradual Training (LGT) method overcomes this by progressively building deep networks while maintaining expressiveness and achieving state-of-the-art results.

Paperid: 116, https://arxiv.org/pdf/2506.17538.pdf   GitHub

Authors:Yile Gu, Rohan Kadekodi, Hoang Nguyen, Keisuke Kamahori, Yiyu Liu, Baris Kasikci

Title: ConsumerBench: Benchmarking Generative AI Applications on End-User Devices

Abstract:
The recent shift in Generative AI (GenAI) applications from cloud-only environments to end-user devices introduces new challenges in resource management, system efficiency, and user experience. This paper presents ConsumerBench, a comprehensive benchmarking framework designed to evaluate the system efficiency and response time of GenAI models running on end-user devices. Unlike existing benchmarks that assume exclusive model access on dedicated GPUs, ConsumerBench simulates realistic multi-application scenarios executing concurrently on constrained hardware. Furthermore, ConsumerBench supports customizable workflows that simulate complex tasks requiring coordination among multiple applications. ConsumerBench captures both application-level metrics, including latency and Service Level Objective (SLO) attainment, and system-level metrics like CPU/GPU utilization and memory bandwidth. Through extensive experiments, ConsumerBench reveals inefficiencies in resource sharing, unfair scheduling under greedy allocation, and performance pitfalls of static model server configurations. The paper also provides practical insights for model developers and system designers, highlighting the benefits of custom kernels tailored to consumer-grade GPU architectures and the value of implementing SLO-aware scheduling strategies.
中文: 本文提出ConsumerBench基准测试框架，通过在终端设备上模拟多应用并发场景来评估生成式AI性能，揭示了资源分配低效问题，并为开发人员提供了优化建议。
English: This paper introduces ConsumerBench, a benchmarking framework that evaluates GenAI performance on end-user devices under realistic multi-application scenarios, revealing resource inefficiencies and providing optimization insights for developers.

Paperid: 117, https://arxiv.org/pdf/2506.17486.pdf   GitHub

Authors:Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar

Title: Distilling On-device Language Models for Robot Planning with Minimal Human Intervention

Abstract:
Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or industrial settings. We present PRISM, a framework for distilling small language model (SLM)-enabled robot planners that run on-device with minimal human supervision. Starting from an existing LLM-enabled planner, PRISM automatically synthesizes diverse tasks and environments, elicits plans from the LLM, and uses this synthetic dataset to distill a compact SLM as a drop-in replacement of the source model. We apply PRISM to three LLM-enabled planners for mapping and exploration, manipulation, and household assistance, and we demonstrate that PRISM improves the performance of Llama-3.2-3B from 10-20% of GPT-4o's performance to over 93% - using only synthetic data. We further demonstrate that the distilled planners generalize across heterogeneous robotic platforms (ground and aerial) and diverse environments (indoor and outdoor). We release all software, trained models, and datasets at https://zacravichandran.github.io/PRISM.

Paperid: 118, https://arxiv.org/pdf/2506.17419.pdf   GitHub

Authors:Jinhao Duan, James Diffenderfer, Sandeep Madireddy, Tianlong Chen, Bhavya Kailkhura, Kaidi Xu

Title: UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making

Abstract:
As Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making in the real world, it is essential to know when to trust LLM decisions. Existing LLM Uncertainty Quantification (UQ) methods are primarily designed for single-turn question-answering formats, resulting in multi-step decision-making scenarios, e.g., LLM agentic system, being underexplored. In this paper, we introduce a principled, information-theoretic framework that decomposes LLM sequential decision uncertainty into two parts: (i) internal uncertainty intrinsic to the current decision, which is focused on existing UQ methods, and (ii) extrinsic uncertainty, a Mutual-Information (MI) quantity describing how much uncertainty should be inherited from preceding decisions. We then propose UProp, an efficient and effective extrinsic uncertainty estimator that converts the direct estimation of MI to the estimation of Pointwise Mutual Information (PMI) over multiple Trajectory-Dependent Decision Processes (TDPs). UProp is evaluated over extensive multi-step decision-making benchmarks, e.g., AgentBench and HotpotQA, with state-of-the-art LLMs, e.g., GPT-4.1 and DeepSeek-V3. Experimental results demonstrate that UProp significantly outperforms existing single-turn UQ baselines equipped with thoughtful aggregation strategies. Moreover, we provide a comprehensive analysis of UProp, including sampling efficiency, potential applications, and intermediate uncertainty propagation, to demonstrate its effectiveness. Codes will be available at https://github.com/jinhaoduan/UProp.
中文: 本文提出UProp框架，将大语言模型序列决策的不确定性分解为内在和外在两部分，在多步决策基准测试中显著优于现有方法。
English: This paper introduces UProp, a novel framework that decomposes LLM sequential decision uncertainty into intrinsic and extrinsic components, significantly outperforming existing methods in multi-step decision-making benchmarks.

Paperid: 119, https://arxiv.org/pdf/2506.17323.pdf   GitHub

Authors:Tamas Bisztray, Bilel Cherif, Richard A. Dubniczky, Nils Gruschka, Bertalan Borsos, Mohamed Amine Ferrag, Attila Kovacs, Vasileios Mavroeidis, Norbert Tihanyi

Title: I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution

Abstract:
Detecting AI-generated code, deepfakes, and other synthetic content is an emerging research challenge. As code generated by Large Language Models (LLMs) becomes more common, identifying the specific model behind each sample is increasingly important. This paper presents the first systematic study of LLM authorship attribution for C programs. We released CodeT5-Authorship, a novel model that uses only the encoder layers from the original CodeT5 encoder-decoder architecture, discarding the decoder to focus on classification. Our model's encoder output (first token) is passed through a two-layer classification head with GELU activation and dropout, producing a probability distribution over possible authors. To evaluate our approach, we introduce LLM-AuthorBench, a benchmark of 32,000 compilable C programs generated by eight state-of-the-art LLMs across diverse tasks. We compare our model to seven traditional ML classifiers and eight fine-tuned transformer models, including BERT, RoBERTa, CodeBERT, ModernBERT, DistilBERT, DeBERTa-V3, Longformer, and LoRA-fine-tuned Qwen2-1.5B. In binary classification, our model achieves 97.56% accuracy in distinguishing C programs generated by closely related models such as GPT-4.1 and GPT-4o, and 95.40% accuracy for multi-class attribution among five leading LLMs (Gemini 2.5 Flash, Claude 3.5 Haiku, GPT-4.1, Llama 3.3, and DeepSeek-V3). To support open science, we release the CodeT5-Authorship architecture, the LLM-AuthorBench benchmark, and all relevant Google Colab scripts on GitHub: https://github.com/LLMauthorbench/.
中文: 本文提出了CodeT5-Authorship模型，用于识别C程序的具体大语言模型作者，并发布了包含32,000个可编译C程序的LLM-AuthorBench基准，在二元和多元分类任务中均实现了高准确率。
English: This paper introduces CodeT5-Authorship, a novel model for identifying the specific LLM authors of C programs, and presents LLM-AuthorBench, a benchmark of 32,000 compilable C programs, achieving high accuracy in both binary and multi-class attribution tasks.

Paperid: 120, https://arxiv.org/pdf/2506.17307.pdf   GitHub

Authors:Zhixiang Chi, Li Gu, Huan Liu, Ziqiang Wang, Yanan Wu, Yang Wang, Konstantinos N Plataniotis

Title: Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation

Abstract:
Few-shot Test-Time Domain Adaptation focuses on adapting a model at test time to a specific domain using only a few unlabeled examples, addressing domain shift. Prior methods leverage CLIP's strong out-of-distribution (OOD) abilities by generating domain-specific prompts to guide its generalized, frozen features. However, since downstream datasets are not explicitly seen by CLIP, solely depending on the feature space knowledge is constrained by CLIP's prior knowledge. Notably, when using a less robust backbone like ViT-B/16, performance significantly drops on challenging real-world benchmarks. Departing from the state-of-the-art of inheriting the intrinsic OOD capability of CLIP, this work introduces learning directly on the input space to complement the dataset-specific knowledge for frozen CLIP. Specifically, an independent side branch is attached in parallel with CLIP and enforced to learn exclusive knowledge via revert attention. To better capture the dataset-specific label semantics for downstream adaptation, we propose to enhance the inter-dispersion among text features via greedy text ensemble and refinement. The text and visual features are then progressively fused in a domain-aware manner by a generated domain prompt to adapt toward a specific domain. Extensive experiments show our method's superiority on 5 large-scale benchmarks (WILDS and DomainNet), notably improving over smaller networks like ViT-B/16 with gains of \textbf{+5.1} in F1 for iWildCam and \textbf{+3.1\%} in WC Acc for FMoW.
中文摘要：本文提出了一种新方法，通过在CLIP冻结特征基础上增加输入空间学习分支，结合逆向注意力和改进的文本特征分散技术，显著提升了小样本测试时域自适应性能，在多个基准测试中取得突破性进展。
English Summary: This paper introduces a novel method that enhances few-shot test-time domain adaptation by learning directly in the input space alongside CLIP's frozen features, using a side branch with revert attention and improved text feature dispersion, achieving significant performance gains on challenging benchmarks.

Paperid: 121, https://arxiv.org/pdf/2506.17302.pdf   GitHub

Authors:Yijun Lin, Theresa Chen, Colby Brungard, Grunwald Sabine, Sue Ives, Matt Macander, Timm Nawrocki, Yao-Yi Chiang, Nic Jelinski

Title: Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning

Abstract:
Fine-scale soil mapping in Alaska, traditionally relying on fieldwork and localized simulations, remains a critical yet underdeveloped task, despite the region's ecological importance and extensive permafrost coverage. As permafrost thaw accelerates due to climate change, it threatens infrastructure stability and key ecosystem services, such as soil carbon storage. High-resolution soil maps are essential for characterizing permafrost distribution, identifying vulnerable areas, and informing adaptation strategies. We present MISO, a vision-based machine learning (ML) model to produce statewide fine-scale soil maps for near-surface permafrost and soil taxonomy. The model integrates a geospatial foundation model for visual feature extraction, implicit neural representations for continuous spatial prediction, and contrastive learning for multimodal alignment and geo-location awareness. We compare MISO with Random Forest (RF), a traditional ML model that has been widely used in soil mapping applications. Spatial cross-validation and regional analysis across Permafrost Zones and Major Land Resource Areas (MLRAs) show that MISO generalizes better to remote, unseen locations and achieves higher recall than RF, which is critical for monitoring permafrost thaw and related environmental processes. These findings demonstrate the potential of advanced ML approaches for fine-scale soil mapping and provide practical guidance for future soil sampling and infrastructure planning in permafrost-affected landscapes. The project will be released at https://github.com/knowledge-computing/Peatland-permafrost.
中文: 本研究提出的MISO视觉机器学习模型，在阿拉斯加高分辨率土壤制图中优于传统方法，为气候变化下的冻土监测和基础设施规划提供了更有效的解决方案。
English: This study introduces MISO, a vision-based machine learning model that outperforms traditional methods in generating high-resolution soil maps for Alaska, enabling better permafrost monitoring and infrastructure planning amid climate change.

Paperid: 122, https://arxiv.org/pdf/2506.17297.pdf   GitHub

Authors:Satyam Mishra, Phung Thao Vi, Shivam Mishra, Vishwanath Bijalwan, Vijay Bhaskar Semwal, Abdul Manan Khan

Title: SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library

Abstract:
We introduce SafeRL-Lite, an open-source Python library for building reinforcement learning (RL) agents that are both constrained and explainable. Existing RL toolkits often lack native mechanisms for enforcing hard safety constraints or producing human-interpretable rationales for decisions. SafeRL-Lite provides modular wrappers around standard Gym environments and deep Q-learning agents to enable: (i) safety-aware training via constraint enforcement, and (ii) real-time post-hoc explanation via SHAP values and saliency maps. The library is lightweight, extensible, and installable via pip, and includes built-in metrics for constraint violations. We demonstrate its effectiveness on constrained variants of CartPole and provide visualizations that reveal both policy logic and safety adherence. The full codebase is available at: https://github.com/satyamcser/saferl-lite.
中文: SafeRL-Lite 是一个开源 Python 库，通过模块化封装和实时解释工具，能够开发具有内置安全约束和可解释性功能的强化学习智能体。
English: SafeRL-Lite is an open-source Python library that enables the development of reinforcement learning agents with built-in safety constraints and explainability features through modular wrappers and real-time explanation tools.

Paperid: 123, https://arxiv.org/pdf/2506.17253.pdf   GitHub

Authors:Chenghan Li, Mingchen Li, Yipu Liao, Ruisheng Diao

Title: MS-TVNet:A Long-Term Time Series Prediction Method Based on Multi-Scale Dynamic Convolution

Abstract:
Long-term time series prediction has predominantly relied on Transformer and MLP models, while the potential of convolutional networks in this domain remains underexplored. To address this gap, we introduce a novel multi-scale time series reshape module, which effectively captures the relationships among multi-period patches and variable dependencies. Building upon this module, we propose MS-TVNet, a multi-scale 3D dynamic convolutional neural network. Through comprehensive evaluations on diverse datasets, MS-TVNet demonstrates superior performance compared to baseline models, achieving state-of-the-art (SOTA) results in long-term time series prediction. Our findings highlight the effectiveness of leveraging convolutional networks for capturing complex temporal patterns, suggesting a promising direction for future research in this field.The code is realsed on https://github.com/Curyyfaust/TVNet.
Chinese: 本研究提出了MS-TVNet，一种多尺度三维动态卷积神经网络，通过有效捕捉多周期模式和变量依赖关系，在长期时间序列预测中实现了最先进的性能。
English: This study introduces MS-TVNet, a multi-scale 3D dynamic convolutional neural network that achieves state-of-the-art performance in long-term time series prediction by effectively capturing multi-period patterns and variable dependencies.

Paperid: 124, https://arxiv.org/pdf/2506.17250.pdf   GitHub

Authors:Fudong Lin, Jiadong Lou, Hao Wang, Brian Jalaian, Xu Yuan

Title: Towards Interpretable Adversarial Examples via Sparse Adversarial Attack

Abstract:
Sparse attacks are to optimize the magnitude of adversarial perturbations for fooling deep neural networks (DNNs) involving only a few perturbed pixels (i.e., under the l0 constraint), suitable for interpreting the vulnerability of DNNs. However, existing solutions fail to yield interpretable adversarial examples due to their poor sparsity. Worse still, they often struggle with heavy computational overhead, poor transferability, and weak attack strength. In this paper, we aim to develop a sparse attack for understanding the vulnerability of CNNs by minimizing the magnitude of initial perturbations under the l0 constraint, to overcome the existing drawbacks while achieving a fast, transferable, and strong attack to DNNs. In particular, a novel and theoretical sound parameterization technique is introduced to approximate the NP-hard l0 optimization problem, making directly optimizing sparse perturbations computationally feasible. Besides, a novel loss function is designed to augment initial perturbations by maximizing the adversary property and minimizing the number of perturbed pixels simultaneously. Extensive experiments are conducted to demonstrate that our approach, with theoretical performance guarantees, outperforms state-of-the-art sparse attacks in terms of computational overhead, transferability, and attack strength, expecting to serve as a benchmark for evaluating the robustness of DNNs. In addition, theoretical and empirical results validate that our approach yields sparser adversarial examples, empowering us to discover two categories of noises, i.e., "obscuring noise" and "leading noise", which will help interpret how adversarial perturbation misleads the classifiers into incorrect predictions. Our code is available at https://github.com/fudong03/SparseAttack.
中文: 本文提出一种新型稀疏攻击方法，通过在l0约束下最小化初始扰动来高效生成高可解释性的对抗样本，在计算效率、可迁移性和攻击强度方面表现优异，同时揭示了两类噪声以解释深度神经网络的脆弱性。
English: This paper introduces a novel sparse attack method that efficiently generates highly interpretable adversarial examples by minimizing initial perturbations under l0 constraints, achieving superior computational efficiency, transferability, and attack strength while revealing two noise categories to explain DNN vulnerabilities.

Paperid: 125, https://arxiv.org/pdf/2506.17249.pdf   GitHub

Authors:Jianing He, Qi Zhang, Duoqian Miao, Yi Kun, Shufeng Hao, Hongyun Zhang, Zhihua Wei

Title: Improving Prediction Certainty Estimation for Reliable Early Exiting via Null Space Projection

Abstract:
Early exiting has demonstrated great potential in accelerating the inference of pre-trained language models (PLMs) by enabling easy samples to exit at shallow layers, eliminating the need for executing deeper layers. However, existing early exiting methods primarily rely on class-relevant logits to formulate their exiting signals for estimating prediction certainty, neglecting the detrimental influence of class-irrelevant information in the features on prediction certainty. This leads to an overestimation of prediction certainty, causing premature exiting of samples with incorrect early predictions. To remedy this, we define an NSP score to estimate prediction certainty by considering the proportion of class-irrelevant information in the features. On this basis, we propose a novel early exiting method based on the Certainty-Aware Probability (CAP) score, which integrates insights from both logits and the NSP score to enhance prediction certainty estimation, thus enabling more reliable exiting decisions. The experimental results on the GLUE benchmark show that our method can achieve an average speed-up ratio of 2.19x across all tasks with negligible performance degradation, surpassing the state-of-the-art (SOTA) ConsistentEE by 28%, yielding a better trade-off between task performance and inference efficiency. The code is available at https://github.com/He-Jianing/NSP.git.
中文摘要：本研究提出的基于确定性感知概率（CAP）的早期退出方法，通过结合类别相关logits和衡量类别无关信息的NSP评分来改进预训练语言模型的早期退出机制，在GLUE基准测试中实现了2.19倍平均加速且性能损失可忽略，以28%优势超越现有最优方法。
English Summary: The proposed Certainty-Aware Probability (CAP) method enhances early exiting in pre-trained language models by integrating class-relevant logits with a novel NSP score that measures class-irrelevant information, achieving a 2.19x average speed-up on GLUE tasks with minimal performance loss and outperforming prior methods by 28%.

Paperid: 126, https://arxiv.org/pdf/2506.17248.pdf   GitHub

Authors:Zequn Yang, Hongfa Wang, Di Hu

Title: Efficient Quantification of Multimodal Interaction at Sample Level

Abstract:
Interactions between modalities -- redundancy, uniqueness, and synergy -- collectively determine the composition of multimodal information. Understanding these interactions is crucial for analyzing information dynamics in multimodal systems, yet their accurate sample-level quantification presents significant theoretical and computational challenges. To address this, we introduce the Lightweight Sample-wise Multimodal Interaction (LSMI) estimator, rigorously grounded in pointwise information theory. We first develop a redundancy estimation framework, employing an appropriate pointwise information measure to quantify this most decomposable and measurable interaction. Building upon this, we propose a general interaction estimation method that employs efficient entropy estimation, specifically tailored for sample-wise estimation in continuous distributions. Extensive experiments on synthetic and real-world datasets validate LSMI's precision and efficiency. Crucially, our sample-wise approach reveals fine-grained sample- and category-level dynamics within multimodal data, enabling practical applications such as redundancy-informed sample partitioning, targeted knowledge distillation, and interaction-aware model ensembling. The code is available at https://github.com/GeWu-Lab/LSMI_Estimator.
中文: LSMI估计器基于逐点信息理论，可精确量化样本级别的多模态交互作用（冗余性、独特性和协同性），实现了细粒度数据分析，并支持知识蒸馏和模型集成等实际应用。
English: The LSMI estimator is introduced to accurately quantify sample-level multimodal interactions—redundancy, uniqueness, and synergy—using pointwise information theory, enabling fine-grained analysis and practical applications like knowledge distillation and model ensembling.

Paperid: 127, https://arxiv.org/pdf/2506.17230.pdf   GitHub

Authors:Yichen Luo, Jia Wang, Dapeng Lan, Yu Liu, Zhibo Pang

Title: MMET: A Multi-Input and Multi-Scale Transformer for Efficient PDEs Solving

Abstract:
Partial Differential Equations (PDEs) are fundamental for modeling physical systems, yet solving them in a generic and efficient manner using machine learning-based approaches remains challenging due to limited multi-input and multi-scale generalization capabilities, as well as high computational costs. This paper proposes the Multi-input and Multi-scale Efficient Transformer (MMET), a novel framework designed to address the above challenges. MMET decouples mesh and query points as two sequences and feeds them into the encoder and decoder, respectively, and uses a Gated Condition Embedding (GCE) layer to embed input variables or functions with varying dimensions, enabling effective solutions for multi-scale and multi-input problems. Additionally, a Hilbert curve-based reserialization and patch embedding mechanism decrease the input length. This significantly reduces the computational cost when dealing with large-scale geometric models. These innovations enable efficient representations and support multi-scale resolution queries for large-scale and multi-input PDE problems. Experimental evaluations on diverse benchmarks spanning different physical fields demonstrate that MMET outperforms SOTA methods in both accuracy and computational efficiency. This work highlights the potential of MMET as a robust and scalable solution for real-time PDE solving in engineering and physics-based applications, paving the way for future explorations into pre-trained large-scale models in specific domains. This work is open-sourced at https://github.com/YichenLuo-0/MMET.
中文: 本文提出多输入多尺度高效变换器（MMET），通过创新的序列解耦和嵌入技术克服了偏微分方程求解中多尺度泛化和计算效率的局限，在多个基准测试中展现出卓越的精度与效率优势。
English: This paper introduces the Multi-input and Multi-scale Efficient Transformer (MMET), a novel framework that overcomes limitations in multi-scale generalization and computational efficiency for solving Partial Differential Equations (PDEs) through innovative sequence decoupling and embedding techniques, demonstrating superior accuracy and efficiency across diverse benchmarks.

Paperid: 128, https://arxiv.org/pdf/2506.17206.pdf   GitHub

Authors:Yukun Huang, Yanning Zhou, Jianan Wang, Kaiyi Huang, Xihui Liu

Title: DreamCube: 3D Panorama Generation via Multi-plane Synchronization

Abstract:
3D panorama synthesis is a promising yet challenging task that demands high-quality and diverse visual appearance and geometry of the generated omnidirectional content. Existing methods leverage rich image priors from pre-trained 2D foundation models to circumvent the scarcity of 3D panoramic data, but the incompatibility between 3D panoramas and 2D single views limits their effectiveness. In this work, we demonstrate that by applying multi-plane synchronization to the operators from 2D foundation models, their capabilities can be seamlessly extended to the omnidirectional domain. Based on this design, we further introduce DreamCube, a multi-plane RGB-D diffusion model for 3D panorama generation, which maximizes the reuse of 2D foundation model priors to achieve diverse appearances and accurate geometry while maintaining multi-view consistency. Extensive experiments demonstrate the effectiveness of our approach in panoramic image generation, panoramic depth estimation, and 3D scene generation.

Paperid: 129, https://arxiv.org/pdf/2506.17007.pdf   GitHub

Authors:Marco Jiralerspong, Esther Derman, Danilo Vucetic, Nikolay Malkin, Bilun Sun, Tianyu Zhang, Pierre-Luc Bacon, Gauthier Gidel

Title: Robust Reinforcement Learning for Discrete Compositional Generation via General Soft Operators

Abstract:
A major bottleneck in scientific discovery involves narrowing a large combinatorial set of objects, such as proteins or molecules, to a small set of promising candidates. While this process largely relies on expert knowledge, recent methods leverage reinforcement learning (RL) to enhance this filtering. They achieve this by estimating proxy reward functions from available datasets and using regularization to generate more diverse candidates. These reward functions are inherently uncertain, raising a particularly salient challenge for scientific discovery. In this work, we show that existing methods, often framed as sampling proportional to a reward function, are inadequate and yield suboptimal candidates, especially in large search spaces. To remedy this issue, we take a robust RL approach and introduce a unified operator that seeks robustness to the uncertainty of the proxy reward function. This general operator targets peakier sampling distributions while encompassing known soft RL operators. It also leads us to a novel algorithm that identifies higher-quality, diverse candidates in both synthetic and real-world tasks. Ultimately, our work offers a new, flexible perspective on discrete compositional generation tasks. Code: https://github.com/marcojira/tgm.
Chinese: 本文针对科学发现中现有强化学习方法的不足，引入了一种处理代理奖励不确定性的稳健算子，提出了一种新算法，能在组合搜索空间中生成更高质量和更多样化的候选对象。
English: This paper addresses the limitations of existing reinforcement learning methods in scientific discovery by introducing a robust operator that handles uncertainty in proxy rewards, leading to a novel algorithm that produces higher-quality and more diverse candidates in combinatorial search spaces.

Paperid: 130, https://arxiv.org/pdf/2506.16853.pdf   GitHub

Authors:Semin Kim, Yeonwoo Cha, Jaehoon Yoo, Seunghoon Hong

Title: Reward-Agnostic Prompt Optimization for Text-to-Image Diffusion Models

Abstract:
We investigate a general approach for improving user prompts in text-to-image (T2I) diffusion models by finding prompts that maximize a reward function specified at test-time. Although diverse reward models are used for evaluating image generation, existing automated prompt engineering methods typically target specific reward configurations. Consequently, these specialized designs exhibit suboptimal performance when applied to new prompt engineering scenarios involving different reward models. To address this limitation, we introduce RATTPO (Reward-Agnostic Test-Time Prompt Optimization), a flexible test-time optimization method applicable across various reward scenarios without modification. RATTPO iteratively searches for optimized prompts by querying large language models (LLMs) \textit{without} requiring reward-specific task descriptions. Instead, it uses the optimization trajectory and a novel reward-aware feedback signal (termed a "hint") as context. Empirical results demonstrate the versatility of RATTPO, effectively enhancing user prompts across diverse reward setups that assess various generation aspects, such as aesthetics, general human preference, or spatial relationships between objects. RATTPO surpasses other test-time search baselines in search efficiency, using up to 3.5 times less inference budget, and, given sufficient inference budget, achieves performance comparable to learning-based baselines that require reward-specific fine-tuning. The code is available at https://github.com/seminkim/RATTPO.
中文摘要：本文提出RATTPO方法，通过大语言模型迭代优化提示并结合奖励感知反馈，无需特定任务描述即可适应不同奖励场景，在文本到图像生成中实现了高效通用的提示优化，显著超越现有测试时搜索基线。
English Summary: This paper introduces RATTPO, a reward-agnostic test-time prompt optimization method that enhances text-to-image generation by iteratively refining prompts using LLMs and reward-aware feedback, achieving superior efficiency and adaptability across diverse reward scenarios without requiring task-specific modifications.

Paperid: 131, https://arxiv.org/pdf/2506.16840.pdf   GitHub

Authors:Zeyneddin Oz, Shreyas Korde, Marius Bock, Kristof Van Laerhoven

Title: FedFitTech: A Baseline in Federated Learning for Fitness Tracking

Abstract:
The rapid evolution of sensors and resource-efficient machine learning models has spurred the widespread adoption of wearable fitness tracking devices. Equipped with inertial sensors, such devices can continuously capture physical movements for fitness technology (FitTech), enabling applications from sports optimization to preventive healthcare. Traditional Centralized Learning approaches to detect fitness activities struggle with data privacy concerns, regulatory restrictions, and communication inefficiencies. In contrast, Federated Learning (FL) enables a decentralized model training by communicating model updates rather than potentially private wearable sensor data. Applying FL to FitTech presents unique challenges, such as data imbalance, lack of labeled data, heterogeneous user activities, and trade-offs between personalization and generalization. To simplify research on FitTech in FL, we present the FedFitTech baseline, under the Flower framework, which is publicly available and widely used by both industry and academic researchers. Additionally, to illustrate its usage, this paper presents a case study that implements a system based on the FedFitTech baseline, incorporating a client-side early stopping strategy and comparing the results. For instance, this system allows wearable devices to optimize the trade-off between capturing common fitness activities and preserving individuals' nuances, thereby enhancing both the scalability and efficiency of privacy-aware fitness tracking applications. The results show that this reduces the overall redundant communications by 13%, while maintaining the overall recognition performance at a negligible recognition cost by 1%. Thus, the FedFitTech baseline creates a foundation for a wide range of new research and development opportunities in FitTech, and it is available as open source at: https://github.com/shreyaskorde16/FedFitTech
中文摘要：联邦学习通过去中心化模型训练解决可穿戴健身设备的数据隐私和通信效率问题，FedFitTech基准在保持识别性能基本不变的同时，将通信开销降低了13%。
English Summary: Federated Learning addresses privacy and efficiency issues in wearable fitness tracking by enabling decentralized model training, with the FedFitTech baseline reducing communication overhead by 13% while maintaining near-identical recognition performance.

Paperid: 132, https://arxiv.org/pdf/2506.16753.pdf   GitHub

Authors:Kosuke Nakanishi, Akihiro Kubo, Yuji Yasui, Shin Ishii

Title: Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation

Abstract:
Recently, robust reinforcement learning (RL) methods designed to handle adversarial input observations have received significant attention, motivated by RL's inherent vulnerabilities. While existing approaches have demonstrated reasonable success, addressing worst-case scenarios over long time horizons requires both minimizing the agent's cumulative rewards for adversaries and training agents to counteract them through alternating learning. However, this process introduces mutual dependencies between the agent and the adversary, making interactions with the environment inefficient and hindering the development of off-policy methods. In this work, we propose a novel off-policy method that eliminates the need for additional environmental interactions by reformulating adversarial learning as a soft-constrained optimization problem. Our approach is theoretically supported by the symmetric property of policy evaluation between the agent and the adversary. The implementation is available at https://github.com/nakanakakosuke/VALT_SAC.
Chinese: 本文提出了一种新颖的离线策略强化学习方法，通过将对抗学习重构为软约束优化问题，利用策略评估的对称性消除了额外环境交互的需求。
English: This paper introduces a novel off-policy reinforcement learning method that reformulates adversarial learning as a soft-constrained optimization problem, eliminating the need for extra environmental interactions by leveraging symmetric policy evaluation properties.

Paperid: 133, https://arxiv.org/pdf/2506.16652.pdf   GitHub

Authors:Guang Yin, Yitong Li, Yixuan Wang, Dale McConachie, Paarth Shah, Kunimatsu Hashimoto, Huan Zhang, Katherine Liu, Yunzhu Li

Title: CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity

Abstract:
Natural language instructions for robotic manipulation tasks often exhibit ambiguity and vagueness. For instance, the instruction "Hang a mug on the mug tree" may involve multiple valid actions if there are several mugs and branches to choose from. Existing language-conditioned policies typically rely on end-to-end models that jointly handle high-level semantic understanding and low-level action generation, which can result in suboptimal performance due to their lack of modularity and interpretability. To address these challenges, we introduce a novel robotic manipulation framework that can accomplish tasks specified by potentially ambiguous natural language. This framework employs a Vision-Language Model (VLM) to interpret abstract concepts in natural language instructions and generates task-specific code - an interpretable and executable intermediate representation. The generated code interfaces with the perception module to produce 3D attention maps that highlight task-relevant regions by integrating spatial and semantic information, effectively resolving ambiguities in instructions. Through extensive experiments, we identify key limitations of current imitation learning methods, such as poor adaptation to language and environmental variations. We show that our approach excels across challenging manipulation tasks involving language ambiguity, contact-rich manipulation, and multi-object interactions.

Paperid: 134, https://arxiv.org/pdf/2506.16627.pdf   GitHub

Authors:Haotian Yin, Aleksander Plocharski, Michal Jan Wlodarczyk, Mikolaj Kida, Przemyslaw Musialski

Title: FlatCAD: Fast Curvature Regularization of Neural SDFs for CAD Models

Abstract:
Neural signed-distance fields (SDFs) are a versatile backbone for neural geometry representation, but enforcing CAD-style developability usually requires Gaussian-curvature penalties with full Hessian evaluation and second-order differentiation, which are costly in memory and time. We introduce an off-diagonal Weingarten loss that regularizes only the mixed shape operator term that represents the gap between principal curvatures and flattens the surface. We present two variants: a finite-difference version using six SDF evaluations plus one gradient, and an auto-diff version using a single Hessian-vector product. Both converge to the exact mixed term and preserve the intended geometric properties without assembling the full Hessian. On the ABC benchmarks the losses match or exceed Hessian-based baselines while cutting GPU memory and training time by roughly a factor of two. The method is drop-in and framework-agnostic, enabling scalable curvature-aware SDF learning for engineering-grade shape reconstruction. Our code is available at https://flatcad.github.io/.

Paperid: 135, https://arxiv.org/pdf/2506.16471.pdf   GitHub

Authors:Tara Akhound-Sadegh, Jungyoon Lee, Avishek Joey Bose, Valentin De Bortoli, Arnaud Doucet, Michael M. Bronstein, Dominique Beaini, Siamak Ravanbakhsh, Kirill Neklyudov, Alexander Tong

Title: Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities

Abstract:
Sampling efficiently from a target unnormalized probability density remains a core challenge, with relevance across countless high-impact scientific applications. A promising approach towards this challenge is the design of amortized samplers that borrow key ideas, such as probability path design, from state-of-the-art generative diffusion models. However, all existing diffusion-based samplers remain unable to draw samples from distributions at the scale of even simple molecular systems. In this paper, we propose Progressive Inference-Time Annealing (PITA), a novel framework to learn diffusion-based samplers that combines two complementary interpolation techniques: I.) Annealing of the Boltzmann distribution and II.) Diffusion smoothing. PITA trains a sequence of diffusion models from high to low temperatures by sequentially training each model at progressively higher temperatures, leveraging engineered easy access to samples of the temperature-annealed target density. In the subsequent step, PITA enables simulating the trained diffusion model to procure training samples at a lower temperature for the next diffusion model through inference-time annealing using a novel Feynman-Kac PDE combined with Sequential Monte Carlo. Empirically, PITA enables, for the first time, equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates with dramatically lower energy function evaluations. Code available at: https://github.com/taraak/pita
中文: 本文提出渐进推理时间退火（PITA）新框架，通过结合温度退火与扩散平滑技术，首次实现了对复杂分子系统的高效平衡采样，并大幅降低了计算成本。
English: The paper introduces Progressive Inference-Time Annealing (PITA), a novel framework that combines temperature annealing and diffusion smoothing to enable efficient equilibrium sampling of complex molecular systems with significantly reduced computational cost.

Paperid: 136, https://arxiv.org/pdf/2506.16406.pdf   GitHub

Authors:Zhiyuan Liang, Dongwen Tang, Yuhao Zhou, Xuanlei Zhao, Mingjia Shi, Wangbo Zhao, Zekai Li, Peihao Wang, Konstantin Schürholt, Damian Borth, Michael M. Bronstein, Yang You, Zhangyang Wang, Kai Wang

Title: Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

Abstract:
Modern Parameter-Efficient Fine-Tuning (PEFT) methods such as low-rank adaptation (LoRA) reduce the cost of customizing large language models (LLMs), yet still require a separate optimization run for every downstream dataset. We introduce \textbf{Drag-and-Drop LLMs (\textit{DnD})}, a prompt-conditioned parameter generator that eliminates per-task training by mapping a handful of unlabeled task prompts directly to LoRA weight updates. A lightweight text encoder distills each prompt batch into condition embeddings, which are then transformed by a cascaded hyper-convolutional decoder into the full set of LoRA matrices. Once trained in a diverse collection of prompt-checkpoint pairs, DnD produces task-specific parameters in seconds, yielding i) up to \textbf{12,000$\times$} lower overhead than full fine-tuning, ii) average gains up to \textbf{30\%} in performance over the strongest training LoRAs on unseen common-sense reasoning, math, coding, and multimodal benchmarks, and iii) robust cross-domain generalization despite never seeing the target data or labels. Our results demonstrate that prompt-conditioned parameter generation is a viable alternative to gradient-based adaptation for rapidly specializing LLMs. Our project is available at \href{https://jerryliang24.github.io/DnD}{https://jerryliang24.github.io/DnD}.
中文: 提出的拖拽式大语言模型（DnD）通过从任务提示直接生成LoRA参数，无需逐任务训练，比全参数微调快12,000倍，并在推理、数学和编程任务中实现性能提升。
English: The proposed Drag-and-Drop LLMs (DnD) method eliminates per-task training by generating LoRA parameters directly from task prompts, achieving up to 12,000× faster adaptation than fine-tuning while improving performance across reasoning, math, and coding tasks.

Paperid: 137, https://arxiv.org/pdf/2506.16402.pdf   GitHub

Authors:Xiaoya Lu, Zeren Chen, Xuhao Hu, Yijin Zhou, Weichen Zhang, Dongrui Liu, Lu Sheng, Jing Shao

Title: IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks

Abstract:
Flawed planning from VLM-driven embodied agents poses significant safety hazards, hindering their deployment in real-world household tasks. However, existing static, non-interactive evaluation paradigms fail to adequately assess risks within these interactive environments, since they cannot simulate dynamic risks that emerge from an agent's actions and rely on unreliable post-hoc evaluations that ignore unsafe intermediate steps. To bridge this critical gap, we propose evaluating an agent's interactive safety: its ability to perceive emergent risks and execute mitigation steps in the correct procedural order. We thus present IS-Bench, the first multi-modal benchmark designed for interactive safety, featuring 161 challenging scenarios with 388 unique safety risks instantiated in a high-fidelity simulator. Crucially, it facilitates a novel process-oriented evaluation that verifies whether risk mitigation actions are performed before/after specific risk-prone steps. Extensive experiments on leading VLMs, including the GPT-4o and Gemini-2.5 series, reveal that current agents lack interactive safety awareness, and that while safety-aware Chain-of-Thought can improve performance, it often compromises task completion. By highlighting these critical limitations, IS-Bench provides a foundation for developing safer and more reliable embodied AI systems. Code and data are released under [this https URL](https://github.com/AI45Lab/IS-Bench).
中文：IS-Bench作为首个评估具身智能体交互安全性的多模态基准，通过对主流视觉语言模型的广泛测试，揭示了当前模型缺乏风险意识以及安全导向推理存在的任务完成度妥协问题。
English: IS-Bench is introduced as the first multimodal benchmark for evaluating interactive safety in embodied agents, revealing current models' lack of risk awareness and the trade-offs of safety-focused reasoning through extensive testing on leading VLMs.

Paperid: 138, https://arxiv.org/pdf/2506.16349.pdf   GitHub

Authors:Nikola Jovanović, Ismail Labiad, Tomáš Souček, Martin Vechev, Pierre Fernandez

Title: Watermarking Autoregressive Image Generation

Abstract:
Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted to watermark their outputs at the token level. In this work, we present the first such approach by adapting language model watermarking techniques to this setting. We identify a key challenge: the lack of reverse cycle-consistency (RCC), wherein re-tokenizing generated image tokens significantly alters the token sequence, effectively erasing the watermark. To address this and to make our method robust to common image transformations, neural compression, and removal attacks, we introduce (i) a custom tokenizer-detokenizer finetuning procedure that improves RCC, and (ii) a complementary watermark synchronization layer. As our experiments demonstrate, our approach enables reliable and robust watermark detection with theoretically grounded p-values.
中文: 本研究首次提出了针对自回归图像生成模型的令牌级水印方法，通过定制化分词器微调和水印同步层解决了逆向循环一致性问题，有效提升了水印对图像变换与攻击的鲁棒性。
English: This study introduces the first token-level watermarking method for autoregressive image generation models, addressing the challenge of reverse cycle-consistency and enhancing robustness against image transformations and attacks through custom tokenizer finetuning and a watermark synchronization layer.

Paperid: 139, https://arxiv.org/pdf/2506.16243.pdf   GitHub

Authors:Abdulvahap Mutlu, Şengül Doğan, Türker Tuncer

Title: Synthetic ALS-EEG Data Augmentation for ALS Diagnosis Using Conditional WGAN with Weight Clipping

Abstract:
Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease, and high-quality EEG data from ALS patients are scarce. This data scarcity, coupled with severe class imbalance between ALS and healthy control recordings, poses a challenge for training reliable machine learning classifiers. In this work, we address these issues by generating synthetic EEG signals for ALS patients using a Conditional Wasserstein Generative Adversarial Network (CWGAN). We train CWGAN on a private EEG dataset (ALS vs. non-ALS) to learn the distribution of ALS EEG signals and produce realistic synthetic samples. We preprocess and normalize EEG recordings, and train a CWGAN model to generate synthetic ALS signals. The CWGAN architecture and training routine are detailed, with key hyperparameters chosen for stable training. Qualitative evaluation of generated signals shows that they closely mimic real ALS EEG patterns. The CWGAN training converged with generator and discriminator loss curves stabilizing, indicating successful learning. The synthetic EEG signals appear realistic and have potential use as augmented data for training classifiers, helping to mitigate class imbalance and improve ALS detection accuracy. We discuss how this approach can facilitate data sharing and enhance diagnostic models.
中文: 本研究采用条件Wasserstein生成对抗网络为ALS患者生成逼真的合成脑电信号，通过缓解数据稀缺和类别不平衡问题来提升ALS检测分类器的训练效果。
English: This study uses a Conditional Wasserstein Generative Adversarial Network to generate realistic synthetic EEG signals for ALS patients, addressing data scarcity and class imbalance to improve machine learning classifier training for ALS detection.

Paperid: 140, https://arxiv.org/pdf/2506.16141.pdf   GitHub

Authors:Yi Chen, Yuying Ge, Rui Wang, Yixiao Ge, Junhao Cheng, Ying Shan, Xihui Liu

Title: GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning

Abstract:
Recent reinforcement learning approaches, such as outcome-supervised GRPO, have advanced Chain-of-Thought reasoning in large language models (LLMs), yet their adaptation to multimodal LLMs (MLLMs) is unexplored. To address the lack of rigorous evaluation for MLLM post-training methods, we introduce SEED-Bench-R1, a benchmark with complex real-world videos requiring balanced perception and reasoning. It offers a large training set and evaluates generalization across three escalating challenges: in-distribution, cross-environment, and cross-environment-task scenarios. Using SEED-Bench-R1, we find that standard GRPO, while improving answer accuracy, often reduces logical coherence between reasoning steps and answers, with only a 57.9% consistency rate. This stems from reward signals focusing solely on final answers, encouraging shortcuts, and strict KL penalties limiting exploration.To address this, we propose GRPO-CARE, a consistency-aware RL framework optimizing both answer correctness and reasoning coherence without explicit supervision. GRPO-CARE introduces a two-tiered reward: (1) a base reward for answer correctness, and (2) an adaptive consistency bonus, computed by comparing the model's reasoning-to-answer likelihood (via a slowly-evolving reference model) against group peers.This dual mechanism amplifies rewards for reasoning paths that are both correct and logically consistent. Replacing KL penalties with this adaptive bonus, GRPO-CARE outperforms standard GRPO on SEED-Bench-R1, achieving a 6.7% performance gain on the hardest evaluation level and a 24.5% improvement in consistency. It also shows strong transferability, improving model performance across diverse video understanding benchmarks. Our work contributes a systematically designed benchmark and a generalizable post-training framework, advancing the development of more interpretable and robust MLLMs.
中文: 本研究提出了用于评估多模态大语言模型后训练方法的基准SEED-Bench-R1，并开发了GRPO-CARE一致性感知强化学习框架，该框架在无需显式监督的情况下显著提升了答案准确性和推理连贯性，实现了明显的性能改进。
English: This study introduces SEED-Bench-R1, a benchmark for evaluating multimodal large language models' post-training methods, and proposes GRPO-CARE, a consistency-aware reinforcement learning framework that enhances both answer accuracy and reasoning coherence, achieving significant performance gains without explicit supervision.

Paperid: 141, https://arxiv.org/pdf/2506.16078.pdf   GitHub

Authors:Tianle Gu, Kexin Huang, Zongqi Wang, Yixu Wang, Jie Li, Yuanqi Yao, Yang Yao, Yujiu Yang, Yan Teng, Yingchun Wang

Title: Probing the Robustness of Large Language Models Safety to Latent Perturbations

Abstract:
Safety alignment is a key requirement for building reliable Artificial General Intelligence. Despite significant advances in safety alignment, we observe that minor latent shifts can still trigger unsafe responses in aligned models. We argue that this stems from the shallow nature of existing alignment methods, which focus on surface-level refusal behaviors without sufficiently altering internal representations. Consequently, small shifts in hidden activations can re-trigger harmful behaviors embedded in the latent space. To explore the robustness of safety alignment to latent perturbations, we introduce a probing method that measures the Negative Log-Likelihood of the original response generated by the model. This probe quantifies local sensitivity in the latent space, serving as a diagnostic tool for identifying vulnerable directions. Based on this signal, we construct effective jailbreak trajectories, giving rise to the Activation Steering Attack (ASA). More importantly, these insights offer a principled foundation for improving alignment robustness. To this end, we introduce Layer-wise Adversarial Patch Training~(LAPT), a fine-tuning strategy that inject controlled perturbations into hidden representations during training. Experimental results highlight that LAPT strengthen alignment robustness without compromising general capabilities. Our findings reveal fundamental flaws in current alignment paradigms and call for representation-level training strategies that move beyond surface-level behavior supervision. Codes and results are available at https://github.com/Carol-gutianle/LatentSafety.
中文摘要：现有AI模型的安全对齐方法过于浅层，易受潜在偏移影响而引发不安全响应，但提出的分层对抗补丁训练（LAPT）通过针对性优化内部表征，有效提升了安全鲁棒性。
English Summary: Current safety alignment methods for AI models are shallow, making them vulnerable to latent shifts that can trigger unsafe responses, but the proposed Layer-wise Adversarial Patch Training (LAPT) enhances robustness by addressing these internal weaknesses.

Paperid: 142, https://arxiv.org/pdf/2506.16001.pdf   GitHub

Authors:Qianru Zhang, Honggang Wen, Ming Li, Dong Huang, Siu-Ming Yiu, Christian S. Jensen, Pietro Liò

Title: AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Prediction

Abstract:
Time series forecasting requires architectures that simultaneously achieve three competing objectives: (1) strict temporal causality for reliable predictions, (2) sub-quadratic complexity for practical scalability, and (3) multi-scale pattern recognition for accurate long-horizon forecasting. We introduce AutoHFormer, a hierarchical autoregressive transformer that addresses these challenges through three key innovations: 1) Hierarchical Temporal Modeling: Our architecture decomposes predictions into segment-level blocks processed in parallel, followed by intra-segment sequential refinement. This dual-scale approach maintains temporal coherence while enabling efficient computation. 2) Dynamic Windowed Attention: The attention mechanism employs learnable causal windows with exponential decay, reducing complexity while preserving precise temporal relationships. This design avoids both the anti-causal violations of standard transformers and the sequential bottlenecks of RNN hybrids. 3) Adaptive Temporal Encoding: a novel position encoding system is adopted to capture time patterns at multiple scales. It combines fixed oscillating patterns for short-term variations with learnable decay rates for long-term trends. Comprehensive experiments demonstrate that AutoHFormer 10.76X faster training and 6.06X memory reduction compared to PatchTST on PEMS08, while maintaining consistent accuracy across 96-720 step horizons in most of cases. These breakthroughs establish new benchmarks for efficient and precise time series modeling. Implementations of our method and all baselines in hierarchical autoregressive mechanism are available at https://github.com/lizzyhku/Autotime.
中文摘要：AutoHFormer是一种分层自回归变换器，通过多尺度时序建模、动态窗口注意力和自适应编码技术，在保持严格因果性的同时实现了高效准确的时间序列预测。
English Summary: AutoHFormer is a hierarchical autoregressive transformer that achieves efficient and accurate time series forecasting through multi-scale temporal modeling, dynamic windowed attention, and adaptive encoding while maintaining strict causality.

Paperid: 143, https://arxiv.org/pdf/2506.15954.pdf   GitHub

Authors:Vinicius Yuiti Fukase, Heitor Gama, Barbara Bueno, Lucas Libanio, Anna Helena Reali Costa, Artur Jordao

Title: One Period to Rule Them All: Identifying Critical Learning Periods in Deep Networks

Abstract:
Critical Learning Periods comprehend an important phenomenon involving deep learning, where early epochs play a decisive role in the success of many training recipes, such as data augmentation. Existing works confirm the existence of this phenomenon and provide useful insights. However, the literature lacks efforts to precisely identify when critical periods occur. In this work, we fill this gap by introducing a systematic approach for identifying critical periods during the training of deep neural networks, focusing on eliminating computationally intensive regularization techniques and effectively applying mechanisms for reducing computational costs, such as data pruning. Our method leverages generalization prediction mechanisms to pinpoint critical phases where training recipes yield maximum benefits to the predictive ability of models. By halting resource-intensive recipes beyond these periods, we significantly accelerate the learning phase and achieve reductions in training time, energy consumption, and CO$_2$ emissions. Experiments on standard architectures and benchmarks confirm the effectiveness of our method. Specifically, we achieve significant milestones by reducing the training time of popular architectures by up to 59.67%, leading to a 59.47% decrease in CO$_2$ emissions and a 60% reduction in financial costs, without compromising performance. Our work enhances understanding of training dynamics and paves the way for more sustainable and efficient deep learning practices, particularly in resource-constrained environments. In the era of the race for foundation models, we believe our method emerges as a valuable framework. The repository is available at https://github.com/baunilhamarga/critical-periods
中文: 本研究提出了一种系统性方法，用于识别深度神经网络训练中的关键学习期，从而可在这些阶段后停止资源密集型技术，实现训练时间减少高达59.67%、二氧化碳排放降低59.47%、成本下降60%，且不影响模型性能。
English: This study introduces a systematic method to identify critical learning periods in deep neural network training, enabling the cessation of resource-intensive techniques after these phases to reduce training time by up to 59.67%, cut CO₂ emissions by 59.47%, and lower costs by 60% without performance loss.

Paperid: 144, https://arxiv.org/pdf/2506.15882.pdf   GitHub

Authors:Sheng Liu, Tianlang Chen, Pan Lu, Haotian Ye, Yizheng Chen, Lei Xing, James Zou

Title: Fractional Reasoning via Latent Steering Vectors Improves Inference Time Compute

Abstract:
Test-time compute has emerged as a powerful paradigm for improving the performance of large language models (LLMs), where generating multiple outputs or refining individual chains can significantly boost answer accuracy. However, existing methods like Best-of-N, majority voting, and self-reflection typically apply reasoning in a uniform way across inputs, overlooking the fact that different problems may require different levels of reasoning depth. In this work, we propose Fractional Reasoning, a training-free and model-agnostic framework that enables continuous control over reasoning intensity at inference time, going beyond the limitations of fixed instructional prompts. Our method operates by extracting the latent steering vector associated with deeper reasoning and reapplying it with a tunable scaling factor, allowing the model to tailor its reasoning process to the complexity of each input. This supports two key modes of test-time scaling: (1) improving output quality in breadth-based strategies (e.g., Best-of-N, majority voting), and (2) enhancing the correctness of individual reasoning chains in depth-based strategies (e.g., self-reflection). Experiments on GSM8K, MATH500, and GPQA demonstrate that Fractional Reasoning consistently improves performance across diverse reasoning tasks and models.
中文：Fractional Reasoning是一种无需训练的框架，通过在推理时缩放潜在引导向量来实现对推理强度的连续控制，从而在多种推理任务中同步提升广度策略和深度策略的测试时计算效果。
English: Fractional Reasoning is a training-free framework that enables continuous control over reasoning intensity during inference by scaling latent steering vectors, improving both breadth-based and depth-based test-time compute strategies across various reasoning tasks.

Paperid: 145, https://arxiv.org/pdf/2506.15722.pdf   GitHub

Authors:Wangzhi Zhan, Jianpeng Chen, Dongqi Fu, Dawei Zhou

Title: UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation

Abstract:
Metamaterials are artificial materials that are designed to meet unseen properties in nature, such as ultra-stiffness and negative materials indices. In mechanical metamaterial design, three key modalities are typically involved, i.e., 3D topology, density condition, and mechanical property. Real-world complex application scenarios place the demanding requirements on machine learning models to consider all three modalities together. However, a comprehensive literature review indicates that most existing works only consider two modalities, e.g., predicting mechanical properties given the 3D topology or generating 3D topology given the required properties. Therefore, there is still a significant gap for the state-of-the-art machine learning models capturing the whole. Hence, we propose a unified model named UNIMATE, which consists of a modality alignment module and a synergetic diffusion generation module. Experiments indicate that UNIMATE outperforms the other baseline models in topology generation task, property prediction task, and condition confirmation task by up to 80.2%, 5.1%, and 50.2%, respectively. We opensource our proposed UNIMATE model and corresponding results at https://github.com/wzhan24/UniMate.
Chinese: UNIMATE作为一种统一的机器学习模型，通过同时处理3D拓扑、密度条件和力学性能，填补了机械超材料设计领域的空白，在生成和预测任务中性能超越基线模型高达80.2%。
English: UNIMATE is a unified machine learning model that addresses the gap in mechanical metamaterial design by simultaneously handling 3D topology, density condition, and mechanical property, outperforming baseline models by up to 80.2% in generation and prediction tasks.

Paperid: 146, https://arxiv.org/pdf/2506.15721.pdf   GitHub

Authors:Junqi Gao, Zhichang Guo, Dazhi Zhang, Dong Li, Runze Liu, Pengfei Li, Kai Tian, Biqing Qi

Title: Bohdi: Heterogeneous LLM Fusion with Automatic Data Exploration

Abstract:
Heterogeneous Large Language Model (LLM) fusion integrates the strengths of multiple source LLMs with different architectures into a target LLM with low computational overhead. While promising, existing methods suffer from two major limitations: 1) reliance on real data from limited domain for knowledge fusion, preventing the target LLM from fully acquiring knowledge across diverse domains, and 2) fixed data allocation proportions across domains, failing to dynamically adjust according to the target LLM's varying capabilities across domains, leading to a capability imbalance. To overcome these limitations, we propose Bohdi, a synthetic-data-only heterogeneous LLM fusion framework. Through the organization of knowledge domains into a hierarchical tree structure, Bohdi enables automatic domain exploration and multi-domain data generation through multi-model collaboration, thereby comprehensively extracting knowledge from source LLMs. By formalizing domain expansion and data sampling proportion allocation on the knowledge tree as a Hierarchical Multi-Armed Bandit problem, Bohdi leverages the designed DynaBranches mechanism to adaptively adjust sampling proportions based on the target LLM's performance feedback across domains. Integrated with our proposed Introspection-Rebirth (IR) mechanism, DynaBranches dynamically tracks capability shifts during target LLM's updates via Sliding Window Binomial Likelihood Ratio Testing (SWBLRT), further enhancing its online adaptation capability. Comparative experimental results on a comprehensive suite of benchmarks demonstrate that Bohdi significantly outperforms existing baselines on multiple target LLMs, exhibits higher data efficiency, and virtually eliminates the imbalance in the target LLM's capabilities. Our code is available at https://github.com/gjq100/Bohdi.git.
中文: Bohdi是一个仅使用合成数据的异构大语言模型融合框架，通过自动领域探索和自适应数据采样克服了现有方法的局限，显著提升了目标模型的性能和数据效率，同时消除了能力不平衡问题。
English: Bohdi is a synthetic-data-only heterogeneous LLM fusion framework that overcomes limitations of existing methods by enabling automatic domain exploration and adaptive data sampling, significantly enhancing the target LLM's performance and data efficiency while eliminating capability imbalance.

Paperid: 147, https://arxiv.org/pdf/2506.15715.pdf   GitHub

Authors:Hanyu Pei, Jing-Xiao Liao, Qibin Zhao, Ting Gao, Shijun Zhang, Xiaoge Zhang, Feng-Lei Fan

Title: NeuronSeek: On Stability and Expressivity of Task-driven Neurons

Abstract:
Drawing inspiration from our human brain that designs different neurons for different tasks, recent advances in deep learning have explored modifying a network's neurons to develop so-called task-driven neurons. Prototyping task-driven neurons (referred to as NeuronSeek) employs symbolic regression (SR) to discover the optimal neuron formulation and construct a network from these optimized neurons. Along this direction, this work replaces symbolic regression with tensor decomposition (TD) to discover optimal neuronal formulations, offering enhanced stability and faster convergence. Furthermore, we establish theoretical guarantees that modifying the aggregation functions with common activation functions can empower a network with a fixed number of parameters to approximate any continuous function with an arbitrarily small error, providing a rigorous mathematical foundation for the NeuronSeek framework. Extensive empirical evaluations demonstrate that our NeuronSeek-TD framework not only achieves superior stability, but also is competitive relative to the state-of-the-art models across diverse benchmarks. The code is available at https://github.com/HanyuPei22/NeuronSeek.
中文摘要：本研究通过用张量分解替代符号回归来优化神经元结构，增强了NeuronSeek框架的稳定性和收敛速度，在多个基准测试中展现出竞争优势，同时为通用近似能力提供了理论保证。
English Summary: This study enhances the NeuronSeek framework by replacing symbolic regression with tensor decomposition to optimize neuron formulations, achieving improved stability, faster convergence, and competitive performance across various benchmarks while providing theoretical guarantees for universal approximation.

Paperid: 148, https://arxiv.org/pdf/2506.15710.pdf   GitHub

Authors:Siru Ouyang, Xinyu Zhu, Zilin Xiao, Minhao Jiang, Yu Meng, Jiawei Han

Title: RAST: Reasoning Activation in LLMs via Small-model Transfer

Abstract:
Reinforcement learning (RL) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs), as evidenced by recent successes such as OpenAI's o1 and Deepseek-R1. However, applying RL at scale remains intimidatingly resource-intensive, requiring multiple model copies and extensive GPU workloads. On the other hand, while being powerful, recent studies suggest that RL does not fundamentally endow models with new knowledge; rather, it primarily reshapes the model's output distribution to activate reasoning capabilities latent in the base model. Building on this insight, we hypothesize that the changes in output probabilities induced by RL are largely model-size invariant, opening the door to a more efficient paradigm: training a small model with RL and transferring its induced probability shifts to larger base models. To verify our hypothesis, we conduct a token-level analysis of decoding trajectories and find high alignment in RL-induced output distributions across model scales, validating our hypothesis. Motivated by this, we propose RAST, a simple yet effective method that transfers reasoning behaviors by injecting RL-induced probability adjustments from a small RL-trained model into larger models. Experiments across multiple mathematical reasoning benchmarks show that RAST substantially and consistently enhances the reasoning capabilities of base models while requiring significantly lower GPU memory than direct RL training, sometimes even yielding better performance than the RL-trained counterparts. Our findings offer new insights into the nature of RL-driven reasoning and practical strategies for scaling its benefits without incurring its full computational cost. The project page of RAST is available at https://ozyyshr.github.io/RAST/.
中文摘要：强化学习通过重塑输出分布而非增加新知识来提升大语言模型的推理能力，而RAST方法可将从小模型习得的概率调整迁移至大模型，从而以更低资源成本显著增强推理性能。
English Summary: Reinforcement learning enhances LLMs' reasoning by reshaping output distributions rather than adding new knowledge, and the proposed RAST method efficiently transfers these adjustments from small to large models to boost performance with lower resource costs.

Paperid: 149, https://arxiv.org/pdf/2506.15703.pdf   GitHub

Authors:Guoqing Chao, Zhenghao Zhang, Lei Meng, Jie Wen, Dianhui Chu

Title: Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance

Abstract:
Federated multi-view clustering has been proposed to mine the valuable information within multi-view data distributed across different devices and has achieved impressive results while preserving the privacy. Despite great progress, most federated multi-view clustering methods only used global pseudo-labels to guide the downstream clustering process and failed to exploit the global information when extracting features. In addition, missing data problem in federated multi-view clustering task is less explored. To address these problems, we propose a novel Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG). Specifically, we designed a dual-head graph convolutional encoder at each client to extract two kinds of underlying features containing global and view-specific information. Subsequently, under the guidance of the fused graph, the two underlying features are fused into high-level features, based on which clustering is conducted under the supervision of pseudo-labeling. Finally, the high-level features are uploaded to the server to refine the graph fusion and pseudo-labeling computation. Extensive experimental results demonstrate the effectiveness and superiority of FIMCFG. Our code is publicly available at https://github.com/PaddiHunter/FIMCFG.
中文：FIMCFG方法通过双头图卷积编码器提取全局与视图特定特征，在融合图指导下整合高层特征并进行伪标签监督聚类，有效解决了联邦不完全多视图聚类中的全局信息利用不足和数据缺失问题，实验证明了其优越性。
English: Federated Incomplete Multi-view Clustering with globally Fused Graph guidance (FIMCFG) addresses limitations in existing methods by integrating global information during feature extraction and tackling missing data through a dual-head graph encoder and fused graph supervision, demonstrating superior performance in experiments.

Paperid: 150, https://arxiv.org/pdf/2506.15701.pdf   GitHub

Authors:Haolin Pan, Hongyu Lin, Haoran Luo, Yang Liu, Kaichun Yao, Libo Zhang, Mingjie Xing, Yanjun Wu

Title: Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning

Abstract:
Compiler auto-tuning optimizes pass sequences to improve performance metrics such as Intermediate Representation (IR) instruction count. Although recent advances leveraging Large Language Models (LLMs) have shown promise in automating compiler tuning, two significant challenges still remain: the absence of high-quality reasoning datasets for agents training, and limited effective interactions with the compilation environment. In this work, we introduce Compiler-R1, the first reinforcement learning (RL)-driven framework specifically augmenting LLM capabilities for compiler auto-tuning. Compiler-R1 features a curated, high-quality reasoning dataset and a novel two-stage end-to-end RL training pipeline, enabling efficient environment exploration and learning through an outcome-based reward. Extensive experiments across seven datasets demonstrate Compiler-R1 achieving an average 8.46% IR instruction count reduction compared to opt -Oz, showcasing the strong potential of RL-trained LLMs for compiler optimization. Our code and datasets are publicly available at https://github.com/Panhaolin2001/Compiler-R1.
Chinese: 本文提出Compiler-R1，一个通过强化学习增强大语言模型进行编译器自动调优的框架，它提供高质量推理数据集和两阶段训练流程，在七个数据集上平均减少8.46%的中间表示指令数。
English: This paper introduces Compiler-R1, a reinforcement learning framework that enhances LLMs for compiler auto-tuning by providing a high-quality reasoning dataset and a two-stage training pipeline, achieving an average 8.46% reduction in IR instruction count across seven datasets.

Paperid: 151, https://arxiv.org/pdf/2506.15695.pdf   GitHub

Authors:Xinxing Ren, Qianbo Zang, Zekun Guo

Title: SimuGen: Multi-modal Agentic Framework for Constructing Block Diagram-Based Simulation Models

Abstract:
Recent advances in large language models (LLMs) have shown impressive performance in mathematical reasoning and code generation. However, LLMs still struggle in the simulation domain, particularly in generating Simulink models, which are essential tools in engineering and scientific research. Our preliminary experiments indicate that LLM agents often fail to produce reliable and complete Simulink simulation code from text-only inputs, likely due to the lack of Simulink-specific data in their pretraining. To address this challenge, we propose SimuGen, a multimodal agent-based framework that automatically generates accurate Simulink simulation code by leveraging both the visual Simulink diagram and domain knowledge. SimuGen coordinates several specialized agents, including an investigator, unit test reviewer, code generator, executor, debug locator, and report writer, supported by a domain-specific knowledge base. This collaborative and modular design enables interpretable, robust, and reproducible Simulink simulation generation. Our source code is publicly available at https://github.com/renxinxing123/SimuGen_beta.
中文摘要：大型语言模型在数学推理和代码生成方面表现出色，但在Simulink模型生成领域存在不足，因此提出SimuGen多模态智能体框架，通过结合视觉图表和领域知识来自动生成准确的仿真代码。
English Summary: Large language models excel in math and coding but struggle with Simulink model generation due to training data gaps, prompting the proposed SimuGen framework that uses multimodal agents and domain knowledge to produce reliable simulations.

Paperid: 152, https://arxiv.org/pdf/2506.15680.pdf   GitHub

Authors:Kaifeng Zhang, Baoyu Li, Kris Hauser, Yunzhu Li

Title: Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos

Abstract:
Modeling the dynamics of deformable objects is challenging due to their diverse physical properties and the difficulty of estimating states from limited visual information. We address these challenges with a neural dynamics framework that combines object particles and spatial grids in a hybrid representation. Our particle-grid model captures global shape and motion information while predicting dense particle movements, enabling the modeling of objects with varied shapes and materials. Particles represent object shapes, while the spatial grid discretizes the 3D space to ensure spatial continuity and enhance learning efficiency. Coupled with Gaussian Splattings for visual rendering, our framework achieves a fully learning-based digital twin of deformable objects and generates 3D action-conditioned videos. Through experiments, we demonstrate that our model learns the dynamics of diverse objects -- such as ropes, cloths, stuffed animals, and paper bags -- from sparse-view RGB-D recordings of robot-object interactions, while also generalizing at the category level to unseen instances. Our approach outperforms state-of-the-art learning-based and physics-based simulators, particularly in scenarios with limited camera views. Furthermore, we showcase the utility of our learned models in model-based planning, enabling goal-conditioned object manipulation across a range of tasks. The project page is available at https://kywind.github.io/pgnd .

Paperid: 153, https://arxiv.org/pdf/2506.15651.pdf   GitHub

Authors:Tevin Wang, Chenyan Xiong

Title: AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning

Abstract:
Rule-based rewards offer a promising strategy for improving reinforcement learning from human feedback (RLHF), but current approaches often rely on manual rule engineering. We present AutoRule, a fully automated method for extracting rules from preference feedback and formulating them into rule-based rewards. AutoRule extraction operates in three stages: it leverages a reasoning model to interpret user preferences, identifies candidate rules from the reasoning chain of these interpretations, and synthesizes them into a unified rule set. Leveraging the finalized rule set, we employ language-model verifiers to compute the fraction of rules satisfied by each output, using this metric as an auxiliary reward alongside the learned reward model during policy optimization. Training a Llama-3-8B model with AutoRule results in a 28.6\% relative improvement in length-controlled win rate on AlpacaEval2.0, and a 6.1\% relative gain in second-turn performance on a held-out MT-Bench subset, compared to a GRPO baseline trained with the same learned reward model but without the rule-based auxiliary reward. Our analysis confirms that the extracted rules exhibit good agreement with dataset preference. We find that AutoRule demonstrates reduced reward hacking compared to a learned reward model when run over two episodes. Finally, our case study suggests that the extracted rules capture unique qualities valued in different datasets. The extracted rules are provided in the appendix, and the code is open-sourced at https://github.com/cxcscmu/AutoRule.
Chinese: AutoRule通过从偏好反馈中自动提取规则并构建规则奖励，结合学习到的奖励模型强化训练，显著提升了模型在AlpacaEval2.0和MT-Bench等基准测试中的性能表现。
English: AutoRule automates the extraction of rules from preference feedback to create rule-based rewards, enhancing reinforcement learning by integrating these with learned reward models and significantly improving model performance on benchmarks like AlpacaEval2.0 and MT-Bench.

Paperid: 154, https://arxiv.org/pdf/2506.15499.pdf   GitHub

Authors:Alaa Anani, Tobias Lorenz, Mario Fritz, Bernt Schiele

Title: Pixel-level Certified Explanations via Randomized Smoothing

Abstract:
Post-hoc attribution methods aim to explain deep learning predictions by highlighting influential input pixels. However, these explanations are highly non-robust: small, imperceptible input perturbations can drastically alter the attribution map while maintaining the same prediction. This vulnerability undermines their trustworthiness and calls for rigorous robustness guarantees of pixel-level attribution scores. We introduce the first certification framework that guarantees pixel-level robustness for any black-box attribution method using randomized smoothing. By sparsifying and smoothing attribution maps, we reformulate the task as a segmentation problem and certify each pixel's importance against $\ell_2$-bounded perturbations. We further propose three evaluation metrics to assess certified robustness, localization, and faithfulness. An extensive evaluation of 12 attribution methods across 5 ImageNet models shows that our certified attributions are robust, interpretable, and faithful, enabling reliable use in downstream tasks. Our code is at https://github.com/AlaaAnani/certified-attributions.
中文摘要：本文首次提出基于随机平滑的认证框架，为任何黑盒归因方法提供像素级鲁棒性保证，确保归因结果在对抗扰动下的可靠性和可解释性。
English Summary: This paper introduces the first certification framework using randomized smoothing to guarantee pixel-level robustness for any black-box attribution method against adversarial perturbations, ensuring reliable and interpretable explanations.

Paperid: 155, https://arxiv.org/pdf/2506.15461.pdf   GitHub

Authors:Nikolay Blagoev, Oğuzhan Ersoy, Lydia Yiyu Chen

Title: All is Not Lost: LLM Recovery without Checkpoints

Abstract:
Training LLMs on decentralized and wimpy computation nodes, e.g., multiple on-spot instances, lowers the training cost and enables model democratization. The inevitable challenge here is the churn of nodes due to failures and the operator's scheduling policies, leading to losing a stage - a part of the model. The conventional approaches to recover from failures are to either use checkpointing, where periodically a copy of the entire model is sent to an additional storage, or redundant computation. These approaches yield significant communication and/or computation overhead even in non-failure cases and scale poorly in settings with large models. In this paper, we propose, CheckFree, an efficient recovery method where a failing stage is substituted by a weighted average of the closest neighboring stages. In contrast to the state of the art, CheckFree requires no additional computation or storage. However, because of the nature of averaging neighbouring stages, it can only recover failures of intermediate stages. We further extend our method to CheckFree+ with out-of-order pipeline execution to tolerate crashes of the first and last stages. Thanks to out-of-order pipelining, behaviour of those stages is mimicked by their neighboring ones, which allows CheckFree+ to recover them by simply copying the weights from the immediate neighbour. To be able to recover the (de)embedding layers, CheckFree+ copies those layers to the neighboring stages, which requires relatively small storage overhead. We extensively evaluate our method on LLaMa models of model sizes from 124M to 1.5B with varying failure frequencies. In the case of low and medium failure rates (5-10%), CheckFree and CheckFree+ outperform both checkpointing and redundant computation in terms of convergence in wall-clock time by over 12%. Both of our proposals can be run via our code available at: https://github.com/gensyn-ai/CheckFree.
中文: CheckFree是一种高效的恢复方法，通过用相邻阶段的加权平均值替代故障阶段，无需额外计算或存储，其增强版CheckFree+通过乱序流水线执行进一步支持首尾阶段故障的恢复。
English: CheckFree is an efficient recovery method that replaces failed stages with weighted averages of neighboring stages, eliminating the need for additional computation or storage, and its enhanced version CheckFree+ extends this capability to handle first and last stage failures through out-of-order pipeline execution.

Paperid: 156, https://arxiv.org/pdf/2506.15448.pdf   GitHub

Authors:Guoguo Ai, Hezhe Qiao, Hui Yan, Guansong Pang

Title: Semi-supervised Graph Anomaly Detection via Robust Homophily Learning

Abstract:
Semi-supervised graph anomaly detection (GAD) utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. Current methods in this line posit that 1) normal nodes share a similar level of homophily and 2) the labeled normal nodes can well represent the homophily patterns in the normal class. However, this assumption often does not hold well since normal nodes in a graph can exhibit diverse homophily in real-world GAD datasets. In this paper, we propose RHO, namely Robust Homophily Learning, to adaptively learn such homophily patterns. RHO consists of two novel modules, adaptive frequency response filters (AdaFreq) and graph normality alignment (GNA). AdaFreq learns a set of adaptive spectral filters that capture different frequency components of the labeled normal nodes with varying homophily in the channel-wise and cross-channel views of node attributes. GNA is introduced to enforce consistency between the channel-wise and cross-channel homophily representations to robustify the normality learned by the filters in the two views. Experiments on eight real-world GAD datasets show that RHO can effectively learn varying, often under-represented, homophily in the small normal node set and substantially outperforms state-of-the-art competing methods. Code is available at https://github.com/mala-lab/RHO.
中文: 提出的RHO框架通过自适应频率响应滤波器和图正态对齐模块，有效学习正常节点中多样化的同质性模式，在八个真实世界图异常检测数据集上显著优于现有最优方法。
English: The proposed RHO framework addresses limitations in semi-supervised graph anomaly detection by introducing adaptive frequency response filters and graph normality alignment to effectively capture diverse homophily patterns in normal nodes, demonstrating superior performance across eight real-world datasets.

Paperid: 157, https://arxiv.org/pdf/2506.15383.pdf   GitHub

Authors:Damin Kühn, Michael T. Schaub

Title: Global Ground Metric Learning with Applications to scRNA data

Abstract:
Optimal transport provides a robust framework for comparing probability distributions. Its effectiveness is significantly influenced by the choice of the underlying ground metric. Traditionally, the ground metric has either been (i) predefined, e.g., as the Euclidean distance, or (ii) learned in a supervised way, by utilizing labeled data to learn a suitable ground metric for enhanced task-specific performance. Yet, predefined metrics typically cannot account for the inherent structure and varying importance of different features in the data, and existing supervised approaches to ground metric learning often do not generalize across multiple classes or are restricted to distributions with shared supports. To address these limitations, we propose a novel approach for learning metrics for arbitrary distributions over a shared metric space. Our method provides a distance between individual points like a global metric, but requires only class labels on a distribution-level for training. The learned global ground metric enables more accurate optimal transport distances, leading to improved performance in embedding, clustering and classification tasks. We demonstrate the effectiveness and interpretability of our approach using patient-level scRNA-seq data spanning multiple diseases.
Chinese: 该方法仅利用分布层面的类别标签学习最优传输的全局基础度量，无需预定义或监督学习即可提升聚类和分类等任务的准确性。
English: The proposed method learns a global ground metric for optimal transport using only distribution-level class labels, enhancing accuracy in tasks like clustering and classification without requiring predefined or supervised metrics.

Paperid: 158, https://arxiv.org/pdf/2506.15378.pdf   GitHub

Authors:J. Thorben Frank, Winfried Ripken, Gregor Lied, Klaus-Robert Müller, Oliver T. Unke, Stefan Chmiela

Title: Sampling 3D Molecular Conformers with Diffusion Transformers

Abstract:
Diffusion Transformers (DiTs) have demonstrated strong performance in generative modeling, particularly in image synthesis, making them a compelling choice for molecular conformer generation. However, applying DiTs to molecules introduces novel challenges, such as integrating discrete molecular graph information with continuous 3D geometry, handling Euclidean symmetries, and designing conditioning mechanisms that generalize across molecules of varying sizes and structures. We propose DiTMC, a framework that adapts DiTs to address these challenges through a modular architecture that separates the processing of 3D coordinates from conditioning on atomic connectivity. To this end, we introduce two complementary graph-based conditioning strategies that integrate seamlessly with the DiT architecture. These are combined with different attention mechanisms, including both standard non-equivariant and SO(3)-equivariant formulations, enabling flexible control over the trade-off between between accuracy and computational efficiency. Experiments on standard conformer generation benchmarks (GEOM-QM9, -DRUGS, -XL) demonstrate that DiTMC achieves state-of-the-art precision and physical validity. Our results highlight how architectural choices and symmetry priors affect sample quality and efficiency, suggesting promising directions for large-scale generative modeling of molecular structures. Code available at https://github.com/ML4MolSim/dit_mc.
中文: DiTMC通过结合图结构条件与等变注意力机制，将扩散变换器应用于分子构象生成，在标准测试中实现了最优精度和物理有效性。
English: DiTMC adapts Diffusion Transformers for molecular conformer generation by integrating graph-based conditioning and equivariant attention, achieving state-of-the-art precision and physical validity on benchmarks.

Paperid: 159, https://arxiv.org/pdf/2506.15346.pdf   GitHub

Authors:A. S. Stankevich, I. B. Petrov

Title: Acoustic Waveform Inversion with Image-to-Image Schrödinger Bridges

Abstract:
Recent developments in application of deep learning models to acoustic Full Waveform Inversion (FWI) are marked by the use of diffusion models as prior distributions for Bayesian-like inference procedures. The advantage of these methods is the ability to generate high-resolution samples, which are otherwise unattainable with classical inversion methods or other deep learning-based solutions. However, the iterative and stochastic nature of sampling from diffusion models along with heuristic nature of output control remain limiting factors for their applicability. For instance, an optimal way to include the approximate velocity model into diffusion-based inversion scheme remains unclear, even though it is considered an essential part of FWI pipeline. We address the issue by employing a Schrödinger Bridge that interpolates between the distributions of ground truth and smoothed velocity models. To facilitate the learning of nonlinear drifts that transfer samples between distributions we extend the concept of Image-to-Image Schrödinger Bridge ($\text{I}^2\text{SB}$) to conditional sampling, resulting in a conditional Image-to-Image Schrödinger Bridge (c$\text{I}^2\text{SB}$) framework. To validate our method, we assess its effectiveness in reconstructing the reference velocity model from its smoothed approximation, coupled with the observed seismic signal of fixed shape. Our experiments demonstrate that the proposed solution outperforms our reimplementation of conditional diffusion model suggested in earlier works, while requiring only a few neural function evaluations (NFEs) to achieve sample fidelity superior to that attained with supervised learning-based approach. The supplementary code implementing the algorithms described in this paper can be found in the repository https://github.com/stankevich-mipt/seismic_inversion_via_I2SB.
Chinese: 深度学习在全波形反演中的应用近期采用扩散模型作为先验分布，但受限于采样效率和启发式控制，本文通过引入条件图像到图像薛定谔桥（cI²SB）框架，以更少神经网络评估实现优于现有方法的样本保真度。
English: Recent advances in deep learning for acoustic Full Waveform Inversion utilize diffusion models as priors, but face limitations in sampling efficiency and control, which are addressed by introducing a conditional Image-to-Image Schrödinger Bridge (cI²SB) framework that outperforms prior methods with fewer neural evaluations.

Paperid: 160, https://arxiv.org/pdf/2506.15271.pdf   GitHub

Authors:Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic

Title: Unlocking Post-hoc Dataset Inference with Synthetic Data

Abstract:
The remarkable capabilities of Large Language Models (LLMs) can be mainly attributed to their massive training datasets, which are often scraped from the internet without respecting data owners' intellectual property rights. Dataset Inference (DI) offers a potential remedy by identifying whether a suspect dataset was used in training, thereby enabling data owners to verify unauthorized use. However, existing DI methods require a private set-known to be absent from training-that closely matches the compromised dataset's distribution. Such in-distribution, held-out data is rarely available in practice, severely limiting the applicability of DI. In this work, we address this challenge by synthetically generating the required held-out set. Our approach tackles two key obstacles: (1) creating high-quality, diverse synthetic data that accurately reflects the original distribution, which we achieve via a data generator trained on a carefully designed suffix-based completion task, and (2) bridging likelihood gaps between real and synthetic data, which is realized through post-hoc calibration. Extensive experiments on diverse text datasets show that using our generated data as a held-out set enables DI to detect the original training sets with high confidence, while maintaining a low false positive rate. This result empowers copyright owners to make legitimate claims on data usage and demonstrates our method's reliability for real-world litigations. Our code is available at https://github.com/sprintml/PostHocDatasetInference.
中文摘要：本研究提出一种生成合成数据的方法，使数据集推断能够有效检测大型语言模型中未经授权的训练数据使用，克服了需要不可得保留数据集的限制，并在多样实验中展现出高准确率和低误报率。
English Summary: This study introduces a method to generate synthetic data that enables Dataset Inference to effectively detect unauthorized use of training data in Large Language Models, overcoming the limitation of requiring unavailable held-out datasets and demonstrating high accuracy with low false positives in diverse experiments.

Paperid: 161, https://arxiv.org/pdf/2506.15263.pdf   GitHub

Authors:Jan van Delden, Julius Schultz, Sebastian Rothe, Christian Libner, Sabine C. Langer, Timo Lüddecke

Title: Minimizing Structural Vibrations via Guided Flow Matching Design Optimization

Abstract:
Structural vibrations are a source of unwanted noise in engineering systems like cars, trains or airplanes. Minimizing these vibrations is crucial for improving passenger comfort. This work presents a novel design optimization approach based on guided flow matching for reducing vibrations by placing beadings (indentations) in plate-like structures. Our method integrates a generative flow matching model and a surrogate model trained to predict structural vibrations. During the generation process, the flow matching model pushes towards manufacturability while the surrogate model pushes to low-vibration solutions. The flow matching model and its training data implicitly define the design space, enabling a broader exploration of potential solutions as no optimization of manually-defined design parameters is required. We apply our method to a range of differentiable optimization objectives, including direct optimization of specific eigenfrequencies through careful construction of the objective function. Results demonstrate that our method generates diverse and manufacturable plate designs with reduced structural vibrations compared to designs from random search, a criterion-based design heuristic and genetic optimization. The code and data are available from https://github.com/ecker-lab/Optimizing_Vibrating_Plates.
Chinese: 本研究提出了一种基于引导流匹配的新颖设计优化方法，通过整合生成模型和代理模型来减少板状结构中的振动，相比传统方法，能更有效地生成可制造且低振动的设计。
English: This study introduces a novel design optimization method using guided flow matching to reduce structural vibrations in plates by integrating generative and surrogate models, which produces manufacturable, low-vibration designs more effectively than traditional approaches.

Paperid: 162, https://arxiv.org/pdf/2506.15068.pdf   GitHub

Authors:Zongxia Li, Yapei Chang, Yuhang Zhou, Xiyang Wu, Zichao Liang, Yoo Yeon Sung, Jordan Lee Boyd-Graber

Title: Semantically-Aware Rewards for Open-Ended R1 Training in Free-Form Generation

Abstract:
Evaluating open-ended long-form generation is challenging because it is hard to define what clearly separates good from bad outputs. Existing methods often miss key aspects like coherence, style, or relevance, or are biased by pretraining data, making open-ended long-form evaluation an underexplored problem. To address this gap, we propose PrefBERT, a scoring model for evaluating open-ended long-form generation in GRPO and guiding its training with distinct rewards for good and bad outputs. Trained on two response evaluation datasets with diverse long-form styles and Likert-rated quality, PrefBERT effectively supports GRPO by offering better semantic reward feedback than traditional metrics ROUGE-L and BERTScore do. Through comprehensive evaluations, including LLM-as-a-judge, human ratings, and qualitative analysis, we show that PrefBERT, trained on multi-sentence and paragraph-length responses, remains reliable across varied long passages and aligns well with the verifiable rewards GRPO needs. Human evaluations confirm that using PrefBERT as the reward signal to train policy models yields responses better aligned with human preferences than those trained with traditional metrics. Our code is available at https://github.com/zli12321/long_form_rl.
Chinese: PrefBERT是一种新颖的评分模型，旨在通过提供语义奖励反馈来评估开放式生成长文本，其表现优于ROUGE-L和BERTScore等传统指标，并在训练策略模型时更好地符合人类偏好。
English: PrefBERT is a novel scoring model designed to evaluate open-ended long-form generation by providing semantic reward feedback, outperforming traditional metrics like ROUGE-L and BERTScore and aligning better with human preferences in training policy models.

Paperid: 163, https://arxiv.org/pdf/2506.14984.pdf   GitHub

Authors:Marissa Dominijanni, Alexander Ororbia, Kenneth W. Regan

Title: Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays

Abstract:
Synaptic delays play a crucial role in biological neuronal networks, where their modulation has been observed in mammalian learning processes. In the realm of neuromorphic computing, although spiking neural networks (SNNs) aim to emulate biology more closely than traditional artificial neural networks do, synaptic delays are rarely incorporated into their simulation. We introduce a novel learning rule for simultaneously learning synaptic connection strengths and delays, by extending spike-timing dependent plasticity (STDP), a Hebbian method commonly used for learning synaptic weights. We validate our approach by extending a widely-used SNN model for classification trained with unsupervised learning. Then we demonstrate the effectiveness of our new method by comparing it against another existing methods for co-learning synaptic weights and delays as well as against STDP without synaptic delays. Results demonstrate that our proposed method consistently achieves superior performance across a variety of test scenarios. Furthermore, our experimental results yield insight into the interplay between synaptic efficacy and delay.
中文: 本研究提出了一种新颖的学习规则，通过扩展脉冲时序依赖可塑性来同时学习脉冲神经网络的突触权重和延迟，实验结果表明该方法在多种测试场景中均表现优异，并揭示了突触效能与延迟之间的相互作用。
English: This study introduces a novel learning rule that extends spike-timing dependent plasticity to simultaneously learn synaptic weights and delays in spiking neural networks, demonstrating superior performance and providing insights into synaptic efficacy-delay interactions across various test scenarios.

Paperid: 164, https://arxiv.org/pdf/2506.14965.pdf   GitHub

Authors:Zhoujun Cheng, Shibo Hao, Tianyang Liu, Fan Zhou, Yutao Xie, Feng Yao, Yuexin Bian, Yonghao Zhuang, Nilabjo Dey, Yuheng Zha, Yi Gu, Kun Zhou, Yuqi Wang, Yuan Li, Richard Fan, Jianshu She, Chengqian Gao, Abulhair Saparov, Haonan Li, Taylor W. Killian, Mikhail Yurochkin, Zhengzhong Liu, Eric P. Xing, Zhiting Hu

Title: Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective

Abstract:
Reinforcement learning (RL) has emerged as a promising approach to improve large language model (LLM) reasoning, yet most open efforts focus narrowly on math and code, limiting our understanding of its broader applicability to general reasoning. A key challenge lies in the lack of reliable, scalable RL reward signals across diverse reasoning domains. We introduce Guru, a curated RL reasoning corpus of 92K verifiable examples spanning six reasoning domains--Math, Code, Science, Logic, Simulation, and Tabular--each built through domain-specific reward design, deduplication, and filtering to ensure reliability and effectiveness for RL training. Based on Guru, we systematically revisit established findings in RL for LLM reasoning and observe significant variation across domains. For example, while prior work suggests that RL primarily elicits existing knowledge from pretrained models, our results reveal a more nuanced pattern: domains frequently seen during pretraining (Math, Code, Science) easily benefit from cross-domain RL training, while domains with limited pretraining exposure (Logic, Simulation, and Tabular) require in-domain training to achieve meaningful performance gains, suggesting that RL is likely to facilitate genuine skill acquisition. Finally, we present Guru-7B and Guru-32B, two models that achieve state-of-the-art performance among open models RL-trained with publicly available data, outperforming best baselines by 7.9% and 6.7% on our 17-task evaluation suite across six reasoning domains. We also show that our models effectively improve the Pass@k performance of their base models, particularly on complex tasks less likely to appear in pretraining data. We release data, models, training and evaluation code to facilitate general-purpose reasoning at: https://github.com/LLM360/Reasoning360
中文: Guru语料库通过涵盖六个领域的9.2万个多样化推理实例，解决了强化学习在语言模型中应用范围有限的问题，其Guru-7B和Guru-32B模型实现了最先进性能，证明强化学习既能激发既有知识，也能在预训练不足的领域促成真正的技能习得。
English: The Guru corpus introduces 92K diverse reasoning examples across six domains to address the limited scope of reinforcement learning in language models, enabling models like Guru-7B and Guru-32B to achieve state-of-the-art performance by demonstrating that RL can both elicit existing knowledge and foster genuine skill acquisition in underrepresented areas.

Paperid: 165, https://arxiv.org/pdf/2506.14866.pdf   GitHub

Authors:Thomas Kuntz, Agatha Duzan, Hao Zhao, Francesco Croce, Zico Kolter, Nicolas Flammarion, Maksym Andriushchenko

Title: OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents

Abstract:
Computer use agents are LLM-based agents that can directly interact with a graphical user interface, by processing screenshots or accessibility trees. While these systems are gaining popularity, their safety has been largely overlooked, despite the fact that evaluating and understanding their potential for harmful behavior is essential for widespread adoption. To address this gap, we introduce OS-Harm, a new benchmark for measuring safety of computer use agents. OS-Harm is built on top of the OSWorld environment and aims to test models across three categories of harm: deliberate user misuse, prompt injection attacks, and model misbehavior. To cover these cases, we create 150 tasks that span several types of safety violations (harassment, copyright infringement, disinformation, data exfiltration, etc.) and require the agent to interact with a variety of OS applications (email client, code editor, browser, etc.). Moreover, we propose an automated judge to evaluate both accuracy and safety of agents that achieves high agreement with human annotations (0.76 and 0.79 F1 score). We evaluate computer use agents based on a range of frontier models - such as o4-mini, Claude 3.7 Sonnet, Gemini 2.5 Pro - and provide insights into their safety. In particular, all models tend to directly comply with many deliberate misuse queries, are relatively vulnerable to static prompt injections, and occasionally perform unsafe actions. The OS-Harm benchmark is available at https://github.com/tml-epfl/os-harm.
中文: OS-Harm 是一个新基准，旨在通过涉及多种操作系统应用的150项任务和与人工评估高度一致的自动化评判器，测试基于大语言模型的计算机使用代理在故意滥用、提示注入攻击和模型不当行为这三类危害中的安全性。
English: OS-Harm is a new benchmark designed to evaluate the safety of LLM-based computer use agents by testing them across three categories of harm—deliberate misuse, prompt injections, and model misbehavior—through 150 tasks involving various OS applications and an automated judge that aligns closely with human evaluations.

Paperid: 166, https://arxiv.org/pdf/2506.14808.pdf   GitHub

Authors:Jenny Schmalfuss, Nadine Chang, Vibashan VS, Maying Shen, Andres Bruhn, Jose M. Alvarez

Title: PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models

Abstract:
Vision language models (VLMs) respond to user-crafted text prompts and visual inputs, and are applied to numerous real-world problems. VLMs integrate visual modalities with large language models (LLMs), which are well known to be prompt-sensitive. Hence, it is crucial to determine whether VLMs inherit this instability to varying prompts. We therefore investigate which prompt variations VLMs are most sensitive to and which VLMs are most agnostic to prompt variations. To this end, we introduce PARC (Prompt Analysis via Reliability and Calibration), a VLM prompt sensitivity analysis framework built on three pillars: (1) plausible prompt variations in both the language and vision domain, (2) a novel model reliability score with built-in guarantees, and (3) a calibration step that enables dataset- and prompt-spanning prompt variation analysis. Regarding prompt variations, PARC's evaluation shows that VLMs mirror LLM language prompt sensitivity in the vision domain, and most destructive variations change the expected answer. Regarding models, outstandingly robust VLMs among 22 evaluated models come from the InternVL2 family. We further find indications that prompt sensitivity is linked to training data. The code will be at https://github.com/NVlabs/PARC.
Chinese: 视觉语言模型对提示变化具有敏感性，类似于大语言模型，其中InternVL2系列在22个评估模型中表现出最强的鲁棒性，这是通过PARC框架在语言和视觉领域进行可靠性和校准分析得出的结论。
English: Vision language models (VLMs) exhibit sensitivity to prompt variations similar to large language models, with the InternVL2 family showing the most robustness among 22 evaluated models, as analyzed through the PARC framework that assesses reliability and calibration across language and vision domains.

Paperid: 167, https://arxiv.org/pdf/2506.14799.pdf   GitHub

Authors:Evdoxia Taka, Debadyuti Bhattacharya, Joanne Garde-Hansen, Sanjay Sharma, Tanaya Guha

Title: Analyzing Character Representation in Media Content using Multimodal Foundation Model: Effectiveness and Trust

Abstract:
Recent advances in AI has made automated analysis of complex media content at scale possible while generating actionable insights regarding character representation along such dimensions as gender and age. Past works focused on quantifying representation from audio/video/text using AI models, but without having the audience in the loop. We ask, even if character distribution along demographic dimensions are available, how useful are those to the general public? Do they actually trust the numbers generated by AI models? Our work addresses these open questions by proposing a new AI-based character representation tool and performing a thorough user study. Our tool has two components: (i) An analytics extraction model based on the Contrastive Language Image Pretraining (CLIP) foundation model that analyzes visual screen data to quantify character representation across age and gender; (ii) A visualization component effectively designed for presenting the analytics to lay audience. The user study seeks empirical evidence on the usefulness and trustworthiness of the AI-generated results for carefully chosen movies presented in the form of our visualizations. We found that participants were able to understand the analytics in our visualizations, and deemed the tool `overall useful'. Participants also indicated a need for more detailed visualizations to include more demographic categories and contextual information of the characters. Participants' trust in AI-based gender and age models is seen to be moderate to low, although they were not against the use of AI in this context. Our tool including code, benchmarking, and the user study data can be found at https://github.com/debadyuti0510/Character-Representation-Media.
中文摘要：本研究开发了一种基于CLIP模型分析媒体角色表征的AI工具，用户研究显示该工具被认为具有实用性，但参与者对AI生成的年龄性别数据信任度普遍中等偏低。
English Summary: This research introduces an AI-powered tool that quantifies character representation in media through CLIP-based analytics and visualizations, finding it useful but revealing moderate to low trust in AI-generated demographics among users.

Paperid: 168, https://arxiv.org/pdf/2506.14796.pdf   GitHub

Authors:Zhangyang Gao, Hao Wang, Cheng Tan, Chenrui Xu, Mengdi Liu, Bozhen Hu, Linlin Chao, Xiaoming Zhang, Stan Z. Li

Title: PFMBench: Protein Foundation Model Benchmark

Abstract:
This study investigates the current landscape and future directions of protein foundation model research. While recent advancements have transformed protein science and engineering, the field lacks a comprehensive benchmark for fair evaluation and in-depth understanding. Since ESM-1B, numerous protein foundation models have emerged, each with unique datasets and methodologies. However, evaluations often focus on limited tasks tailored to specific models, hindering insights into broader generalization and limitations. Specifically, researchers struggle to understand the relationships between tasks, assess how well current models perform across them, and determine the criteria in developing new foundation models. To fill this gap, we present PFMBench, a comprehensive benchmark evaluating protein foundation models across 38 tasks spanning 8 key areas of protein science. Through hundreds of experiments on 17 state-of-the-art models across 38 tasks, PFMBench reveals the inherent correlations between tasks, identifies top-performing models, and provides a streamlined evaluation protocol. Code is available at \href{https://github.com/biomap-research/PFMBench}{\textcolor{blue}{GitHub}}.
中文: 本研究推出PFMBench基准测试，通过评估17个蛋白质基础模型在38项任务中的表现，填补了该领域缺乏标准化评估的空白，揭示了任务间关联并识别出最优模型。
English: This study introduces PFMBench, a comprehensive benchmark addressing the lack of standardized evaluation for protein foundation models by assessing 17 models across 38 tasks to reveal task correlations and top performers.

Paperid: 169, https://arxiv.org/pdf/2506.14767.pdf   GitHub

Authors:Li-Wei Chen, Takuya Higuchi, Zakaria Aldeneh, Ahmed Hussen Abdelaziz, Alexander Rudnicky

Title: A Variational Framework for Improving Naturalness in Generative Spoken Language Models

Abstract:
The success of large language models in text processing has inspired their adaptation to speech modeling. However, since speech is continuous and complex, it is often discretized for autoregressive modeling. Speech tokens derived from self-supervised models (known as semantic tokens) typically focus on the linguistic aspects of speech but neglect prosodic information. As a result, models trained on these tokens can generate speech with reduced naturalness. Existing approaches try to fix this by adding pitch features to the semantic tokens. However, pitch alone cannot fully represent the range of paralinguistic attributes, and selecting the right features requires careful hand-engineering. To overcome this, we propose an end-to-end variational approach that automatically learns to encode these continuous speech attributes to enhance the semantic tokens. Our approach eliminates the need for manual extraction and selection of paralinguistic features. Moreover, it produces preferred speech continuations according to human raters. Code, samples and models are available at https://github.com/b04901014/vae-gslm.
Chinese Summary: 本文提出了一种端到端的变分方法，能自动学习将连续语音属性编码到语义标记中，无需手动特征工程，从而提高了生成语音的自然度。
English Summary: This paper introduces an end-to-end variational method that automatically learns to encode continuous speech attributes into semantic tokens, eliminating manual feature engineering and improving speech naturalness in generated outputs.

Paperid: 170, https://arxiv.org/pdf/2506.14607.pdf   GitHub

Authors:Ziyu Gong, Jim Lim, David I. Inouye

Title: Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization

Abstract:
Distribution matching (DM) is a versatile domain-invariant representation learning technique that has been applied to tasks such as fair classification, domain adaptation, and domain translation. Non-parametric DM methods struggle with scalability and adversarial DM approaches suffer from instability and mode collapse. While likelihood-based methods are a promising alternative, they often impose unnecessary biases through fixed priors or require explicit density models (e.g., flows) that can be challenging to train. We address this limitation by introducing a novel approach to training likelihood-based DM using expressive score-based prior distributions. Our key insight is that gradient-based DM training only requires the prior's score function -- not its density -- allowing us to train the prior via denoising score matching. This approach eliminates biases from fixed priors (e.g., in VAEs), enabling more effective use of geometry-preserving regularization, while avoiding the challenge of learning an explicit prior density model (e.g., a flow-based prior). Our method also demonstrates better stability and computational efficiency compared to other diffusion-based priors (e.g., LSGM). Furthermore, experiments demonstrate superior performance across multiple tasks, establishing our score-based method as a stable and effective approach to distribution matching. Source code available at https://github.com/inouye-lab/SAUB.
中文: 本文提出了一种基于分数的先验分布新方法，用于基于似然的分布匹配，避免了固定先验和显式密度模型的偏差，并在多个任务中展现出更优的稳定性、效率和性能。
English: This paper introduces a novel score-based prior distribution method for likelihood-based distribution matching, which avoids biases from fixed priors and explicit density models, demonstrating improved stability, efficiency, and performance across various tasks.

Paperid: 171, https://arxiv.org/pdf/2506.14606.pdf   GitHub

Authors:Ahmed Heakl, Sarim Hashmi, Chaimaa Abi, Celine Lee, Abdulrahman Mahmoud

Title: Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees

Abstract:
The hardware ecosystem is rapidly evolving, with increasing interest in translating low-level programs across different instruction set architectures (ISAs) in a quick, flexible, and correct way to enhance the portability and longevity of existing code. A particularly challenging class of this transpilation problem is translating between complex- (CISC) and reduced- (RISC) hardware architectures, due to fundamental differences in instruction complexity, memory models, and execution paradigms. In this work, we introduce GG (Guaranteed Guess), an ISA-centric transpilation pipeline that combines the translation power of pre-trained large language models (LLMs) with the rigor of established software testing constructs. Our method generates candidate translations using an LLM from one ISA to another, and embeds such translations within a software-testing framework to build quantifiable confidence in the translation. We evaluate our GG approach over two diverse datasets, enforce high code coverage (>98%) across unit tests, and achieve functional/semantic correctness of 99% on HumanEval programs and 49% on BringupBench programs, respectively. Further, we compare our approach to the state-of-the-art Rosetta 2 framework on Apple Silicon, showcasing 1.73x faster runtime performance, 1.47x better energy efficiency, and 2.41x better memory usage for our transpiled code, demonstrating the effectiveness of GG for real-world CISC-to-RISC translation tasks. We will open-source our codes, data, models, and benchmarks to establish a common foundation for ISA-level code translation research.

Paperid: 172, https://arxiv.org/pdf/2506.14605.pdf   GitHub

Authors:Giacomo Meanti, Thomas Ryckeboer, Michael Arbel, Julien Mairal

Title: Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching

Abstract:
This work addresses image restoration tasks through the lens of inverse problems using unpaired datasets. In contrast to traditional approaches -- which typically assume full knowledge of the forward model or access to paired degraded and ground-truth images -- the proposed method operates under minimal assumptions and relies only on small, unpaired datasets. This makes it particularly well-suited for real-world scenarios, where the forward model is often unknown or misspecified, and collecting paired data is costly or infeasible. The method leverages conditional flow matching to model the distribution of degraded observations, while simultaneously learning the forward model via a distribution-matching loss that arises naturally from the framework. Empirically, it outperforms both single-image blind and unsupervised approaches on deblurring and non-uniform point spread function (PSF) calibration tasks. It also matches state-of-the-art performance on blind super-resolution. We also showcase the effectiveness of our method with a proof of concept for lens calibration: a real-world application traditionally requiring time-consuming experiments and specialized equipment. In contrast, our approach achieves this with minimal data acquisition effort.
中文: 本研究提出一种基于非配对数据集和条件流匹配的图像复原方法，能够同时学习退化模型和复原过程，在去模糊和点扩散函数校准任务中表现优异，且仅需少量数据即可实现。
English: This study introduces a novel image restoration method that uses unpaired datasets and conditional flow matching to learn both the degradation model and restoration process, achieving superior performance in deblurring and PSF calibration tasks with minimal data requirements.

Paperid: 173, https://arxiv.org/pdf/2506.14574.pdf   GitHub

Authors:Mingkang Zhu, Xi Chen, Zhongdao Wang, Bei Yu, Hengshuang Zhao, Jiaya Jia

Title: TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization

Abstract:
Recent advancements in reinforcement learning from human feedback have shown that utilizing fine-grained token-level reward models can substantially enhance the performance of Proximal Policy Optimization (PPO) in aligning large language models. However, it is challenging to leverage such token-level reward as guidance for Direct Preference Optimization (DPO), since DPO is formulated as a sequence-level bandit problem. To address this challenge, this work decomposes the sequence-level PPO into a sequence of token-level proximal policy optimization problems and then frames the problem of token-level PPO with token-level reward guidance, from which closed-form optimal token-level policy and the corresponding token-level reward can be derived. Using the obtained reward and Bradley-Terry model, this work establishes a framework of computable loss functions with token-level reward guidance for DPO, and proposes a practical reward guidance based on the induced DPO reward. This formulation enables different tokens to exhibit varying degrees of deviation from reference policy based on their respective rewards. Experiment results demonstrate that our method achieves substantial performance improvements over DPO, with win rate gains of up to 7.5 points on MT-Bench, 6.2 points on AlpacaEval 2, and 4.3 points on Arena-Hard. Code is available at https://github.com/dvlab-research/TGDPO.
Chinese: 本研究通过将序列级PPO分解为令牌级问题，提出了一种将令牌级奖励指导融入直接偏好优化的方法，在多个基准测试中相比标准DPO实现了显著性能提升。
English: This study introduces a method to integrate token-level reward guidance into Direct Preference Optimization by decomposing sequence-level PPO into token-level problems, achieving significant performance gains over standard DPO across multiple benchmarks.

Paperid: 174, https://arxiv.org/pdf/2506.14562.pdf   GitHub

Authors:Di He, Ajay Jaiswal, Songjun Tu, Li Shen, Ganzhao Yuan, Shiwei Liu, Lu Yin

Title: AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs

Abstract:
Weight decay is a standard regularization technique for training large language models (LLMs). While it is common to assign a uniform decay rate to every layer, this approach overlooks the structural diversity of LLMs and the varying spectral properties across modules. In this paper, we introduce AlphaDecay, a simple yet effective method that adaptively assigns different weight decay strengths to each module of an LLM. Our approach is guided by Heavy-Tailed Self-Regularization (HT-SR) theory, which analyzes the empirical spectral density (ESD) of weight correlation matrices to quantify "heavy-tailedness." Modules exhibiting more pronounced heavy-tailed ESDs, reflecting stronger feature learning, are assigned weaker decay, while modules with lighter-tailed spectra receive stronger decay. Our method leverages tailored weight decay assignments to balance the module-wise differences in spectral properties, leading to improved performance. Extensive pre-training tasks with various model sizes from 60M to 1B demonstrate that AlphaDecay achieves better perplexity and generalization than conventional uniform decay and other adaptive decay baselines. Our code is available at https://github.com/hed-ucas/AlphaDecay.
中文摘要：AlphaDecay是一种自适应权重衰减方法，根据大语言模型各模块的光谱特性分配不同的衰减强度，相比传统均匀衰减方法能有效提升模型性能。
English Summary: AlphaDecay is an adaptive weight decay method that assigns varying decay strengths to different modules of large language models based on their spectral properties, improving performance over uniform decay approaches.

Paperid: 175, https://arxiv.org/pdf/2506.14436.pdf   GitHub

Authors:Shen Yuan, Yin Zheng, Taifeng Wang, Binbin Liu, Hongteng Xu

Title: MoORE: SVD-based Model MoE-ization for Conflict- and Oblivion-Resistant Multi-Task Adaptation

Abstract:
Adapting large-scale foundation models in multi-task scenarios often suffers from task conflict and oblivion. To mitigate such issues, we propose a novel ''model MoE-ization'' strategy that leads to a conflict- and oblivion-resistant multi-task adaptation method. Given a weight matrix of a pre-trained model, our method applies SVD to it and introduces a learnable router to adjust its singular values based on tasks and samples. Accordingly, the weight matrix becomes a Mixture of Orthogonal Rank-one Experts (MoORE), in which each expert corresponds to the outer product of a left singular vector and the corresponding right one. We can improve the model capacity by imposing a learnable orthogonal transform on the right singular vectors. Unlike low-rank adaptation (LoRA) and its MoE-driven variants, MoORE guarantees the experts' orthogonality and maintains the column space of the original weight matrix. These two properties make the adapted model resistant to the conflicts among the new tasks and the oblivion of its original tasks, respectively. Experiments on various datasets demonstrate that MoORE outperforms existing multi-task adaptation methods consistently, showing its superiority in terms of conflict- and oblivion-resistance. The code of the experiments is available at https://github.com/DaShenZi721/MoORE.
中文摘要：本文提出MoORE方法，通过奇异值分解和可学习路由器构建正交专家模型，有效解决多任务适应中的任务冲突与遗忘问题，在各项实验中均优于现有方法。
English Summary: This paper introduces MoORE, a novel multi-task adaptation method that uses singular value decomposition and learnable routers to create orthogonal experts, effectively preventing task conflicts and preserving original task knowledge while outperforming existing approaches.

Paperid: 176, https://arxiv.org/pdf/2506.14263.pdf   GitHub

Authors:Qingyu Song, Wei Lin, Juncheng Wang, Hong Xu

Title: Towards Robust Learning to Optimize with Theoretical Guarantees

Abstract:
Learning to optimize (L2O) is an emerging technique to solve mathematical optimization problems with learning-based methods. Although with great success in many real-world scenarios such as wireless communications, computer networks, and electronic design, existing L2O works lack theoretical demonstration of their performance and robustness in out-of-distribution (OOD) scenarios. We address this gap by providing comprehensive proofs. First, we prove a sufficient condition for a robust L2O model with homogeneous convergence rates over all In-Distribution (InD) instances. We assume an L2O model achieves robustness for an InD scenario. Based on our proposed methodology of aligning OOD problems to InD problems, we also demonstrate that the L2O model's convergence rate in OOD scenarios will deteriorate by an equation of the L2O model's input features. Moreover, we propose an L2O model with a concise gradient-only feature construction and a novel gradient-based history modeling method. Numerical simulation demonstrates that our proposed model outperforms the state-of-the-art baseline in both InD and OOD scenarios and achieves up to 10 $\times$ convergence speedup. The code of our method can be found from https://github.com/NetX-lab/GoMathL2O-Official.
中文: 本文针对学习优化模型在分布外场景中缺乏理论保证的问题，通过证明其收敛性并提出一种新颖的仅使用梯度特征的模型，实现了比现有方法快10倍的收敛速度。
English: This paper addresses the lack of theoretical guarantees for Learning to Optimize (L2O) models in out-of-distribution scenarios by providing proofs of their convergence rates and proposing a novel model with gradient-only features that achieves up to 10 times faster convergence than existing methods.

Paperid: 177, https://arxiv.org/pdf/2506.14198.pdf   GitHub

Authors:Jeremy A. Collins, Loránd Cheng, Kunal Aneja, Albert Wilcox, Benjamin Joffe, Animesh Garg

Title: AMPLIFY: Actionless Motion Priors for Robot Learning from Videos

Abstract:
Action-labeled data for robotics is scarce and expensive, limiting the generalization of learned policies. In contrast, vast amounts of action-free video data are readily available, but translating these observations into effective policies remains a challenge. We introduce AMPLIFY, a novel framework that leverages large-scale video data by encoding visual dynamics into compact, discrete motion tokens derived from keypoint trajectories. Our modular approach separates visual motion prediction from action inference, decoupling the challenges of learning what motion defines a task from how robots can perform it. We train a forward dynamics model on abundant action-free videos and an inverse dynamics model on a limited set of action-labeled examples, allowing for independent scaling. Extensive evaluations demonstrate that the learned dynamics are both accurate, achieving up to 3.7x better MSE and over 2.5x better pixel prediction accuracy compared to prior approaches, and broadly useful. In downstream policy learning, our dynamics predictions enable a 1.2-2.2x improvement in low-data regimes, a 1.4x average improvement by learning from action-free human videos, and the first generalization to LIBERO tasks from zero in-distribution action data. Beyond robotic control, we find the dynamics learned by AMPLIFY to be a versatile latent world model, enhancing video prediction quality. Our results present a novel paradigm leveraging heterogeneous data sources to build efficient, generalizable world models. More information can be found at https://amplify-robotics.github.io/.

Paperid: 178, https://arxiv.org/pdf/2506.14123.pdf   GitHub

Authors:Jonathan Hayase, Alisa Liu, Noah A. Smith, Sewoong Oh

Title: Sampling from Your Language Model One Byte at a Time

Abstract:
Tokenization is used almost universally by modern language models, enabling efficient text representation using multi-byte or multi-character tokens. However, prior work has shown that tokenization can introduce distortion into the model's generations, an issue known as the Prompt Boundary Problem (PBP). For example, users are often advised not to end their prompts with a space because it prevents the model from including the space as part of the next token. While this heuristic is effective in English, the underlying PBP continues to affect languages such as Chinese as well as code generation, where tokens often do not line up with word and syntactic boundaries. In this work, we present an inference-time method to convert any autoregressive LM with a BPE tokenizer into a character-level or byte-level LM. Our method efficiently solves the PBP and is also able to unify the vocabularies of language models with different tokenizers, allowing one to ensemble LMs with different tokenizers at inference time or transfer the post-training from one model to another using proxy-tuning. We demonstrate in experiments that the ensemble and proxy-tuned models outperform their constituents on downstream evals. Code is available at https://github.com/SewoongLab/byte-sampler .
中文: 本文提出了一种推理时方法，可将采用BPE分词器的自回归语言模型转换为字符级或字节级模型，有效解决了提示边界问题，并实现了不同分词器模型间的集成与代理调优。
English: This paper introduces an inference-time method that converts autoregressive language models with BPE tokenizers into character-level or byte-level models, effectively solving the Prompt Boundary Problem and enabling model ensembling and proxy-tuning across different tokenizers.

Paperid: 179, https://arxiv.org/pdf/2506.14003.pdf   GitHub

Authors:Yiwei Chen, Soumyadeep Pal, Yimeng Zhang, Qing Qu, Sijia Liu

Title: Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

Abstract:
Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowledge from a trained model, while maintaining its performance on standard tasks. While unlearning plays a vital role in protecting data privacy, enforcing copyright, and mitigating sociotechnical harms in LLMs, we identify a new vulnerability post-unlearning: unlearning trace detection. We discover that unlearning leaves behind persistent ''fingerprints'' in LLMs, detectable traces in both model behavior and internal representations. These traces can be identified from output responses, even when prompted with forget-irrelevant inputs. Specifically, a simple supervised classifier can reliably determine whether a model has undergone unlearning based solely on its textual outputs. Further analysis shows that these traces are embedded in intermediate activations and propagate nonlinearly to the final layer, forming low-dimensional, learnable manifolds in activation space. Through extensive experiments, we show that forget-relevant prompts enable over 90% accuracy in detecting unlearning traces across all model sizes. Even with forget-irrelevant inputs, large LLMs maintain high detectability, demonstrating the broad applicability of unlearning trace detection. These findings reveal that unlearning leaves measurable signatures, introducing a new risk of reverse-engineering forgotten information when a model is identified as unlearned given an input query. Codes are available at https://github.com/OPTML-Group/Unlearn-Trace.
中文摘要：大语言模型的机器遗忘会在模型输出和内部激活中留下可检测的痕迹，使得遗忘事件能够被高精度识别，这揭示了一种新的安全风险：被遗忘的信息可能通过逆向工程被推测出来。
English Summary: Machine unlearning in large language models leaves detectable traces in model outputs and internal activations, enabling high-accuracy detection of unlearning events and revealing a new vulnerability where forgotten information could potentially be reverse-engineered.

Paperid: 180, https://arxiv.org/pdf/2506.13996.pdf   GitHub

Authors:Stas Bekman, Samyam Rajbhandari, Michael Wyatt, Jeff Rasley, Tunji Ruwase, Zhewei Yao, Aurick Qiao, Yuxiong He

Title: Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences

Abstract:
Long sequences are critical for applications like RAG, long document summarization, multi-modality, etc., and modern LLMs, like Llama 4 Scout, support max sequence length of up to 10 million tokens. However, outside of enterprise labs, long sequence training is challenging for the AI community with limited system support in the open-source space. Out-of-box, even on a modern NVIDIA H100 80GB GPU cluster, training Llama 8B model with sequence over 32K runs out of memory on a basic Hugging Face (HF) model due to two reasons: i) LLM training workloads are not optimized to fully leverage a single GPU memory, ii) existing solutions for leveraging multiple GPU memory are not easily available to HF models, making long sequence training inaccessible. We address this with Arctic Long Sequence Training (ALST). It offers a combination of attention-agnostic single GPU and multi-GPU memory optimizations, that enables it to support out-of-box training of multi-million sequence length for a wide variety of HF models. ALST supports training Meta's Llama 8B model with 500K sequence length on a single H100 GPU, 3.7M on a single 8xH100 GPU node, and over 15M on a 4 node cluster, an increase of over 400x compared to the 32K baseline for the latter. ALST is fully compatible with HF models and open-sourced via Deepspeed https://www.deepspeed.ai/tutorials/ulysses-alst-sequence-pallellism/ and Arctic Training https://github.com/snowflakedb/ArcticTraining/blob/main/projects/sequence-parallelism/README.md.
中文: 北极长序列训练（ALST）方法通过优化单GPU和多GPU内存使用，支持Hugging Face模型开箱即用的数百万标记序列训练，相比32K基准实现了高达400倍的序列长度提升。
English: The Arctic Long Sequence Training (ALST) method enables out-of-box training of multi-million token sequences for Hugging Face models by optimizing single and multi-GPU memory usage, achieving up to 400x longer sequences than the 32K baseline.

Paperid: 181, https://arxiv.org/pdf/2506.13903.pdf   GitHub

Authors:Christel Sirocchi, Damiano Verda

Title: Enhancing interpretability of rule-based classifiers through feature graphs

Abstract:
In domains where transparency and trustworthiness are crucial, such as healthcare, rule-based systems are widely used and often preferred over black-box models for decision support systems due to their inherent interpretability. However, as rule-based models grow complex, discerning crucial features, understanding their interactions, and comparing feature contributions across different rule sets becomes challenging. To address this, we propose a comprehensive framework for estimating feature contributions in rule-based systems, introducing a graph-based feature visualisation strategy, a novel feature importance metric agnostic to rule-based predictors, and a distance metric for comparing rule sets based on feature contributions. By experimenting on two clinical datasets and four rule-based methods (decision trees, logic learning machines, association rules, and neural networks with rule extraction), we showcase our method's capability to uncover novel insights on the combined predictive value of clinical features, both at the dataset and class-specific levels. These insights can aid in identifying new risk factors, signature genes, and potential biomarkers, and determining the subset of patient information that should be prioritised to enhance diagnostic accuracy. Comparative analysis of the proposed feature importance score with state-of-the-art methods on 15 public benchmarks demonstrates competitive performance and superior robustness. The method implementation is available on GitHub: https://github.com/ChristelSirocchi/rule-graph.
中文: 本研究提出了一种全面的规则系统特征贡献分析框架，通过基于图的可视化和新型度量方法提升复杂模型（如医疗领域应用）的可解释性，在临床数据集和公共基准测试中验证了其优越的稳健性能。
English: This study introduces a comprehensive framework for analyzing feature contributions in rule-based systems, featuring graph-based visualization and novel metrics to enhance interpretability in complex models like those used in healthcare, validated on clinical datasets and public benchmarks with robust performance.

Paperid: 182, https://arxiv.org/pdf/2506.13846.pdf   GitHub

Authors:Runtao Liu, Jiahao Zhan, Yingqing He, Chen Wei, Alan Yuille, Qifeng Chen

Title: Fake it till You Make it: Reward Modeling as Discriminative Prediction

Abstract:
An effective reward model plays a pivotal role in reinforcement learning for post-training enhancement of visual generative models. However, current approaches of reward modeling suffer from implementation complexity due to their reliance on extensive human-annotated preference data or meticulously engineered quality dimensions that are often incomplete and engineering-intensive. Inspired by adversarial training in generative adversarial networks (GANs), this paper proposes GAN-RM, an efficient reward modeling framework that eliminates manual preference annotation and explicit quality dimension engineering. Our method trains the reward model through discrimination between a small set of representative, unpaired target samples(denoted as Preference Proxy Data) and model-generated ordinary outputs, requiring only a few hundred target samples. Comprehensive experiments demonstrate our GAN-RM's effectiveness across multiple key applications including test-time scaling implemented as Best-of-N sample filtering, post-training approaches like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Code and data will be released at https://github.com/Visualignment/GAN-RM.
Chinese: 本文提出GAN-RM，一种高效奖励建模框架，通过仅使用少量目标样本与模型生成输出进行判别训练，无需人工偏好标注和显式质量维度设计，并在样本筛选和微调等关键应用中验证了其有效性。
English: This paper introduces GAN-RM, an efficient reward modeling framework that eliminates the need for manual preference annotations and explicit quality engineering by training on a small set of target samples and model-generated outputs, demonstrating effectiveness across various applications like sample filtering and fine-tuning.

Paperid: 183, https://arxiv.org/pdf/2506.13814.pdf   GitHub

Authors:Lufei Liu, Tor M. Aamodt

Title: ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering

Abstract:
Graphics rendering applications increasingly leverage neural networks in tasks such as denoising, supersampling, and frame extrapolation to improve image quality while maintaining frame rates. The temporal coherence inherent in these tasks presents an opportunity to reuse intermediate results from previous frames and avoid redundant computations. Recent work has shown that caching intermediate features to be reused in subsequent inferences is an effective method to reduce latency in diffusion models. We extend this idea to real-time rendering and present ReFrame, which explores different caching policies to optimize trade-offs between quality and performance in rendering workloads. ReFrame can be applied to a variety of encoder-decoder style networks commonly found in rendering pipelines. Experimental results show that we achieve 1.4x speedup on average with negligible quality loss in three real-time rendering tasks. Code available: https://ubc-aamodt-group.github.io/reframe-layer-caching/

Paperid: 184, https://arxiv.org/pdf/2506.13759.pdf   GitHub

Authors:Runpeng Yu, Qi Li, Xinchao Wang

Title: Discrete Diffusion in Large Language and Multimodal Models: A Survey

Abstract:
In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy. This paradigm naturally enables parallel generation, fine-grained output control, and dynamic perception. These capabilities are previously difficult to achieve with AR models. A growing number of industrial-scale proprietary d(M)LLMs, as well as a large number of open-source academic d(M)LLMs, have demonstrated performance comparable to their autoregressive counterparts, while achieving up to 10$\times$ acceleration in inference speed. These developments position discrete diffusion models as a promising alternative to intelligence based on the traditional autoregressive approach. In this work, we present a comprehensive overview of the research in the dLLM and dMLLM domains. We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, list commonly-used modeling methods, and categorize representative models. We further analyze key techniques for training, inference, quantization. We also discuss the trustworthy issues and summarize emerging applications across language, vision-language, and biological domains and etc.. We conclude by discussing future directions for research and deployment. Relative papers are collected in https://github.com/LiQiiiii/Awesome-Discrete-Diffusion-LLM_MLLM
本文系统综述了离散扩散语言与多模态模型，阐明了其相比自回归模型在并行解码速度、精细化控制和动态感知方面的优势，并剖析了其理论框架、关键技术及应用领域。
This survey comprehensively explores discrete diffusion language and multimodal models, highlighting their parallel decoding advantages over autoregressive models in speed, control, and perception while analyzing their frameworks, techniques, and applications.

Paperid: 185, https://arxiv.org/pdf/2506.13727.pdf   GitHub

Authors:Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat, Patrick Kahardipraja, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

Title: Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMs

Abstract:
Large Language Models (LLMs) are central to many contemporary AI applications, yet their extensive parameter counts pose significant challenges for deployment in memory- and compute-constrained environments. Recent works in eXplainable AI (XAI), particularly on attribution methods, suggest that interpretability can also enable model compression by identifying and removing components irrelevant to inference. In this paper, we leverage Layer-wise Relevance Propagation (LRP) to perform attribution-guided pruning of LLMs. While LRP has shown promise in structured pruning for vision models, we extend it to unstructured pruning in LLMs and demonstrate that it can substantially reduce model size with minimal performance loss. Our method is especially effective in extracting task-relevant subgraphs -- so-called ``circuits'' -- which can represent core functions (e.g., indirect object identification). Building on this, we introduce a technique for model correction, by selectively removing circuits responsible for spurious behaviors (e.g., toxic outputs). All in all, we gather these techniques as a uniform holistic framework and showcase its effectiveness and limitations through extensive experiments for compression, circuit discovery and model correction on Llama and OPT models, highlighting its potential for improving both model efficiency and safety. Our code is publicly available at https://github.com/erfanhatefi/SparC3.
中文摘要：本文提出了一个利用层级相关性传播进行归因引导剪枝的统一框架，能够在大幅压缩大语言模型规模的同时保持性能，并实现核心功能电路发现和模型安全修正。
English Summary: This paper presents a holistic framework using Layer-wise Relevance Propagation for attribution-guided pruning of Large Language Models, enabling efficient model compression, circuit discovery, and safety correction while maintaining performance.

Paperid: 186, https://arxiv.org/pdf/2506.13666.pdf   GitHub

Authors:Junfeng Fang, Zijun Yao, Ruipeng Wang, Haokai Ma, Xiang Wang, Tat-Seng Chua

Title: We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems

Abstract:
The development of large language models (LLMs) has entered in a experience-driven era, flagged by the emergence of environment feedback-driven learning via reinforcement learning and tool-using agents. This encourages the emergenece of model context protocol (MCP), which defines the standard on how should a LLM interact with external services, such as \api and data. However, as MCP becomes the de facto standard for LLM agent systems, it also introduces new safety risks. In particular, MCP introduces third-party services, which are not controlled by the LLM developers, into the agent systems. These third-party MCP services provider are potentially malicious and have the economic incentives to exploit vulnerabilities and sabotage user-agent interactions. In this position paper, we advocate the research community in LLM safety to pay close attention to the new safety risks issues introduced by MCP, and develop new techniques to build safe MCP-powered agent systems. To establish our position, we argue with three key parts. (1) We first construct \framework, a controlled framework to examine safety issues in MCP-powered agent systems. (2) We then conduct a series of pilot experiments to demonstrate the safety risks in MCP-powered agent systems is a real threat and its defense is not trivial. (3) Finally, we give our outlook by showing a roadmap to build safe MCP-powered agent systems. In particular, we would call for researchers to persue the following research directions: red teaming, MCP safe LLM development, MCP safety evaluation, MCP safety data accumulation, MCP service safeguard, and MCP safe ecosystem construction. We hope this position paper can raise the awareness of the research community in MCP safety and encourage more researchers to join this important research direction. Our code is available at https://github.com/littlelittlenine/SafeMCP.git.
中文: 模型上下文协议（MCP）作为大语言模型与外部服务交互的标准，引入了第三方恶意服务的安全风险，因此呼吁研究界关注并开发安全的MCP驱动智能体系统。
English: The emergence of model context protocol (MCP) as a standard for LLM interactions with external services introduces significant safety risks from potentially malicious third-party providers, prompting a call for research into developing secure MCP-powered agent systems.

Paperid: 187, https://arxiv.org/pdf/2506.13585.pdf   GitHub GitHub

Authors:MiniMax, :, Aili Chen, Aonian Li, Bangwei Gong, Binyang Jiang, Bo Fei, Bo Yang, Boji Shan, Changqing Yu, Chao Wang, Cheng Zhu, Chengjun Xiao, Chengyu Du, Chi Zhang, Chu Qiao, Chunhao Zhang, Chunhui Du, Congchao Guo, Da Chen, Deming Ding, Dianjun Sun, Dong Li, Enwei Jiao, Haigang Zhou, Haimo Zhang, Han Ding, Haohai Sun, Haoyu Feng, Huaiguang Cai, Haichao Zhu, Jian Sun, Jiaqi Zhuang, Jiaren Cai, Jiayuan Song, Jin Zhu, Jingyang Li, Jinhao Tian, Jinli Liu, Junhao Xu, Junjie Yan, Junteng Liu, Junxian He, Kaiyi Feng, Ke Yang, Kecheng Xiao, Le Han, Leyang Wang, Lianfei Yu, Liheng Feng, Lin Li, Lin Zheng, Linge Du, Lingyu Yang, Lunbin Zeng, Minghui Yu, Mingliang Tao, Mingyuan Chi, Mozhi Zhang, Mujie Lin, Nan Hu, Nongyu Di, Peng Gao, Pengfei Li, Pengyu Zhao, Qibing Ren, Qidi Xu, Qile Li, Qin Wang, Rong Tian, Ruitao Leng, Shaoxiang Chen, Shaoyu Chen, Shengmin Shi, Shitong Weng, Shuchang Guan, Shuqi Yu, Sichen Li, Songquan Zhu, Tengfei Li, Tianchi Cai, Tianrun Liang, Weiyu Cheng, Weize Kong, Wenkai Li, Xiancai Chen, Xiangjun Song, Xiao Luo, Xiao Su, Xiaobo Li, Xiaodong Han, Xinzhu Hou, Xuan Lu, Xun Zou, Xuyang Shen, Yan Gong, Yan Ma, Yang Wang, Yiqi Shi, Yiran Zhong, Yonghong Duan, Yongxiang Fu, Yongyi Hu, Yu Gao, Yuanxiang Fan, Yufeng Yang, Yuhao Li, Yulin Hu, Yunan Huang, Yunji Li, Yunzhi Xu, Yuxin Mao, Yuxuan Shi, Yuze Wenren, Zehan Li, Zelin Li, Zhanxu Tian, Zhengmao Zhu, Zhenhua Fan, Zhenzhen Wu, Zhichao Xu, Zhihang Yu, Zhiheng Lyu, Zhuo Jiang, Zibo Gao, Zijia Wu, Zijian Song, Zijun Sun

Title: MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Abstract:
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax-Text-01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. The M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax-M1 enables efficient scaling of test-time compute. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems including sandbox-based, real-world software engineering environments. In addition to M1's inherent efficiency advantage for RL training, we propose CISPO, a novel RL algorithm to further enhance RL efficiency. CISPO clips importance sampling weights rather than token updates, outperforming other competitive RL variants. Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on 512 H800 GPUs to complete in only three weeks, with a rental cost of just $534,700. We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training. Experiments on standard benchmarks show that our models are comparable or superior to strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex software engineering, tool utilization, and long-context tasks. We publicly release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1.
MiniMax-M1是全球首个开放权重的混合注意力推理模型，具备百万令牌上下文和闪电注意力机制，通过仅需三周的高效强化学习在复杂任务中实现卓越性能。
MiniMax-M1 is the world's first open-weight hybrid-attention reasoning model featuring a million-token context and lightning attention mechanism, achieving superior performance in complex tasks through efficient reinforcement learning completed in just three weeks.

Paperid: 188, https://arxiv.org/pdf/2506.13566.pdf   GitHub

Authors:Jonathan Hoss, Felix Schelling, Noah Klarmann

Title: A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints

Abstract:
The classical Job Shop Scheduling Problem (JSSP) focuses on optimizing makespan under deterministic constraints. Real-world production environments introduce additional complexities that cause traditional scheduling approaches to be less effective. Reinforcement learning (RL) holds potential in addressing these challenges, as it allows agents to learn adaptive scheduling strategies. However, there is a lack of a comprehensive, general-purpose frameworks for effectively training and evaluating RL agents under real-world constraints. To address this gap, we propose a modular framework that extends classical JSSP formulations by incorporating key real-world constraints inherent to the shopfloor, including transport logistics, buffer management, machine breakdowns, setup times, and stochastic processing conditions, while also supporting multi-objective optimization. The framework is a customizable solution that offers flexibility in defining problem instances and configuring simulation parameters, enabling adaptation to diverse production scenarios. A standardized interface ensures compatibility with various RL approaches, providing a robust environment for training RL agents and facilitating the standardized comparison of different scheduling methods under dynamic and uncertain conditions. We release JobShopLab as an open-source tool for both research and industrial applications, accessible at: https://github.com/proto-lab-ro/jobshoplab
中文：本文提出了JobShopLab这一模块化开源框架，通过整合实际生产约束和多目标优化来扩展经典作业车间调度问题，为强化学习智能体训练和动态条件下调度方法的标准化比较提供了统一环境。
English: This paper introduces JobShopLab, a modular and open-source framework that extends classical job shop scheduling by incorporating real-world constraints and multi-objective optimization, providing a standardized environment for training reinforcement learning agents and comparing scheduling methods under dynamic conditions.

Paperid: 189, https://arxiv.org/pdf/2506.13523.pdf   GitHub

Authors:YuQing Xie, Ameya Daigavane, Mit Kotak, Tess Smidt

Title: The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products

Abstract:
$E(3)$-equivariant neural networks have demonstrated success across a wide range of 3D modelling tasks. A fundamental operation in these networks is the tensor product, which interacts two geometric features in an equivariant manner to create new features. Due to the high computational complexity of the tensor product, significant effort has been invested to optimize the runtime of this operation. For example, Luo et al. (2024) recently proposed the Gaunt tensor product (GTP) which promises a significant speedup. In this work, we provide a careful, systematic analysis of a number of tensor product operations. In particular, we emphasize that different tensor products are not performing the same operation. The reported speedups typically come at the cost of expressivity. We introduce measures of expressivity and interactability to characterize these differences. In addition, we realized the original implementation of GTP can be greatly simplified by directly using a spherical grid at no cost in asymptotic runtime. This spherical grid approach is faster on our benchmarks and in actual training of the MACE interatomic potential by 30%. Finally, we provide the first systematic microbenchmarks of the various tensor product operations. We find that the theoretical runtime guarantees can differ wildly from empirical performance, demonstrating the need for careful application-specific benchmarking. Code is available at https://github.com/atomicarchitects/PriceofFreedom.
中文: E(3)等变神经网络中的张量积操作虽经优化提速却以牺牲表达能力为代价，我们的研究发现理论运行时间与实测性能差异显著，并提出一种简化的球面网格方法，在实际训练中效率提升30%。
English: E(3)-equivariant neural networks rely on tensor products for geometric feature interactions, but current optimizations like the Gaunt tensor product sacrifice expressivity for speed, with our analysis revealing that theoretical runtime claims often mismatch empirical performance and proposing a simplified spherical grid method that boosts training efficiency by 30%.

Paperid: 190, https://arxiv.org/pdf/2506.13342.pdf   GitHub

Authors:Wooseok Seo, Seungju Han, Jaehun Jung, Benjamin Newman, Seungwon Lim, Seungbeen Lee, Ximing Lu, Yejin Choi, Youngjae Yu

Title: Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers

Abstract:
Fact verification is essential for ensuring the reliability of LLM applications. In this study, we evaluate 12 pre-trained LLMs and one specialized fact-verifier, including frontier LLMs and open-weight reasoning LLMs, using a collection of examples from 14 fact-checking benchmarks. We share three findings intended to guide future development of more robust fact verifiers. First, we highlight the importance of addressing annotation errors and ambiguity in datasets, demonstrating that approximately 16\% of ambiguous or incorrectly labeled data substantially influences model rankings. Neglecting this issue may result in misleading conclusions during comparative evaluations, and we suggest using a systematic pipeline utilizing LLM-as-a-judge to help identify these issues at scale. Second, we discover that frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance. We therefore recommend future studies include comparisons with these simple yet highly effective baselines. Lastly, despite their effectiveness, frontier LLMs incur substantial costs, motivating the development of small, fine-tuned fact verifiers. We show that these small models still have room for improvement, particularly on instances that require complex reasoning. Encouragingly, we demonstrate that augmenting training with synthetic multi-hop reasoning data significantly enhances their capabilities in such instances. We release our code, model, and dataset at https://github.com/just1nseo/verifying-the-verifiers
中文: 本研究评估了12个预训练大语言模型和一个专业事实核查器，发现解决数据集模糊性、利用少样本示例的前沿模型以及通过合成推理数据增强小型模型的能力，对构建可靠的事实核查系统至关重要。
English: This study evaluates 12 pre-trained LLMs and a specialized fact-verifier, revealing that addressing dataset ambiguities, leveraging frontier LLMs with few-shot examples, and enhancing small models with synthetic reasoning data are crucial for developing robust fact verification systems.

Paperid: 191, https://arxiv.org/pdf/2506.13331.pdf   GitHub

Authors:Badr AlKhamissi, C. Nicolò De Sabbata, Zeming Chen, Martin Schrimpf, Antoine Bosselut

Title: Mixture of Cognitive Reasoners: Modular Reasoning with Brain-Like Specialization

Abstract:
Human intelligence emerges from the interaction of specialized brain networks, each dedicated to distinct cognitive functions such as language processing, logical reasoning, social understanding, and memory retrieval. Inspired by this biological observation, we introduce the Mixture of Cognitive Reasoners (MiCRo) architecture and training paradigm: a modular transformer-based language model with a training curriculum that encourages the emergence of functional specialization among different modules. Inspired by studies in neuroscience, we partition the layers of a pretrained transformer model into four expert modules, each corresponding to a well-studied cognitive brain network. Our Brain-Like model has three key benefits over the state of the art: First, the specialized experts are highly interpretable and functionally critical, where removing a module significantly impairs performance on domain-relevant benchmarks. Second, our model outperforms comparable baselines that lack specialization on seven reasoning benchmarks. And third, the model's behavior can be steered at inference time by selectively emphasizing certain expert modules (e.g., favoring social over logical reasoning), enabling fine-grained control over the style of its response. Our findings suggest that biologically inspired inductive biases involved in human cognition lead to significant modeling gains in interpretability, performance, and controllability.

Paperid: 192, https://arxiv.org/pdf/2506.13320.pdf   GitHub

Authors:Wenlong Wan, Weiying Zheng, Tianyi Xiang, Guiqing Li, Shengfeng He

Title: Action Dubber: Timing Audible Actions via Inflectional Flow

Abstract:
We introduce the task of Audible Action Temporal Localization, which aims to identify the spatio-temporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose $TA^{2}Net$, a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio input. $TA^{2}Net$ also integrates a self-supervised spatial localization strategy during training, combining contrastive learning with spatial analysis. This dual design improves temporal localization accuracy and simultaneously identifies sound sources within video frames. To support this task, we introduce a new benchmark dataset, $Audible623$, derived from Kinetics and UCF101 by removing non-essential vocalization subsets. Extensive experiments confirm the effectiveness of our approach on $Audible623$ and show strong generalizability to other domains, such as repetitive counting and sound source localization. Code and dataset are available at https://github.com/WenlongWan/Audible623.
中文: 本文提出可听动作时序定位任务，旨在识别可听动作的时空坐标，并开发了TA²Net架构，通过运动二阶导数无需音频输入即可检测碰撞时刻，在新基准数据集Audible623上验证了其有效性。
English: This paper introduces Audible Action Temporal Localization, a task identifying spatio-temporal coordinates of audible movements, and proposes TA²Net, a novel architecture using motion's second derivative to detect collision timings without audio input, validated on the new Audible623 benchmark dataset.

Paperid: 193, https://arxiv.org/pdf/2506.13277.pdf   GitHub

Authors:Huayang Li, Yahui Liu, Hongyu Sun, Deng Cai, Leyang Cui, Wei Bi, Peilin Zhao, Taro Watanabe

Title: SeqPE: Transformer with Sequential Position Encoding

Abstract:
Since self-attention layers in Transformers are permutation invariant by design, positional encodings must be explicitly incorporated to enable spatial understanding. However, fixed-size lookup tables used in traditional learnable position embeddings (PEs) limit extrapolation capabilities beyond pre-trained sequence lengths. Expert-designed methods such as ALiBi and RoPE, mitigate this limitation but demand extensive modifications for adapting to new modalities, underscoring fundamental challenges in adaptability and scalability. In this work, we present SeqPE, a unified and fully learnable position encoding framework that represents each $n$-dimensional position index as a symbolic sequence and employs a lightweight sequential position encoder to learn their embeddings in an end-to-end manner. To regularize SeqPE's embedding space, we introduce two complementary objectives: a contrastive objective that aligns embedding distances with a predefined position-distance function, and a knowledge distillation loss that anchors out-of-distribution position embeddings to in-distribution teacher representations, further enhancing extrapolation performance. Experiments across language modeling, long-context question answering, and 2D image classification demonstrate that SeqPE not only surpasses strong baselines in perplexity, exact match (EM), and accuracy--particularly under context length extrapolation--but also enables seamless generalization to multi-dimensional inputs without requiring manual architectural redesign. We release our code, data, and checkpoints at https://github.com/ghrua/seqpe.
中文摘要：SeqPE提出了一种统一且完全可学习的位置编码框架，通过符号序列表示位置索引并采用对比学习与知识蒸馏目标，显著提升了外推能力和多模态适应性。
English Summary: SeqPE introduces a unified, learnable position encoding framework that uses symbolic sequences and complementary objectives to enhance extrapolation and adaptability across various tasks and modalities.

Paperid: 194, https://arxiv.org/pdf/2506.13181.pdf   GitHub

Authors:Philipp Spohn, Leander Girrbach, Jessica Bader, Zeynep Akata

Title: Align-then-Unlearn: Embedding Alignment for LLM Unlearning

Abstract:
As large language models (LLMs) are trained on massive datasets, they have raised significant privacy and ethical concerns due to their potential to inadvertently retain sensitive information. Unlearning seeks to selectively remove specific data from trained models, such as personal information or copyrighted content. Current approaches targeting specific output sequences at the token level often fail to achieve complete forgetting and remain susceptible to prompt rephrasing. We propose Align-then-Unlearn, a novel framework that performs unlearning in the semantic embedding space rather than directly on output tokens. Align-then-Unlearn first augments the LLM with an embedding prediction module trained to anticipate future context representations. Unlearning is then achieved by fine-tuning the model to minimize the similarity between these predicted embeddings and a target embedding that represents the concept to be removed. Initial results show that Align-then-Unlearn effectively removes targeted knowledge with minimal degradation in overall model utility. These findings suggest that embedding-based unlearning offers a promising and robust approach to removing conceptual knowledge. Our code is available at https://github.com/ExplainableML/align-then-unlearn.
Chinese: Align-then-Unlearn框架通过在语义嵌入空间执行遗忘操作，有效移除大型语言模型中的特定知识，同时保持模型整体性能，为解决隐私问题提供了新途径。
English: The Align-then-Unlearn framework addresses privacy concerns in large language models by performing unlearning in the semantic embedding space, effectively removing targeted knowledge while preserving overall model utility.

Paperid: 195, https://arxiv.org/pdf/2506.13160.pdf   GitHub

Authors:Ting Qiao, Yiming Li, Jianbin Li, Yingjia Wang, Leyi Qi, Junfeng Guo, Ruili Feng, Dacheng Tao

Title: CertDW: Towards Certified Dataset Ownership Verification via Conformal Prediction

Abstract:
Deep neural networks (DNNs) rely heavily on high-quality open-source datasets (e.g., ImageNet) for their success, making dataset ownership verification (DOV) crucial for protecting public dataset copyrights. In this paper, we find existing DOV methods (implicitly) assume that the verification process is faithful, where the suspicious model will directly verify ownership by using the verification samples as input and returning their results. However, this assumption may not necessarily hold in practice and their performance may degrade sharply when subjected to intentional or unintentional perturbations. To address this limitation, we propose the first certified dataset watermark (i.e., CertDW) and CertDW-based certified dataset ownership verification method that ensures reliable verification even under malicious attacks, under certain conditions (e.g., constrained pixel-level perturbation). Specifically, inspired by conformal prediction, we introduce two statistical measures, including principal probability (PP) and watermark robustness (WR), to assess model prediction stability on benign and watermarked samples under noise perturbations. We prove there exists a provable lower bound between PP and WR, enabling ownership verification when a suspicious model's WR value significantly exceeds the PP values of multiple benign models trained on watermark-free datasets. If the number of PP values smaller than WR exceeds a threshold, the suspicious model is regarded as having been trained on the protected dataset. Extensive experiments on benchmark datasets verify the effectiveness of our CertDW method and its resistance to potential adaptive attacks. Our codes are at \href{https://github.com/NcepuQiaoTing/CertDW}{GitHub}.
中文: 本文提出CertDW认证数据集水印方法，通过统计指标评估模型预测稳定性，在恶意攻击下仍能实现可靠的数据集所有权验证，并提供可证明的保障。
English: This paper introduces CertDW, a certified dataset watermarking method that ensures reliable dataset ownership verification under malicious attacks by leveraging statistical measures to assess model prediction stability and providing provable guarantees.

Paperid: 196, https://arxiv.org/pdf/2506.13150.pdf   GitHub

Authors:Thomas Möllenhoff, Siddharth Swaroop, Finale Doshi-Velez, Mohammad Emtiyaz Khan

Title: Federated ADMM from Bayesian Duality

Abstract:
ADMM is a popular method for federated deep learning which originated in the 1970s and, even though many new variants of it have been proposed since then, its core algorithmic structure has remained unchanged. Here, we take a major departure from the old structure and present a fundamentally new way to derive and extend federated ADMM. We propose to use a structure called Bayesian Duality which exploits a duality of the posterior distributions obtained by solving a variational-Bayesian reformulation of the original problem. We show that this naturally recovers the original ADMM when isotropic Gaussian posteriors are used, and yields non-trivial extensions for other posterior forms. For instance, full-covariance Gaussians lead to Newton-like variants of ADMM, while diagonal covariances result in a cheap Adam-like variant. This is especially useful to handle heterogeneity in federated deep learning, giving up to 7% accuracy improvements over recent baselines. Our work opens a new Bayesian path to improve primal-dual methods.
Chinese: 本研究提出了一种基于贝叶斯对偶的新方法，从根本上重构了联邦ADMM，通过引入牛顿式和Adam式变体，在异构联邦学习环境中将准确率提升高达7%。
English: The study introduces a novel Bayesian duality approach to fundamentally reformulate federated ADMM, enabling extensions like Newton-like and Adam-like variants that improve accuracy by up to 7% in heterogeneous federated learning settings.

Paperid: 197, https://arxiv.org/pdf/2506.13083.pdf   GitHub

Authors:Qingfeng Chen, Shiyuan Li, Yixin Liu, Shirui Pan, Geoffrey I. Webb, Shichao Zhang

Title: Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach

Abstract:
Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of class probabilities that vary with the depth of the model, leading to unreliable and risky predictions in real-world scenarios. To bridge the gap, in this paper, we propose a novel Evidence Fusing Graph Neural Network (EFGNN for short) to achieve trustworthy prediction, enhance node classification accuracy, and make explicit the risk of wrong predictions. In particular, we integrate the evidence theory with multi-hop propagation-based GNN architecture to quantify the prediction uncertainty of each node with the consideration of multiple receptive fields. Moreover, a parameter-free cumulative belief fusion (CBF) mechanism is developed to leverage the changes in prediction uncertainty and fuse the evidence to improve the trustworthiness of the final prediction. To effectively optimize the EFGNN model, we carefully design a joint learning objective composed of evidence cross-entropy, dissonance coefficient, and false confident penalty. The experimental results on various datasets and theoretical analyses demonstrate the effectiveness of the proposed model in terms of accuracy and trustworthiness, as well as its robustness to potential attacks. The source code of EFGNN is available at https://github.com/Shiy-Li/EFGNN.
Chinese: 本文提出了一种新颖的证据融合图神经网络（EFGNN），通过将证据理论与多跳传播相结合，提高了节点分类的准确性并量化预测不确定性，从而增强了预测的可靠性和抗攻击鲁棒性。
English: This paper introduces a novel Evidence Fusing Graph Neural Network (EFGNN) that integrates evidence theory with multi-hop propagation to enhance node classification accuracy and quantify prediction uncertainty, achieving improved trustworthiness and robustness against attacks.

Paperid: 198, https://arxiv.org/pdf/2506.13064.pdf   GitHub

Authors:Kai Tang, Ji Zhang, Hua Meng, Minbo Ma, Qi Xiong, Fengmao Lv, Jie Xu, Tianrui Li

Title: CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values

Abstract:
Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missing values caused by sensor failures or human errors significantly degrade forecasting accuracy. Prior efforts usually employ an impute-then-forecast paradigm, leading to suboptimal predictions due to error accumulation and misaligned objectives between the two stages. To address this challenge, we propose the Collaborative Imputation-Forecasting Network (CoIFNet), a novel framework that unifies imputation and forecasting to achieve robust MTSF in the presence of missing values. Specifically, CoIFNet takes the observed values, mask matrix and timestamp embeddings as input, processing them sequentially through the Cross-Timestep Fusion (CTF) and Cross-Variate Fusion (CVF) modules to capture temporal dependencies that are robust to missing values. We provide theoretical justifications on how our CoIFNet learning objective improves the performance bound of MTSF with missing values. Through extensive experiments on challenging MSTF benchmarks, we demonstrate the effectiveness and computational efficiency of our proposed approach across diverse missing-data scenarios, e.g., CoIFNet outperforms the state-of-the-art method by $\underline{\textbf{24.40}}$% ($\underline{\textbf{23.81}}$%) at a point (block) missing rate of 0.6, while improving memory and time efficiency by $\underline{\boldsymbol{4.3\times}}$ and $\underline{\boldsymbol{2.1\times}}$, respectively. Our code is available at: https://github.com/KaiTang-eng/CoIFNet.
Chinese: 提出的CoIFNet框架将填补与预测统一起来，在存在缺失值的多元时间序列预测中提升了准确性，相比现有方法实现了显著的性能提升和计算效率。
English: The proposed CoIFNet framework unifies imputation and forecasting to enhance multivariate time series forecasting accuracy in the presence of missing values, achieving significant performance improvements and computational efficiency over existing methods.

Paperid: 199, https://arxiv.org/pdf/2506.13059.pdf   GitHub

Authors:Coleman Hooper, Sebastian Zhao, Luca Manolache, Sehoon Kim, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami

Title: Multipole Attention for Efficient Long Context Reasoning

Abstract:
Large Reasoning Models (LRMs) have shown promising accuracy improvements on complex problem-solving tasks. While these models have attained high accuracy by leveraging additional computation at test time, they need to generate long chain-of-thought reasoning in order to think before answering, which requires generating thousands of tokens. While sparse attention methods can help reduce the KV cache pressure induced by this long autoregressive reasoning, these methods can introduce errors which disrupt the reasoning process. Additionally, prior methods often pre-process the input to make it easier to identify the important prompt tokens when computing attention during generation, and this pre-processing is challenging to perform online for newly generated reasoning tokens. Our work addresses these challenges by introducing Multipole Attention, which accelerates autoregressive reasoning by only computing exact attention for the most important tokens, while maintaining approximate representations for the remaining tokens. Our method first performs clustering to group together semantically similar key vectors, and then uses the cluster centroids both to identify important key vectors and to approximate the remaining key vectors in order to retain high accuracy. We design a fast cluster update process to quickly re-cluster the input and previously generated tokens, thereby allowing for accelerating attention to the previous output tokens. We evaluate our method using emerging LRMs such as Qwen-8B, demonstrating that our approach can maintain accuracy on complex reasoning tasks even with aggressive attention sparsity settings. We also provide kernel implementations to demonstrate the practical efficiency gains from our method, achieving up to 4.5$\times$ speedup for attention in long-context reasoning applications. Our code is available at https://github.com/SqueezeAILab/MultipoleAttention.
Chinese: 提出的多极注意力方法通过仅对关键标记计算精确注意力并对其他标记进行聚类近似，加速大型推理模型的自回归推理过程，在保持精度的同时实现了长上下文应用中高达4.5倍的注意力加速。
English: The proposed Multipole Attention method accelerates autoregressive reasoning in Large Reasoning Models by computing exact attention only for key tokens and approximating others through clustering, maintaining accuracy while achieving up to 4.5× speedup in long-context applications.

Paperid: 200, https://arxiv.org/pdf/2506.13056.pdf   GitHub

Authors:Haibo Qiu, Xiaohan Lan, Fanfan Liu, Xiaohu Sun, Delian Ruan, Peng Shi, Lin Ma

Title: Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model Learning

Abstract:
Recent advancements in large language models (LLMs) have witnessed a surge in the development of advanced reasoning paradigms, which are now being integrated into multimodal large language models (MLLMs). However, existing approaches often fall short: methods solely employing reinforcement learning (RL) can struggle with sample inefficiency and activating entirely absent reasoning capabilities, while conventional pipelines that initiate with a cold-start supervised fine-tuning (SFT) phase before RL may restrict the model's exploratory capacity and face suboptimal convergence. In this work, we introduce \textbf{Metis-RISE} (\textbf{R}L \textbf{I}ncentivizes and \textbf{S}FT \textbf{E}nhances) for multimodal reasoning model learning. Unlike conventional approaches, Metis-RISE distinctively omits an initial SFT stage, beginning instead with an RL phase (e.g., using a Group Relative Policy Optimization variant) to incentivize and activate the model's latent reasoning capacity. Subsequently, the targeted SFT stage addresses two key challenges identified during RL: (1) \textit{inefficient trajectory sampling} for tasks where the model possesses but inconsistently applies correct reasoning, which we tackle using self-distilled reasoning trajectories from the RL model itself; and (2) \textit{fundamental capability absence}, which we address by injecting expert-augmented knowledge for prompts where the model entirely fails. This strategic application of RL for incentivization followed by SFT for enhancement forms the core of Metis-RISE, leading to two versions of our MLLMs (7B and 72B parameters). Evaluations on the OpenCompass Multimodal Reasoning Leaderboard demonstrate that both models achieve state-of-the-art performance among similar-sized models, with the 72B version ranking fourth overall. Please refer to our project page for open-source information.
Chinese: 本文提出Metis-RISE多模态推理模型，该方法先通过强化学习激发模型推理能力，再采用监督微调解决采样效率低和知识缺失问题，在评测中取得了同类模型的最佳性能。
English: This paper introduces Metis-RISE, a novel multimodal reasoning model that starts with reinforcement learning to activate reasoning capabilities before applying supervised fine-tuning to address inefficiencies and knowledge gaps, achieving state-of-the-art performance on benchmarks.

Paperid: 201, https://arxiv.org/pdf/2506.13051.pdf   GitHub

Authors:Can Polat, Hasan Kurban, Erchin Serpedin, Mustafa Kurban

Title: Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning

Abstract:
Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces a multiscale multicrystal dataset with two physically grounded evaluation protocols to stress-test multimodal generative models. The Spatial-Exclusion benchmark withholds all supercells of a given radius from a diverse dataset, enabling controlled assessments of spatial interpolation and extrapolation. The Compositional-Exclusion benchmark omits all samples of a specific chemical composition, probing generalization across stoichiometries. Nine vision--language foundation models are prompted with crystallographic images and textual context to generate structural annotations. Responses are evaluated via (i) relative errors in lattice parameters and density, (ii) a physics-consistency index penalizing volumetric violations, and (iii) a hallucination score capturing geometric outliers and invalid space-group predictions. These benchmarks establish a reproducible, physically informed framework for assessing generalization, consistency, and reliability in large-scale multimodal models. Dataset and code are available at https://github.com/KurbanIntelligenceLab/StressTestingMMFMinCR.
中文摘要：本研究提出一个多尺度多晶体数据集及两项基于物理原理的评估基准，通过空间排除和成分排除协议，系统测试多模态基础模型在晶体学推理中的泛化能力、物理一致性及可靠性。
English Summary: This study introduces a multiscale multicrystal dataset with two physics-grounded benchmarks to evaluate multimodal foundation models' crystallographic reasoning by testing their generalization, physical consistency, and reliability through structured exclusion protocols.

Paperid: 202, https://arxiv.org/pdf/2506.13045.pdf   GitHub

Authors:Haiyang Guo, Fanhu Zeng, Fei Zhu, Jiayi Wang, Xukai Wang, Jingang Zhou, Hongbo Zhao, Wenzhuo Liu, Shijie Ma, Da-Han Wang, Xu-Yao Zhang, Cheng-Lin Liu

Title: Continual Learning for Generative AI: From LLMs to MLLMs and Beyond

Abstract:
The rapid advancement of generative models has empowered modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specific domains. However, these models are fundamentally constrained by \emph{catastrophic forgetting}, \ie~a persistent challenge where models experience performance degradation on previously learned tasks when adapting to new tasks. To address this practical limitation, numerous approaches have been proposed to enhance the adaptability and scalability of generative AI in real-world applications. In this work, we present a comprehensive survey of continual learning methods for mainstream generative AI models, encompassing large language models, multimodal large language models, vision-language-action models, and diffusion models. Drawing inspiration from the memory mechanisms of the human brain, we systematically categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based methods, while elucidating their underlying methodologies and motivations. We further analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones, thereby providing deeper insights into the field. The project page of this paper is available at https://github.com/Ghy0501/Awesome-Continual-Learning-in-Generative-Models.
中文: 本综述系统梳理了生成式AI模型的持续学习方法，通过借鉴人类记忆机制提出三种解决灾难性遗忘的范式，并深入分析了不同模型的训练设置与评估基准。
English: This survey systematically categorizes and analyzes continual learning methods for generative AI models to address catastrophic forgetting, drawing inspiration from human memory mechanisms and examining various training setups and benchmarks.

Paperid: 203, https://arxiv.org/pdf/2506.13021.pdf   GitHub

Authors:Siqi Liang, Yudi Zhang, Yubo Wang

Title: C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation

Abstract:
Sequential recommender systems aim to model users' evolving preferences by capturing patterns in their historical interactions. Recent advances in this area have leveraged deep neural networks and attention mechanisms to effectively represent sequential behaviors and time-sensitive interests. In this work, we propose C-TLSAN (Content-Enhanced Time-Aware Long- and Short-Term Attention Network), an extension of the TLSAN architecture that jointly models long- and short-term user preferences while incorporating semantic content associated with items, such as product descriptions. C-TLSAN enriches the recommendation pipeline by embedding textual content linked to users' historical interactions directly into both long-term and short-term attention layers. This allows the model to learn from both behavioral patterns and rich item content, enhancing user and item representations across temporal dimensions. By fusing sequential signals with textual semantics, our approach improves the expressiveness and personalization capacity of recommendation systems. We conduct extensive experiments on large-scale Amazon datasets, benchmarking C-TLSAN against state-of-the-art baselines, including recent sequential recommenders based on Large Language Models (LLMs), which represent interaction history and predictions in text form. Empirical results demonstrate that C-TLSAN consistently outperforms strong baselines in next-item prediction tasks. Notably, it improves AUC by 1.66%, Recall@10 by 93.99%, and Precision@10 by 94.80% on average over the best-performing baseline (TLSAN) across 10 Amazon product categories. These results highlight the value of integrating content-aware enhancements into temporal modeling frameworks for sequential recommendation. Our code is available at https://github.com/booml247/cTLSAN.
中文: 本文提出的C-TLSAN模型通过将物品语义内容融入长短时注意力层来增强时序偏好建模，在亚马逊数据集上的实验表明其显著超越了现有最优基线模型。
English: This paper introduces C-TLSAN, a sequential recommendation model that enhances temporal preference modeling by integrating item content into both long- and short-term attention layers, achieving significant performance improvements over state-of-the-art baselines on Amazon datasets.

Paperid: 204, https://arxiv.org/pdf/2506.13001.pdf   GitHub

Authors:Christian Zhou-Zheng, Philippe Pasquier

Title: Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV

Abstract:
Existing work in automatic music generation has primarily focused on end-to-end systems that produce complete compositions or continuations. However, because musical composition is typically an iterative process, such systems make it difficult to engage in the back-and-forth between human and machine that is essential to computer-assisted creativity. In this study, we address the task of personalizable, multi-track, long-context, and controllable symbolic music infilling to enhance the process of computer-assisted composition. We present MIDI-RWKV, a novel model based on the RWKV-7 linear architecture, to enable efficient and coherent musical cocreation on edge devices. We also demonstrate that MIDI-RWKV admits an effective method of finetuning its initial state for personalization in the very-low-sample regime. We evaluate MIDI-RWKV and its state tuning on several quantitative and qualitative metrics, and release model weights and code at https://github.com/christianazinn/MIDI-RWKV.
Chinese: 本研究提出了MIDI-RWKV这一新型模型，通过其线性架构和状态调优功能，在边缘设备上实现个性化、可控的符号音乐填充，从而有效促进计算机辅助音乐创作中的协同创作过程。
English: This study introduces MIDI-RWKV, a novel model for personalized and controllable symbolic music infilling that enhances computer-assisted composition by enabling efficient musical co-creation on edge devices through its linear architecture and state tuning capabilities.

Paperid: 205, https://arxiv.org/pdf/2506.12876.pdf   GitHub

Authors:Yan Sun, Qixin Zhang, Zhiyuan Yu, Xikun Zhang, Li Shen, Dacheng Tao

Title: MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on Large Language Models

Abstract:
The rapid scaling of large language models (LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, semi-structured sparsity offers a promising solution by strategically retaining $N$ elements out of every $M$ weights, thereby enabling hardware-friendly acceleration and reduced memory. However, existing (N:M)-compatible approaches typically fall into two categories: rule-based layerwise greedy search, which suffers from considerable errors, and gradient-driven combinatorial learning, which incurs prohibitive training costs. To tackle these challenges, we propose a novel linear-space probabilistic framework named MaskPro, which aims to learn a prior categorical distribution for every $M$ consecutive weights and subsequently leverages this distribution to generate the (N:M)-sparsity throughout an $N$-way sampling without replacement. Furthermore, to mitigate the training instability induced by the high variance of policy gradients in the super large combinatorial space, we propose a novel update method by introducing a moving average tracker of loss residuals instead of vanilla loss. Finally, we conduct comprehensive theoretical analysis and extensive experiments to validate the superior performance of MaskPro, as well as its excellent scalability in memory efficiency and exceptional robustness to data samples. Our code is available at https://github.com/woodenchild95/Maskpro.git.
中文摘要：该摘要提出了一种名为MaskPro的概率框架，通过学习权重分布的稀疏模式并采用创新的梯度稳定方法，有效提升了大型语言模型的推理效率，展现出卓越的性能和可扩展性。
English Summary: The abstract introduces MaskPro, a probabilistic framework designed to enhance inference efficiency in large language models by learning sparsity patterns through categorical distributions and a novel gradient stabilization method, achieving superior performance and scalability.

Paperid: 206, https://arxiv.org/pdf/2506.12779.pdf   GitHub

Authors:Yuxuan Wang, Ming Yang, Ziluo Ding, Yu Zhang, Weishuai Zeng, Xinrun Xu, Haobin Jiang, Zongqing Lu

Title: From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots

Abstract:
Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they struggle to generalize across highly varied behaviors due to conflicting control requirements and mismatched data distributions. In this work, we propose BumbleBee (BB), an expert-generalist learning framework that combines motion clustering and sim-to-real adaptation to overcome these challenges. BB first leverages an autoencoder-based clustering method to group behaviorally similar motions using motion features and motion descriptions. Expert policies are then trained within each cluster and refined with real-world data through iterative delta action modeling to bridge the sim-to-real gap. Finally, these experts are distilled into a unified generalist controller that preserves agility and robustness across all motion types. Experiments on two simulations and a real humanoid robot demonstrate that BB achieves state-of-the-art general whole-body control, setting a new benchmark for agile, robust, and generalizable humanoid performance in the real world. The project webpage is available at https://beingbeyond.github.io/BumbleBee/.

Paperid: 207, https://arxiv.org/pdf/2506.12696.pdf   GitHub

Authors:Xiaoyan Kui, Canwei Liu, Qinsong Li, Zhipeng Hu, Yangyang Shi, Weixin Si, Beiji Zou

Title: TFKAN: Time-Frequency KAN for Long-Term Time Series Forecasting

Abstract:
Kolmogorov-Arnold Networks (KANs) are highly effective in long-term time series forecasting due to their ability to efficiently represent nonlinear relationships and exhibit local plasticity. However, prior research on KANs has predominantly focused on the time domain, neglecting the potential of the frequency domain. The frequency domain of time series data reveals recurring patterns and periodic behaviors, which complement the temporal information captured in the time domain. To address this gap, we explore the application of KANs in the frequency domain for long-term time series forecasting. By leveraging KANs' adaptive activation functions and their comprehensive representation of signals in the frequency domain, we can more effectively learn global dependencies and periodic patterns. To integrate information from both time and frequency domains, we propose the $\textbf{T}$ime-$\textbf{F}$requency KAN (TFKAN). TFKAN employs a dual-branch architecture that independently processes features from each domain, ensuring that the distinct characteristics of each domain are fully utilized without interference. Additionally, to account for the heterogeneity between domains, we introduce a dimension-adjustment strategy that selectively upscales only in the frequency domain, enhancing efficiency while capturing richer frequency information. Experimental results demonstrate that TFKAN consistently outperforms state-of-the-art (SOTA) methods across multiple datasets. The code is available at https://github.com/LcWave/TFKAN.
中文: 提出的时频KAN（TFKAN）通过双分支架构和选择性维度调整整合时域与频域信息，有效提升了长期时间序列预测性能，在多个数据集上超越了现有最优方法。
English: The proposed Time-Frequency KAN (TFKAN) integrates both time and frequency domains using a dual-branch architecture and selective dimension adjustment to enhance long-term time series forecasting, outperforming existing methods across multiple datasets.

Paperid: 208, https://arxiv.org/pdf/2506.12661.pdf   GitHub

Authors:M. H. Maqbool, Moghis Fereidouni, Umar Farooq, A. B. Siddique, Hassan Foroosh

Title: INTERPOS: Interaction Rhythm Guided Positional Morphing for Mobile App Recommender Systems

Abstract:
The mobile app market has expanded exponentially, offering millions of apps with diverse functionalities, yet research in mobile app recommendation remains limited. Traditional sequential recommender systems utilize the order of items in users' historical interactions to predict the next item for the users. Position embeddings, well-established in transformer-based architectures for natural language processing tasks, effectively distinguish token positions in sequences. In sequential recommendation systems, position embeddings can capture the order of items in a user's historical interaction sequence. Nevertheless, this ordering does not consider the time elapsed between two interactions of the same user (e.g., 1 day, 1 week, 1 month), referred to as "user rhythm". In mobile app recommendation datasets, the time between consecutive user interactions is notably longer compared to other domains like movies, posing significant challenges for sequential recommender systems. To address this phenomenon in the mobile app domain, we introduce INTERPOS, an Interaction Rhythm Guided Positional Morphing strategy for autoregressive mobile app recommender systems. INTERPOS incorporates rhythm-guided position embeddings, providing a more comprehensive representation that considers both the sequential order of interactions and the temporal gaps between them. This approach enables a deep understanding of users' rhythms at a fine-grained level, capturing the intricacies of their interaction patterns over time. We propose three strategies to incorporate the morphed positional embeddings in two transformer-based sequential recommendation system architectures. Our extensive evaluations show that INTERPOS outperforms state-of-the-art models using 7 mobile app recommendation datasets on NDCG@K and HIT@K metrics. The source code of INTERPOS is available at https://github.com/dlgrad/INTERPOS.
中文: 本研究提出INTERPOS策略，将节奏引导的位置嵌入整合到基于Transformer的序列推荐系统中，以更全面地捕捉用户交互的顺序和时间间隔，在移动应用推荐任务中展现出卓越性能。
English: The study introduces INTERPOS, a novel strategy that integrates rhythm-guided position embeddings into transformer-based sequential recommender systems to better capture both the order and temporal gaps in user interactions, demonstrating superior performance in mobile app recommendations.

Paperid: 209, https://arxiv.org/pdf/2506.12552.pdf   GitHub

Authors:Zain Muhammad Mujahid, Dilshod Azizov, Maha Tufail Agro, Preslav Nakov

Title: Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts

Abstract:
In an age characterized by the proliferation of mis- and disinformation online, it is critical to empower readers to understand the content they are reading. Important efforts in this direction rely on manual or automatic fact-checking, which can be challenging for emerging claims with limited information. Such scenarios can be handled by assessing the reliability and the political bias of the source of the claim, i.e., characterizing entire news outlets rather than individual claims or articles. This is an important but understudied research direction. While prior work has looked into linguistic and social contexts, we do not analyze individual articles or information in social media. Instead, we propose a novel methodology that emulates the criteria that professional fact-checkers use to assess the factuality and political bias of an entire outlet. Specifically, we design a variety of prompts based on these criteria and elicit responses from large language models (LLMs), which we aggregate to make predictions. In addition to demonstrating sizable improvements over strong baselines via extensive experiments with multiple LLMs, we provide an in-depth error analysis of the effect of media popularity and region on model performance. Further, we conduct an ablation study to highlight the key components of our dataset that contribute to these improvements. To facilitate future research, we released our dataset and code at https://github.com/mbzuai-nlp/llm-media-profiling.
中文: 本研究提出了一种利用大型语言模型评估整个新闻媒体可靠性和政治偏见的新方法，通过关注信息来源特征而非单个声明，改进了传统的核实方式。
English: This study introduces a novel method using large language models to assess the reliability and political bias of entire news outlets, improving upon traditional fact-checking by focusing on source characterization rather than individual claims.

Paperid: 210, https://arxiv.org/pdf/2506.12541.pdf   GitHub

Authors:Catalin E. Brita, Hieu Nguyen, Lohithsai Yadala Chanchu, Domonkos Nagy, Maksim Zhdanov

Title: BSA: Ball Sparse Attention for Large-scale Geometries

Abstract:
Self-attention scales quadratically with input size, limiting its use for large-scale physical systems. Although sparse attention mechanisms provide a viable alternative, they are primarily designed for regular structures such as text or images, making them inapplicable for irregular geometries. In this work, we present Ball Sparse Attention (BSA), which adapts Native Sparse Attention (NSA) (Yuan et al., 2025) to unordered point sets by imposing regularity using the Ball Tree structure from the Erwin Transformer (Zhdanov et al., 2025). We modify NSA's components to work with ball-based neighborhoods, yielding a global receptive field at sub-quadratic cost. On an airflow pressure prediction task, we achieve accuracy comparable to Full Attention while significantly reducing the theoretical computational complexity. Our implementation is available at https://github.com/britacatalin/bsa.
中文：球稀疏注意力通过球树结构将稀疏注意力应用于不规则几何形状，在物理系统预测任务中以次二次复杂度实现了与全注意力相当的精度。
English: Ball Sparse Attention adapts sparse attention to irregular geometries using ball tree structures, achieving full-attention accuracy with sub-quadratic complexity on physical systems.

Paperid: 211, https://arxiv.org/pdf/2506.12524.pdf   GitHub

Authors:Nuwan Bandara, Thivya Kandappu, Archan Misra

Title: Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing

Abstract:
Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and temporal discontinuities. To complement traditional spatial accuracy metrics, we propose a novel Jitter Metric that captures the temporal smoothness of predicted gaze trajectories based on velocity regularity and local signal complexity. Together, these contributions significantly improve the consistency of event-based gaze signals, making them better suited for downstream tasks such as micro-expression analysis and mind-state decoding. Our results demonstrate consistent improvements across multiple baseline models on controlled datasets, laying the groundwork for future integration with multimodal affect recognition systems in real-world environments. Our code implementations can be found at https://github.com/eye-tracking-for-physiological-sensing/EyeLoRiN.
中文: 本文提出了一种与模型无关的优化框架，通过运动感知滤波和光流局部优化来改进基于事件的视线估计，显著提升了视线信号的稳定性以支持认知状态解码，并引入了评估时间平滑度的新型抖动指标。
English: This paper presents a model-agnostic framework that enhances event-based gaze estimation through motion-aware filtering and optical flow refinement, improving signal consistency for cognitive state inference while introducing a novel jitter metric for temporal smoothness evaluation.

Paperid: 212, https://arxiv.org/pdf/2506.12481.pdf   GitHub

Authors:Runhao Zeng, Qi Deng, Ronghao Zhang, Shuaicheng Niu, Jian Chen, Xiping Hu, Victor C. M. Leung

Title: Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation

Abstract:
Test-time adaptation (TTA) aims to boost the generalization capability of a trained model by conducting self-/unsupervised learning during the testing phase. While most existing TTA methods for video primarily utilize visual supervisory signals, they often overlook the potential contribution of inherent audio data. To address this gap, we propose a novel approach that incorporates audio information into video TTA. Our method capitalizes on the rich semantic content of audio to generate audio-assisted pseudo-labels, a new concept in the context of video TTA. Specifically, we propose an audio-to-video label mapping method by first employing pre-trained audio models to classify audio signals extracted from videos and then mapping the audio-based predictions to video label spaces through large language models, thereby establishing a connection between the audio categories and video labels. To effectively leverage the generated pseudo-labels, we present a flexible adaptation cycle that determines the optimal number of adaptation iterations for each sample, based on changes in loss and consistency across different views. This enables a customized adaptation process for each sample. Experimental results on two widely used datasets (UCF101-C and Kinetics-Sounds-C), as well as on two newly constructed audio-video TTA datasets (AVE-C and AVMIT-C) with various corruption types, demonstrate the superiority of our approach. Our method consistently improves adaptation performance across different video classification models and represents a significant step forward in integrating audio information into video TTA. Code: https://github.com/keikeiqi/Audio-Assisted-TTA.
Chinese: 本研究提出了一种音频辅助的视频测试时自适应方法，利用音频生成的伪标签和灵活的自适应周期，在多个受损数据集上显著提升了模型性能。
English: This study introduces an audio-assisted test-time adaptation method for video that leverages audio-generated pseudo-labels and a flexible adaptation cycle to enhance model performance across multiple corrupted datasets.

Paperid: 213, https://arxiv.org/pdf/2506.12468.pdf   GitHub

Authors:Suyeon Kim, SeongKu Kang, Dongwoo Kim, Jungseul Ok, Hwanjo Yu

Title: Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark

Abstract:
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification tasks but struggle with label noise in real-world data. Existing studies on graph learning with label noise commonly rely on class-dependent label noise, overlooking the complexities of instance-dependent noise and falling short of capturing real-world corruption patterns. We introduce BeGIN (Benchmarking for Graphs with Instance-dependent Noise), a new benchmark that provides realistic graph datasets with various noise types and comprehensively evaluates noise-handling strategies across GNN architectures, noisy label detection, and noise-robust learning. To simulate instance-dependent corruptions, BeGIN introduces algorithmic methods and LLM-based simulations. Our experiments reveal the challenges of instance-dependent noise, particularly LLM-based corruption, and underscore the importance of node-specific parameterization to enhance GNN robustness. By comprehensively evaluating noise-handling strategies, BeGIN provides insights into their effectiveness, efficiency, and key performance factors. We expect that BeGIN will serve as a valuable resource for advancing research on label noise in graphs and fostering the development of robust GNN training methods. The code is available at https://github.com/kimsu55/BeGIN.
中文摘要：BeGIN基准通过引入实例相关噪声模拟和全面评估抗噪策略，弥补了现有图学习方法的不足，揭示了LLM模拟噪声的特殊挑战，并强调节点特定参数化对提升图神经网络鲁棒性的关键作用。
English Summary: The BeGIN benchmark addresses limitations in existing graph learning methods by introducing realistic instance-dependent noise simulations and comprehensively evaluating noise-handling strategies, revealing the particular challenges of LLM-based corruption while highlighting node-specific parameterization as key for GNN robustness.

Paperid: 214, https://arxiv.org/pdf/2506.12384.pdf   GitHub

Authors:Zichuan Fu, Xian Wu, Guojing Li, Yingying Zhang, Yefeng Zheng, Tianshi Ming, Yejing Wang, Wanyu Wang, Xiangyu Zhao

Title: Model Merging for Knowledge Editing

Abstract:
Large Language Models (LLMs) require continuous updates to maintain accurate and current knowledge as the world evolves. While existing knowledge editing approaches offer various solutions for knowledge updating, they often struggle with sequential editing scenarios and harm the general capabilities of the model, thereby significantly hampering their practical applicability. This paper proposes a two-stage framework combining robust supervised fine-tuning (R-SFT) with model merging for knowledge editing. Our method first fine-tunes the LLM to internalize new knowledge fully, then merges the fine-tuned model with the original foundation model to preserve newly acquired knowledge and general capabilities. Experimental results demonstrate that our approach significantly outperforms existing methods in sequential editing while better preserving the original performance of the model, all without requiring any architectural changes. Code is available at: https://github.com/Applied-Machine-Learning-Lab/MM4KE.
中文: 本文提出了一种结合鲁棒监督微调和模型合并的两阶段框架，能在不改变架构的情况下有效更新大语言模型的知识并保持其通用能力，在连续编辑任务中显著优于现有方法。
English: This paper introduces a two-stage framework that combines robust supervised fine-tuning with model merging to effectively update knowledge in Large Language Models while preserving their general capabilities, outperforming existing methods in sequential editing without architectural modifications.

Paperid: 215, https://arxiv.org/pdf/2506.12379.pdf   GitHub

Authors:Zichuan Fu, Xian Wu, Yejing Wang, Wanyu Wang, Shanshan Ye, Hongzhi Yin, Yi Chang, Yefeng Zheng, Xiangyu Zhao

Title: Training-free LLM Merging for Multi-task Learning

Abstract:
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen has triggered the development of numerous fine-tuned models tailored for various tasks and languages. In this paper, we explore an important question: is it possible to combine these specialized models to create a unified model with multi-task capabilities. We introduces Hierarchical Iterative Merging (Hi-Merging), a training-free method for unifying different specialized LLMs into a single model. Specifically, Hi-Merging employs model-wise and layer-wise pruning and scaling, guided by contribution analysis, to mitigate parameter conflicts. Extensive experiments on multiple-choice and question-answering tasks in both Chinese and English validate Hi-Merging's ability for multi-task learning. The results demonstrate that Hi-Merging consistently outperforms existing merging techniques and surpasses the performance of models fine-tuned on combined datasets in most scenarios. Code is available at: https://github.com/Applied-Machine-Learning-Lab/Hi-Merging.
中文: 本文提出Hi-Merging方法，通过分层剪枝和缩放将专业大语言模型统一为单一模型，在跨语言多任务学习中优于现有技术且无需额外训练。
English: This paper introduces Hi-Merging, a training-free method that unifies specialized LLMs into a single model through hierarchical pruning and scaling, demonstrating superior multi-task performance across languages compared to existing techniques.

Paperid: 216, https://arxiv.org/pdf/2506.12301.pdf   GitHub

Authors:Yue Wan, Xiaowei Jia, Xiang Lorraine Li

Title: Unveiling Confirmation Bias in Chain-of-Thought Reasoning

Abstract:
Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). However, the effectiveness of CoT reasoning is inconsistent across tasks with different reasoning types. This work presents a novel perspective to understand CoT behavior through the lens of \textit{confirmation bias} in cognitive psychology. Specifically, we examine how model internal beliefs, approximated by direct question-answering probabilities, affect both reasoning generation ($Q \to R$) and reasoning-guided answer prediction ($QR \to A$) in CoT. By decomposing CoT into a two-stage process, we conduct a thorough correlation analysis in model beliefs, rationale attributes, and stage-wise performance. Our results provide strong evidence of confirmation bias in LLMs, such that model beliefs not only skew the reasoning process but also influence how rationales are utilized for answer prediction. Furthermore, the interplay between task vulnerability to confirmation bias and the strength of beliefs also provides explanations for CoT effectiveness across reasoning tasks and models. Overall, this study provides a valuable insight for the needs of better prompting strategies that mitigate confirmation bias to enhance reasoning performance. Code is available at \textit{https://github.com/yuewan2/biasedcot}.
Chinese: 本研究揭示了大型语言模型中的确认偏见会扭曲思维链提示中的推理过程和答案预测，解释了其在不同任务中效果不一致的原因，并提出了需要减少偏见的策略。
English: This study reveals that confirmation bias in large language models skews both the reasoning process and answer prediction in chain-of-thought prompting, explaining its inconsistent effectiveness across tasks and suggesting the need for bias-mitigating strategies.

Paperid: 217, https://arxiv.org/pdf/2506.12284.pdf   GitHub GitHub

Authors:Thomas Walker, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

Title: GrokAlign: Geometric Characterisation and Acceleration of Grokking

Abstract:
A key challenge for the machine learning community is to understand and accelerate the training dynamics of deep networks that lead to delayed generalisation and emergent robustness to input perturbations, also known as grokking. Prior work has associated phenomena like delayed generalisation with the transition of a deep network from a linear to a feature learning regime, and emergent robustness with changes to the network's functional geometry, in particular the arrangement of the so-called linear regions in deep networks employing continuous piecewise affine nonlinearities. Here, we explain how grokking is realised in the Jacobian of a deep network and demonstrate that aligning a network's Jacobians with the training data (in the sense of cosine similarity) ensures grokking under a low-rank Jacobian assumption. Our results provide a strong theoretical motivation for the use of Jacobian regularisation in optimizing deep networks -- a method we introduce as GrokAlign -- which we show empirically to induce grokking much sooner than more conventional regularizers like weight decay. Moreover, we introduce centroid alignment as a tractable and interpretable simplification of Jacobian alignment that effectively identifies and tracks the stages of deep network training dynamics. Accompanying webpage (https://thomaswalker1.github.io/blog/grokalign.html) and code (https://github.com/ThomasWalker1/grokalign).
中文: 本研究阐释了深度网络中的"顿悟"现象是通过网络雅可比矩阵与训练数据的对齐实现的，并提出了GrokAlign这一雅可比正则化方法，相比传统权重衰减等方法能显著加快顿悟过程。
English: This study explains that grokking in deep networks is achieved through the alignment of the network's Jacobians with training data, leading to the introduction of GrokAlign, a Jacobian regularization method that accelerates grokking compared to traditional approaches like weight decay.

Paperid: 218, https://arxiv.org/pdf/2506.12231.pdf   GitHub

Authors:Ella Miray Rajaonson, Mahyar Rajabi Kochi, Luis Martin Mejia Mendoza, Seyed Mohamad Moosavi, Benjamin Sanchez-Lengeling

Title: CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction

Abstract:
Developing improved predictive models for multi-molecular systems is crucial, as nearly every chemical product used results from a mixture of chemicals. While being a vital part of the industry pipeline, the chemical mixture space remains relatively unexplored by the Machine Learning community. In this paper, we introduce CheMixHub, a holistic benchmark for molecular mixtures, covering a corpus of 11 chemical mixtures property prediction tasks, from drug delivery formulations to battery electrolytes, totalling approximately 500k data points gathered and curated from 7 publicly available datasets. CheMixHub introduces various data splitting techniques to assess context-specific generalization and model robustness, providing a foundation for the development of predictive models for chemical mixture properties. Furthermore, we map out the modelling space of deep learning models for chemical mixtures, establishing initial benchmarks for the community. This dataset has the potential to accelerate chemical mixture development, encompassing reformulation, optimization, and discovery. The dataset and code for the benchmarks can be found at: https://github.com/chemcognition-lab/chemixhub
中文: 本文提出CheMixHub分子混合物基准平台，涵盖11项性质预测任务和50万数据点，旨在推动混合物建模发展并加速化学制剂的配方优化与发现进程。
English: This paper introduces CheMixHub, a comprehensive benchmark for molecular mixtures featuring 11 property prediction tasks and 500k data points, designed to advance predictive modeling and accelerate chemical mixture development.

Paperid: 219, https://arxiv.org/pdf/2506.12230.pdf   GitHub

Authors:Yuan-Sen Ting

Title: Statistical Machine Learning for Astronomy -- A Textbook

Abstract:
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that reveals connections between modern data analysis techniques and traditional statistical methods. We show how these techniques emerge from familiar statistical foundations. The consistently Bayesian perspective prioritizes uncertainty quantification and statistical rigor essential for scientific inference in astronomy. The textbook progresses from probability theory and Bayesian inference through supervised learning including linear regression with measurement uncertainties, logistic regression, and classification. Unsupervised learning topics cover Principal Component Analysis and clustering methods. We then introduce computational techniques through sampling and Markov Chain Monte Carlo, followed by Gaussian Processes as probabilistic nonparametric methods and neural networks within the broader statistical context. Our theory-focused pedagogical approach derives each method from first principles with complete mathematical development, emphasizing statistical insight and complementing with astronomical applications. We prioritize understanding why algorithms work, when they are appropriate, and how they connect to broader statistical principles. The treatment builds toward modern techniques including neural networks through a solid foundation in classical methods and their theoretical underpinnings. This foundation enables thoughtful application of these methods to astronomical research, ensuring proper consideration of assumptions, limitations, and uncertainty propagation essential for advancing astronomical knowledge in the era of large astronomical surveys.
这本教材通过贝叶斯推断为天文研究构建了统计机器学习的统一框架，从概率论基础延伸到神经网络等现代技术，始终强调不确定性量化和理论严谨性。
This textbook establishes a unified Bayesian framework for statistical machine learning in astronomy, progressing from foundational probability theory to modern techniques like neural networks while emphasizing uncertainty quantification and theoretical rigor.

Paperid: 220, https://arxiv.org/pdf/2506.12222.pdf   GitHub

Authors:Tony Alex, Sara Ahmed, Armin Mustafa, Muhammad Awais, Philip JB Jackson

Title: SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes

Abstract:
Self-supervised pre-trained audio networks have seen widespread adoption in real-world systems, particularly in multi-modal large language models. These networks are often employed in a frozen state, under the assumption that the SSL pre-training has sufficiently equipped them to handle real-world audio. However, a critical question remains: how well do these models actually perform in real-world conditions, where audio is typically polyphonic and complex, involving multiple overlapping sound sources? Current audio SSL methods are often benchmarked on datasets predominantly featuring monophonic audio, such as environmental sounds, and speech. As a result, the ability of SSL models to generalize to polyphonic audio, a common characteristic in natural scenarios, remains underexplored. This limitation raises concerns about the practical robustness of SSL models in more realistic audio settings. To address this gap, we introduce Self-Supervised Learning from Audio Mixtures (SSLAM), a novel direction in audio SSL research, designed to improve, designed to improve the model's ability to learn from polyphonic data while maintaining strong performance on monophonic data. We thoroughly evaluate SSLAM on standard audio SSL benchmark datasets which are predominantly monophonic and conduct a comprehensive comparative analysis against SOTA methods using a range of high-quality, publicly available polyphonic datasets. SSLAM not only improves model performance on polyphonic audio, but also maintains or exceeds performance on standard audio SSL benchmarks. Notably, it achieves up to a 3.9\% improvement on the AudioSet-2M (AS-2M), reaching a mean average precision (mAP) of 50.2. For polyphonic datasets, SSLAM sets new SOTA in both linear evaluation and fine-tuning regimes with performance improvements of up to 9.1\% (mAP).
中文: 自监督音频模型因主要基于单声道音频训练而在处理现实多声道音频时表现不足，为此提出的SSLAM方法显著提升了多声道音频处理能力，同时保持了在标准基准测试中的优异性能。
English: Self-supervised audio models often underperform with polyphonic audio due to training on monophonic datasets, prompting the development of SSLAM, which enhances performance on complex audio while maintaining excellence on standard benchmarks.

Paperid: 221, https://arxiv.org/pdf/2506.12204.pdf   GitHub

Authors:Wenyue Hua, Dujian Ding, Yile Gu, Yujie Ren, Kai Mei, Minghua Ma, William Yang Wang

Title: Semantic Scheduling for LLM Inference

Abstract:
Conventional operating system scheduling algorithms are largely content-ignorant, making decisions based on factors such as latency or fairness without considering the actual intents or semantics of processes. Consequently, these algorithms often do not prioritize tasks that require urgent attention or carry higher importance, such as in emergency management scenarios. However, recent advances in language models enable semantic analysis of processes, allowing for more intelligent and context-aware scheduling decisions. In this paper, we introduce the concept of semantic scheduling in scheduling of requests from large language models (LLM), where the semantics of the process guide the scheduling priorities. We present a novel scheduling algorithm with optimal time complexity, designed to minimize the overall waiting time in LLM-based prompt scheduling. To illustrate its effectiveness, we present a medical emergency management application, underscoring the potential benefits of semantic scheduling for critical, time-sensitive tasks. The code and data are available at https://github.com/Wenyueh/latency_optimization_with_priority_constraints.
中文: 本文提出了一种针对大语言模型请求的语义调度算法，该算法根据任务内容的重要性确定优先级，并在医疗急救等关键应用中证明了其能有效缩短等待时间。
English: This paper introduces a semantic scheduling algorithm for large language model requests that prioritizes tasks based on their content significance, demonstrating its efficiency in minimizing waiting times for critical applications like medical emergencies.

Paperid: 222, https://arxiv.org/pdf/2506.12176.pdf   GitHub

Authors:Jackson Eshbaugh

Title: Fidelity Isn't Accuracy: When Linearly Decodable Functions Fail to Match the Ground Truth

Abstract:
Neural networks excel as function approximators, but their complexity often obscures the types of functions they learn, making it difficult to explain their behavior. To address this, the linearity score $λ(f)$ is introduced, a simple and interpretable diagnostic that quantifies how well a regression network's output can be mimicked by a linear model. Defined as the $R^2$ value between the network's predictions and those of a trained linear surrogate, $λ(f)$ measures linear decodability: the extent to which the network's behavior aligns with a structurally simple model. This framework is evaluated on both synthetic and real-world datasets, using dataset-specific networks and surrogates. High $λ(f)$ scores reliably indicate alignment with the network's outputs; however, they do not guarantee accuracy with respect to the ground truth. These results highlight the risk of using surrogate fidelity as a proxy for model understanding, especially in high-stakes regression tasks.
中文: 线性度评分λ(f)作为一种可解释的诊断工具，用于量化神经网络输出与线性替代模型的近似程度，但高分值并不保证与真实数据的准确性，警示在高风险回归任务中过度依赖替代模型保真度的风险。
English: The linearity score λ(f) is introduced as an interpretable diagnostic to measure how closely a neural network's outputs align with a linear surrogate model, though high scores don't guarantee accuracy with ground truth, cautioning against over-reliance on surrogate fidelity for model understanding.

Paperid: 223, https://arxiv.org/pdf/2506.12041.pdf   GitHub

Authors:Yewei Liu, Xiyuan Wang, Muhan Zhang

Title: Meta Pruning via Graph Metanetworks : A Meta Learning Framework for Network Pruning

Abstract:
Network pruning, aimed at reducing network size while preserving accuracy, has attracted significant research interest. Numerous pruning techniques have been proposed over time. They are becoming increasingly effective, but more complex and harder to interpret as well. Given the inherent complexity of neural networks, we argue that manually designing pruning criteria has reached a bottleneck. To address this, we propose a novel approach in which we "use a neural network to prune neural networks". More specifically, we introduce the newly developed idea of metanetwork from meta-learning into pruning. A metanetwork is a network that takes another network as input and produces a modified network as output. In this paper, we first establish a bijective mapping between neural networks and graphs, and then employ a graph neural network as our metanetwork. We train a metanetwork that learns the pruning strategy automatically which can transform a network that is hard to prune into another network that is much easier to prune. Once the metanetwork is trained, our pruning needs nothing more than a feedforward through the metanetwork and the standard finetuning to prune at state-of-the-art. Our method achieved outstanding results on many popular and representative pruning tasks (including ResNet56 on CIFAR10, VGG19 on CIFAR100, ResNet50 on ImageNet). Our code is available at https://github.com/Yewei-Liu/MetaPruning
Chinese: 我们提出了一种全新的元学习网络剪枝框架，通过元网络自动学习复杂剪枝规则，无需针对每个任务进行专门训练即可在各种网络上实现最先进的剪枝效果。
English: We introduce a novel meta-learning framework for network pruning that automatically learns complex pruning rules through a metanetwork, achieving state-of-the-art results across various networks without requiring special training for each task.

Paperid: 224, https://arxiv.org/pdf/2506.12040.pdf   GitHub

Authors:Hao Gu, Lujun Li, Zheyu Wang, Bei Liu, Qiyuan Zhu, Sirui Han, Yike Guo

Title: BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook

Abstract:
Binary quantization represents the most extreme form of large language model (LLM) compression, reducing weights to $\pm$1 for maximal memory and computational efficiency. While recent sparsity-aware binarization methods achieve sub-1-bit compression by pruning redundant binary weights, they suffer from three critical challenges: performance deterioration, computational complexity from sparse mask management, and limited hardware compatibility. In this paper, we present BTC-LLM, a novel sub-1-bit LLM quantization framework that leverages adaptive weight transformation and binary pattern clustering to overcome these limitations, delivering both superior accuracy and efficiency. Our approach incorporates two key innovations: (1) a Learnable Transformation that optimizes invertible scaling and rotation matrices to align binarized weights with full-precision distributions, enabling incoherence processing to enhance layer-wise representation quality; (2) a Flash and Accurate Binary Codebook that identifies recurring binary vector clusters, compressing them into compact indices with tailored distance metrics and sign-based centroid updates. This eliminates the need for sparse masks, enabling efficient inference on standard hardware. Our code is available at https://github.com/Chooovy/BTC-LLM.
中文：BTC-LLM是一种新颖的亚1比特量化框架，通过自适应权重变换和二进制模式聚类克服了性能和硬件限制，无需稀疏掩码即可实现卓越效率。
English: BTC-LLM is a novel sub-1-bit quantization framework that overcomes performance and hardware limitations through adaptive weight transformation and binary pattern clustering, achieving superior efficiency without sparse masks.

Paperid: 225, https://arxiv.org/pdf/2506.12015.pdf   GitHub

Authors:Hsi-Che Lin, Yu-Chu Yu, Kai-Po Chang, Yu-Chiang Frank Wang

Title: EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction

Abstract:
Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains. However, fine-tuning large foundation models for domain-specific or personalized tasks remains prohibitively expensive for most users due to the significant memory overhead beyond that of inference. We introduce EMLoC, an Emulator-based Memory-efficient fine-tuning framework with LoRA Correction, which enables model fine-tuning within the same memory budget required for inference. EMLoC constructs a task-specific light-weight emulator using activation-aware singular value decomposition (SVD) on a small downstream calibration set. Fine-tuning then is performed on this lightweight emulator via LoRA. To tackle the misalignment between the original model and the compressed emulator, we propose a novel compensation algorithm to correct the fine-tuned LoRA module, which thus can be merged into the original model for inference. EMLoC supports flexible compression ratios and standard training pipelines, making it adaptable to a wide range of applications. Extensive experiments demonstrate that EMLoC outperforms other baselines across multiple datasets and modalities. Moreover, without quantization, EMLoC enables fine-tuning of a 38B model on a single 24GB consumer GPU-bringing efficient and practical model adaptation to individual users.

Paperid: 226, https://arxiv.org/pdf/2506.12014.pdf   GitHub

Authors:Yuliang Xu, Siming Huang, Mingmeng Geng, Yao Wan, Xuanhua Shi, Dongping Chen

Title: code_transformed: The Influence of Large Language Models on Code

Abstract:
Coding remains one of the most fundamental modes of interaction between humans and machines. With the rapid advancement of Large Language Models (LLMs), code generation capabilities have begun to significantly reshape programming practices. This development prompts a central question: Have LLMs transformed code style, and how can such transformation be characterized? In this paper, we present a pioneering study that investigates the impact of LLMs on code style, with a focus on naming conventions, complexity, maintainability, and similarity. By analyzing code from over 19,000 GitHub repositories linked to arXiv papers published between 2020 and 2025, we identify measurable trends in the evolution of coding style that align with characteristics of LLM-generated code. For instance, the proportion of snake\_case variable names in Python code increased from 47% in Q1 2023 to 51% in Q1 2025. Furthermore, we investigate how LLMs approach algorithmic problems by examining their reasoning processes. Given the diversity of LLMs and usage scenarios, among other factors, it is difficult or even impossible to precisely estimate the proportion of code generated or assisted by LLMs. Our experimental results provide the first large-scale empirical evidence that LLMs affect real-world programming style.
中文：大型语言模型正显著影响现实世界的代码风格，通过对数万个GitHub仓库的分析，发现命名规范等编程风格正呈现与AI生成代码一致的变化趋势。
English: Large Language Models are measurably influencing real-world coding styles, as evidenced by trends in naming conventions and other style elements across thousands of GitHub repositories.

Paperid: 227, https://arxiv.org/pdf/2506.12007.pdf   GitHub

Authors:Paul Setinek, Gianluca Galletti, Thomas Gross, Dominik Schnürer, Johannes Brandstetter, Werner Zellinger

Title: SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts

Abstract:
Neural surrogates for Partial Differential Equations (PDEs) often suffer significant performance degradation when evaluated on unseen problem configurations, such as novel material types or structural dimensions. Meanwhile, Domain Adaptation (DA) techniques have been widely used in vision and language processing to generalize from limited information about unseen configurations. In this work, we address this gap through two focused contributions. First, we introduce SIMSHIFT, a novel benchmark dataset and evaluation suite composed of four industrial simulation tasks: hot rolling, sheet metal forming, electric motor design and heatsink design. Second, we extend established domain adaptation methods to state of the art neural surrogates and systematically evaluate them. These approaches use parametric descriptions and ground truth simulations from multiple source configurations, together with only parametric descriptions from target configurations. The goal is to accurately predict target simulations without access to ground truth simulation data. Extensive experiments on SIMSHIFT highlight the challenges of out of distribution neural surrogate modeling, demonstrate the potential of DA in simulation, and reveal open problems in achieving robust neural surrogates under distribution shifts in industrially relevant scenarios. Our codebase is available at https://github.com/psetinek/simshift
神经PDE代理模型在未见配置下表现不佳，而领域自适应技术可利用目标域的参数化数据（无需真实模拟数据）提升其泛化能力。
Neural PDE surrogates struggle with unseen configurations, but domain adaptation techniques can improve their generalization using parametric data from target domains without ground truth simulations.

Paperid: 228, https://arxiv.org/pdf/2506.11997.pdf   GitHub

Authors:Korbinian Pöppel, Richard Freinschlag, Thomas Schmied, Wei Lin, Sepp Hochreiter

Title: pLSTM: parallelizable Linear Source Transition Mark networks

Abstract:
Modern recurrent architectures, such as xLSTM and Mamba, have recently challenged the Transformer in language modeling. However, their structure constrains their applicability to sequences only or requires processing multi-dimensional data structures, such as images or molecular graphs, in a pre-defined sequential order. In contrast, Multi-Dimensional RNNs (MDRNNs) are well suited for data with a higher level structure, like 2D grids, trees, and directed acyclic graphs (DAGs). In this work, we extend the notion of multi-dimensionality to linear RNNs. We introduce parallelizable Linear Source Transition Mark networks (pLSTMs) using Source, Transition, and Mark gates that act on the line graph of a general DAG. This enables parallelization in analogy to parallel associative scans and the chunkwise-recurrent form of sequential linear RNNs, but for DAGs. For regular grids (1D and 2D), like images, this scheme can be efficiently implemented using einsum operations, concatenations, and padding in logarithmic time. pLSTMs tackle the vanishing/exploding activation/gradient problem for long distances in DAGs via two distinct modes: a directed propagation mode (P-mode) and a diffusive distribution mode (D-mode). To showcase the long-range capabilities of pLSTM, we introduce arrow-pointing extrapolation as a synthetic computer vision task that contains long-distance directional information. We demonstrate that pLSTMs generalize well to larger image sizes, whereas Transformers struggle to extrapolate. On established molecular graph and computer vision benchmarks, pLSTMs also show strong performance. Code and Datasets are available at: https://github.com/ml-jku/plstm_experiments.
Chinese Summary: 本研究提出了可并行化的线性源转换标记网络（pLSTMs），将多维线性RNNs扩展到有向无环图处理，通过并行计算解决长距离依赖问题，在需要方向信息的任务和泛化能力上优于Transformer模型。
English Summary: The study introduces parallelizable Linear Source Transition Mark networks (pLSTMs), which extend multi-dimensional linear RNNs to handle directed acyclic graphs with parallel processing and address long-range dependency issues, outperforming Transformers in tasks requiring directional information and generalization.

Paperid: 229, https://arxiv.org/pdf/2506.11938.pdf   GitHub

Authors:Samuel Simko, Mrinmaya Sachan, Bernhard Schölkopf, Zhijing Jin

Title: Improving Large Language Model Safety with Contrastive Representation Learning

Abstract:
Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to adversarial attacks. While existing defenses often struggle to generalize across varying attack types, recent advancements in representation engineering offer promising alternatives. In this work, we propose a defense framework that formulates model defense as a contrastive representation learning (CRL) problem. Our method finetunes a model using a triplet-based loss combined with adversarial hard negative mining to encourage separation between benign and harmful representations. Our experimental results across multiple models demonstrate that our approach outperforms prior representation engineering-based defenses, improving robustness against both input-level and embedding-space attacks without compromising standard performance. Our code is available at https://github.com/samuelsimko/crl-llm-defense
中文: 本研究提出了一种基于对比表征学习的大语言模型防御框架，通过三元组损失和对抗性负样本挖掘增强模型鲁棒性，在保持标准性能的同时有效抵御输入级和嵌入空间攻击。
English: This study introduces a contrastive representation learning framework for defending Large Language Models against adversarial attacks, utilizing triplet loss and adversarial mining to enhance robustness without sacrificing standard performance.

Paperid: 230, https://arxiv.org/pdf/2506.11902.pdf   GitHub

Authors:Zhenyu Hou, Ziniu Hu, Yujiang Li, Rui Lu, Jie Tang, Yuxiao Dong

Title: TreeRL: LLM Reinforcement Learning with On-Policy Tree Search

Abstract:
Reinforcement learning (RL) with tree search has demonstrated superior performance in traditional reasoning tasks. Compared to conventional independent chain sampling strategies with outcome supervision, tree search enables better exploration of the reasoning space and provides dense, on-policy process rewards during RL training but remains under-explored in On-Policy LLM RL. We propose TreeRL, a reinforcement learning framework that directly incorporates on-policy tree search for RL training. Our approach includes intermediate supervision and eliminates the need for a separate reward model training. Existing approaches typically train a separate process reward model, which can suffer from distribution mismatch and reward hacking. We also introduce a cost-effective tree search approach that achieves higher search efficiency under the same generation token budget by strategically branching from high-uncertainty intermediate steps rather than using random branching. Experiments on challenging math and code reasoning benchmarks demonstrate that TreeRL achieves superior performance compared to traditional ChainRL, highlighting the potential of tree search for LLM. TreeRL is open-sourced at https://github.com/THUDM/TreeRL.
中文摘要：TreeRL提出了一种基于策略树搜索的强化学习框架，通过策略性分支和中间监督提升推理性能，无需单独训练奖励模型。
English Summary: TreeRL introduces an on-policy tree search reinforcement learning framework that enhances reasoning performance through strategic branching and intermediate supervision, eliminating the need for separate reward models.

Paperid: 231, https://arxiv.org/pdf/2506.11798.pdf   GitHub

Authors:Maximilian Kreutner, Marlene Lutz, Markus Strohmaier

Title: Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models

Abstract:
Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse, but have been found to consistently display a progressive left-leaning bias. At the same time, so-called persona or identity prompts have been shown to produce LLM behavior that aligns with socioeconomic groups that the base model is not aligned with. In this work, we analyze whether zero-shot persona prompting with limited information can accurately predict individual voting decisions and, by aggregation, accurately predict positions of European groups on a diverse set of policies. We evaluate if predictions are stable towards counterfactual arguments, different persona prompts and generation methods. Finally, we find that we can simulate voting behavior of Members of the European Parliament reasonably well with a weighted F1 score of approximately 0.793. Our persona dataset of politicians in the 2024 European Parliament and our code are available at https://github.com/dess-mannheim/european_parliament_simulation.
中文摘要：研究表明，零样本角色提示能有效模拟个人投票决策并预测欧洲群体的政策立场，对欧洲议会议员投票行为的模拟加权F1分数达到约0.793。
English Summary: This study demonstrates that zero-shot persona prompting can effectively simulate individual voting decisions and predict policy positions of European groups, achieving a weighted F1 score of approximately 0.793 for simulating European Parliament members' voting behavior.

Paperid: 232, https://arxiv.org/pdf/2506.11769.pdf   GitHub

Authors:Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang

Title: Long-Short Alignment for Effective Long-Context Modeling in LLMs

Abstract:
Large language models (LLMs) have exhibited impressive performance and surprising emergent properties. However, their effectiveness remains limited by the fixed context window of the transformer architecture, posing challenges for long-context modeling. Among these challenges, length generalization -- the ability to generalize to sequences longer than those seen during training -- is a classical and fundamental problem. In this work, we propose a fresh perspective on length generalization, shifting the focus from the conventional emphasis on input features such as positional encodings or data structures to the output distribution of the model. Specifically, through case studies on synthetic tasks, we highlight the critical role of \textbf{long-short alignment} -- the consistency of output distributions across sequences of varying lengths. Extending this insight to natural language tasks, we propose a metric called Long-Short Misalignment to quantify this phenomenon, uncovering a strong correlation between the metric and length generalization performance. Building on these findings, we develop a regularization term that promotes long-short alignment during training. Extensive experiments validate the effectiveness of our approach, offering new insights for achieving more effective long-context modeling in LLMs. Code is available at https://github.com/PKU-ML/LongShortAlignment.
Chinese: 本研究提出了大语言模型中长度泛化的新视角，强调输出分布的长短对齐重要性，通过设计量化指标和正则化方法，有效提升了长上下文任务的性能表现。
English: This research introduces a novel perspective on length generalization in large language models by emphasizing the importance of long-short alignment in output distributions, proposing a metric and regularization method that significantly improves performance on long-context tasks.

Paperid: 233, https://arxiv.org/pdf/2506.11653.pdf   GitHub

Authors:Emre Kavak, Tom Nuno Wolf, Christian Wachinger

Title: DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation

Abstract:
Dataset bias often leads deep learning models to exploit spurious correlations instead of task-relevant signals. We introduce the Standard Anti-Causal Model (SAM), a unifying causal framework that characterizes bias mechanisms and yields a conditional independence criterion for causal stability. Building on this theory, we propose DISCO$_m$ and sDISCO, efficient and scalable estimators of conditional distance correlation that enable independence regularization in black-box models. Across five diverse datasets, our methods consistently outperform or are competitive in existing bias mitigation approaches, while requiring fewer hyperparameters and scaling seamlessly to multi-bias scenarios. This work bridges causal theory and practical deep learning, providing both a principled foundation and effective tools for robust prediction. Source Code: https://github.com/***.
中文: 本文提出因果框架和高效估计器来解决深度学习中的数据集偏差问题，在多种数据集上以最少超参数实现了稳健性能。
English: This paper introduces a causal framework and efficient estimators to mitigate dataset bias in deep learning, achieving robust performance across diverse datasets with minimal hyperparameters.

Paperid: 234, https://arxiv.org/pdf/2506.11586.pdf   GitHub

Authors:Shashank Balla

Title: SecONNds: Secure Outsourced Neural Network Inference on ImageNet

Abstract:
The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and communication costs, rendering them impractical for real-world deployment. We introduce SecONNds, a non-intrusive secure inference framework optimized for large ImageNet-scale Convolutional Neural Networks. SecONNds integrates a novel fully Boolean Goldreich-Micali-Wigderson (GMW) protocol for secure comparison -- addressing Yao's millionaires' problem -- using preprocessed Beaver's bit triples generated from Silent Random Oblivious Transfer. Our novel protocol achieves an online speedup of 17$\times$ in nonlinear operations compared to state-of-the-art solutions while reducing communication overhead. To further enhance performance, SecONNds employs Number Theoretic Transform (NTT) preprocessing and leverages GPU acceleration for homomorphic encryption operations, resulting in speedups of 1.6$\times$ on CPU and 2.2$\times$ on GPU for linear operations. We also present SecONNds-P, a bit-exact variant that ensures verifiable full-precision results in secure computation, matching the results of plaintext computations. Evaluated on a 37-bit quantized SqueezeNet model, SecONNds achieves an end-to-end inference time of 2.8 s on GPU and 3.6 s on CPU, with a total communication of just 420 MiB. SecONNds' efficiency and reduced computational load make it well-suited for deploying privacy-sensitive applications in resource-constrained environments. SecONNds is open source and can be accessed from: https://github.com/shashankballa/SecONNds.
中文：SecONNds提出了一种针对大型神经网络优化的高效安全推理框架，通过创新协议和GPU加速显著降低了计算和通信开销，同时确保隐私保护。
English: SecONNds introduces an efficient secure inference framework optimized for large neural networks, significantly reducing computational and communication overhead while ensuring privacy through novel protocols and GPU acceleration.

Paperid: 235, https://arxiv.org/pdf/2506.11550.pdf   GitHub

Authors:Xiaoyu Ma, Hao Chen, Yongjian Deng

Title: Improving Multimodal Learning Balance and Sufficiency through Data Remixing

Abstract:
Different modalities hold considerable gaps in optimization trajectories, including speeds and paths, which lead to modality laziness and modality clash when jointly training multimodal models, resulting in insufficient and imbalanced multimodal learning. Existing methods focus on enforcing the weak modality by adding modality-specific optimization objectives, aligning their optimization speeds, or decomposing multimodal learning to enhance unimodal learning. These methods fail to achieve both unimodal sufficiency and multimodal balance. In this paper, we, for the first time, address both concerns by proposing multimodal Data Remixing, including decoupling multimodal data and filtering hard samples for each modality to mitigate modality imbalance; and then batch-level reassembling to align the gradient directions and avoid cross-modal interference, thus enhancing unimodal learning sufficiency. Experimental results demonstrate that our method can be seamlessly integrated with existing approaches, improving accuracy by approximately 6.50%$\uparrow$ on CREMAD and 3.41%$\uparrow$ on Kinetic-Sounds, without training set expansion or additional computational overhead during inference. The source code is available at https://github.com/MatthewMaxy/Remix_ICML2025.
中文: 本文提出多模态数据重组方法，通过解耦和重组数据解决模态不平衡问题并增强单模态学习，在不增加计算成本的情况下显著提升了准确率。
English: This paper introduces multimodal Data Remixing, a method that decouples and reassembles data to address modality imbalance and enhance unimodal learning, achieving significant accuracy improvements without extra computational costs.

Paperid: 236, https://arxiv.org/pdf/2506.11543.pdf   GitHub

Authors:Zhuguanyu Wu, Shihe Wang, Jiayi Zhang, Jiaxin Chen, Yunhong Wang

Title: FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation

Abstract:
Post-training quantization (PTQ) has stood out as a cost-effective and promising model compression paradigm in recent years, as it avoids computationally intensive model retraining. Nevertheless, current PTQ methods for Vision Transformers (ViTs) still suffer from significant accuracy degradation, especially under low-bit quantization. To address these shortcomings, we analyze the prevailing Hessian-guided quantization loss, and uncover certain limitations of conventional Hessian approximations. By following the block-wise reconstruction framework, we propose a novel PTQ method for ViTs, dubbed FIMA-Q. Specifically, we firstly establish the connection between KL divergence and FIM, which enables fast computation of the quantization loss during reconstruction. We further propose an efficient FIM approximation method, namely DPLR-FIM, by employing the diagonal plus low-rank principle, and formulate the ultimate quantization loss. Our extensive experiments, conducted across various vision tasks with representative ViT-based architectures on public datasets, demonstrate that our method substantially promotes the accuracy compared to the state-of-the-art approaches, especially in the case of low-bit quantization. The source code is available at https://github.com/ShiheWang/FIMA-Q.
中文摘要：本文提出FIMA-Q这一新型视觉Transformer后训练量化方法，通过快速Fisher信息矩阵近似和分块重建框架解决低比特量化精度损失问题，在多项视觉任务中显著超越现有最优方法。
English Summary: This paper introduces FIMA-Q, a novel post-training quantization method for Vision Transformers that addresses accuracy degradation in low-bit settings by leveraging a fast Fisher Information Matrix approximation and block-wise reconstruction, achieving superior performance across various tasks.

Paperid: 237, https://arxiv.org/pdf/2506.11515.pdf   GitHub

Authors:Xiao Xu, Libo Qin, Wanxiang Che, Min-Yen Kan

Title: Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs

Abstract:
Two-Tower Vision--Language Models (VLMs) have demonstrated strong performance across various downstream VL tasks. While BridgeTower further enhances performance by building bridges between encoders, it \textit{(i)} suffers from ineffective layer-by-layer utilization of unimodal representations, \textit{(ii)} restricts the flexible exploitation of different levels of unimodal semantic knowledge, and \textit{(iii)} is limited to the evaluation on traditional low-resolution datasets only with the Two-Tower VLM architecture. In this work, we propose Manager, a lightweight, efficient and effective plugin that adaptively aggregates insights from different levels of pre-trained unimodal experts to facilitate more comprehensive VL alignment and fusion. First, under the Two-Tower VLM architecture, we introduce ManagerTower, a novel VLM that introduces the manager in each cross-modal layer. Whether with or without VL pre-training, ManagerTower outperforms previous strong baselines and achieves superior performance on 4 downstream VL tasks. Moreover, we extend our exploration to the latest Multimodal Large Language Model (MLLM) architecture. We demonstrate that LLaVA-OV-Manager significantly boosts the zero-shot performance of LLaVA-OV across different categories of capabilities, images, and resolutions on 20 downstream datasets, whether the multi-grid algorithm is enabled or not. In-depth analysis reveals that both our manager and the multi-grid algorithm can be viewed as a plugin that improves the visual representation by capturing more diverse visual details from two orthogonal perspectives (depth and width). Their synergy can mitigate the semantic ambiguity caused by the multi-grid algorithm and further improve performance. Code and models are available at https://github.com/LooperXX/ManagerTower.
中文: 提出的Manager插件通过自适应整合多层级单模态专家知识，在双塔视觉语言模型和多模态大语言模型中均实现了性能提升，并与多网格算法形成互补，增强了视觉细节的捕捉能力。
English: The proposed Manager plugin enhances Two-Tower Vision-Language Models by adaptively integrating multi-level unimodal expertise, achieving superior performance across VL tasks and MLLM architectures while complementing multi-grid algorithms for richer visual representation.

Paperid: 238, https://arxiv.org/pdf/2506.11465.pdf   GitHub

Authors:Haotian Ni, Yake Wei, Hang Liu, Gong Chen, Chong Peng, Hao Lin, Di Hu

Title: RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer

Abstract:
Multimodal learning faces challenges in effectively fusing information from diverse modalities, especially when modality quality varies across samples. Dynamic fusion strategies, such as attention mechanism in Transformers, aim to address such challenge by adaptively emphasizing modalities based on the characteristics of input data. However, through amounts of carefully designed experiments, we surprisingly observed that the dynamic adaptability of widely-used self-attention models diminishes. Model tends to prefer one modality regardless of data characteristics. This bias triggers a self-reinforcing cycle that progressively overemphasizes the favored modality, widening the distribution gap in attention keys across modalities and deactivating attention mechanism's dynamic properties. To revive adaptability, we propose a simple yet effective method Rolling Query (RollingQ), which balances attention allocation by rotating the query to break the self-reinforcing cycle and mitigate the key distribution gap. Extensive experiments on various multimodal scenarios validate the effectiveness of RollingQ and the restoration of cooperation dynamics is pivotal for enhancing the broader capabilities of widely deployed multimodal Transformers. The source code is available at https://github.com/GeWu-Lab/RollingQ_ICML2025.
中文摘要：研究发现多模态学习中的自注意力机制常无法动态适应不同模态质量，反而偏好单一模态形成自我强化偏差，但提出的滚动查询（RollingQ）方法通过轮换查询平衡注意力分配，成功恢复了模型的动态适应性。
English Summary: The study reveals that self-attention mechanisms in multimodal learning often fail to dynamically adapt to varying modality qualities, instead favoring one modality and creating a self-reinforcing bias, but proposes Rolling Query (RollingQ) to restore adaptability by rotating queries to balance attention allocation.

Paperid: 239, https://arxiv.org/pdf/2506.11449.pdf   GitHub

Authors:Abhishek Tyagi, Arjun Iyer, William H Renninger, Christopher Kanan, Yuhao Zhu

Title: Dynamic Sparse Training of Diagonally Sparse Networks

Abstract:
Recent advances in Dynamic Sparse Training (DST) have pushed the frontier of sparse neural network training in structured and unstructured contexts, matching dense-model performance while drastically reducing parameter counts to facilitate model scaling. However, unstructured sparsity often fails to translate into practical speedups on modern hardware. To address this shortcoming, we propose DynaDiag, a novel structured sparse-to-sparse DST method that performs at par with unstructured sparsity. DynaDiag enforces a diagonal sparsity pattern throughout training and preserves sparse computation in forward and backward passes. We further leverage the diagonal structure to accelerate computation via a custom CUDA kernel, rendering the method hardware-friendly. Empirical evaluations on diverse neural architectures demonstrate that our method maintains accuracy on par with unstructured counterparts while benefiting from tangible computational gains. Notably, with 90% sparse linear layers in ViTs, we observe up to a 3.13x speedup in online inference without sacrificing model performance and a 1.59x speedup in training on a GPU compared to equivalent unstructured layers. Our source code is available at https://github.com/horizon-research/DynaDiag/.
中文: DynaDiag提出了一种采用对角线稀疏模式的结构化稀疏训练方法，在保持与无结构化方法同等精度的同时，通过硬件友好的实现获得了显著的计算加速效果。
English: DynaDiag introduces a structured sparse training method using diagonal sparsity patterns that matches the accuracy of unstructured approaches while achieving significant computational speedups through hardware-friendly implementations.

Paperid: 240, https://arxiv.org/pdf/2506.11431.pdf   GitHub

Authors:Jinhee Kim, Seoyeon Yoon, Taeho Lee, Joo Chan Lee, Kang Eun Jeon, Jong Hwan Ko

Title: TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision

Abstract:
The deployment of deep neural networks on edge devices is a challenging task due to the increasing complexity of state-of-the-art models, requiring efforts to reduce model size and inference latency. Recent studies explore models operating at diverse quantization settings to find the optimal point that balances computational efficiency and accuracy. Truncation, an effective approach for achieving lower bit precision mapping, enables a single model to adapt to various hardware platforms with little to no cost. However, formulating a training scheme for deep neural networks to withstand the associated errors introduced by truncation remains a challenge, as the current quantization-aware training schemes are not designed for the truncation process. We propose TruncQuant, a novel truncation-ready training scheme allowing flexible bit precision through bit-shifting in runtime. We achieve this by aligning TruncQuant with the output of the truncation process, demonstrating strong robustness across bit-width settings, and offering an easily implementable training scheme within existing quantization-aware frameworks. Our code is released at https://github.com/a2jinhee/TruncQuant.
中文: TruncQuant是一种创新的训练方案，通过运行时位移使深度神经网络能灵活适应不同位精度，在多种硬件平台上均表现出强大鲁棒性，且易于集成到现有量化感知框架中。
English: TruncQuant is a novel training scheme that enables deep neural networks to adapt flexibly to various bit precisions through runtime bit-shifting, providing robust performance across different hardware platforms while being easily integrated into existing quantization-aware frameworks.

Paperid: 241, https://arxiv.org/pdf/2506.11402.pdf   GitHub

Authors:Pradyut Sekhsaria, Marcel Mateos Salles, Hai Huang, Randall Balestriero

Title: LoRA Users Beware: A Few Spurious Tokens Can Manipulate Your Finetuned Model

Abstract:
Parameter Efficient FineTuning (PEFT), such as Low-Rank Adaptation (LoRA), aligns pre-trained Large Language Models (LLMs) to particular downstream tasks in a resource-efficient manner. Because efficiency has been the main metric of progress, very little attention has been put in understanding possible catastrophic failures. We uncover one such failure: PEFT encourages a model to search for shortcut solutions to solve its fine-tuning tasks. When very small amount of tokens, e.g., one token per prompt, are correlated with downstream task classes, PEFT makes any pretrained model rely predominantly on that token for decision making. While such spurious tokens may emerge accidentally from incorrect data cleaning, it also opens opportunities for malevolent parties to control a model's behavior from Seamless Spurious Token Injection (SSTI). In SSTI, a small amount of tokens correlated with downstream classes are injected by the dataset creators. At test time, the finetuned LLM's behavior can be controlled solely by injecting those few tokens. We apply SSTI across models from three families (Snowflake Arctic, Apple OpenELM, and Meta LLaMA-3) and four diverse datasets (IMDB, Financial Classification, CommonSense QA, and Bias in Bios). Our findings reveal three astonishing behaviors. First, as few as a single token of SSTI is sufficient to steer a model's decision making. Second, for light SSTI, the reliance on spurious tokens is proportional to the LoRA rank. Lastly, with aggressive SSTI, larger LoRA rank values become preferable to small rank values as it makes the model attend to non-spurious tokens, hence improving robustness.

Paperid: 242, https://arxiv.org/pdf/2506.11332.pdf   GitHub

Authors:Sadman Sadeed Omee, Lai Wei, Sourin Dey, Jianjun Hu

Title: Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control

Abstract:
Crystalline materials can form different structural arrangements (i.e. polymorphs) with the same chemical composition, exhibiting distinct physical properties depending on how they were synthesized or the conditions under which they operate. For example, carbon can exist as graphite (soft, conductive) or diamond (hard, insulating). Computational methods that can predict these polymorphs are vital in materials science, which help understand stability relationships, guide synthesis efforts, and discover new materials with desired properties without extensive trial-and-error experimentation. However, effective crystal structure prediction (CSP) algorithms for inorganic polymorph structures remain limited. We propose ParetoCSP2, a multi-objective genetic algorithm for polymorphism CSP that incorporates an adaptive space group diversity control technique, preventing over-representation of any single space group in the population guided by a neural network interatomic potential. Using an improved population initialization method and performing iterative structure relaxation, ParetoCSP2 not only alleviates premature convergence but also achieves improved convergence speed. Our results show that ParetoCSP2 achieves excellent performance in polymorphism prediction, including a nearly perfect space group and structural similarity accuracy for formulas with two polymorphs but with the same number of unit cell atoms. Evaluated on a benchmark dataset, it outperforms baseline algorithms by factors of 2.46-8.62 for these accuracies and improves by 44.8\%-87.04\% across key performance metrics for regular CSP. Our source code is freely available at https://github.com/usccolumbia/ParetoCSP2.
Chinese: 该研究提出ParetoCSP2算法，通过自适应空间群多样性控制和神经网络势能指导，改进了无机多晶型物的晶体结构预测，在准确性和收敛速度上均显著优于基准算法。
English: The study introduces ParetoCSP2, a multi-objective genetic algorithm that enhances crystal structure prediction for inorganic polymorphs by incorporating adaptive space group diversity control and neural network potentials, achieving superior accuracy and convergence compared to baseline methods.

Paperid: 243, https://arxiv.org/pdf/2506.11271.pdf   GitHub

Authors:Kyung Rok Kim, Yansong Wang, Xiaocheng Li, Guanting Chen

Title: Collaborative Prediction: To Join or To Disjoin Datasets

Abstract:
With the recent rise of generative Artificial Intelligence (AI), the need of selecting high-quality dataset to improve machine learning models has garnered increasing attention. However, some part of this topic remains underexplored, even for simple prediction models. In this work, we study the problem of developing practical algorithms that select appropriate dataset to minimize population loss of our prediction model with high probability. Broadly speaking, we investigate when datasets from different sources can be effectively merged to enhance the predictive model's performance, and propose a practical algorithm with theoretical guarantees. By leveraging an oracle inequality and data-driven estimators, the algorithm reduces population loss with high probability. Numerical experiments demonstrate its effectiveness in both standard linear regression and broader machine learning applications. Code is available at https://github.com/kkrokii/collaborative_prediction.
中文: 本研究提出了一种实用算法，通过整合不同来源的数据集，在理论上保证以高概率降低总体损失，并在线性回归和更广泛的机器学习应用中通过数值实验验证了其有效性。
English: This research introduces a practical algorithm that merges datasets from various sources to minimize population loss with theoretical guarantees, validated through numerical experiments in linear regression and broader machine learning tasks.

Paperid: 244, https://arxiv.org/pdf/2506.11262.pdf   GitHub

Authors:Shijie Fang, Hang Yu, Qidi Fang, Reuben M. Aronson, Elaine S. Short

Title: Demonstration Sidetracks: Categorizing Systematic Non-Optimality in Human Demonstrations

Abstract:
Learning from Demonstration (LfD) is a popular approach for robots to acquire new skills, but most LfD methods suffer from imperfections in human demonstrations. Prior work typically treats these suboptimalities as random noise. In this paper we study non-optimal behaviors in non-expert demonstrations and show that they are systematic, forming what we call demonstration sidetracks. Using a public space study with 40 participants performing a long-horizon robot task, we recreated the setup in simulation and annotated all demonstrations. We identify four types of sidetracks (Exploration, Mistake, Alignment, Pause) and one control pattern (one-dimension control). Sidetracks appear frequently across participants, and their temporal and spatial distribution is tied to task context. We also find that users' control patterns depend on the control interface. These insights point to the need for better models of suboptimal demonstrations to improve LfD algorithms and bridge the gap between lab training and real-world deployment. All demonstrations, infrastructure, and annotations are available at https://github.com/AABL-Lab/Human-Demonstration-Sidetracks.
中文: 本研究识别了人类机器人演示中系统性的非最优行为，称为“演示旁路”，将其分为四种类型，并将其模式与任务情境和控制界面相关联，以改进演示学习算法。
English: This research identifies systematic non-optimal behaviors called "demonstration sidetracks" in human robot demonstrations, categorizing them into four types and linking their patterns to task context and control interfaces to improve Learning from Demonstration algorithms.

Paperid: 245, https://arxiv.org/pdf/2506.11170.pdf   GitHub

Authors:Ching Chang, Ming-Chih Lo, Wen-Chih Peng, Tien-Fu Chen

Title: PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation

Abstract:
Multivariate time series data, collected across various fields such as manufacturing and wearable technology, exhibit states at multiple levels of granularity, from coarse-grained system behaviors to fine-grained, detailed events. Effectively segmenting and integrating states across these different granularities is crucial for tasks like predictive maintenance and performance optimization. However, existing time series segmentation methods face two key challenges: (1) the inability to handle multiple levels of granularity within a unified model, and (2) limited adaptability to new, evolving patterns in dynamic environments. To address these challenges, we propose PromptTSS, a novel framework for time series segmentation with multi-granularity states. PromptTSS uses a unified model with a prompting mechanism that leverages label and boundary information to guide segmentation, capturing both coarse- and fine-grained patterns while adapting dynamically to unseen patterns. Experiments show PromptTSS improves accuracy by 24.49% in multi-granularity segmentation, 17.88% in single-granularity segmentation, and up to 599.24% in transfer learning, demonstrating its adaptability to hierarchical states and evolving time series dynamics. Our code is available at https://github.com/blacksnail789521/PromptTSS.
Chinese: PromptTSS是一种新颖的时间序列分割框架，通过采用带有提示机制的统一模型，有效解决了现有方法无法处理多粒度状态和适应动态环境的问题，显著提升了分割精度和迁移学习能力。
English: PromptTSS is a novel framework that addresses the limitations of existing time series segmentation methods by using a unified model with a prompting mechanism to handle multi-granularity states and adapt to evolving patterns, significantly improving accuracy in segmentation and transfer learning tasks.

Paperid: 246, https://arxiv.org/pdf/2506.11164.pdf   GitHub

Authors:Simon Ghyselincks, Valeriia Okhmak, Stefano Zampini, George Turkiyyah, David Keyes, Eldad Haber

Title: Synthetic Geology -- Structural Geology Meets Deep Learning

Abstract:
Visualizing the first few kilometers of the Earth's subsurface, a long-standing challenge gating a virtually inexhaustible list of important applications, is coming within reach through deep learning. Building on techniques of generative artificial intelligence applied to voxelated images, we demonstrate a method that extends surface geological data supplemented by boreholes to a three-dimensional subsurface region by training a neural network. The Earth's land area having been extensively mapped for geological features, the bottleneck of this or any related technique is the availability of data below the surface. We close this data gap in the development of subsurface deep learning by designing a synthetic data-generator process that mimics eons of geological activity such as sediment compaction, volcanic intrusion, and tectonic dynamics to produce a virtually limitless number of samples of the near lithosphere. A foundation model trained on such synthetic data is able to generate a 3D image of the subsurface from a previously unseen map of surface topography and geology, showing increasing fidelity with increasing access to borehole data, depicting such structures as layers, faults, folds, dikes, and sills. We illustrate the early promise of the combination of a synthetic lithospheric generator with a trained neural network model using generative flow matching. Ultimately, such models will be fine-tuned on data from applicable campaigns, such as mineral prospecting in a given region. Though useful in itself, a regionally fine-tuned models may be employed not as an end but as a means: as an AI-based regularizer in a more traditional inverse problem application, in which the objective function represents the mismatch of additional data with physical models with applications in resource exploration, hazard assessment, and geotechnical engineering.
中文: 通过生成式人工智能，利用地表数据和钻孔信息训练神经网络，结合模拟地质活动的合成数据，实现了对地球近地表三维结构的可视化建模。
English: Deep learning is enabling the visualization of the Earth's subsurface by using generative AI to create 3D models from surface data and boreholes, trained on synthetic geological data that mimics natural processes.

Paperid: 247, https://arxiv.org/pdf/2506.11153.pdf   GitHub

Authors:Changxin Ke, Rui Zhang, Shuo Wang, Li Ding, Guangli Li, Yuanbo Wen, Shuoming Zhang, Ruiyuan Xu, Jin Qin, Jiaming Guo, Chenxi Wang, Ling Li, Qi Guo, Yunji Chen

Title: Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

Abstract:
The rise of GPU-based high-performance computing (HPC) has driven the widespread adoption of parallel programming models such as CUDA. Yet, the inherent complexity of parallel programming creates a demand for the automated sequential-to-parallel approaches. However, data scarcity poses a significant challenge for machine learning-based sequential-to-parallel code translation. Although recent back-translation methods show promise, they still fail to ensure functional equivalence in the translated code. In this paper, we propose a novel Mutual-Supervised Learning (MSL) framework for sequential-to-parallel code translation to address the functional equivalence issue. MSL consists of two models, a Translator and a Tester. Through an iterative loop consisting of Co-verify and Co-evolve steps, the Translator and the Tester mutually generate data for each other and improve collectively. The Tester generates unit tests to verify and filter functionally equivalent translated code, thereby evolving the Translator, while the Translator generates translated code as augmented input to evolve the Tester. Experimental results demonstrate that MuSL significantly enhances the performance of the base model: when applied to Qwen2.5-Coder, it not only improves Pass@1 by up to 28.91% and boosts Tester performance by 68.90%, but also outperforms the previous state-of-the-art method CodeRosetta by 1.56 and 6.92 in BLEU and CodeBLEU scores, while achieving performance comparable to DeepSeek-R1 and GPT-4.1. Our code is available at https://github.com/kcxain/musl.
中文: 本文提出了一种互监督学习（MSL）框架，通过翻译器与测试器之间的迭代协作确保功能等价性，显著提升了串行到并行代码转换的性能，并优于现有先进方法。
English: The paper introduces a Mutual-Supervised Learning (MSL) framework that enhances sequential-to-parallel code translation by ensuring functional equivalence through iterative collaboration between a Translator and a Tester, achieving significant performance improvements over existing methods.

Paperid: 248, https://arxiv.org/pdf/2506.11133.pdf   GitHub

Authors:Christos Pantazopoulos, Spyridon Thermos, Gerasimos Potamianos

Title: Monocular 3D Hand Pose Estimation with Implicit Camera Alignment

Abstract:
Estimating the 3D hand articulation from a single color image is an important problem with applications in Augmented Reality (AR), Virtual Reality (VR), Human-Computer Interaction (HCI), and robotics. Apart from the absence of depth information, occlusions, articulation complexity, and the need for camera parameters knowledge pose additional challenges. In this work, we propose an optimization pipeline for estimating the 3D hand articulation from 2D keypoint input, which includes a keypoint alignment step and a fingertip loss to overcome the need to know or estimate the camera parameters. We evaluate our approach on the EgoDexter and Dexter+Object benchmarks to showcase that it performs competitively with the state-of-the-art, while also demonstrating its robustness when processing "in-the-wild" images without any prior camera knowledge. Our quantitative analysis highlights the sensitivity of the 2D keypoint estimation accuracy, despite the use of hand priors. Code is available at the project page https://cpantazop.github.io/HandRepo/

Paperid: 249, https://arxiv.org/pdf/2506.11120.pdf   GitHub

Authors:Hourun Zhu, Chengchao Shen

Title: SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models

Abstract:
In spite of strong performance achieved by LLMs, the costs of their deployment are unaffordable. For the compression of LLMs, gradient-based pruning methods present promising effectiveness. However, in these methods, the gradient computation with one-hot labels ignore the potential predictions on other words, thus missing key information for generative capability of the original model. To address this issue, we introduce a self-distillation loss during the pruning phase (rather than post-training) to fully exploit the predictions of the original model, thereby obtaining more accurate gradient information for pruning. Moreover, we find that, compared to attention modules, the predictions of LLM are less sensitive to multilayer perceptron (MLP) modules, which take up more than $5 \times$ parameters (LLaMA3.2-1.2B). To this end, we focus on the pruning of MLP modules, to significantly compress LLM without obvious performance degradation. Experimental results on extensive zero-shot benchmarks demonstrate that our method significantly outperforms existing pruning methods. Furthermore, our method achieves very competitive performance among 1B-scale open source LLMs. The source code and trained weights are available at https://github.com/visresearch/SDMPrune.
中文: 本文在剪枝过程中引入自蒸馏损失以充分利用原始模型的预测来获得更准确的梯度信息，并专注于剪枝MLP模块，在1B规模的大模型中实现了优异的压缩效果和竞争力。
English: This paper introduces a self-distillation loss during pruning to better utilize the original model's predictions for accurate gradient computation and focuses on pruning MLP modules, achieving superior compression and competitive performance among 1B-scale LLMs.

Paperid: 250, https://arxiv.org/pdf/2506.11110.pdf   GitHub

Authors:Jaeho Lee, Atharv Chowdhary

Title: AssertBench: A Benchmark for Evaluating Self-Assertion in Large Language Models

Abstract:
Recent benchmarks have probed factual consistency and rhetorical robustness in Large Language Models (LLMs). However, a knowledge gap exists regarding how directional framing of factually true statements influences model agreement, a common scenario for LLM users. AssertBench addresses this by sampling evidence-supported facts from FEVEROUS, a fact verification dataset. For each (evidence-backed) fact, we construct two framing prompts: one where the user claims the statement is factually correct, and another where the user claims it is incorrect. We then record the model's agreement and reasoning. The desired outcome is that the model asserts itself, maintaining consistent truth evaluation across both framings, rather than switching its evaluation to agree with the user. AssertBench isolates framing-induced variability from the model's underlying factual knowledge by stratifying results based on the model's accuracy on the same claims when presented neutrally. In doing so, this benchmark aims to measure an LLM's ability to "stick to its guns" when presented with contradictory user assertions about the same fact. The complete source code is available at https://github.com/achowd32/assert-bench.
中文摘要：AssertBench是一个新基准，用于评估当用户将同一证据支持的事实表述为正确或错误时，大型语言模型是否能保持一致性的事实判断，从而测试其不因迎合用户而改变评估的能力。
English Summary: AssertBench is a new benchmark that evaluates whether LLMs maintain consistent factual judgments when users frame the same evidence-backed facts as either correct or incorrect, testing their ability to resist switching evaluations to simply agree with users.

Paperid: 251, https://arxiv.org/pdf/2506.11085.pdf   GitHub

Authors:Justin Asher

Title: LeanExplore: A search engine for Lean 4 declarations

Abstract:
The expanding Lean 4 ecosystem poses challenges for navigating its vast libraries. This paper introduces LeanExplore, a search engine for Lean 4 declarations. LeanExplore enables users to semantically search for statements, both formally and informally, across select Lean 4 packages (including Batteries, Init, Lean, Mathlib, PhysLean, and Std). This search capability is powered by a hybrid ranking strategy, integrating scores from a multi-source semantic embedding model (capturing conceptual meaning from formal Lean code, docstrings, AI-generated informal translations, and declaration titles), BM25+ for keyword-based lexical relevance, and a PageRank-based score reflecting declaration importance and interconnectedness. The search engine is accessible via a dedicated website (https://www.leanexplore.com/) and a Python API (https://github.com/justincasher/lean-explore). Furthermore, the database can be downloaded, allowing users to self-host the service. LeanExplore integrates easily with LLMs via the model context protocol (MCP), enabling users to chat with an AI assistant about Lean declarations or utilize the search engine for building theorem-proving agents. This work details LeanExplore's architecture, data processing, functionalities, and its potential to enhance Lean 4 workflows and AI-driven mathematical research
LeanExplore 搜索引擎通过混合排名策略（结合概念嵌入、词法相关性和声明互连性）实现了对 Lean 4 库的语义搜索，解决了庞大生态系统的导航难题，支持网页访问、API调用及大语言模型集成。
The LeanExplore search engine addresses the challenge of navigating Lean 4's extensive libraries by enabling semantic searches through a hybrid ranking system that combines conceptual embeddings, lexical relevance, and declaration interconnectedness, accessible via web interface, API, and LLM integration.

Paperid: 252, https://arxiv.org/pdf/2506.11052.pdf   GitHub

Authors:Henrik Abgaryan, Tristan Cazenave, Ararat Harutyunyan

Title: ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention

Abstract:
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet their direct application to NP-hard combinatorial problems (CPs) remains underexplored. In this work, we systematically investigate the reasoning abilities of LLMs on a variety of NP-hard combinatorial optimization tasks and introduce ACCORD: Autoregressive Constraint-satisfying generation for COmbinatorial optimization with Routing and Dynamic attention. ACCORD features a novel dataset representation and model architecture that leverage the autoregressive nature of LLMs to dynamically enforce feasibility constraints, coupled with attention-based routing to activate problem-specific LoRA modules. We also present the ACCORD-90k supervised dataset, covering six NP-hard combinatorial problems: TSP, VRP, Knapsack, FlowShop, JSSP, and BinPacking. Extensive experiments demonstrate that our ACCORD model, built on an 8B-parameter Llama backbone, consistently outperforms standard prompting and input-output methods, even when compared to much larger LLMs, such as gpt-4. Ablation studies further show that our output structure enhances solution feasibility. To the best of our knowledge, this is the first large-scale, end-to-end framework for exploring the applications of LLMs to a broad spectrum of combinatorial optimization problems. The codes are publicly available at https://github.com/starjob42/ACCORD
中文: 本文提出ACCORD框架，通过动态满足可行性约束和基于注意力的路由激活专用模块，显著提升大语言模型解决NP难组合优化问题的能力，实验表明其性能优于标准方法及更大规模模型。
English: This paper introduces ACCORD, a novel framework that enhances LLMs' ability to solve NP-hard combinatorial problems by dynamically enforcing feasibility constraints and using attention-based routing to activate specialized modules, demonstrating superior performance over standard methods even with smaller models.

Paperid: 253, https://arxiv.org/pdf/2506.11044.pdf   GitHub

Authors:Jiaqi Zhao, Weili Guan, Ming Li, Miao Zhang

Title: Boost Post-Training Quantization via Null Space Optimization for Large Language Models

Abstract:
Existing post-training quantization methods for large language models (LLMs) offer remarkable success. However, the increasingly marginal performance gains suggest that existing quantization strategies are insufficient to support the development of more compressed models. To inspire new directions for future research, this paper introduces the concept of null space into LLMs quantization. We argue that the quantization error can be effectively alleviated by constraining the post-quantization weight perturbation to lie within the null space of input activations. To prove this idea, we propose a plug-and-play null space projection module for existing milestone PTQ baselines named Q2N. Specifically, we first design an efficient and accurate null space projection approximation method tailored to the characteristics of LLMs. Subsequently, we theoretically derive a closed-form solution for an equivalent vector of the obtained projection matrix, which satisfies practical inference condition while avoiding additional memory overhead. Extensive experiments are conducted on various state-of-the-art LLMs (LLaMA3, DeepSeek, Qwen3) and baselines, demonstrating the effectiveness of both our Q2N and the perspective of null space optimization for LLMs quantization. We view this paper the first step to further alleviate the quantization error based on the insights of null space, hoping it inspiring future researchers to design more advanced quantization methods. Codes are available at https://github.com/zjq0455/q2n.
中文摘要：本文首次将零空间概念引入大语言模型量化领域，提出可即插即用的Q2N模块，通过将量化后权重扰动约束在输入激活的零空间内有效降低误差，并在多个前沿模型上通过实验验证了其有效性。
English Summary: This paper introduces the concept of null space to large language model quantization, proposing a plug-and-play Q2N module that reduces quantization error by constraining weight perturbations within input activations' null space, validated through extensive experiments on leading models.

Paperid: 254, https://arxiv.org/pdf/2506.11038.pdf   GitHub

Authors:Linjie Li, Zhenyu Wu, Yang Ji

Title: MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning

Abstract:
Class-incremental learning (CIL) requires deep learning models to continuously acquire new knowledge from streaming data while preserving previously learned information. Recently, CIL based on pre-trained models (PTMs) has achieved remarkable success. However, prompt-based approaches suffer from prompt overwriting, while adapter-based methods face challenges such as dimensional misalignment between tasks. While the idea of expert fusion in Mixture of Experts (MoE) can help address dimensional inconsistency, both expert and routing parameters are prone to being overwritten in dynamic environments, making MoE challenging to apply directly in CIL. To tackle these issues, we propose a mixture of task-specific experts (MoTE) framework that effectively mitigates the miscalibration caused by inconsistent output dimensions across tasks. Inspired by the weighted feature fusion and sparse activation mechanisms in MoE, we introduce task-aware expert filtering and reliable expert joint inference during the inference phase, mimicking the behavior of routing layers without inducing catastrophic forgetting. Extensive experiments demonstrate the superiority of our method without requiring an exemplar set. Furthermore, the number of tasks in MoTE scales linearly with the number of adapters. Building on this, we further explore the trade-off between adapter expansion and model performance and propose the Adapter-Limited MoTE. The code is available at https://github.com/Franklilinjie/MoTE.
Chinese: 本文提出了一种任务特定专家混合（MoTE）框架，通过任务感知的专家筛选和联合推理机制，有效解决了类增量学习中的提示覆盖和维度不匹配问题，无需样本集即可避免灾难性遗忘。
English: This paper introduces a Mixture of Task-specific Experts (MoTE) framework to address challenges in class-incremental learning, such as prompt overwriting and dimensional misalignment, by leveraging task-aware expert filtering and joint inference to prevent catastrophic forgetting without needing exemplars.

Paperid: 255, https://arxiv.org/pdf/2506.11031.pdf   GitHub

Authors:Zoher Kachwala, Danishjeet Singh, Danielle Yang, Filippo Menczer

Title: Task-aligned prompting improves zero-shot detection of AI-generated images by Vision-Language Models

Abstract:
As image generators produce increasingly realistic images, concerns about potential misuse continue to grow. Supervised detection relies on large, curated datasets and struggles to generalize across diverse generators. In this work, we investigate the use of pre-trained Vision-Language Models (VLMs) for zero-shot detection of AI-generated images. While off-the-shelf VLMs exhibit some task-specific reasoning and chain-of-thought prompting offers gains, we show that task-aligned prompting elicits more focused reasoning and significantly improves performance without fine-tuning. Specifically, prefixing the model's response with the phrase "Let's examine the style and the synthesis artifacts" -- a method we call zero-shot-s$^2$ -- boosts Macro F1 scores by 8%-29%. These gains are consistent for two widely used open-source models and across three recent, diverse datasets spanning human faces, objects, and animals with images generated by 16 different models -- demonstrating strong generalization. We further evaluate the approach across three additional model sizes and observe improvements in most dataset-model combinations -- suggesting robustness to model scale. Surprisingly, self-consistency, a behavior previously observed in language reasoning, where aggregating answers from diverse reasoning paths improves performance, also holds in this setting. Even here, zero-shot-s$^2$ scales better than chain-of-thought in most cases -- indicating that it elicits more useful diversity. Our findings show that task-aligned prompts elicit more focused reasoning and enhance latent capabilities in VLMs, like the detection of AI-generated images -- offering a simple, generalizable, and explainable alternative to supervised methods. Our code is publicly available on github: https://github.com/Zoher15/Zero-shot-s2.
中文: 本研究提出了零样本s²方法，通过任务对齐提示显著提升了视觉语言模型在无需微调的情况下检测AI生成图像的能力，并在多种数据集和模型上展现出强大的泛化性。
English: This study introduces zero-shot-s², a task-aligned prompting method that significantly enhances Vision-Language Models' ability to detect AI-generated images without fine-tuning, demonstrating strong generalization across diverse datasets and models.

Paperid: 256, https://arxiv.org/pdf/2506.10978.pdf   GitHub

Authors:Donghoon Ahn, Jiwon Kang, Sanghyun Lee, Minjae Kim, Jaewon Min, Wooseok Jang, Sangwu Lee, Sayak Paul, Susung Hong, Seungryong Kim

Title: Fine-Grained Perturbation Guidance via Attention Head Selection

Abstract:
Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approaches, attention perturbation has demonstrated strong empirical performance in unconditional scenarios where classifier-free guidance is not applicable. However, existing attention perturbation methods lack principled approaches for determining where perturbations should be applied, particularly in Diffusion Transformer (DiT) architectures where quality-relevant computations are distributed across layers. In this paper, we investigate the granularity of attention perturbations, ranging from the layer level down to individual attention heads, and discover that specific heads govern distinct visual concepts such as structure, style, and texture quality. Building on this insight, we propose "HeadHunter", a systematic framework for iteratively selecting attention heads that align with user-centric objectives, enabling fine-grained control over generation quality and visual attributes. In addition, we introduce SoftPAG, which linearly interpolates each selected head's attention map toward an identity matrix, providing a continuous knob to tune perturbation strength and suppress artifacts. Our approach not only mitigates the oversmoothing issues of existing layer-level perturbation but also enables targeted manipulation of specific visual styles through compositional head selection. We validate our method on modern large-scale DiT-based text-to-image models including Stable Diffusion 3 and FLUX.1, demonstrating superior performance in both general quality enhancement and style-specific guidance. Our work provides the first head-level analysis of attention perturbation in diffusion models, uncovering interpretable specialization within attention layers and enabling practical design of effective perturbation strategies.

Paperid: 257, https://arxiv.org/pdf/2506.10974.pdf   GitHub

Authors:Yixin Ou, Yujie Luo, Jingsheng Zheng, Lanning Wei, Shuofei Qiao, Jintian Zhang, Da Zheng, Huajun Chen, Ningyu Zhang

Title: AutoMind: Adaptive Knowledgeable Agent for Automated Data Science

Abstract:
Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems. LLM-driven data science agents promise to automate the entire machine learning pipeline, yet their real-world effectiveness remains limited. Existing frameworks depend on rigid, pre-defined workflows and inflexible coding strategies; consequently, they excel only on relatively simple, classical problems and fail to capture the empirical expertise that human practitioners bring to complex, innovative tasks. In this work, we introduce AutoMind, an adaptive, knowledgeable LLM-agent framework that overcomes these deficiencies through three key advances: (1) a curated expert knowledge base that grounds the agent in domain expert knowledge, (2) an agentic knowledgeable tree search algorithm that strategically explores possible solutions, and (3) a self-adaptive coding strategy that dynamically tailors code generation to task complexity. Evaluations on two automated data science benchmarks demonstrate that AutoMind delivers superior performance versus state-of-the-art baselines. Additional analyses confirm favorable effectiveness, efficiency, and qualitative solution quality, highlighting AutoMind as an efficient and robust step toward fully automated data science.
中文摘要：AutoMind是一种自适应大型语言模型智能体框架，通过融合专家知识、策略性解决方案探索和动态编码，在自动化数据科学基准测试中展现出卓越性能。
English Summary: AutoMind is an adaptive LLM-agent framework that enhances automated data science by integrating expert knowledge, strategic solution exploration, and dynamic coding, achieving superior performance on benchmarks.

Paperid: 258, https://arxiv.org/pdf/2506.10973.pdf   GitHub

Authors:Julius Berner, Miguel Liu-Schiaffini, Jean Kossaifi, Valentin Duruisseaux, Boris Bonev, Kamyar Azizzadenesheli, Anima Anandkumar

Title: Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning

Abstract:
A wide range of scientific problems, such as those described by continuous-time dynamical systems and partial differential equations (PDEs), are naturally formulated on function spaces. While function spaces are typically infinite-dimensional, deep learning has predominantly advanced through applications in computer vision and natural language processing that focus on mappings between finite-dimensional spaces. Such fundamental disparities in the nature of the data have limited neural networks from achieving a comparable level of success in scientific applications as seen in other fields. Neural operators are a principled way to generalize neural networks to mappings between function spaces, offering a pathway to replicate deep learning's transformative impact on scientific problems. For instance, neural operators can learn solution operators for entire classes of PDEs, e.g., physical systems with different boundary conditions, coefficient functions, and geometries. A key factor in deep learning's success has been the careful engineering of neural architectures through extensive empirical testing. Translating these neural architectures into neural operators allows operator learning to enjoy these same empirical optimizations. However, prior neural operator architectures have often been introduced as standalone models, not directly derived as extensions of existing neural network architectures. In this paper, we identify and distill the key principles for constructing practical implementations of mappings between infinite-dimensional function spaces. Using these principles, we propose a recipe for converting several popular neural architectures into neural operators with minimal modifications. This paper aims to guide practitioners through this process and details the steps to make neural operators work in practice. Our code can be found at https://github.com/neuraloperator/NNs-to-NOs
中文摘要：本文提出神经算子作为神经网络向无限维函数空间映射的原则性扩展，提供了一个实用框架，可将现有神经架构应用于科学计算领域，如求解偏微分方程。
English summary: This paper introduces neural operators as a principled extension of neural networks to handle mappings between infinite-dimensional function spaces, providing a practical framework to adapt existing neural architectures for scientific applications like solving partial differential equations.

Paperid: 259, https://arxiv.org/pdf/2506.10972.pdf   GitHub

Authors:Houyi Li, Wenzhen Zheng, Qiufeng Wang, Zhenyu Ding, Haoying Wang, Zili Wang, Shijie Xuyang, Ning Ding, Shuigeng Zhou, Xiangyu Zhang, Daxin Jiang

Title: Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models

Abstract:
Training Large Language Models (LLMs) is prohibitively expensive, creating a critical scaling gap where insights from small-scale experiments often fail to transfer to resource-intensive production systems, thereby hindering efficient innovation. To bridge this, we introduce Farseer, a novel and refined scaling law offering enhanced predictive accuracy across scales. By systematically constructing a model loss surface $L(N,D)$, Farseer achieves a significantly better fit to empirical data than prior laws (e.g., Chinchilla's law). Our methodology yields accurate, robust, and highly generalizable predictions, demonstrating excellent extrapolation capabilities, improving upon Chinchilla's law by reducing extrapolation error by 433\%. This allows for the reliable evaluation of competing training strategies across all $(N,D)$ settings, enabling conclusions from small-scale ablation studies to be confidently extrapolated to predict large-scale performance. Furthermore, Farseer provides new insights into optimal compute allocation, better reflecting the nuanced demands of modern LLM training. To validate our approach, we trained an extensive suite of approximately 1,000 LLMs across diverse scales and configurations, consuming roughly 3 million NVIDIA H100 GPU hours. We are comprehensively open-sourcing all models, data, results, and logs at https://github.com/Farseer-Scaling-Law/Farseer to foster further research.
中文: Farseer提出了一种改进的扩展定律，显著提升了大型语言模型训练的预测准确性，能够从小规模实验可靠地推断大规模性能，相比Chinchilla定律将外推误差降低了433%。
English: Farseer introduces a refined scaling law that significantly improves predictive accuracy for large language model training, enabling reliable extrapolation from small-scale experiments to large-scale performance and reducing extrapolation error by 433% compared to Chinchilla's law.

Paperid: 260, https://arxiv.org/pdf/2506.10962.pdf   GitHub GitHub

Authors:Yuanhui Huang, Weiliang Chen, Wenzhao Zheng, Yueqi Duan, Jie Zhou, Jiwen Lu

Title: SpectralAR: Spectral Autoregressive Visual Generation

Abstract:
Autoregressive visual generation has garnered increasing attention due to its scalability and compatibility with other modalities compared with diffusion models. Most existing methods construct visual sequences as spatial patches for autoregressive generation. However, image patches are inherently parallel, contradicting the causal nature of autoregressive modeling. To address this, we propose a Spectral AutoRegressive (SpectralAR) visual generation framework, which realizes causality for visual sequences from the spectral perspective. Specifically, we first transform an image into ordered spectral tokens with Nested Spectral Tokenization, representing lower to higher frequency components. We then perform autoregressive generation in a coarse-to-fine manner with the sequences of spectral tokens. By considering different levels of detail in images, our SpectralAR achieves both sequence causality and token efficiency without bells and whistles. We conduct extensive experiments on ImageNet-1K for image reconstruction and autoregressive generation, and SpectralAR achieves 3.02 gFID with only 64 tokens and 310M parameters. Project page: https://huang-yh.github.io/spectralar/.
中文摘要：SpectralAR框架通过谱视角实现视觉序列的因果性，采用嵌套谱标记化将图像转化为有序频谱标记进行自回归生成，以少量标记和参数取得了优异性能。
English Summary: The SpectralAR framework introduces a novel autoregressive visual generation method using spectral tokens to ensure causality and efficiency, achieving state-of-the-art results with minimal tokens and parameters.

Paperid: 261, https://arxiv.org/pdf/2506.10960.pdf   GitHub

Authors:Kangwei Liu, Siyuan Cheng, Bozhong Tian, Xiaozhuan Liang, Yuyang Yin, Meng Han, Ningyu Zhang, Bryan Hooi, Xi Chen, Shumin Deng

Title: ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark

Abstract:
Large language models (LLMs) have been increasingly applied to automated harmful content detection tasks, assisting moderators in identifying policy violations and improving the overall efficiency and accuracy of content review. However, existing resources for harmful content detection are predominantly focused on English, with Chinese datasets remaining scarce and often limited in scope. We present a comprehensive, professionally annotated benchmark for Chinese content harm detection, which covers six representative categories and is constructed entirely from real-world data. Our annotation process further yields a knowledge rule base that provides explicit expert knowledge to assist LLMs in Chinese harmful content detection. In addition, we propose a knowledge-augmented baseline that integrates both human-annotated knowledge rules and implicit knowledge from large language models, enabling smaller models to achieve performance comparable to state-of-the-art LLMs. Code and data are available at https://github.com/zjunlp/ChineseHarm-bench.
中文摘要：本研究提出了一个全面、专业标注的中文有害内容检测基准，涵盖六大类别并基于真实数据构建，同时通过知识增强基线方法，使较小模型能达到与顶尖大语言模型相媲美的性能。
English Summary: This study introduces a comprehensive, professionally annotated benchmark for Chinese harmful content detection, covering six categories and utilizing real-world data, along with a knowledge-augmented baseline that enhances smaller models' performance to match state-of-the-art LLMs.

Paperid: 262, https://arxiv.org/pdf/2506.10948.pdf   GitHub

Authors:Boaz Lavon, Shahar Katz, Lior Wolf

Title: Execution Guided Line-by-Line Code Generation

Abstract:
We present a novel approach to neural code generation that incorporates real-time execution signals into the language model generation process. While large language models (LLMs) have demonstrated impressive code generation capabilities, they typically do not utilize execution feedback during inference, a critical signal that human programmers regularly leverage. Our method, Execution-Guided Classifier-Free Guidance (EG-CFG), dynamically incorporates execution signals as the model generates code, providing line-by-line feedback that guides the generation process toward executable solutions. EG-CFG employs a multi-stage process: first, we conduct beam search to sample candidate program completions for each line; second, we extract execution signals by executing these candidates against test cases; and finally, we incorporate these signals into the prompt during generation. By maintaining consistent signals across tokens within the same line and refreshing signals at line boundaries, our approach provides coherent guidance while preserving syntactic structure. Moreover, the method naturally supports native parallelism at the task level in which multiple agents operate in parallel, exploring diverse reasoning paths and collectively generating a broad set of candidate solutions. Our experiments across diverse coding tasks demonstrate that EG-CFG significantly improves code generation performance compared to standard approaches, achieving state-of-the-art results across various levels of complexity, from foundational problems to challenging competitive programming tasks. Our code is available at: https://github.com/boazlavon/eg_cfg
中文: 我们提出了一种新颖的神经代码生成方法——执行引导的无分类器引导（EG-CFG），通过在推理过程中动态整合实时执行反馈来引导模型生成可执行代码，在各类编程任务中实现了最先进的性能。
English: We introduce Execution-Guided Classifier-Free Guidance (EG-CFG), a novel neural code generation method that dynamically integrates real-time execution feedback during inference to guide the model toward producing executable code, achieving state-of-the-art performance across diverse coding tasks.

Paperid: 263, https://arxiv.org/pdf/2506.10943.pdf   GitHub

Authors:Adam Zweiger, Jyothish Pari, Han Guo, Ekin Akyürek, Yoon Kim, Pulkit Agrawal

Title: Self-Adapting Language Models

Abstract:
Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples. We introduce Self-Adapting LLMs (SEAL), a framework that enables LLMs to self-adapt by generating their own finetuning data and update directives. Given a new input, the model produces a self-edit-a generation that may restructure the information in different ways, specify optimization hyperparameters, or invoke tools for data augmentation and gradient-based updates. Through supervised finetuning (SFT), these self-edits result in persistent weight updates, enabling lasting adaptation. To train the model to produce effective self-edits, we use a reinforcement learning loop with the downstream performance of the updated model as the reward signal. Unlike prior approaches that rely on separate adaptation modules or auxiliary networks, SEAL directly uses the model's own generation to control its adaptation process. Experiments on knowledge incorporation and few-shot generalization show that SEAL is a promising step toward language models capable of self-directed adaptation. Our website and code is available at https://jyopari.github.io/posts/seal.

Paperid: 264, https://arxiv.org/pdf/2506.10915.pdf   GitHub

Authors:Jiancheng Huang, Gengwei Zhang, Zequn Jie, Siyu Jiao, Yinlong Qian, Ling Chen, Yunchao Wei, Lin Ma

Title: M4V: Multi-Modal Mamba for Text-to-Video Generation

Abstract:
Text-to-video generation has significantly enriched content creation and holds the potential to evolve into powerful world simulators. However, modeling the vast spatiotemporal space remains computationally demanding, particularly when employing Transformers, which incur quadratic complexity in sequence processing and thus limit practical applications. Recent advancements in linear-time sequence modeling, particularly the Mamba architecture, offer a more efficient alternative. Nevertheless, its plain design limits its direct applicability to multi-modal and spatiotemporal video generation tasks. To address these challenges, we introduce M4V, a Multi-Modal Mamba framework for text-to-video generation. Specifically, we propose a multi-modal diffusion Mamba (MM-DiM) block that enables seamless integration of multi-modal information and spatiotemporal modeling through a multi-modal token re-composition design. As a result, the Mamba blocks in M4V reduce FLOPs by 45% compared to the attention-based alternative when generating videos at 768$\times$1280 resolution. Additionally, to mitigate the visual quality degradation in long-context autoregressive generation processes, we introduce a reward learning strategy that further enhances per-frame visual realism. Extensive experiments on text-to-video benchmarks demonstrate M4V's ability to produce high-quality videos while significantly lowering computational costs. Code and models will be publicly available at https://huangjch526.github.io/M4V_project.
中文: M4V框架采用多模态Mamba架构，通过创新的令牌重组设计和奖励学习策略，在保持高质量视频生成的同时，将计算成本显著降低45%。
English: The M4V framework introduces a multi-modal Mamba architecture that significantly reduces computational costs by 45% while maintaining high-quality video generation through innovative token re-composition and reward learning strategies.

Paperid: 265, https://arxiv.org/pdf/2506.10892.pdf   GitHub GitHub

Authors:Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin Chiu, Volodymyr Kuleshov

Title: The Diffusion Duality

Abstract:
Uniform-state discrete diffusion models hold the promise of fast text generation due to their inherent ability to self-correct. However, they are typically outperformed by autoregressive models and masked diffusion models. In this work, we narrow this performance gap by leveraging a key insight: Uniform-state diffusion processes naturally emerge from an underlying Gaussian diffusion. Our method, Duo, transfers powerful techniques from Gaussian diffusion to improve both training and sampling. First, we introduce a curriculum learning strategy guided by the Gaussian process, doubling training speed by reducing variance. Models trained with curriculum learning surpass autoregressive models in zero-shot perplexity on 3 of 7 benchmarks. Second, we present Discrete Consistency Distillation, which adapts consistency distillation from the continuous to the discrete setting. This algorithm unlocks few-step generation in diffusion language models by accelerating sampling by two orders of magnitude. We provide the code and model checkpoints on the project page: http://s-sahoo.github.io/duo
中文：Duo方法通过将高斯扩散技术迁移到均匀状态离散扩散模型中，采用课程学习策略加速训练，并引入离散一致性蒸馏实现快速少步生成，在部分基准测试中超越了自回归模型的性能。
English: The Duo method enhances uniform-state discrete diffusion models by transferring techniques from Gaussian diffusion, using curriculum learning to accelerate training and discrete consistency distillation to enable fast few-step generation, outperforming autoregressive models on some benchmarks.

Paperid: 266, https://arxiv.org/pdf/2506.10764.pdf   GitHub

Authors:Xiaozhe Li, Jixuan Chen, Xinyu Fang, Shengyuan Ding, Haodong Duan, Qingwen Liu, Kai Chen

Title: OPT-BENCH: Evaluating LLM Agent on Large-Scale Search Spaces Optimization Problems

Abstract:
Large Language Models (LLMs) have shown remarkable capabilities in solving diverse tasks. However, their proficiency in iteratively optimizing complex solutions through learning from previous feedback remains insufficiently explored. To bridge this gap, we present OPT-BENCH, a comprehensive benchmark designed to evaluate LLM agents on large-scale search space optimization problems. OPT-BENCH includes 20 real-world machine learning tasks sourced from Kaggle and 10 classical NP problems, offering a diverse and challenging environment for assessing LLM agents on iterative reasoning and solution refinement. To enable rigorous evaluation, we introduce OPT-Agent, an end-to-end optimization framework that emulates human reasoning when tackling complex problems by generating, validating, and iteratively improving solutions through leveraging historical feedback. Through extensive experiments on 9 state-of-the-art LLMs from 6 model families, we analyze the effects of optimization iterations, temperature settings, and model architectures on solution quality and convergence. Our results demonstrate that incorporating historical context significantly enhances optimization performance across both ML and NP tasks. All datasets, code, and evaluation tools are open-sourced to promote further research in advancing LLM-driven optimization and iterative reasoning. Project page: \href{https://github.com/OliverLeeXZ/OPT-BENCH}{https://github.com/OliverLeeXZ/OPT-BENCH}.
中文: 本研究提出了OPT-BENCH基准来评估大语言模型在迭代优化任务中的表现，并证明利用历史反馈能显著提升其在机器学习和NP问题上的解决能力。
English: The study introduces OPT-BENCH, a benchmark for evaluating LLM agents on iterative optimization tasks, and demonstrates that leveraging historical feedback significantly improves their performance across machine learning and NP problems.

Paperid: 267, https://arxiv.org/pdf/2506.10707.pdf   GitHub

Authors:Marco Spinaci, Marek Polewczyk, Maximilian Schambach, Sam Thelin

Title: ConTextTab: A Semantics-Aware Tabular In-Context Learner

Abstract:
Tabular in-context learning (ICL) has recently achieved state-of-the-art (SOTA) performance on several tabular prediction tasks. Previously restricted to classification problems on small tables, recent advances such as TabPFN and TabICL have extended its use to larger datasets. Although current table-native ICL architectures are architecturally efficient and well-adapted to tabular data structures, their exclusive training on synthetic data limits their ability to fully leverage the rich semantics and world knowledge contained in real-world tabular data. At the other end of the spectrum, tabular ICL models based on pretrained large language models such as TabuLa-8B integrate deep semantic understanding and world knowledge but are only able to make use of a small amount of context due to inherent architectural limitations. With the aim to combine the best of both these worlds, we introduce ConTextTab, integrating semantic understanding and alignment into a table-native ICL framework. By employing specialized embeddings for different data modalities and by training on large-scale real-world tabular data, our model is competitive with SOTA across a broad set of benchmarks while setting a new standard on the semantically rich CARTE benchmark. Code and model checkpoints are available at: https://github.com/SAP-samples/contexttab
Chinese: ConTextTab是一种新颖的表格原生上下文学习框架，通过专用嵌入和大规模真实数据训练整合语义理解与对齐，在多个基准测试中表现优异，并在CARTE基准上创下新标准。
English: ConTextTab is a novel table-native in-context learning framework that integrates semantic understanding and alignment through specialized embeddings and large-scale real-world data training, achieving competitive performance across benchmarks and setting new standards on the CARTE benchmark.

Paperid: 268, https://arxiv.org/pdf/2506.10680.pdf   GitHub

Authors:Igor Urbanik, Paweł Gajewski

Title: Saturation Self-Organizing Map

Abstract:
Continual learning poses a fundamental challenge for neural systems, which often suffer from catastrophic forgetting when exposed to sequential tasks. Self-Organizing Maps (SOMs), despite their interpretability and efficiency, are not immune to this issue. In this paper, we introduce Saturation Self-Organizing Maps (SatSOM)-an extension of SOMs designed to improve knowledge retention in continual learning scenarios. SatSOM incorporates a novel saturation mechanism that gradually reduces the learning rate and neighborhood radius of neurons as they accumulate information. This effectively freezes well-trained neurons and redirects learning to underutilized areas of the map.
Chinese: 本文提出饱和自组织映射（SatSOM），通过引入饱和机制逐步降低已学习神经元的参数更新，有效缓解持续学习中的灾难性遗忘问题。
English: This paper introduces Saturation Self-Organizing Maps (SatSOM), which enhance continual learning by incorporating a saturation mechanism that reduces learning parameters for experienced neurons, thus mitigating catastrophic forgetting.

Paperid: 269, https://arxiv.org/pdf/2506.10632.pdf   GitHub

Authors:Alexander Lobashev, Dmitry Guskov, Maria Larchenko, Mikhail Tamm

Title: Hessian Geometry of Latent Space in Generative Models

Abstract:
This paper presents a novel method for analyzing the latent space geometry of generative models, including statistical physics models and diffusion models, by reconstructing the Fisher information metric. The method approximates the posterior distribution of latent variables given generated samples and uses this to learn the log-partition function, which defines the Fisher metric for exponential families. Theoretical convergence guarantees are provided, and the method is validated on the Ising and TASEP models, outperforming existing baselines in reconstructing thermodynamic quantities. Applied to diffusion models, the method reveals a fractal structure of phase transitions in the latent space, characterized by abrupt changes in the Fisher metric. We demonstrate that while geodesic interpolations are approximately linear within individual phases, this linearity breaks down at phase boundaries, where the diffusion model exhibits a divergent Lipschitz constant with respect to the latent space. These findings provide new insights into the complex structure of diffusion model latent spaces and their connection to phenomena like phase transitions. Our source code is available at https://github.com/alobashev/hessian-geometry-of-diffusion-models.
Chinese: 本文提出了一种通过重构Fisher信息度量来分析生成模型潜在空间几何结构的新方法，揭示了扩散模型中相变的分形结构，并在热力学量重构方面优于现有基准。
English: This paper introduces a method to reconstruct the Fisher information metric for analyzing latent space geometry in generative models, revealing phase transitions and fractal structures in diffusion models while outperforming baselines in thermodynamic quantity reconstruction.

Paperid: 270, https://arxiv.org/pdf/2506.10622.pdf   GitHub

Authors:Sergio Burdisso, Esaú Villatoro-Tello, Petr Motlicek

Title: SDialog: A Python Toolkit for Synthetic Dialogue Generation and Analysis

Abstract:
The advancement of conversational AI systems relies on the availability of high-quality, flexible, and reproducible synthetic dialogues for training, evaluation, and benchmarking. SDialog is a modular, extensible Python toolkit designed to address the challenges of synthetic dialogue generation and analysis. By leveraging instruction-tuned Large Language Models (LLMs), SDialog provides abstractions for personas, orchestration, and scenario management, enabling the creation of realistic, diverse, and controllable conversational data for research and development. SDialog supports workflows such as multi-agent simulation and scenario-driven generation, and represents a step forward in the standardization of tools and frameworks for synthetic data generation, a crucial advancement for ensuring reproducibility in today's fast-evolving research landscape.
中文：SDialog是一个模块化的Python工具包，它利用指令调优的大语言模型生成真实可控的合成对话，支持多智能体模拟和场景驱动的工作流程，以推动对话AI研究并确保可复现性。
English: SDialog is a modular Python toolkit that uses instruction-tuned LLMs to generate realistic and controllable synthetic dialogues, supporting multi-agent simulations and scenario-driven workflows to advance conversational AI research and ensure reproducibility.

Paperid: 271, https://arxiv.org/pdf/2506.10617.pdf   GitHub

Authors:Reza Karbasi, Masoud Rahimi, Abdol-Hossein Vahabie, Hadi Moradi

Title: Deep Learning-Based Digitization of Overlapping ECG Images with Open-Source Python Code

Abstract:
This paper addresses the persistent challenge of accurately digitizing paper-based electrocardiogram (ECG) recordings, with a particular focus on robustly handling single leads compromised by signal overlaps-a common yet under-addressed issue in existing methodologies. We propose a two-stage pipeline designed to overcome this limitation. The first stage employs a U-Net based segmentation network, trained on a dataset enriched with overlapping signals and fortified with custom data augmentations, to accurately isolate the primary ECG trace. The subsequent stage converts this refined binary mask into a time-series signal using established digitization techniques, enhanced by an adaptive grid detection module for improved versatility across different ECG formats and scales. Our experimental results demonstrate the efficacy of our approach. The U-Net architecture achieves an IoU of 0.87 for the fine-grained segmentation task. Crucially, our proposed digitization method yields superior performance compared to a well-established baseline technique across both non-overlapping and challenging overlapping ECG samples. For non-overlapping signals, our method achieved a Mean Squared Error (MSE) of 0.0010 and a Pearson Correlation Coefficient (rho) of 0.9644, compared to 0.0015 and 0.9366, respectively, for the baseline. On samples with signal overlap, our method achieved an MSE of 0.0029 and a rho of 0.9641, significantly improving upon the baseline's 0.0178 and 0.8676. This work demonstrates an effective strategy to significantly enhance digitization accuracy, especially in the presence of signal overlaps, thereby laying a strong foundation for the reliable conversion of analog ECG records into analyzable digital data for contemporary research and clinical applications. The implementation is publicly available at this GitHub repository: https://github.com/masoudrahimi39/ECG-code.
本文提出了一种采用U-Net分割和自适应数字化技术的两阶段流程，能精准将纸质心电图转换为数字信号，相比基线方法在处理信号重叠情况时表现出显著优势。
This paper introduces a two-stage pipeline using U-Net segmentation and adaptive digitization to accurately convert paper ECG recordings into digital signals, significantly improving performance especially for overlapping signals compared to baseline methods.

Paperid: 272, https://arxiv.org/pdf/2506.10452.pdf   GitHub GitHub

Authors:Guowei Zhong, Ruohong Huan, Mingzhen Wu, Ronghua Liang, Peng Chen

Title: Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts

Abstract:
Recent advancements in Multimodal Emotion Recognition (MER) face challenges in addressing both modality missing and Out-Of-Distribution (OOD) data simultaneously. Existing methods often rely on specific models or introduce excessive parameters, which limits their practicality. To address these issues, we propose a novel robust MER framework, Causal Inference Distiller (CIDer), and introduce a new task, Random Modality Feature Missing (RMFM), to generalize the definition of modality missing. CIDer integrates two key components: a Model-Specific Self-Distillation (MSSD) module and a Model-Agnostic Causal Inference (MACI) module. MSSD enhances robustness under the RMFM task through a weight-sharing self-distillation approach applied across low-level features, attention maps, and high-level representations. Additionally, a Word-level Self-aligned Attention Module (WSAM) reduces computational complexity, while a Multimodal Composite Transformer (MCT) facilitates efficient multimodal fusion. To tackle OOD challenges, MACI employs a tailored causal graph to mitigate label and language biases using a Multimodal Causal Module (MCM) and fine-grained counterfactual texts. Notably, MACI can independently enhance OOD generalization with minimal additional parameters. Furthermore, we also introduce the new repartitioned MER OOD datasets. Experimental results demonstrate that CIDer achieves robust performance in both RMFM and OOD scenarios, with fewer parameters and faster training compared to state-of-the-art methods. The implementation of this work is publicly accessible at https://github.com/gw-zhong/CIDer.
中文摘要：提出的因果推理蒸馏器（CIDer）框架通过自蒸馏和因果推理模块，解决了多模态情感识别中模态缺失和分布外数据的双重挑战，以更少参数和更快训练实现了鲁棒性能。
English Summary: The proposed Causal Inference Distiller (CIDer) framework addresses simultaneous modality missing and Out-Of-Distribution challenges in Multimodal Emotion Recognition through self-distillation and causal inference modules, achieving robust performance with fewer parameters and faster training.

Paperid: 273, https://arxiv.org/pdf/2506.10389.pdf   GitHub

Authors:Yuhang Chen, Zhen Tan, Tianlong Chen

Title: EQA-RM: A Generative Embodied Reward Model with Test-time Scaling

Abstract:
Reward Models (RMs), vital for large model alignment, are underexplored for complex embodied tasks like Embodied Question Answering (EQA) where nuanced evaluation of agents' spatial, temporal, and logical understanding is critical yet not considered by generic approaches. We introduce EQA-RM, a novel generative multimodal reward model specifically architected for EQA, trained via our innovative Contrastive Group Relative Policy Optimization (C-GRPO) strategy to learn fine-grained behavioral distinctions. The generative nature of EQA-RM provides interpretable, structured reward feedback (beyond simple scalars), uniquely enabling test-time scaling to dynamically adjust evaluation granularity, from concise scores to detailed critiques of reasoning and grounding, at inference without retraining. Concurrently, we introduce EQARewardBench, a new benchmark built on OpenEQA for standardized EQA reward model assessment. Demonstrating high sample efficiency, EQA-RM (fine-tuning Qwen2-VL-2B-Instruct) achieves 61.9\% accuracy on EQA-RM-Bench with only 700 samples, outperforming strong proprietary baselines, including Gemini-2.5-Flash, GPT-4o, Claude-3.5-Haiku, and open-sourced state-of-the-art models such as RoVRM and VisualPRM. The code and dataset can be found here https://github.com/UNITES-Lab/EQA-RM.
中文: 本文提出了针对具身问答任务的生成式多模态奖励模型EQA-RM，该模型能提供可解释的反馈并以少量样本实现优于主流模型的性能，同时建立了标准化评估新基准。
English: This paper introduces EQA-RM, a generative multimodal reward model for Embodied Question Answering, which provides interpretable feedback and outperforms leading models with high sample efficiency, alongside a new benchmark for standardized evaluation.

Paperid: 274, https://arxiv.org/pdf/2506.10347.pdf   GitHub

Authors:Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo

Title: LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture

Abstract:
Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-based KGRSs, Self-Supervised Learning (SSL) has been incorporated to address the sparity issue, leading to longer training time. However, through extensive experiments, we reveal that: (1)compared to other KGRSs, the existing GNN-based KGRSs fail to keep their superior performance under sparse interactions even with SSL. (2) More complex models tend to perform worse in sparse interaction scenarios and complex mechanisms, like attention mechanism, can be detrimental as they often increase learning difficulty. Inspired by these findings, we propose LightKG, a simple yet powerful GNN-based KGRS to address sparsity issues. LightKG includes a simplified GNN layer that encodes directed relations as scalar pairs rather than dense embeddings and employs a linear aggregation framework, greatly reducing the complexity of GNNs. Additionally, LightKG incorporates an efficient contrastive layer to implement SSL. It directly minimizes the node similarity in original graph, avoiding the time-consuming subgraph generation and comparison required in previous SSL methods. Experiments on four benchmark datasets show that LightKG outperforms 12 competitive KGRSs in both sparse and dense scenarios while significantly reducing training time. Specifically, it surpasses the best baselines by an average of 5.8\% in recommendation accuracy and saves 84.3\% of training time compared to KGRSs with SSL. Our code is available at https://github.com/1371149/LightKG.
中文: 图神经网络在知识图谱感知推荐系统中应用广泛，但面临稀疏交互和复杂性增加的挑战，因此提出了LightKG这一简化模型，既提升了推荐准确性又显著缩短了训练时间。
English: Graph Neural Networks (GNNs) are widely used in Knowledge Graph-aware Recommender Systems but struggle with sparse interactions and increased complexity, prompting the development of LightKG, a simplified GNN model that enhances performance and reduces training time.

Paperid: 275, https://arxiv.org/pdf/2506.10332.pdf   GitHub

Authors:Aaryam Sharma

Title: Air in Your Neighborhood: Fine-Grained AQI Forecasting Using Mobile Sensor Data

Abstract:
Air pollution has become a significant health risk in developing countries. While governments routinely publish air-quality index (AQI) data to track pollution, these values fail to capture the local reality, as sensors are often very sparse. In this paper, we address this gap by predicting AQI in 1 km^2 neighborhoods, using the example of AirDelhi dataset. Using Spatio-temporal GNNs we surpass existing works by 71.654 MSE a 79% reduction, even on unseen coordinates. New insights about AQI such as the existence of strong repetitive short-term patterns and changing spatial relations are also discovered. The code is available on GitHub.
Chinese: 本文通过使用时空图神经网络精确预测1平方公里区域的空气质量指数，解决了传感器稀疏的局限，将误差降低了79%，并揭示了污染数据中的新规律。
English: This paper tackles the limitations of sparse air-quality sensors by using spatio-temporal graph neural networks to accurately predict AQI in 1 km² areas, achieving a 79% reduction in error and uncovering new patterns in pollution data.

Paperid: 276, https://arxiv.org/pdf/2506.10326.pdf   GitHub

Authors:Cameron Angliss, Jiaxun Cui, Jiaheng Hu, Arrasy Rahman, Peter Stone

Title: A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon

Abstract:
Developing AI agents that can robustly adapt to dramatically different strategic landscapes without retraining is a central challenge for multi-agent learning. Pokémon Video Game Championships (VGC) is a domain with an extraordinarily large space of possible team configurations of approximately $10^{139}$ - far larger than those of Dota or Starcraft. The highly discrete, combinatorial nature of team building in Pokémon VGC causes optimal strategies to shift dramatically depending on both the team being piloted and the opponent's team, making generalization uniquely challenging. To advance research on this problem, we introduce VGC-Bench: a benchmark that provides critical infrastructure, standardizes evaluation protocols, and supplies human-play datasets and a range of baselines - from large-language-model agents and behavior cloning to reinforcement learning and empirical game-theoretic methods such as self-play, fictitious play, and double oracle. In the restricted setting where an agent is trained and evaluated on a single-team configuration, our methods are able to win against a professional VGC competitor. We extensively evaluated all baseline methods over progressively larger team sets and find that even the best-performing algorithm in the single-team setting struggles at scaling up as team size grows. Thus, policy generalization across diverse team strategies remains an open challenge for the community. Our code is open sourced at https://github.com/cameronangliss/VGC-Bench.
中文摘要：VGC-Bench作为新基准被提出，旨在解决AI智能体在宝可梦VGC巨大策略空间中泛化适应的核心难题，现有方法在单队伍配置中表现良好但难以扩展到多队伍场景。
English Summary: VGC-Bench is introduced as a new benchmark to address the challenge of developing AI agents that can generalize across Pokémon VGC's vast strategic landscape, where current methods succeed in single-team scenarios but fail to scale effectively.

Paperid: 277, https://arxiv.org/pdf/2506.10325.pdf   GitHub

Authors:Cheng Wang, Siqi Chen, Donghua Mi, Yang Chen, Yudong Zhang, Yinsheng Li

Title: SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation

Abstract:
Recent advances in medical imaging have established deep learning-based segmentation as the predominant approach, though it typically requires large amounts of manually annotated data. However, obtaining annotations for intracranial hemorrhage (ICH) remains particularly challenging due to the tedious and costly labeling process. Semi-supervised learning (SSL) has emerged as a promising solution to address the scarcity of labeled data, especially in volumetric medical image segmentation. Unlike conventional SSL methods that primarily focus on high-confidence pseudo-labels or consistency regularization, we propose SWDL-Net, a novel SSL framework that exploits the complementary advantages of Laplacian pyramid and deep convolutional upsampling. The Laplacian pyramid excels at edge sharpening, while deep convolutions enhance detail precision through flexible feature mapping. Our framework achieves superior segmentation of lesion details and boundaries through a difference learning mechanism that effectively integrates these complementary approaches. Extensive experiments on a 271-case ICH dataset and public benchmarks demonstrate that SWDL-Net outperforms current state-of-the-art methods in scenarios with only 2% labeled data. Additional evaluations on the publicly available Brain Hemorrhage Segmentation Dataset (BHSD) with 5% labeled data further confirm the superiority of our approach. Code and data have been released at https://github.com/SIAT-CT-LAB/SWDL.
中文摘要：SWDL-Net是一种新型半监督学习框架，通过结合拉普拉斯金字塔边缘锐化和深度卷积上采样，在仅使用少量标注数据的情况下实现了优越的颅内出血分割效果。
English Summary: SWDL-Net is a novel semi-supervised learning framework that combines Laplacian pyramid edge sharpening with deep convolutional upsampling to achieve superior intracranial hemorrhage segmentation using minimal labeled data.

Paperid: 278, https://arxiv.org/pdf/2506.10315.pdf   GitHub

Authors:Paul Janson, Benjamin Therien, Quentin Anthony, Xiaolong Huang, Abhinav Moudgil, Eugene Belilovsky

Title: PyLO: Towards Accessible Learned Optimizers in PyTorch

Abstract:
Learned optimizers have been an active research topic over the past decade, with increasing progress toward practical, general-purpose optimizers that can serve as drop-in replacements for widely used methods like Adam. However, recent advances -- such as VeLO, which was meta-trained for 4000 TPU-months -- remain largely inaccessible to the broader community, in part due to their reliance on JAX and the absence of user-friendly packages for applying the optimizers after meta-training. To address this gap, we introduce PyLO, a PyTorch-based library that brings learned optimizers to the broader machine learning community through familiar, widely adopted workflows. Unlike prior work focused on synthetic or convex tasks, our emphasis is on applying learned optimization to real-world large-scale pre-training tasks. Our release includes a CUDA-accelerated version of the small_fc_lopt learned optimizer architecture from (Metz et al., 2022a), delivering substantial speedups -- from 39.36 to 205.59 samples/sec throughput for training ViT B/16 with batch size 32. PyLO also allows us to easily combine learned optimizers with existing optimization tools such as learning rate schedules and weight decay. When doing so, we find that learned optimizers can substantially benefit. Our code is available at https://github.com/Belilovsky-Lab/pylo
中文: PyLO库通过基于PyTorch的解决方案，使学习优化器在实际应用中更易用且高效，显著提升训练速度并兼容现有优化工具。
English: The PyLO library introduces a PyTorch-based solution to make learned optimizers accessible and practical for real-world applications, offering significant speed improvements and compatibility with existing optimization tools.

Paperid: 279, https://arxiv.org/pdf/2506.10226.pdf   GitHub

Authors:Parsa Rahimi, Sebastien Marcel

Title: ScoreMix: Improving Face Recognition via Score Composition in Diffusion Generators

Abstract:
In this paper, we propose ScoreMix, a novel yet simple data augmentation strategy leveraging the score compositional properties of diffusion models to enhance discriminator performance, particularly under scenarios with limited labeled data. By convexly mixing the scores from different class-conditioned trajectories during diffusion sampling, we generate challenging synthetic samples that significantly improve discriminative capabilities in all studied benchmarks. We systematically investigate class-selection strategies for mixing and discover that greater performance gains arise when combining classes distant in the discriminator's embedding space, rather than close in the generator's condition space. Moreover, we empirically show that, under standard metrics, the correlation between the generator's learned condition space and the discriminator's embedding space is minimal. Our approach achieves notable performance improvements without extensive parameter searches, demonstrating practical advantages for training discriminative models while effectively mitigating problems regarding collections of large datasets. Paper website: https://parsa-ra.github.io/scoremix

Paperid: 280, https://arxiv.org/pdf/2506.10186.pdf   GitHub

Authors:Yuhui Ding, Thomas Hofmann

Title: Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment

Abstract:
Equivariant diffusion models have achieved impressive performance in 3D molecule generation. These models incorporate Euclidean symmetries of 3D molecules by utilizing an SE(3)-equivariant denoising network. However, specialized equivariant architectures limit the scalability and efficiency of diffusion models. In this paper, we propose an approach that relaxes such equivariance constraints. Specifically, our approach learns a sample-dependent SO(3) transformation for each molecule to construct an aligned latent space. A non-equivariant diffusion model is then trained over the aligned representations. Experimental results demonstrate that our approach performs significantly better than previously reported non-equivariant models. It yields sample quality comparable to state-of-the-art equivariant diffusion models and offers improved training and sampling efficiency. Our code is available at https://github.com/skeletondyh/RADM
中文摘要：我们的方法通过放宽严格的等变性约束，利用样本相关的分子对齐构建潜在空间，并采用非等变扩散模型，在保持顶尖生成质量的同时显著提升了3D分子生成的训练与采样效率。
English Summary: Our method enhances 3D molecule generation by relaxing rigid equivariance constraints, achieving state-of-the-art quality with improved efficiency through sample-dependent molecular alignment and non-equivariant diffusion modeling.

Paperid: 281, https://arxiv.org/pdf/2506.10173.pdf   GitHub

Authors:Mohammad Jalali, Haoyu Lei, Amin Gohari, Farzan Farnia

Title: SPARKE: Scalable Prompt-Aware Diversity Guidance in Diffusion Models via RKE Score

Abstract:
Diffusion models have demonstrated remarkable success in high-fidelity image synthesis and prompt-guided generative modeling. However, ensuring adequate diversity in generated samples of prompt-guided diffusion models remains a challenge, particularly when the prompts span a broad semantic spectrum and the diversity of generated data needs to be evaluated in a prompt-aware fashion across semantically similar prompts. Recent methods have introduced guidance via diversity measures to encourage more varied generations. In this work, we extend the diversity measure-based approaches by proposing the Scalable Prompt-Aware Rény Kernel Entropy Diversity Guidance (SPARKE) method for prompt-aware diversity guidance. SPARKE utilizes conditional entropy for diversity guidance, which dynamically conditions diversity measurement on similar prompts and enables prompt-aware diversity control. While the entropy-based guidance approach enhances prompt-aware diversity, its reliance on the matrix-based entropy scores poses computational challenges in large-scale generation settings. To address this, we focus on the special case of Conditional latent RKE Score Guidance, reducing entropy computation and gradient-based optimization complexity from the $O(n^3)$ of general entropy measures to $O(n)$. The reduced computational complexity allows for diversity-guided sampling over potentially thousands of generation rounds on different prompts. We numerically test the SPARKE method on several text-to-image diffusion models, demonstrating that the proposed method improves the prompt-aware diversity of the generated data without incurring significant computational costs. We release our code on the project page: https://mjalali.github.io/SPARKE

Paperid: 282, https://arxiv.org/pdf/2506.10054.pdf   GitHub

Authors:Shangpin Peng, Weinong Wang, Zhuotao Tian, Senqiao Yang, Xing Wu, Haotian Xu, Chengquan Zhang, Takashi Isobe, Baotian Hu, Min Zhang

Title: Omni-DPO: A Dual-Perspective Paradigm for Dynamic Preference Learning of LLMs

Abstract:
Direct Preference Optimization (DPO) has become a cornerstone of reinforcement learning from human feedback (RLHF) due to its simplicity and efficiency. However, existing DPO-based approaches typically treat all preference pairs uniformly, ignoring critical variations in their inherent quality and learning utility, leading to suboptimal data utilization and performance. To address this challenge, we propose Omni-DPO, a dual-perspective optimization framework that jointly accounts for (1) the inherent quality of each preference pair and (2) the model's evolving performance on those pairs. By adaptively weighting samples according to both data quality and the model's learning dynamics during training, Omni-DPO enables more effective training data utilization and achieves better performance. Experimental results on various models and benchmarks demonstrate the superiority and generalization capabilities of Omni-DPO. On textual understanding tasks, Gemma-2-9b-it finetuned with Omni-DPO beats the leading LLM, Claude 3 Opus, by a significant margin of 6.7 points on the Arena-Hard benchmark. On mathematical reasoning tasks, Omni-DPO consistently outperforms the baseline methods across all benchmarks, providing strong empirical evidence for the effectiveness and robustness of our approach. Code and models will be available at https://github.com/pspdada/Omni-DPO.
中文摘要：Omni-DPO是一种新颖的双视角优化框架，通过基于数据固有质量和模型学习动态的自适应加权机制改进直接偏好优化方法，在多个基准测试中实现了卓越性能。
English Summary: Omni-DPO is a novel dual-perspective optimization framework that enhances Direct Preference Optimization by adaptively weighting preference pairs based on both inherent data quality and the model's learning dynamics, achieving superior performance across various benchmarks.

Paperid: 283, https://arxiv.org/pdf/2506.09997.pdf   GitHub

Authors:Chieh Hubert Lin, Zhaoyang Lv, Songyin Wu, Zhen Xu, Thu Nguyen-Phuoc, Hung-Yu Tseng, Julian Straub, Numair Khan, Lei Xiao, Ming-Hsuan Yang, Yuheng Ren, Richard Newcombe, Zhao Dong, Zhengqin Li

Title: DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos

Abstract:
We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM), the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene. Feed-forward scene reconstruction has gained significant attention for its ability to rapidly create digital replicas of real-world environments. However, most existing models are limited to static scenes and fail to reconstruct the motion of moving objects. Developing a feed-forward model for dynamic scene reconstruction poses significant challenges, including the scarcity of training data and the need for appropriate 3D representations and training paradigms. To address these challenges, we introduce several key technical contributions: an enhanced large-scale synthetic dataset with ground-truth multi-view videos and dense 3D scene flow supervision; a per-pixel deformable 3D Gaussian representation that is easy to learn, supports high-quality dynamic view synthesis, and enables long-range 3D tracking; and a large transformer network that achieves real-time, generalizable dynamic scene reconstruction. Extensive qualitative and quantitative experiments demonstrate that DGS-LRM achieves dynamic scene reconstruction quality comparable to optimization-based methods, while significantly outperforming the state-of-the-art predictive dynamic reconstruction method on real-world examples. Its predicted physically grounded 3D deformation is accurate and can readily adapt for long-range 3D tracking tasks, achieving performance on par with state-of-the-art monocular video 3D tracking methods.

Paperid: 284, https://arxiv.org/pdf/2506.09993.pdf   GitHub

Authors:Jaewon Min, Jin Hyeon Kim, Paul Hyunbin Cho, Jaeeun Lee, Jihye Park, Minkyu Park, Sangpil Kim, Hyunhee Park, Seungryong Kim

Title: Text-Aware Image Restoration with Diffusion Models

Abstract:
Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image restoration, often struggle to faithfully reconstruct textual regions in degraded images. Those methods frequently generate plausible but incorrect text-like patterns, a phenomenon we refer to as text-image hallucination. In this paper, we introduce Text-Aware Image Restoration (TAIR), a novel restoration task that requires the simultaneous recovery of visual contents and textual fidelity. To tackle this task, we present SA-Text, a large-scale benchmark of 100K high-quality scene images densely annotated with diverse and complex text instances. Furthermore, we propose a multi-task diffusion framework, called TeReDiff, that integrates internal features from diffusion models into a text-spotting module, enabling both components to benefit from joint training. This allows for the extraction of rich text representations, which are utilized as prompts in subsequent denoising steps. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art restoration methods, achieving significant gains in text recognition accuracy. See our project page: https://cvlab-kaist.github.io/TAIR/

Paperid: 285, https://arxiv.org/pdf/2506.09958.pdf   GitHub

Authors:Sushant Gautam, Michael A. Riegler, PÃ¥l Halvorsen

Title: Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy

Abstract:
Medical Visual Question Answering (MedVQA) is a promising field for developing clinical decision support systems, yet progress is often limited by the available datasets, which can lack clinical complexity and visual diversity. To address these gaps, we introduce Kvasir-VQA-x1, a new, large-scale dataset for gastrointestinal (GI) endoscopy. Our work significantly expands upon the original Kvasir-VQA by incorporating 159,549 new question-answer pairs that are designed to test deeper clinical reasoning. We developed a systematic method using large language models to generate these questions, which are stratified by complexity to better assess a model's inference capabilities. To ensure our dataset prepares models for real-world clinical scenarios, we have also introduced a variety of visual augmentations that mimic common imaging artifacts. The dataset is structured to support two main evaluation tracks: one for standard VQA performance and another to test model robustness against these visual perturbations. By providing a more challenging and clinically relevant benchmark, Kvasir-VQA-x1 aims to accelerate the development of more reliable and effective multimodal AI systems for use in clinical settings. The dataset is fully accessible and adheres to FAIR data principles, making it a valuable resource for the wider research community. Code and data: https://github.com/Simula/Kvasir-VQA-x1 and https://huggingface.co/datasets/SimulaMet/Kvasir-VQA-x1
中文: Kvasir-VQA-x1是一个大规模胃肠道内窥镜数据集，通过15.9万个问题-答案对和模拟影像伪影的视觉增强，提升了临床复杂性和视觉多样性，旨在推动临床多模态AI系统的发展。
English: Kvasir-VQA-x1 is a large-scale gastrointestinal endoscopy dataset that expands clinical complexity and visual diversity through 159,549 question-answer pairs and augmented imaging artifacts, aiming to advance multimodal AI for clinical decision support.

Paperid: 286, https://arxiv.org/pdf/2506.09820.pdf   GitHub

Authors:Chengpeng Li, Zhengyang Tang, Ziniu Li, Mingfeng Xue, Keqin Bao, Tian Ding, Ruoyu Sun, Benyou Wang, Xiang Wang, Junyang Lin, Dayiheng Liu

Title: CoRT: Code-integrated Reasoning within Thinking

Abstract:
Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or inaccurate when handling complex mathematical operations. Addressing these limitations through computational tools (e.g., computation libraries and symbolic solvers) is promising, but it introduces a technical challenge: Code Interpreter (CI) brings external knowledge beyond the model's internal text representations, thus the direct combination is not efficient. This paper introduces CoRT, a post-training framework for teaching LRMs to leverage CI effectively and efficiently. As a first step, we address the data scarcity issue by synthesizing code-integrated reasoning data through Hint-Engineering, which strategically inserts different hints at appropriate positions to optimize LRM-CI interaction. We manually create 30 high-quality samples, upon which we post-train models ranging from 1.5B to 32B parameters, with supervised fine-tuning, rejection fine-tuning and reinforcement learning. Our experimental results demonstrate that Hint-Engineering models achieve 4\% and 8\% absolute improvements on DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-1.5B respectively, across five challenging mathematical reasoning datasets. Furthermore, Hint-Engineering models use about 30\% fewer tokens for the 32B model and 50\% fewer tokens for the 1.5B model compared with the natural language models. The models and code are available at https://github.com/ChengpengLi1003/CoRT.
中文：CoRT是一种后训练框架，通过提示工程集成代码解释器，有效提升大型推理模型在数学推理中的性能，显著减少计算资源消耗并提高准确率。
English: CoRT is a post-training framework that enhances Large Reasoning Models' efficiency in mathematical reasoning by integrating Code Interpreters through Hint-Engineering, achieving significant performance improvements and token reduction.

Paperid: 287, https://arxiv.org/pdf/2506.09668.pdf   GitHub

Authors:Maik Dannecker, Vasiliki Sideri-Lampretsa, Sophie Starck, Angeline Mihailov, Mathieu Milh, Nadine Girard, Guillaume Auzias, Daniel Rueckert

Title: CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain

Abstract:
Magnetic resonance imaging of fetal and neonatal brains reveals rapid neurodevelopment marked by substantial anatomical changes unfolding within days. Studying this critical stage of the developing human brain, therefore, requires accurate brain models-referred to as atlases-of high spatial and temporal resolution. To meet these demands, established traditional atlases and recently proposed deep learning-based methods rely on large and comprehensive datasets. This poses a major challenge for studying brains in the presence of pathologies for which data remains scarce. We address this limitation with CINeMA (Conditional Implicit Neural Multi-Modal Atlas), a novel framework for creating high-resolution, spatio-temporal, multimodal brain atlases, suitable for low-data settings. Unlike established methods, CINeMA operates in latent space, avoiding compute-intensive image registration and reducing atlas construction times from days to minutes. Furthermore, it enables flexible conditioning on anatomical features including GA, birth age, and pathologies like ventriculomegaly (VM) and agenesis of the corpus callosum (ACC). CINeMA supports downstream tasks such as tissue segmentation and age prediction whereas its generative properties enable synthetic data creation and anatomically informed data augmentation. Surpassing state-of-the-art methods in accuracy, efficiency, and versatility, CINeMA represents a powerful tool for advancing brain research. We release the code and atlases at https://github.com/m-dannecker/CINeMA.
Chinese: CINeMA是一种新型框架，通过在潜在空间操作，能在低数据环境下快速构建高分辨率多模态脑图谱，支持对解剖特征的灵活条件设置，并实现分割和数据增强等下游任务。
English: CINeMA is a novel framework that rapidly constructs high-resolution, multimodal brain atlases in low-data settings by operating in latent space, enabling flexible conditioning on anatomical features and supporting downstream tasks like segmentation and data augmentation.

Paperid: 288, https://arxiv.org/pdf/2506.09650.pdf   GitHub GitHub

Authors:Kunyu Peng, Junchao Huang, Xiangsheng Huang, Di Wen, Junwei Zheng, Yufan Chen, Kailun Yang, Jiamin Wu, Chongqing Hao, Rainer Stiefelhagen

Title: HopaDIFF: Holistic-Partial Aware Fourier Conditioned Diffusion for Referring Human Action Segmentation in Multi-Person Scenarios

Abstract:
Action segmentation is a core challenge in high-level video understanding, aiming to partition untrimmed videos into segments and assign each a label from a predefined action set. Existing methods primarily address single-person activities with fixed action sequences, overlooking multi-person scenarios. In this work, we pioneer textual reference-guided human action segmentation in multi-person settings, where a textual description specifies the target person for segmentation. We introduce the first dataset for Referring Human Action Segmentation, i.e., RHAS133, built from 133 movies and annotated with 137 fine-grained actions with 33h video data, together with textual descriptions for this new task. Benchmarking existing action recognition methods on RHAS133 using VLM-based feature extractors reveals limited performance and poor aggregation of visual cues for the target person. To address this, we propose a holistic-partial aware Fourier-conditioned diffusion framework, i.e., HopaDIFF, leveraging a novel cross-input gate attentional xLSTM to enhance holistic-partial long-range reasoning and a novel Fourier condition to introduce more fine-grained control to improve the action segmentation generation. HopaDIFF achieves state-of-the-art results on RHAS133 in diverse evaluation settings. The code is available at https://github.com/KPeng9510/HopaDIFF.git.
中文: 本文开创性地提出文本参照引导的多人物动作分割方法，构建了首个RHAS133数据集，并设计了HopaDIFF框架——通过交叉输入门控注意力机制和傅里叶条件调控实现整体-局部感知的扩散模型，在多样化评估设置中取得了最优性能。
English: This paper introduces a novel textual reference-guided approach for multi-person action segmentation, presenting the RHAS133 dataset and proposing HopaDIFF—a holistic-partial aware diffusion framework that achieves state-of-the-art performance by enhancing long-range reasoning and fine-grained control through cross-input gate attention and Fourier conditioning.

Paperid: 289, https://arxiv.org/pdf/2506.09645.pdf   GitHub

Authors:Tianjun Yao, Haoxuan Li, Zhiqiang Shen, Pan Li, Tongliang Liu, Kun Zhang

Title: Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering

Abstract:
Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains, but their reliability is hindered by the outdated knowledge and hallucinations. Retrieval-Augmented Generation mitigates these issues by grounding LLMs with external knowledge; however, most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning. Knowledge graphs, which represent facts as relational triples, offer a more structured and compact alternative. Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering (KGQA), with a significant proportion adopting the retrieve-then-reasoning paradigm. In this framework, graph-based retrievers have demonstrated strong empirical performance, yet they still face challenges in generalization ability. In this work, we propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA. RAPL addresses these limitations through three aspects: (1) a two-stage labeling strategy that combines heuristic signals with parametric models to provide causally grounded supervision; (2) a model-agnostic graph transformation approach to capture both intra- and inter-triple interactions, thereby enhancing representational capacity; and (3) a path-based reasoning strategy that facilitates learning from the injected rational knowledge, and supports downstream reasoner through structured inputs. Empirically, RAPL outperforms state-of-the-art methods by $2.66\%-20.34\%$, and significantly reduces the performance gap between smaller and more powerful LLM-based reasoners, as well as the gap under cross-dataset settings, highlighting its superior retrieval capability and generalizability. Codes are available at: https://github.com/tianyao-aka/RAPL.
中文: 大语言模型因知识过时和幻觉问题影响可靠性，而RAPL框架通过创新的图检索方法增强结构化推理和泛化能力，有效提升了知识图谱问答的性能。
English: Large Language Models face reliability issues due to outdated knowledge and hallucinations, which the proposed RAPL framework addresses through a novel graph retrieval approach that enhances structured reasoning and generalizability in knowledge graph question answering.

Paperid: 290, https://arxiv.org/pdf/2506.09562.pdf   GitHub

Authors:Songze Li, Mingxuan Zhang, Kang Wei, Shouling Ji

Title: TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning

Abstract:
Deep reinforcement learning (DRL) has achieved remarkable success in a wide range of sequential decision-making domains, including robotics, healthcare, smart grids, and finance. Recent research demonstrates that attackers can efficiently exploit system vulnerabilities during the training phase to execute backdoor attacks, producing malicious actions when specific trigger patterns are present in the state observations. However, most existing backdoor attacks rely primarily on simplistic and heuristic trigger configurations, overlooking the potential efficacy of trigger optimization. To address this gap, we introduce TooBadRL (Trigger Optimization to Boost Effectiveness of Backdoor Attacks on DRL), the first framework to systematically optimize DRL backdoor triggers along three critical axes, i.e., temporal, spatial, and magnitude. Specifically, we first introduce a performance-aware adaptive freezing mechanism for injection timing. Then, we formulate dimension selection as a cooperative game, utilizing Shapley value analysis to identify the most influential state variable for the injection dimension. Furthermore, we propose a gradient-based adversarial procedure to optimize the injection magnitude under environment constraints. Evaluations on three mainstream DRL algorithms and nine benchmark tasks show that TooBadRL significantly improves attack success rates, while ensuring minimal degradation of normal task performance. These results highlight the previously underappreciated importance of principled trigger optimization in DRL backdoor attacks. The source code of TooBadRL can be found at https://github.com/S3IC-Lab/TooBadRL.
Chinese: 深度强化学习面临优化后门攻击的严重威胁，TooBadRL框架通过系统优化触发器的时间、空间和强度维度，显著提升攻击成功率，同时确保正常任务性能影响最小。
English: Deep reinforcement learning faces significant threats from optimized backdoor attacks, as demonstrated by the TooBadRL framework, which systematically enhances trigger effectiveness across temporal, spatial, and magnitude dimensions to achieve high attack success with minimal performance impact.

Paperid: 291, https://arxiv.org/pdf/2506.09548.pdf   GitHub

Authors:Taku Okawara, Kenji Koide, Aoki Takanose, Shuji Oishi, Masashi Yokozuka, Kentaro Uno, Kazuya Yoshida

Title: Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

Abstract:
In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.g., assuming delivery or transportation tasks) and terrain conditions. According to the \textit{neural adaptive leg odometry factor} and online uncertainty estimation of the leg kinematics model-based motion predictions, we jointly solve online training of this kinematics model and odometry estimation on a unified factor graph to retain the consistency of both. The proposed method was verified through real experiments using a quadruped robot in two challenging situations: 1) a sandy beach, representing an extremely featureless area with a deformable terrain, and 2) a campus, including multiple featureless areas and terrain types of asphalt, gravel (deformable terrain), and grass. Experimental results showed that our odometry estimation incorporating the \textit{neural leg kinematics model} outperforms state-of-the-art works. Our project page is available for further details: https://takuokawara.github.io/RAL2025_project_page/
中文: 本研究提出了一种紧密耦合的LiDAR-IMU-腿足里程计系统，通过在线学习的神经腿足运动学模型，在特征缺失环境和可变形地形中实现了更强的适应性，经实验验证其性能优于现有先进方法。
English: This study introduces a tightly coupled LiDAR-IMU-leg odometry system enhanced by an online learning-based neural leg kinematics model, which improves robustness in featureless environments and on deformable terrains through adaptive training and integrated factor graph optimization.

Paperid: 292, https://arxiv.org/pdf/2506.09487.pdf   GitHub

Authors:Taesoo Park, Mungwi Jeong, Mingyu Park, Narae Kim, Junyoung Kim, Mujung Kim, Jisang Yoo, Hoyun Lee, Sanghoon Kim, Soonchul Kwon

Title: BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation

Abstract:
This paper presents a tutorial-style survey and implementation guide of BemaGANv2, an advanced GAN-based vocoder designed for high-fidelity and long-term audio generation. Built upon the original BemaGAN architecture, BemaGANv2 incorporates major architectural innovations by replacing traditional ResBlocks in the generator with the Anti-aliased Multi-Periodicity composition (AMP) module, which internally applies the Snake activation function to better model periodic structures. In the discriminator framework, we integrate the Multi-Envelope Discriminator (MED), a novel architecture we originally proposed, to extract rich temporal envelope features crucial for periodicity detection. Coupled with the Multi-Resolution Discriminator (MRD), this combination enables more accurate modeling of long-range dependencies in audio. We systematically evaluate various discriminator configurations, including MSD + MED, MSD + MRD, and MPD + MED + MRD, using objective metrics (FAD, SSIM, PLCC, MCD) and subjective evaluations (MOS, SMOS). This paper also provides a comprehensive tutorial on the model architecture, training methodology, and implementation to promote reproducibility. The code and pre-trained models are available at: https://github.com/dinhoitt/BemaGANv2.
中文: 本文介绍了BemaGANv2这一改进的GAN声码器，其生成器采用带Snake激活函数的AMP模块，判别器结合新型MED与MRD技术，能实现高质量音频生成，并通过完整评估和开源代码确保可复现性。
English: This paper introduces BemaGANv2, an enhanced GAN vocoder featuring AMP modules with Snake activation in the generator and a novel MED discriminator combined with MRD for superior high-fidelity audio generation, supported by comprehensive evaluations and open-source implementation.

Paperid: 293, https://arxiv.org/pdf/2506.09398.pdf   GitHub GitHub

Authors:Haiyang Yu, Yuchao Lin, Xuan Zhang, Xiaofeng Qian, Shuiwang Ji

Title: Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames

Abstract:
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science. Motivated by the inherent relationship between the off-diagonal blocks of the Hamiltonian matrix and the SO(2) local frame, we propose a novel and efficient network, called QHNetV2, that achieves global SO(3) equivariance without the costly SO(3) Clebsch-Gordan tensor products. This is achieved by introducing a set of new efficient and powerful SO(2)-equivariant operations and performing all off-diagonal feature updates and message passing within SO(2) local frames, thereby eliminating the need of SO(3) tensor products. Moreover, a continuous SO(2) tensor product is performed within the SO(2) local frame at each node to fuse node features, mimicking the symmetric contraction operation. Extensive experiments on the large QH9 and MD17 datasets demonstrate that our model achieves superior performance across a wide range of molecular structures and trajectories, highlighting its strong generalization capability. The proposed SO(2) operations on SO(2) local frames offer a promising direction for scalable and symmetry-aware learning of electronic structures. Our code will be released as part of the AIRS library https://github.com/divelab/AIRS.
中文: QHNetV2通过引入高效的SO(2)等变操作在局部坐标系中实现全局SO(3)等变性，在预测哈密顿矩阵的任务中展现了卓越性能和泛化能力，同时避免了昂贵的SO(3)张量积计算。
English: QHNetV2 introduces efficient SO(2)-equivariant operations within local frames to achieve global SO(3) equivariance for predicting Hamiltonian matrices, demonstrating superior performance and generalization on molecular datasets while eliminating costly SO(3) tensor products.

Paperid: 294, https://arxiv.org/pdf/2506.09373.pdf   GitHub

Authors:Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Yuhui Chen, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao Lu, Yanqing Ma, Shiyin Lu, Qifeng Chen

Title: LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization

Abstract:
The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful intermediary. Despite the predominance of Supervised Fine-Tuning (SFT) methods in current GUI agents for achieving spatial localization, these methods face substantial challenges due to their limited capacity to accurately perceive positional data. Existing strategies, such as reinforcement learning, often fail to assess positional accuracy effectively, thereby restricting their utility. In response, we introduce Location Preference Optimization (LPO), a novel approach that leverages locational data to optimize interaction preferences. LPO uses information entropy to predict interaction positions by focusing on zones rich in information. Besides, it further introduces a dynamic location reward function based on physical distance, reflecting the varying importance of interaction positions. Supported by Group Relative Preference Optimization (GRPO), LPO facilitates an extensive exploration of GUI environments and significantly enhances interaction precision. Comprehensive experiments demonstrate LPO's superior performance, achieving SOTA results across both offline benchmarks and real-world online evaluations. Our code will be made publicly available soon, at https://github.com/AIDC-AI/LPO.
Chinese: 该研究提出位置偏好优化（LPO）方法，通过信息熵和动态奖励机制利用位置数据提升自主代理与图形用户界面的交互精度，在离线和在线评估中均取得了最先进的性能表现。
English: The study introduces Location Preference Optimization (LPO), a novel method that enhances autonomous agents' interaction precision with Graphical User Interfaces by utilizing locational data through information entropy and dynamic rewards, achieving state-of-the-art results in both offline and online evaluations.

Paperid: 295, https://arxiv.org/pdf/2506.09366.pdf   GitHub

Authors:Yuxuan Kuang, Haoran Geng, Amine Elhafsi, Tan-Dzung Do, Pieter Abbeel, Jitendra Malik, Marco Pavone, Yue Wang

Title: SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending

Abstract:
Humanoid robots hold significant potential in accomplishing daily tasks across diverse environments thanks to their flexibility and human-like morphology. Recent works have made significant progress in humanoid whole-body control and loco-manipulation leveraging optimal control or reinforcement learning. However, these methods require tedious task-specific tuning for each task to achieve satisfactory behaviors, limiting their versatility and scalability to diverse tasks in daily scenarios. To that end, we introduce SkillBlender, a novel hierarchical reinforcement learning framework for versatile humanoid loco-manipulation. SkillBlender first pretrains goal-conditioned task-agnostic primitive skills, and then dynamically blends these skills to accomplish complex loco-manipulation tasks with minimal task-specific reward engineering. We also introduce SkillBench, a parallel, cross-embodiment, and diverse simulated benchmark containing three embodiments, four primitive skills, and eight challenging loco-manipulation tasks, accompanied by a set of scientific evaluation metrics balancing accuracy and feasibility. Extensive simulated experiments show that our method significantly outperforms all baselines, while naturally regularizing behaviors to avoid reward hacking, resulting in more accurate and feasible movements for diverse loco-manipulation tasks in our daily scenarios. Our code and benchmark will be open-sourced to the community to facilitate future research. Project page: https://usc-gvl.github.io/SkillBlender-web/.
中文摘要：SkillBlender是一种分层强化学习框架，通过动态融合预训练的基础技能实现多功能人形机器人移动操作，在减少任务特定调整的同时显著优于现有方法，产生更自然可行的动作。
English Summary: SkillBlender is a hierarchical reinforcement learning framework that blends pretrained primitive skills to enable versatile humanoid loco-manipulation with minimal task-specific tuning, outperforming existing methods while producing more natural and feasible movements.

Paperid: 296, https://arxiv.org/pdf/2506.09347.pdf   GitHub

Authors:Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li

Title: ErrorEraser: Unlearning Data Bias for Improved Continual Learning

Abstract:
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction results, we find that data biases simultaneously reduce CL's ability to retain and transfer knowledge. To address this, we propose ErrorEraser, a universal plugin that removes erroneous memories caused by biases in CL, enhancing performance in both new and old tasks. ErrorEraser consists of two modules: Error Identification and Error Erasure. The former learns the probability density distribution of task data in the feature space without prior knowledge, enabling accurate identification of potentially biased samples. The latter ensures only erroneous knowledge is erased by shifting the decision space of representative outlier samples. Additionally, an incremental feature distribution learning strategy is designed to reduce the resource overhead during error identification in downstream tasks. Extensive experimental results show that ErrorEraser significantly mitigates the negative impact of data biases, achieving higher accuracy and lower forgetting rates across three types of CL methods. The code is available at https://github.com/diadai/ErrorEraser.
中文摘要：本文提出ErrorEraser这一持续学习通用插件，通过误差识别和擦除双模块解决数据偏差问题，无需先验知识即可有效消除错误记忆，显著提升新旧任务性能。
English Summary: This paper introduces ErrorEraser, a universal plugin for continual learning that addresses data bias issues by identifying and erasing erroneous memories through two specialized modules, significantly improving performance across tasks.

Paperid: 297, https://arxiv.org/pdf/2506.09340.pdf   GitHub GitHub

Authors:Siheng Li, Zhanhui Zhou, Wai Lam, Chao Yang, Chaochao Lu

Title: RePO: Replay-Enhanced Policy Optimization

Abstract:
Reinforcement learning (RL) is vital for optimizing large language models (LLMs). Recent Group Relative Policy Optimization (GRPO) estimates advantages using multiple on-policy outputs per prompt, leading to high computational costs and low data efficiency. To address this, we introduce Replay-Enhanced Policy Optimization (RePO), which leverages diverse replay strategies to retrieve off-policy samples from a replay buffer, allowing policy optimization based on a broader and more diverse set of samples for each prompt. Experiments on five LLMs across seven mathematical reasoning benchmarks demonstrate that RePO achieves absolute average performance gains of $18.4$ and $4.1$ points for Qwen2.5-Math-1.5B and Qwen3-1.7B, respectively, compared to GRPO. Further analysis indicates that RePO increases computational cost by $15\%$ while raising the number of effective optimization steps by $48\%$ for Qwen3-1.7B, with both on-policy and off-policy sample numbers set to $8$. The repository can be accessed at https://github.com/SihengLi99/RePO.
中文: RePO通过回放策略利用离策略样本进行更高效的策略优化，在适度增加计算成本的同时，相比GRPO实现了显著的性能提升。
English: RePO introduces replay strategies to utilize off-policy samples for more efficient policy optimization in LLMs, achieving significant performance gains over GRPO with a moderate increase in computational cost.

Paperid: 298, https://arxiv.org/pdf/2506.09316.pdf   GitHub

Authors:Yeonju Ro, Zhenyu Zhang, Souvik Kundu, Zhangyang Wang, Aditya Akella

Title: On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention

Abstract:
Large language models (LLMs) excel at capturing global token dependencies via self-attention but face prohibitive compute and memory costs on lengthy inputs. While sub-quadratic methods (e.g., linear attention) can reduce these costs, they often degrade accuracy due to overemphasizing recent tokens. In this work, we first propose dual-state linear attention (DSLA), a novel design that maintains two specialized hidden states-one for preserving historical context and one for tracking recency-thereby mitigating the short-range bias typical of linear-attention architectures. To further balance efficiency and accuracy under dynamic workload conditions, we introduce DSLA-Serve, an online adaptive distillation framework that progressively replaces Transformer layers with DSLA layers at inference time, guided by a sensitivity-based layer ordering. DSLA-Serve uses a chained fine-tuning strategy to ensure that each newly converted DSLA layer remains consistent with previously replaced layers, preserving the overall quality. Extensive evaluations on commonsense reasoning, long-context QA, and text summarization demonstrate that DSLA-Serve yields 2.3x faster inference than Llama2-7B and 3.0x faster than the hybrid Zamba-7B, while retaining comparable performance across downstream tasks. Our ablation studies show that DSLA's dual states capture both global and local dependencies, addressing the historical-token underrepresentation seen in prior linear attentions. Codes are available at https://github.com/utnslab/DSLA-Serve.
中文摘要：DSLA-Serve通过双状态线性注意力机制解决传统线性注意力对近期标记的过度关注问题，并采用自适应蒸馏框架在保持任务性能的同时，实现比同类模型快2.3-3倍的推理速度。
English Summary: DSLA-Serve introduces dual-state linear attention to mitigate linear attention's recency bias and an adaptive distillation framework that accelerates inference by 2.3-3x over comparable models while maintaining task performance.

Paperid: 299, https://arxiv.org/pdf/2506.09237.pdf   GitHub

Authors:Mojtaba Nafez, Amirhossein Koochakian, Arad Maleki, Jafar Habibi, Mohammad Hossein Rohban

Title: PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies

Abstract:
Anomaly Detection (AD) and Anomaly Localization (AL) are crucial in fields that demand high reliability, such as medical imaging and industrial monitoring. However, current AD and AL approaches are often susceptible to adversarial attacks due to limitations in training data, which typically include only normal, unlabeled samples. This study introduces PatchGuard, an adversarially robust AD and AL method that incorporates pseudo anomalies with localization masks within a Vision Transformer (ViT)-based architecture to address these vulnerabilities. We begin by examining the essential properties of pseudo anomalies, and follow it by providing theoretical insights into the attention mechanisms required to enhance the adversarial robustness of AD and AL systems. We then present our approach, which leverages Foreground-Aware Pseudo-Anomalies to overcome the deficiencies of previous anomaly-aware methods. Our method incorporates these crafted pseudo-anomaly samples into a ViT-based framework, with adversarial training guided by a novel loss function designed to improve model robustness, as supported by our theoretical analysis. Experimental results on well-established industrial and medical datasets demonstrate that PatchGuard significantly outperforms previous methods in adversarial settings, achieving performance gains of $53.2\%$ in AD and $68.5\%$ in AL, while also maintaining competitive accuracy in non-adversarial settings. The code repository is available at https://github.com/rohban-lab/PatchGuard .
Chinese: 本研究提出了PatchGuard，一种利用伪异常和视觉Transformer框架的鲁棒异常检测与定位方法，显著提升了对抗攻击的防御能力，在对抗性和标准环境下均实现了显著性能提升。
English: This study introduces PatchGuard, a robust method for anomaly detection and localization that uses pseudo anomalies and a Vision Transformer framework to significantly enhance resistance against adversarial attacks, achieving notable performance improvements in both adversarial and standard settings.

Paperid: 300, https://arxiv.org/pdf/2506.09200.pdf   GitHub

Authors:Val Andrei Fajardo, David B. Emerson, Amandeep Singh, Veronica Chatrath, Marcelo Lotif, Ravi Theja, Alex Cheung, Izuki Matsuba

Title: FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems

Abstract:
Retrieval-augmented generation (RAG) systems have been shown to be effective in addressing many of the drawbacks of relying solely on the parametric memory of large language models. Recent work has demonstrated that RAG systems can be improved via fine-tuning of their retriever and generator models. In this work, we introduce FedRAG, a framework for fine-tuning RAG systems across centralized and federated architectures. FedRAG supports state-of-the-art fine-tuning methods, offering a simple and intuitive interface and a seamless conversion from centralized to federated training tasks. FedRAG is also deeply integrated with the modern RAG ecosystem, filling a critical gap in available tools.
中文: FedRAG是一个框架，支持在集中式和联邦式架构下对检索增强生成系统进行微调，集成了先进方法与现代RAG工具，填补了现有工具的空白。
English: FedRAG is a framework that enables fine-tuning of retrieval-augmented generation systems across both centralized and federated architectures, integrating advanced methods and modern RAG tools to bridge existing gaps.

Paperid: 301, https://arxiv.org/pdf/2506.09193.pdf   GitHub

Authors:Yilin Zhuang, Karthik Duraisamy

Title: LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting

Abstract:
Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representation, and a transformer-based diffusion model produces sequential latent updates with arbitrary hour initialization. The model incorporates Geometric Rotary Position Embedding (GeoRoPE) to account for the Earth's spherical geometry, a dual-stream attention mechanism for efficient conditioning, and sinusoidal temporal embeddings to capture seasonal patterns. LaDCast achieves deterministic and probabilistic skill close to that of the European Centre for Medium-Range Forecast IFS-ENS, without any explicit perturbations. Notably, LaDCast demonstrates superior performance in tracking rare extreme events such as cyclones, capturing their trajectories more accurately than established models. By operating in latent space, LaDCast reduces storage and compute by orders of magnitude, demonstrating a practical path toward forecasting at kilometer-scale resolution in real time. We open-source our code and models and provide the training and evaluation pipelines at: https://github.com/tonyzyl/ladcast.
中文摘要：LaDCast首次提出全球潜在扩散框架用于中期集合天气预报，在保持与主流模型相当准确度的同时大幅降低计算成本，并在追踪极端天气事件方面展现出卓越性能。
English Summary: LaDCast introduces the first global latent-diffusion framework for medium-range ensemble weather forecasting, achieving accuracy comparable to leading models while significantly reducing computational costs and demonstrating superior performance in tracking extreme weather events.

Paperid: 302, https://arxiv.org/pdf/2506.09176.pdf   GitHub

Authors:Haoyuan Cai, Zhenghao Peng, Bolei Zhou

Title: Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism

Abstract:
Interactive Imitation Learning (IIL) allows agents to acquire desired behaviors through human interventions, but current methods impose high cognitive demands on human supervisors. We propose the Adaptive Intervention Mechanism (AIM), a novel robot-gated IIL algorithm that learns an adaptive criterion for requesting human demonstrations. AIM utilizes a proxy Q-function to mimic the human intervention rule and adjusts intervention requests based on the alignment between agent and human actions. By assigning high Q-values when the agent deviates from the expert and decreasing these values as the agent becomes proficient, the proxy Q-function enables the agent to assess the real-time alignment with the expert and request assistance when needed. Our expert-in-the-loop experiments reveal that AIM significantly reduces expert monitoring efforts in both continuous and discrete control tasks. Compared to the uncertainty-based baseline Thrifty-DAgger, our method achieves a 40% improvement in terms of human take-over cost and learning efficiency. Furthermore, AIM effectively identifies safety-critical states for expert assistance, thereby collecting higher-quality expert demonstrations and reducing overall expert data and environment interactions needed. Code and demo video are available at https://github.com/metadriverse/AIM.
中文: 提出的自适应干预机制（AIM）通过代理Q函数自适应地请求专家演示，显著减少了交互式模仿学习中的人工监督需求，与基线方法相比效率提升40%，并能更有效地识别安全关键状态。
English: The proposed Adaptive Intervention Mechanism (AIM) significantly reduces human supervision in Interactive Imitation Learning by using a proxy Q-function to adaptively request expert demonstrations, achieving 40% improvement in efficiency and better identification of safety-critical states compared to baseline methods.

Paperid: 303, https://arxiv.org/pdf/2506.09101.pdf   GitHub

Authors:Míriam Barrabés, Daniel Mas Montserrat, Kapal Dev, Alexander G. Ioannidis

Title: Feature Shift Localization Network

Abstract:
Feature shifts between data sources are present in many applications involving healthcare, biomedical, socioeconomic, financial, survey, and multi-sensor data, among others, where unharmonized heterogeneous data sources, noisy data measurements, or inconsistent processing and standardization pipelines can lead to erroneous features. Localizing shifted features is important to address the underlying cause of the shift and correct or filter the data to avoid degrading downstream analysis. While many techniques can detect distribution shifts, localizing the features originating them is still challenging, with current solutions being either inaccurate or not scalable to large and high-dimensional datasets. In this work, we introduce the Feature Shift Localization Network (FSL-Net), a neural network that can localize feature shifts in large and high-dimensional datasets in a fast and accurate manner. The network, trained with a large number of datasets, learns to extract the statistical properties of the datasets and can localize feature shifts from previously unseen datasets and shifts without the need for re-training. The code and ready-to-use trained model are available at https://github.com/AI-sandbox/FSL-Net.
Chinese: FSL-Net 是一种神经网络，能够无需重新训练即可快速准确地定位大型高维数据集中的特征偏移。
English: FSL-Net is a neural network that accurately and efficiently localizes feature shifts in large, high-dimensional datasets without requiring retraining for new data.

Paperid: 304, https://arxiv.org/pdf/2506.09096.pdf   GitHub

Authors:Chaoyang Zhou, Shunyu Liu, Zengmao Wang, Di Wang, Rong-Cheng Tu, Bo Du, Dacheng Tao

Title: Intra-Trajectory Consistency for Reward Modeling

Abstract:
Reward models are critical for improving large language models (LLMs), particularly in reinforcement learning from human feedback (RLHF) or inference-time verification. Current reward modeling typically relies on scores of overall responses to learn the outcome rewards for the responses. However, since the response-level scores are coarse-grained supervision signals, the reward model struggles to identify the specific components within a response trajectory that truly correlate with the scores, leading to poor generalization on unseen responses. In this paper, we propose to leverage generation probabilities to establish reward consistency between processes in the response trajectory, which allows the response-level supervisory signal to propagate across processes, thereby providing additional fine-grained signals for reward learning. Building on analysis under the Bayesian framework, we develop an intra-trajectory consistency regularization to enforce that adjacent processes with higher next-token generation probability maintain more consistent rewards. We apply the proposed regularization to the advanced outcome reward model, improving its performance on RewardBench. Besides, we show that the reward model trained with the proposed regularization induces better DPO-aligned policies and achieves better best-of-N (BON) inference-time verification results. Our code is provided in https://github.com/chaoyang101/ICRM.
中文: 本文提出轨迹内一致性正则化方法，利用生成概率在响应过程中建立奖励一致性，从而提升奖励模型在基准测试中的表现，并优化策略对齐和推理时验证效果。
English: This paper introduces intra-trajectory consistency regularization, which uses generation probabilities to align rewards across response processes, enhancing reward model performance on benchmarks and improving policy alignment and inference-time verification.

Paperid: 305, https://arxiv.org/pdf/2506.09063.pdf   GitHub

Authors:Shayan Shekarforoush, David B. Lindell, Marcus A. Brubaker, David J. Fleet

Title: Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery

Abstract:
Cryo-EM is a transformational paradigm in molecular biology where computational methods are used to infer 3D molecular structure at atomic resolution from extremely noisy 2D electron microscope images. At the forefront of research is how to model the structure when the imaged particles exhibit non-rigid conformational flexibility and compositional variation where parts are sometimes missing. We introduce a novel 3D reconstruction framework with a hierarchical Gaussian mixture model, inspired in part by Gaussian Splatting for 4D scene reconstruction. In particular, the structure of the model is grounded in an initial process that infers a part-based segmentation of the particle, providing essential inductive bias in order to handle both conformational and compositional variability. The framework, called CryoSPIRE, is shown to reveal biologically meaningful structures on complex experimental datasets, and establishes a new state-of-the-art on CryoBench, a benchmark for cryo-EM heterogeneity methods.

Paperid: 306, https://arxiv.org/pdf/2506.09033.pdf   GitHub

Authors:Haozhen Zhang, Tao Feng, Jiaxuan You

Title: Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning

Abstract:
The rapid emergence of diverse large language models (LLMs) has spurred the development of LLM routers that assign user queries to the most suitable model. However, existing LLM routers typically perform a single-round, one-to-one mapping (\textit{i.e.}, assigning each query to a single model in isolation), which limits their capability to tackle complex tasks that demand the complementary strengths of multiple LLMs. In this paper, we present \textbf{Router-R1}, a reinforcement learning (RL)-based framework that formulates multi-LLM routing and aggregation as a sequential decision process. Router-R1 instantiates the router itself as a capable LLM, leveraging its reasoning ability to interleave "think" actions (internal deliberation) with "route" actions (dynamic model invocation), and integrates each response into its evolving context. To facilitate learning, we employ a lightweight rule-based reward comprising format rewards, final outcome rewards, and a novel cost reward for optimizing the balance between performance and cost, opening a pathway toward enhancing performance-cost trade-offs via RL. Router-R1 also conditions only on simple model descriptors such as pricing, latency, and example performance, enabling strong generalization to unseen model selection. Experiments on seven general and multi-hop QA benchmarks show that Router-R1 outperforms several strong baselines, achieving superior performance while maintaining robust generalization and cost management.
中文: 本文提出Router-R1强化学习框架，通过将多模型路由构建为序列决策过程，动态调用并整合不同大语言模型的优势，在多个基准测试中实现了性能与成本的最优平衡。
English: This paper introduces Router-R1, a reinforcement learning framework that enhances multi-LLM routing by dynamically selecting and aggregating models through sequential decision-making, achieving superior performance and cost efficiency across diverse benchmarks.

Paperid: 307, https://arxiv.org/pdf/2506.08990.pdf   GitHub

Authors:Chenyu Lian, Hong-Yu Zhou, Dongyun Liang, Jing Qin, Liansheng Wang

Title: Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models

Abstract:
Medical vision-language alignment through cross-modal contrastive learning shows promising performance in image-text matching tasks, such as retrieval and zero-shot classification. However, conventional cross-modal contrastive learning (CLIP-based) methods suffer from suboptimal visual representation capabilities, which also limits their effectiveness in vision-language alignment. In contrast, although the models pretrained via multimodal masked modeling struggle with direct cross-modal matching, they excel in visual representation. To address this contradiction, we propose ALTA (ALign Through Adapting), an efficient medical vision-language alignment method that utilizes only about 8% of the trainable parameters and less than 1/5 of the computational consumption required for masked record modeling. ALTA achieves superior performance in vision-language matching tasks like retrieval and zero-shot classification by adapting the pretrained vision model from masked record modeling. Additionally, we integrate temporal-multiview radiograph inputs to enhance the information consistency between radiographs and their corresponding descriptions in reports, further improving the vision-language alignment. Experimental evaluations show that ALTA outperforms the best-performing counterpart by over 4% absolute points in text-to-image accuracy and approximately 6% absolute points in image-to-text retrieval accuracy. The adaptation of vision-language models during efficient alignment also promotes better vision and language understanding. Code is publicly available at https://github.com/DopamineLcy/ALTA.
中文: ALTA方法通过适配预训练的视觉模型，以极低的计算成本实现了医学视觉与语言的高效对齐，在检索和零样本分类任务中表现卓越。
English: The proposed ALTA method efficiently aligns medical vision and language by adapting a pretrained vision model from masked record modeling, achieving superior performance in retrieval and zero-shot classification with minimal computational resources.

Paperid: 308, https://arxiv.org/pdf/2506.08977.pdf   GitHub

Authors:Victoria Hankemeier, Malte Schilling

Title: Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data

Abstract:
Developments in Deep Learning have significantly improved time series forecasting by enabling more accurate modeling of complex temporal dependencies inherent in sequential data. The effectiveness of such models is often demonstrated on limited sets of specific real-world data. Although this allows for comparative analysis, it still does not demonstrate how specific data characteristics align with the architectural strengths of individual models. Our research aims at uncovering clear connections between time series characteristics and particular models. We introduce a novel dataset generated using Gaussian Processes, specifically designed to display distinct, known characteristics for targeted evaluations of model adaptability to them. Furthermore, we present TimeFlex, a new model that incorporates a modular architecture tailored to handle diverse temporal dynamics, including trends and periodic patterns. This model is compared to current state-of-the-art models, offering a deeper understanding of how models perform under varied time series conditions.
Chinese: 深度学习进展通过建模复杂依赖提升了时间序列预测能力，但现有评估未能将数据特性与模型优势关联，为此引入高斯过程生成的数据集和TimeFlex模型，以针对性评估模型适应性。
English: Deep learning advancements have enhanced time series forecasting by modeling complex dependencies, yet current evaluations fail to link data traits with model strengths, prompting the introduction of a Gaussian Processes dataset and TimeFlex model for targeted adaptability assessments.

Paperid: 309, https://arxiv.org/pdf/2506.08939.pdf   GitHub

Authors:Hang Ye, Gaoxiang Duan, Haoran Zeng, Yangxin Zhu, Lingxue Meng, Xiaoying Zheng, Yongxin Zhu

Title: KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting

Abstract:
Multivariate long-term and efficient time series forecasting is a key requirement for a variety of practical applications, and there are complex interleaving time dynamics in time series data that require decomposition modeling. Traditional time series decomposition methods are single and rely on fixed rules, which are insufficient for mining the potential information of the series and adapting to the dynamic characteristics of complex series. On the other hand, the Transformer-based models for time series forecasting struggle to effectively model long sequences and intricate dynamic relationships due to their high computational complexity. To overcome these limitations, we introduce KARMA, with an Adaptive Time Channel Decomposition module (ATCD) to dynamically extract trend and seasonal components. It further integrates a Hybrid Frequency-Time Decomposition module (HFTD) to further decompose Series into frequency-domain and time-domain. These components are coupled with multi-scale Mamba-based KarmaBlock to efficiently process global and local information in a coordinated manner. Experiments on eight real-world datasets from diverse domains well demonstrated that KARMA significantly outperforms mainstream baseline methods in both predictive accuracy and computational efficiency. Code and full results are available at this repository: https://github.com/yedadasd/KARMA
中文摘要：KARMA通过自适应时间通道分解和混合频时分解模块动态提取时序成分，结合多尺度Mamba模块协同处理信息，在多个真实数据集上显著超越了主流基线方法的预测精度与计算效率。
English Summary: KARMA introduces adaptive decomposition modules and Mamba-based blocks to dynamically extract time series components, significantly outperforming existing methods in both accuracy and efficiency across multiple real-world datasets.

Paperid: 310, https://arxiv.org/pdf/2506.08935.pdf   GitHub

Authors:Andrew Shin

Title: Can A Gamer Train A Mathematical Reasoning Model?

Abstract:
While large language models (LLMs) have achieved remarkable performance in various tasks including mathematical reasoning, their development typically demands prohibitive computational resources. Recent advancements have reduced costs for training capable models, yet even these approaches rely on high-end hardware clusters. In this paper, we demonstrate that a single average gaming GPU can train a solid mathematical reasoning model, by integrating reinforcement learning and memory optimization techniques. Specifically, we train a 1.5B parameter mathematical reasoning model on RTX 3080 Ti of 16GB memory that achieves comparable or better performance on mathematical reasoning benchmarks than models several times larger, in resource-constrained environments. Our results challenge the paradigm that state-of-the-art mathematical reasoning necessitates massive infrastructure, democratizing access to high-performance AI research. https://github.com/shinandrew/YouronMath.
Chinese: 该研究通过强化学习和内存优化技术，在单个游戏显卡上成功训练出性能优异的15亿参数数学推理模型，打破了高性能AI研究必须依赖庞大计算资源的传统模式。
English: This research demonstrates that a single gaming GPU can train a competitive 1.5B-parameter mathematical reasoning model using reinforcement learning and memory optimization, challenging the need for massive computational infrastructure.

Paperid: 311, https://arxiv.org/pdf/2506.08902.pdf   GitHub GitHub

Authors:Chongyi Zheng, Seohong Park, Sergey Levine, Benjamin Eysenbach

Title: Intention-Conditioned Flow Occupancy Models

Abstract:
Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the community (including those without data or compute resources to train a model from scratch) to adapt and fine-tune to specific tasks. Applying this same framework to reinforcement learning (RL) is appealing because it offers compelling avenues for addressing core challenges in RL, including sample efficiency and robustness. However, there remains a fundamental challenge to pre-train large models in the context of RL: actions have long-term dependencies, so training a foundation model that reasons across time is important. Recent advances in generative AI have provided new tools for modeling highly complex distributions. In this paper, we build a probabilistic model to predict which states an agent will visit in the temporally distant future (i.e., an occupancy measure) using flow matching. As large datasets are often constructed by many distinct users performing distinct tasks, we include in our model a latent variable capturing the user intention. This intention increases the expressivity of our model, and enables adaptation with generalized policy improvement. We call our proposed method intention-conditioned flow occupancy models (InFOM). Comparing with alternative methods for pre-training, our experiments on $36$ state-based and $4$ image-based benchmark tasks demonstrate that the proposed method achieves $1.8 \times$ median improvement in returns and increases success rates by $36\%$. Website: https://chongyi-zheng.github.io/infom Code: https://github.com/chongyi-zheng/infom
大规模预训练通过基于意图的流匹配模型预测强化学习中具有长期依赖性的未来状态占用，相比现有方法显著提升了任务成功率与回报中位数。
Large-scale pre-training in reinforcement learning addresses long-term action dependencies by modeling future state occupancy with intention-aware flow matching, achieving significant performance gains over existing methods.

Paperid: 312, https://arxiv.org/pdf/2506.08889.pdf   GitHub

Authors:Yizhao Gao, Shuming Guo, Shijie Cao, Yuqing Xia, Yu Cheng, Lei Wang, Lingxiao Ma, Yutao Sun, Tianzhu Ye, Li Dong, Hayden Kwok-Hay So, Yu Hua, Ting Cao, Fan Yang, Mao Yang

Title: SeerAttention-R: Sparse Attention Adaptation for Long Reasoning

Abstract:
We introduce SeerAttention-R, a sparse attention framework specifically tailored for the long decoding of reasoning models. Extended from SeerAttention, SeerAttention-R retains the design of learning attention sparsity through a self-distilled gating mechanism, while removing query pooling to accommodate auto-regressive decoding. With a lightweight plug-in gating, SeerAttention-R is flexible and can be easily integrated into existing pretrained model without modifying the original parameters. We demonstrate that SeerAttention-R, trained on just 0.4B tokens, maintains near-lossless reasoning accuracy with 4K token budget in AIME benchmark under large sparse attention block sizes (64/128). Using TileLang, we develop a highly optimized sparse decoding kernel that achieves near-theoretical speedups of up to 9x over FlashAttention-3 on H100 GPU at 90% sparsity. Code is available at: https://github.com/microsoft/SeerAttention.
Chinese: SeerAttention-R 是一种专为推理模型长解码设计的稀疏注意力框架，在保持接近无损精度的同时，通过优化内核在 H100 GPU 上实现了高达 9 倍的加速。
English: SeerAttention-R is a sparse attention framework designed for efficient long decoding in reasoning models, maintaining near-lossless accuracy with high sparsity and achieving up to 9x speedup on H100 GPUs through optimized kernels.

Paperid: 313, https://arxiv.org/pdf/2506.08884.pdf   GitHub GitHub

Authors:Shiqin Tang, Shujian Yu

Title: InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis

Abstract:
Extracting meaningful latent representations from high-dimensional sequential data is a crucial challenge in machine learning, with applications spanning natural science and engineering. We introduce InfoDPCCA, a dynamic probabilistic Canonical Correlation Analysis (CCA) framework designed to model two interdependent sequences of observations. InfoDPCCA leverages a novel information-theoretic objective to extract a shared latent representation that captures the mutual structure between the data streams and balances representation compression and predictive sufficiency while also learning separate latent components that encode information specific to each sequence. Unlike prior dynamic CCA models, such as DPCCA, our approach explicitly enforces the shared latent space to encode only the mutual information between the sequences, improving interpretability and robustness. We further introduce a two-step training scheme to bridge the gap between information-theoretic representation learning and generative modeling, along with a residual connection mechanism to enhance training stability. Through experiments on synthetic and medical fMRI data, we demonstrate that InfoDPCCA excels as a tool for representation learning. Code of InfoDPCCA is available at https://github.com/marcusstang/InfoDPCCA.
中文: InfoDPCCA是一种动态概率典型相关分析框架，通过信息论目标提取共享和序列特定的潜在表示，在建模相互依赖的序列数据时提升了可解释性和鲁棒性。
English: InfoDPCCA is a dynamic probabilistic CCA framework that uses an information-theoretic objective to extract shared and sequence-specific latent representations, improving interpretability and robustness in modeling interdependent sequential data.

Paperid: 314, https://arxiv.org/pdf/2506.08862.pdf   GitHub

Authors:Zike Wu, Qi Yan, Xuanyu Yi, Lele Wang, Renjie Liao

Title: StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams

Abstract:
Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams is crucial for numerous real-world applications. However, existing methods struggle to jointly address three key challenges: 1) processing uncalibrated inputs in real time, 2) accurately modeling dynamic scene evolution, and 3) maintaining long-term stability and computational efficiency. To this end, we introduce StreamSplat, the first fully feed-forward framework that transforms uncalibrated video streams of arbitrary length into dynamic 3D Gaussian Splatting (3DGS) representations in an online manner, capable of recovering scene dynamics from temporally local observations. We propose two key technical innovations: a probabilistic sampling mechanism in the static encoder for 3DGS position prediction, and a bidirectional deformation field in the dynamic decoder that enables robust and efficient dynamic modeling. Extensive experiments on static and dynamic benchmarks demonstrate that StreamSplat consistently outperforms prior works in both reconstruction quality and dynamic scene modeling, while uniquely supporting online reconstruction of arbitrarily long video streams. Code and models are available at https://github.com/nickwzk/StreamSplat.
中文: StreamSplat是一种开创性的前馈框架，能将未校准视频流在线转换为动态3D高斯溅射表示，在实时重建、动态建模和计算效率方面表现卓越。
English: StreamSplat is a pioneering feed-forward framework that converts uncalibrated video streams into dynamic 3D Gaussian Splatting representations online, excelling in real-time reconstruction, dynamic modeling, and computational efficiency.

Paperid: 315, https://arxiv.org/pdf/2506.08771.pdf   GitHub

Authors:Yuni Susanti, Michael Färber

Title: Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery

Abstract:
Inferring causal relationships between variable pairs is crucial for understanding multivariate interactions in complex systems. Knowledge-based causal discovery -- which involves inferring causal relationships by reasoning over the metadata of variables (e.g., names or textual context) -- offers a compelling alternative to traditional methods that rely on observational data. However, existing methods using Large Language Models (LLMs) often produce unstable and inconsistent results, compromising their reliability for causal inference. To address this, we introduce a novel approach that integrates Knowledge Graphs (KGs) with LLMs to enhance knowledge-based causal discovery. Our approach identifies informative metapath-based subgraphs within KGs and further refines the selection of these subgraphs using Learning-to-Rank-based models. The top-ranked subgraphs are then incorporated into zero-shot prompts, improving the effectiveness of LLMs in inferring the causal relationship. Extensive experiments on biomedical and open-domain datasets demonstrate that our method outperforms most baselines by up to 44.4 points in F1 scores, evaluated across diverse LLMs and KGs. Our code and datasets are available on GitHub: https://github.com/susantiyuni/path-to-causality
中文摘要：本研究提出了一种将知识图谱与大型语言模型相结合的新方法，通过元路径子图排序和零样本提示增强基于知识的因果发现，在多个数据集上相比基线方法F1分数最高提升44.4分。
English Summary: This study introduces a novel approach that integrates Knowledge Graphs with Large Language Models to enhance knowledge-based causal discovery, achieving up to 44.4-point F1 score improvements over baselines through metapath subgraph ranking and zero-shot prompting.

Paperid: 316, https://arxiv.org/pdf/2506.08743.pdf   GitHub

Authors:Michael Färber, David Lamprecht, Yuni Susanti

Title: Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems

Abstract:
Graph Neural Networks (GNNs) have substantially advanced the field of recommender systems. However, despite the creation of more than a thousand knowledge graphs (KGs) under the W3C standard RDF, their rich semantic information has not yet been fully leveraged in GNN-based recommender systems. To address this gap, we propose a comprehensive integration of RDF KGs with GNNs that utilizes both the topological information from RDF object properties and the content information from RDF datatype properties. Our main focus is an in-depth evaluation of various GNNs, analyzing how different semantic feature initializations and types of graph structure heterogeneity influence their performance in recommendation tasks. Through experiments across multiple recommendation scenarios involving multi-million-node RDF graphs, we demonstrate that harnessing the semantic richness of RDF KGs significantly improves recommender systems and lays the groundwork for GNN-based recommender systems for the Linked Open Data cloud. The code and data are available on our GitHub repository: https://github.com/davidlamprecht/rdf-gnn-recommendation
中文: 本研究提出将RDF知识图谱与图神经网络相结合，充分利用其拓扑结构和内容信息，并通过大规模实验证明，利用RDF语义丰富性能显著提升推荐系统的性能。
English: This study proposes integrating RDF knowledge graphs with Graph Neural Networks to leverage both topological and content information, demonstrating through large-scale experiments that utilizing RDF semantic richness significantly enhances recommendation performance.

Paperid: 317, https://arxiv.org/pdf/2506.08652.pdf   GitHub

Authors:Mahesh Godavarti

Title: JoFormer (Journey-based Transformer): Theory and Empirical Analysis on the Tiny Shakespeare Dataset

Abstract:
Transformers have demonstrated remarkable success in sequence modeling, yet effectively incorporating positional information remains a challenging and active area of research. In this paper, we introduce JoFormer, a journey-based Transformer architecture grounded in a recently proposed non-commutative algebra for composing transformations across positions. JoFormer represents relative positions through learnable directional transforms that are sequentially composed along the input, thereby extending and generalizing existing approaches based on relative position representations. We derive the JoFormer attention mechanism from first principles and show that it subsumes standard methods such as rotary transformations as special cases. To evaluate its effectiveness, we compare JoFormer to the RoFormer baseline on the Tiny Shakespeare character-level language modeling task. Our results demonstrate that JoFormer consistently achieves lower perplexity and faster convergence, highlighting the advantages of its more expressive, journey-based treatment of position. Notably, the per-token JoFormer is still a primitive, conceptual variant with layer-independent angles, yet it already demonstrates strong performance-underscoring its promise as a proof of concept for more expressive architectures. We conclude by discussing how JoFormer offers a principled approach to integrating positional structure into Transformer architectures. The code used in this work is available at https://github.com/mahesh-godavarti/joformer.
中文: JoFormer是一种基于可学习方向变换的新型Transformer架构，通过相对位置建模在语言建模任务中实现了比RoFormer更低的困惑度和更快的收敛速度。
English: JoFormer is a novel Transformer architecture that uses learnable directional transforms to model relative positions, achieving lower perplexity and faster convergence than RoFormer in language modeling tasks.

Paperid: 318, https://arxiv.org/pdf/2506.08646.pdf   GitHub

Authors:Mingyu Zheng, Zhifan Feng, Jia Wang, Lanrui Wang, Zheng Lin, Yang Hao, Weiping Wang

Title: TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning

Abstract:
Despite the commendable progress of recent LLM-based data synthesis methods, they face two limitations in generating table instruction tuning data. First, they can not thoroughly explore the vast input space of table understanding tasks, leading to limited data diversity. Second, they ignore the weaknesses in table understanding ability of the target LLM and blindly pursue the increase of data quantity, resulting in suboptimal data efficiency. In this paper, we introduce a progressive and weakness-guided data synthesis framework tailored for table instruction tuning, named TableDreamer, to mitigate the above issues. Specifically, we first synthesize diverse tables and related instructions as seed data, and then perform an iterative exploration of the input space under the guidance of the newly identified weakness data, which eventually serve as the final training data for fine-tuning the target LLM. Extensive experiments on 10 tabular benchmarks demonstrate the effectiveness of the proposed framework, which boosts the average accuracy of Llama3.1-8B-instruct by 11.62% (49.07% to 60.69%) with 27K GPT-4o synthetic data and outperforms state-of-the-art data synthesis baselines which use more training data. The code and data is available at https://github.com/SpursGoZmy/TableDreamer
中文: TableDreamer提出了一种渐进式、弱点引导的数据合成框架，通过生成多样化数据并迭代修正大语言模型在表格理解中的不足，以少量合成数据显著提升了模型性能。
English: TableDreamer introduces a progressive, weakness-guided framework to enhance table instruction tuning by synthesizing diverse data and iteratively addressing LLM weaknesses, significantly improving accuracy with minimal synthetic data.

Paperid: 319, https://arxiv.org/pdf/2506.08641.pdf   GitHub

Authors:Simon Roschmann, Quentin Bouniot, Vasilii Feofanov, Ievgen Redko, Zeynep Akata

Title: Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

Abstract:
Time series classification is a fundamental task in healthcare and industry, yet the development of time series foundation models (TSFMs) remains limited by the scarcity of publicly available time series datasets. In this work, we propose Time Vision Transformer (TiViT), a framework that converts time series into images to leverage the representational power of frozen Vision Transformers (ViTs) pretrained on large-scale image datasets. First, we theoretically motivate our approach by analyzing the 2D patching of ViTs for time series, showing that it can increase the number of label-relevant tokens and reduce the sample complexity. Second, we empirically demonstrate that TiViT achieves state-of-the-art performance on standard time series classification benchmarks by utilizing the hidden representations of large OpenCLIP models. We explore the structure of TiViT representations and find that intermediate layers with high intrinsic dimension are the most effective for time series classification. Finally, we assess the alignment between TiViT and TSFM representation spaces and identify a strong complementarity, with further performance gains achieved by combining their features. Our findings reveal a new direction for reusing vision representations in a non-visual domain. Code is available at https://github.com/ExplainableML/TiViT.
中文: TiViT框架将时间序列转换为图像以利用预训练的视觉变换器，实现了最先进的分类性能，并揭示了在非视觉领域重用视觉表示的新方向。
English: The TiViT framework transforms time series into images to utilize pretrained Vision Transformers, achieving state-of-the-art classification performance and revealing a novel approach for reusing visual representations in non-visual domains.

Paperid: 320, https://arxiv.org/pdf/2506.08618.pdf   GitHub GitHub

Authors:Xianquan Yan, Hakan Akgün, Kenji Kawaguchi, N. Duane Loh, Ching Hua Lee

Title: HSG-12M: A Large-Scale Spatial Multigraph Dataset

Abstract:
Existing graph benchmarks assume non-spatial, simple edges, collapsing physically distinct paths into a single link. We introduce HSG-12M, the first large-scale dataset of $\textbf{spatial multigraphs}-$graphs embedded in a metric space where multiple geometrically distinct trajectories between two nodes are retained as separate edges. HSG-12M contains 11.6 million static and 5.1 million dynamic $\textit{Hamiltonian spectral graphs}$ across 1401 characteristic-polynomial classes, derived from 177 TB of spectral potential data. Each graph encodes the full geometry of a 1-D crystal's energy spectrum on the complex plane, producing diverse, physics-grounded topologies that transcend conventional node-coordinate datasets. To enable future extensions, we release $\texttt{Poly2Graph}$: a high-performance, open-source pipeline that maps arbitrary 1-D crystal Hamiltonians to spectral graphs. Benchmarks with popular GNNs expose new challenges in learning from multi-edge geometry at scale. Beyond its practical utility, we show that spectral graphs serve as universal topological fingerprints of polynomials, vectors, and matrices, forging a new algebra-to-graph link. HSG-12M lays the groundwork for geometry-aware graph learning and new opportunities of data-driven scientific discovery in condensed matter physics and beyond.
中文摘要：本文提出了HSG-12M，这是首个大规模空间多重图数据集，通过保留节点间多条几何路径并基于光谱势数据构建，为几何感知的图学习及科学发现奠定了基础。
English Summary: This paper introduces HSG-12M, the first large-scale spatial multigraph dataset that preserves multiple geometrically distinct paths between nodes, derived from spectral potential data to enable geometry-aware graph learning and scientific discovery.

Paperid: 321, https://arxiv.org/pdf/2506.08607.pdf   GitHub

Authors:Kiran Purohit, V Venktesh, Sourangshu Bhattacharya, Avishek Anand

Title: Sample Efficient Demonstration Selection for In-Context Learning

Abstract:
The in-context learning paradigm with LLMs has been instrumental in advancing a wide range of natural language processing tasks. The selection of few-shot examples (exemplars / demonstration samples) is essential for constructing effective prompts under context-length budget constraints. In this paper, we formulate the exemplar selection task as a top-m best arms identification problem. A key challenge in this setup is the exponentially large number of arms that need to be evaluated to identify the m-best arms. We propose CASE (Challenger Arm Sampling for Exemplar selection), a novel sample-efficient selective exploration strategy that maintains a shortlist of "challenger" arms, which are current candidates for the top-m arms. In each iteration, only one of the arms from this shortlist or the current topm set is pulled, thereby reducing sample complexity and, consequently, the number of LLM evaluations. Furthermore, we model the scores of exemplar subsets (arms) using a parameterized linear scoring function, leading to stochastic linear bandits setting. CASE achieves remarkable efficiency gains of up to 7x speedup in runtime while requiring 7x fewer LLM calls (87% reduction) without sacrificing performance compared to state-of-the-art exemplar selection methods. We release our code and data at https://github.com/kiranpurohit/CASE
中文: 本文提出CASE方法，将示例选择建模为最优臂识别问题，通过高效采样策略在保持性能的同时，将运行速度提升7倍并减少87%的大语言模型调用次数。
English: This paper introduces CASE, a sample-efficient strategy that formulates exemplar selection as a top-m best arms identification problem, achieving up to 7x faster runtime and 87% fewer LLM calls while maintaining performance compared to existing methods.

Paperid: 322, https://arxiv.org/pdf/2506.08592.pdf   GitHub

Authors:Liyan Xu, Zhenlin Su, Mo Yu, Jiangnan Li, Fandong Meng, Jie Zhou

Title: Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings

Abstract:
This work stems from an observed limitation of text encoders: embeddings may not be able to recognize fine-grained entities or events within encoded semantics, resulting in failed retrieval even in simple cases. To examine such behaviors, we first introduce a new evaluation dataset, CapRetrieval, in which passages are image captions and queries are phrases targeting entity or event concepts in diverse forms. Zero-shot evaluation suggests that encoders often struggle with these fine-grained matching, regardless of training sources or model size. Aiming for enhancement, we proceed to finetune encoders with our proposed data generation strategies, enabling a small 0.1B encoder to outperform the state-of-the-art 7B model. Within this process, we further uncover the granularity dilemma, a challenge for embeddings to capture fine-grained salience while aligning with overall semantics. Our dataset, code and models in this work are publicly released at https://github.com/lxucs/CapRetrieval.
中文: 本研究针对文本编码器在识别细粒度实体和事件方面的局限性，通过引入CapRetrieval数据集并提出数据生成策略，使小型编码器性能超越大型模型，同时揭示了嵌入语义中的粒度困境。
English: This study addresses the limitation of text encoders in recognizing fine-grained entities and events by introducing the CapRetrieval dataset and proposing data generation strategies that enable a small encoder to outperform larger models, while also identifying the granularity dilemma in embedding semantics.

Paperid: 323, https://arxiv.org/pdf/2506.08591.pdf   GitHub

Authors:Chengchao Shen, Hourun Zhu, Gongfan Fang, Jianxin Wang, Xinchao Wang

Title: Diversity-Guided MLP Reduction for Efficient Large Vision Transformers

Abstract:
Transformer models achieve excellent scaling property, where the performance is improved with the increment of model capacity. However, large-scale model parameters lead to an unaffordable cost of computing and memory. We analyze popular transformer architectures and find that multilayer perceptron (MLP) modules take up the majority of model parameters. To this end, we focus on the recoverability of the compressed models and propose a Diversity-Guided MLP Reduction (DGMR) method to significantly reduce the parameters of large vision transformers with only negligible performance degradation. Specifically, we conduct a Gram-Schmidt weight pruning strategy to eliminate redundant neurons of MLP hidden layer, while preserving weight diversity for better performance recover during distillation. Compared to the model trained from scratch, our pruned model only requires 0.06\% data of LAION-2B (for the training of large vision transformers) without labels (ImageNet-1K) to recover the original performance. Experimental results on several state-of-the-art large vision transformers demonstrate that our method achieves a more than 57.0\% parameter and FLOPs reduction in a near lossless manner. Notably, for EVA-CLIP-E (4.4B), our method accomplishes a 71.5\% parameter and FLOPs reduction without performance degradation. The source code and trained weights are available at https://github.com/visresearch/DGMR.
中文: 提出的多样性引导多层感知机缩减方法通过剪枝冗余神经元并保持权重多样性，以近乎无损的方式将大型视觉变换器的参数和计算量减少超过57%。
English: The proposed Diversity-Guided MLP Reduction (DGMR) method significantly compresses large vision transformers by pruning redundant MLP neurons while preserving weight diversity, achieving over 57% parameter and FLOPs reduction with negligible performance loss.

Paperid: 324, https://arxiv.org/pdf/2506.08518.pdf   GitHub

Authors:Sunny Gupta, Nikita Jangid, Shounak Das, Amit Sethi

Title: FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching

Abstract:
Domain Generalization (DG) seeks to train models that perform reliably on unseen target domains without access to target data during training. While recent progress in smoothing the loss landscape has improved generalization, existing methods often falter under long-tailed class distributions and conflicting optimization objectives. We introduce FedTAIL, a federated domain generalization framework that explicitly addresses these challenges through sharpness-guided, gradient-aligned optimization. Our method incorporates a gradient coherence regularizer to mitigate conflicts between classification and adversarial objectives, leading to more stable convergence. To combat class imbalance, we perform class-wise sharpness minimization and propose a curvature-aware dynamic weighting scheme that adaptively emphasizes underrepresented tail classes. Furthermore, we enhance conditional distribution alignment by integrating sharpness-aware perturbations into entropy regularization, improving robustness under domain shift. FedTAIL unifies optimization harmonization, class-aware regularization, and conditional alignment into a scalable, federated-compatible framework. Extensive evaluations across standard domain generalization benchmarks demonstrate that FedTAIL achieves state-of-the-art performance, particularly in the presence of domain shifts and label imbalance, validating its effectiveness in both centralized and federated settings. Code: https://github.com/sunnyinAI/FedTail
中文摘要：FedTAIL作为一种联邦领域泛化框架，通过锐度感知优化解决了类别不平衡与目标冲突问题，在领域偏移和标签不平衡场景下实现了最先进的性能表现。
English Summary: FedTAIL is a federated domain generalization framework that tackles class imbalance and conflicting objectives through sharpness-aware optimization, achieving state-of-the-art performance under domain shifts and label imbalance.

Paperid: 325, https://arxiv.org/pdf/2506.08473.pdf   GitHub

Authors:Shuo Yang, Qihui Zhang, Yuyang Liu, Yue Huang, Xiaojun Jia, Kunpeng Ning, Jiayu Yao, Jigang Wang, Hailiang Dai, Yibing Song, Li Yuan

Title: AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin

Abstract:
Large language models (LLMs) are vulnerable to safety risks during fine-tuning, where small amounts of malicious or harmless data can compromise safeguards. In this paper, building on the concept of alignment direction -- defined by the weight difference between aligned and unaligned models -- we observe that perturbations along this direction preserve model safety. In contrast, perturbations along directions orthogonal to this alignment are strongly linked to harmful direction perturbations, rapidly degrading safety and framing the parameter space as a narrow safety basin. Based on this insight, we propose a methodology for safety fine-tuning called AsFT (Anchoring Safety in Fine-Tuning), which integrates a regularization term into the training objective. This term uses the alignment direction as an anchor to suppress updates in harmful directions, ensuring that fine-tuning is constrained within the narrow safety basin. Extensive experiments on multiple datasets show that AsFT outperforms Safe LoRA, reducing harmful behavior by 7.60 percent, improving model performance by 3.44 percent, and maintaining robust performance across various experimental settings. Code is available at https://github.com/PKU-YuanGroup/AsFT
Chinese: 本研究提出AsFT安全微调方法，以对齐方向为锚点将更新约束在狭窄安全区域内，实验表明该方法能有效减少7.60%有害行为并提升3.44%模型性能。
English: The study introduces AsFT, a safety fine-tuning method that uses the alignment direction as an anchor to restrict updates within a narrow safety basin, effectively reducing harmful behavior by 7.60% and improving model performance by 3.44% in experiments.

Paperid: 326, https://arxiv.org/pdf/2506.08464.pdf   GitHub

Authors:Hyunseok Seung, Jaewoo Lee, Hyunsuk Ko

Title: MAC: An Efficient Gradient Preconditioning using Mean Activation Approximated Curvature

Abstract:
Second-order optimization methods for training neural networks, such as KFAC, exhibit superior convergence by utilizing curvature information of loss landscape. However, it comes at the expense of high computational burden. In this work, we analyze the two components that constitute the layer-wise Fisher information matrix (FIM) used in KFAC: the Kronecker factors related to activations and pre-activation gradients. Based on empirical observations on their eigenspectra, we propose efficient approximations for them, resulting in a computationally efficient optimization method called MAC. To the best of our knowledge, MAC is the first algorithm to apply the Kronecker factorization to the FIM of attention layers used in transformers and explicitly integrate attention scores into the preconditioning. We also study the convergence property of MAC on nonlinear neural networks and provide two conditions under which it converges to global minima. Our extensive evaluations on various network architectures and datasets show that the proposed method outperforms KFAC and other state-of-the-art methods in terms of accuracy, end-to-end training time, and memory usage. Code is available at https://github.com/hseung88/mac.
中文: 提出的MAC方法高效近似KFAC中Fisher信息矩阵的Kronecker因子，在多种神经网络上实现了更高的精度、更快的训练速度和更低的内存消耗，并且是首个将Kronecker分解应用于Transformer注意力层的算法。
English: The proposed MAC method efficiently approximates the Kronecker factors in KFAC's Fisher information matrix, achieving superior accuracy, faster training, and lower memory usage across various neural networks while being the first to apply Kronecker factorization to transformer attention layers.

Paperid: 327, https://arxiv.org/pdf/2506.08423.pdf   GitHub

Authors:Utkarsh Pratiush, Austin Houston, Kamyar Barakati, Aditya Raghavan, Dasol Yoon, Harikrishnan KP, Zhaslan Baraissov, Desheng Ma, Samuel S. Welborn, Mikolaj Jakowski, Shawn-Patrick Barhorst, Alexander J. Pattison, Panayotis Manganaris, Sita Sirisha Madugula, Sai Venkata Gayathri Ayyagari, Vishal Kennedy, Ralph Bulanadi, Michelle Wang, Kieran J. Pang, Ian Addison-Smith, Willy Menacho, Horacio V. Guzman, Alexander Kiefer, Nicholas Furth, Nikola L. Kolev, Mikhail Petrov, Viktoriia Liu, Sergey Ilyev, Srikar Rairao, Tommaso Rodani, Ivan Pinto-Huguet, Xuli Chen, Josep Cruañes, Marta Torrens, Jovan Pomar, Fanzhi Su, Pawan Vedanti, Zhiheng Lyu, Xingzhi Wang, Lehan Yao, Amir Taqieddin, Forrest Laskowski, Xiangyu Yin, Yu-Tsun Shao, Benjamin Fein-Ashley, Yi Jiang, Vineet Kumar, Himanshu Mishra, Yogesh Paul, Adib Bazgir, Rama chandra Praneeth Madugula, Yuwen Zhang, Pravan Omprakash, Jian Huang, Eric Montufar-Morales, Vivek Chawla, Harshit Sethi, Jie Huang, Lauri Kurki, Grace Guinan, Addison Salvador, Arman Ter-Petrosyan, Madeline Van Winkle, Steven R. Spurgeon, Ganesh Narasimha, Zijie Wu, Richard Liu, Yongtao Liu, Boris Slautin, Andrew R Lupini, Rama Vasudevan, Gerd Duscher, Sergei V. Kalinin

Title: Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Abstract:
Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1
中文: 显微镜技术产生丰富但格式不一的数据，缺乏标准化工具和集成策略阻碍了高效分析，而黑客松活动通过连接机器学习与显微学领域，推动了基准数据集和数字孪生等解决方案的开发。
English: Microscopy generates rich but inconsistently formatted data, where the lack of standardized tools and integration hinders efficient analysis, though hackathons bridge ML and microscopy communities to develop solutions like benchmarks and digital twins.

Paperid: 328, https://arxiv.org/pdf/2506.08388.pdf   GitHub

Authors:Edoardo Cetin, Tianyu Zhao, Yujin Tang

Title: Reinforcement Learning Teachers of Test Time Scaling

Abstract:
Training reasoning language models (LMs) with reinforcement learning (RL) for one-hot correctness inherently relies on the LM being able to explore and solve its task with some chance at initialization. Furthermore, a key use case of reasoning LMs is to act as teachers for distilling new students and cold-starting future RL iterations rather than being deployed themselves. From these considerations, we introduce a new framework that avoids RL's exploration challenge by training a new class of Reinforcement-Learned Teachers (RLTs) focused on yielding the most effective downstream distillation. RLTs are prompted with both the question and solution to each problem, and tasked to simply "connect-the-dots" with detailed explanations tailored for their students. We train RLTs with dense rewards obtained by feeding each explanation to the student and testing its understanding of the problem's solution. In practice, the raw outputs of a 7B RLT provide higher final performance on competition and graduate-level tasks than existing distillation and cold-starting pipelines that collect and postprocess the reasoning traces of orders of magnitude larger LMs. Furthermore, RLTs maintain their effectiveness when training larger students and when applied zero-shot to out-of-distribution tasks, unlocking new levels of efficiency and re-usability for the RL reasoning framework.
中文摘要：本文提出强化学习教师（RLT）框架，通过训练模型生成针对下游学生的详细解释来规避强化学习的探索难题，相比传统方法能以更小模型实现更优性能，并保持跨任务的有效性。
English Summary: The paper introduces Reinforcement-Learned Teachers (RLTs), a framework that trains reasoning models to generate detailed explanations for distillation, overcoming exploration challenges in RL while achieving superior performance with smaller models compared to traditional methods.

Paperid: 329, https://arxiv.org/pdf/2506.08360.pdf   GitHub

Authors:Hyunseok Seung, Jaewoo Lee, Hyunsuk Ko

Title: NysAct: A Scalable Preconditioned Gradient Descent using Nystrom Approximation

Abstract:
Adaptive gradient methods are computationally efficient and converge quickly, but they often suffer from poor generalization. In contrast, second-order methods enhance convergence and generalization but typically incur high computational and memory costs. In this work, we introduce NysAct, a scalable first-order gradient preconditioning method that strikes a balance between state-of-the-art first-order and second-order optimization methods. NysAct leverages an eigenvalue-shifted Nystrom method to approximate the activation covariance matrix, which is used as a preconditioning matrix, significantly reducing time and memory complexities with minimal impact on test accuracy. Our experiments show that NysAct not only achieves improved test accuracy compared to both first-order and second-order methods but also demands considerably less computational resources than existing second-order methods. Code is available at https://github.com/hseung88/nysact.
中文摘要：NysAct是一种可扩展的一阶梯度预处理方法，通过近似激活协方差矩阵平衡了计算效率与测试精度，在泛化性能上优于一阶和二阶方法，同时显著降低了资源消耗。
English Summary: NysAct is a scalable first-order gradient preconditioning method that balances computational efficiency with improved test accuracy by approximating the activation covariance matrix, outperforming both first-order and second-order methods in generalization while using fewer resources.

Paperid: 330, https://arxiv.org/pdf/2506.08353.pdf   GitHub

Authors:Hyunseok Seung, Jaewoo Lee, Hyunsuk Ko

Title: An Adaptive Method Stabilizing Activations for Enhanced Generalization

Abstract:
We introduce AdaAct, a novel optimization algorithm that adjusts learning rates according to activation variance. Our method enhances the stability of neuron outputs by incorporating neuron-wise adaptivity during the training process, which subsequently leads to better generalization -- a complementary approach to conventional activation regularization methods. Experimental results demonstrate AdaAct's competitive performance across standard image classification benchmarks. We evaluate AdaAct on CIFAR and ImageNet, comparing it with other state-of-the-art methods. Importantly, AdaAct effectively bridges the gap between the convergence speed of Adam and the strong generalization capabilities of SGD, all while maintaining competitive execution times. Code is available at https://github.com/hseung88/adaact.
中文: AdaAct是一种新颖的优化算法，通过根据激活方差调整学习率来提高训练稳定性和泛化能力，有效弥合了Adam与SGD之间的性能差距，同时保持高效运行。
English: AdaAct is a novel optimization algorithm that enhances training stability and generalization by adapting learning rates based on activation variance, effectively bridging the performance gap between Adam and SGD while maintaining competitive efficiency.

Paperid: 331, https://arxiv.org/pdf/2506.08347.pdf   GitHub

Authors:Yinan Huang, Haoteng Yin, Eli Chien, Rongzhe Wei, Pan Li

Title: Differentially Private Relational Learning with Entity-level Privacy Guarantees

Abstract:
Learning with relational and network-structured data is increasingly vital in sensitive domains where protecting the privacy of individual entities is paramount. Differential Privacy (DP) offers a principled approach for quantifying privacy risks, with DP-SGD emerging as a standard mechanism for private model training. However, directly applying DP-SGD to relational learning is challenging due to two key factors: (i) entities often participate in multiple relations, resulting in high and difficult-to-control sensitivity; and (ii) relational learning typically involves multi-stage, potentially coupled (interdependent) sampling procedures that make standard privacy amplification analyses inapplicable. This work presents a principled framework for relational learning with formal entity-level DP guarantees. We provide a rigorous sensitivity analysis and introduce an adaptive gradient clipping scheme that modulates clipping thresholds based on entity occurrence frequency. We also extend the privacy amplification results to a tractable subclass of coupled sampling, where the dependence arises only through sample sizes. These contributions lead to a tailored DP-SGD variant for relational data with provable privacy guarantees. Experiments on fine-tuning text encoders over text-attributed network-structured relational data demonstrate the strong utility-privacy trade-offs of our approach. Our code is available at https://github.com/Graph-COM/Node_DP.
中文摘要：本文提出了一个具有形式化实体级差分隐私保障的关系学习框架，通过自适应梯度裁剪和针对耦合采样的扩展分析，解决了敏感性控制和隐私放大难题。
English Summary: This paper introduces a principled framework for relational learning with formal entity-level differential privacy guarantees, addressing challenges in sensitivity control and privacy amplification through adaptive gradient clipping and extended analysis for coupled sampling.

Paperid: 332, https://arxiv.org/pdf/2506.08316.pdf   GitHub

Authors:Alan N. Amin, Nate Gruver, Andrew Gordon Wilson

Title: Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion

Abstract:
Discrete diffusion models, like continuous diffusion models, generate high-quality samples by gradually undoing noise applied to datapoints with a Markov process. Gradual generation in theory comes with many conceptual benefits; for example, inductive biases can be incorporated into the noising Markov process, and access to improved sampling algorithms. In practice, however, the consistently best performing discrete diffusion model is, surprisingly, masking diffusion, which does not denoise gradually. Here we explain the superior performance of masking diffusion by noting that it makes use of a fundamental difference between continuous and discrete Markov processes: discrete Markov processes evolve by discontinuous jumps at a fixed rate and, unlike other discrete diffusion models, masking diffusion builds in the known distribution of jump times and only learns where to jump to. We show that we can similarly bake in the known distribution of jump times into any discrete diffusion model. The resulting models - schedule-conditioned discrete diffusion (SCUD) - generalize classical discrete diffusion and masking diffusion. By applying SCUD to models with noising processes that incorporate inductive biases on images, text, and protein data, we build models that outperform masking.
中文摘要：该研究通过揭示离散马尔可夫过程中跳跃时间分布的已知特性解释了掩码扩散的优越性，并提出SCUD模型将这一机制融入各类离散扩散过程，在图像、文本和蛋白质数据上实现了性能突破。
English Summary: The study explains that masking diffusion excels in discrete diffusion models by leveraging the known distribution of jump times in discrete Markov processes, and introduces SCUD models that incorporate this insight to outperform existing methods across various data types.

Paperid: 333, https://arxiv.org/pdf/2506.08309.pdf   GitHub

Authors:Katherine Tieu, Dongqi Fu, Zihao Li, Ross Maciejewski, Jingrui He

Title: Learnable Spatial-Temporal Positional Encoding for Link Prediction

Abstract:
Accurate predictions rely on the expressiveness power of graph deep learning frameworks like graph neural networks and graph transformers, where a positional encoding mechanism has become much more indispensable in recent state-of-the-art works to record the canonical position information. However, the current positional encoding is limited in three aspects: (1) most positional encoding methods use pre-defined, and fixed functions, which are inadequate to adapt to the complex attributed graphs; (2) a few pioneering works proposed the learnable positional encoding but are still limited to the structural information, not considering the real-world time-evolving topological and feature information; (3) most positional encoding methods are equipped with transformers' attention mechanism to fully leverage their capabilities, where the dense or relational attention is often unaffordable on large-scale structured data. Hence, we aim to develop Learnable Spatial-Temporal Positional Encoding in an effective and efficient manner and propose a simple temporal link prediction model named L-STEP. Briefly, for L-STEP, we (1) prove the proposed positional learning scheme can preserve the graph property from the spatial-temporal spectral viewpoint, (2) verify that MLPs can fully exploit the expressiveness and reach transformers' performance on that encoding, (3) change different initial positional encoding inputs to show robustness, (4) analyze the theoretical complexity and obtain less empirical running time than SOTA, and (5) demonstrate its temporal link prediction out-performance on 13 classic datasets and with 10 algorithms in both transductive and inductive settings using 3 different sampling strategies. Also, L-STEP obtains the leading performance in the newest large-scale TGB benchmark. Our code is available at https://github.com/kthrn22/L-STEP.
Chinese: 当前图学习中的位置编码方法在适应性、动态信息整合和计算效率方面存在局限，因此我们开发了L-STEP这一可学习的时空模型，该模型在多个数据集和设置下展现出卓越的性能、效率及鲁棒性。
English: Current positional encoding methods in graph learning face limitations in adaptability, dynamic information integration, and computational efficiency, prompting the development of L-STEP, a learnable spatial-temporal model that demonstrates superior performance, efficiency, and robustness across multiple datasets and settings.

Paperid: 334, https://arxiv.org/pdf/2506.08295.pdf   GitHub

Authors:Zhanke Zhou, Xiao Feng, Zhaocheng Zhu, Jiangchao Yao, Sanmi Koyejo, Bo Han

Title: From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?

Abstract:
While existing benchmarks probe the reasoning abilities of large language models (LLMs) across diverse domains, they predominantly assess passive reasoning, providing models with all the information needed to reach a solution. By contrast, active reasoning-where an LLM must interact with external systems to acquire missing evidence or data-has received little systematic attention. To address this shortfall, we present AR-Bench, a novel benchmark designed explicitly to evaluate an LLM's active reasoning skills. AR-Bench comprises three task families-detective cases, situation puzzles, and guessing numbers-that together simulate real-world, agentic scenarios and measure performance across commonsense, logical, and symbolic reasoning challenges. Empirical evaluation on AR-Bench demonstrates that contemporary LLMs exhibit pronounced difficulties with active reasoning: they frequently fail to acquire or leverage the information needed to solve tasks. This gap highlights a stark divergence between their passive and active reasoning abilities. Moreover, ablation studies indicate that even advanced strategies, such as tree-based searching or post-training approaches, yield only modest gains and fall short of the levels required for real-world deployment. Collectively, these findings highlight the critical need to advance methodology for active reasoning, e.g., incorporating interactive learning, real-time feedback loops, and environment-aware objectives for training. The benchmark is publicly available at: https://github.com/tmlr-group/AR-Bench.
中文摘要：AR-Bench是一个专为评估大语言模型主动推理能力设计的新基准，揭示了模型在获取和利用外部信息方面相比被动推理存在显著困难，并强调了改进交互学习等方法的必要性。
English Summary: AR-Bench is a new benchmark designed to evaluate large language models' active reasoning skills, revealing their significant struggles in acquiring and using external information compared to passive reasoning, and highlighting the need for improved methodologies like interactive learning.

Paperid: 335, https://arxiv.org/pdf/2506.08292.pdf   GitHub

Authors:Xie Yi, Zhanke Zhou, Chentao Cao, Qiyu Niu, Tongliang Liu, Bo Han

Title: From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium

Abstract:
Multi-agent frameworks can substantially boost the reasoning power of large language models (LLMs), but they typically incur heavy computational costs and lack convergence guarantees. To overcome these challenges, we recast multi-LLM coordination as an incomplete-information game and seek a Bayesian Nash equilibrium (BNE), in which each agent optimally responds to its probabilistic beliefs about the strategies of others. We introduce Efficient Coordination via Nash Equilibrium (ECON), a hierarchical reinforcement-learning paradigm that marries distributed reasoning with centralized final output. Under ECON, each LLM independently selects responses that maximize its expected reward, conditioned on its beliefs about co-agents, without requiring costly inter-agent exchanges. We mathematically prove that ECON attains a markedly tighter regret bound than non-equilibrium multi-agent schemes. Empirically, ECON outperforms existing multi-LLM approaches by 11.2% on average across six benchmarks spanning complex reasoning and planning tasks. Further experiments demonstrate ECON's ability to flexibly incorporate additional models, confirming its scalability and paving the way toward larger, more powerful multi-LLM ensembles. The code is publicly available at: https://github.com/tmlr-group/ECON.
Chinese: ECON通过将多智能体协调建模为不完全信息博弈，提出了一种结合分布式推理与集中输出的分层强化学习范式，在显著降低通信成本的同时实现了更强的性能与理论保障。
English: ECON introduces a hierarchical reinforcement-learning framework that models multi-LLM coordination as an incomplete-information game, achieving superior performance with a tighter regret bound and eliminating costly inter-agent communication.

Paperid: 336, https://arxiv.org/pdf/2506.08277.pdf   GitHub

Authors:Subba Reddy Oota, Khushbu Pahwa, Prachi Jindal, Satya Sai Srinath Namburi, Maneesh Singh, Tanmoy Chakraborty, Bapi S. Raju, Manish Gupta

Title: Instruction-Tuned Video-Audio Models Elucidate Functional Specialization in the Brain

Abstract:
Recent voxel-wise multimodal brain encoding studies have shown that multimodal large language models (MLLMs) exhibit a higher degree of brain alignment compared to unimodal models in both unimodal and multimodal stimulus settings. More recently, instruction-tuned multimodal models have shown to generate task-specific representations that align strongly with brain activity. However, prior work evaluating the brain alignment of MLLMs has primarily focused on unimodal settings or relied on non-instruction-tuned multimodal models for multimodal stimuli. To address this gap, we investigated brain alignment, that is, measuring the degree of predictivity of neural activity recorded while participants were watching naturalistic movies (video along with audio) with representations derived from MLLMs. We utilized instruction-specific embeddings from six video and two audio instruction-tuned MLLMs. Experiments with 13 video task-specific instructions show that instruction-tuned video MLLMs significantly outperform non-instruction-tuned multimodal (by 15%) and unimodal models (by 20%). Our evaluation of MLLMs for both video and audio tasks using language-guided instructions shows clear disentanglement in task-specific representations from MLLMs, leading to precise differentiation of multimodal functional processing in the brain. We also find that MLLM layers align hierarchically with the brain, with early sensory areas showing strong alignment with early layers, while higher-level visual and language regions align more with middle to late layers. These findings provide clear evidence for the role of task-specific instructions in improving the alignment between brain activity and MLLMs, and open new avenues for mapping joint information processing in both the systems. We make the code publicly available [https://github.com/subbareddy248/mllm_videos].
中文: 指令调优的多模态大语言模型在自然观影场景中显著优于非指令调优模型，其任务特定表征能精确对应大脑层级处理机制，为大脑与计算系统的映射研究开辟了新途径。
English: Instruction-tuned multimodal large language models significantly outperform non-instruction-tuned models in aligning with brain activity during naturalistic movie viewing, showing hierarchical layer correspondence and task-specific representation disentanglement that advances brain-computation mapping.

Paperid: 337, https://arxiv.org/pdf/2506.08167.pdf   GitHub

Authors:Sunny Gupta, Nikita Jangid, Amit Sethi

Title: UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data

Abstract:
Federated Learning (FL) often suffers from severe performance degradation when faced with non-IID data, largely due to local classifier bias. Traditional remedies such as global model regularization or layer freezing either incur high computational costs or struggle to adapt to feature shifts. In this work, we propose UniVarFL, a novel FL framework that emulates IID-like training dynamics directly at the client level, eliminating the need for global model dependency. UniVarFL leverages two complementary regularization strategies during local training: Classifier Variance Regularization, which aligns class-wise probability distributions with those expected under IID conditions, effectively mitigating local classifier bias; and Hyperspherical Uniformity Regularization, which encourages a uniform distribution of feature representations across the hypersphere, thereby enhancing the model's ability to generalize under diverse data distributions. Extensive experiments on multiple benchmark datasets demonstrate that UniVarFL outperforms existing methods in accuracy, highlighting its potential as a highly scalable and efficient solution for real-world FL deployments, especially in resource-constrained settings. Code: https://github.com/sunnyinAI/UniVarFL
Chinese: UniVarFL提出了一种新颖的联邦学习框架，通过双重正则化策略有效缓解本地分类器偏差并提升特征泛化能力，在非独立同分布数据场景下实现了更优的准确性与可扩展性。
English: UniVarFL introduces a novel federated learning framework that employs dual regularization strategies to mitigate local classifier bias and enhance feature generalization, achieving superior accuracy and scalability in non-IID settings.

Paperid: 338, https://arxiv.org/pdf/2506.08164.pdf   GitHub

Authors:Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Sijia Liu, Mingyi Hong

Title: BLUR: A Bi-Level Optimization Approach for LLM Unlearning

Abstract:
Enabling large language models (LLMs) to unlearn knowledge and capabilities acquired during training has proven vital for ensuring compliance with data regulations and promoting ethical practices in generative AI. Although there are growing interests in developing various unlearning algorithms, it remains unclear how to best formulate the unlearning problem. The most popular formulation uses a weighted sum of forget and retain loss, but it often leads to performance degradation due to the inherent trade-off between forget and retain losses. In this work, we argue that it is important to model the hierarchical structure of the unlearning problem, where the forget problem (which \textit{unlearns} certain knowledge and/or capabilities) takes priority over the retain problem (which preserves model utility). This hierarchical structure naturally leads to a bi-level optimization formulation where the lower-level objective focuses on minimizing the forget loss, while the upper-level objective aims to maintain the model's utility. Based on this new formulation, we propose a novel algorithm, termed Bi-Level UnleaRning (\texttt{BLUR}), which not only possesses strong theoretical guarantees but more importantly, delivers superior performance. In particular, our extensive experiments demonstrate that \texttt{BLUR} consistently outperforms all the state-of-the-art algorithms across various unlearning tasks, models, and metrics. Codes are available at https://github.com/OptimAI-Lab/BLURLLMUnlearning.
中文: 本文针对大语言模型的知识消除问题提出分层双级优化框架，通过BLUR算法优先实现特定知识遗忘并保持模型性能，在多项任务中显著优于现有方法。
English: This paper introduces a hierarchical bi-level optimization approach for large language model unlearning, proposing the BLUR algorithm that prioritizes forgetting specific knowledge while maintaining model utility, which significantly outperforms existing methods across various tasks.

Paperid: 339, https://arxiv.org/pdf/2506.08070.pdf   GitHub

Authors:Ziheng Qin, Hailun Xu, Wei Chee Yew, Qi Jia, Yang Luo, Kanchan Sarkar, Danhui Guan, Kai Wang, Yang You

Title: Info-Coevolution: An Efficient Framework for Data Model Coevolution

Abstract:
Machine learning relies heavily on data, yet the continuous growth of real-world data poses challenges for efficient dataset construction and training. A fundamental yet unsolved question is: given our current model and data, does a new data (sample/batch) need annotation/learning? Conventional approaches retain all available data, leading to non-optimal data and training efficiency. Active learning aims to reduce data redundancy by selecting a subset of samples to annotate, while it increases pipeline complexity and introduces bias. In this work, we propose Info-Coevolution, a novel framework that efficiently enables models and data to coevolve through online selective annotation with no bias. Leveraging task-specific models (and open-source models), it selectively annotates and integrates online and web data to improve datasets efficiently. For real-world datasets like ImageNet-1K, Info-Coevolution reduces annotation and training costs by 32\% without performance loss. It is able to automatically give the saving ratio without tuning the ratio. It can further reduce the annotation ratio to 50\% with semi-supervised learning. We also explore retrieval-based dataset enhancement using unlabeled open-source data. Code is available at https://github.com/NUS-HPC-AI-Lab/Info-Coevolution/.
Chinese: 提出的Info-Coevolution框架通过选择性在线标注实现模型与数据的协同进化，在ImageNet-1K数据集上无需性能损失即可降低32%的标注成本，同时消除偏差且无需手动调整比例。
English: The proposed Info-Coevolution framework enables efficient model-data coevolution through selective online annotation, reducing ImageNet-1K annotation costs by 32% without performance loss while eliminating bias and manual ratio tuning.

Paperid: 340, https://arxiv.org/pdf/2506.08065.pdf   GitHub

Authors:Ye Zhu, Duo Xu, Zhiwei Deng, Jonathan C. Tan, Olga Russakovsky

Title: Dynamic Diffusion Schrödinger Bridge in Astrophysical Observational Inversions

Abstract:
We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation. We introduce the Astro-DSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics. By investigating its learning process and prediction performance in both physically simulated data and in real observations (the Taurus B213 data), we present two main takeaways. First, from the astrophysical perspective, our proposed paired DSB method improves interpretability, learning efficiency, and prediction performance over conventional astrostatistical and other machine learning methods. Second, from the generative modeling perspective, probabilistic generative modeling reveals improvements over discriminative pixel-to-pixel modeling in Out-Of-Distribution (OOD) testing cases of physical simulations with unseen initial conditions and different dominant physical processes. Our study expands research into diffusion models beyond the traditional visual synthesis application and provides evidence of the models' learning abilities beyond pure data statistics, paving a path for future physics-aware generative models which can align dynamics between machine learning and real (astro)physical systems.
中文: Astro-DSB模型作为针对天体物理系统定制的扩散薛定谔桥变体，在恒星形成研究中提升了可解释性与预测能力，并证明了生成模型在处理未知天体物理条件时的优越性。
English: The Astro-DSB model, a tailored Diffusion Schrödinger Bridge variant, enhances interpretability and prediction in star formation studies while demonstrating generative modeling's superiority in handling unseen astrophysical conditions.

Paperid: 341, https://arxiv.org/pdf/2506.08063.pdf   GitHub

Authors:Songqiao Hu, Zeyi Liu, Xiao He

Title: Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift

Abstract:
The change in data distribution over time, also known as concept drift, poses a significant challenge to the reliability of online learning methods. Existing methods typically require model retraining or drift detection, both of which demand high computational costs and are often unsuitable for real-time applications. To address these limitations, a lightweight, fast and efficient random vector functional-link network termed Lite-RVFL is proposed, capable of adapting to concept drift without drift detection and retraining. Lite-RVFL introduces a novel objective function that assigns weights exponentially increasing to new samples, thereby emphasizing recent data and enabling timely adaptation. Theoretical analysis confirms the feasibility of this objective function for drift adaptation, and an efficient incremental update rule is derived. Experimental results on a real-world safety assessment task validate the efficiency, effectiveness in adapting to drift, and potential to capture temporal patterns of Lite-RVFL. The source code is available at https://github.com/songqiaohu/Lite-RVFL.
中文: Lite-RVFL是一种轻量级网络，通过指数加权函数强调新数据来适应概念漂移，无需重新训练或漂移检测，并在实际应用中验证了其高效性。
English: Lite-RVFL is a lightweight network that adapts to concept drift by emphasizing recent data through an exponential weighting function, eliminating the need for retraining or drift detection while proving efficient in real-world applications.

Paperid: 342, https://arxiv.org/pdf/2506.08054.pdf   GitHub

Authors:Yiming Wang, Hao Peng, Senzhang Wang, Haohua Du, Chunyang Liu, Jia Wu, Guanlin Wu

Title: STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation

Abstract:
Traffic data imputation is fundamentally important to support various applications in intelligent transportation systems such as traffic flow prediction. However, existing time-to-space sequential methods often fail to effectively extract features in block-wise missing data scenarios. Meanwhile, the static graph structure for spatial feature propagation significantly constrains the models flexibility in handling the distribution shift issue for the nonstationary traffic data. To address these issues, this paper proposes a SpatioTemporal Attention Mixture of experts network named STAMImputer for traffic data imputation. Specifically, we introduce a Mixture of Experts (MoE) framework to capture latent spatio-temporal features and their influence weights, effectively imputing block missing. A novel Low-rank guided Sampling Graph ATtention (LrSGAT) mechanism is designed to dynamically balance the local and global correlations across road networks. The sampled attention vectors are utilized to generate dynamic graphs that capture real-time spatial correlations. Extensive experiments are conducted on four traffic datasets for evaluation. The result shows STAMImputer achieves significantly performance improvement compared with existing SOTA approaches. Our codes are available at https://github.com/RingBDStack/STAMImupter.
Chinese: 本文提出STAMImputer模型，通过动态图生成和专家混合框架有效处理交通数据中的块状缺失问题，在四个数据集上的实验表明其性能显著优于现有最优方法。
English: This paper introduces STAMImputer, a SpatioTemporal Attention Mixture of experts network that effectively handles block-wise missing traffic data through dynamic graph generation and achieves superior performance over existing methods.

Paperid: 343, https://arxiv.org/pdf/2506.08013.pdf   GitHub

Authors:Anh-Quan Cao, Ivan Lopes, Raoul de Charette

Title: StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets

Abstract:
Multi-task learning for dense prediction is limited by the need for extensive annotation for every task, though recent works have explored training with partial task labels. Leveraging the generalization power of diffusion models, we extend the partial learning setup to a zero-shot setting, training a multi-task model on multiple synthetic datasets, each labeled for only a subset of tasks. Our method, StableMTL, repurposes image generators for latent regression. Adapting a denoising framework with task encoding, per-task conditioning and a tailored training scheme. Instead of per-task losses requiring careful balancing, a unified latent loss is adopted, enabling seamless scaling to more tasks. To encourage inter-task synergy, we introduce a multi-stream model with a task-attention mechanism that converts N-to-N task interactions into efficient 1-to-N attention, promoting effective cross-task sharing. StableMTL outperforms baselines on 7 tasks across 8 benchmarks.
中文摘要：StableMTL提出了一种基于扩散模型的零样本多任务学习方法，利用部分标注的合成数据集进行训练，通过统一潜在损失和任务注意力机制促进任务间协同，在多个基准测试中超越基线模型。
English Summary: StableMTL introduces a zero-shot multi-task learning method using diffusion models that trains on synthetic datasets with partial labels and employs a unified latent loss with task-attention for cross-task synergy, outperforming baselines across multiple benchmarks.

Paperid: 344, https://arxiv.org/pdf/2506.07998.pdf   GitHub GitHub

Authors:Boya Zeng, Yida Yin, Zhiqiu Xu, Zhuang Liu

Title: Generative Modeling of Weights: Generalization or Memorization?

Abstract:
Generative models, with their success in image and video generation, have recently been explored for synthesizing effective neural network weights. These approaches take trained neural network checkpoints as training data, and aim to generate high-performing neural network weights during inference. In this work, we examine four representative methods on their ability to generate novel model weights, i.e., weights that are different from the checkpoints seen during training. Surprisingly, we find that these methods synthesize weights largely by memorization: they produce either replicas, or at best simple interpolations, of the training checkpoints. Current methods fail to outperform simple baselines, such as adding noise to the weights or taking a simple weight ensemble, in obtaining different and simultaneously high-performing models. We further show that this memorization cannot be effectively mitigated by modifying modeling factors commonly associated with memorization in image diffusion models, or applying data augmentations. Our findings provide a realistic assessment of what types of data current generative models can model, and highlight the need for more careful evaluation of generative models in new domains. Our code is available at https://github.com/boyazeng/weight_memorization.
中文: 本研究发现，现有生成模型在合成神经网络权重时主要依赖记忆或简单插值训练检查点，未能超越基础方法，这源于数据限制及结构先验利用不足。
English: This study finds that current generative models for synthesizing neural network weights primarily produce memorized or interpolated versions of training checkpoints, failing to outperform simple baselines due to data limitations and inadequate use of structural priors.

Paperid: 345, https://arxiv.org/pdf/2506.07969.pdf   GitHub

Authors:Jacob Helwig, Sai Sreeharsha Adavi, Xuan Zhang, Yuchao Lin, Felix S. Chim, Luke Takeshi Vizzini, Haiyang Yu, Muhammad Hasnain, Saykat Kumar Biswas, John J. Holloway, Narendra Singh, N. K. Anand, Swagnik Guhathakurta, Shuiwang Ji

Title: A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling

Abstract:
We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in which uniform time-stepping is practical, flows approaching and exceeding the speed of sound exhibit sudden changes such as shock waves. In such cases, it is essential to use adaptive time-stepping methods to allow a temporal resolution sufficient to resolve these phenomena while simultaneously balancing computational costs. Here, we propose a two-phase machine learning method, known as ShockCast, to model high-speed flows with adaptive time-stepping. In the first phase, we propose to employ a machine learning model to predict the timestep size. In the second phase, the predicted timestep is used as an input along with the current fluid fields to advance the system state by the predicted timestep. We explore several physically-motivated components for timestep prediction and introduce timestep conditioning strategies inspired by neural ODE and Mixture of Experts. As ShockCast is the first framework for learning high-speed flows, we evaluate our methods by generating two supersonic flow datasets, available at https://huggingface.co/datasets/divelab. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).
中文: 本研究提出ShockCast，一种两阶段机器学习框架，通过自适应时间步长来高效模拟含激波的高速流动，利用时间步长预测和条件策略解决计算挑战。
English: This study introduces ShockCast, a two-phase machine learning framework that employs adaptive time-stepping to efficiently model high-speed flows with shock waves, addressing computational challenges through timestep prediction and conditioning strategies.

Paperid: 346, https://arxiv.org/pdf/2506.07940.pdf   GitHub

Authors:Christopher Subia-Waud

Title: Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation

Abstract:
Current AutoML platforms leave substantial performance untapped. Testing 180 fine-tuning tasks across models from 70M to 70B parameters, we found that HuggingFace AutoTrain, TogetherAI, Databricks, and Google Cloud consistently produce suboptimal configurations. Gradients, built on the Bittensor network, attacks this problem through competition. Independent miners race to find optimal hyperparameters, earning rewards proportional to their models' performance. This tournament drives exploration of configuration spaces that single-strategy methods never examine. In our experiments, Gradients achieved a 100\% win rate against TogetherAI, Databricks, and Google Cloud, and beat HuggingFace AutoTrain in 82.8\% of experiments. Mean improvements reached 42.1\% against commercial platforms. Retrieval-augmented generation tasks saw 30-40\% gains; diffusion models improved 23.4\% on person-specific generation. When miners compete for rewards, they develop optimization strategies that centralized approaches overlook. These findings demonstrate that decentralized systems with economic incentives can systematically outperform traditional AutoML, suggesting market dynamics may be key to achieving superior fine-tuning results. Code is available at https://github.com/rayonlabs/G.O.D.
中文: 现有AutoML平台表现不佳，而基于Bittensor网络的去中心化系统Gradients通过竞争性矿工的经济激励，在超参数优化上表现卓越，平均提升达42.1%，显著优于传统方法。
English: Current AutoML platforms underperform, but Gradients, a decentralized system using competitive miners on the Bittensor network, consistently outperforms them by up to 42.1% through economic incentives that drive superior hyperparameter optimization.

Paperid: 347, https://arxiv.org/pdf/2506.07927.pdf   GitHub

Authors:Jiayi Sheng, Luna Lyu, Jikai Jin, Tony Xia, Alex Gu, James Zou, Pan Lu

Title: Solving Inequality Proofs with Large Language Models

Abstract:
Inequality proving, crucial across diverse scientific and mathematical fields, tests advanced reasoning skills such as discovering tight bounds and strategic theorem application. This makes it a distinct, demanding frontier for large language models (LLMs), offering insights beyond general mathematical problem-solving. Progress in this area is hampered by existing datasets that are often scarce, synthetic, or rigidly formal. We address this by proposing an informal yet verifiable task formulation, recasting inequality proving into two automatically checkable subtasks: bound estimation and relation prediction. Building on this, we release IneqMath, an expert-curated dataset of Olympiad-level inequalities, including a test set and training corpus enriched with step-wise solutions and theorem annotations. We also develop a novel LLM-as-judge evaluation framework, combining a final-answer judge with four step-wise judges designed to detect common reasoning flaws. A systematic evaluation of 29 leading LLMs on IneqMath reveals a surprising reality: even top models like o1 achieve less than 10% overall accuracy under step-wise scrutiny; this is a drop of up to 65.5% from their accuracy considering only final answer equivalence. This discrepancy exposes fragile deductive chains and a critical gap for current LLMs between merely finding an answer and constructing a rigorous proof. Scaling model size and increasing test-time computation yield limited gains in overall proof correctness. Instead, our findings highlight promising research directions such as theorem-guided reasoning and self-refinement. Code and data are available at https://ineqmath.github.io/.

Paperid: 348, https://arxiv.org/pdf/2506.07918.pdf   GitHub

Authors:Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas, Benson Li, Junwei Ma, Jesse C. Cresswell, Rahul G. Krishnan

Title: CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

Abstract:
Causal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands substantial manual effort and domain expertise. We present CausalPFN, a single transformer that amortizes this workflow: trained once on a large library of simulated data-generating processes that satisfy ignorability, it infers causal effects for new observational datasets out-of-the-box. CausalPFN combines ideas from Bayesian causal inference with the large-scale training protocol of prior-fitted networks (PFNs), learning to map raw observations directly to causal effects without any task-specific adjustment. Our approach achieves superior average performance on heterogeneous and average treatment effect estimation benchmarks (IHDP, Lalonde, ACIC). Moreover, it shows competitive performance for real-world policy making on uplift modeling tasks. CausalPFN provides calibrated uncertainty estimates to support reliable decision-making based on Bayesian principles. This ready-to-use model does not require any further training or tuning and takes a step toward automated causal inference (https://github.com/vdblm/CausalPFN).
中文: CausalPFN是一种基于Transformer的模型，通过模拟数据训练实现观测数据的自动因果效应估计，无需手动调整即可在基准测试中取得优异性能，并提供基于贝叶斯原则的校准不确定性评估以支持可靠决策。
English: CausalPFN is a transformer-based model trained on simulated data to provide automated causal effect estimation for observational datasets without requiring manual tuning, delivering superior performance on benchmarks and calibrated uncertainty estimates for reliable decision-making.

Paperid: 349, https://arxiv.org/pdf/2506.07903.pdf   GitHub

Authors:Kevin Rojas, Yuchen Zhu, Sichen Zhu, Felix X. -F. Ye, Molei Tao

Title: Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces

Abstract:
Diffusion models have demonstrated remarkable performance in generating unimodal data across various tasks, including image, video, and text generation. On the contrary, the joint generation of multimodal data through diffusion models is still in the early stages of exploration. Existing approaches heavily rely on external preprocessing protocols, such as tokenizers and variational autoencoders, to harmonize varied data representations into a unified, unimodal format. This process heavily demands the high accuracy of encoders and decoders, which can be problematic for applications with limited data. To lift this restriction, we propose a novel framework for building multimodal diffusion models on arbitrary state spaces, enabling native generation of coupled data across different modalities. By introducing an innovative decoupled noise schedule for each modality, we enable both unconditional and modality-conditioned generation within a single model simultaneously. We empirically validate our approach for text-image generation and mixed-type tabular data synthesis, demonstrating that it achieves competitive performance.
Chinese: 该框架通过解耦噪声调度，使多模态扩散模型能够原生生成跨模态的耦合数据，无需依赖外部预处理，并在单一模型中同时支持无条件生成和条件生成。
English: The proposed framework enables multimodal diffusion models to generate coupled data natively across different modalities without relying on external preprocessing, using a decoupled noise schedule to support both unconditional and conditioned generation within a single model.

Paperid: 350, https://arxiv.org/pdf/2506.07902.pdf   GitHub

Authors:Sifan Wang, Zehao Dou, Tong-Rui Liu, Lu Lu

Title: FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling

Abstract:
Recent advances in generative modeling -- particularly diffusion models and flow matching -- have achieved remarkable success in synthesizing discrete data such as images and videos. However, adapting these models to physical applications remains challenging, as the quantities of interest are continuous functions governed by complex physical laws. Here, we introduce $\textbf{FunDiff}$, a novel framework for generative modeling in function spaces. FunDiff combines a latent diffusion process with a function autoencoder architecture to handle input functions with varying discretizations, generate continuous functions evaluable at arbitrary locations, and seamlessly incorporate physical priors. These priors are enforced through architectural constraints or physics-informed loss functions, ensuring that generated samples satisfy fundamental physical laws. We theoretically establish minimax optimality guarantees for density estimation in function spaces, showing that diffusion-based estimators achieve optimal convergence rates under suitable regularity conditions. We demonstrate the practical effectiveness of FunDiff across diverse applications in fluid dynamics and solid mechanics. Empirical results show that our method generates physically consistent samples with high fidelity to the target distribution and exhibits robustness to noisy and low-resolution data. Code and datasets are publicly available at https://github.com/sifanexisted/fundiff.
中文: FunDiff是一种创新的函数空间生成框架，它结合了潜在扩散与函数自编码器，能够生成连续且符合物理规律的函数，在多种物理应用中实现了理论最优性和鲁棒性。
English: FunDiff is a novel generative framework for function spaces that integrates latent diffusion with function autoencoders to generate continuous, physically consistent functions while ensuring theoretical optimality and robustness across various physical applications.

Paperid: 351, https://arxiv.org/pdf/2506.07865.pdf   GitHub

Authors:Jinxi Li, Ziyang Song, Siyuan Zhou, Bo Yang

Title: FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian Velocity

Abstract:
In this paper, we aim to model 3D scene geometry, appearance, and the underlying physics purely from multi-view videos. By applying various governing PDEs as PINN losses or incorporating physics simulation into neural networks, existing works often fail to learn complex physical motions at boundaries or require object priors such as masks or types. In this paper, we propose FreeGave to learn the physics of complex dynamic 3D scenes without needing any object priors. The key to our approach is to introduce a physics code followed by a carefully designed divergence-free module for estimating a per-Gaussian velocity field, without relying on the inefficient PINN losses. Extensive experiments on three public datasets and a newly collected challenging real-world dataset demonstrate the superior performance of our method for future frame extrapolation and motion segmentation. Most notably, our investigation into the learned physics codes reveals that they truly learn meaningful 3D physical motion patterns in the absence of any human labels in training.
中文: 本文提出FreeGave方法，无需任何物体先验知识，仅通过多视角视频学习复杂动态3D场景的物理特性，采用物理编码和无散度模块估计速度场，在帧预测和运动分割任务中展现出优越性能。
English: This paper introduces FreeGave, a method that learns the physics of complex dynamic 3D scenes from multi-view videos without requiring object priors, using a physics code and divergence-free module to estimate velocity fields and demonstrating superior performance in future frame extrapolation and motion segmentation.

Paperid: 352, https://arxiv.org/pdf/2506.07857.pdf   GitHub

Authors:Zihui Zhang, Weisheng Dai, Hongtao Wen, Bo Yang

Title: LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds

Abstract:
We study the problem of unsupervised 3D semantic segmentation on raw point clouds without needing human labels in training. Existing methods usually formulate this problem into learning per-point local features followed by a simple grouping strategy, lacking the ability to discover additional and possibly richer semantic priors beyond local features. In this paper, we introduce LogoSP to learn 3D semantics from both local and global point features. The key to our approach is to discover 3D semantic information by grouping superpoints according to their global patterns in the frequency domain, thus generating highly accurate semantic pseudo-labels for training a segmentation network. Extensive experiments on two indoor and an outdoor datasets show that our LogoSP surpasses all existing unsupervised methods by large margins, achieving the state-of-the-art performance for unsupervised 3D semantic segmentation. Notably, our investigation into the learned global patterns reveals that they truly represent meaningful 3D semantics in the absence of human labels during training.
中文: 本文提出LogoSP方法，通过结合局部和全局点特征，在频域中依据全局模式对超点进行分组以生成精确的语义伪标签，在无需人工标注的情况下实现了无监督3D语义分割的最优性能。
English: This paper introduces LogoSP, an unsupervised 3D semantic segmentation method that learns from both local and global point features by grouping superpoints based on frequency-domain patterns to generate accurate pseudo-labels, achieving state-of-the-art performance on multiple datasets without human annotations.

Paperid: 353, https://arxiv.org/pdf/2506.07833.pdf   GitHub

Authors:Michael K. Chen, Xikun Zhang, Jiaxing Huang, Dacheng Tao

Title: Improving Large Language Models with Concept-Aware Fine-Tuning

Abstract:
Large language models (LLMs) have become the cornerstone of modern AI. However, the existing paradigm of next-token prediction fundamentally limits their ability to form coherent, high-level concepts, making it a critical barrier to human-like understanding and reasoning. Take the phrase "ribonucleic acid" as an example: an LLM will first decompose it into tokens, i.e., artificial text fragments ("rib", "on", ...), then learn each token sequentially, rather than grasping the phrase as a unified, coherent semantic entity. This fragmented representation hinders deeper conceptual understanding and, ultimately, the development of truly intelligent systems. In response, we introduce Concept-Aware Fine-Tuning (CAFT), a novel multi-token training method that redefines how LLMs are fine-tuned. By enabling the learning of sequences that span multiple tokens, this method fosters stronger concept-aware learning. Our experiments demonstrate significant improvements compared to conventional next-token finetuning methods across diverse tasks, including traditional applications like text summarization and domain-specific ones like de novo protein design. Multi-token prediction was previously only possible in the prohibitively expensive pretraining phase; CAFT, to our knowledge, is the first to bring the multi-token setting to the post-training phase, thus effectively democratizing its benefits for the broader community of practitioners and researchers. Finally, the unexpected effectiveness of our proposed method suggests wider implications for the machine learning research community. All code and data are available at https://github.com/michaelchen-lab/caft-llm
Chinese: 大语言模型受限于逐词预测，但新提出的概念感知微调方法通过多词学习增强了概念理解能力，在多项任务中表现优异。
English: Large language models are limited by next-token prediction, but the new Concept-Aware Fine-Tuning method enables multi-token learning for improved conceptual understanding across various tasks.

Paperid: 354, https://arxiv.org/pdf/2506.07804.pdf   GitHub

Authors:Jie Bao, Chuangyin Dang, Rui Luo, Hanwei Zhang, Zhixin Zhou

Title: Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability

Abstract:
As deep learning models are increasingly deployed in high-risk applications, robust defenses against adversarial attacks and reliable performance guarantees become paramount. Moreover, accuracy alone does not provide sufficient assurance or reliable uncertainty estimates for these models. This study advances adversarial training by leveraging principles from Conformal Prediction. Specifically, we develop an adversarial attack method, termed OPSA (OPtimal Size Attack), designed to reduce the efficiency of conformal prediction at any significance level by maximizing model uncertainty without requiring coverage guarantees. Correspondingly, we introduce OPSA-AT (Adversarial Training), a defense strategy that integrates OPSA within a novel conformal training paradigm. Experimental evaluations demonstrate that our OPSA attack method induces greater uncertainty compared to baseline approaches for various defenses. Conversely, our OPSA-AT defensive model significantly enhances robustness not only against OPSA but also other adversarial attacks, and maintains reliable prediction. Our findings highlight the effectiveness of this integrated approach for developing trustworthy and resilient deep learning models for safety-critical domains. Our code is available at https://github.com/bjbbbb/Enhancing-Adversarial-Robustness-with-Conformal-Prediction.
中文: 本研究提出了OPSA对抗性攻击方法以增加模型不确定性，并开发了OPSA-AT防御策略，通过整合对抗训练与保形预测来提升模型鲁棒性并保持可靠预测性能。
English: This study introduces OPSA, an adversarial attack that increases model uncertainty, and OPSA-AT, a defense strategy using conformal training to enhance robustness and maintain reliable predictions in deep learning models.

Paperid: 355, https://arxiv.org/pdf/2506.07785.pdf   GitHub

Authors:Qi Yang, Chenghao Zhang, Lubin Fan, Kun Ding, Jieping Ye, Shiming Xiang

Title: Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger

Abstract:
Recent advancements in Large Vision Language Models (LVLMs) have significantly improved performance in Visual Question Answering (VQA) tasks through multimodal Retrieval-Augmented Generation (RAG). However, existing methods still face challenges, such as the scarcity of knowledge with reasoning examples and erratic responses from retrieved knowledge. To address these issues, in this study, we propose a multimodal RAG framework, termed RCTS, which enhances LVLMs by constructing a Reasoning Context-enriched knowledge base and a Tree Search re-ranking method. Specifically, we introduce a self-consistent evaluation mechanism to enrich the knowledge base with intrinsic reasoning patterns. We further propose a Monte Carlo Tree Search with Heuristic Rewards (MCTS-HR) to prioritize the most relevant examples. This ensures that LVLMs can leverage high-quality contextual reasoning for better and more consistent responses. Extensive experiments demonstrate that our framework achieves state-of-the-art performance on multiple VQA datasets, significantly outperforming In-Context Learning (ICL) and Vanilla-RAG methods. It highlights the effectiveness of our knowledge base and re-ranking method in improving LVLMs. Our code is available at https://github.com/yannqi/RCTS-RAG.
Chinese: 本研究提出名为RCTS的多模态RAG框架，通过构建富含推理背景的知识库和树搜索重排序方法增强大视觉语言模型，在多项VQA任务中实现了最先进的性能表现。
English: This study introduces RCTS, a multimodal RAG framework that enhances Large Vision Language Models by enriching the knowledge base with reasoning contexts and employing a tree search re-ranking method, achieving state-of-the-art performance on VQA tasks.

Paperid: 356, https://arxiv.org/pdf/2506.07773.pdf   GitHub

Authors:Mohamed Djilani, Nassim Ali Ousalah, Nidhal Eddine Chenni

Title: Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity

Abstract:
We introduce a trend-aware and visually-grounded fashion recommendation system that integrates deep visual representations, garment-aware segmentation, semantic category similarity and user behavior simulation. Our pipeline extracts focused visual embeddings by masking non-garment regions via semantic segmentation followed by feature extraction using pretrained CNN backbones (ResNet-50, DenseNet-121, VGG16). To simulate realistic shopping behavior, we generate synthetic purchase histories influenced by user-specific trendiness and item popularity. Recommendations are computed using a weighted scoring function that fuses visual similarity, semantic coherence and popularity alignment. Experiments on the DeepFashion dataset demonstrate consistent gender alignment and improved category relevance, with ResNet-50 achieving 64.95% category similarity and lowest popularity MAE. An ablation study confirms the complementary roles of visual and popularity cues. Our method provides a scalable framework for personalized fashion recommendations that balances individual style with emerging trends. Our implementation is available at https://github.com/meddjilani/FashionRecommender
该系统通过融合服装视觉分析与用户行为模拟，实现了兼顾个人风格与流行趋势的个性化时尚推荐。
This system combines visual garment analysis with user behavior simulation to deliver personalized fashion recommendations that balance individual style with current trends.

Paperid: 357, https://arxiv.org/pdf/2506.07747.pdf   GitHub

Authors:Adam Breuer

Title: E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time

Abstract:
In this paper, we provide the first practical algorithms with provable guarantees for the problem of inferring the topics assigned to each document in an LDA topic model. This is the primary inference problem for many applications of topic models in social science, data exploration, and causal inference settings. We obtain this result by showing a novel non-gradient-based, combinatorial approach to estimating topic models. This yields algorithms that converge to near-optimal posterior probability in logarithmic parallel computation time (adaptivity) -- exponentially faster than any known LDA algorithm. We also show that our approach can provide interpretability guarantees such that each learned topic is formally associated with a known keyword. Finally, we show that unlike alternatives, our approach can maintain the independence assumptions necessary to use the learned topic model for downstream causal inference methods that allow researchers to study topics as treatments. In terms of practical performance, our approach consistently returns solutions of higher semantic quality than solutions from state-of-the-art LDA algorithms, neural topic models, and LLM-based topic models across a diverse range of text datasets and evaluation parameters.
Chinese: 本文首次提出具有可证明保证的LDA主题推断实用算法，通过创新组合方法实现指数级加速，同时保持可解释性以支持下游因果分析。
English: This paper introduces the first practical algorithms with provable guarantees for LDA topic inference, using a novel combinatorial approach that achieves exponential speedup and maintains interpretability for downstream causal analysis.

Paperid: 358, https://arxiv.org/pdf/2506.07744.pdf   GitHub

Authors:Seungho Baek, Taegeon Park, Jongchan Park, Seungjun Oh, Yusung Kim

Title: Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning

Abstract:
Existing offline hierarchical reinforcement learning methods rely on high-level policy learning to generate subgoal sequences. However, their efficiency degrades as task horizons increase, and they lack effective strategies for stitching useful state transitions across different trajectories. We propose Graph-Assisted Stitching (GAS), a novel framework that formulates subgoal selection as a graph search problem rather than learning an explicit high-level policy. By embedding states into a Temporal Distance Representation (TDR) space, GAS clusters semantically similar states from different trajectories into unified graph nodes, enabling efficient transition stitching. A shortest-path algorithm is then applied to select subgoal sequences within the graph, while a low-level policy learns to reach the subgoals. To improve graph quality, we introduce the Temporal Efficiency (TE) metric, which filters out noisy or inefficient transition states, significantly enhancing task performance. GAS outperforms prior offline HRL methods across locomotion, navigation, and manipulation tasks. Notably, in the most stitching-critical task, it achieves a score of 88.3, dramatically surpassing the previous state-of-the-art score of 1.0. Our source code is available at: https://github.com/qortmdgh4141/GAS.
中文: 提出的图辅助拼接（GAS）框架通过将子目标选择构建为图搜索问题，利用时序距离表示和时序效率指标优化状态转换拼接，在多项任务中显著超越了现有离线分层强化学习方法。
English: The proposed Graph-Assisted Stitching (GAS) framework replaces explicit high-level policy learning with graph-based subgoal selection, utilizing Temporal Distance Representation and Temporal Efficiency metrics to enhance transition stitching and achieve superior performance across various tasks compared to prior offline hierarchical reinforcement learning methods.

Paperid: 359, https://arxiv.org/pdf/2506.07691.pdf   GitHub

Authors:Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

Title: Training Superior Sparse Autoencoders for Instruct Models

Abstract:
As large language models (LLMs) grow in scale and capability, understanding their internal mechanisms becomes increasingly critical. Sparse autoencoders (SAEs) have emerged as a key tool in mechanistic interpretability, enabling the extraction of human-interpretable features from LLMs. However, existing SAE training methods are primarily designed for base models, resulting in reduced reconstruction quality and interpretability when applied to instruct models. To bridge this gap, we propose $\underline{\textbf{F}}$inetuning-$\underline{\textbf{a}}$ligned $\underline{\textbf{S}}$equential $\underline{\textbf{T}}$raining ($\textit{FAST}$), a novel training method specifically tailored for instruct models. $\textit{FAST}$ aligns the training process with the data distribution and activation patterns characteristic of instruct models, resulting in substantial improvements in both reconstruction and feature interpretability. On Qwen2.5-7B-Instruct, $\textit{FAST}$ achieves a mean squared error of 0.6468 in token reconstruction, significantly outperforming baseline methods with errors of 5.1985 and 1.5096. In feature interpretability, $\textit{FAST}$ yields a higher proportion of high-quality features, for Llama3.2-3B-Instruct, $21.1\%$ scored in the top range, compared to $7.0\%$ and $10.2\%$ for $\textit{BT(P)}$ and $\textit{BT(F)}$. Surprisingly, we discover that intervening on the activations of special tokens via the SAEs leads to improvements in output quality, suggesting new opportunities for fine-grained control of model behavior. Code, data, and 240 trained SAEs are available at https://github.com/Geaming2002/FAST.
中文摘要：FAST方法通过将训练过程与指令模型的数据分布和激活模式对齐，显著提升了稀疏自编码器在指令模型上的重建质量和特征可解释性，同时揭示了通过特殊令牌干预来精细调控模型行为的新途径。
English Summary: The proposed FAST method significantly enhances sparse autoencoder performance for instruct models by aligning training with their data distribution and activation patterns, achieving superior reconstruction accuracy and feature interpretability while revealing new opportunities for model behavior control.

Paperid: 360, https://arxiv.org/pdf/2506.07637.pdf   GitHub

Authors:Yuchong Long, Wen Sun, Ningxiao Sun, Wenxiao Wang, Chao Li, Shan Yin

Title: HieraEdgeNet: A Multi-Scale Edge-Enhanced Framework for Automated Pollen Recognition

Abstract:
Automated pollen recognition is vital to paleoclimatology, biodiversity monitoring, and public health, yet conventional methods are hampered by inefficiency and subjectivity. Existing deep learning models often struggle to achieve the requisite localization accuracy for microscopic targets like pollen, which are characterized by their minute size, indistinct edges, and complex backgrounds. To overcome this limitation, we introduce HieraEdgeNet, a multi-scale edge-enhancement framework. The framework's core innovation is the introduction of three synergistic modules: the Hierarchical Edge Module (HEM), which explicitly extracts a multi-scale pyramid of edge features that corresponds to the semantic hierarchy at early network stages; the Synergistic Edge Fusion (SEF) module, for deeply fusing these edge priors with semantic information at each respective scale; and the Cross Stage Partial Omni-Kernel Module (CSPOKM), which maximally refines the most detail-rich feature layers using an Omni-Kernel operator - comprising anisotropic large-kernel convolutions and mixed-domain attention - all within a computationally efficient Cross-Stage Partial (CSP) framework. On a large-scale dataset comprising 120 pollen classes, HieraEdgeNet achieves a mean Average Precision (mAP@.5) of 0.9501, significantly outperforming state-of-the-art baseline models such as YOLOv12n and RT-DETR. Furthermore, qualitative analysis confirms that our approach generates feature representations that are more precisely focused on object boundaries. By systematically integrating edge information, HieraEdgeNet provides a robust and powerful solution for high-precision, high-efficiency automated detection of microscopic objects.
中文: HieraEdgeNet提出了一种多尺度边缘增强框架，通过三个协同模块解决深度学习在定位微观花粉时的局限性，在大规模数据集上实现了卓越的准确性和效率。
English: HieraEdgeNet introduces a multi-scale edge-enhancement framework with three synergistic modules to overcome deep learning challenges in localizing microscopic pollen, achieving superior accuracy and efficiency on a large-scale dataset.

Paperid: 361, https://arxiv.org/pdf/2506.07551.pdf   GitHub

Authors:Mengsong Wu, YaFei Wang, Yidong Ming, Yuqi An, Yuwei Wan, Wenliang Chen, Binbin Lin, Yuqiang Li, Tong Xie, Dongzhan Zhou

Title: CheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning

Abstract:
Large language models (LLMs) have recently demonstrated promising capabilities in chemistry tasks while still facing challenges due to outdated pretraining knowledge and the difficulty of incorporating specialized chemical expertise. To address these issues, we propose an LLM-based agent that synergistically integrates 137 external chemical tools created ranging from basic information retrieval to complex reaction predictions, and a dataset curation pipeline to generate the dataset ChemToolBench that facilitates both effective tool selection and precise parameter filling during fine-tuning and evaluation. We introduce a Hierarchical Evolutionary Monte Carlo Tree Search (HE-MCTS) framework, enabling independent optimization of tool planning and execution. By leveraging self-generated data, our approach supports step-level fine-tuning (FT) of the policy model and training task-adaptive PRM and ORM that surpass GPT-4o. Experimental evaluations demonstrate that our approach significantly improves performance in Chemistry QA and discovery tasks, offering a robust solution to integrate specialized tools with LLMs for advanced chemical applications. All datasets and code are available at https://github.com/AI4Chem/ChemistryAgent .
中文摘要：本研究提出了一种基于大语言模型的化学智能体，通过整合137种专业工具和新型分层进化蒙特卡洛树搜索框架，实现了化学问答与发现任务的性能突破，有效解决了专业工具与大模型融合的挑战。
English Summary: This study introduces a chemistry-focused LLM agent that integrates 137 specialized tools and a novel HE-MCTS framework to significantly enhance chemical QA and discovery tasks through optimized tool utilization and self-improving data generation.

Paperid: 362, https://arxiv.org/pdf/2506.07549.pdf   GitHub

Authors:Zhangchi Zhao, Jun Shu, Deyu Meng, Zongben Xu

Title: Improving Memory Efficiency for Training KANs via Meta Learning

Abstract:
Inspired by the Kolmogorov-Arnold representation theorem, KANs offer a novel framework for function approximation by replacing traditional neural network weights with learnable univariate functions. This design demonstrates significant potential as an efficient and interpretable alternative to traditional MLPs. However, KANs are characterized by a substantially larger number of trainable parameters, leading to challenges in memory efficiency and higher training costs compared to MLPs. To address this limitation, we propose to generate weights for KANs via a smaller meta-learner, called MetaKANs. By training KANs and MetaKANs in an end-to-end differentiable manner, MetaKANs achieve comparable or even superior performance while significantly reducing the number of trainable parameters and maintaining promising interpretability. Extensive experiments on diverse benchmark tasks, including symbolic regression, partial differential equation solving, and image classification, demonstrate the effectiveness of MetaKANs in improving parameter efficiency and memory usage. The proposed method provides an alternative technique for training KANs, that allows for greater scalability and extensibility, and narrows the training cost gap with MLPs stated in the original paper of KANs. Our code is available at https://github.com/Murphyzc/MetaKAN.
中文: MetaKANs通过引入元学习器为KANs生成权重，在多种任务中显著减少了可训练参数和训练成本，同时保持或提升了性能与可解释性。
English: MetaKANs introduce a meta-learner to generate weights for Kolmogorov-Arnold Networks (KANs), significantly reducing trainable parameters and training costs while maintaining or enhancing performance and interpretability across various tasks.

Paperid: 363, https://arxiv.org/pdf/2506.07517.pdf   GitHub

Authors:Shuqiang Zhang, Yuchao Zhang, Jinkun Chen, Haochen Sui

Title: Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems

Abstract:
Recommendation systems (RS) aim to provide personalized content, but they face a challenge in unbiased learning due to selection bias, where users only interact with items they prefer. This bias leads to a distorted representation of user preferences, which hinders the accuracy and fairness of recommendations. To address the issue, various methods such as error imputation based, inverse propensity scoring, and doubly robust techniques have been developed. Despite the progress, from the structural causal model perspective, previous debiasing methods in RS assume the independence of the exogenous variables. In this paper, we release this assumption and propose a learning algorithm based on likelihood maximization to learn a prediction model. We first discuss the correlation and difference between unmeasured confounding and our scenario, then we propose a unified method that effectively handles latent exogenous variables. Specifically, our method models the data generation process with latent exogenous variables under mild normality assumptions. We then develop a Monte Carlo algorithm to numerically estimate the likelihood function. Extensive experiments on synthetic datasets and three real-world datasets demonstrate the effectiveness of our proposed method. The code is at https://github.com/WallaceSUI/kdd25-background-variable.
中文: 本文针对推荐系统中的选择偏差问题，提出了一种基于似然最大化的新型学习算法，通过蒙特卡洛方法建模潜在外生变量，并在合成与真实数据集上验证了其有效性。
English: This paper addresses selection bias in recommendation systems by proposing a novel learning algorithm that models latent exogenous variables using likelihood maximization and a Monte Carlo estimation method, demonstrating effectiveness through synthetic and real-world datasets.

Paperid: 364, https://arxiv.org/pdf/2506.07501.pdf   GitHub

Authors:Libo Wang

Title: Graph-of-Causal Evolution: Challenging Chain-of-Model for Reasoning

Abstract:
In view of the problem that each subchain in the chain-of-model (CoM) relies only on the information of the previous subchain and may lose long-range dependencies due to the causal mask blocking the global context flow between multi-level subchains, this work proposes a graph of causal evolution (GoCE). Its core principle is to map the implicit token representation into a differentiable and sparse causal adjacency matrix, then permeate causal constraints through each layer of calculation using causal-masked attention and causal-MoE. By combining intervention consistency loss test and self-evolution gate, the dynamic balance between causal structure learning and adaptive updating of transformer architecture is realized. The researcher built experimental environments in sandboxes built with Claude Sonnet 4, o4-mini-high, and DeepSeek R1 respectively with the transformer variant architecture introduced in GoCE. It is evaluated on publicly available datasets including CLUTRR, CLADDER, EX-FEVER, and CausalQA and compared with the baseline LLMs. The finding proves that GoCE strengthens the transformer's ability to capture long-range causal dependencies, while the ability to self-evolve is improved. It not only surpasses the design of CoM in terms of design principles, but also provides experience for future research on causal learning and continuous adaptive improvement.
中文: 本研究提出因果演化图（GoCE）以解决链式模型中因因果掩码导致长程依赖丢失的问题，通过将隐式表征映射为稀疏因果邻接矩阵并结合因果注意力机制，增强了Transformer捕捉长程因果依赖与自我演化的能力，其性能优于基线模型。
English: This work introduces the Graph of Causal Evolution (GoCE) to address the loss of long-range dependencies in chain-of-model architectures by mapping token representations into a sparse causal adjacency matrix and integrating causal constraints through attention mechanisms, ultimately enhancing transformers' ability to capture causal relationships and self-evolve beyond baseline models.

Paperid: 365, https://arxiv.org/pdf/2506.07484.pdf   GitHub

Authors:Dasol Hong, Wooju Lee, Hyun Myung

Title: CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization

Abstract:
Prompt tuning, which adapts vision-language models by freezing model parameters and optimizing only the prompt, has proven effective for task-specific adaptations. The core challenge in prompt tuning is improving specialization for a specific task and generalization for unseen domains. However, frozen encoders often produce misaligned features, leading to confusion between classes and limiting specialization. To overcome this issue, we propose a confusion-aware loss (CoA-loss) that improves specialization by refining the decision boundaries between confusing classes. Additionally, we mathematically demonstrate that a mixture model can enhance generalization without compromising specialization. This is achieved using confidence-aware weights (CoA-weights), which adjust the weights of each prediction in the mixture model based on its confidence within the class domains. Extensive experiments show that CoCoA-Mix, a mixture model with CoA-loss and CoA-weights, outperforms state-of-the-art methods by enhancing specialization and generalization. Our code is publicly available at https://github.com/url-kaist/CoCoA-Mix.
Chinese: 提示调优通过仅优化提示来有效适配视觉语言模型，但面临特征错位和泛化难题；提出的CoCoA-Mix方法结合混淆感知损失和权重，在提升专业化和泛化能力方面均优于现有最优方法。
English: Prompt tuning effectively adapts vision-language models by optimizing only the prompt, but faces challenges with misaligned features and generalization; the proposed CoCoA-Mix method, incorporating confusion-aware loss and weights, enhances both specialization and generalization, outperforming state-of-the-art approaches.

Paperid: 366, https://arxiv.org/pdf/2506.07477.pdf   GitHub

Authors:Thomas Zhu, Joshua Clune, Jeremy Avigad, Albert Qiaochu Jiang, Sean Welleck

Title: Premise Selection for a Lean Hammer

Abstract:
Neural methods are transforming automated reasoning for proof assistants, yet integrating these advances into practical verification workflows remains challenging. Hammers are tools that interface with external automatic theorem provers to automate tedious reasoning steps. They have dramatically improved productivity in proof assistants, but the Lean proof assistant still does not have a hammer despite its growing popularity. We present LeanHammer, the first end-to-end domain-general hammer for Lean, built on a novel neural premise selection system for a hammer in dependent type theory. Unlike existing Lean premise selectors, our approach dynamically adapts to user-specific contexts and combines with symbolic proof search and reconstruction to create a practical hammer. With comprehensive evaluations, we show that our premise selector enables LeanHammer to solve 21\% more goals relative to existing premise selectors, and generalize well to diverse domains. Our work bridges the gap between neural retrieval and symbolic reasoning, making formal verification more accessible to researchers and practitioners.
中文摘要：LeanHammer是首个面向Lean证明助手的端到端通用锤子系统，其采用新型神经前提选择技术，能动态适应用户上下文并与符号证明搜索相结合，显著提升了定理自动证明能力。
English Summary: LeanHammer is the first comprehensive hammer for the Lean proof assistant, featuring a novel neural premise selection system that adapts to user contexts and integrates with symbolic proof search to significantly enhance automated theorem proving capabilities.

Paperid: 367, https://arxiv.org/pdf/2506.07467.pdf   GitHub

Authors:Jie Peng, Hongwei Yang, Jing Zhao, Hengji Dong, Hui He, Weizhe Zhang, Haoyu He

Title: Circumventing Backdoor Space via Weight Symmetry

Abstract:
Deep neural networks are vulnerable to backdoor attacks, where malicious behaviors are implanted during training. While existing defenses can effectively purify compromised models, they typically require labeled data or specific training procedures, making them difficult to apply beyond supervised learning settings. Notably, recent studies have shown successful backdoor attacks across various learning paradigms, highlighting a critical security concern. To address this gap, we propose Two-stage Symmetry Connectivity (TSC), a novel backdoor purification defense that operates independently of data format and requires only a small fraction of clean samples. Through theoretical analysis, we prove that by leveraging permutation invariance in neural networks and quadratic mode connectivity, TSC amplifies the loss on poisoned samples while maintaining bounded clean accuracy. Experiments demonstrate that TSC achieves robust performance comparable to state-of-the-art methods in supervised learning scenarios. Furthermore, TSC generalizes to self-supervised learning frameworks, such as SimCLR and CLIP, maintaining its strong defense capabilities. Our code is available at https://github.com/JiePeng104/TSC.
中文: 提出的两阶段对称连通性（TSC）防御方法利用少量干净样本和理论原理，有效清除受后门攻击的模型，在监督学习和自监督学习框架中均展现出强大的防御性能。
English: The proposed Two-stage Symmetry Connectivity (TSC) defense effectively purifies backdoor-infected models using minimal clean samples and theoretical principles, demonstrating robust performance in both supervised and self-supervised learning frameworks.

Paperid: 368, https://arxiv.org/pdf/2506.07435.pdf   GitHub

Authors:Alexander Kolpakov, Igor Rivin

Title: Fast Geometric Embedding for Node Influence Maximization

Abstract:
Computing classical centrality measures such as betweenness and closeness is computationally expensive on large-scale graphs. In this work, we introduce an efficient force layout algorithm that embeds a graph into a low-dimensional space, where the radial distance from the origin serves as a proxy for various centrality measures. We evaluate our method on multiple graph families and demonstrate strong correlations with degree, PageRank, and paths-based centralities. As an application, it turns out that the proposed embedding allows to find high-influence nodes in a network, and provides a fast and scalable alternative to the standard greedy algorithm.
中文: 本研究提出一种高效的力导向布局算法，将图嵌入低维空间，利用径向距离作为中心性度量的替代指标，为识别网络中有影响力的节点提供了一种快速且可扩展的解决方案。
English: The study presents an efficient force layout algorithm that embeds graphs into low-dimensional space, using radial distance as a proxy for centrality measures and offering a scalable alternative for identifying influential nodes.

Paperid: 369, https://arxiv.org/pdf/2506.07400.pdf   GitHub

Authors:Philip R. Liu, Sparsh Bansal, Jimmy Dinh, Aditya Pawar, Ramani Satishkumar, Shail Desai, Neeraj Gupta, Xin Wang, Shu Hu

Title: MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models

Abstract:
The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologist shortages and improve clinical reporting efficiency. However, applying general LLMs to medical imaging remains challenging due to hallucinations, limited interpretability, and insufficient domain-specific medical knowledge, which can potentially reduce clinical accuracy. Although recent approaches combining imaging models with LLM reasoning have improved reporting, they typically rely on a single generalist agent, restricting their capacity to emulate the diverse and complex reasoning found in multidisciplinary medical teams. To address these limitations, we propose MedChat, a multi-agent diagnostic framework and platform that combines specialized vision models with multiple role-specific LLM agents, all coordinated by a director agent. This design enhances reliability, reduces hallucination risk, and enables interactive diagnostic reporting through an interface tailored for clinical review and educational use. Code available at https://github.com/Purdue-M2/MedChat.
中文总结：MedChat是一个多智能体诊断框架，通过结合专业视觉模型与角色定制的大语言模型，提高了青光眼检测的可靠性，减少幻觉现象，并提供交互式临床报告功能。
English Summary: MedChat is a multi-agent diagnostic framework that integrates specialized vision models with role-specific LLMs to enhance glaucoma detection reliability, reduce hallucinations, and provide interactive clinical reporting.

Paperid: 370, https://arxiv.org/pdf/2506.07398.pdf   GitHub

Authors:Guibin Zhang, Muxin Fu, Guancheng Wan, Miao Yu, Kun Wang, Shuicheng Yan

Title: G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems

Abstract:
Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to discover that prevailing MAS memory mechanisms (1) are overly simplistic, completely disregarding the nuanced inter-agent collaboration trajectories, and (2) lack cross-trial and agent-specific customization, in stark contrast to the expressive memory developed for single agents. To bridge this gap, we introduce G-Memory, a hierarchical, agentic memory system for MAS inspired by organizational memory theory, which manages the lengthy MAS interaction via a three-tier graph hierarchy: insight, query, and interaction graphs. Upon receiving a new user query, G-Memory performs bi-directional memory traversal to retrieve both $\textit{high-level, generalizable insights}$ that enable the system to leverage cross-trial knowledge, and $\textit{fine-grained, condensed interaction trajectories}$ that compactly encode prior collaboration experiences. Upon task execution, the entire hierarchy evolves by assimilating new collaborative trajectories, nurturing the progressive evolution of agent teams. Extensive experiments across five benchmarks, three LLM backbones, and three popular MAS frameworks demonstrate that G-Memory improves success rates in embodied action and accuracy in knowledge QA by up to $20.89\%$ and $10.12\%$, respectively, without any modifications to the original frameworks. Our codes are available at https://github.com/bingreeky/GMemory.
中文: G-Memory作为一种层次化、智能的记忆系统，通过三层图结构管理多智能体系统的交互，无需修改原有框架即可显著提升任务成功率和准确性。
English: G-Memory is a hierarchical, agentic memory system designed to enhance multi-agent systems by managing interactions through a three-tier graph structure, which significantly improves task success rates and accuracy without altering existing frameworks.

Paperid: 371, https://arxiv.org/pdf/2506.07364.pdf   GitHub

Authors:Chengchao Shen, Dawei Liu, Jianxin Wang

Title: Multiple Object Stitching for Unsupervised Representation Learning

Abstract:
Contrastive learning for single object centric images has achieved remarkable progress on unsupervised representation, but suffering inferior performance on the widespread images with multiple objects. In this paper, we propose a simple but effective method, Multiple Object Stitching (MOS), to refine the unsupervised representation for multi-object images. Specifically, we construct the multi-object images by stitching the single object centric ones, where the objects in the synthesized multi-object images are predetermined. Hence, compared to the existing contrastive methods, our method provides additional object correspondences between multi-object images without human annotations. In this manner, our method pays more attention to the representations of each object in multi-object image, thus providing more detailed representations for complicated downstream tasks, such as object detection and semantic segmentation. Experimental results on ImageNet, CIFAR and COCO datasets demonstrate that our proposed method achieves the leading unsupervised representation performance on both single object centric images and multi-object ones. The source code is available at https://github.com/visresearch/MultipleObjectStitching.
中文: 本文提出的多对象拼接（MOS）方法通过将单对象图像合成为多对象图像，无需人工标注即可提供额外对象对应关系，从而在多对象图像的无监督表征学习上取得领先性能。
English: The proposed Multiple Object Stitching (MOS) method enhances unsupervised representation learning for multi-object images by synthesizing them from single-object images, providing additional object correspondences without human annotation and achieving superior performance on various datasets.

Paperid: 372, https://arxiv.org/pdf/2506.07288.pdf   GitHub

Authors:Weijie Guan, Haohui Wang, Jian Kang, Lihui Liu, Dawei Zhou

Title: EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network

Abstract:
Graph learning has been crucial to many real-world tasks, but they are often studied with a closed-world assumption, with all possible labels of data known a priori. To enable effective graph learning in an open and noisy environment, it is critical to inform the model users when the model makes a wrong prediction to in-distribution data of a known class, i.e., misclassification detection or when the model encounters out-of-distribution from novel classes, i.e., out-of-distribution detection. This paper introduces Evidential Reasoning Network (EVINET), a framework that addresses these two challenges by integrating Beta embedding within a subjective logic framework. EVINET includes two key modules: Dissonance Reasoning for misclassification detection and Vacuity Reasoning for out-of-distribution detection. Extensive experiments demonstrate that EVINET outperforms state-of-the-art methods across multiple metrics in the tasks of in-distribution classification, misclassification detection, and out-of-distribution detection. EVINET demonstrates the necessity of uncertainty estimation and logical reasoning for misclassification detection and out-of-distribution detection and paves the way for open-world graph learning. Our code and data are available at https://github.com/SSSKJ/EviNET.
中文: EVINET框架通过将Beta嵌入与主观逻辑相结合，有效解决了图学习中的误分类和分布外检测问题，在多项指标上表现优异，推动了开放世界图学习的发展。
English: EVINET is a novel framework that tackles misclassification and out-of-distribution detection in graph learning by integrating Beta embeddings with subjective logic, demonstrating superior performance across multiple metrics and advancing open-world graph learning.

Paperid: 373, https://arxiv.org/pdf/2506.07198.pdf   GitHub

Authors:Tianci Bu, Chuanrui Wang, Hao Ma, Haoren Zheng, Xin Lu, Tailin Wu

Title: GGBall: Graph Generative Model on Poincaré Ball

Abstract:
Generating graphs with hierarchical structures remains a fundamental challenge due to the limitations of Euclidean geometry in capturing exponential complexity. Here we introduce \textbf{GGBall}, a novel hyperbolic framework for graph generation that integrates geometric inductive biases with modern generative paradigms. GGBall combines a Hyperbolic Vector-Quantized Autoencoder (HVQVAE) with a Riemannian flow matching prior defined via closed-form geodesics. This design enables flow-based priors to model complex latent distributions, while vector quantization helps preserve the curvature-aware structure of the hyperbolic space. We further develop a suite of hyperbolic GNN and Transformer layers that operate entirely within the manifold, ensuring stability and scalability. Empirically, our model reduces degree MMD by over 75\% on Community-Small and over 40\% on Ego-Small compared to state-of-the-art baselines, demonstrating an improved ability to preserve topological hierarchies. These results highlight the potential of hyperbolic geometry as a powerful foundation for the generative modeling of complex, structured, and hierarchical data domains. Our code is available at \href{https://github.com/AI4Science-WestlakeU/GGBall}{here}.
中文摘要：GGBall提出了一种基于双曲几何的图生成框架，通过结合双曲向量量化自编码器和黎曼流匹配技术，能更好地捕捉层次结构，在保持拓扑特性方面显著优于现有方法。
English Summary: GGBall introduces a hyperbolic geometry-based framework for graph generation, combining a Hyperbolic Vector-Quantized Autoencoder with Riemannian flow matching to better capture hierarchical structures and significantly outperform existing methods in preserving topological properties.

Paperid: 374, https://arxiv.org/pdf/2506.07165.pdf   GitHub

Authors:Qi Liu, Jingqing Ruan, Hao Li, Haodong Zhao, Desheng Wang, Jiansong Chen, Wan Guanglu, Xunliang Cai, Zhi Zheng, Tong Xu

Title: AMoPO: Adaptive Multi-objective Preference Optimization without Reward Models and Reference Models

Abstract:
Existing multi-objective preference alignment methods for large language models (LLMs) face limitations: (1) the inability to effectively balance various preference dimensions, and (2) reliance on auxiliary reward/reference models introduces computational complexity. To address these challenges, we propose Adaptive Multi-objective Preference Optimization (AMoPO), a novel framework that achieves dynamic balance across preference dimensions. By introducing the multi-objective optimization paradigm to use the dimension-aware generation metrics as implicit rewards, AMoPO aligns LLMs with diverse preferences without additional reward models or reference models. We introduce an adaptive weight assignment mechanism that models the generation space as a Gaussian distribution, allowing dynamic prioritization of preference dimensions. Empirical results demonstrate that AMoPO outperforms state-of-the-art baselines by 28.5%, and the experiments on 7B, 14B, and 32B models reveal the scaling ability of AMoPO. Moreover, additional analysis of multiple dimensions verifies its adaptability and effectiveness. These findings validate AMoPO's capability to achieve dimension-aware preference alignment, highlighting its superiority. Our codes and datasets are available at https://github.com/Javkonline/AMoPO.
中文: AMoPO提出了一种无需辅助奖励模型即可动态平衡大语言模型中多维度偏好的新框架，相比现有方法性能提升28.5%，并在不同规模模型上展现出优秀的扩展能力。
English: AMoPO introduces a novel framework that dynamically balances multiple preference dimensions in large language models without requiring auxiliary reward models, achieving a 28.5% performance improvement over existing methods while demonstrating strong scalability across different model sizes.

Paperid: 375, https://arxiv.org/pdf/2506.07109.pdf   GitHub

Authors:Rong-Xi Tan, Ming Chen, Ke Xue, Yao Wang, Yaoyuan Wang, Sheng Fu, Chao Qian

Title: Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings

Abstract:
The pursuit of universal black-box optimization (BBO) algorithms is a longstanding goal. However, unlike domains such as language or vision, where scaling structured data has driven generalization, progress in offline BBO remains hindered by the lack of unified representations for heterogeneous numerical spaces. Thus, existing offline BBO approaches are constrained to single-task and fixed-dimensional settings, failing to achieve cross-domain universal optimization. Recent advances in language models (LMs) offer a promising path forward: their embeddings capture latent relationships in a unifying way, enabling universal optimization across different data types possible. In this paper, we discuss multiple potential approaches, including an end-to-end learning framework in the form of next-token prediction, as well as prioritizing the learning of latent spaces with strong representational capabilities. To validate the effectiveness of these methods, we collect offline BBO tasks and data from open-source academic works for training. Experiments demonstrate the universality and effectiveness of our proposed methods. Our findings suggest that unifying language model priors and learning string embedding space can overcome traditional barriers in universal BBO, paving the way for general-purpose BBO algorithms. The code is provided at https://github.com/lamda-bbo/universal-offline-bbo.
Chinese: 语言模型的最新进展通过利用其统一嵌入和学习框架，为克服异构数值空间中的传统障碍，实现通用黑盒优化提供了有希望的途径。
English: Recent advances in language models provide a promising path toward universal black-box optimization by leveraging their unified embeddings and learning frameworks to overcome traditional barriers in heterogeneous numerical spaces.

Paperid: 376, https://arxiv.org/pdf/2506.07099.pdf   GitHub

Authors:Wenying He, Jieling Huang, Junhua Gu, Ji Zhang, Yude Bai

Title: Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion

Abstract:
Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments reveal that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.
Chinese Summary: 针对时空数据修复中复杂依赖关系和误差传播的难题，CoFILL创新性地采用条件扩散模型与双流架构，通过并行处理时域和频域特征实现高质量数据补全，在修复精度上显著超越现有最优方法。
English Summary: Spatiotemporal data imputation is challenging due to complex interdependencies and error propagation in existing methods, but CoFILL, a novel conditional diffusion model with dual-stream architecture, overcomes these limitations by generating high-quality imputations that outperform state-of-the-art approaches.

Paperid: 377, https://arxiv.org/pdf/2506.07022.pdf   GitHub

Authors:Leheng Sheng, Changshuo Shen, Weixiang Zhao, Junfeng Fang, Xiaohao Liu, Zhenkai Liang, Xiang Wang, An Zhang, Tat-Seng Chua

Title: AlphaSteer: Learning Refusal Steering with Principled Null-Space Constraint

Abstract:
As LLMs are increasingly deployed in real-world applications, ensuring their ability to refuse malicious prompts, especially jailbreak attacks, is essential for safe and reliable use. Recently, activation steering has emerged as an effective approach for enhancing LLM safety by adding a refusal direction vector to internal activations of LLMs during inference, which will further induce the refusal behaviors of LLMs. However, indiscriminately applying activation steering fundamentally suffers from the trade-off between safety and utility, since the same steering vector can also lead to over-refusal and degraded performance on benign prompts. Although prior efforts, such as vector calibration and conditional steering, have attempted to mitigate this trade-off, their lack of theoretical grounding limits their robustness and effectiveness. To better address the trade-off between safety and utility, we present a theoretically grounded and empirically effective activation steering method called AlphaSteer. Specifically, it considers activation steering as a learnable process with two principled learning objectives: utility preservation and safety enhancement. For utility preservation, it learns to construct a nearly zero vector for steering benign data, with the null-space constraints. For safety enhancement, it learns to construct a refusal direction vector for steering malicious data, with the help of linear regression. Experiments across multiple jailbreak attacks and utility benchmarks demonstrate the effectiveness of AlphaSteer, which significantly improves the safety of LLMs without compromising general capabilities. Our codes are available at https://github.com/AlphaLab-USTC/AlphaSteer.
中文: AlphaSteer是一种基于理论的激活引导方法，通过为恶意提示学习拒绝向量来增强大语言模型安全性，同时利用近零向量保持良性输入的实用性，有效平衡了安全性与性能。
English: AlphaSteer is a theoretically grounded activation steering method that enhances LLM safety by learning refusal vectors for malicious prompts while preserving utility through nearly zero vectors for benign inputs, effectively balancing safety and performance.

Paperid: 378, https://arxiv.org/pdf/2506.06999.pdf   GitHub

Authors:Arun Sharma, Mingzhou Yang, Majid Farhadloo, Subhankar Ghosh, Bharat Jayaprakash, Shashi Shekhar

Title: Towards Physics-informed Diffusion for Anomaly Detection in Trajectories

Abstract:
Given trajectory data, a domain-specific study area, and a user-defined threshold, we aim to find anomalous trajectories indicative of possible GPS spoofing (e.g., fake trajectory). The problem is societally important to curb illegal activities in international waters, such as unauthorized fishing and illicit oil transfers. The problem is challenging due to advances in AI generated in deep fakes generation (e.g., additive noise, fake trajectories) and lack of adequate amount of labeled samples for ground-truth verification. Recent literature shows promising results for anomalous trajectory detection using generative models despite data sparsity. However, they do not consider fine-scale spatiotemporal dependencies and prior physical knowledge, resulting in higher false-positive rates. To address these limitations, we propose a physics-informed diffusion model that integrates kinematic constraints to identify trajectories that do not adhere to physical laws. Experimental results on real-world datasets in the maritime and urban domains show that the proposed framework results in higher prediction accuracy and lower estimation error rate for anomaly detection and trajectory generation methods, respectively. Our implementation is available at https://github.com/arunshar/Physics-Informed-Diffusion-Probabilistic-Model.
中文摘要：本研究提出了一种融合运动学约束的物理信息扩散模型，用于检测GPS欺骗等异常轨迹，在海上和城市数据集中实现了更高的检测精度和更低的误差率。
English Summary: This study introduces a physics-informed diffusion model that incorporates kinematic constraints to detect anomalous trajectories, such as GPS spoofing, achieving higher accuracy and lower error rates in maritime and urban datasets.

Paperid: 379, https://arxiv.org/pdf/2506.06990.pdf   GitHub

Authors:Mingyi Li, Michael R. Metel, Akiko Takeda

Title: Modified K-means Algorithm with Local Optimality Guarantees

Abstract:
The K-means algorithm is one of the most widely studied clustering algorithms in machine learning. While extensive research has focused on its ability to achieve a globally optimal solution, there still lacks a rigorous analysis of its local optimality guarantees. In this paper, we first present conditions under which the K-means algorithm converges to a locally optimal solution. Based on this, we propose simple modifications to the K-means algorithm which ensure local optimality in both the continuous and discrete sense, with the same computational complexity as the original K-means algorithm. As the dissimilarity measure, we consider a general Bregman divergence, which is an extension of the squared Euclidean distance often used in the K-means algorithm. Numerical experiments confirm that the K-means algorithm does not always find a locally optimal solution in practice, while our proposed methods provide improved locally optimal solutions with reduced clustering loss. Our code is available at https://github.com/lmingyi/LO-K-means.
Chinese: 本文分析了K-means算法的局部最优性，并提出了在保持相同计算复杂度的前提下，能够确保算法在广义Bregman散度下收敛到局部最优解的改进方法。
English: This paper analyzes the local optimality of the K-means algorithm and proposes modifications that ensure convergence to locally optimal solutions under general Bregman divergence, maintaining the same computational complexity as the original algorithm.

Paperid: 380, https://arxiv.org/pdf/2506.06985.pdf   GitHub

Authors:Anastasia Koloskova, Youssef Allouah, Animesh Jha, Rachid Guerraoui, Sanmi Koyejo

Title: Certified Unlearning for Neural Networks

Abstract:
We address the problem of machine unlearning, where the goal is to remove the influence of specific training data from a model upon request, motivated by privacy concerns and regulatory requirements such as the "right to be forgotten." Unfortunately, existing methods rely on restrictive assumptions or lack formal guarantees. To this end, we propose a novel method for certified machine unlearning, leveraging the connection between unlearning and privacy amplification by stochastic post-processing. Our method uses noisy fine-tuning on the retain data, i.e., data that does not need to be removed, to ensure provable unlearning guarantees. This approach requires no assumptions about the underlying loss function, making it broadly applicable across diverse settings. We analyze the theoretical trade-offs in efficiency and accuracy and demonstrate empirically that our method not only achieves formal unlearning guarantees but also performs effectively in practice, outperforming existing baselines. Our code is available at https://github.com/stair-lab/certified-unlearning-neural-networks-icml-2025
中文: 本文提出了一种经过认证的机器遗忘方法，通过对保留数据进行噪声微调来提供无需严格假设的形式化遗忘保证，在理论和实践中均表现出优越性能。
English: This paper introduces a certified machine unlearning method that uses noisy fine-tuning on retained data to provide formal unlearning guarantees without restrictive assumptions, demonstrating both theoretical and practical effectiveness.

Paperid: 381, https://arxiv.org/pdf/2506.06944.pdf   GitHub

Authors:Mellon M. Zhang, Glen Chou, Saibal Mukhopadhyay

Title: Polar Hierarchical Mamba: Towards Streaming LiDAR Object Detection with Point Clouds as Egocentric Sequences

Abstract:
Accurate and efficient object detection is essential for autonomous vehicles, where real-time perception requires low latency and high throughput. LiDAR sensors provide robust depth information, but conventional methods process full 360° scans in a single pass, introducing significant delay. Streaming approaches address this by sequentially processing partial scans in the native polar coordinate system, yet they rely on translation-invariant convolutions that are misaligned with polar geometry -- resulting in degraded performance or requiring complex distortion mitigation. Recent Mamba-based state space models (SSMs) have shown promise for LiDAR perception, but only in the full-scan setting, relying on geometric serialization and positional embeddings that are memory-intensive and ill-suited to streaming. We propose Polar Hierarchical Mamba (PHiM), a novel SSM architecture designed for polar-coordinate streaming LiDAR. PHiM uses local bidirectional Mamba blocks for intra-sector spatial encoding and a global forward Mamba for inter-sector temporal modeling, replacing convolutions and positional encodings with distortion-aware, dimensionally-decomposed operations. PHiM sets a new state-of-the-art among streaming detectors on the Waymo Open Dataset, outperforming the previous best by 10\% and matching full-scan baselines at twice the throughput. Code will be available at https://github.com/meilongzhang/Polar-Hierarchical-Mamba .
中文: 提出的极坐标分层Mamba（PHiM）架构通过用感知畸变的Mamba模块替代传统卷积，解决了流式LiDAR检测的低效问题，在Waymo开放数据集中以超越先前最佳方法10%的性能刷新了最高水平，并实现了与全扫描相当的吞吐量。
English: The proposed Polar Hierarchical Mamba (PHiM) architecture addresses inefficiencies in streaming LiDAR detection by replacing conventional convolutions with distortion-aware Mamba blocks, achieving state-of-the-art performance on the Waymo Open Dataset with a 10% improvement over prior methods and matching full-scan throughput.

Paperid: 382, https://arxiv.org/pdf/2506.06884.pdf   GitHub

Authors:Divya Jyoti Bajpai, Manjesh Kumar Hanawal

Title: FREE: Fast and Robust Vision Language Models with Early Exits

Abstract:
In recent years, Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-Language tasks. However, their large size poses challenges for real-world applications where inference latency is a concern. To tackle this issue, we propose employing Early Exit (EE) strategies in VLMs. However, training exit classifiers in VLMs is challenging, particularly with limited labeled training data. To address this, we introduce FREE, an adversarial training approach within a GAN-based framework. Here, each exit consists of a transformer layer and a classifier. The transformer layer is adversarially trained to produce feature representations similar to the final layer, while a feature classifier serves as the discriminator. Our method focuses on performing input-adaptive inference that increases inference speed with minimal drop in performance. Experimental results demonstrate the effectiveness of our approach in enhancing accuracy and model robustness by mitigating overthinking and the phenomenon of mid-crisis that we highlight. We experimentally validate that our method speeds up the inference process by more than 1.51x while retaining comparable performance. The source code is available at https://github.com/Div290/FREE.
Chinese: 本文提出FREE方法，通过基于GAN框架的对抗性训练在视觉语言模型中实现早期退出策略，在保持性能的同时将推理速度提升超过1.51倍。
English: The paper introduces FREE, an adversarial training method using a GAN-based framework to implement Early Exit strategies in Vision-Language Models, significantly accelerating inference by over 1.51x while maintaining performance.

Paperid: 383, https://arxiv.org/pdf/2506.06873.pdf   GitHub

Authors:Armin Behnamnia, Gholamali Aminian, Alireza Aghaei, Chengchun Shi, Vincent Y. F. Tan, Hamid R. Rabiee

Title: Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning

Abstract:
Off-policy learning and evaluation leverage logged bandit feedback datasets, which contain context, action, propensity score, and feedback for each data point. These scenarios face significant challenges due to high variance and poor performance with low-quality propensity scores and heavy-tailed reward distributions. We address these issues by introducing a novel estimator based on the log-sum-exponential (LSE) operator, which outperforms traditional inverse propensity score estimators. Our LSE estimator demonstrates variance reduction and robustness under heavy-tailed conditions. For off-policy evaluation, we derive upper bounds on the estimator's bias and variance. In the off-policy learning scenario, we establish bounds on the regret -- the performance gap between our LSE estimator and the optimal policy -- assuming bounded $(1+ε)$-th moment of weighted reward. Notably, we achieve a convergence rate of $O(n^{-ε/(1+ ε)})$ for the regret bounds, where $ε\in [0,1]$ and $n$ is the size of logged bandit feedback dataset. Theoretical analysis is complemented by comprehensive empirical evaluations in both off-policy learning and evaluation scenarios, confirming the practical advantages of our approach. The code for our estimator is available at the following link: https://github.com/armin-behnamnia/lse-offpolicy-learning.
中文摘要：作者提出了一种基于对数求和指数（LSE）的新型估计器，通过减少方差并在重尾奖励分布下保持鲁棒性，有效解决了离线策略学习和评估中的关键挑战，并提供了理论保证和实证验证。
English Summary: The authors propose a novel log-sum-exponential (LSE) estimator that addresses challenges in off-policy learning and evaluation by reducing variance and demonstrating robustness under heavy-tailed reward distributions, with theoretical guarantees and empirical validation.

Paperid: 384, https://arxiv.org/pdf/2506.06853.pdf   GitHub

Authors:Ilya Kaufman Sirot, Omri Azencot

Title: Curvature Enhanced Data Augmentation for Regression

Abstract:
Deep learning models with a large number of parameters, often referred to as over-parameterized models, have achieved exceptional performance across various tasks. Despite concerns about overfitting, these models frequently generalize well to unseen data, thanks to effective regularization techniques, with data augmentation being among the most widely used. While data augmentation has shown great success in classification tasks using label-preserving transformations, its application in regression problems has received less attention. Recently, a novel \emph{manifold learning} approach for generating synthetic data was proposed, utilizing a first-order approximation of the data manifold. Building on this foundation, we present a theoretical framework and practical tools for approximating and sampling general data manifolds. Furthermore, we introduce the Curvature-Enhanced Manifold Sampling (CEMS) method for regression tasks. CEMS leverages a second-order representation of the data manifold to enable efficient sampling and reconstruction of new data points. Extensive evaluations across multiple datasets and comparisons with state-of-the-art methods demonstrate that CEMS delivers superior performance in both in-distribution and out-of-distribution scenarios, while introducing only minimal computational overhead. Code is available at https://github.com/azencot-group/CEMS.
Chinese: 过参数化深度学习模型通过数据增强等正则化技术实现良好泛化，而提出的曲率增强流形采样（CEMS）方法利用二阶流形近似，以最小计算开销显著提升回归任务的性能。
English: Over-parameterized deep learning models achieve strong generalization through regularization like data augmentation, and the proposed Curvature-Enhanced Manifold Sampling (CEMS) method leverages second-order manifold approximations to significantly improve regression performance with minimal computational cost.

Paperid: 385, https://arxiv.org/pdf/2506.06701.pdf   GitHub

Authors:Fudong Lin, Wanrou Du, Jinchan Liu, Tarikul Milon, Shelby Meche, Wu Xu, Xiaoqi Qin, Xu Yuan

Title: Do Protein Transformers Have Biological Intelligence?

Abstract:
Deep neural networks, particularly Transformers, have been widely adopted for predicting the functional properties of proteins. In this work, we focus on exploring whether Protein Transformers can capture biological intelligence among protein sequences. To achieve our goal, we first introduce a protein function dataset, namely Protein-FN, providing over 9000 protein data with meaningful labels. Second, we devise a new Transformer architecture, namely Sequence Protein Transformers (SPT), for computationally efficient protein function predictions. Third, we develop a novel Explainable Artificial Intelligence (XAI) technique called Sequence Score, which can efficiently interpret the decision-making processes of protein models, thereby overcoming the difficulty of deciphering biological intelligence bided in Protein Transformers. Remarkably, even our smallest SPT-Tiny model, which contains only 5.4M parameters, demonstrates impressive predictive accuracy, achieving 94.3% on the Antibiotic Resistance (AR) dataset and 99.6% on the Protein-FN dataset, all accomplished by training from scratch. Besides, our Sequence Score technique helps reveal that our SPT models can discover several meaningful patterns underlying the sequence structures of protein data, with these patterns aligning closely with the domain knowledge in the biology community. We have officially released our Protein-FN dataset on Hugging Face Datasets https://huggingface.co/datasets/Protein-FN/Protein-FN. Our code is available at https://github.com/fudong03/BioIntelligence.
中文: 本研究提出了Protein-FN蛋白质功能数据集、新型序列蛋白质Transformer（SPT）预测模型，以及可解释AI技术Sequence Score，揭示了SPT模型如何捕捉蛋白质序列中的生物学模式，其最小模型仅用540万参数即实现了优异预测精度。
English: This study introduces a Protein-FN dataset, a novel Sequence Protein Transformer (SPT) model for efficient protein function prediction, and an explainable AI technique called Sequence Score that reveals how SPT models capture biologically meaningful patterns in protein sequences, achieving high accuracy with minimal parameters.

Paperid: 386, https://arxiv.org/pdf/2506.06694.pdf   GitHub

Authors:Yuan Yuan, Yukun Liu, Chonghua Han, Jie Feng, Yong Li

Title: Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning

Abstract:
Foundation models have revolutionized fields such as natural language processing and computer vision by enabling general-purpose learning across diverse tasks and datasets. However, building analogous models for human mobility remains challenging due to the privacy-sensitive nature of mobility data and the resulting data silos across institutions. To bridge this gap, we propose MoveGCL, a scalable and privacy-preserving framework for training mobility foundation models via generative continual learning. Without sharing raw data, MoveGCL enables decentralized and progressive model evolution by replaying synthetic trajectories generated from a frozen teacher model, and reinforces knowledge retention through a tailored distillation strategy that mitigates catastrophic forgetting. To address the heterogeneity of mobility patterns, MoveGCL incorporates a Mixture-of-Experts Transformer with a mobility-aware expert routing mechanism, and employs a layer-wise progressive adaptation strategy to stabilize continual updates. Experiments on six real-world urban datasets demonstrate that MoveGCL achieves performance comparable to joint training and significantly outperforms federated learning baselines, while offering strong privacy protection. MoveGCL marks a crucial step toward unlocking foundation models for mobility, offering a practical blueprint for open, scalable, and privacy-preserving model development in the era of foundation models. To facilitate reproducibility and future research, we have released the code and models at https://github.com/tsinghua-fib-lab/MoveGCL.
中文：MoveGCL是一个通过生成式持续学习实现去中心化移动基础模型训练的隐私保护框架，在保护敏感数据的同时，在异构城市数据集上取得了与联合训练相当的性能表现。
English: MoveGCL is a privacy-preserving framework that enables decentralized training of mobility foundation models through generative continual learning, achieving performance comparable to joint training while protecting sensitive data across heterogeneous urban datasets.

Paperid: 387, https://arxiv.org/pdf/2506.06606.pdf   GitHub

Authors:Xinyu Luo, Cedar Site Bai, Bolian Li, Petros Drineas, Ruqi Zhang, Brian Bullins

Title: Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization

Abstract:
While popular optimization methods such as SGD, AdamW, and Lion depend on steepest descent updates in either $\ell_2$ or $\ell_\infty$ norms, there remains a critical gap in handling the non-Euclidean structure observed in modern deep networks training. In this work, we address this need by introducing a new accelerated $\ell_p$ steepest descent algorithm, called Stacey, which uses interpolated primal-dual iterate sequences to effectively navigate non-Euclidean smooth optimization tasks. In addition to providing novel theoretical guarantees for the foundations of our algorithm, we empirically compare our approach against these popular methods on tasks including image classification and language model (LLM) pretraining, demonstrating both faster convergence and higher final accuracy. We further evaluate different values of $p$ across various models and datasets, underscoring the importance and efficiency of non-Euclidean approaches over standard Euclidean methods. Code can be found at https://github.com/xinyuluo8561/Stacey .
中文: 本文提出了一种名为Stacey的加速$\ell_p$最速下降算法，通过插值原始-对偶迭代序列有效处理深度学习中的非欧几里得优化问题，在图像分类和语言模型预训练任务中展现出比现有方法更快的收敛速度和更高的最终精度。
English: This paper introduces Stacey, an accelerated $\ell_p$ steepest descent algorithm that effectively addresses non-Euclidean optimization in deep learning, demonstrating superior convergence and accuracy over existing methods in both theoretical and empirical evaluations.

Paperid: 388, https://arxiv.org/pdf/2506.06603.pdf   GitHub

Authors:Joseph T Colonel, Carolyn Hagler, Guiselle Wismer, Laura Curtis, Jacqueline Becker, Juan Wisnivesky, Alex Federman, Gaurav Pandey

Title: CAtCh: Cognitive Assessment through Cookie Thief

Abstract:
Several machine learning algorithms have been developed for the prediction of Alzheimer's disease and related dementia (ADRD) from spontaneous speech. However, none of these algorithms have been translated for the prediction of broader cognitive impairment (CI), which in some cases is a precursor and risk factor of ADRD. In this paper, we evaluated several speech-based open-source methods originally proposed for the prediction of ADRD, as well as methods from multimodal sentiment analysis for the task of predicting CI from patient audio recordings. Results demonstrated that multimodal methods outperformed unimodal ones for CI prediction, and that acoustics-based approaches performed better than linguistics-based ones. Specifically, interpretable acoustic features relating to affect and prosody were found to significantly outperform BERT-based linguistic features and interpretable linguistic features, respectively. All the code developed for this study is available at https://github.com/JTColonel/catch.
中文: 本研究将原本用于预测阿尔茨海默病的语音机器学习方法应用于更广泛的认知障碍检测，发现多模态和声学方法，特别是捕捉情感和韵律特征的方法，优于单模态和语言学方法。
English: This study adapts existing speech-based machine learning methods, originally designed for Alzheimer's disease prediction, to detect broader cognitive impairment, finding that multimodal and acoustic approaches, particularly those capturing affect and prosody, outperform unimodal and linguistic methods.

Paperid: 389, https://arxiv.org/pdf/2506.06482.pdf   GitHub

Authors:Zhiyuan Zhao, Juntong Ni, Shangqing Xu, Haoxin Liu, Wei Jin, B. Aditya Prakash

Title: TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness

Abstract:
Time-series forecasting is an essential task with wide real-world applications across domains. While recent advances in deep learning have enabled time-series forecasting models with accurate predictions, there remains considerable debate over which architectures and design components, such as series decomposition or normalization, are most effective under varying conditions. Existing benchmarks primarily evaluate models at a high level, offering limited insight into why certain designs work better. To mitigate this gap, we propose TimeRecipe, a unified benchmarking framework that systematically evaluates time-series forecasting methods at the module level. TimeRecipe conducts over 10,000 experiments to assess the effectiveness of individual components across a diverse range of datasets, forecasting horizons, and task settings. Our results reveal that exhaustive exploration of the design space can yield models that outperform existing state-of-the-art methods and uncover meaningful intuitions linking specific design choices to forecasting scenarios. Furthermore, we release a practical toolkit within TimeRecipe that recommends suitable model architectures based on these empirical insights. The benchmark is available at: https://github.com/AdityaLab/TimeRecipe.
Chinese: TimeRecipe框架通过大量实验在模块级别系统评估时间序列预测方法，揭示了超越现有最优方法的设计选择，并提供了基于实证的架构推荐工具包。
English: The TimeRecipe framework systematically evaluates time-series forecasting models at the module level through extensive experiments, revealing optimal design choices that outperform state-of-the-art methods and providing a toolkit for architecture recommendations.

Paperid: 390, https://arxiv.org/pdf/2506.06454.pdf   GitHub

Authors:Abrar Majeedi, Viswanatha Reddy Gajjala, Satya Sai Srinath Namburi GNVV, Nada Magdi Elkordi, Yin Li

Title: LETS Forecast: Learning Embedology for Time Series Forecasting

Abstract:
Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future prediction. While deep learning has achieved major success in time series forecasting, many existing approaches do not explicitly model the dynamics. To bridge this gap, we introduce DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks. Inspired by empirical dynamic modeling (EDM) and rooted in Takens' theorem, DeepEDM presents a novel deep model that learns a latent space from time-delayed embeddings, and employs kernel regression to approximate the underlying dynamics, while leveraging efficient implementation of softmax attention and allowing for accurate prediction of future time steps. To evaluate our method, we conduct comprehensive experiments on synthetic data of nonlinear dynamical systems as well as real-world time series across domains. Our results show that DeepEDM is robust to input noise, and outperforms state-of-the-art methods in forecasting accuracy. Our code is available at: https://abrarmajeedi.github.io/deep_edm.

Paperid: 391, https://arxiv.org/pdf/2506.06444.pdf   GitHub GitHub

Authors:Ruizhong Qiu, Gaotang Li, Tianxin Wei, Jingrui He, Hanghang Tong

Title: Saffron-1: Safety Inference Scaling

Abstract:
Existing safety assurance research has primarily focused on training-phase alignment to instill safe behaviors into LLMs. However, recent studies have exposed these methods' susceptibility to diverse jailbreak attacks. Concurrently, inference scaling has significantly advanced LLM reasoning capabilities but remains unexplored in the context of safety assurance. Addressing this gap, our work pioneers inference scaling for robust and effective LLM safety against emerging threats. We reveal that conventional inference scaling techniques, despite their success in reasoning tasks, perform poorly in safety contexts, even falling short of basic approaches like Best-of-N Sampling. We attribute this inefficiency to a newly identified challenge, the exploration--efficiency dilemma, arising from the high computational overhead associated with frequent process reward model (PRM) evaluations. To overcome this dilemma, we propose SAFFRON, a novel inference scaling paradigm tailored explicitly for safety assurance. Central to our approach is the introduction of a multifurcation reward model (MRM) that significantly reduces the required number of reward model evaluations. To operationalize this paradigm, we further propose: (i) a partial supervision training objective for MRM, (ii) a conservative exploration constraint to prevent out-of-distribution explorations, and (iii) a Trie-based key--value caching strategy that facilitates cache sharing across sequences during tree search. Extensive experiments validate the effectiveness of our method. Additionally, we publicly release our trained multifurcation reward model (Saffron-1) and the accompanying token-level safety reward dataset (Safety4M) to accelerate future research in LLM safety. Our code, model, and data are publicly available at https://github.com/q-rz/saffron , and our project homepage is at https://q-rz.github.io/p/saffron .
中文: 现有大语言模型安全方法易受越狱攻击，因此本研究提出SAFFRON创新推理扩展框架，通过多叉奖励模型显著提升安全防御能力并降低计算开销。
English: Current safety methods for large language models are vulnerable to jailbreak attacks, so this research introduces SAFFRON, a novel inference scaling approach using a multifurcation reward model to enhance safety while reducing computational costs.

Paperid: 392, https://arxiv.org/pdf/2506.06443.pdf   GitHub

Authors:Luis Pinto

Title: Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers

Abstract:
Pretrained molecular encoders have become indispensable in computational chemistry for tasks such as property prediction and molecular generation. However, the standard practice of relying solely on final-layer embeddings for downstream tasks may discard valuable information. In this work, we challenge this convention by conducting a comprehensive layer-wise analysis of five diverse molecular encoders across 22 ADMET property prediction tasks. Our results demonstrate that embeddings from intermediate layers consistently outperform final-layer representations. Specifically, using fixed embeddings from the optimal intermediate layers improved downstream performance by an average of 5.4%, reaching gains up to 28.6%. Furthermore, finetuning up to these intermediate layers yielded even greater average improvements of 8.5%, with performance increases as high as 40.8%, achieving new state-of-the-art results on several benchmarks. Additionally, a strong positive correlation between fixed embedding performance and finetuning outcomes supports an efficient evaluate-then-finetune approach, enabling identification of optimal layers with reduced computational cost. These findings highlight the importance of exploring the full representational depth of molecular encoders to achieve substantial performance improvements and computational efficiency. The code is made publicly available at https://github.com/luispintoc/Unlocking-Chemical-Insights.
中文: 本研究表明，在ADMET性质预测中，分子编码器的中间层嵌入始终优于最终层表示，固定嵌入可将性能提升高达28.6%，微调更可实现40.8%的增益，同时强相关性为高效选择最优层提供了依据。
English: This study reveals that intermediate-layer embeddings from molecular encoders consistently outperform final-layer representations in ADMET property prediction, with fixed embeddings improving performance by up to 28.6% and fine-tuning achieving gains of up to 40.8%, while demonstrating a strong correlation that enables efficient layer selection.

Paperid: 393, https://arxiv.org/pdf/2506.06409.pdf   GitHub

Authors:Dor Tsur, Carol Xuan Long, Claudio Mayrink Verdun, Hsiang Hsu, Chen-Fu Chen, Haim Permuter, Sajani Vithana, Flavio P. Calmon

Title: HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions

Abstract:
Large language model (LLM) watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate by changing the next-token predictions output by an LLM. The updated (i.e., watermarked) predictions depend on random side information produced, for example, by hashing previously generated tokens. LLM watermarking is particularly challenging in low-entropy generation tasks - such as coding - where next-token predictions are near-deterministic. In this paper, we propose an optimization framework for watermark design. Our goal is to understand how to most effectively use random side information in order to maximize the likelihood of watermark detection and minimize the distortion of generated text. Our analysis informs the design of two new watermarks: HeavyWater and SimplexWater. Both watermarks are tunable, gracefully trading-off between detection accuracy and text distortion. They can also be applied to any LLM and are agnostic to side information generation. We examine the performance of HeavyWater and SimplexWater through several benchmarks, demonstrating that they can achieve high watermark detection accuracy with minimal compromise of text generation quality, particularly in the low-entropy regime. Our theoretical analysis also reveals surprising new connections between LLM watermarking and coding theory. The code implementation can be found in https://github.com/DorTsur/HeavyWater_SimplexWater
中文: 本文提出了一种可优化的水印设计框架，开发了HeavyWater和SimplexWater两种水印技术，能在保持文本质量的同时实现高检测精度，适用于各类语言模型和辅助信息生成方式。
English: This paper introduces an optimization framework for designing tunable LLM watermarks, HeavyWater and SimplexWater, which effectively balance detection accuracy and text quality while being applicable to any language model and side information generation method.

Paperid: 394, https://arxiv.org/pdf/2506.06337.pdf   GitHub

Authors:Ali Murad, Bo Hui, Wei-Shinn Ku

Title: Optimized Local Updates in Federated Learning via Reinforcement Learning

Abstract:
Federated Learning (FL) is a distributed framework for collaborative model training over large-scale distributed data, enabling higher performance while maintaining client data privacy. However, the nature of model aggregation at the centralized server can result in a performance drop in the presence of non-IID data across different clients. We remark that training a client locally on more data than necessary does not benefit the overall performance of all clients. In this paper, we devise a novel framework that leverages a Deep Reinforcement Learning (DRL) agent to select an optimized amount of data necessary to train a client model without oversharing information with the server. Starting without awareness of the client's performance, the DRL agent utilizes the change in training loss as a reward signal and learns to optimize the amount of training data necessary for improving the client's performance. Specifically, after each aggregation round, the DRL algorithm considers the local performance as the current state and outputs the optimized weights for each class, in the training data, to be used during the next round of local training. In doing so, the agent learns a policy that creates an optimized partition of the local training dataset during the FL rounds. After FL, the client utilizes the entire local training dataset to further enhance its performance on its own data distribution, mitigating the non-IID effects of aggregation. Through extensive experiments, we demonstrate that training FL clients through our algorithm results in superior performance on multiple benchmark datasets and FL frameworks. Our code is available at https://github.com/amuraddd/optimized_client_training.git.
中文: 联邦学习在非独立同分布数据下性能会下降，本文提出一种深度强化学习框架，通过优化本地训练数据量来提升客户端性能，同时保护数据隐私。
English: Federated Learning can suffer from performance degradation with non-IID data, but this paper introduces a Deep Reinforcement Learning framework that optimizes local data usage during training to enhance client performance while maintaining privacy.

Paperid: 395, https://arxiv.org/pdf/2506.06315.pdf   GitHub

Authors:Masoud Rahimi, Reza Karbasi, Abdol-Hossein Vahabie

Title: An Open-Source Python Framework and Synthetic ECG Image Datasets for Digitization, Lead and Lead Name Detection, and Overlapping Signal Segmentation

Abstract:
We introduce an open-source Python framework for generating synthetic ECG image datasets to advance critical deep learning-based tasks in ECG analysis, including ECG digitization, lead region and lead name detection, and pixel-level waveform segmentation. Using the PTB-XL signal dataset, our proposed framework produces four open-access datasets: (1) ECG images in various lead configurations paired with time-series signals for ECG digitization, (2) ECG images annotated with YOLO-format bounding boxes for detection of lead region and lead name, (3)-(4) cropped single-lead images with segmentation masks compatible with U-Net-based models in normal and overlapping versions. In the overlapping case, waveforms from neighboring leads are superimposed onto the target lead image, while the segmentation masks remain clean. The open-source Python framework and datasets are publicly available at https://github.com/rezakarbasi/ecg-image-and-signal-dataset and https://doi.org/10.5281/zenodo.15484519, respectively.
中文: 该开源Python框架基于PTB-XL信号生成合成心电图图像数据集，支持心电图数字化、导联区域识别和波形分割等深度学习任务，四类开放数据集可通过GitHub和Zenodo公开获取。
English: This open-source Python framework generates synthetic ECG image datasets from PTB-XL signals to support deep learning tasks like ECG digitization, lead detection, and waveform segmentation, with four publicly available datasets accessible via GitHub and Zenodo.

Paperid: 396, https://arxiv.org/pdf/2506.06310.pdf   GitHub

Authors:Xiaoyu Sun, Yang Yang, Xunde Dong

Title: Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue

Abstract:
In the field of automatic Electrocardiogram (ECG) diagnosis, due to the relatively limited amount of labeled data, how to build a robust ECG pretrained model based on unlabeled data is a key area of focus for researchers. Recent advancements in contrastive learning-based ECG pretrained models highlight the potential of exploiting the additional patient-level self-supervisory signals inherent in ECG. They are referred to as patient contrastive learning. Its rationale is that multiple physical recordings from the same patient may share commonalities, termed patient consistency, so redefining positive and negative pairs in contrastive learning as intrapatient and inter-patient samples provides more shared context to learn an effective representation. However, these methods still fail to efficiently exploit patient consistency due to the insufficient amount of intra-inter patient samples existing in a batch. Hence, we propose a contrastive learning-based ECG pretrained model enhanced by the Patient Memory Queue (PMQ), which incorporates a large patient memory queue to mitigate model degeneration that can arise from insufficient intra-inter patient samples. In order to further enhance the performance of the pretrained model, we introduce two extra data augmentation methods to provide more perspectives of positive and negative pairs for pretraining. Extensive experiments were conducted on three public datasets with three different data ratios. The experimental results show that the comprehensive performance of our method outperforms previous contrastive learning methods and exhibits greater robustness in scenarios with limited labeled data. The code is available at https://github.com/3hiuwoo/PMQ.
中文: 本研究提出了一种基于患者记忆队列增强的心电图对比学习预训练模型，通过解决批次内患者样本不足的问题提升了模型鲁棒性，在标注数据有限的情况下显著优于现有方法。
English: This study introduces a Patient Memory Queue-enhanced contrastive learning model for ECG diagnosis, which improves robustness by addressing insufficient intra-inter patient samples and outperforms previous methods in limited labeled data scenarios.

Paperid: 397, https://arxiv.org/pdf/2506.06293.pdf   GitHub

Authors:Junyi Liu, Stanley Kok

Title: Prediction of Bank Credit Ratings using Heterogeneous Topological Graph Neural Networks

Abstract:
Agencies such as Standard & Poor's and Moody's provide bank credit ratings that influence economic stability and decision-making by stakeholders. Accurate and timely predictions support informed decision-making, regulatory actions, and investor protection. However, a complete interbank connection graph is often unavailable due to privacy concerns, complicating the direct application of Graph Neural Networks (GNNs) for rating prediction. our research utilizes persistent homology to construct a network that captures relationships among banks and combines this with a traditional lending network to create a heterogeneous network that integrates information from both sources, leading to improved predictions. Experiments on a global, real-world dataset validate the effectiveness of HTGNN. This research has implications for investors and regulatory bodies in enhancing proactive risk mitigation and the implementation of effective market interventions.The code can be find at https://github.com/Liu-Jun-Yi/HTGNN.
Chinese: 本研究通过持久同调构建银行关系网络，并将其与借贷网络结合成异质结构，提升了银行信用评级预测的准确性，经真实数据验证有助于投资者和监管机构进行风险管理。
English: This study enhances bank credit rating predictions by using persistent homology to build a bank relationship network and combining it with a lending network into a heterogeneous structure, which is validated on real-world data to aid investors and regulators in risk management.

Paperid: 398, https://arxiv.org/pdf/2506.06231.pdf   GitHub

Authors:Mohammad Jalali, Bahar Dibaei Nia, Farzan Farnia

Title: Towards an Explainable Comparison and Alignment of Feature Embeddings

Abstract:
While several feature embedding models have been developed in the literature, comparisons of these embeddings have largely focused on their numerical performance in classification-related downstream applications. However, an interpretable comparison of different embeddings requires identifying and analyzing mismatches between sample groups clustered within the embedding spaces. In this work, we propose the \emph{Spectral Pairwise Embedding Comparison (SPEC)} framework to compare embeddings and identify their differences in clustering a reference dataset. Our approach examines the kernel matrices derived from two embeddings and leverages the eigendecomposition of the difference kernel matrix to detect sample clusters that are captured differently by the two embeddings. We present a scalable implementation of this kernel-based approach, with computational complexity that grows linearly with the sample size. Furthermore, we introduce an optimization problem using this framework to align two embeddings, ensuring that clusters identified in one embedding are also captured in the other model. We provide numerical results demonstrating the SPEC's application to compare and align embeddings on large-scale datasets such as ImageNet and MS-COCO. The project page is available at https://mjalali.github.io/SPEC/.

Paperid: 399, https://arxiv.org/pdf/2506.06192.pdf   GitHub

Authors:Dimitrios Proios, Alban Bornet, Anthony Yazdani, Jose F Rodrigues, Douglas Teodoro

Title: ICU-TSB: A Benchmark for Temporal Patient Representation Learning for Unsupervised Stratification into Patient Cohorts

Abstract:
Patient stratification identifying clinically meaningful subgroups is essential for advancing personalized medicine through improved diagnostics and treatment strategies. Electronic health records (EHRs), particularly those from intensive care units (ICUs), contain rich temporal clinical data that can be leveraged for this purpose. In this work, we introduce ICU-TSB (Temporal Stratification Benchmark), the first comprehensive benchmark for evaluating patient stratification based on temporal patient representation learning using three publicly available ICU EHR datasets. A key contribution of our benchmark is a novel hierarchical evaluation framework utilizing disease taxonomies to measure the alignment of discovered clusters with clinically validated disease groupings. In our experiments with ICU-TSB, we compared statistical methods and several recurrent neural networks, including LSTM and GRU, for their ability to generate effective patient representations for subsequent clustering of patient trajectories. Our results demonstrate that temporal representation learning can rediscover clinically meaningful patient cohorts; nevertheless, it remains a challenging task, with v-measuring varying from up to 0.46 at the top level of the taxonomy to up to 0.40 at the lowest level. To further enhance the practical utility of our findings, we also evaluate multiple strategies for assigning interpretable labels to the identified clusters. The experiments and benchmark are fully reproducible and available at https://github.com/ds4dh/CBMS2025stratification.
中文: ICU-TSB是首个基于ICU电子健康记录时序表征学习的患者分层综合基准，其分层评估框架表明时序模型能识别临床相关患者群组，但在聚类准确性方面仍存在挑战。
English: ICU-TSB is the first comprehensive benchmark for evaluating patient stratification using temporal representation learning from ICU EHR data, featuring a hierarchical evaluation framework that demonstrates temporal models can identify clinically meaningful patient cohorts while revealing persistent challenges in clustering accuracy.

Paperid: 400, https://arxiv.org/pdf/2506.06155.pdf   GitHub

Authors:Wenyuan Li, Shunlin Liang, Yuxiang Zhang, Liqin Liu, Keyan Chen, Yongzhe Chen, Han Ma, Jianglei Xu, Yichuan Ma, Shikang Guan, Zhenwei Shi

Title: Fine-grained Hierarchical Crop Type Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series: A Large-scale Dataset and Dual-stream Transformer Method

Abstract:
Fine-grained crop type classification serves as the fundamental basis for large-scale crop mapping and plays a vital role in ensuring food security. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining these two modalities remains scarce currently due to challenges in hyperspectral data acquisition and crop types annotation costs. To address these issues, we construct a hierarchical hyperspectral crop dataset (H2Crop) by integrating 30m-resolution EnMAP hyperspectral data with Sentinel-2 time series. With over one million annotated field parcels organized in a four-tier crop taxonomy, H2Crop establishes a vital benchmark for fine-grained agricultural crop classification and hyperspectral image processing. We propose a dual-stream Transformer architecture that synergistically processes these modalities. It coordinates two specialized pathways: a spectral-spatial Transformer extracts fine-grained signatures from hyperspectral EnMAP data, while a temporal Swin Transformer extracts crop growth patterns from Sentinel-2 time series. The designed hierarchical classification head with hierarchical fusion then simultaneously delivers multi-level crop type classification across all taxonomic tiers. Experiments demonstrate that adding hyperspectral EnMAP data to Sentinel-2 time series yields a 4.2% average F1-scores improvement (peaking at 6.3%). Extensive comparisons also confirm our method's higher accuracy over existing deep learning approaches for crop type classification and the consistent benefits of hyperspectral data across varying temporal windows and crop change scenarios. Codes and dataset are available at https://github.com/flyakon/H2Crop.
中文: 本研究提出了H2Crop分层高光谱作物数据集及双流Transformer模型，通过协同处理高光谱与多时相卫星数据，显著提升了细粒度作物分类精度，相关代码与数据已开源。
English: This study introduces H2Crop, a hierarchical hyperspectral crop dataset, and a dual-stream Transformer model that synergistically combines hyperspectral and multi-temporal satellite data to significantly improve fine-grained crop classification accuracy, with codes and data publicly available.

Paperid: 401, https://arxiv.org/pdf/2506.06114.pdf   GitHub

Authors:Xudong Zhang, Renato Cordeiro de Amorim

Title: Scalable unsupervised feature selection via weight stability

Abstract:
Unsupervised feature selection is critical for improving clustering performance in high-dimensional data, where irrelevant features can obscure meaningful structure. In this work, we introduce the Minkowski weighted $k$-means++, a novel initialisation strategy for the Minkowski Weighted $k$-means. Our initialisation selects centroids probabilistically using feature relevance estimates derived from the data itself. Building on this, we propose two new feature selection algorithms, FS-MWK++, which aggregates feature weights across a range of Minkowski exponents to identify stable and informative features, and SFS-MWK++, a scalable variant based on subsampling. We support our approach with a theoretical guarantee under mild assumptions and extensive experiments showing that our methods consistently outperform existing alternatives. Our software can be found at https://github.com/xzhang4-ops1/FSMWK.
中文: 本文提出了Minkowski加权$k$-means++初始化方法及两种基于特征相关性的特征选择算法，通过理论保证和实验验证了其在提升高维数据聚类性能上的显著优势。
English: This paper introduces Minkowski weighted $k$-means++ initialization and two feature selection algorithms that leverage feature relevance to enhance clustering in high-dimensional data, supported by theoretical guarantees and superior experimental results.

Paperid: 402, https://arxiv.org/pdf/2506.06112.pdf   GitHub

Authors:Cheng-Long Wang, Qi Li, Zihang Xiang, Yinzhi Cao, Di Wang

Title: Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness

Abstract:
Growing concerns over data privacy and security highlight the importance of machine unlearning--removing specific data influences from trained models without full retraining. Techniques like Membership Inference Attacks (MIAs) are widely used to externally assess successful unlearning. However, existing methods face two key limitations: (1) maximizing MIA effectiveness (e.g., via online attacks) requires prohibitive computational resources, often exceeding retraining costs; (2) MIAs, designed for binary inclusion tests, struggle to capture granular changes in approximate unlearning. To address these challenges, we propose the Interpolated Approximate Measurement (IAM), a framework natively designed for unlearning inference. IAM quantifies sample-level unlearning completeness by interpolating the model's generalization-fitting behavior gap on queried samples. IAM achieves strong performance in binary inclusion tests for exact unlearning and high correlation for approximate unlearning--scalable to LLMs using just one pre-trained shadow model. We theoretically analyze how IAM's scoring mechanism maintains performance efficiently. We then apply IAM to recent approximate unlearning algorithms, revealing general risks of both over-unlearning and under-unlearning, underscoring the need for stronger safeguards in approximate unlearning systems. The code is available at https://github.com/Happy2Git/Unlearning_Inference_IAM.
中文摘要：提出的插值近似测量（IAM）框架通过泛化拟合差距量化样本级遗忘完整性，有效评估机器遗忘效果，克服了现有方法的计算和粒度限制，并揭示了近似遗忘系统中的普遍风险。
English Summary: The proposed Interpolated Approximate Measurement (IAM) framework efficiently evaluates machine unlearning by quantifying sample-level completeness through generalization-fitting gaps, overcoming computational and granular limitations of existing methods while revealing risks in approximate unlearning systems.

Paperid: 403, https://arxiv.org/pdf/2506.06105.pdf   GitHub

Authors:Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, Robert Tjarko Lange

Title: Text-to-LoRA: Instant Transformer Adaption

Abstract:
While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation of datasets and repeated fine-tuning of the underlying model. Fine-tuning techniques enable practitioners to adapt foundation models for many new applications but require expensive and lengthy training while being notably sensitive to hyperparameter choices. To overcome these limitations, we introduce Text-to-LoRA (T2L), a model capable of adapting large language models (LLMs) on the fly solely based on a natural language description of the target task. T2L is a hypernetwork trained to construct LoRAs in a single inexpensive forward pass. After training T2L on a suite of 9 pre-trained LoRA adapters (GSM8K, Arc, etc.), we show that the ad-hoc reconstructed LoRA instances match the performance of task-specific adapters across the corresponding test sets. Furthermore, T2L can compress hundreds of LoRA instances and zero-shot generalize to entirely unseen tasks. This approach provides a significant step towards democratizing the specialization of foundation models and enables language-based adaptation with minimal compute requirements. Our code is available at https://github.com/SakanaAI/text-to-lora
基础模型通常需要针对特定任务进行微调，这一过程成本高昂且对设置敏感，而Text-to-LoRA (T2L) 通过自然语言描述实现即时适配，能以极低计算量生成高效的LoRA适配器。
Foundation models often need task-specific fine-tuning, which is costly and sensitive to settings, but Text-to-LoRA (T2L) enables on-the-fly adaptation using natural language descriptions, generating effective LoRA adapters with minimal computational effort.

Paperid: 404, https://arxiv.org/pdf/2506.06069.pdf   GitHub

Authors:Maor Ashkenazi, Ofir Brenner, Tal Furman Shohet, Eran Treister

Title: Zero-Shot Detection of LLM-Generated Code via Approximated Task Conditioning

Abstract:
Detecting Large Language Model (LLM)-generated code is a growing challenge with implications for security, intellectual property, and academic integrity. We investigate the role of conditional probability distributions in improving zero-shot LLM-generated code detection, when considering both the code and the corresponding task prompt that generated it. Our key insight is that when evaluating the probability distribution of code tokens using an LLM, there is little difference between LLM-generated and human-written code. However, conditioning on the task reveals notable differences. This contrasts with natural language text, where differences exist even in the unconditional distributions. Leveraging this, we propose a novel zero-shot detection approach that approximates the original task used to generate a given code snippet and then evaluates token-level entropy under the approximated task conditioning (ATC). We further provide a mathematical intuition, contextualizing our method relative to previous approaches. ATC requires neither access to the generator LLM nor the original task prompts, making it practical for real-world applications. To the best of our knowledge, it achieves state-of-the-art results across benchmarks and generalizes across programming languages, including Python, CPP, and Java. Our findings highlight the importance of task-level conditioning for LLM-generated code detection. The supplementary materials and code are available at https://github.com/maorash/ATC, including the dataset gathering implementation, to foster further research in this area.
中文: 本研究提出了一种新颖的零样本检测方法，通过评估近似任务条件下的标记熵来区分LLM生成的代码与人工编写的代码，该方法无需访问生成模型或原始提示，就在多种编程语言中实现了最优性能。
English: This study introduces a novel zero-shot detection method that leverages task-level conditioning to distinguish LLM-generated code from human-written code by evaluating token entropy under approximated task conditions, achieving state-of-the-art performance across multiple programming languages without requiring access to the generator model or original prompts.

Paperid: 405, https://arxiv.org/pdf/2506.06042.pdf   GitHub

Authors:Taoran Yue, Xiaojin Lu, Jiaxi Cai, Yuanping Chen, Shibing Chu

Title: SDS-Net: Shallow-Deep Synergism-detection Network for infrared small target detection

Abstract:
Current CNN-based infrared small target detection(IRSTD) methods generally overlook the heterogeneity between shallow and deep features, leading to inefficient collaboration between shallow fine grained structural information and deep high-level semantic representations. Additionally, the dependency relationships and fusion mechanisms across different feature hierarchies lack systematic modeling, which fails to fully exploit the complementarity of multilevel features. These limitations hinder IRSTD performance while incurring substantial computational costs. To address these challenges, this paper proposes a shallow-deep synergistic detection network (SDS-Net) that efficiently models multilevel feature representations to increase both the detection accuracy and computational efficiency in IRSTD tasks. SDS-Net introduces a dual-branch architecture that separately models the structural characteristics and semantic properties of features, effectively preserving shallow spatial details while capturing deep semantic representations, thereby achieving high-precision detection with significantly improved inference speed. Furthermore, the network incorporates an adaptive feature fusion module to dynamically model cross-layer feature correlations, enhancing overall feature collaboration and representation capability. Comprehensive experiments on three public datasets (NUAA-SIRST, NUDT-SIRST, and IRSTD-1K) demonstrate that SDS-Net outperforms state-of-the-art IRSTD methods while maintaining low computational complexity and high inference efficiency, showing superior detection performance and broad application prospects. Our code will be made public at https://github.com/PhysiLearn/SDS-Net.
中文: 本文提出的SDS-Net通过双分支结构和自适应特征融合模块，有效协同浅层与深层特征，在提升红外小目标检测精度的同时显著提高了计算效率。
English: This paper introduces SDS-Net, a dual-branch network that effectively models shallow and deep features through adaptive fusion, enhancing infrared small target detection accuracy and efficiency while reducing computational costs.

Paperid: 406, https://arxiv.org/pdf/2506.06041.pdf   GitHub

Authors:Joscha Diehl, Rasheed Ibraheem, Leonard Schmitz, Yue Wu

Title: Tensor-to-Tensor Models with Fast Iterated Sum Features

Abstract:
Data in the form of images or higher-order tensors is ubiquitous in modern deep learning applications. Owing to their inherent high dimensionality, the need for subquadratic layers processing such data is even more pressing than for sequence data. We propose a novel tensor-to-tensor layer with linear cost in the input size, utilizing the mathematical gadget of ``corner trees'' from the field of permutation counting. In particular, for order-two tensors, we provide an image-to-image layer that can be plugged into image processing pipelines. On the one hand, our method can be seen as a higher-order generalization of state-space models. On the other hand, it is based on a multiparameter generalization of the signature of iterated integrals (or sums). The proposed tensor-to-tensor concept is used to build a neural network layer called the Fast Iterated Sums (FIS) layer which integrates seamlessly with other layer types. We demonstrate the usability of the FIS layer with both classification and anomaly detection tasks. By replacing some layers of a smaller ResNet architecture with FIS, a similar accuracy (with a difference of only 0.1\%) was achieved in comparison to a larger ResNet while reducing the number of trainable parameters and multi-add operations. The FIS layer was also used to build an anomaly detection model that achieved an average AUROC of 97.3\% on the texture images of the popular MVTec AD dataset. The processing and modelling codes are publicly available at https://github.com/diehlj/fast-iterated-sums.
中文摘要：本文提出了一种基于排列计数中“角树”数学工具的新型张量到张量线性计算层，该层既是状态空间模型的高阶推广，也是迭代积分的多参数扩展，在保持与大型网络相近精度的同时显著减少了参数数量和计算量。
English Summary: This paper introduces a novel tensor-to-tensor layer with linear computational cost using "corner trees" from permutation counting, which serves as both a higher-order generalization of state-space models and a multiparameter extension of iterated integrals, achieving comparable accuracy to larger networks while reducing parameters and computational operations.

Paperid: 407, https://arxiv.org/pdf/2506.06027.pdf   GitHub

Authors:Yuhao Sun, Jiacheng Zhang, Zesheng Ye, Chaowei Xiao, Feng Liu

Title: Sample-Specific Noise Injection For Diffusion-Based Adversarial Purification

Abstract:
Diffusion-based purification (DBP) methods aim to remove adversarial noise from the input sample by first injecting Gaussian noise through a forward diffusion process, and then recovering the clean example through a reverse generative process. In the above process, how much Gaussian noise is injected to the input sample is key to the success of DBP methods, which is controlled by a constant noise level $t^*$ for all samples in existing methods. In this paper, we discover that an optimal $t^*$ for each sample indeed could be different. Intuitively, the cleaner a sample is, the less the noise it should be injected, and vice versa. Motivated by this finding, we propose a new framework, called Sample-specific Score-aware Noise Injection (SSNI). Specifically, SSNI uses a pre-trained score network to estimate how much a data point deviates from the clean data distribution (i.e., score norms). Then, based on the magnitude of score norms, SSNI applies a reweighting function to adaptively adjust $t^*$ for each sample, achieving sample-specific noise injections. Empirically, incorporating our framework with existing DBP methods results in a notable improvement in both accuracy and robustness on CIFAR-10 and ImageNet-1K, highlighting the necessity to allocate distinct noise levels to different samples in DBP methods. Our code is available at: https://github.com/tmlr-group/SSNI.
中文: 本文提出SSNI框架，通过预训练评分网络评估样本偏离干净数据分布的程度，自适应调整每个样本的噪声注入水平，在CIFAR-10和ImageNet-1K数据集上显著提升了基于扩散的净化方法的准确性和鲁棒性。
English: The paper introduces SSNI, a framework that adaptively adjusts noise injection levels in diffusion-based purification methods based on each sample's deviation from the clean data distribution, improving accuracy and robustness on benchmark datasets.

Paperid: 408, https://arxiv.org/pdf/2506.06021.pdf   GitHub

Authors:Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu

Title: Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems

Abstract:
Multi-solid systems are foundational to a wide range of real-world applications, yet modeling their complex interactions remains challenging. Existing deep learning methods predominantly rely on implicit modeling, where the factors influencing solid deformation are not explicitly represented but are instead indirectly learned. However, as the number of solids increases, these methods struggle to accurately capture intricate physical interactions. In this paper, we introduce a novel explicit modeling paradigm that incorporates factors influencing solid deformation through structured modules. Specifically, we present Unisoma, a unified and flexible Transformer-based model capable of handling variable numbers of solids. Unisoma directly captures physical interactions using contact modules and adaptive interaction allocation mechanism, and learns the deformation through a triplet relationship. Compared to implicit modeling techniques, explicit modeling is more well-suited for multi-solid systems with diverse coupling patterns, as it enables detailed treatment of each solid while preventing information blending and confusion. Experimentally, Unisoma achieves consistent state-of-the-art performance across seven well-established datasets and two complex multi-solid tasks. Code is avaiable at https://github.com/therontau0054/Unisoma.
中文: 本文提出Unisoma，一种基于Transformer的显式建模方法，通过结构化模块精确捕捉多固体系统中的物理相互作用，在多个数据集和复杂任务中均实现了最先进的性能。
English: This paper introduces Unisoma, an explicit Transformer-based model that accurately captures physical interactions in multi-solid systems through structured modules, achieving state-of-the-art performance across diverse datasets and tasks.

Paperid: 409, https://arxiv.org/pdf/2506.05968.pdf   GitHub GitHub

Authors:Motoki Omura, Kazuki Ota, Takayuki Osa, Yusuke Mukuta, Tatsuya Harada

Title: Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning

Abstract:
For continuous action spaces, actor-critic methods are widely used in online reinforcement learning (RL). However, unlike RL algorithms for discrete actions, which generally model the optimal value function using the Bellman optimality operator, RL algorithms for continuous actions typically model Q-values for the current policy using the Bellman operator. These algorithms for continuous actions rely exclusively on policy updates for improvement, which often results in low sample efficiency. This study examines the effectiveness of incorporating the Bellman optimality operator into actor-critic frameworks. Experiments in a simple environment show that modeling optimal values accelerates learning but leads to overestimation bias. To address this, we propose an annealing approach that gradually transitions from the Bellman optimality operator to the Bellman operator, thereby accelerating learning while mitigating bias. Our method, combined with TD3 and SAC, significantly outperforms existing approaches across various locomotion and manipulation tasks, demonstrating improved performance and robustness to hyperparameters related to optimality. The code for this study is available at https://github.com/motokiomura/annealed-q-learning.
Chinese: 本研究提出了一种退火方法，在连续动作空间的行动者-评论家框架中从贝尔曼最优算子过渡到贝尔曼算子，既加速了学习过程又缓解了高估偏差，在各种任务中显著提升了性能和鲁棒性。
English: This study introduces an annealing approach that transitions from the Bellman optimality operator to the Bellman operator in actor-critic frameworks for continuous action spaces, accelerating learning while mitigating overestimation bias and significantly improving performance and robustness in various tasks.

Paperid: 410, https://arxiv.org/pdf/2506.05957.pdf   GitHub

Authors:Tianjun Yao, Haoxuan Li, Yongqiang Chen, Tongliang Liu, Le Song, Eric Xing, Zhiqiang Shen

Title: Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization

Abstract:
Graph Neural Networks (GNNs) often encounter significant performance degradation under distribution shifts between training and test data, hindering their applicability in real-world scenarios. Recent studies have proposed various methods to address the out-of-distribution generalization challenge, with many methods in the graph domain focusing on directly identifying an invariant subgraph that is predictive of the target label. However, we argue that identifying the edges from the invariant subgraph directly is challenging and error-prone, especially when some spurious edges exhibit strong correlations with the targets. In this paper, we propose PrunE, the first pruning-based graph OOD method that eliminates spurious edges to improve OOD generalizability. By pruning spurious edges, PrunE retains the invariant subgraph more comprehensively, which is critical for OOD generalization. Specifically, PrunE employs two regularization terms to prune spurious edges: 1) graph size constraint to exclude uninformative spurious edges, and 2) $ε$-probability alignment to further suppress the occurrence of spurious edges. Through theoretical analysis and extensive experiments, we show that PrunE achieves superior OOD performance and outperforms previous state-of-the-art methods significantly. Codes are available at: \href{https://github.com/tianyao-aka/PrunE-GraphOOD}{https://github.com/tianyao-aka/PrunE-GraphOOD}.
Chinese: 本文提出PrunE方法，通过图大小约束和ε概率对齐剪除虚假边，提升图神经网络在分布外场景下的泛化能力，实验证明其显著优于现有最优方法。
English: The paper introduces PrunE, a novel pruning-based method that enhances out-of-distribution generalization in Graph Neural Networks by eliminating spurious edges through graph size constraints and ε-probability alignment, achieving superior performance over existing approaches.

Paperid: 411, https://arxiv.org/pdf/2506.05895.pdf   GitHub

Authors:Adrien Petralia, Paul Boniol, Philippe Charpentier, Themis Palpanas

Title: Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series

Abstract:
Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for electricity suppliers. In this regard, millions of smart meters have been deployed worldwide in the last decade, recording the main electricity power consumed in individual households. This data produces valuable information that can help them reduce their electricity footprint; nevertheless, the collected signal aggregates the consumption of the different appliances running simultaneously in the house, making it difficult to apprehend. Non-Intrusive Load Monitoring (NILM) refers to the challenge of estimating the power consumption, pattern, or on/off state activation of individual appliances using the main smart meter signal. Recent methods proposed to tackle this task are based on a fully supervised deep-learning approach that requires both the aggregate signal and the ground truth of individual appliance power. However, such labels are expensive to collect and extremely scarce in practice, as they require conducting intrusive surveys in households to monitor each appliance. In this paper, we introduce CamAL, a weakly supervised approach for appliance pattern localization that only requires information on the presence of an appliance in a household to be trained. CamAL merges an ensemble of deep-learning classifiers combined with an explainable classification method to be able to localize appliance patterns. Our experimental evaluation, conducted on 4 real-world datasets, demonstrates that CamAL significantly outperforms existing weakly supervised baselines and that current SotA fully supervised NILM approaches require significantly more labels to reach CamAL performances. The source of our experiments is available at: https://github.com/adrienpetralia/CamAL. This paper appeared in ICDE 2025.
中文摘要：提升智能电网管理对应对气候变化至关重要，本文提出CamAL弱监督方法，仅需家电存在信息即可定位用电模式，在减少标注需求的同时显著优于现有技术。
English Summary: Improving smart grid management is vital for climate action, and this paper introduces CamAL, a weakly supervised method that localizes appliance usage patterns using only household presence data, outperforming existing approaches with fewer labels.

Paperid: 412, https://arxiv.org/pdf/2506.05867.pdf   GitHub

Authors:Zhixiong Zhuang, Hui-Po Wang, Maria-Irina Nicolae, Mario Fritz

Title: Stealix: Model Stealing via Prompt Evolution

Abstract:
Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information. Recent methods that use pre-trained diffusion models for data synthesis improve efficiency and performance but rely heavily on manually crafted prompts, limiting automation and scalability, especially for attackers with little expertise. To assess the risks posed by open-source pre-trained models, we propose a more realistic threat model that eliminates the need for prompt design skills or knowledge of class names. In this context, we introduce Stealix, the first approach to perform model stealing without predefined prompts. Stealix uses two open-source pre-trained models to infer the victim model's data distribution, and iteratively refines prompts through a genetic algorithm, progressively improving the precision and diversity of synthetic images. Our experimental results demonstrate that Stealix significantly outperforms other methods, even those with access to class names or fine-grained prompts, while operating under the same query budget. These findings highlight the scalability of our approach and suggest that the risks posed by pre-trained generative models in model stealing may be greater than previously recognized.

Paperid: 413, https://arxiv.org/pdf/2506.05857.pdf   GitHub

Authors:Junpeng Lin, Tian Lan, Bo Zhang, Ke Lin, Dandan Miao, Huiru He, Jiantao Ye, Chen Zhang, Yan-fu Li

Title: Wavelet-based Disentangled Adaptive Normalization for Non-stationary Times Series Forecasting

Abstract:
Forecasting non-stationary time series is a challenging task because their statistical properties often change over time, making it hard for deep models to generalize well. Instance-level normalization techniques can help address shifts in temporal distribution. However, most existing methods overlook the multi-component nature of time series, where different components exhibit distinct non-stationary behaviors. In this paper, we propose Wavelet-based Disentangled Adaptive Normalization (WDAN), a model-agnostic framework designed to address non-stationarity in time series forecasting. WDAN uses discrete wavelet transforms to break down the input into low-frequency trends and high-frequency fluctuations. It then applies tailored normalization strategies to each part. For trend components that exhibit strong non-stationarity, we apply first-order differencing to extract stable features used for predicting normalization parameters. Extensive experiments on multiple benchmarks demonstrate that WDAN consistently improves forecasting accuracy across various backbone model. Code is available at this repository: https://github.com/MonBG/WDAN.
中文: 本文提出WDAN框架，通过小波变换将时间序列分解为趋势和波动分量，并针对各分量应用定制化归一化策略，有效处理非平稳性以提升预测精度。
English: This paper introduces WDAN, a model-agnostic framework that uses wavelet transforms to decompose time series into trends and fluctuations, applying customized normalization to each component to enhance forecasting accuracy by addressing non-stationarity.

Paperid: 414, https://arxiv.org/pdf/2506.05831.pdf   GitHub

Authors:Yihan Xie, Sijing Li, Tianwei Lin, Zhuonan Wang, Chenglin Yang, Yu Zhong, Wenqiao Zhang, Haoyuan Li, Hao Jiang, Fengda Zhang, Qishan Chen, Jun Xiao, Yueting Zhuang, Beng Chin Ooi

Title: Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling

Abstract:
We present Heartcare Suite, a multimodal comprehensive framework for finegrained electrocardiogram (ECG) understanding. It comprises three key components: (i) Heartcare-220K, a high-quality, structured, and comprehensive multimodal ECG dataset covering essential tasks such as disease diagnosis, waveform morphology analysis, and rhythm interpretation. (ii) Heartcare-Bench, a systematic and multi-dimensional benchmark designed to evaluate diagnostic intelligence and guide the optimization of Medical Multimodal Large Language Models (Med-MLLMs) in ECG scenarios. and (iii) HeartcareGPT with a tailored tokenizer Bidirectional ECG Abstract Tokenization (Beat), which compresses raw multi-lead signals into semantically rich discrete tokens via duallevel vector quantization and query-guided bidirectional diffusion mechanism. Built upon Heartcare-220K, HeartcareGPT achieves strong generalization and SoTA performance across multiple clinically meaningful tasks. Extensive experiments demonstrate that Heartcare Suite is highly effective in advancing ECGspecific multimodal understanding and evaluation. Our project is available at https://github.com/DCDmllm/Heartcare-Suite .
中文：Heartcare Suite 是一个用于精细心电图分析的多模态框架，包含全面数据集 Heartcare-220K、评估基准 Heartcare-Bench 以及采用定制分词器的 HeartcareGPT，在临床任务中实现了顶尖性能。
English: Heartcare Suite is a multimodal framework for detailed ECG analysis, featuring a comprehensive dataset (Heartcare-220K), an evaluation benchmark (Heartcare-Bench), and HeartcareGPT with specialized tokenization that achieves state-of-the-art performance in clinical tasks.

Paperid: 415, https://arxiv.org/pdf/2506.05814.pdf   GitHub

Authors:Yogesh Verma, Amauri H. Souza, Vikas Garg

Title: Positional Encoding meets Persistent Homology on Graphs

Abstract:
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional encoding (PE) and Persistent Homology (PH) have emerged as two promising approaches to mitigate this issue. PE schemes endow GNNs with location-aware features, while PH methods enhance GNNs with multiresolution topological features. However, a rigorous theoretical characterization of the relative merits and shortcomings of PE and PH has remained elusive. We bridge this gap by establishing that neither paradigm is more expressive than the other, providing novel constructions where one approach fails but the other succeeds. Our insights inform the design of a novel learnable method, PiPE (Persistence-informed Positional Encoding), which is provably more expressive than both PH and PE. PiPE demonstrates strong performance across a variety of tasks (e.g., molecule property prediction, graph classification, and out-of-distribution generalization), thereby advancing the frontiers of graph representation learning. Code is available at https://github.com/Aalto-QuML/PIPE.
中文摘要：研究表明位置编码和持续同调在图神经网络中表达能力相当，由此开发的PiPE新方法在多项任务中超越了二者的性能。
English Summary: The study demonstrates that positional encoding and persistent homology are equally expressive for graph neural networks, leading to the development of PiPE, a novel method that surpasses both in performance across various tasks.

Paperid: 416, https://arxiv.org/pdf/2506.05758.pdf   GitHub

Authors:Guang-Xing Li

Title: Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery

Abstract:
The development of science has been transforming man's view towards nature for centuries. Observing structures and patterns in an effective approach to discover regularities from data is a key step toward theory-building. With increasingly complex data being obtained, revealing regularities systematically has become a challenge. Correlation is a most commonly-used and effective approach to describe regularities in data, yet for complex patterns, spatial inhomogeneity and complexity can often undermine the correlations. We present an algorithm to derive maps representing the type and degree of correlations, by taking the two-fold symmetry of the correlation vector into full account using the Stokes parameter. The method allows for a spatially resolved view of the nature and strength of correlations between physical quantities. In the correlation view, a region can often be separated into different subregions with different types of correlations. Subregions correspond to physical regimes for physical systems, or climate zones for climate maps. The simplicity of the method makes it widely applicable to a variety of data, where the correlation-based approach makes the map particularly useful in revealing regularities in physical systems and alike. As a new and efficient approach to represent data, the method should facilitate the development of new computational approaches to regularity discovery.
中文: 该研究提出一种算法，利用斯托克斯参数生成反映复杂数据中相关类型与强度的图谱，能实现模式的空间解析和子区域划分，有助于在多个领域提升规律发现的效率。
English: The study introduces an algorithm that uses the Stokes parameter to generate maps depicting the type and strength of correlations in complex data, enabling spatial resolution of patterns and subregions for improved regularity discovery in various fields.

Paperid: 417, https://arxiv.org/pdf/2506.05735.pdf   GitHub

Authors:Rongzhe Wei, Peizhi Niu, Hans Hao-Hsun Hsu, Ruihan Wu, Haoteng Yin, Yifan Li, Eli Chien, Kamalika Chaudhuri, Olgica Milenkovic, Pan Li

Title: Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness

Abstract:
Machine unlearning techniques aim to mitigate unintended memorization in large language models (LLMs). However, existing approaches predominantly focus on the explicit removal of isolated facts, often overlooking latent inferential dependencies and the non-deterministic nature of knowledge within LLMs. Consequently, facts presumed forgotten may persist implicitly through correlated information. To address these challenges, we propose a knowledge unlearning evaluation framework that more accurately captures the implicit structure of real-world knowledge by representing relevant factual contexts as knowledge graphs with associated confidence scores. We further develop an inference-based evaluation protocol leveraging powerful LLMs as judges; these judges reason over the extracted knowledge subgraph to determine unlearning success. Our LLM judges utilize carefully designed prompts and are calibrated against human evaluations to ensure their trustworthiness and stability. Extensive experiments on our newly constructed benchmark demonstrate that our framework provides a more realistic and rigorous assessment of unlearning performance. Moreover, our findings reveal that current evaluation strategies tend to overestimate unlearning effectiveness. Our code is publicly available at https://github.com/Graph-COM/Knowledge_Unlearning.git.
中文摘要：本文提出了一种知识遗忘评估框架，通过知识图谱和大型语言模型作为评判者，更准确地评估大语言模型中已遗忘事实的隐性存留，发现现有方法高估了遗忘效果。
English Summary: This paper introduces a knowledge unlearning evaluation framework that uses knowledge graphs and LLM judges to more accurately assess the implicit persistence of forgotten facts in large language models, revealing that current methods overestimate unlearning effectiveness.

Paperid: 418, https://arxiv.org/pdf/2506.05725.pdf   GitHub

Authors:Fang Wu, Vijay Prakash Dwivedi, Jure Leskovec

Title: Large Language Models are Good Relational Learners

Abstract:
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, yet their application to relational deep learning (RDL) remains underexplored. Existing approaches adapt LLMs by traversing relational links between entities in a database and converting the structured data into flat text documents. Still, this text-based serialization disregards critical relational structures, introduces redundancy, and often exceeds standard LLM context lengths. We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for LLMs within a retrieval-augmented generation (RAG) framework. Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to effectively process and reason over complex entity relationships. Specifically, the GNN encoder extracts a local subgraph around an entity to build feature representations that contain relevant entity relationships and temporal dependencies. These representations are transformed into structured prompts using a denormalization process, effectively allowing the LLM to reason over relational structures. Through extensive experiments, we demonstrate that Rel-LLM outperforms existing methods on key RDL tasks, offering a scalable and efficient approach to integrating LLMs with structured data sources. Code is available at https://github.com/smiles724/Rel-LLM.
Chinese: Rel-LLM提出了一种新颖架构，通过基于图神经网络的编码器在检索增强生成框架中生成结构化关系提示，既保留了数据库的内在关系结构，又使大语言模型能有效处理复杂实体关系，在关键关系深度学习任务上超越了现有方法。
English: Rel-LLM introduces a novel architecture that uses a GNN-based encoder to generate structured relational prompts within a RAG framework, preserving database structures and enabling LLMs to effectively reason over complex entity relationships, outperforming existing methods on key RDL tasks.

Paperid: 419, https://arxiv.org/pdf/2506.05721.pdf   GitHub

Authors:Dumindu Tissera, Omar Awadallah, Muhammad Umair Danish, Ayan Sadhu, Katarina Grolinger

Title: Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data

Abstract:
Multi-label Classification (MLC) assigns an instance to one or more non-exclusive classes. A challenge arises when the dataset contains a large proportion of instances with no assigned class, referred to as negative data, which can overwhelm the learning process and hinder the accurate identification and classification of positive instances. Nevertheless, it is common in MLC applications such as industrial defect detection, agricultural disease identification, and healthcare diagnosis to encounter large amounts of negative data. Assigning a separate negative class to these instances further complicates the learning objective and introduces unnecessary redundancies. To address this challenge, we redesign standard MLC loss functions by deriving a likelihood of any class being present, formulated by a normalized weighted geometric mean of the predicted class probabilities. We introduce a regularization parameter that controls the relative contribution of the absent class probabilities to the any-class presence likelihood in positive instances. The any-class presence likelihood complements the multi-label learning by encouraging the network to become more aware of implicit positive instances and improve the label classification within those positive instances. Experiments on large-scale datasets with negative data: SewerML, modified COCO, and ChestX-ray14, across various networks and base loss functions show that our loss functions consistently improve MLC performance of their standard loss counterparts, achieving gains of up to 6.01 percentage points in F1, 8.06 in F2, and 3.11 in mean average precision, all without additional parameters or computational complexity. Code available at: https://github.com/ML-for-Sensor-Data-Western/gmean-mlc
中文摘要：本研究通过归一化加权几何均值重构多标签分类损失函数，有效处理负数据，在不增加参数或计算复杂度的情况下提升了分类性能。
English Summary: This study redesigns standard multi-label classification loss functions using a normalized weighted geometric mean to handle negative data, improving performance without extra parameters or computational cost.

Paperid: 420, https://arxiv.org/pdf/2506.05713.pdf   GitHub GitHub

Authors:Zhan Zhuang, Xiequn Wang, Wei Li, Yulong Zhang, Qiushi Huang, Shuhao Chen, Xuehao Wang, Yanbin Wei, Yuhe Nie, Kede Ma, Yu Zhang, Ying Wei

Title: Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation

Abstract:
Low-rank adaptation (LoRA) has emerged as a leading parameter-efficient fine-tuning technique for adapting large foundation models, yet it often locks adapters into suboptimal minima near their initialization. This hampers model generalization and limits downstream operators such as adapter merging and pruning. Here, we propose CoTo, a progressive training strategy that gradually increases adapters' activation probability over the course of fine-tuning. By stochastically deactivating adapters, CoTo encourages more balanced optimization and broader exploration of the loss landscape. We provide a theoretical analysis showing that CoTo promotes layer-wise dropout stability and linear mode connectivity, and we adopt a cooperative-game approach to quantify each adapter's marginal contribution. Extensive experiments demonstrate that CoTo consistently boosts single-task performance, enhances multi-task merging accuracy, improves pruning robustness, and reduces training overhead, all while remaining compatible with diverse LoRA variants. Code is available at https://github.com/zwebzone/coto.
中文: CoTo是一种渐进式训练策略，通过逐步提高适配器激活概率来增强LoRA，促进更均衡的优化和更广泛的损失空间探索，从而提升模型泛化能力和下游任务表现。
English: CoTo is a progressive training strategy that enhances LoRA by gradually increasing adapter activation probability, promoting balanced optimization and broader loss landscape exploration to improve generalization and downstream task performance.

Paperid: 421, https://arxiv.org/pdf/2506.05688.pdf   GitHub

Authors:Keinichi Fujita, Shota Horiguchi, Yusuke Ijima

Title: Voice Impression Control in Zero-Shot TTS

Abstract:
Para-/non-linguistic information in speech is pivotal in shaping the listeners' impression. Although zero-shot text-to-speech (TTS) has achieved high speaker fidelity, modulating subtle para-/non-linguistic information to control perceived voice characteristics, i.e., impressions, remains challenging. We have therefore developed a voice impression control method in zero-shot TTS that utilizes a low-dimensional vector to represent the intensities of various voice impression pairs (e.g., dark-bright). The results of both objective and subjective evaluations have demonstrated our method's effectiveness in impression control. Furthermore, generating this vector via a large language model enables target-impression generation from a natural language description of the desired impression, thus eliminating the need for manual optimization. Audio examples are available on our demo page (https://ntt-hilab-gensp.github.io/is2025voiceimpression/).

Paperid: 422, https://arxiv.org/pdf/2506.05664.pdf   GitHub

Authors:Chao Zhang, Li Wang, Samson Lasaulce, Merouane Debbah

Title: BAQ: Efficient Bit Allocation Quantization for Large Language Models

Abstract:
Post-training model quantization is a widely adopted technique for reducing the memory and computational costs of large language models (LLMs). However, most existing methods rely on uniform or heuristic bitwidth assignments, failing to account for the nonuniform sensitivity of weights to quantization noise. In this paper, we propose a novel framework for allocating quantization bitwidths based on sensitivity metrics derived from a Hessian proxy. We make key assumptions, which allow the layer/component-wise loss function to be expressed as an explicit function of the bitwidths. This enables a neat formulation of the bit allocation problem as a convex optimization task, whose closed-form solution adapts precision across weights to minimize the layer-wise quantization loss. Inspecting the solution provides several insights (such as the equal-loss structure), which are then exploited to design the proposed \textbf{BAQ} (Bit Allocation Quantization) algorithm. The proposed algorithm achieves a good trade-off between loss minimization and complexity and allows BAQ to be integrated into standard quantization pipelines with minimal overhead. Experimental results show that BAQ consistently outperforms GPTQ, achieving up to 56$\times$ lower perplexity at the same bitwidth on large language models ranging from 125M to 30B parameters. Leveraging our analytical results derived from solving the optimal bit allocation problem, we also provide a theoretical explanation for the observed gains. All codes of this paper are available at https://github.com/CSU-ModelCompression/BAQ.
中文摘要：本文提出BAQ量化框架，通过基于Hessian的敏感度指标和凸优化实现最优位宽分配，在多种规模的大语言模型上相比GPTQ实现高达56倍的困惑度降低。
English Summary: This paper introduces BAQ, a novel quantization framework that optimally allocates bitwidths using Hessian-based sensitivity metrics and convex optimization, significantly outperforming GPTQ with up to 56× lower perplexity across various LLM sizes.

Paperid: 423, https://arxiv.org/pdf/2506.05587.pdf   GitHub

Authors:Junjie Xing, Yeye He, Mengyu Zhou, Haoyu Dong, Shi Han, Lingjiao Chen, Dongmei Zhang, Surajit Chaudhuri, H. V. Jagadish

Title: MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark

Abstract:
Tables and table-based use cases play a crucial role in many important real-world applications, such as spreadsheets, databases, and computational notebooks, which traditionally require expert-level users like data engineers, data analysts, and database administrators to operate. Although LLMs have shown remarkable progress in working with tables (e.g., in spreadsheet and database copilot scenarios), comprehensive benchmarking of such capabilities remains limited. In contrast to an extensive and growing list of NLP benchmarks, evaluations of table-related tasks are scarce, and narrowly focus on tasks like NL-to-SQL and Table-QA, overlooking the broader spectrum of real-world tasks that professional users face. This gap limits our understanding and model progress in this important area. In this work, we introduce MMTU, a large-scale benchmark with over 30K questions across 25 real-world table tasks, designed to comprehensively evaluate models ability to understand, reason, and manipulate real tables at the expert-level. These tasks are drawn from decades' worth of computer science research on tabular data, with a focus on complex table tasks faced by professional users. We show that MMTU require a combination of skills -- including table understanding, reasoning, and coding -- that remain challenging for today's frontier models, where even frontier reasoning models like OpenAI o4-mini and DeepSeek R1 score only around 60%, suggesting significant room for improvement. We highlight key findings in our evaluation using MMTU and hope that this benchmark drives further advances in understanding and developing foundation models for structured data processing and analysis. Our code and data are available at https://github.com/MMTU-Benchmark/MMTU and https://huggingface.co/datasets/MMTU-benchmark/MMTU.
中文：MMTU是一个包含超过3万个问题、涵盖25种专家级表格任务的大规模基准，旨在全面评估模型对真实表格的理解、推理和操作能力，结果显示即使顶级模型得分也仅约60%，表明仍有巨大改进空间。
English: MMTU is a large-scale benchmark with over 30,000 questions across 25 expert-level table tasks, designed to comprehensively evaluate models' ability to understand, reason, and manipulate real tables, revealing significant room for improvement as even top models score only around 60%.

Paperid: 424, https://arxiv.org/pdf/2506.05542.pdf   GitHub

Authors:Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Aitor Alberdi Escudero, Edward Blake, David Cechak, Luke Cassar, Alessandro Balestrucci, Panagiotis Alexiou

Title: Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data

Abstract:
The adoption of machine learning (ML) and deep learning methods has revolutionized molecular medicine by driving breakthroughs in genomics, transcriptomics, drug discovery, and biological systems modeling. The increasing quantity, multimodality, and heterogeneity of biological datasets demand automated methods that can produce generalizable predictive models. Recent developments in large language model-based agents have shown promise for automating end-to-end ML experimentation on structured benchmarks. However, when applied to heterogeneous computational biology datasets, these methods struggle with generalization and success rates. Here, we introduce Agentomics-ML, a fully autonomous agent-based system designed to produce a classification model and the necessary files for reproducible training and inference. Our method follows predefined steps of an ML experimentation process, repeatedly interacting with the file system through Bash to complete individual steps. Once an ML model is produced, training and validation metrics provide scalar feedback to a reflection step to identify issues such as overfitting. This step then creates verbal feedback for future iterations, suggesting adjustments to steps such as data representation, model architecture, and hyperparameter choices. We have evaluated Agentomics-ML on several established genomic and transcriptomic benchmark datasets and show that it outperforms existing state-of-the-art agent-based methods in both generalization and success rates. While state-of-the-art models built by domain experts still lead in absolute performance on the majority of the computational biology datasets used in this work, Agentomics-ML narrows the gap for fully autonomous systems and achieves state-of-the-art performance on one of the used benchmark datasets. The code is available at https://github.com/BioGeMT/Agentomics-ML.
中文: Agentomics-ML是一种基于智能体的全自动系统，通过自动化机器学习实验流程在计算生物学数据集上实现了更好的泛化能力和成功率，显著缩小了与专家构建模型之间的性能差距。
English: Agentomics-ML is an autonomous agent-based system that automates end-to-end machine learning experimentation for computational biology datasets, outperforming existing agent methods in generalization and success rates while narrowing the performance gap with expert-built models.

Paperid: 425, https://arxiv.org/pdf/2506.05516.pdf   GitHub

Authors:Boyuan Deng, Luca Rossini, Jin Wang, Weijie Wang, Nikolaos Tsagarakis

Title: Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots

Abstract:
Adaptive recovery from fall incidents are essential skills for the practical deployment of wheeled-legged robots, which uniquely combine the agility of legs with the speed of wheels for rapid recovery. However, traditional methods relying on preplanned recovery motions, simplified dynamics or sparse rewards often fail to produce robust recovery policies. This paper presents a learning-based framework integrating Episode-based Dynamic Reward Shaping and curriculum learning, which dynamically balances exploration of diverse recovery maneuvers with precise posture refinement. An asymmetric actor-critic architecture accelerates training by leveraging privileged information in simulation, while noise-injected observations enhance robustness against uncertainties. We further demonstrate that synergistic wheel-leg coordination reduces joint torque consumption by 15.8% and 26.2% and improves stabilization through energy transfer mechanisms. Extensive evaluations on two distinct quadruped platforms achieve recovery success rates up to 99.1% and 97.8% without platform-specific tuning. The supplementary material is available at https://boyuandeng.github.io/L2R-WheelLegCoordination/

Paperid: 426, https://arxiv.org/pdf/2506.05447.pdf   GitHub

Authors:Andrei Mircea, Supriyo Chakraborty, Nima Chitsazan, Milind Naphade, Sambit Sahu, Irina Rish, Ekaterina Lobacheva

Title: Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning

Abstract:
This work aims to understand how scaling improves language models, specifically in terms of training dynamics. We find that language models undergo loss deceleration early in training; an abrupt slowdown in the rate of loss improvement, resulting in piecewise linear behaviour of the loss curve in log-log space. Scaling up the model mitigates this transition by (1) decreasing the loss at which deceleration occurs, and (2) improving the log-log rate of loss improvement after deceleration. We attribute loss deceleration to a type of degenerate training dynamics we term zero-sum learning (ZSL). In ZSL, per-example gradients become systematically opposed, leading to destructive interference in per-example changes in loss. As a result, improving loss on one subset of examples degrades it on another, bottlenecking overall progress. Loss deceleration and ZSL provide new insights into the training dynamics underlying language model scaling laws, and could potentially be targeted directly to improve language models independent of scale. We make our code and artefacts available at: https://github.com/mirandrom/zsl
中文摘要：本研究发现语言模型扩展通过降低损失减速发生的临界点并改善减速后的学习效率，来缓解早期训练中的损失减速现象，该现象源于零和学习机制中的梯度相互抵消问题。
English Summary: This study reveals that language model scaling mitigates early training loss deceleration by reducing its onset loss and improving post-deceleration learning rates, attributing this phenomenon to zero-sum learning dynamics where gradient interference limits progress.

Paperid: 427, https://arxiv.org/pdf/2506.05441.pdf   GitHub

Authors:Kimberley M. Bird, Xujiong Ye, Alan M. Race, James M. Brown

Title: Deep histological synthesis from mass spectrometry imaging for multimodal registration

Abstract:
Registration of histological and mass spectrometry imaging (MSI) allows for more precise identification of structural changes and chemical interactions in tissue. With histology and MSI having entirely different image formation processes and dimensionalities, registration of the two modalities remains an ongoing challenge. This work proposes a solution that synthesises histological images from MSI, using a pix2pix model, to effectively enable unimodal registration. Preliminary results show promising synthetic histology images with limited artifacts, achieving increases in mutual information (MI) and structural similarity index measures (SSIM) of +0.924 and +0.419, respectively, compared to a baseline U-Net model. Our source code is available on GitHub: https://github.com/kimberley/MIUA2025.
中文: 本研究提出了一种基于pix2pix的方法，可从质谱成像数据合成组织学图像，实现有效的单模态配准，并在互信息和结构相似性指标上展现出优于基准模型的性能提升。
English: This study introduces a pix2pix-based method to synthesize histological images from mass spectrometry imaging, enabling effective unimodal registration and demonstrating improved mutual information and structural similarity over baseline models.

Paperid: 428, https://arxiv.org/pdf/2506.05433.pdf   GitHub

Authors:Zikang Liu, Tongtian Yue, Yepeng Tang, Longteng Guo, Junxian Cai, Qingbin Liu, Xi Chen, Jing Liu

Title: Prefix Grouper: Efficient GRPO Training through Shared-Prefix Forward

Abstract:
Group Relative Policy Optimization (GRPO) enhances policy learning by computing gradients from relative comparisons among candidate outputs that share a common input prefix. Despite its effectiveness, GRPO introduces substantial computational overhead when processing long shared prefixes, which must be redundantly encoded for each group member. This inefficiency becomes a major scalability bottleneck in long-context learning scenarios. We propose Prefix Grouper, an efficient GRPO training algorithm that eliminates redundant prefix computation via a Shared-Prefix Forward strategy. In particular, by restructuring self-attention into two parts, our method enables the shared prefix to be encoded only once, while preserving full differentiability and compatibility with end-to-end training. We provide both theoretical and empirical evidence that Prefix Grouper is training-equivalent to standard GRPO: it yields identical forward outputs and backward gradients, ensuring that the optimization dynamics and final policy performance remain unchanged. Empirically, our experiments confirm that Prefix Grouper achieves consistent results while significantly reducing the computational cost of training, particularly in long-prefix scenarios. The proposed method is fully plug-and-play: it is compatible with existing GRPO-based architectures and can be seamlessly integrated into current training pipelines as a drop-in replacement, requiring no structural modifications and only minimal changes to input construction and attention computation. Prefix Grouper enables the use of larger group sizes under the same computational budget, thereby improving the scalability of GRPO to more complex tasks and larger models. Code is now available at https://github.com/johncaged/PrefixGrouper
中文摘要：Prefix Grouper是一种高效训练算法，通过重构自注意力机制消除GRPO中的冗余前缀计算，在保持性能不变的同时显著降低计算成本。
English Summary: Prefix Grouper is an efficient training algorithm that eliminates redundant prefix computations in Group Relative Policy Optimization (GRPO) by restructuring self-attention, maintaining identical performance while significantly reducing computational costs.

Paperid: 429, https://arxiv.org/pdf/2506.05427.pdf   GitHub

Authors:Zishan Shu, Yufan Deng, Hongyu Zhang, Zhiwei Nie, Jie Chen

Title: MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction

Abstract:
Activity cliff prediction is a critical task in drug discovery and material design. Existing computational methods are limited to handling single binding targets, which restricts the applicability of these prediction models. In this paper, we present the Multi-Grained Target Perception network (MTPNet) to incorporate the prior knowledge of interactions between the molecules and their target proteins. Specifically, MTPNet is a unified framework for activity cliff prediction, which consists of two components: Macro-level Target Semantic (MTS) guidance and Micro-level Pocket Semantic (MPS) guidance. By this way, MTPNet dynamically optimizes molecular representations through multi-grained protein semantic conditions. To our knowledge, it is the first time to employ the receptor proteins as guiding information to effectively capture critical interaction details. Extensive experiments on 30 representative activity cliff datasets demonstrate that MTPNet significantly outperforms previous approaches, achieving an average RMSE improvement of 18.95% on top of several mainstream GNN architectures. Overall, MTPNet internalizes interaction patterns through conditional deep learning to achieve unified predictions of activity cliffs, helping to accelerate compound optimization and design. Codes are available at: https://github.com/ZishanShu/MTPNet.
Chinese: MTPNet通过多粒度蛋白质语义引导动态优化分子表征，提出了统一的活性悬崖预测框架，在30个代表性数据集上显著超越现有方法，平均预测精度提升18.95%。
English: MTPNet introduces a unified framework for activity cliff prediction by dynamically optimizing molecular representations through multi-grained protein semantic guidance, significantly outperforming existing methods and improving prediction accuracy by 18.95% on average across 30 datasets.

Paperid: 430, https://arxiv.org/pdf/2506.05426.pdf   GitHub

Authors:Wenhao Wu, Fuhong Liu, Haoru Li, Zican Hu, Daoyi Dong, Chunlin Chen, Zhi Wang

Title: Mixture-of-Experts Meets In-Context Reinforcement Learning

Abstract:
In-context reinforcement learning (ICRL) has emerged as a promising paradigm for adapting RL agents to downstream tasks through prompt conditioning. However, two notable challenges remain in fully harnessing in-context learning within RL domains: the intrinsic multi-modality of the state-action-reward data and the diverse, heterogeneous nature of decision tasks. To tackle these challenges, we propose \textbf{T2MIR} (\textbf{T}oken- and \textbf{T}ask-wise \textbf{M}oE for \textbf{I}n-context \textbf{R}L), an innovative framework that introduces architectural advances of mixture-of-experts (MoE) into transformer-based decision models. T2MIR substitutes the feedforward layer with two parallel layers: a token-wise MoE that captures distinct semantics of input tokens across multiple modalities, and a task-wise MoE that routes diverse tasks to specialized experts for managing a broad task distribution with alleviated gradient conflicts. To enhance task-wise routing, we introduce a contrastive learning method that maximizes the mutual information between the task and its router representation, enabling more precise capture of task-relevant information. The outputs of two MoE components are concatenated and fed into the next layer. Comprehensive experiments show that T2MIR significantly facilitates in-context learning capacity and outperforms various types of baselines. We bring the potential and promise of MoE to ICRL, offering a simple and scalable architectural enhancement to advance ICRL one step closer toward achievements in language and vision communities. Our code is available at https://github.com/NJU-RL/T2MIR.
中文：T2MIR框架通过引入标记级和任务级混合专家机制到基于Transformer的决策模型中，有效解决了情境强化学习中的多模态和任务异质性挑战，并在实验中展现出优于各类基准方法的性能。
English: The T2MIR framework introduces token- and task-wise mixture-of-experts to transformer-based decision models, effectively addressing multi-modality and task heterogeneity in in-context reinforcement learning while demonstrating superior performance over existing methods.

Paperid: 431, https://arxiv.org/pdf/2506.05418.pdf   GitHub

Authors:Kyungsoo Kim, Jeongsoo Ha, Yusung Kim

Title: Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning

Abstract:
Vision-based reinforcement learning requires efficient and robust representations of image-based observations, especially when the images contain distracting (task-irrelevant) elements such as shadows, clouds, and light. It becomes more important if those distractions are not exposed during training. We design a Self-Predictive Dynamics (SPD) method to extract task-relevant features efficiently, even in unseen observations after training. SPD uses weak and strong augmentations in parallel, and learns representations by predicting inverse and forward transitions across the two-way augmented versions. In a set of MuJoCo visual control tasks and an autonomous driving task (CARLA), SPD outperforms previous studies in complex observations, and significantly improves the generalization performance for unseen observations. Our code is available at https://github.com/unigary/SPD.
中文：自预测动态（SPD）方法通过双向增强预测状态转换，能有效提取含干扰图像中的任务相关特征，在视觉控制任务中表现优异，并显著提升对未见观察的泛化能力。
English: The Self-Predictive Dynamics (SPD) method effectively extracts task-relevant features from images with distractions by using dual augmentations to predict transitions, demonstrating superior performance in visual control tasks and enhanced generalization for unseen observations.

Paperid: 432, https://arxiv.org/pdf/2506.05414.pdf   GitHub

Authors:Mingfei Chen, Zijun Cui, Xiulong Liu, Jinlin Xiang, Caleb Zheng, Jingyuan Li, Eli Shlizerman

Title: SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing

Abstract:
3D spatial reasoning in dynamic, audio-visual environments is a cornerstone of human cognition yet remains largely unexplored by existing Audio-Visual Large Language Models (AV-LLMs) and benchmarks, which predominantly focus on static or 2D scenes. We introduce SAVVY-Bench, the first benchmark for 3D spatial reasoning in dynamic scenes with synchronized spatial audio. SAVVY-Bench is comprised of thousands of relationships involving static and moving objects, and requires fine-grained temporal grounding, consistent 3D localization, and multi-modal annotation. To tackle this challenge, we propose SAVVY, a novel training-free reasoning pipeline that consists of two stages: (i) Egocentric Spatial Tracks Estimation, which leverages AV-LLMs as well as other audio-visual methods to track the trajectories of key objects related to the query using both visual and spatial audio cues, and (ii) Dynamic Global Map Construction, which aggregates multi-modal queried object trajectories and converts them into a unified global dynamic map. Using the constructed map, a final QA answer is obtained through a coordinate transformation that aligns the global map with the queried viewpoint. Empirical evaluation demonstrates that SAVVY substantially enhances performance of state-of-the-art AV-LLMs, setting a new standard and stage for approaching dynamic 3D spatial reasoning in AV-LLMs.

Paperid: 433, https://arxiv.org/pdf/2506.05413.pdf   GitHub

Authors:Patrik Czakó, Gábor Kertész, Sándor Szénási

Title: SmoothRot: Combining Channel-Wise Scaling and Rotation for Quantization-Friendly LLMs

Abstract:
We present SmoothRot, a novel post-training quantization technique to enhance the efficiency of 4-bit quantization in Large Language Models (LLMs). SmoothRot addresses the critical challenge of massive activation outliers, by integrating channel-wise scaling with Hadamard transformations. Our technique effectively transforms extreme outliers into quantization-friendly activations, significantly improving quantization accuracy. Experiments conducted on popular LLMs (LLaMA2 7B, LLaMA3.1 8B, and Mistral 7B) demonstrate that SmoothRot consistently reduces the performance gap between quantized and FP16 models by approximately 10-30\% across language generation and zero-shot reasoning tasks, without introducing additional inference latency. Code is available at https://github.com/czakop/smoothrot.
中文: SmoothRot是一种新颖的训练后量化技术，通过将激活异常值转化为量化友好形式，显著提升大型语言模型的4位量化效率，在不增加延迟的情况下将性能差距缩小10-30%。
English: SmoothRot is a novel post-training quantization technique that enhances 4-bit quantization efficiency in Large Language Models by transforming activation outliers into quantization-friendly forms, significantly reducing performance gaps by 10-30% without added latency.

Paperid: 434, https://arxiv.org/pdf/2506.05394.pdf   GitHub

Authors:Hondamunige Prasanna Silva, Federico Becattini, Lorenzo Seidenari

Title: Attacking Attention of Foundation Models Disrupts Downstream Tasks

Abstract:
Foundation models represent the most prominent and recent paradigm shift in artificial intelligence. Foundation models are large models, trained on broad data that deliver high accuracy in many downstream tasks, often without fine-tuning. For this reason, models such as CLIP , DINO or Vision Transfomers (ViT), are becoming the bedrock of many industrial AI-powered applications. However, the reliance on pre-trained foundation models also introduces significant security concerns, as these models are vulnerable to adversarial attacks. Such attacks involve deliberately crafted inputs designed to deceive AI systems, jeopardizing their reliability. This paper studies the vulnerabilities of vision foundation models, focusing specifically on CLIP and ViTs, and explores the transferability of adversarial attacks to downstream tasks. We introduce a novel attack, targeting the structure of transformer-based architectures in a task-agnostic fashion. We demonstrate the effectiveness of our attack on several downstream tasks: classification, captioning, image/text retrieval, segmentation and depth estimation. Code available at:https://github.com/HondamunigePrasannaSilva/attack-attention
基础模型通过为多种任务提供通用且高精度的解决方案正在革新人工智能领域，然而它们对对抗性攻击的脆弱性带来了严重的安全隐患，本研究聚焦CLIP和视觉变换器，通过一种新型的针对变换器结构的攻击验证了这一点。
Foundation models are revolutionizing AI by providing versatile, high-accuracy solutions across various tasks, yet their vulnerability to adversarial attacks poses serious security risks, as demonstrated in this study focusing on CLIP and ViTs with a novel transformer-targeted attack.

Paperid: 435, https://arxiv.org/pdf/2506.05393.pdf   GitHub

Authors:Shenyang Huang, Ali Parviz, Emma Kondrup, Zachary Yang, Zifeng Ding, Michael Bronstein, Reihaneh Rabbany, Guillaume Rabusseau

Title: Are Large Language Models Good Temporal Graph Learners?

Abstract:
Large Language Models (LLMs) have recently driven significant advancements in Natural Language Processing and various other applications. While a broad range of literature has explored the graph-reasoning capabilities of LLMs, including their use of predictors on graphs, the application of LLMs to dynamic graphs -- real world evolving networks -- remains relatively unexplored. Recent work studies synthetic temporal graphs generated by random graph models, but applying LLMs to real-world temporal graphs remains an open question. To address this gap, we introduce Temporal Graph Talker (TGTalker), a novel temporal graph learning framework designed for LLMs. TGTalker utilizes the recency bias in temporal graphs to extract relevant structural information, converted to natural language for LLMs, while leveraging temporal neighbors as additional information for prediction. TGTalker demonstrates competitive link prediction capabilities compared to existing Temporal Graph Neural Network (TGNN) models. Across five real-world networks, TGTalker performs competitively with state-of-the-art temporal graph methods while consistently outperforming popular models such as TGN and HTGN. Furthermore, TGTalker generates textual explanations for each prediction, thus opening up exciting new directions in explainability and interpretability for temporal link prediction. The code is publicly available at https://github.com/shenyangHuang/TGTalker.
中文: 本文提出的TGTalker创新框架使大语言模型能在真实时序图上实现具有竞争力的链接预测，并通过生成文本解释显著提升了模型的可解释性。
English: This paper introduces TGTalker, a novel framework that enables Large Language Models to perform competitive link prediction on real-world temporal graphs while generating textual explanations for enhanced interpretability.

Paperid: 436, https://arxiv.org/pdf/2506.05334.pdf   GitHub

Authors:Mihran Miroyan, Tsung-Han Wu, Logan King, Tianle Li, Jiayi Pan, Xinyan Hu, Wei-Lin Chiang, Anastasios N. Angelopoulos, Trevor Darrell, Narges Norouzi, Joseph E. Gonzalez

Title: Search Arena: Analyzing Search-Augmented LLMs

Abstract:
Search-augmented language models combine web search with Large Language Models (LLMs) to improve response groundedness and freshness. However, analyzing these systems remains challenging: existing datasets are limited in scale and narrow in scope, often constrained to static, single-turn, fact-checking questions. In this work, we introduce Search Arena, a crowd-sourced, large-scale, human-preference dataset of over 24,000 paired multi-turn user interactions with search-augmented LLMs. The dataset spans diverse intents and languages, and contains full system traces with around 12,000 human preference votes. Our analysis reveals that user preferences are influenced by the number of citations, even when the cited content does not directly support the attributed claims, uncovering a gap between perceived and actual credibility. Furthermore, user preferences vary across cited sources, revealing that community-driven platforms are generally preferred and static encyclopedic sources are not always appropriate and reliable. To assess performance across different settings, we conduct cross-arena analyses by testing search-augmented LLMs in a general-purpose chat environment and conventional LLMs in search-intensive settings. We find that web search does not degrade and may even improve performance in non-search settings; however, the quality in search settings is significantly affected if solely relying on the model's parametric knowledge. We open-sourced the dataset to support future research in this direction. Our dataset and code are available at: https://github.com/lmarena/search-arena.
中文: 本研究推出了Search Arena这一大规模人类偏好数据集，用于评估搜索增强语言模型，发现用户偏好受引用数量和来源类型影响，交叉分析表明网络搜索能提升非搜索场景性能，但仅依赖参数知识会显著降低搜索密集型任务的质量。
English: This study introduces Search Arena, a large-scale human-preference dataset for evaluating search-augmented language models, revealing that user preferences are influenced by citation quantity and source type, while cross-analysis shows web search enhances performance in non-search settings but reliance on parametric knowledge alone degrades search-intensive results.

Paperid: 437, https://arxiv.org/pdf/2506.05333.pdf   GitHub

Authors:Ranajoy Sadhukhan, Zhuoming Chen, Haizhong Zheng, Yang Zhou, Emma Strubell, Beidi Chen

Title: Kinetics: Rethinking Test-Time Scaling Laws

Abstract:
We rethink test-time scaling laws from a practical efficiency perspective, revealing that the effectiveness of smaller models is significantly overestimated. Prior work, grounded in compute-optimality, overlooks critical memory access bottlenecks introduced by inference-time strategies (e.g., Best-of-$N$, long CoTs). Our holistic analysis, spanning models from 0.6B to 32B parameters, reveals a new Kinetics Scaling Law that better guides resource allocation by incorporating both computation and memory access costs. Kinetics Scaling Law suggests that test-time compute is more effective when used on models above a threshold than smaller ones. A key reason is that in TTS, attention, rather than parameter count, emerges as the dominant cost factor. Motivated by this, we propose a new scaling paradigm centered on sparse attention, which lowers per-token cost and enables longer generations and more parallel samples within the same resource budget. Empirically, we show that sparse attention models consistently outperform dense counterparts, achieving over 60 points gains in low-cost regimes and over 5 points gains in high-cost regimes for problem-solving accuracy on AIME, encompassing evaluations on state-of-the-art MoEs. These results suggest that sparse attention is essential and increasingly important with more computing invested, for realizing the full potential of test-time scaling where, unlike training, accuracy has yet to saturate as a function of computation, and continues to improve through increased generation. The code is available at https://github.com/Infini-AI-Lab/Kinetics.
中文摘要：本研究提出动力学缩放定律，揭示因忽略内存瓶颈而高估了小模型效能，并通过稀疏注意力机制降低单令牌成本、支持生成长文本，从而优化资源分配。
English Summary: This study introduces the Kinetics Scaling Law, which demonstrates that smaller models' effectiveness is overestimated due to overlooked memory bottlenecks, and proposes sparse attention to optimize resource allocation by reducing per-token costs and enabling longer generations.

Paperid: 438, https://arxiv.org/pdf/2506.05294.pdf   GitHub

Authors:Arnav Kumar Jain, Vibhakar Mohta, Subin Kim, Atiksh Bhardwaj, Juntao Ren, Yunhai Feng, Sanjiban Choudhury, Gokul Swamy

Title: A Smooth Sea Never Made a Skilled $\texttt{SAILOR}$: Robust Imitation via Learning to Search

Abstract:
The fundamental limitation of the behavioral cloning (BC) approach to imitation learning is that it only teaches an agent what the expert did at states the expert visited. This means that when a BC agent makes a mistake which takes them out of the support of the demonstrations, they often don't know how to recover from it. In this sense, BC is akin to giving the agent the fish -- giving them dense supervision across a narrow set of states -- rather than teaching them to fish: to be able to reason independently about achieving the expert's outcome even when faced with unseen situations at test-time. In response, we explore learning to search (L2S) from expert demonstrations, i.e. learning the components required to, at test time, plan to match expert outcomes, even after making a mistake. These include (1) a world model and (2) a reward model. We carefully ablate the set of algorithmic and design decisions required to combine these and other components for stable and sample/interaction-efficient learning of recovery behavior without additional human corrections. Across a dozen visual manipulation tasks from three benchmarks, our approach $\texttt{SAILOR}$ consistently out-performs state-of-the-art Diffusion Policies trained via BC on the same data. Furthermore, scaling up the amount of demonstrations used for BC by 5-10$\times$ still leaves a performance gap. We find that $\texttt{SAILOR}$ can identify nuanced failures and is robust to reward hacking. Our code is available at https://github.com/arnavkj1995/SAILOR .
行为克隆(BC)仅能模仿专家在已演示状态下的行为，缺乏错误恢复能力，而我们提出的L2S方法通过习得世界模型和奖励模型，使智能体具备独立规划与纠错能力，在多项基准测试中显著优于BC方法。
Behavioral cloning (BC) is limited to imitating expert actions only within demonstrated states, lacking recovery strategies for errors, whereas our proposed L2S approach enables agents to plan and recover independently using learned world and reward models, significantly outperforming BC methods across multiple benchmarks.

Paperid: 439, https://arxiv.org/pdf/2506.05188.pdf   GitHub

Authors:Moritz Miller, Bernhard Schölkopf, Siyuan Guo

Title: Counterfactual reasoning: an analysis of in-context emergence

Abstract:
Large-scale neural language models (LMs) exhibit remarkable performance in in-context learning: the ability to learn and reason the input context on the fly without parameter update. This work studies in-context counterfactual reasoning in language models, that is, to predict the consequences of changes under hypothetical scenarios. We focus on studying a well-defined synthetic setup: a linear regression task that requires noise abduction, where accurate prediction is based on inferring and copying the contextual noise from factual observations. We show that language models are capable of counterfactual reasoning in this controlled setup and provide insights that counterfactual reasoning for a broad class of functions can be reduced to a transformation on in-context observations; we find self-attention, model depth, and data diversity in pre-training drive performance in Transformers. More interestingly, our findings extend beyond regression tasks and show that Transformers can perform noise abduction on sequential data, providing preliminary evidence on the potential for counterfactual story generation. Our code is available under https://github.com/moXmiller/counterfactual-reasoning.git .
中文: 研究表明大规模语言模型能够通过噪声溯因在线性回归任务中进行上下文反事实推理，其性能受自注意力机制、模型深度和预训练数据多样性影响，并显示出在反事实故事生成等领域的应用潜力。
English: This research demonstrates that large-scale language models can perform in-context counterfactual reasoning through noise abduction in linear regression tasks, with performance driven by self-attention mechanisms, model depth, and pre-training data diversity, showing potential for applications like counterfactual story generation.

Paperid: 440, https://arxiv.org/pdf/2506.05183.pdf   GitHub

Authors:Zhicheng Yang, Zhijiang Guo, Yinya Huang, Xiaodan Liang, Yiwei Wang, Jing Tang

Title: TreeRPO: Tree Relative Policy Optimization

Abstract:
Large Language Models (LLMs) have shown remarkable reasoning capabilities through Reinforcement Learning with Verifiable Rewards (RLVR) methods. However, a key limitation of existing approaches is that rewards defined at the full trajectory level provide insufficient guidance for optimizing the intermediate steps of a reasoning process. To address this, we introduce \textbf{\name}, a novel method that estimates the mathematical expectations of rewards at various reasoning steps using tree sampling. Unlike prior methods that rely on a separate step reward model, \name directly estimates these rewards through this sampling process. Building on the group-relative reward training mechanism of GRPO, \name innovatively computes rewards based on step-level groups generated during tree sampling. This advancement allows \name to produce fine-grained and dense reward signals, significantly enhancing the learning process and overall performance of LLMs. Experimental results demonstrate that our \name algorithm substantially improves the average Pass@1 accuracy of Qwen-2.5-Math on test benchmarks, increasing it from 19.0\% to 35.5\%. Furthermore, \name significantly outperforms GRPO by 2.9\% in performance while simultaneously reducing the average response length by 18.1\%, showcasing its effectiveness and efficiency. Our code will be available at \href{https://github.com/yangzhch6/TreeRPO}{https://github.com/yangzhch6/TreeRPO}.
中文: 提出的TreeRPO方法通过树采样估计步骤级奖励来增强大语言模型的推理能力，在准确性和效率上相比现有方法均有显著提升。
English: The proposed TreeRPO method enhances LLM reasoning by estimating step-level rewards through tree sampling, significantly improving accuracy and efficiency over existing approaches.

Paperid: 441, https://arxiv.org/pdf/2506.05047.pdf   GitHub

Authors:Viet Nguyen, Changjian Shui, Vijay Giri, Siddarth Arya, Amol Verma, Fahad Razak, Rahul G. Krishnan

Title: Reliably detecting model failures in deployment without labels

Abstract:
The distribution of data changes over time; models operating operating in dynamic environments need retraining. But knowing when to retrain, without access to labels, is an open challenge since some, but not all shifts degrade model performance. This paper formalizes and addresses the problem of post-deployment deterioration (PDD) monitoring. We propose D3M, a practical and efficient monitoring algorithm based on the disagreement of predictive models, achieving low false positive rates under non-deteriorating shifts and provides sample complexity bounds for high true positive rates under deteriorating shifts. Empirical results on both standard benchmark and a real-world large-scale internal medicine dataset demonstrate the effectiveness of the framework and highlight its viability as an alert mechanism for high-stakes machine learning pipelines.
Chinese: 本文提出D3M监控算法，通过预测模型间的分歧来检测动态环境中机器学习模型的性能退化，在非退化偏移下保持低误报率，在退化偏移下实现高检测率，经实验验证可作为高风险机器学习流程的有效预警机制。
English: This paper introduces D3M, a monitoring algorithm that detects when machine learning models need retraining due to performance degradation in dynamic environments, effectively reducing false alarms while ensuring high detection rates for actual deterioration.

Paperid: 442, https://arxiv.org/pdf/2506.05039.pdf   GitHub

Authors:Nicolas Lell, Ansgar Scherp

Title: iN2V: Bringing Transductive Node Embeddings to Inductive Graphs

Abstract:
Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V are limited in their application on new nodes, which restricts them to the transductive setting where the entire graph, including the test nodes, is available during training. We propose inductive node2vec (iN2V), which combines a post-hoc procedure to compute embeddings for nodes unseen during training and modifications to the original N2V training procedure to prepare the embeddings for this post-hoc procedure. We conduct experiments on several benchmark datasets and demonstrate that iN2V is an effective approach to bringing transductive embeddings to an inductive setting. Using iN2V embeddings improves node classification by 1 point on average, with up to 6 points of improvement depending on the dataset and the number of unseen nodes. Our iN2V is a plug-in approach to create new or enrich existing embeddings. It can also be combined with other embedding methods, making it a versatile approach for inductive node representation learning. Code to reproduce the results is available at https://github.com/Foisunt/iN2V .
Chinese: 提出的归纳式node2vec（iN2V）方法通过结合改进的训练和后处理程序，将直推式嵌入扩展到归纳式场景，使节点分类提升最高达6个百分点，并可作为现有嵌入技术的通用插件使用。
English: The proposed inductive node2vec (iN2V) method extends transductive embeddings to an inductive setting by combining modified training with a post-hoc procedure, improving node classification by up to 6 points and serving as a versatile plug-in for existing embedding techniques.

Paperid: 443, https://arxiv.org/pdf/2506.05035.pdf   GitHub GitHub

Authors:Hyeongwon Jang, Changhun Kim, Eunho Yang

Title: TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation

Abstract:
Recent explainable artificial intelligence (XAI) methods for time series primarily estimate point-wise attribution magnitudes, while overlooking the directional impact on predictions, leading to suboptimal identification of significant points. Our analysis shows that conventional Integrated Gradients (IG) effectively capture critical points with both positive and negative impacts on predictions. However, current evaluation metrics fail to assess this capability, as they inadvertently cancel out opposing feature contributions. To address this limitation, we propose novel evaluation metrics-Cumulative Prediction Difference (CPD) and Cumulative Prediction Preservation (CPP)-to systematically assess whether attribution methods accurately identify significant positive and negative points in time series XAI. Under these metrics, conventional IG outperforms recent counterparts. However, directly applying IG to time series data may lead to suboptimal outcomes, as generated paths ignore temporal relationships and introduce out-of-distribution samples. To overcome these challenges, we introduce TIMING, which enhances IG by incorporating temporal awareness while maintaining its theoretical properties. Extensive experiments on synthetic and real-world time series benchmarks demonstrate that TIMING outperforms existing time series XAI baselines. Our code is available at https://github.com/drumpt/TIMING.
中文：近期时间序列可解释人工智能方法常忽略对预测的方向性影响，但传统积分梯度能有效识别关键点；然而现有评估指标存在缺陷，因此我们提出新指标证明其优越性，并引入具有时间感知能力的TIMING方法，在实验中超越现有基准。
English: Recent time series explainable AI methods often miss capturing directional impacts on predictions, but conventional Integrated Gradients (IG) effectively identify critical points; however, their evaluation is flawed, so we propose new metrics showing IG's superiority and introduce TIMING, a temporally-aware IG enhancement that outperforms existing methods.

Paperid: 444, https://arxiv.org/pdf/2506.05032.pdf   GitHub

Authors:Zeming Wei, Yiwen Guo, Yisen Wang

Title: Identifying and Understanding Cross-Class Features in Adversarial Training

Abstract:
Adversarial training (AT) has been considered one of the most effective methods for making deep neural networks robust against adversarial attacks, while the training mechanisms and dynamics of AT remain open research problems. In this paper, we present a novel perspective on studying AT through the lens of class-wise feature attribution. Specifically, we identify the impact of a key family of features on AT that are shared by multiple classes, which we call cross-class features. These features are typically useful for robust classification, which we offer theoretical evidence to illustrate through a synthetic data model. Through systematic studies across multiple model architectures and settings, we find that during the initial stage of AT, the model tends to learn more cross-class features until the best robustness checkpoint. As AT further squeezes the training robust loss and causes robust overfitting, the model tends to make decisions based on more class-specific features. Based on these discoveries, we further provide a unified view of two existing properties of AT, including the advantage of soft-label training and robust overfitting. Overall, these insights refine the current understanding of AT mechanisms and provide new perspectives on studying them. Our code is available at https://github.com/PKU-ML/Cross-Class-Features-AT.
中文: 本文通过分析跨类别特征提出了对抗训练的新视角，揭示了这类特征在实现鲁棒性中的关键作用，并通过类别特征归因解释了鲁棒过拟合等现象。
English: This paper introduces a novel perspective on adversarial training by analyzing cross-class features, revealing their crucial role in achieving robustness and explaining phenomena like robust overfitting through class-wise feature attribution.

Paperid: 445, https://arxiv.org/pdf/2506.05027.pdf   GitHub GitHub

Authors:Kuang He, Wei Tang, Tong Wei, Min-Ling Zhang

Title: Tuning the Right Foundation Models is What you Need for Partial Label Learning

Abstract:
Partial label learning (PLL) seeks to train generalizable classifiers from datasets with inexact supervision, a common challenge in real-world applications. Existing studies have developed numerous approaches to progressively refine and recover ground-truth labels by training convolutional neural networks. However, limited attention has been given to foundation models that offer transferrable representations. In this work, we empirically conduct comprehensive evaluations of 11 foundation models across 13 PLL approaches on 8 benchmark datasets under 3 PLL scenarios. We further propose PartialCLIP, an efficient fine-tuning framework for foundation models in PLL. Our findings reveal that current PLL approaches tend to 1) achieve significant performance gains when using foundation models, 2) exhibit remarkably similar performance to each other, 3) maintain stable performance across varying ambiguity levels, while 4) are susceptible to foundation model selection and adaptation strategies. Additionally, we demonstrate the efficacy of text-embedding classifier initialization and effective candidate label filtering using zero-shot CLIP. Our experimental results and analysis underscore the limitations of current PLL approaches and provide valuable insights for developing more generalizable PLL models. The source code can be found at https://github.com/SEU-hk/PartialCLIP.
中文: 本研究评估了基础模型在偏标签学习中的应用，揭示了其显著的性能优势和局限性，并提出了PartialCLIP这一高效微调框架，以提升模型适应性和标签筛选能力。
English: This study evaluates foundation models in partial label learning, revealing their significant performance benefits and limitations, and introduces PartialCLIP, an efficient fine-tuning framework that enhances model adaptability and label filtering.

Paperid: 446, https://arxiv.org/pdf/2506.04867.pdf   GitHub

Authors:Jônata Tyska Carvalho, Stefano Nolfi

Title: LLMs for sensory-motor control: Combining in-context and iterative learning

Abstract:
We propose a method that enables large language models (LLMs) to control embodied agents by directly mapping continuous observation vectors to continuous action vectors. At the outset, the LLMs generate a control strategy based on a textual description of the agent, its environment, and the intended goal. This strategy is then iteratively refined through a learning process in which the LLMs are repeatedly prompted to improve the current strategy, using performance feedback and sensory-motor data collected during its evaluation. The method is validated on classic control tasks from the Gymnasium library and the inverted pendulum task from the MuJoCo library. The approach proves effective with relatively compact models such as Gpt-oss:120b and Qwen2.5:72b. In most cases, it successfully identifies optimal or near-optimal solutions by integrating symbolic knowledge derived through reasoning with sub-symbolic sensory-motor data gathered as the agent interacts with its environment.
中文: 该方法通过迭代学习生成并优化控制策略，使大型语言模型能够操控具身智能体，并在Gymnasium和MuJoCo任务中通过GPT-oss:120b等模型验证了其有效性。
English: This method enables large language models to control embodied agents by generating and refining control strategies through iterative learning, validated on Gymnasium and MuJoCo tasks using models like GPT-oss:120b and Qwen2.5:72b.

Paperid: 447, https://arxiv.org/pdf/2506.04823.pdf   GitHub

Authors:Svetlana Pavlitska, Jamie Robb, Nikolai Polley, Melih Yazgan, J. Marius Zöllner

Title: Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors

Abstract:
Realistic adversarial attacks on various camera-based perception tasks of autonomous vehicles have been successfully demonstrated so far. However, only a few works considered attacks on traffic light detectors. This work shows how CNNs for traffic light detection can be attacked with printed patches. We propose a threat model, where each instance of a traffic light is attacked with a patch placed under it, and describe a training strategy. We demonstrate successful adversarial patch attacks in universal settings. Our experiments show realistic targeted red-to-green label-flipping attacks and attacks on pictogram classification. Finally, we perform a real-world evaluation with printed patches and demonstrate attacks in the lab settings with a mobile traffic light for construction sites and in a test area with stationary traffic lights. Our code is available at https://github.com/KASTEL-MobilityLab/attacks-on-traffic-light-detection.
中文摘要：本研究展示了使用印刷补丁对交通信号灯检测系统实施现实对抗攻击的方法，在模拟和真实环境中均成功实现了针对性标签翻转和分类攻击。
English Summary: This study demonstrates realistic adversarial attacks on traffic light detection systems using printed patches, successfully achieving targeted label-flipping and classification attacks in both simulated and real-world environments.

Paperid: 448, https://arxiv.org/pdf/2506.04790.pdf   GitHub GitHub

Authors:Yusuke Matsui

Title: LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table

Abstract:
Approximate nearest neighbor search (ANNS) is an essential building block for applications like RAG but can sometimes yield results that are overly similar to each other. In certain scenarios, search results should be similar to the query and yet diverse. We propose LotusFilter, a post-processing module to diversify ANNS results. We precompute a cutoff table summarizing vectors that are close to each other. During the filtering, LotusFilter greedily looks up the table to delete redundant vectors from the candidates. We demonstrated that the LotusFilter operates fast (0.02 [ms/query]) in settings resembling real-world RAG applications, utilizing features such as OpenAI embeddings. Our code is publicly available at https://github.com/matsui528/lotf.
Chinese: LotusFilter是一个后处理模块，通过预计算邻近向量表并贪婪地剔除候选结果中的冗余向量，有效提升近似最近邻搜索结果的多样性，在真实RAG应用中展现出高速处理性能。
English: LotusFilter is a post-processing module that efficiently diversifies approximate nearest neighbor search results by greedily removing redundant vectors using a precomputed cutoff table, operating at high speed in real-world RAG applications.

Paperid: 449, https://arxiv.org/pdf/2506.04765.pdf   GitHub

Authors:Jiachen Tang, Zhonghao Wang, Sirui Chen, Sheng Zhou, Jiawei Chen, Jiajun Bu

Title: OpenGT: A Comprehensive Benchmark For Graph Transformers

Abstract:
Graph Transformers (GTs) have recently demonstrated remarkable performance across diverse domains. By leveraging attention mechanisms, GTs are capable of modeling long-range dependencies and complex structural relationships beyond local neighborhoods. However, their applicable scenarios are still underexplored, this highlights the need to identify when and why they excel. Furthermore, unlike GNNs, which predominantly rely on message-passing mechanisms, GTs exhibit a diverse design space in areas such as positional encoding, attention mechanisms, and graph-specific adaptations. Yet, it remains unclear which of these design choices are truly effective and under what conditions. As a result, the community currently lacks a comprehensive benchmark and library to promote a deeper understanding and further development of GTs. To address this gap, this paper introduces OpenGT, a comprehensive benchmark for Graph Transformers. OpenGT enables fair comparisons and multidimensional analysis by establishing standardized experimental settings and incorporating a broad selection of state-of-the-art GNNs and GTs. Our benchmark evaluates GTs from multiple perspectives, encompassing diverse tasks and datasets with varying properties. Through extensive experiments, our benchmark has uncovered several critical insights, including the difficulty of transferring models across task levels, the limitations of local attention, the efficiency trade-offs in several models, the application scenarios of specific positional encodings, and the preprocessing overhead of some positional encodings. We aspire for this work to establish a foundation for future graph transformer research emphasizing fairness, reproducibility, and generalizability. We have developed an easy-to-use library OpenGT for training and evaluating existing GTs. The benchmark code is available at https://github.com/eaglelab-zju/OpenGT.
图变换器在建模复杂依赖关系方面表现出色，但缺乏全面的基准测试，因此推出OpenGT以进行公平比较和多维分析，推动该领域研究发展。
Graph Transformers have shown strong performance in modeling complex dependencies but lack comprehensive benchmarks, prompting the introduction of OpenGT for fair comparisons and multidimensional analysis to advance research.

Paperid: 450, https://arxiv.org/pdf/2506.04667.pdf   GitHub