From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents

Bo Tang; Yang Zhang; Guomian Zhuang; Wenqiang Wei; Gaoyang Zheng; Lindong Xie; Yanchao Tan; Feiyu Xiong; Qingyu Yang; Edward Chung; Zhiyu li

arXiv · 2607.16621

From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents

Bo Tang·Yang Zhang·Guomian Zhuang·Wenqiang Wei·Gaoyang Zheng·Lindong Xie·Yanchao Tan·Feiyu Xiong·Qingyu Yang·Edward Chung·Zhiyu li

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Abstract

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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BibTeX RIS

Bo Tang, Yang Zhang, Guomian Zhuang, Wenqiang Wei, Gaoyang Zheng, Lindong Xie, Yanchao Tan, Feiyu Xiong, Qingyu Yang, Edward Chung, Zhiyu li. 2026-07-18. From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents. https://arxiv.org/abs/2607.16621

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