Multi-Agent System Optimization

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10 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 130

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ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning
Jun 29, 2026Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara +4Multi-Agent System Optimization Multi-Agent Coordination
1KAIST · 2KRAFTON · University of Toronto +2
GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems
Jun 26, 2026Xiaocheng Yang, Abdulrahman Alrabah, Dilek Hakkani-Tür +1Gradient-Based Attribution Multi-Agent LLM Systems
University of Illinois Urbana-Champaign
MMAO: A Metabolic Multi-Agent Optimizer with Endogenous Resource Allocation for Continuous and Discrete Optimization
Jun 26, 2026Jinliang Xu, Liping MaMulti-Agent System Optimization Metaheuristic Optimization
Department of Disease Control and Prevention, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
QueenBee Planner: Skill-Evolving Communication Topologies for Token-Efficient LLM Multi-Agent Systems
Jun 25, 2026Congjia Tian, Yuhang Yao, Jiaming CuiMulti-Agent LLM Systems Multi-Agent System Optimization
Virginia Tech, Blacksburg, VA · Carnegie Mellon University, Pittsburgh, PA
EvoFlock: evolved inverse design of multi-agent motion
Jun 24, 2026Craig ReynoldsMulti-Agent System Optimization Multi-Agent Systems
unaffiliated researcher
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Jun 23, 2026Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +5Multi-Agent System Optimization LLM Agent Memory
University of Southern California · Iowa State University · Cisco AI Research
MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?
Jun 22, 2026Juyang Bai, Laixi ShiMulti-Agent LLM Systems Multi-Agent System Optimization
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Optimal Order of Multi-Agent and General Many-Body Systems
Jun 18, 2026Jake J. XiaDynamical Systems Multi-Agent System Optimization
Harvard Management Company · Research Affiliate at Massachusetts Institute of Technology
A Multi-Agent system for Multi-Objective constrained optimization
Jun 18, 2026Federica FilippiniMulti-Objective Reinforcement Learning Multi-Agent System Optimization
University of Milano-Bicocca Milan, Italy
Deep-Unfolded Coordination
Jun 18, 2026Hunter Kuperman, Minchan Jung, Rahul V. Ghosh +2Trajectory Optimization Multi-Agent System Optimization
Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology United States
SIGMA: Skill-Incidence Graphs for Compositional Multi-Agent Design
Jun 18, 2026Kun Zeng, Yu Huo, Siyu Zhang +5Multi-Agent System Optimization Multi-Agent Systems
Sun Yat-sen University · School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen · Taiyuan University of Technology +1
Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications
Jun 16, 2026Paresh Dashore, Shreyas Kulkarni, Uttam Gurram +5Multi-Agent LLM Systems Multi-Agent System Optimization
AI Foundations, Capital One
Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning
Jun 12, 2026Pengxin Wang, Lihao Guo, Yi Xie +3Multi-Objective Reinforcement Learning Multi-Agent System Optimization
Department of Electrical and Computer Engineering, University of Arizona
Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Jun 10, 2026Neha Prakriya, Chaojun Hou, Zheng Gong +5LLM Inference Efficiency Multi-Agent System Optimization
Training and Inference Optimization Team AMD
INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration
Jun 9, 2026Ahasan Kabir, Jiaqi Xue, Mengxin Zheng +1Multi-Agent System Optimization Multi-Agent Orchestration
University of Central Florida
FlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse
Jun 9, 2026Lingzhi Yuan, Chenghao Deng, Fangxu Yu +3Multi-Agent LLM Systems Multi-Agent System Optimization
University of Maryland, College Park · Amazon
Decentralized Multi-Agent Systems with Shared Context
Jun 9, 2026Yuzhen Mao, Jerry Gu, Aadi Chauhan +3Multi-Agent LLM Systems Multi-Agent System Optimization
Stanford University
PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback
Jun 7, 2026Chunji Lv, Jiaxi Ye, Yuchen Jiang +2Multi-Agent System Optimization 3D Scene Generation
Beijing Institute of Technology
GRPO Does Not Close the Multi-Agent Coordination Gap
Jun 5, 2026Najmul Hasan, Prashanth BusiReddyGariMulti-Agent System Optimization Multi-Agent Coordination
Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC, USA
What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
Jun 3, 2026Chen Huang, Yuhao Wu, Wenxuan ZhangMulti-Agent LLM Systems Multi-Agent System Optimization
Singapore University of Technology and Design
Streaming Communication in Multi-Agent Reasoning
Jun 3, 2026Zhen Yang, Xiaogang Xu, Wen Wang +3Multi-Agent LLM Systems Multi-Agent System Optimization
HKUST(GZ) · ZJU · Alibaba Group +1
GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation
Jun 3, 2026Yuxiao Ye, Yiwen Zhang, Huiyuan Xie +2Multi-Agent LLM Systems Multi-Agent System Optimization
1Tsinghua University · 2The Hong Kong University of Science and Technology (Guangzhou)
SPOQ: Specialist Orchestrated Queuing for Multi-Agent Software Engineering
Jun 2, 2026Royce Carbowitz, Dheeraj KumarMulti-Agent System Optimization Software Engineering Agents
Pinpoint Technologies LLC
MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency
Jun 2, 2026Saptarshi Mitra, Yifan Zhang, Rachid Karami +5LLM Inference Efficiency Multi-Agent System Optimization
University of California, Irvine, USA · University of Southern California, Los Angeles, USA · Intel, USA
MOC: Multi-Order Communication in LLM-based Multi-Agent Systems
Jun 1, 2026Yao Guan, Lin Wang, Zhihu Lu +3Multi-Agent LLM Systems Multi-Agent System Optimization
Fudan University · Nanjing Normal University · Pennsylvania State University
Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Jun 1, 2026Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders JonssonMulti-Objective Reinforcement Learning Multi-Agent System Optimization
Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus
Jun 1, 2026Wanshuang Gou, Zihan LiuMulti-Agent LLM Systems Multi-Agent System Optimization
Chengdu University
When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding
May 31, 2026Xu Yang, Lunyiu Nie, Ethan Chandra +3Multi-Agent System Optimization Software Engineering Agents
1The University of Texas at Austin · University of Oxford
S $^3$ LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization
May 29, 2026Chao Yin, Youran Dong, Shiqian Ma +2Multi-Agent System Optimization Bilevel Optimization
School of Mathematics, Hohai University, Nanjing, P. R. China · School of Mathematics, Nanjing University, Nanjing, P. R. China · Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, USA
Learning Multi-Agent Coordination via Sheaf-ADMM
May 29, 2026Jeffrey Seely, Bartłomiej Cupiał, Llion JonesMulti-Agent System Optimization Multi-Agent Coordination
Sakana AI, Tokyo, Japan · University of Warsaw · AKCES NCBR

Jun 29, 2026, cs.AIJ/K move · Enter open · S save

ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning

Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara, Scott Sanner+3

1KAIST · 2KRAFTON · University of Toronto+2

Multi-Agent System Optimization Multi-Agent Coordination Decentralized MARL Policy Learning Cooperative MARL Multi-Agent Reinforcement Learning

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Cooperative tasks in Multi-Agent Reinforcement Learning (MARL) require agents to collectively maximize a shared return. Under the Centralized Training with Decentralized Execution (CTDE) paradigm, policy gradients have remained difficult to compute directly. Prior methods largely follow two approaches: independent factorized updates with centralized critics, which lack general joint-improvement guarantees without value decomposition assumptions, or alternating best-response updates, which can converge to suboptimal Nash Equilibria. In this paper, we show the joint policy gradient admits an exact decentralized decomposition of per-agent terms, each formed from per-agent score functions and decentralized critics. Based on this decomposition, we develop Agent-Chained Policy Optimization (ACPO), where actors are trained independently, with their updates together constituting a single step on the joint policy gradient. Central to this result is a serialized view of the simultaneous joint decision in which agents commit actions one at a time, each conditioning on a belief over preceding actions. The belief acts as the coordination mechanism which ties the independent per-agent updates into a joint gradient step. We evaluate ACPO on Multi-Robot Warehouse, SMACv2, and MA-MuJoCo, where it outperforms strong baselines, with the gap widening as the number of agents grows.

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Latest papers 130