RawVLA introduces a streaming neural image signal processor that adaptively renders RAW observations for vision‑language‑action (VLA) policies, focusing on imaging factors that influence embodied behavior. The authors systematically analyze how five ISP dimensions—gain, sensor noise, chromatic response, tonal response, and bit depth—affect action prediction and manipulation success, showing that RAW‑to‑RGB processing significantly shapes outcomes. They also present RawVLA‑Bench, a RAW‑domain manipulation benchmark that evaluates image processing as an explicit variable across clean and adverse conditions, demonstrating that RawVLA maintains performance under standard settings while markedly improving robustness under degraded imaging.
By Shuhong Liu, Heng Zhou, Lingfeng Qian, Yuhao Fang, Xianbao Hou, Qianyu Zhou, Lin Gu, Wei Sui, Jianfei Yang, Ziteng Cui
CogWAM is a cognition‑guided world‑action model that links task reasoning with action learning via a persistent Semantic State, which records completed task events and the active subtask. The model updates this state only on semantic transitions, enabling task‑level context to persist across multiple action chunks. During inference, CogWAM removes the future‑prediction branch and directly generates actions from observations and the maintained state, achieving state‑of‑the‑art performance on RoboDojo and BiCoord and demonstrating efficient closed‑loop dual‑arm manipulation in real‑world experiments.
By Sen Wang, Liu Liu, Xinjiang Wang, Zequn Chen, Haoyi Jiang, Taojun Ding, Tingyang Xiao, Zhizhong Su, Jie Wang, Sanping Zhou
The paper investigates whether input ablations on predictive models can reliably reveal the importance of information for explaining human sequential choice behavior. Using two synthetic bandit tasks with known generating policies, the authors compare GRUs, Transformers, a fine‑tuned LLaMA, and cognitive models under varied reward contributions. They find that while neural models can predict choices well, their responses to ablations often diverge from the true generating process, indicating that predictive accuracy alone does not guarantee faithful model ablations.
By Hanbo Xie
EndoPrior-GS is a new pipeline for dynamic endoscopic reconstruction that combines frame-extracted vision heuristics with depth maps. It creates a joint texture prior using a tool-filtered tissue mask, a non-specular photometric filter, and anatomical salience, which guides primitive initialization and density control. Experiments on EndoNeRF and SCARED datasets show that EndoPrior-GS reduces Flow Error by 27.7% and 25.8% compared to representative methods while maintaining real-time rendering speed and competitive quality.
By Jiaqi Huang, Shidong Wang, Tong Xin, Kabita Adhikari
AdvancedMathBench is a new benchmark suite that evaluates large language models on advanced mathematical proof generation and verification. It includes ProverBench, with 245 problems from undergraduate to doctoral qualifying‑exam levels, and VerifierBench, which tests models’ ability to judge proof validity using 888 expert‑annotated trajectories. The suite features an automatic verification pipeline trained on expert data, and results show that even state‑of‑the‑art models perform poorly, highlighting a gap between generation and verification skills.
By Lingkai Kong, Zijian Wu, Yuzhe Gu, Haiteng Zhao, Zhouqi Hua, Wenyong Huang, Shuang Sun, Zhicheng Xiong, Xiaotian Zhang, Shuya Zhao, Yan Wang, Disheng Xu, Wenwei Zhang, Kai Chen
The paper introduces TempLoc, a Temporal‑aware Localization framework that improves outdoor LiDAR relocalization by leveraging spatio‑temporal consistency across scans. It first predicts point‑wise global coordinates with uncertainties, then estimates inter‑frame correspondences using an attention‑based Prior Coordinate Generation module, and finally fuses these predictions in an uncertainty‑guided manner to produce a more accurate global 6‑DoF pose. Experiments on the NCLT and Oxford RobotCar datasets show that TempLoc significantly outperforms existing state‑of‑the‑art methods.
By Minghang Zhu, Zhijing Wang, Yuxin Guo, Chen Liu, Yongshu Huang, Wen Li, Sheng Ao, Cheng Wang
ESMTrack is a fully end‑to‑end self‑supervised RGB‑T tracking framework that eliminates the need for costly modality‑aligned bounding boxes or offline pseudo‑label generation. It learns discriminative, temporally consistent representations using a grounding triplet loss on the initial annotated frame and a cross‑frame temporal triplet loss on unlabeled search frames, with reliable samples selected via forward‑backward consistency. A three‑branch architecture (fusion, RGB, thermal) and a modality decoupling mechanism mitigate modality dominance bias, enabling competitive state‑of‑the‑art performance, strong cross‑dataset generalization, and real‑time inference on five RGB‑T benchmarks.
By Shenglan Li, Rui Yao, Kunyang Sun, Hong Jia, Yong Zhou, Javen Qinfeng Shi, Xinyu Zhang
StateTape introduces a new framework for long‑horizon coding agents that rewrites the agent’s context as the code repository changes, rather than letting the context grow with every observation. It models the repository as a symbol‑level code graph, using a tape to mark symbols altered by each write and a manager model to resolve stale records. The authors provide theoretical analysis, a new benchmark called TraceBench, and empirical results showing higher resolve rates across six agents and three edit‑heavy benchmarks with minimal computational overhead.
By Ziyang Yu, Liang Zhao, Bowen Zhu, Hasibul Haque
FineSID introduces a new quantization framework for semantic identifier learning in generative recommendation systems. By replacing the traditional Top‑1 hard assignment with a soft, differentiable approach, it distributes gradient updates across all codewords, leading to balanced codebook optimization and reduced identifier collisions. Experiments on public benchmarks show that FineSID improves codebook utilization and recommendation accuracy without relying on complex initialization strategies.
By Song-Li Wu, Weinan Gan, Zhaocheng Du, Xianquan Wang, Jingyi Wang
OmniVCBench is a figure‑centric, source‑traceable benchmark designed to evaluate the interpretation component of Artificial Intelligence Virtual Cells (AIVCs). It comprises 6,077 curated question–answer pairs drawn from scientific figures and experimental contexts, organized into three scientific reasoning tasks that mirror the AIVC Predict–Explain–Discover agenda. The benchmark also introduces AIVC‑Judge, a task‑conditioned MLLM‑as‑a‑judge framework with reference‑aware rubrics, and a Model‑Derived Hard‑Negative Mining strategy to generate multiple‑choice distractors for efficient evaluation.
By Manyu Li, Xunkai Li, Yongfu Xiong, Yi Liu, Rong-Hua Li, Guoren Wang
The paper introduces Reliable Parallel Decoding (RPD) for masked diffusion language models, addressing the unreliability of committing multiple predictions from a single forward pass. Diagnostics reveal that confidence alone is insufficient, as confident end‑sequence predictions can preempt necessary upstream computations, and downstream predictions degrade with upstream uncertainty. RPD selects candidates based on layer‑wise stability and final confidence, committing them under an entropy budget while deferring uncertain predictions, achieving superior throughput and competitive accuracy on LLaDA and Dream benchmarks.
By Zhenghao He, Bohan Liu, Guangzhi Xiong, Aidong Zhang
The paper introduces ProgressCompass, a framework that enhances Embodied Progress Reward Models (PRMs) by providing the necessary contextual information for accurate progress estimation in long manipulation tasks. It presents ContextProgress-Bench, a benchmark with 24 tasks that tests PRMs under three context-dependent scenarios—State Recall, Sequence Tracking, and Recurrence Disambiguation—showing that even history-aware PRMs struggle without proper context. By integrating a context-aware loop that leverages general-purpose vision‑language models, ProgressCompass reduces PRM progress error by up to 82% and improves rank agreement by 76%.
By Jianshu Zhang, Keliang Wu, Chengxuan Qian, Xiyuan Yang, Ce Zhang, Ariel Tian, Anbang Liu, Haoran Lu, Han Liu
FracGen is a fracture‑aware video generation model that creates realistic, controllable fracture dynamics from a single intact image, guided by physics signals. It is trained using FracSim, a simulation framework that extends material point method (MPM) with a continuum damage model to produce paired fracture videos and dense physical fields. The model jointly predicts RGB video and physical maps, employing physics‑informed losses to capture material‑specific fracture behavior and enabling fine‑grained control over tear location, crack speed, and deformation before failure.
By Trong-Tung Nguyen, Jiahan Zhang, Anand Bhattad
The paper introduces a controlled benchmark for evaluating large language models (LLMs) on key‑value pair extraction from documents with varying levels of OCR noise. It tests 136 configurations across five instruction‑tuned open‑weight LLMs, three datasets, and four text‑quality conditions, using deterministic decoding to generate 17,688 document‑level inferences. The study finds that clean‑text performance does not reliably predict real‑world robustness, model rankings can reverse under noisy conditions, and few‑shot demonstrations do not always improve accuracy, highlighting reliability risks in OCR‑to‑LLM pipelines.
By Zahra Anvari
FastVR is a streaming video restoration framework that uses a one‑step diffusion model to achieve strong restoration quality and temporal consistency while processing 1080p video at 11 FPS on a single H20 GPU. It addresses efficiency bottlenecks by combining a lightweight VAE with chunk‑wise causal attention, and improves inference speed and restoration quality through velocity consistency regularization and continuous trajectory learning during training. Experiments demonstrate that FastVR outperforms diffusion baselines in efficiency and achieves state‑of‑the‑art performance on both synthetic and real‑world benchmarks.
By Xiaoxu Chen, Qin Yang, Haoran Bai, Sibin Deng, Ying Chen
Spatial-OPSD is a label‑free self‑improvement framework for vision‑language models that leverages spatial priors such as depth, 3D relations, and camera geometry to provide dense token‑level supervision. During training, a privileged teacher uses these priors while the student learns from only the original visual‑language input, and a recursive round‑wise scheme allows repeated self‑improvement without moving the teacher. Across four VLM families, one round of Spatial‑OPSD improves the five‑benchmark average, and three rounds push a strong spatially specialized model to the open‑source frontier, achieving the highest average among open models and best results on three of five spatial reasoning benchmarks.
By Zhenyu Liu, Zhangquan Chen, Keyi Chen, Mingze Sun, Xiang An, Haodong Jing, Ruqi Huang
LongCat-DeepResearch is a deep research system that merges an enhanced LongCat model with a multi‑agent workflow to produce comprehensive, evidence‑grounded reports. The workflow separates global planning from detailed investigation, using planning agents to create a ResearchSpec and research agents to draft sections in parallel, followed by targeted local revisions guided by global review. The system achieves strong benchmark scores, including 55.25 on DeepResearchBench and 79.83 on ResearchRubrics, and shows benefits from combining planning perspectives and additional editing for readability.
By Meituan LongCat Team, He Zhu, Yue Xu, Wanli Wu, Haolin Ren, Yuxin Bian, Jiarui Zhao, Rongzhi Zhang, Quanchi Weng, Jinghao Cui, Yu Fan, Yuhan Liu, Yunhu Ye, Jiyuan Ren, Fengcheng Yuan, Zhao Yang, Jiacheng Zhang, Yuchuan Dai, Ruixuan Xiao, Haozhe Sun, Xiangyuan Liu, Cheng Sun, Yao Du, Yiming Hao, Hongbo Guo, Shuo He, Lei Wang, Xunliang Cai, Yan Chen, Fan Yang, Lingchuan Liu
arXiv:2609.34879v2 Announce Type: replace
Abstract: Tool agents use large language models to act through external tools, yet successfully executed calls can still leave user requests unfulfilled. Too...
By Xiang Xia, Cheng Yan, Wuyang Zhang, Fan Xu, Zhijun Fan, Shuyuan Zhang, Yanyong Zhang
arXiv:2605.30327v2 Announce Type: replace-cross
Abstract: Frontier reasoning models are produced by post-training base language models with reinforcement learning. Recent work has challenged this by...
By Felix Zhou, Anay Mehrotra, Quanquan C. Liu
The paper introduces the concept of protocol divergence, showing that identical nominal missing rates can lead to vastly different learning regimes in incomplete multi‑view clustering. It critiques existing evaluation practices that ignore observation structure and proposes CRAFT, a train‑once framework that fuses observed views with mask‑aware attention, enabling efficient deployment across multiple missing‑view protocols. Experiments on CUB, MultiFashion, and other benchmarks demonstrate CRAFT’s superior performance and significant computational savings through checkpoint reuse.
By Haolu Liu, Xiyue Wang, Xuanting Xie, Liangjian Wen, Zhao Kang