CoEvolve：多智能体系统中基于反事实引导的记忆与协作共进化框架
CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems
AI 导读
研究者提出 CoEvolve，一种以技能为中心的 LLM 多智能体记忆—协作对齐共进化框架，将可复用的协作模式表示为技能，并通过对比原始与反事实轨迹生成进化信号，协调更新技能库、技能记忆与执行器记忆。
正文 · 原文
Abstract:Large language model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across diverse tasks. Yet their memory and collaboration architectures often remain static, constraining continued adaptation and improvement over time. Recent work addresses this limitation by evolving memory or collaboration based on interaction experience. However, updates to these interdependent components are not necessarily aligned: an evolved workflow may require experience that memory does not provide, while evolved memory may encode guidance that the workflow cannot use. To address this problem, we propose CoEvolve, a skill-centered framework for aligned memory--collaboration evolution. CoEvolve represents reusable local collaboration patterns as skills that link evolving collaboration decisions to their supporting memories. It further compares original and counterfactual trajectories to identify local evolution signals, which coordinate updates to the skill library, skill memory, and executor memories across rounds. Experiments on the ALFWorld and WebShop benchmarks confirm that CoEvolve achieves the best overall performance among the evaluated baselines, while ablation studies further validate the effectiveness of aligned co-evolution and counterfactual-guided evolution.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33539 [cs.AI] |
| (or arXiv:2610.33539v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33539 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xufei Shi [view email]
[v1]
Sat, 24 Oct 2026 08:35:39 UTC (3,366 KB)
来源：arXiv cs.AI · arxiv.org