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，针对大语言模型多智能体系统的研究兴趣日益增长，催生了众多利用多个大语言模型处理复杂任务的框架。然而，现有文献大多仅借用多智能体系统的术语，却未深入其核心理念。在本立场论文中，我们重点指出多智能体系统理论与当前大语言模型多智能体系统实现之间的关键差异，聚焦于四个核心领域：智能体的社会属性、环境设计、协调与通信协议以及涌现行为的度量。我们认为，当前许多大语言模型多智能体系统缺乏自主性、社会交互和结构化环境等多智能体特征，且常依赖过度简化、以大语言模型为中心的架构。若忽视多智能体领域已解决的问题，该领域的发展可能放缓并丧失动力。因此，我们系统分析了这一问题，并梳理了相关研究机遇；我们主张更有效地整合成熟的多智能体系统概念，采用更精确的术语体系，以避免概念误用并把握潜在发展契机。