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

• URL: http://arxiv.org/abs/2602.11583v1
• Date: Thu, 12 Feb 2026 05:07:50 GMT
• Title: The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs
• Authors: Jingdi Chen, Hanqing Yang, Zongjun Liu, Carlee Joe-Wong,
• Abstract summary: 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.<n>We trace how communication approaches have evolved across three major paradigms.<n>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.
• Score: 17.78378587855649
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Grounding Natural Language for Multi-agent Decision-Making with Multi-agentic LLMs [10.186029242664931]
  We extend the capabilities of large language models (LLMs) by integrating them with advancements in multi-agent decision-making algorithms.<n>We propose a systematic framework for the design of multi-agentic large language models (LLMs), focusing on key integration practices.
  arXiv  Detail & Related papers  (2025-08-10T19:53:23Z)
• Communicating Plans, Not Percepts: Scalable Multi-Agent Coordination with Embodied World Models [0.0]
  A central question in Multi-Agent Reinforcement Learning (MARL) is whether to engineer communication protocols or learn them end-to-end.<n>We propose and compare two communication strategies for a cooperative task-allocation problem.<n>Our experiments reveal that while emergent communication is viable in simple settings, the engineered, world model-based approach shows superior performance, sample efficiency, and scalability as complexity increases.
  arXiv  Detail & Related papers  (2025-08-04T21:29:07Z)
• Bidirectional Emergent Language in Situated Environments [4.950411915351642]
  We introduce two novel cooperative environments: Multi-Agent Pong and Collectors. optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one. We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination.
  arXiv  Detail & Related papers  (2024-08-26T21:25:44Z)
• Fully Independent Communication in Multi-Agent Reinforcement Learning [4.470370168359807]
  Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems. We investigate how independent learners in MARL that do not share parameters can communicate. Our results show that, despite the challenges, independent agents can still learn communication strategies following our method.
  arXiv  Detail & Related papers  (2024-01-26T18:42:01Z)
• Large Language Model Enhanced Multi-Agent Systems for 6G Communications [94.45712802626794]
  We propose a multi-agent system with customized communication knowledge and tools for solving communication related tasks using natural language. We validate the effectiveness of the proposed multi-agent system by designing a semantic communication system.
  arXiv  Detail & Related papers  (2023-12-13T02:35:57Z)
• ChatDev: Communicative Agents for Software Development [84.90400377131962]
  ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate. These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
  arXiv  Detail & Related papers  (2023-07-16T02:11:34Z)
• Building Cooperative Embodied Agents Modularly with Large Language Models [104.57849816689559]
  We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments. We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
  arXiv  Detail & Related papers  (2023-07-05T17:59:27Z)
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society [58.04479313658851]
  This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents. We propose a novel communicative agent framework named role-playing. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems.
  arXiv  Detail & Related papers  (2023-03-31T01:09:00Z)
• Learning to Ground Decentralized Multi-Agent Communication with Contrastive Learning [1.116812194101501]
  We introduce an alternative perspective to the communicative messages sent between agents, considering them as different incomplete views of the environment state. We propose a simple approach to induce the emergence of a common language by maximizing the mutual information between messages of a given trajectory in a self-supervised manner.
  arXiv  Detail & Related papers  (2022-03-07T12:41:32Z)
• Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents [83.52684405389445]
  We introduce the collaborative multi-object navigation task CoMON. In this task, an oracle agent has detailed environment information in the form of a map. It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals. We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
  arXiv  Detail & Related papers  (2021-10-12T06:56:11Z)
• The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning [6.18778092044887]
  Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.
  arXiv  Detail & Related papers  (2020-08-06T12:48:08Z)
• Learning Structured Communication for Multi-agent Reinforcement Learning [104.64584573546524]
  This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We propose a novel framework termed as Learning Structured Communication (LSC) by using a more flexible and efficient communication topology.
  arXiv  Detail & Related papers  (2020-02-11T07:19:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.