We propose Re-FORC, an adaptive reward prediction method that, given a query, enables prediction of the expected future rewards as a function of the number of future thinking tokens. Re-FORC trains a lightweight adapter on reasoning models, demonstrating improved prediction with longer reasoning and larger models. Re-FORC enables: 1) early stopping of unpromising reasoning chains, reducing compute by up to 26\% compared to fixed-budget cutoffs, while maintaining accuracy, 2) optimized model and thinking length selection that outperforms the largest model alone--- reaching 1.7 percentage points higher peak accuracy while needing up to 12\% less compute to match the largest model's accuracy, 3) adaptive test-time scaling, which increases accuracy by 9.9 percentage points (on average at maximum compute) over confidence-based baselines. Re-FORC allows dynamic reasoning with length control via cost-per-token thresholds while estimating computation time upfront.
RREDCoT: Segment-Level Reward Redistribution for Reasoning Models
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria（林茨ELLIS单元和LIT人工智能实验室，机器学习研究所，林茨约瑟夫·冯·拉格纳大学，奥地利）；Cognizant AI Lab, San Francisco, USA（认知人工智能实验室，美国旧金山）；NXAI GmbH, Linz, Austria（NXAI公司，奥地利林茨）
Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage
CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（东华大学教育人工智能研究所）
Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts
Department of Computer Science and Engineering, University of Minnesota（明尼苏达大学计算机科学与工程系）；Department of Computer Science and Engineering, University of California Santa Cruz（加州大学圣克鲁兹分校计算机科学与工程系）；Argonne National Laboratory（阿贡国家实验室）
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；University College London（伦敦大学学院）；Yale University（耶鲁大学）；The Hong Kong University of Science and Technology（香港科技大学）；University of Washington（华盛顿大学）
提出Uncertainty of Thoughts (UoT)算法，通过不确定性感知模拟、基于不确定性的奖励和奖励传播机制，增强大型语言模型主动提问寻求信息的能力，在医疗诊断等任务中平均成功率提升38.1%。
SCOUT: Self-Checking and Recovery-Aware Tool-Thought Agents for Ultra-Long Egocentric Video Reasoning
Sun Yat-sen University（中山大学）；Shenzhen Loop Area Institute（深圳河套学院）；Guangdong OPPO Mobile Telecommunications Corp., Ltd.（广东欧珀移动通信有限公司）；OPPO AI Center（OPPO人工智能中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Thought Purity: A Defense Framework For Chain-of-Thought Attack
Huzhou University（湖州大学）；Zhejiang Key Laboratory of Intelligent Education Technology and Application（浙江智能教育技术与应用重点实验室）；University of Science and Technology of China（中国科学技术大学）；Alibaba Group（阿里巴巴集团）；Zhejiang University of Technology（浙江工业大学）
Efficient Process Reward Modeling via Contrastive Mutual Information
Department of Electrical and Computer Engineering, Seoul National University（首尔大学电气与计算机工程系）；Department of Computer Science and Engineering, Konkuk University（建国大学计算机科学与工程系）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, San Diego（加州大学圣地亚哥分校）；Texas A&M University（德克萨斯A&M大学）；Stevens Institute of Technology（史蒂文斯理工学院）
Confidence as a Reward: Transforming LLMs into Reward Models
Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；Xidian University（西安电子科技大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）
