arXiv — NLP / Computation & Language · · 3 min read

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

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Computer Science > Computation and Language

arXiv:2607.16621 (cs)
[Submitted on 18 Jul 2026]

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

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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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