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arXiv 2602.02474cs.CLcs.AIcs.LG
MemSkill：面向自进化智能体的可学习与进化记忆技能
MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents
Nanyang Technological University（南洋理工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Tsinghua University（清华大学）
Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, Wenya Wang
Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors about what to store and how to revise memory, making them rigid under diverse interaction patterns and inefficient on long histories. To this end, we present \textbf{MemSkill}, which reframes these operations as learnable and evolvable memory skills, structured and reusable routines for extracting, consolidating, and pruning information from interaction traces. Inspired by the design philosophy of agent skills, MemSkill employs a \emph{controller} that learns to select a small set of relevant skills, paired with an LLM-based \emph{executor} that produces skill-guided memories. Beyond learning skill selection, MemSkill introduces a \emph{designer} that periodically reviews hard cases where selected skills yield incorrect or incomplete memories, and evolves the skill set by proposing refinements and new skills. Together, MemSkill forms a closed-loop procedure that improves both the skill-selection policy and the skill set itself. Experiments on LoCoMo, LongMemEval, HotpotQA, and ALFWorld demonstrate that MemSkill improves task performance over strong baselines and generalizes well across settings. Further analyses shed light on how skills evolve, offering insights toward more adaptive, self-evolving memory management for LLM agents.
ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval
Shenzhen International Center for Industrial and Applied Mathematics（深圳国际工业与应用数学中心）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Shenzhen Loop Area Institute（深圳河套学院）
CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；McGill University（麦吉尔大学）；Columbia University（哥伦比亚大学）；Zhejiang University（浙江大学）；University of British Columbia（不列颠哥伦比亚大学）
Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；Huazhong University of Science and Technology（华中科技大学）
Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO
University of Chinese Academy of Sciences（中国科学院大学）；East China Normal University（华东师范大学）；Zhongguancun Academy（中关村学院）；Southeast University（东南大学）；Hainan University（海南大学）；Tsinghua University（清华大学）
DIRO & Institut Courtois, Université de Montréal（DIRO与Courtois研究所，蒙特利尔大学）；Mila – Québec AI Institute（魁北克人工智能研究所）；Microsoft Research（微软研究院）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology（东部技术研究所）；University of Science and Technology of China（中国科学技术大学）；Southeast University（东南大学）；Ningbo Institute of Digital Twin（宁波数字孪生研究所）
SkillFocus: Evolving Agent Skills via Capability Decomposition
East China Normal University（华东师范大学）；Nanyang Technological University（南洋理工大学）；Southern University of Science and Technology（南方科技大学）；Shanghai Innovation Institute（上海创新研究院）
Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
University of Maryland（马里兰大学）；University of Southern California（南加州大学）；Good Start Labs（Good Start实验室）；Independent Researcher（独立研究者）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
When Experience Becomes Instruction: Trajectory Poisoning in Self-Evolving Agent Skill Systems
Ant Group（蚂蚁集团）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Hangzhou Dianzi University（杭州电子科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Xi’an Jiaotong University（西安交通大学）
Fudan University（复旦大学）；Shanghai Key Laboratory of Data Science（上海市数据科学重点实验室）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
Tsinghua University（清华大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）
Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery
Southwest Jiaotong University, China（西南交通大学）；The University of Tokyo, Japan（东京大学）；RIKEN, Japan（日本理化学研究所）；Westlake University, China（西湖大学）；Hokkaido University, Japan（北海道大学）
