# Multi-Agent Coordination across Diverse Applications: A Survey

arXiv: https://arxiv.org/abs/2502.14743 · HTML: https://mlsanity.com/paper/2502.14743/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 35 of its 148 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 35)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [NEAT: Neural Attention Fields for End-to-End Autonomous Driving](https://mlsanity.com/paper/2109.04456/) (2021)
- [Multi-Agent Reinforcement Learning is a Sequence Modeling Problem](https://mlsanity.com/paper/2205.14953/) (2022)
- [Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization](https://mlsanity.com/paper/1709.08765/) (2017)
- [Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks](https://mlsanity.com/paper/1812.09755/) (2018)
- [Mindstorms in Natural Language-Based Societies of Mind](https://mlsanity.com/paper/2305.17066/) (2023)
- [Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games](https://mlsanity.com/paper/1703.10069/) (2017)
- [Learning to Schedule Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1902.01554/) (2019)
- [Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis](https://mlsanity.com/paper/2312.05488/) (2023)
- [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [VAIN: Attentional Multi-agent Predictive Modeling](https://mlsanity.com/paper/1706.06122/) (2017)
- [Welfare Diplomacy: Benchmarking Language Model Cooperation](https://mlsanity.com/paper/2310.08901/) (2023)
- [Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving](https://mlsanity.com/paper/1903.10654/) (2019)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Teleoperation of Humanoid Robots: A Survey](https://mlsanity.com/paper/2301.04317/) (2023)

## Built on by (most cited first, 30 of 37)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Measuring Agents in Production](https://mlsanity.com/paper/2512.04123/) (2025)
- [AutoData: A Multi-Agent System for Open Web Data Collection](https://mlsanity.com/paper/2505.15859/) (2025)
- [Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance](https://mlsanity.com/paper/2511.10400/) (2025)
- [From Glue-Code to Protocols: A Critical Analysis of A2A and MCP Integration for Scalable Agent Systems](https://mlsanity.com/paper/2505.03864/) (2025)
- [Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents](https://mlsanity.com/paper/2601.10955/) (2026)
- [Clawdrain: Exploiting Tool-Calling Chains for Stealthy Token Exhaustion in OpenClaw Agents](https://mlsanity.com/paper/2603.00902/) (2026)
- [Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries](https://mlsanity.com/paper/2601.04583/) (2026)
- [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026)
- [MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era](https://mlsanity.com/paper/2601.07526/) (2026)
- [A Taxonomy of Hierarchical Multi-Agent Systems: Design Patterns, Coordination Mechanisms, and Industrial Applications](https://mlsanity.com/paper/2508.12683/) (2025)
- [Multi-User Large Language Model Agents](https://mlsanity.com/paper/2604.08567/) (2026)
- [HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution](https://mlsanity.com/paper/2604.17745/) (2026)
- [InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios](https://mlsanity.com/paper/2509.22502/) (2025)
- [Detecting Perspective Shifts in Multi-agent Systems](https://mlsanity.com/paper/2512.05013/) (2025)
- [Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination](https://mlsanity.com/paper/2508.14635/) (2025)
- [An Empirical Study of Multi-Agent Collaboration for Automated Research](https://mlsanity.com/paper/2603.29632/) (2026)
- [An Auditable Agent Platform For Automated Molecular Optimisation](https://mlsanity.com/paper/2508.03444/) (2025)
- [How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning](https://mlsanity.com/paper/2605.23023/) (2026)
- [Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2607.07989/) (2026)
- [Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Models](https://mlsanity.com/paper/2510.23824/) (2025)
- [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025)
- [Self-Evolving Coordination Protocol in Multi-Agent AI Systems: An Exploratory Systems Feasibility Study](https://mlsanity.com/paper/2602.02170/) (2026)
- [Weak-Link Optimization for Multi-Agent Reasoning and Collaboration](https://mlsanity.com/paper/2604.15972/) (2026)
- [Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents](https://mlsanity.com/paper/2606.21856/) (2026)
- [Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds](https://mlsanity.com/paper/2506.09335/) (2025)
- [Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era](https://mlsanity.com/paper/2506.09755/) (2025)
- [MAPF-World: Action World Model for Multi-Agent Path Finding](https://mlsanity.com/paper/2508.12087/) (2025)
- [MAC: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations](https://mlsanity.com/paper/2512.13154/) (2025)
- [Logic-Driven Semantic Communication for Resilient Multi-Agent Systems](https://mlsanity.com/paper/2601.06733/) (2026)

## Abstract (arXiv)

Multi-agent coordination studies the underlying mechanism enabling the trending spread of diverse multi-agent systems (MAS) and has received increasing attention, driven by the expansion of emerging applications and rapid AI advances. This survey outlines the current state of coordination research across applications through a unified understanding that answers four fundamental coordination questions: (1) what is coordination; (2) why coordination; (3) who to coordinate with; and (4) how to coordinate. Our purpose is to explore existing ideas and expertise in coordination and their connections across diverse applications, while identifying and highlighting emerging and promising research directions. First, general coordination problems that are essential to varied applications are identified and analyzed. Second, a number of MAS applications are surveyed, ranging from widely studied domains, e.g., search and rescue, warehouse automation and logistics, and transportation systems, to emerging fields including humanoid and anthropomorphic robots, satellite systems, and large language models (LLMs). Finally, open challenges about the scalability, heterogeneity, and learning mechanisms of MAS are analyzed and discussed. In particular, we identify the hybridization of hierarchical and decentralized coordination, human-MAS coordination, and LLM-based MAS as promising future directions.