Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, and intelligent problem-solving capabilities. Existing surveys typically categorize LLM-based multi-agent systems (LLM-MAS) according to their application domains or architectures, overlooking the central role of communication in coordinating agent behaviors and interactions. To address this gap, this paper presents a comprehensive survey of LLM-MAS from a communication-centric perspective. Specifically, we propose a structured framework that integrates system-level communication (architecture, goals, and protocols) with system internal communication (strategies, paradigms, objects, and content), enabling a detailed exploration of how agents interact, negotiate, and achieve collective intelligence. Through an extensive analysis of recent literature, we identify key components in multiple dimensions and summarize their strengths and limitations. In addition, we highlight current challenges, including communication efficiency, security vulnerabilities, inadequate benchmarking, and scalability issues, and outline promising future research directions. This review aims to help researchers and practitioners gain a clear understanding of the communication mechanisms in LLM-MAS, thereby facilitating the design and deployment of robust, scalable, and secure multi-agent systems.

翻译：基于大语言模型的多智能体系统因其在复杂、协作及智能问题解决方面的潜力，近期获得了广泛关注。现有综述通常根据应用领域或架构对大语言模型多智能体系统（LLM-MAS）进行分类，却忽视了通信在协调智能体行为与交互中的核心作用。为弥补这一不足，本文从以通信为中心的视角出发，对LLM-MAS进行了全面综述。具体而言，我们提出一个结构化框架，将系统级通信（包括架构、目标和协议）与系统内部通信（包括策略、范式、对象和内容）相整合，从而深入探索智能体如何交互、协商并实现集体智能。通过广泛分析近期文献，我们从多维度识别关键组件，并总结其优势与局限。此外，我们重点阐述了当前面临的挑战，包括通信效率、安全漏洞、基准测试不足以及可扩展性问题，并勾勒出未来有前景的研究方向。本综述旨在帮助研究人员和实践者清晰理解LLM-MAS中的通信机制，从而促进稳健、可扩展且安全的多智能体系统的设计与部署。