arXiv cs.AI· Qinuo Cao, Xin He, Dianxuan Fu, Lizhi Zhang, Zhaoqing Xing, Jiapu Wang, Xin Wang, Na Zou, Yi Chang, Qinggang Zhang·· 14 小时前AI 评分34
EvoTeam：通过演化团队实现多智能体协作
EvoTeam: Multi-Agent Collaboration via Evolving Teams
AI 导读
EvoTeam 是一个以协作为中心的多智能体演化框架，联合演化团队组织、交互规则与个体智能体技能，并维护可复用团队库，按团队专属交互规则选择或创建团队。协作经验在兼容的团队上下文中积累，个体技能可跨团队复用，在四个基准上验证了有效性，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