The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs

2026-02-12

AI Summary

In dynamic, partially observable environments, multi-agent systems must leverage effective communication to reduce uncertainty and enable collaboration. This work proposes a “Five Ws” analytical framework—addressing who communicates, when, what content is shared, and why—to systematically unify and analyze the evolution and design logic of communication mechanisms across three major paradigms: multi-agent reinforcement learning (MARL), emergent communication, and large language models (LLMs). By integrating insights across these paradigms, the study reveals fundamental trade-offs and shared challenges concerning interpretability, generalization, and scalability. It further distills practical communication design patterns and outlines a novel direction toward hybrid collaborative systems that synergistically integrate learning, language, and control.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multi-Robot Systems

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Multi-agent sequential decision-making powers many real-world systems, from autonomous vehicles and robotics to collaborative AI assistants. In dynamic, partially observable environments, communication is often what reduces uncertainty and makes collaboration possible. This survey reviews multi-agent communication (MA-Comm) through the Five Ws: who communicates with whom, what is communicated, when communication occurs, and why communication is beneficial. This framing offers a clean way to connect ideas across otherwise separate research threads. We trace how communication approaches have evolved across three major paradigms. In Multi-Agent Reinforcement Learning (MARL), early methods used hand-designed or implicit protocols, followed by end-to-end learned communication optimized for reward and control. While successful, these protocols are frequently task-specific and hard to interpret, motivating work on Emergent Language (EL), where agents can develop more structured or symbolic communication through interaction. EL methods, however, still struggle with grounding, generalization, and scalability, which has fueled recent interest in large language models (LLMs) that bring natural language priors for reasoning, planning, and collaboration in more open-ended settings. Across MARL, EL, and LLM-based systems, we highlight how different choices shape communication design, where the main trade-offs lie, and what remains unsolved. We distill practical design patterns and open challenges to support future hybrid systems that combine learning, language, and control for scalable and interpretable multi-agent collaboration.

Problem

• multi-agent communication
• collaborative decision-making
• emergent language
• large language models
• scalable collaboration

Innovation

• Multi-Agent Communication
• Five Ws Framework
• Emergent Language
• Large Language Models
• Multi-Agent Reinforcement Learning