Graph-based Agent Memory: Taxonomy, Techniques, and Applications
The Hong Kong Polytechnic University（香港理工大学）；School of Information（信息学院）；Xiamen University（厦门大学）；School of Computing and Information Systems（计算与信息学院）；Singapore Management University（新加坡国立管理学院）；School of Artificial Intelligence（人工智能学院）
Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), where memory can enable knowledge accumulation, iterative reasoning and self-evolution. Among diverse paradigms, graph stands out as a powerful structure for agent memory due to the intrinsic capabilities to model relational dependencies, organize hierarchical information, and support efficient retrieval. This survey presents a comprehensive review of agent memory from the graph-based perspective. First, we introduce a taxonomy of agent memory, including short-term vs. long-term memory, knowledge vs. experience memory, non-structural vs. structural memory, with an implementation view of graph-based memory. Second, according to the life cycle of agent memory, we systematically analyze the key techniques in graph-based agent memory, covering memory extraction for transforming the data into the contents, storage for organizing the data efficiently, retrieval for retrieving the relevant contents from memory to support reasoning, and evolution for updating the contents in the memory. Third, we summarize the open-sourced libraries and benchmarks that support the development and evaluation of self-evolving agent memory. We also explore diverse application scenarios. Finally, we identify critical challenges and future research directions. This survey aims to offer actionable insights to advance the development of more efficient and reliable graph-based agent memory systems. All the related resources, including research papers, open-source data, and projects, are collected for the community in https://github.com/DEEP-PolyU/Awesome-GraphMemory.
LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Nanyang Technological University（南洋理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Hong Kong University of Science and Technology（香港科技大学）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络安全科学与工程学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学中关村人工智能学院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Computing, Faculty of Science and Engineering, Macquarie University（麦考瑞大学计算机学院）；Cyberspace Institute of Advanced Technology, Guangzhou University（广州大学高级技术网络研究所）；Squirrel Ai Learning；Xiaomi Company（小米公司）
LLM Agents in Law: Taxonomy, Applications, and Challenges
Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（国立新加坡大学）；Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；The University of Chicago（芝加哥大学）；Rutgers University（罗格斯大学）；Columbia University（哥伦比亚大学）；New Jersey Institute of Technology（新泽西理工学院）
Taxonomy, Evaluation and Exploitation of IPI-Centric LLM Agent Defense Frameworks
The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University of Technology（浙江工业大学）；Lingnan University（岭南大学）；School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；ZTE Corporation（中兴通讯有限公司）
Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents
Department of Computer Science, New York University, CIMS, New York, USA（纽约大学计算机科学系，CIMS，纽约，美国）；Department of Computer Science, Cleveland State University, Cleveland, USA（克利夫兰州立大学计算机科学系，克利夫兰，美国）
Towards Compositional Generalization of LLMs via Skill Taxonomy Guided Data Synthesis
State Key Laboratory of Complex & Critical Software Environment, Beihang University（复杂与关键软件环境国家重点实验室，北京航空航天大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Beijing Institute of Technology（北京理工大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
Beijing Jiaotong University（北京交通大学）；Lancaster University（兰卡斯特大学）；Max Planck Institute for Informatics, Saarland Informatics Campus（马克斯·普朗克研究所（信息学）萨尔兰州信息学校区）；University of Electronic Science and Technology of China（电子科技大学）
Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions
School of Computer Science and Mathematics, Keele University（基尔大学计算机科学与数学学院）；Cybersecurity Institute, University of Liverpool（利物浦大学网络安全研究所）；Department of Applied Computing IICL, University of Wales Trinity Saint David（威尔士特里尼达大学应用计算系）；Deakin Cyber Research and Innovation Hub, Deakin University（德金大学网络安全研究与创新中心）；Department of Information Systems and Cyber Security, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系与网络安全系）
Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Zhejiang University（浙江大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；City University of Macau（澳门城市大学）；Ant Group（蚂蚁集团）；Griffith University（格里菲斯大学）；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
