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.

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675 papers98 papers in the last four weeks, up 27% on the four weeks before. 0.6% of all new papers.

Agentic Social Affordance Framework (ASAF): Agent Identity Design as a Collaboration Interface in Multi-Agent Systems
Apr 19, 2026Meng-Han LeeMulti-Agent Collaboration Human-AI Interaction
Independent Researcher, Taipei, Taiwan
Phase-Scheduled Multi-Agent Systems for Token-Efficient Coordination
Apr 19, 2026Mohit DubeyMulti-Agent LLM Systems Multi-Agent System Optimization
Open Gigantic
Hive: A Multi-Agent Infrastructure for Algorithm- and Task-Level Scaling
Apr 19, 2026Zizhang Luo, Yuhao Luo, Youwei Xiao +3LLM Inference Efficiency Multi-Agent LLM Systems
Peking University Beijing, China
Formal Foundations of Agentic Business Process Management
Apr 19, 2026Giuseppe De Giacomo, Timotheus Kampik, Lukas Kirchdorfer +2Business Process Automation Multi-Agent Systems
University of Oxford, UK · SAP, Germany · Ume˚a University, Sweden +2
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
The Virtual Roundtable: Multi-Agent Personas Simulating the Dynamics of Human Brainstorming
Apr 17, 2026Tim Dorn, Saara A. Khan, Julie MumfordMulti-Agent Collaboration Multi-Agent Systems
Reality Labs, Meta, Menlo Park, California 94025, USA
Veritas-RPM: Provenance-Guided Multi-Agent False Positive Suppression for Remote Patient Monitoring
Apr 17, 2026Aswini Misro, Vikash Sharma, Shreyank N GowdaHealthcare Remote Patient Monitoring
YouDiagnose Limited / NHS, London, UK · YouDiagnose Limited, London, UK · School of Computer Science, University of Nottingham, Nottingham, UK
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
Apr 17, 2026Yaohui Han, Tianshuo Wang, Zixi Zhao +6Vision-Language Models VLM Reasoning
The Chinese University of Hong Kong · Central South University · Huawei Technologies Co., Ltd +1
SocialGrid: A Benchmark for Planning and Social Reasoning in Embodied Multi-Agent Systems
Apr 17, 2026Hikaru Shindo, Hanzhao Lin, Lukas Helff +2LLM Agent Evaluation AI Agent Benchmarks
Technical University of Darmstadt · German Research Center for Artificial Intelligence · CERTAIN +2
The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity
Apr 16, 2026Sushant Gautam, Annika W. Olstad, Klas H. Pettersen +1Social Media Analysis Multi-Agent Systems
Simula Metropolitan Center for Digital Engineering (SimulaMet), Oslo, Norway · Oslo Metropolitan University, Oslo, Norway · Simula Research Laboratory, Oslo, Norway
Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems
Apr 16, 2026Simeon Allmendinger, Luca Deck, Lucas MuellerAlgorithmic Fairness Multi-Agent Systems
University of Bayreuth · Fraunhofer FIT
CoGrid & the Multi-User Gymnasium: A Framework for Multi-Agent Experimentation
Apr 16, 2026Chase McDonald, Cleotilde GonzalezHuman-AI Interaction Multi-Agent Systems
Department of Social and Decision Sciences · Carnegie Mellon University
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations
Apr 16, 2026Xiangning Yu, Yuwei Guo, Yuqi Hou +2Causal Discovery Causal Structure Learning
College of Intelligence and Computing, Tianjin University, Tianjin, China · Tianjin Key Laboratory of Healthy Habitat and Smart Technology, Tianjin, China · Laboratory of Computation and Analytics of Complex Management Systems, Tianjin University, Tianjin, China
Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration
Apr 15, 2026Sayan Kumar Chaki, Antoine Gourru, Julien VelcinDemographic Bias in Language Models Multi-Agent Systems
Laboratoire Hubert Curien, UMR CNRS 5516, Saint-Étienne, France · École Centrale de Lyon, LIRIS CNRS UMR 5205
Representation Homogeneity and Systemic Instability in AI-Dominated Financial Markets: A Structural Approach
Apr 14, 2026Yimeng Qiu, Qiwei HanAlgorithmic Trading Emergent Behavior in Multi-Agent Systems
VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing
Apr 13, 2026Lucas Stoffl, Benedikt Wiestler, Johannes C. PaetzoldAI Agents for Scientific Discovery Healthcare
Department of Radiology, Weill Cornell Medicine, New York, USA · Cornell Tech, New York, USA · TUM University Hospital, Munich, Germany +2
Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions
Apr 13, 2026Isaac Remy, Caleb Chang, Karen LeungMulti-Agent Systems
University of Washington, Department of Aeronautics and Astronautics · NVIDIA
REGREACT: Self-Correcting Multi-Agent Pipelines for Structured Regulatory Information Extraction
Apr 13, 2026Mohammed Ali, Abdelrahman Abdallah, Adam JatowtLLM Self-Correction Document Information Extraction
University of Innsbruck, Austria
Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach
Apr 13, 2026Maria G. Mendoza, Pan-Yang Su, Bryce L. Ferguson +1Multi-Robot Task Allocation Multi-Agent Resource Allocation
Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States · Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States · Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
Evaluating Cooperation in LLM Social Groups through Elected Leadership
Apr 13, 2026Ryan Faulkner, Anushka Deshpande, David Guzman Piedrahita +2AI Agent Governance Multi-Agent Collaboration
Jinesis Lab, University of Toronto & Vector Institute · EuroSafeAI · ETH Zürich +2
Governance by Design: A Parsonian Institutional Architecture for Internet-Wide Agent Societies
Apr 13, 2026Anbang RuanAI Agent Governance Multi-Agent Systems
NetX Foundation
Prosociality by Coupling, Not Mere Observation: Homeostatic Sharing in an Inspectable Recurrent Artificial Life Agent
Apr 12, 2026Aishik SanyalArtificial Life Emergent Behavior in Multi-Agent Systems
Independent Research Engineer
A Benchmark and Multi-Agent System for Instruction-driven Cinematic Video Compilation
Apr 12, 2026Peixuan Zhang, Chang Zhou, Ziyuan Zhang +8Instruction-Based Video Editing Video Editing Benchmarks
School of Artificial Intelligence, Beijing University of Posts and Telecommunications · AI Technology Center, Online Video Business Unit, Tencent PCG · Tsinghua University +4
AI Organizations are More Effective but Less Aligned than Individual Agents
Apr 11, 2026Judy Hanwen Shen, Daniel Zhu, Siddarth Srinivasan +5Multi-Agent Systems AI Alignment
Constellation Institute · MATS Program · Anthropic
Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Review
Apr 10, 2026Corey Yang-Smith, Ronnie de Souza Santos, Ahmad AbdellatifAlgorithmic Fairness Multi-Agent Systems
University of Calgary, Calgary, Canada
CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation
Apr 10, 2026Aarush Sinha, Arion Das, Soumyadeep Nag +7Strategic Reasoning Risks in Language Models Emergent Behavior in Multi-Agent Systems
University of Copenhagen · IIIT Ranchi · ISI Kolkata +6
Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems
Apr 10, 2026Keyu Li, Jin Gao, Dequan WangAlgorithmic Bias Multi-Agent Systems
Shanghai Jiao Tong University · Shanghai Innovation Institute
Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue
Apr 9, 2026Oleksii BychkovSwarm Robotics Cognitive Architectures for AI Agents
Department of Program Systems and Technologies, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
LOLGORITHM: Funny Comment Generation Agent For Short Videos
Apr 9, 2026Xuan Ouyang, Bouzhou Wang, Senan Wang +3Text Generation Controllable Text Generation
University of New South Wales Sydney, Australia · University of Sydney Sydney, Australia · The University of Hong Kong Hong Kong, China +1

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Agentic Social Affordance Framework (ASAF): Agent Identity Design as a Collaboration Interface in Multi-Agent Systems

Meng-Han Lee

Independent Researcher, Taipei, Taiwan

Multi-Agent Collaboration Human-AI Interaction Multi-Agent Systems Human-AI Collaboration Agentic AI

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As AI systems evolve from single agents to multi-agent architectures, a critical design dimension has been overlooked: how the social identity of individual agents shapes human behavior within the collaboration. This paper introduces the Agentic Social Affordance Framework (ASAF), a theoretical framework extending Social Affordance theory to multi-agent AI systems. We propose that agent identity design functions as a collaboration interface--structuring how users perceive and engage with each agent, and thereby influencing Human-Agent collaboration outcomes. ASAF adopts the analytical separability of the social affordance layer and the engineering orchestration layer as a framing assumption--an organizing distinction that structures design analysis--rather than a testable claim about effect-independence. ASAF comprises three mechanisms: Identity Signaling, Behavioral Priming, and Collaborative Governance, and specifies their boundary conditions through a four-tier Identity Signal Fidelity Spectrum and an individual-difference moderating variable (anthropomorphizing vs. instrumentalizing cognitive style). We situate ASAF relative to affordance theory (Hutchby, 2001), the CASA paradigm (Gambino et al., 2020), and classical multi-agent systems research (Wooldridge & Jennings, 1995), identifying a directional reversal: where classical MAS used roles, norms, and coordination to constrain autonomous agents, ASAF applies the same organizational vocabulary to structure the cognition and oversight of human operators who remain in the loop. ASAF positions social affordance design as a first-class design responsibility that engineering orchestration cannot subsume. We outline directions for empirical validation, including a factorial design characterizing the empirical interaction surface between the social affordance and engineering orchestration layers.