Can Lessons From Human Teams Be Applied to Multi-Agent Systems? The Role of Structure, Diversity, and Interaction Dynamics

• URL: http://arxiv.org/abs/2510.07488v2
• Date: Fri, 10 Oct 2025 01:59:22 GMT
• Title: Can Lessons From Human Teams Be Applied to Multi-Agent Systems? The Role of Structure, Diversity, and Interaction Dynamics
• Authors: Rasika Muralidharan, Haewoon Kwak, Jisun An,
• Abstract summary: We propose a multi-agent framework to examine core aspects of team science: structure, diversity, and interaction dynamics.<n>Our results show that flat teams tend to perform better than hierarchical ones, while diversity has a nuanced impact.
• Score: 3.866688354921775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Agent Systems (MAS) with Large Language Model (LLM)-powered agents are gaining attention, yet fewer studies explore their team dynamics. Inspired by human team science, we propose a multi-agent framework to examine core aspects of team science: structure, diversity, and interaction dynamics. We evaluate team performance across four tasks: CommonsenseQA, StrategyQA, Social IQa, and Latent Implicit Hate, spanning commonsense and social reasoning. Our results show that flat teams tend to perform better than hierarchical ones, while diversity has a nuanced impact. Interviews suggest agents are overconfident about their team performance, yet post-task reflections reveal both appreciation for collaboration and challenges in integration, including limited conversational coordination.

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