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.

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

Kernel-based potential mean-field games with unbiased random Fourier $U$ -statistics
May 28, 2026Yumiharu NakanoMulti-Agent Coordination Mean Field Games
Department of Mathematical and Computing Science, Institute of Science Tokyo
Differentiable Belief-based Opponent Shaping
May 27, 2026Aarav G Sane, Karthik Sivachandran, Rohan PalejaMulti-Agent Coordination Imperfect-Information Games
Department of Computer Science Purdue University
From Task Allocation to Risk Clearing: A Unifying Interface for Mixed Human-Agent Societies
May 26, 2026Vassilis VassiliadesMulti-Agent Coordination Multi-Robot Task Allocation
CYENS - Centre of Excellence, Nicosia, Cyprus
Cost of Structural Learning Under Censored Feedback: A Threshold-Bandit Approach
May 26, 2026Michael Ledford, William RegliMulti-Armed Bandits Multi-Agent Coordination
University of Maryland, College Park
AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems
May 26, 2026Nicole KoenigsteinMulti-Agent LLM Systems Multi-Agent System Optimization
Independent researcher.
Multi-Agent Coordination Adaptation via Structure-Guided Orchestration
May 25, 2026Haoran Li, Shulun Chen, Shaoyuan Sun +1Multi-Agent LLM Systems Multi-Agent System Optimization
Nanjing University · University of Technology Sydney, Sydney, Australia · University of New South Wales, Sydney, Australia
Collaborative Threat-Aware Autonomy (CTAA)
May 25, 2026Rajnikant Sharma, Abhinav Sinha, Isaac WeintraubMulti-Agent Coordination Multi-Agent Planning
Integrated Solutions for Systems Inc. (IS4S), Dayton, Ohio · Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH, 45221, USA · Autonomous Controls Branch, Power and Control Division, Aerospace Systems Directorate, Air Force Research Lab, Wright-Patterson AFB, OH 45433, USA
SPIN: Decentralized Swarm Control via Tensorized Policy Coordination
May 25, 2026Zhaowen FanMulti-Agent Coordination Swarm Robotics
DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs
May 24, 2026Yi Li, Songtao Wei, Dongming Jiang +3Multi-Agent LLM Systems Multi-Agent System Optimization
University of Texas at Dallas · Independent Researcher · University of California, Davis
On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach
May 24, 2026Jiahui Bai, Hai Dong, A. K. QinMulti-Agent Coordination Federated Learning Aggregation
School of Computer Technologies, RMIT University Melbourne, VIC, Australia · School of Science, Computing and Engineering Technologies, Swinburne University of Technology Hawthorn, VIC, Australia
Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
May 24, 2026Chengwei Li, Junlin Liu, Yang GaoMulti-Agent Coordination Reinforcement Learning
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences
CoRe-Code: Collaborative Reinforcement Learning for Code Generation
May 24, 2026Zhihao Dou, Qinjian Zhao, Zhongwei Wan +2RL for Code Generation Multi-Agent Coordination
Department of Computer Science Kean University · Department of Computer Science Case Western Reserve University
PRIMA: Operational Patterns for Resilient Multi-Agent Research with Verifiable Identity and Convergent Feedback
May 23, 2026Sasank AnnapureddyMulti-Agent Coordination Multi-Agent Orchestration
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
May 23, 2026Yuheng Jing, Kai Li, Ziwen Zhang +8RL Benchmarks Multi-Agent Coordination
C2DL, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · University of Science and Technology of China +5
Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development
May 22, 2026Srijita Basu, Viktor Kjellberg, Simin Sun +5Multi-Agent Coordination Software Engineering Agents
Chalmers University of Technology and University of Gothenburg · Scania CV AB · Research & Development, Volvo Car Corporation +1
CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces
May 22, 2026Joydeep ChandraMulti-Agent Coordination Temporal IR
BNRIST, Tsinghua University Beijing, China
Foundation Protocol: A Coordination Layer for Agentic Society
May 22, 2026Bang Liu, Yongfeng Gu, Jiayi Zhang +26Multi-Agent Coordination Multi-Agent Orchestration
FoundationAgents · Université de Montréal & Mila · HKUST(GZ) +8
MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination
May 21, 2026Joss ArmstrongAI Agent Reliability Multi-Agent Coordination
Ericsson, Athlone, Ireland
Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
May 21, 2026Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier +1Aerial Robotics Multi-Agent Coordination
Robotics and Perception Group, University of Zurich, Zurich, Switzerland · Google DeepMind, London, United Kingdom
A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation
May 21, 2026Promise Ekpo, Angelique Taylor, Lekan MoluNash Equilibrium Multi-Agent Coordination
Cornell University, NY. · Bala Cynwyd, PA.
Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning
May 21, 2026Yujin Lin, Yue Yang, Hao WangMulti-Agent Coordination Automated Negotiation
Department of Data Science and AI, Faculty of IT, Monash University, Australia · Monash Energy Institute, Monash University, Australia
Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence
May 21, 2026Fiona Y. Wong, Markus J. BuehlerAI for Science Multi-Agent Coordination
Laboratory for Atomistic and Molecular Mechanics (LAMM) · Department of Biological Engineering · Department of Mechanical Engineering +2
Secure Coordination for Vertiport Sequencing in Advanced Air Mobility
May 20, 2026Jaehan Im, Filippos Fotiadis, Ufuk Topcu +1Multi-Agent Coordination False Data Injection Attacks
Graduate Research Assistant, Department of Aerospace Engineering and Engineering Mechanics · Postdoctoral Researcher, Oden Institute for Computational Engineering & Sciences · Professor, Oden Institute for Computational Engineering & Sciences +1
STEAM: A Training-Free Congestion-Aware Enhancement Framework for Decentralized Multi-Agent Path Finding
May 20, 2026Mingyang Feng, Mengnuo Zhang, Shaoyuan Li +1Multi-Agent System Optimization Multi-Agent Coordination
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China
Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination
May 20, 2026Matthew Low, Jasmine Jerry Aloor, Victoria Marie Tuck +2Collision Avoidance Multi-Agent Coordination
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley · Department of Aeronautics and Astronautics, Massachusetts Institute of Technology · GRASP Laboratory, University of Pennsylvania +1
Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance
May 20, 2026Mehrnaz SabetMulti-Agent Coordination Air Traffic Management
Cornell University, Ithaca, NY 14853 USA
Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections
May 19, 2026Wenzhe Song, Hao ZhangMulti-Agent Coordination Intelligent Transportation Systems
School of Business Stevens Institute of Technology Hoboken, 07030, USA · Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, 15213, USA
Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On
May 18, 2026Yixiang Yao, Yuhang Yao, Xinyi Fan +5Multi-Agent LLM Systems Multi-Agent Coordination
University of Southern California · Carnegie Mellon University · University of Illinois Urbana-Champaign
Equilibrium Selection in Multi-Agent Policy Gradients via Opponent-Aware Basin Entry
May 18, 2026Yevhen Shcherbinin, Arina Redina, Maxim Kalpin +1Nash Equilibrium Multi-Agent Coordination
Bloomsbury Technology, London, United Kingdom · London School of Economics and Political Science, London, United Kingdom · University of Bristol, Bristol, United Kingdom +2
Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
May 18, 2026Sunwoo Lee, Mingu Kang, Yonghyeon Jo +1Adversarial Training Multi-Agent Coordination
Graduate School of Artificial Intelligence, UNIST, Ulsan, South Korea

May 28, 2026J/K move · Enter open · S save

Kernel-based potential mean-field games with unbiased random Fourier $U$ -statistics

Yumiharu Nakano

Department of Mathematical and Computing Science, Institute of Science Tokyo

Multi-Agent Coordination Mean Field Games Schrödinger Bridges Mean-Field Theory

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We study the subclass of potential mean-field games in which the running interaction cost and the terminal target cost are both expressed through reproducing-kernel maximum mean discrepancy (MMD) penalties, and develop a computational framework that exploits this kernel structure. Both costs are estimated from finite-sample empirical distributions using a random Fourier U-statistic representation that is unbiased and has linear cost in the batch size. The drift of the controlled diffusion is parametrized by a neural network and trained via stochastic gradient descent. For population near-minimizers we prove convergence to the terminal-constrained problem as the penalty diverges, and show that the same limit is recovered almost surely when the learned controls are evaluated on independent finite samples under explicit coupling conditions on the penalty, random-feature count and sample size. The framework includes the kernel-MMD-penalty Schr{ö}dinger bridge problem as the special case of a vanishing interaction cost. Numerical experiments illustrate the method on the Schr{ö}dinger bridge problem in dimensions up to one hundred, and on an electric vehicle charging coordination problem with per-vehicle physical heterogeneity, where an aggregate-demand congestion cost represents price-feedback competition at the population level and the terminal MMD penalty shapes the state-of-charge distribution at the deadline.