让大模型把记忆变成可复用的技能，提升长期任务执行能力
From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents
- 将记忆转化为带证据链的可调用技能，支持持续进化
- 通过自反思回填值，使稀疏反馈驱动全程决策优化
- 在多个任务上表现超越现有方法，适合长期任务智能体
当前长周期大模型智能体的记忆系统多仅被动引用过往经验，未能将其转化为可执行能力。本文提出无需训练的内存-技能协同演化框架MSCE，将智能体经验组织为具证据基础的步骤轨迹、可复用的过程策略及陈述性环境认知。MSCE将具有正收益估计的证据支撑的L2策略结晶为可调用技能，保留证据链接、适用边界、决策指引、验证规则和可靠性评估。进一步引入反射加权价值回填机制，通过密集的局部自反思，将稀疏的终局反馈传播至密集的轨迹节点，生成具证据校准的价值以指导记忆与技能演化。在EvoAgentBench与LoCoMo上的实验表明，MSCE显著优于现有技能增强与记忆驱动型智能体基线，展现出强大的跨领域迁移能力和终身演化特性。
原文摘要 · Abstract (English)
Existing memory systems for long-horizon LLM agents often retrieve prior traces as passive context rather than converting them into executable capabilities. In this paper, we propose MSCE, a training-free Memory--Skill Co-Evolution framework that organizes agent experience into grounded step traces, reusable procedural policies, and declarative environmental cognition. MSCE crystallizes evidence-backed L2 policies with positive estimated gain into callable skills that retain evidence links, applicability boundaries, decision guidance, verification rules, and reliability estimates. It further introduces reflection-weighted value backfilling, which propagates sparse terminal feedback through dense local self-reflections to produce evidence-calibrated trace values for governing memory and skill evolution. Experiments on EvoAgentBench and LoCoMo demonstrate that MSCE significantly outperforms state-of-the-art skill-augmented and memory-driven agent baselines, exhibiting strong cross-domain transferability and lifelong-evolution capabilities.
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