EvoTeam: Multi-Agent Collaboration via Evolving Teams

Qinuo Cao, Xin He, Dianxuan Fu, Lizhi Zhang, Zhaoqing Xing, Jiapu Wang, Xin Wang, Na Zou, Yi Chang, Qinggang Zhang

EvoTeam: Multi-Agent Collaboration via Evolving Teams: 23 upvotes on Hugging Face Daily Papers, #12 of 46 papers on 2026-11-04. Day-by-day upvote history.

Upvotes over time

It had 23 upvotes by its first day, 23 upvotes by the next day, 23 upvotes by day 3, 23 upvotes by day 7, and has 23 now. It ranked #36 of 130 papers in its week and #36 of 130 in its month.

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.

Paper page on Hugging Face · arXiv

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