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.

翻译：近期，大语言模型多智能体系统（MAS LLMs）的研究兴趣日益增长，涌现出许多利用多个大语言模型处理复杂任务的框架。然而，大量相关文献仅借用了多智能体系统的术语，却未深入其基础理论。在本立场论文中，我们重点指出了多智能体系统理论与当前MAS LLMs实现之间的关键差异，聚焦于四个核心领域：智能体的社会性、环境设计、协调与通信协议以及涌现行为的度量。我们的立场是，许多MAS LLMs缺乏多智能体特性，如自主性、社会交互和结构化环境，且往往依赖于过度简化、以大语言模型为中心的架构。若忽视多智能体系统文献已解决的问题，该领域的发展可能放缓并失去关注。因此，我们系统分析了这一问题，并概述了相关的研究机遇；我们主张更好地整合既有的多智能体系统概念，采用更精确的术语，以避免误判和错失良机。