From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents
Existing memory systems for long-horizon LLM agents often retrieve prior traces as passive context rather than converting them into executable capabilities. This paper proposes 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 into callable skills and introduces reflection-weighted value backfilling. Experiments show significant improvements over state-of-the-art baselines.
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[Submitted on 18 Jul 2026]
Title:From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents
View a PDF of the paper titled From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents, by Bo Tang and 10 other authors
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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.
Comments: Submitted into EMNLP'2026
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.16621 [cs.CL]
(or arXiv:2607.16621v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.16621
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yang Zhang [view email] [v1] Sat, 18 Jul 2026 03:46:22 UTC (380 KB)
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