arXiv cs.AI· Qinuo Cao, Xin He, Dianxuan Fu, Lizhi Zhang, Zhaoqing Xing, Jiapu Wang, Xin Wang, Na Zou, Yi Chang, Qinggang Zhang·· 2 小时前AI 评分36
EvoTeam：基于团队进化的多智能体协作框架
EvoTeam: Multi-Agent Collaboration via Evolving Teams
AI 导读
EvoTeam 是一个以协作为核心的多智能体系统进化框架，通过执行经验联合进化团队组织、交互规则和个体智能体技能。它维护可复用团队库，在兼容的团队上下文中积累协作经验，同时使个体技能可跨团队复用，解决了现有方法在异构任务上团队重组受限的问题。实验在四个 benchmark 上验证了其有效性，代码已开源。
正文 · 原文
Abstract:LLM-based multi-agent systems (MAS) rely on collaboration among specialized agents to solve complex tasks. Existing methods improve MAS by refining the prompts, skills, and execution strategies of individual agents based on task feedback. However, improving individual agents does not necessarily lead to better team collaboration. Recent studies have therefore extended evolution to the collaboration level, using collective execution experience to adjust team organization, and inter-agent interactions. Nevertheless, evolving collaboration across heterogeneous tasks remains challenging, as existing approaches often constrain team reorganization within a shared structure and reuse interaction rules across incompatible team contexts. In this paper, we propose EvoTeam, a collaboration-centric evolution framework that jointly evolves team organization, interaction rules, and individual agent skills through execution experience. It maintains a library of reusable teams, selecting or creating agent teams and guiding collaboration through team-specific interaction rules. Collaboration experience accumulates within compatible team contexts, while individual skills remain reusable across teams. Experiments across four benchmarks demonstrate its effectiveness. Our code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39813 [cs.AI] |
| (or arXiv:2610.39813v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39813 arXiv-issued DOI via DataCite |
Submission history
From: Xin He [view email]
[v1]
Thu, 29 Oct 2026 10:55:30 UTC (4,253 KB)
来源：arXiv cs.AI · arxiv.org