Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

2025-02-20

AI Summary

Existing research on LLM-driven multi-agent systems (LLM-MAS) lacks a systematic, communication-centric perspective on how natural language interaction enables collective intelligence and adaptive collaboration. Method: We propose the first “communication-centered” analytical framework that bridges system-level dimensions (architecture, paradigm) and mechanism-level components (goals, strategies, content generation), uncovering how coupling among communication elements shapes collaborative flexibility and emergent intelligence. Based on this, we establish the first taxonomy of natural language communication specifically for LLM-MAS. Contribution/Results: The taxonomy identifies three core challenges—scalability, security, and multimodal integration—and derives corresponding design principles and development roadmaps. Our work provides a theoretical foundation and practical guidance for building robust, interpretable, and cross-domain collaborative multi-agent systems.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in reasoning, planning, and decision-making. Building upon these strengths, researchers have begun incorporating LLMs into multi-agent systems (MAS), where agents collaborate or compete through natural language interactions to tackle tasks beyond the scope of single-agent setups. In this survey, we present a communication-centric perspective on LLM-based multi-agent systems, examining key system-level features such as architecture design and communication goals, as well as internal mechanisms like communication strategies, paradigms, objects and content. We illustrate how these communication elements interplay to enable collective intelligence and flexible collaboration. Furthermore, we discuss prominent challenges, including scalability, security, and multimodal integration, and propose directions for future work to advance research in this emerging domain. Ultimately, this survey serves as a catalyst for further innovation, fostering more robust, scalable, and intelligent multi-agent systems across diverse application domains.

Problem

• Enhancing multi-agent systems with LLMs
• Exploring communication strategies in MAS
• Addressing scalability and security in MAS

Innovation

• LLM-based multi-agent systems
• communication-centric architecture design
• scalable and secure MAS