A common approach to solving physical reasoning tasks is to train a value learner on example tasks. A limitation of such an approach is that it requires learning about object dynamics solely from reward values assigned to the final state of a rollout of the environment. This study aims to address this limitation by augmenting the reward value with self-supervised signals about object dynamics. Specifically, we train the model to characterize the similarity of two environment rollouts, jointly with predicting the outcome of the reasoning task. This similarity can be defined as a distance measure between the trajectory of objects in the two rollouts, or learned directly from pixels using a contrastive formulation. Empirically, we find that this approach leads to substantial performance improvements on the PHYRE benchmark for physical reasoning (Bakhtin et al., 2019), establishing a new state-of-the-art.
Structured Role-Aware Policy Optimization for Multimodal Reasoning
School of Computing & Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；School of Computing & Data Science and Institute of Data Science, The University of Hong Kong（计算与数据科学学院和数据科学研究所，香港大学）
Mitigating Overthinking in Large Reasoning Models via Difficulty-aware Reinforcement Learning
Laboratory for Artificial Intelligence and New Forms of Education（人工智能与新教育形式实验室）；National Engineering Research Center of Educational Big Data（教育大数据国家工程研究中心）；Faculty of Artificial Intelligence in Education, Central China Normal University（教育人工智能学院，中央财经大学）；College of Electronics and Information Engineering, South-Central Minzu University（电子与信息工程学院，西南民族大学）
BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
Institute for AI Industry Research (AIR) Tsinghua University（人工智能产业研究院（AIR）清华大学）；Global Innovation Exchange & Department of Automation Tsinghua University（全球创新交流中心及自动化系 清华大学）
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
DISI, University of Trento（特伦托大学DISI学院）；DI, University of Pisa（比萨大学DI学院）；University of Edinburgh（爱丁堡大学）；CIMeC&DISI, University of Trento（特伦托大学CIMeC&DISI学院）
BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
The Conversational AI (CoAI) group, DCST, Tsinghua University（清华大学智能对话AI小组，清华大学数据科学与技术研究院）；Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore（前沿人工智能研究中心，高性能计算研究所，新加坡科技研究局）；The College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness
MARS 4.0 Fellowship, Cambridge AI Safety Hub(CAISH), University of Cambridge（MARS 4.0 Fellow，剑桥人工智能安全中心（CAISH），剑桥大学）；AWS Generative AI Innovation Center, Amazon Web Services, USA（亚马逊生成AI创新中心，亚马逊网络服务，美国）；Google, USA（谷歌，美国）；Stanford University（斯坦福大学）；Northeastern University, Seattle, WA, USA（东北大学，西雅图，华盛顿州，美国）
When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Northeastern University（东北大学）；Harvard University（哈佛大学）；Cornell University（康奈尔大学）；MIT（麻省理工学院）；Fujitsu Research of America（美国富士通研究）；Tsinghua University（清华大学）；Peking University（北京大学）；University of Georgia（佐治亚大学）；Florida International University（佛罗里达国际大学）；EmbodyX Inc（EmbodyX公司）；Cisco Systems（思科系统）
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
Artificial Intelligence Research Center, Chang Gung University（长庚大学人工智能研究中心）；Graduate Institute of Oral Biology, National Taiwan University（国立台湾大学口腔生物学研究所）；National Taiwan University of Science and Technology（国立台湾科技大学）
Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation
Beihang University（北航）；Heilongjiang University（黑龙江大学）；Beijing University of Civil Engineering and Architecture（北京建筑大学）；Yanshan University（燕山大学）；Shandong University（山东大学）；Renmin University of China（中国人民大学）
ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models
Fudan University（复旦大学）；The University of Hong Kong（香港大学）；Nanjing University（南京大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）
Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering
1 School of Internet of Things, Nanjing University of Posts；Telecommunications 2 Faculty of Medicine \& Health, University of New South Wales, Sydney, Australia 3 Dept. of Biomedical Engineering \& Biotechnology, Khalifa University, Abu Dhabi 4 Beijing Information Science；Technology University 5 Shortest Path Technology Co., Ltd.
Heterogeneous Element-Aware Cross-Version Differencing of Scientific Documents via Layout-Aware Alignment and Structure-Aware Reasoning
Beijing Renhe Information Technology Co., Ltd.（北京人和信息技术有限公司）；Key Laboratory of Digital Publishing and Total Process Management of Scientific and Technical Journals（科技期刊数字出版与全程管理重点实验室）
Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models
LARG, Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology（大型语言模型研究组，社会计算与交互机器人研究中心，哈尔滨工业大学）；School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）
