{"id":66450,"url":"https://github.com/kyegomez/awesome-multi-agent-papers","name":"awesome-multi-agent-papers","description":"A compilation of the best multi-agent papers","projects_count":294,"last_synced_at":"2026-10-13T06:00:26.533Z","repository":{"id":223739903,"uuid":"761397238","full_name":"kyegomez/awesome-multi-agent-papers","owner":"kyegomez","description":"A compilation of the best multi-agent 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Simulation \u0026 Agent Societies","Multi-Agent Collaboration \u0026 System Design","Evaluation \u0026 Model Improvement","Application-Specific Multi-Agent Systems","Multi-Agent Frameworks \u0026 Benchmarks","Workflow, Architecture \u0026 Agent Design","Papers","Science","Others","RL","Surveys","Agents for Research","Training","Reinforcement Learning","Finance"],"sub_categories":["Other Domains","Software Engineering","Multimodal","Healthcare \u0026 Medical","Data \u0026 ML","Security"],"readme":"\u003cdiv align=\"left\"\u003e\n  \u003ca href=\"https://swarms.world\"\u003e\n    \u003ch1\u003eAwesome Multi-Agent Papers\u003c/h1\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\u003cp align=\"left\"\u003e\n  \u003ca href=\"https://twitter.com/swarms_corp\"\u003e\n    \u003cpicture\u003e\n      \u003csource srcset=\"https://img.shields.io/badge/Twitter-Follow-1DA1F2?style=for-the-badge\u0026logo=twitter\u0026logoColor=white\" media=\"(prefers-color-scheme: dark)\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Twitter-Follow-1DA1F2?style=for-the-badge\u0026logo=twitter\u0026logoColor=white\" alt=\"Twitter\"\u003e\n    \u003c/picture\u003e\n  \u003c/a\u003e\n\n  \u003ca href=\"https://discord.gg/EamjgSaEQf\"\u003e\n    \u003cpicture\u003e\n      \u003csource srcset=\"https://img.shields.io/badge/Discord-Join-5865F2?style=for-the-badge\u0026logo=discord\u0026logoColor=white\" media=\"(prefers-color-scheme: dark)\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Discord-Join-5865F2?style=for-the-badge\u0026logo=discord\u0026logoColor=white\" alt=\"Discord\"\u003e\n    \u003c/picture\u003e\n  \u003c/a\u003e\n\n  \u003ca href=\"https://github.com/kyegomez/awesome-multi-agent-papers\"\u003e\n    \u003cpicture\u003e\n      \u003csource srcset=\"https://img.shields.io/badge/Awesome%20Multi-Agent%20Papers-Repo-000000?style=for-the-badge\u0026logo=github\u0026logoColor=white\" media=\"(prefers-color-scheme: dark)\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Awesome%20Multi-Agent%20Papers-Repo-000000?style=for-the-badge\u0026logo=github\u0026logoColor=white\" alt=\"Awesome Multi-Agent Papers\"\u003e\n    \u003c/picture\u003e\n  \u003c/a\u003e\n\n  \u003ca href=\"https://swarms.ai\"\u003e\n    \u003cpicture\u003e\n      \u003csource srcset=\"https://img.shields.io/badge/Swarms-Website-0A66C2?style=for-the-badge\u0026logo=google-chrome\u0026logoColor=white\" media=\"(prefers-color-scheme: dark)\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Swarms-Website-0A66C2?style=for-the-badge\u0026logo=google-chrome\u0026logoColor=white\" alt=\"Swarms Website\"\u003e\n    \u003c/picture\u003e\n  \u003c/a\u003e\n\n  \u003ca href=\"https://github.com/kyegomez/swarms\"\u003e\n    \u003cpicture\u003e\n      \u003csource srcset=\"https://img.shields.io/badge/Swarms-Framework-181717?style=for-the-badge\u0026logo=github\u0026logoColor=white\" media=\"(prefers-color-scheme: dark)\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Swarms-Framework-181717?style=for-the-badge\u0026logo=github\u0026logoColor=white\" alt=\"Swarms Framework\"\u003e\n    \u003c/picture\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"left\"\u003e\n  \u003cem\u003e\n    This is an awesome list of the best multi-agent research papers, compiled by the \u003ca href=\"https://github.com/kyegomez/swarms\"\u003eSwarms\u003c/a\u003e Team. Our mission at Swarms is to research multi-agent systems. \u003ca href=\"https://discord.gg/EamjgSaEQf\"\u003eJoin our Discord Now!\u003c/a\u003e\n  \u003c/em\u003e\n\u003c/p\u003e\n\n\n---\n\n## Format\n\n- [Paper Name] [Description] [Link] \n\n----\n\n\n## Multi-Agent Collaboration \u0026 System Design\n- **[Thought Communication in Multiagent Collaboration](https://arxiv.org/pdf/2510.20733)**\n- **[MAS-Zero: Designing Multi-Agent Systems with Zero Supervision](https://arxiv.org/pdf/2505.14996)**\n- **[K-Level Reasoning with Large Language Models](https://arxiv.org/pdf/2402.01521)**\n- **[More Agents is All You Need](https://arxiv.org/pdf/2402.05120.pdf)**\n- **[LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration](https://arxiv.org/pdf/2402.11550)**\n- **[AgentScope: A Flexible yet Robust Multi-Agent Platform](https://arxiv.org/pdf/2402.14034.pdf)**\n- **[Learning to Decode Collaboratively with Multiple Language Models](https://arxiv.org/abs/2403.03870)**\n- **[AIOS: LLM Agent Operating System](https://arxiv.org/pdf/2403.16971.pdf)**\n- **[AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155)**\n- **[Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://arxiv.org/abs/2406.02818)**\n- **[Mixture-of-Agents Enhances Large Language Model Capabilities](https://arxiv.org/abs/2406.04692)**\n- **[EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms](https://arxiv.org/pdf/2406.14228)**\n- **[Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence](https://arxiv.org/abs/2407.07061)**\n- **[Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning](https://arxiv.org/abs/2310.03094)**\n- **[Optimizing Collaboration of LLM based Agents](https://arxiv.org/pdf/2408.13406)**\n- **[LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework](https://arxiv.org/pdf/2409.11393)**\n- **[Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System](https://huggingface.co/papers/2410.08115)**\n- **[SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence](https://arxiv.org/pdf/2506.15672)**\n- **[AGENTSNET: Coordination and Collaborative Reasoning in Multi-Agent LLMs](https://www.arxiv.org/abs/2507.08616)**\n- **[LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra](https://arxiv.org/pdf/2507.15815)**\n- **[SOTOPIA-RL: REWARD DESIGN FOR SOCIAL INTELLIGENCE](https://arxiv.org/abs/2508.03905)**\n- **[Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate](https://arxiv.org/abs/2509.05396)**\n- **[Virtual Agent Economies](https://huggingface.co/papers/2509.10147)**\n- **[Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI](https://arxiv.org/pdf/2509.20175)**\n- **[In-the-Flow Agentic System Optimization for Effective Planning and Tool Use](https://huggingface.co/papers/2510.05592)**\n- **[Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?](https://arxiv.org/html/2502.00674v1)**\n- **[TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture](https://arxiv.org/abs/2510.01279)**\n- **[Achilles Heel of Distributed Multi-Agent Systems](https://arxiv.org/abs/2504.07461)**\n- **[CAMEL: Communicative Agents for \"Mind\" Exploration of Large Language Model Society](https://arxiv.org/pdf/2303.17760.pdf)**\n- **[Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://api.semanticscholar.org/CorpusId:259088724)**\n- **[Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://arxiv.org/pdf/2402.18272.pdf)**\n- **[MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration](https://api.semanticscholar.org/CorpusId:265212971)**\n- **[LLM Harmony: Multi-Agent Communication for Problem Solving](https://api.semanticscholar.org/CorpusId:266725580)**\n- **[Multi-Agent Consensus Seeking via Large Language Models](https://arxiv.org/pdf/2310.20151.pdf)**\n- **[Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents](https://api.semanticscholar.org/CorpusId:267750197)**\n- **[Cooperative Strategic Planning Enhances Reasoning Capabilities in Large Language Models](https://api.semanticscholar.org/CorpusId:273654271)**\n- **[SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents](https://api.semanticscholar.org/CorpusId:273821018)**\n- **[Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10336211)**\n- **[A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis](https://arxiv.org/pdf/2504.12322)**\n- **[Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination](https://kjha02.github.io/publication/cross-env-coop)**\n- **[Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation](https://arxiv.org/abs/2511.02303)**\n- **[The Station: An Open-World Environment for AI-Driven Discovery](https://huggingface.co/papers/2511.06309)**\n- **[Towards a Science of Scaling Agent Systems](https://arxiv.org/abs/2512.08296)**\n- **[RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents](https://arxiv.org/abs/2601.18130)**\n- **[Intelligent AI Delegation](https://huggingface.co/papers/2602.11865)**\n- **[Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook](https://huggingface.co/papers/2602.14299)**\n- **[Multi-agent cooperation through in-context co-player inference](https://huggingface.co/papers/2602.16301)**\n- **[SkillOrchestra: Learning to Route Agents via Skill Transfer](https://huggingface.co/papers/2602.19672)**\n- **[Contextual Counterfactual Credit Assignment for Multi-Agent Reinforcement Learning in LLM Collaboration](https://arxiv.org/abs/2603.06859)** [GitHub](https://github.com/facebookresearch/Hyperagents)\n- **[CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery](https://huggingface.co/papers/2604.01658)**\n- **[TRINITY: An Evolved LLM Coordinator](https://arxiv.org/abs/2512.04695)**\n- **[Learning to Orchestrate Agents in Natural Language with the Conductor](https://arxiv.org/abs/2512.04388)**\n- **[From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company](https://huggingface.co/papers/2604.22446)**\n- **[Can AI Agents Agree?](https://arxiv.org/abs/2603.01213)**\n- **[ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration](https://huggingface.co/papers/2605.03042)**\n- **[Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems](https://huggingface.co/papers/2605.14892)**\n- **[AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning](https://huggingface.co/papers/2605.24486)**\n- **[DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs](https://huggingface.co/papers/2605.25188)**\n- **[AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems](https://huggingface.co/papers/2605.27466)**\n- **[When Cloud Agents Meet Device Agents: Lessons from Hybrid Multi-Agent Systems](https://huggingface.co/papers/2605.30102)**\n- **[Streaming Communication in Multi-Agent Reasoning](https://huggingface.co/papers/2606.05158)**\n- **[What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems](https://huggingface.co/papers/2606.05304)**\n- **[SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research](https://arxiv.org/abs/2606.09730)**\n- **[Decentralized Multi-Agent Systems with Shared Context](https://arxiv.org/abs/2606.10662)**\n- **[Sakana Fugu Technical Report](https://arxiv.org/abs/2606.21228)**\n- **[A Technical Taxonomy of LLM Agent Communication Protocols](https://arxiv.org/abs/2606.19135)**\n- **[Will Scaling Improve Social Simulation with LLMs?](https://arxiv.org/abs/2607.02464)**\n- **[Agensh: Scaling Organizational Intelligence to 1,024 Agents](https://huggingface.co/papers/2609.26781)**\n- **[Self-Organizing Agent Teams Learn to Reason Together](https://www.alphaxiv.org/abs/2609.22682)**\n\n## Multi-Agent Frameworks \u0026 Benchmarks\n\n- **[MALLM: Multi-Agent Large Language Models Framework](https://arxiv.org/abs/2509.11656)**\n- **[AgentGym: Evolving Large Language Model-based Agents across Diverse Environments](https://huggingface.co/papers/2406.04151)**\n- **[Very Large-Scale Multi-Agent Simulation in AgentScope](https://arxiv.org/abs/2407.17789)**\n- **[AgentClinic: A Multimodal Agent Benchmark for AI in Clinical Environments](https://arxiv.org/pdf/2405.07960)**\n- **[MultiAgentBench: Evaluating the Collaboration and Competition of LLM Agents](https://arxiv.org/abs/2503.01935)**\n- **[TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks](https://arxiv.org/abs/2412.14161)**\n- **[BoxingGym: Benchmarking Progress in Automated Experimental Design](https://arxiv.org/abs/2501.01540)**\n- **[Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence](https://arxiv.org/abs/2508.20019)**\n- **[CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards](https://huggingface.co/papers/2510.08529)**\n- **[The Collaboration Gap](https://huggingface.co/papers/2511.02687)**\n- **[API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://api.semanticscholar.org/CorpusId:258179056)**\n- **[AutoAgents: A Framework for Automatic Agent Generation](https://api.semanticscholar.org/CorpusId:263310605)**\n- **[MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems](https://api.semanticscholar.org/CorpusId:276781761)**\n- **[Multi-Agent Collaboration via Evolving Orchestration](https://openreview.net/pdf/9727f658d788c52f49f12ae4b230baf4cf0d4007.pdf)**\n- **[TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration](https://huggingface.co/papers/2601.04544)**\n- **[Context Learning for Multi-Agent Discussion](https://huggingface.co/papers/2602.02350)**\n- **[The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems](https://huggingface.co/papers/2602.15382)**\n- **[EvoSkill: Automated Skill Discovery for Multi-Agent Systems](https://arxiv.org/pdf/2603.02766)**\n\n## Application-Specific Multi-Agent Systems\n\n### Software Engineering\n- **[Automated Unit Test Improvement using Large Language Models](https://arxiv.org/pdf/2402.09171.pdf)**\n- **[Experiential Co-Learning of Software-Developing Agents](https://arxiv.org/abs/2312.17025)**\n- **[ChatDev: Communicative Agents for Software Development](https://arxiv.org/pdf/2307.07924.pdf)**\n- **[MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution](https://arxiv.org/abs/2403.17927)**\n- **[CodeR: Issue Resolving with Multi-Agent and Task Graphs](https://arxiv.org/pdf/2406.01304)**\n- **[From LLMs to LLM-based Agents for Software Engineering: A Survey](https://arxiv.org/abs/2408.02479)**\n- **[CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases](https://www.arxiv.org/abs/2408.03910)**\n- **[Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents](https://arxiv.org/abs/2408.07060)**\n- **[Large Language Model-Based Agents for Software Engineering: A Survey](https://arxiv.org/pdf/2409.02977)**\n- **[AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation](https://arxiv.org/pdf/2409.10737)**\n- **[RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance](https://arxiv.org/pdf/2410.01242)**\n- **[CoAct-1: Computer-using Agents with Coding as Actions](https://arxiv.org/abs/2508.03923)**\n- **[Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization](https://arxiv.org/pdf/2404.02183.pdf)**\n- **[Human-In-the-Loop Software Development Agents](https://api.semanticscholar.org/CorpusId:274150430)**\n- **[LLM-Based Multi-Agent Systems for Software Engineering: Vision and the Road Ahead](https://api.semanticscholar.org/CorpusId:269005211)**\n- **[LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://api.semanticscholar.org/CorpusId:274965784)**\n- **[Tailoring with Targeted Precision: Edit-Based Agents for Open-Domain Procedure Customization](https://api.semanticscholar.org/CorpusId:268231438)**\n- **[Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents](https://api.semanticscholar.org/CorpusId:273482697)**\n- **[Scaling long-running autonomous coding](https://cursor.com/blog/scaling-agents)** A blog by Cursor\n- **[Towards self-driving codebases](https://cursor.com/blog/self-driving-codebases)** By Cursor \n- **[Harness Design for Long-Running Application Development](https://www.anthropic.com/engineering/harness-design-long-running-apps)**\n\n### Healthcare \u0026 Medical\n- **[Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://arxiv.org/abs/2405.02957)**\n- **[MEDCO: Medical Education Copilots Based on A Multi-Agent Framework](https://arxiv.org/pdf/2408.12496)**\n- **[Multi Agent based Medical Assistant for Edge Devices](https://arxiv.org/pdf/2503.05397)**\n- **[Can AI Agents Design and Implement Drug Discovery Pipelines?](https://arxiv.org/abs/2504.19912)**\n- **[Sequential Diagnosis with Language Models](https://arxiv.org/pdf/2506.22405) ||Open-Source Implementation [Code Link](https://github.com/The-Swarm-Corporation/Open-MAI-Dx-Orchestrator)**\n- **[Towards an AI co-scientist](https://storage.googleapis.com/coscientist_paper/ai_coscientist.pdf)** --- [Implementation](https://github.com/The-Swarm-Corporation/AI-CoScientist)\n- **[The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://www.nature.com/articles/s41586-025-09442-9)**\n- **[Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment](https://arxiv.org/abs/2603.21597)**\n- **[Evolving Diagnostic Agents in a Virtual Clinical Environment](https://huggingface.co/papers/2510.24654)**\n- **[MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://api.semanticscholar.org/CorpusId:269303028)**\n- **[Zodiac: A Cardiologist-Level LLM Framework for Multi-Agent Diagnostics](https://api.semanticscholar.org/CorpusId:273098806)**\n- **[MADD: Multi-Agent Drug Discovery Orchestra](https://huggingface.co/papers/2511.08217)**\n- **[SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning](https://arxiv.org/pdf/2511.08151)**\n\n### Data \u0026 ML\n- **[LAMBDA: A Large Model Based Data Agent](https://arxiv.org/abs/2407.17535)**\n- **[Agentic Retrieval-Augmented Generation for Time Series Analysis](https://arxiv.org/abs/2408.14484)**\n- **[Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining](https://huggingface.co/papers/2410.08102)**\n- **[AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML](https://arxiv.org/pdf/2410.02958)**\n- **[AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions](https://arxiv.org/abs/2410.20424)**\n- **[DataLab: A Unified Platform for LLM-Powered Business Intelligence](https://arxiv.org/abs/2412.02205)**\n\n### Security\n- **[BreachSeek: A Multi-Agent Automated Penetration Tester](https://arxiv.org/abs/2409.03789)**\n- **[Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems](https://huggingface.co/papers/2605.27766)**\n- Mind Viruses: Self-Propagating Ideas in Multi-Agent LLM Systems [Link](https://www.alphaxiv.org/abs/2608.10218)\n\n### Multimodal\n- **[Mora: Enabling Generalist Video Generation via A Multi-Agent Framework](https://arxiv.org/pdf/2403.13248)**\n- **[Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation](https://huggingface.co/papers/2406.01014)**\n- **[Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks](https://arxiv.org/abs/2408.03615)**\n- **[Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration](https://huggingface.co/papers/2605.17423)**\n- **[Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players](https://huggingface.co/papers/2605.28816)**\n- **[Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation](https://huggingface.co/papers/2605.29861)**\n- **[MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception](https://arxiv.org/abs/2312.07472)**\n- **[PC-Agent: A Hierarchical Multi-Agent Framework for Complex Task Automation on PC](https://arxiv.org/abs/2502.14282)**\n- **[Improving Large Vision and Language Models by Learning from a Panel of Peers](https://huggingface.co/papers/2509.01610)**\n- **[PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images](https://huggingface.co/papers/2509.25185)**\n- **[UFO: A UI-Focused Agent for Windows OS Interaction](https://api.semanticscholar.org/CorpusId:267636625)**\n- **[Multi-Agents Based on Large Language Models for Knowledge-based Visual Question Answering](https://api.semanticscholar.org/CorpusId:274992537)**\n- **[Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10657391)**\n- **[Two Heads Are Better Than One: Collaborative LLM Embodied Agents for Human-Robot Interaction](https://api.semanticscholar.org/CorpusId:274281016)**\n- **[LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://huggingface.co/papers/2512.20618)**\n\n### Other Domains\n- **[Human-level play in Diplomacy by combining language models with strategic reasoning](https://www.science.org/doi/10.1126/science.ade9097)**\n- **[CulturePark: Boosting Cross-cultural Understanding in Large Language Models](https://arxiv.org/abs/2405.15145)**\n- **[Beyond Human Translation: Multi-Agent Collaboration for Translating Ultra-Long Literary Texts](https://arxiv.org/abs/2405.11804)**\n- **[FanCric: Multi-Agentic Framework for Crafting Fantasy 11 Cricket Teams](https://arxiv.org/pdf/2410.01307)**\n- **[Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Multi-Agent Collaboration](https://arxiv.org/pdf/2410.02507)**\n- **[SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction](https://huggingface.co/papers/2509.00581)**\n- **[CoMet: Metaphor-Driven Covert Communication for Multi-Agent Language Games](https://aclanthology.org/2025.acl-long.389/)**\n- **[Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy](https://proceedings.neurips.cc/paper_files/paper/2024/hash/df2d62b96a4003203450cf89cd338bb7-Abstract-Conference.html)**\n- **[Large Language Model for Participatory Urban Planning](https://api.semanticscholar.org/CorpusId:268032947)**\n- **[Large language model empowered participatory urban planning](https://api.semanticscholar.org/CorpusId:267412207)**\n- **[Agents on the Bench: Large Language Model Based Multi Agent Framework for Trustworthy Digital Justice](https://api.semanticscholar.org/CorpusId:275119074)**\n- **[AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents](https://api.semanticscholar.org/CorpusId:271874640)**\n- **[MiniFed : Integrating LLM-based Agentic-Workflow for Simulating FOMC Meeting](https://api.semanticscholar.org/CorpusId:273532286)**\n- **[TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation](https://doi.org/10.5220/0013321900003890)**\n- **[Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances](https://api.semanticscholar.org/CorpusId:276576185)**\n- **[KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models](https://api.semanticscholar.org/CorpusId:271310037)**\n- **[A Multi-Agent Conversational Recommender System](https://api.semanticscholar.org/CorpusId:267406588)**\n- **[Enhancing Supermarket Robot Interaction: A Multi-Level LLM Conversational Interface for Handling Diverse Customer Intents](https://api.semanticscholar.org/CorpusId:270560396)**\n- **[Challenges Faced by Large Language Models in Solving Multi-Agent Flocking](https://api.semanticscholar.org/CorpusId:269004483)**\n- **[Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10610676)**\n- **[MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation](https://api.semanticscholar.org/CorpusId:274280893)**\n- **[ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise](https://api.semanticscholar.org/CorpusId:273654638)**\n- **[AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions](https://huggingface.co/papers/2602.06008)**\n\n## Evaluation \u0026 Model Improvement\n\n- **[Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Match Human Crowd Accuracy](https://arxiv.org/abs/2402.19379)**\n- **[Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://arxiv.org/abs/2403.04132)**\n- **[Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems](https://arxiv.org/pdf/2403.02419.pdf)**\n- **[Evolutionary Optimization of Model Merging Recipes](https://arxiv.org/html/2403.13187v1)**\n- **[Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models](https://arxiv.org/abs/2404.18796)**\n- **[Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073)**\n- **[On scalable oversight with weak LLMs judging strong LLMs](https://arxiv.org/abs/2407.04622)**\n- **[ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://arxiv.org/abs/2308.07201)**\n- **[RouteLLM: An Open-Source Framework for Cost-Effective LLM Routing](https://lmsys.org/blog/2024-07-01-routellm/)**\n- **[Agent-as-a-Judge: Evaluate Agents with Agents](https://arxiv.org/abs/2410.10934v1)**\n- **[Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debates](https://arxiv.org/pdf/2410.04663)**\n- **[MALT: Improving Reasoning with Multi-Agent LLM Training](https://arxiv.org/abs/2412.01928)**\n- **[Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/pdf/2503.13657)**\n- **[Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent](https://arxiv.org/pdf/2409.11527)**\n- **[Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems](https://huggingface.co/papers/2605.26302)**\n- **[Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling](https://huggingface.co/papers/2605.27030)**\n- **[ResearchMath-14K: Scaling Research-Level Mathematics via Agents](https://huggingface.co/papers/2605.28003)**\n- **[The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind](https://arxiv.org/abs/2506.20664)**\n- **[Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL](https://arxiv.org/abs/2508.13167)**\n- **[Probe by Gaming: A Game-based Benchmark for Assessing Conceptual Knowledge in LLMs](https://arxiv.org/abs/2505.17512)**\n- **[The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://arxiv.org/abs/2509.02547)**\n- **[Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://api.semanticscholar.org/CorpusId:264146255)**\n- **[Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)](https://api.semanticscholar.org/CorpusId:269042958)**\n- **[Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet](https://api.semanticscholar.org/CorpusId:266843945)**\n- **[Reliable Decision-Making for Multi-Agent LLM Systems](https://multiagents.org/2025_artifacts/reliable_decision_making_for_multi_agent_llm_systems.pdf)**\n- **[Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents](https://api.semanticscholar.org/CorpusId:276647196)**\n- **[AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks](https://api.semanticscholar.org/CorpusId:268297202)**\n- **[AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation](https://arxiv.org/abs/2601.00930)**\n- **[Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation](https://arxiv.org/abs/2604.09549)**\n- **[PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation](https://arxiv.org/abs/2606.05697)**\n- **[Recursive Self-Improvement through Multi-Agent Self-Supervision](https://arxiv.org/abs/2610.12176)**\n\n## Social Simulation \u0026 Agent Societies\n\n- **[TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets](https://arxiv.org/abs/2502.01506)**\n- **[Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442)**\n- **[SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents](https://arxiv.org/pdf/2403.08715.pdf)**\n- **[Scaling Instructable Agents Across Many Simulated Worlds](https://huggingface.co/papers/2404.10179)**\n- **[Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://huggingface.co/papers/2406.20094)**\n- **[Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://arxiv.org/abs/2404.16698)**\n- **[From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://arxiv.org/abs/2407.09502)**\n- **[Mindstorms in Natural Language-Based Societies of Mind](https://arxiv.org/abs/2305.17066)**\n- **[Agents' Room: Narrative Generation through Multi-step Collaboration](https://arxiv.org/pdf/2410.02603)**\n- **[GenSim: A General Social Simulation Platform with Large Language Model based Agents](https://arxiv.org/pdf/2410.04360)**\n- **[Large Language Models can Achieve Social Balance](https://arxiv.org/pdf/2410.04054)**\n- **[Cultural Evolution of Cooperation among LLM Agents](https://arxiv.org/pdf/2412.10270)**\n- **[SDPO: Segment-Level Direct Preference Optimization for Social Agents](https://arxiv.org/abs/2501.01821)**\n- **[AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents](https://arxiv.org/abs/2502.08691v1)**\n- **[OASIS: Open Agent Social Interaction Simulations with One Million Agents](https://arxiv.org/abs/2411.11581)**\n- **[LLM-Based Social Simulations Require a Boundary](https://arxiv.org/pdf/2506.19806)**\n- **[Social World Models](https://arxiv.org/abs/2509.00559)**\n- **[Simulating Human-like Daily Activities with Desire-driven Autonomy](https://arxiv.org/abs/2412.06435)**\n- **[Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations](https://aclanthology.org/2025.emnlp-main.1562/)**\n- **[Greater than the Sum of its Parts: Exploring Social Influence of Multi-Agents](https://doi.org/10.1145/3706599.3719973)**\n- **[Multi-Agents Are Social Groups: Investigating Social Influence of Multiple Agents in Human–Agent Interactions](https://dl.acm.org/doi/pdf/10.1145/3757633)**\n- **[Digital Red Queen: Adversarial Program Evolution in Core War with LLMs](https://pub.sakana.ai/drq/)**\n- **[Distributional AGI Safety: Governance Trade-offs in Multi-Agent Systems Under Adversarial Pressure](https://github.com/swarm-ai-safety/swarm)**\n- **[Agentopia: Long-Term Life Simulation and Learning in Agent Societies](https://arxiv.org/abs/2606.07513)**\n- **[CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation](https://arxiv.org/abs/2506.21805)**\n- **[From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models](https://huggingface.co/papers/2608.06020)**\n- A game theory for foundation models shows new paths to rational cooperation through similarity inference [Link](https://arxiv.org/abs/2608.03958)\n- MatrAIx: Simulating the World with 8.3 Billion Persona Agents [LINK](https://huggingface.co/papers/2608.04205)\n- Business Arena: Benchmarking LLM Agents in a Realistic Marketplace [LINK](https://huggingface.co/papers/2608.08621)\n- Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop [LINK](https://huggingface.co/papers/2608.11215)\n\n## Workflow, Architecture \u0026 Agent Design\n\n- **[AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models](https://huggingface.co/papers/2403.15157)**\n- **[AgentInstruct: Toward Generative Teaching with Agentic Flows](https://arxiv.org/abs/2407.03502)**\n- **[SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning](https://arxiv.org/pdf/2409.05556)**\n- **[Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts](https://huggingface.co/papers/2409.13449)**\n- **[AFlow: Automating Agentic Workflow Generation](https://arxiv.org/pdf/2410.10762)**\n- **[Agents Thinking Fast and Slow: A Talker-Reasoner Architecture](https://arxiv.org/pdf/2410.08328)**\n- **[DynaSaur: Large Language Agents Beyond Predefined Actions](https://arxiv.org/pdf/2411.01747)**\n- **[LLMs as Method Actors: A Model for Prompt Engineering and Architecture](https://arxiv.org/abs/2411.05778v1)**\n- **[Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents](https://arxiv.org/abs/2412.13194)**\n- **[Automated Design of Agentic Systems](https://arxiv.org/abs/2408.08435)**\n- **[The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization](https://arxiv.org/pdf/2408.08688)**\n- **[Multi-agent Architecture Search via Agentic Supernet](https://arxiv.org/pdf/2502.04180)**\n- **[Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems](https://arxiv.org/abs/2502.11098)**\n- **[When One LLM Drools, Multi-LLM Collaboration Rules](https://arxiv.org/abs/2502.04506)**\n- **[Enhancing Reasoning with Collaboration and Memory](https://arxiv.org/pdf/2503.05944)**\n- **[Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning](https://huggingface.co/papers/2407.20798)**\n- **[Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence](https://arxiv.org/abs/2410.10934v1)**\n- **[MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding](https://huggingface.co/papers/2503.13964)**\n- **[Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web Reconnaissance, Tool Generation, and Task Execution](https://huggingface.co/papers/2509.21072)**\n- **[Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation](https://huggingface.co/papers/2510.15624)**\n- **[Human-Agent Collaborative Paper-to-Page Crafting for Under $0.1](https://huggingface.co/papers/2510.19600)**\n- **[Search Self-play: Pushing the Frontier of Agent Capability without Supervision](https://huggingface.co/papers/2510.18821)**\n- **[Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication](https://huggingface.co/papers/2510.19995)**\n- **[ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature](https://huggingface.co/papers/2510.20362)**\n- **[LLM Voting: Human Choices and AI Collective Decision Making](https://api.semanticscholar.org/CorpusId:267413124)**\n- **[Planning with Multi-Constraints via Collaborative Language Agents](https://api.semanticscholar.org/CorpusId:270063287)**\n- **[An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-Making](https://api.semanticscholar.org/CorpusId:273502378)**\n- **[Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface](https://api.semanticscholar.org/CorpusId:273023085)**\n- **[Prompt Engineering Through the Lens of Optimal Control](https://api.semanticscholar.org/CorpusId:264426100)**\n- **[A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition](https://api.semanticscholar.org/CorpusId:276618078)**\n- **[Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System](https://api.semanticscholar.org/CorpusId:273346445)**\n- **[PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback](https://arxiv.org/pdf/2502.00988.pdf)**\n- **[Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework](https://arxiv.org/pdf/2403.19735.pdf)**\n\n## Science\n\n- **[GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis](https://arxiv.org/abs/2406.15341)**\n- **[GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis](https://arxiv.org/abs/2507.21035)**\n- **[aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists](https://arxiv.org/abs/2508.15126)**\n- **[ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence](https://huggingface.co/papers/2605.26340)**\n- **[AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation](https://huggingface.co/papers/2605.28655)**\n- **[The AI Scientist: The world's first AI system for automating scientific research](https://arxiv.org/abs/2408.06292)**\n- **[Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive Benchmark](https://huggingface.co/papers/2606.18648)**\n\n## Alignment\n\n- **[The Alignment Waltz: Jointly Training Agents to Collaborate for Safety](https://arxiv.org/abs/2510.08240)**\n- **[MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization](https://api.semanticscholar.org/CorpusId:273233831)**\n\n## Reinforcement Learning\n\n- **[MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning](https://api.semanticscholar.org/CorpusId:276580906)**\n- **[YOLO-MARL: You Only LLM Once for Multi-agent Reinforcement Learning](https://api.semanticscholar.org/CorpusId:273186556)**\n- **[LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions](https://api.semanticscholar.org/CorpusId:269921354)**\n- **[Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning](https://huggingface.co/papers/2601.09667)**\n- **[Scaling Multiagent Systems with Process Rewards](https://huggingface.co/papers/2601.23228)**\n\n## Surveys\n\n- **[Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://api.semanticscholar.org/CorpusId:267412980)**\n- **[A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges](https://api.semanticscholar.org/CorpusId:273218743)**\n- **[Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://api.semanticscholar.org/CorpusId:275471465)**\n- **[A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application](https://api.semanticscholar.org/CorpusId:274981589)**\n- **[A Survey on Large Language Model based Autonomous Agents](https://arxiv.org/pdf/2308.11432.pdf)**\n- **[Large Language Models: A Survey](https://api.semanticscholar.org/CorpusId:267617032)**\n- **[LLM Multi-Agent Systems: Challenges and Open Problems](https://api.semanticscholar.org/CorpusId:267499950)**\n- **[Multi-Agent Coordination across Diverse Applications: A Survey](https://api.semanticscholar.org/CorpusId:276482652)**\n- **[LLM-based Multi-Agent Systems: Techniques and Business Perspectives](https://api.semanticscholar.org/CorpusId:274165614)**\n- **[Small LLMs Are Weak Tool Learners: A Multi-LLM Agent](https://www.aclanthology.org/2024.emnlp-main.929.pdf)**\n\n## Others\n\n- **[PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs](https://huggingface.co/papers/2508.17188)**\n- **[MetaGPT: Meta Programming for Multi-Agent Collaborative Framework](https://doi.org/10.48550/arXiv.2308.00352)**\n- **[Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO](https://arxiv.org/abs/2511.13288)**\n\n## Finance\n\n- **[TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets](https://arxiv.org/abs/2502.01506)**\n- **[HedgeAgents: A Balanced-aware Multi-agent Financial Trading System](https://arxiv.org/abs/2502.13165)**\n\n\n## Agents for Research\n\n- **[Towards Collaborative Autonomous Research](https://agentrxiv.github.io/)**\n\n## Training\n\n- **[Latent Collaboration in Multi-Agent Systems](https://arxiv.org/abs/2511.20639)**\n- **[Recursive Multi-Agent Systems](https://huggingface.co/papers/2604.25917)**\n\n----\n\n# Citations\n\nIn the arxiv_bibtex.bib file, you can find the bibtex citations for all the papers in this repository.\n\n## Cite This List\n\nIf you find this resource useful in your research, please cite it as follows:\n\n**BibTeX:**\n\n```bibtex\n@misc{gomez2024awesome,\n  author       = {Gomez, Kye},\n  title        = {Awesome Multi-Agent Papers: A Compilation of the Best Multi-Agent Research},\n  year         = {2024},\n  publisher    = {GitHub},\n  journal      = {GitHub Repository},\n  howpublished = {\\url{https://github.com/kyegomez/awesome-multi-agent-papers}},\n  note         = {Maintained by the Swarms Team. Contact: kye@swarms.world}\n}\n```\n\n**APA:**\n\n\u003e Gomez, K. (2024). *Awesome Multi-Agent Papers: A Compilation of the Best Multi-Agent Research*. GitHub. https://github.com/kyegomez/awesome-multi-agent-papers\n\n**MLA:**\n\n\u003e Gomez, Kye. \"Awesome Multi-Agent Papers: A Compilation of the Best Multi-Agent Research.\" *GitHub*, 2024, https://github.com/kyegomez/awesome-multi-agent-papers.\n\nFor questions or collaborations, contact: [kye@swarms.world](mailto:kye@swarms.world)\n\n# Contributing\n\nHave a multi-agent paper that isn’t on the list? We welcome your contributions! Please open a Pull Request (PR) to add new papers and help us maintain this comprehensive and up-to-date resource for the multi-agent research community. By contributing, you enable others—especially newcomers—to access the latest research in a single, centralized repository. Thank you for helping the community grow!\n\n# License\n\nThis project is licensed under the [Apache License 2.0](LICENSE).\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/kyegomez%2Fawesome-multi-agent-papers/projects"}