Multi-Agent Communication

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Multi-agent communication examines how agents share information, form common beliefs, and coordinate actions when each may have only a partial view of a task. Approaches include learned signaling in decentralized reinforcement learning, graph-based routing and debate among language-model agents, and compact symbolic or latent messages designed to reduce bandwidth and latency. Researchers evaluate these methods in settings such as cooperative navigation, StarCraft, reasoning and coding tasks, and embodied collaboration with limited or unreliable communication. A central tension is that messages can reveal information teammates need, but added communication can also create overhead, spread errors, reduce diversity, or expose systems to attacks.

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380 papers65 papers in the last four weeks, up 59% on the four weeks before. 0.4% 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
The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search
May 7, 2026David Farr, Iain Cruickshank, Kate Starbird +1Opinion Dynamics Multi-Agent Systems
1*Information School, University of Washington, 1410 NE Campus Parkway, Seattle, 98195, WA, USA. · 2Computer Science, Carnegie Mellon University, 5000 Forbes Avenue,2026 Pittsburgh, 15213, PA, USA. · 3Human Centered Design Engineering, University of Washington, 1410 NE Campus Parkway, Seattle, 98195, WA, USA. +1
Randomness is sometimes necessary for coordination
May 7, 2026Rohan Patil, Jai Malegaonkar, Henrik I. ChristensenMulti-Agent Coordination Multi-Agent Systems
SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking
May 7, 2026Yong Xiao, Haoran Zhou, Yujie Zhou +1Agent Communication Protocols Semantic Communication
School of Elect. Inform. & Commun., Huazhong Univ. of Science & Technology, China · Peng Cheng Laboratory, Shenzhen, China · Pazhou Laboratory (Huangpu), Guangzhou, China +1
Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems
May 7, 2026Huchen Yang, Xinghao Dong, Dan Negrut +1Multi-Agent LLM Systems Multi-Agent System Optimization
University of Wisconsin–Madison
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
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
Breaking the Communication-Accuracy Trade-off: A Sparsified Information Diffusion Framework for Multi-Agent Collaborative Perception
May 1, 2026Jirong Zha, Chenyu Zhao, Nan Zhou +5Bayesian Filtering Cooperative Perception
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Shenzhen Institute of Artificial Intelligence and Robotics, Shenzhen, China, 518000 · Shenzhen Smart City Technology Development Group, Shenzhen, China, 518000
Scaling Video Understanding via Compact Latent Multi-Agent Collaboration
May 1, 2026Kerui Chen, Jinglu Wang, Jianrong Zhang +3Multi-Agent Collaboration Long-Video Understanding
Microsoft Research Asia · Zhejiang University · Guanming Lab.
AONA: A Comprehensive Architecture and Workflow Design for Global Agentic Collaboration
Apr 30, 2026Jinliang Xu, Runkai Zhu, Bingqi Li +3Multi-Agent Collaboration Multi-Agent Communication
China Academy of Information and Communications Technology, No.52, North Huayuan Road, Haidian District, Beijing 100191, China
Recursive Multi-Agent Systems
Apr 28, 2026Jiaru Zou, Rui Pan, Ruizhong Qiu +8Multi-Agent LLM Systems Language Modeling
Stanford University · University of Illinois Urbana-Champaign · NVIDIA +1
A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
Apr 28, 2026Valentin Cuzin-Rambaud, Laetitia Matignon, Maxime MorgeMulti-Agent Communication Multi-Agent Reinforcement Learning
LIRIS, UCBL · Université Lyon 1, INSA Lyon, CNRS, LIRIS, UMR 5205, Lyon, France
Architecture Matters for Multi-Agent Security
Apr 25, 2026Ben Hagag, William L. Anderson, Christian Schroeder de Witt +1Multi-Agent Systems Multi-Agent System Security
Carnegie Mellon University · MATS · Oxford Witt Lab +1
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
DM $^3$ -Nav: Decentralized Multi-Agent Multimodal Multi-Object Semantic Navigation
Apr 23, 2026Amin Kashiri, Atharva Jamsandekar, Yasin YazıcıoğluVision-Language Navigation Multi-Robot Coordination
Northeastern University, Boston, USA · Department of Electrical and Computer Engineering at Northeastern University, Boston, MA
Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems
Apr 23, 2026Ye Yu, Heming Liu, Haibo Jin +3Multi-Agent System Optimization LLM Agent Training
University of Illinois Urbana-Champaign, IL, USA
AI-Gram: When Visual Agents Interact in a Social Network
Apr 23, 2026Andrew ShinMulti-Agent LLM Systems Multi-Agent Systems
Faculty of Science and Technology Keio University Yokohama, Kanagawa Prefecture, Japan
Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems
Apr 21, 2026Hongwei XuLLM Agent Memory Multi-Agent Communication
Revac: A Social Deduction Reasoning Agent
Apr 21, 2026Mihir Shriniwas Arya, Avinash Anish, Aditya RanjanGame-Playing Agents Social Deduction Games
Department of Computer Science and Engineering RV College of Engineering Bangalore · Department of Information Science and Engineering RV College of Engineering Bangalore · Department of Artificial Intelligence and Machine Learning RV College of Engineering Bangalore
Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model
Apr 21, 2026Julian F. Schumann, Johan Engström, Ran Wei +3Collision Avoidance Intelligent Transportation Systems
Department of Cognitive Robotics, Delft University of Technology, Netherlands · Waymo LLC, Mountain View, CA, USA
Gated Coordination for Efficient Multi-Agent Collaboration in Minecraft Game
Apr 21, 2026HuaDong Jian, Chenghao Li, Haoyu Wang +4Multi-Agent LLM Systems Multi-Agent Collaboration
University of Electronic Science and Technology of China Chengdu, China
Poster: EdgeCitadel -- Hybrid NATS-MQTT Orchestration for Edge Multi-Agent Systems
Apr 20, 2026Zhonghao Zhan, Yefan Zhang, Hamed HaddadiMulti-Agent Orchestration Edge Computing
Imperial College London · Independent Researcher
Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
Apr 20, 2026Zixiang Wang, Mengjia Gong, Qiyu Sun +5Multi-Agent Coordination Multi-Agent Systems
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China · School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China · School of Electrical Engineering and Automation, Nantong University, Nantong 226019, China +2
Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation
Apr 20, 2026Nuo Chen, Yicheng Tong, Yuzhe Yang +5Multi-Agent LLM Systems Multi-Agent Collaboration
National University of Singapore · The Chinese University of Hong Kong, Shenzhen
Memory Centric Power Allocation for Multi-Agent Embodied Question Answering
Apr 20, 2026Chengyang Li, Shuai Wang, Kejiang Ye +5Embodied QA Wireless Resource Allocation
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · Southern University of Science and Technology, Shenzhen, China +2
Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration
Apr 18, 2026Sukwon Yun, Jie Peng, Pingzhi Li +5LLM Agent Orchestration Multi-Agent Collaboration
UNC Chapel Hill · Eigent AI · MIT-IBM Watson AI Lab +1
The Virtual Roundtable: Multi-Agent Personas Simulating the Dynamics of Human Brainstorming
Apr 17, 2026Tim Dorn, Saara A. Khan, Julie MumfordMulti-Agent Collaboration Multi-Agent Systems
Reality Labs, Meta, Menlo Park, California 94025, USA
Preregistered Belief Revision Contracts
Apr 16, 2026Saad AlqithamiBelief Revision Dynamic Epistemic Logic
The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity
Apr 16, 2026Sushant Gautam, Annika W. Olstad, Klas H. Pettersen +1Social Media Analysis Multi-Agent Systems
Simula Metropolitan Center for Digital Engineering (SimulaMet), Oslo, Norway · Oslo Metropolitan University, Oslo, Norway · Simula Research Laboratory, Oslo, Norway
NeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration
Apr 16, 2026Yang Zhou, Yash Shetye, Long Quang +8Efficient Neural Network Inference Multi-Robot Systems
New York University, New York, USA · U.S. Army Combat Capabilities Development Command, Army Research Laboratory, Adelphi, MD 20783, USA · Vanderbilt University, Tennessee, USA +1

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.