EvoTeam：通过演化团队实现多智能体协作
EvoTeam: Multi-Agent Collaboration via Evolving Teams
EvoTeam 通过执行经验联合演化团队组织、交互规则和智能体技能，同时保持可重用的团队库，在四个基准测试中展示了有效性。它维护一个可重用团队库，通过特定团队交互规则指导协作，并在兼容的团队上下文中积累协作经验，而个体技能可在不同团队间复用。
Published on Oct 30
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Abstract
EvoTeam evolves multi-agent collaboration by jointly adapting team organization, interaction rules, and agent skills through execution experience while maintaining reusable team libraries.
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 https://github.com/DEEP-JLU/EvoTeam.
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