arXiv cs.AI· Yalun Wu, Qiwei Ma, Bingbing Xu, Dan Zhang, Long Bai, Boyang Wang, Jiatong Li, Zheqi Lv, Shaozu Yuan, Peiying Wang, Jiawei Wang, An Zhang, Tat-Seng Chua·· 3 小时前AI 评分35
EvolveMAS：面向去中心化多智能体系统的统一自进化框架
EvolveMAS: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems
AI 导读
研究者提出 EvolveMAS，一个支持去中心化多智能体系统在测试时适应与经验复用的统一自进化框架。
正文 · 原文
Authors:Yalun Wu, Qiwei Ma, Bingbing Xu, Dan Zhang, Long Bai, Boyang Wang, Jiatong Li, Zheqi Lv, Shaozu Yuan, Peiying Wang, Jiawei Wang, An Zhang, Tat-Seng Chua
Abstract:Decentralized multi-agent systems (MAS) enable large language model agents to interact without centralized control. Agents need to adapt to other agents at test time and reuse accumulated experience to refine their interaction strategies for future interactions across cooperative, mixed-motive, and adversarial settings. However, existing approaches often develop adaptation mechanisms for particular interaction settings. This raises a central question: how can decentralized MAS support test-time adaptation and reliable experience reuse across interaction settings? We propose EvolveMAS, a unified self-evolution framework that supports experience-driven adaptation within and across episodes. EvolveMAS pairs each main agent with a dedicated MemoryAgent for test-time memory governance, which maintains isolated Structured Memory outside the main agent's reasoning context. EvolveMAS organizes adaptation into two complementary processes: Within-Episode Online Adaptation and Cross-Episode Knowledge Evolution. During Within-Episode Online Adaptation, MemoryAgent updates episodic, semantic, and working memory from interaction evidence to inform subsequent actions. During Cross-Episode Knowledge Evolution, the main agent uses memory and episode outcomes to distill reusable interaction knowledge comprising partner profiles and interaction strategies for subsequent episodes. Across five benchmarks and six evaluation configurations spanning the three interaction settings, EvolveMAS achieves the highest normalized average score of 77.2 among the compared methods. Ablations show that test-time memory governance and cross-episode knowledge reuse both improve performance. Interaction traces further illustrate how updated memory and distilled knowledge guide subsequent interactions. These results support experience-driven adaptation in decentralized MAS without updating model parameters.
| Comments: | 27 pages, 9 figures |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34450 [cs.MA] |
| (or arXiv:2610.34450v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34450 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiawei Wang [view email]
[v1]
Sun, 25 Oct 2026 17:09:49 UTC (1,614 KB)
来源：arXiv cs.AI · arxiv.org