Multi-Agent Coordination

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Multi-agent coordination studies how agents with partial information, different capabilities, or competing incentives can act together in settings such as robot teams, traffic networks, resource allocation, and language-model workflows. Common approaches include decentralized and centralized multi-agent reinforcement learning, game-theoretic planning, role and task assignment, and communication through messages, shared memory, or learned coordination policies. Recent work also develops orchestration protocols and benchmarks for language-model agents, measuring not just task success but information use, role adherence, safety, and coordination failures. A central tension is that communication and additional agents can improve complex teamwork but also introduce overhead, propagate errors, or make performance worse; individual reasoning ability alone does not guarantee effective cooperation.

Latest papers

520 papers84 papers in the last four weeks, up 91% on the four weeks before. 0.5% of all new papers.

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
May 8, 2026Valeriy Vyaltsev, Alsu Sagirova, Anton Andreychuk +5Multi-Agent Coordination Robust MAPF
CogAI Lab, Moscow, Russia
TeamBench: Evaluating Agent Coordination under Enforced Role Separation
May 8, 2026Yubin Kim, Chanwoo Park, Taehan Kim +9Multi-Agent Coordination LLM Agent Evaluation
MIT · Google Research · Independent Researcher +1
Randomness is sometimes necessary for coordination
May 7, 2026Rohan Patil, Jai Malegaonkar, Henrik I. ChristensenMulti-Agent Coordination Multi-Agent Systems
Coordination Matters: Process-Aware Evaluation of Cooperative Multi-Agent Reinforcement Learning
May 7, 2026Maria Ana Cardei, Matthew Landers, Afsaneh DoryabMulti-Agent Coordination AI Agent Benchmarks
University of Virginia
Sustaining Cooperation in Populations Guided by AI: A Folk Theorem for LLMs
May 7, 2026Jonathan Shaki, Eden Hartman, Sarit Kraus +1Multi-Agent LLM Systems Multi-Agent Coordination
Bar-Ilan University, Israel
A Statistical Framework for Algorithmic Collective Action with Multiple Collectives
May 7, 2026Claudio Battiloro, Pietro Greiner, Dario Rancati +3Multi-Agent Coordination
Harvard University · Mila and LawZero · Institute of Science and Technology Austria +2
Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs
May 7, 2026Elizabeth Mieczkowski, Alexander Ku, Tiwalayo Eisape +5Multi-Agent Coordination LLM Agent Orchestration
Princeton University · University of Cambridge · MIT +1
Multiagent Stochastic Shortest Path Problem
May 7, 2026Martin Jonáš, Antonín Kučera, Vojtěch Kůr +2Multi-Agent System Optimization Multi-Agent Coordination
Faculty of Informatics, Masaryk University, Czechia
Role-Based Agentic AI for Intent-Driven Network and Service Orchestration
May 6, 2026Juan Parra-Ullauri, Talha Ahmed Khan, Daniel McHugh +4Multi-Agent Coordination Multi-Agent Orchestration
Research & Commercialisation, BT Group, UK.
Coordination as an Architectural Layer for LLM-Based Multi-Agent Systems
May 5, 2026Maksym Nechepurenko, Pavel ShuvalovMulti-Agent LLM Systems Multi-Agent Coordination
Devnull FZCO · Dubai, UAE
Pact: A Choreographic Language for Agentic Ecosystems
May 4, 2026Kiran Gopinathan, Jack Feser, Michelangelo Naim +2Multi-Agent Coordination Game Theory
Basis Research Institute, New York, New York, USA
SOTOPIA-TOM: Evaluating Privacy and Information Management in Multi-Agent Interaction with Theory of Mind
May 4, 2026Yashwanth YS, Ruichen Wang, Shihua Zeng +4Multi-Agent Coordination Theory of Mind
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA · Fujitsu Research of America, Pittsburgh, USA
Sheaf-Theoretic Planning: A Categorical Foundation for Resilient Multi-Agent Autonomous Systems
May 3, 2026Manuel Hernández, Eduardo Sánchez-SotoMulti-Agent Coordination Sheaf Theory
Universidad Tecnol´ogica de la Mixteca
Quality-Aware Exploration Budget Allocation for Cooperative Multi-Agent Reinforcement Learning
May 3, 2026Dahyun Oh, Minhyuk Yoon, H. Jin KimMulti-Agent Coordination Intrinsic Reward Methods
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea
MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning
May 3, 2026Haohan Yu, Jinmiao Cong, Shengzhi Wang +2Multi-Agent Coordination Intrinsic Reward Methods
1Dalian University of Technology · 2Microsoft, Beijing, China
Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation
May 3, 2026Yiheng Yao, Chelsea Zou, Robert D. HawkinsMulti-Agent Coordination Multi-Agent Negotiation
Stanford University
CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making
May 2, 2026Guowei Zou, Haitao Wang, Beiwen Zhang +2Flow Matching Multi-Agent Coordination
Sun Yat-sen University
TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination
May 1, 2026Yi Xie, Siao Liu, Falong Fan +3Multi-Agent System Optimization Multi-Agent Coordination
Department of Electrical & Computer Engineering, University of Arizona · Future Science and Engineering College, Soochow University · INSAIT, Sofia University ”St. Kliment Ohridski” +1
Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning
May 1, 2026Iman Sharifi, Hyeong Tae Kim, Maheed Hatem Ahmed +2Multi-Agent Coordination Multi-Agent Reinforcement Learning
Department of Mechanical and Aerospace Engineering, George Washington University, Washington, DC, USA · Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
May 1, 2026Sizhe Tang, Zuyuan Zhang, Mahdi Imani +1Multi-Agent System Optimization Multi-Agent Coordination
The George Washington University · Northeastern University
Learning to Act and Cooperate for Distributed Black-Box Consensus Optimization
May 1, 2026Zi-Bo Qin, Feng-Feng Wei, Tai-You Chen +1Multi-Agent System Optimization Multi-Agent Coordination
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
The Inverse-Wisdom Law: Architectural Tribalism and the Consensus Paradox in Agentic Swarms
Apr 30, 2026Dahlia Shehata, Ming LiMulti-Agent LLM Systems Multi-Agent Coordination
University of Waterloo · Canada
Preserving Disagreement: Architectural Heterogeneity and Coherence Validation in Multi-Agent Policy Simulation
Apr 29, 2026Ariel SelaMulti-Agent LLM Systems Multi-Agent Coordination
Tel Aviv University
Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest
Apr 28, 2026Abigail O'Neill, Alan Zhu, Mihran Miroyan +2Multi-Agent Coordination Multi-Agent Negotiation
University of California, Berkeley
Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings
Apr 28, 2026Keenan Powell, Peihong Yu, Pratap TokekarMulti-Agent Coordination Reward Shaping
Department of Computer Science University of Maryland, College Park
An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
Apr 27, 2026Moritz Link, Jonathan Hoss, Noah KlarmannMulti-Agent Coordination Combinatorial Optimization
Faculty of Management and Engineering, University of Applied Sciences, 83024 Rosenheim, Germany
MarketBench: Evaluating AI Agents as Market Participants
Apr 26, 2026Andrey Fradkin, Rohit KrishnanMulti-Agent Coordination LLM Agent Evaluation
Boston University and the MIT Initiative on the Digital Economy. · Independent Researcher.
CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
Apr 25, 2026Marcel Hedman, Kale-ab Abebe Tessera, Juan Claude Formanek +5Multi-Agent Coordination Offline RL
Inephany Ltd · Department of Statistics, University of Oxford · The University of Edinburgh +2
Don't Make the LLM Read the Graph: Make the Graph Think
Apr 24, 2026Yuqi Sun, Tianqin Meng, George Liu +4Multi-Agent Coordination Game-Playing Agents
Mindoverflow · University of Waterloo · Carnegie Mellon University +4
Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents
Apr 24, 2026Xirui Li, Ming Li, Yunze Xiao +4Multi-Agent Coordination AI Agent Evaluation
University of Maryland · 2Mohamed bin Zayed University of Artificial Intelligence · 3Carnegie Mellon University

May 8, 2026J/K move · Enter open · S save

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding

Valeriy Vyaltsev, Alsu Sagirova, Anton Andreychuk, Oleg Bulichev+4

CogAI Lab, Moscow, Russia

Multi-Agent Coordination Robust MAPF Multi-Agent Communication Multi-Agent Path Finding

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Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared environment. Although solving MAPF optimally is NP-hard, scalable and efficient solvers are critical for real-world applications such as logistics and search-and-rescue. To this end, the research community has proposed various decentralized suboptimal MAPF solvers that leverage machine learning. Such methods frame MAPF (from a single agent perspective) as a Dec-POMDP where at each time step an agent has to decide an action based on the local observation and typically solve the problem via reinforcement learning or imitation learning. We follow the same approach but additionally introduce a learnable communication module tailored to enhance cooperation between agents via efficient feature sharing. We present the Local Communication for Multi-agent Pathfinding (LC-MAPF), a generalizable pre-trained model that applies multi-round communication between neighboring agents to exchange information and improve their coordination. Our experiments show that the introduced method outperforms the existing learning-based MAPF solvers, including IL and RL-based approaches, across diverse metrics in a diverse range of (unseen) test scenarios. Remarkably, the introduced communication mechanism does not compromise LC-MAPF's scalability, a common bottleneck for communication-based MAPF solvers.