Large language models miss the multi-agent mark

Conference item

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.

Authors: La Malfa, E; La Malfa, G; Marro, S; Zhang, JM; Black, E

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: NuerIPS
• Host title: 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Position Paper Track.
• Publication date: 2025-12-11
• Acceptance date: 2025-09-26
• Event title: 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025)
• Event location: San Diego, CA, USA
• Event website: https://neurips.cc/Conferences/2025
• Event start date: 2025-12-02
• Event end date: 2025-12-07
• Language: English