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Guan Wang, Changling Liu, Chenyu Wang +6 authors
A Hierarchical Recurrent Model architecture with specialized training on instruction-response pairs achieves competitive language modeling performance with significantly reduced computational requirements compared to traditional Transformer-based approaches.
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Dongsheng Ma, Jiayu Li, Zhengren Wang +8 authors
CiteVQA introduces a benchmark for document vision-language models that evaluates both answer accuracy and correct citation of supporting evidence, revealing significant attribution hallucinations in current models.
Xuying Ning, Katherine Tieu, Dongqi Fu +39 authors
This survey frames code as the operational foundation for agent reasoning, action, and verification, organizing agent harnesses across interface, mechanism, and multi-agent scaling layers.
Kaiyi Zhang, Wei Wu, Yankai Lin
Reinforcement learning from verifiable rewards is enhanced through a discriminative token credit assignment method that improves reward-based training by amplifying distinctive token-gradient directions and reducing noise from shared patterns.
Guobin Shen, Xiang Cheng, Chenxiao Zhao +4 authors
Anti-Self-Distillation reverses the direction of knowledge transfer in self-distillation to improve math reasoning efficiency and accuracy.
Jiaqi Liu, Shi Qiu, Mairui Li +32 authors
AutoResearchClaw is a multi-agent autonomous research system that improves scientific discovery through structured debate, self-healing execution, verifiable reporting, human collaboration, and evolutionary learning, outperforming previous systems on a benchmark while maintaining human oversight.
Hanyu Guo, Jiedong Yang, Chao Chen +3 authors
TransitLM dataset enables end-to-end transit route planning using large language models trained on structured transit data, eliminating the need for traditional map-based approaches.
Caixin Kang, Tianyu Yan, Sitong Gong +8 authors
Researchers introduce a new task and dataset for evaluating personality reasoning in multimodal language models, revealing significant gaps between accurate predictions and grounded reasoning processes.
Xiaofei Wen, Wenjie Jacky Mo, Xingyu Fu +6 authors
Video-capable multimodal large language models exhibit apparent audio understanding driven by visual cues rather than actual audio processing, necessitating intervention-based frameworks for diagnosing and improving audio-visual alignment.
Weimin Xiong, Shuhao Gu, Bowen Ye +5 authors
A large-scale GUI dataset was created by automatically extracting interaction trajectories from internet videos, enabling improved performance in GUI agents through pre-training on this diverse collection.
Shijie Lian, Bin Yu, Xiaopeng Lin +10 authors
PhysBrain 1.0 leverages human egocentric video to generate physical commonsense supervision for vision-language-action models, achieving state-of-the-art performance in embodied control tasks through capability-preserving adaptation.
Zhifei Xie, Kaiyu Pang, Haobin Zhang +4 authors
Mega-ASR framework improves robustness in real-world speech recognition through compound-data construction and progressive acoustic-to-semantic optimization techniques.
Hongyi Liu, Haoyan Yang, Tao Jiang +3 authors
SkillsVote is a governance framework for long-horizon LLM agents that manages reusable skills through structured collection, recommendation, and evolution processes.
Kangning Zhang, Shuai Shao, Qingyao Li +8 authors
Multimodal procedural knowledge frameworks enable visual agents to leverage external reusable skills through structured representations combining text, state cards, and visual keyframes, improving decision-making in complex environments.
Yukang Chen, Luozhou Wang, Wei Huang +13 authors
LongLive-2.0 presents an NVFP4-based parallel infrastructure for long video generation that addresses training and inference bottlenecks through sequence-parallel autoregressive training and diffusion model tuning.
Haoran Zhang, Luxin Xu, Zhilin Wang +11 authors
Proactive assistance in personal agent systems requires identifying hidden user intents through sustained multi-turn interactions, which current benchmarks fail to adequately evaluate.
Yanke Zhou, Yiduo Li, Hanlin Tang +6 authors
RTPurbo leverages intrinsic sparsity in full-attention LLMs to achieve efficient long-context inference with minimal training overhead, enabling significant speedups while maintaining near-lossless accuracy.
Jeremías Figueiredo Paschmann, Juan Kaplan, Francisco Nattero +3 authors
Pairwise ranking prompting is reformulated as active learning from noisy comparisons, with improved rankers that enhance ranking quality under call constraints and address position bias through a randomized oracle.
X. Feng, J. Zhu, M. Wu +6 authors
MIGA addresses long video generation challenges by reducing training-inference gaps and enhancing temporal consistency through dual consistency mechanisms.
Jinbiao Wei, Qianran Ma, Yilun Zhao +4 authors
OpenComputer presents a framework for creating verifiable software environments for computer-use agents through integrated state verification, self-improving layers, task synthesis, and evaluation systems across multiple desktop applications.
Rongbin Tan, Fangfang Lin, Zhenlong Yuan +10 authors
IndusAgent is a tool-augmented agentic framework for open-vocabulary industrial anomaly detection that improves performance through structured visual reasoning and dynamic tool utilization.
Fengyi Fu, Mengqi Huang, Shaojin Wu +10 authors
Lance is a unified multimodal model that combines understanding, generation, and editing capabilities for images and videos through collaborative multi-task training and a dual-stream architecture.
Lingdong Kong, Xian Sun, Wei Chow +17 authors
AI systems demonstrate varying reliability across research stages, excelling in structured tasks but struggling with novel ideas and scientific judgment, necessitating human oversight for credible outcomes.
Quanjian Song, Yefeng Shen, Mengting Chen +5 authors
FashionChameleon enables real-time interactive multi-garment video customization through teacher-student distillation and in-context learning techniques while maintaining motion coherence.
Zhongzhu Zhou, Donglin Zhuang, Jisen Li +4 authors
OSCAR is an ultra-low-bit KV cache quantization method that aligns quantization with attention-aware covariance structures, achieving high accuracy and efficiency for long-context LLM serving.
Qisheng Su, Zhen Fang, Shiting Huang +8 authors
Agent Context Compilation (ACC) enhances long-context reasoning in LLMs by converting multi-turn agent trajectories into structured QA pairs, enabling direct supervision of distant context integration without additional annotation.
Yuchen Cai, Ding Cao, Liang Lin +9 authors
On-policy distillation efficiency arises from early establishment of stable update trajectories, with findings leading to a plug-and-play acceleration method achieving 3x training speedup.
Minxuan Lv, Tiehua Mei, Tanlong Du +9 authors
GoLongRL presents an open-source approach for long-context reinforcement learning with diverse reward optimization through capability-oriented data construction and TMN-Reweight methodology.
Ziang Cao, Yinghao Liu, Haitian Li +5 authors
PhysX-Omni presents a unified framework for generating simulation-ready 3D assets with physical properties across multiple categories using a novel geometry representation and evaluation benchmarks.
Kaiwen Luo, Zhenhong Zhou, Leo Wang +31 authors
Large Audio Language Models exhibit significant trustworthiness challenges despite performance advances, requiring comprehensive frameworks addressing security vulnerabilities and defensive strategies.
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