Reasoning about the behaviour of physical objects is a key capability of agents operating in physical worlds. Humans are very experienced in physical reasoning while it remains a major challenge for AI. To facilitate research addressing this problem, several benchmarks have been proposed recently. However, these benchmarks do not enable us to measure an agent's granular physical reasoning capabilities when solving a complex reasoning task. In this paper, we propose a new benchmark for physical reasoning that allows us to test individual physical reasoning capabilities. Inspired by how humans acquire these capabilities, we propose a general hierarchy of physical reasoning capabilities with increasing complexity. Our benchmark tests capabilities according to this hierarchy through generated physical reasoning tasks in the video game Angry Birds. This benchmark enables us to conduct a comprehensive agent evaluation by measuring the agent's granular physical reasoning capabilities. We conduct an evaluation with human players, learning agents, and heuristic agents and determine their capabilities. Our evaluation shows that learning agents, with good local generalization ability, still struggle to learn the underlying physical reasoning capabilities and perform worse than current state-of-the-art heuristic agents and humans. We believe that this benchmark will encourage researchers to develop intelligent agents with advanced, human-like physical reasoning capabilities. URL: https://github.com/Cheng-Xue/Hi-Phy
Cog-Rethinker: Hierarchical Metacognitive Reinforcement Learning for LLM Reasoning
Baidu Inc.（百度公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）
ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models
Chinese Information Processing Laboratory（中国信息处理实验室）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Self-supervised Hierarchical Visual Reasoning with World Model
Department of Electronic Engineering and Information Science, University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous Driving
School of Mechanical and Electronic Engineering, Wuhan University of Technology（武汉理工大学机械与电子工程学院）；Intelligent Transportation Systems Research Center, Wuhan University of Technology（武汉理工大学智能交通系统研究所）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Mechanical Engineering, Southeast University（东南大学机械工程学院）
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）
PhotoCraft: Agentic Reasoning with Hierarchical Self-Evolving Memory for Deep Image Search
Pattern Recognition Center, WeChat AI, Tencent Inc.（微信AI模式识别中心，腾讯公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Zhongguancun Academy（中关村学院）；Nanyang Technological University（南洋理工大学）；Shanghai Jiao Tong University（上海交通大学）
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）
Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage
School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；Faculty of Dentistry, The University of Hong Kong（香港大学牙科学院）；The Prince Philip Dental Hospital（菲利普王子牙科医院）；Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学利滋医学学院）；The Hong Kong University of Science and Technology（香港科学与技术大学）
Hierarchical Reasoning Models: Perspectives and Misconceptions
Center for Brains, Minds, and Machines（大脑、心智与机器中心）；K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center（K. Lisa Yang整合计算神经科学中心）；McGovern Institute for Brain Research（麦戈文脑科学研究所）；Department of Brain and Cognitive Sciences（脑科学与认知科学系）
HiPO: Hierarchical Preference Optimization for Adaptive Reasoning in LLMs
Vellore Institute of Technology（维洛雷理工学院）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）；Northwestern University（西北大学）；Yale University（耶鲁大学）；Algoverse AI Research（Algoverse AI研究）
H2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning
Harbin Institute of Technology（哈尔滨工业大学）；AI Research Center, Midea Group (Shanghai) Co., Ltd.（美的集团（上海）有限公司AI研究中心）；Changchun University of Science and Technology（长春理工大学）
THOR: A Theta-Gamma Hierarchical Oscillatory Reasoning Framework for Multi-hop QA
Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部生物医学工程学院）
Dynamical Systems Theory Behind a Hierarchical Reasoning Model
Department of Radiophysics, University of Nizhny Novgorod（尼日尼诺夫戈罗德大学无线电物理系）；Huawei Nizhny Novgorod Research Center（华为尼日尼诺夫戈罗德研究中心）；Skolkovo Institute of Science and Technology（斯克洛科夫科学与技术研究院）
