Multi-Agent Systems

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Multi-Agent Systems studies how multiple decision-makers interact when they share goals, compete for resources, or have only partial information about one another. Approaches include multi-agent reinforcement learning, game theory and formal verification, decentralized consensus and task allocation, and—more recently—LLM-agent teams organized through roles, workflows, debate, and shared memory. Researchers evaluate these methods in social dilemmas, cooperative robotics and swarms, reasoning and coding benchmarks, and operational settings, measuring success alongside cost, robustness, and fairness. A central open tension is that coordination can improve outcomes but also adds overhead and can spread shared errors, so its benefits depend on the task, communication structure, and agents involved.

Latest papers

677 papers99 papers in the last four weeks, up 29% on the four weeks before. 0.6% of all new papers.

EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium
May 10, 2026Yuqiao Meng, Luoxi Tang, Sakshi Sunil Narvekar +4Multi-Agent Debate Agent Memory Poisoning
Binghamton University, State University of New York, Binghamton, NY, USA · University of Iowa, Iowa City, IA, USA
Internal vs. External: Comparing Deliberation and Evolution for Multi-Agent Constitutional Design
May 9, 2026Hershraj Niranjani, Ujwal Kumar, Phan Xuan TanEvolutionary Optimization Multi-Agent Systems
Department of EECS, University of California, Berkeley, Berkeley, CA, USA · College of Engineering, Shibaura Institute of Technology, Tokyo, Japan
Robust Multi-Agent LLMs under Byzantine Faults
May 9, 2026Haejoon Lee, Vincent-Daniel Yun, Dimitra Panagou +1Multi-Agent LLM Systems Multi-Agent Coordination
Department of Robotics, University of Michigan, Ann Arbor, USA · Thomas Lord Department of Computer Science, University of Southern California, USA
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
May 9, 2026Yulang Chen, Haoxuan Peng, Jinyan Liu +3Multi-Agent LLM Systems Multi-Agent System Optimization
1Shanghai Jiao Tong University · 2Nanjing University · 3The University of Sydney +2
Beyond the All-in-One Agent: Benchmarking Role-Specialized Multi-Agent Collaboration in Enterprise Workflows
May 9, 2026Tao Yu, Hao Wang, Changyu Li +15LLM Agent Evaluation Business Process Automation
BAAI · CASIA · UCAS +2
Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems
May 9, 2026Haixiang, Yang Xu, Jiefu Zhang +4Multi-Agent Systems
Purdue University · The University of Hong Kong · Johns Hopkins University
Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification
May 8, 2026Sarah Wilson, Diem Linh Dang, Usman Ali Moazzam +2AI Agent Evaluation LLM Agents
Columbia University
The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
May 8, 2026William Brach, Federico Torrielli, Stine Lyngsø Beltoft +3Privacy Leakage in Language Models Multi-Agent Systems
University of Turin Dept. of Computer Science Torino, Italy · University of Southern Denmark Odense, Denmark
Insider Attacks in Multi-Agent LLM Consensus Systems
May 8, 2026Xiaolin Sun, Zixuan Liu, Yibin Hu +1Multi-Agent LLM Systems Multi-Agent Systems
Department of Computer Science, Tulane University, New Orleans, United States of America.
Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems
May 8, 2026Lynnette Hui Xian Ng, Iain J. Cruickshank, Adrian Xuan Wei Lim +1Computational Social Science Multi-Agent Systems
Carnegie Mellon University · National University of Singapore
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
Designing Intelligent Enterprise Agents: A Capability-Aligned Multi-Agent Architecture
May 7, 2026John deVadossEnterprise AI Agents AI Agent Architecture
InterWork Alliance Washington DC
Randomness is sometimes necessary for coordination
May 7, 2026Rohan Patil, Jai Malegaonkar, Henrik I. ChristensenMulti-Agent Coordination Multi-Agent Systems
Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
May 7, 2026Hyeongwon Kang, Jeongseob Kim, Jinwoo Park +1LLM-Based Time Series Anomaly Detection Interpretable Anomaly Detection
Department of Industrial and Management Engineering Korea University · Mirae Asset Securities · Department of Industrial Engineering Seoul National University
Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
May 7, 2026Jingjie Ning, Xiaochuan Li, Ji Zeng +2Multi-Agent Systems Self-Improving Agents
School of Computer Science, Carnegie Mellon University
Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game
May 6, 2026Honglei Guo, Yexin Li, Chiyuan Wang +1Game Theory Hierarchical RL
College of Computer Science and Technology Zhejiang University State Key Laboratory of General Artificial Intelligence, BIGAI
Autonomous Synchronization of Discrete-Time Heterogeneous Multiagent Systems
May 6, 2026Wei Hu, Quanyi LiangMulti-Agent Control Multi-Agent Systems
School of Mathematical Sciences, Beihang University, Beijing 100191, China
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.
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence
May 5, 2026Munkhdelgerekh Batzorig, Purevbaatar Ganbold, Kyungbin Park +2Multi-Agent Systems AI Agent Governance
Department of Information Security Engineering, Soonchunhyang University, Asan, South Korea · Department of Mobility Convergence Security, Soonchunhyang University, Asan, South Korea
Pact: A Choreographic Language for Agentic Ecosystems
May 4, 2026Kiran Gopinathan, Jack Feser, Michelangelo Naim +2Multi-Agent Coordination Game Theory
Basis Research Institute, New York, New York, USA
Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representations
May 4, 2026Cameron Berg, Susan L. Schneider, Mark M. BaileyAI Agent Monitoring Spectral Clustering
Reciprocal Research · New York, NY, USA · Center for the Future of AI, Mind, and Society +5
Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces
May 4, 2026Chenchen ZhangMulti-Agent LLM Systems LLM Agent Orchestration
Independent Researcher
When Stress Becomes Signal: Detecting Antifragility-Compatible Regimes in Multi-Agent LLM Systems
May 4, 2026Jose Manuel de la Chica, Juan Manuel Vera, Jairo RodríguezMulti-Agent LLM Systems AI Agent Evaluation
Santander AI Lab
Position: Safety and Fairness in Agentic AI Depend on Interaction Topology, Not on Model Scale or Alignment
May 1, 2026Tanav Singh Bajaj, Nikhil Singh, Karan Anand +1Opinion Dynamics AI Agent Safety
Department of Computer Science, University of British Columbia, Vancouver, Canada · Department of Artificial Intelligence, IIT Hyderabad, Hyderabad, India · Amazon, Delhi, India.
Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure
May 1, 2026Talal Ashraf Butt, Muhammad Iqbal, Razi IqbalAI Accountability Multi-Agent Systems
aHigher Colleges of Technology, Fujairah, United Arab Emirates · bCentral Michigan University, Mount Pleasant, United States of America
Causal Foundations of Collective Agency
Apr 30, 2026Frederik Hytting Jørgensen, Sebastian Weichwald, Lewis HammondAI Agent Safety Causal Abstraction
Copenhagen Causality Lab and Pioneer Centre for AI, University of Copenhagen, Denmark · Cooperative AI Foundation, United Kingdom
A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics
Apr 30, 2026Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah +1Nash Equilibrium Cooperative Game Theory
1DAVID Lab, University of Paris-Saclay, UVSQ, 78035 Versailles, France. · 2TSAM, ASM, Centre for Development of Advanced Technologies, Algiers, Algeria. · 3LIPN, UMR CNRS 7030, Sorbonne Paris Nord University, Villetaneuse, France.
AgentReputation: A Decentralized Agentic AI Reputation Framework
Apr 30, 2026Mohd Sameen Chishti, Damilare Peter Oyinloye, Jingyue LiAI Accountability AI Agent Security
Department of Computer Science NTNU Trondheim, Norway
When Agents Evolve, Institutions Follow
Apr 30, 2026Chao Fei, Hongcheng Guo, Yanghua XiaoMulti-Agent Orchestration Multi-Agent Systems
KAUST · Fudan University
Trace-Level Analysis of Information Contamination in Multi-Agent Systems
Apr 30, 2026Anna Mazhar, Huzaifa Suri, Sainyam GalhotraAgentic Workflows AI Agent Monitoring
Cornell University Ithaca, NY, USA · University of Illinois Urbana-Champaign, IL, USA

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EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium

Yuqiao Meng, Luoxi Tang, Sakshi Sunil Narvekar, Rupali Rajendra Vaje+3

Binghamton University, State University of New York, Binghamton, NY, USA · University of Iowa, Iowa City, IA, USA

Multi-Agent Debate Agent Memory Poisoning Multi-Agent Systems Agent Memory Management Shared-Memory Multi-Agent Systems

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Multi-agent debate (MAD) systems increasingly rely on shared memory to support long-horizon reasoning, but this convenience opens a critical vulnerability: a single corrupted entry can contaminate the downstream memory-augmented reasoning, and debate alone fails to filter such errors. Existing safeguards filter entries via heuristics or LLM-based validation, yet they rely on AI judgments that share the same failure modes and overlook the cross-agent dynamics of MAD. We address this gap by formulating memory updating in MAD as a zero-trust memory game, in which no agent is assumed reliable and the game's equilibrium motivates a principled objective for calibrating memory influence. Guided by this objective, we propose EquiMem, an inference-time calibration mechanism that evaluates each update against the shared memory state and the memory already being used by other agents, using their existing retrieval queries and traversal paths as evidence of calibration. EquiMem instantiates this calibration for both embedding- and graph-based memory, and across diverse benchmarks, MAD frameworks, and memory architectures, it consistently outperforms existing safeguards, remains robust under adversarial agents, and incurs negligible inference overhead.