Multi-Agent Planning

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Multi-Agent Planning studies how multiple robots, vehicles, software agents, or people can coordinate actions to meet shared or competing goals. Methods include search and optimization for task assignment and pathfinding, decentralized planning with communication, reinforcement learning and world models, game-theoretic planning, and language-model agents organized into roles and workflows. Work is evaluated in settings such as warehouse routing, robot navigation, autonomous driving, games, and long-horizon computer-use tasks, with measures of success, safety, efficiency, and coordination. A central challenge is balancing scalability and responsiveness with reliable coordination under partial information, changing conditions, and limited communication, while preserving formal guarantees where needed.

Latest papers

121 papers20 papers in the last four weeks, up 233% on the four weeks before. 0.1% of all new papers.

Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Jun 18, 2026Jannik Hösch, Alessandro Sestini, Florian Fuchs +6Sequential MARL Multi-Agent Coordination
1Electronic Arts (EA) · 2KTH Royal Institute of Technology, Stockholm, Sweden
PersonalPlan: Planning Multi-Agent Systems for Personalized Programming Learning
Jun 17, 2026Zhiyuan Wen, Jiannong Cao, Peng Gao +4Programming Education LLM Agent Training
Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China · JIUTIAN Team, China Mobile Research Institute, Beijing, China · School of Artificial Intelligence, Nankai University, Tianjin, China
Tensor-Coord: Algebraic Decomposition of Joint Plan Tensors for Conflict-Free Multi-Agent LLM Planning
Jun 15, 2026Mudit RastogiMulti-Agent Coordination Large Language Model-Based Robot Planning
University of Michigan, Ann Arbor, USA
AnyGoal: Vision-Language Guided Multi-Agent Exploration for Training-Free Lifelong Navigation
Jun 11, 2026MoniJesu James, Marcelino Julio Fernando, Miguel Altamirano Cabrera +1Multi-Robot Systems Robot Navigation
DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning
Jun 5, 2026Lingyong Yan, Can Xu, Yukun Zhao +13Deep Research Agents Agentic Search
DuMate Team, Baidu AI Cloud
Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems
Jun 4, 2026Cheng Ren, Ming Li, Xinping Guan +1Multi-Agent Coordination Robotics
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China
SPOQ: Specialist Orchestrated Queuing for Multi-Agent Software Engineering
Jun 2, 2026Royce Carbowitz, Dheeraj KumarMulti-Agent System Optimization Software Engineering Agents
Pinpoint Technologies LLC
Multi-Agent Computer Use
Jun 1, 2026Jing Yu Koh, Ruslan Salakhutdinov, Daniel FriedMulti-Agent Coordination Computer-Use Agents
Carnegie Mellon University
CARVE: Certified Affordable Repair of Vetoed Maneuvers via Envelopes for Interactive Driving
May 31, 2026Yifan WangFormal Verification Autonomous Driving Planning
Department of Mechanical Engineering, McGill University, QC, H3A 2T7, Canada
Sentinel: Embodied Cooperative Spatial Reasoning and Planning
May 25, 2026Xiangye Lin, Hongxin Zhang, Ruxi Deng +2AI Agent Benchmarks Multi-Agent Planning
University of Massachusetts Amherst · University of Illinois Urbana-Champaign
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
Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems
May 24, 2026Andy Xu, Yu-Wing TaiMulti-Agent Orchestration Multi-Agent Systems
Dartmouth College
Towards Multi-Turn Dialog Systems for Industrial Asset Operations and Maintenance
May 24, 2026Chengrui Li, Rujing Li, Yitong Bai +1Multi-Agent Orchestration Multi-Turn Dialogue Management
Columbia University
When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
May 22, 2026Zehao Wang, Shilong Jin, Zhao Cao +1Multi-Agent LLM Systems Multi-Agent Planning
College of Intelligence and Computing, Tianjin University, Tianjin, China · School of New Media and Communication, Tianjin University, Tianjin, China · Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China
How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning
May 21, 2026Zeyu He, Hannah Kim, Dan Zhang +1Multi-Agent LLM Systems Process Supervision
Penn State University · Work done during internship at Megagon Labs · Megagon Labs
From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning
May 19, 2026Ahmed Y. Gado, Omar Y. Goba, Alaa Hassanein +2Spatiotemporal Reasoning Autonomous Driving Planning
Computer Science & Engineering Department, German University in Cairo (GUC), Egypt · C-DRiVeS Lab: Cognitive Driving Research in Vehicular Systems, Cairo, Egypt
IFPV: An Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification
May 14, 2026Zhigao Huang, Zhengqing Hu, Dong Chen +5Multi-Agent Planning LLM Agent Planning
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, Henan, China · Engineering Research Center of Intelligent Swarm Systems, Ministry of Education, Zhengzhou, 450001, Henan, China · National Supercomputing Center in Zhengzhou, Zhengzhou, 450001, Henan, China +1
Privacy Preserving Multi Agent Path Finding
May 13, 2026Rotem Lev Lehman, Roni Stern, Guy ShaniGraph-Based Planning Multi-Agent Planning
Ben Gurion University of the Negev · Be’er Sheva, Israel
Decoupled Planning for Multiple Omega-Regular Objectives
May 13, 2026Guy Avni, Thomas A. Henzinger, Kaushik Mallik +2Multi-Agent Coordination Graph-Based Planning
Department of Computer Science, University of Haifa, Israel · Institute of Science and Technology Austria, Klosterneuberg, Austria · IMDEA Software Institute, Spain +2
AssemPlanner: A Multi-Agent Based Task Planning Framework for Flexible Assembly System
May 9, 2026Chenhao Zhang, Chaoran Zhang, Zhaobo Xu +3Multi-Agent Orchestration Robot Task Planning
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations
May 9, 2026Shizhong Zhou, Haifeng Liu, Zheng Zhang +3Collision Avoidance Vehicle Routing
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610000, China · College of Computer Science, Sichuan University, Chengdu 610000, China
Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge
May 8, 2026Dhaval Patel, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula +4Competitive Analysis Multi-Agent Orchestration
1IBM · 2Artificial Intelligence Institute at University of South Carolina · 3Steel Authority of India Limited +2
Many-to-Many Multi-Agent Pickup and Delivery
May 8, 2026Ethan Schneider, Jingkai Chen, Tianyi Gu +3Multi-Agent System Optimization Multi-Robot Task Allocation
Institute of Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA · Symbotic Inc., Wilmington, MA 01887, USA · Northeastern University, Boston, MA 02115, USA
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
From Intent to Execution: Composing Agentic Workflows with Agent Recommendation
May 5, 2026Kishan Athrey, Ramin Pishehvar, Brian Riordan +1LLM Agent Orchestration Multi-Agent Systems
Cisco Systems Inc.
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
Agentic AI for Trip Planning Optimization Application
Apr 30, 2026Tiejin Chen, Ahmadreza Moradipari, Kyungtae Han +2Multi-Agent Orchestration Intelligent Transportation Systems
Toyota Motor North America R&D, InfoTech Labs, USA. · Arizona State University
OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction
Apr 28, 2026Junxing Hu, Tianlong Li, Lei Yu +1Multi-Agent Orchestration AI Agent Monitoring
JD.com Beijing, China
From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
Apr 24, 2026Zhengxu Yu, Yu Fu, Zhiyuan He +5Multi-Agent Orchestration Multi-Agent Systems
1HUAWEI Noah’s Ark Lab · University Liverpool · University College London
BOIL: Learning Environment Personalized Information
Apr 18, 2026Rohan Patil, Henrik I. ChristensenMulti-Agent Control Multi-Agent Planning

Jun 18, 2026J/K move · Enter open · S save

Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution

Jannik Hösch, Alessandro Sestini, Florian Fuchs, Amir Baghi+5

1Electronic Arts (EA) · 2KTH Royal Institute of Technology, Stockholm, Sweden

Sequential MARL Multi-Agent Coordination Game-Playing Agents Hierarchical RL LLM-Guided RL Multi-Agent Planning

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Reinforcement learning (RL) has achieved strong performance in sequential decision-making, yet scaling to complex multi-agent environments remains challenging due to sparse rewards, large state-action spaces, and the difficulty of learning coordinated strategies. We propose a hierarchical architecture where a pretrained large language model (LLM) acts as a centralized strategic controller that selects among specialized RL skill policies for a team of agents, while RL policies handle reactive low-level execution. We evaluate this hybrid system in a competitive 2v2 King of the Hill environment against behavior tree (BT) and ``Flat'' RL (end-to-end training without skill decomposition) baselines. The LLM+RL system achieves task performance statistically equivalent to hand-crafted BT (46.4% vs 51.5% win rate,

p=0.103

) while both significantly outperform Flat RL trained without skill decomposition. A user study (

n=15

) reveals that 60% of participants perceive LLM+RL agents as the most human-like (

p=0.027

), citing behavioral adaptability and tactical variability. These results demonstrate that pretrained LLM reasoning can effectively orchestrate pretrained RL skills, achieving competitive multi-agent coordination and superior perceived believability without manual rule engineering.