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）
Test-time scaling has emerged as a transformative paradigm for enhancing the performance of large reasoning models, enabling dynamic allocation of computational resources during inference. However, as the landscape of reasoning models rapidly expands, a critical question remains: how can we systematically compare and evaluate the test-time scaling capabilities across different models? In this paper, we introduce ARISE (Adaptive Resolution-aware Scaling Evaluation), a novel metric specifically designed to assess the test-time scaling effectiveness of large reasoning models. Unlike existing evaluation approaches, ARISE incorporates two key innovations: (1) sample-level awareness that effectively penalizes negative scaling behaviors where increased computation leads to performance degradation, and (2) a dynamic sampling mechanism that mitigates the impact of accuracy fluctuations and token count instability on the final assessment. We conduct comprehensive experiments evaluating state-of-the-art reasoning models across diverse domains including mathematical reasoning, code generation, and agentic tasks. Our results demonstrate that ARISE provides a reliable and fine-grained measurement of test-time scaling capabilities, revealing significant variations in scaling efficiency across models. Notably, our evaluation identifies Claude Opus as exhibiting superior scaling characteristics compared to other contemporary reasoning models.
ARISE: Agent Reasoning with Intrinsic Skill Evolution in Hierarchical Reinforcement Learning
Department of Electrical and Computer Engineering, George Washington University（乔治华盛顿大学电气与计算机工程系）；Department of Mathematical Sciences, University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）；School of Business, George Washington University（乔治华盛顿大学商学院）
Sycophancy Mitigation Through Reinforcement Learning with Uncertainty-Aware Adaptive Reasoning Trajectories
University of California, Davis（加州大学戴维斯分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Virginia Tech（弗吉尼亚理工大学）；Meta AI；Adobe Research（Adobe研究）
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）
HiDiffTIR: Hierarchical Difficulty-Aware Policy Optimization for Multi-Turn Tool-Integrated Reasoning
State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Meituan（美团）
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）
Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning
Huazhong University of Science and Technology（华中科技大学）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））；Xidian University（西安电子科技大学）；Nanyang Technological University（南洋理工大学）
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）
PAEC: Position-Aware Entropy Calibration for LLM Reasoning in RLVR
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
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.
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Jiao Tong University（上海交通大学）；SenseTime（商汤科技）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Tongji University（同济大学）；Hangzhou Institute for Advanced Study（杭州高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）
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）
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）
SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Merchants Union Consumer Finance Company Limited（招商局联融消费金融股份有限公司）
