EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks
Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；University of Technology Sydney（悉尼科技大学）
While multimodal large language models (MLLMs) have made groundbreaking progress in embodied intelligence, they still face significant challenges in spatial reasoning for complex long-horizon tasks. To address this gap, we propose EmbodiedVSR (Embodied Visual Spatial Reasoning), a novel framework that integrates dynamic scene graph-guided Chain-of-Thought (CoT) reasoning to enhance spatial understanding for embodied agents. By explicitly constructing structured knowledge representations through dynamic scene graphs, our method enables zero-shot spatial reasoning without task-specific fine-tuning. This approach not only disentangles intricate spatial relationships but also aligns reasoning steps with actionable environmental dynamics. To rigorously evaluate performance, we introduce the eSpatial-Benchmark, a comprehensive dataset including real-world embodied scenarios with fine-grained spatial annotations and adaptive task difficulty levels. Experiments demonstrate that our framework significantly outperforms existing MLLM-based methods in accuracy and reasoning coherence, particularly in long-horizon tasks requiring iterative environment interaction. The results reveal the untapped potential of MLLMs for embodied intelligence when equipped with structured, explainable reasoning mechanisms, paving the way for more reliable deployment in real-world spatial applications. The codes and datasets will be released soon.
Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Teaching AI Stepwise Diagnostic Reasoning with Report-Guided Chain-of-Thought Learning
Fujian University of Technology（福建工程学院）；Fujian Provincial Key Laboratory of Big Data Mining and Applications（福建省大数据挖掘与应用重点实验室）；Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences（生物医学成像科学与系统重点实验室，中国科学院）
Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy
Jilin University（吉林大学）；University of Central Florida（中央佛罗里达大学）；Arizona State University（亚利桑那州立大学）；University of Vienna（维也纳大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution
City University of Hong Kong（香港城市大学）；Soochow University（苏州大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Honhai Research Institute（鸿海研究院）
DAG-Math: Graph-of-Thought Guided Mathematical Reasoning in LLMs
Department of Statistics, University of Warwick（沃里克大学统计系）；Google DeepMind（谷歌DeepMind）；Department of Applied Mathematics, Hong Kong Polytechnic University（香港理工大学应用数学系）；School of Mathematical Sciences, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University（上海交通大学数学科学学院）
Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Shenyang Aerospace University（沈阳航空航天大学）；ZGC LAB, Beijing,China（北京ZGC实验室）；Institute for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与网络空间研究院）
Chain Of Thought Compression: A Theoretical Analysis
School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China（山西大学计算机与信息学院）；Queen Mary, University of London, UK（伦敦大学女王学院）；School of Informatics, University of Edinburgh, UK（爱丁堡大学信息学院）
CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning
Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；The Chinese University of Hong Kong（香港中文大学）；Monash University（墨尔本大学）；Independent Researcher（独立研究者）；Shanghai Academy of AI for Science（上海人工智能科学研究院）
Predicting LLM Output Length via Entropy-Guided Representations
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Provable Responsible AI and Data Analytics (PRADA) Lab（可验证负责任人工智能与数据分析实验室）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
