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

Rasika Muralidharan, Haewoon Kwak, Jisun An
10/8/2025
cs.CLcs.AI

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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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Cite this paper

@article{muralidharan2025lessons,
  title  = {Can Lessons From Human Teams Be Applied to Multi-Agent Systems? The Role of Structure, Diversity, and Interaction Dynamics},
  author = {Rasika Muralidharan and Haewoon Kwak and Jisun An},
  year   = {2025},
  eprint = {2510.07488},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.07488v2}
}

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