Multi-Agent Coordination

Follow this topic Ask about this topic

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.

V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach
Apr 24, 2026Pengfei Liu, Jialing Zhou, Yuezu Lv +2Multi-Agent Coordination Safe Motion Planning
School of Automation, Beijing Institute of Technology, Beijing 100081, China · State Key Laboratory of CNS/ATM, Beijing Institute of Technology, Beijing 100081, China · Beijing Institute of Technology (Zhuhai), Zhuhai 519088, China +3
Multi-Agent Empowerment and Emergence of Complex Behavior in Groups
Apr 22, 2026Tristan Shah, Ilya Nemenman, Daniel Polani +1Multi-Agent Coordination Intrinsic Motivation
Department of Computer Science Texas Tech University United States of America · Department of Physics, Department of Biology, Initiative in Theory and Modeling of Living Systems Emory University United States of America · Department of Computer Science, School of Physics, Engineering and Computer Science University of Hertfordshire United Kingdom
AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure
Apr 22, 2026Anbang Ruan, Xing ZhangMulti-Agent Coordination LLM Agent Orchestration
NetX Foundation
SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving
Apr 22, 2026Anjie Qiu, Donglin Wang, Zexin Fang +2Multi-Agent Coordination Autonomous Driving
Institute for Wireless Communication and Navigation
A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing
Apr 22, 2026Samaresh Kumar Singh, Joyjit RoyMulti-Agent System Optimization Multi-Agent Coordination
Independent Researcher · Leander, Texas · Austin, Texas
Quantum Frog: Emergent Cooperation and Difficulty Scaling in a Quantized-Time Cooperative Game
Apr 22, 2026Saad MankariousMulti-Agent Coordination Game-Playing Agents
TeamFusion: Supporting Open-ended Teamwork with Multi-Agent Systems
Apr 21, 2026Jiale Liu, Victor S. Bursztyn, Lin Ai +4Multi-Agent Coordination Multi-Agent Collaboration
Pennsylvania State University · Adobe Research · Columbia University +1
Explicit Trait Inference for Multi-Agent Coordination
Apr 21, 2026Suhaib Abdurahman, Etsuko Ishii, Katerina Margatina +3Multi-Agent LLM Systems Multi-Agent Coordination
University of Southern California · AWS Agentic AI Labs
ClawCoin: An Agentic AI-Native Cryptocurrency for Decentralized Agent Economies
Apr 21, 2026Shaoyu Li, Chaoyu Zhang, Hexuan Yu +2Financial Services Multi-Agent Coordination
Virginia Tech Virginia, USA
Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent Systems
Apr 21, 2026Namyoung So, Seokgyu Jang, Taeuk KimMulti-Agent Coordination OOD Generalization
Department of Computer Science, Hanyang University, Seoul, Republic of Korea
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
ConventionPlay: Capability-Limited Training for Robust Ad-Hoc Collaboration
Apr 20, 2026Abhishek Sriraman, Eleni Vasilaki, Robert LoftinMulti-Agent Coordination Multi-Agent Collaboration
University of Sheffield · Sheffield, United Kingdom
CADMAS-CTX: Contextual Capability Calibration for Multi-Agent Delegation
Apr 20, 2026Chuhan QiaoMulti-Agent Coordination Multi-Agent Systems
Beijing JIAOTONG University · BEIJING, China
Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
Apr 20, 2026Yingtao TianMulti-Agent Coordination Algorithmic Collusion
Sakana AI Tokyo, Japan
Provable Coordination for LLM Agents via Message Sequence Charts
Apr 19, 2026Benedikt Bollig, Matthias Függer, Thomas NowakMulti-Agent LLM Systems Multi-Agent Coordination
Universit´e Paris-Saclay, CNRS, ENS Paris-Saclay, LMF, Gif-sur-Yvette, France · Institut Universitaire de France, Paris, France
Phase-Scheduled Multi-Agent Systems for Token-Efficient Coordination
Apr 19, 2026Mohit DubeyMulti-Agent LLM Systems Multi-Agent System Optimization
Open Gigantic
Safe and Policy-Compliant Multi-Agent Orchestration for Enterprise AI
Apr 19, 2026Vinil Pasupuleti, Shyalendar Reddy Allala, Siva Rama Krishna Varma Bayyavarapu +1Multi-Agent System Optimization Multi-Agent Coordination
International Business Machines · International Business Machines (IBM) · South Carolina, United States +6
Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
Apr 19, 2026Jiuyun Jiang, Yuecheng Hong, Bo Yang +4Multi-Agent Coordination Human Behavior Simulation
Harbin Institute of Technology · The Hong Kong Polytechnic University
Do LLM-derived graph priors improve multi-agent coordination?
Apr 19, 2026Nikunj Gupta, Rajgopal Kannan, Viktor PrasannaMulti-Agent Coordination LLM-Guided RL
University of Southern California, Los Angeles, California, United States · DEVCOM ARL Army Research Oﬀice, Los Angeles, California, United States
From Necklaces to Coalitions: Fair and Self-Interested Distribution of Coalition Value Calculations
Apr 18, 2026Terry R. Payne, Luke RileyCooperative Game Theory Multi-Agent Coordination
School of Computer Science and Informatics, University of Liverpool, Liverpool, UK. · Quant, London, UK.
Complete Cyclic Subtask Graphs for Tool-Using LLM Agents: Flexibility, Cost, and Bottlenecks in Multi-Agent Workflows
Apr 17, 2026Luay Gharzeddine, Samer SaabMulti-Agent Coordination Multi-Agent Orchestration
1Dept of Computer Science, Lebanese American University. · 2*Dept of Electrical and Computer Engineering, Lebanese American University.
The Price of Paranoia: Robust Risk-Sensitive Cooperation in Non-Stationary Multi-Agent Reinforcement Learning
Apr 17, 2026Deep Kumar Ganguly, Chandradithya S Jonnalagadda, Pratham Chintamani +1Multi-Agent Reinforcement Learning Multi-Agent Coordination
Technical University of Munich (TU Munich) · Brown University · Indian Institute of Technology Tirupati (IIT Tirupati)
CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas
Apr 16, 2026Emanuel Tewolde, Xiao Zhang, David Guzman Piedrahita +2Social Dilemmas Cooperative Game Theory
Carnegie Mellon University · Foundations of Cooperative AI Lab (FOCAL) · Jinesis Lab, University of Toronto & Vector Institute +3
On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations
Apr 15, 2026Ziyang WangMulti-Agent Coordination Distributed Inference
Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
Apr 15, 2026Zijian Zhao, Jing Gao, Sen LiCooperative MARL Multi-Agent Reinforcement Learning
1The Hong Kong University of Science and Technology · 2The Hong Kong Polytechnic University · 3The Hong Kong University of Science and Technology (Guangzhou)
DeCoNav: Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation
Apr 14, 2026Sunyao Zhou, Yunzi Wu, Tianhang Wang +5Multi-Agent Coordination Vision-Language Navigation
Institute of Artificial Intelligence (TeleAI), China Telecom · Fudan University · Tongji University
M2HRI: An LLM-Driven Multimodal Multi-Agent Framework for Personalized Human-Robot Interaction
Apr 13, 2026Shaid Hasan, Breenice Lee, Sujan Sarker +1Multi-Agent LLM Systems Multi-Agent Coordination
University of Virginia, VA, USA
PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints
Apr 13, 2026Minjun Park, Donghyun Kim, Hyeonjong Ju +5Multi-Agent Collaboration Multi-Agent Coordination
Department of Artificial Intelligence, Yonsei University
AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web
Apr 13, 2026Shanshan Zhong, Kate Shen, Chenyan XiongWeb Agent Benchmarks Web Agents
Language Technologies Institute, School of Computer Science, Carnegie Mellon University · Anaxi Labs
Toward Explanatory Equilibrium: Verifiable Reasoning as a Coordination Mechanism under Asymmetric Information
Apr 10, 2026Feliks Bańka, Jarosław A. ChudziakMulti-Agent LLM Systems Multi-Agent Coordination
The Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland

Apr 24, 2026J/K move · Enter open · S save

V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach

Pengfei Liu, Jialing Zhou, Yuezu Lv, Guanghui Wen+1

School of Automation, Beijing Institute of Technology, Beijing 100081, China · State Key Laboratory of CNS/ATM, Beijing Institute of Technology, Beijing 100081, China · Beijing Institute of Technology (Zhuhai), Zhuhai 519088, China+3

Multi-Agent Coordination Safe Motion Planning Motion Planning

Open paper AskPDF arXiv

Coordinating the motions of multiple autonomous vehicles (AVs) requires planning frameworks that ensure safety while making efficient use of space and time. This paper presents a new approach, termed variable-time-step spatio-temporal corridor (V-STC), that enhances the temporal efficiency of multi-vehicle coordination. An optimization model is formulated to construct a V-STC for each AV, in which both the spatial configuration of the corridor cubes and their time durations are treated as decision variables. By allowing the corridor's spatial position and time step to vary, the constructed V-STC reduces the overall temporal occupancy of each AV while maintaining collision-free separation in the spatio-temporal domain. Based on the generated V-STC, a dynamically feasible trajectory is then planned independently for each AV. Simulation studies demonstrate that the proposed method achieves safe multi-vehicle coordination and yields more time-efficient motion compared with existing STC approaches.