Large Language Model (LLM) agents commonly condition actions on full action-observation histories, which introduce task-irrelevant information that easily leads to redundant actions and higher inference cost. We propose Progress-Aware Belief Update (PABU), a belief-state framework that compactly represents an agent's state by explicitly modeling task progress and selectively retaining past actions and observations. At each step, the agent predicts its relative progress since the previous round and decides whether the newly encountered interaction should be stored, conditioning future decisions only on the retained subset. Across eight environments in the AgentGym benchmark, and using identical training trajectories, PABU achieves an 81.0% task completion rate, outperforming previous State of the art (SoTA) models with full-history belief by 23.9%. Additionally, PABU's progress-oriented action selection improves efficiency, reducing the average number of interaction steps to 9.5, corresponding to a 26.9% reduction. Ablation studies show that both explicit progress prediction and selective retention are necessary for robust belief learning and performance gains.
PACR: Progressively Ascending Confidence Reward for LLM Reasoning
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Microsoft Research Asia (MSRA)（微软亚洲研究院）；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Nanjing Institute of Software Technology（南京软件技术学院）；Nanjing University of Posts and Telecommunications（南京邮电大学）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing University of Technology（北京工业大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
A Rational Analysis of the Effects of Sycophantic AI
School of Public and International Affairs, Princeton University（公共与国际事务学院，普林斯顿大学）；Princeton University（普林斯顿大学）；Department of Psychology, Princeton University（心理学系，普林斯顿大学）
SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought
SIGS, Tsinghua University（清华大学信息科学与技术学院）；Southern University of Science and Technology（南方科技大学）；Guangdong Laboratory of AI and Digital Economy (SZ)（广东人工智能与数字经济实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）
