Recent interest in Multi-Agent Systems of Large Language Models (MAS LLMs) has led to an increase in frameworks leveraging multiple LLMs to tackle complex tasks. However, much of this literature appropriates the terminology of MAS without engaging with its foundational principles. In this position paper, we highlight critical discrepancies between MAS theory and current MAS LLMs implementations, focusing on four key areas: the social aspect of agency, environment design, coordination and communication protocols, and measuring emergent behaviours. Our position is that many MAS LLMs lack multi-agent characteristics such as autonomy, social interaction, and structured environments, and often rely on oversimplified, LLM-centric architectures. The field may slow down and lose traction by revisiting problems the MAS literature has already addressed. Therefore, we systematically analyse this issue and outline associated research opportunities; we advocate for better integrating established MAS concepts and more precise terminology to avoid mischaracterisation and missed opportunities.
View on arXiv@article{malfa2025_2505.21298,
title={ Large Language Models Miss the Multi-Agent Mark },
author={ Emanuele La Malfa and Gabriele La Malfa and Samuele Marro and Jie M. Zhang and Elizabeth Black and Michael Luck and Philip Torr and Michael Wooldridge },
journal={arXiv preprint arXiv:2505.21298},
year={ 2025 }
}